# Emissions Modeling Technical Support Document:

Tier 3 Motor Vehicle Emission and Fuel StandardsEPA

- 454/R

- 14

- 003 February 2014

Emissions Modeling Technical Support Document:

Tier 3 Motor Vehicle Emission and Fuel Standards

By: Alexis Zubrow, Rich Mason, Alison Eyth

EPA OAQPS Research Triangle Park, North Carolina

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division

Emissions Inventory an Analysis Group Research Triangle Park, NCi 

## TALE OF CONTENTS

ACRONYMS

................................ ................................ ................................ ................................ ................................ ........... III

LIST OF TABLES

................................ ................................ ................................ ................................ ................................ .. VI

LIST OF FIGURES

................................ ................................ ................................ ................................ ............................... VII

1 INTRODUCTION

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

2 2007 EMISSION INVENT ORIES AND APPROACHES

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

2.1 2007 NEI POINT SOURCES ( PTIPM AND PTNONIPM )

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

2.1.1 IPM sector (ptipm)

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

2.1.2 Non

- IPM sector (ptnonipm)

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

2.2 2007 NONPOINT SOURCES ( AFDUST

, AG

, NONPT )

................................ ................................ ................................ ............. 9

2.2.1 Area fugitive dust sector (afdust)

................................ ................................ ................................ ....................... 10

2.2.2 Agricultural ammonia sector (ag)

................................ ................................ ................................ ...................... 10

2.2.3 Other nonpoint sources (nonpt)

................................ ................................ ................................ ......................... 10

2.3 F IRES ( AVEFIRE )

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

2.4 B IOGENIC SOURCES ( BIOG )

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

2.5 2007 MOBILE SOURCES ( ONROAD

, ONROAD

_ RFL

, NONROAD

, C 1 C 2 RAIL

, C 3 MARINE )

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

2.5.1 Onroad non

- refueling (onroad)

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

2.5.2 Onroad refueling (onroad_rfl)

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

2.5.3 Nonroad mobile equipment sources: (nonroad)

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

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

................................ ................. 16

2.5.5 Class 3 commercial marine vessels (c3marine)

................................ ................................ ................................ . 16

2.6 E MISSIONS FROM C ANADA

, M EXICO AND OFFSHORE D RILLING PLATFORMS ( OTHPT

, OTHAR

, OTHON )

......................... 16

2.7 SMOKE

- READY NON

- ANTHROPOGENIC INVENT ORIES FOR CHLORINE

................................ ................................ ......... 17

3 EMISSIONS MODELING S UMMARY

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

3.1 E MISSIONS MODELING O VERVIEW

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

3.2 C HEMICAL S PECIATION

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

3.2.1 VOC speciation

................................ ................................ ................................ ................................ .................. 23

3.2.2 PM speciation

................................ ................................ ................................ ................................ ..................... 29

3.3 T EMPORAL A LLOCATION

................................ ................................ ................................ ................................ .............. 30

3.3.1 FF10 format and inventory resolution

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

3.3.2 Ptipm Temporalization

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

3.3.3 Meteorologically

- based temporalization

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

3.3.4 Onroad and Onroad_rfl Temporalization

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

3.3.5 Additional sector specific details

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

3.4 S PATIAL A LLOCATION

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

3.4.1 Spat ial Surrogates for U.S. emissions

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

3.4.2 Allocation method for airport

- related sources in the U.S.

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

3.4.3 Surrogates for Canada and Mexico emission inventories

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

4 DEVELOPMENT OF FUTUR E YEAR EMISSIONS

................................ ................................ ................................ 43

4.1 S TATIONARY SOURCE PRO JECTIONS : EGU SECTOR ( PTIPM )

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

4.2 S TATIONARY SOURCE PRO JECTIONS : NON

- EGU SECTORS ( PTNONIPM

, NONPT

, AG

, AFDUST )

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

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

................................ ..................... 49

4.2.2 Upstream agricul tural and Livestock adjustments (afdust, ag, nonpt, ptnonipm)

................................ .............. 60

4.2.3 Fuel sulfur rules (nonpt, ptnonipm)

................................ ................................ ................................ ................... 62

4.2.4 Portland Cement NESHAP projections (ptnonipm)

................................ ................................ ........................... 62

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

..................... 63

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

................................ ................................ . 65

4.3 M OBILE SOURCE PROJECT IONS

................................ ................................ ................................ ................................ ..... 66

4.3.1 Onroad mobile (onroad and onroad_rfl)

................................ ................................ ................................ ........... 69

4.3.2 Nonroad mobile (nonroad)

................................ ................................ ................................ ................................ . 73

4. 3.3 Locomotives and Class 1 & 2 commercial marine vessels (c1c2rail)

................................ ................................ 74

4.3.4 Class 3 commercial marine vessels (c3marine)

................................ ................................ ................................ . 78

4.4 C ANADA

, M EXICO

, AND O FFSHORE SOURCES ( OTHAR

, OTHON

, AND OTHPT )

................................ ................................ 78ii

5 EMISSION SUMMARIES

................................ ................................ ................................ ................................ ............ 79

6 REFERENCES

................................ ................................ ................................ ................................ ............................... 99iii 

## 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

BEIS 3.14 Biogenic Emissions Inventory System , version 3.14

BELD3 Biogenic Emissions Land use Database, version 3 Bgal Billion gallons

BPS Bulk Plant Storage

BTP Bulk Terminal (Plant) to Pump

C1/C2 Category 1 and 2 commercial marine vessels

C3 Category 3 (commercial marine vessels)

CAEP Committee on Aviation Environmental Protection

CAIR Clean Air Interstate Rule

CAMD The EPA’s Clean Air Markets Division

CAM X Comprehensive Air Quality Model with Extensions

CAP Criteria Air Pollutant

CARB California Air Resources Board

CB05 Carbon Bond 2005 chemical mechanism

CBM Coal

- bed methane

CEC North American Commission for Environmental Cooperation

CEM Continuous Emissions Monitoring

CEPAM California Emissions Projection Analysis Model

CISWI Commercial and Industrial Solid Waste Incineration

Cl Chlorine CMAQ Community Multiscale Air Quality

CMV Commercial M arine V essel

CO Carbon monoxide

CSAPR Cross

- State Air Pollution Rule EO, E10, E85 0%, 10% and 85% Ethanol blend gasolines, respectively

EBAFM Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol

ECA Emissions Control Area

EEZ Exclusive Economic Zone

EF Emission Factor

EGU Electric G enerating U nits

EIS Emissions Inventory System

EISA Energy Independence and Security Act of 2007

EPA Environmental Protection Agency

EMFAC Emission Factor (California’s onroad mobile model)

FAA Federal Aviation Administration

FAPRI Food and Agriculture Policy and Research Institute

FASOM Forest and Agricultural Section Optimization Model

FCCS Fuel Characteristic Classification System

FIPS Federal Information Processing Standards

FHWA Federal Highway Administration

HAP Hazardous Air Pollutantiv HCl Hydrochloric acid

HDGHG Heavy

- Duty Vehicle Greenhouse Gas Hg Mercury

HMS Hazard Mapping System

HPMS Highway Performance Monitoring System

HWC Hazardous Waste Combustion

HWI Hazardous Waste Incineration

ICAO International Civil Aviation Organization

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

ICR Information Collection Request

I/M Inspection and Maintenance

IMO International Marine Organization

IPAMS Independent Petroleum Association of Mountain States

IPM Integrated Planning Model

ITN Itinerant

LADCO Lake Michigan Air Directors Consortium

LDGHG Light

- Duty Vehicle Greenhouse Gas

LPG Liquified Petroleum Gas

MACT Maximum Achievable Control Technology

MARAMA Mid

- Atlantic Regional Air Management Association

MATS Mercury and Air Toxics Standards

MCIP Meteorology

- Chemistry Interface Proce ssor Mgal Million gallons

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

Management, Regulation and Enforcement (BOEMRE)

MOBILE 6 OTAQ’s model for estimation of onroad mobile emissions factors , replaced by

MOVES2010b

MOVES Motor Vehicle Emissions Simulator

-- OTAQ’s model for estimation of onroad

mobile emissions

– replaces the use of the MOBILE model

MSA Metropolitan Statistical Area

MSAT2 Mobile Source Air Toxics Rule

MTBE Methyl tert

- butyl ether

MWRPO Mid

- west Regional Planning Organization

NCD National County Database

NEEDS National Electric Energy Database System

NEI National Emission Inventory

NESCAUM Northeast States for Coordinated Air Use Management

NESHAP National Emission Standards for Hazardous Air Pollutants

NH 3 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

NO X Nitrogen oxides

NSPS New Source Performance Standards

NSR New Source Reviewv OAQPS The EPA’s Office of Air Quality Planning and Standards

OHH Outdoor Hydronic Heater OTAQ The EPA’s Office of Transportation and Air Quality

ORIS Office of Regulatory Information System

ORD The EPA’s Office of Research and Development

ORL One Record per Line

OTC Ozone Transport Commission

PADD Petroleum Administration for Defense Districts

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

PFC Portable Fuel Container

PM 2.5 Particulate matter less than or equal to 2.5 microns

PM 10 Particulate matter less than or equal to 10 microns ppb, ppm Parts p er billion, parts per m illion

RCRA Resource Conservation and Recovery Act

RBT Refinery to Bulk Terminal

RFS2 Renewable Fuel Standard

RIA Regulatory Impact Analysis

RICE Reciprocating Internal Combustion Engine

RRF Relative Response Factor

RWC Residential Wood Combustion

RPO Regional Planning Organization

RVP Reid Vapor Pressure

SCC Source Classification Code

SEMAP Southeastern Modeling, Analysis, and Planning

SESARM Southeastern States Air Resource Managers SESQ S esquiterpene s SMARTFIRE Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation

SMOKE Sparse Matrix Operator Kernel Emissions

SO 2 Sulfur dioxide

SOA Secondary Organic Aerosol

SI Spark

- ignition

SIP State Implementation Plan

SPDPRO Hourly Speed Profiles for w eekday versus w eekend

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 Rese arch and Forecasting Modelvi 

## List of Tables

Table 1

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

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

Table 2

- 1. Platform sectors starting point for the 2007 platform

................................ ................................ ...... 5

Table 2

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

.. 6

Table 2

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

.................... 9

Table 2

- 4. Toxic

- to

- VOC Ratios for Corn Ethanol Plants

................................ ................................ ................ 9

Table 2

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

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

Table 3

- 1. Key emissions modeling steps by sector.

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

Table 3

- 2. Descriptions of the 2007v5 platform grids

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

Table 3

- 3. Emission model species produced for CB05 w ith SOA for CMAQ and CAM X *

......................... 22

Table 3

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

................................ ........ 23

Table 3

- 5. HAP augmentation for c1c2rail

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

Table 3

- 6. VOC profiles for WRAP Phase III basins

................................ ................................ ..................... 26

Table 3

- 7. Select VOC profiles 2007. 2018 and 2030

................................ ................................ .................... 28

Table 3

- 8. PM model species: AE5 versus AE6

................................ ................................ ............................. 29

Table 3

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

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

Table 3

- 10. U.S. Surrogates available for the 2007 platform.

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

Table 3

- 11. Spatial Surrogates for WRAP Oil and Gas Data

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

Table 3

- 12. Counties included in the WRAP Dataset

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

Table 3

- 13. Spatial Surrogates for Mexico

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

Table 3

- 14. Canadian Spatial Surrogates for 2007

- based platform Canadian Emissions

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

Table 4

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

from the 2007 base case

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

Table 4

- 2. Summary of non

- EGU stationary projections subsections

................................ ............................ 49

Table 4

- 3. Renewable Fuel Volumes Assumed for Stationary Source Adjustments.

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

Table 4

- 4. 20 07 and 2018/2030 corn ethanol plant emissions [tons]

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

Table 4

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

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

Table 4

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

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

Table 4

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

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

Table 4

- 8. Crit eria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)

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

Table 4

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

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

Table 4

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

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

Table 4

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

...................... 54

Table 4

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

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

Table 4

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

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

Table 4

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

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

Table 4

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

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

Table 4

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

................................ ............... 57

Table 4

- 17. Adjustment factors applied to storage and transport emissions

................................ ................... 58

Table 4

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

................................ ...... 58

Table 4

- 19. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with gasoline and diesel fuel production.

................................ ................................ ................................ ......... 59

Table 4

- 20. 2030 adjustment factors applied to petroleum pipelines and refinery emissions associated with gasoline and diesel fuel production.

................................ ................................ ................................ ......... 59

Table 4

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

................................ ........................... 60

Table 4

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

............ 61

Table 4

- 23. Composite NH 3 projection factors to year 2018 and 2030 for animal operations

....................... 61

Table 4

- 24. Summary of fuel sulfur rules by state

................................ ................................ .......................... 62

Table 4

- 25. ISIS

- based cement industry change (tons/yr)

................................ ................................ .............. 63vii Table 4

- 26. Factors used to project 2008 base

- case aircraft emissions to 2020

................................ .............. 65

Table 4

- 27. Overview of Reference and Control Scenarios

................................ ................................ ............ 67

Table 4

- 28. Comparison of MO VES runs

................................ ................................ ................................ ........ 70

Table 4

- 29. CA LEVIII program states

................................ ................................ ................................ ............ 70

Table 4

- 30. Early NLEV states

................................ ................................ ................................ ........................ 71

Ta ble 4

- 31. LEV2 states and MOVES databases

................................ ................................ ............................. 71

Table 4

- 32. RVP bins by representative county

................................ ................................ ............................... 72

Table 4

- 33 . Non

- California year 2018 and 2030 Projection Factors for locomotives and Class 1 and Class 2

Commercial Marine Vessel Emissions

................................ ................................ ................................ ..... 75

Table 4

- 34

. Scalars Applied to Rail Combustion Emissions in 2030 to account for 2017

- 2025 LDGHG

emission standards

................................ ................................ ................................ ................................ .... 76

Table 4

- 35

. Scalars Applied to C1/C2 Combustion Emissions in 2030 to account for 2017

- 2025 LDGHG

emission standards

................................ ................................ ................................ ................................ .... 77

Table 4

- 36 . Cumulative RFS2 and LDGHG adjustments to c 1c2rail sector emissions

................................ .. 77

Table 4

- 37. Growth factors to project the 2007 ECA

- IMO inventory to 2018 and 2030

............................... 78

Table 5

- 1. National and non

- U.S. CAP emissions by sector for 2007 base case

................................ ............ 80

Table 5

- 2. National and non

- U.S. CAP emissions by sector for 20 18 reference case

................................ .... 81

Table 5

- 3. National and non

- U.S. CAP emissions by sector for 2018 control case

................................ ........ 82

Table 5

- 4. National and non

- U.S. CAP emissions by sector for 2030 reference case

................................ .... 83

Table 5

- 5. National and non

- U.S. CAP emissions by sector for 2030 control case

................................ ........ 84

Table 5

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

................................ ................................ .............. 85

Table 5

- 7. NH 3 emissions (tons/yr) for each case and state

................................ ................................ ............ 87

Table 5

- 8. NO X emissions (tons/yr) for each case and state

................................ ................................ ........... 89

Table 5

- 9. PM 2.5 emissions (tons/yr) for each case and state

................................ ................................ .......... 91

Table 5

- 10. PM 10 emissions (tons/yr) for each case and state

................................ ................................ ......... 93

Table 5

- 11. SO 2 emissions (tons/yr) f or each case and state

................................ ................................ ........... 95

Table 5

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

................................ ................................ ......... 97



## List of Figures

Figure 3

- 1. Air quality modeling domains

................................ ................................ ................................ ....... 20 Figure 3

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

- 3. Example of SMOKE

- MOVES temporal variability of NO X emissions

................................ ....... 35 Figure 4

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

................................ ............... 551 

## 1 Introduction

Th e U.S. Environmental Protection Agency (EPA) developed a year 2007 air quality modeling platform in support of the Tier 3 Motor Vehicle Emission and Fuel Standards

. The air quality modeling platform

consists of all of the emissions inventories

, ancillary files n eeded for emissions modeling

, and the meteorological, initial condition, and boundary condition files needed to run the air quality model. Th is platform uses all Criteria Air Pollutants (CAPs) and a select set of Hazardous Air Pollutants (HAPs )

. This

doc ument focuses on the emissions modeling component s of the 2007 platform, including the emission inventories and the ancillary data and the approaches used to transform emission inventories for use in air quality modeling.

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

- BAFM

2007

- Based Platform, Version 5”, also known as the “2007v5” platform. The 2007v5 platform was used to support the Regulatory Impact Assessment (RIA) for the 2012 Final National Ambient Air Quality Standards (NAAQS) for particulate matter less than 2.5 microns (PM 2.5 ). The Technical Support Document (TSD) “Preparation of Emissions Inventories for the Version 5.0, 2007 Emissions Modeling Platform ” contains

man y additional details on the aspects of the Tier 3 and 2007v5 platforms that are shared. The TSD is available from the Emissions Modeling Clearinghouse website, http://www.epa.gov/ttn/chief/emch/ , under

the section entitled “ Particulate Matter ( PM) NAAQS (2007v5 ) Platform ”. The appendices available for the 2007v5 TSD that do not reference the specific PM NAAQS modeling cases are also relevant to the “ Tier 3 ” platform.

Many emissions inventory components of the Tier 3 air quality modeling platform are based on the 2008 National Emissions Inventory version 2, hereafter referred to as the “2008 NEI”, with updated inventory data for some emission sectors. In particular, a version of the Motor Vehicle Emissions S imulator (MOVES) designed to represent the impacts of the Tier 3 Motor Vehicle Emission and Fuel Standards ( MOVESTier3FRM ) was used to generate emission factors for onroad mobile sources. The emissions modeling tool used to create the air quality model

- re ady emissions from the emission inventories was the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system ( http://www.smoke

model.org/index.cfm ) version 3.5 beta. Emissions were created for 36 k m and 12 km national grids.

The gridded meteorological model used for Tier 3 is the Weather Rese arch and Forecasting Model (WRF, http://wrf

- model.org ) version 3.3 , Advanced Research WRF (ARW) core (Skamarock, et al., 20 08) . The

WRF Model is a mesoscale numerical weather prediction system developed for both operational forecasting and atmospheric re search applications

. WRF was run for 2007 over a domain covering the continental

United States at a 36 km and 12 km resolution with 35 vertical layers 2

. Th is meteorological run was different

than the one used for the 2007v5 platform.

The air quality model used for the Tier 3 platform is the Community Multiscale Air Quality (CMAQ) model ( http://www.epa.gov/AMD/CMAQ/ ). CMAQ supports modeling ozone (O 3 ) and particulate matter (PM) and requires hourly and gridded emissions of chemical species from the following inventory pollutants: carbon monoxide (CO), nitrogen oxides (NO X ), volatile organic compounds (VOC), sulfur dioxide (SO 2 ), ammonia (NH 3 ), particulate matter less than or equal to 10 microns (PM 10 ), and individual component species for particulate matter less than or equal to 2.5 microns (PM 2.5 ). The CMAQ version used the chemical mechanism called Carbon Bond 2005 ( CB05 ) with chlorine chemistry, which is part of the “base” version of CMAQ. CB05 allows explicit treatment of benzene, acetaldehyde, formaldehyde, and methanol

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

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

. Th e Tier 3 modeling used the most recent multi

- pollutant version of CMAQ available at the time (CMAQ MP version 5.0.1) the modeling was performed , and an additional set of HAPs (from here out referred to as “CMAQ MP

- lite HAPs”): acrolein, 1,3

- butadiene and naphthalene were modeled

.

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

2018 re ference case, 2018 Tier 3 control case, 2030 reference case and 2030 Tier 3 control case

. Table 1

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

case that is consistent with the methods used in the future

- year base cases and ultimately

, in the future year

reference and control cases for the Final Tier 3 Rule

. For regulatory applications, the 2007 base case is used with the outputs from the 20 18 and 2030 reference cases in the relative response factor (RRF) calculations to identify future areas of nonattainment. For more information on the use of RRFs and air quality modeling,

see “Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5, and Regional Haze”, available from http://www.epa.gov/ttn/scram/guidance/gu ide/final

- 03

- pm

- rh

- guidance.pdf

.

Table 1

- 1

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

Case Name Internal EPA

Abbreviation Description

2007 base case 200 7rg _v5 2007 case created using average

- year wildfires data, smoothed prescribed fires, and an average

- year temporal allocation approach for Electrical Generating Units (EGUs); used for computing relative response factors with 20 18 and 2030 reference scenario(s).

2018 reference

case 2018rg_ref 2 _v5 3 2018 future year reference scenario with EGU emissions that represent the implementation of Clean Air Interstate Rule (CAIR)

and final M ercury and A ir T oxics (MATS)

, with upstream stationary and mobile sources representing the implementation of the EISA/EPAct fuel supply (RFS2 Rule)

2018 Tier 3

control case 2018rg_ctl_v5 2018 Tier 3 control case scenario sharing many aspects of the 2018 reference case, but also representing n ational Tier 3 vehicle

and fuel emissions standards.

2030 reference

case 2030rg_ref_v5 2030 future year reference scenario with EGU emissions that represent the implementation of Clean Air Interstate Rule (CAIR)

and final M ercury and A ir T oxics (MATS), with upstream stationary and mobile sources representin g the implementation of the EISA/EPAct fuel supply (RFS2 Rule)

2030 Tier 3

control case 2030rg_ctl_v5 2030 Tier 3 control case scenario sharing many aspects of the 2030 reference case, but also representing n ational Tier 3 vehicle

and fuel emissions standards.

This document contains five sections. Section 2 describes the inventories input to SMOKE for the 2007 base case. Section 3 describes the emissions modeling and the ancillary files used to process the emission

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

20 18 and 2030 Tier 3 FRM reference and control case inventor ies (projected from 2007). Data summaries

3 The case 2018rg_ref2_v5 is identical to an earlier case that was created for Tier 3 FRM (2018rg_ref_v5), except that the “ref 2” case updated the following modeling sectors: nonpt, onroad, othpt, othar, and othon.3 comparing the 2007 base case and the 20 18 and 2030 reference a nd control case s are provided in Section 5. Section 6 provides references.4 

## 2 2007 Emission Inventories and Approaches

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

case ; future year emissions inve ntory data development is discussed in Section 4

. While providing some background, this section focuses on the differences between the 2007v5 platform and the upda ted Tier 3 platform

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

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

.

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

HAP emissions data are more often augmented by EPA because they are voluntar il y submitted

. For fires, EPA used the SMARTFIRE2 (SF2) system in the 2008 NEI . SF2 assign s all fires as either prescribed burning or wildfire categories and includes improved emission factor estimates for p rescribed burning.

The 2008 NEI includes five data categories : nonpoint (formerly called “stationary area”) sources, point
sources, nonroad mobile sources, onroad mobile sources, and fires. The 2008 NEI TSD uses approximately sixty sectors to further d escribe the emissions. In addition to the NEI data, 2007 biogenic emissions, emissions from the Canadian and Mexican inventories, and other non

- NEI data are included in the 2007 platform. T he non

- NEI emissions component s of the 2007 platform include prim arily year

- 2007 onroad mobile and nonroad mobile emissions, a computed average fires inventory

, and data received from some regional planning organizations (RPOs).

In the 2007v5 platform, some data in the 2008NEIv2 were updated with data from RPOs. The RP Os focused on addressing visibility impairment from a regional perspective and updated related inventory and ancillary

data . A map of these RPOs can be found here: http://www.epa.gov/visibility/reg ional.html . The RPOs most involved in providing data were :

 Mid

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

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

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

- sesarm.org/

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

For the purposes of preparing the air quality model

- ready emissions, the 2007 emissions inventory was split

into inventories for each of the modeling “platform” sectors. The significance of an emissions modeling or “platform” sector is that the data is run through all of the SMOKE programs except the final merge (Mrggrid) independently from the other sectors. The final merge program is then used to co mbine the

sector

- specific gridded, speciated

, hourly emissions together to create CMAQ

- ready emission inputs.

Table 2

- 1 presents the sectors in the 2007 platform and how they generally relate to the 2008 NEI as a starting point. The sector abbreviations are provided in italics. These abbreviations are used in the SMOKE modeling scripts, inventory file names, and throughout the remainder of this document. As discuss ed in greater detail in Table 2

- 2 , the Tier 3 platform emissions platform was modified in specific ways from the original 2007 v5 platform.5 Table 2

- 1

. Platform sectors starting point for the 200 7 platform

Platform Sector:

abbreviation 2008NEI

Sector Description and resolution of the data input to SMOKE

EGU (also called the IPM sector): ptipm Point 2008 NEI point source EGUs that could be mapped to the Integrated Planning Model (IPM) model using the National Electric Energy Database System (NEEDS) version 4.10. Annual resolution.

Non

- EGU (non

IPM sector):

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

Includes all aircraft emissions and some rail yard emissions. Annual

resolution. Agricultural:

a g Nonpoint NH 3 emissions from NEI nonpoint livestock and fertilizer application, county and annual resolution. Area fugitive dust:

a fdust Nonpoint PM 10 and PM 2.5 from fugitive dust sources from the NEI nonpoint inventory. Includes building construction, road constru ct ion, paved roads, unpaved roads and agricultural dust

. C ounty and annual

resolution. P rocessed as a separate sector to allow for the application of a land use based transport fraction and precipitation adjustments

. Class 1 & 2 CMV

and locomotives:

c1c2rail Mobile:

Nonroad Non

- rail maintenance locomotives and category 1 and category 2

commercial marine vessel (CMV) emissions sources from the NEI nonpoint inventory . C ounty and annual resolution.

C3 commercial

marine:

c3marine Mobile:

Nonroad Non

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

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

- Ignition Engines at or Above 30 Liters per Cylinder”, also described as the Emissions Control Area

- International Maritime Organization (ECA

- IMO) study: http://www.epa.gov/otaq/oceanvessels.htm

. (EPA

- 420

- F

- 10

- 041, August 2010). Annual resolution and treated as point source s

. Remaining nonpoint:

n onpt Nonpoint Primarily NEI nonpoint sources not otherwise included in other SMOKE sectors; county and annual resolution.

Nonroad:

n onroad Mobile:

Nonroad Monthly nonroad equipment emissions from the National Mobile

Inventory Model (NMIM) using NONROAD2008 version NR08b. NMIM was used for all states except California. Monthly emissions for California created from annual emissions submitted by the California Air Resources Board (CARB)

. Onroad non

refueling:

o nroad Mobile:

onroad Onroad mobile gasoline and diesel vehicles from parking lots and

moving vehicles. Includes the following modes: exhaust, extended idle, evaporative, permeation, and brake and tire wear. For all states, based on Motor Vehicle Emissions Simulator (MOVES) e mission

factor tables and monthly activity data for 2007

. Onroad refueling: onroad_rfl Mobile:

onroad Onroad mobile gasoline and diesel vehicle refueling emissions for all

states. Based on MOVES emission factor tables and 2007 activity

data

. Average

- fire: avefire N/A Average

- year wildfire and prescribed fire emissions, county and daily

resolution. This sector is used in all modeling cases

. Other point

sources not from

the NEI:

o thpt N/A Point sources from Canada’s 2006 inventory and Mexico’s Phase III 2008 inventory grown from year 1999. I ncludes annual U.S. offshore oil 2008 NEI point source emissions. Annual resolution.

Other non

- NEI nonpoint and

nonroad:

o thar N/A Y ear 2006 Canada (province resolution) and year 2008 (grown from 1999) Mexico Phase III (municipio resolution) nonpoint and nonroad

mobile inventories , annual resolution

.6

Platform Sector:

abbreviation 2008NEI

Sector Description and resolution of the data input to SMOKE

Other non

- NEI

onroad sources:

othon N/A Year 2006 Canada (province resolution) and year 2008 (grown from 1999) Mexico Phase III (municipio resolution) onroad mobile inventories, annual resolution. Biogenic:

b eis N/A Year 2007, hour

- specific, grid cell

- specific emissions generated from the BEIS3.14 model, including emissions in Canada and Mexico.

Table 2

- 2 provides a brief by

- sector overview of the most significant differences between the Tier 3 platform

and the 200 7v5 platform

. The specific by

- sector u pdates to the 2007 platform made for Tier 3 are described in greater detail later in the following subsection s

. The remainder of Section 2 provides details about the data contained in each of the 2007 platform sectors. Different levels of detail are provided fo r different

sectors

, depending on the degree of changes or manipulation of the data needed to prepare it for input to SMOKE, and on whether the Tier 3 2007 platform emissions are significantly different from the original 2007v5 platform.

Table 2

- 2

. Summary of s ignificant c hanges between 2007 v5 platform and 2007 Tier 3 base case by s ector

Platform Sector Summary of Significant Inventory Differences

IPM sector: ptipm  Inclu ded additional HAPs

Non

- IPM sector:

p tnonipm  Updated ethanol inventory

 Replaced oil and gas emissions with Western Regional Air Partnership (WRAP) Phase III year 2008 emissions in select oil and gas basins
Agricultural:

a g  Temporalized to hours using the Tier 3 WRF output

Area fugitive dust:

a fdust  Performed meteorological adjustments with the Tier 3 WRF output

Remaining nonpoint sector:

n onpt  Replaced oil and gas emissions with Western Regional Air Partnership

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

Class 1 & 2 CMV

and locomotives:

c1c2rail  Augmented HAPs in California and RPO data

C3 commercial

marine:

c3marine  Augmented HAPs

Nonroad sector:

n onroad  Used updated fuels

 Augmented HAPs in California

Onroad non

refueling:

o nroad  Used MOVESTier3FRM emission factors

 Used updated fuels

Onroad non

refueling: onroad_rfl  Used MOVESTier3FRM emission factors

 Used updated fuels

Average fires:

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

emissions7 

### 2.1 2 007 NEI point sources (ptipm and ptnonipm)

Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude) are specified, as in the case of an individual facility. A facility may have multiple emission points, which may

be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas). With a couple of minor exceptions, this section describes only NEI point sources within th e contiguous United States. The offshore oil platform (othpt sector) and category 3 CMV emissions (c3marine sector) are also point source

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

.

After removing offshore oil platforms into the othpt sector, EPA created an initial version of two platform sectors from the remaining 2008 NEI point sources for input into SMOKE: the EGU sector

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

- EGU sector

– also called the non

- IPM sector (i.e., ptnonipm). This split

facilitates the use of differen t SMOKE temporal processing and future

- year projection techniques for each of these sectors. The inventory pollutants processed through SMOKE for both the ptipm and ptnonipm sectors
were: CO, NO X , VOC, SO 2 , NH 3 , PM 10 , and PM 2.5 and the following HAPs: H Cl (pollutant code =

7647010 ), C l (code = 7782505 ) , and the CMAQ MP

- lite HAPs (see Section 3.2 for details)

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

the 2007v5 TSD.



