2016 marked the first time that a nationwide intercity passenger rail emissions inventory was created for Amtrak.  The calculation methodology mimics that used for the Class II and III and commuter railroads with a few modifications. Since link-level activity data for Amtrak was unavailable, the default assumption was made to evenly distribute Amtrak's 2016 reported fuel use across all of it diesel-powered route-miles shown in Figure 2-9.  Participating states were instructed that they could alter the fuel use distribution within their jurisdictions by analyzing Amtrak's 2016 national timetable and calculating passenger train-miles for each affected route. Illinois and Connecticut chose to do this and were able to derive activity-based fuel use numbers for their states based on Amtrak's 2016 reported average fuel use of 2.2 gallons per passenger train-mile.  In addition, Connecticut provided supplemental data for selected counties in Massachusetts, New Hampshire, and Vermont.  Amtrak also submitted company-specific fleet mix information and company-specific weighted emission factors were derived.  Amtrak's emission rates were 25% lower than the default Class II and III and commuter railroad emission rate. Details on the computation of the Amtrak emissions are available in the rail specification sheet.
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains

Other Data Sources

The California Air Resources Board (CARB) provided rail inventories for inclusion in the 2016v1 platform. CARB's rail inventories were used in California, in place of the national dataset described above. For rail yards, the national point source rail yard dataset was used to allocate CARB-submitted rail yard emissions to point sources where possible. That is, for each California county with at least one rail yard in the national dataset, the emissions in the national rail yard dataset were adjusted so that county total rail yard emissions matched the CARB dataset. In other words, 2016v1 platform includes county total rail yard emissions from CARB, but the locations of rail yards are based on the national methodology. There are three counties with CARB-submitted rail yard emissions, but no rail yard locations in the national dataset; for those counties, the rail yard emissions were included in the rail sector using SCC<PHONE_NUMBER>.

North Carolina separately provided passenger train (SCC<PHONE_NUMBER>) emissions for use in the platform. We used NC's passenger train emissions instead of the corresponding emissions from the Lake Michigan Air Directors Consortium (LADCO) dataset.

None of these rail inventory sources included HAPs. For VOC speciation, the EPA preferred augmenting the inventory with HAPs and using those HAPs for integration, rather than running the sector as a no-integrate sector. So, Naphthalene, Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM) emissions were added to all rail inventories, including the California inventory, using the same augmentation factors as are used to augment HAPs in the NEI.
Nonroad Mobile Equipment Sources (nonroad)
The mobile nonroad equipment sector includes all mobile source emissions that do not operate on roads, excluding commercial marine vehicles, railways, and aircraft. Types of nonroad equipment include recreational vehicles, pleasure craft, and construction, agricultural, mining, and lawn and garden equipment. Nonroad equipment emissions were computed by running the MOVES2014b model, which incorporates the NONROAD2008 model. MOVES2014b replaced MOVES2014a in August 2018, and incorporates updated nonroad engine population growth rates, nonroad Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels. MOVES2014b provides a complete set of HAPs and incorporates updated nonroad emission factors for HAPs. MOVES2014b was used for all states other than California and Texas, which developed their own emissions using their own tools. VOC and PM speciation profile assignments are determined by MOVES and applied by SMOKE.

MOVES2014b provides estimates of NONHAPTOG along with the speciation profile code for the NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant code in the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation profile code. One of the speciation profile codes is `95335a' (lowercase `a'); the corresponding inventory pollutant is NONHAPTOG95335A (uppercase `A') because SMOKE does not support inventory pollutant names with lowercase letters. Since speciation profiles are applied by SCC and pollutant, no changes to SMOKE were needed to use the inventory file with this profile information. This approach was not used for California or Texas, because the datasets in those states included VOC.

MOVES2014b, unlike MOVES2014a, also provides estimates of PM2.5 by speciation profile code for the PM2.5 emission source, using PM25_#### as the pollutant code in the FF10 inventory file, where #### is a speciation profile code. To facilitate calculation of coarse particulate matter (PMC) within SMOKE, and to help create emissions summaries, an additional pollutant representing total PM2.5 called PM25TOTAL was added to the inventory. As with VOC / TOG, this approach is not used for California or Texas.

MOVES2014b outputs emissions data in county-specific databases, and a post-processing script converts the data into FF10 format. Additional post-processing steps were performed as follows:
 County-specific FF10s were combined into a single FF10 file.
 Emissions were aggregated from the more detailed SCCs modeled in MOVES to the SCCs modeled in SMOKE. A list of the aggregated SMOKE SCCs is in Appendix A of the 2016v1 nonroad specification sheet.
 To reduce the size of the inventory, HAPs that are not needed for air quality modeling, such as dioxins and furans, were removed from the inventory.
 To reduce the size of the inventory further, all emissions for sources (identified by county/SCC) for which total CAP emissions are less than 1*10[-10] were removed from the inventory. The MOVES model attributes a very tiny amount of emissions to sources that are actually zero, for example, snowmobile emissions in Florida. Removing these sources from the inventory reduces the total size of the inventory by about 7%.
 Gas and particulate components of HAPs that come out of MOVES separately, such as naphthalene, were combined.
 VOC was renamed VOC_INV so that SMOKE does not speciate both VOC and NONHAPTOG, which would result in a double count.
 PM25TOTAL, referenced above, was also created at this stage of the process.
 California and Texas emissions from MOVES were deleted and replaced with the CARB- and TCEQ-supplied emissions, respectively.
Emissions for airport ground support vehicles (SCCs ending in -8005), and oil field equipment (SCCs ending in -10010), were removed from the mobile nonroad inventory, to prevent a double count with the ptnonipm and np_oilgas sectors, respectively.

National Updates: Agricultural and Construction Equipment Allocation

The methodology for developing Agricultural equipment allocation data for the 2016v1 platform was developed by the North Carolina Department of Environmental Quality (NCDEQ). EPA updated the Construction equipment allocation data for the v1 platform.

NCDEQ compiled regional and state-level Agricultural sector fuel expenditure data for 2016 from the US Department of Agriculture, National Agricultural Statistics Service (NASS), August 2018 publication, "Farm Production Expenditures 2017 Summary." This resource provides expenditures for each of 5 major regions that cover the Continental U.S., as well as state-level data for 15 major farm producing states. Because of the limited coverage of the NASS source relative to that in MOVES, it was necessary to identify a means for estimating the 2016 Agricultural sector allocation data for the following States and Territories from a different source:  Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands. The approach for these areas is described below.

