## Technical Support Document (TSD)

Preparation of Emissions Inventories for the Version 6.2

,

20 11 Emissions Modeling Platform

August

, 201 5

U.S. Environmental Protection Agency

Office of Air and Radiation Office of Air Quality Plan ning and Standards Air Quality Assessment Division

Contacts: Alison Eyth , Jeff Vukovichii 

## TABLE OF CONTENTS

LIST OF TABLES

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

LIST OF FIGURES

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

LIST OF APPENDICES

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

ACRONYMS

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

1 INTRODUCTION

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

2 2011 EMISSION INVENT ORIES AND APPROACHES

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

2.1 2011 NEI POINT SOURCES ( PTEGU

, PT

_ OILGAS AND PTNONIPM )

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

2.1.1 EGU sector (ptegu)

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

2.1.2 Point source oil and gas sector (pt_oilgas)

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

2.1.3 Non

- IPM sector (ptnonipm)

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

2.2 2011 NONPOINT SOURCES ( AFDUST

, AG

, AGFIRE

, NP

_ OILGAS

, RWC

, NONPT )

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

2.2.1 Area fugitive dust sector (afdust)

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

2.2.2 Agricultural ammonia sector (ag)

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

2.2.3 Agricultural fires (agfire)

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

2.2.4 Nonpoint source oil and gas sector (np_oilgas)

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

2.2.5 Residential wood combustion sector (rwc)

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

2.2.6 Other nonpoint sources sector (nonpt)

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

2.3 2011 ONROAD MOBILE SOURCE S ( ONROAD )

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

2.3.1 Onroad (onroad)

................................ ................................ ................................ ................................ ......... 2 1

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

, C 3 MARINE

, NONROAD )

................................ ................................ ...... 24

2.4.1 Category 1/Category 2 Commercial Marine Vessels and Locomotives and (c1c2r ail)

................................ ... 24

2.4.2 Category 3 commercial marine vessels (c3marine)

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

2.4.3 Nonroad mobile equipment sources: (nonroad)

................................ ................................ ............................ 27

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

- U.S.

SOURCES

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

2.5.1 Point sources from o ffshore C3 CMV, drilling platforms, Canada and Mexico (othpt)

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

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

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

2.5.3 Onroad mobile sources from Canada and Mexico (othon)

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

2.6 F IRES ( PTWILDFIRE

, PTPRESCFIRE )

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

2.7 B IOGENI C SOURCES ( BEIS )

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

2.8 SMOKE

- READY NON

- ANTHROPOGENIC INVENT ORIES FOR CHLORINE

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

3 EMISSIONS MODELING S UMMARY

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

3.1 E MISSIONS MODELING O VERVIEW

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

3.2 C HEMICAL S PECIATION

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

3.2.1 VOC speciation

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

3.2.1.1 The combination of HAP BAFM (benzene, acetaldehyde, formaldehyde and methanol) and VOC for VOC speciation 42

3.2.1.2 Co unty specific profile combinations (GSPRO_COMBO)

................................ ................................ ......................... 44

3.2.1.3 Additional sector specific details

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

3.2.1.4 Future year speciation

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

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

3.2.3 NO X speciation

................................ ................................ ................................ ................................ ............ 55

3.3 T EMPORAL A LLOC ATION

................................ ................................ ................................ ................................ ....... 55

3.3.1 Use of FF10 format for finer than annual emissions

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

3.3.2 Electric Generating Utility temporalization (ptegu)

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

3.3.2.1 Base year temporal allocation of EGUs

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

3.3.2.2 Future year temporal allocation of EGUs

................................ ................................ ................................ .................. 60
3.3.3 Residential Wood Combustion Temporalization (rwc)

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

3.3.4 Agricultural Ammonia Temporal Profiles (ag)

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

3.3.5 Onroad mobile temporalization (onroad)

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

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

......... 77iii

3.4 S PATIAL A LLOCATION

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

3.4.1 Spatial Surrogates for U.S. emissions

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

3.4.2 All ocation method for airport

- related sources in the U.S.

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

3.4.3 Surrogates for Canada and Mexico emission inventories

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

4 DEVELOP MENT OF 2017 AND 202 5 BASE

- CASE EMISSIONS

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

4.1 EGU SECTOR PROJECTIONS : PTEGU

................................ ................................ ................................ ........................ 94

4.2 N ON

- EGU P OINT AND NEI N ONPOINT SECTOR PROJE C TIONS : AFDUST

, AG

, C 1 C 2 RAIL

, C 3 MARINE

, NONPT

, NP

_ OILGAS

, PTNONIPM

, PT

_ OILGAS

, RWC

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

4.2.1 CoST Background: Used for NEI non

- EGU Point and Nonpoint sectors

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

4.2.2 CoST Plant CLOSURE packet (ptnonipm, pt_oilgas)

................................ ................................ .................. 100

4.2.3 CoST PROJECTION packets (afdust, ag, c1c2rail, c3marine, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)

. 101

4.2.3.1 Paved and unpaved roads VMT growth (afdust)

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

4.2.3.2 Livestock population growth (ag)

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

4.2.3.3 Locomotives and Category 1 & 2 commercial marine vessels (c1c2rail, ptnonipm)

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

4.2.3.4 Category 3 commercial marine vessels (c3marine, othpt)

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

4.2.3.5 U pstream distribution, pipelines and refineries ( nonpt, ptnonipm, pt_oilgas)

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

4.2.3.6 Oil and gas and industrial sou rce growth from 2011v6.0 NODA ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

................. 108 Natural Gas Consumption and Crude Oil Production

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

Oth er Fuels

................................ ................................ ................................ ................................ ................................ ........... 114 Oil/Gas Plays

................................ ................................ ................................ ................................ ................................ ....... 115 Remaining Areas

................................ ................................ ................................ ................................ ................................ ... 116

4.2.3.7 Data fro m comments on previous platforms ( nonpt, ptnonipm, pt_oilgas)

................................ ................................ 119

4.2.3.8 Aircraft (ptnonipm)

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

4.2.3.9 Cement manufacturing (pt nonipm)

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

4.2.3.10 Corn ethanol plants (ptnonipm)

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

4.2.3.11 Residential wood combustion (rwc)

................................ ................................ ................................ ........................ 126
4.2.4 CoST CONTROL packets ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

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

4.2.4.1 Oil and gas NSPS ( np_oilgas, pt_oilgas)

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

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

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

4.2.4.3 RICE NSPS ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

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

4.2.4.4 ICI Boilers ( nonpt, ptnonipm, pt_oilgas )

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

4.2.4.5 Fuel sulfur rules ( nonpt, ptnonipm, pt_oilgas )

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

4.2.4.6 Natural gas turbines NO X NSPS ( ptnonipm, pt_oilgas )

................................ ................................ ............................ 140

4.2.4.7 Process heaters NO X NSPS ( ptnonipm, pt_oilgas )

................................ ................................ ................................ ... 142

4.2.4.8 Arizona Regional Haze controls (ptnonipm)

................................ ................................ ................................ ............ 143

4.2.4.9 CISWI (ptnonipm)

................................ ................................ ................................ ................................ ................. 144

4.2.4.10 Data from comments on previous platfor ms ( nonpt, ptnonipm, pt_oilgas )

................................ ................................ 144
4.2.5 Stand

- alone future year inventories (nonpt, ptnonipm)

................................ ................................ ............... 146

4.2.5.1 Portable fuel containe rs (nonpt)

................................ ................................ ................................ .............................. 146

4.2.5.2 Biodiesel plants (ptnonipm)

................................ ................................ ................................ ................................ .... 147

4.2.5.3 Cellulosic plants (nonpt)

................................ ................................ ................................ ................................ ........ 148

4.2.5.4 New cement plants (nonpt, ptnonipm)

................................ ................................ ................................ ..................... 150
4.3 M OBILE SOURCE PROJECT IONS

................................ ................................ ................................ ............................. 151

4.3.1 Onroad mobile (o nroad)

................................ ................................ ................................ ............................ 151

4.3.1.1 Future activity data

................................ ................................ ................................ ................................ ................ 152

4.3.1.2 Set up and Run MOVES to create EFs

................................ ................................ ................................ .................... 154

4.3.1.3 National and California adjustments

................................ ................................ ................................ ....................... 155
4.4 N ONROAD MOBILE SOURCE PROJECTIONS ( NONROAD )

................................ ................................ ........................... 156

4.5 “O THER E MISSIONS ”: O FFSHORE C ATEGORY 3 COMMERCIAL MARINE VE SSELS AND DRILLING P LATFORMS

, C ANADA AND
M EXICO ( OTHPT

, OTHAR

, AND OTHON )

................................ ................................ ................................ .......................... 157

5 EMISSION SUMMARIES

................................ ................................ ................................ ................................ .... 159

6 REFERENCES

................................ ................................ ................................ ................................ ...................... 170iv 

## 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

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

Table 2

- 2. Summary of differences between 2011v6.2 platform and 2011NEIv2 emission s by sector

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

Table 2

- 3. Point source oil and gas sector NAICS Codes

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

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

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

Table 2

- 6

. SCCs in the afdust p latform sector

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

Table 2

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

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

Table 2

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

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

Table 2

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

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

Table 2

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

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

Table 2

- 11. Onroad emission aggregate processes

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

Table 2

- 12. 2011NEIv2 SCCs extracted for the starting point in c1c2rail development

............................... 24

Table 2

- 13. Growth factors to project the 2002 ECA

- IMO inventory to 2011

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

Table 2

- 14. 2011 Platform SCCs representing emissions in the ptprescfire and ptwildfire modeling sectors 30

Table 2

- 15. Large fires apportioned to multiple grid cells

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

Table 2

- 16. Meteorological variables required by BEIS 3.61

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

Table 3

- 1. Key emissions modeling steps by sector.

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

Table 3

- 2. Descriptions of the platform grids

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

Table 3

- 3. Emission model species produced for CB6 for CAM X *

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

Table 3

- 4. Cmaq2camx mapping file

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

Table 3

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

................................ ...... 44

Table 3

- 6. MOVES integrated species in M

- profiles

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

Table 3

- 7. VOC profiles for WRAP Phase III basins

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

Table 3

- 8. National VOC profiles for oil and gas

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

Table 3

- 9. Counties included in the WRAP Dataset

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

Table 3

- 10. Select VOC profiles 2011 vs 2017 vs 2025

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

Table 3

- 11. Onroad M

- profiles

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

Table 3

- 12. MOVES Process IDs

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

Table 3

- 13. MOVES Fuel subtype IDs

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

Table 3

- 14. MOVES Regclass IDs

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

Table 3

- 15. PM model species: AE5 versus AE6

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

Table 3

- 16. NO X speciation profiles

................................ ................................ ................................ ........... 55

Table 3

- 17. Temporal settings used for the pla tform sectors in SMOKE

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

Table 3

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

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

Table 3

- 19. Off

- Network Mobil e Source Surrogates

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

Table 3

- 20. Spatial Surrogates for Oil and Gas Sources

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

Table 3

- 21. Selected 2011 CAP emissions by secto r for U.S. Surrogates*

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

Table 3

- 22. Canadian Spatial Surrogates

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

Table 3

- 23. CAPs Allocated to Mexican and Canadian Spatial S urrogates

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

Table 4

- 1. Control strategies and growth assumptions for creating the 2017 and 2025 base

- case emissions

inventories from the 2011 base case

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

Table 4

- 2. Subset of CoST Packet Matching Hierarchy

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

Table 4

- 3. Summary of non

- EGU stationary projections subsections

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

Table 4

- 4. Reductions from all facility/unit/stack

- level closures by modeling sector

................................ .. 100

Table 4

- 5. NH 3 projection factors and total impacts to years 2017 and 2025 for animal operations

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

Table 4

- 6. Non

- California projection factors for locomotives and Category 1 and Category 2 Commercial

Marine Vessel Emissions

................................ ................................ ................................ ................... 103v Table 4

- 7. Difference in c1c2rail sector emissions between 2011 and future years

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

Table 4

- 8. Growth factors to project the 2011 ECA

- IMO inventory to 2017 and 2025

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

Table 4

- 9. Difference in c3marine sector and othpt C3 CMV emissions between 2011 and future years

.... 105

Table 4

- 10. Petroleum pipelines & refineries and production storage and transport factors and reductions 107

Table 4

- 11. Sources of new industrial source growth factor data from the 2011v6.0 NODA

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

Table 4

- 12. Summary of “EPA” Projection Approaches for IC Engines/Gas Turbines and ICI Boilers/Process

Heaters

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

Table 4

- 13. NAICS Codes for which NAICS/SC C

- level Growth Factors were developed

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

Table 4

- 14. AEO Oil/Gas Plays

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

Table 4

- 15. Industrial source projections net impacts from 2011v6.0 NODA

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

Table 4

- 16. Impact of 2011v6.0 projection factors for Texas

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

Table 4

- 17. NEI SCC to FAA TAF ITN aircraft categories used for aircraft projections

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

Table 4

- 18 . National aircraft emission projection summary

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

Table 4

- 19. U.S. Censu s Division ISMP

- based projection factors for existing kilns

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

Table 4

- 20. ISMP

- based cement industry projected emissions

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

Tab le 4

- 21. 2011 and 2017/2025 corn ethanol plant emissions [tons]

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

Table 4

- 22. Non

- West Coast RWC projection factors, including NSPS impacts

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

Table 4

- 23. Cumulative national RWC emissions from growth, retirements and NSPS impacts

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

Table 4

- 24. Assumed retirement rates and new source emission factor ratios for new so urces for various

NSPS rules

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

Table 4

- 25. NSPS VOC oil and gas reductions from projected pre

- control 2018 and 2025 grown values

... 131

Table 4

- 26. Summary RICE NESHAP SI and CI percent reductions prior to 2011NEIv2 analysis

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

Table 4

- 27. National by

- sector reductions from RICE Reconsideration controls (tons)

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

Table 4

- 28. RICE NSPS Analysis and resulting 2011v6.2 new emission rates used to compute controls

.... 134

Table 4

- 29 . National by

- sector redu ctions from RICE NSPS controls (tons)

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

Table 4

- 30. Facility types potentially subject to Boiler MACT reductions

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

Table 4

- 31. National

- level, with Wisconsin exceptions, ICI boiler adjustment factors by base fuel type

.... 138

Table 4

- 32. New York and New Jersey NO X ICI Boiler Rules that supersede national approach

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

Table 4

- 33. Summary of ICI Boiler reductions

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

Table 4

- 34. State Fuel Oil Sulfur Rules data provided by MANE

- VU

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

Table 4

- 35. Summary of fuel sulfur rule impacts on SO 2 emissions

................................ ........................... 140

Table 4

- 36. Stationary gas turbines NSPS analysis and resulting 2011v6.2 new emission rates used to compute controls

................................ ................................ ................................ ............................... 141

Table 4

- 37. National by

- sector NO X reductions from Stationary Natural Gas Turbine NSPS controls

........ 142

Table 4

- 38. Process Heaters NSPS analysis and 2011v6.2 new emission rates used to compute controls

... 143

Table 4

- 39. National by

- sector NO X reductions from Process Heaters NSPS con trols

................................ 143

Table 4

- 40. Summary of remaining ptnonipm and pt_oilgas reductions

................................ ..................... 145

Table 4

- 41. PFC emissions for 2011, 2018 and 2025 [tons]

................................ ................................ ....... 147

Table 4

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

................................ ................................ . 147

Table 4

- 43. 2018/2025 biodiesel plant emissions [ton s]

................................ ................................ ............. 148

Table 4

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

........................... 148

Table 4

- 45. Toxic Emission Fact ors for Cellulosic Plants (Tons/RIN gallon)

................................ ............. 149

Table 4

- 46. 2017/2025 cellulosic plant emissions [tons]

................................ ................................ ............ 149

Table 4

- 47. New cellul osic plants NOX emissions provided by Iowa DNR.

................................ .............. 149

Table 4

- 48. Locations of new ISMP

- generated cement kilns

................................ ................................ ...... 150

Table 4

- 49. ISMP

- generated new permitted and non

- permitted emissions

................................ ................. 151

Table 4

- 50. Projection factors for 2018 and 2025 VMT (in millions of miles)

................................ ........... 152

Table 4

- 51. Inputs for MOVES runs for 2018 and 2025

................................ ................................ ............. 154

Table 4

- 52. CA LEVIII program states

................................ ................................ ................................ ...... 155

Table 5

- 1. National by

- sect or CAP emissions summaries for the 2011 evaluation case

.............................. 160vi Table 5

- 2. National by

- sector CAP emissions summaries for the 2017 base case

................................ ....... 161

Table 5

- 3. National by

- sector CAP emissions summaries for the 2025 base case

................................ ....... 162

Table 5

- 4. National by

- sector CO emissions (tons/yr) summaries and percent change

............................... 163

Table 5

- 5. National by

- sector NH 3 emissions (tons/yr) summaries and percent change

.............................. 164

Table 5

- 6. National by

- sector NO

x emissions (tons/yr) summ aries and percent change

.............................. 165

Table 5

- 7. National by

- sector PM 2.5 emissions (tons/yr) summaries and percent change

............................ 166

Table 5

- 8. N ational by

- sector PM 10 emissions (tons/yr) summaries and percent change

............................. 167

Table 5

- 9. National by

- sector SO 2 emissions (tons/yr) summaries and percent change

............................... 168

Table 5

- 10. National by

- sector VOC emissions (tons/yr) summaries and percent change

........................... 169



## List of Figures

Figure 2

- 1. Impact of adjust ments to fugitive dust emissions due to transport fraction, precipitation, and

cumulative

................................ ................................ ................................ ................................ ........... 17 Figure 2

- 2. Illustration of regional modeling domains in ECA

- IMO study

................................ .................. 26 Figure 2

- 3. Annual NO emissions output from BEIS 3.61 for 2011

................................ ............................ 32 Figure 2

- 4. Annual isoprene emissions output from BEIS 3.61 for 2011

................................ ..................... 32 Figure 3

- 1. Air quality modeling domains

................................ ................................ ................................ ... 37 Figure 3

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

................................ ......... 43 Figure 3

- 3. Eliminating unmeasured spikes in CEMS data

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

- 4. Seasonal diurnal profiles for EGU emissions in a Virginia Region

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

- 5. IPM Regions for EPA Base Case v5.14

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

- 6. Month

- to

- day profiles for different fuels in a West Texas Region

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

- 7. Future year emissions follow pattern of base year emissions

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

- 8. Excess emissions apportioned to hours less than ma ximum

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

- 9. Adjustment to Hours Less than Maximum not Possible, Regional Profile Applied

.................... 65 Figure 3

- 10. Regional Profi le Applied, but Exceeds Maximum in Some Hours

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

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

.......................... 67 Figure 3

- 12. RWC diurnal temporal profile

................................ ................................ ................................ 68 Figure 3

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

................................ ........................... 69 Figure 3

- 14. Day

- of

- week temporal profiles for OHH and Recreational RWC

................................ ............ 69 Figure 3

- 15. Annual

- to

- month temporal profiles for OHH and recreational RWC

................................ ....... 70 Figure 3

- 16. Example of animal NH 3 emissions temporalization approach, summed to daily emi ssions

...... 71 Figure 3

- 17. Example of SMOKE

- MOVES temporal variability of NO X emissions

................................ .... 72 Figure 3

- 18. Previous onroad diurnal week day profiles for urban roads

................................ ...................... 73 Figure 3

- 19. Use of submitted versus new national default profiles

................................ ............................. 74 Figure 3

- 20. Updated natio nal default profiles for LDGV vs. HHDDV, urban restricted

............................. 75 Figure 3

- 21. Updated national default profiles for day of week

................................ ................................ ... 7 6

Fi gure 3

- 22 . Combination long

- haul truck restricted and hoteling profile

................................ .................... 77 Figure 3

- 23. Agricultural burning diurnal temporal profile

................................ ................................ ......... 78 Figure 4

- 1. Oil and gas plays with AEO projection data

................................ ................................ ............ 116 Figure 4

- 2. Oil and Gas NEMS Regions

................................ ................................ ................................ .... 117 Figure 4

- 3. Cement sec tor trends in domestic production versus normalized emissions

.............................. 124 Figure 4

- 4. Light Duty VMT growth rates

................................ ................................ ................................ 154vii 

## List of Appendices

Appendix A: Nonpoint Oil and G as NEI SCCs Appendix B: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT Appendix C : Future Animal Population Projection Methodology



## Acronyms

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

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

AEO Annual Energy Outlook

BAFM Benzene, Acetaldehyde, Formaldehyde and Methanol

BEIS Biogenic Emissions Inventory System

BELD Biogenic Emissions Land use Database Bgal Billion gallons

BPS Bulk Plant Storage

BTP Bulk Terminal (Plant) t o Pump

C1/C2 Category 1 and 2 commercial marine vessels

C3 Category 3 (commercial marine vessels)

CAEP Committee on Aviation Environmental Protection

CAIR Clean Air Interstate Rule

CAMD EPA ’s Clean Air Markets Division

CAM X Comprehensive Air Quality Model with Extensions

CAP Criteria Air Pollutant

CARB California Air Resources Board

CB05 Carbon Bond 2005 chemical mechanism

CBM Coal

- bed methane

CEC North American Commission for Environmental Cooperation

CEM 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 gasoline , respectively

EBAFM Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol

ECA Emissions Control Area

EEZ Exclusive Economic Zone

EF Emission Factor

EGU Electric G enerating U nits

EIS Emissions Inve ntory System

EISA Energy Independence and Security Act of 2007

EPA Environmental Protection Agency

EMFAC Emission Factor (California’s onroad mobile model)

FAA Federal Aviation Administrationviii

FAPRI Food and Agriculture Policy and Research Institute

F ASOM Forest and Agricultural Section Optimization Model

FCCS Fuel Characteristic Classification System

FF10 Flat File 2010

FIPS Federal Information Processing Standards

FHWA Federal Highway Administration

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 Industri al/Commercial/Institutional (boilers and process heaters)

ICR Information Collection Request

IDA Inventory Data Analyzer

I/M Inspection and Maintenance

IMO International Marine Organization

IPAMS Independent Petroleum Association of Mountain States

IPM Integrated Planning Model

ITN Itinerant

LADCO Lake Michigan Air Directors Consortium

LDGHG Light

- Duty Vehicle Greenhouse Gas

LPG Liquefied Petroleum Gas

MACT Maximum Achievable Control Technology

MARAMA Mid

- Atlantic Regional Air Management Assoc iation

MATS Mercury and Air Toxics Standards

MCIP Meteorology

- Chemistry Interface Proce ssor Mgal Million gallons

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

Management, Regulation and Enforcement (BOEMRE)

MOVES Motor Vehicle Emi ssions Simulator

MSA Metropolitan Statistical Area

MSAT2 Mobile Source Air Toxics Rule

MTBE Methyl tert

- butyl ether

MWRPO Mid

- west Regional Planning Organization

NCD National County Database

NEEDS National Electric Energy Database System

NEI Nationa l Emission Inventory

NESCAUM Northeast States for Coordinated Air Use Management

NESHAP National Emission Standards for Hazardous Air Pollutants

NH 3 Ammonia

NIF NEI Input Format

NLCD National Land Cover Database

NLEV National Low Emission Vehicle pro gram

nm nautical mile

NMIM National Mobile Inventory Model

NOAA National Oceanic and Atmospheric Administrationix

NODA Notice of Data Availability

NONROAD OTAQ’s model for estimation of nonroad mobile emissions

NO X Nitrogen oxides

NSPS New Source Perf ormance Standards

NSR New Source Review 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 Combust ion 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 Reso urce 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 Imple mentation Plan

SPDPRO Hourly Speed Profiles for weekday versus weekend

SPPD Sector Policies and Programs Division

TAF Terminal Area Forecast TCEQ Texas Commission on Environmental Quality

TOG Total Organic Gas

TSD Technical support document

ULSD Ul

tra Low Sulfur Diesel

USDA United States Department of Agriculture

VOC Volatile organic compounds

VMT Vehicle miles traveled

VPOP Vehicle Population

WRAP Western Regional Air Partnership

WRF Weather Rese arch and Forecasting Model1 

## 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 2 ( 2011NEIv2 ) . The air quality modeling platform consists of all the emissions inventories and ancillary data files used for emissions modeling, as well as the meteorological, initial condition, and boundary condition files needed to run the air quality

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

which incl udes 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 2011NEIv2 , although there are some differences between the platform inventories and the 2011NEIv2 emissions.

This 2011 modeling platform includes all criteria air pollutants and precursors (CAPs) and the following hazardous air pollutants (HAPs): chlorine (Cl), hydrogen chloride (HCl), benzene, acetaldehyde, formaldehyde and methanol. The latter four HAPs are also abbreviated as BAFM. This platform is called

the “CAP

- BAFM 2011

- Based Platform, version 6.2 ” because it is primarily a CAP platform with BAFM species included. Here, “version 6.2 ” de notes an evolution from the 2011

- based platform, version 6 .0

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

that is described is referred to as the “2011 v6.2 platform” or “2011v 6.2 ”.

The 2011 v 6.2 platform was used to support ozone transport modeling for the 2008 National Ambient Air Quality Standards (NAAQS) , the 2015 NAAQS for ozone

, along with other special studies . The air quality model used for th ese rule s is the Comprehen sive Air Quality Model with Extensions (CAM X ) model ( http://www.camx.com/ ), version 6.1 0

. H owever , emissions are first processed into a format compatible with for the Community Multiscale Air Quality (CMAQ) model ( http://www.epa.gov/AMD/CMAQ/CMAQdocumentation.html ), version 5.0. 2 and th ose emissions are

converted to CAM X

- ready format. The 2011 National Air Toxics Assessment (NATA) was developed using a s imilar modeling platform with some variations such as a focu s on toxic air pollutants and will be

described in the Technical Support Document ( TSD ) “EPA’s 2011 National

- Scale Air Toxics

Assessment”.

Both CAM X and CMAQ support modeling ozone (O 3 ) and par ticulate matter (PM), and require as input hourly and gridded emissions of chemical species that correspond to CAPs and specific HAPs. The chemical mechanism used by CAM X for this platform is called Carbon Bond version 6 revision 2 (CB 6 r2 ) and includes imp ortant reactions for simulating ozone formation, NOx cycling, and formation of secondary aerosol species (Hildebrant Ruiz and Yarwood, 2013) . CB6 provides several revisions to the previous carbon bond version (CB05) through inclusion of four new explicit o rganic species: benzene, propane, acetylene and acetone; along with updates to reaction chemistry for those species and several

other VOCs. Note that because CMAQ does not currently support CB6, the CMAQ

- format emissions produced during the emissions mode ling process cannot actually be used to run CMAQ. To create files usable with CMAQ, the inventories would instead need to be processed using CB05 speciation.

T he 20 11 v 6.2 platform consists of t hree ‘complete’ emissions cases : the 20 11 base case (i.e.,

20 11eh_cb6v2 _v6)

, the 2017 base case (i.e., 201 7 e h_cb6v2 _v6) and the 2025 base case (i.e.,
2025eh_cb6v2 _v6)

. In the case abbreviations, the 2011

, 2017 and 2025 are the year represented by the emissions; the “e” stands for evaluation, meaning that year

- speci fic data for fir es and electric generating units ( EGUs ) are used, and the “h ” represents that this was the eighth set of emissions modeled for a 2011

- based modeling 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 consistent with the methods used2

in corresponding future

- year cases
, including the 2017 and 2025 future year base case s
, as well as any additional future year control and source apportionment cases. This 2011v6.2 TSD is available on EPA’s Emissions Modeling Clearinghouse (EMCH) website, http://www.epa.gov/ttn/chief/emch/ , under the

section entitled “2011

- based Modeling Pla tform (2011v6 Platform)”.

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

from the 2017 and 2025 base case s in the relative response factor (RRF) calculations to identify future

areas of nonattainm ent. For more information on the use of RRFs and air quality modeling , see “Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5, and Regional Haze”, available from http://www.epa.gov/ttn/scram/guidance/guide/final

- 03

- pm

- rh

- guidance.pdf

.

Table 1

- 1

. List of cases in the 2011 Version 6 Emissions Modeling Platf orm

Case Name Abbreviation Description

20 11 base case 2011eh_cb6v2 _v 6 20 11 case relevant for air quality model evaluation purposes and for computing relative response factors with 2017 and

2025 scenario(s). Uses 2011NEIv2 and some other inventory

data , w ith hourly 2011 continuous emissions monitoring System ( CEMS ) data for Electrical Generating Units (EGUs)

, hourly onroad mobile emissions, and 2011 day

- specific wild and prescribed fire data.

2017 base case 2017eh_cb6v2 _v 6 2017 “base case” scenario, rep resenting the best estimate for

2017 that incorporates estimates of the impact of current “on

the

- books” regulations

.

2025 base case 2025eh_cb6v2 _v6 2025 “base case” scenario, representing the best estimate for

2025 that incorporates estimates of the impa ct of current “on

the

- books” regulations

.

All of the above cases use the same version of the 2011 meteorology and the cases are sometimes referred to with “_11g” after the emissions portion of the case name. A special version of the 2017eh_cb6v2_v6

case called 2 017eh_ussa_cb6v2_v6_11g was prepared for use with the CAMx OSAT/APCA feature that

allowed the contribution of 2017 base case NOX and VOC emissions from all sources in each state to projected 2017 ozone concentrations at air quality monitoring sites to be quantified. The emissions for the case are equivalent to those in the 2017eh_cb6v2_v6 case, except that the emission sources are tagged according to their origin by state or sector

. The steps for setting up the 2017eh_cb6v2_v6 source apportionment c ase included:

1) prepare files for the source groups to track (e.g., anthropogenic emissions from each state, non

geographic sector

- specific tags for biogenic, fugitive dust, fire, and non

- US emissions); 2) rerun all sectors in SMOKE using the specified source groups; 3) create CAMx point source files for source groups tracked only by sector; 4) convert SMOKE outputs to CAMx point source files using the tags assigned by SMOKE; and 5) merge all of the point source files together into a single CAMx mrgpt file for each day.

More information on processing for source apportionment is available at ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/smoke_scri pts_utils/ README_smoke_source_apportionment.txt

.

The year 2018 was used as the analytic year for the preliminary modeling performed with the 2011v6.0 platform because at the onset of the modeling assessment, that year aligned with the December 20183

att ainment date for Moderate ozone nonattainment areas. However, subsequent to the completion of the 2018 modeling, EPA issued the final 2008 Ozo ne NAAQS SIP Requirements Rule, which revised the attainment deadline for ozone nonattainment areas currently desi gnated as Moderate for the 2008 ozone NAAQS to July 2018. The EPA established this deadline in the 2015 Ozone SIP Requirements Rule after previously establishing a deadline of December 31, 2018, that was vacated by the D.C. Circuit in Natural Resources Def

ense Council v. EPA. In order to demonstrate attainment by the revised attainment deadline, the demonstration would have to be based on design values calculated using 2015 through 2017 ozone season data, since the July 2018 deadline does not afford a full ozone season of measured data. Therefore, the EPA has adopted 2017 as the analytic year for the updated ozone transport modeling in the 2011v6.2 platform. Because projected emission inventories had already been prepared for the year 2018 by the

time of th e Natural Resources Defense Council v. EPA decision, the projected emissions for some sectors were adjusted to the year 2017 using methods appropriate to those sectors.

The emissions data in the 2011v6.2 platform are primarily based on the 2011NEIv2 for p oint sources
, nonpoint sources, commercial marine vessels (CMV) , nonroad mobile sources and fires. The onroad mobile source emissions are similar to those in the 2011NEIv2, but were generated using the released

2014 version of the Motor Vehicle Emissions Simulator ( MOVES2014 ) with support for CB6 ( http://www.epa.gov/otaq/models/moves/ ), while the 2011NEIv2 emissions were generated using a slightly earlier version of MOVES2014 and had a different treatme nt of E

- 85 vehicles. The 2011v 6.2 platform cases implement comments received on the Federal Register notices is sued for 2011 and 2018

emission s in the 2011v6.0 platform: 78 FR 70935 and 79 FR 2437, respectively. High

- level descriptions of implemented comme nts are included in the relevant subsections. The 2011v6.2 platform 2011 and 2017 emissions and air quality model results were released for review and comment in the Federal Register notice 80 FR 46271

. The updates in the 2011v6.2 platform include the foll owing:

- Emissions updates based on comments submitted during the notices of data availability for the 2011 and 2018 emissions modeling platforms and the development of the 2011NEIv2 that resulted in changes to all sectors of emissions in 2011NEIv2;

- Use of updated EPA emissions data sets in the 2011NEIv2 for many sectors , including oil and gas, residential wood combustion (RWC), commercial marine vessels, and oil platforms in the
Gulf of Mexico;

- Use of MOVES 2014 for base and future year onroad mobile source emissions, along with updated input databases based on comments and new EPA data sources

, and updates to speciation, spatial surrogates, and temporal profiles for onroad mobile sources ;

- Use of the Biogenic Emissions Inventory System ( BEIS ) version 3.61 fo r biogenic emissions

, including updated land use information ;

- Use of the Integrated Planning Model ( IPM ) version 5.14 for future year EGU emissions and updated EGU temporalization methods ;

- Updated emissions for Canada and Mexico; and

- Updated pro

jections fo r stationary sources.

The primary emissions modeling tool used to create the air quality model

- ready emissions was the

SM OKE modeling system ( http://www.smoke

- model.org/ ). S MOKE version 3. 6.5 was used to create
emissions files for a 12

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

- 1

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

- Chem ( http://geos

- chem.org/ ) and were consistent with those used for the 2011v6.1 platform used for the proposed 2015 ozone NAAQS

. Electronic copies of the data used as input to SMOKE for the 20114

Platform are available from the Emissions Modeling Clearinghouse website, http://www.epa.gov/ttn/chief/emch/ under the 2011

- based modeling platform section.

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

Rese arch and Forecasting Model (WRF, http://wrf

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

. WRF was run for 2011

over a domain covering the continental United States at a 12km resolution with 35 vertical layers

. The

WRF data were collapsed to 25 layers prior to running the emissions and air quality models. The run for this platf orm included high resolution sea surface temperature data from the Group for High Resolution Sea Surface Temperature (GHRSST) (see https://www.ghrsst.org/ ) and is given the EPA meteorological case label “11g” and are consistent with those used for the 2011v6.1 platform

.

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 development of the 2017 and 2025 inventor ies (projected

from 20 11 ). Data summaries comparing the 20 11

, 2017 and 2025 base case s are provided in Section 5. Section 6 provides references. The Appendices pro vide additional details about specific technical

methods.5 

## 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 201 1NEIv2

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

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

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

combustion ; and increased wildfire emissions. Documentation for the 2011NEIv2 , 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 sup ports tracking release point ( e.g., stack) coordinates separately from facility coordinates. EPA collaborat ed extensively with S/L/T agencies to ensure a high quality of data in the 2011NEIv2 . Tangible benefits of this collaboration are seen in improved data quality, improved completeness and reduced duplication between point and nonpoint source categories such as industrial boilers. O nroad mobile source emission s in the

20 11 NEI v 2 were developed using a pre

- release version of MOVES2014 ; however, the 2011 emissions modeling platform used the released version of MOVES2014

, with added support for CB6

. 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 : poi nt sources, nonpoint (formerly called “stationary area”)

sources, nonroad mobile sources, onroad mobile sources, and events c onsist ing of fires. The 2011NEIv2

uses 60 sectors to further describe the emissions

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

- NEI data sources are included in the 20 11 platform.

As expl ained below, the major differences between the 2011 NEIv2 and the 2011 platform include: a

different version of MOVES

- based onroad mobile source emissions, meteorologically

- adjusted road dust

emissions , CEMS data for EGUs, and emissions for areas outside t he U.S. In addition, the modeling platform uses more temporally

- resolved emissions than the NEI for many sectors.

Fire emissions in 2011NEIv2 were developed based on Version 2 of the Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconcil iation (SMARTFIRE) system (Sul livan, 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,

w hich impacted the emissions values and diurnal emissions pattern. Recent updates to SMARTFIRE include improved emission factors for prescribed burning.

For the purposes of preparing the air quality model

- ready emissions, the 2011NEIv2 was split into fin er

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 mer ge program then combines the sector

- specific gridded, speciated

, hourly emissions together to create CMAQ

- ready emission inputs. For CAMx applications, the CMAQ

- ready emissions are then converted into the format needed by CAMx using a convertor program

.6

Table 2

- 1 presents the sectors in the 20 11 platform and how they generally relate to the 2011NEIv2 as a starting point. As discussed in greater detail in Table 2

- 2 , the emissions in some of these sectors were modified from the 2011NEIv2 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, and throughout the remainder of this d ocument.

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 to SMOKE

EGU non

- peaking

units :

p t eg u Point 20 11 NEI v2 point source EGUs

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

, where the

units are match ed to the NEI

. Other pollutants are scaled from

20 11 NEI v 2 using CEMS heat input

. Emissions for all sources n ot
matched to CEMS data come from 2011NEIv2

. For future year emissions, these units are mapped to the Integrated Planning Model (IPM) model results using a cross reference to the National Electric Energy D atabase System (NEEDS) version 5 .1 4

. Annual resolu tion for

non

- matched sources, hourly for CEMS sources

. Point source oil and gas: pt_oilgas Point 2011NEI v 2 point sources that include oil and gas production emissions processes. Annual resolution.

Remaining n on

EGU point :

p tnonipm Point All 2011 NEI v 2 p oint source records not matched to the ptegu or 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 Nonpoint NH 3 emissions f rom 2011 NEI v 2 nonpoint livestock and fertilizer application, county and annual resolution. Agricultural fires: agfire Nonpoint 2011NEIv2 agricultural fire sources . County and monthly resolution.

Area fugitive dust:

a fdust Nonpoint PM 10 and PM 2.5 fugiti ve dust sources from the 2011 NEI v 2 nonpoint inventory ; including building construction, road construct ion, 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.

Biogenic:

Beis Nonpoint Year 20 11 , hour

- specific, grid cell

- specific emissions ge nerated from

the BEIS3. 61 model with in SMOKE , including emissions in Canada

and Mexico. Category 1 & 2

CMV and

locomotives:

c1c2rail Nonpoint L ocomotives and category 1 (C1) and category 2 (C2) commercial marine vessel (CMV) emissions sources from the 201 1 NEI v 2 nonpoint inventory

. C ounty and annual resolution.

commercial

marine:

c3marine Nonpoint Category 3 ( C3 ) CMV emissions from the 2011NEIv2 from the

2011NEIv2 ; see othpt sector for all non

- U.S. emissions.

Remaining nonpoint:

n onpt Nonpoint 2011 NEI v 2 nonpoint sources not included in other platform sectors; county and annual resolution.

Nonpoint source oil and gas: np_oilgas Nonpoint 2011NEIv2 nonpoint sources from oil and gas

- related processes. County and annual resolution.7

Platform Sector:

abbreviation NEI Data

Category Description and resolution of the data input to SMOKE

Residential Wood

Combu stion:

rwc Nonpoint 2011NEIv2 NEI nonpoint sources with Residential Wood Combustion (RWC) processes. County and annual resolution.

Nonroad:

nonroad Nonroad 2011NEIv 2 nonroad equipment emissions developed with the National Mobile Inventory Model (NMIM) us ing NONROAD2008 version

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

, which submitted their own emissions for the 2011NEI v 2

. County and monthly resolution.

Onroad:

onroad O nroad 2011 o nroad mobile source gasoline and diesel vehicles from parking lots and moving vehicles. Includes the following modes: exhaust, extended idle, auxiliary power units, evaporative, permeation, refueling, and brake and tire wear. For all states except California, based on monthly MOVES emissions tables fro m MOVES2014

. 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).

Point source fires:

pt presc fire Fires Point source da y

- specific prescribed fires for 20 11 computed using

SMARTFIRE 2, except for Georgia and Florida

- submitted emissions

. Consistent with 2011NEIv 2

. Note that CMAQ requires all fire emissions to be in a single sector (ptfire) to receive the fire

- specif ic treatment for plume rise , so prescribed and wild must be merged

.

Point source fires:
pt wild fire Fires Point source da y

- specific wild fires for 20 11 computed using SMARTFIRE2, except for Georgia and Florida

- submitted emissions

. Consistent with 2011NEIv2

. Other dust

sources not from

the 2011 NEI: Othafdust N/A Fugitive dust sources from Canada’s 2010 inventory.

Other point

sources not from

the 2011 NEI: othpt N/A Point sources from Canada’s 20 10 inventory and Mexico’s 20 08 inventory, annual resolution. Also includes all non

- U.S. C3 CMV and

U.S. offshore oil production

.

Other non

- NEI nonpoint and

nonroad:

othar N/A Annual year 20 10 Canada (province resolution) and year 20 08 Mexico (municipio resolution) nonpoint and nonroad mobile inventories.

Other no n

- NEI

onroad sources:

othon N/A Year 20 10 Canada (province resolution) and year 20 08 Mexico (municipio resolution) onroad mobile inventories, annual resolution.

Table 2

- 2 provides a brief by

- sector overview of the most significa nt differences between the 20 11 emissions platform and the 2011NEIv2

. O nly those sectors with significant differences between the

2011NEIv2 and the 2011 emissions modeling platform are listed

. The specific by

- sector u pdates to the

20 11 platform are descr ibed in greater detail later in this section under each by

- sector subsection

.8

Table 2

- 2

. Summary of differences between 20 11 v6.2 platform and 2011NEIv2 emissions by s ector

Platform Sector Summary of Signif icant Inventory Differences of 20 11 Platform vs

. 2011NEIv2 Area fugitive dust: afdust 1) Replaced EPA

- provided emission estimates for paved and unpaved road dust

with “non

- met eorologically

- adjusted” emissions , then a djusted emissions to reflect land use (tra nsport) and meteorological effects such as rain and snow

cover which significantly reduce s PM emissions. These adjusted emissions are

known as the afdust_adj sector

. Biogenic:

beis 1) Biogenic emissions changed from 3.60 to 3.61

Remaining nonpoint sector:
nonpt 1) Spli t the 2011NEIv2 nonpoint file into the platform sectors afdust, ag, agfire, beis, np_oil gas, rwc
, c3 marine

, and c1c2rail

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

values , whereas the NEI only stores annual va lues

. Nonroad sector:

nonroad 1) M onthly rather than annual emissions that match 2011NEIv2 totals

.

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

EPA’s 20 11 nonroad estimates.

Onroad sector : O nroad 1) For all states except California: Year 20 11 emissions based on emission

factors from the released version of MOVES2014 , as opposed to the early

MOVES 2014 which was used for the 2011NEIv2

. Developed with SMOKE

MOVES using 20 11 meteorology. 2) Platform contains correction to diesel refueling emissions 3) Platform uses E

- 85 activity data and emission factors, where for NEI E

85 was not included. 4) For California: merged in 20 11 California data to post

- adjust SMOKE

MOVES data (discussed later)

. EGUs :

p t egu 1) Based on 2011NEIv2 and 2011 CEMS data ana lysis by EPA and states, 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 data processing

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

. Hourly NO X an d SO 2 CEMS data replaces annual NO X and SO 2 NEI data in the air quality model inputs for 2011 and scaled hourly data in future years.

Non

- EGUs

sector: ptnonipm 1) Additional matches to IPM_YN codes and ORIS facility codes caused several

sources to move out of ptnonipm and into the pt egu sector. Th e goal is to prevent double counting of EGU emissions in the future years.

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 2011v6.2 “readme” file indicates the particular zipped files associated with each platform sector. A number of reports were deve loped for the 2011 platform. Descriptions of the available data and reports are available

from the FTP site at ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform . The types of reports inclu de state summaries of inventory pollutants and model species by modeling platform sector , county annual totals by modeling platform sector, daily NOx and VOC emissions by sector and total, and state

- SCC

sector summaries. . A comparison of the complete li st of inventory files, ancillary files, and parameter settings for the 2011v6.2 platform modeling cases is also available

.

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

Different levels of det ail are provided for different sectors depending on the availability of reference information for the data, the degree of changes or manipulation of the data needed to prepare it for input to SMOKE, and whether the 2011 platform emissions are significantly different from the 2011NEIv 2

.9



### 2.1 20 11 NEI point sources (pt egu, 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 f acility may have multiple

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

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

. A comprehensive description o f how EGU

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

2011NEIv2 TSD.

The point source file us ed for the modeling platform is exported from EIS into the Flat File 2010 (FF10) format that is compatible with SMOKE (see http://www.cmascenter.org/smoke /documentation/3.5.1/

html/ch08s02s10.html#d0e44906 ). After moving offshore oil platforms into the othpt sector , and dropping sources without true locations (i.e., their FIPS code ends in 777) , initial version s of the other

four platfor m point source sect ors w ere created from the remaining 2011NEIv2 point sources . The point

sectors are : the EGU sector for non

- peaking units (ptegu), point source oil and gas extraction

- related emissions (pt_oilgas) and the remaining non

- EGU sector also called the non

- IPM (p tnonipm) sector. The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal processing and future

- year projection techniques. The oil and gas sector emissions (pt_oilgas) were processed separately for summary tracking purposes and distinct future

- year projection techniques from the remaining non

- EGU emissions (ptnonipm).

The inventory pollutants processed through SMOKE for all point source sectors were: CO, NO X , VOC,

SO 2 , NH 3 , PM 10 , and PM 2.5 and the followin g 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) of the VOC HAP pollutants from the inventory ( VOC integ ration is discussed in detail in Section 0 ).

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

pt egu sources with CEMS data (that could be match ed to the 2011NEIv2 )

, 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 sector , dail y emissions were created using an approach described in Section 2.1.1, and IPM region

- and pollutant

- specific diurnal profiles were applied to create hourly emissions.

