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

### EPA-454/R-11-011 November 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 Divisioniiiiv 

## 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 f actors 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 th e 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) .......................................................................... 2 0 2.5.1 Portable fuel containers ...................................................................................................... .......... 20 2.5.2 Onroad refueling .............................................................................................................. ............. 20 2.6 Other sources (biogenics, ot hpt, 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) ............................. 33 4.2.1 Ethanol plants (ptnonipm) ..................................................................................................... ....... 35 4.2.2 Biodiesel plants (ptnonipm) ................................................................................................... ...... 35 4.2.3 Portable fuel c ontainers (nonpt) .............................................................................................. ..... 36 4.2.4 Cellulosic fuel production (nonpt) ............................................................................................ .... 36 4.2.5 Ethanol transport and distribution (nonpt) ................................................................................... 37 4.2.6 Onroad refueling (nonpt) ...................................................................................................... ........ 37 4.2.7 Refinery adjustments (ptnonipm) ............................................................................................... .. 38 4.2.8 Ethanol transport gasoline a nd blends (ptnonipm, nonpt) ............................................................ 38 4.2.9 Upstream agricultural adjustments (afdust, ag, nonpt, ptnonipm) ............................................... 38 4.2.10 Livestock emissions growth (ag, afdust) .................................................................................. 39 4.2.11 Residential wood combus tion growth (nonpt) ......................................................................... 39 4.2.12 Aircraft growth (ptnonipm) .................................................................................................... .. 40 4.2.13 Stationary source control programs, consent decrees & settlements, and plant closures (ptnonipm, nonpt) ............................................................................................................. ........................ 41 4.2.14 Oil and gas projections in TX, OK, and non-California WRAP states (nonpt) ....................... 46 4.3 Onroad mobile source projections (onroad) ..................................................................................... 46 4.3.1 California LEV ................................................................................................................ ............. 47 4.4 Nonroad mobile source projections (nonroad, alm_no_c3, seca_c3) ............................................... 47 4.4.1 Emissions generated with th e NONROAD model (nonroad) ...................................................... 47 4.4.2 Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3) ..................................... 48v 4.4.3 Class 3 commercial marine vessels (seca_c3) .............................................................................. 50 4.5 Canada, Mexico, and offshore source s (othar, othon, and othpt) ..................................................... 51 4.6 Reference case emission summaries ............................................................................................. ... 51 5 MATS Control Case ............................................................................................................. .................. 57 6 References .................................................................................................................... ........................... 61



## 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 m odeling for the final MATS 2005v4.3 platform 3 Table 2-2. Model species produced by SMOKE fo r 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 stat es to the Petroleum Administration for Defense Districts 8 Table 2-5. Gasoline parameter categories 9 Table 2-6. States adopting Ca lifornia emission standards 10 Table 2-7. Summary of count y grouping characteristics for representative counties 11 Table 2-8. Updated 1,3-butadiene to VOC ratio for 2- stroke snowmobiles for NM IM’s gasoline categories15 Table 2-9. Criteria for groupi ng representative counties fo r nonroad mobile analysis 16 Table 2-10. NONROAD model te mperature (F) categories 17 Table 2-11. NONROAD NMIM runs 17 Table 2-12. Summary of NON ROAD 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 SO 2 emissions (tons/year) for states by sector 21 Table 2-16. 2005 base year PM 2.5 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 assump tions 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 co rn ethanol plant emissions 35 Table 4-4. 2017 biodiesel plant emissions 36 Table 4-5. PFC emissions for 2017 36 Table 4-6. 2017 cellulo sic plant emissions 37 Table 4-7. VOC losses (Emissions) due to ethanol transport and distribution 37 Table 4-8. Onroad gasoline and diesel refueling emissions 38 Table 4-9. Impact of refinery adjustments on 2017 emissions 38 Table 4-10. Upstream agricultural emissi on increases due to RFS2 fuels in 2017 38 Table 4-11. Growth factors from year 2005 to 2017 for animal operations 39 Table 4-12. Projection factors for growing year 2005 residen tial wood combustion sources 40 Table 4-13. Impact of year 2017 pr ojection factor error on resident ial wood combustion estimates 40 Table 4-14. Factors used to project 2 005 base-case aircraft emissions to 2017 41 Table 4-15. Summary of non-EGU emi ssion reductions applied to the 2005 inventory due to unit and plant closures 42 Table 4-16. Future-year ISIS-based cement indus try annual reductions (ton s/yr) for the non-EGU (ptnonipm) sector 44 Table 4-17. State-level n on-MACT boiler reductions from ICR data gathering 44vi Table 4-18. National impact of RICE controls on non-EGU projections 45 Table 4-19. Impact of fuel sulfur (SO 2 ) controls on 2017 non-EGU projections 45 Table 4-20. Oil and gas NO X and SO 2 emissions for 2005 and 2017 including additional reductions due to the RICE NESHAP 46 Table 4-21. Factors applied to year 2005 emissions to project locomotives and class 1 and class 2 commercial marine vessel emissions to 2017 49 Table 4-22. Additional class 1 railroad and C1/C2 CMV emissions from RFS2 fuel volume changes 50 Table 4-23. NO X , SO 2 , and PM 2.5 factors to project class 3 CMV emissions for 2017 51 Table 4-24. Summary of modeled base case SO 2 and PM 2.5 annual emissions (tons/year) for 48 states by

sector 52 Table 4-25. Reference case SO 2 emissions (tons/year) for states by sector 53 Table 4-26. Reference case PM 2.5 emissions (tons/year) for states by sector 54 Table 4-27. Future year baseline EGU CAP emissions (tons/year) by state 55 Table 5-1. Summary of emissions changes for the MATS AQ mode ling in the lower 48 states 57 Table 5-2. EGU emissions totals for the Mmdele d MATS control case in the lower 48 states 57 Table 5-3. State-specific changes in annual EGU SO 2 for the lower 48 states 58 Table 5-4. State-specific changes in annual EGU PM 2.5 for the lower 48 states 60



## LIST OF APPENDICES

APPENDIX A : Ancillary Datasets and Parameters Used for Each MATS Modeling Case

APPENDIX B: Inventory Data Files Used for Each MA TS Modeling Case – SMOKE Input Inventory
Datasets

APPENDIX C: Summary of MATS Rule 2017 Base Case Non-EGU Control Prog rams, Closures and Projectionsvii 

## 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 (f ormerly 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 Partnership1 

## 1 Introduction to the Modeling Platform

This Technical Support Document (TSD ) describes the development of the emissions inventories used as inputs to the air quality m odeling that the U.S. Environmental Protec tion Agency (EPA) performed to assess the impact of the Mercury and Air Toxics Standard s (MATS). This document provides the details of emissions modeling done to support th e development of the Regulatory Im pact Assessment (RIA) for the MATS. The emissions processing described herein and the corresponding ai r quality modeling were used to develop benefit-per-ton scaling factor s for the benefits calculation as described in the RIA. More information on this approach can be found in Appendi x 5C of the RIA and in the Air Quality Modeling Technical Support Document (TSD). The emissions i nventories 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 require d by the Community Multi-scale Ai r Quality (CMAQ) model. CMAQ simulates the numerous physical and chemical processes i nvolved in the formation, transport, and destruction of ozone, particulate ma tter and air toxics.

As part of the analysis for this rulemaking, the mode ling system was used to cal culate daily and annual PM 2.5 concentrations, 8-hr maximum ozone and visi bility impairment. Model predictions of PM 2.5 and ozone are used in a relative sense to estimate scenario -specific, future-year design values of PM 2.5 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) th e 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 mode l, including the settings and data used for the model, including emissions data, mete orology, initial conditions, a nd boundary conditions. The 2005-based air quality modeling platform used for th e proposed utility NESHAP RIA includes 2005 base year emissions and 2005 meteor ology for modeling ozone and PM 2.5 with CMAQ. In support of this rule, EPA modeled the air quality in the Eastern and the We stern United States using two separate model runs, each with a horizontal grid resolu tion of 12 km x 12 km. These 12 km modeling domains were “nested” within a modeling domain covering the remainder of th e 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 suppor t of the RIA is version 4.3 and is referred to as the 2005v4.3 platform. It is an upda te to the 2005-based platform, ve rsion 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 fo r 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 a nd for the appropriate a nd necessary finding. The 2005v4.3 platform builds upon the 2005-based platform, version 4.2, which was the ve rsion of the platform used for the final CrossState Air Pollution Rule and incorporated changes ma de in response to public comments on the proposed version of that rule.

Table 1-1 provides a high-level summar y of the three emissions cases that were modeled in support of the final rule RIA. The form of the fuel used for mob ile sources is a key discriminator between the cases. Therefore, the mobile source emissions are described with respect to the impacts of the Energy Independence2 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 pr e-EISA/EPAct fuel supply for mobile sources, and aver age year fires data. Air quality outputs from this case are used to compute relative response factors w ith 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 aver age 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/E PAct fuel supply (RFS2), along with average year fire data.

In the remainder of this document, we provide a desc ription of the approaches taken for the emissions in support of air quality modeling for the MATS. In Section 2, we describe th e 2005v4.3 platform custom configurations, ancilla ry data and 2005 inventory differences from the v4.2 platform. In Section 3, we describe the speciation differences among each of th e cases run. In Section 4, we describe the 2017 Reference (i.e., future year baseline) case as co mpared to the 2005 base ca se. Appendix A provides a comparison of the ancillary datasets and parameters used for the various MA TS emissions cases, and Appendix B compares the emissions inventory and other input data files used for each of the MATS cases.


## 2 2005 Emission Inventories and Their Preparation

As mentioned previously, the 2005 emi ssions modeling approach for MATS us ed much of the same data and approaches as the 2005v4.2 platform. In this section, we identify the diffe rences 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. Sectio n 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 emi ssions data are used for this rulemaking. All of the documentation provided here desc ribes 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 ye ar 2005 emissions scenario. This approach is very similar to that CSAPR Final Rule (formerly known as the “Transport Rule”). A base case approach uses3 average year fires and EGU temporal profiles from thr ee 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 e ach year, and EGU shutdowns and high use on high energy demand days also vary by year. By using a base case approach, these two asp ects of the inventory are maintained into the future year modeling and theref ore do not introduce potentiall y spurious year-specific artifacts into the air quality modeling estimates. Fo r MATS, the same biogenic emissions data as the 2005v4.2 platform was used for the 2005 case, and also for bo th future-year cases. The only significant data changes between the 2005 and the 2 017 future-year MATS case are the emission inventories and speciation approaches.

Table 2-1 below lists the platform sectors used for the MA TS modeling platform. It also indicates which platform sectors include HAP emissi ons and the associated sectors fr om 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 pres cribed fire emissions, county and annual resolution. Yes

Agricultural sector:

ag Nonpoint Ammonia (NH 3 ) emissions from NEI nonpoint livestock and fertilizer application. No

Area fugitive dust
sector: afdust Nonpoint PM 10 and PM 2.5 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 comme rcial marine vessel (CMV) emissions sources, county and annual resolution. Aircraft emissions are no longer in this s ector and are now included in the Non-EGU sector (as point sour ces); also, category 3 CMV emissions are no longer in this s ector 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/oceanve ssels.htm). Utilized final projections from 2002, developed for the C3 ECAProposal to the International Maritime Organization (EPA-420-F10-041, August 2010). Yes4 Platform Sector 2005 NEI Sector Description Contains HAP emissions? Onroad Mobile: onroad Mobile: onroad + Motor Vehicle Emissions Simulator (MOVES) emission factors created to account for hourly-bas ed meteorology dependencies at a select number of representative counties. Includ es local input information such as fuels, te mperatures, vehicle fleet, speed distributions and controls. Emi ssion 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 20 06 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.


### 2.1 Custom configuration for emissions modeling for MATS
Unlike the 2005v4.2 platform, the configuration for MA TS modeling included additional hazardous air pollutants (HAPs) and used slightly re vised 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 platfo rm had a few additional custom asp ects 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 specia tion across the MATS future-year ca ses as compared to 2005. Section 3 provides a detailed account of these differences. The future-year ancillar y data were largely the same as those in 2005, with no substantial diffe rences for most modeling sectors. The exception to this is onroad mobile, which in MATS processing, required several ne w ancillary input files to support the SMOKE to MOVES modules; these are discussed in detail in Section 2.4. A ll other ancillary data files not required for SMOKE to MOVES processing ca n otherwise be found at the 2005-based platform website ( http://www.epa.gov/ttn/chief/emch/index.html#2005 ).5 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
NO X NO Nitrogen oxide NO2 Nitrogen dioxide HONO Nitrous acid SO 2 SO2 Sulfur dioxide SULF Sulfuric acid vapor
NH 3 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

PM 10 PMC Coarse PM > 2.5 microns and  10 microns
PM 2.5 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, BU TADIENE13, ETHANOL and FO RM_PRIMARY are the extra “CMAQ-lite” HAPs that are not in the v4.2 platform.6 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 platformSpeciation crossreferences and Speciation profiles The MATS 2005v4.3 data files are confi gured 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. Th erefore, the MATS data files include profiles for additional VOC HAP species. Speciation VOC to TOG conversion profiles Added MATS-specific VOC to TO G 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 upda ted to support SMOKE to MOVES pollutants and modes, the MP “lite” version of CM AQ, and, to accept inventory Ethanol (ETOH). The 2005v4.2 platform data file is configured to support only the non-MP version.



### 2.2 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 f actors 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 fact ors with the activity data to compute the actual emissions. In brief, our approach was to use the met4moves program to identif y 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, app lies speciation profiles to develop inventories for pollutants not included in MOVES and temporally and spat ially allocates emissions to individual grid cells for CMAQ input.



### 2.2.1 MOVES
For MATS, EPA used a version of the MOVES 2010a mode l 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 in cluded some minor updates to emission rates and some changes designed to make the model run more effici ently. All updates are desc ribed 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 descri be 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 / Na tional Center for Atmospheric Res earch 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 maintaini ng dynamic consistency between the meteorological model and chemistry mechanisms. The hourly gridded meteor ological data was postprocessed by met4moves to create maximum temperatur e ranges, average relative humidity, and a series of diurnal temperature profiles. MOVES was run for each temperature bin and diur nal profile. See Sections 2.2.4.1 and 2.2.4.3 for details.

Vehicle population data is a requ ired input for MOVES when modeli ng on a county basis. Using the technical guidance provided to states by EPA, the cont ractor generated appropriate estimates for vehicle populations for use in the MOVES databases using the county specific VMT and national average ratios of7 vehicle populations versus vehicle VMT from the MOVE S 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 C onformity" (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 co vers the 50 United States (U.S.), Wa shington 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 cooperati on with the Office of Transportation and Air Quality (OTAQ) in Ann Arbor, Michigan.

OTAQ has developed a consolidated modeling system known as the Na tional 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 th e important input parameters specific to each county. The data in the NMIM county database (NCD) are used to develo p 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 ar e 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 (MOVE S) was used. This required conversion of the NCD database parameters to a format consistent w ith MOVES. A contractor was given the assignment (TranSystems, Contract N o. EP-D-06-001, WA 4-65) to convert th e NCD database to MOVES formatted input databases. This was accomplished with the assist ance 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 conve rted to MOVES format for use at the county scale consistent with the da tabases created using the MOVES Co unty Data Manager (CDM), except for the information regarding fuel properties. Th e fuel properties were updated using more recent information and methods specifically for this rule a nd described elsewhere in th is 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.



### 2.2.2 Representing counties Although EPA compiles county specific databases for a ll 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 ha s been accomplished in this rule using a concept called "representing counties".

In this approach, we group counties that have similar properties and th erefore would have similar emission rates. Then, we explicitly model onl y 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 c ounties in the group. This appr oach dramatically reduces the number of modeling runs required to generate inve ntories and still takes into account differences between counties.8 As described in Section 2.2.4, in order to generate onr oad mobile emissions, MOVES was run in conjunction with the EPA SMOKE model to generate "grid" leve l inventories for use in air quality modeling. SMOKE uses emission rates (not inventorie s) to generate inventory estimate s within each grid. Since SMOKE handles the differences in the fleet mix, temperatur es, speeds and VMT versus location and time, MOVES can be run in the "emission rate" mode. As a resu lt, when counties are groupe d, they can be grouped independently of fleet mix, speeds and temperature. Th is greatly increases the num ber of counties that can be in each grouping, since temperature is a factor that varies among the counties 1 . For this analysis, we grouped counties with similar fuel, emission standard s, altitude, and inspecti on and maintenance (I/M)

programs.

The information used to group the counties was de rived from the NMIM inputs used for the 2005 NEI onroad and nonroad mobile sectors. For the onroad portion of the inventor y estimates for the rule, including all base and control scenar ios, the current EPA highway mobile s ource emission model (MOVES) was used. This required conversion of the NCD database pa rameters 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 da ta 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 th e 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 cate gories. Then by grouping counties within the same categories, you get groups of counties that have the similar paramete rs. Counties were sorted into their Petroleum Administration for Defense District s (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 w ithin the same PADD. Tabl e 2-4 below shows the PADDs and the states within each PADD.

Table 2-4. Allocation of states to the Petroleu m 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

1 This differs from the calculation of nonroad inventories where te mperature was considered in the choice of representing county

.9 PADD State FIPS State Name Abbreviation
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 va lues used for the bins ar e described in Table 2-5.

Table 2-5 . Gasoline parameter categories10 Gasoline Parameter Category ID Mini mum 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 standa rds 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 standard s will be treated separately when c hoosing representing counties. Table 2-6 shows the states with Ca lifornia emission standards.

Table 2-6 . States adopting Calif ornia 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. A ltitude is a field in the County table of the MOVESDB20101006 database. Counties ar e either high (H) or low (L) a ltitude based on the criteria set11 forth by EPA certification procedures (4,000 feet above sea level). The result is a set of county groups with similar fuel, emission standards, alt itude 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 character istics for representative counties County Grouping Charac teristic 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 fu el 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 standard s 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 administer ed separately and have a different program design. Altitude Counties are either high or low altitude based on the criteria set forth by EPA cer tification 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 qualit y 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 repr esentative county and fuel month were performed independently of one another on different computer pr ocessors each accessing a MySQL database specific to that run.



### 2.2.3 SMOKE-MOVES inputs Both MOVES and SMOKE require meteorological data . The program met4moves takes gridded hourly meteorological data, the repr esentative counties, and the representa tive fuel months and produces separate meteorological products for MOVES and SMOKE. Met4 moves uses the representa tive 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 correspond ing counties in that county group for the number of months that correspond to the fuel month 2 . 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 taki ng an average over th ese same grid cells.

2 Spatial surrogates are used in determining which grid cells to pull in calculating the various meteorological statistics. The se spatial surrogates both map counties to grid cells. The spatial surrogates further limit the grid cells by determining whether some of the grid cells should not be included in the calculation of temperature range. For example, if some of the county has no ro ads or population, e.g. high mountains, then there is no reason to include it in the temperature range for onroad emissions.12 For rate-per-profile (RPP), SMOKE-MOVES uses the change in temperature over the day, the diurnal profile, instead of the temp erature at the hour of processing. Met4m oves create a series of diurnal profiles based on the extent of the temperature range and the si ze of the temperature bins. For MOVES, these diurnal profiles will span the full range of temperatures for that representativ e 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 temperat ure ranges are county specific, not ba sed on the representative county or county group. Met4moves can be run in daily or m onthly mode for producing SM OKE input. In monthly mode, the temperature range is determ ined by looking at the range of temp eratures over the whole month for that specific county. Therefore, ther e is one temperature range per county per month. While in daily mode, the temperature range is determined by evaluating the rang e 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 rate s produced by MOVES, SMOKE requires county VMT, population, and average speed by road t ype to calculate the necessary emi ssions for air quality modeling. VMT by county and Source Classifica tion Code (SCC) was developed us ing 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 count y were derived from the default national average speed distributions found in the default MOVES2010a da tabase AvgSpeedDistributio n table. These average speeds are the average speeds developed for the prev ious 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 togeth er using the default nati onal 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.



### 2.2.4 Generating emission factors for SMOKE After representative counties and fuel months were chosen, the met4moves scri pt was executed to produce the set of MOVES RunSpecs and meteorology tables th at would ultimately gene rate a set of SMOKE lookup tables encompassing the full range of temperatures for all the counties and m onths in each group. OTAQ also provided VMT, population, and averag e 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 (Met 4Moves), 2) construct a set of M OVES RunSpecs that produce lookup tables by temperature and average speed (runspec_generator), 3) pro cess 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 humi dity for each county and month. The inputs to13 met4moves are the hourly gridded temperature and humidity genera ted by the meteorological model used for CMAQ along with the list of represen tative counties and fuel months. For each representative county and fuel-mont h, 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 count y 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 configure d, along zonemonthhour tables in CSV format. Specifications were generated for the three types of MOVES processes: rate-perdistance (RPD), rate-per-profile (RPP), and rate -per-vehicle (RPV). R un 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 range s provided by the met4moves output. For RPP, a second series of run specifications were created for each representative c ounty, 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 c ounty databases, the zonemonthhour databases, other user-supplied databases, and the run specifications. The tool imp lemented 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 info rmation into a form that could be used by the compute server.

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

5) Once the MOVES runs were complete, OTAQ ran th e 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 perfor med additional calculations to support SMOKE processing of CMAQ ready model emi ssions: speciating HONO from NO and NO 2 , 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 (exha ust, evaporative, permeation, break wear, and tire wear) 3

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.



### 2.2.5 Running SMOKE for onroad mobile Running SMOKE using emission factor s (EF) from MOVES required the development of a new set of functionality. The central SMOKE program that perf orms this new analysis is movesmrg which takes activity data, meteorol ogical 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).

3 The moves2smk postprocessor also corrects the extended idle emissions for RPV by merging in data from a separate national extended idle run and replaces missing EF from RPD due to missing SCCroadtypes in some reference counties.14 The emissions process RPD is for modeling the on-netw ork emissions. This includes the following modes: vehicle exhaust, evaporation, permeation, break wear, and tire wear. For RPD, the ac tivity data is monthly VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus weekend (SPDPRO) 4 . The SMOKE program temporal takes vehicle and roadtype sp ecific temporal profiles a nd distributes the monthly VMT to day of the week and hour. Movesmrg read s the speed data for that county and SCC and the temperature from the gridded hourly data and uses th ese 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. Th is is repeated for each pollutant and SCC in that grid cell.

The emission process RPV is for modeling the off-netw ork 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 gri dded hourly data and uses the temperature plus SCC and the hour of the day to look up the appropriate EF fr om 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-networ k emissions for parked vehicles. This includes the mode vehicle evaporative (f uel vapor venting). For RPP, the activity data is VPOP. Movesmrg reads the county based diurnal temperat ure 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 a nd 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 cr eated: RPD, RPV, and RPP. SMOKE was run so that for each model day it would read in a single EF table (b ased on the appropriate fuel month), pr ocess all the counties that are part of the county group (i.e. are represented by that represen tative 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 emi ssions processes (RPD, RPV, and RPP). Post-processing scripts were developed to in tegrate the process specific intermediate files into model-ready intermediate files for the onroad sector. These files were on nationa l 12km domain, to support the CMAQ runs they were further processed to create an aggregated 36km s ector specific model-ready file and 2 12km domains (12EUS1 and 12WUS1).



### 2.3 Nonroad mobile sources (n onroad, alm_no_c3, seca_c3)

The nonroad sectors include a wide-ra nge of mobile emission sources ra nging from locomotives, marine vessels, construction and farming equipment to hand-held lawn tools. As disc ussed in Section 5, nonroad upstream impacts also impact the post-EPAct/EISA/R FS2/reference case (anti-backsliding) reflecting increased ethanol production resulti ng in fuel volume increases for locomotives and C1/C2 CMV emissions.



