DRAFT



## Technical Support Document (TSD)

Preparation of Emissions Inventories for the Version 6 .0,

20 11 Emissions Modeling Platform

February 26

, 201 4

U.S. Environmental Protection Agency

Office of Air and Radiation Office of Air Qual ity Planning and Standards Air Quality Assessment Division

Contacts: Rich Mason, Alison Eyth, Alexis Zubrow

, Darin DelVecchioDRAFT

ii 

## TABLE OF CONTENTS

LIST OF TABLES

................................ ................................ ................................ ................................ ................................ .. IV

LIST OF FIGURES

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

LIST OF A PPENDICES

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

ACRONYMS

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

1 INTRODUCTION

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

2 2011 EMISSION INVENTORIES AND APPROACHES

................................ ................................ ............................ 3

2.1 2011 NEI POINT SOURCES ( PTEGU

, PTEGU

_ PK

, PT

_ OILGAS AND PTNONIPM )

................................ ................................ ... 8

2.1.1 EGU non

-

peaking units sector (ptegu)

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

2.1.2 EGU peaking units sector (ptegu_pk)

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

2.1.3 Point source oil and gas sector (pt_oilgas)

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

2. 1.4 Non

- IPM sector (ptnonipm)

................................ ................................ ................................ ............................... 13

2.2 2011 NONPOINT SOURCES ( AFDUST

, AG

, NP

_ OILGAS

, RWC

, NONPT )

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

2.2.1 Area fugitive dust sector (afdust)

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

2.2.2 Agricultural ammonia sector (ag)

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

2.2.3 Nonpoint source oil and gas sector (np_oilgas)

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

2.2.4 Residential wood combustion sector (rwc)

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

2.2.5 Other nonpoint sources sector (nonpt)

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

2.3 2011 ONROAD MOBILE SOURCE S ( ONROAD

, ONROAD

_ RFL )

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

2.3.1 Onroad non

- refueling (onroad)

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

2.3.2 Onroad refueling (onroad_rfl)

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

2.4 2011 NONROAD MOBILE SOURC ES ( C 1 C 2 RAIL

, C 3 MARINE

, NONROAD )

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

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

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

2.4.2 Class 3 commercial marine vessels (c3marine)

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

2.4.3 Nonroad mobile equipment sources: (nonroad)

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

2.5 “O THER E MISSIONS ”: O FFSHORE C LASS 3 COMMERCIAL MARINE VE SSELS AND DRILLING P LATFORMS AND NON

- U.S.

SOURCES

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

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

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

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

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

2.5.3 Onroad mobile sources from Canada and Mexico (othon)

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

2.6 F IRES ( PTFIRE )

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

2.7 B IOGENIC SOURCES ( BIOG )

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

2.8 SMOKE

- READY NON

- ANTHROPOGENIC INVENT ORIES FOR CHLORINE

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

3 EMISSIONS MODELING S UMMARY

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

3.1 E MISSIONS MODELING O VERVIEW

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

3.2 C HEMICAL S PECIATION

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

3.2.1 VOC speciation

................................ ................................ ................................ ................................ .................. 41

3.2.2 PM speciation

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

3.2.3 NO X speciation

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

3.3 T EMPORAL A LLOCATION

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

3.3.1 Use of FF10 format for finer than annual emissions

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

3.3.2 Electric Generating Utility temporalization (ptegu, ptegu_pk)

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

3.3.3 Residential Wood Combustion Temporalization (rwc)

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

3.3.4 Agricultural Ammonia Temporal Profiles (ag)

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

3.3.5 Onroad mobile temporalization (onroad, onroad_rfl)

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

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

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

3.4 S PATIAL A LLOCATION

................................ ................................ ................................ ................................ .................. 68

3.4.1 Spatial Surrogates for U.S. emissions

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

3.4.2 Allocation method for airport

- related sources in the U.S.

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

3.4.3 Surrogates for Canada and Mexico emission inventories

................................ ................................ .................. 73DRAFT

i ii 4 DEVELOPMENT OF 2018 BASE

- CASE EMISSIONS

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

4.1 S TATIONARY SOURCE PRO JECTIONS : EGU SECTORS ( PTEGU

, PTEGU

_ PK )

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

4.2 S TATIONARY SOURCE PRO JECTIONS : NON

- EGU SECTORS ( AFDUST

, AG

, NONPT

, NP

_ OILGAS

, PTNONIPM

, PT

_ OILGAS

, RWC )

84

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

................................ ..................... 86

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

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

4.2.3 Residential wood combustion growth (nonpt)

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

4.2. 4 Oil and Gas projections (np_oilgas, pt_oilgas)

................................ ................................ ................................ . 99

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

................................ ................................ ................. 104

4.2.6 Fuel sulfur rules (nonpt, ptnonipm)

................................ ................................ ................................ ................. 106

4.2.7 Industrial Boiler MACT reconsideration (ptnonipm)

................................ ................................ ....................... 108

4.2.8 Portland Cement NESHAP projections (ptnonipm)

................................ ................................ ......................... 110

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

................................ ............................... 113

4.2.10 Aircraft projections (ptnonipm)

................................ ................................ ................................ ........................ 118

4.2.11 Remaining non

- EGU controls and closures (ptnonipm)

................................ ................................ .................. 120

4.3 M OBILE SOURCE PROJECT IONS

................................ ................................ ................................ ................................ ... 121

4.3.1 Onroad mobile (on road and onroad_rfl)

................................ ................................ ................................ ......... 121

4.4 N ONROAD MOBILE SOURCE PROJECTIONS ( C 1 C 2 RAIL

, C 3 MARINE

, NONROAD )

................................ ............................ 125

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

................................ .............................. 125

4.4.2 Class 3 commercial marine vessels (c3marine)

................................ ................................ ............................... 128

4. 4.3 Other nonroad mobile sources (nonroad)

................................ ................................ ................................ ........ 129

4.5 “O THER E MISSIONS ”: O FFSHORE C LASS 3 COMMERCIAL MARINE VE SSELS AND DRILLING P LATFO RMS

, C ANADA AND

M EXICO ( OTHPT

, OTHAR

, AND OTHON )

................................ ................................ ................................ ................................ 130

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

...................... 130

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

.......................... 130

5 EMISSION SUMMARIES

................................ ................................ ................................ ................................ .......... 131

6 REFERENCES

................................ ................................ ................................ ................................ ............................. 141DRAFT

iv 

## List of Tables

Table 1

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

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

Table 2

- 1. Platform sectors for the 2011 emissions modeling platform

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

Table 2

- 2. Summary of significan t changes between 2011 platform and 2011NEIv1 by sector

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

Table 2

- 3. Point source oil and gas sector SCCs

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

Table 2

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

.................. 13

Table 2

- 5. Toxic

- to

- VOC Ratios for Corn Ethanol Plants

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

Table 2

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

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

Table 2

- 7

. SCCs in the afdust platform sector

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

Table 2

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

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

Table 2

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

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

Table 2

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

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

Table 2

- 1 1. SCCs in the Residential Wood Combustion Sector (rwc)*

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

Table 2

- 12. Onroad emission modes

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

Table 2

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

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

Table 2

- 14. Growth factor adjustment factors to project the 2002 ECA

- IMO inventory to 2011

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

Table 2

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

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

Table 3

- 1. Key emissions modeling steps by sector.

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

Table 3

- 2. Descriptions of the platform grids

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

Table 3

- 3. Emission model species produced for CB05 with SOA for CMAQ5.0.1 and CAM X *

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

Table 3

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

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

T able 3

- 5. VOC profiles for WRAP Phase III basins

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

Table 3

- 6. National VOC profiles for oil and gas

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

Table 3

- 7. Counties included in the WRAP Dataset

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

Table 3

- 8. Select VOC profiles 2011 versus 2018

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

Table 3

- 9. PM model species: AE5 versus AE6

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

Table 3

- 10. MOVES exhaust PM species versus AE5 species

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

Table 3

- 11. NO X speciation profiles

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

Table 3

- 12. Temp oral settings used for the platform sectors in SMOKE

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

Table 3

- 13. Mapping of MOVES to SMOKE road types

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

Table 3

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

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

Table 3

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

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

Table 3

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

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

Table 3

- 17. Canadian Spatial Surrogates

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

Table 3

- 18.CAPs Allocated to Mexican and Canadian Spatial Surrogates

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

Table 4

- 1. Control strategies and growth assumptions for creating the 2018 base

- case emissions inventories

from the 2011 base case

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

Table 4

- 2. Subset of CoST Packet Matching Hierarchy

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

Table 4

- 3. Summary of non

- EGU stationary projections subsections

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

Table 4

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

................................ ..... 86

Table 4

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

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

Table 4

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

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

Table 4

- 7. 2018 biodiesel plant emissions [tons]

................................ ................................ ............................ 88

Tab le 4

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

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

Table 4

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

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

Table 4

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

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

Table 4

- 11. 2018 cellulosic plant emissions [tons]

................................ ................................ ......................... 90

Table 4

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

................................ ...... 90

Table 4

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

................................ ........... 92DRAFT

v Table 4

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

................................ ...... 92

Table 4

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

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

Table 4

- 16. Adjustment factors applied to storage and transport em issions

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

Table 4

- 17. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with gasoline and diesel fuel production.

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

Table 4

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

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

Table 4

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

............ 96

Table 4

- 20. Composite NH 3 projection factors to year 2018 for animal operations

................................ ....... 96

Table 4

- 21. Non

- West Coast RWC projection factors to year 2018

................................ ............................... 98

Table 4

- 22. National RWC impacts f or PM 2.5 and NO X from 2011 to 2018

................................ .................. 99

Table 4

- 23. AEO

- based 2018 Projection Factors

................................ ................................ .......................... 101

Table 4

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

................................ ........ 102

Table 4

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

Table 4

- 26. Projected by

- state NO X and VOC 2011 and 2018 Oil and Gas sector emissions

...................... 103

Table 4

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

............ 105

Table 4

- 28. National by

- sector reductions from RICE Reconsideration Controls

................................ ........ 106

Table 4

- 29. Summary of fuel sulfur rules by state

................................ ................................ ......................... 108

Table 4

- 30. Facility types potentially subject to Boiler MACT reductions

................................ ................... 109

Table 4

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

................................ ........... 109

Table 4

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

.... 110

Table 4

- 33. Locations of new ISIS

- generated cement kilns

................................ ................................ ........... 111

Table 4

- 34. U.S. Census Division ISIS

- based projection factors for existing kiln s

................................ ..... 112

Table 4

- 35. ISIS

- based cement industry change (tons/yr)

................................ ................................ ............ 112

Table 4

- 36. Impacts of most non

- EGU point source state comments received in 2013

............................... 114

Table 4

- 37. Minor source ptnonipm sector NAICS

- level projections for Texas

................................ .......... 115

Table 4

- 38. Minor source nonpt sector projections for Texas

................................ ................................ ....... 115

Table 4

- 39. Target company

- wide reductions from OECA consent decree information

.............................. 117

Table 4

- 40. Default national

- level fac tors used to project 2011 base

- case aircraft emissions to 2018

........ 118

Table 4

- 41. Increases in aircraft emissions by year 2018 from airport

- specific and national

- level methods 119

Table 4

- 42. Reductions from all EIS

- based and remaining information facility/unit

- level closures

............. 121

Table 4

- 43. Projection factors for 2018 V MT (in millions of miles)

................................ ............................. 122

Table 4

- 44. Comparison of MOVES runs for 2018

................................ ................................ ....................... 123

Table 4

- 45. CA LEVIII program states

................................ ................................ ................................ .......... 123

Table 4

- 46. Non

- California year 2018 intermediate projection factors for locomotives and Class 1 and

Class 2 Commercial Marine Vessel Emissions

................................ ................................ ...................... 126

Table 4

- 47. EISA mandate emission adjustments in 2018

................................ ................................ ............. 127

Table 4

- 48. Difference in c1c2rail sector emissions between 2011 a nd 2018

................................ .............. 128

Table 4

- 49. Growth factors to project the 2011 ECA

- IMO inventory to 2018

................................ ............. 128

Table 5

- 1. National by

- sector CAP emissions summaries for 2011 evaluation case

................................ .... 132

Table 5

- 2. National by

- sector CAP emissions summaries for 2018 base case

................................ ............. 133

Table 5

- 3. National by

- sector CO emissions (tons/yr) summaries with differences

................................ ..... 134

Table 5

- 4. National by

- sector NH 3 emissions (tons/yr) summaries with differences

................................ ... 135

Table 5

- 5. Natio nal by

- sector NO X emissions (tons/yr) summaries with differences

................................ ... 136

Table 5

- 6. National by

- sector PM 2.5 emissions (tons/yr) summaries with differences

................................ . 137

Table 5

- 7. National by

- sector PM 10 emissions (tons/yr) summaries with differen ces

................................ .. 138

Table 5

- 8. National by

- sector SO 2 emissions (tons/yr) summaries with differences

................................ .... 139

Table 5

- 9. National by

- sector VOC emissions (tons/yr) summaries with differences

................................ .. 140DRAFT

vi 

## List of Figures

Figure 2

- 1. January PM 2.5 afdust emissions: raw 2008 NEI (top), after application of transport fraction (middle) and final post

- meteorological adjusted (bottom)

................................ ................................ ....... 19 Figure 2

- 2. Illustration of regional modeling domains in ECA

- IMO study

................................ .................... 30 Figure 2

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

................................ ............................... 35 Figure 2

- 4. An nual isoprene emissions output from BEIS 3.14 for 2011

................................ ....................... 35 Figure 3

- 1. Air quality modeling domains

................................ ................................ ................................ ....... 38 Figure 3

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

................................ .......... 42 Figure 3

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

................................ ................................ ....................... 55 Figur e 3

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

............................ 57 Figure 3

- 5. RWC diurnal temporal profile

................................ ................................ ................................ ...... 58 Figure 3

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

................................ ............................... 59 Figure 3

- 7. Day

- of

- week temporal profiles for OHH and Recreational RWC

................................ ............... 59 Figure 3

- 8. Annual

- to

- month temporal profiles for OHH and recreational RWC

................................ .......... 60 Figure 3

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

- 10. Example of SMOKE

- MOVES temporal variability of NO X emissions

................................ ..... 62 Figure 3

- 11. Previous onroad diurnal weekday profiles for urban roads

................................ ........................ 63 Figure 3

- 12. Variation in MOVES diurnal profiles

................................ ................................ ......................... 63 Figure 3

- 13. Use of submitted versus new national default profiles

................................ ............................... 64 Figure 3

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

............... 66 Figure 3

- 15. Agricultural burning diurnal temporal profile

................................ ................................ ............ 68 Figure 4

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

................................ ............... 92 Figure 4

- 2. Oil and Gas NEMS Regions

................................ ................................ ................................ ....... 100 Figure 4

- 3. Cement sector trends in domestic production versus nor malized emissions

.............................. 111



## List of Appendices

A ppendix A: Nonpoint Oil and Gas NEI SCCs Appendix B: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT

A ppendix C : Crosswalk b etween AE6 Profile Codes and SPECIATE 4.3 P rofile C odes Appendix D: Memo Describing the Differences in MOVES speciated PM and CMAQ PM Appendix E: CAP emissions by surrogate and sector Appendix F : SMOKE Input Data Files and Parameters Used in the 2011 Evaluation and 2018 Base Cases Appendix G : Future Animal Population Projection Methodology, Updated 07/24/12 Appen dix H : Boiler MA CT ICR Fuels Cross

- Reference to NEI SCCsDRAFT

vii 

## 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 Annu al 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 Data base, 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 A ir Interstate Rule

CAMD 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 Amer ican Commission for Environmental Cooperation

CEM S Continuous Emissions Monitoring System

CEPAM California Emissions Projection Analysis Model

CISWI Commercial and Industrial Solid Waste Incineration

Cl Chlorine CMAQ Community Multiscale Air Quality

CMV Commercial M arine V essel

CO Carbon monoxide

CSAPR Cross

- State Air Pollution Rule

E0 , E10, E85 0%, 10% and 85% Ethanol blend gasolines, respectively

EBAFM Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol

ECA Emissions Control Area

EEZ Exc lusive 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 F ederal Aviation Administration

FAPRI Food and Agriculture Policy and Research Institute

FASOM Forest and Agricultural Section Optimization Model

FCCS Fuel Characteristic Classification System

FF10 Flat File 20 10

FIPS Federal Information Processing Sta ndards

FHWA Federal Highway AdministrationDRAFT

viii HAP Hazardous Air Pollutant

HCl Hydrochloric acid

HDGHG Heavy

- Duty Vehicle Greenhouse Gas Hg Mercury

HMS Hazard Mapping System

HPMS Highway Performance Monitoring System

HWC Hazardous Waste Combustion

HWI Hazardous Waste Incineration

ICAO International Civil Aviation Organization

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

ICR Information Collection Request

IDA Inventory Data Analyzer

I/M Inspection and Maintenance

IMO Inte rnational 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

MA CT 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 th e Bureau of Energy

Management, Regulation and Enforcement (BOEMRE)

MOVES Motor Vehicle Emissions Simulator (2010b)

-- OTAQ’s model for estimation

of onroad mob ile 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 Electri c 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

N LEV 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 ReviewDRAFT

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

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

ORIS Office of Regulatory Information System

ORD EPA ’s Office of Research and Development

ORL One Record per Line

OTC Ozone Transport Commission

PADD Petroleum Administration for Defense Districts

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

PFC Portable Fuel Container

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

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 Env ironmental 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 Governor s’ Association

WRAP Western Regional Air Partnership

WRF Weather Rese arch and Forecasting ModelDRAFT

1 

## 1 Introduction

Th e U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform for 2011 based on the 2011 National Emissions Inventory, v ersion 1 (2011NEIv1) . The air quality modeling plat form consists of all the emissions inventories and ancillary data files used for emissions modeling, as well as the meteorological, initial condition, and boundary condition files needed to run the air quality

model. This document focuses on the emissions modeling component of the 2011 modeling platform, which includes the emission inventories, the ancillary data files , and the approaches used to transform inventories for use in air quality modeling. Many emissions inventory components of this air quality modeling platform are based on the 2011NEIv1, although there are some differences between the platform inventories and the 2011NEIv1 emissions.

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

h azardous air pollutants (HAPs): chlorine (Cl), hydrogen chloride (HCl), benzene, acetaldehyde, formaldehyde and methanol. The latter four HAPs are also abbreviated as BAFM. This platform is called

the “CAP

- BAFM 2011

- Based Platform, version 6” because it is primarily a CAP platform with BAFM species included. Here, “version 6” denotes an evolution from the 2007

- based platform, version 5, with substantial improvements due to the use of newer data and methods. For the rest of this document, the platform th at is described is referred to as the “2011 platform” or “2011v6”. Future updates to the 2011 platform will include a version qualifier such as “2011 Platform v6.1”, and so on.

The first use of the 2011 platform is for the proposed rule related to the transport of ozone that will focus on helping states in the eastern United States meet the 2008 National Ambient Air Quality Standards (NAAQS) for ozone. The air quality model used for this rule is the Comprehensive Air Quality Model

with Extensions (CAM X ) model ( http://www.camx.com/ ), version 6. 10 ; however , emissions are first processed for the Community Multiscale Air Quality (CMAQ) model ( http://www.epa.gov/AMD/CMAQ/ ), version 5.0.1 and then converted to CAM X - ready format. Both

CAM X and CMAQ support modeling ozone (O 3 ) and particulate matter (PM), and require as input hourly and gridded emissions of chemical species that correspond to CAPs and specific HAPs. The chemical

mecha nism used by CAM X for this platform is called Carbon Bond 2005 (CB05) with chlorine chemistry. CB05 allows explicit treatment of BAFM and includes HAP emissions of HCl and Cl.

T he 20 11 platform consists of t wo ‘complete’ emissions cases : the 20 11 base cas e (i.e., 2011ed_v6) and

the 20 18 base case (i.e., 2018ed_v6)

. In the case abbreviations, the 2011 and 2018 are the year represented by the emissions; the “e” stands for evaluation, meaning that year

- specific data for fires and EGUs are used, and the “d” r epresents that this was the fourth set of emissions modeled for the 2011v6 platform. Table 1

- 1 provides more information on these emissions cases. The purpose of the 20 11 base

case is to represent the year 2011 in a manner consi stent with the methods used in corresponding future

year cases
, including the 2018 future year base case
, as well as any additional future year control and source apportionment cases.

For regulatory applications, the outputs from the 20 11 base case are used in conjunction with the outputs

from the 20 18 base case in the relative response factor (RRF) calculations to identify future areas of

nonattainment. For more information on the use of RRFs and air quality modeling , see “Guidance on the

Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5, and Regional Haze”, available from http://www.epa.gov/ttn/scram/guidance/guide/fin al

- 03

- pm

- rh

guidance.pdf

. This document is available on EPA’s Emissions Modeling Clearinghouse website, http://www.epa.gov/ttn/chief/emch/ , under the section entitled “2011

- based Modeling Platform (2011v6 Platform)”.DRAFT

2 Table 1

- 1

. List of cases in the 2011 Version 6 Emissions Modeling Platform

Case Name Abbreviation Description

20 11 base case 20 11ed _v 6 20 11 case relevant for air quality model evaluation purposes and for computing relative response factors with 2018 scenario(s).

Uses 2011NEIv1 and some other inventory data , with hourly 2011 continuous emissions monitoring System ( CEMS ) data for Electrical Generating Units (EGUs) , hourly onroad mobile emissions, a nd 2011 day

- specific wild and prescribed fire data.

20 18 base case 20 18ed _v 6 20 18 “base case” scenario, representing the best estimate for the future year that incorporates estimates of the impact of current

“on

- the

- books” regulations, without including implementation of

controls needed to attain current PM 2.5 annual and 24

- hour

standards (35 ppm and 15 ppm
, respectively) and o zone 8

- hour

standard (75 ppb)

.

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

1) P oint and nonpoi nt sources are based on the 20 11 NEI v 1

. 2) Onroad mobile sources are based on year 20 11 emissions computed using the Sparse Matrix Operator Kernel Emission (SMOKE) interface to emission factors developed with the version of

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

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

. 3) N onroad mobile sources are based on the 2011NEI v1

, except for some additions of VOC in

Californ ia wh ere there were HAP emissions but no VOCs in 2011NEIv1

. 4) Commercial marine vessels (CMV) are based on the 2011NEI v1 except for 2011 California Air Resources Board ( C ARB ) inventory in California, 2010 regiona l planning organization (RPO)

inventories in t he Midwest

, and a separate year

- 2002

- based (projected to 2011) inventory for Class

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

.

The primary emissions modeling tool used to cre ate the air quality model

- ready emissions was SM OKE modeling system ( http://www.smoke

- model.org/ ). S MOKE version 3. 5 .1 was used to create emissions

files for a 12

- km national grid that includes all of the contiguo us states “12US2”, shown in Figure 3

- 1

. B oundary conditions for this grid were obtained from a 2011 run of GEOS

- Chem ( http://geos

- chem.org/ )

. Electronic copies of the data used as input to SMOKE for the 2011 Platform ar e available from the Emissions Modeling Clearinghouse website, http://www.epa.gov/ttn/chief/emch/

.

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

Rese arch a nd Forecasting Model (WRF, http://wrf

- model.org ) version 3.4 , Advanced Research WRF (ARW) core (Skamarock, et al., 2008) . The WRF Model is a mesoscale numerical weather prediction system developed for both operational for ecasting and atmospheric re search applications

. WRF was run

for 2011 over a domain covering the continental United States at a 12km resolution with 35 vertical layers. The data was collapsed to 25 layers prior to running the emissions and air quality mode ls.

This document contains five sections and several appendices. Section 2 describes the 20 11 inventories input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used with the emission inventories. Section 4, describes the de velopment of the 20 18 inventory (projected from 20 11 ). Data summaries comparing the 20 11 base case and 20 18 base case are provided in Section 5. Section 6 provides references. The Appendices provide additional details about specific technical methods.DRAFT

3 

## 2 20 11 Emission Inventories and Approaches

This section describes the 20 11 emissions data that make up the 2011 platform. The starting point for the

2011 stationary so urce emission inputs is the 2011 NEI v1

. Emissions of NO X , SO 2 , VOC and PM

emissions decreas e from values in the 2008 NEI version 3 for most source sectors

, with a couple of notable exceptions including increased industrial NO X , VOC and CO associated with increased oil and gas sector emissions and improved emission estimates; slightly increased V OC, CO and NH 3 from fuel

combustion ; and increased wildfire emissions. Documentation for the 2011NEIv1, including a Technical Support Document (TSD), is available at http:/ /www.epa.gov/ttn/chief/net/2011inventory.html#inventorydoc

.

The NEI data for CAPs are largely compiled from data submitted by state, local and tribal (S/L/T) air agencies. HAP emissions data are also from the state data, but are often augmented by EPA because they

are voluntar il y submitted

. EPA use s the Emissions Inventory System (EIS) to compile the NEI. EIS includes hundreds of automated QA checks to help improve data quality, and also supports tracking release point ( e.g., stack) coordinates separa tely from facility coordinates. EPA collaborat ed extensively with S/L/T agencies to ensure a very high quality of data in the 2011NEI v1 . Tangible benefits of this collaboration are seen in improved data quality from past first version inventories

, improv ed completeness

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

mobile source emission s in the 20 11 NEI v1 were developed using M OVES 2010b; however, the 2011

emissions modeling platform used a differen t version of MOVES

, hence forth referred to as “MOVESTier3NPRM”, that facilitated the representation of the proposed Tier 3 standards in future years

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

year 2011.

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

sources, nonroad mobile sources, onroad mobile sources, and events c onsist ing of fires. The 20 11 NEI v1

uses 60 sectors to further describe the em issions

, with an additional biogenic sector generated from a summation of the gridded, hourly 2011 biogenic data used in the modeling platform . In addition to the NEI data, emissions from the Canadian and Mexican inventories and several other non

- NEI data sources are included in the 20 11 platform. As explain ed below, the non

- NEI emissions component to the 20 11 platform primarily includes : different version of MOVES

- based onroad mobile source emissions, non

meteorologically

- adjusted road dust, year

- 20 10 co mmercial marine vessel (CMV) emissions in the

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

.

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

Rean alysis Tool for Fire Incident Reconciliation (SMARTFIRE) system (Sullivan, et al., 2008). SMARTFIRE 2 was the first version of SMARTFIRE to assign all fires as either prescribed burning or wildfire categories. In past inventories, a significant number of fires were published as unclassified, which impacted the emissions values and diurnal emissions pattern. Recent updates to SMARTFIRE include improved emission factors for prescribed burning.

For the purposes of preparing the air quality model

- ready emis sions, the 20 11 NEIv1 was split into finer

grained sectors used for emissions modeling . The significance of an emissions modeling or “platform

sector ” is that the data are run through all of the SMOKE programs except the final merge (Mrggrid) independently from the other sectors. The final merge program then combines the sector

- specific gridded, speciated

, hourly emissions together to create CMAQ

- ready emission inputs. For CAMx applications, the CMAQ

- ready emissions are then converted into the format neede d by CAMx by a convertor program

.DRAFT

4 Table 2

- 1 presents the sectors in the 20 11 platform and how they generally relate to the 20 11 NEI v1 as a starting point. As discussed in greater detail in Table 2

- 2 , the emissions in some of these sectors were

modified from the 2011NEIv1 emissions for the 20 11 modeling platform. The platform sector abbreviations are provided in italics. These abbreviations are used in the SMOKE modeling scripts, inventory file names, an d throughout the remainder of this document.

Table 2

- 1

. Platform sectors for the 20 11 emissions modeling platform

Platform Sector:

abbreviation NEI Data

Category Description and resolution of the data input t o SMOKE

EGU non

- peaking

units :

p t egu Point 20 11 NEI point source EGUs determined to operate as non

- peaking

units based on criteria discussed in Section 2.1. For future year emissions, t hese units are mapped to the Integrated Planning Model (IPM) model us ing the National Electric Energy D atabase System (NEEDS) version 5 .1 3

. The 2011NEIv1 emissions are replaced with hourly 20 11 CEM S values for NO X and SO 2

, where the units match

. Other pollutants are scaled from 20 11 NEI v1 using CEMS heat input

. Emissions f

or all n on

- CEM S sources come from 20 11 NEI v1

. Annual

resolution for non

- CEMS sources, hourly for CEMS sources

. EGU peaking

units :

p t egu_pk Point Same as ptegu sector, but limited to EGUs that are determined to operate as peaking units, as discussed in Sec tion 2.1. All sources in

this sector have CEMS data for 2011 and are therefore hourly

. Point source oil and gas: pt_oilgas Point 2011NEI v1 point sources with oil and gas production emissions

processes.

Remaining n on

EGU point :

p tnonipm Point All 2011 N EI v1 point source records not matched to the ptegu, ptegu_pk

, and pt_oilgas sector s , except for offshor e point sources that

are in the othpt sector . Includes all aircraft emissions and some rail yard emissions. Annual resolution. Agricultural:

a g Nonpoi nt NH 3 emissions from 2011 NEI v1 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 2011 NEI v1 nonpoint inventory including building constru ction, road construct ion,

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

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

. Instead, the emissions modeling adjustment applies a

transport fraction and a meteorology

- based ( precipitation and snow/ice cover) zero

- out. C ounty and annual resolution. Nonp oint source oil and gas: np_oilgas Nonpoint 2011NEI v1 nonpoint sources from oil and gas

- related processes

. County and annual resolution.

Residential Wo od

Combustion:

rwc Nonpoint 2011 NEIv1 NEI nonpoint sources with Residential Wood Combustion (RWC) processes . County and annual resolution.

Class 1 & 2 CMV

and locomotives:

c1c2rail Nonpoint L ocomotives and primarily category 1 (C1) and category 2 (C2)

co mmercial marine vessel (CMV) emissions sources from the

2011 NEI v1 nonpoint inventory

. Midwestern states’ CMV emissions, including Class 3 sources, are from a separate year 2010 emissions inventory. C ounty and annual resolution.DRAFT

5 Platform Sector:

abbreviation NEI Data

Category Description and resolution of the data input t o SMOKE

commercial

marine:

c3mari ne Nonpoint Category 3 ( C3 ) CMV emissions projected to 2011 from year 2002

values

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

d eveloped for the rule called “ Control of Emissions from New Marine Compression

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

- International Maritime Organization (ECA

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

. (E PA

- 420

- F

- 10

- 041, August 2010). U.S. st ates

- only emissions (zero in Midwest) ; see othpt

sector for all non

- U.S. emissions. Treated as point sources to reflect shipping lanes, a nnual resolution

. Remaining nonpoint:

n onpt Nonpoint 2011 NEI v1 nonpoint sources not otherwise removed from modeling o r included in other platform sectors; county and annual resolution.

Nonroad:

nonroad Nonroad 2011NEIv1 nonroad equipment emissions developed with the National Mobile Inventory Model (NMIM) using NONROAD2008 version

NR08 a . NMIM was used for all states ex cept California and Texas

, which submitted their own emissions to the 2011NEI v1

. County and monthly resolution.

Onroad non

refueling:

onroad O nroad 2011 o nroad mobile source gasoline and diesel vehicles from parking lots and moving vehicles. Includes t he following modes: exhaust, extended idle, evaporative, permeation, and brake and tire wear. For all states except California and Texas , based on monthly MOVES

emissions tables from MOVESTier3NPRM

. Texas emissions are

from the 2011NEIv1 based on MOVES 2 010b , and California

emissions are based on Emission Factor (EMFAC). MOVES

- based emissions computed for each hour and model grid cell using monthly

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

Onroad refueling: onroad_rfl O nroad 2011NEIv1 o n road mobile gasoline and diesel vehicle refueling

emissions for all states. Based on MOVES 2010b emissions tables. Computed hourly based on temperature and for each model grid cell.

Point source fires: ptfire Fires Point source da y

- specific wildfires an d prescribed fires for 20 11 computed using SMARTFIRE 2, except for Georgia

- submitted

emissions

. Consistent with 2011NEIv1.

Other point

sources not from

the 2011 NEI: othpt N/A Point sources from Canada’s 2006 inventory and Mexico’s Phase III

20 12 inventor y, annual resolution. Mexico’s inventory is year 2012

and grown from y ear 1999 (ERG, 2009; Wolf, 2009)

. Also includes

all non

- U.S. C3 CMV and U.S. offshore oil production, which are unchanged from the 2008 NEI point source annual emissions.

Other non

- NE I nonpoint and

nonroad:

othar N/A Annual year 2006 Canada (province resolution) and year 20 12 (grown from 1999) Mexico Phase III (municipio resolution) nonpoint and

nonroad mobile inventories.

Other non

- NEI

onroad sources:

othon N/A Year 2006 Canada (pro vince resolution) and year 20 12 (grown from 1999) Mexico Phase III (municipio resolution) onroad mobile inventories, annual resolution.

Biogenic:

beis Nonpoint Year 20 11 , hour

- specific, grid cell

- specific emissions generated from

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

Mexico.DRAFT

6 Table 2

- 2 provides a brief by

- sector overview of the most significant differences between the 20 11 emissions platform and the 20 11 NEI v1

. O nly those sectors with significant differe nces between the

2011NEI v1 and the 2011 emissions modeling platform are listed . For some sectors, such as non

- EGU point ( ptnonipm ) , these changes are very minor and local ized . In contrast, other sectors such as C3

commercial marine ( c3marine ) are either completely replaced or have significant and detailed edits based on review of available alternative data. The specific by

- sector u pdates to the 20 11 platform are described in greater detail later in this section under each by

- sector subsection. A spreadsh eet quantifying the

differences between the 2011 NEI v1 and modeling platform is available on CHIEF under ftp://ftp.epa.gov/EmisInventory/2011v 6/v1platform/reports/ 2011NEIv1_versus_2011ed_differences.xlsx

.

Table 2

- 2

. Summary of s ignificant c hanges between 20 11 platform and 20 11 NEI v1 by s ector

Platform Sector Summary of Significant Inventory Dif ferences of 20 11 Platform vs

. 20 11 NEI v1

IPM sector s :

p t egu & ptegu_pk 1) Based on 2011 NEI v1 and 2011 CEMS data analysis, added ORIS Boiler IDs to

some units (greater than 1,000 tons of NO X or SO 2 ) with missing or incorrect

values to allow for hourly CEMS da ta processing

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

. 3) Hourly NO X and SO 2 CEMS data replaces annual NO X and SO 2 NEI data in the air quality model inputs. Remaining non

EGU (IPM)

sector: ptnonipm 1) Based on items above (ptegu & pt egu_pk), made additional matches to IPM_YN codes and ORIS facility codes that caused several sources to move into the pt egu and ptegu_pk sector s . This edit prevents double counting of EGU emissions in the future years. 2) Included 20 11 ethanol plant facili ties from EPA’s Office of Transportation and Air Quality (OTAQ) that were not identified in the 20 11 NEI v1

. Area fugitive dust: afdust 1) Replaced EPA

- provided emission estimates for paved and unpaved road dust

with “non

- met

- adjusted” emissions; i.e., the me teorology/precipitation

reduction included in the 2011 NEI v1 is backed

- out. 2) All emissions in this sector are processed ( adjusted ) to reflect land use (transport) and meteorological effects such as rain and snow cover that significantly reduce PM emissions i nput to the air quality model. These adjusted emissions are known as the afdust_adj emissions. Remaining nonpoint sector:

nonpt 1) Spli t the 2011 NEI v1 nonpoint file into the platform sectors afdust, ag, np_oil gas, rwc
, c3 marine

, and c1c2rail

. 2) Used agricultur al fires emissions from daily inventory aggregated to monthly

values , whereas the NEI only stores annual values

. Class 1 & 2 CMV

and locomotives:

c1c2rail 1) Replaced Midwest RPO states c1c2 CMV emissions with comprehensive year 2010 RPO inventory

. 2) Replaced all California estimates with year

- 20 11 CARB estimates.

C3 commercial

marine:

c3marine 1) Used non

- 2011 NEI v1

- based data. R ather used year

- 20 11 point sources as projected from 2002 from the ECA

- IMO project

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

Nonroad sector:

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

in California

. 2) Texas : replaced with annual 20 11 Texas data apportioned to months using

EPA’s 20 11 nonroad estimates.DRAFT

7 Platform Sector Summary of Significant Inventory Dif ferences of 20 11 Platform vs

. 20 11 NEI v1

Onroad non

re fueling:

onroad 1) For all states except California and Texas : Year 20 11 emissions for all pollutants and modes (exhaust , evaporative, permeation, tire and brake wear) 2
from all vehicle types are based on emission factors from the version of

MOVES Tier3NPRM

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

2011 NEI v1 . Processed with 20 11 meteorology using SMOKE

- MOVES (discussed later). 2) For California and Texas : merged in 20 11 California and Texas data to post

adjust SMOKE

- MOVES data (discussed later)

. Onro ad non

refueling: onroad_rfl For all states including California: Year 20 11 emissions for all pollutants AND all

vehicle types are based on MOVES2010b emission factor tables p rocessed with

20 11 meteorology using SMOKE

- M OVES (discussed later ). Therefore, these

emissions are i dentical to the 2011 NEI v1 for states that did not submit refueling

emissions , but are inconsistent with 2011 NEI v1 for states that did submit point and nonpoint refueling since the 2011 NEI v1 kept state

- submissions over EPA data.

The emission inventories in SMOKE input format for the 2011 base case are available from the Emissions Modeling Clearinghouse website http://www.epa.gov/ttn/chief/emch/index.html#2011 . The i nventories “readme” file indicates the particular zipped files associated with each platform sector. A number of reports were developed for the 2011 platform. Descriptions of the available data and reports are available

from the FTP site at ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform . The types of reports include state summaries of inventory pollutants and model species by modeling platform sector for 2011 and 2018

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

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

“ 2011ed_2018ed_comparison_6jan2014.xlsx ”. CAP emission totals by county, month, and modeling platf orm sector are available in the files “ 2011ed_county_monthly_report_CAPs.xlsx ” and

“ 2018ed_county_monthly_report_CAPs.xlsx ”. Summaries by state and source classification code (SCC), including SCC descriptions, by modeling sector for 2011 and 2018 are avail able at the FTP site: ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/State

- SCC

- Summaries/ . A comparison of the complete list of inventory files, ancilla ry files, and parameter settings for the 2011 and 2018 modeling

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

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

Different levels o f detail are provided for different sectors depending on the availability of reference information for the data, the degree of changes or manipulation of the data needed to prepare it for input to SMOKE, and whether the 2011 platform emissions are signific antly different from the 2011NEIv1.

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

8 

### 2.1 20 11 NEI point sources (pt egu, ptegu_pk, pt_oilgas and ptnonipm)

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

emission release points, which may be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and

sometimes b urns natural gas). With a couple of minor exceptions, this section describes only NEI point sources within the contiguous United States. The offshore oil platform (othpt sector) and category 3 CMV emissions (c3marine and othpt sector s ) are processed by S MOKE as point source inventories

, as described in Section 2.5.1 and Section 2. 4.2 , respectively

. A comprehensive description on how EGU

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

2011NEIv1 TSD.

The point source file used for the modeling platform is exported from EIS into the Flat File 2010 (FF10) format that is compatible with SMOKE (see http://www.cmascen ter.org/smoke/documentation/3.5.1/html/ch08s02s10.html#d0e44906 ). After moving offshore oil platforms into the othpt sector, initial version s of the other four platfor m point

source sectors w ere created from the remaining 2011 NEI v1 point sources . The poin t sectors are : the

EGU sector for non

- peaking units (ptegu), the EGU sector for peaking units (ptegu_pk), point source oil and gas extraction

- related emissions (pt_oilgas) and the remaining non

- EGU sector also called the non

IPM (ptnonipm) sector. The EG U emissions are split out to facilitate the use of distinct SMOKE temporal processing and future

- year projection techniques. The EGU sectors are further split into “peaking” (ptegu_pk) and non

- peaking units to allow for better analysis of the impact of pe aking units

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

- year projection techniques from the remaining non

- EGU emissions (ptnonipm).

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

summa r ies are available on the Emissions Modeling Clearinghouse website. A summary report of stack parameters for the point source sectors, including cross references to CEMS data via ORIS IDs, can be

found in the file ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2011_emissions/2011ec_stack_param eter_re port.xlsx

. A comparison of the 2011NEIv1 EGU emissions with the 2011 CEMS data is available in the same directory in the file “ 2011_EGUs_NEI_CEMS.xlsx ”

.

The inventory pollutants processed through SMOKE for both all point source sectors were: CO, NO X , VOC, SO 2 , NH 3 , PM 10 , and PM 2.5 and the following HAPs: HCl (pollutant code = 7647010 ), and C l (code = 7782505 ). T he inventory BAFM from these sectors was not used, instead VOC was speciated to these pollutants without any use (i.e., integration) an d the VOC HAP pollutants from the inventory were ignored ( VOC integration is discussed in detail in Section 3.2.1.1 ).

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

pt egu and ptegu_pk sources with CEMS data (that could be match ed to the 2011 NEI v1 )

, 20 11 hourly

CEMS NO X and SO 2 emissions were used (rather than NEI emissions) and for all other pollutants

annual emissions were used as

- is from the NEI , but were allocated to hou r ly values us ing heat input

CEMS data . For the non

- CEMS sources in the ptegu and ptegu_pk sectors , daily emissions were created using an approach described in Section 2.1.1, and IPM region

- and pollutant

- specific diurnal profiles were applied to create hourl y emissions.DRAFT

9 C hanges made to the point

- based sectors from the 20 11 NEI v1 for the 20 11 platform were briefly

described in Table 2

- 2 . One of these changes involved splitting the stacks, units and facilities into the ptnonipm , pt_ oilgas, ptegu and ptegu_pk sectors. Sources were included in the pt egu or ptegu_pk

sector s only when it was determined that these sources were reflected in the future

- year IPM output data. These changes and other updates to the point source sectors for t he 20 11 platform are discussed in the following sections.



### 2.1.1 EGU non

- peaking units s ector (pt egu )

The pt egu and ptegu_pk (see Section 2.1.2) sector s contain emissions from EGUs in the 20 11 NEI v1 point inventory that could be matched to units found in the NEEDS v5.13 database

. It was necessary to put these EGUs into separate sector s in the platform because IPM projects future emissions for the

EGUs defined in the NEEDS database , and emissions for sources in the ptegu and ptegu_pk sectors are replaced with IPM ou tputs in the future year modeling case

. Sources not matched to units found in

NEEDS are placed in to the pt_oilgas (see Section 2.1.3) or ptnonipm sector (see Section 2.1.4) and are projected to the future year using projection and control factors. It is i mportant that the matching between the NEI and NEEDS database be as complete as possible because there can be double counting of emissions in the future year if emissions for units are projected by IPM are not properly matched to

the units in the NEI

.

I n the SMOKE point flat file, e mission records for sources that have been matched to the NEEDS

database have a value filled in to the IPM_YN column. Many of these matches are stored within EIS. In some cases, it was difficult to match the sources between the databases due to different facility names in the two data systems and due to differences in how the units are defined, thereby resulting in matches that are not always one

- to

- one. Some additional matches were made in the modeling platform to

accommodate s ome of these situations as described later in this section . The NEEDS v5.13 database can be found at http://www.epa.gov/powersectormodeling/BaseCasev513.html , along with additional

info rmation about IPM

.

Some units in the ptegu and ptegu_pk sectors are matched to CEMS data via ORIS facility codes and boiler ID. For these units, SMOKE replaces the 2011 emissions of NO X and SO 2 with the CEMS

emissions

, thereby ignoring the annual values s pecified in the NEI. For other pollutants, the hourly

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

parameters, stack locations, and SCC codes for these sources come from the NEI. Because the se attributes are obtained from the NEI, the chemical speciation of VOC and PM 2.5 for the sources is selected based on the SCC or in some cases, based on unit

- specific data. If CEMS data exists for a unit, but the unit is not matched to the NEI, the CEMS data for that unit is not used in the modeling platform

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

source would still be modeled using the annual emission value in the NEI

. EIS stores many matches

from EIS units to the ORIS facility codes and boiler IDs used to reference the CEMS data. Some additional matches were made in the modeling platform as described later in this section

.

In the SMOKE point flat file, e mission records for point sources matched to C EMS data have values

filled in to the ORIS_FACILITY_CODE an d ORIS_BOILER_ID columns. The CEMS data in SMOKE

ready fo r mat is available at http://ampd.epa.gov/ampd/ near the bottom of the “Prepackaged Data” tab.

M any s maller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can be match ed to the NEI due to inconsistencies in the way a unit is defined between the NEI and CAMD

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

systems

. Also, the NEEDS database of units modeled by IPM includes many smaller emitting EGUsDRAFT

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

NEEDS databas e than have CEMS data.

For sources not matched to CEMS data (“non

- CEMS ” sources), daily emissions were computed from the NEI annual emissions using average CEMS data profiles specific to fuel type, pollutant 3 , and IPM region. To allocate to hour, diurnal profiles were created using average CEMS data for heat input specific to fuel type and IPM region. For future

- year scenarios, there are no CEMS data available for specific units, but the shape of the CEMS profiles is preserved for sources that are carried into the future year. This method keeps the temporal behavior of the base and future year cases as consistent as possible. See Section 3.3.2 for more details on the temporalization approach for ptegu sources.

Finding addition al matches between the NEI, NEEDS, and CEMS data

Several analytical steps were performed to better link the NEEDS units to the 2011 NEI v1 , along with implementing better matching to the CEMS data cross

- referenced using “ORIS” facility and boiler IDs

. The steps described in the 2011 NEI v1 TSD have some detail on how the values in the IPM _YN column were assigned. For the modeling platform, an initial ptipm /ptnonipm split w as determined using the

values in the SMOKE point source flat file variable “IPM_YN” , wh ich is populated based on an EIS alternative facility identifier. Because EIS expects the matches to be one

- to

- one for an entire unit, if the units are not defined in the same way in EIS and NEEDS, it is not possible to store the match in EIS.
Some IPM_YN values in the SMOKE input file were therefore manually updated based on units that had been matched to IPM units in past modeling platforms, but for which the alternative facility IDs in

EIS did not includ e a code for IPM matching. These additions were us ually needed due to one

- to

- many

or many

- to

- one relationships between units in the EIS and the NEEDS data bases

.

Additional matches between the NEI and NEEDS were identified by identifying units in IPM outputs that were not yet matched to NEI data, and by looking for units identified in the NEI with facility type

codes identifying them as EGUs or facility names that indicated they were EGUs. In each case, priority was given to units with larger emissions (e.g., > 300TPY of NO

x or SO 2 ). The units in each data set that did not yet have matches within the same county were compared to one another on the basis of their plant names and locations. In some cases, IDs were similar but were mismatched only due to a missing leading zero in one of the databases. In other cases, a facility level match was specified, but a unit/boiler level match was not yet identified and therefore the units at the facility were compared to one another on the basis of design capacity and naming. For any new matches that were found, va lues that represented the NEEDS IDs were filled in to the IPM_YN in the modeling platform flat files. When possible, these

matches were loaded into EIS.

A similar matching process was used to identify additional matches between the 2011 NEI v1 and CEMS

dat a

. To determine whether a NEI unit match ed a CEMS unit , the CEMS units were compared to

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

, locations, and total emissions of NO

x and SO 2

. A dditional CEM S matches that were found were applied

to the FF10 file by specifying value s for ORIS_FACILITY_CODE, ORIS_BOILER_ID

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

field for these units.

