Emissions Modeling Technical Support 

Document: Tier 3 Motor Vehicle 

Emission and Fuel Standards 
By: 

Alexis Zubrow, Rich Mason, Alison Eyth

Emissions Inventory an Analysis Group 

Air Quality Assessment Division 

Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency 

Research Triangle Park, NC

Emissions Modeling Technical Support 

Document: Tier 3 Motor Vehicle 

Emission and Fuel Standards 

EPA-454/R-14-003 

February 2014
i

TABLE OF CONTENTS

ACRONYMS ...........................................................................................................................................................................III

LIST OF TABLES .................................................................................................................................................................. VI

LIST OF FIGURES ............................................................................................................................................................... VII

1 INTRODUCTION ............................................................................................................................................................ 1

2 2007 EMISSION INVENTORIES AND APPROACHES ............................................................................................ 4

2.1 2007 NEI POINT SOURCES (PTIPM AND PTNONIPM) ......................................................................................................... 7

2.1.1 IPM sector (ptipm) ............................................................................................................................................... 7

2.1.2 Non-IPM sector (ptnonipm) ................................................................................................................................. 7

2.2 2007 NONPOINT SOURCES (AFDUST, AG, NONPT)............................................................................................................. 9

2.2.1 Area fugitive dust sector (afdust) .......................................................................................................................10

2.2.2 Agricultural ammonia sector (ag) ......................................................................................................................10

2.2.3 Other nonpoint sources (nonpt) .........................................................................................................................10

2.3 FIRES (AVEFIRE) ...........................................................................................................................................................11

2.4 BIOGENIC SOURCES (BIOG) ...........................................................................................................................................11

2.5 2007 MOBILE SOURCES (ONROAD, ONROAD_RFL, NONROAD, C1C2RAIL, C3MARINE) ...................................................12

2.5.1 Onroad non-refueling (onroad)..........................................................................................................................12

2.5.2 Onroad refueling (onroad_rfl) ...........................................................................................................................14

2.5.3 Nonroad mobile equipment sources: (nonroad) ................................................................................................. 15

2.5.4 Class 1/Class 2 Commercial Marine Vessels and Locomotives and (c1c2rail) .................................................16

2.5.5 Class 3 commercial marine vessels (c3marine) ................................................................................................. 16

2.6 EMISSIONS FROM CANADA, MEXICO AND OFFSHORE DRILLING PLATFORMS (OTHPT, OTHAR, OTHON) .........................16

2.7 SMOKE-READY NON-ANTHROPOGENIC INVENTORIES FOR CHLORINE .........................................................................17

3 EMISSIONS MODELING SUMMARY ......................................................................................................................18

3.1 EMISSIONS MODELING OVERVIEW................................................................................................................................18

3.2 CHEMICAL SPECIATION ................................................................................................................................................21

3.2.1 VOC speciation ..................................................................................................................................................23

3.2.2 PM speciation.....................................................................................................................................................29

3.3 TEMPORAL ALLOCATION..............................................................................................................................................30

3.3.1 FF10 format and inventory resolution ...............................................................................................................31

3.3.2 Ptipm Temporalization .......................................................................................................................................32

3.3.3 Meteorologically-based temporalization............................................................................................................32

3.3.4 Onroad and Onroad_rfl Temporalization ..........................................................................................................34

3.3.5 Additional sector specific details .......................................................................................................................35

3.4 SPATIAL ALLOCATION..................................................................................................................................................35

3.4.1 Spatial Surrogates for U.S. emissions ................................................................................................................36

3.4.2 Allocation method for airport-related sources in the U.S. .................................................................................39

3.4.3 Surrogates for Canada and Mexico emission inventories..................................................................................39

4 DEVELOPMENT OF FUTURE YEAR EMISSIONS ................................................................................................43

4.1 STATIONARY SOURCE PROJECTIONS: EGU SECTOR (PTIPM) .........................................................................................47

4.2 STATIONARY SOURCE PROJECTIONS: NON-EGU SECTORS (PTNONIPM, NONPT, AG, AFDUST) .......................................47

4.2.1 RFS2 upstream future year inventories and adjustments (nonpt, ptnonipm) .....................................................49

4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm) ..............................................60

4.2.3 Fuel sulfur rules (nonpt, ptnonipm) ...................................................................................................................62

4.2.4 Portland Cement NESHAP projections (ptnonipm) ...........................................................................................62

4.2.5 Controls, Closures and consent decrees from CSAPR and NODA Comments (nonpt, ptnonipm) .....................63

4.2.6 All other PROJECTION and CONTROL packets (ptnonipm, nonpt) ................................................................. 65

4.3 MOBILE SOURCE PROJECTIONS .....................................................................................................................................66

4.3.1 Onroad mobile (onroad and onroad_rfl) ...........................................................................................................69

4.3.2 Nonroad mobile (nonroad) ................................................................................................................................. 73

4.3.3 Locomotives and Class 1 & 2 commercial marine vessels (c1c2rail) ................................................................74

4.3.4 Class 3 commercial marine vessels (c3marine) ................................................................................................. 78

4.4 CANADA, MEXICO, AND OFFSHORE SOURCES (OTHAR, OTHON, AND OTHPT) ................................................................78
ii

5 EMISSION SUMMARIES ............................................................................................................................................79

6 REFERENCES ...............................................................................................................................................................99
iii

Acronyms 

ACI Activated Carbon Injection

AE5 CMAQ Aerosol Module, version 5, introduced in CMAQ v4.7

AE6 CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0

AEO Annual Energy Outlook

AIM Architectural and Industrial Maintenance (coatings)

ARW Advanced Research WRF

BAFM Benzene, Acetaldehyde, Formaldehyde and Methanol

BEIS3.14 Biogenic Emissions Inventory System, version 3.14

BELD3 Biogenic Emissions Land use Database, version 3

Bgal Billion gallons

BPS Bulk Plant Storage

BTP Bulk Terminal (Plant) to Pump

C1/C2 Category 1 and 2 commercial marine vessels

C3 Category 3 (commercial marine vessels)

CAEP Committee on Aviation Environmental Protection

CAIR Clean Air Interstate Rule

CAMD The EPA’s Clean Air Markets Division

CAMX Comprehensive Air Quality Model with Extensions

CAP Criteria Air Pollutant

CARB California Air Resources Board

CB05 Carbon Bond 2005 chemical mechanism

CBM Coal-bed methane

CEC North American Commission for Environmental Cooperation

CEM Continuous Emissions Monitoring

CEPAM California Emissions Projection Analysis Model

CISWI Commercial and Industrial Solid Waste Incineration

Cl Chlorine

CMAQ Community Multiscale Air Quality

CMV Commercial Marine Vessel

CO Carbon monoxide

CSAPR Cross-State Air Pollution Rule

EO, E10, E85 0%, 10% and 85% Ethanol blend gasolines, respectively

EBAFM Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol

ECA Emissions Control Area

EEZ Exclusive Economic Zone

EF Emission Factor

EGU Electric Generating Units

EIS Emissions Inventory System

EISA Energy Independence and Security Act of 2007

EPA Environmental Protection Agency

EMFAC Emission Factor (California’s onroad mobile model)

FAA Federal Aviation Administration

FAPRI Food and Agriculture Policy and Research Institute

FASOM Forest and Agricultural Section Optimization Model

FCCS Fuel Characteristic Classification System

FIPS Federal Information Processing Standards

FHWA Federal Highway Administration

HAP Hazardous Air Pollutant
iv

HCl Hydrochloric acid

HDGHG Heavy-Duty Vehicle Greenhouse Gas

Hg Mercury

HMS Hazard Mapping System

HPMS Highway Performance Monitoring System

HWC Hazardous Waste Combustion

HWI Hazardous Waste Incineration

ICAO International Civil Aviation Organization

ICI Industrial/Commercial/Institutional (boilers and process heaters)

ICR Information Collection Request 

I/M Inspection and Maintenance

IMO International Marine Organization

IPAMS Independent Petroleum Association of Mountain States

IPM Integrated Planning Model

ITN Itinerant 

LADCO Lake Michigan Air Directors Consortium

LDGHG Light-Duty Vehicle Greenhouse Gas

LPG Liquified Petroleum Gas

MACT Maximum Achievable Control Technology

MARAMA Mid-Atlantic Regional Air Management Association

MATS Mercury and Air Toxics Standards

MCIP Meteorology-Chemistry Interface Processor

Mgal Million gallons

MMS Minerals Management Service (now known as the Bureau of Energy 

Management, Regulation and Enforcement (BOEMRE)

MOBILE6 OTAQ’s model for estimation of onroad mobile emissions factors, replaced by 

MOVES2010b

MOVES Motor Vehicle Emissions Simulator -- OTAQ’s model for estimation of onroad 

mobile emissions – replaces the use of the MOBILE model

MSA Metropolitan Statistical Area

MSAT2 Mobile Source Air Toxics Rule

MTBE Methyl tert-butyl ether

MWRPO Mid-west Regional Planning Organization

NCD National County Database

NEEDS National Electric Energy Database System

NEI National Emission Inventory

NESCAUM Northeast States for Coordinated Air Use Management

NESHAP National Emission Standards for Hazardous Air Pollutants

NH3 Ammonia

NIF NEI Input Format

NLCD National Land Cover Database

NLEV National Low Emission Vehicle program

nm nautical mile

NMIM National Mobile Inventory Model

NOAA National Oceanic and Atmospheric Administration

NODA Notice of Data Availability

NONROAD OTAQ’s model for estimation of nonroad mobile emissions

NOX Nitrogen oxides

NSPS New Source Performance Standards

NSR New Source Review
v

OAQPS The EPA’s Office of Air Quality Planning and Standards

OHH Outdoor Hydronic Heater

OTAQ The EPA’s Office of Transportation and Air Quality

ORIS Office of Regulatory Information System

ORD The EPA’s Office of Research and Development

ORL One Record per Line

OTC Ozone Transport Commission

PADD Petroleum Administration for Defense Districts

PF Projection Factor, can account for growth and/or controls

PFC Portable Fuel Container

PM2.5 Particulate matter less than or equal to 2.5 microns

PM10 Particulate matter less than or equal to 10 microns

ppb, ppm Parts per billion, parts per million

RCRA Resource Conservation and Recovery Act

RBT Refinery to Bulk Terminal

RFS2 Renewable Fuel Standard

RIA Regulatory Impact Analysis

RICE Reciprocating Internal Combustion Engine

RRF Relative Response Factor

RWC Residential Wood Combustion

RPO Regional Planning Organization

RVP Reid Vapor Pressure

SCC Source Classification Code

SEMAP Southeastern Modeling, Analysis, and Planning

SESARM Southeastern States Air Resource Managers

SESQ Sesquiterpenes

SMARTFIRE Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation

SMOKE Sparse Matrix Operator Kernel Emissions

SO2 Sulfur dioxide

SOA Secondary Organic Aerosol

SI Spark-ignition

SIP State Implementation Plan

SPDPRO Hourly Speed Profiles for weekday versus weekend

SPPD Sector Policies and Programs Division

TAF Terminal Area Forecast 

TCEQ Texas Commission on Environmental Quality

TOG Total Organic Gas

TSD Technical support document

ULSD Ultra Low Sulfur Diesel

USDA United States Department of Agriculture

VOC Volatile organic compounds

VMT Vehicle miles traveled

VPOP Vehicle Population

WGA Western Governors’ Association

WRAP Western Regional Air Partnership

WRF Weather Research and Forecasting Model
vi

List of Tables 

Table 1-1. List of base cases run in the Tier 3 FRM Emissions Modeling Platform ........................................ 2

Table 2-1. Platform sectors starting point for the 2007 platform ...................................................................... 5

Table 2-2. Summary of significant changes between 2007v5 platform and 2007 Tier 3 base case by sector .. 6

Table 2-3. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced) .................... 9

Table 2-4. Toxic-to-VOC Ratios for Corn Ethanol Plants ................................................................................ 9

Table 2-5. 2007 Platform SCCs representing emissions in the ptfire and avefire modeling sectors .............. 11

Table 3-1. Key emissions modeling steps by sector. ....................................................................................... 19

Table 3-2. Descriptions of the 2007v5 platform grids .................................................................................... 20

Table 3-3. Emission model species produced for CB05 with SOA for CMAQ and CAMX* ......................... 22

Table 3-4. Integration approach for BAFM and EBAFM for each platform sector ........................................ 23

Table 3-5. HAP augmentation for c1c2rail ..................................................................................................... 25

Table 3-6. VOC profiles for WRAP Phase III basins ..................................................................................... 26

Table 3-7. Select VOC profiles 2007. 2018 and 2030 .................................................................................... 28

Table 3-8. PM model species: AE5 versus AE6 ............................................................................................. 29

Table 3-9. Temporal settings used for the platform sectors in SMOKE ......................................................... 31

Table 3-10. U.S. Surrogates available for the 2007 platform. ......................................................................... 36

Table 3-11. Spatial Surrogates for WRAP Oil and Gas Data .......................................................................... 37

Table 3-12. Counties included in the WRAP Dataset ..................................................................................... 37

Table 3-13. Spatial Surrogates for Mexico ...................................................................................................... 39

Table 3-14. Canadian Spatial Surrogates for 2007-based platform Canadian Emissions ............................... 40

Table 4-1. Control strategies and growth assumptions for creating the 2018 and 2030 emissions inventories 

from the 2007 base case ........................................................................................................................... 45

Table 4-2. Summary of non-EGU stationary projections subsections ............................................................ 49

Table 4-3. Renewable Fuel Volumes Assumed for Stationary Source Adjustments. ..................................... 50

Table 4-4. 2007 and 2018/2030 corn ethanol plant emissions [tons] .............................................................. 50

Table 4-5. Emission Factors for Biodiesel Plants (Tons/Mgal) ...................................................................... 51

Table 4-6. 2018/2030 biodiesel plant emissions [tons] ................................................................................... 51

Table 4-7. PFC emissions for 2007, 2018, and 2030 [tons] ............................................................................ 52

Table 4-8. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon) ............................... 53

Table 4-9. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon) ................................................. 53

Table 4-10. 2018 and 2030 cellulosic plant emissions [tons] ......................................................................... 53

Table 4-11. 2018 and 2030 VOC working losses (Emissions) due to ethanol transport [tons] ...................... 54

Table 4-12. RVPs Assumed for 2018 ethanol and gasoline volumes with EISA ........................................... 56

Table 4-13. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA ...................................... 56

Table 4-14. RVPs Assumed for 2030 ethanol and gasoline volumes with EISA ........................................... 56

Table 4-15. RVPs Assumed for 2030 ethanol and gasoline volumes without EISA ...................................... 56

Table 4-16. Storage and Transport Vapor Loss Emission Factors (g/mmBtu) ............................................... 57

Table 4-17. Adjustment factors applied to storage and transport emissions ................................................... 58

Table 4-18. Impact of VOC losses from reduced gasoline production due to EISA ...................................... 58

Table 4-19. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with 

gasoline and diesel fuel production. ......................................................................................................... 59

Table 4-20. 2030 adjustment factors applied to petroleum pipelines and refinery emissions associated with 

gasoline and diesel fuel production. ......................................................................................................... 59

Table 4-21. Impact of refinery adjustments on 2007 emissions [tons] ........................................................... 60

Table 4-22. Adjustments to modeling platform agricultural emissions for the Tier 3 reference case ............ 61

Table 4-23. Composite NH3 projection factors to year 2018 and 2030 for animal operations ....................... 61

Table 4-24. Summary of fuel sulfur rules by state .......................................................................................... 62

Table 4-25. ISIS-based cement industry change (tons/yr) .............................................................................. 63
vii

Table 4-26. Factors used to project 2008 base-case aircraft emissions to 2020 .............................................. 65

Table 4-27. Overview of Reference and Control Scenarios ............................................................................ 67

Table 4-28. Comparison of MOVES runs ........................................................................................................ 70

Table 4-29. CA LEVIII program states ............................................................................................................ 70

Table 4-30. Early NLEV states ........................................................................................................................ 71

Table 4-31. LEV2 states and MOVES databases ............................................................................................. 71

Table 4-32. RVP bins by representative county ............................................................................................... 72

Table 4-33. Non-California year 2018 and 2030 Projection Factors for locomotives and Class 1 and Class 2 

Commercial Marine Vessel Emissions ..................................................................................................... 75

Table 4-34. Scalars Applied to Rail Combustion Emissions in 2030 to account for 2017-2025 LDGHG 

emission standards .................................................................................................................................... 76

Table 4-35. Scalars Applied to C1/C2 Combustion Emissions in 2030 to account for 2017-2025 LDGHG 

emission standards .................................................................................................................................... 77

Table 4-36. Cumulative RFS2 and LDGHG adjustments to c1c2rail sector emissions .................................. 77

Table 4-37. Growth factors to project the 2007 ECA-IMO inventory to 2018 and 2030 ............................... 78

Table 5-1. National and non-U.S. CAP emissions by sector for 2007 base case ............................................ 80

Table 5-2. National and non-U.S. CAP emissions by sector for 2018 reference case .................................... 81

Table 5-3. National and non-U.S. CAP emissions by sector for 2018 control case ........................................ 82

Table 5-4. National and non-U.S. CAP emissions by sector for 2030 reference case .................................... 83

Table 5-5. National and non-U.S. CAP emissions by sector for 2030 control case ........................................ 84

Table 5-6. CO emissions (tons/yr) for each case and state .............................................................................. 85

Table 5-7. NH3 emissions (tons/yr) for each case and state ............................................................................ 87

Table 5-8. NOX emissions (tons/yr) for each case and state ........................................................................... 89

Table 5-9. PM2.5 emissions (tons/yr) for each case and state .......................................................................... 91

Table 5-10. PM10 emissions (tons/yr) for each case and state ......................................................................... 93

Table 5-11. SO2 emissions (tons/yr) for each case and state ........................................................................... 95

Table 5-12. VOC emissions (tons/yr) for each case and state ......................................................................... 97

List of Figures 

Figure 3-1. Air quality modeling domains ....................................................................................................... 20

Figure 3-2. Example of new animal NH3 emissions temporalization approach, summed to daily emissions 34

Figure 3-3. Example of SMOKE-MOVES temporal variability of NOX emissions ....................................... 35

Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD) ............................................... 55
1

1 Introduction 

The U.S. Environmental Protection Agency (EPA) developed a year 2007 air quality modeling platform in 

support of the Tier 3 Motor Vehicle Emission and Fuel Standards. The air quality modeling platform 

consists of all of the emissions inventories, ancillary files needed for emissions modeling, and the 

meteorological, initial condition, and boundary condition files needed to run the air quality model. This

platform uses all Criteria Air Pollutants (CAPs) and a select set of Hazardous Air Pollutants (HAPs). This 

document focuses on the emissions modeling components of the 2007 platform, including the emission 

inventories and the ancillary data and the approaches used to transform emission inventories for use in air 

quality modeling. 

The Tier 3 modeling platform was developed by implementing specific modifications to the “CAP-BAFM 

2007-Based Platform, Version 5”, also known as the “2007v5” platform. The 2007v5 platform was used to 

support the Regulatory Impact Assessment (RIA) for the 2012 Final National Ambient Air Quality Standards 

(NAAQS) for particulate matter less than 2.5 microns (PM2.5). The Technical Support Document (TSD) 

“Preparation of Emissions Inventories for the Version 5.0, 2007 Emissions Modeling Platform” contains 

many additional details on the aspects of the Tier 3 and 2007v5 platforms that are shared. The TSD is 

available from the Emissions Modeling Clearinghouse website, http://www.epa.gov/ttn/chief/emch/, under 

the section entitled “Particulate Matter (PM) NAAQS (2007v5) Platform”. The appendices available for the 

2007v5 TSD that do not reference the specific PM NAAQS modeling cases are also relevant to the “Tier 3” 

platform. 

Many emissions inventory components of the Tier 3 air quality modeling platform are based on the 2008 

National Emissions Inventory version 2, hereafter referred to as the “2008 NEI”, with updated inventory data 

for some emission sectors. In particular, a version of the Motor Vehicle Emissions Simulator (MOVES) 

designed to represent the impacts of the Tier 3 Motor Vehicle Emission and Fuel Standards 

(MOVESTier3FRM) was used to generate emission factors for onroad mobile sources. The emissions 

modeling tool used to create the air quality model-ready emissions from the emission inventories was the 

Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (http://www.smokemodel.org/index.cfm) version 3.5 beta. Emissions were created for 36 km and 12 km national grids. 

The gridded meteorological model used for Tier 3 is the Weather Research and Forecasting Model (WRF, 

http://wrf-model.org) version 3.3, Advanced Research WRF (ARW) core (Skamarock, et al., 2008). The 

WRF Model is a mesoscale numerical weather prediction system developed for both operational forecasting 

and atmospheric research applications. WRF was run for 2007 over a domain covering the continental 

United States at a 36 km and 12 km resolution with 35 vertical layers2. This meteorological run was different 

than the one used for the 2007v5 platform. 

The air quality model used for the Tier 3 platform is the Community Multiscale Air Quality (CMAQ) model 

(http://www.epa.gov/AMD/CMAQ/). CMAQ supports modeling ozone (O3) and particulate matter (PM) 

and requires hourly and gridded emissions of chemical species from the following inventory pollutants: 

carbon monoxide (CO), nitrogen oxides (NOX), volatile organic compounds (VOC), sulfur dioxide (SO2), 

ammonia (NH3), particulate matter less than or equal to 10 microns (PM10), and individual component 

species for particulate matter less than or equal to 2.5 microns (PM2.5). The CMAQ version used the 

chemical mechanism called Carbon Bond 2005 (CB05) with chlorine chemistry, which is part of the “base” 

version of CMAQ. CB05 allows explicit treatment of benzene, acetaldehyde, formaldehyde, and methanol 

 

2

 For more details on the meteorological models and the run see section 2.5.1.2. 
2

(BAFM) and includes anthropogenic HAP emissions of hydrochloric acid (HCl) and chlorine (Cl). The 

Tier 3 modeling used the most recent multi-pollutant version of CMAQ available at the time (CMAQ MP 

version 5.0.1) the modeling was performed, and an additional set of HAPs (from here out referred to as 

“CMAQ MP-lite HAPs”): acrolein, 1,3-butadiene and naphthalene were modeled. 

The emissions and modeling effort for the Tier 3 platform consists of five emissions cases: 2007 base case, 

2018 reference case, 2018 Tier 3 control case, 2030 reference case and 2030 Tier 3 control case. Table 1-1

provides more information on these emissions cases. The purpose of 2007 base case is to provide a 2007 

case that is consistent with the methods used in the future-year base cases and ultimately, in the future year 

reference and control cases for the Final Tier 3 Rule. For regulatory applications, the 2007 base case is used 

with the outputs from the 2018 and 2030 reference cases in the relative response factor (RRF) calculations to 

identify future areas of nonattainment. For more information on the use of RRFs and air quality modeling, 

see “Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM 2.5, and Regional Haze”, available from 

http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf. 

Table 1-1. List of base cases run in the Tier 3 FRM Emissions Modeling Platform

Case Name

Internal EPA 

Abbreviation Description

2007 base case 2007rg_v5 2007 case created using average-year wildfires data, smoothed 

prescribed fires, and an average-year temporal allocation approach 

for Electrical Generating Units (EGUs); used for computing 

relative response factors with 2018 and 2030 reference scenario(s).

2018 reference 

case

2018rg_ref2_v532018 future year reference scenario with EGU emissions that 

represent the implementation of Clean Air Interstate Rule (CAIR) 

and final Mercury and Air Toxics (MATS), with upstream 

stationary and mobile sources representing the implementation of 

the EISA/EPAct fuel supply (RFS2 Rule)

2018 Tier 3 

control case

2018rg_ctl_v5 2018 Tier 3 control case scenario sharing many aspects of the 

2018 reference case, but also representing national Tier 3 vehicle 

and fuel emissions standards.

2030 reference 

case

2030rg_ref_v5 2030 future year reference scenario with EGU emissions that 

represent the implementation of Clean Air Interstate Rule (CAIR) 

and final Mercury and Air Toxics (MATS), with upstream 

stationary and mobile sources representing the implementation of 

the EISA/EPAct fuel supply (RFS2 Rule)

2030 Tier 3 

control case

2030rg_ctl_v5 2030 Tier 3 control case scenario sharing many aspects of the 

2030 reference case, but also representing national Tier 3 vehicle 

and fuel emissions standards.

This document contains five sections. Section 2 describes the inventories input to SMOKE for the 2007 base 

case. Section 3 describes the emissions modeling and the ancillary files used to process the emission 

inventories into a form that can be used by the air quality model. Section 4, describes the development of the 

2018 and 2030 Tier 3 FRM reference and control case inventories (projected from 2007). Data summaries 

 

3

 The case 2018rg_ref2_v5 is identical to an earlier case that was created for Tier 3 FRM (2018rg_ref_v5), except that the “ref2” 

case updated the following modeling sectors: nonpt, onroad, othpt, othar, and othon. 
3

comparing the 2007 base case and the 2018 and 2030 reference and control cases are provided in Section 5. 

Section 6 provides references. 
4

2 2007 Emission Inventories and Approaches 

This section describes the 2007 emissions data created for input to SMOKE that is part of the 2007 base 

case; future year emissions inventory data development is discussed in Section 4. While providing some 

background, this section focuses on the differences between the 2007v5 platform and the updated Tier 3 

platform. The starting point for the 2007 stationary source emission inputs is the 2008 NEI version 2, for 

which a draft TSD is available from http://www.epa.gov/ttn/chief/net/2008inventory.html. 

The NEI data are largely compiled from data submitted by state, local and tribal (S/L/T) agencies for CAPs. 

HAP emissions data are more often augmented by EPA because they are voluntarily submitted. For fires, 

EPA used the SMARTFIRE2 (SF2) system in the 2008 NEI. SF2 assigns all fires as either prescribed 

burning or wildfire categories and includes improved emission factor estimates for prescribed burning. 

The 2008 NEI includes five data categories: nonpoint (formerly called “stationary area”) sources, point 

sources, nonroad mobile sources, onroad mobile sources, and fires. The 2008 NEI TSD uses approximately 

sixty sectors to further describe the emissions. In addition to the NEI data, 2007 biogenic emissions, 

emissions from the Canadian and Mexican inventories, and other non-NEI data are included in the 2007 

platform. The non-NEI emissions components of the 2007 platform include primarily year-2007 onroad 

mobile and nonroad mobile emissions, a computed average fires inventory, and data received from some 

regional planning organizations (RPOs). 

In the 2007v5 platform, some data in the 2008NEIv2 were updated with data from RPOs. The RPOs focused 

on addressing visibility impairment from a regional perspective and updated related inventory and ancillary 

data. A map of these RPOs can be found here: http://www.epa.gov/visibility/regional.html. The RPOs most 

involved in providing data were: 

 Mid-Atlantic Regional Air Management Association (MARAMA): http://www.marama.org/

 Midwest Regional Planning Organization (MWRPO): http://www.ladco.org/

 Southeastern States Air Resource Managers (SESARM): http://www.metro4-sesarm.org/

 Western Regional Air Partnership (WRAP): http://www.wrapair2.org/

For the purposes of preparing the air quality model-ready emissions, the 2007 emissions inventory was split 

into inventories for each of the modeling “platform” sectors. The significance of an emissions modeling or 

“platform” sector is that the data is run through all of the SMOKE programs except the final merge 

(Mrggrid) independently from the other sectors. The final merge program is then used to combine the 

sector-specific gridded, speciated, hourly emissions together to create CMAQ-ready emission inputs. 

Table 2-1 presents the sectors in the 2007 platform and how they generally relate to the 2008 NEI as a 

starting point. The sector abbreviations are provided in italics. These abbreviations are used in the SMOKE 

modeling scripts, inventory file names, and throughout the remainder of this document. As discussed in 

greater detail in Table 2-2, the Tier 3 platform emissions platform was modified in specific ways from the 

original 2007v5 platform. 
5

Table 2-1. Platform sectors starting point for the 2007 platform

Platform Sector: 

abbreviation

2008NEI 

Sector Description and resolution of the data input to SMOKE

EGU (also called 

the IPM sector):

ptipm 

Point 2008 NEI point source EGUs that could be mapped to the Integrated 

Planning Model (IPM) model using the National Electric Energy 

Database System (NEEDS) version 4.10. Annual resolution.

Non-EGU (nonIPM sector):

ptnonipm

Point All NEI point source records not matched to the ptipm sector. 

Includes all aircraft emissions and some rail yard emissions. Annual 

resolution.

Agricultural:

ag

Nonpoint NH3 emissions from NEI nonpoint livestock and fertilizer application, 

county and annual resolution. 

Area fugitive dust:

afdust

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

inventory. Includes building construction, road construction, paved 

roads, unpaved roads and agricultural dust. County and annual 

resolution. Processed as a separate sector to allow for the application 

of a land use based transport fraction and precipitation adjustments.

Class 1 & 2 CMV 

and locomotives:

c1c2rail

Mobile: 

Nonroad

Non-rail maintenance locomotives and category 1 and category 2 

commercial marine vessel (CMV) emissions sources from the NEI 

nonpoint inventory. County and annual resolution.

C3 commercial 

marine:

c3marine

Mobile: 

Nonroad

Non-NEI, year 2007 category 3 (C3) CMV emissions projected from 

year 2002. Developed for the rule called “Control of Emissions from 

New Marine Compression-Ignition Engines at or Above 30 Liters per 

Cylinder”, also described as the Emissions Control Area- International 

Maritime Organization (ECA-IMO) study: 

http://www.epa.gov/otaq/oceanvessels.htm. (EPA-420-F-10-041, 

August 2010). Annual resolution and treated as point sources.

Remaining 

nonpoint:

nonpt

Nonpoint Primarily NEI nonpoint sources not otherwise included in other 

SMOKE sectors; county and annual resolution. 

Nonroad:

nonroad

Mobile: 

Nonroad

Monthly nonroad equipment emissions from the National Mobile 

Inventory Model (NMIM) using NONROAD2008 version NR08b. 

NMIM was used for all states except California. Monthly emissions 

for California created from annual emissions submitted by the 

California Air Resources Board (CARB).

Onroad nonrefueling:

onroad

Mobile: 

onroad

Onroad mobile gasoline and diesel vehicles from parking lots and 

moving vehicles. Includes the following modes: exhaust, extended 

idle, evaporative, permeation, and brake and tire wear. For all states, 

based on Motor Vehicle Emissions Simulator (MOVES) emission

factor tables and monthly activity data for 2007. 

Onroad refueling:

onroad_rfl

Mobile: 

onroad

Onroad mobile gasoline and diesel vehicle refueling emissions for all 

states. Based on MOVES emission factor tables and 2007 activity 

data.

Average-fire:

avefire 

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

resolution. This sector is used in all modeling cases.

Other point 

sources not from 

the NEI:

othpt

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

2008 inventory grown from year 1999. Includes annual U.S. offshore 

oil 2008 NEI point source emissions. Annual resolution.

Other non-NEI 

nonpoint and 

nonroad:

othar

N/A Year 2006 Canada (province resolution) and year 2008 (grown from 

1999) Mexico Phase III (municipio resolution) nonpoint and nonroad 

mobile inventories, annual resolution.
6

Platform Sector: 

abbreviation

2008NEI 

Sector Description and resolution of the data input to SMOKE

Other non-NEI 

onroad sources:

othon

N/A Year 2006 Canada (province resolution) and year 2008 (grown from 

1999) Mexico Phase III (municipio resolution) onroad mobile 

inventories, annual resolution.

Biogenic:

beis

N/A Year 2007, hour-specific, grid cell-specific emissions generated from 

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

Table 2-2 provides a brief by-sector overview of the most significant differences between the Tier 3 platform 

and the 2007v5 platform. The specific by-sector updates to the 2007 platform made for Tier 3 are described 

in greater detail later in the following subsections. The remainder of Section 2 provides details about the 

data contained in each of the 2007 platform sectors. Different levels of detail are provided for different 

sectors, depending on the degree of changes or manipulation of the data needed to prepare it for input to 

SMOKE, and on whether the Tier 3 2007 platform emissions are significantly different from the original 

2007v5 platform. 

