2008 National Emissions Inventory, version 3

Technical Support Document

September 2013 - DRAFT
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Table 51: Nonroad SCCs included in 2008 NEI that were not in S/L/T agency submittals

State SCC Description

ID 2268010010 CNG Industrial Equipment Other Oil Field Equipment

ID 2265007015 Off-highway Vehicle Gasoline, 4-Stroke Logging Equipment Forest Eqp –

Feller/Bunch/Skidder

ID 2270010010 Off-highway Vehicle Diesel Industrial EquipmentOther Oil Field Equipment

ID 2265010010 Off-highway Vehicle Gasoline, 4-Stroke Industrial Equipment Other Oil Field 

Equipment

ID 2265007010 Off-highway Vehicle Gasoline, 4-Stroke Logging Equipment Shredders : 6 HP

ID 2270007015 Off –highway Vehicle Diesel Logging Equipment Forest Eqp – Feller/Bunch/Skidder

ID 2260007005 Off-highway Vehicle Gasoline, 2-Stroke Logging Equipment Chain Saws : 6 HP

Most agencies did not submit HAPs, and therefore the data in the2008 NEI came from the EPA-created

data. We considered whether including EPA data for HAPs but state and/or state plus EPA data for CAPs 

could cause any problems. Since the 2008 NEI for criteria VOC and PM is always larger than the EPA 

VOC or PM for any state, we can be assured that the 2008 NEI criteria VOC will always be larger than the 

sum of the 2008 NEI VOC HAPs, and that the 2008 NEI criteria PM will always be larger than the sum of 

the 2008 NEI PM HAPs. Nevertheless, there is still an inconsistency between CAPs and HAPs because of 

the different approaches used to compute each of them.

The California submittal differed dramatically from EPA dataset in SCC and pollutant coverage due to 

being estimated with a different model. The two data sources could not be merged without numerous 

double counts. Only California data were used in this case. The 2008 NEI in California does not agree 

well with the rest of the country.

o Example:

 California nonroad data does not include NH3, and therefore it is missing from the 2008

NEI as well

The EPA dataset does not include tribal areas. Therefore the 2008 NEI contains only tribal submission 

data and includes only the SCCs and pollutants submitted by tribes, which can be different from the 

county data. 

Agencies emissions are likely to capture local scale details that EPA data may not, particularly because 

most the agencies submitting emissions did not submit input data. Some agency data differ significantly 

from EPA’s.

o Example: 

 Delaware and New York SO2 are each about 300% higher than EPA, perhaps indicating 

higher sulfur fuel usage than EPA assumed.

4.6 On-road – all Diesel and Gasoline vehicles

This section includes the description of four EIS sectors:

Mobile – On-road – Diesel Heavy Duty Vehicles

Mobile – On-road – Diesel Light Duty Vehicles

Mobile – On-road – Gasoline Heavy Duty Vehicles

Mobile – On-road – Gasoline Light Duty Vehicles

They are treated here in a single section because the methods used are the same across all sectors.
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4.6.1 Sector Description

The four sectors for on-road mobile sources include emissions from motorized vehicles that are normally 

operated on public roadways. This includes passenger cars, motorcycles, minivans, sport-utility vehicles, lightduty trucks, heavy-duty trucks, and buses. The sectors include emissions from parking areas as well as emissions 

while the vehicles are moving.

SCCs starting with 22010 define the light duty gasoline vehicles including motorcycles, with the exception of 

SCCs starting with 220107, which define the heavy duty gasoline vehicles. SCCs starting with 22300 define the 

light duty diesel vehicles, with the exception of SCCs starting with 223007 that define the heavy duty diesel 

vehicles.

The 2008 NEI v1 and past NEIs included emissions from the MOBILE6 model. The 2008 NEI v2 and v3 are the 

first NEI to include emissions from the MOVES model.

4.6.2 Sources of data overview and selection hierarchy

All 2008 NEI on-road estimates were calculated by EPA using MOVES, except in California. Table 52 shows the 

selection hierarchy

Table 52: 2008 NEI on-road mobile selection hierarchy

Priority Dataset Name Dataset Content

1

2008_EPA_MOBILE EPA’s MOVES2010b-based estimates

Exception: California

1

EPA Correction Dataset – Onroad Overwrites submitted emissions that do not conform to 

pollutant and emissions types expected

2

Responsible Agency Dataset Submitted on-road emissions

California submitted emissions to the NEI based on the EMFAC model, which is a separately EPA-approved 

model to be used only in California. Because California’s emissions were calculated with a different model, the 

emissions are not congruent with the rest of the country in terms of SCCs used, pollutants present, and emission 

type coverage.