For the Continental U.S., NCDEQ first allocated the remainder of the regional fuel expenditures to states in each region for which state-level data are not reported. For this allocation, NCDEQ relied on 2012 fuel expenditure data from NASS' 2012 Census of Agriculture (note that 2017 data were not yet available at the time of this effort). The next step to developing county-level allocation data for agricultural equipment was to multiply the state-level fuel expenditure estimates by county-level allocation ratios. These allocation ratios were computed from county-level fuel expenditure data from the NASS' 2012 Census of Agriculture. There were 17 counties for which fuel expenditure data were withheld in the Census of Agriculture. For these counties, NDEQ allocated the fuel expenditures that were not accounted for in the applicable state via a surrogate indicator of fuel expenditures. For most states, the 2012 Census of Agriculture's total machinery asset value was the surrogate indicator used to perform the allocation. This indicator was found to have the strongest correlation to agricultural sector fuel expenditures based on analysis of 2012 state-level Census of Agriculture values for variables analyzed (correlation coefficient of 0.87). Because the analyzed surrogate variables were not available for the two counties in New York without fuel expenditure data, farm sales data from the 2012 Census of Agriculture were used in the allocation procedure for these counties.

For Alaska and Hawaii, NCDEQ estimated 2016 state-level fuel production expenditures by first applying the national change in fuel expenditures between 2012 and 2016 from NASS' "Farm Production Expenditures" summary publications to 2012 state expenditure data from the 2012 Census of Agriculture. Next, NCDEQ applied an adjustment factor to account for the relationship between national 2012 fuel expenditures as reported by the Census of Agriculture and those reported in the Farm Production Expenditures Summary. Hawaii's state-level fuel expenditures were allocated to counties using the same approach as the states in the Continental U.S. (i.e., county-level fuel expenditure data from the NASS' 2012 Census of Agriculture). Alaska's fuel expenditures total was allocated to counties using a different approach because the 2012 Census of Agriculture reports fuel expenditures data for a different list of counties than the one included in MOVES. To ensure consistency with MOVES, NCDEQ allocated Alaska's fuel expenditures based on the current allocation data in MOVES, which reflect 2002 harvested acreage data from the Census of Agriculture.

Because NCDEQ did not identify any source of fuel expenditures data for Puerto Rico or the U.S. Virgin Islands, the county allocation percentages that are represented by the 2002 MOVES allocation data were used for these territories.

For the Construction sector, MOVES2014b uses estimates of 2003 total dollar value of construction by county to allocate national Construction equipment populations to the state and local levels. However, the 2016 Nonroad Collaborative Work Group sought to update the surrogate data used to geographically allocate Construction equipment with a more recent data source thought to be more reflective of emissions-generating Construction equipment activity at the county level: acres disturbed by residential, non-residential, and road construction activity.

The nonpoint sector of the 2014 National Emissions Inventory (NEI) includes estimates of Construction Dust (PM2.5), for which acreage disturbed by residential, non-residential, and road construction activity is a function. The 2014 NEI Technical Support Document includes a description of the methods used to estimate acreage disturbed at the county level by residential, non-residential, and road construction activity, for the 50 states.

Acreage disturbed by residential, non-residential, and road construction were summed together to arrive at a single value of acreage disturbed by Construction activities at the county level. County-level acreage disturbed were then summed together to arrive at acreage disturbed at the state level. State totals were then summed to arrive at a national total of acreage disturbed by Construction activities.

Puerto Rico and the U.S. Virgin Islands are not included in the Construction equipment geographic allocation update, so their relative share of the national population of Construction equipment remains the same as MOVES2014b defaults.

For both the Agricultural and Construction equipment sectors, the surrogatequant and surrogateyearID fields in the model's nrstatesurrogate table, which allocates equipment from the state- to the county-level, were populated with the county-level surrogates described above (fuel expenditures in 2016 for Agricultural equipment; acreage disturbed by construction activity in 2014 for Construction equipment). In addition, the nrbaseyearequippopulation table, which apportions the model's national equipment populations to the state level, was adjusted so that each state's share of the MOVES2014b base-year national populations of Agricultural and Construction equipment is proportional to each state's share of national acreage disturbed by construction activity (Construction equipment) and agricultural fuel expenditures (Agricultural equipment). Additionally, the model's nrsurrogate table, which defines the surrogate data used in the nrstatesurrogate table, was updated to reflect the 2016v1 changes to the Agricultural and Construction equipment sectors.

Updated nrsurrogate, nrstatesurrogate, and nrbaseyearequippopulation tables, along with instructions for utilizing these tables in MOVES runs, are available for download from EPA's ftp site: ftp://newftp.epa.gov/air/emismod/2016/v1/reports/nonroad/.

State-Supplied Nonroad Data

As shown Table 2-31 several state and local agencies provided nonroad inputs for use in the 2016v1 platform. Additionally, per the table footnotes, EPA reviewed data submitted by state and local agencies for the 2014 and 2017 National Emissions Inventories and utilized that information where appropriate (data specific to calendar years 2014 and 2017 were not used in 2016v1).

Table 2-31. Submitted nonroad input tables by agency
stateid
State or County(ies) in the Agency
                nrbaseyearequippopulation
(source populations)
                   nrdayallocation
(allocation to day type)
                      nrfuelsupply
(allocation of fuels)
                       nrgrowthindex
(population growth)
               nrhourallocation
(allocation to diurnal pattern)
                    nrmonthallocation
(seasonal allocation)
                       nrsourceusetype
(yearly activity)
                  nrstatesurrogate
(allocations to counties)
                      countyyear
(Stage II information)
                     nrequipmenttype
(surrogate selection)
                    nrsurrogate
(surrogate identification)
                                       4
ARIZONA - Maricopa Co.
                                       A

                                       D
                                       D
                                       D
                                       D
                                       D
                                       9
CONNECTICUT
                                       A

                                      13
GEORGIA

                                       D

                                       D

                                      16
IDAHO

                                       C

                                      17
ILLINOIS

                                       E

                                      18
INDIANA

                                       C

                                       E

                                      19
IOWA

                                       C

                                       E

                                      26
MICHIGAN

                                       C

                                       E

                                      27
MINNESOTA

                                       C

                                       E

                                      29
MISSOURI

                                       E

                                      36
NEW YORK
                                       D
                                       D

                                       D
                                       D
                                       D
                                       D
                                       D

                                      39
OHIO

                                       C

                                       E

                                      49
UTAH
                                       B
                                       D

                                       D
                                       D

                                       F

                                      53
WASHINGTON

                                       D

                                       D
                                       D
                                      55
WISCONSIN

                                       E

[A] Submitted data with modification: updated the year ID to 2016.
[B] Submitted data with modification: deleted records that were not snowmobile source types 1002-1010.
[C] NEI 2014v2 data used for 2016v1 platform.
[D] Submitted data.
[E] Spreadsheet "ladco_nei2017_nrmonthallocation.xlsx."
[F] Submitted data with modification: deleted records that were not the snowmobile surrogate ID 14.

Emissions Inside California and Texas

California nonroad emissions were provided by CARB for the years 2016, 2023, and 2028.

All California nonroad inventories are annual, with monthly temporalization applied in SMOKE. Emissions for oil field equipment (SCCs ending in -10010) were removed from the California inventory in order to prevent a double count with the np_oilgas sector.