### 2.1.1 IPM sector ( ptipm )

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

found in the May 2012 version 4.10 of the NEEDS database ( http://www.epa.gov/airmarkets/progsregs/epa

ipm/BaseCasev410.html#needs ). IPM provides future

- year emission inventories for the universe of EGUs

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

EGU sources and future

- year EGU emissions for sources which are forecasted by IPM and (2) to avoid double counting when projecting point source emissions to future years. A comprehensive description on

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

2008 NEI documentation (EPA, 2012a)

. The ptipm sector is identical to the 2007v5 platform except for the

inclusio n of CMAQ MP

- lite HAPs (see Section 3.2 for details) in the 2007 model

- ready files 4

.



### 2.1.2 Non

- IPM sector (ptnonipm)

With several exceptions, the non

- IPM (ptnonipm) sect or contains the remaining 2008 NEI point sources that

were not include d in the IPM (ptipm) sector. The ptnonipm sector contains all sources not reflected in future year IPM inventories. For the most part, the ptnonipm sector reflect s the non

- EGU componen t of the NEI point inventory; however, as previously discussed, it is likely that some small low

- emitting EGUs that are not reflected in the CEMs database are present in the ptnonipm sector.

The re are numerous modifications between the published 2008 NEI and the 200 7 ptnonipm inventory used for modeling . More details on some of these modifications can be found in the 2007v5 TSD. The differences between the 2007v5 ptnonipm and the 2007 ptnonipm for this base case are limited to the following:

 Integration of BAFM (see Section 3.2.1.1 for details)

 Inclusion of the CMAQ MP

- lite HAPs (see Section 3.2 for details)

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

 Removal of one cement kiln because of unreasonable emissions 5

 Modification to the S outh D akota point inventory to include 2005 NEI emissions for sources missing

in the 2008 NEI.

 Updated ethanol facilities (see below)

 Updated oil and gas (see below)

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

2008 NEI were removed from the set prior to including them in the 2007 base case . Locations and FIPS codes for these ethanol plants were verified using web searches and Google Earth. These emissions are

included in the 2007 case as a separate FF10

- format inventory for HAPs and CAPs. Emission rates for the

facilities were obtained from EPA’s spreadsheet model for upstream impacts developed for the Renewable Fuel Standard ( RFS2 ) rule (EPA, 2010a). The p lant emission rates for criteria pollutants used to estimate impacts are given in Table 2

- 3 . Toxic emission rates were estimated by applying toxic

- to

- VOC ratios in

Table 2

- 4 to the VOC emission rates shown in Table 2

- 3

. For air toxics other than ethanol, toxic

- to

- VOC

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

Table 2

- 3 and Table 2

- 4 were multiplied by facility production estimates for 2007

, 2018 (via 2017 emission factors) , and 2030 based on analyses performed for the industry characterization described in Chapter 1 of the RFS2 final rule regulatory impact analysis (RIA)

.

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

a nd non

- point format emissions for several major basins in Colorado and Montana, New Mexico, Texas, Utah and Wyoming. These basins are listed here: Denver

- Julesburg, Uinta, San Juan (North and South), Piceance, Southwest Wyoming (Green River), Powder River , Wind River and Permian. A map showing the geographic area of these basins is provided at: http://www.eia.gov/oil_gas/rpd/shale_gas.jpg

.

The WRAP oil and gas Phase III project was co

- sponsored b y the Independent Petroleum Association of Mountain States (IPAMS) and is based on survey outreach efforts. Survey coverage varied, and survey data were generally reflected as point sources in the inventory. Unpermitted sources were based somewhat on

sur veys but also on activity and emission factor estimates and were generally reflected as nonpoint (nonpt sector) sources.

Overall, the Phase III project estimated emissions for a couple dozen source types, including drilling rigs, compressor stations, he aters and boilers, tank breathing venting and flashing, pneumatic devices, well and pipeline/compressor fugitive emissions, dehydrators, amine units, truck loading and other miscellaneous

sources. Phase III emissions include basin

- specific speciation, sur rogates and hence SCCs to account for the different products extracted: oil, gas and coal

- bed methane (CBM). To prevent possible double

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

co mprise the 9 basins in the WRAP Phase III inventories. The list of oil and gas SCCs that were removed from the point (and nonpoint) 2008 NEI are provided in Appendix A of the 2007v5 TSD 6

.

5 The cement kiln had emissions orders of magnitude higher than in any other year in the NEI. The particular facility is facil ity ID
4773111, FIPS 26017.

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

Table 2

- 3

. Corn Eth anol Plant Criteria Pollutant Emission Factors (grams per gallon produced)

Corn Ethanol Plant Type Year VOC CO NO X PM 10 PM 2.5 SO 2 NH 3

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

2030 2.29 0.58 0.94 0.94 0.23 0.01 0.00
Dry Mill NG (wet distillers grains with solubles (DGS)) 2005, 2017 2.27 0.37 0.63 0.91 0.20 0.00 0.00

2030 2.27 0.37 0.60 0.91 0.20 0.00 0.00

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

2030 2.29 0.62 1.00 0.94 0.23 0.01 0.00

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

2030 2.27 0.39 0.63 0.91 0.20 0.00 0.00

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

2030 2.31 2.65 3.68 3.64 1.54 3.48 0.00

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

2030 2.28 1.68 2.34 2.62 1.03 2.21 0.00

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

2030 2.42 2.55 3.65 1.28 0.36 0.14 0.00 Dry Mill Biomass (wet DGS) 2005, 2017 2.35 1.62 2.32 1.12 0.28 0.09 0.00

2030 2.35 1.62 2.32 1.12 0.28 0.09 0.00

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

2030 2.33 1.04 1.68 1.00 0.29 0.01 0.00

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

2030 2.33 3.50 4.86 4.53 1.98 4.60 0.00

Table 2

- 4

. Toxic

- to

- VOC Ratios for Corn Ethanol Plants

Acetaldehyde Acrolein Benzene 1,3

- Butadiene Formaldehyde

Wet Mill NG 0.02580 0.00131 0.00060 2.82371E

- 08 0.00127

Wet Mill Coal 0.08242 0.00015 0.00048 2.82371E

- 08 0.00108 Dry Mill NG 0.01089 0.00131 0.00060 2.82371E

- 08 0.00127 Dry Mill Coal 0.02328 0.00102 0.00017 2.82371E

- 08 0.00119



### 2.2 2007 nonpoint sources (afdust, ag, nonpt)

The nonpoint sectors use the 2008 NEI as a starting point. EPA created several sectors from the 2008 NEI nonpoint inventory , and this section describes the stationary nonpoint sources. Class 1 & Class 2 (c1c2) and Class 3 (c3) commercial marine vessels and locomotives are also in the 2008 NEI nonpoint data category. However, these mobile sources are included in the mobile documentation in Section s 2.5.4 2.5.5 as the

c1c2rail and c3marine sectors , respectively

.

T he nonpoint tribal

- submitted emissions were removed to prevent possible double counting with the county

level emissions and also because spatial surrogates for tribal data were not available . Because the tribal nonpoint emissions are small, these omissions will have a limited impact on the results at the 12

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

NEI website ( EPA , 2012 a )

.

The following subsections describe the partitioning of the 2008 NEI nonpoint inventory into the 2007v5 modeling platform sectors, and also the differences between the nonpoint emissions in the 2007v5 platform

and the 2007 base case

.10 

### 2.2.1 Area fugitive dust sector (afdust)

The area

- source fugitive dust (afdust) sector contains PM 10 and PM 2.5 emission estimates for nonpoint SCCs identified by the EPA staff as dust sources. Categories included in the afdust sector are paved roads, unpaved r oads and airstrips, construction (residential, industrial, road and total), agriculture production, and mining and quarrying. It does not include fugitive dust from grain elevators because these are elevated point

sources.

This sector is separated from o ther nonpoint sectors to allow for the application of “transport fraction,” and meteorology/precipitation

- based reductions. These adjustments are applied via sector

- specific scripts, beginning with land use

- based gridded transport fractions and then subse quent daily zero

- outs for days where at least 0.01 inches of precipitation occurs or days when there is snow cover on the ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are subject to transport.

This meth odology 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 Emissi ons Modeling Platform (Adelman, 2012). The precipitation adjustment is then applied to remove all emissions for days where measureable rain occurs. Both the transport fraction and meteorological adjustments are based on the gridded resolution of the plat form; therefore, different emissions will result from different grid resolutions. Application of the transport fraction and meteorological adjustments reduces the overestimation of fugitive dust impacts in the grid modeling as compared to ambient samples

.

For more details on this approach and the differences between the 2007 base case and the 2008 NEI, see the 2007v5 TSD. The afdust sector is identical to the 2007v5 platform except for the fact that the meteorological adjustments were computed using the update WRF outputs.



### 2.2.2 Agricultural ammonia sector (ag)

The agricultural NH 3 “ag” sector is based on livestock and agricultural fertilizer application emissions from

the 2008 NEI nonpoint inventory. The ag sector is identical to 2007v5 platform (see 2007v5 TSD for details) except for the temporalization of animal NH 3

. An updated temporal allocation methodology for animal NH 3 that allocates emissions down to the hourly level by taking into account tem perature and wind speed was incorporated into the 2007 platform (see

Section 3.3.3 for more details)

.



### 2.2.3 Other nonpoint sources (nonpt)

Stationary nonpoint sources th at were not subdivided into the afdust or ag sectors were assigned to the “nonpt” sector. A ll fire emissions from the 2008 NEI nonpoint inventory were removed and replaced with

SMARTFIRE emissions

, described in Section 2.3. Additionally, locomotives and CMV mobile sources from the 2008 NEI nonpoint inventory are described in Section 2.5.

For more details on the development of the nonpt sector see the 2007v5 TSD. The differences between the 2007v5 nonpt and the 2007 nonpt for this base case are limited to the following:

 Replaced 2008 NEI oil and gas emissions (SCCs beginning with “23100”) with year 2008 Phase III oil and gas emissions for several basins in the WRAP RPO states. These WRAP Phase III emissions contain point and nonpoint formatted data are discussed in greater detail in Section 2.1.2 and here: http://www.wrapair2.org/PhaseIII.aspx

. These changes w ere made in counties affected by the WRAP

data.11

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

Section 3.2.1.3 and 3.4.1 , respectively).

 Included the CMAQ MP

- lite HAPs (see Section 3.2 for details)

 Updated the temporalization of the residential wood combustion (RWC) inventories to account for the updated meteorology (see Section 3.3.3 for details).



### 2.3 Fires (avefire)

Wildfire and prescribed burning emissions are contained in the ptfire and avefire sectors. The ptfire sector has emissions provided at geographic coordinates (point locatio ns) and has daily emissions values, whereas the avefire sector contains county

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

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

sector was used instead of point fires

. T he SCCs in Table 2

- 5 are considered “fires”

– note that the complete SCC description includes “Miscellaneous Area So urces” as the first tier level description.

Table 2

- 5

. 2007 Platform SCCs representing emissions in the ptfire and avefire modeling sectors

SCC SCC Description

2810001000 Other Combustion; Forest Wildfires; Total

2810015000 Other Combustion; Prescribed Burning for Forest Management; Total

2811015000 Other Combustion

- as Event; Prescribed Burning for Forest Management; Total

2811090000 Other Combustion

- as Event; Prescribed Forest Burning ;Unspecified

The a vefire sector exclude s agricultural burning and other open burning sources, which are included in the

nonpt sector. The agricultural burning and other open burning sources are left in the nonpt sector because these categories were not factored into the de velopment of the average fire sector. Additionally, the emissions are much lower and their year

- to

- year variability is much lower than that of wildfires and non

agricultural prescribed/managed burns.

The purpose of the avefire sector is to represent emissions for a typical year’s fires for use in projection year inventories, since the location and degree of future

- year fires are not known. This approach keeps the fires

information constant between the 2007 base case and future

- year cases to eliminate large and uncertain differences between those cases that would be caused by changing the fires. Using an average of multiple

years of data reduces the possibility that a single

- year's high or low fire act ivity would unduly affect future

year model

- predicted concentrations. Emissions are day

- specific but aggregated to county

- level where spatial surrogates will allocate the fires to forest and crop/pasture land. The creation of the avefire daily nonpoint i nventory is distinct for prescribed burning and wildfires. For more details on Fire Averaging Tool and the various smoothing techniques, see the 2007v5 TSD.

For this 2007 base case (and future years), EPA used 4 years (2007 through 2010) of fire data fro m the Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) version 2 for both prescribed and wildfires and used a 29 day averaging period.



### 2.4 Biogenic sources (biog)

The biogenic emissions were computed based on 2007 meteor ology data using the Biogenic Emission Inventory System, version 3.14 (BEIS3.14) model within SMOKE. The BEIS3.14 model creates gridded, hourly, model

- species emissions from vegetation and soils. It estimates CO, VOC (most notably isoprene, terpine, and sesquiterpene ) , and NO emissions for the U.S., Mexico, and Canada. The BEIS3.14 model is12 described further i n http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_cmas08.ppt

. The 2007v5 biog sector and the biog sector for this 2007 base case, the meteorology differed between the two cases. The changes in meteorology will impact both the total emissi ons and the temporalization and spatial distribution of these emissions. The BIOSEASONS file was also updated using the meteorology data for this

case.



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

For the 2007 base case , as indica ted in Table 2

- 1

, EPA separated the 2007 onroad emissions into two sectors: (1) “onroad” and (2) “onroad_rfl”. As discussed in Section 2.5.2, the onroad and onroad_ rfl sectors are processed separately to allow for different spatial allocation to be applied to onroad refueling ( using a gas station s urrogate ) versus onroad vehicles ( using surrogates based on roads and population). All onroad and onroad refueling emiss ions are generated using a new SMOKE

- MOVES emissions modeling framework that leverage s MOVES generated outputs ( http://www.epa.gov/otaq/models/moves/index.htm ) and hourly meteorology.

The nonr oad sector is based on NMIM except for California which uses data provided by the California Air Resources Board (CARB). All nonroad emissions are compiled at the county/SCC level. NMIM (EPA, 2005) creates the nonroad emissions on a month

- specific basis that accounts for temperature, fuel types, and

other variables that vary by month.

The locomotive and commercial marine vessel (CMV) emissions are divided into two nonroad sectors: “c1c2rail” and “c3marine”. The c1c2rail sector includes all railway and most rail yard emissions as well as the gasoline and diesel

- fueled Class 1 and Class 2 CMV emissions. The c3marine sector emissions contain the larger residual fueled ocean

- going vessel Class 3 CMV emissions and are treated as point emissions with

an elev ated release component; all other nonroad emissions are treated as county

- specific low

- level

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

.

All tribal data from the mobile sectors have been dropped because EPA do es not have spatial surrogate data for tribal regi ons
, the data may be double

- counted with emissions submitted by state and local agencies for the same areas, and the emissions are small.



### 2.5.1 Onroad non

- refueling (onroad)

For the 2007 base case , EPA estimated emissions for every county in the continental U. S 7

. EPA used a modeling framework that took into account the strong temperature sensitivity of the onroad emissions. Specifically, EPA used county

- specific inputs and tools that integrated the MOVES model with the SMOKE 8
emission inventory model to take advantage of the gridded hourly temperature information available from meteorology modeling used for air quality modeling. This integrated “SMOKE

- MOVES” tool was developed by EPA in 2010 and is in use by states and region al planning organizations for regional air quality modeling. SMOKE

- MOVES requires emission rate “lookup” tables generated by MOVES that differentiate emissions by process (running, start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of day, etc. To generate the MOVES emission rates that could be applied across the U.S., an automated process

ran MOVES to produ ce emission factors by temperature and speed for 146 “representative counties,” to which every other county could be mappe d. Using the MOVES emission rates, SMOKE selected appropriate emissions rates for each county, hourly temperature, SCC, and speed bin and multiplied the

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

8 A beta version of SMOKE v3.5 was used for modeling the Tier 3 FRM. The release version is available at: http://www.smoke

model.org/index.cfm13 emission rate by activity (VMT (vehicle miles travelled) or vehicle population) to produce emissions. These calculations were done for every county, grid cell, and hour in the continental U.S.

SMOKE

- MOVES can be used with different versions of the MOVES model. For the 2007 platform, EPA used the latest publically released version: MOVES2010b ( http://www.epa.gov/otaq/models/moves/index.htm ). However, since the release of MOVES2010b, EPA has continued to collect and analyze emissions data. In particular, EPA completed two major studies of fuel effects on emissions in Tier 2 light

- duty gasoline vehicles, and an important collection of studies on evaporative emissions. Because fuels and evaporative emissions are affected by Tier 3 standards, it was very important to include these data in the mod eling. Therefore, in estimating the impact of the Tier 3 vehicle and fuel standards on air quality for the future years, MOVES2010b was updated to include the results from these studies, along with numerous other updates. Furthermore, the new model incor porated the changes that reflect recent EPA rules on light

- duty and heavy

- duty greenhouse gas emissions. These changes are

documented in the docket for the NPRM 9 and in the docket for this rule 10

.

The following inputs and methodologies are identical to t he 2007v5 platform 11 and are detailed in the

2007v5 TSD:

 Activity data (VMT, VPOP, speed)

 Representative counties

 Fuel Months

 Local MOVES inputs

 Procedure for running MOVES to create emission factors

 Procedure for running SMOKE to create emissions

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

fuels

, meteorology, and extended idle adjustments.

2.5.1.1 Fuels

Although state

- submitted NMIM and MOVES input data may have include d information about fuel properties, the MOVES runs for the 2007 base case were run using a set of fuel properties for each county in

2007 generated by EPA . These data were developed using a combination of purchased fuel survey data, proprietary fuel refine ry information , ethanol and other biofuel production levels, and known federal and local regulatory constraints. The fuel supply used in the Tier 3 FRM varies significantly from that used in the NPRM, including the introduction of a new approach to aggreg ating fuels by region. For more information regarding this new approach to fuels, please refer to the Tier 3 FRM, Chapter 7.1.3.2. The fuel supplied used in the Tier 3 FRM varies slightly from that used in the 2007v5, including less ethanol over all

with a slightly dif

ferent regional distribution, which more closely match ed the new AEO regions. The fuel properti es themselves are the same in 2007v5 and in Tier 3 FRM ( 2007 base case )

.

2.5.1.2 Temperature and humidity

Ambient temperature can have a large impact on emi ssions. Low temperatures are associated with high start emissions for many pollutants. High temperatures are associated with greater running emissions due to the higher engine load of air conditioning. High temperatures also are associated with higher e vaporative

emissions.

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

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

11 Tier 3 NPRM was based on the 2005 platform and hence inputs such as the representative c ounties, activity data, and local MOVES inputs were updated as part of the development of the 2007 platform (used for Tier 3 FRM).14 The 36

- km and 12

- km gridded meteorological input data for the entire year of 2007 covering the continental

United States were derived from simulations of version 3. 3 of the Weather Rese arch and Forecasting Model (WRF, http://wrf

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

Model is a mesoscale numerical weather prediction system developed for both operational forecasting and atmospheric re search applica tions

. The Meteorology

- Chemistry Interface Proce ssor (MCIP) version 4.1.2 ( http://www.cmascenter.org/help/model_docs/mcip/4.1/ReleaseNotes ) was used as the software for

maintain ing dynamic consistency between the meteorological model , the emissions model, and air quality chemistry model

. The meteorology run used for the 2007 base year 12 is different than what was used for the 2007v5 platform. Specifically it used newer versions of WRF and MCIP and 25 vertical layers 13 instead of

the 24 used in 2007v5

.

The SMOKE

- MOVES tool Met4moves reads the gridded, hourly meteorological data (output from MCIP) to generate a list of the maximum temperature ranges, average relative humidity, and temperature profiles that

are needed for MOVES to create the emission

- factor lookup tables. For more details on Met4moves and the processing of meteorology for SMOKE

- MOVES see the 2007v5 TSD.

2.5.1.3 Extended idle adjustments and SMOKE

- MOVES

Emissions from t he e xtended idling of long haul trucks are a subset of the exhaust emissions. These emissions are typically from trucks

, which have traveled across county and state boundaries. Federal rules require that truck drivers may not drive more than 10 hours without rest. These long haul trucks are known to stop for these rest periods at truck stops along their routes and idle their trucks for hours while they rest. The MOVES model generates an estimate of the total number of extended idling hours and emissions for every county. However, when MOVES is run using the County scale, the extended idling rate (in grams per hour per truck) is not adjusted to account for allocation of the extended idling to counties where interstate

travel occurs.

SMOKE has an optional in put that adjusts emissions (CFPRO) by county, SCC, and mode. To account for the extended idle adjustment, EPA created an adjustment file that applies these allocation factors by county for extended idle and for the long haul type SCCs (SCC7 2230073 and 22 30074) only (see the 2011v1 NEI TSD for more details).

SMOKE

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

estimate 2007 emi ssions that incorporates these adjustments.



### 2.5.2 Onroad refueling (onroad_rfl)

Onroad refue ling is modeled very similarly to other onroad emissions (see Section 2.5.1 ).

MOVES Tier3FRM can produce EF s for refueling. These EF s are at the resolution of the onroad SCCs. The refueling EF s were run separately from the other onroad mobile sources to allow for d ifferent spatial

allocation

. To facilitate this, the EF s from the r efueling process were separated out into RPD refueling and RPV refueling tables 14

. EPA then r an SMOKE

- MOVES using these EF tables as inputs and spatially

allocate d the results based on a g as stations surrogate (see Section 3.4.1 )

.

Lastly, the Mrggrid SMOKE program combine d RPD refueling and RPV refueling into a single onroad_rfl model ready output for final processing with the other sectors prior to use in CMAQ.

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

13 WRF was run at 35 layers and MCIP post

- process ed it to 25 layers.

14 The Moves2smk post

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

### 2.5.3 Nonroad mobile equipment sources: (nonroad)

This sector includes mont hly exhaust, evaporative and refueling emissions from nonroad engines (not including commercial marine, aircraft, and lo comotives) that are derived from NMIM for all states except

California. Y ear

- 2007 inventories from CARB were used for California after the completion of several preprocessing steps discussed in the 2007v5 TSD

.

NMIM (non

- California) nonroad

NMIM ran the version of NONROAD

, NR08b , which models all in

- force nonroad controls, including the marine spark ignited (SI) and small SI engine final rule, published May 200 9 (EPA, 200 8 ). This version of NONROAD is very similar to the publicly released ve rsion, but it can model ethanol blends up to E20. The NMIM version is NMIM20090504d, which has the same results as the publicly

- released NMIM version NMIM20090504a. The underlying National County Database (NCD) is NCD20101201 a , but with 2007 meteorology inserted into the countymonthhour table. NCD20101201 a includes state inputs for the 2008

NEI.

The NMIM run, abs2007basenr

, includes the lower 48 states plus Washington D.C. ; it excludes Alaska, Hawaii, Puerto Rico and the Virgin Islands. To conserve p rocessing time, NMIM was run using 392 county groups. The county groups are in the same state and have the same fuels and similar temperature regimes. The county from each group with the highest VMT was chosen as the representing county. All counties are mapped to their representing county in the MySQL table countymap392 . The fuels database, countryyearmonth2007_Baseline_0906012 , is a conversion to NMIM format of the MOVES fuels for the 2007 base case (see Section 2.5.1.1 )

.

As with the onroad e missions, NMIM provides nonroad emissions for VOC by three emission modes: exhaust, evaporative and refueling. Unlike the onroad sector, refueling emissions from nonro ad sources are

not separated into a different sector.

The EPA ran NMIM to create county

- SCC emissions and removed California emissions because they were replaced with a CARB inventory. Emissions were converted from monthly totals to SMOKE

- ready FF10

form at ( http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch08s02s04.html ) monthly average

- day

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

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

2010 a ). The nonroad sector emissions in California are developed us ing a modular approach and include all rulemakings and updates in place by December 2010. These emissions were developed using Version 1 of the California Emissions Projection Analysis Model (CEPAM) which support various California off

- road regulations su ch 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, 20 10b ).

EPA converted the CARB

- supplied nonroad annual inventory to mo nthly emissions values by using the aforementioned EPA NMIM monthly inventories to compute monthly ratios by pollutant and SCC. Some adjustments to the CARB inventory were needed to convert the provided total organic gas ( TOG ) to VOC and augment the HAPs

. See Section 3.2.1.3 for details on speciation of California nonroad data.16 

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

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

3/residual

- fuel (C3) CMV and railway maintenance. The “c1c2” portion of this sector name refers to the Class I/II CMV emissions, not the railway emissions. Railwa y maintenance emissions are included in the

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

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

2007v5 c1c2rail and the 2007 c1c2ra il for this base case are limited to the following:

 Updated an outdated FIPS in the RPO rail inventory. Specifically changed Clifton Forge, VA (51560) to Alleghany County, VA (51005)

 Augmented the CARB c1c2rail and RPO rail inventories to include the CMA Q MP

- lite HAPs (see

Section 3.2 and 3.2.1.3 for details)



### 2.5.5 Class 3 comm ercial marine vessels (c3marine)

The c 3 marine sector emissions data were developed based on a 4

- km resolution ASCII raster format dataset

used since the Emissions Control Area

- International Marine Organization ( ECA

- IMO ) project began in

2005 , then known as the Sulfur Emissions Control Area (SECA)

. These emissions consist of large marine diesel engines (at or above 30 liters/cylinder) that until very recently , were allowed to meet relatively modest emission requirements, often burning residual fuel. The emissions in this sector are comprised of primarily foreign

- flagged ocean

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

inc ludes these ships in several intra

- port modes (cruising, hoteling, reduced speed zone, maneuvering, and idling) and underway mode and includes near

- port auxiliary engines. This sector is identical to the 2007v5 platform except for the addition of naphthal ene (see Section 3.2.1.3 ). For more details on the development of this sector’s emissions, see the 2007v5 TSD.



### 2.6 Emissions from Canada, Mexico and offshore drilling platforms (othpt, othar, othon)

The emissions from Canada, Mexico, and offshore drilling platforms are included as part of three emissions modeling sectors: othpt, othar, and othon.

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

- county geographic FIPS, and the third and fourth characters provide the SMOKE source types: “pt” for point, “ar” for “area and nonroad mobile”, and “on” for onroad mobile. All “oth” emiss ions are CAP

- only inventories.

For mo re details on the development of the “oth” sectors see the 2007v5 TSD. The differences between the 2007v5 “oth” and the 2007 “oth” for this base case are limited to the following:

 o thar has updated spatial surrogates (see Sections 3.4.3 )

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

 o thpt has updated speciation (see Sections 3.2.1.3 )17 

### 2.7 SMOKE

- re ady 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

availa ble and were not modified other than the name “CHLORINE” was changed to “CL2” because that is the name required by the CMAQ model.18 

## 3 Emissions Modeling Summary

Both the CMAQ and CAM X 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 necessar y 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 t ransforms 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, sp atial 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 acro ss 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 pro vides some basic information about the tools and data files used for emissions modeling as part of the modeling platform. In Section 2 , the emissions inventories and how they

differ from the 2007v5 platform were described. In Section 3, the descriptions of data are limited to the ancillary data SMOKE uses to perform the emissions modeling steps.

SMOKE version 3. 5 beta was used to pre

- process the emissions inven tories into emissions inputs for CMAQ. For sectors that have plume rise, the in

- line emissions capability of the air quality models was used, thereby creating source

- based and two

- dimensional gridded emissions files that are much smaller than full

three

- di mensional 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 E missions 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 (Mrg grid) 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 f rom 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

SMO KE area

- to

- point feature to grid the emissions. The “Speciation” column indicates that all sectors use the SMOKE speciation step, though biogenics speciation is done within the Tmpbeis3 program and not as a separate SMOKE step. The “Inventory resolution” column shows the inventory temporal resolution from which SMOKE needs to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory; instead, activity data and emission factors are used in combination with me teorological 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 that19 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 e ach model

- ready emissions sector. The height of the plume rise determines the model layer into which the emissions are placed. The c3marine, and othpt sectors are the only

sectors that contain only “in

- line” emissions, meaning that all of the emissions ar e 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

a g S urrogates Yes a nnual (some monthly)

a fdust S urrogates Yes Annual

a vefire S urrogates Yes daily

beis P re

- gridded

land use in BEIS 3.14 computed hourly

c1c2rail S urrogates Yes Annual

c3 marine Point Yes Annual in

- line

n onpt S urrogates &

area

- to

- point Yes annual (some monthly)

n onroad S urrogates &

area

- to

- point Yes Monthly

o thar Surrogates Yes Annual

o n road Surrogates Yes computed hourly

on road_rfl Surrogates Yes computed hourly

o thon S urrogates Yes Annual

o thpt Point Yes Annual in

- line

p tipm Point Yes Daily in

- line

p tnonipm Point Yes Annual in

- line

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

rise. For the 2007v5 platform

, no grouping was performed because grouping combined with “in

- line” processing will not give identical results as “offli ne” (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

- li ne and offline is to avoid the use of grouping

.

EPA ran SMOKE for the 36

- km CON tinental U nited S tates “CONUS” modeling domain for the boundary

conditions and for the smaller CONUS US 12

- km modeling domain (12US2) shown in Figure 3

- 1 and

described in Table 3

- 2

.20 Figure 3

- 1

. Air quality modeling domain s

Both grids use a Lambert

- Conformal projection, with Alpha = 33o, Beta = 45o and Gamma =

- 97o, with a

center of X =

- 97o and Y = 40o.

Table 3

- 2

. Descriptions of the 200 7v5 platform g rids

Common

Name Grid

Cell Size Description

(see Figure 3

- 1 ) Grid name Parameters listed in SMOKE grid description (GRIDDESC) file:

p rojection name , xorig, yorig, xcell, ycell, ncols, nrows, nthik

Continental 36km grid (36US1) 36km Entire conterminous US plus some of

Mexico/Canada 36US1_148X112 'LAM_40N97W',

- 2736000,

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

Continental 12km grid 12 km Entire conterminous US plus some of

Mexico/Canada 12US1_459X299 ‘LAM_40N97W',

- 2556000,

- 1728000, 12.D3, 12.D3, 459, 299, 1

US 12 km or

“smaller”

CONUS

- 12 12 km Smaller 12km

CONUS plus some of

Mexico/Canada 12US2 ‘LAM_40N97W',

- 2412000 ,

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

Section 3.4 provides the details on the spatial surrogates and area

- to

- point data used to accomplish spatial

allocation with SMOKE.21 

### 3.2 Chemical Speciation

The emissions modeling step for chemical speciation creates “model species” needed by the air quality model for a specific chemical mechanism. These model species are either individual chemical compounds or groups of species, called “model species.” The chemical mechanism used for the 200 7 platform is th e CB05 mechanism (Yarwood, 2005). The same base chemical mechanism is used with CMAQ and CAM X , but the implementation differs slightly between the two models. The specific versions of CMAQ and CAMx used in applications of this platform include secondary o rganic 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 CAM X . It should be noted that the BENZENE model species is not part of CB05 in that the concentrations of BENZENE do not provide any feedback into the chemical reactions (i.e., it is not “inside” the chemical mechanism). Rather, benzene is used as a reactive tracer and as such is impacted by the CB05 chemistry. BENZENE, along with several reactive CB05 species (such as TOL and XYL) plays a role in SOA

formation. Unlike the 2007v5 platform, the Tier3 FRM modeling included additional hazardous air pollutants (HAPs) and used slightly revised speciation. A “lite” version 15 of the multi

- pollutant CMAQ (Version 5.0.1 ) was used that required additional HAP species 16 (see Table 3

- 3 for details): ACROLEIN,

ALD 2 _PRIMARY, BUTADIENE13, ETOH , FORM_PRIMARY, and NAPHTHALENE

.