During the 2008 NEI development cycle for on-road mobile emissions, EPA requested that S/L/T agencies submit 

NMIM inputs for use in an EPA 2008 NEI NMIM run to generate MOBILE6-based emissions. At the start of the 

2008 NEI cycle, the MOVES model had not yet been released for criteria pollutants and the input formats were 

not stable, and so it was not possible for EPA to collect the MOVES input formats or MOVES-based emissions. A 

summary of the NMIM input submittals and EPA’s conversion of VMT to inputs is described in Section 3.2.3 of 

the project documentation for EPA’s mobile data (E.H. Pechan, 2011). EPA used the NMIM inputs to update the 

EPA NMIM input database. If an agency submitted on-road emissions (which includes VMT data) rather than 

NMIM inputs, then EPA compiled the VMT from this submittal for use in EPA’s NMIM run. EPA used the NMIM 

database to create 2008 on-road emissions using NMIM, which were used in version 1 and 1.5 of the 2008 NEI 

along with any emissions submitted by agencies that did not provide NMIM inputs. 
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After the formal 2008 NEI submission period had ended, EPA provided S/L/T agencies the opportunity to provide 

MOVES inputs. A few states provided these data, which were used in subsequent data development steps 

described below. No agencies submitted MOVES-based emissions estimates. EPA converted the NMIM 

database for input to MOVES and then overlaid these data with the MOVES inputs provided by some states. The 

resulting database was the starting point for the MOVES-based emissions described below, and as described, 

EPA continued to make changes to the database prior to running MOVES for the NEI. The MOVES databases did 

not change between 2008 v2 and 2008 v3.

Several tribes submitted data based on the MOBILE6 model, but these data were not included in the NEI 

selection because of the switch to a MOVES-based inventory. The tribal data are available in EIS. These tribes 

were: the Kootenai Tribe of Idaho, the Eastern Band of Cherokee Indians, the Nez Perce Tribe, the Northern 

Cheyenne Tribe, the Pueblo of Laguna, New Mexico, and the Shoshone-Bannock Tribes of the Fort Hall 

Reservation of Idaho.

4.6.3 Spatial coverage and data sources for the sector

The on-road mobile sectors include emissions in every state, Puerto Rico, and the US Virgin Islands.

[Placeholder for CAP and HAP maps and associated observations]

4.6.4 EPA-developed on-road mobile emissions data for the continental U.S.

For the 2008 NEI, EPA estimated emissions for every county in the U.S. except for California. For the continental 

U.S., we used a modeling framework that took into account the strong temperature sensitivity of the on-road 

emissions. Specifically, we used county-specific inputs and tools that integrated the MOVES model with the 

SMOKE19 emission inventory model to take advantage of the gridded hourly temperature information available 

from meteorology and air quality modeling. This integrated “SMOKE-MOVES” tool was developed by EPA in 

2010 and is in use by states and regional planning organizations for regional air quality modeling. SMOKEMOVES requires emission rate “lookup” tables generated by MOVES that differentiate emissions by process 

(running, start, vapor venting, etc.), vehicle type, temperature, speed, hour of day, etc. To generate the MOVES 

emission rates that could be applied across the U.S., EPA used an automated process to run MOVES to produce

emission factors by temperature and speed for 146 “representative counties,” to which every other county 

could be mapped, as detailed below. Using the MOVES emission rates, SMOKE selected appropriate emissions 

rates for each county, hourly temperature, SCC, and speed bin and multiplied the emission rate by activity (VMT 

(vehicle miles travelled) or vehicle population) to produce emissions. These calculations were done for every 

county, grid cell, and hour in the continental U.S. and aggregated to produce continental U.S. emissions. The 

MOVES “RunSpec” files (that tells MOVES what to run for each representative county) are available in the file 

“RepCounty_Runspecs.zip” (see Section 8.1 for access information). A full listing of datasets available as 

supporting information for the on-road MOVES runs is available in Section 8.1 and these are referenced in the 

subsections below.