Texas nonroad emissions were provided by the Texas Commission on Environmental Quality for the years 2016, 2023, and 2028, using TCEQ's TexN2 tool. This tool facilitates the use of detailed Texas-specific nonroad equipment population, activity, fuels, and related data as inputs for MOVES2014b, and accounts for Texas-specific emission adjustments such as the Texas Low Emission Diesel (TxLED) program.

Nonroad Updates from State Comments
The 2016 Nonroad Collaborative Work group received a small number of comments on the 2016beta inventory, all of which were addressed and implemented in the 2016v1 nonroad inventory:
       Georgia Department of Natural Resources: incorporate updated fuel supply (nrfuelsupply table) for 45 Georgia counties, to reflect the removal of summer Reid Vapor Pressure restrictions in 2016; utilize updated geographic allocation factors (nrstatesurrogate table) for the Commercial, Lawn & Garden (commercial, public, and residential), Logging, Manufacturing, Golf Carts, Recreational, Railroad Maintenance Equipment and A/C/Refrigeration sectors, using data from the U.S. Census Bureau and U.S. Forest Service.
       Lake Michigan Air Directors Consortium (LADCO): update seasonal allocation of agricultural equipment activity (nrmonthallocation table) for Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, and Wisconsin.
       Texas Commission on Environmental Quality: replace MOVES2014b nonroad emissions for Texas with emissions calculated with TCEQ's TexN2 model.
 Alaska Department of Environmental Conservation: remove emissions as calculated by MOVES2014b for several equipment sector-county/census areas combinations in Alaska, due to an absence of nonroad activity (see Table 2-32).

       Table 2-32. Alaska counties/census areas for which nonroad equipment sector-specific emissions are removed in 2016v1
Nonroad Equipment Sector

Counties/Census Areas (FIPS) for which equipment sector emissions are removed in 2016v1
Agricultural

Aleutians East (02013), Aleutians West (02016), Bethel Census Area (02050), Bristol Bay Borough (02060), Dillingham Census Area (02070), Haines Borough (02100), Hoonah-Angoon Census Area (02105), Ketchikan Gateway (02130), Kodiak Island Borough (02150), Lake and Peninsula (02164), Nome (02180), North Slope Borough (02185), Northwest Arctic (02188), Petersburg Borough (02195), Pr of Wales-Hyder Census Area (02198), Sitka Borough (02220), Skagway Borough (02230), Valdez-Cordova Census Area (02261), Wade Hampton Census Area (02270), Wrangell City + Borough (02275), Yakutat City + Borough (02282), Yukon-Koyukuk Census Area (02290)
Logging

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

Aleutians East (02013), Aleutians West (02016), Bethel Census Area (02050), Bristol Bay Borough (02060), Dillingham Census Area (02070), Haines Borough (02100), Hoonah-Angoon Census Area (02105), Juneau City + Borough (02110), Ketchikan Gateway (02130), Kodiak Island Borough (02150), Lake and Peninsula (02164), Nome (02180), ), North Slope Borough (02185), Northwest Arctic (02188), Petersburg Borough (02195), Pr of Wales-Hyder Census Area (02198), Sitka Borough (02220), Southeast Fairbanks (02240), Wade Hampton Census Area (02270), Wrangell City + Borough (02275), Yakutat City + Borough (02282), Yukon-Koyukuk Census Area (02290)

2016 Fires (ptfire, ptagfire)
Multiple types of fires are represented in the modeling platform.  These include wild and prescribed fires that are grouped into the ptfire sector, and agricultural fires that comprise the ptagfire sector.  All ptfire and ptagfire fires are in the United States.  Fires outside of the United States are described in the ptfire_othna sector later in this document.
Wild and Prescribed Fires (ptfire)
Wildfires and prescribed burns that occurred during the inventory year are included in the year 2016 version 1 (2016v1) inventory as event and point sources. The point agricultural fires inventory (ptagfire) is described in a separate section. For purposes of emission inventory preparation, wildland fire (WLF) is defined as any non-structure fire that occurs in the wildland.  The wildland is defined an area in which human activity and development are essentially non-existent, except for roads, railroads, power lines, and similar transportation facilities.  Wildland fire activity is categorized by the conditions under which the fire occurs. These conditions influence important aspects of fire behavior, including smoke emissions. In the 2016v1 inventory, data processing was conducted differently depending on the fire type, as defined below:
 Wildfire (WF): any fire started by an unplanned ignition caused by lightning; volcanoes; other acts of nature; unauthorized activity; or accidental, human-caused actions, or a prescribed fire that has developed into a wildfire.
 Prescribed (Rx) fire: any fire intentionally ignited by management actions in accordance with applicable laws, policies, and regulations to meet specific land or resource management objectives.  Prescribed fire is one type of fire fuels treatment. Fire fuels treatments are vegetation management activities intended to modify or reduce hazardous fuels. Fuels treatments include prescribed fires, wildland fire use, and mechanical treatment.
The SCCs used for the ptfire sources are shown in Table 2-33. The ptfire inventory includes separate SCCs for the flaming and smoldering combustion phases for wildfire and prescribed burns.  Note that prescribed grassland fires or Flint Hills, Kansas have their own SCC in the 2016v1 inventory.  The year 2016 fire season also included some major wild grassland fires. These wild grassland fires were assigned the standard wildfire SCCs shown in Table 2-33.
Table 2-33. SCCs included in the ptfire sector for the 2016v1 inventory
                                      SCC
                                  Description
2801500170
Grassland fires; prescribed
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland wildfires)
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming

National Fire Information Data

Numerous fire information databases are available from U.S. national government agencies.  Some of the databases are available via the internet while others must be obtained directly from agency staff.  Table 2-34 provides the national fire information databases that were used for the 2016v1 ptfire inventory, including the website where the 2016 data were downloaded.
Table 2-34. National fire information databases used in 2016v1 ptfire inventory
Dataset Name
Fire Types
Format
Agency
Coverage
Source
Hazard Mapping System (HMS)
WF/RX
CSV
NOAA
North America
https://www.ospo.noaa.gov/Products/land/hms.html
Geospatial Multi-Agency Coordination(GeoMAC)
WF
SHP
USGS
Entire US
https://www.geomac.gov/GeoMACTransition.shtml
Incident Command System Form 209: Incident Status Summary (ICS-209)
WF/RX
CSV
Multi
Entire US
https://fam.nwcg.gov/fam-web/
National Association of State Foresters (NASF)
WF
CSV
Multi
Participating US states
https://fam.nwcg.gov/fam-web/  (see Public Access Reports, Free Data Extract, then NASF State Data Extract)
Monitoring Trends in Burn Severity (MTBS)
WF/RX
SHP
USGS, USFS
Entire US
https://www.mtbs.gov/direct-download
Forest Service Activity Tracking System (FACTS)
RX
SHP
USFS
Entire US
Hazardous Fuel Treatment Reduction: Polygon at https://data.fs.usda.gov/geodata/edw/ datasets.php
US Fish and Wildland Service (USFWS) fire database
WF/RX
CSV
USFWS
Entire US
Direct communication with USFWS

The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and
Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information
Service (NESDIS) as a tool to identify fires over North America in an operational environment. The system utilizes geostationary and polar orbiting environmental satellites.  Automated fire detection algorithms are employed for each of the sensors. When possible, HMS data analysts apply quality control procedures for the automated fire detections by eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via a thorough examination of the satellite imagery.