Table 2-2. Summary of significant changes between 2007v5 platform and 2007 Tier 3 base case by sector

Platform Sector Summary of Significant Inventory Differences 

IPM sector:

ptipm  Included additional HAPs

Non-IPM sector:

ptnonipm

 Updated ethanol inventory

 Replaced oil and gas emissions with Western Regional Air Partnership 

(WRAP) Phase III year 2008 emissions in select oil and gas basins

Agricultural:

ag

 Temporalized to hours using the Tier 3 WRF output

Area fugitive dust:

afdust  Performed meteorological adjustments with the Tier 3 WRF output

Remaining 

nonpoint sector:

nonpt

 Replaced oil and gas emissions with Western Regional Air Partnership 

(WRAP) Phase III year 2008 emissions in select oil and gas basins

Class 1 & 2 CMV 

and locomotives:

c1c2rail

 Augmented HAPs in California and RPO data

C3 commercial 

marine:

c3marine

 Augmented HAPs 

Nonroad sector:

nonroad

 Used updated fuels

 Augmented HAPs in California

Onroad nonrefueling:

onroad

 Used MOVESTier3FRM emission factors

 Used updated fuels

Onroad nonrefueling:

onroad_rfl

 Used MOVESTier3FRM emission factors

 Used updated fuels

Average fires:

avefire

 Used 2007 through 2010 SMARTFIRE 2 data to generate average fire 

emissions
7

2.1 2007 NEI point sources (ptipm and ptnonipm) 

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

specified, as in the case of an individual facility. A facility may have multiple emission points, which may 

be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple 

processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas). With a couple 

of minor exceptions, this section describes only NEI point sources within the contiguous United States. The 

offshore oil platform (othpt sector) and category 3 CMV emissions (c3marine sector) are also point source 

formatted inventories that are discussed in Section 2.6 and Section 2.5.5, respectively. 

After removing offshore oil platforms into the othpt sector, EPA created an initial version of two platform 

sectors from the remaining 2008 NEI point sources for input into SMOKE: the EGU sector – also called the 

IPM sector (i.e., ptipm) and the non-EGU sector – also called the non-IPM sector (i.e., ptnonipm). This split 

facilitates the use of different SMOKE temporal processing and future-year projection techniques for each of 

these sectors. The inventory pollutants processed through SMOKE for both the ptipm and ptnonipm sectors 

were: CO, NOX, VOC, SO2, NH3, PM10, and PM2.5 and the following HAPs: HCl (pollutant code = 

7647010), Cl (code = 7782505), and the CMAQ MP-lite HAPs (see Section 3.2 for details). For more 

details on the development of these sectors and the differences between the inventories and the 2008 NEI see 

the 2007v5 TSD. 

2.1.1 IPM sector (ptipm)

The ptipm sector contains emissions from EGUs in the 2008 NEI point inventory that were matched to units 

found in the May 2012 version 4.10 of the NEEDS database (http://www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev410.html#needs). IPM provides future-year emission inventories for the universe of EGUs 

contained in the NEEDS database. This matching was done (1) to provide consistency between the 2007 

EGU sources and future-year EGU emissions for sources which are forecasted by IPM and (2) to avoid 

double counting when projecting point source emissions to future years. A comprehensive description on 

how EGU emissions were characterized and estimated in the 2008 NEI can be found in Section 3.10 in the 

2008 NEI documentation (EPA, 2012a). The ptipm sector is identical to the 2007v5 platform except for the 

inclusion of CMAQ MP-lite HAPs (see Section 3.2 for details) in the 2007 model-ready files4.

2.1.2 Non-IPM sector (ptnonipm)

With several exceptions, the non-IPM (ptnonipm) sector contains the remaining 2008 NEI point sources that 

were not included in the IPM (ptipm) sector. The ptnonipm sector contains all sources not reflected in future 

year IPM inventories. For the most part, the ptnonipm sector reflects the non-EGU component of the NEI 

point inventory; however, as previously discussed, it is likely that some small low-emitting EGUs that are 

not reflected in the CEMs database are present in the ptnonipm sector. 

There are numerous modifications between the published 2008 NEI and the 2007 ptnonipm inventory used 

for modeling. More details on some of these modifications can be found in the 2007v5 TSD. The 

differences between the 2007v5 ptnonipm and the 2007 ptnonipm for this base case are limited to the 

following: 

 Integration of BAFM (see Section 3.2.1.1 for details) 

 Inclusion of the CMAQ MP-lite HAPs (see Section 3.2 for details) 

 

4

 Note, these additional HAPs are not in the future year scenarios because IPM does not produce estimates for them. 
8

 Removal of one cement kiln because of unreasonable emissions5

 Modification to the South Dakota point inventory to include 2005 NEI emissions for sources missing 

in the 2008 NEI. 

 Updated ethanol facilities (see below) 

 Updated oil and gas (see below) 

Additional Ethanol facilities

An updated set of corn ethanol facilities was developed for year 2008. Any facilities already included in the 

2008 NEI were removed from the set prior to including them in the 2007 base case. Locations and FIPS 

codes for these ethanol plants were verified using web searches and Google Earth. These emissions are 

included in the 2007 case as a separate FF10-format inventory for HAPs and CAPs. Emission rates for the 

facilities were obtained from EPA’s spreadsheet model for upstream impacts developed for the Renewable 

Fuel Standard (RFS2) rule (EPA, 2010a). The plant emission rates for criteria pollutants used to estimate 

impacts are given in Table 2-3. Toxic emission rates were estimated by applying toxic-to-VOC ratios in 

Table 2-4 to the VOC emission rates shown in Table 2-3. For air toxics other than ethanol, toxic-to-VOC 

ratios were developed using emission inventory data from the 2005 NEI (EPA, 2009a). Emission rates in 

Table 2-3 and Table 2-4 were multiplied by facility production estimates for 2007, 2018 (via 2017 emission 

factors), and 2030 based on analyses performed for the industry characterization described in Chapter 1 of 

the RFS2 final rule regulatory impact analysis (RIA). 

WRAP Phase III oil and gas emissions

The Western Regional Air Partnership (WRAP) RPO created year 2008 “Phase III” oil and gas sector point 

and non-point format emissions for several major basins in Colorado and Montana, New Mexico, Texas, 

Utah and Wyoming. These basins are listed here: Denver-Julesburg, Uinta, San Juan (North and South), 

Piceance, Southwest Wyoming (Green River), Powder River, Wind River and Permian. A map showing the 

geographic area of these basins is provided at: http://www.eia.gov/oil_gas/rpd/shale_gas.jpg. 

The WRAP oil and gas Phase III project was co-sponsored by the Independent Petroleum Association of 

Mountain States (IPAMS) and is based on survey outreach efforts. Survey coverage varied, and survey data 

were generally reflected as point sources in the inventory. Unpermitted sources were based somewhat on 

surveys but also on activity and emission factor estimates and were generally reflected as nonpoint (nonpt 

sector) sources. 

Overall, the Phase III project estimated emissions for a couple dozen source types, including drilling rigs, 

compressor stations, heaters and boilers, tank breathing venting and flashing, pneumatic devices, well and 

pipeline/compressor fugitive emissions, dehydrators, amine units, truck loading and other miscellaneous 

sources. Phase III emissions include basin-specific speciation, surrogates and hence SCCs to account for the 

different products extracted: oil, gas and coal-bed methane (CBM). To prevent possible double-counting of 

oil and gas sector emissions, all oil and gas emissions were removed from the 2008 NEI for counties that 

comprise the 9 basins in the WRAP Phase III inventories. The list of oil and gas SCCs that were removed 

from the point (and nonpoint) 2008 NEI are provided in Appendix A of the 2007v5 TSD6

.

 

5

 The cement kiln had emissions orders of magnitude higher than in any other year in the NEI. The particular facility is facility ID 

4773111, FIPS 26017. 

6

 The 2007v5 platform used the 2006 WRAP Phase III inventory. 
9

Table 2-3. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced) 

Corn Ethanol Plant Type Year VOC CO NOX PM10 PM2.5 SO2 NH3

Dry Mill Natural Gas (NG) 2005, 2017 2.29 0.58 0.99 0.94 0.23 0.01 0.00

2030 2.29 0.58 0.94 0.94 0.23 0.01 0.00

Dry Mill NG (wet distillers 

grains with solubles (DGS))

2005, 2017 2.27 0.37 0.63 0.91 0.20 0.00 0.00

2030 2.27 0.37 0.60 0.91 0.20 0.00 0.00

Dry Mill Biogas 2005, 2017 2.29 0.62 1.05 0.94 0.23 0.01 0.00

2030 2.29 0.62 1.00 0.94 0.23 0.01 0.00

Dry Mill Biogas (wet DGS) 2005, 2017 2.27 0.39 0.67 0.91 0.20 0.00 0.00

2030 2.27 0.39 0.63 0.91 0.20 0.00 0.00

Dry Mill Coal 2005, 2017 2.31 2.65 4.17 3.81 1.71 4.52 0.00

2030 2.31 2.65 3.68 3.64 1.54 3.48 0.00

Dry Mill Coal (wet DGS) 2005, 2017 2.31 2.65 2.65 2.74 1.14 2.87 0.00

2030 2.28 1.68 2.34 2.62 1.03 2.21 0.00

Dry Mill Biomass 2005, 2017 2.42 2.55 3.65 1.28 0.36 0.14 0.00

2030 2.42 2.55 3.65 1.28 0.36 0.14 0.00

Dry Mill Biomass (wet 

DGS)

2005, 2017 2.35 1.62 2.32 1.12 0.28 0.09 0.00

2030 2.35 1.62 2.32 1.12 0.28 0.09 0.00

Wet Mill NG 2005, 2017 2.35 1.62 1.77 1.12 0.28 0.09 0.00

2030 2.33 1.04 1.68 1.00 0.29 0.01 0.00

Wet Mill Coal 2005, 2017 2.33 1.04 5.51 4.76 2.21 5.97 0.00

2030 2.33 3.50 4.86 4.53 1.98 4.60 0.00

Table 2-4. Toxic-to-VOC Ratios for Corn Ethanol Plants 

Acetaldehyde Acrolein Benzene 1,3-Butadiene Formaldehyde

Wet Mill NG 0.02580 0.00131 0.00060 2.82371E-08 0.00127

Wet Mill Coal 0.08242 0.00015 0.00048 2.82371E-08 0.00108

Dry Mill NG 0.01089 0.00131 0.00060 2.82371E-08 0.00127

Dry Mill Coal 0.02328 0.00102 0.00017 2.82371E-08 0.00119

2.2 2007 nonpoint sources (afdust, ag, nonpt) 

The nonpoint sectors use the 2008 NEI as a starting point. EPA created several sectors from the 2008 NEI 

nonpoint inventory, and this section describes the stationary nonpoint sources. Class 1 & Class 2 (c1c2) and 

Class 3 (c3) commercial marine vessels and locomotives are also in the 2008 NEI nonpoint data category. 

However, these mobile sources are included in the mobile documentation in Sections 2.5.4 2.5.5 as the 

c1c2rail and c3marine sectors, respectively. 

The nonpoint tribal-submitted emissions were removed to prevent possible double counting with the countylevel emissions and also because spatial surrogates for tribal data were not available. Because the tribal 

nonpoint emissions are small, these omissions will have a limited impact on the results at the 12-km scales 

used for this modeling. The documentation for the nonpoint sector of the 2008 NEI is available on the 2008 

NEI website (EPA, 2012a). 

The following subsections describe the partitioning of the 2008 NEI nonpoint inventory into the 2007v5 

modeling platform sectors, and also the differences between the nonpoint emissions in the 2007v5 platform 

and the 2007 base case. 
10

2.2.1 Area fugitive dust sector (afdust) 

The area-source fugitive dust (afdust) sector contains PM10 and PM2.5 emission estimates for nonpoint SCCs 

identified by the EPA staff as dust sources. Categories included in the afdust sector are paved roads, 

unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production, and 

mining and quarrying. It does not include fugitive dust from grain elevators because these are elevated point 

sources. 

This sector is separated from other nonpoint sectors to allow for the application of “transport fraction,” and 

meteorology/precipitation-based reductions. These adjustments are applied via sector-specific scripts, 

beginning with land use-based gridded transport fractions and then subsequent daily zero-outs for days 

where at least 0.01 inches of precipitation occurs or days when there is snow cover on the ground. The land 

use data used to reduce the NEI emissions explains the amount of emissions that are subject to transport. 

This methodology is discussed in (Pouliot, et. al., 2010), 

http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot_pres.pdf, and in Fugitive Dust Modeling for 

the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation adjustment is then applied to 

remove all emissions for days where measureable rain occurs. Both the transport fraction and 

meteorological adjustments are based on the gridded resolution of the platform; therefore, different 

emissions will result from different grid resolutions. Application of the transport fraction and meteorological 

adjustments reduces the overestimation of fugitive dust impacts in the grid modeling as compared to ambient 

samples.

For more details on this approach and the differences between the 2007 base case and the 2008 NEI, see the 

2007v5 TSD. The afdust sector is identical to the 2007v5 platform except for the fact that the meteorological 

adjustments were computed using the update WRF outputs. 

2.2.2 Agricultural ammonia sector (ag) 

The agricultural NH3 “ag” sector is based on livestock and agricultural fertilizer application emissions from 

the 2008 NEI nonpoint inventory. The ag sector is identical to 2007v5 platform (see 2007v5 TSD for 

details) except for the temporalization of animal NH3.

An updated temporal allocation methodology for animal NH3 that allocates emissions down to the hourly 

level by taking into account temperature and wind speed was incorporated into the 2007 platform (see 

Section 3.3.3 for more details). 

2.2.3 Other nonpoint sources (nonpt)

Stationary nonpoint sources that were not subdivided into the afdust or ag sectors were assigned to the 

“nonpt” sector. All fire emissions from the 2008 NEI nonpoint inventory were removed and replaced with 

SMARTFIRE emissions, described in Section 2.3. Additionally, locomotives and CMV mobile sources 

from the 2008 NEI nonpoint inventory are described in Section 2.5. 

For more details on the development of the nonpt sector see the 2007v5 TSD. The differences between the 

2007v5 nonpt and the 2007 nonpt for this base case are limited to the following: 

 Replaced 2008 NEI oil and gas emissions (SCCs beginning with “23100”) with year 2008 Phase III 

oil and gas emissions for several basins in the WRAP RPO states. These WRAP Phase III emissions 

contain point and nonpoint formatted data are discussed in greater detail in Section 2.1.2 and here: 

http://www.wrapair2.org/PhaseIII.aspx. These changes were made in counties affected by the WRAP 

data. 
11

 Updated speciation and spatial surrogates were used to support the addition of the Permian basin (see 

Section 3.2.1.3 and3.4.1, respectively). 

 Included the CMAQ MP-lite HAPs (see Section 3.2 for details) 

 Updated the temporalization of the residential wood combustion (RWC) inventories to account for 

the updated meteorology (see Section 3.3.3 for details). 

2.3 Fires (avefire) 

Wildfire and prescribed burning emissions are contained in the ptfire and avefire sectors. The ptfire sector 

has emissions provided at geographic coordinates (point locations) and has daily emissions values, whereas 

the avefire sector contains county-summed inventories also at daily resolution. EPA used the ptfire sector in 

the 2007v5 evaluation case but not for this 2007 base case. For the 2007 and future base cases, the avefire 

sector was used instead of point fires. The SCCs in Table 2-5 are considered “fires” – note that the complete 

SCC description includes “Miscellaneous Area Sources” as the first tier level description. 

Table 2-5. 2007 Platform SCCs representing emissions in the ptfire and avefire modeling sectors 

SCC SCC Description

2810001000 Other Combustion; Forest Wildfires; Total

2810015000 Other Combustion; Prescribed Burning for Forest Management; Total

2811015000 Other Combustion-as Event; Prescribed Burning for Forest Management; Total

2811090000 Other Combustion-as Event; Prescribed Forest Burning ;Unspecified

The avefire sector excludes agricultural burning and other open burning sources, which are included in the 

nonpt sector. The agricultural burning and other open burning sources are left in the nonpt sector because 

these categories were not factored into the development of the average fire sector. Additionally, the 

emissions are much lower and their year-to-year variability is much lower than that of wildfires and nonagricultural prescribed/managed burns. 

The purpose of the avefire sector is to represent emissions for a typical year’s fires for use in projection year 

inventories, since the location and degree of future-year fires are not known. This approach keeps the fires 

information constant between the 2007 base case and future-year cases to eliminate large and uncertain 

differences between those cases that would be caused by changing the fires. Using an average of multiple 

years of data reduces the possibility that a single-year's high or low fire activity would unduly affect futureyear model-predicted concentrations. Emissions are day-specific but aggregated to county-level where 

spatial surrogates will allocate the fires to forest and crop/pasture land. The creation of the avefire daily 

nonpoint inventory is distinct for prescribed burning and wildfires. For more details on Fire Averaging Tool 

and the various smoothing techniques, see the 2007v5 TSD. 

For this 2007 base case (and future years), EPA used 4 years (2007 through 2010) of fire data from the 

Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) version 2 for 

both prescribed and wildfires and used a 29 day averaging period. 

2.4 Biogenic sources (biog) 

The biogenic emissions were computed based on 2007 meteorology data using the Biogenic Emission 

Inventory System, version 3.14 (BEIS3.14) model within SMOKE. The BEIS3.14 model creates gridded, 

hourly, model-species emissions from vegetation and soils. It estimates CO, VOC (most notably isoprene, 

terpine, and sesquiterpene), and NO emissions for the U.S., Mexico, and Canada. The BEIS3.14 model is 
12

described further in http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_cmas08.ppt. The 

2007v5 biog sector and the biog sector for this 2007 base case, the meteorology differed between the two 

cases. The changes in meteorology will impact both the total emissions and the temporalization and spatial 

distribution of these emissions. The BIOSEASONS file was also updated using the meteorology data for this 

case. 

2.5 2007 mobile sources (onroad, onroad_rfl, nonroad, c1c2rail, c3marine) 

For the 2007 base case, as indicated in Table 2-1, EPA separated the 2007 onroad emissions into two sectors: 

(1) “onroad” and (2) “onroad_rfl”. As discussed in Section 2.5.2, the onroad and onroad_rfl sectors are 

processed separately to allow for different spatial allocation to be applied to onroad refueling (using a gas 

station surrogate) versus onroad vehicles (using surrogates based on roads and population). All onroad and 

onroad refueling emissions are generated using a new SMOKE-MOVES emissions modeling framework that 

leverages MOVES generated outputs (http://www.epa.gov/otaq/models/moves/index.htm) and hourly 

meteorology. 

The nonroad sector is based on NMIM except for California which uses data provided by the California Air 

Resources Board (CARB). All nonroad emissions are compiled at the county/SCC level. NMIM (EPA, 

2005) creates the nonroad emissions on a month-specific basis that accounts for temperature, fuel types, and 

other variables that vary by month. 

The locomotive and commercial marine vessel (CMV) emissions are divided into two nonroad sectors: 

“c1c2rail” and “c3marine”. The c1c2rail sector includes all railway and most rail yard emissions as well as 

the gasoline and diesel-fueled Class 1 and Class 2 CMV emissions. The c3marine sector emissions contain 

the larger residual fueled ocean-going vessel Class 3 CMV emissions and are treated as point emissions with 

an elevated release component; all other nonroad emissions are treated as county-specific low-level 

emissions (i.e., are in model layer 1). 

All tribal data from the mobile sectors have been dropped because EPA does not have spatial surrogate data 

for tribal regions, the data may be double-counted with emissions submitted by state and local agencies for 

the same areas, and the emissions are small. 

2.5.1 Onroad non-refueling (onroad) 

For the 2007 base case, EPA estimated emissions for every county in the continental U.S7. EPA used a 

modeling framework that took into account the strong temperature sensitivity of the onroad emissions. 

Specifically, EPA used county-specific inputs and tools that integrated the MOVES model with the SMOKE8

emission inventory model to take advantage of the gridded hourly temperature information available from 

meteorology modeling used for air quality modeling. This integrated “SMOKE-MOVES” tool was 

developed by EPA in 2010 and is in use by states and regional planning organizations for regional air quality 

modeling. SMOKE-MOVES requires emission rate “lookup” tables generated by MOVES that differentiate 

emissions by process (running, start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of 

day, etc. To generate the MOVES emission rates that could be applied across the U.S., an automated process 

ran MOVES to produce emission factors by temperature and speed for 146 “representative counties,” to 

which every other county could be mapped. Using the MOVES emission rates, SMOKE selected 

appropriate emissions rates for each county, hourly temperature, SCC, and speed bin and multiplied the 

 

7

 EPA estimated California as well, this is different than the approach for the 2007v5 platform. 

8

 A beta version of SMOKE v3.5 was used for modeling the Tier 3 FRM. The release version is available at: http://www.smokemodel.org/index.cfm
13

emission rate by activity (VMT (vehicle miles travelled) or vehicle population) to produce emissions. These 

calculations were done for every county, grid cell, and hour in the continental U.S. 

SMOKE-MOVES can be used with different versions of the MOVES model. For the 2007 platform, EPA 

used the latest publically released version: MOVES2010b 

(http://www.epa.gov/otaq/models/moves/index.htm). However, since the release of MOVES2010b, EPA has 

continued to collect and analyze emissions data. In particular, EPA completed two major studies of fuel 

effects on emissions in Tier 2 light-duty gasoline vehicles, and an important collection of studies on 

evaporative emissions. Because fuels and evaporative emissions are affected by Tier 3 standards, it was very 

important to include these data in the modeling. Therefore, in estimating the impact of the Tier 3 vehicle and 

fuel standards on air quality for the future years, MOVES2010b was updated to include the results from 

these studies, along with numerous other updates. Furthermore, the new model incorporated the changes that 

reflect recent EPA rules on light-duty and heavy-duty greenhouse gas emissions. These changes are 

documented in the docket for the NPRM9

and in the docket for this rule10.

The following inputs and methodologies are identical to the 2007v5 platform11 and are detailed in the 

2007v5 TSD: 

 Activity data (VMT, VPOP, speed) 

 Representative counties 

 Fuel Months 

 Local MOVES inputs 

 Procedure for running MOVES to create emission factors 

 Procedure for running SMOKE to create emissions 

The following inputs differed between the 2007 base case and the 2007v5 modeling and are detailed below: 

fuels, meteorology, and extended idle adjustments. 

2.5.1.1 Fuels 

Although state-submitted NMIM and MOVES input data may have included information about fuel 

properties, the MOVES runs for the 2007 base case were run using a set of fuel properties for each county in 

2007 generated by EPA. These data were developed using a combination of purchased fuel survey data, 

proprietary fuel refinery information, ethanol and other biofuel production levels, and known federal and 

local regulatory constraints. The fuel supply used in the Tier 3 FRM varies significantly from that used in 

the NPRM, including the introduction of a new approach to aggregating fuels by region. For more 

information regarding this new approach to fuels, please refer to the Tier 3 FRM, Chapter 7.1.3.2. The fuel 

supplied used in the Tier 3 FRM varies slightly from that used in the 2007v5, including less ethanol overall 

with a slightly different regional distribution, which more closely matched the new AEO regions. The fuel 

properties themselves are the same in 2007v5 and in Tier 3 FRM (2007 base case). 

2.5.1.2 Temperature and humidity 

Ambient temperature can have a large impact on emissions. Low temperatures are associated with high start 

emissions for many pollutants. High temperatures are associated with greater running emissions due to the 

higher engine load of air conditioning. High temperatures also are associated with higher evaporative 

emissions. 

 

9

 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM” 

10 U.S. EPA. 2014. “Memorandum to Docket: Updates to MOVES for the Tier 3 FRM Analysis” 

11 Tier 3 NPRM was based on the 2005 platform and hence inputs such as the representative counties, activity data, and local 

MOVES inputs were updated as part of the development of the 2007 platform (used for Tier 3 FRM). 
14

The 36-km and 12-km gridded meteorological input data for the entire year of 2007 covering the continental 

United States were derived from simulations of version 3.3 of the Weather Research and Forecasting Model 

(WRF, http://wrf-model.org), Advanced Research WRF (ARW) core (Skamarock, et al., 2008). The WRF 

Model is a mesoscale numerical weather prediction system developed for both operational forecasting and 

atmospheric research applications. The Meteorology-Chemistry Interface Processor (MCIP) version 4.1.2 

(http://www.cmascenter.org/help/model_docs/mcip/4.1/ReleaseNotes) was used as the software for 

maintaining dynamic consistency between the meteorological model, the emissions model, and air quality 

chemistry model. The meteorology run used for the 2007 base year12 is different than what was used for the 

2007v5 platform. Specifically it used newer versions of WRF and MCIP and 25 vertical layers13 instead of 

the 24 used in 2007v5. 

The SMOKE-MOVES tool Met4moves reads the gridded, hourly meteorological data (output from MCIP) to 

generate a list of the maximum temperature ranges, average relative humidity, and temperature profiles that 

are needed for MOVES to create the emission-factor lookup tables. For more details on Met4moves and the 

processing of meteorology for SMOKE-MOVES see the 2007v5 TSD. 

2.5.1.3 Extended idle adjustments and SMOKE-MOVES 

Emissions from the extended idling of long haul trucks are a subset of the exhaust emissions. These 

emissions are typically from trucks, which have traveled across county and state boundaries. Federal rules 

require that truck drivers may not drive more than 10 hours without rest. These long haul trucks are known 

to stop for these rest periods at truck stops along their routes and idle their trucks for hours while they rest. 

The MOVES model generates an estimate of the total number of extended idling hours and emissions for 

every county. However, when MOVES is run using the County scale, the extended idling rate (in grams per 

hour per truck) is not adjusted to account for allocation of the extended idling to counties where interstate 

travel occurs. 

SMOKE has an optional input that adjusts emissions (CFPRO) by county, SCC, and mode. To account for 

the extended idle adjustment, EPA created an adjustment file that applies these allocation factors by county 

for extended idle and for the long haul type SCCs (SCC7 2230073 and 2230074) only (see the 2011v1 NEI 

TSD for more details). 

SMOKE-MOVES, specifically Movesmrg, uses the adjustment factor file (CFPRO) for extended idle to 

estimate 2007 emissions that incorporates these adjustments. 

2.5.2 Onroad refueling (onroad_rfl)

Onroad refueling is modeled very similarly to other onroad emissions (see Section2.5.1). 

MOVESTier3FRM can produce EFs for refueling. These EFs are at the resolution of the onroad SCCs. The 

refueling EFs were run separately from the other onroad mobile sources to allow for different spatial 

allocation. To facilitate this, the EFs from the refueling process were separated out into RPD refueling and 

RPV refueling tables14. EPA then ran SMOKE-MOVES using these EF tables as inputs and spatially 

allocated the results based on a gas stations surrogate (see Section 3.4.1). 

Lastly, the Mrggrid SMOKE program combined RPD refueling and RPV refueling into a single onroad_rfl 

model ready output for final processing with the other sectors prior to use in CMAQ. 

 

12 Note the meteorology is consistent across all 3years: 2007, 2018 and 2030. 

13 WRF was run at 35 layers and MCIP post-processed it to 25 layers. 

14 The Moves2smk post-processing script has command line arguments that will either consolidate or split out the refueling EF. 
15

2.5.3 Nonroad mobile equipment sources: (nonroad)

This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not 

including commercial marine, aircraft, and locomotives) that are derived from NMIM for all states except 

California. Year-2007 inventories from CARB were used for California after the completion of several 

preprocessing steps discussed in the 2007v5 TSD. 

NMIM (non-California) nonroad

NMIM ran the version of NONROAD, NR08b, which models all in-force nonroad controls, including the 

marine spark ignited (SI) and small SI engine final rule, published May 2009 (EPA, 2008). This version of 

NONROAD is very similar to the publicly released version, but it can model ethanol blends up to E20. The 

NMIM version is NMIM20090504d, which has the same results as the publicly-released NMIM version 

NMIM20090504a. The underlying National County Database (NCD) is NCD20101201a, but with 2007 

meteorology inserted into the countymonthhour table. NCD20101201a includes state inputs for the 2008 

NEI. 

The NMIM run, abs2007basenr, includes the lower 48 states plus Washington D.C. ; it excludes Alaska, 

Hawaii, Puerto Rico and the Virgin Islands. To conserve processing time, NMIM was run using 392 county 

groups. The county groups are in the same state and have the same fuels and similar temperature regimes. 

The county from each group with the highest VMT was chosen as the representing county. All counties are 

mapped to their representing county in the MySQL table countymap392. The fuels database, 

countryyearmonth2007_Baseline_0906012, is a conversion to NMIM format of the MOVES fuels for the 

2007 base case (see Section 2.5.1.1).

As with the onroad emissions, NMIM provides nonroad emissions for VOC by three emission modes: 

exhaust, evaporative and refueling. Unlike the onroad sector, refueling emissions from nonroad sources are 

not separated into a different sector. 

The EPA ran NMIM to create county-SCC emissions and removed California emissions because they were 

replaced with a CARB inventory. Emissions were converted from monthly totals to SMOKE-ready FF10 

format (http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch08s02s04.html) monthly average-day 

based on the number of days in each month. EPA retained only CAPs and the necessary HAPs: BAFM, 

acrolein, butadiene, and naphthalene. 

California nonroad

California year 2007 nonroad emissions were provided by CARB and are documented in a staff report (ARB, 

2010a). The nonroad sector emissions in California are developed using a modular approach and include all 

rulemakings and updates in place by December 2010. These emissions were developed using Version 1 of 

the California Emissions Projection Analysis Model (CEPAM) which support various California off-road 

regulations such as in-use diesel retrofits (ARB, 2007), Diesel Risk-Reduction Plan (ARB, 2000) and 2007 

State Implementation Plans (SIPS) for the South Coast and San Joaquin Valley air basins (ARB, 2010b).

EPA converted the CARB-supplied nonroad annual inventory to monthly emissions values by using the 

aforementioned EPA NMIM monthly inventories to compute monthly ratios by pollutant and SCC. Some 

adjustments to the CARB inventory were needed to convert the provided total organic gas (TOG) to VOC 

and augment the HAPs. See Section 3.2.1.3 for details on speciation of California nonroad data. 
16

2.5.4 Class 1/Class 2 Commercial Marine Vessels and Locomotives and (c1c2rail) 

The c1c2rail sector contains locomotive and commercial marine vessel (CMV) sources, except for category 

3/residual-fuel (C3) CMV and railway maintenance. The “c1c2” portion of this sector name refers to the 

Class I/II CMV emissions, not the railway emissions. Railway maintenance emissions are included in the 

nonroad sector. The C3 CMV emissions are in the c3marine sector. 

For more details on the development of the c1c2rail sector see the 2007v5 TSD. The differences between the 

2007v5 c1c2rail and the 2007 c1c2rail for this base case are limited to the following: 

 Updated an outdated FIPS in the RPO rail inventory. Specifically changed Clifton Forge, VA 

(51560) to Alleghany County, VA (51005) 

 Augmented the CARB c1c2rail and RPO rail inventories to include the CMAQ MP-lite HAPs (see 

Section 3.2 and 3.2.1.3 for details) 

2.5.5 Class 3 commercial marine vessels (c3marine) 

The c3marine sector emissions data were developed based on a 4-km resolution ASCII raster format dataset 

used since the Emissions Control Area-International Marine Organization (ECA-IMO) project began in 

2005, then known as the Sulfur Emissions Control Area (SECA). These emissions consist of large marine 

diesel engines (at or above 30 liters/cylinder) that until very recently, were allowed to meet relatively modest 

emission requirements, often burning residual fuel. The emissions in this sector are comprised of primarily 

foreign-flagged ocean-going vessels, referred to as Category 3 (C3) CMV ships. The c3marine inventory 

includes these ships in several intra-port modes (cruising, hoteling, reduced speed zone, maneuvering, and 

idling) and underway mode and includes near-port auxiliary engines. This sector is identical to the 2007v5 

platform except for the addition of naphthalene (see Section 3.2.1.3). For more details on the development 

of this sector’s emissions, see the 2007v5 TSD. 

2.6 Emissions from Canada, Mexico and offshore drilling platforms (othpt, 

othar, othon) 

The emissions from Canada, Mexico, and offshore drilling platforms are included as part of three emissions 

modeling sectors: othpt, othar, and othon. 

The “oth” refers to the fact that these emissions are usually “other” than those in the U.S. state-county 

geographic FIPS, and the third and fourth characters provide the SMOKE source types: “pt” for point, “ar” 

for “area and nonroad mobile”, and “on” for onroad mobile. All “oth” emissions are CAP-only inventories. 

For more details on the development of the “oth” sectors see the 2007v5 TSD. The differences between the 

2007v5 “oth” and the 2007 “oth” for this base case are limited to the following: 

 othar has updated spatial surrogates (see Sections 3.4.3) 

 othon has updated speciation and spatial surrogates (see Sections 3.2.1.3 and 3.4.3, respectively) 

 othpt has updated speciation (see Sections 3.2.1.3) 
17

2.7 SMOKE-ready non-anthropogenic inventories for chlorine 

The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cl2) 

concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution were 

available and were not modified other than the name “CHLORINE” was changed to “CL2” because that is 

the name required by the CMAQ model. 
18

3 Emissions Modeling Summary 

Both the CMAQ and CAMX models require hourly emissions of specific gas and particle species for the 

horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To provide 

emissions in the form and format required by the model, it is necessary to “pre-process” the “raw” emissions 

(i.e., emissions input to SMOKE) for the sectors described above in Section 2. In brief, the process of 

emissions modeling transforms the emissions inventories from their original temporal resolution, pollutant 

resolution, and spatial resolution into the hourly, speciated, gridded resolution required by the air quality 

model. Emissions modeling includes temporal allocation, spatial allocation, and pollutant speciation. In 

some cases, emissions modeling also includes the vertical allocation of point sources, but many air quality 

models also perform this task because it greatly reduces the size of the input emissions files if the vertical 

layer of the sources does not need to be included. 