EPA used a different approach for states and territories outside the lower 48 states. For Alaska, Hawaii, Puerto 

Rico and the Virgin Islands, EPA ran MOVES in “inventory mode” for each county and month, using countyspecific inputs. More information is provided Section 4.6.5.

 

19 SMOKE v3.1 was used for the 2008 NEI v3. The current version is available at: http://www.smoke-model.org/index.cfm
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SMOKE-MOVES can be used with different versions of the MOVES model. For the 2008 NEI v3, EPA used the 

latest publically released version: MOVES2010b (http://www.epa.gov/otaq/models/moves/index.htm). This 

version of the model included improvements to handling of refueling and extended idle emissions, addressed 

errors in the MOVES2010a emission rates for ammonia (NH3), nitrous oxide (NO) and nitrogen dioxide (NO2), 

and included the capability to model additional hazardous air pollutants. Details on the changes to air toxics are 

detailed in a separate technical report (US EPA, 2012). See the MOVES website for full documentation on 

MOVES2010b. Using SMOKE-MOVES for creating the NEI requires numerous steps, as described in the sections 

below:

Determine which counties will be used to represent other counties in the MOVES runs (see 

Section 4.6.4.1)

Determine which months will be used to represent other month’s fuel characteristics (see 

Section 4.6.4.2)

Create MOVES inputs needed only for MOVES runs (see Sections 4.6.4.3 and 4.6.4.4 ). MOVES requires 

county-specific information on vehicle populations, age distributions, and inspection-maintenance 

programs for each of the representative counties.

Create inputs needed both by MOVES and by SMOKE, including a list of temperatures and activity data

(see Sections 4.6.4.5 and 4.6.4.6).

Run MOVES to create emission factor tables (see Section 4.6.4.7)

Run SMOKE to apply the emission factors to activities to calculate emissions (see Section 4.6.4.8)

Aggregate the results at the county-SCC level for the NEI (see Section 4.6.4.9)

4.6.4.1 Representative counties

Although EPA compiles county-specific databases for all counties in the nation, actual county-specific data is 

rare. Instead, much of our “county” data is based on state-wide estimates or national defaults. For the NEI, 

rather than explicitly modeling every county in the nation, we have done detailed modeling for some counties 

and less detailed estimates for the other counties. This approach dramatically reduces the number of modeling 

runs required to generate inventories and still takes into account important differences between counties.

In this approach, we group counties that have similar properties that would result in similar emission rates. We 

explicitly model only one county in the group (the “representative” county) to determine emission rates. These 

rates are then used in combination with county specific activity and meteorology data, to generate inventories 

for all of the counties in the group. The grouping of counties was based on several characteristics as 

summarized in Table 53 below.
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Table 53: Characteristics for Grouping Counties

County Grouping Characteristic Description

PADD

Petroleum Administration for Defense Districts (PADDs). PADD 

1 is divided into three sub-PADD groupings and each sub-group 

is treated as a separate PADD (1a, 1b and 1c). Each state 

belongs to a PADD and all counties in any state are within the 

same PADD.

Fuel Parameters

Weighted average gasoline fuel properties for January and July 

2008, including RVP, sulfur level, ethanol fraction and percent 

benzene

Emission Standards

Some states have adopted California highway vehicle emission 

standards or plan to adopt them. Since implementation of the 

standards varies, each state with California standards is 

treated separately.

Inspection/Maintenance Programs

Counties were grouped within a state according to whether or 

not they had an inspection/maintenance (I/M) program. All

I/M programs within a state were considered as a single 

program, even though each county may be administered 

separately and have a different program design.

Altitude

Counties were categorized as high or low altitude based on the 

criteria set forth by EPA certification procedures (4,000 feet 

above sea level).

Fleet Age The weighted average age of passenger cars.

Total VMT County total vehicle miles traveled.

The result is a set of 146 county groups with similar fuel, emission standards, altitude, I/M programs and fleet 

age. For each group, the county with the highest total VMT was chosen as the representative county for the 

group (this VMT is not used to calculate the emissions however). The representative counties for the 2008 NEI 

v3 match those that were used for the 2007v5 platform, but the v3 representative counties have a different 

mapping from what was used in the 2008 NEI v2. A summary of the representative counties is available in the 

spreadsheet included in “MCXREF_2008v3.zip” and the MOVES County Database Manager databases are 

available in the file “RepCounty_Counties.zip” (see Section 8.1 for access information). 