C hanges made to the point

- based sectors from the 2011NEIv2 for the 20 11 platform were described in

Table 2

- 2 . Some of these changes involved splitting the stacks, units and facilities into the ptnonipm

, pt_oilgas and ptegu sectors. Sources were included in the pt egu sector only when it was determined that

these s ources were reflected in the future

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



### 2.1.1 EGU s ector (pt egu )

The pt egu sector contain s emissions from EGUs in the 20 11NEIv2 point inventory that could be

matched to units found in the NEEDS v5.1 4 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 NEEDS10

database , and emissio ns for sources in the ptegu sector are replaced with IPM outputs 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. 2 ) or ptnonipm (see Section 2.1. 3 ) sectors and are projected to t he future year using projection and

control factors. It is important that the matching between the NEI and NEEDS database be as complete as possible because there can be double counting of emissions in the future year if emissions for units are projected b y IPM are not properly matched to the units in the NEI

. The 2011v6.1 platform included a ptegu_pk sector, but this was determined not to add significant information and therefore all identified EGUs are in the ptegu sector in the 2011v6.2 platform.

In th e 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 dat abases due to different facility names in the two data systems and due to differences in how the units are defined, thereby resulting in matches that are not always one

- to

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

accommodate some of these situations as described later in this section . The NEEDS v5.1 4 database can be found at http://www.epa.gov/powersectormodeling/psmodel514.html , along with additional

informati on about IPM

. The cross reference between NEEDS 5.14 units and those in the NEI can be

found here: ftp://ftp.epa.gov/EmisInven tory/2011v6/v2platform/reports/ ipm_to_flat_file_xref_2011NEIv2_IPM5.14_20150113.xlsx and is used when IPM outputs are postprocessed to create flat files for modeling. An updated version with additional matches to be used

with later versions of IPM called ipm_to_flat_file_xref_2011NEIv2_Updated_20150710.xlsx is also

available.

Some units in the ptegu sector are matched to CEMS d ata via ORIS facility codes and boiler ID. Many additional units were matched to CEMS data than were matched for the 2011v6.1 platform. For matched units, SMOKE replaces the 2011 emissions of NO X and SO 2 with the CEMS emissions

, thereby ignoring the annual values specified in the NEI. For other pollutants, the hourly CEMS heat input data are used to allocate the NEI annual emissions to hourly values. A ll stack parameters, stack locations,

and SCC codes for these sources come from the NEI. Because the se at tributes 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 not matched to a CEMS unit, the emissions from that source are still modeled using the

annual emission value in the NEI

. EIS stores many matches from EIS u nits 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 CEMS 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 an y smaller 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 identificatio n such as inconsistent plant names in the two data

systems

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

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

NEEDS data base than have CEMS data. The temporalization of EGU units matched to CEMS is based on the CEMS data in the base and future years are based on the base year CEMS data for those units, whereas regional profiles are used for the remaining units. More detail can be found in Section 3.3.2.11

For sources not matched to CEMS data

, daily emissions were computed from the NEI annual emissions using average CEMS data profiles specific to fuel type, pollutant 2 , and IPM region. To allocate

emissions to each hour of the day , 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 tha t 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.

Fi nding additional matches between the NEI, NEEDS, and CEMS data

Several analytical steps were performed to better link the NEEDS units to the 2011NEIv2 , along with implementing better matching to the CEMS data cross

- referenced using “ORIS” facility and boi ler IDs

. The steps described in the 2011NEIv2 TSD have some detail on how the values in the IPM _YN column were assigned. For the modeling platform, an initial ptegu /ptnonipm split w as determined using the

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

- to

- one for an entire unit, if the units are not defined in the same way in EIS and NEEDS, one

- to

- many or many

- to

- m any matches can

onl y be stored in EIS with specified “end dates” and will not export directly to the flat file. However,

one

- to

- many and many

- to

- many matches to the IPM_YN values were placed in to the SMOKE input file through a postprocessing step. This requires the addition al of additional “dummy” records in the

SMOKE file that will be overlaid with CEMS data when SMOKE is run.

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 look ing 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, values that represented the NEEDS IDs were filled in to the IPM_YN in the modeling platform flat files. When possible, these

matches were loaded into EIS.

A similar matching process was used to identify additional matches between the 2011NEIv2 and CEMS

data

. 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 CEMS mat ches 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.

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. Many new CEMS assignments

2 The year to day profiles use NOx and SO2 CEMS for NOx and SO2, respectively. For all oth er pollutants, they use heat input CEMS data.12

were loaded into EIS for use in future inventories

. Note that SM OKE can perform matches of CEMS

data down to the stack or release point

- level , which is finer than unit

- level

.



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

The pt_oilgas sector was separated from the ptnonipm sector by selecting sources with specific NAIC S

codes shown in Table 2

- 3

. This was done differently than in the 2011v6.1 platform and resulted in more sources and emissions in the pt_oilgas sector and fewer sources and emissions in the ptnonipm sector.

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. N onpoint oil and gas emissi ons 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 3.2 0 of the 2011NEIv2 TSD.

Table 2

- 3

. Point source oil and gas sector N AICS Codes

NAICS NAICS description

2111 Oil and Gas Extraction

2212 Natural Gas Distribution

4862 Pipeline Transportation of Natural Gas

21111 Oil and Gas Extraction

22121 Natural Gas Distribution

48611 Pipeline Transportation of Crude Oil

48621 Pipeline Transportation of Natural Gas

211111 Crude Petroleum and Natural Gas Extraction

211112 Natural Gas Liquid Extraction

213111 Drilling Oil and Gas Wells

213112 Support Activities for Oil and Gas Operations

221210 Natural Gas Distribution

486110 Pipeline Transportation of Crude Oil

486210 Pipeline Transportation of Natural Gas



### 2.1.3 Non

- IPM sector (ptnonipm)

Except for some minor exceptions, the non

- IPM (ptnonipm) sector contains the 2011NEIv2 point

sources that are not in t he ptegu 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 ptno nipm sector. The sector includes some ethanol plants that have been identified by EPA and require special treatment in the future

cases as they are impacted by mobile source rules

.

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

. Sources with state/county FIPS code ending with “777” are in the 2011NEIv2 but are not includ ed in any modeling sectors. These sources typically 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 as is needed for mod eling. Therefore, these sources are dropped from the point

- based sectors in the modeling platform.13

EPA estimates for e thanol facilities

Ethanol plants are important facilities for mobile source rules that have impact development work, EPA developed a list of corn ethanol facilities for 2011

. E thanol facilities that were not in 2011 NEI v1 were added into 2011NEIv2. Locations and FIPS codes for these ethanol plants were verified using web searches and Google Earth. EPA believes that some of these sources were not originally included in the NEI as point sources because they do not meet the 100 ton/year potential

- to

- emit threshold for NEI point

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

emission rates for criteria pollutants used to estimate impacts for years 2011 ( and are assumed to be the same in 2018 and 2025) are given in Table 2

- 4

.

Table 2

- 4

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

Corn Ethanol Plant Type VOC CO NO X PM 10 PM 2.5 SO 2 NH 3 Dry Mill Natural Gas (NG) 2.29 0.58 0.99 0.94 0.23 0.01 0.0 0

Dry Mill NG (wet distillers grains with solubles (DGS)) 2.27 0.37 0.63 0.91 0.20 0.00 0.00

Dry Mill Biogas 2.29 0.62 1.05 0.94 0.23 0.01 0.00

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

Dry Mill Coal 2.31 2.65 4.17 3.81 1.71 4.52 0.0 0

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

Dry Mill Biomass 2.42 2.55 3.65 1.28 0.36 0.14 0.00

Dry Mill Biomass (wet DGS) 2.35 1.62 2.32 1.12 0.28 0.09 0.00

Wet Mill NG 2.35 1.62 1.77 1.12 0.28 0.09 0.00

Wet Mill Coal 2.33 1.04 5.51 4.76 2.21 5.97 0.00

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

- 5 were multiplied by facility production estimates for 2011 and 2018 based on analyses performed for the industry

characteriza tion 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, agfire, np_oilgas, rwc, nonpt)

S everal modeling platform sectors were created from the 2011NEIv2 nonpoint inventory . T his sect ion

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

, and C3 CMV are also

in cluded the 2011NEIv2 nonpoint data category , but are mobile sources that are described in Section s 2.4.1 and 2.4.2 as the c1c2rail and c3marine sectors , respe ctively

. The 2011NEIv2 TSD available from http://www.epa.gov/ttn/chief/net/2011inventory.html includes documentation for the nonpoint sector of

the 2011NEIv2

.14

T he nonpoint tribal

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

- counting with county

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

available

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

at the 12

- km scales used for this platform

.

The following subsections descri be how the sources in the 2011NEIv2 nonpoint inv entory were separated into 2011 modeling platform sectors, along with any data that were updated replaced with

non

- NEI data.



### 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 doe s 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 separate d from other nonpoint sectors to allow for the application of a “tran sport fraction,” and meteorological /precipitation reductions. These adjustments are applied with a script that applies land use

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

land use data used to reduce the NEI emissions determines the amount of emissions that are subject to transport. This me thodology 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 Em issions Modeling Platform ” (Adelman, 2012). Both the transport fraction and meteorological adjustments are based on the gridded resolution of the platform (e.g., 12km grid cells) ; therefore, different emissions will result if the process were applied to d ifferent 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 processin g , 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

- 6

.

Table 2

- 6

. SCCs in the afdust platform sector

SCC SCC Description

2275085000 Mobile Sources;Aircraft;Unpaved Airstrip s;Total

2294000000 Mobile Sources;Paved Roads;All Paved Roads;Total: Fugitives

2294000002 Mobile Sources;Paved Roads;All Paved Roads;Total: Sanding/Salting

- Fugitives

2296000000 Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives

2296 005000 Mobile Sources;Unpaved Roads;Public Unpaved Roads;Total: Fugitives

2296010000 Mobile Sources;Unpaved Roads;Industrial Unpa ved Roads;Total: Fugitives

2311000000 Industrial Processes;Construction: SIC 15

- 17;All Processes;Total

2311010000 Industrial Processes;Construction: SIC 15

- 17;Residential;Total

2311020000 Industrial Processes;Construction: SIC 15

- 17;Industrial/Commercial/Institutional;Total15

SCC SCC Description

23110300 00 Industrial Processes;Construction: SIC 15

- 17;Road Construction;Total

2311040000 Industrial Processes;Construction: SIC 15

- 17;S pecial Trade Construction;Total

2325000000 Industrial Processes;Mining and Quarrying: SIC 14;All Processes;Total

2325020000 Industrial Processes;Mining and Quarrying: SIC 14;Crushed and Broken Stone;Total

2325030000 Industrial Processes;Mining and Quarrying: SIC 14;Sand and Gravel;Total

2801000000 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

- Crops;Total

2801000002 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

- Crops;Planting

2801000003 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

- Crops;Tilling

2801000005 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

-

Crops;Harvesting

2801000008 Miscellaneous Area Sources;Agriculture Production

- Crops;Agriculture

-

Crops;Transport

2805001000 Misc ellaneous Area Sources;Agriculture Production

- Livestock;Beef cattle

- finishing

operations on feedlots (drylots);Dust Kicked

- up by Hooves (use 28

- 05

- 020,

- 001,

- 002, or

- 003 for Waste

The dust emissions in the modeling platform are not the same as the 2011NEIv2 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 platform meteorologi cal adjustment . Due to this, in the platform the paved and unpaved road emissions

data used did not includ e any precipitation

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

- specific transport fractions an d meteorological adjustments can be applied. Where states submitted afdust data, it was assumed that the state

- submitted data were not met

adjusted and therefore the meteorological adjustments were still applied

. Thus , it is possible 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 meteorological adjustments for the 2 011NEIv2 are shown

in Table 2

- 7 , where the starting inventory numbers include unadjusted paved and unpaved road dust, so they do not match the NEI values because those include a different type of adjustment. The amount of

t he redu ction ranges from about 7 % in New Hampshire to about 7 2 % in Nevada.

Table 2

- 7

. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory

State Unadjusted

PM10 Unadjusted

PM2_5 Change in

PM10 C hange in

PM2_5 PM10

Reduction PM2_5

Reduction

Alabama 378,874 47,158

- 310,750

- 38,597 18% 18%

Arizona 237,361 30,015

- 78,519

- 9,778 67% 67%

Arkansas 421,958 58,648

- 305,611

- 40,757 28% 31%

California 255,889 38,664

- 119,035

- 17,930 54% 54%

Colorado 24 4,630 40,421

- 130,598

- 20,991 47% 48%

Connecticut 29,067 4,393

- 25,877

- 3,912 11% 11%

Delaware 11,548 1,968

- 8,219

- 1,396 29% 29%

D.C. 2,115 337

- 1,596

- 254 25% 25%

Florida 292,797 39,637

- 181,017

- 24,333 38% 39%
Georgia 733,478 90,041

- 593,644

- 72,02 8 19% 20%

Idaho 432,116 49,294

- 291,880

- 32,897 33% 33%

Illinois 763,665 123,680

- 472,806

- 76,086 38% 39%16

State Unadjusted

PM10 Unadjusted

PM2_5 Change in

PM10 C hange in

PM2_5 PM10

Reduction PM2_5

Reduction

Indiana 603,152 85,151

- 435,027

- 60,660 28% 29%

Iowa 590,528 96,070

- 339,349

- 54,855 43% 43%

Kansas 747,242 118,726

- 352,559

- 54,760 53% 54%

Ken tucky 199,744 29,496

- 160,640

- 23,511 20% 20%

Louisiana 236,787 35,730

- 162,780

- 24,086 31% 33%

Maine 50,547 7,016

- 43,643

- 6,078 14% 13% Maryland 65,701 10,215

- 49,481

- 7,691 25% 25%

Massachusetts 205,561 22,444

- 177,808

- 19,370 14% 14% Michigan 462, 324 61,969

- 353,225

- 47,137 24% 24%

Minnesota 336,791 64,253

- 217,036

- 41,145 36% 36% Mississippi 956,702 107,965

- 782,249

- 86,685 18% 20%

Missouri 1,063,992 130,995

- 780,488

- 94,576 27% 28%

Montana 385,541 50,583

- 266,046

- 33,521 31% 34%

Nebraska 591 ,457 85,206

- 316,918

- 45,198 46% 47%

Nevada 160,699 20,477

- 47,147

- 5,688 71% 72%
New Hampshire 25,540 3,766

- 23,836

- 3,515 7% 7%
New Jersey 24,273 5,412

- 19,215

- 4,255 21% 21%

New Mexico 924,497 95,871

- 352,117

- 36,344 62% 62%

New York 274,114 37,493

- 236,431

- 31,990 14% 15%

North Carolina 186,650 33,409

- 146,918

- 26,184 21% 22%

North Dakota 354,107 59,113

- 218,630

- 36,286 38% 39%

Ohio 414,902 64,609

- 319,831

- 49,298 23% 24%

Oklahoma 733,750 87,864

- 385,344

- 44,585 48% 49%
Oregon 348,093 40,596

- 268,605

- 30,516 23% 25%
Pennsylvania 208,246 30,344

- 179,991

- 26,158 14% 14%

Rhode Island 4,765 731

- 3,628

- 564 24% 23%

South Carolina 259,350 31,494

- 198,175

- 24,002 24% 24%

South Dakota 262,935 44,587

- 155,938

- 26,215 41% 41%

Tennessee 139,731 25,35 7

- 107,964

- 19,514 23% 23%

Texas 2,573,687 304,551

- 1,278,053

- 146,122 50% 52%

Utah 196,551 21,589

- 113,837

- 12,464 42% 42%

Vermont 67,690 7,563

- 61,423

- 6,855 9% 9% Virginia 131,798 19,374

- 108,700

- 15,895 18% 18%
Washington 174,969 27,999

- 99,720

- 1 5,425 43% 45% West Virginia 85,956 10,652

- 79,745

- 9,888 7% 7%

Wisconsin 239,851 41,669

- 164,113

- 28,542 32% 32% Wyoming 434,090 45,350

- 264,580

- 27,467 39% 39%

Domain Total 18,525,814 2,489,943

- 11,790,743

- 1,566,004 36% 37%

Figure 2

- 1 shows the impact of each step of the adjustment for 2011. T he reductions due to the transport fraction adjustments alone are shown at the top of Figure 2

- 1 . The reductions due to the precipitation adjustments are sho wn in the middle of Figure 2

- 1 . The cumulative emission reductions after both transport fraction and meteorological adjustments are shown at the bottom of Figure 2

- 1

. The top plot

shows how the transport fraction has a larger reduction effect in the east

, where forested areas are more effective at reducing PM transport than in many western areas. The middle plot shows how the meteorological impacts of precipitation, along with snow cover in the north, fur ther reduce the dust

emissions.17

Figure 2

- 1

. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and

cumulative18



### 2.2.2 Agricultural ammonia sector (ag)

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

the 2011NEIv2 nonpoint inventory. The livestock and fertilizer emissions in this sector are based only

on the SCC s listed in Table 2

- 8 and Table 2

- 9

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

.

Table 2

- 8

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

SCC SCC Descri ption*

2805001100 Beef cattle

- finishing operations on feedlots (drylots);Confinement

2805001200 Beef cattle

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

2805001300 Beef cattle

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

2805002000 Beef cattle production composite;Not Elsewhere Classified

2805003100 Beef cattle

- finishing operations on pasture/range;Confinement

2805007100 Poultry production

- layers with dry manure management systems;Confinemen t

2805007300 Poultry production

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

2805008100 Poultry production

- layers with wet manure management systems;Confinement

2805008200 Poultry production

- layers with wet manure managemen t systems;Manure handling and storage

2805008300 Poultry production

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

2805009100 Poultry production

- broilers;Confinement

2805009200 Poultry production

- broilers;Manure handling and storage

2805009300 Poultry production

- broilers;Land application of manure

2805010100 Poultry production

- turkeys;Confinement

2805010200 Poultry production

- turkeys;Manure handling and storage

2805010300 Poultry production

- turkeys;Land application of manure

2805018000 Dairy cattle composite;Not Elsewhere Classified

2805019100 Dairy cattle

- flush dairy;Confinement

2805019200 Dairy cattle

- flush dairy;Manure handling and storage

2805019300 Dairy cattle

- flush dairy;Land application of manure

2805020000 Cattle and Calves Waste Emissions;Milk Total

280502000 2 Cattle and C alves Waste Emissions:Beef Cows

2805021100 Dairy cattle

- scrape dairy;Confinement

2805021200 Dairy cattle

- scrape dairy;Manure handling and storage

2805021300 Dairy cattle

- scrape dairy;Land application of manure

2805022100 Dairy cattle

- deep pit dairy;Confinement

2805022200 Dairy cattle

- deep pit dairy;Manure handling and storage

2805022300 Dairy cattle

- deep pit dairy;Land application of manure

2805023100 Dairy ca ttle

- drylot/pasture dairy;Confinement

2805023200 Dairy cattle

- drylot/pasture dairy;Manure handling and storage

2805023300 Dairy cattle

- drylot/pasture dairy;Land application of manure

2805025000 Swine production composite;Not Elsewhere Classified ( see also 28

- 05

- 039,

- 047,

- 053)

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

- 05

- 007,

- 008,

- 009)

2805030003 Poultry Waste Emissions;Layers

2805030004 Poultry Waste Emissions;Broilers

2805030007 Poultry Waste Emissions;Ducks

2805030008 Poultry Waste Emissions;Geese

2805030009 Poultry Waste Emissions;Turkeys

2805035000 Horses and Ponies Waste Emissions;Not Elsewhere Classified

2805039100 Swine production

- operations with lagoons (unspecified animal age);Confinement

280503 9200 Swine production

- operations with lagoons (unspecified animal age);Manure handling and storage19

SCC SCC Descri ption*

2805039300 Swine production

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

2805040000 Sheep and Lambs Waste Emissions;Tota l

2805045000 Goats Waste Emissions;Not Elsewhere Classified

2805045002 Goats Waste Emissions;Angora Goats

2805045003 Goats Waste Emissions;Milk Goats

2805047100 Swine production

- deep

- pit house operations (unspecified animal age);Confinement

28050473 00 Swine production

- deep

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

2805053100 Swine production

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

– Livestock”

Table 2

- 9

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

SCC SCC Description*

2801700001 Anhydrous Ammonia

2801700002 Aqueous Ammonia

2801700003 Nitrogen Solutions

2801700004 Urea

2801700005 Ammonium Nitrate

2801700006 Ammonium Sulfate

2801700007 Ammonium Thiosulfate

2801700008 Other Straight Nitrate

2801700009 Ammonium Phosphates

2801700010 N

- P

- K (multi

- grade nutrient fertilizers)

2801700011 Calci um Ammonium Nitrate

2801700012 Potassium Nitrate

2801700013 Diammonium Phosphate

2801700014 Monoammonium Phosphate

2801700015 Liquid Ammonium Polyphosphate

2801700099 Miscellaneous Fertilizers

* All descriptions include “Miscellaneous Area Sources;

A griculture Production

– Crops; Fertilizer Application ” as the beginning of the description.



### 2.2.3 Agricultural fires (agfire)

The agricultural fire (agfire) sector contains emissions from agricultural fires. These emissions were placed into the sector based o n their SCC code. All SCCs starting with 28015 were included. The first three levels of descriptions for these SCCs are: Fir es

- Agricultural Field Burning; Miscellaneous Area

Sources ; Agriculture Production

- Crops

- as nonpoint ; Agricultural Field Burni ng

- whole field set on

fire

. The SCC 2801500000 does not specify the crop type or burn method, while the more specific SCCs specify field or orchard crops

, and in some cases the specific crop being grown. For more information on how emissions for agric ultural fires were developed in the 2011NEIv2, see Section 5.2

of the 2011NEIv2 TSD.



### 2.2.4 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 fo r all counties with 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, p neumatic pumps and other devices,20

storage tanks, flares, truck loading, compressor engines , and dehydrators

. For more information on the development of the oil and gas emissions in the 2011NEIv2 , see Section 3.2 0 of the 2011NEIv2 TSD. A complete list of S CCs for the np_oilgas modeling platform sector is provided in Appendix A

. See the pt_oilgas sector (section 2.1.2 ) for more information on point source oil and gas sources. Updates were made to the speciation, temporalization , and spatial allocation of sources in the 2011v6.2 platform based

on comments that were received. S ections 3.2, 3.3, and 3.4 provide additional details.



### 2.2.5 Residential wood combustion sector (rwc)

The residential wood combustion (rwc) sector includes resid ential 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

chimneas

. F

ree s tanding woodstoves and inserts are further differentiated into three categories: conventional (not EPA certified); EPA certified, catalytic; and EPA certified, noncatalytic. Generally speaking, the conventional units were constructed prior to 1988. Units c onstructed 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 2011NEIv2 TSD.

The SCCs in the rwc sector are shown in Table 2

- 10

. Some reductions to Clark County, NV were implemented in the modeling platform as a result of data review for NATA , but these are not reflected in

the 2011NEIv2.

Table 2

- 10

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

SCC SCC Description

2104008100 SSFC;Residential;Wood;Fireplace: general

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

- EPA certified

2104008220 SSFC;Residential;W ood;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;Woo dstove: freestanding, non

- EPA certified

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

- catalytic

2104008330 SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, catalytic

2104008400 SSFC;Residential;Wood;Woodstove: pellet

- fired, general (freestanding or FP insert)

210400842 0 SSFC;Residential;Wood;Woodstove: pellet

- fired, EPA certified (freestanding or FP

insert)

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

- fired, non

- EPA certified

2104008610 SSFC;Re sidential;Wood;Hydronic heater: outdoor

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

- pits, chimeas, etc)

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



### 2.2.6 Other nonpoint so urces 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 2011NEIv2 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;21

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

 solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic marking, textile pro duction, 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, p lastics, industrial processes, personal care products, household products, adhesives and sealants;

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

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

 storage and transport of chemi cals;

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

 agricultural burning and orchard heating;

 misce llaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair shops.



### 2.3 2011 onroad mobile sources (onroad)

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

- utility vehicles, light

- duty trucks, heavy

- duty trucks, and buses . The sources are further divide d between diesel

, gasoline , E

- 85, and compressed natural gas (CNG) vehicles. The sector characterizes emission s from parked vehicle

processes (e.g. starts, hot soak, and extended idle) as well as from on

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

. Except for California, all onroad emissions are generated using the

SMOKE

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

. The SCCs used in the 2011v6.2 platform are significantly different th an what was used in previous platforms. The new onroad SCCs were designed to be more consistent with MOVES. For more details, see the 2011NEIv2 TSD. Updates to speciation, temporal allocation, and spatial allocation for the onroad sector were made in the 2011v6.2 platform as a result of comments received and upgrades to the platform. S ections 3.2, 3.3, and

3.4 contain more details.



### 2.3.1 Onroad (onroad)

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

- road emissions. Specifically, EPA used MOVES inputs for representative counties, vehicle miles traveled (VMT) and vehicle population (VPOP) data for all counties, along with tools that integrated the MOVES model with SMOKE. In this way, it was po ssible to take advantage of the gridded hourly temperature information available from meteorology modeling used for air quality modeling. The “SMOKE

- MOVES” integration tool was developed by EPA in 2010 and is used for regional air quality modeling of onro ad 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 gen erate the MOVES emission rates that could be applied22

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 appropriate emissions rates for each county, hourly temperature, SCC, and speed bin and multiplied the emission rate by activity (VMT (vehicle miles travelled)

, VPOP (vehicle population)) , or HOTE LING (hours of exten ded idle) to produce emissions. These calculations were done for every county and grid cell, in the continental U.S. for each hour of the year

.

The SMOKE

- MOVES process for creating the model

- ready emissions consists of the following steps:

1) Determine wh ich counties will be used to represent other counties in the MOVES runs

. 2) Determine which months will be used to represent oth er 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 temperatures and activity data.

5) Run MOVES to create emission factor tables for the tempe ratures found in each county

. 6) Run SMOKE to apply the emission factors to activity data (VMT

, VPOP , and HOTELING ) to

calculate emissions based on the gridded hourly temperatures in the meteorological data.

7) Aggregate the results to the county

- SCC level for s ummaries and quality assurance

.

The onroad emissions inputs are similar to the emissions in the onroad data category of the 2011NEIv 2

, described in more detail in Section 4.6 of the 2011NEIv 2 TSD . Spe cifically the platform and

2011 NEIv 2 have identical:

 MO VES County databases (CDBs)

 Representative counties

 Fuel months

 Meteorology

 Activity data (VMT, VPOP, speed , HOTE LING )

T here are some key differences between the two onroad emission inventories:

 The 2011 platform used a different 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 2 011 platform had separate activity data for E

- 85. For the 2011 NEIv2, E

- 85 VMT and VPOP were combined with gasoline VMT and VPOP because the CDBs were not generating a complete set of EF for the E

- 85 sources. EPA corrected the seeding script and regenera ted the representative CDBs so that the EF tables had a complete set of EF for all fuel types.

 The seeding of some representative counties left out the generation of combination long

- haul trucks on rural restricted highways and therefore for no emission fa ctors were generated and as a result hoteling emissions were zero. To mitigate this, the hoteling emission factors were taken from other nearby counties.23

 The MOVES code and MOVES default database for the platform were version 20141021cb6

and movesdb2014102 1cb6, respectively. Also f or the platform, EPA updated the internal speciation of TOG to provide revised CB05 and CB6 model

- species for air quality modeling.

 The 2011 platform used an updated fuel da tabase: movesdb20141021_fuelsupply

.

 The treatment of Cal ifornia emissions differs between the two inventories (see below for more details).

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

MOVES runs were generated at the same level of detail, but the NEI emissions were aggr egated

into 2 all

- inclusive modes: refueling and all other modes

. In addition, the NEI SCCs were aggregated over roads to all parking and all road emissions. The list of modes (or aggregate processes) used in the v6.2 platform and the corresponding MOVES processes mapped to them

are listed in Table 2

- 11

.

Table 2

- 11

. Onroad emission aggregate processes

A ggregate process D escription MOVES process IDs

40 All brake and tire wear 9;10

5 3 All extended idle exhaust 17;90

62 All refueling 18;19

72 All exhaust and evaporative except refueling and hoteling 1;2;11;12;13;15;16

91 Auxiliary Power Units 91

One reason that brake and tire wear was split out from the other processes was to allo w for better modeling of the impacts of electric vehicles in future years, since these vehicles still have brake and tire wear emissions, but do not have exhaust, evaporative, or refueling emissions. For more detailed

information on methods used to develo p the onroad emissions and input data set s and on running

SMOKE

- MOVES

, see the 2011NEIv 2 TSD.

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

- supplied annual emissions (from the 2011NEIv 2 ) with EPA developed SMOKE

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

- MOVES. The basic steps involved in temporally allocating California’s onroad emissions based on SMOKE

MOVES results were:

1) Run CA using EPA inputs through SMOKE

- MOVES to produce hourly 2011 emissions

hereafter known as “EPA estimates”

. These EPA estimates for CA are run in a separate sec tor called “onroad_ca”.

2) Calculate ratios between state

- supplied emissions and EPA estimates 3 . For California, these

were calculated for each county/ comparison SCC6 4 / pollutant combination . These were not

3 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 for NO, NO 2 , HONO and use d the PM 2.5 ratio for P EC, PNO 3 , POC, PSO 4 etc (For

more details on NO X and PM speciation, see Sections 3.2.3 and 0 ) . 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

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

4 Comparison SCC6 are aggregate codes to classify fuels and vehicle type. These were created to make a consistent comparison between the MOBILE6 era SCCs (2011 NEIv1 and before) and MOVES SCCs (2011 NEIv2). See the 2011

NEIv2 TSD for more details.24

calculated at a greater resolution because Californ ia’s emissions did not provide data for all vehicle type s.

3) Create an adjustment factor file (CFPRO) that includes EPA

- to

- state estimate ratios.

4) Rerun CA through SMOKE

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

T hrough t his process

, adjust ed model

- ready files were created that sum to California’s annual totals

, but have the temporal and spatial patterns reflecting the highly resolved meteorology and SMOKE

- MOVES. After adjusting the emissions, this sector is called “onroad_ca_adj”. Note tha t in emission summaries,

the emissions from the “onroad” and “onroad_ca_adj” sectors are summed and designated as the

emissions for the onroad sector.

An additional step was taken for the refueling emissions. Colorado submitted point emissions for gasoli ne refueling for some counties 5 . For these counties, EPA zeroed out the onroad estimates of gasoline refueling (SCC 2201*62) so that the states’ point emissions would take precedence. The onroad refueling emissions were zeroed out using the adjustment fa ctor file (CFPRO) and Movesmrg.



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

The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions (nonroad) and locomotive and commercial marine vessel (CMV) emissions divid ed into two nonroad

sectors: “c1c2rail” and “c3marine”.



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

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

locomotives . Railway ma intenance emissions are included in the nonroad sector. The “c1c2” portion of this sector name refers to the Category 1 and 2 CMV emissions, not the railway emissions. The C3

CMV emissions are in the c3marine and othpt sector s

. All emissions in this sect or are annual and at the

county

- SCC resolution

. As discussed in Table 2

- 1 and Table 2

- 2 , the modeling platform emissions for

the c1c2rail SCCs were extracted from the 2011NEIv2 nonpoint inventory using t he SCCs listed in

Table 2

- 12

. Th e emissions include the offshore portion of the C1 and C2 commercial marine sources

, including fishing vessels and oil rig support vessels in the Gulf of Mexico. Emissions that occur outside

of stat e waters are not assigned to states

. For more information on CMV and locomotive sources in the NEI, see Section 4.3 and Section 4.4 of the 2011NEIv2 TSD , respectively

.

Table 2

- 12

. 2011NEIv2 SCCs extracted f or 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 Loc omotives: Class I Operations

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

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

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

22850020 10 Railroad Equipment; Diesel; Yard Locomotives

5 There were 53 counties in Colorado that had point emissions for gasoline refueling. Outside Colorado, it was determined that refueling emissions in the 2011 NEIv2 point did not significantly o verlap the refueling emissions in onroad.25



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

The Category 3 (C3) CMV sources in the c3marine sector of the 2011v6.2 platform run on residual oil

and use the SCCs 2280 003100 and 2280003200 for port and underway emissions , respectively, and are

consistent with the 2011NEIv2. Emissions for this sector use state

- submitted values and EPA

- developed

emissions in areas where states did not submit. A change in the 2011v6.2 pla tform is to restrict this sector only to include emissions in state

- waters and to treat the emissions as nonpoint sources instead of point sources. Thus, the c3marine emissions are placed in layer 1 and allocated to grid cells using spatial

surrogates

. Not e that there are no Category 3 CMV emissions in the Great Lakes states, as all the vessels in that region are smaller than C3. The development of the ECA

- IMO

- based C3 CM V inventory is

discussed below. Canadian emissions, C3 CMV emissions outside of state w aters, and non

- U.S. emissions farther offshore than U.S. waters are processed in the “othpt” sector (see Section 2.5.1 )

.

The EPA

- estimated C3 CMV emissions were developed based on a 4

- km resolution ASCII raster format dataset that preserves shipping lan es. This dataset has been used since the Emissions Control Area

International Marine Organization ( ECA

- IMO ) project began in 2005 , although it was then known as the Sulfur Emissions Control Area (SECA)

. The ECA

- IMO emissions consist of large marine diese l engines (at or above 30 liters/cylinder) that until recently were allowed to meet relativel y modest emission requirements and as a result these ships would often burn residual fuel in that region . The emissions in this sector are comprised of primarily foreign

- flagged ocean

- going vessels , referred to as

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

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

, and includes near

- port auxiliary engine emission 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

- cyl inder displacement at or above 30 liters , and the establishment of Emission Control

Areas is available from http://www.epa.gov/oms/oceanvessels.htm . The base year ECA inventory is

2002 and consists o f 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. Thes e growth factors ar e provided in Table

2

- 13

. 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). Technically, the EEZ FIPS are not really “FIPS” state

- county codes, but are treated as such in the inventory and emissions processing.

Table 2

- 13

. Growth factors to project th e 2002 ECA

- IMO inventory to 2011

Region EEZ FIPS NO X PM 10 PM 2.5 VOC 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 0.524

South Pacific (SP) 85002 1.367 0.525 0.521 1.565 1.562 0.611

Great Lakes (GL) n/a 1.072 0.394 0.390 1.177 1.176 0.415

Outside ECA 98001 1.341 1.457 1.457 1.457 1.457 1.45726 Figure 2

- 2

. Illustration of regional modeling domains in ECA

- IMO study

The emissions were converted to SMOKE point source inventory format as described in http://www.epa.gov/ttn/chief/conference/ei17/session6/mas on.pdf

, allowing for the emissions to be allocated to modeling layers above the surface layer. 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

mil es (nm) from the coast, assigned stack parameters, and monthly ASCII raster dataset emissions were used to create monthly temporal profiles. All no n

- US , non

- EEZ emissions (i.e., in waters considered
outside of the 200 nm EEZ, and hence out of the U.S. and Canadian ECA

- IMO controllable domain)

we re simply assigned a dummy state/county FIPS code =98001 , and were projected to year 2011 using

the “Outside ECA” factors in Table 2

- 13

.

The assignment of U.S. state/county FIPS codes was restricted to state

- federal water boundaries data from the Mineral Management Service (MMS) that extend approximately 3 to 10 nautical miles (nm) off shore. Emissions outside the 3 to 10 mile MMS boundary, but within the approximately 200 nm EEZ

boundari es in Figure 2

- 2 , were projected to year 2011 using the same regional adjustment factors as the U.S. emissions; however, the state/county FIPS codes were assigned as “EEZ” codes and tho se 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 Midwest states and othe r 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 t he Midwest RPO are processed in the “c1c2rail” sector.

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

do main used by EPA in recent years 6

.

6 The extent of the “36US1” domain is similar to the full geographic region shown in Figure 3

- 1. Note that this domain is not specifically used in this 2011 platform, although spatial surrogates that can be used wi th it are provided.27

The original ECA

- IMO inventory did not delineate between ports and underway emissions (or other C3 modes such as hoteling, maneuvering, reduced

- speed zone, and idling). However, a U.S. ports spatial surrogate dataset w as used to assign the ECA

- IMO emissions to ports and underway SCCs 2280003100 and 2280003200, respectively. This had no effect on temporal allocation or speciation because all C3 CMV emissions, unclassified/total, port and underway, share the same tempora l and speciation profiles.

See Section 3.2.1.3 for more details on c3marine speciation and Section 3.3.6 for details on temporal

allocation.

For California, the ECA

- IMO 2011 emissions were scaled to match those provided by CARB for year 2011 because CARB has had distinct projection and control approaches for this sector since 2002. These CARB C3 CMV emissions are documented in a staff report available at: http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf . The CMV emissions obtained from the CARB nonroad mobile dataset include the 2011 regulations to reduce emissions from diesel engines on

commercial harbor craft operated within California waters and 24 nautical miles of the California shoreline. These emissions were developed using Version 1 of the California Emissions Projection Analysis Model (CEPAM) that supports various California off

- road regulations. The locomotive

emissions were obtained from the CARB trains dataset “ ARMJ_RF#2002_ANNUAL_TRAINS.txt ”. Documentation of the CARB offroad mobile methodology, including c1c2rail sector data, is provided at: http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles

.



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

The nonroad equipment emissions are equivalent to the emissions in the nonroad data category of the

2011NEIv 2

, 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

NMIM for all states ex cept California and Texas

. Additional details on the development of the

2011NEIv 2 nonroad emissions are available in Section 4.5 the 2011NEIv 2 TSD. Note that the nonroad

emissions for 2011NEIv2 are the same as those in the 2011NEIv1 for all states except Delaware.

California year 20 11 nonroad emissions were submitted to the 2011NEIv2 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 up dates 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 Implemen tation Plans (SIPS) for the South Coast and San Joaquin Valley air basins (ARB, 2010b).

T he CARB

- supplied nonroad annual inventory emissions values were converted to monthly values by using the aforementioned EPA NMIM monthly inventories to compute monthl y 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 invent ories had a more consistent coverage of sources

. Some VOC

emissions were added to California to account for situations when VOC HAP emissions were included in the inventory, but there were no VOC emissions. These additional VOC emissions were computed by summing benzene, acetaldehyde, and formaldehyde for the specific sources.28

Texas year 2011 nonroad emissions were also submitted to the NEI. The 2011NEIv2 nonroad annual inventory emissions values were converted to monthly va lues by using EPA ’s NMIM m onthly inventories to compute monthly ratios by county, SCC7 , mode, and poll 7

.

Some updates to spatial surrogate assignments for the nonroad sector were made in the 2011v6.2 platform in response to comments. Details can be found in Section 3.4.



### 2.5 “Other Emi

ssions”: Offshore Category 3 commercial marine vessels and drilling platforms and non

- U.S. sources

The emissions from Canada, Mexico, and non

- U.S. offshore Category 3 Commercial Marine Vessels (C3 CMV) and drilling platforms are included as part of three e missions modeling sectors: othpt, othar, and othon. The “oth” refers to the fact that these emissions are usually “other” than those in the U.S.

state

- county geographic FIPS, and the third and fourth characters provide the SMOKE source types: “pt” for po int, “ar” for “area and nonroad mobile”, and “on” for onroad mobile.



### 2.5.1 Point sources from offshore C3 CMV

, drilling platforms

, Canada and Mexico (othpt)

As discussed in Section 2.4.2, the ECA

- IMO

- based C3 CMV emissions outside of state waters are processed i n the othpt sector. These C3 CMV emissions include those assigned to U.S. federal waters, Canada, those assigned to the Exclusive Economic Zone ( EEZ; defined as those emissions beyond the

U.S. Federal waters approximately 3

- 10 miles offshore, and extendin g to about 200 nautical miles from the U.S. coastline), along with any other offshore emissi ons. T hese emissions are developed in the same

way as the EPA

- dataset described in the c3marine sector (see Section 2.4.2 )

. Emissions in U.S. waters are aggregated into large regions and included in the 2011NEIv2 using special FIPS codes. Because these emissions are treat as point sources, shipping lane routes can be preserved and they may be

allocated to air quality m

odel layers highe r than layer 1

.

For Can a dian point sources, 2010 emissions provided by Environment Canada were used. Note that VOC was not provided for Canadian point sources, but any VOC emissions were speciated into CB05 species. Temporal profiles and speciated emissi ons were also provided. Point sources in Mexico were compiled based on the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014 a ). The p oint

source emissions in the 2008 inventory were converted to English units and into the FF10 format that

could b e read by SMOKE , missing stack parameters were gapfilled using SCC

- based defaults

, and latitude and longitude coordinates were verified and adjusted if they were not consistent with the reported municipality

. Note that t here are no explicit HAP emissions i n this inventory.

T he othpt sector also includes point source offshore oil and gas drilling platforms that are beyond U.S.

state

- county boundaries in the Gulf of Mexico. For these offshore emissions, data from the 2011NEIv2

were used

.



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

, othafdust )

For Canada area and nonroad mobiles sources

, year

- 2010 emissions provided by Environment Canada

were used , including C3 CMV emissions

. An overlap between these emissions and the U.S. C3 CMV

7 If there was no match at county/SCC7/mode/poll, the allocation would fall back to state/SCC7/mode/poll. If that did not find a match, then state/SCC7 was used. For a few situations, that would also fail to match and the monthly emis sions were

allocated with a similar SCC7.29

emissions in the St. Lawrence River resulted in a double count in the 2011v6.2 platform cases. The Canadian inventory included fugitive dust emissions that do not incorporate either a transportable

fraction or met eorological

- based adjustments. To properly account for these issues, a separate sector
called othafdust was created and modeled using the same adjustments as are done for U.S. sources (see Section 2.2.1 for more details). Updated Shapefiles for creating spatial surrogates for Canada were also provided.

A rea and nonroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones
de Mexico, 2008 (ERG, 2014 a ). The 2008 emissions were quality assured for completeness

, SCC assignments were made when needed

, the pollutants expected for the various processes were reviewed

, and adjustments were made to ensure that PM 10 was greater than or equal to PM 2.5 . The resulting inventory was written using English units to the nonpoint FF10 format that could be read by SMOKE.

Note that unlike the U.S. in ventories, there are no explicit HAPs in the nonpoint or nonroad inventories for Canada and Mexico, and therefore all HAPs are created from speciation.



### 2.5.3 Onroad mobile sources from Canada and Mexico (othon)

Onroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014 a ). SCCs compatible with the 2011NEIv2 were assigned to the 2008 onroad
mobile source emissions in Mexico, and it was enforced that PM 10 be greater than or equal to PM 2.5

. Quality assurance of the onroad mobile source emissions data revealed that Baja California, Michoacán, and Nuevo León had significantly high per capita emissions for all pollutants and should be considered

to be outliers . The emissions for these states were replaced with va lues computed based on the average per capita emissions for the remaining states. T he data were written using English units to the nonpoint FF10 format that could be read by SMOKE.

For Canada, year

- 2010 emissions provided by Environment Canada were used. Note that unlike the U.S. inventories, there are no explicit HAPs in the onroad inventories for Canada and Mexico, and therefore all HAPs are created from speciation.



### 2.6 Fires (pt wild fire

, ptprescfire

)

In the 2011v6.2 platform, w ildfire s are in the ptwildfi re sector and prescribed burning emissions are contained in the pt presc fire sector. The sectors were split from the single ptfire sector used in previous emissions to aid in the analysis of the impacts of the different types of fires. Fire emissions are specified at geographic coordinates (point locations) and ha ve daily emissions values. The pt wild fire and ptprescfire sector s exclude agricultural burning and other open burning sources that are included in the nonpt sector. Emissions are day

- specific an d include satellite

- derived latitude/longitude of the fire’s origin and other parameters associated with the emissions such as acres burned and fuel load, which allow estimation of plume rise. Emissions for the SCCs listed in Table 2

- 14 are treated as point sources and are consistent with the fires stored in the Events data category of the 2011NEIv2

. Emissions for

SCC 2811015000 are in the ptprescfire sector, while the rest are in the ptwildfire sector. For more

information on the development of the 2011NEIv2 fire inventory, see Section 5.1 of the 2011NEIv2

TSD.30

Table 2

- 14

. 20 11 Platform SCCs representing emissions in the pt presc fire and ptwildfire modeling

sector s

SCC SCC Description *

2810001000 Other Combustion; Forest Wildfires; Total

2810001001 Other Combustion; Forest Wildfires; Wildland fire use

2811015000 Other Combustion

- as Event; Prescribed Burning for Forest Management; Total * The first tier level of the SCC Description is “Miscellaneous Area Sources”

The point source day

- 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 locati on 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 emissions from wildfires and prescribed burns on a daily basis. The method involves the reconciliation of ICS

- 209 reports (Incident Status Summary Reports) with satellite

- based fire detections to determine spat ial and temporal information about the

fires. A functional diagram of the SMARTFIRE 2 process of reconciling fires with ICS

- 209 reports is available in the documentation (Raffuse, et al., 2007) . Once the fire reconciliation process is completed,

the emis sions 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

2011NEIv2 TSD. These changes mad e the data in the ptfire inventory consistent with the data in the

2011NEIv2

.

An update in the 2011v6.2 platform was to split fires over 20,000 acres into the respective grid cells that they overlapped. The idea of this was to prevent all emissions fro m going into a single grid cell, when in reality the fire was more dispersed than a single point. The large fires were each projected as a circle

over the area centered on the specified latitude and longitude

, and then apportioned into the grid cells they overlapped . T he area of each of the “ subfires ” was computed in proportion to the overlap with that grid cell. The se “subfires” were given new names that were the same as the original, but with “_a”, “_b”, “_c”, and “_d” appended as needed

. The FIPS state and county codes and fire IDs for the f ifteen

fires apportioned to multiple grid cells are shown in Table 2

- 15

.

Table 2

- 15

. Large f

ires apportioned to multiple grid cells

County FIP S F ire ID

32007 SF11C1774898

32007 SF11C1775252

32013 SF11C1774993

35027 SF11C1760072

35027 SF11C1760460

46065 SF11C1503125

48003 SF11C1718109

48081 SF11C1742329

48125 SF11C174935831 County FIP S F ire ID

48243 SF11C1738273

48243 SF11C1747162

48353 SF11C1759082

48371 SF11C1750272

48415 SF11C1742358

56013 SF11C1791126



### 2.7 Biogenic sources ( beis )

B iogenic emissions were computed based on the same 11g version of the 20 11 meteorology data used for the air quality modeling, and were developed using the Biogenic Emission In ventory System, version 3.61 (BEIS3.61 ) within SMOKE

. This was an update from the emissions in the 2011v6.1 platform that

used BEIS 3.14 , and from the 2011NEIv2 that used BEIS 3.60 . Like BEIS 3.14, BEIS3.61 creates gridded, hourly, model

- species emission s from vegetation and soils. It estimates CO, VOC (most notably isoprene, t erpe ne, and sesquiterpene ) , and NO emissions for the contiguous U.S. and for portions of M exico and Canada.