The HMS product used for the 2016v1 inventory consisted of daily comma-delimited files containing fire detect information including latitude-longitude, satellite used, time detected, and other information.  The Visible Infrared Imaging Radiometer Suite (VIIRS) satellite fire detects were introduced into the HMS in late 2016.  Since it was only available for a small portion of the year, the VIIRS fire detects were removed for the entire year for consistency. In the 2016alpha inventory, the grassland fire detects were put in the point agricultural fire sector (ptagfire). As there were a few significant grassland wildfires in Kansas and Oklahoma in year 2016, all grassland fire detects were included in the ptfire sector for the 2016v1 inventory. These grassland fires were processed through Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework.
GeoMAC (Geospatial Multi-Agency Coordination) is an online wildfire mapping application designed for fire managers to access maps of current U.S. fire locations and perimeters. The wildfire perimeter data is based upon input from incident intelligence sources from multiple agencies, GPS data, and infrared (IR) imagery from fixed wing and satellite platforms.
The Incident Status Summary, also known as the "ICS-209" is used for reporting specific information on significant fire incidents. The ICS-209 report is a critical interagency incident reporting tool giving daily `snapshots' of the wildland fire management situation and individual incident information which include fire behavior, size, location, cost, and other information.  Data from two tables in the ICS-209 database were merged and used for the 2016v1 ptfire inventory: the SIT209_HISTORY_INCIDENT_209_REPORTS table contained daily 209 data records for large fires, and the SIT209_HISTORY_INCIDENTS table contained summary data for additional smaller fires.
The National Association of State Foresters (NASF) is a non-profit organization composed of the directors of forestry agencies in the states, U.S. territories, and District of Columbia to manage and protect state and private forests, which encompass nearly two-thirds of the nation's forests. The NASF compiles fire incident reports from agencies in the organization and makes them publicly available. The NASF fire information includes dates of fire activity, acres burned, and fire location information.
Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map the burn severity and extent of large fires across the U.S. from 1984 to present. The MTBS data includes all fires 1,000 acres or greater in the western United States and 500 acres or greater in the eastern United States. The extent of coverage includes the continental U.S., Alaska, Hawaii, and Puerto Rico. Fire occurrence and satellite data from various sources are compiled to create numerous MTBS fire products. The MTBS Burned Areas Boundaries Dataset shapefiles include year 2016 fires and that are classified as either wildfires, prescribed burns or unknown fire types. The unknown fire type shapes were omitted in the 2016v1 inventory development due to temporal and spatial problems found when trying to use these data.
The US Forest Service (USFS) compiles a variety of fire information every year. Year 2016 data from the USFS Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and used for 2016v1 emissions inventory development. This database includes information about activities related to fire/fuels, silviculture, and invasive species. The FACTS database consists of shapefiles for prescribed burns that provide acres burned,and start and ending time information.
The US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their federal lands every year. Year 2016 data were acquired from USFWS through direct communication with USFWS staff and were used for 2016v1 emissions inventory development.   The USFWS fire information provided fire type, acres burned, latitude-longitude, and start and ending times.

State/Local/Tribal Fire Information
During the 2016 emissions modeling platform development process, S/L/T agencies were invited by EPA and 2016 Inventory Collaborative Fire Workgroup to submit all fire occurrence data for use in developing the 2016v1 fire inventory.  A template form containing the desired format for data submittals was provided to S/L/T air agencies. The list of S/L/T agencies that submitted fire data is provided in Table 2-35.  Data from nine individual states and one Indian Tribe were used for the 2016v1 ptfire inventory.
Table 2-35. List of S/L/T agencies that submitted fire data for 2016v1 with types and formats.
S/L/T agency name
Fire Types
Format
NCDEQ
WF/RX
CSV
KDHE
RX/AG
CSV
CO Smoke Mgmt Program
RX
CSV
Idaho DEQ
AG
CSV
Nez Perce Tribe
AG
CSV
GA DNR
ALL
EIS
MN
RX/AG
CSV
WA ECY
AG
CSV
NJ DEP
WF/RX
CSV
Alaska DEC
WF/RX
CSV
The data provided by S/L/T agencies were evaluated by EPA and further feedback on the data submitted by the state was requested at times. Table 2-36 provides a summary of the type of data submitted by each S/L/T agency and includes spatial, temporal, acres burned and other information provided by the agencies.
Table 2-36. Brief description of fire information submitted for 2016v1 inventory use.
S/L/T agency name
Fire Types
Description
NCDEQ
WF/RX
Fire type, period-specific, latitude-longitude and acres burned information. Technical direction was to remove all fire detects that were not reconciled with any other national or state agency database.
Kansas DHE
RX/AG
Day-specific, county-centroid located, acres burned for Flint Hills prescribed burns for Feb 27-May 4 time period. Reclassified fuels for some agricultural burns.  A grassland gridding surrogate was used to spatially allocate the day-specific grassland fire emissions.
Colorado Smoke Mgmt Program
RX
Day-specific, latitude-longitude, and acres burned for prescribed burns
Idaho DEQ
AG
Day-specific, latitude-longitude, acres burned for agricultural burns. Total replacement of 2016 alpha fire inventory for Idaho.
Nez Perce Tribe
AG
Day-specific, latitude-longitude, acres burned for agricultural burns. Total replacement of 2016 alpha fire inventory within the tribal area boundary.
Georgia DNR
ALL
Data submitted included all fires types via EIS. The wildfire and prescribed burn data were provided as daily, point emissions sources. The agricultural burns were provided as day-specific point emissions sources.
Minnesota
RX/AG
Corrected latitude-longitude, day-specific and acres burned for some prescribed and agricultural burns.
Washington ECY
AG
Month-specific, latitude-longitude, acres burned, fuel loading and emissions for agricultural burns. Not day-specific so allocation to daily implemented by EPA. WA state direction included to continue to use the 2014NEIv2 pile burns that were included in the non-point sector for 2016v1.
New Jersey DEP
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and prescribed burns.
Alaska DEC
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and prescribed burns.