As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across 

sectors, and may be hourly, daily, monthly, or annual total emissions. The spatial resolution, which also can 

be different for different sectors, may be individual point sources, county/province/municipio totals, or 

gridded emissions. This section provides some basic information about the tools and data files used for 

emissions modeling as part of the modeling platform. In Section 2, the emissions inventories and how they 

differ from the 2007v5 platform were described. In Section 3, the descriptions of data are limited to the 

ancillary data SMOKE uses to perform the emissions modeling steps. 

SMOKE version 3.5 beta was used to pre-process the emissions inventories into emissions inputs for 

CMAQ. For sectors that have plume rise, the in-line emissions capability of the air quality models was used, 

thereby creating source-based and two-dimensional gridded emissions files that are much smaller than full 

three-dimensional gridded emissions files. For quality assurance of the emissions modeling steps, emissions 

totals by specie for the entire model domain are output as reports that are then compared to reports generated 

by SMOKE on the input inventories to ensure that mass is not lost or gained during the emissions modeling 

process. 

3.1 Emissions modeling Overview 

When preparing emissions for the air quality model, emissions for each sector are processed separately 

through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sectorspecific emissions across sectors. The SMOKE settings in the run scripts and the data in the SMOKE 

ancillary files control the approaches used by the individual SMOKE programs for each sector. Table 3-1

summarizes the major processing steps of each platform sector. The “Spatial” column shows the spatial 

approach used: here “point” indicates that SMOKE maps the source from a point location (i.e., latitude and 

longitude) to a grid cell; “surrogates” indicates that some or all of the sources use spatial surrogates to 

allocate county emissions to grid cells; and “area-to-point” indicates that some of the sources use the 

SMOKE area-to-point feature to grid the emissions. The “Speciation” column indicates that all sectors use 

the SMOKE speciation step, though biogenics speciation is done within the Tmpbeis3 program and not as a 

separate SMOKE step. The “Inventory resolution” column shows the inventory temporal resolution from 

which SMOKE needs to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is 

no input inventory; instead, activity data and emission factors are used in combination with meteorological 

data to compute hourly emissions. 

Finally, the “plume rise” column indicates the sectors for which the “in-line” approach is used. These 

sectors are the only ones with emissions in aloft layers based on plume rise. The term “in-line” means that 
19

the plume rise calculations are done inside of the air quality model instead of being computed by SMOKE. 

The air quality model computes the plume rise using the stack data and the hourly air quality model inputs 

found in the SMOKE output files for each model-ready emissions sector. The height of the plume rise 

determines the model layer into which the emissions are placed. The c3marine, and othpt sectors are the only 

sectors that contain only “in-line” emissions, meaning that all of the emissions are placed in aloft layers and 

there are no emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE. 

Table 3-1. Key emissions modeling steps by sector. 

Platform sector

Spatial Speciation

Inventory

resolution Plume rise

ag Surrogates Yes annual

(some monthly)

afdust Surrogates Yes Annual

avefire Surrogates Yes daily

beis Pre-gridded 

land use in BEIS3.14 computed hourly

c1c2rail Surrogates Yes Annual

c3marine Point Yes Annual in-line

nonpt Surrogates &

area-to-point Yes annual(some monthly)

nonroad Surrogates &

area-to-point Yes Monthly

othar Surrogates Yes Annual

onroad Surrogates Yes computed hourly

onroad_rfl Surrogates Yes computed hourly

othon Surrogates Yes Annual

othpt Point Yes Annual in-line

ptipm Point Yes Daily in-line

ptnonipm Point Yes Annual in-line

SMOKE has the option of grouping sources so that they are treated as a single stack when computing plume 

rise. For the 2007v5 platform, no grouping was performed because grouping combined with “in-line” 

processing will not give identical results as “offline” (i.e., when SMOKE creates 3-dimensional files). This 

occurs when stacks with different stack parameters or lat/lons are grouped, thereby changing the parameters 

of one or more sources. The most straightforward way to get the same results between in-line and offline is 

to avoid the use of grouping. 

EPA ran SMOKE for the 36-km CONtinental United States “CONUS” modeling domain for the boundary 

conditions and for the smaller CONUS US 12-km modeling domain (12US2) shown in Figure 3-1 and 

described in Table 3-2. 
20

Figure 3-1. Air quality modeling domains

Both grids use a Lambert-Conformal projection, with Alpha = 33o, Beta = 45o and Gamma = -97o, with a 

center of X = -97o and Y = 40o. 

Table 3-2. Descriptions of the 2007v5 platform grids

Common 

Name

Grid 

Cell Size

Description 

(see Figure 3-1) Grid name

Parameters listed in SMOKE grid 

description (GRIDDESC) file:

 projection name, xorig, yorig, 

 xcell, ycell, ncols, nrows, nthik

Continental 

36km grid 

(36US1)

36km

Entire conterminous 

US plus some of 

Mexico/Canada

36US1_148X112 'LAM_40N97W', -2736000, -

2088000, 36.D3, 36.D3, 148, 112, 1

Continental 

12km grid 12 km

Entire conterminous 

US plus some of 

Mexico/Canada

12US1_459X299 ‘LAM_40N97W', -2556000, -1728000, 

12.D3, 12.D3, 459, 299, 1

US 12 km or 

“smaller” 

CONUS-12

12 km

Smaller 12km 

CONUS plus some of 

Mexico/Canada

12US2 ‘LAM_40N97W', -2412000 , -

1620000, 12.D3, 12.D3, 396, 246, 1

Section 3.4 provides the details on the spatial surrogates and area-to-point data used to accomplish spatial 

allocation with SMOKE. 
21

3.2 Chemical Speciation 

The emissions modeling step for chemical speciation creates “model species” needed by the air quality 

model for a specific chemical mechanism. These model species are either individual chemical compounds or 

groups of species, called “model species.” The chemical mechanism used for the 2007 platform is the CB05 

mechanism (Yarwood, 2005). The same base chemical mechanism is used with CMAQ and CAMX, but the 

implementation differs slightly between the two models. The specific versions of CMAQ and CAMx used in 

applications of this platform include secondary organic aerosol (SOA) and HONO enhancements. 

From the perspective of emissions preparation, the CB05 with SOA mechanism is the same as was used in 

the 2007 platform. Table 3-3 lists the model species produced by SMOKE for use in CMAQ and CAMX. It 

should be noted that the BENZENE model species is not part of CB05 in that the concentrations of 

BENZENE do not provide any feedback into the chemical reactions (i.e., it is not “inside” the chemical 

mechanism). Rather, benzene is used as a reactive tracer and as such is impacted by the CB05 chemistry. 

BENZENE, along with several reactive CB05 species (such as TOL and XYL) plays a role in SOA 

formation. Unlike the 2007v5 platform, the Tier3 FRM modeling included additional hazardous air 

pollutants (HAPs) and used slightly revised speciation. A “lite” version15 of the multi-pollutant CMAQ 

(Version 5.0.1) was used that required additional HAP species16 (see Table 3-3 for details): ACROLEIN, 

ALD2_PRIMARY, BUTADIENE13, ETOH, FORM_PRIMARY, and NAPHTHALENE . 

The approach for speciating PM2.5 emissions supports both CMAQ 4.7.1 and CMAQ 5.0 and includes 

speciation of PM2.5 into a larger set of PM model species than is listed above (see the 2007v5 TSD and 

Section 3.2.2 for details). The TOG and PM2.5 speciation factors that are the basis of the chemical speciation 

approach were developed from the SPECIATE4.3 database (http://www.epa.gov/ttn/chief/software/speciate),

EPA's repository of TOG and PM speciation profiles of air pollution sources. However, a few of the profiles 

used in this modeling will be published in later versions of the SPECIATE database after the release of this 

documentation. 

The approach for speciating NOX into NO, NO2, and HONO is consistent with the 2007v5 platform (see the 

2007v5 TSD for details). 

The SPECIATE database development and maintenance is a collaboration involving the EPA’s ORD, 

OTAQ, and the Office of Air Quality Planning and Standards (OAQPS), and Environment Canada (EPA, 

2006a). The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical 

compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles for 

PM2.5. 

 

15 Note, that the MP version of CMAQ allows the user to control the list of HAPs that they wish to explicitly model. 

16 These additional HAPs are referred to in this document as “CMAQ MP-lite HAPs”. 
22

Table 3-3. Emission model species produced for CB05 with SOA for CMAQ and CAMX*

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 for VOC speciation

ALD2_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

CH4 Methane17

ETH Ethene

ETHA Ethane

ETOH Ethanol

FORM Formaldehyde from VOC speciation

FORM_PRIMARY Formaldehyde from the HAP inventory

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

ISOP Isoprene

MEOH Methanol

NAPHTHALENE Naphthalene from the HAP inventory

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

PAR Paraffin carbon bond

TOL Toluene and other monoalkyl aromatics

XYL Xylene and other polyalkyl aromatics

VOC species from the biogenics 

model that do not map to model 

species above

SESQ Sesquiterpenes

TERP Terpenes

PM10 PMC Coarse PM > 2.5 microns and  10 microns

PM2.5

18 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)

19

PCL Particulate chloride

PNA Particulate sodium

 

17 Technically, CH4 is not a VOC but part of TOG. Although we derive emissions of CH4, the AQ models do not use these 

emissions because the anthropogenic emissions are dwarfed by the CH4 already in the atmosphere. 

18 For CMAQ 5.0, PM2.5 is speciated into a finer set of PM components. Listed in this table are the AE5 species 

19 These emissions are created outside of SMOKE 
23

*The same species names are used for the CAMX model with exceptions as follows:

1. CL2 is not used in CAMX

2. CAMX particulate sodium is NA (in CMAQ it is PNA)

3. CAMX uses different names for species that are both in CBO5 and SOA for the following: TOLA=TOL, XYLA=XYL, 

ISP=ISOP, TRP=TERP. They are duplicate species in CAMX that are used in the SOA chemistry. CMAQ uses the same 

names in CB05 and SOA for these species.

4. CAMX uses a different name for sesquiterpenes: CMAQ SESQ = CAMX SQT

5. CAMX uses particulate species uses different names for organic carbon, coarse particulate matter and other particulate 

mass as follows: CMAQ POC = CAMX POA, CMAQ PMC = CAMX CPRM, CMAQ PMFINE= CAMX FCRS, and CMAQ 

PMOTHR = CAMx FPRM

3.2.1 VOC speciation 

3.2.1.1 The combination of HAP BAFM (benzene, acetaldehyde, formaldehyde and 

methanol) and VOC for VOC speciation 

The VOC speciation includes HAP emissions from the NEI in the speciation process. Instead of speciating 

VOC to generate all of the species listed in Table 3-3, EPA integrated emissions of four specific HAPs, 

benzene, acetaldehyde, formaldehyde and methanol (collectively known as “BAFM”) from the NEI with the 

NEI VOC. The integration process (described in more detail in the 2007v5 TSD) combines these HAPs with 

the VOC in a way that does not double count emissions and uses the HAP inventory directly in the speciation 

process. The basic process is to subtract the specified HAPs from VOC and to use a special integrated 

profile to speciate the remainder of VOC to the model species excluding the specific HAPs. Generally, the 

HAP emissions from the NEI are considered to be more representative of emissions of these compounds than 

their generation via VOC speciation. 

Specific sectors fell into 3 categories: all sources are speciated from VOC directly (no integration), all 

sources are speciated with BAFM or EBAFM (ethanol plus BAFM) coming from the inventory (full 

integration), or some sources have BAFM and other sources do not (partial integration). See Table 3-4 for 

the integration status of each of the modeling sectors. 

Table 3-4. Integration approach for BAFM and EBAFM for each platform sector

Platform 

Sector

Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A), 

Formaldehyde (F), Methanol (M), and Ethanol (E)

ptipm No integration 

ptnonipm Partial integration (BAFM)

avefire No integration 

Ag N/A – sector contains no VOC 

Afdust N/A – sector contains no VOC

Nonpt Partial integration (BAFM and EBAFM)

nonroad Partial integration (BAFM). Except for California: no integration

c1c2rail Partial integration (BAFM)

c3marine Full integration (BAFM)

Onroad Full integration (EBAFM and BAFM)

Biog N/A – sector contains no inventory pollutant "VOC"; but rather specific VOC species

Othpt No integration 

Othar No integration

othon No integration 

More details on the integration of specific sectors and additional details of the speciation are provided in 

Section3.2.1.3 and the 2007v5 TSD. 
24

3.2.1.2 County specific profile combinations (GSPRO_COMBO) 

EPA used the SMOKE feature to compute speciation profiles from mixtures of other profiles in userspecified proportions. The combinations are specified in the GSPRO_COMBO ancillary file by pollutant 

(including pollutant mode, e.g., EXH__VOC), state and county (i.e., state/county FIPS code) and time period 

(i.e., month). 

EPA used this feature for onroad and nonroad mobile and gasoline-related related stationary sources 

whereby the emission sources use fuels with varying ethanol content, and therefore the speciation profiles 

require different combinations of gasoline profiles, e.g. E0 and E10 profiles. Since the ethanol content varies 

spatially (e.g., by state or county), temporally (e.g., by month) and by modeling year (future years have more 

ethanol) the feature allows combinations to be specified at various levels for different years. SMOKE 

computes the resultant profile using the fraction of each specific profile assigned by county, month and 

emission mode. 

The GSREF file indicates that a specific source uses a combination file with the profile code “COMBO”. 

Because the GSPRO_COMBO file does not differentiate by SCC and there are various levels of integration 

across sectors, we typically have a sector specific GSPRO_COMBO. For the onroad and onroad_rfl sectors, 

the GSPRO_COMBO uses E-profiles (i.e. there is EBAFM integration). Different profile combinations are 

specified by the mode (e.g. exhaust, evaporative, refueling, etc.) by changing the pollutant name (e.g. 

EXH__NONHAPTOG, EVP__NONHAPTOG, RFL__NONHAPTOG). For the nonpt sector, there is a 

combination of BAFM and EBAFM integration. Due to the lack of SCC in the GSPRO_COMBO, the only 

way to differentiate the sources that should use BAFM integrated profiles versus E-profiles is by changing 

the pollutant name. For example, we changed the pollutant name for the PFC future year inventory so the 

integration would use EVP__NONHAPVOC to correctly select the E-profile combinations while other 

sources used NONHAPVOC to select the typical BAFM profiles. 

3.2.1.3 Additional sector specific details 

The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some sectors 

there is no integration (VOC is speciated directly), for some sectors there is full integration (all sources are 

integrated), and for other sectors there is partial integration (some sources are not integrated and other 

sources are integrated). The integrated HAPs are either BAFM (ethanol not subtracted from VOC with 

BAFM HAPs) or EBAFM (ethanol and BAFM HAPs subtracted from VOC). Table 3-4 summarizes the 

integration for each platform sector. The additional CMAQ MP-lite HAPs were evaluated and, where it was 

needed, augmented in the mobile sectors, with details provided below.20

For the c1c2rail sector, EPA integrated BAFM for most sources from the 2008 NEI. There were a few 

sources that had zero BAFM; therefore, they were processed as no integrate. The RPO and CARB 

inventories did not include HAPs; therefore, EPA processed all non-NEI source emissions in the c1c2rail 

sector as no integrate. For California, EPA converted the CARB inventory TOG to VOC by dividing the 

inventory TOG by the available VOC-to-TOG speciation factor. For CARB and the RPO inventories, EPA 

augmented the inventories to include the CMAQ MP-lite HAPs. In Table 3-5, “geography” indicates 

whether the emission factor is applied nationally or to a subset of the country and “speciation base” is the 

pollutant value to use in the emissions calculation. 

 

20 EPA analyzed the presence of acroleine, 1,3-butadiene, and naphthalene in the nonpt and point sectors and found that the 

coverage was very inconsistent; therefore the decision was made to augment only the mobile sectors where it was needed. 
25

Table 3-5. HAP augmentation for c1c2rail

Pollutant

Speciation 

Fraction

Speciation 

Base Geography SCC SCC Description

1,3 Butadiene 6.146E-05 PM10-PRI California 2285002007

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class II / III Operations

1,3 Butadiene 6.146E-05 PM10-PRI California 2285002006

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class I Operations

Acrolein 8.547E-05 PM10-PRI California 2285002007

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class II / III Operations

Acrolein 8.547E-05 PM10-PRI California 2285002006

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class I Operations

Napthalene 1.851E-03 PM10-PRI California 2285002007

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class II / III Operations

Napthalene 1.851E-03 PM10-PRI California 2285002006

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class I Operations

Napthalene 2.576E-03 PM10-PRI 49 States 2285002006

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class I Operations

Napthalene 2.576E-03 PM10-PRI 49 States 2285002007

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class II / III Operations

Acrolein 4.594E-03 PM10-PRI 49 States 2285002007

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class II / III Operations

Acrolein 4.594E-03 PM10-PRI 49 States 2285002006

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class I Operations

1,3 Butadiene 4.774E-03 PM10-PRI 49 States 2285002006

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class I Operations

1,3 Butadiene 4.774E-03 PM10-PRI 49 States 2285002007

Railroad Equipment; Diesel; Line Haul 

Locomotives: Class II / III Operations

Acrolein 2.625E-03 VOC National 2280002100 Marine Vessels; Commercial; Diesel; Port

Acrolein 2.188E-03 VOC National 2280002200

Marine Vessels; Commercial; Diesel; 

Underway

Naphthalene 1.051E-03 PM25-PRI National 2280002100 Marine Vessels; Commercial; Diesel; Port

Naphthalene 8.756E-04 PM25-PRI National 2280002200

Marine Vessels; Commercial; Diesel; 

Underway

For the c3marine sector, EPA computed HAPs directly from the CAP inventory; therefore, the entire sector 

is integrated to use the VOC BAFM HAP species directly, rather than VOC speciation profiles. There is no 

methanol in the VOC speciation, but the remaining VOC BAF HAPs and the CMAQ MP-lite HAPs21

emissions are derived from the following equations: 

Benzene = VOC * 9.795E-06

Acetaldehyde = VOC * 2.286E-04 

Formaldehyde = VOC * 1.5672E-03 

Naphthalene = PM2.5 * 1.990E-5

For the onroad and onroad_rfl sectors, there are series of unique speciation issues. First, SMOKE-MOVES 

(see the 2007v5 TSD) is used to estimate these sectors, meaning that both the MEPROC and INVTABLE 

files are involved in controlling which pollutants are ingested and speciated. Second, these sectors have 

estimates of TOG as well as VOC; therefore, TOG can be speciated directly. Third, the gasoline sources use 

 

21 The c3marine sources do not emit acrolein or 1,3-butadiene. 
26

full integration of EBAFM (i.e. use E-profiles) and the diesel sources use full integration of BAFM. Fourth, 

the onroad sector utilizes 7 different modes for speciation: exhaust, extended idle, auxiliary power units 

(APU), evaporative, permeation (gasoline vehicles only), brake wear, and tire wear. The onroad_rfl sector 

utilizes an eighth mode, refueling. Fifth, the gasoline exhaust profiles were updated to 8750a (revision to 

Gasoline Exhaust - Reformulated gasoline) and 8751a (revision to Gasoline Exhaust - E10 ethanol 

gasoline).22 Sixth, the CMAQ MP-lite HAPs are produced directly from the SMOKE-MOVES processing. 

For the nonroad sector, CNG or LPG sources (SCC beginning with 2268 or 2267) were not integrated 

because NMIM computed only VOC and no HAPs were available for these SCCs. All other nonroad 

sources were integrated. For California, EPA converted the CARB inventory TOG to VOC by dividing the 

inventory TOG by the available VOC-to-TOG speciation factor. SMOKE later applies the same VOC-toTOG factor prior to computing speciated emissions. The CARB-based nonroad data includes exhaust and 

evaporative mode-specific data for VOC, but does not contain refueling. The CARB inventory also does not 

include HAP estimates; therefore all California nonroad emissions are processed as no integrate so that the 

HAP species are generated by speciating the TOG emissions. The CMAQ MP-lite HAPS are produced 

directly by NMIM. In California, the CMAQ MP-lite HAPs were augmented by applying state-wide SCC 

HAP to VOC ratios based on EPA’s NMIM estimates. 

For the ptnonipm sector, there is partial integration limited to the 2007 ethanol inventory (SCC 30125010), 

which includes BAFM . In the future year, there is also partial integration because both the ethanol and 

biodiesel inventories (SCC 30125010) provided by OTAQ include BAFM. See the 2007v5 TSD for 

additional details on this sector. 

For the oil and gas sources in ptnonipm and nonpt, the WRAP Phase III sources have basin-specific VOC 

speciation that takes into account the distinct composition of gas. ENVIRON developed these basin-specific 

profiles using gas composition analysis data obtained from operators through surveys. ENVIRON separated 

out emissions and speciation from conventional/tight sands/shale gas from coal‐bed methane (CBM) gas 

sources. Table 3-6 lists the basin and gas composition specific profiles used for the WRAP Phase III 

inventory.23

Table 3-6. VOC profiles for WRAP Phase III basins

Profile Code Description

SSJCB South San Juan Basin Produced Gas Composition for CBM Wells

SSJCO South San Juan Basin Produced Gas Composition for Conventional Wells

WRBCO Wind River Basin Produced Gas Composition for Conventional Wells

PRBCB Powder River Basin Produced Gas Composition for CBM Wells

PRBCO Powder River Basin Produced Gas Composition for Conventional Wells

DJFLA D-J Basin Flashing Gas Composition for Condensate

DJVNT D-J Basin Produced Gas Composition

UNT01 Uinta Basin Gas Composition at CBM Wells

UNT02 Uinta Basin Gas Composition at Conventional Wells

UNT03 Uinta Basin Flashing Gas Composition for Oil

UNT04 Uinta Basin Flashing Gas Composition for Condensate

PNC01 Piceance Basin Gas Composition at Conventional Wells

 

22 These revised profiles are expected to be in the yet to be released SPECIATE 4.4. 

23 Profile PRM01 was used in Tier 3 but not in the 2007v5 modeling. All other profiles are the same between the two cases. 
27

Profile Code Description

PNC02 Piceance Basin Gas Composition at Oil Wells

PNC03 Piceance Basin Flashing Gas Composition for Condensate

SWFLA SW Wyoming Basin Flash Gas Composition

SWVNT SW Wyoming Basin Vented Gas Composition

PRM01 Permian Basin Produced Gas Composition

SWE01 Wyoming Flashing Gas Composition

Othon and othpt used a GSPRO_COMBO that included a gasoline exhaust profile updated to 8750a (revision 

to Gasoline Exhaust - Reformulated gasoline). 

For the remaining sectors, see the 2007v5 TSD for speciation details. 

3.2.1.4 Future year speciation 

The VOC speciation approach used for the future year case is customized to account for the impact of fuel 

changes. These changes affect the onroad, onroad_rfl, nonroad, and parts of the nonpt and ptnonipm sectors. 

Speciation profiles for VOC in the nonroad, onroad and onroad_rfl sectors that account for the changes in 

ethanol content of fuels across years. The actual fuel formulations used can be found in Sections 2.5.1.1 and 

4.3.1.2. For 2007, EPA used “COMBO” profiles to model combinations of profiles for E0 and E10 fuel use. 

For 2018 and 2030, EPA used “COMBO” profiles to model combinations of E10, E15, and E85 fuel use. 

The speciation of onroad exhaust VOC additionally accounts for changes in the fraction of the vehicle fleet 

meeting different vehicle standards over time; currently, different exhaust profiles are available for pre-Tier 

2 versus Tier 2 and later vehicles. Thus for onroad gasoline, VOC speciation uses different COMBO profiles 

to take into account both the increase in ethanol use, and the increase in vehicles meeting Tier 2 and later 

standards in the future case. 

The speciation changes from fuels in the nonpt sector are for PFCs and fuel distribution operations 

associated with the BTP distribution. For these sources, ethanol may be mixed into the fuels; therefore, 

speciation is expect to change across years. The speciation changes from fuels in the ptnonipm sector 

include BTP distribution operations inventoried as point sources. RBT fuel distribution and BPS speciation 

does not change across the modeling cases because this is considered upstream from the introduction of 

ethanol into the fuel. For PFC, ethanol was present in the future inventories and therefore EBAFM profiles 

were used to integrate ethanol in the speciation. Mapping of fuel distribution SCCs to PFC, BTP, BPS, and 

RBT emissions categories can be found in Appendix B of the 2007v5 TSD. 

Error! Reference source not found. Table 3-7 summarizes the different profiles utilized for the fuel-related 

sources in each of the sectors for 2007 and the future year cases. This table indicates when “E-profiles” were 

used instead of BAFM integrated profiles. The term “COMBO” indicates that a combination of the profiles 

listed was used to speciate that subcategory using the GSPRO_COMBO file. Note, the speciation for the 

Tier3 2018 control case is identical to the 2018 reference case and the speciation for the 2030 control case is 

identical to the 2030 reference case. Although many of the profiles making up the COMBO are the same 

between 2018 and 2030, the ratio of the profiles changes between the two years. 
28

Table 3-7. Select VOC profiles 2007. 2018 and 2030

Sector

Subcategory 2007 2018 2030

onroad gasoline 

exhaust

COMBO: COMBO: COMBO:

8750aE

Pre-Tier 2 E0 

exhaust 8751aE

Pre-Tier 2 E10 

exhaust 8751aE Pre-Tier 2 E10 exhaust

8751aE

Pre-Tier 2 E10 

exhaust 8757E Tier 2 E10 Exhaust 8757E Tier 2 E10 Exhaust

8756E

Tier 2 E0 

Exhaust 8758E Tier 2 E15 Exhaust 8758E Tier 2 E15 Exhaust

8757E

Tier 2 E10 

Exhaust 8855E Tier 2 E85 Exhaust 8855E Tier 2 E85 Exhaust

onroad gasoline 

evaporative

COMBO: COMBO: COMBO:

8753E E0 Evap 8754E E10 Evap 8754E E10 Evap

8754E E10 Evap 8872E E15 Evap 8872E E15 Evap

8934E E85 Evap 8934E E85 Evap

onroad gasoline 

permeation

COMBO: COMBO: COMBO:

8766E E0 evap perm 8769E E10 evap perm 8769E E10 evap perm

8769E E10 evap perm 8770E E15 evap perm 8770E E15 evap perm

8934E E85 Evap 8934E E85 Evap

onroad_rfl gasoline 

refueling

COMBO: COMBO: COMBO:

8869E E0 Headspace 8870E E10 Headspace 8870E E10 Headspace

8870E

E10 

Headspace 8871E E15 Headspace 8871E E15 Headspace

8934E E85 Evap 8934E E85 Evap

onroad

diesel 

exhaust 8774

Pre-2007 MY 

HDD exhaust 877P0

Weighted diesel 

exhaust for 2018 87730T3

Weighted diesel 

exhaust for 2030

onroad

diesel 

extended 

idle 8774

Pre-2007 MY 

HDD exhaust 877EIT3

Weighted diesel 

extended idle for 2018 877EIT3

Weighted diesel 

extended idle for 2018

onroad

auxiliary 

power units N/A 8774

Pre-2007 MY HDD 

exhaust 8774

Pre-2007 MY HDD 

exhaust

onroad

diesel 

evaporative 4547

Diesel 

Headspace 4547 Diesel Headspace 4547 Diesel Headspace

onroad_rfl

diesel 

refueling 4547

Diesel 

Headspace 4547 Diesel Headspace 4547 Diesel Headspace

nonroad gasoline 

exhaust

COMBO: 8751a

Pre-Tier 2 E10 

exhaust 8751a Pre-Tier 2 E10 exhaust

8750a

Pre-Tier 2 E0 

exhaust

8751a

Pre-Tier 2 E10 

exhaust

nonroad gasoline 

evaporative

COMBO: 8754 E10 evap 8754 E10 evap

8753 E0 evap

8754 E10 evap

nonroad gasoline 

refueling

COMBO: 8870 E10 Headspace 8870 E10 Headspace

8869 E0 Headspace

8870 E10 Headspace
29

Sector

Subcategory 2007 2018 2030

nonroad

diesel 

exhaust 8774

Pre-2007 MY 

HDD exhaust 8774

Pre-2007 MY HDD 

exhaust 8774

Pre-2007 MY HDD 

exhaust

nonroad

diesel 

evaporative 4547

Diesel 

Headspace 4547 Diesel Headspace 4547 Diesel Headspace

nonroad

diesel 

refueling 4547

Diesel 

Headspace 4547 Diesel Headspace 4547 Diesel Headspace

nonpt/

ptnonipm PFC

COMBO: COMBO: COMBO:

8869E E0 Headspace 8870E E10 Headspace 8870E E10 Headspace

8870E E10 Headspace 8871E E15 Headspace 8871E E15 Headspace

8934E E85 Evap 8934E E85 Evap

nonpt/

ptnonipm BTP

COMBO: COMBO: COMBO:

8869 E0 Headspace 8870 E10 Headspace 8870 E10 Headspace

8870 E10 Headspace 8871 E15 Headspace 8871 E15 Headspace

8934 E85 Evap 8934 E85 Evap

nonpt/

ptnonipm BPS/RBT 8869 E0 Headspace 8869 E0 Headspace 8869 E0 Headspace

3.2.2 PM speciation 

In addition to VOC profiles, the SPECIATE database also contains the PM2.5 speciated into both individual 

chemical compounds (e.g., zinc, potassium, manganese, lead), and into the “simplified” PM2.5 components 

used in the air quality model. For CMAQ 4.7.1 modeling, these “simplified” components (AE5) are all that 

is needed. For CMAQ 5.0.1, there is a new thermodynamic equilibrium aerosol modeling tool 

(ISORROPIA) v2 mechanism that needs additional PM components (AE6), which are further subsets of 

PMFINE (see Table 3-8). EPA speciated PM2.5 so that it included both AE5 and AE6 PM model species 

without causing any double counting. Therefore, emissions from these scenarios can be used with either 

CMAQ 4.7.1 or CMAQ 5.0.1. 

Table 3-8. PM model species: AE5 versus AE6 

species name species description AE5 AE6

POC organic carbon Y Y

PEC elemental carbon Y Y

PSO4 sulfate Y Y

PNO3 nitrate Y Y

PMFINE unspeciated PM2.5 Y N

PNH4 ammonium N Y

PNCOM non-carbon organic matter N Y

PFE iron N Y

PAL aluminum N Y

PSI silica N Y

PTI titanium N Y

PCA calcium N Y

PMG magnesium N Y

PK potassium N Y

PMN manganese N Y
30

species name species description AE5 AE6

PNA sodium N Y

PCL chloride N Y

PH2O water N Y

PMOTHR unspeciated PM2.5 N Y

The majority of the PM profiles come from the 911XX series, which include updated AE6 speciation24. 

Unlike the 2007v5 platform, the profile numbers used in the Tier 3 runs are consistent with SPECIATE 4.3. 

Although the profile numbers changed, the underlying profiles (namely the percentage of AE6 components) 

did not change between 2007v5 and this 2007 base case (see the 2007v5 TSD for details on the earlier profile 

names). This change in profile numbers impacts most sectors with PM emissions25.

3.3 Temporal Allocation 

Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer 

temporal resolution, such converting annual emissions to hourly emissions. While the total emissions are 

important, the timing of the occurrence of emissions is also essential for accurately simulating ozone, PM, 

and other pollutant concentrations in the atmosphere. Many emissions inventories are annual or monthly in 

nature. Temporalization takes these annual emissions and distributes them to the month, and then distributes 

the monthly emissions to the day, and the daily emissions to the hour. This process is typically done by 

applying temporal profiles to the inventories in this order: monthly, day of the week, and diurnal. 

The temporal profiles and associated cross references used to create the hourly emissions inputs for the air 

quality model were similar to those used for the 2007v5 platform. New methodologies introduced in this 

platform and updated profiles are discussed in this section. Temporal factors are typically applied to the 

inventory by some combination of country, state, county, SCC, and pollutant. The following values are used 

in Error! Not a valid bookmark self-reference.: the value “all” means that hourly emissions computed for 

every day of the year and that emissions potentially have day-of-year variation. The value “week” means 

that hourly emissions computed for all days in one “representative” week, representing all weeks for each 

month. This means emissions have day-of-week variation, but not week-to-week variation within the month. 