For each county group, SMOKE-MOVES generated a set of emission rates that varied by SCC (vehicle type and 

road type), fuel, speed, temperature, and humidity; thus, we did not need to consider the fleet mix, fuel, speed, 

temperature range, or humidity in our grouping characteristics. This greatly increased the number of counties 

that can be grouped, and reduced the number of MOVES runs required.

4.6.4.2 Fuel months

The concept of a fuel month is used to indicate when a particular set of fuel properties should be used in a 

MOVES simulation. Similar to the reference county, the fuel month reduces the computational time of MOVES 

by using a single month to represent a set of months. Because there are winter fuels and summer fuels, EPA 

used January to represent October through April and July to represent May through September. For example, if 

the grams/mile exhaust emission rates in January are identical to February’s rates for a given reference county, 
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and temperature (as well as other factors), then we use a single fuel month to represent January and February. 

In other words, only one of the months needs to be modeled through MOVES. The hour-specific VMT, 

temperature and other factors for February are still used to calculate emissions in February, but the emission 

factors themselves do not need to be created since one month can represent the other month sufficiently. The 

fuel months used for each representative county are available in the spreadsheet included in 

“MFMREF_2008v3.zip” (see Section 8.1 for access information).

4.6.4.3 Fuels

Although state-submitted NMIM and MOVES input data may have included information about fuel properties, 

the MOVES runs for the 2008 NEI were run using a set of fuel properties for each county in 2008 generated by 

EPA. We developed these data using a combination of purchased fuel survey data, proprietary fuel refinery 

information and known federal and local regulatory constraints.

The 2008 fuels generated by EPA (dated 9/23/2011) were developed by interpolating between a 2005 reference 

fuel supply and a 2017 fuel supply that had been developed for use in EPA regulation development, using yearby-year gasoline fuel property regulations (such as sulfur and benzene control) and projected national ethanol 

penetration levels per year based on the 2011 Ethanol Industry Outlook (Renewable Fuels Association, 2011).

EPA made adjustments to align 10% ethanol (E10) fuel properties in interpolated years.

The following list provides a step-by-step outline of the interpolation steps applied to create the 2008 fuel 

supply database.

1) Methyl tertiary butyl ether, ethyl tert-butyl ether, and teriary amyl methyl ether fuel blends were 

removed from the 2005 fuel supply and replaced with appropriate E10 (a mixture of 10% ethanol and 

90% gasoline) levels and properties found from refinery modeling.

2) Reformulated Gasoline (RFG) areas were adjusted to contain only E10 blends and associated fuel 

properties.

3) Ethanol blends from 2005 were removed and replaced with appropriate properties found from the 

updated refinery modeling used to generate the 2017 fuel supply.

4) Gasoline sulfur levels were reduced to 30 ppm for all counties outside of the Geographic Phase-in Area 

(GPA). Counties within the GPA remain at the sulfur levels found in the 2005 reference case. The 

counties in the GPA are defined in the Code of Federal Regulations (CFR Title 40 Section 80.215).

5) E10 market share was adjusted by county to a minimum market share of 45%. Counties with market 

share above 45%, including RFG counties, remain at the higher market share.

6) Diesel fuel was carried over from the 2005 fuel supply. 

4.6.4.4 Other local MOVES inputs

In addition to fuels and the information also needed by SMOKE (in the following sections), MOVES also required 

inputs such as age distribution and I/M program descriptions for each of the representative counties. At the 

county level, these inputs provide an opportunity to assure that the model properly accounts for the most 
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recent available local data. When these data were available from the state-supplied NMIM inputs, we 

converted the NMIM data (version NCD20101201) for use in MOVES. EPA manually imported the 2008 data 

from Delaware and Utah into a MOVES format. Only data related to VMT, vehicle populations, speed 

distributions and age distributions were imported. Fuel data submitted by states was not used for the 2008 NEI 

in order to use the latest EPA estimates and make selecting representing counties easier. Similarly, 

meteorological data from states were not used, since the NEI calculations used the SMOKE generated 

meteorological data instead. Other state data from the NMIM data format were not used because of the 

project schedule and resource constraints.