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

11 As a result of identifying additional matches through this analysis , many EGUs that otherwise would have remained in the ptnonipm sector were moved to the ptegu sector. Some of the new CEMS assignments were loaded into EIS for use in future inventories

, but some could not be loaded into EIS because they we re not one

- to

- one matches (e.g. multiple EIS units corresponding to a single CEMS unit). Note that SMOKE is more flexible when associating CEMS data with the inventory than EIS is, and can perform matche s down to the stack or release point level.



### 2.1.2 EGU peaking units sector (ptegu_pk)

The ptegu_pk sector includes sources identified by EPA as peaking units

. The units were separated into this sector to facilitate analyses of the impact of peaking units. Asi de from their inclusion in this sector, in all other ways they are treated in the same way as CEMS sources in the ptegu sector because all of

them are matched to CEMS data

. To identify units for inclusion in this sector

, EPA made sure they

satisfied two t ests : (1) the capacity factor was less than 10% over a 3 year average (2010

- 2012), and (2) the capacity factor was less than 20% in each of the 3 years. Here

, “c apacity factor ” means either: (1) The ratio of a unit's actual annual electric output (express ed in MWe/hr) to the unit's nameplate capacity (or maximum observed hourly gross load (in MWe/hr) if greater than the nameplate capacity) times 8760 hours; or (2) The ratio of a unit's annual heat input (in million B TU s or equivalent units of measure) to t he unit's maximum rated hourly heat input rate (in million B TU s per hour or equivalent units of measure) times 8,760 hours. The list of units in the ptegu_pk sector is provided in the file (see

the file ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2011_emissions/Peakers_CAMD_2011.080
213_NEI_IPM_match.xls ).



### 2.1.3 Point source oil and gas sector (pt_oilgas)

The pt_oilgas se ctor includes sources with the SCCs specified in the list in Table 2

- 3

. The emissions

and other source characteristics in the pt_oilgas sector are submitted by states, while EPA developed a dataset of nonpoint oil and gas emissions for each county in the U.S. with oil and gas activity that was available for states to use. The nonpoint emissions can be found in the np_oilgas sector. More information on the development of the 2011 oil and gas emissions can be found in Section 3.21 of the

2011NEIv1 TSD.

Ta ble 2

- 3

. Point source oil and gas sector SCCs

SCC SCC Description*

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

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

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

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

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

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

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

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

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

31000402 IP;OGP;Process Heaters;Residual Oil

31000403 IP;OGP;Process Heaters;Crude Oil

31000404 IP;OGP;Process Heaters;Natural GasDRAFT

12 SCC SCC Description*

31000405 IP;OGP;Process Heaters;Process Gas

31000406 IP;OGP;Process Heaters;Propane/Butane

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

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

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

310 00414 IP;OGP;Process Heaters;Natural Gas: Steam Generators

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

31000502 IP;OGP;Liquid Waste Treatment;Liquid

- Liquid Separator

31000503 IP;OGP;Liquid Waste Treatment;Oil

- Water Separator

3100050 4 IP;OGP;Liquid Waste Treatment;Oil

- Sludge

- Waste Water Pit

31000506 IP;OGP;Liquid Waste Treatment;Oil

- Water Separation Wastewater Holding Tanks

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

31088802 IP;OGP;Fugitive Emissions;Specify in Co mments Field

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

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

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

31088811 IP;OGP;Fugitive Emissions;Fugitive Emissions

3 1700101 Industrial Processes;NGTS;Natural Gas Transmission and Storage Facilities;Pneumatic Controllers

Low Bleed

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

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

40400302 PSE;PLS;OGFSWT;Fixe d Roof Tank: Working Loss

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

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

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

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

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

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

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

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

40400314 PSE;PLS;OGFSWT;Fixed Roof Tank, Specialty Chem

- working+breathing+flashing losses

40400 315 PSE;PLS;OGFSWT;Fixed Roof Tank, Produced water, working+breathing+flashing losses

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

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

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

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

40400324 PSE;PLS;OGFSWT;External Floating Ro of Tank, Specialty Chem

- working+breathing+flashing losses

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

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

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

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

40400334 PSE;PLS;OGFSWT;Internal Floating Roof Tank, S pecialty Chem

- working+breathing+flashing lossesDRAFT

13 SCC SCC Description*

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

* IP;OGP = Industrial Processes;Oil and Gas Production and PSE;PLS;OGFSWT=Petroleum and Solvent Evapor ation;Petroleum Liquids Storage (non

- Refinery);Oil and Gas Field Storage and Working Tanks



### 2.1.4 Non

- IPM sector (ptnonipm)

Except for some minor exceptions, the non

- IPM (ptnonipm) sector contains the 2011 NEI v1 point

sources included in the modeling platform that are not in the ptegu, ptegu_pk, or pt_oilgas sectors. For the most part, the ptnonipm sector reflect s the non

- EGU sources of the NEI point inventory; however, it is likely that some small low

- emitting EGUs not matched to the NEEDS database or to CEMS data are present in the ptnonipm sector. The sector also includes some ethanol plants that have been identified by EPA but are not in 2011NEIv1.

The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on paved or unp aved roads at industrial facilities, coal handling at coal mines , and grain elevators

. Some point sources in the 2011 NEI v1 that are not included in any modeling sectors are:

S ources with state/county FIPS code ending with “777” . These sources represent mobile (temporary) asphalt plants that are only reported for some states, and are generally in a fixed location for only a part of the year and are therefore difficult to allocate to specific places and days for modeling

. Therefore, t hese sources are drop ped from the point

- based sectors in the modeling platform.

O ffshore oil records with FIPS=85000 were not updated from the 2008 NEIv 3 and are processed in the othpt sector as discussed in Section 2.5.1

.
Additional Ethanol facilities

Another difference betwee n the 2011 NEI v1 data and the modeling platform is the addition of some ethanol production facilities identified by EPA but were not found in the NEI . For some rule development work, EPA developed a list of corn ethanol facilities for 2011

. Many of the se ethanol

facilities were included in the 2011 NEI v1 , but those that were not matched were added to the ptnonipm sector in a separate FF10

- format inventory file . Locations and FIPS codes for these ethanol plants were verified using web searches and Google Ea rth. EPA believes that some of these sources are not included in the NEI as point sources because they do not meet the 100 ton/year potential

- to

- emit threshold for NEI point sources. In other cases, EPA is following up with states to evaluate whether the state data should include these point sources.

Emission rates for the ethanol plants were obtained from EPA ’s updated spreadsheet model for upstream impacts developed for the Renewable Fuel Standard ( RFS2 ) rule (EPA, 2010a). Plant emission rates for

cri teria pollutants used to estimate impacts for years 2011 and 2018 are given in Table 2

- 4

.

Table 2

- 4

. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produce d)

Corn Ethanol Plant Type VO

C CO NO

X PM 1

0 PM 2.

5 SO 2 NH

3

Dry Mill Natural Gas (NG) 2.29 0.5

8 0.99 0.94 0.23 0.0

1 0.00

Dry Mill NG (wet distillers grains with solubles (DGS)) 2.27 0.3

7 0.63 0.91 0.20 0.0

0 0.00DRAFT

14 Corn Ethanol Plant Type VO

C CO NO

X PM 1

0 PM 2.

5 SO 2 NH

3

Dry Mill Biogas 2.29 0.6

2 1.05 0.94 0.23 0.0

1 0.00

Dry Mill Biogas (wet DGS) 2.27 0.3

9 0.67 0.91 0.20 0.0

0 0.00

Dry Mill Coal 2.31 2.6

5 4.17 3.81 1.71 4.5

2 0.00

Dry Mill Coal (wet DGS) 2.31 2.6

5 2.65 2.74 1.14 2.8

7 0.00

Dry Mill Biomass 2.42 2.5

5 3.65 1.28 0.36 0.1

4 0.00

Dry Mill Biomass (wet D GS) 2.35 1.6

2 2.32 1.12 0.28 0.0

9 0.00

Wet Mill NG 2.35 1.6

2 1.77 1.12 0.28 0.0

9 0.00

Wet Mill Coal 2.33 1.0

4 5.51 4.76 2.21 5.9

7 0.00

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

- 5 were m ultiplied by facility production estimates for 2011 and 2018 based on analyses performed for the industry characterization described in Chapter 1 of the RFS2 final rule regulatory impact analysis. For air toxics except ethanol, the toxic

- to

- VOC ratios were developed using emission i nventory data from the 2005 NEI (EPA, 2009a).

Table 2

- 5

. Toxic

- to

- VOC Ratios for Corn Ethanol Plants

Acetaldehyde Acrolein Benzene 1,3

- Butadiene Formaldehyde

Wet Mill NG 0.02580 0.00131 0.00060 2.82371E

- 08 0.00127

Wet Mill Coal 0.08242 0.00015 0.00048 2.82371E

- 08 0.00108 Dry Mill NG 0.01089 0.00131 0.00060 2.82371E

- 08 0.00127 Dry Mill Coal 0.02328 0.00102 0.00017 2.82371E

- 08 0.00119



### 2.2 20 11 nonpoint sources (afdust, ag, np_oilg as, rwc, nonpt)

S everal modeling platform sectors were created from the 2011 NEI v1 nonpoint inventory . T his section

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

, and C3 CMV are also

in cluded the 2011 NEI v1 nonpoint data category , bu t are mobile sources that are described in Section s 2.4.1 and 2.4.2 as the c1c2rail and c3marine sectors , respectively

. The 2011NEIv1 TSD available from http://www.epa.gov/ttn/chief/net/2011i nventory.html includes documentation for the nonpoint sector of

the 2011NEI v1

.

T he nonpoint tribal

- submitted emissions are dropped during spatial processing with SMOKE due to the configuration of the spatial surrogates. Part of the reason for this is to prevent possible double

- counting with county

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

available

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

at the 12

- km scale s used for this platform

.DRAFT

15 The emissions modeling sector inventories start with the NEI data

. S everal source categories were not included in the modeling platform inventories for the following reasons: 1) these sources are only reported by a small numb er of states or agencies, 2) these sources are ‘atypical’ and have small

emissions , and/or 3) EPA has have other data th e Agency believe s to be more accurate. Table 2

- 6 provides a list of SCCs removed from the nonpoint sectors

, justification for their removal

, and the

national annual NO X , VOC and NH 3 emission totals. The following subsections descri be how the remaining sources in the 2011NEIv1 nonpoint inventory were separated into 2011 modeling platform sectors, along with any data that were updated replaced with non

- NEI data.

Table 2

- 6

. 20 11 NEI v 1 nonpoint sources removed from the 20 11 platform

SCC Description Reason for

Removal NO X * VOC * NH 3 *

2280003100 Marine Vessels, Comme rcial; Residual; Port

emissions Replaced with OTAQ ECA

IMO dataset

- see Section 2.4.2 62,906 2,41 1 23

2280003200 Marine Vessels, Commercial; Residual; Underway

emissions 81 7,367 30,846 151

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

emissions NOT

reduced via precipitation

– see Section 2.2.1 2294010000 Paved Roads; All Other Public Paved Roads; Total:

Fugitives

2501060100 Gasoline Stage 2 refueling: Total Replaced with

MOVE S 2010b

- - based estimates

– see Section 2.3.2 154,349

2501060101 Gasoline Stage 2 refueling: Displacement

Loss/Uncontrolled 6,731

2501060102 Gasoline Stage 2 refueling: Displacement

Loss/Controlled 6,890

2501060103 Gasoline Stage 2 refueling: Spillage 2,771

2810005001 Managed Burning, Slash (Logging Debris);Pile

Burning Replaced with

SMARTFIRE 2

estimates

- see Section 2.6 84.5 95

2810005002 Managed Burning, Slash (Logging Debris);Broadcast

Burning 0 0

2810020000 Prescribed Rangeland Burning; Unspecified

2810090000 Open Fire; Not categorized

2275087000 Aircraft; In

- flight (non

- Landing

- Takeoff cycle);Total

Dropped because they are a typical and sparsely

reported

categor ies with

small emis sions 2806010000 Domestic Animals Waste Emissions; Cats; Total 294

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

2807020001 Wild Animals Waste Emissions; Bears; Black Bears 3

2807020002 Wild Animals Waste Emissions; Bears; Grizzly Bears 0

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

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

2807040000 Wild Animals Waste Emissions; Birds; Total 0

2810003000 Cigarette Smoke; Total 2 43 4

28100100 00 Human Perspiration and Respiration; Total 2,742

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

2830010000 Catastrophic/Accidental Releases; Transportation

Accidents; Total 0

2862000000 Swimming Pools; Total (Commercial, Reside ntial,

Public);Total 198

* Emission units are short tonsDRAFT

16 

### 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 EPA staff as dust sources. Categories included in the afdust sector are paved roads, unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production,

and mining and quarrying. It does not include fugitive dust from grain elevators , coal handling at coal mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as point sources so they are properly located

.

T he afdust sector is separated from other nonpoint sectors to allow for the application of a “t ran sport fraction,” and meteorological /precipitation reductions. These adjustments are applied with a script that applies land use

- based gridded transport fractions followed by another script that zeroes out emissions for days on which at least 0.01 inche s of precipitation occurs or there is snow cover on the ground. The

land use data used to reduce the NEI emissions determines the amount of emissions that are subject to transport. This methodology is discussed in (Pouliot, et. al., 2010 )

, http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot_pres.pdf , and in “ Fugitive Dust Modeling for the 2008 Emissions Modeling Platform ” (Adelman, 2012). The purpose of applyin g the transport fraction and meteorological adjustments is to reduce the overestimation of fugitive dust in the grid modeling as compared to ambient observations. Both the transport fraction and meteorological adjustments are based on the gridded resolutio n of the platform (e.g., 12km grid cells) ; therefore,

different emissions will result if the process were applied to different grid resolutions. A limitation of the transport fraction approach is the lack of monthly variability that would be expected with seasonal changes in vegetative cover. W hile wind speed and direction are not accounted for in the emissions processing , the hourly variability due to soil moisture, snow cover and precipitation is accounted for in the subsequent meteorological adjustment.

The sources in the afdust sector are for SCCs and pollutant codes (i.e., PM 10 and PM 2.5 ) that are

considered to be “fugitive” dust sources . The se SCCs are provided in Table 2

- 7

.

Table 2

- 7

. SCCs in the afdust platform sector

SCC SCC Description

2275085000 Industrial Processes;Construction: SIC 15

- 17;All Processes;Vehicle Traffic

2294000000 Industrial Processes;Construction: SIC 15

- 17;Industrial/Commercial/Institutional;Total

2294005000 Industrial Processes;Construction: SIC 15

- 17;Residential;Total

2294010000 Industrial Processes;Construction: SIC 15

- 17;Road Construction;Total

2296000000 Industrial Processes;Construction: SIC 15

- 17;Special Trade Construction;Total

2296 005000 Industrial Processes;Mining and Quarrying: SIC 14;All Processes;Total

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

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

2 311010000 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

-

Crops;Harvesting

2311020000 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

- Crops;Planting

2311030000 Miscellaneous Area Sources;Agriculture Product ion

- Crops;Agriculture

- Crops;Tilling

2311040000 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

- Crops;Total

2325000000 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

- Crops;Transport

2325020000 Miscella neous Area Sources;Agriculture Production

- Livestock;Beef cattle

- finishing

operations on feedlots (drylots);Dust Kicked

- up by Hooves (use 28

- 05

- 020,

- 001,

- 002, or

- 003 for WasteDRAFT

17 SCC SCC Description

2325030000 Mobile Sources;Aircraft;Unpaved Airstrips;Total

2801000000 M obile Sources;Paved Roads;All Other Public Paved Roads;Total: Fugitives

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

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

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

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

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

The dust emissions in the modeling platform are not the same as the 2011 NEI v1 emissions because t he NEI paved and unpaved road dust emissions include a built

- in precipitation reduction that is based on average meteorological data , which is at a coarser temporal and spatial resolution than the modeling platf orm meteorological adjustment . Due to this, in the platform paved and unpaved road emissions

data was used that did not includ e any precipitation

- based reduction . This allow s the entire sector to be processed consistently so that the same grid

- specific tr ansport fractions and meteorological adjustments can be applied. Where states submitted afdust data, the state

- submitted data was used assuming that it

was not met

- adjusted and therefore the meteorological adjustments were still applied

. Thus , it is possib le

that the se sources may have been adjusted twice. Even with that possibility , air quality modeling shows that in general, dust is frequently overestimate d in the air quality modeling results

.

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

Table 2

- 8 , where the starting inventory numbers include unadjusted paved and unpaved road dust, so they do not match the NEI values which include a different type of adjustment. The amoun t of the reduction ranges from about 6% in New Hampshire to almost 73% in Nevada. Figure 2

- 1 shows the impact of each step of the adjust ment for January 2008

, using the 2008 NEI as an example . The raw

NEI afdust PM 2.5 emissions

– prior to transport fraction meteorological adjustments

– are shown at the top of Figure 2

- 1 . The afdust emissions after the application of the transport fraction, but prior to meteorological adjustments are shown in the middle of Figure 2

- 1 . Finally, the resulting emissions after both transport fraction and meteorological adjustments are shown at the bottom of Figure 2

- 1

. The top and middle plots show how the transport fractio n has a larger reduction effect in the east where forested areas are more effective at reducing PM transport than in many western areas. Comparing the bottom

and middle plot s shows how the meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust emissions

.

Table 2

- 8

. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory

State Unadjusted

PM10 Unadjusted

PM2_5 Change

in PM10 Change in

PM2_5 PM10

Redu ction PM2_5

Reduction

Alabama 378,873 47,158

- 310,412

- 38,554 18.1% 18.2%

Arizona 237,361 30,015

- 78,365

- 9,757 67.0% 67.5%

Arkansas 421,958 58,648

- 305,667

- 40,779 27.6% 30.5%

California 255,889 38,664

- 119,728

- 18,039 53.2% 53.3%

Colorado 244,630 40 ,421

- 130,902

- 21,038 46.5% 48.0%

Connecticut 29,067 4,393

- 26,045

- 3,938 10.4% 10.4%

Delaware 11,477 2,046

- 8,004

- 1,437 30.3% 29.8%

District of Columbia 2,115 337

- 1,597

- 254 24.5% 24.6%

Florida 292,797 39,636

- 181,252

- 24,357 38.1% 38.5%DRAFT

18 State Unadjusted

PM10 Unadjusted

PM2_5 Change

in PM10 Change in

PM2_5 PM10

Redu ction PM2_5

Reduction
Georgia 73 3,478 90,041

- 593,397

- 71,996 19.1% 20.0%

Idaho 432,116 49,294

- 295,315

- 33,293 31.7% 32.5%

Illinois 763,665 123,680

- 478,200

- 76,944 37.4% 37.8%

Indiana 603,153 85,151

- 440,718

- 61,488 26.9% 27.8%

Iowa 590,528 96,070

- 342,678

- 55,397 42.0% 42.3%

Kans as 748,652 118,902

- 353,498

- 54,883 52.8% 53.8%
Kentucky 199,744 29,496

- 160,887

- 23,547 19.5% 20.2%

Louisiana 236,787 35,730

- 162,460

- 24,039 31.4% 32.7%

Maine 50,547 7,016

- 44,059

- 6,137 12.8% 12.5%
Maryland 49,225 8,361

- 37,212

- 6,290 24.4% 24.8%

M assachusetts 205,561 22,444

- 179,580

- 19,567 12.6% 12.8%

Michigan 462,324 61,969

- 360,229

- 48,087 22.1% 22.4%

Minnesota 336,791 64,253

- 221,558

- 42,026 34.2% 34.6%
Mississippi 956,702 107,965

- 781,604

- 86,622 18.3% 19.8%

Missouri 1,064,146 130,995

- 781 ,001

- 94,632 26.6% 27.8%

Montana 385,541 50,583

- 270,346

- 34,134 29.9% 32.5%

Nebraska 591,457 85,206

- 317,684

- 45,301 46.3% 46.8%

Nevada 152,191 19,538

- 44,038

- 5,350 71.1% 72.6%
New Hampshire 25,540 3,766

- 23,951

- 3,532 6.2% 6.2%

New Jersey 24,273 5, 412

- 19,282

- 4,270 20.6% 21.1%

New Mexico 924,497 95,871

- 352,163

- 36,350 61.9% 62.1%

New York 274,114 37,493

- 239,314

- 32,394 12.7% 13.6%

North Carolina 186,650 33,409

- 146,997

- 26,199 21.2% 21.6%

North Dakota 354,107 59,113

- 224,340

- 37,233 36.6% 37. 0%

Ohio 414,902 64,609

- 324,179

- 49,996 21.9% 22.6%

Oklahoma 733,749 87,864

- 385,316

- 44,579 47.5% 49.3%

Oregon 348,093 40,596

- 272,169

- 30,935 21.8% 23.8%
Pennsylvania 208,246 30,344

- 181,086

- 26,319 13.0% 13.3%

Rhode Island 4,765 731

- 3,679

- 572 22. 8% 21.7%

South Carolina 259,350 31,494

- 198,329

- 24,020 23.5% 23.7%

South Dakota 262,935 44,587

- 158,320

- 26,623 39.8% 40.3%

Tennessee 139,732 25,357

- 108,109

- 19,543 22.6% 22.9%

Texas 2,573,682 304,550 - 1,275,075

- 145,799 50.5% 52.1%

Utah 196,554 21, 589

- 114,478

- 12,534 41.8% 41.9%

Vermont 67,690 7,563

- 61,971

- 6,917 8.4% 8.5%
Virginia 131,797 19,374

- 108,734

- 15,900 17.5% 17.9%

Washington 174,969 27,999

- 101,341

- 15,685 42.1% 44.0%
West Virginia 85,956 10,652

- 79,843

- 9,900 7.1% 7.1%

Wisconsin 2 39,851 41,669

- 166,313

- 28,920 30.7% 30.6%
Wyoming 434,090 45,350

- 267,536

- 27,773 38.4% 38.8%DRAFT

19 Figure 2

- 1

. January PM 2.5 afdust emissions: raw 2008 NEI (top), after application of transport fraction

(midd le) and final post

- meteorological adjusted (bottom)DRAFT

20 

### 2.2.2 Agricultural ammonia sector (ag)

The agricultural NH 3 ( ag ) sector includes livestock and agricultural fertilizer application emissions from

the 2011 NEI v1 nonpoint inventory. The livestock and ferti lizer emissions in this sector are based only

on the SCC s listed in Table 2

- 9 and T able 2

- 10

. The “ag” sector includes all of the NH 3 emissions from fertilizer from the NEI. However, the “ag” sector doe s not include all of the livestock ammonia emissions, as there are also a small amount of NH 3 emissions from livestock feedlots in the ptnonipm inventory (as point sources) in California (175 tons) and Wisconsin (125 tons)

.

Table 2

- 9

. Livestock SCCs extracted from the NEI to create the ag sector

SCC SCC Description*

2805001100 Beef cattle

- finishing operations on feedlots (drylots);Confinement

2805001200 Beef cattle

- finishing operations on feedlots (dry lots);Manure handling and storage

2805001300 Beef cattle

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

2805002000 Beef cattle production composite;Not Elsewhere Classified

2805003100 Beef cattle

- finishing operations on pastur e/range;Confinement

2805007100 Poultry production

- layers with dry manure management systems;Confinement

2805007300 Poultry production

- layers with dry manure management systems;Land application of manure

2805008100 Poultry production

- layers with we t manure management systems;Confinement

2805008200 Poultry production

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

2805008300 Poultry production

- layers with wet manure management systems;Land application of manure

2805009100 Poultry production

- broilers;Confinement

2805009200 Poultry production

- broilers;Manure handling and storage

2805009300 Poultry production

- broilers;Land application of manure

2805010100 Poultry production

- turkeys;Confinement

2805010200 Poultry p roduction

- turkeys;Manure handling and storage

2805010300 Poultry production

- turkeys;Land application of manure

2805018000 Dairy cattle composite;Not Elsewhere Classified

2805019100 Dairy cattle

- flush dairy;Confinement

2805019200 Dairy cattle

- fl ush dairy;Manure handling and storage

2805019300 Dairy cattle

- flush dairy;Land application of manure

2805020000 Cattle and Calves Waste Emissions;Milk Total

2805021100 Dairy cattle

- scrape dairy;Confinement

2805021200 Dairy cattle

- scrape dairy;Man ure handling and storage

2805021300 Dairy cattle

- scrape dairy;Land application of manure

2805022100 Dairy cattle

- deep pit dairy;Confinement

2805022200 Dairy cattle

- deep pit dairy;Manure handling and storage

2805022300 Dairy cattle

- deep pit dair y;Land application of manure

2805023100 Dairy cattle

- drylot/pasture dairy;Confinement

2805023200 Dairy cattle

- drylot/pasture dairy;Manure handling and storage

2805023300 Dairy cattle

- drylot/pasture dairy;Land application of manure

2805025000 Swin e production composite;Not Elsewhere Classified (see also 28

- 05

- 039,

- 047,

- 053)

2805030000 Poultry Waste Emissions;Not Elsewhere Classified (see also 28

- 05

- 007,

- 008,

- 009)

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

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

2805030003 Poultry Waste Emissions;Layers

2805030004 Poultry Waste Emissions;Broilers

2805030007 Poultry Waste Emissions;Ducks

2805030008 Poultry Waste Em issions;Geese

2805030009 Poultry Waste Emissions;Turkeys

2805035000 Horses and Ponies Waste Emissions;Not Elsewhere Classified

2805039100 Swine production

- operations with lagoons (unspecified animal age);Confinement

2805039200 Swine production

- oper ations with lagoons (unspecified animal age);Manure handling and storage

2805039300 Swine production

- operations with lagoons (unspecified animal age);Land application of manureDRAFT

21 SCC SCC Description*

2805040000 Sheep and Lambs Waste Emissions;Total

2805045000 Goats Waste Em issions;Not Elsewhere Classified

2805045002 Goats Waste Emissions;Angora Goats

2805045003 Goats Waste Emissions;Milk Goats

2805047100 Swine production

- deep

- pit house operations (unspecified animal age);Confinement

2805047300 Swine production

- deep

- p it house operations (unspecified animal age);Land application of manure

2805053100 Swine production

- outdoor operations (unspecified animal age);Confinement * All SCC Descriptions begin “Miscellaneous Area Sources;Agriculture Production

– Livestock”

T able 2

- 10

. Fertilizer SCCs extracted from the NEI for inclusion in the “ag” sector

SCC SCC Description*

2801700001 Anhydrous Ammonia

2801700002 Aqueous Ammonia

2801700003 Nitrogen Solutions

2801700004 Ure a
2801700005 Ammonium Nitrate

2801700006 Ammonium Sulfate

2801700007 Ammonium Thiosulfate

2801700008 Other Straight Nitrate

2801700009 Ammonium Phosphates

2801700010 N

- P

- K (multi

- grade nutrient fertilizers)

2801700011 Calcium Ammonium Nitrate

28017 00012 Potassium Nitrate

2801700013 Diammonium Phosphate

2801700014 Monoammonium Phosphate

2801700015 Liquid Ammonium Polyphosphate

2801700099 Miscellaneous Fertilizers

* All descriptions include “Miscellaneous Area Sources; Agriculture Production

– Cr ops; Fertilizer Application ” as the beginning of the description.



### 2.2.3 Nonpoint source oil and gas sect o r (np_oilgas)

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

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

m any S/L/T agencies also submitted nonpoint oil and gas data. The types of sources covered include drill rigs, workover rigs, artificial lift, hydraulic fracturing engines, pneumatic pumps an d other devices, storage tanks, flares, truck loading, compressor engines , and dehydrators

. For more information on the development of the oil and gas emissions in the 2011 NEI v1 , see Section 3.21 of the 2011NEIv1 TSD. A complete list of SCCs for the np_oi lgas modeling platform sector is provided in Appendix A

. See the pt_oilgas sector (section 2.1.3 ) for more information on point source oil and gas sources.



### 2.2.4 Residential wood combustion sector (rwc)

The residential wood combusti on (rwc) sector includes residential wood burning devices such as fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic heaters (also know n as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and

chimeneas. F ree standing woodstoves and inserts are further differentiated into three categories:DRAFT

22 conventional (not EPA certified); EPA certified, catalytic; and EPA certified, noncatalytic. Generally speaking, the conventional units were con structed prior to 1988. Units constructed after 1988 had to meet EPA emission standards and they are either catalytic or non

- catalytic. For more information on the development of the residential wood combustion emissions , see Section 3.14 of the 2011NEIv1 TSD.

The SCCs in the rwc sector are shown in Table 2

- 11

.

Table 2

- 11

. SCCs in the Residential Wood Combustion Sector (rwc)*

SCC SCC Description

2104008100 SSFC;Residential;Wood;Firep lace: general

2104008210 SSFC;Residential;Wood;Woodstove: fireplace inserts; non

- EPA certified

2104008220 SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; non

- catalytic

2104008230 SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; catalytic

2104008300 SSFC;Residential;Wood;Woodstove: freestanding, general

2104008310 SSFC;Residential;Wood;Woodstove: freestanding, non

- EPA certified

2104008320 SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, non

- catalytic

2 104008330 SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, catalytic

2104008400 SSFC;Residential;Wood;Woodstove: pellet

- fired, general (freestanding or FP insert)

2104008510 SSFC;Residential;Wood;Furnace: Indoor, cordwood

- fired, non

- EPA cert ified

2104008610 SSFC;Residential;Wood;Hydronic heater: outdoor

2104008700 SSFC;Residential;Wood;Outdoor wood burning device, NEC (fire

- pits, chimeas, etc)

2104009000 SSFC;Residential;Firelog;Total: All Combustor Types * SSFC=Stationary Source Fuel Com bustion



### 2.2.5 Other nonpoint sources sector (nonpt)

Stationary nonpoint sources that were not subdivided into the afdust

, ag , np_oilgas, or rwc sectors were assigned to the “nonpt” sector. L ocomotives and CMV mobile sources from the 2011 NEI v1 nonpoint inventory are described in Section 2.4.1

. The re are too many SCCs to list all of them individually, but

the types of sources in the nonpt sector include :

stationary sour ce fuel combustion , including industrial, commercial, and residential ;
chemical manufacturing;

industrial processes such as commercial cooking, metal production, mineral processes, petroleum refining, wood products, fabricated metals, and refrigeration;

solvent utilization for surface coatings such as architectural coatings, auto refinishing, tra ffic marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances, and motor vehicles;

solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;

solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care products, household products, adhesives and sealants;

solvent utilization for asphalt application and roofing, and pesticide application;

storage and transport of petroleum for uses su ch as portable gas cans, bulk terminals, gasoline service stations, aviation, and marine vessels;

storage and transport of chemi cals;DRAFT

23 waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;

agricultural burning a nd orchard heating;

miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair shops.

Most sources in this sector have annual emissions that are temporal ly allocated to hourly values using temporal profiles. The annual a gricultural burning estimates are treated as monthly values. The annual

values in the 2011NEIv1 were split into monthly emissions by aggregating the data up to monthly values

from daily estimates of emissions.



### 2.3 2011 onroad mobile sources (onroad, onroad_rf l)

O n road mobile sources include emissions from motorized vehicles that are normally operated on public roadways. Th ese include passenger cars, motorcycles, minivans, sport

- utility vehicles, light

- duty trucks, heavy

- duty trucks, and buses . The sources ar e further divide d between diesel and gasoline vehicles.

The sector characterizes emissions from off

- network processes (e.g. starts, hot soak, and extended idle)

as well as from on

- network processes ( i.e., from vehicles moving along the roads)

. For the 20 11 platform, as indicated in Table 2

- 1 , the 2011 onroad emissions are separated into two sectors: (1) “onroad” and (2) “onroad_rfl”. The onroad and onroad_rfl sectors are processed separately to allow for different spatial alloc ation to be applied to onroad refueling , which is allocated using a gas station

surrogate

, versus onroad vehicles , which are allocated using surrogates based on roads and population. Except for California and Texas, all onroad and onroad refueling emissio ns are generated using the

SMOKE

- MOVES emissions modeling framework that leverages MOVES generated outputs ( http://www.epa.gov/otaq/models/moves/index.htm ) and hourly meteorology. All tribal dat a from the mobile sectors have been dropped because the emissions are small, the emissions could be double

counted with state

- provided onroad emissions, all tribal data was developed using the older model
MOBILE6, and because spatial surrogate data is not currently available.



### 2.3.1 Onroad non

- refueling (onroad)

For the continental U.S., EPA used a modeling framework that took into account the temperature sensitivity of the on

- road emissions. Specifically, EPA used MOVES inputs for representative counties,

vehi cle miles traveled (VMT) and vehicle population (VPOP) data for all counties, along with tools that integrated the MOVES model with SMOKE. In this way, it was possible to take advantage of the gridded hourly temperature information available from meteorol ogy modeling used for air quality modeling. The “SMOKE

- MOVES” integration tool was developed by EPA in 2010 and is in use by states and regional planning organizations for regional air quality modeling of onroad mobile sources

. SMOKE

- MOVES requires that emission rate “lookup” tables be generated by MOVES which differentiate emissions by process ( i.e., running, start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of day, etc. To generate the MOVES emission rates that could be app lied across the U.S., EPA used an automated process to run MOVES to produ ce emission factors by temperature and speed for a series of “representative counties,” to which every other county was mapped. Using the MOVES emission rates, SMOKE select s appropri ate emissions rates for each county, hourly temperature, SCC, and speed bin and multiplied the emission rate by activity (VMT (vehicle miles travelled) or VPOP (vehicle population)) to produce emissions. These calculations were done for every county and g rid cell, in the continental U.S. for each hour of the year

.

Using SMOKE

- MOVES for creating the model

- ready emissions requires numerous steps:DRAFT

24 1) Determine which counties will be used to represent other counties in the MOVES runs

2) Determine which months wi ll be used to represent other month’s fuel characteristics

3) Create MOVES inputs needed only by MOVES. MOVES requires county

- specific information
on vehicle populations, age distributions, and inspection

- maintenance programs for each of the
representative counties.

4) Create inputs needed both by MOVES and by SMOKE, including a list of temperatures and
activity data

5) Run MOVES to create emission factor tables

6) Run SMOKE to apply the emission factors to activity data (VMT and VPOP) to calculate

emissions

7) Aggrega te the results to the county

- SCC level for summaries and quality assurance

The onroad emissions inputs are similar to the emissions in the onroad data category of the 2011NEIv1

, described in more detail in Section 4.6 of the 2011NEIv1 TSD . Spe cifically th e platform and

2011 NEIv1 have identical:

MOVES County databases (CDBs)

Fuels Representative counties

Fuel months Meteorology Activity data (VMT, VPOP, speed) Extended idle adjustments

Despite the commonalities, there are some key differences between the t wo onroad emission inventories:

The 2011NEIv1 used MOVES2010b to create the emission factor (EF) tables , while t he 2011 platform used the MOVESTier3NPRM (specifically, model “Moves20110414a” and the default database “movestier3db20110512”) for most EFs. T he exceptions are that refueling (described in the next section) and extended idle EFs were generated using the MOVE S 2010b model for

both inventories because MOVESTier3NPRM did not create these emission rates for these

modes

. The 2011 platform used a dif ferent post

- processor to create EF s for SMOKE because the pollutants needed for speciation and running CMAQ are different than what is needed for the

NEI. For example, the NEI needs a much larger set of HAPs and the modeling platform requires

emissions for the components of PM 2.5

. The treatment of Texas and California emissions differs between the two inventories (see below for more details).

The list of emission modes differ between the two inventories. Both SMOKE

- MOVES runs were generated at the same level of detail, but the NEI emissions were aggregated into 4 all

inclusive modes: exhaust (includ ing extended idle), evaporative (includ ing permeation), brake wear, and tire wear. The list of modes and the corresponding MOVES processes mapped to

them are listed in Table 2

- 12

.DRAFT

25 Table 2

- 12

. Onroad emission modes

M ode D escription MOVES process IDs

EXH Exhaust, includ ing running and starts, exclud ing extended idle 1;2;15;16

EXT Extende d idle exhaust from long

- haul trucks 17;90

EVP Evaporative emissions, including vapor venting and fuel leaks, exclud ing permeation 12;13

EPM Evaporative permeation 11

RFL Refueling 18;19

BRK Brake wear 9

TIR Tire wear 10

For more detailed informati on on methods used to develop the onroad emissions and input data set s and

on running SMOKE

- MOVES

, see the 2011NEIv1 TSD.

The California and Texas onroad emissions were created through a hybrid approach of combining state

supplied annual emissions (from t he 2011NEIv1) with EPA developed SMOKE

- MOVES runs. Through this approach, the platform was able to reflect California’s unique rules and Texas’ detailed modeling, while leveraging the more detailed SCCs and the highly resolved spatial patterns, temporal pa tterns, and speciation from SMOKE

- MOVES. The basic steps involved in temporally allocating California’s and

Texas’ onroad emissions based on SMOKE

- MOVES results were:

1) Run CA/TX using EPA inputs through SMOKE

- MOVES to produce hourly 2011 emissions

hereafte r known as “EPA estimates”

. These EPA estimates for CA/TX are run in a separate sector called “onroad_catx”.

2) Calculate ratios between state

- supplied emissions and EPA estimates 4

. For Texas, these ratios

were calculated for e ach county/SCC7 (fuel and vehi cle type) /pollutant combination. For California, these were calculated for each county/SCC3 (fuel type) /pollutant combination . These were not calculated at a greater resolution because California’s emissions did not provide data

for all vehicle type s.

3) Cr eate an adjustment factor file (CFPRO) that includes EPA

- to

- state estimate ratios. For extended idle adjustments, each specific state ratio (county/SCC Group (7 or 3) /pollutant) was multiplied by the extended idle adjustment factor (see the 2011NEIv1 TSD f or details).

4) Rerun CA/TX through SMOKE

- MOVES using EPA inputs and the new adjustment factor file.

T hrough t his process

, adjusted model

- ready files were created that sum to California’s and Texas’

annual totals

, but have the temporal and spatial patterns r eflecting the highly resolved meteorology and

SMOKE

- MOVES. After adjusting the emissions, this sector is called “onroad_catx_adj”. Note that in emission summaries, the emissions from the “onroad” and “onroad_catx_adj” sectors are summed and designated as the emissions for the onroad sector.

4 These ratios were created for all matching pollutants. These ratios were d uplicate d for all appropriate modeling species. For example, EPA use d the NO X ratio f or NO, NO 2 , HONO and use d the PM 2.5 ratio for PEC, PNO 3 , POC, PSO 4 , and

PMFINE (For more details on NO X and PM speciation, see Sections 3.2.3 and 3.2.2 ) . For VOC model

- species, if there was

an exact match (e.g.

, BENZENE) , EPA used that HAP pollutant ratio. F or other VOC

- based model

- species that didn’t exist in the NEI inventory, EPA use d VOC ratios.DRAFT

26 

### 2.3.2 Onroad refueling (onroad_rfl)

Onroad refueling is modeled very similarly to other onroad emissions. As noted in the onroad section (Section 2.3.2 ), the refueling emissions were generated via MOVES2010b. The onroad_rfl emissions are spatially allocated to gas station locations (see Section 3.4.1 ). . Because the refueling emission factors use the same SCCs as the other onroad models, refueling was run in a separate sector from the

other onr oad mobile sources to allow for the different spatial allocation. To facilitate this, the refueling EFs were separated from the other emission factors into rate

- per

- distance (RPD) refueling and rate

- per

vehicle (RPV) refueling tables 5 . SMOKE

- MOVES was ru n using these EF tables as inputs, and spatially allocated using a gas stations spatial surrogate

. Lastly, the SMOKE program Mrggrid combined RPD refueling and RPV refueling into a single onroad_rfl model ready output for final processing with

the other se ctors prior to use in CMAQ. EPA SMOKE

- MOVES generated emissions for onroad refueling were used without any adjustments for all states, including California and Texas. These emissions were used instead of state submissions to provide a consistent approach nationwide and also because most states did not submit refueling emissions for diesel fuel. Since the 2011NEIv1 includes the

state

- submitted emissions, the platform and the NEI refueling emissions in the nonpoint category are

consistent for states that did not submit emissions, but are inconsistent for states that submitted refueling

emissions.



### 2.4 2011 nonroad mobile sources (c1c2rail, c3marine, nonroad)

The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions (nonroad) and lo comotive and commercial marine vessel (CMV) emissions divided into two nonroad

sectors: “c1c2rail” and “c3marine”.



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

The c1c2rail sector contains locomotive and smaller CMV sources, ex cept for railway maintenance

locomotives and C3 CMV sources outside of the Midwest states . The “c1c2” portion of this sect or name refers to the Class 1 and 2 CMV emissions, not the railway emissions. Railway maintenance emissions

are included in the nonr oad sector. The C3 CMV emissions are in the c3marine sector

. All emissions in this sector are annual and at the county

- SCC resolution

.

The starting point for the c1c2rail sector is the 20 11 NEI v1 nonpoint inventory for all but specific

Midwest states

, wh ich are instead derived from the Great Lakes 2010 CMV inventory . As discussed in

Table 2

- 1 and Table 2

- 2 , the modeling platform emissions for the c1c2rail SCCs were extracted from the NEI nonpoint invent ory. For more information on CMV sources in the NEI, see Section 4.3 of the

2011NEIv1 TSD. For more information on locomotives, see Section 4.4 of the 2011NEIv1 TSD. Table

2

- 13 lists the NEI SCCs included in the c1c2rail sector of the modeling platform

.

Table 2

- 13

. 20 11 NEI v1 SCCs extracted for the starting point in c1c2rail development

SCC Description: Mobile Sources prefix for all

2280002100 Marine Vessels; Commercial; Diesel; Port

2280002200 Marine Vessels; Commercial; Diesel; Underway

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

228500200 7 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations

228500200 8 Railroad Equipme nt; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)

5 The Moves2smk post

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

EF.DRAFT

27 228500200 9 Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines

22850020 10 Railroad Equipment; Diesel; Yard Locomotives

Some differences exist between the 2011NEIv1 and the modeli ng platform for this sector due to the availability of alternative data. The differences follow.

Replaced California C1/C2 CMV and rail data with CARB data

As discussed in Section 2.4 , the CARB provided year 20 11 and corresponding future year emissions f or all mobile sources, including C1/C2 CMV and locomotives

. These emissions were documented in a staff report available at: http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadi sor.pdf

.

The C1/C2 CMV emissions obtained from the CARB nonroad mobile dataset include the 20 11 regulations to reduce emissions from diesel engines on commercial harbor craft operated within

California waters and 24 nautical miles of the California bas eline. These emissions were developed using Version 1 of the California Emissions Projection Analysis Model (CEPAM) that supports various

California off

- road regulations. The locomotive emissions were obtained from the CARB trains dataset

“ ARMJ_RF#2002_A NNUAL_TRAINS.txt ”. Documentation of the CARB offroad mobile methodology, including c1c2rail sector data, is provided at: http://www.arb.ca.gov/msei/categories.htm#offroad_m otor_vehicles

. T he TOG in the CARB inventory was mapped to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor according to the SCC of the source . See Section 3.2.1.3 for more details on c1c2rail speciation. The RPO and CARB inventories did not include HAPs; therefore, all non

- NEI source emissions in the

c1c2rail sector were processed using VOC speciation only, rather than use the inventory BAFM

.

Replaced all CMV in the Midwest RPO

Y ear

- 20 10 emis sions were received from the Lake Michigan Air Directors Consortium for tug boats, Great Lakes vessels (“Lakers”) and inland waterway s for states within the Midwest RPO and Minnesota, hereafter simply referred to as “ MWRPO ” ( http://www.ladco.org/ )

. The states in the MWRPO are: Illinois, Indiana, Michigan, Minnesota, Ohio and Wisconsin. These MWRPO CMV emissions include coverage for bordering states/counties along the inland waterways such as the Mississippi and Ohio

rive rs in Iowa, Missouri, Kentucky, West Virginia, Pennsylvania and New York. The LADCO 2010 inventory was used to replace EPA

- estimated CMV emissions in the MWRPO states, but was not used to replace the 2011 NEI v1 emissions in the bordering non

- MWRPO states.

Some modifications to the MW RPO CMV data were made prior to SMOKE processing:

E missions provided at the level of NEI Shape IDs were aggregated to county

- level

. T he 2011 NEI v1 was used to determine which counties had ports; for those counties that had

por ts , 90% of emissions in the MWPRO inventory were assigned as underway (SCC=2280002200) and 10% were assigned as port emissions (SCC=2280002100)

. E missions were converted to short tons and PM2.5 was added by assuming it is equal to 92% of

PM10 at the sugges tion of the MWRPO. Tugs were assigned a unique SCC (2280002021) to allow for unique spatial allocation (see Section 3.4.1)

. Tugs were assigned from MWRPO total to counties based on 2011 NEI v1 county

- level activity information for tug vessels

.DRAFT

28 Because the G reat Lakes vessels include all CMV activity on the Great Lakes

, EPA

- estimated C3 CMV (c3marine) sector emissions (discussed in the following section) in the MWRPO states were removed to avoid potential double

- counting of C3 CMV with the LADCO inventory in the MWRPO states.



### 2.4.2 Class 3 commercial marine vessels (c3marine)

The U.S

. C3 CMV inventory was d eveloped 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)

. Th e ECA

- IMO data are used instead

of the 2011 NEI v1 data for the modeling platform because accompanying estimates of emission projections for future years are available. In addition, the inven tory preserves shipping lanes in federal

waters while these are not stored within the NEI data . Keeping the sources in this sector separate from

smaller CMV sources allows for the emissions to be elevated above the surface layer within the AQ

model

. The E CA

- IMO data are used for all states with C3 CMV emissions. For the MWPRO states, the

ECA

- IMO C3 CMV emissions in the Great Lakes are assumed to be misclassified as C3 vessels for

which emissions are included in the c1c2rail sector as part of the LADCO inv entory, therefore the ECA

IMO emissions are not included in the c3marine sector

.

The development of this ECA

- IMO

- based C3 CMV inventory is discussed below; however, all non

- U.S. emissions (Canad ian emissions and emissions farther offshore than U.S. waters ) are processed in the “othpt” sector, discussed later in Section 2.5.1. This splitting of the C3 CMV emissions from the farther

offshore emissions allows for easier summaries of U.S.

- only and state or county total emissions.

The ECA

- IMO emissions consis t of large marine diesel engines (at or above 30 liters/cylinder) that until recently , were allowed to meet relatively modest emission requirements, and often burn residual fuel. The emissions in this sector are comprised of primarily foreign

- flagged ocea n

- going vessels , referred to

as C3 CMV ships. The c3marine inventory includes these ships in several intra

- port modes ( i.e., cruising, hoteling, reduced speed zone, maneuvering, and idling) and an underway mode

, and includes

near

- port auxiliary engine emi ssion s. An overview of the C3 ECA Proposal to the International Maritime Organization (EPA

- 420

- F

- 10

- 041, August 2010 ) project and future

- year goals for reduction of

NO X , SO 2 , and PM C3 emissions can be found at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf . The resulting ECA

- IMO coordinated

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

cylinder displacement at or above 30 liters , and the establishment of Emission Control Areas is at: http://www.epa.gov/oms/oceanvessels.htm

.