The value “mwdss” means hourly emissions for one representative Monday, representative weekday 

(Tuesday through Friday), representative Saturday, and representative Sunday for each month. This means 

emissions have variation between Mondays, other weekdays, Saturdays and Sundays within the month, but 

not week-to-week variation within the month. The value “aveday” means hourly emissions computed for 

one representative day of each month, meaning emissions for all days within a month are the same. Special 

situations with respect to temporalization are described in the following subsections. 

Table 3-9 summarizes the temporal aspects of emissions modeling by comparing the key approaches used for 

temporal processing across the sectors. The temporal aspects of SMOKE processing are controlled through 

(a) the L_TYPE (temporal type) and M_TYPE (merge type) settings used, and (b) the temporal profiles 

themselves. In the table, “Daily temporal approach” refers to the temporal approach for getting daily 

emissions from the inventory using the SMOKE Temporal program. The values given are the values of the 

SMOKE L_TYPE setting. The “Merge processing approach” refers to the days used to represent other days 

in the month for the merge step. If this is not “all”, then the SMOKE merge step runs only for representative 

 

24 The exceptions are 5674 (Marine Vessel – Marine Engine – Heavy Fuel Oil) used for c3marine and 92018 (Draft Cigarette 

Smoke – Simplified) used in nonpt. 

25 It impacts the profile names (numbers) used in the sector, but it does not impact the fraction of AE6 PM species and hence will 

not impact the air quality model-ready files. 
31

days, which could include holidays as indicated by the right-most column. The values given are those used 

for the SMOKE M_TYPE setting (see below for more information). 

The following values are used in Error! Not a valid bookmark self-reference.: the value “all” means that 

hourly emissions computed for every day of the year and that emissions potentially have day-of-year 

variation. The value “week” means that hourly emissions computed for all days in one “representative” 

week, representing all weeks for each month. This means emissions have day-of-week variation, but not 

week-to-week variation within the month. The value “mwdss” means hourly emissions for one 

representative Monday, representative weekday (Tuesday through Friday), representative Saturday, and 

representative Sunday for each month. This means emissions have variation between Mondays, other 

weekdays, Saturdays and Sundays within the month, but not week-to-week variation within the month. The 

value “aveday” means hourly emissions computed for one representative day of each month, meaning 

emissions for all days within a month are the same. Special situations with respect to temporalization are 

described in the following subsections. 

Table 3-9. Temporal settings used for the platform sectors in SMOKE

Platform 

sector short 

name

Inventory 

resolutions

Monthly 

profiles 

used?

Daily 

temporal 

approach

Merge 

processing 

approach

Process 

Holidays as 

separate days

ptipm Daily all all Yes

ptnonipm Annual yes mwdss mwdss Yes

othpt Annual yes mwdss mwdss

nonroad Monthly mwdss mwdss Yes

othar Annual yes week week

c1c2rail Annual yes mwdss mwdss

c3marine Annual yes aveday aveday

onroad annual & monthly1all all Yes

onroad_rfl annual & monthly2all all Yes

othon annual yes week week

nonpt annual & monthly yes all all Yes

ag annual & monthly yes all all Yes

afdust_adj annual yes week all Yes

avefire daily all all Yes

biog hourly n/a all Yes

1. Note the annual and monthly “inventory” actually refers to the activity data (VMT and VPOP) for 

onroad. The actual emissions are computed on an hourly basis.

2. Note the annual and monthly “inventory” actually refers to the activity data (VMT and VPOP) for 

onroad_rfl. The actual emissions are computed on an hourly basis.

See Section 3.3.5 for more details on the temporalization and inventory resolution of specific sectors. 

In addition to the resolution, temporal processing includes a ramp-up period for several days prior to January 

1, 2007, which is intended to mitigate the effects of initial condition concentrations. The ramp-up period 

was 10 days (December 22-31, 2006). For most non-EGU sectors, our approach used the emissions from 
32

December 2007 to fill in surrogate emissions for the end of December 2006. In particular, we used 

December 2007 emissions (representative days) for December 2006. For biogenic emissions, we processed 

December 2006 emissions using 2006 meteorology. 

3.3.1 FF10 format and inventory resolution

The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for 

monthly, daily, and hourly emissions inventories than previous formats supported. Previously, to process 

monthly inventory data required the use of 12 separate inventory files. With the FF10 format, a single 

inventory file can contain emissions for all 12 months and the annual emissions in a single record. This 

helps simplify the management of numerous inventories. Similarly, daily and hourly FF10 inventories 

contain individual records with data for all days in a month and all hours in a day, respectively. 

SMOKE 3.5 prevents the application of temporal profiles on top of the “native” resolution of the inventory. 

For example, a monthly inventory should not have annual to month temporalization applied to it; rather, it 

should only have month-to-day and diurnal temporalization. This becomes particularly important when 

specific sectors have a mix of annual, monthly, daily, and/or hourly inventories (e.g. the nonpt sector). The 

flags that control temporalization for a mixed set of inventories are discussed in the SMOKE documentation. 

The modeling platform sectors that make use of monthly values in the FF10 files are nonroad, onroad, and 

the ag burning inventory within the nonpt sector. 

3.3.2 Ptipm Temporalization

The approach for temporalization of the ptipm sector (EGUs) has not changed from the 2005 v4.3 platform, 

and is consistent with the method described in the 2007v5 TSD. However, the importance of this sector 

warrants a restating of the methodology. 

Daily emissions were computed from the annual emissions using a structured query language (SQL) program 

and state-average CEM data. To allocate the annual emissions to each month, state-specific, three-year 

averages of 2006-2008 CEM data were created. These average annual-to-month factors were assigned to 

sources by state. To allocate the monthly emissions to each day, the 2007 CEM data was used to compute 

state-specific month-to-day factors, averaged across all units in each state. The factors were applied to the 

annual emissions to calculate the daily emissions outside of SMOKE, and the resulting daily inventories 

were used as inputs to SMOKE. 

The daily-to-hourly allocation was performed in SMOKE using diurnal profiles. The state-specific and 

pollutant-specific diurnal profiles were created by using the 2007 CEM data to create state-specific, day-tohour factors, averaged over the whole year and all units in each state. The diurnal factors were calculated for 

SO2 emissions, NOX emissions, and heat input. The SO2 and NOX-specific factors were used to temporally 

allocate those pollutants, and the factors created from the hourly heat input data were used to allocate all 

other pollutants. The resulting profiles were assigned by state and pollutant. The same procedures and 

factors were used to allocate the base and future year emissions. 

3.3.3 Meteorologically-based temporalization 

A significant improvement over previous platforms was the introduction of meteorologically-based 

temporalization. There are many factors that impact the timing of when emissions occur. The benefits of 

considering meteorology in support of temporalization are: (1) a meteorological dataset consistent with the 

one used by the AQ model is available (e.g., outputs from WRF); (2) the meteorological model data is highly 

resolved in terms of spatial resolution; and (3) the meteorological variables vary at hourly resolution and can 

therefore be translated into hour-specific temporalization. Because the WRF output data for this study was 
33

from a different run than that described in the 2007v5 platform TSD, any meteorologically-based 

temporalization factors, adjustments, and other meteorological effects were recomputed for the Tier 3 runs. 

This included updating the BIOSEASON file used for biogenic emissions. 

The SMOKE program GenTPRO provides a method for developing meteorology-based temporalization. 

Currently, the program can utilize three types of temporal algorithms: annual-to-day temporalization for 

residential wood combustion (RWC), month-to-hour temporalization for agricultural livestock ammonia, and 

a generic meteorology-based algorithm for other situations. For the 2011 platform, meteorological-based 

temporalization was used for portions of the rwc sector and for livestock within the ag sector. GenTPRO 

reads in gridded meteorological data (output from MCIP) along with spatial surrogates, and uses the 

specified algorithm to produce a new temporal profile that can be input into SMOKE. The meteorological 

variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend on the selected 

algorithm and the run parameters. For more details on the development of these algorithms and running 

GenTPRO, see the GenTPRO documentation and the SMOKE documentation at 

http://www.cmascenter.org/smoke/documentation/3.1/GenTPRO_TechnicalSummary_Aug2012_Final.pdf

For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal 

allocation of emissions to days. We ran GenTPRO so that it created annual-to-day temporal profiles for the 

RWC sources within the nonpt sector. These generated profiles distribute the annual RWC emissions to the 

coldest days of the year. On days where the minimum temperature does not drop below a user defined 

threshold, RWC emissions are zero. Conversely, the program temporally allocates the largest percentage of 

emissions to the coldest days. Similar to other temporal allocation profiles, the total annual emissions do not 

change, only the distribution of the emissions within the year is affected. The default 50  ̊F threshold was 

used for the majority of the states, and a 60  ̊F threshold for the following states: Alabama, Arizona, 

California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas. 

For the agricultural livestock NH3 algorithm, the GenTPRO algorithm is based on an equation derived by 

Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation was 

updated from that described in the 2007v5 TSD, and is based on observations from the TES satellite 

instrument with the GEOS-Chem model and its adjoint to estimate diurnal NH3 emission variations from 

livestock as a function of ambient temperature, aerodynamic resistance, and wind speed. The equations are: 

Ei,h = [161500/Ti,h x e(-1380/T

i,h

)

] x ARi,h

PEi,h = Ei,h / Sum(Ei,h) 

where 

 PEi,h = Percentage of emissions in county i on hour h

 Ei,h = Emission rate in county i on hour h

 Ti,h = Ambient temperature (Kelvin) in county i on hour h

 Vi,h = Wind speed (meter/sec) in county i (minimum wind speed is 0.1 meter/sec) 

 ARi,h = Aerodynamic resistance in county i

GenTPRO was run using the “BASH_NH3” profile method to create month-to-hour temporal profiles for 

these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly 
34

emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized to 

the month26. 

Figure 3-2 compares the daily emissions for Minnesota from the “old” approach (i.e., uniform monthly 

profiles) with the “new” approach (i.e., GenTPRO generated month-to-hour profiles). Although the 

GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the same between the 

two approaches.

Figure 3-2. Example of new animal NH3 emissions temporalization approach, summed to daily emissions

For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to 

reduce the total emissions based on meteorological conditions. These adjustments are applied through 

sector-specific scripts, beginning with the application of land use-based gridded transport fractions and then 

subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the ground. The 

land use data used to reduce the NEI emissions explains the amount of emissions that are subject to transport. 

This methodology is discussed in (Pouliot, et. al., 2010, 

http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot_pres.pdf, and in Fugitive Dust Modeling for 

the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation adjustment is applied to remove 

all emissions for days where measureable rain occurs. Therefore, the afdust emissions vary day-to-day based 

on the precipitation and/or snow cover for that grid cell and day. Both the transport fraction and 

meteorological adjustments are based on the gridded resolution of the platform; therefore, somewhat 

different emissions will result from different grid resolutions. Application of the transport fraction and 

meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as 

compared to ambient samples. 

3.3.4 Onroad and Onroad_rfl Temporalization 

For the onroad and onroad_rfl sectors, meteorology was not used in the development of the temporal 

profiles; rather, meteorology impacts the actual calculation of the hourly emissions through the program 

Movesmrg. The result is that the emissions vary at the hourly level by grid cell. More specifically, the onnetwork (RPD) and the off-network (RPV) exhaust, evaporative, and evaporative permeation modes use the 

gridded meteorology (MCIP) directly. Movesmrg determined the temperature for that hour and grid cell and 

used it to select the appropriate EF for that SCC/pollutant/mode. For the off-network RPP, Movesmrg used 

the Met4moves output for SMOKE (daily minimum and maximum temperatures by county) to determine the 

appropriate EF for that hour and SCC/pollutant. The result was that the emissions varied hourly by county. 

 

26 SMOKE v3.5.1 will correctly read in a monthly inventory and apply GenTPRO ag NH3 month-to-hour temporalization. When 

the emissions were developed for this sector, we were using SMOKE v3.1 beta that incorrectly applied an annual-to-month 

temporal profile on top of a monthly inventory when temporalizing with GenTPRO ag NH3 profiles. As an interim solution, a flat 

monthly profile was applied to the states with a monthly ag NH3 inventory. 
35

The combination of these three processes (RPD, RPV, and RPP) is the total onroad emissions, while the 

combination of the two processes (RPD, RPV) for the refueling mode only is the total onroad_rfl emissions. 

Both sectors show a strong meteorological influence on their temporal patterns (see Sections 2.5.1.2 and 

Error! Reference source not found. for more details). 

Figure 3-3 illustrates the difference between temporalization of the onroad sector used in previous platforms 

and that from SMOKE-MOVES. In the plot, the “MOVES” inventory is a monthly inventory that is 

temporalized by SCC to day-of-week and hour. Similar temporalization is done for the VMT in SMOKEMOVES, but the meteorologically varying EFs add an additional variable signal on top of the 

temporalization. Note how the MOVES emissions have a repeating pattern within the month, while the 

SMOKE-MOVES shows day-to-day (and hour-to-hour) variability. In addition to tracking the 

meteorological influence, SMOKE-MOVES does not show the artificial jumps between the months. 

Figure 3-3. Example of SMOKE-MOVES temporal variability of NOX emissions 

For the onroad and onroad_rfl sectors, the “inventories” referred to in The following values are used in 

Error! Not a valid bookmark self-reference.: the value “all” means that hourly emissions computed for 

every day of the year and that emissions potentially have day-of-year variation. The value “week” means 

that hourly emissions computed for all days in one “representative” week, representing all weeks for each 

month. This means emissions have day-of-week variation, but not week-to-week variation within the month. 

The value “mwdss” means hourly emissions for one representative Monday, representative weekday 

(Tuesday through Friday), representative Saturday, and representative Sunday for each month. This means 

emissions have variation between Mondays, other weekdays, Saturdays and Sundays within the month, but 

not week-to-week variation within the month. The value “aveday” means hourly emissions computed for 

one representative day of each month, meaning emissions for all days within a month are the same. Special 

situations with respect to temporalization are described in the following subsections. 

Table 3-9 are actually the activity data inventories. For RPP and RPV processes, the VPOP inventory is 

annual and does not need temporalization. For RPD, the VMT inventory is monthly and is temporalized to 

day of the week and then to hourly VMT through temporal profiles. In addition, the RPD processes used a 

speed profile (SPDPRO) that had vehicle speed by hour for typical weekday and weekend. In addition, 
36

RPD, RPV, and RPP all have additional temporal variability due to the meteorological based emissions 

calculated through Movesmrg.

3.3.5 Additional sector specific details 

For a discussion of additional sector-specific details related to temporalization, see Section 3.3.4 of the 

2007v5 platform TSD. The topics discussed include updates made in the 2007 platform to agricultural 

burning and residential wood combustion diurnal profiles. 

3.4 Spatial Allocation

The methods used to perform spatial allocation for the 2007 platform are summarized in this section. For the 

2007 platform, spatial factors are typically applied by country and SCC. As described in Section 3.1, spatial 

allocation was performed for national 12-km and 36-km domains. To do this, SMOKE used national 12-km 

and 36-km spatial surrogates and a SMOKE area-to-point data file. For the U.S., the surrogates were 

updated to use 2010-based data wherever possible. For Mexico, we used the same spatial surrogates as were 

used for the 2005 platform. For Canada we used a set of Canadian surrogates provided by Environment 

Canada, also unchanged from the 2005v4.3 platform. The U.S., Mexican, and Canadian 12-km surrogates 

cover the entire CONUS domain 12US1 shown in Figure 3-1. When SMOKE runs, it windows the 

surrogates to the area needed, such as the 12US2 domain. For the original 2007v5 platform, SMOKE was 

run on the 12US1 grid and windowed to 12US2 prior to air quality modeling. For the Tier 3 runs, SMOKE 

was actually run on the 12US2 domain. The remainder of this subsection provides further detail on the origin 

of the data used for the spatial surrogates and the area-to-point data. 

3.4.1 Spatial Surrogates for U.S. emissions 

There are 69 spatial surrogates available for spatially allocating U.S. county-level emissions to the 12-km 

grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point approach overrides 

the use of surrogates for some sources. Table 3-10 lists the codes and descriptions of the surrogates. 

Table 3-10. U.S. Surrogates available for the 2007 platform.

Code Surrogate Description Code Surrogate Description

N/A Area-to-point approach (see 3.3.1.2) 520 Commercial plus Industrial plus Institutional

100 Population 525

Golf Courses + Institutional +Industrial +

Commercial

110 Housing 527 Single Family Residential

120 Urban Population 530 Residential - High Density

130 Rural Population 535

Residential + Commercial + Industrial + 

Institutional + Government

137 Housing Change 540 Retail Trade 

140 Housing Change and Population 545 Personal Repair 

150 Residential Heating - Natural Gas 550 Retail Trade plus Personal Repair 

160 Residential Heating – Wood 555

Professional/Technical plus General 

Government 

165

0.5 Residential Heating - Wood plus 0.5 Low 

Intensity Residential 560 Hospital 

170 Residential Heating - Distillate Oil 565 Medical Office/Clinic 

180 Residential Heating – Coal 570 Heavy and High Tech Industrial 

190 Residential Heating - LP Gas 575 Light and High Tech Industrial 

200 Urban Primary Road Miles 580 Food, Drug, Chemical Industrial

210 Rural Primary Road Miles 585 Metals and Minerals Industrial 

220 Urban Secondary Road Miles 590 Heavy Industrial 
37

Code Surrogate Description Code Surrogate Description

230 Rural Secondary Road Miles 595 Light Industrial 

240 Total Road Miles 596 Industrial plus Institutional plus Hospitals

250 Urban Primary plus Rural Primary 600 Gas Stations

255 0.75 Total Roadway Miles plus 0.25 Population 650 Refineries and Tank Farms

260 Total Railroad Miles 675 Refineries and Tank Farms and Gas Stations

270 Class 1 Railroad Miles 680

Oil & Gas Wells, IHS Energy, Inc. and 

USGS

280 Class 2 and 3 Railroad Miles 700 Airport Areas

300 Low Intensity Residential 710 Airport Point

310 Total Agriculture 720 Military AIports

312 Orchards/Vineyards 800 Marine Ports

320 Forest Land 801 NEI Ports

330 Strip Mines/Quarries 802 NEI Shipping Lanes

340 Land 807 Navigable Waterway Miles

350 Water 810 Navigable Waterway Activity

400 Rural Land Area 850 Golf Courses

500 Commercial Land 860 Mines

505 Industrial Land 870 Wastewater Treatment Facilities

510 Commercial plus Industrial 880 Drycleaners

515 Commercial plus Institutional Land 890 Commercial Timber

The surrogates in bold have been updated with 2010-based data, including 2010 census data at the block 

group level, 2010 American Community Survey Data for heating fuels, 2010 TIGER/Line data for railroads 

and roads, and 2010 National Transportation Atlas Data for ports and navigable waterways. Not all of the 

available surrogates are used to spatially allocate sources in the 2007 platform; that is, some surrogates 

shown in Table 3-10 were not assigned to SCCs used in the 2007 platform. Alternative surrogates for ports 

(801) and shipping lanes (802) were developed from the 2008 NEI shapefiles: Ports_032310_wrf and 

ShippingLanes_111309FINAL_wrf. These new surrogates were used in the 2007 platform for c1 and c2 

commercial marine emissions instead of the standard 800 and 810 surrogates, respectively. Note that the 800 

surrogate was used for nonpoint SCCs starting with 250502, which are related to the storage and transfer of 

petroleum products. 

Specific updates made to the surrogates for the Tier 3 runs include updating of the land use-based 300-series 

surrogates, gas stations (600), construction and mining (861), and dry cleaners (880). Updates to the oil and 

gas surrogates (689-699) were made as described below, and updated surrogates were used for Mexico. 

The creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and 

updated documentation for it is available at http://www.ie.unc.edu/cempd/projects/mims/spatial/ and 

http://www.cmascenter.org/help/documentation.cfm?MODEL=spatial_allocator&VERSION=3.6&temp_id=

99999. Note that the surrogate methodology “gapfills” surrogates with less spatial coverage in terms of 

counties with surrogates that have more spatial coverage. This helps ensure no emissions are dropped during 

SMOKE processing. Because the land use-based surrogates were updated and are used to gapfill many 

other surrogates, there were some changes to most of the remaining 500 and 600 series surrogates in terms of 

using new gapfiling values. 

For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, which the offnetwork (RPP and RPV) emissions were allocated to parking areas. For the onroad_rfl sector, the emissions 

were spatially allocated to gas station locations. 
38

For the oil and gas sources in the nonpt sector, the WRAP Phase III sources have detailed basin-specific 

spatial surrogates shown in Table 3-11. The remaining oil and gas sources used the 2005-based surrogate 

“Oil & Gas Wells, IHS Energy, Inc. and USGS” (680) developed for oil and gas SCCs. The surrogates in 

Table 3-11 were applied for the counties listed in Table 3-12. For the Tier 3 runs, surrogate data for the 

Permian basin was added.

Table 3-11. Spatial Surrogates for WRAP Oil and Gas Data

Country Code Surrogate Description

USA 699 Gas production at CBM wells

USA 698 Well count - gas wells

USA 697 Oil production at gas wells

USA 696 Gas production at gas wells

USA 695 Well count - oil wells

USA 694 Oil production at Oil wells

USA 693 Well count - all wells

USA 692 Spud count

USA 691 Well count - CBM wells

USA 690 Oil production at all wells

USA 689 Gas production at all wells

Table 3-12. Counties included in the WRAP Dataset

FIPS State County

08001 CO Adams 

08005 CO Arapahoe 

08007 CO Archuleta 

08013 CO Boulder 

08014 CO Broomfield 

08029 CO Delta 

08031 CO Denver 

08039 CO Elbert 

08043 CO Fremont 

08045 CO Garfield 

08051 CO Gunnison 

08059 CO Jefferson

08063 CO Kit Carson 

08067 CO La Plata 

08069 CO Larimer 

08073 CO Lincoln 

08075 CO Logan 

08077 CO Mesa 

08081 CO Moffat 

08087 CO Morgan 

08095 CO Phillips 

08097 CO Pitkin

FIPS State County

08103 CO Rio Blanco 

08107 CO Routt 

08115 CO Sedgwick 

08121 CO Washington 

08123 CO Weld 

08125 CO Yuma 

30003 MT Big Horn 

30075 MT Powder River 

35005 NM Chaves

35015 NM Eddy

35015 NM Lea

35031 NM Mc Kinley 

35039 NM Rio Arriba 

35041 NM Roosevelt

35043 NM Sandoval 

35045 NM San Juan 

48003 TX Andrews

48033 TX Borden

48079 TX Cochran

48081 TX Coke

48103 TX Crane

48105 TX Crockett

FIPS State County

48107 TX Crosby

48109 TX Culberson

48115 TX Dawson

48125 TX Dickens

48135 TX Ector

48141 TX El Paso

48151 TX Fisher

48165 TX Gaines

48169 TX Garza

48173 TX Glasscock

48219 TX Hockley

48227 TX Howard

48229 TX Hudspeth

48235 TX Irion

48263 TX Kent

48269 TX King

48301 TX Loving

48303 TX Lubbock

48305 TX Lynn

48317 TX Martin

48329 TX Midland

48335 TX Mitchell
39

FIPS State County

48353 TX Nolan

48371 TX Pecos

48383 TX Reagan

48389 TX Reeves

48413 TX Schleicher

48415 TX Scurry

48431 TX Sterling

48435 TX Sutton

48445 TX Terry

48451 TX Tom Green

48461 TX Upton

48475 TX Ward

48495 TX Winkler

48501 TX Yoakum

FIPS State County

49007 UT Carbon 

49009 UT Daggett 

49013 UT Duchesne 

49015 UT Emery 

49019 UT Grand 

49043 UT Summit 

49047 UT Uintah 

56001 WY Albany 

56005 WY Campbell 

56007 WY Carbon 

56009 WY Converse 

56011 WY Crook 

56013 WY Fremont 

56019 WY Johnson 

FIPS State County

56023 WY Lincoln 

56025 WY Natrona 

56027 WY Niobrara 

56033 WY Sheridan 

56035 WY Sublette 

56037 WY Sweetwater 

56041 WY Uinta 

56045 WY Weston 

3.4.2 Allocation method for airport-related sources in the U.S. 

There are numerous airport-related emission sources in the 2008 NEI, such as aircraft, airport ground support 

equipment, and jet refueling. The 2007 platform includes the aircraft emissions as point sources. For the 

2007 platform, the SMOKE “area-to-point” approach was used for airport ground support equipment 

(nonroad sector) and jet refueling (nonpt sector). The approach is described in detail in the 2002 platform 

documentation: http://www.epa.gov/scram001/reports/Emissions%20TSD%20Vol1_02-28-08.pdf. 

The ARTOPNT file that lists the nonpoint sources to locate using point data was unchanged from the 2005-

based platform. 

3.4.3 Surrogates for Canada and Mexico emission inventories 

The Mexican spatial surrogates were updated from the 207v5 platform to use the surrogates shown in Table 

3-13. The same surrogates for Canada were used to spatially allocate the 2006 Canadian emissions as were 

used for the 2005v4.2 platform. The spatial surrogate data came from Environment Canada, along with cross 

references. The surrogates they provided were outputs from the Surrogate Tool (previously referenced). Per 

Environment Canada, the surrogates are based on 2001 Canadian census data. The Canadian surrogates used 

for this platform are listed in Table 3-14. We added the leading “9” to the surrogate codes to avoid duplicate 

surrogate numbers with U.S. surrogates. 

Table 3-13. Spatial Surrogates for Mexico 

Srg code Description

22 MEX Total Road Miles

10 MEX Population

12 MEX Housing

14 MEX Residential Heating - Wood

16 MEX Residential Heating - Distillate Oil
40

20 MEX Residential Heating - LP Gas

22 MEX Total Road Miles

24 MEX Total Railroads Miles

26 MEX Total Agriculture

28 MEX Forest Land

32 MEX Commercial Land

34 MEX Industrial Land

36 MEX Commercial plus Industrial Land

38 MEX Commercial plus Institutional Land

40

Residential (RES1-

4)+Comercial+Industrial+Institutional+

Government

42 MEX Personal Repair (COM3)

44 MEX Airports Area

46 MEX Marine Ports

48 Brick Kilns - Mexico

50 Mobile sources - Border Crossing - Mexico

Table 3-14. Canadian Spatial Surrogates for 2007-based platform Canadian Emissions

Code Description Code Description

9100 Population 9493 Warehousing and storage

9101 Total dwelling 9494 Total Transport and warehouse

9102 Urban dwelling 9511 Publishing and information services

9103 Rural dwelling 9512 Motion picture and sound recording industries

9104 Total Employment 9513 Broadcasting and telecommunications

9106 ALL_INDUST 9514 Data processing services

9111 Farms 9516 Total Info and culture

9113 Forestry and logging 9521 Monetary authorities - central bank

9114 Fishing hunting and trapping 9522 Credit intermediation activities

9115 Agriculture and forestry activities 9523 Securities commodity contracts and other 

financial investment activities

9116 Total Resources 9524 Insurance carriers and related activities

9211 Oil and Gas Extraction 9526 Funds and other financial vehicles

9212 Mining except oil and gas 9528 Total Banks

9213 Mining and Oil and Gas Extract activities 9531 Real estate

9219 Mining-unspecified 9532 Rental and leasing services

9221 Total Mining 9533 Lessors of non-financial intangible assets 

(except copyrighted works)

9222 Utilities 9534 Total Real estate

9231 Construction except land subdivision and land 

development

9541 Professional scientific and technical services

9232 Land subdivision and land development 9551 Management of companies and enterprises
41

Code Description Code Description

9233 Total Land Development 9561 Administrative and support services

9308 Food manufacturing 9562 Waste management and remediation services

9309 Beverage and tobacco product manufacturing 9611 Education Services

9313 Textile mills 9621 Ambulatory health care services

9314 Textile product mills 9622 Hospitals

9315 Clothing manufacturing 9623 Nursing and residential care facilities

9316 Leather and allied product manufacturing 9624 Social assistance

9321 Wood product manufacturing 9625 Total Service

9322 Paper manufacturing 9711 Performing arts spectator sports and related 

industries

9323 Printing and related support activities 9712 Heritage institutions

9324 Petroleum and coal products manufacturing 9713 Amusement gambling and recreation 

industries

9325 Chemical manufacturing 9721 Accommodation services

9326 Plastics and rubber products manufacturing 9722 Food services and drinking places

9327 Non-metallic mineral product manufacturing 9723 Total Tourism

9331 Primary Metal Manufacturing 9811 Repair and maintenance

9332 Fabricated metal product manufacturing 9812 Personal and laundry services

9333 Machinery manufacturing 9813 Religious grant-making civic and professional 

and similar organizations

9334 Computer and Electronic manufacturing 9814 Private households

9335 Electrical equipment appliance and component 

manufacturing

9815 Total other services

9336 Transportation equipment manufacturing 9911 Federal government public administration

9337 Furniture and related product manufacturing 9912 Provincial and territorial public administration 

(9121 to 9129)

9338 Miscellaneous manufacturing 9913 Local municipal and regional public 

administration (9131 to 9139)

9339 Total Manufacturing 9914 Aboriginal public administration

9411 Farm product wholesaler-distributors 9919 International and other extra-territorial public 

administration

9412 Petroleum product wholesaler-distributors 9920 Total Government

9413 Food beverage and tobacco wholesalerdistributors9921 Commercial Fuel Combustion

9414 Personal and household goods wholesalerdistributors9922 TOTAL DISTRIBUTION AND RETAIL

9415 Motor vehicle and parts wholesaler-distributors 9923 TOTAL INSTITUTIONAL AND 

GOVERNEMNT

9416 Building material and supplies wholesalerdistributors9924 Primary Industry

9417 Machinery equipment and supplies wholesalerdistributors9925 Manufacturing and Assembly
42

Code Description Code Description

9418 Miscellaneous wholesaler-distributors 9926 Distribution and Retail (no petroleum)

9419 Wholesale agents and brokers 9927 Commercial Services

9420 Total Wholesale 9928 Commercial Meat cooking

9441 Motor vehicle and parts dealers 9929 HIGHJET

9442 Furniture and home furnishings stores 9930 LOWMEDJET

9443 Electronics and appliance stores 9931 OTHERJET

9444 Building material and garden equipment and 

supplies dealers

9932 CANRAIL

9445 Food and beverage stores 9933 Forest fires

9446 Health and personal care stores 9941 PAVED ROADS

9447 Gasoline stations 9942 UNPAVED ROADS

9448 clothing and clothing accessories stores 9943 HIGHWAY

9451 Sporting goods hobby book and music stores 9944 ROAD

9452 General Merchandise stores 9945 Commercial Marine Vessels

9453 Miscellaneous store retailers 9946 Construction and mining

9454 Non-store retailers 9947 Agriculture Construction and mining

9455 Total Retail 9950 Intersection of Forest and Housing

9481 Air transportation 9960 TOTBEEF

9482 Rail transportation 9970 TOTPOUL

9483 Water Transportation 9980 TOTSWIN

9484 Truck transportation 9990 TOTFERT

9485 Transit and ground passenger transportation 9993 Trail

9486 Pipeline transportation 9994 ALLROADS

9487 Scenic and sightseeing transportation 9995 30UNPAVED_70trail

9488 Support activities for transportation 9996 Urban area

9491 Postal service 9997 CHBOISQC

9492 Couriers and messengers 9991 Traffic
43

4 Development of Future Year Emissions 

This section describes the methods used for developing the emissions for the 2018 and 2030 future-year 

scenarios. Within the 2018 and 2030 cases, the future year emissions for the Tier 3 FRM “reference” and 

“control” cases are the same for all stationary sources. For 2018 and 2030, the Tier 3 FRM control case 

emissions include Tier 3 engine and fuel controls that impact emissions for onroad mobile and nonroad 

mobile sources. EPA analyzed emission impacts of the Tier 3 vehicle emissions and fuel standards by 

comparing projected emissions for future years without the Tier 3 rule (reference scenario) to projected 

emissions for future years with the Tier 3 standards in place (control scenario). For more details on the 

differences between the reference and control scenarios, see the onroad and nonroad sections (Section 4.3.1

and 4.3.2, respectively). 

The future scenarios’ projection methodologies vary by sector. The 2018 and 2030 reference scenarios 

represent predicted emissions in the absence of any further controls beyond those Federal and State measures 

already promulgated, or under reconsideration before emissions processing began in March, 2013. The 

future base-case scenario reflects projected economic changes and fuel usage for EGU and mobile sectors. 

