Emissions Inventory for Air Quality Modeling 

Technical Support Document: Proposed Tier 3 

Emissions Standards
EPA-454/R-13-002

March 2013

Emissions 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

March 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 sources (afdust, ag, avefire, nonpt)........................................................................20

2.5.1 Portable fuel containers............................................................................................................20

2.5.2 Onroad refueling ......................................................................................................................20

2.6 Other sources (biogenics, othpt, othar, and othon)........................................................................21

3 VOC Speciation Changes that Represent Fuel Changes..................................................................21

4 2017 and 2030 Reference Cases.........................................................................................................25

4.1 Stationary 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)...........................................................................50
ii

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 VOC ratio for 2-stroke snowmobiles for NMIM’s gasoline categories15

Table 2-9. Criteria for grouping representative counties for nonroad mobile analysis 16

Table 2-10. NONROAD model temperature (F) categories 17

Table 2-11. NONROAD NMIM runs 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 increases due to RFS2 fuels in 2017 and 2030 [tons] 37

Table 4-11. Growth factors from year 2005 to 2017 and 2030 for animal operations 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] 43
iii

Table 4-18. National impact of RICE controls on non-EGU projections 44

Table 4-19. Impact of fuel sulfur (SO2) 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. NOX, SO2, PM2.5 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. National (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. emissions (tons/year) by sector 55

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 Datasets
iv

ACRONYMS

AEO Annual Energy Outlook

BEIS Biogenic Emission Inventory System

bps Bulk plant storage

btp Bulk plant terminal-to-pump

C3 Category 3 (commercial marine vessels)

CAP Criteria Air Pollutant

CMAQ Community Multiscale Air Quality

CSAPR Cross-State Air Pollution (formerly Transport) Rule

E0 0% Ethanol gasoline (by volume)

E10 10% Ethanol gasoline

E15 15% Ethanol gasoline

EGU Electric Generating Utility

EISA Energy Independence and Security Act of 2007

EPAct Energy Policy Act of 2005

FAA Federal Aviation Administration

FIPS Federal Information Processing Standard

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 Rulemaking

OAQPS EPA’s Office of Air Quality Planning and Standards

ORL One Record per Line (a SMOKE input format)

MP Multipollutant

NO Nitric oxide

NO2 Nitrogen dioxide

NOX Nitrogen oxides

PFC Portable Fuel Container

PEC Elemental carbon component of PM2.5

PMFINE Leftover “Other”, or “crustal” component of PM2.5

PNO3 Particulate nitrate component of PM2.5

PSO4 Particulate sulfate component of PM2.5

POC Organic carbon component of PM2.5

rbt Refinery-to-bulk terminal

RFS2 Revised annual renewable fuel standard (mandate)

SMOKE Sparse Matrix Operator Kernel Emissions

SCC Source Category Code

TAF Terminal Area Forecast
v

TSD Technical Support Document

VOC Volatile Organic Compound

WRAP Western Regional Air Partnership
1

1 Introduction to the Modeling Platform

This Technical Support Document (TSD) describes the development of the emissions inventories used as 

inputs to the air quality modeling that the 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 NPRM”. The emissions inventories were generated using the Sparse Matrix Operator Kernel Emissions 

(SMOKE) modeling system (http://www.smoke-model.org/index.cfm) version 2.7 and processed into the 

form required by the Community Multi-scale Air Quality (CMAQ) model. CMAQ simulates the numerous 

physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate 

matter and air toxics. 

As part of the analysis for this rulemaking, the modeling system was used to calculate daily and annual PM2.5

concentrations, 8-hr maximum ozone, visibility impairment and seasonal and annual concentrations for the 

following HAPs: acetaldehyde, acrolein, benzene, 1,3-butadiene, ethanol and formaldehyde. Model 

predictions of PM2.5 and ozone are used in a relative sense to estimate scenario-specific, future-year design 

values of PM2.5 and ozone. These are combined with monitoring data to estimate population-level exposures 

to changes in ambient concentrations for use in estimating health and welfare effects. In this document, we 

provide an overview of (1) the emissions components of the modeling platform, (2) the development of the 

2005 base year emissions, (3) the development of the future year 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 for modeling ozone, PM2.5 and other model species 

with CMAQ. In support of this rule, EPA modeled the air quality in the Eastern and the Western United 

States using two separate model runs, each with a horizontal grid resolution of 12 km x 12 km. These 12 km 

modeling domains were “nested” within a modeling domain covering the remainder of the lower 48 states 

and surrounding areas using a grid resolution of 36 x 36 km. The results from the 36-km modeling were 

used to provide incoming “boundary” for the 12km grids. Additional details on the non-emissions portion of 

this 2005v4.3 modeling platform are described in the air quality modeling TSD. 

Version 4.3 of the 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 platform 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

2005ct 2005 calendar year case / scenarios that use an average year 

temporal allocation approach for Electrical Generating Units 

(EGUs), a pre-EISA/EPAct fuel supply for mobile sources, and 

average year fires data. Air quality outputs from this case are used 

to compute relative response factors with the 2017 future year 

reference case scenarios.

2017

reference case

2017ct_ref 2017 future year reference scenario with 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 2017 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 Pollution 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 fuels 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 among each of the cases run. In Section 4, we describe the 2017 and 

2030 Reference (i.e., future year baseline) cases 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 for each of the Tier 3 NPRM

cases.

2 2005 Emission Inventories and Their Preparation

As mentioned previously, the 2005 emissions modeling approach for the Tier 3 NPRM used much of the 

same data and approaches as the 2005v4.2 platform. In this section, we identify the differences between the 

data used for the Tier 3 NPRM 2005v4.3 platform and that used for the 2005v4.2 platform. Section 2.1

provides ancillary data differences that impact multiple sectors. Section 2.2 discusses the new approach used 

for emissions preprocessing and processing for all onroad mobile sources. Section 2.3 discusses the updated 

nonroad mobile components. Sections 2.4 and 2.5 provide differences for the point and nonpoint (area) 

inventories, respectively. 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 described in the Final Cross-State Air 

Pollution Rule TSD (http://www.epa.gov/ttn/chief/emch/index.html#2005), also known as the CAP-BAFM 

2005-based Version 4.2 Platform (i.e., 2005v4.2). However, some different emissions data are used for this 

rulemaking. All of the documentation provided here describes what was done differently and specifically for 

the Tier 3 NPRM in contrast to what 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-specific variability in some components of the inventory. 

For example, large fires vary in location and day of the year each year, and EGU shutdowns and high use on 

high energy demand days also vary by year. By using a base case approach, these two aspects of the 

inventory are maintained into the future year modeling but do not introduce potentially spurious yearspecific artifacts into the air quality modeling estimates. The biogenic emissions data were the same as those 

used for the 2005v4.2 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 cases 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 subsections refer to these platform sectors to identify the emissions differences 

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

Table 2-1. Sectors used in emissions modeling for the Tier 3 NPRM 2005v4.3 platform

Platform Sector

2005 NEI 

Sector Description

Contains HAP 

emissions?

IPM sector: ptipm Point NEI EGU units at facilities mapped to the IPM model using the 

National Electric Energy Database System (NEEDS) database.

Yes

Non-IPM sector: 

ptnonipm

Point+ All NEI point source units not matched to the ptipm sector, 

including airports.

Yes

Average-fire sector: 

avefire 

N/A Average-year wildfire and prescribed fire emissions, county and 

annual resolution. 

Yes

Agricultural sector: 

ag

Nonpoint Ammonia (NH3) emissions from NEI nonpoint livestock and 

fertilizer application.

No

Area fugitive dust 

sector: afdust

Nonpoint PM10 and PM2.5 emissions from fugitive dust sources in the NEI 

nonpoint inventory.

No

Remaining nonpoint 

sector: nonpt

Nonpoint+ All U.S. nonpoint (i.e. inventoried at the county-level) sources not 

otherwise included in other emissions modeling sectors.

Yes

Nonroad sector: 

nonroad

Mobile: 

Nonroad

Monthly nonroad emissions from the NONROAD model version 

NR08b and National Mobile Inventory Model (NMIM) software 

version NMIM20090504b and NMIM and Meteorology database 

version NCD20101201Tier3. Nonroad version is equivalent to 

NONROAD2008a used in 2005v4.2 for future year 2017; 

however, E15 fuels are allowed for the 2030 cases. 

Yes

C1 & C2 CMV and 

locomotives: 

alm_no_c3

Mobile: 

Nonroad

Primarily 2002 NEI non-rail maintenance locomotives, and 

category 1 and category 2 commercial marine vessel (CMV) 

emissions sources, county and annual resolution. Aircraft 

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

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

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

seca_c3 sector.

Yes

C3 commercial 

marine: seca_c3

Mobile: 

nonroad

Annual point source-formatted, year 2005 category 3 (C3) CMV 

emissions, developed for the rule called “Control of Emissions 

from New Marine Compression-Ignition Engines at or Above 30 

Liters per Cylinder”, usually described as the Emissions Control 

Area (ECA) study (http://www.epa.gov/otaq/oceanvessels.htm). 

Utilized final projections from 2002, developed for the C3 ECA 

Proposal to the International Maritime Organization (EPA-420-F10-041, August 2010).

Yes
4

Platform Sector

2005 NEI 

Sector Description

Contains HAP 

emissions?

Onroad Mobile: 

onroad

Mobile: 

onroad+

Motor Vehicle Emissions Simulator (MOVES) emission factors 

created using the Tier 3 NPRM version to account for hourlybased meteorology dependencies at a select number of 

representative counties. Includes local input information such as 

fuels, temperatures, vehicle fleet, speed distributions and controls. 

Emission factors are combined with activity data and gridded 

temperature via SMOKE to produce gridded emissions. These 

emissions are discussed extensively in Section 2.2.

Yes

Biogenic: biog N/A Hour-specific, grid cell-specific emissions generated from the 

BEIS3.14 model, including emissions in Canada and Mexico. 

Unchanged from the 2005v4 platform, and the same data are used 

for all future year scenarios.

No

Other point sources 

not from the NEI: 

othpt

N/A Point sources from Canada’s 2006 inventory and Mexico’s Phase 

III 1999 inventory, annual resolution. Also includes annual U.S. 

offshore oil 2005v2 NEI point source emissions. Unchanged from 

the 2005v4 platform, and the same data are used for all future year 

scenarios.

No

Other nonpoint and 

nonroad not from 

the NEI: othar

N/A Annual year 2006 Canada (province resolution) and year 1999 

Mexico Phase III (municipio resolution) nonpoint and nonroad 

mobile inventories. Unchanged from the 2005v4 platform, and 

the same data is used for all future year scenarios.

No

Other onroad 

sources not from the 

NEI: othon

N/A Year 2006 Canada (province resolution) and year 1999 Mexico 

Phase III (municipio resolution) onroad mobile inventories, 

annual resolution. Unchanged from the 2005v4 platform, and the 

same data 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 v1 or v2. 

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 (HAPs) and used slightly revised ancillary speciation data. Both of these differences are 

described in this section.

