Emissions Modeling for the Final Mercury and Air Toxics Standards
Technical Support Document

                                                               EPA-454/R-11-011
                                                                  December 2011

Emissions Modeling for the Final Mercury and Air Toxics Standards
Technical Support Document

                                      By:
                                  Alison Eyth
                                  Rich Mason
                                 Alexis Zubrow

                     U.S. Environmental Protection Agency
                          Office of Air and Radiation
                 Office of Air Quality Planning and Standards
                        Air Quality Assessment Division

                               TABLE OF CONTENTS

LIST OF TABLES	v
LIST OF APPENDICES	vi
ACRONYMS	vii
1	Introduction to the Modeling Platform	1
2	2005 Emission Inventories and Their Preparation	2
2.1	Custom configuration for emissions modeling for MATS	4
2.2	Onroad mobile sources (onroad)	6
2.2.1	MOVES	6
2.2.2	Representing counties	7
2.2.3	SMOKE-MOVES inputs	11
2.2.4	Generating emission factors for SMOKE	12
2.2.5	Running SMOKE for onroad mobile	13
2.3	Nonroad mobile sources (nonroad, alm_no_c3, seca_c3)	14
2.3.1	Emissions generated with the NONROAD model (nonroad)	14
2.3.2	Locomotives and commercial marine vessels (alm_no_c3, seca_c3)	18
2.4	2005 point sources (ptipm and ptnonipm sectors)	18
2.4.1	Ethanol plants (ptnonipm)	19
2.5	2005 nonpoint sources (afdust, ag, avefire, nonpt)	20
2.5.1	Portable fuel containers	20
2.5.2	Onroad refueling	20
2.6	Other sources (biogenics, othpt, othar, and othon)	21
2.7	Emissions summaries for 2005 base case	21
3	VOC Speciation Changes that Represent Fuel Changes	23
4	2017 Reference Case	27
4.1	Stationary source projections:  EGU sector (ptipm)	33
4.2	Stationary source projections:  non-EGU sectors (ptnonipm, nonpt, ag, afdust)	34
4.2.1	Ethanol plants (ptnonipm)	35
4.2.2	Biodiesel plants (ptnonipm)	35
4.2.3	Portable fuel containers (nonpt)	37
4.2.4	Cellulosic fuel production (nonpt)	37
4.2.5	Ethanol transport and distribution (nonpt)	38
4.2.6	Onroad refueling (nonpt)	38
4.2.7	Refinery adjustments (ptnonipm)	39
4.2.8	Ethanol transport gasoline and blends (ptnonipm, nonpt)	39
4.2.9	Upstream agricultural adjustments (afdust, ag, nonpt, ptnonipm)	39
4.2.10	Livestock emissions growth (ag, afdust)	40
4.2.11	Residential wood combustion growth (nonpt)	40
4.2.12	Aircraft growth (ptnonipm)	41
4.2.13	Stationary source control programs, consent decrees & settlements, and plant closures (ptnonipm, nonpt)	42
4.2.14	Oil and gas projections in TX, OK, and non-California WRAP states (nonpt)	47
4.3	Onroad mobile source projections (onroad)	47
4.3.1	California LEV	48
4.4	Nonroad mobile source projections (nonroad, alm_no_c3, seca_c3)	48
4.4.1	Emissions generated with the NONROAD model (nonroad)	48
4.4.2	Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3)	49
4.4.3	Class 3 commercial marine vessels (seca_c3)	51
4.5	Canada, Mexico, and offshore sources (othar, othon, and othpt)	52
4.6	Reference case emission summaries	52
5	MATS Control Case	58
6	References	62

                                 LIST OF TABLES

Table 1-1.  List of cases run in support of the MATS air quality modeling for the RIA	2
Table 2-1.  Sectors used in emissions modeling for the final MATS 2005v4.3 platform	3
Table 2-2.  Model species produced by SMOKE for CB05 with SOA for the MATS platform	5
Table 2-3.  Description of differences in ancillary data (unrelated to SMOKE to MOVES) between the MATS 2005 case and the 2005v4.2 platform	6
Table 2-4.  Allocation of states to the Petroleum Administration for Defense Districts	8
Table 2-5.  Gasoline parameter categories	9
Table 2-6.  States adopting California emission standards	10
Table 2-7.  Summary of county grouping characteristics for representative counties	11
Table 2-8.  Updated 1,3-butadiene to VOC ratio for 2-stroke snowmobiles for NMIM's gasoline categories	15
Table 2-9.  Criteria for grouping representative counties for nonroad mobile analysis	16
Table 2-10.  NONROAD model temperature (F) categories	17
Table 2-11.  NONROAD NMIM runs	17
Table 2-12.  Summary of NONROAD modeling components	18
Table 2-13.  2005 ethanol plant emissions	19
Table 2-14.  2005 U.S. emissions (tons/year) by sector	21
Table 2-15.  2005 base year SO2  emissions (tons/year) for states by sector	21
Table 2-16.  2005 base year PM2.5 emissions (tons/year) for states by sector	22
Table 3-1.  Summary of VOC speciation profile approaches by sector across cases	25
Table 4-1.  Control strategies and growth assumptions for creating the 2017 reference case emissions inventories from the 2005 base case	30
Table 4-2.  MATS reference case mobile source-related projection methods	34
Table 4-3.  2017 reference case corn ethanol plant emissions	35
Table 4-4.  2017 biodiesel plant emissions	37
Table 4-5.  PFC emissions for 2017	37
Table 4-6.  2017 cellulosic plant emissions	38
Table 4-7.  VOC losses (Emissions) due to ethanol transport and distribution	38
Table 4-8.  Onroad gasoline and diesel refueling emissions	39
Table 4-9.  Impact of refinery adjustments on 2017 emissions	39
Table 4-10.  Upstream agricultural emission increases due to RFS2 fuels in 2017	39
Table 4-11.  Growth factors from year 2005 to 2017 for animal operations	40
Table 4-12.  Projection factors for growing year 2005 residential wood combustion sources	41
Table 4-13.  Impact of year 2017 projection factor error on residential wood combustion estimates	41
Table 4-14.  Factors used to project 2005 base-case aircraft emissions to 2017	42
Table 4-15.  Summary of non-EGU emission reductions applied to the 2005 inventory due to unit and plant closures	43
Table 4-16.  Future-year ISIS-based cement industry annual reductions (tons/yr)  for the non-EGU (ptnonipm) sector	45
Table 4-17.  State-level non-MACT boiler reductions from ICR data gathering	45
Table 4-18.  National impact of RICE controls on non-EGU projections	46
Table 4-19.  Impact of fuel sulfur (SO2) controls on 2017 non-EGU projections	46
Table 4-20.  Oil and gas NOX and SO2 emissions for 2005 and 2017 including additional reductions due to the RICE NESHAP	47
Table 4-21.  Factors applied to year 2005 emissions to project locomotives and class 1 and class 2 commercial marine vessel emissions to 2017	50
Table 4-22.  Additional class 1 railroad and C1/C2 CMV emissions from RFS2 fuel volume changes	51
Table 4-23.  NOX, SO2, and PM2.5 factors to project class 3 CMV emissions for 2017	52
Table 4-24.  Summary of modeled base case SO2 and PM2.5 annual emissions (tons/year) for 48 states by sector	53
Table 4-25.  Reference case SO2 emissions (tons/year) for states by sector	54
Table 4-26.  Reference case PM2.5 emissions (tons/year) for states by sector	55
Table 4-27.  Future year baseline EGU CAP emissions (tons/year) by state	56
Table 5-1.  Summary of emissions changes for the MATS AQ modeling in the lower 48 states	58
Table 5-2.  EGU emissions totals for the Mmdeled MATS control case in the lower 48 states	58
Table 5-3.  State-specific changes in annual EGU SO2 for the lower 48 states	59
Table 5-4.  State-specific changes in annual EGU PM2.5 for the lower 48 states	61

                               LIST OF APPENDICES
APPENDIX A: Ancillary Datasets and Parameters Used for Each MATS Modeling Case
APPENDIX B: Inventory Data Files Used for Each MATS Modeling Case  -  SMOKE Input Inventory Datasets
APPENDIX C: Summary of MATS Rule 2017 Base Case Non-EGU Control Programs, Closures and Projections

                                    ACRONYMS
AEO	Annual Energy Outlook
BEIS	Biogenic Emission Inventory System
bps	Buly plant storage
btp	Bulk plant terminal-to-pump
C3	Category 3 (commercial marine vessels)
CAP	Criteria Air Pollutant
CMAQ	Community Multiscale Air Quality
CSAPR	Cross-State Air Pollution (formerly Transport) Rule
E0	0% Ethanol gasoline (by volume)
E10	10% Ethanol gasoline
E15	15% Ethanol gasoline
EGU	Electric Generating Utility
EISA	Energy Independence and Security Act of 2007
EPAct	Energy Policy Act of 2005
FAA	Federal Aviation Administration
FIPS	Federal Information Processing Standard
HAP	Hazardous Air Pollutant
HDGHG	Heavy Duty Greenhouse Gas
HONO	HNO2, nitrous acid
IPM	Integrated Planning Model
LDGHG	Light Duty Greenhouse Gas
MOBILE6	Mobile Source Emission Factor Model, version 6
MOVES	Motor Vehicle Emissions Simulator
MY	Model Year
NEEDS	National Electric Energy Database System
NEI	National Emission Inventory
NMIM	National Mobile Inventory Model
OAQPS	EPA's Office of Air Quality Planning and Standards
ORL	One Record per Line (a SMOKE input format)
MP	Multipollutant
NO	Nitric oxide
NO2	Nitrogen dioxide
NOX	Nitrogen oxides
PFC 	Portable Fuel Container
PEC	Elemental carbon component of PM2.5
PMFINE	Leftover "Other", or "crustal" component of PM2.5
PNO3	Particulate nitrate component of PM2.5
PSO4	Particulate sulfate component of PM2.5
POC	Organic carbon component of PM2.5
rbt	Refinery-to-bulk terminal
RFS2	Revised annual renewable fuel standard (mandate)
SMOKE	Sparse Matrix Operator Kernel Emissions
SCC	Source Category Code
TAF	Terminal Area Forecast
TSD	Technical Support Document
VOC	Volatile Organic Compound
WRAP	Western Regional Air Partnership

    Introduction to the Modeling Platform
This Technical Support Document (TSD) describes the development of the emissions inventories used as inputs to the air quality modeling that the U.S. Environmental Protection Agency (EPA) performed to assess the impact of the Mercury and Air Toxics Standards (MATS).  This document provides the details of emissions modeling done to support the development of the Regulatory Impact Assessment (RIA) for the MATS.  The emissions processing described herein and the corresponding air quality modeling were used to develop benefit-per-ton scaling factors for the benefits calculation as described in the RIA.  More information on this approach can be found in Appendix 5C of the RIA and in the Air Quality Modeling Technical Support Document (TSD).  The emissions inventories were using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (http://www.smoke-model.org/index.cfm) version 2.7 processed into the form required by the Community Multi-scale Air Quality (CMAQ) model.  CMAQ simulates the numerous physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and air toxics.

