# 2008 National Emissions Inventory, version 3 Technical Support Document

September 201 3

- DRAFT112 Table 51 : Nonroad SCCs included in 2008 NEI that were not in S/L/T agency s ubmittal s S tate 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 Equipment Other 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 Dies el 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 c onsidered 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 cri teria 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 differe nt 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 count s. Only California data were used in this case. The 2008 NE I 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 SCC s and pollutants submitted by tribes, which can be different from the county data

. Agencies emissions are likely to capture local scale detail s 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.113

4.6.1 Sector Description The four sectors for on

- road mobile sources include emission s from motorized vehicles that are normally operated on public roadways. This includes passenger cars, motorcycles, minivans, sport

- utility vehicles, light

duty trucks, heavy

- duty trucks, and buses. The sectors include emissions from parking areas as wel l 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. Tab le 52 s hows the selection hierarchy

Tab le 52 : 2008 NEI on

- road mobile selection h ierarchy Priority Dataset Name Dataset Content

1 2008_EPA_MOBILE EPA’s MOVES2010b

- based estimates

Exception : California

1 EPA Correction Dataset

– O nroad Overwrites submi tted emissions that do not conform to pollutant and emis sions 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 Calif ornia. 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 do cumentation 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 submitt al 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.114

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 b ecause 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 Mexic o, 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 map s 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 t ook 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 SMOKE 19 emission inventory model to take advantage of the gridded hourly temperatu re 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. SMOKE

MOVES requires emissi on 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 u sed an automated process to run MOVES to produ ce 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 emissi ons 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 i n 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 appr oach for states and territories outside the lower 48 states. F or Alaska, Hawaii, Puerto Rico and the Virgin Islands, EPA ran MOVES in “inventory mode” for each county and month, using county

specific 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.cfm115

SMOKE

- MOVES can be used with different versions of the MOVES model. For the 2008 NEI v3 , EPA used the latest publically release d version: MOVES 2010 b ( 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 i ncluded 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 f ull documentation on MOVES2010b

. Using SMOKE

- MOVES for crea ting 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 represen t other month’s fuel characteristics (see Section 4.6.4.2 ) Create MOVES inputs needed only for MOVES runs ( see Section s 4.6.4.3 and 4.6.4.4 ). MOVES requires county

- spe cific 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 Sect ion 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, a ctual county

- specific data is rare. Instead, much of our “county” data is based on state

- wide estimates or national defaults. For the NEI,

r ather than explicitly model ing every county in the nation, we have done detailed modeling for some counties and le ss detailed estimates for the other counties. This approach dramatically reduces the number of modeling runs required to generate inventories and still takes into accoun t important differences between counties.
In this approach, we group counties that hav e similar properties that would result in similar emission rates. W e 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 meteo rology 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.116 Table 53 : Character istics 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 s tate 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.

Altitu de 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.

T he r esult is a set of 146 county groups with similar fuel, emission standards, altitude, I/M program s and fleet age. For each group, the county with the highest total VMT was chos en 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 representativ e counties is available in the spreadsheet included in “MCXREF_2008 v3 .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 groupin g characteristics. This greatly increased the number of counties that can be grouped , and reduce d 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 M OVES 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 ,117

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 the mselves 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 th e spreadsheet included in “MFMREF_2008 v3 .zip” (see Section 8.1 for access information)

.
4.6.4.3 Fuels Although state

- submitted NMIM and MOVES input data may have include d 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 d eveloped 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 devel oped by interpolating between a 200 5 reference fuel supply and a 2017 fuel supply that had been developed for use in EPA regulation development, using year

by

- year gasoline fuel property regulations (such as sulfur and benzene control) and projected national ethanol penetration levels per y ear based on the 2011 Ethanol Industry Outlook (R enewable F uels A ssociation , 2011 ). EPA made a djustments to align 10% ethanol (E10) fuel pro perties in interpolated years.

The f ollowing list provides a step

- by

- step out line of the interpolation steps applie d 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) l evels 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 up dated 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 c ase. 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 t he higher market share.