### 2.3.1 Emissions generated with the NONROAD model (nonroad)

Most nonroad emissions are were estimated usi ng the EPA’s NONROAD model, as run by the EPA’s NMIM. NONROAD is EPA’s model for calculating emi ssions from nonroad equipment, except for aircraft,

4 If the SPDPRO is available, the hourly speed takes preceden ce over the average speed in the SPEED inventory. Due to an oversight, SPDPRO was not used in the base and future-years m odeling. A later sensitivity was run including the SPDPRO input for the base year which found that the use of hourly speed sl ightly increased the emissions for most pollutants (e.g. nationall y NO
x showed a 0.8% increase, VOC showed a 1% increase, and PM 2.5 showed a 3% increase).15 locomotives, and commercial marine vessels. Th e 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 ba sed 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 desi gned to run NONROAD for “represe ntative counties” and to use individual county activity to develop national inventorie s. 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 speci al versions of countyyearmonth, gasoline, and diesel, which were copied into the sta ndard versions of these tables in or der to run the model. The fuels in NCD2010201Tier3 were devel oped from the fuels used for onroad ve hicles, as described in Section 2.2.1.

Similarly, a special countymonthhour table that contained 2005 meteorol ogy was copied into the standard countymonthhour table. The use of the countymonthhour table for meteor ology was selected by the RunSpec setting useYearlyWeatherDataSelected=" false." We also made a minor ch ange for snowmobiles: the SCC toxics table in the NMIM County Database (NCD) wa s updated to correct 1,3-butad iene exhaust emissions for 2-stroke snowmobiles (SCC 2260001020), 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 ru ral areas during winter due to snow mobile emissions, available at: http://www.epa.gov/otaq/r enewablefuels/420r10006.pdf . The increases were based on data from only three engines, which showed unusually high1,3-butadiene em issions with 10% ethanol (Eth oxygenate). Other data suggests that this increase is hi ghly 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 ga s is used where the fuel contains ethanol which is greater than or equa l to 5% by volume or Ethyl Tertiary Butyl Ether (ETBE) is greater than or equal to 5% by volume. MTBE gas is used where th e fuel contains MTBE which is greater than or equal to 12% by volume or Tertiary Amine Methyl Ether (T AME) 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 le ss 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 tabl e was developed to use representing counties in the NMIM runs. The algorithm for producing representing c ounties for NMIM was identical to that used for MOVES except that ten degree temperature bins were added to the criteria. The result was 293 repres enting counties. Finally, NMIM does not estimate ethanol emi ssions, so the inventory for this pollutant was from the chemical speciation that is obtained by post-processing using SMOKE16 2.3.1.1 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 Mode l for only one of those counties and then using those emission factors for all the co unties in the group. For this analys is, 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 a nd summer fuels, January was chosen as the fuel-month to represent the seven months October through April, a nd July was chosen to represent the five months MaySeptember. The total number of county-months for wh ich NMIM runs needed to be performed was thus 293 times 12 months = 3,516 county-months for each scenario-y ear. If NMIM runs were performed for all U.S. counties and months, there would be 3141 countie s (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 characteri stics listed in Table 2-9.

Table 2-9. Criteria for grouping representative c ounties 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 cr eated 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 countie s 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 in17 Table 2-10, ten degree Fahrenheit (F) bins were create d for min and max temperatures for each month. All counties in each group had all min and max temperatures fo r all twelve months in th e same bins. The lowest interval includes all temperatures be low -10 degrees F. The highest inte rval includes all temperatures above 100 degrees F.18 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 wa s chosen as the one with the highest VMT. The same set of 293 county groups and representing counties was used fo r all years and scenarios.

2.3.1.2 Fuel inputs for NONROAD runs For the nonroad mobile portion of the inventory estimat e for the rule, the NMIM county database (NCD) developed for the 2005 NEI, with one exception of the c ounty-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. Prac tically, this means conve rting the fuelsupply and fuelformulation tables from MOVES into the countyy earmonth, gasoline, and dies el tables in the NCD. In 2005, onroad and nonroad gasoline formula tions are assumed to be identical.

While MOVES allows for multiple gasoline fuels, each w ith 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. Wh en there are multiple MOVES gasoline fuels for a single county-month, non-oxygena te MOVES fuel properties are multipli ed by market share and summed to produce the fuel property in the gaso line table. Individual non-ethano l oxygenates and California ethanol occur in only one fuel per county-month in MOVES, so the volume and market shar e 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.

2.3.1.3 NMIM runs

Table 2-11 shows the NMIM runs th at 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

MOVES fuels were used for future year cases and converted to NMIM format. However, for 2017, EPA Case Year Run Name Base 2005 Tier3Base2005Nr Reference 2017 Tier3Ref2017e10Nr19 assumed that nonroad equipment would use only E10. Th e details of the fuels conversion from MOVES to NMIM was discussed above. Ta ble 2-12 describes the component s in the NONROAD/NMIM system common to all MATS modeling scenarios.

Table 2-12. Summary of NONROA D 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 Hi storical 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.



### 2.3.2 Locomotives and commercial ma rine vessels (alm_no_c3, seca_c3) The year 2005 emissions from these sources used for th is rule are the same as they were for the Final Rulemaking: Greenhouse Gas Emissions Standards and Fu el Efficiency Standards for Medium- and HeavyDuty 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/tr ansportrulefinal_eitsd_28jun2011.pdf. The procedures for calculating emissions from locomotives and C1 /C2 commercial marine were developed for the Locomotive Marine Rule (2008) and ar e detailed in the RIA “Final Ru le: Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ig nition 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

. 

### 2.4 2005 point sources (ptipm and ptnonipm sectors)
Point sources are sources of emissions for which speci fic 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 so urces within the contiguous United States. The offshore oil platform (othpt sector) and category 3 CMV em issions (seca_c3 sector) are also po int 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 2.3.2.20 After removing offshore oil platforms (othpt sector), we created two platform sectors from the remaining 2005v2 NEI point sources for input into SMOKE: the E GU sector – also called the Integrated Planning Model (IPM) sector (i.e., ptipm) and the non-EGU sect or – also called the non-IPM sector (i.e., ptnonipm). This split facilitates the use of different SMOKE tem poral processing and future-y ear projection techniques for each of these sectors. The inventory pollutant s processed through SMOKE for both ptipm and ptnonipm sectors were: CO, NO X , VOC, SO 2 , NH 3 , PM 10 , and PM 2.5 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., integrati on) of VOC HAP pollutants from the inventory (integration is discussed in detail in Section 3).

The ptnonipm emissions were provided to SMOKE as a nnual emissions. The ptipm emissions for the base case were input to SMOKE as daily emissions. Th e 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 a ll MATS scenarios, includi ng 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 repl aced with MOVES-based onroad refueling emissions (discussed in Section 2.5.2).



### 2.4.1 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 pl ants represent a “Low Ethanol” scenario needed to produce onl y 4 billion gallons of ethanol, essent ially a scenario without a future year RFS2 mandate (or MATS). All ethanol plants were assigned or corrected (after quality assurance analyses) coordinates based on analys is using searches of company web sites and Google Earth verification for many sites. Emissions were ca lculated based on plant design capacity and emission factors based on fuel type (e.g., coal, natural gas). Finally, because ben zene, acetaldehyde and formaldehyde (BAF) emissions were directly computed for these sources, unlike the re st of the ptnonipm sector, we treated these ethanol plants as VOC integrate sources. A summary of the ethanol plant emi ssions 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

NO X 8,204

PM 10 10,107

PM 2.5 3,691

SO 2 9,001 VOC 10,75421 

### 2.5 2005 nonpoint sources (afdust, ag, avefire, nonpt)
The year 2005 area-source fugitive dust (af dust), agricultural animal and fertilizer NH 3 (ag), and average (typical)-year fires (avefire) emissi ons are the same as those used in the CSAPR Final (2005v4.2) emission modeling platform. Nonpoint sources th at were not subdivided into the afdust, ag, or avefire sectors were assigned to the “nonpt” sector, and mo st of these sources are also unc hanged for MATS modeling. The 2005 nonpoint sources that change are limited to portable fuel contai ners (PFCs) and onroad refueling.



### 2.5.1 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 futu re year scenarios. Therefore, we scaled year 2017 reference case etha nol emissions by the ratio of 2005 to 2017 base total VOC emissions to compute year 2005 ethanol emissions:

Ethanol_2005 = Ethanol_2017referen ce * (VOC_2005 / VOC_2017reference)



### 2.5.2 Onroad refueling
As mentioned in Section 2.2, NEI-bas ed 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 emissi on inventories for refueling from on-road vehicles in calendar years 2005 and 2017 for MATS reference and control cases were ge nerated for air quality modeling. The refueling inventory in cludes emissions from spillage loss and displacement vapor loss. For this analysis, the refueling emissions were estimate d using the revised versi on of EPA’s Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all twelve months.

In an effort to reduce MOVES runtime, the “represe nting counties” approach wa s 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 co unties based on Petroleum Admini stration for Defense Districts (PADD), fuel parameters, usage of California emissi on standards, Inspection/Ma intenance programs, and altitude. One additional paramete r included in developing the repres enting counties for refueling was temperature.

Temperature bins with increments of ten degr ees F were created for the minimum and maximum temperatures for each month using the temperatur es 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 fo r 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 wa s used for all years and scenarios.

MOVES was run in inventory mode for only the repr esenting counties using th e county-specific on-road data, such as vehicle miles travel led, fleet age distribution, speed di stribution, and meteorology, available from the NCD. The customized fuel inputs, discussed in Section 2.2.2.1, were used for each of the representing counties.

The resulting refueling emission inventories for 238 repr esenting 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 represen ting counties were applied to the represented counties and multiplied by the county-specif ic activity to generate the inventories for all counties.22 

### 2.6 Other sources (biogenics, othpt, othar, and othon)

All emissions from Canada, Mexico, and Offshore Dril ling platforms (othpt, otha r, and othon), and all nonanthropogenic inventories (biogenics and ocean ch lorine) are unchanged from the 2005v4.2 (used for the Final CSAPR and HDGHG FRM) emissions modeling platform. The same em issions are used for all MATS scenarios and years.



### 2.7 Emissions summaries for 2005 base case
Once developed, the emissions inventories were proce ssed to provide the hourly, gridded emissions for the model-species needed by CMAQ. Table 2-14 provide s summaries of the 2005 U.S. emissions inventories modeled for this rule by sector. Table 2-15 a nd Table 2-16 provide state-level summaries of SO 2 , and PM 2.5 by sector. Note that the nonroad columns include emi ssions from traditional nonr oad 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 NO X SO 2 PM 2.5 PM 10 NH 3 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 SO 2 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 32 371,45823 State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 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 PM 2.5 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 1,468 161,66924 State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 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 8,607 11,139 1,601 84,529 North Carolina 16,967 12,665 49,551 6,272 8,939 9,870 104,264 North Dakota 6,397 598 41,504 4,552 976 934 54,962 Ohio 53,572 12,847 52,348 9,847 11,785 316 140,715 Oklahoma 1,411 6,246 90,047 3,765 4,559 6,644 112,672 Oregon 412 8,852 58,145 3,741 3,375 65,350 139,874 Pennsylvania 55,547 16,263 44,607 7,565 11,058 454 135,494 Rhode Island 10 256 1,289 394 577 14 2,540 South Carolina 14,455 4,779 26,598 3,491 5,061 9,163 63,548 South Dakota 390 2,982 33,678 2,910 1,056 7,062 48,079 Tennessee 12,856 21,912 32,563 5,072 8,514 3,934 84,851 Texas 21,464 37,563 194,036 21,361 29,859 21,578 325,861 Tribal 0 1,569 0 0 0 0 1,569 Utah 5,055 3,595 14,761 1,627 2,703 27,412 55,153 Vermont 37 337 6,943 479 605 696 9,098 Virginia 12,357 11,455 38,140 5,968 6,661 5,659 80,241 Washington 2,396 4,618 45,599 6,697 6,721 4,487 70,519 West Virginia 26,377 5,154 14,778 1,702 1,930 3,050 52,991 Wisconsin 5,233 7,967 37,277 6,083 6,783 994 64,337 Wyoming 8,068 10,298 31,645 1,455 1,103 15,686 68,254 Total 496,877 433,346 2,110,298 268,745 301,073 684,035 4,294,373 

## 3 VOC Speciation Changes that Represent Fuel Changes

A significant detail that is different in each of th e MATS modeling cases than in the 2005v4.2 emissions modeling is the VOC speciation profiles used to sp lit total VOC emissions into the VOC model species needed for CMAQ. In this section, we summarize the various speciation prof ile information used in configuring the various cases.

A major change between the 2005v4.2 platform and the MATS base and fu ture modeling is the integration of ethanol for key sectors and specific inve ntories. In the previous platform , the inventories for specific sources had benzene, acetaldehyde, formaldehyde and/or metha nol (BAFM). These emissions would be integrated, namely their emissions would come from the inve ntory not from speciating VOC. To prevent double counting, BAFM would be removed from VOC, leavi ng the remainder (NONHAPVOC) to be speciated to other components (i.e. non-BAFM species). See s ection 3.1.2.1 of the 2005v4 plat form for more details25 ftp://ftp.epa.gov/EmisInventor y/2005v4/2005_emissions_tsd_07jul2010.pdf . In the MATS modeling, if ethanol was present in the inventorie s, it would also be integrated. To differentiate when a source was integrating BAFM versus EBAFM (ethan ol in addition to BAFM), the specia tion profiles is re ferred to as an “E-profile”, for example pre-Tier 2 vehicles E10 gasoline exhaust specia tion profile 8751 versus 8751E. For the onroad sector, ethanol is integrated for all emi ssions from gasoline vehicles. For the nonpt sector, ethanol is integrated for refueling and portable fuel containers (PFCs). In the future-year case, the nonpt sector includes a cellulosic corn ethanol and biodiese l inventory (SCC 30125010) in which ethanol is integrated. For fuel distribution ope rations associated with the bulk-plan t-to-pump (btp) distribution, ethanol is speciated from VOC because the nonpoint in ventories do not include ethanol specifically.

The onroad sector has some additional changes to VOC speciation. Instead of speciating VOC, SMOKEMOVES uses TOG instead of VOC. Therefore, SMOKE does not need to convert VOC to TOG before creating NONHAPTOG and performing a dditional speciation. A second change in VOC speciation is the differentiation of a new mode. In pr evious platforms, onroad mobile em issions were divided into exhaust and evaporative modes. For the MATS base and future years, gasoline vehicle’s evaporative mode is further divided into permeation specific emi ssions and evaporative. Similar to evaporative and exhaust profiles, these profiles change between the base and future y ear cases. Additional update s include headspace vapor speciation utilizes a combination of the E10 headspace vapor profile and E0 headspace vapor profile as opposed to using solely E0 for 2005 5 , and a new Heavy Duty Diesel vehicle exhaust mode profile for pre2007 model year (MY) vehicles that replaces an older 2004-vintage medium-duty diesel profile. See Table 3-1 for more details.

The VOC speciation approach is customi zed to account for the impact of fuel changes in the future year case. These changes affect the onroad sector, the nonroad s ector, and parts of the nonpt and ptnonipm sectors. These fuel changes and vehicle changes are implemen ted by using different VOC profiles and combination of profiles between the base and futu re cases. The speciation changes from fuels in the nonpt sector are for refueling, portable fuel containers (PFCs), and fuel distribution operations associated with the bulk-plant-topump (btp) distribution. The speciat ion changes from fuels in the ptnon ipm sector include btp distribution operations inventoried as point sources 6 . Refinery to bulk terminal (rbt) fuel distribution speciation does not change across the modeling cases because this is consid ered upstream from the in troduction of ethanol into the fuel 7 . Mapping of fuel distribution SCCs to btp and rbt emissions categories ca n be found in Appendix A of the revised annual Renewable Fuel Standard (R FS2) Emissions Inventory for Air Quality Modeling Technical Support Document (EPA Report No. 420-R-10-005, January 2010, http://www.epa.gov/otaq/r enewablefuels/420r10005.pdf ).

Table 3-1 summarizes the different prof iles utilized for the fuel-related sources in each of the sectors for 2005 and the future year cases. A comparison of th e 2005v4.2 platform with the MATS 2005 case is also included. Appendix A lists ancillary input da ta set names used for MATS emissions.

5 This was an oversight in th e 2005v4.2 platform corrected for this modeling effort.

6 VOC speciation is customized by using different speciation profiles in the base versus future year cases. For some sources related to the mobile sector and fuel distribution, a combination of profiles are specified by county, month and mode (e.g. exh aust, evaporative, permeation). SMOKE calculates a resultant profile by calculating the fraction of each profile by month, county, a nd mode. The GSPRO_COMBO ancillary file controls this featur e in SMOKE. The GSPRO_COMBO file represents the county specific mixture of fuels, for example the mixture of E10 and E0. For the nonpt sector, a further complication in developing t he GSPRO_COMBO is differentiating the sources th at integrate ethanol (i.e. use E-profiles) and those that do not integrate ethanol

. By using the mode for refueling (RFL__VOC) and PFC (EVP__VOC), these ethanol integrated sectors can be differentiated from btp (VOC).

7 We also identified bulk plant storage (bps) as an upstream so urce that is pre-addition of ethanol and uses the same speciation profile as rbt.26Table 3-1.Summary of VOC speciation profile approaches by sector across casesCategory 2005v4.2 2005 MATS 2017 MATS referenceOnroad GasolineExhaust COMBO COMBO COMBO8750 Pre-Tier 2 E0 exhaust 8750E Pre-Tier2 E0 exhaust 8751E Pre-Tier 2 E10 exhaust8751 Pre-Tier 2 E10 exhaust 8751E Pre-Tier2 E10 exhaust 8757E Tier 2 E10 Exhaust8758E Tier 2 E15 ExhaustEvaporative COMBO COMBO (All evap except permeation) COMBO (All evap except permeation)8753 E0 Evap 8753E E0 Evap 8754E E10 Evap8754 E10 Evap 8754E E10 Evap 8872E E15 EvapCOMBO (Permeation evap) COMBO (Permeation evap)8766E E0 evap perm 8769E E10 evap perm8769E E10 evap perm 8770E E15 evap permRefueling 8762 E0 Headspace composite COMBO COMBO8869E E0 Headspace 8870E E10 Headspace8870E E10 Headspace 8871E E15 HeadspaceOnroad DieselExhaust 4674 2004 MDD exhaust 8774 Pre-2007 MY HDDexhaust877T3 Pre & Post 2007 MY HDDexhaustWeighted 8774 and 8775 profilesEvaporative 4547 Diesel Headspace 4547Diesel Headspace 4547 Diesel HeadspaceRefuel 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel HeadspaceNonroad GasolineExhaust COMBO COMBO 8751 Pre-Tier 2 E10 exhaust8750 Pre-Tier 2 E0 exhaust8750 Pre-Tier 2 E0 exhaust8751 Pre-Tier 2 E10 exhaust8751 Pre-Tier 2 E10 exhaustEvaporative COMBO COMBO 8754 E10 Evap8753 E0 evap 8753 E0 evap8754 E10 evap 8754 E10 evapRefueling 8762 E0 Headspace composite COMBO 8870 E10 Headspace8869 E0 Headspace8870 E10 Headspace27Category 2005v4.2 2005 MATS 2017 MATS referenceNonroad DieselExhaust 4674 2004 MDD exhaust 8774 Pre-2007 MY HDDexhaust8774 Pre-2007 MY HDD exhaustEvaporative 4547 Diesel Headspace 4547Diesel Headspace 4547 Diesel HeadspaceRefueling 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel HeadspacePFC8762 E0 Headspace composite COMBO 8870E E10 Headspace8869E E0 Headspace8870E E10 HeadspaceAircraft5565 Aircraft Exhaust 5565* Aircraft Exhaust 5565* Aircraft Exhaust* Updated version in SPECIATE 4.3* Updated version in SPECIATE 4.3Locomotives4674 2004 MDD exhaust 8774 Pre-2007 MY HDDexhaust8774 Pre-2007 MY HDD exhaustMarine2480 Ship Channel Downwind 2480 Ship Channel Downwind 2480 Ship Channel DownwindBTP8762 E0 Headspace composite COMBO COMBO8869 E0 Headspace 8870 E10 Headspace8870 E10 Headspace 8871 E15 HeadspaceRBT/BPS8762 E0 Headspace composite 8869E0 Headspace 8869 E0 HeadspaceEthanol Plants1188 fermentation process 8776 EthanolFuel Prod 8776 Ethanol Fuel Proda8776E Ethanol Fuel Prodbacorn ethanol andbiodiesel ptnonipmbcellulosic ethanol & cellulosic diesel nonpt28 

## 4 2017 Reference Case

The 2017 reference case represents the future, including the implementation of emissions impacts of the fuel volumes mandated by the 2005 EPAct and 2007 EISA and fi nalized in the RFS2 program. The reference case includes MSAT2 and LDGHG but does not in clude HDGHG impacts. The 2017 reference case assumes 21.6 billion gallons of renewable fuels ( 24 billion ethanol-equivalent gallons due to volume increases of ethanol), with 17.8 bill ion gallons of E10 and E15, 1.5 billio n gallons of biodiesel, 0.2 billion gallons of renewable diesel, and 2.2 billion gallons of cellulosic diesel . The fuel changes required upstream emissions estimates and adjustments in addition to the downstream changes to onroad and nonroad mobile source emissions. For nonroad mobile sources, onroad mobile including refueling sources, OTAQ-generated emissions were provided to reflec t the reference case fuels.

The 2017 reference case uses many of th e same growth and control assumpti ons as those for the Final CrossState Air Pollution Rule (CSAPR), because other th an onroad mobile, nonroad mobile, onroad refueling, PFC, and ethanol plant sources, both MATS a nd CSAPR use the same 2005v4.2-based emissions inventories. There are some differences between the 2012 and 2014 ba se case projections in CSAPR and the 2017 reference case for MATS:

1) 2017 includes some additional controls that were promulgated after 2014, (e.g., post-2014 consent decrees and fuel sulfur rule s in a couple of states).

2) Growth factors for several sources are year-spe cific; so while the methodology is the same as CSAPR, the future year emissions estimates diffe r (e.g., oil and gas in a co uple states, residential wood combustion).

3) Onroad refueling uses year and scenario-specifi c (i.e., reference) MOVES emissions for all MATS modeling, rather than NEI emissions.

4) There is a new dataset of ethanol plants that re place a limited set of NEI ethanol plants in 2005v4.2- based CSAPR 2012 and 2014 projections. These MATS reference case emissions are the same for the 2005 and 2017 base case.

5) Minor errors identified after CS APR modeling was complete were fi xed (e.g, we include agricultural dust projections for the couple of stat es that provided point source farms).

The remainder of Section 4 is very similar to Sect ion 4 in the CSAPR emissions modeling TSD, available from ftp://ftp.epa.gov/EmisInventory/2005v4_2/ transportrulefinal_eitsd_28jun2011.pdf , but with the updates described above.

The future case projection methodolog ies vary by sector. For EGU emi ssions (ptipm sector), the emissions reflect state rules and federal consent decrees th rough December 1, 2010. For onr oad mobile sources, all national measures for which data were available at the time of modeling have been included. The future case scenarios reflect projected economic changes and fuel usage for EGU and mobile sectors. For nonEGU point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, a nd afdust sectors), local control programs that might have been necessary for areas to attain the 1997 PM 2.5 NAAQS annual standard, 2006 PM NAAQS (24-hour) standard, and th e 1997 ozone NAAQS are generally not included in the future basecase projections for most states. One exception are some NO X and VOC reductions associated with the New York, Virginia, and Connecticut State Implementation Plans (SIP), which were added as part of the comments received from the CSAPR and a larger effort to start including more local control information on stationary non-EGU sources; this is described further in Section 4.2. The following bullets summarize the29 projection methods used for sources in the various sectors, while additional details and data sources are given in Table 4-1.

 IPM sector (ptipm): Unit-specific estimates from IPM, version 4.10.

 Non-IPM sector (ptnonipm): Pr ojection factors and percent reduc tions reflect CSAPR (Transport Rule) comments and emission reductions due to c ontrol programs, plant closures, consent decrees and settlements, and 1997 and 2001 ozone State Impl ementation Plans in NY, CT, and VA. We also used projection approaches for point -source livestock, and aircraft that are consistent with projections used for the sectors that contain the bulk of these emissions. Term inal area forecast (TAF) data aggregated to the national level were used fo r aircraft to account for projected changes in landing/takeoff activity. Year-specifi c speciation was applied to some portions of this sector and was discussed in Section 3.