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

not included in the 2005v4.2 platform. A “lite” version of the multi-pollutant CMAQ (Version 4.7) was used 

that required emissions only for the species listed in the footnote of Table 2-2. In addition to the model 

species differences, the 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 future-year ancillary data were largely the 

same as those in 2005, with no substantial differences for most modeling sectors. The exception to this is 

onroad mobile, which 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 gas-phase chlorine

HCl HCL Hydrogen Chloride (hydrochloric acid) gas

CO CO Carbon monoxide

NOX NO Nitrogen oxide

NO2 Nitrogen dioxide

HONO Nitrous acid

SO2 SO2 Sulfur dioxide

SULF Sulfuric acid vapor

NH3 NH3 Ammonia

VOC ACROLEIN* Acrolein from the HAP inventory

ALD2 Acetaldehyde from VOC speciation

ALD_PRIMARY* Acetaldehyde from the HAP inventory

ALDX Propionaldehyde and higher aldehydes

BENZENE Benzene (not part of CB05)

BUTADIENE13*1,3-butadiene from the HAP inventory

ETH Ethene

ETHA Ethane

ETOH Ethanol, from select inventories provided by OTAQ

FORM Formaldehyde

FORM_PRIMARY*Formaldehyde from the HAP inventory

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

ISOP Isoprene

MEOH Methanol

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

PAR Paraffin carbon bond

TOL Toluene and other monoalkyl aromatics

XYL Xylene and other polyalkyl aromatics

Various additional 

VOC species from the 

biogenics model which 

do not map to the 

above model species

SESQ Sesquiterpenes

TERP Terpenes

PM10 PMC Coarse PM > 2.5 microns and 10 microns

PM2.5 PEC Particulate elemental carbon 2.5 microns

PNO3 Particulate nitrate 2.5 microns

POC Particulate organic carbon (carbon only) 2.5 microns

PSO4 Particulate Sulfate 2.5 microns

PMFINE Other particulate matter 2.5 microns

Sea-salt species (non –

anthropogenic 

emissions)

PCL Particulate chloride

PNA Particulate sodium

- ACROLEIN, ALD2_PRIMARY, BUTADIENE13, ETHANOL and FORM_PRIMARY are the extra 

“CMAQ-lite” HAPs that are not in the v4.2 platform.
6

Table 2-3. Description of differences in 2005 case ancillary data (unrelated to SMOKE to MOVES)

between the 2005v4.3 and 2005v4.2 platforms

Ancillary Data Type Difference between 2005v4.2 platform and 2005v4.3 platform

Speciation crossreferences and 

Speciation profiles

The Tier 3 NPRM 2005v4.3 data files are configured to support the multipollutant (MP) version of CMAQ, whereas the 2005v4.2 platform data files are

configured to support only 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 onroad 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). The 2005v4.2 platform data file is configured to support only 

the non-MP version.

2.2 Onroad mobile sources (onroad)

For each scenario, emissions from cars, trucks and motorcycles were estimated by using the EPA’s Motor 

Vehicle Emission Simulator (MOVES) to create emission factors that were then input to the Sparse Matrix 

Operator Kernel Emissions system (SMOKE). The SMOKE-MOVES integration tools combined the county 

and temperature-specific emission factors with the activity data to compute the actual emissions. In brief, 

our approach was to use the met4moves program to identify a set of temperatures that needed emission rates. 

For each scenario, we ran MOVES repeatedly to produce emission rates by temperature, Source 

Classification Code (SCC), speed bin, and representing county. The moves2smk tool then reformatted the 

MOVES rates and selected the appropriate rates for each county and month. Movesmrg multiplies the 

emission rates by county VMT or vehicle population, applies speciation profiles to develop inventories for 

pollutants not included in MOVES and temporally, and spatially allocates emissions to individual grid cells 

for CMAQ input.

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 included updated information on how fuel parameters impact vehicle emissions and 

updates on our understanding of evaporative emissions. It also included some minor updates to emission 

rates and some changes designed to make the model run more efficiently. All updates are described in detail 

in a memorandum to the docket (U.S. EPA 2013, Memorandum to Docket: Updates to MOVES for the Tier 

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

The gridded meteorological input data for the entire year of 2005 were derived from simulations of the 

Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model (MM5), a 

limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and 

thermodynamic equations which govern atmospheric motions. The Meteorology-Chemistry Interface 

Processor (MCIP) version 3.4 was used as the software for maintaining dynamic consistency between the 

meteorological model and chemistry mechanisms. The hourly gridded meteorological data was postprocessed 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 diurnal 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 average 
7

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: http://www.epa.gov/otaq/models/moves/index.htm.

The county inputs used for the rule were derived from the inputs used for the 2005 National Emissions 

Inventory (NEI). This inventory covers the 50 United States (U.S.), Washington DC, Puerto Rico and the

U.S. Virgin Islands. The NEI was created by the EPA's Emission Inventory and Analysis Group (EIAG) in 

Research Triangle Park, North Carolina, in cooperation with the Office of Transportation and Air Quality 

(OTAQ) in Ann Arbor, Michigan.

OTAQ has developed a consolidated modeling system known as the National Mobile Inventory Model 

(NMIM) 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 within NMIM. The basis for the 

2005 default vehicle miles traveled (VMT) is data supplied by the Federal Highway Administration 

(FHWA), as well as publicly available data from FHWA's Highway Statistics series. Details of how the 

NCD was developed are documented for the NEI "Documentation for the 2005 Mobile National Emissions 

Inventory, Version 2 (December 2008)", which can be obtained on EPA web site:

http://www.epa.gov/ttn/chief/net/2005inventory.html.

For the onroad portion of the inventory estimates for the rule, including all base and control scenarios, the 

current EPA highway mobile source emission model (MOVES) was used. This required conversion of the 

NCD parameters to a format consistent with MOVES. A contractor was given the assignment (TranSystems, 

Contract No. EP-D-06-001, WA 4-65) to convert the NCD to MOVES formatted input databases. This was 

accomplished with the assistance of converters designed for this purpose. These converters are available on 

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

All of the county-specific onroad data available in the NCD was converted to MOVES format for use at the 

county scale consistent with the databases created using the MOVES County Data Manager (CDM), except 

for the information regarding fuel properties. The fuel properties were updated using more recent 

information and methods specifically for this rule and described elsewhere in this document. Any table 

entries in the NCD that contained national average default information from the MOBILE6 model were 

replaced with the more recent national average default information used by MOVES.

2.2.2 Representing counties

Although EPA compiles county-specific databases for all counties in the nation, many of the states can 

provide little or no county-specific information for most counties. Rather than explicitly model every county 

in the nation (there are over 3,000 counties), we have performed detailed modeling for some counties and 

less detailed estimates for the other counties. In this rule, this 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 specific activity 

and meteorology data to generate emissions estimates for all of the counties in the group. This approach 

dramatically reduces the number of modeling runs required to generate inventories and still takes into 

account differences between counties.
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 used in air quality modeling. SMOKE uses 

emission rates (not inventories) to generate inventory estimates within each grid. Since SMOKE handles the 

differences in the fleet mix, temperatures, speeds and VMT versus location and time, MOVES can be run in 

the "emission rate" (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 counties1. For this analysis, 

we grouped counties with similar fuel, emission standards, altitude, and inspection and maintenance (I/M)

programs.

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

onroad and nonroad mobile sectors. For the onroad portion of the inventory estimates for the rule, including 

all base and control scenarios, the current EPA highway mobile source emission model (MOVES) was used. 

This required conversion of the NCD parameters to a format consistent with MOVES.

The NCD also does not contain county-specific information regarding vehicle populations and there are no 

default values. Vehicle population data is a required input for MOVES when modeling on a county basis. 

Using the technical guidance provided to states by EPA, the contractor generated appropriate estimates for 

vehicle populations for use in the MOVES databases using the county specific VMT and national average 

ratios of vehicle populations versus vehicle VMT from the MOVES application. This method is described in 

Section 3.3 of the document, "Technical Guidance on the Use of MOVES2010 for Emission Inventory 

Preparation in State Implementation Plans and Transportation Conformity" (EPA-420-B-10-023, April 

2010), which is available at: http://www.epa.gov/otaq/models/moves/index.htm.

The grouping of counties uses a tree algorithm, which is conceptually simple. In the tree algorithm, all 

counties are assigned to various categories. Then, by grouping counties within the same categories, you get 

groups of counties that have the similar parameters. Counties were sorted into their Petroleum 

Administration for Defense Districts (PADDs). PADD 1 is divided into three sub-PADD groupings and 

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

counties in any state are within the same PADD. Table 2-4 below shows the PADDs and the states within 

each PADD.

Table 2-4. Allocation of states to the Petroleum Administration for Defense Districts

PADD State FIPS State Name Abbreviation

1a 09 CONNECTICUT CT

1a 23 MAINE ME

1a 25 MASSACHUSETTS MA

1a 33 NEW HAMPSHIRE NH

1a 44 RHODE ISLAND RI

1a 50 VERMONT VT

1b 10 DELAWARE DE

1b 11 DISTRICT OF COLUMBIA DC

1b 24 MARYLAND MD

1b 34 NEW JERSEY NJ

1b 36 NEW YORK NY

1b 42 PENNSYLVANIA PA

 

1 This differs from the calculation of nonroad inventories where temperature was considered in the choice of representing county.
9

PADD State FIPS State Name Abbreviation

1c 12 FLORIDA FL

1c 13 GEORGIA GA

1c 37 NORTH CAROLINA NC

1c 45 SOUTH CAROLINA SC

1c 51 VIRGINIA VA

1c 54 WEST VIRGINIA WV

1c 72 PUERTO RICO PR

1c 78 VIRGIN ISLANDS VI

2 17 ILLINOIS IL

2 18 INDIANA IN

2 19 IOWA IA

2 20 KANSAS KS

2 21 KENTUCKY KY

2 26 MICHIGAN MI

2 27 MINNESOTA MN

2 29 MISSOURI MO

2 31 NEBRASKA NE

2 38 NORTH DAKOTA ND

2 39 OHIO OH

2 40 OKLAHOMA OK

2 46 SOUTH DAKOTA SD

2 47 TENNESSEE TN

2 55 WISCONSIN WI

3 01 ALABAMA AL

3 05 ARKANSAS AR

3 22 LOUISIANA LA

3 28 MISSISSIPPI MS

3 35 NEW MEXICO NM

3 48 TEXAS TX

4 08 COLORADO CO

4 16 IDAHO ID

4 30 MONTANA MT

4 49 UTAH UT

4 56 WYOMING WY

5 02 ALASKA AK

5 04 ARIZONA AZ

5 06 CALIFORNIA CA

5 15 HAWAII HI

5 32 NEVADA NV

5 41 OREGON OR

5 53 WASHINGTON WA

The counties in each PADD were sorted into fuel groups using the January fuel properties and the July fuel 

properties. The fuel supply and fuel formulation data were taken from the 2005 fuels developed for the rule. 