T he ECA

- IMO emissions data were converted to SMOKE point

- source ORL input format as described

in http://www.epa.gov/ttn/chief/conference/ei17/session6/mason.pdf

. As described in the paper, the

ASCII raster dataset was converted to latitude

- longitude, mapped to state/county FIPS codes that extended up to 200 nautical miles (nm) from the coast, assigned stack parameters, and monthly ASCII raster dataset emissions were used to create monthly temporal profiles. Counties were assigned as extending up to 200nm from t he coast because this was the distance to the edge of the U.S. Exclusive Economic Zone (EEZ), a distance that defines the outer limits of ECA

- IMO controls for these vessels.

The base year ECA inventory is 2002 and consists of these CAPs: PM 10 , PM 2.5 , CO, CO 2 , NH 3 , NO X , SO X (assumed to be SO 2 ), and h ydrocarbons (assumed to be VOC). EPA developed regional growth (activity

- based) factors that were applied to create the 20 11 inventory from the 2002 data. These growth factors are provided in The emissions were converted to SMOKE point source inventory format

,DRAFT

29 allowing for the emissions to be allocated to modeling layers above the surface layer. All no n

- US , non

EEZ emissions (i.e., in waters considered outside of the 20 0 nm EEZ, and hence out of the U.S. and

Canadian ECA

- IMO controllable domain) we re simply assigned a dummy state/county FIPS

code =98001 , and were projected to year 2011 using the “Outside ECA” factors in Error! Not a valid

bookmark self

- reference.

. The SMOKE

- ready data have been cropped from the original ECA

- IMO entire northwestern quarter of the globe to cover only the large continental U.S. 36

- km “36US1” air quality model domain , the largest domain used by EPA in recent years

. . The geographic regions listed in the table are shown in Figure 2

- 2 . The East Coast and Gulf Coast regions were divided along a line roughly through Key Largo (longitude 80 ° 26’ West).

The Canadian near

- shore emissions were assigned to province

- leve l FIPS codes and paired those to region classifications for British Columbia (North Pacific), Ontario (Great Lakes) and Nova Scotia (East Coast).

Table 2

- 14

. Growth factor adjustment factor s to project the 2002 ECA

- IMO inventory to 20 11

* Technically, these are not really “FIPS” state

- county codes, but are treate d as such in the inventory and emissions processing.

The assignment of U.S. state/county FIPS codes was restricted to state

- federal water boundaries data from the Mineral Management Serv ice (MMS) that extend approximately 3 to 10 nautical miles (nm) off

s hore. Emissions outside the 3 to 10 mile MMS boundary

, but within the approximately 200 nm EEZ

boundar ies in Figure 2

- 2

, were projected to year 20 11 using the same regional adjustment factors as the U.S. emissions; however, the s tate/county FIPS codes were assigned as “EEZ” codes and these emissions processed in the “othpt” sector (see Section 2.5.1) . Note that state boundaries in the Great Lakes are an exception, extending through the middle of each lake such that all emissions in the Great Lakes are assigned to a U.S. county or Ontario. This holds true for MWRPO states and other states such as Pennsylvania and New York. The classification of emissions to U.S. and Canadian FIPS codes is

needed to avoid double

- counting of C3 CMV U .S. emissions in the Great Lakes because, as discussed in the previous section, all CMV emissions in the Midwest RPO are processed in the “c1c2rail” sector

. Region EEZ

FIPS NO X PM 10 PM 2.5 VOC (HC) CO SO 2 East Coast (EC) 85004 1.301 0.500 0.496 1.501 1.501 0.536
Gulf Coast (GC) 85003 1.114 0.428 0.423 1.288 1.288 0.461
North Pacific (NP) 85001 1.183 0.467 0.458 1.353 1.353 0.52 4
South Pacific (SP) 85002 1.367 0.525 0.521 1.565 1.562 0.611
Great Lakes (GL) n/a 1.072 0.394 0.390 1.177 1.176 0.415

Outside ECA 98001 1.341 1.457 1.457 1.457 1.457 1.457DRAFT

30 Figure 2

- 2

. Illustration of regional modeling doma ins in ECA

- IMO study

The emissions were converted to SMOKE point source inventory format

, allowing for the emissions to be allocated to modeling layers above the surface layer. All no n

- US , non

- EEZ emissions (i.e., in waters

considered outside of the 20 0 nm EEZ, and hence out of the U.S. and Canadian ECA

- IMO controllable domain) we re simply assigned a dummy state/county FIPS code =98001 , and were projected to year 2011 using the “Outside ECA” factors in Error! Not a valid bookmark self

- reference.

. The SMOKE

ready data have been cropped from the original ECA

- IMO entire northwestern quarter of the globe to cover only the large continental U.S. 36

- km “36US1” air quality model domain , the largest domain used by EPA in recent years 6

.

The original ECA

- IMO inventory did not delineate between ports and underway emissions (or other C3 modes such as hoteling, maneuvering, reduced

- speed zone, and idling)

. H owever, a U.S. ports spatial surrogate dataset was used to assign the ECA

- IMO emissi ons to ports and underway SCCs 2280003100 and 2280003200, respectively. This ha d no effect on temporal allocation or speciation because all C3

CMV emissions, unclassified/total, port and underway, share the same temporal and speciation profiles.

See Sect ion 3.2.1.3 for more details on c3marine speciation and Section 3.3.6 for details on temporal

allocation

.



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

The nonroad equipment emissions are equivalent t o the emissions in the nonroad data category of the 2011NEIv1, with the exception that the modeling platform emissions also include monthly totals. All nonroad emissions are compiled at the county/SCC level. NMIM (EPA, 2005) creates the nonroad

emissions on a month

- specific basis that accounts for temperature, fuel types, and other variables that vary by month. Th e nonroad sector includes mont hly exhaust, evaporative and refueling emissions from

nonroad engines (not including commercial marine, aircraft, and locomotives) that EPA derived from

6 T he extent of the “36US1” domain is similar to the full geographic region shown in Figure 3

- 1. Note that this domain is not specifically used in this 2011 platform, although spatial surrogates that can be used with it are provided.DRAFT

31 NMIM for all states except California and Texas

. Additional details on the development of the

2011NEIv1 nonroad emissions are available in Section 4.5 the 2011NEIv1 TSD.

California year 20 11 nonroad emissions were submitted to the 2011 NEI v1 and are also documented in a staff report (ARB, 2010a). The nonroad sector emissions in California we re developed using a modular approach and include all rulemakings and updates in place by December 2010. These emissions were developed using Version 1 of the CEPAM which support s various California off

- road regulations such

as in

- use diesel retrofits (ARB, 2007), Diesel Risk

- Reduction Plan (ARB, 2000) and 2007 State Implementation Plans (SIPS) for the South Coast and San Joaquin Valley air basins (ARB, 2010b).

T he CARB

- supplied 2011NEIv1 nonroad annual inventory emissions values were converted to monthly values by using the aforementioned EPA NMIM monthly inventories to compute monthly ratios by

county, SCC 7 (fuel, engine type, and equipment type group) , mode, and pollutant

. SCC7 ratios were used because the SCCs in the CARB inventory did not align with many of the SCCs in EPA NMIM inventory. By aggregating up to SCC7, the two inventories had a more consistent coverage of sourc es

. Some VOC emissions were added to California to account for situations when VOC HAP emissions were included in the inventory, but there were no VOC emissions. These additional VOC emissions were computed by summing benzene, acetaldehyde, and formaldeh yde for the specific sources.

Texas year 2011 nonroad emissions were also submitted to the NEI. The 2011NEIv1 nonroad annual inventory emissions values were converted to monthly va lues by using EPA ’s NMIM monthly inventories to compute monthly ratios by county, SCC7 , mode, and poll 7

.



### 2.5 “Other Emissions”: Offshore Class 3 commercial marine vessels and drilling platforms and non

- U.S. sources

The emissions from Canada, Mexico, and non

- U.S. offshore Class 3 Commercial Marine Vessels (C3 CMV) and drilling pla tforms 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 provi de the SMOKE source types: “pt” for point, “ar” for “area and nonroad mobile”, and “on” for onroad mobile.

For Canada, year

- 2006 Canadian emissions were the starting point with the addition of s everal

modifications to the se inventories. The SCCs in these inventories were changed to the generic 39999999 and the industrial code information was removed to preserve confidentiality. The Canadian point sources are split into three inventory files:

ptinv_canada_point_2006_orl_13aug2013_v3_orl.txt: contains point sources for all pollutants except VOC;

ptinv_canada_point_cb5_2006_orl_13aug2013_v1_orl.txt: contains VOC emissions split into CB05 species;

ptinv_canada_point_uog_2006_orl_02mar2009_v0_orl.txt: contains oil and gas

- related sources.

7 If there was no match a t county/SCC7/mode/poll, the allocation would fall back to state/SCC7/mode/poll. If that did not find a match, then state/SCC7 was used. For a few situations, that would also fail to match and the monthly emissions were

allocated with a similar SCC7.DRAFT

32 For Mexico

, emissions for year 2012 are projections of their 1999 inventory originally developed by

Eastern Research Group Inc., (ERG, 2006; ERG, 2009; Wolf, 2009 ) as part of a partnership between Mexico's Secretariat of the Environment and Natural Resources (Secretaría de Medi o Ambiente y

Recursos Naturales

- SEMARNAT) and National Institute of Ecology (Instituto Nacional de Ecología

INE), the U.S. EPA, the Western Governors' Association (WGA), and the North American Commission for Environmental Cooperation (CEC). This inventory includes emissions from all states in Mexico. A background on the development of year

- 20 12 Mexico emissions from the 1999 inventory is available at: http://www.wrapair.org/forums/e f/inventories/MNEI/index.html

.



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

As discussed in Section 2.4.2, the ECA

- IMO

- based C3 CMV emissions for non

- U.S. states are processed in the othpt sector. These C3 CMV e missions include those assigned to Canada, those assigned to the Exclusive Economic Zone ( EEZ, defined as those emissions just beyond U.S. waters approximately 3

- 10 miles offshore, extending to about 200 nautical miles from the U.S. coastline), and

all oth er offshore emissions

– far offshore and non

- U.S. T hese emissions are included in the othpt sector for simplicity of creating U.S.

- only emissions summaries. Otherwise, these emissions are developed in

the same way as the U.S. C3 CMV emissions in the c3mar ine sector.

For Canad ian point sources, other than some minor formatting changes, the Canada

- provided year

- 2006

emissions were modified as follows :

i. S peciated VOC emissions from the Acid Deposition and Oxidant Model ( ADOM ) chemical

mechanism were not inclu ded because EPA modeling uses speciated emissions from the CB5

chemical mechanism

, which Canada also provided.

ii. Excessively high CO emissions were removed from Babine Forest Products Ltd (British Columbia SMOKE plantid=’5188’) in the point inventory. This change was made at EPA’s discretion because the value of the emissions was impossibly large.

iii. The county part of the state/county FIPS code field in the SMOKE inputs were modified in the point inventory from “000” to “001” to enable matching to existing tem poral profiles.

iv. An update to the 2007 platform version was the removal of t hree units that closed in 2010: Grand Lake Generating Station in New Brunswick (PLANTID=’1708’, POINTID=’130011’),

Raffinerie de Montreal

- Est in Quebec (PLANTID=’3127’, POINITD=’532 02982’) and Kidd Metallurgical Site in Ontario (PLANTID=’2815’, POINTID=’ON500004’).

Mexico point

- format year

- 2012 inventories projected from the 1999 Mexico NEI were used essentially

as

- is with only minor formatting changes. The othpt sector also include s point source offshore oil and gas drilling platforms that are beyond U.S. state

- county boundaries in the Gulf of Mexico. For these offshore emissions, the 2008 NEI v ersion 3 point source inventory data were used because the 2011 data were not yet availa ble. This is consistent with the 2011 NEIv1

. U pdated offshore oil and gas drilling

emissions are expected to be incorporated in to v ersion 2 of the 2011 NEI. The 2008

- based offshore

emission sources were provided by the Mineral Management Services (MMS).



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

For Canada

, year

- 2006 emissions provided by Canada and unchanged from EPA 2007 platform we re used . Inventory files were provided for area fugitive dust, agricultural, commercial marine, railro ad, nonroad, aircraft, and other area sources. The following adjustments were made to the original files:DRAFT

33 i. W ildfires or prescribed burning were not included because Canada does not include these inventory data in their modeling. Note that SMARTFIRE 2 is us ed for U.S. sources only.

ii. I n

- flight aircraft emissions were not included because these sources are not included in the U.S. modeling.

iii. A 75% reduction (“transport fraction”) was applied to PM for the road dust, agricultural, and

construction emissions in th e Canadian “afdust” inventory. This approach is more simplistic than the county

- specific approach used for the U.S., but a comparable approach was not

available for Canada.

iv. Wind erosion (SCC=2730100000) and cigarette smoke (SCC=2810060000) emissions were removed from the nonpoint (nonpt) inventory because these emissions are not modeled in the U.S. inventory.

v. Quebec PM 2.5 emissions (2,000 tons/yr) were removed for one SCC (2305070000) for Industrial Processes, Mineral Processes, Gypsum, Plaster Products du e to corrupt fields after conversion to SMOKE input format.

vi. C3 CMV SCCs (22800030X0) records were removed because, as discussed in Section 2.5.1, these emissions are included in the (ECA

- IMO derived) othpt sector, which covers not only

emissions close to Canada but also emissions far at sea. Canada was involved in the inventory development of the ECA

- IMO C3 CMV inventory.

For Mexico nonpoint

- format year

- 2012 inventories, the only significant modification was the removal of domestic ammonia (SCC=555555555 5) (ERG, 2009; Wolf, 2009).



### 2.5.3 Onroad mobile sources from Canada and Mexico (othon)

Both year

- 2006 Canada and year

- 2012 Mexico inventories (ERG, 2009; Wolf, 2009) were converted from their original SMOKE One

- Record per Line ( ORL ) and Inventory Data Analyzer ( IDA ) format s, respectively, in to the SMOKE Flat File 20 10 (FF10) inventory format : http://www.cmascenter.org/smoke/documentation/3.5/html/ch08s02s07.html . Otherwise, the se inventories were used as

- is. The emission values in the Canada

- provided Canadian inventories were unchanged from the 2007 platform.



### 2.6 Fires (ptfire)

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

emis sions provided at geographic coordinates (point locations) and has daily emissions values. The ptfire sector excludes agricultural burning and other open burning sources that are included in the nonpt sector. Emissions are day

- specific and include satell ite

- derived latitude/longitude of the fire’s origin and other parameters associated with the emissions such as acres burned and fuel load, which allow estimation of plume rise. Emissions for the SCCs listed in Table 2

- 15 are treat ed as point sources and are consistent with the fires stored in the Events data category of the 2011NEIv1. For more information on the development of the 2011NEIv1 fire inventory, see Section 5.1 of the 2011NEIv1 TSD.

Table 2

- 15

. 20 11 Platform SCCs representing emissions in the ptfire modeling sector

SCC SCC Description *

2810001000 Other Combustion; Forest Wildfires; Total

2810015000 Other Combustion; Prescribed Burning for Forest Management; Total

2811015 000 Other Combustion

- as Event; Prescribed Burning for Forest Management; Total

2811090000 Other Combustion

- as Event; Prescribed Forest Burning ;UnspecifiedDRAFT

34 * The first tier level of the SCC Description is “Miscellaneous Area Sources”

The point source d ay

- specific emission estimates for 20 11 fires rely on SMARTFIRE 2 (Sullivan, et al., 2008), which use s the National Oceanic and Atmospheric Administration’s (NOAA’s) Hazard Mapping System (HMS) fire location information as input . Additional inputs include the CONSUMEv3.0 software application ( J oint Fire Science Program, 2009 ) and the Fuel Characteristic Classification System (FCCS) fuel

- loading database to estimate fire emission s from wildfires and prescribed burns on a daily basis. The method involves the reconciliation of ICS

- 209 reports (Incident Status Summary Reports) with satellite

- based fire detections to determine spatial and temporal information about the

fires. A func tional diagram of the SMARTFIRE 2 process of reconciling fires with ICS

- 209 reports is available in the documentation (Raffuse, et al., 2007) . Once the fire reconciliation process is completed, the emissions are calculated using the U.S. Forest Service’s CONSUMEv3.0 fuel consumption model

and the FCCS fuel

- loading database in the BlueSky Framework (Ottmar, et. al., 2007)

.

SMARTFIRE 2 estimates were used directly for all states except Georg ia and Florida. For Georgia, the

satellite

- derived emissions were removed from the ptfire inventory and replaced with a separa te state

supplied ptfire inventory

. Adjustments were also made to Florida as described in Section 5.1.4 of the 2011NEIv1 TSD. These changes made the data in the ptfire inventory consistent with t he data in the

2011NEIv1

.



### 2.7 Biogenic sources (biog)

The biogenic emissions were computed based on 20 11 meteorology data using the Biogenic Emission Inventory System, version 3.14 (BEIS3.14) model within SMOKE. The BEIS3.14 model creates gridded, hourly, mod el

- species emissions from vegetation and soils. It estimates CO, VOC (most notably isoprene, t erpe ne, and sesquiterpene ) , and NO emissions for the U.S., Mexico, and Canada. The

BEIS3.14 model is described further in: http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_cmas08.ppt

. The inputs to BEIS include:

Temperature data at 2 meters

, which were obtained from the meteorological input files to the air quality model,

Land

- use data from the Biogenic Emissions Land use Database, version 3 (BELD3). BELD3 data provides data on the 230 vegetation classes at 1

- km resolution over most of North America.

To provide a sense of the scope and spatial distribution of the emissions, p lots of annual BEIS outputs for isoprene and NO for 20 11 are shown in Figure 2

- 3 and Figure 2

- 4 , respectively

.



### 2.8 SMOKE

- ready non

- anthropogenic inventories for chlorine

The ocean chlorine gas emission estimates are based on the build

- up of molecular chlorine (Cl 2 ) concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution

were available and were not modified other than the model

- species name “CHLOR INE” was changed to

“CL2” to support CMAQ model ing

.DRAFT

35 Figure 2

- 3

. Annual NO emissions output from BEIS 3.14 for 20 11

Figure 2

- 4

. Annual i soprene emissions o utput from BEIS 3.14 for 2011DRAFT

36 

## 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 necessary to “pre

- process” the “raw” emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2 . In brief, the process of e missions modeling transforms the emissions inventories from their original temporal resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded resolution required by the air quality model. Emissions modeling includes temp oral allocation, spatial allocation, and pollutant speciation. In some cases, emissions modeling also include s the vertical allocation of point

sources , but many air quality models also perform this task because it greatly reduces the size of the input emi ssions files if the vertical layer of the sources does not need to be included.

As seen in Section 2 , the temporal resolution s of the emissions inventories input to SMOKE var y across sectors, and may be hourly, daily, monthly , or annual total emissions. The spatial resolution, which also can be different for different sectors, may be individual point sources

, county /province/municipio totals

, or gridded emissions . This section provides some basic information about the tools and data files used for emissions modeling as part of the modeling platform. In Section 2

, the emissions inventories and how they differ from the 2011 NEI v1 were described. In Section 3, the descriptions of data are limited to the ancillary data SMOKE uses to perform the emissions modeling steps. Note that all SMOKE inputs

for the 2011v6 platform are available from the CHIEF Emissions Modeling Clearinghouse website (see

Section 1 ).

SMOKE version 3

. 5. 1 was used to pre

- process the raw emissions inventories into emissions inputs for CMAQ. For projects that used CAMx, the CMAQ emissions were converted into the CAMx formats using CAMx convertor programs. For sectors that have plume rise, the in

- line em issions capability of the air quality model s was used, which allows the creat ion of source

- based and two

- dimensional gridded

emissions files that are much smaller than full three

- dimensional gridded emissions files. For quality

assurance of the emissions modeling steps, e missions 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 m issions modeling Overview

When preparing emissions for the air quality model, emissions for e ach sector are processed separately through SMOKE, and then the final merge program (Mrggrid) is run to combine the model

- ready,

sector

- specific emissions across s ectors. 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 platfo rm sector. The “Spatial” column shows the spatial approach used : here “point” indicates that SMOKE maps the source from a point location (i.e., latitude and longitude) to a grid cell; “surrogates” indicates that some or all of the sources use spatial surr ogates to allocate county emissions to grid cells; and “area

- to

- point” indicates that some of

the sources use the SMOKE area

- to

- point feature to grid the emissions (further described in Section

3.4.2 ). The “Speciation” column indicates that all sectors us e the SMOKE speciation step, though biogenics speciation is done within the Tmpbeis3 program and not as a separate SMOKE step. The “Inventory resolution” column shows the inventory temporal resolution from which SMOKE needs to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory ;DRAFT

37 i nstead

, activity data and emission factors are used in combination with meteorological data to compute hourly emissions.

Finally, the “plume rise” column indicates the sector s for which the “in

- line” approach is used. These sectors are the only ones with emissions in aloft layers based on plume rise. The term “in

- line” means that the plume rise calculations are done inside of the air quality model instead of being computed b y SMOKE. T he air quality model computes the plume rise using the stack data and the hourly air quality model inputs found in the SMOKE output files for each model

- ready emissions sector. The height of the plume rise determines the model layer into which the emissions are placed. The c3 marine , othpt, and ptfire sector s are the only sector s with only “in

- line” emissions, meaning that all of the emissions are placed in aloft layers and there are no emissions for th ose sector s in the two

- dimensional, lay er

- 1 files created by SMOKE.

Table 3

- 1

. Key emissions modeling steps by sector

.

Platform sector Spatial Speciation Inventory

resolution Plume rise

a fdust S urrogates Yes annual

a g S urrogates Yes a nnual (some mo nthly)

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

np_oilgas S urrogates Yes annual

o n road Surrogates Yes monthly activity, computed hourly

on road_rfl Surrogates Yes monthly activity, computed hourly

o thar Surrogates Yes annual

o thon S urrogates Yes annual

o thpt Point Yes annual in

- line pt_oilgas Point Yes an nual in

- line

p t egu Point Yes daily & hourly in

- line

p t egu_pk Point Yes daily & hourly in

- line ptfire Point Yes daily in

- line

p tnonipm Point Yes annual in

- line

rwc Surrogates Yes annual

SMOKE has the option of grouping sources so that they are treate d as a single stack when computing plume rise. For the 2011 platform

, no grouping was performed because grouping combined with “in

line” processing will not give identical results as “offline” processing (i.e., when SMOKE creates 3

- dimensional files). Th is occurs when stacks with different stack parameters or lat/lons are grouped, thereby changing the parameters of one o r more sources

. The most straightforward way to get the same

results between in

- line and offline is to avoid the use of grouping.DRAFT

38 SMOKE was run for the smaller 12

- km CON tinental U nited S tates “CONUS” modeling domain (12US2) shown in Figure 3

- 1 and boundary conditions were obtained from a 2011 run of GEOS

- Chem.

Figure 3

- 1

. Air quality modeli ng 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 describes the grids for the t wo domains.

Table 3

- 2

. Descriptions of the 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 , xo rig, yorig, xcell, ycell, ncols, nrows, nthik

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

Mexico/Canada 12US1_459X299 ‘LAM_40N97W',

- 2556000,

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

US 12 km or

“smaller”

CONUS

- 12 12 km Smaller 12km

CONUS plus some of

Mexico/Canada 12US2 ‘LAM_40N97W',

- 2412000 ,

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

Section 3.4 provides the details on the spatial surrogates and area

- to

- point data used to accomplish spatial allocation with S MOKE.DRAFT

39 

### 3.2 Chemical Speciation

The emissions modeling step for chemical speciation creates the “model species” needed by the air quality model for a specific chemical mechanism. These model species are either individual chemical compounds or groups of species , called “model species.” The chemical mechanism used for the 2011 platform is the CB05 mechanism (Yarwood, 2005). The same base chemical mechanism is used with in

both CMAQ and CAM X , but the implementation differs slightly between the two models. The spe cific versions of CMAQ and CAMx used in applications of this platform include secondary organic aerosol ( SOA) and HONO enhancements.

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

in the 200 7 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 chemic al 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 format ion.

The TOG and PM 2.5 speciation factors that are the basis of the chemical speciation approach were developed from the SPECIATE4.3 database ( http://www.epa.gov/ttn/chief/software/speciate ) whi ch is

EPA 's repository of TOG and PM speciation profiles of air pollution sources. However, a few of the profiles used in the v 6 platform will be published in later versions of the SPECIATE database after the

release of this documentation. The SPECIATE da tabase development and maintenance is a collaboration involving EPA ’s ORD, OTAQ, and the Office of Air Quality Planning and Standards (OAQPS)

, in cooperation with Environment Canada (EPA, 2006a). The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical compounds, VOC

- to

- TOG conversion factors associated with the TOG profiles, and speciation profiles for PM 2.5

.

Speciation profiles and cross

- references for this platform are available in spreadsheet form from ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/speciation_profiles/ . The profiles are in the

Excel files “ gspro_2011.xlsx ” and “ gspro_combo_2011.xlsx, gsref_201 1.xlsx ” . The cross reference

information is in “ gsref_2011.xlsx ” , and differences between 2011 and 2018 speciation profile s are shown in “ 2011ed_2018ed_gspro_differences.xlsx ”

. A spreadsheet showing emission totals for each speciation profile by modeling sector is available in the file

“ 2011ed_speciation_profile_CAPs_feb112014.xlsx ”

.DRAFT

40 Table 3

- 3

. Emission m odel s pecies produced for CB05 with SOA for CMAQ 5.0 .1 and CAM X *

Inventory Pollutant Model Species Model species description

C l 2 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 ALD2 Acetaldehyde

ALDX Propionaldehyde and higher aldehydes

BENZENE Benzene (not part of CB05)

CH4 Methane 8

ETH Ethene

ETHA Ethane

ETOH Ethanol

FORM Formaldehyde

IOLE Internal olefin carbon bond (R

- C=C

- R)

ISOP Isoprene

MEOH Methanol

OLE Terminal olefin carbon bond (R

- C=C)

PAR Paraffin carbon bond

TOL Toluene and other monoalkyl aromatics

XYL Xylene and other polyalkyl aromatics VOC species from the biogenics

model that do not m ap to model species above SESQ Sesquiterpenes

TERP Terpenes

PM 10 PMC Coarse PM > 2.5 microns and 10 microns

PM 2.5 9 PEC Particulate elemental carbon 2.5 microns

PNO3 Particulate nitrate 2.5 microns

POC Particulate organic carbon (carbon only) 2.5 microns

PSO4 Particulate Sulfate 2.5 microns

PMFINE Other particulate matter 2.5 microns

Sea

- s alt species (non

– anthropogenic ) 10 PCL Particulate chloride

PNA Particulate sodium
*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 dupli cate 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 organi c carbon, coarse particulate 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

8 T echnically, CH4 is not a VOC but part of TOG. Although emissions of CH4 are derived, the AQ models do not use these emissions because the anthropogenic emissions are dwarfed by the CH4 already in the atmosphere.

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

10 These emissions are created outside of SMOKEDRAFT

41 PMOTHR= CAM X 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 2011 NEI v1 in the speciation process. Instead of speciating VOC to generate all of the species listed in Table 3

- 3 , emissions of four specific HAPs : benzene, acetaldehyde, formaldehyde and methanol (collectively known as “BAFM”) from the NEI were “integrated” with the NEI VOC. The integration process (described in more detail below) combines these HAPs with the VOC in a way that does not double count emissions and uses the HAP inventory directly in the speciation process. The basic process is to subtract the specified HAPs

emissions mass from VOC emissions mass and to then use a special “ integrated ” profile to speciate the

remainder of VOC to t he model species excluding the specific HAPs. EPA believes that generally, the HAP emissions from the NEI are more representative of emissions of these compounds than their generation via VOC speciation.

T he BAFM HAPs ( benzene, acetaldehyde, formaldehyde and methanol ) were chosen because , with the exception of BENZENE, they are the only explicit VOC HAPs in the base version of CMAQ 5.0.1 ( CAPs only with chlorine chemistry) model. Explicit means that they are not lumped chemical groups

like the other CB05 species. These “explicit VOC HAPs” are model species that participate in the modeled chemistry using the CB05 chemical mechanism. The use of these HAP emission estimates along with VOC is called “HAP

- CAP integration”. BENZENE was chosen because it is a model species in the base version of CMAQ 5.0.1, and there was a desire to keep its emissions consistent

between multi

- pollutant and base versions of CMAQ.

For specific sources, especially within the onroad and onroad_rfl sectors, the integration inclu ded

ethanol. To differentiate when a source was integrating BAFM versus EBAFM (ethanol in addition to BAFM), the speciation profiles that do not include ethanol are referred to as an “E

- profile” (to be used when the ethanol comes from the inventory pollut ant)

. F or example

, use E10 headspace gasoline evaporative speciation profile 8763 whe n ethanol is speciated from VOC, but use 8763E whe n ethanol is obtained directly from the inventory.

The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats other than PTDAY (the format used for the ptfire sector). SMOKE allows the user to specify both the particular HAPs to integrate via the INVTABLE and the particular sources to integrate via the NHAPEXCLUDE file (which actually pr ovides the sources to be excluded from integration 11 ). For the “integrate d ” sources, SMOKE subtracts the “integrate d ” HAPs from the VOC (at the source level) to compute emissions for the new pollutant “NONHAPVOC.” The user provides NONHAPVOC

- to

NONHAPTOG factors and NONHAPTOG speciation profiles 12 . SMOKE computes NONHAPTOG and then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC species not including the integrated HAPs. After determining if a sector is to be in tegrated, if all sources

11 In SMOKE version 3.5, the options to specify sources for integration are expanded so that a user can specify the particular

sources to inc lude or exclude from integration, and there are settings to include or exclude all sources within a sector. In addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, but it is missing BAFM or VOC, SMOKE will now raise an error.

12 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified lis t of pollutants, for example BAFM.DRAFT

42 have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a NHAPEXCLUDE file. If on the other hand, certain sources do not have the necessary HAPs, then an NHAPEXCLUDE file must be provid ed based on the evaluation of each source’s pollutant mix. EPA

considered CAP

- HAP integration for all sectors and developed “integration criteria” for some of them (see Section 3.2.1.3 for details).

The process of partial int egration for BAFM is illustrated in Figure 3

- 2 that the BAFM records in the input inventories do not need to be removed from any sources in a partially integrated sector becau s e SMOKE does this automatically using the INVTABLE con figuration

. For EBAFM integration, this process is identical to that shown in the figure except for the addition of ethanol (E) to the list of subtracted HAP pollutants. For full integration, the process would be very similar except that the

NHAPEXCLUDE f

ile would not be used and all sources in the sector would be integrated.

Figure 3

- 2

. Process of integrating BAFM with VOC for use in VOC Speciation

In SMOKE, the INVTABLE allows the user to specify both th e particular HAPs to integrate. T wo different types of INVTABLE files are included for use with different sectors of the platform. For

sectors that had no integration across the entire sector (see Table 3

- 4 ), EPA created a “no HA P use” INVTABLE in which the “KEEP” flag is set to “N” for BAFM pollutants. Thus, any BAFM pollutants in the inventory input into SMOKE are automatically dropped. This approach both avoids double

counting of these species and assumes that the VOC speciat ion is the best available approach for these species for sectors using th is approach. The second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep the inventory BAFM pollutants and indicates that they

are to be i ntegrated with VOC . This is done by setting the “VOC or TOG component” field to “V” for all four HAP pollutants. T his type of INVTABLE is further differentiated into a version for those

sectors that integrate BAFM and another for those that integrate EBAF M , such as the onroad and
onroad_rfl sectors.DRAFT

43 Table 3

- 4

. Integration approach for BAFM and EBAFM for each platform sector

Platform

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

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

ptegu No integration ptegu_pk No integration ptnonipm No integration ptfire No integration

othar No integration

othon No integration

ag N/A

– sector contains no VOC

afdust N/A

– sector con tains no VOC biog N/A

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

nonpt Partial integration (BAFM and EBAFM) np_oilgas Partial integration (BAFM) pt_oilgas Partial integration (BAFM)

rwc Partial integration (BAFM)

nonroad Partial integration (BAFM)

c1c2rail Partial integration (BAFM) othpt Partial integration (BAFM)

c3marine Full integration (BAFM)

onroad Full integration (EBAFM and BAFM)
onroad_rfl Full integration (EBAFM and BAFM)

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

Section 3.2.1.3

.

3.2.1.2 County specific profile combinations (GSPRO_COMBO)

SMOKE can compute speciation profiles from mixtures of other profiles in u ser

- 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). T his feature was used to spe ciate onroad and nonroad mobile and gasoline

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

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

- profiles (i.e. there is EBAFM integration). Different profile comb inations are specified by the mode (e.g. exhaust, evaporative, refueling, etc.) by changing the pollutant name (e.g. EXH__NONHAPTOG, EVP__NONHAPTOG, RFL__NONHAPTOG). For the nonpt sector, a combination of BAFM and EBAFM integration is used . Due to the l ack of SCC

specificity in the GSPRO_COMBO, the only way to differentiate the sources that should use BAFM integrated profiles versus E

- profiles is by changing the pollutant name. For example, EPA changed the pollutant name for the PFC future year inventor y so the integration would use EVP__NONHAPVOC toDRAFT

44 correctly select the E

- profile combinations

, while other sources used NONHAPVOC to select the typical BAFM profiles.

3.2.1.3 Additional sector specific details

The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some sectors there is no integration (VOC is speciated directly), for some sectors there is full integration (all sources are integrated), and for other sectors there is partial integration (some sources are not integ rated and other sources are integrated). The integrated HAPs are either BAFM (BAFM HAPs subtracted from VOC) or EBAFM ( ethanol and BAFM HAPs subtracted from VOC ). Table 3

- 4 summarizes the integration for each platform sector.

For the c1c2rail sector, EPA integrated BAFM for most sources from the 2011NEIv1 . There were a few sources that had zero BAFM; therefore, they were not integrate d . The MWRPO and CARB inventories (see Section 2.4.1 ) did not i nclude HAPs; therefore, all non

- NEI source emissions in the c1c2rail sector

were not integrated . For California, the CARB inventory TOG was converted to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor.

For the othpt sect or, the C3 marine sources (see Section 2.4.2 ) are integrated. HAPs in this sector are derived identically to the U.S. c3marine sector. T he rest of the sources in othpt are not integrated

, thus the sector is partial ly integrated

.

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

MOVES (see Sections 2.3.1 and 2.3.2 ) is used to estimate these sectors

, meaning both the MEPROC

and INVTABLE files are inv olved in controlling which pollutants are ingested and speciated. Second, these sectors have estimates of TOG as well as VOC; therefore, TOG can be speciated directly. Third, the gasoline sources use full integration of EBAFM (i.e. use E

- profiles) and t h e diesel sources use full integration of BAFM. Fourth, the onroad sector utilizes 6 different modes for speciation: exhaust, extended idle, evaporative, permeation (gasoline vehicles only), brake wear, and tire wear (See Table

2

- 12 for more details). The onroad_rfl sector utilizes an additional mode : refueling. Fifth, the gasoline exhaust profiles were updated to 8750a (r evision to Gasoline Exhaust

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

- E10 ethanol ga soline ) 13 . Sixth, for California and Texas, EPA applied adjustment factors to SMOKE

- MOVES to produce California and Texas adjusted model

- ready files (see Section 2.3.1 for details). By applying the ratios through SMOKE

- MOVES, the CARB and Texas inventories are essentially speciated to match EPA estimated speciation grid cell by grid cell. T he future year CARB inventor ies did not have BAFM, so EPA estimates of BAFM were adjusted using VOC adjustment factors for California only.

For the nonroad sector, CNG or LPG sources (SCC s beginning with 2268 or 2267) are not integrated because NMIM computed only VOC and not any HAPs for these SCCs. All other nonroad sources were integrated except in California. For California, the CARB inve ntory TOG was converted to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor. SMOKE later applies the

same VOC

- to

- TOG factor prior to computing speciated emissions. The CARB

- based nonroad data includes exhaust and evaporativ e mode

- specific data for VOC, but does not contain refueling. The CARB inventory does not include HAP estimates for all sources; therefore, the sources which have VOC but do not have BAFM or BAFM is greater than VOC are not integrated . The remaining sour ces are integrated

. T he future year CARB inventories did not have BAFM so all sources for California were

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

45 not integrate d

. Similar to onroad, the gasoline exhaust profiles were updated to 8750a and 8751a (this is

true nation

- wide).

For the ptnonipm sector , the 2011 and 2018 runs were not integrated . This was an oversight

— it should have been partial integration because the 2011 ethanol inventory (SCC 30125010) provided by OTAQ includes BAFM. In the future year, ptnonipm should be partial ly integrate d bec ause both the ethanol and biodiesel inventories (SCC 30125010) provided by OTAQ include BAFM. Aircraft emissions use the profile 5565b which is chemically equivalent to 5565 (aircraft exhaust) in SPECIATE 4.3 database.

T he profile numbers are differentia ted from each other because a draft version of 5565 was used in previous modeling platforms.

For most sources in the rwc sector, the VOC emissions were greater than or equal to BAFM

, and BAFM

was not zero , so th os e sources were integrated , although a few specific sources that did not meet these criteria could not be integrated

.

For the oil and gas sources in np_oilgas and pt_oilgas, the basins studied in WRAP Phase III have basin

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

these basin

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

from coal

‐ bed methane (CBM) gas sourc es

. Table 3

- 5 lists the basin and gas composition specific profiles used for the sources in the WRAP Phase III basins. F or oil and gas sources outside of the WRAP Phase III basins, the profiles did not vary by region or basin (s ee Table 3

- 6 ). Table 3

- 7 lists the

WRAP Phase III counties

.

Table 3

- 5

. VOC profiles for WRAP Phase III basins

Profile Code Description

DJFLA D

- J Basin Fla shing Gas Composition for Condensate

DJVNT D

- J Basin Produced Gas Composition

PNC01 Piceance Basin Gas Composition at Conventional Wells

PNC02 Piceance Basin Gas Composition at Oil Wells

PNC03 Piceance Basin Flashing Gas Composition for Condensate

PRBCO Powder River Basin Produced Gas Composition for Conventional Wells

PRM01 Permian Basin Produced Gas Composition

SSJCO South San Juan Basin Produced Gas Composition for Conventional Wells

SWE01 Wyoming Flashing Gas Composition

SWFLA SW Wyoming Ba sin Flash Gas Composition

SWVNT SW Wyoming Basin Vented Gas Composition

UNT02 Uinta Basin Gas Composition at Conventional Wells

UNT03 Uinta Basin Flashing Gas Composition for Oil

UNT04 Uinta Basin Flashing Gas Composition for Condensate

WRBCO Wind River Basin Produced Gas Composition for Conventional Wells

Table 3

- 6

. National VOC profiles for oil and gas

profile Description

0000 Over All Average

0001 External Combustion Boiler

- Residual OilDRAFT

46 profile Description

00 02 External Combustion Boiler

- Distillate Oil

0003 External Combustion Boiler

- Natural Gas

0004 External Combustion Boiler

- Refinery Gas

0007 Natural Gas Turbine

0008 Reciprocating Diesel Engine

0051 Flares

- Natural Gas

0296 Fixed Roo f Tank

- Crude Oil Production

1001 Internal Combustion Engine

- Natural Gas

1010 Oil and Gas Production

- Fugitives

- Unclassified

1011 Oil and Gas Production

- Fugitives

- Valves and Fittings

- Liquid Service

1012 Oil and Gas Production

- Fugi tives

- Valves and Fittings

- Gas Service

1207 Well Heads (Water Flood) Composite

2487 Composite of 7 Emission Profiles from Crude Oil Storage Tanks

- 1993

2489 Composite of 15 Fugitive Emission Profiles from Petroleum Storage Facilities

- 1993

Table 3

- 7

. Counties included in the WRAP Dataset

FIPS Sta te County

08 001 CO Adams

08 005 CO Arapahoe

08 007 CO Archuleta

08 013 CO Boulder

08 014 CO Broomfie ld

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 Jeffer son

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

08 103 CO Rio Blanco

08 107 CO Routt

08 115 CO Sedgwick

08 121 CO Washington FIPS Sta te County

08 123 CO Weld

08 125 CO Yuma

30003 MT Big Horn

30075 MT Powder River

35005 NM Chaves

35015 NM Eddy

35015 N M 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

4810 7 TX Crosby

48109 TX Culberson

48115 TX Dawson

48125 TX Dickens

48135 TX Ector

48141 TX El Paso

48151 TX Fisher

48165 TX Gaines FIPS Sta te County

48169 TX Garza

48173 TX Glasscock

48219 TX Hockley

48227 TX Howard

48229 TX Hudspeth

48235 TX Irion

48263 TX Kent

48269 TX King

48301 TX Loving

48303 TX Lubbock

48305 TX Lynn

48317 TX Martin

48329 TX Midland

48335 TX Mitchell

48353 TX Nolan

48371 TX Pecos

48383 TX Reagan

48389 TX Reeves

48413 TX Schleicher

48415 TX Scurry

48431 TX Sterling

48435 TX Sutt on

48445 TX Terry

48451 TX Tom Green

48461 TX Upton

48475 TX WardDRAFT

47 FIPS Sta te County

48495 TX Winkler

48501 TX Yoakum

49007 UT Carbon

49009 UT Daggett

49013 UT Duchesne

49015 UT Em ery

49019 UT Grand

49043 UT Summit FIPS Sta te County

49047 UT Uintah

56001 WY Albany

56005 WY Campbell

56007 WY Carbon

56009 WY Converse

56011 WY Crook

56013 WY Fremont

56019 WY Johnson FIPS 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

For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. The 2011 platform uses BEIS3.14, which includes a new species (SES Q) that was mapped to the model species SESQT. The profile code associated with BEIS3.14 profiles for use with CB05 uses the profile : “B10C5.”

For the nonpt sector, where VOC emissions were greater than or equal to BAFM and BAFM was not

zero
, the sources were integrated. For portable fuel containers (PFCs) and fuel distribution operations

associated with the b ulk

- plant

- to

- pump (BTP) distribution, ethanol may be mixed into the fuels; therefore,

county

- and month

- specific COMBO speciation was used (via th e GSPRO_COMBO file). Refinery to bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation are considered upstream from the introduction of ethanol into the fuel; therefore a single profile is sufficient for these sources. No

refined information on potential VOC speciation differences between cellulosic diesel and cellulosic

ethanol sources was available, t herefore cellulosic diesel and cellulosic ethanol sources used the same SCC (30125010: Industrial Chemical Manufacturing, Ethanol b y Fermentation production) for VOC speciation as was used for corn ethanol plants. For the future year, PFC and the cellulosic sources were integrated EBAFM (i.e. used E

- profiles) because ethanol was present in those inventories.

3.2.1.4 Future year speciation

Th e 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 nonro ad, onroad and onroad_rfl sectors account for the changes in ethanol content of fuels across years. A description of the actual fuel formulations for 2011 can be found

in the 2011NEIv1 TSD

, and for 2018 see Section 4.3

. For 2011, EPA used “COMBO” profile s to model

combination s of profiles for E0 and E10 fuel use. For 2018, EPA used “COMBO” profiles to model

combinations of E10 and E85 fuel use. The speciation of onroad exhaust VOC also accounts for a portion of the vehicle fleet meeting Tier 2 standards in that different exhaust profiles are available for pre

- Tier 2

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

into account both the increase in ethanol use , and the increase in Tier 2 vehicles in t he future case

.

The speciation changes from fuels in the nonpt sector are for PFCs and fuel distribution operations associated with the BTP distribution. For these sources, ethanol may be mixed into the fuels , in which

case speciation would change acro ss years. The speciation changes from fuels in the ptnonipm sectorDRAFT

48 include BTP distribution operations inventoried as point sources. RBT fuel distribution and BPS speciation does not change across the modeling cases because this is considered upstream fr om the

introduction of ethanol into the fuel. For PFC s , ethanol was present in the future inventories and therefore EBAFM profiles were used to integrate ethanol in the speciation. The m apping of fuel distribution SCCs to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix B.

Tab le 3

- 8 summarizes the different profiles utilized for the fuel

- related sources in each of the sectors for 2011 and the future year case. This table indicates when “E

- profiles” were used instead of BAFM integrated profiles. The term “COMBO” indicates that a combination of the profiles listed was used to speciate that subcategory using the GSPRO_COMBO file.

Tab le 3

- 8

. Select VOC profiles 20 11 versus 2018

S ector subcategory 2011 2018

onroad gasoline

exhaust COMBO: COMBO:

8750aE Pre

- Tier 2 E0 exhaust 8751E Pre

- Tier 2 E10 exhaust

8751aE Pre

- Tier 2 E10 exhaust 8757E Tier 2 E10 Exhaust

8756E Tier 2 E0 Exhaust 8758E Tier 2 E15 Exhaus t

8757E Tier 2 E10 Exhaust 8855E Tier 2 E85 Exhaust

onroad gasoline

evaporative COMBO: COMBO

8753E E0 Evap 8754E E10 Evap

8754E E10 Evap 8872E E15 Evap

8934E E85 Evap

onroad gasoline

permeation COMBO: COMBO

8766E E0 evap perm 8 769E E10 evap perm

8769E E10 evap perm 8770E E15 evap perm

8934E E85 Evap

onroad_rfl gasoline

refueling COMBO: COMBO

8869E E0 Headspace 8870E E10 Headspace

8870E E10 Headspace 8871E E15 Headspace

8934E E85 Evap

onroad diesel

exha ust 87710 Weighted diesel exhaust for

201 0 877P0 Weighted diesel exhaust

for 2020

onroad diesel

extended

idle 877P0 Weighted diesel exhaust for

2020 877EIT3 Weighted diesel extended

idle for 2018

onroad diesel

evaporative 4547 Diesel Headspace 4547 Diese l Headspace

onroad_rfl diesel

refueling 4547 Diesel Headspace 4547 Diesel Headspace

nonroad gasoline

exhaust COMBO: 8751a Pre

- Tier 2 E10 exhaust

8750a Pre

- Tier 2 E0 exhaust

8751a Pre

- Tier 2 E10 exhaust

nonroad gasoline

evaporative COMBO: 8754 E10 evap

8753 E0 evapDRAFT

49 S ector subcategory 2011 2018

8754 E10 evap

nonroad gasoline

refueling COMBO: 8870 E10 Headspace

8869 E0 Headspace

8870 E10 Headspace

nonroad diesel

exhaust 8774 Pre

- 2007 MY HDD exhaust 8774 Pre

- 2007 MY HDD exhaust

nonroad die sel

evaporative 4547 Diesel Headspace 4547 Diesel Headspace

nonroad diesel

refueling 4547 Diesel Headspace 4547 Diesel Headspace

nonpt/ptnonipm PFC COMBO: COMBO

8869E E0 Headspace 8870E E10 Headspace

8870E E10 Headspace 8871E E15 Headspace

8934E E85 Evap

nonpt/ptnonipm BTP COMBO: COMBO

8869 E0 Headspace 8870 E10 Headspace

8870 E10 Headspace 8871 E15 Headspace

8934 E85 Evap

nonpt/ptnonipm BPS/RBT 8869 E0 Headspace 8869 E0 Headspace



### 3.2.2 PM speciation

3.2.2.1 AE5 versus AE6 speciati on In addition to VOC profiles, t he 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 “simplified” components (AE5) are all that is needed. For CMAQ 5.0.1, there is a new thermodynamic equilibrium aerosol modeling tool (ISORROPIA) v2 mechanism that needs additional PM components (AE6), which are

further subsets of PMFIN E ( see Table 3

- 9 ). EPA speciated PM 2.5 so that it included both AE5 and AE6

PM model species without causing any double count ing . Therefore, emissions from this platform can be

used with either CMAQ 4.7.1 or CMAQ 5.0.1.

