Where Fertadjusted,crop is the FEST-C 12km grid cell adjusted fertilization rate, FertSubmitted,crop is the USDA or State submitted state mean annual application data for the specified crop, in kg ha[-1], FERTFEST-C,crop is the initial FEST-C 12km grid cell fertilization rate for the state being considered, ncrop is the number of grid cells with fertilization use for the specified crop in the state, and Fertmax,crop is the maximum fertilization rate estimated from EPIC for the crop.
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions

Fertilizer Activity Data

The following activity parameters were input into the EPIC model:

 Grid cell meteorological variables from WRF (see Table 3)
 Initial soil profiles/soil selection
 Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn, silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans, spring wheat, winter wheat, canola, and other crops (e.g.. lettuce, tomatoes, etc.)
 Fertilizer sales to establish the type/composition of nutrients applied
 Management scenarios for the 10 USDA production regions. These include irrigation, tile drainage, intervals between forage harvest, fertilizer application method (injected versus surface applied), and equipment commonly used in these production regions.

The WRF meteorological model was used to provide grid cell meteorological parameters for year 2016 using a national 12-km rectangular grid covering the continental U.S. The meteorological parameters in Table 2-12 were used as EPIC model inputs.
Table 2-12.  Source of input variables for EPIC
EPIC input variable
Variable Source
Daily Total Radiation (MJ m[2] )
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s[-1] )
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ

Initial soil nutrient and pH conditions in EPIC were based on the 1992 USDA Soil Conservation Service (CSC) Soils-5 survey. The EPIC model then was run for 25 years using current fertilization and agricultural cropping techniques to estimate soil nutrient content and pH for the 2016 EPIC/WRF/CMAQ simulation.

The presence of crops in each model grid cell was determined through the use of USDA Census of Agriculture data (2012) and USGS National Land Cover data (2011). These two data sources were used to compute the fraction of agricultural land in a model grid cell and the mix of crops grown on that land.

Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2014 Association of American Plant Food Control Officials (AAPFCO, http://www.aapfco.org/publications.html). AAPFCO data were used to identify the composition (e.g., urea, nitrate, organic) of the fertilizer used, and the amount applied is estimated using the modeled crop demand. These data were useful in making a reasonable assignment of what kind of fertilizer is being applied to which crops.

Management activity data refers to data used to estimate representative crop management schemes. The USDA Agricultural Resource Management Survey (ARMS, https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Ag_Resource_Management/) was used to provide management activity data. These data cover 10 USDA production regions and provide management schemes for irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn, silage corn, cottonoats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans, spring wheat, winter wheat, canola, and other crops (e.g. lettuce, tomatoes, etc.).

Fertilizer Emission Factors

The emission factors were derived from the 2016 CMAQ FEST-C outputs adjusted using USDA Economic Research Service (ERS) state and crop specific reported annual fertilizer rates. Total fertilizer emission factors for each month and county were computed by taking the ratio of total fertilizer NH3 emissions (short tons) to total nitrogen fertilizer application (short tons).
12 km by 12 km gridded NH3 emissions were mapped to a county shape file polygon.  The cell was assigned to a county if the grid centroid fell within the county boundary.
Nonpoint Oil and Gas Sector (np_oilgas)
While the major emissions sources associated with oil and gas collection, processing, and distribution have traditionally been included in the National Emissions Inventory (NEI) as point sources (e.g., gas processing plants, pipeline compressor stations, and refineries), the activities occurring "upstream" of these types of facilities have not been as well characterized in the NEI.  Here, upstream activities refer to emission units and processes associated with the exploration and drilling of oil and gas wells, and the equipment used at the wellsite to then extract the product from the well and deliver it to a central collection point or processing facility. The types of unit processes found at upstream sites include separators, dehydrators, storage tanks, and compressor engines.

The nonpoint oil and gas (np_oilgas) sector, which consists of oil and gas exploration and production sources, both onshore and offshore (state-owned only). In the 2016v1 platform, these emissions are mostly based on the EPA Oil and Gas Tool run with data specific to the year 2016, with some states submitting their own inventory data. Because of the growing importance of these emissions, special consideration is given to the speciation, spatial allocation, and monthly temporalization of nonpoint oil and gas emissions, instead of relying on older, more generalized profiles.

EPA Oil and Gas Tool

EPA developed the 2016 Nonpoint Oil and Gas Emission Estimation Tool (the "Tool") to estimate the non-point oil and gas inventory for the 2016v1 platform.  The Tool was previously used to estimate emissions for the 2014 NEI. Year 2016 oil and gas activity data were supplied to EPA by some state air agencies, and where state data were not supplied to EPA, EPA populated the 2016v1 inventory with the best available data. The Tool is an Access database that utilizes county-level activity data (e.g. oil production and well counts), operational characteristics (types and sizes of equipment), and emission factors to estimate emissions.    The Tool creates a CSV-formatted emissions dataset covering all national nonpoint oil and gas emissions. This dataset is then converted to FF10 format for use in SMOKE modeling.  A separate report named "2016 Nonpoint Oil and Gas Emission Estimation Tool V1_0 December_2018.docx" was generated that provides technical details of how the tool was applied for the 2016v1 platform.

In the 2016beta platform, it was found that the number of active wells in the state of Illinois was too high (~48,000 total wells).   After various discussions and other communications with the Illinois Environmental Protection Agency (IEPA), a more accurate number of active of wells (~20,000 total wells) was obtained and the new data were used in a rerun of the Oil and Gas Tool to produce new emissions for the state of Illinois. These new emissions estimates for Illinois are in the 2016v1 modeling platform. The reduction in total number of active wells resulted in NOX and VOC emissions being reduced by about 14,000 tons and 48,000 tons, respectively, in 2016v1 when compared to 2016beta emissions.

Nonpoint Oil and Gas Alternative Datasets

Some states provided, or recommended use of, a separate emissions inventory for use in 2016v1 platform instead of emissions derived from the EPA Oil and Gas Tool. For example, the California Air Resources Board (CARB) developed their own np_oilgas emissions inventory for 2016 for California that were used for the 2016v1 platform.

In Pennsylvania for the 2016v1 modeling platform, the emissions associated with unconventional wells for year 2016 were supplied by the Pennsylvania Department of Environmental Protection (PA DEP). The Oil and Gas Tool was used to produce the conventional well emissions for 2016. Together these unconventional and conventional well emissions represent the total non-point oil and gas emissions for Pennsylvania. The resulting NOX emissions for Pennsylvania were increased by about 16,000 tons in 2016v1 when compared to the 2016beta emissions.   The VOC emissions were reduced by about 56,000 tons in 2016v1 due to these emissions changes in Pennsylvania.

Colorado Department of Public Health and Environment (CDPHE) requested that the 2014NEIv2 be projected to 2016 instead of using data from the EPA Oil and Gas Tool. For Colorado projections were applied to CO, NOX, PM, and SO2, but not VOC.  VOC emissions for year 2016 were assumed to equal year 2014 levels for Colorado.  Projection factors for Colorado are listed in Table 2-13 and are based on historical production trends.

Oklahoma Department of Environmental Quality requested that np_oilgas emissions from 2014NEIv2 be projected to 2016 for all source except lateral compressors. Projection factors for Oklahoma np_oilgas production, based on historical production data, are listed in Table 2-13. For lateral compressor emissions in Oklahoma, the EPA Oil and Gas Tool inventory for 2016 was used, except with a 72% cut applied to all emissions. Exploration np_oilgas emissions in Oklahoma are based on the EPA Oil and Gas Tool inventory for 2016, without modification.
Table 2-13. 2014NEIv2-to-2016 oil and gas projection factors for CO and OK.
State/region
Emissions type
                                    Factor
Pollutant(s)
Colorado
Oil
                                    +22.0%
CO, NOX, SO2
Colorado
Natural Gas
                                     +3.5%
CO, NOX, PM, SO2
Colorado
Combination Oil + NG
                                    +12.8%
CO, NOX, PM, SO2
Oklahoma
Oil Production
                                     +6.9%
All
Oklahoma
Natural Gas Production
                                     +5.9%
All
Oklahoma
Combination Oil + NG Production
                                     +6.4%
All
Oklahoma
Coal Bed Methane Production
                                    -30.0%
All

Residential Wood Combustion (rwc)
The RWC sector includes residential wood burning devices such as fireplaces, fireplaces with inserts, free standing woodstoves, pellet stoves, outdoor hydronic heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepits and chimneys.  Free standing woodstoves and inserts are further differentiated into three categories: 1) conventional (not EPA certified); 2) EPA certified, catalytic; and 3) EPA certified, noncatalytic. Generally, the conventional units were constructed prior to 1988.  Units constructed after 1988 had to meet EPA emission standards and they are either catalytic or non-catalytic.  The source classification codes (SCCs) in the RWC sector are listed in Table 2-14.
Table 2-14. 2016 v1 platform SCCs for RWC sector
SCC
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Description
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Fireplace: general
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts; non-EPA certified
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts; EPA certified; non-catalytic
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: fireplace inserts; EPA certified; catalytic
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: freestanding, non-EPA certified
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: freestanding, EPA certified, non-catalytic
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: freestanding, EPA certified, catalytic
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Woodstove: pellet-fired, general (freestanding or FP insert)
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Furnace: Indoor, cordwood-fired, non-EPA certified
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Hydronic heater: outdoor
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Wood
Outdoor wood burning device, NEC (fire-pits, chimeas, etc)
<PHONE_NUMBER>
Stationary Source Fuel Combustion
Residential
Firelog
Total: All Combustor Types

For all states other than California, Washington, and Oregon RWC emissions from the NEI2014v2 were projected to 2016 using projection factors derived by MARAMA based on implementing the projection methodology from EPA's 2011 platform into a spreadsheet tool. Projection factors are by SCC and SCC-pollutant; SCC-only factors (i.e., factors that do not specify a pollutant) are applied to all pollutants without an SCC-pollutant factor. Table 2-15 lists the SCC-based projection factors applied to RWC sources.
Table 2-15. Projection factors for RWC by SCC
SCC

SCC description
Pollutant

                                 2014-to-2016
2104008100
Fireplace: general

                                     2.00%
2104008210
Woodstove: fireplace inserts; non-EPA certified

                                    -3.40%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
PM10-PRI
                                     2.29%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
PM25-PRI
                                     2.29%
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic

                                     5.25%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
PM10-PRI
                                     2.44%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
PM25-PRI
                                     2.44%
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic

                                     5.25%
2104008310
Woodstove: freestanding, non-EPA certified
CO
                                    -2.35%
2104008310
Woodstove: freestanding, non-EPA certified
PM10-PRI
                                    -2.17%
2104008310
Woodstove: freestanding, non-EPA certified
PM25-PRI
                                    -2.17%
2104008310
Woodstove: freestanding, non-EPA certified
VOC
                                    -2.06%
2104008310
Woodstove: freestanding, non-EPA certified

                                    -2.35%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic
PM10-PRI
                                     2.29%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic
PM25-PRI
                                     2.29%
2104008320
Woodstove: freestanding, EPA certified, non-catalytic

                                     5.25%
2104008330
Woodstove: freestanding, EPA certified, catalytic
PM10-PRI
                                     2.47%
2104008330
Woodstove: freestanding, EPA certified, catalytic
PM25-PRI
                                     2.47%
2104008330
Woodstove: freestanding, EPA certified, catalytic

                                     5.25%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)
PM10-PRI
                                    14.40%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)
PM25-PRI
                                    14.40%
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)

                                    14.38%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
CO
                                    -9.70%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM10-PRI
                                    -6.15%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
PM25-PRI
                                    -6.15%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified
VOC
                                    -9.74%
2104008510
Furnace: Indoor, cordwood-fired, non-EPA certified

                                    -9.70%
2104008610
Hydronic heater: outdoor
PM10-PRI
                                     2.99%
2104008610
Hydronic heater: outdoor
PM25-PRI
                                     2.99%
2104008610
Hydronic heater: outdoor

                                     2.00%
2104008700
Outdoor wood burning device, NEC (fire-pits, chimineas, etc)

                                     2.00%
2104009000
Fire log total

                                     2.00%

For California, Oregon, and Washington, the RWC emissions were held constant at NEI2014v2 levels for 2016. This approach is consistent with the RWC projections used in the EPA's 2011 emissions modeling platform.