The fuel parameters used for grouping and the ranges of 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 parameter categories

Gasoline Parameter Category ID Minimum Value (>=) Maximum Value (<)

Reid Vapor Pressure (psi) 1 0 7.3

2 7.3 8.2

3 8.2 9.2

4 9.2 100

Sulfur (ppm) 1 0 50

2 50 100

3 100 110

4 110 1000

Ethanol (volume percent) 1 0 3

2 3 8

3 8 100

Benzene (volume percent) 1 0 1

2 1 1.5

3 1.5 2

4 2 10

Some states have adopted California 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 varies between these 

states, each state with California standards must be modeled independently from the other states with 

California standards as well. Each state with California standards will be treated separately when choosing 

representing counties. Table 2-6 shows the states with California emission standards.

Table 2-6. States adopting California emission standards

State ID State Name Abbreviation CA Program Begins

06 California CA 1994

25 Massachusetts MA 1995

36 New York NY 1996

50 Vermont VT 2000

23 Maine ME 2001

09 Connecticut CT 2008

42 Pennsylvania PA 2008

44 Rhode Island RI 2008

41 Oregon OR 2009

53 Washington WA 2009

34 New Jersey NJ 2009

24 Maryland MD 2011

10 Delaware DE 2014

35 New Mexico NM 2016

The counties in each PADD-fuel group were sorted into groups with and without I/M vehicle inspection 

programs. I/M programs were determined using the 2005 calendar year entries in the IMCoverage table of 

the MOVES database. The I/M category is the state in which the county resides. All I/M programs within a 

state were considered as a single program, even though each county may be administered separately and 

have a different program design.
11

Altitude was also added as its own category. Altitude is a field in the County table of the 

MOVESDB20101006 database. Counties are either high (H) or low (L) altitude based on the criteria set 

forth by EPA certification procedures (4,000 feet above sea level). The result is a set of county groups with 

similar fuel, emission standards, altitude and I/M program. Then the county with the highest VMT in each 

group is chosen as the representing county. The categories are summarized below in Table 2-7.

Table 2-7. Summary of county grouping characteristics for representative counties

County Grouping Characteristic Description

PADD Petroleum Administration for Defense Districts (PADDs). 

PADD 1 is divided into three sub-PADD groupings and 

each sub-group is treated as a separate PADD (1a, 1b and 

1c). Each state belongs to a PADD and all counties in any 

state are within the same PADD.

Fuel Parameters Average gasoline fuel properties for January and July 

2005, including RVP, sulfur level, ethanol fraction and 

percent benzene.

Emission Standards. Some states have adopted California LEV highway 

vehicle emission standards or plan to adopt them. Since 

implementation of the standards varies, each state with 

California standards is treated separately.

Inspection/Maintenance Programs Counties were grouped within a state according to whether 

or not they had an I/M program. All I/M programs within 

a state were considered as a single program, even though 

each county may be administered separately and have a 

different program design.

Altitude Counties are either high or low altitude based on the 

criteria set forth by EPA certification procedures (4,000 

feet above sea level).

Using these criteria, a set of 106 counties were selected to represent the nation. Of these, only 103 were 

needed to model the 48 states included in the 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 counties (excluding 

AK and HI) times 12 months = 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 processors, with each accessing a MySQL database specific to that run.

2.2.3 SMOKE-MOVES inputs

Both MOVES and SMOKE require meteorological data. The program met4moves takes gridded hourly 

meteorological data, the representative counties, and the representative fuel 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 month, it determines all the grid cells that fall within the corresponding counties in that

county group for the number of months that correspond to the fuel month2. The temperature range is then 

determined by looking at the minimum and maximum temperature across all these grid cells for all hours in 

that time period. Relative humidity is calculated by taking an average over these same grid cells.

 

2

Spatial surrogates are used in determining which grid cells to pull in calculating the various meteorological statistics. These 

spatial surrogates both map counties to grid cells. The spatial surrogates further limit the grid cells by determining whether some 

of the grid cells should not be included in the calculation of temperature range. For example, if some of the county has no roads or 

population, e.g. high mountains, then there is no reason to include it in the temperature range for onroad emissions.
12

For rate-per-profile (RPP) 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 range 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 these temperature ranges are countyspecific, 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 whole month for that 

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

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

output for the daily mode is one temperature range per county per day. Typically, the SMOKE input 

produced in monthly mode will have larger temperature ranges for each county than when it is run in daily 

mode. For the Tier 3 NPRM runs, met4moves was run in daily mode.

In addition 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) was 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 distributions found in the default MOVES2010a database AvgSpeedDistribution table. These average 

speeds are the average speeds developed for the previous EPA highway vehicle emission factor model, 

MOBILE6. The same speed data was used for the base and future year cases.

In MOVES, there is a distribution of average speeds for each hour of the day for each road type. The 

average speeds in these distributions were used to calculate an overall average speed for each hour of the 

day. These hourly average speeds were weighted together using the default national average hourly vehicle 

miles traveled (VMT) distribution found in the MOVES default database HourlyVMTFraction table, to 

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

each county.

2.2.4 Generating emission factors for SMOKE

After representative counties and fuel months were chosen, the met4moves script was executed to produce 

the set of MOVES RunSpecs and meteorology tables that would ultimately generate a set of SMOKE lookup 

tables encompassing the full range of temperatures for all the counties and months in each group. OTAQ 

also provided VMT, population, and average speed tables for every county. 

The onroad model-ready emissions were produced by running SMOKE-MOVES using 103 representative

counties and two fuel months. SMOKE-MOVES is a series of scripts and programs that 1) produce 

meteorological data for MOVES (Met4Moves), 2) construct a set of MOVES RunSpecs that produce lookup 

tables by temperature and average speed (runspec_generator), 3) process the MOVES lookup tables into a 

SMOKE-ready format (moves2smkEF), and 4) runs SMOKE. The way that OTAQ used SMOKE-MOVES 
13

differs somewhat from the way that SMOKE-MOVES was initially designed to be run. The full sequence of 

events was the following:

1) OAQPS ran met4moves for a nation-wide 12 km grid. This generated the temperatures needed for the

emission factor lookup tables and an average humidity for each county and month. The inputs to 

met4moves are the hourly gridded temperature and humidity generated by the meteorological model 

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

representative county and fuel-month, met4moves queries all the grid cells in all the represented 

county-months to find the full range of temperatures and profiles needed and averages the relative 

humidity. 

2) OTAQ ran the runSpec_generator Perl script, (runspec_generator_v0.3_04Nov2010.plx). The inputs 

to this process include the representative county list, fuel month list, temperature bin size (=10 

degrees here), and the outputs from met4moves. The runspec_generator script produced MOVES 

run-specifications that control how the MOVES run is configured, along with zonemonthhour tables

in CSV format. Specifications were generated for the three types of MOVES processes: rate-perdistance (RPD), rate-per-profile (RPP), and rate-per-vehicle (RPV). Run specifications were 

generated as needed to simulate the range of conditions reflected in the meteorological inputs. For 

RPD and RPV, a series of run specifications were created for each representative county, one for 

each temperature bin covering the temperature ranges provided by the met4moves output. For RPP, a 

second series of run specifications were created for each representative county, one for each diurnal 

profile provided by the met4moves output. The input data specific to each county were loaded into 

databases called “scaleinputdatabases”, and the zonemonthhour tables were also loaded into 

databases.

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

databases, and the run specifications. The tool implemented LEV programs into the specifications as 

appropriate and also modified the pollutant-process associations in the run specifications to meet the 

needs of 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 servers.

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 

movesmrg program. The postprocessor also performed additional calculations to support SMOKE 

processing of CMAQ ready model emissions: speciating HONO from NO and NO2, speciating the 

AE5 PM species (PEC, POC, PNO3, PSO4, PMFINE, and PMC for break and tire wear), and 

aggregating the detailed MOVES modes into 5 broader modes (exhaust, evaporative, permeation, 

break wear, and tire wear)3.

6) OAQPS downloaded the emission factors from the server and executed SMOKE programs to produce

gridded, hourly, speciated emissions for CMAQ. See the next section for details.

 

3 The moves2smk postprocessor also corrects the extended idle emissions for RPV by merging in data from a separate national 

extended idle run and replaces missing EF from RPD due to missing SCCroadtypes in some reference counties.
14

2.2.5 Running SMOKE for onroad mobile

Running SMOKE using emission factors (EF) from MOVES required the development of a new set of 

functionality. The central SMOKE program that performs this new analysis is movesmrg which takes 

activity data, meteorological data, and the EF to produce gridded emissions. SMOKE is run independently

for each of the three processes: rate-per-distance (RPD), rate-per-vehicle (RPV) and rate-per-profile (RPP).

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

vehicle exhaust, evaporation, permeation, break wear, and tire wear. For RPD, the activity data is monthly 

VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus weekend (SPDPRO)4. The 

SMOKE program temporal takes vehicle and roadtype specific temporal profiles and distributes the monthly 

VMT to day of the week and hour. Movesmrg reads the speed data for that county and SCC and the 

temperature from the gridded hourly data and uses these values to look-up the appropriate EF from the 

representative county’s EF table. It then multiplies this EF by 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 emissions process RPV is for modeling the off-network emissions. This includes the following modes: 

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

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

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

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

repeats for each pollutant and SCC in that grid cell.

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

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

county based diurnal temperature range from met4moves output for SMOKE. It uses this temperature range 

to determine the most similar idealized diurnal profile from the EF table using the temperature min and max, 

SCC, and hour of the day. It then multiplies this EF by the VPOP for that SCC and FIPS to calculate the 

emissions for that grid cell and hour. This repeats for each pollutant and SCC within the county. For more 

details on processing RPD, RPV, and RPP in SMOKE, see:

http://www.smoke-model.org/version3.1/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 were created: RPD, RPV, and RPP. SMOKE was run so that for each model day 

it would read in a single EF table (based on the appropriate fuel month), process all the counties that are part 

of the county group (i.e. are represented by that representative county), then read the next representative

county EF table, etc. After all days in the model year were looped over, SMOKE has generated a separate 

set of daily intermediate files for each of the emissions processes (RPD, RPV, and RPP). Post-processing 

scripts were developed to integrate the process specific intermediate files into model-ready intermediate files 

for the onroad sector. These files were generated for a national 12km domain. To support the CMAQ runs

they were further processed to create an aggregated 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. nationally NOx

showed a 0.8% increase, VOC showed a 1% increase, and PM2.5 showed a 3% increase). 
15

2.3 Nonroad mobile sources (nonroad, alm_no_c3, seca_c3)

The nonroad sectors include a wide-range of mobile emission sources ranging from locomotives, marine 

vessels, construction and farming equipment to hand-held lawn tools. As discussed in Section 4, nonroad 

upstream impacts also impact the (post-EPAct/EISA/RFS2) Tier 3 NPRM future year cases, reflecting 

increased ethanol production resulting in fuel volume increases for locomotives and C1/C2 CMV emissions.

2.3.1 Emissions generated with the NONROAD model (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 model and extensive documentation can be 

found at http://www.epa.gov/otaq/nonrdmdl.htm. NMIM is a program that references a national database of 

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

once for every county and month requested by the user and collects the results in an output database.

Information and downloads for NMIM can be found at http://www.epa.gov/otaq/nmim.htm. Rather than 

running every county, NMIM is designed to run NONROAD for “representative counties” and to use 

individual county activity data to develop national inventories.