The approach for speciating PM 2.5 emissions supports both CMAQ 4.7.1 and CMAQ 5.0 and includes speciation of PM 2.5 into a larger set of PM model species than is li sted above (see the 2007v5 TSD and

Section 3.2.2 for details ). The TOG and PM 2.5 speciation factors that are the basis of the chemical speciation approach were dev eloped from the SPECIATE4.3 database ( http://www.epa.gov/ttn/chief/software/speciate )

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

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

documentation.

The approach for speci ating NO X into NO, NO 2 , and HONO is consistent with the 2007v5 platform (see the 2007v5 TSD for details).

The SPECIATE database development and maintenance is a collaboration involving the EPA ’s ORD, OTAQ, and the Office of Air Quality Planning and Standa rds (OAQPS) , and Environment Canada (EPA, 2006a). The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical compounds, VOC

- to

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

PM 2.5

.

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

16 These additional HAPs are referred to in this document as “CMAQ MP

- lite HAPs”.22

Table 3

- 3

. Emission m odel s pecies produced for CB05 with SOA for CMAQ and CAM X *

Inventory Pollutant Model Species Model species description

CL2 CL2 Atomic gas

- phase chlorine

HCl HCL Hydrogen Chloride (hydrochloric acid) gas

CO CO Carbon monoxide

NO X NO Nitrogen oxide

NO2 Nitrogen dioxide

HONO Nitrous acid

SO 2 SO2 Sulfur dioxide

SULF Sulfuric acid vapor

NH 3 NH3 Ammonia

VOC ACROLEIN Acrolein from the HAP inventory

ALD2 Acetaldehyde for VOC speciation

ALD 2 _PRIMARY Acetaldehyde from the HAP inventory

ALDX Propionaldehyde and higher aldehydes

BENZENE Benzene (not part of CB05)

BUTADIENE13 1,3

- butadiene from the HAP inventory

CH4 Methane 17

ETH Ethene

ETHA Ethane

ETOH Ethanol

FORM Formaldehyde from VOC speciation
FORM_PRIMARY Formaldehyde from the HAP inventory

IOLE Internal olefin carbon bond (R

- C=C

- R)

ISOP Isoprene

MEOH Methanol

NAPHTHALENE Naphthalene from the HAP inventory

OLE Terminal olefin carbon bond (R

- C=C)

PAR Paraffin carbon bond

TOL Toluene and other monoalkyl aromatics

XYL Xylene and other polyalkyl aromatics VOC species from the biogenics

model that do not map to model species above SESQ Sesquiterpenes

TERP Terpenes

PM 10 PMC Coarse PM > 2.5 microns and  10 microns

PM 2.5 18 PEC Particulate elemental carbon  2.5 microns

PNO3 Particulate nitrate  2.5 microns

POC Particulate organic carbon (carbon only)  2.5

microns

PSO4 Particulate Sulfate  2.5 microns

PMFINE Other particulate matter  2.5 microns

Sea

- s alt species (non

– anthropogenic ) 19 PCL Particulate chloride

PNA Particulate sodium

17 Technically, CH4 is not a VOC but part of TOG. Although we derive emissions of CH4, the AQ models do not use these emissions because the anthropogenic emissions are d warfed by the CH4 already in the atmosphere.

18 For CMAQ 5.0, PM 2.5 is speciated into a finer set of PM components. Listed in this table are the AE5 species

19 These emissions are created outside of SMOKE23

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

1. CL2 is not used in CAM X 2. CAM X particulate sodium is NA (in CMAQ it is PNA)

3. CAM X 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 CAM X that are used in the SOA chemistry. CMAQ uses the same names in CB05 and SOA for these species.

4

. CAM X uses a different name for sesquiterpenes: CMAQ SESQ = CAM X SQT

5

. CAM X uses particulate species uses different names for organic carbon, coarse par ticulate matter and other particulate mass as follows: CMAQ POC = CAM X POA, CMAQ PMC = CAM X C PR M

, CMAQ PMFINE= CAM X FCRS, and CMAQ

PMOTHR = CAMx FPRM



### 3.2.1 VOC speciation

3.2.1.1 The c ombination of HAP BAFM (benzene, acetaldehyde, formaldehyde and methanol) and VOC for VOC s peciation

The VOC speciation includes HAP emissions from the NEI in the speciation process. I nstead of speciating VOC to generate all of the species listed in Table 3

- 3

, EPA integrated emissions of four specific HAPs, benzene, acetaldehyde, formaldehyde and methanol ( collectively known as “ BAFM ” ) from the NEI with the NEI VOC. The integration process (described in more detail in the 2007v5 TSD ) combines the s e 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 HAP s from VOC and to use a special integrated profile to speciate the remainder of VOC to the model species excluding the specific HAP s

. G enerally, the

HAP emissions from the NEI are considered to be more representative of emissions of these compounds than their generation via VOC speciation.

Specific sectors fell into 3 categories : all sources are speciated from VOC directly (no integration), all sources are speciated with BAFM or EBAFM (ethanol plus BAFM) coming from the inventory (full integration), or some sources have BAFM and other sources do not (partial integration). See Table 3

- 4 for the integration status of each of the modeling sectors.

Table 3

- 4

. Integration approach for BAFM and EBAFM for each platform sector

Platform

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

Formaldehyde (F), Methanol (M), and Ethanol (E) ptipm No integration ptnonipm Partial integration (BAFM)

avefire No integration

A g N/A

– sector contains no VOC

A fdust N/A

– sector contains no VOC

N onpt Partial integration (BAFM and EB A FM)

nonroad Partial integration (BAFM). Except f or California: no integration

c1c2rail Partial integration (BAFM)

c3marine Full integration (BAFM)

O nroad Full integration (EBAFM and BAFM)

B iog N/A

– sector contains no inventory pollutant "VOC"; but rather specific VOC species

O thpt No integration

O thar No integration

othon No integration

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

Section 3.2.1.3 and the 2007v5 TSD

.24 3.2.1.2 County specific profile combinations (GSPRO_COMBO)

EPA used the SMOKE feature to compute speciation profiles from mixtures of other profiles in 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).

EPA used this feature for onroad and nonroad mobile and gasoline

- related related stationary sources whereby the emission sources use fuels with varying ethanol content, and therefore the speciation pr ofiles require different combinations of gasoline profiles, e.g. E0 and E10 profiles. Since the ethanol content varies spatially (e.g., by state or county), temporally (e.g., by month) and by modeling year (future years have more ethanol) the feature allo ws combinations to be specified at various levels for different years. SMOKE computes the resultant profile using the fraction of each specific profile assigned by county, month and

emission mode.

The GSREF file indicates that a specific source uses a combination file with the profile code “COMBO”. Because the GSPRO_COMBO file does not differentiate by SCC and there are various levels of integration across sectors, we typically have a sector specific GSPRO_COMBO. For the onroad and onroad_rfl sectors,
the GSPRO_COMBO uses E

- profiles (i.e. there is EBAFM integration). Different profile combinations are specified by the mode (e.g. exhaust, evaporative, refueling, etc.) by changing the pollutant name (e.g. EXH__NONHAPTOG, EVP__NONHAPTOG, RFL__NONHAPTOG) . For the nonpt sector, there is a combination of BAFM and EBAFM integration. Due to the lack of SCC in the GSPRO_COMBO, the only way to differentiate the sources that should use BAFM integrated profiles versus E

- profiles is by changing the pollutant nam e. For example, w e changed the pollutant name for the PFC future year inventory so the integration would use EVP__NONHAPVOC to correctly select the E

- profile combinations while other sources used NONHAPVOC to select the typical BAFM profiles.

3.2.1.3 Additional sector specific details

The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some sectors there is no integration (VOC is speciated directly), for some sectors there is full integration (all sources are integrated), and for other sectors there is partial integration (some sources are not integrated and other sources are integrated). The integrated HAPs are either BAFM ( ethanol not subtracted from VOC with

BAFM HAPs ) or EBAFM ( ethanol and BAFM HAPs subtracted from VO C ). Table 3

- 4 summarizes the integration for each platform sector. The additional CMAQ MP

- lite HAPs were evaluated and

, where it was

needed

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

For the c1c2rail sector, EPA integrated BAFM for most sources from the 2008 NEI. There were a few sources that had zero BAFM; therefore, they were processed as no integrate. The RPO and CARB inventories did not include HAPs; therefore, EPA processed all non

- NEI source emissions in the c1c2rail sector as no integrate. For California, EPA converted the CARB inventory TOG to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor. For CARB and the RPO inventories, EPA augmented the inventories to include the CMAQ MP

- lite HAPs. In Table 3

- 5 , “geography” indicates whether the emission factor is applied nationally or to a subset of the country an d “speciation base” is the pollutant value to use in the emissions calculation.

20 EPA analyzed the presence of acroleine, 1,3

- butadi ene, and naphthalene in the nonpt and point sectors and found that the coverage was very inconsistent; therefore the decision was made to augment only the mobile sectors where it was needed.25 Table 3

- 5

. HAP augmentation for c1c2rail

Pollutant Speciation

Fraction Speciation

Base Geography SCC SCC Description

1,3 Butadiene 6.146E

- 05 PM10

- PRI California 2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations

1,3 Butadiene 6.146E

- 05 PM10

- PRI California 2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations

Acrolein 8.547E

- 05 PM10

- PRI California 2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations

Acrolein 8.547E

- 05 PM10

- PRI California 2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations

Napthalene 1.851E

- 03 PM10

- PRI California 2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations

Napthalene 1.851E

- 03 PM10

- PRI California 2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations

Napthalene 2.576E

- 03 PM10

- PRI 49 States 2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations

Napthalene 2.576E

- 03 PM10

- PRI 49 States 2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations

Acrolein 4.594E

- 03 PM10

- PRI 49 States 2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations

Acrolein 4.594E

- 03 PM10

- PRI 49 States 2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations

1,3 Butadiene 4.774E

- 03 PM10

- PRI 49 States 2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations

1,3 Butadiene 4.774E

- 03 PM10

- PRI 49 States 2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations

Acrolein 2.625E

- 03 VOC N ational 2280002100 Marine Vessels; Commercial; Diesel; Port

Acrolein 2.188E

- 03 VOC N ational 2280002200 Marine Vessels; Commercial; Diesel; Underway

Naphthalene 1.051E

- 03 PM25

- PRI N ational 2280002100 Marine Vessels; Commercial; Diesel; Port

Naphthalene 8.756E

- 04 PM25

- PRI N ational 2280002200 Marine Vessels; Commercial; Diesel; Underway

For the c3marine sector, EPA computed HAPs directly from the CAP inventory; therefore, the entire sector is integrated to use the VOC BAFM HAP species directly, rather than VOC speciation profiles. There is no methanol in the VOC speciation, but the remaining VOC BAF HAPs and the CMAQ MP

- lite HAPs 21
emissions are derived from the following equations:

Benzene = VOC * 9.79 5E

- 06 Acetaldehyde = VOC * 2.286E

- 04 Formaldehyde = VOC * 1.5672E

- 03 Naphthalene = PM2.5 * 1.99 0 E

- 5

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

- MOVES (see the 2007v5 TSD ) is used to estimate these sectors, mean ing that both the MEPROC and INVTABLE

files are involved in controlling which pollutants are ingested and speciat ed . Second

, these sectors have estimates of TOG as well as VOC; therefore, TOG can be speciated directl y. Third, the gasoline sources use

21 The c3marine sources do not emit acrolein or 1,3

- butadiene.26 full integration of EBAFM (i.e. use E

- profiles) and the diesel sources use full integration of BAFM. Fourth,

the onroad sector utilize s 7 different modes for speciation: exhaust, extended idle, auxiliary power units (AP U), evaporative, permeation (gasoline vehicles only), brake wear, and tire wear. The onroad_rfl sector

utilize s a n eighth mode, refueling. Fifth, the gasoline exhaust profiles were updated to 8750a (r evision to

Gasoline Exhaust

- Reformulated gasoline ) a nd 8751a (revision to Gasoline Exhaust

- E10 ethanol gasoline ). 22 Sixth, the CMAQ MP

- lite HAPs are produced directly from the SMOKE

- MOVES processing.

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

because NMIM computed only VOC and no HAPs were available for these SCCs. All other nonroad sources were integrated. For California, EPA converted the CARB inventory TOG to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor. SMOKE later applies the same VOC

- 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 also does not

include HAP e stimates; therefore all California nonroad emissions are processed as no integrate so that the HAP species are generated by speciating the TOG emissions

. The CMAQ MP

- lite HAPS are produced directly by NMIM. In California, the CMAQ MP

- lite HAPs were augmen ted by applying state

- wide SCC

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

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

, which includes BAFM . In the future year, there is also partial in tegration because both the ethanol and biodiesel inventories (SCC 30125010) provided by OTAQ include BAFM. See the 2007v5 TSD for

additional details on this sector

.

For the oil and gas sources in ptnonipm and nonpt, the WRAP Phase III sources have basi n

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

- specific profiles using gas composition analysis data obtained from operators through surveys. ENVIRON separated out emissions and speciation from conventional/tight sands/shale gas from coal

‐ bed methane (CBM) gas

sources

. Table 3

- 6 lists the basin and gas composition specific profiles used for the WRAP P hase III inventory. 23

Table 3

- 6

. VOC profiles for WRAP Phase III basins

Profile Code Description

SSJCB South San Juan Basin Produced Gas Composition for CBM Wells

SSJCO South San Juan Basin Produced Gas Composition for Conventional Wells

WRBCO Wind River Basin Produced Gas Composition for Conventional Wells

PRBCB Powder River Basin Produced Gas Composition for CBM Wells

PRBCO Powder River Basin Produced Gas Composition for Conventional Wells

DJFLA D

- J Basin Flashing Gas Composition for Condensate

DJVNT D

- J Basin Produced Gas Composition

UNT01 Uinta Basin Gas Composition at CBM Wells

UNT02 Uinta Basin Gas Composition at Conventional Wells

UNT03 Uinta Basin Flashing Gas Composition for Oil

UNT04 Uinta Basin Flashing Gas Composition for Condensate

PNC01 Piceance Basin Gas Composition at Conventional Wells

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

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

Profile Code Description

PNC02 Piceance Basin Gas Composition at Oil Wells

PNC03 Piceance Basin Flashing Gas Composition for Condensate

SWFLA SW Wyoming Basin Flash Gas Composition

SWVNT SW Wyoming Basin Vented Gas Composition

PRM01 Permian Basin Produced Gas Composition

SWE01 Wyoming Flashing Gas Composition

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

to Gasoline Exhaust

- Reformulated gasoline )

.

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

3.2.1.4 Future year speciation

The VOC speciation approach used for the future year case is customized to account for the impact of fuel changes. These changes affect the onroad, onroad_rfl, nonroad, and parts of the nonpt and ptnonipm sectors.

Speciation profiles for VOC in the nonroad, onroad and onroad_rfl sectors that account for the changes in ethanol content of fuels across years. The actual fuel formulations used can be found in Section s 2.5.1.1 and

4.3.1.2

. For 2007, EPA used “COMBO” profiles to model combination s of profile s for E0 and E10 fuel use.

For 2018 and 2030

, EPA use d “ COMBO ” profiles to model combinations of E10 , E15, and E85 fuel use. The speciation of onroad exhaust VOC additionally accounts for changes in the fraction of the vehicle fleet meeting different vehicle standards over time; currently, diff erent exhaust profiles are available for pre

- Tier

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

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

standards in the future case

.

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

associated with the BTP distribution. For these sources

, ethanol may be mixed into the fuels ; therefore

, specia tion is expect to change across years. The speciation changes from fuels in the ptnonipm sector include BTP distribution operations inventoried as point sour ces. RBT fuel distribution and BPS speciation does not change across the modeling cases because t his is considered upstream from the introduction of

ethanol into the fuel. For PFC , ethanol was present in the future inventories and therefore EBAFM profiles were used to integrate ethanol in the speciation. Mapping of fuel distribution SCCs to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix B of the 2007v5 TSD

. Error! Reference source not found. Tab le 3

- 7 summarizes the different profiles utilized for the fuel

- related sources in each of the sectors for 2007 and the future year case s . This table indicates when “E

- profiles” were

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

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

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

identical to the 2030 reference case

. Although many of the profiles making up the COMBO are the same between 2018 and 2030, the ratio of the profiles changes between the two years.28 Tab le 3

- 7

. Select VOC profiles 2007. 2018 and 2030

S ector S ub

category 2007 2018 2030

o nroad gasoline

exhaust COMBO: COMBO: COMBO:

8750aE Pre

- Tier 2 E0

exhaust 8751 a E Pre

- Tier 2 E10

exhaust 8751 a E Pre

- Tier 2 E10 exhaust

8751aE Pre

- Tier 2 E10

exhaust 8757E Tier 2 E10 Exhaust 8757E Tier 2 E1 0 Exhaust

8756E Tier 2 E0

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

8757E Tier 2 E10

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

o nroad gasoline

evaporative COMBO: COMBO : COMBO :

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

8754E E10 Evap 8872E E15 Evap 8872E E15 Evap

8934E E85 Evap 8934E E85 Evap

onroad gasoline

permeation COMBO: COMBO : COMBO :

8766E E0 evap perm 8769E E10 evap perm 8769E E10 evap perm

8769E E10 evap perm 8770E E15 evap perm 8770E E15 evap perm

8934E E85 Evap 8934E E85 Evap

onroad_rfl gasoline

refueling COMBO: COMBO : COMBO :

8869E E0 Headspace 8870E E10 Headspace 8870E E10 Headspace

8870E E10

Headspace 8871E E15 Headspace 8871E E15 Headspace

8934E E85 Evap 8934E E85 Evap

onroad diesel

exhaust 8774 Pre

- 2007 MY

HDD exhaust 877P0 Weighted diesel

exhaust for 2018 87730T3 Weighted diesel

exhaust for 2030

onroad diesel

extended

idle 8774 Pre

- 2007 MY

HDD exhaust 877EIT3 Weighted diesel

extended idle for 2018 877EIT3 Weighted diesel

extended idle for 2018

onroad auxiliary

power units N/A 8774 Pre

- 2007 MY HDD

exhaust 8774 Pre

- 2007 MY HDD

exhaust

onroad diesel

evaporative 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace

onroad_rfl diesel

refueling 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel H eadspace

nonroad gasoline

exhaust COMBO: 8751a Pre

- Tier 2 E10

exhaust 8751a Pre

- Tier 2 E10 exhaust

8750a Pre

- Tier 2 E0

exhaust

8751a Pre

- Tier 2 E10

exhaust

nonroad gasoline

evaporative COMBO: 8754 E10 evap 8754 E10 evap

8753 E0 evap

8754 E10 evap

nonroad gasoline

refueling COMBO: 8870 E10 Headspace 8870 E10 Headspace

8869 E0 Headspace

8870 E10 Headspace29

S ector S ub

category 2007 2018 2030

nonroad diesel

exhaust 8774 Pre

- 2007 MY

HDD exhaust 8774 Pre

- 2007 MY HDD

exhaust 8774 Pre

- 2007 MY HDD

exhaust

nonroad diesel

evaporative 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace

nonroad diesel

refueling 4547 Diesel Headspace 4547 Diesel Headspace 4547 Diesel Headspace

nonpt/ ptnonipm PFC COMBO: COMBO : COMBO :

8869E E0 Headspace 8870E E10 Headspace 8870E E10 Headspace

8870E E10 Headspace 8871E E15 Headspace 8871E E15 Headspace

8934E E85 Evap 8934E E85 Evap

nonpt/ ptnonipm BTP COMBO: COMBO : COMBO :

8869 E0 Headspace 8870 E10 Headspace 8870 E10 Headspace

8870 E10 Headspace 8871 E15 Headspace 8871 E15 Headspace

8934 E85 Evap 8934 E85 Evap
nonpt/ ptnonipm BPS/RBT 8869 E0 Headspace 8869 E0 Headspace 8869 E0 Headspace



### 3.2.2 PM speciation

In addition to VOC profiles, the SPECIATE database also contains the PM 2.5 speciated into both individual chemical compounds (e.g., zinc, potassium, manganese, lead), and into the “simplified” PM 2.5 components used in the air quality model. For CMAQ 4.7.1 modeling, these “s implified” components (AE5) are all that is needed. For CMAQ 5.0.1, there is a new thermodynamic equilibrium aerosol modeling tool (ISORROPIA) v2 mechanism that needs additional PM components (AE6), which are further subsets of PMFINE (see Table 3

- 8 ). EPA speciated PM 2.5 so that it included both AE5 and AE6 PM model species without causing any double counting. Therefore, emissions from these scenarios can be used with either CMAQ 4.7.1 or CMAQ 5.0.1.

Table 3

- 8

. PM model species: AE5 versus AE6

species name species description AE5 AE6

POC organic carbon Y Y

PEC elemental carbon Y Y

PSO4 sulfate Y Y

PNO3 nitrate Y Y

PMFINE unspeciated PM 2.5 Y N

PNH4 ammonium N Y

PNCOM non

- carbon organic matter N Y

PFE iron N Y

PAL aluminum N Y

PSI silica N Y

PTI titanium N Y

PCA calcium N Y

PMG magnesium N Y

PK potassium N Y

PMN manganese N Y30 species name species description AE5 AE6

PNA sodium N Y

PCL chloride N Y

PH2O water N Y

PMOTHR unspeciated PM 2.5 N Y

The majority of the PM profiles come from the 911XX series, which include updated AE6 speciation 24

. Unlike the 2007v5 platform, t he profile numbers used in the Tier 3 runs are consistent with SPECIATE 4.3. Although the profile numbers changed, the underlying profiles (namely the percentage of AE6 components) did not change between 2007v5 and this 2007 base case (see the 2007v5 TSD for details on the earlier profile names). This change in profile numbers impacts most sec tors with PM emissions 25

.



### 3.3 Temporal Allocation

Temporal allocation (i.e., temporalization ) is the process of distributing aggregated emissions to a finer temporal resolution , such converting annual emissions to hourly emissions. While the total emissions ar e 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 ann ual emissions and distributes them to the month, and then distributes the monthly emissions to the day, and the daily emissions to the hour. This process is typically done by applying temporal profiles to the inventories in this order: monthly, day of the week, and diurnal.

The temporal profiles and associated cross references used to create the hourly emissions inputs for the air quality model were similar to those used for the 2007v5 platform. N ew methodologies introduced in this platform and updated p rofiles are discussed in this section

. T emporal factors are typically applied to the inventory by some combination of country, state, county, SCC, and pollutant. The following values are used

in Error! Not a valid bookmark self

- reference. : t he 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 a ll 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) , representa tive Saturday

, and repre sentative 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 emission s computed for one representative day of each month, mean ing emissions for all days within a month are the same. Special situations with respect to temporalization are described in the following subsections.

Table 3

- 9 summarizes the temporal aspects of emissions modeling by comparing the key approaches used for temporal processing across the sectors. The temporal aspects of SMOKE processing are controlled through (a) the L_TYPE (temporal type) and M_TYPE (merge type) settings used, and (b) the temporal profiles

themselv e s. 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 run s only for representative

24 The exceptions are 5674 (Marine Vessel

– Marine Eng ine

– Heavy Fuel Oil) used for c3marine and 92018 ( Draft Cigarette

Smoke

– Simplified ) used in nonpt.

25 It impacts the profile names (numbers) used in the sector, but it does not impact the fraction of AE6 PM species and hence wi ll not impact the air quali ty model

- ready files.31 days, which could include holidays as indic ated by the right

- most column. The values given are those used

for the SMOKE M_TYPE setting (see below for more information)

.

The following values are used in Error! Not a valid bookmark self

- reference. : t he 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 a ll 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) , representa tive Saturday

, and

repre sentative 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 emission s computed for one representative day of each month, mean ing emissions for all days within a month are the same. Special situations with respect to temporalization are described in the following subsections.

Table 3

- 9

. Temporal s ettings u sed for the platform s ectors in SMOKE

Platform

sector short

name Inventory

resolutions Monthly

profiles

used? Daily

temporal approach Merge

processing approach Process

Holidays as separate days

ptipm D aily all all Y es ptnonipm A nnual yes mwdss mwdss Y es othpt A nnual yes mwdss mwdss

nonroad M onthly mwdss mwdss Y es

othar A nnual yes week week

c1c2rail A nnual yes mwdss mwdss

c3marine A nnual yes aveday aveday

onroad annual & monthly 1 all all Y es

onroad_rfl annual & monthly 2 all all Y es

othon annual yes week week

nonpt annual & monthly yes all all Y es

ag annual & monthly yes all all Y es afdust_adj annual yes week all Y es

avefire daily all all Y es biog hourly n/a all Y es

1. Note the annual and monthly “inventory” actually refers to the activity data (VMT and VPOP) for onroad. The actual emissions are computed on an hourly basis. 2. Note the annual and monthly “inventory” actually refers to the activity data (VMT and VPOP) for onroad_rfl. The actual emissions are computed on an hourly basis.

See Section 3.3.5 for more details on the temporalization and inventory resolution of specific sectors.

In addition to the resolution, temporal processing includes a ramp

- up period for several days prior to January 1, 200 7 , which is intended to mitigate the effects of initial condition concentrations. The ramp

- up period was 10 days (December 22

- 31, 2006). For most non

- EGU sectors, our approach used the emissions from32 December 200 7 to fill in surrogate emiss ions for the end of December 200 6 . In particular, we used

December 200 7 emissions (representative days) for December 200 6 . For biogenic emissions, we processed

December 200 6 emissions using 200 6 meteorology.



### 3.3.1 FF10 format and inventory resolution

The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for monthly, daily, and hourly emissions inventories than previous formats supported. Previously, to process monthly inventory data required the use of 12 separate inve ntory 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

cont ain individual records with data for all days in a month and all hours in a day, respectively.

SMOKE 3.5 prevents the application of temporal profiles on top of the “native” resolution of the inventory. For example, a monthly inventory should not have a nnual 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 s ector). The flags that control temporalization for a mixed set of inventories are discussed in the SMOKE documentation. The modeling platform sectors that make use of monthly values in the FF10 files are nonroad, onroad, and the ag burning inventory with in the nonpt sector.



### 3.3.2 Ptipm Temporalization

T he approach for temporalization of the ptipm sector (EGUs) has not changed from the 2005 v4.3 platform

, and is consistent with the method described in the 2007v5 TSD. However , the importance of this sector

warra nts a restating of the methodology.

D aily emissions were computed from the annual emissions using a structured query language (SQL) program

and state

- average CEM data. To allocate the annual emissions to each month, state

- specific, three

- year

averages of 2006

- 2008 CEM data were created . These average annual

- to

- month factors were assigned to sources by state. To allocate the monthly emissions to each day, the 2007 CEM data was used to compute

state

- specific month

- to

- day factors, averaged across all un its in each state. The factors were applied to the

annual emissions to calculate the daily emissions outside of SMOKE

, and the resulting daily inventor ies

were used as input s to SMOKE.

The daily

- to

- hourly allocation was performed in SMOKE using diurnal profiles. T he state

- specific and pollutant

- specific diurnal profiles were created by using the 2007 CEM data to create state

- specific, day

- to

hour factors, averaged over the whole year and all units in each state. The d iurnal factors were calculated for

SO 2 emissions, NO X emissions

, and heat input. The SO 2 and NO X - specific factors were used to temporally allocate those pollutants, and the factors created from the hourly heat input data were used to allocate all other pollutants

. T he resulting profiles were assigned by state and pollutant. The same procedures and factors were used to allocate the base and future year emissions.



### 3.3.3 Meteorologically

- based temporalization

A significant improv ement over previous platforms was the introduction of meteorologically

- based temporalization. T here are many factors that impact the timing of when emissions occur. The benefits of considering meteorology in support of temporalization are: (1) a meteorological dataset consistent with the

one used by the AQ model is available (e.g. , outputs from WRF); (2) the meteorological model data is highly resolved in terms of spatial resolution; and (3) the meteorological variables vary at hourly resolution and can therefore be translate d in to hour

- specific temporal ization. Because the WRF output data for this study was33 from a different run than that described in the 2007v5 platform TSD, any meteorological l y

- based temporalizat ion factors, adjustments, and other meteorological effects were recomputed for the Tier 3 r uns. This included updating the BIOSEASON file used for biogenic emissions.

The SMOKE program GenTPRO provides a method for developing meteorology

- based temporalization. Currently, the program can utilize three types of temporal algorithms: annual

- to

- day temporalization for residential wood combustion (RWC), month

- to

- hour tempor alization for agricultural livestock ammonia, and a generic meteorology

- based algorithm for other situations. For the 2011 platform, meteorological

- based temporalization was used for portions of the rwc sector and for livestock within the ag sector. GenT PRO 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 te mporal profile (hourly, daily, etc.) depend on the selected algorithm and the run parameters. For more details on the development of these algorithms and running GenTPRO, see the GenTPRO documentation and the SMOKE documentation at http://www.cmascenter.org/smoke/documentation/3.1/GenTPRO_TechnicalSummary_Aug2012_Final.pdf

For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal allocation of emissions to days. We ran GenTPRO so that it created annual

- to

- day temporal profile s for the RWC sources within the nonpt sector. These generated profiles distribute the annual RWC emissions to the coldest days of the year . On days where the minimum temperature does not drop below a user defined threshold, RWC emissions are zero. Conversely, the program temporally allocates the largest percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual emis sions do not change, only the distribution of the emissions within the year is affected

. The default 50  ̊ F threshold was used for the majority of the states, and a 60  ̊ F threshold for the following states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas.

For the agricultural livestock NH 3 algorithm, the GenTPRO algorithm is based on an equation derived by Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation was updated from that described in the 2007v5 TSD, and is based on observations from the TES satellite

instrument with the GEOS

- Chem model and its adjoint to estimate diurnal NH3 emission variations from

livestock as a function of ambient temperature, aerodynamic resistance, and wind speed. The equations are:

E i,h = [161500/T i,h x e (

- 1380/T i,h ) ] x AR i,h

PE i,h = E i,h / Sum(E i,h )

where

 PE i,h = Percentage of emissions in county i on hour h

 E i,h = Emission rate in county i on hour h

 T i,h = Ambient temperature (Kelvin) in county i on hour h

 V i,h = Wind speed (meter/sec) in county i (minimum wind speed is 0.1 meter/sec)

 AR i,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 monthly34 emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized to

the month 26

.