After the 2014NEIv2 was published, it was determined that the 2014NEIv2 RWC inventory was missing woodstove emissions for certain pollutants in Idaho. The missing emissions for woodstove SCCs<PHONE_NUMBER>,<PHONE_NUMBER>,<PHONE_NUMBER>,<PHONE_NUMBER> were added to the inventory prior to projecting it to 2016 for the v1 platform.
Nonpoint (nonpt)
The starting point for the 2016v1 platform nonpt inventory is the 2014NEIv2, including all nonpoint sources that are not included in the afdust, ag, cmv_c1c2, cmv_c3, np_oilgas, rail, or rwc sectors. The types of sources in the nonpt sector include, but are not limited to:
 stationary source fuel combustion, including industrial, commercial, and residential and orchard heaters;
 commercial sources such as commercial cooking;
 industrial processes such as chemical manufacturing, metal production, mineral processes, petroleum refining, wood products, fabricated metals, and refrigeration;
 solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances, and motor vehicles;
 solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;
 solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care products, household products, adhesives and sealants;
 solvent utilization for asphalt application and roofing, and pesticide application;
 storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline service stations, aviation, and marine vessels;
 storage and transport of chemicals;
 waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
 cellulosic biorefining;
 miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair shops.
The nonpoint emissions in 2016v1 platform are equivalent to those in the 2014NEIv2 except for the following changes:

Nonpoint projection to 2016 inside MARAMA region

2014-to-2016 projection packets for all nonpoint sources were provided by MARAMA for the following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV.

New Jersey provided their own projection factors for projection from 2014 to 2016 which were mostly the same as those provided by MARAMA, except for three SCCs with differences (SCCs:<PHONE_NUMBER>,<PHONE_NUMBER>,<PHONE_NUMBER>). For those three SCCs, the projection factors provided by New Jersey were used instead of the MARAMA factors.

Nonpoint projection to 2016 outside MARAMA region

In areas outside of the MARAMA states, historical census population, sometimes by county and sometimes by state, was used to project select nonpt sources from the 2014NEIv2 to 2016v1 platform. The population data was downloaded from the US Census Bureau. Specifically, the "Population, Population Change, and Estimated Components of Population Change: April 1, 2010 to July 1, 2017" file (https://www2.census.gov/programs-surveys/popest/datasets/2010-2017/counties/totals/co-est2017-alldata.csv). A ratio of 2016 population to 2014 population was used to create a growth factor that was applied to the 2014NEIv2 emissions with SCCs matching the population-based SCCs listed in Table 2-16. Positive growth factors (from increasing population) were not capped, but negative growth factors (from decreasing population) were flatlined for no growth.
Table 2-16. 2016v1 platform SCCs for Census-based growth
SCC
Tier 1 Description
Tier 2 Description
Tier 3
Description
Tier 4
Description
2302002100
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Charbroiling
Conveyorized Charbroiling
2302002200
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Charbroiling
Under-fired Charbroiling
2302003000
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Total
2302003100
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Flat Griddle Frying
2302003200
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Clamshell Griddle Frying
2401001000
Solvent Utilization
Surface Coating
Architectural Coatings
Total: All Solvent Types
2401002000
Solvent Utilization
Surface Coating
Architectural Coatings - Solvent-based
Total: All Solvent Types
2401003000
Solvent Utilization
Surface Coating
Architectural Coatings - Water-based
Total: All Solvent Types
2401100000
Solvent Utilization
Surface Coating
Industrial Maintenance Coatings
Total: All Solvent Types
2401200000
Solvent Utilization
Surface Coating
Other Special Purpose Coatings
Total: All Solvent Types
2425000000
Solvent Utilization
Graphic Arts
All Processes
Total: All Solvent Types
2425010000
Solvent Utilization
Graphic Arts
Lithography
Total: All Solvent Types
2425020000
Solvent Utilization
Graphic Arts
Letterpress
Total: All Solvent Types
2425030000
Solvent Utilization
Graphic Arts
Rotogravure
Total: All Solvent Types
2425040000
Solvent Utilization
Graphic Arts
Flexography
Total: All Solvent Types
2440020000
Solvent Utilization
Miscellaneous Industrial
Adhesive (Industrial) Application
Total: All Solvent Types
2460000000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Processes
Total: All Solvent Types
2460100000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Personal Care Products
Total: All Solvent Types
2460200000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Household Products
Total: All Solvent Types
2460400000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Automotive Aftermarket Products
Total: All Solvent Types
2460500000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Coatings and Related Products
Total: All Solvent Types
2460600000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Adhesives and Sealants
Total: All Solvent Types
2460800000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All FIFRA Related Products
Total: All Solvent Types
2460900000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
Miscellaneous Products (Not Otherwise Covered)
Total: All Solvent Types
2461800000
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Total: All Solvent Types
2461800001
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Surface Application
2461800002
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Soil Incorporation
2461870999
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: Non-Agricultural
Not Elsewhere Classified
2465800000
Solvent Utilization
Miscellaneous Non-industrial: Consumer
Pesticide Application
Total: All Solvent Types
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Permeation
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Evaporation (includes Diurnal losses)
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Spillage During Transport
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Refilling at the Pump - Vapor Displacement
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Refilling at the Pump - Spillage
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Permeation
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Evaporation (includes Diurnal losses)
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Spillage During Transport
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Refilling at the Pump - Vapor Displacement
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Refilling at the Pump - Spillage
<PHONE_NUMBER>
Waste Disposal
Treatment and Recovery
Wastewater Treatment, Public Owned
Total Processed
<PHONE_NUMBER>
Waste Disposal
Treatment and Recovery
TSDFs, All TSDF Types
Total: All Processes
<PHONE_NUMBER>
Miscellaneous Area Sources
Other Combustion
Residential Grilling
Total
<PHONE_NUMBER>
Miscellaneous Area Sources
Other Combustion
Cremation
Humans
2016 Onroad Mobile sources (onroad)
Onroad mobile source include emissions from motorized vehicles operating on public roadways.  These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks, and buses.  The sources are further divided by the fuel they use, including diesel, gasoline, E-85, and compressed natural gas (CNG) vehicles.  The sector characterizes emissions from parked vehicle processes (e.g., starts, hot soak, and extended idle) as well as from on-network processes (i.e., from vehicles as they move along the roads).  Except for California, all onroad emissions are generated using the SMOKE-MOVES emissions modeling framework that leverages MOVES-generated emission factors, county and SCC-specific activity data, and hourly meteorological data.  The onroad source classification codes (SCCs) in the modeling platform are more finely resolved than those in the National Emissions Inventory (NEI). The NEI SCCs distinguish vehicles and fuels.  The SCCs used in the model platform also distinguish between emissions processes (i.e., off-network, on-network, and extended idle), and road types.

Onroad emissions were computed with SMOKE-MOVES by multiplying specific types of vehicle activity data by the appropriate emission factors. This section includes discussions of the activity data and the emission factor development. The vehicles (aka source types) for which MOVES computes emissions are shown in Table 2-17. SMOKE-MOVES was run for specific modeling grids.  Emissions for the contiguous U.S. states and Washington, D.C., were computed for a grid covering those areas. Emissions for Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands were computed by running SMOKE-MOVES for distinct grids covering each of those regions and are included in the onroad_nonconus sector. In some summary reports these non-CONUS emissions are aggregated with emissions from the onroad sector.
Table 2-17. MOVES vehicle (source) types
                              MOVES vehicle type
                                  Description
                               HPMS vehicle type
                                      11
Motorcycle
                                      10
                                      21
Passenger Car
                                      25
                                      31
Passenger Truck
                                      25
                                      32
Light Commercial Truck
                                      25
                                      41
Intercity Bus
                                      40
                                      42
Transit Bus
                                      40
                                      43
School Bus
                                      40
                                      51
Refuse Truck
                                      50
                                      52
Single Unit Short-haul Truck
                                      50
                                      53
Single Unit Long-haul Truck
                                      50
                                      54
Motor Home
                                      50
                                      61
Combination Short-haul Truck
                                      60
                                      62
Combination Long-haul Truck
                                      60

Onroad Activity Data Development

SMOKE-MOVES uses vehicle miles traveled (VMT), vehicle population (VPOP), and hours of hoteling, to calculate emissions. These datasets are collectively known as "activity data". For each of these activity datasets, first a national dataset was developed; this national dataset is called the "EPA default" dataset. Second, data submitted by state agencies were incorporated where available, in place of the EPA default data. EPA default activity was used for California, but the emissions were scaled to California-supplied values during the emissions processing.  The agencies for which submitted VMT and VPOP data were used for 2016 platforms are shown in Table 2-18 along with the timing of the submission: 2014v1 or 2016 beta or 2016 v1.  Data submitted for the 2014 NEI were adjusted before they were used for 2016 platforms.
Table 2-18. Submitted data used to prepare onroad activity data
Agency
                                   2016 VMT
                                   2016 VPOP
Alaska
                                 yes (2014v1)
                                 yes (2014v1)
Arizona - Maricopa
                                 yes (2014v1)
                                 yes (2014v1)
Arizona - Pima
                                   yes (v1)
                                   yes (v1)
Colorado
                                  yes (beta)
                                   yes (v1)
Connecticut
                                  yes (beta)
                                 yes (2014v1)
Delaware
                                 yes (2014v1)
                                 yes (2014v1)
District of Columbia
                                 yes (2014v1)
                                 yes (2014v1)
Georgia
                                  yes (beta)
                                  yes (beta)
Idaho
                                 yes (2014v1)
                                 yes (2014v1)
Illinois - Chicago area
                                   yes (v1)
                                   yes (v1)
Illinois - rest of state
                                  yes (beta)
                                 yes (2014v1)
Indiana - Louisville area
                                   yes (v1)

Kentucky - Jefferson
                                   yes (v1)
                                 yes (2014v1)
Kentucky - Louisville exurbs
                                   yes (v1)

