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Technical Support Document (TSD)

Preparation of Emissions Inventories for the Version 6.0, 

2011 Emissions Modeling Platform

February 26, 2014

U.S. Environmental Protection Agency

Office of Air and Radiation

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Contacts: 

Rich Mason, Alison Eyth, Alexis Zubrow, Darin DelVecchio
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TABLE OF CONTENTS

LIST OF TABLES ..................................................................................................................................................................IV

LIST OF FIGURES ................................................................................................................................................................ VI

LIST OF APPENDICES ........................................................................................................................................................ VI

ACRONYMS..........................................................................................................................................................................VII

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

2 2011 EMISSION INVENTORIES AND APPROACHES ............................................................................................3

2.1 2011 NEI POINT SOURCES (PTEGU, PTEGU_PK, PT_OILGAS AND PTNONIPM)...................................................................8

2.1.1 EGU non-peaking units sector (ptegu).................................................................................................................9

2.1.2 EGU peaking units sector (ptegu_pk) ................................................................................................................11

2.1.3 Point source oil and gas sector (pt_oilgas)........................................................................................................11

2.1.4 Non-IPM sector (ptnonipm) ...............................................................................................................................13

2.2 2011 NONPOINT SOURCES (AFDUST, AG, NP_OILGAS, RWC, NONPT)..............................................................................14

2.2.1 Area fugitive dust sector (afdust) .......................................................................................................................16

2.2.2 Agricultural ammonia sector (ag)......................................................................................................................20

2.2.3 Nonpoint source oil and gas sector (np_oilgas).................................................................................................21

2.2.4 Residential wood combustion sector (rwc).........................................................................................................21

2.2.5 Other nonpoint sources sector (nonpt)...............................................................................................................22

2.3 2011 ONROAD MOBILE SOURCES (ONROAD, ONROAD_RFL)...........................................................................................23

2.3.1 Onroad non-refueling (onroad)..........................................................................................................................23

2.3.2 Onroad refueling (onroad_rfl)...........................................................................................................................26

2.4 2011 NONROAD MOBILE SOURCES (C1C2RAIL, C3MARINE, NONROAD) .........................................................................26

2.4.1 Class 1/Class 2 Commercial Marine Vessels and Locomotives and (c1c2rail) .................................................26

2.4.2 Class 3 commercial marine vessels (c3marine) .................................................................................................28

2.4.3 Nonroad mobile equipment sources: (nonroad).................................................................................................30

2.5 “OTHER EMISSIONS”: OFFSHORE CLASS 3 COMMERCIAL MARINE VESSELS AND DRILLING PLATFORMS AND NON-U.S.

SOURCES ................................................................................................................................................................................31

2.5.1 Point sources from offshore C3 CMV and drilling platforms and Canada and Mexico (othpt) ........................32

2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar)................................................................32

2.5.3 Onroad mobile sources from Canada and Mexico (othon)................................................................................33

2.6 FIRES (PTFIRE)..............................................................................................................................................................33

2.7 BIOGENIC SOURCES (BIOG)...........................................................................................................................................34

2.8 SMOKE-READY NON-ANTHROPOGENIC INVENTORIES FOR CHLORINE .........................................................................34

3 EMISSIONS MODELING SUMMARY ......................................................................................................................36

3.1 EMISSIONS MODELING OVERVIEW................................................................................................................................36

3.2 CHEMICAL SPECIATION ................................................................................................................................................39

3.2.1 VOC speciation ..................................................................................................................................................41

3.2.2 PM speciation.....................................................................................................................................................49

3.2.3 NOX speciation ...................................................................................................................................................51

3.3 TEMPORAL ALLOCATION..............................................................................................................................................52

3.3.1 Use of FF10 format for finer than annual emissions .........................................................................................53

3.3.2 Electric Generating Utility temporalization (ptegu, ptegu_pk)..........................................................................54

3.3.3 Residential Wood Combustion Temporalization (rwc) ......................................................................................56

3.3.4 Agricultural Ammonia Temporal Profiles (ag) ..................................................................................................60

3.3.5 Onroad mobile temporalization (onroad, onroad_rfl).......................................................................................61

3.3.6 Additional sector specific details (afdust, beis, c1c2rail, c3marine, nonpt, ptfire)............................................67

3.4 SPATIAL ALLOCATION..................................................................................................................................................68

3.4.1 Spatial Surrogates for U.S. emissions................................................................................................................69

3.4.2 Allocation method for airport-related sources in the U.S. .................................................................................72

3.4.3 Surrogates for Canada and Mexico emission inventories..................................................................................73
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4 DEVELOPMENT OF 2018 BASE-CASE EMISSIONS.............................................................................................78

4.1 STATIONARY SOURCE PROJECTIONS: EGU SECTORS (PTEGU, PTEGU_PK) ....................................................................84

4.2 STATIONARY SOURCE PROJECTIONS: NON-EGU SECTORS (AFDUST, AG, NONPT, NP_OILGAS, PTNONIPM, PT_OILGAS, RWC)

84

4.2.1 RFS2 upstream future year inventories and adjustments (nonpt, ptnonipm) .....................................................86

4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm)..............................................95

4.2.3 Residential wood combustion growth (nonpt)....................................................................................................97

4.2.4 Oil and Gas projections (np_oilgas, pt_oilgas) .................................................................................................99

4.2.5 RICE NESHAP (nonpt, ptnonipm, np_oilgas, pt_oilgas).................................................................................104

4.2.6 Fuel sulfur rules (nonpt, ptnonipm) .................................................................................................................106

4.2.7 Industrial Boiler MACT reconsideration (ptnonipm).......................................................................................108

4.2.8 Portland Cement NESHAP projections (ptnonipm).........................................................................................110

4.2.9 State comments and consent decrees/settlements (nonpt, ptnonipm) ...............................................................113

4.2.10 Aircraft projections (ptnonipm)........................................................................................................................118

4.2.11 Remaining non-EGU controls and closures (ptnonipm) ..................................................................................120

4.3 MOBILE SOURCE PROJECTIONS ...................................................................................................................................121

4.3.1 Onroad mobile (onroad and onroad_rfl) .........................................................................................................121

4.4 NONROAD MOBILE SOURCE PROJECTIONS (C1C2RAIL, C3MARINE, NONROAD)............................................................125

4.4.1 Locomotives and Class 1 & 2 commercial marine vessels (c1c2rail)..............................................................125

4.4.2 Class 3 commercial marine vessels (c3marine) ...............................................................................................128

4.4.3 Other nonroad mobile sources (nonroad)........................................................................................................129

4.5 “OTHER EMISSIONS”: OFFSHORE CLASS 3 COMMERCIAL MARINE VESSELS AND DRILLING PLATFORMS, CANADA AND 

MEXICO (OTHPT, OTHAR, AND OTHON) ................................................................................................................................130

4.5.1 Point sources from offshore C3 CMV and drilling platforms and Canada and Mexico (othpt) ......................130

4.5.2 Area, nonroad mobile and onroad mobile sources from Canada and Mexico (other, othon)..........................130

5 EMISSION SUMMARIES ..........................................................................................................................................131

6 REFERENCES .............................................................................................................................................................141
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List of Tables

Table 1-1. List of cases in the 2011 Version 6 Emissions Modeling Platform.................................................2

Table 2-1. Platform sectors for the 2011 emissions modeling platform ...........................................................4

Table 2-2. Summary of significant changes between 2011 platform and 2011NEIv1 by sector......................6

Table 2-3. Point source oil and gas sector SCCs..............................................................................................11

Table 2-4. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced) ..................13

Table 2-5. Toxic-to-VOC Ratios for Corn Ethanol Plants..............................................................................14

Table 2-6. 2011NEIv1 nonpoint sources removed from the 2011 platform ...................................................15

Table 2-7. SCCs in the afdust platform sector ................................................................................................16

Table 2-8. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory..................................17

Table 2-9. Livestock SCCs extracted from the NEI to create the ag sector....................................................20

Table 2-10. Fertilizer SCCs extracted from the NEI for inclusion in the “ag” sector.....................................21

Table 2-11. SCCs in the Residential Wood Combustion Sector (rwc)* .........................................................22

Table 2-12. Onroad emission modes...............................................................................................................25

Table 2-13. 2011NEIv1 SCCs extracted for the starting point in c1c2rail development................................26

Table 2-14. Growth factor adjustment factors to project the 2002 ECA-IMO inventory to 2011 ..................29

Table 2-15. 2011 Platform SCCs representing emissions in the ptfire modeling sector.................................33

Table 3-1. Key emissions modeling steps by sector........................................................................................37

Table 3-2. Descriptions of the platform grids .................................................................................................38

Table 3-3. Emission model species produced for CB05 with SOA for CMAQ5.0.1 and CAMX*.................40

Table 3-4. Integration approach for BAFM and EBAFM for each platform sector........................................43

Table 3-5. VOC profiles for WRAP Phase III basins .....................................................................................45

Table 3-6. National VOC profiles for oil and gas ...........................................................................................45

Table 3-7. Counties included in the WRAP Dataset .......................................................................................46

Table 3-8. Select VOC profiles 2011 versus 2018 ..........................................................................................48

Table 3-9. PM model species: AE5 versus AE6 .............................................................................................49

Table 3-10. MOVES exhaust PM species versus AE5 species.......................................................................50

Table 3-11. NOX speciation profiles................................................................................................................51

Table 3-12. Temporal settings used for the platform sectors in SMOKE.......................................................52

Table 3-13. Mapping of MOVES to SMOKE road types ...............................................................................65

Table 3-14. U.S. Surrogates available for the 2011 modeling platform..........................................................69

Table 3-15. Spatial Surrogates for WRAP and Marcellus Shale Oil and Gas Data ........................................70

Table 3-16. Total CAP emissions by sector for U.S. Surrogates .....................................................................71

Table 3-17. Canadian Spatial Surrogates ........................................................................................................73

Table 3-18.CAPs Allocated to Mexican and Canadian Spatial Surrogates......................................................75

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

from the 2011 base case ...........................................................................................................................80

Table 4-2. Subset of CoST Packet Matching Hierarchy .................................................................................83

Table 4-3. Summary of non-EGU stationary projections subsections ............................................................85

Table 4-4. Renewable Fuel Volumes Assumed for Stationary Source Adjustments. .....................................86

Table 4-5. 2011 and 2018 corn ethanol plant emissions [tons].......................................................................87

Table 4-6. Emission Factors for Biodiesel Plants (Tons/Mgal) ......................................................................87

Table 4-7. 2018 biodiesel plant emissions [tons]............................................................................................88

Table 4-8. PFC emissions for 2011 and 2018 [tons].......................................................................................89

Table 4-9. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)...............................89

Table 4-10. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon) ...............................................89

Table 4-11. 2018 cellulosic plant emissions [tons] .........................................................................................90

Table 4-12. 2018 VOC working losses (Emissions) due to ethanol transport [tons]......................................90

Table 4-13. RVPs Assumed for 2018 ethanol and gasoline volumes with EISA ...........................................92
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Table 4-14. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA ......................................92

Table 4-15. Storage and Transport Vapor Loss Emission Factors (g/mmBtu)...............................................93

Table 4-16. Adjustment factors applied to storage and transport emissions...................................................93

Table 4-17. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with 

gasoline and diesel fuel production. .........................................................................................................95

Table 4-18. Impact of refinery adjustments on 2011 emissions [tons] ...........................................................95

Table 4-19. Adjustments to modeling platform agricultural emissions for the Tier 3 reference case ............96

Table 4-20. Composite NH3 projection factors to year 2018 for animal operations.......................................96

Table 4-21. Non-West Coast RWC projection factors to year 2018...............................................................98

Table 4-22. National RWC impacts for PM2.5 and NOX from 2011 to 2018 ..................................................99

Table 4-23. AEO-based 2018 Projection Factors..........................................................................................101

Table 4-24. Oil and Gas sector VOC 2018 Projection Factors for NSPS sources........................................102

Table 4-25. Projected national Oil and Gas sector 2011 and 2018 emissions, summed point and nonpoint 103

Table 4-26. Projected by-state NOX and VOC 2011 and 2018 Oil and Gas sector emissions......................103

Table 4-27. Summary RICE NESHAP SI and CI percent reductions prior to 2011NEIv1 analysis ............105

Table 4-28. National by-sector reductions from RICE Reconsideration Controls........................................106

Table 4-29. Summary of fuel sulfur rules by state .........................................................................................108

Table 4-30. Facility types potentially subject to Boiler MACT reductions ...................................................109

Table 4-31. Default Boiler MACT fuel percent % reductions by ICR fuel type ...........................................109

Table 4-32. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions....110

Table 4-33. Locations of new ISIS-generated cement kilns...........................................................................111

Table 4-34. U.S. Census Division ISIS-based projection factors for existing kilns .....................................112

Table 4-35. ISIS-based cement industry change (tons/yr) ............................................................................112

Table 4-36. Impacts of most non-EGU point source state comments received in 2013 ...............................114

Table 4-37. Minor source ptnonipm sector NAICS-level projections for Texas..........................................115

Table 4-38. Minor source nonpt sector projections for Texas.......................................................................115

Table 4-39. Target company-wide reductions from OECA consent decree information..............................117

Table 4-40. Default national-level factors used to project 2011 base-case aircraft emissions to 2018 ........118

Table 4-41. Increases in aircraft emissions by year 2018 from airport-specific and national-level methods119

Table 4-42. Reductions from all EIS-based and remaining information facility/unit-level closures.............121

Table 4-43. Projection factors for 2018 VMT (in millions of miles).............................................................122

Table 4-44. Comparison of MOVES runs for 2018 .......................................................................................123

Table 4-45. CA LEVIII program states..........................................................................................................123

Table 4-46. Non-California year 2018 intermediate projection factors for locomotives and Class 1 and 

Class 2 Commercial Marine Vessel Emissions......................................................................................126

Table 4-47. EISA mandate emission adjustments in 2018.............................................................................127

Table 4-48. Difference in c1c2rail sector emissions between 2011 and 2018 ..............................................128

Table 4-49. Growth factors to project the 2011 ECA-IMO inventory to 2018.............................................128

Table 5-1. National by-sector CAP emissions summaries for 2011 evaluation case ....................................132

Table 5-2. National by-sector CAP emissions summaries for 2018 base case .............................................133

Table 5-3. National by-sector CO emissions (tons/yr) summaries with differences.....................................134

Table 5-4. National by-sector NH3 emissions (tons/yr) summaries with differences...................................135

Table 5-5. National by-sector NOX emissions (tons/yr) summaries with differences...................................136

Table 5-6. National by-sector PM2.5 emissions (tons/yr) summaries with differences.................................137

Table 5-7. National by-sector PM10 emissions (tons/yr) summaries with differences..................................138

Table 5-8. National by-sector SO2 emissions (tons/yr) summaries with differences....................................139

Table 5-9. National by-sector VOC emissions (tons/yr) summaries with differences..................................140
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List of Figures

Figure 2-1. January PM2.5 afdust emissions: raw 2008 NEI (top), after application of transport fraction 

(middle) and final post-meteorological adjusted (bottom).......................................................................19

Figure 2-2. Illustration of regional modeling domains in ECA-IMO study....................................................30

Figure 2-3. Annual NO emissions output from BEIS 3.14 for 2011...............................................................35

Figure 2-4. Annual isoprene emissions output from BEIS 3.14 for 2011.......................................................35

Figure 3-1. Air quality modeling domains .......................................................................................................38

Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation ..........................................42

Figure 3-3. IPM Regions for EPA Base Case v5.13 .......................................................................................55

Figure 3-4. Example of RWC temporalization in 2007 using a 50 versus 60  ̊F threshold ............................57

Figure 3-5. RWC diurnal temporal profile ......................................................................................................58

Figure 3-6. Diurnal profile for OHH, based on heat load (BTU/hr) ...............................................................59

Figure 3-7. Day-of-week temporal profiles for OHH and Recreational RWC ...............................................59

Figure 3-8. Annual-to-month temporal profiles for OHH and recreational RWC..........................................60

Figure 3-9. Example of new animal NH3 emissions temporalization approach, summed to daily emissions 61

Figure 3-10. Example of SMOKE-MOVES temporal variability of NOX emissions.....................................62

Figure 3-11. Previous onroad diurnal weekday profiles for urban roads........................................................63

Figure 3-12. Variation in MOVES diurnal profiles.........................................................................................63

Figure 3-13. Use of submitted versus new national default profiles...............................................................64

Figure 3-14. Updated national default profiles for LDGV vs. HHDDV, urban restricted weekday...............66

Figure 3-15. Agricultural burning diurnal temporal profile ............................................................................68

Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD) ...............................................92

Figure 4-2. Oil and Gas NEMS Regions.......................................................................................................100

Figure 4-3. Cement sector trends in domestic production versus normalized emissions..............................111

List of Appendices

Appendix A: Nonpoint Oil and Gas NEI SCCs

Appendix B: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT

Appendix C: Crosswalk between AE6 Profile Codes and SPECIATE 4.3 Profile Codes

Appendix D: Memo Describing the Differences in MOVES speciated PM and CMAQ PM

Appendix E: CAP emissions by surrogate and sector

Appendix F: SMOKE Input Data Files and Parameters Used in the 2011 Evaluation and 2018 Base Cases

Appendix G:Future Animal Population Projection Methodology, Updated 07/24/12

Appendix H:Boiler MACT ICR Fuels Cross-Reference to NEI SCCs
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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 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

CEMS Continuous Emissions Monitoring System

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

E0, 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

FF10 Flat File 2010

FIPS Federal Information Processing Standards

FHWA Federal Highway Administration
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HAP Hazardous Air Pollutant

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 

IDA Inventory Data Analyzer

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)

MOVES Motor Vehicle Emissions Simulator (2010b) -- 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
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OAQPS EPA’s Office of Air Quality Planning and Standards

OHH Outdoor Hydronic Heater

OTAQ EPA’s Office of Transportation and Air Quality

ORIS Office of Regulatory Information System

ORD 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

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
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1 Introduction

The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform for 2011 

based on the 2011 National Emissions Inventory, version 1 (2011NEIv1). The air quality modeling 

platform consists of all the emissions inventories and ancillary data files used for emissions modeling, as 

well as the meteorological, initial condition, and boundary condition files needed to run the air quality 

model. This document focuses on the emissions modeling component of the 2011 modeling platform, 

which includes the emission inventories, the ancillary data files, and the approaches used to transform 

inventories for use in air quality modeling. Many emissions inventory components of this air quality

modeling platform are based on the 2011NEIv1, although there are some differences between the 

platform inventories and the 2011NEIv1 emissions. 

This 2011 modeling platform includes all criteria air pollutants and precursors (CAPs) and the following 

hazardous air pollutants (HAPs): chlorine (Cl), hydrogen chloride (HCl), benzene, acetaldehyde, 

formaldehyde and methanol. The latter four HAPs are also abbreviated as BAFM. This platform is called 

the “CAP-BAFM 2011-Based Platform, version 6” because it is primarily a CAP platform with BAFM 

species included. Here, “version 6” denotes an evolution from the 2007-based platform, version 5, with 

substantial improvements due to the use of newer data and methods. For the rest of this document, the 

platform that is described is referred to as the “2011 platform” or “2011v6”. Future updates to the 2011 

platform will include a version qualifier such as “2011 Platform v6.1”, and so on. 

The first use of the 2011 platform is for the proposed rule related to the transport of ozone that will focus 

on helping states in the eastern United States meet the 2008 National Ambient Air Quality Standards 

(NAAQS) for ozone. The air quality model used for this rule is the Comprehensive Air Quality Model 

with Extensions (CAMX) model (http://www.camx.com/), version 6.10; however, emissions are first 

processed for the Community Multiscale Air Quality (CMAQ) model 

(http://www.epa.gov/AMD/CMAQ/), version 5.0.1 and then converted to CAMX-ready format. Both 

CAMX and CMAQ support modeling ozone (O3) and particulate matter (PM), and require as input hourly 

and gridded emissions of chemical species that correspond to CAPs and specific HAPs. The chemical 

mechanism used by CAMX for this platform is called Carbon Bond 2005 (CB05) with chlorine chemistry. 

CB05 allows explicit treatment of BAFM and includes HAP emissions of HCl and Cl.

The 2011 platform consists of two ‘complete’ emissions cases: the 2011 base case (i.e., 2011ed_v6) and 

the 2018 base case (i.e., 2018ed_v6). In the case abbreviations, the 2011 and 2018 are the year 

represented by the emissions; the “e” stands for evaluation, meaning that year-specific data for fires and 

EGUs are used, and the “d” represents that this was the fourth set of emissions modeled for the 2011v6 

platform. Table 1-1 provides more information on these emissions cases. The purpose of the 2011 base 

case is to represent the year 2011 in a manner consistent with the methods used in corresponding futureyear cases, including the 2018 future year base case, as well as any additional future year control and 

source apportionment cases. 

For regulatory applications, the outputs from the 2011 base case are used in conjunction with the outputs 

from the 2018 base case 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-rhguidance.pdf. This document is available on EPA’s Emissions Modeling Clearinghouse website, 

http://www.epa.gov/ttn/chief/emch/, under the section entitled “2011-based Modeling Platform 

(2011v6 Platform)”.
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Table 1-1. List of cases in the 2011 Version 6 Emissions Modeling Platform

Case Name Abbreviation Description

2011 base case 2011ed_v6

2011 case relevant for air quality model evaluation purposes and 

for computing relative response factors with 2018 scenario(s). 

Uses 2011NEIv1 and some other inventory data, with hourly 

2011 continuous emissions monitoring System (CEMS) data for 

Electrical Generating Units (EGUs), hourly onroad mobile

emissions, and 2011 day-specific wild and prescribed fire data. 

2018 base case 2018ed_v6

2018 “base case” scenario, representing the best estimate for the 

future year that incorporates estimates of the impact of current 

“on-the-books” regulations, without including implementation of 

controls needed to attain current PM2.5 annual and 24-hour 

standards (35 ppm and 15 ppm, respectively) and ozone 8-hour 

standard (75 ppb).

A brief summary of the emissions data used in the 2011v6 platform follows: 

1) Point and nonpoint sources are based on the 2011NEIv1.

2) Onroad mobile sources are based on year 2011 emissions computed using the Sparse Matrix 

Operator Kernel Emission (SMOKE) interface to emission factors developed with the version of 

Motor Vehicle Emissions Simulator (MOVES) that represents the proposed Tier 3 Vehicle 

Emission and Fuel Standards (http://www.epa.gov/OTAQ/tier3.htm).

3) Nonroad mobile sources are based on the 2011NEIv1, except for some additions of VOC in 

California where there were HAP emissions but no VOCs in 2011NEIv1.

4) Commercial marine vessels (CMV) are based on the 2011NEIv1 except for 2011 California Air 

Resources Board (CARB) inventory in California, 2010 regional planning organization (RPO) 

inventories in the Midwest, and a separate year-2002-based (projected to 2011) inventory for Class 

3 CMV vessels. Additional minor changes were made to point sources as described in Section 2.1.

The primary emissions modeling tool used to create the air quality model-ready emissions was SMOKE

modeling system (http://www.smoke-model.org/). SMOKE version 3.5.1 was used to create emissions 

files for a 12-km national grid that includes all of the contiguous states “12US2”, shown in Figure 3-1. 

Boundary conditions for this grid were obtained from a 2011 run of GEOS-Chem (http://geos-chem.org/). 

Electronic copies of the data used as input to SMOKE for the 2011 Platform are available from the 

Emissions Modeling Clearinghouse website, http://www.epa.gov/ttn/chief/emch/.

The gridded meteorological model used for the emissions modeling was developed using the Weather 

Research and Forecasting Model (WRF, http://wrf-model.org) version 3.4, 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 2011 over a domain covering the continental United States at a 12km resolution with 35 vertical 

layers. The data was collapsed to 25 layers prior to running the emissions and air quality models.

This document contains five sections and several appendices. Section 2 describes the 2011 inventories 

input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used with the 

emission inventories. Section 4, describes the development of the 2018 inventory (projected from 2011). 

Data summaries comparing the 2011 base case and 2018 base case are provided in Section 5. Section 6 

provides references. The Appendices provide additional details about specific technical methods. 
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2 2011 Emission Inventories and Approaches

This section describes the 2011 emissions data that make up the 2011 platform. The starting point for the 

2011 stationary source emission inputs is the 2011NEIv1. Emissions of NOX, SO2, VOC and PM 

emissions decrease from values in the 2008 NEI version 3 for most source sectors, with a couple of 

notable exceptions including increased industrial NOX, VOC and CO associated with increased oil and 

gas sector emissions and improved emission estimates; slightly increased VOC, CO and NH3 from fuel 

combustion; and increased wildfire emissions. Documentation for the 2011NEIv1, including a Technical 

Support Document (TSD), is available at 

http://www.epa.gov/ttn/chief/net/2011inventory.html#inventorydoc. 

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

agencies. HAP emissions data are also from the state data, but are often augmented by EPA because they 

are voluntarily submitted. EPA uses the Emissions Inventory System (EIS) to compile the NEI. EIS 

includes hundreds of automated QA checks to help improve data quality, and also supports tracking 

release point (e.g., stack) coordinates separately from facility coordinates. EPA collaborated extensively

with S/L/T agencies to ensure a very high quality of data in the 2011NEIv1. Tangible benefits of this 

collaboration are seen in improved data quality from past first version inventories, improved completeness 

and avoided duplication between point and nonpoint source categories such as industrial boilers. Onroad 

mobile source emissions in the 2011NEIv1 were developed using MOVES2010b; however, the 2011 

emissions modeling platform used a different version of MOVES, hence forth referred to as 

“MOVESTier3NPRM”, that facilitated the representation of the proposed Tier 3 standards in future years.

When given the same inputs, these two versions of MOVES produce similar emissions estimates for the 

year 2011.

The 2011 NEI includes five data categories: point sources, nonpoint (formerly called “stationary area”) 

sources, nonroad mobile sources, onroad mobile sources, and events consisting of fires. The 2011NEIv1

uses 60 sectors to further describe the emissions, with an additional biogenic sector generated from a 

summation of the gridded, hourly 2011 biogenic data used in the modeling platform. In addition to the 

NEI data, emissions from the Canadian and Mexican inventories and several other non-NEI data sources 

are included in the 2011 platform. As explained below, the non-NEI emissions component to the 2011

platform primarily includes: different version of MOVES-based onroad mobile source emissions, nonmeteorologically-adjusted road dust, year-2010 commercial marine vessel (CMV) emissions in the 

Midwest (http://www.ladco.org/), and Class 3 CMV data developed by EPA.

Fire emissions in 2011NEIv2 were developed based on Version 2 of the Satellite Mapping Automated 

Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) system (Sullivan, et al., 2008). 

SMARTFIRE 2 was the first version of SMARTFIRE to assign all fires as either prescribed burning or 

wildfire categories. In past inventories, a significant number of fires were published as unclassified, 

which impacted the emissions values and diurnal emissions pattern. Recent updates to SMARTFIRE 

include improved emission factors for prescribed burning. 

For the purposes of preparing the air quality model-ready emissions, the 2011NEIv1 was split into finergrained sectors used for emissions modeling. The significance of an emissions modeling or “platform 

sector” is that the data are run through all of the SMOKE programs except the final merge (Mrggrid) 

independently from the other sectors. The final merge program then combines the sector-specific gridded, 

speciated, hourly emissions together to create CMAQ-ready emission inputs. For CAMx applications, the 

CMAQ-ready emissions are then converted into the format needed by CAMx by a convertor program.
DRAFT

4

Table 2-1 presents the sectors in the 2011 platform and how they generally relate to the 2011NEIv1 as a 

starting point. As discussed in greater detail in Table 2-2, the emissions in some of these sectors were 

modified from the 2011NEIv1 emissions for the 2011 modeling platform. The platform 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.

Table 2-1. Platform sectors for the 2011 emissions modeling platform

Platform Sector: 

abbreviation

NEI Data 

Category Description and resolution of the data input to SMOKE

EGU non-peaking 

units:

ptegu

Point

2011 NEI point source EGUs determined to operate as non-peaking 

units based on criteria discussed in Section 2.1. For future year 

emissions, these units are mapped to the Integrated Planning Model 

(IPM) model using the National Electric Energy Database System 

(NEEDS) version 5.13. The 2011NEIv1 emissions are replaced with 

hourly 2011 CEMS values for NOX and SO2, where the units match. 

Other pollutants are scaled from 2011NEIv1 using CEMS heat input. 

Emissions for all non-CEMS sources come from 2011NEIv1. Annual 

resolution for non-CEMS sources, hourly for CEMS sources.

EGU peaking 

units:

ptegu_pk

Point

Same as ptegu sector, but limited to EGUs that are determined to 

operate as peaking units, as discussed in Section 2.1. All sources in 

this sector have CEMS data for 2011 and are therefore hourly.

Point source oil 

and gas: 

pt_oilgas

Point 2011NEIv1 point sources with oil and gas production emissions 

processes.

Remaining nonEGU point:

ptnonipm

Point

All 2011NEIv1 point source records not matched to the ptegu, 

ptegu_pk, and pt_oilgas sectors, except for offshore point sources that 

are in the othpt sector. Includes all aircraft emissions and some rail 

yard emissions. Annual resolution.

Agricultural:

ag

Nonpoint NH3 emissions from 2011NEIv1 nonpoint livestock and fertilizer 

application, county and annual resolution. 

Area fugitive dust:

afdust Nonpoint

PM10 and PM2.5 from fugitive dust sources from the 2011NEIv1 

nonpoint inventory including building construction, road construction, 

and agricultural dust, and road dust; however, unpaved and paved road 

dust emissions differ from the NEI in that do not have a precipitation 

adjustment. Instead, the emissions modeling adjustment applies a 

transport fraction and a meteorology-based (precipitation and snow/ice 

cover) zero-out. County and annual resolution. 

Nonpoint source 

oil and gas: 

np_oilgas

Nonpoint 2011NEIv1 nonpoint sources from oil and gas-related processes. 

County and annual resolution.

Residential Wood 

Combustion:

rwc

Nonpoint 2011NEIv1 NEI nonpoint sources with Residential Wood Combustion 

(RWC) processes. County and annual resolution.

Class 1 & 2 CMV 

and locomotives:

c1c2rail

Nonpoint

Locomotives and primarily category 1 (C1) and category 2 (C2) 

commercial marine vessel (CMV) emissions sources from the 

2011NEIv1 nonpoint inventory. Midwestern states’ CMV emissions, 

including Class 3 sources, are from a separate year 2010 emissions 

inventory. County and annual resolution.
DRAFT

5

Platform Sector: 

abbreviation

NEI Data 

Category Description and resolution of the data input to SMOKE

commercial 

marine:

c3marine

Nonpoint

Category 3 (C3) CMV emissions projected to 2011 from year 2002

values. These emissions are not from the NEI, but rather were 

developed for the rule called “Control of Emissions from New Marine 

Compression-Ignition Engines at or Above 30 Liters per Cylinder”, 

usually described as the Emissions Control Area- International 

Maritime Organization (ECA-IMO) study: 

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

August 2010). U.S. states-only emissions (zero in Midwest); see othpt 

sector for all non-U.S. emissions. Treated as point sources to reflect 

shipping lanes, annual resolution.

Remaining 

nonpoint:

nonpt

Nonpoint 2011NEIv1 nonpoint sources not otherwise removed from modeling or 

included in other platform sectors; county and annual resolution. 

Nonroad:

nonroad Nonroad

2011NEIv1 nonroad equipment emissions developed with the National 

Mobile Inventory Model (NMIM) using NONROAD2008 version 

NR08a. NMIM was used for all states except California and Texas, 

which submitted their own emissions to the 2011NEIv1. County and 

monthly resolution.

Onroad nonrefueling:

onroad

Onroad

2011 onroad mobile source 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 except California and Texas, based on monthly MOVES 

emissions tables from MOVESTier3NPRM. Texas emissions are 

from the 2011NEIv1 based on MOVES 2010b, and California

emissions are based on Emission Factor (EMFAC). MOVES-based 

emissions computed for each hour and model grid cell using monthly 

and annual activity data (e.g., VMT, vehicle population). 

Onroad refueling:

onroad_rfl Onroad

2011NEIv1 onroad mobile gasoline and diesel vehicle refueling 

emissions for all states. Based on MOVES 2010b emissions tables.

Computed hourly based on temperature and for each model grid cell.

Point source fires:

ptfire Fires

Point source day-specific wildfires and prescribed fires for 2011

computed using SMARTFIRE 2, except for Georgia-submitted 

emissions. Consistent with 2011NEIv1.

Other point 

sources not from 

the 2011 NEI:

othpt

N/A

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

2012 inventory, annual resolution. Mexico’s inventory is year 2012 

and grown from year 1999 (ERG, 2009; Wolf, 2009). Also includes 

all non-U.S. C3 CMV and U.S. offshore oil production, which are 

unchanged from the 2008 NEI point source annual emissions.

Other non-NEI 

nonpoint and 

nonroad:

othar

N/A

Annual year 2006 Canada (province resolution) and year 2012 (grown 

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

nonroad mobile inventories.

Other non-NEI 

onroad sources:

othon

N/A

Year 2006 Canada (province resolution) and year 2012 (grown from 

1999) Mexico Phase III (municipio resolution) onroad mobile 

inventories, annual resolution.

Biogenic:

beis Nonpoint

Year 2011, hour-specific, grid cell-specific emissions generated from 

the BEIS3.14 model with SMOKE, including emissions in Canada and 

Mexico.
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6

Table 2-2 provides a brief by-sector overview of the most significant differences between the 2011

emissions platform and the 2011NEIv1. Only those sectors with significant differences between the 

2011NEIv1 and the 2011 emissions modeling platform are listed. For some sectors, such as non-EGU 

point (ptnonipm), these changes are very minor and localized. In contrast, other sectors such as C3 

commercial marine (c3marine) are either completely replaced or have significant and detailed edits based 

on review of available alternative data. The specific by-sector updates to the 2011 platform are described 

in greater detail later in this section under each by-sector subsection. A spreadsheet quantifying the 

differences between the 2011NEIv1 and modeling platform is available on CHIEF under 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/ 2011NEIv1_versus_2011ed_differences.xlsx. 

Table 2-2. Summary of significant changes between 2011 platform and 2011NEIv1 by sector

Platform Sector Summary of Significant Inventory Differences of 2011 Platform vs.

2011NEIv1

IPM sectors:

ptegu & ptegu_pk

1) Based on 2011NEIv1 and 2011 CEMS data analysis, added ORIS Boiler IDs to 

some units (greater than 1,000 tons of NOX or SO2) with missing or incorrect 

values to allow for hourly CEMS data processing.

2) Added CEMS matches to additional units identified as CEMS sources.

3) Hourly NOX and SO2 CEMS data replaces annual NOX and SO2 NEI data in 

the air quality model inputs.

Remaining nonEGU (IPM)

sector:

ptnonipm

1) Based on items above (ptegu & ptegu_pk), made additional matches to 

IPM_YN codes and ORIS facility codes that caused several sources to move 

into the ptegu and ptegu_pk sectors. This edit prevents double counting of 

EGU emissions in the future years. 

2) Included 2011 ethanol plant facilities from EPA’s Office of Transportation and 

Air Quality (OTAQ) that were not identified in the 2011 NEIv1.

Area fugitive dust:

afdust

1) Replaced EPA-provided emission estimates for paved and unpaved road dust 

with “non-met-adjusted” emissions; i.e., the meteorology/precipitation 

reduction included in the 2011NEIv1 is backed-out.

2) All emissions in this sector are processed (adjusted) to reflect land use 

(transport) and meteorological effects such as rain and snow cover that 

significantly reduce PM emissions input to the air quality model. These 

adjusted emissions are known as the afdust_adj emissions.

Remaining 

nonpoint sector:

nonpt

1) Split the 2011NEIv1 nonpoint file into the platform sectors afdust, ag, 

np_oilgas, rwc, c3marine, and c1c2rail.

2) Used agricultural fires emissions from daily inventory aggregated to monthly

values, whereas the NEI only stores annual values.

Class 1 & 2 CMV 

and locomotives:

c1c2rail

1) Replaced Midwest RPO states c1c2 CMV emissions with comprehensive year 

2010 RPO inventory.

2) Replaced all California estimates with year-2011 CARB estimates.

C3 commercial 

marine:

c3marine

1) Used non-2011NEIv1-based data. Rather used year-2011 point sources as 

projected from 2002 from the ECA-IMO project.

2) Midwest RPO states replaced with 2010 RPO inventory (see c1c2rail sector).

Nonroad sector:

nonroad

1) States other than Texas: monthly rather than annual + small VOC adjustments 

in California.

2) Texas: replaced with annual 2011 Texas data apportioned to months using 

EPA’s 2011 nonroad estimates.
DRAFT

7

Platform Sector Summary of Significant Inventory Differences of 2011 Platform vs.

2011NEIv1

Onroad nonrefueling:

onroad

1) For all states except California and Texas: Year 2011 emissions for all 

pollutants and modes (exhaust, evaporative, permeation, tire and brake wear)2

from all vehicle types are based on emission factors from the version of

MOVESTier3NPRM, as opposed to MOVES 2010b which was used for the 

2011NEIv1. Processed with 2011 meteorology using SMOKE-MOVES 

(discussed later). 

2) For California and Texas: merged in 2011 California and Texas data to postadjust SMOKE-MOVES data (discussed later). 

Onroad nonrefueling:

onroad_rfl

For all states including California: Year 2011 emissions for all pollutants AND all 

vehicle types are based on MOVES2010b emission factor tables processed with 

2011 meteorology using SMOKE-MOVES (discussed later). Therefore, these 

emissions are identical to the 2011NEIv1 for states that did not submit refueling 

emissions, but are inconsistent with 2011NEIv1 for states that did submit point and 

nonpoint refueling since the 2011NEIv1 kept state-submissions over EPA data.

The emission inventories in SMOKE input format for the 2011 base case are available from the Emissions 

Modeling Clearinghouse website http://www.epa.gov/ttn/chief/emch/index.html#2011. The inventories 

“readme” file indicates the particular zipped files associated with each platform sector. A number of 

reports were developed for the 2011 platform. Descriptions of the available data and reports are available 

from the FTP site at ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform. The types of reports include 

state summaries of inventory pollutants and model species by modeling platform sector for 2011 and 2018 

in the Microsoft®Excel®files “2011ed_v6_11f_state_sector_totals.xlsx” and 

“2018ed_v6_11f_state_sector_totals.xlsx”, with a comparison of the emissions in two cases in the file 

“2011ed_2018ed_comparison_6jan2014.xlsx”. CAP emission totals by county, month, and modeling 

platform sector are available in the files “2011ed_county_monthly_report_CAPs.xlsx” and 

“2018ed_county_monthly_report_CAPs.xlsx”. Summaries by state and source classification code (SCC), 

including SCC descriptions, by modeling sector for 2011 and 2018 are available at the FTP site: 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/State-SCC-Summaries/. A comparison of the 

complete list of inventory files, ancillary files, and parameter settings for the 2011 and 2018 modeling 

cases is available in the file “2011ed_2018ed_case_inputs.xlsx”.

The remainder of Section 2 provides details about the data contained in each of the 2011 platform sectors. 

Different levels of detail are provided for different sectors depending on the availability of reference 

information for the data, the degree of changes or manipulation of the data needed to prepare it for input 

to SMOKE, and whether the 2011 platform emissions are significantly different from the 2011NEIv1.

 

2

For the extended idle mode, used MOVES2010b emissions factors. See onroad section below for details.
DRAFT

8

2.1 2011 NEI point sources (ptegu, ptegu_pk, pt_oilgas 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 release 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 and othpt sectors) are processed by SMOKE as point source inventories, as 

described in Section 2.5.1 and Section 2.4.2, respectively. A comprehensive description on how EGU 

emissions were characterized and estimated in the 2011 NEI can be found in Section 3.10 in the 

2011NEIv1 TSD.