Figure 3

- 2 compares the daily emissions for Minnesota from the “old” approach ( i.e., uniform monthly profile s ) with the “new” approach ( i.e., GenTPRO generated month

- to

- hour pr ofiles). Although the GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the same between the two approaches.

Figure 3

- 2

. Example of new animal NH 3 emissions temporalization approach, summed to daily emissions

For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to reduce the total emissions based on meteorological conditions. These adjustment s 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 u sed 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.g ov/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. Theref ore, the afdust emissions vary day

- to

- day based on the precipitation and/or snow cover for that grid cell and day. Both the transport fraction and meteorological adjustments are based on the gridded resolution of the platform; therefore, somewhat

differe nt 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 a s compared to ambient samples.



### 3.3.4 Onroad and Onroad_rfl Temporalization

For the onroad and onroad_rfl sectors, meteorology wa s not used in the development of the temporal profiles; rather

, meteo rology impacts the actual calculation of the hourly emissions through the program Movesmrg. The result is that the e missions vary at the hourly level by grid cell. More specifically, the on

network ( RPD ) and the off

- network ( RPV) exhaust, evaporative, and evaporative permea tion modes use the gridded meteorology (MCIP) directly. Movesmrg determine d the temperature for that hour and grid cell and

use d it to select the appropriate EF for that SCC/pollutant/mode. For the off

- network RPP, Movesmrg use d

the M et4moves output for SMOKE (daily minimum and maximum temperatures by county) to determine the

ap propriate EF for that hour and SCC/pollutant

. The result was that the emissions var ied hourly by county.

26 SMOKE v3.5.1 will correctly read in a monthly inventory and apply GenTPRO ag NH3 month

- to

- hour temporalization. When the emissions were developed for this sector, we were using SMOKE v3.1 beta that incorrectly applied an annual

- to

- month temporal profile on top of a monthly inventory when temporalizing with GenTPRO ag NH 3 profiles. As an interim solution, a flat monthly profile was applied to the states with a monthly ag NH3 inventory.35 The combination of these three processes (RPD, RPV, and RPP) is the total onroad emissions, while the combination of the two process es (RPD, RPV) for the refueling mode only is the total onroad_rfl emissions. Both sectors show a strong meteorological influence on their temporal patterns (see Sections 2.5.1.2 a nd

Error! Reference source not found. for more details).

Figure 3

- 3 illustrates the difference betwee n temporalization of the onroad sector used in previous platforms

and that from SMOKE

- MOVES. In the plot, the “MOVES” inventory is a monthly inventory that is temporalized by SCC to day

- of

- week and hour. Similar temporalization is done for the VMT in SMO KE

MOVES, but the meteorologically varying EF s add an additional variable signal on top of the temporalization. Note how the MOVES emissions have a repeating pattern within the month, while the

SMOKE

- MOVES shows day

- to

- day (and hour

- to

- hour) variability. In addition to tracking the meteorological influence, SMOKE

- MOVES does not show the artificial jumps between the months.

Figure 3

- 3

. Example of SMOKE

- MOVES temporal variability of NO X emissions

For the onroad and onroad_rfl sectors, the “inventories” referred to in The following values are used in

Error! Not a valid bookmark self

- reference. : t he 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 a ll 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) , representa tive Saturday

, and repre sentative 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 emission s computed for one representative day of each month, mean ing emissions for all days within a month are the same. Special situations with respect to temporalization are described in the following subsections.

Table 3

- 9 are actually the activity data inventories. For RPP and RPV processes, the VPOP in ventory is annual and does not need temporalization. For RPD, the VMT inventory is monthly and is temporalized to day of the week and then to hourly VMT through temporal profiles. In addition, the RPD processes used a speed profile (SPDPRO) that had vehi cle speed by hour for typical weekday and weekend. In addition,36 RPD, RPV, and RPP all have additional temporal variability due to the meteorological based emissions calculated through Movesmrg.



### 3.3.5 Additional s ector specific details

For a discussion of addit i onal sector

- specific details related to temporalization, see Section 3.3.4 of the 2007v5 platform TSD. The topics discussed include updates made in the 2007 platform to agricultural burning and residential wood combustion diurnal profiles.



### 3.4 Spatial Allocati on

The methods used to perform spatial allocation for the 2007 platform are summarized in this section

. For the

2007 platform , spatial factors are typically applied by count r y and SCC

. As described in Section 3.1

, spatial

allocation was performed for national 12

- km and 36

- km domain s

. To do this, SMOKE used national 12

- km

and 36

- km spatial surrogates and a SMOKE area

- to

- point data file. For the U.S., the surrogates were updated to use 2010

- based data wherever possible. For Mexico, we used the same spatial surrogates as were used for the 2005 platform. For Canada we used a set of Canadian surrogates provided by Environment Canada, also unchanged from the 2005v4.3 platform. The U.S., Mexican, and Canadian 12

- km surrogates

cover the entire CONUS domain 12US1 shown in Figure 3

- 1

. When SMOKE runs, it windows the

sur rogates to the area needed, such as the 12US2 domain. For the original 2007v5 platform, SMOKE was run on the 12US1 grid and windowed to 12US2 prior to air quality modeling. For the Tier 3 runs, SMOKE was actually run on the 12US2 domain. The remainder of this subsection provides further detail on the origin of the data used for the spatial surrogates and the area

- to

- point data.



### 3.4.1 Spatial Surrogates for U.S. e missions

There are 6 9 spatial surrogates available for spatially allocating U.S. county

- level emissions to the 12

- km grid cells used by the air quality model. As described in Section 3.4.2 , an area

- to

- point approach overrides the use of surrogates for some sources. Table 3

- 10 lists the codes and descriptions of the surrogates.

Table 3

- 10

. U.S. Surrogates available for the 2007 p latform

.

Co de 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 Industrial37

Co de Surrogate Description Code Surrogate Description

230 Rural Secondary Road Miles 595 Light Industrial

240 Total Road Miles 596 Industrial plus Institutional plus Hospitals

250 Urban Primary plus Rural Primary 600 Gas Stations

255 0.75 Total Roadway Miles plus 0.25 Population 650 Refineries and Tank Farms

260 Total Railroad Miles 675 Refineries and Tank Farms and Gas Stations

270 Class 1 Railroad Miles 680 Oil & Gas Wells, IHS Energy, Inc. and

USGS

280 Class 2 and 3 Railroad Miles 700 Airport Areas

300 Low Intensity Residential 710 Airport Point

310 Total Agriculture 720 Military AIports

312 Orchards/Vineyards 800 Marine Ports

320 Forest Land 801 NEI Ports

330 Strip Mines/Quarries 802 NEI Shipping Lanes

340 Land 807 Navigable Waterway Miles

350 Water 810 Navigable Waterway Activity

400 Rural Land Area 850 Golf Courses

500 Commercial Land 860 Mines

505 Industrial Land 870 Wastewater Treatment Facilities

510 Commercial plus Industrial 880 Drycleaners

515 Commercial plus Institutional Land 890 Commercial Timber

The surrogates in bold have been updated with 2010

- based data, including 2010 census data at the block group level, 2010 American Community Survey Data for heating fuels, 2010 TIGER/Line data for railroads and roads, and 2010 National Transportation Atlas Data for ports and navigable waterways. Not all of the available surrogates are used to spatially allocate sources in the 2007 platform; that is, some surrogates

shown in Table 3

- 10 were not assigned to SCCs used in the 2007 platform. Alternative surrogates for ports (801) and shipping lanes (802) were developed from the 2008 NEI shapefiles: Ports_032310_wrf and ShippingLanes_111309FINAL_wrf. These new surrogates we re used in the 2007 platform for c1 and c2

commercial marine emissions instead of the standard 800 and 8 10 surrogates, respectively. Note that the 800 surrogate was used for nonpoint SCCs starting with 250502, which are related to the storage and transfer of petroleum products.

Specific updates made to the surrogates for the Tier 3 runs include updating of the land use

- based 300

- series surrogates, gas stations (600), construction and mining (861), and dry cleaners (880). Updates to the oil and gas surrogates (689

- 699) were made a s described below, and updated surrogates were used for Mexico.

The creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and

u pdated documentation for it is available at http://www.ie.unc.edu/cempd/projects/mims/spatial/ and http://www.cmascenter.org/help/documentation.cfm?MODEL=spatia l_allocator&VERSION=3.6&temp_id=

99999

. Note that the surrogate methodology “gapfills” surrogates with less spatial coverage in terms of counties with surrogates that have more spatial coverage. This helps ensure no emissions are dropped during SMOKE proc essing. Because the land use

- based surrogates were updated and are used to gapfill many other surrogates, there were some changes to most of the remaining 500 and 600 series surrogates in terms of using new gapfiling values.

For the onroad sector, the o n

- network (RPD) emissions were s patially allocated to roadways, which the off

network (RPP and RPV) emissions were allocated to parking areas. For the onroad_rfl sector, the emissions were spatially allocated to gas station locations.38 For the oil and gas sources in the nonpt sector, the WRAP Phase III sources have detailed basin

- specific spatial surrogates shown in Table 3

- 11

. The remain ing oil and gas sources use d the 2005

- based surrogate “Oil & Gas Wells, IHS Energy, Inc. and USGS” (680) developed for oil and gas SCCs. The surrogates in

Table 3

- 11 were applie d for the counties listed in Table 3

- 12

. For the Tier 3 runs, surrogate data for the

Permian basin was added.

Table 3

- 11

. Spatial Surrogates for WRAP Oil and Gas Data

Country Code Surrogate Description

USA 699 Gas production at CBM wells

USA 698 Well count

- gas wells

USA 697 Oil production at gas wells

USA 696 Gas production at gas wells

USA 695 Well count

- oil wells

USA 694 Oil production at Oil wells

USA 693 Well count

- all wells

USA 692 Spud count

USA 691 Well count

- CBM wells

USA 690 Oil production at all wells

USA 689 Gas production at all wells

Table 3

- 12

. Counties included in the W RAP Dataset

FIPS Sta te County

08 001 CO Adams

08 005 CO Arapahoe

08 007 CO Archuleta

08 013 CO Boulder

08 014 CO Broomfield

08 029 CO Delta

08 031 CO Denver

08 039 CO Elbert

08 043 CO Fremont

08 045 CO Garfield

08 051 CO Gunnison

08 059 CO Jefferson

08 063 CO Kit Carson

08 067 CO La Plata

08 069 CO Larimer

08 073 CO Lincoln

08 075 CO Logan

08 077 CO Mesa

0808 1 CO Moffat

0808 7 CO Morgan

08 095 CO Phillips

08 097 CO Pitkin FIPS Sta te County

08 103 CO Rio Blanco

08 107 CO Routt

08 115 CO Sedgwick

08 121 CO Washington

08 123 CO Weld

08 125 CO Yuma

30003 MT Big Horn

30075 MT Powder River

35005 NM Chaves

35015 NM Eddy

35015 NM Lea

35031 NM Mc Kinley

35039 NM Rio Arriba

35041 NM Roosevelt

35043 NM Sandoval

35045 NM San Juan

48003 TX Andrews

48033 TX Borden

48079 TX Cochran

48081 TX Coke

48103 TX Crane

48105 TX Crockett FIPS Sta te County

48107 TX Crosby

48109 TX Culberson

48115 TX Dawson

48125 TX Dickens

48135 TX Ector

48141 TX El Paso

48151 TX Fisher

48165 TX Gaines

48169 TX Garza

48173 TX Glasscock

48219 TX Hockley

48227 TX Howard

48229 TX Hudspeth

48235 TX Irion

48263 TX Kent

48269 TX King

48301 TX Loving

48303 TX Lubbock

48305 TX Lynn

48317 TX Martin

48329 TX Midland

48335 TX Mitchell39

FIPS Sta te County

48353 TX Nolan

48371 TX Pecos

48383 TX Reagan

48389 TX Reeves

48413 TX Schleicher

48415 TX Scurry

48431 TX Sterling

48435 TX Sutton

48445 TX Terry

48451 TX Tom Green

48461 TX Upton

48475 TX Ward

48495 TX Winkler

48501 TX Yoakum FIPS Sta te County

49007 UT Carbon

49009 UT

Daggett

49013 UT

Duchesne

49015 UT

Emery

49019 UT

Grand

49043 UT

Summit

49047 UT

Uintah

56001 WY Albany

56005 WY Campbell

56007 WY Carbon

56009 WY Converse

56011 WY Crook

56013 WY Fremont

56019 WY Johnson FIPS Sta te County

56023 WY Lincoln

56025 WY Natrona

56027 WY Niobrara

56033 WY Sheridan

56035 WY Sublette

56037 WY Sweetwater

56041 WY Uinta

56045 WY Weston



### 3.4.2 Allocation m ethod for airport

- related source s in the U.S.

There are numerous airport

- related emission sources in the 200 8 NEI, such as aircraft, airport ground support equipment, and jet refueling. The 2007 platform includes the aircraft emissions as point sources. For the

200 7 platform, the SMOKE “area

- to

- point” approach was used for airport ground support equipment (nonr oad sector) and jet refueling (nonpt sector). The approach is described in detail in the 2002 platform

documentation : http://www.epa.gov/scram001/reports/Emissions%20TS D%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 e mission i nventories

The Mexican spatial surrogates were updated from the 207v5 platform to use the surrogates shown in Table

3

- 13

. T he same surrogates for Canada were used to spatially allocate the 2006 Canadian emissions as were used for the 2005 v4.2 platform. The spatial surrogate data came from Environment Canada, along with cross references. The surrogates they provided were outputs from the Surrogate Tool (previously referenced). Per

Environm ent Canada, the surrogates are based on 2001 Canadian census data . The Canadian surrogates used for this platform are listed in Table 3

- 14

. We added the leading “9” t o the surrogate codes to avoid duplicate surrogate numbers with U.S. surrogates.

Table 3

- 13

. Spatial Surrogates for Mexico

Srg code Description

22 MEX Total Road Miles

10 MEX Population

12 MEX Housing

14 MEX Residential Heating

- Wood

16 MEX Residential Heating

- Distillate Oil40 20 MEX Residential Heating

- LP Gas

22 MEX Total Road Miles

24 MEX Total Railroads Miles

26 MEX Total Agriculture

28 MEX Forest Land

32 MEX Commercial Land

34 MEX Industrial Land

36 MEX Commercial plus Industrial Land

38 MEX Commercial plus Institutional Land

40 Residential (RES1

- 4)+Comercial+Industrial+Institutional+

Government

42 MEX Personal Repair (COM3)

44 MEX Airports Area

46 MEX Marine Ports

48 Brick Kilns

- Mexico

50 Mobile sources

- Border Crossing

- Mexico

Table 3

- 14

. Canadian Spatial Surrogates for 200 7

- based platform Canadian Emissions

Code Description Code Description

9100 Population 9493 Warehousing and storage

9101 Total dwelling 9494 Total Transport and warehouse

9102 Urban dwelling 9511 Publishing and information services

9103 Rural dwelling 9512 Motion picture and sound recording industries

9104 Total Employment 9513 Broadcasting and telecommunications

9106 ALL_INDUST 9514 Data processing services

9111 Farms 9516 Total Info and culture

9113 Forestry and logging 9521 Monetary authorities

- central bank

9114 Fishing hunting and trapping 9522 Credit intermediation activities

9115 Agriculture and forestry activities 9523 Securities commodity contracts and other

financial investment activities

9116 Total Resources 9524 Insurance carriers and related activities

9211 Oil and Gas Extraction 9526 Funds and other financial vehicles

9212 Mining except oil and gas 9528 Total Banks

9213 Mining and Oil and Gas Extract activities 9531 Real estate

9219 Mining

- unspecified 9532 Rental and leasing services

9221 Total Mining 9533 Lessors of non

- financial intangible assets (except copyrighted works)

9222 Utilities 9534 Total Real estate

9231 Construction except land subdivision and land

development 9541 Professional scientific and technical services

9232 Land subdivision and land development 9551 Management of companies and enterprises41 Code Description Code Description

9233 Total Land Development 9561 Administrative and support services

9308 Food manufacturing 9562 Waste management and remediation services

9309 Beverage and tobacco product manufacturing 9611 Education Services

9313 Textile mills 9621 Ambulatory health care services

9314 Textile product mills 9622 Hospitals

9315 Clothing manufacturing 9623 Nursing and residential care facilities

9316 Leather and allied product manufacturing 9624 Social assistance

9321 Wood product manufacturing 9625 Total Service

9322 Paper manufacturing 9711 Performing arts spectator sports and related

industries

9323 Printing and related support activities 9712 Heritage institutions

9324 Petroleum and coal products manufacturing 9713 Amusement gambling and recreation

industries

9325 Chemical manufacturing 9721 Accommodation services

9326 Plastics and rubber products manufacturing 9722 Food services and drinking places

9327 Non

- metallic mineral product manufacturing 9723 Total Tourism

9331 Primary Metal Manufacturing 9811 Repair and maintenance

9332 Fabricated metal product manufacturing 9812 Personal and laundry services

9333 Machinery manufacturing 9813 Religious grant

- making civic and professional

and similar organizations

9334 Computer and Electronic manufacturing 9814 Private households

9335 Electrical equipment appliance and component

manufacturing 9815 Total other services

9336 Transportation equipment manufacturing 9911 Federal government public administration

9337 Furniture and related product manufacturing 9912 Provincial and territorial public administration (9121 to 9129)

9338 Miscellaneous manufacturing 9913 Local municipal and regional public administration (9131 to 9139)

9339 Total Manufacturing 9914 Aboriginal public administration

9411 Farm product wholesaler

- distributors 9919 International and other extra

- territorial public

administration

9412 Petroleum product wholesaler

- distributors 9920 Total Government

9413 Food beverage and tobacco wholesaler

distributors 9921 Commercial Fuel Combustion

9414 Personal and household goods wholesaler

distributors 9922 TOTAL DISTRIBUTION AND RETAIL

9415 Motor vehicle and parts wholesaler

- distributors 9923 TOTAL INSTITUTIONAL AND

GOVERNEMNT

9416 Building material and supplies wholesaler

distributors 9924 Primary Industry

9417 Machinery equipment and supplies wholesaler

distributors 9925 Manufacturing and Assembly42 Code Description Code Description

9418 Miscellaneous wholesaler

- distributors 9926 Distribution and Retail (no petroleum)

9419 Wholesale agents and brokers 9927 Commercial Services

9420 Total Wholesale 9928 Commercial Meat cooking

9441 Motor vehicle and parts dealers 9929 HIGHJET

9442 Furniture and home furnishings stores 9930 LOWMEDJET

9443 Electronics and appliance stores 9931 OTHERJET

9444 Building material and garden equipment and

supplies dealers 9932 CANRAIL

9445 Food and beverage stores 9933 Forest fires

9446 Health and personal care stores 9941 PAVED ROADS

9447 Gasoline stations 9942 UNPAVED ROADS

9448 clothing and clothing accessories stores 9943 HIGHWAY

9451 Sporting goods hobby book and music stores 9944 ROAD

9452 General Merchandise stores 9945 Commercial Marine Vessels

9453 Miscellaneous store retailers 9946 Construction and mining

9454 Non

- store retailers 9947 Agriculture Construction and mining

9455 Total Retail 9950 Intersection of Forest and Housing

9481 Air transportation 9960 TOTBEEF

9482 Rail transportation 9970 TOTPOUL

9483 Water Transportation 9980 TOTSWIN

9484 Truck transportation 9990 TOTFERT

9485 Transit and ground passenger transportation 9993 Trail

9486 Pipeline transportation 9994 ALLROADS

9487 Scenic and sightseeing transportation 9995 30UNPAVED_70trail

9488 Support activities for transportation 9996 Urban area

9491 Postal service 9997 CHBOISQC

9492 Couriers and messengers 9991 Traffic43 

## 4 Development of Future Year Emissions

This section describes the methods used for developing the emissions for the 2018 and 2030 future

- year scenarios. Within the 2018 and 2030 cases, the future year emissions for the Tier 3 FRM “reference” and

“c ontrol” cases are the same for all stationary sources. For 2018 and 2030, the Tier 3 FRM control case emissions include Tier 3 engine and fuel controls that impact emissions for onroad mobile and nonroad

mobile sources. EPA analyzed emission impacts of the Tier 3 vehicle emissions and fuel standards by comparing projected emissions for future years without the Tier 3 rule (reference scenario) to projected emissions for future years with the Tier 3 standards in place (control scenario). For more details on the differences between the reference and control scenarios, see the onroad and nonroad sections (Section 4.3.1

and 4.3.2 , respectively).

The future scenarios’ projection methodologies vary by sector. T he 201 8 and 2030 reference scenarios represent predicted emissions in the absence of any further controls beyond those Federal and State measures already promulgated, or under reconsideration before emissions processing began in March, 2013

. The

future base

- cas e scenario reflects projected economic changes and fuel usage for EGU and mobile sectors.

The 2020 (used as a surrogate for 2018) and 2030 EGU projected inventor ies represent demand growth, fuel resource availability, generating technology cost and perfor mance, and other economic factors affecting power sector behavior. It also reflects the expected 2020 and 2030 emissions effects due to environmental rules and regulations, consent decrees and settlements, plant closures, control devices updated since 200 7,

and forecast unit construction through the calendar year 2020 and 2030

. In this analysis, the projected EGU

emissions include the Final M ercury and A ir T oxics (MATS) rule announced on December 21, 2011

, the

Clean Air Interstate Rule issued on March 10, 2005 , including impacts of electric vehicle penetration resulting from the Light

- duty Greenhouse Gas (LDGHG) Rule

.

For the other mobile sources (c1c2rail and c3marine sectors), all national measures for which data were available at the time of modeling have been included.

For nonEGU point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, and afdust sectors), local control programs are generally not includ ed in the future base

- case projections for most states unless information was provided by the states

. One exception are some NO X and VOC reductions associated with the New York, Virginia, and Connecticut State Implementation Plans (SIP), that were added a s part of a larger effort to start including more local control information on stationary non

- EGU sources; this is

described further in Section 4.2 . T he following bullets summarize the projection methods used for sources in

the various sectors , while additional details and data sources are given in the following subsections and Table

4

- 1

. For information on control/growth strategies that are not different from the 2007v5 platform, please reference the 2007v5 TSD, available at: http://ep a.gov/ttn/chief/emch/2007v5/2007v5_2020base_EmisMod_TSD_13dec2012.pdf

 IPM sector (ptipm): Unit

- specific e stimates from IPM, version 4.10 Final MATS with CAIR and the penetration of electric vehicles anticipated due to the LDGHG rule

.

 N on

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

- State Ai r Pollution Rule (CSAPR) comments and emission reductions due to national rules, control programs, plant closures, consent decrees and settlements , and 1997 and 2001 ozone State Implementation Plans in NY, CT, and VA. A lso used projection approaches for corn ethanol and biodiesel plants, refineries and upstream impacts from the Energy Independence and Security Act of 2007 (EISA)

. Terminal

area forecast (TAF) data ag gregated to the national level were used for aircraft to account for projected changes in landing/takeoff activity.44

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

 Agricultural sector (ag): Projection factors for livestock estimates based on expect ed changes in animal population from 2005 Department of Agriculture data, updated based on personal

communication with EPA experts in July 2012 ; fertilizer application NH 3 emissions projections include upstream impacts EISA.

 Area fugitive dust sector (afdu st): Projection factors for dust categories related to livestock estimates based on expected changes in animal population and upstream impacts from EISA

.

 Remaining Nonpoint sector (nonpt): Projection factors that implement Cross State Air Pollution Rule

comments and reflect emission reductions due to control programs. Residential wood combustion projections are based on growth in lower

- emitting stoves and a reduction in higher emitting stoves. PFC projection factors reflecting impact of the final Mobile Source Air Toxics ( MSAT 2) rule. Upstream impacts from EISA, including post

- 2007 cellulosic ethanol plants are also reflected.

 Nonroad mobile sector (nonroad): Other than for California, this sector uses data from a run of

NMIM that utilized NONROAD2008 b , using future

- year equipment population estimates and control

programs to the year 2018 and 2030 and using national level inputs

. Fuels are consistent with the

onroad sector. F inal controls from the final locomotive

- marine and large spark ignition OTAQ r ules

are included . California

- specific data were prov ided by CARB

.

 L ocomotive, and non

- Class 3 commercial marine sector ( c1c2rail ): For all states except California,

p rojection factors for Class 1 and Class 2 commercial marine and locomotives which refle ct final

locomotive

- marine controls. California projected year

- 2017 (used for 2018) and 2030 inventory data were provided by CARB. Additional RFS2

- related county

- level emissions adjustments were applied

to reflect different fuel volume characteristics fr om increased ethanol fuel transport on rail and

commercial marine vessels.

 Class 3 commercial marine vessel s (c3marine): Base

- year 2007 emissions grown and controlled to

2018 and 2030 , incorporating controls based on Emissions Control Area (ECA) and Inter national Marine Organization (IMO) global NO X and SO 2 controls.

 Onroad mobile, not including refueling (on road ): focus of the rule, see Section 4.3.1

.

 Onroad refueling mode (on road_rfl ): Uses the same projection approach as the onroad sector and processing as described in Section 2.5.2

.

 Other onroad (othar ): No growth or control for Canada because data are not available from Canada.

Mexico inventory data were grown from 1999 to year s 2018 and 2030

.

 Other nonroad/nonpoint (oth on ): No growth or con trol for Canada

. Mexico inventory data were grown from 1999 to year s 2018 and 2030

.

 Other p oint (othpt): No growth or control for Canada and offshore oil

. Mexico inventory data were grown from 1999 to year s 2018 and 2030

.

 Biogenic: 2007 emissions used for all future

- year scenarios.

Table 4

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

the U.S. 201 8 and 2030 scenario emissions from the 2007 base

- case inventories. T he control, closures, projection packets (datasets) used to create stationary non

- EGU and c1c2rail sector 2018 and 2030 future

year s scenario inventories from the 2007 base case are descr ibed in the following sections

. These datasets (i.e., “ packets ”) were processed through the EPA Control Strategy Tool (CoST) to create future year

inventories. CoST is described here: http://www.epa.gov/ ttnecas1/cost.htm

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

2018 and 2030 s cenarios . 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 ass ociated with the emissions categories are provided in parentheses. If a strategy is identical to the 2007v5 platform, it is noted in the Section as

“2007v5” and is documented in the 2007v5 TSD.

Table 4

- 1

. Control strategies and growth assumptions for creating the 2018 and 2030 emissions inventories

from the 2007 base case

Control Strategies and/or growth assumptions (grouped by standard and approach used to apply to the inventory) CAPs

affected Section

Non

- EGU Point (pt nonipm sector) Controls and Growth Assumptions
Ethanol plants that account for increase d ethanol production due to EISA mandate

All Error!

Referen

ce

source

not

found.
Biodiesel plants producing 1.6 billion gallons of production due to EISA mandate All 4.2.1.2 Ethanol distribution vapor losses adjustments due to EISA mandate VOC 0 Refinery upstream adjustments from EISA mandate All 4.2.1.7

Livestock e missions g rowth from year 200 8 to 2018 and 2030

, also including upstream RFS2 impacts on agricultural

- related activities such as pesticide and fertilizer production All 4.2.2

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

, SO 2 2007v5

State fuel sulfur content rules for fuel oil

– as of July, 2012, effective only in Maine, Massachusetts, New Jersey, New York and Vermont SO 2 4.2.3

Industrial/Commercial/Institutional Boilers and Process Heaters MACT with Reconsideration

Amendments CO, PM,

SO 2 , VOC 2007v5

NESHAP: Portland Cement ( 09/09/10)

– plant level based on Industrial Sector Integrated Solutions (ISIS) policy emissions in 2013. The ISIS results are from the ISIS

- Cement model runs for the NESHAP and NSPS analysis of July 28, 2010 and include closures

. All

4.2.4

Future baseline inventory improvements received from a 2005 platform NODA and comments from the CSAPR proposal, including local controls, fuel switching, unit closures and consent decrees Al l 4.2.5

Facility and unit closures obtained from various sources such as states, industry and web posting, EPA staff and post

- 2008 inventory submittals : effective prior to spring 2012 All 2007v5

Aircraft growth via Itinerant (ITN) operations at airports to 2018 and 2030 All 4.2.6.1

Emission r eductions resulting from controls put on specific boiler units (not due to MACT) after

200 8 , identified through analysis of the control data gathered from the Information Collection Request ( ICR ) from the Industrial/Commercial/Institutional Boiler NESHAP. SO 2 2007v5

New York o zone SIP c ontrols NO X 2007v5 Boat Manufacturing MACT rule, VOC: national applied by SCC VOC 2007v5

Lafarge and Saint Gobain consent decrees NO X , PM, SO 2 2007v5

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

– OECA) apportioned to plants owned/operated by the c ompanies CO,

NO X , PM, SO 2 , VOC 2007v5

Refinery Consent Decrees: plant/ unit controls NO X , 2007v546 Control Strategies and/or growth assumptions (grouped by standard and approach used to apply to the inventory) CAPs

affected Section

SO 2 Commercial and Industrial Solid Waste Incineration (CISWI) revised NSPS PM, SO 2 2007v5 Hazardous Waste Incineration (HWI), Phase I and II PM 2007v5

Nonpoint ( afdust, ag and nonpt sector s ) Controls and Growth Assumptions

MSAT2 and RFS2 impacts on portable fuel container growth and control from 2007 to 2018 and

2030 VOC 0

Cellulosic ethanol and diesel emissions from EISA mandate All 4.2.1. 4 Ethanol transport working losses inventory from EISA mandate VOC 0 Ethanol distribution vapor losses adjustments due to EISA mandate VOC 0

Livestock emissions g rowth from year 200 8 to 2018 and 2030

, also including upstream RFS2 impacts on agricultural

- related activities such as pesticide and fertilizer production All 4.2.2

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

, SO 2 2007v5

State fuel sulfur content rules for fuel oil

– as of July, 2012, effective only in Maine, Massachusetts, New Jersey, New York and Vermont SO 2 4.2.3

Residential wood c ombustion growth and c hange

- outs from year 2008 to 2018 All 2007v5

Future baseline inventory improvements received from a 2005 platform NODA and comments from the CSAPR proposal, reflecting local controls NO X , VOC 4.2.5

New York o zone SIP c ontrols

NO X 2007v5 Er

ror!

Referen

ce

source

not

found.
Texas oil and gas projections to year 2018 All 4.2.6.2

Onroad Mobile Controls ( All national in

- force regulations are modeled. The list includes key recent mobile control strategies but is not exhaustive.)