Maine
                                 yes (2014v2)
                                 yes (2014v2)
Maryland
                                  yes (beta)
                                  yes (beta)
Massachusetts
                                   yes (v1)
                                   yes (v1)
Michigan - Detroit area
                                  yes (beta)
                                 yes (2014v1)
Michigan - rest of state
                                  yes (beta)
                                 yes (2014v1)
Minnesota
                                  yes (beta)
                                 yes (2014v1)
Missouri
                                 yes (2014v1)
                                 yes (2014v1)
Nevada - Clark
                                  yes (beta)
                                  yes (beta)
Nevada - Washoe
                                 yes (2014v1)
                                 yes (2014v1)
New Hampshire
                                  yes (beta)
                                  yes (beta)
New Jersey
                                  yes (beta)
                                   yes (v1)
New Mexico - Bernalillo
                                 yes (2014v1)
                                 yes (2014v1)
New York
                                 yes (2014v1)
                                 yes (2014v1)
North Carolina
                                  yes (beta)
                                  yes (beta)
Ohio
                                 yes (2014v1)
                                 yes (2014v1)
Oregon
                                 yes (2014v1)
                                 yes (2014v1)
Pennsylvania
                                  yes (beta)
                                  yes (beta)
Rhode Island
                                 yes (2014v1)
                                 yes (2014v1)
South Carolina
                                  yes (beta)
                                  yes (beta)
Tennessee - Davidson
                                 yes (2014v1)
                                 yes (2014v1)
Tennessee - Knox
                                 yes (2014v1)
                                 yes (2014v1)
Tennessee - rest of state
                                 yes (2014v2)
                                 yes (2014v2)
Texas
                                 yes (2014v1)
                                 yes (2014v1)
Vermont
                                 yes (2014v2)
                                 yes (2014v2)
Virginia
                                  yes (beta)
                                 yes (2014v2)
Washington
                                 yes (2014v2)
                                 yes (2014v2)
West Virginia
                                  yes (beta)
                                  yes (beta)
Wisconsin
                                  yes (beta)
                                  yes (beta)

Vehicle Miles Traveled (VMT)

EPA calculated default 2016 state VMT by projecting the 2014NEIv2 platform VMT to 2016. The 2014NEIv2 Technical Support Document has details on the development of those VMT (https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-technical-support-document-tsd). The data projected to 2016 were used for states that did not submit 2016 VMT data. Projection factors to grow state VMT from 2014 to 2016 were based on state-level VMT data from the Federal Highway Administration (FHWA) VM-2 reports (https://www.fhwa.dot.gov/policyinformation/statistics/2014/vm2.cfm and https://www.fhwa.dot.gov/policyinformation/statistics/2016/vm2.cfm). For most states, separate factors were calculated for urban VMT and rural VMT. Some states have a very different distribution of urban activity versus rural activity between 2014NEIv2 and the FHWA data, due to inconsistencies in the definition of urban versus rural. For those states, a single state-wide projection factor based on total FHWA VMT across all road types was applied to all VMT independent of road type. The following states used a single state-wide projection factor to adjust the VMT to 2016 levels: AK, GA, IN, ME, MA, NE, NM, NY, ND, TN, and WV. Also, state-wide projection factors in Texas and Utah were developed from alternative VMT datasets provided by their respective Departments of Transportation. The VMT projection factors for all states are provided in Table 2-19.

Table 2-19. Factors applied to project VMT from 2014 to 2016 to prepare default activity data
State
                                  Rural roads
                                  Urban roads
                           Projection Factor Source
Alabama
                                     5.36%
                                     5.47%
                             FHWA VM-2 urban/rural
Alaska
                                     8.27%
                                     8.27%
                                FHWA VM-2 total
Arizona
                                     1.07%
                                     6.35%
                             FHWA VM-2 urban/rural
Arkansas
                                     4.80%
                                     5.36%
                             FHWA VM-2 urban/rural
California
                                     1.06%
                                     2.39%
                             FHWA VM-2 urban/rural
Colorado
                                     5.97%
                                     6.67%
                             FHWA VM-2 urban/rural
Connecticut
                                     1.33%
                                     1.45%
                             FHWA VM-2 urban/rural
Delaware
                                     4.42%
                                     6.75%
                             FHWA VM-2 urban/rural
District of Columbia
                                     0.00%
                                     2.68%
                             FHWA VM-2 urban/rural
Florida
                                    10.27%
                                     6.64%
                             FHWA VM-2 urban/rural
Georgia
                                    10.10%
                                    10.10%
                                FHWA VM-2 total
Hawaii
                                     6.14%
                                     4.21%
                             FHWA VM-2 urban/rural
Idaho
                                     5.51%
                                     7.80%
                             FHWA VM-2 urban/rural
Illinois
                                     3.40%
                                     1.96%
                             FHWA VM-2 urban/rural
Indiana
                                     5.02%
                                     5.02%
                                FHWA VM-2 total
Iowa
                                     6.17%
                                     6.05%
                             FHWA VM-2 urban/rural
Kansas
                                     2.42%
                                     6.52%
                             FHWA VM-2 urban/rural
Kentucky
                                     2.52%
                                     3.26%
                             FHWA VM-2 urban/rural
Louisiana
                                    -5.49%
                                     7.10%
                             FHWA VM-2 urban/rural
Maine
                                     3.75%
                                     3.75%
                                FHWA VM-2 total
Maryland
                                     4.98%
                                     4.75%
                             FHWA VM-2 urban/rural
Massachusetts
                                     7.42%
                                     7.42%
                                FHWA VM-2 total
Michigan
                                     5.62%
                                     0.66%
                             FHWA VM-2 urban/rural
Minnesota
                                     2.66%
                                     2.97%
                             FHWA VM-2 urban/rural
Mississippi
                                     1.83%
                                     4.96%
                             FHWA VM-2 urban/rural
Missouri
                                     4.70%
                                     4.17%
                             FHWA VM-2 urban/rural
Montana
                                     3.32%
                                     4.34%
                             FHWA VM-2 urban/rural
Nebraska
                                     5.54%
                                     5.54%
                                FHWA VM-2 total
Nevada
                                     8.30%
                                     5.30%
                             FHWA VM-2 urban/rural
New Hampshire
                                     5.00%
                                     3.65%
                             FHWA VM-2 urban/rural
New Jersey
                                     5.41%
                                     2.83%
                             FHWA VM-2 urban/rural
New Mexico
                                    10.01%
                                    10.01%
                                FHWA VM-2 total
New York
                                    -4.90%
                                    -4.90%
                                FHWA VM-2 total
North Carolina
                                     7.47%
                                     8.41%
                             FHWA VM-2 urban/rural
North Dakota
                                    -7.35%
                                    -7.35%
                                FHWA VM-2 total
Ohio
                                     4.61%
                                     5.42%
                             FHWA VM-2 urban/rural
Oklahoma
                                     4.72%
                                     1.23%
                             FHWA VM-2 urban/rural
Oregon
                                     8.05%
                                     4.84%
                             FHWA VM-2 urban/rural
Pennsylvania
                                    -4.30%
                                     4.73%
                             FHWA VM-2 urban/rural
Rhode Island
                                     3.26%
                                     3.26%
                             FHWA VM-2 urban/rural
South Carolina
                                     9.70%
                                     8.89%
                             FHWA VM-2 urban/rural
South Dakota
                                     3.23%
                                     2.64%
                             FHWA VM-2 urban/rural
Tennessee
                                     6.29%
                                     6.29%
                                FHWA VM-2 total
Texas
                                     7.82%
                                     7.82%
                                     TxDOT
Utah
                                    11.62%
                                    11.62%
                                     UDOT
Vermont
                                     5.55%
                                     2.24%
                             FHWA VM-2 urban/rural
Virginia
                                    -4.93%
                                     9.78%
                             FHWA VM-2 urban/rural
Washington
                                     6.86%
                                     4.43%
                             FHWA VM-2 urban/rural
West Virginia
                                     2.21%
                                     2.21%
                                FHWA VM-2 total
Wisconsin
                                     4.15%
                                     9.32%
                             FHWA VM-2 urban/rural
Wyoming
                                    -1.38%
                                    -1.53%
                             FHWA VM-2 urban/rural
Puerto Rico
                                     0.00%
                                     0.00%
                               No FHWA VM-2 data
Virgin Islands
                                     0.00%
                                     0.00%
                               No FHWA VM-2 data

For the 2016v1 platform, VMT data submitted by state and local agencies were incorporated and used in place of EPA defaults, as described below.  Note that VMT data need to be provided to SMOKE for each county and SCC.  The onroad SCCs characterize vehicles by MOVES fuel type, vehicle (aka source) type, emissions process, and road type.  Any VMT provided at a different resolution than this were converted to a full county-SCC resolution to prepare the data for processing by SMOKE.

Air agencies from CO, CT, GA, IL, MD, NJ, NC, VA, WI, and Pima County (AZ) provided 2016 VMT data by county and Highway Performance Monitoring Systems (HPMS) vehicle type to be used for the 2016beta and 2016v1 platforms. That level of detail is sufficient for MOVES, but SMOKE also needs VMT broken out by MOVES vehicle type (which is more detailed than HPMS vehicle type), and by fuel type, and road type. To get VMT at the resolution needed by SMOKE, the county-HPMS VMT data provided by the states were loaded into the county databases (CDBs) that are used to run MOVES. MOVES CDBs include fuel type splits, road type splits, and VPOP by MOVES vehicle type. Using those tables, county-HPMS VMT data were converted into the county-SCC VMT data that are needed by SMOKE. One exception to the use of local data in these states was for North Carolina, where EPA default VMT for buses was used along with state-submitted VMT for other vehicle types.

South Carolina and Massachusetts submitted VMT by county-HPMS using the same HPMS splits in every county in the state. Unlike Massachusetts, South Carolina did not provide county-specific road type splits. Instead, a new set of county-specific HPMS splits was developed from the EPA default VMT. For all HPMS types except 25 (light cars and trucks), county-HPMS ratios were calculated from the EPA default VMT, and then scaled up or down so that the overall state-HPMS ratio would match South Carolina's state-HPMS ratio. For HPMS type 25, the county-HPMS ratios were set equal to the remainder within each county so that all ratios within each county sum to 1.0. The new VMT by county-HPMS varies by county while respecting the state-wide HPMS splits in South Carolina's original VMT dataset. The VMT was then split to full SCC level using a similar procedure as other states that submitted VMT at the county-HPMS level.

Pennsylvania and New Hampshire submitted VMT for the 2016beta platform at the full county-SCC level, already in the FF10 format needed by SMOKE. These data were used directly for the 2016v1 platform, except for the redistribution of light duty VMT (see last item in this subsection).

Michigan and Minnesota submitted 2016 VMT by county and by road type for the 2016beta platform. Fuel type and vehicle type distributions from the EPA default VMT were used to convert these data to full SCC.

West Virginia submitted county total VMT only for the 2016beta platform. Fuel, vehicle, and road type distributions from the EPA default VMT were used to convert their data to full SCC.

For the 2016beta platform, Clark County, NV, submitted VMT by county and MOVES vehicle type, which is more detailed than HPMS vehicle type, but nevertheless cannot be imported into MOVES CDBs as easily to facilitate the creation of VMT at the full SCC detail. Fuel type and road type distributions from the EPA default VMT were used to convert these data to full SCC.

For the 2016v1 platform, VMT was provided by:
 Massachusetts (by HPMS, to override what was provided for beta)
 Chicago area (8 counties, by HPMS/road; excluded motorcycles)
 Louisville area (5 counties, county totals restricted/unrestricted)
 Pima County AZ (by HPMS)

Some of the provided data were adjusted following quality assurance, as described below in the VPOP section.

A final step was performed on all state-submitted VMT. The distinction between a "passenger car" (MOVES vehicle type 21) versus a "passenger truck" (MOVES vehicle type 31) versus a "light commercial truck" (MOVES vehicle type 32) is not always consistent between different datasets. This distinction can have a noticeable effect on the resulting emissions, since MOVES emission factors for passenger cars are quite different than those for passenger trucks and light commercial trucks.