BEIS3.61 has some important updates from BEIS 3.14. These include th e incorporation of Version 4 of the Biogenic Emissions Land use Database (BELD4), and the incorporation of a canopy model to

estimate leaf

- level temperatures (Pouliot and Bash, 2015). BEIS version 3.61 includes a two

- layer canopy model. Layer structure va ries with light intensity and solar zenith angle. Both layers of the canopy model include estimates of sunlit and shaded leaf area based on solar zenith angle and light intensity, direct and diffuse solar radiation, and leaf temperature (Bash et al., 2015)

. The new algorithm requires additional meteorological variables over previous versions of BEIS. The variables output from

the Meteorology

- Chemistry Interface Proce ssor (MCIP) that are used to convert WRF outputs to CMAQ inputs are shown in Tab le 2

- 16

.

BELD4 is based on an updated version of the USDA

- USFS Forest Inventory and Analysis (FIA) vegetation speciation based data from 2002 to 2013 from the Forest Inventory and Analysis version 5.1

. Canopy coverage is based on the Landsa t satellite NLCD product from 2001, since no canopy product was developed for the 2006 NLCD. The FIA includes approximately 250,000 representative plots of species fraction data that are within approximately 75 km of one another in areas identified as fore st by the NLCD canopy coverage. The 2006 NLCD provides land cover information with a native data grid spacing of 30 meters. For land areas outside the conterminous United States, 500 meter grid spacing land cover data from the Moderate Resolution Imaging S pect r oradiometer (MODIS) is used

.

To provide a sense of the scope and spatial distribution of the emissions, plots of annual BEIS outputs for isoprene and NO for 2011 are shown in Figure 2

- 3 and Figure 2

- 4 , respectively.32 Figure 2

- 3

. Annual NO emissions output from BEIS 3.61 for 20 11

Figure 2

- 4

. Annual i soprene emissions output from BEIS 3.61 for 201133 Tab le 2

- 16

. Meteorological variables required by BEIS 3.61

Variable Description

LAI leaf

- area index

PRSFC surface pressure

Q2 mixing ratio at 2 m

RC convective pcpn per met TSTEP

RGRND solar rad rea ching sfc

RN nonconvec. pcpn per met TSTEP

RSTOMI inverse of bulk stomatal resistance

SLYTP soil texture type by USDA category

SOIM1 volumetric soil moisture in top cm

SOIT1 soil temperature in top cm

TEMPG skin temperature at ground

USTAR cell averaged friction velocity

RADYNI inverse of aerodynamic resistance

TEMP2 temperature at 2 m



### 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 “CHLORINE” was changed to

“CL2” to support CMAQ model ing

.34 

## 3 Emissions Modeling Sum mary

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 b y the model, it is necessary to “pre

- process” the “raw” emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2 . In brief, the process of emissions modeling transforms the emissions inventories from their original temporal resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded resolution required by the air quality model. Emissions modeling includes temporal allocation, spatial allocation, and pollutant spec iation. 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 emissions files if the vertical layer s of the sources are not 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, may be

indiv idual point sources

, county /province/municipio totals , or gridded emissions and varies by sector

. 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

2011NEIv2 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 inp uts for the 2011v6 platform are available from the CHIEF Emissions Modeling Clearinghouse website (see Section 1 ).

SMOKE version 3. 6.5 was used to pre

- process the raw emissions inventories into emissions inputs for CMAQ. For p rojects that used CAMx, the CMAQ emissions were converted into the CAMx formats using CAMx convertor programs. For sectors that have plume rise, the in

- line emissions capability of the air quality 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 tha t are then compared to reports generated by SMOKE on the input inventories to

ensure that mass is not lost or gained during the emissions modeling process.



### 3.1 E missions modeling Overview

When preparing emissions for the air quality model, emissions for 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 sectors. The SMOKE settings in the run scripts and the data in the SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.

Table 3

- 1 summarizes the major processing steps of each platform sector. The “Spatial” column shows the spatial approach used : here “point” indicates that SM OKE maps the source from a point location (i.e., latitude and longitude) to a grid cell; “surrogates” indicates that some or all of the sources use spatial surrogates to allocate county emissions to grid cells; and “area

- to

- point” indicates that some of

th e sources use the SMOKE area

- to

- point feature to grid the emissions (further described in Section

3.4.2 ). The “Speciation” column indicates that all sectors use the SMOKE speciation step, though biogenics speciation is done within the Tmpbeis3 program and not as a separate SMOKE step. The “Inventory resolution” column shows the inventory temporal resolution from which SMOKE needs to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory ; i nstead

, activity data and emission factors are used in combination with meteorological data to compute hourly emissions.35

Finally, the “plume rise” column indicates the sectors for which the “in

- line” approach is used. These sectors are the only ones with emissions in a loft layers based on plume rise. The term “in

- line” means that the plume rise calculations are done inside of the air quality model instead of being computed by

SMOKE. 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 othpt sector has only

“in

- line” emissions, meaning that all of the emissions are treated as elevated sources and there are no

emissions for th ose sector s in the two

- dimensional, lay er

- 1 files created by SMOKE. Day

- specific point fires are treated separately. For CMAQ modeling, fire plume rise is done within CMAQ itself , but for CAMx, the plume rise is done by running SMOKE to create a three

- dimensional output file and then those emissions are postprocessed into a point source format that CAMx can read. In either case, after plume rise is applied, there will be emission s in every layer from the ground up to the top of the plume.

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 annual agfire Surrogates Yes monthly

b eis P re

- gridded

land use in BEIS 3.14 computed hourly

c1c2rail S urrogates Yes annual

c3 marine Surrogates Yes annual in

- line

n onpt S urrogates &

area

- to

- point Yes annual

n onroad S urrogates &

area

- to

- point Yes monthly

np_oilgas S urrogates Yes annual

o n road Surrogates Yes monthly activity, computed hourly

othafdust Surrogates Yes annual

o thar Surrogates Yes annual

o thon S urrogates Yes annual

o thpt Point Yes annual in

- line pt_oilg as Point Yes annual in

- line

p t egu Point Yes daily & hourly in

- line

p t prescfire Point Yes daily in

- line

p t wild fire Point Yes daily in

- line

p tnonipm Point Yes annual in

- line

r wc Surrogates Yes annual

SMOKE has the option of grouping sources so that th ey are treated 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

- dimensiona l files). This 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 gro uping.36

To prepare fires for CAMx using a plume rise algorithm that is consistent with the algorithms in SMOKE and CMAQ, the following steps are performed:

1) The ptfire inventories are run through SMOKE programs to read the inventories, speciate, temporaliz e, and grid the emissions

. 2) The SMOKE program laypoint is used to estimate the plume height and layer fractions for each fire (see https://www.cmascenter.org/smoke/documentation/3.6.5/html/ch06s06.html#sect_programs_ laypoint_plume_rise_fires ).

3) The emissions are gridded and layered, and then written as three

- dimensional netCDF CMAQ ready files.

4) Species in the CMAQ

- formatted file are converted to CAMx species using the spcmap

program

. 5) The netCDF fire files are converted to a CAMx “PTSOURCE” type file where each grid cell centroid represents one stack using the cmaq2uam program. Note: each virtual stack has default stack parameters of 1 m height, 1 m diameter, 273 K temperature, and 1 m/s velocity. Also, a n individual virtual stack point (grid cell centroid) will have all of the emissions for the grid cell divided up into layers with an effective plume height at each layer. Only the layers that contain emissions are kept for each virtual stack.

6) The program pthtq is run to add an effective plume height based on the cell center height from the METCRO3D (ZH).

7) The resulting PTSOURCE files have emissions as a stack at (x,y) that to up to layer z that is derived from the CMAQ 3D file, and are merged with the PTSOURCE sector files from

other sectors into a single PTSOURCE file with stacks for all point sources. This file , along

with the 2D emissions file, is input into the CAMx model.

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.37 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.38



### 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 CB6 mechanism (Yarwood, 2010 ). The 2011v6.2 platform is the first EPA modeling platform to use CB6 ; previous platforms used CB05 and earlier versions of the carbon bond mech anism. The key difference in CB6 from CB05 from an emissions modeling perspective is that is has additional lumped and explicit model species. The specific version of CAMx used in applications of this platform include secondary organic aerosol ( SOA) and H ONO enhancements. In addition, this platform generates the PM 2.5 model species associated with the CMAQ Aerosol Module, version 6 (AE6)

, though they are not used by CAM X

. Table 3

- 3 lists the model species produced by SMOKE in th e 2011v6.2 platform and Table 3

- 4 provides the cmaq2camx mapping file used to convert the SMOKE generated model species to the appropriate inputs for CAM X

.

The TOG and PM 2.5 speciation factors that are the basis of the chemical s peciation approach were developed from the SPECIATE 4. 4 database ( http://www.epa.gov/ttn/chief/software/speciate )

, which is

EPA 's repository of TOG and PM speciation profiles of air pollution s ources. However, a few of the profiles used in the v 6 .2 platform will be published in later versions of the SPECIATE database after the

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

- to

- TOG conversion fact ors associated with the TOG profiles, and speciation profiles for PM 2.5

.

Some updates to speciation made in the 2011v6.2 platform include the following ( the subsections below

contain more details on the specific changes):

 VOC speciation profile cross ref erence assignments for nonpoint oil and gas sources were updated based on comm ents;

 VOC and PM speciation for onroad mobile sources occurs within MOVES2014;

 t he latest CB6 chemical mechanism was used;

 o nly AE6 PM sp ecies are included, where previously both AE5 and AE6 species were generated;

 t he 2010 Canadian point source inventories in the othpt use CB05 speciation as it was p rovided from Environment Canada; and

 s peciation for onroad mobile sources in Mexico was updated to be more consistent with that used

in the United States.

Speciation profiles and cross

- references for the 2011v6 .2 platform are available in the SMOKE input files for the 2011v6.2 platform. Totals of each model species by state and sector can be found in the

state

- sector totals workbook s for the respective cases. In addition, the county

- monthly reports for each case include EC and OC, and t he 2011eh_county_SCC7_sector_CAP_PM.xlsx workbook contains speciated PM by county and the first seven digits of the SCC code

.39 Table 3

- 3

. Emission m odel s pecies produced for CB6 for 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

N O X NO Nitrogen oxide

NO2 Nitrogen dioxide

HONO Nitrous acid

SO 2 SO2 Sulfur dioxide

SULF Sulfuric acid vapor

NH 3 NH3 Ammonia

VOC ACET Acetone

ALD2 Acetaldehyde

ALDX Propionaldehyde and higher aldehydes

BENZ Benzene

CH4 Methane 8

ETH Ethene

ETHA Ethane

ETHY Ethyne

ETOH Ethanol

FORM Formaldehyde

KET Ketone Groups

IOLE Internal olefin carbon bond (R

- C=C

- R)

ISOP Isoprene

MEOH Methanol

OLE Terminal olefin carbon bond (R

- C=C)

PAR Paraffin car bon bond

PRPA Propane

TOL Toluene and other monoalkyl aromatics

XYL Xylene and other polyalkyl aromatics
VOC species from the biogenics

model that do not map to model species above SESQ Sesquiterpenes

TERP Terpenes

PM 10 PMC Coarse PM > 2. 5 microns and  10 microns

8 Technically, CH4 is not a VOC but part of TOG. Although emissions of CH4 are derived, the AQ models do not use these emissions because the anthropogenic emissions are dwarfed by the CH4 already in the atmosphere

.40

Inventory Pollutant Model Species Model species description

PM 2.5 PAL Aluminum

PCA Calcium

PCL C hloride

PEC Particulate elemental carbon  2.5 microns

PFE Iron

PK P otassium

PH2O Water

PMG Magnesium

PMN M anganese PMOTHR PM 2.5 not in other AE6 species

PNA S odium

P NCOM N on

- carbon organic matter

PNO3 Particulate nitrate  2.5 microns

PNH4 Ammonium

POC Particulate organic carbon (carbon only)  2.5 microns

PSI Silica

PSO4 Particulate Sulfate  2.5 microns

PTI Titanium

Sea

- s alt species (non

– anthro pogenic ) 9 PCL Particulate chloride

PNA Particulate sodium

* Notes : 1. CL2 is not used in CAM X and is provided above because of its use in CMAQ

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

3. CAM X uses different names for species that are both in CB 6 and SOA for the following: TOLA=TOL, XYLA=XYL, ISP=ISOP, TRP=TERP. They are duplicate species in CAM X that are used in the SOA chemistry. CMAQ uses the same names in CB05 and SOA for these species.

4

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

5

. CAM X particulate species have different names for organic carbon, coarse particulate matter and other particulate mass:

CAM X uses POA

, C PR

M

, FCRS, and FPRM , respectively.

9 These emissions are created outside of SMOKE41 Table 3

- 4

. Cmaq2camx mapping file

CMAQ Species CMAQ to

CAMx

Factor CAMx

Species Units CMAQ

Species CMAQ

to CAMx

Factor CAMx

Species Units

SO2 1 SO2 moles/hr UNR 1 NR moles/hr

SULF 1 SULF moles/hr NR 1 NR moles/hr

NH3 1 NH3 moles/hr TOL 1 TOLA moles/hr

CO 1 C O moles/hr XYL 1 XYLA moles/hr

NO 1 NO moles/hr PSO4 1 PSO4 g/hr

NO2 1 NO2 moles/hr PH2O 1 PH2O g/hr

HONO 1 HONO moles/hr PNH4 1 PNH4 g/hr

CL2 1 CL2 moles/hr PNO3 1 PNO3 g/hr

HCL 1 HCL moles/hr PEC 1 PEC g/hr

CH4 1 CH4 moles/hr POC 1 POC g/hr

PAR 1 PAR moles/hr PMOTHR 1 PMOTHR g/hr

ETHA 1 ETHA moles/hr PMC 1 CPRM g/hr

MEOH 1 MEOH moles/hr ISOP 1 ISP moles/s

ETOH 1 ETOH moles/hr TERP 1 TRP moles/s

ETH 1 ETH moles/hr SESQ 1 SQT moles/s

OLE 1 OLE moles/hr PCL 1 PCL g/hr

IOLE 1 IOLE moles/hr PNCOM 1 PNCOM g/hr

ISOP 1 ISOP moles/hr PAL 1 PAL g/hr

TERP 1 TERP moles/hr PCA 1 PCA g/hr

FORM 1 FORM moles/hr PFE 1 PFE g/hr

ALD2 1 ALD2 moles/hr PMG 1 PMG g/hr

ALDX 1 ALDX moles/hr PK 1 PK g/hr

TOL 1 TOL moles/hr PMN 1 PMN g/hr

XYL 1 XYL moles/hr PSI 1 PSI g/hr

PRPA 1 PRPA moles/hr PTI 1 PTI g/hr

ETHY 1 ETHY moles/hr PNA 1 NA g/hr

BENZ 1 BENZ moles/hr POC 1 POA g/hr

ACET 1 ACET moles/hr PNCOM 1 POA g/hr

KET 1 KET moles/hr



### 3.2.1 VOC speciation

The concept of VOC speciation is to use emission source

- r elated speciation profiles to convert VOC to

TOG , to speciate TOG into individual chemical compounds , and to use a chemical mechanism mapping

file to aggregate the c hemical compounds to the chemical mechanism model species

. The chemical

mechanism mapping f

ile is typically developed by the developer of the chemical mechanism.

SMOKE uses profiles that convert inventory species and TOG directly to the model species. The

SMOKE

- ready profiles are generated from the Speciation Tool which uses the “raw” (TOG to chemical compounds) SPECIATE profiles and the chemical mechanism mapping file.

For the 2011v6.2 platform, Greg Yarwood ( ENVIRON ) provided the CB6 chemical mapping file based

on the latest mechanism table for CB05 from Bill Carter. This CB6 mapping file included some42

corrections to the CB05 profiles used in the 2011v6.1 platform

. Similarly to previous platforms

, HAP

VOC inventory species were used in the VOC speciation process for some sectors as described below

.

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

The VOC speciation includes HAP emissions from the 2011NEIv2 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 combines these HAPs with the VOC in a way

that does not double count emiss ions and uses the HAP inventory directly in the speciation process. The basic process is to subtract the specified HAPs emissions mass from the VOC emissions mass

, and to

then use a special “ integrated ” profile to speciate the remainder of VOC to the mode l species excluding the specific HAPs. EPA believes that the HAP emissions in the NEI are often more representative of

emissions than HAP emissions generated via VOC speciation , although this varies by sector

.

T he BAFM HAPs ( benzene, acetaldehyde, formal dehyde and methanol ) were chosen for in tegration in previous platforms because , with the exception of BENZENE 10

, they are the only explicit VOC HAPs in

the base version of the CMAQ 5.0. 2 ( CAPs only with chlorine chemistry) model . These remain appropriate f

or the 2011v6.2 platform since they are all explicit in CAMx

. Explicit means that they are not lumped chemical groups like PAR, IOLE and several other CB6 model species. These “explicit VOC HAPs” are model species that participate in the modeled chemistr y using the CB6 chemical

mechanism. The use of inventory HAP emissions along with VOC is called “HAP

- CAP integration”.

For specific sources, especially within the nonpt sector, the integration included ethanol. To differentiate when a source was integ rating BAFM versus EBAFM (ethanol in addition to BAFM), the speciation profiles that do not include ethanol are referred to as an “E

- profile” and should be used when ethanol comes from the inventory

. F or example

, the E10 headspace gasoline evaporative spec iation profile 8763 should be used whe n ethanol is speciated from VOC, but 8763E should be used 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 provides the sources to be excluded fr om 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 pro files 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 integrated, if all sources have the ap propriate 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

10 BENZENE was chosen to keep its emissions consistent between the multi

- pollutant and base versions of CMAQ

11 In SMOKE version 3.6.5, the options to specify sources for integration are expanded so that a use r can specify the particular sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector. In addition, the error checking is significantly stricter for integrated sources. If a source is suppos ed 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.43

NHAPEXCLUDE file must be provided based on the evaluation of each s ource’s pollutant mix. EPA

considered CAP

- HAP integration for all sectors and developed “integration criteria” for some of them (see Section 3.2.1.3 for details).

The process of partial integration for BAFM is illustrated in Figure 3

- 2 that the BAFM records in the input inventories do not need to be removed from any sources in a partially integrated sector becau s e SMOKE does this automatically using the INVTABLE configuration

. For EBAFM integration, t his process is identical to that shown in the figure except for the addition of ethanol (E) to the list of subtracted HAP pollutants. For full integration, the process would be very similar except that the

NHAPEXCLUDE file would not be used and all source s in the sector would be integrated.

Figure 3

- 2

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

In SMOKE, the INVTABLE allows the user to specify both the particular HAPs to integrate. 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

- 5 ), EPA created a “no HAP use”

INVTABLE in which the “KEEP” flag is set to “N” for BAFM pollutants. Thus, any BAFM pollutants in the inventory input into SMOKE are automatically dropped. This approach both avoids double

counting of these species and assumes that the VOC speciation is the best available approach f or 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 integrated 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 EBAFM.44

Table 3

- 5

. 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

pt nonipm No integration ptprescfire No integration

pt wild fire No integration

othafdust No integration

othar No integration

othon No integration

a g N/A

– sector contains no VOC

afdust N/A

– sector contains no VOC

b iog N/A

– sector contains no in ventory pollutant "VOC"; but rather specific VOC species agfire Partial integration (BAFM)

c1c2rail Partial integration (BAFM)

c3marine Partial integration (BAFM)

nonpt Partial integration (BAFM and EBAFM)

nonroad Partial integration (BAFM) np_oilga s Partial integration (BAFM) pt_oilgas Partial integration (BAFM)

r wc Partial integration (BAFM) othpt Partial integration (BAFM)

onroad Full integration ( internal to MOVES )

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

Section 3.2.1.3

.

3.2.1.2 County specific profile combinations (GSPRO_COMBO)

SMOKE can compute speciation profiles from mixtures of other profiles in user

- specified proportions. The combinations are spe cified 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 speciate nonroad mobile and gasoline

- related related st ationary sources

that use fuels with varying ethanol content . In these cases, the speciation profiles require different combinations of gasoline profiles, e.g. E0 and E10 profiles. Since the ethanol content varies spatially (e.g., by state or county), tem porally (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 prof ile 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 int egrati on across sectors, sector

- s pecific GSPRO_COMBO files are used . Different profile combinations are specified by the mo de (e.g. exhaust, evaporative ) and by changing the pollutant name (e.g. EXH__NONHAPTOG, EVP__NONHAPTOG). For the nonpt sector, a combina tion of BAFM and EBAFM integration is used . Due to the lack 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 ch anged the pollutant name for the PFC future year45

inventory so the integration would use EVP__NONHAPVOC to correctly select the E

- profile

combinations

, while other sources used NONHAPVOC to select the typical BAFM profiles.

3.2.1.3 Additional sector specific detail s
The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some

s ectors there is no integration and VOC is speciated directly; for some sec tors there is full integration meaning all sources are integ rated; and for other sector s there is partial integration meaning some sources are not integrated a nd 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

- 5 above summarizes the integration method for each platform sector.

For the c1c2rail sector, EPA integrated BAFM for most sources from the 2011NEIv2 . There were a few sources that had zero BAFM; therefore, they were not integrate d . The M WRPO and CARB inventories (see Section 2.4.1 ) did not include HAPs; therefore, all non

- NEI source emissions in the c1c2rail sector

were not integrated . For California, the CARB inventory TOG was converted to VOC by dividing th e inventory TOG by the available VOC

- to

- TOG speciation factor.

For the othpt sector, the C3 marine sources (see Section 2.4.2 ) are integrated. HAPs in this sector are derived identically to the U.S. c3marine sector. T he rest of the sources in othpt ar e not integrated

, thus the sector is partial ly integrated

.

For the onroad sector, there are series of unique speciation issues. First, SMOKE

- MOVES (see Section

2.3.1 ) is used to create emissions for these sectors and both the MEPROC and INVTABLE files are involved in controlling which pollutants are processed . Second, the speciation occurs within MOVES
itself, not within SMOKE

. The advantage of using MOVES to speciate VOC is that during the internal calculation of MOVES, the model has complete information on the characteristics of the fleet and fuels (e.g. model year, ethanol content, process, etc.) , thereby allowing it to more accurately make use of specific speciation profiles. This means that MOVES produces EF tables that include inventory pollutants (e.g. TOG) and model

- ready species (e.g. PAR, OLE, etc) 13 . SMOKE essentially calculates

the model

- ready spe cies by using the appropriate emission factor without further speciation 14

. Third, MOVES’ internal speciation use s full integration of an extended list of HAPs beyond EBAFM (called

“M

- profiles”). The M

- profiles integration is very similar to BAFM integration explained above except that the integration calculation (see Figure 3

- 2 ) is performed on emissions factors instead of on emissions. The list of integrated HAP s is described in Table 3

- 6 . An additional run of the speciation tool was necessary to create the M

- profiles that were then loaded into the MOVES default datab ase. Fourth , for California EPA applied adjustment factors to SMOKE

- MOVES to produce California adjusted model

- ready files (see Section 2.3.1 for details). By applying the ratios through SMOKE

MOVES, the CARB inventories are e ssentially speciated to match EPA estimated speciation.

13 Because t he EF table has the speciation “baked” into the factors, all counties that are in the county group (i.e. are mapped to that reference county) will have the same speciation.

14 For more details on the use of model

- ready EF, see the SMOKE 3.6.5 documentation: https://www.cmascenter.org/smoke/documentation/3.6.5/html/46 Table 3

- 6

. MOVES integrated species in M

- profiles

MOVES ID Pollutant Name

5 Methane (CH4)

20 Benzene

21 Ethanol

22 MTBE

24 1,3

- Butadiene

25 Forma ldehyde

26 Acetaldehyde

27 Acrolein

40 2,2,4

- Trimethylpentane

41 Ethyl Benzene

42 Hexane

43 Propionaldehyde

44 Styrene

45 Toluene

46 Xylene

185 Naphthalene gas

For the nonroad sector, CNG or LPG sources (SCC s beginning with 2268 or 2267) are no t integrated because NMIM computed only VOC and not any HAPs for these SCCs. All other nonroad sources were integrated except in California. For California, the CARB inventory TOG was converted to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor. SMOKE later applies the

same VOC

- to

- TOG factor prior to computing speciated emissions. The CARB

- based nonroad data includes exhaust and evaporative mode

- specific data for VOC, but does not contain refueling. The CARB inventory doe s not includ e HAP estimates for all sources. T herefore, the sources which have

VOC but not BAFM

, or for which BAFM is greater than VOC

, are not integrated and t he remaining sources are integrated

. T he future year CARB inventories did not have BAFM so all s ources for

California were not integrate d

. The gasoline exhaust profiles were updated to 8750a and 8751a (this is

true nation

- wide).

For the ptnonipm sector, the 2011

, 2017 and 2025 runs were not integrated . This was an oversight

— it should have been pa rtial integration because the 2011 ethanol inventory (SCC 30125010) provided by OTAQ includes BAFM. In the future year s , ptnonipm should be partial ly integrate d because both the ethanol and biodiesel inventories (SCC 30125010) provided by OTAQ include BAF M. Aircraft emissions use the profile 5565. In previous versions of the platform, a significant amount of VOC emissions associated with the pulp and paper and the chemical manufacturing industries did not have specific profiles assigned to them (i.e. they had the default VOC profile 0000). To address this, EPA

and Environ developed industry wide average profiles to improve the speciation of these significant sources of VOC. The two new composite profiles are “ Composite Profile

– Chemical Manufacturing (9 5325) ” and “Composite Profile

– Pulp and Paper Mills” (95326) 15

.

15 These profiles are expected to be included in SPECIATE 4.5.47

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 n ot meet these criteria could not be integrated

.

For the oil and gas sources in the np_oilgas and pt_oilgas sectors , the basins studied in WRAP Phase III

have basin

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

d eveloped these basin

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

‐ bed methane (CBM) gas sources

. Table 3

- 7 list s 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 n ot vary by region or basin (see Table 3

- 8 ). Table 3

- 9 lists the

WRAP Phase III counties

.

Table 3

- 7

. VOC profiles for WRAP Phase III basins

Profile Code Des cription

DJFLA D

- J Basin Flashing Gas Composition for Condensate

DJVNT D

- J Basin Produced Gas Composition

PNC01 Piceance Basin Gas Composition at Conventional Wells

PNC02 Piceance Basin Gas Composition at Oil Wells

PNC03 Piceance Basin Flashing Ga s 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

SWFLA SW Wyoming Basin Flash G as 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

- 8

. National VOC profiles for oil and gas

profile Description

0000 Over All Average

0001 External Combustion Boiler

- Residual Oil

0002 External Combustion Boiler

- Distillate Oil

0003 External Combustion Boiler

- Natural Gas

0004 External Combustion Boiler

- Refinery Gas

0007 Natural Gas Turbine

0008 Reciprocating Diesel Engine

0051 Flares

- Natural Gas

0296 Fixed Roof Tank

- Cr ude 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 Service48

profile Description

1012 Oil and Gas Production

- Fugitives

- Val ves 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

8489 Natur al Gas Production

8950 Natural Gas Transmission

Table 3

- 9

. 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 Broomfield

08 029 CO Delta

08 031 CO Denver

08 039 CO Elbert

08 043 CO Fremont

08 045 CO Garfield

08 051 CO Gunnison

08 059 CO Jefferson

08 063 CO Kit Carson

08 067 CO La Plata

08 069 CO Larimer

08 073 CO Lincoln

08 075 CO Logan

08 077 CO Mesa

0808 1 CO Moffat

0808 7 CO Morgan

08 095 CO Phillips

08 097 CO Pitkin

08 103 CO Rio Blanco

08 107 CO Rout t

08 115 CO Sedgwick

08 121 CO Washington

08 123 CO Weld

08 125 CO Yuma

30003 MT Big Horn

30075 MT Powder River

35005 NM Chaves

35015 NM Eddy

35015 NM Lea FIPS Sta te County

35031 NM Mc Kinley

35039 NM Rio Arriba

35041 NM Roosevelt

35043 NM Sandoval

35045 NM San Juan

48003 TX Andrews

48033 TX Borden

48079 TX Cochran

48081 TX Coke

48103 TX Crane

48105 TX Crockett

48107 TX Crosby

48109 TX Culberson

48115 TX Dawson

48125 TX Dickens

48135 TX Ector

48141 TX El Paso

48151 TX Fisher

48165 TX Gaines

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 FIPS Sta te County

48353 TX Nolan

48371 TX Pecos

48383 TX Reagan

48389 TX Reeves

48413 TX Schleicher

48415 TX Scurry

48431 TX Sterling

48435 TX Sutton

48445 TX Terry

48451 TX Tom Green

48461 TX Upton

48475 TX Ward

48495 TX Winkler

48501 TX Yoakum

49007 UT Carbon

49009 UT Daggett

49013 UT Duchesne

49015 UT Emery

49019 UT Grand

49043 UT Summit

49047 UT Uintah

56001 WY Albany

56005 WY Campbe ll

56007 WY Carbon

56009 WY Converse

56011 WY Crook

56013 WY Fremont

56019 WY Johnson

56023 WY Lincoln

56025 WY Natrona

56027 WY Niobrara

56 033 WY Sheridan

56035 WY Sublette49

FIPS Sta te County

56037 WY Sweetwater FIPS Sta te County

56041 WY Uinta FIPS Sta te County

56045 WY Weston

Although the intention was to apply WRAP speciation everywhere in the WRAP region ( Table 3

- 9 ), a SMOKE processing error meant that for select sources in select counties the national VOC profiles for oil and gas were used 16

. For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. The 2011 platform uses BEIS3. 61 , which includes a new species (SESQ) that was mapped to the model species SESQT. The profile code associated with BEIS3. 61 for use with CB05 is “B10C5”

, while the profile for use with CB6 is “B10C6”. The main difference between the profiles is t he explicit

treatment of acetone emissions in B 1 0 C6.

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

as sociated 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 the GSPRO_COMBO file). Refinery to bulk terminal (RBT) fuel distribution and bulk plant stor age (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 cellulosi c ethanol sources was available, t herefore cellulosic diesel and cellulosic ethanol sources used the same SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC speciation as was used for corn ethanol plants. For the future year, PFC and the cellulosic sources were integrated EBAFM (i.e. used E

- profiles) because ethanol was present in those inventories.

3.2.1.4 Future year speciation

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

Speciation profiles for VOC in the nonroad sector account for the changes in ethanol content of fuels

across years. A description of the actua l fuel formulations for 2011 can be found in the 2011NEIv2 TSD

, and for 2017 and 2025 see Section 4.3

. For 2011, EPA used “COMBO” profiles to model combination s of profiles for E0 and E10 fuel use. For 2017 and 2025 , EPA assumed E10 fuel use for all nonr oad gasoline processes

.

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 c hange across years. The speciation changes from fuels in the ptnonipm sector include BTP distribution operations inventoried as point sources. RBT fuel distribution and BPS speciation does not change across the modeling cases because this is considered u pstream from the

introduction of ethanol into the fuel. For PFC s , ethanol was present in the future inventories and therefore

16 The sources were accidentally not included in the list of no

- integrated sources/counties. At the time of the modeling, EPA only had no

- integrate WRAP profiles (VOC); therefore the incorrectly assigned integrated sources (NONHAPVOC) defaulted to the national integrated profiles for oil and gas. This impacted np_oilgas but not pt_oilgas.50

EBAFM profiles were used to integrate ethanol in the future year speciation. The m apping of fuel distribution SCCs to PFC, BTP, B PS, and RBT emissions categories can be found in Appendix B.

Tab le 3

- 10 summarizes the different profiles utilized for the fuel

- related sources in each of the sectors for 2011 and the future year case s . 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. Although some of the component profiles are

the same betwe en 2017 and 2025, for example “ nonpt, BTP ”, the proportion of each profile within the GSPRO_COMBO differs between the two future years.

Tab le 3

- 10

. Select VOC profiles 2011 vs 2017 vs 2025

sector S ub

categor y 2011 2017 2025

nonroad gasoline

exhaust COMBO 8751a Pre

- Tier 2 E10

exhaust 8751a Pre

- Tier 2 E10

exhaust

8750a Pre

- Tier 2 E0

exhaust

8751a Pre

- Tier 2

E10 exhaust

nonroad gasoline

evap

orative COMBO 8754 E10 evap 8754 E10 evap

875 3 E0 evap

8754 E10 evap

nonroad gasoline

refueling COMBO 8870 E10 Headspace 8870 E10 Headspace

8869 E0

Headspace

8870 E10

Headspace

nonroad diesel

exhaust 8774 Pre

- 2007 MY

HDD exhaust 8774 Pre

- 2007 MY

HDD exhaust 87 74 Pre

- 2007 MY HDD

exhaust

nonroad diesel

evap

orative 4547 Diesel

Headspace 4547 Diesel
Headspace 4547 Diesel Headspace

nonroad diesel

refueling 4547 Diesel

Headspace 4547 Diesel
Headspace 4547 Diesel Headspace

nonpt/

ptnonipm PFC COMBO

8869 E0

Headspace 8870E E10 Headspace 8870E E10 Headspace

8870 E10

Headspace

nonpt/

ptnonipm BTP COMBO COMBO COMBO

8869 E0

Headspace 8870 E10 Headspace 8870 E10 Headspace

8870 E10

Headspace 8871 E15 Headspace 8871 E15 Headspace

8934 E85 Evap 8934 E85 Evap51

sector S ub

categor y 2011 2017 2025 nonpt/

ptnonipm BPS/RBT 8869 E0 Headspace 8869 E0 Headspace 8869 E0 Headspace

The speciation of onroad VOC occurs within MOVES. MOVES take s into account fuel type and properties, emission standards as they affect diffe rent vehicle types and model years , and specific emission processes. A description of the actual fuel formulations for 2011 ca n be found in the 2011NEIv2 TSD; for 2017 and 2025 see Section 4.3. Table 3

- 11 describes all of the M

- p rofiles available to MOVES depending on the model year range, MOVES process (processID), fuel sub

- type (fuelSubTypeID), and regulatory class (regClassID). Table 3

- 12 to Table 3

- 14 describe the meaning of these MOVES codes. For a specific representative county and future year, there will be a different mix of these profiles. For example, for HD diesel exhaust, the emissions will use a combination of profiles 8774M and 8775M depending on the proportion of HD vehicles that are pre

- 2007 model years (MY) in that particular county. As that county is projected farther into the future, the proportion of pre

- 2007 MY vehicles will decrease. A second example, for gasoline exhaust (not including E

- 85), the emissio ns will use a combination of profiles 8756M, 8757M, 8758M, 8750aM, and 8751aM. Each representative county has a different mix of these key properties and therefore has a unique combination of the specific M

- profiles.

Table 3

- 11

. Onroad M

- profiles

Profile Profile Description Model Year

range processID fuelSubTypeID regClassID

1001M CNG Exhaust 1940

- 2050 1,2,15,16 30 48

4547M Diesel Headspace 1940

- 2050 11 20,21,22 0

4547M Diesel Headspace 1940

- 2050 12,13,18, 19 20,21,22 10,20,30,40,41, 42,46,47,48

8753M E0 Evap 1940

- 2050 12,13,19 10 10,20,30,40,41,42, 46,47,48

8754M E10 Evap 1940

- 2050 12,13,19 12,13,14 10,20,30,40,41, 42,46,47,48

8756M Tier 2 E0 Exhaust 2001

- 2050 1,2,15,16 10 20,30

8757M Tier 2 E10 Exhaust 2001

- 2050 1,2,15,16 12,13,14 20,30

8758M Tier 2 E15 Exhaust 1940

- 2050 1,2,15,16 15,18 10,20,30,40,41, 42,46,47,48

8766M E0 evap permeation 1940

- 2050 11 10 0

8769M E10 evap permeation 1940

- 2050 11 12,13,14 0

8770M E15 evap permeation 1940

- 2050 11 15,18 0

8774M Pre

- 2007 MY HDD

exhaust 1940

- 2006 1,2,15,16,17,9

0 20,21,22 40,41,42,46,47,48

8774M Pre

- 2007 MY HDD

exhaust 1940

- 2050 91 20,21,22 46,47

8774M Pre

- 2007 MY HDD

exhaust 1940

- 2006 1,2,15,16 20,21,22 20,30

8775M 2007+ MY HDD exhaust 2007

- 2050 1,2 ,15,16 20,21,22 20,30

8775M 2007+ MY HDD exhaust 2007

- 2050 1,2,15,16,17,9

0 20,21,22 40,41,42,46,47,48

8855M Tier 2 E85 Exhaust 1940

- 2050 1,2,15,16 50,51,52 10,20,30,40,41, 42,46,47,4852

Profile Profile Description Model Year

range processID fuelSubTypeID regClassID

8869M E0 Headspace 1940

- 2050 18 10 10,20,30,40,41, 42,46,47,48

8870M E10 Headspace 1940

- 2050 18 12,13,14 10,20,30,40,41, 42,46,47,48

8871M E15 Headspace 1940

- 2050 18 15,18 10,20,30,40,41, 42,46,47,48

8872M E15 Evap 1940

- 2050 12,13,19 15,18 10,20,30,40,41, 42,46,47,48

8934M E85 Evap 1940

- 2050 11 50,51,52 0

8934M E85 Eva p 1940

- 2050 12,13,18,19 50,51,52 10,20,30,40,41, 42,46,47,48

8750aM Pre

- Tier 2 E0 exhaust 1940

- 2000 1,2,15,16 10 20,30

8750aM Pre

- Tier 2 E0 exhaust 1940

- 2050 1,2,15,16 10 10,40,41,42,46,47,48

8751aM Pre

- Tier 2 E10 exhaust 1940

- 2000 1,2,15,16 11,12,13,14 20,30

8751aM Pre

- Tier 2 E10 exhaust 1940

- 2050 1,2,15,16 11,12,13,14,15,1

8 10,40,41,42,46,47,48

Table 3

- 12

. MOVES Process IDs

P rocess ID P rocess N ame

1 Running Exhaust

2 Start Exhaust

11 Evap Permeation

12 Evap Fuel Vapor Venting

13 Evap Fuel Leaks

15 Crankcase Running Exhaust

16 Crankcase Start Exhaust

17 Crankcase Extended Idle Exhaust

18 Refueling Displacement Vapor Loss

19 Refueling Spillage Loss

20 Evap Tank Permeation

21 Evap Hose Permeatio n

22 Evap RecMar Neck Hose Permeation

23 Evap RecMar Supply/Ret Hose Permeation

24 Evap RecMar Vent Hose Permeation

30 Diurnal Fuel Vapor Venting

31 HotSoak Fuel Vapor Venting

32 RunningLoss Fuel Vapor Venting

40 Nonroad

90 Extended Idle Exhaust

9 1 Auxiliary Power Exhaust53 Table 3

- 13

. MOVES Fuel subtype IDs

F uel S ubtype ID F uel S ubtype D esc riptions

10 Conventional Gasoline

11 Reformulated Gasoline (RFG)

12 Gasohol (E10)

13 Gasohol (E8)

14 Gasohol (E5)

15 Gasohol (E15)

18 Ethanol (E20)

20 Conventional Diesel Fuel

21 Biodiesel (BD20)

22 Fischer

- Tropsch Diesel (FTD100)

30 Compressed Natural Gas (CNG)

50 Ethanol

51 Ethanol (E85)

52 Ethanol (E70)

Table 3

- 14

. MOVES Regclass IDs

R e g. C lass ID Regulatory Class Description

0 Doesn’t Matter

10 Motorcycles

20 Light Duty Vehicles

30 Light Duty Trucks

40 Class 2b Trucks with 2 Axles and 4 Tires (8,500 lbs < GVWR <= 10,000 lbs)

41 Class 2b Trucks wi th 2 Axles and at least 6 Tires or Class 3 Trucks (8,500 lbs < GVWR <= 14,000 lbs)

42 Class 4 and 5 Trucks (14,000 lbs < GVWR <= 19,500 lbs)

46 Class 6 and 7 Trucks (19,500 lbs < GVWR <= 33,000 lbs)

47 Class 8a and 8b Trucks (GVWR > 33,000 lbs)

48 Urba n Bus (see CFR Sec 86.091_2)



### 3.2.2 PM speciation

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 u sed in the air quality model. We speciated PM 2.5 into the AE6 species associated with CMAQ 5.0.1 and later versions

. While provided in the platform, they are not used in CAM X but rather

converted to the PM 2.5 species based on the cmaq2camx file presented in Table 3

- 4

. Table 3

- 15 shows the mapping of AE5 and AE6 for historical reference. The majority of the 2011 platform PM profiles come from the 911XX series which include updated AE6 speciation 17 . The

2 011eh_cb6v2, 2017eh_cb6v2, and 2025eh_cb6v2 state

- sector totals workbooks include state totals of the

PM emissions for each state for the sectors that include PM.

17 The exceptions are 5674 (Marine Vessel

– Marin e Engine

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

Smoke

– Simplified ) used in nonpt.54 Table 3

- 15

. PM model species: AE5 versus AE6

S pecies name S pecies description AE5 AE6

POC organic carbon Y Y

PEC elemental carbon Y Y

PSO4 S ulfate Y Y

PNO3 N itrate Y Y

PMFINE unspeciated PM 2.5 Y N

PNH4 A mmonium N Y

PNCOM non

- carbon organic matter N Y

PFE I ron N Y

PAL A luminum N Y

PSI S ilic a N Y

PTI T itanium N Y

PCA C alcium N Y

PMG M agnesium N Y

PK potassium N Y

PMN M anganese N Y

PNA S odium N Y

PCL C hloride N Y

PH2O W ater N Y

PMOTHR PM 2.5 not in other AE6 species N Y

For the onroad sector, for all processes except brake and tire w ear, PM speciation occurs within MOVES itself, not within SMOKE (similar to the VOC speciation described above). The advantage of using MOVES to speciate PM is that during the internal calculation of MOVES, the model has complete

information on the charac teristics of the fleet and fuels (e.g. model year, sulfur content, process, etc.) to accurately match to specific profiles. This means that MOVES produces EF tables that include total PM (e.g. PM 10 and PM 2.5 ) and speciated PM (e.g. PEC, PFE, etc). SMOKE essentially calculates the PM components by using the appropriate EF without further speciation 18

. For onroad brake and tire wear, the PM is speciated in the moves2smk postprocessor that prepares the emission factors for processing in

SMOKE. The formulas for this are based on the standard speciation factors that would otherwise be used

in SMOKE via the profiles 91134 for brake wear and 91150 for tire wear :

POC = 0.4715 * PM25TIRE + 0.107 * PM25BRAKE

PEC = 0.22 * PM25TIRE + 0.0261 * PM25BRAKE

PNO3 = 0.0015 * PM25TIRE + 0.0016 * PM25BRAKE

PSO4 = 0.0311 * PM25TIRE + 0.0334 * PM25BRAKE

PNH4 = 0.00019 * PM25TIRE + 0.00003 * PM25BRAKE

PNCOM = 0.1886 * PM25TIRE + 0.0428 * PM25BRAKE

For California onroad emissions , adjustment factors were applied to SMOKE

- MOVES t o produce California adjusted model

- ready files (see Section 2.3.1 for details). California did not supply speciated

18 Unlike previous platforms, the PM components (e.g. POC) are now consistently defined between MOVES2014 and CMAQ.

For more details on the use of mo del

- ready EF, see the SMOKE 3.6.5 documentation: https://www.cmascenter.org/smoke/documentation/3.6.5/html/55

PM, therefore the adjustment factors applied to PM2.5 were also applied to the speciated PM components. By ap plying the ratios through SMOKE

- MOVES, the CARB inventories are essentially speciated to match EPA estimated speciation.



### 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 onroad ( 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

- 16 g ives the split factor for these two profiles. The onroad sector does not use the “HONO” profile to speciate NO X . MOVES2014 produces speciated
NO, NO 2 , and HONO by source, including emission factors for these species in the emission factor tables used by S MOKE

- 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

.

Table 3

- 16

. NO X speciation profiles

P rofile pollutant species split facto r
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



### 3.3 Temporal Allocation

Temporal allocation (i.e., temporalization ) is the process of distributing aggregated emissions to a finer temporal resolution

, thereby co nverting annual emissions to hourly emissions. While the total emissions are important , the timing of the occurrence of emissions is also essential for accurately simulating ozone, PM, and other pollutant concentrations in the atmosphere. Many emissions inventories are annual or monthly in nature. Temporalization takes these aggregated emissions and if needed distributes them to the month, and then distributes the monthly emissions to the day and the daily emissions to the hour s of each day . This process is typically done by applying temporal profiles to the inventories in this order: monthly, day of the week, and diurnal. A summary of emissions by temporal profile and sector for the 2011eh case

is available from ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/reports/ 2011eh_emissions_by_temporal_profile.xlsx

.