The point source file used for the modeling platform is exported from EIS into the Flat File 2010 (FF10) 

format that is compatible with SMOKE (see 

http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch08s02s10.html#d0e44906). After 

moving offshore oil platforms into the othpt sector, initial versions of the other four platform point 

source sectors were created from the remaining 2011NEIv1 point sources. The point sectors are: the 

EGU sector for non-peaking units (ptegu), the EGU sector for peaking units (ptegu_pk), point source oil 

and gas extraction -related emissions (pt_oilgas) and the remaining non-EGU sector also called the nonIPM (ptnonipm) sector. The EGU emissions are split out to facilitate the use of distinct SMOKE 

temporal processing and future-year projection techniques. The EGU sectors are further split into 

“peaking” (ptegu_pk) and non-peaking units to allow for better analysis of the impact of peaking units. 

The oil and gas sector emissions (pt_oilgas) were processed separately for summary tracking purposes 

and unique future-year projection techniques from the remaining non-EGU emissions (ptnonipm).

In addition to the emissions summaries described in Section 1, two other specialized point source

summaries are available on the Emissions Modeling Clearinghouse website. A summary report of stack 

parameters for the point source sectors, including cross references to CEMS data via ORIS IDs, can be 

found in the file

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2011_emissions/2011ec_stack_parameter_re

port.xlsx. A comparison of the 2011NEIv1 EGU emissions with the 2011 CEMS data is available in the 

same directory in the file “2011_EGUs_NEI_CEMS.xlsx”. 

The inventory pollutants processed through SMOKE for both all point source sectors were: CO, NOX, 

VOC, SO2, NH3, PM10, and PM2.5 and the following HAPs: HCl (pollutant code = 7647010), and Cl

(code = 7782505). The inventory BAFM from these sectors was not used, instead VOC was speciated 

to these pollutants without any use (i.e., integration) and the VOC HAP pollutants from the inventory

were ignored (VOC integration is discussed in detail in Section 3.2.1.1).

The ptnonipm and pt_oilgas sector emissions were provided to SMOKE as annual emissions. For those 

ptegu and ptegu_pk sources with CEMS data (that could be matched to the 2011NEIv1), 2011 hourly 

CEMS NOX and SO2 emissions were used (rather than NEI emissions) and for all other pollutants 

annual emissions were used as-is from the NEI, but were allocated to hourly values using heat input

CEMS data. For the non-CEMS sources in the ptegu and ptegu_pk sectors, daily emissions were created 

using an approach described in Section 2.1.1, and IPM region- and pollutant-specific diurnal profiles 

were applied to create hourly emissions. 
DRAFT

9

Changes made to the point-based sectors from the 2011NEIv1 for the 2011 platform were briefly 

described in Table 2-2. One of these changes involved splitting the stacks, units and facilities into the 

ptnonipm, pt_oilgas, ptegu and ptegu_pk sectors. Sources were included in the ptegu or ptegu_pk 

sectors only when it was determined that these sources were reflected in the future-year IPM output 

data. These changes and other updates to the point source sectors for the 2011 platform are discussed in 

the following sections.

2.1.1 EGU non-peaking units sector (ptegu)

The ptegu and ptegu_pk (see Section 2.1.2) sectors contain emissions from EGUs in the 2011NEIv1

point inventory that could be matched to units found in the NEEDS v5.13 database. It was necessary to 

put these EGUs into separate sectors in the platform because IPM projects future emissions for the 

EGUs defined in the NEEDS database, and emissions for sources in the ptegu and ptegu_pk sectors are 

replaced with IPM outputs in the future year modeling case. Sources not matched to units found in 

NEEDS are placed into the pt_oilgas (see Section 2.1.3) or ptnonipm sector (see Section 2.1.4) and are 

projected to the future year using projection and control factors. It is important that the matching 

between the NEI and NEEDS database be as complete as possible because there can be double counting 

of emissions in the future year if emissions for units are projected by IPM are not properly matched to 

the units in the NEI. 

In the SMOKE point flat file, emission records for sources that have been matched to the NEEDS 

database have a value filled into the IPM_YN column. Many of these matches are stored within EIS. In 

some cases, it was difficult to match the sources between the databases due to different facility names in 

the two data systems and due to differences in how the units are defined, thereby resulting in matches 

that are not always one-to-one. Some additional matches were made in the modeling platform to 

accommodate some of these situations as described later in this section. The NEEDS v5.13 database can 

be found at http://www.epa.gov/powersectormodeling/BaseCasev513.html, along with additional 

information about IPM.

Some units in the ptegu and ptegu_pk sectors are matched to CEMS data via ORIS facility codes and 

boiler ID. For these units, SMOKE replaces the 2011 emissions of NOX and SO2 with the CEMS

emissions, thereby ignoring the annual values specified in the NEI. For other pollutants, the hourly 

CEMS heat input data are used to allocate the NEI annual emissions to hourly values. All stack 

parameters, stack locations, and SCC codes for these sources come from the NEI. Because these

attributes are obtained from the NEI, the chemical speciation of VOC and PM2.5 for the sources is 

selected based on the SCC or in some cases, based on unit-specific data. If CEMS data exists for a unit, 

but the unit is not matched to the NEI, the CEMS data for that unit is not used in the modeling platform. 

However, if the source exists in the NEI and is just not matched to a CEMS unit, the emissions from that 

source would still be modeled using the annual emission value in the NEI. EIS stores many matches 

from EIS units to the ORIS facility codes and boiler IDs used to reference the CEMS data. Some 

additional matches were made in the modeling platform as described later in this section. 

In the SMOKE point flat file, emission records for point sources matched to CEMS data have values 

filled into the ORIS_FACILITY_CODE and ORIS_BOILER_ID columns. The CEMS data in SMOKEready format is available at http://ampd.epa.gov/ampd/ near the bottom of the “Prepackaged Data” tab. 

Many smaller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can 

be matched to the NEI due to inconsistencies in the way a unit is defined between the NEI and CAMD 

datasets, or due to uncertainties in source identification such as inconsistent plant names in the two data 

systems. Also, the NEEDS database of units modeled by IPM includes many smaller emitting EGUs
DRAFT

10

that are not included in the CAMD hourly CEMS programs. Therefore, there will be more units in the 

NEEDS database than have CEMS data.

For sources not matched to CEMS data (“non-CEMS” sources), daily emissions were computed from 

the NEI annual emissions using average CEMS data profiles specific to fuel type, pollutant3, and IPM 

region. To allocate to hour, diurnal profiles were created using average CEMS data for heat input 

specific to fuel type and IPM region. For future-year scenarios, there are no CEMS data available for 

specific units, but the shape of the CEMS profiles is preserved for sources that are carried into the future 

year. This method keeps the temporal behavior of the base and future year cases as consistent as 

possible. See Section 3.3.2 for more details on the temporalization approach for ptegu sources.

Finding additional matches between the NEI, NEEDS, and CEMS data

Several analytical steps were performed to better link the NEEDS units to the 2011NEIv1, along with 

implementing better matching to the CEMS data cross-referenced using “ORIS” facility and boiler IDs. 

The steps described in the 2011NEIv1 TSD have some detail on how the values in the IPM_YN column

were assigned. For the modeling platform, an initial ptipm/ptnonipm split was determined using the 

values in the SMOKE point source flat file variable “IPM_YN”, which is populated based on an EIS 

alternative facility identifier. Because EIS expects the matches to be one-to-one for an entire unit, if the 

units are not defined in the same way in EIS and NEEDS, it is not possible to store the match in EIS. 

Some IPM_YN values in the SMOKE input file were therefore manually updated based on units that 

had been matched to IPM units in past modeling platforms, but for which the alternative facility IDs in 

EIS did not include a code for IPM matching. These additions were usually needed due to one-to-many 

or many-to-one relationships between units in the EIS and the NEEDS databases. 

Additional matches between the NEI and NEEDS were identified by identifying units in IPM outputs 

that were not yet matched to NEI data, and by looking for units identified in the NEI with facility type 

codes identifying them as EGUs or facility names that indicated they were EGUs. In each case, priority 

was given to units with larger emissions (e.g., > 300TPY of NOx or SO2). The units in each data set that 

did not yet have matches within the same county were compared to one another on the basis of their 

plant names and locations. In some cases, IDs were similar but were mismatched only due to a missing 

leading zero in one of the databases. In other cases, a facility level match was specified, but a unit/boiler 

level match was not yet identified and therefore the units at the facility were compared to one another on 

the basis of design capacity and naming. For any new matches that were found, values that represented 

the NEEDS IDs were filled in to the IPM_YN in the modeling platform flat files. When possible, these 

matches were loaded into EIS.

A similar matching process was used to identify additional matches between the 2011NEIv1 and CEMS

data. To determine whether a NEI unit matched a CEMS unit, the CEMS units were compared to 

facilities in the NEI that were not yet identified as a CEMS unit on the basis of their county FIPS codes, 

locations, and total emissions of NOx and SO2. Additional CEMS matches that were found were applied 

to the FF10 file by specifying values for ORIS_FACILITY_CODE, ORIS_BOILER_ID. Because IPM 

uses a concatenation of the ORIS facility code and boiler ID, values were also filled in to the IPM_YN 

field for these units. 

 

3 The year to day profiles use NOx and SO2 CEMS for NOx and SO2, respectively. For all other pollutants, they use heat 

input CEMS data.
DRAFT

11

As a result of identifying additional matches through this analysis, many EGUs that otherwise would 

have remained in the ptnonipm sector were moved to the ptegu sector. Some of the new CEMS

assignments were loaded into EIS for use in future inventories, but some could not be loaded into EIS 

because they were not one-to-one matches (e.g. multiple EIS units corresponding to a single CEMS

unit). Note that SMOKE is more flexible when associating CEMS data with the inventory than EIS is, 

and can perform matches down to the stack or release point level. 

2.1.2 EGU peaking units sector (ptegu_pk)

The ptegu_pk sector includes sources identified by EPA as peaking units. The units were separated into 

this sector to facilitate analyses of the impact of peaking units. Aside from their inclusion in this sector, 

in all other ways they are treated in the same way as CEMS sources in the ptegu sector because all of 

them are matched to CEMS data. To identify units for inclusion in this sector, EPA made sure they

satisfied two tests: (1) the capacity factor was less than 10% over a 3 year average (2010-2012), and (2) 

the capacity factor was less than 20% in each of the 3 years. Here, “capacity factor” means either: (1) 

The ratio of a unit's actual annual electric output (expressed in MWe/hr) to the unit's nameplate capacity 

(or maximum observed hourly gross load (in MWe/hr) if greater than the nameplate capacity) times 

8760 hours; or (2) The ratio of a unit's annual heat input (in million BTUs or equivalent units of 

measure) to the unit's maximum rated hourly heat input rate (in million BTUs per hour or equivalent 

units of measure) times 8,760 hours. The list of units in the ptegu_pk sector is provided in the file (see 

the file 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2011_emissions/Peakers_CAMD_2011.080

213_NEI_IPM_match.xls). 

2.1.3 Point source oil and gas sector (pt_oilgas)

The pt_oilgas sector includes sources with the SCCs specified in the list in Table 2-3. The emissions 

and other source characteristics in the pt_oilgas sector are submitted by states, while EPA developed a 

dataset of nonpoint oil and gas emissions for each county in the U.S. with oil and gas activity that was 

available for states to use. The nonpoint emissions can be found in the np_oilgas sector. More 

information on the development of the 2011 oil and gas emissions can be found in Section 3.21 of the 

2011NEIv1 TSD.

Table 2-3. Point source oil and gas sector SCCs

SCC SCC Description*

31000309 IP;OGP;Natural Gas Processing Facilities;Compressor Seals

31000310 IP;OGP;Natural Gas Processing Facilities;Pump Seals

31000311 IP;OGP;Natural Gas Processing Facilities;Flanges and Connections

31000321 IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Niagaran Formation (Mich.)

31000322 IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Prairie du Chien Formation (Mich.)

31000323 IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Antrim Formation (Mich.)

31000324 IP;OGP;Natural Gas Processing Facilities;Pneumatic Controllers Low Bleed

31000325 IP;OGP;Natural Gas Processing Facilities;Pneumatic Controllers High Bleed >6 scfm

31000401 IP;OGP;Process Heaters;Distillate Oil (No. 2)

31000402 IP;OGP;Process Heaters;Residual Oil

31000403 IP;OGP;Process Heaters;Crude Oil

31000404 IP;OGP;Process Heaters;Natural Gas
DRAFT

12

SCC SCC Description*

31000405 IP;OGP;Process Heaters;Process Gas

31000406 IP;OGP;Process Heaters;Propane/Butane

31000411 IP;OGP;Process Heaters;Distillate Oil (No. 2): Steam Generators

31000412 IP;OGP;Process Heaters;Residual Oil: Steam Generators

31000413 IP;OGP;Process Heaters;Crude Oil: Steam Generators

31000414 IP;OGP;Process Heaters;Natural Gas: Steam Generators

31000415 IP;OGP;Process Heaters;Process Gas: Steam Generators

31000502 IP;OGP;Liquid Waste Treatment;Liquid - Liquid Separator

31000503 IP;OGP;Liquid Waste Treatment;Oil-Water Separator

31000504 IP;OGP;Liquid Waste Treatment;Oil-Sludge-Waste Water Pit

31000506 IP;OGP;Liquid Waste Treatment;Oil-Water Separation Wastewater Holding Tanks

31088801 IP;OGP;Fugitive Emissions;Specify in Comments Field

31088802 IP;OGP;Fugitive Emissions;Specify in Comments Field

31088803 IP;OGP;Fugitive Emissions;Specify in Comments Field

31088804 IP;OGP;Fugitive Emissions;Specify in Comments Field

31088805 IP;OGP;Fugitive Emissions;Specify in Comments Field

31088811 IP;OGP;Fugitive Emissions;Fugitive Emissions

31700101

Industrial Processes;NGTS;Natural Gas Transmission and Storage Facilities;Pneumatic Controllers 

Low Bleed

40400300 PSE;PLS;OGFSWT;Fixed Roof Tank: Flashing Loss

40400301 PSE;PLS;OGFSWT;Fixed Roof Tank: Breathing Loss

40400302 PSE;PLS;OGFSWT;Fixed Roof Tank: Working Loss

40400303 PSE;PLS;OGFSWT;External Floating Roof Tank with Primary Seals: Standing Loss

40400304 PSE;PLS;OGFSWT;External Floating Roof Tank with Secondary Seals: Standing Loss

40400305 PSE;PLS;OGFSWT;Internal Floating Roof Tank: Standing Loss

40400306 PSE;PLS;OGFSWT;External Floating Roof Tank: Withdrawal Loss

40400307 PSE;PLS;OGFSWT;Internal Floating Roof Tank: Withdrawal Loss

40400311 PSE;PLS;OGFSWT;Fixed Roof Tank, Condensate, working+breathing+flashing losses

40400312 PSE;PLS;OGFSWT;Fixed Roof Tank, Crude Oil, working+breathing+flashing losses

40400313 PSE;PLS;OGFSWT;Fixed Roof Tank, Lube Oil, working+breathing+flashing losses

40400314 PSE;PLS;OGFSWT;Fixed Roof Tank, Specialty Chem-working+breathing+flashing losses

40400315 PSE;PLS;OGFSWT;Fixed Roof Tank, Produced water, working+breathing+flashing losses

40400316 PSE;PLS;OGFSWT;Fixed Roof Tank, Diesel, working+breathing+flashing losses

40400321 PSE;PLS;OGFSWT;External Floating Roof Tank, Condensate, working+breathing+flashing losses

40400322 PSE;PLS;OGFSWT;External Floating Roof Tank, Crude Oil, working+breathing+flashing losses

40400323 PSE;PLS;OGFSWT;External Floating Roof Tank, Lube Oil, working+breathing+flashing losses

40400324 PSE;PLS;OGFSWT;External Floating Roof Tank, Specialty Chem-working+breathing+flashing losses

40400325 PSE;PLS;OGFSWT;External Floating Roof Tank, Produced water, working+breathing+flashing losses

40400326 PSE;PLS;OGFSWT;External Floating Roof Tank, Diesel, working+breathing+flashing losses

40400331 PSE;PLS;OGFSWT;Internal Floating Roof Tank, Condensate, working+breathing+flashing losses

40400332 PSE;PLS;OGFSWT;Internal Floating Roof Tank, Crude Oil, working+breathing+flashing losses

40400334 PSE;PLS;OGFSWT;Internal Floating Roof Tank, Specialty Chem-working+breathing+flashing losses
DRAFT

13

SCC SCC Description*

40400335 PSE;PLS;OGFSWT;Internal Floating Roof Tank, Produced water, working+breathing+flashing losses

*IP;OGP = Industrial Processes;Oil and Gas Production and

 PSE;PLS;OGFSWT=Petroleum and Solvent Evaporation;Petroleum Liquids Storage (non-Refinery);Oil and Gas 

Field Storage and Working Tanks

2.1.4 Non-IPM sector (ptnonipm)

Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the 2011NEIv1 point 

sources included in the modeling platform that are not in the ptegu, ptegu_pk, or pt_oilgas sectors. For 

the most part, the ptnonipm sector reflects the non-EGU sources of the NEI point inventory; however, it 

is likely that some small low-emitting EGUs not matched to the NEEDS database or to CEMS data are 

present in the ptnonipm sector. The sector also includes some ethanol plants that have been identified 

by EPA but are not in 2011NEIv1.

The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on 

paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Some 

point sources in the 2011NEIv1 that are not included in any modeling sectors are: 

Sources with state/county FIPS code ending with “777”. These sources represent mobile 

(temporary) asphalt plants that are only reported for some states, and are generally in a fixed 

location for only a part of the year and are therefore difficult to allocate to specific places and 

days for modeling. Therefore, these sources are dropped from the point-based sectors in the 

modeling platform.

Offshore oil records with FIPS=85000 were not updated from the 2008NEIv3 and are processed 

in the othpt sector as discussed in Section 2.5.1.

Additional Ethanol facilities

Another difference between the 2011NEIv1 data and the modeling platform is the addition of some 

ethanol production facilities identified by EPA but were not found in the NEI. For some rule 

development work, EPA developed a list of corn ethanol facilities for 2011. Many of these ethanol 

facilities were included in the 2011NEIv1, but those that were not matched were added to the ptnonipm 

sector in a separate FF10-format inventory file. Locations and FIPS codes for these ethanol plants were 

verified using web searches and Google Earth. EPA believes that some of these sources are not included 

in the NEI as point sources because they do not meet the 100 ton/year potential-to-emit threshold for 

NEI point sources. In other cases, EPA is following up with states to evaluate whether the state data 

should include these point sources.

Emission rates for the ethanol plants were obtained from EPA’s updated spreadsheet model for upstream 

impacts developed for the Renewable Fuel Standard (RFS2) rule (EPA, 2010a). Plant emission rates for 

criteria pollutants used to estimate impacts for years 2011 and 2018 are given in Table 2-4. 

Table 2-4. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced)

Corn Ethanol Plant Type VO

C

CO NO

X

PM1

0

PM2.

5

SO2

NH

3

Dry Mill Natural Gas (NG) 2.29 0.5

8

0.99 0.94 0.23 0.0

1

0.00

Dry Mill NG (wet distillers grains with solubles 

(DGS)) 2.27 0.37

0.63 0.91 0.20 0.0

0

0.00
DRAFT

14

Corn Ethanol Plant Type VO

C

CO NO

X

PM1

0

PM2.

5

SO2

NH

3

Dry Mill Biogas 2.29 0.6

2

1.05 0.94 0.23 0.0

1

0.00

Dry Mill Biogas (wet DGS) 2.27 0.3

9

0.67 0.91 0.20 0.0

0

0.00

Dry Mill Coal 2.31 2.6

5

4.17 3.81 1.71 4.5

2

0.00

Dry Mill Coal (wet DGS) 2.31 2.6

5

2.65 2.74 1.14 2.8

7

0.00

Dry Mill Biomass 2.42 2.5

5

3.65 1.28 0.36 0.1

4

0.00

Dry Mill Biomass (wet DGS) 2.35 1.6

2

2.32 1.12 0.28 0.0

9

0.00

Wet Mill NG 2.35 1.6

2

1.77 1.12 0.28 0.0

9

0.00

Wet Mill Coal 2.33 1.0

4

5.51 4.76 2.21 5.9

7

0.00

Air toxic emission rates were estimated by applying toxic to VOC ratios in Table 2-5 were multiplied by 

facility production estimates for 2011 and 2018 based on analyses performed for the industry 

characterization described in Chapter 1 of the RFS2 final rule regulatory impact analysis. For air toxics 

except ethanol, the toxic-to-VOC ratios were developed using emission inventory data from the 2005 

NEI (EPA, 2009a).

Table 2-5. 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 2011 nonpoint sources (afdust, ag, np_oilgas, rwc, nonpt)

Several modeling platform sectors were created from the 2011NEIv1 nonpoint inventory. This section 

describes the stationary nonpoint sources. Locomotives, C1 and C2 CMV, and C3 CMV are also 

included the 2011NEIv1 nonpoint data category, but are mobile sources that are described in Sections

2.4.1 and 2.4.2 as the c1c2rail and c3marine sectors, respectively. The 2011NEIv1 TSD available from 

http://www.epa.gov/ttn/chief/net/2011inventory.html includes documentation for the nonpoint sector of 

the 2011NEIv1.

The nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE due to the 

configuration of the spatial surrogates. Part of the reason for this is to prevent possible double-counting 

with county-level emissions and also because spatial surrogates for tribal data are not currently 

available. These omissions are not expected to have an impact on the results of the air quality modeling 

at the 12-km scales used for this platform. 
DRAFT

15

The emissions modeling sector inventories start with the NEI data. Several source categories were not 

included in the modeling platform inventories for the following reasons: 1) these sources are only 

reported by a small number of states or agencies, 2) these sources are ‘atypical’ and have small

emissions, and/or 3) EPA has have other data the Agency believes to be more accurate. Table 2-6

provides a list of SCCs removed from the nonpoint sectors, justification for their removal, and the 

national annual NOX, VOC and NH3 emission totals. The following subsections describe how the 

remaining sources in the 2011NEIv1 nonpoint inventory were separated into 2011 modeling platform 

sectors, along with any data that were updated replaced with non-NEI data. 

Table 2-6. 2011NEIv1 nonpoint sources removed from the 2011 platform

SCC Description Reason for 

Removal NOX

* VOC* NH3*

2280003100 Marine Vessels, Commercial; Residual; Port 

emissions

Replaced with 

OTAQ ECAIMO dataset -see 

Section 2.4.2

62,906 2,411 23

2280003200 Marine Vessels, Commercial; Residual; Underway 

emissions 817,367 30,846 151

2294000000 Paved Roads; All Paved Roads; Total: Fugitives Replaced with 

emissions NOT 

reduced via 

precipitation –

see Section 2.2.1

2294010000 Paved Roads; All Other Public Paved Roads; Total: 

Fugitives

2501060100 Gasoline Stage 2 refueling: Total

Replaced with 

MOVES 2010b--

based estimates –

see Section 2.3.2

154,349

2501060101 Gasoline Stage 2 refueling: Displacement 

Loss/Uncontrolled 6,731

2501060102 Gasoline Stage 2 refueling: Displacement 

Loss/Controlled 6,890

2501060103 Gasoline Stage 2 refueling: Spillage 2,771

2810005001 Managed Burning, Slash (Logging Debris);Pile 

Burning Replaced with 

SMARTFIRE 2

estimates -see 

Section 2.6

84.5 95

2810005002 Managed Burning, Slash (Logging Debris);Broadcast 

Burning 0 0

2810020000 Prescribed Rangeland Burning; Unspecified

2810090000 Open Fire; Not categorized

2275087000 Aircraft; In-flight (non-Landing-Takeoff cycle);Total

Dropped because 

they are atypical 

and sparselyreported 

categories with 

small emissions

2806010000 Domestic Animals Waste Emissions; Cats; Total 294

2806015000 Domestic Animals Waste Emissions; Dogs; Total 1,674

2807020001 Wild Animals Waste Emissions; Bears; Black Bears 3

2807020002 Wild Animals Waste Emissions; Bears; Grizzly Bears 0

2807025000 Wild Animals Waste Emissions; Elk; Total 1,425

2807030000 Wild Animals Waste Emissions; Deer; Total 1,431

2807040000 Wild Animals Waste Emissions; Birds; Total 0

2810003000 Cigarette Smoke; Total 2 43 4

2810010000 Human Perspiration and Respiration; Total 2,742

2830000000 Catastrophic/Accidental Releases; All; Total 0 167 0

2830010000 Catastrophic/Accidental Releases; Transportation 

Accidents; Total 0

2862000000 Swimming Pools; Total (Commercial, Residential, 

Public);Total 198

* Emission units are short tons
DRAFT

16

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 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, coal handling at coal 

mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as

point sources so they are properly located. 

The afdust sector is separated from other nonpoint sectors to allow for the application of a “transport 

fraction,” and meteorological/precipitation reductions. These adjustments are applied with a script that 

applies land use-based gridded transport fractions followed by another script that zeroes out emissions

for days on which at least 0.01 inches of precipitation occurs or there is snow cover on the ground. The 

land use data used to reduce the NEI emissions determines 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 purpose of applying the 

transport fraction and meteorological adjustments is to reduce the overestimation of fugitive dust in the 

grid modeling as compared to ambient observations. Both the transport fraction and meteorological 

adjustments are based on the gridded resolution of the platform (e.g., 12km grid cells); therefore, 

different emissions will result if the process were applied to different grid resolutions. A limitation of 

the transport fraction approach is the lack of monthly variability that would be expected with seasonal 

changes in vegetative cover. While wind speed and direction are not accounted for in the emissions 

processing, the hourly variability due to soil moisture, snow cover and precipitation is accounted for in 

the subsequent meteorological adjustment.

The sources in the afdust sector are for SCCs and pollutant codes (i.e., PM10 and PM2.5) that are 

considered to be “fugitive” dust sources. These SCCs are provided in Table 2-7.

Table 2-7. SCCs in the afdust platform sector

SCC SCC Description

2275085000 Industrial Processes;Construction: SIC 15 - 17;All Processes;Vehicle Traffic

2294000000 Industrial Processes;Construction: SIC 15 - 17;Industrial/Commercial/Institutional;Total

2294005000 Industrial Processes;Construction: SIC 15 - 17;Residential;Total

2294010000 Industrial Processes;Construction: SIC 15 - 17;Road Construction;Total

2296000000 Industrial Processes;Construction: SIC 15 - 17;Special Trade Construction;Total

2296005000 Industrial Processes;Mining and Quarrying: SIC 14;All Processes;Total

2296010000 Industrial Processes;Mining and Quarrying: SIC 14;Crushed and Broken Stone;Total

2311000070 Industrial Processes;Mining and Quarrying: SIC 14;Sand and Gravel;Total

2311010000

Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -

Crops;Harvesting

2311020000 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Planting

2311030000 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Tilling

2311040000 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Total

2325000000 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Transport

2325020000

Miscellaneous Area Sources;Agriculture Production - Livestock;Beef cattle - finishing 

operations on feedlots (drylots);Dust Kicked-up by Hooves (use 28-05-020, -001, -002, or 

-003 for Waste
DRAFT

17

SCC SCC Description

2325030000 Mobile Sources;Aircraft;Unpaved Airstrips;Total

2801000000 Mobile Sources;Paved Roads;All Other Public Paved Roads;Total: Fugitives

2801000002 Mobile Sources;Paved Roads;All Paved Roads;Total: Fugitives

2801000003 Mobile Sources;Paved Roads;Interstate/Arterial;Total: Fugitives

2801000005 Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives

2801000008 Mobile Sources;Unpaved Roads;Industrial Unpaved Roads;Total: Fugitives

2805001000 Mobile Sources;Unpaved Roads;Public Unpaved Roads;Total: Fugitives

The dust emissions in the modeling platform are not the same as the 2011NEIv1 emissions because the 

NEI paved and unpaved road dust emissions include a built-in precipitation reduction that is based on 

average meteorological data, which is at a coarser temporal and spatial resolution than the modeling 

platform meteorological adjustment. Due to this, in the platform paved and unpaved road emissions 

data was used that did not include any precipitation-based reduction. This allows the entire sector to be 

processed consistently so that the same grid-specific transport fractions and meteorological adjustments

can be applied. Where states submitted afdust data, the state-submitted data was used assuming that it 

was not met-adjusted and therefore the meteorological adjustments were still applied. Thus, it is possible 

that these sources may have been adjusted twice. Even with that possibility, air quality modeling shows 

that in general, dust is frequently overestimated in the air quality modeling results.

The total impacts of the transport fraction and meteorological adjustments for 2011NEIv1 are shown in

Table 2-8, where the starting inventory numbers include unadjusted paved and unpaved road dust, so 

they do not match the NEI values which include a different type of adjustment. The amount of the 

reduction ranges from about 6% in New Hampshire to almost 73% in Nevada. Figure 2-1 shows the 

impact of each step of the adjustment for January 2008, using the 2008 NEI as an example. The raw 

NEI afdust PM2.5 emissions – prior to transport fraction meteorological adjustments – are shown at the 

top of Figure 2-1. The afdust emissions after the application of the transport fraction, but prior to 

meteorological adjustments are shown in the middle of Figure 2-1. Finally, the resulting emissions after 

both transport fraction and meteorological adjustments are shown at the bottom of Figure 2-1. The top 

and middle plots show how the transport fraction has a larger reduction effect in the east where forested 

areas are more effective at reducing PM transport than in many western areas. Comparing the bottom 

and middle plots shows how the meteorological impacts of precipitation, along with snow cover in the 

north, further reduce the dust emissions.

Table 2-8. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory 

State

Unadjusted 

PM10

Unadjusted

PM2_5

Change 

in PM10

Change in 

PM2_5

PM10

Reduction

PM2_5

Reduction

Alabama 378,873 47,158 -310,412 -38,554 18.1% 18.2%

Arizona 237,361 30,015 -78,365 -9,757 67.0% 67.5%

Arkansas 421,958 58,648 -305,667 -40,779 27.6% 30.5%

California 255,889 38,664 -119,728 -18,039 53.2% 53.3%

Colorado 244,630 40,421 -130,902 -21,038 46.5% 48.0%

Connecticut 29,067 4,393 -26,045 -3,938 10.4% 10.4%

Delaware 11,477 2,046 -8,004 -1,437 30.3% 29.8%

District of Columbia 2,115 337 -1,597 -254 24.5% 24.6%

Florida 292,797 39,636 -181,252 -24,357 38.1% 38.5%
DRAFT

18

State

Unadjusted 

PM10

Unadjusted

PM2_5

Change 

in PM10

Change in 

PM2_5

PM10

Reduction

PM2_5

Reduction

Georgia 733,478 90,041 -593,397 -71,996 19.1% 20.0%

Idaho 432,116 49,294 -295,315 -33,293 31.7% 32.5%

Illinois 763,665 123,680 -478,200 -76,944 37.4% 37.8%

Indiana 603,153 85,151 -440,718 -61,488 26.9% 27.8%

Iowa 590,528 96,070 -342,678 -55,397 42.0% 42.3%

Kansas 748,652 118,902 -353,498 -54,883 52.8% 53.8%

Kentucky 199,744 29,496 -160,887 -23,547 19.5% 20.2%

Louisiana 236,787 35,730 -162,460 -24,039 31.4% 32.7%

Maine 50,547 7,016 -44,059 -6,137 12.8% 12.5%

Maryland 49,225 8,361 -37,212 -6,290 24.4% 24.8%

Massachusetts 205,561 22,444 -179,580 -19,567 12.6% 12.8%

Michigan 462,324 61,969 -360,229 -48,087 22.1% 22.4%

Minnesota 336,791 64,253 -221,558 -42,026 34.2% 34.6%

Mississippi 956,702 107,965 -781,604 -86,622 18.3% 19.8%

Missouri 1,064,146 130,995 -781,001 -94,632 26.6% 27.8%

Montana 385,541 50,583 -270,346 -34,134 29.9% 32.5%

Nebraska 591,457 85,206 -317,684 -45,301 46.3% 46.8%

Nevada 152,191 19,538 -44,038 -5,350 71.1% 72.6%

New Hampshire 25,540 3,766 -23,951 -3,532 6.2% 6.2%

New Jersey 24,273 5,412 -19,282 -4,270 20.6% 21.1%

New Mexico 924,497 95,871 -352,163 -36,350 61.9% 62.1%

New York 274,114 37,493 -239,314 -32,394 12.7% 13.6%

North Carolina 186,650 33,409 -146,997 -26,199 21.2% 21.6%

North Dakota 354,107 59,113 -224,340 -37,233 36.6% 37.0%

Ohio 414,902 64,609 -324,179 -49,996 21.9% 22.6%

Oklahoma 733,749 87,864 -385,316 -44,579 47.5% 49.3%

Oregon 348,093 40,596 -272,169 -30,935 21.8% 23.8%

Pennsylvania 208,246 30,344 -181,086 -26,319 13.0% 13.3%

Rhode Island 4,765 731 -3,679 -572 22.8% 21.7%

South Carolina 259,350 31,494 -198,329 -24,020 23.5% 23.7%

South Dakota 262,935 44,587 -158,320 -26,623 39.8% 40.3%

Tennessee 139,732 25,357 -108,109 -19,543 22.6% 22.9%

Texas 2,573,682 304,550

-

1,275,075 -145,799 50.5% 52.1%

Utah 196,554 21,589 -114,478 -12,534 41.8% 41.9%

Vermont 67,690 7,563 -61,971 -6,917 8.4% 8.5%

Virginia 131,797 19,374 -108,734 -15,900 17.5% 17.9%

Washington 174,969 27,999 -101,341 -15,685 42.1% 44.0%

West Virginia 85,956 10,652 -79,843 -9,900 7.1% 7.1%

Wisconsin 239,851 41,669 -166,313 -28,920 30.7% 30.6%

Wyoming 434,090 45,350 -267,536 -27,773 38.4% 38.8%
DRAFT

19

Figure 2-1. January PM2.5 afdust emissions: raw 2008 NEI (top), after application of transport fraction 

(middle) and final post-meteorological adjusted (bottom)
DRAFT

20

2.2.2 Agricultural ammonia sector (ag)

The agricultural NH3 (ag) sector includes livestock and agricultural fertilizer application emissions from 

the 2011NEIv1 nonpoint inventory. The livestock and fertilizer emissions in this sector are based only 

on the SCCs listed in Table 2-9 and Table 2-10. The “ag” sector includes all of the NH3 emissions from 

fertilizer from the NEI. However, the “ag” sector does not include all of the livestock ammonia 

emissions, as there are also a small amount of NH3 emissions from livestock feedlots in the ptnonipm 

inventory (as point sources) in California (175 tons) and Wisconsin (125 tons). 

Table 2-9. Livestock SCCs extracted from the NEI to create the ag sector

SCC SCC Description*

2805001100 Beef cattle - finishing operations on feedlots (drylots);Confinement

2805001200 Beef cattle - finishing operations on feedlots (drylots);Manure handling and storage

2805001300 Beef cattle - finishing operations on feedlots (drylots);Land application of manure

2805002000 Beef cattle production composite;Not Elsewhere Classified

2805003100 Beef cattle - finishing operations on pasture/range;Confinement

2805007100 Poultry production - layers with dry manure management systems;Confinement

2805007300 Poultry production - layers with dry manure management systems;Land application of manure

2805008100 Poultry production - layers with wet manure management systems;Confinement

2805008200 Poultry production - layers with wet manure management systems;Manure handling and storage

2805008300 Poultry production - layers with wet manure management systems;Land application of manure

2805009100 Poultry production - broilers;Confinement

2805009200 Poultry production - broilers;Manure handling and storage

2805009300 Poultry production - broilers;Land application of manure

2805010100 Poultry production - turkeys;Confinement

2805010200 Poultry production - turkeys;Manure handling and storage

2805010300 Poultry production - turkeys;Land application of manure

2805018000 Dairy cattle composite;Not Elsewhere Classified

2805019100 Dairy cattle - flush dairy;Confinement

2805019200 Dairy cattle - flush dairy;Manure handling and storage

2805019300 Dairy cattle - flush dairy;Land application of manure

2805020000 Cattle and Calves Waste Emissions;Milk Total

2805021100 Dairy cattle - scrape dairy;Confinement

2805021200 Dairy cattle - scrape dairy;Manure handling and storage

2805021300 Dairy cattle - scrape dairy;Land application of manure

2805022100 Dairy cattle - deep pit dairy;Confinement

2805022200 Dairy cattle - deep pit dairy;Manure handling and storage

2805022300 Dairy cattle - deep pit dairy;Land application of manure

2805023100 Dairy cattle - drylot/pasture dairy;Confinement

2805023200 Dairy cattle - drylot/pasture dairy;Manure handling and storage

2805023300 Dairy cattle - drylot/pasture dairy;Land application of manure

2805025000 Swine production composite;Not Elsewhere Classified (see also 28-05-039, -047, -053)

2805030000 Poultry Waste Emissions;Not Elsewhere Classified (see also 28-05-007, -008, -009)

2805030001 Poultry Waste Emissions;Pullet Chicks and Pullets less than 13 weeks old

2805030002 Poultry Waste Emissions;Pullets 13 weeks old and older but less than 20 weeks old

2805030003 Poultry Waste Emissions;Layers

2805030004 Poultry Waste Emissions;Broilers

2805030007 Poultry Waste Emissions;Ducks

2805030008 Poultry Waste Emissions;Geese

2805030009 Poultry Waste Emissions;Turkeys

2805035000 Horses and Ponies Waste Emissions;Not Elsewhere Classified

2805039100 Swine production - operations with lagoons (unspecified animal age);Confinement

2805039200 Swine production - operations with lagoons (unspecified animal age);Manure handling and storage

2805039300 Swine production - operations with lagoons (unspecified animal age);Land application of manure
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SCC SCC Description*

2805040000 Sheep and Lambs Waste Emissions;Total

2805045000 Goats Waste Emissions;Not Elsewhere Classified

2805045002 Goats Waste Emissions;Angora Goats

2805045003 Goats Waste Emissions;Milk Goats

2805047100 Swine production - deep-pit house operations (unspecified animal age);Confinement

2805047300 Swine production - deep-pit house operations (unspecified animal age);Land application of manure

2805053100 Swine production - outdoor operations (unspecified animal age);Confinement

* All SCC Descriptions begin “Miscellaneous Area Sources;Agriculture Production – Livestock”

Table 2-10. Fertilizer SCCs extracted from the NEI for inclusion in the “ag” sector

SCC SCC Description*

2801700001 Anhydrous Ammonia

2801700002 Aqueous Ammonia

2801700003 Nitrogen Solutions

2801700004 Urea

2801700005 Ammonium Nitrate

2801700006 Ammonium Sulfate

2801700007 Ammonium Thiosulfate

2801700008 Other Straight Nitrate

2801700009 Ammonium Phosphates

2801700010 N-P-K (multi-grade nutrient fertilizers)

2801700011 Calcium Ammonium Nitrate

2801700012 Potassium Nitrate

2801700013 Diammonium Phosphate

2801700014 Monoammonium Phosphate

2801700015 Liquid Ammonium Polyphosphate

2801700099 Miscellaneous Fertilizers

* All descriptions include “Miscellaneous Area Sources; 

Agriculture Production – Crops; Fertilizer Application” as 

the beginning of the description.

2.2.3 Nonpoint source oil and gas sector (np_oilgas)

The nonpoint oil and gas (np_oilgas) sector contains onshore and offshore oil and gas emissions. EPA

estimated emissions for all counties with 2011 oil and gas activity data with the Oil and Gas Tool, and 

many S/L/T agencies also submitted nonpoint oil and gas data. The types of sources covered include 

drill rigs, workover rigs, artificial lift, hydraulic fracturing engines, pneumatic pumps and other devices, 

storage tanks, flares, truck loading, compressor engines, and dehydrators. For more information on the 

development of the oil and gas emissions in the 2011NEIv1, see Section 3.21 of the 2011NEIv1 TSD. 