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

4.6.4.4 Other l ocal MOVES i nputs 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 most118

recent available local data. When the s e data were available from the state

- s upplied 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 distributio ns 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, s ince the NEI calculations u sed 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 ca ses where MOVES input data were provided, we used that data. At their req uest, we converted 2007 data (already in MOVES format) submitte d by Florida and Shelby County, Tennessee for use in calendar year 2008, augmenting with 2008 calendar year VMT, population and average speed estimates. Extens ive 2008 data were provided by Te xas , 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 h umidity Ambient temperature can have a large impact on emissions. Low temperatur es 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.

T he 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 Rese arch 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 re search applications

. The Meteorology

- Chemistry Interface Proce ssor (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 mode l , 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 temper ature profiles that are needed for MOVES to create the emission

- factor lookup tables. “ Temperature profiles ” are arrays of 24119

temperatures that describe how temperatures change over a day, and t hey are used by MOVES to estimate vapor venting emissions

. T he 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 Section s 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 mappe d 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 generat ed 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. Met4mov es 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 ), t he 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. I n 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 deter mined 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 p ublicly 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 p opulation, and s peed
SMOKE requires county

- specific VMT, popula tion, 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, m ost of the VMT information used was converted to a MOVES format from data originally supplied to E PA 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 f or that purpose.
The average speeds provided to SMOKE for each county were derived from the default national average speed distributions found in the default MOVES2010 b database AvgSpeedDistribution table. These average speeds are120

the average speeds devel oped for the previous EPA highway vehicle e mission 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 s peeds 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 fo r 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 th is 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 missi ng ratios . Next we applied these ratios and the MOVES2010 b (MOVESdb201 21030 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. F inally, 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 vehic le 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

- vehicle s. Using this ratio and the MOVES2010b (MOVESdb201 21030 sccvtypedistribution for model year 2008, we allocated the MOVES default population to SCC.
The MOVES MySQL databases th at 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 coun ty 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 gen erator 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 gene rate 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.smoke

model.org/version3.1/html/ch05s02s04.html

.121

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

- M OVES program performs this function by combin ing 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, coverin g 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, mon thly speed (SPEED) , and hourly speed profiles for weekday versus weekend (SPDPRO) 20 . The SMOKE
program T emporal takes vehicle and roadtype specific temporal profiles and distributes the monthly VMT to day of the week and hour. Movesmrg reads the speed dat a 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 ( EF s) from the representative county’s EF table. It then multiplies this EF by temporalized VMT to calculat e 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 evaporat ive 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 f or 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 (M et4moves’ output for SMOKE ) . It uses this temperature range to determin e 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 eac h 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 fo r 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 hourl y speed takes precedence over the average monthly speed.122

A select set of m etals were generated through a separate process. Instead of h aving 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 E F s.

Table 54 : Pollutants estimated through national emission factors NEI pollutant Description 16065831 Chromium III 18540299 Chromium (VI) 7439965 Manganese 7440020 Nicke l 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 M OVES “ inventory mode” to crea te emissions f or 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 MOV E S 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, t he file

“ akhiprvi_temperatures.zip ” c ontains the MySQL database containing the tables that describ e 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 invent ory. 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 w idespread 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 process es that are dependent on VMT) for the above counties. Checked the consistency of VMT with vehicle population and i dentified counties in which there was VMT but no vehicle population . Updated the vehicle population in consultation with OTAQ for the following123

FIPS ( 16061, 30069, 31005, 51610, and 51685) . Reran RPP and RPV (processes that a re 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 road typedistribution 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 vehi cle 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 sta tes into M OVES 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. Gen erated new EFs for these two reference counties and reran RPD, RPP, and RPV for all of A rkansas and Louisiana

. Compared the on

- road results to simi lar results from the previous version of the 2008 NEI (v2 and v1)

. The previous version was prepared using MOBILE6. We found numerous differences bet ween the two sets of results. Detailed comparisons by state, county and SCC vehicle type showed that mos t 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 ammon ia 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 v 3 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 wa s 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 wi th inventory inputs.