In SMOKE 3.6.5 and in the 2011v6.2 platform, more readable and flexible file s formats are used for temporal profiles and cross references

. The profiles and cross references were initially created by converting the 2011v6.1 platform temporal profiles into the new formats, and then any specific adjustments for the 2011v6.2 platform were made. T he t emporal factors applied to the inventory are selected using some combination of country, state, county, SCC, and pollutant. Table 3

- 17 summarizes the temporal aspects of emissions modeling by comparing the key app roaches 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 b y the right

- most column. The values given are those used for the SMOKE M_TYPE setting (see below for more information)

.56 Table 3

- 17

. 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

afdust_adj Annual Y es week all Yes

A g Annual Y es all all Yes

agfire Monthly week week Yes

beis Hourly n/a al l Yes

c1c2rail Annual Y es mwdss mwdss

c3marine Annual Y es aveday aveday

nonpt Annual Y es week week Yes

nonroad Monthly mwdss mwdss Yes

np_oilgas Annual yes mwdss mwdss Yes

onroad Annual & monthly 1 all all Yes

onroad_ca_adj Annual & monthly 1 all all Yes

othar Annual yes week week

othon Annual yes week week

othpt Annual yes mwdss mwdss

pt_oilgas Annual yes mwdss mwdss Yes

ptegu Daily & hourly all all Yes

ptnonipm Annual yes mwdss mwdss Yes

ptprescfire Daily all all Yes

ptwil dfire Daily all all Yes

rwc Annual no met

- based all Yes

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

The following values are used in the table: The value “ all ” means that hourly emissions are computed for every day of the year and that emissions potentially have day

- of

- year variation. The value “week” means

that hourly emissions computed for all days in one “representative” week, repr esenting 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, other weekdays, Saturdays and Sundays within the month, but not week

- to

- week variation within the month . The value “aveday” means hourl y 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.

In addition to the resolution, tempo ral processing includes a ramp

- 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 Dec ember 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 meteorol ogy.57 

### 3.3.1 Use of FF10 format for finer than annual emissions

The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for monthly, daily, and hourly emissions inventories than prior formats supported . Previously, process i ng 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 i nventories. Similarly, daily and hourly FF10 inventories

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

SMOKE prevents the application of temporal profiles on top of the “native” resolution of the inve ntory. For example, a monthly invent ory 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, m onthly, daily, and/or hourly inventories. The flags that control temporalization for a mixed set of inventories are discussed in the SMOKE documentation. The modeling platform sectors that make use of monthly values in the FF10 files are agfire, nonroad, onroad

, and ptegu

.



### 3.3.2 Electric Generating Utility temporalization (ptegu)

3.3.2.1 Base year temporal allocation of EGUs

The 2011NEIv2 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. Several updates were made to EGU temporalization in the 2011v6.2 pl atform. First, the CEMS data were processed using a tool that

reviewed the data quality fl ags that indicate the data were not measured. Unmeasured data can cause

errone ously high values in the CEMS data. If the data were not measured at specific hours , and those

values were found to be more the 3 times the annual mean for that unit , the data for those hours were replaced with annual mean values (Adelman, et al

. , 2012)

. These adjusted CEMS data were then used for the remainder of the temporalization process described below (see Figure 3

- 3 for an example) . Another update in the 2011v6.2 platform was the incorporation of winter and summer season s i nto the development of the diurnal profiles as opposed to using data for the entire year. Analysis of the hourly CEMS data revealed that there were different diurnal patterns in winter versus summer in many areas. Typically a single mid

- day peak is visibl e in the summer, while there are morning and evening peaks in

the winter as shown in Figure 3

- 4

.

The temporal allocation procedure is differentiated by whether or not the source could be directly

matched to a CEMS unit via ORIS facility code and boiler ID. Prior to temporal allocation, as many sources as possible were matched to CEMS data via ORIS facility code and boiler ID. Units were considered matches if the FIPS state/county code matched, the facility name was similar, and the NOx

and SO 2 emissions were similar. EIS stores a base set of previously matched units via alternate facility

and unit IDs

. Additions to these matches were made for the 2011v6.2 platform. For any units that are matched, the ORIS facility and boiler ID columns of the point FF10 inventory files are filled with the information on the rows for the corresponding NEI unit. Note that f or units matched to CEMS data,

annual totals of the ir emissions may be different than the annual values in 2011NEIv2 because t he CEMS data actually replaces the inventory data for the seasons in which the CEMS are operating

. If a CEMS

matched unit is determined to be a partial year reporter, as can happen for sources that run CEMS only in the summer, emissions totaling the diffe rence between the annual emissions and the total CEMS

emissions are allocated to the non

- summer months.58 Figure 3

- 3

. Eliminating unmeasured spikes in CEMS data

Figure 3

- 4

. Seasonal diurnal profiles for EGU emissions in a Virginia Region

For sources not matched to CEMS units

, the allocation of annual emissions to months and then days are done outside of SMOKE and then daily emissions are output to day

- speci fic inventory files

. For these

units , the allocation of the inventory annual emissions to months is done using average fuel

- specific

season

- to

- month factors generated for each of the 64 IPM regions shown in Figure 3

- 5 . These fact ors are59

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 , SO 2 , and heat input. An overall composite profile was also computed and was used

when there were no CEM S unit s with the specified fuel in the region containing the unit. For both CEMS

matched units and units not matched to CEMS

, NO

x and SO 2 CEM S data are used to allocate NO

x and

SO 2 emissions to daily emissions

, respectively, while heat input data are used to allocate emissions of all other pollutants.

Figure 3

- 5

. IPM Regions for EPA Base Case v5.1 4

Daily temporal allocation o f units matched to CEMS was performed using a procedure similar to the approach to allocate emissions to months in that the CEMS data replaces the inventory data for each pollutant . For units without CEMS data, emissions we re allocated from month to day u sing IPM

- region

and fuel

- specific average month

- to

- day factors based on the 2011 CEM S data. Separate month

- to

- day

allo cation factors were computed for each month of the year using heat input for the fuels coal, natural gas, and “ other ” in each region

. 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 are used to allocate all other pollutants. An example of month

- to

- day profiles for gas, coal, and an overall composite for a region in

western Texas is shown in Figure 3

- 6

.60

Figure 3

- 6

. Month

- to

- day profiles for different fuels in a West Texas Region

For units matched to CEMS data , hourly emissions use the hourly CEMS values for NOx and SO2, while other pollutants are allocated according to heat input values . For units not matched to CEMS data, temporal profiles from days to hours are computed based on the season

- , region

- and fuel

- specific average

d ay

- to

- hour factors derived from the CEMS data for those fuels and regions using the appropriate subset

of data

. For the unmatched units, CEMS heat input data are used to allocate all pollutants (including NO

x and SO 2 ) because the heat input data was genera lly found to be more complete than the pollutant

- specific

data. S MOKE then allocates the daily emissions data to hours using the temporal profiles obtained from

the CEMS data for the analysis base year (i.e., 2011 in this case)

.

3.3.2.2 Future year temporal alloc ation of EGUs

IPM provides unit

- level emission projections of average winter (representing October through April) and

average summer (representing May through September) values. To use these data in an air quality model,

the unit

- level data must first be converted to into hourly values through the temporal allocation process. 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 a re done outside of SMOKE and the third step is done by SMOKE using daily emissions files created from the first two steps. The approach in the 2011v6.2 platform maximizes the use of the CEMS data from the air quality analysis year (e.g., 2011).

The goal o f the temporal allocation process is to reflect the variability in the unit

- level emissions that can impact air quality over seasonal, daily, or hourly time scales, in a manner compatible with incorporating

future

- year emission projections into future

- year air quality modeling. The temporal allocation process is applied to the seasonal emission projections obtained from an IPM modeling scenario. IPM represents

two seasons: summer ( May through September ) and winter (October through April)

. IPM unit

- level

par sed files contain seasonal and annual totals of SO 2 , NO X , CO 2 , Hg

, and HCl emissions (computed directly within IPM ), while PM 2.5 , PM 10 , VOC, NH 3 , and CO emissions are calculated using a post

-61

processing tool 19 based on each unit’s projected fuel use and con figuration , coupled with pollutant

specific emission factors. 20 W hen calculating PM emissions , the p ost

- processing tool utilizes specific data assumptions such as the ash and sulfur content of the coal projected to be used at the unit

. The tool creates

a Fl at File (in a comma

- separated value or .csv file format) that provides the starting point for developing emission inputs to an air quality model.

The result ing Flat File contains all of the endogenous ly

- determined and post

- calculated unit

- level

emissions combined with stack parameters ( i.e., stack location and other characteristics consistent with information found in the National Emissions Inventory (NEI)). A cross reference is used to map the units in NEEDS to the stack parameter and facility, unit, rele ase point, and process identifiers used in the National Emissions Inventory (NEI). The cross reference also maps sources to the hourly Continuous

Emissions Monit oring System (CEMS) data used to temporally allocate the emissions in the base year air quality modeling. This cross reference has been updated for the v5.14 platform through collaboration with EPA, regional planning organizations, and states and is also used to determine which emissions sources in the NEI sources have future year emissions predicte d by IPM , and is available at ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/reports/ipm_to_flat_file_xref_2011NEIv2_IPM5.14_

2 0150113.xlsx

.

Emissions from point sources for which emissions are not predicted by IPM are carried forward into the future year modeling platform using other projection methods. Therefore, if the NEI and IPM sources are not properly matched, double

- co unting could result because the future year emissions output from IPM in the future year are treated as a full replacement for the base year emissions, although only for the emissions processes estimated by IPM. Note: any feedback to EPA on future updates to the cross reference should be provided based on this updated version that will be used with later versions of IPM: ftp://ftp. epa.gov/EmisInventory/2011v6/v2platform/reports/ ipm_to_flat_file_xref_2011NEIv2_Updated_20150710.xlsx

.

In order to support the temporal allocation process and other requirements of modeling point sources, the Flat File output from the IPM postprocessor specifies annual and monthly emissions for each stack; however, since IPM projections are only modeled for two seasons comprising multiple months each, monthly emissions cannot be precisely specified in the Flat File. Instead, the monthly values in the Fl at File output from the postprocessor are computed by multiplying the average summer day and average winter day emissions predicted by IPM by the number of days in the respective month. In summary, the monthly emission values shown in the Flat File are no t intended to represent an actual month

- to

- month emission pattern; instead, they are interim values that have translated IPM’s seasonal projections into

month

- level data that serve as a starting point for the temporal allocation process.

The monthly emis sions within the Flat File undergo a multi

- step temporal allocation process to yield the hourly emission values at each unit, as is needed for air quality modeling: summer/ winter value to month, month to day, and day to hour . For sources not matched to uni t

- specific CEMS data, t he first two steps are done outside of SMOKE and the third step to get to hourly values is done by SMOKE using daily the emissions files created from the first two steps. For each of these three temporal allocation steps, NO

x and SO 2 CEM S data are used to allocate NO

x and SO 2 emissions, while CEM S heat input data are used to allocate all other pollutants. The approach defined here gives priority to temporalization based on the base year CEMS data to the maximum extent possible.

19 Documentation of this tool can be found at www.epa.gov/powersectormodeling

20 For more info rmation on EPA emission factors see http://www.epa.gov/ttnchie1/ap42/62

Prior to using the 2011 CEMS data to develop monthly, daily, and hourly profiles, the CEMS data were processed through a tool that found data quality flags that indicated the data were measured (see Section

3.3.2.1 ). These adj usted CEMS data were used to compute the monthly, daily, and hourly profiles

described below.

For units in NEEDS that are matched to units in the National Emission Inventory (NEI), and for which

CEMS data are avai lable, the emissions are tempora lized base d on the CEMS data for that unit and pollutant. For units that are not matched to the NEI or for which CEMS data are not available, the allocation of the IPM seasonal emissions to months is done using average fuel

- specific season

- to

- month factors generate d for each of the 64 IPM regions shown in Figure 3

- 5 . These factors are based on a single year of CEMS data for the modeling base year associated with the air quality modeling analysis being performed, such as 2011. Note that IP M uses load data (reflecting the shape of demand) corresponding to the load in each IPM region that occurred in the base year of the air quality modeling analysis, such as

2011. The fuels used for creating the profiles for a region are coal, natural gas, a nd other , where the other fuels used include oil and wood and vary by region . Separate profiles are computed for NO

x , SO 2 , and heat input. An overall composite profile across all fuels is also computed and can be used in the event that a region has too few units of a fuel type to make a reasonable average profile, or in the case when a unit changes fuels between the base and future year and there were previously no units with that fuel in the region containing the unit

.

The monthly emission values in th e Flat File are first reallocated across the months in that season to align

the month

- to

- month emission pattern at each stack with historic seasonal emission patterns. 21 While this reallocation affects the monthly pattern of each unit’s future

- year seasonal emissions, the seasonal totals are held equal to the IPM projection for that unit and season. Second, the reallocated monthly emission values at each stack are disaggregated down to the daily level consistent with historic daily emission patterns in the g iven month at the given stack using separate profiles for NO

x , SO 2 , and heat input. This process helps to capture the influence of meteorological episodes that cause electricity demand to vary from day

- to

- day, as well as weekday

- weekend effects that change demand during the course of a given
week. Third, this data set of emission values for each day of the year at each unit is input into SMOKE, which uses temporal profiles to disaggregate the daily values into specific values for each hour of the

year.

For units without or not matched to CEMS data, or for which the CEMS data are found to be unsuitable for use in the future year, emissions are allocated from month to day using IPM

- region and fuel

- specific average month

- to

- day factors based on CEM S data from the base year of the air quality modeling analysis. These instances include units that did not operate in the base year or for which it may not have been possible to match the unit in NEEDS with a specific unit in the NEI. EPA uses average emission

pr ofiles for some units with CEMS data in the base year when one of the following cases is true : (1) units are projected to have substantially increased emissions in the future year compared to its emissions in the base (historic) year 22 ; (2) CEMS data are on ly available for a limited number of hours in that base year; (3) units change fuels in the future year; (4) the unit is new in the future year; or (5) units experienced atypical conditions during the base year, such as lengthy downtimes for maintenance or installation of

21 For example, the total emissions for a unit in May would not typically be the same as the total emissions for the same unit i n July, e ven though May and July are both in the summer season and the number of days in those months is the same. This is because the weather changes over the course of each season, and thus the operating behavior of a specific unit can also vary throughout each s eason. Therefore, part of the temporal allocation process is intended to create month

- specific emissions totals

that reflect this intra

- seasonal variation in unit operation and associated emissions.

22 In such instances, EPA does not use that unit’s CEMS da ta for temporal allocation in order to avoid assigning large increases in emissions over short time periods in the unit’s hourly emission profile.63

controls. The temporal profiles that map emissions from days to hours are computed based on the region

and fuel

- specific seasonal (i.e., winter and summer) average day

- to

- hour factors derived from the CEMS data for those fuels and regions using only heat input data for that season

. Only heat input is used because it is the variable that is the most complete in the CEMS data. SMOKE uses these profiles to allocate the daily emissions data to hours.

The emissions from units for which unit

- specific profiles are not deemed appropriate, and for units in the IPM outputs that are not specifically matched to units in the base year, are temporally allocated to hours reflecting patterns typical of the region in which the unit is located. Analysis o f CEMS data for units in each of the 64 IPM regions revealed that there were differences in the temporal patterns of historic emission data that correlate with fuel type (e.g., coal, gas, and other), time of year, pollutant, season (i.e.

winter versus summ er) and region of the country. The correlation of the temporal pattern with fuel type is explained by the relationship of units’ operating practices with the fuel burned. For example, coal units take longer to ramp up and ramp down than natural gas units, and some oil units are used only when electricity demand cannot otherwise be met. Geographically, the patterns were less dependent on state location than they were on IPM regional location. For temporal allocation of emissions at these units, Figure 3

- 6 provides an example of daily coal, gas, and composite profiles in one IPM region

. EPA developed seasonal average emission profiles, each derived from base year CEMS data for each season across all units sharing both IPM region and f uel type. 23 Figure 3

- 4 provides an example of seasonal profiles that allocate daily emissions to hours in one IPM region. These average day

- to

- hour temporal profiles were also used for sources during seasons of the year for which t here were no CEMS data

available , but for which IPM predicted emissions in that season

. This situation can occur for multiple reasons, including how the CEMS was run at each source in the base year.

For units that do have CEMS data in the base year and ar e matched to units in the IPM output, the base year CEMS data are scaled so that their seasonal emissions match the IPM

- projected totals . In particular, the fraction of the unit’s seasonal emissions in the base year is computed for each hour of the season, and then applied to the seasonal emissions in the future year. This is accomplished outside of SMOKE by creating data in the same format as CEMS data for NOx, SO2, and heat input. Any pollutants other than NOX and SO2 are temporalized within SMOKE using h eat input as a surrogate. Distinct factors are used for the fuels coal, natural gas, and “other”

. This procedure yields future

- year hourly data that have the same temporal pattern as the base year CEMS data while matching future

- year seasonal total emissi ons for each stack to IPM’s future

- year projections (see example in Figure 3

- 7 )

.

In cases when the emissions for a particular unit are projected to be substantially higher in the future year than in the base year, the proportion al scaling method to match the emission patterns in the base year described above can yield emissions for a unit that are much higher than the historic maximum emissions for that unit. To address this issue for the 2017 case, the maximum measured emissions of NO

x and SO 2 in the period of 2011

- 2014 were computed. The temporalized emissions were then evaluated at each hour to determine whether they were above this cumulative maximum. The amount of “excess emissions” over the maximum was then computed. For uni ts for which the “excess emissions” could be reallocated to other hours, those emissions were distributed evenly to hours that were below the maximum. Those hourly emissions were then reevaluated against the maximum, and the procedure of reallocating the

e xcess emissions to other hours was repeated until all of the hours had emissions below the maximum, whenever possible (see example in Figure 3

- 8 ). Note: this reallocation technique is new in the 2011v6.2

23 EPA also uses a n overall composite profile across all fuels for each IPM region in instances where a unit i s projected to burn a fuel for which EPA cannot construct an average emission profile (because there were no other units in that IPM region whose historic CEMS data represent emissions from burning th at fuel )

.64

platform and was only used in the 2017eh case, not in the 2025eh case, as it was developed after modeling for the 2025eh case was completed.

Figure 3

- 7

. Future year emissions follow pattern of base year emissions

Figure 3

- 8

. Excess emissions apportioned to hours less than maximum65

Using the above approach, it was not always possible to reallocate excess emissions to hours below the historic maximum, such as when the total seasona l emissions of NOx or SO2 for a unit divided by the number of hours of operation are greater than the 2011

- 2014 maximum emissions level. For these units,

the regional fuel

- specific average profile was applied to all pollutants, including heat input, for th at season (see example in Figure 3

- 9 ). An exception to this is if the fuel for that unit is not gas or coal. In that case, the composite (non

- fuel

- specific) profile was used for that unit. This is because many sources

that used “other” fuel profiles had very irregular shapes due to a small number of sources in the region, and the allocated emissions frequently still exceeded the 2011

- 2014 maximum. Note that it was not possible for SMOKE to use regional profiles for some pollutant s and adjusted CEMS data for other pollutants for the same unit / season, therefore all pollutants are assigned to regional profiles when regional profiles are needed. Also note that for some units, some hours still exceed the 2011

- 2014 annual

maximum fo r the unit even after regional profiles were applied (see example in Figure 3

- 10 ).

For more information on the development of IPM emission estimates and the temporalization of those,

see http://www.epa.gov/powersectormodeling/psmodel514.html , in particular the Air Quality Modeling Flat File Documentation and accompanying inputs.

Figure 3

- 9

. Adjustment to Hou rs Less than Maximum not Possible, Regional Profile Applied66

Figure 3

- 10

. Regional Profile Applied, but Exceeds Maximum in Some Hours



### 3.3.3 Residential Wood Combustion Temporalization (rwc)

There are many f

actors 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 consistent with that used by the AQ model is avail able (e.g.

, outputs from WRF); (2) the meteorological model data are 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 SMO KE program GenTPRO provides a method for developing meteorology

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

- to

- day temporalization for residential wood combustion ( RWC )

, month

- to

- hour temporalizati on for agricultural livestock ammonia, and a generic meteorology

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

based temporalization was used for portions of the rwc sector and for livestock within the ag sector.

GenTPRO r eads 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 tempora l 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 documentation at http://www.cmascenter.org/smoke/documentation/3.1/GenTPRO_TechnicalSummary_Aug2012_Final.pd

f and http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch05s03s07.html , respectively

.

In the 2011v6.2 platform and in SMOKE 3.6.5, the temporal profile format has been updated. GenTPRO now produces separate files including the monthly tempora l profiles (ATPRO_MONTHLY) and day

- of

month temporal profiles (ATPRO_DAILY), instead of a single ATPRO_DAILY with day

- of

- year temporal profiles as it did in SMOKE 3.5. The results are the same either way, so the temporal profiles67

themselves are effectively the same in 2011v6.2 as they were in 2011v6.0 since the meteorology is the same, but they are formatted differently.

For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal allocation of emissions to days. GenTPRO was used to create an annual

- to

- day temporal profile for the RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of the year

. On days where the minimum temperature does not drop below a user

- defined threshold, RWC emiss ions

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 change, only the distribu tion 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

- 11 illustrates the impact of changing the temperature thr eshold 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 distributes a small amount of emissions to the days that have a minimum temperature between 50 and

60  ̊ F.

Figure 3

- 11

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

The diurnal profile for used for most RWC sources (see Figure 3

- 12 ) 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 outd oor temporal profiles from counties in Delaware and aggregating them into a single RWC diurnal profile. This new profile was compared to a concentration based analysis of aethalometer measurements in Rochester, NY (Wang et al. 2011) for

various seasons an d day of the week and found that the new RWC profile generally tracked the concentration based temporal patterns.68 Figure 3

- 12

. RWC diurnal temporal profile

The temporalizatio n for “Outdoor Hydronic Heate rs” (i.e.,“OHH”, SCC=2104008610) and “Outdoor wood burning device, NEC (fire

- pits, chimneas, etc. )” ( i.e., “recreational RWC”, SCC=21040087000) were updated because the meteorological

- based temporalization used for the rest of the rwc sector did not

agree with observations for how these appliances are used.

For OHH, the annual

- to

- month, day

- of

- week and diurnal profiles were modified based on information in the New York State Energy Research and Development Authority (NYSERDA) “Environmental, Energy

Market , and Health Characterization of Wood

- Fired Hydronic Heater Technologies, Final Report” (NYSERDA, 2012) as well as a Northeast States for Coordinated Air Use Management (NESCAUM) report “Assessment of Outdoor Wood

- fired Boilers” (NESCAUM, 2006). A Minneso ta 2008 Residential Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional

annual

- to

- month, day

- of

- week and diurnal activity information for OHH as well as recreational RWC

usage.

The diurnal profile for OHH, shown in Figure 3

- 13 is based on a conventional single

- stage heat load unit burning red oak in Syracuse, New York. As shown in Figure 3

- 14 , the NESCAUM report describes how for individual units, OHH are highly variable day

- to

day but that in the aggregate, these emissions have no day

- of

- week variation. In contrast, the day

- of

week profile for recreational RWC follows a typical “recreational” profile with emissions peaked on

weekends.

Annual

- to

- month temporaliza tion for OHH as well as recreational RWC were computed from the MN DNR survey (MDNR, 2008) and are illustrated in Figure 3

- 15 . OHH emissions still exhibit strong seasonal variability, but do not drop to zero because many units op erate year round for water and pool heating. In contrast to all other RWC appliances, recreational RWC emissions are used far more frequently during the warm season.69 Figure 3

- 13

. Diurnal profile for OHH, ba sed on heat load (BTU/hr)

Figure 3

- 14

. 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 Heater70

Figure 3

- 15

. Annual

- to

- month temporal profiles for OHH and recreational RWC



### 3.3.4 Agricultural Ammonia 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 equa tions are:

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

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

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

where

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

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

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

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

 AR i,h = Aerodynamic resistance in county i

GenTPRO was run using the “BASH_NH3” profile method to create month

- to

- hour temporal profile s for

these sources. Because these profiles distribu te to the hour ba sed on monthly emissions, the monthly

emissions are obtained from a monthly inventory

, or from an annual inventory that has been temporalized

to the month. Fi gure 3

- 16 compares the daily emissions for Minnesota f rom 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.

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 Heater71

Fi gure 3

- 16

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



### 3.3.5 Onroad mobile temporalization (onroad)

For the onroad sector, the temporal distribution of emissions is a comb ination of more traditional temporal profiles and the influence of meteorology. This section will discuss both the meteorological influences and the updates to the diurnal temporal profiles for the 2011 v6.2 platform.

Meteorology is not used in the develop ment of the temporal profiles, but rather it impacts the calculation of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the hourly level by grid cell. More specifically, the on

- network (RPD) and the off

- network parked vehicle (RPV , RPH, and RPP) processes use the gridded meteorology (MCIP) directly. Movesmrg determines the temperature for each hour and grid cell and uses that information to select the appropriate emission factor (EF) for the specified SCC/pollu tant/mode combination. In the 2011 platform (and the

2011 NEI v 2 ), 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

combi nation of these four processes (RPD, RPV, RPH, and RPP) is the total onroad sector emissions.

The onroad sector show a strong meteorological influence on their temporal patterns (see the 2011NEIv2 TSD for more details).

Figure 3

- 17 illustrates the difference between temporalization of the onroad sector used in the 2005 and earlier platforms and the meteorological influence via SMOKE

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

- 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 temporal ization 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 day

- to

- day (and hour

- to

- hour) variability. In addition, the MOVES emissions have an art ificial jump between months which is due to the inventory providing new emissions for each month that are then temporalized within the month but not between months. The SMOKE

- MOVES emissions have a

smoother transition between the months.72

Figure 3

- 17

. Example of SMOKE

- MOVES temporal variability of NO X emissions

For the onroad sector, t he “inventories” referred to in Table 3

- 17 actually consist of activity data , not
emissions . For R PP and RPV processes, the VPOP inventory is annual and does not need temporalization. For RPD, the VMT inventory is monthly and was temporalized to days of the week and then to hourly VMT through temporal profiles. The RPD processes require a speed profil e (SPDPRO) that consists of vehicle speed by hour for a typical weekday and weekend day. Unlike other sectors, the temporal profiles and SPDPRO will impact not only the distribution of emissions through time but also the total emissions.

Because SMOKE

- MOV ES (for RPD ) calculates emissions from VMT, speed and meteorology, if one shifted the VMT or speed to different hours, it would align with different temperatures and hence
different EF. In other words, two SMOKE

- MOVES runs with identical annual VMT, meteor ology, and MOVES EF, will have different total emissions if the temporalization of VMT changes. For RPH, the HOTELING inventory is monthly and was temporalized to days of the week and to hour of the day through temporal profiles. This is an analogous proce ss to RPD except that speed is not included in the

calculation of RPH.

In previous platforms, the diurnal profile for VMT 24 varied by road type but not by vehicle type (see Figure 3

- 18 ). These profiles were used throughout the nat ion.

24 These profiles were used in the 2007 platfor m and proceeding platforms.73 Figure 3

- 18

. Previous onroad diurnal weekday profiles for urban roads

D iurnal profiles that could differentiate by vehicle type as well as by road type and would potentially vary over geography were desired

. In the development of the 2011 v6 .0 25 platform, the EPA updated these profiles to include information submitted by states in their MOVES county databases (CDBs). The

2011NEIv2 process provided an opportunity to update the se diurnal profile with new information submitted by states , to supple ment the data with additional sources, and to refine the methodology

.

States submitted MOVES county databases (CDBs) that included information on the distribution of VMT by hour of day and by day of week 26 (see the 2011NEIv2 TSD for details on the submittal process for onroad). EPA mined the state submitted MOVES CDBs for non

- default diurnal profiles 27 . The list of potential diurnal profiles was then analyzed to see whether the profiles varied by vehicle type, ro ad type, weekday vs. weekend, and by county within a state . For the MOVES diurnal profiles, EPA only considered the state profiles that varied significantly by both vehicle and road types. Only those profiles that passed this criteria were used in that st ate or used in developing default temporal profiles. The Vehicle Travel Information System (VTRIS) is a repository for reported traffic count data to the Federal Highway Administration (FHWA). EPA used 2012 VTRIS data to create additional temporal profiles for states that did not submit temporal information in their CDBs or where those profiles did not pass the

varian ce criteria. The VTRIS data were used to create state specific diurnal profiles by HPMS vehicle and road type. EPA created distinct diurnal pr ofiles for weekdays, Saturday and Sunday along with day of the week profiles 28

.

EPA attempted to maximize the use of state and/or county specific diurnal profiles (either from MOVES or VTRIS)

. Where there was no MOVES or VTRIS data , then a new default pr ofile would be used (see below for description of new profiles). This analysis was done separately for weekdays and for weekends, therefore some areas had submitted profiles for weekdays but defaults for weekends. The

25 These profiles that were generated from MOVES submittals only were used for the v6 and v6.1 platforms. See their respective TSDs for more details.

26 The MOVES tables are the hourvmtfraction and the dayvmtfraction.

27 Further Q A 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.

28 Note, the day of the week profiles (ie. Monday vs Tuesday vs etc) are only from the VTRIS data. The MOVES CDBs only have weekday vs weekend profiles so they were not included in calculating a new national default day of the week profile.74

result was a set of profiles that va ried geographically depending on the source of the profile and the characteristics of the profiles (see Figure 3

- 19 ).

Figure 3

- 19

. Use of submitted versus new national default profi les

A new set of diurnal profiles was developed from the submitted profiles that varied by both vehicle type and road type. For the purposes of constructing the national default diurnal profiles, EPA created individual profiles for each state (averagin g over the counties within) to create a single profile by state, vehicle type, road type, and the day (i.e. weekday vs Saturday vs Sunday)

. The source of the underlying profiles was either MOVES or VTRIS data (see Figure 3

- 19 ). Th e states individual profiles were averaged together to create a new default profile 29

. Figure 3

- 20 shows two new national default profiles for light duty gas vehicles (LDGV, SCC 6 2201 21 ) and combination long

- haul diesel trucks (HHD DV, SCC 6

220 262 ) on restricted urban roadways (interstates and freeways )

. The blue lines indicate the weekday profile, the green the Saturday profile, and the red the Sunday profile. In comparison , the new default profile s for weekdays places more LDGV VMT ( upper plot) in the rush hours while placing HHDDV VMT ( lower plot) predominately in the middle of the day with a longer tail into the evening hours and early morning.

29 Note that the states were weighted equally in the average indepe ndent of the size of the state or the variation in submitted county data.75

Figure 3

- 20

. Updated national defaul t profiles for LDGV vs. HHDDV, urban restricted

In addition to creating diurnal profiles, EPA developed day of week profiles using the VTRIS data. The creation of the state and national profiles was similar to the diurnal profiles (described above)

. Figure76

3

- 21 shows a set of national default profiles for rural restricted roads (top plot) and urban unrestricted roads (lower plot). Each vehicle type is a different color on the plots.

Figure 3

- 21

. Updated national default profiles for day of week

In addition to creating diurnal profiles for VMT, EPA developed a natio nal profile for hote ling. EPA averaged all the combination long

- haul truck profiles on res tricted roads (urban and rural) for weekdays to create a single national restricted profile (blue line in Figure 3

- 22 ). This was then inverte d to create a profile for hotel ing (green line in Figure 3

- 22 ) . This single nat ional profile was used for hote ling77

irrespective of location.

Figure 3

- 22

. Combination long

- haul truck restricted and hote ling profile

For California, CARB supplied diurnal profiles t hat varied by vehicle type, day of the week 30 , and air basin. These CARB specific profiles were used in developing EPA estimates for California. Although

EPA adjusted the total emissions to match California’s submittal to the 2011NEIv2 , the temporalizatio n of these emissions t ook 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 onroad
emissions, see Section 2.3.1 and the 2011NEIv2 TSD.



### 3.3.6 Additional s ector specific details ( afdust, beis

, c1c2rail, c3marine, nonpt

, ptnonipm, ptfire , np_oilgas )

For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to

reduce the total emis sions based on meteorological conditions. These adjustments are applied through

sector

- specific scripts, beginning with the application of land use

- based gridded transport fractions and then subsequent zero

- outs for hours during which precipitation occurs or there is snow cover on the ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are subject to transport. This methodology is discussed in (Pouliot, et al., 2010, http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot_pres.pdf , and in “ Fugitive Dust Modeling for the 2008 Emissions Modeling Platform ” (Adelman, 2012). The precipitation adjustment is applied to

remove all emissions for days where measureable rain occurs. Therefore, the afdust emissions vary day

to

- day based on the precipitation and/or snow cover for that grid cell and day. Both the transport fraction

and meteorological adjustments are based on the g ridded resolution of the platform; therefore, somewhat

30 California’s diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and Tuesday, Wednesday, Thursday had the same profile.78

different emissions will result from different grid resolutions. Application of the transport fraction and meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid m odeling a s compared to ambient samples.

Biogenic emissions in the beis sector vary by every day of the year because they are developed using meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are computed using appropriate emission factors according to the vegetation in each model grid cell, while taking the meteorological data into account.

For the c1c2rail and c3marine 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 agfire sector, the emissions were 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 t he fact that burning occurs during the daylight hours

- see Figure 3

- 23 (McCarty et al., 2009) . This puts most of the emissions during the work day and suppresses the emissions during the middle of the night. A u niform profile for each day of the week was used for all agricultural burning emissions in all states , 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.

Updates were made to temporal profiles for the ptnonipm sector in the 2011v6.2 platform based on comments and data review by EPA staff. Temporal profiles for small airports (i.e., non

- commercial) were updated to eliminate emissions between 10pm and 6am due to a lack of tower operations . Industrial process that are not likely to shut down on Sundays such as those at cement plants were assigned to other more realistic profiles that included emissions on Sundays. This also affected e missions on holidays because Sunday emissions are also used on holidays.

Figure 3

- 23

. Agricultural burning diurnal temporal profile79

For the pt wild fire and ptpresc fire sector s , the inventories are in the d aily point fire format ORL PTDAY. The ptfire sector is used in the model evaluation case (2011e h and in the future base case (201 7 e h ). The 2007 and earlier platforms had additional regulatory cases that used averaged fires and temporally averaged EGU emis sions, 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 in ventory 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 t o create a monthly inventory.

Some cross reference updates for t emporalization of the np_oilgas sector were made in the 2011v6.2 platform to assign np_oilgas sources to 24 hour per day, 7 days a week based on comments received.



### 3.4 Spatial Allocation

The methods used to perform spatial allocation are summarized in this sec tion

. For the modeling platform , spatial factors are typically applied by county and SCC

. As described in Section 3.1

, spatial

allocation was performed for a national 12

- km domain

. To accomplish this, SMOKE used national 12

- km spatial surrogates and a SMOKE area

- to

- point data file. For the U.S., EPA updated surrogates to use circa

2010

- 2011 data wherever possible. For Mexico, updated spatial surrogates were used as described below

. For Canada surrogates provided by Environment Canada were used and are unchanged from the 2007 platform

. The U.S., Mexican, and Canadian 12

- km surrogates cover the entire CONUS domain 12US1

shown in Figure 3

- 1 . Additional documentation on the 2011 spatial surrogates is avai lable at ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/spatial_surrogates/ in the files US_SpatialSurrogate_Documentation_v0 70115 .pdf and US_S patialSurrogate_Workbook _v072115 .xlsx

. The remainder of this subsection provides further detail on the origin of the data used for the spatial surrogates and the area

- to

- point data.



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

There are more than 70 s patial surrogates available for spat ially 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 a limited set of sources. Table 3

- 18 lists the codes and descriptions of the surrogates. Many surrogates use circa 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 2 006 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 v2 shapefiles: Ports_032310_wrf and ShippingLanes_111309FINAL_wrf, but also included shipping lane data in the Great Lakes and support vessel activity data in the Gulf of Mexico.

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

documentation for it is available at http://www.ie.unc.edu/cempd/projects/mims/spatial/ and https://www.cmascenter.org/sa

- tools/documentation/4.2/html/srgtool/SurrogateToolUserGuide_4_2.htm

.80 Table 3

- 18

. U.S. Surrogates available for the 2011 modeli ng p latform

.

Code Surrogate Description Code Surrogate Description

N/A Area

- to

- point approach (see 3.3.1.2) 520 Commercial plus Industrial plus Institutional

100 Population 525 Golf Courses + Institutional +Industrial +

Commercial

110 Housing 527 Single Family Residential

120 Urban Population 530 Residential

- High Density

130 Rural Population 535 Residential + Commercial + Industrial +

Institutional + Government

137 Housing Change 540 Retail Trade

140 Housing Change and Population 545 Personal Repa ir

150 Residential Heating

- Natural Gas 550 Retail Trade plus Personal Repair

160 Residential Heating

– Wood 555 Professional/Technical plus General

Government

165 0.5 Residential Heating

- Wood plus 0.5 Low Intensity Residential 560 Hospital

170 Residential Heating

- Distillate Oil 565 Medical Office/Clinic

180 Residential Heating

– Coal 570 Heavy and High Tech Industrial

190 Residential Heating

- LP Gas 575 Light and High Tech Industrial

200 Urban Primary Road Miles 580 Food, Drug, Chemical Industrial

210 Rural Primary Road Miles 585 Metals and Minerals Industrial

220 Urban Secondary Road Miles 590 Heavy Industrial

230 Rural Secondary Road Miles 595 Light Industrial

240 Total Road Miles 596 Industrial plus Institutional plus Hospita ls

250 Urban Primary plus Rural Primary 600 Gas Stations

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

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

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

USGS

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 Tot al 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

4 00 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 secto r, the on

- network (RPD) emissions were spatially allocated to roadways, and the off

network (RPP and RPV) emissions were allocated to population. The refueling emissions were spatially allocated to gas station locations (surrogate 600)

. On

- network (i.e., on

- roadway) mobile source emissions were assigned to the following surrogates: r ural restricted access to rural primary road miles ( 210 )

, rural

unrestricted access to 231, urban restricted access to urban primary road miles ( 200 ) , and urban

unrestricted a ccess to 221. Off

- network emissions were spatially allocated according to the mapping in81

Table 3

- 19

. In the 2011v6.2 platform, e missions from the extended (i.e., overnight) idling of trucks were assigned to a new s urrogate 205 that is based on locations of overnight truck parking spaces.

Table 3

- 19

. Off

- Network Mobile Source S urrogates
Source type Source Type name Surrogate ID

11 Motorcycle 535

21 Passenger Car 535

31 Passenger Truck 535

32 Light Commercial Truck 510

41 Intercity Bus 258

42 Transit Bus 259

43 School Bus 506

51 Refuse Truck 507

52 Single Unit Short

- haul Truck 256

53 Single Unit Long

- haul Truck 257

54 Motor Home 526

61 Combination Short

- haul Truck 256

62 Combination Long

- haul Truck 257

For the oil and gas sources in the np_oilgas sector , the spatial surrogates were updated to those shown in

Table 3

- 20 using 2011 data consistent with what was used to develop the 201 1NEI nonpoint oil and gas

emissions

. Note that the “Oil & Gas Wells, IHS Energy, Inc. and USGS” (680) is older and based on

circa

- 2005 data. These surrogates were based on the same GIS data of well locations and related

attributes as was used to develop t he 2011NEIv2 data for the oil and gas sector . The data sources

included Drilling Info (DI) Desktop’s HPDI database (Drilling Info, 2012) aggregated to grid cell levels, along with data from Oil and Gas Commission (OGC) websites. Well c ompletion data from HPDI was supplemented by implementing the methodology for counting oil and gas well completions developed for

the U.S. Nat ional Greenhouse Gas Inventory . Under that methodology, both completion date and date of first production from HPDI were used to ident i fy wells completed during 2011. In total, over 1.08 million unique well locations were compiled from the various data sources. The well locations cover 33 states and
1,193 counties (ERG, 2014 b )

. Although basically the same surrogates were used

, s ome minor updates to the oil and gas surrogates were made in the 2011v 6.2 platform to correct some mis

- located emissions.

Table 3

- 20

. Spatial Surrogates for Oil and Gas Sources

Surrogate Code Surrogate Description

6 81 Spud count

- Oil Wells

682 Spud count

- Horizontally

- drilled wells

683 Produced Water at all wells

684 Completions at Gas and CBM Wells

685 Completions at Oil Wells

686 Completions at all wells

687 Feet drilled at all wells

688 Spud count

- Gas a nd CBM Wells

689 Gas production at all wells

692 Spud count

- All Wells82 693 Well count

- all wells

694 Oil production at oil wells

695 Well count

- oil wells

697 Oil production at Gas and CBM Wells

698 Well counts

- Gas and CBM Wells

Some spatial surrogate cross reference updates were made between the 2011v6.1 platform and the 2011v6.2 platform aside from the reworking of the onroad mobile source surrogates described above. These updates included the following:

 Nonroad SCCs using spatial surrog ate 525 (50% commercial + industrial + institutional, 50% golf courses) were changed to 520 (100% commercial + industrial + institutional). The golf course surrogate 850, upon which 525 is partially based, is incomplete and subject to hot spots ;

 Some nonro ad SCCs for commercial equipment in New York County had assignments updated to surrogate 340 ;

 Commercial lawn and garden equipment w as updated to use surrogate 520; and

 Some county

- specific assignments for RWC were updated to use surrogate 300

.

Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is, some surrogates shown in Table 3

- 18 were not assigned to any SCCs, although many of the “unused” surrogates are actually used to “ gap fill ” other surrogates that are used . When the source data for a surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in those counties to ensure that no emissions are dropped when the spatial surrog ates are applied to the

emission inventories. Table 3

- 21 shows the CAP emissions (i.e., NH 3 , NOx, PM 2.5 , SO 2 , and VOC) by

sector , with rows for each sector listed in order of most emissions to least CAP emissions

.

Table 3

- 21

. Selected 2011 CAP emissions by sector for U .S. Surrogates *

Sector ID Description NH3 NOX PM2_5 SO2 VOC

afdust 130 Rural Population 0 0 1,089,422 0 0

afdust 310 T 0 otal Agriculture 0 0 895,786 0 0

afdust 240 Total Road Miles 0 0 286,188 0 0

afdust 140 Housing Change and

Population 0 0 159,485 0 0

afdust 330 Strip Mines/Quarries 0 0 58,959 0 0

afdust 400 Rural Land Area 0 0 1 0 0

a g 310 Total Agriculture 3,502,246 0 0 0 0

a gfire 310 Total Agriculture 3,287 42,326 92,754 15,470 73,858

a gfire 312 Orchards/Vineyards 27 432 1,082 753 799

a gfire 320 Forest Land 7 8 121 0 124

c1c2rail 271 NTAD Class 1 2 3 Railroad

Density 332 734,683 22,636 7,390 38,304

c1c2rail 806 Of fshore Shipping NEI2011

NOx 329 504,779 16,146 7,272 12,151

c1c2rail 820 Ports NEI2011 NOx 19 56,363 1,866 834 1,666

c1c2rail 280 Class 2 and 3 Railroad Miles 13 41,963 948 287 1,622

c1c2rail 261 NTAD Total Railroad Density 2 16,636 379 260 92583

Sector ID Description NH3 NOX PM2_5 SO2 VOC

c3marin e 806 Offshore Shipping NEI2011

NOx 27 77,281 5,143 50,309 3,150

c3marine 820 Ports NEI2011 NOx 41 54,101 3,900 36,064 1,998

nonpt 100 Population 4,137 0 0 0 1,196,465

nonpt 505 Industrial Land 35,360 195,282 124,150 111,849 114,391

nonpt 600 Gas Stati ons 0 0 0 0 416,448

nonpt 310 Total Agriculture 0 0 614 0 363,385

nonpt 535 Residential + Commercial +

Industrial + Institutional +

Government 23 366 1,283 0 327,986

nonpt 515 Commercial plus Institutional

Land 1,408 177,903 18,637 58,287 21,915

nonpt 150 Residential Heating

- Natural

Gas 40,775 217,560 4,785 1,443 13,031

nonpt 510 Commercial plus Industrial 4 178 27 109 224,110

nonpt 140 Housing Change and

Population 3 23,423 65,897 29 134,887

nonpt 300 Low Intensity Residential 3,847 18,334 90,706 3,048 40,003

nonpt 527 Single Family Residential 0 0 0 0 153,528

nonpt 590 Heavy Industrial (IND1) 10 4,373 5,419 1,131 138,575

nonpt 170 Residential Heating

- Distillate

Oil 2,045 40,842 4,523 88,432 1,394

nonpt 650 Refineries and Tank Farms 0 0 0 0 1 29,572

nonpt 500 Commercial Land 2,367 2 85,404 585 26,183

nonpt 250 Urban Primary plus Rural

Primary 0 0 0 0 102,207

nonpt 595 Light Industrial (IND2) 0 1 244 0 79,169

nonpt 545 Personal Repair (COM3) 0 0 93 0 60,289

nonpt 190 Residential Heating

- L P Gas 136 38,705 224 705 1,432

nonpt 240 Total Road Miles 0 27 602 0 32,152

nonpt 520 Commercial plus Industrial plus

Institutional 0 0 0 0 14,965

nonpt 801 Port Areas 0 0 0 0 12,469

nonpt 580 Food, Drug, Chemical

Industrial (IND3) 0 610 313 171 10,535

nonpt 180 Residential Heating

– Coal 247 1,033 605 7,931 1,233

nonpt 880 Drycleaners 0 0 0 0 7,053

nonpt 870 Wastewater Treatment

Facilities 1,003 0 0 0 4,671

nonpt 555 Professional/Technical (COM4)

plus General Government (GOV1) 0 0 0 0 2,865

nonpt 400 Rural Land Area 2,855 0 0 0 0

nonpt 312 Orchards/Vineyards 0 441 117 1,806 262

nonpt 575 Light and High Tech Industrial

(IND2 + IND5) 0 0 0 0 2,538

nonpt 260 Total Railroad Miles 0 0 0 0 2,195

nonpt 700 Airport area 0 0 0 0 1,956

nonpt 540 Retail Trade (COM1) 0 0 0 0 1,371

nonpt 675 Refineries and Tank Farms and

Gas Stations 0 0 0 0 1,20384

Sector ID Description NH3 NOX PM2_5 SO2 VOC

nonpt 585 Metals and Minerals Industrial

(IND4) 0 23 140 8 443

nonpt 320 Forest Land 0 85 287 0 97

nonpt 330 Strip Mines/Quarries 0 4 0 0 48

nonpt 560 Hospit al (COM6) 0 0 0 0 10

nonroad 350 Water 213 143,096 12,395 337 614,637

nonroad 400 Rural Land Area 157 25,658 16,711 194 620,786

nonroad 140 Housing Change and

Population 554 537,250 45,058 1,255 78,526

nonroad 310 Total Agriculture 481 488,224 39,037 9 10 57,473

nonroad 520 Commercial plus Industrial plus

Institutional 205 70,541 16,361 288 255,836

nonroad 510 Commercial plus Industrial 382 131,572 9,888 348 139,291

nonroad 300 Low Intensity Residential 106 26,637 4,324 138 202,928

nonroad 505 Indust rial Land 452 146,871 5,809 411 32,978

nonroad 100 Population 40 39,475 2,824 85 5,030

nonroad 890 Commercial Timber 19 12,979 1,486 38 8,680

nonroad 850 Golf Courses 12 2,394 112 17 7,092

nonroad 860 Mines 2 2,931 341 5 594

nonroad 261 NTAD Total Rai lroad Density 2 2,673 310 5 568

np_oilgas 698 Well count

- gas and CBM

wells 0 378,342 6,608 2,636 566,281

np_oilgas 694 Oil production at oil wells 0 1,847 0 12,602 729,489

np_oilgas 695 Well count

- oil wells 0 103,585 3,299 96 452,404

np_oilgas 697 Oil production at gas and CBM

wells 0 3,275 203 35 465,498

np_oilgas 686 Completions at all wells 0 8,926 333 44 106,170

np_oilgas 689 Gas production at all wells 0 39,256 2,323 225 62,716

np_oilgas 693 Well count

- all wells 0 29,867 436 141 51,389

np _oilgas 687 Feet drilled at all wells 0 44,820 1,449 119 9,714

np_oilgas 683 Produced Water at all wells 0 0 0 0 45,033

np_oilgas 692 Spud count

- all wells 0 29,816 417 520 4,686

np_oilgas 685 Completions at Oil Wells 0 360 11 381 28,194

np_oilgas 688 Spud count

- Gas and CBM

Wells 0 0 0 0 16,115

np_oilgas 684 Completions at Gas and CBM

Wells 0 1,532 46 1,497 11,706

np_oilgas 681 Spud count

- Oil Wells 0 0 0 0 6,700

np_oilgas 682 Spud count

- Horizontally

drilled wells 0 5,526 208 9 349

np_oilgas 6 80 Oil and Gas Wells 0 10 0 0 55

np_oilgas 400 Rural Land Area 0 0 0 0 50

onroad 221 Urban Unrestricted Roads 49,896 1,580,588 66,861 12,781 407,127

onroad 535 Residential + Commercial +

Industrial + Institutional +

Government 675,029 13,884 982 1,349, 381

onroad 231 Rural Unrestricted Roads 30,644 1,245,765 42,117 6,945 224,361

onroad 200 Urban Primary Road Miles 26,961 938,015 37,845 5,855 150,425

onroad 210 Rural Primary Road Miles 12,396 799,645 24,520 2,811 76,29585

Sector ID Description NH3 NOX PM2_5 SO2 VOC

onroad 205 Extended Idle Locati ons 908 336,235 6,874 119 75,323

onroad 510 Commercial plus Industrial 132,109 2,225 156 213,658

onroad 600 Gas Stations 190,698

onroad 256 Off

- Network Short

- Haul

Trucks 11,019 271 10 17,088

onroad 526 Residential

- Non

- Institutional 741 19 1 2, 415

onroad 257 Off

- Network Long

- Haul Trucks 431 38 2 1,423

onroad 506 Education 346 28 1 1,050

onroad 507 Heavy Light Construction

Industrial Land 33 2 0 98

onroad 259 Transit Bus Terminals 8 4 0 101

onroad 258 Intercity Bus Terminals 18 1 0 27

rwc 165 0.5 Residential Heating

- Wood

plus 0.5 Low Intensity

Residential 19,260 33,660 374,085 8,838 433,797

rwc 300 Low Intensity Residential 412 745 6,742 107 8,301



### 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 and airport ground support equipment emissions as point sources. For the modeling platform, EPA used t he SMOKE “area

- to

- point” approach for only jet refueling in the nonpt sector. The following SCCs use this approach: 2501080050 and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/roc ket engine firing and testing). The ARTOPNT approach is described in detail in the 2002 platform documentation : http://www.epa.gov/scram001/reports/Emissions%20TSD%20Vol1_02

- 28

- 08.pdf

. The ARTOPNT file

that lists the nonpoint sources to lo cate using point data were unchanged 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 2010 Canadian emissions have been updated in the

2011v6.2 platform. The spatial surrogate data came from Environment Canada, along with cross references. The surrogates they provided were outputs from the Surrogate Tool (previously referenced). The Canadian surrogates used for this platform are listed in Table 3

- 22

. Th e leading “9” was added to the surrogate codes to avoid duplicate surrogate numbers with U.S. surrogates. S urrogates for Mexico are circa 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

- 23 . The entries in this table are for the othar sector except for the “ MEX Total Road Miles ” and t he “ CAN traffic ” rows, which are for the othon

sector.