A complete list of SCCs for the np_oilgas modeling platform sector is provided in Appendix A. See the 

pt_oilgas sector (section 2.1.3) for more information on point source oil and gas sources.

2.2.4 Residential wood combustion sector (rwc)

The residential wood combustion (rwc) sector includes residential wood burning devices such as 

fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic 

heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and 

chimeneas. Free standing woodstoves and inserts are further differentiated into three categories: 
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conventional (not EPA certified); EPA certified, catalytic; and EPA certified, noncatalytic. Generally 

speaking, the conventional units were constructed prior to 1988. Units constructed after 1988 had to 

meet EPA emission standards and they are either catalytic or non-catalytic. For more information on the 

development of the residential wood combustion emissions, see Section 3.14 of the 2011NEIv1 TSD. 

The SCCs in the rwc sector are shown in Table 2-11.

Table 2-11. SCCs in the Residential Wood Combustion Sector (rwc)*

SCC SCC Description

2104008100 SSFC;Residential;Wood;Fireplace: general

2104008210 SSFC;Residential;Wood;Woodstove: fireplace inserts; non-EPA certified

2104008220 SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; non-catalytic

2104008230 SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; catalytic

2104008300 SSFC;Residential;Wood;Woodstove: freestanding, general

2104008310 SSFC;Residential;Wood;Woodstove: freestanding, non-EPA certified

2104008320 SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, non-catalytic

2104008330 SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, catalytic

2104008400 SSFC;Residential;Wood;Woodstove: pellet-fired, general (freestanding or FP insert)

2104008510 SSFC;Residential;Wood;Furnace: Indoor, cordwood-fired, non-EPA certified

2104008610 SSFC;Residential;Wood;Hydronic heater: outdoor

2104008700 SSFC;Residential;Wood;Outdoor wood burning device, NEC (fire-pits, chimeas, etc)

2104009000 SSFC;Residential;Firelog;Total: All Combustor Types

* SSFC=Stationary Source Fuel Combustion

2.2.5 Other nonpoint sources sector (nonpt)

Stationary nonpoint sources that were not subdivided into the afdust, ag, np_oilgas, or rwc sectors were 

assigned to the “nonpt” sector. Locomotives and CMV mobile sources from the 2011NEIv1 nonpoint 

inventory are described in Section 2.4.1. There are too many SCCs to list all of them individually, but 

the types of sources in the nonpt sector include:

stationary source fuel combustion, including industrial, commercial, and residential; 

chemical manufacturing;

industrial processes such as commercial cooking, metal production, mineral processes, petroleum 

refining, wood products, fabricated metals, and refrigeration; 

solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic 

marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances, 

and motor vehicles; 

solvent utilization for degreasing of furniture, metals, auto repair, electronics, and 

manufacturing;

solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care 

products, household products, adhesives and sealants; 

solvent utilization for asphalt application and roofing, and pesticide application; 

storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline 

service stations, aviation, and marine vessels; 

storage and transport of chemicals;
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waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;

agricultural burning and orchard heating;

miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair 

shops.

Most sources in this sector have annual emissions that are temporally allocated to hourly values using 

temporal profiles. The annual agricultural burning estimates are treated as monthly values. The annual 

values in the 2011NEIv1 were split into monthly emissions by aggregating the data up to monthly values 

from daily estimates of emissions.

2.3 2011 onroad mobile sources (onroad, onroad_rfl)

Onroad mobile sources include emissions from motorized vehicles that are normally operated on public 

roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, 

heavy-duty trucks, and buses. The sources are further divided between diesel and gasoline vehicles. 

The sector characterizes emissions from off-network processes (e.g. starts, hot soak, and extended idle)

as well as from on-network processes (i.e., from vehicles moving along the roads). For the 2011 

platform, as indicated in Table 2-1, the 2011 onroad emissions are separated into two sectors: (1) 

“onroad” and (2) “onroad_rfl”. The onroad and onroad_rfl sectors are processed separately to allow for 

different spatial allocation to be applied to onroad refueling, which is allocated using a gas station 

surrogate, versus onroad vehicles, which are allocated using surrogates based on roads and population. 

Except for California and Texas, all onroad and onroad refueling emissions are generated using the 

SMOKE-MOVES emissions modeling framework that leverages MOVES generated outputs 

(http://www.epa.gov/otaq/models/moves/index.htm) and hourly meteorology. All tribal data from the 

mobile sectors have been dropped because the emissions are small, the emissions could be doublecounted with state-provided onroad emissions, all tribal data was developed using the older model 

MOBILE6, and because spatial surrogate data is not currently available. 

2.3.1 Onroad non-refueling (onroad)

For the continental U.S., EPA used a modeling framework that took into account the temperature 

sensitivity of the on-road emissions. Specifically, EPA used MOVES inputs for representative counties, 

vehicle miles traveled (VMT) and vehicle population (VPOP) data for all counties, along with tools that 

integrated the MOVES model with SMOKE. In this way, it was possible to take advantage of the 

gridded hourly temperature information available from meteorology modeling used for air quality 

modeling. The “SMOKE-MOVES” integration tool was developed by EPA in 2010 and is in use by 

states and regional planning organizations for regional air quality modeling of onroad mobile sources. 

SMOKE-MOVES requires that emission rate “lookup” tables be generated by MOVES which 

differentiate emissions by process (i.e., 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., EPA used an automated process to run MOVES to produce emission factors by 

temperature and speed for a series of “representative counties,” to which every other county was 

mapped. Using the MOVES emission rates, SMOKE selects appropriate emissions rates for each 

county, hourly temperature, SCC, and speed bin and multiplied the emission rate by activity (VMT 

(vehicle miles travelled) or VPOP (vehicle population)) to produce emissions. These calculations were 

done for every county and grid cell, in the continental U.S. for each hour of the year. 

Using SMOKE-MOVES for creating the model-ready emissions requires numerous steps:
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1) Determine which counties will be used to represent other counties in the MOVES runs 

2) Determine which months will be used to represent other month’s fuel characteristics 

3) Create MOVES inputs needed only by MOVES. MOVES requires county-specific information 

on vehicle populations, age distributions, and inspection-maintenance programs for each of the 

representative counties.

4) Create inputs needed both by MOVES and by SMOKE, including a list of temperatures and 

activity data 

5) Run MOVES to create emission factor tables

6) Run SMOKE to apply the emission factors to activity data (VMT and VPOP) to calculate 

emissions

7) Aggregate the results to the county-SCC level for summaries and quality assurance

The onroad emissions inputs are similar to the emissions in the onroad data category of the 2011NEIv1, 

described in more detail in Section 4.6 of the 2011NEIv1 TSD. Specifically the platform and 

2011NEIv1 have identical:

MOVES County databases (CDBs)

Fuels

Representative counties

Fuel months

Meteorology

Activity data (VMT, VPOP, speed)

Extended idle adjustments

Despite the commonalities, there are some key differences between the two onroad emission inventories:

The 2011NEIv1 used MOVES2010b to create the emission factor (EF) tables, while the 2011 

platform used the MOVESTier3NPRM (specifically, model “Moves20110414a” and the default 

database “movestier3db20110512”) for most EFs. The exceptions are that refueling (described 

in the next section) and extended idle EFs were generated using the MOVES2010b model for 

both inventories because MOVESTier3NPRM did not create these emission rates for these 

modes. 

The 2011 platform used a different post-processor to create EFs for SMOKE because the 

pollutants needed for speciation and running CMAQ are different than what is needed for the 

NEI. For example, the NEI needs a much larger set of HAPs and the modeling platform requires 

emissions for the components of PM2.5.

The treatment of Texas and California emissions differs between the two inventories (see below 

for more details). 

The list of emission modes differ between the two inventories. Both SMOKE-MOVES runs 

were generated at the same level of detail, but the NEI emissions were aggregated into 4 allinclusive modes: exhaust (including extended idle), evaporative (including permeation), brake 

wear, and tire wear. The list of modes and the corresponding MOVES processes mapped to 

them are listed in Table 2-12.
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Table 2-12. Onroad emission modes

Mode Description MOVES process IDs

EXH Exhaust, including running and starts, excluding extended idle 1;2;15;16

EXT Extended idle exhaust from long-haul trucks 17;90

EVP

Evaporative emissions, including vapor venting and fuel leaks, excluding

permeation 12;13

EPM Evaporative permeation 11

RFL Refueling 18;19

BRK Brake wear 9

TIR Tire wear 10

For more detailed information on methods used to develop the onroad emissions and input data sets and 

on running SMOKE-MOVES, see the 2011NEIv1 TSD.

The California and Texas onroad emissions were created through a hybrid approach of combining statesupplied annual emissions (from the 2011NEIv1) with EPA developed SMOKE-MOVES runs. Through 

this approach, the platform was able to reflect California’s unique rules and Texas’ detailed modeling, 

while leveraging the more detailed SCCs and the highly resolved spatial patterns, temporal patterns, and 

speciation from SMOKE-MOVES. The basic steps involved in temporally allocating California’s and 

Texas’ onroad emissions based on SMOKE-MOVES results were:

1) Run CA/TX using EPA inputs through SMOKE-MOVES to produce hourly 2011 emissions

hereafter known as “EPA estimates”. These EPA estimates for CA/TX are run in a separate 

sector called “onroad_catx”.

2) Calculate ratios between state-supplied emissions and EPA estimates4. For Texas, these ratios 

were calculated for each county/SCC7 (fuel and vehicle type)/pollutant combination. For 

California, these were calculated for each county/SCC3 (fuel type)/pollutant combination. These 

were not calculated at a greater resolution because California’s emissions did not provide data 

for all vehicle types.

3) Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios. For 

extended idle adjustments, each specific state ratio (county/SCC Group (7 or 3)/pollutant) was 

multiplied by the extended idle adjustment factor (see the 2011NEIv1 TSD for details).

4) Rerun CA/TX through SMOKE-MOVES using EPA inputs and the new adjustment factor file.

Through this process, adjusted model-ready files were created that sum to California’s and Texas’ 

annual totals, but have the temporal and spatial patterns reflecting the highly resolved meteorology and 

SMOKE-MOVES. After adjusting the emissions, this sector is called “onroad_catx_adj”. Note that in 

emission summaries, the emissions from the “onroad” and “onroad_catx_adj” sectors are summed and 

designated as the emissions for the onroad sector.

 

4 These ratios were created for all matching pollutants. These ratios were duplicated for all appropriate modeling species. 

For example, EPA used the NOX ratio for NO, NO2, HONO and used the PM2.5 ratio for PEC, PNO3, POC, PSO4, and 

PMFINE (For more details on NOX and PM speciation, see Sections 3.2.3 and 3.2.2). For VOC model-species, if there was 

an exact match (e.g., BENZENE), EPA used that HAP pollutant ratio. For other VOC-based model-species that didn’t exist 

in the NEI inventory, EPA used VOC ratios.
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2.3.2 Onroad refueling (onroad_rfl)

Onroad refueling is modeled very similarly to other onroad emissions. As noted in the onroad section 

(Section 2.3.2), the refueling emissions were generated via MOVES2010b. The onroad_rfl emissions 

are spatially allocated to gas station locations (see Section 3.4.1). . Because the refueling emission 

factors use the same SCCs as the other onroad models, refueling was run in a separate sector from the 

other onroad mobile sources to allow for the different spatial allocation. To facilitate this, the refueling 

EFs were separated from the other emission factors into rate-per-distance (RPD) refueling and rate-pervehicle (RPV) refueling tables5

. SMOKE-MOVES was run using these EF tables as inputs, and 

spatially allocated using a gas stations spatial surrogate. Lastly, the SMOKE program Mrggrid 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. EPA SMOKE-MOVES generated emissions for onroad 

refueling were used without any adjustments for all states, including California and Texas. These 

emissions were used instead of state submissions to provide a consistent approach nationwide and also 

because most states did not submit refueling emissions for diesel fuel. Since the 2011NEIv1 includes the 

state-submitted emissions, the platform and the NEI refueling emissions in the nonpoint category are 

consistent for states that did not submit emissions, but are inconsistent for states that submitted refueling 

emissions.

2.4 2011 nonroad mobile sources (c1c2rail, c3marine, nonroad)

The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions 

(nonroad) and locomotive and commercial marine vessel (CMV) emissions divided into two nonroad 

sectors: “c1c2rail” and “c3marine”.

2.4.1 Class 1/Class 2 Commercial Marine Vessels and Locomotives and 

(c1c2rail)

The c1c2rail sector contains locomotive and smaller CMV sources, except for railway maintenance

locomotives and C3 CMV sources outside of the Midwest states. The “c1c2” portion of this sector name 

refers to the Class 1 and 2 CMV emissions, not the railway emissions. Railway maintenance emissions 

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

this sector are annual and at the county-SCC resolution.

The starting point for the c1c2rail sector is the 2011NEIv1 nonpoint inventory for all but specific 

Midwest states, which are instead derived from the Great Lakes 2010 CMV inventory. As discussed in 

Table 2-1 and Table 2-2, the modeling platform emissions for the c1c2rail SCCs were extracted from the 

NEI nonpoint inventory. For more information on CMV sources in the NEI, see Section 4.3 of the 

2011NEIv1 TSD. For more information on locomotives, see Section 4.4 of the 2011NEIv1 TSD. Table 

2-13 lists the NEI SCCs included in the c1c2rail sector of the modeling platform.

Table 2-13. 2011NEIv1 SCCs extracted for the starting point in c1c2rail development

SCC Description: Mobile Sources prefix for all

2280002100 Marine Vessels; Commercial; Diesel; Port

2280002200 Marine Vessels; Commercial; Diesel; Underway

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

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

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

 

5 The Moves2smk post-processing script has command line arguments that will either consolidate or split out the refueling 

EF.
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2285002009 Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines

2285002010 Railroad Equipment; Diesel; Yard Locomotives

Some differences exist between the 2011NEIv1 and the modeling platform for this sector due to the 

availability of alternative data. The differences follow.

Replaced California C1/C2 CMV and rail data with CARB data

As discussed in Section 2.4, the CARB provided year 2011 and corresponding future year emissions for 

all mobile sources, including C1/C2 CMV and locomotives. These emissions were documented in a 

staff report available at: http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf. 

The C1/C2 CMV emissions obtained from the CARB nonroad mobile dataset include the 2011

regulations to reduce emissions from diesel engines on commercial harbor craft operated within 

California waters and 24 nautical miles of the California baseline. These emissions were developed 

using Version 1 of the California Emissions Projection Analysis Model (CEPAM) that 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 mobile 

methodology, including c1c2rail sector data, is provided at:

http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles. The TOG in the CARB inventory 

was mapped to VOC by dividing the inventory TOG by the available VOC-to-TOG speciation factor

according to the SCC of the source. See Section 3.2.1.3 for more details on c1c2rail speciation. The 

RPO and CARB inventories did not include HAPs; therefore, all non-NEI source emissions in the 

c1c2rail sector were processed using VOC speciation only, rather than use the inventory BAFM.

Replaced all CMV in the Midwest RPO

Year-2010 emissions were received from the Lake Michigan Air Directors Consortium for tug boats, 

Great Lakes vessels (“Lakers”) and inland waterways for states within the Midwest RPO and Minnesota, 

hereafter simply referred to as “MWRPO” (http://www.ladco.org/). The states in the MWRPO are: 

Illinois, Indiana, Michigan, Minnesota, Ohio and Wisconsin. These MWRPO CMV emissions include 

coverage for bordering states/counties along the inland waterways such as the Mississippi and Ohio 

rivers in Iowa, Missouri, Kentucky, West Virginia, Pennsylvania and New York. The LADCO 2010 

inventory was used to replace EPA-estimated CMV emissions in the MWRPO states, but was not used 

to replace the 2011NEIv1 emissions in the bordering non-MWRPO states. 

Some modifications to the MWRPO CMV data were made prior to SMOKE processing: 

Emissions provided at the level of NEI Shape IDs were aggregated to county-level.

The 2011NEIv1 was used to determine which counties had ports; for those counties that had 

ports, 90% of emissions in the MWPRO inventory were assigned as underway 

(SCC=2280002200) and 10% were assigned as port emissions (SCC=2280002100).

Emissions were converted to short tons and PM2.5 was added by assuming it is equal to 92% of 

PM10 at the suggestion of the MWRPO.

Tugs were assigned a unique SCC (2280002021) to allow for unique spatial allocation (see 

Section 3.4.1).

Tugs were assigned from MWRPO total to counties based on 2011NEIv1 county-level activity 

information for tug vessels.
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Because the Great Lakes vessels include all CMV activity on the Great Lakes, EPA-estimated C3 CMV 

(c3marine) sector emissions (discussed in the following section) in the MWRPO states were removed to 

avoid potential double-counting of C3 CMV with the LADCO inventory in the MWRPO states.

2.4.2 Class 3 commercial marine vessels (c3marine)

The U.S. C3 CMV inventory was 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). The ECA-IMO data are used instead 

of the 2011NEIv1 data for the modeling platform because accompanying estimates of emission

projections for future years are available. In addition, the inventory preserves shipping lanes in federal 

waters while these are not stored within the NEI data. Keeping the sources in this sector separate from 

smaller CMV sources allows for the emissions to be elevated above the surface layer within the AQ 

model. The ECA-IMO data are used for all states with C3 CMV emissions. For the MWPRO states, the 

ECA-IMO C3 CMV emissions in the Great Lakes are assumed to be misclassified as C3 vessels for 

which emissions are included in the c1c2rail sector as part of the LADCO inventory, therefore the ECAIMO emissions are not included in the c3marine sector.

The development of this ECA-IMO-based C3 CMV inventory is discussed below; however, all non-U.S. 

emissions (Canadian emissions and emissions farther offshore than U.S. waters) are processed in the 

“othpt” sector, discussed later in Section 2.5.1. This splitting of the C3 CMV emissions from the farther 

offshore emissions allows for easier summaries of U.S.-only and state or county total emissions.

The ECA-IMO emissions consist of large marine diesel engines (at or above 30 liters/cylinder) that until 

recently, were allowed to meet relatively modest emission requirements, and often burn residual fuel. 

The emissions in this sector are comprised of primarily foreign-flagged ocean-going vessels, referred to 

as C3 CMV ships. The c3marine inventory includes these ships in several intra-port modes (i.e., 

cruising, hoteling, reduced speed zone, maneuvering, and idling) and an underway mode, and includes 

near-port auxiliary engine emissions. An overview of the C3 ECA Proposal to the International 

Maritime Organization (EPA-420-F-10-041, August 2010) project and future-year goals for reduction of 

NOX, SO2, and PM C3 emissions can be found at: 

http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf. The resulting ECA-IMO coordinated 

strategy, including emission standards under the Clean Air Act for new marine diesel engines with percylinder displacement at or above 30 liters, and the establishment of Emission Control Areas is at: 

http://www.epa.gov/oms/oceanvessels.htm. 

The ECA-IMO emissions data were converted to SMOKE point-source ORL input format as described 

in http://www.epa.gov/ttn/chief/conference/ei17/session6/mason.pdf. As described in the paper, the 

ASCII raster dataset was converted to latitude-longitude, mapped to state/county FIPS codes that 

extended up to 200 nautical miles (nm) from the coast, assigned stack parameters, and monthly ASCII 

raster dataset emissions were used to create monthly temporal profiles. Counties were assigned as 

extending up to 200nm from the coast because this was the distance to the edge of the U.S. Exclusive 

Economic Zone (EEZ), a distance that defines the outer limits of ECA-IMO controls for these vessels.

The base year ECA inventory is 2002 and consists of these CAPs: PM10, PM2.5, CO, CO2, NH3, NOX, 

SOX (assumed to be SO2), and hydrocarbons (assumed to be VOC). EPA developed regional growth 

(activity-based) factors that were applied to create the 2011 inventory from the 2002 data. These growth 

factors are provided in The emissions were converted to SMOKE point source inventory format,
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29

allowing for the emissions to be allocated to modeling layers above the surface layer. All non-US, nonEEZ emissions (i.e., in waters considered outside of the 200 nm EEZ, and hence out of the U.S. and 

Canadian ECA-IMO controllable domain) were simply assigned a dummy state/county FIPS

code=98001, and were projected to year 2011 using the “Outside ECA” factors in Error! Not a valid 

bookmark self-reference.. The SMOKE-ready data have been cropped from the original ECA-IMO 

entire northwestern quarter of the globe to cover only the large continental U.S. 36-km “36US1” air 

quality model domain, the largest domain used by EPA in recent years. 

. The geographic regions listed in the table are shown in Figure 2-2. The East Coast and Gulf Coast 

regions were divided along a line roughly through Key Largo (longitude 80°26’ West). 

The Canadian near-shore emissions were assigned to province-level FIPS codes and paired those to 

region classifications for British Columbia (North Pacific), Ontario (Great Lakes) and Nova Scotia (East 

Coast). 

Table 2-14. Growth factor adjustment factors to project the 2002 ECA-IMO inventory to 2011

* Technically, these are not really “FIPS” state-county codes, but are treated as such in the 

inventory and emissions processing.

The assignment of U.S. state/county FIPS codes was restricted to state-federal water boundaries data 

from the Mineral Management Service (MMS) that extend approximately 3 to 10 nautical miles (nm) off 

shore. Emissions outside the 3 to 10 mile MMS boundary, but within the approximately 200 nm EEZ 

boundaries in Figure 2-2, were projected to year 2011 using the same regional adjustment factors as the 

U.S. emissions; however, the state/county FIPS codes were assigned as “EEZ” codes and these 

emissions processed in the “othpt” sector (see Section 2.5.1). Note that state boundaries in the Great 

Lakes are an exception, extending through the middle of each lake such that all emissions in the Great 

Lakes are assigned to a U.S. county or Ontario. This holds true for MWRPO states and other states such 

as Pennsylvania and New York. The classification of emissions to U.S. and Canadian FIPS codes is 

needed to avoid double-counting of C3 CMV U.S. emissions in the Great Lakes because, as discussed in 

the previous section, all CMV emissions in the Midwest RPO are processed in the “c1c2rail” sector.

Region

EEZ 

FIPS NOX PM10 PM2.5

VOC 

(HC) CO SO2

East Coast (EC) 85004 1.301 0.500 0.496 1.501 1.501 0.536

Gulf Coast (GC) 85003 1.114 0.428 0.423 1.288 1.288 0.461

North Pacific (NP) 85001 1.183 0.467 0.458 1.353 1.353 0.524

South Pacific (SP) 85002 1.367 0.525 0.521 1.565 1.562 0.611

Great Lakes (GL) n/a 1.072 0.394 0.390 1.177 1.176 0.415

Outside ECA 98001 1.341 1.457 1.457 1.457 1.457 1.457
DRAFT

30

Figure 2-2. Illustration of regional modeling domains in ECA-IMO study

The emissions were converted to SMOKE point source inventory format, allowing for the emissions to 

be allocated to modeling layers above the surface layer. All non-US, non-EEZ emissions (i.e., in waters 

considered outside of the 200 nm EEZ, and hence out of the U.S. and Canadian ECA-IMO controllable 

domain) were simply assigned a dummy state/county FIPS code=98001, and were projected to year 

2011 using the “Outside ECA” factors in Error! Not a valid bookmark self-reference.. The SMOKEready data have been cropped from the original ECA-IMO entire northwestern quarter of the globe to 

cover only the large continental U.S. 36-km “36US1” air quality model domain, the largest domain used 

by EPA in recent years6. 

The original ECA-IMO inventory did not delineate between ports and underway emissions (or other C3 

modes such as hoteling, maneuvering, reduced-speed zone, and idling). However, a U.S. ports spatial 

surrogate dataset was used to assign the ECA-IMO emissions to ports and underway SCCs 2280003100 

and 2280003200, respectively. This had no effect on temporal allocation or speciation because all C3 

CMV emissions, unclassified/total, port and underway, share the same temporal and speciation profiles. 

See Section 3.2.1.3 for more details on c3marine speciation and Section 3.3.6 for details on temporal 

allocation.

2.4.3 Nonroad mobile equipment sources: (nonroad)

The nonroad equipment emissions are equivalent to the emissions in the nonroad data category of the 

2011NEIv1, with the exception that the modeling platform emissions also include monthly totals. 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 nonroad sector includes monthly exhaust, evaporative and refueling emissions from 

nonroad engines (not including commercial marine, aircraft, and locomotives) that EPA derived from 

 

6 The extent of the “36US1” domain is similar to the full geographic region shown in Figure 3-1. Note that this domain is not 

specifically used in this 2011 platform, although spatial surrogates that can be used with it are provided.
DRAFT

31

NMIM for all states except California and Texas. Additional details on the development of the 

2011NEIv1 nonroad emissions are available in Section 4.5 the 2011NEIv1 TSD.

California year 2011 nonroad emissions were submitted to the 2011NEIv1 and are also documented in a 

staff report (ARB, 2010a). The nonroad sector emissions in California were 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 CEPAM which supports 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).

The CARB-supplied 2011NEIv1 nonroad annual inventory emissions values were converted to monthly 

values by using the aforementioned EPA NMIM monthly inventories to compute monthly ratios by 

county, SCC7 (fuel, engine type, and equipment type group), mode, and pollutant. SCC7 ratios were 

used because the SCCs in the CARB inventory did not align with many of the SCCs in EPA NMIM 

inventory. By aggregating up to SCC7, the two inventories had a more consistent coverage of sources. 

Some VOC emissions were added to California to account for situations when VOC HAP emissions 

were included in the inventory, but there were no VOC emissions. These additional VOC emissions 

were computed by summing benzene, acetaldehyde, and formaldehyde for the specific sources. 

Texas year 2011 nonroad emissions were also submitted to the NEI. The 2011NEIv1 nonroad annual 

inventory emissions values were converted to monthly values by using EPA’s NMIM monthly 

inventories to compute monthly ratios by county, SCC7, mode, and poll7.

2.5 “Other Emissions”: Offshore Class 3 commercial marine vessels and 

drilling platforms and non-U.S. sources

The emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial Marine Vessels (C3 

CMV) and 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.

For Canada, year-2006 Canadian emissions were the starting point with the addition of several 

modifications to these inventories. The SCCs in these inventories were changed to the generic 

39999999 and the industrial code information was removed to preserve confidentiality. The Canadian 

point sources are split into three inventory files:

ptinv_canada_point_2006_orl_13aug2013_v3_orl.txt: contains point sources for all pollutants 

except VOC;

ptinv_canada_point_cb5_2006_orl_13aug2013_v1_orl.txt: contains VOC emissions split into 

CB05 species;

ptinv_canada_point_uog_2006_orl_02mar2009_v0_orl.txt: contains oil and gas-related sources.

 

7

If there was no match at county/SCC7/mode/poll, the allocation would fall back to state/SCC7/mode/poll. If that did not 

find a match, then state/SCC7 was used. For a few situations, that would also fail to match and the monthly emissions were 

allocated with a similar SCC7.
DRAFT

32

For Mexico, emissions for year 2012 are projections of their 1999 inventory originally developed by 

Eastern Research Group Inc., (ERG, 2006; ERG, 2009; Wolf, 2009) as part of a partnership between 

Mexico's Secretariat of the Environment and Natural Resources (Secretaría de Medio Ambiente y 

Recursos Naturales-SEMARNAT) and National Institute of Ecology (Instituto Nacional de EcologíaINE), the U.S. EPA, the Western Governors' Association (WGA), and the North American Commission 

for Environmental Cooperation (CEC). This inventory includes emissions from all states in Mexico. A 

background on the development of year-2012 Mexico emissions from the 1999 inventory is available at: 

http://www.wrapair.org/forums/ef/inventories/MNEI/index.html.

2.5.1 Point sources from offshore C3 CMV and drilling platforms and Canada and 

Mexico (othpt)

As discussed in Section 2.4.2, the ECA-IMO-based C3 CMV emissions for non-U.S. states are 

processed in the othpt sector. These C3 CMV emissions include those assigned to Canada, those 

assigned to the Exclusive Economic Zone (EEZ, defined as those emissions just beyond U.S. waters 

approximately 3-10 miles offshore, extending to about 200 nautical miles from the U.S. coastline), and 

all other offshore emissions –far offshore and non-U.S. These emissions are included in the othpt sector 

for simplicity of creating U.S.-only emissions summaries. Otherwise, these emissions are developed in 

the same way as the U.S. C3 CMV emissions in the c3marine sector.

For Canadian point sources, other than some minor formatting changes, the Canada-provided year-2006 

emissions were modified as follows:

i. Speciated VOC emissions from the Acid Deposition and Oxidant Model (ADOM) chemical 

mechanism were not included because EPA modeling uses speciated emissions from the CB5 

chemical mechanism, which Canada also provided.

ii. Excessively high CO emissions were removed from Babine Forest Products Ltd (British 

Columbia SMOKE plantid=’5188’) in the point inventory. This change was made at EPA’s

discretion because the value of the emissions was impossibly large.

iii. The county part of the state/county FIPS code field in the SMOKE inputs were modified in the 

point inventory from “000” to “001” to enable matching to existing temporal profiles.

iv. An update to the 2007 platform version was the removal of three units that closed in 2010: Grand 

Lake Generating Station in New Brunswick (PLANTID=’1708’, POINTID=’130011’), 

Raffinerie de Montreal-Est in Quebec (PLANTID=’3127’, POINITD=’53202982’) and Kidd 

Metallurgical Site in Ontario (PLANTID=’2815’, POINTID=’ON500004’).

Mexico point-format year-2012 inventories projected from the 1999 Mexico NEI were used essentially 

as-is with only minor formatting changes. The othpt sector also includes point source offshore oil and 

gas drilling platforms that are beyond U.S. state-county boundaries in the Gulf of Mexico. For these 

offshore emissions, the 2008 NEI version 3 point source inventory data were used because the 2011 data 

were not yet available. This is consistent with the 2011NEIv1. Updated offshore oil and gas drilling 

emissions are expected to be incorporated into version 2 of the 2011 NEI. The 2008-based offshore 

emission sources were provided by the Mineral Management Services (MMS).

2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar)

For Canada, year-2006 emissions provided by Canada and unchanged from EPA 2007 platform were 

used. Inventory files were provided for area fugitive dust, agricultural, commercial marine, railroad, 

nonroad, aircraft, and other area sources. The following adjustments were made to the original files:
DRAFT

33

i. Wildfires or prescribed burning were not included because Canada does not include these 

inventory data in their modeling. Note that SMARTFIRE 2 is used for U.S. sources only.

ii. In-flight aircraft emissions were not included because these sources are not included in the U.S. 

modeling.

iii. A 75% reduction (“transport fraction”) was applied to PM for the road dust, agricultural, and 

construction emissions in the Canadian “afdust” inventory. This approach is more simplistic 

than the county-specific approach used for the U.S., but a comparable approach was not 

available for Canada.

iv. Wind erosion (SCC=2730100000) and cigarette smoke (SCC=2810060000) emissions were 

removed from the nonpoint (nonpt) inventory because these emissions are not modeled in the 

U.S. inventory.

v. Quebec PM2.5 emissions (2,000 tons/yr) were removed for one SCC (2305070000) for Industrial 

Processes, Mineral Processes, Gypsum, Plaster Products due to corrupt fields after conversion to 

SMOKE input format. 

vi. C3 CMV SCCs (22800030X0) records were removed because, as discussed in Section 2.5.1, 

these emissions are included in the (ECA-IMO derived) othpt sector, which covers not only 

emissions close to Canada but also emissions far at sea. Canada was involved in the inventory 

development of the ECA-IMO C3 CMV inventory.

For Mexico nonpoint-format year-2012 inventories, the only significant modification was the removal of 

domestic ammonia (SCC=5555555555) (ERG, 2009; Wolf, 2009). 

2.5.3 Onroad mobile sources from Canada and Mexico (othon)

Both year-2006 Canada and year-2012 Mexico inventories (ERG, 2009; Wolf, 2009) were converted 

from their original SMOKE One-Record per Line (ORL) and Inventory Data Analyzer (IDA) formats, 

respectively, into the SMOKE Flat File 2010 (FF10) inventory format: 

http://www.cmascenter.org/smoke/documentation/3.5/html/ch08s02s07.html. Otherwise, these 

inventories were used as-is. The emission values in the Canada-provided Canadian inventories were 

unchanged from the 2007 platform.

2.6 Fires (ptfire)

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

emissions provided at geographic coordinates (point locations) and has daily emissions values. The 

ptfire sector excludes agricultural burning and other open burning sources that are included in the nonpt 

sector. Emissions are day-specific and include satellite-derived latitude/longitude of the fire’s origin and 

other parameters associated with the emissions such as acres burned and fuel load, which allow 

estimation of plume rise. Emissions for the SCCs listed in Table 2-15 are treated as point sources and 

are consistent with the fires stored in the Events data category of the 2011NEIv1. For more information 

on the development of the 2011NEIv1 fire inventory, see Section 5.1 of the 2011NEIv1 TSD.

Table 2-15. 2011 Platform SCCs representing emissions in the ptfire modeling sector

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
DRAFT

34

* The first tier level of the SCC Description is “Miscellaneous Area Sources” 

The point source day-specific emission estimates for 2011 fires rely on SMARTFIRE 2 (Sullivan, et al., 

2008), which uses the National Oceanic and Atmospheric Administration’s (NOAA’s) Hazard Mapping 

System (HMS) fire location information as input. Additional inputs include the CONSUMEv3.0 

software application (Joint Fire Science Program, 2009) and the Fuel Characteristic Classification 

System (FCCS) fuel-loading database to estimate fire emissions from wildfires and prescribed burns on 

a daily basis. The method involves the reconciliation of ICS-209 reports (Incident Status Summary 

Reports) with satellite-based fire detections to determine spatial and temporal information about the 

fires. A functional diagram of the SMARTFIRE 2 process of reconciling fires with ICS-209 reports is 

available in the documentation (Raffuse, et al., 2007). Once the fire reconciliation process is completed, 

the emissions are calculated using the U.S. Forest Service’s CONSUMEv3.0 fuel consumption model 

and the FCCS fuel-loading database in the BlueSky Framework (Ottmar, et. al., 2007).

SMARTFIRE 2 estimates were used directly for all states except Georgia and Florida. For Georgia, the 

satellite-derived emissions were removed from the ptfire inventory and replaced with a separate statesupplied ptfire inventory. Adjustments were also made to Florida as described in Section 5.1.4 of the 

2011NEIv1 TSD. These changes made the data in the ptfire inventory consistent with the data in the 

2011NEIv1.

2.7 Biogenic sources (biog)

The biogenic emissions were computed based on 2011 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, terpene, and sesquiterpene), and NO emissions for the U.S., Mexico, and Canada. The 

BEIS3.14 model is described further in: 

http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_cmas08.ppt.

The inputs to BEIS include:

Temperature data at 2 meters, which were obtained from the meteorological input files to the air

quality model,

Land-use data from the Biogenic Emissions Land use Database, version 3 (BELD3). BELD3 

data provides data on the 230 vegetation classes at 1-km resolution over most of North America.

To provide a sense of the scope and spatial distribution of the emissions, plots of annual BEIS outputs 

for isoprene and NO for 2011 are shown in Figure 2-3 and Figure 2-4, respectively.

2.8 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 model-species name “CHLORINE” was changed to 

“CL2” to support CMAQ modeling. 
DRAFT

35

Figure 2-3. Annual NO emissions output from BEIS 3.14 for 2011

Figure 2-4. Annual isoprene emissions output from BEIS 3.14 for 2011
DRAFT

36

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 2011NEIv1 were described. In Section 3, the descriptions of data are limited to

the ancillary data SMOKE uses to perform the emissions modeling steps. Note that all SMOKE inputs 

for the 2011v6 platform are available from the CHIEF Emissions Modeling Clearinghouse website (see 

Section 1).

SMOKE version 3.5.1 was used to pre-process the raw emissions inventories into emissions inputs for 

CMAQ. For projects that used CAMx, the CMAQ emissions were converted into the CAMx formats 

using CAMx convertor programs. For sectors that have plume rise, the in-line emissions capability of 

the air quality models was used, which allows the creation of 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, 

sector-specific 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 (further described in Section

3.4.2). 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;
DRAFT

37

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 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, othpt, and 

ptfire sectors are the only sectors with 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

afdust Surrogates Yes annual

ag Surrogates Yes annual

(some monthly)

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

np_oilgas Surrogates Yes annual

onroad Surrogates Yes monthly activity, 

computed hourly

onroad_rfl Surrogates Yes monthly activity, 

computed hourly

othar Surrogates Yes annual

othon Surrogates Yes annual

othpt Point Yes annual in-line

pt_oilgas Point Yes annual in-line

ptegu Point Yes daily & hourly in-line

ptegu_pk Point Yes daily & hourly in-line

ptfire Point Yes daily in-line

ptnonipm Point Yes annual in-line

rwc Surrogates Yes annual

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

plume rise. For the 2011 platform, no grouping was performed because grouping combined with “inline” processing will not give identical results as “offline” processing (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.
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38

SMOKE was run for the smaller 12-km CONtinental United States “CONUS” modeling domain 

(12US2) shown in Figure 3-1 and boundary conditions were obtained from a 2011 run of GEOS-Chem. 

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 describes the grids for the two domains.

Table 3-2. Descriptions of the 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 

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.
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39

3.2 Chemical Speciation

The emissions modeling step for chemical speciation creates the “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 2011 

platform is the CB05 mechanism (Yarwood, 2005). The same base chemical mechanism is used within 

both 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.

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) which is 

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

profiles used in the v6 platform will be published in later versions of the SPECIATE database after the 

release of this documentation. The SPECIATE database development and maintenance is a collaboration 

involving EPA’s ORD, OTAQ, and the Office of Air Quality Planning and Standards (OAQPS), in 

cooperation with 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. 

Speciation profiles and cross-references for this platform are available in spreadsheet form from 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/speciation_profiles/. The profiles are in the 

Excel files “gspro_2011.xlsx” and “gspro_combo_2011.xlsx, gsref_2011.xlsx”. The cross reference 

information is in “gsref_2011.xlsx”, and differences between 2011 and 2018 speciation profiles are 

shown in “2011ed_2018ed_gspro_differences.xlsx”. A spreadsheet showing emission totals for each 

speciation profile by modeling sector is available in the file 

“2011ed_speciation_profile_CAPs_feb112014.xlsx”.
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40

Table 3-3. Emission model species produced for CB05 with SOA for CMAQ5.0.1 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 ALD2 Acetaldehyde

ALDX Propionaldehyde and higher aldehydes

BENZENE Benzene (not part of CB05)

CH4 Methane8

ETH Ethene

ETHA Ethane

ETOH Ethanol

FORM Formaldehyde

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

ISOP Isoprene

MEOH Methanol

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

PAR Paraffin carbon bond

TOL Toluene and other monoalkyl aromatics

XYL Xylene and other polyalkyl aromatics

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

9

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)

10

PCL Particulate chloride

PNA Particulate sodium

*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 

 

8 Technically, CH4 is not a VOC but part of TOG. Although emissions of CH4 are derived, the AQ models do not use these 

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

9

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

10 These emissions are created outside of SMOKE
DRAFT

41

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 2011NEIv1 in the speciation process. Instead of 

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

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

were “integrated” with the NEI VOC. The integration process (described in more detail below) 

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 

emissions mass from VOC emissions mass and to then use a special “integrated” profile to speciate the 

remainder of VOC to the model species excluding the specific HAPs. EPA believes that generally, the 

HAP emissions from the NEI are more representative of emissions of these compounds than their 

generation via VOC speciation.

The BAFM HAPs (benzene, acetaldehyde, formaldehyde and methanol) were chosen because, with the 

exception of BENZENE, they are the only explicit VOC HAPs in the base version of CMAQ 5.0.1

(CAPs only with chlorine chemistry) model. Explicit means that they are not lumped chemical groups 

like the other CB05 species. These “explicit VOC HAPs” are model species that participate in the 

modeled chemistry using the CB05 chemical mechanism. The use of these HAP emission estimates 

along with VOC is called “HAP-CAP integration”. BENZENE was chosen because it is a model 

species in the base version of CMAQ 5.0.1, and there was a desire to keep its emissions consistent 

between multi-pollutant and base versions of CMAQ. 