Fire Emissions Estimation Methodology
The national and S/L/T data mentioned earlier were used to estimate daily wildfire and prescribed burn emissions from flaming combustion and smoldering combustion phases for the 2016v1 inventory. Flaming combustion is more complete combustion than smoldering and is more prevalent with fuels that have a high surface-to-volume ratio, a low bulk density, and low moisture content. Smoldering combustion occurs without a flame, is a less complete burn, and produces some pollutants, such as PM2.5, VOCs, and CO, at higher rates than flaming combustion. Smoldering combustion is more prevalent with fuels that have low surface-to-volume ratios, high bulk density, and high moisture content. Models sometimes differentiate between smoldering emissions that are lofted with a smoke plume and those that remain near the ground (residual emissions), but for the purposes of the 2016v1 inventory the residual smoldering emissions were allocated to the smoldering SCCs listed in Table 2-33. The lofted smoldering emissions were assigned to the flaming emissions SCCs in Table 2-33.
Figure 2-10 is a schematic of the data processing stream for the 2016v1 inventory for wildfire and prescribe burn sources. The ptfire inventory sources were estimated using Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and Blue Sky Framework. SMARTFIRE2 is an algorithm and database system that operate within a geographic information system (GIS). SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified GIS database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on the strengths of both data types while avoiding double-counting of fire events. At its core, SMARTFIRE2 is an association engine that links reports covering the same fire in any number of multiple databases. In this process, all input information is preserved, and no attempt is made to reconcile conflicting or potentially contradictory information (for example, the existence of a fire in one database but not another).
For the 2016v1 inventory, the national and S/L/T fire information was input into SMARTFIRE2 and then merged and associated based on user-defined weights for each fire information dataset. The output from SMARTFIRE2 was daily acres burned by fire type, and latitude-longitude coordinates for each fire. The fire type assignments were made using the fire information datasets. If the only information for a fire was a satellite detect for fire activity, then the flow described in Figure 2-11 was used to make fire type assignment by state and by month.
Figure 2-10. Processing flow for fire emission estimates in the 2016v1 inventory

Figure 2-11. Default fire type assignment by state and month in cases where a satellite detect is only source of fire information.

The BlueSky Modeling Framework version 3.5 (revision #38169) was used to calculate fuel loading and consumption, and emissions using various models depending on the available inputs as well as the desired results. The contiguous United States and Alaska, where Fuel Characteristic Classification System (FCCS) fuel loading data are available, were processed using the modeling chain described in 	Figure 2-12. The Fire Emissions Production Simulator (FEPS) in the Bluesky Framework generated all of the CAP emission factors for wildland fires used in the 2016v1 inventory.    The HAPs were derived from regional emissions factors from Urbanski (2014).
	Figure 2-12.  Blue Sky Modeling Framework

For the 2016v1 inventory, the FCCSv2 spatial vegetation cover was upgraded to the LANDFIRE v1.4 fuel vegetation cover (See: https://www.landfire.gov/fccs.php). The FCCSv3 fuel bed characteristics were implemented along with LANDFIREv1.4 to provide better fuel classification for the BlueSky Framework. The LANDFIREv1.4 raster data were aggregated from the native resolution and projection to 200 meter resolution using a nearest-neighbor methodology. Aggregation and reprojection was required to allow these data to work in the BlueSky Framework.
Point source Agriculture Fires (ptagfire)
The point source agricultural fire (ptagfire) inventory sector contains daily agricultural burning emissions. Daily fire activity was derived from the NOAA Hazard Mapping System (HMS) fire activity data.  The agricultural fires sector includes SCCs starting with `28015'. The first three levels of descriptions for these SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 2) Agriculture Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on fire.  The SCC<PHONE_NUMBER> does not specify the crop type or burn method, while the more specific SCCs specify field or orchard crops and, in some cases, the specific crop being grown. The SCCs for this sector listed are in Table 2-37.
Table 2-37. SCCs included in the ptagfire sector for the 2016v1 inventory
                                      SCC
                                  Description
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Unspecified crop type and Burn Method
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crops Unspecified
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Alfalfa: Backfire Burning
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Barley: Burning Techniques Not Significant
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Bean (red): Headfire Burning
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Corn: Burning Techniques Not Important
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Double Crop Winter Wheat and Corn
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;DoubleCrop Corn and Soybeans
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Cotton: Burning Techniques Not Important
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Grasses: Burning Techniques Not Important
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Fallow
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Hay (wild): Backfire Burning
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Pea: Backfire Burning
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Rice: Burning Techniques Not Significant
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Sugar Cane: Burning Techniques Not Significant
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Field Crop is Wheat: Backfire Burning
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;DoubleCrop Winter Wheat and Cotton
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;DoubleCrop Winter Wheat and Soybeans
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop Unspecified
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop is Apple
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop is Cherry
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop is Peach
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Orchard Crop is Pear
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Vine Crop Unspecified
<PHONE_NUMBER>
Miscellaneous Area Sources;Agriculture Production - Crops - as nonpoint;Agricultural Field Burning - whole field set on fire;Forest Residues Unspecified

The EPA estimated biomass burning emissions using remote sensing data. These estimates were then reviewed by the states and revised as resources allowed. As many states did not have the resources to estimate emissions for this sector, remote sensing was necessary to fill in the gaps for regions where there was no other source of data. Crop residue emissions result from either pre-harvest or post-harvest burning of agricultural fields. The crop residue emission inventory for 2016 is day-specific and includes geolocation information by crop type. The method employed and described here is based on the same methods employed in the 2014 NEI with a few minor updates. It should be noted that grassland fires were moved from the agricultural burning inventory sector to the prescribed and wildland fire sector for 2016beta and 2016v1 inventories. This was done to prevent double-counting of fires and because the largest fire (acres burned) in 2016 was a wild grassland fire in Kansas.

Daily, year-specific agricultural burning emissions were derived from HMS fire activity data, which contains the date and location of remote-sensed anomalies. As point source inventories, the locations of the fires are identified with latitude-longitude coordinates for specific fire events. The HMS activity data were filtered using 2016 USDA cropland data layer (CDL). Satellite fire detects over agricultural lands were assumed to be agricultural burns and assigned a crop type. Detects that were not over agricultural lands were output to a separate file for use in the point source wildfire (ptfire) inventory sector. Each detect was assigned an average size of between 40 and 80 acres based on crop type. The assumed field sizes are found in Table 2-38.

Table 2-38. Assumed field size of agricultural fires per state(acres)
State
                                  Field Size
Alabama
                                      40
Arizona
                                      80
Arkansas
                                      40
California
                                      120
Colorado
                                      80
Connecticut
                                      40
Delaware
                                      40
Florida
                                      60
Georgia
                                      40
Idaho
                                      120
Illinois
                                      60
Indiana
                                      60
Iowa
                                      60
Kansas
                                      80
Kentucky
                                      40
Louisiana
                                      40
Maine
                                      40
Maryland
                                      40
Massachusetts
                                      40
Michigan
                                      40
Minnesota
                                      60
Mississippi
                                      40
Missouri
                                      60
Montana
                                      120
Nebraska
                                      60
Nevada
                                      40
New Hampshire
                                      40
New Jersey
                                      40
New Mexico
                                      80
New York
                                      40
North Carolina
                                      40
North Dakota
                                      60
Ohio
                                      40
Oklahoma
                                      80
Oregon
                                      120
Pennsylvania
                                      40
Rhode Island
                                      40
South Carolina
                                      40
South Dakota
                                      60
Tennessee
                                      40
Texas
                                      80
Utah
                                      40
Vermont
                                      40
Virginia
                                      40
Washington
                                      120
West Virginia
                                      40
Wisconsin
                                      40
Wyoming
                                      80

Another feature of the ptagfire database is that the satellite detections for 2016 were filtered out to exclude areas covered by snow during the winter months.  To do this, the daily snow cover fraction per grid cell was extracted from a 2016 meteorological Weather Research Forecast (WRF) model simulation. The locations of fire detections were then compared with this daily snow cover file. For any day in which a grid cell had snow cover, the fire detections in that grid cell on that day were excluded from the inventory.   Due to the inconsistent reporting of fire detections for year 2016 from the Visible Infrared Imaging Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as VIIRS or Suomi National Polar-orbiting Partnership satellite were excluded.  In addition, certain crop types (corn and soybeans) were excluded from the following states: Iowa, Kansas, Indiana, Illinois, Michigan, Missouri, Minnesota, Wisconsin, and Ohio. Kansas was not included in this list in the 2014NEI but added for 2016.   The reason for these crop types being excluded is because states have indicated that these crop types are not burned.