National Onroad Rules:

All onroad control programs finalized as of the date of the model run, including most recently: Light

- Duty Greenhouse Gas Rule: October, 2012 Heavy (and Medium)

- Duty Greenhouse Gas Rule: September , 2011

Renewable Fuel Standard : March , 2010 Light Duty Greenhouse Gas Rule: May , 2010

C orporate

- Average Fuel Economy standards for 2008

- 2011 , April, 2010 2007 Onroad Heavy

- Duty Rule : February, 2009 Final Mobile Source Air Toxics Rule (MSAT2) : February, 2007

Tier 2 Rule : Signature date February, 2000

National Low Emission Vehicle Program (NLEV): March, 1998 All 4.3

Local Onroad Programs: California LEVIII Program Ozone Transport Commission (OTC) LEV Program : January,1995 Inspection and Maintenance programs Fuel programs (also affect gasoline nonroad equipment) Stage II refueling control programs VOC 4.3

Nonroad Mobile Controls ( All national in

- force regulations are modeled. The list includes recent key mobile control strategies but is not exhaustive.)

National Nonroad Controls:

All nonroad control programs finalized as of the date of the model run, including most recently: All 4.3.247 Control Strategies and/or growth assumptions (grouped by standard and approach used to apply to the inventory) CAPs

affected Section
Emissions Standards for New Nonroad Spark

- Ignition Engines, Equipment, and Vessels: October,

2008 Control of Emissions from Nonroad Large Spark

- Ignition Engines, a nd Recreational Engines (Marine and Land

- Based), November 8, 2002 27

Clean Air Nonroad Diesel Final Rule

– Tier 4 : June, 2004 28

Locomotives: Control of Emissions of Air Pollution from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008

Clean Air Nonroad Diesel Final Rule

– Tier 4 : May, 2004 All 4.3.3

Commercial Marine: Category 3 marine diesel engines Clean Air Act and International Maritime Organization standard s : April, 2010 Control of Emissions of Air Pollution from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008

Clean Air Nonroad Diesel Final Rule

– Tier 4 : May, 2004 All 4.3.4

The ancillary input data in the future

- year scenarios are very similar to those used in the 2007 base case except for the speciation profiles used for gasoline

- related sources, which change in the future to account for increased ethanol usage in gasoline (see Section 3.2.1.4 for details).



### 4.1 Stationary so urce projections: EGU sector (ptipm)

The future

- year data for the ptipm sector used in the air quality modeling were created by the Integrated Planning Model (IPM) version 4.10 (v4.10) Final MATS (Mercury and Air Toxics Standards) http://www.epa.gov/airmarkets/progsregs/epa

- ipm/toxics.html ) . IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. IPM 4.10 was u pdated from the previous version to include adjustments to assumptions regarding the performance of acid gas control technologies, new costs imposed on fuel

- switching (e.g., bituminous to sub

- bituminous), correction of lignite availability to some plants, incorporation of planned retirements, implementation of a scrubber upgrade option, and the availability of a scrubber retrofit to waste

- coal fired fluidized bed combustion units without an existing scrubber.

The scenario used for this modeling represents CAIR, MATS, and the penetration of electric vehicles anticipated due to the LDGHG rule. IPM v4.10 Final MATS originally included the Cross

- State Air Pollution Rule (CSAPR), but the rule was stayed by the D.C. circuit court pending judicial review. Therefore, the original CAIR rule was included in the run for this project. Electric vehicle penetration expected from the Light

- duty Greenhouse Gas Rule was also included.

T he Boiler MACT reconsideration was not r epresented in the 2020 IPM dataset because the rule was not final at the time the IPM modeling was performed. Further details on the future

- year EGU emissions inventory used for this rule can be found in the incremental documentation of the IPM v.4.1 0 plat form

, available a t http://www.epa.gov/airmarkets/progsregs/epa

- ipm/transport.html

. .

While NO

x , SO 2 , and mercury are specific outputs from IPM, d irectly emitted PM emissions (i .e., PM 2.5 and

PM 10 ) from the EGU sector are computed via a post processing routine that applies emission factors to the

27 https://www.federalregister.gov/articles/20 02/11/08/02

- 23801/control

- of

- emissions

- from

- nonroad

- large

- spark

- ignition

- engines

and

- recreational

- engines

- marine

- and

28 http://www.epa.gov/otaq/nonroad

- diesel.htm48 IPM

- estimated fuel throughput based on fuel, configuration and controls to compute the filterable and condensable components of PM.



### 4.2 S tationary source projections: non

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

To project U.S. stat ionary sources other than the ptipm sector, EPA applied growth factors and/or controls to certain categories within the ptnonipm, nonpt, ag and afdust platform sectors. This subsection provides details on the data and projection methods used for these sectors. In estimating future

- year emissions, EPA

assumed that emissions growth does not track with economic growth for many stationary non

- IPM sources. This “n o

- growth” assumption is based on an examination of historical emissions and economic data. For more details on the projection methodology and justification for the approaches, see the 2007v5 TSD.

Year

- specific projection factors (PROJECTION packets) fo r year 20 18 and 2030 were used for creating the

scenarios unless noted otherwise. The contents of these projection packets (and control reductions ) are provided in the following sections where feasible. However, some sectors used growth or control factor s that varied geographically and their contents could not be provided in the following sections (e.g., facilities and units subject to the Boiler MACT reconsideration has thousands of records). If the growth or control factors for a sector are not provide d in a table in this document, they are available as a “projection”

, “control” , or “closures” packet for input to SMOKE on the 2007v5 p latform website

. This document only summarizes the impact of controls and projections that differ between Tier 3 and the previous modeling for PM NAAQS (2007v5 platform) . The following projection, control or closure packets had identical or nearly identical impact in 2018 (as well as 2030) for Tier 3 as in 2020 of PM NAAQS modeling and are detailed in

the 2007v5 TSD :

 RICE NESHAP

 Industrial Boiler MACT reconsideration

 Residential wood combustion growth

 R emaining non

- EGU plant closures

 Boiler reductions not associated with the MACT rule

 NY Ozone SIP controls

 Boat manufacturing MACT

 Lafarge and St Gobain settlements

 OECA consent decrees

 Refinery consent decrees

 CISWI/HWI controls

This section is divided into several subsections that are summarized in Table 4

- 2

. Note that for som e

sources, future year inventories were used rather than projection or control packets.49 Table 4

- 2

. Summary of non

- EGU stationary projections subsections

Subsection Title Sector(s) Brief Description

4.2.1 RFS2 upstream future year

inventories and adjustments nonpt ptnonipm 1) Point and non

- point inventories received from

OTAQ that account for the upstream impact of

the RFS2 and the EISA mandate.

2) Point and non

- point adjustment factors that we apply to the 2007 inventory to reflect RFS2

4.2.2 Agricultural and livestock

adjustments, including

RFS2 afdust, ag,

nonpt, ptnonipm Adjustment factors to all ag

- related sources that also

reflect upstream RFS2 impacts on ag

- related

processes impacted by increased ethanol use

Error!

Reference

source not

found. Fuel sulfur rules nonpt ptnonipm Control packet reflecting state and local fuel sulfur

rules, including ULSD

4.2.3 Portland cement NESHAP

projections ptnonipm Year

- 2013 ISIS policy case reflecting closures, controls at existing kilns and an inventory containing

new kilns constructed after 2008 that account for shifting capacity from some closed units to open units

Error!

Reference

source not

found. CSAPR and NODA

comments nonpt ptnonipm Post

- 2008 controls, ad justments, and closures received in response to preparing the 2005 NEI for a future year baseline. These are not reflective of CSAPR; but rather of non

- EGU future year

information received from comments.

4.2.4 All other PROJECTION

and CONTROL packets nonpt ptnonipm All other non

- EGU stationary source PROJECTION

and CONTROL packets not covered in previous

subsections.



### 4.2.1 RFS2 upstream future year inve ntories and adjustments (nonpt, ptnonipm)

EPA incorporated adjustments for some stationary source categories to account for impacts of the Energy Independence and Security Act (EISA) renewable fuel standards mandate in the Renewable Fuel Standards Program (RFS2; EPA, 2010a) ), as estimated by Annual Energy Outlook (AEO) 2013, as well as impacts of

recent 2017

- 2025 light duty vehicle greenhouse gas emission standards and heavy

- duty greenhouse gas

standards . These mand ate s 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 pr oduction 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/distri bution terminals, and distribution of finished fuels to retail outlets. These impacts are

accounted for in the 2018 and 2030 inventories. A portion of these impacts are discussed in this section, with additional impacts due to transport discussed in the onroad and c1c2rail sectors (see Section 4.3.1.1 and

4.3.3 , respectively ). There are also impacts on domestic crude emissions upstream of petroleum refineries, due to displacement of gasoline and d iesel fuel with biofuels, but these are not accounted for in these projections as these data were not available. Greenhouse gas s tandards also affect production and distribution of gasoline and diesel fuels, but the impacts of these rules will be very small in 2018 and were

not accounted for. Where it was feasible, EPA included the impact of these greenhouse gas standards in the

203 0 estimates.50 EPA assume d that an unad justed 2018 inventory, which does not account for the impacts of the EISA renewable fuel mandate, would have comparable ethanol volumes to 2007, approximately 6.9 billion gallons

. However, analyses done to support the RFS2 rule (EPA, 2010a) suggest a significant increase in renewable

fuel volumes in 2018 and 2030 (see Table 4

- 3 ). Adjustments applied to the inventories (described in the following subsections) reflect the impacts on emissions due to the difference betwe en the 2007 ethanol

volume s and the renewable fuel volumes shown in Table 4

- 3

. In 2018, EPA assu med 1 Bgal (billion gallons) of ethanol would be used as E85, 10 Bgal as E10 , and about 4 Bgal as E15

. In 2030, EPA assumed 1.4 Bgal of ethanol would be used as E85, 6.8 Bga l as E10, and 6.5 Bgal as E15.

Table 4

- 3

. Renewable Fuel Volumes Assumed for Stationary Source Adjustments.

Renewable Fuel 2018 Volume (Bgal) 2030 Volume (Bgal)

Corn Ethanol 14.7 14.4

Cellulosic Ethanol 0.235 0.235 Imported Ethanol 1. 061 0.707

Biodiesel 1. 887 1.887

Renewable Diesel 0. 236 1.179

Cellulosic Diesel 0.290 0.915

4.2.1.1 Corn Ethanol plants inventory (ptnonipm)

Future year inventor ies : “ethanol_plants_2018_NEI” and “ethanol_plants_2018_OTAQ _revised ”

As discussed in Section 2.1.2

, EPA supplemented the 2007 NEI with corn ethanol plants that EPA/OTAQ developed. The 2007 emissions were projected to account for the increased domestic corn ethanol production assumed in this modeling, specifically an increase from 6.9 Bgal in 2007 to approximately 15 Bgal in 2018 and 2030. An industry characterization (EPA, 2012b) was also used to project the 2007 inventory to fut ure years, based on new plants, changes in production capacity since 2007

, or changes in

progress. Table 4

- 4 provides the summaries of estimated emissions for the c orn ethanol plants in year 2007

and 2018 29

. Note, EPA estimated the same amount of ethanol plant production in the future years for 2018 and 2030 and assumed that the control scenarios would have limited to no impact on the ethanol production.

Table 4

- 4

. 20 07 and 2018 /2030 c orn ethanol p lant e missions [tons]

pollutant 2007 2018/2030

1,3

- Butadiene 0.0015 0.0021

Acrolein 30 70

Formaldehyde 31 65

Benzene 13 26

Acetaldehyde 746 857

Nap h thalene 0.028 0.030

CO 12,821 21,811

NH3 1,036 1,072

NOX 13,403 25,618

PM10 11,468 21,054

PM2.5 4,452 7,827

SO2 16,930 20,235

29 The 2007 emissions are the sum of the NEI and OTAQ facilities. The same is true for 2018.51 VOC 19,271 41,146

4.2.1.2 Biodiesel plants inventory (ptnonipm)

New Future year inventory: “Biodiesel_Plants_2018_ff10”

EPA/OTAQ developed an inventory of biodiesel plants for 2018 and 2030. Plant location and production

volume data came from the Tier 3 proposed rule. 30

, 31 The total volume of biodiesel came from the AEO 2013 early release, 1.3 BG for 2018 and 2030 . To reach the total volume of biodiesel, plants that had current production volumes were assumed to be at 100% production and the remaining volume was split among plants with planned production. These emission factors in Table 4

- 5 are in tons per million gallons (Mgal) and were obtained from EPA’s spreadsheet model for upstream EISA impacts developed for the RFS2 rule (EPA, 2010a). Inventories were modeled as point sources with Google Earth and web searching validating facility coordinates and correcting state

- county FIPS. Table 4

- 6 provides the 2018 and 2030 biodiesel plant

em issions estimates. Emissions in 2007 are assumed to be near zero, and HAP emissions in 2018 and 2030 are nearly zero. Note, EPA estimated the same amount of biodiesel plant production in the future years for

2018 and 2030 and assumed that the control sce narios would have limited to no impact on the biodiesel production.

Table 4

- 5

. Emission Fac tors for Biodiesel Plants (Tons/Mgal )

Pollutant Emission Factor

VOC 4.3981E

- 02

CO 5.0069E

- 01

NO X 8.0790E

- 01

PM 10 6.8240E

- 02

PM 2.5 6.8240E

- 02

SO 2 5.9445E

- 03

NH 3 0
Acetaldehyde 2.4783E

- 07

Acrolein 2.1290E

- 07

Benzene 3.2458E

- 08

1,3

- Butadiene 0
Formaldehyde 1.5354E

- 06

Ethanol 0

Table 4

- 6

. 2018 /2030 biodiesel p lant e missions [tons]

Pollutant 20 18 /2030

CO 649

NO X 1048

PM 10 89

PM 2.5 89

SO 2 8

30 US EPA 2013. Draft Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA

- 420

- D

- 13

- 002.

31 Cook, R. 2012. Development of Air Qua lity Reference Case Upstream and Portable Fuel Container Inventories for Tier 3 Proposal . Memorandum to Docket EPA

- HQ

- OAR

- 20 10

- 0 162.52 VOC 57

4.2.1.3 Portable fuel container inventory (nonpt)

Future year inventor ies : “2018_PFC_inventory_FF10_revision2” , “2030_PFC_inventory_FF10_revision2”

EPA used future

- year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and

modeled for the EPA ’s MSAT 2 rule (EPA, 2007a ). The 10 PFC SCCs are summarized below ( note that the full SCC descriptions for these SCCs include “Storage and Tra nsport; 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

 250101 2015 Commercial Portable Fuel Containers: Refilling at the Pump: Spillage

The large majority of spillage emissions occur when refueling equipment, and this is already included in the nonroad equipment inventory. Thus we did not include these emissions i n the PFC inventory for this rule. Vapor displacement for nonroad equip ment container refueling was also subtracted from vapor displacement in the PFC inventory to avoid double counting these emissions

. The future

- year emissions reflect projected

increas es in fuel consumption, state programs to reduce PFC emissions, standards promulgated in the

MSAT 2 rule, and impacts of the EISA on gasoline volatility. OTAQ provided year 2018 and 2030 PFC emissions that include estimated Reid Vapor Pressure (RVP) and ox ygenate impacts on VOC emissions, and more importantly, large increases in ethanol emissions from EISA . Because the future year PFC inventories contain ethanol in addition to benzene, EPA developed a VOC E

- profile that integrated ethanol and benzene;

see Section 3.2.1.1 and Section 3.2.1.4 for more details. Emissions for 200 7, 2018 , and 2030 are provided in

Table 4

- 7

.

Table 4

- 7

. PFC e missions for 2007, 2018 , and 2030 [tons]

pollutant 2007 2018 2030

Benzene 1,049 645 829

Naphthalene 0.6 7.1 9.3

Ethanol 3,719 4,969

VOC 220,472 29,119 37,947

4.2.1.4 Cellulosic fuel production inventory (nonpt)

New Future year inventor ies : “2018_cellulosic_inventory” , “2030_cellulosic_inventory”

Depending on available feedstock, cellulosic plants are likely to produce fuel through either a biochemical process or a thermochemical process. OTAQ developed county

- level inventories for biochemical and thermochemical cellulosic fuel production for 2018 and 2030 to reflect AEO2013 (early release) renewable

fuel volumes. Emissions factors for each cellulosic biofuel refinery reflect the fuel production technology used rather than the fuel produced. Emission rates in Table 4

- 8 and Table 4

- 9 were used to develop53 cellulosic plant inventories. Criteria pollutant emission rates are in tons per RIN gallon. Emission factors

from the cellulosic diesel work in the Tier 3 NPRM were used as the emission factors for the thermochemical plants in the FRM modeling

. C ellulosic ethanol VOC and related HAP emission factors

from the Tier 3 NPRM were used as the biochemical VOC and related HAP emission factors

. Because the future year cellulosi c inventory contains ethanol, we developed a VOC E

- profile that integrated ethanol, see

Sections 3.2.1.1 and 3.2.1.3 for more details.

P lants were treated as area sources spread across the entire area of whatever county they were considered to be located in. Cellulosic biofuel refinery siting was based on utilizing the lowest cost feedstock, accounting for the cost of the feedstock itself as well as feedstock storage and the transportation of the feedstock to the cellulosic biofuel refinery. The total number of cellulosic biofuel refineries was projected using volumes

from AEO2013 (early release) . The methodology used to determine most likely plant locations is described

in Section 1.8.1.3 of the RFS2 RIA (EPA, 2010a). Table 4

- 10 provides the 2018 and 2030 cellulosic plant

emissions estimates.

Table 4

- 8

. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/ RIN gal lon )

Cellulosic Plant Type VOC CO NO

x PM 10 PM 2.5 SO

x NH 3

Thermochemical 5.92E

- 07 8.7E

- 06 1.31E

- 05 1.56E

- 06 7.81E

- 07 1.17E

- 06 1.44E

- 10

Biochemical 1.82E

- 06 1.29E

- 05 1.85E

- 05 3.08E

- 06 1.23E

- 06 6.89E

- 07 0

Table 4

- 9

. Toxic Emission Factors for Cellulosic Plants (Tons/ RIN gal lon )

Plant Type Acetaldehyde Acrolein Benzene 1,3

- Butadiene Formaldehyde Ethanol

Thermochemical 2.95E

- 08 1.27E

- 09 9.61E

- 10 0 5.07E

- 09 2.09E

- 07

Biochemical 3.98E

- 07 1.11E

- 08 1.39E

- 08 0 2.28E

- 08 6.41E

- 07

Table 4

- 10

. 2018 and 2030 c ellulosic plant e missions [tons]

pollutant 2018 2030

Acrolein 0.9 3.1

Formaldehyde 3.5 10.3

Benzene 0.7 2.9

Acetaldehyde 20.6 86.1

CO 6,087 15,196

Ethanol 146 397

NH3 0.1 0.2

NOX 9,199 22,867

PM10 1,088 2,792

PM2.5 547 1,372

SO2 819 1,941

VOC 414 1,125

4.2.1.5 Ethanol working loss inventory (nonpt)

New Future year inventor ies : “Ethanol_transport_vapor_2018rg_ref”

, “Ethanol_transport_vapor_2030rg_ref”54 These inventor ies were provided by OTAQ to represent upstream impacts of loading and unloading at ethanol terminals. Emissions are entirely evaporative and were computed by county for truck, rail and waterway loading and unloading and intermodal transfers (e.g., highway to rail). Inventory totals are

summa rized in Table 4

- 11 . The leading descriptions are “Industrial Processes; Food and Agriculture;

Ethanol Production” for each SCC.

Table 4

- 11

. 20 18 and 2030 V OC working losses (Emissions) due to ethanol transport [tons]

SCC description 2018 2030

30205031 Denatured Ethanol Storage Working Loss 23,420 22,396

30205052 Ethanol Loadout to Truck 14,425 13,794

30205053 Ethanol Loadout to Railcar 10,484 10,025

4.2.1.6 Vapor losses from Ethanol transport and distribution (nonpt, ptnonipm)

Packet s : “PROJECTION_ 2008 _2018_distribution_upstream_OTAQ_Tier3FRM”

, “PROJECTION_2008_2030_distribution_upstream_OTAQ_Tier3FRM” OTAQ developed county

- level inventories for ethanol transport and distribution for 2018 and 2030 to account for losses for the processes such as truck, rail and waterways loading/unloading and intermodal transfers such as highway

- to

- rail, highways

- to

- water ways, and all other possible combinations of transfers. These emissions are entirely evaporative and therefore limited to VOC and HAPs in VOC

.

2018 and 2030 inventor ies which included EISA impacts were developed by adjusting the 2007 platform inventory. Impacts of the light

- duty greenhouse gas rule are small enough to ignore for 2018. EISA adjustments were made using an updated version of EPA’s spreadsheet model for upstream emission impacts, developed for the RFS2 rule 32

. The develop ment of emission fa ctors and fuel volumes to make these adjustments with the RFS2 impacts spreadsheet are described below

.

V apor loss VOC emission factors (EFs) for gasoline were first developed, based on invent ory estimates from the 2005 NEI (EPA, 2009a). Total volume o f 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) component s

. Emissions for the BTP component are greater than the RBT and BPS components.

Total nationwide emissions for these components were divided by the energy content of the total volume of gasoline distributed in 2005 to obtain emission factors in grams per million metric British Thermal Units (g/mmBTU). Total volume of gasoline was based on gasoline sales as reported by the Energy Information

Administration. 33 In addition to gasoline VOC emission factors for the RBT/BPS components, emission factors were developed for the BTP component, for 10% ethanol

, 15% ethanol , and 85% ethanol . Emission factors were calculated by applying adjustment factors to the gasoline EFs. The BTP adjustment factors were based on an algorithm from the 1994 On

- Board Refueling Vapor Recovery Rule (EPA, 1994) :

EF (g/gal) = exp[

- 1.2798

- 0.0049(ΔT) + 0.0203(Td) + 0.1315(RVP)]

H ere delta T is the difference in temperature between the fu el in the tank and the fuel being dispensed, and Td is the temperature of the gasoline being dispensed. We assumed delta T is zero, and the temperature of

the fuel bei ng dispensed averages 60 oF over the year.

32 U.S. EPA. 2013. Spreadsheet “upstream_emissions_rev T3.xls.

33 Source: Energy Information Administration. 2006. Annual Energy Outlook 2006. Report #:DOE/EIA

- 0383(2006) Available at <http://www.eia.gov/oiaf/archive/aeo06/aeoref_tab.html>55 Average summer RVPs at the Petroleum Admin istration for Defense Districts (PADD) level was used to calculate adjustments. The U.S. is broken into five PADDs for petroleum products data collection purposes via the U.S. Energy Information Administration; see: http://www.eia.gov/oog/info/twip/padddef.html . These PADD regions are shown in Figure 4

- 1

.

Figure 4

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

All counties within a PADD received the same adjustment for BTP emissions. Volumes for each fuel type and summer RVPs for 2018 with EISA impacts are provided in Table 4

- 1 2 while volumes without EISA are in Table 4

- 13

. Volumes with and without EISA for 2030 are provided in Table 4

- 14 and Table 4

- 16

. These volumes and RVPs were obtained from analyse s done for the Tier 3 rule. These two sets of volumes were used to estimate emissions using an updated version of the RFS2 impacts spreadsheet ( EPA, 2013a)

.56

Table 4

- 1 2

. RVPs Assumed for 2018 ethanol and ga soline volumes with EISA

Table 4

- 13

. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA

Table 4

- 14

. RVPs Assumed for 2030 ethanol and gasoline volumes with EISA

PADD Total

Fuel

Volume Gasoline

Volume Ethanol

Volume E10 Only

Volume E15 Only

Volume E85(74)

Only

Volume Wtd.

RVP Wtd.

E10 RVP Wtd.

E15

RVP Wtd. E85

RVP

1 3.92 E+ 10 3.41 E+ 10 5.02 E+ 09 2.43 E+ 10 1.43 E+ 10 6.02 E+ 08 8.444 8.763 7.962 7

2 2.97 E+ 1 0 2.58 E+ 10 3.91 E+ 09 1.62 E+ 10 1.31 E+ 10 4.56 E+ 08 8.942 9.333 8.525 7

3 1.95 E+ 10 1.68 E+ 10 2.63 E+ 09 9.32 E+ 09 9.85 E+ 09 2.98 E+ 08 8.245 8.644 7.905 7

4 4.69 E+ 0 9 4.08 E+ 09 6.14 E+ 08 2.64 E+ 09 1.98 E+ 09 7.20 E+ 07 9.063 9.524 8.524 7

5 2.05 E+ 10 1.81 E+ 10 2.47 E+ 09 1.60 E+ 10 4.24 E+ 09 3.17 E+ 08 7.884 7.93 7.774 7

T otal 1.14 E+ 11 9.89 E+ 10 1.46 E+ 10 6.84 E+ 10 4.34 E+ 10 1.75 E+ 09 8.464 8.716 8.126 7

Table 4

- 15

. RVPs Assumed for 2030 ethanol and gasoline volumes without EISA PADD Total

Fuel

Volume Gasoline

Volume Ethanol

Volume E10 Only

Volume E15 Only

Volume E85(74)

Only

Volume Weighte

d RVP Weighted

E10 RVP Weighted

E15 RVP Weighted

E85 RVP

1 4.50E+10 3.99E+10 5.18E+09 3.71E+10 7.43E+09 4.73E+08 8.536 8.651 8.056 7

2 3.46E+10 3.03E+10 4.25E+09 2.31E+10 1.11E+10 3.63E+08 9.051 9.35 8.493 7

3 2.14E+10 1.89E+10 2.48E+09 1.72E+10 3.95E+09 2.25E+08 8.399 8.515 7.973 7

4 5.07E+09 4.48E+09 5.89E+08 4.06E+09 9.53E+08 5.33E+07 9.322 9.536 8.536 7

5 2.35E+10 2.08E+10 2.71E+09 1.91E+10 4.13E+09 2.47E+08 7.906 7.974 7.645 7

Total 1.30E+11 1.14E+11 1.52E+10 1.01E+11 2.75E+10 1.36E+09 8.567 8.696 8.175 7

PADD Total Fuel

Volume Ethanol

Volume Gasoline

Volume E0 Only

Volume E10 Only

Volume Weighted

RVP Weighted E0

RVP Weighted E10

RVP

1 4.42E+10 2.47E+09 4.17E+10 1.95E+10 2.47E+10 8.63 8.301 8.883

2 3.38E+10 1.92E+09 3.19E+10 1.47E+10 1.92E+10 9.48 8.925 9.907

3 2.09E+10 1.02E+09 1.99E+10 1.07E+10 1.02E+10 8.77 8.391 9.166

4 4.96E+09 2.54E+08 4.71E+09 2.43E+09 2.54E+09 9.18 8.674 9.674

5 2.30E+10 1.27E+09 2.17E+10 1.03E+10 1.27E+10 7.77 7.602 7.911

Total 1.27E+11 6.93E+09 1.20E+11 5.76E+10 6.93E+10 8.75 8.372 9.059

PADD Total Fuel

Volume Ethanol

Volume Gasoline

Volume E0 Only

Volume E10 Only

Volume Weighted

RVP Weighted

E0 RVP Weighted

E10 RVP

1 3.83E+10 2.47E+09 3.59E+10 1.36E+10 2.47E+10 8.68 8.301 8.883

2 2.90E+10 1.92E+09 2.71E+10 9.88E+09 1.92E+10 9.57 8.925 9.907

3 1.89E+10 1.02E+09 1.79E+10 8.78E+09 1.02E+10 8.81 8.391 9.166

4 4.57E+09 2.54E+08 4.32E+09 2.04E+09 2.54E+09 9.23 8.674 9.674

5 2.02E+10 1.27E+09 1.89E+10 7.48E+09 1.27E+10 7.80 7.602 7.911

T otal 1.11E+11 6.93E+09 1.04E+11 4.18E+10 6.93E+10 8.80 8.372 9.05957 A b enzene g/mmgal emission factor for 2018 and 2030 w as 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 est imate g/mmBTU emission factors based on the energy content of E0

, E10 , and E15 gasoline. Aside from energy content, we did not account for the effect of other fuel parameters on emission rates for E0, E10, and E15 blends . Thus, the E1 0 emission rate is slightly higher than the E0 rate due to the lower energy content of E1 0, and the E15 emission rate is higher still

. The E85 emission rate was estimated for the RFS2 rule. Emission facto rs are summarized

in Table 4

- 16

.

Table 4

- 16

. Sto rage 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 e mission factors for VOC and benzene were used in conjunction with an updated version of EPA’s spreadsheet model for upstream emission impacts, developed for the RFS2 rule, to estimate PADD

- level inventory changes of the changes in gasoline volume in 2018 with 2007 ethanol volumes versus projected

volumes with EISA. VOC inventory changes were used to develop nationwide adjustment factors that were applied to modeling platform inventory SCCs associated with storage and transport processes (see Table

4

- 17 ). Benzene emission estimates were obtained either by application of the adjustments in Table 4

- 17 or through speciation of VOC in SMOKE

.

A similar approach was used t o develop adjustment factors for 2030. However, in addition to the impacts of EISA, the 2017

- 2025 light

- duty greenhouse gas rule will significantly reduce gasoline production. The impact of this rule was small enough to ignore for 2018, but quite signifi cant in 2030. To account for impacts of this rule in 2030, an additional scalar of 0.7916 was applied.

Ethanol emissions were estimated in SMOKE by applying the ethanol to VOC ratios from headspace profiles

to VOC emissions for E10 and E15, and an evapora tive emissions profile for E85 . These ratios are 0.065 for

E10

, 0.272 for E15 , and 0.61 for E85 . The E10 and E15 profiles were obtained from an ORD analysis of fuel samples from the EPAct exhaust test program 34 and have been submitted for incorporation i nto 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 pr ofiles between 2007 and the future years, see Section

3.2.1.4 Error! Reference source not found.

.

After developing emissions for 2018 with EISA volumes versus 2018 without EISA volumes, and 2030 with EISA and the light

- duty greenhouse gas rule versus 2030 without those rules, EPA create d ratios of these two cases to apply against the 2007 platform emissions. From this, EPA created 2018 and 2030 reference

cases.

34 U. S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from En closed Fuel Tanks. Office of Research and Development and Office of Transportation and Air Quality. Report No. EPA

- 420

- R

- 11

- 018. EPA Docket EPA

- HQ

- OAR

- 2011

- 0135.58 Table 4

- 17

. Adjustment factors applied to storage and transport emissions

Ye ar Process PADD Pollutant Adjustment

Factor

2018 BTP 1 VOC 0.9515

benzene 0.9905

2 VOC 0.9619

benzene 0.9882

3 VOC 0.9778

benzene 0.9879

4 VOC 0.8983

benzene 0.9885

5 VOC 0.9430

benzene 0.9901

RBT/BPS All VOC 0.9553

benzene 0.9893

2030 BTP 1 VOC 0.8005

benzene 0.7871

2 VOC 0.8436

benzene 0.7865

3 VOC 0.8318

benzene 0.7824

4 VOC 0.7905

benzene 0.7840

5 VOC 0.7700

benzene 0.7888

RBT/BPS All VOC 0.8114

benzene 0.7863

It should be noted that these adjustment factors are based on summer RVP, but applied to emissions for the whole calendar year. However, higher RVPs in winter corresponding to lower temperatures result in roughly the same vapor pressure of the fuel and roughly the sam e propensity to evaporate. Significant evaporative emissions are not expected from storage and transport of biodiesel, renewable or cellulosic diesel fuel due to their low volatility. The cumulative impacts are a reduction in VOC emissions from 2007 leve ls (see Table

4

- 18 ). See 2007v5 TSD, Appendix B for the complete cross

- walk between SCC, and state

- SCC for BTP components, and each type of petroleum transport and st orage.