Table 3

- 9

. PM model species: AE5 versus AE6

species name species description AE5 AE6

POC organic carbon Y Y

PEC elemental carbon Y Y

PSO4 sulfate Y Y

PNO3 nitrate Y Y

PMFINE unspeciated PM 2.5 Y N

PNH4 ammonium N Y

PNCOM non

- carbon org anic matter N Y

PFE iron N Y

PAL aluminum N YDRAFT

50 species name species description AE5 AE6

PSI silica N Y

PTI titanium N Y

PCA calcium N Y

PMG magnesium N Y

PK potassium N Y

PMN manganese N Y

PNA sodium N Y

PCL chloride N Y

PH2O water N Y

PMOTHR PM 2.5 not in other AE6 species N Y

The ma jority of the 2011 platform PM profiles come from the 911XX series which include updated AE6 speciation 14

. Appendix C contains a c rosswalk between AE6 profile codes and SPECIATE 4.3 profile

codes

. Summaries of 2011 and 2018 emissions by county, month, and sector speciated according to AE5 and AE6 speciation profiles are available from ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/ in the files 2011ed_county_monthly_report_P M_AE5.xlsx, 2011ed_county_monthly_report_PM_AE6.xlsx, 2018ed_county_monthly_report_PM_AE5.xlsx, and 2018ed_county_monthly_report_PM_AE6.xlsx. In addition, the file 2011ed_speciation_profiles_6feb2014.xlsx shows the total PM2.5 apportioned to each

sector.

3.2.2.2 O nroad PM speciation

Unlike other sectors, the onroad sector has pre

- speciated PM. This speciated PM comes from the MOVES model and is processed through the SMOKE

- MOVES system (see Section 2.3.1 ). Unfortunately, the MOVES spec iated PM does not map 1

- to

- 1 to the AE5 speciation (nor the AE6 speciation) needed for CMAQ modeling. Table 3

- 10 shows the relationship between MOVES 15 exhaust

PM 2.5 related species and CMAQ AE5 PM species.

Table 3

- 10

. MOVES exhaust PM species versus AE5 species

MOVES2010b Pollutant Name Variable name for Equations Relation to AE5 model species

Primary Exhaust PM2.5

– Total PM25_TOTAL
Primary PM 2.5 - Organic Carbon PM25OM Sum of POC, PNO3 and PMFINE
Primary PM 2.5 - Elemental Carbon PM25EC PEC
Primary PM 2.5 - Sulfate Particulate PM25SO4 PSO4

MOVES s pecies are related as follow s:

PM25_TOTAL = PM25EC + PM25OM + PSO4

The five CMAQ AE5 species also sum to total PM 2.5 :

14 The exceptions are 5674 (Marine Vessel

– Marine Engine

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

Smoke

– Simplified ) used in nonpt.

15 The Tier3 NPRM MOVES model has the same PM components as MOVES2010b. MOVES2014 is expected to have a one

to

- one mapping of PM species to CMAQ PM species.DRAFT

51 PM 2.5 = PO C+PEC+PNO3+PSO4+PMFINE

The basic problem is to differentiate MOVES species “PM25OM” into the component AE5 species (POC, PNO3 and PMFINE). The Moves2smkEF post

- processor script takes the MOVES species (EF tables) and calculates the appropriate AE5 PM 2.5 s pecies and converts them into a format that is appropriate for SMOKE (see http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch05s02s04.html for details

on the Mov es2smkEF script). For a more detailed discussion of the derivation of these equations, see Appendix D.

For brake wear and tire wear PM, total PM 2.5 (not speciated) comes directly from MOVES. These PM modes are speciated by SMOKE. PMFINE from onroad exh aust is further speciated by SMOKE into the component AE6 species.

For California and Texas, adjustment factors were applied to SMOKE

- MOVES to produce California and Texas adjusted model

- ready files (see Section 2.3.1 for deta ils). California and Texas did not supply speciated PM, therefore the adjustment factors applied to PM2.5 were also applied to the speciated PM components. By applying the ratios through SMOKE

- MOVES, the CARB and Texas inventories are essentially speciate d to match EPA estimated speciation grid cell by grid cell.



### 3.2.3 NO X 

### speciation

NO X can be speciated into NO, NO2, and/or HONO. For the non

- mobile sources, EPA use d a single profile “NHONO” to split NO X into NO and NO 2 . For the mobile sources except for onroa d ( including nonroad, c1c2rail, c3marine

, othon sectors ) and for specific SCCs in othar and ptnonipm

, the profile “HONO” splits NO X into NO, NO 2 , and HONO. Table 3

- 11 gives the split factor for these two profiles.

Table 3

- 11

. NO X speciation profiles

profile pollutant species split factor

HONO NOX NO2 0.092

HONO NOX NO 0.9

HONO NOX HONO 0.008

NHONO NOX NO2 0.1

NHONO NOX NO 0.9

The onroad sector does not use the “HONO” profile to speciate NO X . MOVES2010b produces speciated
NO, NO 2 , and HONO by source, including emission factors for these species in the emission factor tables used by SMOKE

- MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NO X . The NO

fraction varies by heavy duty versus light duty, fuel type, and model year. The NO 2 fraction = 1

– NO

– HONO. For more details on the NO X fractions within MOVES, see http://www.epa.gov/otaq/models /moves/documents/420r12022.pdf

. HONO is not calculated directly by the Tier 3 proposal version of MOVES. For these EF tables, the calculation of HONO and the NO 2 fraction are calculated externally by the moves2smk script 16

. The SMOKE

- MOVES system then models these species directly without further speciation.

16 A specific version of the moves2smk script was developed to do this calculation of HONO. The typical version assumes that HONO was calculated directly by MOVES2010b.DRAFT

52 

### 3.3 Temporal Allocation

Temporal allocation (i.e., temporalization ) is the process of distributing aggregated emissions to a finer temporal resolution

, thereby converting annual emissions to hourly emis sions. While the total emissions are important , the timing of the occurrence of emissions is also essential for accurately simulating ozone, PM, and other pollutant concentrations in the atmosphere. Many emissions inventories are annual or monthly in nat ure. Temporalization takes these aggregated emissions and if needed distributes them to the month, and then distributes the monthly emissions to the day and the daily emissions to the hour. This process is typically done by applying temporal profiles to t he inventories in this order: monthly, day of the week, and diurnal.

The temporal profiles and associated cross references used to create the hourly emissions inputs for the

2011 air quality model ing platform were similar to those used for the 2007 platfo rm. T he t emporal

factors applied to the inventory are selected using some combination of country, state, county, SCC, and pollutant. Table 3

- 12 summarizes the temporal aspects of emissions modeling by comparing the key approache s used for temporal processing across the sectors. In the table, “ Daily temporal approach ” refers to the temporal approach for getting daily emissions from the inventory using the SMOKE Temporal program. The values given are the values of the SMOKE L_TYPE setting. The “ Merge processing approach ” refers to the days used to represent other days in the month for the merge step. If

this is not “all”, then the SMOKE merge step run s only for representative days, which could include holidays as indicated by the right

- most column. The values given are those used for the SMOKE M_TYPE setting (see below for more information)

.

Table 3

- 12

. Temporal 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

ptegu D aily & hourly all all Y es

p t egu_pk D aily & hourly a ll all Y es

p tnonipm A nnual yes mwdss mwdss Y es pt_oilgas Annual yes mwdss mwdss Y es ptfire D aily a ll all Y es

o thpt A nnual yes mwdss mwdss

n onroad M onthly mwdss mwdss Y es

othar A nnual yes week week

c1c2rail A nnual yes mwdss mwdss

c3marine A nnual yes aveday aveday

o nroad A nnual & monthly 1 all all Y es

onroad_rfl A nnual & monthly 2 all all Y es

o thon A nnual yes week week

n onpt A nnual & monthly yes all all Y es

np_oilgas Annual yes mwdss mwdss Yes

rwc Annual no met

- based All Yes

ag A nnual yes all all Y esDRAFT

53 Platform

sector short

name Inventory

resolutions Monthly

profiles

used? Daily

temporal approach Merge

processing approach Process

Holidays as separate days
afdust_adj A nnual yes week all Y e s

beis H ourly 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.

The following values are used in the table: The value “ all ” means that hourly emissions computed for every day of the year and that emissions potential ly have day

- of

- year variation. The value “week” means

that hourly emissions computed for all days in one “representative” week, representing all weeks for each

month . This means emissions have day

- of

- week variation, but not week

- to

- week variation within the

month . The value “ mwdss ” means hourly emissions for one representative Monday, representative weekday (Tuesday through Friday) , representative Saturday

, and repre sentative Sunday for each month.

This means emissions have variation between Mondays, oth er weekdays, Saturdays and Sundays within the month, but not week

- to

- week variation within the month . The value “aveday” means hourly emissions computed for one representative day of each month, mean ing emissions for all days within a

month are the same. Special situations with respect to temporalization are described in the following

subsections.

Spreadsheets summarizing the temporal profiles and cross

- references used in the modeling are available with the other 2011 platform reports in ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/temporal_profiles/ with the profiles in the files tpro_2011.xlsx, tpro_onroad_2011.xlsx, tpro_rwc_2011.xlsx, and the cross references in
tref_2011.xlsx. The file 2011ed_temporal_profiles_CAPs_feb112014.xlsx shows the emissions assigned to each of the profiles for sectors other than onroad, ptegu, and ptegu_pk.

In addition to the resolution, temporal processing includes a ram p

- up period for several days prior to January 1, 20 11 , which is intended to mitigate the effects of initial condition concentrations. The ramp

- up period was 10 days (December 22

- 31, 2010). For most sectors, emissions from December 2011 were

used to fill in surrogate emissions for the end of December 2010. In particular, December 2011 emissions (representative days) were used for December 2010. For biogenic emissions, December 2010 emissions were processed using 2010 meteorology.



### 3.3.1 Use of FF10 format for f iner than annual emissions

The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for monthly, daily, and hourly emissions inventories than previous formats supported . Previously, to process monthly inventory data required the use of 12 separate inventory files. With the FF10 format, a single inventory file can contain emissions for all 12 months and the annual emissions in a single record. This helps simplify the management of numerous inventories. Similarly, da ily and hourly FF10 inventories

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

SMOKE 3. 5. 1 prevents the application of temporal profiles on top of the “native” resolution of the inventory. For example, a monthly inventory should not have annual to month temporalization applied to
it ; rather, it should only have month

- to

- day and diurnal temporalization. This becomes particularly important when specific sectors have a mix of annual, monthly, daily, and/o r hourly inventories (e.g. the nonpt sector). The flags that control temporalization for a mixed set of inventories are discussed in theDRAFT

54 SMOKE documentation. The modeling platform sectors that make use of monthly values in the FF10 files are nonroad, onr oad, and the ag burning inventory within the nonpt sector

.



### 3.3.2 Electric Generating Utility temporalization (ptegu, ptegu_pk)

3.3.2.1 Base year temporal allocation of EGUs

The 2011NEI v1 annual EGU emissions are allocated to hourly emissions using the following 3

- step methodology: annual value to month, month to day, and day to hour. The temporal allocation procedure is differentiated by whether or not the source could be directly matched to a CEMS unit via ORIS facility

code and boiler ID. Prior to temporal allocatio n, as many sources as possible were matched to CEMS

data via ORIS facility code and boiler ID. EIS stores a base set of previously matched units via alternate facility and unit IDs. For any units not yet matched, reports were generated by unit to identif y potential

matches with the NEI. The reports included FIPS state/county code, facility name, and NOx and SO 2 emissions. Units were considered matches if the FIPS state/county code matched, the facility name was similar, and the NOx and SO 2 emissions wer e similar.

For sources not matched to CEM S measurements, the first two steps of the allocation are done outside of

SMOKE. For sources in the ptegu and ptegu_pk sectors that are matched to CEMS data, annual totals of

the emissions may be different than the annual values in 2011NEIv1 because the CEMS data actually replaces the inventory data

. All units in the ptegu_pk sector with non

- zero emissions for 2011 were

matched to CEMS data.

For units not matched to CEMS data, the allocation of the inventory annua l emissions to months is done using average fuel

- specific season

- to

- month factors generated for each of the 64 IPM regions shown in Figure 3

- 3 . These factors are based 2011 CEMS data only. In each re gion, separate factors were developed for the fuels coal , natural gas, and “ other ”, where the types of fuels included in “other” vary by region . Separate profiles we re computed for NO

x and SO 2 , and heat input. An overall composite profile

was also computed and was used in a few cases in which the fuel

- specific profile wa s too irregular

, or there were no CEM S unit s with the specified fuel in the region containing the unit. For both CEMS and

non

- CEMS matched units, NO

x and SO 2 CEM S data are used to allocate NO

x and SO 2 emissions, while

CEM S heat input data is used to allocate all other pollutants.

Daily “temporal allocation” of units with CEMS was performed using a procedure similar to that in the first step in that the CEMS data replaces the inventory data for each pollutant . For units without CEMS data, emiss ions we re allocated from month to day using IPM

- region and fuel

- specific average month

- to

day factors based on the 2011 CEM S data. Separate allo cation factors were computed for NOx, SO 2 , and heat input for the fuels coal, natural gas, and other

. For both CEMS and non

- CEMS matched units, NO

x and SO 2 CEM S data are used to allocate NO

x and SO 2 emissions, while CEM S heat input data is used to allocate all other pollutants.

For units with associated CEMS data , hourly emissions use the hourly CEMS values a s described above for NOx and SO2, while other pollutants are allocated according to heat input values . For units without CEMS data, temporal profiles from days to hours are computed based on the region

- and fuel

- specific average day

- to

- hour factors derive d from the CEMS data for those fuels and regions using data from the entire year. For non

- CEMS matched units, CEMS heat input data is used to allocate all pollutants (including NO

x and SO 2 ). S MOKE then allocates the daily emissions data to hours using the profiles

obtained from the CEMS data for the analysis base year.DRAFT

55 Figure 3

- 3

. IPM Regions for EPA Base Case v5.13

3.3.2.2 Future year temporal allocation of EGUs

IPM provides unit

- level emission projections of average winter (representing October through April) and average summer (representing May through September) values. These annualized emissions are allocated to hourly emissions using a 3

- step methodology: annualized summer/winter value to month, month to day, and day to hour. The first two steps are done outside of SMOKE and the third step is done by SMOKE using daily emissions files created from the first two steps. This approach maximizes the use of

the CEMS data from the air quality analysis year (e.g., 2011)

.

For CEMS matched units, the 2011 based CEMS were scaled so that their seasonal emissions matched IPM totals. In other words, EPA created a set of artificial CEMS data which had the same temporal pattern as 2011, but for which the seasonal total emissions matched IPM’s predictions for 2018. Except for the scaling of CEMS data, the procedure for allocating the emissions of CEMS matched units is the same as the base year (see Section 3.3.2.1 ). For sources not matched t o CEMS units , the allocation of the IPM seasonal emissions to months was done using average fuel

- specific season

- to

- month factors generated for each of the 64 IPM regions shown in Figure 3

- 3. These factors are based on a single year of

CEMS data consistent with the modeling base year, in this case 2011. Similar to the base year, profiles were created for coal, natural gas, and “other” fuel. For each fuel, separate profiles were computed for

NO

x , SO 2 , and heat input. An overall composite profile was also com puted in the event that a fuel

- specific profile was too irregular or in the case when a unit changed fuels between the base and future year andDRAFT

56 there were previously no units with that fuel in the specific region. Except for the season

- to

- month allocation, the procedure for allocating the emissions of non

- CEMS matched units is the same as the base

year.

Units with year

- specific impacts in the season

- to

- month allocations, such as long

- duration downtimes for

maintenance or installation of controls that occ ur only in one year were temporalized using average profiles instead of using the anomalous profile for the base year. These situations are determined by analysis of the base and future year data. Note that IPM uses load data (reflecting the shape of deman d) corresponding to the load in each IPM region that occurred in the base year of the air quality modeling analysis, such as 2011.

Some refinements to the above approach were made in some special cases:

When emissions were substantially higher for units w ith limited hours of operation in the base year, an averaged profile was used.

When a unit switched fuels in the future year to a fuel not used in the base year, the profile was selected according to the new type of fuel. If the unit was a CEMS unit in t he base year, it was

treated as non

- CEMS in the future year.

When a CEMS unit operated in only one season in the base year, but IPM predicted that there were emissions in both seasons, an average profile was used for the future year unit during both

season s. New units coming on line use d the appropriate region and fuel

- specific profiles

Units that are not new but had no emissions in 2011 were treated like new units

.

For more information on the development of IPM emission estimates and the temporalization o f those

,

see http://www.epa.gov/powersectormodeling/BaseCasev513.html , in particular the Air Quality Modeling Flat File Documentation and accompanying inputs.



### 3.3.3 Residential Wood Combust ion Temporalization (rwc)

There are many factors that impact the timing of when emissions occur , and for some sectors this includes meteorology . The benefits of utilizing meteorology as method for temporaliz ation are: (1) a meteorological dataset consiste nt with that used by the AQ model is available (e.g. outputs from WRF); (2) the meteorological model data is highly resolved in terms of spatial resolution; and (3) the meteorological variables vary at hourly resolution and can therefore be translate d in to hour

- specific temporalization.

The SMOKE program GenTPRO provides a method for developing meteorology

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

- to

- day temporalization for

residential wood combu stion ( RWC )

, month

- to

- hour temporalization for agricultural livestock ammonia, and a generic meteorology

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

based temporalization was used for portions of the rwc sector and for live stock within the ag sector.

GenTPRO reads in gridded meteorolog ical data ( output from MCIP) along with spatial surrogates

, and

uses the specified algorithm to produce a new temporal profile that can be input into SMOKE. The meteorological variables and the resolution of the generated temporal profi le (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 docu mentation at http://www.cmascenter.org/smoke/documentation/3.1/ GenTPRO_TechnicalSummary_Aug2012_Final.pdfDRAFT

57 and http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch05s03s07.html , respectively

.

For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal

allocation of emissions to days. GenTPRO was used to create an annual

- to

- day temporal profile for the RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of the year

. On days where the minimum temperature does not drop below a user

- define d threshold, RWC emissions

for most sources in the sector are zero. Conversely, the program temporally allocates the largest percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual

emis sions do not cha nge, only the distribution of the emissions within the year is affected

. The temperature threshold for rwc emissions was 50  ̊ F f or most of the country, and 60  ̊ F for the following states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and

Texas.

Figure 3

- 4 illustrates the impact of changing the temperature thre shold for a warm climate county. The plot shows the temporal fraction by day for Duval County, Florida for the first four months of 2007

. The

default 50  ̊ F threshol d creates large spikes on a few days, while the 60  ̊ F threshold dampens these spikes

and d istributes a small amount of emissions to the days that have a minimum temperature between 50 and

60  ̊ F.

Figure 3

- 4

. Example of RWC temporalization in 2007 using a 50 versus 60  ̊F threshold

The diurnal pr ofile for used for most RWC sources (see Figure 3

- 5 ) places more of the RWC emissions in the morning and the evening when people are typically using these sources. This profile is based on a

2004 MANE

- VU survey based temporal profiles (see http://www.marama.org/publications_folder/ResWoodCombustion/Final_report.pdf ). This profile was created by averaging three indoor and three RWC outdoor temporal profiles from coun ties in Delaware and aggregating them into a single RWC diurnal profile. This new profile was compared to a concentration based analysis of aethalometer measurements in Rochester, NY (Wang et al. 2011) for various seasons and day of the week and found tha t the new RWC profile generally tracked the concentration based temporal patterns.DRAFT

58 Figure 3

- 5

. RWC diurnal temporal profile

The temporalizatio n for “Outdoor Hydronic Heaters” (i.e.,“OHH”, SCC=2104008610) and “Outdoor wood burning device, NEC (fire

- pits, chimneas, etc)” ( i.e., “recreational RWC”,SCC=21040087000) were updated because the meteorological

- based temporalization used for the rest of the rwc sector did not agree with observations for how these ap pliances are used.

For OHH, the annual

- to

- month, day

- of

- week and diurnal profiles were modified based on information in the New York State Energy Research and Development Authority (NYSERDA) “Environmental, Energy
Market, and Health Characterization of W ood

- Fired Hydronic Heater Technologies, Final Report” (NYSERDA, 2012) as well as a Northeast States for Coordinated Air Use Management (NESCAUM) report “Assessment of Outdoor Wood

- fired Boilers” (NESCAUM, 2006). A Minnesota 2008 Residential

Fuelwood Asses sment Survey of individual household responses (MDNR, 2008) provided additional

annual

- to

- month, day

- of

- week and diurnal activity information for OHH as well as recreational RWC

usage.

The diurnal profile for OHH, shown in Figure 3

- 6 is based on a conventional single

- stage heat load unit burning red oak in Syracuse, New York. As shown in Figure 3

- 7 , the NESCAUM report describes how for individual units, OHH are highly variable day

- to

- day but that in the a ggregate, these emissions have no day

- of

- week variation. In contrast, the day

- of

- week profile for recreational RWC follows a typical “recreational” profile with emissions peaked on weekends.

Annual

- to

- month temporalization for OHH as well as recreational RWC were computed from the MN DNR survey (MDNR, 2008) and are illustrated in Figure 3

- 8 . OHH emissions still exhibit strong seasonal variability, but do not drop to zero because many units operate year round for water and pool

he ating. In contrast to all other RWC appliances, recreational RWC emissions are used far more frequently during the warm season.DRAFT

59 Figure 3

- 6

. Diurnal profile for OHH, based on heat load (BTU/hr)

Figure 3

- 7

. Day

- of

- week temporal profiles for OHH and Recreational RWC 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 Heat Load (BTU/hr)

0 50 100 150 200 250 300 350 Fire Pits/Chimineas Day

- of

- Week Profile

Fire Pit/Chimenea

Outdoor Hydronic HeaterDRAFT

60 Figure 3

- 8

. Annual

- to

- month temporal profiles for OHH and recreational RWC



### 3.3.4 Agricultural Am monia Temporal Profiles (ag)

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 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 ambi ent temperature, aerodynamic

resistance , and wind speed

. The equations are:

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

- 138 0/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 w ind 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 profile s for

these sources. Because these profiles distribute to the hour ba sed on monthly emissio ns, the monthly

emissions are obtained from a monthly inventory

, or from an annual inventory that has been temporalized

to the month 17

.

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

- to

- hour temporalization. However, SMOKE v3.5 beta incorrectly applied an annual

- to

- month temporal profile on top of a monthly inventory when

0 10 20 30 40 50 60 70 80 90 100

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Millions Monthly Temporal Activity for OHH & Recreational RWC

Fire Pit/Chimenea

Outdoor Hydronic HeaterDRAFT

61 Figure 3

- 9 compares the daily emissions for Minnesota from the “old” approach (uniform monthly profile) with the “new” approach (GenTPRO generated month

- to

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

Figure 3

- 9

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

emissions



### 3.3.5 Onroad mobile temporalization (onroad, onroad_rfl)

For the onroad and onroad_rfl sectors, the temporal distribution of emissions is a combination of more traditional temp oral profiles and the influence of meteorology. This section will discuss both the meteorological influences and the updates to the diurnal temporal profiles for the 2011 platform.

Meteorology is not used in the development of the temporal profiles, but r ather it impacts the calculation of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the hourly level by grid cell. More specifically, the on

- network (RPD) and the off

- network (RPV and RPP) processes use the gri dded meteorology (MCIP) directly. Movesmrg determines the temperature for each hour and grid cell and uses that information to select the appropriate emission factor (EF) for the specified SCC/pollutant/mode combination. In the previous platform, RPP use d county level minimum and maximum temperature ranges for the day to determine the appropriate EF. This potentially overestimated the temperature range for any particular grid cell, which would result in increased emissions for vapor

venting. In the 2011 platform (and the 2011 NEI v1 ), RPP was updated to use the gridded minimum and maximum temperature for the day. This more spatially resolved temperature range produces more accurate emissions for each grid cell. The combination of these three processes (RP D, RPV, and RPP) is the total onroad sector emissions, while the combination of the two processes (RPD, RPV) for the refueling mode only is the total onroad_rfl sector emissions. Both sectors show a strong meteorological influence on their temporal patter ns (see the 2011NEIv1 TSD for more details).

Figure 3

- 10 illustrates the difference between temporalization of the onroad sector used in the 2005 and earlier platforms and the meteorological influence via SMOKE

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

- of

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

- MOVES, but the meteorologically varying EFs add an additional variation on top of the temporalization . Note, the SMOKE

- MOVES run is based on the 2005 platform and previous temporalization of VMT to facilitate the comparison of the results. In the figure, the MOVES emissions have a repeating pattern within the month, while the SMOKE

- MOVES shows

temporalizing with GenTP RO ag NH 3 profiles. As an interim solution, a flat monthly profile was applied to the states with a monthly ag NH3 inventory.DRAFT

62 day

- to

- d ay (and hour

- to

- hour) variability. In addition, the MOVES emissions have an artificial jump between months which is due to the inventory providing new emissions for each month which are then temporalized within the month but not between months. The SMOKE

- MOVES emissions have a

smoother transition between the months.

Figure 3

- 10

. Example of SMOKE

- MOVES temporal variability of NO X emissions

For the onroad and onroad_rfl sectors, the “inventories” referred to in Table 3

- 12 actually consist of activity data. For RPP and RPV processes, the VPOP inventory is annual and does not need temporalization. For RPD, the VMT inventory is monthly and was temporalized to days of the week and

th en to hourly VMT through temporal profiles. The RPD processes require a speed profile (SPDPRO) that consists of vehicle speed by hour for a typical weekday and weekend day. Unlike other sectors, the temporal profiles and SPDPRO will impact not only the d istribution of emissions through time but also

the total emissions. Because SMOKE

- MOVES’ process RPD calculates emissions from VMT, speed and meteorology, if one shifted the VMT or speed to different hours, it would align with different temperatures and h ence different EF. In other words, two SMOKE

- MOVES runs with identical annual VMT, meteorology, and MOVES EF, will have different total emissions if the temporalization of VMT changes.

In previous platforms, the diurnal profile for VMT 18 varied by road ty pe but not by vehicle type (see Figure 3

- 11 ). These profiles were used throughout the nation.

18 These same profiles were used for onroad emissions in the 2005 platform.DRAFT

63 Figure 3

- 11

. Previous onroad diurnal weekday profiles for urban roads

EPA wanted to create new diurnal profiles that could differentiate by vehicle type as well as by road type and would potentially vary over geography. The 2011NEI v1 process provided an opportunity to update the diurnal profile with information submitted by states. S tates submitted MOVES county databases (CDBs) that included information on the distribution of VMT by hour of day and by day of week 19 (see

the 2011NEIv1 TSD for details on the submittal process for onroad). EPA decided not to update the day of week profi le because MOVES only differentiated weekday versus weekend while the default SMOKE profiles differentiated each of the 7 days. EPA mined the state submitted MOVES CDBs for non

- default diurnal profiles 20 . The list of potential diurnal profiles was then an alyzed to see whether the profiles varied by vehicle type, road type, weekday vs. weekend, and by county within a state (see Figure 3

- 12 ).

Figure 3

- 12

. Variation in MOVES diurnal pr ofiles

19 The MOVES tables are the hourvmtfraction and the d ayvmtfraction.

20 Further QA was done to remove duplicates and profiles that were missing two or more hours. If they were missing a single hour, the missing hour could be calculated by subtracting all other hours fractions from 1.DRAFT

64 EPA attempted to maximize the use of state and/or county specific diurnal profiles. If a specific state or county’s profiles varied by vehicle type or/and road type, then the submitted profile was used. If the profile had less variability than the old SMOKE defaults (i.e. neither varied by vehicle type nor road type), then a new default profile would be used (see below for description of new profiles). This analysis was done separately for weekdays and for weekends, therefore some areas had sub mitted profiles for weekdays but defaults for weekends. The result was a set of profiles that varied geographically depending on whether or not the profile was submitted and the characteristics of the profiles (see Figure

3

- 13 ).

Figure 3

- 13

. Use of submitted versus new national default profiles

A new set of diurnal profiles was developed from the submitted profiles that varied by both vehicle type and road type. Before developin g the national profiles, there needs to be a mapping between MOVES road types and SMOKE road types (i.e., the last three digits of the SCC) and between MOVES source types and SMOKE vehicle types. The mapping between road types is relatively straight forwa rd (see

Tabl e 3

- 13 ). Basically the road types are consolidated into 4 types in MOVES, therefore the new profiles will not differentiate at the level of the SMOKE road type. For example, the SMOKE “urban interstate” (SCC LAST 3 = 230 ) will have the same profile as the SMOKE “urban other freeways and expressways” (SCC LAST 3 = 250). The mapping between MOVES source type and SMOKE vehicle type is more complicated; it is a many

- to

- many mapping (see the 2011NEIv1 TSD for more details).DRAFT

65 Tabl e 3

- 13

. Mapping of MOVES to SMOKE road types

MOVES roadtype ID Description SMOKE

SCC LAST 3 Description

2 Rural Restricted Access 110 Rural Interstate: Total

3 Rural Unrestricted Access 130 Rural Other Prin cipal Arterial: Total

150 Rural Minor Arterial: Total

170 Rural Major Collector: Total

190 Rural Minor Collector: Total

210 Rural Local: Total

4 Urban Restricted Access 230 Urban Interstate: Total

250 Urban Other Freeways and Expressways: Total

5 Urban Unrestricted Access 270 Urban Other Principal Arterial: Total

290 Urban Minor Arterial: Total

310 Urban Collector: Total

330 Urban Local: Total

For the purposes of constructing the SMOKE diurnal profiles, all MOVES profiles for t he road type and for any overlapping source types are averaged together to create a single diurnal profile for a specific county, SMOKE road type, SMOKE vehicle type, and weekday or weekend . This process is also used for creating SMOKE versions of the su bmitted profile in the non

- default regions (described above). The states that submitted profiles that varied by vehicle and road types for weekdays were: Idaho, Maine, Michigan, New Jersey, Ohio

, and Pennsylvania. The states that submitted profiles that varied by vehicle and road types for weekends were: Idaho, Maine, and Michigan. EPA created individual profiles for each state (averaging over the counties within) to create a single profile by state, vehicle type, road type, and weekday or weekend. The states individual profiles were averaged together to create a new default profile 21

. Figure 3

- 14 shows two new default profiles for light duty gas vehicles (LDGV, SCC7 2201001 ) and heavy, heavy duty diesel vehicles (HHDDV, SCC7 2 230074 ) on restricted urban roadways (interstates and freeways, SCC LAST 3 = 230 and 250) for weekdays. The grey lines are the individual state profiles, the black line is the new default profile, and the 2 colored lines are the previous SMOKE default profile s. Note that there are two previous SMOKE profiles for this road type, but that they don’t vary by vehicle. In contrast, the new default profile does vary by vehicle and places more LDGV VMT (left plot) in the rush hours while placing HHDDV VMT (right pl ot) predominately in the middle of the day with a longer tail into the evening hours and early morning. For a full list of the default profiles, see ftp://ftp.epa.go v/EmisInventory/2011v6/v1platform/reports/temporal_profiles/ onroad_default_hourly_profile_plots_2011ed.zip

.

21 Note that the states we re weighted equally in the average independent of the size of the state or the variation in submitted county data.DRAFT

66 Figure 3

- 14

. Updated national default profiles for LDGV vs. HHDDV, urban restricted weekday

For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week 22 , and air basin. These CARB specific profiles were used in developing EPA estimates for California. For Texas, the profiles used were a combination of state suppl ied (via MOVES CDBs) and new national defaults. Although EPA adjusted the total emissions to match California’s and Texas’ submittals to the

22 California’s diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and Tuesday, Wednesday, T hursday had the same profile.DRAFT

67 2011 NEI v1 , the temporalization of these emissions took into account both the state specific VMT profiles

and the SMOKE

- MOVES process of incorporating meteorology. For more details on the adjustments to California’s and Texas’ onroad emissions, see the 2011NEIv1 TSD.



### 3.3.6 Additional s ector specific details ( afdust, beis

, c1c2rail, c3marine, nonpt

, ptfire )

For the afdust s ector, meteorology is not used in the development of the temporal profiles, but it is used to reduce the total emissions based on meteorological conditions. These adjustments are applied through

sector

- specific scripts, beginning with the application of l and use

- based gridded transport fractions and then subsequent zero

- outs for hours during which precipitation occurs or there is snow cover on the ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are subject to transport. This methodology is discussed in (Pouliot, et. al., 2010, http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot_pres.pdf , and in Fugitive Dust Model ing for the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation adjustment is applied to remove all emissions for days where measureable rain occurs. Therefore, the afdust emissions vary day

to

- day based on the precipitation and/or snow c over for that grid cell and day. Both the transport fraction

and meteorological adjustments are based on the gridded resolution of the platform; therefore, somewhat different emissions will result from different grid resolutions. Application of the tran sport fraction and meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling a s compared to ambient samples.

Biogenic emissions in the beis sector vary by every day of the year because they are developed using

me teorological data including temperature, surface pressure, and radiation/cloud data. The emissions are computed using appropriate emission factors according to the vegetation in each model grid cell, while taking the meteorological data into account.

For the c1c2rail and c3marine sector s , emissions are allocated with flat monthly and day of week profiles, and most emissions are also allocated with flat hourly profiles.

For the nonpt sector , most the inventories are annual except for the agricultural burni ng (SCC 2801500000) inventory which was allocated to months by adding up the available values for each day of

the month

. For all agricultural burning, the diurnal temporal profile used reflected the fact that burning occurs during the daylight hours

- see Figure 3

- 15 (McCarty et al., 2009) . This puts most of the emissions during the work day and suppresses the emissions during the middle of the night. All states use d a uniform profile for each day of the week for all agricultural burning emissions, except for the following

states that for which EPA used state

- specific day of week profiles: Arkansas, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, and Texas.DRAFT

68 Figure 3

- 15

. Agricultural burning diurnal temporal profile

For the ptfire sector, the inventories are in the daily point fire format ORL PTDAY. The ptfire sector is used in the model evaluation case (2011ed and in the future base case (2018ed). T he 2007 and earlier platforms had additional regulatory cases that used averaged fires and temporally averaged EGU emissions, but the 2011 platform uses base year

- specific (i.e., 2011) data for both cases.

For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly inventories from output from NMIM. For California, a monthly inventory was created from CARB’s annual inventory using EPA

- estimated NMIM monthly results to compute monthly ratios by pollutant and

SCC7 and these ratios were applied to the CARB inventory to create a monthly inventory. .



### 3.4 Spatial Allocation

The methods used to perform spatial allocation are summarized in this section

. For the modeling platform , spatial factors are typically applied by co unty and SCC

. As described in Section 3.1

, spatial

allocation was performed for a national 12

- km domain

. To accomplish this, SMOKE used national 12

- km spatial surrogates and a SMOKE area

- to

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

circa 2010

- 2011 data wherever possible. For Mexico, updated spatial surrogates were used as described

below . For Canada surrogates provided by Environment Canada were used and are unchanged from the 2007 platform

. The U.S., Me xican, and Canadian 12

- km surrogates cover the entire CONUS domain

12US1 shown in Figure 3

- 1

. The remainder of this subsection provides further detail on the origin of the data used for the spatial surrogates and the area

- to

- poin t data.

Additional documentation on the 2011 spatial surrogates is available at ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/spatial_surrogates/ in th e files US_SpatialSurrogate_Documentation_v091113.pdf and US_SpatialSurrogate_Workbook_v093013.xlsx

. The spatial cross reference file is in gsref_2011.xlsx. Plots of the spatial surrogates are available in all_surrogate_maps_2011platform_12US1_v2.pdf . Not e that these are plots of the surrogate fractions summed by grid cell, so grid cells that overlap multiple counties can show values greater than one. TheseDRAFT

69 maps are only to give an idea of the spatial distribution of the surrogates. Allocations of CAP emis sions to each of the surrogate codes is given in 2011ed_spatial_surrogate_CAPs_feb112014.xlsx

.



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

There are more than 70 s patial surrogates available for spatially allocating U.S. county

- level emissions to

the 12

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

- to

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

- 14 lists the codes and descriptions of the surrogates. The surrogates in bol d 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 f

or railroads and roads, the 2006 National Land Cover Database, 2011 gas station and dry cleaner data, and the 2012 National Transportation Atlas Data for rail

- lines, ports and navigable

waterways

. Surrogates for ports (801) and shipping lanes (802) were developed based on the 2011NEI v1 shapefiles: Ports_032310_wrf and ShippingLanes_11130 9FINAL_wrf, but also included shipping lane data in the Great Lakes and support vessel activity data in the Gulf of Mexico.

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

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

d=99999

.

Table 3

- 14

. U.S. Surrogates available for the 2011 modeling p latform

.

Code Surrogate Description Code Surrogate De scription

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 Tra de 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/C linic

180 Residential Heating

– Coal 570 Heavy and High Tech Industrial

190 Residential Heating

- LP Gas 575 Light and High Tech Industrial

200 Urban Primary Road Miles 580 Food, Drug, Chemical Industrial

210 Rural Primary Road Miles 585 Metals and Minerals Industrial

220 Urban Secondary Road Miles 590 Heavy Industrial

230 Rural Secondary Road Miles 595 Light Industrial

240 Total Road Miles 596 Industrial plus Institutional plus Hospitals

250 Urban Primary plus Rural Primary 600 Gas Stations

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

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

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

70 Code Surrogate Description Code Surrogate De scription

USGS

261 NTAD Total Railroad Density 700 Airport Areas

271 NTAD Class 1, 2, 3 Railroad Density 710 Airport Points

280 Class 2 and 3 Railroad Miles 720 Military Airports

300 Low Intensity Residential 800 Marine Ports

310 Total Agriculture 801 NEI Ports

312 Orchards/Vineyards 802 NEI Shipping Lanes

320 Forest Land 807 Navigable Waterway Miles

330 Strip Mines/Quarries 808 Gulf Tug Zone Area

340 Land 810 Navigable Waterway Activity

350 Water 812 Midwest Shipping Lanes

400 Rural Land Area 850 Golf Courses

500 Commercial Land 860 Mines

505 Industrial Land 870 Wastewater Treatment Facilities

510 Commercial plus Industrial 880 Drycleaners

515 Commercial plus Institutional Land 890 Commercial Timber

For the onroad sector, the on

- network (RPD) emissions were spatially allocate d to roadways, and the off

network (RPP and RPV) emissions were allocated to population. For the onroad_rfl sector, the emissions were spatially allocated to gas station locations. For the oil and gas sources in the nonpt sector, the WRAP

Phase III source s have detailed 2008

- based basin

- specific spatial surrogates shown in Table 3

- 15

. These surrogates were also updated in the northeast Marcellus Shale region using 2011 data. Any remaining oil and gas sources still use the 2005

- ba sed surrogate “Oil & Gas Wells, IHS Energy, Inc. and USGS” (680) developed for oil and gas SCCs. The surrogates in Table 3

- 15 were applied for the counties listed in

Table 3

- 7

.

Table 3

- 15

. Spatial Surrogates for WRAP and Marcellus Shale Oil and Gas Data

Country Code Surrogate Description

USA 689 Gas production at all wells

USA 690 Oil production at all wells

USA 691 Well count

- CBM wells

USA 692 Spud c ount

USA 693 Well count

- all wells

USA 694 Oil production at Oil wells

USA 695 Well count

- oil wells

USA 696 Gas production at gas wells

USA 697 Oil production at gas wells

USA 698 Well count

- gas wells

USA 699 Gas production at CBM wells

Not a ll of the available surrogates are used to spatially allocate sources in the modeling platform; that is, some surrogates shown in Table 3

- 14 were not assigned to any SCCs, although many of the “unused” surrogates are actually used to “ gap fill ” other surrogates that are assigned. When the source data for a surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in those counties to ensure that no emissions are dropped when the spati al surrogates are applied to the

emission inventories. Table 3

- 16 shows the total of CAP emissions (i.e., NH 3 , NOx, PM 2.5 , SO 2 , andDRAFT

71 VOC) by sector. Appendix E shows the emissions totals by surrogate code and sector for most of th e CAPs. Some sectors use only one surrogate, those are listed below:

ag: Total Agriculture (#310) with 3,524,607 tons of NH3;
onroad_rfl: G as Stations (#600) with 157,629 tons of VOC; and rwc: 0.5 Residential Heating

- Wood plus 0.5 Low Intensity Resident ial (#165) with 903,651 tons

of CAPs.

Table 3

- 16

. Total CAP emissions by sector for U .S. Surrogates

C ode Description afdust c1c2rail nonpt nonroad np_oilgas o nroad

100 Population 1,221,647 47,453 2,742,952

120 Urban Population 521,778

130 Rural Population 1,102,192 282,583

140 Housing Change and
Population 162,157 224,499 662,641

150 Residential Heating

- Natural

Gas 278,120

165 0.5 Residential Heating

- Wood plus 0.5 Low Intensity

Res idential

170 Residential Heating

- Distillate Oil 142,700

180 Residential Heating

– Coal 12,791

190 Residential Heating

- LP Gas 42,196

200 Urban Primary Road Miles 2,534,014

210 Rural Primary Road Miles 1,609,013

220 U rban Secondary Road

Miles 281,011

230 Rural Secondary Road Miles 491,782

240 Total Road Miles 287,531

240 Total Road Miles 6,825

250 Urban Primary plus Rural
Primary 102,793

260 Total Railroad Miles 2,195

261 NTAD Total Railroad Density 15,271 3,559

271 NTAD Class 1 2 3 Railroad
Density 802,720

280 Class 2 and 3 Railroad Miles 45,114

300 Low Intensity Residential 162,807 234,134

310 Total Agriculture 896,741 709,177 586,125

312 Orchards/Vineyards 5,720

320 Forest Land 347

330 Strip Mines/Quarries 59,782

350 Water 770,993

400 Rural Land Area 1 1,188 663,518

500 Commercial Land 115,905

505 Industrial Land 747,634 186,522

510 Commercial plus Industrial 225,266 281,481

515 Commercial plus

Institutional Land 294,718

520 Commercial plus Industrial
plus Institutional 11,252 122,236DRAFT

72 C ode Description afdust c1c2rail nonpt nonroad np_oilgas o nroad

525 Golf Courses plus
Institutional plus Industrial
plus Commercial 0 220,996

527 Single Family Residential 0

535 Residential + Commercial +

Industrial + Institutional +

Government 334,251

540 Retail Trade (COM1) 1,375

545 Personal Repair (COM3) 63,005

555 Professional/Technical
(COM4) plus General Government (GOV1) 2,872

560 Hospital (COM6 ) 9

575 Light and High Tech Industrial (IND2 + IND5) 2,554

580 Food, Drug, Chemical Industrial (IND3) 11,626

585 Metals and Minerals Industrial (IND4) 615

590 Heavy Industrial (IND1) 156,032

595 Light Industrial (IND2) 80,48 4

600 Gas Stations 413,518

650 Refineries and Tank Farms 130,222

675 Refineries and Tank Farms

and Gas Stations 1,203

680 Oil and Gas Wells 116,568

692 Spud count 77,009

693 Well count

- all wells 8,599

694 Oil produ ction at Oil wells 1,241,682

695 Well count

- oil wells 221,759

696 Gas production at gas wells 1,130,789

697 Oil production at gas wells 1

698 Well count

- gas wells 294,244

700 Airport Areas 32,030

801 Port Areas 12, 578

802 Shipping Lanes 571,927

808 Gulf Tug Zone Area 5,503

820 Ports NEI2011 NOx 76,008

850 Golf Courses 9,628

860 Mines 3,874

870 Wastewater Treatment

Facilities 6,018

880 Drycleaners 10,026

890 Commercial Ti mber 23,202



### 3.4.2 Allocation m ethod for airport

- related source s in the U.S.

There are numerous airport

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

the modeling platform, EPA used the SMOKE “area

- to

- point” approach for only airport ground support equipment (nonroad sector), and jet refueling (nonpt sector). The approach is described in detail in theDRAFT

73 2002 platform doc umentation : http://www.epa.gov/scram001/reports/Emissions%20TSD%20Vol1_02

- 28

- 08.pdf

.

The ARTOPNT file that lists the nonpoint sources to locate using point data was unc hanged from the

2005

- based platform.



### 3.4.3 Surrogates for Canada and Mexico e mission i nventories

The surrogates for Canada to spatially allocate the 2006 Canadian emissions a re unchanged from the 200 7 platform. The spatial surrogate data came from Environment Canada, along with cross references. The surrogates they provided were outputs from the Surrogate Tool (previously referenced). Per Environment Canada, the surrogates are based on 2001 Canadian census data . The Canadian surrogates used for this platfor m are listed in Table 3

- 17

. Th e leading “9” was added to the surrogate codes to avoid duplicate surrogate numbers with U.S. surrogates. Some new surrogates for Mexico became available in the 2011 platform. The surrogates are circ a 1999 and 2000 and were b a sed on data obtained from the Sistema Municpal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999. Most of the CAPs allocated to the Mexico and Canada surrogates are shown in Table 3

- 18 . The entries in this table are for the othar sector except for the MEX Total Road Miles and The CAN traffic rows, which

are for the othon sector.