The 2020 (used as a surrogate for 2018) and 2030 EGU projected inventories represent demand growth, fuel 

resource availability, generating technology cost and performance, and other economic factors affecting 

power sector behavior. It also reflects the expected 2020 and 2030 emissions effects due to environmental 

rules and regulations, consent decrees and settlements, plant closures, control devices updated since 2007, 

and forecast unit construction through the calendar year 2020 and 2030. In this analysis, the projected EGU 

emissions include the Final Mercury and Air Toxics (MATS) rule announced on December 21, 2011, the 

Clean Air Interstate Rule issued on March 10, 2005, including impacts of electric vehicle penetration 

resulting from the Light-duty Greenhouse Gas (LDGHG) Rule. 

For the other mobile sources (c1c2rail and c3marine sectors), all national measures for which data were 

available at the time of modeling have been included. 

For nonEGU point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, and afdust sectors), local 

control programs are generally not included in the future base-case projections for most states unless 

information was provided by the states. One exception are some NOX and VOC reductions associated with 

the New York, Virginia, and Connecticut State Implementation Plans (SIP), that were added as part of 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 the following subsections and Table 

4-1. For information on control/growth strategies that are not different from the 2007v5 platform, please 

reference the 2007v5 TSD, available at: 

http://epa.gov/ttn/chief/emch/2007v5/2007v5_2020base_EmisMod_TSD_13dec2012.pdf

 IPM sector (ptipm): Unit-specific estimates from IPM, version 4.10 Final MATS with CAIR and the 

penetration of electric vehicles anticipated due to the LDGHG rule. 

 Non-IPM sector (ptnonipm): Projection factors and percent reductions reflect Cross-State Air 

Pollution Rule (CSAPR) comments and emission reductions due to national rules, control programs, 

plant closures, consent decrees and settlements, and 1997 and 2001 ozone State Implementation Plans 

in NY, CT, and VA. Also used projection approaches for corn ethanol and biodiesel plants, refineries 

and upstream impacts from the Energy Independence and Security Act of 2007 (EISA). Terminal 

area forecast (TAF) data aggregated to the national level were used for aircraft to account for 

projected changes in landing/takeoff activity. 
44

 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, updated based on personal 

communication with EPA experts in July 2012; fertilizer application NH3 emissions projections 

include upstream impacts EISA. 

 Area fugitive dust sector (afdust): Projection factors for dust categories related to livestock estimates 

based on expected changes in animal population and upstream impacts from EISA. 

 Remaining Nonpoint sector (nonpt): Projection factors that implement Cross State Air Pollution Rule 

comments and reflect emission reductions due to control programs. Residential wood combustion 

projections are 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. 

Upstream impacts from EISA, including post-2007 cellulosic ethanol plants are also reflected. 

 Nonroad mobile sector (nonroad): Other than for California, this sector uses data from a run of 

NMIM that utilized NONROAD2008b, using future-year equipment population estimates and control 

programs to the year 2018 and 2030 and using national level inputs. Fuels are consistent with the 

onroad sector. Final controls from the final locomotive-marine and large spark ignition OTAQ rules 

are included. California-specific data were provided by CARB. 

 Locomotive, and non-Class 3 commercial marine sector (c1c2rail): For all states except California, 

projection factors for Class 1 and Class 2 commercial marine and locomotives which reflect final 

locomotive-marine controls. California projected year-2017 (used for 2018) and 2030 inventory data 

were provided by CARB. Additional RFS2-related county-level emissions adjustments were applied 

to reflect different fuel volume characteristics from increased ethanol fuel transport on rail and 

commercial marine vessels. 

 Class 3 commercial marine vessels (c3marine): Base-year 2007 emissions grown and controlled to 

2018 and 2030, incorporating controls based on Emissions Control Area (ECA) and International 

Marine Organization (IMO) global NOX and SO2 controls. 

 Onroad mobile, not including refueling (onroad): focus of the rule, see Section 4.3.1.

 Onroad refueling mode (onroad_rfl): Uses the same projection approach as the onroad sector and 

processing as described in Section 2.5.2.

 Other onroad (othar): No growth or control for Canada because data are not available from Canada. 

Mexico inventory data were grown from 1999 to years 2018 and 2030. 

 Other nonroad/nonpoint (othon): No growth or control for Canada. Mexico inventory data were 

grown from 1999 to years 2018 and 2030.

 Other point (othpt): No growth or control for Canada and offshore oil. Mexico inventory data were 

grown from 1999 to years 2018 and 2030.

 Biogenic: 2007 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 U.S. 2018 and 2030 scenario emissions from the 2007 base-case inventories. The control, closures, 

projection packets (datasets) used to create stationary non-EGU and c1c2rail sector 2018 and 2030 future 

years scenario inventories from the 2007 base case are described in the following sections. These datasets 

(i.e., “packets”) were processed through the EPA Control Strategy Tool (CoST) to create future year 

inventories. CoST is described here: http://www.epa.gov/ttnecas1/cost.htm. 
45

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 

2018 and 2030 scenarios. 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. If a strategy is identical to the 2007v5 platform, it is noted in the Section as 

“2007v5” and is documented in the 2007v5 TSD. 

Table 4-1. Control strategies and growth assumptions for creating the 2018 and 2030 emissions inventories 

from the 2007 base case 

Control Strategies and/or growth assumptions

(grouped by standard and approach used to apply to the inventory)

CAPs

affected Section

Non-EGU Point (ptnonipm sector) Controls and Growth Assumptions

Ethanol plants that account for increased ethanol production due to EISA mandate

All

Error! 

Referen

ce 

source 

not 

found.

Biodiesel plants producing 1.6 billion gallons of production due to EISA mandate All 4.2.1.2

Ethanol distribution vapor losses adjustments due to EISA mandate VOC 0

Refinery upstream adjustments from EISA mandate All 4.2.1.7

Livestock emissions growth from year 2008 to 2018 and 2030, also including upstream RFS2 

impacts on agricultural-related activities such as pesticide and fertilizer production All 4.2.2

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations NOX, 

CO, PM, 

SO2

2007v5

State fuel sulfur content rules for fuel oil – as of July, 2012, effective only in Maine, Massachusetts, 

New Jersey, New York and Vermont

SO2

4.2.3

Industrial/Commercial/Institutional Boilers and Process Heaters MACT with Reconsideration 

Amendments

CO, PM, 

SO2, 

VOC

2007v5

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.

All

4.2.4

Future baseline inventory improvements received from a 2005 platform NODA and comments from 

the CSAPR proposal, including local controls, fuel switching, unit closures and consent decrees

All

4.2.5

Facility and unit closures obtained from various sources such as states, industry and web posting, 

EPA staff and post-2008 inventory submittals: effective prior to spring 2012

All 2007v5

Aircraft growth via Itinerant (ITN) operations at airports to 2018 and 2030 All 4.2.6.1

Emission reductions resulting from controls put on specific boiler units (not due to MACT) after 

2008, identified through analysis of the control data gathered from the Information Collection 

Request (ICR) from the Industrial/Commercial/Institutional Boiler NESHAP.

SO2 2007v5

New York ozone SIP controls NOX 2007v5

Boat Manufacturing MACT rule, VOC: national applied by SCC VOC 2007v5

Lafarge and Saint Gobain consent decrees NOX, 

PM, SO2

2007v5

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

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

CO, 

NOX, 

PM, SO2, 

VOC

2007v5

Refinery Consent Decrees: plant/unit controls NOX, 2007v5
46

Control Strategies and/or growth assumptions

(grouped by standard and approach used to apply to the inventory)

CAPs

affected Section

SO2

Commercial and Industrial Solid Waste Incineration (CISWI) revised NSPS PM, SO2 2007v5

Hazardous Waste Incineration (HWI), Phase I and II PM 2007v5

Nonpoint (afdust, ag and nonpt sectors) Controls and Growth Assumptions

MSAT2 and RFS2 impacts on portable fuel container growth and control from 2007 to 2018 and 

2030 VOC 0

Cellulosic ethanol and diesel emissions from EISA mandate All 4.2.1.4

Ethanol transport working losses inventory from EISA mandate VOC 0

Ethanol distribution vapor losses adjustments due to EISA mandate VOC 0

Livestock emissions growth from year 2008 to 2018 and 2030, also including upstream RFS2 

impacts on agricultural-related activities such as pesticide and fertilizer production All 4.2.2

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations NOX, 

CO, PM, 

SO2

2007v5

State fuel sulfur content rules for fuel oil –as of July, 2012, effective only in Maine, Massachusetts, 

New Jersey, New York and Vermont SO2 4.2.3

Residential wood combustion growth and change-outs from year 2008 to 2018 All 2007v5

Future baseline inventory improvements received from a 2005 platform NODA and comments from 

the CSAPR proposal, reflecting local controls

NOX, 

VOC 4.2.5

New York ozone SIP controls

NOX

2007v5Er

ror! 

Referen

ce 

source 

not 

found.

Texas oil and gas projections to year 2018 All 4.2.6.2

Onroad Mobile Controls 

(All national in-force regulations are modeled. The list includes key recent mobile control strategies but is 

not exhaustive.)

National Onroad Rules:

All onroad control programs finalized as of the date of the model run, including most recently:

Light-Duty Greenhouse Gas Rule: October, 2012

Heavy (and Medium)-Duty Greenhouse Gas Rule: September, 2011

Renewable Fuel Standard: March, 2010

Light Duty Greenhouse Gas Rule: May, 2010

Corporate-Average Fuel Economy standards for 2008-2011, April, 2010

2007 Onroad Heavy-Duty Rule: February, 2009

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

Tier 2 Rule: Signature date February, 2000

National Low Emission Vehicle Program (NLEV): March, 1998

All 4.3

Local Onroad Programs:

California LEVIII Program

Ozone Transport Commission (OTC) LEV Program: January,1995

Inspection and Maintenance programs

Fuel programs (also affect gasoline nonroad equipment)

Stage II refueling control programs

VOC 4.3

Nonroad Mobile Controls 

(All national in-force regulations are modeled. The list includes recent key mobile control strategies but is 

not exhaustive.)

National Nonroad Controls:

All nonroad control programs finalized as of the date of the model run, including most recently: All 4.3.2
47

Control Strategies and/or growth assumptions

(grouped by standard and approach used to apply to the inventory)

CAPs

affected Section

Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels: October, 

2008

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

(Marine and Land-Based), November 8, 200227

Clean Air Nonroad Diesel Final Rule – Tier 4: June, 200428

Locomotives:

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

Less than 30 Liters per Cylinder: March, 2008

Clean Air Nonroad Diesel Final Rule – Tier 4: May, 2004

All 4.3.3

Commercial Marine:

Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards:

April, 2010

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

Less than 30 Liters per Cylinder: March, 2008

Clean Air Nonroad Diesel Final Rule – Tier 4: May, 2004

All 4.3.4

The ancillary input data in the future-year scenarios are very similar to those used in the 2007 base case 

except for the speciation profiles used for gasoline-related sources, which change in the future to account for 

increased ethanol usage in gasoline (see Section 3.2.1.4 for details). 

4.1 Stationary source projections: EGU sector (ptipm) 

The future-year data for the ptipm sector used in the air quality modeling were created by the Integrated 

Planning Model (IPM) version 4.10 (v4.10) Final MATS (Mercury and Air Toxics Standards) 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/toxics.html). IPM is a multiregional, dynamic, 

deterministic linear programming model of the U.S. electric power sector. IPM 4.10 was updated from the 

previous version to include adjustments to assumptions regarding the performance of acid gas control 

technologies, new costs imposed on fuel-switching (e.g., bituminous to sub-bituminous), correction of lignite 

availability to some plants, incorporation of planned retirements, implementation of a scrubber upgrade 

option, and the availability of a scrubber retrofit to waste-coal fired fluidized bed combustion units without 

an existing scrubber. 

The scenario used for this modeling represents CAIR, MATS, and the penetration of electric vehicles 

anticipated due to the LDGHG rule. IPM v4.10 Final MATS originally included the Cross-State Air 

Pollution Rule (CSAPR), but the rule was stayed by the D.C. circuit court pending judicial review. 

Therefore, the original CAIR rule was included in the run for this project. Electric vehicle penetration 

expected from the Light-duty Greenhouse Gas Rule was also included. 

The Boiler MACT reconsideration was not represented in the 2020 IPM dataset because the rule was not 

final at the time the IPM modeling was performed. Further details on the future-year EGU emissions 

inventory used for this rule can be found in the incremental documentation of the IPM v.4.10 platform, 

available at http://www.epa.gov/airmarkets/progsregs/epa-ipm/transport.html. . 

While NOx, SO2, and mercury are specific outputs from IPM, directly emitted PM emissions (i.e., PM2.5 and 

PM10) from the EGU sector are computed via a post processing routine that applies emission factors to the 

 

27 https://www.federalregister.gov/articles/2002/11/08/02-23801/control-of-emissions-from-nonroad-large-spark-ignition-enginesand-recreational-engines-marine-and 

28 http://www.epa.gov/otaq/nonroad-diesel.htm
48

IPM-estimated fuel throughput based on fuel, configuration and controls to compute the filterable and 

condensable components of PM. 

4.2 Stationary source projections: non-EGU sectors (ptnonipm, nonpt, ag, 

afdust) 

To project U.S. stationary sources other than the ptipm sector, EPA 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. In estimating future-year emissions, EPA 

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. For 

more details on the projection methodology and justification for the approaches, see the 2007v5 TSD. 

Year-specific projection factors (PROJECTION packets) for year 2018 and 2030 were used for creating the 

scenarios unless noted otherwise. The contents of these projection packets (and control reductions) 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., facilities 

and units subject to the Boiler MACT reconsideration has thousands of records). If the growth or control 

factors for a sector are not provided in a table in this document, they are available as a “projection”,

“control”, or “closures” packet for input to SMOKE on the 2007v5 platform website. This document only 

summarizes the impact of controls and projections that differ between Tier 3 and the previous modeling for 

PM NAAQS (2007v5 platform). The following projection, control or closure packets had identical or nearly 

identical impact in 2018 (as well as 2030) for Tier 3 as in 2020 of PM NAAQS modeling and are detailed in 

the 2007v5 TSD:

 RICE NESHAP 

 Industrial Boiler MACT reconsideration 

 Residential wood combustion growth 

 Remaining non-EGU plant closures 

 Boiler reductions not associated with the MACT rule 

 NY Ozone SIP controls 

 Boat manufacturing MACT 

 Lafarge and St Gobain settlements 

 OECA consent decrees 

 Refinery consent decrees 

 CISWI/HWI controls 

This section is divided into several subsections that are summarized in Table 4-2. Note that for some 

sources, future year inventories were used rather than projection or control packets.
49

Table 4-2. Summary of non-EGU stationary projections subsections 

Subsection Title Sector(s) Brief Description

4.2.1 RFS2 upstream future year 

inventories and 

adjustments

nonpt

ptnonipm

1) Point and non-point inventories received from 

OTAQ that account for the upstream impact of 

the RFS2 and the EISA mandate.

2) Point and non-point adjustment factors that we 

apply to the 2007 inventory to reflect RFS2

4.2.2 Agricultural and livestock 

adjustments, including 

RFS2

afdust, ag, 

nonpt, 

ptnonipm

Adjustment factors to all ag-related sources that also 

reflect upstream RFS2 impacts on ag-related 

processes impacted by increased ethanol use

Error! 

Reference 

source not 

found.

Fuel sulfur rules nonpt

ptnonipm

Control packet reflecting state and local fuel sulfur 

rules, including ULSD

4.2.3 Portland cement NESHAP 

projections

ptnonipm Year-2013 ISIS policy case reflecting closures, 

controls at existing kilns and an inventory containing 

new kilns constructed after 2008 that account for 

shifting capacity from some closed units to open units

Error! 

Reference 

source not 

found.

CSAPR and NODA 

comments

nonpt

ptnonipm

Post-2008 controls, adjustments, and closures 

received in response to preparing the 2005 NEI for a 

future year baseline. These are not reflective of 

CSAPR; but rather of non-EGU future year 

information received from comments.

4.2.4 All other PROJECTION 

and CONTROL packets

nonpt

ptnonipm

All other non-EGU stationary source PROJECTION 

and CONTROL packets not covered in previous 

subsections.

4.2.1 RFS2 upstream future year inventories and adjustments (nonpt, ptnonipm) 

EPA incorporated adjustments for some stationary source categories to account for impacts of the Energy 

Independence and Security Act (EISA) renewable fuel standards mandate in the Renewable Fuel Standards 

Program (RFS2; EPA, 2010a) ), as estimated by Annual Energy Outlook (AEO) 2013, as well as impacts of 

recent 2017-2025 light duty vehicle greenhouse gas emission standards and heavy-duty greenhouse gas 

standards. These mandates not only impact emissions associated with highway vehicles and nonroad engines, 

but also emissions associated with point and nonpoint sources. The "upstream" emission impacts of the 

renewable fuels mandate are associated with all stages of biofuel production and distribution, including 

biomass production (agriculture, forestry), fertilizer and pesticide production and transport, biomass 

transport, biomass refining (corn or cellulosic ethanol production facilities), biofuel transport to 

blending/distribution terminals, and distribution of finished fuels to retail outlets. These impacts are 

accounted for in the 2018 and 2030 inventories. A portion of these impacts are discussed in this section, 

with additional impacts due to transport discussed in the onroad and c1c2rail sectors (see Section 4.3.1.1 and 

4.3.3, respectively). There are also impacts on domestic crude emissions upstream of petroleum refineries, 

due to displacement of gasoline and diesel fuel with biofuels, but these are not accounted for in these 

projections as these data were not available. Greenhouse gas standards also affect production and 

distribution of gasoline and diesel fuels, but the impacts of these rules will be very small in 2018 and were 

not accounted for. Where it was feasible, EPA included the impact of these greenhouse gas standards in the 

2030 estimates. 
50

EPA assumed that an unadjusted 2018 inventory, which does not account for the impacts of the EISA 

renewable fuel mandate, would have comparable ethanol volumes to 2007, approximately 6.9 billion gallons. 

However, analyses done to support the RFS2 rule (EPA, 2010a) suggest a significant increase in renewable 

fuel volumes in 2018 and 2030 (see Table 4-3). Adjustments applied to the inventories (described in the 

following subsections) reflect the impacts on emissions due to the difference between the 2007 ethanol 

volumes and the renewable fuel volumes shown in Table 4-3. In 2018, EPA assumed 1 Bgal (billion gallons) 

of ethanol would be used as E85, 10 Bgal as E10, and about 4 Bgal as E15. In 2030, EPA assumed 1.4 Bgal 

of ethanol would be used as E85, 6.8 Bgal as E10, and 6.5 Bgal as E15. 

Table 4-3. Renewable Fuel Volumes Assumed for Stationary Source Adjustments. 

Renewable Fuel 2018 Volume (Bgal) 2030 Volume (Bgal)

Corn Ethanol 14.7 14.4

Cellulosic Ethanol 0.235 0.235

Imported Ethanol 1.061 0.707

Biodiesel 1.887 1.887

Renewable Diesel 0.236 1.179

Cellulosic Diesel 0.290 0.915

4.2.1.1 Corn Ethanol plants inventory (ptnonipm) 

Future year inventories: “ethanol_plants_2018_NEI” and “ethanol_plants_2018_OTAQ_revised” 

As discussed in Section 2.1.2, EPA supplemented the 2007 NEI with corn ethanol plants that EPA/OTAQ 

developed. The 2007 emissions were projected to account for the increased domestic corn ethanol 

production assumed in this modeling, specifically an increase from 6.9 Bgal in 2007 to approximately 15 

Bgal in 2018 and 2030. An industry characterization (EPA, 2012b) was also used to project the 2007 

inventory to future years, based on new plants, changes in production capacity since 2007, or changes in 

progress. Table 4-4 provides the summaries of estimated emissions for the corn ethanol plants in year 2007 

and 201829

. Note, EPA estimated the same amount of ethanol plant production in the future years for 2018 

and 2030 and assumed that the control scenarios would have limited to no impact on the ethanol production. 

Table 4-4. 2007 and 2018/2030 corn ethanol plant emissions [tons] 

pollutant 2007 2018/2030

1,3-Butadiene 0.0015 0.0021

Acrolein 30 70

Formaldehyde 31 65

Benzene 13 26

Acetaldehyde 746 857

Naphthalene 0.028 0.030

CO 12,821 21,811

NH3 1,036 1,072

NOX 13,403 25,618

PM10 11,468 21,054

PM2.5 4,452 7,827

SO2 16,930 20,235

 

29 The 2007 emissions are the sum of the NEI and OTAQ facilities. The same is true for 2018. 
51

VOC 19,271 41,146

4.2.1.2 Biodiesel plants inventory (ptnonipm) 

New Future year inventory: “Biodiesel_Plants_2018_ff10” 

EPA/OTAQ developed an inventory of biodiesel plants for 2018 and 2030. Plant location and production 

volume data came from the Tier 3 proposed rule.30,31 The total volume of biodiesel came from the AEO 

2013 early release, 1.3 BG for 2018 and 2030. To reach the total volume of biodiesel, plants that had current 

production volumes were assumed to be at 100% production and the remaining volume was split among 

plants with planned production. These emission factors in Table 4-5 are in tons per million gallons (Mgal) 

and were obtained from EPA’s spreadsheet model for upstream EISA impacts developed for the RFS2 rule 

(EPA, 2010a). Inventories were modeled as point sources with Google Earth and web searching validating 

facility coordinates and correcting state-county FIPS. Table 4-6 provides the 2018 and 2030 biodiesel plant 

emissions estimates. Emissions in 2007 are assumed to be near zero, and HAP emissions in 2018 and 2030 

are nearly zero. Note, EPA estimated the same amount of biodiesel plant production in the future years for 

2018 and 2030 and assumed that the control scenarios would have limited to no impact on the biodiesel 

production. 

Table 4-5. Emission Factors for Biodiesel Plants (Tons/Mgal) 

Pollutant Emission Factor

VOC 4.3981E-02

CO 5.0069E-01

NOX 8.0790E-01

PM10 6.8240E-02

PM2.5 6.8240E-02

SO2 5.9445E-03

NH3 0

Acetaldehyde 2.4783E-07

Acrolein 2.1290E-07

Benzene 3.2458E-08

1,3-Butadiene 0

Formaldehyde 1.5354E-06

Ethanol 0

Table 4-6. 2018/2030 biodiesel plant emissions [tons] 

Pollutant 2018/2030

CO 649

NOX 1048

PM10 89

PM2.5 89

SO2 8

 

30 US EPA 2013. Draft Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA-420-D-13-002. 

31 Cook, R. 2012. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for Tier 3 

Proposal. Memorandum to Docket EPA-HQ-OAR-2010-0162. 
52

VOC 57

4.2.1.3 Portable fuel container inventory (nonpt) 

Future year inventories: “2018_PFC_inventory_FF10_revision2”, “2030_PFC_inventory_FF10_revision2” 

EPA used future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and 

modeled for the EPA’s MSAT2 rule (EPA, 2007a). The 10 PFC SCCs are summarized below (note that the 

full SCC descriptions for these SCCs include “Storage and Transport; Petroleum and Petroleum Product 

Storage” as the beginning of the description). 

 2501011011 Residential Portable Fuel Containers: Permeation 

 2501011012 Residential Portable Fuel Containers: Evaporation 

 2501011013 Residential Portable Fuel Containers: Spillage During Transport 

 2501011014 Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement 

 2501011015 Residential Portable Fuel Containers: Refilling at the Pump: Spillage 

 2501012011 Commercial Portable Fuel Containers: Permeation 

 2501012012 Commercial Portable Fuel Containers: Evaporation 

 2501012013 Commercial Portable Fuel Containers: Spillage During Transport 

 2501012014 Commercial Portable Fuel Containers: Refilling at the Pump: Vapor Displacement 

 2501012015 Commercial Portable Fuel Containers: Refilling at the Pump: Spillage 

The large majority of spillage emissions occur when refueling equipment, and this is already included in the 

nonroad equipment inventory. Thus we did not include these emissions in the PFC inventory for this rule. 

Vapor displacement for nonroad equipment container refueling was also subtracted from vapor displacement 

in the PFC inventory to avoid double counting these emissions. The future-year emissions reflect projected 

increases in fuel consumption, state programs to reduce PFC emissions, standards promulgated in the 

MSAT2 rule, and impacts of the EISA on gasoline volatility. OTAQ provided year 2018 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 EISA. Because the future year PFC inventories 

contain ethanol in addition to benzene, EPA developed a VOC E-profile that integrated ethanol and benzene; 

see Section 3.2.1.1 and Section 3.2.1.4for more details. Emissions for 2007, 2018, and 2030 are provided in 

Table 4-7. 

Table 4-7. PFC emissions for 2007, 2018, and 2030 [tons] 

pollutant 2007 2018 2030

Benzene 1,049 645 829

Naphthalene 0.6 7.1 9.3

Ethanol 3,719 4,969

VOC 220,472 29,119 37,947

4.2.1.4 Cellulosic fuel production inventory (nonpt) 

New Future year inventories: “2018_cellulosic_inventory”, “2030_cellulosic_inventory” 

Depending on available feedstock, cellulosic plants are likely to produce fuel through either a biochemical 

process or a thermochemical process. OTAQ developed county-level inventories for biochemical and 

thermochemical cellulosic fuel production for 2018 and 2030 to reflect AEO2013 (early release) renewable 

fuel volumes. Emissions factors for each cellulosic biofuel refinery reflect the fuel production technology 

used rather than the fuel produced. Emission rates in Table 4-8 and Table 4-9 were used to develop 
53

cellulosic plant inventories. Criteria pollutant emission rates are in tons per RIN gallon. Emission factors 

from the cellulosic diesel work in the Tier 3 NPRM were used as the emission factors for the 

thermochemical plants in the FRM modeling. Cellulosic ethanol VOC and related HAP emission factors 

from the Tier 3 NPRM were used as the biochemical VOC and related HAP emission factors. Because the 

future year cellulosic inventory contains ethanol, we developed a VOC E-profile that integrated ethanol, see 

Sections 3.2.1.1 and 3.2.1.3 for more details. 

Plants were treated as area sources spread across the entire area of whatever county they were considered to 

be located in. Cellulosic biofuel refinery siting was based on utilizing the lowest cost feedstock, accounting 

for the cost of the feedstock itself as well as feedstock storage and the transportation of the feedstock to the 

cellulosic biofuel refinery. The total number of cellulosic biofuel refineries was projected using volumes 

from AEO2013 (early release). The methodology used to determine most likely plant locations is described 

in Section 1.8.1.3 of the RFS2 RIA (EPA, 2010a). Table 4-10 provides the 2018 and 2030 cellulosic plant 

emissions estimates. 

Table 4-8. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon) 

Cellulosic Plant 

Type

VOC CO NOx PM10 PM2.5 SOx NH3

Thermochemical 5.92E-07 8.7E-06 1.31E-05 1.56E-06 7.81E-07 1.17E-06 1.44E-10

Biochemical 1.82E-06 1.29E-05 1.85E-05 3.08E-06 1.23E-06 6.89E-07 0

Table 4-9. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon) 

Plant Type Acetaldehyde Acrolein Benzene 1,3-Butadiene Formaldehyde Ethanol

Thermochemical 2.95E-08 1.27E-09 9.61E-10 0 5.07E-09 2.09E-07

Biochemical 3.98E-07 1.11E-08 1.39E-08 0 2.28E-08 6.41E-07

Table 4-10. 2018 and 2030 cellulosic plant emissions [tons] 

pollutant 2018 2030

Acrolein 0.9 3.1

Formaldehyde 3.5 10.3

Benzene 0.7 2.9

Acetaldehyde 20.6 86.1

CO 6,087 15,196

Ethanol 146 397

NH3 0.1 0.2

NOX 9,199 22,867

PM10 1,088 2,792

PM2.5 547 1,372

SO2 819 1,941

VOC 414 1,125

4.2.1.5 Ethanol working loss inventory (nonpt) 

New Future year inventories: “Ethanol_transport_vapor_2018rg_ref”, 

“Ethanol_transport_vapor_2030rg_ref” 
54

These inventories were provided by OTAQ to represent upstream impacts of loading and unloading at 

ethanol terminals. Emissions are entirely evaporative and were computed by county for truck, rail and 

waterway loading and unloading and intermodal transfers (e.g., highway to rail). Inventory totals are 

summarized in Table 4-11. The leading descriptions are “Industrial Processes; Food and Agriculture; 

Ethanol Production” for each SCC. 

Table 4-11. 2018 and 2030 VOC working losses (Emissions) due to ethanol transport [tons] 

SCC description 2018 2030

30205031 Denatured Ethanol Storage Working Loss 23,420 22,396

30205052 Ethanol Loadout to Truck 14,425 13,794

30205053 Ethanol Loadout to Railcar 10,484 10,025

4.2.1.6 Vapor losses from Ethanol transport and distribution (nonpt, ptnonipm) 

Packets: “PROJECTION_2008_2018_distribution_upstream_OTAQ_Tier3FRM”, 

“PROJECTION_2008_2030_distribution_upstream_OTAQ_Tier3FRM” 

OTAQ developed county-level inventories for ethanol transport and distribution for 2018 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. 

These emissions are entirely evaporative and therefore limited to VOC and HAPs in VOC. 

2018 and 2030 inventories which included EISA impacts were developed by adjusting the 2007 platform 

inventory. Impacts of the light-duty greenhouse gas rule are small enough to ignore for 2018. EISA 

adjustments were made using an updated version of EPA’s spreadsheet model for upstream emission 

impacts, developed for the RFS2 rule32. The development of emission factors and fuel volumes to make 

these adjustments with the RFS2 impacts spreadsheet are described below. 

Vapor loss VOC emission factors (EFs) for gasoline were first developed, based on inventory estimates from 

the 2005 NEI (EPA, 2009a). Total volume of gasoline was based on gasoline sales as reported by the Energy 

Information Administration (2006). Emissions were partitioned into refinery to bulk terminal (RBT), bulk 

plant storage (BPS), and bulk terminal to gasoline dispensing pump (BTP) components. Emissions for the 

BTP component are greater than the RBT and BPS components. 

Total nationwide emissions for these components were divided by the energy content of the total volume of 

gasoline distributed in 2005 to obtain emission factors in grams per million metric British Thermal Units 

(g/mmBTU). Total volume of gasoline was based on gasoline sales as reported by the Energy Information 

Administration.33 In addition to gasoline VOC emission factors for the RBT/BPS components, emission 

factors were developed for the BTP component, for 10% ethanol, 15% ethanol, and 85% ethanol. Emission 

factors were calculated by applying adjustment factors to the gasoline EFs. The BTP adjustment factors 

were based on an algorithm from the 1994 On-Board Refueling Vapor Recovery Rule (EPA, 1994): 

EF (g/gal) = exp[-1.2798 - 0.0049(ΔT) + 0.0203(Td) + 0.1315(RVP)] 

Here delta T is the difference in temperature between the fuel in the tank and the fuel being dispensed, and 

Td is the temperature of the gasoline being dispensed. We assumed delta T is zero, and the temperature of 

the fuel being dispensed averages 60 oF over the year. 

 

32 U.S. EPA. 2013. Spreadsheet “upstream_emissions_rev T3.xls. 

33 Source: Energy Information Administration. 2006. Annual Energy Outlook 2006. Report #:DOE/EIA-0383(2006) Available at 

<http://www.eia.gov/oiaf/archive/aeo06/aeoref_tab.html> 
55

Average summer RVPs at the Petroleum Administration for Defense Districts (PADD) level was used to 

calculate adjustments. The U.S. is broken into five PADDs for petroleum products data collection purposes 

via the U.S. Energy Information Administration; see: http://www.eia.gov/oog/info/twip/padddef.html. These 

PADD regions are shown in Figure 4-1. 

Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD) 

All counties within a PADD received the same adjustment for BTP emissions. Volumes for each fuel type 

and summer RVPs for 2018 with EISA impacts are provided in Table 4-12 while volumes without EISA are 

in Table 4-13. Volumes with and without EISA for 2030 are provided in Table 4-14 and Table 4-16. These 

volumes and RVPs were obtained from analyses done for the Tier 3 rule. These two sets of volumes were 

used to estimate emissions using an updated version of the RFS2 impacts spreadsheet (EPA, 2013a). 
56

Table 4-12. RVPs Assumed for 2018 ethanol and gasoline volumes with EISA 

Table 4-13. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA 

Table 4-14. RVPs Assumed for 2030 ethanol and gasoline volumes with EISA 

PADD

Total 

Fuel 

Volume

Gasoline 

Volume

Ethanol 

Volume

E10 Only 

Volume

E15 Only 

Volume

E85(74) 

Only 

Volume

Wtd.

RVP

Wtd.

E10 RVP

Wtd.