To ensure consistency in the 21/31/32 splits across the country, all state-submitted VMT for MOVES vehicle types 21, 31, and 32 (all of which are part of HPMS vehicle type 25) was summed, and then re-split using the 21/31/32 splits from the EPA default VMT. VMT for each source type as a percentage of total 21/31/32 VMT was calculated by county from the EPA default VMT. Then, state-submitted VMT for 21/31/32 was summed and then resplit according to those percentages.

This was done for all states and counties listed above which submitted VMT for 2016. Most of the states listed above did not provide VMT down to the source type, so splitting the light-duty vehicle VMT does not create an inconsistency with state-provided data in those states. Exceptions are New Hampshire and Pennsylvania: those two states provided SCC-level VMT, but these were reallocated to 21/31/32 so that the splits are performed in a consistent way across the country. The 21/31/32 splits in the EPA default VMT can be traced back to the 2014NEIv2 VPOP data obtained from IHS-Polk.

Speed Activity (SPEED/SPDIST)

In SMOKE 4.7, SMOKE-MOVES was updated to use speed distributions similarly to how they are used when running MOVES in inventory mode. This new speed distribution file, called SPDIST, specifies the amount of time spent in each MOVES speed bin for each county, vehicle (aka source) type, road type, weekday/weekend, and hour of day.  This file contains the same information at the same resolution as the Speed Distribution table used by MOVES but is reformatted for SMOKE.  Using the SPDIST file results in a SMOKE emissions calculation that is more consistent with MOVES than the old hourly speed profile (SPDPRO) approach, because emission factors from all speed bins can be used, rather than interpolating between the two bins surrounding the single average speed value for each hour as is done with the SPDPRO approach.

As was the case with the previous SPDPRO approach, the SPEED inventory that includes a single overall average speed for each county, SCC, and month, must still be read in by the SMOKE program Smkinven.  SMOKE requires the SPEED dataset to exist even when speed distribution data are available, even though only the speed distribution data affects the selection of emission factors. The SPEED dataset is carried over from 2014NEIv2, while the SPDIST dataset is new for the 2016v1 platform. Both are based on a combination of the Coordinating Research Council (CRC) A-100 data and MOVES CDBs.

Vehicle Population (VPOP)

The EPA default VPOP dataset was based on the EPA default VMT dataset described above. For each county, fuel type, and vehicle type, a VMT/VPOP ratio (miles per vehicle per year) was calculated based on the 2014NEIv2 VMT and VPOP datasets. That ratio was applied to the 2016 EPA default VMT, to produce an EPA default VPOP projection.

As with VMT, several state and local agencies submitted VPOP data for the beta and v1 platforms, and those data were used in place of the EPA default VPOP. The VPOP SCCs used by SMOKE are similar to the VMT SCCs, except the emissions process is represented as "00" because it is not relevant to vehicle population data.

For the 2016 beta platform, GA, MD, MA, NJ, NC, WI, and Pima County AZ provided VPOP data for the year 2016 by county and MOVES vehicle type. That level of detail is sufficient for MOVES, but SMOKE also needs VPOP broken out by fuel type. To get VPOP by full SCC, the county-vehicle VPOP data provided by the states were loaded into the MOVES CDBs. Using fuel type tables in the CDBs, it is possible to take county-vehicle VPOP data and create county-SCC VPOP data at the resolution needed by SMOKE. For Massachusetts, based on quality assurance checks, modifications to their VPOP like those done for their VMT were not needed. Wisconsin provided VPOP for 2016 by county and HPMS vehicle type instead of by MOVES vehicle type, but the same procedure was applied as for other states in this group. For North Carolina, EPA default VPOP data were used for buses along with the state-submitted VPOP for other vehicle types, consistent with the VMT.

West Virginia and Clark County, Nevada also provided VPOP for the 2016 beta platform by county and MOVES vehicle type. Because they did not provide VMT by county-HPMS, these data were not put into MOVES databases for splitting.  Instead, the VPOP data were split to full SCC using county-vehicle to county-SCC ratios calculated from the 2016 beta VMT - not the EPA default VMT, but the final VMT incorporating state data and split to full SCC within MOVES CDBs. So effectively, MOVES CDBs were used to split their VPOP to full SCC, but only indirectly. West Virginia's VPOP dataset did not include any intercity buses (MOVES vehicle type 41), thus intercity bus VPOP data were taken from the EPA default VPOP.

The FF10-formatted county-SCC VPOP data provided by Pennsylvania and New Hampshire for the 2016 beta platform were used for the 2016v1 platform.

EPA default VPOP data were used for the states that submitted VMT but did not submit VPOP (CT, IL, MI, MN, and VA). The new VMT that South Carolina provided, in addition to the recalculation of HPMS splits between counties, introduced some issues with VMT/VPOP ratios when comparing the 2016beta VMT with EPA default beta VPOP. The largest VMT/VPOP ratio issues were for HD vehicles. Because the light-duty (LD) VPOP data are based on the IHS-Polk registration data, only the heavy-duty (HD) VPOP data were modified for South Carolina using the EPA defaults. For HD VPOP in South Carolina: new VPOP = EPA default VPOP * (SC-submitted VMT / EPA default VMT). In other words, the same changes that were made to the VMT as a result of the new state data were also made to the VPOP on a percentage basis. This preserves VMT/VPOP ratios for HD vehicles in South Carolina compared to the EPA default data. This procedure resulted in some changes to the overall HD VPOP total in South Carolina, both at the county level and state level.

VPOP by source type was not re-split among the LD types 21/31/32. This is consistent with the 2016beta platform, in which all state-submitted VMT was re-split, but state-submitted VPOP at the source type level or better was not.

For 2016v1, VPOP data were provided for:
 Massachusetts (by HPMS)
 Chicago area (8 counties, by source type)
 Colorado (by source type)
 New Jersey (by source type)
 Pima County, AZ (by source type)

The state-submitted VMT and VPOP data underwent several modifications based on quality assurance:

Colorado:

 There was a lot of inconsistency between the VMT and VPOP when it was broken down into individual vehicle types. Colorado indicated that we shouldn't put too much stock into the HPMS->vehicle breakdowns in their VPOP data. So, we summed their VPOP to HPMS type and re-split to vehicle type based on splits from beta VPOP.
 Due to concerns about VMT/VPOP ratios for long haul source types (41, 53, 62), we recalculated the VPOP from VMT using average national VMT/VPOP ratios from 2014v2: 53,000 for 41s; 18,600 for 53s, and 68,000 for 62s. We also recalculated the 52 VPOP as old 52+53 VPOP minus new 53 VPOP. In one county (08019), 52 VPOP ended up negative, so we increased the 53 VMT/VPOP ratio (which decreased the VPOP) for that county only.
 There were also some VMT/VPOP ratios at the county level for HPMS vehicle types 42, 43, and 61 that were greater than 150,000 miles/year. For these, we increased the VPOP for these county-vehicle combinations so that the VMT/VPOP ratio would never exceed 150,000. This affected 6 county-vehicle combinations, mostly with small VPOP.

Chicago area:

 Chicago provided separate VMT for HPMS vehicle types 20 and 30, which were summed and re-split based on 2016beta platform VMT to keep LD vehicle type distributions consistent.
 Motorcycles VMT and VPOP were taken from the 2016beta platform.
 Based on email communication and number comparison, the provided Chicago area bus VMT (submitted as total buses), appear to include only data for bust types 41 and 42 only and not 43 (school). So, the bus VMT were allocated to the 41and 42 types and school bus VMT (43) were carried forward from 2016beta.
 For bus VPOP, Chicago did not provide intercity buses, so those were carried forward from 2016beta, but their transit and school bus VPOP values were retained.
 The provided 50/60 VPOP appeared to be much too low, so we recalculated it based on their VMT combined with average VMT/VPOP ratios: 24,000 for 51s; 10,000 for 52s; 18,600 for 53s; 4,000 for 54s; 57,000 for 61s and 68,000 for 62s.
 Counties 17063 and 17093 had VPOP for 41/42 but no VMT. We added VMT from the 2016beta platform for these county-vehicle combinations. The VMT for 41 was carried forward from 2016beta to 2016v1. For 42, the 2016v1 VMT = beta VMT * (v1 VPOP / beta VPOP).

Pima County: The provided 50/60 VPOP was not based on vehicle registrations, so we recalculated based on their VMT combined with average VMT/VPOP ratios (as was done for Chicago).

Hoteling Hours (HOTELING)

Hoteling hours activity is used to calculate emissions from extended idling and auxiliary power units (APUs) for heavy duty diesel vehicles. Many states have commented that EPA estimates of hoteling hours, and therefore emissions resulting from hoteling are higher than they could realistically be in reality given the available parking spaces. Therefore, recent hoteling activity datasets, including the 2014NEIv2, 2016 beta, and 2016v1 platforms, incorporate reductions to hoteling activity data based on the availability of truck stop parking spaces in each county, as described below. For 2016v1, hoteling hours were recomputed using a new factor identified by EPA's Office of Transportation and Air Quality as more appropriate based on recent studies.

The method used in 2016v1 is the following:
 Start with 2016v1 VMT for 62 on restricted roads, by county.
 Multiply that by 0.007248 hours/mile (Sonntag, 2018). This results in about 73.5% less hoteling hours as compared to the 2014v2 approach.
 Apply parking space reductions as has been done for 2016beta, except for states that requested we not do that (CO, ME, NJ, NY).

Hoteling hours were adjusted down in counties for which there were more hoteling hours assigned to the county than could be supported by the known parking spaces.  To compute the adjustment, we started with the hoteling hours for the county as computed by the above method, and then we applied reductions directly to the 2016 hoteling hours based on known parking space availability so that there were not more hours assigned to the county than the available parking spaces could support if they were full every hour of every day.

A dataset of truck stop parking space availability with the total number of parking spaces per county was used in the computation of the adjustment factors. This same dataset is used to develop the spatial surrogate for hoteling emissions. For the 2016v1 platform, the parking space dataset includes several updates compared to 2016beta platform, based on information provided by some states (e.g., MD). Since there are 8,784 hours in the year 2016; the maximum number of possible hoteling hours in a particular county is equal to 8,784 * the number of parking spaces in that county. Hoteling hours for each county were capped at that theoretical maximum value for 2016 in that county, with some exceptions as outlined below.

Because the truck stop parking space dataset may be incomplete in some areas, and trucks may sometimes idle in areas other than designated spaces, it was assumed that every county has at least 12 parking spaces, even if fewer parking spaces are found in the parking space dataset. Therefore, hoteling hours are never reduced below 105,408 hours for the year in any county. If the unreduced hoteling hours were already below that maximum, the hours were left unchanged; in other words, hoteling activity are never increased as a result of this analysis.

A handful of high activity counties that would otherwise be subject to a large reduction were analyzed individually to see if their parking space count seemed unreasonably low. In the following counties, the parking space count and/or the reduction factor was manually adjusted:

 17043 / DuPage IL (instead of reducing hoteling by 89%, applied no adjustment)
 39061 / Hamilton OH (parking spot count increased to 20 instead of the minimum 12)
 47147 / Robertson TN (parking spot count increased to 52 instead of just 26)
 51015 / Augusta VA (parking space count increased to 48 instead of the minimum 12)
 51059 / Fairfax VA (parking spot count increased to 20 instead of the minimum 12)

Georgia and New Jersey submitted hoteling activity for the 2016v1 platform. For these states, the EPA default projection was replaced with their state data. New Jersey provided their hoteling activity in a series of HotellingHours MOVES-formatted tables, which include separate activity for weekdays and weekends and for each month and which have units of hours-per-week. These data first needed to be converted to annual totals by county.