For specific sources, especially within the onroad and onroad_rfl sectors, the integration included 

ethanol. To differentiate when a source was integrating BAFM versus EBAFM (ethanol in addition to 

BAFM), the speciation profiles that do not include ethanol are referred to as an “E-profile” (to be used 

when the ethanol comes from the inventory pollutant). For example, use E10 headspace gasoline 

evaporative speciation profile 8763 when ethanol is speciated from VOC, but use 8763E when ethanol is 

obtained directly from the inventory. 

The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats other 

than PTDAY (the format used for the ptfire sector). SMOKE allows the user to specify both the 

particular HAPs to integrate via the INVTABLE and the particular sources to integrate via the 

NHAPEXCLUDE file (which actually provides the sources to be excluded from integration11). For the 

“integrated” sources, SMOKE subtracts the “integrated” HAPs from the VOC (at the source level) to 

compute emissions for the new pollutant “NONHAPVOC.” The user provides NONHAPVOC-toNONHAPTOG factors and NONHAPTOG speciation profiles12. SMOKE computes NONHAPTOG and 

then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC 

species not including the integrated HAPs. After determining if a sector is to be integrated, if all sources 

 

11 In SMOKE version 3.5, the options to specify sources for integration are expanded so that a user can specify the particular 

sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector. In 

addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, but it is 

missing BAFM or VOC, SMOKE will now raise an error.

12 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified list 

of pollutants, for example BAFM.
DRAFT

42

have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a 

NHAPEXCLUDE file. If on the other hand, certain sources do not have the necessary HAPs, then an

NHAPEXCLUDE file must be provided based on the evaluation of each source’s pollutant mix. EPA 

considered CAP-HAP integration for all sectors and developed “integration criteria” for some of them 

(see Section 3.2.1.3 for details).

The process of partial integration for BAFM is illustrated in Figure 3-2 that the BAFM records in the 

input inventories do not need to be removed from any sources in a partially integrated sector because 

SMOKE does this automatically using the INVTABLE configuration. For EBAFM integration, this 

process is identical to that shown in the figure except for the addition of ethanol (E) to the list of 

subtracted HAP pollutants. For full integration, the process would be very similar except that the 

NHAPEXCLUDE file would not be used and all sources in the sector would be integrated.

Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation

In SMOKE, the INVTABLE allows the user to specify both the particular HAPs to integrate. Two 

different types of INVTABLE files are included for use with different sectors of the platform. For 

sectors that had no integration across the entire sector (see Table 3-4), EPA created a “no HAP use” 

INVTABLE in which the “KEEP” flag is set to “N” for BAFM pollutants. Thus, any BAFM pollutants 

in the inventory input into SMOKE are automatically dropped. This approach both avoids doublecounting of these species and assumes that the VOC speciation is the best available approach for these 

species for sectors using this approach. The second INVTABLE, used for sectors in which one or more 

sources are integrated, causes SMOKE to keep the inventory BAFM pollutants and indicates that they 

are to be integrated with VOC. This is done by setting the “VOC or TOG component” field to “V” for 

all four HAP pollutants. This type of INVTABLE is further differentiated into a version for those 

sectors that integrate BAFM and another for those that integrate EBAFM, such as the onroad and 

onroad_rfl sectors.
DRAFT

43

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)

ptegu No integration 

ptegu_pk No integration 

ptnonipm No integration

ptfire No integration 

othar No integration

othon No integration 

ag N/A – sector contains no VOC 

afdust N/A – sector contains no VOC

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

nonpt Partial integration (BAFM and EBAFM)

np_oilgas Partial integration (BAFM)

pt_oilgas Partial integration (BAFM)

rwc Partial integration (BAFM)

nonroad Partial integration (BAFM)

c1c2rail Partial integration (BAFM)

othpt Partial integration (BAFM)

c3marine Full integration (BAFM)

onroad Full integration (EBAFM and BAFM)

onroad_rfl Full integration (EBAFM and BAFM)

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

Section 3.2.1.3.

3.2.1.2 County specific profile combinations (GSPRO_COMBO)

SMOKE can compute speciation profiles from mixtures of other profiles in user-specified 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).This feature was used to speciate onroad and nonroad mobile and gasoline-related related 

stationary sources that use fuels with varying ethanol content. In these cases, 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 GSPRO_COMBO 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, sector specific GSPRO_COMBO files are used. 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, a combination of BAFM and EBAFM integration is used. Due to the lack of SCCspecificity 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, EPA changed the 

pollutant name for the PFC future year inventory so the integration would use EVP__NONHAPVOC to 
DRAFT

44

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 (BAFM HAPs subtracted from 

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

integration for each platform sector. 

For the c1c2rail sector, EPA integrated BAFM for most sources from the 2011NEIv1. There were a few 

sources that had zero BAFM; therefore, they were not integrated. The MWRPO and CARB inventories 

(see Section 2.4.1) did not include HAPs; therefore, all non-NEI source emissions in the c1c2rail sector 

were not integrated. For California, the CARB inventory TOG was converted to VOC by dividing the 

inventory TOG by the available VOC-to-TOG speciation factor. 

For the othpt sector, the C3 marine sources (see Section 2.4.2) are integrated. HAPs in this sector are 

derived identically to the U.S. c3marine sector. The rest of the sources in othpt are not integrated, thus 

the sector is partially integrated.

For the onroad and onroad_rfl sectors, there are series of unique speciation issues. First, SMOKEMOVES (see Sections 2.3.1 and 2.3.2) is used to estimate these sectors, meaning 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 full integration of EBAFM (i.e. use E-profiles) and the diesel sources use full 

integration of BAFM. Fourth, the onroad sector utilizes 6 different modes for speciation: exhaust, 

extended idle, evaporative, permeation (gasoline vehicles only), brake wear, and tire wear (See Table 

2-12 for more details). The onroad_rfl sector utilizes an additional 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)

13. Sixth, for California and Texas, EPA 

applied adjustment factors to SMOKE-MOVES to produce California and Texas adjusted model-ready 

files (see Section 2.3.1 for details). By applying the ratios through SMOKE-MOVES, the CARB and 

Texas inventories are essentially speciated to match EPA estimated speciation grid cell by grid cell. The 

future year CARB inventories did not have BAFM, so EPA estimates of BAFM were adjusted using 

VOC adjustment factors for California only.

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

because NMIM computed only VOC and not any HAPs for these SCCs. All other nonroad sources were 

integrated except in California. For California, the CARB inventory TOG was converted to VOC by 

dividing the inventory TOG by the available VOC-to-TOG speciation factor. SMOKE later applies the 

same VOC-to-TOG 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 does not include HAP estimates for all sources; therefore, the sources which have 

VOC but do not have BAFM or BAFM is greater than VOC are not integrated. The remaining sources 

are integrated. The future year CARB inventories did not have BAFM so all sources for California were 

 

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

45

not integrated. Similar to onroad, the gasoline exhaust profiles were updated to 8750a and 8751a (this is 

true nation-wide).

For the ptnonipm sector, the 2011 and 2018 runs were not integrated. This was an oversight— it should 

have been partial integration because the 2011 ethanol inventory (SCC 30125010) provided by OTAQ 

includes BAFM. In the future year, ptnonipm should be partially integrated because both the ethanol 

and biodiesel inventories (SCC 30125010) provided by OTAQ include BAFM. Aircraft emissions use 

the profile 5565b which is chemically equivalent to 5565 (aircraft exhaust) in SPECIATE 4.3 database. 

The profile numbers are differentiated from each other because a draft version of 5565 was used in 

previous modeling platforms.

For most sources in the rwc sector, the VOC emissions were greater than or equal to BAFM, and BAFM 

was not zero, so those sources were integrated, although a few specific sources that did not meet these 

criteria could not be integrated.

For the oil and gas sources in np_oilgas and pt_oilgas, the basins studied in WRAP Phase III have basinspecific 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 3-5 lists the basin and gas composition specific 

profiles used for the sources in the WRAP Phase III basins. For oil and gas sources outside of the 

WRAP Phase III basins, the profiles did not vary by region or basin (see Table 3-6). Table 3-7 lists the 

WRAP Phase III counties.

Table 3-5. VOC profiles for WRAP Phase III basins

Profile Code Description

DJFLA D-J Basin Flashing Gas Composition for Condensate

DJVNT D-J Basin Produced Gas Composition

PNC01 Piceance Basin Gas Composition at Conventional Wells

PNC02 Piceance Basin Gas Composition at Oil Wells

PNC03 Piceance Basin Flashing Gas Composition for Condensate

PRBCO Powder River Basin Produced Gas Composition for Conventional Wells

PRM01 Permian Basin Produced Gas Composition

SSJCO South San Juan Basin Produced Gas Composition for Conventional Wells

SWE01 Wyoming Flashing Gas Composition

SWFLA SW Wyoming Basin Flash Gas Composition

SWVNT SW Wyoming Basin Vented Gas Composition

UNT02 Uinta Basin Gas Composition at Conventional Wells

UNT03 Uinta Basin Flashing Gas Composition for Oil

UNT04 Uinta Basin Flashing Gas Composition for Condensate

WRBCO Wind River Basin Produced Gas Composition for Conventional Wells

Table 3-6. National VOC profiles for oil and gas

profile Description

0000 Over All Average 

0001 External Combustion Boiler - Residual Oil 
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46

profile Description

0002 External Combustion Boiler - Distillate Oil 

0003 External Combustion Boiler - Natural Gas 

0004 External Combustion Boiler - Refinery Gas 

0007 Natural Gas Turbine 

0008 Reciprocating Diesel Engine 

0051 Flares - Natural Gas 

0296 Fixed Roof Tank - Crude Oil Production 

1001 Internal Combustion Engine - Natural Gas 

1010 Oil and Gas Production - Fugitives - Unclassified 

1011 Oil and Gas Production - Fugitives - Valves and Fittings - Liquid Service 

1012 Oil and Gas Production - Fugitives - Valves and Fittings - Gas Service 

1207 Well Heads (Water Flood) Composite 

2487 Composite of 7 Emission Profiles from Crude Oil Storage Tanks - 1993 

2489 Composite of 15 Fugitive Emission Profiles from Petroleum Storage Facilities - 1993 

Table 3-7. 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

08103 CO Rio Blanco 

08107 CO Routt 

08115 CO Sedgwick 

08121 CO Washington 

FIPS State County

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

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

FIPS State County

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

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
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FIPS State County

48495 TX Winkler

48501 TX Yoakum

49007 UT Carbon 

49009 UT Daggett 

49013 UT Duchesne 

49015 UT Emery 

49019 UT Grand 

49043 UT Summit 

FIPS State County

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 

For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. The 2011 

platform uses BEIS3.14, which includes a new species (SESQ) that was mapped to the model species 

SESQT. The profile code associated with BEIS3.14 profiles for use with CB05 uses the profile: 

“B10C5.”

For the nonpt sector, where VOC emissions were greater than or equal to BAFM and BAFM was not 

zero, the sources were integrated. For portable fuel containers (PFCs) and fuel distribution operations 

associated with the bulk-plant-to-pump (BTP) distribution, ethanol may be mixed into the fuels; therefore, 

county- and month-specific COMBO speciation was used (via the GSPRO_COMBO file). Refinery to 

bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation are considered upstream 

from the introduction of ethanol into the fuel; therefore a single profile is sufficient for these sources. No

refined information on potential VOC speciation differences between cellulosic diesel and cellulosic 

ethanol sources was available, therefore cellulosic diesel and cellulosic ethanol sources used the same 

SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC 

speciation as was used for corn ethanol plants. For the future year, PFC and the cellulosic sources were 

integrated EBAFM (i.e. used E-profiles) because ethanol was present in those inventories.

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 account for the changes in 

ethanol content of fuels across years. A description of the actual fuel formulations for 2011 can be found 

in the 2011NEIv1 TSD, and for 2018 see Section 4.3. For 2011, EPA used “COMBO” profiles to model

combinations of profiles for E0 and E10 fuel use. For 2018, EPA used “COMBO” profiles to model 

combinations of E10 and E85 fuel use. The speciation of onroad exhaust VOC also accounts for a portion 

of the vehicle fleet meeting Tier 2 standards in that different exhaust profiles are available for pre-Tier 2

versus Tier 2 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 Tier 2 vehicles 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, in which 

case speciation would change across years. The speciation changes from fuels in the ptnonipm sector 
DRAFT

48

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 PFCs, ethanol was present in the future inventories and therefore 

EBAFM profiles were used to integrate ethanol in the speciation. The mapping of fuel distribution SCCs 

to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix B.

Table 3-8 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for 

2011 and the future year case. 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. 

Table 3-8. Select VOC profiles 2011 versus 2018

Sector subcategory 2011 2018

onroad gasoline 

exhaust

COMBO: COMBO:

8750aE Pre-Tier 2 E0 exhaust 8751E Pre-Tier 2 E10 exhaust

8751aE Pre-Tier 2 E10 exhaust 8757E Tier 2 E10 Exhaust

8756E Tier 2 E0 Exhaust 8758E Tier 2 E15 Exhaust

8757E Tier 2 E10 Exhaust 8855E Tier 2 E85 Exhaust

onroad gasoline 

evaporative

COMBO: COMBO

8753E E0 Evap 8754E E10 Evap

8754E E10 Evap 8872E E15 Evap

8934E E85 Evap

onroad gasoline 

permeation

COMBO: COMBO

8766E E0 evap perm 8769E E10 evap perm

8769E E10 evap perm 8770E E15 evap perm

8934E E85 Evap

onroad_rfl gasoline 

refueling

COMBO: COMBO

8869E E0 Headspace 8870E E10 Headspace

8870E E10 Headspace 8871E E15 Headspace

8934E E85 Evap

onroad

diesel 

exhaust 87710

Weighted diesel exhaust for 

2010 877P0

Weighted diesel exhaust 

for 2020

onroad

diesel 

extended 

idle 877P0

Weighted diesel exhaust for 

2020 877EIT3

Weighted diesel extended 

idle for 2018

onroad

diesel 

evaporative 4547 Diesel Headspace 4547 Diesel Headspace

onroad_rfl

diesel 

refueling 4547 Diesel Headspace 4547 Diesel Headspace

nonroad gasoline 

exhaust

COMBO: 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

8753 E0 evap
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49

Sector subcategory 2011 2018

8754 E10 evap

nonroad gasoline 

refueling

COMBO: 8870 E10 Headspace

8869 E0 Headspace

8870 E10 Headspace

nonroad

diesel 

exhaust 8774 Pre-2007 MY HDD exhaust 8774 Pre-2007 MY HDD exhaust

nonroad

diesel 

evaporative 4547 Diesel Headspace 4547 Diesel Headspace

nonroad

diesel 

refueling 4547 Diesel Headspace 4547 Diesel Headspace

nonpt/ptnonipm PFC

COMBO: COMBO

8869E E0 Headspace 8870E E10 Headspace

8870E E10 Headspace 8871E E15 Headspace

8934E E85 Evap

nonpt/ptnonipm BTP

COMBO: COMBO

8869 E0 Headspace 8870 E10 Headspace

8870 E10 Headspace 8871 E15 Headspace

8934 E85 Evap

nonpt/ptnonipm BPS/RBT 8869 E0 Headspace 8869 E0 Headspace

3.2.2 PM speciation

3.2.2.1 AE5 versus AE6 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 3-9). EPA speciated PM2.5 so that it included both AE5 and AE6 

PM model species without causing any double counting. Therefore, emissions from this platform can be 

used with either CMAQ 4.7.1 or CMAQ 5.0.1. 

Table 3-9. 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
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50

species name species description AE5 AE6

PSI silica N Y

PTI titanium N Y

PCA calcium N Y

PMG magnesium N Y

PK potassium N Y

PMN manganese N Y

PNA sodium N Y

PCL chloride N Y

PH2O water N Y

PMOTHR PM2.5 not in other AE6 species N Y

The majority of the 2011 platform PM profiles come from the 911XX series which include updated AE6 

speciation14. Appendix C contains a crosswalk between AE6 profile codes and SPECIATE 4.3 profile 

codes. Summaries of 2011 and 2018 emissions by county, month, and sector speciated according to AE5 

and AE6 speciation profiles are available from 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/ in the files 

2011ed_county_monthly_report_PM_AE5.xlsx, 2011ed_county_monthly_report_PM_AE6.xlsx, 

2018ed_county_monthly_report_PM_AE5.xlsx, and 2018ed_county_monthly_report_PM_AE6.xlsx. In 

addition, the file 2011ed_speciation_profiles_6feb2014.xlsx shows the total PM2.5 apportioned to each 

sector.

3.2.2.2 Onroad PM speciation

Unlike other sectors, the onroad sector has pre-speciated PM. This speciated PM comes from the 

MOVES model and is processed through the SMOKE-MOVES system (see Section 2.3.1). 

Unfortunately, the MOVES speciated PM does not map 1-to-1 to the AE5 speciation (nor the AE6 

speciation) needed for CMAQ modeling. Table 3-10 shows the relationship between MOVES15 exhaust 

PM2.5 related species and CMAQ AE5 PM species.

Table 3-10. MOVES exhaust PM species versus AE5 species

MOVES2010b Pollutant Name Variable name 

for Equations Relation to AE5 model species

Primary Exhaust PM2.5 – Total PM25_TOTAL

Primary PM2.5 - Organic Carbon PM25OM Sum of POC, PNO3 and PMFINE

Primary PM2.5 - Elemental Carbon PM25EC PEC

Primary PM2.5 - Sulfate Particulate PM25SO4 PSO4

MOVES species are related as follows: 

PM25_TOTAL = PM25EC + PM25OM + PSO4

The five CMAQ AE5 species also sum to total PM2.5: 

 

14 The exceptions are 5674 (Marine Vessel – Marine Engine – Heavy Fuel Oil) used for c3marine and 92018 (Draft Cigarette 

Smoke – Simplified) used in nonpt.

15 The Tier3 NPRM MOVES model has the same PM components as MOVES2010b. MOVES2014 is expected to have a oneto-one mapping of PM species to CMAQ PM species.
DRAFT

51

PM2.5 = POC+PEC+PNO3+PSO4+PMFINE

The basic problem is to differentiate MOVES species “PM25OM” into the component AE5 species (POC, 

PNO3 and PMFINE). The Moves2smkEF post-processor script takes the MOVES species (EF tables) 

and calculates the appropriate AE5 PM2.5 species and converts them into a format that is appropriate for 

SMOKE (see http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch05s02s04.html for details 

on the Moves2smkEF script). For a more detailed discussion of the derivation of these equations, see 

Appendix D.

For brake wear and tire wear PM, total PM2.5 (not speciated) comes directly from MOVES. These PM 

modes are speciated by SMOKE. PMFINE from onroad exhaust is further speciated by SMOKE into the 

component AE6 species.

For California and Texas, adjustment factors were applied to SMOKE-MOVES to produce California and 

Texas adjusted model-ready files (see Section 2.3.1 for details). California and Texas did not supply 

speciated PM, therefore the adjustment factors applied to PM2.5 were also applied to the speciated PM 

components. By applying the ratios through SMOKE-MOVES, the CARB and Texas inventories are 

essentially speciated to match EPA estimated speciation grid cell by grid cell.

3.2.3 NOX speciation

NOX can be speciated into NO, NO2, and/or HONO. For the non-mobile sources, EPA used a single 

profile “NHONO” to split NOX into NO and NO2. For the mobile sources except for onroad (including 

nonroad, c1c2rail, c3marine, othon sectors) and for specific SCCs in othar and ptnonipm, the profile 

“HONO” splits NOX into NO, NO2, and HONO. Table 3-11 gives the split factor for these two profiles.

Table 3-11. NOX speciation profiles

profile pollutant species split factor

HONO NOX NO2 0.092

HONO NOX NO 0.9

HONO NOX HONO 0.008

NHONO NOX NO2 0.1

NHONO NOX NO 0.9

The onroad sector does not use the “HONO” profile to speciate NOX. MOVES2010b produces speciated 

NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables 

used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOX. The NO 

fraction varies by heavy duty versus light duty, fuel type, and model year. The NO2 fraction = 1 – NO –

HONO. For more details on the NOX fractions within MOVES, see 

http://www.epa.gov/otaq/models/moves/documents/420r12022.pdf. HONO is not calculated directly by 

the Tier 3 proposal version of MOVES. For these EF tables, the calculation of HONO and the NO2

fraction are calculated externally by the moves2smk script16. The SMOKE-MOVES system then models 

these species directly without further speciation.

 

16 A specific version of the moves2smk script was developed to do this calculation of HONO. The typical version assumes that 

HONO was calculated directly by MOVES2010b.
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3.3 Temporal Allocation

Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer 

temporal resolution, thereby 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 aggregated emissions and if needed 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 

2011 air quality modeling platform were similar to those used for the 2007 platform. The temporal 

factors applied to the inventory are selected using some combination of country, state, county, SCC, and 

pollutant. Table 3-12 summarizes the temporal aspects of emissions modeling by comparing the key 

approaches used for temporal processing across the sectors. 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 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). 

Table 3-12. 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

ptegu Daily & hourly all all Yes

ptegu_pk Daily & hourly all all Yes

ptnonipm Annual yes mwdss mwdss Yes

pt_oilgas Annual yes mwdss mwdss Yes

ptfire Daily all all 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

np_oilgas Annual yes mwdss mwdss Yes

rwc Annual no met-based All Yes

ag Annual yes all all Yes
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53

Platform 

sector short 

name

Inventory 

resolutions

Monthly 

profiles 

used?

Daily 

temporal 

approach

Merge 

processing 

approach

Process 

Holidays as 

separate days

afdust_adj Annual yes week all Yes

beis 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.

The following values are used in the table: 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. 

Spreadsheets summarizing the temporal profiles and cross-references used in the modeling are available 

with the other 2011 platform reports in 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/temporal_profiles/ with the profiles in the 

files tpro_2011.xlsx, tpro_onroad_2011.xlsx, tpro_rwc_2011.xlsx, and the cross references in 

tref_2011.xlsx. The file 2011ed_temporal_profiles_CAPs_feb112014.xlsx shows the emissions assigned 

to each of the profiles for sectors other than onroad, ptegu, and ptegu_pk.

In addition to the resolution, temporal processing includes a ramp-up period for several days prior to 

January 1, 2011, which is intended to mitigate the effects of initial condition concentrations. The ramp-up 

period was 10 days (December 22-31, 2010). For most sectors, emissions from December 2011 were 

used to fill in surrogate emissions for the end of December 2010. In particular, December 2011 emissions 

(representative days) were used for December 2010. For biogenic emissions, December 2010 emissions 

were processed using 2010 meteorology.

3.3.1 Use of FF10 format for finer than annual emissions

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 3.5.1 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 
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54

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 Electric Generating Utility temporalization (ptegu, ptegu_pk)

3.3.2.1 Base year temporal allocation of EGUs

The 2011NEIv1 annual EGU emissions are allocated to hourly emissions using the following 3-step 

methodology: annual value to month, month to day, and day to hour. The temporal allocation procedure 

is differentiated by whether or not the source could be directly matched to a CEMS unit via ORIS facility 

code and boiler ID. Prior to temporal allocation, as many sources as possible were matched to CEMS

data via ORIS facility code and boiler ID. EIS stores a base set of previously matched units via alternate 

facility and unit IDs. For any units not yet matched, reports were generated by unit to identify potential 

matches with the NEI. The reports included FIPS state/county code, facility name, and NOx and SO2

emissions. Units were considered matches if the FIPS state/county code matched, the facility name was 

similar, and the NOx and SO2 emissions were similar.

For sources not matched to CEMS measurements, the first two steps of the allocation are done outside of 

SMOKE. For sources in the ptegu and ptegu_pk sectors that are matched to CEMS data, annual totals of 

the emissions may be different than the annual values in 2011NEIv1 because the CEMS data actually 

replaces the inventory data. All units in the ptegu_pk sector with non-zero emissions for 2011 were 

matched to CEMS data.

For units not matched to CEMS data, the allocation of the inventory annual emissions to months is done 

using average fuel-specific season-to-month factors generated for each of the 64 IPM regions shown in 

Figure 3-3. These factors are based 2011 CEMS data only. In each region, separate factors were 

developed for the fuels coal, natural gas, and “other”, where the types of fuels included in “other” vary by 

region. Separate profiles were computed for NOx and SO2, and heat input. An overall composite profile 

was also computed and was used in a few cases in which the fuel-specific profile was too irregular, or 

there were no CEMS units with the specified fuel in the region containing the unit. For both CEMS and 

non-CEMS matched units, NOx and SO2 CEMS data are used to allocate NOx and SO2 emissions, while 

CEMS heat input data is used to allocate all other pollutants. 

Daily “temporal allocation” of units with CEMS was performed using a procedure similar to that in the 

first step in that the CEMS data replaces the inventory data for each pollutant. For units without CEMS 

data, emissions were allocated from month to day using IPM-region and fuel-specific average month-today factors based on the 2011 CEMS data. Separate allocation factors were computed for NOx, SO2, and 

heat input for the fuels coal, natural gas, and other. For both CEMS and non-CEMS matched units, NOx

and SO2 CEMS data are used to allocate NOx and SO2 emissions, while CEMS heat input data is used to 

allocate all other pollutants. 

For units with associated CEMS data, hourly emissions use the hourly CEMS values as described above

for NOx and SO2, while other pollutants are allocated according to heat input values. For units without 

CEMS data, temporal profiles from days to hours are computed based on the region- and fuel-specific 

average day-to-hour factors derived from the CEMS data for those fuels and regions using data from the 

entire year. For non-CEMS matched units, CEMS heat input data is used to allocate all pollutants 

(including NOx and SO2). SMOKE then allocates the daily emissions data to hours using the profiles 

obtained from the CEMS data for the analysis base year. 
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55

Figure 3-3. IPM Regions for EPA Base Case v5.13

3.3.2.2 Future year temporal allocation of EGUs

IPM provides unit-level emission projections of average winter (representing October through April) and 

average summer (representing May through September) values. These annualized emissions are allocated 

to hourly emissions using a 3-step methodology: annualized summer/winter value to month, month to 

day, and day to hour. The first two steps are done outside of SMOKE and the third step is done by 

SMOKE using daily emissions files created from the first two steps. This approach maximizes the use of

the CEMS data from the air quality analysis year (e.g., 2011).

For CEMS matched units, the 2011 based CEMS were scaled so that their seasonal emissions matched 

IPM totals. In other words, EPA created a set of artificial CEMS data which had the same temporal 

pattern as 2011, but for which the seasonal total emissions matched IPM’s predictions for 2018. Except 

for the scaling of CEMS data, the procedure for allocating the emissions of CEMS matched units is the 

same as the base year (see Section 3.3.2.1). For sources not matched to CEMS units, the allocation of the 

IPM seasonal emissions to months was done using average fuel-specific season-to-month factors 

generated for each of the 64 IPM regions shown in Figure 3-3. These factors are based on a single year of 

CEMS data consistent with the modeling base year, in this case 2011. Similar to the base year, profiles 

were created for coal, natural gas, and “other” fuel. For each fuel, separate profiles were computed for 

NOx, SO2, and heat input. An overall composite profile was also computed in the event that a fuel-specific 

profile was too irregular or in the case when a unit changed fuels between the base and future year and 
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56

there were previously no units with that fuel in the specific region. Except for the season-to-month 

allocation, the procedure for allocating the emissions of non-CEMS matched units is the same as the base 

year. 

Units with year-specific impacts in the season-to-month allocations, such as long-duration downtimes for 

maintenance or installation of controls that occur only in one year were temporalized using average 

profiles instead of using the anomalous profile for the base year. These situations are determined by 

analysis of the base and future year data. Note that IPM uses load data (reflecting the shape of demand) 

corresponding to the load in each IPM region that occurred in the base year of the air quality modeling 

analysis, such as 2011.

Some refinements to the above approach were made in some special cases:

When emissions were substantially higher for units with limited hours of operation in the base 

year, an averaged profile was used. 

When a unit switched fuels in the future year to a fuel not used in the base year, the profile was 

selected according to the new type of fuel. If the unit was a CEMS unit in the base year, it was 

treated as non-CEMS in the future year.

When a CEMS unit operated in only one season in the base year, but IPM predicted that there 

were emissions in both seasons, an average profile was used for the future year unit during both 

seasons.

New units coming on line used the appropriate region and fuel-specific profiles

Units that are not new but had no emissions in 2011 were treated like new units.

For more information on the development of IPM emission estimates and the temporalization of those, 

see http://www.epa.gov/powersectormodeling/BaseCasev513.html, in particular the Air Quality Modeling 

Flat File Documentation and accompanying inputs. 

3.3.3 Residential Wood Combustion Temporalization (rwc)

There are many factors that impact the timing of when emissions occur, and for some sectors this includes 

meteorology. The benefits of utilizing meteorology as method for temporalization are: (1) a

meteorological dataset consistent with that 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.

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, meteorologicalbased 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
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57

and http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch05s03s07.html, respectively.

For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal 

allocation of emissions to days. GenTPRO was used to create an annual-to-day temporal profile for the 

RWC sources. These generated profiles distribute 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

for most sources in the sector 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

temperature threshold for rwc emissions was 50  ̊F for most of the country, and 60  ̊F for the following 

states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and 

Texas.

Figure 3-4 illustrates the impact of changing the temperature threshold for a warm climate county. The 

plot shows the temporal fraction by day for Duval County, Florida for the first four months of 2007. The 

default 50  ̊F threshold creates large spikes on a few days, while the 60  ̊F threshold dampens these spikes 

and distributes a small amount of emissions to the days that have a minimum temperature between 50 and 

60  ̊F.

Figure 3-4. Example of RWC temporalization in 2007 using a 50 versus 60  ̊F threshold

The diurnal profile for used for most RWC sources (see Figure 3-5) places more of the RWC emissions in 

the morning and the evening when people are typically using these sources. This profile is based on a 

2004 MANE-VU survey based temporal profiles (see 

http://www.marama.org/publications_folder/ResWoodCombustion/Final_report.pdf). This profile was 

created by averaging three indoor and three RWC outdoor temporal profiles from counties in Delaware 

and aggregating them into a single RWC diurnal profile. This new profile was compared to a 

concentration based analysis of aethalometer measurements in Rochester, NY (Wang et al. 2011) for 

various seasons and day of the week and found that the new RWC profile generally tracked the 

concentration based temporal patterns.
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58

Figure 3-5. RWC diurnal temporal profile

The temporalization for “Outdoor Hydronic Heaters” (i.e.,“OHH”, SCC=2104008610) and “Outdoor 

wood burning device, NEC (fire-pits, chimneas, etc)” (i.e., “recreational RWC”,SCC=21040087000) were 

updated because the meteorological-based temporalization used for the rest of the rwc sector did not agree 

with observations for how these appliances are used. 

For OHH, the annual-to-month, day-of-week and diurnal profiles were modified based on information in 

the New York State Energy Research and Development Authority (NYSERDA) “Environmental, Energy 

Market, and Health Characterization of Wood-Fired Hydronic Heater Technologies, Final Report” 

(NYSERDA, 2012) as well as a Northeast States for Coordinated Air Use Management (NESCAUM) 

report “Assessment of Outdoor Wood-fired Boilers” (NESCAUM, 2006). A Minnesota 2008 Residential 

Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional 

annual-to-month, day-of-week and diurnal activity information for OHH as well as recreational RWC 

usage.

The diurnal profile for OHH, shown in Figure 3-6 is based on a conventional single-stage heat load unit 

burning red oak in Syracuse, New York. As shown in Figure 3-7, the NESCAUM report describes how 

for individual units, OHH are highly variable day-to-day but that in the aggregate, these emissions have 

no day-of-week variation. In contrast, the day-of-week profile for recreational RWC follows a typical 

“recreational” profile with emissions peaked on weekends.

Annual-to-month temporalization for OHH as well as recreational RWC were computed from the MN 

DNR survey (MDNR, 2008) and are illustrated in Figure 3-8. OHH emissions still exhibit strong 

seasonal variability, but do not drop to zero because many units operate year round for water and pool 

heating. In contrast to all other RWC appliances, recreational RWC emissions are used far more 

frequently during the warm season.
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59

Figure 3-6. Diurnal profile for OHH, based on heat load (BTU/hr)

Figure 3-7. Day-of-week temporal profiles for OHH and Recreational RWC

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

Heat Load (BTU/hr)

0

50

100

150

200

250

300

350

Fire Pits/Chimineas Day-of-Week Profile

Fire Pit/Chimenea

Outdoor Hydronic Heater
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60

Figure 3-8. Annual-to-month temporal profiles for OHH and recreational RWC

3.3.4 Agricultural Ammonia Temporal Profiles (ag)

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 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 

emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized 

to the month17. 

 

17 SMOKE v3.5.1 will correctly read in a monthly inventory and apply GenTPRO ag NH3 month-to-hour temporalization. 

However, SMOKE v3.5 beta incorrectly applied an annual-to-month temporal profile on top of a monthly inventory when 

0

10

20

30

40

50

60

70

80

90

100

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Millions

Monthly Temporal Activity for OHH & Recreational RWC

Fire Pit/Chimenea

Outdoor Hydronic Heater
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61

Figure 3-9 compares the daily emissions for Minnesota from the “old” approach (uniform monthly 

profile) with the “new” approach (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 3-9. Example of new animal NH3 emissions temporalization approach, summed to daily

emissions

3.3.5 Onroad mobile temporalization (onroad, onroad_rfl)

For the onroad and onroad_rfl sectors, the temporal distribution of emissions is a combination of more 

traditional temporal profiles and the influence of meteorology. This section will discuss both the 

meteorological influences and the updates to the diurnal temporal profiles for the 2011 platform.

Meteorology is not used in the development of the temporal profiles, but rather it impacts the 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 on-network (RPD) and the off-network (RPV and RPP) 

processes use the gridded meteorology (MCIP) directly. Movesmrg determines the temperature for each 

hour and grid cell and uses that information to select the appropriate emission factor (EF) for the specified 

SCC/pollutant/mode combination. In the previous platform, RPP used county level minimum and 

maximum temperature ranges for the day to determine the appropriate EF. This potentially overestimated 

the temperature range for any particular grid cell, which would result in increased emissions for vaporventing. In the 2011 platform (and the 2011NEIv1), RPP was updated to use the gridded minimum and 

maximum temperature for the day. This more spatially resolved temperature range produces more 

accurate emissions for each grid cell. The combination of these three processes (RPD, RPV, and RPP) is 

the total onroad sector emissions, while the combination of the two processes (RPD, RPV) for the 

refueling mode only is the total onroad_rfl sector emissions. Both sectors show a strong meteorological 

influence on their temporal patterns (see the 2011NEIv1 TSD for more details).

Figure 3-10 illustrates the difference between temporalization of the onroad sector used in the 2005 and 

earlier platforms and the meteorological influence via 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 SMOKE-MOVES, but the meteorologically varying EFs add an 

additional variation on top of the temporalization. Note, the SMOKE-MOVES run is based on the 2005 

platform and previous temporalization of VMT to facilitate the comparison of the results. In the figure, 

the MOVES emissions have a repeating pattern within the month, while the SMOKE-MOVES shows 

 

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. 
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62

day-to-day (and hour-to-hour) variability. In addition, the MOVES emissions have an artificial jump 

between months which is due to the inventory providing new emissions for each month which are then 

temporalized within the month but not between months. The SMOKE-MOVES emissions have a 

smoother transition between the months.

Figure 3-10. Example of SMOKE-MOVES temporal variability of NOX emissions

For the onroad and onroad_rfl sectors, the “inventories” referred to in Table 3-12 actually consist of 

activity data. For RPP and RPV processes, the VPOP inventory is annual and does not need 

temporalization. For RPD, the VMT inventory is monthly and was temporalized to days of the week and 

then to hourly VMT through temporal profiles. The RPD processes require a speed profile (SPDPRO) 

that consists of vehicle speed by hour for a typical weekday and weekend day. Unlike other sectors, the 

temporal profiles and SPDPRO will impact not only the distribution of emissions through time but also 

the total emissions. Because SMOKE-MOVES’ process RPD calculates emissions from VMT, speed and 

meteorology, if one shifted the VMT or speed to different hours, it would align with different 

temperatures and hence different EF. In other words, two SMOKE-MOVES runs with identical annual 

VMT, meteorology, and MOVES EF, will have different total emissions if the temporalization of VMT 

changes.

In previous platforms, the diurnal profile for VMT18 varied by road type but not by vehicle type (see 

Figure 3-11). These profiles were used throughout the nation. 

 

18 These same profiles were used for onroad emissions in the 2005 platform.
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Figure 3-11. Previous onroad diurnal weekday profiles for urban roads

EPA wanted to create new diurnal profiles that could differentiate by vehicle type as well as by road type 

and would potentially vary over geography. The 2011NEIv1 process provided an opportunity to update 

the diurnal profile with information submitted by states. States submitted MOVES county databases 

(CDBs) that included information on the distribution of VMT by hour of day and by day of week19 (see 

the 2011NEIv1 TSD for details on the submittal process for onroad). EPA decided not to update the day 

of week profile because MOVES only differentiated weekday versus weekend while the default SMOKE 

profiles differentiated each of the 7 days. EPA mined the state submitted MOVES CDBs for non-default 

diurnal profiles20. The list of potential diurnal profiles was then analyzed to see whether the profiles 

varied by vehicle type, road type, weekday vs. weekend, and by county within a state (see Figure 3-12).

Figure 3-12. Variation in MOVES diurnal profiles

 

19 The MOVES tables are the hourvmtfraction and the dayvmtfraction.

20 Further QA was done to remove duplicates and profiles that were missing two or more hours. If they were missing a single 

hour, the missing hour could be calculated by subtracting all other hours fractions from 1.
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64

EPA attempted to maximize the use of state and/or county specific diurnal profiles. If a specific state or 

county’s profiles varied by vehicle type or/and road type, then the submitted profile was used. If the 

profile had less variability than the old SMOKE defaults (i.e. neither varied by vehicle type nor road 

type), then a new default profile would be used (see below for description of new profiles). This analysis 

was done separately for weekdays and for weekends, therefore some areas had submitted profiles for 

weekdays but defaults for weekends. The result was a set of profiles that varied geographically 

depending on whether or not the profile was submitted and the characteristics of the profiles (see Figure 

3-13).