 Average fires sector (avefire): No growth or control.

 Agricultural sector (ag) : Projection factors for livestock estim ates based on expected changes in animal population from 2005 Department of Agri culture data; no growth or control for NH 3 emissions from fertilizer application.

 Area fugitive dust sector (afdust): Projection factor s for dust categories related to livestock estimates based on expected changes in animal population; no growth or c ontrol for other cate gories in this

sector.

 Remaining Nonpoint sector (nonpt) : Projection factors that imple ment CSAPR Proposal comments and reflect emission reductions due to control pr ograms. Residential wood combustion projections based on growth in lower-emitting stoves and a reduction in higher emitting stoves. PFC projection factors reflecting impact of the final Mobile Source Air Toxics (M SAT2) rule and include ethanol emissions. Gasoline stage II onroad refueling emissi ons obtained directly from MOVES. Oil and gas projection estimates are provided for the non-California Western Re gional Air Partnership (WRAP) states as well as Oklahoma and Texas. Year-speci fic speciation was applied to some portions of this sector and was discus sed in Section 3.

 Nonroad mobile sector (nonroad): Same versi on of the NONROAD2008a, in cluding same set of 293 county groups and representing c ounties as the 2005 base case. Future-year equipment population estimates and control programs (final locomotive-marine and small spark ignition) to 2017 are included. The only differences between the MATS fu ture case runs are the fuels used, specifically, the ratio of E10 and E15 fuels. Year-specific speciation was applied to some portions of this sector and is discussed in Section 3.

 Locomotive, and non-Class 3 commercial marine sect or (alm_no_c3): Projecti on factors for Class 1 and Class 2 commercial marine and locomotives which reflect CSAPR Proposal comments and activity growth and final locomotive-marine controls.

 Class 3 commercial marine vessel sector (seca_c3 ): Base-year 2005 emissions grown and controlled to 2017, incorporating CSAPR Proposal comments a nd controls based on Emissions Control Area (ECA) and International Marine Organization (IMO) global NO X and SO 2 controls.

 Onroad mobile sector uses a version MOVES devel oped for the Tier 3 Proposal that incorporates new car and light truck greenhouse gas emissions st andards (LDGHG) affecting model years 2012 and later (published May 7, 2010). Thes e emissions also include RFS2 fuels. VOC speciation uses different future-year values to take into account bot h the increase in ethanol us e, and the existence of Tier 2 vehicles that use a differe nt speciation profile. This sect or includes all non-refueling onroad mobile emissions (exhaust, evaporative, brake wear and tire wear modes). SMOKE-MOVES was used in a similar configuration as the 2005 base case to apportion MOVES emissions factors into hourly gridded temperatur e-adjusted emissions.30  Other nonroad/nonpoint (othar): No growth or control.

 Other onroad sector (othon): No growth or control.

 Other nonroad/nonpoint (othar): No growth or control.

 Other point (othpt): No growth or control.

 Biogenic: 2005 emissions used for all future-year scenarios.31 Table 4-1 summarizes the control strate gies and growth assumptions by source type that were used to create the 2017 reference-case emissions from the 2005v4.2 base-cas e inventories. These future year base case projections and controls are also included in the MATS reference and control cases. All Mexico, Canada, and offshore oil emissions are unchanged in all fu ture cases from those in the 2005 base case.

Lists of the control, closures, pr ojection packets (datasets) used to create the MATS 2017 future case inventories from the 2005 MATS base case are provided in Appendix C.

The remainder of this section is organized either by source sector or by specific emissions category within a source sector for which a distinct se t of data were used or developed for the purpose of projections for the MATS Rule. This organization allows consolidation of the discussion of the emissions categories that are contained in multiple sectors, because the data and approaches used across the sectors are consistent and do not need to be repeated. Sector na mes associated with the emissions cat egories are provided in parentheses.32 Table 4-1. Control strategies and growth assumptions for creating the 2017 reference case emissions inventories from the 2005 base case Control Strategies and/or growth assumptions (grouped by affected pollutants or standard and approach used to apply to the inventory) Pollutants affected Approach/ Reference Non-EGU Point (ptnonipm sector) projection approaches MACT rules, national, VOC: national applied by SCC, MACT Boat Manufacturing Wood Building Produ cts Surface Coating Generic MACT II: Spandex Production, Ethylene manufacture Large Appliances Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents, Explosives, Phthalate Plasticizers, Polyes ter Resins, Polymerized Vinylidene Chloride Reinforced Plastics Asphalt Processing & Roofing Iron & Steel Foundries Metal: Can, Coil Metal Furniture Miscellaneous Metal Parts & Products Municipal Solid Waste Landfills Paper and Other Web Plastic Parts Plywood and Composite Wood Products Carbon Black Production Cyanide Chemical Manufacturing Friction Products Manufacturing Leather Finishing Operations Miscellaneous Coating Manufacturing Organic Liquids Distribution (Non-Gasoline) Refractory Products Manufacturing Sites Remediation VOC EPA, 2007a

Consent decrees on companies (based on in formation from the Office of Enforcement and Compliance Assurance – OECA) apportioned to plants owned/operated by the companies VOC, CO, NOx, PM, SO 2 1

DOJ Settlements: plant SCC controls for: Alcoa, TX Premcor (formerly Motiva), DE All 2

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO 2 3 Hazardous Waste Combustion PM 4 Municipal Waste Combustor Reductions –plant level PM 5 Hospital/Medical/Infectious Waste Incinerator Regulations NO X , PM, SO 2 EPA, 2005 Large Municipal Waste Combustors – growth applied to specific plants All (including Hg) 5 MACT rules, plant-level, VOC: Auto Plants VOC 6 MACT rules, plant-level, PM & SO 2 : Lime Manufacturing PM, SO 2 7 MACT rules, plant-level, PM: Taconite Ore PM 8 Livestock Emissions Growth from year 2002 to year 2017 (some farms in the point inventory) NH 3 , PM 9

NESHAP: Portland Cement (09/09/10) – plant level based on Industrial Sector Integrated Solutions (ISIS) policy emissions in 2013. The ISIS results are from the ISIS-Cement model runs for the NESHAP and NSPS analysis of July 28, 2010 and include closures. Hg, NO X , SO2, PM, HCl 10; EPA, 2010

New York ozone SIP controls VOC, NO X , HAP VOC 11

Additional plant and unit closures provided by state, regional, and the EPA agencies and additional consent decrees. Include s updates from CSAPR comments. All 12

Emission reductions resulting from controls put on specific boiler units (not due to MACT) after 2005, identified th rough analysis of the control data gathered from the Information Collection Request (ICR) from the Industrial/Commercial/Institutional NO X , SO 2 , HCl Section 4.2.13.233 Boiler NESHAP. Reciprocating Internal Combustion Engines (RICE) NESHAP NO X , CO, PM, SO 2 13

Ethanol plants that account fo r increased ethanol production due to RFS2 mandate All 14 State fuel sulfur content rules for fuel oil –effective only in Maine, New Jersey, and New York SO 2 15

Nonpoint (nonpt sector) projection approaches
Municipal Waste Landfills: projection f actor of 0.25 applied All EPA, 2007a Livestock Emissions Growth from year 2002 to 2017 NH 3 , PM 9 New York, Connecticut, and Virginia ozone SIP controls VOC 11, 16
RICE NESHAP NO X , CO, VOC, PM, SO 2 13

State fuel sulfur content rules for fuel oil –effective only in Maine, New Jersey, and New York SO 2 15

Residential Wood Combustion Growth and Change-outs from year 2005 to 2017 All 17 Gasoline and diesel fuel Stage II refue ling via MOVES2010a month-specific inventories for 2017 with assumed RFS2 and LDGHG fuels VOC, Benzene, Ethanol 18

Portable Fuel Container Mobile Source Air Toxics Rule 2 (MSAT2) inventory growth and control from year 2005 to 2017 VOC 19

Use Phase II WRAP 2018 Oil and Gas VOC, SO 2 , NO X , CO Section 4.2.14 Use 2008 Oklahoma and Texas Oil and Gas, and apply year 2017 projections for TX, and RICE NESHAP controls to Oklahoma emissions. VOC, SO 2 , NO X , CO, PM Section 4.2.14

APPROACHES/REFERENCES- Non-EGU Stationary Sources:

1. Appendix B in the MATS Proposal TSD: http://www.epa.gov/ttn/chief/emch/toxics /proposed_toxics_rule_appendices.pdf

2. For Alcoa consent decree, used http:// cfpub.epa.gov/complian ce/cases/index.cfm; for Motiva: used information sent by State of Delaware

3. Used data provided by the EPA, OAQPS, Sector Policies and Programs Division (SPPD).

4. Obtained from Anne Pope, the US EPA - Hazardous Waste Incinerators criteria and hazardous air pollutant controls carried over from 2002 Platform, v3.1.

5. Used data provided by the EPA, OAQPS SPPD expert.

6. Percent reductions and plants to receive reductions based on recommendations by rule lead engineer, and are consistent with the reference: EPA, 2007a

7. Percent reductions recommended are determined from the exis ting plant estimated baselines and estimated reductions as shown in the Federal Register Notice for the rule. SO 2 percent reduction are computed by 6,147/30,783 = 20% and
PM 10 and PM 2.5 reductions are computed by 3,786/13,588 = 28%

8. Same approach as used in the 2006 Clean Air Interstate Rule (CAIR), which estimated reductions of “PM emissions by 10,538 tpy, a reduction of about 62%.” Used same list of plants as were identified based on tonnage and SCC from CAIR: http://www.envinfo.com/caai n/June04updat es/tiop_fr2.pdf

9. Except for dairy cows and turkeys (no growth), based on animal population growth estimates from the US Department of Agriculture (USDA) and the Food and Agriculture Policy and Research Institute. See Section 4.2.10.

10. Data files for the cement sector provided by Elineth Torr es, the EPA-SPPD, from the an alysis done for the Cement NESHAP: The ISIS documentation and analysis for the ceme nt NESHAP/NSPS is in the docket of that rulemakingdocket # EPA-HQ-OAR-2002-005. The Cement NESHAP is in the Federal Register: September 9, 2010 (Volume 75, Number 174, Page 54969-55066

11. New York NO X and VOC reductions obtained from Appendix J in NY Department of Environmental Conservation Implementation Plan for Ozone (February 2008): http://www.dec.ny.gov/docs/ air_pdf/NYMASIP7final.pdf

. 12. Appendix D of Cross-State Air Pollution Rule: ftp://ftp.epa.gov/EmisInventory/2005v4_2/tran sportrulefinal_eitsd_appendices_28jun2011.pdf

13. Appendix F in the Proposed (Mercury and Air) Toxics Rule TSD: http://www.epa.gov/ttn/chief/emch/toxics /proposed_toxics_rule_appendices.pdf

14. The 2008 data used came from Illinois’ submittal of 2008 emissions to the NEI.34 15. Based on available, enforceable state sulfur rules as of November, 2010: http://www.ilta.org/LegislativeandRegulatory/ MVNRLM/NEUSASulfur%20Rules_09.2010.pdf

, http://www.mainelegislature.org/legis/ bills/bills_124th/billpdfs/SP062701.pdf

, http://switchboard.nrdc.org/blogs/rkasse l/governor_paterson_signs_new_la.html

, http://green.blogs.nytimes.com/2010/07/2 0/new-york-mandates-cleaner-heating-oil/

16. VOC reductions in Connecticut and Virg inia obtained from CSAPR comments.

17. Growth and Decline in woodstove types based on industry trade group data, See Section 4.2.11.

18. MOVES (2010a) results for onroad refuelin g including activity growth from VMT, Stage II control programs at gasoline stations, and phase in of newer vehicles with onboard Stage II vehicle controls. http://www.epa.gov/otaq/models/moves/index.htm

19. VOC, benzene, and ethanol emissions for 2017 based on MSAT2 rule and ethanol fuel assumptions (EPA, 2007b)

Onroad mobile and nonr oad mobile controls (list includes all key mobile control strategies but is not exhaustive)

National Onroad Rules: Tier 2 Rule: Signature date February, 2000 2007 Onroad Heavy-Duty Rule: February, 2009 Final Mobile Source Air Toxics Rule (MSAT2): February, 2007 Renewable Fuel Standard: March, 2010 Light Duty Greenhouse Gas Rule: May, 2010 all 1

Local Onroad Programs: National Low Emission Vehicle Program (NLEV): March, 1998 Ozone Transport Commission (OTC) LEV Program: January,1995 VOC 2

National Nonroad Controls: Clean Air Nonroad Diesel Final Rule – Tier 4: June, 2004 Control of Emissions from Nonroad Large-Spark Ignition Engines and Recreational Engines (Marine and Land Based): “Pentathalon Rule”: November, 2002 Clean Bus USA Program: October, 2007 Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder: October, 2008 all 3,4,5

Aircraft (emissions are in the nonEGU point inventory) : Itinerant (ITN) op erations at airports to 2017 all 6

Locomotives: Energy Information Administration (EIA) fuel consumption projections for freight rail Clean Air Nonroad Diesel Final Rule – Tier 4: June 2004 Locomotive Emissions Final Rulemaking, December 17, 1997 Control of Emissions of Air Pollution from Locomotives and Marine: May 2008 all EPA, 2009; 3; 4; 5

Commercial Marine: Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards (April, 30, 2010) –also includes CSAPR comments. EIA fuel consumption projections for diesel-fueled vessels Clean Air Nonroad Diesel Final Rule – Tier 4 Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999 Tier 1 Marine Diesel Engines, February 28, 2003 all 7, 3; EPA, 2009

APPROACHES/REFERENCE S – Mobile Sources

1. http://epa.gov/otaq/hwy.htm

2. Only for states submitting these inputs: http://www.epa.gov/otaq/lev-nlev.htm

3. http://www.epa.gov/nonroad-diesel/2004fr.htm

4. http://www.epa.gov/cleanschoolbus/

5. http://www.epa.gov/otaq/marinesi.htm

6. Federal Aviation Administration (FAA) Terminal Area Forecast (TAF) System, January 2010: http://www.apo.data.fa a.gov/main/taf.asp

7. http://www.epa.gov/otaq/oceanvessels.htm35 

### 4.1 Stationary source projections: EGU sector (ptipm)
The future-year data for the ptipm sector used in the air quality modeling were created using version 4.10 (v4.10) of the Integrated Planning Model (IPM) ( http://www.epa.gov/airmarkt/progsregs/epaipm/index.html ). The IPM is a multiregional, dynamic, dete rministic linear programmi ng model of the U.S. electric power sector. Version 4.10 reflects federal and state rules and binding, enforceable consent decrees through December of 2010. The 2017 IPM emissions refl ect the CSAPR as finalized in July 2011. The future year baseline emissions do not reflect the final Mercur y and Air Toxics (MATS) rule, but the control case emissions do reflect the rule. Neither case reflects the Boiler MACT regulatory assumptions.

IPM v4.10 Final incorporates informa tion on existing controls collected through the Information Collection Request (ICR), and information from comments received on the IPM-related Notice of Data Availability (NODA) published on September 1, 2010. IPM v4.10 also included the addition of over 20 GW of existing Activated Carbon Injection (A CI) reported to the EPA via the ICR. Units with SO 2 or NO X advanced controls (e.g., scrubber, SCR) that were not required to run for comp liance with Title IV, New Source Review (NSR), state settlements, or st ate-specific rules were modeled by IP M to either operate those controls or not based on economic efficiency parameters.

Updates to IPM 4.10 (with respect to the version released in the IPM NODA version) include adjustments to assumptions regarding the performance of acid ga s control technologies, new costs imposed on fuelswitching (e.g., bituminous to sub-bituminous), correc tion of lignite availabi lity to some plants, incorporation of additional planned retirements, a more inclusive implementation of the scrubber upgrade option, and the availability of a scru bber retrofit to waste-coal fired fl uidized bed combustion units without an existing scrubber. Further details on the future-year EGU emissions inve ntory used for this rule can be found in the incremental documentation of the IPM v.4.10 platform, available at http://www.epa.gov/airmarkets/pro gsregs/epa-ipm/BaseCasev410.html

.

Directly emitted PM emissions (i.e., PM 2.5 and PM 10 ) from the EGU sector are computed via a post processing routine which applies emission factors to the IPM-estimated fuel throughput based on fuel, configuration and controls to com pute the filterable and condensable components of PM. This methodology is documented in the IPM CSAPR TSD.



### 4.2 Stationary source projections: no n-EGU sectors (ptnonipm, nonpt, ag, afdust)
To project U.S. stationary sources other than the ptipm sector, we applied growth fa ctors and/or controls to certain categories within the ptnon ipm, nonpt, ag and afdust platform s ectors. This subsection provides details on the data and projection methods used for thes e sectors. The MATS future year scenarios also required obtaining and preprocessing numerous other inputs that we re ceived directly from OTAQ.

In estimating future-year emissions, we assumed that em issions growth does not track with economic growth for many stationary non-IPM sources. This “no-growth” assumption is base d on an examination of historical emissions and economic data. More details on the rationa le for this approach can be found in Appendix D of the Regulatory Impact Assessment for the PM NAAQS rule (EPA, 2006).

The starting point for projecting the 2005 MATS emissions was to use similar emission projection methodologies as used for the 2005v4.2 platform for th e Final CSAPR, which inco rporated responses to public comments on the modeling inventories. Th e 2012 and 2014 projection factors developed for the CSAPR (see http://www.epa.gov/ttn/chief/emch/index.html#final ) were updated to reflect year 2017.36 Year-specific projection factors fo r years 2017 were created. Growth factors (and control factors) are provided in the following sections wh ere feasible. However, some sector s used growth or control factors that varied geographically and their contents could not be provided in the following sections (e.g., gasoline distribution varies by stat e and pollutant and has hundreds of records).

Table 4-2 lists the stationary non-EGU inputs and projection factors that were applied to account for the year 2017 RFS2 mandate impacts on emissions to the reference case. These inputs are discussed in more detail in Section 4.2.1 through Section 4.2.9. All other stationary non-EGU projecti ons, controls and plant closure information not related to the RFS2 impacts are discussed in Section 4.2.10 through Section 4.2.13. All stationary non-EGU emissions in the 2017 reference case are uncha nged in the 2017 control case (see Section 5); therefore, we will simply note that these emissions are “year 2017” rather than the more cumbersome “year 2017 reference case”.

Table 4-2. MATS reference case mobile source-related projection methods Input Type Sector(s) Description Corn ethanol plants SMOKE ORL file that replaces 2005 base case ORL file ptnonipm Based on RFS2 analysis and production volumes. Point source format.

Biodiesel plants SMOKE ORL file ptnonipm A ccounts for facilities with current production capacities, to support RFS2 biodiesel production. Point source format. Cellulosic fuel production SMOKE ORL file nonpt Accounts for cellulosic ethanol and cellulosic diesel to support RFS2 cellulosic production. County-level (nonpoint) format. Ethanol transport and distribution SMOKE ORL file nonpt Accounts for ethanol vapor losses and spillage at any point in the transport and distribution chain. C ounty-level (nonpoint) format. Portable Fuel Containers (PFCs) SMOKE ORL nonpt NONROAD-mode l based emissions from PFCs, including vapor displacement, tank permeation, and diurnal evaporation. Countylevel (nonpoint) format. Onroad refueling SMOKE ORL file nonpt MOVES-based gasoline and diesel fuel spillage and displacem ent vapor losses. County-level (nonpoint) format, monthly resolution. Refinery adjustments Projection factors ptnonipm Not in ba se cases, accounts for changes in various refinery processes due to incorporation of RFS2 fuels. Ethanol transport gasoline & ethanol blends Projection factors nonpt, ptnonipm Not in base cases, accounts for RFS impacts on emissions from bulk plant storage, refinery to bulk terminal, and bulk terminal to pump. Upstream agricultural adjustments Projection factors afdust, ag, nonpt, ptnonipm Not in base cases, accounts for changes in ag burning/dust, fertilizer application/production, livestock dust/waste and pesticide application/production.37 

### 4.2.1 Ethanol plants (ptnonipm) As discussed in Section 2.4.1, we replaced all corn et hanol plants that OTAQ had supplied from the RFS2 rule –see Section 2.1.2 in the CSAPR Final TSD- with those recently compiled for 2005 and a year 2017 without the RFS2 mandate (not sepa rately modeled for this rule). Additional ethanol plants cited for development in support of increased ethanol production for RFS2 are the cause for the increased number of facilities and emissions in the refere nce case. Table 4-3 provides the summaries for the corn ethanol plants in the 2017 reference case.

Table 4-3. 2017 reference case corn ethanol plant emissions

Pollutant Tons 1,3-Butadiene 0.0011

Acrolein 41.7 Formaldehyde 45.2

Benzene 20.3 Acetaldehyde 643.2 CO 14,847

NO X 20,035

PM 10 21,639

PM 2.5 6,825

SO 2 11,299 VOC 35,459



### 4.2.2 Biodiesel pl ants (ptnonipm) OTAQ developed an inventory of existing biodiesel plants for 2017 that were sited at existing plant locations in support of producing biodiesel fuels for the RFS2 mandate. The RFS2 calls for 1.45 billion gallons per year (Bgal) of biodiesel fuel production by year 2017. Only plants wi th current production capacities were assumed to be operating in 2017. Total plant capacity at these existing facilities is limited to just over 1 Bgal. There was no attempt to site future year plan ts to account for the need to match biodiesel production needed for RFS2. Therefore, OTAQ applied a scalar adjustment (of 1.41) to each individua l biodiesel plant to match the 2017 production target of 1.45 Bgal. Once f acility-level production ca pacities were scaled, emission factors were applied base d on an assumed natural gas combus tion process. Inventories were modeled as point sources with Googl e Earth and web searchi ng validating facility co ordinates and correcting state-county FIPS.38 Table 4-4 provides the 2017 biodies el plant emissions estimates.39 Table 4-4. 2017 biodiesel plant emissions

Pollutant Tons
Acrolein 3.09E-04 Formaldehyde 2.23E-03

Benzene 4.71E-05 Acetaldehyde 3.59E-04

CO 726

NO X 1,171

PM 10 99

PM 2.5 99

SO 2 9

VOC 64



### 4.2.3 Portable fuel containers (nonpt) OTAQ provided year 2017 PFC emissi ons that include estimated Reid Vapor Pressure (RVP) and oxygenate impacts on VOC emissions, and more importantly, large increases in ethanol emissions from RFS2. These emission estimates also include refueling from the NONROAD model for gas can vapor displacement, changes in tank permeation and diurnal emissions from evaporation. Because these PFC inventories contain ethanol, we developed a VOC E- profile that integrated ethanol, see Sec tion 3 for more details. Emissions for 2017 are provided in Table 4-5.

Table 4-5. PFC emissions for 2017
Pollutant Tons VOC 123,186 Benzene 1,368 Ethanol 11,565



### 4.2.4 Cellulosic fuel production (nonpt) OTAQ developed county-level inventories for cellulosi c diesel and cellulosic et hanol production for 2017 to satisfy RFS2 production. The methodology for building cellulosic plant emissions inventories is fairly similar conceptually to that for building the biodiesel plant inventories. First, we assume that cellulosic diesel and cellulosic ethanol are pr oduced in the same counties where current production capacity exists, based on RFS2 FRM inventories. Total county pr oduction capacities was over 16 Bgal; therefore, OTAQ applied a scalar adjustment (of 0.246) to each counties production ca pacity to match the 2017 production target of 3.93 Bgal (2.2 Bgal di esel and 1.69Bgal ethanol). Once county-level cellulosic production capacities were scaled down to match the 2017 target, emission factors were applied based on an assumed natural gas combustion process.40 Table 4-6 provides the year 2017 cell ulosic plant emissions estimates.41 Table 4-6. 2017 cellulosic plant emissions

Pollutant Tons

Acrolein 21 Formaldehyde 58

Benzene 27 Acetaldehyde 786 CO 42,839 Ethanol 1,875

NH 3 0.5

NO X 64,062

PM 10 7,533

PM 2.5 3,796

SO 2 4,973 VOC 5,336

We had no refined information on potential VOC speci ation differences between cellulosic diesel and cellulosic ethanol sources. Therefore, we summed up cellulosic diesel and cellulosic ethanol sources and used the same SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC speciation as was used for corn ethanol plants. However, these cellulosic i nventories contain ethanol; therefore we developed a VOC E-profile that inte grated ethanol, see Section 3 for more details.



