# E missions Inventory for Air Quality Modeling Technical Support Document : Proposed Tier 3

Emissions StandardsEPA

- 4 54 /R

- 1 3

- 0 02

March 201 3



## E missions Inventory for Air Quality Modeling Technical Support

Document : Proposed Tier 3 Emissions Standards

By:

Rich Mason

Alexis Zubrow Alison Eyth

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 27711

M arch 2013

### (This page intentionally left blank)i 

## TABLE OF CONTENTS

LIST OF TABLES

................................ ................................ ................................ ................................ ...... ii

LIST OF APPENDICES

................................ ................................ ................................ ............................ iii

ACRONYMS

................................ ................................ ................................ ................................ .............. iv

1 Introduction to the Modeling Platform

................................ ................................ .............................. 1

2 2005 Emission Inventories and Their Preparation

................................ ................................ ............. 2

2.1 Custom configuration for emissions modeling for Tier 3 NPRM

................................ .................... 4

2.2 Onroad mobile sources (onroad)

................................ ................................ ................................ .... 6

2.2.1 MOVES

................................ ................................ ................................ ................................ ..... 6

2.2.2 Representing counties

................................ ................................ ................................ ................ 7

2.2.3 SMOKE

- MOVES inputs

................................ ................................ ................................ .......... 11

2.2.4 Generating emission factors for SMOKE

................................ ................................ ................. 12

2.2.5 Running SMOKE for onroad mobile

................................ ................................ ........................ 14

2.3 Nonroad mobile sources (nonroad, alm_no_c3, seca_c3)

................................ ............................. 15

2.3.1 Emissions generated with the NONROAD model (nonroad)

................................ .................... 15

2.3.2 Locomotives and commercial marine vessels (alm_no_c3, seca_c3)

................................ ......... 18

2.4 2005 point sources (ptipm and ptnonipm sectors)

................................ ................................ ......... 19

2.4.1 Ethanol plants (ptnonipm)

................................ ................................ ................................ ........ 19

2.5 2005 nonpoint so urces (afdust, ag, avefire, nonpt)

................................ ................................ ........ 20

2.5.1 Portable fuel containers

................................ ................................ ................................ ............ 20

2.5.2 Onroad refueling

................................ ................................ ................................ ...................... 20

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

................................ ................................ ........ 21

3 VOC Speciation Changes that Represent Fuel Changes

................................ ................................ .. 21

4 2017 and 2030 Reference Cases

................................ ................................ ................................ ......... 25

4.1 Station ary source projections: EGU sector (ptipm)

................................ ................................ ...... 31

4.2 Stationary source projections: non

- EGU sectors (ptnonipm, nonpt, ag, afdust)

............................ 32

4.2.1 Ethanol plants (ptnonipm)

................................ ................................ ................................ ........ 33

4.2.2 Biodiesel plants (ptnonipm)

................................ ................................ ................................ ...... 34

4.2.3 Portable fuel containers (nonpt)

................................ ................................ ................................ 34

4.2.4 Cellulosic fuel production (nonpt)

................................ ................................ ............................ 35

4.2.5 Ethanol transport and distribution (nonpt)

................................ ................................ ................ 36

4 .2.6 Onroad refueling (nonpt)

................................ ................................ ................................ .......... 36

4.2.7 Refinery adjustments (ptnonipm)

................................ ................................ ............................. 36

4.2.8 Ethanol transport gasoline and blends (ptnonipm, nonpt)

................................ .......................... 37

4.2.9 Upstream agricultural adjustments (afdust, ag, nonpt, ptnonipm)

................................ .............. 37

4.2.10 Livestock emissions growth (ag, afdust)

................................ ................................ ............... 37

4.2 .11 Residential wood combustion growth (nonpt)

................................ ................................ ....... 38

4.2.12 Aircraft growth (ptnonipm)

................................ ................................ ................................ .. 39

4.2.13 Stationary source control programs, consent decrees & settlements, and plant closures (ptnonipm, nonpt)

................................ ................................ ................................ ................................ 40

4.2.14 Oil and gas projections in TX, OK, and non

- California WRAP states (nonpt)

....................... 45

4.3 Onroad mobile source projections (onroad)

................................ ................................ .................. 45

4.3.1 California LEV

................................ ................................ ................................ ........................ 46

4.4 Nonroad mobile source projections (nonroad, alm_no_c3, seca_c3)

................................ ............. 46

4.4.1 Emissions generated with the NONROAD model (nonroad)

................................ .................... 47

4.4.2 Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3)

................................ .... 48

4.4.3 Class 3 commercial marine vessels (seca_c3)

................................ ................................ ........... 50ii

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

................................ ................... 50

5 2017 and 2030 Tier 3 Control Cases

................................ ................................ ................................ . 51

5.1 Non

- EGU stationary source projections (nonpt)

................................ ................................ ........... 51

5 .2 Onroad mobile (onroad)

................................ ................................ ................................ ............... 52

5.3 Nonroad mobile (nonroad)

................................ ................................ ................................ ........... 52

5.3.1 Emissions generated with the NONROAD model (nonroad)

................................ .................... 52

6 Tier 3 emissions summaries

................................ ................................ ................................ ............... 53

7 References

................................ ................................ ................................ ................................ .......... 56



## LIST OF TABLES

Table 1

- 1. List of cases run in support of the Proposed Tier 3 air quality modeling 2

Table 2

- 1. Sectors used in emissions modeling for the Tier 3 NPRM 2005v4.3 platform 3

Table 2

- 2. Model species produced by SMOKE for CB05 with SOA for the Tier 3 NPRM platform 5

Table 2

- 3. Description of differences in 2005 case ancillary data (unrelated to SMOKE to MOVES) between the 2005v4.3 and 2005v4.2 platforms 6

Table 2

- 4. Allocation of states to the Petroleum Administration for Defense Districts 8

Table 2

- 5. Gasoline parameter categories 10

Table 2

- 6. States adopting California emission standards 10

Table 2

- 7. Summary of county grouping characteristics for representative counties 11

Table 2

- 8. Updated 1,3

- butadiene to VO C ratio for 2

- stroke snowmobiles for NMIM’s gasoline categories 15

Table 2

- 9. Criteria for grouping representative counties for n onroad mobile analysis 16

Table 2

- 10. NONROAD model temperature (F) categories 17

Table 2

- 11. NONROAD NMIM runs 18

Table 2

- 12. Summary of NONROAD modeling components 18

Table 2

- 13. 2005 ethanol plant emissions 20

Table 3

- 1. Summary of VOC speciation profile approaches by sector across cases 23

Table 4

- 1. Control strategies and growth assumptions for creating the Tier 3 NPRM 2017 and 2030

reference case emissions inventories from the 2005 base case 28

Table 4

- 2. Tier 3 NPRM reference case stationary non

- EGU source

- related projection methods 33

Table 4

- 3. 2017 and 2030 corn ethanol plant emissions [tons] 34

Table 4

- 4. 2017 and 2030 biodiesel plant emissions [tons] 34

Table 4

- 5. PFC emissions for 2017 and 2030 [tons] 35

Table 4

- 6. 2017 and 2030 cellulosic plant emissions [tons] 35

Table 4

- 7. 2017 and 2030 VOC losses (Emissions) due to ethanol transport and distribution [tons] 36

Table 4

- 8. Tier 3 NPRM Reference case onroad gasoline and diesel refueling emissions [tons] 36

Table 4

- 9. Impact of refinery adjustments on 2017 and 2030 emissions [tons] 37

Table 4

- 10. Upstream agricultural emission incre ases due to RFS2 fuels in 2017 and 2030 [tons] 37

Table 4

- 11. Growth factors from year 2005 to 2017 and 2030 for animal operatio ns 38

Table 4

- 12. Projection factors for growing year 2005 residential wood combustion sources 39

Table 4

- 13. Impact of year 2017 projection factor error on residential wood combustion estimates 39

Table 4

- 14. Factors used to project 2005 base

- case aircraft emissions to 2017 and 2030 40

Table 4

- 15. Summary of non

- EGU emission reductions applied to the 2005 inventory due to unit and plant

closures 41

Table 4

- 16. Future

- year ISIS

- based cement industry annual reductions [tons/yr] for the non

- EGU (ptnonipm) sector 43

Table 4

- 17. State

- level non

- MACT boiler reductions from ICR data gathering [tons] 43iii Table 4

- 18. National impact of RICE controls on non

- EGU projections 44

Table 4

- 19. Impact of fuel sulfur (SO 2 ) controls on 2017 and 2030 non

- EGU projections [tons] 44

Table 4

- 20. Oil and gas emissions for 2005, 2017 and 2030 including additional reductions due to the RICE

NESHAP 45

Table 4

- 21 . Factors applied to year 2005 emissions to project locomotives and class 1 and class 2

commercial marine vessel emissions to 2017 and 2030 48

Table 4

- 22. Additional class 1 railroad and C1/C2 CMV emissions from RFS2 fuel volume changes 49

Table 4

- 23. NO X , SO 2 , PM 2.5 and VOC factors to project class 3 CMV emissions for 2017 and 2030 50

Table 6

- 1. National (49

- state) 2005 U.S. emissions (tons/year) by sector 53

Table 6

- 2. National (49

- state) 2017 Reference Case U.S. emissions (tons/year) by sector 54

Table 6

- 3. Natio nal (49

- state) 2030 Reference Case U.S. emissions (tons/year) by sector 54

Table 6

- 4. National (49

- state) 2017 Tier 3 Control Case U.S. emissions (tons/year) by sector 54

Table 6

- 5. National (49

- state) 2030 Tier 3 Control Case U.S. emi ssions (tons/year) by sector 5 5



## LIST OF APPENDICES

APPENDIX A : Ancillary Datasets and Parameters Used for Each Tier 3 NPRM Modeling Case

APPENDIX B: Inventory Data Files Used for Each Tier 3 NPRM Modeling Case

– SMOKE Input

Inventory Datasetsiv 

## ACRONYMS

AEO Annual Energy Outlook

BEIS Biogenic Emission Inventory System bps Bul

k 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 (former ly 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

FI PS Federal Information Processing Standard

FRM Final Rulemaking

HAP Hazardous Air Pollutant

HDGHG Heavy Duty Greenhouse Gas

HONO HNO2, nitrous acid

IPM Integrated Planning Model

LDGHG Light Duty Greenhouse Gas

LEV (California) Low

- Emission Vehicle Program

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

NPRM Notice for Proposed Rulema king 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

PE C Elemental carbon component of PM2.5

PMF INE 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

r bt Refinery

- to

- bulk terminal

RFS2 R evised annual renewable fuel standard (m andate)

SMOKE Sparse Matrix Operator Kernel Emissions

SCC Source Category Code

TAF Terminal Area Forecastv TSD Technical Support Document

VOC Volatile Organic Compound

WRAP Western Regional Air Partnership1 

## 1 Introduction to the M odeling P latform

This Technic al Support Document (TSD) describes the development of the emissions inventories used as inputs to the air quality modeling that t h e U.S. Environmental Protection Agency (EPA) performed to assess the impact of the proposed Tier 3 vehicle and fuel emission standards for cars and trucks

. This document provides the details of emissions modeling done to support the development of the Regulatory Impact Assessment (RIA) for the Tier 3 Notice of Proposed Rulemaking (NPRM) , hereafter referred to as the “Tier

3 NPR M” . The emissions inventories were generated using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system ( http://www.smoke

- model.org/index.cfm ) v ersion 2. 7 and processed into the form requir ed by the Community Multi

- scale Air Quality (CMAQ) model. CMAQ simulates the numerous physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate

matter and air toxics.

As part of the analysis for this ru lemaking, the modeling system was used to calculate daily and annual PM 2.5
concent rations, 8

- hr maximum ozone, visibility impairment and seasonal and annual concentrations for the following HAPs: acetaldehyde, acrolein, benzene, 1,3

- butadiene, ethanol and formaldehyde

. 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 amb ient concentrations for use in estimating health and welfare effects. In this document, we provide an overview of (1) the emissions components of the modeling platform, (2) the development of the 2005 base year emissions, (3) the development of the future year 2017 and 2030 baseline emissions, and (4) the development of the future year 2017 and 2030 control case emissions.

A modeling platform is the collection of the inputs to an air quality model, including emissions data, meteorology, initial conditions , and boundary conditions. The 2005

- based air quality modeling platform includes 2005 base year emissions and 2005 meteorology f or modeling ozone, PM 2.5 and other model species with CMAQ. In support of this rule, EPA modeled the air quality in the Easter n and the Western United States using two separate model runs, each with a horizontal grid resolution of 12 km x 12 km. These 12 km modeling domains were “nested” within a modeling domain covering the remainder of the lower 48 states and surrounding areas using a grid resolution of 36 x 36 km. The results from the 36

- km modeling were used to provide incoming “boundary” for the 12km grids. Additional details on the non

- emissions portion of

th is 2005v4.3 modeling platform are described in the air quality m odeling TSD.

Version 4.3 of t he 2005

- based air quality modeling platform was used for the Tier3 NPRM and is referred to as the 2005v4.3 platform. The 2005v4.3 platform builds upon the 2005

- based platform, version 4.2, which was the version of the platfo rm used for the final Cross

- State Air Pollution Rule (CSAPR) and incorporated changes made in response to public comments on the proposed version of that rule. The Technical Support

Document “Preparation of Emissions Inventories for the Version 4. 2 , 2005

- based Platform” (see http://www.epa.gov/ttn/chief/emch/index.html#final ) provides information on the platform used for the proposed version of this rule.

Table 1

- 1 provides a high

- level summary of the five emissions cases that were modeled in support of the

Tier 3 NPRM

. The form of the fuel used for mobile sources is a key discriminator between the cases. Therefore, the mobile source emissions are described with respect to the impacts of the Energy Independence and Security Act of 2007 (EISA) and the Energy Policy Act of 2005 (EPAct ) on mobile source fuels.2

Table 1

- 1

. List of cases run in support of the Proposed Tier 3 air quality modeling

Case Name Internal EPA

Abbreviation Description

2005 base

case 2005c t 2005 calendar year case / scenarios that use an average year temporal allocation approach for Electrical Generating Units (EGUs), a pre

- EISA/EPA ct fuel supp ly for mobile sources, and average year fires data. Air quality outputs from this case are used to compute relative response factors with the 2017 future year

reference case scenarios.

2017

reference case 2017ct_ref 2017 future year reference scenario wit h EGU emissions that represent the implementation of the Cross

- State Air Pollution Rule (CSAPR) and upstream stationary and mobile sources representing the implementation of the EISA/ EPAct fuel supply (RFS2 Rule).

2017 Tier 3

control case 2017ct_ ctla 201 7 Tier 3 control case scenario representing national Tier 3

vehicle and fuels emissions standards.

2030

reference case 2030ct_ref_csapr 2030 future year reference scenario with EGU emissions that represent the implementation of the Cross

- State Air Polluti on Rule (CSAPR) and upstream stationary and mobile sources representing the implementation of the EISA/EPAct fuel supply (RFS2 Rule).

2030 Tier 3

control case 2030ct_ctl_csapr 2030 Tier 3 control case scenario representing national Tier 3

vehicle and fuel s emissions standards.

In the remainder of this document, we provide a description of the approach taken to generate the emissions in support of air quality modeling for the Tier 3 NPRM . In Section 2 , we describe the 2005v4.3 platform custom configurations, ancillary data and 2005 inventory differences from the v4 .2 platform. In Section 3

, we describe the speciation differences amon g each of the cases run . In Section 4 , we describe the 20 17 and

2030 Reference (i.e., future year baseline) case s as compared to the 2005 base case. In Section 5, we describe the 2017 and 2030 Tier 3 Control cases as compared to the 2017 and 2030 Reference cases.

Emission summaries for all Tier 3 NPRM scenarios are provided in Section 6. Appendix A provides a comparison of the ancillary datasets and parameters used for the various Tier 3 NPRM emissions cases, and Appendix B compares the emissions inventory and other input data files used f or each of the Tier 3 NPRM

cases.



## 2 2005 Emission I nventories and Their P reparation

As mentioned previously, t he 2005 e missions modeling approach for the Tier 3 NPRM used much of the same data and approaches as the 2005v4.2 platform. In this section, we ide ntify the differences between the

data used for the Tier 3 NPRM 2005v4.3 platform and that used for the 2005v4.2 platform. Section 2.1 provide s ancillary data differences that impact multiple sectors . Section 2. 2 discusses th e new approach used for emissions preprocessing and processing for all onr oad mobile sources. Section 2.3 discusses the updated nonroad mobile components. Sections 2.4 and 2. 5 provide differences for the point and nonpoint (area) inventories, respectivel y. Section 2.6 discusses other emissions categories such as biogenic and non

- U.S.

sources.

The data used in the 2005 emissions case is often the same as those d escribed 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 P latform (i.e., 2005v4.2). However, some different emissions data are used for this rulemaking. All of the documentatio n provided here describes what was done differently and specifically for

the Tier 3 NPRM in contrast to wh at was done for the 2005v4.2 platform.3 For the Tier 3 NPRM , a 2005 base case approach was used for the year 2005 emissions scenario. This approach is very similar to that taken for the CSAPR Final Rule (formerly known as the “Transport Rule”)

. A base case approach uses average year fires and EGU temporal profiles developed from three years of EGU

data. We use a base case approach to reduce year

- sp ecific variability in some components of the inventory. For example, large fires vary in location and day of the year each year, and EGU shutdowns and high use on high energy demand days also vary by year. By using a base case approach, these two aspects of the inventory are maintained in to the future year modeling but do not introduce potentially spurious year

specific artifacts in to the air quality modeling estimates. T he biogenic emissions data were the same as those

used for the 2005 v4.2 platform and were also the same for the 2005 case and for both future

- year cases. The only significant data changes between the 2005 and the 2017 and 2030 future

- year Tier 3 reference and

control case s are the emission inventories and speciation approaches.

Table 2

- 1 below lists the platform sectors used for the Tier 3 NPRM modeling platform. It also indicates

which platform sectors include HAP emissions and the associated sectors from the National Emission Inventory (NEI) . Subsequent sub s ections refer to these platform sectors to identify the emissions differences

between the 2005 v4 .2 platform and the Tier 3 NPRM 2005v4.3

- based platform.

Table 2

- 1

. Sectors u sed in e missions m odeling for the T ier 3 NPRM 2005v4.3 p latform

Platform Sector 2005 NEI

S ector Description Contains HAP

emissions?

IPM sector: ptipm Point NEI EGU units at facilities mapped to the IPM model using the National Electric Energy Dat abase 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 prescr ibed 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: non

pt 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 Natio nal Mobile Inventory Model (NMIM) software

version NMIM20090504b and NMIM and Meteorology database

version NCD20101201Tier3. Nonroad version i s equivalent to

NONROAD2008a used in 2005v4.2 for future year 2017 ; however, E15 fuels are allowed for the 2030 c ases

. Yes

C1 & C2 CMV and

locomotive s : alm_no_c3 Mobile:

Nonroad Primarily 2002 NEI non

- rail maintenance locomotives, and

category 1 and category 2 commercial marine vessel (CMV) emissions sources, county and annual resolution. Aircraft

emissions are no longer in this sector and are now included in the

Non

- EGU sector (as point sources) ; also, category 3 CMV

emissions are no longer in this sector and are now contained in the
seca_c3 sector. Yes

C3 commercial

marine: seca_c3 Mobile:

nonroad Annual point s ource

- 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 (htt p://www.epa.gov/otaq/oceanvessels.htm). Utilized final projections from 2002, developed for the C3 ECA Proposal to the International Maritime Organization (EPA

- 420

- F

10

- 041, August 2010). Yes4

Platform Sector 2005 NEI

S ector Description Contains HAP

emissions?

Onroad Mobile :

on road Mobile:

onroad + Motor Vehicle Emissions Simulator (MOVES) emission factors

created using the Tier 3 NPRM version to account for hourly

based meteorology dependencies at a select number of representative counties. Includes local input information such as fuels, temperatures, vehicle fleet, spee d distributions and controls. Emission factors are combined with activity data and gridded temperature via SMOKE to produce gridded emissions. These emissions are discussed extensively in Section 2. 2

. Yes

Biogenic: biog N/A Hour

- specific, grid cell

- spe cific emissions generated from the BEIS3.14 model, including emissions in Canada and Mexico. Unchanged from the 2005v4 platform , and the same data are used for all future year scenarios

. No

Other point sources

not from the NEI: othpt N/A Point sources from Canada’s 2006 inventory and Mexico’s Phase III 1999 inventory, annual resolution. Also includes annual U.S. offshore oil 2005v2 NEI point source emissions. Unchanged from the 2005v4 platform , and the same data are used for all future year

scenarios

. No

Other nonpoint and

nonroad not from

the NEI: othar N/A Annual year 2006 Canada (province resolution) and year 1999 Mexico Phase III (municipio resolution) nonpoint and nonroad mobile inventories. Unchanged from the 2005v4 platform , and

the same data is used for all future year scenarios

. No

Other onroad

sources not from the

NEI: othon N/A Year 2006 Canada (province resolution) and year 1999 Mexico Phase III (municipio resolution) onroad mobile inventories, annual resolution. Unchanged from the 200 5v4 platform , and the

same data are used for all future year scenarios

. No

+ Some data included in modeling sector has been revised beyond what is included in the 2005 NEI v 1 or v 2.



### 2.1 Custom configuration for emissions modeling for Tier 3 NPRM

Unlike the 2005v4.2 platform, the configuration for Tier 3 NPRM modeling included additional hazardous air pollutants ( HAP s) and used slightly revised ancillary speciation data. Both of these differences are

described in this section.

Table 2

- 2 lists the additional HAP pollutants processed for the Tier 3 NPRM 2005v4.3 platform

, which were

not included in the 2005 v4 .2 platform. A “lite” version of the multi

- pollutant CMAQ (Version 4.7) was used that required emissions only for the species l isted in the footnote of Table 2

- 2

. In addition to the model species differences, the Tier 3 NPRM platform had a few additional custom aspects in the 2005 cases. Table

2

- 3 lists the datasets used by the ( Tier 3 NPRM ) 2005v4.3 platform that are different from the 2005v4.2 platform.

Another consideration is the speciation across the Tier 3 NPRM future

- year cases as compared to 2005. Section 3 provides a detailed account of these differences. The futur e

- year ancillary data were largely the same as those in 2005, with no substantial differences for most modeling sectors. The exception to this is onroad mobile, which required several new ancillary input files to support the SMOKE to MOVES modules;

these are discussed in detail in Section 2.4. All other ancillary data files not required for SMOKE to MOVES processing can otherwise be found at the 2005

- based platform website ( http://www.epa .gov/ttn/chief/emch/index.html#2005 ).5

Table 2

- 2

. Model species produced by SMOKE for CB05 with SOA for the Tier 3 NPRM platform

Inventory Pollutant Model Species Model species description

CL2 CL2 Atomic g as

- 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 t he 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 inventor ies 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, BUTADIENE13, ETHANOL and FORM_PRIMARY are the extra “CMAQ

- lite” HAPs that are not in the v 4.2 platform.6

Table 2

- 3

. Description of differences in 2005 case ancillary data (unrelated to SMOKE to MOVES)

between the 2005v4.3 and 2005v4.2 platform s

Ancillary Data Type Difference between 2005v4.2 pl atform and 2005v4.3 platform Speciation cross

references and Speciation profiles The Tier 3 NPRM 2005v4.3 data files are configured to support the multi

pollutant (MP) version of CMAQ, whereas the 2005v4.2 platform data file s are configured to support onl y the non

- MP version. Therefore, the Tier 3 NPRM data files include profiles for additional VOC HAP species. Speciation VOC to TOG conversion profiles Added Tier 3

- specific VOC to TOG and nonHAP VOC to nonHAP TOG assignments SCC Descriptions Added onroa d diesel SCCs representing start and idle modes (223007X000) Inventory tables The Tier 3 NPRM data file was updated to support SMOKE to MOVES pollutants and modes, the MP “lite” version of CMAQ , and, to accept inventory Ethanol (ETOH). T he 2005v4.2 platf orm data file is confi gured to support only

the non

- MP version.



### 2.2 Onroad m obile s ources (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 fac tors that were then input to the Sparse Matrix Operator Kernel Emissions system (SMOKE). The SMOKE

- MOVES i ntegration t ool s combined the county and temperature

- specific emission factors with the activity data to compute the actual emissions

. In brief,

our approach was to use the met4moves program to identify a set of temperatures that needed emission rates. For each scenario, we ran MOVES repeatedly to produce emission rates by temperature, Source Classification Code (SCC), speed bin, and representing cou nty. The moves2smk tool then reformatted the MOVES rates and selected the appropriate rates for each county and month. Movesmrg multiplies the emission rates by county VMT or vehicle population, applies speciation profiles to develop inventories for poll utants not included in MOVES and temporally

, and spatially allocates emissions to individual grid cells for CMAQ input

.



### 2.2.1 MOVES

For the Tier 3 NPRM , EPA used a version of the MOVES 2010a model that was enhanced for the rule . This

version o fthe model includ ed updated information on how fuel parameters impact vehicle emissions and updates on our understand ing of evaporative emissions. It also included some minor updates to emission rates and some changes designed to make the model run more efficiently

. All updates are described in detail

in a memorandum to the docket ( U.S. EPA 201 3 , Memorandum to Docket: Updates to MOVES for the Tier

3 NPRM )

. The following sections describe inputs to the MOVES model that were specific to this analysis.