Figure 3-13. Use of submitted versus new national default profiles

A new set of diurnal profiles was developed from the submitted profiles that varied by both vehicle type 

and road type. Before developing the national profiles, there needs to be a mapping between MOVES 

road types and SMOKE road types (i.e., the last three digits of the SCC) and between MOVES source 

types and SMOKE vehicle types. The mapping between road types is relatively straight forward (see 

Table 3-13). Basically the road types are consolidated into 4 types in MOVES, therefore the new profiles 

will not differentiate at the level of the SMOKE road type. For example, the SMOKE “urban interstate” 

(SCCLAST3=230) will have the same profile as the SMOKE “urban other freeways and expressways” 

(SCCLAST3=250). The mapping between MOVES source type and SMOKE vehicle type is more 

complicated; it is a many-to-many mapping (see the 2011NEIv1 TSD for more details). 
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Table 3-13. Mapping of MOVES to SMOKE road types

MOVES 

roadtype ID Description

SMOKE 

SCCLAST3 Description

2 Rural Restricted Access 110 Rural Interstate: Total

3 Rural Unrestricted Access

130 Rural Other Principal Arterial: Total

150 Rural Minor Arterial: Total

170 Rural Major Collector: Total

190 Rural Minor Collector: Total

210 Rural Local: Total

4 Urban Restricted Access 230 Urban Interstate: Total

250 Urban Other Freeways and Expressways: Total

5 Urban Unrestricted Access

270 Urban Other Principal Arterial: Total

290 Urban Minor Arterial: Total

310 Urban Collector: Total

330 Urban Local: Total

For the purposes of constructing the SMOKE diurnal profiles, all MOVES profiles for the road type and 

for any overlapping source types are averaged together to create a single diurnal profile for a specific 

county, SMOKE road type, SMOKE vehicle type, and weekday or weekend . This process is also used 

for creating SMOKE versions of the submitted profile in the non-default regions (described above). The 

states that submitted profiles that varied by vehicle and road types for weekdays were: Idaho, Maine, 

Michigan, New Jersey, Ohio, and Pennsylvania. The states that submitted profiles that varied by vehicle 

and road types for weekends were: Idaho, Maine, and Michigan. EPA created individual profiles for 

each state (averaging over the counties within) to create a single profile by state, vehicle type, road type, 

and weekday or weekend. The states individual profiles were averaged together to create a new default 

profile21. Figure 3-14 shows two new default profiles for light duty gas vehicles (LDGV, SCC7 2201001) 

and heavy, heavy duty diesel vehicles (HHDDV, SCC7 2230074) on restricted urban roadways 

(interstates and freeways, SCCLAST3=230 and 250) for weekdays. The grey lines are the individual state 

profiles, the black line is the new default profile, and the 2 colored lines are the previous SMOKE default 

profiles. Note that there are two previous SMOKE profiles for this road type, but that they don’t vary by 

vehicle. In contrast, the new default profile does vary by vehicle and places more LDGV VMT (left plot) 

in the rush hours while placing HHDDV VMT (right plot) predominately in the middle of the day with a 

longer tail into the evening hours and early morning. For a full list of the default profiles, see 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/temporal_profiles/

onroad_default_hourly_profile_plots_2011ed.zip.

 

21 Note that the states were weighted equally in the average independent of the size of the state or the variation in submitted 

county data. 
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Figure 3-14. Updated national default profiles for LDGV vs. HHDDV, urban restricted weekday

For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week22, and air 

basin. These CARB specific profiles were used in developing EPA estimates for California. For Texas, 

the profiles used were a combination of state supplied (via MOVES CDBs) and new national defaults. 

Although EPA adjusted the total emissions to match California’s and Texas’ submittals to the 

 

22 California’s diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and 

Tuesday, Wednesday, Thursday had the same profile.
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2011NEIv1, the temporalization of these emissions took into account both the state specific VMT profiles 

and the SMOKE-MOVES process of incorporating meteorology. For more details on the adjustments to 

California’s and Texas’ onroad emissions, see the 2011NEIv1 TSD.

3.3.6 Additional sector specific details (afdust, beis, c1c2rail, c3marine, nonpt, 

ptfire)

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 dayto-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.

Biogenic emissions in the beis sector vary by every day of the year because they are developed using 

meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are 

computed using appropriate emission factors according to the vegetation in each model grid cell, while 

taking the meteorological data into account.

For the c1c2rail and c3marine sectors, emissions are allocated with flat monthly and day of week profiles, 

and most emissions are also allocated with flat hourly profiles.

For the nonpt sector, most the inventories are annual except for the agricultural burning (SCC 

2801500000) inventory which was allocated to months by adding up the available values for each day of 

the month. For all agricultural burning, the diurnal temporal profile used reflected the fact that burning 

occurs during the daylight hours - see Figure 3-15 (McCarty et al., 2009). This puts most of the emissions 

during the work day and suppresses the emissions during the middle of the night. All states used a 

uniform profile for each day of the week for all agricultural burning emissions, except for the following 

states that for which EPA used state-specific day of week profiles: Arkansas, Kansas, Louisiana, 

Minnesota, Missouri, Nebraska, Oklahoma, and Texas. 
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Figure 3-15. Agricultural burning diurnal temporal profile

For the ptfire sector, the inventories are in the daily point fire format ORL PTDAY. The ptfire sector is 

used in the model evaluation case (2011ed and in the future base case (2018ed). The 2007 and earlier 

platforms had additional regulatory cases that used averaged fires and temporally averaged EGU 

emissions, but the 2011 platform uses base year-specific (i.e., 2011) data for both cases.

For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly 

inventories from output from NMIM. For California, a monthly inventory was created from CARB’s 

annual inventory using EPA-estimated NMIM monthly results to compute monthly ratios by pollutant and 

SCC7 and these ratios were applied to the CARB inventory to create a monthly inventory. .

3.4 Spatial Allocation

The methods used to perform spatial allocation are summarized in this section. For the modeling 

platform, spatial factors are typically applied by county and SCC. As described in Section 3.1, spatial 

allocation was performed for a national 12-km domain. To accomplish this, SMOKE used national 12-

km spatial surrogates and a SMOKE area-to-point data file. For the U.S., EPA updated surrogates to use 

circa 2010-2011 data wherever possible. For Mexico, updated spatial surrogates were used as described 

below. For Canada surrogates provided by Environment Canada were used and are unchanged from the 

2007 platform. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain

12US1 shown in Figure 3-1. 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.

Additional documentation on the 2011 spatial surrogates is available at 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/spatial_surrogates/ in the files 

US_SpatialSurrogate_Documentation_v091113.pdf and US_SpatialSurrogate_Workbook_v093013.xlsx. 

The spatial cross reference file is in gsref_2011.xlsx. Plots of the spatial surrogates are available in 

all_surrogate_maps_2011platform_12US1_v2.pdf. Note that these are plots of the surrogate fractions 

summed by grid cell, so grid cells that overlap multiple counties can show values greater than one. These 
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maps are only to give an idea of the spatial distribution of the surrogates. Allocations of CAP emissions to 

each of the surrogate codes is given in 2011ed_spatial_surrogate_CAPs_feb112014.xlsx.

3.4.1 Spatial Surrogates for U.S. emissions

There are more than 70 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 3-14 lists the codes and descriptions of 

the surrogates. 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, the 2006 National Land Cover Database, 2011 gas station and 

dry cleaner data, and the 2012 National Transportation Atlas Data for rail-lines, ports and navigable 

waterways. Surrogates for ports (801) and shipping lanes (802) were developed based on the 2011NEIv1

shapefiles: Ports_032310_wrf and ShippingLanes_111309FINAL_wrf, but also included shipping lane 

data in the Great Lakes and support vessel activity data in the Gulf of Mexico. 

The creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and 

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_i

d=99999. 

Table 3-14. U.S. Surrogates available for the 2011 modeling 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 

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 
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Code Surrogate Description Code Surrogate Description

USGS

261 NTAD Total Railroad Density 700 Airport Areas

271 NTAD Class 1, 2, 3 Railroad Density 710 Airport Points

280 Class 2 and 3 Railroad Miles 720 Military Airports

300 Low Intensity Residential 800 Marine Ports

310 Total Agriculture 801 NEI Ports

312 Orchards/Vineyards 802 NEI Shipping Lanes 

320 Forest Land 807 Navigable Waterway Miles

330 Strip Mines/Quarries 808 Gulf Tug Zone Area

340 Land 810 Navigable Waterway Activity

350 Water 812 Midwest Shipping Lanes

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

For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, and the offnetwork (RPP and RPV) emissions were allocated to population. For the onroad_rfl sector, the emissions 

were spatially allocated to gas station locations. For the oil and gas sources in the nonpt sector, the WRAP 

Phase III sources have detailed 2008-based basin-specific spatial surrogates shown in Table 3-15. These 

surrogates were also updated in the northeast Marcellus Shale region using 2011 data. Any remaining oil 

and gas sources still use the 2005-based surrogate “Oil & Gas Wells, IHS Energy, Inc. and USGS” (680)

developed for oil and gas SCCs. The surrogates in Table 3-15 were applied for the counties listed in 

Table 3-7.

Table 3-15. Spatial Surrogates for WRAP and Marcellus Shale Oil and Gas Data

Country Code Surrogate Description

USA 689 Gas production at all wells

USA 690 Oil production at all wells

USA 691 Well count - CBM wells

USA 692 Spud count

USA 693 Well count - all wells

USA 694 Oil production at Oil wells

USA 695 Well count - oil wells

USA 696 Gas production at gas wells

USA 697 Oil production at gas wells

USA 698 Well count - gas wells

USA 699 Gas production at CBM wells

Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is, 

some surrogates shown in Table 3-14 were not assigned to any SCCs, although many of the “unused” 

surrogates are actually used to “gap fill” other surrogates that are assigned. When the source data for a 

surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in 

those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the 

emission inventories. Table 3-16 shows the total of CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and 
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71

VOC) by sector. Appendix E shows the emissions totals by surrogate code and sector for most of the 

CAPs. Some sectors use only one surrogate, those are listed below:

ag: Total Agriculture (#310) with 3,524,607 tons of NH3;

onroad_rfl: Gas Stations (#600) with 157,629 tons of VOC; and

rwc: 0.5 Residential Heating - Wood plus 0.5 Low Intensity Residential (#165) with 903,651 tons 

of CAPs.

Table 3-16. Total CAP emissions by sector for U.S. Surrogates

Code Description afdust c1c2rail nonpt nonroad np_oilgas onroad

100 Population 1,221,647 47,453 2,742,952

120 Urban Population 521,778

130 Rural Population 1,102,192 282,583

140

Housing Change and 

Population 162,157 224,499 662,641

150

Residential Heating - Natural 

Gas 278,120

165

0.5 Residential Heating -

Wood plus 0.5 Low Intensity 

Residential

170

Residential Heating -

Distillate Oil 142,700

180 Residential Heating – Coal 12,791

190 Residential Heating - LP Gas 42,196

200 Urban Primary Road Miles 2,534,014

210 Rural Primary Road Miles 1,609,013

220

Urban Secondary Road 

Miles 281,011

230 Rural Secondary Road Miles 491,782

240 Total Road Miles 287,531

240 Total Road Miles 6,825

250

Urban Primary plus Rural 

Primary 102,793

260 Total Railroad Miles 2,195

261 NTAD Total Railroad Density 15,271 3,559

271

NTAD Class 1 2 3 Railroad 

Density 802,720

280 Class 2 and 3 Railroad Miles 45,114

300 Low Intensity Residential 162,807 234,134

310 Total Agriculture 896,741 709,177 586,125

312 Orchards/Vineyards 5,720

320 Forest Land 347

330 Strip Mines/Quarries 59,782

350 Water 770,993

400 Rural Land Area 1 1,188 663,518

500 Commercial Land 115,905

505 Industrial Land 747,634 186,522

510 Commercial plus Industrial 225,266 281,481

515

Commercial plus 

Institutional Land 294,718

520

Commercial plus Industrial 

plus Institutional 11,252 122,236
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Code Description afdust c1c2rail nonpt nonroad np_oilgas onroad

525

Golf Courses plus 

Institutional plus Industrial 

plus Commercial 0 220,996

527 Single Family Residential 0

535

Residential + Commercial + 

Industrial + Institutional + 

Government 334,251

540 Retail Trade (COM1) 1,375

545 Personal Repair (COM3) 63,005

555

Professional/Technical 

(COM4) plus General 

Government (GOV1) 2,872

560 Hospital (COM6) 9

575

Light and High Tech 

Industrial (IND2 + IND5) 2,554

580

Food, Drug, Chemical 

Industrial (IND3) 11,626

585

Metals and Minerals 

Industrial (IND4) 615

590 Heavy Industrial (IND1) 156,032

595 Light Industrial (IND2) 80,484

600 Gas Stations 413,518

650 Refineries and Tank Farms 130,222

675

Refineries and Tank Farms 

and Gas Stations 1,203

680 Oil and Gas Wells 116,568

692 Spud count 77,009

693 Well count - all wells 8,599

694 Oil production at Oil wells 1,241,682

695 Well count - oil wells 221,759

696 Gas production at gas wells 1,130,789

697 Oil production at gas wells 1

698 Well count - gas wells 294,244

700 Airport Areas 32,030

801 Port Areas 12,578

802 Shipping Lanes 571,927

808 Gulf Tug Zone Area 5,503

820 Ports NEI2011 NOx 76,008

850 Golf Courses 9,628

860 Mines 3,874

870

Wastewater Treatment 

Facilities 6,018

880 Drycleaners 10,026

890 Commercial Timber 23,202

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

There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support 

equipment, and jet refueling. The modeling platform includes the aircraft emissions as point sources. For

the modeling platform, EPA used the SMOKE “area-to-point” approach for only airport ground support 

equipment (nonroad sector), and jet refueling (nonpt sector). The approach is described in detail in the 
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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 surrogates for Canada to spatially allocate the 2006 Canadian emissions are unchanged from the 2007

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 3-17. The leading “9” was added to the surrogate codes to avoid duplicate 

surrogate numbers with U.S. surrogates. Some new surrogates for Mexico became available in the 2011 

platform. The surrogates are circa 1999 and 2000 and were based on data obtained from the Sistema 

Municpal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999. 

Most of the CAPs allocated to the Mexico and Canada surrogates are shown in Table 3-18. The entries in 

this table are for the othar sector except for the MEX Total Road Miles and The CAN traffic rows, which 

are for the othon sector.

Table 3-17. Canadian Spatial Surrogates 

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

9233 Total Land Development 9561 Administrative and support services
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Code Description Code Description

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 wholesalerdistributors9923 TOTAL INSTITUTIONAL AND 

GOVERNEMNT

9416 Building material and supplies wholesalerdistributors9924 Primary Industry

9417 Machinery equipment and supplies 

wholesaler-distributors

9925 Manufacturing and Assembly

9418 Miscellaneous wholesaler-distributors 9926 Distribution and Retail (no petroleum)
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Code Description Code Description

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

Table 3-18.CAPs Allocated to Mexican and Canadian Spatial Surrogates 

Srg code Description NH3 NOX PM 2_5 SO2 VOC

22 MEX Total Road Miles 15,965 370,867 34,396 13,713 375,276

10 MEX Population 0 0 0 0 431,231

12 MEX Housing 0 161,013 17,483 2,123 452,685

14 MEX Residential Heating - Wood 0 20,093 211,525 2,859 380,572

16 MEX Residential Heating - Distillate Oil 0 38 0 11 2

20 MEX Residential Heating - LP Gas 0 25,303 787 63 614

22 MEX Total Road Miles 0 0 0 0 3,513

24 MEX Total Railroads Miles 0 74,969 1,669 663 2,824

26 MEX Total Agriculture 679,212 164,144 72,372 2,127 43,958

28 MEX Forest Land 0 16,224 67,683 660 79,018

32 MEX Commercial Land 0 125,211 7,726 0 286,982

34 MEX Industrial Land 0 45,831 5,684 59,201 133,440

36 MEX Commercial plus Industrial Land 0 0 0 0 332,495
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Srg code Description NH3 NOX PM 2_5 SO2 VOC

38 MEX Commercial plus Institutional Land 0 6,400 216 84 28,293

40

Residential (RES1-

4)+Commercial+Industrial+Institutional+

Government 0 8 20 0 241,710

42 MEX Personal Repair (COM3) 0 0 0 0 33,616

44 MEX Airports Area 0 14,639 0 1,149 6,857

46 MEX Marine Ports 0 124,951 2,991 1,482 1,099

48 Brick Kilns - Mexico 0 776 6,691 0 10,244

50 Mobile sources - Border Crossing - Mexico 0 454 0 0 2,668

9100 CAN Population 603 0 276 0 304

9101 CAN total dwelling 643 46,256 12,783 14,698 32,944

9106 CAN ALL_INDUST 133 21,526 381 3,921 2

9113 CAN Forestry and logging 1,582 8,561 28,622 1,809 36,114

9115 CAN Agriculture and forestry activities 160 239,553 25,318 9,092 26,526

9116 CAN Total Resources 0 17 0 0 5

9212 CAN Mining except oil and gas 0 0 5,391 0 0

9221 CAN Total Mining 42 2,292 45,374 728 26

9222 CAN Utilities 189 14,882 369 1,124 255

9233 CAN Total Land Development 17 20,789 1,928 981 2,551

9308 CAN Food manufacturing 0 0 0 0 4,535

9323 CAN Printing and related support activities 0 0 0 0 25,203

9324 CAN Petroleum and coal products manufacturing 0 0 2,402 0 0

9327

CAN Non-metallic mineral product 

manufacturing 0 238 7,708 2,941 1,218

9331 CAN Primary Metal Manufacturing 0 98 5,062 12 6

9412 CAN Petroleum product wholesaler-distributors 0 0 0 0 70,125

9416

CAN Building material and supplies wholesalerdistributors 2 0 1,461 3,259 560

9448 CAN clothing and clothing accessories stores 0 0 0 0 328

9562

CAN Waste management and remediation 

services 165 893 1,596 1,998 16,551

9921 CAN Commercial Fuel Combustion 494 33,816 2,750 35,471 850

9924 CAN Primary Industry 0 0 0 0 219,282

9925 CAN Manufacturing and Assembly 0 0 0 0 139,227

9931 CAN OTHERJET 9 14,388 548 1,139 7,629

9932 CAN CANRAIL 109 122,694 4,093 5,737 3,304

9942 CAN UNPAVED ROADS 40 3,462 3,499 48 152,674

9945 CAN Commercial Marine Vessels 28 45,454 6,404 14,325 61,139

9946 CAN Construction and mining 247 156,770 10,070 5,667 17,180
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Srg code Description NH3 NOX PM 2_5 SO2 VOC

9947 CAN Agriculture Construction and mining 19 37,452 536 26 32,683

9950 CAN Intersection of Forest and Housing 1,053 11,700 120,045 1,671 173,130

9960 CAN TOTBEEF 176,156 0 7,420 0 317,394

9970 CAN TOTPOUL 74,204 0 2 0 264

9980 CAN TOTSWIN 122,094 0 996 0 3,186

9990 CAN TOTFERT 178,791 0 9,279 0 0

9991 CAN traffic 22,294 550,896 10,888 5,548 285,104

9994 CAN ALLROADS 0 0 55,468 0 0

9995 CAN 30UNPAVED_70trail 0 0 106,707 0 0

9996 CAN urban_area 0 0 284 0 0
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4 Development of 2018 Base-Case Emissions

This section describes the methods used for developing the 2018 future-year base-case emissions. The future 

base-case projection methodologies vary by sector. With the exceptions discussed in Section 4.2, the 2018 

base case represents 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 

November, 2013. The future base-case scenario reflects projected economic changes and fuel usage for 

EGU and mobile sectors. The 2018 EGU projected inventory represents demand growth, fuel resource 

availability, generating technology cost and performance, and other economic factors affecting power sector 

behavior. It also reflects the expected 2018 emissions effects due to environmental rules and regulations, 

consent decrees and settlements, plant closures, control devices updated since 2011, and forecast unit 

construction through the calendar year 2018. In this analysis, the projected EGU emissions include the Final 

Mercury and Air Toxics (MATS) rule announced on December 21, 2011 and the Clean Air Interstate Rule 

(CAIR) issued March 10, 2005. More information on the EGU base case can be found at 

http://www.epa.gov/powersectormodeling/BaseCasev513.html. 

For mobile sources (onroad, onroad_rfl, nonroad, c1c2rail and c3marine sectors), all national measures for 

which data were available at the time of modeling have been included. The Tier 3 standards proposed in 

March, 2013 are represented (see http://www.epa.gov/otaq/tier3.htm). Efforts made to include some regional 

haze and state-reported local controls as part of a larger effort to include more local control information on 

stationary non-EGU sources are 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.

EGU sector (ptegu and ptegu_pk): Unit-specific estimates from IPM version 5.13, including CAIR

and Final MATS.

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

during the development of the Cross-State Air Pollution Rule (CSAPR) along with emission 

reductions due to national and local rules, control programs, plant closures, consent decrees and 

settlements. Projection approaches for corn ethanol and biodiesel plants, refineries and upstream 

impacts represent the Energy Independence and Security Act of 2007 (EISA). Airport-specific 

terminal area forecast (TAF) data were used for aircraft to account for projected changes in 

landing/takeoff activity.

Point and nonpoint oil and gas sectors (pt_oilgas and np_oilgas): Regional projection factors by 

product type using Annual Energy Outlook (AEO) 2013 projections to year 2018. Cobenefits of 

stationary engines CAP-cobenefit reductions (RICE NESHAP) and New Source Performance 

Standards (NSPS) VOC controls reflected for select source categories. 

Fires sector (ptfire): No growth or control – 2011 estimates used directly.

Agricultural sector (ag): Projection factors for livestock estimates based on expected changes in 

animal population from 2005 Department of Agriculture data, updated according to 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.

Residential Wood Combustion (rwc): Projection factors that reflect assumed growth of wood 

burning appliances based on sales data, equipment replacement rates and change outs. These changes 

include a growth in lower-emitting stoves and a reduction in higher emitting stoves.
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Remaining Nonpoint sector (nonpt): Projection factors implement comments received during Cross 

State Air Pollution Rule development and emission reductions due to control programs. 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 reflected.

Nonroad mobile sector (nonroad): Other than for California and Texas, this sector uses data from a 

run of NMIM that utilized NONROAD2008a with database “NCD20130731_nei2018dv1”, using 

future-year equipment population estimates and control programs to the year 2018 and inputs that 

were either state-supplied as part of the 2011NEIv1 process or using national level inputs. Final 

controls from the final locomotive-marine and small spark ignition OTAQ rules are included. 

California and Texas-specific data were provided by CARB and TCEQ, respectively.

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 reflect final 

locomotive-marine controls and RFS2 adjustments. California projected year-2017 inventory data 

were provided by CARB.

Class 3 commercial marine vessel (c3marine): Base-year 2011 emissions grown and controlled to 

2018, incorporating controls based on Emissions Control Area (ECA) and International Marine 

Organization (IMO) global NOX and SO2 controls.

Onroad mobile, not including refueling (onroad): MOVES2010b (extended idle mode) and 

MOVESTier3NPRM-based emissions factors for year 2018 were developed using the same 

representative counties, state-supplied data, meteorology, and procedures that were used to produce 

the 2011 emission factors described in Section 2.3.1. California and TCEQ-specific data were 

provided by CARB and TCEQ, respectively. This sector includes all non-refueling onroad mobile 

emissions (exhaust, extended idle, evaporative, evaporative permeation, brake wear and tire wear 

modes).

Onroad refueling mode (onroad_rfl): the same projection approach is used as for the onroad sector 

and processing is described in Section 2.3.2, in that emission factors are from MOVES2010b and that 

California and Texas did not include state supplied emissions.

Other onroad (othar): No growth or control for Canada because data are not available. Mexico 

inventory data were grown from year 1999 to 2018.

Other nonroad/nonpoint (othon): No growth or control for Canada. Mexico inventory data were 

grown from year 1999 to 2018.

Other point (othpt): No growth or control for Canada and offshore oil. Mexico inventory data were 

grown from 1999 to year 2018. Non-U.S. C3 CMV data projected using the same methodology as 

the c3marine sector.

Biogenic: 2011 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 base-case emissions from the 2011v6 base-case inventories. Lists of the control, closures, 

projection packets (datasets) used to create 2018 future year base-case scenario inventories from the 2011 

base case are provided in ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/2018emissions/CoST_packets/

in the file 2018ed_Projections_with_CoST_23dec13.xlsx. These packets were processed through EPA

Control Strategy Tool (CoST) to create future year inventories. CoST is described here:

http://www.epa.gov/ttnecas1/cost.htm. The CoST packets are formatted in the same way as those needed for 

SMOKE and are available on the 2011v6 web site within the CoST_packets directory mentioned above in 

the file 2018ed_CoST packets.zip. Summaries of the emissions changes resulting from all CoST packets 

(control programs, projections and closures) can be found in the reports directory of the 2011/2018 emissions 
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modeling platform release: ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2018_emissions/ in 

the files 2018ed_CoST_ptnonipm_facility_summary.xlsx and 2018ed_CoST_sector_summaries.xlsx. 

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

from the 2011 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 and pt_oilgas sectors) Controls and Growth Assumptions

Ethanol plants that account for increased ethanol production due to EISA mandate All 4.2.1.1

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 4.2.1.6

Refinery upstream adjustments from EISA mandate All 4.2.1.7

Livestock emissions growth from year 2011 to 2018, also including upstream RFS2 impacts on 

agricultural-related activities such as pesticide and fertilizer production

All 4.2.2

Oil and gas production AEO-based regional growth factors and VOC NSPS controls All 4.2.4

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations NOX, 

CO, PM, 

SO2

4.2.3

State fuel sulfur content rules for fuel oil – as of July, 2012, effective only in Maine, Massachusetts, 

New Jersey, New York and Vermont

SO2

4.2.6

Industrial/Commercial/Institutional Boilers and Process Heaters MACT with Reconsideration 

Amendments

CO, PM, 

SO2, 

VOC

4.2.7

NESHAP: Portland Cement census-division level based on Industrial Sector Integrated Solutions 

(ISIS) policy emissions to year 2018. The ISIS results are from the ISIS-Cement model runs for the 

NESHAP and NSPS analysis of August 2013 and include closures.

All

4.2.8

Future baseline inventory improvements received from states and a 2005 platform NODA and 

comments from the CSAPR proposal, including local controls, fuel switching, unit closures and 

consent decrees

All

4.2.9

Facility and unit closures obtained from various sources such as states, industry and web posting, 

EPA staff and post-2011 inventory submittals

All 4.2.10

Aircraft growth via Itinerant (ITN) operations at airports to 2018 All 4.2.10

Boat Manufacturing MACT rule, VOC: national applied by SCC VOC 4.2.11.1

Lafarge and Saint Gobain consent decrees NOX, 

PM, SO2

4.2.9.3

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

4.2.9.3

Refinery Consent Decrees: plant/unit controls

NOX, 

SO2

4.2.9.3

Commercial and Industrial Solid Waste Incineration (CISWI) revised NSPS PM, SO2 4.2.11.2

Nonpoint (afdust, ag, nonpt np_oilgas, and rwc sectors) Controls and Growth Assumptions

MSAT2 and RFS2 impacts on portable fuel container growth and control from 2011 to 2018 VOC 4.2.1.3

Cellulosic ethanol and diesel emissions from EISA mandate All 4.2.1.4

Ethanol transport working losses inventory from EISA mandate VOC 4.2.1.5

Ethanol distribution vapor losses adjustments due to EISA mandate VOC 4.2.1.6

Livestock emissions growth from year 2011 to 2018, also including upstream RFS2 impacts on 

agricultural-related activities such as pesticide and fertilizer production

All 4.2.2

Oil and gas production AEO-based regional growth factors and VOC NSPS controls All 4.2.4

Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations NOX, 

CO, PM, 

4.2.3
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Control Strategies and/or growth assumptions

(grouped by standard and approach used to apply to the inventory)

CAPs 

affected Section

SO2

State fuel sulfur content rules for fuel oil – as of July, 2012, effective only in Maine, Massachusetts, 

New Jersey, New York and Vermont

SO2

4.2.6

Residential wood combustion growth and change-outs from year 2011 to 2018 All 4.2.3

Future baseline inventory improvements received from states NOX, 

VOC 4.2.9

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:

Proposed Tier-3 Standards: March, 2013

Light-Duty Greenhouse Rule: March, 2013

Heavy (and Medium)-Duty Greenhouse Gas Rule: August, 2011

Renewable Fuel Standard: February, 2010

Light Duty Greenhouse Gas Rule: April, 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:

Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels: October, 

2008

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 4.4

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 4.4.1

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 4.4.2

A quick background on the Control Strategy Tool (CoST)

CoST is used to apply most non-EGU projection/growth factors, controls and facility/unit/stack-level 

closures to the 2011 emissions modeling inventories to create year 2018 inventories for the following 

sectors: afdust, ag, c1c2rail, nonpt, np_oilgas, ptnonipm, pt_oilgas and rwc. The CoST training manual is 
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available at: http://www.cmascenter.org/help/model_docs/cost/2.5/CoST_UsersGuide_2012-08-

01_Final.pdf. The CoST development document, which is a more thorough but dated document of how to 

build and format CoST input files (packets) is available at: 

http://www.epa.gov/ttnecas1/models/CoST_Development%20Document_2010-06-09.pdf. The CoST packets 

used for 2018ed are available here: The CoST packets themselves can be found in 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/2018emissions/CoST_packets/. 

CoST allows the user to apply projection factors, controls and closures at various geographic and inventory 

key field resolutions. CoST provides the user with the ability to perform numerous quality assurance 

routines as well as create SMOKE-ready future year inventories. There are also available linkages to 

existing and user-defined control measures databases and it is up to the user to determine how control 

strategies are developed and applied. EPA typically creates individual CoST datasets or “packets” that 

represent specific intended purposes. For example, aircraft projections for airports are in a separate 

PROJECTION packet from residential wood combustion sales/appliance turnover-based projections. CoST 

uses three packet types as described below:

1. CLOSURE: Applied first in CoST. This packet can be used to zero-out (close) point source 

emissions at resolutions as broad as a facility to as specific as a stack. EPA used these types of 

packets for known post-2011 controls as well as information on closures provided by states on 

specific facilities, units or stacks. This packet type is only used in the ptnonipm sector in the 2011 

platform.

2. PROJECTION: This packet allows the user to increase or decrease emissions for virtually any 

geographic and/or inventory source level. Projection factors are applied as simple scalars to the 2011 

emissions inventories prior to the application of any possible subsequent CONTROLs. A 

PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable 

when information is based more on activity assumptions rather than known controls. EPA used

PROJECTION packet(s) in every non-EGU modeling sector in the 2011 platform.

3. CONTROL: These packets are applied after any/all CLOSURE and PROJECTION packet entries. 

The user has similar level of control as PROJECTION packets regarding specificity of geographic 

and/or inventory source level application. Control factors are expressed as a percent reduction (0 to 

100) and can be applied in addition to any pre-existing inventory control, or as a replacement control 

where inventory controls are first backed out prior to the application of a more-stringent replacement 

control. 

As mentioned above, CoST first applies any/all CLOSURE information for point sources, then applies 

PROJECTION packet information, followed by CONTROL packets. A hierarchy is used by CoST to 

separately apply PROJECTION and CONTROL packets. In short, in a separate process for PROJECTION 

and CONTROL packets, more specific information is applied in lieu of less-specific information in ANY 

other packets. For example, a facility-level PROJECTION factor will be replaced by a unit-level, or facility 

and pollutant-level PROJECTION factor. It is important to note that this hierarchy does not apply intrapacket types; for example, CONTROL packet entries are applied irrespective of PROJECTION packet 

hierarchies. A more specific example: a state/SCC-level PROJECTION factor will be applied before a 

stack/pollutant-level CONTROL factor that impacts the same inventory record. However, an inventory 

source that is subject to a CLOSURE packet record is removed from consideration of subsequent 

PROJECTION and CONTROL packets.

The implication for this hierarchy and intra-packet independence is important to understand and quality 

assure when creating future year strategies. For example, with consent decrees, settlements and state 
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comments, the goal is typically to achieve a targeted reduction (from the 2011NEIv1) or a targeted 2018 

emissions value. Therefore, as encountered with this 2018 base case, consent decrees and state comments 

for specific cement kilns (expressed as CONTROL packet entries), needed to be applied instead of (not in 

addition to) the more general approach of the PROJECTION packet entries for cement manufacturing. By 

processing CoST control strategies with PROJECTION and CONTROL packets separated by type of 

measure/program and also by consent decree and state comments, it is possible to show actual changes from 

the 2011 inventory to the 2018 inventory for each packet.

Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a 

hierarchal matching approach, a sample subset of which is shown in Table 4-2, to assign PROJECTION 

factors to the inventory. For example, a packet entry with Ranking=1 will supersede all other potential 

inventory matches from other packets. CoST then computes the projected emissions from all PROJECTION 

packet matches and then performs a similar routine for all CONTROL packets. Therefore, when 

summarizing “emissions reduced” from CONTROL packets, it is important to note that these reductions are 

not relative to the 2011 inventory, but rather, to the intermediate inventory after application of any/all 

PROJECTION packet matches. It is also important not all 70+ hierarchy options are shown. The fields listed 

in Table 4-2 are not necessarily named the same in CoST, but rather are similar to those in the SMOKE FF10 

inventories; for example, “REGION_CD” is the county-state-county FIPS code (e.g., Harris county Texas is 

48201) and “STATE” would be the 2-digit state FIPS code with three trailing zeros (e.g., Texas is 48000).

Table 4-2. Subset of CoST Packet Matching Hierarchy

Rank Matching Hierarchy Inventory Type

1 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID, SCC, POLL point

2 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID, POLL point

3 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, SCC, POLL point

4 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, POLL point

5 REGION_CD, FACILITY_ID, UNIT_ID, SCC, POLL point

6 REGION_CD, FACILITY_ID, UNIT_ID, POLL point

7 REGION_CD, FACILITY_ID, SCC, POLL point

8 REGION_CD, FACILITY_ID, POLL point

9 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID point

10 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID point

11 REGION_CD, FACILITY_ID, UNIT_ID point

12 REGION_CD, FACILITY_ID point

13 REGION_CD, NAICS, SCC, POLL point, nonpoint

14 REGION_CD, NAICS, POLL point, nonpoint

15 STATE, NAICS, POLL point, nonpoint

16 REGION_CD, NAICS point, nonpoint

17 NAICS point, nonpoint

18 REGION_CD, SCC, POLL point, nonpoint

19 STATE, SCC, POLL point, nonpoint

20 SCC, POLL point, nonpoint

21 REGION_CD, SCC point, nonpoint

22 STATE, SCC point, nonpoint

23 SCC point, nonpoint

24 REGION_CD, POLL point, nonpoint

25 REGION_CD point, nonpoint

26 STATE, POLL point, nonpoint

27 STATE point, nonpoint

28 POLL point, nonpoint
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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 base case. 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.

A list of inventory datasets used for this and all cases is provided in Table G-1 in Appendix G. The ancillary 

input data in the future-year scenarios are very similar to those used in the 2011 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. Table G-1 of Appendix G also shows the differences between ancillary input data 

sets between the 2011 base case and the future-year scenario. The specific speciation profile changes are 

discussed in Section 3.2.1. Table G-2 in Appendix G also provides the values for the main parameters used 

in the emissions modeling cases. 

4.1 Stationary source projections: EGU sectors (ptegu, ptegu_pk)

The future-year data for the ptipm sector used in the air quality modeling were created by the Integrated 

Planning Model (IPM) version 5.13 (v5.13) Final MATS (Mercury and Air Toxics Standards) of 

(http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html). The IPM is a multiregional, dynamic, 

deterministic linear programming model of the U.S. electric power sector. Version 5.13 reflects state rules, 

consent decrees and announced shutdowns through August, 2013. IPM 5.13 was significantly updated from 

the previous version 4.10 and represents electricity demand projections for the Annual Energy Outlook 

(AEO) 2013. The scenario used for this modeling represents the implementation of the Clean Air Interstate 

Rule, the Mercury and Air Toxics Standards, and the final actions EPA has taken to implement the Regional 

Haze Rule. More details on the 2018 base case can be found at 

http://www.epa.gov/powersectormodeling/BaseCasev513.html. 

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 IPM-estimated fuel throughput based on fuel, 

configuration and controls to compute the filterable and condensable components of PM. This methodology 

is documented in the air quality modeling flat file documentation available here: 

http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Methodology.pdf. As part of the development 

of the flat file, a cross reference between the 2011NEIv1 and IPM is used to help populate stack parameters 

and other related information. This cross reference is available from 

http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Inputs.xls. The emissions in the flat file 

created from the IPM outputs are temporalized into the hourly emissions needed by the air quality model as 

described in Section 3.3.2.

4.2 Stationary source projections: non-EGU sectors (afdust, ag, nonpt, 

np_oilgas, ptnonipm, pt_oilgas, rwc)

To project U.S. stationary sources other than the ptipm sector, growth factors and/or controls were applied to 

certain categories within the afdust, ag, nonpt, np_oilgas, ptnonipm, pt_oilgas and rwc platform sectors. This

subsection provides details on the data and projection methods used for these sectors. In estimating futureyear 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. While EPA is working toward improving the projection approach in future 

emissions platforms, the Agency is still using the no-growth assumption for the 2011 platform unless states 
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provided specific growth factors for 2018. More details on the rationale for this approach can be found in 

Appendix D of the Regulatory Impact Assessment for the PM NAAQS rule (EPA, 2006b). 

For many sources, EPA applied emissions reduction factors (CONTROL packets) to the 2011 base case

emissions for particular sources in the ptnonipm, nonpt and two oil and gas sectors (np_oilgas and pt_oilgas) 

to reflect the impact of stationary-source national and local-scale control programs including consent 

decrees. Information on plant, unit and stack closures (CLOSURE packets) is restricted to the ptnonipm 

sector. Some of the controls described in this section were obtained from comments on the Cross-State Air 

Pollution Rule (CSAPR) proposal. 

The contents of the controls, local adjustments and closures for the 2018 base case are described in the 

following subsections. Detailed summaries of the impacts of all control programs, local adjustments and 

closures are provided in ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2018_emissions/. Yearspecific projection factors (PROJECTION packets) for year 2018 were used for creating the 2018 base case 

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). Regardless of whether the growth or 

control factors for a sector are provided in a table in this document, they are available as “projection”,

“control”, or “closures” packets for input to SMOKE on the 2011v6 platform website in 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/2018emissions/CoST_packets/. This section is divided 

into several subsections that are summarized in Table 4-3. Note that future year inventories were used rather 

than projection or control packets for some sources.

Table 4-3. 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 EPA

applied to the 2011 inventory to reflect RFS2 in 

2018.

4.2.2 Upstream agricultural and 

livestock adjustments

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.

4.2.3 Residential wood 

combustion projections

rwc Adjustment factors that reflect the change in RWC 

emissions by appliance type, including wood stove 

change-outs and accounting for estimated future sales 

and replacement rates.

4.2.4 Oil and Gas projections np_oilgas, 

pt_oilgas

Projection packet reflecting regional AEO-based 

growth for oil and gas production as well as VOC 

NSPS controls for select sources.

4.2.3 RICE NESHAP controls nonpt, 

np_oilgas, 

ptnonipm, 

pt_oilgas

Control packet reflecting RICE NESHAP with 

reconsideration amendments.

4.2.6 Fuel sulfur rule controls nonpt

ptnonipm

Control packet reflecting state and local fuel sulfur 

rules, including ULSD.

4.2.7 Industrial Boiler MACT ptnonipm Control packet reflecting ICI Boiler MACT 
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Subsection Title Sector(s) Brief Description

reconsideration controls reconsideration reductions.

4.2.8 Portland cement NESHAP 

projections

ptnonipm Year-2018 ISIS policy case reflecting the Portland 

Cement NESHAP, including closures, controls at 

existing kilns and an inventory containing new kilns 

constructed after 2011 that account for shifting 

capacity from some closed units to open units.

4.2.3 State comments and 

consent decrees/settlements

nonpt, 

ptnonipm

Projection and control packets reflecting numerous 

sources of consent decree/settlement information as 

well as state comments and data regarding 2018.

4.2.9 Aircraft projections ptnonipm Airport-specific projections to year 2018 based on 

FAA itinerary activity estimates.

4.2.10 Remaining non-EGU 

controls and closures

ptnonipm All other controls and plant/unit/stack closures 

information 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). 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 inventories. 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.

Based on the Annual Energy Outlook 2013 (early release) energy use of 15.47 quad (1015 BTU) (Department 

of Energy, 2012), EPA estimated the 2011 ethanol volume as 11.1 billion gallons (Bgal). EPA assumes that 

an unadjusted 2018 inventory, which does not account for the impacts of the EISA renewable fuel mandate, 

would have comparable ethanol volumes to 2011. However, analyses done to support the RFS2 rule (EPA, 

2010a) suggest a significant increase in renewable fuel volumes in 2018 (see Table 4-4). Adjustments 

applied to the inventories (described in the following subsections) reflect the impacts on emissions due to the 

difference between the 2011 ethanol volumes and the renewable fuel volumes shown in Table 4-4. In 2018, 

EPA assumed 1 Bgal of ethanol would be used as E85, 10 Bgal as E10, and about 4 Bgal as E15.