### 4.2.5 Ethanol transport a nd distribution (nonpt) OTAQ developed county-level inventories for ethanol transport and distribution for 2017 to account for losses for the processes such as truck, rail and waterw ays loading/unloading and intermodal transfers such as highway-to-rail, highways-to-waterways , and all other possible combinations of transfers. Emission rates were applied based on June 2008 AP-42 factors and etha nol versus gasoline vapor mass equations. These emissions are entirely evaporative and therefore limited to VOC and are summarized in Table 4-7. The leading descriptions are “Industria l Processes; Food and Agriculture; Ethanol Production” for each SCC.

Table 4-7. VOC losses (Emissions) due to etha nol transport and distribution SCC Description Tons 30205031 Denatured Ethanol Storage Working Loss 27,763 30205052 Ethanol Loadout to Truck 19,069 30205053 Ethanol Loadout to Railcar 9,610



### 4.2.6 Onroad refueling (nonpt)
As discussed in Section 2.5.2, the re fueling inventory includes gasoline and diesel fuel emissions from spillage loss and displacement vapor loss. For this an alysis, 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. The same set of repr esentative counties and temperatures we re used for all MATS scenarios. VMT, fleet age distribution and speed distribution were developed for 2017. Because these refueling inventories contain ethanol, we developed a VOC E-profile that inte grated ethanol, see Section 3 for more details. A summary of the 2017 onroad mobile refueling emissions is provided in42 Table 4-8.43 Table 4-8. Onroad gasoline and diesel refueling emissions Fuel Type Pollutant Tons Gasoline VOC 63,759 Diesel VOC 12,962

Gasoline Benzene 161 Gasoline Ethanol 8,735



### 4.2.7 Refinery adjustments (ptnonipm) Refinery emissions were adjusted for changes in fuel s due to the RFS2 mandate. These adjustments were provided by OTAQ and impact processe s such as process heaters, ca talytic cracking units, blowdown systems, wastewater treatment, condensers, cooling towe rs, flares and fugitive emissions. The impact of the RFS2-based reductions is shown in Table 4-9.

Table 4-9. Impact of refinery adjustments on 2017 emissions Pollutant Reductions (tons) CO 12,674

NO X 20,183

PM 10 4,367

PM 2.5 2,525

SO 2 13,846 VOC 3,693



### 4.2.8 Ethanol transport gasoline and blends (ptnonipm, nonpt) Emissions changes in the transport of changing fuel s from the RFS2 mandate impact several processes including bulk plant storage (BPS), re finery to bulk terminal (RBT) and bulk terminal to pump (BTP). These impacts, provided by OTAQ, result in approximate ly 15,000 tons of VOC reductions in 2017 for these

processes.


### 4.2.9 Upstream agricultural adjustme nts (afdust, ag, nonpt, ptnonipm) Changes in domestic biofuel volumes , resulting from the RFS2 fuels ma ndate, impact upstream agriculturalrelated source categories in several emissions modeling sectors. Thes e source categories include fertilizer application, pesticide applica tion and livestock waste (NH 3 only), agricultural tilli ng, unloading and livestock dust (PM only) and fertilizer produc tion mixing and blending, pesticid e production and ag ricultural burning (all pollutants). As seen in Table 4-10, the cumulati ve impact of these source- specific changes is a net increase in emissions for upstream agricultural sources.

Table 4-10. Upstream agricultural emission incr eases due to RFS2 fuels in 2017 Pollutant Increases (tons)

CO 302

NH 3 45,272

NO X 363

PM 10 42,934

PM 2.5 6,500

SO 2 69

VOC 1644 

### 4.2.10 Livestock emissions growth (ag, afdust)
Growth in ammonia (NH 3 ) and dust (PM 10 and PM 2.5 ) emissions from livestock in the ag, afdust and ptnonipm sectors was based on projections of growth in animal population. Table 4-11Table 4-11 provides the growth factors from the 2005 base-case emissions to all MATS year 2017 scenarios for animal categories applied to the ag, afdust, and ptnonipm sectors for livestock-related SCCs.

Except for dairy cows and turkey pr oduction, the animal projection factor s are derived from national-level animal population projections from the U.S. Depa rtment of Agriculture (USDA) and the Food and Agriculture Policy and Research Institute (FAPRI). For dairy cows and turkeys, we assumed that there would be no growth in emissions. This assumption was based on an analysis of historical trends in the number of such animals compared to production rate s. Although productions ra tes have increased, the number of animals has declined. Thus, we do not be lieve that production forecasts provide representative estimates of the future number of co ws and turkeys; therefore, we did not use these forecasts for estimating future-year emissions from these animals. In particular , the dairy cow population is projected to decrease in the future as it has for the past few decades; howev er, milk production will be increasing over the same period. Note that the ammonia emissions from dairies are not directly related to animal population but also nitrogen excretion. With the cow numbers going dow n and the production going up we suspect the excretion value will be changing, but we assumed no change because we did not have a quantitative estimate.

The inventory for livestock emissions used 2002 emissi ons values therefore, our projection method projected from 2002 rather than from 2005.

Appendix E in the 2002v3 platform documentation pr ovides the animal population data and regression curves used to derive the growth factors: http://www.epa.gov/scram001/reports/Emi ssions%20TSD%20Vol2_Appendices_01-15-08.pdf . Appendix F in the same document provides the cross references of livestock sources in the ag, afdust and ptnonipm sectors to the animal categor ies in Table 4-11Table 4-11.

Table 4-11. Growth factors from year 2005 to 2017 for animal operations Animal Category Projection Factor Dairy Cow 1.0000 Beef 1.0206 Pork 1.0893 Broilers 1.3442 Turkeys 1.0000 Layers 1.2406 Poultry Average 1.2674 Overall Average 1.0935



### 4.2.11 Residential wood co mbustion growth (nonpt) We projected residential wood comb ustion (RWC) emissions based on the expected increase in the number of low-emitting wood stoves and the corresponding decrea se in other types of wood stoves. As newer, cleaner woodstoves replace older, hi gher-polluting wood stoves, there will be an overall reduction of the emissions from these sources. The a pproach cited here was developed as part of a modeling exercise to estimate the expected benefits of the woodstoves change-out program ( http://www.epa.gov/burnwise ). Details of this approach can be found in Section 2.3.3 of the PM NAAQS Regulatory Impact Analysis (EPA, 2006).

The specific assumptions we made were:45  Fireplaces, source category code (SCC)=2104008001: increase 1%/year

 Old woodstoves, SCC=2104008002, 2104008010, or 2104008051: decrease 2%/year

 New woodstoves, SCC=2104008003, 2104008004, 2104008030, 2104008050, 2104008052 or 2104008053: increase 2%/year

For the general woodstoves and fireplaces categor y (SCC 2104008000) we computed a weighted average distribution based on 19.4% fire places, 71.6% old woodstoves, 9.1% new woodstoves using 2002v3 Platform missions for PM 2.5 . These fractions are based on the fracti on of emissions from these processes in the states that did not have the “general woodstoves and fireplaces” SCC in the 2002v3 NEI. This approach results in an overall decrease of 1.056% per year for this source category.

We discovered an interpolation error in the year 2017 projection factors for RWC af ter air quality modeling. Table 4-12 presents the projection factors used to project the 2005 base case (2002 emissions) for RWC, including these 2017 errors. Table 4-13shows the nationa l impact (tons) of the 2017 pr ojection fact or error.

Table 4-12. Projection factors for growing year 2005 residential wood combustion sources

SCC SCC Description Erroneous 2017 Factor Correct 2017 Factor 2104008000 Total: Woodstoves and Fireplaces 0.45 0.84 2104008001 Fireplaces: General 0.65 1.15 2104008070 Outdoor Wood Burning Equipment 2104008002 Fireplaces: Insert; non-EPA certified
0.36 0.70 2104008010 Woodstoves: General 2104008051 Non-catalytic Woodst oves: Non-EPA certified 2104008003 Fireplaces: Insert; EPA certified; non-catalytic

0.74 1.30 2104008004 Fireplaces: Insert; EP A certified; catalytic 2104008030 Catalytic Woodstoves: General 2104008050 Non-catalytic Woodst oves: EPA certified 2104008052 Non-catalytic Woodstoves: Low Emitting 2104008053 Non-catalytic Woodst oves: Pellet Fired

Table 4-13. Impact of year 2017 projection factor e rror on residential wood combustion estimates

Pollutant 2005 Emissions Erroneous 2017 Emissions Erroneous 2017 Reductions Correct 2017 Emissions Correct 2017 Reductions

NO X 38,292 18,023 20,270 33,545 4,747

PM 2.5 381,362 174,769 206,593 326,706 54,656

SO 2 5,302 2,529 2,773 4,697 605 VOC 569,950 242,126 327,824 450,990 118,959



### 4.2.12 Aircraft growth (ptnonipm) These 2005 point-source emissions are projected to futu re years by applying activity growth using data on itinerant (ITN) operations at airports. The ITN operations are defined as aircraft take-offs whereby the aircraft leaves the airport vicinity and lands at another airport, or airc raft landings where by the aircraft has arrived from outside the airport vici nity. We used projected ITN info rmation available from the Federal Aviation Administration’s (FAA) Terminal Area Forecast (TAF) System: http://www.apo.data.faa.gov/main/taf.asp (publication date January 2010). Th is information is available for approximately 3,300 individual airports, for all year s up to 2030. We aggreg ated and applied this information at the national level by summing the air port-specific (U.S. airports only) ITN operations to46 national totals by year and by aircra ft operation, for each of the four av ailable operation types: commercial, general, air taxi and military. We computed growth factors for each operation type by dividing future-year ITN by 2005-year ITN. We assigned factors to inventory SCCs based on the operation type.

The methods that the FAA used for developing the ITN data in the TAF are documented in: http://www.faa.gov/data_research/ aviation/aerospace_forecasts/2009- 2025/media/2009%20Forecast%20Doc.pdf

Table 4-14 provides the national growth factors for aircraft; all factors are applied to year 2005 emissions. For example, year 2017 commercial aircraft emissions are 12.88% higher than year 2005 emissions. The same aircraft factors were used for each of the year-specifi c scenarios: low-ethanol, reference and control.

Table 4-14. Factors used to project 2005 ba se-case aircraft emissions to 2017 SCC SCC Description Projection Factor 2275001000 Military aircraft 1.0229 2275020000 Commercial aircraft 1.1288 2275050000 General aviation 0.8918 2275060000 Air taxi 0.8620

27501015 Internal Combustion Engines;Fixed Wing Aircraft L & TO Exhaust;Military;Jet Engine: JP-5 1.0229

27502001 Internal Combustion Engines;Fixed Wing Aircraft L & TO Exhaust;Commercial;Piston Engine: Aviation Gas 1.1288

27502011 Internal Combustion Engines;Fixed Wing Aircraft L & TO Exhaust;Commercial;Jet Engine: Jet A 1.1288

27505001 Internal Combustion Engines;Fixed Wing Aircraft L & TO Exhaust;Civil;Piston E ngine: Aviation Gas 0.8918

27505011 Internal Combustion Engines;Fixed Wing Aircraft L & TO Exhaust;Civil;Jet Engine: Jet A 0.8918

27601014 Internal Combustion Engines;Rotary Wing Aircraft L & TO Exhaust;Military;Jet Engine: JP-4 1.0229

27601015 Internal Combustion Engines;Rotary Wing Aircraft L & TO Exhaust;Military;Jet Engine: JP-5 1.0229

We did not apply growth factors to any point sources with SCC 2760201 1 (Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; Commercia l; Jet Engine: Jet A) becau se the facility names associated with these point sources app eared to represent industrial faciliti es rather than airports. This SCC is only in one county, Santa Barbar a, California (Sta te/County FIPS 06083).

None of our aircraft emission projections account for any control programs. We considered the NO X standard adopted by the International Civil Av iation Organization’s (IC AO) Committee on Aviation Environmental Protection (CAEP) in Februa ry 2004, which is expected to reduce NO X by approximately 2% in 2015 and 3% in 2020. However, this rule, signed July 2011 (see http://www.epa.gov/otaq/aviation.htm ), was not adopted as an EPA (or U.S.) rule prior to MATS modeling; therefore, the effects of this rule were not included in the future-year emissions projections.



### 4.2.13 Stationary source control program s, consent decrees & settlements, and plant closures (ptnonipm, nonpt) We applied emissions reduction factor s to the 2005 emissions for partic ular sources in the ptnonipm and nonpt sectors to reflect the impact of stationary -source control programs in cluding consent decrees, settlements, and plant closures. Some of the controls described in this section were obtaine d from comments on the CSAPR proposal. Detailed summaries of the impacts of the control programs are provided in47 Appendix D of the CSAPR TSD: ftp://ftp.epa.gov/EmisInventory/2005v4_2/tr ansportrulefinal_eitsd_appendices_28jun2011.pdf

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Controls from the NO X SIP call were assumed to have been implemented by 2005 and captured in the 2005 base case (2005v2 point invent ory). This assumption was confirmed by review of the 2005 NEI that showed reductions from Large Boiler/Turbines and Large Inte rnal Combustion Engines in the Northeast states covered by the NO X SIP call. The future-year base controls consist of the following:

 We did not include MACT rules where compliance dates were prior to 2005, because we assumed these were already reflected in the 2005 inventory. The EPA OAQPS Sector Policies and Programs Division (SPPD) provided all controls information rela ted to the MACT rules, and this information is as consistent as possible with the preamble emissions reduction percen tages for these rules.

 Various emissions reductions from the CSAPR comments, including but not limited to: fuel switching at units, shutdowns, futu re-year emission limits, ozone SIP VOC controls for some sources in Virginia and Connecticut, and state and local control programs.

 Evolutionary information gathering of plant closures (i.e., emissions we re zeroed out for future years) were also included where information indicated that the plant was actually closed after the 2005 base year and prior to CSAPR and MATS modeling that began in the spring of 2011. We also applied unit and plant closures received from the CSAPR comment s. However, plants projected to close in the future (post-2010) were not removed in the future years because these projec tions can be inaccurate due to economic improvements. We also applied cement kiln (uni t) and cement plant closures discussed later in Section 4.2.6.1. More detailed in formation on the overall state-level impacts of all control programs and projection data sets, including units and plants cl osed in the 2017 reference case ptnonipm inventories are provided in Appendix D of the Final CSAPR TSD: ftp://ftp.epa.gov/EmisInventory/2005v4_2/tr ansportrulefinal_eitsd_appendices_28jun2011.pdf . The magnitude of all unit and plant closures on the non-EGU point (ptnonipm) sector 2005 base-case emissions is shown in Table 4-15 below. These sa me reductions are seen in all MATS future year scenarios.

Table 4-15. Summary of non-EGU emission redu ctions applied to the 2005 i nventory due to unit and plant closures

CO NH 3 NO X PM 10 PM 2.5 SO 2 VOC
Reductions 125,162 636 109,237 21,143 12,600 190,734 26,750

 In addition to plant closures, we included the eff ects of the Department of Justice Settlements and Consent Decrees on the non-EGU (ptnonipm) sector em issions. We also included estimated impacts of HAP standards per Section 112, 129 of th e Clean Air Act on the non-EGU (ptnonipm) and nonpoint (nonpt) sector emissions, ba sed on expected CAP co-benefits to sources in these sectors.

 Numerous controls have compliance dates beyond 2008; these include refine ry and the Office of Compliance and Enforcement (OECA) consent decrees, Department of Justice (DOJ) settlements, as well as most national VOC MACT controls. A dditional OECA consent decree information is provided in Appendix B of the Proposed Toxics Rule TSD: http://www.epa.gov/ttn/chief/emch/toxi cs/proposed_toxics_rule_appendices.pdf . The detailed data used are available at the website listed in Section 1.

 Refinery consent decrees contro ls at the facility and SCC le vel (collected through internal coordination on refineries by the EPA).

 Fuel sulfur fuel limits were enforceab le for Maine, New Jersey and New York.48  Criteria air pollutant (cap) re ductions a cobenefit to RICE NESHAP controls, including SO 2 RICE cobenefit controls.

 We applied New York State Impl ementation Plan available cont rols for the 1997 8-hour Ozone standard for non-EGU point and nonpoint NO X and VOC sources based on NY State Department of Environmental Conservation February 2008 guidance. These reductions are found in Appendix J in: http://www.dec.ny.gov/docs/air_pdf/NYMASIP7final.pdf . See Section 3.2.6 in the CSAPR TSD: ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf

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Most of the control programs were applied as replacement controls, which means that any existing percent reductions (“baseline control effici ency”) reported in the NEI were re moved prior to the addition of the percent reductions due to these control programs. Exceptions to replacement controls are “additional” controls, which ensure that the c ontrolled emissions match desired re ductions regardless of the baseline control efficiencies in the NEI. We used the “additional controls” approach for many permit limits, settlements and consent decrees where specific plant and multiple-plant-level reductions/targets were desired and at municipal waste landfills where VOC was reduced 75% via a MACT control using projection factors of 0.25.

4.2.13.1 Reductions from the Port land Cement NESHAP (ptnonipm)
As indicated in Table 4-1, the Industrial Sectors Inte grated Solutions (ISIS) model (EPA, 2010) was used to project the cement industry component of the ptnoni pm emissions modeling sect or to 2013. There were no future year estimates for 2017, so 2013 estimates were us ed for all future year MATS modeling scenarios. This approach provided reductions of criteria and h azardous air pollutants, including mercury. The ISIS cement emissions were developed in support for th e Portland Cement NESHAPs and the NSPS for the Portland cement manufacturing industry.

The ISIS model produced a Portland Cement NESHAP policy case of multi-pollutant emissions for individual cement kilns (emission inventory units) that were rele vant for years 2013 through 2017. These ISIS-based emissions included information on new cement kilns, facility and unit- level closures, and updated policy case emissions at existing cement kilns. The un its that opened or closed be fore 2010 were included in the projections as were the ISIS-based policy case pr edictions of emissions reductions and activity growth.

The ISIS model results for the future show a continua tion of the recent trend in the cement sector of the replacement of lower capacity, ineffi cient wet and long dry k ilns with bigger and more efficient preheater and precalciner kilns. Multiple regulatory requirement s such as the NESHAP and NSPS currently apply to the cement industry to reduce CAP and HAP emissi ons. Additionally, state and local regulatory requirements might apply to individual cement faciliti es depending on their locations relative to ozone and
PM 2.5 nonattainment areas. The ISIS model provides the emission reduction stra tegy that balances: 1) optimal (least cost) industry operati on, 2) cost-effective controls to meet the demand for cement, and 3) emission reduction requirements over the time period of interest.49 Table 4-1650 Table shows the magnitude of the ISIS-based cement industry reductions in the future-year emissions that represent 2013 (and 2017 for MATS), a nd the impact that thes e reductions have on total stationary non-EGU point source (ptnonipm) emissions.51 Table 4-16. Future-year ISIS-based cement industry annual reductions (tons/yr) for the non-EGU (ptnonipm) sector

Pollutant Cement Industry emissions in 2005 (tons) Reductions in 2017 (tons) Percent Reduction % NO X 193,000 56,740 2.4%
PM 2.5 14,400 7,840 1.8%
SO 2 128,400 106,000 5.0% VOC 6,900 5,570 0.4% HCl 2,900 2,220 4.5%

4.2.13.2 Boiler reductions not associated with the MACT rule (ptnonipm) The Boiler MACT ICR collected data on existing contro ls. We used an early version of a data base developed for that rulemaking entitled “surve y_database_2008_results2. mdb” (EPA-HQ-OAR-2002-0058- 0788) which is posted under the Technical Inform ation for the Boiler MACT major source rule ( http://www.epa.gov/ttn/ atw/boiler/boilerpg.html ). We extracted all non-EGU stationary (ptnonipm) controls that were installed after 2005, determined a percent reduction, and verified with source owners that these controls were actively in use. In many situations we learned that th e controls were on site but were not in use. A summary of the plant-uni t specific reductions that were ve rified to be actively in use are summarized in Table 4-17. All redu ctions are promulgated by the present day, and therefore these reductions are the same for all MATS future year scenarios.

Table 4-17. State-level non-MACT boiler reduc tions from ICR data gathering

State Pollutant Pre-controlled Emissions (tons) Controlled Emissions (tons) Reductions in 2017 (tons) Percent Reduction % Michigan NO X 907 544 363 40
North Carolina SO 2 652 65 587 90 Virginia SO 2 3379 338 3041 90 Washington SO 2 639 383 256 40
North Carolina HCl 31 3 28 90

4.2.13.3 RICE NESHAP (ptnonipm and nonpt) There are three rulemakings for National Emission St andards for Hazardous Air Pollutants (NESHAP) for Reciprocating Internal Combustion Engines (RICE). These rules redu ce HAPs from existing and new RICE sources. In order to meet the standards, existing sour ces with certain types of e ngines will need to install controls. In addition to reducing HAPs, these controls also reduce CAPs, specifically, CO, NO X , VOC, PM, and SO 2 . In 2014 and beyond, compliance dates have passed fo r all three rules; thus all three rules are included in the 2017 reference case emissions projection.

The rules can be found at http://www.epa.gov/tt n/atw/rice/ricepg.html and are listed below:

 National Emission Standards for Hazardous Air Po llutants for Reciprocatin g Internal Combustion Engines; Final Rule (69 FR 33473) published 06/15/04

 National Emission Standards for Hazardous Air Po llutants for Reciprocatin g Internal Combustion Engines; Final Rule (FR 9648 ) published 03/03/10

 National Emission Standards for Hazardous Air Po llutants for Reciprocatin g Internal Combustion Engines; Final Rule (75 FR 51570) published 08/20/201052 The difference among these three rules is that they focus on different types of engines, different facility types (major for HAPs, versus area for HAPs) and different engine sizes based on horsepower (HP). In addition, they have different compliance dates. We projec t CAPs from the 2005 NEI RICE sources, based on the requirements of the rule for existing sources only b ecause the inventory includes only existing sources and the current projection approach does not estimate emissions from new sources.

A complete discussion on the methodology to estimate RICE controls is provide d in Appendix F in the Proposed MATS Rule TSD: http://www.epa.gov/ttn/chief/emch/toxi cs/proposed_toxics_rule_appendices.pdf . Impacts of the RICE controls on stationary non-EGU emissions (nonpt and ptnonipm sect ors), excluding WRAP, Texas, and Oklahoma oil and gas emissions (see Section 4.2.7) are provided in Table 4-18. These reductions are promulgated before year 2017, and th erefore these reductions are the same for all MATS future year scenarios.

Table 4-18. National impact of RICE cont rols on non-EGU projections

CO NO X PM 10 PM 2.5 SO 2 VOC
Reductions 116,434 111,749 1,595 1,368 21,957 14,669

4.2.13.4 Fuel sulfur rules (ptnonipm and nonpt) Fuel sulfur rules that were signed (enforceable) at the ti me of the emissions processing are limited to Maine, New Jersey and New York. Several other states have fuel sulfur rules that were in development but not finalized prior to the final CSAPR and proposed MATS emissions processing: http://www.ilta.org/LegislativeandRegul atory/MVNRLM/NEUSASulfur%20Rules_09.2010.pdf

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The fuel sulfur content for all home heating oil SCCs in 2005 is assumed to by 3000 part per million (ppm). Effective July 1, 2012, New York requires all heating oil sold in New York to c ontain no more than 15ppm of sulfur, thus reducing SO 2 emissions by 99.5% for post-2012 projectio ns. These New York sulfur content reductions are further discussed here: http://switchboard.nrdc.org/blogs/rka ssel/governor_paters on_signs_new_la.html

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The New Jersey year 2017 standard of 15ppm (assu ming 500ppm baseline for Ke rosene) sulfur content yields a 96.25% SO 2 emissions reduction for kerosene (fuel #1). The New Jersey sulfur content reductions are discussed here: http://njtoday.net/2010/09/01/nj-adopts-rule -limiting-sulfur-content-in-fuel-oil/

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For MATS year 2017 projections, the Ma ine fuel sulfur rule, effective in year 2016, reduces sulfur to50 ppm from 3,000 ppm in 2005, resulting in a 98.3% reduction for the 2017 scenario. The impact of these fuel sulfur content reductions on SO 2 is shown in Table 4-19. These year-s pecific reductions ar e the same for all MATS scenarios: low-etha nol, reference and control.