The gridded meteoro logical input data for the entire year of 2005 were derived from simulations of the Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model (MM5), a limited

- area, nonhydrostatic, terrain

- following system that solves for the full set of physical and thermodynamic equations which govern atmospheric motions. The Meteorology

- Chemistry Interface Processor (MCIP) version 3.4 was used as the software for maintaining dynamic consistency between the meteorological model and chemistr y mechanisms

. The hourly gridded meteorological data was post

processed by met4moves to determine the maximum temperature ranges, average relative humidity, and a series of diurnal temperature profiles . MOVES was then run for each temperature bin and diu rnal profile to compute the required emission factors

.

Vehicle population data is a required input for MOVES when modeling on a county basis. Using the technical guidance provided to states by EPA, estimates for vehicle populations were generated for use in the

MOVES databases . The populations were computed using the county

- specific VMT and national average7 ratios of vehicle population to 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: h ttp://www.epa.gov/otaq/models/moves/index.htm

.

The county inputs used for the rule were derived from the inputs used for the 2005 National Emissions Inventory (NEI). This inventory covers the 50 United States (U.S.), Washington DC, Puerto Rico and the

U .S. Virgin Islands. The NEI was created by the EPA's Emission Inventory and Analysis Group (EI A G) in Research Triangle Park, North Carolina, in cooperation with the Office of Transportation and Air Quality (OTAQ) in Ann Arbor, Michigan.

OTAQ has develope d a consolidated modeling system known as the National Mobile Inventory Model (NMIM ) to calculate emissions from onroad highway mobile source and nonroad mobile sources. NMIM

documentation is available at http://www.epa.gov/otaq/models/nmim/420r05024.pdf

. NMIM includes a

county

- level database with the important input parameters specific to each county. The data in the NMIM county database (NCD) are used to develop NONROAD model input files w ithin NMIM. The basis for the 2005 default vehicle miles traveled (VMT) is data supplied by the Federal Highway Administration (FHWA), as well as publicly available data from FHWA's Highway Statistics series. Details of how the NCD was developed are docu mented for the NE I "Documentation for the 2005 Mobile National Emissions Inventory, Version 2 (December 2008)", which ca n be obtained on EPA web site : http://www.epa.gov/ttn/chief/net/2005inv entory.html

.

For the onroad portion of the inventory estimates for the rule, including all base and control scenarios, the current EPA highway mobile source emission model (MOVES) was used. This required conversion of the NCD parameters to a format cons istent with MOVES. A contractor was given the assignment ( TranSystems

, Contract No. EP

- D

- 06

- 001, WA 4

- 65) to convert the NCD to MOVES formatted input databases. This was accomplished with the assistance of converters designed for this purpose. These con verters are available on

the EPA web site at: http://www.epa.gov/otaq/models/moves/tools.htm

.

All of the county

- specific onroad data available in the NCD was converted to MOVES format for use at the county scale consistent with the databases created using the MOVES County Data Manager (CDM), except for the information regarding fuel properties. The fuel properties were updated using more recent information and methods specifically for this rule and described elsewhere in this document. Any table entries in the NCD that contained national average default information from the MOBILE6 model were replaced with the more recent national average default information used by MOVES.



### 2.2.2 Representing c ounties

Although EPA compiles county

- specific databases for all counties in the nation, many of the states can provide little or no county

- specific information for most counties. Rather than explicitly model every county in the nation ( there are over 3,000 counti es), we have performed detailed modeling for some counties and

less detailed estimates for the other counties. In this rule, t his has been accomplished using a concept called "representing counties".

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

accoun t differences between counties

.8 As described in Section 2.2.4, to generate onroad mobile emissions, MOVES was run in conjunction with the EPA SMOKE model to generate the gridded inventories us e d in air quality modeling. SMOKE uses emission rates (not inventories) to generate inventor y estimates within each grid. Since SMOKE handles the differences in the fleet mix, temperatures, speeds and VMT versus location and time, MOVES can be run in

the "emission rate" (i.e. emissions factor) mode. As a result, when counties are grouped, they can be grouped independently of fleet mix, speeds and temperature. This greatly increases the number of counties that can be in each grouping, since temperature is a factor that varies among the counties 1

. For this analysis, we grouped counties with simi lar fuel, emission standards, altitude

, and inspection and maintenance ( I/M )

program s.

The information used to group the counties was derived from the NMIM inputs used for the 2005 NEI

onroad and nonroad mobile sectors . For the onroad portion of the inve ntory estimates for the rule, including all base and control scenarios, the current EPA highway mobile source emission model (MOVES) was used. This required conversion of the NCD parameters to a format consistent with MOVES.

The N CD also does not contain county

- specific information regarding vehicle populations and there are no default values. Vehicle population data is a required input for MOVES when modeling on a county basis. Using the technical guidance provided to states by EPA, the contractor gene rated appropriate estimates for vehicle populations for use in the MOVES databases using the county specific VMT and national average ratios of vehicle populations versus vehicle VMT from the MOVES application. This method is described in

Section 3.3 of t he 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 at: http://www.epa.gov/otaq/models/moves/index.htm

.

The grouping of counties uses a tree algorithm, which is conceptually simple. In the tree algorithm, all counties are assigned to various categories. Then

, by grouping counties within the sam e categories, you get groups of counties that have the similar parameters. Counties were sorted into their Petroleum Administration for Defense Districts (PADDs). PADD 1 is divided into three sub

- PADD groupings and

each sub

- group is treated as a separate PADD (1a, 1b and 1c). Each state belongs to a PADD and all counties in any state are within the same PADD. Table 2

- 4 below shows the PADDs and the states within

each PADD.

Table 2

- 4

. Allocation of s tates to the Petroleum A dministration for Defense Districts

PADD State FIPS State Name Abbreviation

1a 0 9 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 DEL AWARE 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 temperature was considered in the c hoice 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 7 2 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 4 6 SOUTH DAKOTA SD

2 47 TENNESSEE TN

2 55 WISCONSIN WI

3 0 1 ALABAMA AL

3 0 5 ARKANSAS AR

3 22 LOUISIANA LA

3 28 MISSISSIPPI MS

3 35 NEW MEXICO NM

3 48 TEXAS TX

4 0 8 COLORADO CO

4 16 IDAHO ID

4 30 MONTANA MT

4 49 UTAH UT

4 56 WYOMING WY

5 0 2 ALA SKA AK

5 0 4 ARIZONA AZ

5 0 6 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 supp ly and fue l formulation data were taken from the 2005 fuels developed for the rule. The fuel parameters used for grouping and the ranges of values used for the bins are described in Table 2

- 5 ; the parameter categories in Table 2

- 5 are used in all calendar years for Tier 3 NPRM

.10 Table 2

- 5

. Gasoline p arameter c ategories

Gasoline Parameter Category ID Minimum Value (>=) Maximum Value (<) Reid Vapor Pressure (psi) 1 0 7. 3

2 7.3 8.2

3 8.2 9.2

4 9.2 100 Sulfur (ppm) 1 0 50

2 50 100

3 100 110

4 110 1000 Ethanol (volume percent) 1 0 3

2 3 8

3 8 100 Benzene (volume percent) 1 0 1

2 1 1.5

3 1.5 2

4 2 10

Some states have adopted California Low

- Emissions Vehicle (LEV) highway vehicle emission standards or plan to adopt them. Since the emission rates in these states will be different than in neighboring states, they must be modeled separately. Also, because the implementation of California standards vari es between these states, each state with California standards must be modeled independently from the other states with California standards as well. Each state with California standards will be treated separately when choosing representing counties. Table 2

- 6 shows the states with California emission standards.

Table 2

- 6

. States a dopting California emission s tandards

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 PA DD

- fuel group were sorted into groups with and without I/M vehicle inspec tion 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.11 Altitude was also adde d as its own category. Altitude is a field in the County table of the MOVESDB20101006 database. Counties are either high (H) or low (L) altitude based on the criteria set forth by EPA certification procedures (4,000 feet above sea level). The result is a set of county groups with similar fuel, emission standards, altitude and I/M program. Then the county with the highest VMT in each group is chosen as the representing county. The categories are summarized below in T able 2

- 7

.

T able 2

- 7

. Summary of c ounty g rouping c haracteristics for r epresentative c ounties County Grouping Characteristic Description

PADD Petroleum Administration for Defense Districts (PADDs).

PADD 1 is divided int o three sub

- PADD groupings and

each sub

- group is treated as a separate PADD (1a, 1b and 1c). Each state belongs to a PADD and all counties in any

state are within the same PADD.

Fuel Parameters Average gasoline fuel properties for January and July
2005, including RVP, sulfur level, ethanol fraction and percent benzene.

Emission Standards. Some states have adopted California LEV highway vehicle emission standards or plan to adopt them. Since implementation of the standards varies, each state with

Califor nia 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 ma y be administered separately and have a different program design.

Altitude Counties are either high or low altitude based on the criteria set forth by EPA certification procedures (4,000 feet above sea level).

Using these criteria, a set of 106 counties were selected to represent the nation. Of these, only 103 were needed to model the 48 states included in the modeling domain for the air quality analysis inventory. If MOVES runs were performed for all U.S. counties and months, there would be 3

, 141 count ies (excluding AK and HI) times 12 mo nths = 37,692 county

- months rather than the 1

, 236 needed with representative

counties

. The MOVES runs for each representative county and fuel month were performed independently of one another on different computer proc essors , with each accessing a MySQL database specific to that run.



### 2.2.3 SMOKE

- MOVES i nputs

Both MOVES and SMOKE re quire meteorological data

. The program met4moves takes gridded hourly meteorological data, the representative counties, and the representative fue l months as inputs and produces

separate meteorological products for MOVES and SMOKE. Met4moves uses the representative counties and fuel months to determine the full range of meteorology in that county group. For each representative county and fuel mont h, it determines all the grid cells that fall within the corresponding counties in that county group for the number of months that correspond to the fuel month 2 . The temperature range is then determined by looking at the minimum and maximum temperature ac ross all these grid cells for all hours in that time period. Relative humidity is calculated by taking an average over these 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 gr id 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 roads or population, e.g. high mountains, then there is no reason to include it in the tempera ture range for onroad emissions.12 For rate

- per

- profile (RPP) process emissions , SMOKE

- MOVES uses the change in temperature over the day (i.e., the diurnal profile), instead of the temperature at the hour of processing. Met4moves create a series of diurnal profiles based on the extent of the temperature range and the size of the temperature bins

. For MOVES, these diurnal profiles span the full rang e of temperatures for that representative county and fuel

month. To satisfy SMOKE ’s needs in computing RPP emissions , met4moves creates a minimum and maximum temperature range for each county in the domain . Note that thes e temperature ranges are county

s pecific, and are not based on the representative county or county group

.

Met4moves can be run in daily or monthly mode for producing SMOKE input. In monthly mode, the temperature range is determined by looking at the range of temperatures over the whol e month for that specific county . Therefore

, there is one temperature range per county per month. While in daily mode, the temperature range is determined by evaluating the range of temperatures in tha t 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 Tier 3 NPRM runs, met4moves was run in daily mode.

In additi on to the lookup tables of emission rates produced by MOVES, SMOKE requires county

- specific VMT, population, and average speed by road type to calculate the necessary emissions for air quality modeling. VMT by county and Source Classification Code (SCC) w as developed using MOVES2010a and the National County Database. The National County Database (NCD20101201) contained the most recent
estimates of 2005 VMT and the best available estimates of allocation of VMT from national to the county

level at the time the modeling was performed . Accordingly, for the 2005 base year, the estimates of VMT by county and SCC were taken directly from the NCD.

The average speeds provided to SMOKE for each county were derived from the default national average speed distribu tions found in the default MOVES2010a database AvgSpeedDistribution table. These average speeds are the average speeds developed for the previous EPA highway vehicle emission factor model,

MOBILE6. The same speed data was used for the base and future yea r 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 wer e weighted together using the default national average hourly vehicle miles traveled (VMT) distribution found in the MOVES default database HourlyVMTFraction table, to calculate an average speed for each road type. This average speed by road type was prov ided to SMOKE for each county.



### 2.2.4 Generating e mission f actors for SMOKE

After representative counties and fuel months were chosen, the met4moves script was executed to produce the set of MOVES RunSpecs and meteorology tables that would ultimately generate a s et of SMOKE lookup tables encompassing the full range of temperatures for all the counties and months in each group. OTAQ also provided VMT, population, and average speed tables for every county.

The onroad model

- ready emissions were produced by runnin g SMOKE

- MOVES using 103 representative

counties and two fuel months. SMOKE

- MOVES is a series of scripts and programs that 1) produce meteorological data for MOVES (Met4Moves), 2) construct a set of MOVES RunSpecs that produce lookup tables by temperature and average speed (runspec_generator), 3) process the MOVES lookup tables into a

SMOKE

- ready format (moves2smkEF) , and 4) runs SMOKE. The way that OTAQ used SMOKE

- MOVES13 differs somewhat from the way that SMOKE

- MOVES was initially designed to be run. The full sequence of

events wa s the following :

1) OAQPS ran m et4 m

oves for a nation

- wide 12 km grid. This generated the temperatures needed for the

emission factor lookup tables and an average humidity for each county and month

. The inputs to
met4moves are the h ourly gridded temperature and humidity generated by the meteorological model

used for CMAQ along with the list of representative counties and fuel months. For each representative county and fuel

- month, met4m oves queries all the grid cells in all the repre sented

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, ( runspe c_generator_v0.3_04Nov2010.plx). The inputs to this process include the representativ e county list, fuel month list, temperature bin size (=10 degrees here), and the outputs from met4moves. The run s pec_generator script produce d MOVES

run

- s pec ifications that control how the MOVES run is configured, along with zonemonthhour table s in CSV for mat . Specifications were generated for the three types of MOVES processes: rate

- per

distance (RPD) , rate

- per

- profile (RPP) , and rate

- per

- vehicle (RPV) . Run specifications were generated as needed to simulate the range of conditions reflected in the meteor ological inputs

. For RPD and RPV, a series of run specifications were created for each representative county, one for each temperature bin covering the temperature ranges provided by the met4moves output. For RPP, a second series of run specifications we re created for each representative county, one for each diurnal profile provided by the met4moves output. The input data specific to each county were loaded into databases called “scaleinputdatabases”, and the zonemonthhour tables were also loaded into

da tabases.

3) OTAQ ran a tool to read the county databases, the zonemonthhour databases, other user

- supplied

databases , and the run specifications. The tool implemented LEV programs into the specifications as appropriate and also modified the pollutant

- proces s associations in the run specifications to meet the

needs of the Tier 3 NPRM

. The tool then packaged the information into a form that could be used by the compute server.

4) OTAQ issued the command to start the required MOVES runs for each county and fuel month on the

compute server s

.

5) Once the MOVES runs were complete, OTAQ ran the moves2smkEF postprocessor to reformat the MySQL tables into the emission factor tables in CSV format that are readable by the SMOKE

moves m rg program. The postprocessor also perf ormed additional calculations to support SMOKE processing of CMAQ ready model emissions: 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 mod es into 5 broader modes (exhaust, 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 d etails.

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 co unties.14 

### 2.2.5 Running SMOKE for o nroad mobile

Running SMOKE using emission factors ( EF) from MOVES required the development of a new set of functionality. The central SMOKE program that performs this new analysis is movesmrg which takes activity data, meteorolo gical data, and the EF to produce gridded emissions. SMOKE is run independently for each of the three processes: rate

- per

- distance (RPD), rate

- per

- vehicle (RPV) and rate

- per

- profile (RPP).

The emissions process RPD is for modeling the on

- network emission s. This includes the following modes: vehicle exhaust, evaporation, permeation, break wear, and tire wear. For RPD, the activity data is monthly VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus weekend (SPDPRO) 4 . The SMOKE progra m temporal takes vehicle and roadtype specific temporal profiles and distributes the monthly VMT to day of the week and hour. Movesmrg reads the speed data for that county and SCC and the temperature from the gridded hourly data and uses these values to l oo k

- up the appropriate EF from the representative county’s EF table. It then multiplies this EF by hourly temporalized VMT to calculate the emissions for that grid cell and hour. This is repeated for each pollutant and SCC in that grid cell.

The emissio n s process RPV is for modeling the off

- network emissions. This includes the following modes: vehicle exhaust, evaporative, and permeation For RPV, the activity data is vehicle population (VPOP)

. Movesmrg reads the temperature from the gridded hourly da ta and uses the temperature plus SCC and the hour of the day to look up the appropriate EF from the representative county’s EF table. It then multiplies this EF by the VPOP for that SCC and FIPS to calculate the emissions for that grid cell and hour. Thi s repeats for each pollutant and SCC in that grid cell.

The emission s process RPP is for modeling the off

- network emissions for parked vehicles. This includes the mode vehicle evaporative (fuel vapor venting). For RPP, the activity data is VPOP. Movesm rg reads the county based diurnal temperature range from met4moves output for SMOKE. It uses this temperature range to determine the most similar idealized diurnal profile from the EF table using the temperature min and max, SCC, and hour of the day. It then multiplies this EF by the VPOP for that SCC and FIPS to calculate the emissions for that grid cell and hour. This repeats for each pollutant and SCC within the county. For more

details on processing RPD, RPV, and RPP in SMOKE, see : http://www.smoke

- model.org/version3.1/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 w ere created: RPD, RPV, and RPP. SMOKE was run so that for each model day it would read in a single EF table (based on the appropriate fuel month), process all the counties that are part of the county group (i.e. are represented by that representative coun ty), then read the next representative county EF table, etc. After all days in the model year were looped over, SMOKE has generated a separate set of daily intermediate files for each of the emissions processes (RPD, RPV, and RPP). Post

- processing script s were developed to integrate the process specific intermediate files into model

- ready intermediate files

for the onro ad sector. These files were generated for a national 12km domain. T o support the CMAQ runs they were further processed to create an aggre gated 36km sector specific model

- ready file and two 12km domains (12EUS1 and 12WUS1).

4 If the SPDPRO is available, the hourly speed takes precedence over the average speed in the SPEED inventory. Due to an oversight, SPDPRO was not used in the base and future

- years modeling. A later sensitivity was run including the SPDPRO input for the base year which found that the use of hourly speed slightly increased the emissions for most pollutants (e.g. nationa lly NO

x showed a 0.8% increase, VOC showed a 1% increase, and PM 2.5 showed a 3% increase).15 

### 2.3 Nonroad m obile s ources (nonroad, alm_no_c3, seca_c3)

The nonroad sector s include a wide

- range of mobile emission sources ranging from locomotives , marine vessels, const ruction and farming equipment to hand

- held lawn tools. As discussed in Section 4 , nonroad

u pstream impacts also impact the ( post

- EPAct/EISA/ RFS2 ) Tier 3 NPRM future year case s , reflecting increased ethanol production resulting in fuel volume increases for locomotives and C1/C2 CMV emissions

.



### 2.3.1 Emissions g enerated with the NONROAD m o del ( nonroad )

Most nonroad emissions were estimated using the NONROAD model, as run by EPA’s NMIM. NONROAD is EPA’s model for calculating emissions from nonroad equipment, except for aircraft, locomotives, and commercial marine vessels. The NONROAD m odel and extensive documentation can be

found at http://www.epa.gov/otaq/nonrdmdl.htm

. NMIM is a program that references a national database of

county

- month data, writes county

- month input files for NONROAD based on that data, runs NONROAD

once for eve ry county and month requested by the user and collects the results in an output database

. Information and downloads for NMIM can be fo und at http://www.epa.gov/otaq/nmim.htm

. Rather than running every county, NMIM is designed to run NONROAD for “representative counties” and to use individual county activity data to develop national inventor ies.

Inputs for NMIM runs were stored in the NMIM County Database (NCD ). The NCD version used for this modeling was NCD20101201Tier3. This NCD is based on NCD20101201, which is the version that includes all updates from the 2008 National Emission Inve ntory process described at http://www.epa.gov/ttn/chief/net/2008inventory.html

. F or this analysis, updates were made to the underlying

fue l supply for the Tier 3 NPRM future year reference case s

. Thus, t he NCD20101201Tier3 contained special versions of county yearmonth, gasoline, and diesel . The fuels in the NCD2010201Tier3 were developed from the fuels used for onroad vehicl es, as described in Section 2.2.1.

A special countymonthhour tabl e that contained 2005 meteorology was copied into the standard countymonthhour table. The use of the countymonthhour table for meteorology was selected by the RunSpec setting useYearlyWeatherDataSelected="false." A minor change was also made for snowmobil es: the SCC toxics table in the NMIM County Database (NCD) was updated to correct 1,3

- butadiene exhaust emissions

for 2

- stroke snowmobiles (SCC 2260001020), as shown in Table 2

- 8 below. This correction addressed an

issue identif

ied in air quality modeling for the RFS2 rule, where unexpected increases in ambient concentrations were observed in rural areas during winter due to snowmobile emissions , available at: http ://www.epa.gov/otaq/renewablefuels/420r10006.pdf

. The increases were based on data from only three engines, which showed unusually high1,3

- butadiene emissions with 10% ethanol (Eth oxygenate) . Other data suggests that this increase is highly unlikely to be representative of the in

- use fleet as a whole; thus

results were corrected to those in the “NCD20101201Tier3” column. In Table 2

- 8

, Base Gasoline represents cases where the fuel type is not Eth, MTBE or RFG. Eth gas is used where the fuel contains ethanol which is greater than or equal to 5% by volume or Ethyl Tertiary Butyl Ether (ETBE) is greater than or equal to 5% by volume. MTBE gas is used where the fuel contains MTBE which is greater than or equal to 12% by

volume or Tertiary Amine Methyl Ether (TAME) is greater than or equal to 13% by volume. Finally, RFG gas is used where the fuel is RFG and where the fuel contains oxygenate greater than 5% by volume and where the fuel contains MTBE which is less than 12% by volume or TAME is less than 13% by volume.

Table 2

- 8

. Updated 1,3

- b utadiene to VOC r atio for 2

- stroke s nowmobiles for NMIM’s gasoline c ategories

Fuel Type NCD20101201 NCD20101201Tier3

Base Gas oline 0.0012 0.0012

E thyl Tertiary Butyl Ether ( Eth ) Gas 0.00732 0.0012 Methyl T ertiary Butyl Ether ( MTBE ) Gas 0.0012 0.0012 Reformulated Gasoline ( RFG ) Gas 0.0012 0.001216 A special county map table was developed to use representing counties in the NMIM runs. The algorithm for producing representing counties for NMIM was identical to that used for MOVES except that ten degree temperature bins were added to the criteria. The result was 293 representing counties. Finally, NMIM does not estimate ethanol emissions, so the inve ntory for this pollutant was from the chemical speciation that is obtained by post

- processing through SMOKE

.

2.3.1.1 Representing c ounties for NONROAD

“Representing counti es” is a way of saving NMIM run

- time by grouping together similar counties and generating emi ssion factors by running the NONROAD m odel for only one of those counties and then using those emission factors for all the counties in the group. For this analysis, 293 county groups were developed. The counties in each group were in the same state, had similar fuels in both summer and winter, and had similar I/M programs. Since there are winter fuels and summer fuels, January was chosen as the fuel

- month to represent the seven months October through April, and July was chosen to represent the five mont hs May

September. The total number of county

- months for which NMIM runs needed to be performed was thus 293 times 12 months = 3,516 county

- months for each scenario

- year. If NMIM runs were performed for all U.S. counties and months, there would be 3141 co unties (excluding AK and HI) times 12 mo nths = 37,692

county

- months. Representing c ounties were chosen for NONROAD m odel NMIM runs by grouping

counties based on the characteristics listed in Table 2

- 9

.

Table 2

- 9

. Criteria for g rouping r epresentative c ounties for n onroad mobile a nalysis

Characteristic Grouping Criteria

Petroleum Administration for Defense District (PADD) All counties in a group must be in the same PADD.

Gasoline par ameters Fuel bins were created for RVP, sulfur, benzene, and ethanol. All counties in each group had all of these fuel properties in the same bins for all twelve months. Inspection/Maintenance Programs Counties with I/M programs were grouped with other

c ounties 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 ca lculated on a grid basis, as the highway mobile sources were. S o
, when running NMIM for nonroad emissions, the representing counties must also account for temperatures. The temperatures are taken from the 2005 calendar year values in the CountyYearMonthHo ur table of the

NCD20100602 NMIM database. As shown in17 Table 2

- 10 , t en

- degree F ahrenheit (F) bins were created for min and max temperatures for each month. All counties in each group had all min and max temperatures for all twel ve months in the same bins. The lowest interval includes all temperatures below

- 10 degrees F. The highest interval includes all temperatures above 100 degrees F

.18 Table 2

- 10

. NONROAD m odel t emperature (F) c ategories Temperature

Bin Minimum

Temperature (>=) Maximum

Temperature (<)

1

- 20

- 10

2

- 10 0

3 0 10

4 10 20

5 20 30

6 30 40

7 40 50

8 50 60

9 60 70

10 70 80

11 80 90

12 90 100

13 100 200

Once counties were grouped, the representing county was chosen as the one with the highest VMT. The same set of 293 county groups and representing counties was used for all years and scenarios.