As part of the analysis for this rulemaking, the modeling system was used to calculate daily and annual PM2.5 concentrations, 8-hr maximum ozone and visibility impairment. Model predictions of PM2.5 and ozone are used in a relative sense to estimate scenario-specific, future-year design values of PM2.5 and ozone. These are combined with monitoring data to estimate population-level exposures to changes in ambient concentrations for use in estimating health and welfare effects.  In this document, we provide an overview of (1) the emissions components of the modeling platform, (2) the development of the 2005 base year emissions, (3) the development of the future year baseline emissions, and (4) the development of the future year control case emissions.

A modeling platform is the collection of the inputs to an air quality model, including the settings and data used for the model, including emissions data, meteorology, initial conditions, and boundary conditions.  The 2005-based air quality modeling platform used for the proposed utility NESHAP RIA includes 2005 base year emissions and 2005 meteorology for modeling ozone and PM2.5 with CMAQ.  In support of this rule, EPA modeled the air quality in the Eastern and the Western United States using two separate model runs, each with a horizontal grid resolution of 12 km x 12 km.  These 12 km modeling domains were "nested" within a modeling domain covering the remainder of the lower 48 states and surrounding areas using a grid resolution of 36 x 36 km.  The results from the 36-km modeling were used to provide incoming "boundary" for the 12km grids.  Additional details on the non-emissions portion of the 2005v4.3 modeling platform used for the RIA are described in the air quality modeling TSD.

The 2005-based air quality modeling platform used in support of the RIA is version 4.3 and is referred to as the 2005v4.3 platform.  It is an update to the 2005-based platform, version 4.1 (i.e., 2005v4.1) used for the proposal modeling and for the appropriate and necessary finding. The Technical Support Document "Preparation of Emissions Inventories for the Version 4.1, 2005-based Platform" (see http://www.epa.gov/ttn/chief/emch/index.html#toxics) provides information on the platform used for the proposed version of this rule and for the appropriate and necessary finding.  The 2005v4.3 platform builds upon the 2005-based platform, version 4.2, which was the version of the platform used for the final Cross-State Air Pollution Rule and incorporated changes made in response to public comments on the proposed version of that rule.

Table 1-1 provides a high-level summary of the three emissions cases that were modeled in support of the final rule RIA.  The form of the fuel used for mobile sources is a key discriminator between the cases.  Therefore, the mobile source emissions are described with respect to the impacts of the Energy Independence and Security Act of 2007 (EISA) and the Energy Policy Act of 2005 (EPAct) on mobile source fuels.

Table 1-1.  List of cases run in support of the MATS air quality modeling for the RIA
Case Name
Internal EPA Abbreviation
Description
2005 base case
2005ct
2005 calendar year case / scenarios that uses an average year temporal allocation approach for Electrical Generating Units (EGUs), a pre-EISA/EPAct fuel supply for mobile sources, and average year fires data. Air quality outputs from this case are used to compute relative response factors with the 2017 future year reference case scenarios.
2017 reference case
2017ct_ref
2017 future year baseline scenario with EGU emissions that represent the implementation of the Cross-State Air Pollution Rule (CSAPR) and mobile sources representing the implementation of the EISA/EPAct fuel supply (RFS2 Rule) along with average year fire data.
2017 control case
2017ct_ref_mats
2017 "control" or remedy case scenario with EGU emissions that represent the implementation of both CSAPR and MATS, and mobile sources representing implementation of the EISA/EPAct fuel supply (RFS2), along with average year fire data.
In the remainder of this document, we provide a description of the approaches taken for the emissions in support of air quality modeling for the MATS.  In Section 2, we describe the 2005v4.3 platform custom configurations, ancillary data and 2005 inventory differences from the v4.2 platform.  In Section 3, we describe the speciation differences among each of the cases run.  In Section 4, we describe the 2017 Reference (i.e., future year baseline) case as compared to the 2005 base case. Appendix A provides a comparison of the ancillary datasets and parameters used for the various MATS emissions cases, and Appendix B compares the emissions inventory and other input data files used for each of the MATS cases.
    2005 Emission Inventories and Their Preparation
As mentioned previously, the 2005 emissions modeling approach for MATS used much of the same data and approaches as the 2005v4.2 platform.  In this section, we identify the differences between the data used for the MATS 2005v4.3 platform and that used for the 2005v4.2 platform.  Section 2.1 provides ancillary data differences that impact multiple sectors.  Section 2.2 discusses the new approach used for emissions preprocessing and processing for all onroad mobile sources.  Section 2.3 discusses the updated nonroad mobile components.  Sections 2.2 and 2.65 provide differences for the nonpoint and nonpoint (area) inventories, respectively.

The data used in the 2005 emissions case is often the same as those described in the Final Cross-State Air Pollution Rule TSD (http://www.epa.gov/ttn/chief/emch/index.html#2005), also known as the CAP-BAFM 2005-based Version 4.2 Platform (i.e., 2005v4.2). However, some different emissions data are used for this rulemaking.  All of the documentation provided here describes what was done differently and specifically for the MATS in contrast to what was done for the 2005v4.2 platform.

In MATS, we used a 2005 base case approach for the year 2005 emissions scenario.  This approach is very similar to that CSAPR Final Rule (formerly known as the "Transport Rule").  A base case approach uses average year fires and EGU temporal profiles from three years of EGU data.  We use a base case approach because we want to reduce year-specific variability in some components of the inventory.  For example, large fires vary in location and day of the year each year, and EGU shutdowns and high use on high energy demand days also vary by year.  By using a base case approach, these two aspects of the inventory are maintained into the future year modeling and therefore do not introduce potentially spurious year-specific artifacts into the air quality modeling estimates.  For MATS, the same biogenic emissions data as the 2005v4.2 platform was used for the 2005 case, and also for both future-year cases.  The only significant data changes between the 2005 and the 2017 future-year MATS case are the emission inventories and speciation approaches.

Table 2-1 below lists the platform sectors used for the MATS modeling platform.  It also indicates which platform sectors include HAP emissions and the associated sectors from the National Emission Inventory (NEI).  Subsequent subsections refer to these platform sectors to identify the emissions differences between the 2005v4.2 platform and the MATS 2005v4.3-based platform.
Table 2-1.  Sectors used in emissions modeling for the final MATS 2005v4.3 platform
Platform Sector
2005 NEI Sector
Description
Contains HAP emissions?
IPM sector: ptipm
Point
NEI EGU units at facilities mapped to the IPM model using the National Electric Energy Database System (NEEDS) database.
Yes
Non-IPM sector: ptnonipm
Point[+]
All NEI point source units not matched to the ptipm sector, including airports.
Yes
Average-fire sector:   avefire
N/A
Average-year wildfire and prescribed fire emissions, county and annual resolution.
Yes
Agricultural sector: ag
Nonpoint
Ammonia (NH3) emissions from NEI nonpoint livestock and fertilizer application.
No
Area fugitive dust sector: afdust
Nonpoint
PM10 and PM2.5 emissions from fugitive dust sources in the NEI nonpoint inventory.
No
Remaining nonpoint sector: nonpt
Nonpoint[+]
All U.S. nonpoint (i.e. inventoried at the county-level) sources not otherwise included in other emissions modeling sectors.
Yes
Nonroad sector:  nonroad
Mobile: Nonroad
Monthly nonroad emissions from the NONROAD model version NR08b and National Mobile Inventory Model (NMIM) software version NMIM20090504b and NMIM and Meteorology database version NCD20101201Tier3.  Nonroad version is equivalent to NONROAD2008a used in 2005v4.2 for future year 2017.
Yes
C1 & C2 CMV and locomotives: alm_no_c3
Mobile: Nonroad
Primarily 2002 NEI non-rail maintenance locomotives, and category 1 and category 2 commercial marine vessel (CMV) emissions sources, county and annual resolution.  Aircraft emissions are no longer in this sector and are now included in the Non-EGU sector (as point sources); also, category 3 CMV emissions are no longer in this sector and are now contained in the seca_c3 sector.
Yes
C3 commercial marine: seca_c3
Mobile: nonroad
Annual point source-formatted, year 2005 category 3 (C3) CMV emissions, developed for the rule called "Control of Emissions from New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder", usually described as the Emissions Control Area (ECA) study (http://www.epa.gov/otaq/oceanvessels.htm).  Utilized final projections from 2002, developed for the C3 ECA Proposal to the International Maritime Organization (EPA-420-F-10-041, August 2010).
Yes
Onroad Mobile:  onroad
Mobile: onroad[+]
Motor Vehicle Emissions Simulator (MOVES) emission factors created to account for hourly-based meteorology dependencies at a select number of representative counties.  Includes local input information such as fuels, temperatures, vehicle fleet, speed distributions and controls.  Emission factors are combined with activity data and gridded temperature via SMOKE to produce gridded emissions. These emissions are discussed extensively in Section 2.2.
Yes
Biogenic:  biog
N/A
Hour-specific, grid cell-specific emissions generated from the BEIS3.14 model, including emissions in Canada and Mexico.  Unchanged from the 2005v4 platform, and the same data are used for all future year scenarios.
No
Other point sources not from the NEI:   othpt
N/A
Point sources from Canada's 2006 inventory and Mexico's Phase III 1999 inventory, annual resolution.  Also includes annual U.S. offshore oil 2005v2 NEI point source emissions.  Unchanged from the 2005v4 platform, and the same data are used for all future year scenarios.
No
Other nonpoint and nonroad not from the NEI: othar
N/A
Annual year 2006 Canada (province resolution) and year 1999 Mexico Phase III (municipio resolution) nonpoint and nonroad mobile inventories.  Unchanged from the 2005v4 platform, and the same data is used for all future year scenarios.
No
Other onroad sources not from the NEI:  othon
N/A
Year 2006 Canada (province resolution) and year 1999 Mexico Phase III (municipio resolution) onroad mobile inventories, annual resolution.  Unchanged from the 2005v4 platform, and the same data is used for all future year scenarios.
No
  + Some data included in modeling sector has been revised beyond what is included in the 2005 NEI v1 or v2.
Custom configuration for emissions modeling for MATS
Unlike the 2005v4.2 platform, the configuration for MATS modeling included additional hazardous air pollutants (HAPs) and used slightly revised ancillary speciation data.  Both of these differences are described in this section.