Table 3

- 22

. Canadian Spatial Surrogates

Code Canadian Surrogate D escription Code Description

9100 Population 92424 BARLEY

9101 total dwelling 92425 BUCWHT

9103 rural dwelling 92426 CANARY

9106 ALL_INDUST 92427 CANOLA

9111 Farms 92428 CHICPEA

9113 Forestry and logging 92429 CORNGR

9211 Oil and Gas Extraction 92425 BUCWHT86

Code Canadian Surrogate D escription Code Description

9212 Mining except oil and gas 92430 CORNSI

9221 Total Mining 92431 DFPEAS

9222 Utilities 92432 FLAXSD

9233 Total Land Development 92433 FORAGE

9308 Food manufacturing 92434 LENTIL

9321 Wood product manufacturing 92435 MUSTSD

9323 Printing and related support activities 92436 MXDGRN

9324 Petroleum and coal products manufacturing 92437 OATS

9327 Non

- metallic mineral product manufacturing 92438 ODFBNS

9331 Primary Metal Manufacturing 92439 OTTAME

9412 Petroleum product wholesaler

- distributors 92440 POTATS

9416 Building material and supplies wholes aler

distributors 92441 RYEFAL

9447 Gasoline stations 92442 RYESPG

9448 clothing and clothing accessories stores 92443 SOYBNS

9481 Air transportation 92444 SUGARB

9482 Rail transportation 92445 SUNFLS

9562 Waste management and remediation services 924 46 TOBACO

9921 Commercial Fuel Combustion 92447 TRITCL

9924 Primary Industry 92448 WHITBN

9925 Manufacturing and Assembly 92449 WHTDUR

9932 CANRAIL 92450 WHTSPG

9941 PAVED ROADS 92451 WHTWIN

9942 UNPAVED ROADS 92452 BEANS

9945 Commercial Marine Vess els 92453 CARROT

9946 Construction and mining 92454 GRPEAS

9948 Forest 92455 OTHVEG

9950 Combination of Forest and Dwelling 92456 SWCORN

9955 UNPAVED_ROADS_AND_TRAILS 92457 TOMATO

9960 TOTBEEF 92430 CORNSI

9970 TOTPOUL 92431 DFPEAS

9980 TOTSWIN 9243 2 FLAXSD

9990 TOTFERT 92433 FORAGE

9996 urban_area 92434 LENTIL

9997 CHBOISQC 92435 MUSTSD

91201 traffic_bcw 92436 MXDGRN

92401 BULLS 92437 OATS

92402 BFCOWS 92438 ODFBNS

92403 BFHEIF 92439 OTTAME

92404 CALFU1 92440 POTATS

92405 FDHEIF 92441 RYEFA L

92406 STEERS 92442 RYESPG

92407 MLKCOW 92443 SOYBNS

92408 MLKHEIF 92444 SUGARB87

Code Canadian Surrogate D escription Code Description

92409 MBULLS 92445 SUNFLS

92410 MCALFU1 92446 TOBACO

92412 BROILER 92447 TRITCL

92413 LAYHEN 92448 WHITBN

92414 TURKEY 92449 WHTDUR

92416 BOARS 92450 WHTSPG

92417 GR WPIG 92451 WHTWIN

92418 NURPIG 92452 BEANS

92419 SOWS 92453 CARROT

92421 IMPAST 92454 GRPEAS

92422 UNIMPAST 92455 OTHVEG

92423 ALFALFA 92456 SWCORN

92457 TOMATO

Table 3

- 23

. CAPs Allocated to Mexican and Canadian Spatial Surrogates

Code Mexican or Canadian Surrogate 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 R esidential Heating

- Wood 0 20,093 211,525 2,859 380,572

16 MEX Residential Heating

- Distillate Oil 0 38 0 11 2

20 MEX Residential Heating

- LP Gas 0 25,303 787 63 614

22 MEX Total Road Miles 0 0 0 0 3,513

24 MEX Total Railroads Miles 0 74,969 1,669 6 63 2,824

26 MEX Total Agriculture 679,212 164,144 72,372 2,127 43,958

28 MEX Forest Land 0 16,224 67,683 660 79,018

32 MEX Commercial Land 0 125,211 7,726 0 286,982

34 MEX Industrial Land 0 45,831 5,684 59,201 133,440

36 MEX Commercial plus Industrial Land 0 0 0 0 332,495

38 MEX Commercial plus Institutional Land 0 6,400 216 84 28,293

40 Residential (RES1

- 4)+Com m ercial+Industrial+

Institutional+ Government 0 8 20 0 241,710

42 MEX Personal Repair (COM3) 0 0 0 0 33,616

44 MEX Airports Area 0 14,639 0 1,149 6,857

46 MEX Marine Ports 0 124,951 2,991 1,482 1,099

48 Brick Kilns

- Mexico 0 776 6,691 0 10,244

50 Mobile sources

- Border Crossing

- Mexico 0 454 0 0 2,668

9100 CAN Population 603 0 276 0 304

9101 CAN total dwelling 643 46,256 12,783 14,698 32,944

9106 CAN ALL_INDUST 133 21,526 381 3,921 2

9113 CAN Forestry and logging 1,582 8,561 28,622 1,809 36,114

9115 CAN Agriculture and forestry activities 160 239,553 25,318 9,092 26,526

9116 CAN Total Resources 0 17 0 0 588

Code Mexican or Canadian Surrogate Description NH 3 NO X PM 2_5 SO 2 VOC

9212 CAN Mining except oi l 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 whole saler

- 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 accessories stores 0 0 0 0 328

9562 CAN Waste management and remediation

services 165 893 1,596 1,998 16 ,551

9921 CAN Commercial Fuel Combustion 494 33,816 2,750 35,471 850

9924 CAN Primary Industry 0 0 0 0 219,282

9925 CAN Manufacturing and Assembly 0 0 0 0 139,227

9931 CAN OTHERJET 9 14,388 548 1,139 7,629

9932 CAN CANRAIL 109 122,694 4,093 5,737 3,30 4

9942 CAN UNPAVED ROADS 40 3,462 3,499 48 152,674

9945 CAN Commercial Marine Vessels 28 45,454 6,404 14,325 61,139

9946 CAN Construction and mining 247 156,770 10,070 5,667 17,180

9947 CAN Agriculture Construction and mining 19 37,452 536 26 32,683

9 950 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 0 089 

## 4 Development of 201 7 and 2025 Base

- Case Emissions

The emission inventories for the future years of 2017 and 202 5 have been developed using projection methods that are specific to the type of emission source. Future emissions are projected from the 2011 base case either by running models to estimate future year emissions from specifi c types of emission sources (e.g

. , EGUs, and onroad and nonroad mobile sources), or for other types of sources by adjusting the base year emissions according to the best estimate of changes expected to occ ur in the intervening years (e.g ., non

- EGU point and nonpoint sources). For some sec tors, the same emissions are used in the base and future years, such as biogenic, fire, and Canadian emissions. For the remaining sectors, rules and specific legal obligations that go into effect in the intervening years, along with changes in activity for the sector, are considered when possible. The 2017 and 2025 base case emission inventories represent predicted emissions that account for Federal and State measures promulgated or under reconsideration by December, 2014.

Emissions inventories for neighbor ing countries used in our modeling are included in the 2011v6.2 platform, specifically 2008, 2018, and 2025 emissions inventories for Mexico, and 2010 emissions inventories for

Canada. The meteorological data used to create and temporalize emissions for th e future year cases is held constant and represents the year 2011. With the exception of speciation profiles for mobile sources and temporal profiles for EGUs, the same ancillary data files are used to prepare the future year emissions

inventories for air quality modeling as were used to prepare the 2011 base year inventories

.

Emission projections for EGUs were developed using IPM version 5.14 and are reflected in an air quality modeling

- ready flat file taken from the EPA Base Case v.5.14 . The NEEDS databas e is an important input to IPM in that contains the generation unit records used for the model plants that represent existing and planned/committed units in EPA modeling applications of IPM. NEEDS includes basic geographic, operating, air emissions, and ot her data on these generating units and has been updated for the EPA’s version 5.14 power sector modeling platform based on comments received on the notices of data availability for the 2011 and 2018 emissions modeling platforms and through other sources of data . The EGU emission projections in the flat file format, the corresponding NEEDS database, and user guides and documentation are available with the

information for the EPA’s Power Sector Modeling Platform v.5.14 at http://www.epa.gov/powersectormodeling

Note: after October 1, 2015, the new site will be http://www.epa.gov/airmarkets/powersectormodeling.html

. Th e projected EGU emi ssions include the Final M ercury and A ir T oxics (MATS) rule announced on December
21, 2011 and the Cross

- State Air Pollution Rule (CSAPR) issued July 6, 2011. Note that the Clean Power Plan

is not included in the 2017 or 2025 base cases.

To project future emissions for onroad and nonroad m obile sources, the EPA used MOVES2014 and NMIM, respectively. The EPA obtained future year projected emissions for these sectors by running the MOVES and NMIM models using year

- specific information about fuel mixtures, act ivity data, and the impacts of national

and state

- level rules and control programs. Development of the future year onroad and nonroad emissions requires a substantial amount of lead time and resources. EPA had already prepared the emissions projections

for 2018 when EPA revised the attainment deadline for Moderate nonattainment areas to July 2018 in the 2008 Ozone SIP Requirements Rule, as discussed above, which effectively required the agency to adjust its projection year for this rulemaking to 2017. Thus, for purposes of the 2011v6.2 platform , the EPA calculated

the 2017 emissions from mobile sources by adjusting the 2018 emissions to represent 2017 using factors derived from national scale runs of MOVES and NMIM, respectively. T he agency anticipates that for the final rule to address interstate transport for the 2008 ozone standard , the mobile source emissions for 2017 that will be used in the air quality modeling will be generated by running these for models the year 2017

.90 For non

- EGU point and nonpoint sources, projections of 2017 emissions were developed by starting with the 2011 emissions inventories and applying adjustments that represent the impact of national, state, and local rules coming into effect in the years 2012 through 2017, along with the i mpacts of planned shutdowns, the construction of new plants, specific information provided by states, and specific legal obligations resolving alleged environmental violations, such as consent decrees. Changes in activity are considered for sectors such as oil and gas, residential wood combustion, cement kilns, livestock, aircraft, commercial marine vessels and

locomotives. Efforts were made to include some regional haze and state

- reported local controls as part of a larger effort to include more local cont rol 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 ar e given in the following subsections and in Table 4

- 1

.

 EGU sector (pt egu ): Unit

- specific e stimates from IPM version 5. 14

, including CASPR

, Final MATS

, Regional Haze rule, and Cooling Water Intakes Rule

.

 N on

- IPM sector (ptnonipm): Closures, p rojection factors and percent reductions reflect comments
received from the notices of d ata a vailability for the 2011 and 2018 emissions modeling platforms, along

with emission reductions due to national and local rules, control programs, plant closures, consent
decrees and settlements

. P rojection for corn ethanol and biodiesel plants, refineries and upstream impacts take into account Annual Energy Outlook (AEO) fuel volume projections

. Airport

- specific

t erminal area forecast (TAF) data were used for aircraft to account for projected changes in landing/takeoff activity. For year 2017, due to the late change of modeling years from 2018 to 2017, most projection information was obtained for year 2018 but projections were processed for the year 2017, meaning that controls with known compliance dates in year 2018 were not applied

.

 Point and nonpoint oil and gas sectors (pt_oilgas and np_oilgas): Regional projection factors by product and consumption indicators using information from AEO 201 4 projections to year s 2018 and 2025 , as well as comments received on the notices of data availability for the 2011 and 2018 emissions modeling platforms

. Cobenefits of stationary engines CAP

- cobenefit reductions (RICE NESHAP) and controls

from New Source Performance S tandards (NSPS) are reflected for select source categories.

 Biogenic (beis): 2011 emissions are used for all future

- year scenarios and are computed with the same

“11 g ” meteorology as is used for the air quality modeling

.

 F ires sector s ( pt wild fire , ptpresc fire , agfire ): No growth or control

– 2011 estimates are used directly

.

 Agricultural sector (ag): Year 2017 and 2025 p rojection 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

.

 Area fugitive dust sector (afdust): For livestock PM emissions, p rojection factors for dust categories

related to livestock estimates based on expected changes in animal population . For unpaved and paved road dust, c ounty

- level VMT project ions to 2017 and 2025 were considered

.

 Remaining Nonpoint sector (nonpt): P rojection factors and percent reductions reflect comments received

from the notices of data availability for the 2011 and 2018 emissions modeling platforms, a long with

emission reductions due to national and local rules/ control programs

. PFC projection factors reflecting impact of the final Mobile Source Air Toxics ( MSAT 2) rule. Upstream impacts from AEO fuel volume

, including cellulosic ethanol plants

, are ref lected. For year 2017, due to the late change of modeling years from 2018 to 2017, most projection information was obtained for year 2018 and used as

- is without interpolation to 2017.

 Residential Wood Combustion (rwc): Year 2017 and 2025 p rojection f actors reflect assumed growth of wood burning appliances based on sales data, equipment replacement rates and change outs. These91

changes include the 2

- stage NSPS for Residential Wood Heaters, resulting in growth in lower

- emitting stoves and a reduction in higher emitting stoves.

 L ocomotive, and non

- Category 3 commercial marine sector ( c1c2rail ): Year 2017 and 2025 p rojection

factors for Category 1 and Category 2 commercial marine and locomotives reflect final locomotive

marine controls.

 Category 3 commer cial mar ine vessel (c3marine): Base

- year 2011 emissions grown and controlled to

201 7 and 2025 , incorporating controls based on Emissions Control Area (ECA) and International Marine Organization (IMO) global NO X and SO 2 controls.

 Nonroad mobile sector (nonroad): O ther than for California and Texas

, this sector uses data from a run

of NMIM that utilized NONROAD2008a , using future

- year equipment population estimates and control

programs to year s 2018 and 2025. The year 2018 data were adjusted to 2017 using national a djustment

factors. The inputs were either state

- supplied as part of the 2011NEIv2 process or using national level inputs, with only minor updates for 2011NEIv2

. F inal controls from the final locomotive

- marine and small spark ignition rules are included . C alifornia and Texas

- specific data were prov ided by CARB and TCEQ, respectively

.

 Onroad mobile (onroad) : MOVES2014

- based emissions factors for year s 2018 and 2025 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

. The 2018 emissions were adjusted to 2017 using national adjustment factors. Data for California were provided by the California

Air Reso urces Board

.

 Other p oint (othpt) , nonpoint/nonroad (othar), onroad (othon) : For Canada, year 2010 inventories were used for 2017 and 2025 because no future year projected inventories were available. Mexico inventory data were projected from year 2008 to 2018 (used for 2017) and 2025. C3 CMV data projected using the same methodology as the c3marine sector. Offshore oil platforms emission held constant at 2011

levels.

Table 4

- 1 summarizes the growth and control assumptions by sour ce type that w ere used to create the U.S. 201 7

and 2025 base

- case emissions from the 2011v 6.2 base year inventorie s. T he control, closures and projection packets ( i.e., data sets) used to create the 201 7 and 2025 future year base

- case scenario inventories from the

2011 base case are provided on the EMCH web site and are discussed in more detail in the s ections listed in

Table 4

- 1

. These packets were processed through EPA ’s Control Strategy Tool (CoST) to c reate future year

emission inventories. CoST is described here: http://www.epa.gov/ttnecas1/cost.htm and discussed in context to this emissions modeling platform in Section 4.2.1. The last column in Table 4

- 1 indicates the order of the CoST

strategy u sed for the source/packet type. F or some sectors ( e.g., ptnonipm), multiple CoST strategies are needed to produce the future year inventory because the same source category may be subject to multiple projecti on or control packet s

. For example, the “Loco

- marine” projection factors are applied in a second control strategy for the ptnonipm sector, while f or the c1c2rail sector, these same projection factors can be applied in the first ( and only ) control strategy. Thus, i n Table 4

- 1, p ackets with a “1” in the CoST s trategy column are applied in the first strategy, while packets with a “2” in the CoST strategy column are applied in a second strategy that is run

on an intermediate inventory output from the first stra tegy.

The remainder of this section is organized by broad NEI sectors with further stratification by the types of packets (e.g., projection, control, closure packets) and whether emissions are projected via a stand

- alone model (e.g., EGUs use the IPM mode l and onroad mobile uses MOVES), using CoST, or by other mechanisms. EGU projections are discussed in Section 4.1. All NEI non

- EGU Point and Nonpoint sector projections (including all commercial marine vessels, locomotives and aircraft) are described in S ection 4.2, along with some background on CoST. Onroad and nonroad mobile projections are discussed in Sections 4.3 and 4.4 , respectively. Finally, projections for all “other” sources, primarily out side the U.S., are described in Section 4.5

.92

Table 4

- 1

. Control strategies and growth assumptions for creating the 201 7 and 2025 base

- case emissions

inventories from the 2011 base case

Description of growth, control, closure data, or, new inventory Sector(s) Packet Type CAPs impacted Section(s) CoST Strategy

Non

- EGU Point (ptnonipm and pt_oilgas sectors) Controls and Growth Assumptions Facility, unit and stack closures, primarily from the Emissions Inventory System (EIS) ptnonipm, pt_oilgas CLOSURE All 4.2.2 1

"Loco

- marine rule": Growth and control to years 2017 and 2025 from Locomotives and Marine Compression

- Ignition Engines Les s than 30 Liters per Cylinder: March, 2008 ptnonipm,

c1c2rail PROJECTION All 4.2.3.3 2,

1

Upstream RFS2/EISA/LDGHG impacts on gas distribution, pipelines and refineries to years 2018 and 2025 ptnonipm, pt_oilgas,

non pt PROJECTION All 4.2.3.5 2

AEO growth to 2018 and 2025: industrial sources, including oil and gas play

- level projections ptnonipm, pt_oilgas,
nonpt, np_oilgas PROJECTION All 4.2.3.6 1
Aircraft growth via Itinerant (ITN) operations at airports to

2017 and 2025 ptnonipm PROJECTION All 4.2.3.8 1
Corn Ethanol plants adjusted via AEO volume projections to

2017 and 2025 ptnoni pm PROJECTION All 4.2.3.10 1

NESHAP: Portland Cement census

- division level based on Industrial Sector Integrated Solutions (ISIS) policy emi ssions to years 2018 and 2025. The ISIS results are from the ISIS

Ceme nt model runs for the NESHAP and NSPS analysis of August 2013 and include closures and growth. ptnonipm,

nonpt PROJECTION

& new

inventories for

new kilns All 4.2.3.9 &

4.2.5.4 1 &

n/a

NESHAP: RICE (reciprocating internal combustion engines)

with reconsideration amendments ptnonipm, pt_oilgas,

nonpt, np_oilgas CONTROL CO,

NO X , PM, SO 2 , VOC 4.2.4.2 1

NSPS: oil and gas pt_oi lgas, np_oilgas CONTROL VOC 4.2.4.1 1

NSPS: RICE ptnonipm, pt_oilgas,

nonpt, np_oilgas CONTROL CO,

NO X , VOC 4.2.4.3 2

NSPS: Gas turbines ptnonipm, pt_oilgas CONTROL NO X 4.2.4.6 1

NSPS: Process heaters ptnonipm, pt_oilgas CONTROL NO X 4.2.4.7 1

Industrial/Commercial/Institutional Boiler MACT with

Reconsideration Amendments + loc al programs nonpt, ptnonipm, pt_oilgas CONTROL CO,

NO X , PM, SO 2 , VOC 4.2.4.4 1

State fuel sulfur content rules for fuel oil

– via 2018 NODA comments, effective only in most northeast states nonpt, ptnonipm,
pt_ oilgas CONTROL SO 2 4.2.4.5 1 State comments: from previous platforms (including consent decrees) and 2018 NODA (search for ‘EPA

- HQ

- OAR

- 2013

- 0809’ at regulations.gov) nonpt, ptnonipm, pt_oilgas PROJECTION

&

CONT ROL All 4.2.3.6

, 4.2.3.7

, 4. 2.4.10 1 Commercial and Industrial Solid Waste Incineration (CISWI)

revised NSPS ptnonipm CO NTROL SO2 4.2.4.9 1 Arizona Regional haze controls ptnonipm CONTROL NO X , SO 2 4.2.4.8 1 New biodiesel plants in year 2018 ptnonipm new inventory All 4.2.5.2 n/a

Nonpoint (afdust, ag, nonpt, np_oilgas and rwc sectors) Controls and Growth Assumptions

AEO

- based VMT growth for paved and unpaved roads afdust PROJECTION PM 4.2.3.1 193

Description of growth, control, closure data, or, new inventory Sector(s) Packet Type CAPs impacted Section(s) CoST Strategy Livestock emissions growth from year 2011 to years 2017 and

2025 ag PROJECTION NH 3 4.2.3.2 1

Upstream RFS2/EISA/LDGHG impacts on gas distribution, pipelines and refineries to years 2018 and 2025 ptnonipm, pt_oi lgas,

nonpt PROJECTION All 4.2.3.5 2

AEO growth to 2018 and 2025: industrial sources, including oil and gas play

- level projections ptnonipm, pt_oilgas,
nonpt, np_oilgas PROJECTION All 4.2.3.6 1

NESHAP: RICE (reciprocating internal combustion engines)

with reconsideration amendments ptnonipm, pt_oilgas,

nonpt, np_oilgas CONTROL CO,

NO X , PM, SO 2 , VOC 4.2.4.2 1

NSPS: oil and gas pt_oilgas, np_oilgas CONTROL VOC 4.2.4.1 1

NSPS: RICE ptnonipm, pt_oilgas,

nonpt, np_oilgas CONTROL CO,

NO X , VOC 4.2.4.3 2
Residential wood combustion growth a nd change

- outs from year 2011 to years 2018 and 2025 rwc PROJECTION All 4.2.3.11 1

Industrial/Commercial/Institutional Boiler MACT with Reconsideration Amendments + local programs nonpt, ptnonipm, pt_oilgas CON TROL CO,

NO X , PM, SO 2 , VOC 4.2.4.4 1

State fuel sulfur content rules for fuel oil

– via 2018 NODA comments, effective only in most northeast states nonpt, ptnonipm, pt_oilgas CONTROL SO 2 4.2.4.5 1 State comments: from previous platforms (including consent decrees) and 2018 NODA (search for ‘EPA

- HQ

- OAR

- 2013

- 0809’ at regulations.gov) nonpt, ptnonipm, pt_oilgas PROJECTION

&

CONTROL All 4.2.3.6

, 4.2.3.7

, 4.2.4.10 1 MSAT2 and RFS2 impacts with state comments on PFC (portable fuel container) growth and control from 2011 to years

2018 and 20 25 nonpt new inventory All 4.2.5.1 n/a New cellulosic plants in year 2018 nonpt new inventory All 4.2.5.3 n/a
Onroad Mobile (onroad sector) Controls and Growth Assu mptions

All national in

- force regulations are modeled. The list includes recent key mobile source regulations but is not exhaustive.

National Onroad Rules:
All onroad control programs finalized as of the date of the model run, including most recently:

on road n/a All 4.3 n/a Tier

- 3 Vehicle Emissions and Fuel Standards Program: March,

2014 2017 and Later Model Year Light

- Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy

Standards : Oc tober, 2012

Greenhouse Gas Emissions Standards and Fuel Efficiency

Standards for Medium

- and Heavy

- Duty Engines and

Vehicles : September, 2011 Regulation of Fuels and Fuel Additives: Modifications to Renewable Fuel Standard Program (RFS2): Dec ember , 2010 Light

- Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards;

Final Rule for Model

- Year 2012

- 2016: May, 2010 Final Mobile Source Air Toxics Rule (MSAT2): February,

200794

Description of growth, control, closure data, or, new inventory Sector(s) Packet Type CAPs impacted Section(s) CoST Strategy
Local Onroad Program s: California LEVIII Program

onroad n/a All 4.3 n/a Ozone Transport Commission (OTC) LEV Program: January,1995
Inspection and Maintenance programs

Fuel programs (also affect gasoline nonroad equi pment)

Stage II refueling control programs

Nonroad Mobile (c1c2rail, c3marine, nonroad sectors) Controls and Growth Assumptions

All national in

- force regulations are modeled. The list includes recent key mobile source regulations but is not exh austive.

National Nonroad Controls:

All nonroad control programs finalized as of the date of the model run, including most recently:

nonroad n/a All 4.4 n/a Emissions Standards for New Nonroad Spark

- Ignition Engines, Equipment, and Vessels: October, 2008
Growth and control to years 2017 and 2025 from Locomotives and Marine Compression

- Ignition Engines Les s than 30 Liters per Cylinder: March, 2008

Clean Air Nonroad Diesel Final Rule

– Tier 4: May, 2004

Locomotives:
Growth and control to years 2017 and 2025 from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008 c1c2rail, ptnonipm PROJECTION All 4.2.3.3 1,

2

Clean Air Nonroad Diesel Final Rule

– Tier 4: May, 2004 c1c2rail n/a All 4.4 n/a

Commercial Marine:
Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards: Apr il, 2010 c3marine PROJECTION All 4.2.3.4 1 Growth and control to years 2017 and 2025 from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008 c1c2rail, ptnonipm PR OJECTION All 4.2.3.3 1,

2

Clean Air Nonro ad Diesel Final Rule

– Tier 4: May, 2004 nonroad n/a All 4.4 n/a



### 4.1 EGU sector projection s : ptegu

Th e future

- year data for t he ptegu sector used in the air quality modeling were created by the Integrated Planning Model (IPM) version 5. 14 (v5.1 4 )

. The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. IPM v ersion 5 .1 4 refl ects state rules, c onsent decrees

and announced shutdowns through December, 2014 , in addition the NEEDS database was upd a ted based on

comments received on the notices of data availability for the 2011 and 2018 emissions modeling platforms

. IPM

5.1 4 was upd ated from the previous version 5.13 and represents electricity demand projections for the Annual Energy Outlook (AEO) 201 4

. The scenario used for this mo deling represents the implementation of the Cross

State Air Pollution Rule (CSAPR)

, the Me rcury and Air Toxics Standards (MATS)

, the final actions EPA has taken to implement the Regional Haze Rule , and the Cooling Water Intakes R ule and Combustion Residuals

fro m Electric Utilities (CCR)

. More details on the IPM v5.1 4 base case scenarios can be found at http://epa.gov/powersectormodeling/psmodel514.html

.

Directly emitted PM emissions (i.e., PM 2.5 and PM 10 ) from the EGU sector are computed via a post processing

routine that applies emission factors to the IPM

- estimated fuel throughput based on fuel, configuration and controls to compute the filterable and condensable components of PM. This postprocessing step also apportions95 the regional emissions down to the unit

- level emissions used for ai r quality modeling. A single IPM run was postprocessed once for each output y ear to get results for 2018 and 2025. For i nformation on how the 2018 emissions were adjusted to represent 2017, see: http://www.epa.gov/airmarkets/documents/ipm/Adjusted2017.pdf

.

From the unit

- level parsed file, a flat file is created that is used as the input to SMOKE and processed into the format needed by the air quality model. As part of the develop ment of the flat file, a cross reference between the

2011NEIv2 and IPM is used to populate stack parameters and other related information for matched sources

. The flat file creation methodology is documented in the air quality modeling flat file documentat ion available

here: http://www.epa.gov/airmarkets/documents/ipm/FlatFile_Methodology.pdf . The cross reference is

available from http://www.epa.gov/airmarkets/documents/ipm/FlatFile_Inputs.xls

, with additional information

available in ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/reports/ ipm_to_flat_file_xref_2011NEIv2_IPM5.14_20150113.xlsx

. The emissions in the flat file created based on the IPM outputs are temporalized in to the hourly emissions needed by the air qu ality model a s described in Section

3.3.2.



### 4.2 Non

- EGU Point and NEI Nonpoint sector projections : afdust, ag, c1c2rail, c3marine, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc

To project all U.S. non

- EGU stat ionary sources

, facility/unit closures information, and growth (PROJECTION)

factors and/or controls were applied to certain categories within the afdust, ag, c1c2rail, c3marine, nonpt, np_oilgas, ptnonipm, pt_oilgas and rwc platform sectors. Some facility or sub

- facility

- level closure information was also appl ied to the point sources. There are also a handful of situations where new inventories were generated for sources that did not exist in the 2011 NEI (e.g., biodiesel and cellulosic plants, yet

- to

- be constructed cement kilns). This subsection provides detai ls on the data and projection methods used for these

sectors.

In recent platforms, EPA has assumed that emissions growth for most industrial sources d id not track with economic growth for m

ost stationary non

- IPM sources (EPA, 2006b )

. This “no

- growth” ass umption wa s based

on an examination of historical emissions and economic data. Recently however, EPA has received growth (and control) data from numerous states and regional planning organizations for many industrial sources, including the rapidly

- changing oil and gas sector. EPA provided a Notice of Data Availability for the 2011v6.0 emissions modeling platform and projected 2018 inventory in January, 2014 (docket EPA

- HQ

- OAR

- 2013

- 0809) . EPA requested comment on the future year growth and control assumption s used to develop the 2018 inventories

. One of the most frequent comments EPA received was to use the growth factors information that numerous states either provi ded or deferred to growth factors provided by broader regi on

- level efforts. In an attempt to

m ake the projections approach as consistent as possible across all states, EPA decided to expand this effort to

all states for some of the most

- significant industrial sources (see Section 4.2.3.6 )

.

Because much of the projecti ons and controls data are developed independent ly from how EPA defines its emissions modeling sectors, this section is organized primarily by the type of projections data , with secondary consideration given to the emissions modeling sector . For example, in dustrial source growth factors are applicable to 4 emissions modeling sectors. The rest of this section is organized in the order that the EPA uses

CoST in combination with other methods to produce future year inventories: 1) for point sources, apply plant (facility or sub

- facility

- level) closure information vi a CoST, 2) apply all PROJECTION packets via CoST (multiplicative factors that could cause increases or decreases) , 3) apply all percent reduction

- based CONTROL packets via CoST, and 4) append all othe r future

- year inventories not generated via CoST. This organization allows consolidation of the discussion of the emissions categories that are contained in multiple sectors, because the data and approaches used across the sectors are consistent a nd do not need to be repeated. Sector names

associated with the CoST p ackets are provided in parentheses.96 

### 4.2.1 CoST Background: Used for NEI non

- EGU Point and Nonpoint sectors

CoST is used to apply most non

- EGU projection/growth factors, controls and facility/unit/stack

- level closures

to the 2011 NEI

- based emissions modeling inventories to create inventories for years 201 7 and 2025 for the following sectors: afdust, ag, c1c2rail, c3marine, nonpt, np_oilgas , ptnonipm, pt_oilgas and rwc. The CoST training manual is availa ble at: http://www.cmascenter.org/help/model_docs/cost/2.5/CoST_UsersGuide_2012

- 08

- 01_Final.pdf

. The CoST development document, which is a more thorough albeit 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

- 0 6

- 09.pdf

.

CoST allows the user to apply projection (growth) factors, controls and closures at various geographic and

in ventory key field resolutions. Each of these CoST datasets, also called “packets” or “programs”, provides the user with the ability to perform numerous quality assurance assessments as well as create SMOKE

- ready future year inventories. Future year inventories are created for each emissions modeling sector via a CoST “strategy”

and each strategy includes all 2011 inventories and applicab le CoST packets. For reasons discussed later, some emissions modeling sectors require multiple CoST strategies to account for the compound ing of control programs that impact the same type of sources. There are also available linkages to existing and user

- d efined control measures databases and it is up to the user to determine how control strategies are developed and applied. EPA typically creates individual CoST packets that represent specific intended purposes. For example, aircraft projections for airport s are in a separate PROJECTION packet from residential wood combustion sales/applian ce turnover

- based projections. CoST uses three packet types as described below:

1. C LOSURE: Applied first in CoST. This packet can be used to zero

- out (close) point source emi ssions at

resolutions as broad as a facil ity to as specific as a stack. EPA use s these types of packets for known

post

- 2011 controls as well as information on closures provided by states on specific facilit ies, units or

stacks. This packet type is only use d in the ptnonipm and pt_oilgas sector s

.

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 multiplicative factors to the

2011 emissions in ventories prior to the application of any possible subsequent CONTROLs. A PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable when information is based more on activity assumptions rather than known control measures. EPA use s PROJECTION packet(s) in every non

- EGU modeling sector.

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

i nven tory 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.

All of these packets are stored as data sets within the Emissions Modeling Framework (EMF) and use comma

delimited formats. As mentioned above, CoST first applies any/all CLOSURE information for point sources, then applies PROJECTION packet information, fol lowed by CONTROL packets. A hierarchy is used by CoST to separately apply P ROJECTION 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 pol lutant

- level PROJECTION factor. It is important to note that this hierarchy does not apply between packet types; for example, CONTROL packet entries are applied irrespective of PROJECTION packet hierarchies. A more specific example: a state/SCC

- level PROJECTION factor will be applied before a stack/pollutant

- level CONTROL factor that impa cts the same inventory record. However, an inventory source that is subject to a CLOSURE packet record is removed from consideration of subsequent PROJECTION and CONTROL packets.97 The implication for this hierarchy and intra

- packet independence is importa nt to understand and quality assure when creating fu ture year strategies. For example, with consent decrees, settlements and state comments, the goal is typically to achieve a targeted reduction (from the 2011NEI ) or a targeted future

- year emissions value. Therefore, as encountered with this 201 7 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 ent ries for cement manufacturing. By processing CoST control strategies with PROJECTION and CONTROL packets separated by the type of broad measure/program, it is possible to show actual changes from the 2011 inventory to the 201 7 and 2025 inventory due to each packet.

Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a hierarchal matching approach to assign PROJEC TION factors to the inventory. For example, a packet entry with Ranking=1 will supersede all other poten tial invento ry matches from other packets. CoST then computes the projected emissions from all PROJECTION packet matches and then performs a similar ro utine for all CONTROL packets. Therefore, when summarizing “emissions reduced” from CONTROL packets, it i s important to note that these reductions are not relative to the 2011 inventory, but rather, to the intermediate inventory after application of any/ all PROJECTION packet matches (and CLOSURES). A subset of the more than 70 hierarchy options is shown in Table 4

- 2 , although t he fields in Table 4

- 2 are not necessarily named the same in CoST, but rather are similar to those in the SMOKE FF10 inventories. F or example, “REGION_CD” is the county

- state

- county FIP S code (e.g., Harris county Texas is 48201) and “STATE” would be the 2

- digit state FIPS code with three trailing zeroes (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, U NIT_ID, REL_POINT_ID, PROCESS_ID point

10 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID point

11 REGION_CD, FACILITY_ID, UNIT_ID point

12 REGION_CD, FACILITY_ID point

13 REGION_CD, NAICS, SCC, POLL point, nonpoint

14 REGION_CD, NAICS, POLL point, nonp oint

15 STATE, NAICS, POLL point, nonpoint

16 REGION_CD, NAICS point, nonpoint

17 NAICS point, nonpoint

18 REGION_CD, SCC, POLL point, nonpoint

19 STATE, SCC, POLL point, nonpoint

20 SCC, POLL point, nonpoint

21 REGION_CD, SCC point, nonpoint

22 ST ATE, SCC point, nonpoint

23 SCC point, nonpoint

24 REGION_CD, POLL point, nonpoint

25 REGION_CD point, nonpoint

26 STATE, POLL point, nonpoint98 Ran k Matching Hierarchy Inventory Type

27 STATE point, nonpoint

28 POLL point, nonpoint

T he contents of the controls, local adjustments and clos ures for the 201 7 and 2025 base case s are described in the following subsections

. Year

- specific projection factors (PROJECTION packets) for year s 2017 and 2025 were used for creating the 201 7 and 2025 base case s unless noted otherwise. The contents of a fe w of these projection packets (and control reductions ) are provided in the following sub sections where feasible. However,

most sectors used growth or control factors that varied geographically and their contents could not be provided in the following secti ons (e.g., facilities and units subject to the Boiler MACT reconsideration has thousands of records). The remainder of Section 4.2 is divided into several subsections that are summarized in Table 4

- 3

. Note that future year inventories were used rather than projection or control packets for some sources.

Table 4

- 3

. Summary of non

- EGU stationary projections subsections

Subsection Title Sector(s) Brief Description

4.2.2 CoST Plant CLO SURE

packet ptnonipm, pt_oilgas All facility/unit/stack closures information,

primarily from Emissions Inventory System (EIS),

but also includes information from states and other organizations.

4.2.3 CoST PROJECTION

packets Al l I ntroduc es and summariz es national impacts of all CoST PROJECTION packets to years 2017 and

2025.

4.2.3.1 Paved and unpaved roads

VMT growth A fdust PROJECTION packet: county

- level resolution, based on VMT growth.

4.2.3.2 Livestock population

growth A g PROJECTION packet: national, by

- animal type resolution, based on animal population projections.

4.2.3.3 Locomotives and Category

1 & 2 commercial marine

vessels c1c2rai l

, ptnonipm PROJECTION packet: national, by

- equipment type

and pollutant, based on c umulative growth and control impacts from rulemaking.

4.2.3.4 Category 3 commercial

marine vessels c3marine PROJECTION packet: region

- level by

- pollutant, based on cumulative growth and control impacts from rulemaking.

4.2.3.5 OTAQ upstream

distribution, pipelines and

refineries nonpt

, ptnonipm, pt_oilgas PROJECTION packet: national, by

- broad source

category, based o n upstream impacts from mobile source rulemakings

. 4.2.3.6 Oil and gas and industrial

source growth nonpt, np_oilgas, ptnonipm, pt_oilgas Several PROJECTION packets: varying geographic

resolutions from state, county, to oil/ga s play

- level and by

- process/fuel

- type applications. Data derived

from AEO2014 with several modifications

. 4.2.3.7 Data from comments on

previous platforms nonpt, ptnonipm Several PROJECTION packets: varying geographic

resolut ions and by

- process/fuel

- type applications. Data derived from various sources in response to the previous (2011v6.0) version of the emissions modeling platform.

4.2.3.8 Aircraft ptnonipm PROJECTION packet: by

- airport for all direct matches to FAA Terminal Area Forecast data, with

state

- level factors for non

- matching NEI airports.99 Subsection Title Sector(s) Brief Description

4.2.3.9 Cement m anufacturing ptnonipm PROJECTION packet: by

- kiln projections based on Industrial Sectors Integrated Sol utions (ISIS) model of demand growth and Portland Cement NESHAP

. 4.2.3.10 Corn ethanol plants ptnonipm PROJECTION packet: national, based on 2014 AEO renewable fuel production forecast.

4.2.3.11 R esidential wood

combustion rwc PROJECTION packet: national with exceptions, based on appliance type sales growth estimates and retirement assumptions and impacts of recent NSPS.

4.2.4 CoST CONTROL packets nonpt, np_oilgas,

ptn onipm, pt_oilgas Introduces and summarizes national impacts of all

CoST CONTROL packets to years 2017 and 2025.

4.2.4.1 Oil and gas NSPS np_oilgas, pt_oilgas CONTROL packet: national, oil and gas NSPS

impacting VOC only for so me activities.

4.2.4.2 RICE NESHAP nonpt, np_oilgas, ptnonipm, pt_oilgas CONTROL packet: national, reflects NESHAP

amendments on compression and spark ignition stationary reciprocating internal combustion engines (RICE).

4.2.4.3 RICE NSPS nonpt, np_oilgas, ptnonipm, pt_oilgas CONTROL packet: state and county

- level new

source RICE controls, whose reductions by

definition, are a function of growth factors and also equipment retirement assumptions

. 4.2.4.4 ICI Boilers nonpt, ptnonipm, pt_oilgas CONTROL packet: by

- fuel, and for point sources,

by

- facility

- type controls impacting Industrial and Commercial/Institutional boilers from rulemaking

and state

- provided information.

4.2.4.5 Fuel sulfur rules nonpt, ptnonipm, pt_oilgas CONTROL packet: state and MSA

- level fuel sulfur

control programs provided by several northeastern

U.S. states.

4.2.4.6 Natural gas turbines NSPS ptnonipm, pt_oilgas CONTROL packet: state and county

- level new

source natural gas turbine controls, whose reductions by

- definition, are a function of growth

factors and also equipment retirement assumptions.

4.2.4.7 Process h eaters NSPS ptnonipm, pt_oilgas CONTROL packet: state and county

- level new

source process heaters controls, whose reductions by

- definition, are a function of growth factors and also equipment retirement assumptions.

4.2.4.8 Ar izona Regional Haze ptnonipm CONTROL packet: Regional haze controls for Arizona provided by Region 9.

4.2.4.9 CISWI ptnonipm CONTROL packet reflecting EPA solid waste rule

cobenefits.

4.2.4.10 Dat a from comments on

previous platforms nonpt, ptnonipm, pt_oilgas CONTROL packets for all other programs,

including Regional Haze, consent
decrees/settlements, and other information from states/other agencies in prior platforms.

4.2.5 Stand

- alone future year

inventories nonpt, ptnonipm Introduction to future

- year inventories not generated

via CoST strategies/packets.100 Subsection Title Sector(s) Brief Description

4.2.5.1 Portable fuel containers nonpt Reflects impacts of Mobile Source Air Toxics (MSAT2) on PFCs.

4.2.5.2 Biodiesel plants ptnonipm Year 2018 new biodiesel plants provided by OTAQ reflecting planned sited

- plants production volumes.

4.2.5.3 Cellulosic plants nonpt Year 2018 ne w cellulosic ethanol plants based on cellulosic biofuel refinery siting provided by OTAQ

and 2018 NODA.