In the few cases where MOVES input data were provided, we used that data. At their request, we converted 

2007 data (already in MOVES format) submitted by Florida and Shelby County, Tennessee for use in calendar 

year 2008, augmenting with 2008 calendar year VMT, population and average speed estimates. Extensive 2008 

data were provided by Texas, but these data were not easily converted to MOVES format, so EPA did not have 

time to include these data. EPA also received additional data from Connecticut, but the data were received too 

late to be included. When state-supplied MOVES data were not available, we used MOVES databases created 

from the NMIM database for 2008 discussed earlier.

When state-supplied data were not available either in the 2008 NMIM database or from subsequent 

submissions, we used MOVES defaults. In the state-provided data, EPA identified errors in age distributions 

provided for two counties in Arkansas (FIPS codes 05015, 05143) which resulted in anomalous results. Those 

age distributions were replaced with default distributions prior to the final run of MOVES for the NEI.

For the continental U.S., all of these MOVES inputs were organized by representative counties. This means that 

only the counties used to represent other counties had specific information for the MOVES runs. As listed in 

Section 8.1, the MOVES input data for the representative counties are available in several sets of files provided 

with the supporting data for this documentation.

4.6.4.5 Temperature and humidity

Ambient temperature can have a large impact on emissions. Low temperatures are associated with high start 

emissions for many pollutants. High temperatures are associated with greater running emissions due to the 

higher engine load of air conditioning. High temperatures also are associated with higher evaporative 

emissions.

The 12-km gridded meteorological input data for the entire year of 2008 covering the continental United States 

were derived from simulations of version 3.1 of the Weather Research and Forecasting Model (WRF, 

http://wrf-model.org), Advanced Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale 

numerical weather prediction system developed for both operational forecasting and atmospheric research 

applications. The Meteorology-Chemistry Interface Processor (MCIP) version 3.6

(http://www.cmascenter.org/help/model_docs/mcip/3.6/ReleaseNotes) was used as the software for 

maintaining dynamic consistency between the meteorological model, the emissions model, and air quality 

chemistry model. 

EPA applied the SMOKE-MOVES tool Met4moves to the gridded, hourly meteorological data (output from MCIP) 

to generate a list of the maximum temperature ranges, average relative humidity, and temperature profiles that 

are needed for MOVES to create the emission-factor lookup tables. “Temperature profiles” are arrays of 24 
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temperatures that describe how temperatures change over a day, and they are used by MOVES to estimate 

vapor venting emissions. The hourly gridded meteorological data (output from MCIP) was also used directly by 

SMOKE (Section 4.6.4.7).

The temperature lists were organized based on the representative counties and fuel months as described in 

Sections 4.6.4.1 and 4.6.4.2, respectively. Temperatures were analyzed for all of the counties that are mapped 

to the representative counties, i.e., for the county groups, and for all the months that were mapped to the fuel 

months. EPA used Met4moves to determine the minimum and maximum temperatures in a county group for 

the January fuel month and for the July fuel month, and the minimum and maximum temperatures for each 

hour of the day. Met4moves also generated idealized temperature profiles using the minimum and maximum 

temperatures and 10 degree intervals. In addition to the meteorological data, the representative counties and 

the fuel months, Met4moves uses spatial surrogates to determine which grid cells from the meteorological data 

to collect temperature and relative humidity statistics. For example, if a county had a mountainous area with no 

roads, this would be excluded from the meteorological statistics.

The treatment of humidity was simpler. Met4moves calculated an average day-time (6 am to 6 pm) relative 

humidity for the county group for the months mapped to July and for the months mapped to January. The 

humidity was also averaged over the grid cells intersecting the counties in the county group. When the emission 

factors are applied by SMOKE (Section 4.6.4.7), the appropriate (July or January) humidity was used for all runs 

of the county group.

Met4moves can be run in daily or monthly mode for producing SMOKE input. In monthly mode, the 

temperature range is determined by looking at the range of temperatures over the whole month for that 

specific county. Therefore, there is one temperature range per county per month. While in daily mode, the 

temperature range is determined by evaluating the range of temperatures in that county for that day. The 

output for the daily mode is one temperature range per county per day and is a more detailed approach for 

modeling the vapor venting emissions. EPA ran Met4moves in daily mode for 2008 NEI.