Table 2-2 lists the additional HAP pollutants processed for the MATS 2005v4.3 platform, which were not included in the 2005v4.2 platform.  A "lite" version of the multi-pollutant CMAQ (Version 4.7) was used that required emissions only for the species listed in the footnote of Table 2-2. In addition to the model species differences, the MATS platform had a few additional custom aspects in the 2005 cases.  Table 2-3 lists the datasets used by the 2005v4.3 platform that are different from the 2005v4.2 platform.

Another consideration is the speciation across the MATS future-year cases as compared to 2005.  Section 3 provides a detailed account of these differences.  The future-year ancillary data were largely the same as those in 2005, with no substantial differences for most modeling sectors.  The exception to this is onroad mobile, which in MATS processing, required several new ancillary input files to support the SMOKE to MOVES modules; these are discussed in detail in Section 2.4.  All other ancillary data files not required for SMOKE to MOVES processing can otherwise be found at the 2005-based platform website (http://www.epa.gov/ttn/chief/emch/index.html#2005).

Table 2-2.  Model species produced by SMOKE for CB05 with SOA for the MATS platform
Inventory Pollutant
Model Species
Model species description
CL2
CL2
Atomic gas-phase chlorine
HCl
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOX
NO
Nitrogen oxide

NO2
Nitrogen dioxide

HONO
Nitrous acid
SO2
SO2
Sulfur dioxide

SULF
Sulfuric acid vapor
NH3
NH3
Ammonia
VOC
ACROLEIN[*]
Acrolein from the HAP inventory

ALD2
Acetaldehyde from VOC speciation

ALD_PRIMARY[*]
Acetaldehyde from the HAP inventory

ALDX
Propionaldehyde and higher aldehydes

BENZENE
Benzene (not part of CB05)

BUTADIENE13[*]
1,3-butadiene from the HAP inventory

ETH
Ethene

ETHA
Ethane

ETOH
Ethanol, from select inventories provided by OTAQ

FORM
Formaldehyde

FORM_PRIMARY[*]
Formaldehyde from the HAP inventory

IOLE
Internal olefin carbon bond (R-C=C-R)

ISOP
Isoprene

MEOH
Methanol

OLE
Terminal olefin carbon bond (R-C=C)

PAR
Paraffin carbon bond

TOL
Toluene and other monoalkyl aromatics

XYL
Xylene and other polyalkyl aromatics
Various additional VOC species from the biogenics model which do not map to the above model species
SESQ
Sesquiterpenes

TERP
Terpenes
PM10
PMC
Coarse PM > 2.5 microns and  10 microns
PM2.5
PEC
Particulate elemental carbon  2.5 microns

PNO3
Particulate nitrate  2.5 microns

POC
Particulate organic carbon (carbon only)  2.5 microns

PSO4
Particulate Sulfate  2.5 microns

PMFINE
Other particulate matter   2.5 microns
Sea-salt species (non  - anthropogenic emissions)
PCL
Particulate chloride

PNA
Particulate sodium
   * - ACROLEIN, ALD2_PRIMARY, BUTADIENE13, ETHANOL and FORM_PRIMARY are the extra "CMAQ-lite" HAPs that are not in the v4.2 platform.

Table 2-3.  Description of differences in ancillary data (unrelated to SMOKE to MOVES) between the MATS 2005 case and the 2005v4.2 platform
Ancillary Data Type
Difference between 2005v4.2 platform and MATS platform
Speciation cross-references and Speciation profiles
The MATS 2005v4.3 data files are configured to support the multi-pollutant (MP) version of CMAQ, whereas the 2005v4.2 platform data file is configured to support only the non-MP version.  Therefore, the MATS data files include profiles for additional VOC HAP species.
Speciation VOC to TOG conversion profiles
Added MATS-specific VOC to TOG and nonHAP VOC to nonHAP TOG assignments
SCC Descriptions
Added onroad diesel SCCs representing start and idle modes (223007X000)
Inventory tables
The MATS data file was updated to support SMOKE to MOVES pollutants and modes, the MP "lite" version of CMAQ, and, to accept inventory Ethanol (ETOH).  The 2005v4.2 platform data file is configured to support only the non-MP version.
Onroad mobile sources (onroad)
For each scenario, emissions from cars, trucks and motorcycles were estimated by using the EPA's Motor Vehicle Emission Simulator (MOVES) to create emission factors that were then input to the Sparse Matrix Operator Kernel Emissions system (SMOKE). The SMOKE-MOVES Integration Tools combined the county and temperature-specific emission factors with the activity data to compute the actual emissions.  In brief, our approach was to use the met4moves program to identify a set of temperatures that needed emission rates.  For each scenario, we then ran MOVES repeatedly to produce emission rates by temperature, Source Classification Code (SCC), speed bin, and representing county.  The moves2smk tool then reformatted the MOVES rates and selected the appropriate rates for each county and month.  Movesmrg then multiplies the emission rates by county VMT or vehicle population, applies speciation profiles to develop inventories for pollutants not included in MOVES and temporally and spatially allocates emissions to individual grid cells for CMAQ input.
MOVES
For MATS, EPA used a version of the MOVES 2010a model that was enhanced for the proposed Tier 3 rule.  This model included updated information on how fuel parameters impact vehicle emissions and updates on our understanding of evaporative emissions.  It also included some minor updates to emission rates and some changes designed to make the model run more efficiently.  All updates are described in detail in a memorandum to the docket (U.S. EPA 2012, Memorandum to Docket:  Updates to MOVES for the Tier 3 NPRM).  The following sections describe inputs to the MOVES model that were specific to this analysis.

The gridded meteorological input data for the entire year of 2005 were derived from simulations of the Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model (MM5), a limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and thermodynamic equations which govern atmospheric motions.  The Meteorology-Chemistry Interface Processor (MCIP) version 3.4 was used as the software for maintaining dynamic consistency between the meteorological model and chemistry mechanisms.  The hourly gridded meteorological data was post-processed by met4moves to create maximum temperature ranges, average relative humidity, and a series of diurnal temperature profiles.  MOVES was run for each temperature bin and diurnal profile. See Sections <IP_ADDRESS> and <IP_ADDRESS> for details.

Vehicle population data is a required input for MOVES when modeling on a county basis.  Using the technical guidance provided to states by EPA, the contractor generated appropriate estimates for vehicle populations for use in the MOVES databases using the county specific VMT and national average ratios of vehicle populations versus vehicle VMT from the MOVES application.  This method is described in Section 3.3 of the document, "Technical Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State Implementation Plans and Transportation Conformity" (EPA-420-B-10-023, April 2010), which is available on the EPA web site at: http://www.epa.gov/otaq/models/moves/index.htm

The county inputs used for the rule were derived from the inputs used for the 2005 National Emissions Inventory (NEI).  This inventory covers the 50 United States (U.S.), Washington DC, Puerto Rico and U.S. Virgin Islands.  The NEI was created by the EPA's Emission Inventory and Analysis Group (EIAG) in Research Triangle Park, North Carolina, in cooperation with the Office of Transportation and Air Quality (OTAQ) in Ann Arbor, Michigan.

OTAQ has developed a consolidated modeling system known as the National Mobile Inventory Model (NMIM) for calculation of emissions from onroad highway mobile source and nonroad mobile sources.  NMIM documentation is available at http://www.epa.gov/otaq/models/nmim/420r05024.pdf.  NMIM includes a county-level database with the important input parameters specific to each county.  The data in the NMIM county database (NCD) are used to develop MOBILE6.2 and NONROAD model input files within NMIM.  The basis for the 2005 default vehicle miles traveled (VMT) is data supplied by the Federal Highway Administration (FHWA), as well as publicly available data from FHWA's Highway Statistics series.  Details of how the NCD was developed are documented for the NEI "Documentation for the 2005 Mobile National Emissions Inventory, Version 2 (December 2008)", which can be obtained on EPA web site: http://www.epa.gov/ttn/chief/net/2005inventory.html

For the onroad portion of the inventory estimates for the rule, including all base and control scenarios, the current EPA highway mobile source emission model (MOVES) was used.  This required conversion of the NCD database parameters to a format consistent with MOVES.  A contractor was given the assignment (TranSystems, Contract No. EP-D-06-001, WA 4-65) to convert the NCD database to MOVES formatted input databases.  This was accomplished with the assistance of converters designed for this purpose.  These converters are available on the EPA web site at: http://www.epa.gov/otaq/models/moves/tools.htm

All of the county specific onroad data available in the NCD was converted to MOVES format for use at the county scale consistent with the databases created using the MOVES County Data Manager (CDM), except for the information regarding fuel properties.  The fuel properties were updated using more recent information and methods specifically for this rule and described elsewhere in this document.  Any table entries in the NCD that contained national average default information from the MOBILE6 model were replaced with the more recent national average default information used by MOVES.
Representing counties
Although EPA compiles county specific databases for all counties in the nation, many of the states can provide little or no county specific information for most counties.  Rather than explicitly model every county in the nation (there are over 3,000 counties), we have performed detailed modeling for some counties and less detailed estimates for the other counties.  This has been accomplished in this rule using a concept called "representing counties".