Crop type-specific emissions factors were applied to each daily fire to calculate criteria and hazardous pollutant emissions. In all prior NEIs for this sector, the HAP emission factors and the VOC emission factors were known to be inconsistent. The HAP emission factors were copied from the HAP emission factors for wildfires in the 2014 NEI and in the 2016 beta and version 1 modeling platforms. The VOC emission factors were scaled from the CO emission factors in the 2014 NEI and the 2016 beta and version 1 modeling platforms.  See Pouliot et al, 2017 for a complete table of emission factors and fuel loading by crop type.

Heat flux values for computing fire plume rise were calculated using the size and assumed fuel loading of each daily fire.  Emission factors and fuel loading by crop type are available in Table 1 of Pouliot et al. (2017).  This information is needed for a plume rise calculation within a chemical transport modeling system. In prior NEIs including the 2014 NEI, all the emissions were placed into layer 1 (i.e. ground level).

The daily agricultural and open burning emissions were converted from a tabular format into the SMOKE-ready daily point Flat File 2010 (FF10) format. The daily emissions were also aggregated into annual values by location and converted into the annual point flat file format.
2016 Biogenic Sources (beis)
Biogenic emissions for the entire year 2016 were developed using the Biogenic Emission Inventory System version 3.61 (BEIS3.61) within SMOKE.  The landuse input into BEIS3.61 is the Biogenic Emissions Landuse Dataset (BELD) version 4.1 which is based on an updated version of the USDA-USFS Forest Inventory and Analysis (FIA) vegetation speciation-based data from 2001 to 2014 from the FIA version 5.1.

BEIS3.61 has some important updates from BEIS 3.14.  These include the incorporation of Version 4.1 of the Biogenic Emissions Landuse Database (BELD4), and the incorporation of a canopy model to estimate leaf-level temperatures (Pouliot and Bash, 2015).  BEIS3.61 includes a two-layer canopy model. Layer structure varies with light intensity and solar zenith angle.  Both layers of the canopy model include estimates of sunlit and shaded leaf area based on solar zenith angle and light intensity, direct and diffuse solar radiation, and leaf temperature (Bash et al., 2016).  The new algorithm requires additional meteorological variables over previous versions of BEIS.  The variables output from the Meteorology-Chemistry Interface Processor (MCIP) that are used for BEIS3.61 processing are shown in Table 2-39.   The 2016 version 1 of the BEIS3 modeling for year 2016 included processing for both a 36km (36US3) and 12km domain (12US1) (see Figure 3-1Error! Reference source not found.).    The 12US2 modeling domain can also be supported by taking a subset or window of the 12US1 BEIS3 emissions dataset.
Table 2-39. Hourly Meteorological variables required by BEIS 3.61
                                   Variable

                                  Description
LAI

leaf-area index
PRSFC

surface pressure
Q2

mixing ratio at 2 m
RC

convective precipitation
RGRND

solar rad reaching sfc
RN

nonconvective precipitation
RSTOMI

inverse of bulk stomatal resistance
SLYTP

soil texture type by USDA category
SOIM1

volumetric soil moisture in top cm
SOIT1

soil temperature in top cm
TEMPG

skin temperature at ground
USTAR

cell averaged friction velocity
RADYNI

inverse of aerodynamic resistance
TEMP2

temperature at 2 m

SMOKE-BEIS3 modeling system consists of two programs named: 1) Normbeis3 and 2) Tmpbeis3.   Normbeis3 uses emissions factors and BELD4 landuse to compute gridded normalized emissions for chosen model domain (see Figure 2-13).  The emissions factor file (B360FAC) contains leaf-area-indices (LAI), dry leaf biomass, winter biomass factor, indicator of specific leaf weight, and normalized emission fluxes for 35 different species/compounds.    The BELD4 file is the gridded landuse for 276 different landuse types.    The output gridded domain is the same as the input domain for the land use data.   Output emission fluxes (B3GRD) are normalized to 30 °C, and isoprene and methyl-butenol fluxes are also normalized to a photosynthetic active radiation of 1000 umol/m[2]s.
Figure 2-13. Normbeis3 data flows

The normalized emissions output from Normbeis3 (B3GRD) are input into Tmpbeis3 along with the MCIP meteorological data, chemical speciation profile to use for desired chemical mechanism, and BIOSEASON file used to indicate how each day in year 2016 should be treated, either as summer or winter.   Figure 2-14 illustrates the data flows for the Tmpbeis3 program.  The output from Tmpbeis includes gridded, speciated, hourly emissions both in moles/second (B3GTS_L) and tons/hour (B3GTS_S).
Figure 2-14. Tmpbeis3 data flow diagram.

Biogenic emissions do not use an emissions inventory and do not have SCCs.   The gridded land use data, gridded meteorology, an emissions factor file, and a speciation profile are further described in the next section.
Sources Outside of the United States
The emissions from Canada and Mexico and other areas outside of the U.S. are included in these emissions modeling sectors:  othpt, othar, othafdust, othptdust, onroad_can, onroad_mex, and ptfire_othna.  The "oth" refers to the fact that these emissions are usually "other" than those in the NEI, and the remaining characters provide the SMOKE source types: "pt" for point, "ar" for "area and nonroad mobile," "afdust" for area fugitive dust (Canada only), and "ptdust" for point fugitive dust. Because Canada and Mexico onroad mobile emissions are modeled differently from each other, they are separated into two sectors: onroad_can and onroad_mex.  Emissions for Mexico are based on the Inventario Nacional de Emisiones de Mexico, 2008 projected to year 2016 (ERG, 2014a). Additional details for these sectors can be found in the 2016v1 platform specification sheets.
Point Sources in Canada and Mexico (othpt)
Canadian point sources were taken from the ECCC 2015 emission inventory, including upstream oil and gas emissions,  agricultural ammonia and VOC, along with point source emissions from Mexico's 2008 inventory projected to 2014 and 2018 and then interpolated to 2016. The Canadian point source inventory is pre-speciated for the CB6 chemical mechanicsm.  Also for Canada, agricultural data were originally provided on a rotated 10-km grid for the 2016beta platform.  These were smoothed out so as to avoid the artifact of grid lines in the processed emissions.  The data were monthly resolution for Canadian agricultural and airport emissions, along with some Canadian point sources, and annual resolution for the remainder of Canada and all of Mexico.
Fugitive Dust Sources in Canada (othafdust, othptdust)
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities, were provided by Environment and Climate Change Canada (ECCC) as part of their 2015 emission inventory.  Different source categories were provided as gridded point sources and area (nonpoint) source inventories.  Following consultation with ECCC, construction dust emissions in the othafdust inventory were reduced to levels compatible with their 2010 inventory.