4.2.5.4 New cement plants nonpt, ptnonipm Year 2018 and 2025 ISIS policy case

- derived new

cement kilns, permitted (point) and model

- generated based on shifted capacity from some closed units to open units (nonpt)



### 4.2.2 CoST Plant CLOSURE packet ( ptnonipm , pt_oilgas )

Packet: “ CLOSURES_2011v6.2_v3_23feb2015.txt ” This packet contains facility, unit and stack

- level closure information derived from the following sources:

1. Emissions Inventory System (EIS) facilities report from December 20, 2014 with closure status equal to “PS” (permanent shutdown)

2. EIS unit

- level report from Novem ber 29, 2014 with status = ‘PS’

3. Concatenation of all 2011v 6.0 closures information ; see Section 4.2.11.3 at: http://www.epa.gov/ttn/chief/emch/2011v6/outreach/2011v6_2018base_EmisMod_TSD_26feb2014.pdf

4. Comme nts from states and regional planning organizations.

The EIS sources, accessible at http://www.epa.gov/ttnchie1/eis/gateway/ , r eport post

- 2011 permanent facility /unit shutdowns through the date of th e EIS report, assuming reporting agencies provided the

information. The 2011v6.0 closure information is from a concatenation of previous facility and unit

- level

closure information used in the 2008 NEI

- based emissions modeling platform ( http://epa.gov/ttn/chief/emch/2007v5/2007v5_2020base_EmisMod_TSD_13dec2012.pdf ). In addition, comments on the 2011v6.0 emissions modeling platform received by states and other agen cies indicated the removal of some closure information (keep open). Ultimately, all data were updated to match the SMOKE FF10 inventory key fields, with all duplicates removed, and a single CoST packet was generated. T he cumulative

reductions in emissions from this packet are shown in Table 4

- 4

. Note that these reductions are the same for both 2017 and 2025 future years.

Table 4

- 4

. Red uctions from all facility/unit /stac k

- level closures by modeling sector

Pollutant ptnonipm pt_oilgas Cumulative

CO 7,542 1,473 9,015

NH 3 727 0 727

NO X 12,250 2,300 14,550

PM 10 4,940 108 5,048

PM 2.5 3,882 104 3,986

SO 2 39,491 184 39,675

VOC 12,695 641 13,336101 

### 4.2.3 CoST PROJECTION packets (a fdust, ag, c1c2rail, c3marine, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)

As previously discussed, for point inventories, after application of any/all CLOSURE packet information, the next step in running a CoST control strategy is the application of all C oST PROJECTION packets. Regardless of inventory type (point or nonpoint), the PROJECTION packets applied prior to the CoST packets. For several emissions modeling sectors (afdust, ag, c1c2rail, c3marine and rwc), there is only one CoST PROJECTION packet. F or all other sectors, there are several differe nt sources of PROJECTIONS data and therefore there are multiple PROJECTION packets that are concatenated and quality

- assured for duplicates and applicability to the inventories in the CoST strategy. The PROJEC TION (and CONTROL) packets were separated into a few “key” control program types to allow for quick summaries of these distinct control programs. The remainder of this section is broken out by CoST packet, with the exception of discussion of the various p ackets used for oil and gas and industrial source projections; these packets are a mix of different sources of data that targeted similar

sources.

4.2.3.1 Paved and unpaved roads VMT growth (afdust)

Packet:

For 2017: “ PROJECTION_2011_20 17 _AFDUST_VMT_2011v6.2_10ma r2015.txt ”

For 2025: “ PROJECTION_2011_2025_AFDUST_VMT_2011v6.2_10mar2015.txt ”

We received comments from the 2018 NODA ( search for ‘EPA

- HQ

- OAR

- 2013

- 0809’ at www.regulations.gov ) suggesting we grow emissions from pa ved an d unpaved road dust as a function of vehicle miles traveled (VMT). The future year VMT used to project this sector was consistent with the VMT used for the onroad mobile source modeling as described in Section 4.3.1.1

. Th e resulting national sector

- total increase in PM 2.5 emissions are provided in Table 4

- 5. Note that this packet does not impact any other sources of fugitive dust emissions in the afdust sector (e.g., no impact on construction dust, mining and quarrying, et c.).

Table 4

- 5. Increase in total afdust PM 2.5 emissions from VMT projections

2011 Emissions 2017 Emissions 2025 Emissions % Increase 2017 % Increase 2025

2,510,307 2,623,572 2,792,127 4.5% 11.2%

4.2.3.2 Livestock population growth (ag)

Packet:

For 2017: “ PROJEC TION_2011_2017_ag_2011v6.2_no_RFS2_04feb2015.txt ”

For 2025: “ PROJECTION_2011_2025_ag_2011v6.2_no_RFS2_23jan2015.txt ”

EPA estimated animal population growth in ammonia (NH 3 ) emissions from livestock in the ag sector

. Except for dairy cows and turkey product ion, 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

a nd Research Institute (FAPRI). This methodology was initiated in 2005 for t he 2005 NEI, but was updated on July 24, 2012 in support of the 2007v5 platform (EPA, 2012). For dairy cows

, EPA assumed that there would be no growth in emissions based on little change in U.S. dairy cow populations from year 2011 through 2025 according t o linear regression analyses of the FAPRI projections. This assumption was based on an analysis of

historical trends in the number of such animals compared to production rates. Although productions rates have increased, the number of animals has declined. Based on this analysis, EPA concluded that production forecasts do not provi de representative estimates of the future number of cows and turkeys; therefore, these forecasts

were not used for estimating future

- year emissions from these animals. In particu lar, the dairy cow population102 is projected to decrease in the future as it has for the past few decades; however, milk production will be

in creasing over the same period. Note that the ammonia emissions from dairies are not directly related to animal popul atio n but also nitrogen excretion. With the cow numbers going down and the production going up the

excretion value will change , but no change was assumed because a quantitative estimate was not available

. Appendix C provides the animal population data and regression curves used to derive the growth factors.

The projection factors by animal category and ag sector total impacts are provided in Table 4

- 5

. As discussed below, dairy cows are assumed to have no growth in animal populat ion, and therefore the projection factor for

these animals is 1.0 (no growth)

. Impacts from the renewable fuels mandate are not included in projections for

this sector.

Table 4

- 5

. NH 3 projection factors and to tal impacts to years 2017 and 2025 for a nimal o perations

Animal Category 2017 Projection Factor s & Total Emissions 2025 Projection Factor s & Total Emissions Dairy Cow 1.0 1.0

Beef 0.988 0.974

Pork 1.069 1.118

Broilers 1.087 1.130 Turkeys 0.947 0.921 Layers 1.063 1.094 Poultry Average 1.057 1.085 Overall Average 1.026 1.041

Total Emissions 3,584,419 3,617,575

% Increase from 2011 1.8% 2.7%

4.2.3.3 Locomotives and Category 1 & 2 commercial marine vessels (c1c2rail, ptnonipm)

Packet:

For 2017: “ PROJECTION_2 011v6.2_2017 _c1c2rail_BASE_06feb2015.txt ”

For 2025: “ PROJECTION_2011v6.2_2025_c1c2rail_BASE_06feb2015.txt ”

There are two components used to cr eate projection factors for year s 201 7 and 2025. The first component of the future year c1c2rail inventory is the non

- California data proj ected from the 2011 base case. The second component is the CARB

- supplied year 2011, 2017 and 2025 data for California.

For all states outside of California, national projection factors by SCC and pollutant between 2011 and future

y ears reflect the May 2004 “Tier 4 emissions standards and fuel requirements” ( http://www.epa.gov/otaq/documents/nonroad

- diesel/420r04007.pdf ) as well as the March 2008 “Final

lo comotive

- marine rule” controls ( http://www.epa.gov/otaq/regs/nonroad/420f08004.pdf )

. The future

- year c1c2rail emissions account for increased fuel consumption based on Energy Information A dministration (EIA) fuel consumption projections for freight, and emissions reductions resulting from emissions standards from the

Final Locomotive

- Marine rule (EPA, 2009d). 30 For locomotives, EPA applied HAP factors for VOC HAPs by using VOC projection fac tors to obtain 1,3

- butadiene, acetaldehyde, acro lein, benzene, and formaldehyde. Similar to locomotives, C1/C2 VOC HAPs were projected based on the VOC factor. C1/C2 diesel emissions

were p rojected based on the Final L ocomotive Marine rule national

- level factors

. These non

- California projection ratios are provided in Table 4

- 6

. Note that projection factors for “...Yard Locomotives”

30 This rule lowered diesel sulfur content and tightened emission standards for existing and new locomotives and marine diesel

emissions to lower future

- year PM, SO 2 , and NO X , and is documented at: http: //www.epa.gov/otaq/marine.htm#2008final

.103 (SCC=2285002010) are applied to the ptnonipm (point inventory) “yard locomotives” (SCC=28500201)

repo rted by a couple of states in the 2011 NEI.

Table 4

- 6

. Non

- California projection f actors for locomotives and Category 1 and Category 2 Commercial

Marine Vessel Emissions

SCC Description Poll 201 7

Factor 2025

F actor

2280002XXX Marine Vessels, Commercial; Diesel; Underway & port emissions CO 0.957 0.955

2280002XXX Marine Vessels, Commercial; Diesel; Underway & port emissions NO X 0. 801 0. 537

2280002XXX Marine Vessels, Commercial; Diesel; Underway & port emissio ns PM 0. 712 0. 491

2280002XXX Marine Vessels, Commercial; Diesel; Underway & port emissions SO 2 0. 157 0. 069

2280002XXX Marine Vessels, Commercial; Diesel; Underway & port emissions VOC 0. 814 0. 523

2285002006 Railroad Equipment; Diesel; Line Haul Locomoti ves: Class I Operations CO 1.100 1.249

2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations NO X 0. 840 0. 621

2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations PM 0. 716 0. 457

2285002006 Railroad E quipment; Diesel; Line Haul Locomotives: Class I Operations SO 2 0. 031 0. 036

2285002006 Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations VOC 0. 656 0. 430

2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Oper ations CO 1.100 1.249

2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations NO X 1.056 1.065

2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations PM 1.025 1.012

2285002007 Railroad Equi pment; Diesel; Line Haul Locomotives: Class II / III Operations SO 2 0.031 0.036

2285002007 Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations VOC 1.100 1.249

2285002008 Railroad Equipment; Diesel; Line Haul Locomotives: Passenge r Trains (Amtrak) CO 1. 049 1. 118

2285002008 Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak) NO X 0.703 0.458

2285002008 Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak) PM 0.661 0.337

2285002008 Ra ilroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak) SO 2 0. 030 0. 033

2285002008 Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak) VOC 0.600 0.275

2285002009 Railroad Equipment; Diesel; Line Haul Locomotive s: Commuter Lines CO 1.049 1.118

2285002009 Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines NO X 0.703 0.458

2285002009 Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines PM 0.661 0.337

2285002009 Railroad Equipment; Dies el; Line Haul Locomotives: Commuter Lines SO 2 0.030 0.033

2285002009 Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines VOC 0.600 0.275

2285002010 Railroad Equipment; Diesel; Yard Locomotives CO 1. 100 1. 249

2285002010 Railroad Equipment; Diesel; Yard Locomotives NO X 0. 980 0. 837

2285002010 Railroad Equipment; Diesel; Yard Locomotives PM 0. 951 0. 810

2285002010 Railroad Equipment; Diesel; Yard Locomotives SO 2 0. 032 0. 036

2285002010 Railroad Equipment; Diesel; Yard Locomotives VOC 0. 950 0. 7 67

For California projections

, CARB provided to EPA the locomotive, and Category 1 and 2 commercial marine

emissions used to reflect year s 2011, 2017 and year 2025 . These CARB inventories include d nonroad rules reflected in the December 2010 Rulemaking I nventory ( http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf ), those in the March 2011 Rule Inventory, the

Off

- Road Construction Rule Inventory for “In

- Use Diesel”, cargo handling equipment rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm ), and the 2007 and 2010 regulations to reduce emissions diesel engines on commercial harbor craf t operated within California waters and 24 nautical mil es of the

California baseline.

The California C1/C2 CMV emissions were obtained from the CARB nonroad mobile dataset

“ ARMJ_RF#2002_ANNUAL_MOBILE.txt ”. These emissions were developed using Version 1 of the CEPAM which supports various California off

- road regu lations. The locomotive emissions were obtained from the

CARB trains dataset “ ARMJ_RF#2002_ANNUAL_TRAINS.txt ”. Documentation of the CARB off

- road methodology, including c1c2rail sector data, is prov ided here: http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles

. EPA converted the CARB inventory TOG104 to VOC by dividing the inventory TOG by the available VO C

- to

- TOG speciation factor. The CARB year

- 2011 inventory (provided with the 2017 and 2025 emissions) did not match the CARB

- submitted inventory in Version 2 of the 2011 NEI; therefore, we used the CARB 2011/2017/2025 data to compute projection ratios that were then applied to the 2011 emissions modeling platform (2011NEIv2). California projection factors were “capped” at 2.5; we found that those counties/SCCs/pollutants with projection factors greater than 2 (100% increase) were all under 100 tons for any g iven pollutant. The California projection factors are county

- level and therefore not provided here.

The non

- California projection factors were applied to all “offshore” c1c2 CMV emissions. These offshore emissions, in the 2011 NEI, start at the end of stat e waters, and extend out to the Economic Exclusion Zone (EEZ). A summary of the national impact for US (including California) and offshore c1c2rail sector emissions are provided in Table 4

- 7

.

Table 4

- 7

. Difference in c1c2rail sector emissions between 2011 and future years

Region

Pollutant 2011 2017 2025 Difference

2017

- 2011 Difference

2025

- 2011

U.S. CO 185,074 198,436 219,537 13,362 34,463

U.S. NO X 1,097,162 933,414 691,890

- 163, 749

- 405,273

U.S. PM 10 36,079 26,484 18,034

- 9,594

- 18,045

U.S. PM 2.5 33,713 24,741 16,850

- 8,972

- 16,863

U.S. SO 2 12,869 1,202 905

- 11,667

- 11,963

U.S. VOC 48,810 34,956 24,777

- 13,854

- 24,032

Offshore CO 66,395 63,515 63,390

- 2,880

- 3,005

Offshore NO X 326,631 261,637 175,482

- 64,994

- 151,149

Offshore PM 10 10,795 7,687 5,297

- 3,108

- 5,499

Offshore PM 2.5 10,471 7,457 5,138

- 3,015

- 5,334

Offshore SO 2 4,014 632 278

- 3,382

- 3,736

Offshore VOC 7,472 6,080 3,910

- 1,392

- 3,562

4.2.3.4 Category 3 commercial mar ine vessels (c3marine, othpt)

Packet:

For 2017: “ PROJECTION_2011_2017_C3_CMV_ECA_IMO_2011v6.2_10feb2015.txt ” For 2025: “PROJECTION_2011_2025 _C3_CMV_ECA_IMO_2011v6.2_10feb2015.txt

As discussed in Section 2.4.2 , t he EPA estimates for C3 CMV emissions data were developed for year 2002 and projected to year 2011 for the 2011 base case and used where states did not submit data to Version 2 of the 2011

NEI. P ollutant and geographic

- specific projection factors to year 2011 were applied

, along with projection factors to years 2017 and 2025 that reflect assumed growth and final ECA

- IMO controls. These emissions estimates reflect EPA’s coordinated strategy for large marine vessels. More information on EPA’s coordinated strategy for large m arine vessels can be found in our Category 3 Marine Diesel Engines and Fuels regulation published in April 2010. That rule, as well as information about the North American and U.S. Caribbean Sea ECAs, designated by amendment to MARPOL Annex VI, can be fou nd here: http://www.epa.gov/otaq/oceanvessels.htm

.

Projection factors for creating the year 2017 and 2025 c3marine inventories from the 2011 base case are provided in Table 4

- 8 . Background on the region and Exclusive Economic Zone (EEZ) FIPS is provided in the discussion on the c3marine inventory for 2011

– Section 2.4.2 . For example, Washington state emissions are grown the same as all No rth Pacif ic offshore emissions.105

Table 4

- 8

. Growth factors to project the 20 11 ECA

- IMO inventory to 20 17 and 2025

Region EEZ

(Offshore)

FIPS Year 2 017 and 2025 Adjustments Relative to 2011

CO NO X PM 10 PM 25 SO 2 VOC

North Pacific (NP) 85001 2017 1.215 0.989 0.168 0.167 0.047 1.215

2025 1.575 0.798 0.217 0.216 0.061 1.575

South Pacific (SP) 85002 2017 1.350 1.084 0.187 0.185 0.053 1.351

2025 2.027 0.909 0.285 0.283 0.081 2.028

East Coast (EC) 85004 2017 1.302 1.039 0.177 0.176 0.047 1.302

2025 1.852 0.838 0.252 0.250 0.066 1.852

Gulf Coast (GC) 85003 2017 1.187 0.947 0.162 0.161 0.042 1.187

2025 1.492 0.679 0.204 0.202 0.053 1.492

Great Lakes (GL) n/a 2017 1.106 0.972 0.146 0.145 0.039 1.106

2025 1.2 66 0.940 0.168 0.166 0.045 1.266

Outside ECA 98001 2017 1.298 1.182 1.298 1.298 1.298 1.298

2025 1.858 1.463 0.409 0.405 0.337 1.858

As discussed in Section 2.4.2, e missions outside the 3 to 10 mile coastal boundary but within the approximately

200 n m EEZ boundaries were projected to years 201 7 and 2025 using the same regional adjustment factors as the U.S. emissions; however, the FIPS codes were assigned as “EEZ” FIPS and these emissions are processed in the “othpt” s ector (see Section 2.5.1 and 4.5. 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 assigne d to a U.S. county or Ontario. The classification of emissions to U.S. and Canadian FIPS cod es is needed to avoid double

counting of Canadian

- provided C3 CMV emissions in the Great Lakes.

The cumulative impact of these ECA

- IMO projections and controls to the U.S

. + near

- offshore (c3marine sector) and far

- offshore emissions (othpt sector ) in 2017 and 2025 is provided in Table 4

- 9

.

Table 4

- 9

. Difference in c3marine sector and othpt C3 CMV emissions between 2011 and future years

Region Pollutant 2011

emissions 2017

emissions 20 25

emissions Difference

2017

- 2011 Difference

2025

- 2011

U.S. + near offshore CO 13,705 17,057 22,941 3,353 9,237

U.S. + near offshore NO X 145,729 147,369 121,177 1,640

- 24,552

U.S. + near offshore PM 10 11,370 2,013 2,606

- 9,357

- 8,764

U.S. + near of

fshore PM 2.5 10,148 1,790 2,322

- 8,358

- 7,826

U.S. + near offshore SO 2 95,306 5,093 5,998

- 90,213

- 89,309

U.S. + near offshore VOC 5,645 7,047 9,515 1,401 3,870

far

- offshore (othpt sector) CO 251,654 324,915 461,376 73,261 209,722

far

- offshore (othpt s ector) NO X 2,935,480 3,361,627 3,864,871 426,147 929,391

far

- offshore (othpt sector) PM 10 216,192 249,379 83,553 33,187

- 132,639

far

- offshore (othpt sector) PM 2.5 198,750 229,369 76,127 30,619

- 122,623

far

- offshore (othpt sector) SO 2 1,617,893 1,825,762 486,412 207,870

- 1,131,480

far

- offshore (othpt sector) VOC 106,860 137,972 195,924 31,112 89,064106

4.2.3.5 U pstream distribution, pipelines and refineries ( nonpt , ptnonipm, pt_oilgas)

Packet : For 2017: “PROJECTION_2011_2017_OTAQ_upstream_GasDist_pipelines_refine ries_2011v6.2_05feb2015.txt” For 2025: “PROJECTION_2011_2025_OTAQ_upstream_GasDist_pipelines_refineries_2011v6.2_05feb2015.txt”

To account for projected increases in renewable fuel volumes due to the Renewable Fuel Standards (RFS2)/EISA (EPA, 2010a) and d ecreased gasoline volumes due to RFS2 and light

- duty greenhouse gas

standards as quantified in AEO 2014 ( http://www.eia.gov/forecasts/archive/aeo14/ )

, EPA developed county

level inventory adjustme nts for gasoline and gasoline/ethanol blend transport and distribution for 2018 and

2025

. Here, 2018 adjustments are used for 2017. These adjustments account for losses during truck, rail and waterways loading/unloading and intermodal transfers such as hi ghway

- to

- rail, highways

- to

- waterways, and all other possible combinations of transfers. Adjustments for 2018 only account for impacts of RFS2 , and the 2025 adjustments also account for additional impacts of greenhouse gas emission standards for motor vehic les (EPA, 2012b) on transported volumes. These emissions are entirely evaporative and therefore limited to VOC.

A 2018 inventory which included impacts of the EISA mandate was developed by applying adjustment factors

to the 2011 NEIv2 inventory

. These adju stments were made using an updated version of EPA’s model for upstream emission impacts, developed for the RFS2 rule 31

. The methodology used to make these adjustments is

described in a 2014 memorandum included in the docket for the EPA Tier 3 rule (EPA, 201 4)

. 32

Ethanol emissions were estimated in SMOKE by applying the ethanol to VOC ratios from headspace profiles to

VOC emissions for E10 and E15, and an evaporative emissions profile for E85. These ratios are 0.065 for E10

, 0.272 for E15 , and 0.61 for E85. The E10 and E15 profiles were obtained from an ORD analysis of fuel samples

from EPA ct exhaust test program 33 and were submitted for incorporation into the EPA’s SPECIATE database. The E85 profile was obtained from data collected as part of the CRC E

- 80 tes t program (Environ, 2008) and was also submitted into EPA’s SPECIATE database. For more details on the change in speciation profiles
between 2011 and 2018, see Section 3.2.1.4

.

Pipeline usage and refinery emissions were adjuste d to account for impacts of the 2017

- 2025 light duty vehicle greenhouse gas emission standards, as well as renewable fuel volume projections. These adjustments were developed by EPA OTAQ and impact processes such as process heaters, catalytic cracking unit s, blowdown systems, wastewater treatment, condensers, cooling towers, flares and fugitive emissions. Calculation of the emission inventory impacts of decreased gasoline and diesel production, due to renewable fuel volume projections, on nationwide refiner y emissions was done in EPA’s spreadsheet model for upstream emission impacts (EPA, 2009b) . Emission inventory changes reflecting these impacts were used to develop adjustment factors that were applied to inventories for each petroleum refinery in the U.S. These impacts of decreased production were assumed to be spread evenly across all U. S. refineries. Toxic emissions were estimated in SMOKE by applying speciation to VOC emissions. It should be noted that the a djustment factors are estimated

relative to t hat 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.

31 U.S. EPA. 2013. Spreadsheet “upstream_emissions_rev T3.xls.

32 U. S. EPA. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for the Tier 3 Final

Rule. Memorandum from R ich Cook, Margaret Zawacki and Zoltan Jung to the Docket. February 25, 2014. Docket EPA

- HQ

- OAR

2011

- 0135.

33 U. S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. Office of Research and Development and Office of Tra nsportation and Air Quality. Report No. EPA

- 420

- R

- 11

- 018. EPA Docket EPA

- HQ

- OAR

- 2011

- 0135.107 The resulti ng adjustments for pipelines, refineries and the gasoline distribution processes (RBT, BPS and BTP) are provided in Table 4

- 10 . Separate adjustments were applied to refinery to bulk terminal (RBT), bulk plant storage (BPS), and b ulk terminal to gasoline dispensing pump (BTP) component s . Emissions for the BTP component are greater than the RBT and BPS components. See Appendix B for the complete cross

- walk
between SCC, for all component t ype s of petroleum transport and storage compo nent s . An additional adjustment was applied for 2025 at a national scale to account for impacts of gasoline volume reductions of the

2017

- 2025 light

- duty greenhouse gas rule.

Notice that the “2011 Emissions” are not the same in Table 4

- 10 . This is because these “2011” emissions are actually an intermediate set up projections applied after a first CoST strategy used to apply most other PROJECTION and CONTROL packets. We decided to first apply these other packets because we have mul tiple PROJECTION and CONTROL programs that impact the same emission sources. For this example, we applied

year

- specific industrial sector AEO

- based growth (discussed in the next section) with our first CoST strategy, then applied these “EISA” adjustments o n the results of this first CoST strategy. Similarly, we have IC Engine (RICE) existing (NESHAP) as well as new source (NSPS) controls that need to be applied in separate strategies. Alternatively, we could have made “compound” CoST packets that combine th ese PROJECTION (and CONTROL) factors, but preferred to keep these packets separate for transparency. If we tried to process the multiple packets affecting the same sources in a single CoST strategy, CoST would either fail if the packet

entries were are the same key

- field resolution (duplicate error), or, if packets were at a different key

- field resolution, CoST would only apply the packet entry with higher priority according to Table 4

- 2

.

Table 4

- 10

. P etroleum pipelines & refineries and production storage and transport factors and reductions

Poll Year F actors

2011

Emissions Reduction %

Reduction Pipelines &

Refineries RBT BTP/BPS

CO 2018 0.9896 n/a 53,527 557 1.0%

2025 0.9295 53,206 3,733 7.0%

NO X 2018 0.9877 n/a 68,406 838 1.2%

2025 0.9172 62,713 5,191 8.3%

PM 10 2018 0.9938 n/a 24,486 152 0.6%

2025 0.9578 24,140 1,017 4.2%

PM 2.5 2018 0.9940 n/a 21,602 130 0.6%

2025 0.9592 21,269 867 4.1%

SO 2 2018 0.9909 n/a 79,3 91 719 0.9%

2025 0.9386 78,766 4,812 6.1%

VOC 2018 0.9934 0.9940 0.9581 750,311 21,629 2.9%

2025 0.9555 0.8172 0.7878 744,678 143,906 19.3%108

4.2.3.6 Oil and gas and industrial source growth from 2011v6.0 NODA ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

2017 Pac kets:

“ PROJECTION_2011v6.2_2018_nonpoint_SCC_SRAcapped_05dec2014 ”

“ PROJECTION_2011v6.2_2018_SCC_NONPOINT_LADCO_09dec2014 ”

“ PROJECTION_2011v6.2_2018_SCC_NONPOINT_SCA_orig_CAPPED_09dec2014 ”

“ PROJECTION_2011v6.2_2018_SRAcapped_POINT_05dec2014 ”

“ PROJECTION_201 1v6.2_2018_SCC_POINT_LADCO_09dec2014 ”

“ PROJECTION_2011v6.2_2018_SCC_POINT_SCA_orig_CAPPED_09dec2014 ”

“ PROJECTION_2011v6.2_2018_NAICS_SCC_SCA_orig_NEI_matched_CAPPED2.5_04dec2014 ”

2025 Packets: “PROJECTION_2011v6.2_2025 _nonpoint_SCC_SRAcapped_05dec2014 ”

“PR OJECTION_2011v6.2_2025 _SCC_NONPOINT_LADCO_09dec2014 ” “PROJECTION_2011v6.2_2025 _SCC_NONPOINT_SCA_orig_CAPPED_09dec2014 ” “PROJECTION_2011v6.2_2025 _SRAcapped_POINT_05dec2014 ” “PROJECTION_2011v6.2_2025 _SCC_POINT_LADCO_09dec2014 ” “PROJECTION_2011v6.2_2025 _SCC_P OINT_SCA_orig_CAPPED_09dec2014 ” “PROJECTION_2011v6.2_2025 _NAICS_SCC_SCA_orig_NEI_matched_CAPPED2.5_04dec2014 ”

The EPA provided a Notice of Data Availability (NODA, search for the docket ‘EPA

- HQ

- OAR

- 2013

- 0809’ on

re gulations.gov ) for the 2011v6.0 emissions modeling platform and projected 2018 inventory in January, 2014. A significant number of the comments were about the EPA’s “no growth” assumption for industrial stationary

sources and about the current projection approach for oil and gas sources that was applied similarly to 5 broad geographic (NEMS) regions and limited to only oil and gas drilling activities.

With limited exceptions, the EPA has used a no

- growth assumption for all industrial non

- EGU emissions sin ce the 2005 NEI

- based emissions modeling platform (EPA, 2006). However, comments provided to the EPA for this platform (via the NODA) and for previous modeling platform s suggested that this approach was

insufficient. In addition, the NO X Budget program, wh ich had a direct impact on post

- 2002 emissions reductions, is in full compliance in the 2011 NEI. This means that additional la rge

- scale industrial reductions should not be expected beyond 2011 in the absence of on

- the

- books state and federal rules.

In res ponse to the comments about EPA’s no

- growth approach, the EPA developed industrial sector activity

based growth factors. In response to the NODA , many states have additionally provided detailed activity

- based projection factors for industrial sources, incl uding oil and gas sources . To develop the methods described here

, we have blended the state

- provided growth factors with the EPA

- developed industrial sector growth factors

. This approach has attempted to balance using the specific information that is avail able with EPA’s interest in consistency for a given sector and technical credibility

. Table 4

- 11 lists the resulting data sources for industrial

sector non

- EGU growth factors that EPA applied to this emissions modeling platform. Note that this list do es

no t include the revised “projection packets” from the 2011v6 .0

- based platform, which were also sent to the EPA via the docket p rocess. That additional data were considered and included in our projections as well

, and are discussed separately in Section 4.2.3.7

.

Ultimately, there were 3 broad sources of projection information for industrial sources, including oil and gas; these sources are referenced as the following for simplicity (e.g., re alizing that not all data from Mid

- Atlan tic Regional Air Management Association ( MARAMA ) states are limited to MARAMA states):109

1) EPA: Reflects EPA

- sponsored data provided by a contractor (SC&A, 2014a; SC&A, 2014b)

. Packet file

names for these data include “SCA”.

2) MARAMA: Reflects data submitted on behalf of Atlantic seaboard states from North Carolina through
Maine, and extending west through Pennsylvania and West Virginia. Packet file names for these data

include “SRA” (SRA, 2014)

. 3) LADCO: Reflects data submitted on behalf of Lake Michigan Air Dire ctors Consortium ( LADCO ) states (MN, WI, MI, IL, IN, OH). Projection data from this data source are reflected in packet name s containing “LADCO” (Alpine Geophysics, 2014)

.

Table 4

- 11

. Sources of new industrial source growth f actor data f

rom the 2011v6.0 NODA

Abbrev. Source Geographic

Resolution Inventory

Resolution Use/Caveat

EPA 2014 AEO fuel

consumption/production for EPA “priority” categories: IC Engines,

Gas Turbines and ICI

Boilers/Process Heaters Census

Division NAICS/SCC

or SCC Impacts almost all non

- EGU stationary industrial and

commercial sources. These data are used where LADCO and MARAMA data (below) are not also provided. Growth factors are “capped” to 1.25 (25% cumulative growth) and outlier

value s (e.g., commercial residual oil) set to no

- growth, consistent with MARAMA growth factor data (below).

EPA 2014 AEO Crude Oil

Production, Natural Gas

Production and Lease plant fuel + pipeline fuel natural gas consumption AEO

Oil/Gas Play

- level

and “Rest of

Census

Division” NAICS/SCC

or SCC Impacts both point and nonpoint oil and gas sectors as well as

some non

- EGU point sources not in the pt_oilgas sector. These data supersede any/all other projections data, including

LADCO/MARAMA information with minor e xceptions (PA drilling, NY state no

- growth , point source MARAMA data ). These data also “cap” growth factors to 2.5 (150% cumulative growth ).

MARAMA MARAMA /SRA/states State or county for nonpoint and facility and

below for most point

sources Facility and

s ub

- facility for point,

SCC

- level for nonpoint Many projection factors a re unchanged from the 2011v6.0

platform ; however, many new data provided were included in our 2011v6.2 platform

. However, the provided oil and gas projection data w ere not used, as we o pt ed for sing le data

source “ EPA ” approach. Emissions assigned a cap of 1.25 for

many non

- oil/gas production related sources (regardless of future year); we retain this cap and also apply this cap to all

non

- oil and gas sources.

LADCO LADCO /Alpine/states State or

county SCC Most projection factors are unchanged from 2011v6.0 platform

. Oil and Gas factors from 2014 AEO but state

- level only. We only used the new growth factor data, essentially limited to a couple priority (LADCO) source categories and did NO T use any oil and gas production

- based projections data, opting instead to use the single data source “ EPA ” approach. As a result, these data have very limited impact on point and nonpoint oil and gas projections.

A discussion of each projection component in Table 4

- 11 is discussed below.

“ EPA ” factors

As previously discussed, the EPA cr eate d a nationally

- consistent set of industrial source projection factors for several future years. We relied on 2014 AEO fuel consumption/produc tion projections data to develop growth factors for our first two “priority” source categories: IC Engines/Gas Turbines an d ICI Boilers/Process Heaters.

Table 4

- 12 provides a summary of the approaches used. We selected specific AE O energy consumption data

sets that reflect the best available indicator of ea ch source’s emissions activity. The discussion below provides110 more details on the approaches summarized in Table 4

- 12

. This discussion is generally orga nized from the most straightforward approach, to the most complex. Growth factors were only developed for the specific NAICS

codes that the AEO identifies as associated with economic sector

- specific fuel consumption projections. These

NAICS codes are liste d in Table 4

- 13

. The EPA mapped and expand the resulting NAICS/SCC and SCC growth factors to all relevant SCCs in the 2011v6.2 inventories. In cases where the AEO data showed a fuel type with zero consumption in each year, we set the growth factors for these fuels to “ 1 .0” for each relevant

year. This approach holds the growth flat for those fuels rather than zeros out the emissions in future years.

For all applicable c ommercial/ i nstitutional fuel combustion priority category SCCs, we compiled 2014 AEO regional c ommercial sector energy consumption projections data by fuel type for 2011, 2018, 2025 and 2030, and computed growth factors for each projection year/fuel type as the ratio of each future year’s energy consumption to 2011 en ergy consumption. The AEO’s c ommercial sector fuel consumption regions are

e quivalent to Census Divisions (see www2.census.gov/geo/pdfs/maps

- data/maps/reference/us_regdiv .pdf )

. Because Census Divisions are groups of state s
, c ommercial/ i nstitutional fuel combustion growth factors were developed at the state

- level, with each state in a given division assigned the same growth factors.111

Table 4

- 12

. Summary of “ EPA ” Projection Approaches for IC Engines/Gas Turbines and ICI Boilers/Process Heaters

Source Category Industry Sector 1 NAICS Code(s) Fuel Type(s) Summary of Approach

Commercial/

Institutional All AEO Commercial sector e nergy consumption projections by fuel type by Census Division

Industrial Food 311

All Calculate national energy intensity factors by AEO industry sector and fuel type; multiply by projected industry sector outpu t at Census Division level to yield estimate d regional energy consumption by sector/fuel type; and calculate regional growth factors by fuel type from these estimates Paper 322

Refining 32411

Bulk Chemical 3251, 3252, 3253

Glass 3272

Cement 32731, 32741

Iron and Steel 3311

Aluminum 3313

Metal Based Durables 332

- 336

Other Manufacturing all other 31

- 33

Other Agriculture 111,112,113,115

All Calculate national energy intensity factors by AEO industry sector and fuel type; multiply by projected industry sector outpu t a t Census Division level to yield estimated regional energy consumption by sector/fuel type; and calculate regional growth facto rs by fuel type from these estimates Construction 233

- 238

Mining 211, 2121, 2122

- 2123

Oil/Gas Production 2 211, 213111, 213112, 2212, 4861, 4862, Natural

Gas AEO Oil/Gas Play

- level : Calculate ratios of national oil/gas production to o il/ g as sector constant dollar output; multiply ratios by

AEO Census Division

- level o il/ g as sector constant dollar output to estimate Census D ivision

- level oil/gas production; calculate

Census Division

- level ratios representing the sum of lease plant fuel + pipeline fuel natural gas consumption to the estimated volume of oil/gas produced; multiply these ratios by the volume of oil/gas produced i n each applicable oil/gas play to yield oil/gas play

- level projections of lease plant fuel + pipeline fuel natural gas consumption; finally, calculate play

- level growth factors from

these estimates.

Rest of Nation : Calculate “rest

- of

- Census Division” cons umption estimates for pipeline fuel natural gas + lease and plant fuel by subtracting oil/gas play consumption estimates from the Census Division estimates published in the AEO. This yields nine sets of growth factors for each year, corresponding to each o f the nine Census Divisions.

All other AEO Oil/Gas Plays : Calculate the national ratio of o il/ g as sector constant dollar output to the volume of oil and gas produced for each year; multiply these national ratios by AEO estimates of oil and gas product ion in each oil/gas play to yield estimates of

o il/ g as sector constant dollar output in each play; multiply the o il/ g as sector constant dollar output estimates for each play by ratios of national Mining sector fuel consumption to national o il/ g as sector co nstant dollar output (this p rocedure develops

estimates of m ining sector energy consumption by fuel type within each oil/gas play); and calculate growth factors by play/fuel type from these estimates.

Rest of Nation : develop “rest

- of

- Supply Region” oil/ga s production estimates by subtracting the AEO’s oil/gas production estimates for these plays from AEO’s total o il/ g as Supply region production estimates (the residual production values are then

used to calculate the “rest

- of

- Supply Region” growth factors). This yields six sets of growth factors for each year, correspo nding

to each of the AEO’s six o il/ g as Supply Regions.

All Other All AEO total energy consumption projections by fuel type and Census Division

Notes: Though not displayed in this table, a no

- growth assumption was applied to 3 rocket engine SCCs, and applied AEO transportation sector fuel consumption projections to aircraft and railroad equipmen t

- related SCCs.

1 Identified based either on NAICS code or SCC/NAICS code

- see text for discussion.

2 In addition to priority category SCC records with oil/gas production NAICS codes, f actors will also be applied to n atural gas p roduction SCCs (e.g., 31000203

- Natural Gas Production/Compressors).112

Table 4

- 13

. NAICS Codes for which NAICS/SCC

- level Growth Factors were developed

NAICS

Code NAICS Code Description

111 Crop Production

112 Vegetable and Melon Farming

113 Fruit and Tree Nut Farming

115 Support Activities for Agriculture and Forestry

211 Oil and Gas Extraction

2121 Coal Mining

2122 Iron Ore Mining

2123 Nonmetallic Mineral Mining and Quarrying

213111 Drilling Oil and Gas Wells

213112 Support Activities for Oil and Gas Operations

2212 Natural Gas Distribution

233 Nonresidential Building Const ruction

234 Heavy Construction

235 Special Trade Contractors

236 Construction of Buildings

237 Heavy and Civil Engineering Construction

238 Specialty Trade Contractors

311 Food Manufacturing

312 Beverage and Tobacco Product Manufacturing

313 Textil e Mills

314 Textile Product Mills

315 Apparel Manufacturing

316 Leather and Allied Product Manufacturing

321 Wood Product Manufacturing

322 Paper Manufacturing

323 Printing and Related Support Activities

32411 Petroleum Refineries

32412 Asphalt Pav ing, Roofing, and Saturated Materials Manufacturing

32419 Other Petroleum and Coal Products Manufacturing

3251 Basic Chemical Manufacturing

3252 Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments Manufacturing

3253 Pesticide, Fertil izer, and Other Agricultural Chemical Manufacturing

3254 Pharmaceutical and Medicine Manufacturing

3255 Paint, Coating, and Adhesive Manufacturing

3256 Soap, Cleaning Compound, and Toilet Preparation Manufacturing

3259 Other Chemical Product and Prepar ation Manufacturing

326 Plastics and Rubber Products Manufacturing

3272 Glass and Glass Product Manufacturing

32731 Cement Manufacturing

32732 Ready

- Mix Concrete Manufacturing

32733 Concrete Pipe, Brick, and Block Manufacturing

32739 Other Concrete P roduct Manufacturing113

NAICS

Code NAICS Code Description

32741 Lime Manufacturing

3279 Other Nonmetallic Mineral Product Manufacturing

3311 Iron and Steel Mills and Ferroalloy Manufacturing

3312 Steel Product Manufacturing from Purchased Steel

3313 Alumina and Aluminum Production and Pr ocessing

3314 Nonferrous Metal (except Aluminum) Production and Processing

3315 Foundries

332 Fabricated Metal Product Manufacturing

333 Machinery Manufacturing

334 Computer and Electronic Product Manufacturing

335 Electrical Equipment, Appliance, an d Component Manufacturing

336 Transportation Equipment Manufacturing

337 Furniture and Related Product Manufacturing

339 Miscellaneous Manufacturing

4861 Pipeline Transportation of Crude Oil

4862 Pipeline Transportation of Natural Gas

For all IC Eng ine/Gas Turbines and ICI Boilers/Process Heaters SCCs in the 2011 NEI that have no NAICS
code, or a NAICS code for which there is no economic sector

- specific AEO energy forecast available, we compiled growth factors based on the SCC process description . Fo r example, SCC 10200101 ( External
Combustion Boilers; Industrial; Anthracite Coal; Pulverized Coal ) is assigned to AEO’s Total Industrial sector coal consumption projections.

For all oil and g as p roduction priority category SCCs (e.g., SCC 23100xxxxx) and all other priority category

SCCs with o il/ g as production

- related NAICS codes, we u se d two separate approaches for AEO oil/gas plays (see Table 4

- 14 ) . The first approach is specific to natural gas consumption, and the other approac h is for consumption of all other individual fuel types. Because the AEO does not have information on the counties that comprise each of the oil/gas plays represented by the AEO data

, we developed the necessary county lists using an internet search of oil/ gas play maps and other related information. For counties not included in one

of the AEO data’s oil/gas plays, “rest

- of

- Census Division” oil/gas consumption estimates were computed to develop growth factors for projecting energy consumption for all fuel ty pes except natural gas (e.g., residual and distillate oil, coal, LPG, kerosene )

. Details on the oil/gas production sector growth factor development

methods are described below.114 Table 4

- 14

. AEO Oil/Gas Plays

T ight Oil Play* Shale Gas Play

Austin Chalk Antrim Avalon/Bone Springs Bakken

Bakken Barnett Eagle Ford Eagle Ford Monterey Fayetteville

Niobrara Haynesville/Bossier Sprayberry Marcellus Wolfcamp

Woodford
* AEO also publishes estimates for an “O ther” tight oil plays category, however, EIA was unable to provide a list of what plays should be considered included in this “Other” category. “Tight” oil (not to be confused with oil, or “kerogen” shale) refers to heterogeneous formations

of low

- permeabi lity that vary widely over relatively short distances.

Natural Gas Consumption and Crude Oil Production

For natural gas consumption

- related growth factor records, we developed oil/gas production area (“play”)

- level estimates of natural gas consumption and “rest

- of

- Census Division” estimates, which were applied to counties outside these play areas. For each relevant year, w e first calculated from AEO’s projections, the ratio of national oil/gas production (in B TU s) to national o i l/g as sector constant dollar output. These values were then multiplied by AEO projections of Census Division

- level oil/gas sector constant dollar output to yield Division

- level estimates of oil/gas production by year (in B TUs ). Next, we computed AEO Census

Division

- level ratios of the sum of pipeline fuel natural gas + lease and plant fuel consumption 35 to the estimated volume of oil/gas produced in each year. Then we develop ed oil/gas play

- level projections of pipeline fuel natural gas + lease and plant fuel consumption by multiplying the Census Division

- level ratios by the AEO estimates of oil/gas production in each play by year. Growth factors for each play/forecast year were developed by dividing each forecast year’s estimated consumption of pipeline fuel natural gas + lease and plan t fuel by the estimated 2011 total volume of these fuels.

We calculate d “rest

- of

- Census Division” consumption estimates for pipeline fuel natural gas + lease and plant fuel by subtracting the oil/gas play consumption estimates from the Census Division esti mates published in the AEO. This yielded nine sets of growth factors for each year, corresponding to each of the nine Census Divisions. These growth factors reflect “ rest

- of

- Census Division” estimated consumption of pipeline fuel natural gas + lease and pl ant fuel, and were applied t o the non

- AEO oil/gas play counties in

each Division

.

Crude oil production growth factors were also generated for crude oil production

- specific sources (SCCs). These growth factors were generated at similar spatial resolution a s natural gas consumption factors: oil/gas production “play”

- level and rest of Census Division resolution. These data are based on AEO reference table “14” Crude Oil Production and Supply projections.

Other Fuels

The AEO prepares fuel c onsumption projectio ns for the m ining sector, which is comprised of the Oil & Gas Extraction & Support Activities , Coal Mining, and Other Mining and Quarrying sectors. On a national basis,

AEO data indicate that the Oil & Gas Extraction & Support Activities sector contributed 85 percent of total

35 While pipeline fuel natural gas should be self

- explanatory, lease and plant fuel refers to “natural gas used in well, field, and lease operations, in natural gas processing plant machinery, and for liquefaction in export facilities.”115 m ining sector output (NAICS codes 211, 2121, 2122

- 2123) in the 2011 NEI , and is projected to account for a similar contribution (86 percent) in 2030. Estimates of mining sector energy consumption by fuel type for the AEO oil and gas pl ays were developed, and assumed that m ining sector fuel consumption growth rates approximate growth rates in the oil/gas sector for these plays. Because of concerns that o il/ g as production may not represent as significant a contribution to total m ining act ivity in other areas (outside oil/gas plays)

, we chose not to use estimated Mining sector fuel consumption data to project oil/gas production sector fuel consumption in other areas of the country.