Table 4

- 18

. Impact of VOC losses from reduced gasoline production due to EISA

Sector 2018 Reductions 2030 Reductions ptnonipm 2,039 7,884

nonpt 22,082 97,030

Total 24,121 104,914

4.2.1.7 Pipeline and Refin ery EISA adjustments (ptnonipm)

Packet s : “PROJECTION_ 2018rg_ref_pipelines_refineries ”

, “PROJECTION_2030rg_ref_pipelines_refineries”59 Pipeline usage

, refinery , and bulk terminal emissions were adjusted for changes in fuels due to EISA and reductions in gasoline and diesel volumes due to greenhouse gas emission standards . These adjustments were developed by EPA/OTAQ and impact processes such as process heaters, catalytic cracking units, blowdown systems, wastewater treatment, condenser s, cooling towers, flares and fugitive emissions.

Calculation of the emission inventory impacts of decreased gasoline and diesel production, due to EISA and
greenhouse gas rules

, on nationwide refinery emissions was done in the updated version of EPA's
spreadsheet model for upstream emission impacts. Emission inventory changes reflecting EISA and
greenhouse gas rules implementation were used to develop adjustment factors that were applied to
inventories for each petroleum refinery in the U.S. ( Table 4

- 19 and Table 4

- 20 ) . These impacts of decreased
production were assumed to be s pread evenly across all U. S. refineries. Toxic emissions were estimated in
SMOKE by applying speciation to VOC emissions. It should be noted that the a djustment factors in Table

4

- 19 are estimated relative to that portion of refinery emissions associated with gasoline and diesel fuel
production. Production of jet fuel, still gas and other products also produce emissions. If these emissions
were included, the adjust ment factors would not be as large. The impact of the EISA and greenhouse gas

rules is shown in6 0 Table 4

- 21

.

Table 4

- 19

. 2018 a djustment f actors a pplied to petroleum pipelines and refinery emissions associated with gasoline and diesel fuel production.

Pollutant Pipelines Refineries Both

CO 0. 996 0.978 0.974

NO X 0. 982 0.987 0.969

PM 10 0. 997 0.984 0.981

PM 2.5 0. 998 0.979 0.977

SO 2 0. 998 0.978 0.976

NH 3 n/a 0.952 n/a

VOC 0.9 99 0.972 0.971

Table 4

- 20

. 2030 a djustment f actors a pplied to petroleum pipelines and refinery emissions associated with gasoline and diesel fuel production.

Pollutant Pipelines Refineries Both

CO 0. 981 0.773 0.763

NO X 0. 890 0.781 0.695

PM 10 0. 985 0.779 0.767

PM 2.5 0. 989 0.775 0.766

SO 2 0. 986 0.774 0.763

NH 3 n/a 0.750 n/a

VOC 0.9 94 0.769 0.76461 Table 4

- 21

. Impact of r efinery adjustments on 2007 e missions [tons]

pollutant reductions

2018 reductions

2030

CO 2,148 19,952

NH3 111 572

NOX 2,884 28,116

PM10 515 6,185

PM2.5 555 5,503

SO2 3,401 33,913

VOC 1,891 15,341



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

Packet s : “PROJECTION_2008_ 2018 _ag_including_upstream_OTAQ” ; “PROJECTION_2008_2030_ag_including_upstream_OTAQ”

Inventory adjustments were previously developed for 2030 as part of final RFS2 rule modeling 35 . 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. I mpacts on farm equipment emissions were not accounted for, however. Emission rates from the GREET model (fertilizer and pesticide production) 36 or based on the 2002 National Emissions Inventory (fertilizer and pesticide application, agricultural dust, liv estock waste) were combined with estimates of agricultural impacts from FASOM (Forest and Agricultural Section Optimization Model). Since FASOM modeling used a reference case of 13.2 billion gallons of ethanol, impacts used in the modeling for this rule

are underestimates.

Adjustment factors are provided in

35 U. S. Environmental Protection Agency. 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. Assessment and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI. Report No. EPA

- 420

- R

- 10

- 006, February, 2010. Available at <http://www.epa.gov/otaq/fuels/r enewablefuels/regulations.htm>.

36 GREET, v ersion 1.8c. Available at < http://greet.es.anl.gov/ >.62 Table 4

- 22

. These adjustments were applied equally to all counties having any of the affected sources. This

is an area of uncertainty in the inventories, since there would likely be variation from one county to another depending on how much of the predicted agricultural changes occurred in which counties. By using percent change adjustments rather than att empting to calculate absolute ton changes in each county, EPA attempted to minimize the inventory distortions that could occur if the calculated change for a given county was out of proportion to the reference case emissions for that county. For instance, a different approach could estimate reductions that were larger than the reference case NEI emissions, since there was no linkage between the NEI inventories and the FASOM modeling. The specific sources (SCCs) and affected pollutants that these adjustments were applied to are listed in a docket reference 37

.

37 U. S. EPA. 2011. Spreadsheet “agricultural sector adjustments.xls.” Docket EPA

– HQ

– OAR

– 2011

– 0135

.63

Table 4

- 22

. Adjustments to modeling platform a gricultural e missions for the Tier 3 reference c ase

Source Description 2018 Adjustment 2030 Adjustment
Nitrogen Fertilizer Application 1.0 242 1.0573

Fertilizer Production 1.0 603 1.0603

Pesticide Production 0.9 544 0.9954
Tilling/Harvesting Dust 1.0 079 1.0265
Agricultural Burning 1.000 0 1.000 0

Livestock Dust 0.9 868 0.9983

Livestock Waste 0.99 01 0.9983
For the animal waste sources, EPA also estimate d animal population growth in ammonia (NH 3 ) and dust

(PM 10 and PM 2.5 ) emissions from livestock in the ag and afdust and ptnonipm sectors . Therefore, a
composite set of projection factors is needed for animal operations that also reflect the minor 0.99% decrease
resulting from the EISA mandate. These composite projection factors by animal category are provided in

Table 4

- 23 . As discussed below, Dairy Cows and Turkeys are assumed to have no growth in animal
population, and therefore the projection factor for these animals is the same as the upstream ag riculture

related projection factor. The PROJECTION packet used for these sources, including the cross

- reference to
the animal categories listed in Table 4

- 23 and the source categories in64 Table 4

- 22

.

Table 4

- 23

. Composite NH 3 projection factors to year 2018 and 2030 for a nimal o perations

Animal Category 2018 Factors 2030 Factors Dairy Cow 0.9901 0.9983

Beef 0.9927 0.9460

Pork 1.0798 1.1614

Broilers 1.1399 1.1795 Turkeys 0.9050 0.8953 Layers 1.0974 1.1281 Poultry Average 1.0865 1.1149 Overall Average 1.0428 1.0505

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

- level animal population projections from the U.S. Department of Agriculture (USDA) and the Food and Agriculture Policy and Research Institute (FAPRI). Thi s methodology was initiated in 2005 for the 2005 NEI, but was updated on July 24, 2012 in support of the 2007v5 platform (EPA, 2012 )

. For dairy cows

, EPA assumed that there would be no growth in emissions based on little change in U.S. dairy cow populatio ns from year 2007 through 2030 according to linear regression analyses of the FAPRI projections. This assumption was based on an analysis of historical trends in the number of such animals compared to production rates. Although productions rates have inc reased, the number of animals has declined. Based on this analysis, EPA concluded that production forecasts do not provi de representative estimates of the future number of cows and turkeys; therefore, EPA did not use these forecasts for estimating future

- year emissions from these animals. In particular, the dairy cow population is projected to decrease in the future as it has for the past few decades; however, milk production will be increasing over the same period. Note that the

ammonia emissions from d airies are not directly related to animal population but also nitrogen excretion. With the cow numbers going down and the production going up

, the excretion value will likely chang e , but

EPA assumed no change because of the lack of a quantitative estimate

. Appendix H of the 2007v5 platform

TSD provides the animal population data and regression curves used to derive the growth factors.



### 4.2.3 Fuel sulfur rules (nonpt, ptnonipm)

Packet s : CONTROL_SULF_2020_2007v5 ; CONTROL_SULF_2030_2007v5

Fuel sulfur rules that were signed by July, 2012 are limited to Maine, Massachusetts, New Jersey, New York and Vermont. The fuel limits for these states are incremental starting after year 2012, but are fully implemented by year 2018 in all of these states. In 2030, New York C ity had additional controls implemented, mandating that all sources burn ULSD (15 ppm). For more details on these rules, see the 2007v5 TSD.A summary of the sulfur rules by state, with emissions reductions is provided in Table 4

- 24

.

Table 4

- 24

. Summary of fuel sulfur rules by state

State/

Metro Fuel %

reduction

2018 %

reduction

2030 2008

Emissions 2018

Emissions 2018

Reductions 2030

Emissions 2030

Reductions

ME Distillate 99.5 99.5 12,076 1,056 11,021 1,056 11,021 ME Residual 75 75

MA Distillate 99.5 99.5 17,265 86 17,178 86 17,178

NJ Distillate 99.5 99.5 7,285 45 7,240 45 7,240 NJ Kerosene 96.25 96.25

NY Distillate 99.5 99.5 54,093 655 53,439 274 53,81965

NYC Residual 75 99.25

VT Distillate 99.5 99.5 2,018 10 2,008 10 2,008



### 4.2.4 Portland Cement NESHAP projections (ptnonipm)

As indicated in Table 4

- 1 , the Industrial Sectors Integrated Solutions (ISIS) model (EPA, 2010b) was used to project the cement industry component of the ptnonipm emissions modeling sector to 2013. This approach

prov ided reductions of criteria and hazardous air pollutants, including mercury (Hg). The ISIS cement emissions were developed in support for the Portland Cement NESHAPs and the NSPS for the Portland cement manufacturing industry.

The ISIS model produced a P ortland Cement NESHAP policy case of multi

- pollutant emissions for individual cement kilns (emission inventory units) that were relevant for years 2013 through 2017; however, no additional policy case scenario for later future years (i.e., 2018) are availa ble. Therefore, the 2013 policy

case is used for the 2018 and 2030 base case. These ISIS

- based emissions are reflected using CoST packets and a cement inventory for new kilns:

1) Inventory: “cement_newkilns_ISIS2013_2007v5_POINT_ff10”

Contains information on new cement kilns constructed after year 2008. This inventory was accidentally not included in projections for all future year scenarios. 2) Packet: “CLOSURES_cement_ISIS_2007v5_2013policy” Contains facility and unit

- level closures, 3) Packet: “PROJECTION_ISIS2013_cement_2007v5“ Contains updated policy case emissions at existing cement kilns which we include via projection factors. The units that opened or closed before 2010 were included in the 2020 base case.

The ISIS model results for the future show a continuation of the recent trend in the cement sector of the replacement of lower capacity, inefficient wet and long dry kilns with bigger and more efficient preheater and precalciner kilns. Multiple regulatory requirements such as the NESH AP 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

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

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

- 25 shows the magnitude of the

ISIS

- based cement industry reductions in the future

- year emissions that represent 2018 and 2030, and the impact that these reductions have on total stationary non

- EGU point source (ptnonipm) emissions. The impact of accidentall y not including the inventory of new kilns in future year modeling is quantified below. This error has the most significant impact on NO X emissions; however, nationally, NO X from cement kilns would still decrease from 2008 by over 50% had these new kilns b een included in the future year scenarios.

Table 4

- 25

. ISIS

- based cement industry change (tons/yr)

Pollutant Cement Industry

emissions in 2008 Cement Industry

projected emissions in

2018 & 2030 New cement kil ns

erroneously dropped from inventory % decrease in

Cement projections

CO 46,317 8,713 0 81%

NH 3 270 77 0 71%

NO X 156,579 57,477 17,699 63%

PM 10 6,621 1,005 2 85%

PM 2.5 3,689 800 1 78%66

SO 2 98,277 22,287 1,543 77%

VOC 6,954 1,131 135 84%



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

EPA released a Notice of Data Availability (NODA) after the CSAPR proposal to seek comments and improvements from states and outside agencies. The goal was to improve th e future baseline emissions modeling platform prior to processing the Final CSAPR. EPA received several control programs and other responses that were used for future year projections. However, this effort was performed on a version of the 2005 modeling platform, which used the NEI2005v2 as a base year starting point for future year projections. Now with the 2007 platform using the 2008 NEI for most non

- EGU point and nonpoint sources, many of these controls and data improvements were removed from this 20 07 base case projection. But for those controls, closures and consent decree information that are implemented after 2008, EPA used these controls/data after mapping them to the correct SCCs and/or facilities in the 2008 NEI. This subsection

breaks down t he controls used for the nonpt and ptnonipm sectors separately, and also describes the consent decrees separately. EPA used July 1, 2008 as the cut

- off date for assuming whether controls were included in the 2007 modeling platform (2008 NEI). For example , if a control had a compliance date of December

2008 it was assumed that the 2008 NEI emissions did not reflect this control and would need to reflect this control in our 2018 or 2030 scenarios. It is important to note that these controls are not compreh ensive for all state/counties and source categories. These only represent post

- year 2008 controls for those areas and categories where EPA received usable feedback from the CSAPR comments and related 2005 platform

NODA.

Nonpoint controls : packet “CONTROL _CSAPR_nonpoint_ 2018 _2007v5” The remaining nonpt sector CSAPR comments controls with compliance dates after 2008 are limited to state

level Ozone Transport Commission (OTC) VOC controls in Connecticut and local controls around Richmond Virginia. These con trols target many of the same sources in the previously

- discussed NY SIP ozone control packet: AIM coating, Mobile Equipment Repair and Refinishing, Adhesives and Sealants and Consumer Products. Cumulatively, these controls reduce VOC by approximately 1,40 0 tons. This control packet also impacts Texas oil and gas drill rigs (SCC 2310000220), were though factors for these emissions were available through year 2021, EPA only projected these to 2018. The impact in TX is a reduction of

NO X by 17,244 tons, PM 2 .5 by 1,158 tons and VOC by 1,470 tons with minor reductions for other pollutants. The control packet is used for both 2018 and 2030 and has the same impact in both years.

Ptnonipm controls : packet “ CONTROL_CSAPR_ptnonipm_2020_2007v5 ”

EPA created a CONTR OL packet for the ptnonipm sector that contains reductions needed to achieve post

year

- 2008 emissions values from the CSAPR response to comments. These reductions reflect fuel switching, cleaner fuels, and permit targets via specific information on contro l equipment and unit and facility zero

- outs in the following states: California, Delaware, Georgia, New Hampshire, New York and Virginia. Cumulatively, these controls reduce NO X about 1,000 tons and SO 2 by approximately 4,100 tons. The control packet is u sed for both 2018 and 2030 and has the same impact in both years.

Ptnonipm closures: packet “CLOSURES_TR1_2008NEIv2” This packet contains observed unit and facility

- level closures based on CSAPR comments. This packet includes only units that reported by states as closed prior to receipt of the CSAPR comments in year 2012 or

sooner. EPA found a couple of units in the 2008 NEI

- based inventory that were reported as closed in year 2007; therefore, the compliance dates in this packet range from 2007 to 2012. EPA also retained all year

2007 closures to allow for this packet to potentially be used on RPO year

- 2007 point inventories. All67 closures were provided for the 2005 NEI facilit y and unit identifier codes. EPA matched these

units/facilities to the 2008 N EI using the “agy_facility_id” and “agy_point_id” codes in the NEI and searching the EIS for closure information. Overall, these facility and unit closures reduced NO X , SO 2 and

PM 2.5 emissions by approximately 8,800, 1,300 and 50,000 tons respectively dis tributed amongst the following states: Alabama, Arkansas, Delaware, Georgia, Illinois, Maine, Massachusetts, Missouri, New Hampshire, South Carolina, Texas, Virginia and West Virginia. The closure packet is used for both 2018 and 2030 and has the same imp act in both years.

Ptnonipm projection: packet “PROJECTION_CSAPR_WVunit_ptnonipm_2020_2007v5” This packet contains the only post

- 2008 unit

- level growth projection resulting from CSAPR comments. The Sunoco Chemicals Neal Plant in Wayne County West Virginia replaced a 155MM Btu/hour coal

- fired boiler with a 96.72 MM Btu/hour natural gas

- fired unit i n 2010. The shutdown of the coal boiler was included in the CLOSURES_TR1_2008NEIv2” packet just discussed and simply added the emissions from the new natural gas unit to an existing unit by computing the new cumulative total from the new and old natural g as units. The closing of the coal

- fired boiler removed 51 tons of NO X and 234 tons of SO 2 while this packet resulted in only 28 more tons of NO X and minimal emissions from PM and SO 2 . The projection packet is

used for both 2018 and 2030 and has the same impact in both years.

Consent decrees (ptnonipm): packet “CONTROLS_CSAPR_consent_2008NEIv2” These controls reflect consent decree and settlements that were identified in our preparation of the Final CSAPR emissions modeling platform. These controls generally consist of one or more facilities and target future year reductions. After removing all consent decrees with compliance dates prior to late

- 2008, EPA matched the remaining controls to the 2008 NEI using a combination of EIS facility codes, “agy_facility_id”, “agy_point_id” and searching the EIS. Then, the percent reductions were recomputed such that the future year emissions would match those for facilities originally projected from the 2005 platform. EPA did not retain consent decree controls if the emissions in 2007 (2008 NEI) were less than the controlled future year

emissions based on t he 2005 platform. The remaining consent decree controls are in sixteen states (AL, CA, IN, KS, KY, LA, MI, MS, MO, OH, OK, TN, TX, UT, WI, WY) and accounted for 3,835 tons of NO X and 37,368 tons of SO 2 cumulative reductions in 2018 and approximately 3,411 tons of NO X and 36,878 tons of

SO 2 cumulative reductions in 2030. The control packet is used for both 2018 and 2030, but compliance

dates for 2 facilities are in late

- 2018; therefore these controls were not applied in 2018 but were applied for

2030.



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

This section describes all remaining non

- EGU stationary sources not already discussed. These control packets and projection packets generally have lesser national

- level impact on future year projectio ns than those items above. However, some of the consent decrees discussed below have significant local impacts.

4.2.6.1 Aircraft growth (ptnonipm)

Packet s : “PROJECTION_2008_ 2018 _aircraft” ; “PROJECTION_2008_2030_aircraft”

A ircraft emissions are contained in th e ptnonipm inventory. These 200 8 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 a irport vicinity and lands at another airport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. We used projected ITN information available from the Federal Aviation Administration’s (FAA) Terminal Area Forecast (TAF ) System: http://www.apo.data.faa.gov/main/taf.asp (publication date March, 2012 ). This information is available for approximately 3

, 300 individual ai rports, for all years up to 2030

. EPA68 aggregated and applied this information at the national level by summing the airport

- specific (U.S. airports only) ITN operations to national totals by year and by aircraft operation, for each of the four available operation types: commercial, general, ai r taxi, military. Growth factors were computed for each operation type by dividing future

- year 2018 and 2030 ITN by 200 8

- year ITN. EPA assigned factors to inventory SCCs based on the operation type.

The methods that the FAA used for developing the ITN data in the TAF are documented in: http://www.faa.gov/about/office_org/headquarters_offices/apl/aviation_fore casts/taf_reports/media/TAF_su mmary_report_FY20112040.pdf

. Table 4

- 26 provides the national growth factors for aircraft; all factors are applied to year 200 8 emissions

. For example, year 2018 commercial aircraft emissions are 7.5% higher than year 2008 emissions

.

None of the aircraft emission projections account for any control programs. EPA considered the NO X standard adopted by the International Civil Aviation Organization’s (ICAO) Committee on Aviation Environmental Protection (CAEP) in February 2004, which is expected to reduce NO X by approximately 3%

in 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule; therefore, the effects of this

rule were not included in the future

- year emissions projections.

Table 4

- 26

. F actors used to project 200 8 base

- case aircraft emissions to 2020

SCC Description 2018 2030

2270008005 Commercial Aircraft: Diesel Airport Ground Support Equipment, Air Ground Support

Equipment 1.0750 1.3502

2275000000 All Aircraft Types and Operations 1.0750 1.3502

2275001000 Military Aircraft, Total 1.0620 1.0623

2275020000 Commercial Aviation, Total 1.0750 1.3502

2275050000 General Aviation, Total 0.9206 0.9709

2275050011 General Aviation, Piston 0.9206 0.9709

2275050012 General Aviation, Turbine 0.9206 0.9709

2275060000 Air Taxi, Total 0.9381 1.0965

2275060011 Air Taxi, Total: Air Taxi, Piston 0.9381 1.0965

2275060012 Air Taxi, Total: Air Taxi, Turbine 0.9381 1.0965

2275070000 Commercial Aircraft: Aircraft Auxiliary Power Units, Total 1.0750 1.3502

27501014 Military aircraft: Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Military;

Jet Engine: JP

- 4 1.0620 1.0623

27501015 Military aircraft, This SCC is in 2005v2: Internal Combustion Engines; Fixed Wing Aircraft L

& TO Exhaust; Military; Jet Engine: JP

- 5 1.0620 1.0623

27502001 C ommercial Aircraft, Total, This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed

Wing Aircraft L & TO Exhaust; Commercial; Piston Engine: Aviation Gas 1.0750 1.3502

27502011 Commercial Aircraft, Total, This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed

Wing Aircraft L & TO Exhaust; Commercial; Jet Engine: Jet A 1.0750 1.3502

27505001 General Aviation Total. This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed

Wing Aircraft L & TO Exhaust; Civil; Piston Engine: Aviatio n Gas 0.9206 0.9709

27505011 General Aviation Total. This SCC is in 2002 NEI: Internal Combustion Engines; Fixed Wing

Aircraft L & TO Exhaust; Civil; Jet Engine: Jet A 0.9206 0.9709

27601014 Military aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust;

Military; Jet Engine: JP

- 4 1.0620 1.0623

27601015 Military aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust;

Military; Jet Engine: JP

- 5 1.0620 1.0623

27602011 Commercial aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust;

Commercial; Jet Engine: Jet A 1.0750 1.350269 4.2.6.2 Oil and gas projections in TX, and non

- California WRAP states (nonpt)

EPA used a suite of intermediate

- year projected WRAP Phase III oil and gas emissions for all fu ture year
scenarios. These intermediate

- year inventories are 2008 for the Permian basin, and 2010, 2012 or 2015 for the remaining WRAP area basins. These point and nonpoint inventories are discussed in the 2007 base case

Sections 2.1.2 and 2.2.3 , respectively. Summaries of these mid

- term projections are posted on the WRAP Phase III oil and gas project website: http://www.wrapair2.org/PhaseIII.aspx . As discussed in Section

4.2.6.2

, for drilling rig operations in the remaining counties in Texas (non

- Permian basin), EPA applied year

2018 projections from a TCEQ report ( http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/ei/5820783985FY0901

- 20090715

- ergi

- Drilling_Rig_EI.pdf ).



### 4.3 Mobile source projections

EPA analyzed emission impacts of the Tier 3 vehicle emission s and fuel standards by comparing projected emissions for future years without the Tier 3 rule (reference scenario) to projected emissions for future years with the Tier 3 standards in place (control scenario). Table 4

- 27 presents an overview of the reference and control scenarios for calendar years 2018 and 2030. Both scenarios reflect the renewable fuel volumes and market fractions projected by the An nual Energy Outlook 2013 Report (“AEO2013”) 38

38 U.S. Energy Information Administration, Annual Energy Outl ook 2013 (April 15, 2013)70 Table 4

- 27

. Overview of Reference and Control Scenarios

Reference Scenario Control Scenario

2018 Renewable Fuels: AEO 2013 a 17.5 B gallons renewable fuels (18.3 B ethanol

- equivalent gallons): 16.0 B gallons ethanol: E10 b , E15 c , E85 d

Fuel Sulfur Level: 30 ppm (10 ppm California)

Fleet: e 96 percent Tier 2 and older vehicles 4 percent LEV III vehicles Renewable Fuels: AEO 2013 a 17.5 B gallons renewable fuels (18.3 B ethanol

- equivalent gallons): 16.0 B gallons ethanol: E10 b , E15 c , E85 d

Fuel Sulfur Level: 10 ppm

Fleet: e 86 percent Tier 2 and older vehicles 14 percent Tier 3/LEV III vehicles

2030 Renewable Fuels: AEO 2013 a 17.6 B gallons renewable fuels (18.6 B ethanol

- equivalent gallons): 15.3 B gallons ethanol: E10 b , E15 c , E85 d

Fuel Sulfur Level: 30 ppm (10 ppm California)

Fleet: e 76 percent Tier 2 and older vehicles 24 percent LEV III vehicles Renewable Fuels: AEO 2013 a 17.6 B gallons renewable fuels (18.6 B ethanol

- equivalent gallons): 15.3 B gallons ethanol: E10 b , E15 c , E85 d

Fuel Sulfur Level: 10 ppm

Fleet: e 21 percent Tier 2 and older vehicles 79 percent Tier 3/LEV III vehicles

a U.S. Energy Information Administration, Annual Energy Outlook 2013 (April 15, 2013)

b Gasoline containing 10 percent ethanol by volume

c Gasoline containing 15 percent ethanol by volume

d Gasoline containing up to 85 percent ethanol by volume (74 percent nominal used in this ana lysis)

e Fraction of the vehicle population

The reference scenarios assumed an average fuel sulfur level of 30 ppm in accordance with the Tier 2 gasoline sulfur standards. Under the Tier 3 program, federal gasoline will contain no more than 10 ppm

sulfu r on an annual average basis by January 1, 2017, and therefore EPA assumed a nationwide fuel sulfur level of 10 ppm for both future year control cases.

EPA assumed a continuation of the existing Tier 2 standards for model years 2017 and later in modeling

emissions for the reference scenario, with the exception of California and Section 177 states that have adopted the LEV III program. The Tier 3 control scenario modeled the suite of exhaust and evaporative emission standards for light

- duty vehicles (LDVs ), light duty trucks (LDTs: 1

- 4), medium passenger vehicles (MDPVs) and large pick

- ups and vans (Class 2b and 3 trucks) including:

 Fleet average Federal Test Procedure (FTP) NMOG+NO X standards of 30 mg/mi for LDVs, LDTs and MDPVs, phasing in from MYs 201 7 to 2025 for the light

- duty fleet under 6,000 lbs. GVWR and phasing in from MYs 2018 to 2025 for the light

- duty fleet over 6,000 lbs. GVWR, and MDPVs

 Fleet average Supplemental Federal Test Procedure (SFTP) NMOG+NO X standards of 50 mg/mi for
LDVs, LDTs an d MDPVs, phasing in from MYs 2017 to 2025 for the light

- duty fleet under 6,000 lbs. GVWR and phasing in from MYs 2018 to 2025 for the light

- duty fleet over 6,000 lbs. GVWR,

and MDPVs

 Per

- vehicle FTP PM standard of 3 mg/mi for LDVs, LDTs and MDPVs, phasing in from MYs 2017 to 2022 for the light

- duty fleet under 6,000 lbs. GVWR and phasing in from MYs 2018 to 2022 for the light

- duty fleet over 6,000 lbs. GVWR, and MDPVs71

 Per

- vehicle US06

- only PM standard of 10 mg/mi for LDVs, LDTs and MDPVs through MY2021

and of 6 mg/mi for MY2022 and later model years

 New standards for Class 2b and 3 trucks phasing in by MY 2022 including NMOG+NO X declining fleet average, and more stringent PM standards

 More stringent evaporative emission standards for diurnal plus hot soak emissions, a new canister bleed test and emission standard, and new requirements addressing evaporative leaks on in

- use vehicles.

 New refueling emission control requirements for all complete HDGVs equal to or less than 14,000 lbs GVWR (i.e., Class 2b/3 HDG Vs), starting in the 2018 model year, and for all larger HDGVs by the 2022 model year

Implementation of the Tier 3 standards is aligned with the model year 2017

- 2025 Light

- Duty GHG

standards 39 to achieve significant criteria pollutant and GHG emissions reductions while providing regulatory certainty and compliance efficiency to the auto and oil industries. Accordingly, the analyses for

the Tier 3 rule include the final LD GHG standards in both the reference and control scenarios, and thus account for their impacts on the future vehicle fleet and future fuel consumption.

The analysis described here accounts for the following national onroad rules:

 Tier 2 Motor Vehicle Emissions Standards and G asoline Sulfur Control Requirements (65 FR 6698, February 10, 2000)

 Heavy

- Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, January 18, 2001)

 Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007)

 Reg ulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program (75 FR 14670, March 26, 2010)

 Light

- Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy

Standards for 2012

- 2016 (75 FR 25324, May 7, 2010)

 Gre enhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium

- and Heavy

- Duty Engines and Vehicles (76 FR 57106, September 15, 2011)

 2017 and Later Model Year Light

- Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standar ds (77 FR 62623, October 15, 2012)

In addition, the modeling accounts for state and local rules including local fuel standards, Inspection/Maintenance programs, Stage II refueling controls, the National Low Emission Vehicle Program (NLEV), and the Ozone T ransport Commission (OTC) LEV Program. Furthermore, the Tier 3 emissions modeling for both the national inventory and air quality analysis includes California’s LEV III program and

its associated emission reductions from both California and the states tha t adopted the LEV III program, in

the baseline scenario.

Onroad mobile sources are comprised of several components and are discussed in the next subsection ( 4.3.1 ). Monthly nonroad mobile emission projections are discussed in subsection 4.3.2 . Locomotives and Class 1 and Class 2 commercial marine vessel (C1/C2 CMV) proj ections are discussed in subsection 4.3.3 , and Class 3 (C3) CMV projected emissions are discussed in subsection 4.3.4

.

39 2017 and Later Model Year Light

- Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule (77 FR 62623

– 63200 ), October 2012.72 

### 4.3.1 Onroad mobile (onroad and onroad_rfl)

The onroad emissions for 2018 and 2030 use the same SMOKE

- MOVES system as for the base year (see

Section s 2.5.1 and 2.5.2 ). Meteorology, speed, spatial and temporal surrogates, representative counties, and

fuel months were the same as for 2 007, discussed above.