For Georgia we were going to bring forward their beta HOTELING but found it was now much too large compared to other states once the new hoteling factor was implemented. After discussion with Georgia Department of Natural Resources staff, we agreed to recalculate from VMT for all counties except for those where parking > 0 and restricted VMT = 0. In those counties, Georgia's 2016beta hoteling were reduced by 73.5% (the same reduction factor applied to the rest of the country).

Alaska Department of Natural Resources staff requested that we zero out hoteling activity in several counties due to the nature of driving patterns in their region.  In addition, there are no hoteling hours or other emissions from long-haul combination trucks in Hawaii, Puerto Rico, or the Virgin Islands.

All parking space counts are the same as 2016beta except Maryland, which submitted an update for 2016v1.

The states of Colorado, Maine, New Jersey, and New York requested that no reductions be applied to the hoteling activity based on parking space availability. For these states, we did not apply any reductions based on parking space availability and left the hours that were computed using the updated method for 2016v1; or in the case of New Jersey, their submitted activity; unchanged. Otherwise, the submitted data from New Jersey would have been subject to reductions. The submitted data from Georgia did not exceed the maximum value in any county, so their submitted data did not need to be reduced.

Finally, the county total hoteling must be split into separate values for extended idling (SCC<PHONE_NUMBER>) and APUs (SCC<PHONE_NUMBER>). New Jersey's submittal of hoteling activity specified a 30% APU split, and this was used for all New Jersey counties. For the rest of the country, a 12.4% APU split was used for the year 2016, meaning that APUs are used for 12.4% of the hoteling hours.

Onroad Emission Factor Table Development

MOVES2014b was run in emission rate mode to create emission factor tables using CB6 speciation for the years 2016, 2020, 2023, and 2028, for all representative counties and fuel months. MOVES was run for all counties in Alaska, Hawaii, and Virgin Islands, and for a single representative county in Puerto Rico.  The county databases (CDBs) used to run MOVES to develop the emission factor tables were updated from those used in the 2016beta platform.

Age distributions are a key input to MOVES in determining emission rates. The age distributions for 2016v1 were updated based on vehicle registration data obtained from the CRC A-115 project, subject to reductions for older vehicles determined according to CRC A-115 methods but using additional age distribution data that became available as part of the 2017 NEI submitted input data.  One of the findings of CRC project A-115 is that IHS data contain higher vehicle populations than state agency analyses of the same Department of Motor Vehicles data, and the discrepancies tend to increase with increasing vehicle age (i.e., there are more older vehicles in the IHS data). The CRC project dealt with the discrepancy by releasing datasets based on raw (unadjusted) information and adjusted sets of age distributions, where the adjustments reflected the differences in population by model year of 2014 IHS data and 2014 submitted data from a single state.

For the 2016 platform and 2017 NEI, EPA repeated the CRC's assessment of IHS vs. state discrepancies but with updated 2017 information and for more states.  The 2017 light-duty vehicle (LDV) populations from the CRC A-115 project were compared by model year to the populations submitted by state/local (S/L) agencies for the 2017 NEI.  The comparisons by model year were used to develop adjustment factors that remove older age LDVs from the IHS dataset. Out of 31 S/L agencies that provided data, 16 provided LDV population and age distributions with snapshot dates of January 2017, July 2017, or 2018.  The other 15 had either unknown or older (back to 2013) data pull dates, so were not a fair comparison to the 2017 IHS data.

We reviewed the population by model year comparisons for each of the 16 geographic areas vs. IHS separately for source type 21 and for source type 31 plus 32 together. We reallocated the S/L agency populations of cars (source type 21) and light trucks (source types 31 and 32) to match IHS car and light-duty truck splits by county for consistent VIN decoding.  We also removed the state of Georgia from the pool of S/L agencies used to calculate the adjustment factors to avoid its influence on a pooled geographic adjustment.  Georgia already works closely with IHS on VIN decoding, and as a result, their submittal matched IHS.  The IHS data are higher than the pooled state data by 6.5 percent for cars and 5.9 percent for light trucks.

We calculated the vehicle age distribution adjustment factors as one minus the fraction of vehicles to remove from IHS to equal the state data, with two exceptions.  The model year range 2006/2007 to 2017 receives no adjustment and the model year 1987 receives a capped adjustment that equals the adjustment to 1988.  Table 2-20 below shows the fraction of vehicles to keep by model year based on this analysis.  The adjustments were applied to the 2016 IHS-based age distributions from CRC project A-115 prior to use in 2016v1.  In addition, we removed the county-specific fractions of antique license plate vehicles present in the registration summary from IHS.  Nationally, the prevalence of antique plates is only 0.8 percent, but as high as 6 percent in some states (e.g., Mississippi).

Table 2-20. Older Vehicle Adjustments Showing the Fraction of IHS Vehicle Populations to Retain for 2016v1 and 2017 NEI
Model Year
Cars
Light Trucks
pre-1989
                                                                          0.675
                                                                          0.769
                                                                           1989
                                                                          0.730
                                                                          0.801
                                                                           1990
                                                                          0.732
                                                                          0.839
                                                                           1991
                                                                          0.740
                                                                          0.868
                                                                           1992
                                                                          0.742
                                                                          0.867
                                                                           1993
                                                                          0.763
                                                                          0.867
                                                                           1994
                                                                          0.787
                                                                          0.842
                                                                           1995
                                                                          0.776
                                                                          0.865
                                                                           1996
                                                                          0.790
                                                                          0.881
                                                                           1997
                                                                          0.808
                                                                          0.871
                                                                           1998
                                                                          0.819
                                                                          0.870
                                                                           1999
                                                                          0.840
                                                                          0.874
                                                                           2000
                                                                          0.838
                                                                          0.896
                                                                           2001
                                                                          0.839
                                                                          0.925
                                                                           2002
                                                                          0.864
                                                                          0.921
                                                                           2003
                                                                          0.887
                                                                          0.942
                                                                           2004
                                                                          0.926
                                                                          0.953
                                                                           2005
                                                                          0.941
                                                                          0.966
                                                                           2006
                                                                              1
                                                                          0.987
2007-2017
                                                                              1
                                                                              1

In addition to removing the older and antique plate vehicles from the IHS data, we accounted for 25 counties that were outliers because their fleet age was significantly younger than typical. We limited our outlier identification to LDV source types 21, 31, and 32, because they're the most important. Many rural counties also have outliers for low-population source types such as Transit Bus and Refuse Truck; these do not have much of an impact on the inventory overall and reflect sparse data in low-population areas and therefore do not require correction.

The most extreme examples of LDV outliers were Light Commercial Truck age distributions where over 50 percent of the population in the entire county is 0 and 1 years old. These sorts of young fleets can happen if the headquarters of a leasing or rental company is the owner/entity of a relatively large number of vehicles relative to the county-wide population.  While the business owner of thousands of new vehicles may reside in a single county, the vehicles likely operate in broader areas without being registered where they drive. To avoid creating artificial low spots of LDV emissions in these outlier counties, we flagged all counties above a 0.35 fraction of new vehicles and excluded their age distribution from the final set of grouped age distributions that went into the 2016v1 CDBs.

The 2016 age distributions were then grouped using a population-weighted average of the source type populations of each county in the representative county group.  The end-product was age distributions for each of the 13 source types in each of the 315 representative counties for 2016v1.  It should be noted that the long-haul truck source types 53 (Single Unit) and 62 (Combination Unit) are a nationwide average due to the long-haul nature of their operation.

Input data tables provided by states were reviewed before they were used.  Some submitted data tables were found to be from previous emissions modeling platforms, primarily NEI 2014v2, 2016 alpha, or 2016 beta, and these were not explicitly used as most were already incorporated into the CDBs.  All average speed distributions in 2016v1 came from the CRC A-100 study, and most age distributions (other than accepted submittals for New Jersey, Pima County, Arizona, and Wisconsin) came from methods described above for 2016 v1.  The following submitted MOVES input data (other than the activity data discussed above) were incorporated into the 2016v1 base year MOVES CDBs:

 Chicago (IL) Metropolitan Agency for Planning: FF10 VMT, FF10 VPOP, Month/Day VMT Fraction, Ramp Fractions
 Georgia Department of Natural Resources:  Fuel Supply (county assignments to fuel type groups)
 Louisville (KY) Metro Air Pollution Control District:  Road Type Distributions, Ramp Fractions
 Maryland Department of the Environment:	Truck Stop Locations (these affect the spatial surrogate but not the MOVES run)
 New Jersey Department of Environmental Protection: Age Distribution
 Pima (AZ) Association of Governments: Age Distribution, I/M Coverage, Day VMT Fraction, Road Type Distribution
 Wisconsin Department of Natural Resources:  Age Distribution, I/M Coverage

Once the input data were incorporated into the CDBs, a new set of representative counties was developed.  Each county in the continental U.S. was classified according to its state, altitude (high or low), fuel region, the presence of inspection and maintenance programs, the mean light-duty age, and the fraction of ramps.  A binning algorithm was executed to identify "like counties", and then specific requests for representative county groups by states were honored from the states of Maryland, New York, New Jersey, Wisconsin, Michigan, and Georgia.  The final result was 315 representative counties (up from 304 in 2016 beta) as shown in Figure 2-3.  The representative counties themselves changed substantially; of the 315 representative counties, 145 were not representative counties in 2016 beta.  The CDBs for these 145 counties were developed from the 2014NEIv2 counties and updated to represent the year 2016. For more information on the development of the 2016 age distributions and representative counties and the review of the input data, see the memoranda "Onroad 2016v1 documentation_20191007" and "RepCountiesFor2016v1-2017_13jun2019" (ERG, 2019).

Figure 2-3. Representative Counties in 2016v1

To create the 2016v1 emission factors, MOVES was run separately for each representative county and fuel month for each temperature bin needed for calendar year 2016.  The CDBs used to run MOVES include the state-specific control measures such as the California low emission vehicle (LEV) program, except that fuels were updated to represent calendar year 2016.  In addition, the range of temperatures run along with the average humidities used were specific to the year 2016. The MOVES results were post-processed into CSV-formatted emission factor tables that can be read by SMOKE-MOVES.

Onroad California Inventory Development

The California Air Resources Board (CARB) provided their own onroad emissions inventories based on their EMFAC2017 model. EMFAC2017 was run by CARB for model years 2016, 2023, 2028, and 2035. Details on how SMOKE-MOVES emissions were adjusted to match the CARB-based 2016 inventory are provided in the Emissions Processing Requirements section of this document.
2016 Nonroad Mobile sources (cmv, rail, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions (nonroad), locomotive (rail) and CMV emissions.

Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)
The cmv_c1c2 inventory sector contains small to medium-size engine CMV emissions. Category 1 and Category 2 (C1C2) marine diesel engines typically range in size from about 700 to 11,000 hp. These engines are used to provide propulsion power on many kinds of vessels including tugboats, towboats, supply vessels, fishing vessels, and other commercial vessels in and around ports. They are also used as stand-alone generators for auxiliary electrical power on many types of vessels. Category 1 represents engines up to 7 liters per cylinder displacement. Category 2 includes engines from 7 to 30 liters per cylinder.