E15 

RVP

Wtd. E85 

RVP

1 3.92E+10 3.41E+10 5.02E+09 2.43E+10 1.43E+10 6.02E+08 8.444 8.763 7.962 7

2 2.97E+10 2.58E+10 3.91E+09 1.62E+10 1.31E+10 4.56E+08 8.942 9.333 8.525 7

3 1.95E+10 1.68E+10 2.63E+09 9.32E+09 9.85E+09 2.98E+08 8.245 8.644 7.905 7

4 4.69E+09 4.08E+09 6.14E+08 2.64E+09 1.98E+09 7.20E+07 9.063 9.524 8.524 7

5 2.05E+10 1.81E+10 2.47E+09 1.60E+10 4.24E+09 3.17E+08 7.884 7.93 7.774 7

Total 1.14E+11 9.89E+10 1.46E+10 6.84E+10 4.34E+10 1.75E+09 8.464 8.716 8.126 7

Table 4-15. RVPs Assumed for 2030 ethanol and gasoline volumes without EISA 

PADD

Total 

Fuel 

Volume

Gasoline 

Volume

Ethanol 

Volume

E10 Only 

Volume

E15 Only 

Volume

E85(74) 

Only 

Volume

Weighte

d RVP

Weighted 

E10 RVP

Weighted 

E15 RVP

Weighted 

E85 RVP

1 4.50E+10 3.99E+10 5.18E+09 3.71E+10 7.43E+09 4.73E+08 8.536 8.651 8.056 7

2 3.46E+10 3.03E+10 4.25E+09 2.31E+10 1.11E+10 3.63E+08 9.051 9.35 8.493 7

3 2.14E+10 1.89E+10 2.48E+09 1.72E+10 3.95E+09 2.25E+08 8.399 8.515 7.973 7

4 5.07E+09 4.48E+09 5.89E+08 4.06E+09 9.53E+08 5.33E+07 9.322 9.536 8.536 7

5 2.35E+10 2.08E+10 2.71E+09 1.91E+10 4.13E+09 2.47E+08 7.906 7.974 7.645 7

Total 1.30E+11 1.14E+11 1.52E+10 1.01E+11 2.75E+10 1.36E+09 8.567 8.696 8.175 7

PADD

Total Fuel 

Volume

Ethanol 

Volume

Gasoline 

Volume

E0 Only 

Volume

E10 Only 

Volume

Weighted 

RVP

Weighted E0 

RVP

Weighted E10 

RVP

1 4.42E+10 2.47E+09 4.17E+10 1.95E+10 2.47E+10 8.63 8.301 8.883

2 3.38E+10 1.92E+09 3.19E+10 1.47E+10 1.92E+10 9.48 8.925 9.907

3 2.09E+10 1.02E+09 1.99E+10 1.07E+10 1.02E+10 8.77 8.391 9.166

4 4.96E+09 2.54E+08 4.71E+09 2.43E+09 2.54E+09 9.18 8.674 9.674

5 2.30E+10 1.27E+09 2.17E+10 1.03E+10 1.27E+10 7.77 7.602 7.911

Total 1.27E+11 6.93E+09 1.20E+11 5.76E+10 6.93E+10 8.75 8.372 9.059

PADD

Total Fuel 

Volume

Ethanol 

Volume

Gasoline 

Volume

E0 Only 

Volume

E10 Only 

Volume

Weighted 

RVP

Weighted 

E0 RVP

Weighted 

E10 RVP

1 3.83E+10 2.47E+09 3.59E+10 1.36E+10 2.47E+10 8.68 8.301 8.883

2 2.90E+10 1.92E+09 2.71E+10 9.88E+09 1.92E+10 9.57 8.925 9.907

3 1.89E+10 1.02E+09 1.79E+10 8.78E+09 1.02E+10 8.81 8.391 9.166

4 4.57E+09 2.54E+08 4.32E+09 2.04E+09 2.54E+09 9.23 8.674 9.674

5 2.02E+10 1.27E+09 1.89E+10 7.48E+09 1.27E+10 7.80 7.602 7.911

Total 1.11E+11 6.93E+09 1.04E+11 4.18E+10 6.93E+10 8.80 8.372 9.059
57

A benzene g/mmgal emission factor for 2018 and 2030 was based on benzene inventory projections used in 

the 2011 Cross-State Air Pollution Rule and projected gasoline volumes obtained from the Annual Energy 

Outlook 2011 Early Release Overview. This emission factor was used to estimate g/mmBTU emission 

factors based on the energy content of E0, E10, and E15 gasoline. Aside from energy content, we did not 

account for the effect of other fuel parameters on emission rates for E0, E10, and E15 blends. Thus, the E10 

emission rate is slightly higher than the E0 rate due to the lower energy content of E10, and the E15 emission 

rate is higher still. The E85 emission rate was estimated for the RFS2 rule. Emission factors are summarized 

in Table 4-16. 

Table 4-16. Storage and Transport Vapor Loss Emission Factors (g/mmBtu) 

Process Fuel Benzene

BTP

E0 0.250

E10 0.259

E15 0.264

E85 0.023

RBT/BPS E0 0.059

These emission factors for VOC and benzene were used in conjunction with an updated version of EPA’s 

spreadsheet model for upstream emission impacts, developed for the RFS2 rule, to estimate PADD-level 

inventory changes of the changes in gasoline volume in 2018 with 2007 ethanol volumes versus projected 

volumes with EISA. VOC inventory changes were used to develop nationwide adjustment factors that were 

applied to modeling platform inventory SCCs associated with storage and transport processes (see Table 

4-17). Benzene emission estimates were obtained either by application of the adjustments in Table 4-17 or 

through speciation of VOC in SMOKE. 

A similar approach was used to develop adjustment factors for 2030. However, in addition to the impacts of 

EISA, the 2017-2025 light-duty greenhouse gas rule will significantly reduce gasoline production. The 

impact of this rule was small enough to ignore for 2018, but quite significant in 2030. To account for 

impacts of this rule in 2030, an additional scalar of 0.7916 was applied. 

Ethanol emissions were estimated in SMOKE by applying the ethanol to VOC ratios from headspace profiles 

to VOC emissions for E10 and E15, and an evaporative emissions profile for E85. These ratios are 0.065 for 

E10, 0.272 for E15, and 0.61 for E85. The E10 and E15 profiles were obtained from an ORD analysis of 

fuel samples from the EPAct exhaust test program34 and have been submitted for incorporation into EPA’s 

SPECIATE database. The E85 profile was obtained from data collected as part of the CRC E-80 test 

program (Environ, 2008) and has also been submitted for incorporation into EPA’s SPECIATE database. 

For more details on the change in speciation profiles between 2007 and the future years, see Section 

3.2.1.4Error! Reference source not found.. 

After developing emissions for 2018 with EISA volumes versus 2018 without EISA volumes, and 2030 with 

EISA and the light-duty greenhouse gas rule versus 2030 without those rules, EPA created ratios of these 

two cases to apply against the 2007 platform emissions. From this, EPA created 2018 and 2030 reference 

cases. 

 

34 U.S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. Office of Research and 

Development and Office of Transportation and Air Quality. Report No. EPA-420-R-11-018. EPA Docket EPA-HQ-OAR-2011-

0135.
58

Table 4-17. Adjustment factors applied to storage and transport emissions 

Year Process PADD Pollutant

Adjustment 

Factor

2018

BTP

1

VOC 0.9515

benzene 0.9905

2

VOC 0.9619

benzene 0.9882

3

VOC 0.9778

benzene 0.9879

4

VOC 0.8983

benzene 0.9885

5

VOC 0.9430

benzene 0.9901

RBT/BPS All VOC 0.9553

benzene 0.9893

2030

BTP

1

VOC 0.8005

benzene 0.7871

2

VOC 0.8436

benzene 0.7865

3

VOC 0.8318

benzene 0.7824

4

VOC 0.7905

benzene 0.7840

5

VOC 0.7700

benzene 0.7888

RBT/BPS All VOC 0.8114

benzene 0.7863

It should be noted that these adjustment factors are based on summer RVP, but applied to emissions for the 

whole calendar year. However, higher RVPs in winter corresponding to lower temperatures result in roughly 

the same vapor pressure of the fuel and roughly the same propensity to evaporate. Significant evaporative 

emissions are not expected from storage and transport of biodiesel, renewable or cellulosic diesel fuel due to 

their low volatility. The cumulative impacts are a reduction in VOC emissions from 2007 levels (see Table 

4-18). See 2007v5 TSD, Appendix B for the complete cross-walk between SCC, and state-SCC for BTP 

components, and each type of petroleum transport and storage. 

Table 4-18. Impact of VOC losses from reduced gasoline production due to EISA

Sector 2018 Reductions 2030 Reductions

ptnonipm 2,039 7,884

nonpt 22,082 97,030

Total 24,121 104,914

4.2.1.7 Pipeline and Refinery EISA adjustments (ptnonipm) 

Packets: “PROJECTION_2018rg_ref_pipelines_refineries”, 

“PROJECTION_2030rg_ref_pipelines_refineries” 
59

Pipeline usage, refinery, and bulk terminal emissions were adjusted for changes in fuels due to EISA and 

reductions in gasoline and diesel volumes due to greenhouse gas emission standards. These adjustments 

were developed by EPA/OTAQ and impact processes such as process heaters, catalytic cracking units, 

blowdown systems, wastewater treatment, condensers, cooling towers, flares and fugitive emissions. 

Calculation of the emission inventory impacts of decreased gasoline and diesel production, due to EISA and 

greenhouse gas rules, on nationwide refinery emissions was done in the updated version of EPA's 

spreadsheet model for upstream emission impacts. Emission inventory changes reflecting EISA and 

greenhouse gas rules implementation were used to develop adjustment factors that were applied to 

inventories for each petroleum refinery in the U.S. (Table 4-19 and Table 4-20). These impacts of decreased 

production were assumed to be spread evenly across all U. S. refineries. Toxic emissions were estimated in 

SMOKE by applying speciation to VOC emissions. It should be noted that the adjustment factors in Table 

4-19 are estimated relative to that portion of refinery emissions associated with gasoline and diesel fuel 

production. Production of jet fuel, still gas and other products also produce emissions. If these emissions 

were included, the adjustment factors would not be as large. The impact of the EISA and greenhouse gas 

rules is shown in 
60

Table 4-21. 

Table 4-19. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with 

gasoline and diesel fuel production. 

Pollutant Pipelines Refineries Both

CO 0.996 0.978 0.974

NOX 0.982 0.987 0.969

PM10 0.997 0.984 0.981

PM2.5 0.998 0.979 0.977

SO2 0.998 0.978 0.976

NH3 n/a 0.952 n/a

VOC 0.999 0.972 0.971

Table 4-20. 2030 adjustment factors applied to petroleum pipelines and refinery emissions associated with 

gasoline and diesel fuel production. 

Pollutant Pipelines Refineries Both

CO 0.981 0.773 0.763

NOX 0.890 0.781 0.695

PM10 0.985 0.779 0.767

PM2.5 0.989 0.775 0.766

SO2 0.986 0.774 0.763

NH3 n/a 0.750 n/a

VOC 0.994 0.769 0.764
61

Table 4-21. Impact of refinery adjustments on 2007 emissions [tons] 

pollutant

reductions 

2018

reductions 

2030

CO 2,148 19,952

NH3 111 572

NOX 2,884 28,116

PM10 515 6,185

PM2.5 555 5,503

SO2 3,401 33,913

VOC 1,891 15,341

4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm) 

Packets: “PROJECTION_2008_2018_ag_including_upstream_OTAQ”; 

“PROJECTION_2008_2030_ag_including_upstream_OTAQ” 

Inventory adjustments were previously developed for 2030 as part of final RFS2 rule modeling35. For the 

Tier 3 proposal, adjustments for 2017 were scaled by the ratio of 2017 renewable fuel volumes versus 2030 

volumes. Although 2018 was modeled for this rule rather than 2017, EPA continued to use the 2017 

adjustments. Impacts on farm equipment emissions were not accounted for, however. Emission rates from 

the GREET model (fertilizer and pesticide production)36 or based on the 2002 National Emissions Inventory 

(fertilizer and pesticide application, agricultural dust, livestock waste) were combined with estimates of 

agricultural impacts from FASOM (Forest and Agricultural Section Optimization Model). Since FASOM 

modeling used a reference case of 13.2 billion gallons of ethanol, impacts used in the modeling for this rule 

are underestimates. 

Adjustment factors are provided in 

 

35 U. S. Environmental Protection Agency. 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. 

Assessment and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI. Report No. EPA-420-R-10-006, 

February, 2010. Available at <http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm>. 

36 GREET, version 1.8c. Available at < http://greet.es.anl.gov/>.
62

Table 4-22. These adjustments were applied equally to all counties having any of the affected sources. This 

is an area of uncertainty in the inventories, since there would likely be variation from one county to another 

depending on how much of the predicted agricultural changes occurred in which counties. By using percent 

change adjustments rather than attempting to calculate absolute ton changes in each county, EPA attempted 

to minimize the inventory distortions that could occur if the calculated change for a given county was out of 

proportion to the reference case emissions for that county. For instance, a different approach could estimate 

reductions that were larger than the reference case NEI emissions, since there was no linkage between the 

NEI inventories and the FASOM modeling. The specific sources (SCCs) and affected pollutants that these 

adjustments were applied to are listed in a docket reference37.

 

37 U. S. EPA. 2011. Spreadsheet “agricultural sector adjustments.xls.” Docket EPA–HQ–OAR–2011–0135. 
63

Table 4-22. Adjustments to modeling platform agricultural emissions for the Tier 3 reference case 

Source Description 2018 Adjustment 2030 Adjustment

Nitrogen Fertilizer Application 1.0242 1.0573

Fertilizer Production 1.0603 1.0603

Pesticide Production 0.9544 0.9954

Tilling/Harvesting Dust 1.0079 1.0265

Agricultural Burning 1.0000 1.0000

Livestock Dust 0.9868 0.9983

Livestock Waste 0.9901 0.9983

For the animal waste sources, EPA also estimated animal population growth in ammonia (NH3) and dust 

(PM10 and PM2.5) emissions from livestock in the ag and afdust and ptnonipm sectors. Therefore, a 

composite set of projection factors is needed for animal operations that also reflect the minor 0.99% decrease 

resulting from the EISA mandate. These composite projection factors by animal category are provided in 

Table 4-23. As discussed below, Dairy Cows and Turkeys are assumed to have no growth in animal 

population, and therefore the projection factor for these animals is the same as the upstream agriculturerelated projection factor. The PROJECTION packet used for these sources, including the cross-reference to 

the animal categories listed in Table 4-23 and the source categories in 
64

Table 4-22. 

Table 4-23. Composite NH3 projection factors to year 2018 and 2030 for animal operations 

Animal Category 2018 Factors 2030 Factors

Dairy Cow 0.9901 0.9983

Beef 0.9927 0.9460

Pork 1.0798 1.1614

Broilers 1.1399 1.1795

Turkeys 0.9050 0.8953

Layers 1.0974 1.1281

Poultry Average 1.0865 1.1149

Overall Average 1.0428 1.0505

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). This methodology was initiated in 2005 for the 2005 

NEI, but was updated on July 24, 2012 in support of the 2007v5 platform (EPA, 2012). For dairy cows, 

EPA assumed that there would be no growth in emissions based on little change in U.S. dairy cow 

populations from year 2007 through 2030 according to linear regression analyses of the FAPRI projections. 

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. Based on 

this analysis, EPA concluded that production forecasts do not provide representative estimates of the future 

number of cows and turkeys; therefore, EPA 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, the excretion value will likely change, but 

EPA assumed no change because of the lack of a quantitative estimate. Appendix H of the 2007v5 platform 

TSD provides the animal population data and regression curves used to derive the growth factors. 

4.2.3 Fuel sulfur rules (nonpt, ptnonipm)

Packets: CONTROL_SULF_2020_2007v5; CONTROL_SULF_2030_2007v5 

Fuel sulfur rules that were signed by July, 2012 are limited to Maine, Massachusetts, New Jersey, New York 

and Vermont. The fuel limits for these states are incremental starting after year 2012, but are fully 

implemented by year 2018 in all of these states. In 2030, New York City had additional controls 

implemented, mandating that all sources burn ULSD (15 ppm). For more details on these rules, see the 

2007v5 TSD.A summary of the sulfur rules by state, with emissions reductions is provided inTable 4-24.

Table 4-24. Summary of fuel sulfur rules by state 

State/

Metro

Fuel % 

reduction 

2018

% 

reduction 

2030

2008 

Emissions

2018 

Emissions

2018 

Reductions

2030 

Emissions

2030 

Reductions

ME Distillate 99.5 99.5 12,076 1,056 11,021 1,056 11,021 ME Residual 75 75

MA Distillate 99.5 99.5 17,265 86 17,178 86 17,178

NJ Distillate 99.5 99.5 7,285 45 7,240 45 7,240 NJ Kerosene 96.25 96.25

NY Distillate 99.5 99.5 54,093 655 53,439 274 53,819
65

NYC Residual 75 99.25

VT Distillate 99.5 99.5 2,018 10 2,008 10 2,008

4.2.4 Portland Cement NESHAP projections (ptnonipm)

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

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

provided reductions of criteria and hazardous air pollutants, including mercury (Hg). 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; however, 

no additional policy case scenario for later future years (i.e., 2018) are available. Therefore, the 2013 policy 

case is used for the 2018 and 2030 base case. These ISIS-based emissions are reflected using CoST packets 

and a cement inventory for new kilns: 

1) Inventory: “cement_newkilns_ISIS2013_2007v5_POINT_ff10” 

Contains information on new cement kilns constructed after year 2008. This inventory was 

accidentally not included in projections for all future year scenarios. 

2) Packet: “CLOSURES_cement_ISIS_2007v5_2013policy” 

Contains facility and unit-level closures, 

3) Packet: “PROJECTION_ISIS2013_cement_2007v5“ 

Contains updated policy case emissions at existing cement kilns which we include via projection 

factors. The units that opened or closed before 2010 were included in the 2020 base case. 

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 

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. Table 4-25 shows the magnitude of the 

ISIS-based cement industry reductions in the future-year emissions that represent 2018 and 2030, and the 

impact that these reductions have on total stationary non-EGU point source (ptnonipm) emissions. The 

impact of accidentally not including the inventory of new kilns in future year modeling is quantified below. 

This error has the most significant impact on NOX emissions; however, nationally, NOX from cement kilns 

would still decrease from 2008 by over 50% had these new kilns been included in the future year scenarios. 

Table 4-25. ISIS-based cement industry change (tons/yr) 

Pollutant

Cement Industry 

emissions in 2008

Cement Industry 

projected emissions in 

2018 & 2030

New cement kilns 

erroneously dropped 

from inventory

% decrease in 

Cement 

projections

CO 46,317 8,713 0 81%

NH3 270 77 0 71%

NOX 156,579 57,477 17,699 63%

PM10 6,621 1,005 2 85%

PM2.5 3,689 800 1 78%
66

SO2 98,277 22,287 1,543 77%

VOC 6,954 1,131 135 84%

4.2.5 Controls, Closures and consent decrees from CSAPR and NODA Comments 

(nonpt, ptnonipm)

EPA released a Notice of Data Availability (NODA) after the CSAPR proposal to seek comments and 

improvements from states and outside agencies. The goal was to improve the future baseline emissions 

modeling platform prior to processing the Final CSAPR. EPA received several control programs and other 

responses that were used for future year projections. However, this effort was performed on a version of the 

2005 modeling platform, which used the NEI2005v2 as a base year starting point for future year projections. 

Now with the 2007 platform using the 2008 NEI for most non-EGU point and nonpoint sources, many of 

these controls and data improvements were removed from this 2007 base case projection. But for those 

controls, closures and consent decree information that are implemented after 2008, EPA used these 

controls/data after mapping them to the correct SCCs and/or facilities in the 2008 NEI. This subsection 

breaks down the controls used for the nonpt and ptnonipm sectors separately, and also describes the consent 

decrees separately. EPA used July 1, 2008 as the cut-off date for assuming whether controls were included 

in the 2007 modeling platform (2008 NEI). For example, if a control had a compliance date of December 

2008 it was assumed that the 2008 NEI emissions did not reflect this control and would need to reflect this 

control in our 2018 or 2030 scenarios. It is important to note that these controls are not comprehensive for 

all state/counties and source categories. These only represent post-year 2008 controls for those areas and 

categories where EPA received usable feedback from the CSAPR comments and related 2005 platform 

NODA.

Nonpoint controls: packet “CONTROL_CSAPR_nonpoint_2018_2007v5” 

The remaining nonpt sector CSAPR comments controls with compliance dates after 2008 are limited to statelevel Ozone Transport Commission (OTC) VOC controls in Connecticut and local controls around 

Richmond Virginia. These controls target many of the same sources in the previously-discussed NY SIP 

ozone control packet: AIM coating, Mobile Equipment Repair and Refinishing, Adhesives and Sealants and 

Consumer Products. Cumulatively, these controls reduce VOC by approximately 1,400 tons. This control 

packet also impacts Texas oil and gas drill rigs (SCC 2310000220), were though factors for these emissions 

were available through year 2021, EPA only projected these to 2018. The impact in TX is a reduction of 

NOX by 17,244 tons, PM2.5 by 1,158 tons and VOC by 1,470 tons with minor reductions for other pollutants. 

The control packet is used for both 2018 and 2030 and has the same impact in both years. 

Ptnonipm controls: packet “CONTROL_CSAPR_ptnonipm_2020_2007v5” 

EPA created a CONTROL packet for the ptnonipm sector that contains reductions needed to achieve post 

year-2008 emissions values from the CSAPR response to comments. These reductions reflect fuel 

switching, cleaner fuels, and permit targets via specific information on control equipment and unit and 

facility zero-outs in the following states: California, Delaware, Georgia, New Hampshire, New York and 

Virginia. Cumulatively, these controls reduce NOX about 1,000 tons and SO2 by approximately 4,100 tons. 

The control packet is used for both 2018 and 2030 and has the same impact in both years. 

Ptnonipm closures: packet “CLOSURES_TR1_2008NEIv2” 

This packet contains observed unit and facility-level closures based on CSAPR comments. This packet 

includes only units that reported by states as closed prior to receipt of the CSAPR comments in year 2012 or 

sooner. EPA found a couple of units in the 2008 NEI-based inventory that were reported as closed in year 

2007; therefore, the compliance dates in this packet range from 2007 to 2012. EPA also retained all year2007 closures to allow for this packet to potentially be used on RPO year-2007 point inventories. All 
67

closures were provided for the 2005 NEI facility and unit identifier codes. EPA matched these 

units/facilities to the 2008 NEI using the “agy_facility_id” and “agy_point_id” codes in the NEI and 

searching the EIS for closure information. Overall, these facility and unit closures reduced NOX, SO2 and 

PM2.5 emissions by approximately 8,800, 1,300 and 50,000 tons respectively distributed amongst the 

following states: Alabama, Arkansas, Delaware, Georgia, Illinois, Maine, Massachusetts, Missouri, New 

Hampshire, South Carolina, Texas, Virginia and West Virginia. The closure packet is used for both 2018 

and 2030 and has the same impact in both years. 

Ptnonipm projection: packet “PROJECTION_CSAPR_WVunit_ptnonipm_2020_2007v5” 

This packet contains the only post-2008 unit-level growth projection resulting from CSAPR comments. The 

Sunoco Chemicals Neal Plant in Wayne County West Virginia replaced a 155MM Btu/hour coal-fired boiler 

with a 96.72 MM Btu/hour natural gas-fired unit in 2010. The shutdown of the coal boiler was included in 

the CLOSURES_TR1_2008NEIv2” packet just discussed and simply added the emissions from the new 

natural gas unit to an existing unit by computing the new cumulative total from the new and old natural gas 

units. The closing of the coal-fired boiler removed 51 tons of NOX and 234 tons of SO2 while this packet 

resulted in only 28 more tons of NOX and minimal emissions from PM and SO2. The projection packet is 

used for both 2018 and 2030 and has the same impact in both years. 

Consent decrees (ptnonipm): packet “CONTROLS_CSAPR_consent_2008NEIv2” 

These controls reflect consent decree and settlements that were identified in our preparation of the Final 

CSAPR emissions modeling platform. These controls generally consist of one or more facilities and target 

future year reductions. After removing all consent decrees with compliance dates prior to late-2008, EPA 

matched the remaining controls to the 2008 NEI using a combination of EIS facility codes, “agy_facility_id”, 

“agy_point_id” and searching the EIS. Then, the percent reductions were recomputed such that the future 

year emissions would match those for facilities originally projected from the 2005 platform. EPA did not 

retain consent decree controls if the emissions in 2007 (2008 NEI) were less than the controlled future year 

emissions based on the 2005 platform. The remaining consent decree controls are in sixteen states (AL, CA, 

IN, KS, KY, LA, MI, MS, MO, OH, OK, TN, TX, UT, WI, WY) and accounted for 3,835 tons of NOX and 

37,368 tons of SO2 cumulative reductions in 2018 and approximately 3,411 tons of NOX and 36,878 tons of 

SO2 cumulative reductions in 2030. The control packet is used for both 2018 and 2030, but compliance 

dates for 2 facilities are in late-2018; therefore these controls were not applied in 2018 but were applied for 

2030. 

4.2.6 All other PROJECTION and CONTROL packets (ptnonipm, nonpt) 

This section describes all remaining non-EGU stationary sources not already discussed. These control 

packets and projection packets generally have lesser national-level impact on future year projections than 

those items above. However, some of the consent decrees discussed below have significant local impacts. 

4.2.6.1 Aircraft growth (ptnonipm) 

Packets: “PROJECTION_2008_2018_aircraft”; “PROJECTION_2008_2030_aircraft” 

Aircraft emissions are contained in the ptnonipm inventory. These 2008 point-source emissions 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 March, 2012). This 

information is available for approximately 3,300 individual airports, for all years up to 2030. EPA 
68

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, military. Growth factors were computed for each operation 

type by dividing future-year 2018 and 2030 ITN by 2008-year ITN. EPA 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/about/office_org/headquarters_offices/apl/aviation_forecasts/taf_reports/media/TAF_su

mmary_report_FY20112040.pdf. Table 4-26 provides the national growth factors for aircraft; all factors are 

applied to year 2008 emissions. For example, year 2018 commercial aircraft emissions are 7.5% higher than 

year 2008 emissions. 

None of the aircraft emission projections account for any control programs. EPA 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 3% 

in 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule; therefore, the effects of this 

rule were not included in the future-year emissions projections. 

Table 4-26. Factors used to project 2008 base-case aircraft emissions to 2020 

SCC Description 2018 2030

2270008005

Commercial Aircraft: Diesel Airport Ground Support Equipment, Air Ground Support 

Equipment 1.0750 1.3502

2275000000 All Aircraft Types and Operations 1.0750 1.3502

2275001000 Military Aircraft, Total 1.0620 1.0623

2275020000 Commercial Aviation, Total 1.0750 1.3502

2275050000 General Aviation, Total 0.9206 0.9709

2275050011 General Aviation, Piston 0.9206 0.9709

2275050012 General Aviation, Turbine 0.9206 0.9709

2275060000 Air Taxi, Total 0.9381 1.0965

2275060011 Air Taxi, Total: Air Taxi, Piston 0.9381 1.0965

2275060012 Air Taxi, Total: Air Taxi, Turbine 0.9381 1.0965

2275070000 Commercial Aircraft: Aircraft Auxiliary Power Units, Total 1.0750 1.3502

27501014

Military aircraft: Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Military; 

Jet Engine: JP-4

1.0620 1.0623

27501015

Military aircraft, This SCC is in 2005v2: Internal Combustion Engines; Fixed Wing Aircraft L 

& TO Exhaust; Military; Jet Engine: JP-5

1.0620 1.0623

27502001

Commercial Aircraft, Total, This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed 

Wing Aircraft L & TO Exhaust; Commercial; Piston Engine: Aviation Gas 1.0750 1.3502

27502011

Commercial Aircraft, Total, This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed 

Wing Aircraft L & TO Exhaust; Commercial; Jet Engine: Jet A 1.0750 1.3502

27505001

General Aviation Total. This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed 

Wing Aircraft L & TO Exhaust; Civil; Piston Engine: Aviation Gas 0.9206 0.9709

27505011

General Aviation Total. This SCC is in 2002 NEI: Internal Combustion Engines; Fixed Wing 

Aircraft L & TO Exhaust; Civil; Jet Engine: Jet A 0.9206 0.9709

27601014

Military aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; 

Military; Jet Engine: JP-4

1.0620 1.0623

27601015

Military aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; 

Military; Jet Engine: JP-5

1.0620 1.0623

27602011

Commercial aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; 

Commercial; Jet Engine: Jet A 1.0750 1.3502
69

4.2.6.2 Oil and gas projections in TX, and non-California WRAP states (nonpt) 

EPA used a suite of intermediate-year projected WRAP Phase III oil and gas emissions for all future year 

scenarios. These intermediate-year inventories are 2008 for the Permian basin, and 2010, 2012 or 2015 for 

the remaining WRAP area basins. These point and nonpoint inventories are discussed in the 2007 base case 

Sections 2.1.2 and 2.2.3, respectively. Summaries of these mid-term projections are posted on the WRAP 

Phase III oil and gas project website: http://www.wrapair2.org/PhaseIII.aspx. As discussed in Section 

4.2.6.2, for drilling rig operations in the remaining counties in Texas (non-Permian basin), EPA applied year2018 projections from a TCEQ report 

(http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/ei/5820783985FY0901-

20090715-ergi-Drilling_Rig_EI.pdf).

4.3 Mobile source projections 

EPA analyzed emission impacts of the Tier 3 vehicle emissions and fuel standards by comparing projected 

emissions for future years without the Tier 3 rule (reference scenario) to projected emissions for future years 

with the Tier 3 standards in place (control scenario). Table 4-27 presents an overview of the reference and 

control scenarios for calendar years 2018 and 2030. Both scenarios reflect the renewable fuel volumes and 

market fractions projected by the Annual Energy Outlook 2013 Report (“AEO2013”)38

 

38 U.S. Energy Information Administration, Annual Energy Outlook 2013 (April 15, 2013) 
70

Table 4-27. Overview of Reference and Control Scenarios

Reference Scenario Control Scenario

2018

Renewable Fuels: AEO 2013a

 17.5 B gallons renewable fuels 

 (18.3 B ethanol-equivalent gallons):

16.0 B gallons ethanol: E10 b, E15c, E85d

Fuel Sulfur Level: 30 ppm (10 ppm California)

Fleet: e

 96 percent Tier 2 and older vehicles

 4 percent LEV III vehicles

Renewable Fuels: AEO 2013a

 17.5 B gallons renewable fuels 

 (18.3 B ethanol-equivalent gallons):

16.0 B gallons ethanol: E10 b, E15c, E85d

Fuel Sulfur Level: 10 ppm

Fleet: e

 86 percent Tier 2 and older vehicles

 14 percent Tier 3/LEV III vehicles 

2030

Renewable Fuels: AEO 2013a

 17.6 B gallons renewable fuels 

 (18.6 B ethanol-equivalent gallons):

15.3 B gallons ethanol: E10 b, E15c, E85d

Fuel Sulfur Level: 30 ppm (10 ppm California)

Fleet: e

 76 percent Tier 2 and older vehicles

 24 percent LEV III vehicles

Renewable Fuels: AEO 2013a

 17.6 B gallons renewable fuels 

 (18.6 B ethanol-equivalent gallons):

15.3 B gallons ethanol: E10 b, E15c, E85d

Fuel Sulfur Level: 10 ppm

Fleet: e

 21 percent Tier 2 and older vehicles

 79 percent Tier 3/LEV III vehicles

a U.S. Energy Information Administration, Annual Energy Outlook 2013 (April 15, 2013) 

b Gasoline containing 10 percent ethanol by volume 

c

 Gasoline containing 15 percent ethanol by volume 

d

 Gasoline containing up to 85 percent ethanol by volume (74 percent nominal used in this analysis) 

e

 Fraction of the vehicle population 

The reference scenarios assumed an average fuel sulfur level of 30 ppm in accordance with the Tier 2 

gasoline sulfur standards. Under the Tier 3 program, federal gasoline will contain no more than 10 ppm 

sulfur on an annual average basis by January 1, 2017, and therefore EPA assumed a nationwide fuel sulfur 

level of 10 ppm for both future year control cases. 