The resulting temperatures provided to the representative counties are available in the file 

“RepCounty_temperatures.zip” (see Section 8.1 for access information). The gridded, hourly temperature data 

used are publicly available only upon request and with provision of a disk media to copy these very large 

datasets (contact info.chief@epa.gov).

4.6.4.6 VMT, vehicle population, and speed

SMOKE requires county-specific VMT, population, and average speed by SCC to calculate the gridded or county 

emissions. Unlike the other inputs that are needed just for the representative counties, these inputs are needed 

for every county. When available, VMT and vehicle population estimates were obtained from data submitted by 

states. The state submitted input data are discussed in Section 4.6.4.4. As described above, most of the VMT 

information used was converted to a MOVES format from data originally supplied to EPA as NMIM input data. 

Data obtained from the NCD did not contain vehicle population data. When population data were not available, 

the vehicle population data were derived from the state supplied VMT data using methodologies provided in 

MOVES guidance for that purpose.

The average speeds provided to SMOKE for each county were derived from the default national average speed 

distributions found in the default MOVES2010b database AvgSpeedDistribution table. These average speeds are 
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the average speeds developed for the previous EPA highway vehicle emission factor model, MOBILE6. EPA used 

the MOVES distribution of average speeds for each hour of the day for each road type to calculate an overall 

average speed for each hour of the day. These hourly average speeds were weighted together using the default 

national average hourly VMT distribution found in the MOVES default database HourlyVMTFraction table, to 

calculate an average speed for each road type. This average speed by road type was provided to SMOKE for 

each county.

SMOKE requires VMT by county and SCC, but MOVES is not based on the traditional NEI SCCs. Because the VMT 

in each MOVES county database is by the broader category of “HPMSVtype”, it was necessary to allocate this 

VMT to the SCCs. We did this by running MOVES at the national level for 2008 with MOVES defaults. Then we 

used the activity output to determine default ratio of sourcetype VMT to HPMSVtype VMT. We also used this 

output to determine ratios of sourcetype/fueltype to sourcetype VMT. We used the 

NCD20110908.baseyearvmt to determine “roadtype ratios” i.e., allocation from MOVES roadtypes to 

SCCroadtypes by county and SCCvtype (same as P5vclass). Because some ratios were missing, we used ratios for 

cars (vtype=1) to fill in any missing ratios . Next we applied these ratios and the MOVES2010b

(MOVESdb20121030 sccvtypedistribution for model year 2008 to allocate VMT to SCCVtype. And we used 

roadtype ratios previously derived from NCD to allocate countyroadtype VMT to SCC roadtypes. Finally, we 

used county-specific monthvmtfractions to allocate VMT to each month.

Vehicle populations also had to be allocated to SCC. We started with state-provided (or default) MOVES inputs 

on vehicle populations by county. These were provided by vehicle sourcetype. We had to allocate this 

population by fueltype and to the various SCC categories. To do this, we ran MOVES at the national level for 

2008 with default inputs. This generated activity by sourcetype and fuel type, so we could determine the

default split of each sourcetype between gasoline and diesel fueled- vehicles. Using this ratio and the

MOVES2010b (MOVESdb20121030 sccvtypedistribution for model year 2008, we allocated the MOVES default 

population to SCC. 

The MOVES MySQL databases that include the VMT and vehicle population used for the representative counties 

are listed in Section 8.1. The SMOKE input VMT, vehicle population, speed data, and hourly speed profiles used 

to estimate emissions for every county are the same as what was used in 2008 NEI v2 and are available in the 

files “VMT_NEI_2008_updated2_18jan2012_v3.zip”, “VPOP_NEI_2008_18jan2012_v3.zip”, 

“SPEED_2008NEI_18nov2011_v0.zip”, and “spdpro_2008nei_18nov2011_v0.zip” (see Section 8.1 for access 

information).

4.6.4.7 Run MOVES to create emission factors

EPA used the SMOKE-MOVES driver scripts to run MOVES for each of the representative counties, fuel-months, 

and the listed temperatures and temperature profiles. The runspec generator created a series of runspecs 

(MOVES jobs) based on the outputs from Met4moves. Specifically, the script used a 5 degree bin and the 

minimum and maximum temperature ranges from Met4moves and used the idealized diurnal profiles from 

Met4moves to generate a series of MOVES runs that captured the full range of temperatures for each 

representative county. The SMOKE-MOVES driver scripts resulted in three emission factors (EF) tables for each 

representative county and fuel month: rate per distance (RPD), rate per vehicle (RPV), and rate per profile (RPP). 