In this approach, we group counties that have similar properties and therefore would have similar emission rates.  Then, we explicitly model only one county in the group (the "representing" county) to determine the rates.  These representative rates are then used, in combination with county specific activity and meteorology data to generate emissions estimates for all of the counties in the group.  This approach dramatically reduces the number of modeling runs required to generate inventories and still takes into account differences between counties.

As described in Section 2.2.4, in order to generate onroad mobile emissions, MOVES was run in conjunction with the EPA SMOKE model to generate "grid" level inventories for use in air quality modeling.  SMOKE uses emission rates (not inventories) to generate inventory estimates within each grid.  Since SMOKE handles the differences in the fleet mix, temperatures, speeds and VMT versus location and time, MOVES can be run in the "emission rate" mode.  As a result, when counties are grouped, they can be grouped independently of fleet mix, speeds and temperature.  This greatly increases the number of counties that can be in each grouping, since temperature is a factor that varies among the counties.  For this analysis, we grouped counties with similar fuel, emission standards, altitude, and inspection and maintenance (I/M) programs.

The information used to group the counties was derived from the NMIM inputs used for the 2005 NEI onroad and nonroad mobile sectors.  For the onroad portion of the inventory estimates for the rule, including all base and control scenarios, the current EPA highway mobile source emission model (MOVES) was used.  This required conversion of the NCD database parameters to a format consistent with MOVES.

The NCD also does not contain county specific information regarding vehicle populations and there are no default values.  Vehicle population data is a required input for MOVES when modeling on a county basis.  Using the technical guidance provided to states by EPA, the contractor generated appropriate estimates for vehicle populations for use in the MOVES databases using the county specific VMT and national average ratios of vehicle populations versus vehicle VMT from the MOVES application.  This method is described in Section 3.3 of the document, "Technical Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State Implementation Plans and Transportation Conformity" (EPA-420-B-10-023, April 2010), which is available on the EPA web site at: http://www.epa.gov/otaq/models/moves/index.htm

The grouping of counties uses a tree algorithm, which is conceptually simple.  In the tree algorithm, all counties are assigned to various categories.  Then by grouping counties within the same categories, you get groups of counties that have the similar parameters.  Counties were sorted into their 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.  Table 2-4 below shows the PADDs and the states within each PADD.
Table 2-4.  Allocation of states to the Petroleum Administration for Defense Districts
PADD
State FIPS
State Name
Abbreviation
1a
09
CONNECTICUT
CT
1a
23
MAINE
ME
1a
25
MASSACHUSETTS
MA
1a
33
NEW HAMPSHIRE
NH
1a
44
RHODE ISLAND
RI
1a
50
VERMONT
VT
1b
10
DELAWARE
DE
1b
11
DISTRICT OF COLUMBIA
DC
1b
24
MARYLAND
MD
1b
34
NEW JERSEY
NJ
1b
36
NEW YORK
NY
1b
42
PENNSYLVANIA
PA
1c
12
FLORIDA
FL
1c
13
GEORGIA
GA
1c
37
NORTH CAROLINA
NC
1c
45
SOUTH CAROLINA
SC
1c
51
VIRGINIA
VA
1c
54
WEST VIRGINIA
WV
1c
72
PUERTO RICO
PR
1c
78
VIRGIN ISLANDS
VI
2
17
ILLINOIS
IL
2
18
INDIANA
IN
2
19
IOWA
IA
2
20
KANSAS
KS
2
21
KENTUCKY
KY
2
26
MICHIGAN
MI
2
27
MINNESOTA
MN
2
29
MISSOURI
MO
2
31
NEBRASKA
NE
2
38
NORTH DAKOTA
ND
2
39
OHIO
OH
2
40
OKLAHOMA
OK
2
46
SOUTH DAKOTA
SD
2
47
TENNESSEE
TN
2
55
WISCONSIN
WI
3
01
ALABAMA
AL
3
05
ARKANSAS
AR
3
22
LOUISIANA
LA
3
28
MISSISSIPPI
MS
3
35
NEW MEXICO
NM
3
48
TEXAS
TX
4
08
COLORADO
CO
4
16
IDAHO
ID
4
30
MONTANA
MT
4
49
UTAH
UT
4
56
WYOMING
WY
5
02
ALASKA
AK
5
04
ARIZONA
AZ
5
06
CALIFORNIA
CA
5
15
HAWAII
HI
5
32
NEVADA
NV
5
41
OREGON
OR
5
53
WASHINGTON
WA
The counties in each PADD were sorted into fuel groups using the January fuel properties and the July fuel properties.  The fuel supply and fuel formulation data were taken from the 2005 fuels developed for the rule.  The fuel parameters used for grouping and the ranges of values used for the bins are described in Table 2-5.
                   Table 2-5.  Gasoline parameter categories
Gasoline Parameter
Category ID
Minimum Value (>=)
Maximum Value (<)
Reid Vapor Pressure (psi)
1
0
7.3

2
7.3
8.2

3
8.2
9.2

4
9.2
100
Sulfur (ppm)
1
0
50

2
50
100

3
100
110

4
110
1000
Ethanol (volume percent)
1
0
3

2
3
8

3
8
100
Benzene (volume percent)
1
0
1

2
1
1.5

3
1.5
2

4
2
10
Some states have adopted California highway vehicle emission standards or plan to adopt them.  Since the emission rates in these states will be different than in neighboring states, they must be modeled separately.  Also, because the implementation of California standards varies between these states, each state with California standards must be modeled independently from the other states with California standards as well.  Each state with California standards will be treated separately when choosing representing counties.  Table 2-6 shows the states with California emission standards.
           Table 2-6.  States adopting California emission standards
State ID
State Name
Abbreviation
CA Program Begins
06
California
CA
1994
25
Massachusetts
MA
1995
36
New York
NY
1996
50
Vermont
VT
2000
23
Maine
ME
2001
09
Connecticut
CT
2008
42
Pennsylvania
PA
2008
44
Rhode Island
RI
2008
41
Oregon
OR
2009
53
Washington
WA
2009
34
New Jersey
NJ
2009
24
Maryland
MD
2011
10
Delaware
DE
2014
35
New Mexico
NM
2016
The counties in each PADD-fuel group were sorted into groups with and without I/M vehicle inspection programs.  I/M programs were determined using the 2005 calendar year entries in the IMCoverage table of the MOVES database.  The I/M category is the state in which the county resides.  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 was also added as its own category.  Altitude is a field in the County table of the MOVESDB20101006 database.  Counties are either high (H) or low (L) altitude based on the criteria set forth by EPA certification procedures (4,000 feet above sea level).  The result is a set of county groups with similar fuel, emission standards, altitude and I/M program.  Then the county in with the highest VMT in each group is chosen as the representing county.  The categories are summarized below in Table 2-7.
Table 2-7.  Summary of county grouping characteristics for representative 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
Average gasoline fuel properties for January and July 2005, 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 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 are either high or low altitude based on the criteria set forth by EPA certification procedures (4,000 feet above sea level).
Using these criteria, a set of 106 counties were selected to represent the nation.  Of these, only 103 were needed to model the 48 states included in the air quality analysis inventory.  If MOVES runs were performed for all U.S. counties and months, there would be 3141 counties (excluding AK and HI) times 12 months = 37,692 county-months.  The MOVES runs for each representative county and fuel month were performed independently of one another on different computer processors each accessing a MySQL database specific to that run.
SMOKE-MOVES inputs
Both MOVES and SMOKE require meteorological data.  The program met4moves takes gridded hourly meteorological data, the representative counties, and the representative fuel months and produces separate meteorological products for MOVES and SMOKE.  Met4moves uses the representative counties and fuel months to determine the full range of meteorology in that county group.  For each representative county and fuel month, it determines all the grid cells that fall within the corresponding counties in that county group for the number of months that correspond to the fuel month.  The temperature range is then determined by looking at the minimum and maximum temperature across all these grid cells for all hours in that time period.  Relative humidity is calculated by taking an average over these same grid cells.

For rate-per-profile (RPP), SMOKE-MOVES uses the change in temperature over the day, the diurnal profile, instead of the temperature at the hour of processing.  Met4moves create a series of diurnal profiles based on the extent of the temperature range and the size of the temperature bins.  For MOVES, these diurnal profiles will span the full range of temperatures for that representative county and fuel month.  For SMOKE processing of RPP, met4moves creates a minimum and maximum temperature range for each county in the domain.  Note that these temperature ranges are county specific, not based on the representative county or 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.  Typically, the SMOKE input produced in monthly mode will have larger temperature ranges for each county than when it is run in daily mode. For the MATS runs, met4moves was run in daily mode.