The cmv_c1c2 inventory sector contains sources that traverse state and federal waters that are in the 2017NEI along with emissions from surrounding areas of Canada, Mexico, and international waters.  The cmv_c1c2 sources are modeled as point sources but using plume rise parameters that cause the emissions to be released in the ground layer of the air quality model.

The cmv_c1c2 sources within state waters are identified in the inventory with the Federal Information Processing Standard (FIPS) county code for the state and county in which the vessel is registered. The cmv_c1c2 sources that operate outside of state waters but within the Emissions Control Area (ECA) are encoded with a state FIPS code of 85.  The ECA areas include parts of the Gulf of Mexico, and parts of the Atlantic and Pacific coasts.  The cmv_c1c2 sources in the 2016v1 inventory are categorized as operating either in-port or underway and as main and auxiliary engines are encoded using the SCCs listed in Table 2-21.
Table 2-21. 2016v1 platform SCCs for cmv_c1c2 sector
SCC
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Description
<PHONE_NUMBER>
C1/C2
Diesel
Port
Main
<PHONE_NUMBER>
C1/C2
Diesel
Port
Auxiliary
<PHONE_NUMBER>
C1/C2
Diesel
Underway
Main
<PHONE_NUMBER>
C1/C2
Diesel
Underway
Auxiliary

Category 1 and 2 CMV emissions were developed for the 2017 NEI, The 2017 NEI emissions were developed based signals from Automated Identification System (AIS) transmitters. AIS is a tracking system used by vessels to enhance navigation and avoid collision with other AIS transmitting vessels.  The USEPA Office of Transportation and Air Quality received AIS data from the U.S. Coast Guard (USCG) in order to quantify all ship activity which occurred between January 1 and December 31, 2017. The provided AIS data extends beyond 200 nautical miles from the U.S. coast (Figure 2-4). This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the North American ECA, although some non-ECA activity are captured as well.
Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed)

The AIS data were compiled into five-minute intervals by the USCG, providing a reasonably refined assessment of a vessel's movement. For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions would be less. The ability to track vessel movements through AIS data and link them to attribute data, has allowed for the development of an inventory of very accurate emission estimates. These AIS data were used to define the locations of individual vessel movements, estimate hours of operation, and quantify propulsion engine loads. The compiled AIS data also included the vessel's International Marine Organization (IMO) number and Maritime Mobile Service Identifier (MMSI); which allowed each vessel to be matched to their characteristics obtained from the Clarksons ship registry (Clarksons, 2018).

USEPA used the engine bore and stroke data to calculate cylinder volume. Any vessel that had a calculated cylinder volume greater than 30 liters was incorporated into the USEPA's new Category 3 Commercial Marine Vessel (C3CMV) model. The remaining records were assumed to represent Category 1 and 2 (C1C2) or non-ship activity.  The C1C2 AIS data were quality assured including the removal of duplicate messages, signals from pleasure craft, and signals that were not from CMV vessels (e.g., buoys, helicopters, and vessels that are not self-propelled).  Following this, there were 422 million records remaining.

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

      Emissionsinterval=Time (hr)intervalx Power(kW)xEF(gkWh)xLLAF 	(1)

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

Next, vessels were identified in order determine their vessel type, and thus their vessel group, power rating, and engine tier information which are required for the emissions calculations. See the 2017 NEI documentation for more details on this process.  Following the identification, 108 different vessel types were matched to the C1C2 vessels. Vessel attribute data was not available for all these vessel types, so the vessel types were aggregated into 13 different vessel groups for which surrogate data were available as shown in Table 2-22.  11,302 vessels were directly identified by their ship and cargo number. The remaining group of miscellaneous ships represent 13 percent of the AIS vessels (excluding recreational vessels) for which a specific vessel type could not be assigned.
Table 2-22. Vessel groups in the cmv_c1c2 sector
                                 Vessel Group
                              NEI Area Ship Count
Bulk Carrier
                                                                             37
Commercial Fishing
                                                                          1,147
Container Ship
                                                                              7
Ferry Excursion
                                                                            441
General Cargo
                                                                          1,498
Government
                                                                          1,338
Miscellaneous
                                                                          1,475
Offshore support
                                                                          1,149
Reefer
                                                                             13
Ro Ro
                                                                             26
Tanker
                                                                            100
Tug
                                                                          3,994
Work Boat
                                                                             77
Total in Inventory:
                                                                         11,302

As shown in Equation (1), power is an important component of the emissions computation. Vessel-specific installed propulsive power ratings and service speeds were pulled from Clarkson's ship registry and adopted from the Global Fishing Watch (GFW) dataset when available. However, there is limited vessel specific attribute data for most of the C1C2 fleet. This necessitated the use of surrogate engine power and load factors, which were computed for each vessel group shown in Table 2.  In addition to the power required by propulsive engines, power needs for auxiliary engines were also computed for each vessel group.  Emissions from main and auxiliary engines are inventoried with different SCCs as shown in Table 2-21.

The final components of the emissions computation equation are the emission factors and the low load adjustment factor.  The emission factors used in this inventory take into consideration the EPA's marine vessel fuel regulations as well as exhaust standards that are based on the year that the vessel was manufactured to determine the appropriate regulatory tier. Emission factors in g/kWhr by tier for NOx, PM10, PM2.5, CO, CO2, SO2 and VOC were developed using Tables 3-7 through 3-10 in USEPA's (2008) Regulatory Impact Analysis on engines less than 30 liters per cylinder. To compile these emissions factors, population-weighted average emission factor were calculated per tier based on C1C2 population distributions grouped by engine displacement. Boiler emission factors were obtained from an earlier Entec study (Entec, 2004).  If the year of manufacture was unknown then it was assumed that the vessel was Tier 0, such that actual emissions may be less than those estimated in this inventory. Without more specific data, the magnitude of this emissions difference cannot be estimated.

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

For more information on the emission computations for 2017, see the supporting documentation for the 2017 NEI C1C2 CMV emissions.  The emissions from the 2017 NEI were adjusted to represent 2016 in the cmv_c1c2 sector using factors derived from U.S. Army Corps of Engineers national vessel Entrance and Clearance data by applying a factor of 0.98 to all pollutants. For consistency, the same methods were used for California, Canadian, and other non-U.S. emissions.
Category 3 Commercial Marine Vessels (cmv_c3)
The cmv_c3 inventory is brand new for the 2016v1 platform.  It was developed in conjunction with the CMV inventory for the 2017 NEI.  This sector contains large engine CMV emissions. Category 3 (C3) marine diesel engines are those at or above 30 liters per cylinder, typically these are the largest engines rated at 3,000 to 100,000 hp. C3 engines are typically used for propulsion on ocean-going vessels including container ships, oil tankers, bulk carriers, and cruise ships. Emissions control technologies for C3 CMV sources are limited due to the nature of the residual fuel used by these vessels.  The cmv_c3 sector contains sources that traverse state and federal waters; along with sources in waters not covered by the NEI in surrounding areas of Canada, Mexico, and international waters.

The cmv_c3 sources that operate outside of state waters but within the federal Emissions Control Area (ECA) are encoded with a FIPS state code of 85, with the "county code" digits representing broad regions such as the Atlantic, Gulf of Mexico, and Pacific.  The ECA areas include parts of the Gulf of Mexico, and parts of the Atlantic and Pacific coasts.  CMV C3 sources around Puerto Rico, Hawaii and Alaska, which are outside the ECA areas, are included in the 2016v1 inventory but are in separate files from the emissions around the continental United States (CONUS). The cmv_c3 sources in the 2016v1 inventory are categorized as operating either in-port or underway and are encoded using the SCCs listed in Table 2-23 and distinguish between diesel and residual fuel, in port areas versus underway, and main and auxiliary engines.  In addition to C3 sources in state and federal waters, the cmv_c3 sector includes emissions in waters not covered by the NEI (FIPS = 98) and taken from the "ECA-IMO-based" C3 CMV inventory. The ECA-IMO inventory is also used for allocating the FIPS-level emissions to geographic locations for regions within the domain not covered by the AIS selection boxes as described in the next section.
Table 2-23. 2016v1 platform SCCs for cmv_c3 sector
                                      SCC
Tier 1 Description
Tier 2 Description
Tier 3 Description
Tier 4 Description
<PHONE_NUMBER>
C3
Diesel
Port
Main
<PHONE_NUMBER>
C3
Diesel
Port
Auxiliary
<PHONE_NUMBER>
C3
Diesel
Underway
Main
<PHONE_NUMBER>
C3
Diesel
Underway
Auxiliary
<PHONE_NUMBER>
C3
Residual
Port
Main
<PHONE_NUMBER>
C3
Residual
Port
Auxiliary
<PHONE_NUMBER>
C3
Residual
Underway
Main
<PHONE_NUMBER>
C3
Residual
Underway
Auxiliary

Prior to creation of the 2017 NEI, "The EPA received Automated Identification System (AIS) data from United States Coast Guard (USCG) in order to quantify all ship activity which occurred between January 1 and December 31, 2017. The International Maritime Organization's (IMO's) International Convention for the Safety of Life at Sea (SOLAS) requires AIS to be fitted aboard all international voyaging ships with gross tonnage of 300 or more, and all passenger ships regardless of size (IMO, 2002). In addition, the USCG has mandated that all commercial marine vessels continuously transmit AIS signals while transiting U.S. navigable waters. As the vast majority of C3 vessels meet these requirements, any omitted from the inventory due to lack of AIS adoption are deemed to have a negligible impact on national C3 emissions estimates. The activity described by this inventory reflects ship operations within 200 nautical miles of the official U.S. baseline. This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone and the North American ECA, although some non-ECA activity is captured as well (Figure 2-4).

The 2017 NEI data were computed based on the AIS data from the USGS for the year of 2017.  The AIS data were coupled with ship registry data that contained engine parameters, vessel power parameters, and other factors such as tonnage and year of manufacture which helped to separate the C3 vessels from the C1C2 vessels.  Where specific ship parameters were not available, they were gap-filled. The types of vessels that remain in the C3 data set include: bulk carrier, chemical tanker, liquified gas tanker, oil tanker, other tanker, container ship, cruise, ferry, general cargo, fishing, refrigerated vessel, roll-on/roll-off, tug, and yacht.

Prior to use, the AIS data were reviewed - data deemed to be erroneous were removed, and data found to be at intervals greater than 5 minutes were interpolated to ensure that each ship had data every five minutes. The five-minute average data provide a reasonably refined assessment of a vessel's movement. For example, using a five-minute average, a vessel traveling at 25 knots would be captured every two nautical miles that the vessel travels. For slower moving vessels, the distance between transmissions would be less.

The emissions were calculated for each C3 vessel in the dataset for each 5-minute time range and allocated to the location of the message following to the interval. Emissions were calculated according to Equation 1.

      Emissionsinterval=Time (hr)intervalx Power(kW)xEF(gkWh)xLLAF 	(1)

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

Emissions were computed according to a computed power need (kW) multiplied by the time (hr) and by an engine-specific emission factor (g/kWh) and finally by a low load adjustment factor that reflects increasing propulsive emissions during low load operations.