4.3.1.1 VMT and vehicle population

EPA estimate s of total national Vehicle Miles Travelled (VMT) in 20 18 and 2030 came from DOE's Annual Energy Outlook (AEO) 201 3 early release ( http://www.eia.gov/forecasts/aeo/ ). The VMT was allocated

between ve hicle types using a version of MOVES2010b that had been modified with VMT growth factors

from the AEO and with historical data from FHWA ( http://www.fhwa.dot.gov/policyinformation/statistics.cfm ). The growth rates by SCC were applied to the VMT values from the 2007 base year in each county to generate the future year VMT estimates. These

VMT values were normalized suc h that the total national VMT from the growth calculations matched the total VMT estimates in the AEO for 2018 and 2030. Vehicle populations by county, month and vehicle type were estimated by dividing annual VMT by national estimates of annual VMT per veh icle.

Tank trucks are used to transport ethanol mandated by EISA from production facilities to bulk terminals and from terminals to bulk plants and dispensing facilities. Impacts of this activity on emissions from tank trucks transporting ethanol (Class 8 trucks) are accounted for in these inventories by adjusting VMT used in

SMOKE

- MOVES

. The VMT a djustments were derived from an Oak Ridge National Laboratory analysis of ethanol transport (Oak Ridge National Laboratory, 2009)

, scaled to account for the di fference in ethanol

volume for AEO 2013 future year projections versus the 2007 platform volume of 8.7 billion gallons

. It should be noted that the Oak Ridge analysis only addressed ethanol transport and did not account for impacts

of other biofuels on tr ansportation activity. Impacts from the 2017

- 2025 light duty vehicle GHG emission standards were assumed to be negligible in 2018 and were accounted for with a 0.997 scalar in 2030.

4.3.1.2 Set up and Run MOVES to create EF

Emission factor tables were created by running SMOKE

- MOVES using the same procedures and models as

2007 (see Section 2.5.1 and the 2007v5 TSD ) . The same meteorology and the same representative counties

were used . Changes between 2007, 2018, and 2030 are predominantly VMT, fuels, national and local rules,

and the model

- year distribution of the fleet, which is built into MOVES. F leet turnover result ed in a greater
fraction of newer vehicles meeting stri cter emission standards.

A detailed list of the differences betwee n the runs for each case are described in73 Table 4

- 28

.74 Table 4

- 28

. Comparison of MOVES runs

Year 2007 2018 2018 2030 2030

Case base ref c tl ref Ctl

Version 20130130 20130609 20130610 20130603 20130604

Run Name abs2007base_201

30130 tier3frm2018ref_20

130609 tier3frm2018ctl_20

130610 tier3frm2030ref_20

130603 tier3frm2030ctl_20

130604
MOVES

code moves20121002f moves20121002f moves20121002f moves20121002f moves20121002f

MOVES

default

database movesdb20121002

f movesdb20121002

k_truncatedgfre movesdb20121002

k_truncatedgfre movesdb20121002

k_truncatedgfre movesdb20121002

k_truncatedgfre

CDBs 2007PFCdb146Re

pCnties20120402 2018PM146Counti

es20120724 2018PM146Counti

es20120724 2030146Counties2

0130312 2030146Counties2

0130312 Early NLEV early_nlev N/A N/A N/A N/A

LEV rates tier3_lev_standard

s_YYYY lev3_standards_SS

_20130603 lev3_standards_SS

_20130603 lev3_standards_SS

_20130603 lev3_standards_S

S_20130603

VMT and

VPOP nationaldefaultvmt

pop_20120410 nationaldefaultvmtp

op_20120410 nationaldefaultvmtp

op_20120410 nationaldefaultvmtp

op_20120410 nationaldefaultvmt

pop_20120410

GFRE N/A non

- LEV: tier3frm3030gfre

LEV: tier3frm3010gfre_0

53013 tier3frm3010gfre_0

53013 non

- LEV:

tier3frm3030gfre

LEV: tier3frm3010gfre_0

53013 tier3frm3010gfre_0

53013

hcspeciation N/A tier3frm_ref2018_h cspec_M_rvpB tier3frm_ref2018_h

cspec_M_rvpB tier3frm_ref2030_h

cspec_M_rvpB tier3frm_ref2030_h

cspec_M_rvpB

Fuels 2007_Baseline_09

062012 tier3frm2018reffuel

s_02252013 tier3frm2018ctrlfuel

s_03152013 tier3frm2030reffuel

s_03072013 tier3frm2030ctrlfuel

s_03152013

Tier 3

control rates N/A N/A tier3ctldbs_060313 N/A tier3ctldbs_060313

The following states were modeled as having adopted the California LEV II and LEV III program s (see

Table 4

- 29 )

Table 4

- 29

. CA LEVIII program states

FIPS State Name

06 California

09 Connecticut

10 Delaware

23 Maine

24 Maryland

25 Massachusetts

34 New Jersey

36 New York

41 Oregon

42 Pennsylvania

44 Rhode Island

50 Vermont

53 Washington75 Early NLEV refers to states which adopted the California “low emission vehicle” (LEV) standards in the 1990’s “early”, since the California LEV standards were adopted nationally (NLEV) starting in 2001

. The following states were modeled as using the early N LEV program in 2007 (see Table 4

- 30 ).

Table 4

- 30

. Early N LEV states

FIPS State Name

0 9 CONNECTICUT

10 DELAWARE

11 DISTRICT OF COLUMBIA

23 MAINE

24 MARYLAND

33 NEW HAMPSHIRE

34 NEW JERSEY

42 PENNSYLVANIA

44 RHODE ISLAND

50 VERMONT

51 VIRGINIA

The following table indicates when the LEV states adopted the LEV2 standard s and lists the emission

standards databases used in the 2007 base run (see Table 4

- 31 )

Table 4

- 31

. LEV2 states and MOVES databases

FIPS State Name Database

6 CALIFORNIA tier3_lev_standards_1994

9 CONNECTICUT tier3_lev_standards_2008

10 DELAWARE tier3_lev_standards_2014

23 MAINE tier3_lev_standards_2001

24 MARYLAND tier3_lev_standards_2011

25 MASSACHUSETTS tier3_lev_standards_1995

34 NEW JERSEY tier3_lev_standards_2009

35 NEW MEXICO tier3_lev_standards_2016

36 NEW YORK tier3_lev_standards_1996

41 OREGON tier3_lev_standards_2009

42 PENNSYLVANIA tier3_lev_standards_2008

44 RHODE ISLAND tier3_lev_standards_2008

50 VERMONT tier3_lev_standards_2000

53 WASHINGTON tier3_lev_standards_2009

The use of RVP bins for specifying hydrocarb on speciation (HCspeciation) database for January and July for both 2018 and 2030 runs are listed by representative county (see Table 4

- 32 ) Because the hydrocarbon speciation is dependent on the level of RVP, four RVP bins were generated based on RVP of the fuels in each representative county for each month. The HCspeciation database contains hydrocarbon speciation profiles for NMOG and VOC and applies only to fuels containing 70% or more ethanol by volume (i.e., E85).76 Table 4

- 32

. RVP bins by representative county

FIPS Jan Jul FIPS Jan Jul FIPS Jan Jul FIPS Jan Jul

1073 2 2 23019 2 3 39041 3 4 47163 2 4

4013 2 1 23031 2 2 39049 3 4 48001 2 2

4019 2 4 24005 2 1 39061 3 3 48003 2 4

4021 2 4 24015 2 1 39113 3 3 48005 2 2

6025 2 1 24029 2 1 39119 3 4 48011 2 4

6037 2 1 24033 2 1 39151 3 4 48039 1 1

8031 3 3 24043 2 4 40143 2 4 48047 2 4

8041 3 4 24045 2 4 41005 3 3 48139 2 2

8101 3 4 25017 2 1 41019 3 4 48141 2 1

9003 2 1 26081 3 4 41029 3 4 48143 2 4

10003 2 1 26163 3 2 41037 3 4 48221 2 2

10005 2 1 27003 4 4 41039 3 4 48251 2 2

11001 2 1 27053 4 4 41047 3 3 48439 1 1

12011 2 3 27111 4 4 41051 3 3 49005 3 4

12033 2 4 27137 4 4 41067 3 3 49009 3 4

12086 2 3 27145 4 4 41071 3 4 49011 3 3

13015 2 2 27165 4 4 42003 3 2 49017 3 4

13051 2 4 29095 2 2 42005 3 2 49035 3 3

13121 2 2 29189 2 1 42009 2 4 49043 3 4

16001 3 4 32003 2 4 42017 2 1 49045 3 4

16027 3 4 32005 2 4 42019 3 2 49049 3 4

17031 2 1 32007 2 4 42039 3 4 49057 3 4

17093 2 1 32009 2 4 42043 2 4 50007 2 4

18019 3 3 32027 2 4 42049 3 4 51036 2 1

18089 2 1 32031 2 3 42051 3 2 51041 2 1

18097 3 4 33009 2 4 42055 2 4 51059 2 1

19095 2 4 33015 2 1 42063 3 4 51087 2 1

19133 2 4 34003 2 1 42071 2 4 51107 2 1

19153 2 4 35001 2 4 42073 3 4 51683 2 1

19163 2 4 35013 2 4 42089 2 4 51740 2 1

20091 2 2 36029 2 3 42091 2 1 53033 3 4

21029 2 1 36103 2 1 42101 2 1 53063 3 4

21111 2 1 37051 2 4 42129 3 2 53067 3 4

21117 2 1 37081 2 3 44007 2 1 55025 4 4

22033 2 3 37119 2 3 46099 4 4 55079 2 1

22047 2 3 39023 3 3 47157 3 3 55117 4 4

23005 2 2 39035 3 4

Fuels were projected into the future using estimates from the AEO2013 ( http://www.eia.gov/forecasts/aeo/ ), release dates April 15 th

- May 2 nd 2013. . The fuel supply used in the Tier 3 FRM varies significantly from that used in the NPRM, including the introduction of a new approach to aggregating fuels by region. For more information regarding this new approach to fuels, please refer to the Tier 3 FR M, Chapter 7.1.3.2. Reference fuels contain E10, E15, and E85 at a market share fraction determined regionally from the AEO2013 report. Additional fuel properties for the reference case s are based on local fuel surveys as well as EPA fuel compliance and c ertification reports. All sulfur levels were set at 30ppm in the reference case s in order to reduce possible calculation artifacts. Fuel property changes associated with this sulfur adjustment were also calculated appropriately by region. The Tier 3 contro l case s adjust this sulfur level to 10ppm, as well as adjusting associated fuel properties with this sulfur reduction. Market shares between E10, E15 and77 E85 remain unchanged between the reference and the control case s but changed across the years (2018

ve rsus 2030)

.

4.3.1.3 Long Haul truck adjustments and SMOKE

- MOVES

A set of adjustments were calculated for SMOKE

- MOVES to estimate 2018 and 2030 emissions in each

county to account for extended idle and the use of auxiliary power units (APU)

. These adjustments use the same approach as was used in 2007 (see Section 2.5.1.3 and the 2011 NEI v1 TSD for details) except for the vehicle population ( VPOP ) was updated to be consisten t with 2018 or 2030. These adjustments are by county, vehicle type (long

- haul truck SCCs only), and mode (extended idle or APU only) and impacted the RPV process only. The set of adjustments created for the extended idle mode are identical for APU becaus e it was assumed that the distribution of APU usage and emissions would mirror extended idle activity

.

SMOKE

- MOVES, specifically Movesmrg, uses the adjustment factor file (CFPRO) for extended idle and APU to estimate 2018 and 2030 emissions that incorpora tes each of these adjustments.



### 4.3.2 Nonroad mobile (nonroad)

The nonroad sector includes a wide range of mobile emission sources ranging from construction equipment

to hand

- held lawn tools. In the nonroad sector, the only emissions that are directly affected by the Tier 3 regulation are the emissions from gasoline

- powered equipment such as lawn

- mowers, recreational boats and

all

- terrain vehicles. Their SO 2 emissions are reduced with the decrease in gasoline sulfur levels. As with onroad, reference and control case emissions were generated using the fuel supply inputs reflecting the projected fuel volumes from AEO2013.

Gasoline and land

- based diesel nonroad emissions were estimated using EPA’s NONROAD 2008b model 40

,

as ru n by the EPA’s consolidated modeling system known as the National Mobile Inventory Model (NMIM). 41 The fuels in the NMIM database, NCD2010201a, were developed from the reference and control fuels used for onroad vehicles, as described in Section 4.3.1.2 . O nroad and nonroad gasoline formulations

are assumed to be identical for all years

. In 201 8 and 2030 , nonroad equipment is assumed to use E10 only. For all years, the reference case included the higher sulfur reference gasoline and the control case met the

sulfur limits.

This sector includes mont hly exhaust, evaporative and refueling emissions from nonroad engines (not including commercial marine, aircraft, and l ocomotives) derived from NMIM for all states except California.

The version of NONROAD models all in

- force nonroad controls (see nonroad section under Table 4

- 1 )

. Not

included are volu ntary local programs such as e ncouraging either no refueling or evening refueling on Ozone Action Days

.

California nonroad emissions

40 This version of NONROAD is very similar to the publicly released version, but it can model ethanol blends up to E20. The NMIM version is NMIM20090504d, which has the same results as the publicly

- released NMIM version NMIM20090504a. The underlying Nationa l County Database (NCD) is NCD20101201a, but with 2007 meteorology inserted into the countymonthhour table. NCD20101201a includes state inputs for the 2008 NEI.

41 U.S. EPA. 2005 EPA’s National Inventory Model (NMIM), A Consolidated Emissions Modeling Sy stem for MOBILE6 and

NONROAD ; EPA420

- R

- 05

- 024; Office of Transportation and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/models/nmim/420r05024.pdf78 Similar to the 2007 base n onroa d mobile, NMIM was not used to generate future

- year nonroad emissions for California, other than for NH 3

. EPA used NMIM for California future nonroad NH 3 emissions because

CARB did not provide these data for any nonroad vehicle types. For the rest of the pollutants, EPA

converted the CARB

- supplied 2017 (surrogate for 2018) and 2030 nonroad annual inventor ies to monthly emissions values by using the NMIM monthly inventories to compute monthly ratios by pollutant and SCC. Some adjustment s to the CARB inventory were needed to convert the provided TOG to VOC and to augment
the HAPs . See Section 3.2.1.3 for details on speciation of California nonroad data. The CARB nonroad emissions include nonroad rules reflected in the December 2010 Rulemaking Inventory ( http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf ) and those in the March 2011 Rule Inventory, the Off

- Road Construction Rule Inventory for “In

- Use Diesel”

.



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

Recall from Section 2.5.4 that there are several non

- NEI components to the c1c2rail sector in the 2007 base case. There are three distinct approaches used to craft year 2018 and 2030 inventories from the 2007 base case. The first component of the 20 18 and 2030 c1c2rail inventories is the non

- California data projected from the 2007 base case. The second component is the CARB

- supplied year 2017 (used for 2018 scenarios) and year 2030 data for California. The third component is a set of OTAQ

- provided county

- specific emissions adjustments that account for different fuel transport characteristics resulting from the EISA (RFS2) mandate. Specifically, these adjustments reduce finished petroleum

- based fuel transport by rail and barge (CMV) and add ethanol

- based finished fuel transport by rail and barge.

Step 1: Project non

- California CMV and rail emissions

Packet: “PROJECTION_2008_20 18 _c1c2rail” ; “PROJECTION_2008_20 30 _c1c2rail”

In this step, a projection packet creates an intermediate set of year 2018 and year 2030 emissions, respectively, for all states except California. This packet does not reflect emission impacts from ethanol volume impacts from the EISA (RFS2) mandate; the EISA impacts are applied for all states in Step 3. This packet consists o f national projection factors by SCC and pollutant between 2007 and 2018 and 2030 that reflect the May 2004 “Tier 4 emissions standards and fuel requirements” ( http://www.epa.gov/ otaq/documents/nonroad

- diesel/420r04007.pdf ) as well as the March 2008 “F inal

loco motive

- marine rule” controls ( http://www.epa.gov/otaq/regs/nonroad/420f08004.pdf )

.

Projection factors are based on year 2008 rather than year 2007 for a couple of reasons. First, many states
with large c1c2rail emissions utilize the 2008 NEI emissions; Texas is one example. Second, the year 2007
emissions are mostly lower than the 2008 RIA summaries, and these emissions generally decrease in the
future. By choosing year 2008, the projections are unlikely to overly

- reduce emissions by years 2018 and
2030. In addition, the 2007 platform emissions are often much different than the RIA em issions for any
year. EPA OTAQ experts determined that the 2007 platform estimates were more up

- to date and likely more

reliable than the RIA estimates in 2007/2008 and 2018 and 2030. However, the controls and hence the

relative reductions in the RIA are expected to be fairly close to what would be expected from the 2007
platform. Therefore, EPA simply apply the ratio of the RIA 2018 or 2030 to 2008 emissions to project the
2007 platform emissions. These projection ratios are provided in79 Table 4

- 33

.80 Table 4

- 33

. Non

- California year 2018 and 2030 Projection Factors for locomotives and Class 1 and Class 2

Commercial Marine Vessel Emissions

SCC Description Poll utant 2018 2030

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions CO 0.9284 0.9506

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NO X 0.7042 0.3931

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM 10 0.6429 0.3696

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM 2.5 0.6429 0.3696

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions SO 2 0.1213 0.0647

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions VOC 0.7529 0.4065

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations CO 1.1720 1.4180

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NO X 0.7523 0.4426

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 10 0.6168 0.2939

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 2.5 0.6168 0.2939

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations SO 2 0.0334 0.0405

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations VOC 0.5453 0.2914

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations CO 1.1709 1.4174

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NO X 1.1092 1.1014

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 10 1.0700 1.0250

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 2.5 1.0722 1.0206

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO 2 0.0349 0.0349

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations VOC 1.1740 1.4189

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) CO 1.0832 1.1915

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NO X 0.5292 0.2723

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 10 0.5457 0.1926

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 2.5 0.5445 0.1908

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO 2 0.0286 0.0343

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) VOC 0.4735 0.1527

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines CO 1.0832 1.1918

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NO X 0.5292 0.2723

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM 10 0.5444 0.1918

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM 2.5 0.5446 0.1906

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines SO 2 0.0335 0.0335

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines VOC 0.4730 0.1535

2285002010 Railroad Equipment;Diesel;Yard Locomotives CO 1.1720 1.4180

2285002010 Railroad Equipment;Diesel;Yard Locomotives NO X 0.9948 0.7533

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 10 0.9562 0.7271

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 2.5 0.9567 0.7269

2285002010 Railroad Equipment;Diesel;Yard Locomotives SO 2 0.0337 0.0404

2285002010 Railroad Equipment;Diesel;Yard Locomotives VOC 0.9511 0.6851

The future

- year locomotive emissions account for increased fuel consumption based on Energy Information Administration (EIA) fuel consumption projections for freight rail, and emissions reductions resulting from

emissions standards from the Final Locomotive

- Marine rule (E PA, 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, SO 2 , and NO X , 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, acro lein, benzene, and formaldehyde. C1/C2 CMV diesel emissions (SCC = 2280002100 and
2280002200) were p rojected based on the Final L ocomotive Marine rule national

- level factors provided in

Table 4

- 33 . Similar t o locomotives, VOC HAPs were projected based on the VOC factor.81 Step 2: Intermediate California year 2018 and year 2030 inventories

Obtained from CARB, the locomotive, and class 1 and 2 commercial marine emissions used for California reflect year 2017 and year 2030 and include nonroad rules reflected in the December 2010 Rulemaking Inventory ( http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf ), those in the March 20 11 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 an d 2010 regulations to reduce emissions diesel engines on commercial harbor craft operated within California waters and 24

nautical miles of the California baseline.

The C1/C2 CMV emissions for both 2017 and 2030 were obtained from the CARB nonroad mobil e 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_TRAIN S.txt ”. Documentation of the CARB offroad methodology, including c1c2rail sector data, is provided here: http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles

. EPA converted the CARB inventory TOG to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor. Note th ere

was no attempt to modify year 2017 CARB emissions to year 2018 via linear interpolation or other schemes.

EPA simply assi gned year 2018 emissions as the 2017 CARB submittal with these medications as well as those discussed next in step 3.

Step 3: Adjusting intermediate year 2018 and 2030 c1c2rail emissions to reflect the EISA mandate and

LD GHG emission standards

R ail category 1 and commercial marine activity are impacted by both the EISA mandate and the 2017

- 2025 light

- duty vehicle greenhouse gas emission standards. The inventories were adjusted to account for both.

Inventories were adjusted to account for (a) differ ences in C1 / C2 CMV and rail emission rates in 201 8 and 2030 versus 2007, (b) the difference in ethanol volume impacts for AEO 2013 future year projections versus the 2007 platform volume of 8.7 billion gallons and (c) impacts on gasoline production and dis tribution from

the 2017

- 2025 light duty vehicle GHG emissions standards (EPA, 2013b)

. Adjustments to C1 / C2 CMV and rail emission rates were calculated using ton/ton

- mile e mission factors that were obtained from the Tier 3 proposed rule inventory and AEO 2 013 projected domestic shipping estimates (EPA, 2013c; Energy Information Administration, 2013). The ethanol volume impacts were allocated to individual counties using factors developed from the ORNL analysis (ORNL, 2013)

. Impacts on C1 / C2 CMV and rail em issions from

the 2017

- 2025 light duty vehicle GHG emission standards were calculated using the RFS2 impacts spreadsheet. Impacts from the 2017

- 2025 light duty vehicle GHG emission standards were assumed to be negligible in 2018 but were accoun ted for by t he scalars in Table 4

- 34 and Table 4

- 35 for 2030.

Table 4

- 34

. Scalars Applied to Rail Combustion Emissions in 2030 to account for 2017

- 2025 LDGHG

emission standards

Pollutant Scalar

CO 0.998

NO X 0.998

PM 2.5 0.997

PM 10 0.996

SO 2 0.997

VOC 0.99782

Table 4

- 35

. Scalars Applied to C1/C2 Combustion Emissions in 2030 to account for 2017

- 2025 LDGHG

emission standards

Pollutant Scalar

CO 0.996

NO X 0.993

PM 2.5 0.994

PM 10 0.993

SO 2 0.991

VOC 0.978

These emissions from updated ethanol volumes via RFS/EISA and LDGHG are not included in the previously

- discussed non

- California loco

- marine rule

- based projections (Step 1) and CARB 2017 and 2030 inventories (Step 2). Nationally, these adjusted emissions a re modest.

On average, for year 2018 rail emissions, the impact of the adjustment for transporting more ethanol is an increase in emissions by 1.24%, and the adjustment for transporting less gasoline is

- 0.04%, leaving a small

increase in rail emissions in year 2018. For year 2030 rail emissions , the impact of the adjustment for transporting more ethanol is an increase in emissions by 0.92%, and the adjustment for transporting less gasoline is

- 0.06%, and the adjustment for the LD GHG rule is a reduction of 0.24%, leaving a small increase in emissions in year 2030. These small increases in rail emissions are evident in Table 4

- 36 . For CMV emissions in year 2018, the impact of transporting more ethanol by 0.21% is offset by the adjustment for transporting less gasoline by

- 1.32%. For 2030, as shown in the “2030 Error” column in Table 4

- 36 , we erroneously misapplied these RFS2 as well as LDGHG decreasing emissions adjustments: increased ethanol by 0.19%, decreased gasoline by 2.05% and LDGHG rule reductions of

- 0.78%. In short, in year 2030,

national total NO X emissions from C1/C2 CMV are 2,678 tons larger than intended, a 0.6% error for C1/C2 CMV nationally.

Table 4

- 36

. Cumulative RFS2 and LDGHG adjustments to c 1c2r ail sector e missions

Pollutant Source 2018

Adjustment Final

2018 2030

Applied adjustment Final

2030 Intended

2030 2030

Error

CO cmv

- 855 88,226 0 92,593 90,360 2,233

CO rail 1,715 152,424 1,296 185,916 185,916 0

NH 3 cmv

- 2 218 0 225 221 4

NH 3 rail 5 358 5 357 357 0

NO X cmv

- 3,635 343,725 0 197,537 192,590 4,947

NO X rail 8,346 683,017 2,678 430,577 430,577 0

PM 10 cmv

- 139 11,584 0 7,060 6,869 192

PM 10 rail 198 18,530 31 9,672 9,672 0

PM 2.5 cmv

- 155 11,102 0 6,773 6,504 269

PM 2.5 rail

- 10 16,857

- 36 8,826 8,826 0

SO 2 cmv

- 296 5,380 0 4,249 3,870 379

SO 2 rail 80 482

- 7 478 478 0

VOC cmv

- 136 11,452 0 7,484 7,293 191

VOC rail 357 28,272 91 17,143 17,143 083 

### 4.3.4 Class 3 commercial marine vessels (c3marine)

As discussed in Section 2.5.5 , t he c3marine sector emissions data were developed for year 2002 and projected to year 2007 for the 2007 base case. The ECA

- IMO project provides pollutant and geographic

specific projection factors to year 2007, and also projection factors to years 2018 and 2030 that reflect assumed growth and final ECA

- IMO controls. The ECA

- IMO rule, published in December 2009, applies to Category 3 (C3) diesel engines (engines with per cylinder displacemen t at or above 30 liters) installed on

U.S. vessels. The ECA

- IMO rule includes an implementation of Tier 2 and Tier 3 NO X limits for C3 engines beginning in 2011 and 2016, respectively. The ECA

- IMO rule also imposes fuel sulfur limits of 1,000 ppm (0.1%) by 2015 in the ECA region

- generally within 200 nautical miles of the U.S. and Canadian coastlines, as well as 5,000 ppm (0.5%) for “global” areas

– those areas outside the ECA region. For comparison, with the exception of some local areas, year 2007 sulfu r content limits are as high as 15,000 ppm (1.5%) in U.S. waters and 45,000 ppm (4.5%) in global areas. More information on the ECA

- IMO rule can be found in the Category 3 marine diesel engines Regulatory Impact Assessment: http://www.epa.gov/otaq/oceanvessels.htm

.

Projection factors for creating the year 2018 and year 2030 c3marine inventories from the 2007 base case are provided in Table 4

- 37 . Background on the region and EEZ FIPS is provided in the discussion on the c3marine inventory for 2007

– Section 2.5.5 . The impact of the Tier 2 and Tier 3 NO X engine standards is less noticeable because of the inevitable delay in fleet turnover for these new engines; however, the

immediate and drastic cuts in fuel sulfur content are obvious. VOC and CO are mostly unaffected by the engine and fuel standards, thus providing an idea on how much these emissions would have grown without

ECA

- IMO controls. VOC HAPs are assigned the same growth rates as VOC.

Table 4

- 37

. Growth factors to project the 2007 ECA

- IMO inventory to 2018 and 2030

Region EEZ FIPS Year NO X PM 10 PM 2.5 VOC CO SO 2

East Coast (EC) 85004 2018 1.166 0.221 0.219 1.623 1.623 0.058

2030 0.900 0.373 0.373 2.752 2.752 0.098

Gulf Coast (GC) 85003 2018 0.984 0.187 0.186 1.370 1.370 0.049

2030 0.633 0.264 0.265 1.930 1.930 0.069

North Pacific (NP) 85001 2018 1.061 0.197 0.196 1.429 1.429 0.055

2030 0.773 0.293 0.297 2.128 2.126 0.082

South Pacific (SP) 85002 2018 1.255 0.241 0.239 1.731 1.730 0.068

2030 1.009 0.456 0.457 3.290 3.254 0.128

Great Lakes (GL) n/a 2018 1.023 0.159 0.158 1.204 1.204 0.043

2030 1.013 0.195 0.195 1.474 1.474 0.052

Outside ECA 98001 2018 1.401 1.606 1.606 1.606 1.606 1.606

2030 2.083 0.612 0.606 2.778 2.778 0.506



### 4.4 Canada, Mexico, and Offshore sources (othar, othon, and othpt)

Emissions for Canada and offshore sources were not projected to future years, and are therefore the same as those used in the 2007 base case. Canada did not provide future

- year emissions that were consistent with the base year emissions. The Mexico emiss ions are based on year 1999 but projected to years 2018 and 2030. A background on the development of year

- 2018 and year

- 2030 Mexico emissions from the 1999 inventory is

available at: http://www.wrapair.org/forums/ef/inventories/MNEI/index.html

.84 

## 5 Emission Summaries

The following tables summarize the emissions for the 2007 base case , 2018 referenc e and control cases, and

the 2030 reference and control cases . These summaries are pr ovided for the contiguou s U.S. and for the portions of Canada and Mexico inside the smaller 12km domain (12US 2 ) discussed in Section 3 .1

. The

afdust sector emissio ns represent the summaries after application of both the land use (transport fractio n ) and meteorological adjustments (see Section 2.2.1 ) ; therefore, this sector is called “afdust

- adj” in these

summaries. The onroad and onroad refueling (onroad_rfl) sector totals are post

- SMOKE

- MOVES totals, representing air quality model

- ready emission totals. Biogenic emissions totals are only given within the

United States. The “c3marine

- US” sector represents c3marine sector emissions with U.S. FIPS only; these extend to roughly 3

- 5 miles offshore and include all U.S. waters in the Great Lakes along with all U.S. ports.

The Offshore c3marine represents all non

- U.S. c3marine emissions outside of U.S. state waters. The

c3marine sector is discussed in Section 2.5.5

. The “Off

- shore othpt” sector is the n on

- Canada, no n

- Mexico component of the othpt sector , i.e., the offshore oil platform emissions from the 2008 NEI.

National emission totals by air quality model

- ready sector are provided for all CAP emissions for the 2007

base case and 2007 evaluation case in Table 5

- 1 . The total of all U.S. emissions in all sectors is provided. The lower portion of the table provides the non

- U.S. emissions including subtotals for Ca nada, Mexico, and

all non

- U.S. emissions. Tables 5

- 2 through 5

- 5 provide similar CAP emission totals by sector for the 2018 reference case, 2018 control case, 2030 reference case, and 2030 control case, respectively

.