Oil/Gas Plays

As listed in Table 4

- 14 and displayed in Figure 4

- 1

, the AEO reports oil and natural gas production projections for a number of individual oil and gas plays. The first step in estimating oil/gas sector energy consumption projections for these plays was to calculate, for each relevant year, the national ratio of Oil/Gas sector constant dollar output to the volume of oil and gas produced (expressed in B TU s) from the AEO. We then estimated Oil/Gas sector constant dollar output in each oil/gas play by mu ltiplying these national ratios by AEO estimates of oil and gas production (B TU s) in each oil/gas play. These oil/gas play

- level output estimates were then multiplied by year

- specific national AEO ratios of Mining sector fuel consumption to

Oil/Gas sector output, which produced Mining sector fuel consumption estimates by play/year. As noted above, these Mining sector estimates are deemed to be reasonable surrogates for Oil/Gas sector activity in the AEO oil/gas play areas.116 Figure 4

- 1

. Oil and gas plays with AEO projection data

Remaining Areas

For all non

- AEO gas/oil play counties, we developed “rest

- of

- Supply Region ” (National Energy Modeling System, or “NEMS”) oil/gas production estimates as the surrogate g rowth indicator for projecting fuel consumption in the Oil/Gas sector. These NEMS regions are shown in Figure 4

- 2

. Since we address oil/gas play areas separately, this approach reflects oil/gas production occurring outside these a reas. To accomplish this, we subtracted the AEO’s oil/gas production estimates for these plays from AEO’s total Oil/Gas Supply region production estimates, and used the result to calculat e the growth factors for areas outside the oil/gas plays

. After imple menting this procedure

, we produced six sets of “rest

- of

- Oil/Gas Supply Region ” growth

factors (one for each of the six Supply Regions) for application to counties in each supply region not in one of the AEO oil/gas plays. All oil and gas production relate d growth factors were capped at 2.5 (150% increase) . Note that r aw AEO growth factors of 4

- 6+ were not uncommon for 2018 and 2025. The cap

addresses concerns that the uncertainty in these projections was too great to allow such dramatic growth.117 Figure 4

- 2

. Oil and Gas NEMS Regions

“MARAMA” factors

The MARAMA states provided usable projections and controls data for 15 states (SRA, 2014)

. The growth data for oil and gas

- specific processes (SCC

- level) wer e not used . Rather, we used the EPA approach for nonpoint oil and gas emissions because it was more comprehens ive and consistent nationally. We used the

MARAMA facility and sub

- facility (point inventory) growth factors ; only a small subset of these factors impact oil and gas point sources (facilities). We capped growth factors related to employment growth for both point and nonpoint factors at 1.25 (25% cumulative growth ) to prevent excessive overestimation of

future

- year emissions

.

The MARAMA point invent ory projection factors were included in our projection packets at facility and

sub

- facility resolution and supersede any/all “ EPA ”

- based SCC and NAICS/SCC projection factors (recall

the C o ST hierarchy described in Section 4.2.1 ). We used t he other (i.e., non

- oil and gas) MARAMA nonpoint projection factors which cover a large portion of the nonpoint sector s , including ICI fuel combustion, construction and mining, surface coating and degreasing processes, dry cleaners and waste

wa ter treatment.

“LADCO” factors118 Similar to the MARAMA data, the LADCO (Midwest RPO) states provided usable projections and controls

data for some states: MI, OH, IN. IL, WI, MN, IA, MO and KY (Alpine Geophysics, 2014)

. We did not use

t he growth data for oi l and gas

- specific processes (SCC

- level). Rather, we used the EPA approach for nonpoint oil and gas emissions because it was more comprehensive and consistent nationally. The only facility or sub

- facility growth factors for LADCO’s “priority” sou rce catego ries that remained were limited to (hydraulic) frac sanding mining and petroleum mining. Our use of the n onpoint factors were limited to agriculture tilling and pesticide application, degreasing operations and residential wood combustion (RWC); we split ou t RWC factors and merged with our “rest of the country” set of projection factors (discussed in

Section 4.2.3.11 )

. The se growth fact ors were all deemed reasonable, in that the values were well under 2.0

- a 100% increase and did not rely on employment growth projections

, and as such these were used essentially

as

- is. Note that the impact of these factors will be seen more for R WC than other stationary non

- EGU emissions modeling sectors

.

Net impacts of projection factors

Net i mpac ts of these projection packets for each of the modeling sectors is provided in Table 4

- 15

. There are a couple of items to note:

1) A ll projection factors are for years 2018 and 2025; we u se d 2018 factors for year 2017.

2) The “ 2011 E mi ssions Subject to PROJECTION factors” in Table 4

- 15 are different in the 2017 and

2025 projection processes/routines ( CoST strategies ) because other previously

- applied PROJECTIONS or CLOSURES data differ for year 2017 and 2025, ma king the remaining 2011 emissions subject to these specific PROJECTION packets different.

3) T he largest increases are to the nonpoint oil and gas sector; however, these are not the final future year impacts

, as significant new and existing controls, discusse d in 4.2.4

, have not yet been applied in

cr e ating the future

- year emissions values shown in this table

.

Table 4

- 15

. I ndustrial source projections net impacts from 2011v6.0 NODA

Pol l Sector 2011 Emissions Subject to

PROJECTION factors Intermediate Projected (not yet controlled)

Emissions Difference (Future

- 2011) % Difference

(Future

- 2011)

For 2017 For 2025 2017 2025 2017 2025 2017 2025

CO nonpt 672,183 673,783 698,363 720,949 26,179 47,167 4% 7%

CO np_oilgas 532,211 532,211 683,021 795,874 150,811 263,663 28% 50%

CO pt_oilgas 228,664 228,577 233,940 307,594 5,276 79,016 2% 35%

CO ptnonipm 572,046 568,599 611,005 625,670 38,959 57,071 7% 10%

CO Total 2,005,104 2,003,169 2,2 26,329 2,450,086 221,224 446,917 11% 22%

NH 3 nonpt 15,938 15,938 16,465 16,581 528 643 3% 4%

NH 3 pt_oilgas 222 222 165 207

- 57

- 15

- 26%

- 7%

NH 3 ptnonipm 12,364 12,233 12,958 13,113 593 880 5% 7%

NH 3 Total 28,524 28,392 29,588 29,900 1,064 1,508 4% 5%

NO X nonpt 477,769 472,942 497,051 498,126 19,281 25,184 4% 5%

NO X np_oilgas 576,626 576,626 775,875 902,334 199,250 325,709 35% 56%119

Pol l Sector 2011 Emissions Subject to

PROJECTION factors Intermediate Projected (not yet controlled)

Emissions Difference (Future

- 2011) % Difference

(Future

- 2011)

For 2017 For 2025 2017 2025 2017 2025 2017 2025

NO X pt_oilgas 522,192 522,026 476,963 622,751

- 45,229 100,725

- 9% 19%

NO X ptnonipm 586,160 575,044 610,221 615,481 24,061 40,437 4% 7%

NO X Total 2,162,747 2,146,637 2,360,110 2,638,692 197,362 492,055 9% 23%

PM 10 nonpt 260,831 260,792 278,565 293,743 17,734 32,951 7% 13%

PM 10 np_oilgas 15,270 15,270 19,537 23,691 4,267 8,421 28% 55%

PM 10 pt_oilgas 13,266 13,243 12,726 15 ,837

- 540 2,594

- 4% 20%

PM 10 ptnonipm 130,488 128,764 141,695 146,538 11,207 17,775 9% 14%

PM 10 Total 419,855 418,069 452,524 479,809 32,669 61,740 8% 15%

PM 2.5 nonpt 209,188 209,206 223,913 236,507 14,725 27,302 7% 13%

PM 2.5 np_oilgas 13,761 13,761 17 ,847 20,865 4,086 7,104 30% 52%

PM 2.5 pt_oilgas 13,020 12,997 12,494 15,549

- 526 2,551

- 4% 20%

PM 2.5 ptnonipm 105,943 104,219 115,864 119,787 9,921 15,568 9% 15%

PM 2.5 Total 341,912 340,183 370,117 392,708 28,205 52,525 8% 15%

SO 2 nonpt 248,793 249,431 251,323 247,483 2,531

- 1,948 1%

- 1%

SO 2 np_oilgas 17,598 17,598 36,797 38,206 19,199 20,607 109% 117%

SO 2 pt_oilgas 51,588 51,477 54,423 55,180 2,835 3,703 5% 7%

SO 2 ptnonipm 514,616 512,441 499,393 504,215

- 15,223

- 8,226

- 3%

- 2%

SO 2 Total 832,595 830 ,948 841,937 845,084 9,342 14,135 1% 2%

VOC nonpt 874,870 874,951 885,095 891,556 10,226 16,605 1% 2%

VOC np_oilgas 2,417,715 2,417,715 3,737,520 4,389,231 1,319,805 1,971,516 55% 82%

VOC pt_oilgas 131,691 131,395 137,535 167,923 5,843 36,528 4% 28%

VO C ptnonipm 131,016 129,910 140,787 142,621 9,771 12,711 7% 10%

VOC Total 3,555,292 3,553,970 4,900,937 5,591,332 1,345,645 2,037,361 38% 57%

4.2.3.7 Data from comments on previous platforms ( nonpt, ptnonipm , pt_oilgas )

2017 Packet s : “PROJECTION_2011v6.2_2018_T CEQ_v6_leftovers_NONPOINT_30jan2015.txt”

“ PROJECTION_VA_ME_TCEQ_AL_comments_2011v6_2018_03dec2013.txt”

“ PROJECTION_TCEQ_ptnonipm_NAICS_comments_2011v6_2018 ”

2025 Packets: “PROJECTION_2011v6.2_2025_TCEQ_v6_leftovers_NONPOINT_30jan2015.txt”

“ PROJECTION_VA_M E_TCEQ_AL_comments_2011v6_2019_03dec2013.txt ”

” PROJECTION_TCEQ_ptn onipm_NAICS_comments_2011v6_2025”

Th ese projection packet s includes projection factors used in the development of the 2011v6.0 emissions modeling platform, specifically, those discussed in S ection 4.2.9 in the 2011v6.1 emissions modeling platform TSD (EPA, 2014b)

. Most of these data were originally received from the Texas Commission on Environmental Quality (TCEQ).120 TCEQ nonpoint projection data

Packet: PROJECTION_2011v6.2_20YY_TCEQ_v6_leftove rs_NONPOINT_30jan2015.txt, where “YY” is 18 or 25 for year 2018 (2017 as well) and 2025 respectively.

Most of th e “old” TCEQ nonpoint projections data are superseded by the 2011v6.0 NODA data , particularly

the SCC

- level growth factors discussed in Section 4.2.3.6

. We removed all TCEQ projection factors for SCCs that overlapped. The remaining TCEQ nonpoint projection data are unchanged from the 2011v6.0 emissions modeling platform.

State comments from 2013

Packet: PROJECTION_VA_ ME_TCEQ_AL_comments_2011v6_20 YY _03dec2013.txt , where “YY” is 18 or 19 for year 2018 (used for 2017) and year 2019 (used for 2025 projections), respectively.

This packet includes comments received prior to 2011v6.0 emissions modeling platform processing fro m Alabama, Maine, Texas and Virginia. These projections data target specific point sources in each of these states, generally impacting only a couple of facilities/units in a couple of counties in each state.

TCEQ point county/NAICS projections data

Packe t: PROJECTION_TCEQ_ptn onipm_NAICS_comments_2011v6_20YY, where “YY” is 18 or 25 for year 2018 (2017 as well) and 2025 respectively.

This packet was provided by TCEQ for minor point source emissions. Projections are applied by county and

NAICS codes and are based on gross product projections for various types of industry, population and economy.com data. We did not apply these to oil and gas sources, opting to instead use the approach

discussed in Section 4.2.3.6 ; in fact, most of these entries are not used because they are lower in the CoST hierarchy tha n the county/NAICS/SCC projection factors discussed in the same section.

Summary impacts A summary of the impacts of these three projection factors for the three affected sectors a re provided in

Table 4

- 16

. Most of these impacts are in Texas. Note that the 2011 emissions differ for 2017 and 2025 projection scenarios; one reason for this is that projection factors equal to 1.0 (no change) were dropped from

t he TCEQ projections data; for some sources, year 2017 (2018) factors were equal to 1.0 while year 2025 factors were not, and vice versa. When this happens, emissions in 2011 do not appear in 2017 but they do for

2025 summaries.

Table 4

- 16 . Impact of 2011v6.0 projection factors for Texas

Poll Sector 2011 Emissions Subject to

PROJECTION

factors Projected

Emissions Tons Difference

(Future

- 2011) % Difference

(Future

- 2011)

For

2017 For

2025 2017 2025 2017 20 25 2017 2025

CO nonpt 61,056 59,457 74,709 75,350 13,653 15,893 22% 27%

CO pt_oilgas 6,096 6,181 6,097 6,120 1

- 61 0%

- 1%

CO ptnonipm 54,891 66,329 73,297 100,156 18,406 33,827 34% 51%

CO T otal 122,043 131,967 154,103 181,626 32,060 49,659 26% 38%

NH 3 nonpt 2,443 2,444 2,485 2,511 42 67 2% 3%

NH 3 pt_oilgas 32 32 29 27

- 3

- 5

- 9%

- 16%

NH 3 ptnonipm 1,754 2,179 2,259 3,170 505 991 29% 45%121

Poll Sector 2011 Emissions Subject to

PROJECTION

factors Projected

Emissions Tons Difference

(Future

- 2011) % Difference

(Future

- 2011)

For

2017 For

2025 2017 2025 2017 20 25 2017 2025

NH 3 T otal 4,229 4,655 4,773 5,708 544 1,053 13% 23%

NO X nonpt 21,650 26,477 22,619 28,182 969 1,705 4% 6%

NO X pt_o ilgas 4,423 4,587 4,322 4,320

- 101

- 267

- 2%

- 6%

NO X ptnonipm 54,820 70,536 74,610 104,624 19,790 34,088 36% 48%

NO X T otal 80,893 101,600 101,551 137,126 20,658 35,526 26% 35%

PM 10 nonpt 20,101 20,140 24,535 27,003 4,434 6,863 22% 34%

PM 10 pt_oilgas 1,0 71 1,103 1,040 1,060

- 31

- 43

- 3%

- 4%

PM 10 ptnonipm 16,002 22,293 20,484 30,777 4,482 8,484 28% 38%

PM 10 T otal 37,174 43,536 46,059 58,840 8,885 15,304 24% 35%

PM 2.5 nonpt 15,672 15,655 19,439 21,629 3,767 5,974 24% 38%

PM 2.5 pt_oilgas 1,037 1,069 1,005 1,022

- 32

- 47

- 3%

- 4%

PM 2.5 ptnonipm 12,528 18,024 16,237 24,993 3,709 6,969 30% 39%

PM 2.5 T otal 29,237 34,748 36,681 47,644 7,444 12,896 25% 37%

SO 2 nonpt 4,415 3,776 4,367 3,649

- 48

- 127

- 1%

- 3%

SO 2 pt_oilgas 9,077 9,172 8,757 8,634

- 320

- 538

- 4%

- 6 %

SO 2 ptnonipm 22,376 44,050 27,964 56,702 5,588 12,652 25% 29%

SO 2 T otal 35,868 56,998 41,088 68,985 5,220 11,987 15% 21%

VOC nonpt 259,815 259,734 276,439 286,184 16,624 26,450 6% 10%

VOC pt_oilgas 14,796 15,178 14,399 13,914

- 397

- 1,264

- 3%

- 8%

VOC ptnonipm 51,020 70,148 68,586 103,803 17,566 33,655 34% 48%

VOC T otal 325,631 345,060 359,424 403,901 33,793 58,841 10% 17%

4.2.3.8 Aircraft (ptnonipm)

Packet:

For 201 7 : “PROJECTION_2011_2017 _aircraft_ST_and_by_airport_22jan2015.txt ”

For 2025: “ PROJECTION_201 1_2025_aircraft_ST_and_by_airport_22jan2015.txt ”

A ircraft emissions are contai ned 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. T he ITN operations are defined as aircraft take

- offs whereby the aircraft leaves the airport vicinity and lands at another airport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. EPA used projected ITN information a vailable from the Federal Aviation Administration’s (FAA) Terminal Area Forecast (TAF) System: http://www.apo.data.faa.gov/main/taf.asp (publication date March, 2014). This

information is available f or approximately 3

, 300 individual ai rports, for all years up to 2040. 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_FY2013

- 2040.pdf

.122 None of our aircraft emission projections account for any control progr ams . 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 h as 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 state

- level aggregation, used primari ly for airports with very little activity, by ITN operation type (commercial, general aviation, military and air taxi) to be used as a default method for projecting from 2011 to future years. The second set of projection factors was by airport, where EPA p rojects emissions for each individual airport with significant ITN activity.

Where NEI facility identifiers were not matched to FAA airport identifiers we simply summed the ITN operations to state totals by year and aircraft operation and computed projec tion factors as future

- year ITN to

year

- 201 1 ITN . EPA assigned factors to inventory SCCs based on the operation type shown in Table 4

- 17

.

Table 4

- 17

. NEI SCC to FAA TAF ITN aircraft c a tegories used for air craft projections

SCC Description FAA ITN Type

2265008005 Commercial Aircraft: 4

- stroke Airport Ground Support Equipment Commercial

2267008005 Commercial Aircraft: LPG Airport Ground Support Equipment Commercia l

2268008005 Commerci al Aircraft: CNG Airport Ground Support Equipment Commercia l

2270008005 Commercial Aircraft: Diesel Airport Ground Support Equipment Commercia l

2275000000 All Aircraft Types and Operations Commercia l

2275001000 Military Aircraft, Total Military

2275020 000 Commercial Aviation, Total Commercia l

2275050011 General Aviation, Piston General

2275050012 General Aviation, Turbine General

2275060011 Air Taxi, Total: Air Taxi, Piston Air Taxi

2275060012 Air Taxi, Total: Air Taxi, Turbine Air Taxi

2275070000 Commercial Aircraft: Aircraft Auxiliary Power Units, Total Commercia l

27501015 Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust;

Military; Jet Engine: JP

- 5 Military

27502011 Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust;

Commercial; Jet Engine: Jet A Commercia l

27505001 Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil;

Piston Engine: Aviation Gas General

27505011 Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil;

Jet Engine: Jet A General

Most NEI airports matched FAA TAF identifiers and therefore use airport

- specific projection factors. We
applied a cap on projection factors of 2.0 (100% increase) for state

- level defaults and 5.0 for airport

- specific
entries. None of the large st airports/larger

- emitters had projection factors close to these caps. A national
summary of aircraft emissions between 2011 and future years 2017 and 2025 are provided in Table 4

- 1 8

.

.123 Table 4

- 1 8

. National aircraft emission projection summary

Emissions Difference % Difference

2011 2017 2025 2017 2025 2017 2025

CO 489,854 509,687 565,678 19,833 75,824 4.0% 15.5%

NO X 120,968 131,678 161,297 10,710 40,328 8.9% 33.3%

PM 10 9,164 9, 373 10,121 208 956 2.3% 10.4%

PM 2.5 7,891 8,084 8,787 194 896 2.5% 11.4%

SO 2 14,207 15,256 18,536 1,049 4,329 7.4% 30.5%

VOC 32,023 33,601 38,591 1,578 6,568 4.9% 20.5%

4.2.3.9 Cement manufacturing (ptn onipm)

Packet:

For 2017: “ PROJECTION_2011_2018_ISIS_cement _by_CENSUS_DIVISION_04dec2013.txt “

For 2025: “ PROJECTION_2011_2025_ISIS_cement_by_CENSUS_DIVISION_25nov2013.txt ”

As indicated in Table 4

- 1 , the Industrial Sectors Modeling Platform (IS MP ) (EPA, 2010b) was used to project the cemen t industry component of the ptnonipm emissions mod eling sector to 2018 and 2025; we used year 2018 as a surrogate for year 2017. This approach provided reductions of criteria and select hazardous air pollut ants. The ISMP cement emissions were developed in support for the Portland Cement NESHAPs and the NSPS for the Portland cement manufacturing industry.

The ISMP model produced a Portland Cement NESHAP policy case of multi

- pollutant emissions for individual cement kilns (emission inventory units) that were relevant for years 2015 through 2030. These

ISMP

- based emissions are reflected using a CoST packet for all existing kilns that are not impacted by more

local information f

rom states (or consent decrees). ISMP also generates new cement kilns that are permi tted (point inventory) and not

- permitted, but generated based on ISMP assumptions on demand and infrastructure (nonpt inventory). These new cement kilns are discussed in Section 4.2.5.4

.

The PROJECTION packets c ontain U.S. cen sus division level based projection factors for each NEI unit (kiln) based on ISMP updated policy case emiss ions at existing cement kilns. The units that closed before 2018 (and 2025) are included in the 2018 (and 2025) base case but are included in other CoST packets that reflect state comments and consent decrees (discussed in Section 4.2.4.10 ).

The ISMP model, version August 2013 w as used for these projections. Recent data updates include updated matching of kilns to better capture recent retirements, capacity additions and projections of capacity additions from Portland Cement Association (PCA) Plant Information Summary of December 31, 2010 and

feedback from Portland Cement NE SHAP reconsideration comments. Updated cement con sumption projections are based on a post

- recession (July 2012) PCA long

- term cement consumption o utlook. Updated

emissions controls in 2015 from the NESHAP are also reflected. Overall, as seen in Figure 4

- 3 , domestic production of cement grows significantly between 2011 and 2015, then more slowly through 2018.
Meanwhile, emissions from NESHAP

- regulated pollutants such as PM and SO 2 drop significantly based on regulated emissions rates. Emissions for NO X increase, though not as much as production because the ISMP model continues the recent trend in the cement sector of the replacement of lower capacity, inefficient wet and long dry kilns with bigger and more efficient pr eheater and precalciner kilns.124

Figure 4

- 3

. Cement sector trends in domestic production versus normalized emissions

Multiple regulatory requirements such as the NESHAP and NSPS currently apply to the cement industry to

reduce CAP and HAP emissions. Addit ionally, 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 ISMP model provides the emission reduction strategy that balances: 1) optimal (le ast cost) industry operation, 2) cost

- effective controls to meet the demand for cement, and 3) emission reduction requirements over the time period of interest. The first step in using ISMP 2018 and 2025 projected emissions is matching the kilns in future ye ars to those

in the 2011 NEI. While ISMP provides by

- kiln emissions for each future year, EPA cement kilns experts
preferred that the Agency project existing cement kilns based on a more

- smooth geographic approach to
reduce the “on”/”off” switching that ISMP assigns to each kiln based on pr oduction and capacity demands. It
would be inefficient and unrealistic to project existing cement kilns to operate as essentially 0% or 100%
capacity based strictly on ISMP output. Therefore, EPA developed a U.S. Censu s Division approach where

ISMP emissions in 2011 and future y ears, that matched the 2011 NEI (e.g., not new ISMP 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 2018/2011 and 2025/2011 emissions ratios for each pollutant and geographic area. The projection

ra tios, provided in Table 4

- 19 , were then applied to all 2011 NEI cement kilns

– except for kilns where
specific local information (e.g., consent decrees/settlements/local information) was available

.

0 50 100 150 200 250

Domestic Production

PM trend

NOX trend

SO2 trend125

Table 4

- 19

. U.S. Census Division ISMP

- based projection factors for existing kilns

Region Division NO X PM SO 2 VOC

2018 2025 2018 2025 2018 2025 2018 2025

Midwest East North Central 2.024 2.053 0.106 0.144 1.800 3.034 0.527 0.670

Midwes t West North Central 0.930 1.279 0.614 0.673 0.695 1.262 0.317 0.492

Northeast Middle Atlantic 1.853 1.221 0.058 0.119 0.904 0.867 0.561 0.569

Northeast New England 2.560 2.560 0.004 0.004 3.563 3.563 0.713 0.713

South East South Central 0.999 0.999 0.1 09 0.109 0.402 0.402 0.323 0.323

South South Atlantic 1.042 1.077 0.284 0.339 0.911 0.936 0.413 0.420

South West South Central 1.220 1.526 0.079 0.174 0.484 0.664 0.225 0.252

West Mountain 1.453 1.321 2.542 1.032 1.917 1.366 0.310 0.345

West Pacific 1. 465 1.465 0.001 0.006 0.300 0.251 0.321 0.290

Table 4

- 20 shows the magnitude of the ISMP census division based projected cement industry emissions at existing NEI facilities from 2011 to future years 2018 and 2025 ; we use 2018 pr ojected emissions for year

2017 . Additional new kiln emissions generated by ISMP are discussed in Section 0

. There are some local exceptions where EPA did not use ISMP

- based projections for cement kilns where local information from consent decrees/settlements and state comments were used instead. Cement kilns projected using these non

ISMP information are not reflected here in Table 4

- 20

.

Table 4

- 20

. ISMP

- b ased cement industry projected emissions

Emissions Tons Difference % Difference

2011 2017 2025 2017 2025 2017 2025

NO X 53,240 70,432 75,680 17,193 22,440 32.3% 42.1%

PM 10 2,954 1,185 1,033

- 1,769

- 1,921

- 59.9%

- 65.0%

PM 2.5 1,709 717 657

- 992

- 1,052

- 58.0%

- 61.6%

SO 2 15,876 18,053 25,579 2,177 9,702 13.7% 61.1%

VOC 2,503 861 1,026

- 1,642

- 1,477

- 65.6%

- 59.0%

4.2.3.10 Corn ethanol plants (ptnonipm)

Packet:

For 2017: “ PROJECTION_2011_2017 _Corn_Ethanol_Plants_AEO2014_Table17_2011v6.2_19feb2015.txt “

For 2025: “P ROJECTION_2011_2025 _Corn_Ethanol_Plants_AEO2014_Table17_2011v6.2_19feb2015.txt ”

We used the AEO 2014 renewable forecast projections of “From Corn and Other Starch” to compute national year 2017 and 2025 growth in ethanol plant production. Per OTAQ directio n, we exempted two facilities ( ‘Highwater Ethanol LLC’ in Redwood county MN and ‘Life Line Foods LLC

- St. Joseph’ in Buchanan county MO ) from these projections; future year emissions were equal to their 2011 NEI v2 values

for these two facilities.

The 2011 corn ethanol plant emissions were projected to account for the change in domestic corn ethanol production between 2011 and future years, from approximately 13.9 Bgal (billion gallons) in 2011 to 13.0 Bgal by 201 7 and 13.2 Bgal in 2025 , based on AEO 2014 p rojections . The projection was applied to all pollutants and all facilities equally. Table 4

- 21 provides the summaries of estimated emissions for the corn

etha nol plants in 2011 and future years 2017 and 2025

.126 Table 4

- 21

. 20 11 and 201 7 /2025 c orn ethanol p lant e missions [tons]

Emissions Difference % Change

20 11 20 17 2025 20 17 2025 20 17 2025

CO 1,172 1,092 1,111

- 80

- 61

- 6.9%

- 5.2%

NO X 1,552 1,445 1,471

- 107

- 81

- 6.9%

- 5.2%

PM 10 1, 386 1,290 1,314

- 96

- 72

- 6.9%

- 5.2%

PM 2.5 339 316 322

- 23

- 17

- 6.9%

- 5.2%

SO 2 12 11 11

- 1

- 1

- 6.9%

- 5.2%

VOC 3,379 3,147 3,204

- 232

- 175

- 6.9%

- 5.2%

4.2.3.11 Residential wood combustion (rwc)

Packet: For 2017: “PROJECTION_2011_2017_RWC_2011v6.2_03mar2015.txt”

For 2025: “ PROJECTION_2011_2025_RWC_2011v6.2_03mar2015.txt ”

EPA applied the recently

- promulgated national New Source Performance Standards (NSPS) for wood stoves to the Residential Wood Combustion (RWC) projections methodology for this platform. To learn m ore about the strengthened NSPS for residential wood heaters, see http://www2.epa.gov/residential

- wood

heaters/regulatory

- actions

- residential

- wood

- heate rs . EPA projected residential wood combustion (RWC)

emissions to years 2017 and 2025 based on expected increase s and decreases in various residential wood burning appliances. As newer, cleaner woodstoves replace some older, higher

- polluting wood stoves, t here

will be an overall reduction of the emissions from older “dirty” stoves but an overall increase in total RWC due to population and sales trends in all other types of wood burning devices such as indoor furnaces and outdoor hydronic heaters (OHH)

. It i s important to note that our RWC projection methodology does not explicitly account for state or local residential wood control programs. There are a number more

- stringent state and local rules in place in 2011, specifically in California, Oregon and Washi ngton. However, at this time, EPA does not have enough detailed information to calculate state specific or local area growth rates. Therefore, with the exception of California, Oregon and Washington, EPA is using national level growth

rates for each RWC SC C category. After discussions with California air districts, regional office contacts and EPA experts, EPA decided to hold RWC emissions flat (unchanged) for all SCCs in Cal ifornia, Oregon and Washington.

Assumed Appliance Growth and Replacement Rates

The development of projected growth in RWC emissions to years 201 7 and 2025 starts with the projected growth in RWC appliances derived from year 2012 appliance shipments reported in the Regulatory Impact Analysis (RIA) for Proposed Residential Wood Heaters NSP S 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 bas ed on 2008 shipment data and revenue forecasts from a Frost & Sullivan Market Report (Frost & Sulliv an, 2010). Next, to be consistent with the RIA (EPA, 2013b), growth rates for new appliances for certified wood stoves, pellet stoves, indoor furnaces and O HH were based on forecasted revenue (real GDP) growth rate of 2.0% per year from 2013 through 2025 as predicted by the U.S. Bureau of Economic Analysis (BEA, 2012). While this approach is not perfectly correlated, in the absence of specific shipment projec tions, the RIA assumes the overall trend in the projection is reasonable. The growth rates for appliances not listed in the RIA (fireplaces, outdoor wood burning devices (not elsewhere classified) and residential fire logs) are estimated based on the avera ge growth in the number of houses between 2002 and 2012, about 1% (U.S. Census, 2012).127 In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the replacement of older, e xisting appliances are needed. Based on long lifeti mes, no replacement of fireplaces, outdoor wood burning devices (not elsewhere classified) or res idential fire logs is assumed. It is assumed that 95% of new woodstoves will replace older non

- EPA certified freestanding stoves (pre

- 1988 NSPS) and 5% will re place existing EPA

- certified catalytic and non

- catalytic stoves that currently meet the 1988 NSPS (Houck, 2011).

EPA RWC NSPS experts assume that 10% of new pellet stoves and OHH replace older units and that because of their short lifespan, that 10% of ind oor fu rnaces are replaced each year; t hese are the same assumptions used since the 2007 emissions m odeling platform (EPA, 2012d). The resulting growth factors for these appliance types varies by appliance type and also by pollutant because the emission rat es, from EPA RWC tool ( EPA, 2013rwc ), vary by appliance type and pollutant. F or EPA certified units, the projection factors for PM are lower than t hose for all other pollutants. The projection factors also vary because the total number of existing units in 2011 varies gr eatly between appliance types.

NSPS Overview

The residential wood heaters NSPS Final Rule was promulgated on February 3, 2015. This rule does not affect existing woodstoves or other wood burning devices; however, it does provide more stringe nt emissions standards for new woodstoves, outdoor hydronic heaters and indoor wood

- burning forced air furnaces. New

“Phase 1” less

- polluting heater standards begin in 2015, with even more

- stringent Phase 2 standards beginning in 2020. The associated reduc ed emission rates for each appliance type (SCC) are applied to all new units sold, some of which are assumed to replace retired units, since year 2015.

Currently the 1988 NSPS limits primary PM 2.5 emissions from adjustable burn rate stoves, including firep lace inserts and freestanding woodstoves, to 7.5 grams/hour (g/hr) for non

- catalytic stoves and 4.1 g/hr for catalytic stoves. The final NSPS limit s PM 2.5 emissions for room heaters, which include adjustable and single burn rate stoves and pellet stoves to 4.5 g/hr in 201 5 and 1.3 g/hr in 20 20 . In addition, the final NSPS

limit s PM 2.5 emissions from hydronic heaters to 0.32 lb/MMBtu heat output in 201 5 , and 0.06 lb/MMBtu in

20 20 . The final NSPS limit s PM 2.5 emissions from indoor furnaces to 0.93 lb/MMBtu in 201 5 and

0.06/MMBtu in 2020

.

Emission factors were estimated from the 2011v2 NEI based on tons of emissions per appliance for PM 2.5 , VOC and CO. This calculation was based on estimated appliance (SCC) population and total emissions by

SCC. EPA

- certified w ood stove emission factors are provided in the wood heaters NSPS RIA Tables 4

- 3, 4

- 7 and 4

- 11 for PM 2.5 , VOC and CO, respectively. For all RWC appliances subject to the NSPS, baseline RIA emission factors, when lower than the computed emission factors (201 1 NEI), are used for new appliances sold between 2012 and 2014. Starting in 2015, Phase 1 emission limits are 60% stronger (0.45 g/hr / 0.75 g/hr) than the RIA baseline emission factors. There are also different standards for catalytic versus non

catalytic EPA

- certified stoves. Similar calculations are performed for Phase 2 emission limits that begin in 2020 and for different emission rates for different appliance types. Because the 2011 NEI and RIA baseline (2012

- 2014) emission factors vary by pollutant, al l RWC appliances subject to the NSPS have pollutant

specific “projection” factors. We realize that these “projection” factors are a composite of growth, retirements and potentially emission factors in 4 increments. More detailed documentation on the EPA RW C Projection Tool, including information on baseline, new appliances pre

- NSPS, and Phase 1 and Phase 2 emission factors, is available up on request.

Caveats and Results

California, Oregon and Washington have state

- level RWC control programs, inclu ding local burn bans in place. Without an ability to incorporate significant local RWC control programs/burn bans for a future year128 inventory, EPA left RWC emissions unchanged in th e future for all three states. The RWC projections

factors for states other than Cali fornia, Oregon and Washington are provided in Table 4

- 22

. VOC HAPs use the same projection factors as VOC, PM 10 uses the same factor as PM 2.5 , and all other pollutants use the CO projection factor. Note that appliance types not su bject to the wood heaters NSPS (e.g., fire pits, fire logs ) have pollutant

- independent projection factors because there is no assumed change in future year emission

factors.

Table 4

- 22

. Non

- West Coast RWC pro jection factors , including NSPS impacts

Default if pollutant not

defined PM VOC and

VOC HAPs CO and

remaining

CAPs

2017 2025 2017 2025 2017 2025 2017 2025

2104008100 Fireplace: general 1.062 1.149

2104008210 Woodstove: fireplace inserts; non

E PA certified 0.903 0.754

2104008220 Woodstove: fireplace inserts; EPA

certified; non

- catalytic 1.171 1.26 1.071 1.113

2104008230 Woodstove: fireplace inserts; EPA

certified; catalytic 1.171 1.317 1.076 1.145

2104008310 Woodstove: freestand ing, non

- EPA

certified 0.932 0.795 0.937 0.791 0.94 0.809 0.932 0.795

2104008320 Woodstove: freestanding, EPA

certified, non

- catalytic 1.171 1.26 1.071 1.113

2104008330 Woodstove: freestanding, EPA

certified, catalytic 1.171 1.317 1.077 1.146

21 04008400 Woodstove: pellet

- fired, general 1.55 1.952 1.551 2.014

2104008510 Furnace: Indoor, cordwood

- fired,

non

- EPA certified 0.746 0.12 0.831 0.147 0.745 0.119 0.746 0.12

2104008610 Hydronic heater: outdoor 1.062 1.038 1.094 1.074

2104008700 O utdoor wood burning device, NEC 1.062 1.149

2104009000 Residential Firelog Total: All

Combustor Types 1.062 1.149

National emission summaries for the RWC sector in 2011, 2017 and 2025 are provided in Table 4

- 23 . For direct PM, the NSPS emission factor reductions mostly offset the growth in appliances by year 2017; by year 2025, continued penetration of Phase 1 and 2 standards and replacement of non

- EPA certified units with

much cleaner EPA

- certified appliances result in a net reduction for primary PM.

Table 4

- 23

. Cumulative national RWC emissions from growth, retirements and NSPS impacts

Pollutant Emissions Difference % Difference

2011 2017 2025 2017

- 2011 2025

- 2011 2 017

- 2011 2025

- 2011

CO 2,527,054 2,513,621 2,332,033

- 13,434

- 195,021

- 0.5%

- 7.7%

NH 3 19,759 19,611 18,210

- 148

- 1,549

- 0.7%

- 7.8%

NO X 34,577 35,652 34,863 1,074 285 3.1% 0.8%

PM 10 382,817 385,356 357,054 2,539

- 25,763 0.7%

- 6.7%

PM 2.5 382,591 385,1 19 356,802 2,528

- 25,789 0.7%

- 6.7%

SO 2 8,977 8,837 7,626

- 140

- 1,351

- 1.6%

- 15.1%

VOC 444,349 439,738 409,947

- 4,610

- 34,402

- 1.0%

- 7.7%129 

### 4.2.4 CoST CONTROL packets ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

The final step in a CoST control strategy, after appli cation of any/all CLOSURE packet(s) (point inventories only) and any/all PROJECTION packet(s) is the application of CoST CONTROL packets. While some controls are embedded in our PROJECTION packets (e.g., NSPS controls for RWC and loco

- marine controls

for r ail and commercial marines vessels), we attempted to separate out the control (program) component in our modeling platform where feasible. In our platform, CoST control packets only impact the nonpt, np_oilgas, ptnonipm and pt_oilgas sectors.

There are sev eral different sources of CONTROL data that are concatenated and quality

- assured for duplicates and applicability to the inventories in the CoST strateg ies . We broke up the CONTROL (and PROJECTION) packets into a few “key” control program types to allow fo r quick summaries of these distinct control programs. The remainder of this section is broken out by CoST packet, with the exception of discussion of the various packets gathered from previous versions of the emissions modeling platform; these packets are a mix of different sources of data, only some of which have not been replaced by more recent information gathered for this platform.

For future

- year NSPS controls (oil and gas, RICE, Natural Gas Turbines, and Process Heaters), we attempted to control only new sources/equipment using the following equation to account fo r growth and retirement of existing sources and the differences between the new and existing source emission rates.

Qn = Qo { [ (1 + Pf ) t

– 1 ] Fn + ( 1

- Ri ) t Fe + [ 1

- ( 1

- Ri ) t ] Fn ] } Equation 1

w here:

Qn = emissions in projection year Qo = emissions in base year Pf = growth rate expressed as ratio (e.g., 1.5=50% cumulative growth) t = number of years between base and future years

Fn = emission factor ratio for new sources Ri = retirement rate, expressed as whole number (e.g., 3.3%=0.033) Fe = emission factor ratio for existing sources

The first term in Equation 1 represents new source growth and controls, the second term accounts for

retirement and controls for existing sources, and the third term accounts for replacement source controls. For computing the CoST % reductions (Control Efficiency), the simplified Equation 2 was used for 2025 projections:

Control_Efficiency 2025 (%) = 100 * (1

- [(Pf 2025

- 1)*Fn + (1

- Ri) 14 + (1

- (1

- Ri) 14 )*Fn]/ Pf 2025 ) Equation 2

Here, the existing source emissions factor (Fe) is set to 1.0, 2025 (future year) minus 2011 (base year) is 14, and new source emission factor (Fn) is the ratio of the NSPS emission factor to the existing emissio n factor.

Table 4

- 24 shows the values for Retirement rate and new source emission factors (Fn) for each NSPS regulation and other conditions within; this table also provides the subsection where the CONTROL packets

are discussed.130

Table 4

- 24

. Assumed r etirement rates and new source e mission factor ratios for new sources for various

NSPS rules

NSPS

Rule TSD

Section Retirement

Rate years (%/year ) Pollutants

Impacted Applied where? New So urce Emission Factor (Fn)

Oil and

Gas 4.2.4.1 No assumption VOC Storage Tanks: 70.3% reduction in growth

- only (>1.0) 0.297

Gas Well Completions: 95% control (regardless) 0.05

Pneumatic controllers, not high

bleed >6s cfm or low

- bleed: 77% reduction in growth

- only (>1.0) 0.23

Pneumatic controllers, high

- bleed

>6scfm or low

- bleed: 100% reduction in growth

- only (>1.0) 0.00

Compressor Seals: 79.9% reduction in growth

- only (>1.0) 0.201

RICE 4.2.4.3 40, (2.5%) NO X Lean burn: PA, all other states 0.25, 0.606
Rich Burn: PA, all other states 0.1, 0.069 Combined (average) LB/RB: PA,

other states 0.175, 0.338

CO Lean burn: PA, all other states 1.0 (n/a),

0.889

Rich Burn: P A, all other states 0.15, 0.25 Combined (average) LB/RB: PA,

other states 0.575, 0.569

VOC Lean burn: PA, all other states 0.125, n/a

Rich Burn: PA, all other states 0.1, n/a Combined (average) LB/RB: PA,

other states 0.1125, n/a

Gas

Tu rbines 4.2.4.6 45 (2.2%) NO X California and NO X SIP Call

states 0.595

All other states 0.238

Process

Heaters 4.2.4.7 30 (3.3%) NO X Nationally to Process Heater

SCCs 0.41

4.2.4.1 Oil and gas NSPS ( np _oilgas, pt_oilgas)

Packet:

For 2017: “ CONT ROL_2011v6.2_2018 _OilGas_VOC_NSPS_12dec2014.txt ”

For 2025: “CONTROL_2011v6.2_2025_OilGas_VOC_NSPS_12dec2014.txt”

For oil and gas NSPS controls

, with the exception of gas well completions (a 95% control) the assum ption of no equipment retirements through year 2025 dictates that NSPS controls are applied to the growth component only of any PROJECTION factors. For example, if a growth factor i s 1.5 for storage tanks ( indicating a 50% increase activity), then, using Table 4

- 24 , the 70.3% VOC NSPS control to this new growth will result in a 23.4% control: 100 *(70.3 * (1.5

- 1) / 1.5); this yields an “effective” growth rate (combined PROJECTION and CONTROL) of 1.1485, or, a 70.3% reduction from 1.5 to 1.0. The impacts of131 all non

- drilling completion VOC NSPS controls are therefore greater where growth in oil and gas production is assumed highest. Conversely, for oil and gas basins with assumed negative growth in activity/production,

VOC NSPS contr ols will be limited to well completions only. Because these impacts are so geographically varying, we are providing the VOC NSPS reductions by each of the 6 broad NEMS regions, with Texas and New Mexico aggregated because these states include multiple NEMS regions (see Figure 4

- 2 ). These reductions are year

- specific because projection factors for these sources are year

- specific. For the 2017 modeling case, because the projection factors are for year 2018, the reductions are based o n year 2018 growth as well.

Table 4

- 25

. NSPS VOC oil and gas reductions from projected pre

- control 2018 and 2025 grown values

Region Pre

- NSPS Emissions Post

- NSPS Emissions NSPS Reductions NSPS %

Reductions

2 018 2025 2018 2025 2018 2025 2018 2025

Gulf Coast 75,152 137,990 57,951 110,054 17,201 27,937 23% 20%

Midcontinent 7,882 222,288 4,238 189,474 3,644 32,814 46% 15%

New Mexico/ Texas 1,184,448 1,389,112 716,616 881,695 467,832 507,417 39% 37%

Northeast 3 3,300 33,327 14,903 14,909 18,397 18,418 55% 55%
Rocky Mountains 517,266 993,574 313,729 529,956 203,536 463,618 39% 47%

West Coast 15 15 1 1 14 14 95% 95%

Overall 1,818,063 2,776,306 1,107,438 1,726,089 710,625 1,050,217 39% 38%

4.2.4.2 RICE NESHAP ( nonpt, n p_oilgas, ptnonipm, pt_oilgas)

Packet: “ CONTROL_2011v6.2_RICE_NESHAP_v2_30jan2015.txt ”

There are two rulemakings for National Emission Standards for Hazardous Air Pollutants (NESHAP) for Reciprocating Internal Combustion Engines (RICE). These rules reduce HAPs from existing and new RICE sources. In order to meet the standards, existing sources with certain types of engines will need to install controls. In addition to reducing HAPs, these controls have co

- benefits that also reduce CAPs, specifically, CO, NO X , VOC, PM, and SO 2 . In 2014 and beyond, compliance dates have passed for both rules and are thus included in emissions projections. These RICE reductions also reflect the Reconsideration Amendments (proposed in January, 2012), which result in significantl y 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:

 National Emission Standards for Hazardo us Air Pollutants for Reciprocating Internal Combustion Engines; Final Rule (FR 9648 ) published 03/03/10

 National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion Engines; Final Rule (75 FR 51570) published 08/20/2010

T he difference among these t wo 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 both are after 2011 and fully implemented prior to 201 7 . EPA project s CAPs from the 2011NEIv 2 RICE sources , based on the requirements of the rule for existing sources only because the inventory includes only existing sources . EPA estimates the NSPS (new so urce) impacts132 from RICE regulations in a separate CONTROL packet and CoST strategy; the RICE NSPS is discussed in

the next section

.

The Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression Ignition (CI) Engines NE SHAP: Final Report (EPA, 2013ci) is available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/RICE_NESHAPreconsideration_Comp ression_Ignition_Engines_ RIA_final2013_EPA.pdf

. The Regulatory Impact Analysis (RIA) for Reconsideration of the Existing Stationary Spark Ignition (SI) RICE NESHAP: Final Report (EPA, 2013si) is available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/NESHAP_RICE_Spark_Ignition_RIA_finalreconsideration2013_ EPA.pdf

. Together, EPA calls these the RICE NESHAP amendmen t RIA’s for SI and CI eng ines. From these RICE NESHAP RIA documents, EPA obtained cumulative RICE reductions for all SCCs rep resented by CI and SI engines. These aggregate reductions and percent reductions from baseline emissions (not the

2011NEIv 2 ) are provided in Table 4

- 26

.

Table 4

- 26

. Summary RICE NESHAP SI and CI percent reductions prior to 2011NEI v 2 analysis

CO NO X PM SO 2 VOC RIA Baseline: SI engines 637,756 932,377 127,170 RIA Reductions: SI engine s 22,211 9,648 9,147 RIA Baseline: CI engines 81,145 19,369 11,053 79,965 RIA Reductions: CI engines 14,238 2,818 5,100 27,142

RIA Cumulative Reductions 36,449 9,638 2,818 5,100 36,289

SI % reduction 3.5% 1.0% n/a n/a 7.2%

CI % reduction 17.5% n/a 14.5% 46.1% 33.9%

These RIA percent reductions were used as an upper

- bound for reducing emissions from RICE SCCs in the

2011NEIv 2 point and nonpoint modeling sectors (ptnonipm, nonpt, pt_oilgas and np_oilgas). To begin with,

the RIA inventories are base d on the 2005 NEI, so EPA wanted to ensure that our 2011 reductions did not exceed those in the RICE RIA documents. For the 2011 platform EPA worked with EPA RICE NESHAP experts and developed a fairly simple approach to estimate RICE NESHAP reductions. Mos t SCCs in the inventory are not broken down by horsepower size range, mode of operation (e.g., emergency mode), nor major versus area source type. Therefore, EPA summed NEI emissions nationally by

- SCC for RICE sources

and also for sources that were at leas t partially IC engines (e.g., “Boiler and IC engines”). Then, EPA applied the RIA percent reductions to the 2011NEIv 2 for SCCs where national totals exceeded 100 tons; EPA chose 100 tons as a threshold, assuming there would be little to no application of R ICE NESHAP controls on

smaller existing sources.