The resulting emissions were available at 5-minute intervals.  Code was developed to aggregate these emissions to modeling grid cells and up to hourly levels so that the emissions data could be input to SMOKE for emissions modeling with SMOKE.  Within SMOKE, the data were speciated into the pollutants needed by the air quality model, but since the data were already in the form of point sources at the center of each grid cell, and they were already hourly, no other processing was needed within SMOKE.  SMOKE requires an annual inventory file to go along with the hourly data, so those files were also generated for each year.

On January 1st, 2015, the ECA initiated a fuel sulfur standard which regulated large marine vessels to use fuel with 1,000 ppm sulfur or less. These standards are reflected in the cmv_c3 inventories.

There were some areas needed for modeling that the AIS request boxes did not cover (see Figure 2-4).  These include a portion of the St. Lawrence Seaway transit to the Great Lakes, a small portion of the Pacific Ocean far offshore of Washington State, portions of the southern Pacific Ocean around off the coast of Mexico, and the southern portion of the Gulf of Mexico that is within the 36-km domain used for air quality modeling. In addition, a determination had to be made regarding whether to use the existing Canadian CMV inventory or the more detailed AIS-based inventory.  In 2016v1, the AIS-based inventory was used in the areas for which data were available, and the areas not covered were gap-filled with inventory data from the 2016beta platform, which included data from Environment Canada and the 2011 ECA-IMO C3 inventory.

For the gap-filled areas not covered by AIS selections or the Environment Canada inventory, the 2016beta nonpoint C3 inventory was converted to a point inventory to support plume rise calculations for C3 vessels. The nonpoint emissions were allocated to point sources using a multi-step allocation process because not all of the inventory components had a complete set of county-SCC combinations. In the first step, the county-SCC sources from the nonpoint file were matched to the county-SCC points in the 2011 ECA-IMO C3 inventory. The ECA-IMO inventory contains multiple point locations for each county-SCC. The nonpoint emissions were allocated to those points using the PM2.5 emissions at each point as a weighting factor.

Cmv_c3 underway emissions that did not have a matching FIPS in the ECA-IMO inventory were allocated using the 12 km 2014 offshore shipping activity spatial surrogate (surrogate code 806). Each county with underway emissions in the area inventory was allocated to the centroids of the cells associated with the respective county in the surrogate. The emissions were allocated using the weighting factors in the surrogate.

The resulting point emissions centered on each grid cell were converted to an annual point 2010 flat file format (FF10). Pictures of the emissions are shown in Section 7 of this document. A set of standard stack parameters were assigned to each release point in the cmv_c3 inventory. The assigned stack height was 65.62 ft, the stack diameter was 2.625 ft, the stack temperature was 539.6 °F, and the velocity was 82.02 ft/s. Emissions were computed for each grid cell needed for modeling.

Adjustment of the 2017 NEI CMV C3 to 2016

Because the NEI emissions data were for 2017, an analysis was performed of 2016 versus 2017 entrance and clearance data (ERG, 2019a). Annual, monthly, and daily level data were reviewed. Annual ratios of entrance and clearance activity were developed for each ship type as shown in Table 2-24.  For vessel types with low populations (C3 Yacht, tug, barge, and fishing vessels), an annual ratio of 0.98 was applied.
Table 2-24. 2017 to 2016 projection factors for C3 CMV
Ship Type
                                Annual Ratio[a]
Barge
                                     1.551
Bulk Carrier
                                     1.067
Chemical Tanker
                                     1.031
Container Ship
                                    1.0345
Cruise
                                     1.008
Ferry Ro Pax
                                     1.429
General Cargo
                                     0.888
Liquified Gas Tanker
                                     1.192
Miscellaneous Fishing
                                     0.932
Miscellaneous Other
                                     1.015
Offshore
                                     0.860
Oil Tanker
                                     1.101
Other Tanker
                                     1.037
Reefer
                                     0.868
Ro Ro
                                     1.007
Service Tug
                                     1.074
	[a] Above ratios are applied to the 2017 emission values to estimate 2016 values

The cmv_c3 projection factors were pollutant-specific and region-specific. Most states are mapped to a single region with a few exceptions.  Pennsylvania and New York were split between the East Coast and Great Lakes, Florida was split between the Gulf Coast and East Coast, and Alaska was split between Alaska East and Alaska West. The non-federal factors listed in this table were applied to sources outside of U.S. federal waters (FIPS 98). Volatile Organic Compound (VOC) Hazardous Air Pollutant (HAP) emissions were projected using the VOC factors. NH3 emissions were held constant at 2014 levels.
Rail Sources (rail)
The rail sector includes all locomotives in the NEI nonpoint data category. The 2016v1 inventory SCCs are shown in Table 2-25.  This sector excludes railway maintenance activities.  Railway maintenance emissions are included in the nonroad sector.  The point source yard locomotives are included in the ptnonipm sector.  In 2014NEIv2, rail yard locomotive emissions were present in both the nonpoint (rail sector) and point (ptnonipm sector) inventories.  For the 2016v1 platform, rail yard locomotive emissions are only in the point inventory / ptnonipm sector.  Therefore, SCC<PHONE_NUMBER> is not present in the 2016v1 platform rail sector, except in three California counties. The California Air Resources Board (CARB) submitted rail emissions, including rail yards, for 2016v1 platform. In three counties, CARB's rail yard emissions could not be mapped to point source rail yards, and so those counties' emissions were included in the rail sector.
Table 2-25. 2016v1 SCCs for the Rail Sector
SCC
Sector
Description: Mobile Sources prefix for all
<PHONE_NUMBER>
                                     rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
<PHONE_NUMBER>
                                     rail
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
<PHONE_NUMBER>
                                     rail
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)
<PHONE_NUMBER>
                                     rail
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
<PHONE_NUMBER>
                                     rail
Railroad Equipment; Diesel; Yard Locomotives (nonpoint)
                                   28500201
                                     rail
Railroad Equipment; Diesel; Yard Locomotives (point)

Class I Line-haul Methodology

In 2008 air quality planners in the eastern US formed the Eastern Technical Advisory Committee (ERTAC) for solving persistent emissions inventory issues. This work is the fourth inventory created by the ERTAC rail group. For the 2016 inventory, the Class I railroads granted ERTAC Rail permission to use the confidential link-level line-haul activity GIS data layer maintained by the Federal Railroad Administration (FRA).  In addition, the Association of American Railroads (AAR) provided national emission tier fleet mix information.  This allowed ERTAC Rail to calculate weighted emission factors for each pollutant based on the percentage of the Class I line-haul locomotives in each USEPA Tier level category.  These two datasets, along with 2016 Class I line-haul fuel use data reported to the Surface Transportation Board (Table 2-26), were used to create a link-level Class I emissions inventory, based on a methodology recommended by Sierra Research. Rail Fuel Consumption Index (RFCI) is a measure of fuel use per ton mile of freight.  This link-level inventory is nationwide in extent, but it can be aggregated at either the state or county level.
Table 2-26. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016
                               Class I Railroads
               2016 R-1 Reported Locomotive
Fuel Use (gal/year)
                                     RFCI
                                (ton-miles/gal)
                                 Adjusted RFCI
                                (ton-miles/gal)

                                  Line-Haul*
                                   Switcher

                                     BNSF
                                 1,243,366,255
                                  40,279,454
                                      972
                                      904
                               Canadian National
                                 102,019,995
                                   6,570,898
                                     1,164
                                     1,081
                               Canadian Pacific
                                  56,163,697
                                   1,311,135
                                     1,123
                                     1,445
                              CSX Transportation
                                 404,147,932
                                  39,364,896
                                     1,072
                                     1,044
                             Kansas City Southern
                                  60,634,689
                                   3,211,538
                                      989
                                      995
                               Norfolk Southern
                                 437,110,632
                                  28,595,955
                                      920
                                      906
                                 Union Pacific
                                 900,151,933
                                  85,057,080
                                     1,042
                                     1,095
                                    Totals:
                                3,203,595,133
                                  204,390,956
                                     1,006
                                      993
* Includes work trains; Adjusted RFCI values calculated from FRA gross ton-mile data as described on page 7.   RFCI total is ton-mile weighted mean.

Annual default emission factors for locomotives based on operating patterns ("duty cycles") and the estimated nationwide fleet mixes for both switcher and line-haul locomotives are available.   However, Tier level fleet mixes vary significantly between the Class I and Class II/III railroads.  As can be seen in Figure 2-5 and Figure 2-6, Class I railroad activity is highly regionalized in nature and is subject to variations in terrain across the country which can have a significant impact on fuel efficiency and overall fuel consumption.

Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)

Figure 2-6. Class I Railroads in the United States[5]

For the 2016 inventory, the AAR provided a national line-haul Tier fleet mix profile representing the entire Class I locomotive fleet.  A locomotive's Tier level determines its allowable emission rates based on the year when it was built and/or re-manufactured.  The national fleet mix data was then used to calculate weighted average in-use emissions factors for the line-haul locomotives operated by the Class I railroads as shown in Table 2-27.
Table 2-27. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal)
                                  Tier Level
                              AAR Fleet Mix Ratio
                                     PM10
                                      HC
                                      NOx
                                      CO
Uncontrolled (pre-1973)
                                   0.047494
                                     6.656
                                     9.984
                                     270.4
                                    26.624
Tier 0 (1973-2001)
                                   0.188077
                                     6.656
                                     9.984
                                    178.88
                                    26.624
Tier 0+ (Tier 0 rebuilds)
                                   0.141662
                                     4.16
                                     6.24
                                    149.76
                                    26.624
Tier 1 (2002-2004)
                                   0.029376
                                     6.656
                                     9.776
                                    139.36
                                    26.624
Tier 1+ (Tier 1 rebuilds)
                                   0.223147
                                     4.16
                                     6.032
                                    139.36
                                    26.624
Tier 2 (2005-2011)
                                   0.124536
                                     3.744
                                     5.408
                                    102.96
                                    26.624
Tier 2+ (Tier 2 rebuilds)
                                   0.093607
                                     1.664
                                     2.704
                                    102.96
                                    26.624
Tier 3 (2012-2014)
                                   0.123113
                                     1.664
                                     2.704
                                    102.96
                                    26.624
Tier 4 (2015 and later)
                                   0.028988
                                     0.312
                                     0.832
                                     20.8
                                    26.624
                             2016 Weighted EF's
                                   1.000000
                                     4.117
                                     6.153
                                    138.631
                                    26.624
Based on values in EPA Technical Highlights:  Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April 2009.
Weighted Emission Factors (EF) per pollutant for each gallon of fuel used (grams/gal or lbs/gal) were calculated for the US Class I locomotive fleet based on the percentage of line-haul locomotives certified at each regulated Tier level (Equation 1).
                                 Equation (1)
where:
	EFi	= 	Weighted Emission Factor for pollutant i for Class I locomotive fleet (g/gal).
    	EFiT	=	Emission Factor for pollutant i for locomotives in Tier T (g/gal) (Table 4).
	fT	=		Percentage of the Class I locomotive fleet in Tier T expressed as a ratio.

While actual engine emissions will vary within Tier level categories, the approach described above likely provides reasonable emission estimates, as locomotive diesel engines are certified to meet the emission standards for each Tier.  It should be noted that actual emission rates may increase over time due to engine wear and degradation of the emissions control systems.  In addition, locomotives may be operated in a manner that differs significantly from the conditions used to derive line-haul duty-cycle estimates.