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

Renewable Fuel Volume (Bgal)

Corn Ethanol 14.7

Cellulosic Ethanol 0.235

Imported Ethanol 1.061

Biodiesel 1.887

Renewable Diesel 0.236

Cellulosic Diesel 0.290
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4.2.1.1 Corn Ethanol plants inventory (ptnonipm)

Future year inventories: “ethanol_plants_2018ed_NEI” and “ethanol_plants_2018ed_OTAQ”

As discussed in Section 2.1.4, EPA supplemented the 2011 NEI with corn ethanol plants that EPA OTAQ

identified. The 2011 emissions were projected to account for the increased domestic corn ethanol production 

assumed in this modeling, specifically an increase from 13.9 Bgal in 2011 to 14.7 Bgal by 2018. The 

projection was applied to all pollutants and all facilities equally. Table 4-5 provides the summaries of 

estimated emissions for the corn ethanol plants in year 2011 and 201823.

Table 4-5. 2011 and 2018 corn ethanol plant emissions [tons]

Pollutant 2011 2018

CO 15,934 16,858

NH3 726 768

NOX 18,048 19,095

PM10 10,602 11,217

PM2.5 5,995 6,343

SO2 34,608 36,294

VOC 19,654 21,115

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 in support of producing biodiesel fuels for 

the EISA mandate. Plant location and production volume data came from the Tier 3 proposed rule.24,25 The 

total volume of biodiesel came from the AEO 2013 early release, 1.3 BG for 2018. 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. Once facility-level production 

capacities were scaled, emission factors were applied based on soybean oil feedstock. These emission 

factors in Table 4-6 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 4-7 provides the 2018 biodiesel plant emissions estimates. Emissions in 2011 are assumed to 

be near zero, and HAP emissions in 2018 are nearly zero.

Table 4-6. 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

 

23 The 2011 emissions are the sum of the NEI and OTAQ facilities. The same is true for 2018.

24 US EPA 2013. Draft Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA-420-D-13-002. 

25 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.
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Pollutant Emission Factor

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 4-7. 2018 biodiesel plant emissions [tons]

Pollutant 2018

CO 649

NOX 1048

PM10 89

PM2.5 89

SO2 8

VOC 57

4.2.1.3 Portable fuel container inventory (nonpt)

Future year inventory: “2018_PFC_inventory_FF10_revision2”

EPA used future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and 

modeled for 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 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 PFC emissions that include estimated Reid Vapor Pressure (RVP) and oxygenate 

impacts on VOC emissions, and more importantly, large increases in ethanol emissions from RFS2. These 

emission estimates also include refueling from the NONROAD model for gas can vapor displacement, 

changes in tank permeation and diurnal emissions from evaporation. Because 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 for more details. Emissions for 2011 and 2018 are provided in Section 5.
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Table 4-8. PFC emissions for 2011 and 2018 [tons]

Pollutant 2011 2018

VOC 198,395 29,119

Benzene 786 645

Ethanol 0 3,719

4.2.1.4 Cellulosic fuel production inventory (nonpt)

New Future year inventory: “2018_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 to reflect AEO2013er 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 4-9 and Table 4-10 were used to develop 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. Cellulosic 

ethanol VOC and related HAP emission factors from the Tier 3 NPRM were used as the biochemical VOC 

and related HAP emission factors.

26 Because the future year cellulosic inventory contains ethanol, a VOC 

E-profile that integrated ethanol was used, 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 4-11 provides the year 2018 cellulosic plant 

emissions estimates.

Table 4-9. 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 4-10. 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

 

26 It should be noted that in the Tier 3 NPRM we meant to use different cellulosic ethanol non-VOC CAP emission factors 

depending on which feedstock the plant was using but instead used the same emission factors (based on a forest waste feedstock) 

for all the plants. This was corrected by using emission factors for the non-VOC CAPS that were based on a stover feedstock for 

the biochemical plants.
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Table 4-11. 2018 cellulosic plant emissions [tons]

Pollutant Emissions

Acrolein 1

Formaldehyde 4

Benzene 1

Acetaldehyde 21

CO 6,088

Ethanol 146

NH3 0.1

NOX 9,199

PM10 1,088

PM2.5 547

SO2 819

VOC 414

4.2.1.5 Ethanol working loss inventory (nonpt)

New Future year inventory: “Ethanol_transport_vapor_2018rg_ref_v1”

This inventory was 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 4-12. The leading descriptions are “Industrial Processes; Food and Agriculture; Ethanol Production” 

for each SCC.

Table 4-12. 2018 VOC working losses (Emissions) due to ethanol transport [tons]

SCC Description Emissions

30205031 Denatured Ethanol Storage Working Loss 23,420

30205052 Ethanol Loadout to Truck 14,425

30205053 Ethanol Loadout to Railcar 10,484

4.2.1.6 Vapor losses from Ethanol transport and distribution (nonpt, ptnonipm)

Packet: “PROJECTION_2011_2018_distribution_upstream_OTAQ_Tier3FRM”

OTAQ developed county-level inventories for ethanol transport and distribution for 2018 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.

A 2018 inventory which included EISA impacts was developed by adjusting the 2007 platform inventory. 

These adjustments were made using an updated version of EPA’s spreadsheet model for upstream emission 

impacts, developed for the RFS2 rule27. Below EPA describes how the Agency developed emission factors 

and fuel volumes to make these adjustments with the RFS2 impacts spreadsheet. 

 

27 U.S. EPA. 2013. Spreadsheet “upstream_emissions_rev T3.xls.
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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.28-In addition to gasoline VOC emission factors for the RBT/BPS components, emission 

factors were developed for the BTP component, for 10% ethanol and 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. EPA assumed delta T is zero, and the temperature of 

the fuel being dispensed averages 60 oF over the year. 

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.

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 4-13 while volumes without EISA are 

in Table 4-14. 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 the RFS2 impacts spreadsheet (see below for details).

 

28 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>
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Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD)

Table 4-13. RVPs Assumed for 2018 ethanol and gasoline volumes with EISA

Table 4-14. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA

PADD

Total 

Fuel 

Volume

Gasoline 

Volume

Ethanol 

Volume

E10 Only 

Volume

E15 Only 

Volume

E85(74) 

Only 

Volume

Weight

ed 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
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A benzene g/mmgal emission factor for 2018 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, EPA 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 4-17.

Table 4-15. 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 

4-16). Benzene emission estimates were obtained either by application of the adjustments in Table 4-16 or 

through speciation of VOC in SMOKE.

Table 4-16. 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

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 
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fuel samples from EPAct exhaust test program29 and have been submitted for incorporation into the 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 2011 and 2018, see Section 3.2.1.4.

After developing emissions from the 2018 with EISA volumes versus the 2018 without EISA volumes, EPA 

created ratios of these two cases to apply against the 2007 platform emissions. This created a 2018 reference 

case. For the 2011 platform, EPA scaled the same sources so that their total emissions matched the 2018 

reference case.

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 VOC reductions of approximately 26,075 tons across the 

nonpt sector and 2,681 tons in the ptnonipm sector in 2018 for these processes. See Appendix B for the 

complete cross-walk between SCC, and state-SCC for BTP components, and each type of petroleum 

transport and storage.

4.2.1.7 Pipeline and Refinery EISA adjustments (ptnonipm)

Packet: “PROJECTION_pipelines_refineries_2018ed”

Pipeline usage and refinery emissions were adjusted for changes in fuels due to the EISA. 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. 

A portion of these impacts are discussed in this section, with additional impacts due to transport discussed in 

the onroad and c1c2rail sectors (see Sections 4.3.1 and 4.4.1, respectively).

Calculation of the emission inventory impacts of decreased gasoline and diesel production, due to EISA, on 

nationwide refinery emissions was done in EPA’s spreadsheet model for upstream emission impacts (EPA, 

2009b). Emission inventory changes reflecting EISA implementation were used to develop adjustment 

factors that were applied to inventories for each petroleum refinery in the U.S. (Table 4-17). 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 4-17 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-based 

reductions is shown in Table 4-18.

 

29 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.
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Table 4-17. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with 

gasoline and diesel fuel production.

Pollutant Pipelines Refineries Both

CO 0.9964 0.9776 0.9741

NOX 0.9819 0.9867 0.9688

PM10 0.9967 0.9839 0.9806

PM2.5 0.9975 0.9789 0.9765

SO2 0.9981 0.9781 0.9763

NH3 n/a 0.9517 n/a

VOC 0.999 0.9719 0.9710

Table 4-18. Impact of refinery adjustments on 2011 emissions [tons]

Pollutant Reductions 2018

CO 1,233

NH3 135

NOX 2,256

PM10 466

PM2.5 495

SO2 1,986

VOC 1,515

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

Packet: “PROJECTION_2011_2018_ag_including_upstream_OTAQ_25nov2013_v1”

Inventory adjustments were previously developed for 2030 as part of final RFS2 rule modeling30. 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)31 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 Table 4-19. 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 has 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 

 

30 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>.

31 GREET, version 1.8c. Available at < http://greet.es.anl.gov/>.
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that county. For instance, a different approach could estimate reductions that were larger than the reference 

case emissions, since there was no linkage between the 2011NEIv1 inventories and the FASOM modeling. 

The specific sources (SCCs) and affected pollutants that these adjustments were applied to are listed in a 

docket reference32.

Table 4-19. Adjustments to modeling platform agricultural emissions for the Tier 3 reference case

Source Description Adjustment

Nitrogen fertilizer application 1.0242

Fertilizer production, mixing/blending 1.0603

Pesticide production 0.9544

Agricultural tilling/loading dust 1.0079

Agricultural burning 1.000

Livestock dust 0.9868

Livestock waste 0.9901

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 4-20. 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 4-20 and the source categories in Table 4-19 is provided on the 2011v6 

platform website in ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/2018emissions/CoST_packets/. 

Table 4-20. Composite NH3 projection factors to year 2018 for animal operations

Animal Category Projection Factor

Dairy Cow 0.9901

Beef 0.9851

Pork 1.0582

Broilers 1.0904

Turkeys 0.9290

Layers 1.0629

Poultry Average 1.0557

Overall Average 1.0310

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) and 2011 to 2018 

animal population projections were computed for these 2011v6 projections to year 2018. 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 2011 through 2018 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 

 

32 U. S. EPA. 2011. Spreadsheet “agricultural sector adjustments.xls.” Docket EPA–HQ–OAR–2011–0135.
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number of cows and turkeys; therefore, these forecasts were not used 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 change, but no 

change was assumed because a quantitative estimate was not available. Appendix G provides the animal 

population data and regression curves used to derive the growth factors.

4.2.3 Residential wood combustion growth (nonpt)

Packet: “PROJECTION_2011v6_2018bau_RWC_25nov2013.txt”

EPA used a “business as usual” (BAU) approach to Residential Wood Combustion (RWC) projections that 

does not account for national New Source Performance Standards (NSPS) for wood stoves, since they are 

currently in the comment-seeking process from proposal (EPA, 2013a and available at: 

http://www2.epa.gov/residential-wood-heaters/proposed-new-source-performance-standards-residentialwood-heaters). EPA projected residential wood combustion (RWC) emissions to the year 2018 based on 

expected increases and decreases in various residential wood burning appliances. As newer, cleaner 

woodstoves replace some older, higher-polluting wood stoves, there will be an overall reduction of the 

emissions from older “dirty” stoves but an overall increase in total RWC due to population and sales trends 

in all other types of wood burning devices such as indoor furnaces and outdoor hydronic heaters (OHH). It 

is important to note that our RWC projection methodology does not explicitly account for state or local 

residential wood control programs. There are a number state and local rules in place, specifically in 

California, Oregon and Washington. However, at this time, EPA does not have enough detailed information 

to calculate state specific or local area growth rates. Therefore, with the exception of California, Oregon and 

Washington, EPA is using national level growth rates for each RWC SCC category. After discussions with 

California air districts, regional office contacts and EPA experts, EPA decided to simply hold RWC 

emissions flat (unchanged) for all SCCs in California, Oregon and Washington. 

The development of projected growth in RWC emissions to year 2018 starts with the projected growth in 

RWC appliances derived from year 2012 appliance shipments reported in the Regulatory Impact Analysis 

(RIA) for Proposed Residential Wood Heaters NSPS Revision Final Report (EPA, 2013b), also available at: 

http://www2.epa.gov/sites/production/files/2013-12/documents/ria-20140103.pdf. The 2012 shipments are 

based on 2008 shipment data and revenue forecasts from a Frost & Sullivan Market Report (Frost & 

Sullivan, 2010). Next, to be consistent with the RIA (EPA, 2013b), growth rates for new appliances for 

certified wood stoves, pellet stoves, indoor furnaces and OHH were based on forecasted revenue (real GDP) 

growth rate of 2.0% per year from 2013 through 2018 as predicted by the U.S. Bureau of Economic Analysis 

(BEA, 2012). While this approach is not perfectly correlated, in the absence of specific shipment 

projections, the RIA assumes the overall trend in the projection is reasonable. The growth rates for 

appliances not listed in the RIA (fireplaces, outdoor wood burning devices (not elsewhere classified) and 

residential fire logs) are estimated based on the average growth in the number of houses between 2002 and 

2012, about 1% (U.S. Census, 2012).

In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the replacement 

of older, existing appliances are needed. Based on long lifetimes, no replacement of fireplaces, outdoor 

wood burning devices (not elsewhere classified) or residential fire logs is assumed. It is assumed that 95% 

of new woodstoves will replace older non-EPA certified freestanding stoves (pre-1988 NSPS) and 5% will 

replace existing EPA-certified catalytic and non-catalytic stoves that currently meet the 1988 NSPS (Houck, 

2011).
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EPA RWC NSPS experts assume that 10% of new pellet stoves and OHH replace older units and that 

because of their short lifespan, that 10% of indoor furnaces are replaced each year. These are the same 

assumptions used in the 2007 emissions modeling platform (EPA, 2012d). The resulting growth factors for 

these appliance types varies by appliance type and also by pollutant because the emission rates, from EPA

RWC tool (EPA, 2013rwc), vary by appliance type and pollutant. For our non-NSPS projection approach, 

the projection factors are the same for all pollutants except for EPA certified woodstoves of all types. For 

EPA certified units, the projection factors for PM are lower than those for all other pollutants. The 

projection factors also vary because the total number of existing units in 2011 varies greatly between 

appliance types. 

California did not report detailed SCCs in the 2011NEIv1, simply reporting emissions from general 

fireplaces (SCC=2104008100) and general woodstoves (SCC=2104008300). California, Oregon and 

Washington also have state-level RWC control programs, including local burn bans in place. Without 

appliance counts in California at specific appliance types (e.g., certified versus non-certified), and an 

inability to incorporate significant local RWC control programs/burn bans for a future year inventory, EPA

decided to leave all RWC emissions unchanged in the future for all three states. The RWC projections 

factors for 2018 for states other than California, Oregon and Washington are provided in Table 4-23. EPAcertified woodstoves (inserts and freestanding) utilize different projection factors for direct PM than all other 

pollutants. 

Table 4-21. Non-West Coast RWC projection factors to year 2018

Pollutant SCC Description

Projection 

Factor

All 2104008100 Fireplace: general 1.072

All 2104008210 Woodstove: fireplace inserts; non-EPA certified 0.897

PM 2104008220 Woodstove: fireplace inserts; EPA certified; non-catalytic 1.076

All other 2104008220 Woodstove: fireplace inserts; EPA certified; non-catalytic 1.181

PM 2104008230 Woodstove: fireplace inserts; EPA certified; catalytic 1.081

All other 2104008230 Woodstove: fireplace inserts; EPA certified; catalytic 1.181

All 2104008300 Woodstove: freestanding, general 1.171

All 2104008310 Woodstove: freestanding, non-EPA certified 0.98

PM 2104008320 Woodstove: freestanding, EPA certified, non-catalytic 1.076

All other 2104008320 Woodstove: freestanding, EPA certified, non-catalytic 1.181

PM 2104008330 Woodstove: freestanding, EPA certified, catalytic 1.081

All other 2104008330 Woodstove: freestanding, EPA certified, catalytic 1.181

All 2104008400 Woodstove: pellet-fired, general (freestanding or FP insert) 1.645

All 2104008510 IF: Indoor Furnaces: cordwood-fired, non-EPA certified 1.103

All 2104008610 OHH: Outdoor Hydronic heaters 1.237

All

2104008700

Outdoor wood burning device, NEC (e.g., fire-pits, 

chimneas) 1.072

All 2104009000 Residential firelog total; all combustor types 1.072

The national impact of RWC projections for PM2.5 and NOX by SCC are provided in Table 4-24. 

Regionally, these impacts vary considerably depending on the distribution of these appliance types. For 

example, RWC emissions increase more in areas with a higher proportion on OHH (Great Lakes and 

Northeast).
DRAFT

99

Table 4-22. National RWC impacts for PM2.5 and NOX from 2011 to 2018

SCC Description

PM2.5 NOX

2011 2018 Difference 2011 2018 Difference

2104008100 Fireplace: general 66,699 70,422 3,723 7,263 7,673 410

2104008210

Woodstove: fireplace inserts; 

non-EPA certified

49,336 44,788 -4,548 4,514 4,098 -416

2104008220

Woodstove: fireplace inserts; 

EPA certified; non-catalytic

11,420 12,130 710 1,328 1,525 197

2104008230

Woodstove: fireplace inserts; 

EPA certified; catalytic

4,205 4,482 277 412 473 61

2104008300

Woodstove: freestanding, 

general

9,525 9,525 0 1,079 1,079 0

2104008310

Woodstove: freestanding, nonEPA certified83,842 82,321 -1,521 7,672 7,533 -139

2104008320

Woodstove: freestanding, EPA 

certified, non-catalytic

17,998 19,129 1,131 2,094 2,407 313

2104008330

Woodstove: freestanding, EPA 

certified, catalytic

10,838 11,520 682 1,063 1,212 149

2104008400

Woodstove: pellet-fired, 

general (freestanding or FP 

insert)

1,747 2,719 972 2,169 3,377 1,208

2104008510

IF: Indoor Furnaces:

cordwood-fired, non-EPA 

certified

26,432 29,052 2,620 1,759 1,933 174

2104008610

OHH: Outdoor Hydronic 

heaters

79,163 97,910 18,747 2,290 2,832 542

2104008700

Outdoor wood burning 

device, NEC (e.g., fire-pits, 

chimneas)

20,573 21,916 1,343 2,266 2,414 148

2104009000

Residential firelog total; all 

combustor types

6,511 6,939 428 1,762 1,877 115

Total Total RWC 388,289 412,853 24,564 35,671 38,433 2,762

4.2.4 Oil and Gas projections (np_oilgas, pt_oilgas)

Packet: PROJECTION_2011v6_2018_oilgas_27nov2013.txt

The oil and gas point (pt_oilgas) and nonpoint (np_oilgas) sectors are modeled separately from the remaining 

point (ptnonipm) and nonpoint (nonpt) sector emissions primarily to better track/isolate and summarize the 

oil and gas projections from 2011 to future years. EPA is aware that these emissions inventories are subject 

to much scrutiny in the base year (2011) as well as growth and control assumptions in the coming years. Our 

initial approach at projecting these emissions is a simple regional-level Annual Energy Outlook (AEO) 2013-

based methodology with some associated VOC reduction factors for sources that would be subject to New 

Source Performance Standards (NSPS). The methodology EPA describes here was a result of a coordinated 

effort between EPA OAQPS and EPA Office of Atmospheric Programs (OAP) Climate Change Division 

(CCD). 

The AEO-2013 regional growth factors are based on 2011 to 2018 oil production, gas production and 

combined oil and gas production trends, available in Supplemental tables for regional detail, Table 131 and 

Table 132 at: http://www.eia.gov/forecasts/aeo/tables_ref.cfm. These National Energy Modeling System
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(NEMS) regions are shown in Figure 4-2 and demonstrate one of the many limitations of this projection 

strategy: projections are not based on oil/gas basin but rather, much larger geographic regions. A countyNEMS region cross-walk was developed to assign counties in New Mexico and Texas to specific NEMS 

regions. 

Figure 4-2. Oil and Gas NEMS Regions

The AEO-2013 provides regional growth factors for oil production and gas production; however, numerous 

sources (SCCs) in the 2011 platform are ambiguous regarding the type of product being extracted/produced. 

These sources were assigned to a combined oil and gas category set of factors where oil and natural gas 

production levels were summed using a barrel-of-oil equivalent of 0.178 barrels of crude oil to 1000 cubic 

feet of natural gas. The AEO-based projection factors for each products type and NEMS region, provided in 

Table 4-25, are applied to for all pollutants and SCCs in the point and nonpoint oil and gas sector 

inventories, with the exception of VOC for select SCCs. The two character region codes (e.g., “NE” for 

Northeast region) are relevant in the following discussion on VOC projection factors.
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Table 4-23. AEO-based 2018 Projection Factors

Region Oil Gas Oil/Gas

Northeast (NE) 1.238 1.596 1.572 

Gulf Coast (GC) 1.853 1.246 1.368 

Midcontinent (MC) 1.165 0.910 0.955 

Southwest (SW) 1.391 1.043 1.173 

Rocky Mountains (RM) 1.642 1.098 1.243 

West Coast (WC) 0.865 0.993 0.888 

For select VOC processes, SCCs were identified that were likely to be affected by NSPS and verified with 

EPA OAP and OAQPS oil and gas sector experts. NSPS reductions for VOC-only were applied in 

composite with AEO-based regional growth factors to create a set of “net” growth factors. These NSPS 

VOC reductions are consistent with EPA OAP-led Climate Action Report, available at: 

http://www.state.gov/e/oes/rls/rpts/car6/index.htm. The VOC NSPS reductions specifically, are discussed in 

Section 2 of the “Methodologies for U.S. Greenhouse Gas Emissions Projections” document available at: 

http://www.state.gov/documents/organization/219472.pdf. These composite projection factors for VOC 

NSPS sources are provided in Table 4-24. 

There were several assumptions in the application of NSPS VOC reductions. NSPS VOC reductions were 

only applied to increase (if any) of emissions from 2011 to 2018 as provided by the AEO projection factor. 

If AEO-based gas or oil production was projected to decrease in 2018 versus 2011, then NSPS reductions 

had no impact. One exception, highlighted in Table 4-24, is for natural gas well completions; these “oneshot” activities are generally short-term year to year processes and therefore NSPS reductions are applied to 

the entire 2018 projected estimates. Other important assumptions are:

Emissions change linearly with production-level changes (AEO projections)

In the absence of local/state rules, existing equipment will continue to be used and there is no 

replacement of capital that would be affected by the NSPS; the NSPS only affects growth for 

processes other than natural gas well completions.

Engine-related regulatory impacts are accounted for separately (see RICE NESHAP in the following 

section)

EPA did not attempt to account for or quantify the potential reductions due to the oil and natural gas 

NESHAP

Secondary emissions related to NSPS reductions were not accounted for (e..g, NOX emissions arising 

from the combustion of VOC emissions)

EPA acknowledges that these assumptions are not ideal, particularly the linear scaling of production changes 

to emissions for all processes. EPA hopes that future refinement of this methodology, particularly for large 

processes with highly-reactive pollutants such as glycol dehydrators, improve this aspect of oil and gas 

projections. 
DRAFT

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Table 4-24. Oil and Gas sector VOC 2018 Projection Factors for NSPS sources

SCCs SCC Level 4 NSPS Source

NSPS 

Reduction Resource Region

AEO 

Factor

VOC 

NSPS 

(Final)

Factor

2310030210;

2310030300;

2310021010

Gas Well Tanks - Flashing & 

Standing/Working/Breathing, 

Uncontrolled; 

Gas Well Water Tank Losses;

Storage Tanks: Condensate

Storage

Tanks 70.3%

Gas

NE 1.596 1.177

GC 1.246 1.073

MC 0.910 0.910

SW 1.043 1.013

RM 1.098 1.029

WC 0.993 0.993

2310010200;

2310011020

Oil Well Tanks - Flashing & 

Standing/Working/Breathing;

Storage Tanks: Crude Oil

Oil

NE 1.238 1.071

GC 1.853 1.253

MC 1.165 1.049

SW 1.391 1.116

RM 1.642 1.191

WC 0.865 0.865

31000222;

2310121700;

2310021601;

2310021602

Drilling and Well Completion;

Gas Well Completion: All Processes;

Gas Well Venting - Initial 

Completions;

Gas Well Venting – Recompletions

Gas Well 

Completions 95.0% Gas

NE 1.596 0.080

GC 1.246 0.062

MC 0.910 0.045

SW 1.043 0.052

RM 1.098 0.055

WC 0.993 0.050

2310021300 Gas Well Pneumatic Devices

Pneumatic 

controllers

77.0% Gas

NE 1.596 1.137

GC 1.246 1.056

MC 0.910 0.910

SW 1.043 1.010

RM 1.098 1.023

WC 0.993 0.993

31000325;

31000324

Pneumatic Controllers High Bleed 

>6 scfm;

Pneumatic Controllers Low Bleed

100.0% Gas

NE 1.596 1.000

GC 1.246 1.000

MC 0.910 0.910

SW 1.043 1.000

RM 1.098 1.000

WC 0.993 0.993

2310010300 Oil Well Pneumatic Devices 77.0% Oil

NE 1.238 1.055

GC 1.853 1.196

MC 1.165 1.038

SW 1.391 1.090

RM 1.642 1.148

WC 0.865 0.865

31000309 Compressor Seals Compressor

Seals 79.9% Gas

NE 1.596 1.120

GC 1.246 1.049

MC 0.910 0.910

SW 1.043 1.009

RM 1.098 1.020

WC 0.993 0.993
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The national impact of the AEO-based projections for oil and gas sector point and nonpoint inventories is 

shown in Table 4-27. As previously-discussed, these projections vary by product type, NEMS region and for 

some processes for VOC NSPS controls. The smaller percent increase in VOC due to NSPS controls is 

evident. The larger percent growth in NH3 is an artifact of Virginia containing 77 of the 112 tons; Virginia 

being in the Northeast NEMS region, has gas production increasing 59.6% according to AEO projections in

Table 4-23. 

Table 4-25. Projected national Oil and Gas sector 2011 and 2018 emissions, summed point and nonpoint

Pollutant 2011 2018 Increase Percent Difference

CO 662,762 807,868 145,107 22%

NH3 112 159 47 42%

NOX 670,245 817,136 146,891 22%

PM10 23,587 29,248 5,661 24%

PM2.5 19,008 23,537 4,529 24%

SO2 72,337 91,054 18,716 26%

VOC 2,360,726 2,664,887 304,161 13%

National, and even NEMS-region projections are not representative of the trends in each state for many of 

the reasons already discussed. Emissions projections also vary by state depending on the proportion of gas, 

oil and gas/oil (undefined) emissions in the inventory for that state. For this reason, EPA provided VOC and 

NOX state-level projections for the oil and gas sector in Table 4-28.

Table 4-26. Projected by-state NOX and VOC 2011 and 2018 Oil and Gas sector emissions

State

VOC 

2011

VOC 

2018

VOC 

Difference

NOX

2011

NOX

2018

NOX 

Difference

Alabama 22,445 26,400 3,954 11,309 14,178 2,869

Arizona 78 90 12 15 18 3

Arkansas 9,019 8,140 -879 11,573 10,790 -783

California 15,829 14,203 -1,626 3,179 2,976 -203

Colorado 238,717 209,628 -29,090 29,860 37,636 7,776

Connecticut 1 1 0 0 0 0

Delaware 0 0 0 0 0 0

Florida 3,179 3,846 667 180 237 58

Georgia 3 4 1 16 25 9

Idaho 7 9 2 0 0 0

Illinois 27,313 31,029 3,715 9,293 14,121 4,828

Indiana 9,694 11,574 1,880 6,189 9,648 3,459

Iowa 36 33 -3 2 2 0

Kansas 94,601 96,185 1,584 56,613 53,677 -2,936

Kentucky 24,958 32,322 7,364 24,448 38,803 14,355

Louisiana 117,324 143,754 26,429 49,701 65,351 15,649

Maine 16 26 9 0 0 0

Maryland 8 11 3 12 19 7

Massachusetts 22 35 13 0 0 0

Michigan 28,377 35,758 7,382 17,682 28,002 10,320

Minnesota 0 0 0 0 0 0

Mississippi 26,248 33,407 7,159 3,492 4,558 1,066

Missouri 79 81 2 17 17 0
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Note, the national and state-level summaries provided in Table 4-25and Table 4-26 do not include reductions 

from the RICE NESHAP. EPA is also aware that early release AEO 2014 projections (available at: 

http://www.eia.gov/forecasts/aeo/er/pdf/0383er(2014).pdf) became available in December 2013. Overall, it 

appears that oil production increases significantly in the draft AEO 2014 compared to the AEO 2013 

projections, about 22% higher by 2019 in the draft AEO 2014 projection versus the AEO 2013 projection. 

There appears to be less significant increase, about 11%, in projections for natural gas in the draft 2014 AEO 

versus AEO 2013.

4.2.5 RICE NESHAP (nonpt, ptnonipm, np_oilgas, pt_oilgas)

Packet: CONTROL_RICE_incl_SO2_2007v5_27nov2013.txt

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

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

sources. In order to meet the standards, existing sources with certain types of engines will need to install 

controls. In addition to reducing HAPs, these controls have co-benefits that also reduce CAPs, specifically, 

CO, NOX, VOC, PM, and SO2. In 2014 and beyond, compliance dates have passed for all three rules; thus 

all three rules are included in the emissions projection. These RICE reductions also reflect the recent 

(proposed January, 2012) Reconsideration Amendments, which results in significantly less stringent NOX

controls (fewer reductions) than the 2010 final rules.

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

State

VOC 

2011

VOC 

2018

VOC 

Difference

NOX

2011

NOX

2018

NOX 

Difference

Nebraska 2,374 2,495 121 901 864 -37

Nevada 440 553 113 20 26 6

New 

Hampshire 0 0 0 0 0 0

New Jersey 13 21 7 1 2 0

New Mexico 139,402 148,382 8,980 42,277 46,874 4,597

New York 8,222 11,379 3,157 606 944 338

North Dakota 96,873 117,943 21,070 6,375 8,225 1,851

Ohio 10,321 11,996 1,675 322 507 184

Oklahoma 231,536 220,820 -10,717 83,337 77,766 -5,571

Oregon 45 45 -1 41 41 0

Pennsylvania 18,947 25,959 7,012 40,604 64,481 23,877

South 

Carolina 9 14 5 0 0 0

South Dakota 1,444 1,718 274 256 304 47

Tennessee 2,700 3,182 482 1,548 2,432 884

Texas 969,231 1,150,990 181,759 193,512 226,694 33,182

Utah 131,497 157,230 25,733 21,128 26,013 4,885

Virginia 6,628 9,509 2,880 9,577 15,285 5,708

Washington 0 0 0 0 0 0

West Virginia 43,422 60,296 16,873 30,346 48,391 18,045

Wisconsin 0 0 0 1 1 0

Wyoming 42,977 53,237 10,260 2,785 3,330 544

Total 2,360,726 2,664,887 304,161 670,245 817,136 146,891
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105

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion 

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

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion 

Engines; Final Rule (FR 9648) published 03/03/10

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion 

Engines; Final Rule (75 FR 51570) published 08/20/2010

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

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

have different compliance dates, though all are after 2011 and fully implemented prior to 2018. EPA

projects CAPs from the 2011NEIv1 RICE sources, based on the requirements of the rule for existing sources

only because the inventory includes only existing sources and the current projection approach does not 

estimate emissions from new sources.

The Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression 

Ignition (CI) Engines NESHAP: Final Report (EPA, 2013ci) is available at:

http://www.epa.gov/ttn/ecas/regdata/RIAs/RICE_NESHAPreconsideration_Compression_Ignition_Engines_

RIA_final2013_EPA.pdf. The Regulatory Impact Analysis (RIA) for Reconsideration of the Existing 

Stationary Spark Ignition (SI) RICE NESHAP: Final Report (EPA, 2013si) is available at:

http://www.epa.gov/ttn/ecas/regdata/RIAs/NESHAP_RICE_Spark_Ignition_RIA_finalreconsideration2013_

EPA.pdf. Together, EPA calls these the RICE NESHAP amendment RIA’s for SI and CI engines. From 

these RICE NESHAP RIA documents, EPA obtained cumulative RICE reductions for all SCCs represented 

by CI and SI engines. These aggregate reductions and percent reductions from baseline emissions (not the 

2011NEIv1) are provided in Table 4-27.

Table 4-27. Summary RICE NESHAP SI and CI percent reductions prior to 2011NEIv1 analysis

CO NOX PM SO2 VOC

RIA Baseline: SI engines 637,756 932,377 127,170

RIA Reductions: SI engines 22,211 9,648 9,147

RIA Baseline: CI engines 81,145 19,369 11,053 79,965

RIA Reductions: CI engines 14,238 2,818 5,100 27,142

RIA Cumulative Reductions 36,449 9,638 2,818 5,100 36,289

SI % reduction 3.5% 1.0% n/a n/a 7.2%

CI % reduction 17.5% n/a 14.5% 46.1% 33.9%

These RIA percent reductions were used as an upper-bound for reducing emissions from RICE SCCs in the 

2011NEIv1 point and nonpoint modeling sectors (ptnonipm, nonpt, pt_oilgas and np_oilgas). To begin with, 

the RIA inventories are based on the 2005 NEI, so EPA wanted to ensure that our 2011 reductions did not 

exceed those in the RICE RIA documents. For the 2011 platform EPA worked with EPA RICE NESHAP 

experts and developed a fairly simple approach to estimate RICE NESHAP reductions. Most SCCs in the 

inventory are not broken down by horsepower size range, mode of operation (e.g., emergency mode), nor 

major versus area source type. Therefore, EPA summed NEI emissions nationally by-SCC for RICE sources 

and also for sources that were at least partially IC engines (e.g., “Boiler and IC engines”). Then, EPA

applied the RIA percent reductions to the 2011NEIv1 for SCCs where national totals exceeded 100 tons; 

EPA chose 100 tons as a threshold arbitrarily, assuming there would be little to no application of RICE 

NESHAP controls on smaller sources. Next, EPA aggregated these national reductions by engine type (CI 
DRAFT

106

vs. SI) and pollutant and compared these to the RIA reductions. As expected, for most pollutants and engine 

types, our cumulative reductions were significantly less than those in the RIA. The only exception was for 

SO2 CI engines, where EPA opted to scale the RIA percent reduction from 46.1% to 10.2% for four broad 

nonpoint SCCs that were not restricted to only RICE engines. These four SCCs were the “Boilers and IC 

Engines” or “All processes” that would presumably contain some fraction of non-RICE component. 

Reducing the SO2 percent reduction for these four SCCs resulted in slightly less than 5,100 tons of SO2

reductions overall from only RICE NESHAP controls. However, more specific CoST projection packets 

would later override these RICE NESHAP reductions. Recall the CoST hierarchy discussed earlier; these 

RICE NESHAP reductions are national by pollutant and SCC and thus easily overridden by more-specific 

information such as state-level fuel sulfur rules (discussed in the next section). Impacts of the RICE 

NESHAP controls on nonpt, ptnonipm, pt_oilgas and np_oilgas sector emissions are provided in Table 4-28.

Table 4-28. National by-sector reductions from RICE Reconsideration Controls

Pollutant Nonpoint

Oil & Gas

(np_oilgas)

Point

Oil & Gas

(pt_oilgas)

Nonpoint

(nonpt)

Point

(ptnonipm) Total

CO 1,865 95 8,838 7,167 17,964

NOX 1,101 94 1,976 2,033 5,205

PM10 0 0 1,201 300 1,501

PM2.5 0 0 1,120 282 1,402

SO2 1,699 0 1,571 1,049 4,319

VOC 6,249 52 1,304 4,074 11,679

4.2.6 Fuel sulfur rules (nonpt, ptnonipm)

Packet: CONTROL_SULF_2011v6_2018_27nov2013.txt

Fuel sulfur rules that were signed by November, 2013 are limited to Connecticut, Maine, Massachusetts, 

New Jersey, New York, Pennsylvania and Vermont. The fuel limits for these states are incremental starting 

after year 2012, but are fully implemented before June 30, 2018 in all of these states. Other states in the 

Northeast and Mid-Atlantic had pending sulfur rules but were not finalized prior to November, 2013 -the 

completion date of the 2011 platform year-2018 projection. Background on most of these enforceable and 

pending fuel sulfur rules can be found here: 

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

recent update to the status of fuel sulfur rules is provided here: 

http://www.eia.gov/todayinenergy/detail.cfm?id=5890#.

Connecticut

A public hearing on proposed regulations on fuel sulfur limits for heating oil via Connecticut State Agencies 

section 221-174-19b was held on October 9, 2013 (see 

http://www.ct.gov/deep/cwp/view.asp?A=2586&Q=530284). Effective July 1, 2018 maximum fuel sulfur 

content limits for distillate, residual and kerosene fuels go into effect. For distillate fuel oil or distillate fuel 

oil blended with biodiesel, these new limits must not exceed 15 ppm, a 99.5% reduction from 3000 ppm in 

the baseline and down from 500 ppm effective July 1, 2014. Residual oil or residual fuel oil blended with 

biodiesel fuel must not exceed 3000 ppm, a 70% reduction from today’s 1% fuel content assumption for 

smaller stationary sources. For kerosene, a 15 ppm limit replaces the existing 500 ppm limit, a 97% 

reduction.
DRAFT

107

Maine

The Maine Law Legislative Document (LD) 1662 sets a fuel sulfur rule effective January 1, 2014 that 

reduces sulfur to 15 ppm for distillate fuel, resulting in a 99.5% reduction from 3,000 ppm assumed in year 

2008. Maine Law LD 1662 also states that #5 and #6 fuel oils must not exceed 0.5% by weight (500 ppm), 

which is a 75% reduction from an assumed 2% baseline sulfur content in 2008. These Maine sulfur content 

reductions are discussed here: 

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

Massachusetts

The Massachusetts Department of Environmental Protection issued a commitment in their State 

Implementation Plan (SIP) to adopt Phase 2 ultra low sulfur diesel (ULSD) limits by year 2016. Similar to 

Maine, this will reduce the sulfur content in distillate fuel to 15 ppm, a 99.5% reduction from the 3,000 ppm 

baseline. Additional details on the phase-in of ULSD can be found here: 

http://www.mass.gov/dep/service/online/boilwbk.pdf

New Jersey

The New Jersey Department of Environmental Protection adopted sulfur fuel content rules for kerosene and 

home heating distillate oil. For distillate oil, the ULSD limit of 15 ppm yields a 99.5% reduction from the 

3,000 ppm baseline. For kerosene, the same 15 ppm limit is adopted, resulting in a 97% reduction from an 

assumed 2,000 ppm baseline. More details on these fuel sulfur limits in New Jersey can be found here: 

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

New York

New York also signed a law requiring ULSD to replace distillate heating oil #2, which results in a fuel sulfur 

content limit of 15 ppm, a 99.5% reduction from the 3,000 ppm baseline. The ULSD law (A.8642-

A/S.1145-C) can be found here: 

http://switchboard.nrdc.org/blogs/rkassel/governor_paterson_signs_new_la.html and here: 

http://green.blogs.nytimes.com/2010/07/20/new-york-mandates-cleaner-heating-oil/. New York City also 

includes limits by year 2015 on #4 and #6 residual oils, where fuel sulfur content must not exceed 0.5% by 

weight (500 ppm), a 75% reduction from an assumed 2% baseline sulfur content in 2008. By 2030, these 

sources must burn ULSD (15 ppm). The NYC updated Air Code, updated from the NY DEP is discussed 

here: http://www.nyc.gov/html/dep/html/news/dep_stories_p3-109.shtml. 

Pennsylvania

Legislation has been proposed in Pennsylvania that would reduce allowable sulfur levels to 15 ppm for 

distillate oil, a 99.5% reduction from the 3,000 ppm baseline. While EPA typically do not include proposed 

rulemakings in our base projection scenarios without direction from state agencies, the existence of similar, 

finalized standards in neighboring Northeast census region states such as New Jersey and New York suggest 

this will become finalized prior to 2018. EPA can revise this, and potential application to other fuels, based 

on state comment or regulatory changes.