In addition to the lookup tables of emission rates produced by MOVES, SMOKE requires county VMT, population, and average speed by road type to calculate the necessary emissions for air quality modeling.
VMT by county and Source Classification Code (SCC) was developed using MOVES2010a and the National County Database.  The National County Database (NCD20101201) has our most recent estimates of 2005 VMT and our best estimates of allocation of VMT from national to the county level.  Accordingly, for the 2005 base year, our estimates of VMT by county and SCC were taken directly from the NCD.

The average speeds provided to SMOKE for each county were derived from the default national average speed distributions found in the default MOVES2010a database AvgSpeedDistribution table.  These average speeds are the average speeds developed for the previous EPA highway vehicle emission factor model, MOBILE6.  The same speed data was used for the base and future year cases.

In MOVES, there is a distribution of average speeds for each hour of the day for each road type.  The average speeds in these distributions were used 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 vehicle miles traveled (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.
Generating emission factors for SMOKE
After representative counties and fuel months were chosen, the met4moves script was executed to produce the set of MOVES RunSpecs and meteorology tables that would ultimately generate a set of SMOKE lookup tables encompassing the full range of temperatures for all the counties and months in each group.  OTAQ also provided VMT, population, and average speed tables for every county.

The onroad model-ready emissions were produced by running SMOKE-MOVES using 103 representative counties and two fuel months.  SMOKE-MOVES is a series of scripts and programs that 1) produce meteorological data for MOVES (Met4Moves), 2) construct a set of MOVES RunSpecs that produce lookup tables by temperature and average speed (runspec_generator), 3) process the MOVES lookup tables into a SMOKE-ready format (moves2smkEF), and 4) runs SMOKE.  The way that OTAQ used SMOKE-MOVES differs somewhat from the way that SMOKE-MOVES was initially designed to be run.  The full sequence of events was the following:

   1) OAQPS ran met4moves for a nation-wide 12 km grid. This generated the temperatures needed for the emission factor lookup tables and an average humidity for each county and month.  The inputs to met4moves are the hourly gridded temperature and humidity generated by the meteorological model used for CMAQ along with the list of representative counties and fuel months.  For each representative county and fuel-month, met4moves queries all the grid cells in all the represented county-months to find the full range of temperatures and profiles needed and averages the relative humidity.

   2) OTAQ ran the runSpec_generator Perl script, (runspec_generator_v0.3_04Nov2010.plx).  The inputs to this process include the representative county list, fuel month list, temperature bin size (=10 degrees here), and the outputs from met4moves. The runspec_generator script produced MOVES run-specifications that control how the MOVES run is configured, along zonemonthhour tables in CSV format.  Specifications were generated for the three types of MOVES processes: rate-per-distance (RPD), rate-per-profile (RPP), and rate-per-vehicle (RPV). Run specifications were generated as needed to simulate the range of conditions reflected in the meteorological inputs.  For RPD and RPV, a series of run specifications were created for each representative county, one for each temperature bin covering the temperature ranges provided by the met4moves output.  For RPP, a second series of run specifications were created for each representative county, one for each diurnal profile provided by the met4moves output.  The input data specific to each county were loaded into databases called "scaleinputdatabases", and the zonemonthhour tables were also loaded into databases.

   3) OTAQ ran a tool to read the county databases, the zonemonthhour databases, other user-supplied databases, and the run specifications.  The tool implemented LEV programs into the specifications as appropriate and also modified the pollutant-process associations in the run specifications to meet the needs of MATS. The tool then packaged the information into a form that could be used by the compute server.

   4) OTAQ issued the command to kick off the required MOVES runs for each county and fuel month on the compute server.

   5) Once the MOVES runs were complete, OTAQ ran the moves2smkEF postprocessor to reformat the MySQL tables into the emission factor tables in CSV format that is readable by the SMOKE movesmrg program. The postprocessor also performed additional calculations to support SMOKE processing of CMAQ ready model emissions:  speciating HONO from NO and NO2, speciating the AE5 PM species (PEC, POC, PNO3, PSO4, PMFINE, and PMC for break and tire wear), and aggregating the detailed MOVES modes into 5 broader modes (exhaust, evaporative, permeation, break wear, and tire wear)

   6) OAQPS downloaded the emission factors from the server and executed SMOKE programs to produce gridded, hourly, speciated emissions for CMAQ.  See the next section for details.
Running SMOKE for onroad mobile
Running SMOKE using emission factors (EF) from MOVES required the development of a new set of functionality.  The central SMOKE program that performs this new analysis is movesmrg which takes activity data, meteorological data, and the EF to produce gridded emissions.  SMOKE is run independently for each of the three processes: rate-per-distance (RPD), rate-per-vehicle (RPV) and rate-per-profile (RPP).

The emissions process RPD is for modeling the on-network emissions.  This includes the following modes: vehicle exhaust, evaporation, permeation, break wear, and tire wear.  For RPD, the activity data is monthly VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus weekend (SPDPRO).  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 data and uses these values to look-up the appropriate EF 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 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 from met4moves output for SMOKE.  It uses this temperature range to determine the most 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.  For more details on processing RPD, RPV, and RPP in SMOKE, see:
http://www.smoke-model.org/version3.0/html/ch02s08s04.html.

MOVES was run for a series of representative counties and fuel months.  For each representative county and fuel month, three EF tables were created: RPD, RPV, and RPP.  SMOKE was run so that for each model day it would read in a single EF table (based on the appropriate fuel month), process all the counties that are part of the county group (i.e. are represented by that representative county), then read the next representative county EF table, etc.  After all days in the model year were looped over, SMOKE has generated a separate set of daily intermediate files for each of the emissions processes (RPD, RPV, and RPP).  Post-processing scripts were developed to integrate the process specific intermediate files into model-ready intermediate files for the onroad sector.  These files were on national 12km domain, to support the CMAQ runs they were further processed to create an aggregated 36km sector specific model-ready file and 2 12km domains (12EUS1 and 12WUS1).
Nonroad mobile sources (nonroad, alm_no_c3, seca_c3)
The nonroad sectors include a wide-range of mobile emission sources ranging from locomotives, marine vessels, construction and farming equipment to hand-held lawn tools.  As discussed in Section 5, nonroad upstream impacts also impact the post-EPAct/EISA/RFS2/reference case (anti-backsliding) reflecting increased ethanol production resulting in fuel volume increases for locomotives and C1/C2 CMV emissions.
Emissions generated with the NONROAD model (nonroad)
Most nonroad emissions are were estimated using the EPA's NONROAD model, as run by the EPA's NMIM.  NONROAD is EPA's model for calculating emissions from nonroad equipment, except for aircraft, locomotives, and commercial marine vessels.  The NONROAD Model and extensive documentation can be found at http://www.epa.gov/otaq/nonrdmdl.htm.  NMIM is a program that references a national database of county-month data, writes county-month input files for NONROAD based on that data, runs NONROAD once for every county and month requested by the user, and collects the results in an output database.  Information and downloads for NMIM can be found at http://www.epa.gov/otaq/nmim.htm.  Rather than running every county, NMIM is designed to run NONROAD for "representative counties" and to use individual county activity to develop national inventories.  Inputs for NMIM runs were stored in the NMIM County Database (NCD).  The NCD version is NCD20101201Tier3.  This NCD is based on NCD20101201, which is the version of the NCD that includes all updates from the 2008 National Emission Inventory process: http://www.epa.gov/ttn/chief/net/2008inventory.html.

In particular, for this analysis, we made updates to the underlying fuel supply for the post EISA/EPAct reference case.  The NCD20101201Tier3 contained special versions of countyyearmonth, gasoline, and diesel, which were copied into the standard versions of these tables in order to run the model.  The fuels in NCD2010201Tier3 were developed from the fuels used for onroad vehicles, as described in Section 2.2.1.

Similarly, a special countymonthhour table that contained 2005 meteorology was copied into the standard countymonthhour table. The use of the countymonthhour table for meteorology was selected by the RunSpec setting useYearlyWeatherDataSelected="false."  We also made a minor change for snowmobiles:  the SCC toxics table in the NMIM County Database (NCD) was updated to correct 1,3-butadiene exhaust emissions for 2-stroke snowmobiles (SCC<PHONE_NUMBER>), as shown in Table 2-8 below.  This correction addressed an issue identified in air quality modeling for the RFS2 rule, where unexpected increases in ambient concentrations were observed in rural areas during winter due to snowmobile emissions, available at: http://www.epa.gov/otaq/renewablefuels/420r10006.pdf.   The increases were based on data from only three engines, which showed unusually high1,3-butadiene emissions with 10% ethanol (Eth oxygenate).  Other data suggests that this increase is highly unlikely to be representative of the in-use fleet as a whole; thus results were corrected to those in the "NCD20101201Tier3" column.  In Table 2-but, Base Gasoline represents cases where the fuel type is not Eth, MTBE or RFG.  Eth gas is used where the fuel contains ethanol which is greater than or equal to 5% by volume or Ethyl Tertiary Butyl Ether (ETBE) is greater than or equal to 5% by volume.  MTBE gas is used where the fuel contains MTBE which is greater than or equal to 12% by volume or Tertiary Amine Methyl Ether (TAME) is greater than or equal to 13% by volume.  Finally, RFG gas is used where the fuel is RFG and where the fuel contains oxygenate greater than 5% by volume and where the fuel contains MTBE which is less than 12% by volume or TAME is less than 13% by volume.
Table 2-8.  Updated 1,3-butadiene to VOC ratio for 2-stroke snowmobiles for NMIM's gasoline categories
Fuel Type
NCD20101201
NCD20101201Tier3
Base Gasoline
0.0012
0.0012
Ethyl Tertiary Butyl Ether (Eth) Gas
0.00732
0.0012
Methyl ertiary Butyl Ether (MTBE) Gas
0.0012
0.0012
Reformulated Gasoline (RFG) Gas
0.0012
0.0012
In addition, a special countymap table was developed to use representing counties in the NMIM runs.  The algorithm for producing representing counties for NMIM was identical to that used for MOVES except that ten degree temperature bins were added to the criteria.  The result was 293 representing counties.  Finally, NMIM does not estimate ethanol emissions, so the inventory for this pollutant was from the chemical speciation that is obtained by post-processing using SMOKE
Representing counties for NONROAD
"Representing counties" is a way of saving NMIM run time by grouping together similar counties and generating emission factors by running the NONROAD Model for only one of those counties and then using those emission factors for all the counties in the group.  For this analysis, 293 county groups were developed.  The counties in each group were in the same state, had similar fuels in both summer and winter, and had similar I/M programs.  Since there are winter fuels and summer fuels, January was chosen as the fuel-month to represent the seven months October through April, and July was chosen to represent the five months May-September.  The total number of county-months for which NMIM runs needed to be performed was thus 293 times 12 months = 3,516 county-months for each scenario-year.  If NMIM runs were performed for all U.S. counties and months, there would be 3141 counties (excluding AK and HI) times 12 months = 37,692 county-months.  Representing counties were chosen for NONROAD-Model NMIM runs by grouping counties based on the characteristics listed in Table 2-9.
Table 2-9.  Criteria for grouping representative counties for nonroad mobile analysis
Characteristic
Grouping Criteria
Petroleum Administration for Defense District (PADD)
All counties in a group must be in the same PADD.
Gasoline parameters
Fuel bins were created for RVP, sulfur, benzene, and ethanol.  All counties in each group had all of these fuel properties in the same bins for all twelve months.
Inspection/Maintenance Programs
Counties with I/M programs were grouped with other counties with I/M programs in the same state.
Altitude
All counties in the group must be in the same altitude category (high or low).
Temperatures
All counties in the group must have similar temperatures, as detailed below.
Nonroad inventories are not calculated on a grid basis, as the highway mobile sources were, so when running NMIM for nonroad emissions, the representing counties must also account for temperatures.  The temperatures are taken from the 2005 calendar year values in the CountyYearMonthHour table of the NCD20100602 NMIM database.  As shown in Table 2-10, ten degree Fahrenheit (F) bins were created for min and max temperatures for each month.  All counties in each group had all min and max temperatures for all twelve months in the same bins.  The lowest interval includes all temperatures below -10 degrees F.  The highest interval includes all temperatures above 100 degrees F.