Table 3

- 17

. Canadian Spatial Surrogates

Code Description Code Description

9100 Population 9493 Warehousing and storage

9101 Total dwelling 9494 Total Transport and warehouse

9102 Urban dwelling 9511 Publishing and information services

9103 Rural dwelling 9512 Motion picture and sound recording

in dustries

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 tr apping 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 Extr action 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 no n

- financial intangible assets (except copyrighted works)

9222 Utilities 9534 Total Real estate

9231 Construction except land subdivision and land

development 9541 Professional scientific and technical services

9232 Land subdivision and land development 9551 Management of companies and enterprises

9233 Total Land Development 9561 Administrative and support servicesDRAFT

74 Code Description Code Description

9308 Food manufacturing 9562 Waste management and remediation services

9309 Beverage and tobacco product manufacturing 9611 Education Servi ces

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 Wo od 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 Touri sm

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

933 4 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 Furn iture 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

distribut ors 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 supp lies wholesaler

distributors 9924 Primary Industry

9417 Machinery equipment and supplies

wholesaler

- distributors 9925 Manufacturing and Assembly

9418 Miscellaneous wholesaler

- distributors 9926 Distribution and Retail (no petroleum)DRAFT

75 Code Description Code Description

9419 Wholesale agents and brokers 9927 Commercial Services

9420 Total Wholesale 9928 Commercial Meat cooking

9441 Motor vehicle and parts dealers 9929 HIGHJET

9442 Furniture and home furnishings stores 9930 LOWMEDJET

9443 Electronics and appliance stores 9931 OTHERJET

944 4 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 access ories 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 a nd 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

949 2 Couriers and messengers 9991 Traffic

Table 3

- 18

. CAPs Allocated to Mexican and Canadian Spatial Surrogates

S rg code Description NH 3 NO X PM 2_5 SO 2 VOC

22 MEX Total Road Miles 15,965 370,867 34,396 13,713 375,276

10 MEX Population 0 0 0 0 431,231

12 MEX Housing 0 161,013 17,483 2,123 452,685

14 MEX Residential Heating

- Wood 0 20,093 211,525 2,859 380,572

16 MEX Residential Heating

- Distillate Oil 0 38 0 11 2

20 MEX Residential Heating

- LP Gas 0 25,3 03 787 63 614

22 MEX Total Road Miles 0 0 0 0 3,513

24 MEX Total Railroads Miles 0 74,969 1,669 663 2,824

26 MEX Total Agriculture 679,212 164,144 72,372 2,127 43,958

28 MEX Forest Land 0 16,224 67,683 660 79,018

32 MEX Commercial Land 0 125,211 7,726 0 286,982

34 MEX Industrial Land 0 45,831 5,684 59,201 133,440

36 MEX Commercial plus Industrial Land 0 0 0 0 332,495DRAFT

76 S rg code Description NH 3 NO X PM 2_5 SO 2 VOC

38 MEX Commercial plus Institutional Land 0 6,400 216 84 28,293

40 Residential (RES1

- 4)+Com m ercial+Industrial+Institutional+

Governme nt 0 8 20 0 241,710

42 MEX Personal Repair (COM3) 0 0 0 0 33,616

44 MEX Airports Area 0 14,639 0 1,149 6,857

46 MEX Marine Ports 0 124,951 2,991 1,482 1,099

48 Brick Kilns

- Mexico 0 776 6,691 0 10,244

50 Mobile sources

- Border Crossing

- Mexico 0 45 4 0 0 2,668

9100 CAN Population 603 0 276 0 304

9101 CAN total dwelling 643 46,256 12,783 14,698 32,944

9106 CAN ALL_INDUST 133 21,526 381 3,921 2

9113 CAN Forestry and logging 1,582 8,561 28,622 1,809 36,114

9115 CAN Agriculture and forestry activiti es 160 239,553 25,318 9,092 26,526

9116 CAN Total Resources 0 17 0 0 5

9212 CAN Mining except oil and gas 0 0 5,391 0 0

9221 CAN Total Mining 42 2,292 45,374 728 26

9222 CAN Utilities 189 14,882 369 1,124 255

9233 CAN Total Land Development 17 20,789 1,928 981 2,551

9308 CAN Food manufacturing 0 0 0 0 4,535

9323 CAN Printing and related support activities 0 0 0 0 25,203

9324 CAN Petroleum and coal products manufacturing 0 0 2,402 0 0

9327 CAN Non

- metallic mineral product manufacturing 0 238 7,708 2 ,941 1,218

9331 CAN Primary Metal Manufacturing 0 98 5,062 12 6

9412 CAN Petroleum product wholesaler

- distributors 0 0 0 0 70,125

9416 CAN Building material and supplies wholesaler

distributors 2 0 1,461 3,259 560

9448 CAN clothing and clothing accesso ries stores 0 0 0 0 328

9562 CAN Waste management and remediation

services 165 893 1,596 1,998 16,551

9921 CAN Commercial Fuel Combustion 494 33,816 2,750 35,471 850

9924 CAN Primary Industry 0 0 0 0 219,282

9925 CAN Manufacturing and Assembly 0 0 0 0 139,227

9931 CAN OTHERJET 9 14,388 548 1,139 7,629

9932 CAN CANRAIL 109 122,694 4,093 5,737 3,304

9942 CAN UNPAVED ROADS 40 3,462 3,499 48 152,674

9945 CAN Commercial Marine Vessels 28 45,454 6,404 14,325 61,139

9946 CAN Construction and mining 247 15 6,770 10,070 5,667 17,180DRAFT

77 S rg code Description NH 3 NO X PM 2_5 SO 2 VOC

9947 CAN Agriculture Construction and mining 19 37,452 536 26 32,683

9950 CAN Intersection of Forest and Housing 1,053 11,700 120,045 1,671 173,130

9960 CAN TOTBEEF 176,156 0 7,420 0 317,394

9970 CAN TOTPOUL 74,204 0 2 0 264

9980 CAN TOTSWIN 122,094 0 996 0 3,186

9990 CAN TOTFERT 178,791 0 9,279 0 0

9991 CAN traffic 22,294 550,896 10,888 5,548 285,104

9994 CAN ALLROADS 0 0 55,468 0 0

9995 CAN 30UNPAVED_70trail 0 0 106,707 0 0

9996 CAN urban_area 0 0 284 0 0DRAFT

78 

## 4 Development of 2018 Base

- Case Emissions

This section describes the methods used for developing the 2018 future

- year base

- case emissions. The future

base

- case projection methodologies vary by sector. With the exceptions discus sed in Section 4.2, the 2018

base case re presents predicted emissions in the absence of any further controls beyond those Federal and

Sta te measures already promulgated or under reconsideration before emissions processing began in November, 2013. The future base

- case scenario reflects projected economic changes and fuel usage for

EGU and mobile sectors. The 2018 EGU projected inventory represents demand growth, fuel resource availability, generating technology cost and performance, and other economic factors affecting power sector

behavior. It also reflects the expected 2018 emissions effects due to environmental rules and regulations, consent decrees and settlements, plant closures, control devices updated since 2011, and forecast unit construction through the calendar year 20 18 . In this analys is, the projected EGU emissions include the Final

M ercury and A ir T oxics (MATS) rule announced on December 21, 2011 and the Clean Air Interstate Rule (CAIR) issue d March 10, 2005. More information on the EGU base case can be found at http://www.epa.gov/powersectormodeling/BaseCasev513.html

.

For mobile sources (onroad, onroad_rfl, nonroad, c1c2rail and c3marine sectors), all national measures for

which data were available at the time of modeling have been included

. The Tier 3 standards proposed in March, 2013 are represented (see http://www.epa.gov/otaq/tier3.htm ). Efforts made to include some regional

haze and state

- reported local contro ls as part of a larger effort to include more local control information on stationary non

- EGU sources are 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

.

EGU sector (pt egu and ptegu_ pk ): Unit

- specific e stimates from IPM version 5.13

, including CAIR

and Final MATS.

N on

- IPM sect or (ptnonipm): Projection factors and percent reductions reflect comments received during the development of the Cross

- State Air Pollution Rule (CSAPR) along with emission

reductions due to national and local rules, control programs, plant closures, conse nt decrees and

settlements

. P rojection approaches for corn ethanol and biodiesel plants, refineries and upstream impacts represent the Energy Independence and Security Act of 2007 (EISA)

. Airport

- specific

t erminal area forecast (TAF) data were used for a ircraft to account for projected changes in landing/takeoff activity.

Point and nonpoint oil and gas sectors (pt_oilgas and np_oilgas): Regional projection factors by product type using Annual Energy Outlook (AEO) 2013 projections to year 2018. Cobenefit s of stationary engines CAP

- cobenefit reductions (RICE NESHAP) and New Source Performance Standards (NSPS) VOC controls reflected for select source categories.

F ires sector ( ptfire ): No growth or control

– 2011 estimates used directly

. Agricultural secto r (ag): Projection factors for livestock estimates based on expected changes in animal population from 2005 Department of Agriculture data, updated according to EPA experts in July 2012 ; fertilizer application NH 3 emissions projections include upstream im pacts EISA.

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

. Residential Wood Combustion (rwc): Projection factors tha t reflect assumed growth of wood burning appliances based on sales data, equipment replacement rates and change outs

. These changes

include a growth in lower

- emitting stoves and a reduction in higher emitting stoves.DRAFT

79 Remaining Nonpoint sector (nonpt): P rojection factors implement comments received during Cross

State Air Pollution Rule development and emission reductions due to control programs. PFC projection factors reflecting impact of the final Mobile Source Air Toxics ( MSAT 2) rule. Upstream impacts from EISA, including post

- 2007 cellulosic ethanol plants are reflected.

Nonroad mobile sector (nonroad): Other than for California and Texas

, this sector uses data from a

run of NMIM that utilized NONROAD2008a with database “ NCD20130731_nei2018dv1 ” , usin g future

- year equipment population estimates and control programs to the year 2018 and inputs that

were either state

- supplied as part of the 2011NEIv1 process or using national level inputs

. F inal

controls from the final locomotive

- marine and small spark ignition OTAQ rules are included

. California and Texas

- specific data were prov ided by CARB and TCEQ, respectively

. 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 reflect final

locomotive

- marine controls and RFS2 adjustments

. California projected year

- 2017 inventory data were provided by CARB.

Class 3 commercial marine vessel (c3marine): Base

- year 2011 emissions grown and contr olled to 2018, incorporating controls based on Emissions Control Area (ECA) and International Marine Organization (IMO) global NO X and SO 2 controls.

Onroad mobile, not including refueling (on road ): MOVES2010 b (extended idle mode) and

MOVESTier3NPRM

- based emissions factors for year 2018 were developed using the same representative counties, state

- supplied data, meteorology, and procedures that were used to produce

the 20 11 emission factors described in Section 2.3.1

. California and TCEQ

- specific data were provided by CARB and TCEQ, respectively. T his sector includes all non

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

Onroad refueling mode (on ro ad_rfl ): the same projection approach is used as for the onroad sector and processing i s described in Section 2.3.2

, in that emission factors are from MOVES2010b and that California and Texas did not include state supplied emi ssions.

Other onroad (othar ): No growth or control for Canada because data are not available. Mexico inventory data were grown from year 1999 to 2018.

Other nonroad/nonpoint (oth on ): No growth or control for Canada. Mexico inventory data were grown fro m year 1999 to 2018.

Other p oint (othpt): No growth or control for Canada and offshore oil. Mexico inventory data were grown from 1999 to year 2018. Non

- U.S. C3 CMV data projected using the same methodology as

the c3marine sector.

Biogenic: 2011 emissi ons used for all future

- year scenarios.

Table 4

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

the U.S. 2018 base

- case emissions from the 2011v6 base

- case inventories. Lists of the control, closures, projection packets (datasets) used to create 2018 future year base

- case scenario inventories from the 2011

base case are provided in ftp://ft p.epa.gov/EmisInventory/2011v6/v1platform/2018emissions/CoST_packets/

in the file 2018ed_Projections_with_CoST_23dec13.xlsx . These packets were processed through EPA Control Strategy Tool (CoST) to create future year inventories. CoST is described here: http://www.epa.gov/ttnecas1/cost.htm . The CoST packets are formatted in the same way as those needed for

SMOKE and are available on the 2011v6 web site within the CoST_packets directory mentioned above i n the file 2018ed_CoST packets.zip . Summaries of the emissions changes resulting from all CoST packets (control programs, projections and closures) can be found in the reports directory of the 2011/2018 emissionsDRAFT

80 modeling platform release : ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2018_emissions/ in the files 2018ed_CoST_ptnonipm_facility_summary.xlsx and 2018ed_CoST_sector_summaries.xlsx.

Table 4

- 1

. Control strategies and growth assumptions for creating the 2018 base

- case emissions inventories

from the 2011 base case

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

affected Section

Non

- EGU Point (pt nonipm and pt_oilgas sectors) Controls and Growth Assumptions
Ethanol plants that account for increase d ethanol production due to EISA mandate All 4.2.1.1 Biodiesel plants producing 1.6 billion gallons of production due to EISA mandate All 4.2.1.2 Ethanol distribution vapor losses adjustments due to EISA mandate VOC 4.2.1.6 Refinery upstream adjustments from EISA mandate All 4.2.1.7

Livestock e missions g rowth from year 20 11 to 2018, also including upstream RFS2 impacts on agricultural

- related activities such as p esticide and fertilizer production All 4.2.2

Oil and gas production AEO

- based regional growth factors and VOC NSPS controls All 4.2.4 Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsideratio ns NO X , CO, PM

, SO 2 4.2.3

State fuel sulfur content rules for fuel oil

– as of July, 2012, effective only in Maine, Massachusetts, New Jersey, New York and Vermont SO 2 4.2.6

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

Amendments CO, PM,

SO 2 , VOC 4.2.7

NESHAP: Portland Cement census

- division level based on Industrial Sector Integrated Solutions (ISIS) policy emissions to year 2018 . The ISIS results are from the ISIS

- Cement model runs for the NESHAP and NSPS analysis of August 2013 and include closures

. All

4.2.8

Future baseline inventory improvements received from states and a 2005 platform NODA and comments from the CSAPR proposal, including local controls, fuel switching, unit closures and

consent decrees All

4.2.9

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

- 2011 inventory submittals All 4.2.10

Aircraft growth via Itinerant (ITN) operations at airports to 20 18 All 4.2.10

Boat Manufacturing MACT rule, VOC: national applied by SCC VOC 4.2.11.1

Lafarge and Saint Gobain consent decrees NO X , PM, SO 2 4.2. 9.3

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 4.2.9.3

Refinery Consent Decrees: plant/ unit controls NO X , SO 2 4.2.9.3

Commercial and Industrial Solid Waste Incineration (CISWI) r evised NSPS PM, SO 2 4.2.11.2 Nonpoint ( afdust, ag, nonpt np_oilgas, and rwc sector s ) Controls and Growth Assumptions

MSAT2 and RFS2 impacts on portable fuel container growth and control from 2011 to 2018 VOC 4.2.1.3

Cellulosic ethanol and diesel emissions from EISA mandate All 4.2.1.4 Ethanol transport working losses inventory from EISA mandate VOC 4.2.1.5

Ethanol distr ibution vapor losses adjustments due to EISA mandate VOC 4.2.1

. 6

Livestock emissions g rowth from year 20 11 to 2018, also including upstream RFS2 impacts on agricultural

- related activities such as pesticide and fertilizer production All 4.2.2

Oil and gas production AEO

- based regional growth factors and VOC NSPS controls All 4.2.4 Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations NO X , CO, PM

, 4.2.3DRAFT

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

affected Section

SO 2 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 4.2.6

Residential wood c ombustion growth and c hange

- outs from year 2011 to 2018 All 4.2.3

Future baseline inventory improvements received from states NO X , VOC 4.2.9

Onroad Mobile Controls (All national in

- force regulations are modeled. The list includes key rec ent mobile control strategies but is not exhaustive.)

National Onroad Rules: All onroad control programs finalized as of the date of the model run, including most recently: Proposed Tier

- 3 Standards: March, 2013 Light

- Duty Greenhouse Rule: March, 2013

Hea vy (and Medium)

- Duty Greenhouse Gas Rule: August, 2011

Renewable Fuel Standard : February, 2010 Light Duty Greenhouse Gas Rule: April, 2010 Corporate

- Average Fuel Economy standards for 2008

- 2011, April, 2010 2007 Onroad Heavy

- Duty Rule : February, 2009

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

Tier 2 Rule : Signature date February, 2000 National Low Emission Vehicle Program (NLEV): March, 1998 All 4.3

Local Onroad Programs:

California LEVIII Program Ozone Transport Commission (OTC) LEV Program : January,1995 Inspection and Maintenance programs Fuel programs (also affect gasoline nonroad equipment) Stage II refueling control programs VOC 4.3

Nonroad Mobile Controls (All national in

- force regulations are modeled. The list includes recent key mobile control strategies but is not exhaustive.)

National Nonroad Controls: All nonroad control programs finalized as of the date of the model run, including most recently: Emissions Standards for New Nonroad Spark

- Ignition Engines, Equipment, and Vessels: October,

2008 Control of Emissions of Air Pollution from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008

C lean Air Nonroad Diesel Final Rule

– Tier 4 : May, 2004 All 4.4

Locomotives: Control of Emissions of Air Pollution from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: Mar ch, 2008

Clean Air Nonroad Diesel Final Rule

– Tier 4 : May, 2004 All 4.4.1

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

Contr ol of Emissions of Air Pollution from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008

Clean Air Nonroad Diesel Final Rule

– Tier 4 : May, 2004 All 4.4.2

A qui ck background on t he Control Strategy Tool (CoST)

CoST is used to apply most non

- EGU projection/growth factors, controls and facility/unit/stack

- level closures to the 2011 emissions modeling inventories to create year 2018 inventories for the following

se ctors: afdust, ag, c1c2rail, nonpt, np_oilgas, ptnonipm, pt_oilgas and rwc. The CoST training manual isDRAFT

82 available at: http://www.cmascenter.org/help/mode l_docs/cost/2.5/CoST_UsersGuide_2012

- 08

- 01_Final.pdf . The CoST development document, which is a more thorough but dated document of how to build and format CoST input files (packets) is available at: http://www.epa.gov/ttnecas1/models/CoST_Development%20Document_2010

- 06

- 09.pdf . The CoST packets used for 2018ed are available here: The CoST packets themselves can be found in ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/2018emissions/CoST_packets/

.

CoST allows the user to apply projection factors, controls and closures at various geographic and inventory key field resolut ions. CoST provides the user with the ability to perform numerous quality assurance

routines as well as create SMOKE

- ready future year inventories. There are also available linkages to existing and user

- defined control measures databases and it is up to the user to determine how control strategies are developed and applied. EPA typically create s individual CoST datasets or “packets” that represent specific intended purposes. For example, aircraft projections for airports are in a separate PROJECTION pac ket from residential wood combustion sales/appliance turnover

- based projections. CoST

uses three packet types as described below:

1. CLOSURE: Applied first in CoST. This packet can be used to zero

- out (close) point source

emissions at resolutions as broad a s a facility to as specific as a stack. EPA use d these types of packets for known post

- 2011 controls as well as information on closures provided by states on specific facilities, units or stacks. This packet type is only used in the ptnonipm sector in th e 2011 platform.

2. PROJECTION: This packet allows the user to increase or decrease emissions for virtually any geographic and/or inventory source level. Projection factors are applied as simple scalars to the 2011 emissions inventories prior to the applicat ion of any possible subsequent CONTROLs. A PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable when information is based more on activity assumptions rather than known controls. EPA use d PROJECTION packet(s) in ever y non

- EGU modeling sector in the 2011 platform.

3. CONTROL: These packets are applied after any/all CLOSURE and PROJECTION packet entries. The user has similar level of control as PROJECTION packets regarding specificity of geographic and/or inventory source level application. Control factors are expressed as a percent reduction (0 to 100) and can be applied in addition to any pre

- existing inventory control, or as a replacement control where inventory controls are first backed out prior to the application of a more

- stringent replacement

control.

As mentioned above, CoST first applies any/all CLOSURE information for point sources, then applies PROJECTION packet information, followed by CONTROL packets. A hierarchy is used by CoST to separately apply PROJEC TION and CONTROL packets. In short, in a separate process for PROJECTION and CONTROL packets, more specific information is applied in lieu of less

- specific information in ANY other packets. For example, a facility

- level PROJECTION factor will be replaced by a unit

- level, or facility and pollutant

- level PROJECTION factor. It is important to note that this hierarchy does not apply intra

packet types; for example, CONTROL packet entries are applied irrespective of PROJECTION packet hierarchies. A more spec ific example: a state/SCC

- level PROJECTION factor will be applied before a stack/pollutant

- level CONTROL factor that impacts the same inventory record. However, an inventory source that is subject to a CLOSURE packet record is removed from consideration of subsequent PROJECTION and CONTROL packet s.

The implication for this hierarchy and intra

- packet independence is important to understand and quality assure when creating future year strategies. For example, with consent decrees, settlements and stateDRAFT

83 c omments, the goal is typically to achieve a targeted reduction (from the 2011NEI v1 ) or a targeted 2018 emissions value. Therefore, as encountered with this 2018 base case, consent decrees and state comments for specific cement kilns ( expressed as CONTROL packet entries ) , needed to be applied instead of (not in addition to) the more general approach of the PROJECTION packet entries for cement manufacturing. By processing CoST control strategies with PROJECTION and CONTROL packets separated by type of

measu re/program and also by consent decree and state comments, it is possible to show actual changes from the 2011 inventory to the 2018 inventory for each packet

.

Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a

hier archal matching approach, a sample subset of which is shown in Table 4

- 2 , to assign PROJECTION factors to the inventory. For example, a packet entry with Ranking=1 will supersede all other potential inventory matches from other p ackets. CoST then computes the projected emissions from all PROJECTION packet matches and then performs a similar routine for all CONTROL packets. Therefore, when summarizing “emissions reduced” from CONTROL packets, it is important to note that these re ductions are not relative to the 2011 inventory, but rather, to the intermediate inventory after application of any/all PROJECTION packet matches. It is also important not all 70+ hierarchy options are shown. The fields listed

in Table 4

- 2 are not necessarily named the same in CoST, but rather are similar to those in the SMOKE FF10 inventories; for example, “REGION_CD” is the county

- state

- county FIPS code (e.g., Harris county Texas is 48201) and “STATE” would be the 2

- digit state FIPS code with three trailing zeros (e.g., Texas is 48000).

Table 4

- 2

. Subset of CoST Packet Matching Hierarchy

Ran k Matching Hierarchy Inventory Type

1 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS _ID, SCC, POLL point

2 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID , POLL point

3 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID , SCC, POLL point

4 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID , POLL point

5 REGION_CD, FACILITY_ID , UNIT_ID

, SCC, POLL point

6 REGION_CD, FACILITY_ID, UNIT_ID, POLL point

7 REGION_CD, FACILITY_ID

, SCC, POLL point

8 REGION_CD, FACILITY_ID, POLL point

9 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID point

10 REGION_CD, FACILITY_ID, UNIT_ID, REL_POI NT_ID point

11 REGION_CD, FACILITY_ID, UNIT_ID point

12 REGION_CD, FACILITY_ID point

13 REGION_CD, NAICS, SCC, POLL point, nonpoint

14 REGION_CD, NAICS, POLL point, nonpoint

15 STATE, NAICS, POLL point, nonpoint

16 REGION_CD, NAICS point, nonpoint

1 7 NAICS point, nonpoint

18 REGION_CD, SCC, POLL point, nonpoint

19 STATE, SCC, POLL point, nonpoint

20 SCC, POLL point, nonpoint

21 REGION_CD, SCC point, nonpoint

22 STATE, SCC point, nonpoint

23 SCC point, nonpoint

24 REGION_CD, POLL point, nonpoin t

25 REGION_CD point, nonpoint

26 STATE, POLL point, nonpoint

27 STATE point, nonpoint

28 POLL point, nonpointDRAFT

84 The remainder of this section is organized either by source sector or by specific emissions category within a

source sector for which a dis tinct set of data were used or developed for the purpose of projections for the 2018 base case. This organization allows consolidation of the discussion of the emissions categories that are contained in multiple sectors, because the data and approaches us ed across the sectors are consistent and do not need to be repeated. Sector names associated with the emissions categories are provided in parentheses.

A list of inventory datasets used for this and all cases is provided in Table G

- 1 in Appendix G

. The a ncillary input data in the future

- year scenarios are very similar to those used in the 2011 base case except for the speciation profiles used for gasoline

- related sources, which change in the future to account for increased ethanol usage in gasoline. Tabl e G

- 1 of Appendix G also shows the differences between ancillary input data

sets between the 2011 base case and the future

- year scenario. The specific speciation profile changes are

discussed in Section 3.2.1. Table G

- 2 in Appendix G also provides the va lues for the main parameters used in the emissions modeling cases.



### 4.1 Stationary source projections: EGU sectors (ptegu, ptegu_pk)

The future

- year data for the ptipm sector used in the air quality modeling were created by the Integrated Planning Model (IPM ) version 5.13 (v5.13) Final MATS (Mercury and Air Toxics Standards) of ( http://www.epa.gov/airmarkt/progsregs/epa

- ipm/index.html ). The IPM is a multiregional, dynamic,

deterministic li near programming model of the U.S. electric power sector. V ersion 5 .1 3 reflects state rules,

c onsent decrees and announced shutdowns through August, 2013

. IPM 5.13 was significantly updated from the previous version 4.10 and represents electricity demand projections for the Annual Energy Outlook (AEO) 2013. The scenario used for this mo deling represents the implementation of the Clean Air Interstate Rule, the Me rcury and Air Toxics Standards, and the final actions EPA has taken to implement the Regional

Ha ze Rule. More details on the 2018 base case can be found at http://www.epa.gov/powersectormodeling/BaseCasev513.html

.

Directly emitted PM emissions (i.e., PM 2.5 and PM 10 ) from the EG U sector are computed via a post processing routine that applies emission factors to the IPM

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

is documented in the air quality modeling flat file documentation available here: http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Methodology.pdf

. As part of the development

o f the flat file, a cross reference between the 2011 NEI v1 and IPM is used to help populate stack parameters

and other related information. This cross reference is available from http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Inputs.xls

. The emissions in the flat file created from the IPM outputs are temporalized in to the hourly emissions needed by the air quality model a s described in Section 3.3.2.



### 4.2 Stationary sour

ce projections: non

- EGU sectors (afdust, ag, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)

To project U.S. stat ionary sources other than the ptipm sector, growth factors and/or controls were applied to certain categories within the afdust, ag, nonpt, np_oil gas, ptnonipm, pt_oilgas and rwc platform sectors. This subsection provides details on the data and projection methods used for these sectors. In estimating future

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

- IPM sources. This “no

- growth” assumption is based on an examination of historical

emissions and economic data. While EPA is working toward improving the projection approach in future

emissions platforms, the Agency is still using the no

- growth assumption for th e 2011 platform unless statesDRAFT

85 provided specific growth factors for 2018

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

For m any sources, EPA applied emissions reduction factors (CONTROL packets) to the 2011 base case emissions for particular sources in the ptnonipm

, nonpt and two oil and gas sectors (np_oilgas and pt_oilgas) to reflect the impact of stationary

- source national a nd local

- scale control programs including consent

decrees. Information on plant , unit and stack closures (CLOSURE packets) is restricted to the ptnonipm

sector

. Some of the controls described in this section were obtained from comments on the Cross

- State Air Pollution Rule (CSAPR) proposal.

T he contents of the controls, local adjustments and closures for the 2018 base case are described in the following subsections . Detailed summaries of the impacts of all control programs, local adjustments and

closu res are provided in ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2018_emissions/

. Year

specific projection factors (PROJECTION packets) for year 2018 were used for creating the 2018 base case

unless noted otherwise. The contents of these projection packets (and control reductions ) are provided in the following sections where feasible. However, some sectors used growth or control factors that varied

geogra phically and their contents could not be provided in the following sections (e.g., facilities and units subject to the Boiler MACT reconsideration has thousands of records). Regardless of whether the growth or control factors for a sector are provided in a table in this document, they are available as “projection”

, “control” , or “closures” packet s for input to SMOKE on the 2011v6 p latform website in ftp://ftp.epa.gov /EmisInventory/2011v6/v1platform/2018emissions/CoST_packets/ . This section is divided

into several subsections that are summarized in Table 4

- 3 . Note that future year inventories were used rather than projection or control packets for some sources.

Tabl e 4

- 3

. Summary of non

- EGU stationary projections subsections

Subsection Title Sector(s) Brief Description

4.2.1 RFS2 upstream future year

inventories and adjustments nonpt ptnoni pm 1) Point and non

- point inventories received from

OTAQ that account for the upstream impact of

the RFS2 and the EISA mandate. 2) Point and non

- point adjustment factors that EPA appl ied to the 2011 inventory to reflect RFS2 in

2018.

4.2.2 Upstream agricultural and

livestock adjustments afdust, ag,

nonpt, ptnonipm Adjustment factors to all ag

- related sources that also

reflect upstream RFS2 impacts on ag

- related processes impacted by increased ethanol use.

4.2.3 Residential wood

comb ustion projections rwc Adjustment factors that reflect the change in RWC emissions by appliance type, including wood stove change

- outs and accounting for estimated future sales and replacement rates.

4.2.4 Oil and Gas projections np_oilgas, pt_oilgas Proj ection packet reflecting regional AEO

- based

growth for oil and gas production as well as VOC

NSPS controls for select sources.

4.2.3 RICE NESHAP controls nonpt, np_oilgas, ptnonipm, pt_oilgas Control packet reflecting RICE NES HAP with

reconsideration amendments.

4.2.6 Fuel sulfur rule controls nonpt ptnonipm Control packet reflecting state and local fuel sulfur

rules, including ULSD.

4.2.7 Industrial Boiler MACT ptnonipm Control packet reflecting ICI Boiler MACTDRAFT

86 Subsection Title Sector(s) Brief Description

recons ideration controls reconsideration reductions.

4.2.8 Portland cement NESHAP

projections ptnonipm Year

- 2018 ISIS policy case reflecting the Portland Cement NESHAP, including cl osures, controls at existing kilns and an inventory containing new kilns constructed after 2011 that account for shifting capacity from some closed units to open units.

4.2.3 State comments and

consent decrees/settlements nonp t, ptnonipm Projection and control packets reflecting numerous

sources of consent decree/settlement information as

well as state comments and data regarding 2018

. 4.2.9 Aircraft projections ptnonipm Airport

- specific projection s to year 2018 based on FAA itinerary activity estimates.

4.2.10 Remaining non

- EGU

controls and closures ptnonipm All other controls and plant/unit/stack closures information not covered in previous subsections



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

EPA incorporated adjustments for some stationary source categories to account for impacts of the Energy Independence and Security Act (EISA) renewable fuel standards mandate in the Renewable Fuel St andards Program (RFS2; EPA, 2010a ). These mandate 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 mandat e are associated with all stages of biofuel production and distribution, including biomass production (agriculture, forestry), fertilizer and pesticide production and transport, biomass transport, biomass refining (corn or cellulosic ethanol production fac ilities), biofuel transport to blending/distribution terminals, and distribution of finished fuels to retail outlets. These impacts

are accounted for in the 2018 inventories. There are also impacts on domestic crude emissions upstream of petroleum refine ries, 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 standards also affect production and distribution of gasoline and diesel fuels, but the impacts of these rules will be very small in

2018 and were not accounted for.

Based on the Annual Energy Outlook 201 3 (early release) energy use of 15.47 quad (10 15 BTU) (Department of Energy, 2012), EPA estimated the 20 11 ethanol volume as 11.1 billi on gallons (Bgal ). EPA assumes 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 2011 . However, analyses done to support the RFS2 rule (EPA, 2010a) sugge st a significant increase in renewable fuel volumes in 2018 (see Table 4

- 4)

. Adjustments applied to the inventories (described in the following subsections) reflect the impacts on emissions due to the

difference between the 20 11 ethanol volume s and the re newable fuel volumes shown in Table 4

- 4

. In 2018,

EPA assu med 1 Bgal of ethanol would be used as E85, 10 Bgal as E10 , and about 4 Bgal as E15

.

Table 4

- 4

. Renewable Fuel Volumes Assumed for Stationary Source A djustments.

Renewable Fuel Volume (Bgal)

Corn Ethanol 14.7

Cellulosic Ethanol 0.235 Imported Ethanol 1. 061

Biodiesel 1. 887

Renewable Diesel 0. 236

Cellulosic Diesel 0.290DRAFT

87 4.2.1.1 Corn Ethanol plants inventory (ptnonipm)

Future year inventor ies : “ethanol_plan ts_2018ed_NEI” and “ethanol_plants_2018ed_OTAQ”

As discussed in Section 2.1.4

, EPA supplemented the 2011 NEI with corn ethanol plants that EPA OTAQ

identified . The 2011 emissions were projected to account for the increased do mestic corn ethanol production assumed in this modeling, specifically an increase from 13.9 Bgal in 2011 to 14.7 Bgal by 2018. The projection was applied to all pollutants and all facilities equally. Table 4

- 5 provides the summar ies of estimated emissions for the corn ethanol plants in year 2011 and 2018 23

.

Table 4

- 5

. 20 11 and 2018 c orn ethanol p lant e missions [tons]

Pollutant 20 11 20 18

CO 15,934 16,858

NH 3 726 768

NO X 18,048 19,09 5

PM 10 10,602 11,217

PM 2.5 5,995 6,343

SO 2 34,608 36,294

VOC 19,654 21,115

4.2.1.2 Biodiesel plants inventory (ptnonipm)

New Future year inventory: “Biodiesel_Plants_2018_ff10”

EPA OTAQ developed an inventory of biodiesel plants for 2018 in support of produc ing biodiesel fuels for

the EISA mandate

. Plant location and production volume data came from the Tier 3 proposed rule. 24

, 25 The total volume of biodiesel came from the AEO 2013 early release, 1.3 BG for 2018. To reach the total volume of biodiesel, plant s that had current production volumes were assumed to be at 100% production and the remaining volume was split among plants with planned production. Once facility

- level production capacities were scaled, emission factors were applied based on soybean oil feedstock. These emission

factors in Table 4

- 6 are in tons per million gallons (Mgal) and were obtained from EPA ’s spreadsheet model for upstream EISA impacts developed for the RFS2 rule (EPA, 2010a). Inventories were modeled as point sources with Google Earth and web searching validating facility coordinates and correcting state

- county
FIPS. Table 4

- 7 provides the 2018 biodiesel plant emissions estimates. Emissions in 2011 are assumed to be near zero, and HAP emissions in 2018 are nearly zero.

Table 4

- 6

. Emission 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 6.8240 E

- 02

SO 2 5.9445E

- 03

23 The 2011 emissions are the sum of the NEI and OTAQ facilities. The same is true for 2018.

24 US EPA 2013. Draft Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA

- 420

- D

- 13

- 002.

25 Cook, R . 2012. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for Tier 3 Proposal . Memorandum to Docket EPA

- HQ

- OAR

- 20 10

- 0 162.DRAFT

88 Pollutant Emission Factor

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

- 7

. 2018 biodiesel p lant e missions [tons]

Pollutant 2 0 18

CO 649

NO X 1048

PM 10 89

PM 2.5 89

SO 2 8

VOC 57

4.2.1.3 Portable fuel container inventory (nonpt)

Future year inventory: “2018_PFC_inventory_FF10_revision2”

EPA used future

- year VOC emissions from Portable Fuel Containers (PFCs) from inventories develope d and

modeled for 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 Transport; Petroleum and Petroleum Product Storage” as the beginning of the description).

250 1011011 Residential Portable Fuel Containers: Permeation

2501011012 Residential Portable Fuel Containers: Evaporation

2501011013 Residential Portable Fuel Containers: Spillage During Transport

2501011014 Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement

2501011015 Residential Portable Fuel Containers: Refilling at the Pump: Spillage

2501012011 Commercial Portable Fuel Containers: Permeation

2501012012 Commercial Portable Fuel Containers: Evaporation

2501012013 Commercial Portable Fuel Containers: Spillage During Transport

2501012014 Commercial Portable Fuel Containers: Refilling at the Pump: Vapor Displacement

2501012015 Commercial Portable Fuel Containers: Refilling at the Pump: Spillage

The future

- year emissions refle ct projected increases 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 PFC emissions that include estimated Reid Vapor Pressure ( RVP) and oxygenate impacts on VOC emissions, and more importantly, large increases in ethanol emissions from RFS2. These emission estimates also include refueling from the NONROAD model for gas can vapor displacement, changes in tank permeation and diurna l emissions from evaporation. Because the future year PFC inventories contain ethanol in addition to benzene, EPA developed a VOC E

- profile that integrated ethanol and benzene; see Section 3.2.1.1 for more details. Emissions for 2011 and 2018 are provided in Section 5.DRAFT

89 Table 4

- 8

. PFC e missions for 2011 and 2018 [tons]

Pollutant 20 11 20 18

VOC 198,395 29,119

Benzene 786 645

Ethanol 0 3,719

4.2.1.4 Cellulosic fuel production inventory ( nonpt)

New Future year inventory: “2018_cellulosic_inventory”

Depending on available feedstock, cellulosic plants are likely to produce fuel through either a biochemical process or a thermochemical process. OTAQ developed county

- level inventories for bi ochemical and thermochemical cellulosic fuel production for 2018 to reflect AEO2013er renewable fuel volumes. Emissions factors for each cellulosic biofuel refinery reflect the fuel production technology used rather than the fuel produced. Emission rates in Table 4

- 9 and Table 4

- 10 were used to develop cellulosic plant inventories. Criteria pollutant emission rates are in tons per RIN gallon. Emission factors from the cellulosic

diesel work in the Tier 3 NPRM were used as the emission factors for the thermochemical plants

. 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

. 26 Because the future year cellulosic i nventory contains ethanol, a VOC

E

- profile that integrated ethanol was used , see Sections 3.2.1.1 and 3.2.1.3 for more details.

Plants were treated as area sources spread across the entire area of whatever county they were considered to be located in. Cellulosic biofuel refinery siting was based on utilizing the lowest cost feedstock, accounting for the cost of the feedstock itself as well as feedstock storage and the transportation of the feedsto ck 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 RI A (EPA, 2010a). Table 4

- 11 provides the year 2018 cellulosic plant

emissions estimates.

Table 4

- 9

. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/ RIN gal lon )

Cellulo sic 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

- 10

. To xic 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

26 It should be noted that in the Tier 3 NPRM we meant to use different cellulosic ethanol no n

- VOC CAP emission factors depending on which feedstock the plant was using but instead used the same emission factors (based on a forest waste feedstoc k) for all the plants. This was corrected by using emission factors for the non

- VOC CAPS that were base d on a stover feedstock for the biochemical plants.DRAFT

90 Table 4

- 11

. 2018 c ellulosic plant e missions [tons]

Pollutant Emissions

Acrolein 1
Formaldehyde 4

Benzene 1
Acetaldehyde 21

CO 6,088

Ethanol 146

NH 3 0.1

NO X 9,199

PM 10 1,088

PM 2.5 547

SO 2 819

VOC 414

4.2.1.5 Ethanol working loss inventory (nonpt)

New Future year inventory: “Ethanol_transport_vapor_2018rg_ref_v1”

This inventory was provided by OTAQ to represent upstream impacts of loading and unloading at ethanol terminals. Emissions are entirely evaporative and were computed by county for truck, rail and waterway loading and unloading and intermodal transfers (e.g., highway to rail). Inventory totals are summarized in

Table 4

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

for each SCC.

Table 4

- 12

. 20 18 V OC working losses (Emissions) due to ethanol transport [tons]

SCC Description Emissions

30205031 Denatured Ethanol Storag e Working Loss 23,420

30205052 Ethanol Loadout to Truck 14,425

30205053 Ethanol Loadout to Railcar 10,484

4.2.1.6 Vapor losses from Ethanol transport and distribution (nonpt, ptnonipm)

Packet : “PROJECTION_2011_2018_distribution_upstream_OTAQ_Tier3FRM”

OTAQ de veloped county

- level inventories for ethanol transport and distribution for 2018 to account for losses for the processes such as truck, rail and waterways loading/unloading and intermodal transfers such as highway

- to

- rail, highways

- to

- waterways, and all ot her possible combinations of transfers. These emissions are entirely evaporative and therefore limited to VOC.

A 2018 inventory which included EISA impacts was developed by adjusting the 2007 platform inventory. These adjustments were made using an upda ted version of EPA’s spreadsheet model for upstream emission impacts, developed for the RFS2 rule 27 . Below EPA describe s how the Agency developed emission factors and fuel volumes to make these adjustments with the RFS2 impacts spreadsheet.

27 U.S. EPA. 2013. Spreadsheet “upstream_emissions_rev T3.xls.DRAFT

91 V apor loss V OC emission factors (EFs) for gasoline were first developed, based on invent ory estimates from the 2005 NEI (EPA, 2009a). Total volume of gasoline was based on gasoline sales as reported by the Energy

Information Administration (2006)

. Emissions were par titioned 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 com ponents were divided by the energy content of the total volume of gasoline distributed in 2005 to obtain emission factors in grams per million metric British Thermal Units (g/mmBTU). ) . Total volume of gasoline was based on gasoline sales as reported by the Energy Information

Administration. 28

- In addition to gasoline VOC emission factors for the RBT/BPS components, emission factors were developed for the BTP component, for 10% ethanol and 15% ethanol , and 85% ethanol

. Emission factors were calculated by applying adjustment factors to the gasoline EFs. The BTP adjustment factors were based on an algorithm from the 1994 On

- Board Refueling Vapor Recovery Rule (EPA, 1994) :

EF (g/gal) = exp[

- 1.2798

- 0.0049(ΔT) + 0.0203(Td) + 0.1315(RVP)]

H ere delta T is the difference in temperature between the fuel in the tank and the fuel being dispensed, and Td is the temperature of the gasoline being dispensed. EPA assumed delta T is zero, and the temperature of

the fuel bei ng dispensed averages 60 oF over the year.

Average summer RVPs at the Petroleum Administration for Defense Districts (PADD) level was used to calculate adjustments. The U.S. is broken into five PADDs for petro leum products data collection purposes via the U.S. Energy Information Administration; see: http://www.eia.gov/oog/info/twip/padddef.html . These PADD regions are shown in Figure 4

- 1

.

All counties within a PADD received the same adjustment for BTP emissions. Volumes for each fuel type and summer RVPs for 2018 with EISA impacts are provided in Table 4

- 13 while volumes without EISA are in Table 4

- 14

. These volumes and RVPs were obtained from analyses done for the Tier 3 rule. These two sets of volumes were used to estimate emissions using the RFS2 impacts spreadsheet (see below for details).

28 Source: Energy Information Administration. 2006. Annual Energy Outlook 2006. Report #:DOE/EIA

- 0383(2006) Available at <http://www.eia. gov/oiaf/archive/aeo06/aeoref_tab.html>DRAFT

92 Figure 4

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

Table 4

- 13

. RVPs Assumed for 2018 ethanol and gasoline volumes with EISA

Table 4

- 14

. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA PADD Total

Fuel

Volume Gasoline

Vol ume Ethanol

Volume E10 Only

Volume E15 Only

Volume E85(74)

Only

Volume Weight

ed RVP Weighted

E10 RVP Weighted

E15 RVP Weighted

E85 RVP

1 4.50E+10 3.99E+10 5.18E+09 3.71E+10 7.43E+09 4.73E+08 8.536 8.651 8.056 7

2 3.46E+10 3.03E+10 4.25E+09 2.31E+10 1.11E +10 3.63E+08 9.051 9.35 8.493 7

3 2.14E+10 1.89E+10 2.48E+09 1.72E+10 3.95E+09 2.25E+08 8.399 8.515 7.973 7

4 5.07E+09 4.48E+09 5.89E+08 4.06E+09 9.53E+08 5.33E+07 9.322 9.536 8.536 7

5 2.35E+10 2.08E+10 2.71E+09 1.91E+10 4.13E+09 2.47E+08 7.906 7.974 7 .645 7

T otal 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 Gaso line

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+1 0 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

T otal 1.27E+11 6.93E+09 1.20E+11 5.76E+10 6.93E+10 8.75 8.372 9.059DRAFT

93 A b enzene g/mmgal emission factor for 2018 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/m mBTU emission factors based on the energy content of E0

, E10 , and E15 gasoline. Aside from energy content, EPA did not account for the effect of other fuel parameters on emission rates for E0, E10, and E15 blends . Thus, the E1 0 emission rate is slightl y 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

- 17

.

Table 4

- 15

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

Process Fuel Benzene

BTP E0 0.250

E10 0.259

E15 0.264

E85 0.023

RBT/BPS E0 0.059

These e mission factors for VOC and benzene were used in conjunc tion 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 E ISA. VOC inventory changes were used to develop nationwide adjustment factors that were applied to modeling platform inventory SCCs associated with storage and transport processes (see Table

4

- 16 ). Benzene emission estimates wer e obtained either by application of the adjustments in Table 4

- 16 or through speciation of VOC in SMOKE

.

Table 4

- 16

. Adjustment factors applied to storage and transport emissions

Yea r Process PADD Pollutant Adjustment

Factor

2018 BTP 1 VOC 0.9515

benzene 0.9905

2 VOC 0.9619

benzene 0.9882

3 VOC 0.9778

benzene 0.9879

4 VOC 0.8983

benzene 0.9885

5 VOC 0.9430

benzene 0.9901

RBT/BPS All VOC 0.9553

benzene 0.9893

Ethanol emissions were estimated in SMOKE by applying the ethanol to VOC ratios from headspace profiles

to VOC emissions for E10 and E15, and an evaporative emissions profile for E85 . These ratios are 0.065 for

E10

, 0.272 for E15 , and 0.61 for E85 . The E10 and E15 profiles were obtained from an ORD analysis ofDRAFT

94 fuel samples from EPA ct exhaust test program 29 and have been submitted for incorporation into the EPA’s

SPECIATE database. The E85 profile was obtained from data collected as p art of the CRC E

- 80 test

program (Environ, 2008) and has also been submitted for incorporation into EPA’s SPECIATE database. For more details on the change in speciation profiles between 2011 and 2018, see Section 3.2.1.4

.

Aft er developing emissions from the 2018 with EISA volumes versus the 2018 without EISA volumes, EPA created ratios of these two cases to apply against the 2007 platform emissions. This created a 2018 reference

case. For the 2011 platform, EPA scaled the sa me sources so that their total emissions matched the 2018

reference case.

It should be noted that these adjustment factors are based on summer RVP, but applied to emissions for the whole calendar year. However, higher RVPs in winter corresponding to lowe r temperatures result in roughly the same vapor pressure of the fuel and roughly the same propensity to evaporate. Significant evaporative emissions are not expected from storage and transport of biodiesel, renewable or cellulosic diesel fuel due to

their low volatility. The cumulative impacts are VOC reductions of approximately 26,075 tons across the nonpt sector and 2,681 tons in the ptnonipm sector in 2018 for these processes. See Appendix B for the complete cross

- walk between SCC, and state

- SCC for B TP components, and each type of petroleum transport and storage.

4.2.1.7 Pipeline and Refinery EISA adjustments (ptnonipm)

Packet: “PROJECTION_pipelines_refineries_2018ed”

Pipeline usage and refinery emissions were adjusted for changes in fuels due to the EISA. These adjustments were developed by EPA OTAQ and impact processes such as process heaters, catalytic cracking units, blowdown systems, wastewater treatment, condensers, cooling towers, flares and fugitive emissions. A portion of these impacts are discusse d in this section, with additional impacts due to transport discussed in the onroad and c1c2rail sectors (see Sections 4.3.1 and 4.4.1 , respectively)

.

Calculation of the emission inventory impacts o f decreased gasoline and diesel production, due to EISA, on nationwide refinery emissions was done in EPA’s spreadsheet model for upstream emission impacts (EPA, 2009b) . Emission inventory changes reflecting EISA implementation were used to develop adjust ment factors that were applied to inventories for each petroleum refinery in the U.S. ( Table 4

- 17 ) . These impacts of decreased production were assumed to be spread evenly across all U. S. refineries. Toxic emissions were

estimat ed in SMOKE by applying speciation to VOC emissions. It should be noted that the a djustment

factors in Table 4

- 17 are estimated relative to that portion of refinery emissions associated with gasoline and diesel fuel production. Production of jet fuel, still gas and other products also produce emissions. If these emissions were included, the adjustment factors would not be as large. The impact of the EISA

- based

reductions is shown in Table 4

- 18

.