2.3.1.2 Fuel inputs for NONROAD r uns
For the nonroad mobile portion of the inventory estimate s for the rule, the NMIM coun ty database (NCD) developed for the 2005 NEI , with the one exception of the county

- specific fuel properties, was used to

calculate nonroad emissions. The f uels that w ere developed for use with MOVES to compute onroad mobile

emissions for the Tier 3 NPRM ( see Section 2.2 ) were converted to NMIM fuels. Practically, this means the fuelsupply and fuelformulation tables from MOVES were converted into the countyyearmonth, gasoline, and

diesel tables in the NCD

. For the year 2005 modeling , onroad and nonroad ga soline formulations are

assumed to be identical.

MOVES allows for multiple gas oline fuels, each with a market

- share , for a single county

- month , t he market shares always sum to one. The NCD allows only one fuel per county month, but, for each of the fou r 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 the market

- share is less than one, the

remainder of the market is non

- oxygenated (con ventional) gasoline. When there are multiple MOVES gasoline fuels for a single county

- month, non

- oxygenate MOVES fuel properties are multiplied by market share and summed to produce the fuel property in the gasoline table. Individual non

- ethanol oxygenat es and California ethanol occur in only one fuel per county

- month in MOVES, so the volume and market share are transferred to the appropriate columns for that oxygenate in NMIM. In states other than California, for multiple ethanol volumes with volume per cents less than 10, the product of market

- share and volume percent is averaged and then divided by 10, resul ting in a market share for E10.

2.3.1.3 NMIM r uns

Table 2

- 11 shows the NMIM runs that were per formed to generate the NONROAD m odel county

- month results for both national inventories and air quality modeling inventories.19 Table 2

- 11

. NONROAD NMIM r uns

Case Year Run Name

Base 2005 Tier3Base2005Nr

Reference 2017 Tier3Ref2017e10Nr

Referen ce 2030 Tier3Ref2030NrAdj2

Control 2017 Tier3Ctla2017e10Nr

Control 2030 Tier3Ctl2030NrAdj2

MOVES fuels were used for future year cases and converted to NMIM format. However, for 2017, EPA

assumed that nonroad equipment would use only E10. For 2030, E PA assumed that nonroad equipment would use only E15. The details of the fuels conversion from MOVES to NMIM were discussed above.

Table 2

- 12 describes the components in the NONROAD/NMIM system common to all Tier 3 NPRM modeling scenarios.

Table 2

- 12

. Summary of NONROAD m odeling c omponents

Model Version Description

NONROAD NR08b This is identical to the official NONROAD2008, ( see: http://www.epa.gov/otaq/nonrdmdl.htm#model ) except it was modified to allow modeling of emissions on E15

fuels in 2030 . The existing fuel effects algorithm was

retained.

NMIM Code NMIM20090504b This is the same as the official NMIM2008a software, (available at http://www.epa.gov/otaq/nmim.htm ) except the NONROAD model was updated to NRO8b.

NMIM Database NCD20101201Tier3 This is based on NCD20101201, which was developed

for the 2008 NEI. It was adapted to model the desired

scenarios

. Meteorology NCD20101201Tier3 Historical data for calendar year 2005 from the

National Climatic Data Center. County temperatures were determined by weighting nearby temperature stations by their distan ce from the population

- based centroid of each county.



### 2.3.2 Locomotives and commercial m arine v essels (alm_no_c3, seca_c3)

The year 2005 emissions for locomotive and commercial marine vessel sources used for this rule are the same as they were for the Final Rul emaking: Greenhouse Gas Emissions Standards and Fuel Efficiency

Standards for Medium

- and Heavy

- Duty Engines and Vehicles signed on August 9, 2011 and available at http://www.epa.gov/oms /climate/regulations.htm#1

- 2

, and the Final Cross

- State Air Pollution (CSAPR)

Rule : ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.p df

. The procedures for calculating emissions from locomotives and C1/C2 commercial marine were developed for the Locomotive Marine Rule (2008) and are detailed in the RIA “ Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine Comp ression

- Ignition Engines Less Than 30 Liters per Cylinder ”, published May
6, 200 8 and republished June 30, 2008, and available at: http://www.epa.gov/oms/locomotives.htm#2008final

. The pr ocedures 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

.20 

### 2.4 2005 point sources (ptipm and ptnonipm sectors)

Point sources are sources of emissions for which geographic coordinates (e.g., latitude/lon gitude) are specified, as in the case of an individual facility. A facility may have multiple emission points that may be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas). Note that this section describes only NEI point sources within the contiguous United States. The offshore oil platform (othpt sector) and category 3 CMV emissions (seca_c3 sector) are also po int source formatted inventories but are unchanged for the Tier 3 NPRM modeling from the modeling of other recent rules . Discussion of the seca_c3 and othpt sector emissions can be found in the Final CSAPR TSD referenced in Section 2.3.2

.

After removing offshore oil platforms (othpt sector), two platform sectors were created from the remaining 2005v2 NEI point sources to provide input s into SMOKE: the EGU sector

– also called the Integrated Planning Model (IPM) sector (i.e., ptipm) and the non

- EGU sector

– also called the non

- IPM sector (i.e., ptnonipm). This split facilitates the use of different SMOKE temporal processing and future

- year projection techniques for each of these sectors , along with the replacement of ptipm emissions with outputs from IPM

i n emissions cases for future years . The inventory pollutants 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 C l 2 (code = 7782505 ). We did not utilize BAFM from these sectors as we chose to speciate VOC without any use (i.e., integration) of VOC HAP pollutants from the inventory

. I ntegration is discussed in detail in Section 3

.

The ptnonipm emissions were provided to SMOKE as annual emis sions. The ptipm emissions for the base

case were input to SMOKE as daily emissions. The ptipm emissions are unchan ged from those in the

2005v4.2

– the basis for the Final CSAPR and Heavy Duty Greenhouse Gas ( HDGHG ) FRM

- emission modeling platform. Howev er, for the ptnonipm sector for all Tier 3 NPRM scenarios, including year 2005 emissions, additional known ethanol plants were included that were not in 2005v4.2. We also removed all onroad refueling emissions as these were replaced with MOVES

- based onroa d refueling emissions (discussed in Section 2.5.2).



### 2.4.1 Ethanol plants (ptnonipm)

The ethanol plant information originally used in the RFS2 rule was updated

. All ethanol plants were assigned coordinates based on analysis using searches of company web sites and Google Earth verification for many sites. Emissions were calculated based on plant design capacity and emission factors based on production process and energy source (e.g. dry mill natural gas, wet mill coal, etc.) . Finally, because benzene, acetaldehyd e and formaldehyde (BAF) emissions were directly computed for these sources, we treated these ethanol plants as VOC integrate sources , unlike the rest of the ptnonipm sector . A summary of the ethanol plant emissions used in the 2005 scenario is provided i n21 Table 2

- 13

. More details are provided in a memorandum to the docket (Cook, 2012).22 Table 2

- 13

. 2005 ethanol p lant e missions

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 10,754



### 2.5 2005 n onpoint s ources (afdust, ag, avefire, nonpt)

The year 2005 area

- source fugitive dust (afdust), agricultural animal and fertili zer NH 3 (ag), and average (typical)

- year fires (avefire) emissions are the same as those used in the CSAPR Final (2005v4.2) emission modeling platform. Nonpoint sources that were not subdivided into the afdust, ag, or avefire sectors were assigned to the “nonpt” sector , and most of these sources are also unchanged for Tier 3 NPRM modeling. The 2005 nonpoint sources that change in this study are limited to portable fuel containers (PFCs) and onroad refueling.



### 2.5.1 Portable fuel containers

Year 2005 PFC emission s are unchanged from the CSAPR Final inventory except for the addition of ethanol. Ethanol emissions were not provided for 2005, but were supplied for future year scenarios. Therefore, we scaled year 2017 pre

- RFS2 (not Tier 3 NPRM reference case ) ethanol emissions by the ratio of 2005 to 2017

pre

- RFS2 VOC emissions to compute year 2005 ethanol emissions as follows :

Ethanol_2005 = Ethanol_2017 (pre

- RFS2) * [ VOC_2005 / VOC_2017 (pre

- RFS2 ) ]



### 2.5.2 Onroad r efueling

As mentioned in Section 2. 2 , NEI

- based onroad refue ling emissions were replaced with estimates from the revised version of EPA’s Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all

twelve months. This section describes how the emission inventories for refueling from on

- road vehicles in calendar years 2005

, 2017 and 2030 f or Tier 3 NPRM reference and control cases were generated for air quality modeling. The refueling inventory includes emissions from spillage loss and displacement vapor loss. For this analysis, the refueling emissi ons were estimated using the revised Tier 3 version of EPA’s Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all twelve months.

In an effort to reduce MOVES runtime, the “representing counties” approach was used instead of running

e very single county in the lower 48 states. As described in Section 2. 2 for onroad counties, w e selected representing counties by grouping counties based on Petroleum Administration for Defense Districts (PADD), fuel parameters, usage of California emissio n standards, I/M programs, and altitude. One additional parameter included in developing the representing counties for refueling was temperature.

Temperature bins with increments of ten degrees F were created for the min imum and max imum temperatures for each month using the temperatures from the 2005 calendar year values in the23 CountyYearMonthHour table of the NMIM County Database (NCD) NCD20100602 NMIM database. All counties in each group had min and max temperatures for all twelve months in the same te mperature bins.

Once counties were grouped, the representing county was chosen as the one with the highest VMT

, resulting in total of 238 counties. The same set of county groups and representing counties was used for all years and

scenarios.

MOVES was run in inventory mode for only the representing counties using the county

- specific on

- road data, such as vehicle miles travelled, fleet age distribution, speed distribution, and meteorology, available

fro m the NCD . The customized fuel inputs, discussed i n Section 2.2.2.1 , were used for each of the representing counties.

The resulting refueling emission inventories for 238 representing counties in U.S. short tons were converted to emission factors by dividing the inventory by the corresponding activity in each representing county. Then, the calculated emission factors from the representing counties were applied to the represented counties and multiplied by the county

- specific activity to generate the inventories for all counties.



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

All emissions from Canada, Mexico, and Offshore Drilling platforms (othpt, othar, and othon), and all non

anthropogenic inventories (biogenics and ocean chlorine) are unchanged from the 2005v4.2 (used for the

Final CSAPR and HDGHG FRM ) emissions modeling platform. The same emissions are used for all Tier 3

NPRM scenarios and years.



## 3 VOC S peciation C hanges that Represent Fuel C hanges

A significant detail that is different in each of the Tier 3 NPRM modeling cases and different from the 2005v4.2 emissions modeling is the VOC speciation profiles used to split total VOC emissions into the VOC model species needed for CMAQ. In this section

, we summarize the various speciation profile information used in configuring the v arious cases

.

A major change between the 2005v4.2 platform and the Tier 3 NPRM base and future modeling is the integration of ethanol for key sectors and specific inventories. In the previous platform, the inventories for specific sources had benzene, acetaldehyde, for maldehyde and/or methanol (BAFM). These emissions

would be integrated, namely their emissions would come from the inventory not from speciating VOC. To prevent double counting, BAFM would be removed from VOC, leaving the remainder (NONHAPVOC) to be speci ated to other components (i.e. non

- BAFM species) . See section 3.1.2.1 of the 2005v4 platform for

more details ftp://ftp.epa.gov/EmisInventory/2005v4/2005_emissions_tsd_07 jul2010.pdf

.

In the Tier 3 NPRM modeling, if ethanol was present in the inventories, it would also be integrated. To differentiate when a source was integrating BAFM versus EBAFM (ethanol in addition to BAFM), the speciation profiles which do not incl ude ethanol are referred to as an “E

- profile”, for example pre

- Tier 2

vehicles E10 gasoline exhaust speciation profile 8751 where ethanol is speciated from VOC, versus 8751 E where ethanol is obtained directly from the inventory

. For the onroad sector, eth anol is integrated for all emissions 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 biodiesel

invento ry (SCC 30125010 ) in which ethanol is integrated . For fuel distribution operations associated with

the b ulk

- plant

- to

- pump (btp) distribution , ethanol is speciated from VOC

– thus a “BAFM” profile

- because

the fuel distribution operations in the nonpoint in ventories are NEI

- based and therefore do not include ethanol specifically because the NEI does not provide ethanol as a pollutant

.24 The onroad sector has some additional changes to VOC speciation. Instead of speciating VOC directly

, SMOKE

- MOVES uses TOG i nstead of VOC. Therefore, SMOKE does not need to convert VOC to TOG before creating NONHAPTOG and performing additional speciation

. A second change in VOC speciation is

the differentiation of a new mode . In previous platforms, onroad mobile emissions we re divided into

exhaust

, evaporative and refueling modes . For the Tier 3 NPRM base and future years, gasoline vehicle’s evaporative mode is further divided into permeation

- specific emissions and evaporative . Similar to evaporative and exhaust profiles, t hese profiles change between the base and future year cases. Additional updates 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 pre

- 2007 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 customized to ac count for the impact of fuel changes in the future year

case s . These changes affect the on road sector, the nonroad sector, and parts of the nonpt and ptnonipm

sectors. These fuel changes and vehicle changes are implemented by using different VOC profiles and

combination s of profiles between the base and future cases. The speciation changes from fuels in the nonpt

sector are for refueling, cellulosic ethanol and cellulosic diesel, portable fuel containers (PFCs)

, and fuel distribution operations associate d with the b ulk

- plant

- to

- pump (btp) distr ibution. The speciation changes from fuels in the ptnonipm sector include btp distribution operations inventoried as point sources. Refinery to bulk terminal (rbt) fuel distribution and bulk plant storage (bps) sp eciation does not change across the modeling cases because this is considered upstream from the introduction of ethanol into the fuel. Mapping of fuel distribution SCCs to btp and rbt emissions categories can be found in Appendix B of the Technical Suppor t Document (TSD) Preparation of Emissions Inventories for the Version 5.0, 2007 Emissions Modeling Platform, http://epa.gov/ttn/chief/emch/2007v5/2007v5_TSD_Appendices_14dec12.pdf

.

Note that VOC speciation is customized by using different speciation profi les 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. exhaust, evaporative, permeation). SMOKE calculates a resultant profile by calculating the fraction of each profile by month, county, and mode. The GSPRO_COMBO ancillary file controls this feature 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 the GSPRO_COMBO is differentiating the sources that 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 et hanol integrated sectors can be differentiated from btp (VOC).

Table 3

- 1 summarizes the different profiles utilized for the fuel

- related sources in each of the sectors for 2005 and the future year cases. A comparison of the 2005 v4.2 platform with the Tier 3 NPRM 2005 case is

also included. Appendi x A lists ancillary input data set names used for Tier 3 NPRM emissions modeling

.

5 This was an oversight in the 2005v4.2 platform corrected for this modeling effort.25 Table 3

- 1

. Summary of VOC speciation profile approach es by sector across cases

Category 2005v4.2 2005 Tier 3 NPRM 2017 Tier 3 reference & control 2030 reference & control

Onroad Gasoline

Exhaust COMBO COMBO COMBO 8758E Tier2 E15 Exhaust

8750 Pre

- Tier 2 E0 E xhaust 8750E Pre

- Tier 2 E0 E xhaust 8751E Pre

- T ier 2 E10 E xhaust

8751 Pre

- Tier 2 E10 E xhaust 8751E Pre

- Tier 2 E10 E xhaust 8757E Tier 2 E10 Exhaust

8758E Tier 2 E15 Exhaust

Evaporative COMBO COMBO (All evap except permeation) COMBO (All evap except permeation) 8872E E15 Evap

8753 E0 Ev ap 8753E E0 Evap 8754E E10 Evap

8754 E10 Evap 8754E E10 Evap 8872E E15 Evap

COMBO (Permeation evap) COMBO (Permeation evap) 8770E E15 Evap permeation

8766E E0 E vap perm 8769E E10 E vap perm

8769E E10 E vap perm 8770E E15 E vap perm

Refu eling 8762 E0 Headspace composite COMBO COMBO 8871E E15 Headspace

8869E E0 Headspace 8870E E10 Headspace

8870E E10 Headspace 8871E E15 Headspace

Onroad Diesel

Exhaust 4674 2004 MDD exhaust 8774 Pre

- 2007 MY HDD exhaust 877T3 Pre & Post 2007 MY HDD

exhaust 8775 2007+ MY HDD Exh

877RM Weighted HDD Exh a

Weighted 8774 and 8775 profiles 877RH Weighted HDD Exh b

a Class 6&7 HDDVs

b Class 8a&8b HDDVs

Evaporative 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Head space 4547 Diesel Headspace

Refuel 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace

Nonroad Gasoline

Exhaust COMBO COMBO 8751 Pre

- Tier 2 E10 exhaust 8758 Tier 2 E15 exhaust

8750 Pre

- Tier 2 E0 exhaust 8750 Pre

- T ier 2 E0 exhaust

8751 Pre

- Tier 2 E10 exhaust 8751 Pre

- Tier 2 E10 exhaust

Evaporative COMBO COMBO 8754 E10 Evap 8872 E15 Evap

8753 E0 evap 8753 E0 evap

8754 E10 evap 8754 E10 evap26

Category 2005v4.2 2005 Tier 3 NPRM 2017 Tier 3 refere nce & control 2030 reference & control

Refueling 8762 E0 Headspace composite COMBO 8870 E10 Headspace 8871 E15 Headspace

8869 E0 Headspace

8870 E10 Headspace

Nonroad Diesel

Exhaust 4674 2004 MDD exhaust 8774 Pre

- 2007 MY HDD exhaust 8774 Pre

- 2007 MY HDD exhaust 8774 Pre

- 2007 MY HDD

exhaust Evaporative 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace

Refueling 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace

PF C 8762 E0 Headspace composite COMBO 8870E E10 Headspace 8871 E E15 Headspace

8869E E0 Headspace

8870E E10 Headspace

Aircraft 5565 Aircraft Exhaust 5565* Aircraft Exhaust 5565* Aircraft Exhaust 5565* Aircraft Exhaust

* Updated version in SPECIATE 4.3 * Updated version in SPECIATE 4.3 * Updated version in SPECIATE

4.3

Locomotives 4674 2004 MDD exhaust 8774 Pre

- 2007 MY HDD exhaust 8774 Pre

- 2007 MY HDD exhaust 8774 Pre

- 2007 MY HDD

exhaust

Marine 2480 Ship Channel

Downwind 2480 Ship Channel Downwind 2480 Ship Channel Downwind 2480 Ship Channel

Downwind

BTP 8762 E0 Headspace composite COMBO COMBO 8871 E15 Headspace

8869 E0 Headspace 8870 E10 Headspace

8870 E10 Headspace 8871 E15 Headspace

RBT/BPS 8762 E0 Headspace composite 8869 E0 Headspace 8869 E0 Headspace 8869 E0 Headspace

Ethanol Plants 1188 fermentation process 8776 Ethanol Fuel Prod 8776 Ethanol Fuel Prod a 8776 Ethanol Fuel Prod a

8776E Ethanol Fuel Prod b 8776E Ethanol Fuel Prod b

a corn ethanol and biodiesel p tnonipm a corn ethanol and biodiesel ptnonipm

b cellulosic ethanol & cellulosic diesel

nonpt b cellulosic ethanol & cellulosic diesel nonpt27 

## 4 20 17 and 2030 Reference Case s

The 2017 and 2030 Tier 3 NPRM reference case s represent emissions in the fu ture, including emissions impacts of the fuel volumes mandated by the 2005 EPAct and 2007 EISA and finalized in the RFS2 program. The reference case s include MSAT2 and LDGHG but do not include HDGHG impacts. The 2017 reference case assumes 21.6 billion g allons of renewable fuels (24 billion ethanol

- equivalent gallons due to volume increases of ethanol), with 17.8 billion gallons of E10 and E15, 1.5 billion gallons of biodiesel, 0.2 billion gallons of renewable diesel, and 2.2 billion gallons of cellulosic diesel. By the year 2030, the reference case assumes 30.5 billion gallons of renewable fuels (36 billion ethanol

- equivalent gallons due to volume increases of ethanol), with 22.2 billion gallons of E15 (no E10), 1.7 billion gallons of biodiesel, 0.2

bill ion gallons of renewable diesel, and 6.5 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, and for onroad mobile including refueling sources, OTAQ

- generated emissions were provided to reflect the reference case fuels.

The 20 17 and 2030 reference case s use many of the same growth and control assumptions as those for the

Final Cross

- St ate Air Pollution Rule (CSAPR), because other than onroad mobile , nonroad mobile, onroad refueling, PFC, and ethanol plant sources, both Tier 3 NPRM and CSAPR use the same 2005v4.2

- based

emissions inventories. There are some differences between the shared projection inputs from the 2012 and

2014 base case projections in CSAPR and the 20 17 and 2030 reference case s for Tier 3 NPRM :

1) 20 17 includes some additional controls that were promulgated after 2014

, (e.g., post

- 2014 consent

decrees and fuel sulfur rules in a couple of states). Likewise, 2030 includes some additional controls promulgated after 2017.

2) Growth factors for several sources are year

- specific; so while the methodology is the same as CSAPR, the future year emissions estimates differ (e.g., o il an d gas in a couple states, residential wood combustion).

3) Onroad refueling uses year and scenario

- specific ( i.e., reference) MOVES emissions for all Tier 3

NPRM modeling, rather than NEI emissions.

4) There is a new dataset of ethanol plants that replace s a lim ited set of NEI ethanol plants in 2005v4.2

- based CSAPR 2012 and 2014 projections. These Tier 3 reference case emissions are different in

2017 and 2030 and also in the 2005 base case

. 5) Minor errors identified after CSAPR modeling was complete were fixed ( e. g. , we include agricultural dust projections for the couple of states that provided point source farms).

There are other new inputs unique to the Tier 3 NPRM reference cases that were not part of the CSAPR projections. Examples of these are RFS2 upstream inputs such as biodiesel and cellulosic ethanol plants. These new inputs and projections for Tier 3 NPRM reference cases are discussed later in this section. The remainder of Section 4 is very similar to Section 4 in the CSAPR emissions modeling TSD

, ava ilable from ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf

, but with the updates just

discussed

.

The future case projection meth odologies vary by sector. For EGU emissions (ptipm sector), the emissions

reflect state rules and federal consent decrees through De cember 1, 2010. For onroad mobile sources , all national measures for which data were available at the time of modeling hav e 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, and afdust sectors), local control28 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 the 1997 ozone NAAQS are generally not included in the future base

case projections for most states. E xception s are some NO X and VOC reduc tions associated with the New York, Virginia, and Connecticut St ate 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 sou rces; this is described further in Section 4.2 . T he following bullets summarize the 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 e stimates from IPM, version 4.10.

Non

- IPM sector (ptnonipm): Projection factors and percent reductions r eflect CSAPR ( Transport

Rule ) comments and emission reductions due to control programs, plant closures, consent decrees

and settlements , and 1997 and 2001 ozone State Implementation 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. Terminal area forecast (TAF) data aggregated to the national level were used for aircraft to account for projected changes in landing/takeoff activity. Year

- specific 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 estimates based on expected changes in animal population from 2005 Department of Agriculture data ; no growth or control for NH 3 emissions from fertilizer application.

Area fugi tive dust sector (afdust): Projection factors for dust categories related to livestock estimates based on expected changes in animal population; no growth or control for other categories in this

sector.

Remaining Nonpoint sector (nonpt): Projection facto rs that implement CSAPR Proposal comments and reflect emission reductions due to control programs. Residential wood combustion projections based on growth in lower

- emitting stoves and a reduction in higher emitting stoves. PFC projection

factors reflecti ng impact of the final Mobile Source Air Toxics ( MSAT2) rule and include ethanol

emissions . Gasoline stage II onroad refueling emissions obtained directly from MOVES . Oil and gas projection estimates are provided for the non

- California Western Regional A ir Partnership ( WRAP )

states as well as Oklahoma and Texas. Year

- specific speciation was applied to some portions of this

sector and wa s discussed in Section 3

. Nonroad mobile sector (nonroad): Same version of the NONROAD2008a

, including same set of 293 county groups and representing counties as the 2005 base case . F uture

- year equipment population estimates and control programs (final locomotive

- marine and small spark ignition) to years 2017 and

2030 are included

. The only differences between the Tier 3 NPRM future 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 sector (alm_no_c3): Projection 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 20 17 and 2030 , incorporating CSAPR Proposal comments and controls based on Emissions Control Area (ECA) and International Marine Organization (IMO) global NO X and SO 2 controls.

Onroad mobile : uses a version of MOVES developed for the Tie r 3 NPRM that incorporates new car and light truck greenhouse gas emissions standards (LDGHG) affecting model years 2012 and later29

(published May 7, 2010). These emissions also include RFS2 fuels. VOC speciation uses different

future

- year values to take into account both the increase in ethanol use, and the existence of Tier 2 vehicles that use a different speciation profile. T his sector includes all non

- refueling onroad mobile emissions (exhaust, evaporative, brake wear and tire wear modes). SMOKE

- MOVE S was used in a similar configuration as the 2005 base case to apportion MOVES emissions factors into hourly gridded temperature

- adjusted emissions.