             Table 2-10.  NONROAD model temperature (F) categories
Temperature Bin
Minimum Temperature (>=)
Maximum Temperature (<)
1
-20
-10
2
-10
0
3
0
10
4
10
20
5
20
30
6
30
40
7
40
50
8
50
60
9
60
70
10
70
80
11
80
90
12
90
100
13
100
200
Once counties were grouped, the representing county was chosen as the one with the highest VMT.  The same set of 293 county groups and representing counties was used for all years and scenarios.
Fuel inputs for NONROAD runs
For the nonroad mobile portion of the inventory estimate for the rule, the NMIM county database (NCD) developed for the 2005 NEI, with one exception of the county-specific fuel properties, was used to calculate nonroad emissions.  Fuels were developed for MOVES (onroad mobile) for the MATS Rule (see Section 2.2) and were converted to NMIM fuels.  Practically, this means converting the fuelsupply and fuelformulation tables from MOVES into the countyyearmonth, gasoline, and diesel tables in the NCD.
In 2005, onroad and nonroad gasoline formulations are assumed to be identical.

While MOVES allows for multiple gasoline fuels, each with a market share, for a single county-month.  The market shares always sum to one.  The NCD allows only one fuel per county month, but, for each of the four oxygenates (ETOH, MTBE, TAME, and ETBE), the NCD has columns for both volume percent and market share.  The sum of these market shares is less than or equal to one.  If less than one, the remainder of the market is non-oxygenated (conventional) gasoline.  When there are multiple MOVES gasoline fuels for a single county-month, non-oxygenate MOVES fuel properties are multiplied by market share and summed to produce the fuel property in the gasoline table.  Individual non-ethanol oxygenates and California ethanol occur in only one fuel per county-month in MOVES, so the volume and market share are transferred to the appropriate columns for that oxygenate in NMIM.  In states other than California, for multiple ethanol volumes with volume percents less than 10, the product of market share and volume percent is averaged and then divided by 10, resulting in a market share of E10.
NMIM runs
Table 2-11 shows the NMIM runs that were performed to generate the NONROAD Model county-month results for both national inventories and air quality modeling inventories.
                        Table 2-11.  NONROAD NMIM runs
Case
Year
Run Name
Base
2005
Tier3Base2005Nr
Reference
2017
Tier3Ref2017e10Nr
MOVES fuels were used for future year cases and converted to NMIM format.  However, for 2017, EPA assumed that nonroad equipment would use only E10.  The details of the fuels conversion from MOVES to NMIM was discussed above.  Table 2-12 describes the components in the NONROAD/NMIM system common to all MATS modeling scenarios.
              Table 2-12.  Summary of NONROAD modeling components
Model
Version
Description
NONROAD
NR08b
This is identical to the official NONROAD2008, (available at http://www.epa.gov/otaq/nonrdmdl.htm#model) except it was modified to allow modeling of emissions on E15 fuels.  The existing fuel effects algorithm was retained.
NMIM Code
NMIM20090504b
This is the same as the official NMIM2008a software, (available at http://www.epa.gov/otaq/nmim.htm) except the NONROAD model was updated to NRO8b.
NMIM Database
NCD20101201Tier3
This is based on NCD20101201, which was developed for the 2008 NEI.  It was adapted to model the desired scenarios.
Meteorology
NCD20101201Tier3
Historical data for calendar year 2005 from the National Climatic Data Center.  County temperatures were determined by weighting nearby temperature stations by their distance from the population-based centroid of each county.
Locomotives and commercial marine vessels (alm_no_c3, seca_c3)
The year 2005 emissions from these sources used for this rule are the same as they were for the Final Rulemaking: Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles signed on August 9, 2011 and available at http://www.epa.gov/oms/climate/regulations.htm#1-2, and the Final Cross-State Air Pollution (CSAPR) Rule:  ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf.  The procedures for calculating emissions from locomotives and C1/C2 commercial marine were developed for the Locomotive Marine Rule (2008) and are detailed in the RIA "Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines Less Than 30 Liters per Cylinder", published May 6, 2008 and republished June 30, 2008, and available at: http://www.epa.gov/oms/locomotives.htm#2008final.  The procedures used for calculating C3 commercial marine emissions are those developed in the recent C3 "Final Rule: Control of Emissions from New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder", published April 30, 2010 and available at: http://www.epa.gov/oms/oceanvessels.htm#car-ems.
2005 point sources (ptipm and ptnonipm sectors)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude) are specified, as in the case of an individual facility.  A facility may have multiple emission points, which may be characterized as units such as boilers, reactors, spray booths, kilns, etc.  A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas).  Note that this section describes only NEI point sources within the contiguous United States.  The offshore oil platform (othpt sector) and category 3 CMV emissions (seca_c3 sector) are also point source formatted inventories but are unchanged for MATS modeling.  Discussion of the seca_c3 and othpt sector emissions can be found in the Final CSAPR TSD referenced in Section 2.3.2.

After removing offshore oil platforms (othpt sector), we created two platform sectors from the remaining 2005v2 NEI point sources for input into SMOKE: the EGU sector  -  also called the Integrated Planning Model (IPM) sector (i.e., ptipm) and the non-EGU sector  -  also called the non-IPM sector (i.e., ptnonipm).  This split facilitates the use of different SMOKE temporal processing and future-year projection techniques for each of these sectors.  The inventory pollutants processed through SMOKE for both ptipm and ptnonipm sectors were:  CO, NOX, VOC, SO2, NH3, PM10, and PM2.5 and the following HAPs:  HCl (pollutant code = 7647010), and CL2 (code = 7782505).  We did not utilize BAFM from these sectors as we chose to speciate VOC without any use (i.e., integration) of VOC HAP pollutants from the inventory (integration is discussed in detail in Section 3).

The ptnonipm emissions were provided to SMOKE as annual emissions.  The ptipm emissions for the base case were input to SMOKE as daily emissions.  The ptipm emissions are unchanged from those in the 2005v4.2 (basis for the Final CSAPR and Heavy Duty Greenhouse Gas (HDGHG) FRM) emission modeling platform.  However, for the ptnonipm sector for all MATS scenarios, including year 2005 emissions, we included additional known ethanol plants that were not previously included in 2005v4.2.  We also removed all onroad refueling emissions as these were replaced with MOVES-based onroad refueling emissions (discussed in Section 2.5.2).
Ethanol plants (ptnonipm)
We replaced all ethanol plants that OTAQ had supplied from the RFS2 rule  - see Section 2.1.2 in the CSAPR Final TSD- with those recently compiled for the 2005 case for MATS.  These plants represent a "Low Ethanol" scenario needed to produce only 4 billion gallons of ethanol, essentially a scenario without a future year RFS2 mandate (or MATS).  All ethanol plants were assigned or corrected (after quality assurance analyses) coordinates based on analysis using searches of company web sites and Google Earth verification for many sites.  Emissions were calculated based on plant design capacity and emission factors based on fuel type (e.g., coal, natural gas).  Finally, because benzene, acetaldehyde and formaldehyde (BAF) emissions were directly computed for these sources, unlike the rest of the ptnonipm sector, we treated these ethanol plants as VOC integrate sources.  A summary of the ethanol plant emissions used in the 2005 scenario is provided in Table 2-13.
                   Table 2-13.  2005 ethanol plant emissions
Pollutant
Emissions
1,3-Butadiene
                                                                         0.0003
Acrolein
                                                                           10.5
Formaldehyde
                                                                           13.3
Benzene
                                                                            5.7
Acetaldehyde
                                                                          314.4
CO
                                                                          7,023
NOX
                                                                          8,204
PM10
                                                                         10,107
PM2.5
                                                                          3,691
SO2
                                                                          9,001
VOC
                                                                         10,754
2005 nonpoint sources (afdust, ag, avefire, nonpt)
The year 2005 area-source fugitive dust (afdust), agricultural animal and fertilizer NH3 (ag), and average (typical)-year fires (avefire) emissions are the same as those used in the CSAPR Final (2005v4.2) emission modeling platform.  Nonpoint sources that were not subdivided into the afdust, ag, or avefire sectors were assigned to the "nonpt" sector, and most of these sources are also unchanged for MATS modeling.  The 2005 nonpoint sources that change are limited to portable fuel containers (PFCs) and onroad refueling.
Portable fuel containers
Year 2005 PFC emissions are unchanged from the CSAPR Final inventory except for the addition of ethanol.  Ethanol emissions were not provided for 2005, but were supplied for future year scenarios.  Therefore, we scaled year 2017 reference case ethanol emissions by the ratio of 2005 to 2017 base total VOC emissions to compute year 2005 ethanol emissions:

Ethanol_2005 = Ethanol_2017reference * (VOC_2005 / VOC_2017reference)
Onroad refueling
As mentioned in Section 2.2, NEI-based onroad refueling emissions were replaced with estimates from the revised version of EPA's Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all twelve months.  This section describes how the emission inventories for refueling from on-road vehicles in calendar years 2005 and 2017 for MATS reference and control cases were generated for air quality modeling.  The refueling inventory includes emissions from spillage loss and displacement vapor loss.  For this analysis, the refueling emissions were estimated using the revised version of EPA's Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all twelve months.

In an effort to reduce MOVES runtime, the "representing counties" approach was used instead of running every single county in the lower 48 states.  As described in Section 2.2 for onroad counties, we selected representing counties by grouping counties based on Petroleum Administration for Defense Districts (PADD), fuel parameters, usage of California emission standards, Inspection/Maintenance programs, and altitude.  One additional parameter included in developing the representing counties for refueling was temperature.

Temperature bins with increments of ten degrees F were created for the minimum and maximum temperatures for each month using the temperatures from the 2005 calendar year values in the CountyYearMonthHour table of the NMIM County Database (NCD) NCD20100602 NMIM database.  All counties in each group had min and max temperatures for all twelve months in the same temperature bins.

Once counties were grouped, the representing county was chosen as the one with the highest VMT, resulting in total of 238 counties.  The same set of county groups and representing counties was used for all years and scenarios.

MOVES was run in inventory mode for only the representing counties using the county-specific on-road data, such as vehicle miles travelled, fleet age distribution, speed distribution, and meteorology, available from the NCD.  The customized fuel inputs, discussed in Section <IP_ADDRESS>, were used for each of the representing counties.

The resulting refueling emission inventories for 238 representing counties in U.S. short tons were converted to emission factors by dividing the inventory by the corresponding activity in each representing county.  Then, the calculated emission factors from the representing counties were applied to the represented counties and multiplied by the county-specific activity to generate the inventories for all counties.
Other sources (biogenics, othpt, othar, and othon)
All emissions from Canada, Mexico, and Offshore Drilling platforms (othpt, othar, and othon), and all non-anthropogenic inventories (biogenics and ocean chlorine) are unchanged from the 2005v4.2 (used for the Final CSAPR and HDGHG FRM) emissions modeling platform.  The same emissions are used for all MATS scenarios and years.
Emissions summaries for 2005 base case
Once developed, the emissions inventories were processed to provide the hourly, gridded emissions for the model-species needed by CMAQ.  Table 2-14 provides summaries of the 2005 U.S. emissions inventories modeled for this rule by sector.  Table 2-15 and Table 2-16 provide state-level summaries of SO2, and PM2.5 by sector.  Note that the nonroad columns include emissions from traditional nonroad sources that are found "on-land", along with commercial marine sources. The nonpoint columns include area fugitive dust, agriculture, and other nonpoint emissions.
            Table 2-14.  2005 U.S. emissions (tons/year) by sector
Emissions Sector
                                      NOX
                                      SO2
                                     PM2.5
                                     PM10
                                      NH3
                                      CO
                                      VOC
Agriculture

                                                                      3,251,990

Area fugitive Dust

                                                                      1,030,391
                                                                      8,858,992

Average fires
                                                                        189,428
                                                                         49,094
                                                                        684,035
                                                                        796,229
                                                                         36,777
                                                                      8,554,551
                                                                      1,958,992
Commercial marine
Category 3 (US)
                                                                        130,164
                                                                         97,485
                                                                         10,673
                                                                         11,628