29 U. S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. Office of Research and Development and Office of Transportation and Air Quality. Report No. EPA

- 420

- R

- 11

- 018. EPA Docket EPA

- HQ

- OAR

- 2011

- 0135.DRAFT

95 Table 4

- 17

. 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. 9964 0.9776 0.9741

NO X 0. 9819 0.9867 0.9688

PM 10 0. 9967 0.9839 0.9806

PM 2.5 0. 9975 0.9789 0.9765

SO 2 0. 9981 0.9781 0.9763

NH 3 n/a 0.9517 n/a

VOC 0.9 99 0.9719 0.9710

Table 4

- 18

. Impact of r efinery adjustments on 2011 e missions [tons ]

Pollutant Reductions 2018

CO 1,233

NH 3 135

NO X 2,256

PM 10 466

PM 2.5 495

SO 2 1,986

VOC 1,515



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

Packet: “PROJECTION_2011_2018_ag_including_upstream_OTAQ_25nov2013_v1”

Inve ntory adjustments were previously developed for 2030 as part of final RFS2 rule modeling 30 . For the Tier 3 proposal, adjustments for 2017 were scaled by the ratio of 2017 renewable fuel volumes versus 2030

volumes. Although 2018 was modeled for this rule rather than 2017, EPA continued to use the 2017 adjustments. Impacts on farm equipment emissions were not accounted for, however. Emission rates from the GREET model (fertilizer and pesticide production) 31 or based on the 2002 National Emissions Inventor y (fertilizer and pesticide application, agricultural dust, livestock waste) were combined with estimates of agricultural impacts from FASOM (Forest and Agricultural Section Optimization Model). Since FASOM modeling used a reference case of 13.2 billion gallons of ethanol, impacts used in the modeling for this rule

are underestimates.

Adjustment factors are provided in Table 4

- 19

. These adjustments were applied equally to all counties having any of the affected sources. This is an area of uncertainty in the inventories, since there would likely be variation from one county to another depending on how much of the predicted agricultural changes

occurred in which counties. By using percent change adjustments rather than attempt ing to calculate absolute ton changes in each county, EPA has attempted to minimize the inventory distortions that could occur if the calculated change for a given county was out of proportion to the reference case emissions for

30 U. S. Environmental Protection Agency. 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. Assessment and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI. R eport No. EPA

- 420

- R

- 10

- 006, February, 2010. Available at <http://www.epa.gov/otaq/fuels/r enewablefuels/regulations.htm>.

31 GREET, version 1.8c. Available at < http://greet.es.anl.gov/ >.DRAFT

96 that county. For instance, a different approach could estimate reductions that were larger than the reference case emissions, since there was no linkage between the 2011 NEI v1 inventories and the FASOM modeling. The specific sources (SCCs) and affected pollutants that these adjustmen ts were applied to are listed in a

docket reference 32

.

Table 4

- 19

. Adjustments to modeling platform a gricultural e missions for the Tier 3 reference c ase

Source Description Adjustment
Nitrogen f

ertilizer a ppli cation 1.0242
Fertilizer p roduction , mixing/blending 1.0603

Pesticide p roduction 0.9544
Ag ricultural t illing/loading dust 1.0079
Agricultural b urning 1.000

Livestock d ust 0.9 868

Livestock w aste 0.9901

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% dec rease resulting from the EISA mandate. These composite projection factors by animal category are provided in

Table 4

- 20

. As discuss ed below, d airy c ows and t urkeys are assumed to have no growth in animal population, and therefor e the projection factor for these animals is the same as the upstream agriculture

related projection factor

. The PROJECTION packet used for these sources, includ ing the cross

- reference to the animal categories listed in Table 4

- 20 and the source categories in Table 4

- 19 is provided on the 2011v6 platform website in ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/2 018emissions/CoST_packets/

.

Table 4

- 20

. Composite NH 3 projection factors to year 2018 for a nimal o perations

Animal Category Projection Factor Dairy Cow 0.9901

Beef 0.9851

Pork 1.0582

Broilers 1.0904 Turkeys 0.9290 Layers 1.0629 Poultry Average 1.0557 Overall Average 1.0310

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

- level animal population projections from the U.S. Department of Agriculture (USDA) and the Food and Agriculture Policy and Research Institute (FAPRI). This methodology was initiated in 2005 for the 2005 NEI, but was updated on July 24, 2012 in support of the 2007v5 platform (EPA, 2012) and 2011 to 2018 animal population projectio ns were computed for these 2011v6 projections to year 2018. For dairy cows

, EPA assumed that there would be no growth in emissions based on little change in U.S. dairy cow populations from year 2011 through 2018 according to linear regression analyses of the FAPRI projections. This assumption was based on an analysis of historical trends in the number of such animals compared to production rates. Although productions rates have increased, the number of animals has declined. Based on this analysis, EPA c oncluded that production forecasts do not provi de representative estimates of the future

32 U. S. EPA. 2011. Spreads heet “agricultural sector adjustments.xls.” Docket EPA

– HQ

– OAR

– 2011

– 0135

.DRAFT

97 number of cows and turkeys; therefore, these forecasts were not used for estimating future

- year emissions from these animals. In particular, the dairy cow population is projected to decrease in the future as it has for the past few decades; however, milk production will be increasing over the same period. Note that the ammonia emissions from dairies are not directly related to animal population but also nitrogen excre tion. With the cow numbers going down and the production going up the excretion value will change , but no change was assumed because a quantitative estimate was not available

. Appendix G provides the animal population data and regression curves used to d erive the growth factors.



### 4.2.3 Residential wood combustion growth (nonpt)

Packet: “ PROJECTION_20 11v6_2018bau _RWC _25nov2013.txt”

EPA used a “business as usual” (BAU) approach to Residential Wood Combustion (RWC) projections that

does not account for national Ne w Source Performance Standards (NSPS) for wood stoves, since they are currently in the comment

- seeking process from proposal (EPA, 2013a and available at: http://www2.epa.gov/residential

- wood

- heaters/proposed

- new

- source

- performance

- standards

- residential

wood

- heaters ). EPA projected residential wood combustion (RWC) emissions to the year 2018 based on expected increase s and decreases in various residential wood burning appliances . As newer, cleaner woodstoves replace some older, higher

- polluting wood stoves, there will be an overall reduction of the

emissions from older “dirty” stoves but an overall increase in total RWC due to popul ation and sales trends in all other types of wood burning devices such as indoor furnaces and outdoor hydronic heaters (OHH)

. It is important to note that our RWC projection methodology does not explicitly account for state or local

residential wood contr ol programs. There are a number state and local rules in place, specifically in California, Oregon and Washington. However, at this time, EPA does not have enough detailed information to calculate state specific or local area growth rates. Therefore, wi th the exception of California, Oregon and Washington, EPA is using national level growth rates for each RWC SCC category. After discussions with California air districts, regional office contacts and EPA experts, EPA decided to simply hold RWC

emissions flat (unchanged) for all SCCs in California, Oregon and Washington.

The development of projected growth in RWC emissions to year 2018 starts with the projected growth in RWC appliances derived from year 2012 appliance shipments reported in the Regulator y Impact Analysis (RIA) for Proposed Residential Wood Heaters NSPS Revision Final Report (EPA, 2013b), also available at: http://www2.epa.gov/sites/production/files/ 2013

- 12/documents/ria

- 20140103.pdf . The 2012 shipments are based on 2008 shipment data and revenue forecasts from a Frost & Sullivan Market Report ( Frost &

Sullivan , 2010). Next, to be consistent with the RIA (EPA, 2013b), growth rates for new appliance s for certified wood stoves, pellet stoves, indoor furnaces and OHH were based on forecasted revenue (real GDP) growth rate of 2.0% per year from 2013 through 2018 as predicted by the U.S. Bureau of Economic Analysis (BEA, 2012). While this approach is no t perfectly correlated, in the absence of specific shipment projections, the RIA assumes the overall trend in the projection is reasonable. The growth rates for appliances not listed in the RIA (fireplaces, outdoor wood burning devices (not elsewhere clas sified) and

residential fire logs ) are estimated based on the average growth in the number of houses between 2002 and 2012, about 1% (U.S. Census, 2012).

In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the replac ement of older, existing appliances are needed. Based on long lifetimes, no replacement of fireplaces, outdoor wood burning devices (not elsewhere classified) or residential fire logs is assumed . It is assumed that 95% of new woodstoves will replace olde r non

- EPA certified freestanding stoves (pre

- 1988 NSPS) and 5% will replace existing EPA

- certified catalytic and non

- catalytic stoves that currently meet the 1988 NSPS (Houck, 2011).DRAFT

98 EPA RWC NSPS experts assume that 10% of new pellet stoves and OHH repla ce older units and that because of their short lifespan, that 10% of indoor furnaces are replaced each year. These are the same assumptions used in the 2007 emissions modeling platform (EPA, 2012 d ). The resulting growth factors for these appliance types varies by appliance type and also by pollutant because the emission rates, from EPA RWC tool (EPA, 2013rwc), vary by appliance type and pollutant. For our non

- NSPS projection approach, the projection factors are the same for all pollutants except for EPA certified woodstoves of all types. For EPA certified units, the projection factors for PM are lower than those for all other pollutants. The projection factors also vary because the total number of existing units in 2011 varies greatly between appliance types.

California did not report detailed SCCs in the 2011NEI v1 , simply reporting emissions from general fireplaces (SCC=2104008100) and general woodstoves (SCC=2104008300). California, Oregon and Washington also have state

- level RWC control programs, including local burn bans in place. Without appliance counts in California at specific appliance types (e.g., certified versus non

- certified), and an inability to incorporate significant local RWC control programs/burn bans for a future year inventory, EP A decided to leave all RWC emissions unchanged in the future for all three states. The RWC projections factors for 2018 for states other than California, Oregon and Washi ngton are provided in Table 4

- 23 . EPA

certified woodstoves (inserts and freestanding) utilize different projection factors for direct PM than all other pollutants.

Table 4

- 21

. Non

- West Coast RWC projection factors to year 2018

Pollutant SCC Description Projection

Factor

All 2104008100 Fireplace: general 1.072

All 2104008210 Woodstove: fireplace inserts; non

- EPA certified 0.897

PM 2104008220 Woodstove: fireplace inserts; EPA certified; non

- catalytic 1.076

All other 2104008220 Woodstove: fireplace inserts; EPA ce rtified; non

- catalytic 1.181

PM 2104008230 Woodstove: fireplace inserts; EPA certified; catalytic 1.081

All other 2104008230 Woodstove: fireplace inserts; EPA certified; catalytic 1.181

All 21040083 0 0 Woodstove: freestanding, general 1.171

All 21040083 10 Woodstove: freestanding, non

- EPA certified 0.98

PM 2104008320 Woodstove: freestanding, EPA certified, non

- catalytic 1.076

All other 2104008320 Woodstove: freestanding, EPA certified, non

- catalytic 1.181

PM 2104008330 Woodstove: freestanding, EPA cert ified, catalytic 1.081

All other 2104008330 Woodstove: freestanding, EPA certified, catalytic 1.181

All 2104008400 Woodstove: pellet

- fired, general (freestanding or FP insert) 1.645

All 2104008510 IF: Indoor Furnace s : cordwood

- fired, non

- EPA certified 1 .103

All 2104008610 OHH: Outdoor Hydronic heater s 1.237

All

2104008 700 Outdoor wood burning device, NEC (e.g., fire

- pits,

chimneas ) 1.072

All 21040 09000 Residential firelog total; all combustor types 1.072

The national impact of RWC projections for PM 2.5 and NO X by SCC are provided in Table 4

- 24

. Regionally, these impacts vary considerably depending on the distribution of these appliance types. For example, RWC emissions increase more in areas with a higher proportion on OHH (Great Lakes and Northeast).DRAFT

99 Table 4

- 22

. National RWC impacts for PM 2.5 and NO X from 2011 to 2018

SCC Description PM 2.5 NO X 2011 2018 Difference 2011 2018 Difference

2104008100 Fireplace: general 66,699 70,422 3,723 7,263 7,673 410

2104008210 Woodstove: fireplace inserts;

non

- EPA certified 49,336 44,788

- 4,548 4,514 4,098

- 416

2104008220 Woodstove: fireplace inserts;

EPA certified; non

- catalytic 11,420 12,130 710 1,328 1,525 197

2104008230 Woodstove: f ireplace inserts; EPA certified; catalytic 4,205 4,482 277 412 473 61

21040083 0 0 Woodstove: freestanding,

general 9,525 9,525 0 1,079 1,079 0

2104008310 Woodstove: freestanding, non

EPA certified 83,842 82,321

- 1,521 7,672 7,533

- 139

2104008320 Woodstov e: freestanding, EPA

certified, non

- catalytic 17,998 19,129 1,131 2,094 2,407 313

2104008330 Woodstove: freestanding, EPA

certified, catalytic 10,838 11,520 682 1,063 1,212 149

2104008400 Woodstove: pellet

- fired,

general (freestanding or FP insert) 1,747 2,719 972 2,169 3,377 1,208

2104008510 IF: Indoor Furnace s :

cordwood

- fired, non

- EPA

certified 26,432 29,052 2,620 1,759 1,933 174

2104008610 OHH: Outdoor Hydronic

heater s 79,163 97,910 18,747 2,290 2,832 542

2104008 700 Outdoor wood burning

device, NEC (e.g., fire

- pits,

chimneas ) 20,573 21,916 1,343 2,266 2,414 148

21040 09000 Residential firelog total; all

combustor types 6,511 6,939 428 1,762 1,877 115

Total Total RWC 388,289 412,853 24,564 35,671 38,433 2,762



### 4.2.4 Oil and Gas projections (np_oilgas, pt_ oilgas)

Packet: PROJECTION_2011v6_2018_oilgas_27nov2013.txt

The oil and gas point (pt_oilgas) and nonpoint (np_oilgas) sectors are modeled separately from the remaining point (ptnonipm) and nonpoint (nonpt) sector emissions primarily to better track/isola te and summarize the oil and gas projections from 2011 to future years. EPA is aware that these emissions inventories are subject to much scrutiny in the base year (2011) as well as growth and control assumptions in the coming years. Our initial approach at projecting these emissions is a simple regional

- level Annual Energy Outlook (AEO) 2013

- based methodology with some associated VOC reduction factors for sources that would be subject to New Source Performance Standards (NSPS). The methodology EPA descr ibe s here was a result of a coordinated

effort between EPA OAQPS and EPA Office of Atmospheric Programs (OAP) Climate Change Division (CCD).

The AEO

- 2013 regional growth factors are based on 2011 to 2018 oil production, gas production and

combined oil an d gas production trends, available in Supplemental tables for regional detail, Table 131 and

Table 132 at: http://www.eia.gov/forecasts/aeo/tables_ref.cfm . These National Energy Modeling SystemDRAFT

100 (NEMS) regions are shown in Figure 4

- 2 and demonstrate one of the many limitations of this projection strategy: projections are not based on oil/gas basin but rather, much larger geographic regions. A county

NEMS region cross

- walk was developed to assig n counties in New Mexico and Texas to specific NEMS regions.

Figure 4

- 2

. Oil and Gas NEMS Regions

The AEO

- 2013 provides regional growth factors for oil production and gas production; however, numerous

s ources (SCCs) in the 2011 platform are ambiguous regarding the type of product being extracted/produced. These sources were assigned to a combined oil and gas category set of factors where oil and natural gas production levels were summed using a barrel

- o f

- oil equivalent of 0.178 barrels of crude oil to 1000 cubic feet of natural gas. The AEO

- based projection factors for each products type and N EMS region, provided in

Table 4

- 25 , are applied to for all pollutants and SCCs in the point and nonpoint oil and gas sector inventories, with the exception of VOC for select SCCs. The two character region codes (e.g., “NE” for Northeast region) are relevant in the following discussion on VOC projection factors.DRAFT

101 Table 4

- 23

. AEO

- based 2018 Projection Factors

Region Oil Gas Oil/Gas

Northeast (NE) 1.238 1.596 1.572

Gulf Coast (GC) 1.853 1.246 1.368

Midcontinent (MC) 1.165 0.910 0.955

Southwest (SW) 1.391 1.043 1.173
Rocky Mountai ns (RM) 1.642 1 .098 1.243

West Coast (WC) 0.865 0.993 0.888

For select VOC processes, SCCs were identified that were likely to be affected by NSPS and verified with EPA OAP and OAQPS oil and gas sector experts. NSPS reductions for VOC

- only were a pplied in composite with AEO

- based regional growth factors to create a set of “net” growth factors. These NSPS

VOC reductions are consistent with EPA OAP

- led Climate Action Report, available at: http://www.state.gov/e/oes/rls/rpts/car6/index.htm . The VOC NSPS reductions specifically, are discussed in Section 2 of the “Methodologies for U.S. Greenhouse Gas Emissions Projections” document available at: http://www.state.gov/documents/organization/219472.pdf . These composite projection factors for VOC

NSPS sources are provided in Table 4

- 24

.

There were several assumptions in the application of NSPS VOC reductions. NSPS VOC reductions were only applied to increase (if any) of emissions from 2011 to 2018 as provided by the AEO projection factor.

If AEO

- based gas or oil production was projected to decrease in 2018 versus 2011, then NSPS reductions

ha d no impact. One ex ception, highlighted in Table 4

- 24 , is for natural gas well completions; these “one

shot” activities are generally short

- term year to year processes and therefore NSPS reductions are applied to

the entire 2018 projected estimates. Other important assumptions are:

Emissions change linearly with production

- level changes (AEO projections) In the absence of local/state rules, existing equipment will continue to be used and there is no replacement of capital that wo uld be affected by the NSPS; the NSPS only affects growth for processes other than natural gas well completions. Engine

- related regulatory impacts are accounted for separately (see RICE NESHAP in the following section)

EPA did not attempt to account for or quantify the potential reductions due to the oil and natural gas

NESHAP Secondary emissions related to NSPS reductions were not accounted for (e..g, NO X emissions arising from the combustion of VOC emissions)

EPA acknowledge s that these assumptions are no t ideal, particularly the linear scaling of production changes to emissions for all processes. EPA hope s that future refinement of this methodology, particularly for large processes with highly

- reactive pollutants such as glycol dehydrators, improve this aspect of oil and gas projections.DRAFT

102 Table 4

- 24

. Oil and Gas sector VOC 2018 Projection Factors for NSPS sources

SCCs SCC Level 4 NSPS Source NSPS

Reduction Resource Region AEO

Factor VOC

NSPS (Final)

Facto r

2310030210;
2310030300;

2310021010 Gas Well Tanks

- Flashing &

Standing/Working/Breathing, Uncontrolled; Gas Well Water Tank Losses; Storage Tanks: Condensate Storage

Tanks 70.3% Gas NE 1.596 1.177

GC 1.246 1.073

MC 0.910 0.910

SW 1.043 1.013

RM 1.098 1.029

WC 0.993 0.993

2310010200;

2310011020 Oil Well Tanks

- Flashing &

Standing/Working/Breathing; Storage Tanks: Crude Oil Oil NE 1.238 1.071

GC 1.853 1.253

MC 1.165 1.049

SW 1.391 1.116

RM 1.642 1.191

WC 0.865 0.865

31000222;
2310121700;
2310021601;

2310021602 Drilling and Well Completion;

Gas Well Completion: All Processes;

Gas Well Venting

- Initial Completions; Gas Well Venting

– Recompletions Gas Well Completions 95.0% Gas NE 1.596 0.080

GC 1.246 0.062

MC 0.910 0.045

SW 1.043 0.052

RM 1.098 0.055

WC 0.993 0.050

2310021300 Gas Well Pneumatic Devices

Pneumatic

controllers 77.0% Gas NE 1.596 1.137

GC 1.246 1.056

MC 0.910 0.910

SW 1.043 1.010

RM 1.098 1.023

WC 0.993 0.993

31000325;

31000324 Pneumatic Controllers High Bleed

>6 scfm;

Pneumatic Controllers Low Bleed 100.0% Gas NE 1.596 1.000

GC 1.246 1.000

MC 0.910 0.910

SW 1.043 1.000

RM 1.098 1.000

WC 0.993 0.993

2310010300 Oil Well Pneumatic Devices 77.0% Oil NE 1.238 1.055

GC 1.853 1.196

MC 1.165 1.038

SW 1.391 1.090

RM 1.642 1.148

WC 0.865 0.865

31000309 Compressor Seals Compressor

Seals 79.9% Gas NE 1.596 1.120

GC 1.246 1.049

MC 0.910 0.910

SW 1.043 1.009

RM 1.098 1.020

WC 0.993 0.993DRAFT

103 The national impact of the AEO

- based projections for oil and gas sector point and nonpoint inventories is

sho wn in Table 4

- 27 . As previously

- discussed, these projections vary by product type, NEMS region and for some processes for VOC NSPS controls. The smaller percent increase in VOC due to NSPS controls is evident. The larger percen t growth in NH 3 is an artifact of Virginia containing 77 of the 112 tons; Virginia being in the Northeast NEMS region, has gas production increasing 59.6% according to AEO projections in

Table 4

- 23

.

Table 4

- 25

. Projected national Oil and Gas sector 2011 and 2018 emissions, summed point and nonpoint

Pollutant 2011 2018 Increase Percent Difference

CO 662,762 807,868 145,107 22%

NH 3 112 159 47 42%

NO X 670,245 817,136 146,891 22%

P M 10 23,587 29,248 5,661 24%

PM 2.5 19,008 23,537 4,529 24%

SO 2 72,337 91,054 18,716 26%

VOC 2,360,726 2,664,887 304,161 13%

National, and even NEMS

- region projections are not representative of the trends in each state for many of the reasons already di scussed. Emissions projections also vary by state depending on the proportion of gas, oil and gas/oil (undefined) emissions in the inventory for that state. For this reason, EPA provide d VOC and

NO X state

- level projections for the oil and gas sector in Table 4

- 28

.

Table 4

- 26

. Projected by

- state NO X and VOC 2011 and 2018 Oil and Gas sector emissions

State VOC

2011 VOC

2018 VOC

Difference NO X

2011 NO X

2018 NO X

Difference

Alabama 22 ,445 26,400 3,954 11,309 14,178 2,869

Arizona 78 90 12 15 18 3

Arkansas 9,019 8,140

- 879 11,573 10,790

- 783

California 15,829 14,203

- 1,626 3,179 2,976

- 203

Colorado 238,717 209,628

- 29,090 29,860 37,636 7,776

Connecticut 1 1 0 0 0 0

Delaware 0 0 0 0 0 0

Florida 3,179 3,846 667 180 237 58 Georgia 3 4 1 16 25 9

Idaho 7 9 2 0 0 0

Illinois 27,313 31,029 3,715 9,293 14,121 4,828

Indiana 9,694 11,574 1,880 6,189 9,648 3,459

Iowa 36 33

- 3 2 2 0

Kansas 94,601 96,185 1,584 56,613 53,677

- 2,936

Kentuck y 24,958 32,322 7,364 24,448 38,803 14,355

Louisiana 117,324 143,754 26,429 49,701 65,351 15,649

Maine 16 26 9 0 0 0 Maryland 8 11 3 12 19 7

Massachusetts 22 35 13 0 0 0 Michigan 28,377 35,758 7,382 17,682 28,002 10,320

Minnesota 0 0 0 0 0 0

Mississ ippi 26,248 33,407 7,159 3,492 4,558 1,066

Missouri 79 81 2 17 17 0DRAFT

104 Note, the national and state

- level summaries provided in Table 4

- 25 and Table 4

- 26 do not include reductions

from the RICE NESHAP. EPA is also aware that early release AEO 2014 projections (available at: http://www.eia.gov/forecasts/aeo/er/pdf/0383er(2014).pdf) became available in December 2013. Overall, it appears that oil production increases sig nificantly in the draft AEO 2014 compared to the AEO 2013 projections, about 22% higher by 2019 in the draft AEO 2014 projection versus the AEO 2013 projection. There appears to be less significant increase, about 11%, in projections for natural gas in th e draft 2014 AEO

versus AEO 2013.



### 4.2.5 RICE NESHAP (nonpt, ptnonipm, np_oilgas, pt_oilgas)

Packet: CONTROL_RICE_incl_SO2_2007v5_27nov2013.txt

There are three rulemakings for National Emission Standards for Hazardous Air Pollutants (NESHAP) for Reciprocating In ternal Combustion Engines (RICE). These rules reduce HAPs from existing and new RICE sources. In order to meet the standards, existing sources with certain types of engines will need to install controls. In addition to reducing HAPs, these controls have co

- benefits that also reduce CAPs, specifically,
CO, NO X , VOC, PM, and SO 2 . In 2014 and beyond, compliance dates have passed for all three rules; thus all three rules are included in the emissions projection. These RICE reductions also reflect the recen t (proposed January, 2012) Reconsideration Amendments, which results in significantly less stringent NO X controls (fewer reductions) than the 2010 final rules.

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

State VOC

2011 VOC

2018 VOC

Difference NO X

2011 NO X

2018 NO X

Difference

Nebraska 2,374 2,495 121 901 864

- 37

Nevada 440 553 113 20 26 6

New Hampshire 0 0 0 0 0 0 New Jersey 13 21 7 1 2 0

New Mexico 139,402 148,382 8,980 42,277 46,874 4,597

New York 8,222 11,379 3,157 606 944 338

North Dakota 96,873 117,943 21,070 6,375 8,225 1,851

Ohio 10,321 11,996 1,675 322 507 184

Oklahoma 231,536 220,820

- 10,717 83,337 77,766

- 5,571 Oregon 45 45

- 1 41 41 0 Pennsylvania 18,947 25,959 7,012 40,604 64,481 23,877

South

Carolina 9 14 5 0 0 0

South Dakota 1,444 1,718 274 256 304 47

Tennessee 2,700 3,182 482 1,548 2,432 884

Texas 969,231 1,150,990 181,759 193,512 226,694 33,182

Utah 131,497 1 57,230 25,733 21,128 26,013 4,885 Virginia 6,628 9,509 2,880 9,577 15,285 5,708 Washington 0 0 0 0 0 0 West Virginia 43,422 60,296 16,873 30,346 48,391 18,045

Wisconsin 0 0 0 1 1 0 Wyoming 42,977 53,237 10,260 2,785 3,330 544

Total 2,360,726 2,664,88 7 304,161 670,245 817,136 146,891DRAFT

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

National Emission Standards for Hazardous Air Pollutants for Re ciprocating Internal Combustion Engines; Final Rule (FR 9648 ) published 03/03/10

National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion Engines; Final Rule (75 FR 51570) published 08/20/2010

The difference among thes e three rules is that they focus on different types of engines, different facility types (major for HAPs, versus area for HAPs) and different engine sizes based on horsepower

. In addition, they have different compliance dates, though all are after 2011 an d fully implemented prior to 2018. EPA project s CAPs from the 2011 NEI v1 RICE sources , based on the requirements of the rule for existing sources only because the inventory includes only existing sources and the current projection approach does not

estimat e emissions from new sources.

The Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression Ignition (CI) Engines NESHAP: Final Report (EPA, 2013ci) is available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/RICE_NESHAPreconsideration_Compression_Ignition_Engines_ RIA_final2013_EPA.pdf . The Regulatory Impact Analysis (RIA) for Reconsiderat ion of the Existing Stationary Spark Ignition (SI) RICE NESHAP: Final Report (EPA, 2013si) is available at: http://www.epa.gov/ttn/ecas/re gdata/RIAs/NESHAP_RICE_Spark_Ignition_RIA_finalreconsideration2013_ EPA.pdf . Together, EPA call s these the RICE NESHAP amendment RIA’s for SI and CI engines. From
these RICE NESHAP RIA documents, EPA obtained cumulative RICE reductions for all SCCs repre sented by CI and SI engines. These aggregate reductions and percent reductions from baseline emissions (not the

20 11NEI v1 ) are provided in Table 4

- 27

.

Table 4

- 27

. Summary RICE NESH AP SI and CI percent reductions prior to 2011NEI v1 analysis

CO NO X PM SO 2 VOC RIA Baseline: SI engines 637,756 932,377 127,170 RIA Reductions: SI engines 22,211 9,648 9,147 RIA Baseline: CI engines 81,145 19,369 11,053 79,965

RIA Reductions: CI e ngines 14,238 2,818 5,100 27,142

RIA Cumulative Reductions 36,449 9,638 2,818 5,100 36,289

SI % reduction 3.5% 1.0% n/a n/a 7.2%

CI % reduction 17.5% n/a 14.5% 46.1% 33.9%

These RIA percent reductions were used as an upper

- bound for reducing emission s from RICE SCCs in the

2011NEI v1 point and nonpoint modeling sectors (ptnonipm, nonpt, pt_oilgas and np_oilgas). To begin with, the RIA inventories are based on the 2005 NEI, so EPA wanted to ensure that our 2011 reductions did not

exceed those in the RI CE RIA documents. For the 2011 platform EPA worked with EPA RICE NESHAP experts and developed a fairly simple approach to estimate RICE NESHAP reductions. Most SCCs in the inventory are not broken down by horsepower size range, mode of operation (e.g., e mergency mode), nor major versus area source type. Therefore, EPA summed NEI emissions nationally by

- SCC for RICE sources and also for sources that were at least partially IC engines (e.g., “Boiler and IC engines”). Then, EPA applied the RIA percent redu ctions to the 2011NEI v1 for SCCs where national totals exceeded 100 tons;

EPA chose 100 tons as a threshold arbitrarily, assuming there would be little to no application of RICE NESHAP controls on smaller sources. Next, EPA aggregated these national reduc tions by engine type (CIDRAFT

106 vs. SI) and pollutant and compared these to the RIA reductions. As expected, for most pollutants and engine types, our cumulative reductions were significantly less than those in the RIA. The only exception was for SO2 CI engines , where EPA opted to scale the RIA percent reduction from 46.1% to 10.2% for four broad nonpoint SCCs that were not restricted to only RICE engines. These four SCCs were the “Boilers and IC Engines” or “All processes” that would presumably contain some fr action of non

- RICE component. Reducing the SO 2 percent reduction for these four SCCs resulted in slightly less than 5,100 tons of SO 2 reductions overall from only RICE NESHAP controls. However, more specific CoST projection packets

would later override t hese RICE NESHAP reductions. Recall the CoST hierarchy discussed earlier; these RICE NESHAP reductions are national by pollutant and SCC and thus easily overridden by more

- specific

information such as state

- level fuel sulfur rules (discussed in the next s ection). Impacts of the RICE NESHAP controls on nonpt, ptnonipm, pt_oilgas and np_oilgas sector emissions are provided in Table 4

- 28

.

Table 4

- 28

. National by

- sector reductions from RICE Reconsideration Controls

Pollutant Nonpoint

Oil & Ga s (np_oilgas) Point

Oil & Gas (pt_oilgas) Nonpoint

(nonpt) Point

(ptnonipm) Total

CO 1,865 95 8,838 7,167 17, 964

NO X 1,101 94 1,976 2,033 5, 205

PM 10 0 0 1,201 300 1,501

PM 2.5 0 0 1,120 282 1,402

SO 2 1,699 0 1,571 1,049 4,319

VOC 6,249 52 1,304 4,074 11,67 9



### 4.2.6 Fuel sulfur rules (nonpt, ptnonipm)

Packet: CONTROL_SULF_2011v6_2018_27nov2013.txt

Fuel sulfur rules that were signed by November, 2013 are limited to Connecticut, Maine, Massachusetts,

New Jersey, New York, Pennsylvania and Vermont. The fuel limits for these states are incremental starting after year 2012, but are fully implemented before June 30, 2018 in all of these states. O ther states in the

Northeast and Mid

- Atlantic had pending sulf ur rules but were not finalized prior to November, 2013

- the completion date of the 2011 platform year

- 2018 projection. Background on most of these enforceable and pending fuel sulfur rules can be found here : http://www.ilta.org/LegislativeandRegulatory/MVNRLM/NEUSASulfur%20Rules_09.2010.pdf . A more recent update to the status of fuel sulfur rules is provided here: http://www.eia.gov/todayinenergy/detail.cfm?id=5890#

.

Connecticut A public hearing on proposed regulations on fuel sulfur limits for heating oil via Connecticut State Agencies

section 221

- 174

- 19b was held on October 9, 2013 (see http://www.ct.gov/deep/cwp/view.asp?A=2586&Q=530284 ). Effective July 1, 2018 maximum fuel sulfur content limits for distillate, residual and kerosene fuels go into effect. For distillate fuel oil or distillate fuel oil blended with biodiesel, these new limits must not exceed 15 ppm, a 99.5% reduction from 3000 ppm in the baseline and down from 500 ppm effective July 1, 2014. Residual oil or residual fuel oil blended with

biodiesel fuel must n ot exceed 3000 ppm, a 70% reduction from today’s 1% fuel content assumption for smaller stationary sources. For kerosene, a 15 ppm limit replaces the existing 500 ppm limit, a 97%

reduction.DRAFT

107 Maine The Maine Law Legislative Document (LD) 1662 sets a fuel sulfur rule effective January 1, 2014 that reduces sulfur to 15 ppm for distillate fuel, resulting in a 99.5% reduction from 3,000 ppm assumed in year 2008. Maine Law LD 1662 also states that #5 and #6 fuel oils must not exceed 0.5% by weight (500 ppm),

w hich is a 75% reduction from an assumed 2% baseline sulfur content in 2008. These Maine sulfur content

reductions are discussed here: http://www.mainelegislature.o rg/legis/bills/bills_124th/billpdfs/SP062701.pdf

.

Massachusetts The Massachusetts Department of Environmental Protection issued a commitment in their State Implementation Plan (SIP) to adopt Phase 2 ultra low sulfur diesel (ULSD) limits by year 2016. Similar to Maine, this will reduce the sulfur content in distillate fuel to 15 ppm, a 99.5% reduction from the 3,000 ppm baseline. Additional details on the phase

- in of ULSD can be found here: http://www.mass.gov/dep/service/online/boilwbk.pdf

New Jersey The New Jersey Department of Environmental Protection adopted sulfur fuel content rules for kerosene and home heating distillate oil. For distillate oil, the ULSD limit of 15 ppm yields a 9 9.5% reduction from the 3,000 ppm baseline. For kerosene, the same 15 ppm limit is adopted, resulting in a 97% reduction from an assumed 2,000 ppm baseline. More details on these fuel sulfur limits in New Jersey can be found here: http://njtoday.net/2010/09/01/nj

- adopts

- rule

- limiting

- sulfur

- content

- in

- fuel

- oil/

New York New York also signed a law requiring ULSD to replace distillate heating oil #2, which resul ts in a fuel sulfur content limit of 15 ppm, a 99.5% reduction from the 3,000 ppm baseline. The ULSD law (A.8642

- A/S.1145

- C) can be found here: http://switchboard.n rdc.org/blogs/rkassel/governor_paterson_signs_new_la.html and here: http://green.blogs.nytimes.com/2010/07/20/new

- york

- mandates

- cleaner

- heating

- oil/ . New York City also includes limits by year 2015 on #4 and #6 residual oils, where fuel sulfur content must not exceed 0.5% by weight (500 ppm), a 75% reduction from an assumed 2% baseline sulfur content in 2008. By 2030, these sources must burn ULSD (15 ppm). The NYC updated Air Code, updated from the NY DEP is discussed

here: http://www.nyc.gov/html/dep/html/news/dep_stories_p3

- 109.shtml

.

Pennsylvania Legislation has been proposed in P ennsylvania that would reduce allowable sulfur levels to 15 ppm for distillate oil, a 99.5% reduction from the 3,000 ppm baseline. While EPA typically do not include proposed rulemakings in our base projection scenarios without direction from state agenci es, the existence of similar, finalized standards in neighboring Northeast census region states such as New Jersey and New York suggest this will become finalized prior to 2018. EPA can revise this, and potential application to other fuels, based

on state comment or regulatory changes.

Vermont Vermont ULSD fuel and date requirements for home heating oil are similar to those adopted in Massachusetts: a 99.5% reduction to 15 ppm from the 3,000 ppm baseline.

A summary of the sulfur rules by state, with emi ssions reductions is provided in Table 4

- 31

.DRAFT

108 Table 4

- 29

. Summary of fuel sulfur rules by state

State/

Metro Fuel %

reduction 2011

Emissions 20 18

Emissions 2018

Reductions

CT Distilla te 99.5
12,535 347 12,188 CT Kerosene 97

CT Residual 70

ME Distillate 99.5 7,041 706 6,335 ME Residual 75

MA Distillate 99.5 19,540 98 19,443

NJ Distillate 99.5 6,146 31 6,115 NJ Kerosene 96.25

NY Distillate 99.5 32,984 1,027 31,957 NY C Residual 75

PA Distillate 99.5 14,634 73 14,561

VT Distillate 99.5 997 5 992



### 4.2.7 Industrial Boiler MACT reconsideration (ptnonipm)

Packet: CONTROL_BlrMACT_ptnonipm_20XX_2011v6

The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Ru le, hereafter simply r eferred to as the “Boiler MACT” was promulgated on January 31, 2013 based on reconsideration

. B ackground

information on the Boiler MACT can be found at: http://www.epa.gov/ ttn/atw/boiler/boilerpg.html . The Boiler MACT promulgates national emission standards for the control of HAPs (NESHAP) for new and existing industrial, commercial, and institutional (ICI) boilers and process heaters at major sources of HAPs. The expecte d cobenefit for CAPs at these facilities is significant and greatest for SO 2 with lesser impacts for direct PM, CO and VOC.

Boiler MACT reductions were computed from a non

- NEI database of ICI boilers. As seen in the Boiler MACT Reconsideration RIA ( http://www.epa.gov/ttn/atw/boiler/boilersriaproposalrecon111201.pdf ), this

Boiler MACT Information Collection Request (ICR) dataset computed over 558,000 tons of SO 2 reductions by year 2015. However, the Boiler MACT ICR database and reductions are based on the assumption that if a

unit could burn oil, it did burn oil, and often to capacity. With high oil prices and many of these units also able to burn cheaper natural gas, the 20 11 NEIv1 inventory has a lot more gas combustion and a lot less oil combustion than the boiler MACT database. For this reason, EPA decided to target units that potentially could be subject to the Boiler MACT and compute preliminary reductions for several C APs prior to building a control packet.

Step 1: E xtract facilities /sources potentially subject to Boiler MACT

EPA did not attempt to map each ICR unit to the NEI units, instead choosing to use a more general approach to extract NEI sources that would be p otentially subject to, and hence have emissions reduced by the Boiler MACT. The NEI includes a field that indicates whether a facility is a major source of HAPs and/or CAPs. This field in our FF10 point inventory modeling file is called “FACIL_CATEGORY_C ODE” and the possible values for that field are shown in Table 4

- 30

. Because the Boiler MACT rule applies to only major sources of HAPs, EPA restricted the universe of facilities potentially subject to the Boiler MACT to those

cla ssified as HAP major or unknown (UNK). The third column indicates whether the facility was a candidate for extraction as being potentially subject to the Boiler MACT.DRAFT

109 Table 4

- 30

. Facility types potentially su bject to Boiler MACT reductions

Code Facility

Category Subject

to Boiler

MACT? Description

CAP CAP Major N Facility is Major based upon 40 CFR 70 Major Source definition paragraph 2 (100 tpy any CAP. Also meets paragraph 3 definition, but NOT paragraph 1 definition).

HAP HAP Major Y Facility is Major based upon only 40 CFR 70 Major Source definition paragraph 1 (10/25 tpy HAPs).

HAPCAP HAP and

CAP Major Y Facility meets both paragraph 1 and 2 of 40 CFR 70 Major Source definitions (10/25 tpy HAPs and 100 tpy any CAP).

HAPOZN HAP and O3

n/a Major Y Facility meets both paragraph 1 and 3 of 40 CFR 70 Major Source definitions (10/25 tpy HAPs and Ozone n/a area lesser tons for NO X or VOC).

NON Non

- Major N Facility's Potential To Emit is below all 40 CFR 70 Ma jor Source threshold

definitions without a FESOP.

OZN O3 n/a Major N Facility is Major based upon only 40 CFR 70 Major Source definition paragraph 3 (Ozone n/a area lesser tons for NO X or VOC).

SYN Synthetic

non

- Major N Facility has a FESOP which limits its Potential To Emit below all three 40 CFR 70 Major Source definitions.

UNK Unknown N Facility category per 40 CFR 70 Major Source definitions is unknown.

From these facilities EPA extracted records (process level / release point level emissions) from our modeling file with industrial, commercial, institutional boiler or process heater SCCs. A complete list of these SCCs is provided in Appendix H . The resultant data are the NEI sources potentially subject to the Boiler MACT.

Step 2: Match fuel types and control reductions to the NEI SCCs After obtaining the subset of 2011 NEI v1 sources potentially subject to the Boiler MACT, EPA assigned each inventory SCC to a fuel type. The reductions are based on the ICR fuel types and associated controls from

an A pril 2010 “Baseline Memo.pdf” memorandum available on the Regulations.gov website ( http://www.regulations.gov/#!documentDetail;D=EPA

- HQ

- OAR

- 2002

- 0058

- 0802 ) under docket # EPA

HQ

- OAR

- 2002

- 0058

- 0802 . These ICR fuel types and associated default controls were mapped to SCCs in our inventory using the cross

- walk provided in Table 4

- 31 . The previously

- mentioned Appendix H also maps the complete list of inventory SCCs to these ICR fuel categories.

Table 4

- 31

. Default Boiler MACT fuel percent % reductions by ICR fuel type

ICR Fuel Category SCC Fuel Category(s) CO PM 2.5 SO 2 VOC

C oal coal, petroleum coke, w aste coal 98.9 95.8 95 98.9

gas 1 (other) gasified coal, hydrogen, liquified petroleum gas

(LPG), propane/butane, refinery gas 1 1 1 1 gas 2 digester gas, gas, landfill gas, process gas 99.97 0 95 99.97

B agasse B agasse 95.3 90 95 95.3 dry biomass W ood 95.8 99.1 95 95.8 gas 1 (natural gas) natural gas, unknown 1 1 1 1

heavy liquid coal

- based Synfuel, crude oil, liquid waste,

methanol, residual oil, waste oil 99.9 98.3 95 99.9 light liquid distillate oil, gasoline, kerosene, oil, other oil 99.9 93 95 9 9.9

wet biomass solid waste, wood/bark waste 85.5 99.2 95 85.5

The impacts of these Boiler MACT reductions on the controllable facilities and units are provided in Table

4

- 32

. Controls were applied as “replacement” controls to prevent over

- control of units that had existingDRAFT

110 controls. However, this assumes that the inventory correctly reflects units with controls, so it is likely that

some units that are not recorded as controlled in the 2011NEI v1 but are actually controlled wer e reduced more than they should have. Overall, the CO and PM 2.5 reductions are reasonably close to the year

- 2015 expected reductions in the Boiler MACT Reconsideration RIA:

h ttp://www.epa.gov/ttn/atw/boiler/boilersriaproposalrecon111201.pdf . It is worth noting that the SO 2 reductions in the preamble ( http://www.epa.gov/ttn/atw/boiler/fr21mr11m.pdf ) were estimated at 442,000 tons; the additional SO 2 reductions in the reconsideration are from an additional cobenefit from more stringent HCl controls. The 2011NEI v1 SO 2 emissions are actually less than the estimated Boiler MACT reductions, likely a result of numerous u nits undergoing fuel switching from coal or oil to natural gas via changing energy prices between the Boiler MACT RIA analyses and the 2011NEI v1 . It is also worth noting

that EPA did not attempt to quantify the reductions of nonpoint ICI boiler emissions from Boiler MACT

controls.

Table 4

- 32

. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions

Pollutant 20 11 Emissions 20 18 Emissions Reductions RIA Reductions

CO 267,685 66,682 2 01,003 187,000

PM 2.5 3 4

, 586 10, 819 2 4

, 654 25,601

SO 2 301

, 748 35

, 55 3 276,195 558,430

VOC 19, 2 95 6, 984 1 2

, 311 n/a



### 4.2.8 Portland Cement NESHAP projections (ptnonipm)

As indicated in Table 4

- 1 , the Industrial Sectors Integrated Solutio ns (ISIS) model (EPA, 2010b) was used to project the cement industry component of the ptnonipm emissions modeling sector to 2018. This approach provided reductions of criteria and select hazardous air pollutants. The ISIS cement emissions were developed in support for the Portland Cement NESHAPs and the NSPS for the Portland cement manufacturing industry.

The ISIS model produced a Portland Cement NESHAP policy case of multi

- pollutant emissions for individual cement kilns (emission inventory units) that w ere relevant for years 2015 through 2030. These

ISIS

- based emissions are reflected using a CoST packet for all existing kilns that are not impacted by more local information from states (or consent decrees)

– see next section

- and two cement inventories fo r new kilns:

1) Inventory: “cement_newkilns_year2018_from_ISIS2013_NEI2011v1” Contains information on new cement kilns constructed after year 2011, 2) Inventory : “ cement_newkilns_year_2018_from_ISIS2013_NEI2011v1_NONPOINT _v0”

Contains information ISIS

- generated , but not

- permitted, new cement kilns constructed after year
2011, 3) Packet: “PROJECTION_2011_2018_ISIS_cement_by_CENSUS_DIVISION_04dec2013.txt“ Contains U.S. census division level based projection factors for each NEI unit (kiln) based on ISIS updated polic y case emissions at existing cement kilns. The units that closed before 2018 are included in the 2018 base case but are included in other CoST packets that reflect state comments and consent decrees (discussed in the next section).

The ISIS model, versio n August 2013 was used for these projections. Recent data updates include updated matching of kilns to better capture recent retirements, capacity additions and projections of capacity additions from Portland Cement Association (PCA) Plant Information Sum mary of December 31, 2010 and feedback from Portland Cement NESHAP reconsideration comments. Updated cement consumption projections are based on a post

- recession (July 2012) PCA long

- term cement consumption outlook. UpdatedDRAFT

111 emissions controls in 2015 fro m the NESHAP are also reflected. Overall, as seen in Figure 4

- 3 , domestic production of cement grows significantly between 2011 and 2015, then more slowly through 2018. Meanwhile, emissions from NESHAP

- regulated pollutants such as PM and SO 2 drop signifi cantly based on regulated emissions rates. Emissions for NO X increase, though not as much as production because the ISIS model continues the recent trend in the cement sector of the replacement of lower capacity, inefficient wet and long dry kilns with bi gger and more efficient preheater and precalciner kilns.

Figure 4

- 3

. Cement sector trends in domestic production versus normalized emissions

Multiple regulatory requirements such as the NESHAP and NSPS c urrently 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 areas.

The ISI S model provides the emission reduction strategy that balances: 1) optimal (least cost) industry operation, 2) cost

- effective controls to meet the demand for cement, and 3) emission reduction requirements over the time period of interest.

The first step i n using ISIS 2018 projected emissions the cement industry is matching the kilns in year 2018

to those in the 2011NEI v1 . For kilns that were new in 2018, EPA used two different approaches for modeling. For kilns already permitted, known locations (coordin ates) allowed us to process these as point sources. However, the ISIS model also created “generic” kilns in specific geographically strategic locations (counties) to cover the need for increased production/capacity in future years. Because these generic kilns are not permitted and the location in these counties is uncertain, EPA decided to model these as county

- level to avoid placing large emissions sources from a model (ISIS) artifact in one grid cell. These nonpoint source kilns were then spatially all ocated based on industrial land activity in the county. A list of all new point and nonpoint inventory cement kilns in 2018 are provided in Table 4

- 33 . There are additional new kilns generated by ISIS beyond year 2018 that are n ot shown in this table.