After the MOVES runs were completed, the post-processor Moves2smk converted the MySQL tables into EF files 

that can be read by SMOKE. For more details, see the SMOKE documentation: http://www.smokemodel.org/version3.1/html/ch05s02s04.html. 
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4.6.4.8 Run SMOKE to create emissions

Lastly, EPA generated air quality model ready emissions at a gridded and hourly resolution. The Movemrg 

SMOKE-MOVES program performs this function by combining activity data, meteorological data, and emission 

factors to produce gridded, hourly emissions. EPA ran Movesmrg for each of the three sets of emission factor 

tables (RPD, RPV, and RPP). During the Movesmrg run, the program uses the hourly, gridded temperature (for 

RPD and RPV) or daily temperature profile (for RPP) to select the proper emissions rates and compute emissions. 

These calculations were done for all counties and SCCs in the SMOKE inputs, covering the continental U.S. 

The emissions process RPD is for modeling the on-network emissions. This includes the following modes: vehicle 

exhaust, evaporation, evaporative permeation, brake wear, and tire wear. For RPD, the activity data is monthly 

VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus weekend (SPDPRO)20. The SMOKE 

program Temporal takes vehicle and roadtype specific temporal profiles and distributes the monthly VMT to day 

of the week and hour. Movesmrg reads the speed data for that county and SCC and the temperature from the 

gridded hourly (MCIP) data and uses these values to look-up the appropriate emission factors (EFs) from the 

representative county’s EF table. It then multiplies this EF by temporalized VMT to calculate the emissions for 

that grid cell and hour. This is repeated for each pollutant and SCC in that grid cell.

The emission process RPV is for modeling the off-network emissions. This includes the following modes: vehicle 

exhaust, evaporative, and evaporative permeation. For RPV, the activity data is vehicle population (VPOP). 

Movesmrg reads the temperature from the gridded hourly data and uses the temperature plus SCC and the hour 

of the day to look up the appropriate EF from the representative county’s EF table. It then multiplies this EF by 

the VPOP for that SCC and FIPS to calculate the emissions for that grid cell and hour. This repeats for each 

pollutant and SCC in that grid cell.

The emission process RPP is for modeling the off-network emissions for parked vehicles. This includes the mode 

vehicle evaporative (fuel vapor venting). For RPP, the activity data is VPOP. Movesmrg reads the county based 

diurnal temperature range (Met4moves’ output for SMOKE). It uses this temperature range to determine a

similar idealized diurnal profile from the EF table using the temperature min and max, SCC, and hour of the day. 

It then multiplies this EF by the VPOP for that SCC and FIPS to calculate the emissions for that grid cell and hour. 

This repeats for each pollutant and SCC within the county. 

The result of the Movesmrg processing is hourly, gridded data suitable for use in air quality modeling as well as 

daily reports for the three processing streams (RPD, RPV, and RPP). The results include emissions for every 

county in the continental U.S., rather than just for the representative counties.

4.6.4.9 Post-Processing to Generate Annual Inventory

For the purposes of the NEI, EPA needed emissions data by county, SCC, pollutant, and emission type (exhaust, 

evaporative, brake wear, and tire wear). EPA developed and used a set of scripts to combine the emissions from 

the three sets of reports and from all days to create the annual inventory.

 

20 If the SPDPRO file is available, the hourly speed takes precedence over the average monthly speed. 
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A select set of metals were generated through a separate process. Instead of having county, process, and 

temperature specific emission factors, a national EF for each pollutant/SCC combination was multiplied by the 

appropriate VMT for a specific county to create annual emissions for that pollutant. Table 54 lists the pollutants 

that we estimated using national EFs.

Table 54: Pollutants estimated through national emission factors

NEI pollutant Description

16065831 Chromium III

18540299 Chromium (VI)

7439965 Manganese

7440020 Nickel

7440382 Arsenic

The on-road emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands, which EPA generated via MOVES in 

inventory mode (see Section 4.6.5) were appended to the on-road inventory generated from SMOKE-MOVES. 

The emissions for metals and dioxins were also appended to the on-road inventory to create the final emissions. 