Table 4-19. Impact of fuel sulfur (SO 2 ) controls on 2017 non-EGU projections State Reductions (tons) Maine 8,323 New Jersey 998 New York 54,431 Total 63,75153 

### 4.2.14 Oil and gas projections in TX, OK, and non-California WRAP states (nonpt) For the 2005v4.2 platform, we incorporated updated 2005 oil and gas emissions from Texas and Oklahoma. For Texas oil and gas production, we used year 2017 estimates from the Texas Commission of Environmental Quality (TCEQ) and used them as described in: http://www.tceq.state.tx.us/asse ts/public/implementation/air/am /contracts/reports/ei/5820783985FY0901- 20090715-ergi-Drilling_Rig_EI.pdf

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We also received 2008 data for Oklahoma that we used as the best available data to represent 2017. We utilized the latest available future year, year 2018, Phase II WRAP oil and gas emissions data for the nonCalifornia Western Regional Air Part nership (WRAP) states to repres ent 2017. RICE NESHAP reductions, discussed earlier in this section, which are effective by year 2014, were applied to the year 2008 Oklahoma oil and gas inventory but not applie d to the 2017 TCEQ oil and gas esti mates or 2018 WRAP Phase II oil and gas inventory.

For Oklahoma, we applied CO, NO X , SO 2 and VOC emissions reductions from the RICE NESHAP, which we assumed has some applicability to this industry (see Appendix F in the Proposed Toxics Rule TSD: http://www.epa.gov/ttn/chief/emch/toxi cs/proposed_toxics_rule_appendices.pdf ). All these year-specific oil and gas projection estimates are the sa me for all MATS scenarios: low-et hanol, reference and control. Table 4-20 shows the 2005 and 2017 NO X and SO 2 emissions including RICE reductions for Oklahoma.

Table 4-20. Oil and gas NO X and SO 2 emissions for 2005 and 2017 including additional reductions due to the RICE NESHAP

NO X PM 2.5 SO 2 VOC 2005 2017 2005 2017 2005 2017 2005 2017 Alaska 836 453 62 1 68 12 Arizona 13 15 37 49 Colorado 32,188 33,517 350 11 35,500 43,639 Montana 10,617 13,880 640 6 9,187 14,110 Nevada 71 63 1 0 105 163 New Mexico 61,674 74,648 369 12 215,636 267,846 North Dakota 6,040 20,869 688 4 8,988 17,968 Oklahoma 39,668 42,402 1,918 2,231 1,014 2 155,908 163,598 Oregon 61 44 19 14 South Dakota 566 557 43 0 370 562 Texas 42,854 34,772 2,945 1,085 5,977 36 4,337 2,800 Utah 6,896 6,297 149 1 43,403 81,890 Wyoming 36,172 34,142 541 3 166,939 304,748 Total 237,656 261,659 4,862 3,316 9,834 76 640,498 897,400



### 4.3 Onroad mobile source projections (onroad)
The same version of MOVES and SMOKE-MOVES Integr ation Tool was used to create all MATS onroad emission scenarios. Section 2.2 de scribes these components in suppor t of year 2005 processing. This section will only address the differences related to creating and processing year 2017 reference case emissions. Speciation changes for all scenarios are discu ssed in Section 3.

Inputs for temperatures (Section 2.2.1), the represen tative counties and fuel m onths (Section 2.2.2), the overall parallel processing pr ocedures (Section 2.2.3), speed da ta (Section 2.2.4), and SMOKE-MOVES54 configurations (Section 2.2.4) were pr eviously discussed and were the same for all MATS scenarios. However, year-specific MOVES inputs were obtained for fuels and California LEV standards, and SMOKE inputs of VMT and vehicle populations were year-specific and are described below.

For the 2017 VMT inventory, MOVES2010a was run with default inputs to genera te total national VMT by SCC. But, because MOVES uses a static (1999) defa ult allocation of VMT to county, MOVES was not used for these allocations. Instead, the 2017 county VMT was created by interpolating between the NCD VMT values for 2015 and those for 2020 and computing the NCD fraction for each county, then multiplying these fractions by the MOVES VMT. The VMT was also adju sted to account for increas ed onroad transportation of ethanol fuels and the resulting incr ease in travel by large tanker trucks.

Vehicle populations by county and S CC were developed similarly to th e VMT, using MOVES to generate national totals for each year and using the NCD to a llocate to county. However, the NCD does not include population estimates, so we used MOVES to generate the 2005 national population and we assumed that, for each calendar year (2005 and 2017) and for each SCC, the allocation of national vehicle population to county was proportional to the allocation of VMT (summed across road types).

The MOVES 2017 emissions used for MATS reflect onr oad mobile control progr ams including the LightDuty Vehicle Tier 2 Rule and the Mobile Source Air Toxics (M SAT2) final rule.



### 4.3.1 California LEV The list of States which have implemented programs to requi re the sale of vehicles in their state certified for sale in California began with the information stor ed in the modeling inputs used for the 2008 National Emission Inventory (NEI) stored in the National Mobile Inventory Mode l (NMIM) County database. This information was reviewed and updated by states during the process of developing the national inventory for calendar year 2008. This information was supplemen ted with information from a "Dear Manufacturer" letter, "Sales of Californi a-certified 2008-2010 Model Y ear Vehicles (Cross-Border Sales Policy)" (October 29,2007) produced by the Compliance and Innovative St rategies Division of the US Environmental Protection Agency which describes the areas that have recently implemen ted a California standards program. This information was used to generate emission rate table inputs for the MOVE S model for each of these areas using the guidance provided in the document, "Instructions for Using LEV and NLEV Inputs for MOVES" (EPA-420-B-10-003, January 2010) provided to St ates with the MOVES mode l. For calendar year 2017, areas that had implemented California standards w ould still have these progr ams in place in calendar year 2017. More information on the st ates that have implemented Califor nia LEV standards can be found at: http://www.dieselnet.com/standards/us/#cal

. 

### 4.4 Nonroad mobile source projections (nonroad, alm_no_c3, seca_c3)
The components of the nonroad mobile sectors are di scussed in Section 2.3. Nonroad mobile emissions reductions for MATS include year-specific regulati ons affecting locomotives, various nonroad engines including diesel engines and various marine engine types, fuel sulfur content, and evaporative emissions. This section discusses the changes due to the N ONROAD/NMIM system (nonroad sector) and additional C1/C2 CMV and locomotive emissions from volume in creases resulting from in corporation of larger amounts in renewable fuels in the 2017 reference case.



### 4.4.1 Emissions generated with the NONROAD model (nonroad)
As discussed in Section 2.3.1, most nonroad emissions are were estimated using the EPA’s NONROAD model, as run by the EPA’s consolidated modeling sy stem known as the Nationa l Mobile Inventory Model (NMIM). NONROAD is EPA’s mode l for calculating emissions from nonroad equipment, except for aircraft, locomotives, and commercial marine vessel s. Like the onroad em issions, the NONROAD/NMIM55 system provides nonroad emissions for VOC by three emi ssion modes: exhaust, evaporative and refueling. Unlike the onroad sector, nonroad refueling emissions for nonroad sources are no t included in the nonpoint (nonpt) sector and so are re tained in this sector.

The same temperatures and repres entative counties were used for all NONROAD model-generated MATS scenarios. For 2017, E10 and E15 are available in ever y county, but nonroad equipment is assumed to burn only E10. To generate the NMIM fuels, the E10 fuel was copied from MOVES to NMIM, and the E10 oxygenate was assigned a market share of 1. Highway di esel fuel sulfur levels are copied directly from MOVES to NMIM. Nonroad diesel fuel su lfur levels are retained from NMIM.

Section 2.3.1.3 provides a cross-walk of the nonroad m obile NMIM emission scenarios used in MATS; as previously discussed, the only differe nce between these scenarios are th e increases in activity (based on NONROAD model default growth estimates of future-y ear equipment population) an d changes in fuels and engines that reflect implementation of national regulations and local contro l programs that impact each year differently due to engine turnover. For year 2017 , EPA assumed that nonroad equipment would use only E10. Although the NONROAD Model estimates changes in VOC production from E15, NMIM calculates toxics as if the fuel were E10. Emission estimates fo r ethanol come from speciation of VOC in the SMOKE model. These ethanol adjustments for nonroad engine s running on E15 came from the EPAct Phase 1 data.

We have not included voluntary programs in our pr ojections such as programs encouraging either no refueling or evening refueling on Oz one Action Days and diesel retrofit programs. The national regulations incorporated in all MATS future year scenario s are those promulgated prior to December 2009, and beginning about 1990. Recent rules include:

 “Clean Air Nonroad Diesel Final Rule - Tier 4”: (http://www.epa.gov/nonr oaddiesel/2004fr.htm ), published June 29, 2004, and,

 Control of Emissions from Nonroad Large Spark-I gnition Engines, and Recr eational Engines (Marine and Land-Based), November 8, 2002 (“Pentathalon Rule”).

 OTAQ’s Locomotive Marine Rule, March 2008: ( http://www.epa.gov/otaq /regs/nonroad/420f08004.htm )

 OTAQ’s Small Engine Spark Ignitio n (“Bond”) Rule, November 2008: ( http://www.epa.gov/otaq/equip-ld.htm )

All future year nonroad emissions used NMIM data that are based on AEO2009 fuels and the same NMIM county database NCD20101201Tier3. We converted emissi ons from monthly totals to monthly average-day values based the on number of days in each month. Only criteria and select HAPs (benzene, acetaldehyde, butadiene, acrolein, and formaldehyde) were retained when creating SMOKE one record per line (ORL) files.



### 4.4.2 Locomotives and Cl ass 1 & 2 commercial marine vessels (alm_no_c3)

Aircraft emissions reside in the nonEGU point inventory (ptnonip m), and the projection factor s used to create year 2017 create year 2017 estimates, are discussed in Section 4.2. The remaining 2005 NEI emission s for locomotives and Class 1 and Class 1 and Class 2 commercial marine ve ssel (C1/C2 CMV) use year-specific projection estimates. Base future year Base future year locomotive and C1/C2 CMV emissions were calculated using projec tion factors that were computed based computed based on national, annual summaries of emissions in 2002 and 2017. Some additional emissions were then were then factored in due to changes in fuels. These national summaries were used to create national by-pollutant, by-SCC

pollutant, by-SCC projection factors; these factors include final locomotive-m arine controls and are provided in Table

in Table 4-21 . Modest additive Class I railroa d and C1/C2 CMV emissions that a ccount for RFS2 volume increases in56 the MATS reference scenario were then a dded into the reference case due to the volume differences in corn, cellulosic and imported ethanol and cellulosic di esel fuels. These additional emissions are summarized in57 Table 4-22.

Table 4-21 . Factors applied to year 2005 emissions to project locomotives and class 1 and class 2 commercial marine vessel emissions to 2017

SCC SCC Description Pollutant Pro j ection Factor 2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions CO 0.938 2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NH 3 1.144 2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NO X 0.700 2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM 10 0.642 2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM 2.5 0.653 2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions SO 2 0.087 2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions VOC 0.786 2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations CO 1.334 2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NH 3 1.325 2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NO X 0.627 2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 10 0.578 2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 2.5 0.586 2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations SO 2 0.005 2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations VOC 0.589 2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations CO 0.328 2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NH 3 1.325 2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NO X 0.352 2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 10 0.286 2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 2.5 0.288 2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO 2 0.001 2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations VOC 0.315 2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) CO 1.071 2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NH 3 1.325 2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NO X 0.496 2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 10 0.461 2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 2.5 0.463 2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO 2 0.005 2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) VOC 0.475 2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines CO 1.057 2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NH 3 1.325 2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NO X 0.489 2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM 10 0.455 2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM 2.5 0.455 2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines SO 2 0.005 2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines VOC 0.469 2285002010 Railroad Equipment;Diesel;Yard Locomotives CO 1.341 2285002010 Railroad Equipment;Diesel;Yard Locomotives NH 3 1.325 2285002010 Railroad Equipment;Diesel;Yard Locomotives NO X 1.128 2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 10 0.914 2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 2.5 0.934 2285002010 Railroad Equipment;Diesel;Yard Locomotives SO 2 0.006 2285002010 Railroad Equipment;Diesel;Yard Locomotives VOC 1.50958 Table 4-22. Additional class 1 railroad a nd C1/C2 CMV emissions from RFS2 fuel volume changes

Pollutant 2017 Class 1 Rail (tons) 2017 C1/C2 CMV (tons) 1,3-Butadiene 0.83 0.01

Acrolein 0.80 0.08 Formaldehyde 11.12 3.21

Benzene 0.66 0.44 Acetaldehyde 4.83 1.59 CO 1,250 197

NH 3 3.93 0.62

NO X 5,731 890

PM 10 141 29

PM 2.5 136 27

SO 2 2.96 3.96

VOC 257 21

The future-year locomotive emissions account for increased fuel consumption based on Energy Information Administration (EIA) fuel consumpti on projections for freight rail, and emissions reductions resulting from emissions standards from the Final Locomotive-Marine rule (EPA, 2009). This rule lowered diesel sulfur content and tightened emission standa rds for existing and new locomotives and marine diesel emissions to lower future-year PM, SO 2 , and NO X , and is documented at: http://www.epa.gov/otaq/regs/nonroad/420f08004.htm . Voluntary retrofits under the National Clean Diesel Campaign ( http://www.epa.gov/otaq/diesel/index.htm ) are not included in our projections.

We applied HAP factors for VOC HAPs by using th e VOC projection factors to obtain 1,3-butadiene, acetaldehyde, acrolein, benzene, and formaldehyde.

Class 1 and 2 CMV gasoline emissions (SCC = 2280004000) were not changed for future-year processing. C1/C2 diesel emissions (SCC = 2280002100 and 2280002200) were projected based on the Final Locomotive Marine rule national-le vel factors provided in Table 4-21. Similar to locomotives, VOC HAPs were projected based on the VOC factor.

Delaware provided updated future-year NO X , SO 2 , and PM emission estimates for C1/C2 CMV as part of the Transport Rule comments. These updated emissions were applied to the 2017 i nventory and override the C1/C2 projection factors in Table 4-21.



### 4.4.3 Class 3 commercial marine vessels (seca_c3) The seca_c3 sector emissions data were provided by OTAQ in an ASCII raster format used since the SO 2 Emissions Control Area-Internationa l Marine Organization (ECA-IMO) pr oject began in 2005. The (S)ECA Category 3 (C3) commercial marine vessel 2002 base-cas e emissions were projected to year 2005 for the 2005 base case and to 2017, which includes ECA-IMO cont rols. An overview of the ECA-IMO project and future-year goals for reduction of NO X , SO 2 , and PM C3 emissions can be found at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420f09015.htm

The resulting coordinated strategy, including emission standards unde r the Clean Air Act for new marine diesel engines with per-cylinder displacement at or above 30 liters, and the establishment of Emission Control Areas is at: http://www.epa.gov/oms/oceanvessels.htm59 These projection factors vary depe nding on geographic region and pollu tant; where VOC HAPs are assigned the same growth rates as VOC. The projection factors used to create th e 2017 seca_c3 sector emissions are provided in Table 4-23. No te that these factors ar e relative to 2002. Factors relative to 2005 can be computed from the 2002-2005 factors.

The geographic regions are described in th e ECA Proposal technical support document: http://www.epa.gov/oms/regs/ nonroad/marine/ci/420r09007-chap2.pdf . These regions extend up to 200 nautical miles offshore, though less at international boundaries. North and South Pacifi c regions are divided by the Oregon-Washington border, and East Coast and Gu lf Coast regions are divided east-west by roughly the upper Florida Keys just southwest of Miami.

The factors to compute HAP emission are based on em issions ratios discussed in the 2005v4 documentation ( ftp://ftp.epa.gov/EmisInventor y/2005v4/2005_emissions_tsd_07jul2010.pdf ). As with the 2005 base case, this sector uses CAP-HAP VOC integration.

Table 4-23. NO X , SO 2 , and PM 2.5 factors to project class 3 CMV emissions for 2017 Region NO X SO 2 PM 2.5 VOC Alaska East 1.409 0.062 0.203 1.631 Alaska West 1.469 1.571 1.571 1.571 East Coast 1.435 0.070 0.264 1.955 Gulf Coast 1.120 0.055 0.207 1.529 Hawaii East 1.539 0.078 0.268 2.036 Hawaii West 1.725 2.037 2.035 2.037 North Pacific 1.240 0.064 0.222 1.644 South Pacific 1.573 0.084 0.293 2.114 Great Lakes 1.106 0.046 0.171 1.302 Outside ECA 1.585 1.891 1.891 1.891



### 4.5 Canada, Mexico, and offshore sources (othar, othon, and othpt)

Emissions for Canada, Mexico, and offshore sources were not projected to future ye ars, and are therefore the same as those used in the 2005 base case for all MATS scenarios. Therefore, the Mexico emissions are based on year 1999, offshore oil is based on year 2005, a nd Canada is based on year 2006. For both Mexico and Canada, their responsible agencies did not provide future-year emissions that were consistent with the base year emissions.



### 4.6 Reference case emission summaries60 Table 4-24 shows a summary of the 2005 and modeled re ference case emissions for the lower 48 states.61 Table 4-25 and Table 4-26 provide summaries of SO 2 and PM 2.5 in the 2017 baseline for each sector by state. Table 4-27shows the future year baseline EGU emissi ons by state for all criteria air pollutants.62 Table 4-24. Summary of modeled base case SO 2 and PM 2.5 annual emissions (tons/year) for 48 states by

sector

Source Sector SO 2 Emissions 2005 2017

EGU Point 10,380,883 3,281,364

Non-EGU Point 2,030,759 1,534,991 Nonpoint 1,216,362 1,125,985

Nonroad 446,831 15,759

On-road 168,480 29,288 Average Fire 49,094 49,094

Total SO 2 , All Sources 14,292,410 6,036,480

Source Sector PM 2.5 Emissions 2005 2017

EGU Point 496,877 276,430

Non-EGU Point 433,346 411,437 Nonpoint 2,110,298 1,912,757

Nonroad 268,745 150,221

On-road 301,073 129,416 Average Fire 684,035 684,035

Total PM 2.5 , All Sources 4,294,373 3,564,29663 Table 4-25. Reference case SO 2 emissions (tons/year) for states by sector

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total Alabama 186,084 63,053 52,341 146 569 983 303,177 Arizona 36,996 24,191 2,467 59 724 2,888 67,324 Arkansas 92,804 12,160 26,801 123 314 728 132,929 California 5,346 21,046 67,846 3,311 2,087 6,735 106,370 Colorado 74,255 1,415 6,210 50 532 1,719 84,181 Connecticut 3,581 1,833 18,149 100 311 4 23,978 Delaware 2,835 4,770 1,018 500 91 6 9,220 District of Columbia 5 686 1,505 3 38 0 2,237 Florida 117,702 49,082 70,073 1,255 2,111 7,018 247,241 Georgia 96,712 44,248 55,946 192 1,158 2,010 200,266 Idaho 182 17,133 2,894 23 162 3,845 24,240 Illinois 118,217 81,683 5,650 295 1,107 20 206,971 Indiana 200,969 73,930 59,771 150 760 24 335,604 Iowa 85,178 22,865 19,929 86 324 25 128,407 Kansas 45,740 10,288 36,140 57 294 103 92,622 Kentucky 116,927 23,530 33,852 215 463 364 175,350 Louisiana 142,447 129,730 2,669 1,449 447 892 277,634 Maine 2,564 14,285 2,007 72 149 150 19,226 Maryland 29,786 33,562 40,642 494 593 32 105,110 Massachusetts 15,133 17,077 24,907 266 565 93 58,041 Michigan 163,168 48,697 42,185 448 995 91 255,584 Minnesota 52,380 24,742 14,635 220 558 631 93,164 Mississippi 34,865 24,284 6,635 208 396 1,051 67,440 Missouri 178,143 33,757 44,680 191 722 186 257,679 Montana 24,018 7,212 1,875 25 106 1,422 34,657 Nebraska 70,910 6,885 7,899 58 202 105 86,058 Nevada 14,140 2,132 12,028 27 200 1,346 29,873 New Hampshire 6,719 2,471 7,284 21 137 38 16,671 New Jersey 9,042 6,700 9,528 686 757 61 26,774 New Mexico 10,211 7,813 2,719 26 262 3,450 24,480 New York 14,653 45,222 71,060 659 1,466 113 133,173 North Carolina 71,113 58,517 21,713 197 890 696 153,125 North Dakota 105,344 9,915 5,559 36 71 66 120,991 Ohio 180,935 93,600 19,777 373 1,093 22 295,799 Oklahoma 141,433 27,873 7,731 49 501 469 178,056 Oregon 13,211 9,790 9,508 218 361 4,896 37,985 Pennsylvania 126,316 64,697 67,650 427 1,060 32 260,183 Rhode Island 0 2,745 3,338 33 85 1 6,202 South Carolina 103,694 28,536 13,310 294 500 646 146,980 South Dakota 29,711 1,655 10,301 23 86 498 42,273 Tennessee 33,080 59,145 32,624 154 757 277 126,037 Texas 249,748 129,667 108,633 1,146 2,483 1,178 492,855
Tribal 0 676 0 0 0 0 676 Utah 34,912 6,599 3,365 27 291 1,934 47,128 Vermont 264 902 5,283 8 129 49 6,634 Virginia 51,004 50,387 32,439 275 849 399 135,353 Washington 5,569 19,780 6,885 881 633 407 34,156 West Virginia 84,344 32,458 14,322 64 178 215 131,582 Wisconsin 50,777 61,080 6,260 122 633 70 118,94164 State EGU NonEGU Nonpoint Nonroad Onroad Fires Total Wyoming 48,198 20,491 5,944 18 87 1,106 75,844 Total 3,281,364 1,534,991 1,125,985 15,759 29,288 49,094 6,036,480