Table 5

- 6 provides total emission s for CO by state for all five cases: 2007 base case, 2018 reference and

control case s, and 2030 reference and control cases

. Tables 5

- 7 through 5

- 12 provide the same summaries

for NH 3 , NO X , PM 2.5 , PM 10 , SO 2 and VOC, respectively. Note that all of these tables use average fire

emissions and do not include biogenic emissions

. Emission totals by state for each modeling platform

sector , for CAPs and air quality model spe cies, can be found in the se spreadsheets available in the docket: 2007rg _state_totals.xlsx , 2018rg_ref 2 _state_totals.xlsx

, 2018rg_ctl_state_totals.xlsx

, 2030rg_ref

_state_totals.xlsx , and 2030 rg_ctl_state_totals.xlsx

.85

Table 5

- 1

. National and non

- U.S. CAP emissions by sector for 2007 base case

US Totals

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,124,268 863,738

ag 3,595,613

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 218,827 557 1,338,134 43,832 41,015 48,805 61,547

nonpt 4,329,593 155,297 1,199,385 768,986 677,339 406,290 6,529,064

nonroad 17,834,128 1,915 1,877,955 187,707 178,342 100,652 2,517,282

onroad 38,519,970 140,897 7,612,080 365,017 282,304 40,008 4,273,876

avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

ptipm 703,760 25,427 3,357,349 437,210 329,585 9,136,112 38,071

ptnonipm 2,963,103 67,997 2,136,694 582,397 406,476 1,586,719 1,101,615

c3marine 12,724 138,033 12,476 11,452 104,822 4,902

Total 89,451,787 4,288,703 18,953,168 10,382,352 4,366,917 11,559,214 59,137,081

Non

- US Totals
Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 410,176 109,861 171,735 71,082 47,115 53,424 450,935

Mexico othon 2,685,132 14,114 326,165 11,805 9,115 5,462 192,045

Mexico othpt 100,075 0 343,480 120,802 89,358 731,675 77,255

Mexico Subtotal 3,195,383 123,975 841,380 203,690 145,588 790,560 720,235

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 2,607 31,870 2,633 2,402 19,504 1,109

Offshore

c3marine 55,599 674,615 55,891 51,386 417,293 23,635

Total 7,924,652 531,791 2,575,119 1,146,033 494,033 2,124,917 1,988,33786

Table 5

- 2

. National and non

- U.S. CAP emissions by sector for 2018 reference case

US Totals:

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,139,289 866,710

ag 3,717,417

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 236,042 567 1,008,203 29,525 27,390 5,693 39,272

nonpt 4,613,119 157,772 1,185,288 808,719 715,966 317,310 6,423,764

nonroad 12,702,891 2,276 1,067,481 106,715 100,588 2,670 1,408,935

onroad* 16,144,911 80,053 2,495,021 191,678 111,049 26,089 1,800,164

avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

ptipm 857,784 40,255 1,941,441 295,290 233,699 2,131,278 46,057

ptnonipm 2,639,792 68,053 2,010,662 536,133 368,215 957,178 1,013,798

c3marine 18,960 151,489 2,530 2,304 5,734 7,290

Total 62,083,182 4,367,393 11,153,123 9,970,338 4,002,588 3,581,760 55,350,004

Non

- US Totals
Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 527,942 109,840 226,387 70,937 47,201 19,287 577,084

Mexico othon 2,572,443 15,225 304,436 13,185 10,317 3,177 178,219

Mexico othpt 148,760 0 544,711 170,913 127,736 1,066,523 94,351

Mexico Subtotal 3,249,146 125,065 1,075,534 255,035 185,254 1,088,987 849,654

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 3,750 34,364 518 469 1,054 1,595

Offshore

c3marine 87,463 807,580 32,180 29,537 190,969 37,173

Total 8,011,422 532,881 2,944,732 1,171,552 509,917 2,178,569 2,131,77987

Table 5

- 3

. National and non

- U.S. CAP emissions by sector for 2018 control case

US Totals:

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,139,289 866,710

ag 3,717,417

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 236,042 567 1,008,203 29,525 27,390 5,693 39,272

nonpt 4,613,119 157,772 1,185,288 808,719 715,966 317,310 6,423,764

nonroad 12,702,891 2,276 1,067,481 106,715 100,588 1,912 1,408,935

onroad* 15,893,583 80,053 2,248,595 191,209 110,617 11,493 1,757,359

avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

ptipm 857,784 40,255 1,941,441 295,290 233,699 2,131,278 46,057

ptnonipm 2,639,792 68,053 2,010,662 536,133 368,215 957,178 1,013,798

c3marine 18,960 151,489 2,530 2,304 5,734 7,290

Total 61,831,854 4,367,393 10,906,697 9,969,869 4,002,157 3,566,405 55,307,199

Non

- US Totals
Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 527,942 109,840 226,387 70,937 47,201 19,287 577,084

Mexico othon 2,572,443 15,225 304,436 13,185 10,317 3,177 178,219

Mexico othpt 148,760 0 544,711 170,913 127,736 1,066,523 94,351

Mexico Subtotal 3,249,146 125,065 1,075,534 255,035 185,254 1,088,987 849,654

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 3,750 34,364 518 469 1,054 1,595

Offshore

c3marine 87,463 807,580 32,180 29,537 190,969 37,173

Total 8,011,422 532,881 2,944,732 1,171,552 509,917 2,178,569 2,131,77988

Table 5

- 4

. National and non

- U.S. CAP emissions by sector for 2030 reference case

US Totals:

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,174,969 873,719

ag 3,784,658

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 273,741 574 617,480 16,392 15,270 4,633 24,372

nonpt 4,622,228 157,772 1,198,956 809,404 716,460 318,053 6,353,099

N onroad 13,926,036 2,702 715,617 68,626 63,839 3,045 1,182,812

onroad* 14,434,283 84,796 1,367,429 175,639 82,137 22,310 1,260,883

A vefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

P tipm 1,064,259 49,461 1,997,303 300,645 238,259 2,188,169 52,061

P tnonipm 2,693,137 67,596 2,022,539 532,186 364,598 935,044 1,001,777

c3marine 29,906 116,493 3,972 3,654 9,104 11,479

Total 61,913,273 4,448,559 9,329,353 9,942,292 3,934,603 3,616,164 54,497,206

Non

- US Totals
Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 794,133 109,861 326,219 75,903 51,815 9,909 798,874

Mexico othon 2,673,052 17,507 293,017 17,129 13,873 3,499 180,518

Mexico othpt 222,044 0 812,593 249,006 185,682 1,552,126 119,095

Mexico Subtotal 3,689,230 127,368 1,431,829 342,038 251,370 1,565,534 1,098,487

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 5,650 26,704 780 718 1,584 2,405

Offshore

c3marine 146,574 784,389 23,561 21,600 83,949 62,442

Total 8,512,517 535,184 3,270,176 1,250,198 568,346 2,548,627 2,406,69189

Table 5

- 5

. National and non

- U.S. CAP emissions by sector for 2030 control case

US Totals:

Sector CO NH3 NOX PM10 PM25 SO2 VOC

afdust 6,174,969 873,719

ag 3,784,658

biogenic 6,522,111 1,049,976 40,283,861

c1c2rail 273,741 574 617,480 16,392 15,270 4,633 24,372

nonpt 4,622,228 157,772 1,198,956 809,404 716,460 318,053 6,353,099

nonroad 13,926,036 2,702 715,617 68,626 63,839 2,176 1,182,812

onroad* 10,780,871 84,796 1,018,962 166,388 73,619 10,040 1,079,042

avefire 18,347,571 300,999 243,561 1,860,459 1,576,667 135,806 4,326,863

ptipm 1,064,259 49,461 1,997,303 300,645 238,259 2,188,169 52,061

ptnonipm 2,693,137 67,596 2,022,539 532,186 364,598 935,044 1,001,777

c3marine 29,906 116,493 3,972 3,654 9,104 11,479

Total 58,259,861 4,448,559 8,980,886 9,933,042 3,926,085 3,603,025 54,315,365

Non

- US Totals
Country/Sector CO NH3 NOX PM10 PM2.5 SO2 VOC*

Canada othar 2,809,975 386,148 463,154 810,685 248,902 61,190 932,086

Canada othon 1,207,227 6,123 150,856 6,402 5,199 3,679 94,610

Canada othpt 571,728 15,546 338,967 65,952 39,787 831,669 155,906

Canada Subtotal 4,588,930 407,816 952,977 883,039 293,889 896,539 1,182,601

Mexico othar 794,133 109,861 326,219 75,903 51,815 9,909 798,874

Mexico othon 2,673,052 17,507 293,017 17,129 13,873 3,499 180,518

Mexico othpt 222,044 0 812,593 249,006 185,682 1,552,126 119,095

Mexico Subtotal 3,689,230 127,368 1,431,829 342,038 251,370 1,565,534 1,098,487

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 5,650 26,704 780 718 1,584 2,405

Offshore

c3marine 146,574 784,389 23,561 21,600 83,949 62,442

Total 8,512,517 535,184 3,270,176 1,250,198 568,346 2,548,627 2,406,69190 Table 5

- 6

. CO emissions (tons/yr) for each case and state

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 1,924,241 1,360,917 1,351,229 1,355,227 1,255,631

Arizona 1,466,047 957,043 944,935 1,011,463 871,117

Arkansas 1,600,242 1,212,877 1,206,827 1,205,866 1,154,433

California 6,868,040 5,236,858 5,217,036 5,027,026 5,027,026

Colorado 1,190,074 809,875 792,847 872,740 763,605

Connecticut 574,605 357,385 354,137 333,723 332,897

Delaware 162,704 105,413 104,551 93,324 93,129

District of
Columbia 60,932 35,900 35,291 36,923 30,370

Florida 5,022,400 3,138,544 3,105,671 3,230,725 2,815,295

Georgia 3,792,250 2,457,784 2,438,401 2,418,199 2,227,785

Idaho 1,953,101 1,772,944 1,767,029 1,772,829 1,738,622

Illinois 2,288,411 1,436,945 1,421,535 1,493,876 1,330,020

Indiana 1,860,666 1,204,836 1,191,445 1,214,686 1,095,670

Iowa 906,397 498,846 492,379 498,959 455,151

Kansas 1,085,160 754,279 748,374 757,743 706,892

Kentucky 1,313,600 920,767 913,119 919,051 844,092

Louisiana 1,901,576 1,443,889 1,436,282 1,461,086 1,378,073

Maine 417,956 251,190 248,837 230,967 230,019

Maryland 1,054,787 708,961 704,541 666,789 666,962

Massachusetts 876,162 604,516 598,218 578,757 577,788

Michigan 2,970,746 1,688,062 1,668,056 1,628,993 1,486,651

Minnesota 2,785,008 2,148,692 2,137,462 2,094,983 2,011,938

Mississippi 1,170,077 835,928 829,347 836,297 765,258

Missouri 2,144,179 1,422,916 1,411,751 1,422,427 1,311,400

Montana 1,330,031 1,214,531 1,210,079 1,213,708 1,193,598

Nebraska 531,194 310,062 306,155 316,475 283,905

Nevada 724,335 518,145 513,004 542,295 490,562

New Hampshire 304,620 209,280 206,548 215,511 194,314

New Jersey 1,352,241 902,273 893,855 867,908 865,761

New Mexico 677,284 453,892 448,885 445,239 413,092

New York 2,641,178 1,649,663 1,632,656 1,615,599 1,608,909

North Carolina 3,273,181 1,954,088 1,932,231 1,907,338 1,749,607

North Dakota 293,318 209,600 208,195 206,779 195,525

Ohio 3,087,510 1,726,198 1,703,457 1,707,663 1,536,234

Oklahoma 1,686,238 1,214,110 1,204,794 1,221,253 1,133,433

Oregon 2,201,777 1,749,839 1,740,219 1,670,492 1,665,95591 State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Pennsylvania 2,460,443 1,440,750 1,427,500 1,353,066 1,350,221

Rhode Island 151,235 106,761 105,767 101,731 101,681

South Carolina 1,424,534 898,530 890,759 906,112 822,767

South Dakota 321,482 236,051 234,446 234,663 220,361

Tennessee 1,788,332 1,035,150 1,022,408 1,012,970 898,967

Texas 5,466,088 3,430,870 3,388,430 3,632,059 3,119,557

Utah 824,426 589,517 580,111 590,348 538,323

Vermont 188,782 147,145 145,493 140,898 140,486

Virginia 1,837,272 1,146,294 1,134,028 1,174,277 1,039,507

Washington 2,215,115 1,341,308 1,322,137 1,175,059 1,166,307

West Virginia 655,801 432,831 429,529 427,734 401,919

Wisconsin 1,396,496 919,043 910,135 957,000 861,066

Wyoming 656,091 562,905 559,282 562,888 546,433

TOTAL 82,878,364 55,764,204 55,269,404 55,361,723 51,708,31292 Table 5

- 7

. NH 3 emissions (tons/yr) for each case and state

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 78,589 84,806 84,806 86,662 86,662

Arizona 41,818 41,331 41,331 42,090 42,090

Arkansas 135,328 142,800 142,800 145,796 145,796

California 393,315 390,595 390,595 395,474 395,474

Colorado 74,561 74,495 74,495 74,938 74,938

Connecticut 5,158 4,612 4,612 4,691 4,691

Delaware 14,044 15,331 15,331 15,764 15,764

District of
Columbia 366 281 281 308 308

Florida 63,410 60,707 60,707 62,042 62,042

Georgia 116,050 123,001 123,001 125,862 125,862

Idaho 130,482 130,704 130,704 130,326 130,326

Illinois 119,703 122,450 122,450 126,561 126,561

Indiana 98,051 101,698 101,698 104,955 104,955

Iowa 301,952 316,176 316,176 327,804 327,804

Kansas 161,525 162,607 162,607 162,849 162,849

Kentucky 60,996 62,786 62,786 63,724 63,724

Louisiana 84,106 85,334 85,334 86,389 86,389

Maine 7,264 7,353 7,353 7,482 7,482

Maryland 31,585 32,929 32,929 34,163 34,163

Massachusetts 7,229 6,421 6,421 6,700 6,700

Michigan 69,890 69,713 69,713 71,021 71,021

Minnesota 210,960 214,414 214,414 220,096 220,096

Mississippi 68,874 73,643 73,643 75,479 75,479

Missouri 139,500 142,734 142,734 145,793 145,793

Montana 72,677 73,473 73,473 74,049 74,049

Nebraska 179,531 182,275 182,275 182,928 182,928

Nevada 10,483 10,706 10,706 10,761 10,761

New Hampshire 2,303 2,214 2,214 2,266 2,266

New Jersey 11,949 11,042 11,042 11,530 11,530

New Mexico 43,094 42,928 42,928 43,343 43,343

New York 51,428 48,036 48,036 49,238 49,238

North Carolina 187,827 198,254 198,254 206,640 206,640

North Dakota 86,696 88,085 88,085 89,930 89,930

Ohio 111,991 114,657 114,657 117,372 117,372

Oklahoma 112,918 116,127 116,127 118,004 118,004

Oregon 64,640 65,193 65,193 65,684 65,684

Pennsylvania 80,223 81,438 81,438 83,198 83,198

Rhode Island 1,135 1,097 1,097 1,123 1,123

South Carolina 39,330 40,054 40,054 41,038 41,038

South Dakota 134,157 136,513 136,513 139,008 139,00893 State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 42,899 43,506 43,506 43,838 43,838

Texas 320,410 323,482 323,482 326,720 326,720

Utah 42,676 43,006 43,006 42,859 42,859

Vermont 8,404 8,395 8,395 8,507 8,507

Virginia 51,371 51,793 51,793 52,776 52,776

Washington 53,330 53,641 53,641 54,200 54,200

West Virginia 16,526 17,102 17,102 17,436 17,436

Wisconsin 120,740 120,384 120,384 122,140 122,140

Wyoming 26,768 26,877 26,877 26,851 26,851

TOTAL 4,288,260 4,367,201 4,367,201 4,448,408 4,448,40894 Table 5

- 8

. NO X emissions (tons/yr) for each case and state

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 419,234 229,845 224,086 194,697 186,751

Arizona 321,030 166,137 162,417 131,427 123,261

Arkansas 244,131 156,429 152,943 134,335 129,259

California 1,013,990 534,773 534,207 401,302 401,302

Colorado 316,568 193,930 188,903 169,439 160,990

Connecticut 100,231 47,836 45,256 35,480 33,745

Delaware 47,738 22,292 21,481 16,641 16,142

District of
Columbia 14,360 6,129 5,797 4,603 3,930

Florida 924,488 415,734 396,122 307,302 278,307

Georgia 648,360 309,555 295,898 232,426 215,596

Idaho 97,617 68,588 66,444 58,280 55,122

Illinois 706,849 374,954 364,591 308,293 291,621

Indiana 578,412 303,868 296,821 253,053 242,065

Iowa 277,985 181,738 178,314 147,307 142,454

Kansas 313,735 187,508 184,433 153,618 149,064

Kentucky 423,640 210,206 205,730 179,350 172,780

Louisiana 547,743 386,244 382,129 313,834 307,329

Maine 73,939 45,873 44,558 38,031 37,127

Maryland 244,074 110,763 106,299 86,801 84,079

Massachusetts 184,511 98,027 94,083 78,391 75,973

Michigan 596,927 322,122 312,688 274,422 260,185

Minnesota 436,436 251,574 245,143 203,733 195,604

Mississippi 264,622 152,848 148,935 128,683 123,054

Missouri 484,132 250,506 242,841 197,412 186,391

Montana 150,854 90,351 88,666 75,197 72,858

Nebraska 232,064 154,028 152,036 114,138 111,001

Nevada 113,273 68,916 66,421 56,506 52,503

New Hampshire 53,845 28,002 26,788 24,553 21,983

New Jersey 279,205 156,200 150,033 126,532 122,477

New Mexico 242,966 214,189 211,475 193,579 189,554

New York 512,170 288,870 278,693 233,250 225,541

North Carolina 470,574 240,195 229,296 189,772 174,568

North Dakota 158,552 109,343 108,580 90,989 89,772

Ohio 780,928 380,073 367,931 308,996 292,899

Oklahoma 422,925 276,827 271,902 246,670 239,147

Oregon 191,065 117,844 114,051 71,781 69,227

Pennsylvania 649,142 382,391 373,752 323,768 318,208

Rhode Island 24,475 13,179 12,437 9,836 9,418

South Carolina 260,234 147,083 142,073 126,278 119,008

South Dakota 72,894 47,905 47,011 36,593 35,20995 State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 453,075 196,382 188,871 151,166 140,425

Texas 1,643,416 1,049,545 1,027,205 915,168 877,443

Utah 217,960 159,486 156,268 142,187 137,588

Vermont 23,019 13,139 12,430 10,261 9,800

Virginia 409,546 233,194 225,015 186,346 171,417

Washington 338,856 195,727 189,118 120,291 115,573

West Virginia 283,713 133,980 132,031 118,343 115,759

Wisconsin 313,623 181,705 176,143 161,856 152,732

Wyoming 245,053 202,613 201,270 185,500 183,713

TOTAL 17,824,174 10,108,647 9,849,620 8,268,417 7,919,95196 Table 5

- 9

. PM 2.5 emissions (tons/yr) for each case and state

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 108,906 106,546 106,536 105,571 105,391

Arizona 74,270 69,748 69,738 68,653 68,436

Arkansas 108,112 104,947 104,941 104,175 104,073

California 399,796 378,847 378,829 373,435 373,435

Colorado 59,418 58,602 58,625 57,615 57,347

Connecticut 11,334 10,251 10,243 9,806 9,803

Delaware 6,098 4,945 4,943 4,762 4,761

District of
Columbia 1,606 1,283 1,282 1,173 1,156

Florida 167,089 140,539 140,509 137,516 136,937

Georgia 167,573 160,387 160,368 158,003 157,632

Idaho 149,633 148,313 148,322 147,911 147,826

Illinois 114,168 103,352 103,280 101,102 100,555

Indiana 111,294 91,698 91,658 91,207 90,845

Iowa 65,220 57,874 57,863 56,419 56,278

Kansas 114,378 110,828 110,820 109,983 109,854

Kentucky 76,841 76,393 76,371 75,463 75,273

Louisiana 148,650 136,251 136,244 132,771 132,630

Maine 13,644 12,574 12,570 12,117 12,116

Maryland 33,292 24,795 24,770 24,009 23,994

Massachusetts 24,024 21,679 21,663 20,663 20,656

Michigan 92,529 89,602 89,545 87,489 86,974

Minnesota 182,166 176,876 176,837 174,485 174,123

Mississippi 67,639 63,514 63,507 62,644 62,521

Missouri 113,722 104,744 104,718 102,905 102,623

Montana 114,123 115,121 115,126 113,412 113,360

Nebraska 54,712 52,061 52,055 50,565 50,466

Nevada 47,003 47,297 47,291 46,785 46,684

New Hampshire 11,396 11,424 11,418 11,329 11,251

New Jersey 32,028 24,540 24,520 23,033 23,023

New Mexico 73,249 73,117 73,111 72,514 72,402

New York 67,804 59,517 59,483 56,787 56,772

North Carolina 131,243 109,172 109,152 107,654 107,324

North Dakota 45,406 47,772 47,767 46,969 46,914

Ohio 130,887 93,624 93,558 91,451 90,922

Oklahoma 119,448 113,347 113,336 112,259 112,075

Oregon 136,821 134,681 134,692 133,082 133,101

Pennsylvania 121,002 73,505 73,474 71,428 71,413

Rhode Island 3,551 3,283 3,280 3,154 3,153

South Carolina 67,530 54,106 54,098 53,836 53,683

South Dakota 34,265 32,640 32,636 32,079 32,02397 State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 70,324 62,282 62,261 60,406 60,155

Texas 304,810 287,235 287,156 280,355 279,426

Utah 42,657 43,349 43,361 42,052 41,932

Vermont 8,798 10,084 10,081 9,934 9,933

Virginia 56,025 51,254 51,225 50,234 49,896

Washington 81,218 74,509 74,529 72,302 72,341

West Virginia 55,146 37,131 37,124 36,655 36,583

Wisconsin 52,086 53,545 53,507 53,172 52,783

Wyoming 88,542 83,455 83,459 82,734 82,690

TOTAL 4,361,473 4,002,642 4,001,882 3,934,060 3,925,54298 Table 5

- 10

. PM 10 emissions (tons/yr) for each case and state

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 183,182 178,922 178,911 178,468 178,272

Arizona 221,304 216,564 216,553 216,217 215,981

Arkansas 213,094 209,261 209,254 209,464 209,354

California 767,701 746,021 746,001 741,688 741,688

Colorado 167,205 166,169 166,194 166,316 166,024

Connecticut 14,555 13,095 13,085 12,687 12,683

Delaware 8,759 8,981 8,979 8,814 8,813

District of
Columbia 2,263 1,917 1,916 1,814 1,796

Florida 293,334 267,282 267,250 266,055 265,425

Georgia 293,557 280,599 280,578 279,207 278,803

Idaho 255,419 253,805 253,814 253,162 253,069

Illinois 360,962 349,384 349,306 349,836 349,241

Indiana 249,264 232,131 232,088 233,654 233,260

Iowa 231,578 223,006 222,994 223,494 223,341

Kansas 431,017 427,337 427,329 429,770 429,630

Kentucky 127,958 127,539 127,514 127,013 126,807

Louisiana 213,933 198,283 198,275 195,160 195,006

Maine 18,899 17,372 17,368 16,930 16,928

Maryland 47,375 37,502 37,474 36,883 36,867

Massachusetts 50,126 47,953 47,935 47,152 47,144

Michigan 183,520 179,905 179,843 178,640 178,081

Minnesota 379,202 370,541 370,499 369,565 369,172

Mississippi 135,088 130,000 129,992 129,689 129,555

Missouri 313,634 301,513 301,485 300,857 300,552

Montana 240,329 242,981 242,987 241,375 241,319

Nebraska 244,975 242,296 242,290 242,488 242,380

Nevada 207,718 208,009 208,002 207,635 207,525

New Hampshire 13,604 13,375 13,369 13,332 13,247

New Jersey 42,034 34,824 34,801 33,564 33,554

New Mexico 499,439 499,598 499,591 499,279 499,157

New York 110,972 100,461 100,425 98,415 98,399

North Carolina 188,502 162,820 162,799 161,721 161,362

North Dakota 184,477 187,463 187,458 188,449 188,389

Ohio 228,328 190,228 190,156 189,335 188,760

Oklahoma 395,287 387,332 387,320 387,718 387,518

Oregon 202,601 200,067 200,080 198,682 198,702

Pennsylvania 155,601 104,914 104,881 103,180 103,163

Rhode Island 4,623 4,348 4,345 4,248 4,246

South Carolina 126,413 110,963 110,955 111,480 111,314

South Dakota 120,429 119,324 119,319 119,870 119,80999 State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 112,667 104,066 104,043 102,637 102,364

Texas 1,214,912 1,197,608 1,197,521 1,195,116 1,194,108

Utah 134,321 134,782 134,795 133,513 133,383

Vermont 11,723 13,032 13,028 12,912 12,912

Virginia 89,603 83,362 83,330 82,778 82,411

Washington 155,262 148,701 148,723 147,304 147,346

West Virginia 71,742 52,483 52,475 52,054 51,975

Wisconsin 125,130 123,545 123,504 124,019 123,597

Wyoming 328,164 316,276 316,281 315,609 315,562

TOTAL 10,371,781 9,967,938 9,967,113 9,939,248 9,929,998100 Table 5

- 11

. SO 2 emissions (tons/yr) for each case and state

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 532,060 120,900 120,556 127,284 127,008

Arizona 95,311 63,737 63,547 65,920 65,743

Arkansas 96,013 53,257 53,084 56,039 55,890

California 63,232 47,581 47,579 49,907 49,907

Colorado 74,021 24,643 24,328 25,224 24,933

Connecticut 20,638 13,765 13,603 14,143 14,013

Delaware 45,503 8,554 8,506 8,286 8,248

District of
Columbia 1,685 906 882 898 879

Florida 405,072 102,249 100,910 108,181 107,040

Georgia 698,480 135,516 134,826 132,415 131,818

Idaho 28,009 23,180 23,072 23,168 23,073

Illinois 409,821 187,550 186,932 189,244 188,731

Indiana 815,212 227,783 227,371 237,607 237,259

Iowa 189,267 78,977 78,817 81,301 81,157

Kansas 138,105 48,411 48,244 49,836 49,688

Kentucky 417,969 174,597 174,335 179,587 179,376

Louisiana 252,229 146,547 146,284 138,417 138,188

Maine 24,257 6,038 5,953 6,056 5,987

Maryland 312,223 49,690 49,385 50,476 50,225

Massachusetts 84,476 13,227 12,908 13,390 13,126

Michigan 427,357 200,680 200,117 211,957 211,496

Minnesota 129,765 83,686 83,355 88,071 87,797

Mississippi 95,879 37,150 36,918 38,561 38,366

Missouri 418,246 189,346 188,954 191,943 191,609

Montana 38,639 20,432 20,369 18,784 18,729

Nebraska 77,017 57,286 57,181 58,990 58,894

Nevada 18,667 14,363 14,206 14,387 14,245

New Hampshire 51,031 9,421 9,342 9,791 9,723

New Jersey 61,773 15,013 14,554 14,664 14,279

New Mexico 34,258 25,728 25,580 26,093 25,967

New York 240,983 58,409 57,631 58,321 57,641

North Carolina 440,967 116,993 116,434 116,411 115,949

North Dakota 149,603 32,717 32,676 32,833 32,797

Ohio 1,111,660 206,919 206,273 211,543 211,001

Oklahoma 148,867 75,536 75,257 72,933 72,688

Oregon 31,045 26,354 26,152 13,415 13,244

Pennsylvania 1,102,035 189,787 189,263 200,627 200,180

Rhode Island 4,576 3,727 3,674 3,733 3,689

South Carolina 217,175 66,623 66,343 67,591 67,357

South Dakota 13,097 14,981 14,932 15,638 15,595101 State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 297,935 63,903 63,502 64,567 64,236

Texas 651,329 236,570 235,013 207,330 205,952

Utah 35,663 28,388 28,232 26,626 26,490

Vermont 4,173 2,346 2,292 2,426 2,379

Virginia 258,286 72,522 72,082 74,142 73,757

Washington 36,638 16,137 15,781 16,421 16,106

West Virginia 422,049 85,518 85,418 91,986 91,910

Wisconsin 207,922 53,394 53,044 60,050 59,760

Wyoming 115,330 51,648 51,596 49,837 49,794

TOTAL 11,545,544 3,582,687 3,567,299 3,617,050 3,603,918102 Table 5

- 12

. VOC emissions (tons/yr) for each case and state

State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Alabama 394,478 312,021 311,865 295,361 290,621

Arizona 299,405 226,453 229,150 219,068 212,501

Arkansas 367,862 314,065 313,618 301,947 299,537

California 1,493,672 1,203,729 1,214,353 1,155,135 1,151,387

Colorado 359,741 265,489 265,909 259,229 254,687

Connecticut 91,124 56,030 55,574 49,410 49,018

Delaware 30,272 19,775 19,737 17,633 17,506

District of

Columbia 11,564 7,841 7,783 7,516 7,224

Florida 970,251 685,446 685,391 630,355 610,660

Georgia 731,394 580,549 578,596 541,521 532,475

Idaho 491,700 465,738 465,639 458,300 456,974

Illinois 490,297 358,934 357,262 340,282 332,759

Indiana 348,813 264,712 263,731 247,129 241,765

Iowa 189,536 142,561 141,889 132,241 130,142

Kansas 238,892 199,124 198,562 189,268 186,925

Kentucky 284,125 224,140 223,611 210,998 207,531

Louisiana 502,713 427,256 427,040 406,847 402,894

Maine 80,956 56,005 55,813 48,750 48,542

Maryland 172,580 111,856 111,432 101,205 100,363

Massachusetts 180,220 128,399 127,673 118,733 117,986

Michigan 565,194 398,966 397,029 357,995 351,667

Minnesota 622,926 515,939 514,442 481,519 477,844

Mississippi 267,564 213,246 213,264 199,541 196,050

Missouri 423,126 323,326 322,071 302,292 297,093

Montana 293,547 276,893 276,801 272,858 272,065

Nebraska 105,934 82,394 81,972 75,541 74,071

Nevada 152,771 132,532 134,495 132,199 129,597

New Hampshire 57,530 39,034 38,770 35,049 33,985

New Jersey 261,426 177,994 177,196 164,288 163,145

New Mexico 311,387 280,189 280,751 273,093 271,173

New York 535,040 337,635 336,195 307,325 305,271

North Carolina 620,323 474,236 473,183 440,230 432,671

North Dakota 64,768 52,510 52,365 49,120 48,609

Ohio 479,576 317,531 315,292 283,813 276,248

Oklahoma 512,690 450,517 449,649 435,508 431,452

Oregon 435,314 387,230 386,735 367,051 366,503

Pennsylvania 455,475 336,441 337,248 312,623 311,092

Rhode Island 27,159 18,437 18,318 16,762 16,627

South Carolina 292,949 220,191 219,953 205,620 201,702

South Dakota 81,613 70,455 70,331 67,162 66,511103 State 2007rg 2018rg_ref 2018rg_ctl 2030rg_ref 2030rg_ctl

Tennessee 354,294 256,459 255,849 235,103 229,819

Texas 2,414,946 2,158,320 2,155,608 2,108,224 2,083,716

Utah 297,616 287,999 288,306 279,249 276,992

Vermont 30,384 22,924 22,977 20,511 20,367

Virginia 360,687 265,016 264,007 247,344 240,525

Washington 353,447 277,515 276,658 239,200 238,165

West Virginia 117,626 88,643 88,413 82,210 81,028

Wisconsin 337,259 241,006 240,083 222,276 217,957

Wyoming 251,477 260,241 260,208 256,390 255,740

TOTAL 18,813,646 15,013,942 15,002,796 14,201,023 14,019,183104 

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