Vermont

Vermont ULSD fuel and date requirements for home heating oil are similar to those adopted in 

Massachusetts: a 99.5% reduction to 15 ppm from the 3,000 ppm baseline.

A summary of the sulfur rules by state, with emissions reductions is provided in Table 4-31.
DRAFT

108

Table 4-29. Summary of fuel sulfur rules by state

State/

Metro

Fuel % 

reduction

2011

Emissions

2018

Emissions

2018

Reductions

CT Distillate 99.5

CT Kerosene 97 12,535 347 12,188

CT Residual 70

ME Distillate 99.5 7,041 706 6,335 ME Residual 75

MA Distillate 99.5 19,540 98 19,443

NJ Distillate 99.5 6,146 31 6,115 NJ Kerosene 96.25

NY Distillate 99.5 32,984 1,027 31,957 NYC Residual 75

PA Distillate 99.5 14,634 73 14,561

VT Distillate 99.5 997 5 992

4.2.7 Industrial Boiler MACT reconsideration (ptnonipm)

Packet: CONTROL_BlrMACT_ptnonipm_20XX_2011v6

The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Rule, hereafter simply referred 

to as the “Boiler MACT” was promulgated on January 31, 2013 based on reconsideration. Background 

information on the Boiler MACT can be found at: http://www.epa.gov/ttn/atw/boiler/boilerpg.html. The 

Boiler MACT promulgates national emission standards for the control of HAPs (NESHAP) for new and 

existing industrial, commercial, and institutional (ICI) boilers and process heaters at major sources of HAPs. 

The expected cobenefit for CAPs at these facilities is significant and greatest for SO2 with lesser impacts for 

direct PM, CO and VOC.

Boiler MACT reductions were computed from a non-NEI database of ICI boilers. As seen in the Boiler 

MACT Reconsideration RIA (http://www.epa.gov/ttn/atw/boiler/boilersriaproposalrecon111201.pdf), this 

Boiler MACT Information Collection Request (ICR) dataset computed over 558,000 tons of SO2 reductions 

by year 2015. However, the Boiler MACT ICR database and reductions are based on the assumption that if a 

unit could burn oil, it did burn oil, and often to capacity. With high oil prices and many of these units also 

able to burn cheaper natural gas, the 2011NEIv1 inventory has a lot more gas combustion and a lot less oil 

combustion than the boiler MACT database. For this reason, EPA decided to target units that potentially 

could be subject to the Boiler MACT and compute preliminary reductions for several CAPs prior to building 

a control packet.

Step 1: Extract facilities/sources potentially subject to Boiler MACT

EPA did not attempt to map each ICR unit to the NEI units, instead choosing to use a more general approach 

to extract NEI sources that would be potentially subject to, and hence have emissions reduced by the Boiler 

MACT. The NEI includes a field that indicates whether a facility is a major source of HAPs and/or CAPs. 

This field in our FF10 point inventory modeling file is called “FACIL_CATEGORY_CODE” and the 

possible values for that field are shown in Table 4-30. Because the Boiler MACT rule applies to only major 

sources of HAPs, EPA restricted the universe of facilities potentially subject to the Boiler MACT to those 

classified as HAP major or unknown (UNK). The third column indicates whether the facility was a 

candidate for extraction as being potentially subject to the Boiler MACT.
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Table 4-30. Facility types potentially subject to Boiler MACT reductions

Code Facility 

Category

Subject 

to Boiler 

MACT?

Description

CAP CAP Major N Facility is Major based upon 40 CFR 70 Major Source definition paragraph 2 

(100 tpy any CAP. Also meets paragraph 3 definition, but NOT paragraph 1 

definition).

HAP HAP Major Y Facility is Major based upon only 40 CFR 70 Major Source definition 

paragraph 1 (10/25 tpy HAPs).

HAPCAP HAP and 

CAP Major

Y Facility meets both paragraph 1 and 2 of 40 CFR 70 Major Source definitions 

(10/25 tpy HAPs and 100 tpy any CAP).

HAPOZN HAP and O3 

n/a Major

Y Facility meets both paragraph 1 and 3 of 40 CFR 70 Major Source definitions 

(10/25 tpy HAPs and Ozone n/a area lesser tons for NOX or VOC).

NON Non-Major N Facility's Potential To Emit is below all 40 CFR 70 Major Source threshold 

definitions without a FESOP.

OZN O3 n/a Major N Facility is Major based upon only 40 CFR 70 Major Source definition 

paragraph 3 (Ozone n/a area lesser tons for NOX or VOC).

SYN Synthetic 

non-Major

N Facility has a FESOP which limits its Potential To Emit below all three 40 

CFR 70 Major Source definitions.

UNK Unknown N Facility category per 40 CFR 70 Major Source definitions is unknown.

From these facilities EPA extracted records (process level / release point level emissions) from our modeling 

file with industrial, commercial, institutional boiler or process heater SCCs. A complete list of these SCCs is 

provided in Appendix H. The resultant data are the NEI sources potentially subject to the Boiler MACT.

Step 2: Match fuel types and control reductions to the NEI SCCs

After obtaining the subset of 2011NEIv1 sources potentially subject to the Boiler MACT, EPA assigned each 

inventory SCC to a fuel type. The reductions are based on the ICR fuel types and associated controls from 

an April 2010 “Baseline Memo.pdf” memorandum available on the Regulations.gov website 

(http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2002-0058-0802) under docket # EPAHQ-OAR-2002-0058-0802. These ICR fuel types and associated default controls were mapped to SCCs in 

our inventory using the cross-walk provided in Table 4-31. The previously-mentioned Appendix H also 

maps the complete list of inventory SCCs to these ICR fuel categories.

Table 4-31. Default Boiler MACT fuel percent % reductions by ICR fuel type

ICR Fuel Category SCC Fuel Category(s) CO PM2.5 SO2 VOC

Coal coal, petroleum coke, waste coal 98.9 95.8 95 98.9

gas 1 (other)

gasified coal, hydrogen, liquified petroleum gas 

(LPG), propane/butane, refinery gas 1 1 1 1

gas 2 digester gas, gas, landfill gas, process gas 99.97 0 95 99.97

Bagasse Bagasse 95.3 90 95 95.3

dry biomass Wood 95.8 99.1 95 95.8

gas 1 (natural gas) natural gas, unknown 1 1 1 1

heavy liquid

coal-based Synfuel, crude oil, liquid waste, 

methanol, residual oil, waste oil 99.9 98.3 95 99.9

light liquid distillate oil, gasoline, kerosene, oil, other oil 99.9 93 95 99.9

wet biomass solid waste, wood/bark waste 85.5 99.2 95 85.5

The impacts of these Boiler MACT reductions on the controllable facilities and units are provided in Table 

4-32. Controls were applied as “replacement” controls to prevent over-control of units that had existing 
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controls. However, this assumes that the inventory correctly reflects units with controls, so it is likely that 

some units that are not recorded as controlled in the 2011NEIv1 but are actually controlled were reduced 

more than they should have. Overall, the CO and PM2.5 reductions are reasonably close to the year-2015 

expected reductions in the Boiler MACT Reconsideration RIA: 

http://www.epa.gov/ttn/atw/boiler/boilersriaproposalrecon111201.pdf. It is worth noting that the SO2

reductions in the preamble (http://www.epa.gov/ttn/atw/boiler/fr21mr11m.pdf) were estimated at 442,000 

tons; the additional SO2 reductions in the reconsideration are from an additional cobenefit from more 

stringent HCl controls. The 2011NEIv1 SO2 emissions are actually less than the estimated Boiler MACT 

reductions, likely a result of numerous units undergoing fuel switching from coal or oil to natural gas via 

changing energy prices between the Boiler MACT RIA analyses and the 2011NEIv1. It is also worth noting 

that EPA did not attempt to quantify the reductions of nonpoint ICI boiler emissions from Boiler MACT 

controls.

Table 4-32. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions

Pollutant 2011 Emissions 2018 Emissions Reductions RIA Reductions

CO 267,685 66,682 201,003 187,000

PM2.5 34,586 10,819 24,654 25,601

SO2 301,748 35,553 276,195 558,430

VOC 19,295 6,984 12,311 n/a

4.2.8 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 2018. This approach 

provided reductions of criteria and select hazardous air pollutants. 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 2015 through 2030. These 

ISIS-based emissions are reflected using a CoST packet for all existing kilns that are not impacted by more 

local information from states (or consent decrees) –see next section- and two cement inventories for new 

kilns:

1) Inventory: “cement_newkilns_year2018_from_ISIS2013_NEI2011v1”

Contains information on new cement kilns constructed after year 2011,

2) Inventory: “cement_newkilns_year_2018_from_ISIS2013_NEI2011v1_NONPOINT_v0”

Contains information ISIS-generated, but not-permitted, new cement kilns constructed after year 

2011,

3) Packet: “PROJECTION_2011_2018_ISIS_cement_by_CENSUS_DIVISION_04dec2013.txt“

Contains U.S. census division level based projection factors for each NEI unit (kiln) based on ISIS 

updated policy case emissions at existing cement kilns. The units that closed before 2018 are 

included in the 2018 base case but are included in other CoST packets that reflect state comments and 

consent decrees (discussed in the next section). 

The ISIS model, version August 2013 was used for these projections. Recent data updates include updated 

matching of kilns to better capture recent retirements, capacity additions and projections of capacity 

additions from Portland Cement Association (PCA) Plant Information Summary of December 31, 2010 and 

feedback from Portland Cement NESHAP reconsideration comments. Updated cement consumption 

projections are based on a post-recession (July 2012) PCA long-term cement consumption outlook. Updated 
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emissions controls in 2015 from the NESHAP are also reflected. Overall, as seen in Figure 4-3, domestic 

production of cement grows significantly between 2011 and 2015, then more slowly through 2018. 

Meanwhile, emissions from NESHAP-regulated pollutants such as PM and SO2 drop significantly based on 

regulated emissions rates. Emissions for NOX increase, though not as much as production because the ISIS 

model continues 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. 

Figure 4-3. Cement sector trends in domestic production versus normalized emissions

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. 

The first step in using ISIS 2018 projected emissions the cement industry is matching the kilns in year 2018 

to those in the 2011NEIv1. For kilns that were new in 2018, EPA used two different approaches for 

modeling. For kilns already permitted, known locations (coordinates) allowed us to process these as point 

sources. However, the ISIS model also created “generic” kilns in specific geographically strategic locations 

(counties) to cover the need for increased production/capacity in future years. Because these generic kilns 

are not permitted and the location in these counties is uncertain, EPA decided to model these as county-level 

to avoid placing large emissions sources from a model (ISIS) artifact in one grid cell. These nonpoint source 

kilns were then spatially allocated based on industrial land activity in the county. A list of all new point and 

nonpoint inventory cement kilns in 2018 are provided in Table 4-33. There are additional new kilns 

generated by ISIS beyond year 2018 that are not shown in this table.

Table 4-33. Locations of new ISIS-generated cement kilns

ISIS ID Permitted? Facility Name FIPS State County

FLNEW2 Y Vulcan 12001 FL Aluchua

GANEW1 Y Houston American Cement 13153 GA Houston

NCNEW1 Y Titan America LLC 37129 NC New Hanover

NewGA2 N n/a 13153 GA Houston

0

20

40

60

80

100

120

140

160

180

2011 2015 2016 2017 2018

Domestic Production

PM trend

NOX trend

SO2 trend
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NewPA8 N n/a 42011 PA Berks

NewSC1 N n/a 45035 SC Dorchester

NewTX1 N n/a 48029 TX Bexar

NewTX10 N n/a 48091 TX Comal

NewWA1 N n/a 53033 WA King

While ISIS provides by-kiln emissions for each future year, EPA cement kilns experts preferred that the 

Agency project existing cement kilns based on a more-smooth geographic approach to reduce the “on”/”off” 

switching that ISIS assigns to each kiln based on production and capacity demands. It would be inefficient 

and unrealistic to project existing cement kilns to operate as essentially 0% or 100% capacity based strictly 

on ISIS output. Therefore, EPA developed a U.S. Census Division approach where ISIS emissions in 2011

and 2018, that matched the 2011NEIv1 (e.g., not new ISIS kilns), were aggregated by pollutant for each year 

within each of the 9 census divisions in the contiguous U.S. 

(http://www.eia.gov/consumption/commercial/images/cendivco.gif). These aggregate emissions were used 

to create a 2018/2011 emissions ratio for each pollutant and geographic area. The projection ratios, provided 

in Table 4-34, were then applied to all 2011NEIv1 cement kilns –except for kilns where specific local 

information (e.g., consent decrees/settlements/local information). 

Table 4-34. U.S. Census Division ISIS-based projection factors for existing kilns

Region Division NOX PM2.5 SO2 VOC

Midwest East North Central 2.024 0.106 1.800 0.527

Midwest West North Central 0.930 0.614 0.695 0.317

Northeast Middle Atlantic 1.853 0.058 0.904 0.561

Northeast New England 2.560 0.004 3.563 0.713

South East South Central 0.999 0.109 0.402 0.323

South South Atlantic 1.042 0.284 0.911 0.413

South West South Central 1.220 0.079 0.484 0.225

West Mountain 1.453 2.542 1.917 0.310

West Pacific 1.465 0.001 0.300 0.321

For all ISIS future year emissions, PM10 is assigned as 0.85 of total PM provided by ISIS, and PM2.5 is 

assigned as 0.45 of total PM. All new ISIS-generated kilns, point and nonpoint format, are assigned as 

Precalciner kilns (SCC=30500623). While ISIS provides emissions for mercury, EPA did not retain these in 

our modeling.

Table 4-35 shows the magnitude of the ISIS-based cement industry emissions changes between the 

2011NEIv1 and 2018 projection scenario. Kilns that matched the 2011NEIv1 were simply projected to year 

2018 based on U.S. census division aggregate changes in ISIS predictions. There are some local exceptions 

where EPA did not use ISIS-based projections for cement kilns where local information from consent 

decrees/settlements and state comments were used instead. Cement kilns projected using these non-ISIS 

information are not reflected here in Table 4-35. EPA also split out ISIS-based new kilns in 2018 with 

permitted (as of August 2013) kilns modeled as point sources and “generic” ISIS-generated kilns as nonpoint 

sources. 

Table 4-35. ISIS-based cement industry change (tons/yr)

Pollutant 2011 2018 New kilns in 2018 Total Difference 2018 -2011
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113

NEIv1 projected Permitted

(point)

ISIS-generated 

(nonpoint)

2018

NOX 53,874 71,205 3,751 6,836 81,792 27,919

PM2.5 1,772 722 8 15 745 -1,027

SO2 17,065 18,629 1,775 3,263 23,667 6,602

VOC 2,690 903 91 167 1,161 -1,529

4.2.9 State comments and consent decrees/settlements (nonpt, ptnonipm)

This subsection describes the numerous (12 in all) CoST PROJECTION and CONTROL packets developed 

to reflect a wide range of information on future year non-EGU point and nonpoint source projections. In 

general, this information is derived from:

comments received from the Cross-State Air Pollution proposal

local and state comments over the past several years, 

consent decrees and settlements, and 

EPA staff data mining and analyses

4.2.9.1 Comments from Cross-State Air Pollution Rule (2010)

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 2005NEIv2 as a base year starting point for future year projections. 

Now with the 2011 platform using the 2011NEIv1 for most non-EGU point and nonpoint sources, many of 

these controls and data improvements were removed from this 2018 base case projection. But for those 

controls, closures and consent decree information that are implemented after 2011, EPA used these 

controls/data after EPA mapped them to the correct SCCs and/or facilities in the 2011 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, 2011 as the cut-off date for assuming whether controls were included 

in the 2011 NEI. For example, if a control had a compliance date of December 2011 EPA would assume that 

the 2011 NEI emissions did not reflect this control and EPA would need to reflect this control in our 2018 

base case. It is important to note that these controls are not comprehensive for all state/counties and source 

categories. These only represent post-year 2011 controls for those areas and categories where EPA received 

usable feedback from the CSAPR comments and related 2005 platform NODA.

Packet: “CONTROLS_CSAPR_consent_2011v6.csv”

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 EPA removed all consent decrees with compliance dates prior to late-2011, 

EPA matched the remaining controls to the 2011 NEI using a combination of EIS facility codes, 

“agy_facility_id”, “agy_point_id” and searching the EIS. Then, EPA recomputed the percent reductions 

such that the future year emissions would match those for facilities originally projected from the 2005 NEIbased platform –these consent decrees were released from 2007 through 2010, when the 2005 NEI was the 

general baseline. EPA did not retain consent decree controls if the emissions in the 2011NEI were less than 

the controlled future year emissions based on the 2005 platform. EPA were left with consent decree controls 

in twelve states (AL, CA, IN, KS, KY, LA, MI, MS, OH, TN, TX, WY) that accounted for 2,515 tons of 

NOX and 11,014 tons of SO2 cumulative reductions.
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Packet: “CONTROL_CSAPR_ptnonipm_2011v6_22nov2013.txt”

EPA created a CONTROL packet for the ptnonipm sector that contains reductions needed to achieve post 

year-2011 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: Georgia, New Hampshire, New York and Virginia. Cumulatively, 

these controls reduce NOX by 655 tons and SO2 by 7,221 tons. 

Packet: “PROJECTION_CSAPR_WVunit_ptnonipm_2012_2011v6_21nov2013.txt”

This packet contains the only post-2011 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. This closure is already reflected in the 2011 NEI; 

however, in 2012, a new natural gas unit was slated to operate and therefore EPA scaled emissions at an 

existing natural gas boiler to match these 2012 emission targets provided to us by West Virginia via CSAPR 

comments. This packet simply results in an extra 22.5 tons of NOX and minimal increased emissions for PM 

and SO2.

4.2.9.2 State comments since spring of 2013

The following packets were derived from information received from several states since the spring of 2013 

regarding point and nonpoint projections to year 2018.

Packets:

“PROJECTION_VA_ME_TCEQ_AL_comments_2011v6_2018_03dec2013.txt” 

“CONTROL_VA_ME_TCEQ_comments_2011v6_2018_03dec2013.txt”

These packets represent primarily local closures and expected changes in future year emissions, in some 

cases, specified as year 2018, but otherwise simply to be used rather than the 2011 NEI values for general 

future year modeling. These comments from Alabama, Maine, Texas and Virginia were received in the 

spring through early fall of 2013. The CONTROL packet was used for specific stack/unit closures and 

emissions reductions. Deciding which packet type to use (PROJECTION or CONTROL) for applying state 

comments in CoST is fairly subjective. EPA is forced to use PROJECTION packets when emissions 

increase, and if EPA can get away with using only 1 type of packet (PROJECTION or CONTROL) for a 

single source of comments, AND, the choice does not result in different final projected values, then the 

packet type that best fits was used. For example, if a set of state comments results in emissions increasing 

and decreasing at various stacks and other CoST packets do not apply, then the packet type choice does not 

matter. If, however, EPA chose to represent emission decreases as a PROJECTION packet entry, and 

another CoST CONTROL packet applies to that source, then EPA are applying two different sources of 

reductions –not ideal. Our goal is for state comments to pass through to the final future year inventory as-is. 

For this reason, EPA does not quantify emission changes for these packets separately. The cumulative 

impact of these emissions is shown in Table 4-36. Note that the widespread Texas NAICS-level economicbased growth factors and impacts are discussed separately.

Table 4-36. Impacts of most non-EGU point source state comments received in 2013

State Pollutant 2011NEIv1 2018 Projection Change

Alabama NOX 2,941 3,062 120

Alabama SO2 1,156 1,168 12

Maine NOX 178 45 -134

Maine SO2 2,069 666 -1,463
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Texas NOX 3,337 712 -2,625

Texas SO2 8,461 229 -8,233

Texas VOC 469 65 -404

Virginia NOX 8,065 4,531 -3,534

Virginia SO2 1,646 2 -1,644

Packet: “PROJECTION_TCEQ_ptnonipm_NAICS_comments_2011v6_2018_04dec2013.txt”

This packet represents county-specific economic-based NAICS-level projections provided by the Texas 

Commission on Environmental Quality (TCEQ) for minor source emissions. Growth factors are based on 

projections of gross product for various types of industry, population and various economy.com data. EPA

did not apply these projections to oil and gas sources, opting to use the consistent regional/fuel-based 

approach discussed in Section 4.2.4. A summary of these minor source ptnonipm sector projection impacts 

for Texas are provided in Table 4-37.

Table 4-37. Minor source ptnonipm sector NAICS-level projections for Texas

Pollutant 2011NEIv1 2018 Projection Change

CO 114,817 136,696 21,879

NH3 2,099 2,619 520

NOX 138,389 157,997 19,609

PM10 21,146 26,044 4,898

PM2.5 17,301 21,384 4,084

SO2 21,432 28,033 6,601

VOC 62,386 79,671 17,285

Packet: “PROJECTION_TCEQ_AREA_comments_2011v6_2018_04dec2013.txt”

This packet represents nonpt sector 2011-based projections for year 2018 for Texas as provided by TCEQ. 

These county-level and SCC-specific projections are based on a combination of economy.com and Annual 

Energy Outlook (AEO) data. EPA did not apply these projections to oil and gas sources, opting to use the 

consistent regional/fuel-based approach discussed in Section 4.2.4. EPA also did not apply these projections 

to the Residential Wood Combustion sector which were the same for every RWC SCC and county, opting 

instead to use our national-based but SCC-specific approach discussed in Section 4.2.3. A summary of these 

nonpt sector changes in Texas is provided in Table 4-38.

Table 4-38. Minor source nonpt sector projections for Texas

Pollutant 2011NEIv1 2018 Projection Change

CO 68,967 83,299 14,333

NH3 2,659 2,720 60

NOX 32,581 34,329 1,748

PM10 19,999 24,416 4,416

PM2.5 15,520 19,268 3,747

SO2 9,099 8,805 -293

VOC 239,657 256,046 16,389

4.2.9.3 Consent decrees and settlements

These packets were derived in prior emissions modeling platforms, dating back to the 2005 NEI and 2008 

NEI. EPA updated this information based on information in the 2011NEIv1 and analysis for compliance 

dates. Many of these consent decrees were already in place in 2011 and therefore removed from 
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consideration for projections. New information (e.g., Cabot Corporation) has also been obtained since the 

spring of 2013 and has been included in our projections. Consent decrees or settlements released after 

November are not included.EPA also does not reflect consent decrees that do not have obvious quantifiable 

reductions for important emissions modeling pollutants (CAPs).

Packet: “CONTROL_ConsentDecree_Cabot_BlackPowderPlants_03dec2013_v0.txt”

This Cabot Corporation Clean Air Act settlement (release date of November 19, 2013) targets NOX and SO2

reductions of 1,975 and 12,380 tons, respectively, from three carbon black manufacturing plants in Louisiana 

and Texas. More information on this settlement can be found at:

http://www2.epa.gov/enforcement/cabot-corporation-clean-air-act-settlement#reductions.

Because EPA did not have specific stack-level information on this settlement, the Agency apportioned the 

total reductions proportionally to each of the three facilities such that each process in all the facilities was 

assigned the same percent reduction and that the cumulative NOX and SO2 reductions would be achieved.

Packet: “CONTROLS_Refineries_additional_consent_2011NEI_v1_25nov2013_v1.txt”

This packet consists of two settlements. The BP Whiting settlement (released May 23, 2012) is available at:

http://www2.epa.gov/enforcement/bp-whiting-settlement. The Marathon Petroleum Company, Detroit 

Refinery environmental mitigation project (released April 5, 2012) is available at:

http://www2.epa.gov/enforcement/marathon-petroleum-company-lp-and-catlettsburg-refining-llc-settlement.

The initial application of these settlements was to the 2008 NEI. Therefore, to be consistent with previous 

future year estimates for these facilities, EPA modified existing computed reductions from the 2008 such that 

future year estimates from the 2011NEIv1 matched those done with the 2008 NEI. These settlements reduce 

NOX by 78 tons at the Detroit Refinery and NOX and SO2 by 780 and 150 tons, respectively, at the Indiana 

BP Whiting facility.

Packet: “CONTROL_OECA_2011v6_25nov2013.txt”

The Office of Enforcement and Compliance Assurance (OECA) provided emission reduction information for 

several consent decrees while EPA was preparing emissions for the 2005 NEI-based modeling platform 

(http://www.epa.gov/ttn/chief/emch/toxics/proposed_toxics_rule_main.pdf). The press releases for these 

consent decrees are available on EPA’s enforcement website (http://www.epa.gov/enforcement/) and some 

were available with quantitative emission reductions that EPA was able to convert into a control packet. 

These petroleum refinery settlements are available at:

http://www2.epa.gov/enforcement/petroleum-refinery-national-case-results. These settlements were released 

in the 2003-2010 time period and include information for a few corporations but with aggregate reductions 

over numerous facilities under these companies and subsidiaries. Therefore, EPA developed an initial table 

of 2008 NEI emissions summed over all affected facilities for each company. Then EPA merged the multifacility expected reductions from each of these consent decrees to develop an overall future year (postcompliance date) emissions estimate for each company after all controls/reductions are implemented. Using 

this methodology, the emissions reductions were apportioned to each plant owned/operated by each company 

using the same percent reduction from the 2005 NEI emissions.

Now that EPA is using the 2011 NEI, the Agency2011NEIv1, expected that some of these consent decree 

controls/reductions would have already been applied by 2011. EPA did not want to over-control any 

particular plant. Therefore, EPA computed facility-specific reductions based on the controlled emissions 
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from the 2008 NEI. For example, as seen in Table 4-37, SO2 emissions at all Cargill facilities were reduced 

about 24% in the 2008 NEI: from 6,921 tons to 5,280 tons. In the 2011NEIv1, SO2 emissions at these same 

Cargill facilities totaled 6,263 tons, so only approximately 1,000 tons, a 16% cumulative reduction over all 

Cargill facilities, were needed to achieve the 5,280 consent decree target. 

The column “2008 NEI Controlled” in Table 4-39 was our target for year 2018 emissions. However, many 

of these facilities are ethanol plants and are therefore projected separately using EPA OTAQ’s national 

projections for ethanol plants (see Section 4.2.1). This is a biggest issue for the Cargill facilities, a majority 

of which are defined as ethanol plants. Note in Table 4-39, the “applicable” (non-ethanol plants) 2011NEIv1

emissions available for OECA consent decree controls is significantly less than the sum of all Cargill facility 

emissions. The discrepancies between actual and applicable 2011 NEI emissions for the other OECA 

facilities are primarily a result of CoST hierarchy assignments. In short, more-specific (more resolved than 

facility/pollutant of the OECA packet) control information from other CoST packets are used for some of 

these stacks/units/facilities.

Table 4-39. Target company-wide reductions from OECA consent decree information

Corporation Pollutant 2008 NEI 

(tons)

2008 NEI

Controlled 

(tons)

Reductions 

from 2008

(tons)

2011NEIv1

Emissions

(tons)

2011NEIv1

applicable

(tons)

Actual 

2018 

Reductions

Cargill CO 10,889 262 10,627 6,045 401 394

NOX 2,265 1,478 787 1,714 806 111

SO2 6,921 5,280 1,642 6,263 849 172

Conoco Phillips NOX 14,331 7,334 6,997 9,391 9,070 2,932

Sunoco NOX 4,506 1,975 2,531 3,235 3,154 1,231

PM2.5 1,030 585 445 1,072 714 379

Valero NOX 8,212 6,109 2,103 6,676 4,913 966

PM2.5 2,554 1,955 599 2,338 1,883 718

SO2 11,479 2,903 8,575 6,040 4,807 3,367

Total CO 10,889 262 10,627 6,045 401 394

NOX 29,314 16,896 12,418 21,016 17,943 5,240

PM2.5 3,584 2,540 1,044 3,410 2,597 1,097

SO2 18,400 8,183 10,217 12,303 5,656 3,539

Packet: “CONTROL_LaFarge_StGobain_ptnonipm_2011v6_22nov2013.txt”

This control packet includes settlements for all 15 U.S. plants owned by Saint-Gobain Containers, Inc., the 

nation’s second largest container glass manufacturer, and all 13 U.S. plants owned by the Lafarge Company 

and two subsidiaries, the nation’s second largest manufacturer of Portland cement. These settlements, 

released January 21, 2010, are the first system-wide settlements for these sectors under the Clean Air Act and 

require pollution control upgrades, acceptance of enforceable emission limits, and payment of civil penalties. 

The settlements require various NOX and SO2 controls, some of which (SO2 scrubbers) also reduce PM 

emissions. A couple of Lafarge kilns were also scheduled to be shut down. One of these units was 

shutdown prior to 2011 and as expected, is not in the 2011NEIv1. However, a Lafarge kiln in Joppa, Illinois 

was unexpectedly found in the 2011NEIv1 and communication with the Illinois DEP indicated that this unit 

was not closed as of the summer of 2012. More information on the Lafarge settlement can be found here:

http://www2.epa.gov/enforcement/lafarge-north-america-inc-clean-air-act-settlement. More information on 

the Saint-Gobain settlement is available here:

http://www2.epa.gov/enforcement/saint-gobain-containers-inc-clean-air-act-settlement. Many of the 

controls for the units at these facilities were implemented prior to 2011 and were therefore removed from the 
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CONTROL packet; however, cumulatively, there is still significant reductions post-2011: 9,210 tons of NOX, 

214 tons of PM2.5 and 11,777 tons of SO2.

4.2.9.4 EPA staff data mining

Packet: “CONTROLS_Regional_Haze_2011v6.csv”

This packet includes a set of NOX and SO2 reductions provided by EPA’s OAQPS Air Quality Policy 

Division (AQPD) visibility experts. These reductions reflect expected emissions reductions and future year 

caps for facilities of various industries (e.g., cement kilns, taconite, steel, pulp and paper and mining 

industries) in the following states: Georgia, Idaho, Michigan, Minnesota, Montana, New York, Ohio, 

Tennessee, Virginia and Wisconsin. Cumulatively, 28,589 tons of NOX and 20,698 tons of SO2 are reduced 

by these controls. 

4.2.10 Aircraft projections (ptnonipm)

Aircraft emissions are contained in the ptnonipm inventory. These 2011 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. EPA

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, 2013). This 

information is available for approximately 3,300 individual airports, for all years up to 2030. 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.

None of our 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% 

by 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule; therefore, its effects were not 

included in the future-year emissions projections.

EPA developed two sets of projection factors for aircraft. The first set was a simple national (U.S.) 

aggregation, used primarily for airports with very little activity, by ITN operation type (commercial, general 

aviation, military and air taxi) to be used as a default method for projecting from 2011 to 2018. The second 

set of projection factors was by airport, where EPA projected project emissions for each individual airport 

with significant ITN activity. 

Packet: “PROJECTION_2011_2018_aircraft_21nov2013.txt”

In this case, EPA simply summed the ITN operations to national totals by year and aircraft operation and 

computed projection factors as future-year 2018 ITN by 2011-year ITN. EPA assigned factors to inventory 

SCCs based on the operation type shown in Table 4-40.

Table 4-40. Default national-level factors used to project 2011 base-case aircraft emissions to 2018

SCC Description

Projection 

Factor

2265008005 Commercial Aircraft: 4-stroke Airport Ground Support Equipment 1.1741

2267008005 Commercial Aircraft: LPG Airport Ground Support Equipment 1.1741

2268008005 Commercial Aircraft: CNG Airport Ground Support Equipment 1.1741

2270008005 Commercial Aircraft: Diesel Airport Ground Support Equipment 1.1741
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SCC Description

Projection 

Factor

2275000000 All Aircraft Types and Operations 1.1741

2275001000 Military Aircraft, Total 0.9972

2275020000 Commercial Aviation, Total 1.1741

2275050000 General Aviation, Total 1.0199

2275050011 General Aviation, Piston 1.0199

2275050012 General Aviation, Turbine 1.0199

2275060000 Air Taxi, Total 0.9417

2275060011 Air Taxi, Total: Air Taxi, Piston 0.9417

2275060012 Air Taxi, Total: Air Taxi, Turbine 0.9417

2275070000 Commercial Aircraft: Aircraft Auxiliary Power Units, Total 1.1741

27501015

Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Military; Jet Engine: 

JP-5 0.9972

27502011

Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Commercial; Jet 

Engine: Jet A 1.1741

27505001

Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil; Piston 

Engine: Aviation Gas 1.0199

27505011

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

Jet A 1.0199

Packet: “PROJECTION_2011_2018_aircraft_by_airport_21nov2013.txt”

The second set of projection factors was by airport, where EPA projected emissions for each individual 

airport based on the following criteria:

ITN activity in year 2011 are greater than 1000 for any of the four available modes: commercial, 

general aviation, military and air taxi;

ITN airport matched to 2011NEIv1

ITN activity is not the same for 2011, 2018 and 2025 AND 2035. The rational here is that these ITN 

data add no value if 2011 ITN data are used for all future years. These airports were projected based 

on the national default method.

A hierarchal assignment was applied when the airport emissions in the NEI did not match the type of 

ITN information. For example, if an airport in the 2011NEIv1 contained only general aviation 

emissions (based on NEI SCC), and the ITN data for that airport did not contain general aviation, 

then commercial aviation activity was used to project these emissions. There were 11 of 15 possible 

hierarchal assignments used in our projection methodology where EPA assigned a “fall-back” ITN 

projection method to an NEI airport SCC, and most of these assignments were linked to very small 

NEI emissions.

A summary of the national impact of airport-specific and default national projection approaches for airports 

is provided in Table 4-41. Most of the significant airports, and hence increased emissions, are projected via 

the airport-specific projection packet. Overall, aircraft NOX emissions increase approximately 17% between 

2011 and 2018.

Table 4-41. Increases in aircraft emissions by year 2018 from airport-specific and national-level methods

CO NOX PM2.5 SO2 VOC

2011NEIv1 total 456,212 111,575 7,362 12,563 29,687

Airport-specific increases 34,334 18,897 420 2,024 2,950

National approach increases 2,254 137 31 12 64
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Total Increase in 2018 36,558 19,034 451 2,036 3,014

4.2.11 Remaining non-EGU controls and closures (ptnonipm)

This section describes all remaining non-EGU stationary source reductions and closures not already 

discussed. These CONTROL packets and CLOSURE packets generally have lesser national-level impact on 

future year projections than many of the items above. However, these impacts can be significant locally –

particularly plant closures. 

4.2.11.1Boat Manufacturing MACT (ptnonipm)

Packet: CONTROL_MACT_BoatManuf_2007v5_03aug2012.txt

EPA included MACT rules where compliance dates were 2011 or later. EPA OAQPS Sector Policies and 

Programs Division (SPPD) provided all controls information related to the MACT rules, and this information 

is as consistent as possible with the preamble emissions reduction percentages for these rules.

A 32% reduction to VOC and VOC BAFM HAPs was applied to the Boat Manufacturing SCCs in the 

ptnonipm inventory. Compliance with the MACT reduction is expected to occur by use of low HAP resins 

and gel coats and use of non-atomized resin spray application systems. Documentation on this control is 

provided in the Guidance for Estimating VOC and NOX Emission Changes from MACT Rules document 

(EPA, 2007b). The national impact of these reductions is 411 tons of VOC.

4.2.11.2CISWI/HWI controls (ptnonipm)

Packet: CONTROL_CISWI_2011v6_22nov2013.txt

On March 21, 2011, EPA promulgated the revised NSPS and emission guidelines for Commercial and 

Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was 

granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the standards 

re-development included the 5-year technology review of the new source performance standards and 

emission guidelines required under Section 129 of the Clean Air Act (CAA). The history of the CISWI 

implementation is documented here: http://www.epa.gov/ttn/atw/129/ciwi/ciwipg.html. Baseline and CISWI 

rule impacts associated with the CISWI rule are documented here: 

http://www.epa.gov/ttn/atw/129/ciwi/baseline_emission_reductions_memo.pdf. EPA mapped the units from 

the CISWI baseline and controlled dataset to the 2011NEIv1 inventory and because the baseline CISWI 

emissions and the 2011NEIv1 emissions were not the same, EPA computed percent reductions such that our 

future year emissions matched the CISWI controlled dataset values. CISWI reductions limited to SO2

reductions of 1,427 and 1,413 tons in Arkansas and Louisiana, respectively.

4.2.11.3Remaining facility closures

Packets: 

“CLOSURES_EIS_2011NEIv1_sep2013_25nov2013_v1.txt” &

“CLOSURES_2008_Merged_12nov2013_v0.txt”

This section describes two CLOSURE packets. The first “EIS” packet is from a September 11, 2013 

Emissions Inventory System (http://www.epa.gov/ttnchie1/eis/gateway/) report of post-2011 permanent 

facility shutdowns, based on facility status code “PS”. The second “Merged” packet is from a concatenation 

of previous facility and unit-level closure information used in the 2008 NEI-based emissions modeling 

platform (http://epa.gov/ttn/chief/emch/2007v5/2007v5_2020base_EmisMod_TSD_13dec2012.pdf). The 
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“EIS” closures impact facilities in 12 states while the “Merged” packet closures are spread out over 34 states. 

The cumulative reductions in emissions from this packet are shown in Table 4-42.

Table 4-42. Reductions from all EIS-based and remaining information facility/unit-level closures

Pollutant Reductions

CO 1,420

NH3 441

NOX 3,117

PM10 1,858

PM2.5 1,613

SO2 26,073

VOC 2,207

4.3 Mobile source projections

Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2018 using a 

combination of the NMIM and the SMOKE-MOVES models. The 2018 onroad emissions account for 

changes in activity data and the impact of on-the-books rules including: the Light-Duty Vehicle Tier 2 Rule

(EPA, 2000), the 2007 Heavy Duty Diesel Rule (http://www.epa.gov/otaq/highway-diesel/), the Mobile 

Source Air Toxics (MSAT2) Rule (EPA, 2007a), the Renewable Fuel Standard (RFS2) (EPA, 2010a), the 

LD GHG/CAFE standards for 2012-2016 (EPA, 2010c), the Heavy-Duty Vehicle Greenhouse Gas Rule

(EPA, 2011a), and the Tier 3 Notice of Proposed Rulemaking (NPRM) (http://www.epa.gov/otaq/tier3.htm). 

Local inspection and maintenance (I/M) and other onroad mobile programs are included such as California 

LEVIII, the National Low Emissions Vehicle (LEV) and Ozone Transport Commission (OTC) LEV 

regulations(http://www.epa.gov/otaq/lev-nlev.htm). 

Nonroad mobile emissions reductions for these years include reductions to locomotives, various nonroad 

engines including diesel engines and various marine engine types, fuel sulfur content, and evaporative 

emissions standards.

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 4.4. Locomotives and Class 1 and 

Class 2 commercial marine vessel (C1/C2 CMV) projections are discussed in subsection 4.5, and Class 3 

(C3) CMV projected emissions are discussed in subsection 4.4.2.

4.3.1 Onroad mobile (onroad and onroad_rfl)

The onroad emissions for 2018 use the same SMOKE-MOVES system as for the base year (see Sections 

2.3.1 and 2.3.2). Meteorology, speed, spatial and temporal surrogates, representative counties, and fuel 

months were the same as for 2011, discussed above.