Table 4-26. Reference case PM 2.5 emissions (tons/year) for states by sector

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total Alabama 13,154 17,052 33,235 2,403 2,217 13,938 81,999 Arizona 3,889 3,809 20,214 2,674 2,762 37,151 70,498 Arkansas 2,838 10,527 33,486 2,042 1,242 10,315 60,450 California 475 20,693 73,607 14,875 13,492 97,302 220,443 Colorado 3,845 7,037 19,868 2,350 2,387 24,054 59,540 Connecticut 400 222 6,838 1,038 1,414 56 9,968 Delaware 434 772 1,207 383 375 87 3,259 District of Columbia 1 172 536 139 154 0 1,002 Florida 12,723 24,620 50,472 8,100 7,652 99,484 203,050 Georgia 13,445 12,105 59,412 3,803 4,863 24,082 117,711 Idaho 36 2,076 40,288 1,186 714 52,808 97,108 Illinois 8,587 13,471 70,775 6,885 4,926 277 104,922 Indiana 22,354 13,570 72,501 3,491 3,380 344 115,640 Iowa 4,298 7,000 51,684 3,348 1,519 349 68,198 Kansas 3,199 6,895 136,633 2,872 1,268 1,468 152,335 Kentucky 12,078 10,353 26,811 2,717 2,059 5,155 59,173 Louisiana 3,093 30,865 27,082 5,107 1,673 12,647 80,467 Maine 355 3,543 8,213 881 750 2,127 15,869 Maryland 3,969 6,382 18,960 1,975 2,492 531 34,310 Massachusetts 1,465 2,123 23,729 1,914 2,590 1,324 33,145 Michigan 8,102 11,688 43,055 4,696 4,949 1,283 73,773 Minnesota 2,598 9,867 68,121 4,483 2,882 8,943 96,893 Mississippi 2,201 10,492 31,474 2,337 1,525 14,897 62,926 Missouri 7,061 6,384 69,722 3,954 3,059 2,636 92,816 Montana 3,870 2,562 28,479 1,332 492 17,311 54,048 Nebraska 2,358 2,834 44,904 2,967 919 1,483 55,465 Nevada 2,505 4,032 9,351 1,319 857 19,018 37,083 New Hampshire 1,130 464 8,981 576 663 534 12,348 New Jersey 2,452 2,520 8,559 2,929 3,244 865 20,569 New Mexico 3,153 1,442 49,789 1,148 1,103 48,662 105,298 New York 2,331 4,859 44,334 5,032 6,723 1,601 64,879 North Carolina 9,983 12,656 43,398 3,583 3,521 9,870 83,011 North Dakota 5,870 795 40,802 2,126 383 934 50,910 Ohio 18,920 12,353 47,811 5,302 5,013 316 89,715 Oklahoma 3,530 5,695 88,862 2,029 2,006 6,644 108,767 Oregon 381 8,869 39,503 2,148 1,627 65,350 117,877 Pennsylvania 16,727 14,874 38,523 4,582 4,854 454 80,014 Rhode Island 4 256 1,070 222 383 14 1,949 South Carolina 9,997 4,527 23,430 1,932 1,929 9,163 50,978 South Dakota 737 2,399 32,697 1,339 416 7,062 44,650 Tennessee 5,053 21,553 28,449 2,939 3,057 3,934 64,985 Texas 21,677 34,648 187,604 11,901 9,289 21,578 286,698 Tribal 1 1,568 0 0 0 0 1,569 Utah 4,524 3,530 13,978 963 1,318 27,412 51,72465 State EGU NonEGU Nonpoint Nonroad Onroad Fires Total Vermont 67 336 4,930 307 653 696 6,989 Virginia 4,529 10,165 32,254 3,507 3,446 5,659 59,561 Washington 1,444 4,421 35,706 3,328 2,874 4,487 52,259 West Virginia 13,602 4,281 12,951 1,048 762 3,050 35,695 Wisconsin 5,323 7,853 27,656 3,161 3,148 994 48,135 Wyoming 5,662 10,225 30,812 850 392 15,686 63,626 Total 276,430 411,437 1,912,757 150,221 129,416 684,035 3,564,296

Table 4-27. Future year baseline EGU CA P emissions (tons/year) by state

State CO NO X VOC SO 2 NH 3 PM 10 PM 2.5 Alabama 27,024 64,064 1,524 186,084 1,472 16,686 13,154 Arizona 16,797 36,971 825 36,996 1,163 5,038 3,889 Arkansas 9,925 36,297 658 92,804 560 3,507 2,838 California 45,388 20,910 1,031 5,346 2,519 580 475 Colorado 9,006 50,879 636 74,255 398 4,605 3,845 Connecticut 9,180 2,738 139 3,581 313 431 400 Delaware 4,256 2,452 132 2,835 119 580 434
District of Columbia 67 11 2 5 3 1 1 Florida 72,915 83,174 2,253 117,702 3,997 19,098 12,723 Georgia 16,537 43,778 1,293 96,712 903 18,668 13,445 Idaho 1,532 613 41 182 57 38 36 Illinois 51,862 56,128 3,091 118,217 1,437 9,926 8,587 Indiana 30,587 106,881 2,295 200,969 1,317 33,816 22,354 Iowa 8,316 42,698 791 85,178 452 5,735 4,298 Kansas 5,066 25,163 683 45,740 305 3,996 3,199 Kentucky 37,287 71,259 1,604 116,927 928 16,279 12,078 Louisiana 32,626 33,509 852 142,447 1,427 3,677 3,093 Maine 12,789 6,121 306 2,564 269 366 355 Maryland 13,446 17,933 533 29,786 301 5,322 3,969 Massachusetts 7,128 7,991 279 15,133 395 1,915 1,465 Michigan 25,856 66,846 1,497 163,168 874 11,056 8,102 Minnesota 9,365 36,867 746 52,380 460 3,034 2,598 Mississippi 9,704 27,319 440 34,865 469 3,113 2,201 Missouri 16,499 52,464 1,714 178,143 740 9,093 7,061 Montana 5,266 20,946 338 24,018 198 6,117 3,870 Nebraska 4,691 28,898 542 70,910 292 2,948 2,358 Nevada 9,677 15,627 438 14,140 953 3,095 2,505 New Hampshire 5,667 4,908 206 6,719 207 1,234 1,130 New Jersey 25,831 11,178 823 9,042 747 2,948 2,452 New Mexico 9,079 65,189 574 10,211 570 3,833 3,153 New York 19,731 21,172 731 14,653 1,076 3,248 2,331 North Carolina 17,367 44,141 1,076 71,113 654 13,368 9,983 North Dakota 7,437 53,778 867 105,344 383 6,757 5,870 Ohio 33,481 93,150 2,005 180,935 1,317 25,688 18,920 Oklahoma 26,165 47,454 957 141,433 1,073 4,457 3,530 Oregon 5,905 10,828 203 13,211 381 446 381 Pennsylvania 38,767 123,501 2,023 126,316 1,522 22,117 16,727 Rhode Island 1,748 456 44 0 136 7 4 South Carolina 10,305 37,516 726 103,694 515 14,469 9,99766 State CO NO X VOC SO 2 NH 3 PM 10 PM 2.5 South Dakota 742 14,293 129 29,711 48 764 737 Tennessee 10,693 16,982 862 33,080 406 6,313 5,053 Texas 78,317 145,182 4,975 249,748 5,304 31,404 21,677
Tribal 601 73 15 0 47 2 1 Utah 5,632 67,476 526 34,912 279 5,843 4,524 Vermont 1,868 458 52 264 25 69 67 Virginia 30,205 39,408 821 51,004 1,115 5,404 4,529 Washington 7,183 14,284 326 5,569 346 1,706 1,444 West Virginia 15,496 54,247 1,320 84,344 658 18,415 13,602 Wisconsin 19,247 35,179 1,137 50,777 649 6,503 5,323 Wyoming 9,087 71,380 970 48,198 481 7,385 5,662

TOTAL 873,344 1,930,769 46,050 3,281,364 40,259 371,101 276,430 * Emission estimates apply to all fossil Electric Gene rating Units, including those with capacity < 25MW67 

## 5 MATS Control Case

For the future year control case (i .e., policy case) air quality modeling , the emissions for all sectors were unchanged from the base case modeling except for those from EGUs. The IPM model was used to prepare the future year policy case for EGU emissions. The ai r quality modeling for MATS relied on EGU emission projections from an interim IPM platform base d on the Cross-state Air Pollution Rule version 4.10_FTransport, and was subsequently updated during th e rulemaking process. The updates made include: updated assumptions regarding the removal of HCl by alka line fly ash in subbituminous and lignite coals; an update to the fuel-based mercury em ission factor for petroleum coke, which was corrected based on reexamination of the 1999 ICR data; updated capital cost for new nuclear capacity and nuclear life extension costs; corrected variable operating and maintenance cost (VOM) for AC I retrofits; adjusted coal rank availability for some units, consistent with EIA Fr om 923 (2008); updated state rules in Washington and Colorado; and numerous unit-level revisions based on comments received thr ough the notice and comment process. Additional details on the version of IPM used to develop the control case are available in Chapter 3.

The changes in EGU SO 2 , and PM 2.5 emissions as a result of the polic y case for the lower 48 states are summarized in Table 5-1. Table 5-2 shows the CAP em issions for the modeled MATS control case by State. State-specific differen ce summaries of EGU SO 2 and PM 2.5 for the sum of the lower 48 states are shown in Table 5-3 and Table 5-4 respectively.

Table 5-1. Summary of emissions changes for the MA TS AQ modeling in the lower 48 states Future Year EGU Emissions SO 2 PM 2.5 Base Case EGU Emissions (tons) 3,281,364 276,430 Control Case EGU Emissions (tons) 1,866,247 223,320

Reductions to Base Case in Control case (tons) 1,415,117 53,110 Percentage Reduction of Base EGU Emissions 43% 19%
Total Man-made Emissions* Total Base Case Emissions (tons) 6,036,480 3,564,296 Total Control Case Emissions (tons) 4,621,363 3,511,186 Percentage Reduction of All Man-made Emissions 23% 1%

* In this table, man-made emissions includes average fires.

Table 5-2. EGU emissions totals for the Modeled MATS control case in the lower 48 states

State CO NO X VOC SO 2 NH 3 PM 10 PM 2.5 Alabama 20,873 61,863 1,313 68,517 1,235 9,734 7,844 Arizona 13,238 34,804 749 23,459 921 4,264 3,494 Arkansas 9,036 35,788 642 35,112 490 1,696 1,593 California 56,360 27,159 1,307 5,041 2,548 1,057 942 Colorado 8,219 44,409 582 19,564 358 3,492 2,859 Connecticut 8,017 2,800 136 1,400 313 439 412 Delaware 1,312 2,527 67 4,160 93 3,056 1,455
District of Columbia Florida 66,378 61,676 2,055 64,791 3,482 16,434 11,377 Georgia 14,217 41,006 1,197 78,197 790 11,165 9,742 Idaho 1,523 609 41 182 56 38 36 Illinois 24,365 50,655 2,353 103,867 1,050 7,309 6,588 Indiana 17,061 102,045 1,872 156,781 1,110 29,683 20,38868 State CO NO X VOC SO 2 NH 3 PM 10 PM 2.5 Iowa 7,340 41,247 747 48,030 410 3,318 2,947 Kansas 4,683 22,136 623 22,767 282 2,504 2,263 Kentucky 25,911 70,126 1,476 125,430 882 12,544 10,635 Louisiana 28,171 31,655 767 30,509 1,261 2,003 1,899 Maine 10,992 5,683 302 1,372 267 342 331 Maryland 4,283 16,554 400 18,091 211 3,851 3,143 Massachusetts 5,408 7,211 226 5,033 344 1,702 1,267 Michigan 18,792 60,982 1,215 82,834 718 8,261 6,893 Minnesota 8,699 34,942 709 33,214 430 3,332 2,936 Mississippi 8,782 20,749 410 15,975 397 1,949 1,720 Missouri 12,249 52,755 1,605 95,965 686 5,216 4,809 Montana 2,223 19,758 264 6,399 133 2,637 1,727 Nebraska 4,493 28,180 533 34,631 277 2,152 1,828 Nevada 7,178 14,382 336 6,372 725 2,626 2,073 New Hampshire 6,781 4,862 232 2,102 232 1,336 1,264 New Jersey 8,350 7,699 315 6,404 546 2,020 1,583 New Mexico 7,987 64,922 545 9,984 554 2,961 2,750 New York 18,725 20,863 699 28,174 1,086 3,123 2,350 North Carolina 15,195 35,309 1,033 59,551 602 8,885 7,988 North Dakota 7,266 53,267 858 23,889 371 5,940 5,051 Ohio 29,956 85,565 1,852 139,208 1,229 19,599 15,823 Oklahoma 26,687 44,725 892 44,602 970 2,293 2,056 Oregon 6,002 9,671 198 3,565 379 241 233 Pennsylvania 24,865 104,906 1,645 93,606 1,349 17,330 14,080 Rhode Island 1,721 443 43 0 134 7 4 South Carolina 9,826 37,849 725 40,901 459 9,627 6,963 South Dakota 641 14,290 117 2,483 41 260 245 Tennessee 5,551 16,931 723 42,666 334 6,721 5,272 Texas 71,475 138,086 4,444 105,958 4,774 25,359 17,601
Tribal 266 32 7 0 21 1 1 Utah 4,003 65,286 474 17,007 241 4,755 3,896 Vermont 1,868 458 52 264 25 69 67 Virginia 26,778 37,255 707 33,704 748 5,306 4,506 Washington 6,334 3,834 179 854 254 183 176 West Virginia 13,923 47,836 1,263 66,857 632 14,321 11,572 Wisconsin 16,124 32,865 1,012 28,322 578 4,725 3,969 Wyoming 7,516 71,135 932 28,456 467 5,946 4,671

TOTAL 707,640 1,789,790 40,875 1,866,247 35,493 281,811 223,320

Table 5-3. State-specific changes in annual EGU SO 2 for the lower 48 states

State Future year baseline SO 2 (tons) Future Year Policy Case SO 2 (tons) EGU SO 2 reduction (tons) EGU SO 2 reduction (%) Alabama 186,084 68,517 117,568 63% Arizona 36,996 23,459 13,537 37% Arkansas 92,804 35,112 57,692 62% California 5,346 5,041 305 6% Colorado 74,255 19,564 54,690 74% Connecticut 3,581 1,400 2,181 61% Delaware 2,835 4,160 -1,324 -47%69 District of Columbia 5 0 5 100% Florida 117,702 64,791 52,911 45% Georgia 96,712 78,197 18,515 19%
Idaho 182 182 0 0% Illinois 118,217 103,867 14,350 12% Indiana 200,969 156,781 44,189 22% Iowa 85,178 48,030 37,148 44% Kansas 45,740 22,767 22,973 50% Kentucky 116,927 125,430 -8,503 -7% Louisiana 142,447 30,509 111,938 79% Maine 2,564 1,372 1,191 46% Maryland 29,786 18,091 11,695 39% Massachusetts 15,133 5,033 10,100 67% Michigan 163,168 82,834 80,334 49% Minnesota 52,380 33,214 19,165 37% Mississippi 34,865 15,975 18,890 54% Missouri 178,143 95,965 82,177 46% Montana 24,018 6,399 17,618 73% Nebraska 70,910 34,631 36,279 51% Nevada 14,140 6,372 7,768 55% New Hampshire 6,719 2,102 4,618 69% New Jersey 9,042 6,404 2,638 29% New Mexico 10,211 9,984 228 2% New York 14,653 28,174 -13,521 -92% North Carolina 71,113 59,551 11,562 16% North Dakota 105,344 23,889 81,455 77% Ohio 180,935 139,208 41,727 23% Oklahoma 141,433 44,602 96,831 68% Oregon 13,211 3,565 9,646 73% Pennsylvania 126,316 93,606 32,710 26%
Rhode Island 0 0 0 N/A South Carolina 103,694 40,901 62,793 61% South Dakota 29,711 2,483 27,228 92% Tennessee 33,080 42,666 -9,586 -29% Texas 249,748 105,958 143,790 58%
Tribal 0 0 0 N/A Utah 34,912 17,007 17,905 51%
Vermont 264 264 0 0% Virginia 51,004 33,704 17,300 34% Washington 5,569 854 4,716 85% West Virginia 84,344 66,857 17,488 21% Wisconsin 50,777 28,322 22,454 44% Wyoming 48,198 28,456 19,742 41%
TOTAL 3,281,364 1,866,247 1,415,11770 Table 5-4. State-specific changes in annual EGU PM 2.5 for the lower 48 states

State Future year baseline PM2.5 (tons) Future Year Policy Case PM 2.5 (tons) EGU PM 2.5 reduction (tons) EGU PM 2.5 reduction (%)
Alabama 13,154 7,844 5,310 40%
Arizona 3,889 3,494 395 10%
Arkansas 2,838 1,593 1,246 44%

California 475 942 -467 -98%
Colorado 3,845 2,859 985 26%

Connecticut 400 412 -12 -3%
Delaware 434 1,455 -1,021 -235%

District of Columbia 1 0 1 100%
Florida 12,723 11,377 1,346 11% Georgia 13,445 9,742 3,703 28%

Idaho 36 36 0 0%
Illinois 8,587 6,588 2,000 23%
Indiana 22,354 20,388 1,966 9%
Iowa 4,298 2,947 1,351 31%
Kansas 3,199 2,263 936 29% Kentucky 12,078 10,635 1,443 12%
Louisiana 3,093 1,899 1,193 39%

Maine 355 331 24 7% Maryland 3,969 3,143 826 21%
Massachusetts 1,465 1,267 198 14% Michigan 8,102 6,893 1,210 15%
Minnesota 2,598 2,936 -339 -13% Mississippi 2,201 1,720 481 22%
Missouri 7,061 4,809 2,252 32%
Montana 3,870 1,727 2,143 55%
Nebraska 2,358 1,828 530 22%
Nevada 2,505 2,073 432 17% New Hampshire 1,130 1,264 -134 -12% New Jersey 2,452 1,583 868 35%
New Mexico 3,153 2,750 403 13%
New York 2,331 2,350 -19 -1%
North Carolina 9,983 7,988 1,995 20%
North Dakota 5,870 5,051 819 14%
Ohio 18,920 15,823 3,097 16%
Oklahoma 3,530 2,056 1,474 42% Oregon 381 233 148 39% Pennsylvania 16,727 14,080 2,646 16%

Rhode Island 4 4 0 2%
South Carolina 9,997 6,963 3,033 30%

South Dakota 737 245 492 67%
Tennessee 5,053 5,272 -219 -4%
Texas 21,677 17,601 4,077 19%

Tribal 1 1 1 56%
Utah 4,524 3,896 627 14%71 State Future year baseline PM2.5 (tons) Future Year Policy Case PM 2.5 (tons) EGU PM 2.5 reduction (tons) EGU PM 2.5 reduction (%)

Vermont 67 67 0 0% Virginia 4,529 4,506 24 1% Washington 1,444 176 1,268 88% West Virginia 13,602 11,572 2,031 15%
Wisconsin 5,323 3,969 1,354 25% Wyoming 5,662 4,671 991 17%

TOTAL 276,430 223,320 53,110



## 6 References

EPA, 2005. Clean Air Interstate Rule Emissions Inventory Technical Support Document, U.S. Environmental Protection Agency, Office of Ai r Quality Planning and Standards, March 2005. Available at http://www.epa.gov/cair/pdfs/finaltech01.pdf

. EPA, 2006. Regulatory Impact Anal yses, 2006 National Ambient Air Quality Standards for Particle Pollution. U.S. Environmental Protection Agency, Office of Air Quality Pl anning and Standards, October, 2006. Docket # EPA-HQ-OAR- 2001-0017, # EPAHQ-OAR-2006-0834. Available at http://www.epa.gov/ttn/ecas/ria.html

. EPA, 2007a. Guidance for Estimating VOC and NOx Emission Changes from MACT Rules, U.S. Environmental Protection Agency Office of Air Qu ality Planning and Standards, Air Quality Policy Division, Research Triangle Park, NC 27711, EPA-457/B-07-001, May 2007. Available at http://www.epa.gov/ttn/naaqs/ozone/o3i mp8hr/documents/guidance/200705_epa457_b-07- 001_emission_changes_mact_rules.pdf