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 Inventory process described at

http://www.epa.gov/ttn/chief/net/2008inventory.html. For this analysis, updates were made to the underlying 

fuel supply for the Tier 3 NPRM future year reference cases. Thus, the NCD20101201Tier3 contained 

special versions of countyyearmonth, gasoline, and diesel. The fuels in the NCD2010201Tier3 were 

developed from the fuels used for onroad vehicles, as described in Section 2.2.1.

A special countymonthhour table that contained 2005 meteorology was copied into the standard 

countymonthhour table. The use of the countymonthhour table for meteorology was selected by the RunSpec 

setting useYearlyWeatherDataSelected="false." A minor change was also made for snowmobiles: the SCC 

toxics table in the NMIM County Database (NCD) was updated to correct 1,3-butadiene exhaust emissions 

for 2-stroke snowmobiles (SCC 2260001020), as shown in Table 2-8 below. This correction addressed an 

issue identified in air quality modeling for the RFS2 rule, where unexpected increases in ambient 

concentrations were observed in 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-butadiene to VOC ratio for 2-stroke snowmobiles for NMIM’s gasoline categories

Fuel Type NCD20101201 NCD20101201Tier3

Base Gasoline 0.0012 0.0012

Ethyl Tertiary Butyl Ether (Eth) Gas 0.00732 0.0012

Methyl Tertiary Butyl Ether (MTBE) Gas 0.0012 0.0012

Reformulated Gasoline (RFG) Gas 0.0012 0.0012
16

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 inventory for this pollutant was from the chemical speciation that is

obtained by post-processing through SMOKE.

2.3.1.1 Representing counties for NONROAD

“Representing counties” is a way of saving NMIM run-time by grouping together similar counties and 

generating emission factors by running the NONROAD model for only one of those counties and then using 

those emission factors for all the counties in the group. For this analysis, 293 county groups were developed. 

The counties in each group were in the same state, had similar fuels in both summer and winter, and had 

similar I/M programs. Since there are winter fuels and summer fuels, January was chosen as the fuel-month 

to represent the seven months October through April, and July was chosen to represent the five months MaySeptember. The total number of county-months for which NMIM runs needed to be performed was thus 293 

times 12 months = 3,516 county-months for each scenario-year. If NMIM runs were performed for all U.S. 

counties and months, there would be 3141 counties (excluding AK and HI) times 12 months = 37,692 

county-months. Representing counties were chosen for NONROAD model NMIM runs by grouping 

counties based on the characteristics listed in Table 2-9.

Table 2-9. Criteria for grouping representative counties for nonroad mobile analysis

Characteristic Grouping Criteria

Petroleum Administration for Defense 

District (PADD)

All counties in a group must be in the same PADD.

Gasoline parameters Fuel bins were created for RVP, sulfur, benzene, and 

ethanol. All counties in each group had all of these fuel 

properties in the same bins for all twelve months.

Inspection/Maintenance Programs Counties with I/M programs were grouped with other 

counties with I/M programs in the same state.

Altitude All counties in the group must be in the same altitude 

category (high or low).

Temperatures All counties in the group must have similar temperatures, as 

detailed below.

Nonroad inventories are not calculated on a grid basis, as the highway mobile sources were. So, when 

running NMIM for nonroad emissions, the representing counties must also account for temperatures. The 

temperatures are taken from the 2005 calendar year values in the CountyYearMonthHour table of the 

NCD20100602 NMIM database. As shown in 
17

Table 2-10, ten-degree Fahrenheit (F) bins were created for min and max temperatures for each month. All 

counties in each group had all min and max temperatures for all twelve months in the same bins. The lowest 

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

100 degrees F.
18

Table 2-10. NONROAD model temperature (F) categories

Temperature 

Bin

Minimum 

Temperature (>=)

Maximum 

Temperature (<)

1 -20 -10

2 -10 0

3 0 10

4 10 20

5 20 30

6 30 40

7 40 50

8 50 60

9 60 70

10 70 80

11 80 90

12 90 100

13 100 200

Once counties were grouped, the representing county 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 runs

For the nonroad mobile portion of the inventory estimates for the rule, the NMIM county database (NCD) 

developed for the 2005 NEI, with the one exception of the county-specific fuel properties, was used to 

calculate nonroad emissions. The fuels that were 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 gasoline formulations are 

assumed to be identical. 

MOVES allows for multiple gasoline fuels, each with a market-share, for a single county-month, the market 

shares always sum to one. The NCD allows only one fuel per county month, but, for each of the four 

oxygenates (ETOH, MTBE, TAME, and ETBE), the NCD has columns for both volume percent and market 

share. The sum of these market shares is less than or equal to one. If the market-share is less than one, the 

remainder of the market is non-oxygenated (conventional) gasoline. When there are multiple MOVES 

gasoline fuels for a single county-month, non-oxygenate MOVES fuel properties are multiplied by market 

share and summed to produce the fuel property in the gasoline table. Individual non-ethanol oxygenates and 

California ethanol occur in only one fuel per county-month in MOVES, so the volume and market share are 

transferred to the appropriate columns for that oxygenate in NMIM. In states other than California, for 

multiple ethanol volumes with volume percents less than 10, the product of market-share and volume percent 

is averaged and then divided by 10, resulting in a market share for E10.

2.3.1.3 NMIM runs

Table 2-11 shows the NMIM runs that were performed to generate the NONROAD model county-month 

results for both national inventories and air quality modeling inventories.
19

Table 2-11. NONROAD NMIM runs

Case Year Run Name

Base 2005 Tier3Base2005Nr

Reference 2017 Tier3Ref2017e10Nr

Reference 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, EPA 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 modeling components

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 distance from the population-based 

centroid of each county.

2.3.2 Locomotives and commercial marine vessels (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 Rulemaking: Greenhouse Gas Emissions Standards and Fuel Efficiency 

Standards for Medium- and Heavy-Duty Engines and Vehicles signed on August 9, 2011 and available at 

http://www.epa.gov/oms/climate/regulations.htm#1-2, and the Final Cross-State Air Pollution (CSAPR) 

Rule: ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf. The procedures for 

calculating emissions from locomotives and C1/C2 commercial marine were developed for the Locomotive 

Marine Rule (2008) and are detailed in the RIA “Final Rule: Control of Emissions of Air Pollution from 

Locomotives and Marine Compression-Ignition Engines Less Than 30 Liters per Cylinder”, published May 

6, 2008 and republished June 30, 2008, and available at: 

http://www.epa.gov/oms/locomotives.htm#2008final. The procedures used for calculating C3 commercial 

marine emissions are those developed in the recent C3 “Final Rule: Control of Emissions from New Marine 

Compression-Ignition Engines at or Above 30 Liters per Cylinder”, published April 30, 2010 and available

at: http://www.epa.gov/oms/oceanvessels.htm#car-ems.
20

2.4 2005 point sources (ptipm and ptnonipm sectors)

Point sources are sources of emissions for which geographic coordinates (e.g., latitude/longitude) 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 point 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 inputs 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 

in emissions cases for future years. The inventory pollutants processed through SMOKE for both ptipm and 

ptnonipm sectors were: CO, NOX, VOC, SO2, NH3, PM10, and PM2.5 and the following HAPs: HCl 

(pollutant code = 7647010), and Cl2 (code = 7782505). We did not utilize BAFM from these sectors as we 

chose to speciate VOC without any use (i.e., integration) of VOC HAP pollutants from the inventory.

Integration is discussed in detail in Section 3.

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

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

2005v4.2 –the basis for the Final CSAPR and Heavy Duty Greenhouse Gas (HDGHG) FRM- emission 

modeling platform. However, 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 onroad 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, 

acetaldehyde 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 in
21

Table 2-13. More details are provided in a memorandum to the docket (Cook, 2012).
22

Table 2-13. 2005 ethanol plant emissions

Pollutant Emissions

1,3-Butadiene 0.0003

Acrolein 10.5

Formaldehyde 13.3

Benzene 5.7

Acetaldehyde 314.4

CO 7,023

NOX 8,204

PM10 10,107

PM2.5 3,691

SO2 9,001

VOC 10,754

2.5 2005 nonpoint sources (afdust, ag, avefire, nonpt)

The year 2005 area-source fugitive dust (afdust), agricultural animal and fertilizer NH3 (ag), and average 

(typical)-year fires (avefire) emissions are the same as those used in the CSAPR Final (2005v4.2) emission 

modeling platform. Nonpoint sources that were not subdivided into the afdust, ag, or avefire sectors were 

assigned to the “nonpt” sector, and most of these sources are also unchanged for 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 emissions are unchanged from the CSAPR Final inventory except for the addition of ethanol. 

Ethanol emissions were not provided for 2005, but were supplied for future year scenarios. Therefore, we 

scaled year 2017 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 refueling

As mentioned in Section 2.2, NEI-based onroad refueling emissions were replaced with estimates from the 

revised version of EPA’s Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all 

twelve months. This section describes how the emission inventories for refueling from on-road vehicles in 

calendar years 2005, 2017 and 2030 for 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 emissions 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 

every single county in the lower 48 states. As described in Section 2.2 for onroad counties, we selected 

representing counties by grouping counties based on Petroleum Administration for Defense Districts 

(PADD), fuel parameters, usage of California emission standards, 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 minimum and maximum

temperatures for each month using the temperatures from the 2005 calendar year values in the 
23

CountyYearMonthHour table of the NMIM County Database (NCD) NCD20100602 NMIM database. All 

counties in each group had min and max temperatures for all twelve months in the same temperature bins.

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

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

scenarios. 

MOVES was run in inventory mode for only the representing counties using the county-specific on-road 

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

from the NCD. The customized fuel inputs, discussed in Section 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 nonanthropogenic 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 Speciation Changes that Represent Fuel Changes

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 various 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, formaldehyde 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 speciated 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_07jul2010.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 include 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 8751E

where ethanol is obtained directly from the inventory. For the onroad sector, ethanol 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 

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

the bulk-plant-to-pump (btp) distribution, ethanol is speciated from VOC –thus a “BAFM” profile- because 

the fuel distribution operations in the nonpoint inventories 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 instead 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 were 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, these 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 20055, and a new Heavy Duty Diesel vehicle 

exhaust mode profile for pre-2007 model year (MY) vehicles that replaces an older 2004-vintage mediumduty diesel profile. See Table 3-1 for more details.