Table 4

- 33

. Locations of new ISIS

- generated cement kilns

ISIS ID Permitted? Facility Name FIPS State County

FLNEW2 Y Vulcan 12001 FL Aluchua

GANEW1 Y Houston American Cement 13153 GA Houston

NCNEW1 Y Titan America LLC 37129 NC New Hanover

NewGA2 N n/a 13153 GA Houston 0 20 40 60 80 100 120 140 160 180

2011 2015 2016 2017 2018 Domestic Production

PM trend

NOX trend

SO2 trendDRAFT

112 NewPA8 N n/a 42011 PA Berks

NewSC1 N n/a 45035 SC Dorchester

NewTX1 N n/a 48029 TX Bexar

NewTX10 N n/a 48091 TX Comal

NewWA1 N n/a 53033 WA King

While ISIS provid es by

- kiln emissions for each future year, EPA cement kilns experts preferred that the Agency project existing cement kilns based on a more

- smooth geographic approach to reduce the “on”/”off” switching that ISIS assigns to each kiln based on production and capacity demands. I t would be inefficient and unrealistic to project existing cement kilns to operate as essentially 0% or 100% capacity based strictly on ISIS output. Therefore, EPA developed a U.S. Census Division approach where ISIS emissions in 2011 and 2018, that matched the 2011NEI v1 (e.g., not new ISIS kilns), were aggregated by pollutant for each year within each of the 9 census divisions in the contiguous U.S. ( http://www .eia.gov/consumption/commercial/images/cendivco.gif ). These aggregate emissions were used to create a 2018/2011 emissions ratio for each pollutant and geographic area. The projection ratios, provided

in Table 4

- 34 , were then ap plied to all 2011NEI v1 cement kilns

– except for kilns where specific local information (e.g., consent decrees/settlements/local information).

Table 4

- 34

. U.S. Census Division ISIS

- based projection factors f or existing kilns

Region Division NO X PM 2.5 SO 2 VOC

Midwest East North Central 2.024 0.106 1.800 0.527

Midwest West North Central 0.930 0.614 0.695 0.317

Northeast Middle Atlantic 1.853 0.058 0.904 0.561

Northeast New England 2.560 0.004 3.563 0.713

S outh East South Central 0.999 0.109 0.402 0.323

South South Atlantic 1.042 0.284 0.911 0.413

South West South Central 1.220 0.079 0.484 0.225

West Mountain 1.453 2.542 1.917 0.310

West Pacific 1.465 0.001 0.300 0.321

For all ISIS future year emissions , PM 10 is assigned as 0.85 of total PM provided by ISIS, and PM 2.5 is assigned as 0.45 of total PM. All new ISIS

- generated kilns, point and nonpoint format, are assigned as Precalciner kilns (SCC=30500623). While ISIS provides emissions for mercury, EPA did not retain these in our modeling.

Table 4

- 35 shows the magnitude of the ISIS

- based cement industry emissions changes between the

2011NEI v1 and 2018 projection scenario. Kilns that matched the 2011NEI v1 were simply projected t o year 2018 based on U.S. census division aggregate changes in ISIS predictions. There are some local exceptions

where EPA did not use ISIS

- based projections for cement kilns where local information from consent

decrees/settlements and state comments were used instead. Cement kiln s projected using these non

- ISIS

information are not reflected here in Table 4

- 35

. EPA also split out ISIS

- based new kilns in 2018 with permitted (as of August 2013) kilns modeled as point sources and “ generic” ISIS

- generated kilns as nonpoint

sources.

Table 4

- 35

. ISIS

- based cement industry change (tons/yr)

Pollutant 2011 2018 New kilns in 2018 Total Difference 2018

- 2011DRAFT

113 NEI v1 projected Permitted

(point) ISIS

- generated

(nonpoint) 2018

NO X 53,874 71,205 3,751 6,836 81,792 27,919

PM 2.5 1,772 722 8 15 745

- 1,027

SO 2 17,065 18,629 1,775 3,263 23,667 6,602

VOC 2,690 903 91 167 1,161

- 1,529



### 4.2.9 State comments and consent decrees/settlements (nonpt, ptnoni pm)

This subsection describes the numerous (12 in all) CoST PROJECTION and CONTROL packets developed to reflect a wide range of information on future year non

- EGU point and nonpoint source projections. In general, this information is derived from:

c omment s received from the Cross

- State Air Pollution proposal local and state comments over the past several years,
consent decrees and settlements, and EPA staff data mining and analyses

4.2.9.1 Comments from Cross

- State Air Pollution Rule (2010)

EPA released a Notice of Data Availability (NODA) after the CSAPR proposal to seek comments and improvements from states and outside agencies. The goal was to improve the future baseline emissions modeling platform prior to processing the Final CSAPR. EPA received several co ntrol 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 2005 NEI v2 as a base year starting point for future year projections.

Now with the 2011 pla tform using the 2011 NEI v1 for most non

- EGU point and nonpoint sources, many of these controls and data improvements were removed from this 2018 base case projection. But for those controls, closures and consent decree information that are impleme nted after 2011, EPA used these

controls/data after EPA mapped them to the correct SCCs and/or facilities in the 2011 NEI. This subsection breaks down the controls used for the nonpt and ptnonipm sectors separately, and also describes the consent

decrees separately. EPA used July 1, 2011 as the cut

- off date for assuming whether controls were included in the 2011 NEI. For example, if a control had a compliance date of December 2011 EPA would assume that

the 2011 NEI emissions did not reflect this control and EPA would need to reflect this control in our 2018 base case. It is important to note that these controls are not comprehensive for all state/counties and source categories. These only represent post

- year 2011 controls for those areas and categories where EPA received usable feedback from the CSAPR comments and related 2005 platform NODA.

Packet: “ CONTROLS_CSAPR_consent_2011v6.csv ”

These controls reflect consent decree and settlements that were identified in our preparation of the Final

CSAPR emission s modeling platform. These controls generally consist of one or more facilities and target future year reductions. After EPA removed all consent decrees with compliance dates prior to late

- 2011,

EPA matched the remaining controls to the 2011 NEI using a combination of EIS facility codes, “agy_facility_id”, “agy_point_id” and searching the EIS. Then, EPA recomputed the percent reductions such that the future year emissions would match those for facilities originally projected from the 2005 NEI

based platf

orm

– these consent decrees were released from 2007 through 2010, when the 2005 NEI was the general baseline. EPA did not retain consent decree controls if the emissions in the 2011NEI were less than the controlled future year emissions based on the 2005 p latform. EPA were left with consent decree controls in twelve states (AL, CA, IN, KS, KY, LA, MI, MS, OH, TN, TX, WY) that accounted for 2,515 tons of

NO X and 11,014 tons of SO 2 cumulative reductions.DRAFT

114 Packet: “CONTROL_CSAPR_ptnonipm_2011v6_22nov2013.txt”

EPA created a CONTROL packet for the ptnonipm sector that contains reductions needed to achieve post

year

- 2011 emissions values from the CSAPR response to comments. These reductions reflect fuel switching, cleaner fuels, and permit targets via specific in formation on control equipment and unit and facility zero

- outs in the following states: Georgia, New Hampshire, New York and Virginia. Cumulatively,

these controls reduce NO X by 655 tons and SO 2 by 7,221 tons.

Packet: “ PROJECTION_CSAPR_WVunit_ptnonipm_2 012_2011v6_21nov2013.txt ”

This packet contains the only post

- 2011 unit

- level growth projection resulting from CSAPR comments. The Sunoco Chemicals Neal Plant in Wayne County West Virginia replaced a 155MM Btu/hour coal

- fired boiler

with a 96.72 MM Btu/hou r natural gas

- fired unit in 2010. This closure is already reflected in the 2011 NEI; however, in 2012, a new natural gas unit was slated to operate and therefore EPA scaled emissions at an existing natural gas boiler to match these 2012 emission targets p rovided to us by West Virginia via CSAPR comments. This packet simply results in an extra 22.5 tons of NO X and minimal increased emissions for PM

and SO 2

.

4.2.9.2 State comments since spring of 2013

The following packets were derived from information received fro m several states since the spring of 2013 regarding point and nonpoint projections to year 2018.

Packet s :

“ PROJECTION_VA_ME_TCEQ_AL_comments_2011v6_2018_03dec2013.txt ”

“ CONTROL_VA_ME_TCEQ_comments_2011v6_2018_03dec2013.txt ”

These packets represent primari ly local closures and expected changes in future year emissions, in some cases, specified as year 2018, but otherwise simply to be used rather than the 2011 NEI values for general future year modeling. These comments from Alabama, Maine, Texas and Virgini a were received in the spring through early fall of 2013. The CONTROL packet was used for specific stack/unit closures and emissions reductions. Deciding which packet type to use (PROJECTION or CONTROL) for applying state comments in CoST is fairly subje ctive. EPA is forced to use PROJECTION packets when emissions increase, and if EPA can get away with using only 1 type of packet (PROJECTION or CONTROL) for a single source of comments, AND, the choice does not result in different final projected values, then the packet type that best fits was used . For example, if a set of state comments results in emissions increasing and decreasing at various stacks and other CoST packets do not apply, then the packet type choice does not matter. If, however, EPA cho se to represent emission decreases as a PROJECTION packet entry, and another CoST CONTROL packet applies to that source, then EPA are applying two different sources of

reductions

– not ideal. Our goal is for state comments to pass through to the final futu re year inventory as

- is. For this reason, EPA does not quantify emission changes for these packets separately. The cumulative impact of these emissions is shown in Table 4

- 36 . Note that the widespread Texas NAICS

- level economic

based growth factors and impacts are discussed separately.

Table 4

- 36

. Impacts of most non

- EGU point source state comments received in 2013

State Pollutant 2011 NEI v1 2018 Projection Change

Alabama NO X 2,941 3,062 120

Alabama SO 2 1,156 1,168 12

Maine NO X 178 45

- 134

Maine SO 2 2,069 666

- 1,463DRAFT

115 Texas NO X 3,337 712

- 2,625

Texas SO 2 8,461 229

- 8,233

Texas VOC 469 65

- 404 Virginia NO X 8,065 4,531

- 3,534 Virginia SO 2 1,646 2

- 1,644

Packet: “ PROJECTION_TCEQ _ptnonipm_NAICS_comments_2011v6_2018_04dec2013.txt ”

This packet represents county

- specific economic

- based NAICS

- level projections provided by the Texas Commission on Environmental Quality (TCEQ) for minor source emissions. Growth factors are based on

pro jections of gross product for various types of industry, population and various economy.com data. EPA did not apply these projections to oil and gas sources, opting to use the consistent regional/fuel

- based approach discussed in Section 4.2.4. A summary of these minor source ptnonipm sector projection impacts for Texas are provided in Table 4

- 37

.

Table 4

- 37

. Minor source ptnonipm sector NAICS

- level projections for Texas

Pollutant 2011NEI v1 2018 Projection Change

CO 114,817 136,696 21,879

NH 3 2,099 2,619 520

NO X 138,389 157,997 19,609

PM 10 21,146 26,044 4,898

PM 2.5 17,301 21,384 4,084

SO 2 21,432 28,033 6,601

VOC 62,386 79,671 17,285

Packet: “ PROJECTION_TCEQ_AREA_comments_2011v6_2018_04dec2013.txt ” This packet represents nonpt sector 2011

- based projections for year 2018 for Texas as provided by TCEQ. These county

- level and SCC

- specific projectio ns are based on a combination of economy.com and Annual Energy Outlook (AEO) data. EPA did not apply these projections to oil and gas sources, opting to use the

consistent regional/fuel

- based approach discussed in Section 4.2.4. EPA also did not apply these projections to the Residential Wood Combustion sector which were the same for every RWC SCC and county, opting

instead to use our national

- based but SCC

- specific approa ch discussed in Section 4.2.3. A summary of these nonpt sector changes in Texas is provided in Table 4

- 38

.

Table 4

- 38

. Minor source nonpt sector projections for Texas

Pollutant 2011 NEI v1 2018 Projection Change

CO 68,967 83,299 14,333

NH 3 2,659 2,720 60

NO X 32,581 34,329 1,748

PM 10 19,999 24,416 4,416

PM 2.5 15,520 19,268 3,747

SO 2 9,099 8,805

- 293

VOC 239,657 256,046 16,389

4.2.9.3 Consent decrees and settlements

These packets were de rived in prior emissions modeling platforms, dating back to the 2005 NEI and 2008

NEI. EPA updated this information based on information in the 2011NEI v1 and analysis for compliance dates. Many of these consent decrees were already in place in 2011 and t herefore removed fromDRAFT

116 consideration for projections. New information (e.g., Cabot Corporation) has also been obtained since the spring of 2013 and has been included in our projections. Consent decrees or settlements released after

November are not includ ed. EPA also does not reflect consent decrees that do not have obvious quantifiable reductions for important emissions modeling pollutants (CAPs).

Packet: “ CONTROL_ConsentDecree_Cabot_BlackPowderPlants_03dec2013_v0.txt ”

This Cabot Corporation Clean Air Ac t settlement (release date of November 19, 2013) targets NO X and SO 2 reductions of 1,975 and 12,380 tons, respectively, from three carbon black manufacturing plants in Louisiana

and Texas. More information on this settlement can be found at: http://www2.epa.gov/enforcement/cabot

- corporation

- clean

- air

- act

- settlement#reductions

.

Because EPA did not have specific stack

- level information on this settlement, t he Agency apportioned the total reductions proportionally to each of the three facilities such that each process in all the facilities was assigned the same percent reduction and that the cumulative NO X and SO 2 reductions would be achieved.

Packet: “ CONTR OLS_Refineries_additional_consent_2011NEI_v1_25nov2013_v1.txt ”

This packet consists of two settlements. The BP Whiting settlement (released May 23, 2012) is available at: http://www2.epa. gov/enforcement/bp

- whiting

- settlement . The Marathon Petroleum Company, Detroit Refinery environmental mitigation project (released April 5, 2012) is available at: http://www2.epa.gov/enforcement/marathon

- petroleum

- company

- lp

- and

- catlettsburg

- refining

- llc

- settlement

.

The initial application of these settlements was to the 2008 NEI. Therefore, to be consistent with previous future year esti mates for these facilities, EPA modified existing computed reductions from the 2008 such that future year estimates from the 2011NEI v1 matched those done with the 2008 NEI. These settlements reduce

NO X by 78 tons at the Detroit Refinery and NO X and SO 2 by 780 and 150 tons, respectively, at the Indiana BP Whiting facility.

Packet: “ CONTROL_OECA_2011v6_25nov2013.txt ”

The Office of Enforcement and Compliance Assurance (OECA) provided emission reduction information for

several consent decrees while EPA was pr eparing emissions for the 2005 NEI

- based modeling platform ( http://www.epa.gov/ttn/chief/emch/toxics/proposed_toxics_rule_main.pdf ). The press releases for these

consent decrees are available on EPA ’s enforcement website ( http://www.epa.gov/enforcement/ ) and some were available with quantitative emission reductions that EPA was able to convert into a control packet. These petr oleum refinery settlements are available at: http://www2.epa.gov/enforcement/petroleum

- refinery

- national

- case

- results . These settlements were released

in the 2003

- 2010 time period and include information for a few corporations but with aggregate reductions over numerous facilities under these companies and subsidiaries. Therefore, EPA developed an initial table

of 2008 NEI emissions summed over all affected facilities f or each company. Then EPA merged the multi

facility expected reductions from each of these consent decrees to develop an overall future year (post

compliance date) emissions estimate for each company after all controls/reductions are implemented. Using

t his methodology, the emissions reductions were apportioned to each plant owned/operated by each company using the same percent reduction from the 2005 NEI emissions.

Now that EPA is using the 2011 NEI, the Agency 2011NEI v1 , expected that some of these con sent decree controls/reductions would have already been applied by 2011. EPA did not want to over

- control any particular plant. Therefore, EPA computed facility

- specific reductions based on the controlled emissionsDRAFT

117 from the 2008 NEI. For example, as see n in Table 4

- 37, SO 2 emissions at all Cargill facilities were reduced about 24% in the 2008 NEI: from 6,921 tons to 5,280 tons. In the 2011NEI v1 , SO 2 emissions at these same Cargill facilities totaled 6,263 tons, so only approximately 1,000 tons, a 16% c umulative reduction over all Cargill facilities, were needed to achieve the 5,280 consent decree target.

The column “2008 NEI Controlled” in Table 4

- 39 was our target for year 2018 emissions. However, many

of these facilities are ethanol plants and are therefore projected separately using EPA OTAQ’s national projections for ethanol plants (see Section 4.2.1). This is a biggest issue for the Cargill facilities, a majority of which are defined as ethanol plants. Note in Table 4

- 39

, the “applicable” (non

- ethanol plants) 2011NEI v1 emissions available for OECA consent decree controls is significantly less than the sum of all Cargill facility

emissions. T he discrepancies between actual and applicable 2011 NEI emissions for the other OECA

facilities are primarily a result of CoST hierarchy assignments. In short, more

- specific (more resolved than facility/pollutant of the OECA packet) control information from other CoST packets are used for some of

these sta cks/units/facilities.

Table 4

- 39

. Target company

- wide reductions from OECA consent decree information

Corporation Pollutant 2008 NEI (tons) 2008 NEI

Controlled (tons) Reductions

from 2008 (tons) 2011 NEI v1

Emi ssions (tons) 2011 NEI v1

applicable (tons) Actual

2018

Reductions Cargill CO 10,889 262 10,627 6,045 401 394

NO X 2,265 1,478 787 1,714 806 111

SO 2 6,921 5,280 1,642 6,263 849 172 Conoco Phillips NO X 14,331 7,334 6,997 9,391 9,070 2,932

Sunoco NO X 4,5 06 1,975 2,531 3,235 3,154 1,231

PM 2.5 1,030 585 445 1,072 714 379

Valero NO X 8,212 6,109 2,103 6,676 4,913 966

PM 2.5 2,554 1,955 599 2,338 1,883 718

SO 2 11,479 2,903 8,575 6,040 4,807 3,367

Total CO 10,889 262 10,627 6,045 401 394

NO X 29,314 16, 896 12,418 21,016 17,943 5,240

PM 2.5 3,584 2,540 1,044 3,410 2,597 1,097

SO 2 18,400 8,183 10,217 12,303 5,656 3,539

Packet: “ CONTROL_LaFarge_StGobain_ptnonipm_2011v6_22nov2013.txt ”

This control packet includes settlements for all 15 U.S. plants owned by Saint

- Gobain Containers, Inc., the nation’s second largest container glass manufacturer, and all 13 U.S. plants owned by the Lafarge Company and two subsidiaries, the nation’s second largest manufacturer of Portland cement. These settlements

, released January 21, 2010, are the first system

- wide settlements for these sectors under the Clean Air Act and require pollution control upgrades, acceptance of enforceable emission limits, and payment of civil penalties. The settlements require various NO X and S O 2 controls, some of which (SO 2 scrubbers) also reduce PM emissions. A couple of Lafarge kilns were also scheduled to be shut down. One of these units was shutdown prior to 2011 and as expected, is not in the 2011NEI v1 . However, a Lafarge kiln in Joppa

, Illinois was unexpectedly found in the 2011NEI v1 and communication with the Illinois DEP indicated that this unit was not closed as of the summer of 2012. More information on the Lafarge settlement can be found here: http://www2.epa.gov/enforcement/lafarge

- north

- america

- inc

- clean

- air

- act

- settlement . More information on

the Saint

- Gobain settlement is available here: http://www2.epa.gov/enforcement/saint

- gobain

- containers

- inc

- clean

- air

- act

- settlement . Many of the controls for the units at these facilities were implemented prior to 2011 and were therefore removed f rom theDRAFT

11 8 CONTROL packet; however, cumulatively, there is still significant reductions post

- 2011: 9,210 tons of NO X , 214 tons of PM 2.5 and 11,777 tons of SO 2

.

4.2.9.4 EPA staff data mining

Packet: “ CONTROLS_Regional_Haze_2011v6.csv ” This packet includes a set of NO X and SO 2 reductions provided by EPA ’s OAQPS Air Quality Policy Division (AQPD) visibility experts. These reductions reflect expected emissions reductions and future year caps for facilities of various industries (e.g., cement kilns, taconite, steel, pulp and paper and mining industries) in the following states: Georgia, Idaho, Michigan, Minnesota, Montana, New York, Ohio, Tennessee, Virginia and Wisconsin. Cumulatively, 28,589 tons of NO X and 20,698 tons of SO 2 are reduced by these controls.



### 4.2.10 Aircraft pr ojections (ptnonipm)

A ircraft emissions are contained in the ptnonipm inventory. These 20 11 point

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

- offs whereby the aircraft leaves the airport vicinity and lands at another airport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. EPA used projected ITN information available from the Federal Aviation Administration’s (FAA) Terminal Area Forecast (TAF) System: http://www.apo.data.faa.gov/main/taf.asp (publication date March, 2013 ). This information is available for approximately 3

, 300 ind ividual ai rports, for all years up to 2030 . The methods that the FAA used for developing the ITN data in the TAF are documented in: http://www.faa.gov/about/office_org/headquarters_offices/apl/aviation_forecasts/taf_reports/media/TAF_su mmary_report_FY20112040.pdf

.

None of our aircraft emission projections account for any control programs

. EPA considered the 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% by 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule; therefore, its effects were not

included in the future

- year emissions projections.

EPA developed two sets of projection factors for aircraft. The first set was a simple national (U.S.) aggregation, used primarily for airports with v ery little activity, by ITN operation type (commercial, general aviation, military and air taxi) to be used as a default method for projecting from 2011 to 2018. The second set of projection factors was by airport, where EPA project ed project emissions fo r each individual airport with significant ITN activity.

Packet: “ PROJECTION_2011_2018_aircraft_21nov2013.txt ”

In this case, EPA simply summed the ITN operations to national totals by year and aircraft operation and computed projection factors as futur e

- year 2018 ITN by 20 11

- year ITN

. EPA assigned factors to inventory SCCs based on the operation type shown in Table 4

- 40

.

Table 4

- 40

. Default national

- level factors used to project 20 11 base

- case aircraft emissions to 2018

SCC Description Projection

Factor

2265008005 Commercial Aircraft: 4

- stroke Airport Ground Support Equipment 1.1741

2267008005 Commercial Aircraft: LPG Airport Ground Support Equipment 1.1741

2268008005 Commercia l Aircraft: CNG Airport Ground Support Equipment 1.1741

2270008005 Commercial Aircraft: Diesel Airport Ground Support Equipment 1.1741DRAFT

119 SCC Description Projection

Factor

2275000000 All Aircraft Types and Operations 1.1741

2275001000 Military Aircraft, Total 0.9972

2275020000 Commercial Aviation, Total 1.1741

2275050000 General Aviation, Total 1.0199

2275050011 General Aviation, Piston 1.0199

2275050012 General Aviation, Turbine 1.0199

2275060000 Air Taxi, Total 0.9417

2275060011 Air Taxi, Total: Air Taxi, Piston 0.9417

2275060012 A ir Taxi, Total: Air Taxi, Turbine 0.9417

2275070000 Commercial Aircraft: Aircraft Auxiliary Power Units, Total 1.1741

27501015 Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Military; Jet Engine:

JP

- 5 0.9972

27502011 Internal Combustio n Engines; Fixed Wing Aircraft L & TO Exhaust; Commercial; Jet

Engine: Jet A 1.1741

27505001 Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil; Piston

Engine: Aviation Gas 1.0199

27505011 Internal Combustion Engines; Fixed Wing Aircr aft L & TO Exhaust; Civil; Jet Engine:

Jet A 1.0199

Packet: “ PROJECTION_2011_2018_aircraft_by_airport_21nov2013.txt ”

The second set of projection factors was by airport, where EPA project ed emissions for each individual airport based on the following cr iteria: ITN activity in year 2011 are greater than 1000 for any of the four available modes: commercial, general aviation, military and air taxi;

ITN a irport matched to 2011NEI v1 ITN activity is not the same for 2011, 2018 and 2025 AND 2035. The rational here is that these ITN data add no value if 2011 ITN data are used for all future years. These airports were projected based

on the national default method. A hierarchal assignment was applied when the airport emissions in the NEI did not match the type o f ITN information. For example, if an airport in the 2011NEI v1 contained only general aviation emissions (based on NEI SCC), and the ITN data for that airport did not contain general aviation, then commercial aviation activity was used to project these em issions. There were 11 of 15 possible hierarchal assignments used in our projection methodology where EPA assigned a “fall

- back” ITN projection method to an NEI airport SCC, and most of these assignments were linked to very small

NEI emissions.

A summary of the national impact of airport

- specific and default national projection approaches for airports is provided in Table 4

- 41 . Most of the significant airports, and hence increased emissions, are projected via the airport

- specifi c projection packet. Overall, aircraft NO X emissions increase approximately 17% between

2011 and 2018.

Table 4

- 41

. Increases in aircraft emissions by year 2018 from airport

- specific and national

- level metho ds

CO NO X PM 2.5 SO 2 VOC

2011 NEI v1 total 456,212 111,575 7,362 12,563 29,687 Airport

- specific increases 34,334 18,897 420 2,024 2,950 National approach increases 2,254 137 31 12 64DRAFT

120 Total Increase in 2018 36,558 19,034 451 2,036 3,014



### 4.2.11 Remaining non

- EGU controls and closures (ptnonipm)

This section describes all remaining non

- EGU stationary source reductions and closures not already discussed. These CONTROL packets and CLOSURE packets generally have lesser national

- level impact on future year projection s than many of the items above. However, these impacts can be significant locally

– particularly plant closures.

4.2.11.1 Boat Manufacturing MACT (ptnonipm)

Packet: CONTROL_MACT_BoatManuf_2007v5_03aug2012.txt

EPA include d MACT rules where compliance dates were 20 11 or later. EPA OAQPS Sector Policies and Programs Division (SPPD) provided all controls information related to the MACT rules, and this information is as consistent as possible with the preamble emissions reduction percentages for these rules.

A 32% re duction to VOC and VOC BAFM HAPs was applied to the Boat Manufacturing SCCs in the ptnonipm inventory. Compliance with the MACT reduction is expected to occur by use of low HAP resins and gel coats and use of non

- atomized resin spray application systems. Documentation on this control is provided in the Guidance for Estimating VOC and NO X Emission Changes from MACT Rules document (EPA, 2007b). The national impact of these reductions is 411 tons of VOC.

4.2.11.2 CISWI/HWI controls (ptnonipm)

Packet: CONTROL_CISWI_2 011v6_22nov2013.txt

On March 21, 2011, EPA promulgated the revised NSPS and emission guidelines for Commercial and Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the standards

re

- development included the 5

- year technology review of the new source performance standards and emission guidelines required under Section 129 of the Clean Air Act (CAA). The histo ry of the CISWI implementation is documented here: http://www.epa.gov/ttn/atw/129/ciwi/ciwipg.html . Baseline and CISWI rule impacts associated with the CISWI rule are documented here: http://www.epa.gov/ttn/atw/129/ciwi/baseline_emission_reductions_memo.pdf

. EPA mapped the units from

the CISWI baseline and controlled dataset to the 2011 NEIv1 inventory and be cause the baseline CISWI

emissions and the 2011 NEIv1 emissions were not the same, EPA computed percent reductions such that our future year emissions matched the CISWI controlled dataset values. CISWI reductions limited to SO 2 reductions of 1,427 and 1,41 3 tons in Arkansas and Louisiana, respectively.

4.2.11.3 Remaining facility closures

Packets: “CLOSURES_EIS_2011NEIv1_sep2013_25nov2013_v1.txt” & “CLOSURES_2008_Merged_12nov2013_v0.txt”

This section describes two CLOSURE packets. The first “EIS” packet is from a September 11, 2013 Emissions Inventory System ( http://www.epa.gov/ttnchie1/eis/gateway/ ) report of post

- 2011 permanent facility shutdowns, based on facility status code “PS”. The second “Merged” packe t is from a concatenation of previous facility and unit

- level closure information used in the 2008 NEI

- based emissions modeling platform ( http://epa.gov/ttn/chief/e mch/2007v5/2007v5_2020base_EmisMod_TSD_13dec2012.pdf ). TheDRAFT

121 “EIS” closures impact facilities in 12 states while the “Merged” packet closures are spread out over 34 states. The cumulative reductions in emissions from this packet are shown in Table 4

- 42

.

Table 4

- 42

. Reductions from all EIS

- based and remaining information facility/unit

- level closures

Pollutant Reductions

CO 1,420

NH 3 441

NO X 3,117

PM 10 1,858

PM 2.5 1,613

SO 2 26,073

VOC 2,207



### 4.3 Mobile source projections

Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2018 using a

combination of the NMIM and the SMOKE

- MOVES models. The 2018 onroad emissions account for changes in activity dat a and the impact of on

- the

- books rules including: the Light

- Duty Vehicle Tier 2 Rule (EPA, 2000) , the 2007 Heavy Duty Diesel Rule ( http://www.epa.gov/otaq/highway

- diesel/ ) , the Mobile

Source Air Toxics (MSAT2) Rule (EPA, 2007a) , the Renewable Fuel Standard (RFS2) (EPA, 2010a) , the

LD GHG/CAFE standards for 2012

- 2016 (EPA, 2010c) , the Heavy

- Duty Vehicle Greenhouse Gas Rule (EPA, 2011a) , and the Tier 3 Notice of Proposed Rulemaking (NPRM) ( http://www.epa.gov/otaq/tier3.htm )

. Local inspection and maintenance (I/M) and other onroad mobile programs are included such as California
LEVIII, the National Low Emissions Vehicle (LEV) and Ozone Transport Commission (OT C) LEV regulations ( http://www.epa.gov/otaq/lev

- nlev.htm )

.

Nonroad mobile emissions reductions for these years include reductions to locomotives, various nonroad engines including diesel engines and vari ous marine engine types, fuel sulfur content, and evaporative

emissions standards.

Onroad mobile sources are comprised of several components and are discussed in the next subsection ( 4.3.1 ). Monthly nonroad mobile emission pr ojections are discussed in subsection 4.4 . Locomotives and Class 1 and Class 2 commercial marine vessel (C1/C2 CMV) projections are discussed in subsection 4.5 , and Class 3 (C3) CMV projected emiss ions are discussed in subsection 4.4.2

.



### 4.3.1 Onroad mobile (onroad and onroad_rfl)

The onroad emissions for 2018 use the same SMOKE

- MOVES system as for the base year (see Sections

2.3.1 and 2.3.2 ). Mete orology, speed, spatial and temporal surrogates, representative counties, and fuel

months were the same as for 20 11 , discussed above.

4.3.1.1 VMT and vehicle population

E stimate s of total national Vehicle Miles Travelled (VMT) in 20 18 came from DOE's Annual Energy Outlook (AEO) 201 3 ( http://www.eia.gov/forecasts/aeo/ ) transportation projections ( http://www.eia.gov/forecasts/aeo/sector_transportati on.cfm ), specifically the reference case (release dates April 15 th

- May 2 nd 2013). Trends were developed by calculating ratios between 2011 AEO and 2018 AEO estimates and renormalizing the trends so that a projection of the 2011NEIv1 VMT would match the A EO’s 2018 total VMT (across all vehicle types). These ratios were developed for light versus heavy duty and for gasoline versus diesel vehicle types. The projection factors, the national 2011NEIv1 VMT (“VMT_2011”) by vehicle type (SCC7), and the default future VMT (“VMT_2018) by vehicle type are show in Table 4

- 43

.DRAFT

122 Table 4

- 43

. Projection factors for 2018 VMT (in millions of miles)

Classification SCC7 Description VMT 2011 ratio VM T 2018
light_gas 2201001 Light Duty Gasoline Vehicles (LDGV) 1,595, 751 1.0226 1,631, 840
light_gas 2201020 Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5) 682, 930 1.0226 698, 375
light_gas 2201040 Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5) 351, 812 1.0226 359,768
heavy_gas 2201070 Heavy Duty Gasoline Vehicles 2B thru 8B & Buses (HDGV) 98, 334 1.1056 108,714
light_gas 2201080 Motorcycles (MC) 19, 744 1.0226 20,190
light_diesel 2230001 Light Duty Diesel Vehicles (LDDV) 4,764 3.8885 18,526
light_d iesel 2230060 Light Duty Diesel Trucks 1 thru 4 (M6) (LDDT) 13,389 3.8885 52,063
heavy_diesel 2230071 Heavy Duty Diesel Vehicles (HDDV) Class 2B 6, 080 1.2753 7,753
heavy_diesel 2230072 Heavy Duty Diesel Vehicles (HDDV) Class 3, 4, & 5 30, 625 1.2753 39,05 5
heavy_diesel 2230073 Heavy Duty Diesel Vehicles (HDDV) Class 6 & 7 48, 998 1.2753 62,486
heavy_diesel 2230074 Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B 131,503 1.2753 167,704
heavy_diesel 2230075 Heavy Duty Diesel Buses (School & Transit) 8,938 1 .2753 11, 399

These national SCC7 ratios were applied to the 2011NEIv1 VMT to create an EPA estimate of 2018 VMT at the county, SCC level. The following states/regional organizations provided projected VMT that supplanted EPA’s projected VMT: Connecticut , Georgia, Maine, Maryland 33 , Michigan, and SEMCOG. Michigan and SEMCOG provided county total VMT. For these estimates, EPA ’s 2018 SCC fraction by county (which takes into account the AEO growth by vehicle and fuel type) were used to distribute the organiz ation’s total VMT to SCC. For the rest of the states, the VMT was either in MOVES county database (CDB) format or was already distributed to SMOKE SCC (Georgia only 34 ). For these counties, EPA used the state supplied distribution of VMT to vehicle type an d road type (these distributions tended to match their 2011 submittals).

Vehicle population (VPOP) was developed by creating VMT/VPOP ratios from the 2011NEIv1 VMT and

2011NEIv1 VPOP at the county, vehicle type (SCC7) level. These ratios were applied to the 2018 VMT (including state updates) to create a 2018 VPOP. The one exception was Georgia that supplied VPOP for the

20 counties in the Atlanta area.

4.3.1.2 Set up and Run MOVES to create EF

Emission factor tables were created by running SMOKE

- MOVES using the same procedures and mo dels as

described for 2011 (see the 2011NEIv1 TSD and Section 2.3 ) . The same meteorology and the same representative counties were used. Changes between 2011 and 2018 are predominantly VMT

, fuels , national

and local rules, and the model

- year distribution of the fleet, which is built into MOVES. F leet turnover

result ed in a greater fraction of newer vehicles meeting stricter emission standards.

Similar to the 2011 onroad run, two MOVES runs were needed to model 2018: MOVES2010b was used for refueling and extended idle modes and Tier3NPRM (version of the MOVES model for the Tier 3 proposal)

was used for all other modes. The similarities and differences between the two runs are described in Table

4

- 44

.

33 Maryland’s CDBs were missing VMT projections for Howard County (FIPS 24027). This was discovered at a late date; therefore EPA estimates of VMT and VPOP were used for this one Mary land county.

34 Georgia supplied VMT for the 20 counties in the Atlanta area. For the rest of Georgia, EPA estimates were used.DRAFT

123 Table 4

- 44

. Comparison of MOVES runs for 2018

Element T3NPRM MOVES2010b

Code MOVES20110414a MOVES20120410

Default database movestier3db20110512 movesdb20121030

VMT and VPOP CDBs and state DBs for 26 states Same as t3nprm

Hydrocarbon speciation T3FRM2018_natinv_HCspec_SS_M Same as t3nprm

Fuels tier3frm_2018_09192013_NOE85forOAQPS Same as t3nprm

CA LEVIII ca_standards_SS_20130617 (16 states) Same as t3nprm

Tier 3 controls tier3ctldbs_060313 Default

The follow ing states were modeled as having adopted the California LEV III program (see Table 4

- 45 )

Table 4

- 45

. CA LEVIII program states

FIPS State Name

06 California

09 Connecticut

10 De laware

23 Maine

24 Maryland

33 New Hampshire

34 New Jersey

36 New York

41 Oregon

42 Pennsylvania

44 Rhode Island

50 Vermont

53 Washington

Fuels were projected into the future using estimates from the AEO2013 ( http://www.eia.gov/forecasts/aeo/ ), release dates April 15 th

- May 2 nd 2013. The AEO2013 projection includes partial implementation of RFS2 in 2018 and assumes that all fuels have an ethanol content of E10 or greater. The regional fu els in 2011 were projected to 2018 so that some of the regional variation is preserved but the totals match AEO2013. E85 fuels were removed from the database and the other fuels were appropriately adjusted because the T3NPRM

version of MOVES is unable to model E85 directly (see Section 3.2.1.2 for a description of how emissions were adjusted to account for E85). For details on the 2018 speciation of onroad, which is strongly dependent on the fuels, see Section 3.2.1.4

.

4.3.1.3 National, California , and Texas adjustments

A set of adjustments were done in SMOKE

- MOVES to create 2018 emissions: HDGHG and E85, extended idle, California emissions, and Texas emissions.

B ecause the version of MOVES used in the Tier 3 NPRM analysis does not have the capability to model Heavy

- Duty Vehicle Greenhouse Gas Rule ( HDGHG ) and E85, an interim version of MOVES incorporating updates for HDGHG and E85 was used to derive the adjustment factors. The medium and heavy dutyDRAFT

124 gr eenhouse gas program set in place by the rule begins with 2014 model year and increases in stringency through 2018. These changes primarily affect long haul diesel, but other heavy

- duty source types are also affected slightly. For additional details, see the documentation for HDGHG rulemaking. 35 The HDGHG rule was implemented in the interim version of MOVES through three key elements. They are: (a) revised running emission rates for total energy, (b) new aerodynamic coefficients and vehicle masses for us e in MOVES operating mode generation, (c) auxiliary power units (APUs) that largely replace extended idle in long haul trucks and are added to MOVES as a new process for combination long haul trucks. The affected

MOVES tables are “ EmissionRate ” and “ Sourc eUseTypePhysics ”.

EPA conducted a statistical analysis examining the effect of E85 on emissions in comparison to E10. The

dataset included 21 flex

- fuel vehicles that were tested on both E10 and E85 over LA92 drive cycle. The study found that there were no statistically significant difference in THC, NMOG, VOC, NO

x , CO and PM between E10 and E85 (α=0.05). For toxics, E85 showed a statistically significant decrease in emissions of
benzene and 1,3

- butadiene, and a statistically significant increase in emissions of acetaldehyde, formaldehyde, and ethanol. The results from the study were incorporated into the version of MOVES used to develop the adjustment factors by updating the “ GeneralFuelRatioExpression ” and “ HCSpecation ” tables in MOVES default database. The modeling of E85 also used updated “ AVFT ”, “ FuelFormul ation ”,

“ FuelSupply ”, and “ FuelUsageFraction ” tables, reflecting the renewable fuel volumes and market fractions projected by the Annual Energy Outlook 2013 (AEO2013) Report. 36

The two scenarios modeled to develop the adjustment factors were ‘pre

- HDGHG/wi thout E85’ and ‘post

HDGHG/with E85’. All input databases other than the ones related to HDGHG and E85, described above, were kept the same between the two runs. The emissions inventories for both scenarios were based on an inventory run at the national scale. MOVES was run separately for January and July in calendar year 2018 37

. These adjustment factors were national by vehicle type (SCC7), process, month, pollutant, and mode. These adjustments were only for the rate

- per

- distance (RPD) and rate

- per

- veh icle (RPV) processes 38

.

The second set of adjustment factors w as for extended idle. This uses the same approach as was used in 2011 (see the 2011NEIv1 TSD for details) except for the VPOP was updated to be consistent with 2018. These adjustments were by county, vehicle type (long

- haul truck SCCs only), and mode (extended idle only) and impacted the RPV process only.

The third set of adjustment factors w as meant to incorporate 2018 emissions provided by Cali fornia

. T he same approach as was used in 2011 w as used to match the emissions totals provided by CARB (see Section

2.3 .1 ). The o nly differences between the 2011 approach and that applied for 2018 are that the latter uses the

2018 emissions from CARB and the 2018 SMOKE

- MOVES output (EPA estimates) , whe re t he 2018

“ CARB emissions ” were cr eated by interpolating between the 2017 and 2020 CARB emissions. The provided CARB emissions were produced from working draft versions of EMFAC2011

- LD and

EMFAC2011

- HD and include the following heavy duty regulations: ch ip reflash, extended idling, public fleet, trash trucks, drayage trucks, and trucks and buses. It does not include the GHG/smartway regulations

35 Final Regulatory Impact Analysis , Chapter 5 (RIA) (PDF) (553 pp, EPA

- 420

- R

- 11

- 901, August 2011)

36 U.S. Energy Information Admi nistration, Annual Energy Outlook 2013 (April 15, 2013)

37 The January adjustment factors were used for all the winter fuel months and the July adjustment factors were used for all the

summer fuel months.

38 During QA of the adjustment factors, an error was discovered in extended idle for July and evaporative in RPD, RPV, and RPP. For extended idle, the adjustment factors were replaced with January’s adjustment factors. For evaporative, the adjustment f actors
were set to 1 for both months. These modificati ons to the adjustment factors are expected to have minimum impact on the 2018

emissions.DRAFT

125 for trucks, or the low carbon fuel standard. These adjustment factors are by county, SCC3, pollutant and impact all processes (RPD, RPV, RPP).

The fourth set of adjustment factors w as meant to incorporate emissions provided by Texas. Conceptually,

EPA used the trend of 2011 to 2018 based on EPA ’s estimates to project Texas’ submitted emissions for

2011. Mathemat ically, this is equivalent to taking the Texas adjustment factors derived for 2011 (see

Section 2.3.1 for details) and applying them directly to EPA ’s 2018 run. These adjustment factors are by county, SCC7, pollutant and impa ct all processes.

Because these adjustment factors are multiplicative, a single set of adjustment factors may be created by multiplying the 4 adjustment factors together taking care to match process (RPD, RPV, or RPP), mode, pollutant, SCC, and county. M ovesmrg uses the composite adjustment factor file (CFPRO) to estimate 2018 emissions that incorporates each of these adjustments (or a subset of them depending on county, mode, and process).



### 4.4 Nonroad mobile source projections (c1c2rail, c3marine, nonroad)

The projection of locomotives and Class 1 and 2 commercial marine vessels to 2018 is described in

Section 4.4.1. These sources are treated in shapes in the NEI but are considered at the county

- level in the modeling platform. The projection of the larger Class 3 commercial marine vessels, treated as point sources in the modeling platform, is described in Section 4.4.2. Most of the remaining sources in the nonroad sector are projected by running the NMIM model with fuels and vehicle populations appropriate to 2018, as

described in Section 4.4.3.



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

There are three distinct components used to craft year 2018 inventories from the 2011 base case. The first

component of the 2018 c1c2rail inventory i s the non

- California data projected from the 2011 base case. The second component is the CARB

- supplied year 2017 data for California. The third component is a set of EPA OTAQ

- provided county

- specific emissions adjustments that account for different fuel transport characteristics resulting from the EISA (RFS2) mandate. Specifically, these adjustments reduce finished petroleum

- based fuel transport by rail and barge (CMV) and add ethanol

- based finished fuel transport by rail and barge.

Step 1: Project no n

- California CMV and rail emissions

Packet: “PROJECTION_20 11 _20 18 _c1c2rail _BASE_noRFS2_05dec2013.txt ”

This packet creates an intermediate set of year 2018 emissions for all states except California. This packet does not reflect emission impacts from eth anol volume impacts from the EISA (RFS2) mandate; the EISA impacts are applied for all states in Step 3. This packet consists of national projection factors by SCC and pollutant between 2011 and 2018 that reflect the May 2004 “Tier 4 emissions standards a nd 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 )

. These projection ratios are provided in Table 4

- 46

.DRAFT

126 Table 4

- 46

. Non

- California year 2018 intermediate projection f actors for locomotives and Class 1 and

Class 2 Commercial Marine Vessel Emissions

SCC Description Pollutant Projection

Factor

2280002XXX Marine Vessels, Commercial;Diesel;Underway & port emissions CO 0.9525

2280002XXX Marine Vess els, Commercial;Diesel;Underway & port emissions NO X 0.7623

2280002XXX Marine Vessels, Commercial;Diesel;Underway & port emissions PM 0.6755

2280002XXX Marine Vessels, Commercial;Diesel;Underway & port emissions SO 2 0.1275

2280002XXX Marine Vessels, Com mercial;Diesel;Underway & port emissions VOC 0.7715

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations CO 1.175

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NO X 0.8123

2285002006 Railroad Equ ipment;Diesel;Line Haul Locomotives: Class I Operations PM 0.6764

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations SO 2 0.0319

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations VOC 0.6116

2285002 007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations CO 1.175

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NO X 1.0576

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 1.0241

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO 2 0.0319

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations VOC 1.1175

2285002008 Railroad Equipment;Di esel;Line Haul Locomotives: Passenger Trains (Amtrak) CO 1.0574

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NO X 0.6635

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 0.60 52

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO 2 0.0303

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) VOC 0.5316

2285002009 Railroad Equipment;Diesel;Line Haul Locomotiv es: Commuter Lines CO 1.0574

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NO X 0. 6635

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM 0. 6052

2285002009 Railroad Equipment;Diesel;Line Haul Locomo tives: Commuter Lines SO 2 0. 0303

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines VOC 0. 5316

2285002010 Railroad Equipment;Diesel;Yard Locomotives CO 1. 175

2285002010 Railroad Equipment;Diesel;Yard Locomotives NO X 0. 9767

22850 02010 Railroad Equipment;Diesel;Yard Locomotives PM 0. 9436

2285002010 Railroad Equipment;Diesel;Yard Locomotives SO 2 0.0 320

2285002010 Railroad Equipment;Diesel;Yard Locomotives VOC 0. 9388

The future

- year locomotive emissions account for increased fuel consumption based on Energy Information Administration (EIA) fuel consumption projections for freight rail, and emissions reductions resulting from

emissions standards from the Final Locomotive

- Marine rule (EPA, 2009d). This rule lowered diesel sulfur

co ntent 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.h tm#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 diesel emissions (SCC = 2280002100 and 2280002200) were p rojected based on the Final L ocomotive Marine rule national

- level factors

. Similar to locomotives, VOC HAPs were projected based on the VOC factor.

Step 2: Intermediate California year 2018 inventory

Obtained from CARB, the locomotive, and class 1 and 2 commercial marine emissions used for California reflect year 2017 and include nonroad rules reflected in the December 2010 Rulemaking InventoryDRAFT

127 ( http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf ), those in the March 2011 Rule Inventory,

the Off

- Road Construction Rule Inventory for “In

- Use Diesel”, cargo handling equipment rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/car go.htm ), and the 2007 and 2010 regulations to reduce emissions diesel engines on commercial harbor craft operated within California waters and 24 nautical miles

of the California baseline.

The C1/C2 CMV emissions were obtained from the CARB nonroad mob ile 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_TRA INS.txt ”. Documentation of the CARB offroad methodology, including c1c2rail sector data, is provided here: http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles

. EPA converted the CARB inventory TOG to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor.

Step 3: A djusting intermediate 2018 c1c2rail emissions to reflect th e EISA mandate

R ail and barges are used to transport ethanol from production facilities to bulk terminal s. To account for emissions associated with this transport, 2022 RFS2 rule rail impacts were adjusted to account for

differences in ethanol volumes and locomotive emission rates between 2007 and 2018

. T here is only a small difference, not quantified, between ethanol volumes in 2007 and 2011. Emission factors used to make adjustments were obtained from an EPA locomotive emission factor fact sheet (EPA, 2009 e ).