. EPA. 2007b. National Scale Modeling for the Final Mobi le Source Air Toxics Rule , Office of Air Quality Planning and Standards, Emissions Analysis and Monitoring Divi sion, Research Triangle Park, NC 27711, EPA 454/R-07-002, February 2007. Available at http://www.epa.gov/otaq/regs/toxics/454r07002.pdf
EPA, 2009. Regulatory Impact Analys is: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression I gnition Engines Less than 30 Liters Pe r Cylinder. U.S. Environmental Protection Agency Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, EPA420-R-08- 001a, May 2009. Available at: http://www.epa.gov/otaq/regs/nonroad/420r08001a.pdf
EPA, 2010. Technical Support Document: The Industrial Sectors Integrated Soluti ons (ISIS) Mo del and the Analysis for the National Emission Standard s for Hazardous Air Pollutants and New Source Performance Standards for the Portland Cement Manufacturing Industry, U.S. Environmental Protection Agency, Sectors Policies and Program Di vision and Air Pollution Prevention and Control Division, Research Triangle Park, NC 27711, August 2010.72APPENDIX AAncillary Datasets and Parameters Used for Each MATS Modeling CaseThe ancillary data files used for the MATS cases are shown in Table A-1. The Input name column gives a brief designator for thedataset. TheEnvironment Variable column gives the name of the environment variable that is used by SMOKE tospecify the input. The Sectorcolumnspecifies the modeling sector forthe dataset. The remaining columns show the data set name and version used in the 2005 basecase and 2017reference case.To match the Datasets and Versions listed in this table to actual data files, combine the Dataset name and the version number in the followingpattern: <Dataset Name>_<Date>_<Version number>.txt, where <Date>is the last date of change for that version and will have a unique valuefor the combination of Dataset Name and Version number.Table A-2 shows the parameters used for the MATS modeling cases.The columns are the same as in Table A-1 except that the Program is notshown. Many of the parameters apply to all programs, or all programs for the specified processing sector. The values for thecontrol case arenot shown, but they are the same asthose used for the 2017 reference case.Table A-1.List of ancillary data setsassociated with the MATSmodeling cases.Input NameEnvironmentVariable Program Sector 2005 Base Case 2017 Reference CaseArea-to-point data ARTOPNT smkinven artopnt_2002detroit [v0] artopnt_2002detroit [v0]BEIS3 emission factors B3FAC Tmpbeis3 beis beis3_efac_v3.14 [v0] beis3_efac_v3.14 [v0]Biogenic gridding surrogate for reports12EUS1 BGPRO Smkmerge beisbgpro_12EUS1 (/garnet/oaqps)[v0] bgpro_12EUS1 (/garnet/oaqps) [v0]Biogenic gridding surrogate for reports36US1 BGPRO Smkmerge beis bgpro_36US1 (/garnet/oaqps) [v0] bgpro_36US1 (/garnet/oaqps) [v0]Biogenic land use, file A, 12EUS1 BELD3_A Normbeis3 beisLANDA_EUS12_279X240(/garnet/oaqps) [v0]LANDA_EUS12_279X240(/garnet/oaqps) [v0]Biogenic land use, file A, 36US1 BELD3_A Normbeis3 beisLANDA_US36_148X112(/garnet/oaqps) [v0]LANDA_US36_148X112(/garnet/oaqps) [v0]Biogenic land use, file B, 12EUS1 BELD3_B Normbeis3 beisLANDB_EUS12_279X240(/garnet/oaqps) [v0]LANDB_EUS12_279X240(/garnet/oaqps) [v0]Biogenic land use, file B, 36US1 BELD3_B Normbeis3 beisLANDB_US36_148X112(/garnet/oaqps) [v0]LANDB_US36_148X112(/garnet/oaqps) [v0]Biogenic land use, totals, 12EUS1 BELD3_TOT Normbeis3 beisLAND_TOTALS_EUS12_279X240 (/garnet/oaqps) [v0]LAND_TOTALS_EUS12_279X240(/garnet/oaqps) [v0]Biogenic land use, totals, 36US1 BELD3_TOT Normbeis3 beisLAND_TOTALS_US36_148X112(/garnet/oaqps) [v0]LAND_TOTALS_US36_148X112(/garnet/oaqps) [v0]73Bioseasons file 12EUS1 BIOSEASON Tmpbeis3 beisbioseason.cmaq.2005b_12km(/garnet/oaqps) [v0]bioseason.cmaq.2005b_12km(/garnet/oaqps) [v0]Bioseasons file 36US1 mcip v3.4 beta4 b BIOSEASON Tmpbeis3 beisbioseason.cmaq.2005b_36km(/garnet/oaqps) [v0]bioseason.cmaq.2005b_36km(/garnet/oaqps) [v0]CEM annually summed data CEMSUM smkinven ptipmcemsum_ptipm_2005(/orchid/share) [v0]cemsum_ptipm_2005 (/garnet/oaqps)[v0]Combination profilesGSPRO_COMBO Spcmat gspro_combo_2005 [v6] gspro_combo_2005 [v6]Combination profiles - nonptGSPRO_COMBO Spcmat nonptgspro_combo_tier3_2005_base_nonpt_v2 [v2]gspro_combo_tier3_2017_ref_nonpt[v1]Combination profiles - nonroadGSPRO_COMBO Spcmat nonroad gspro_combo_tier3_2005_base_nonroad_v2 [v0]Combination profiles – onroadGSPRO_COMBO Spcmat onroadgspro_combo_tier3_2005_base_onroad_v2 [v0]gspro_combo_tier3_2017_ref_onroad [v2]Combination profiles - ptnonipm (same asnonpt)GSPRO_COMBO Spcmat ptnonipmgspro_combo_tier3_2005_base_nonpt_v2 [v2]gspro_combo_tier3_2005_base_nonpt_v2 [v2]Country, State, County Information COSTCY smkinven costcy_for_2002 [v5] costcy_for_2002 [v5]Elevation Configuration File for PointSourcesPELVCONFIG Laypoint pelvconfig_inline_allpts [v1] pelvconfig_inline_allpts [v1]Elevation Configuration File for seca_c3sectorPELVCONFIG Laypoint seca_c3 pelvconfig_seca_c3[v1] pelvconfig_seca_c3 [v1]Grid Description List GRIDDESC Grdmat griddesc_lambertonly [v39] griddesc_lambertonly [v39]Gridding surrogates CAN-MEX 12km SRGPRO Grdmat othonCanada_12km_revised(/garnet/oaqps) [v0]Canada_12km_revised(/garnet/oaqps) [v0]Gridding surrogates CAN-MEX 12km SRGPRO Grdmat otharCanada_12km_revised(/garnet/oaqps) [v0]Canada_12km_revised(/garnet/oaqps) [v0]Gridding surrogates CAN-MEX 36km SRGPRO Grdmat otharCanada_36km_revised(/garnet/oaqps) [v0]Canada_36km_revised(/garnet/oaqps) [v0]Gridding surrogates CAN-MEX 36km SRGPRO Grdmat othonCanada_36km_revised(/garnet/oaqps) [v0]Canada_36km_revised(/garnet/oaqps) [v0]Gridding surrogates USA 12km SRGPRO GrdmatUSA-CAN-MEX_12km(/garnet/oaqps) [v0]USA-CAN-MEX_12km(/garnet/oaqps) [v0]Gridding surrogates USA 36km SRGPRO GrdmatUSA-CAN-MEX_36km(/garnet/oaqps) [v0]USA-CAN-MEX_36km(/garnet/oaqps) [v0]GSCNV - pollutant to pollutantconversions GSCNV Spcmat gscnv_cb05_soa [v2] gscnv_cb05_soa [v3]GSPRO speciated MOVES PMGSPROTMP_L Spcmat gspro_speciated_pm [v3] gspro_speciated_pm [v3]GSREF speciated PMGSREFTMP_L Spcmat gsref_speciated_pm [v2] gsref_speciated_pm [v2]Holidays table HOLIDAYS Temporal holidays [v0] holidays [v0]74Inventory Table - HAPCAP EBAFMintegration CMAQ-lite v4.7 N1e HDGHG INVTABLE smkinven onroadinvtable_hapcap_cb05soa [v13] invtable_hapcap_cb05soa [v13]Inventory Table - HAPCAP EBAFMintegration CMAQ-lite v4.7 N1e HDGHG INVTABLE smkinven nonptinvtable_hapcap_cb05soa [v13] invtable_hapcap_cb05soa [v13]Inventory Table - HAPCAP integrationCMAQ-lite v4.7 N1e HDGHG INVTABLE smkinven invtable_hapcap_cb05soa [v12] invtable_hapcap_cb05soa [v12]Inventory Table -no-BAFM CMAQ-litev4.7 N1e HDGHG INVTABLE smkinven avefireinvtable_hapcap_cb05_no_bafm[v3]invtable_hapcap_cb05_no_bafm[v3]Inventory Table -no-BAFM CMAQ-litev4.7 N1e HDGHG INVTABLE smkinven ptipminvtable_hapcap_cb05_no_bafm[v3]invtable_hapcap_cb05_no_bafm[v3]List of sectors for mrggrid SECTORLIST Mrggrid sectorlist_2005ct_05b [v3] sectorlist_2017ct_ref_05b [v1]List of sectors for mrggrid SECTORLIST Mrggrid sectorlist_2005ct_05b [v2] sectorlist_2017ct_ref_05b [v0]List of sectors for mrggrid SECTORLIST Mrggrid sectorlist_2005ct_05b [v1] sectorlist_2017ct_ref_05b [v0]MACT Description MACTDESC Smkreport mactdesc_2002v3 [v1] mactdesc_2002v3 [v1]Meteorology temperature profiles METMOVES movesmrg onroadSMOKE_DAILY_12MERGEUS1_2005 [v0]SMOKE_DAILY_12MERGEUS1_2005 [v0]Mobile codes file default MCODES smkinven mcodes [v1] mcodes [v1]MOVES county cross-reference MCXREF movesmrg onroad MCXREF_tier3 [v0] MCXREF_tier3 [v0]MOVES Emission Factor Table list MRCLIST movesmrg onroadmrclist_RPV_05jul2011_2005ct_05b [v0]mrclist_RPV_01jul2011_2017ct_ref_05b [v0]MOVES Emission Factor Table list MRCLIST movesmrg onroadmrclist_RPD_20may2011_2005ct_05b [v0]mrclist_RPD_10jun2011_2017ct_ref_05b [v0]MOVES Emission Factor Table list MRCLIST movesmrg onroadmrclist_RPP_20may2011_2005ct_05b [v0]mrclist_RPP_10jun2011_2017ct_ref_05b [v0]MOVES Emission Factor Tables EFTABLES movesmrg onroadEFtables_20110520_Tier3Base2005[v0]EFtables_20110610_Tier3Ref2017[v0]MOVES Emission Factor Tables EFTABLES movesmrg onroadEFtables_20110705_Tier3Base2005_RPVfix [v0]EFtables_20110701_Tier3Ref2017_RPVfix [v0]MOVES processes and pollutants MEPROC movesmrg onroad meproc_RPP_mplite [v0] meproc_RPP_mplite [v0]MOVES processes and pollutants MEPROC movesmrg onroad meproc_RPV_mplite [v1] meproc_RPV_mplite [v1]MOVES processes and pollutants MEPROC movesmrg onroad meproc_RPD_mplite [v2] meproc_RPD_mplite [v2]MOVES reference county fuel month MFMREF movesmrg onroad MFMREF_tier3 [v0] MFMREF_tier3 [v0]NAICS descriptions NAICSDESC Smkreport naicsdesc [v0] naicsdesc [v0]NHAPEXCLUDE alm_no_c3NHAPEXCLUDE smkinven alm_no_c3 nhapexclude_alm_no_c3_pf4 [v1] nhapexclude_alm_no_c3_pf4 [v1]NHAPEXCLUDE avefireNHAPEXCLUDE smkinven avefire nhapexclude_everything [v0] nhapexclude_everything [v0]NHAPEXCLUDE nonptNHAPEXCLUDE smkinven nonptnhapexclude_nonpt_pf4_addpesticides [v3]nhapexclude_nonpt_pf4_addpesticides [v3]75NHAPEXCLUDE NONROADNHAPEXCLUDE smkinven nonroad nhapexclude_nonroad_pf4[v0] nhapexclude_nonroad_pf4 [v0]NHAPEXCLUDE ptnonipmNHAPEXCLUDE smkinven ptnonipmnhapexclude_ptnonipm_include_30125010 [v0]nhapexclude_ptnonipm_include_30125010 [v0]NHAPEXCLUDE seca_c3NHAPEXCLUDE smkinven seca_c3 nhapexclude_nothing [v0] nhapexclude_nothing [v0]nonpoint & nonroad surrogate xref AGREF Grdmat amgref_us_can_mex_revised [v11] amgref_us_can_mex_revised [v13]onroad surrogate xref default MGREF Grdmat amgref_us_can_mex_revised [v11] amgref_us_can_mex_revised [v13]ORIS Description ORISDESC smkinven orisdesc [v0] orisdesc [v0]SCC descriptions SCCDESC smkinven sccdesc_pf31 [v12] sccdesc_pf31 [v12]SIC descriptions SICDESC Smkreport sic_desc [v0] sic_desc [v0]Smkmerge representative dates filesMRGDATE_FILES Run scriptmerge_dates_2005 (/garnet/oaqps)[v0]merge_dates_2005 (/garnet/oaqps)[v0]Speciation profiles additional for SMOKEMOVESGSPROTMP_O Spcmat onroad gspro_new_for_smoke-moves [v0] gspro_new_for_smoke-moves [v0]Speciation profiles Canada PMGSPROTMP_J Spcmat othptgspro_pm25_canada_2006_point[v0]gspro_pm25_canada_2006_point[v0]Speciation profiles for biogenicsGSPROTMP_K Spcmat beis gspro_biogenics [v1] gspro_biogenics [v1]Speciation profiles for HGGSPROTMP_H Spcmat gspro_hg [v2] gspro_hg [v2]Speciation profiles for INTEGRATEHAPSGSPROTMP_F Spcmatgspro_integratehaps_cb05_tx_pf4[v1]gspro_integratehaps_cb05_tx_pf4[v1]Speciation profiles for NONHAPTOGGSPROTMP_E Spcmat gspro_nonhaptog_cb05 [v3] gspro_nonhaptog_cb05 [v3]Speciation profiles for NONHAPTOGw/ETOH integrationGSPROTMP_E Spcmat onroadgspro_nonhaptog_cb05_eprofiles[v0]gspro_nonhaptog_cb05_eprofiles[v0]Speciation profiles for NONHAPTOGw/ETOH integrationGSPROTMP_E Spcmat nonptgspro_nonhaptog_cb05_eprofiles[v0]gspro_nonhaptog_cb05_eprofiles[v0]Speciation profiles for NOXGSPROTMP_G Spcmat gspro_nox_hono_pf4 [v0] gspro_nox_hono_pf4 [v0]Speciation profiles for PM2.5GSPROTMP_C Spcmat gspro_pm25 [v2] gspro_pm25 [v2]Speciation profiles for SO2-SULFGSPROTMP_B Spcmat gspro_sulf [v1] gspro_sulf [v1]Speciation profiles for TOGGSPROTMP_D Spcmat gspro_tog_cb05_soa [v3] gspro_tog_cb05_soa [v3]Speciation profiles Other VOC HAPGSPROTMP_M Spcmatgspro_other_hapvoc_no_benz-benz[v0]gspro_other_hapvoc_no_benz-benz[v0]Speciation profiles speciated VOCGSPROTMP_I Spcmat gspro_speciated_voc [v0] gspro_speciated_voc [v0]76Speciation profiles staticGSPROTMP_A Spcmat gspro_static_cmaq [v12] gspro_static_cmaq [v12]Speciation xref CAP staticGSREFTMP_A Spcmat gsref_static_cap_pf4 [v1] gsref_static_cap_pf4 [v1]Speciation xref for Canada PMGSREFTMP_N Spcmat othptgsref_pm25_canada_2006_point[v3]gsref_pm25_canada_2006_point[v3]Speciation xref for Integrate-HAPsstatic GSREFTMP_J Spcmatgsref_static_integratehap_emv4[v2] gsref_static_integratehap_emv4 [v2]Speciation xref for NONHAPVOC, notyear-specificGSREFTMP_H Spcmat nonptgsref_nonhapvoc_general_hdghg[v3]Speciation xref for NONHAPVOC, notyear-specificGSREFTMP_H Spcmatgsref_nonhapvoc_general_hdghg[v2]gsref_nonhapvoc_general_hdghg[v2]Speciation xref for NONHAPVOC, yearspecific GSREFTMP_I Spcmat gsref_nonhapvoc_2005_hdghg [v2]gsref_nonhapvoc_2017_ref_tier3[v1]Speciation xref for NONHAPVOC, yearspecific GSREFTMP_ISpcmat nonptgsref_nonhapvoc_2017_ref_tier3[v2]Speciation xref for PM2.5 diesel SCCs butdo not produce dieselGSREFTMP_D Spcmat gsref_no_dieselpm [v3] gsref_no_dieselpm [v3]Speciation xref for PM2.5 non-diesel SCCsGSREFTMP_E Spcmat gsref_pm25_pf4_nondiesel [v14] gsref_pm25_pf4_nondiesel [v14]Speciation xref for SMOKE-MOVES notTOGGSREFTMP_P Spcmat onroadgsref_new_for_smokemoves_otherthantog [v0]gsref_new_for_smoke-moves_otherthantog [v0]Speciation xref for SMOKE-MOVES TOGGSREFTMP_O Spcmat onroadgsref_new_for_smoke-moves_tog[v1]gsref_2017_for_smoke_moves_tog[v1]Speciation xref for SO2-SULFGSREFTMP_B Spcmat gsref_sulf [v0] gsref_sulf [v0]Speciation xref for speciated VOCGSREFTMP_M Spcmat onroad gsref_speciated_voc [v2] gsref_speciated_voc [v2]Speciation xref for speciated VOCGSREFTMP_M Spcmat othpt gsref_speciated_voc [v2] gsref_speciated_voc [v2]Speciation xref for VOC, not year-specificGSREFTMP_F Spcmat gsref_voc_general_hdghg [v3] gsref_voc_general_hdghg [v3]Speciation xref for VOC, year-specificGSREFTMP_G Spcmat gsref_voc_2005_hdghg [v4] gsref_voc_2017_ref_tier3 [v3]Speciation xref HGGSREFTMP_K Spcmat gsref_hg [v8] gsref_hg [v8]Speciation xref static NOX -- HONO formobile sourcesGSREFTMP_C Spcmat gsref_static_nox_hono_pf4 [v6] gsref_static_nox_hono_pf4 [v6]Stack replacement PSTK smkinven pstk [v0] pstk [v0]surrogate descriptions (works for all grids) SRGDESC Grdmat othon srgdesc_36km_revised [v1] srgdesc_36km_revised [v1]surrogate descriptions (works for all grids) SRGDESC Grdmat srgdesc_12km [v2] srgdesc_12km [v2]surrogate descriptions (works for all grids) SRGDESC Grdmat othar srgdesc_36km_revised [v1] srgdesc_36km_revised [v1]77Temporal profiles, all nonpoint andnonroad ATPRO Temporal amptpro_2005_us_can_revised [v2] amptpro_2005_us_can_revised [v2]Temporal profiles, all point PTPRO Temporal amptpro_2005_us_can_revised [v2] amptpro_2005_us_can_revised [v2]Temporal profiles, onroad default MTPRO Temporal amptpro_2005_us_can_revised [v2] amptpro_2005_us_can_revised [v2]Temporal xref, all nonpoint and nonroad ATREF Temporal amptref_v3_3_revised [v12] amptref_v3_3_revised [v12]Temporal xref, onroad mobile default MTREF Temporal amptref_v3_3_revised [v12] amptref_v3_3_revised [v12]Temporal xref, othpt PTREF Temporal othpt ptref_othpt [v4] ptref_othpt [v4]Temporal xref, point default PTREF Temporal amptref_v3_3_revised [v12] amptref_v3_3_revised [v12]Temporal xref, ptipm only PTREF Temporal Ptipm ptref_ptipm_us [v0] ptref_ptipm_us [v0]Table A-2.Parameters used in the MATS casesParameter Name Environment Variable Sector2005 Base Case 2017 Reference CaseAll months across all sectors ALL_MONTHS 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12BEIS3 version BEIS3_VERSION beis 3.14 3.14Biogenics land area surrogateAREA_SURROGATE_NUM beis 340 340Biogenics speciation profile code BIOG_SPRO beis B10C5 B10C5Check for duplicate sourcesRAW_DUP_CHECK ptfire N NCheck for duplicate sources RAW_DUP_CHECK ptnonipm N NCheck for duplicate sourcesRAW_DUP_CHECK ptipm N NCheck for duplicate sources RAW_DUP_CHECK Y YCheck for duplicate sourcesRAW_DUP_CHECK alm_no_c3 NCheck for duplicate sourcesRAW_DUP_CHECK nonpt NCheck for duplicate sourcesRAW_DUP_CHECK othon N NCheck for duplicate sourcesRAW_DUP_CHECK othpt N NCheck for duplicate sourcesRAW_DUP_CHECK othar N NCheck stack parameters for missing CHECK_STACKS_YN ptfire N NConvective rainfall variable for Pleim-Xiu RC_VAR beis RC RCCount of underscores for Daily dataprefix NAMEBREAK_DAILY ptipm 9 10Custom merge outputSMKMERGE_CUSTOM_OUTPUT Y YCustom merge output - MOVESMOVESMRG_CUSTOM_OUTPUT onroad Y YDefault surrogate code SMK_DEFAULT_SRGID 100 10078Default surrogate code SMK_DEFAULT_SRGID afdust 340 340Don't need spinup for most sectors SPINUP_DURATION ptipm 0Don't need spinup for most sectors SPINUP_DURATION nonpt 0 0Don't need spinup for most sectors SPINUP_DURATION ptipmDon't need spinup for most sectors SPINUP_DURATION ptnonipm 0 0Don't need spinup for most sectors SPINUP_DURATION nonroad 0 0Don't need spinup for most sectors SPINUP_DURATION othpt 0 0Don't need spinup for mostsectors SPINUP_DURATION seca_c3Don't need spinup for most sectors SPINUP_DURATION seca_c3 0Don't speciate zero emission SCCs NO_SPC_ZERO_EMIS ptnonipm Y YDon't speciate zero emission SCCsNO_SPC_ZERO_EMIS nonpt Y YDon't use day-specific emissionDAY_SPECIFIC_YN ptipm N NDon't use pollutant conversionPOLLUTANT_CONVERSION onroad N NEGU daily type EGU_TYPE model_performance model_performanceEMF queue options EMF_QUEUE_OPTIONS #NAME? #NAME?Emission rate model SMK_EF_MODEL onroad MOVES MOVESFill annual values FILL_ANNUAL nonroad Y YFill annual valuesFILL_ANNUAL N NFill annual values FILL_ANNUAL nonpt Y YFire-specific plume rise calculations FIRE_PLUME_YN ptfire Y YFormula for Smkinven SMKINVEN_FORMULA PMC=PM10-PM2_5 PMC=PM10-PM2_5Formula for Smkinven SMKINVEN_FORMULA agFormula for Smkinven SMKINVEN_FORMULA nonroadEXH__PMC=EXH__PM10-EXH__PM2_5EXH__PMC=EXH__PM10-EXH__PM2_5Formula for Smkinven SMKINVEN_FORMULA onroadInclude market penetration MRG_MARKETPEN_YN N NI/O API Sphere type IOAPI_ISPH 19 19Laypoint uses Elevpoint to set sources for plumerise calc SMK_SPECELEV_YN Y YMatch full SCCs FULLSCC_ONLY Y YMaximum errors printed SMK_MAXERROR 10000 10000Maximum warnings printed SMK_MAXWARNING 10 1079MCIP name abbreviation MCIPNAME MCIP_v3.4beta4 MCIP_v3.4beta4Merge by day MRG_BYDAY ptnonipm P PMerge by day MRG_BYDAY seca_c3 P PMerge by day MRG_BYDAY othpt P PMerge type M_TYPE Mwdss mwdssMerge type M_TYPE ptipm All allMerge type M_TYPE ptnonipm Mwdss mwdssMerge type M_TYPE ptfire All allMerge type M_TYPE avefire Aveday avedayMerge type M_TYPE ag Aveday avedayMerge type M_TYPE afdust Week weekMerge type M_TYPE onroad All allMerge type M_TYPE nonptfire Aveday avedayMerge type M_TYPE othpt Mwdss mwdssMerge type M_TYPE othon Week weekMerge type M_TYPE seca_c3 Aveday avedayMerge type M_TYPE beis All allModel output format OUTPUT_FORMAT $EMF_AQM $EMF_AQMNonhap Type NONHAP_TYPE nonpt VOC VOCNonhap Type NONHAP_TYPE ptnonipm VOC VOCNonhap Type NONHAP_TYPE avefire VOC VOCNonhap Type NONHAP_TYPE nonroad VOC VOCNonhap Type NONHAP_TYPE onroad TOG TOGNonhap Type NONHAP_TYPE alm_no_c3 VOC VOCNonhap Type NONHAP_TYPE seca_c3 VOC VOCNumber of emissions layers SMK_EMLAYS 10 10Ocean Chlorine filename extension EXT mrggrid .ncf .ncfOutput county biogenic totals BIO_COUNTY_SUMS beis Y YOutput county/SCC totals MRG_REPSRC_YN onroad Y YOutput county totals MRG_REPCNY_YN N NOutput county totals MRG_REPCNY_YN YOutput county totals MRG_REPCNY_YN onroad Y Y80Output SCC totals MRG_REPSCC_YN onroad Y YOutput state biogenic totals BIO_STATE_SUMS beis Y YOutput state totals MRG_REPSTA_YN Y YOutput state totals MRG_REPSTA_YN NOutput state totals MRG_REPSTA_YN onroad N NOutput time zone OUTZONE 0 0Platform name PLATFORM v4.3 v4.3Pleim-Xiu land surface used? PX_VERSION beis Y YPlume-in-grid method SMK_PING_METHOD 0 0Pressure variable name PRES_VAR beis PRSFC PRSFCPTDAY file name case DAILY_CASE ptipm 2005ck 2005ckRadiation/cloud variable name RAD_VAR beis RGRND RGRNDRenormalize temporal profiles RENORM_TPROF Y YReport default profiles used REPORT_DEFAULTS Y YRun holidays RUN_HOLIDAYS ag N NRun holidays RUN_HOLIDAYS avefire N NRun holidays RUN_HOLIDAYS Y YRun holidays RUN_HOLIDAYS seca_c3 N NRun holidays RUN_HOLIDAYS alm_no_c3 N NRun holidays RUN_HOLIDAYS othon N NRun holidays RUN_HOLIDAYS othpt N NRun holidays RUN_HOLIDAYS othar N NRun holidays RUN_HOLIDAYS nonptfire N NRun holidays RUN_HOLIDAYS afdust Y YRun in inline mode INLINE_MODE Both bothRun in inline mode SECA_C3 INLINE_MODE seca_c3 Only onlyRun script for Smkmerge annual totals RUN_PYTHON_ANNUAL Y YSmkmerge reports units MRG_TOTOUT_UNIT tons/dySMOKE-MOVES processing mode MOVES_TYPE onroad RPD RPDSMOKE-MOVES processing modeMOVES_TYPE onroad RPP RPPSMOKE-MOVES processing mode MOVES_TYPE onroad RPV RPVSoil moisture variable for Pleim-Xiu SOIM1_VAR beis SOIM1 SOIM181Soil temperature