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 p oint (othpt): No growth or control.

Biogenic: 2005 emissions used for all future

- year scenarios.

Table 4

- 1 summarizes the control strategies and growth assumptions by source type that were used to create

the 20 17 and 2030 reference

- case emissions from the 2005v4. 3 base

- case inventories. EGU projections are discussed separately in the next section. These future year reference case projections and controls are also

i ncluded in the Tier 3 NPRM control cases. All Mexico, Canada, and offshore oil emissions are unchanged

in all future cases from those in the 2005 base case

.

The remainder of this section is organized either by source sector or by specific emissions cat egory within a source sector for which a distinct set of data were used or developed for the purpose of projections for the

Tier 3 NPRM . This organization allows consolidation of the discussion of the emissions categories that are contained in multiple se ctors, because the data and approaches used across the sectors are consistent and do not need to be repeated. Sector names associated with the emissions categories are provided in parentheses.30

Table 4

- 1

. Co ntrol strategies and growth assumptions for creating the Tier 3 NPRM 20 17 and 2030

reference case emissions inventories from the 2005 base case Control Strategies and/or growth assumptions ( g rouped by a ffected p ollutants or s tandard and a pproach u sed to

a p ply to the i nventory) Pollutants

a ffected Approach/

R eference

Non

- EGU Point (pt nonipm sector) p rojection a pproaches MACT rules, national, VOC: national applied by SCC, MACT Boat Manufacturing Wood Building Products Surface Coating Generic MACT II: Spand ex Production, Ethylene manufacture Large Appliances Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents, Explosives, Phthalate Plasticizers, Polyester Resins, Polymerized Vinylidene Chloride

Reinforced Plastics Asphalt Processing & R oofing

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, 2007 a

Consent decrees on c ompanies (based on informa tion from the Office of Enforcement and Compliance Assurance

– OECA) apportioned to plants owned/operated by the

c ompanies 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

– gro wth 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 s 20 17 and 2030 (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 1 0 ; EPA,

2010

New York o zone SIP c ontrols VOC, NO X , HAP VOC 1 1

Additional p lant and u nit closures provided by state, regional, and the EPA agencies and

additio nal consent decrees. Includes updates from C SAPR comments. All 1 231 Control Strategies and/or growth assumptions ( g rouped by a ffected p ollutants or s tandard and a pproach u sed to

a p ply to the i nventory) Pollutants

a ffected Approach/

R eference

Emission r eductions resulting from controls put on specific boiler units (not due to MACT) after 2005, identified through analysis of the control data gathered from the

Information Collect ion Request ( ICR ) from the Industrial/Commercial/Institutional

Boiler NESHAP. NO X , SO 2 , HCl

Section

4.2. 13.2

Reciprocating Internal Combustion Engines (RICE) NESHAP NO X , CO, PM

, SO 2 1 3

Ethanol plants that account for increased ethanol production due to R FS2 mandate All 1 4

State fuel sulfur content rules for fuel oil

– effective only in Maine, New Jersey, and New

York SO 2 1 5

Nonpoint ( nonpt sector) p rojection a pproaches

Municipal Waste Landfills: projection factor of 0.25 applied All EPA, 2007 a Livestoc k Emissions Growth from year 2002 to years 2017 and 2030 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 years 2017

and 2030 All 1 7

Gasoline and diesel fuel Stage II refueling via MOVES2010 Tier 3 month

- specific

inventories for 2017 and 2030 with assumed RFS2 and LDGHG fuels VOC , Benzene,

Ethanol 1 8

Portable Fuel Container Mobile Source Air Toxics Rule 2 ( MSAT2 ) inventory growth and control from year 2005 to years 2017 and 2030 VOC 1 9

Use Phase II WRAP 2018 Oil and Gas for both 2017 and 2030 VOC, SO 2 , NO X , CO Section

4 .2. 14 Use 2008 Oklahoma and Texas Oil and Gas, and apply year 2017 and 2021 projections for TX (last year available used as surrogate for 2030), 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/compliance/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

- H azardous W ast e I ncinerators criteria and hazardous air pollutant controls carried over from 2002 Platform, v3

. 1.

5. Used data provide d by the EPA, OAQPS SPPD expert

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

7. Percent reductions recommended are determined from the existing 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 estimate d reductions of “ PM emissions by 10,538 tpy, a reduction of about 62%. ” Used same list of p lants as were identified based on tonnage and SCC from

CAIR : http://www.envinfo.com/caain/June04updates/tiop_fr2.pdf

9. Except for dairy cows and turkeys (no growth), based o n animal popula tion 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 Torres, the EPA

- SPPD, from the analysis done for the Ce ment
NES H AP: The ISIS documentation and analysis for the cement NESHAP/NSPS is in the docket of that rulemaking

docket # EPA

- HQ

- OAR

- 2002

- 005. The Cement NESHAP is in the Federal Register: September 9, 2010 (Volume 75,32

Number 174

, Page 5496 9

- 55066

11. New Yo rk 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/NYMASIP7fi nal.pdf

. 12. Appendix D of Cross

- State Air Pollution Rule: ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_appendices_28jun2011.pdf

13. App endix 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.

15. Based on available, enforceable state sulfur rules as of November, 2010: http://www.ilta.org/LegislativeandR egulatory/MVNRLM/NEUSASulfur%20Rules_09.2010.pdf

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

, http://switchboard.nrdc.org/blogs/rkassel/governor_paterson_signs_new_la.html

, http://green.blogs.nyt imes.com/2010/07/20/new

- york

- mandates

- cleaner

- heating

- oil/

16. VOC reductions in Connecticut and Virginia 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 onr oad refueling 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/mo dels/moves/index.htm

19. VOC

, benzene , and ethanol emissions for 20 1 7 and 2030 based on MSAT2 rule and ethanol fuel assumptions (EPA,

2007b )

Control Strategies and/or growth assumptions ( g rouped by a ffected p ollutants or s tandard and a pproach u sed to

a p ply to the i nventory) Pollutants

a ffected Approach/

R eference

Onroad mobile and n onroad m obile c ontrols (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

Cor porate Average Fuel Economy standards for 2008

- 2011 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 Die sel 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 Poll ution from Locomotives and Marine Compression

- Ignition Engines Le ss than 30 Liters per Cylinder: October, 2008 Locomotive and marine rule (May 6, 2008) Marine SI rule (October 4, 1996) Nonroad large SI and recreational engine rule (November 8, 2002)

Nonroa d SI rule (October 8, 2008) Phase 1 nonroad SI rule (July 3, 1995) Tier 1 nonroad diesel rule (June 17, 2004) all 3,4,5

Aircraft (emissions are in the nonEGU point inventory) : Itinerant (ITN) operations at airports to years 2017 and 2030 all 633 Control Strategies and/or growth assumptions ( g rouped by a ffected p ollutants or s tandard and a pproach u sed to

a p ply to the i nventory) Pollutants

a ffected Approach/

R eference

Locomotiv es: Energy Information Administration (EIA) fuel consumption projections for freight rail

Clean Air Nonroad Diesel Final Rule

– Tier 4 : June 2004

Locomotive Em iss ions Final Rulemaking, December 17, 1997 Locomotive rule: April 16, 2008

Control of Emissions of Air Pollution from Locomotives and Marine: May 2008 all EPA, 2008 ; 3; 4; 5

Commercial Marine: Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards (April, 30, 2010)

– also includes C SAPR comments.
EIA fuel c onsumption projections for diesel

- fueled vessels

Clean Air Nonroad Diesel Final Rule

– Tier 4 Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999 Locomotive and marine rule (May 6, 2008) Tier 1 Marine Diesel Engines, February 28, 20 03 all 7, 3; EPA,

2008

APPROACHES/REFERENCES

– 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/otaq/nonroad

- diesel.htm

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

5. http://ww w.epa.gov/otaq/marinesi.htm

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

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



### 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

Final of the Integrated Planning Model (IPM ) ( http://www.epa.gov/airmarkt/progsregs/epa

- ipm/index.html ). The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power

sector. V ersion 4. 1 0 Final reflects federal and state rules and binding, enforceable consent decrees through

December of 2010. The 2017 IPM emissions reflect the CSAPR as finalized in July 2011 ; the 2030 IPM emissions reflected the CSAPR proposal 6

. Both th e reference and control case emissions do not reflect the

final Mercury and Air Toxics (MATS) r ule o r the Boiler MACT regulatory assumptions. Therefore, the

same year

- specific IPM emissions are used in the reference and control cases for Tier 3 NPRM.

Version 4.10 Final reflects state rules and consent decrees through December 1, 2010, information obtained from the 2010 Information Collection Request (ICR), and information from comments received on the IPM

related Notice of Data Availability (NODA) published on September 1, 2010. Notably, IPM 4.1 0 Final included the addition of over 20 GW of existing Activated Carbon Injection (ACI) for coal

- fired EGUs reported to EPA via the ICR. Additional unit

- level updates that identified existing pollution controls (such

as scrubber s) were also made based on the ICR and on comments from the IPM NODA. Units with SO 2 or NO X advanced controls (e.g., scrubber, SCR) that were not required to run for compliance with Title IV, New Source Review (NSR), state settlements, or state

- specific r ules were modeled by IPM to either operate those controls or not based on economic efficiency parameters. The IPM run for the reference (and control)

case s modeled with CMAQ assumed that 100% of the HCl found in the coal was emitted into the

6 The intention was to use the final CSAPR in the 2030 scenarios. Although this was an oversight, subsequent analysis showed that the differences in NOx between proposal and final would have a minimal impact on AQ results.34 atmosphere. However, in the final IPM results for the rule, neutralization of 75% of the available HCl was

in cluded based on recent findings

.

Further details on the reference case EGU emissions inventory used for this rule can be found in the IPM

v.4.10 Documentation , available at http://www.epa.gov/airmarkets/progsregs/epa

- ipm/transport.html

. The

reference case s modeled in IPM for this rule includes estimates of emissions reductions that wi ll result from

the Cross

- State Air Pollution Rule (CSAPR)

. However, reductions from the Boiler MACT rule were not represented this modeling because the rule was stayed at the time the modeling was performed. A complete list of state regulations, NSR sett lements, and state settlements included in the IPM modeling is given in Appendices 3

- 2, 3

- 3, and 3

- 4 beginning on p. 68 of http://www.epa.gov/airmarkets/prog sregs/epa

ipm/CSAPR/docs/DocSuppv410_FTransport.pdf

. For the 2017 reference (and control) case EGU emissions, the IPM outputs for 2020, which are also representative of the year 2017, were used. These emissions were very similar to the year 2015 emissio ns output from the same IPM modeling case. For the 2030 cases, IPM outputs were year 2030.

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

- estim ated fuel throughput based on fuel, configuration and controls to compute the filterable and condensable components of PM. This methodology

is documented in the IPM CSAPR TSD.



### 4.2 Stationary source projections: non

- EGU sectors (ptnonipm, nonpt, ag, afdust)

T o project U.S. stat ionary sources other than the ptipm sector, we applied growth factors and/or controls to certain categories within the ptnonipm, nonpt, ag and afdust platform sectors. This subsection provides details on the data and projection methods used for these sectors. The Tier 3 NPRM future year scenarios also required obtaining and preprocessing numerous other inputs that we received directly from OTAQ.

In estimating future

- year emissions, we assumed that emissions growth does not track with economic growth for many stationary non

- IPM sources. This “no

- growth” assumption is based on an examination of historical

emissions and economic data

. More details on the rationale for this approach can be found in Appendix D of the Regulatory Impact As sessment f or the PM NAAQS rule (EPA, 2006 ).

The starting point for projecting the 2005 Tier 3 NPRM emissions was to use similar emission projection methodologies as used for the 2005v4 .2 platform for the Final CSAPR , which incorporated responses to public comments on the modeling inventories . The 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 s 2017 and

2030

.

Year

- specific projection factors for year s 2017 and 2030 were created . Growth factors (and control factors) are provided in the following sections where feasible. However, some sectors used growth or control factors that varied geograph ically and their contents could not be provided in the following sections (e.g., gasoline distribution varies by state and pollutant and has hundreds of records).

Table 4

- 2 lists the stationary non

- EGU inputs and projection fac tors that were applied to account for year 2017 and 2030 RFS2 mandate impacts on emissions to the reference and control case s . These inputs are discussed in more detail in Section 4.2.1 through Section 4.2.9. All other stationary non

- EGU projections,

con trols and plant closure information not related to the RFS2 impacts are discussed in Section 4.2.10 through Section 4.2.1 4

. With the exception of onroad refueling emissions, all other stationary non

- EGU

emissions in the 2017 and 2030 reference case s are u nchanged in the 2017 and 2030 control case s (see35 Section 5); therefore, with the exception of onroad refueling, we will simply note that these emissions are “year 2017” or “year 2030” rather than the more cumbersome “year 2017 reference case” and “year 203 0 reference case”, respectively

.

Table 4

- 2

. Tier 3 NPRM reference case stationary non

- EGU source

- related p rojection methods Input Type Sector(s) Description Corn ethanol plants SMOKE ORL file that

replaces 2 0 05 base case

ORL file P tnonipm Based on RFS2 analysis and production

volumes. Point source format.

Biodiesel plants SMOKE ORL file P tnonipm Accounts for facilities with current production capacities, to support RFS2 biodiesel production. Point source format.

Cellulosic fuel production SMOKE ORL file N onpt Accounts for cellulosic ethanol and cellulosic diesel to support RFS2 cellulosic production. County

- level (nonpoint) format. Ethanol transport

and distribution SMOKE ORL file N onpt Accounts for eth anol vapor losses and spillage at any point in the transport and distribution chain. County

- level (nonpoint)

format.

Portable Fuel Containers (PFCs) SMOKE ORL N onpt NONROAD

- model based emissions from PFCs, including vapor displacement, tank permeation, and diurnal evaporation. County

level (nonpoint) format. Onroad refueling SMOKE ORL file nonpt MOVES

- based gasoline and diesel fuel spillage and displacement vapor losses. County

- level (nonpoint) format, monthly

resolution. This is the only non

- EGU

c omponent that has different emissions in the Tier 3 control cases compared to the reference cases. Refinery adjustments Projection factors P tnonipm A ccounts for changes in various refinery processes due to incorporation of RFS2 fuels. Ethanol transport

g asoline & ethanol

blends Projection factors nonpt, ptnonipm A ccounts 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, ptnoni pm A ccounts for changes in ag burning/dust, fertilizer application/production, livestock dust/waste and pesticide application/production.



### 4.2.1 Ethanol plants (ptnonipm)

As discussed in Section 2.4.1, we replaced all corn ethanol plants that OTAQ had supplied f rom the RFS2

rule

– see Section 2.1.2 in the CSAPR Final TSD

- with those more recently compiled for the 2005 Tier 3

NPRM

. Additional ethanol plants cited for development in support of increased ethanol production for

RFS2 are the cause for the increased nu mber of facilities and emissions in years 2017 and 2030

. Table 4

- 3 provides the summaries for the corn ethanol plants in the 2017 and 2030 case s

.36 Table 4

- 3

. 2017 and 2030 c orn ethan ol p lant e missions [tons]

Pollutant 2017 2030

1,3

- Butadiene 0.0011 0.0003

Acrolein 41.7 10. 5
Formaldehyde 45.2 13.3

Benzene 20.3 5.7
Acetaldehyde 643.2 314.4

CO 14,847 7,023

NO X 20,035 7,396

PM 10 21,639 10

, 10 7

PM 2.5 6,825 3

, 691

SO 2 11,299 9

, 001

VOC 35,459 10

, 754



### 4.2.2 Biodiesel plants (ptnonipm)

OTAQ developed an inventory of biodiesel plants for 2017 and 2030 that were sited at existing plant locations in support of producing biodiesel fuels for the RFS2 mandate. The RFS2 rule estimated 1.45

billion gallons per year (Bgal) of biodiesel fuel production by year 2017 and 1.67 Bgal by year 2030 . Only plants with current production capacities were assumed to be operating in 2017 and 2030. Total plant capacity at these existing facilities is limited to j ust over 1 Bgal. There was no attempt to site future year plants to account for the need to match biodiesel production needed for RFS2. Therefore, OTAQ applied

scalar adjustments to each individual biodiesel plant to match the 2017 and 2030 production ta rgets of 1.45 Bgal and 1.67 Bgal respectively: 1.41 for 2017 and 1.63 for 2030. Once facility

- level production capacities were scaled, emission factors were applied based on soybean oil feedstock . Inventories were modeled as point sources with Google Ea rth and web searching validating facility coordinates and correcting state

county FIPS. Table 4

- 4 provides the 2017 and 2030 biodiesel plant emissions estimates.

Table 4

- 4

. 2017 and 2030 biodiesel p lant e missions [tons]

Pollutant 2017 2030

Acrolein 3.09E

- 04 3.56E

- 04
Formaldehyde 2.23E

- 03 2.56E

- 03

Benzene 4.71E

- 05 5.42E

- 05
Acetaldehyde 3.59E

- 04 4.14E

- 04

CO 726 836

NO X 1,171 1,349

PM 10 99 114

PM 2.5 99 114

SO 2 9 10

VOC 64 73



### 4.2.3 Portable fuel containers (nonpt)

OTAQ provided year 2017 and 2030 PFC emis sions that include estimated Rei d Vapor Pressure (RVP) and oxygenate impacts on VOC emissions, and more importantly, large increases in ethanol emissions from

RFS2. Exist i ng invento ries were adjusted to account for impacts of RVP and ethanol from RFS2 , using37 adjustment factors derived from modeling done with the NONROAD2008b model. NONROAD was run at the national level using average nationwide fuel parameters for an all E0 case, the low ethanol base case, and the reference case. The percent change in refueling emissions from gasoline equipment was used to adjust the vapor displacement emissions, the percent change in tank permeation was used for PFC permeation, and the percent cha nge in diurnal emissions was used for evaporation. Because these PFC inventories contain ethanol, we developed a VOC E

- profile that integrated ethanol, see Section 3 for more details. Emissions for

2017 and 2030 are provided in Table 4

- 5

.

Table 4

- 5

. PFC e missions for 2017 and 2030 [tons]

Pollutant 2017 2030

VOC 123,186 146,593

Benzene 1,368 1,622

Ethanol 11,565 31,632



### 4.2.4 Cellulosic fuel production (nonpt)

OTAQ developed county

- leve l inventories for cellulosic diesel and cellulosic ethanol production for 2017

and 2030 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 produced in the same counties where current production capacity exists, based on RFS2 FRM inventories. Design capacities for 2022 used in the RFS 2 rule air quality modeling w ere adjusted to account for differences with estimated volumes of cellulos ic fuels produced for 2017 and 2030, using final RFS2 rule data. Since the f inal RFS2 rule assumed about 57 percent of cellulosic
fuel nationwide was cellulosic diesel, with the rem ainder cellulosic ethanol, we assumed this split would apply to every plant. In reality, however, depending on available feedstocks, plants are likely to produce one

fuel or the other. E mission factors were applied based on an assumed natural gas combusti on process. Table 4

- 6 provides the year 2017 and 2030 cellulosic plant emissions estimates.

Table 4

- 6

. 2017 and 2030 c ellulosic plant e missions [tons]

Pollutant 2017 2030

Acrolein 21 61
Formaldehyde 58 168

Benzene 27 79
Acetaldehyde 786 2,286

CO 42,839 124,336

Ethanol 1,875 5,530

NH 3 0.5 1.6

NO X 64,062 185,745

PM 10 7,533 21,862

PM 2.5 3,796 10,986

SO 2 4,973 14,475

VOC 5,336 15,489

We had no refined information on potenti al VOC speciation 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 pr oduction) for VOC speciation as was used for corn ethanol plants. However, these cellulosic inventories contain ethanol; therefore we developed a VOC E

- profile that integrated ethanol , see Section 3 for more details.38 

### 4.2.5 Ethanol transport and distribution (no npt)

OTAQ developed county

- level inventories for vapor losses from ethanol transport and distribution for 2017

and 2030 to account for losses for the processes such as truck, rail and waterways 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 ethanol versus gasoline vapor mass equations. These emissions are entirely evaporative and therefore limited t o VOC and are summarized

in Table 4

- 7 . The leading descriptions are “Industrial Processes; Food and Agriculture; Ethanol Production”

for each SCC.

Table 4

- 7

. 2017 and 2030 V OC losse s (Emissions) due to e thanol t ransport and d istribution [tons]

SCC Description 2017 2030

30205031 Denatured Ethanol Storage Working Loss 27,763 34,642

30205052 Ethanol Loadout to Truck 19,069 23,794

30205053 Ethanol Loadout to Railcar 9,610 11,991



### 4.2.6 Onro ad refueling (nonpt)

As discussed in Section 2.5.2, the refueling inventory includes gasoline and diesel fuel emissions from spillage loss and displacement vapor loss. For this analysis, the refueling emissions were estimated using the

revised version of EPA’s Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all twelve months. The same set of representative counties and temperatures were used for all Tier 3 NPRM scenarios. VMT, fleet age distribution and speed distribution were deve loped for 2017 and 2030

. Because these refueling inventories contain ethanol, we developed a VOC E

- profile that integrated ethanol, see

Section 3 for more details. A summary of the 2017 and 2030 onroad mobile refueling emissions is provided

in Table 4

- 8

. As discussed earlier in Section 4.2 and later in Section 5, these are the only stationary emissions component s of the Tier 3 NPRM inventories that are (very slightly) changed in the Tier 3 control

cases.

Table 4

- 8

. Tier 3 NPRM Reference case o nroad g asoline and d ie sel r efueling e missions [tons]

Fuel Type Pollutant 2017 2030

Gasoline VOC 63,759 40,781

Diesel VOC 12,962 16,449

Gasoline Benzene 161 91

Gasoline Ethanol 8,735 7,253



### 4.2.7 Refinery adjustments (ptnonipm)

Refinery emissions were adjusted for changes in fuels due to the RFS2 mandate. These adjustments were provided by OTAQ and impact processes such as process heaters, catalytic cracking units, blowdo w n systems, wastewater tre atment, condensers, cooling towers, flares and fugitive emissions. The impact of the

RFS2

- based reductio ns is shown in39 Table 4

- 9

.40

Table 4

- 9

. Impact of r efinery adjustments on 2017 a nd 2030 e missions [tons]

Pollutant Reductions 2017 Reductions 2030

CO 12,674 13,602

NO X 20,183 34,850

PM 10 4,367 7,550

PM 2.5 2,525 4,365

SO 2 13,846 24,014

VOC 3,693 6,428



### 4.2.8 Ethanol transport gasoline and blends (ptnonipm, nonpt)

Emission s changes in t he transport of changing fuels from the RFS2 mandate impact several processes including bulk plant storage (BPS), refinery to bulk terminal (RBT) and bulk terminal to pump (BTP). These impacts, provided by OTAQ, result in approximately 15,000 tons of VOC reductions in 2017 and 46,000

tons in 2030 for these processes.



### 4.2.9 Upstream agricultural adjustments (afdust, ag, nonpt, ptnonipm)

Changes in domestic biofuel volumes

, resulting from the RFS2 fuels mandate

, impact upstream agricultural

related source categori es in several emissions modeling sectors. These source categories include fertilizer application, pesticide application and livestock waste (NH 3 only), agricultural tilling, unloading and livestock dust (PM only) and fertilizer production mixing and blend ing, pesticide production and agricultural burning (all pollutants). As seen in Table 4

- 10 , the cumulative impact of these source

- specific changes is a net increase in emissions for upstream agricultural sources.

Table 4

- 10

. Upstream agricultural emission increases due to RFS2 fuels in 2017 and 2030 [tons]

Pollutant Increases 2017 Increases 2030

CO 302 416

NH 3 45,272 61,793

NO X 363 500

PM 10 42,934 59,004

PM 2.5 6,500 8,972

SO 2 6 9 95

VOC 16 23



### 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.41 Table 4

- 11 provides the growth factors from the 2005 base

- case emissions to y ear 2017 and 2030 scenarios for animal categories applied to the ag

, afdust , and ptnonipm secto rs for livestock

- related SCCs.42

Table 4

- 11

. Growth f

actors from year 2005 to 2017 and 2030 for animal o perations

Animal Category 2017 2030 Dairy Cow 1.0000 1.0000

Beef 1.0206 1.0385

Pork 1.0893 1.1666

Broilers 1.3442 1.6426 Turkeys 1.0000 1.0000 Layers 1.2406 1.4491 Poultry Average 1.26 74 1.4991 Overall Average 1.0935 1.1745

Except for dairy cows and turkey production, the animal projection factors are derived from national

- level animal population projections from the U.S. Department of Agriculture (USDA) and the Food and Agriculture P olicy 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 rates. Although p roductions rates have increased, the number of animals has declined. Thus, we do not believe that production forecasts provide representative estimates of the future number of cows and turkeys; therefore, we did not use these forecasts for estimating

futu re

- 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; however, milk production will be increasing over the same period. Note that the ammonia emissions fro m dairies are not directly related to animal population but also nitrogen excretion. With the cow numbers going down and the production going up we suspect the excretion value will be changing, but we assumed no change because we did not have a quantitati ve estimate.