In EPA ’s RFS2 final rule, impacts of these modes of transport of ethanol on combustion emissions from the

C1 and C2 CMV and rail inventor ies were estimated for 20 18 , based AEO 2013 projections on the difference between ethanol volumes mandated by EISA versus RFS1 rule volumes (EPA, 2010a). RFS2 rule imp acts were adjusted to account for (a) differences in rail and barges emission rates in 2018 versus 2022 , and (b) the

difference in e thanol volume impacts for 2018 under EISA versus the 8.7 billion gallons assumed for the intermediate 2018 inventory . Emiss ion factors used to make these adjustments were obtained from analyses done to support the 2010 Category 3 Marine Diesel Rule (EPA, 2009f). The adjusted national inventory impacts were allocated to individual counties using factors developed from the Oak Ridge analysis of ethanol transport (Oak Ridge National Laboratory, 2009)

. These impacts were th en applied to the unadjusted inventory.

These emissions from updated ethanol volumes are not included in the previously

- discussed non

- California

loco

- marine r ule

- based projections (Step 1) and CARB 2017 inventory (Step 2). Nationally, these additional

emissions are modest and are shown in Table 4

- 47 . The overall difference between 2011 and 2018 c1c2rail sector emissions are provided in Table 4

- 48 . These sector totals include all U.S. states as well as offshore

and Puerto Rico.

Table 4

- 47

. EISA mandate emission adjustments in 2018

Pollutant C1/C2 CMV Locomotives

CO

- 855 1,715

NH 3 - 2 5

NO X - 3,635 8,346

PM 10 - 139 198

PM 2.5 - 155

- 10

SO 2 - 296 80

VOC

- 136 357DRAFT

128 Table 4

- 48

. Difference in c1c2rail sector emissions between 2011 and 2018

Pollutant 2011 2018 Difference

CO 242,771 255,496

- 12,725

NH 3 707 712 5

NO X 1,392,532 1,129,284

- 263,248

PM 10 46,142 31,963

- 14,179

PM 2.5 43,491 29,893

- 13,598

SO 2 23,160 3,161

- 19,999

VOC 56,543 33,334

- 23,209



### 4.4.2 Class 3 commercial marine vessels (c3marine)

As discussed in Sectio n 2.4.2 , t he c3marine sector emissions data were developed for year 2002 and projected to year 2011 for the 2011 base case. The ECA

- IMO project provides pollutant and geographic

specific projection factors to year 2011, and al so projection factors to year 2018 that reflect assumed growth

and final ECA

- IMO controls. The ECA

- IMO rule, published in December 2009, applies to Category 3 (C3) diesel engines (engines with per cylinder displacement at or above 30 liters) installed on U.S. vessels. The

ECA

- IMO rule includes an implementation of Tier 2 and Tier 3 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 w ithin 200 nautical miles of the U.S. and Canadian coastlines, as well as 5,000 ppm (0.5%) for “global” areas

– those areas outside the ECA region. For comparison, with the exception of some local areas, year 2011 sulfur content limits are as high as 15,000 ppm (1.5%) in U.S. waters and 45,000 ppm (4.5%) in global areas. More information on the ECA

- IMO rule can be found in the Category 3 marine diesel engines Regulatory Impact Assessment: http://www.epa. gov/otaq/oceanvessels.htm

.

Projection factors for creating the year 2018 c3marine inventory from the 2011 base case are provided in

Table 4

- 49 . Background on the region and Exclusive Economic Zone (EEZ) FIPS is provided in th e discussion on the c3marine inventory for 2011

– Section 2.4.2 . 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

- 49

. Growth factors to project the 20 11 ECA

- IMO inventory to 20 18

2 018 Adjustments Relative to 2011

Region EEZ

FIPS NO X PM 10 PM 2.5 VOC (HC) CO SO 2 East Coast (EC) 85004 1.06 8 0.556 0.556 1.361 1.361 0.136 Gulf Coast (GC) 85003 0.960 0.504 0.504 1.222 1.222 0.122 North Pacific (NP) 85001 1.014 0.501 0.501 1.255 1.255 0.126 South Pacific (SP) 85002 1.121 0.593 0.593 1.421 1.420 0.144 Great Lakes (GL) n/a 1.027 0.444 0.444 1 .125 1.125 0.113

Outside ECA 98001 1.217 1.356 1.356 1.356 1.356 1.356

As discussed in Section 2.4.2, e missions outside the 3 to 10 mile coastal boundary but within the approximately 200 nm EEZ boundaries were projected to year 2018 using the same regio nal adjustment factors as the U.S. emissions; however, the FIPS codes were assigned as “EEZ” FIPS and these, as well as Canada C3 CMV, emissions are processed in the “othpt” sector (see Section 2.5.1 and 4.4.1). Note that stateDRAFT

129 boundaries in the Great Lak es are an exception, extending through the middle of each lake such that all emissions in the Great Lakes are assigned to a U.S. county or Ontario. The classification of emissions to

U.S. and Canadian FIPS codes is needed to avoid double

- counting of C3 CM V U.S. emissions in the Great Lakes because, as discussed in Section 2.4.1, all CMV emissions in the Midwest RPO are processed in the

“c1c2rail” sector.



### 4.4.3 Other nonroad mobile sources (nonroad)

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

and Texas

. Similar to the onroad emissions, NMIM provides nonroad emissions for VOC by three emission

modes: exh au st, evaporative and refueling.

With the exception of California and Texas , U.S. emissions for the nonroad sector (defined as the equipment types covered by the NONROAD model ) were created using a consistent NMIM

- bas ed approach as was

used for 2011. Spe cifically, NMIM utilized NONROAD2008a including future

- year equipment population estimates, control programs to the year 2018, and inputs either state

- supplied as part of the 2011NEIv1 process or national level inputs

. Fuels for 2018 were assumed to be E10 everywhere for nonroad equipment. The fuels were developed from the MOVES fuels, which in turn were developed to be consistent with AEO2013 projections for 2018. The databases used in this run were NMIM county database

“ NCD20130731_nei2018dv1 ” and fuels database “ tier3frm2018ctrlfuels_03152013_ e10fuelsNMIM .” The

2018 emissions account for increases in activity (based on NONROAD model default growth estimates of

future

- year equipment population )

, changes in fuels and engines that reflect implementation of national regulations and local control programs that impact each year differently due to engine turnover

. For details on the 2018 speciation of nonroad, see Section 3.2.1.4

.

The version of NONROAD used was the current publ ic release, NR08a, which models all in

- force nonroad

controls. Recent rules include:

“Clean Air Nonroad Diesel Final Rule

- Tier 4” , published June, 2004 : http://www.epa.gov/otaq/nonroad

- diesel.htm Control of Emissions from Nonroad Large Spark

- Ignition Engines, and Recreational Engines (Marine

and Land

- Based), November 8, 2002 (“Pentathalon Rule”). OTAQ’s Small Engine Spark Ignition (“Bond”) Rule, October, 2008: http://www.epa.gov/otaq/smallsi.htm

Not included are volu ntary local programs such as e ncouraging either no refueling or evening refueling on Ozone Action Days

.

California and Texas nonroad emissions

Similar to the 2011 base nonroad mobile, NMIM was not used to generate future

- year nonroad emissions for California, other than for 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 pollutant s, the CARB

supplied 2017 nonroad annual inventory were distributed to monthly emissions values by using the 2018 NMIM monthly inventories to compute monthly ratios by county, SCC7, mode and pollutant, which was consistent with the approach in 2011 (see Se ction 2.4.3 ) . Some adjustments to the CARB inventory were needed to convert the provided TOG to VOC. See Section 3 .2.1.3 for details on speciation of California

nonroad data see Section 3.2.1.3 ) 39

. . The CARB nonroad emissions include nonroad rules reflected in the

39 In addition, airport equipment was removed from CARB’s inventory because these sources were modeled elsewhere.DRAFT

130 December 2010 Rulemaking Inventory ( http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf ) and those in the March 2011 Rule Inventory, the Off

- Road Construction Rule Inventory for “In

- Use Diesel”.

For Texas, EPA combined Texas’ submitted estimates for 2011 with EPA projections of nonroad emissions into 2018. Specifically, projections were based on state

- wide SCC7, mode, poll ratios 40 of 2018 NMIM to 2011 NMIM. These ratios were then applied to Texas’ submitted 2011 emissions inventory, which had already been distributed to a monthly inventory (see Section 2.4.3 ), to create a 2018 monthly nonroad inventory.



### 4.5 “Other Emis s ions”: Offshore Class 3 commercial marine vessels and drilling platforms, Canada and Mexico (othpt, othar, and othon)

Recall from Section 2.5, that emissions from Canada, Mexico, and non

- U.S. offshore Class 3 Commercial Marine Vessels (C3 CMV) and drilling platforms are included as part of three emissions modeling sectors: othpt, othar, and othon. Non C3 CMV emissions for Canada and offshore sources were not projected to future ye ars, and are therefore the same as those used in the 2011 base case. Canada did not provide future

year emissions that were consistent with the base year emissions. The Mexico emissions are based on year 1999 but projected to year 2018. A background on the development of year

- 2018 Mexico emissions from the 1999 inventory is available at: http://www.wrapair.org/forums/ef/inventories/MNEI/index.html

.



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

As discussed in Section 2.5.1, the ECA

- IMO

- based C3 CMV emissions for non

- U.S. states are processed in the othpt sector. These C3 CMV emissions include those assigned to Canada, those assigned to t he Exclusive Economic Zone (defined as those emissions just beyond U.S. waters approximately 3

- 10 miles offshore, extending to about 200 nautical miles from the U.S. coastline), and all other offshore emissions

– far offshore and non

- U.S. EPA process ed the se emissions in the othpt sector for simplicity of creating U.S.

- only emissions summaries. Otherwise, these emissions are processed in the same way as the U.S. C3 CMV emissions in the c3marine sector. The projection factors for the othpt C3 CMV emissions vary by geographic and region as shown in Table 4

- c3. C3 CMV emissions in British Columbia were assigned as North Pacific, Ontario as Great Lakes, and all other eastern Canada provinces as East Coast.

Mexico point

- format year

- 2018 inventories are used e ssentially as

- is with only minor formatting changes. The othpt sector also includes point source offshore oil and gas drilling platforms that are beyond U.S. state

county boundaries in the Gulf of Mexico. EPA used emissions from the 2008NEI v 2 point sourc e inventory

for both 2011 and 2018. EPA expect s updated offshore oil and gas drilling emissions in the next version of the 2011 NEI (Version 2).



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

Both year

- 2006 Canada an d year

- 2018 Mexico inventories were converted from their original SMOKE One

Record per Line (ORL) and Inventory Data Analyzer (IDA) formats, respectively, to SMOKE Flat File 10 (FF10) inventory format: http://www.cmascenter.org/smoke/documentation/3.5/html/ch08s02s07.html

. Otherwise, these inventories were used as

- is.

40 These ratios were initially attempted by county/SCC7 /mode/pollutant, but due to significantly different distributions of certain source types between EPA and TCEQ’s emissions, this created unreasonable growth in certain areas. The above approach was used except in the following, relatively limited conditio ns. If a state/SCC7/mode/pollutant was in EPA 2018 emissions but not in EPA’s 2011 emissions, 2018 EPA emissions were used in the final inventory. If a state/SCC7/mode/pollutant was in TCEQ’s 2011 emissions but was not in EPA’s 2018 emissions, then state/ SCC3/mode/pollutant ratios were used to project to 2018.DRAFT

131 

## 5 Emission Summaries

The following tables summarize emissi ons differences between the 2011 evaluation case and the 2018 base case. These summaries are provided at the national level by sector for the contiguous U.S. and for the portions of Canada and Mexico inside the smaller 12km domain (12US2) discussed in Section 3.1 . The

afdu st sector emissions represent the summaries after application of both the land use (transport fraction) and meteorological adjustments (see Section 2.2.1 ); therefore, this sector is called “afdust_ adj” in these

summaries. The onroad and onroad refueling (onroad_rfl) sector totals are post

- SMOKE

- MOVES totals, representing air quality model

- ready emission totals, and the onroad portion include CARB emissions for

California. The “c3marine

- US” sector represents c3marine sector emi ssions with U.S. FIPS only; these extend to roughly 3

- 5 miles offshore and all U.S. waters in the Great Lakes and also include all U.S. ports. The “c3marine, EEZ component” represents all non

- U.S. c3marine emissions that are within the (up to) 200

nautica l mile Exclusive Economic Zone (EEZ) boundary but outside of U.S. state waters. Finally, the “c3marine, non

- US non

- EEZ component” represents all non

- U.S. emissions outside of the (up to) 200nm offshore boundary, including all Canadian and Mexican c3marine emissions. The c3marine sector is

discussed in Section 2.4.2 . The “Off

- shore othpt” sector is the non

- Canada, no

- Mexico component of the othpt sector

– the offshore oil platform emissions from the 2008 NEI.

National emission totals by air quality model

- ready sector are provided fo r all CAP emissions for the 2011

evaluation case in Table 5

- 1

. Th e total of all sectors in the 2011 evaluation case are listed as “Con U.S. Total w/ ptfire”. Table 5

- 2 provides national emissions totals by sector for all C APs in the 2018 base case.

Table 5

- 3 provides national

- by sector emission summaries for CO for all the cases: 2011 evaluation case and

2018 base case. Table 5

- 4

, Table 5

- 5

, Table 5

- 6

, Table 5

- 7

, Table 5

- 8 and Table 5

- 9 provide the same

summaries for NH 3 , NO X , PM 2.5 , PM 10 , SO 2 and VOC, respectively. These national tables also include differences and percent differences for each modeling sector between the 2011 evaluation case and the 2018

base case. Note that the same ptfire emissions are used in bot h ca s es

.DRAFT

132 Table 5

- 1

. National by

- sector CAP emissions summaries for 2011 evaluation case

Sector CO NH3 NOX PM10 PM2_5 SO2 VOC
afdust_adj 18,502,317 2,487,403

ag 3,517,371

c1c2rail 1 73,347 484 1,051,169 34,865 32,557 18,975 48,818

c3marine 13,403 131,301 4,384 3,992 40,692 5,110

nonpt 3,046,375 142,323 832,166 715,709 533,248 392,638 3,792,612
np_oilgas 642,182 0 653,219 21,756 17,200 17,195 2,273,214

nonroad 13,952,389 2,627 1,6 30,409 162,420 154,660 4,031 2,024,633

onroad 25,473,866 118,124 5,666,702 285,112 205,145 27,915 2,287,603

onroad_rfl 157,629
ptfire 22,580,113 362,910 347,103 2,362,132 2,005,142 177,107 5,174,593

ptegu 724,444 21,944 1,990,884 266,633 193,877 4,614,299 32,376
ptegu_pk 8,662 425 21,941 2,159 1,886 28,476 783

ptnonipm 2,568,080 74,847 1,771,835 494,639 339,240 1,071,982 873,159
pt_oilgas 22,218 112 22,091 1,887 1,857 55,273 89,755

rwc 2,578,229 20,343 35,672 389,019 388,288 8,986 446,972

Con U.S. Total 71,783,308 4,261,510 14,154,492 23,243,031 6,364,495 6,457,569 17,207,256

Off

- shore to EEZ* 130,419 0 610,664 16,961 15,525 133,606 81,286

Non

- US SECA C3 17,169 0 202,516 17,199 15,823 127,563 7,297

Canada othar 2,810,350 386,147 462,996 810

, 747 248,907 61, 179 932,322

Canada othon 3,303,239 17,572 392,209 11,075 7,712 4,046 199,939 Canada othpt ** 560,661 15,543 369,993 65,782 39,828 825,675 157,170

Mexico othar 439,901 109,861 189,592 69,523 23,600 26,559 499,145

Mexico othon 423,978 3,24 7 76,880 7,593 6,970 1,413 73,888 Mexico othpt 116,609 0 414,399 137,512 101,884 828,418 83,838

Non

- US Total 7,802,326 532,370 2,719,249 1,136,392 460,249 2,008,459 2,034,885 * “Offshore to EEZ” includes both the offshore point emissions, and the “Offsh ore to EEZ” c3marine emissions ** Canadian c3 emissions are included in “Canada othpt”DRAFT

133 Table 5

- 2

. National by

- sector CAP emissions summaries for 2018 base case

Sector CO NH3 NOX PM10 PM2_5 SO2 VOC
afdust_ adj 6,741,452 923,168

ag 3,596,908

c1c2rail 189,355 489 869,089 24,346 22,508 2,628 33,334

c3marine 17,518 136,147 2,338 2,129 5,354 6,678

nonpt 3,058,148 142,384 847,975 720,106 536,477 304,514 3,634,506 np_oilgas 782,408 0 795,491 27,248 21,565 25,488 2,555,021

nonroad 12,377,375 2,900 1,071,612 107,005 100,949 1,868 1,360,554

onroad 15,469,627 86,826 2,647,482 208,598 125,173 12,418 1,133,928
onroad_rfl 74,386 ptfire 22,580,113 362,910 347,103 2,362,132 2,005,142 177,107 5,174, 593

ptegu 752,467 39,629 1,486,128 256,679 199,186 1,443,777 39,227 ptegu_pk 11,249 439 9,954 247 215 3,432 313 ptnonipm 2,419,986 75,822 1,768,859 466,566 316,328 720,681 870,202 pt_oilgas 25,493 159 25,970 2,062 2,026 64,076 106,345

rwc 2,736,854 21 ,485 38,434 413,597 412,852 10,018 466,259

Con U.S. Total 60,420,596 4,329,951 10,044,245 11,332,376 4,667,720 2,771,361 15,455,347

Off

- shore to EEZ* 146,323 635,570 9,630 8,841 18,746 88,045

Non

- US SECA C3 23,318 246,579 23,327 21,462 173,124 9,896

Canada othar 2,810,350 386,147 462,996 810,747 248,907 61,179 932,322

Canada othon 3,303,239 17,572 392,209 11,075 7,712 4,046 199,939 Canada othpt 56 1,438 15,543 370,944 65,276 39,370 818,374 157,501

Mexico othar 527,917 109,840 226,341 70,916 47,191 19,286 577,078

Mexico othon 397,197 4,465 46,794 9,420 8,591 659 62,948 Mexico othpt 148,758 544,690 170,910 127,734 1,066,482 94,351

Non

- US Total 7,918,540 533,567 2,926,123 1,171,301 509,808 2,161,896 2,122,080DRAFT

134 Table 5

- 3

. National by

- sector CO emissions (tons/yr) summaries with differences

Sector 2011 CO 2018 CO 2018

- 2011 % Change

afdust_ adj

ag

c1c2rail 173,347 189,355 16,008 9%

c3marine

- US 13,403 17,518 4,115 31%

nonpt 3,046,37 5 3,058,148 11,773 0%

np_oilgas 642,182 782,408 140,226 22%

nonroad 13,952,389 12,377,375

- 1,575,014

- 11%

onroad 25,473,866 15,469,627

- 10,004,239

- 39%

onroad_rfl

ptfire 22,580,113 22,580,113 0 0%

ptegu 724,444 752,467 28,023 4%

ptegu_pk 8,6 62 11,249 2,587 30%

ptnonipm 2,568,080 2,419,986

- 148,094

- 6%

pt_oilgas 22,218 25,493 3,275 15%

rwc 2,578,229 2,736,854 158,625 6%

Con U.S. Total 71,783,308 60,420,593

- 11,362,715

- 16%

Off

- shore to EEZ* 130,419 146,323 15,904 12%

Non

- US SECA C3 17,1 69 23,318 6,149 36%

Canada othar 2,810,350 2,810,350 0 0%

Canada othon 3,303,239 3,303,239 0 0%

Canada othpt** 560,661 561,438 777 0%

Mexico othar 439,901 527,917 88,016 20%

Mexico othon 423,978 397,197

- 26,781

- 6%

Mexico othpt 116,609 148,758 32,149 28%

Non

- US Total 7,802,326 7,918,540 116,214 1%DRAFT

135 Table 5

- 4

. National by

- sector NH 3 emissions (tons/yr) summaries with differences

Sector 2011 NH 3 2018 NH 3 2018

- 2011 % Change

afdust_ adj

ag 3,51 7,371 3,596,908 79,537 2%

c1c2rail 484 489 5 1%

c3marine

- US

nonpt 142,323 142,384 61 0%

np_oilgas

nonroad 2,627 2,900 273 10%

onroad 118,124 86,826

- 31,298

- 26%

onroad_rfl

ptfire 362,910 362,910 0 0%

ptegu 21,944 39,629 17, 685 81%

ptegu_pk 425 439 14 3%

ptnonipm 74,847 75,822 975 1%

pt_oilgas 112 159 47 42%

rwc 20,343 21,485 1,142 6%

Con U.S. Total 4,261,510 4,329,951 68,441 2%

Off

- shore to EEZ*

Non

- US SECA C3

Canada othar 386,147 386,147 0 0%

Canad a othon 17,572 17,572 0 0%

Canada othpt** 15,543 15,543 0 0%

Mexico othar 109,861 109,840

- 21 0%

Mexico othon 3,247 4,465 1,218 38%

Mexico othpt

Non

- US Total 532,370 533,567 1,197 0%DRAFT

136 Table 5

- 5

. National by

- sector NO X emissions (tons/yr) summaries with differences

Sector 2011 NO X 2018 NO X 2018

- 2011 % Change

afdust_ adj

ag

c1c2rail 1,051,169 869,089

- 182,080

- 17%

c3marine

- US 131,301 136,147 4,846 4%

nonpt 832,166 847,975 15, 809 2%

np_oilgas 653,219 795,491 142,272 22%

nonroad 1,630,409 1,071,612

- 558,797

- 34%

onroad 5,666,702 2,647,482

- 3,019,220

- 53%

onroad_rfl

ptfire 347,103 347,103 0 0%

ptegu 1,990,884 1,486,128

- 504,756

- 25%

ptegu_pk 21,941 9,954

- 11,987

- 5 5%

ptnonipm 1,771,835 1,768,859

- 2,976 0%

pt_oilgas 22,091 25,970 3,879 18%

rwc 35,672 38,434 2,762 8%

Con U.S. Total 14,154,492 10,044,244

- 4,110,248

- 29%

Off

- shore to EEZ* 610,664 635,570 24,906 4%

Non

- US SECA C3 202,516 246,579 44,063 22%

Canada othar 462,996 462,996 0 0%

Canada othon 392,209 392,209 0 0%

Canada othpt** 369,993 370,944 951 0%

Mexico othar 189,592 226,341 36,749 19%

Mexico othon 76,880 46,794

- 30,086

- 39%

Mexico othpt 414,399 544,690 130,291 31%

Non

- US Total 2,719,249 2,926, 123 206,874 8%DRAFT

137 Table 5

- 6

. National by

- sector PM 2.5 emissions (tons/yr) summaries with differences

Sector 2011 PM 2.5 2018 PM 2.5 2018

- 2011 % Change

afdust_ adj 913,525 923,168 9,643 1%

ag

c1c2rai l 32,557 22,508

- 10,049

- 31%

c3marine

- US 3,992 2,129

- 1,863

- 47%

nonpt 533,248 536,477 3,229 1%

np_oilgas 17,200 21,565 4,365 25%

nonroad 154,660 100,949

- 53,711

- 35%

onroad 205,145 125,173

- 79,972

- 39%

onroad_rfl

ptfire 2,005,142 2,005,142 0 0%

ptegu 188,811 199,186 10,375 5%

ptegu_pk 1,886 215

- 1,671

- 89%

ptnonipm 339,240 316,328

- 22,912

- 7%

pt_oilgas 1,857 2,026 169 9%

rwc 388,288 412,852 24,564 6%

Con U.S. Total 4,785,551 4,667,718

- 117,833

- 2%

Off

- shore to EEZ* 15,525 8,841

- 6,68 4

- 43%

Non

- US SECA C3 15,823 21,462 5,639 36%

Canada othar 248,907 248,907 0 0%

Canada othon 7,712 7,712 0 0%

Canada othpt** 39,828 39,370

- 458

- 1%

Mexico othar 23,600 47,191 23,591 100%

Mexico othon 6,970 8,591 1,621 23%

Mexico othpt 101,884 127,73 4 25,850 25%

Non

- US Total 460,249 509,808 49,559 11%DRAFT

138 Table 5

- 7

. National by

- sector PM 10 emissions (tons/yr) summaries with differences

Sector 2011 PM 10 2018 PM 10 2018

- 2011 % Change

afdust_ adj 6,663,357 6,741,452 78,095 1%

ag

c1c2rail 34,865 24,346

- 10,519

- 30%

c3marine

- US 4,384 2,338

- 2,046

- 47%

nonpt 715,709 720,106 4,397 1%

np_oilgas 21,756 27,248 5,492 25%

nonroad 162,420 107,005

- 55,415

- 34%

onroad 285,112 208,598

- 76,514

- 27%

onroad _rfl

ptfire 2,362,132 2,362,132 0 0%

ptegu 266,633 256,679

- 9,954

- 4%

ptegu_pk 2,159 247

- 1,912

- 89%

ptnonipm 494,639 466,566

- 28,073

- 6%

pt_oilgas 1,887 2,062 175 9%

rwc 389,019 413,597 24,578 6%

Con U.S. Total 11,404,072 11,332,376

- 71,69 6

- 1%

Off

- shore to EEZ* 16,961 9,630

- 7,331

- 43%

Non

- US SECA C3 17,199 23,327 6,128 36%

Canada othar 810,747 810,747 0 0%

Canada othon 11,075 11,075 0 0%

Canada othpt** 65,782 65,276

- 506

- 1%

Mexico othar 69,523 70,916 1,393 2%

Mexico othon 7,593 9, 420 1,827 24%

Mexico othpt 137,512 170,910 33,398 24%DRAFT

139 Table 5

- 8

. National by

- sector SO 2 emissions (tons/yr) summaries with differences

Sector 2011 SO 2 2018 SO 2 2018

- 2011 % Change

afdust_ adj

ag

c1c2rail 18,975 2,628

- 16,347

- 86%

c3marine

- US 40,692 5,354

- 35,338

- 87%

nonpt 392,638 304,514

- 88,124

- 22%

np_oilgas 17,195 25,488 8,293 48%

nonroad 4,031 1,868

- 2,163

- 54%

onroad 27,915 12,418

- 15,497

- 56%

onroad_rfl

ptfire 177,1 07 177,107 0 0%

ptegu 4,614,299 1,443,777

- 3,170,522

- 69%

ptegu_pk 28,476 3,432

- 25,044

- 88%

ptnonipm 1,071,982 720,681

- 351,301

- 33%

pt_oilgas 55,273 64,076 8,803 16%

rwc 8,986 10,018 1,032 11%

Con U.S. Total 6,457,569 2,771,361

- 3,686,208

- 57%

Of f

- shore to EEZ* 133,606 18,746

- 114,860

- 86%

Non

- US SECA C3 127,563 173,124 45,561 36%

Canada othar 61,179 61,179 0 0%

Canada othon 4,046 4,046 0 0%

Canada othpt** 825,675 818,374

- 7,301

- 1%

Mexico othar 26,559 19,286

- 7,273

- 27%

Mexico othon 1,413 6 59

- 754

- 53%

Mexico othpt 828,418 1,066,482 238,064 29%

Non

- US Total 2,008,459 2,161,896 153,437 8%DRAFT

140 Table 5

- 9

. National by

- sector VOC emissions (tons/yr) summaries with differences

Sector 2011 VOC 2018 VOC 2018

- 2011 % Change

afdust_ adj

ag

c1c2rail 48,818 33,334

- 15,484

- 32%

c3marine

- US 5,110 6,678 1,568 31%

nonpt 3,792,612 3,634,506

- 158,106

- 4%

np_oilgas 2,273,214 2,555,021 281,807 12%

nonroad 2,024,633 1,360,554

- 664,079

- 33%

o nroad 2,287,603 1,133,928

- 1,153,675

- 50%

onroad_rfl 157,629 74,386

ptfire 5,174,593 5,174,593 0 0%

ptegu 32,376 39,227 6,851 21%

ptegu_pk 783 313

- 470

- 60%

ptnonipm 873,159 870,202

- 2,957 0%

pt_oilgas 89,755 106,345 16,590 18%

rwc 446,972 466,2 59 19,287 4%

Con U.S. Total 17,207,257 15,455,346

- 1,751,911

- 10%

Off

- shore to EEZ* 81,286 88,045 6,759 8%

Non

- US SECA C3 7,297 9,896 2,599 36%

Canada othar 932,322 932,322 0 0%

Canada othon 199,939 199,939 0 0%

Canada othpt** 157,170 157,501 331 0%

Mexico othar 499,145 577,078 77,933 16%

Mexico othon 73,888 62,948

- 10,940

- 15%

Mexico othpt 83,838 94,351 10,513 13%

Non

- US Total 2,034,885 2,122,080 87,195 4%DRAFT

141 

## 6 References

Adelman, Z. 2012. Memorandum: Fugitive Dust Modeling for the 2008 Emission s Modeling Platform

. UNC Institute for the Environment, Chapel Hill, NC. September, 28, 2012.

Anderson, G.K.; Sandberg, D.V; Norheim, R.A., 2004. Fire Emission Production Simulator (FEPS) User's

Guide. Available at http://www.fs.fed.us/pnw/fera/feps/FEPS_users_guide.pdf

ARB, 2000. “Risk Reduction Plan to Reduce Particulate Matter Emissions from Diesel

- Fueled Engines and Vehicles”. California Environmental Protection Agency Air Resources Board, Mobile Source Control Division, Sacramento, CA. October, 2000. Available at: http://www.arb.ca.gov/diesel/documents/rrpFinal.pdf

.

ARB, 2007. “Proposed Regulation for In

- Use Off

- Ro ad Diesel Vehicles”. California Environmental Protection Agency Air Resources Board, Mobile Source Control Division, Sacramento, CA. April,

2007. Available at: http://www.arb.ca.gov/regac t/2007/ordiesl07/isor.pdf

ARB, 2010a. “Proposed Amendments to the Regulation for In

- Use Off

- Road Diesel

- Fueled Fleets and the

Off

- Road Large Spark

- Ignition Fleet Requirements”. California Environmental Protection Agency
Air Resources Board, Mobile Sourc e Control Division, Sacramento, CA. October, 2010. Available

at: http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf

.

ARB, 2010b. “Estimate of Premature Deaths Assoc iated with Fine Particle Pollution (PM2.5) in California Using a U.S. Environmental Protection Agency Methodology”. California Environmental Protection Agency Air Resources Board, Mobile Source Control Division, Sacramento, CA. August, 2010.

Available a t: http://www.arb.ca.gov/research/health/pm

- mort/pm

- report_2010.pdf

.

BEA, 2012. “ 2013 Global Outlook projections prepared by the Conference Board in November 2012 ”. U.S.

Bureau of Economic Analysis . Available from: http://www.conference

board.org/data/globaloutlook.cfm

.

Bullock Jr., R, and K. A. Brehme (2002) “Atmospheric mercury simulation using the CMAQ model : formulation description and analysis of wet deposition results.” Atmospheric Environment 36, pp

2135

– 2146.

Department of Energy, 2012 Annual Energy Outlook 2012, Early Release. Report No. DOE/EIA

0383(2012), June 2012. Available at: http://www.eia.gov/forecasts/aeo/

ERG, 2006. Mexico National Emissions Inventory, 1999: Final , prepared by Eastern Research Group for Secratariat of the Environment and Natural Resources and the National Institute of Ecology , Mexico, October 11, 2006. Available at: http://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_report.pdf

ERG, 2009. Development of Mexico National Emissions I nventory Projecions for 2008, 2012, and 2023.

Available at: http://www.azdeq.gov/environ/air/plan/download/dmneip.pdf

. Energy Information Administration. 2006. Annual Energy Outlook 2 006. Report #:DOE/EIA

- 0383(2006)

Available at: http://www.eia.gov/oiaf/archive/aeo06/aeoref_tab.html

.DRAFT

142 Energy Information Administration. 2010. Annual Energy Outlook 2011 Early Release Overview. U.S. Department of Energy, December 2010. Report # DOE/EIA

- 0383ER (2011). Available at: http://www.eia.doe.gov/forecasts/aeo/tables_ref.cfm

Environ Corp. 2008. Emission Profiles for EPA SPECIATE Database, Part 2: EPAct Fuels (Evaporative Emissions). Prepared for U. S. EPA, Office of Transportation and Air Quality, September 30, 2008.

EPA, 1994. Onboard Refueling Vapor Recovery for Motor Vehicles, Fact Sheet. April, 1994. Avail able at: http://www.epa.gov/oms/regs/ld

- hwy/onboard/orvrfact.txt

EPA, 2000. Light

- Duty Vehicle, Light

- Duty Truck, and Medium

- Duty Passenger Vehicle Tier 2 Exhaust

Emission Standards. O ffice of Transportation and Air Quality, Assessment and Standards Division. Ann Arbor, MI 48105. Available at: http://www.epa.gov/otaq/standards/light

- duty/tier2 stds.htm

EPA, 2005. EPA’s National Inventory Model (NMIM), A Consolidated Emissions Modeling System for

MOBILE6 and NONROAD , U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and Standards D ivision. Ann Arbor, MI 48105, EPA420

- R

- 05

- 024, December

2005. Available at http://www.epa.gov/otaq/models/nmim/420r05024.pdf

.

EPA 2006a. SPECIATE 4.0, Speciation Database Development Documen t, Final Report

, U.S. Environmental Protection Agency, Office of Research and Development

, National Risk Management Research Laboratory, Research Triangle Park, NC 27711

, EPA600

- R

- 06

- 161, February 2006.

Available at http://www.epa.gov/ttn/chief/software/speciate/speciate4/documentation/speciatedoc_1206.pdf

. EPA, 2006b. Regulatory Impact Analyses, 2006 National Ambient Air Quality Standards for Particl e Pollution. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, October, 2006. Docket # EPA

- HQ

- OAR

- 2001

- 0017, # EPAHQ

- OAR

- 2006

- 0834. Available at http://www.epa.gov/ttn/ecas /ria.html

. EPA. 2007a. National Scale Modeling for the Final Mobile Source Air Toxics Rule, Office of Air Quality Planning and Standards, Emissions Analysis and Monitoring Division, Research Triangle Park, NC 27711, EPA 454/R

- 07

- 002, February 2007. Avail able at http://www.epa.gov/otaq/regs/toxics/454r07002.pdf

EPA, 2007b. Guidance for Estimating VOC and NO X Emission Changes from MACT Rules, U.S. Environmental Protection Agency Office of Air Quality Planning and Standards, Air Quality Policy Division, Research Triangle Park, NC 27711, EPA

- 457/B

- 07

- 001, May 2007. Available at http://www.epa.gov/ttn/naaqs/ozone/o3imp8hr/documents/guidance/200705_epa457_b

- 07

- 001_emission_changes_mact_rules.pdf

. EPA, 2008. Regulatory Impact Analysis: Control of Emissions from Marine SI and Small SI Engines, Vessels, and Equipment. U. S. Environmental Protection Agency Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, EPA420

- R

- 08

- 014, September,

2008. Available at: http://www.epa.gov/otaq/regs/nonroad/marinesi

- equipld/420r08014.pdf

EPA, 2009a. 2005 National Emissions Inventory, Version 2. Available at: http://www.epa.gov/ttn/chief/net/2005inv entory.html

. EPA. 2009b. “Impact Calculations RFS

- Docket.xls.” Available on the RFS2 Docket EPA

– HQ

– OAR

– 2010

– 0133 at: http://www.regulations.gov/#!home;tab=search

. EPA, 2009c. Exhaust Emission Profiles for EPA SPECIATE Database: Energy Policy Act (EPAct) Low

Level Ethanol Fuel Blends and Tier 2 Light

- Duty Vehicles. Assessment and Standards Division, Office of Transportation and Air Quality. Report No. EPA

- 420

- R

- 09

- 002.DRAFT

143 EPA, 2009d. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder. U.S. Environmental Protection Agency Office of Transportation and Air Quality, Assessment and Standar ds Division, Ann Arbor, MI 48105, EPA420

- R

- 08

- 001a, May 2009. Available at: http://www.epa.gov/otaq/regs/nonroad/420r08001a.pdf

EPA, 2009e. Emission Factors for Locomotives. Office of Tr ansportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI EPA

- 420

- F

- 09

- 25. Available at: http://www.epa.gov/otaq/regs/nonroad/locomotv/420f09025.pdf

. EPA, 2009f . Regulatory Impact Analysis: Control of Emissions of Air Pollution from Category 3 Marine Diesel Engines. Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI. Report No. EPA

- 420

- R

- 09

- 019. Available at: http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09019.pdf

. EPA. 2010a. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. Assessment and Standards Division, Office of Tr ansportation and Air Quality, Ann Arbor, MI. Report No. EPA

- 420

- R

- 10

- 006, February, 2010. Available at: http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm.

EPA, 2010b. Technical Support Document: The Industrial Sectors Integrated Solutions (I SIS) Model and the Analysis for the National Emission Standards for Hazardous Air Pollutants and New Source Performance Standards for the Portland Cement Manufacturing Industry, U.S. Environmental Protection Agency, Sectors Policies and Program Division an d Air Pollution Prevention and Control Division, Research Triangle Park, NC 27711, August 2010.

EPA, 2010c. Light

- Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards, Final Rule. April, 2010. Available at: http://www.epa.gov/otaq/regs/ld

hwy/greenhouse/ld

- ghg.htm

EPA, 2011a. Heavy

- Duty Vehicle Greenhouse Gas (HDGHG) Emissions Inventory for Air Quality Modeling Technical Support Document, Offi ce of Air Quality Planning and Standards, Air Quality Assessment Division, Research Triangle Park, NC. Report No. EPA

- 420

- R

- 11

- 008, August 2011.

Available at: http://www.epa.gov/otaq/cl imate/documents/420r11008.pdf

EPA. 2011b. Cross

- State Air Pollution Rule Final CAP

- BAFM 2005

- Based Platform, Version 4.2.

Available at: http://www.epa.gov/ttn/chief/emch/index.html#fi nal

EPA, 2012a. 2008 National Emissions Inventory, version 2 Technical Support Document. Office of Air Quality Planning and Standards, Air Quality Assessment Division, Research Triangle Park, NC.

Available at: http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc

EPA, 2012b. Regulatory Impact Analysis: Final Rulemaking for 2017

- 2025 Light

- Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards, Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI. Report No. EPA

- 420

- R

- 12

- 016, August, 2012. Available at: http://www.epa.go v/otaq/climate/documents/420r12016.pdf

EPA. 2012c

. “upstream emissions_2020 AEO.xls”,” upstream emissions_2020 PM NAAQS rev.xls,” Available on the PM NAAQS Docket [EPA

- HQ

- OAR

- 2010

- 0955] at: http ://www.regulations.gov/#!home;tab=search

. EPA, 2012d. Preparation of Emission Inventories for the Version 5.0, 2007 Emissions Modeling Platform Technical Support Document. Available from: http://epa.gov/ttn/chief/emch/2007v5/2007v5_2020base_EmisMod_TSD_13dec2012.pdf

.DRAFT

144 EPA, 2013ci. Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression Ignition (CI) Engines NESHAP: Final Rep ort. Available from: available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/RICE_NESHAPreconsideration_Compression_Ignition_ E ngines_RIA_final2013_EPA.pdf

. EPA, 2013rwc. “ 2011 Residential Wood Combustion Tool version 1.1, September 2013 ” , available from US EPA, OAQPS, EIAG

. EPA, 2013si. Regulatory Impact Analysis (RIA) for Reconsideration of the Existing Stationary Spark Ignit ion (SI) RICE NESHAP: Final Report. Available from http://www.epa.gov/ttn/ecas/regdata/RIAs/NESHAP_RICE_Spark_Ignition_RIA_finalreconsider atio n2013_EPA.pdf

. Frost & Sullivan, 2010. “Project: Market Research and Report on North American Residential Wood Heaters, Fireplaces, and Hearth Heating Products Market (P.O. # PO1

- IMP403

- F&S). Final Report April 26, 2010” , pp. 31

- 32 . Prepared by Fros t & Sullivan, Mountain View, CA 94041.

Houck, 2011; “Dirty

- vs. Clean

- Burning? What percent of freestanding wood heaters in use in the U.S. today are still old, uncertified units?” Hearth and Home, December 2011.

Joint Fire Science Program

, 2009. Consume 3 .0

-- a software tool for computing fuel consumption. Fire Science Brief. 66, June 2009. Consume 3.0 is available at: http://www.fs.fed.us/pnw/fera/research/smoke/consume/index.s html

Kochera, A., 1997. “Residential Use of Fireplaces,” Housing Economics, March 1997, 10

- 11. Also see: http://www.epa.gov/ttnchie1/conference/ei10/area/houck.pdf

. LADCO, 2012. “R egional Air Quality Analyses for Ozone, PM2.5, and Regional Haze: Base C Emissions Inventory (September 12, 2011)”. Lake Michigan Air Directors Consortium, Rosemont, IL 60018.

Available at: http://www.ladco.org/tech/emis/basecv8/Base_C_Emissions_Documentation_Sept_12.pdf

McCarty, J.L., Korontzi, S., Jutice, C.O., and T. Loboda. 2009. The spatial and temporal distribution of crop residue burning in the contiguous United States. Science of the Total Environment, 407 (21): 5701

- 5712.

McKenzie, D.; Raymond, C.L.; Kellogg, L.

- K.B.; Norheim, R.A; Andreu, A.G.; Bayard, A.C.; Kopper, K.E.; Elman. E. 2007. Mapping fuels at multiple scales: landscape application of the Fue l Characteristic Classification System. Canadian Journal of Forest Research. 37:2421

- 2437. Oak Ridge National Laboratory, 2009. Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints. U.S. Department of Energy, March 2009 . Docket No. EPA

– HQ

– OAR

– 2010

– 0133.

MDNR, 2008; “A Minnesota 2008 Residential Fuelwood Assessment Survey of individual household responses”. Minnesota Department of Natural Resources. Available from http://files.dnr.state.mn.us/forestry/um/residentialfuelwoodassessment07_08.pdf

. NESCAUM, 2006; “Assessment of Outdoor Wood

- fired Boilers” (NESCAUM, 2006). Northeast States for Coordinated Air Use Management (NESCAUM ) report. Available from http://www.nescaum.org/documents/assessment

- of

- outdoor

- wood

- fired

- boilers/2006

- 1031

- owb

repor t_revised

-

june2006

- appendix.pdf

. NYSERDA, 2012; “Environmental, Energy Market, and Health Characterization of Wood

- Fired Hydronic Heater Technologies, Final Report”. New York State Energy Research and Development Authority (NYSERDA). Available from: http://www.nyserda.ny.gov/Publications/Case

- Studies/

- /media/Files/Publications/Research/Environmental/Wood

- F ired

- Hydronic

- Heater

- Tech.ashx

.DRAFT

145 Oak Ridge National Laboratory, 2009. Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints. U.S. Department of Energy, March 2009. Docket No. EPA

– HQ

– OAR

– 2010

– 0133.

Ottmar, R.D.; Sandberg , D.V.; Bluhm, A. 2003. Biomass consumption and carbon pools. Poster. In: Galley, K.E.M., Klinger, R.C.; Sugihara, N.G. (eds.) Proceedings of Fire Ecology, Prevention, and Management. Misc. Pub. 13, Tallahassee, FL: Tall Timbers Research Station.

Ottmar, R.D.; Prichard, S.J.; Vihnanek, R.E.; Sandberg, D.V. 2006. Modification and validation of fuel consumption models for shrub and forested lands in the Southwest, Pacific Northwest, Rockes, Midwest, Southeast, and Alaska. Final report, JFSP Project 98

- 1

- 9

- 06

.

Ottmar, R.D.; Sandberg, D.V.; Riccardi, C.L.; Prichard, S.J. 2007. An Overview of the Fuel Characteristic Classification System

– Quantifying, Classifying, and Creating Fuelbeds for Resource Planning. Canadian Journal of Forest Research. 37(12): 2383

- 23 93. FCCS is available at: http://www.fs.fed.us/pnw/fera/fccs/index.shtml

Pouliot, G., H. Simon, P. Bhave, D. Tong, D. Mobley, T. Pace, and T. Pierce . (2010) “Assessing the Anthropogenic Fugitiv e Dust Emission Inventory and Temporal Allocation Using an Updated Speciation of Particulate Matter.” International Emission Inventory Conference, San Antonio, TX.

Available at http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot.pdf

Raffuse, S., D. Sullivan, L. Chinkin, S. Larkin, R. Solomon, A. Soja, 2007. Integration of Satellite

- Detected and Incident Command Reported Wildfire Information into BlueSky, June 27, 200 7. Available at: http://getbluesky.org/smartfire/docs.cfm

Russell, A.G. and G.R. Cass, 1986. Verification of a Mathematical Model for Aerosol Nitrate and Nitric Acid Formation and Its Use for Control Measure Evaluation , Atmospheric Environment, 20: 2011

- 2025.

SESARM, 2012a. “Development of the 2007 Base Year and Typical Year Fire Emission Inventory for the Southeastern States”, Air Resources Managers, Inc., Fire Methodology, AMEC Environment and

Infra structure, Inc. AMEC Project No.: 6066090326, April, 2012

SESARM, 2012b. “Area and Nonroad 2007 Base Year Inventories. Revised Final Report”, Contract No. S

2009

- 06

- 01, Prepared by Transystems Corporation, January 2012. Available at: http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&ved=0CDAQFjA C&url=ftp%3A%2F%2Fwsip

- 70

- 164

- 45

- 196.dc.dc.cox.net%2Fpublic%2FSESARM%2FRevised%2520Final%2FSESAR M%2520Base%252 0Year%2520Revised%2520Final%2520Report_Jan2012.docx&ei=xU

AUPu1F4WA0AHC5YHYCg&usg=AFQjCNFhjgx3Ej

hbfYmMUP4zGI_HBiqZA&sig2=hWWN0m3WYPSO28QSzn5BIA

.

Skamarock, W., J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang, J. Powers, 2008. A Description of the Advanced Research WRF Version 3. NCAR Technical Note. National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder, CO. June 2008.

Available at: http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf

Sullivan D.C., Raffuse S.M., Pryden D.A., Craig K.J., Reid S.B., Wheeler N.J.M., Chinkin L.R., Larkin N.K., Solomon R., and Strand T. (2008) Development and applications of systems for m odeling emissions and smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17thDRAFT

146 International Emissions Inventory Conference, Portland, OR, June 2

- 5. Available at: http://www.epa.g ov/ttn/chief/conferences.html

U.S. Census, 2012; “ 2012 Statistical Abstract, Construction and Housing: Ho using Units and

Characteristics”. U.S. Census Bureau. Available from: http://www.census.gov/compendia/statab/cats/construction_housing/housing_units_and_characteristi

cs.html

Wang, Y., P. Hopke, O. V. Rattigan, X. Xia, D. C. Chalupa, M. J. Utell. (2011) “Characterization of

Residential Wood Combustion Particles Using th e Two

- Wavelength Aethalometer”, Environ. Sci. Technol., 45 (17), pp 7387

– 7393

Wolf, et al, 2009. Developing Mexico National Emissions Inventory Projections for the Future Years of
2008, 2012, and 2030 . Available at: http://www.epa.gov/ttnchie1/conference/ei18/session2/wolf.pdf

. Yarwood, G., S. Rao, M. Yocke, and G. Whitten, 2005: Updates to the Carbon Bond Chemical Mechanism: CB05. Final Report to the US EPA, RT

- 0400675. Available at http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf

. Zue, Henze, et al, 2013. “Constraining U.S. Ammonia Emissions using TES Remote Sensin g Observations

and the GEOS

- Chem adjoint model”, Journal of Geophysical Research: Atmospheres, 118: 1

- 14.