EPA assumed a continuation of the existing Tier 2 standards for model years 2017 and later in modeling 

emissions for the reference scenario, with the exception of California and Section 177 states that have 

adopted the LEV III program. The Tier 3 control scenario modeled the suite of exhaust and evaporative 

emission standards for light-duty vehicles (LDVs), light duty trucks (LDTs: 1-4), medium passenger vehicles 

(MDPVs) and large pick-ups and vans (Class 2b and 3 trucks) including: 

 Fleet average Federal Test Procedure (FTP) NMOG+NOX standards of 30 mg/mi for LDVs, LDTs 

and MDPVs, phasing in from MYs 2017 to 2025 for the light-duty fleet under 6,000 lbs. GVWR and 

phasing in from MYs 2018 to 2025 for the light-duty fleet over 6,000 lbs. GVWR, and MDPVs 

 Fleet average Supplemental Federal Test Procedure (SFTP) NMOG+NOX standards of 50 mg/mi for 

LDVs, LDTs and MDPVs, phasing in from MYs 2017 to 2025 for the light-duty fleet under 6,000 

lbs. GVWR and phasing in from MYs 2018 to 2025 for the light-duty fleet over 6,000 lbs. GVWR, 

and MDPVs 

 Per-vehicle FTP PM standard of 3 mg/mi for LDVs, LDTs and MDPVs, phasing in from MYs 2017 

to 2022 for the light-duty fleet under 6,000 lbs. GVWR and phasing in from MYs 2018 to 2022 for 

the light-duty fleet over 6,000 lbs. GVWR, and MDPVs 
71

 Per-vehicle US06-only PM standard of 10 mg/mi for LDVs, LDTs and MDPVs through MY2021 

and of 6 mg/mi for MY2022 and later model years 

 New standards for Class 2b and 3 trucks phasing in by MY 2022 including NMOG+NOX declining 

fleet average, and more stringent PM standards 

 More stringent evaporative emission standards for diurnal plus hot soak emissions, a new canister 

bleed test and emission standard, and new requirements addressing evaporative leaks on in-use 

vehicles. 

 New refueling emission control requirements for all complete HDGVs equal to or less than 14,000 

lbs GVWR (i.e., Class 2b/3 HDGVs), starting in the 2018 model year, and for all larger HDGVs by 

the 2022 model year 

Implementation of the Tier 3 standards is aligned with the model year 2017-2025 Light-Duty GHG 

standards39 to achieve significant criteria pollutant and GHG emissions reductions while providing 

regulatory certainty and compliance efficiency to the auto and oil industries. Accordingly, the analyses for 

the Tier 3 rule include the final LD GHG standards in both the reference and control scenarios, and thus 

account for their impacts on the future vehicle fleet and future fuel consumption. 

The analysis described here accounts for the following national onroad rules: 

 Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, 

February 10, 2000) 

 Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements 

(66 FR 5002, January 18, 2001) 

 Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007) 

 Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program (75 FR 

14670, March 26, 2010) 

 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy 

Standards for 2012-2016 (75 FR 25324, May 7, 2010) 

 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty 

Engines and Vehicles (76 FR 57106, September 15, 2011) 

 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average 

Fuel Economy Standards (77 FR 62623, October 15, 2012) 

In addition, the modeling accounts for state and local rules including local fuel standards, 

Inspection/Maintenance programs, Stage II refueling controls, the National Low Emission Vehicle Program 

(NLEV), and the Ozone Transport Commission (OTC) LEV Program. Furthermore, the Tier 3 emissions 

modeling for both the national inventory and air quality analysis includes California’s LEV III program and 

its associated emission reductions from both California and the states that adopted the LEV III program, in 

the baseline scenario. 

Onroad mobile sources are comprised of several components and are discussed in the next subsection (4.3.1). 

Monthly nonroad mobile emission projections are discussed in subsection 4.3.2. Locomotives and Class 1 

and Class 2 commercial marine vessel (C1/C2 CMV) projections are discussed in subsection 4.3.3, and Class 

3 (C3) CMV projected emissions are discussed in subsection 4.3.4.

 

39 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; 

Final Rule (77 FR 62623–63200), October 2012. 
72

4.3.1 Onroad mobile (onroad and onroad_rfl) 

The onroad emissions for 2018 and 2030 use the same SMOKE-MOVES system as for the base year (see 

Sections 2.5.1 and 2.5.2). Meteorology, speed, spatial and temporal surrogates, representative counties, and 

fuel months were the same as for 2007, discussed above. 

4.3.1.1 VMT and vehicle population 

EPA estimates of total national Vehicle Miles Travelled (VMT) in 2018 and 2030 came from DOE's Annual 

Energy Outlook (AEO) 2013 early release (http://www.eia.gov/forecasts/aeo/). The VMT was allocated 

between vehicle types using a version of MOVES2010b that had been modified with VMT growth factors 

from the AEO and with historical data from FHWA 

(http://www.fhwa.dot.gov/policyinformation/statistics.cfm). The growth rates by SCC were applied to the 

VMT values from the 2007 base year in each county to generate the future year VMT estimates. These 

VMT values were normalized such that the total national VMT from the growth calculations matched the 

total VMT estimates in the AEO for 2018 and 2030. Vehicle populations by county, month and vehicle type 

were estimated by dividing annual VMT by national estimates of annual VMT per vehicle. 

Tank trucks are used to transport ethanol mandated by EISA from production facilities to bulk terminals and 

from terminals to bulk plants and dispensing facilities. Impacts of this activity on emissions from tank trucks 

transporting ethanol (Class 8 trucks) are accounted for in these inventories by adjusting VMT used in 

SMOKE-MOVES. The VMT adjustments were derived from an Oak Ridge National Laboratory analysis of 

ethanol transport (Oak Ridge National Laboratory, 2009), scaled to account for the difference in ethanol 

volume for AEO 2013 future year projections versus the 2007 platform volume of 8.7 billion gallons. It 

should be noted that the Oak Ridge analysis only addressed ethanol transport and did not account for impacts 

of other biofuels on transportation activity. Impacts from the 2017-2025 light duty vehicle GHG emission 

standards were assumed to be negligible in 2018 and were accounted for with a 0.997 scalar in 2030. 

4.3.1.2 Set up and Run MOVES to create EF 

Emission factor tables were created by running SMOKE-MOVES using the same procedures and models as 

2007 (see Section 2.5.1 and the 2007v5 TSD). The same meteorology and the same representative counties 

were used. Changes between 2007, 2018, and 2030 are predominantly VMT, fuels, national and local rules, 

and the model-year distribution of the fleet, which is built into MOVES. Fleet turnover resulted in a greater 

fraction of newer vehicles meeting stricter emission standards. 

A detailed list of the differences between the runs for each case are described in 
73

Table 4-28. 
74

Table 4-28. Comparison of MOVES runs 

Year 2007 2018 2018 2030 2030

Case base ref ctl ref Ctl

Version 20130130 20130609 20130610 20130603 20130604

Run Name abs2007base_201

30130

tier3frm2018ref_20

130609

tier3frm2018ctl_20

130610

tier3frm2030ref_20

130603

tier3frm2030ctl_20

130604

MOVES 

code

moves20121002f moves20121002f moves20121002f moves20121002f moves20121002f

MOVES 

default 

database

movesdb20121002

f

movesdb20121002

k_truncatedgfre

movesdb20121002

k_truncatedgfre

movesdb20121002

k_truncatedgfre

movesdb20121002

k_truncatedgfre

CDBs 2007PFCdb146Re

pCnties20120402

2018PM146Counti

es20120724

2018PM146Counti

es20120724

2030146Counties2

0130312

2030146Counties2

0130312

Early NLEV early_nlev N/A N/A N/A N/A

LEV rates tier3_lev_standard

s_YYYY

lev3_standards_SS

_20130603

lev3_standards_SS

_20130603

lev3_standards_SS

_20130603

lev3_standards_S

S_20130603

VMT and 

VPOP

nationaldefaultvmt

pop_20120410

nationaldefaultvmtp

op_20120410

nationaldefaultvmtp

op_20120410

nationaldefaultvmtp

op_20120410

nationaldefaultvmt

pop_20120410

GFRE N/A non-LEV: 

tier3frm3030gfre

LEV: 

tier3frm3010gfre_0

53013

tier3frm3010gfre_0

53013

non-LEV: 

tier3frm3030gfre

LEV: 

tier3frm3010gfre_0

53013

tier3frm3010gfre_0

53013

hcspeciation N/A tier3frm_ref2018_h

cspec_M_rvpB

tier3frm_ref2018_h

cspec_M_rvpB

tier3frm_ref2030_h

cspec_M_rvpB

tier3frm_ref2030_h

cspec_M_rvpB

Fuels 2007_Baseline_09

062012

tier3frm2018reffuel

s_02252013

tier3frm2018ctrlfuel

s_03152013

tier3frm2030reffuel

s_03072013

tier3frm2030ctrlfuel

s_03152013

Tier 3 

control rates

N/A N/A tier3ctldbs_060313 N/A tier3ctldbs_060313

The following states were modeled as having adopted the California LEV II and LEV III programs (see 

Table 4-29) 

Table 4-29. CA LEVIII program states 

FIPS State Name

06 California

09 Connecticut

10 Delaware

23 Maine

24 Maryland

25 Massachusetts

34 New Jersey

36 New York

41 Oregon

42 Pennsylvania

44 Rhode Island

50 Vermont

53 Washington
75

Early NLEV refers to states which adopted the California “low emission vehicle” (LEV) standards in the 

1990’s “early”, since the California LEV standards were adopted nationally (NLEV) starting in 2001. The 

following states were modeled as using the early NLEV program in 2007 (see Table 4-30).

Table 4-30. Early NLEV states 

FIPS State Name

09 CONNECTICUT

10 DELAWARE

11 DISTRICT OF COLUMBIA

23 MAINE

24 MARYLAND

33 NEW HAMPSHIRE

34 NEW JERSEY

42 PENNSYLVANIA

44 RHODE ISLAND

50 VERMONT

51 VIRGINIA

The following table indicates when the LEV states adopted the LEV2 standards and lists the emission 

standards databases used in the 2007 base run (see Table 4-31) 

Table 4-31. LEV2 states and MOVES databases 

FIPS State Name Database

6 CALIFORNIA tier3_lev_standards_1994

9 CONNECTICUT tier3_lev_standards_2008

10 DELAWARE tier3_lev_standards_2014

23 MAINE tier3_lev_standards_2001

24 MARYLAND tier3_lev_standards_2011

25 MASSACHUSETTS tier3_lev_standards_1995

34 NEW JERSEY tier3_lev_standards_2009

35 NEW MEXICO tier3_lev_standards_2016

36 NEW YORK tier3_lev_standards_1996

41 OREGON tier3_lev_standards_2009

42 PENNSYLVANIA tier3_lev_standards_2008

44 RHODE ISLAND tier3_lev_standards_2008

50 VERMONT tier3_lev_standards_2000

53 WASHINGTON tier3_lev_standards_2009

The use of RVP bins for specifying hydrocarbon speciation (HCspeciation) database for January and July for 

both 2018 and 2030 runs are listed by representative county (see Table 4-32) Because the hydrocarbon 

speciation is dependent on the level of RVP, four RVP bins were generated based on RVP of the fuels in 

each representative county for each month. The HCspeciation database contains hydrocarbon speciation 

profiles for NMOG and VOC and applies only to fuels containing 70% or more ethanol by volume (i.e., 

E85). 
76

Table 4-32. RVP bins by representative county 

FIPS Jan Jul FIPS Jan Jul FIPS Jan Jul FIPS Jan Jul

1073 2 2 23019 2 3 39041 3 4 47163 2 4

4013 2 1 23031 2 2 39049 3 4 48001 2 2

4019 2 4 24005 2 1 39061 3 3 48003 2 4

4021 2 4 24015 2 1 39113 3 3 48005 2 2

6025 2 1 24029 2 1 39119 3 4 48011 2 4

6037 2 1 24033 2 1 39151 3 4 48039 1 1

8031 3 3 24043 2 4 40143 2 4 48047 2 4

8041 3 4 24045 2 4 41005 3 3 48139 2 2

8101 3 4 25017 2 1 41019 3 4 48141 2 1

9003 2 1 26081 3 4 41029 3 4 48143 2 4

10003 2 1 26163 3 2 41037 3 4 48221 2 2

10005 2 1 27003 4 4 41039 3 4 48251 2 2

11001 2 1 27053 4 4 41047 3 3 48439 1 1

12011 2 3 27111 4 4 41051 3 3 49005 3 4

12033 2 4 27137 4 4 41067 3 3 49009 3 4

12086 2 3 27145 4 4 41071 3 4 49011 3 3

13015 2 2 27165 4 4 42003 3 2 49017 3 4

13051 2 4 29095 2 2 42005 3 2 49035 3 3

13121 2 2 29189 2 1 42009 2 4 49043 3 4

16001 3 4 32003 2 4 42017 2 1 49045 3 4

16027 3 4 32005 2 4 42019 3 2 49049 3 4

17031 2 1 32007 2 4 42039 3 4 49057 3 4

17093 2 1 32009 2 4 42043 2 4 50007 2 4

18019 3 3 32027 2 4 42049 3 4 51036 2 1

18089 2 1 32031 2 3 42051 3 2 51041 2 1

18097 3 4 33009 2 4 42055 2 4 51059 2 1

19095 2 4 33015 2 1 42063 3 4 51087 2 1

19133 2 4 34003 2 1 42071 2 4 51107 2 1

19153 2 4 35001 2 4 42073 3 4 51683 2 1

19163 2 4 35013 2 4 42089 2 4 51740 2 1

20091 2 2 36029 2 3 42091 2 1 53033 3 4

21029 2 1 36103 2 1 42101 2 1 53063 3 4

21111 2 1 37051 2 4 42129 3 2 53067 3 4

21117 2 1 37081 2 3 44007 2 1 55025 4 4

22033 2 3 37119 2 3 46099 4 4 55079 2 1

22047 2 3 39023 3 3 47157 3 3 55117 4 4

23005 2 2 39035 3 4

Fuels were projected into the future using estimates from the AEO2013 (http://www.eia.gov/forecasts/aeo/), 

release dates April 15th-May 2nd 2013. . The fuel supply used in the Tier 3 FRM varies significantly from 

that used in the NPRM, including the introduction of a new approach to aggregating fuels by region. For 

more information regarding this new approach to fuels, please refer to the Tier 3 FRM, Chapter 7.1.3.2. 

Reference fuels contain E10, E15, and E85 at a market share fraction determined regionally from the 

AEO2013 report. Additional fuel properties for the reference cases are based on local fuel surveys as well as 

EPA fuel compliance and certification reports. All sulfur levels were set at 30ppm in the reference cases in 

order to reduce possible calculation artifacts. Fuel property changes associated with this sulfur adjustment 

were also calculated appropriately by region. The Tier 3 control cases adjust this sulfur level to 10ppm, as 

well as adjusting associated fuel properties with this sulfur reduction. Market shares between E10, E15 and 
77

E85 remain unchanged between the reference and the control cases but changed across the years (2018 

versus 2030). 

4.3.1.3 Long Haul truck adjustments and SMOKE-MOVES 

A set of adjustments were calculated for SMOKE-MOVES to estimate 2018 and 2030 emissions in each 

county to account for extended idle and the use of auxiliary power units (APU). These adjustments use the 

same approach as was used in 2007 (see Section 2.5.1.3 and the 2011 NEI v1 TSD for details) except for the 

vehicle population (VPOP) was updated to be consistent with 2018 or 2030. These adjustments are by 

county, vehicle type (long-haul truck SCCs only), and mode (extended idle or APU only) and impacted the 

RPV process only. The set of adjustments created for the extended idle mode are identical for APU because 

it was assumed that the distribution of APU usage and emissions would mirror extended idle activity. 

SMOKE-MOVES, specifically Movesmrg, uses the adjustment factor file (CFPRO) for extended idle and 

APU to estimate 2018 and 2030 emissions that incorporates each of these adjustments. 

4.3.2 Nonroad mobile (nonroad) 

The nonroad sector includes a wide range of mobile emission sources ranging from construction equipment 

to hand-held lawn tools. In the nonroad sector, the only emissions that are directly affected by the Tier 3 

regulation are the emissions from gasoline-powered equipment such as lawn-mowers, recreational boats and 

all-terrain vehicles. Their SO2 emissions are reduced with the decrease in gasoline sulfur levels. As with 

onroad, reference and control case emissions were generated using the fuel supply inputs reflecting the 

projected fuel volumes from AEO2013. 

Gasoline and land-based diesel nonroad emissions were estimated using EPA’s NONROAD2008b model40, 

as run by the EPA’s consolidated modeling system known as the National Mobile Inventory Model 

(NMIM).41 The fuels in the NMIM database, NCD2010201a, were developed from the reference and control 

fuels used for onroad vehicles, as described in Section 4.3.1.2. Onroad and nonroad gasoline formulations 

are assumed to be identical for all years. In 2018 and 2030, nonroad equipment is assumed to use E10 only. 

For all years, the reference case included the higher sulfur reference gasoline and the control case met the 

sulfur limits. 

This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not 

including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California. 

The version of NONROAD models all in-force nonroad controls (see nonroad section under Table 4-1). Not

included are voluntary local programs such as encouraging either no refueling or evening refueling on Ozone 

Action Days. 

California nonroad emissions

 

40 This version of NONROAD is very similar to the publicly released version, but it can model ethanol blends up to E20. The 

NMIM version is NMIM20090504d, which has the same results as the publicly-released NMIM version NMIM20090504a. The 

underlying National County Database (NCD) is NCD20101201a, but with 2007 meteorology inserted into the countymonthhour 

table. NCD20101201a includes state inputs for the 2008 NEI. 

41 U.S. EPA. 2005 EPA’s National Inventory Model (NMIM), A Consolidated Emissions Modeling System for MOBILE6 and 

NONROAD; EPA420-R-05-024; Office of Transportation and Air Quality, Ann Arbor, MI. 

http://www.epa.gov/otaq/models/nmim/420r05024.pdf 
78

Similar to the 2007 base nonroad mobile, NMIM was not used to generate future-year nonroad emissions for 

California, other than for NH3. EPA used NMIM for California future nonroad NH3 emissions because 

CARB did not provide these data for any nonroad vehicle types. For the rest of the pollutants, EPA 

converted the CARB-supplied 2017 (surrogate for 2018) and 2030 nonroad annual inventories to monthly 

emissions values by using the NMIM monthly inventories to compute monthly ratios by pollutant and SCC. 

Some adjustments to the CARB inventory were needed to convert the provided TOG to VOC and to augment 

the HAPs. See Section 3.2.1.3 for details on speciation of California nonroad data. The CARB nonroad 

emissions include nonroad rules reflected in the December 2010 Rulemaking Inventory 

(http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf) and those in the March 2011 Rule 

Inventory, the Off-Road Construction Rule Inventory for “In-Use Diesel”. 

4.3.3 Locomotives and Class 1 & 2 commercial marine vessels (c1c2rail) 

Recall from Section 2.5.4 that there are several non-NEI components to the c1c2rail sector in the 2007 base 

case. There are three distinct approaches used to craft year 2018 and 2030 inventories from the 2007 base 

case. The first component of the 2018 and 2030 c1c2rail inventories is the non-California data projected 

from the 2007 base case. The second component is the CARB-supplied year 2017 (used for 2018 scenarios) 

and year 2030 data for California. The third component is a set of OTAQ-provided county-specific 

emissions adjustments that account for different fuel transport characteristics resulting from the EISA 

(RFS2) mandate. Specifically, these adjustments reduce finished petroleum-based fuel transport by rail and 

barge (CMV) and add ethanol-based finished fuel transport by rail and barge. 

Step 1: Project non-California CMV and rail emissions

Packet: “PROJECTION_2008_2018_c1c2rail”; “PROJECTION_2008_2030_c1c2rail” 

In this step, a projection packet creates an intermediate set of year 2018 and year 2030 emissions, 

respectively, for all states except California. This packet does not reflect emission impacts from ethanol 

volume impacts from the EISA (RFS2) mandate; the EISA impacts are applied for all states in Step 3. This 

packet consists of national projection factors by SCC and pollutant between 2007 and 2018 and 2030 that 

reflect the May 2004 “Tier 4 emissions standards and fuel requirements” 

(http://www.epa.gov/otaq/documents/nonroad-diesel/420r04007.pdf) as well as the March 2008 “Final 

locomotive-marine rule” controls (http://www.epa.gov/otaq/regs/nonroad/420f08004.pdf). 

Projection factors are based on year 2008 rather than year 2007 for a couple of reasons. First, many states 

with large c1c2rail emissions utilize the 2008 NEI emissions; Texas is one example. Second, the year 2007 

emissions are mostly lower than the 2008 RIA summaries, and these emissions generally decrease in the 

future. By choosing year 2008, the projections are unlikely to overly-reduce emissions by years 2018 and 

2030. In addition, the 2007 platform emissions are often much different than the RIA emissions for any 

year. EPA OTAQ experts determined that the 2007 platform estimates were more up-to date and likely more 

reliable than the RIA estimates in 2007/2008 and 2018 and 2030. However, the controls and hence the 

relative reductions in the RIA are expected to be fairly close to what would be expected from the 2007 

platform. Therefore, EPA simply apply the ratio of the RIA 2018 or 2030 to 2008 emissions to project the 

2007 platform emissions. These projection ratios are provided in 
79

Table 4-33. 
80

Table 4-33. Non-California year 2018 and 2030 Projection Factors for locomotives and Class 1 and Class 2 

Commercial Marine Vessel Emissions 

SCC Description Pollutant 2018 2030

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions CO 0.9284 0.9506

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NOX 0.7042 0.3931

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM10 0.6429 0.3696

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM2.5 0.6429 0.3696

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions SO2 0.1213 0.0647

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions VOC 0.7529 0.4065

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations CO 1.1720 1.4180

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NOX 0.7523 0.4426

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM10 0.6168 0.2939

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM2.5 0.6168 0.2939

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations SO2 0.0334 0.0405

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations VOC 0.5453 0.2914

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations CO 1.1709 1.4174

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NOX 1.1092 1.1014

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM10 1.0700 1.0250

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM2.5 1.0722 1.0206

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO2 0.0349 0.0349

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations VOC 1.1740 1.4189

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) CO 1.0832 1.1915

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NOX 0.5292 0.2723

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM10 0.5457 0.1926

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM2.5 0.5445 0.1908

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO2 0.0286 0.0343

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) VOC 0.4735 0.1527

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines CO 1.0832 1.1918

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NOX 0.5292 0.2723

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM10 0.5444 0.1918

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM2.5 0.5446 0.1906

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines SO2 0.0335 0.0335

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines VOC 0.4730 0.1535

2285002010 Railroad Equipment;Diesel;Yard Locomotives CO 1.1720 1.4180

2285002010 Railroad Equipment;Diesel;Yard Locomotives NOX 0.9948 0.7533

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM10 0.9562 0.7271

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM2.5 0.9567 0.7269

2285002010 Railroad Equipment;Diesel;Yard Locomotives SO2 0.0337 0.0404

2285002010 Railroad Equipment;Diesel;Yard Locomotives VOC 0.9511 0.6851

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, 2009d). 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/marine.htm#2008final.

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

acetaldehyde, acrolein, benzene, and formaldehyde. C1/C2 CMV diesel emissions (SCC = 2280002100 and 

2280002200) were projected based on the Final Locomotive Marine rule national-level factors provided in 

Table 4-33. Similar to locomotives, VOC HAPs were projected based on the VOC factor. 
81

Step 2: Intermediate California year 2018 and year 2030 inventories

Obtained from CARB, the locomotive, and class 1 and 2 commercial marine emissions used for California 

reflect year 2017 and year 2030 and include nonroad rules reflected in the December 2010 Rulemaking 

Inventory (http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf), those in the March 2011 Rule 

Inventory, the Off-Road Construction Rule Inventory for “In-Use Diesel”, cargo handling equipment rules in 

place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm), and the 2007 and 2010 regulations to 

reduce emissions diesel engines on commercial harbor craft operated within California waters and 24 

nautical miles of the California baseline. 

The C1/C2 CMV emissions for both 2017 and 2030 were obtained from the CARB nonroad mobile dataset 

“ARMJ_RF#2002_ANNUAL_MOBILE.txt”. These emissions were developed using Version 1 of the 

CEPAM which supports various California off-road regulations. The locomotive emissions were obtained 

from the CARB trains dataset “ARMJ_RF#2002_ANNUAL_TRAINS.txt”. Documentation of the CARB 

offroad methodology, including c1c2rail sector data, is provided here: 

http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles. EPA converted the CARB inventory 

TOG to VOC by dividing the inventory TOG by the available VOC-to-TOG speciation factor. Note there 

was no attempt to modify year 2017 CARB emissions to year 2018 via linear interpolation or other schemes. 

EPA simply assigned year 2018 emissions as the 2017 CARB submittal with these medications as well as 

those discussed next in step 3. 

Step 3: Adjusting intermediate year 2018 and 2030 c1c2rail emissions to reflect the EISA mandate and 

LD GHG emission standards

Rail category 1 and commercial marine activity are impacted by both the EISA mandate and the 2017-2025 

light-duty vehicle greenhouse gas emission standards. The inventories were adjusted to account for both. 

Inventories were adjusted to account for (a) differences in C1/C2 CMV and rail emission rates in 2018 and 

2030 versus 2007, (b) the difference in ethanol volume impacts for AEO 2013 future year projections versus 

the 2007 platform volume of 8.7 billion gallons and (c) impacts on gasoline production and distribution from 

the 2017-2025 light duty vehicle GHG emissions standards (EPA, 2013b). Adjustments to C1/C2 CMV and 

rail emission rates were calculated using ton/ton-mile emission factors that were obtained from the Tier 3 

proposed rule inventory and AEO 2013 projected domestic shipping estimates (EPA, 2013c; Energy 

Information Administration, 2013). The ethanol volume impacts were allocated to individual counties using 

factors developed from the ORNL analysis (ORNL, 2013). Impacts on C1/C2 CMV and rail emissions from 

the 2017-2025 light duty vehicle GHG emission standards were calculated using the RFS2 impacts 

spreadsheet. Impacts from the 2017-2025 light duty vehicle GHG emission standards were assumed to be 

negligible in 2018 but were accounted for by the scalars in Table 4-34 and Table 4-35 for 2030. 

Table 4-34. Scalars Applied to Rail Combustion Emissions in 2030 to account for 2017-2025 LDGHG 

emission standards 

Pollutant Scalar

CO 0.998

NOX 0.998

PM2.5 0.997

PM10 0.996

SO2 0.997

VOC 0.997
82

Table 4-35. Scalars Applied to C1/C2 Combustion Emissions in 2030 to account for 2017-2025 LDGHG 

emission standards 

Pollutant Scalar

CO 0.996

NOX 0.993

PM2.5 0.994

PM10 0.993

SO2 0.991

VOC 0.978

These emissions from updated ethanol volumes via RFS/EISA and LDGHG are not included in the 

previously-discussed non-California loco-marine rule-based projections (Step 1) and CARB 2017 and 2030 

inventories (Step 2). Nationally, these adjusted emissions are modest. 

On average, for year 2018 rail emissions, the impact of the adjustment for transporting more ethanol is an 

increase in emissions by 1.24%, and the adjustment for transporting less gasoline is -0.04%, leaving a small 

increase in rail emissions in year 2018. For year 2030 rail emissions, the impact of the adjustment for 

transporting more ethanol is an increase in emissions by 0.92%, and the adjustment for transporting less 

gasoline is -0.06%, and the adjustment for the LD GHG rule is a reduction of 0.24%, leaving a small increase 

in emissions in year 2030. These small increases in rail emissions are evident in Table 4-36. For CMV 

emissions in year 2018, the impact of transporting more ethanol by 0.21% is offset by the adjustment for 

transporting less gasoline by -1.32%. For 2030, as shown in the “2030 Error” column in Table 4-36, we 

erroneously misapplied these RFS2 as well as LDGHG decreasing emissions adjustments: increased ethanol 

by 0.19%, decreased gasoline by 2.05% and LDGHG rule reductions of -0.78%. In short, in year 2030, 

national total NOX emissions from C1/C2 CMV are 2,678 tons larger than intended, a 0.6% error for C1/C2 

CMV nationally. 

Table 4-36. Cumulative RFS2 and LDGHG adjustments to c1c2rail sector emissions 

Pollutant Source

2018 

Adjustment

Final 

2018

2030 

Applied 

adjustment

Final 

2030

Intended 

2030

2030 

Error

CO cmv -855 88,226 0 92,593 90,360 2,233

CO rail 1,715 152,424 1,296 185,916 185,916 0

NH3 cmv -2 218 0 225 221 4

NH3 rail 5 358 5 357 357 0

NOX cmv -3,635 343,725 0 197,537 192,590 4,947

NOX rail 8,346 683,017 2,678 430,577 430,577 0

PM10 cmv -139 11,584 0 7,060 6,869 192

PM10 rail 198 18,530 31 9,672 9,672 0

PM2.5 cmv -155 11,102 0 6,773 6,504 269

PM2.5 rail -10 16,857 -36 8,826 8,826 0

SO2 cmv -296 5,380 0 4,249 3,870 379

SO2 rail 80 482 -7 478 478 0

VOC cmv -136 11,452 0 7,484 7,293 191

VOC rail 357 28,272 91 17,143 17,143 0
83

4.3.4 Class 3 commercial marine vessels (c3marine) 

As discussed in Section 2.5.5, the c3marine sector emissions data were developed for year 2002 and 

projected to year 2007 for the 2007 base case. The ECA-IMO project provides pollutant and geographicspecific projection factors to year 2007, and also projection factors to years 2018 and 2030 that reflect 

assumed growth and final ECA-IMO controls. The ECA-IMO rule, published in December 2009, applies to 

Category 3 (C3) diesel engines (engines with per cylinder displacement at or above 30 liters) installed on 

U.S. vessels. The ECA-IMO rule includes an implementation of Tier 2 and Tier 3 NOX limits for C3 engines 

beginning in 2011 and 2016, respectively. The ECA-IMO rule also imposes fuel sulfur limits of 1,000 ppm 

(0.1%) by 2015 in the ECA region -generally within 200 nautical miles of the U.S. and Canadian coastlines, 

as well as 5,000 ppm (0.5%) for “global” areas –those areas outside the ECA region. For comparison, with 

the exception of some local areas, year 2007 sulfur content limits are as high as 15,000 ppm (1.5%) in U.S. 

waters and 45,000 ppm (4.5%) in global areas. More information on the ECA-IMO rule can be found in the 

Category 3 marine diesel engines Regulatory Impact Assessment: 

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

Projection factors for creating the year 2018 and year 2030 c3marine inventories from the 2007 base case are 

provided in Table 4-37. Background on the region and EEZ FIPS is provided in the discussion on the 

c3marine inventory for 2007 –Section 2.5.5. The impact of the Tier 2 and Tier 3 NOX engine standards is 

less noticeable because of the inevitable delay in fleet turnover for these new engines; however, the 

immediate and drastic cuts in fuel sulfur content are obvious. VOC and CO are mostly unaffected by the 

engine and fuel standards, thus providing an idea on how much these emissions would have grown without 

ECA-IMO controls. VOC HAPs are assigned the same growth rates as VOC. 

Table 4-37. Growth factors to project the 2007 ECA-IMO inventory to 2018 and 2030 

Region EEZ FIPS Year NOX PM10 PM2.5 VOC CO SO2

East Coast (EC) 85004 2018 1.166 0.221 0.219 1.623 1.623 0.058

2030 0.900 0.373 0.373 2.752 2.752 0.098

Gulf Coast (GC) 85003 2018 0.984 0.187 0.186 1.370 1.370 0.049

2030 0.633 0.264 0.265 1.930 1.930 0.069

North Pacific (NP) 85001 2018 1.061 0.197 0.196 1.429 1.429 0.055

2030 0.773 0.293 0.297 2.128 2.126 0.082

South Pacific (SP) 85002 2018 1.255 0.241 0.239 1.731 1.730 0.068

2030 1.009 0.456 0.457 3.290 3.254 0.128

Great Lakes (GL) n/a 2018 1.023 0.159 0.158 1.204 1.204 0.043

2030 1.013 0.195 0.195 1.474 1.474 0.052

Outside ECA 98001 2018 1.401 1.606 1.606 1.606 1.606 1.606

2030 2.083 0.612 0.606 2.778 2.778 0.506

4.4 Canada, Mexico, and Offshore sources (othar, othon, and othpt) 

Emissions for Canada and offshore sources were not projected to future years, and are therefore the same as 

those used in the 2007 base case. Canada did not provide future-year emissions that were consistent with the 

base year emissions. The Mexico emissions are based on year 1999 but projected to years 2018 and 2030. A 

background on the development of year-2018 and year-2030 Mexico emissions from the 1999 inventory is 

available at: http://www.wrapair.org/forums/ef/inventories/MNEI/index.html. 
84

5 Emission Summaries 

The following tables summarize the emissions for the 2007 base case, 2018 reference and control cases, and 

the 2030 reference and control cases. These summaries are provided for the contiguous U.S. and for the 

portions of Canada and Mexico inside the smaller 12km domain (12US2) discussed in Section 3.1. The 

afdust sector emissions represent the summaries after application of both the land use (transport fraction) and 

meteorological adjustments (see Section 2.2.1); therefore, this sector is called “afdust-adj” in these 

summaries. The onroad and onroad refueling (onroad_rfl) sector totals are post-SMOKE-MOVES totals, 

representing air quality model-ready emission totals. Biogenic emissions totals are only given within the 

United States. The “c3marine-US” sector represents c3marine sector emissions with U.S. FIPS only; these 

extend to roughly 3-5 miles offshore and include all U.S. waters in the Great Lakes along with all U.S. ports. 