The VOC speciation approach is customized to account for the impact of fuel changes in the future year 

cases. These changes affect the onroad 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 

combinations 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 associated with the bulk-plant-to-pump (btp) distribution. 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) speciation 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 

Support 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 profiles in the base versus future year 

cases. For some sources related to the mobile sector and fuel distribution, a combination of profiles are 

specified by county, month and mode (e.g. 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 ethanol 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 2005v4.2 platform with the Tier 3 NPRM 2005 case is 

also included. Appendix 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 approaches 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 Exhaust 8750E Pre-Tier 2 E0 Exhaust 8751E Pre-Tier 2 E10 Exhaust

8751 Pre-Tier 2 E10 Exhaust 8751E Pre-Tier 2 E10 Exhaust 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 Evap 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 Evap perm 8769E E10 Evap perm

8769E E10 Evap perm 8770E E15 Evap perm

Refueling 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 Exha

Weighted 8774 and 8775 profiles 877RH Weighted HDD Exhb

a Class 6&7 HDDVs

b Class 8a&8b HDDVs

Evaporative 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace 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-Tier 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 evap
26

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

PFC 8762 E0 Headspace composite COMBO 8870E E10 Headspace 8871E 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 Proda

8776 Ethanol Fuel Proda

8776E Ethanol Fuel Prodb8776E Ethanol Fuel Prodb

a

corn ethanol and biodiesel ptnonipm acorn ethanol and biodiesel 

ptnonipm

b

cellulosic ethanol & cellulosic diesel 

nonpt

b

cellulosic ethanol & cellulosic 

diesel nonpt
27

4 2017 and 2030 Reference Cases

The 2017 and 2030 Tier 3 NPRM reference cases represent emissions in the future, including emissions 

impacts of the fuel volumes mandated by the 2005 EPAct and 2007 EISA and finalized in the RFS2 

program. The reference cases include MSAT2 and LDGHG but do not include HDGHG impacts. The 2017 

reference case assumes 21.6 billion gallons of renewable fuels (24 billion ethanol-equivalent gallons due to 

volume increases of ethanol), with 17.8 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 

billion 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 2017 and 2030 reference cases use many of the same growth and control assumptions as those for the 

Final Cross-State 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 2017 and 2030 reference cases for Tier 3 NPRM:

1) 2017 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., oil and 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 replaces a limited 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, available from 

ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf, but with the updates just 

discussed.

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

reflect state rules and federal consent decrees through December 1, 2010. For onroad mobile sources, all 

national measures for which data were available at the time of modeling have been included. The future case 

scenarios reflect projected economic changes and fuel usage for EGU and mobile sectors. For nonEGU 

point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, and afdust sectors), local control 
28

programs that might have been necessary for areas to attain the 1997 PM2.5 NAAQS annual standard, 2006 

PM NAAQS (24-hour) standard, and the 1997 ozone NAAQS are generally not included in the future basecase projections for most states. Exceptions are some NOX and VOC reductions associated with the New 

York, Virginia, and Connecticut State Implementation Plans (SIP), which were added as part of the 

comments received from the CSAPR and a larger effort to start including more local control information on 

stationary non-EGU sources; this is described further in Section 4.2. The following bullets summarize 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 estimates from IPM, version 4.10.

Non-IPM sector (ptnonipm): Projection factors and percent reductions reflect 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 NH3

emissions from fertilizer application.

Area fugitive 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 factors 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 reflecting 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 Air Partnership (WRAP)

states as well as Oklahoma and Texas. Year-specific speciation was applied to some portions of this 

sector and was 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. Future-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 2017 and 2030, incorporating CSAPR Proposal comments and controls based on Emissions 

Control Area (ECA) and International Marine Organization (IMO) global NOX and SO2 controls.

Onroad mobile: uses a version of MOVES developed for the Tier 3 NPRM that incorporates new car 

and light truck greenhouse gas emissions standards (LDGHG) affecting model years 2012 and later 
29

(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. This sector includes all non-refueling onroad mobile 

emissions (exhaust, evaporative, brake wear and tire wear modes). SMOKE-MOVES was used in a 

similar configuration as the 2005 base case to apportion MOVES emissions factors into hourly 

gridded 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 point (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 2017 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 

included 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 category 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 sectors, 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. Control strategies and growth assumptions for creating the Tier 3 NPRM 2017 and 2030 

reference case emissions inventories from the 2005 base case

Control Strategies and/or growth assumptions

(grouped by affected pollutants or standard and approach used to 

apply to the inventory)

Pollutants 

affected

Approach/ 

Reference

Non-EGU Point (ptnonipm sector) projection approaches

MACT rules, national, VOC: national applied by SCC, MACT

Boat Manufacturing 

Wood Building Products Surface Coating

Generic MACT II: Spandex 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 & Roofing

Iron & Steel Foundries

Metal: Can, Coil

Metal Furniture

Miscellaneous Metal Parts & Products

Municipal Solid Waste Landfills

Paper and Other Web

Plastic Parts

Plywood and Composite Wood Products

Carbon Black Production

Cyanide Chemical Manufacturing

Friction Products Manufacturing

Leather Finishing Operations

Miscellaneous Coating Manufacturing

Organic Liquids Distribution (Non-Gasoline)

Refractory Products Manufacturing

Sites Remediation

VOC EPA, 2007a

Consent decrees on companies (based on information from the Office of Enforcement 

and Compliance Assurance – OECA) apportioned to plants owned/operated by the 

companies

VOC, CO, NOx, 

PM, SO2

1

DOJ Settlements: plant SCC controls for:

Alcoa, TX 

Premcor (formerly Motiva), DE

All 2

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2 3

Hazardous Waste Combustion PM 4

Municipal Waste Combustor Reductions –plant level PM 5

Hospital/Medical/Infectious Waste Incinerator Regulations NOX, PM, SO2 EPA, 2005

Large Municipal Waste Combustors – growth applied to specific plants All (including Hg) 5

MACT rules, plant-level, VOC: Auto Plants VOC 6

MACT rules, plant-level, PM & SO2: Lime Manufacturing PM, SO2 7

MACT rules, plant-level, PM: Taconite Ore PM 8

Livestock Emissions Growth from year 2002 to years 2017 and 2030 (some farms in the 

point inventory)

NH3, 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, NOX, SO2, 

PM, HCl 10; EPA, 

2010

New York ozone SIP controls VOC, NOX,

HAP VOC 11

Additional plant and unit closures provided by state, regional, and the EPA agencies and 

additional consent decrees. Includes updates from CSAPR comments.

All 12
31

Control Strategies and/or growth assumptions

(grouped by affected pollutants or standard and approach used to 

apply to the inventory)

Pollutants 

affected

Approach/ 

Reference

Emission reductions 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 Collection Request (ICR) from the Industrial/Commercial/Institutional 

Boiler NESHAP.

NOX, SO2, HCl

Section 

4.2.13.2

Reciprocating Internal Combustion Engines (RICE) NESHAP NOX, CO, PM, 

SO2

13

Ethanol plants that account for increased ethanol production due to RFS2 mandate All 14

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

York

SO2

15

Nonpoint (nonpt sector) projection approaches

Municipal Waste Landfills: projection factor of 0.25 applied All EPA, 2007a

Livestock Emissions Growth from year 2002 to years 2017 and 2030 NH3, PM 9

New York, Connecticut, and Virginia ozone SIP controls VOC 11, 16

RICE NESHAP NOX, CO, VOC, 

PM, SO2

13

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

York

SO2

15

Residential Wood Combustion Growth and Change-outs from year 2005 to years 2017

and 2030

All 17

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 18

Portable Fuel Container Mobile Source Air Toxics Rule 2 (MSAT2) inventory growth 

and control from year 2005 to years 2017 and 2030

VOC 19

Use Phase II WRAP 2018 Oil and Gas for both 2017 and 2030 VOC, SO2, NOX, 

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, SO2, NOX, 

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 - Hazardous Waste Incinerators criteria and hazardous air pollutant controls 

carried over from 2002 Platform, v3.1. 

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

6. Percent reductions and plants to receive reductions based on recommendations by rule lead engineer, and are consistent 

with the reference: EPA, 2007a

7. Percent reductions recommended are determined from the existing plant estimated baselines and estimated reductions as 

shown in the Federal Register Notice for the rule. SO2 percent reduction are computed by 6,147/30,783 = 20% and 

PM10 and PM2.5 reductions are computed by 3,786/13,588 = 28%

8. Same approach as used in the 2006 Clean Air Interstate Rule (CAIR), which estimated reductions of “PM emissions by 

10,538 tpy, a reduction of about 62%.” Used same list of plants as were identified based on tonnage and SCC from 

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

9. Except for dairy cows and turkeys (no growth), based on animal population growth estimates from the US Department 

of Agriculture (USDA) and the Food and Agriculture Policy and Research Institute. See Section 4.2.10.

10. Data files for the cement sector provided by Elineth Torres, the EPA-SPPD, from the analysis done for the Cement 

NESHAP: The ISIS documentation and analysis for the cement NESHAP/NSPS is in the docket of that rulemakingdocket # EPA-HQ-OAR-2002-005. The Cement NESHAP is in the Federal Register: September 9, 2010 (Volume 75, 
32

Number 174, Page 54969-55066

11. New York NOX and VOC reductions obtained from Appendix J in NY Department of Environmental Conservation 

Implementation Plan for Ozone (February 2008): http://www.dec.ny.gov/docs/air_pdf/NYMASIP7final.pdf.

12. Appendix D of Cross-State Air Pollution Rule: 

ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_appendices_28jun2011.pdf

13. Appendix F in the Proposed (Mercury and Air) Toxics Rule TSD: 

http://www.epa.gov/ttn/chief/emch/toxics/proposed_toxics_rule_appendices.pdf

14. The 2008 data used came from Illinois’ submittal of 2008 emissions to the NEI.

15. Based on available, enforceable state sulfur rules as of November, 2010: 

http://www.ilta.org/LegislativeandRegulatory/MVNRLM/NEUSASulfur%20Rules_09.2010.pdf , 

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

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

http://green.blogs.nytimes.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 onroad 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/models/moves/index.htm

19. VOC, benzene, and ethanol emissions for 2017 and 2030 based on MSAT2 rule and ethanol fuel assumptions (EPA, 

2007b)

Control Strategies and/or growth assumptions

(grouped by affected pollutants or standard and approach used to 

apply to the inventory)

Pollutants 

affected

Approach/ 

Reference

Onroad mobile and nonroad mobile controls 

(list includes all key mobile control strategies but is not exhaustive)

National Onroad Rules:

Tier 2 Rule: Signature date February, 2000

2007 Onroad Heavy-Duty Rule: February, 2009

Final Mobile Source Air Toxics Rule (MSAT2): February, 2007

Renewable Fuel Standard: March, 2010

Light Duty Greenhouse Gas Rule: May, 2010

Corporate 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 Diesel Final Rule – Tier 4: June, 2004

Control of Emissions from Nonroad Large-Spark Ignition Engines and Recreational 

Engines (Marine and Land Based): “Pentathalon Rule”: November, 2002

Clean Bus USA Program: October, 2007

Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition 

Engines Less than 30 Liters per Cylinder: October, 2008

Locomotive and marine rule (May 6, 2008)

Marine SI rule (October 4, 1996)

Nonroad large SI and recreational engine rule (November 8, 2002)

Nonroad 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 6
33

Control Strategies and/or growth assumptions

(grouped by affected pollutants or standard and approach used to 

apply to the inventory)

Pollutants 

affected

Approach/ 

Reference

Locomotives:

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

Clean Air Nonroad Diesel Final Rule – Tier 4: June 2004

Locomotive Emissions Final Rulemaking, December 17, 1997

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 CSAPR comments.