variable for Pleim-Xiu SOILT_VAR beis SOIT1 SOIT1Soil type variable for Pleim-Xiu ISLTYP_VAR beis SLTYP SLTYPSort inventory EVs by letter SORT_LIST_EVS othpt Y YSort inventory EVs by letter SORT_LIST_EVS avefire Y YSort inventory EVs by letter SORT_LIST_EVS ptipm Y YSpeciation type name SPC $EMF_SPC $EMF_SPCSpinup Duration SPINUP_DURATION 10 10Spinup Duration SPINUP_DURATION 3 3Temperature variable nameTMPR_VAR beis TEMP2 TEMP2Temperature variable name - MOVES TVARNAME onroad TEMP2 TEMP2Temporal type L_TYPE Mwdss mwdssTemporal type L_TYPE ptipm All allTemporal type L_TYPE ptfire All allTemporal type L_TYPE avefire Aveday avedayTemporal type L_TYPE ag Aveday avedayTemporal type L_TYPE afdust Week weekTemporal type L_TYPE onroad All allTemporal type L_TYPE nonptfire Aveday avedayTemporal type L_TYPE othon Week weekTemporal type L_TYPE seca_c3 Aveday avedayTemporal type L_TYPE beis All allUse area-to-point SMK_ARTOPNT_YN alm_no_c3 Y YUse area-to-point SMK_ARTOPNT_YN nonpt Y YUse area-to-point SMK_ARTOPNT_YN nonroad Y YUse average day emissions SMK_AVEDAY_YN N NUse day-specific emission DAY_SPECIFIC_YN ptipm Y YUse day-specific emission DAY_SPECIFIC_YN ptfire Y YUse hourly plume rise data HOURLY_FIRE_YN ptfire Y YUse NHAPEXCLUDE file SMK_PROCESS_HAPS alm_no_c3 PARTIAL PARTIALUse NHAPEXCLUDE file SMK_PROCESS_HAPS seca_c3 ALL ALLUse NHAPEXCLUDE file SMK_PROCESS_HAPS onroad ALL ALLUse NHAPEXCLUDE file SMK_PROCESS_HAPS nonroad PARTIAL PARTIAL82Use NHAPEXCLUDE file SMK_PROCESS_HAPS avefire NONE NONEUse NHAPEXCLUDE file SMK_PROCESS_HAPS ptnonipm PARTIAL PARTIALUse NHAPEXCLUDE file SMK_PROCESS_HAPS nonpt PARTIAL PARTIALUse pollutant conversionPOLLUTANT_CONVERSION Y YWestern hemisphere? WEST_HSPHERE Y YWrite zero emissions WRITE_ANN_ZERO ptfire Y YWrite zero emissions WRITE_ANN_ZERO ptipm Y YZip merged model-ready files GZIP_OUTPUTS mrggrid Y YBase Year2005 2005Downstream ModelCMAQ v4.7 N5c CMAQ v4.7 N5cEnd Date & Time12/31/2005 23:59 12/31/2005 23:59Future Year0 2017Last Modified Date13:57.7 22:39.1Meteorological Year2005 2005ModelSMOKE SMOKEModeling RegionNational National# of emission layers14 14# of met layers14 14Speciationcmaq_cb05_tx cmaq_cb05_txStart Date1/1/2005 0:00 1/1/2005 0:00Version2.7 2.783APPENDIX BInventory Data Files Used for Each MATS Modeling Case – SMOKE Input Inventory DatasetsThe emissions inventory data files used for the MATS cases areshown in Table B-1. The Input namecolumn gives a brief designator for theinventory. The Sector column specifies the modeling sector for the inventory. The remaining columns show the data set name and versionused in the 2005 base case and 2017 reference cases. The datasets used for the 2017 control case are identical to the 2017 reference case,except for the following replacements:Inventory ptipm CAP used PTINV_EPA410FINAL_BC_226_summer_2015_01SEP2011_ORL [v0],Inventory ptipm daily data (CEM sources) used ptday_ptipm_cem_2017ct_ref_mats_05b [v0], andInventory ptipm daily data (nonCEM sources) used ptday_ptipm_noncem_2017ct_ref_mats_05b [v0].To match the Datasets and Versions listed in this table to actual data files, combine the Dataset name and the version number in the followingpattern: <Dataset Name>_<Date>_<Version number>.txt, where <Date>is the last date of change for that version and will have a unique valuefor the combination of Dataset Name and Version number.Table B-1.List of inventory data setsassociated with the MATSmodeling cases.Input name Sector 2005 BaseCase 2017 Reference CaseInventory afdust CAP afdust afdust_2002ad_xportfrac [v0] afdust_2017ct_ref [v0]Inventory ag CAP Ag ag_cap2002nei [v0] ag_cap2017ct_ref [v0]Inventory alm_no_c3 CAP alm_no_c3 lm_no_c3_cap2002v3 [v1] lm_no_c3_cap2017ct_lowE [v0]Inventory alm_no_c3 HAP alm_no_c3 lm_no_c3_hap2002v4 [v0] lm_no_c3_hap2017ct_lowE [v0]Inventory avefire CAP avefire avefire_2002ce [v0] avefire_2002ce [v0]Inventory avefire HAP avefire avefire_2002_hap [v0] avefire_2002_hap [v0]Inventory C1/C2 additional CAP/HAP alm_no_c3 c1c2_additional_2017ct_ref_caphap_25jul2011 [v0]Inventory fire list ptfire ptfire_2005ag_tox [v0] ptfire_2005ag_tox [v0]Inventory nonpt CAP and HAP (PFC only) nonpt pfc_2002_caphap_wETOH [v1] pfc_2017_ref_caphap_23aug2011 [v0]Inventory nonpt CAP/HAP Cellulosic Biodieselplants for Tier3 nonptcellulosic_ETOH_Biodiesel_2017ct_ref_caphap_29jul2011 [v0]Inventory nonpt CAP/HAP Ethanol Transport forTier3 nonptEthanol_transport_vapor_2017ct_ref_caphap_25jul2011 [v0]Inventory nonpt CAP (no PFC, no refueling) nonpt nonpt_pf4_cap_nopfc [v6] nonpt_pf4_cap_nopfc_2017ct_ref [v0]Inventory nonpt CAP: TX and OK Oil and Gas nonptnonpt_cap_2005_TCEQ_Oklahoma_OilGas[v0]nonpt_cap_2017ct_lowE_TCEQ_Oklahoma_OilGas [v0]84Inventory nonpt CAP: WRAP Oil and Gas nonpt nonpt_cap_2005_WRAP_OilGas [v0] nonpt_cap_2018PhaseII_WRAP_OilGas [v0]Inventory nonpt HAP (no PFC, no refueling) nonptnonpt_pf4_hap_nopfc_nobafmpesticidesplus[v4]nonpt_pf4_hap_nopfc_nobafmpesticidesplus_2017ct_ref [v0]Inventory nonpt Refueling from MOVES, April nonptrfl_moves_wETOH_2005ct_apr_18may2011[v0] rfl_moves_wETOH_2017ct_ref_apr_27jul2011 [v0]Inventory nonpt Refueling from MOVES, August nonptrfl_moves_wETOH_2005ct_aug_18may2011[v0]rfl_moves_wETOH_2017ct_ref_aug_27jul2011[v0]Inventory nonpt Refueling from MOVES,December nonptrfl_moves_wETOH_2005ct_dec_18may2011[v0]rfl_moves_wETOH_2017ct_ref_dec_27jul2011[v0]Inventory nonpt Refueling from MOVES,February nonptrfl_moves_wETOH_2005ct_feb_18may2011[v0] rfl_moves_wETOH_2017ct_ref_feb_27jul2011 [v0]Inventory nonpt Refueling from MOVES, January nonptrfl_moves_wETOH_2005ct_jan_18may2011[v0] rfl_moves_wETOH_2017ct_ref_jan_27jul2011 [v0]Inventory nonpt Refueling from MOVES, July nonptrfl_moves_wETOH_2005ct_jul_18may2011[v0] rfl_moves_wETOH_2017ct_ref_jul_27jul2011 [v0]Inventory nonpt Refueling from MOVES, June nonptrfl_moves_wETOH_2005ct_jun_18may2011[v0] rfl_moves_wETOH_2017ct_ref_jun_27jul2011 [v0]Inventory nonpt Refueling from MOVES, March nonptrfl_moves_wETOH_2005ct_mar_18may2011[v0]rfl_moves_wETOH_2017ct_ref_mar_27jul2011[v0]Inventory nonpt Refueling from MOVES, May nonptrfl_moves_wETOH_2005ct_may_18may2011[v0]rfl_moves_wETOH_2017ct_ref_may_27jul2011[v0]Inventory nonpt Refueling from MOVES,November nonptrfl_moves_wETOH_2005ct_nov_18may2011[v0]rfl_moves_wETOH_2017ct_ref_nov_27jul2011[v0]Inventory nonpt Refueling from MOVES,October nonptrfl_moves_wETOH_2005ct_oct_18may2011[v0] rfl_moves_wETOH_2017ct_ref_oct_27jul2011 [v0]Inventory nonpt Refueling from MOVES,September nonptrfl_moves_wETOH_2005ct_sep_18may2011[v0]rfl_moves_wETOH_2017ct_ref_sep_27jul2011[v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif April nonroadnonroad_cmaq_lite_2005ct_apr_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_apr_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif August nonroadnonroad_cmaq_lite_2005ct_aug_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_aug_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif December nonroadnonroad_cmaq_lite_2005ct_dec_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_dec_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif February nonroadnonroad_cmaq_lite_2005ct_feb_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_feb_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif January nonroadnonroad_cmaq_lite_2005ct_jan_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_jan_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif July nonroadnonroad_cmaq_lite_2005ct_jul_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_jul_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif June nonroadnonroad_cmaq_lite_2005ct_jun_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_jun_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif March nonroadnonroad_cmaq_lite_2005ct_mar_19may2011[v0]nonroad_cmaq_lite_2017ct_ref_mar_20jul2011[v0]85Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif May nonroadnonroad_cmaq_lite_2005ct_may_19may2011[v0]nonroad_cmaq_lite_2017ct_ref_may_20jul2011[v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif November nonroadnonroad_cmaq_lite_2005ct_nov_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_nov_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif October nonroadnonroad_cmaq_lite_2005ct_oct_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_oct_20jul2011 [v0]Inventory nonroad cap+CMAQ-lite HAPs US,incl Calif September nonroadnonroad_cmaq_lite_2005ct_sep_19may2011[v0] nonroad_cmaq_lite_2017ct_ref_sep_20jul2011 [v0]Inventory onroad RPD onroad VMT_tier3_2005 [v0] VMT_tier3_2017_ref_cntl [v3]Inventory onroad RPD onroad VMT_tier3_2005 [v0] VMT_tier3_2017_ref_cntl [v3]Inventory onroad RPD onroad VMT_tier3_2005 [v0] VMT_tier3_2017_ref_cntl [v3]Inventory onroad RPD onroad SPEED_tier3 [v0] SPEED_tier3 [v0]Inventory onroad RPD onroad VMT_tier3_2005 [v0] VMT_tier3_2017_ref_cntl [v3]Inventory onroad RPP onroad VPOP_tier3_2005 [v0] VPOP_tier3_2017 [v0]Inventory onroad RPV onroad VPOP_tier3_2005 [v0] VPOP_tier3_2017 [v0]Inventory othar nonpoint CAP Mexico borderstates othar nonpt mexico border1999 [v0] nonpt mexico border1999 [v0]Inventory othar nonpoint CAP Mexico interiorstates othar nonpt mexico interior1999 [v0] nonpt mexico interior1999 [v0]Inventory othar nonroad CAP Mexico borderstates othar nonroad mexico border1999 [v0] nonroad mexico border1999 [v0]Inventory othar nonroad CAP Mexico interiorstates othar nonroad mexico interior1999 [v0] nonroad mexico interior1999 [v0]Inventory othon CAP Mexico border states othon onroad mexico border1999 [v0] onroad mexico border1999 [v0]Inventory othon CAP Mexico interior states othon onroad mexico interior1999 [v0] onroad mexico interior1999 [v0]Inventory othon CAP onroad Canada othon canada_onroad_cap_2006 [v0] canada_onroad_cap_2006 [v0]Inventory othpt CAP Mexico border states othpt mexico_border99 [v1] mexico_border99 [v1]Inventory othpt CAP Mexico interior states othpt mexico_interior99 [v0] mexico_interior99 [v0]Inventory othpt CAP offshore othptptnonipm_offshore_oil_cap2005v2_20nov2008[v0] ptnonipm_offshore_oil_cap2005v2_20nov2008 [v0]Inventory ptipm CAP ptipm ptipm_2005cs_cap_27dec2010.txt [v1]Inventory ptipm CAP ptipmPTINV_EPA410FINAL_BC_58_summer_2020_21MAY2011_ORL [v0]Inventory ptipm CAP ptipmInventory ptipm daily data (CEM sources) ptipmptday_ptipm_caphap_cem_2005cs_05b(/garnet/oaqps) [v0] ptday_ptipm_caphap_cem_2017ct_05b [v0]Inventory ptipm daily data (nonCEM sources) ptipmptday_ptipm_caphap_noncem_2005cs_05b(/garnet/oaqps) [v0] ptday_ptipm_caphap_noncem_2017ct_05b [v0]86Inventory ptipm HAP ptipm ptipm_2005cs_hap_27dec2010.txt [v0]Inventory ptnonipm CAP ptnonipmptnonipm_xportfrac_cap2005v2_2005cs_orl[v7] ptnonipm_xportfrac_cap2017ct_ref [v0]Inventory ptnonipm CAPHAP biodiesel plantadditions forTier3 ptnonipmbiodiesel_plants_2017ct_ref_caphap_29jul2011[v0]Inventory ptnonipm CAPHAP ethanol plantadditions forTier3 ptnonipmethanol_plants_2005ct_2017ct_lowE_caphap[v0] ethanol_plants_2017ct_ref_caphap_19jul2011 [v0]Inventory ptnonipm cement capHg ptnonipmptnonipm_capHG_cementISIS_2016cr_16AUG2010 [v0]Inventory ptnonipm HAP ptnonipm ptnonipm_hap2005v2_2005cs_orl [v6] ptnonipm_hap2017ct_ref [v0]Inventory rail additional CAP/HAP for Tier3ref/ctl alm_no_c3 rail_additional_2017ct_ref_caphap_26jul2011 [v0]Inventory seca_c3 BAF HAPs Canada seca_c3eca_imo_CANADA_SCC_fix_vochaps_2005_09DEC2010 [v0] eca_imo_CANADA_SCC_fix_vochaps_2017 [v0]Inventory seca_c3 BAF HAPs US includes EEZand offshore FIPS seca_c3eca_imo_fixFIPS_US_andSCC_fix_vochaps_2005_09DEC2010 [v0]eca_imo_fixFIPS_US_andSCC_fix_vochaps_2017[v0]Inventory seca_c3 CAP Canada seca_c3eca_imo_CANADA_SCC_fix_caps_2005_09DEC2010 [v0] eca_imo_CANADA_SCC_fix_caps_2017 [v0]Inventory seca_c3 CAP US + EEZ + Offshorenon-Canada seca_c3eca_imo_fixFIPS_US_wDE_andSCC_fix_caps_2005_09DEC2010 [v0]eca_imo_fixFIPS_US_wDE_andSCC_fix_caps_2017 [v0]ORL Nonpoint Inventory - Afdust Canada 2006 othar canada_afdust_xportfrac_cap_2006 [v0] canada_afdust_xportfrac_cap_2006 [v0]ORL Nonpoint Inventory - Ag Canada 2006 othar canada_ag_cap_2006 [v0] canada_ag_cap_2006 [v0]ORL Nonpoint Inventory - Aircraft Canada 2006 othar canada_aircraft_cap_2006 [v0] canada_aircraft_cap_2006 [v0]ORL Nonpoint Inventory - Commercial MarineCanada 2006 othar canada_marine_cap_2006 [v0] canada_marine_cap_2006 [v0]ORL Nonpoint Inventory - Nonroad Canada 2006 othar canada_offroad_cap_2006 [v0] canada_offroad_cap_2006 [v0]ORL Nonpoint Inventory - Oarea Canada 2006 othar canada_oarea_cap_2006 [v3] canada_oarea_cap_2006 [v3]ORL Nonpoint Inventory - Rail Canada 2006 othar canada_rail_cap_2006 [v0] canada_rail_cap_2006 [v0]ORL Point Inventory - Point 2006 othpt canada_point_2006_orl [v2] canada_point_2006_orl [v2]ORL Point Inventory - Point CB5 2006 othpt canada_point_cb5_2006_orl [v0] canada_point_cb5_2006_orl [v0]ORL Point Inventory - Upstream Oil & Gas 2006 othpt canada_point_uog_2006_orl [v0] canada_point_uog_2006_orl [v0]Appendix CSummary of MATS Rule 2017 Base Case Non-EGUControl Programs, Closures and ProjectionsLists of control, closure and projection packet datasets used to create MATS year 2017 base case inventories from the 2005 MATSbase case areprovided in Tables C-1 and C-2.Table C-1.Datasets used to create MATS 2017 reference case inventories for non-EGU point sourcesName Type Dataset Version DescriptionCLOSURES LotusNotes, ABCG, plus Timin2016crPlantClosureCLOSURES_LotusNotes_Linda_Timin_2016cr_23AUG2010 1Plant and unit closures identified through EPAreview.CLOSURES TR1 comments and consent decrees2014csPlantClosure CLOSURES_TR1_2014cs_01FEB2011 0Plant and unit closures through 2014 identified asa result of Transport Rule comments.CLOSURES cement ISIS 2013 policyPlantClosureCLOSURES_cementISIS_2016cr_17AUG2010 1Cement plant and unit closures identified via theISIS 2013 policy case.closures: 2005 to 2012ckPlantClosureCLOSURES_2005ck_to_2012ck_CoST_format 0Plant and unit closures identified 2008 or before.CONTROL ADDITIONAL OECA 2005cr to2016cr ControlCONTROLS_additional_NEIpf4_OECA_2005cr_2016cr_29JUL2010 1Controls that implement OECA consent decrees.CONTROL REPLACE DOJ 2005cr to 2016cr ControlCONTROLS_replacement_NEIpf4_DOJ_2005cr_2016cr_02AUG2010.txt 0Controls resulting from the 2002v3 DOJ Texassettlement.CONTROL REPLACE HWI 2005cr to 2016cr ControlCONTROLS_replacement_NEIpf4_HWI_2005cr_2016cr_02AUG2010.txt 1Hazardous Waste Incinerator controls for CAPsand Haps carried over from 2002v31.CONTROL REPLACE IndustrialBoilernonMACT 2005cr to 2016cr ControlCONTROLS_replacement_IndBoilers_nonMACT_by2008_20AUG2010 0Industrial boiler controls not related to applicationof the MACT but derived from the Boiler MACTICR database dated 4/30/10.CONTROL REPLACE LMWC 2005cr to 2016cr ControlCONTROLS_replacement_NEIpf4_LMWC_2005cr_2016cr_02AUG2010.txt 0Controls for large municipal combustors carriedover from 2002v31.CONTROL REPLACE MACT 2005cr to 2016cr ControlCONTROLS_replacement_NEIpf4_MACT_2005cr_2016cr_02AUG2010.txt 0MACT controls carried over from 2002v3 andupdated as appropriate.CONTROL REPLACE NY SIP 2005cr to 2016cr ControlCONTROLS_replacement_NYSIP_O3_SCC_2016cr_26AUG2010 0Controls that reflect enforceable controls for NOxand VOC from the New York ozone SIP.CONTROL REPLACE Refineries 2005cr to2016cr ControlCONTROLS_replacement_NEIpf4_refineries_2005cr_2016cr_02AUG2010.txt 1Controls for refineriesspecified by EPA expertrefinery staff.CONTROL RICE 2016cr_05b ControlCONTROLS_replacement_RICE_2016cr_21SEP2010 1Controls for 2014 and 2016 that represent threeseparate RICE NESHAPsCONTROL RICE SO2 2014cs_05b ControlCONTROLS_replacement_RICE_SO2_2014cs_05JAN2011 1SO2reductions from the Ultra-low Sulfur Dieselrequirement for CI enginesCONTROL SULF rules: ME, NY, NJ 2017ONLY ControlCONTROLS_SULF_rules_2017only_03FEB2011 0SO2reductions due to statesulfur content rules forfuel oil.CONTROL St Gobain and LaFarge 2017 ControlCONTROLS_rep_Lafarge_StGobain_2017cs_25JAN2011.txt 0Controls for NOX, SO2, PM., and HCl resultingfrom Saint Gobain and Lafarge consent decreesCONTROL TR1 Final CONTROL packet: 2017 Control CONTROLS_TR1_2017 0Controls for TCEQ oil and gas and non-ISISrelated cement controls.CONTROL TR1 Final consent decrees 20XX ControlCONTROLS_additional_TR1final_consent_decrees_2005cs_to_20XXcs.csv 0Controls related to consent decrees identifiedduring the Transport Rule comment period.CONTROL cement ISIS 2013 policy ControlCONTROLS_replacement_cementISIS_2016cr_17AUG2010 0Controls for cement plants based on 2013 ISISpolicy casePROJECTION 2005 to 2017 ag emissionsProjection PROJECTION_2005_2017_ag 0Projection factors for agriculture based on animalpopulation stats.PROJECTION LMWC 2005cr to 2016crProjectionPROJECTION_2005cr_2016cr_LMWC_29JUL2010 0Projection factors for Solid and Liquid MunicipalWaste Combustors.PROJECTION TR1 comments 2005cs to 20XXcs-ptnonipmProjectionPROJECTION_2005cs_20XX_TR1_ptnonipm_01FEB2011 0Projection factors derived from Transport Rulecomments.PROJECTION aircraft 2005 to 2017 JAN2010FAATAFProjectionPROJECTION aircraft 2005 to 2017 JAN2010FAATAF 0Projection factors for aircraft derived from theFAA Terminal Area Forecast System.PROJECTION cement ISIS 2013 policyProjectionPROJECTION_cementISIS_2016cr_17AUG2010 0Projection factors that implement the 2013 ISISpolicy case for cement.PROJECTION RWC and landfills 2005 to 2017BADProjectionPROJECTION_2005_2017_RWC_landfills_BAD 0 Projection factors for residential wood combustionPROJECTION Tier3 Proposal 2017 low-E to2017 REF transport BPS BTP RBTProjectionPROJECTION_2017ct_REF_Tier3prop_transport_scalars_28jul20110Projection factors for transport of renewable fuelblends from bulk plant to storage, refinery to bulkterminal and bulk terminal to pumpPROJECTION Tier3 Proposal 2017 low-E to2017 REF-CTL agProjectionPROJECTION_2017ct_REF_Tier3prop_ag_scalars_26jul2011 0Projection factors accounting for changes inbiofuel volumes on upstream agricultural sourcesPROJECTION Tier3 Proposal 2017 low-E to2017 REF-CTL refineriesProjectionPROJECTION_2017ct_REF_Tier3prop_refinery_scalars_26jul2011 0Projection factors accounting for refinery processchanges from renewable fuelsTable C-2.Datasets used to create MATS 2017 reference case inventories for nonpoint sourcesControl Program Name Type Dataset Version DescriptionCONTROL REPLACE NYSIP 2005cr to 2016cr ControlCONTROLS_replacement_NYSIP_O3_SCC_2016cr_26AUG2010 0Controls that reflect enforceablecontrols for NOx and VOC from theNew York ozone SIP.CONTROL RICE2016cr_05b ControlCONTROLS_replacement_RICE_2016cr_21SEP2010 1Controls for 2014 and 2016 that represent three separate RICENESHAPsCONTROL RICE SO22014cs_05b ControlCONTROLS_replacement_RICE_SO2_2014cs_05JAN2011 1SO2reductions from the Ultra-low Sulfur Diesel requirement for CIenginesCONTROL SULF rules:ME, NY, NJ 2017 ONLY ControlCONTROLS_SULF_rules_2017only_03FEB2011 0SO2reductions due to state sulfur content rules for fuel oil.CONTROL TR1 FinalCONTROL packet: 2017 Control CONTROLS_TR1_2017 0 Controls for TCEQ oil and gas and non-ISIS related cement controls.PROJECTION 2005 to2017ag sector Projection PROJECTION_2005_2017_ag 0 Projection factors for agriculture based on animal population stats.PROJECTION RWC andlandfills 2005 to 2017 BAD ProjectionPROJECTION_2005_2017_RWC_landfills_BAD 0 Projection factors for residential wood combustion and landfills.PROJECTION aircraft 2005to 2017 JAN2010 FAATAF ProjectionPROJECTION aircraft 2005 to 2017JAN2010 FAATAF 0Projection factors for aircraft derived from the FAA Terminal AreaForecast System.PROJECTION Tier3Proposal 2017 low-E to 2017REF transport BPS BTPRBT ProjectionPROJECTION_2017ct_REF_Tier3prop_transport_scalars_28jul2011 0Projection factors for transport of renewable fuel blends from bulkplant to storage, refinery to bulkterminal and bulk terminal to pumpPROJECTION Tier3Proposal 2017 low-E to 2017REF-CTL ag ProjectionPROJECTION_2017ct_REF_Tier3prop_ag_scalars_26jul2011 0Projection factors accounting for changes in biofuel volumes onupstream agricultural sourcesPROJECTION Tier3Proposal 2017 low-E to 2017REF-CTL refineries ProjectionPROJECTION_2017ct_REF_Tier3prop_refinery_scalars_26jul2011 0Projection factors accounting for refinery process changes fromrenewable fuels