4.3.1.1 VMT and vehicle population

Estimates of total national Vehicle Miles Travelled (VMT) in 2018 came from DOE's Annual Energy

Outlook (AEO) 2013 (http://www.eia.gov/forecasts/aeo/) transportation projections 

(http://www.eia.gov/forecasts/aeo/sector_transportation.cfm), specifically the reference case (release dates 

April 15th-May 2nd 2013). Trends were developed by calculating ratios between 2011 AEO and 2018 AEO 

estimates and renormalizing the trends so that a projection of the 2011NEIv1 VMT would match the AEO’s 

2018 total VMT (across all vehicle types). These ratios were developed for light versus heavy duty and for 

gasoline versus diesel vehicle types. The projection factors, the national 2011NEIv1 VMT (“VMT_2011”) 

by vehicle type (SCC7), and the default future VMT (“VMT_2018) by vehicle type are show in Table 4-43. 
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Table 4-43. Projection factors for 2018 VMT (in millions of miles)

Classification SCC7 Description VMT 2011 ratio VMT 2018

light_gas 2201001 Light Duty Gasoline Vehicles (LDGV) 1,595,751 1.0226 1,631,840

light_gas 2201020 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5) 682,930 1.0226 698,375

light_gas 2201040 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5) 351,812 1.0226 359,768

heavy_gas 2201070 Heavy Duty Gasoline Vehicles 2B thru 8B & Buses (HDGV) 98,334 1.1056 108,714

light_gas 2201080 Motorcycles (MC) 19,744 1.0226 20,190

light_diesel 2230001 Light Duty Diesel Vehicles (LDDV) 4,764 3.8885 18,526

light_diesel 2230060 Light Duty Diesel Trucks 1 thru 4 (M6) (LDDT) 13,389 3.8885 52,063

heavy_diesel 2230071 Heavy Duty Diesel Vehicles (HDDV) Class 2B 6,080 1.2753 7,753

heavy_diesel 2230072 Heavy Duty Diesel Vehicles (HDDV) Class 3, 4, & 5 30,625 1.2753 39,055

heavy_diesel 2230073 Heavy Duty Diesel Vehicles (HDDV) Class 6 & 7 48,998 1.2753 62,486

heavy_diesel 2230074 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B 131,503 1.2753 167,704

heavy_diesel 2230075 Heavy Duty Diesel Buses (School & Transit) 8,938 1.2753 11,399

These national SCC7 ratios were applied to the 2011NEIv1 VMT to create an EPA estimate of 2018 VMT at 

the county, SCC level. The following states/regional organizations provided projected VMT that supplanted 

EPA’s projected VMT: Connecticut, Georgia, Maine, Maryland33, Michigan, and SEMCOG. Michigan and 

SEMCOG provided county total VMT. For these estimates, EPA’s 2018 SCC fraction by county (which 

takes into account the AEO growth by vehicle and fuel type) were used to distribute the organization’s total 

VMT to SCC. For the rest of the states, the VMT was either in MOVES county database (CDB) format or 

was already distributed to SMOKE SCC (Georgia only34). For these counties, EPA used the state supplied 

distribution of VMT to vehicle type and road type (these distributions tended to match their 2011 submittals).

Vehicle population (VPOP) was developed by creating VMT/VPOP ratios from the 2011NEIv1 VMT and 

2011NEIv1 VPOP at the county, vehicle type (SCC7) level. These ratios were applied to the 2018 VMT 

(including state updates) to create a 2018 VPOP. The one exception was Georgia that supplied VPOP for the 

20 counties in the Atlanta area.

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 

described for 2011 (see the 2011NEIv1 TSD and Section 2.3). The same meteorology and the same 

representative counties were used. Changes between 2011 and 2018 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.

Similar to the 2011 onroad run, two MOVES runs were needed to model 2018: MOVES2010b was used for 

refueling and extended idle modes and Tier3NPRM (version of the MOVES model for the Tier 3 proposal) 

was used for all other modes. The similarities and differences between the two runs are described in Table

4-44.

 

33 Maryland’s CDBs were missing VMT projections for Howard County (FIPS 24027). This was discovered at a late date; 

therefore EPA estimates of VMT and VPOP were used for this one Maryland county.

34 Georgia supplied VMT for the 20 counties in the Atlanta area. For the rest of Georgia, EPA estimates were used.
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Table 4-44. Comparison of MOVES runs for 2018

Element T3NPRM MOVES2010b

Code MOVES20110414a MOVES20120410

Default database movestier3db20110512 movesdb20121030

VMT and VPOP CDBs and state DBs for 26 states Same as t3nprm

Hydrocarbon speciation T3FRM2018_natinv_HCspec_SS_M Same as t3nprm

Fuels tier3frm_2018_09192013_NOE85forOAQPS Same as t3nprm

CA LEVIII ca_standards_SS_20130617 (16 states) Same as t3nprm

Tier 3 controls tier3ctldbs_060313 Default

The following states were modeled as having adopted the California LEV III program (see Table 4-45)

Table 4-45. CA LEVIII program states

FIPS State Name

06 California

09 Connecticut

10 Delaware

23 Maine

24 Maryland

33 New Hampshire

34 New Jersey

36 New York

41 Oregon

42 Pennsylvania

44 Rhode Island

50 Vermont

53 Washington

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 AEO2013 projection includes partial implementation of RFS2 in 

2018 and assumes that all fuels have an ethanol content of E10 or greater. The regional fuels in 2011 were 

projected to 2018 so that some of the regional variation is preserved but the totals match AEO2013. E85 

fuels were removed from the database and the other fuels were appropriately adjusted because the T3NPRM 

version of MOVES is unable to model E85 directly (see Section 3.2.1.2 for a description of how emissions 

were adjusted to account for E85). For details on the 2018 speciation of onroad, which is strongly dependent 

on the fuels, see Section 3.2.1.4.

4.3.1.3 National, California, and Texas adjustments

A set of adjustments were done in SMOKE-MOVES to create 2018 emissions: HDGHG and E85, extended 

idle, California emissions, and Texas emissions. 

Because the version of MOVES used in the Tier 3 NPRM analysis does not have the capability to model 

Heavy-Duty Vehicle Greenhouse Gas Rule (HDGHG) and E85, an interim version of MOVES incorporating 

updates for HDGHG and E85 was used to derive the adjustment factors. The medium and heavy duty 
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greenhouse gas program set in place by the rule begins with 2014 model year and increases in stringency 

through 2018. These changes primarily affect long haul diesel, but other heavy-duty source types are also 

affected slightly. For additional details, see the documentation for HDGHG rulemaking.35 The HDGHG 

rule was implemented in the interim version of MOVES through three key elements. They are: (a) revised 

running emission rates for total energy, (b) new aerodynamic coefficients and vehicle masses for use in 

MOVES operating mode generation, (c) auxiliary power units (APUs) that largely replace extended idle in 

long haul trucks and are added to MOVES as a new process for combination long haul trucks. The affected 

MOVES tables are “EmissionRate” and “SourceUseTypePhysics”.

EPA conducted a statistical analysis examining the effect of E85 on emissions in comparison to E10. The 

dataset included 21 flex-fuel vehicles that were tested on both E10 and E85 over LA92 drive cycle. The 

study found that there were no statistically significant difference in THC, NMOG, VOC, NOx, CO and PM 

between E10 and E85 (α=0.05). For toxics, E85 showed a statistically significant decrease in emissions of 

benzene and 1,3-butadiene, and a statistically significant increase in emissions of acetaldehyde, 

formaldehyde, and ethanol. The results from the study were incorporated into the version of MOVES used 

to develop the adjustment factors by updating the “GeneralFuelRatioExpression” and “HCSpecation” tables 

in MOVES default database. The modeling of E85 also used updated “AVFT”, “FuelFormulation”, 

“FuelSupply”, and “FuelUsageFraction” tables, reflecting the renewable fuel volumes and market fractions 

projected by the Annual Energy Outlook 2013 (AEO2013) Report.36

The two scenarios modeled to develop the adjustment factors were ‘pre-HDGHG/without E85’ and ‘postHDGHG/with E85’. All input databases other than the ones related to HDGHG and E85, described above, 

were kept the same between the two runs. The emissions inventories for both scenarios were based on an 

inventory run at the national scale. MOVES was run separately for January and July in calendar year 201837. 

These adjustment factors were national by vehicle type (SCC7), process, month, pollutant, and mode. These 

adjustments were only for the rate-per-distance (RPD) and rate-per-vehicle (RPV) processes38.

The second set of adjustment factors was for extended idle. This uses the same approach as was used in 

2011 (see the 2011NEIv1 TSD for details) except for the VPOP was updated to be consistent with 2018. 

These adjustments were by county, vehicle type (long-haul truck SCCs only), and mode (extended idle only) 

and impacted the RPV process only.

The third set of adjustment factors was meant to incorporate 2018 emissions provided by California. The 

same approach as was used in 2011 was used to match the emissions totals provided by CARB (see Section

2.3.1). The only differences between the 2011 approach and that applied for 2018 are that the latter uses the 

2018 emissions from CARB and the 2018 SMOKE-MOVES output (EPA estimates), where the 2018 

“CARB emissions” were created by interpolating between the 2017 and 2020 CARB emissions. The 

provided CARB emissions were produced from working draft versions of EMFAC2011-LD and 

EMFAC2011-HD and include the following heavy duty regulations: chip reflash, extended idling, public 

fleet, trash trucks, drayage trucks, and trucks and buses. It does not include the GHG/smartway regulations 

 

35 Final Regulatory Impact Analysis, Chapter 5 (RIA) (PDF) (553 pp, EPA-420-R-11-901, August 2011)

36 U.S. Energy Information Administration, Annual Energy Outlook 2013 (April 15, 2013)

37 The January adjustment factors were used for all the winter fuel months and the July adjustment factors were used for all the

summer fuel months.

38 During QA of the adjustment factors, an error was discovered in extended idle for July and evaporative in RPD, RPV, and RPP. 

For extended idle, the adjustment factors were replaced with January’s adjustment factors. For evaporative, the adjustment factors 

were set to 1 for both months. These modifications to the adjustment factors are expected to have minimum impact on the 2018 

emissions.
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for trucks, or the low carbon fuel standard. These adjustment factors are by county, SCC3, pollutant and 

impact all processes (RPD, RPV, RPP).

The fourth set of adjustment factors was meant to incorporate emissions provided by Texas. Conceptually, 

EPA used the trend of 2011 to 2018 based on EPA’s estimates to project Texas’ submitted emissions for 

2011. Mathematically, this is equivalent to taking the Texas adjustment factors derived for 2011 (see 

Section 2.3.1 for details) and applying them directly to EPA’s 2018 run. These adjustment factors are by 

county, SCC7, pollutant and impact all processes.

Because these adjustment factors are multiplicative, a single set of adjustment factors may be created by 

multiplying the 4 adjustment factors together taking care to match process (RPD, RPV, or RPP), mode, 

pollutant, SCC, and county. Movesmrg uses the composite adjustment factor file (CFPRO) to estimate 2018 

emissions that incorporates each of these adjustments (or a subset of them depending on county, mode, and 

process).

4.4 Nonroad mobile source projections (c1c2rail, c3marine, nonroad)

The projection of locomotives and Class 1 and 2 commercial marine vessels to 2018 is described in 

Section 4.4.1. These sources are treated in shapes in the NEI but are considered at the county-level in the 

modeling platform. The projection of the larger Class 3 commercial marine vessels, treated as point sources 

in the modeling platform, is described in Section 4.4.2. Most of the remaining sources in the nonroad sector 

are projected by running the NMIM model with fuels and vehicle populations appropriate to 2018, as 

described in Section 4.4.3.

4.4.1 Locomotives and Class 1 & 2 commercial marine vessels (c1c2rail)

There are three distinct components used to craft year 2018 inventories from the 2011 base case. The first 

component of the 2018 c1c2rail inventory is the non-California data projected from the 2011 base case. The 

second component is the CARB-supplied year 2017 data for California. The third component is a set of EPA

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_2011_2018_c1c2rail_BASE_noRFS2_05dec2013.txt”

This packet creates an intermediate set of year 2018 emissions 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 2011 and 2018 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). 

These projection ratios are provided in Table 4-46.
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Table 4-46. Non-California year 2018 intermediate projection factors for locomotives and Class 1 and 

Class 2 Commercial Marine Vessel Emissions

SCC Description Pollutant Projection 

Factor

2280002XXX Marine Vessels, Commercial;Diesel;Underway & port emissions CO 0.9525

2280002XXX Marine Vessels, Commercial;Diesel;Underway & port emissions NOX 0.7623

2280002XXX Marine Vessels, Commercial;Diesel;Underway & port emissions PM 0.6755

2280002XXX Marine Vessels, Commercial;Diesel;Underway & port emissions SO2 0.1275

2280002XXX Marine Vessels, Commercial;Diesel;Underway & port emissions VOC 0.7715

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations CO 1.175

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NOX 0.8123

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 0.6764

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations SO2 0.0319

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations VOC 0.6116

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations CO 1.175

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NOX 1.0576

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 1.0241

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO2 0.0319

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations VOC 1.1175

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) CO 1.0574

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NOX 0.6635

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 0.6052

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO2 0.0303

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) VOC 0.5316

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines CO 1.0574

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NOX 0.6635

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM 0.6052

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines SO2 0.0303

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines VOC 0.5316

2285002010 Railroad Equipment;Diesel;Yard Locomotives CO 1.175

2285002010 Railroad Equipment;Diesel;Yard Locomotives NOX 0.9767

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 0.9436

2285002010 Railroad Equipment;Diesel;Yard Locomotives SO2 0.0320

2285002010 Railroad Equipment;Diesel;Yard Locomotives VOC 0.9388

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 diesel emissions (SCC = 2280002100 and 

2280002200) were projected based on the Final Locomotive Marine rule national-level factors. Similar to 

locomotives, VOC HAPs were projected based on the VOC factor.

Step 2: Intermediate California year 2018 inventory

Obtained from CARB, the locomotive, and class 1 and 2 commercial marine emissions used for California 

reflect year 2017 and include nonroad rules reflected in the December 2010 Rulemaking Inventory 
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(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 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. 

Step 3: Adjusting intermediate 2018 c1c2rail emissions to reflect the EISA mandate

Rail and barges are used to transport ethanol from production facilities to bulk terminals. To account for 

emissions associated with this transport, 2022 RFS2 rule rail impacts were adjusted to account for 

differences in ethanol volumes and locomotive emission rates between 2007 and 2018. There is only a small 

difference, not quantified, between ethanol volumes in 2007 and 2011. Emission factors used to make 

adjustments were obtained from an EPA locomotive emission factor fact sheet (EPA, 2009e). 

In EPA’s RFS2 final rule, impacts of these modes of transport of ethanol on combustion emissions from the 

C1 and C2 CMV and rail inventories were estimated for 2018, based AEO 2013 projections on the difference 

between ethanol volumes mandated by EISA versus RFS1 rule volumes (EPA, 2010a). RFS2 rule impacts 

were adjusted to account for (a) differences in rail and barges emission rates in 2018 versus 2022, and (b) the 

difference in ethanol volume impacts for 2018 under EISA versus the 8.7 billion gallons assumed for the 

intermediate 2018 inventory. Emission factors used to make these adjustments were obtained from analyses 

done to support the 2010 Category 3 Marine Diesel Rule (EPA, 2009f). The adjusted national inventory 

impacts were allocated to individual counties using factors developed from the Oak Ridge analysis of ethanol 

transport (Oak Ridge National Laboratory, 2009). These impacts were then applied to the unadjusted

inventory.

These emissions from updated ethanol volumes are not included in the previously-discussed non-California 

loco-marine rule-based projections (Step 1) and CARB 2017 inventory (Step 2). Nationally, these additional 

emissions are modest and are shown in Table 4-47. The overall difference between 2011 and 2018 c1c2rail 

sector emissions are provided in Table 4-48. These sector totals include all U.S. states as well as offshore 

and Puerto Rico.

Table 4-47. EISA mandate emission adjustments in 2018

Pollutant C1/C2 CMV Locomotives

CO -855 1,715

NH3 -2 5

NOX -3,635 8,346

PM10 -139 198

PM2.5 -155 -10

SO2 -296 80

VOC -136 357
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Table 4-48. Difference in c1c2rail sector emissions between 2011 and 2018

Pollutant 2011 2018 Difference

CO 242,771 255,496 -12,725

NH3 707 712 5

NOX 1,392,532 1,129,284 -263,248

PM10 46,142 31,963 -14,179

PM2.5 43,491 29,893 -13,598

SO2 23,160 3,161 -19,999

VOC 56,543 33,334 -23,209

4.4.2 Class 3 commercial marine vessels (c3marine)

As discussed in Section 2.4.2, the c3marine sector emissions data were developed for year 2002 and 

projected to year 2011 for the 2011 base case. The ECA-IMO project provides pollutant and geographicspecific projection factors to year 2011, and also projection factors to year 2018 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 2011 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 c3marine inventory from the 2011 base case are provided in

Table 4-49. Background on the region and Exclusive Economic Zone (EEZ) FIPS is provided in the 

discussion on the c3marine inventory for 2011 –Section 2.4.2. 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 4-49. Growth factors to project the 2011 ECA-IMO inventory to 2018

2018 Adjustments Relative to 2011

Region

EEZ 

FIPS NOX PM10 PM2.5

VOC 

(HC) CO SO2

East Coast (EC) 85004 1.068 0.556 0.556 1.361 1.361 0.136

Gulf Coast (GC) 85003 0.960 0.504 0.504 1.222 1.222 0.122

North Pacific (NP) 85001 1.014 0.501 0.501 1.255 1.255 0.126

South Pacific (SP) 85002 1.121 0.593 0.593 1.421 1.420 0.144

Great Lakes (GL) n/a 1.027 0.444 0.444 1.125 1.125 0.113

Outside ECA 98001 1.217 1.356 1.356 1.356 1.356 1.356

As discussed in Section 2.4.2, emissions outside the 3 to 10 mile coastal boundary but within the 

approximately 200 nm EEZ boundaries were projected to year 2018 using the same regional adjustment 

factors as the U.S. emissions; however, the FIPS codes were assigned as “EEZ” FIPS and these, as well as 

Canada C3 CMV, emissions are processed in the “othpt” sector (see Section 2.5.1 and 4.4.1). Note that state 
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boundaries in the Great Lakes are an exception, extending through the middle of each lake such that all 

emissions in the Great Lakes are assigned to a U.S. county or Ontario. The classification of emissions to 

U.S. and Canadian FIPS codes is needed to avoid double-counting of C3 CMV U.S. emissions in the Great 

Lakes because, as discussed in Section 2.4.1, all CMV emissions in the Midwest RPO are processed in the 

“c1c2rail” sector.

4.4.3 Other nonroad mobile sources (nonroad)

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

and Texas. Similar to the onroad emissions, NMIM provides nonroad emissions for VOC by three emission 

modes: exhaust, evaporative and refueling.

With the exception of California and Texas, U.S. emissions for the nonroad sector (defined as the equipment 

types covered by the NONROAD model) were created using a consistent NMIM-based approach as was 

used for 2011. Specifically, NMIM utilized NONROAD2008a including future-year equipment population 

estimates, control programs to the year 2018, and inputs either state-supplied as part of the 2011NEIv1

process or national level inputs. Fuels for 2018 were assumed to be E10 everywhere for nonroad equipment. 

The fuels were developed from the MOVES fuels, which in turn were developed to be consistent with 

AEO2013 projections for 2018. The databases used in this run were NMIM county database 

“NCD20130731_nei2018dv1” and fuels database “tier3frm2018ctrlfuels_03152013_ e10fuelsNMIM .” The 

2018 emissions account for increases in activity (based on NONROAD model default growth estimates of 

future-year equipment population), changes in fuels and engines that reflect implementation of national 

regulations and local control programs that impact each year differently due to engine turnover. For details 

on the 2018 speciation of nonroad, see Section 3.2.1.4.

The version of NONROAD used was the current public release, NR08a, which models all in-force nonroad 

controls. Recent rules include:

“Clean Air Nonroad Diesel Final Rule - Tier 4”, published June, 2004: 

http://www.epa.gov/otaq/nonroad-diesel.htm

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

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

OTAQ’s Small Engine Spark Ignition (“Bond”) Rule, October, 2008: 

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

Not included are voluntary local programs such as encouraging either no refueling or evening refueling on 

Ozone Action Days. 

California and Texas nonroad emissions

Similar to the 2011 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, the CARBsupplied 2017 nonroad annual inventory were distributed to monthly emissions values by using the 2018

NMIM monthly inventories to compute monthly ratios by county, SCC7, mode and pollutant, which was 

consistent with the approach in 2011 (see Section 2.4.3). Some adjustments to the CARB inventory were 

needed to convert the provided TOG to VOC. See Section 3.2.1.3 for details on speciation of California 

nonroad data see Section 3.2.1.3)

39

. . The CARB nonroad emissions include nonroad rules reflected in the 

 

39 In addition, airport equipment was removed from CARB’s inventory because these sources were modeled elsewhere.
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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”.

For Texas, EPA combined Texas’ submitted estimates for 2011 with EPA projections of nonroad emissions 

into 2018. Specifically, projections were based on state-wide SCC7, mode, poll ratios40 of 2018 NMIM to 

2011 NMIM. These ratios were then applied to Texas’ submitted 2011 emissions inventory, which had 

already been distributed to a monthly inventory (see Section 2.4.3), to create a 2018 monthly nonroad 

inventory.

4.5 “Other Emissions”: Offshore Class 3 commercial marine vessels 

and drilling platforms, Canada and Mexico (othpt, othar, and othon)

Recall from Section 2.5, that emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial 

Marine Vessels (C3 CMV) and drilling platforms are included as part of three emissions modeling sectors: 

othpt, othar, and othon. Non C3 CMV emissions for Canada and offshore sources were not projected to 

future years, and are therefore the same as those used in the 2011 base case. Canada did not provide futureyear emissions that were consistent with the base year emissions. The Mexico emissions are based on year 

1999 but projected to year 2018. A background on the development of year-2018 Mexico emissions from 

the 1999 inventory is available at: http://www.wrapair.org/forums/ef/inventories/MNEI/index.html. 

4.5.1 Point sources from offshore C3 CMV and drilling platforms and Canada and 

Mexico (othpt)

As discussed in Section 2.5.1, the ECA-IMO-based C3 CMV emissions for non-U.S. states are processed in 

the othpt sector. These C3 CMV emissions include those assigned to Canada, those assigned to the 

Exclusive Economic Zone (defined as those emissions just beyond U.S. waters approximately 3-10 miles 

offshore, extending to about 200 nautical miles from the U.S. coastline), and all other offshore emissions –

far offshore and non-U.S. EPA processed these emissions in the othpt sector for simplicity of creating U.S.-

only emissions summaries. Otherwise, these emissions are processed in the same way as the U.S. C3 CMV 

emissions in the c3marine sector. The projection factors for the othpt C3 CMV emissions vary by 

geographic and region as shown in Table 4-c3. C3 CMV emissions in British Columbia were assigned as 

North Pacific, Ontario as Great Lakes, and all other eastern Canada provinces as East Coast.

Mexico point-format year-2018 inventories are used essentially as-is with only minor formatting changes. 

The othpt sector also includes point source offshore oil and gas drilling platforms that are beyond U.S. statecounty boundaries in the Gulf of Mexico. EPA used emissions from the 2008NEIv2 point source inventory 

for both 2011 and 2018. EPA expects updated offshore oil and gas drilling emissions in the next version of 

the 2011 NEI (Version 2). 

4.5.2 Area, nonroad mobile and onroad mobile sources from Canada and Mexico 

(other, othon)

Both year-2006 Canada and year-2018 Mexico inventories were converted from their original SMOKE OneRecord per Line (ORL) and Inventory Data Analyzer (IDA) formats, respectively, to SMOKE Flat File 10 

(FF10) inventory format: http://www.cmascenter.org/smoke/documentation/3.5/html/ch08s02s07.html. 

Otherwise, these inventories were used as-is.

 

40 These ratios were initially attempted by county/SCC7/mode/pollutant, but due to significantly different distributions of certain 

source types between EPA and TCEQ’s emissions, this created unreasonable growth in certain areas. The above approach was 

used except in the following, relatively limited conditions. If a state/SCC7/mode/pollutant was in EPA 2018 emissions but not in 

EPA’s 2011 emissions, 2018 EPA emissions were used in the final inventory. If a state/SCC7/mode/pollutant was in TCEQ’s 

2011 emissions but was not in EPA’s 2018 emissions, then state/SCC3/mode/pollutant ratios were used to project to 2018.
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5 Emission Summaries

The following tables summarize emissions differences between the 2011 evaluation case and the 2018 base 

case. These summaries are provided at the national level by sector 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, and the onroad portion include CARB emissions for 

California. The “c3marine-US” sector represents c3marine sector emissions with U.S. FIPS only; these 

extend to roughly 3-5 miles offshore and all U.S. waters in the Great Lakes and also include all U.S. ports. 

The “c3marine, EEZ component” represents all non-U.S. c3marine emissions that are within the (up to) 200 

nautical mile Exclusive Economic Zone (EEZ) boundary but outside of U.S. state waters. Finally, the 

“c3marine, non-US non-EEZ component” represents all non-U.S. emissions outside of the (up to) 200nm 

offshore boundary, including all Canadian and Mexican c3marine emissions. The c3marine sector is 

discussed in Section 2.4.2. The “Off-shore othpt” sector is the non-Canada, no-Mexico component of the 

othpt sector –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 2011

evaluation case in Table 5-1. The total of all sectors in the 2011 evaluation case are listed as “Con U.S. 

Total w/ ptfire”. Table 5-2 provides national emissions totals by sector for all CAPs in the 2018 base case.

Table 5-3 provides national-by sector emission summaries for CO for all the cases: 2011 evaluation case and 

2018 base case. Table 5-4, Table 5-5, Table 5-6, Table 5-7, Table 5-8 and Table 5-9 provide the same 

summaries for NH3, NOX, PM2.5, PM10, SO2 and VOC, respectively. These national tables also include 

differences and percent differences for each modeling sector between the 2011 evaluation case and the 2018

base case. Note that the same ptfire emissions are used in both cases.
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Table 5-1. National by-sector CAP emissions summaries for 2011 evaluation case

Sector CO NH3 NOX PM10 PM2_5 SO2 VOC

afdust_adj 18,502,317 2,487,403

ag 3,517,371

c1c2rail 173,347 484 1,051,169 34,865 32,557 18,975 48,818

c3marine 13,403 131,301 4,384 3,992 40,692 5,110

nonpt 3,046,375 142,323 832,166 715,709 533,248 392,638 3,792,612

np_oilgas 642,182 0 653,219 21,756 17,200 17,195 2,273,214

nonroad 13,952,389 2,627 1,630,409 162,420 154,660 4,031 2,024,633

onroad 25,473,866 118,124 5,666,702 285,112 205,145 27,915 2,287,603

onroad_rfl 157,629

ptfire 22,580,113 362,910 347,103 2,362,132 2,005,142 177,107 5,174,593

ptegu 724,444 21,944 1,990,884 266,633 193,877 4,614,299 32,376

ptegu_pk 8,662 425 21,941 2,159 1,886 28,476 783

ptnonipm 2,568,080 74,847 1,771,835 494,639 339,240 1,071,982 873,159

pt_oilgas 22,218 112 22,091 1,887 1,857 55,273 89,755

rwc 2,578,229 20,343 35,672 389,019 388,288 8,986 446,972

Con U.S. Total 71,783,308 4,261,510 14,154,492 23,243,031 6,364,495 6,457,569 17,207,256

Off-shore to EEZ* 130,419 0 610,664 16,961 15,525 133,606 81,286

Non-US SECA C3 17,169 0 202,516 17,199 15,823 127,563 7,297

Canada othar 2,810,350 386,147 462,996 810,747 248,907 61,179 932,322

Canada othon 3,303,239 17,572 392,209 11,075 7,712 4,046 199,939

Canada othpt** 560,661 15,543 369,993 65,782 39,828 825,675 157,170

Mexico othar 439,901 109,861 189,592 69,523 23,600 26,559 499,145

Mexico othon 423,978 3,247 76,880 7,593 6,970 1,413 73,888

Mexico othpt 116,609 0 414,399 137,512 101,884 828,418 83,838

Non-US Total 7,802,326 532,370 2,719,249 1,136,392 460,249 2,008,459 2,034,885

* “Offshore to EEZ” includes both the offshore point emissions, and the “Offshore to EEZ” c3marine emissions

** Canadian c3 emissions are included in “Canada othpt”
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Table 5-2. National by-sector CAP emissions summaries for 2018 base case

Sector CO NH3 NOX PM10 PM2_5 SO2 VOC

afdust_adj 6,741,452 923,168

ag 3,596,908

c1c2rail 189,355 489 869,089 24,346 22,508 2,628 33,334

c3marine 17,518 136,147 2,338 2,129 5,354 6,678

nonpt 3,058,148 142,384 847,975 720,106 536,477 304,514 3,634,506

np_oilgas 782,408 0 795,491 27,248 21,565 25,488 2,555,021

nonroad 12,377,375 2,900 1,071,612 107,005 100,949 1,868 1,360,554

onroad 15,469,627 86,826 2,647,482 208,598 125,173 12,418 1,133,928

onroad_rfl 74,386

ptfire 22,580,113 362,910 347,103 2,362,132 2,005,142 177,107 5,174,593

ptegu 752,467 39,629 1,486,128 256,679 199,186 1,443,777 39,227

ptegu_pk 11,249 439 9,954 247 215 3,432 313

ptnonipm 2,419,986 75,822 1,768,859 466,566 316,328 720,681 870,202

pt_oilgas 25,493 159 25,970 2,062 2,026 64,076 106,345

rwc 2,736,854 21,485 38,434 413,597 412,852 10,018 466,259

Con U.S. Total 60,420,596 4,329,951 10,044,245 11,332,376 4,667,720 2,771,361 15,455,347

Off-shore to EEZ* 146,323 635,570 9,630 8,841 18,746 88,045

Non-US SECA C3 23,318 246,579 23,327 21,462 173,124 9,896

Canada othar 2,810,350 386,147 462,996 810,747 248,907 61,179 932,322

Canada othon 3,303,239 17,572 392,209 11,075 7,712 4,046 199,939

Canada othpt 561,438 15,543 370,944 65,276 39,370 818,374 157,501

Mexico othar 527,917 109,840 226,341 70,916 47,191 19,286 577,078

Mexico othon 397,197 4,465 46,794 9,420 8,591 659 62,948

Mexico othpt 148,758 544,690 170,910 127,734 1,066,482 94,351

Non-US Total 7,918,540 533,567 2,926,123 1,171,301 509,808 2,161,896 2,122,080
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Table 5-3. National by-sector CO emissions (tons/yr) summaries with differences

Sector 2011 CO 2018 CO 2018-2011 % Change 

afdust_adj

ag

c1c2rail 173,347 189,355 16,008 9%

c3marine-US 13,403 17,518 4,115 31%

nonpt 3,046,375 3,058,148 11,773 0%

np_oilgas 642,182 782,408 140,226 22%

nonroad 13,952,389 12,377,375 -1,575,014 -11%

onroad 25,473,866 15,469,627 -10,004,239 -39%

onroad_rfl

ptfire 22,580,113 22,580,113 0 0%

ptegu 724,444 752,467 28,023 4%

ptegu_pk 8,662 11,249 2,587 30%

ptnonipm 2,568,080 2,419,986 -148,094 -6%

pt_oilgas 22,218 25,493 3,275 15%

rwc 2,578,229 2,736,854 158,625 6%

Con U.S. Total 71,783,308 60,420,593 -11,362,715 -16%

Off-shore to EEZ* 130,419 146,323 15,904 12%

Non-US SECA C3 17,169 23,318 6,149 36%

Canada othar 2,810,350 2,810,350 0 0%

Canada othon 3,303,239 3,303,239 0 0%

Canada othpt** 560,661 561,438 777 0%

Mexico othar 439,901 527,917 88,016 20%

Mexico othon 423,978 397,197 -26,781 -6%

Mexico othpt 116,609 148,758 32,149 28%

Non-US Total 7,802,326 7,918,540 116,214 1%
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Table 5-4. National by-sector NH3 emissions (tons/yr) summaries with differences

Sector 2011 NH3 2018 NH3 2018-2011 % Change 

afdust_adj

ag 3,517,371 3,596,908 79,537 2%

c1c2rail 484 489 5 1%

c3marine-US

nonpt 142,323 142,384 61 0%

np_oilgas

nonroad 2,627 2,900 273 10%

onroad 118,124 86,826 -31,298 -26%

onroad_rfl

ptfire 362,910 362,910 0 0%

ptegu 21,944 39,629 17,685 81%

ptegu_pk 425 439 14 3%

ptnonipm 74,847 75,822 975 1%

pt_oilgas 112 159 47 42%

rwc 20,343 21,485 1,142 6%

Con U.S. Total 4,261,510 4,329,951 68,441 2%

Off-shore to EEZ*

Non-US SECA C3

Canada othar 386,147 386,147 0 0%

Canada othon 17,572 17,572 0 0%

Canada othpt** 15,543 15,543 0 0%

Mexico othar 109,861 109,840 -21 0%

Mexico othon 3,247 4,465 1,218 38%

Mexico othpt

Non-US Total 532,370 533,567 1,197 0%
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Table 5-5. National by-sector NOX emissions (tons/yr) summaries with differences

Sector 2011 NOX 2018 NOX 2018-2011 % Change 

afdust_adj

ag

c1c2rail 1,051,169 869,089 -182,080 -17%

c3marine-US 131,301 136,147 4,846 4%

nonpt 832,166 847,975 15,809 2%

np_oilgas 653,219 795,491 142,272 22%

nonroad 1,630,409 1,071,612 -558,797 -34%

onroad 5,666,702 2,647,482 -3,019,220 -53%

onroad_rfl

ptfire 347,103 347,103 0 0%

ptegu 1,990,884 1,486,128 -504,756 -25%

ptegu_pk 21,941 9,954 -11,987 -55%

ptnonipm 1,771,835 1,768,859 -2,976 0%

pt_oilgas 22,091 25,970 3,879 18%

rwc 35,672 38,434 2,762 8%

Con U.S. Total 14,154,492 10,044,244 -4,110,248 -29%

Off-shore to EEZ* 610,664 635,570 24,906 4%

Non-US SECA C3 202,516 246,579 44,063 22%

Canada othar 462,996 462,996 0 0%

Canada othon 392,209 392,209 0 0%

Canada othpt** 369,993 370,944 951 0%

Mexico othar 189,592 226,341 36,749 19%

Mexico othon 76,880 46,794 -30,086 -39%

Mexico othpt 414,399 544,690 130,291 31%

Non-US Total 2,719,249 2,926,123 206,874 8%
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Table 5-6. National by-sector PM2.5 emissions (tons/yr) summaries with differences

Sector 2011 PM2.5 2018 PM2.5 2018-2011 % Change 

afdust_adj 913,525 923,168 9,643 1%

ag

c1c2rail 32,557 22,508 -10,049 -31%

c3marine-US 3,992 2,129 -1,863 -47%

nonpt 533,248 536,477 3,229 1%

np_oilgas 17,200 21,565 4,365 25%

nonroad 154,660 100,949 -53,711 -35%

onroad 205,145 125,173 -79,972 -39%

onroad_rfl

ptfire 2,005,142 2,005,142 0 0%

ptegu 188,811 199,186 10,375 5%

ptegu_pk 1,886 215 -1,671 -89%

ptnonipm 339,240 316,328 -22,912 -7%

pt_oilgas 1,857 2,026 169 9%

rwc 388,288 412,852 24,564 6%

Con U.S. Total 4,785,551 4,667,718 -117,833 -2%

Off-shore to EEZ* 15,525 8,841 -6,684 -43%

Non-US SECA C3 15,823 21,462 5,639 36%

Canada othar 248,907 248,907 0 0%

Canada othon 7,712 7,712 0 0%

Canada othpt** 39,828 39,370 -458 -1%

Mexico othar 23,600 47,191 23,591 100%

Mexico othon 6,970 8,591 1,621 23%

Mexico othpt 101,884 127,734 25,850 25%

Non-US Total 460,249 509,808 49,559 11%
DRAFT

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Table 5-7. National by-sector PM10 emissions (tons/yr) summaries with differences

Sector 2011 PM10 2018 PM10 2018-2011 % Change 

afdust_adj 6,663,357 6,741,452 78,095 1%

ag

c1c2rail 34,865 24,346 -10,519 -30%

c3marine-US 4,384 2,338 -2,046 -47%

nonpt 715,709 720,106 4,397 1%

np_oilgas 21,756 27,248 5,492 25%

nonroad 162,420 107,005 -55,415 -34%

onroad 285,112 208,598 -76,514 -27%

onroad_rfl

ptfire 2,362,132 2,362,132 0 0%

ptegu 266,633 256,679 -9,954 -4%

ptegu_pk 2,159 247 -1,912 -89%

ptnonipm 494,639 466,566 -28,073 -6%

pt_oilgas 1,887 2,062 175 9%

rwc 389,019 413,597 24,578 6%

Con U.S. Total 11,404,072 11,332,376 -71,696 -1%

Off-shore to EEZ* 16,961 9,630 -7,331 -43%

Non-US SECA C3 17,199 23,327 6,128 36%

Canada othar 810,747 810,747 0 0%

Canada othon 11,075 11,075 0 0%

Canada othpt** 65,782 65,276 -506 -1%

Mexico othar 69,523 70,916 1,393 2%

Mexico othon 7,593 9,420 1,827 24%

Mexico othpt 137,512 170,910 33,398 24%
DRAFT

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Table 5-8. National by-sector SO2 emissions (tons/yr) summaries with differences

Sector 2011 SO2 2018 SO2 2018-2011 % Change 

afdust_adj

ag

c1c2rail 18,975 2,628 -16,347 -86%

c3marine-US 40,692 5,354 -35,338 -87%

nonpt 392,638 304,514 -88,124 -22%

np_oilgas 17,195 25,488 8,293 48%

nonroad 4,031 1,868 -2,163 -54%

onroad 27,915 12,418 -15,497 -56%

onroad_rfl

ptfire 177,107 177,107 0 0%

ptegu 4,614,299 1,443,777 -3,170,522 -69%

ptegu_pk 28,476 3,432 -25,044 -88%

ptnonipm 1,071,982 720,681 -351,301 -33%

pt_oilgas 55,273 64,076 8,803 16%

rwc 8,986 10,018 1,032 11%

Con U.S. Total 6,457,569 2,771,361 -3,686,208 -57%

Off-shore to EEZ* 133,606 18,746 -114,860 -86%

Non-US SECA C3 127,563 173,124 45,561 36%

Canada othar 61,179 61,179 0 0%

Canada othon 4,046 4,046 0 0%

Canada othpt** 825,675 818,374 -7,301 -1%

Mexico othar 26,559 19,286 -7,273 -27%

Mexico othon 1,413 659 -754 -53%

Mexico othpt 828,418 1,066,482 238,064 29%

Non-US Total 2,008,459 2,161,896 153,437 8%
DRAFT

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Table 5-9. National by-sector VOC emissions (tons/yr) summaries with differences

Sector 2011 VOC 2018 VOC 2018-2011 % Change 

afdust_adj

ag

c1c2rail 48,818 33,334 -15,484 -32%

c3marine-US 5,110 6,678 1,568 31%

nonpt 3,792,612 3,634,506 -158,106 -4%

np_oilgas 2,273,214 2,555,021 281,807 12%

nonroad 2,024,633 1,360,554 -664,079 -33%

onroad 2,287,603 1,133,928 -1,153,675 -50%

onroad_rfl 157,629 74,386

ptfire 5,174,593 5,174,593 0 0%

ptegu 32,376 39,227 6,851 21%

ptegu_pk 783 313 -470 -60%

ptnonipm 873,159 870,202 -2,957 0%

pt_oilgas 89,755 106,345 16,590 18%

rwc 446,972 466,259 19,287 4%

Con U.S. Total 17,207,257 15,455,346 -1,751,911 -10%

Off-shore to EEZ* 81,286 88,045 6,759 8%

Non-US SECA C3 7,297 9,896 2,599 36%

Canada othar 932,322 932,322 0 0%

Canada othon 199,939 199,939 0 0%

Canada othpt** 157,170 157,501 331 0%

Mexico othar 499,145 577,078 77,933 16%

Mexico othon 73,888 62,948 -10,940 -15%

Mexico othpt 83,838 94,351 10,513 13%

Non-US Total 2,034,885 2,122,080 87,195 4%
DRAFT

141

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