EIA fuel consumption projections for diesel-fueled vessels

Clean Air Nonroad Diesel Final Rule – Tier 4

Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999

Locomotive and marine rule (May 6, 2008)

Tier 1 Marine Diesel Engines, February 28, 2003

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://www.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. Version 4.10 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 proposal6. Both the reference and control case emissions do not reflect the 

final Mercury and Air Toxics (MATS) rule or 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 IPMrelated Notice of Data Availability (NODA) published on September 1, 2010. Notably, IPM 4.10 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 scrubbers) were also made based on the ICR and on comments from the IPM NODA. Units with SO2 or 

NOX 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 rules were modeled by IPM to either operate 

those controls or not based on economic efficiency parameters. The IPM run for the reference (and control) 

cases 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 

included 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 cases modeled in IPM for this rule includes estimates of emissions reductions that will 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 settlements, 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/progsregs/epaipm/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 emissions output from the same IPM modeling case. For the 2030 cases, IPM 

outputs were year 2030.

Directly emitted PM emissions (i.e., PM2.5 and PM10) from the EGU sector are computed via a post 

processing routine which applies emission factors to the IPM-estimated fuel throughput based on fuel, 

configuration and controls to 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)

To project U.S. stationary 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 Assessment for 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 years 2017 and 

2030.

Year-specific projection factors for years 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 geographically 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 factors that were applied to account for year 

2017 and 2030 RFS2 mandate impacts on emissions to the reference and control cases. These inputs are 

discussed in more detail in Section 4.2.1 through Section 4.2.9. All other stationary non-EGU projections, 

controls and plant closure information not related to the RFS2 impacts are discussed in Section 4.2.10 

through Section 4.2.14. With the exception of onroad refueling emissions, all other stationary non-EGU 

emissions in the 2017 and 2030 reference cases are unchanged in the 2017 and 2030 control cases (see 
35

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 2030 

reference case”, respectively.

Table 4-2. Tier 3 NPRM reference case stationary non-EGU source-related projection methods

Input Type Sector(s) Description

Corn ethanol plants SMOKE ORL file that 

replaces 2005 base case 

ORL file

Ptnonipm Based on RFS2 analysis and production 

volumes. Point source format.

Biodiesel plants SMOKE ORL file Ptnonipm Accounts for facilities with current production 

capacities, to support RFS2 biodiesel 

production. Point source format.

Cellulosic fuel 

production

SMOKE ORL file Nonpt Accounts for cellulosic ethanol and cellulosic 

diesel to support RFS2 cellulosic production. 

County-level (nonpoint) format.

Ethanol transport 

and distribution

SMOKE ORL file Nonpt Accounts for ethanol vapor losses and 

spillage at any point in the transport and 

distribution chain. County-level (nonpoint) 

format.

Portable Fuel 

Containers (PFCs)

SMOKE ORL Nonpt NONROAD-model based emissions from 

PFCs, including vapor displacement, tank 

permeation, and diurnal evaporation. Countylevel (nonpoint) format.

Onroad refueling SMOKE ORL file nonpt MOVES-based gasoline and diesel fuel 

spillage and displacement vapor losses. 

County-level (nonpoint) format, monthly 

resolution. This is the only non-EGU 

component that has different emissions in the 

Tier 3 control cases compared to the 

reference cases.

Refinery 

adjustments

Projection factors Ptnonipm Accounts for changes in various refinery 

processes due to incorporation of RFS2 fuels.

Ethanol transport 

gasoline & ethanol 

blends

Projection factors nonpt, 

ptnonipm

Accounts for RFS impacts on emissions from 

bulk plant storage, refinery to bulk terminal, 

and bulk terminal to pump.

Upstream 

agricultural 

adjustments

Projection factors afdust, ag, 

nonpt, 

ptnonipm

Accounts 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 from 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 number 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 cases.
36

Table 4-3. 2017 and 2030 corn ethanol plant emissions [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

NOX 20,035 7,396

PM10 21,639 10,107

PM2.5 6,825 3,691

SO2 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 just 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 targets 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 Earth and web searching validating facility coordinates and correcting statecounty FIPS. Table 4-4 provides the 2017 and 2030 biodiesel plant emissions estimates.

Table 4-4. 2017 and 2030 biodiesel plant emissions [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

NOX 1,171 1,349

PM10 99 114

PM2.5 99 114

SO2 9 10

VOC 64 73

4.2.3 Portable fuel containers (nonpt)

OTAQ provided year 2017 and 2030 PFC emissions that include estimated Reid Vapor Pressure (RVP) and 

oxygenate impacts on VOC emissions, and more importantly, large increases in ethanol emissions from 

RFS2. Existing inventories were adjusted to account for impacts of RVP and ethanol from RFS2, using 
37

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 change 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 emissions 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-level 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 RFS2 rule air quality 

modeling were adjusted to account for differences with estimated volumes of cellulosic fuels produced for 

2017 and 2030, using final RFS2 rule data. Since the final RFS2 rule assumed about 57 percent of cellulosic 

fuel nationwide was cellulosic diesel, with the remainder 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. Emission factors were applied based on an assumed natural gas combustion process. 

Table 4-6 provides the year 2017 and 2030 cellulosic plant emissions estimates.

Table 4-6. 2017 and 2030 cellulosic plant emissions [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

NH3 0.5 1.6

NOX 64,062 185,745

PM10 7,533 21,862

PM2.5 3,796 10,986

SO2 4,973 14,475

VOC 5,336 15,489

We had no refined information on potential 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 production) 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 (nonpt)

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 to 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 VOC losses (Emissions) due to ethanol transport and distribution [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 Onroad 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 developed 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 components 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 onroad gasoline and diesel refueling emissions [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, blowdown 

systems, wastewater treatment, condensers, cooling towers, flares and fugitive emissions. The impact of the 

RFS2-based reductions is shown in
39

Table 4-9.
40

Table 4-9. Impact of refinery adjustments on 2017 and 2030 emissions [tons]

Pollutant Reductions 2017 Reductions 2030

CO 12,674 13,602

NOX 20,183 34,850

PM10 4,367 7,550

PM2.5 2,525 4,365

SO2 13,846 24,014

VOC 3,693 6,428

4.2.8 Ethanol transport gasoline and blends (ptnonipm, nonpt)

Emissions changes in the 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 agriculturalrelated source categories in several emissions modeling sectors. These source categories include fertilizer 

application, pesticide application and livestock waste (NH3 only), agricultural tilling, unloading and livestock 

dust (PM only) and fertilizer production mixing and blending, 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

NH3 45,272 61,793

NOX 363 500

PM10 42,934 59,004

PM2.5 6,500 8,972

SO2 69 95

VOC 16 23

4.2.10 Livestock emissions growth (ag, afdust)

Growth in ammonia (NH3) and dust (PM10 and PM2.5) 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 year 2017 and 2030 scenarios 

for animal categories applied to the ag, afdust, and ptnonipm sectors for livestock-related SCCs.
42

Table 4-11. Growth factors from year 2005 to 2017 and 2030 for animal operations

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 1.2674 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 Policy and Research Institute (FAPRI). For dairy cows and turkeys, we assumed that there 

would be no growth in emissions. This assumption was based on an analysis of historical trends in the 

number of such animals compared to production rates. Although productions 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 

future-year emissions from these animals. In particular, the dairy cow population is projected to decrease in 

the future as it has for the past few decades; however, milk production will be increasing over the same 

period. Note that the ammonia emissions from dairies are not directly related to animal population but also 

nitrogen excretion. With the cow numbers going 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 quantitative estimate.

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

from 2002 rather than from 2005.

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

curves 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 in 
43

Table 4-11
44

Table 4-11.

4.2.11 Residential wood combustion growth (nonpt)

We projected residential 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://www.epa.gov/burnwise). 

Details of this approach can be found in Section 2.3.3 of the PM NAAQS Regulatory Impact Analysis (EPA, 

2006).

The specific assumptions we made were:

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

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

 New woodstoves, SCC=2104008003, 2104008004, 2104008030, 2104008050, 2104008052 or 

2104008053: increase 2%/year

For the general woodstoves and fireplaces 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 PM2.5. These fractions are based on the fraction of emissions from these processes in 

the states that did not have the “general woodstoves and fireplaces” SCC in the 2002v3 NEI. This approach 

results in an overall decrease of 1.056% per year for this source category. 

We discovered an interpolation error in the year 2017 projection factors for RWC after air quality modeling. 

Table 4-12 presents the projection factors used to project the 2005 base case (2002 emissions) for RWC, 

including these 2017 errors. Table 4-13 shows the national impact (tons) of the 2017 projection factor error.

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

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

2104008010 Woodstoves: General 0.36 0.70 0.44

2104008051 Non-catalytic Woodstoves: Non-EPA certified

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 residential wood combustion estimates

Pollutant

2005 

Emissions

Erroneous 2017 

Emissions

Erroneous 2017 

Reductions

Correct 2017 

Emissions

Correct 2017 

Reductions

NOX 38,292 18,023 20,270 33,545 4,747
45

PM2.5 381,362 174,769 206,593 326,706 54,656

SO2 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 whereby 

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 years up to 2030. 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: commercial, 

general, air taxi and military. We computed growth factors for each operation type by dividing future-year 

ITN by 2005-year ITN. We assigned factors to inventory SCCs based on the operation type. 

The methods that the FAA used for developing the ITN data in the TAF are documented in:

http://www.faa.gov/data_research/aviation/aerospace_forecasts/2009-

2025/media/2009%20Forecast%20Doc.pdf

Table 4-14 provides the national growth factors for aircraft; all factors are applied to year 2005 emissions. 

For example, year 2017 commercial aircraft emissions are 12.88% higher than year 2005 emissions. The 

same aircraft factors were used for each of the year-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

2275001000 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 sources 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. This 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 NOX

standard adopted by the International Civil Aviation Organization’s (ICAO) Committee on Aviation 

Environmental Protection (CAEP) in February 2004, which is expected to reduce NOX 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 modeling; therefore, the effects of this rule 

were not included in the future-year emissions projections.

4.2.13 Stationary source control programs, consent decrees & settlements, and 

plant closures (ptnonipm, nonpt)

We applied emissions reduction factors to the 2005 emissions for particular sources in the ptnonipm and 

nonpt sectors to reflect the impact of stationary-source control programs including consent decrees, 

settlements, 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 NOX SIP call were assumed to have been implemented by 2005 and captured 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 NOX SIP call. The future-year base controls consist of the following:

We did not include MACT rules where compliance dates were prior to 2005, because we assumed 

these were already reflected in the 2005 inventory. The EPA OAQPS Sector Policies and Programs 

Division (SPPD) provided all controls information 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 2011. We also 

applied unit and plant closures 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 cement 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 2017

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_28jun2011.pdf. The 

magnitude of all unit and plant closures on the non-EGU point (ptnonipm) sector 2005 base-case 

emissions is shown in Table 4-15 below. These same reductions are seen in all Tier 3 NPRM future 

year scenarios.

Table 4-15. Summary of non-EGU emission reductions applied to the 2005 inventory due to unit and plant 

closures

CO NH3 NOX PM10 PM2.5 SO2 VOC

Reductions 125,162 636 109,237 21,143 12,600 190,734 26,750
47

In addition 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 (collected 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

SO2 RICE 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 NOX and VOC sources based on NY State Department of 

Environmental Conservation February 2008 guidance. These reductions are found in Appendix J in: 

http://www.dec.ny.gov/docs/air_pdf/NYMASIP7final.pdf (see Section 3.2.6 in the CSAPR TSD: 

ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf.)