This complete inventory was submitted to the EIS as the EPA estimates for the on-road sector. The resulting EIS 

dataset is named “2008_EPA_MOBILE”.

4.6.5 EPA-developed on-road mobile emissions data for Alaska, Hawaii, Puerto Rico and the 

Virgin Islands

Since the meteorology domain used by EPA for running SMOKE-MOVES covered only the continental U.S., EPA 

used the MOVES “inventory mode” to create emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands. 

These runs used the average monthly hourly temperatures and humidity values available in the MOVES 

database as derived from the NMIM database described above (see Section 4.6.4.4). These emissions 

characterized all pollutants including a full set of metals and dioxins.

The MOVES inputs used for these emissions are available as described in Section 8.1. The file 

“AKHIPRVI_Counties.zip” contains the MOVES county database manager databases, and the file 

“AKHIPRVI_Runspecs.zip” contains the run specifications used to run MOVES. Lastly, the file 

“akhiprvi_temperatures.zip” contains the MySQL database containing the tables that describe the temperatures 

and relative humidity values used for these states and territories. 

4.6.6 Summary of quality assurance methods

EPA did a series of checks and comparisons against both the inputs and the resulting emissions to quality assure 

the on-road inventory. The following is a list of the more significant checks and resulting corrections:

Checked the VMT data by comparing the 2008 with a 2005 based activity data. Also analyzed the ratio 

VMT to vehicle population to look for extreme values. Identified widespread errors in ID and NV. 

Found additional problems in two counties in CA and 10 counties in VA. Updated the VMT in 

consultation with OTAQ. Reran RPD (the processes that are dependent on VMT) for the above counties.

Checked the consistency of VMT with vehicle population and identified counties in which there was VMT 

but no vehicle population. Updated the vehicle population in consultation with OTAQ for the following 
123

FIPS (16061, 30069, 31005, 51610, and 51685). Reran RPP and RPV (processes that are dependent on 

vehicle population) for these counties.

Many counties in Texas had identical extremely high populations and VMT. We reran all 254 Texas 

counties using older data.

Three counties in Florida and one in Tennessee were missing monthvmt and roadtypedistribution tables 

and had to be re-run using MOVES default values.

The county databases for Norton City VA (51720) listed zero VMT and zero population. We substituted 

county data from the 2005 NEI.

Cottonwood, MN (27033) had an unreasonably low vehicle population of only 82 vehicles. Instead, we 

used VMT from NCD 20101201 and Population/VMT ratios from the 2005 NEI.

Three counties in Florida (12086, 12033, 12057) had populations and VMT that were inconsistent with 

independent sources. We substitute more consistent county inputs provided in spring 2011.

Identified a large number of missing SCCs in the activity data for Georgia. Determined that there was a 

truncation problem in the conversion of the MOBILE6 activity data submitted by the states into MOVES 

activity data. Returned to the original state submitted VMT data and reprocessed it for SMOKE-MOVES.

Identified errors in age distributions in two counties in Arkansas (FIPS codes 05015, 05143) which 

resulted in anomalous results. The state supplied age distributions were replaced with default 

distributions prior to the final run of MOVES for the NEI. Generated new EFs for these two reference 

counties and reran RPD, RPP, and RPV for all of Arkansas and Louisiana.

Compared the on-road results to similar results from the previous version of the 2008 NEI (v2 and v1). 

The previous version was prepared using MOBILE6. We found numerous differences between the two 

sets of results. Detailed comparisons by state, county and SCC vehicle type showed that most of the 

differences were due to updated input data from the states, or to differences between the two emission 

models. In particular, based on an updated understanding of vehicle emissions, the MOVES model 

generally predicts much higher NOx, PM and ammonia emissions compared to the MOBILE6 model. 

And, the MOVES model generally predicts lower emissions of hydrocarbons and carbon monoxide

(Beardsley, 2010). These trends were evident in the comparisons of the two NEI versions. 

Compared the 2008 NEI v3 with a similar run done for 2005 using 2005 inputs. In general, this 

comparison indicated the expected growth of emissions over those three years. It also identified an 

error in two county-specific age distributions that were fixed before the 2008 NEI was finalized, and 

identified errors in county VMT and populations that we were able to repair before finalizing the 

inventory. 

Air toxic results were quality assured by back-calculating toxics ratios from inventory outputs to ensure 

they were consistent with inventory inputs.