The inventory for livestock emissions used 2002 emissions values therefore, our projection method projected

fr om 2002 rather than from 2005.

Appendix E in the 2002v3 platform documentation provides the animal population data and regression

c urves used to derive the growth factors: http://www.epa.gov/scram001/reports/Emissions%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 categories in43 Table 4

- 1144 Table 4

- 11

.



### 4.2.11 Residential wood combustion growth (nonpt)

We projected resident ial wood combustion (RWC) emissions based on the expected increase in the number

of low

- emitting wood stoves and the corresponding decrease in other types of wood stoves. As newer, cleaner woodstoves replace older, higher

- polluting wood stoves, there will be an overall reduction of the emissions from these sources. The approach cited here was developed as part of a modeling exercise to estimate the expected benefits of the woodstoves change

- out program ( http://w ww.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 spe cific assumptions we made were:

 Fireplaces, source category code ( SCC ) =2104008001: increase 1%/y ea r

 Old woodstove s, SCC=2104008002, 2104008010, or 2104008051: decrease 2%/y ea r

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

For the general woodstoves and fireplaces category (SCC 2104008000) we computed a weighted average distribution based on 19.4% fireplaces, 71.6% old woodstoves, 9.1% new woodstoves using 2002v3

Platform missions for PM 2.5 . These fractions are based on the fraction of emissions from these processes in the states that did not have the “g eneral woodstoves and fireplaces” SCC in the 2002 v3 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 after air quality modeli ng. Table 4

- 12 p resents the projection factors used to project the 2005 base case (2002 emissions) for RWC

, including these 2017 errors. Table 4

- 13 shows the national impact (tons) of the 2017 projection factor error

.

Table 4

- 12

. Projection f

actors for growing year 2005 r esidential wood c ombustion s ources

SCC SCC Description Erroneous

2017 Correct

2017 2030

2104008000 Total: Woodstoves and Fireplaces 0.45 0

. 84 0. 70

2104008001 Fireplaces: General 0.65 1. 15 1. 28 2104008070 Outdoor Wood Burning Equipment

2104008002 Fireplaces: Insert; non

- EPA certified

0.36 0. 70 0. 44 2104008010 Woodstoves: General

2104008051 Non

- catalytic Woodstoves: Non

- EPA certifie d

2104008003 Fireplaces: Insert; EPA certified; non

- catalytic

0.74 1. 30 1. 56 2104008004 Fireplaces: Insert; EPA certified; catalytic

2104008030 Catalytic Woodstoves: General

2104008050 Non

- catalytic Woodstoves: EPA certified

2104008052 Non

- catalytic Woodstoves: Low Emitting

2104008053 Non

- catalytic Woodstoves: Pellet Fired

Table 4

- 13

. Impact of year 2017 projection factor error on r esidential wood c ombustion estimates

Pollutant 2005

Em issions Erroneous 2017

Emissions Erroneous 2017

Reductions Correct 2017

Emissions Correct 2017

Reductions

NO X 38,292 18,023 20,270 33,545 4,74745

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)

The 2005 point

- source emissions for aircraft are projected to future years by applying activity growth using data on itinerant (ITN) operations at airports. The ITN operations are defined as aircraft take

- offs wher eby the aircraft leaves the airport vicinity and lands at another airport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. We used projected ITN information 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 ). This information is available for approximately 3

, 300 individual airports, for all year s up to 20 30 . We aggregated and applied this information at the national level by summing the airport

- specific (U.S. airports only) ITN operations to national totals by year and by aircraft operation, for each of the four available operation types : commer cial, 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 airc r a ft factors were used for each of the year

- specific scenarios : reference and control.

Table 4

- 14

. Factors used to project 2005 base

- case aircraft emissions to 2017 and 2030

SCC SCC Description 2017 2030

22750 01000 Military aircraft 1.0229 1.0275

2275020000 Commercial aircraft 1.1288 1.5059

2275050000 General aviation 0.8918 0.9916

2275060000 Air taxi 0.8620 1.0259

27501015 Internal Combustion Engines;Fixed Wing Aircraft L &

TO Exhaust;Military;Jet Engine: JP

- 5 1.0229 1.0275

27502001 Internal Combustion Engines;Fixed Wing Aircraft L &

TO Exhaust;Commercial;Piston Engine: Aviation Gas 1.1288 1.5059

27502011 Internal Combustion Engines;Fixed Wing Aircraft L &

TO Exhaust;Commercial;Jet Engine: Jet A 1.1288 1. 5059

27505001 Internal Combustion Engines;Fixed Wing Aircraft L &

TO Exhaust;Civil;Piston Engine: Aviation Gas 0.8918 0.9916

27505011 Internal Combustion Engines;Fixed Wing Aircraft L &

TO Exhaust;Civil;Jet Engine: Jet A 0.8918 0.9916

27601014 Internal Combustion Engines;Rotary Wing Aircraft L &

TO Exhaust;Military;Jet Engine: JP

- 4 1.0229 1.0275

27601015 Internal Combustion Engines;Rotary Wing Aircraft L &

TO Exhaust;Military;Jet Engine: JP

- 5 1.0229 1.0275

We did not apply growth factors to any point s ources with SCC 27602011 (Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; Commercial; Jet Engine: Jet A) because the facility names associated with these point sources appeared to represent industrial facilities rather than airports. Thi s SCC is only in one county, Santa Barbara, California (State/County FIPS 06083).46 None of our aircraft emission projections account for any control programs. We considered the NO X standard adopted by the International Civil Aviation Organization’s (ICAO) Committee on Aviation Environmental Protection (CAEP) in February 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 Tier 3 NPRM mod eling ; therefore, the effects of this rule

were not included in the future

- year emissions projections.



### 4.2.13 Stationary s ource control programs, consent decrees & settleme nts, and plant closures (ptnonipm, nonpt)

We applied emissions reduction factors to the 200 5 emissions for particular sources in the ptnonipm and nonpt sectors to reflect the impact of stationary

- source control programs including consent decrees,

settlemen ts, and plant closures. Some of the controls described in this section were obtained from comments

on the CSAPR proposal. Detailed summaries of the impacts of the control programs are provided in Appendix D of the CSAPR TSD: ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_appendices_28jun2011.pdf

.

Controls from the NO X SIP call were assumed to have been implemented by 2005 and captur ed in the 2005

base case (2005v2 point inventory). This assumption was confirmed by review of the 2005 NEI that showed reductions from Large Boiler/Turbines and Large Internal Combustion Engines in the Northeast states covered by the NO X SIP call. The fu ture

- 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) pr ovided all controls information related to the MACT rules, and this information is as consistent as possible with the preamble emissions reduction percentages for these rules.

Various emissions reductions from the CSAPR comments, including but not limited to: fuel switching at units, shutdowns, future

- year emission limits, ozone SIP VOC controls for some sources in Virginia and Connecticut, and state and local control programs were included.

Evolutionary information regarding plant closures (i.e., emissions were zeroed out for future years)

was also included where information indicated that the plant was actually closed after the 2005 base year and prior to CSAPR and Tier 3 NPRM modeling that began in the spring of 201 1

. We also applied unit and plant closu res received from the CSAPR comments. However, plants projected to

close in the future (post

- 2010 ) were not removed in the future years because these projections can be inaccurate due to economic improvements

. We also applied cement kiln (unit) and cemen t plant

closures discussed later in Section 4.2.6.1 . More detailed information on the overall state

- level impacts of all control programs and projection datasets, including units and plants closed in the 201 7

and 2030 reference (same in the control) case ptnonipm inventories are provided in Appendix D of

the Final CSAPR TSD: ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_appendices_28j un2011.pdf . The magnitude of all unit and plant closures on the non

- EGU point (ptnonipm) sector 2005 base

- case emissions is shown in Tab le 4

- 15 below. These same reductions are seen in all Tier 3 NPRM future year scenarios.

Tab le 4

- 15

. Summary of n on

- EGU e mission r eductions applied to the 2005 i nventory due to u nit and p lant

c losures

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 26,75047

In add ition to plant closures, we included the effects of the Department of Justice Settlements and

Consent Decrees on the non

- EGU ( ptnonipm ) sector emissions. We also included estimated impacts of HAP standards per Section 112, 129 of the Clean Air Act on the non

- EGU ( ptnonipm ) and nonpoint ( nonpt ) sector emissions, based on expected CAP co

- benefits to sources in these sectors.

Numerous controls have compliance dates beyond 2008; these include refinery and the Office of Compliance and Enforcement (OECA) consent decrees, Department of Justice (DOJ) settlements, as

well as most national VOC MACT controls. Additional OECA consent decree information is provided in Appendix B of the Proposed Toxics Rule TSD: http://www.epa.gov/ttn/chief/emch/toxics/proposed_toxics_rule_appendices.pdf

. The detailed data

used are available at the website listed in Section 1.

Refinery consent decree controls at the facility and SCC level (collec ted through internal coordination on refineries by the EPA) were included

.

Fuel sulfur fuel limits were enforceable for Maine , New Jersey and New York.

Criteria air pollutant (cap) reductions which are a cobenefit to RICE NESHAP controls , including

SO 2 RIC E cobenefit controls , were included

. We applied New York State Implementation Plan available controls 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 (s ee Section 3.2.6 in the CSAPR TSD: ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf

. )

Most of the control programs were applied as replacement controls, which means that any existing percent reductions ( “baseline control efficiency”) reported in the NEI were removed prior to the addition of the percent reductions due to these control programs. Exceptions to replacement controls are “additional”
controls, which ensure that the controlled emissions match d esired reductions 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 R eductions from the Portland Cement NESHAP (ptnonipm)

As indicated in Table 4

- 1 , the Industrial Sectors Integrated Sol utions (ISIS) model (EPA, 2010) was used to project the cement industry component of the ptnonipm emissions modeling sector to 20 13. There were no future year estimates for 2017 or 2030 , so 2013 estimates were used for all future year Tier 3 NPRM modeling scenarios . This approach provided reductions of criteria and hazardous air pollutants, including mercury. The ISIS cement emissions were developed in support for the Portland Cement NESHAPs and the NSPS for the Portland cement manufacturing industry.

T he ISIS model produced a Portland Cement NESHAP policy case of multi

- pollutant emissions for individual cement kilns (emission inventory units) that were relevant for years 2013 through 2017. These

ISIS

- based emissions included information on new cement k ilns, facility and unit

- level closures, and updated policy case emissions at existing cement kilns. The units that opened or closed before 2010 were included in

the projections as were t he ISIS

- based policy case predictions of emissions reductions and act ivity growth.

The ISIS model results for the future show a continuation of the recent trend in the cement sector of the replacement of lower capacity, inefficient wet and long dry kilns with bigger and more efficient preheater and precalciner kilns. Mult iple regulatory requirements such as the NESHAP and NSPS currently apply to48 the cement industry to reduce CAP and HAP emissions. Additionally, state and local regulatory requirements might apply to individual cement facilities depending on their locations relative to ozone and

PM 2.5 nonattainment areas. The ISIS model provides the emission reduction strategy that balances: 1) optimal (least cost) industry operation, 2) cost

- effective controls to meet the demand for cement, and 3) emission reduction requir ements over the time period of interest.49 Table 4

- 16 shows the magnitude of the ISIS

- based cement industry reductions in the future

- year emissions, and the impact that these reductions have on total stationary non

- EGU point source (ptnonipm) emissions.50

Table 4

- 16

. Future

- year ISIS

- based cem ent industry annual reductions [tons/yr] for the non

- EGU (ptnonipm) sector

Pollutant Cement Industry

emissions in 2005 Reductions in

20 17 & 2030 Pe rcent

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 controls. We used an early version of a database developed for that rulemaking entitled “ survey_database_2008_results 2 .mdb ” ( EPA

- HQ

- OAR

- 2002

- 0058

- 0788) which is posted under the Technical Information 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 contr ols were actively in use . In many situations we learned that the controls were on site but were not

in use . A summary of the plant

- unit specific reductions that were verified to be actively in use are

summarized in Table 4

- 17

. All reductions are promulgated by the present day, and therefore these

reductions are the same for all Tier 3 NPRM future year scenarios.

Table 4

- 17

. State

- level non

- MACT b oiler r eductions from ICR d ata g athe ring [tons]

State Pollutant Pre

- controlled

Emissions Controlled

Emissions Reductions in

2017 & 2030 Percent

Reduction
Michigan NO X 907 544 363 40

North Carolina SO 2 652 65 587 90
Virginia SO 2 3

, 379 338 3

, 041 90
Washington SO 2 639 383 256 40

North Caro lina HC l 31 3 28 90

4.2.13.3 RICE NESHAP (ptnonipm and nonpt)

There are three rulemakings for National Emission Standards for Hazardous Air Pollutants (NESHAP) for Reciprocating Internal Combustion Engines (RICE). These rules reduce HAPs from existing and new RIC E sources. In order to meet the standards, existing sources with certain types of engines 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, complian ce dates have passed for all three rules; thus all three rules are

included in the 2017 and 2030 emissions projection s

.

The rules can be found at http://www.epa.gov/ttn/atw/rice/ricepg.html and ar e listed below:

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion Eng ines; Final Rule (69 FR 33473) published 06/15/04

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combusti on Engines; Final Rule (FR 9648 ) published 03/03/10

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion Engines; Final Rule (75 FR 51570) published 08/20/201051 The difference among these three rules is that they f ocus 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 project CAPs from the 2005 NEI RICE sources , ba sed on the requirements of the rule for existing sources only because 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 provided in Appendix F in the Proposed MATS Rule TSD: http://www.epa.gov/ttn/chief/emch/toxics/proposed_toxics_rule_appendices.pdf . Impacts of the RICE controls on stationary non

- EGU emissions (nonpt and ptnonipm sectors), 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 therefore these reductions are the same for all Tier 3 NPRM future year

scenarios.

Table 4

- 18

. National impact of RICE c ontrols on non

- EGU p rojections

CO NO X PM 10 PM 2.5 SO 2 VOC

Reductions 116,434 111,74 9 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 time of the Tier 3 NPRM emissions processing are limited to Maine, New Jersey and New York. Several other states have fuel sulfur r ules that were in development but not finalized prior to the final CSAPR and proposed Tier 3 emissions processing: http://www.ilta.org/LegislativeandRegulator y/MVNRLM/NEUSASulfur%20Rules_09.2010.pdf

.

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 contain no more than 15 ppm of sulfur, thus reducing SO 2 emissions by 99.5% for post

- 2012 (2017 and 2030) projections. These New

York sulfur content reductions are further discussed here: http://switchboard.nrdc.org/blogs/rkassel/governor_paterson_signs_new_la.html

.

The New Jersey year 2017 standard of 15ppm (assuming 500ppm baseline for Kerosene ) sulfur content yields a 96.25% SO 2 emissions reduction for kerosene (fuel #1). The New Jers ey sulfur content reductions

are discussed here: http://njtoday.net/2010/09/01/nj

- adopts

- rule

- limiting

- sulfur

- content

- in

- fuel

- oil/

.

For Tier 3 year 2017 proje ctions, the Maine fuel sulfur rule, effective in year 2016, reduces sulfur to50 ppm from 3,000 ppm in 2005, resulting in a 98.3% reduction for all Tier 3 year 2017 scenarios. A more stringent

Maine fuel sulfur rule effective January 1, 2018 reduces sulfur to 15ppm from 3,000 ppm in 2005, resulting in a 99.5% reduction for all Tier 3 year 2030 scenarios. These Maine sulfur content reductions are discussed

here: http ://www.mainelegislature.org/legis/bills/bills_124th/billpdfs/SP062701.pdf . The impact of these

fuel sulfur content reductions on SO2 is shown in Table 4

- 19 . These year

- specific reductions are the same

for all Tier 3 scenarios: low

- ethanol, reference and control.

Table 4

- 19

. Impact of f

uel s ulfur (SO 2 ) c o ntrols on 2017 and 2030 non

- EGU p rojections [tons]

State 2017 Reductions 2030 Reductions

Maine 8,323 18,470 New Jersey 998 998

New York 54,431 54,431

Total 63,751 73,89852 

### 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 and for year 2030 we used the last available future year, year 2021, estimates from the Texas Commission of Environmental Quality (TCEQ) and used

them as described in: http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/ei/5820783985FY0901

- 20090715

- ergi

- Drilling_Rig_EI.pdf

.

We also received 2008 data for Oklahoma that we used as the best available data to represent 2017 and 2030

. We utilize d the latest available future year, year 2018, Phase II WRAP oil and gas emissions data for the

non

- California Western Regional Air Partnership (WRAP) states to re present both 2017 and 2030 . 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 applied to the 20 17 and 2021 TCEQ oil and gas estimates

or 2018 W RAP Phase II oil and gas inventory.

For Oklahoma, we applied CO, NO X , SO 2 a nd VOC emissions reductions from the RICE N ESHAP, 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/toxics/proposed_toxics_rule_appendices.pdf )

. All these year

- specific oil and gas projection estimates are the same for the reference and control Tier 3 N PRM scenarios. Table 4

- 20

shows the 2005 , 2017 and 2030 emissions including RICE reductions for Oklahoma

.

Table 4

- 20

. Oil and g as e missions for 2005 , 2017 and 2030 including additio nal reductions due to the RICE

NESHAP

NOX PM2.5 SO2 VOC

2005 2017 2030 2005 2017 2030 2005 2017 2030 2005 2017 2030

Alaska 836 453 453 62 1 1 68 12 12

Arizona 13 15 15 37 49 49

Colorado 32,188 33,517 33,517 350 11 11 35,500 43,63 9 43,639

Montana 10,617 13,880 13,880 640 6 6 9,187 14,110 14,110

Nevada 71 63 63 1 0 0 105 163 163

New Mexico 61,674 74,648 74,648 369 12 12 215,636 267,846 267,846

North Dakota 6,040 20,869 20,869 688 4 4 8,988 17,968 17,968

Okla homa 39,668 42,402 42,402 1,918 2,231 2,231 1,014 2 2 155,908 163,598 163,598
Oregon 61 44 44 19 14 14

South Dakota 566 557 557 43 0 0 370 562 562

Texas 42,854 34,772 26,061 2,945 1,085 435 5,977 36 33 4,337 2,800 1,504

Utah 6,896 6,297 6,297 149 1 1 43,403 81,890 81,890
Wyoming 36,172 34,142 34,142 541 3 3 166,939 304,748 304,748

Total 237,656 261,659 252,948 4,862 3,316 2,666 9,834 76 73 640,498 897,400 896,104



### 4.3 Onroad m obile source projections (onroad)

The same version s o f MOVES and SMOKE

- MOVES were used to create all Tier 3 NPRM onroad emission scenarios. Section 2.2 describes these components in support of year 2005 processing. This section will only address the differences related to creating and processing year 2017 and 2030 Tier 3 NPRM reference

case emissions . Section 5 will discuss the differences related to creating and processing year 2017 and 2030

Tier 3 NPRM control case emissions . Speciation changes for all scenarios are discussed in Section 3.53 I nputs for t emperatures (Section 2.2.1)

, the representative counties and fuel months (Section 2.2.2), the overall parallel processing procedures (Section 2.2.3), speed data (Section 2.2.4 ), and SMOKE

- MOVES configurations (Section 2.2.4) were previously discussed and w ere the same for all Tier 3 NPRM 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 and 2030 VMT in ventor ies , MOVES2010a was run with default inputs to generate total national VMT by SCC. But, because MOVES uses a static (1999) default allocation of VMT to county,

MOVES was not used for these allocations. Instead, the 2017 county VMT was created by in terpolating 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. (Unlike MOVES, t he NCD accounts for geographical shifts in activity over time.) For 203 0, NCD values for 2030 were used directly. The VMT was also adjusted to account for increased onroad transportation of ethanol fuels and the resulting increase in travel by large tanker trucks.

Vehicle populations by county and SCC were developed simil arly to the VMT, using MOVES to generate national totals for each year and using the NCD to allocate 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, 2017 and 2030 ) 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 and 2030 emissions used for Tier 3 NPRM reference cases re flect onroad mobile control

programs that were final at the time the modeling was done. This includ ed the Light

- Duty Vehicle Tier 2

Rule and the Mobile Source Air Toxics (MSAT2) final rule. MOVES used fuel sulfur levels of 3 0ppm in all

states in the Tier 3 reference scenarios (except California which was modeled with MOVES2010a default

fuels )

. In terms of fleet composition, both reference scenarios assumed 100% Tier 2 and older vehicles.



### 4.3.1 California LEV

The list of s tates which have implemented programs to require the sale of vehicles in their state certified for sale in California began with the information stored in the modeling inputs used for the 2008 National Emission Inventory (NEI) stored in the National Mobile Inventory Model (NMIM) County databa se. This information was reviewed and updated by states during the process of developing the national inventory for calendar year 2008. This information was supplemented with information from a "Dear Manufacturer"
letter, "Sales of California

- certified 2 008

- 2010 Model Year Vehicles (Cross

- Border Sales Policy)" (October 29,2007) produced by the Compliance and Innovative Strategies Division of the US Environmental Protection Agency which describes the areas that have recently implemented a California standa rds program. This information was used to generate emission rate table inputs for the MOVES 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 States with the MOVES model. For calendar year 2017, areas that had implemented California standards would still have these programs in place in calendar

year s 2017 and 2030

. See Table 2

- 6 for a list of these states and dates of implementation . More information on the states that have implemented California LEV standards can be found at: http://www.dieselnet.com/standards/us/#cal

.



### 4.4 Nonroad mobile source pr ojections (nonroad , alm_no_c3, seca_c3 )

The components of the nonroad mobile sectors are discussed in Section 2.3. Nonroad mobile emissions

reductions for the Tier 3 NPRM reference cases include year

- specific regulations affecting locomotives,

various non road engines including diesel engines and various marine engine types, fuel sulfur content, and54 evaporative emissions. This section discusses the changes due to the NONROAD/NMIM system (nonroad sector) and additional C1/C2 CMV and locomotive emissions fro m volume increases resulting from incorporation of larger amounts of renewable fuels in the 2017 and 2030 reference case s

.



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

As discussed in Section 2.3.1, most nonroad emissions were estimated using the E PA’s NONROAD model,

run via EPA’s consolidated modeling system known as the National Mobile Inventory Model (NMIM). NONROAD is EPA’s model for calculating emissions from nonroad equipment, except for aircraft,
locomotives, and commercial marine vessels. Like the onroad emissions, the NONROAD/ NMIM system provides nonroad emissions for VOC by three emission modes: exhaust, evaporative and refueling. Unlike
the onroad sector, nonroad refueling emissions for nonroad sources are not included in the nonpoint ( nonpt)

sector and so are retained in th e nonroad sector.

The same temperatures and representative counties were used for all NONROAD model

- generated Tier 3

NPRM scenarios for all years as were used for the 2005 base case, describe in section 2.3.1

. For 2 017, E10 and E15 are available in every county, but nonroad equipment is assumed to burn only E10. For 2030, EPA assumed that nonroad equipment would use only E15. To generate the NMIM fuels, the E10 fuel was copied from MOVES to NMIM, and the E10 oxygen ate was assigned a market share of 1. Nonroad diesel fuel

sulfur levels are retained from NMIM.

Table 2

- 11 in Section 2.3.1.3 lists the NMIM emission scenarios used in the Tier 3 NPRM modeling. The only difference between the reference scenarios are the increases in activity between calendar years (based

on NONROAD model default growth estimates of future

- year equipment population ) and changes in fuels and engines that reflect implementation of national regulations and local control programs that impact e ach year differently due to engine turnover

. For year 2017, EPA assumed that nonroad equipment would use only E10. The 2030 modeling, EPA assumed use of E15 fuels for both the reference and control scenarios.

For all scenarios, the NONROAD sulfur levels are taken from the NCD

. Although the NONROAD Model estimates changes in VOC emissions from E15, NMIM calculates toxics as if the fuel were E10. EPA calculated adjustment factors based on highway effects to apply to certain toxic emissions to correct for the

use of E15 in 2030. Emission estimates for ethanol come from speciation of VOC in the SMOKE model. These ethanol adjustments for nonroad engines running on E15 came from the EPAct Phase 1 data.

We have not included volu ntary nonroad programs in our projections such as programs encouraging either no refueling or evening refueling on Ozone Action Days and diesel retrofit programs. The national nonroad regulations incorporated in all Tier 3 NPRM future year scenarios are those promulgated prior to Dec ember

200 9

, and beginning about 1990. Recent rules include:

“Clean Air Nonroad Diesel Final Rule

- Tier 4”: (http://www.epa.gov/nonroaddiesel/2004fr.htm ), published June 29, 2004, and,

Control of Emissions from Nonroad Large Spark

- Ignition Engines, and R ecreational Engines (Marine

and Land

- Based), November 8, 2002 (“Pentathalon Rule”).

OTAQ’s Locomotive Marine Rule , March 2008 : ( http://www.epa.gov/otaq/nonrdmdl.htm )

OTAQ’s Smal l Engine Spark Ignition (“Bond”) Rule , November 2008 : ( http://www.epa.gov/otaq/smallsi.htm )

A ll future year nonroad emissions used NMIM data that are based on A EO2009 fuels and the same NMIM county databa se NCD20101201Tier3 . We converted e missions from monthly totals to monthly average

- day

values based the on number of days in each month. Only criteria and select HAPs (benzene, acetaldehyde,55 butadiene, acrolein, and formaldehyde) were retained when creat ing SMOKE one record per line ( ORL )

files.