Emission factors for other pollutants are not Tier-specific because these pollutants are not directly regulated by USEPA's locomotive emission standards.  PM2.5 was assumed to be 97% of PM10 [4], the ratio of volatile organic carbon (VOC) to (hydrocarbon) HC was assumed to be 1.053, and the emission factors used for sulfur dioxide (SO2) and ammonia (NH3)were 0.0939 g/gal[4] and 83.3 mg/gal[6], respectively.  The 2016 SO2 emission factor is based on the nationwide adoption of 15 ppm ultra-low sulfur diesel (ULSD) fuel by the rail industry.

The remaining steps to compute the Class 1 rail emissions involved calculating class I railroad-specific rail fuel consumption index values and calculating emissions per link. The final
link-level emissions for each pollutant were then aggregated by state/county FIPS code and then converted into an FF10 format used by SMOKE.  More detail on these steps is described in the specification sheet for the 2016v1 rail sector emissions.

Rail yard Methodology

Rail yard emissions were computed based on fuel use and/or yard switcher locomotive counts for the class I rail companies for all of the rail yards on their systems.  Three railroads provided complete rail yard datasets: BNSF, UP, and KCS.  CSX provided switcher counts for its 14 largest rail yards. This reported activity data was matched to existing yard locations and data stored in USEPA's Emissions Inventory System (EIS) database.  All existing EIS yards that had activity data assigned for prior years, but no reported activity data for 2016 were zeroed out.  New yard data records were generated for reported locations that were not found in EIS.  Special care was made to ensure that the new yards added to EIS did not duplicate existing data records.  Data for non-Class I yards was carried forward from the 2014 NEI.

Since the railroads only supplied switcher counts, average fuel use per switcher values were calculated for each railroad.  This was done by dividing each company's 2016 R-1 yard fuel use total by the number of switchers reported for each railroad.  These values were then used to allocate fuel use to each yard based on the number of switchers reported for that location.  Table 2-28 summarizes the 2016 yard fuel use and switcher data for each Class I railroad.  The emission factors used for rail yard switcher engines are shown in Table 2-29.
Table 2-28. Surface Transportation Board R-1 Fuel Use Data  -  2016
                                   Railroad
                                2016 R-1 Yard
                                Fuel Use (gal)
                        ERTAC calculated Fuel Use (gal)
                             Identified Switchers
                       ERTAC per Switcher Fuel Use (gal)
                                     BNSF
                                  40,279,454
                                  40,740,317
                                      442
                                    92,173
                                     CSXT
                                  39,364,896
                                  43,054,795
                                      455
                                    94,626
                                      CN
                                   6,570,898
                                   6,570,898
                                      103
                                    63,795
                                      KCS
                                   3,211,538
                                   3,211,538
                                      176
                                    18,247
                                      NS
                                  28,595,955
                                  28,658,528
                                      458
                                    62,573
                                     CPRS
                                   1,311,135
                                   1,311,135
                                      70
                                    18,731
                                      UP
                                  85,057,080
                                  85,057,080
                                     1286
                                    66,141
                                 All Class I's
                                  204,390,956
                                  208,604,291
                                     2,990
                                    69,767

Table 2-29. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)[4]
                                  Tier Level
                                  AAR Fleet
                                   Mix Ratio
                                     PM10
                                      HC
                                      NOx
                                      CO
Uncontrolled (pre-1973)
                                    0.2601
                                     6.688
                                    15.352
                                    264.48
                                    27.816
Tier 0 (1973-2001)
                                    0.2361
                                     6.688
                                    15.352
                                    191.52
                                    27.816
Tier 0+ (Tier 0 rebuilds)
                                    0.2599
                                     3.496
                                     8.664
                                    161.12
                                    27.816
Tier 1 (2002-2004)
                                    0.0000
                                     6.536
                                    15.352
                                    150.48
                                    27.816
Tier 1+ (Tier 1 rebuilds)
                                    0.0476
                                     3.496
                                     8.664
                                    150.48
                                    27.816
Tier 2 (2005-2011)
                                    0.0233
                                     2.888
                                     7.752
                                    110.96
                                    27.816
Tier 2+ (Tier 2 rebuilds)
                                    0.0464
                                     1.672
                                     3.952
                                    110.96
                                    27.816
Tier 3 (2012-2014)
                                    0.1018
                                     1.216
                                     3.952
                                     68.4
                                    27.816
Tier 4 (2015 and later)
                                    0.0247
                                     0.228
                                     1.216
                                     15.2
                                    27.816
                             2016 Weighted EF's
                                    0.9999
                                     4.668
                                    11.078
                                   178.1195
                                    27.813
Based on values in EPA Technical Highlights:  Emission Factors for Locomotives, EPA Office of Transportation and Air Quality, EPA-420-F-09-025, April 2009.  AAR fleet mix ratios did not add up to 1.0000, which caused a small error for the CO weighted emission factor as shown above.

In addition to the Class I rail yards, Emission estimates were calculated for four large Class III railroad hump yards which are among the largest classification facilities in the United States.  These four yards are located in Chicago (Belt Railway of Chicago-Clearing and Indiana Harbor Belt-Blue Island) and Metro-East St. Louis (Alton & Southern-Gateway and Terminal Railroad Association of St. Louis-Madison).  Figure 2-7 shows the spatial distribution of active yards in the 2016v1 and 2017 NEI inventories.
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States

Class II and III Methodology

There are approximately 560 Class II and III Railroads operating in the United States, most of which are members of the American Short Line and Regional Railroad Association (ASLRRA).  While there is a lot of information about individual Class II and III railroads available online, a significant amount of effort would be required to convert this data into a usable format for the creation of emission inventories.  In addition, the Class II and III rail sector has been in a constant state of flux ever since the railroad industry was deregulated under the Staggers Act in 1980.  Some states have conducted independent surveys of their Class II and III railroads and produced emission estimates, but no national level emissions inventory existed for this sector of the railroad industry prior to ERTAC Rail's work for the 2008 NEI.

Class II and III railroad activities account for nearly 4 percent of the total locomotive fuel use in the combined ERTAC Rail emission inventories and for approximately 35 percent of the industry's national freight rail track mileage.  These railroads are widely dispersed across the country and often utilize older, higher emitting locomotives than their Class I counterparts.  Class II and III railroads provide transportation services to a wide range of industries.  Individual railroads in this sector range from small switching operations serving a single industrial plant to large regional railroads that operate hundreds of miles of track. Figure 2-8 shows the distribution of Class II and III railroads and commuter railroads across the country.  This inventory will be useful for regional and local modeling, helps identify where Class II and III railroads may need to be better characterized, and provides a strong foundation for the future development of a more accurate nationwide short line and regional railroad emissions inventory.  A picture of the locations of class II and III railroads is shown in Figure 2-8. The data sources, calculations, and assumptions used to develop the Class II and III inventory are described in the 2016v1 rail specification sheet.
Figure 2-8. Class II and III Railroads in the United States[5]

Commuter Rail Methodology
Commuter rail emissions were calculated in the same way as the Class II and III railroads. The primary difference is that the fuel use estimates were based on data collected by the Federal Transit Administration (FTA) for the National Transit Database.  2016 fuel use was then estimated for each of the commuter railroads shown in Table 2-30 by multiplying the fuel and lube cost total by 0.95, then dividing the result by Metra's average diesel fuel cost of $1.93/gallon.  These fuel use estimates were replaced with reported fuel use statistics for MARC (Maryland), MBTA (Massachusetts), Metra (Illinois), and NJT (New Jersey). The commuter railroads were separated from the Class II and III railroads so that the appropriate SCC codes could be entered into the emissions calculation sheet.
Table 2-30. Expenditures and fuel use for commuter rail
                                   FRA Code
                                    System
                                 Cities Served
                                Propulsion Type
                                DOT Fuel &
                                  Lube Costs
                          Reported/Estimated Fuel Use
                                     ACEX
Altamont Corridor Express
San Jose / Stockton
                                    Diesel
                                   $889,828
                                  437,998.24
                                     CMRX
Capital MetroRail
Austin
                                    Diesel
                                    No data
                                      n/a
                                     DART
A-Train
Denton
                                    Diesel
                                      $0
                                     0.00
                                     DRTD
Denver RTD: A&B Lines
Denver
                                   Electric
                                      $0
                                     0.00
                                     JPBX
Caltrain
San Francisco / San Jose
                                    Diesel
                                  $7,002,612
                                 3,446,881.55
                                      LI
MTA Long Island Rail Road
New York
                              Electric and Diesel
                                  $13,072,158
                                 6,434,481.92
                                     MARC
MARC Train
Baltimore / Washington, D.C.
                              Diesel and Electric
                                  $4,648,060
                                 4,235,297.57
                                     MBTA
MBTA Commuter Rail
Boston / Worcester / Providence
                                    Diesel
                                  $37,653,001
                                 12,142,826.00
                                     MNCW
MTA Metro-North Railroad
New York / Yonkers / Stamford
                              Electric and Diesel
                                  $13,714,839
                                 6,750,827.49
                                     NICD
NICTD South Shore Line
Chicago / South Bend
                                   Electric
                                   $181,264
                                     0.00
                                     NIRC
Metra
Chicago
                              Diesel and Electric
                                  $52,460,705
                                 25,757,673.57
                                      NJT
New Jersey Transit
New York / Newark / Trenton / Philadelphia
                              Electric and Diesel
                                  $38,400,031
                                 16,991,164.00
                                     NMRX
New Mexico Rail Runner
Albuquerque / Santa Fe
                                    Diesel
                                  $1,597,302
                                  786,236.74
                                     CFCR
SunRail
Orlando
                                    Diesel
                                   $856,202
                                  421,446.58
                                     MNRX
Northstar Line
Minneapolis
                                    Diesel
                                   $708,855
                                  348,918.26
                                   Not Coded
SMART
San Rafael-Santa Rosa (Opened 2017)
                                    Diesel
                                      n/a
                                     0.00
                                     NRTX
Music City Star
Nashville
                                    Diesel
                                   $456,099
                                  224,504.69
                                     SCAX
Metrolink
Los Angeles / San Bernardino
                                    Diesel
                                  $19,245,255
                                 9,473,052.98
                                     SDNR
NCTD Coaster
San Diego / Oceanside
                                    Diesel
                                  $1,489,990
                                  733,414.77
                                     SDRX
Sounder Commuter Rail
Seattle / Tacoma
                                    Diesel
                                  $1,868,019
                                  919,491.22
                                     SEPA
SEPTA Regional Rail
Philadelphia
                                   Electric
                                   $483,965
                                     0.00
                                      SLE
Shore Line East
New Haven
                                    Diesel
                                    No data
                                      n/a
                                     TCCX
Tri-Rail
Miami / Fort Lauderdale / West Palm Beach
                                    Diesel
                                  $5,166,685
                                 2,543,186.92
                                     TREX
Trinity Railway Express
Dallas / Fort Worth
                                    Diesel
                                    No data
                                      n/a
                                      UTF
UTA FrontRunner
Salt Lake City / Provo
                                    Diesel
                                  $4,044,265
                                 1,990,700.39
                                     VREX
Virginia Railway Express
Washington, D.C.
                                    Diesel
                                  $3,125,912
                                 1,538,661.35
                                     WSTX
Westside Express Service
Beaverton
                                    Diesel
                                    No data
                                      n/a
*Reported fuel use values were used for MARC, MBTA, Metra, and New Jersey Transit.

Intercity Passenger Methodology (Amtrak)