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 desired 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 Reductions from the Portland Cement NESHAP (ptnonipm)

As indicated in Table 4-1, the Industrial Sectors Integrated Solutions (ISIS) model (EPA, 2010) was used to 

project the cement industry component of the ptnonipm emissions modeling sector to 2013. 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.

The 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 kilns, 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 the ISIS-based policy case predictions of emissions reductions and activity 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. Multiple regulatory requirements such as the NESHAP and NSPS currently apply to 
48

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 

PM2.5 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 requirements 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 cement industry annual reductions [tons/yr] for the non-EGU 

(ptnonipm) sector

Pollutant

Cement Industry 

emissions in 2005

Reductions in 

2017 & 2030

Percent 

Reduction

NOX 193,000 56,740 2.4%

PM2.5 14,400 7,840 1.8%

SO2 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_results2.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 controls 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 boiler reductions from ICR data gathering [tons]

State Pollutant

Pre-controlled 

Emissions

Controlled 

Emissions

Reductions in 

2017 & 2030

Percent 

Reduction

Michigan NOX 907 544 363 40

North Carolina SO2 652 65 587 90

Virginia SO2 3,379 338 3,041 90

Washington SO2 639 383 256 40

North Carolina HCl 31 3 28 90

4.2.13.3 RICE NESHAP (ptnonipm and nonpt)

There are three rulemakings for National Emission Standards for Hazardous Air Pollutants (NESHAP) for 

Reciprocating Internal Combustion Engines (RICE). These rules reduce HAPs from existing and new RICE 

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, NOX, VOC, PM, 

and SO2. In 2014 and beyond, compliance dates have passed for all three rules; thus all three rules are 

included in the 2017 and 2030 emissions projections. 

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

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion 

Engines; Final Rule (69 FR 33473) published 06/15/04

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion 

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 08/20/2010
51

The difference among these three rules is that they focus on different types of engines, different facility types 

(major for HAPs, versus area for HAPs) and different engine sizes based on horsepower (HP). In addition, 

they have different compliance dates. We project CAPs from the 2005 NEI RICE sources, based 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 controls on non-EGU projections

CO NOX PM10 PM2.5 SO2 VOC

Reductions 116,434 111,749 1,595 1,368 21,957 14,669

4.2.13.4 Fuel sulfur rules (ptnonipm and nonpt)

Fuel sulfur rules that were signed (enforceable) at the time of the Tier 3 NPRM emissions processing are 

limited to Maine, New Jersey and New York. Several other states have fuel sulfur rules that were in 

development but not finalized prior to the final CSAPR and proposed Tier 3 emissions processing: 

http://www.ilta.org/LegislativeandRegulatory/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 15ppm 

of sulfur, thus reducing SO2 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% SO2 emissions reduction for kerosene (fuel #1). The New Jersey sulfur content reductions 

are discussed here: http://njtoday.net/2010/09/01/nj-adopts-rule-limiting-sulfur-content-in-fuel-oil/.

For Tier 3 year 2017 projections, 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 fuel sulfur (SO2) controls on 2017 and 2030 non-EGU projections [tons]

State 2017 Reductions 2030 Reductions

Maine 8,323 18,470

New Jersey 998 998

New York 54,431 54,431

Total 63,751 73,898
52

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 utilized 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 represent 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 2017 and 2021 TCEQ oil and gas estimates 

or 2018 WRAP Phase II oil and gas inventory.

For Oklahoma, we applied CO, NOX, SO2 and VOC emissions reductions from the RICE NESHAP, which 

we assumed has some applicability to this industry (see Appendix F in the Proposed Toxics Rule TSD: 

http://www.epa.gov/ttn/chief/emch/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 NPRM scenarios. Table 4-20

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

Table 4-20. Oil and gas emissions for 2005, 2017 and 2030 including additional 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 43,639 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

Oklahoma 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 mobile source projections (onroad)

The same versions of 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

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 

configurations (Section 2.2.4) were previously discussed and were 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 inventories, 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 interpolating 

between the NCD VMT values for 2015 and those for 2020 and computing the NCD fraction for each 

county, then multiplying these fractions by the MOVES VMT. (Unlike MOVES, the NCD accounts for 

geographical shifts in activity over time.) For 2030, 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 similarly 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 reflect onroad mobile control 

programs that were final at the time the modeling was done. This included the Light-Duty Vehicle Tier 2 

Rule and the Mobile Source Air Toxics (MSAT2) final rule. MOVES used fuel sulfur levels of 30ppm 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 states 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 database. 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 2008-2010 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 standards 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 

years 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 projections (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 nonroad engines including diesel engines and various marine engine types, fuel sulfur content, and 
54

evaporative emissions. This section discusses the changes due to the NONROAD/NMIM system (nonroad 

sector) and additional C1/C2 CMV and locomotive emissions from volume increases resulting from 

incorporation of larger amounts of renewable fuels in the 2017 and 2030 reference cases.

4.4.1 Emissions generated with the NONROAD model (nonroad)

As discussed in Section 2.3.1, most nonroad emissions were estimated using the EPA’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 the 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 2017, 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 oxygenate 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 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 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 voluntary 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 December 

2009, 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 Recreational 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 Small Engine Spark Ignition (“Bond”) Rule, November 2008: 

(http://www.epa.gov/otaq/smallsi.htm)

All future year nonroad emissions used NMIM data that are based on AEO2009 fuels and the same NMIM 

county database NCD20101201Tier3. We converted emissions 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 creating 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, are 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 future year locomotive and C1/C2 CMV emissions were calculated using 

projection 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-SCC projection factors; these factors include final locomotivemarine 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 scenarios 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 class 1 and class 2 

commercial marine vessel emissions to 2017 and 2030

SCC SCC Description Pollutant 2017 2030

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

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NH3 1.144 1.285

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NOX 0.700 0.372

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM10 0.642 0.350

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM2.5 0.653 0.356

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions SO2 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 NH3 1.325 1.627

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NOX 0.627 0.357

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM10 0.578 0.260

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM2.5 0.586 0.263

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations SO2 0.005 0.006

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations 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 NH3 1.325 1.627

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NOX 0.352 0.350

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM10 0.286 0.272

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM2.5 0.288 0.275

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO2 0.001 0.001

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

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

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NH3 1.325 1.627

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NOX 0.496 0.241

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM10 0.461 0.148

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM2.5 0.463 0.149

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO2 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 Lines CO 1.057 1.172

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NH3 1.325 1.627

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NOX 0.489 0.237

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM10 0.455 0.146

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM2.5 0.455 0.146
56

SCC SCC Description Pollutant 2017 2030

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines SO2 0.005 0.005

2285002009 Railroad Equipment;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 NH3 1.325 1.627

2285002010 Railroad Equipment;Diesel;Yard Locomotives NOX 1.128 0.851

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM10 0.914 0.690

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM2.5 0.934 0.704

2285002010 Railroad Equipment;Diesel;Yard Locomotives SO2 0.006 0.007

2285002010 Railroad Equipment;Diesel;Yard Locomotives VOC 1.509 1.074

Table 4-22. Additional class 1 railroad and C1/C2 CMV emissions from RFS2 fuel volume changes

Pollutant

2017 Class 1

Rail (tons)

2017 C1/C2 

CMV (tons)

2030 Class 1

Rail (tons)

2030 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

NH3 3.93 0.62 5.99 0.95

NOX 5,731 890 4,298 642

PM10 141 29 86 21

PM2.5 136 27 83 20

SO2 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 projections 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 diesel emissions to 

lower future-year PM, SO2, and NOX, and is documented at: http://www.epa.gov/otaq/nonrdmdl.htm. 

Voluntary retrofits under the National Clean Diesel Campaign (http://www.epa.gov/otaq/diesel/index.htm) 

are not included in our projections.

We applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3-butadiene, 

acetaldehyde, acrolein, benzene, and formaldehyde.

Class 1 and 2 CMV gasoline emissions (SCC = 2280004000) were not changed for future-year processing. 

C1/C2 diesel emissions (SCC = 2280002100 and 2280002200) were projected based on the Final 

Locomotive Marine rule national-level factors provided in Table 4-21. Similar to locomotives, VOC HAPs 

were projected based on the VOC factor.

Delaware provided updated future-year NOX, SO2, 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 inventories 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 SO2

Emissions Control Area-International 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 includes ECA-IMO controls. The resulting coordinated 

strategy, including emission standards under the Clean Air Act for new marine diesel engines with percylinder 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.

The 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 boundaries. 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 emissions 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 uses CAP-HAP VOC integration.

Table 4-23. NOX, SO2, PM2.5 and VOC factors to project class 3 CMV emissions for 2017 and 2030

Region

NOX SO2 PM2.5 VOC

2017 2030 2017 2030 2017 2030 2017 2030

Alaska East 1.409 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 offshore sources (othar, othon, and othpt)

Emissions for Canada, Mexico, and offshore sources were not projected to future 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 and 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 Cases

The 2017 and 2030 Tier 3 NPRM control (hereafter simply referred to as the “control”) cases represent the 

future with implementation of all RFS2 impacts 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 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 billion gallons of renewable diesel, and 6.5 

billion gallons of cellulosic diesel.

The only notable 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 light-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 mobile, 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 changes 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 emissions, the only update is to use different onroad refueling emissions. These monthlyresolution county-level (nonpoint sector) refueling emissions are MOVES-based gasoline and diesel fuel 

spillage and include displacement vapor losses. A summary of the 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 Refueling 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 7,252
59

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 

configurations (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. 

New 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 vehicles and 80% Tier 3 vehicles in the 2030 control scenario. Other than fuels and the 

application of Tier 3 vehicle emission standards, (described in the 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 the 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 locomotive 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 emissions 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, 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 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 county, 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 fuels were converted from MOVES to NMIM as described for the 2005 base case, assigned 

a market share 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 from the reference case) are converted 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 calendar 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 

model 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 summaries

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 

emissions inventories modeled for this rule 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 and 
62

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

Emissions Sector NOX SO2 PM2.5 PM10 NH3 CO VOC

Agriculture 3,251,990

Area fugitive Dust 1,030,391 8,858,992

Average fires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992

Commercial marine 

Category 3 (US) 130,164 97,485 10,673 11,628 11,862 4,570

EGU 3,729,161 10,380,883 496,877 602,236 21,995 603,788 41,089

Locomotive/ marine 1,922,723 153,068 56,666 59,342 773 270,007 67,690

Non-EGU Point 2,213,471 2,030,759 433,346 647,873 158,342 3,201,418 1,279,308

Nonpoint 1,696,902 1,216,362 1,079,906 1,349,639 133,962 7,410,946 7,560,061

Nonroad 2,031,527 196,277 201,406 210,767 1,971 20,742,873 2,806,422

Onroad 8,235,002 168,480 301,073 369,911 144,409 41,117,658 3,267,931

US TOTAL 20,148,378 14,292,410 4,294,373 12,906,616 3,750,218 81,913,104 16,986,064
63

Table 6-2. National (49-state) 2017 Reference Case U.S. emissions (tons/year) by sector

Emissions Sector NOX SO2 PM2.5 PM10 NH3 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 NOX SO2 PM2.5 PM10 NH3 CO VOC

Agriculture 3,702,527

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 7,290,413

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 (tons/year) by sector

Emissions Sector NOX SO2 PM2.5 PM10 NH3 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 1,169,213

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 13,224,807
64

Table 6-5. National (49-state) 2030 Tier 3 Control Case U.S. emissions (tons/year) by sector

Emissions Sector NOX SO2 PM2.5 PM10 NH3 CO VOC

Agriculture 3,702,527

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 765,026 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 12,420,167
65

7 References

Cook, R, 2012. Development of Air Quality Reference Case Upstream and Portable Fuel Container 

Inventories for Tier 3 Proposal. Memorandum to the Docket, December 12, 2012. Docket EPA-HQOAR-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 2005. 