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

Aircraft emissions reside in the nonEGU point inventory (ptnonipm), and the projection factors used to create year 2017 and year 2030 estimates, ar e discussed in Section 4.2. The remaining 2005 NEI emissions for locomotives and Class 1 and Class 2 commercial marine vessel (C1/C2 CMV) use year

- specific projection estimates. Base f uture year locomotive and C1/C2 CMV emissions were calculated using

pr ojection factors that were computed based on national

, annual summaries of emissions in 2002

, 2017 and

2030

. Some additional emissions were then factored in due to changes in fuels. These national summaries were used to create national by

- pollutant, by

- S CC pr ojection factors; these factors include final loco motive

marine controls and are provided in Table 4

- 21

. Modest additive Class I railroad and C1/C2 CMV emissions

that account for RFS2 volume increases in the Tier 3 future year reference scenario s were then added into the reference case due to the volume differences in corn, cellulosic and imported ethanol and cellulosic diesel

fuels. These additional emissions are summarized in

Table 4

- 22

.

Table 4

- 21 . Factors applied to year 2005 emissions to project locomotives and c lass 1 and c lass 2

commercial m arine vessel e missions to 2017 and 2030

SCC SCC Description Poll utant 201 7 2030

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions CO 0.938 0.956

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NH 3 1.144 1.285

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NO X 0.700 0.372

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM 10 0.642 0.350

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM 2.5 0.653 0.356

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port e missions SO 2 0.087 0.045

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions VOC 0.786 0.402

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations CO 1.334 1.640

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NH 3 1.325 1.627

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NO X 0.627 0.357

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 10 0.578 0.260

2285002006 Rai lroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 2.5 0.586 0.263

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations SO 2 0.005 0.006

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operatio ns VOC 0.589 0.293

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations CO 0.328 0.403

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NH 3 1.325 1.627

2285002007 Railroad Equipment;D iesel;Line Haul Locomotives: Class II / III Operations NO X 0.352 0.350

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 10 0.286 0.272

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Opera tions PM 2.5 0.288 0.275

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO 2 0.001 0.001

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations VOC 0.315 0.387

2285002008 Railroad Equip ment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) CO 1.071 1.188

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NH 3 1.325 1.627

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NO X 0.496 0.241

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 10 0.461 0.148

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 2.5 0.463 0.149

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO 2 0.005 0.005

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) VOC 0.475 0.136

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter L ines CO 1.057 1.172

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NH 3 1.325 1.627

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NO X 0.489 0.237

2285002009 Railroad Equipment;Diesel;Line Haul Loco motives: Commuter Lines PM 10 0.455 0.146

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM 2.5 0.455 0.14656

SCC SCC Description Poll utant 201 7 2030

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines SO 2 0.005 0.005

2285002009 Railroad Equipmen t;Diesel;Line Haul Locomotives: Commuter Lines VOC 0.469 0.134

2285002010 Railroad Equipment;Diesel;Yard Locomotives CO 1.341 1.649

2285002010 Railroad Equipment;Diesel;Yard Locomotives NH 3 1.325 1.627

2285002010 Railroad Equipment;Diesel;Yard Locomotiv es NO X 1.128 0.851

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 10 0.914 0.690

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 2.5 0.934 0.704

2285002010 Railroad Equipment;Diesel;Yard Locomotives SO 2 0.006 0.007

2285002010 Railroad Equipment;Diesel;Yard Locomotives VOC 1.509 1.074

Table 4

- 22

. Additional class 1 r ailroad and C1/C2 CMV e missions from RFS2 f uel v olume c hanges

Pollutant 2017 C lass 1

Rail (tons) 2017 C1/C2

CMV (tons) 2030 C lass 1

Rail (tons) 20 30 C1/C2

CMV (tons)

1,3

- Butadiene 0.83 0.01 0.56 0.01

Acrolein 0.80 0.08 0.54 0.05
Formaldehyde 11.12 3.21 7.50 2.23

Benzene 0.66 0.44 0.45 0.30
Acetaldehyde 4.83 1.59 3.26 1.11

CO 1,250 197 1,906 272

NH 3 3.93 0.62 5.99 0.95

N O X 5,731 890 4,298 642

PM 10 141 29 86 21

PM 2.5 136 27 83 20

SO 2 2.96 3.96 4.51 5.99

VOC 257 21 173 15

The future

- year locomotive emissions account for increased fuel consumption based on Energy Information Administration (EIA) fuel consumption project ions for freight rail, and emissions reductions resulting from

emissions standards from the Final Locomotive

- Marine rule ( EPA, 2008 ). This rule lowered diesel sulfur

content and tightened emission standards for existing and new locomotives and marine dies el emissions to

lower future

- year PM, SO 2 , and NO X , and is documented at: http://www.epa.gov/otaq/nonrdmdl.htm

. Voluntary retrofits under the National Clean Diesel Campaign ( http://www.epa.gov/otaq/die sel/index.htm ) are not included in our projections.

We applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3

- butadiene, acetaldehyde, acro lein, 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 p rojected based on the Final

L ocomotive Marine rule national

- level factors provided in Table 4

- 21 . Similar to locomotiv es, 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 and 2030 inventor ies and override the C1/C2 projection factors in Table 4

- 21

.57 

### 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

- Internat ional Marine Organization ( ECA

- IMO ) project began in 2005. The (S)ECA Category 3 ( C3 ) commercial marine vessel 2002 base

- case emissions were projected to year 2005 for the

2005 base case and to 2017 and 2030 , which include s ECA

- IMO controls. The resulti ng coordinated

strategy, including emission standards under 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.htm

These projection factors vary depending on geographic region and pollutant; where VOC HAPs are assigned the same growth rates as VOC. The projection factors used to create the 2017 and 2030 seca_c3 sector emissions are provided in Table 4

- 23

. Note that these factors are relative to 2002. Factors relative to 2005 can be computed from the 2002

- 2005 factors.

T he geographic regions are described in the 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 bound aries. North and South Pacific regions are divided by the Oregon

- Washington border, and East Coast and Gulf 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/EmisInventory/2005v4/2005_emissions_tsd_07jul2010.pdf ) . As with the 2005 base case

, this sector u ses CAP

- HAP VOC integration.

Table 4

- 23

. NO X , SO 2 , PM 2.5 and VOC f actors to p roject c lass 3 CMV emissions for 2017 and 2030

Region NO X SO 2 PM 2.5 VOC

2017 2030 2017 2030 2017 2030 2017 2030

Alaska East 1.4 09 1.702 0.062 0.095 0.203 0.312 1.631 2.487

Alaska West 1.469 2.052 1.571 0.456 1.571 0.571 1.571 2.396

East Coast 1.435 1.072 0.070 0.123 0.264 0.470 1.955 3.464

Gulf Coast 1.120 0.688 0.055 0.079 0.207 0.303 1.529 2.217

Hawaii East 1.539 1 .416 0.078 0.147 0.268 0.506 2.036 3.839

Hawaii West 1.725 2.783 2.037 0.733 2.035 0.871 2.037 3.842

North Pacific 1.240 0.874 0.064 0.098 0.222 0.348 1.644 2.528

South Pacific 1.573 1.232 0.084 0.166 0.293 0.589 2.114 4.225

Great Lakes 1.106 1.090 0.046 0.057 0.171 0.214 1.302 1.621

Outside ECA 1.585 2.427 1.891 0.623 1.891 0.745 1.891 3.417



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

Emissions for Canada, Mexico, and offshore sources were not projected to futur e years, and are therefore the

same as those used in the 2005 base case for all Tier 3 future year scenarios . Therefore, the Mexico emissions are based on year 1999, offshore oil is based on year 2005, and Canada is based on year 2006. For

both Mexico an d Canada, their responsible agencies did not provide future

- year emissions that were consistent with the base year emissions.58 

## 5 2017 and 2030 Tier 3 Control Case s

The 2017 and 2030 Tier 3 NPRM control (hereafter simply referred to as the “control”) cases represent the future with implementation of all RFS2 impact s discussed in Section 4 plus the inclusion of Tier 3 fuel sulfur reductions and phasing in of Tier 3 vehicle controls. Similar to the 2017 and 2030 base cases discussed in
Section 4, the control cases also include MSAT2 and LDGHG but do not include HDGHG impacts.

Similar to the 2017 reference case, the 2017 control case assumes 21.6 billion gallons of renewable fuels (24

billion ethanol

- equivalent gallons due to volume increases of ethanol), with 17.8 billion gallons of E10 and E15, 1.5 billion gallons of biodiesel, 0.2 billion gallons of renewable diesel, and 2.2 billion gallons of cellulosic diesel. By the year 2030, the control (and reference) case assumes 30.5 billion gallons of

renewable fue ls (36 billion ethanol

- equivalent gallons due to volume increases of ethanol), with 22.2 billion gallons of E15 (no E10), 1.7 billion gallons of biodiesel, 0.2 billion gallons of renewable diesel, and 6.5 billion gallons of cellulosic diesel.

The only not able differences between the reference and control scenarios for both 2017 and 2030 are the fuel sulfur levels and fleet composition. For both 2017 and 2030, fuel sulfur levels are 30ppm (though 10ppm in California) in the reference case and 10ppm in all states in the Tier 3 control scenarios. For both the 2017 and 2030 reference scenarios, fleet composition is composed entirely (100%) of Tier 2 and older vehicles. For the Tier 3 control scenarios, new emission standards for model year 2014 and later lig ht

- duty motor vehicles result in assumed 93% Tier 2 and older vehicles (fraction of vehicle population) and 7% Tier 3 vehicles in 2017 and 20% Tier 2 and older vehicles and 80% Tier 3 vehicles by 2030.

Tier 3 standards are expected to impact onroad mobi le, and to a much smaller extent, nonroad mobile in

2017 and 2030

, and onroad refueling emissions in 2030. However, all other upstream sources, including portable fuel containers, are not expected to be affected. Therefore, the year 2017 and 2030 Tier 3 control and reference case emissions are the same for several components of the modeling inventory. This section will address only those components that are different between the reference and control scenarios in years

2017 and 2030

.

VOC speciation chan ges between these control cases and the reference cases are discussed in Section 3.



### 5.1 Non

- EGU stationary source projections (nonpt)

The 2017 Tier 3 control case non

- EGU emissions are unchanged from the 2017 reference case. For the 2030

Tier 3 control case e missions, the only update is to use different onroad refueling emission s . These monthly

resolution county

- level (nonpoint sector) refueling emissions are MOVES

- based gasoline and diesel fuel spillage and include displacement vapor losses. A summary of th e onroad refueling emissions in the 2030 control and reference cases is provided in Table 6

- 1; note that there is a negligible difference in refueling

emissions between the 2030 reference and 2030 control cases.

Table 6

- 1

. Onroad Gasoline and Diesel Refue ling Emissions for 2017 and 2030 Reference Cases

Fuel Type Pollutant 2017 Reference

and Control 2030

Reference 2030

Control

Gasoline VOC 63,759 40,781 40,777

Diesel VOC 12,962 16,449 16,450

Gasoline Benzene 161 91 91

Gasoline Ethanol 8,735 7,253 7,25259 

### 5.2 Onroad mobile (onroad)

The same version of MOVES and SMOKE

- MOVES Integration Tool was used to create all Tier 3 NPRM onroad emission scenarios. Section 2.2 describes these components in support of year 2005 processing. This section will only address the differences related to creating and processing year 2017 and 2030 Tier 3 NPRM control case emissions. Section 4 discussed the differences related to creating and processing year 2017 and 2030 Tier 3 NPRM reference case emissions. Speciation changes for all scenarios are discussed in Section 3.

Inputs for temperatures (Section 2.2.1), the representative counties and fuel months (Section 2.2.2), the overall parallel processing procedures (Section 2.2.3), speed data (Section 2.2.4), and SMOKE

- MOVES configu rations (Section 2.2.4) were previously discussed and were the same for all Tier 3 NPRM scenarios. However, SMOKE inputs of VMT and vehicle populations were year

- specific but were consistent between reference and control (Section 4.3 )

.

MOVES used fuel sulfur levels of 10ppm in all states in the Tier 3 control scenarios , including California

. N ew emission standards for model year 2014 and later light

- duty motor vehicles result in assumed 93% Tier 2 and older vehicles (fraction of vehicle population) and 7% Tier 3 vehicles in the 2017 control scenario and

20% Tier 2 and older veh icles and 80% Tier 3 vehicles in the 2030 control scenario . Other than fuels and the application of Tier 3 vehicle emission standards

, (described in th e RIA for the rule) the MOVES runs were

identical between the reference and control scenarios of the same year

.



### 5.3 Nonroad mobile (nonroad)

The components of the nonroad mobile sectors are discussed in Section 2.3. Nonroad mobile emissions

reductions for t he Tier 3 NPRM control cases are restricted to various nonroad engines including diesel engines, fuel sulfur content, and evaporative emissions. This section discusses the changes due to the NONROAD/NMIM system (nonroad sector). The C1/C2 CMV and l ocomot ive emissions (alm_no_c3) and C3 CMV (seca_c3 sector) are unchanged in the control cases and use the same emissions as the 2017 and

2030 reference cases.



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

As discussed in Section 2.3.1, most nonroad emiss ions are were estimated using the EPA’s NONROAD model, as run by the EPA’s consolidated modeling system known as the National Mobile Inventory Model (NMIM). NONROAD is EPA’s model for calculating emissions from nonroad equipment, except for
aircraft, loco motives, and commercial marine vessels. Like the onroad emissions, the NONROAD/ NMIM

system provides nonroad emissions for VOC by three emission modes: exhaust, evaporative and refueling.
Unlike the onroad sector, nonroad refueling emissions for nonroad s ources are not included in the nonpoint (nonpt) sector and so are retained in this sector.

The same temperatures and representative counties were used for all NONROAD model

- generated Tier 3

NPRM scenarios. For 2017, E10 and E15 are available in every cou nty, but nonroad equipment is assumed to burn only E10. For 2030, nonroad equipment is assumed to burn only E15

. To generate the NMIM fuels

in 2017 the E10 fuel s were co nverted from MOVES to NMIM as described for the 2005 base case , assigned

a market sha re of 1. In 2030, the E15 fuels were used. For both calendar years, the controlled sulfur levels were carried over to the nonroad case where they affected the sulfate emissions. Highway diesel fuel sulfur

levels (unchanged f

rom the reference case) are co nverted directly from MOVES to NMIM. Nonroad diesel

fuel sulfur levels are retained from NMIM.60 Section 2.3.1.3 provides a cross

- walk of the nonroad mobile NMIM emission scenarios used in the Tier 3 NPRM modeling; the only difference between the calenda r year scenarios are the increases in activity (based

on NONROAD model default growth estimates of future

- year equipment population ) and changes in fuels and engines that reflect implementation of national regulations and local control programs that impact each year differently due to engine turnover

. For year 2017, EPA assumed that nonroad equipment would use only E10. The 2030 modeling, EPA assumed use of E15 fuels. For both control scenario years, the NONROAD gasoline sulfur level was set to equal the level in the onroad fuel. Although the NONROAD

m odel estimates changes in VOC production from E15, NMIM calculates toxics as if the fuel were E10.

EPA calculated adjustment factors based on highway effects to apply to certain toxic emissions to correct for the use of E15 in 2030. Emission estimates for ethanol come from speciation of VOC in the SMOKE model. These ethanol adjustments for nonroad engines running on E15 came from the EPAct Phase 1 data for highway gasoline vehicles

.



## 6 Tier 3 emissions summ aries

Once developed, the emissions inventories were processed to provide the hourly, gridded emissions for the

model

- species needed by CMAQ

. Table 6

- 1 provides national

- level summaries of the 2005 U.S. CAP

emission s inventories modeled for this rul e by sector for the lower 48

- states and D.C

.61 Table 6

- 2 and Table 6

- 3 provide these national summaries of the 2017 and 2030 Reference case U.S. CAP inventories by sector. Table 6

- 4 and62 Table 6

- 5 provide national summaries of the 2017 and 2030 Tier 3 Control case U.S. CAP inventories by

sector. Alaska and Hawaii emissions summaries are not included in this TSD because they are outside of the air quality modeling domain and modeling sectors that utilize domain

- specific meteorology, such as onroad mobile, are needed to compute model

- ready emissions.

Table 6

- 1

. National (49

- state) 2 005 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 mari ne 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 2,030,759 433,346 647,873 158,34 2 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 20,14 8,378 14,292,410 4,294,373 12,906,616 3,750,218 81,913,104 16,986,06463

Table 6

- 2

. National (49

- state) 2017 Reference Case U.S. emissions (tons/year) by sector

Emissions Sector NO X SO 2 PM 2.5 PM 10 NH 3 CO VOC

Agriculture 3,505,410
Area fugitive Dust 1,037,079 8,904,386
Average fires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992

Commercial marine Category 3 (US) 155,281 5,880 2,201 2,417 18,274 7,028

EGU 1,930,769 3,281 ,364 276,430 371,101 40,259 873,344 46,050

Locomotive/ marine 1,302,445 7,143 35,648 36,770 957 299,265 46,664

Non

- EGU Point 2,003,736 1,534,991 411,437 618,157 159,867 2,995,095 1,169,826
Nonpoint 1,713,238 1,125,985 875,678 1,145,768 130,258 5,854,632 7,167,620

Nonroad 1,140,942 2,736 112,372 118,463 2,403 13,551,846 1,509,698

Onroad 3,204,871 29,288 129,416 194,597 85,378 18,690,890 1,397,668

US TOTAL 11,640,709 6,036,480 3,564,297 12,187,889 3,961,309 50,837,897 13,303,546

Table 6

- 3

. National (49

- state) 2030 Reference Case U.S. emissions (tons/year) by sector

Emissions Sector NO X SO 2 PM 2.5 PM 10 NH 3 CO VOC

Agriculture 3,702,527
Area fugitive Dust 1,039,699 8,922,577
Average f ires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992

Commercial marine Category 3 (US) 115,719 9,740 3,619 3,933 29,906 11,479

EGU 2,028,844 3,746,573 296,789 394,471 48,773 1,091,438 53,468

Locomotive/ marine 741,611 3,951 18,467 19,049 1, 129 338,728 25,006

Non

- EGU Point 2,013,274 1,519,548 411,693 617,377 161,546 3,065,975 1,171,769
Nonpoint 1,837,589 1,137,029 986,447 1,267,217 132,659 6,728,522 7,290,413

Nonroad 765,026 3,154 68,308 72,989 2,902 12,921,772 1,209,534

Onroad 1,846,571 30,526 88,516 166,158 90,104 17,021,674 911,513

US TOTAL 9,538,062 6,499,615 3,597,572 12,260,000 4,176,416 49,752,567 12,632,174

Table 6

- 4

. National (49

- state) 2017 Tier 3 Control Case U.S. emissions (ton s/year) by sector

Emissions Sector NO X SO 2 PM 2.5 PM 10 NH 3 CO VOC

Agriculture 3,505,410
Area fugitive Dust 1,037,079 8,904,386
Average fires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992

Commercial marine Category 3 (U S) 155,281 5,880 2,201 2,417 18,274 7,028

EGU 1,930,769 3,281,364 276,430 371,101 40,259 873,344 46,050

Locomotive/ marine 1,302,445 7,143 35,648 36,770 957 299,265 46,664

Non

- EGU Point 2,003,736 1,534,991 411,437 618,157 159,867 2,995,095 1,169,213
Nonpoint 1,713,238 1,125,985 875,678 1,145,768 130,258 5,854,632 7,145,569

Nonroad 1,140,942 1,969 112,372 118,463 2,403 13,551,846 1,498,122

Onroad 2,966,589 14,621 130,778 196,150 85,378 18,055,099 1,353,170

US TOTAL 11,402,428 6,021,046 3,565,659 12, 189,442 3,961,309 50,202,106 13,224,80764

Table 6

- 5

. National (49

- state) 2030 Tier 3 Control Case U.S. emissions (tons/year) by sector

Emissions Sector NO X SO 2 PM 2.5 PM 10 NH 3 CO VOC

Agriculture 3,7 02,527
Area fugitive Dust 1,039,699 8,922,577
Average fires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992

Commercial marine Category 3 (US) 115,719 9,740 3,619 3,933 29,906 11,479

EGU 2,028,844 3,746,573 296,789 394,471 48, 773 1,091,438 53,468

Locomotive/ marine 741,611 3,951 18,467 19,049 1,129 338,728 25,006

Non

- EGU Point 2,013,274 1,519,548 411,693 617,377 161,546 3,065,975 1,171,769
Nonpoint 1,837,589 1,137,029 986,447 1,267,217 132,659 6,728,522 7,290,410

Nonroad 76 5,026 2,257 68,308 72,989 2,902 12921772.4 1,209,452

Onroad 1,371,925 15,068 83,842 161,173 90,104 11984061.3 699,592

US TOTAL 9,063,416 6,483,260 3,592,898 12,255,016 4,176,416 44,714,954 12,420,16765 

## 7 References

Cook, R, 2012. Development of Air Qual ity Reference Case Upstream and Portable Fuel Container Inventories for Tier 3 Proposal . Memorandum to the Docket, December 12, 2012. Docket EPA

- HQ

OAR

- 2011

- 0135.

EPA, 2005. Clean Air Interstate Rule Emissions Inventory Technical Support Document, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, March 2 005.

Available at http://www.epa.gov/cair/pdfs/finaltech01.pdf

. EPA, 2006. Regulatory Impact Analyses, 2006 N ational Ambient Air Quality Standards for Particle Pollution. U.S. Environmental Protection Agency, Office of Air Quality Planning 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, 2007 a

. Guidance for Estimating VOC and NOx Emission Changes from MACT Rules, U.S. Environmental Protection Agency Office of Air Quality 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/o3imp8hr/docu ments/guidance/200705_epa457_b

- 07

- 001_emission_changes_mact_rules.pdf

.

EPA. 2007 b

. National Scale Modeling for the Final Mobile Source Air Toxics Rule, Office of Air Quality Planning and Standards, Emissions Analysis and Monitoring Division, Research Tri angle Park, NC 27711, EPA 454/R

- 07

- 002, February 2007. Available at http://www.epa.gov/otaq/regs/toxics/420f07017.pdf

EPA, 200 8

. Final Regulatory Impact Analysis: Control of Emissions of A ir Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per 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 200 8 . Available at: http://www.epa.gov/otaq/nonrdmdl.htm

EPA, 2010. Technical Support Document: The Industrial Sectors Integrated Solutions (ISIS) Model and the Analysis for the National Emission Standards for Hazardous Air Pollutants and New Source Performance Standards for the Portland Cement Manufacturing Industry, U.S. Environmental Protection Agency, Sectors Policies and Program Division and Air Pollution Prevention and Control

Divis ion, Research Triangle Park, NC 27711, August 2010.A

- 1 

## E missions Inventory for Air Quality Modeling Technical Support

Document : Proposed Tier 3 Emissions Standards

Appendix A

Ancillary Datasets and Parameters Used for Each Tier 3 Modeling Case

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 27711

M arch 2013A

- 2 The ancillary data files used for the Tier 3 NPRM cases are shown in Table A

- 1. The Input name column gives a brief designator for the dataset. The Environment Variable column gives the name of the environment variable that is used by SMOKE to specify the input. The S ector column specifies the modeling sector for the dataset. The remaining columns show the data set n ame and version used in the 2005 ba se case and 2017 and

2030 reference and control case s

. Most ancillary data sets are the same for all future year scenarios; we indicate where data sets differ.

To match the Datasets and Versions listed in this table to actual data files, combine the Dataset name and the version number in the following pattern: <Dataset Name>_<Date>_<Version number>.txt, where <Date> is the last date of change for that version and will have a unique value for

the combination of Dataset Na me and Version number.

Table A

- 2 shows the parameters used for the Tier 3 NPRM modeling cases ; these parameters are the same for all future year scenarios . The

columns are the same as in Table A

- 1 except that the Program is not shown. Many of the paramet ers apply to all programs, or all programs for the specified processing sector.

Table A

- 1. List of ancillary data sets associated with the Tier 3 NPRM modeling cases.