                                                                         11,862
                                                                          4,570
EGU
                                                                      3,729,161
                                                                     10,380,883
                                                                        496,877
                                                                        602,236
                                                                         21,995
                                                                        603,788
                                                                         41,089
Locomotive/ marine
                                                                      1,922,723
                                                                        153,068
                                                                         56,666
                                                                         59,342
                                                                            773
                                                                        270,007
                                                                         67,690
Non-EGU Point
                                                                      2,213,471
                                                                      2,030,759
                                                                        433,346
                                                                        647,873
                                                                        158,342
                                                                      3,201,418
                                                                      1,279,308
Nonpoint
                                                                      1,696,902
                                                                      1,216,362
                                                                      1,079,906
                                                                      1,349,639
                                                                        133,962
                                                                      7,410,946
                                                                      7,560,061
Nonroad
                                                                      2,031,527
                                                                        196,277
                                                                        201,406
                                                                        210,767
                                                                          1,971
                                                                     20,742,873
                                                                      2,806,422
Onroad
                                                                      8,235,002
                                                                        168,480
                                                                        301,073
                                                                        369,911
                                                                        144,409
                                                                     41,117,658
                                                                      3,267,931
US TOTAL
                                                                     20,148,378
                                                                     14,292,410
                                                                      4,294,373
                                                                     12,906,616
                                                                      3,750,218
                                                                     81,913,104
                                                                     16,986,064
  Table 2-15.  2005 base year SO2  emissions (tons/year) for states by sector
State
                                      EGU
                                    NonEGU
                                   Nonpoint
                                    Nonroad
                                    Onroad
                                     Fires
                                     Total
Alabama
                                                                        460,123
                                                                         66,373
                                                                         52,325
                                                                          5,622
                                                                          3,554
                                                                            983
                                                                        588,980
Arizona
                                                                         52,733
                                                                         23,966
                                                                          2,571
                                                                          6,151
                                                                          3,622
                                                                          2,888
                                                                         91,931
Arkansas
                                                                         66,384
                                                                         13,039
                                                                         27,260
                                                                          5,678
                                                                          1,918
                                                                            728
                                                                        115,008
California
                                                                            601
                                                                         33,097
                                                                         77,672
                                                                         40,222
                                                                          4,526
                                                                          6,735
                                                                        162,852
Colorado
                                                                         64,174
                                                                          1,550
                                                                          6,810
                                                                          4,897
                                                                          2,948
                                                                          1,719
                                                                         82,098
Connecticut
                                                                         10,356
                                                                          1,831
                                                                         18,455
                                                                          2,557
                                                                          1,337
                                                                              4
                                                                         34,540
Delaware
                                                                         32,378
                                                                         34,859
                                                                          1,030
                                                                          2,657
                                                                            486
                                                                              6
                                                                         71,416
District of Columbia
                                                                          1,082
                                                                            686
                                                                          1,559
                                                                            414
                                                                            205
                                                                              0
                                                                          3,947
Florida
                                                                        417,321
                                                                         57,429
                                                                         70,490
                                                                         31,190
                                                                         12,388
                                                                          7,018
                                                                        595,836
Georgia
                                                                        616,063
                                                                         52,827
                                                                         56,829
                                                                          9,224
                                                                          6,939
                                                                          2,010
                                                                        743,893
Idaho
                                                                              0
                                                                         17,151
                                                                          2,915
                                                                          2,304
                                                                            902
                                                                          3,845
                                                                         27,117
Illinois
                                                                        330,382
                                                                        131,357
                                                                          5,395
                                                                         19,305
                                                                          6,881
                                                                             20
                                                                        493,339
Indiana
                                                                        878,979
                                                                         86,337
                                                                         59,775
                                                                          9,437
                                                                          4,641
                                                                             24
                                                                      1,039,194
Iowa
                                                                        130,264
                                                                         41,010
                                                                         19,832
                                                                          8,838
                                                                          2,036
                                                                             25
                                                                        202,004
Kansas
                                                                        136,520
                                                                         12,926
                                                                         36,381
                                                                          8,035
                                                                          1,978
                                                                            103
                                                                        195,943
Kentucky
                                                                        502,731
                                                                         25,808
                                                                         34,229
                                                                          6,943
                                                                          3,240
                                                                            364
                                                                        573,315
Louisiana
                                                                        109,875
                                                                        165,705
                                                                          2,378
                                                                         25,451
                                                                          2,902
                                                                            892
                                                                        307,202
Maine
                                                                          3,887
                                                                         18,512
                                                                          9,969
                                                                          1,625
                                                                            963
                                                                            150
                                                                         35,106
Maryland
                                                                        283,205
                                                                         34,988
                                                                         40,864
                                                                          9,353
                                                                          3,016
                                                                             32
                                                                        371,458
Massachusetts
                                                                         84,234
                                                                         19,620
                                                                         25,261
                                                                          6,524
                                                                          2,669
                                                                             93
                                                                        138,402
Michigan
                                                                        349,877
                                                                         76,510
                                                                         42,066
                                                                         14,626
                                                                          8,253
                                                                             91
                                                                        491,423
Minnesota
                                                                        101,678
                                                                         24,603
                                                                         14,747
                                                                         10,409
                                                                          2,934
                                                                            631
                                                                        155,002
Mississippi
                                                                         75,047
                                                                         29,892
                                                                          6,796
                                                                          5,930
                                                                          2,590
                                                                          1,051
                                                                        121,306
Missouri
                                                                        284,384
                                                                         78,308
                                                                         44,573
                                                                         10,464
                                                                          4,901
                                                                            186
                                                                        422,816
Montana
                                                                         19,715
                                                                         11,056
                                                                          2,600
                                                                          3,813
                                                                            874
                                                                          1,422
                                                                         39,480
Nebraska
                                                                         74,955
                                                                          7,910
                                                                          7,659
                                                                          9,199
                                                                          1,510
                                                                            105
                                                                        101,337
Nevada
                                                                         53,363
                                                                          2,253
                                                                         12,477
                                                                          2,880
                                                                            656
                                                                          1,346
                                                                         72,975
New Hampshire
                                                                         51,445
                                                                          3,155
                                                                          7,408
                                                                            789
                                                                            746
                                                                             38
                                                                         63,580
New Jersey
                                                                         57,044
                                                                          7,639
                                                                         10,726
                                                                         13,321
                                                                          3,038
                                                                             61
                                                                         91,830
New Mexico
                                                                         30,628
                                                                          7,831
                                                                          3,193
                                                                          3,541
                                                                          1,801
                                                                          3,450
                                                                         50,445
New York
                                                                        180,847
                                                                         58,426
                                                                        125,158
                                                                         15,666
                                                                          6,258
                                                                            113
                                                                        386,468
North Carolina
                                                                        512,231
                                                                         59,433
                                                                         22,020
                                                                          8,766
                                                                          6,287
                                                                            696
                                                                        609,433
North Dakota
                                                                        137,371
                                                                          9,582
                                                                          6,455
                                                                          5,986
                                                                            533
                                                                             66
                                                                        159,994
Ohio
                                                                      1,116,095
                                                                        115,155
                                                                         19,810
                                                                         15,425
                                                                          7,336
                                                                             22
                                                                      1,273,843
Oklahoma
                                                                        110,081
                                                                         40,482
                                                                          8,556
                                                                          5,015
                                                                          3,039
                                                                            469
                                                                        167,642
Oregon
                                                                         12,304
                                                                          9,825
                                                                          9,845
                                                                          5,697
                                                                          1,790
                                                                          4,896
                                                                         44,357
Pennsylvania
                                                                      1,002,203
                                                                         83,375
                                                                         68,349
                                                                         11,999
                                                                          6,266
                                                                             32
                                                                      1,172,224
Rhode Island
                                                                            176
                                                                          2,743
                                                                          3,365
                                                                            816
                                                                            254
                                                                              1
                                                                          7,354
South Carolina
                                                                        218,781
                                                                         31,495
                                                                         13,489
                                                                          7,719
                                                                          3,589
                                                                            646
                                                                        275,719
South Dakota
                                                                         12,215
                                                                          1,702
                                                                         10,347
                                                                          3,412
                                                                            623
                                                                            498
                                                                         28,797
Tennessee
                                                                        266,148
                                                                         65,693
                                                                         32,714
                                                                          6,288
                                                                          5,538
                                                                            277
                                                                        376,659
Texas
                                                                        534,949
                                                                        223,625
                                                                        115,192
                                                                         34,944
                                                                         16,592
                                                                          1,178
                                                                        926,480
Tribal
                                                                              3
                                                                          1,511
                                                                              0
                                                                              0
                                                                              0
                                                                              0
                                                                          1,515
Utah
                                                                         34,813
                                                                          9,132
                                                                          3,577
                                                                          2,439
                                                                          1,890
                                                                          1,934
                                                                         53,784
Vermont
                                                                              9
                                                                            902
                                                                          5,385
                                                                            385
                                                                            342
                                                                             49
                                                                          7,073
Virginia
                                                                        220,287
                                                                         69,401
                                                                         32,923
                                                                         10,095
                                                                          4,600
                                                                            399
                                                                        337,705
Washington
                                                                          3,409
                                                                         24,211
                                                                          7,254
                                                                         18,810
                                                                          3,343
                                                                            407
                                                                         57,433
West Virginia
                                                                        469,456
                                                                         46,710
                                                                         14,589
                                                                          2,133
                                                                          1,378
                                                                            215
                                                                        534,481
Wisconsin
                                                                        180,200
                                                                         66,807
                                                                          6,369
                                                                          7,163
                                                                          3,647
                                                                             70
                                                                        264,256
Wyoming
                                                                         89,874
                                                                         22,321
                                                                          6,721
                                                                          2,674
                                                                            721
                                                                          1,106
                                                                        123,417
Total
                                                                     10,380,883
                                                                      2,030,759
                                                                      1,216,362
                                                                        446,831
                                                                        168,480
                                                                         49,094
                                                                     14,292,410
 Table 2-16.  2005 base year PM2.5 emissions (tons/year) for states by sector
State
                                      EGU
                                    NonEGU
                                   Nonpoint
                                    Nonroad
                                    Onroad
                                     Fires
                                     Total
Alabama
                                                                         23,366
                                                                         19,498
                                                                         35,555
                                                                          4,142
                                                                          5,775
                                                                         13,938
                                                                        102,273
Arizona
                                                                          7,418
                                                                          3,940
                                                                         21,402
                                                                          4,486
                                                                          6,920
                                                                         37,151
                                                                         81,316
Arkansas
                                                                          1,688
                                                                         10,820
                                                                         34,744
                                                                          3,803
                                                                          3,102
                                                                         10,315
                                                                         64,472
California
                                                                            347
                                                                         21,517
                                                                         94,200
                                                                         22,815
                                                                         26,501
                                                                         97,302
                                                                        262,682
Colorado
                                                                          4,342
                                                                          7,116
                                                                         25,340
                                                                          3,960
                                                                          4,377
                                                                         24,054
                                                                         69,189
Connecticut
                                                                            562
                                                                            224
                                                                         11,460
                                                                          1,740
                                                                          2,544
                                                                             56
                                                                         16,586
Delaware
                                                                          2,169
                                                                          1,810
                                                                          1,590
                                                                            818
                                                                            922
                                                                             87
                                                                          7,397
District of Columbia
                                                                             17
                                                                            172
                                                                            589
                                                                            277
                                                                            367
                                                                              0
                                                                          1,421
Florida
                                                                         24,217
                                                                         25,193
                                                                         52,955
                                                                         15,035
                                                                         16,241
                                                                         99,484
                                                                        233,125
Georgia
                                                                         28,057
                                                                         12,666
                                                                         63,133
                                                                          6,504
                                                                         12,449
                                                                         24,082
                                                                        146,892
Idaho
                                                                              0
                                                                          2,072
                                                                         41,492
                                                                          2,140
                                                                          1,402
                                                                         52,808
                                                                         99,914
Illinois
                                                                         16,585
                                                                         15,155
                                                                         74,045
                                                                         12,880
                                                                         12,574
                                                                            277
                                                                        131,516
Indiana
                                                                         34,439
                                                                         14,124
                                                                         74,443
                                                                          6,515
                                                                          7,585
                                                                            344
                                                                        137,450
Iowa
                                                                          8,898
                                                                          6,439
                                                                         54,312
                                                                          6,969
                                                                          3,468
                                                                            349
                                                                         80,436
Kansas
                                                                          5,549
                                                                          7,387
                                                                        138,437
                                                                          5,719
                                                                          3,109
                                                                          1,468
                                                                        161,669
Kentucky
                                                                         19,830
                                                                         10,453
                                                                         31,245
                                                                          4,762
                                                                          5,566
                                                                          5,155
                                                                         77,010
Louisiana
                                                                          5,599
                                                                         32,201
                                                                         28,164
                                                                          9,440
                                                                          4,288
                                                                         12,647
                                                                         92,339
Maine
                                                                             52
                                                                          3,783
                                                                         15,037
                                                                          1,363
                                                                          1,759
                                                                          2,127
                                                                         24,120
Maryland
                                                                         15,417
                                                                          6,768
                                                                         23,323
                                                                          3,410
                                                                          5,504
                                                                            531
                                                                         54,952
Massachusetts
                                                                          3,110
                                                                          2,245
                                                                         31,116
                                                                          3,293
                                                                          5,913
                                                                          1,324
                                                                         47,001
Michigan
                                                                         11,022
                                                                         12,926
                                                                         47,722
                                                                          8,561
                                                                         13,006
                                                                          1,283
                                                                         94,520
Minnesota
                                                                          3,262
                                                                         10,538
                                                                         73,990
                                                                          8,541
                                                                          6,842
                                                                          8,943
                                                                        112,116
Mississippi
                                                                          2,029
                                                                         10,602
                                                                         34,217
                                                                          4,133
                                                                          4,195
                                                                         14,897
                                                                         70,074
Missouri
                                                                          6,471
                                                                          6,966
                                                                         76,419
                                                                          7,230
                                                                          7,665
                                                                          2,636
                                                                        107,388
Montana
                                                                          2,398
                                                                          2,729
                                                                         30,096
                                                                          2,654
                                                                          1,347
                                                                         17,311
                                                                         56,536
Nebraska
                                                                          1,246
                                                                          2,340
                                                                         45,661
                                                                          5,848
                                                                          2,620
                                                                          1,483
                                                                         59,198
Nevada
                                                                          3,341
                                                                          4,095
                                                                          9,920
                                                                          2,212
                                                                          1,290
                                                                         19,018
                                                                         39,876
New Hampshire
                                                                          2,586
                                                                            568
                                                                         13,316
                                                                            907
                                                                          1,512
                                                                            534
                                                                         19,423
New Jersey
                                                                          4,625
                                                                          2,588
                                                                         13,623
                                                                          5,042
                                                                          5,963
                                                                            865
                                                                         32,707
New Mexico
                                                                          5,583
                                                                          1,460
                                                                         50,698
                                                                          1,959
                                                                          2,861
                                                                         48,662
                                                                        111,224
New York
                                                                          9,648
                                                                          4,994
                                                                         48,540