Gridded point source emissions resulting from land tilling due to agricultural activities were provided as part of the ECCC 2015 emission inventory.  The provided wind erosion emissions were removed.  The data were originally provided on a rotated 10-km grid for the 2016 beta platform, but these were smoothed so as to avoid the artifact of grid lines appearing in the emissions output from SMOKE. The othptdust emissions have a monthly resolution.
A transport fraction adjustment that reduces dust emissions based on land cover types was applied to both point and nonpoint dust emissions, along with a meteorology-based (precipitation and snow/ice cover) zero-out of emissions when the ground is snow covered or wet.
Nonpoint and Nonroad Sources in Canada and Mexico (othar)
ECCC provided year 2015 Canada province, and in some cases sub-province, resolution emissions from for nonpoint and nonroad sources. The nonroad sources were monthly while the nonpoint and rail emissions were annual.  For Mexico, year 2016 Mexico nonpoint and nonroad inventories at the municipio resolution were interpolated from 2014 and 2018 inventories that were projected from their 2008 inventory.  All Mexico inventories were annual resolution.  Canadian CMV inventories that had been included in this sector in past modeling platforms are now included in the cmv_c1c2 and cmv_c3 sectors as point sources.
Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)
ECCC provided monthly year 2015 onroad emissions for Canada at the province resolution or sub-province resolution depending on the province.  For Mexico, monthly year 2016 onroad inventories at the municipio resolution were used.  The Mexico onroad emissions are based on MOVES-Mexico runs for 2014 and 2018 that were then interpolated to 2016
 Fires in Canada and Mexico (ptfire_othna)
Annual point source 2016 day-specific wildland emissions for Mexico, Canada, Central America, and Caribbean nations were developed from a combination of the Fire Inventory from NCAR (FINN) daily fire emissions and fire data provided by Environment Canada when available.  Environment Canada emissions were used for Canada wildland fire emissions for April through November and FINN fire emissions were used to fill in the annual gaps from January through March and December.  Only CAP emissions are provided in the ptfire_othna sector inventories.

For FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural burning, all other fire detections and assumed to be wildfires.  All wildland fires that are not defined as agricultural are assumed to be wild fires rather than prescribed.  FINN fire detects less than 50 square meters (0.012 acres) are removed from the inventory.  The locations of FINN fires are geocoded from latitude and longitude to FIPS code.
 Ocean Chlorine
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cl2) concentrations in oceanic air masses (Bullock and Brehme, 2002).  Data at 36 km and 12 km resolution were available and were not modified other than the model-species name "CHLORINE" was changed to "CL2" to support CMAQ modeling.

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; totals by county (U.S.), province (Canada), or municipio (Mexico); or gridded emissions.  This section provides some basic information about the tools and data files used for emissions modeling as part of the modeling platform.  For additional details that may not be covered in this section, see the specification sheets provided with the 2016v1platform as many will contain additional sector-specific information.
 Emissions modeling Overview
SMOKE version 4.7 was used to process the raw emissions inventories into emissions inputs for each modeling sector into a format compatible with CMAQ, 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 quality assurance of the emissions modeling steps, emissions totals by specie for the entire model domain are output as reports that are then compared to reports generated by SMOKE on the input inventories to ensure that mass is not lost or gained during the emissions modeling process.

When preparing emissions for the air quality model, emissions for each sector are processed separately through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-specific 2-D gridded emissions across sectors.  The SMOKE settings in the run scripts and the data in the SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.  Table 3-1 summarizes the major processing steps of each platform sector with the columns as follows.

The "Spatial" column shows the spatial approach used: "point" indicates that SMOKE maps the source from a point location (i.e., latitude and longitude) to a grid cell; "surrogates" indicates that some or all of the sources use spatial surrogates to allocate county emissions to grid cells; and "area-to-point" indicates that some of the sources use the SMOKE area-to-point feature to grid the emissions (further described in Section 3.4.2).

The "Speciation" column indicates that all sectors use the SMOKE speciation step, though biogenics speciation is done within the Tmpbeis3 program and not as a separate SMOKE step.

The "Inventory resolution" column shows the inventory temporal resolution from which SMOKE needs to calculate hourly emissions.  Note that for some sectors (e.g., onroad, beis), there is no input inventory; 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

afdust_ak_adj (36US3 only)
                                  Surrogates
                                      Yes
                                    annual

ag
                                  Surrogates
                                      Yes
                                    monthly

airports
                                     Point
                                      Yes
                                    annual
                                     None
beis
                             Pre-gridded land use
                                  in BEIS3.61
                                computed hourly

cmv_c1c2
                                  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

onroad_ca_adj
                                  Surrogates
                                      Yes
                       monthly activity, computed hourly

onroad_nonconus (36US3 only)
                                  Surrogates
                                      Yes
                       monthly activity, computed hourly

onroad_can
                                  Surrogates
                                      Yes
                                    monthly

onroad_mex
                                  Surrogates
                                      Yes
                                    monthly

othafdust_adj
                                  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

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 run 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. All grids use a Lambert-Conformal projection, with Alpha = 33º, Beta = 45º and Gamma = -97º, with a center of X = -97º and Y = 40º.  Table 3-2 describes the grids for the three domains.
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

Figure 3-1. Air quality modeling domains

 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 model species XYL, resulting in explicit 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.

Table 3-3. Emission model species produced for CB6 for CMAQ
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HCl
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOX
NO
Nitrogen oxide

NO2
Nitrogen dioxide

HONO
Nitrous acid
SO2
SO2
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

PNO3
Particulate nitrate  2.5 microns

POC
Particulate organic carbon (carbon only)  2.5 microns

PSO4
Particulate Sulfate  2.5 microns

PAL
 Aluminum

PCA
Calcium

PCL
Chloride

PFE
Iron

PK
Potassium

PH2O
Water

PMG
Magnesium

PMN
Manganese

PMOTHR
PM2.5 not in other AE6 species

PNA
Sodium

PNCOM
Non-carbon organic matter

PNH4
Ammonium

PSI
Silica

PTI
Titanium
Sea-salt species (non  - anthropogenic)
PCL
Particulate chloride

PNA
Particulate sodium

The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were developed from the SPECIATE 4.5 database (https://www.epa.gov/air-emissions-modeling/speciate-2), 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 2011v6.3 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;
 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 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.
 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 integration).  For the "integrated" sources, SMOKE subtracts the "integrated" HAPs from the VOC (at the source level) to compute emissions for the new pollutant "NONHAPVOC."  The user provides NONHAPVOC-to-NONHAPTOG factors and NONHAPTOG speciation profiles.  SMOKE computes NONHAPTOG and then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC species not including the integrated HAPs.  After determining if a sector is to be integrated, if all sources have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a NHAPEXCLUDE file.  If, on the other hand, certain sources do not have the necessary HAPs, then an NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix.  The EPA considered CAP-HAP integration for all sectors in determining whether sectors would have full, no or partial integration (see Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation).  For sectors with partial integration, all sources are integrated other than those that have either the sum of NBAFM > VOC or the sum of NBAFM = 0.