Input N ame Environment

Variable Program Sector 2005 Base Case 2017 & 2030 Reference /Con trol

Case s

Area

- 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 reports

12EUS1 BGPRO Smkmerge beis b gpro_12EUS1 (/garnet/oaqps) [v0] bgpro_12EUS1 (/garnet/oaqps) [v0] Biogenic gridding surrogate for reports

36US1 BGPRO Smkmerge beis bgpro_36US1 (/garnet/oaqps) [v0] bgpro_36US1 (/garnet/oaqps) [v0]

Biogenic land use, file A, 12EUS1 BELD3_A Normbeis3 bei s LANDA_EUS12_279X240 (/garnet/oaqps) [v0] LANDA_EUS12_279X240 (/garnet/oaqps) [v0]

Biogenic land use, file A, 36US1 BELD3_A Normbeis3 beis LANDA_US36_148X112 (/garnet/oaqps) [v0] LANDA_US36_148X112 (/garnet/oaqps) [v0]

Biogenic land use, file B, 12EUS1 BELD3_B Normbeis3 beis LANDB_EUS12_279X240 (/garnet/oaqps) [v0] LANDB_EUS12_279X240 (/garnet/oaqps) [v0]

Biogenic land use, file B, 36US1 BELD3_B Normbeis3 beis LANDB_US36_148X112 (/garnet/oaqps) [v0] LANDB_US36_148X112 (/garnet/oaqps) [v0]

Biogenic land use, totals, 12EUS1 BELD3_TOT Normbeis3 beis LAND_TOTALS_EUS12_279X24 0 (/garnet/oaqps) [v0] LAND_TOTALS_EUS12_279X240 (/garnet/oaqps) [v0]

Biogenic land use, totals, 36US1 BELD3_TOT Normbeis3 beis LAND_TOTALS_US36_148X112 (/garnet/oaqps) [v0] LAND_TOTALS _US36_148X112 (/garnet/oaqps) [v0]

Bioseasons file 12EUS1 BIOSEASON Tmpbeis3 beis bioseason.cmaq.2005b_12km (/garnet/oaqps) [v0] bioseason.cmaq.2005b_12km (/garnet/oaqps) [v0]

Bioseasons file 36US1 mcip v3.4 beta4 b BIOSEASON Tmpbeis3 beis bioseason.cmaq .2005b_36km (/garnet/oaqps) [v0] bioseason.cmaq.2005b_36km (/garnet/oaqps) [v0]A

- 3

Input N ame Environment

Variable Program Sector 2005 Base Case 2017 & 2030 Reference /Con trol

Case s

CEM annually summed data CEMSUM smkinven ptipm cemsum_ptipm_2005 (/orchid/share) [v0] cemsum_ptipm_2005 (/garnet/oaqps) [v0]

Combination profiles GSPRO_COM

BO Spcmat gspro_co mbo_2005 [v6] gspro_combo_2005 [v6]

Combination profiles

- nonpt GSPRO_COM

BO Spcmat nonpt gspro_combo_tier3_2005_base_no npt_v2 [v2] 2017 Reference and Control: gspro_combo_tier3_2017_ref_nonpt [v1]

Combination profiles

– onroad GSPRO_COM

BO Spcmat onroad gspro_combo_tier3_2005_base_on road_v2 [v0] 2017 Reference and Control: gspro_combo_tier3_2017_ref_onroad [v2]

Combination profiles

- ptnonipm (same as nonpt) GSPRO_COM

BO Spcmat ptnonipm gspro_combo_tier3_2005_base_no npt_v2 [v2] 2017 Reference and Control: 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 Point

Sources PELVCONFIG Laypoint pelvconfig_inline_allpts [v1] pelvconfig_inline_a llpts [v1] Elevation Configuration File for seca_c3

sector PELVCONFIG Laypoint seca_c3 pelvconfig_seca_c3 [v1] pelvconfig_seca_c3 [v1]

Grid Description List GRIDDESC Grdmat griddesc_lambertonly [v39] griddesc_lambertonly [v39]

Gridding surrogates CA N

- MEX 12km SRGPRO Grdmat othon Canada_12km_revised (/garnet/oaqps) [v0] Canada_12km_revised (/garnet/oaqps) [v0]

Gridding surrogates CAN

- MEX 12km SRGPRO Grdmat othar Canada_12km_revised (/garnet/oaqps) [v0] Canada_12km_revised (/garnet/oaqps) [v0]

Grid ding surrogates CAN

- MEX 36km SRGPRO Grdmat othar Canada_36km_revised (/garnet/oaqps) [v0] Canada_36km_revised (/garnet/oaqps) [v0]

Gridding surrogates CAN

- MEX 36km SRGPRO Grdmat othon Canada_36km_revised (/garnet/oaqps) [v0] Canada_36km_revised (/garnet/ oaqps) [v0]

Gridding surrogates USA 12km SRGPRO Grdmat USA

- CAN

- MEX_12km (/garnet/oaqps) [v0] USA

- CAN

- MEX_12km (/garnet/oaqps) [v0]

Gridding surrogates USA 36km SRGPRO Grdmat USA

- CAN

- MEX_36km (/garnet/oaqps) [v0] USA

- CAN

- MEX_36km (/garnet/oaqps) [v0]

GSCNV

- pollutant to pollutant conversions GSCNV Spcmat gscnv_cb05_soa [v2] 2017 Reference & Control: gscnv_cb05_soa [v3]

2030 Reference & Control: gscnv_cb05_soa [v4]

GSPRO speciated MOVES PM GSPROTMP_

L Spcmat gspro_speciated_pm [v3] gspro_speciated _pm [v3]

GSREF speciated PM GSREFTMP_

L Spcmat gsref_speciated_pm [v2] gsref_speciated_pm [v2]A

- 4

Input N ame Environment

Variable Program Sector 2005 Base Case 2017 & 2030 Reference /Con trol

Case s
Holidays table HOLIDAYS Temporal holidays [v0] holidays [v0] Inventory Table

- HAPCAP EBAFM integration CMAQ

- lite v4.7 N1e HDGHG INVTABLE smkinven onroad invtable_hapcap_cb05soa [v13] invtable_hapcap_cb05soa [v13] Inventory Table

- HAPCAP EBAFM integration CMAQ

- lite v4.7 N1e HDGHG INVTABLE smkinven nonpt invtable_hapcap_cb05soa [v13] invtable_hapcap_cb05soa [v13] Inventory Table

- HAPCAP integration

CMA Q

- lite v4.7 N1e HDGHG INVTABLE smkinven invtable_hapcap_cb05soa [v12] invtable_hapcap_cb05soa [v12] Inventory Table

- no

- BAFM CMAQ

- lite

v4.7 N1e HDGHG INVTABLE smkinven avefire invtable_hapcap_cb05_no_bafm [v3] invtable_hapcap_cb05_no_bafm [v3]

Invento ry Table

- no

- BAFM CMAQ

- lite

v4.7 N1e HDGHG INVTABLE smkinven ptipm invtable_hapcap_cb05_no_bafm [v3] invtable_hapcap_cb05_no_bafm [v3]

List of sectors for mrggrid SECTORLIST Mrggrid sectorlist_2005ct_05b [v3] 2017 Reference: sectorlist_2017ct_ref_05b [v 1]

2017 Control: sectorlist_2017ct_ctla_05b [v1]

2030 Reference: sectorlist_2030ct_ref_csapr_05b [v1]

2030 Control: sectorlist_2030ct_ctl_csapr_05b [v1]

MACT Description MACTDESC Smkreport mactdesc_2002v3 [v1] mactdesc_2002v3 [v1]

Meteorology temperat ure profiles METMOVES movesmrg onroad SMOKE_DAILY_12MERGEUS1 _2005 [v0] SMOKE_DAILY_12MERGEUS1_20 05 [v0]

Mobile codes file default MCODES smkinven mcodes [v1] mcodes [v1]

MOVES county cross

- reference MCXREF movesmrg onroad MCXREF_tier3 [v0] MCXREF_tier 3 [v0]

MOVES Emission Factor Table list MRCLIST movesmrg onroad mrclist_RPV_05jul2011_2005ct_0 5b [v0] 2017 Reference: mrclist_RPV_01jul2011_2017ct_ref_0 5b [v0]

2017 Control: mrclist_RPV_08aug2011_2017ct_ctla _05b [v0]

2030 Reference: mrclist_RPV_06jul2011 _2030ct_ref_0 5b [v0]

2030 Control: mrclist_RPV_03aug11_2030ct_ctl_05 b [v0]A

- 5

Input N ame Environment

Variable Program Sector 2005 Base Case 2017 & 2030 Reference /Con trol

Case s

MOVES Emission Factor Table list MRCLIST movesmrg onroad mrclist_RPD_20may2011_2005ct_ 05b [v0] 2017 Reference: mrclist_RPD_10jun2011_2017ct_ref_ 05b [v0]

2017 Control:
mrclist_RP D_08aug2011_2017ct_ctla _05b [v0]

2030 Reference: mrclist_RPD_06jul2011_2030ct_ref_0 5b [v0]

2030 Control: mrclist_RPD_03aug11_2030ct_ctl_05 b [v0]

MOVES Emission Factor Table list MRCLIST movesmrg onroad mrclist_RPP_20may2011_2005ct_ 05b [v0] 2017 Reference : mrclist_RPP_10jun2011_2017ct_ref_0 5b [v0]

2017 Control: mrclist_RPP_08aug2011_2017ct_ctla_ 05b [v0]

2030 Reference: mrclist_RPP_06jul2011_2030ct_ref_0 5b [v0]

2030 Control: mrclist_RPP_03aug11_2030ct_ctl_05 b [v1]A

- 6

Input N ame Environment

Variable Program Sector 2005 Base Case 2017 & 2030 Reference /Con trol

Case s

MOVES Emission Factor Tables EFTABLES mov esmrg onroad EFtables_20110520_Tier3Base200 5 [v0]
EFtables_20110705_Tier3Base200 5_RPVfix [v0] 2017 Reference:
EFtables_20110610_Tier3Ref2017 [v0]
EFtables_20110701_Tier3Ref2017_R PVfix [v0]

2017 Control: EFtables_20110830_Tier3Ctla2017 [v0]

2030 Reference:
EFtables_20110706_Tier3Ref2030_R PP [v0]
EFtables_20110706_Tier3Ref2030_R PD [v0]
EFtables_20110706_Tier3Ref2030_R PV [v0]

2030 Control:
EFtables_20110803_Tier3Ctl2030 [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_c3 NHAPEXCLU

DE smkinven alm_no_c3 nhapexclude_alm_no_c3_pf4 [v1] nhapexclude_alm_no_c3_pf4 [v1]

NHAPE XCLUDE avefire NHAPEXCLU

DE smkinven avefire nhapexclude_everything [v0] nhapexclude_everything [v0]

NHAPEXCLUDE nonpt NHAPEXCLU

DE smkinven nonpt nhapexclude_nonpt_pf4_addpestici des [v3] nhapexclude_nonpt_pf4_addpesticides [v3]

NHAPEXCLUDE NONROAD NHAPE XCLU

DE smkinven nonroad nhapexclude_nonroad_pf4 [v0] nhapexclude_nonroad_pf4 [v0]

NHAPEXCLUDE ptnonipm NHAPEXCLU

DE smkinven ptnonipm nhapexclude_ptnonipm_include_30 125010 [v0] nhapexclude_ptnonipm_include_3012 5010 [v0]

NHAPEXCLUDE seca_c3 NHAPEXCLU

DE s mkinven 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 [v1 5 ]

onroad surrogate xref default MGREF Grdmat amgref_us_can_mex_revised [v11 ] amgref_us_can_mex_revised [v1 5 ]

ORIS Description ORISDESC smkinven orisdesc [v0] orisdesc [v0]A

- 7

Input N ame Environment

Variable Program Sector 2005 Base Case 2017 & 2030 Reference /Con trol

Case s
SCC descriptions SCCDESC smkinven sccdesc_pf31 [v12] sccdesc_pf31 [v12]

SIC descriptions SICDESC Smkreport sic_desc [v0] sic_desc [v0]

Smkmerge repr esentative dates files MRGDATE_F

ILES Run script merge_dates_2005 (/garnet/oaqps) [v0] merge_dates_2005 (/garnet/oaqps) [v0] Speciation profiles additional for SMOKE

MOVES GSPROTMP_

O Spcmat onroad gspro_new_for_smoke

- moves [v0] gspro_new_for_smoke

- moves [v0]

Speciation profiles Canada PM GSPROTMP_J Spcmat othpt gspro_pm25_canada_2006_point [v0] gspro_pm25_canada_2006_point [v0]

Speciation profiles for biogenics GSPROTMP_

K Spcmat beis gspro_biogenics [v1] gspro_biogenics [v1]

Speciation profiles for HG GSPROTMP_

H Spcmat gspro_hg [v2] gspro_hg [v2]

Speciation profiles for INTEGRATE HAPS GSPROTMP_

F Spcmat gspro_integratehaps_cb05_tx_pf4 [v1] gspro_integratehaps_cb05_tx_pf4 [v1]

Speciation profiles for NONHAPTOG GSPROTMP_

E Spcmat gspro_nonhaptog_c b05 [v3] gspro_nonhaptog_cb05 [v3] Speciation profiles for NONHAPTOG w/ETOH integration GSPROTMP_

E Spcmat onroad gspro_nonhaptog_cb05_eprofiles [v0] gspro_nonhaptog_cb05_eprofiles [v0] Speciation profiles for NONHAPTOG w/ETOH integration GSPROTMP_

E Spc mat nonpt gspro_nonhaptog_cb05_eprofiles [v0] gspro_nonhaptog_cb05_eprofiles [v0]

Speciation profiles for NOX GSPROTMP_

G Spcmat gspro_nox_hono_pf4 [v0] gspro_nox_hono_pf4 [v0]

Speciation profiles for PM2.5 GSPROTMP_

C Spcmat gspro_pm25 [v2] gspro_pm2 5 [v2]

Speciation profiles for SO2

- SULF GSPROTMP_

B Spcmat gspro_sulf [v1] gspro_sulf [v1]

Speciation profiles for TOG GSPROTMP_

D Spcmat gspro_tog_cb05_soa [v3] gspro_tog_cb05_soa [v3]

Speciation profiles Other VOC HAP GSPROTMP_

M Spcmat gspro_oth er_hapvoc_no_benz

- benz [v0] gspro_other_hapvoc_no_benz

- benz [v0]

Speciation profiles speciated VOC GSPROTMP_I Spcmat gspro_speciated_voc [v0] gspro_speciated_voc [v0]

Speciation profiles static GSPROTMP_

A Spcmat gspro_static_cmaq [v12] gspro_static_ cmaq [v12]

Speciation xref CAP static GSREFTMP_

A Spcmat gsref_static_cap_pf4 [v1] gsref_static_cap_pf4 [v1]

Speciation xref for Canada PM GSREFTMP_

N Spcmat othpt gsref_pm25_canada_2006_point [v3] gsref_pm25_canada_2006_point [v3]

Speciation xref for Integrate

- HAPs static GSREFTMP_J Spcmat gsref_static_integratehap_emv4 [v2] gsref_static_integratehap_emv4 [v2]A

- 8

Input N ame Environment

Variable Program Sector 2005 Base Case 2017 & 2030 Reference /Con trol

Case s Speciation xref for NONHAPVOC, not

year

- specific GSREFTMP_

H Spcmat nonpt 2017 Reference & Control: gsref_nonhapvoc_general_hdghg [v3 ]

Speciation xref for NONHAPVOC, not

year

- specific GSREFTMP_

H Spcmat gsref_nonhapvoc_general_hdghg [v2] 2017 Reference & Control: gsref_nonhapvoc_general_hdghg [v2]

2030 Reference & Control: gsref_nonhapvoc_general_tier3_2030_ ref [v1]

Speciation xref for NONHAPVOC, year

specific GSREFTMP_I Spcmat gsref_nonhapvoc_2005_hdghg [v2] 2017 Reference & Control: gsref_nonhapvoc_2017_ref_tier3 [v1]

2030 Reference & Control: gsref_nonhapvoc_2030_ref_tier3 [v3] Speciation xref for NONHAPVOC, year

specific GSREF TMP_I Spcmat nonpt 2017 Reference & Control: gsref_nonhapvoc_2017_ref_tier3 [v2] Speciation xref for PM2.5 diesel SCCs but do not produce diesel GSREFTMP_

D Spcmat gsref_no_dieselpm [v3] gsref_no_dieselpm [v3]

Speciation xref for PM2.5 non

- diesel SCCs GSREFTMP_

E Spcmat gsref_pm25_pf4_nondiesel [v14] gsref_pm25_pf4_nondiesel [v14] Speciation xref for SMOKE

- MOVES not

TOG GSREFTMP_P Spcmat onroad gsref_new_for_smoke

moves_otherthantog [v0] gsref_new_for_smoke

moves_otherthantog [v0]

Speciation xref f

or SMOKE

- MOVES TOG GSREFTMP_

O Spcmat onroad gsref_new_for_smoke

- moves_tog [v1] 2017 Reference and Control: gsref_2017_for_smoke_moves_tog [v1]

2030 Reference and Control: gsref_2030_ref_for_smoke_moves_to g [v1]

Speciation xref for SO2

- SULF GSREFTMP_

B Spc mat gsref_sulf [v0] gsref_sulf [v0]

Speciation xref for speciated VOC GSREFTMP_

M Spcmat onroad gsref_speciated_voc [v2] gsref_speciated_voc [v2]

Speciation xref for speciated VOC GSREFTMP_

M Spcmat othpt gsref_speciated_voc [v2] gsref_speciated_voc [v2 ]

Speciation xref for VOC, not year

- specific GSREFTMP_F Spcmat gsref_voc_general_hdghg [v3] 2017 Reference and Control: gsref_voc_general_hdghg [v3]

2030 Reference & Control: gsref_voc_general_tier3_2030_ref [v1]

Speciation xref for VOC, year

- specific GSREFTMP_

G Spcmat gsref_voc_2005_hdghg [v4] 2017 Reference & Control: gsref_voc_2017_ref_tier3 [v3]

2030 Reference & Control: gsref_voc_2030_ref_tier3 [v1]A

- 9

Input N ame Environment

Variable Program Sector 2005 Base Case 2017 & 2030 Reference /Con trol

Case s

Speciation xref HG GSREFTMP_

K Spcmat gsref_hg [v8] gsref_hg [v8] Speciation xref static NOX

-- HONO for

mobile sources GSREFTMP_

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]

Tempora l profiles, all nonpoint and nonroad 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, onr oad 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 Tempo ral 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, p tipm only PTREF Temporal P tipm ptref_ptipm_us [v0] ptref_ptipm_us [v0]

Table A

- 2. Parameters used in the Tier 3 NPRM c ases

Parameter Name Environment Variable Sector 2005 Base Case 2017 /2030 Reference and Control

Case s

All 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 12

BEIS3 version BEIS3_VERSION beis 3.14 3.14

Biogenics land area surrogate AREA_SURROGATE_NU

M beis 340 340

Biogenics speciation profile code BIOG_SPRO beis B10C5 B10C5

Check for duplicate sou rces RAW_DUP_CHECK ptfire N N

Check for duplicate sources RAW_DUP_CHECK ptnonipm N N

Check for duplicate sources RAW_DUP_CHECK ptipm N N

Check for duplicate sources RAW_DUP_CHECK Y Y

Check for duplicate sources RAW_DUP_CHECK alm_no_c3 N

Check for du plicate sources RAW_DUP_CHECK nonpt NA

- 10

Parameter Name Environment Variable Sector 2005 Base Case 2017 /2030 Reference and Control

Case s
Check for duplicate sources RAW_DUP_CHECK othon N N

Check for duplicate sources RAW_DUP_CHECK othpt N N

Check for duplicate sources RAW_DUP_CHECK othar N N

Check stack parameters for missing CHECK_STACKS_YN ptfire N N

Convective rainfall variable for Pleim

- Xiu RC_VAR beis RC RC

Count of underscores for Daily data prefix NAMEBREAK_DAILY ptipm 9 10

Custom merge output SMKMERGE_CUSTOM_O

UTPUT Y Y

Custom merge output

- MOVES MOVESMRG_CUSTOM_O

UTPUT onroad Y Y

Defau lt surrogate code SMK_DEFAULT_SRGID 100 100

Default surrogate code SMK_DEFAULT_SRGID afdust 340 340

Don't need spinup for most sectors SPINUP_DURATION ptipm 0

Don't need spinup for most sectors SPINUP_DURATION nonpt 0 0

Don't need spinup for most sec tors SPINUP_DURATION ptipm

Don't need spinup for most sectors SPINUP_DURATION ptnonipm 0 0

Don't need spinup for most sectors SPINUP_DURATION nonroad 0 0

Don't need spinup for most sectors SPINUP_DURATION othpt 0 0

Don't need spinup for most sectors SPINUP_DURATION seca_c3

Don't need spinup for most sectors SPINUP_DURATION seca_c3 0

Don't speciate zero emission SCCs NO_SPC_ZERO_EMIS ptnonipm Y Y

Don't speciate zero emission SCCs NO_SPC_ZERO_EMIS nonpt Y Y

Don't use day

- specific emission DAY_SPE CIFIC_YN ptipm N N

Don't use pollutant conversion POLLUTANT_CONVERSI

ON onroad N N

EGU daily type EGU_TYPE model_performance model_performance

EMF queue options EMF_QUEUE_OPTIONS #NAME? #NAME?

Emission rate model SMK_EF_MODEL onroad MOVES MOVES

Fill annual values FILL_ANNUAL nonroad Y Y

Fill annual values FILL_ANNUAL N N

Fill annual values FILL_ANNUAL nonpt Y YA

- 11

Parameter Name Environment Variable Sector 2005 Base Case 2017 /2030 Reference and Control

Case s

Fire

- specific plume rise calculations FIRE_PLUME_YN ptfire Y Y

Formula for Smkinven SMKINVEN_FORMULA PMC=PM10

- PM2_5 PMC=PM10

- PM2_5

Form ula for Smkinven SMKINVEN_FORMULA ag

Formula for Smkinven SMKINVEN_FORMULA nonroad EXH__PMC=EXH__PM10

- EXH__PM2_5 EXH__PMC=EXH__PM10

- EXH__PM2_5

Formula for Smkinven SMKINVEN_FORMULA onroad

Include market penetration MRG_MARKETPEN_YN N N

I/O API S phere type IOAPI_ISPH 19 19 Laypoint uses Elevpoint to set sources for plume

rise calc SMK_SPECELEV_YN Y Y

Match full SCCs FULLSCC_ONLY Y Y

Maximum errors printed SMK_MAXERROR 10000 10000

Maximum warnings printed SMK_MAXWARNING 10 10

MCIP name ab breviation MCIPNAME MCIP_v3.4beta4 MCIP_v3.4beta4

Merge by day MRG_BYDAY ptnonipm P P

Merge by day MRG_BYDAY seca_c3 P P

Merge by day MRG_BYDAY othpt P P

Merge type M_TYPE Mwdss M wdss

Merge type M_TYPE ptipm All A ll

Merge type M_TYPE ptnonipm Mwdss M wdss

Merge type M_TYPE ptfire All A ll

Merge type M_TYPE avefire Aveday A veday

Merge type M_TYPE ag Aveday A veday

Merge type M_TYPE afdust Week W eek

Merge type M_TYPE onroad All A ll

Merge type M_TYPE nonptfire Aveday A veday

Merge type M_TYPE othpt Mwdss M wdss

Merge type M_TYPE othon Week W eek

Merge type M_TYPE seca_c3 Aveday A veday

Merge type M_TYPE beis All A ll

Model output format OUTPUT_FORMAT $EMF_AQM $EMF_AQMA

- 12

Parameter Name Environment Variable Sector 2005 Base Case 2017 /2030 Reference and Control

Case s
Nonhap Type NONHAP_TYPE nonpt VOC VOC

Nonhap Type NONHAP_TYPE ptnonipm VOC VOC

Nonhap Type NONHAP_TYPE avefire VOC VOC

Nonhap Type NONHAP_TYPE nonroad VOC VOC

Nonhap Type NONHAP_TYPE onroad TOG TOG

Nonhap Type NONHAP_TYPE alm_no_c3 VOC VOC

Nonhap Type NONHAP_TYPE seca_c3 VOC VOC

Number of emissions layers SMK_EMLAYS 10 10

Ocea n Chlorine filename extension EXT mrggrid .ncf .ncf

Output county biogenic totals BIO_COUNTY_SUMS beis Y Y

Output county/SCC totals MRG_REPSRC_YN onroad Y Y

Output county totals MRG_REPCNY_YN N N

Output county totals MRG_REPCNY_YN Y

Output county t otals MRG_REPCNY_YN onroad Y Y

Output SCC totals MRG_REPSCC_YN onroad Y Y

Output state biogenic totals BIO_STATE_SUMS beis Y Y

Output state totals MRG_REPSTA_YN Y Y

Output state totals MRG_REPSTA_YN N

Output state totals MRG_REPSTA_YN onroad N N

O utput time zone OUTZONE 0 0

Platform name PLATFORM v4.3 v4.3

Pleim

- Xiu land surface used? PX_VERSION beis Y Y

Plume

- in

- grid method SMK_PING_METHOD 0 0

Pressure variable name PRES_VAR beis PRSFC PRSFC

PTDAY file name case DAILY_CASE ptipm 2005ck 20 05ck

Radiation/cloud variable name RAD_VAR beis RGRND RGRND

Renormalize temporal profiles RENORM_TPROF Y Y

Report default profiles used REPORT_DEFAULTS Y Y

Run holidays RUN_HOLIDAYS ag N N

Run holidays RUN_HOLIDAYS avefire N NA

- 13

Parameter Name Environment Variable Sector 2005 Base Case 2017 /2030 Reference and Control