Available at http://www.epa.gov/cair/pdfs/finaltech01.pdf.

EPA, 2006. Regulatory Impact Analyses, 2006 National 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, 2007a. 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/documents/guidance/200705_epa457_b-07-

001_emission_changes_mact_rules.pdf.

EPA. 2007b. National Scale Modeling for the Final Mobile Source Air Toxics Rule, Office of Air Quality 

Planning and Standards, Emissions Analysis and Monitoring Division, Research Triangle Park, NC 

27711, EPA 454/R-07-002, February 2007. Available at 

http://www.epa.gov/otaq/regs/toxics/420f07017.pdf

EPA, 2008. Final Regulatory Impact Analysis: Control of Emissions of Air 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 2008. 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 

Division, Research Triangle Park, NC 27711, August 2010.
A-1

Emissions 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

March 2013
A-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 Sector column 

specifies the modeling sector for the dataset. The remaining columns show the data set name and version used in the 2005 base case and 2017 and 

2030 reference and control cases. 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 Name 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 parameters 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 Name

Environment 

Variable Program Sector 2005 Base Case

2017 & 2030 Reference/Control

Cases

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

bgpro_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 beis

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 Name

Environment 

Variable Program Sector 2005 Base Case

2017 & 2030 Reference/Control

Cases

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_combo_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_allpts [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 CAN-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]

Gridding 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 Name

Environment 

Variable Program Sector 2005 Base Case

2017 & 2030 Reference/Control

Cases

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 

CMAQ-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]

Inventory 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 [v1]

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 temperature 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_tier3 [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 Name

Environment 

Variable Program Sector 2005 Base Case

2017 & 2030 Reference/Control

Cases

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_RPD_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 Name

Environment 

Variable Program Sector 2005 Base Case

2017 & 2030 Reference/Control

Cases

MOVES Emission Factor Tables EFTABLES movesmrg 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]

NHAPEXCLUDE 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

NHAPEXCLU

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 smkinven seca_c3 nhapexclude_nothing [v0] nhapexclude_nothing [v0]

nonpoint & nonroad surrogate xref AGREF Grdmat amgref_us_can_mex_revised [v11] amgref_us_can_mex_revised [v15]

onroad surrogate xref default MGREF Grdmat amgref_us_can_mex_revised [v11] amgref_us_can_mex_revised [v15]

ORIS Description ORISDESC smkinven orisdesc [v0] orisdesc [v0]
A-7

Input Name

Environment 

Variable Program Sector 2005 Base Case

2017 & 2030 Reference/Control

Cases

SCC descriptions SCCDESC smkinven sccdesc_pf31 [v12] sccdesc_pf31 [v12]

SIC descriptions SICDESC Smkreport sic_desc [v0] sic_desc [v0]

Smkmerge representative dates files

MRGDATE_F

ILES Run script

merge_dates_2005 (/garnet/oaqps) 

[v0]

merge_dates_2005 (/garnet/oaqps) 

[v0]

Speciation profiles additional for SMOKEMOVESGSPROTMP_

O Spcmat onroad gspro_new_for_smoke-moves [v0] gspro_new_for_smoke-moves [v0]

Speciation profiles Canada 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_cb05 [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 Spcmat 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_pm25 [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_other_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 Name

Environment 

Variable Program Sector 2005 Base Case

2017 & 2030 Reference/Control

Cases

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, yearspecific 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, yearspecific GSREFTMP_I Spcmat nonpt2017 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_smokemoves_otherthantog [v0]gsref_new_for_smokemoves_otherthantog [v0]

Speciation xref for 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 Spcmat 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 Name

Environment 

Variable Program Sector 2005 Base Case

2017 & 2030 Reference/Control

Cases

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]

Temporal 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, onroad default MTPRO Temporal

amptpro_2005_us_can_revised 

[v2] amptpro_2005_us_can_revised [v2]

Temporal xref, all nonpoint and nonroad ATREF Temporal amptref_v3_3_revised [v12] amptref_v3_3_revised [v12]

Temporal xref, onroad mobile default MTREF Temporal amptref_v3_3_revised [v12] amptref_v3_3_revised [v12]

Temporal xref, othpt PTREF Temporal othpt ptref_othpt [v4] ptref_othpt [v4]

Temporal xref, point default PTREF Temporal amptref_v3_3_revised [v12] amptref_v3_3_revised [v12]

Temporal xref, ptipm only PTREF Temporal Ptipm ptref_ptipm_us [v0] ptref_ptipm_us [v0]

Table A-2. Parameters used in the Tier 3 NPRM cases

Parameter Name Environment Variable Sector 2005 Base Case

2017/2030 Reference and Control

Cases

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 sources 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 duplicate sources RAW_DUP_CHECK nonpt N
A-10

Parameter Name Environment Variable Sector 2005 Base Case

2017/2030 Reference and Control

Cases

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

Default 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 sectors 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_SPECIFIC_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 Y
A-11

Parameter Name Environment Variable Sector 2005 Base Case

2017/2030 Reference and Control

Cases

Fire-specific plume rise calculations FIRE_PLUME_YN ptfire Y Y

Formula for Smkinven SMKINVEN_FORMULA PMC=PM10-PM2_5 PMC=PM10-PM2_5

Formula 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 Sphere 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 abbreviation 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 Mwdss

Merge type M_TYPE ptipm All All

Merge type M_TYPE ptnonipm Mwdss Mwdss

Merge type M_TYPE ptfire All All

Merge type M_TYPE avefire Aveday Aveday

Merge type M_TYPE ag Aveday Aveday

Merge type M_TYPE afdust Week Week

Merge type M_TYPE onroad All All

Merge type M_TYPE nonptfire Aveday Aveday

Merge type M_TYPE othpt Mwdss Mwdss

Merge type M_TYPE othon Week Week

Merge type M_TYPE seca_c3 Aveday Aveday

Merge type M_TYPE beis All All

Model output format OUTPUT_FORMAT $EMF_AQM $EMF_AQM
A-12

Parameter Name Environment Variable Sector 2005 Base Case

2017/2030 Reference and Control

Cases

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

Ocean 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 totals 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

Output 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 2005ck

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 N
A-13

Parameter Name Environment Variable Sector 2005 Base Case

2017/2030 Reference and Control

Cases

Run holidays RUN_HOLIDAYS 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 Both

Run in inline mode SECA_C3 INLINE_MODE seca_c3 Only Only

Run script for Smkmerge annual totals RUN_PYTHON_ANNUAL Y Y

Smkmerge reports units MRG_TOTOUT_UNIT tons/dy

SMOKE-MOVES processing 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 SOILT_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

Speciation 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 Mwdss Mwdss

Temporal type L_TYPE ptipm All All

Temporal type L_TYPE ptfire All All

Temporal type L_TYPE avefire Aveday Aveday
A-14

Parameter Name Environment Variable Sector 2005 Base Case

2017/2030 Reference and Control

Cases

Temporal type L_TYPE ag Aveday Aveday

Temporal type L_TYPE afdust Week Week

Temporal type L_TYPE onroad All All

Temporal type L_TYPE nonptfire Aveday Aveday

Temporal type L_TYPE othon Week Week

Temporal type L_TYPE seca_c3 Aveday Aveday

Temporal type L_TYPE beis All All

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-point 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_HAPS 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 emissions 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 23:59
A-15

Parameter Name Environment Variable Sector 2005 Base Case

2017/2030 Reference and Control

Cases

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 2.7
B-1

Emissions Inventory for Air Quality Modeling Technical Support 

Document: Proposed Tier 3 Emissions Standards

Appendix B

Inventory Data Files 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

March 2013
B-2

The emissions inventory data files used for the Tier 3 cases are shown in Table B-1. The Input name column 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 cases are identical to the 2017 and 2030

reference cases, 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 version 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 Cases

2030 Reference/Control Cases

Inventory afdust CAP afdust afdust_2002ad_xportfrac [v0] afdust_2017ct_ref [v0] afdust_2030ct_ref [v0]

Inventory ag CAP ag ag_cap2002nei [v0] ag_cap2017ct_ref [v0] ag_cap2030ct_ref [v0]

Inventory alm_no_c3 CAP

alm_no_

c3 lm_no_c3_cap2002v3 [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_2002ce [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]

Inventory 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_29jul2011 [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 nonpt 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_Oklah

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 Cases

2030 Reference/Control Cases

Inventory nonpt HAP (no PFC, no 

refueling) 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]

2030 Reference: 

rfl_moves_wETOH_2030ct_ref_apr_27

jul2011 [v0]

2030 Control: 

rfl_moves_wETOH_2030ct_ctl_apr_28

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_aug_2

7jul2011 [v0]

2030 Control: 

rfl_moves_wETOH_2030ct_ctl_aug_2

8jul2011 [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_28

jul2011 [v0]

Inventory nonpt Refueling from 

MOVES, February nonpt

rfl_moves_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_28

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_28

jul2011 [v0]

Inventory nonpt Refueling from 

MOVES, July nonpt

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_28j

ul2011 [v0]

Inventory nonpt Refueling from 

MOVES, June 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 Cases

2030 Reference/Control Cases

2030 Control: 

rfl_moves_wETOH_2030ct_ctl_jun_28

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

8jul2011 [v0]

Inventory nonpt Refueling from 

MOVES, 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

8jul2011 [v0]

Inventory nonpt Refueling from 

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

8jul2011 [v0]

Inventory nonpt Refueling 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_28

jul2011 [v0]

Inventory nonpt 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_28

jul2011 [v0]

Inventory 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_ref_aug_20

2030 Reference: 

nonroad_cmaq_lite_2030ct_ref_aug_03
B-5

Input name Sector 2005 Base Case 2017 Reference/Control Cases

2030 Reference/Control Cases

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, incl 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_cmaq_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

sep2011 [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 Reference: 

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 Cases

2030 Reference/Control Cases

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] nonpt 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 border1999 [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 Cases

2030 Reference/Control Cases

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 CAP 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_cap2005v2_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_summer_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_ptipm_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 Control: 

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_xportfrac_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

ptnonip

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_cementISIS_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_26j

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_vochaps
B-8

Input name Sector 2005 Base Case 2017 Reference/Control Cases

2030 Reference/Control Cases

aps_2005_09DEC2010 [v0] _2017 [v0] _2030_08FEB2011 [v0]

Inventory 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_09DEC2010 [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_2006 [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

Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA-454/R-13-002

March, 2013