In this platform, we create NBAFM species from the no-integrate source VOC emissions using speciation profiles.  Figure 3-2 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 methyl tert-butyl ether (MTBE).

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

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
airport
No integration, 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_c1c2
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)
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
onroad_mex
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 onroad_mex 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 are +split by speciation profile.  Taking into account that integrated species were subtracted out by MOVES already, the appropriate speciation profiles are then applied in SMOKE to get the VOC model species.  HAP integration for nonroad uses the same additional HAPs and ethanol as for onroad.
 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 GSPRO_COMBO 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., 0% ethanol (E0) and 10% ethanol (E10) profiles. Since the ethanol content varied spatially (e.g., by state or county), temporally (e.g., by month), and by modeling year (future years have more ethanol), the GSPRO_COMBO feature allowed combinations to be specified at various levels for different years. The GSPRO_COMBO is no longer needed for nonroad sources outside of California because nonroad emissions within MOVES have the speciation profiles built into the results, so there is no need to assign them via the GSREF or GSPRO_COMBO feature.  For the 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.

Starting with 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%
Ontario (Southern)
                                     7.93%
Prince Edward Island
                                     0.00%
Québec
                                     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.
 Additional sector specific considerations for integrating HAP emissions from inventories into speciation
The decision to integrate HAPs into the speciation was made on a sector by sector basis.  For some sectors, there is no integration and VOC is speciated directly; for some sectors, there is full integration meaning all sources are integrated; and for other sectors, there is partial integration, meaning some sources are not integrated and other sources are integrated.  The integrated HAPs are either NBAFM or, in the case of MOVES (onroad, nonroad, and MOVES-Mexico), a larger set of HAPs plus ethanol are integrated.  Table 3-4 above summarizes the integration method for each platform sector.

For the rail sector, the EPA integrated NBAFM for most sources.  Some SCCs had zero BAFM and, therefore, they were not integrated.  These were SCCs provided by states for which EPA did not do HAP augmentation<PHONE_NUMBER>,<PHONE_NUMBER> and<PHONE_NUMBER>) 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).  SMOKE essentially calculates the model-ready species by using the appropriate emission factor without further speciation.  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
                                      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=<PHONE_NUMBER> (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.  Since 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 3,900 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 BEIS model species SESQT (Sesquiterpenes).  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.
 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 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 to 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.  The 2016 platform uses the latest version of the WRAP profiles. These profiles are denoted with an _R suffix, and reflect newer data and corrections to older WRAP profiles.  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 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. A minor change in 2016v1 was to use county-specific profile assignments from SCC<PHONE_NUMBER> for the SCCs<PHONE_NUMBER>,<PHONE_NUMBER> in Pennsylvania.

In addition to region-specific assignments, multiple profiles were assigned to particular county/SCC combinations using the SMOKE feature discussed in <IP_ADDRESS>. 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<PHONE_NUMBER> (gas well dehydrators) consists of process, reboiler, and/or flaring emissions.  There are not separate SCCs for the flared portion of the process or the reboiler.  However, the VOC associated with these three portions can have very different speciation profiles.  Therefore, it is necessary to have an estimate of the amount of VOC from each of the portions (process, flare, reboiler) so that the appropriate speciation profiles can be applied to each portion.  The Nonpoint Oil and Gas Emission Estimation Tool generates an intermediate file which provides flare, non-flare (process), and reboiler (for dehydrators) emissions for six source categories that have flare emissions: by county FIPS and SCC code for the U.S.  From these emissions 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

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

 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 E0 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
                                                                           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
                                nonpt/
ptnonipm

                                  PFC and BTP

COMBO

8869
E0 Headspace

8870
E10 Headspace
                                nonpt/
ptnonipm
      Bulk plant storage (BPS) and refine-to-bulk terminal (RBT) sources
8869
E0 Headspace
The speciation of onroad VOC occurs completely within MOVES.  MOVES  accounts for 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 (fuelSubTypeID), 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 8751aM.  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
FuelSubTypeID
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
E15 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
91
20, 21, 22
46,47
8774M
Pre-2007 MY HDD exhaust
1940-2006
1,2,15,16
20, 21, 22
20,30
8775M
2007+ MY HDD exhaust
2007-2050
1,2,15,16
20, 21, 22
20,30
8775M
2007+ MY HDD exhaust
2007-2050
1,2,15,16,17,90
20, 21, 22
40,41,42,46,47,48
8855M
Tier 2 E85 Exhaust
1940-2050
1,2,15,16
50, 51, 52
10,20,30,40,41, 42,46,47,48
8869M
E0 Headspace
1940-2050
18
10
10,20,30,40,41, 42,46,47,48
8870M
E10 Headspace
1940-2050
18
12,13,14
10,20,30,40,41, 42,46,47,48
8871M
E15 Headspace
1940-2050
18
15,18
10,20,30,40,41, 42,46,47,48
8872M
E15 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
8751aM
Pre-Tier 2 E10 exhaust
1940-2000
1,2,15,16
11,12,13,14
20,30
8751aM
Pre-Tier 2 E10 exhaust
1940-2050
1,2,15,16
11,12,13,14,15, 18
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
                                                                             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 (E15)
                                                                             18
Ethanol (E20)
                                                                             20
Conventional Diesel Fuel
                                                                             21
Biodiesel (BD20)
                                                                             22
Fischer-Tropsch Diesel (FTD100)
                                                                             30
Compressed Natural Gas (CNG)
                                                                             50
Ethanol
                                                                             51
Ethanol (E85)
                                                                             52
Ethanol (E70)
Table 3-13.  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 GSPRO_COMBO 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.
 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 speciation.  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

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

 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., PM10 and PM2.5) and speciated PM (e.g., PEC, PFE, etc).  SMOKE essentially calculates the PM components by using the appropriate EF without further speciation.  The specific profiles used within MOVES include two CNG profiles, 45219 and 45220, which were added to SPECIATE4.5.  A list of profiles is provided in the technical document, "Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in MOVES2014" (EPA, 2015c).

For onroad brake and tire wear, the PM is speciated in the moves2smk postprocessor that prepares the emission factors for processing in SMOKE.  The formulas for this are based on the standard speciation factors from brake and tire wear profiles, which were updated from the v6.3 platform based on data from a Health Effects Institute report (Schauer, 2006).  Table 3-14 shows the differences in the v7.1 and v6.3 profiles.
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