Case s
Run holidays RUN_HOLI DAYS Y Y

Run holidays RUN_HOLIDAYS seca_c3 N N

Run holidays RUN_HOLIDAYS alm_no_c3 N N

Run holidays RUN_HOLIDAYS othon N N

Run holidays RUN_HOLIDAYS othpt N N

Run holidays RUN_HOLIDAYS othar N N

Run holidays RUN_HOLIDAYS nonptfire N N

Run holidays RUN_HOLIDAYS afdust Y Y

Run in inline mode INLINE_MODE Both B oth

Run in inline mode SECA_C3 INLINE_MODE seca_c3 Only O nly

Run script for Smkmerge annual totals RUN_PYTHON_ANNUAL Y Y

Smkmerge reports units MRG_TOTOUT_UNIT tons/dy

SMOKE

- MOVES proces sing mode MOVES_TYPE onroad RPD RPD

SMOKE

- MOVES processing mode MOVES_TYPE onroad RPP RPP

SMOKE

- MOVES processing mode MOVES_TYPE onroad RPV RPV

Soil moisture variable for Pleim

- Xiu SOIM1_VAR beis SOIM1 SOIM1

Soil temperature variable for Pleim

- Xiu SOIL T_VAR beis SOIT1 SOIT1

Soil type variable for Pleim

- Xiu ISLTYP_VAR beis SLTYP SLTYP

Sort inventory EVs by letter SORT_LIST_EVS othpt Y Y

Sort inventory EVs by letter SORT_LIST_EVS avefire Y Y

Sort inventory EVs by letter SORT_LIST_EVS ptipm Y Y

Specia tion type name SPC $EMF_SPC $EMF_SPC

Spinup Duration SPINUP_DURATION 10 10

Spinup Duration SPINUP_DURATION 3 3

Temperature variable name TMPR_VAR beis TEMP2 TEMP2

Temperature variable name

- MOVES TVARNAME onroad TEMP2 TEMP2

Temporal type L_TYPE M wdss M wdss

Temporal type L_TYPE ptipm All A ll

Temporal type L_TYPE ptfire All A ll

Temporal type L_TYPE avefire Aveday A vedayA

- 14

Parameter Name Environment Variable Sector 2005 Base Case 2017 /2030 Reference and Control

Case s
Temporal type L_TYPE ag Aveday A veday

Temporal type L_TYPE afdust Week W eek

Temporal type L_TYPE onroad All A ll

Temporal typ e L_TYPE nonptfire Aveday A veday

Temporal type L_TYPE othon Week W eek

Temporal type L_TYPE seca_c3 Aveday A veday

Temporal type L_TYPE beis All A ll

Use area

- to

- point SMK_ARTOPNT_YN alm_no_c3 Y Y

Use area

- to

- point SMK_ARTOPNT_YN nonpt Y Y

Use area

- to

- p oint SMK_ARTOPNT_YN nonroad Y Y

Use average day emissions SMK_AVEDAY_YN N N

Use day

- specific emission DAY_SPECIFIC_YN ptipm Y Y

Use day

- specific emission DAY_SPECIFIC_YN ptfire Y Y

Use hourly plume rise data HOURLY_FIRE_YN ptfire Y Y

Use NHAPEXCLUDE file SMK_PROCESS_HAPS alm_no_c3 PARTIAL PARTIAL

Use NHAPEXCLUDE file SMK_PROCESS_HAPS seca_c3 ALL ALL

Use NHAPEXCLUDE file SMK_PROCESS_HAPS onroad ALL ALL

Use NHAPEXCLUDE file SMK_PROCESS_HAPS nonroad PARTIAL PARTIAL

Use NHAPEXCLUDE file SMK_PROCESS_HA PS avefire NONE NONE

Use NHAPEXCLUDE file SMK_PROCESS_HAPS ptnonipm PARTIAL PARTIAL

Use NHAPEXCLUDE file SMK_PROCESS_HAPS nonpt PARTIAL PARTIAL

Use pollutant conversion POLLUTANT_CONVERSI

ON Y Y

Western hemisphere? WEST_HSPHERE Y Y

Write zero emissio ns WRITE_ANN_ZERO ptfire Y Y

Write zero emissions WRITE_ANN_ZERO ptipm Y Y

Zip merged model

- ready files GZIP_OUTPUTS mrggrid Y Y

Base Year 2005 2005

Downstream Model CMAQ v4.7 N5c CMAQ v4.7 N5c

End Date & Time 12/31/2005 23:59 12/31/2005 23:59A

- 15

Parameter Name Environment Variable Sector 2005 Base Case 2017 /2030 Reference and Control

Case s

Future Year 0 2017

Last Modified Date 13:57.7 22:39.1

Meteorological Year 2005 2005

Model SMOKE SMOKE

Modeling Region National National

# of emission layers 14 14

# of met layers 14 14

Speciation cmaq_cb05_tx cmaq_cb05_tx

Start Date 1/1/2005 0:00 1/1/2005 0:00

Version 2.7 2.7B

- 1 

## E missions Inventory for Air Quality Modeling Technical Support

Document : Proposed Tier 3 Emissions Standards

Appendix B

Inventory Data F iles Used for Each Tier 3 Modeling Case

– SMOKE Input Inventory Datasets

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 27711

M arch 2013B

- 2 The emissions inventory data files used for the Tier 3 cases are shown in Table B

- 1. The Input name c olumn gives a brief designator for the inventory. The Sector column specifies the modeling sector for the inventory. The remaining columns show the data set name and version used

in the 2005 base case and 2017 reference cases. The datasets used for the 2017 and 2030 control case s are identical to the 2017 and 2030

reference case s, respectively , except for the following replacements:

Onroad mobile inventories (onroad sector) Onroad refueling inventories (nonpt sector)
Nonroad mobile inventories (nonroad sector)

To match the Datasets and Versions listed in this table to actual data files, combine the Dataset name and the version number in the following pattern: <Dataset Name>_<Date>_<Version number>.txt, where <Date> is the last date of change for that v ersion and will have a unique value for

the combination of Dataset Name and Version number

.

Table B

- 1

. List of inventory data sets associated with the Tier 3 NPRM modeling cases.

Input name Sector 2005 Base Case 2017 Reference /Control Case s 2030 Referenc e /Control Case s

Inventory afdust CAP afdust afdust_2002ad_xportfrac [v0] afdust_2017ct_ref [v0] afdust_2030ct_ref [v0]

Inventory ag CAP a g ag_cap2002nei [v0] ag_cap2017ct_ref [v0] ag_cap2030ct_ref [v0]

Inventory alm_no_c3 CAP alm_no_

c3 lm_no_c3_cap2002v 3 [v1] lm_no_c3_cap2017ct_lowE [v0] lm_no_c3_cap2030cs [v0]

Inventory alm_no_c3 HAP alm_no_

c3 lm_no_c3_hap2002v4 [v0] lm_no_c3_hap2017ct_lowE [v0] lm_no_c3_hap2030cs [v0]

Inventory avefire CAP avefire avefire_2002ce [v0] avefire_2002ce [v0] avefire_2002c e [v0]

Inventory avefire HAP avefire avefire_2002_hap [v0] avefire_2002_hap [v0] avefire_2002_hap [v0]

Inventory C1/C2 additional CAP/HAP alm_no_

c3 c1c2_additional_2017ct_ref_caphap_25 jul2011 [v0] c1c2_additional_2030ct_ref_caphap_25 jul2011 [v0]

Inven tory nonpt CAP and HAP (PFC only) nonpt pfc_2002_caphap_wETOH [v1] pfc_2017_ref_caphap_23aug2011 [v0] pfc_2030_ref_caphap_23aug2011 [v0]

Inventory nonpt CAP/HAP Cellulosic Biodiesel plants for Tier3 nonpt cellulosic_ETOH_Biodiesel_2017ct_re f_caphap_29jul 2011 [v0] cellulosic_ETOH_Biodiesel_2030ct_re f_caphap_29jul2011 [v0] Inventory nonpt CAP/HAP Ethanol Transport for Tier3 nonpt Ethanol_transport_vapor_2017ct_ref_c aphap_25jul2011 [v0] Ethanol_transport_vapor_2030ct_ref_c aphap_25jul2011 [v0] Inventory no npt CAP (no PFC, no refueling) nonpt nonpt_pf4_cap_nopfc [v6] nonpt_pf4_cap_nopfc_2017ct_ref [v0] nonpt_pf4_cap_nopfc_2030ct_ref [v0] Inventory nonpt CAP: TX and OK

Oil and Gas nonpt nonpt_cap_2005_TCEQ_Oklahoma _OilGas [v0] nonpt_cap_2017ct_lowE_TCEQ_Okl ah oma_OilGas [v0] nonpt_cap_2030ct_lowE_TCEQ_Oklah oma_OilGas [v0] Inventory nonpt CAP: WRAP Oil

and Gas nonpt nonpt_cap_2005_WRAP_OilGas [v0] nonpt_cap_2018PhaseII_WRAP_OilGa s [v0] nonpt_cap_2018PhaseII_WRAP_OilGa s [v0]B

- 3

Input name Sector 2005 Base Case 2017 Reference /Control Case s 2030 Referenc e /Control Case s
Inventory nonpt HAP (no PFC, no

r efueling) nonpt nonpt_pf4_hap_nopfc_nobafmpestic idesplus [v4] nonpt_pf4_hap_nopfc_nobafmpesticide splus_2017ct_ref [v0] nonpt_pf4_hap_nopfc_nobafmpesticide splus_2030ct_ref [v0]

Inventory nonpt Refueling from MOVES, April nonpt rfl_moves_wETOH_2005ct_apr_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_apr_27 jul2011 [v0] 20 30 Reference: rfl_moves_wETOH_2030ct_ref_apr_27 jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _apr_2 8 jul2011 [v0]

Inventory nonpt Refueling from MOVES, August nonpt rfl_moves_wETOH_2005ct_ aug_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_aug_2 7jul2011 [v0] 2030 Reference:
rfl_moves_wETOH_2030ct_ref_a ug _2 7jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl_aug _2

8 jul2011 [v0]

Inventory nonpt Refueling from MOVES, December nonpt rfl_moves_wETOH _2005ct_dec_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_dec_2 7jul2011 [v0] 2030 Reference:
rfl_moves_wETOH_2030ct_ref_ dec _2 7jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _dec _2 8 jul2011 [v0]

Inventory nonpt Refueling from MOVES, February nonpt rfl_mov es_wETOH_2005ct_feb_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_feb_27 jul2011 [v0] 2030 Reference:
rfl_moves_wETOH_2030ct_ref_feb _27 jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _feb _2 8 jul2011 [v0]

Inventory nonpt Refueling from MOVES, January nonpt rfl_moves_wETOH_2005ct_jan_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_jan_27 jul2011 [v0] 2030 Reference: rfl_moves_wETOH_2030ct_ref_jan _27 jul2011 [v0]

2030 Control: rfl_moves_wETOH_2030ct_ ctl _jan _2 8 jul2011 [v0]

Inventory nonpt Refueling from MOVES, July no npt rfl_moves_wETOH_2005ct_jul_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_jul_27 jul2011 [v0] 2030 Reference: rfl_moves_wETOH_2030ct_ref_jul _27 jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _jul _2 8 j ul2011 [v0]

Inventory nonpt Refueling from
MOVES, Jun e nonpt rfl_moves_wETOH_2005ct_jun_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_jun_27 jul2011 [v0] 2030 Reference: rfl_moves_wETOH_2030ct_ref_jun _27 jul2011 [v0]B

- 4

Input name Sector 2005 Base Case 2017 Reference /Control Case s 2030 Referenc e /Control Case s

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _jun _2 8 jul2011 [v0]

Inventory nonpt Refueling from
MOVES, March nonpt rfl_moves_wETOH_2005ct_mar_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_mar_2 7jul2011 [v0] 2030 Reference:
rfl_moves_wETOH_2030ct_ref_mar _2 7jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _mar _2

8 jul2011 [v0]

Inventory nonpt Refueling from

M OVES, May nonpt rfl_moves_wETOH_2005ct_may_1 8may2011 [v0] rfl_moves_wETOH_2017ct_ref_may_2 7jul2011 [v0] 2030 Reference: rfl_moves_wETOH_2030ct_ref_may _2 7jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _may _2

8 jul2011 [v0]

Inventory nonpt Refueling fro m MOVES, November nonpt rfl_moves_wETOH_2005ct_nov_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_nov_2 7jul2011 [v0] 2030 Reference:
rfl_moves_wETOH_2030ct_ref_nov _2 7jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _nov _2

8 jul2011 [v0]

Inventory nonpt Refue ling from MOVES, October nonpt rfl_moves_wETOH_2005ct_oct_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_oct_27 jul2011 [v0] 2030 Reference:
rfl_moves_wETOH_2030ct_ref_oct _27 jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _oct _2 8 jul2011 [v0]

Inventory nonp t Refueling from MOVES, September nonpt rfl_moves_wETOH_2005ct_sep_18 may2011 [v0] rfl_moves_wETOH_2017ct_ref_sep_2 7jul2011 [v0] 2030 Reference: rfl_moves_wETOH_2030ct_ref_sep _2 7jul2011 [v0]

2030 Control:
rfl_moves_wETOH_2030ct_ ctl _sep _2 8 jul2011 [v0]

Inven tory nonroad cap+CMAQ

- lite HAPs US, incl Calif April nonroad nonroad_cmaq_lite_2005ct_apr_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref_apr_20 jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla_apr_0 8sep2011 [v0] 2030 Reference:
nonroad_ cmaq_lite_2030ct_ref_apr_03 aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl_apr_03 aug2011 [v0] Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif August nonroad nonroad_cmaq_lite_2005ct_aug_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_r ef _aug_ 20 2030 Reference:

nonroad_cmaq_lite_2030ct_ref _aug_ 03B

- 5

Input name Sector 2005 Base Case 2017 Reference /Control Case s 2030 Referenc e /Control Case s
jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _aug_ 0 8sep2011 [v0] aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _aug_ 03 aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, inc l Calif December nonroad nonroad_cmaq_lite_2005ct_dec_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref _dec_ 20 jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _dec_ 0 8sep2011 [v0] 2030 Reference: nonroad_cmaq_lite_2030ct_ref _dec_ 03 aug2011 [ v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _dec_ 03 aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif February nonroad nonroad_cmaq_lite_2005ct_feb_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref _feb_ 20 jul2011 [v0]

2017 Control : nonroad_cmaq_lite_2017ct_ctla _feb_ 0 8sep2011 [v0] 2030 Reference: nonroad_cmaq_lite_2030ct_ref _feb_ 03 aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _feb_ 03 aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif January nonroad nonroad_cm aq_lite_2005ct_jan_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref _may_ 2 0jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _may_ 08sep2011 [v0] 2030 Reference: nonroad_cmaq_lite_2030ct_ref _may_ 0 3aug2011 [v0]

2030 Control: nonroad_cmaq_lite _2030ct_ctl _may_ 0 3aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif July nonroad nonroad_cmaq_lite_2005ct_jul_19m ay2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref _jul_ 20j ul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _jul_ 08

s ep2011 [v0] 2030 Reference: nonroad_cmaq_lite_2030ct_ref _jul_ 03a ug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _jul_ 03a ug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif June nonroad nonroad_cmaq_lite_2005ct_jun_19 may2011 [v0] 2017 Ref erence: nonroad_cmaq_lite_2017ct_ref _jun_ 20 jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _jun_ 0 8sep2011 [v0] 2030 Reference: nonroad_cmaq_lite_2030ct_ref _jun_ 03 aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _jun_ 03 aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif March nonroad nonroad_cmaq_lite_2005ct_mar_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref _mar_ 2 0jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _mar_ 0 8sep2011 [v0] 2030 Reference: nonroad_cmaq_ lite_2030ct_ref _mar_ 0 3aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _mar_ 03 aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite nonroad nonroad_cmaq_lite_2005ct_may_19 2017 Reference: 2030 Reference:B

- 6

Input name Sector 2005 Base Case 2017 Reference /Control Case s 2030 Referenc e /Control Case s
HAPs US, incl Calif May may2011 [v0] nonroad_cmaq_lite_2017ct_ref _may_ 2 0jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _may_ 08sep2011 [v0] nonroad_cmaq_lite_2030ct_ref _may_ 0 3aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _may_ 0 3aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif November nonroad nonroad_cmaq_lite_2005ct_nov_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref _nov_ 20 jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _nov_ 0 8sep2011 [v0] 2030 Reference: nonroad_cmaq_lite_2030ct_ref _nov_ 03 aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _nov_ 03 aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif October nonroad nonroad_cmaq_lite_2005ct_oct_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref _oct_ 20j ul2011 [v0]

2017 Control:

nonroad _cmaq_lite_2017ct_ctla _oct_ 0 8sep2011 [v0] 2030 Reference: nonroad_cmaq_lite_2030ct_ref _oct_ 03 aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct_ctl _oct_ 03 aug2011 [v0]

Inventory nonroad cap+CMAQ

- lite HAPs US, incl Calif September nonroad nonroad_cmaq_lite _2005ct_sep_19 may2011 [v0] 2017 Reference: nonroad_cmaq_lite_2017ct_ref _sep_ 20 jul2011 [v0]

2017 Control: nonroad_cmaq_lite_2017ct_ctla _sep_ 0 8sep2011 [v0] 2030 Reference: nonroad_cmaq_lite_2030ct_ref _sep_ 03 aug2011 [v0]

2030 Control: nonroad_cmaq_lite_2030ct _ctl _sep_ 03 aug2011 [v0]

Inventory onroad RPD onroad VMT_tier3_2005 [v0] VMT_tier3_2017_ref_cntl [v3] VMT_tier3_2030_ref_cntl [v1]

Inventory onroad RPD onroad SPEED_tier3 [v0] SPEED_tier3 [v0] SPEED_tier3 [v0]

Inventory onroad RPP onroad VPOP_tier3_2005 [v0] VPOP_tier3_2017 [v0] VPOP_tier3_2030 [v0]

Inventory onroad RPV onroad VPOP_tier3_2005 [v0] VPOP_tier3_2017 [v0] VPOP_tier3_2030 [v0]

Inventory othar nonpoint CAP

Mexico border states othar nonpt mexico border1999 [v0] nonpt mexico border1999 [v0] no npt mexico border1999 [v0] Inventory othar nonpoint CAP

Mexico interior states othar nonpt mexico interior1999 [v0] nonpt mexico interior1999 [v0] nonpt mexico interior1999 [v0] Inventory othar nonroad CAP Mexico

border states othar nonroad mexico border 1999 [v0] nonroad mexico border1999 [v0] nonroad mexico border1999 [v0] Inventory othar nonroad CAP Mexico

interior states othar nonroad mexico interior1999 [v0] nonroad mexico interior1999 [v0] nonroad mexico interior1999 [v0] Inventory othon CAP Mexico border

states othon onroad mexico border1999 [v0] onroad mexico border1999 [v0] onroad mexico border1999 [v0] Inventory othon CAP Mexico interior

states othon onroad mexico interior1999 [v0] onroad mexico interior1999 [v0] onroad mexico interior1999 [v0]B

- 7

Input name Sector 2005 Base Case 2017 Reference /Control Case s 2030 Referenc e /Control Case s

Inventory othon CAP onroad Canada othon canada_onroad_cap_2006 [v0] canada_onroad_cap_2006 [v0] canada_onroad_cap_2006 [v0] Inventory othpt CAP Mexico border

states othpt mexico_border99 [v1] mexico_border99 [v1] mexico_border99 [v1] Inventory othpt CA P Mexico interior

states othpt mexico_interior99 [v0] mexico_interior99 [v0] mexico_interior99 [v0]

Inventory othpt CAP offshore othpt ptnonipm_offshore_oil_cap2005v2_ 20nov2008 [v0] ptnonipm_offshore_oil_cap2005v2_20 nov2008 [v0] ptnonipm_offshore_oil_cap2 005v2_20 nov2008 [v0]

Inventory ptipm CAP ptipm ptipm_2005cs_cap_27dec2010.txt [v1] PTINV_EPA410FINAL_BC_58_summ er_2020_21MAY2011_ORL [v0] 2030 Reference Case:
PTINV_EPA410FINAL_BC_58_summ er_2030_27MAY2011_ORL [v0]

2030 Control Case:
PTINV_EPA410_BC_15b_su mmer_20 30_02FEB2011_ORL [v0]

Inventory ptipm daily data (CEM sources) ptipm ptday_ptipm_caphap_cem_2005cs_ 05b (/garnet/oaqps) [v0] ptday_ptipm_caphap_cem_2017ct_05b [v0] 2030 Reference Case: ptday_ptipm_caphap_cem_2030ct_low E [v0]

2030 Control: ptday_ptip m_caphap_cem_2030cs_hdg hg_ref_05b [v0]

Inventory ptipm daily data (nonCEM sources) ptipm ptday_ptipm_caphap_noncem_2005 cs_05b (/garnet/oaqps) [v0] ptday_ptipm_caphap_noncem_2017ct_ 05b [v0] 2030 Reference: ptday_ptipm_caphap_noncem_2030ct_l owE [v0]

2030 Co ntrol: ptday_ptipm_caphap_noncem_2030cs_ hdghg_ref_05b [v0]

Inventory ptipm HAP ptipm ptipm_2005cs_hap_27dec2010.txt [v0]

Inventory ptnonipm CAP ptnonip

m ptnonipm_xportfrac_cap2005v2_20

05cs_orl [v7] ptnonipm_xportfrac_cap2017ct_ref [v0] ptnonipm_xportfr ac_cap2030ct_ref [v0] Inventory ptnonipm CAPHAP biodiesel plant additions forTier3 ptnonip

m biodiesel_plants_2017ct_ref_caphap_2 9jul2011 [v0] biodiesel_plants_2030ct_ref_caphap_2 9jul2011 [v0] Inventory ptnonipm CAPHAP ethanol plant additions forTier3 pt nonip

m ethanol_plants_2005ct_2017ct_low

E_caphap [v0] ethanol_plants_2017ct_ref_caphap_19j ul2011 [v0] ethanol_plants_2030ct_ref_caphap_19j ul2011 [v0]

Inventory ptnonipm cement capHg ptnonip

m ptnonipm_capHG_cementISIS_2016cr _16AUG2010 [v0] ptnonipm_capHG_c ementISIS_2016cr _16AUG2010 [v0]

Inventory ptnonipm HAP ptnonip

m ptnonipm_hap2005v2_2005cs_orl

[v6] ptnonipm_hap2017ct_ref [v0] ptnonipm_hap2030ct_ref [v0] Inventory rail additional CAP/HAP

for Tier3 ref/ctl alm_no_

c3 rail_additional_2017ct_ref_caphap_26 j ul2011 [v0] rail_additional_2030ct_ref_caphap_26j ul2011 [v0]

Inventory seca_c3 BAF HAPs Canada seca_c3 eca_imo_CANADA_SCC_fix_voch eca_imo_CANADA_SCC_fix_vochaps eca_imo_CANADA_SCC_fix_vochapsB

- 8

Input name Sector 2005 Base Case 2017 Reference /Control Case s 2030 Referenc e /Control Case s
aps_2005_09DEC2010 [v0] _2017 [v0] _2030_08FEB2011 [v0]

Invento ry seca_c3 BAF HAPs US

includes EEZ and offshore FIPS seca_c3 eca_imo_fixFIPS_US_andSCC_fix_ vochaps_2005_09DEC2010 [v0] eca_imo_fixFIPS_US_andSCC_fix_vo chaps_2017 [v0] eca_imo_fixFIPS_US_andSCC_fix_vo chaps_2030_08FEB2011 [v0]

Inventory seca_c3 CAP Canada seca_c3 eca_imo_CANADA_SCC_fix_caps _2005_09DEC2010 [v0] eca_imo_CANADA_SCC_fix_caps_20 17 [v0] eca_imo_CANADA_SCC_fix_caps_20 30_08FEB2011 [v0] Inventory seca_c3 CAP US + EEZ +

Offshore non

- Canada seca_c3 eca_imo_fixFIPS_US_wDE_andSC C_fix_caps_2005_09DEC201 0 [v0] eca_imo_fixFIPS_US_wDE_andSCC_f ix_caps_2017 [v0] eca_imo_fixFIPS_US_wDE_andSCC_f ix_caps_2030_08FEB2011 [v0] ORL Nonpoint Inventory

- Afdust

Canada 2006 othar canada_afdust_xportfrac_cap_2006 [v0] 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] canada_ag_cap_2006 [v0] ORL Nonpoint Inventory

- Aircraft

Canada 2006 othar canada_aircraft_cap_2006 [v0] canada_aircraft_cap_2006 [v0] canada_aircraft_cap_2006 [v0] ORL Nonpoint Inventory

- Commercial Marine Canada 2006 othar canada_marine_cap_2006 [v0] 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] canada_offroad_cap_2006 [v0] ORL Nonpoint Inventory

- Oarea

Canada 2006 othar canada_oarea_cap_2006 [v3] canada_oarea_cap_2006 [v3] canada_oarea_cap_2006 [v3] ORL Nonpoint Inventory

- Rail

Canada 2006 othar canada_rail_cap_2 006 [v0] 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] canada_point_2006_orl [v2] ORL Point Inventory

- Point CB5

2006 othpt canada_point_cb5_2006_orl [v0] 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] canada_point_uog_2006_orl [v0]United States

Environmental Protection Agenc y Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC Publication No. EPA

- 454/R

- 1 3

- 002 March, 2013