The Offshore c3marine represents all non-U.S. c3marine emissions outside of U.S. state waters. The 

c3marine sector is discussed in Section 2.5.5. The “Off-shore othpt” sector is the non-Canada, non-Mexico 

component of the othpt sector, i.e., the offshore oil platform emissions from the 2008 NEI. 

National emission totals by air quality model-ready sector are provided for all CAP emissions for the 2007 

base case and 2007 evaluation case in Table 5-1. The total of all U.S. emissions in all sectors is provided. 

The lower portion of the table provides the non-U.S. emissions including subtotals for Canada, Mexico, and 

all non-U.S. emissions. Tables 5-2 through 5-5 provide similar CAP emission totals by sector for the 2018 

reference case, 2018 control case, 2030 reference case, and 2030 control case, respectively. 

Table 5-6 provides total emissions for CO by state for all five cases: 2007 base case, 2018 reference and 

control cases, and 2030 reference and control cases. Tables 5-7 through 5-12 provide the same summaries 

for NH3, NOX, PM2.5, PM10, SO2 and VOC, respectively. Note that all of these tables use average fire 

emissions and do not include biogenic emissions. Emission totals by state for each modeling platform 

sector, for CAPs and air quality model species, can be found in these spreadsheets available in the docket: 

2007rg_state_totals.xlsx, 2018rg_ref2_state_totals.xlsx, 2018rg_ctl_state_totals.xlsx, 

2030rg_ref_state_totals.xlsx, and 2030rg_ctl_state_totals.xlsx. 
85

Table 5-1. National and non-U.S. CAP emissions by sector for 2007 base case 

US Totals

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,124,268 863,738

ag 3,595,613

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 218,827 557 1,338,134 43,832 41,015 48,805 61,547

nonpt 4,329,593 155,297 1,199,385 768,986 677,339 406,290 6,529,064

nonroad 17,834,128 1,915 1,877,955 187,707 178,342 100,652 2,517,282

onroad 38,519,970 140,897 7,612,080 365,017 282,304 40,008 4,273,876

avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

ptipm 703,760 25,427 3,357,349 437,210 329,585 9,136,112 38,071

ptnonipm 2,963,103 67,997 2,136,694 582,397 406,476 1,586,719 1,101,615

c3marine 12,724 138,033 12,476 11,452 104,822 4,902

Total 89,451,787 4,288,703 18,953,168 10,382,352 4,366,917 11,559,214 59,137,081

Non-US Totals

Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 410,176 109,861 171,735 71,082 47,115 53,424 450,935

Mexico othon 2,685,132 14,114 326,165 11,805 9,115 5,462 192,045

Mexico othpt 100,075 0 343,480 120,802 89,358 731,675 77,255

Mexico Subtotal 3,195,383 123,975 841,380 203,690 145,588 790,560 720,235

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 2,607 31,870 2,633 2,402 19,504 1,109

Offshore 

c3marine 55,599 674,615 55,891 51,386 417,293 23,635

Total 7,924,652 531,791 2,575,119 1,146,033 494,033 2,124,917 1,988,337
86

Table 5-2. National and non-U.S. CAP emissions by sector for 2018 reference case 

US Totals: 

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,139,289 866,710

ag 3,717,417

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 236,042 567 1,008,203 29,525 27,390 5,693 39,272

nonpt 4,613,119 157,772 1,185,288 808,719 715,966 317,310 6,423,764

nonroad 12,702,891 2,276 1,067,481 106,715 100,588 2,670 1,408,935

onroad* 16,144,911 80,053 2,495,021 191,678 111,049 26,089 1,800,164

avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

ptipm 857,784 40,255 1,941,441 295,290 233,699 2,131,278 46,057

ptnonipm 2,639,792 68,053 2,010,662 536,133 368,215 957,178 1,013,798

c3marine 18,960 151,489 2,530 2,304 5,734 7,290

Total 62,083,182 4,367,393 11,153,123 9,970,338 4,002,588 3,581,760 55,350,004

Non-US Totals

Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 527,942 109,840 226,387 70,937 47,201 19,287 577,084

Mexico othon 2,572,443 15,225 304,436 13,185 10,317 3,177 178,219

Mexico othpt 148,760 0 544,711 170,913 127,736 1,066,523 94,351

Mexico Subtotal 3,249,146 125,065 1,075,534 255,035 185,254 1,088,987 849,654

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 3,750 34,364 518 469 1,054 1,595

Offshore 

c3marine 87,463 807,580 32,180 29,537 190,969 37,173

Total 8,011,422 532,881 2,944,732 1,171,552 509,917 2,178,569 2,131,779
87

Table 5-3. National and non-U.S. CAP emissions by sector for 2018 control case 

US Totals: 

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,139,289 866,710

ag 3,717,417

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 236,042 567 1,008,203 29,525 27,390 5,693 39,272

nonpt 4,613,119 157,772 1,185,288 808,719 715,966 317,310 6,423,764

nonroad 12,702,891 2,276 1,067,481 106,715 100,588 1,912 1,408,935

onroad* 15,893,583 80,053 2,248,595 191,209 110,617 11,493 1,757,359

avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

ptipm 857,784 40,255 1,941,441 295,290 233,699 2,131,278 46,057

ptnonipm 2,639,792 68,053 2,010,662 536,133 368,215 957,178 1,013,798

c3marine 18,960 151,489 2,530 2,304 5,734 7,290

Total 61,831,854 4,367,393 10,906,697 9,969,869 4,002,157 3,566,405 55,307,199

Non-US Totals

Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 527,942 109,840 226,387 70,937 47,201 19,287 577,084

Mexico othon 2,572,443 15,225 304,436 13,185 10,317 3,177 178,219

Mexico othpt 148,760 0 544,711 170,913 127,736 1,066,523 94,351

Mexico Subtotal 3,249,146 125,065 1,075,534 255,035 185,254 1,088,987 849,654

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 3,750 34,364 518 469 1,054 1,595

Offshore 

c3marine 87,463 807,580 32,180 29,537 190,969 37,173

Total 8,011,422 532,881 2,944,732 1,171,552 509,917 2,178,569 2,131,779
88

Table 5-4. National and non-U.S. CAP emissions by sector for 2030 reference case 

US Totals: 

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,174,969 873,719

ag 3,784,658

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 273,741 574 617,480 16,392 15,270 4,633 24,372

nonpt 4,622,228 157,772 1,198,956 809,404 716,460 318,053 6,353,099

Nonroad 13,926,036 2,702 715,617 68,626 63,839 3,045 1,182,812

onroad* 14,434,283 84,796 1,367,429 175,639 82,137 22,310 1,260,883

Avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

Ptipm 1,064,259 49,461 1,997,303 300,645 238,259 2,188,169 52,061

Ptnonipm 2,693,137 67,596 2,022,539 532,186 364,598 935,044 1,001,777

c3marine 29,906 116,493 3,972 3,654 9,104 11,479

Total 61,913,273 4,448,559 9,329,353 9,942,292 3,934,603 3,616,164 54,497,206

Non-US Totals

Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 794,133 109,861 326,219 75,903 51,815 9,909 798,874

Mexico othon 2,673,052 17,507 293,017 17,129 13,873 3,499 180,518

Mexico othpt 222,044 0 812,593 249,006 185,682 1,552,126 119,095

Mexico Subtotal 3,689,230 127,368 1,431,829 342,038 251,370 1,565,534 1,098,487

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 5,650 26,704 780 718 1,584 2,405

Offshore 

c3marine 146,574 784,389 23,561 21,600 83,949 62,442

Total 8,512,517 535,184 3,270,176 1,250,198 568,346 2,548,627 2,406,691
89

Table 5-5. National and non-U.S. CAP emissions by sector for 2030 control case 

US Totals:

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,174,969 873,719

ag 3,784,658

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 273,741 574 617,480 16,392 15,270 4,633 24,372

nonpt 4,622,228 157,772 1,198,956 809,404 716,460 318,053 6,353,099

nonroad 13,926,036 2,702 715,617 68,626 63,839 2,176 1,182,812

onroad* 10,780,871 84,796 1,018,962 166,388 73,619 10,040 1,079,042

avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

ptipm 1,064,259 49,461 1,997,303 300,645 238,259 2,188,169 52,061

ptnonipm 2,693,137 67,596 2,022,539 532,186 364,598 935,044 1,001,777

c3marine 29,906 116,493 3,972 3,654 9,104 11,479

Total 58,259,861 4,448,559 8,980,886 9,933,042 3,926,085 3,603,025 54,315,365

Non-US Totals

Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 794,133 109,861 326,219 75,903 51,815 9,909 798,874

Mexico othon 2,673,052 17,507 293,017 17,129 13,873 3,499 180,518

Mexico othpt 222,044 0 812,593 249,006 185,682 1,552,126 119,095

Mexico Subtotal 3,689,230 127,368 1,431,829 342,038 251,370 1,565,534 1,098,487

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 5,650 26,704 780 718 1,584 2,405

Offshore 

c3marine 146,574 784,389 23,561 21,600 83,949 62,442

Total 8,512,517 535,184 3,270,176 1,250,198 568,346 2,548,627 2,406,691
90

Table 5-6. CO emissions (tons/yr) for each case and state 

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 1,924,241 1,360,917 1,351,229 1,355,227 1,255,631

Arizona 1,466,047 957,043 944,935 1,011,463 871,117

Arkansas 1,600,242 1,212,877 1,206,827 1,205,866 1,154,433

California 6,868,040 5,236,858 5,217,036 5,027,026 5,027,026

Colorado 1,190,074 809,875 792,847 872,740 763,605

Connecticut 574,605 357,385 354,137 333,723 332,897

Delaware 162,704 105,413 104,551 93,324 93,129

District of 

Columbia 60,932 35,900 35,291 36,923 30,370

Florida 5,022,400 3,138,544 3,105,671 3,230,725 2,815,295

Georgia 3,792,250 2,457,784 2,438,401 2,418,199 2,227,785

Idaho 1,953,101 1,772,944 1,767,029 1,772,829 1,738,622

Illinois 2,288,411 1,436,945 1,421,535 1,493,876 1,330,020

Indiana 1,860,666 1,204,836 1,191,445 1,214,686 1,095,670

Iowa 906,397 498,846 492,379 498,959 455,151

Kansas 1,085,160 754,279 748,374 757,743 706,892

Kentucky 1,313,600 920,767 913,119 919,051 844,092

Louisiana 1,901,576 1,443,889 1,436,282 1,461,086 1,378,073

Maine 417,956 251,190 248,837 230,967 230,019

Maryland 1,054,787 708,961 704,541 666,789 666,962

Massachusetts 876,162 604,516 598,218 578,757 577,788

Michigan 2,970,746 1,688,062 1,668,056 1,628,993 1,486,651

Minnesota 2,785,008 2,148,692 2,137,462 2,094,983 2,011,938

Mississippi 1,170,077 835,928 829,347 836,297 765,258

Missouri 2,144,179 1,422,916 1,411,751 1,422,427 1,311,400

Montana 1,330,031 1,214,531 1,210,079 1,213,708 1,193,598

Nebraska 531,194 310,062 306,155 316,475 283,905

Nevada 724,335 518,145 513,004 542,295 490,562

New Hampshire 304,620 209,280 206,548 215,511 194,314

New Jersey 1,352,241 902,273 893,855 867,908 865,761

New Mexico 677,284 453,892 448,885 445,239 413,092

New York 2,641,178 1,649,663 1,632,656 1,615,599 1,608,909

North Carolina 3,273,181 1,954,088 1,932,231 1,907,338 1,749,607

North Dakota 293,318 209,600 208,195 206,779 195,525

Ohio 3,087,510 1,726,198 1,703,457 1,707,663 1,536,234

Oklahoma 1,686,238 1,214,110 1,204,794 1,221,253 1,133,433

Oregon 2,201,777 1,749,839 1,740,219 1,670,492 1,665,955
91

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Pennsylvania 2,460,443 1,440,750 1,427,500 1,353,066 1,350,221

Rhode Island 151,235 106,761 105,767 101,731 101,681

South Carolina 1,424,534 898,530 890,759 906,112 822,767

South Dakota 321,482 236,051 234,446 234,663 220,361

Tennessee 1,788,332 1,035,150 1,022,408 1,012,970 898,967

Texas 5,466,088 3,430,870 3,388,430 3,632,059 3,119,557

Utah 824,426 589,517 580,111 590,348 538,323

Vermont 188,782 147,145 145,493 140,898 140,486

Virginia 1,837,272 1,146,294 1,134,028 1,174,277 1,039,507

Washington 2,215,115 1,341,308 1,322,137 1,175,059 1,166,307

West Virginia 655,801 432,831 429,529 427,734 401,919

Wisconsin 1,396,496 919,043 910,135 957,000 861,066

Wyoming 656,091 562,905 559,282 562,888 546,433

TOTAL 82,878,364 55,764,204 55,269,404 55,361,723 51,708,312
92

Table 5-7. NH3 emissions (tons/yr) for each case and state 

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 78,589 84,806 84,806 86,662 86,662

Arizona 41,818 41,331 41,331 42,090 42,090

Arkansas 135,328 142,800 142,800 145,796 145,796

California 393,315 390,595 390,595 395,474 395,474

Colorado 74,561 74,495 74,495 74,938 74,938

Connecticut 5,158 4,612 4,612 4,691 4,691

Delaware 14,044 15,331 15,331 15,764 15,764

District of 

Columbia 366 281 281 308 308

Florida 63,410 60,707 60,707 62,042 62,042

Georgia 116,050 123,001 123,001 125,862 125,862

Idaho 130,482 130,704 130,704 130,326 130,326

Illinois 119,703 122,450 122,450 126,561 126,561

Indiana 98,051 101,698 101,698 104,955 104,955

Iowa 301,952 316,176 316,176 327,804 327,804

Kansas 161,525 162,607 162,607 162,849 162,849

Kentucky 60,996 62,786 62,786 63,724 63,724

Louisiana 84,106 85,334 85,334 86,389 86,389

Maine 7,264 7,353 7,353 7,482 7,482

Maryland 31,585 32,929 32,929 34,163 34,163

Massachusetts 7,229 6,421 6,421 6,700 6,700

Michigan 69,890 69,713 69,713 71,021 71,021

Minnesota 210,960 214,414 214,414 220,096 220,096

Mississippi 68,874 73,643 73,643 75,479 75,479

Missouri 139,500 142,734 142,734 145,793 145,793

Montana 72,677 73,473 73,473 74,049 74,049

Nebraska 179,531 182,275 182,275 182,928 182,928

Nevada 10,483 10,706 10,706 10,761 10,761

New Hampshire 2,303 2,214 2,214 2,266 2,266

New Jersey 11,949 11,042 11,042 11,530 11,530

New Mexico 43,094 42,928 42,928 43,343 43,343

New York 51,428 48,036 48,036 49,238 49,238

North Carolina 187,827 198,254 198,254 206,640 206,640

North Dakota 86,696 88,085 88,085 89,930 89,930

Ohio 111,991 114,657 114,657 117,372 117,372

Oklahoma 112,918 116,127 116,127 118,004 118,004

Oregon 64,640 65,193 65,193 65,684 65,684

Pennsylvania 80,223 81,438 81,438 83,198 83,198

Rhode Island 1,135 1,097 1,097 1,123 1,123

South Carolina 39,330 40,054 40,054 41,038 41,038

South Dakota 134,157 136,513 136,513 139,008 139,008
93

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 42,899 43,506 43,506 43,838 43,838

Texas 320,410 323,482 323,482 326,720 326,720

Utah 42,676 43,006 43,006 42,859 42,859

Vermont 8,404 8,395 8,395 8,507 8,507

Virginia 51,371 51,793 51,793 52,776 52,776

Washington 53,330 53,641 53,641 54,200 54,200

West Virginia 16,526 17,102 17,102 17,436 17,436

Wisconsin 120,740 120,384 120,384 122,140 122,140

Wyoming 26,768 26,877 26,877 26,851 26,851

TOTAL 4,288,260 4,367,201 4,367,201 4,448,408 4,448,408
94

Table 5-8. NOX emissions (tons/yr) for each case and state 

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 419,234 229,845 224,086 194,697 186,751

Arizona 321,030 166,137 162,417 131,427 123,261

Arkansas 244,131 156,429 152,943 134,335 129,259

California 1,013,990 534,773 534,207 401,302 401,302

Colorado 316,568 193,930 188,903 169,439 160,990

Connecticut 100,231 47,836 45,256 35,480 33,745

Delaware 47,738 22,292 21,481 16,641 16,142

District of 

Columbia 14,360 6,129 5,797 4,603 3,930

Florida 924,488 415,734 396,122 307,302 278,307

Georgia 648,360 309,555 295,898 232,426 215,596

Idaho 97,617 68,588 66,444 58,280 55,122

Illinois 706,849 374,954 364,591 308,293 291,621

Indiana 578,412 303,868 296,821 253,053 242,065

Iowa 277,985 181,738 178,314 147,307 142,454

Kansas 313,735 187,508 184,433 153,618 149,064

Kentucky 423,640 210,206 205,730 179,350 172,780

Louisiana 547,743 386,244 382,129 313,834 307,329

Maine 73,939 45,873 44,558 38,031 37,127

Maryland 244,074 110,763 106,299 86,801 84,079

Massachusetts 184,511 98,027 94,083 78,391 75,973

Michigan 596,927 322,122 312,688 274,422 260,185

Minnesota 436,436 251,574 245,143 203,733 195,604

Mississippi 264,622 152,848 148,935 128,683 123,054

Missouri 484,132 250,506 242,841 197,412 186,391

Montana 150,854 90,351 88,666 75,197 72,858

Nebraska 232,064 154,028 152,036 114,138 111,001

Nevada 113,273 68,916 66,421 56,506 52,503

New Hampshire 53,845 28,002 26,788 24,553 21,983

New Jersey 279,205 156,200 150,033 126,532 122,477

New Mexico 242,966 214,189 211,475 193,579 189,554

New York 512,170 288,870 278,693 233,250 225,541

North Carolina 470,574 240,195 229,296 189,772 174,568

North Dakota 158,552 109,343 108,580 90,989 89,772

Ohio 780,928 380,073 367,931 308,996 292,899

Oklahoma 422,925 276,827 271,902 246,670 239,147

Oregon 191,065 117,844 114,051 71,781 69,227

Pennsylvania 649,142 382,391 373,752 323,768 318,208

Rhode Island 24,475 13,179 12,437 9,836 9,418

South Carolina 260,234 147,083 142,073 126,278 119,008

South Dakota 72,894 47,905 47,011 36,593 35,209
95

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 453,075 196,382 188,871 151,166 140,425

Texas 1,643,416 1,049,545 1,027,205 915,168 877,443

Utah 217,960 159,486 156,268 142,187 137,588

Vermont 23,019 13,139 12,430 10,261 9,800

Virginia 409,546 233,194 225,015 186,346 171,417

Washington 338,856 195,727 189,118 120,291 115,573

West Virginia 283,713 133,980 132,031 118,343 115,759

Wisconsin 313,623 181,705 176,143 161,856 152,732

Wyoming 245,053 202,613 201,270 185,500 183,713

TOTAL 17,824,174 10,108,647 9,849,620 8,268,417 7,919,951
96

Table 5-9. PM2.5 emissions (tons/yr) for each case and state 

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 108,906 106,546 106,536 105,571 105,391

Arizona 74,270 69,748 69,738 68,653 68,436

Arkansas 108,112 104,947 104,941 104,175 104,073

California 399,796 378,847 378,829 373,435 373,435

Colorado 59,418 58,602 58,625 57,615 57,347

Connecticut 11,334 10,251 10,243 9,806 9,803

Delaware 6,098 4,945 4,943 4,762 4,761

District of 

Columbia 1,606 1,283 1,282 1,173 1,156

Florida 167,089 140,539 140,509 137,516 136,937

Georgia 167,573 160,387 160,368 158,003 157,632

Idaho 149,633 148,313 148,322 147,911 147,826

Illinois 114,168 103,352 103,280 101,102 100,555

Indiana 111,294 91,698 91,658 91,207 90,845

Iowa 65,220 57,874 57,863 56,419 56,278

Kansas 114,378 110,828 110,820 109,983 109,854

Kentucky 76,841 76,393 76,371 75,463 75,273

Louisiana 148,650 136,251 136,244 132,771 132,630

Maine 13,644 12,574 12,570 12,117 12,116

Maryland 33,292 24,795 24,770 24,009 23,994

Massachusetts 24,024 21,679 21,663 20,663 20,656

Michigan 92,529 89,602 89,545 87,489 86,974

Minnesota 182,166 176,876 176,837 174,485 174,123

Mississippi 67,639 63,514 63,507 62,644 62,521

Missouri 113,722 104,744 104,718 102,905 102,623

Montana 114,123 115,121 115,126 113,412 113,360

Nebraska 54,712 52,061 52,055 50,565 50,466

Nevada 47,003 47,297 47,291 46,785 46,684

New Hampshire 11,396 11,424 11,418 11,329 11,251

New Jersey 32,028 24,540 24,520 23,033 23,023

New Mexico 73,249 73,117 73,111 72,514 72,402

New York 67,804 59,517 59,483 56,787 56,772

North Carolina 131,243 109,172 109,152 107,654 107,324

North Dakota 45,406 47,772 47,767 46,969 46,914

Ohio 130,887 93,624 93,558 91,451 90,922

Oklahoma 119,448 113,347 113,336 112,259 112,075

Oregon 136,821 134,681 134,692 133,082 133,101

Pennsylvania 121,002 73,505 73,474 71,428 71,413

Rhode Island 3,551 3,283 3,280 3,154 3,153

South Carolina 67,530 54,106 54,098 53,836 53,683

South Dakota 34,265 32,640 32,636 32,079 32,023
97

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 70,324 62,282 62,261 60,406 60,155

Texas 304,810 287,235 287,156 280,355 279,426

Utah 42,657 43,349 43,361 42,052 41,932

Vermont 8,798 10,084 10,081 9,934 9,933

Virginia 56,025 51,254 51,225 50,234 49,896

Washington 81,218 74,509 74,529 72,302 72,341

West Virginia 55,146 37,131 37,124 36,655 36,583

Wisconsin 52,086 53,545 53,507 53,172 52,783

Wyoming 88,542 83,455 83,459 82,734 82,690

TOTAL 4,361,473 4,002,642 4,001,882 3,934,060 3,925,542
98

 Table 5-10. PM10 emissions (tons/yr) for each case and state 

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 183,182 178,922 178,911 178,468 178,272

Arizona 221,304 216,564 216,553 216,217 215,981

Arkansas 213,094 209,261 209,254 209,464 209,354

California 767,701 746,021 746,001 741,688 741,688

Colorado 167,205 166,169 166,194 166,316 166,024

Connecticut 14,555 13,095 13,085 12,687 12,683

Delaware 8,759 8,981 8,979 8,814 8,813

District of 

Columbia 2,263 1,917 1,916 1,814 1,796

Florida 293,334 267,282 267,250 266,055 265,425

Georgia 293,557 280,599 280,578 279,207 278,803

Idaho 255,419 253,805 253,814 253,162 253,069

Illinois 360,962 349,384 349,306 349,836 349,241

Indiana 249,264 232,131 232,088 233,654 233,260

Iowa 231,578 223,006 222,994 223,494 223,341

Kansas 431,017 427,337 427,329 429,770 429,630

Kentucky 127,958 127,539 127,514 127,013 126,807

Louisiana 213,933 198,283 198,275 195,160 195,006

Maine 18,899 17,372 17,368 16,930 16,928

Maryland 47,375 37,502 37,474 36,883 36,867

Massachusetts 50,126 47,953 47,935 47,152 47,144

Michigan 183,520 179,905 179,843 178,640 178,081

Minnesota 379,202 370,541 370,499 369,565 369,172

Mississippi 135,088 130,000 129,992 129,689 129,555

Missouri 313,634 301,513 301,485 300,857 300,552

Montana 240,329 242,981 242,987 241,375 241,319

Nebraska 244,975 242,296 242,290 242,488 242,380

Nevada 207,718 208,009 208,002 207,635 207,525

New Hampshire 13,604 13,375 13,369 13,332 13,247

New Jersey 42,034 34,824 34,801 33,564 33,554

New Mexico 499,439 499,598 499,591 499,279 499,157

New York 110,972 100,461 100,425 98,415 98,399

North Carolina 188,502 162,820 162,799 161,721 161,362

North Dakota 184,477 187,463 187,458 188,449 188,389

Ohio 228,328 190,228 190,156 189,335 188,760

Oklahoma 395,287 387,332 387,320 387,718 387,518

Oregon 202,601 200,067 200,080 198,682 198,702

Pennsylvania 155,601 104,914 104,881 103,180 103,163

Rhode Island 4,623 4,348 4,345 4,248 4,246

South Carolina 126,413 110,963 110,955 111,480 111,314

South Dakota 120,429 119,324 119,319 119,870 119,809
99

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 112,667 104,066 104,043 102,637 102,364

Texas 1,214,912 1,197,608 1,197,521 1,195,116 1,194,108

Utah 134,321 134,782 134,795 133,513 133,383

Vermont 11,723 13,032 13,028 12,912 12,912

Virginia 89,603 83,362 83,330 82,778 82,411

Washington 155,262 148,701 148,723 147,304 147,346

West Virginia 71,742 52,483 52,475 52,054 51,975

Wisconsin 125,130 123,545 123,504 124,019 123,597

Wyoming 328,164 316,276 316,281 315,609 315,562

TOTAL 10,371,781 9,967,938 9,967,113 9,939,248 9,929,998
100

Table 5-11. SO2 emissions (tons/yr) for each case and state 

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 532,060 120,900 120,556 127,284 127,008

Arizona 95,311 63,737 63,547 65,920 65,743

Arkansas 96,013 53,257 53,084 56,039 55,890

California 63,232 47,581 47,579 49,907 49,907

Colorado 74,021 24,643 24,328 25,224 24,933

Connecticut 20,638 13,765 13,603 14,143 14,013

Delaware 45,503 8,554 8,506 8,286 8,248

District of 

Columbia 1,685 906 882 898 879

Florida 405,072 102,249 100,910 108,181 107,040

Georgia 698,480 135,516 134,826 132,415 131,818

Idaho 28,009 23,180 23,072 23,168 23,073

Illinois 409,821 187,550 186,932 189,244 188,731

Indiana 815,212 227,783 227,371 237,607 237,259

Iowa 189,267 78,977 78,817 81,301 81,157

Kansas 138,105 48,411 48,244 49,836 49,688

Kentucky 417,969 174,597 174,335 179,587 179,376

Louisiana 252,229 146,547 146,284 138,417 138,188

Maine 24,257 6,038 5,953 6,056 5,987

Maryland 312,223 49,690 49,385 50,476 50,225

Massachusetts 84,476 13,227 12,908 13,390 13,126

Michigan 427,357 200,680 200,117 211,957 211,496

Minnesota 129,765 83,686 83,355 88,071 87,797

Mississippi 95,879 37,150 36,918 38,561 38,366

Missouri 418,246 189,346 188,954 191,943 191,609

Montana 38,639 20,432 20,369 18,784 18,729

Nebraska 77,017 57,286 57,181 58,990 58,894

Nevada 18,667 14,363 14,206 14,387 14,245

New Hampshire 51,031 9,421 9,342 9,791 9,723

New Jersey 61,773 15,013 14,554 14,664 14,279

New Mexico 34,258 25,728 25,580 26,093 25,967

New York 240,983 58,409 57,631 58,321 57,641

North Carolina 440,967 116,993 116,434 116,411 115,949

North Dakota 149,603 32,717 32,676 32,833 32,797

Ohio 1,111,660 206,919 206,273 211,543 211,001

Oklahoma 148,867 75,536 75,257 72,933 72,688

Oregon 31,045 26,354 26,152 13,415 13,244

Pennsylvania 1,102,035 189,787 189,263 200,627 200,180

Rhode Island 4,576 3,727 3,674 3,733 3,689

South Carolina 217,175 66,623 66,343 67,591 67,357

South Dakota 13,097 14,981 14,932 15,638 15,595
101

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 297,935 63,903 63,502 64,567 64,236

Texas 651,329 236,570 235,013 207,330 205,952

Utah 35,663 28,388 28,232 26,626 26,490

Vermont 4,173 2,346 2,292 2,426 2,379

Virginia 258,286 72,522 72,082 74,142 73,757

Washington 36,638 16,137 15,781 16,421 16,106

West Virginia 422,049 85,518 85,418 91,986 91,910

Wisconsin 207,922 53,394 53,044 60,050 59,760

Wyoming 115,330 51,648 51,596 49,837 49,794

TOTAL 11,545,544 3,582,687 3,567,299 3,617,050 3,603,918
102

Table 5-12. VOC emissions (tons/yr) for each case and state 

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 394,478 312,021 311,865 295,361 290,621

Arizona 299,405 226,453 229,150 219,068 212,501

Arkansas 367,862 314,065 313,618 301,947 299,537

California 1,493,672 1,203,729 1,214,353 1,155,135 1,151,387

Colorado 359,741 265,489 265,909 259,229 254,687

Connecticut 91,124 56,030 55,574 49,410 49,018

Delaware 30,272 19,775 19,737 17,633 17,506

District of 

Columbia 11,564 7,841 7,783 7,516 7,224

Florida 970,251 685,446 685,391 630,355 610,660

Georgia 731,394 580,549 578,596 541,521 532,475

Idaho 491,700 465,738 465,639 458,300 456,974

Illinois 490,297 358,934 357,262 340,282 332,759

Indiana 348,813 264,712 263,731 247,129 241,765

Iowa 189,536 142,561 141,889 132,241 130,142

Kansas 238,892 199,124 198,562 189,268 186,925

Kentucky 284,125 224,140 223,611 210,998 207,531

Louisiana 502,713 427,256 427,040 406,847 402,894

Maine 80,956 56,005 55,813 48,750 48,542

Maryland 172,580 111,856 111,432 101,205 100,363

Massachusetts 180,220 128,399 127,673 118,733 117,986

Michigan 565,194 398,966 397,029 357,995 351,667

Minnesota 622,926 515,939 514,442 481,519 477,844

Mississippi 267,564 213,246 213,264 199,541 196,050

Missouri 423,126 323,326 322,071 302,292 297,093

Montana 293,547 276,893 276,801 272,858 272,065

Nebraska 105,934 82,394 81,972 75,541 74,071

Nevada 152,771 132,532 134,495 132,199 129,597

New Hampshire 57,530 39,034 38,770 35,049 33,985

New Jersey 261,426 177,994 177,196 164,288 163,145

New Mexico 311,387 280,189 280,751 273,093 271,173

New York 535,040 337,635 336,195 307,325 305,271

North Carolina 620,323 474,236 473,183 440,230 432,671

North Dakota 64,768 52,510 52,365 49,120 48,609

Ohio 479,576 317,531 315,292 283,813 276,248

Oklahoma 512,690 450,517 449,649 435,508 431,452

Oregon 435,314 387,230 386,735 367,051 366,503

Pennsylvania 455,475 336,441 337,248 312,623 311,092

Rhode Island 27,159 18,437 18,318 16,762 16,627

South Carolina 292,949 220,191 219,953 205,620 201,702

South Dakota 81,613 70,455 70,331 67,162 66,511
103

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 354,294 256,459 255,849 235,103 229,819

Texas 2,414,946 2,158,320 2,155,608 2,108,224 2,083,716

Utah 297,616 287,999 288,306 279,249 276,992

Vermont 30,384 22,924 22,977 20,511 20,367

Virginia 360,687 265,016 264,007 247,344 240,525

Washington 353,447 277,515 276,658 239,200 238,165

West Virginia 117,626 88,643 88,413 82,210 81,028

Wisconsin 337,259 241,006 240,083 222,276 217,957

Wyoming 251,477 260,241 260,208 256,390 255,740

TOTAL 18,813,646 15,013,942 15,002,796 14,201,023 14,019,183
104

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United States

Environmental Protection

Agency

Office of Air Quality Planning and 

Standards

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA-454/R14-003

February, 2014