Next, EPA aggregated these national reductions by engine type (CI vs. SI) and pollutant and compared these to the RIA reductions. As expected, for most pollutants and engine types, the cumulative reduction s were significant ly less than those in the RIA. T he only exception was for SO 2 CI engines, where EPA scale d the RIA percent reduction from 46.1% to 1 4

. 4 % for four broad nonpoint SCCs that were not restricted to only RICE engines. These four SCCs were the “Boilers and IC Engines” or “All processes” that would presumably contain some fraction of n on

- RICE component. This had minimal impact as sulfur content in distillate fuel for many IC engine types has decreased significantly since 2005. Reducing the SO 2 pe rcent reduction for these four SCCs resulted in slightly less than 5,100 tons of SO 2 reductions overall f rom only

RICE NESHAP controls. However, more specific CoST projection packets would later override these RICE

N ESHAP reductions for SO 2

. Recall the CoS T 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 (d iscussed in the next section).133 Additional comments from the NODA were also implemented; specifically, CO controls were modified for a couple of distillate

- fueled industrial/commercial boiler sources. Impacts of the RICE NESHAP controls on nonpt, ptnonipm, pt_oilgas and np_oilgas sector emissions are provided in Table 4

- 27

.

Table 4

- 27

. National by

- sector reductions from RICE Reconsideration c ontrols (tons)

Pollutant Year Nonpoint

Oil & Gas (np_oilgas) Point

Oil & Gas (pt_oilgas) Nonpoint

(nonpt) Poin t

(ptnonipm) Total

CO 2018 7,993 4,533 3,214 6,146 21,886

CO 2025 8,783 6,146 3,215 6,394 24,538

NO X 2018 2,240 1,749 192 81 4,262

NO X 2025 2,441 2,411 197 84 5,133

PM 10 2018 0 8 929 305 1,242

PM 10 2025 0 10 922 311 1,243

PM 2.5 2018 0 8 828 289 1,12 4

PM 2.5 2025 0 10 821 294 1,124

SO 2 2018 0 9 71 296 376

SO 2 2025 0 14 71 294 378

VOC 2018 1,989 2,874 586 942 6,392

VOC 2025 2,224 4,217 588 956 7,986

4.2.4.3 RICE NSPS ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

Packet: For 2017: “CONTROL_2011v6.2_2018_RICE_NS PS_29dec2014.txt” For 2025: “CONTROL_2011v6.2_2025_RICE_NSPS_29dec2014.txt”

Controls for existing RICE source emissions were discussed in the previous section. This section discusses control for new equipment sources, NSPS controls that impact CO, NO X and VOC. EPA emission requirements for stationary engines differ according to whether the engine is new or existing, whether the engine is located at an area source or major source, and whether the engine is a compression ignition or a spark ignition engine. S park ignition engines are further subdivided by power cycle, two vs. four stroke, and whether the engine is rich burn or lean burn.

RICE engines in the NO X SIP Call area are covered by state regulations implementing those requirements.

EPA estimated that N O X emissions within the control region were expected to be reduced by about 53,000 tons per five month ozone season in 2007 from what they would otherwise be without this program. Federal rules affecting RICE included the NESHAP for RICE 40 CFR Part 63, Su bpart ZZZZ, NSPS for Stationary Spark Ignition IC engines 40 CFR Part 60 Subpart JJJJ, and NSPS for Compression Ignition IC engines 40 CFR Part 60 Subpart IIII. SI engine operators were affected by the NSPS if the engine was constructed after June 12, 2006 , with some of the smaller engines affected by the NSPS 1

- 3 years later. The recommended RICE equipment lifetime is 30 to 40 years depending on web searches. We chose 40 years as a conservative

estimate.

The 2011 estimates of the RICE engine average emissi on rates for lean burn and rich burn engines was developed using the stationary engine manufacturers data submitted to EPA for the NSPS analysis (Parise, 2005). Emission factors by pollutant for engines 500

- 1200 horsepower (hp) were used to develop the av erage134 emission rates. The analysis was organized this way because lean vs. rich burn engine type is such a significant factor in the NO X emissions rate. Any state emission regulations that require stationary RICE engines to achieve emission levels lower th an the 2012 NSPS could be included by using lower new source emission ratios that account for the additional emission reductions associated with having more stringent state permit rules. Information is provided for Pennsylvania in Table 4

- 28 . That information shows that the PA regulations have different emission standards for lean burn vs. rich burn engines, and that the emission limits also vary by engine size (100

- 500 hp or greater than 500 hp). While some of the newer RICE SCCs (oil and gas sector in particular) allow states to indicate whether engines are lean vs. rich burn, some SCCs lump these two together. None of the RICE point source SCCs have information about engine sizes. However, the EPA regulatory impact analysis for t he RICE NSPS and NESHAP analysis (RTI, 2007) provides a table that shows the NO X (CO, NMHC and HAP emission estimates are provided as well) emissions in 2015 by engine size, along with engine populations by size. In the future, more rigorous analysis can u se this table to develop computations of weighted average emission reductions by rated hp to state regulations like Pennsylvania’s.

Table 4

- 28

. RICE NSPS Analysis and resulting 2011v6.2 new emission rates use d to compute c ontrols

Engine type & fuel Max Engine

Power Geographic

Applicability Emission standards g/HP

- hr

NO X CO VOC
2011 pop lean burn 500

- 1200 hp 1.65 2.25 0.7 2011 pop rich burn 500

- 1200 hp 14.5 8 0.45

Non

- Emerg. SI NG and Non

- E. SI Lean

Bu rn LPG (except LB 500≤HP<1,350) HP≥100 2006 NSPS 2.0 4.0 1.0

Non

- Emerg. SI NG and Non

- E. SI Lean Burn LPG (except LB 500≤HP<1,350) HP≥100 2012 NSPS 1.0 2.0 0.7

HP≥100 PA (Previous GP

- 5) 2.0 2.0 2.0

New NG Lean Burn 100<HP<500 PA (New GP

- 5) 1.0 2.0 0.7

New NG Lean Burn HP >500 PA (New GP

- 5) 0.5 2.0 0.25

New NG Rich Burn 100<HP<500 PA (New GP

- 5) 0.25 0.3 0.2

New NG Rich Burn HP >500 PA (New GP

- 5) 0.2 0.3 0.2

HP≥100 Maryland 1.5

HP>7500 Colorado 1.2

- 2

Wyoming None None None

Notes : the above table compares the criteria pollutant emission standards from the recent NSPS with the emission limits from selected states for stationary IC engines to determine whether future year emission rates are likely to be significantly lower than fo r the ex isting engine population. States in the NO X SIP Call region instituted NO X emission limits for large engines well before 2011. Most of the values in the above table come from an analysis posted on the PA DEP website. The state emission limits listed above are those in place prior to 2011. Some states (like PA) have instituted tougher RICE emission limits for new and modified engines more rec ently.

Note 2 : Wyoming exempts all but the largest RICE engines from emission limits.

Note 3 : PA has had a size limi t for new RICE engines of 1500 hp until recently (i.e., not engines bigger than 1500 hp can be installed).

Their new General Permit

- 5 removed the engines size cap, but requires new or modified larger engines to be cleaner (i.e., has emission

limits lower t han the NSPS). PA expects that the new emission limits will result in an increase in larger engines being installed, and bringing the average emission rate much lower than it is currently. New source Emissions Rate (Fn): Controls % =100 * (1

- Fn) NO X CO VO C
Pennsylvania NG

- Comb. LB & RB 0.175 0.575 0.113

All other states NG

- Comb. LB & RB 0.338 0.569 1.278

Pennsylvania NG

- lean burn 0.250 1.000 0.125

All other states NG

- lean burn 0.606 0.889 1.000

Pennsylvania NG

- rich burn 0.100 0.150 0.100

Al l other states NG

- rich burn 0.069 0.250 1.556135 We applied NSPS reduction for lean burn, rich burn and “combined” (not specified). We also computed

scaled

- down (less

- stringent) NSPS controls for SCCs that were “IC engines + Boilers” because boiler

emission s are not subject to RICE NSPS. For these SCCs, we used the 2011NEIv2 point inventory to aggregate eligible (fuel and type) boiler and IC engine emissions for each pollutant. We found that for CI engines, almost all emissions were boiler

- related; therefore , there are no CI engine RICE NSPS reductions for “IC engines + Boilers”. For SI engines, we found that approximately 9% of NOX, 10% Of CO and 19% of VOC “IC engines + Boilers” were IC engines; these splits were then applied to the NSPS reductions in

Table 4

- 28 . Finally, we limited RICE NSPS

- eligible sources (SCCs) to those that have at least 100 tons nationally for NO X , CO or VOC, and ignored resulting controls that were under 1%.

PA DEP staff note that until recently they have li mited RICE engines to a maximum of 1500 hp. That cap is

lifted under the new General Permit

- 5 regulations. With that cap lifting, PA expects that new applications will choose to install larger engines which have lower emission limits. However, that potenti al effect will be difficult to capture with no information about how this might occur. These controls were then plugged into Equation 2 (see Section 4.2.4 ) as a function of the projection factor. Resulting controls greater than or equal

to 1% were retained. Note that where new Emissions Factors >=1.0 (uncontrolled, as represented by red cells

at the bottom of Table 4

- 28 ), no RICE NSPS controls were computed. National RICE NSPS reductions from projected pre

- NSPS 2018 and 2025 inventories are shown in Table 4

- 29

.

Table 4

- 29

. National by

- sector reductions from RICE NSPS controls (tons)

Pollutant Year Nonpoint

Oil & Gas (np_oilgas) Poi nt Oil

& Gas (pt_oilgas) Nonpoint

(nonpt) Point

(ptnonipm) Total

NSPS

Reductions Pre

- NSPS

Total

Emissions NSPS %

Reduction

CO 2018 93,422 19,321 758 861 114,363 503,266 23%

CO 2025 161,075 51,249 2,277 1,366 215,968 762,028 28%

NOX 2018 156,942 44,949 1 ,685 1,242 204,818 777,041 26%

NOX 2025 252,770 122,603 3,903 2,063 381,339 1,072,744 36%

VOC 2018 1,656 422 0 1 2,079 3,986 52%

VOC 2025 1,838 484 0 2 2,324 3,990 58%

4.2.4.4 ICI Boilers ( nonpt, ptnonipm, pt_oilgas )

Packet s : CONTROL_2011v6.2_20xx_BoilerMACT _POINT_v2_30jan2015.txt CONTROL_2011v6.2_20xx_BoilerMACT_NONPT_08jan2015.txt

The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Rule, hereafter simply referred to as the “Boiler MACT” was promulgated on January 31, 2013 based on recon sideration. Background

information on the Boiler MACT can be found at: http://www.epa.gov/ttn/atw/boiler/boilerpg.html

. The Boiler MACT promulgates national emission standards for the control of HAPs (NESHAP) for new and existing industrial, commercial, and institutional (ICI) boilers and process hea ters at major sources of HAPs. The expected cobenefit for CAPs at these facilities is significant and greatest for SO 2 with lesser impacts for

dire ct PM, CO and VOC. These packets address only the expected cobenefits to existing ICI boilers.

Boiler MACT reductions were computed from a no n

- 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 reductions136 by year 2015. However, the Boi ler MACT ICR database and reductions are based on the assumption that if a

unit could burn oil, it did bu rn oil, and often to capacity. With high oil prices and many of these units also able to burn cheaper natural gas, the 2011NEIv2 inventory has a lot mo re gas combustion and a lot less oil combustion than the boiler MACT database. For this reason, EPA decided to target units that potentially could be subject to the Boiler MACT and compute preliminary reductions for several CAPs prior to building

a control packet.

Step 1: E xtract facilities /sources potentially subject to Boiler MACT This step is only applicable to point inventory sources. EPA did not attempt to map each ICR unit to the NEI units, instead choosing to use a more general approach to extract N EI sources that would be potentially subject to, and hence have emissions reduced by the Boiler MACT. The NEI includes a field that indicates whether a facility is a maj or source of HAPs and/or CAPs. This field in our FF10 point inventory modeling

file is called “FACIL_CATEGORY_CODE” and the possible values for that field are shown in Table 4

- 30

. Because the Boiler MACT rule applies to only major sources of HAPs, EPA restricted the universe of facilities potentially subject to the Boiler MACT to those classified as HAP major or unknown (UNK). The third column indicates whether the facility was a candidate for extraction as being potentially subject to the

Boiler MACT.

Table 4

- 30

. Faci lity types potentially subject to Boiler MACT reductions

Code Facility

Category Subject

to Boiler

MACT? Description

CAP CAP Major N Facility is Major based upon 40 CFR 70 Major Source definition paragraph 2 (100 tpy any CAP. Also meets paragraph 3 definit ion, 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 Major Source threshold

definitions without a FESOP.

OZN O3 n/a Major N Facility is Major based upon only 40 CFR 70 Major Source definition paragraph 3 (Ozone n/a area lesser tons for 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.

Step 2: Obtain “MARAMA” control information

From the (point inventory) f acilities extracted in Step 1 , we merged in ICI Boiler controls/adj ustments developed under MARAMA in support of the Ozone Transport Commission (OTC) 2 007 mode ling platform future year analyses. These adjustments are discussed in a W hite Paper “White Paper for ICI Blr Emissions

V6.doc ” (available upon request) . This white paper provides methodology and summary future year adjustments and emission estimates based on the OTC 2007 platform for the purpose of estimating emissions changes in ICI point and nonpoi nt sources due to the Boiler MACT, 1

- hour SO2 NAAQS and economic factors related to natural gas prices

. Th is MARAMA approach relies on Council of Industrial Boilers (CIBO ) analysis of cost

- effectiveness of boiler controls and retrofits in orde r to maintain the Boiler MACT. In short, the CIBO analyses showed that many ICI boilers were converting (or replacing coal units) to natural gas137 rather than applying more costly controls. Specifically, CIBO determined that 63% of coal units found it

more economical to replace their coal boilers with natural gas boilers.

ICI boilers were categorized by fuel: Light Oil (distillate), Heavy Oil (residual), Pulverized Coal and Stoker/Other Coal. Next, AP42 ( http://www.epa.gov /ttn/chief/ap42/ ) emission factors for each fuel was converted to consistent units (lb/MMBtu) via heat content and these normalized emission factors were used to develop emission factor ratios of natural gas to non

- natural gas fuel type. Finally, the esti mated number of ICI boiler replacements and retrofits were used to create weighted

- average adjustment (“control”) factors

from these normalized emission factor ratios. This methodology makes the following assumptions:

 Natural Gas NO X emissions rates: 0.10 lb/MMBtu for new boiler s
, 0.19 61 lb/MMBtu for burner retrofit s

 Natural gas emission rates for SO 2 , PM 2.5 and VOC are the same for both boiler replacement and

burner retrofits

 Any unit that finds it economical to replace the entire boiler, will do so. Those that don't replace the boiler but find it economical to retrofit the burner will do so. Other units remain unchanged for NO X , SO 2 , PM 2.5 and VOCs emissions.

 Analyses are based on OTC 2007 modeling platform and applied to 2011 emissions modeling plat form

Step 3: Merge control information with 2011 NEI and apply state NODA comments

EPA analyzed the SCCs in the OTC 2007 inventories and tweaked the SCC mapping of these ICI boiler adjustments to map to those in the 2011 NEI point and nonpoint inventory wi th non

- zero emissions. EPA also removed some duplicate and incorrect mappings and expanded the SCC mapping in some cases to SCCs that were in the NEI but not the OTC inventory (and thus missing from the analysis) . In addition, the MARAMA approach only incl udes adjustments for NOX, PM and SO2. Therefore, EPA merged in existing VOC, CO, HCl controls (applying VOC controls to VOC HAPs as well) from the 2011v6.0 emissions modeling platform (see Section 4.2.7 in the 2011v6.0 Emissions Modeling TSD ) to the same s et of facilities (point) and SCCs as those for the pollutants provided by the MARAMA approach.

Some states commented on the 2011v6.0 ICI boiler controls via the 2018 NODA ( docket # EPA

- HQ

- OAR

2013

- 0809 on http://w ww.regulations.gov ). Wisconsin provided alternative SO 2 , VOC and HCl controls for stoker and pulverized coal fueled units. The national

- level and Wisconsin

- specific ICI boiler adjustments, applied at the unit

- level for point sources and by SCC (and state for Wisconsin) are provided in Table 4

- 31 ; note that we applied the same national

- level adjustments to CO, NO X and PM for coal units in Wisconsin.

New Yo rk and New Jersey, via the MARAMA comment/data to the 2018 NODA, provided boi ler rule NO X reductions that also supersede these nationally

- applied factors. The NJ and NY factors are provided in Table

4

- 32 ; note that NJ controls apply only to nonpoint sources and that NY controls vary by fuel for point

sourc es

.

The impacts of these ICI boiler reductions are provided in Table 4

- 33 . Overall, the CO and PM 2.5 reductions are reasonably close to the year

- 2015 expected reductions in the Boiler MACT Reconsideration RIA: http://www.epa.gov/ttn/atw/boiler/boilersriaproposalrecon111201.pdf . It is worth noting that the 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 co

- benefit from more stringent HCl controls. The 2011NEIv2 SO 2 emissions are actually less than t he estimated Boiler MACT reductions, likely a result of numerous units undergoing fuel switching from coal or oil to natural gas

.138

Table 4

- 31

. National

- level, with Wisconsin exceptions, ICI boiler adjustment f actors by base fuel type

Unit/Fuel Type Default % Reduction (Adjustments)

CO NOX PM SO2 VOC HCl

Stoker Coal 98.9 70.7 96 97.4 98.9 95

Pulverized Coal 98.9 60.6 72.2 73 98.9 95

Residual Oil 99.9 57 92.4 97.1 99.9 95

Distillate Oil 99.9 38.8 68.4 99.9 99.9 88.6

Wisconsin: Stoker Coal 98.9 70.7 96 30 0 45

Wisconsin: Pulverized Coal 98.9 60.6 72.2 30 0 45

Table 4

- 32

. New York and New Jersey NO X ICI Boiler Rules that supersede national approach

NJ and NY B oiler Rule controls NOX %

Reduction

New Jersey Small Boiler Rule (nonpoint only): Default for Distillate, Residual, natural gas and LPG 25
New York Small Boiler Rule (nonpoint only): Default for Distillate, Residual, natural gas and LPG 10

NY Boiler Rul e: Industrial /Distillate Oil /< 10 Million Btu/hr 10

NY Boiler Rule: Industrial /Residual Oil /10

- 100 Million Btu/hr 33.3

NY Boiler Rule: Electric Gen /Residual Oil /Grade 6 Oil: Normal Firing 40

NY Boiler Rule: Electric Gen /Natural Gas /Boilers, < 10 0 Million Btu/hr except Tangent 50
NY Boiler Rule: Electric Gen /Natural Gas /Boilers, 100 Million Btu/hr except Tangent 60

NY Boiler Rule: Industrial /Bitum Coal /Cyclone Furnace 66.7

NY Boiler Rule: Industrial /Natural Gas /> 100 Million Btu/hr 70

NY Boiler Rule: Electric Gen /Bituminous Coal /Pulverized Coal: Dry Bottom 73.3

Table 4

- 33

. Summary of ICI Boiler reductions

Year Pollutant Emissions Eligible

for Control Controlled (Final)

Emissions Reduction s

(tons) %

Reductions

2017 CO 35,118 336 34,782 99.0%

2017 NO X 128,943 64,596 64,347 49.9%

2017 PM 10 34,592 7,401 27,191 78.6%

2017 PM 2.5 13,973 2,504 11,469 82.1%

2017 SO 2 273,449 29,451 243,998 89.2%

2017 VOC 1,778 43 1,735 97.6%

2025 CO 35,883 346 35,537 99.0%

2025 NO X 130,862 65,259 65,603 50.1%

2025 PM 10 35,729 7,700 28,030 78.4%

2025 PM 2.5 14,273 2,572 11,701 82.0%

2025 SO 2 272,425 30,579 241,846 88.8%

2025 VOC 1,791 43 1,747 97.6%139

4.2.4.5 Fuel sulfur rules ( nonpt, ptnonipm, pt_oilgas )

Packet: CONTROL_2011v6.2_20xx_Fuel_Sulfur_Rules_09jan2015.txt

Fuel sulfur rules, based on web searching and t he 2011 emissions modeling NODA comments are currently

limited to the following states: Connecticut, Delaware, Maine, Massachusetts, New Jersey, New York, Pennsylvania , Rhode Island and Vermont. The fuel limits for these states are incremental starting after year 2012, but are fully implemented b y July 1, 2018 in all of these states.

A summary of all fuel sulfur rules provided back to EPA by the 2011 emiss ions modeling NODA comments is provided in Table 4

- 34

. State

- specific control factors were computed for distillate, residual and #4 fuel oil using each state’s baseline sulfur contents and the sulfur content in the rules. For most states, the baseline

sulfur content was 3

, 000 ppm (0.3%) for distillate oil, and 2.25% for residual and #4 oil. However, many states had lower baseline sulfur contents for residual oil, which varied by state and county. SRA used state

or county

- specific baseline residual oil sulfur contents to calculate a state

- or county

- specific control factors

for residual oil (SRA, 2014)

.

Table 4

- 34

. State Fuel Oil Sulfur Rules data provided by MANE

- VU

State Reference

Co nnecticut Section 22a

- 174

- 19a. Control of sulfur dioxide emissions from power plants and other large stationary sources

of air pollution: Distillate and Residual: 3000 ppm effective April 15, 2014.

Section 22a

– 174

- 19b. Fuel Sulfur Content Limitations for Stationary Sources (except for sources subject to

Section 22a

- 174

- 19a). Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018 Residual: 1.0% effective July 1, 2014; 0.3% effective July 1, 2018

Connecticut General Statute 16a

- 21a. S ulfur content of home heating oil and off

- road diesel fuel. Number 2 heating oil and off

- road diesel fuel: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018

See: http://www.ct.gov/deep/cwp/view.asp?a=2684&Q=322184&deepNav_GID=1619

Delaware 1108 Sulfur Dioxide Emissions from Fuel Burning Equipment

Distillate: 15 ppm effective July 1, 2017 Residual: 0.5% effective July 1, 2017

#4 Oi l: 0.25% effective July 1, 2017

See: http://regulations.delaware.gov/AdminCode/title7/1000/1100/1108.shtml

Maine Chapter l06: Low Sulfur Fuel

Distillate: 500 ppm effective July 1, 2014; 15 ppm ef fective July 1, 2018 Residual: 0.5% effective July 1, 2018

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

. Massachusetts 310 CMR 7.05 (1)(a)1: Table 1 : Sulfur Content Limit of Liquid Fossil Fuel

Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018 Residual: 1.0% effective July 1, 2014; 0.5% effective July 1, 2018

See: http://www.mass.gov/eea/docs/dep/service/regulations/310cmr07.pdf New Jersey Title 7, Chapter 27, Subchapter 9 Sulfur in Fuels

Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2016

Residual: 0.5% or 0.3%, depending on county, effective July 1, 2014 #4 Oil: 0.25% effective July 1, 2014

See: http://www.nj.gov/dep/aqm/rules27.html

New York Subpart 225

- 1 Fuel Composition and Use

- Sulfur Lim itations

Distillate: 15 ppm effective July 1, 2016 Residual: 0.3% in New York City effective July 1, 2014; 0.37% in Nassau, Rockland and Westchester counties effective July 1, 2014; 0.5% remainder of state effective July 1, 2016

See: http://www.nyc.gov/html/dep/html/news/dep_stories_p3

- 109.shtml and http://green.blogs.nytimes.com /2010/07/20/new

- york

- mandates

- cleaner

- heating

- oil/?_r=1 and http://switchboard.nrdc.org/blogs/rkassel/governor_paterson_signs_new_la.html Pennsylvania § 123.22 . Combustion units

Distillate: 500 ppm effective July 1, 2016 Residual: 0.5% effective July 1, 2016140 #4 Oi l: 0.25% effective July 1, 2016

See: http://www.pacode.com/secure/data/0 25/chapter123/s123.22.html

Rhode Island Air Pollution Control Regulations No. 8 Sulfur Content of Fuels

Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018 Residual: 0.5% effective July 1, 2018

See: http://www.dem.ri.gov/pubs/regs/regs/air/air08_14.pdf

Vermont 5

- 221(1) Sulfur Limitations in Fuel

Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018 Residual: 0.5% effective July 1, 2018

#4 O il: 0.25% effective July 1, 2018

See: http://www.epa.gov/region1/topics/air/sips/vt/VT_Section5_221.pdf

A summary of the sulfur rules by state, with emissions reductions is provided in Table 4

- 35

. Most of these reductions (98+%) occur in the nonpt sector; a small amount of reductions occur in the ptnonipm sector (approximately 1,600 tons in 2025 and 580 tons in 2017), and a negligible amount of re ductions occur in the pt_oilgas sector. Note that these reductions are based on intermediate 2017 and 2025 inventories, those grown from 2011 to the specific future years. In addition, with implementation (effective) dates after June 30, 2017 for several o f these rules, there are more eligible sources for control in 2025 than 2017.

Table 4

- 35

. Summary of fuel sulfur rule impacts on SO 2 emissions

Year Emissions Eligible

for Control Controlled (Final)

Emissions Reductions % Reductions

2017 76,177 12,849 63,328 83.1%

2025 99,975 21,172 78,803 78.8%

4.2.4.6 Natural gas turbines NO X NSPS ( ptnonipm, pt_oilgas )

Packet: For 2017: “CONTROL_2011v6.2_2018_NOX_GasTurbines_16dec2014.txt”

For 2025: “ CONTROL_2011v6.2_2025 _NOX_Gas Turbines_16dec2014.txt”

These controls were generated based on examination of emission limits for stationary combustion turbines that are not in the power sector. In 2006, EPA promulgated standards of performance for new stationary

combustion turbines in 4 0 CFR subpart KKKK. The standards reflect changes in NO X emission control technologies and turbine design since standards for these units were originally promulgated in 40 CFR part 60, subpart GG. The 2006 NSPSs affecting NO X and SO 2 were established at le vels that bring the emission limits up

- to

- date with the performance of current combustion turbines. Stationary combustion turbines were also regulated by the NO X SIP (State Implementation Plan) Call, which required affected gas turbines to

reduce their NO X emissions by 60 percent.

Table 4

- 36 compares the 2006 NSPS emission limits with the NO X RACT regulations in selected states

within the NO X SIP Call region. The map showing the states and partial

- states in the NO X SIP Call Progra m can be found at: http://www3.epa.gov/airmarkets/progress/reports/program_basics.html . We assigned only those counties in Alabama, Michigan and Missouri as NO X SIP call ba sed on the map on page 8. The state

NO X RACT regulations summary (Pechan, 2001) is from a year 2001 analysis, so some states may have updated their rules since that time.141

Table 4

- 36

. Stationary gas t urbines NS PS a nalysis and resulting 2011v6.2 new emission rates used to

compute c ontrols

NO X Emission Limits for New Stationary Combustion Turbines

Firing Natural Gas <50

MMBTU/hr 50

- 850

MMBTU/hr >850

MMBTU/hr

Federal NSPS 100 25 15 ppm

State RACT Re gulations 5

- 100

MMBTU/hr 100

- 250

MMBTU/hr >250

MMBTU/hr

Connecticut 225 75 75 ppm

Delaware 42 42 42 ppm
Massachusetts 65* 65 65 ppm New Jersey 50* 50 50 ppm
New York 50 50 50 ppm New Hampshire 55 55 55 ppm

* Only applies to 25

- 100 MMBTU/hr

Notes: The above state RACT table is from a 2001 analysis. The current NY State regulations have the same

emission limits. New source emission rate (Fn) NO X ratio Control (%)

NO X SIP Call states plus CA = 25 / 42 = 0.595 40.5%

Other states = 25 / 105 = 0.23 8 76.2%

Regarding stationary gas turbine lifetimes, the Integrated Planning Model (IPM) financial modeling documentation lists the book life of combustion turbines as 30 years, with a debt life of 15 years, and a US MACRS Depreciation Schedule of 15 year s (EPA, 2013). This same documentation lists the book life of nuclear units at 40 years. IPM uses a 60 year lifetime for nuclear units in its simulations of unit retirements. Using the same relationship between estimated lifetime and book life for nuclear units of 1.5, the estimated lifetime for a combustion turbine would be 45 years. This is the same as an annual retirement rate of 2.2

percent.

For projection factor development, the existing source emission ratio was set to 1.0 for combustion turbines.

The new source emission ratio for the NO X SIP Call states and California is the ratio of state NO X emission
limit to the Federal NSPS. A complicating factor in the above is the lack of size information in the stationary

source SCCs. Plus, the size classificat ions in the NSPS do not match the size differentiation used in state air
emission regulations. We accepted a simplifying assumption that most industrial applications of combustion

turbines are in the 100

- 250 MMBtu/hr size range, and computed the new source emission rates as the NSPS

emission limit for 50

- 850 MMBtu/hr units divided by the state emission limits. We used a conservative new
source emission ratio by using the lowest state emission limit of 42 ppmv (Delaware). This yields a new

source emission ra tio of 25/42, or 0.595 (40.5% reduction) for states with existing combu stion turbine

emission limits. States without existing turbine NO X limits would have a lower new source emission ratio

- the uncontrolled emission rate (105 ppmv via AP

- 42) divided into 25 ppmv = 0.238 (76.2% reduction). This
control was then plugged into Equation 2 (see Section 4.2.4 ) as a function of the year

- specific projection
factor. Resulting controls greater than or equal to 1% were included in our proj ections. National Process
Heaters NSPS reductions from projected pre

- NSPS 2018 and 2025 inventories are shown in Table 4

- 37

.142

Table 4

- 37

. National by

- sector NO X reductions from Station ary Natural Gas Turbine NSPS controls

Sector Pre

- NSPS Emissions NSPS Reductions NSPS % Reductions

2018 2025 2018 2025 2018 2025

Non

- EGU Point (ptnonipm) 14,238 15,593 2,400 4,405 17% 28%

Point Oil & Gas (pt_oilgas) 70,267 75,310 8,783 24,536 12% 33%

T otal 84,505 90,903 11,183 28,941 13% 32%

4.2.4.7 Process heaters NO X NSPS ( ptnonipm, pt_oilgas )

Packet: For 2017: “CONTROL_2011v6.2_2018_NOX_Process_heaters_09dec2014.txt”

For 2025: “C ONTROL_2011v6.2_2025 _NOX_Process_heaters_09dec2014.txt”

Process heaters are us ed throughout refineries and chemical plants to raise the temperature of feed materials to meet reaction or distillation requirements. Fuels are typically residual oil, distillate oil, refinery gas, or natural gas. In some sense, process heaters can be con sidered as emission control devices because they can be used to control process streams by recovering the fuel value while destroying the VOC. The criteria pollutants of most concern for process heaters are NO X and SO 2

.

In 2011, process heaters have not b een subject to regional control programs like the NO X SIP Call, so most
of the emission controls put in

- place at refineries and chemical plants have resulted from RACT regulations
that were implemented as part of SIPs to achieve ozone NAAQS in specific are as, and refinery consent
decrees. The boiler/process heater NSPS established NO X emission limits for new and modified process
heaters. These emission limits are displayed in Table 4

- 38

.

In order to develop a relationship between t he typical process heater emission rates in 2011 compared with what the NSPS will require of new and modified sources, an analysis of the materials in the EPA docket ( EPA

- HQ

- OAR

- 2007

- 0011 ) for the NSPS was performed. This docket contained an EPA memorandum that

estimated the NO X emissions impacts for process heaters. Table 1 in that memo

— titled: Summary of Representative Baseline NO X Concentrations for Affected Process Heaters. That analysis can be used to establish an effective 2011 process heater NO X emis sion rate, although the information that EPA

- SPPD used

in the revised NO X impact estimates probably uses data from a few years before 2011. It is likely that the

data used are representative of 2011 emissions because the only wide

- ranging program that has affected process heater emission rates recently have been consent decrees, and the emission reductions associated with these agreements should have been achieved before 2011. However, the compliance schedules are

company

- specific, and differ by company, so it is difficult to make overarching conclusions about when compliance occurred.143

Table 4

- 38

. Process Heaters NSPS a nalysis and 2011v6.2 new emission rates used to compute c ontrols

NO X emission rate Existing (F e) Fraction at this rate

Average PPMV Natural

Draft Forced

Draft

80 0.4 0

100 0.4 0.5

150 0.15 0.35

200 0.05 0.1

240 0 0.05
Cumulative, weighted: Fe 104.5 134.5 119.5

NSPS Standard 40 60

New Source NO X ratio (Fn) 0.383 0.446 0.414

NS PS Control (%) 61.7 55.4 58.6

EPA states that because it “does not have much data on the precise proportion of process heaters that are forced versus natural draft, so the nationwide impacts are expressed as a range bounded by these two scenarios”. (Scen ario 1 assumes all of the process heaters are natural draft process heaters and Scenario 2 assumes all of the process heaters are forced draft process heaters.)

For computations, the existing source emission ratio (Fe) was set to 1.0. The computed (averag e) NO X emission factor ratio for new sources (Fn) is 0.41 (58.6% control). The retirement rate is the inverse of the expected unit lifetime. There is limited information in the literature about process heater lifetimes. This

information was reviewed at the time that the Western Regional Air Partnership (WRAP) developed its initial regional haze program emission projections, and energy technology models used a 20 year lifetime for most refinery equipment. However it was noted that in practice, heaters would probably have a lifetime that was on the order of 50 percent above that estimate. Therefore, a 30 year lifetime was used to estimate the effects of process heater growth and retirement. This yields a 3.3 percent retirement rate. This control was then plugg ed into Equation 2 (see Section 4.2.4 ) as a function of the year

- specific projection factor. Resulting controls greater than or equal to 1% were retained. National Process Heaters NSPS reductions from projected pre

- NSPS 2018 an d 2025 inventories are shown in Table 4

- 39

.

Table 4

- 39

. National by

- sector NO X reductions from Process Heaters NS PS controls

Sector Pre

- NSPS Emissions NSPS Reductions NSPS % Reduction s

2018 2025 2018 2025 2018 2025

Non

- EGU Point (ptnonipm) 74,129 75,936 14,200 21,306 19% 28%

Point Oil & Gas (pt_oilgas) 7,086 7,170 1,114 1,816 16% 25%

Total 81,215 83,106 15,313 23,123 19% 28%

4.2.4.8 Arizona Regional Haze controls (ptnonipm)

Packet: CONT ROL_2011v6.2_20xx_AZ_Regional_Haze_PT_24feb2015.txt

U.S. EPA Region 9 provided regional haze Federal Implementation Plan (FIP) controls for a few industrial facilities. Information on these controls are available in the Federal Register (EPA

- R09

- OAR

- 2013

- 0 588;144 FRL

- 9912

- 97

- OAR) at http://www.federalregister.com . These non

- EGU controls have implementation dates between September 2017 and December 2018 and therefore do not reduce emissions in year 2017 projection s. Year 2025 emissions are reduced at 5 smelter and cement units : NO X by 1,722 tons and SO 2 by 26,423 tons.

4.2.4.9 CISWI (ptnonipm)

Packet: CONTROL_CISWI_2011v6_22nov2013.txt

On March 21, 2011, EPA promulgated the revised NSPS and emission guidelines for Commer cial and Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the standards

re

- development included the 5

- year technology review of the new source performance standards and emission guidelines required under Section 129 of the Clean Air Act (CAA). The history of the CISWI implementation is documented here: http://www.epa.gov/ttn/atw/129/ciwi/ciwipg.html . Baseline and CISWI rule impacts associated with the CISWI rule are documented here: http://www.epa.gov/ttn/atw/12 9/ciwi/baseline_emission_reductions_memo.pdf . EPA mapped the units from the CISWI baseline and controlled dataset to the 2011 NEI inventory and because the baseline CISWI emissions and the 2011 NEI emissions were not the same, EPA computed percent reducti ons such that our future year emissions matched the CISWI controlled dataset values. CISWI controls are applied in Arkansas and Louisiana only, totaling 3,100 and 3,552 tons of SO 2 reductions in years 2017 and 2025 respectively. The reductions are greater in year 2025 because they are applied to year

- specific projected (grown)

emissions.

4.2.4.10 Data from comments on previous platforms ( nonpt, ptnonipm, pt_oilgas )

Packets:

“ CONTROL_2011v6.2_20xx_Consent_Decrees_State_comments_2018docket_pt_v2_09mar2015.txt ”

“ CONTRO L_2011v6.2_20xx_State_comments_2018docket_nonpt_15jan2015.txt ”

All remaining non

- EGU point and nonpoint controls are discussed in this section. For the nonpoint sector,

these controls are limited to comments /data

- responses on the previous (2011v6.0) emiss ions modeling platforms

, and the 2018 NODA process

. For point sources, controls include data from the 2018 NODA process as well as a concatenation of all remaining controls not already discussed. T hese controls are split into separate packets for point and nonpoint sources.

Nonpoint packet : ( CONTROL_2011v6.2_20xx_State_comments_2018docket_nonpt_15jan2015.txt ) This packet contains all nonpoint controls not already discussed in previous sections (e.g., Fuel Sulfur rules, ICI boilers) provided in response to the 2018 NODA, and is restricted to VOC controls for Delaware, Massachusetts, Pennsylvania and Virginia, with the great majority of these controls restricted to Virginia. These VOC controls cover various state programs and rules such as Auto refinishing, A dhesives and Surface Coatings. Cumulatively, these VOC controls reduce nonpoint VOC by approximately 3,900 tons in 2017 and 4,100 tons in 2025.

Point packet: ( CONTROL_2011v6.2_20xx_Consent_Decrees_State_comments_2018docket_pt_v2_09mar2015.txt )

This packet contains all point controls not already discussed in previous sections (e.g., Fuel Sulfur rules, ICI boilers) . This packet includes new controls information provided in response to the 2018 NODA as well as “legacy” controls from the 2011v6.0 emissions mod eling platform from numerous sources such as settlement145 and consent decree data gathering efforts, comments received during the Cross

- State Air Pollution Rule rulemaking process , regional haze modeling, and stack

- specific control information provided by TC EQ.

New control information from the 2018 NODA responses is primarily limited to VOC controls from several states: Delaware, Massachusetts, New Jersey, Pennsylvania and Virginia. However, we also received comments with revised compliance dates, removal of existing control information, and updated controls from local settlements. The CONTROL packet comments field provides information on the source of new control

information

, where available.

The “old” control information includes information discussed in pr eviou s emissions modeling platforms; these CONTROL packet components are discussed in Section 4.2.9 in the 2011v6.1 emissions modeling platform TSD ( EPA, 2014b).

Cumulative ptnonipm and pt_oilgas reductions to 2017 and 2025 pre

- controlled (projection facto rs already applied) from this CONTROL packet are shown in Table 4

- 40 . While these controls impact both the ptnonipm and pt_oilgas sector, almost all reductions are in the ptnonipm sector; pt_oilgas NO X is reduced by 1,300 tons in 2025, and VOC by 172 tons in 2025. Reductions to pt_oilgas for PM and SO2 are under 100 tons cumulatively.

Table 4

- 40

. Summary of remaining ptnonipm and pt_oilgas reductions

Year Pollutant Emissions

Eligible f or Control Controlled

(Final)

Emissions Reductions %

Reductions

2017 CO 5,783 764 5,019 86.8%

2017 NH3 32 0 32 100.0%

2017 NOX 85,009 42,609 42,401 49.9%

2017 PM10 4,007 1,932 2,075 51.8%

2017 PM2.5 3,582 1,759 1,823 50.9%

2017 SO2 121,735 26,155 95, 580 78.5%

2017 VOC 2,741 2,234 507 18.5%

2025 CO 6,092 712 5,380 88.3%

2025 NH3 757 251 505 66.8%

2025 NOX 88,790 43,415 45,375 51.1%

2025 PM10 4,089 1,927 2,162 52.9%

2025 PM2.5 3,642 1,748 1,893 52.0%

2025 SO2 125,367 26,755 98,612 78.7%

2025 VOC 3,145 2,326 819 26.0%

We discovered an error in year 2017 processing for the pt_oilgas sector. We created a second version of the point CONTROL packet after processing year 2025

, but prior to year 2017. For the “v2” packet, we changed the compliance d ates from January 1, 2018 to January 1, 2017 for the ptnonipm sector processing because compliance dates after June 30, 2017 would not be applied for year 2017 projections. This was applied correctly for the ptnonipm sector but the pt_oilgas sector used an older version of the point CONTROL packet that had the 1/1/2018 compliance dates. These controls, limited to information provided by TCEQ (for Texas) were therefore not applied to the pt_oilgas sector; however, this impact is assumed small146 because: 1) the impact was very small in 2025, 2) the pre

- controlled emissions in 2025 were larger than 2017 because of smaller oil and gas growth rates in 2017 than 2025, and 3) it is not clear how many of these stacks in the pt_oilgas sector were controlled by other pa ckets as a result. Regarding item 3, recall that the CoST hierarchy for applying CONTROL packet information. The intended control data in this packet is very specific (stack

- level), so it supersedes other possible less

- specifically applied control informat ion that could apply to several of these stacks, particularly RICE NESHAP (SCC/pollutant

- level) and ICI boilers (unit/SCC/pollutant

- level) control data.



### 4.2.5 Stand

- alone future year inventories (nonpt, ptnonipm)

This section discusses future year NEI non

- EGU po int and nonpoint emission inventories that were not created via CoST strategies/programs/packets. These inventories are either new to the future years because they did not exist in 2011 (e.g., new cement kilns, biodiesel and cellulosic plants), or are a co mplete replacement to the year 2011 NEI inventory in the case of portable fuel containers. N ew non

- EGU facili ties provided by South Carolina via the 2018 NODA on the 2011v6.0 platform were mistakenly omitted from both year 2017 and 2025 emissions modeling processing. Cumulatively, these new facilities would have

added approximately 200 tons of NO X , and under 100 tons of each of the remaining CAPs

.

4.2.5.1 Portable fuel containers (nonpt)

Future year inventory:

For 2017: “ pfc_2018_2011v6.2_ff10_28jan2015_v0.csv ”

For 2025: “pfc_2025_2011v6.2_ff10_28jan2015_v0.csv”

EPA used future

- year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and

modeled for EPA ’s MSAT 2 rule (EPA, 2007a). The 6 PFC SCCs are summarized below ( note that the full

SCC descriptions for these SCCs include “Storage and Transport; Petroleum and Petroleum Product Storage” as the beginning of the description).

 2501011011 Residential Portable Fuel Containers: Permeation

 2501011012 Residential Portable Fuel Containers: Evapo ration

 2501011014 Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement

 2501012011 Commercial Portable Fuel Containers: Permeation

 2501012012 Commercial Portable Fuel Containers: Evaporation

 2501012014 Commercial Portable Fuel Containers: Refilling at the Pump: Vapor Displacement

The future

- year emissions reflect projected increases in fuel consumption, state programs to reduce PFC emissions, standards promulgated in the MSAT 2 rule, and impacts of the RFS2 standards on gasolin e volatility. EPA developed year 2018 and 2025 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 includ e gas can vapor displacement, tank permeation and diurnal emissions from evaporation. Because the future year PFC inventories contain ethanol in addition to benzene, EPA developed a VOC E

- profile that integrated ethanol and benzene; see Section 0 for more

details. Note that spillage emissions were not projected and were carried forward from 2011. We received comments and PFC projections data for year 2018 from MARAMA as part of the 2011v6.0 emissions modeling platform NODA (see: http://www.regulations.gov/#!docketDetail;D=EPA

- HQ

- OAR

- 2013

- 0809 ). We used these projection factors to project MARAMA state PFC emissions to year 2018 and then projected to year 2025 using the existing ratios 2025 to 2018 PFC emissions provided by OTAQ. We used commercial software to blend the MARAMA projection factors and existing PFC inventories to create year 2018 and147 2025 PFC inventories for this platform. A summary o f the resulting PFC emissions for 2011, 2018 (used for 2017) and 2025 are provided in Table 4

- 41

.

Table 4

- 41

. PFC e missions for 2011, 201 8 and 2025 [tons]

Emissions Difference % Cha nge
2011 2018 2025 2018 2025 2018 2025

VOC 171,963 32,158 37,617

- 139,805

- 134,347

- 81.3%

- 78.1%

Benzene 742 654 758

- 88 15

- 11.9% 2.1%

Ethanol 0 3,719 4,448 n/a

4.2.5.2 Biodiesel plants (ptnonipm)

New Future year inventory: “Biodiesel_Plants_2018_ff10”

EPA OTAQ developed an inventory of biodiesel plants for 2018. Plant location and production volume data

came from the Tier 3 proposed rule. 36

, 37 The total volume of biodiesel came from the AEO 2013 early release, 1.3 BG for 2018. To reach the total volume of bio diesel, plants that had current production volumes

were assumed to be at 100% production and the remaining volume was split among plants with planned production. Once facility

- level production capacities were scaled, emission factors based on soybean oil

f eedstock were applied. These emission factors in Table 4

- 42 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

- 43 provides the 2018 biodiesel plant emissions estimates. Since biofuels were not projected to change significantly between 2017 and 2025 the year 2018 inventory was used for both year 2017 and year 2025. Emissions in 2011 are assumed to be near zero, and HAP emissions in 2017 and 2025 are nearly zero.

Table 4

- 42

. Emission Fac tors for Biodiesel Plants (Tons/Mgal )

Pollutant Emission Factor

VOC 4.3981E

- 02

CO 5.0069E

- 01

NO X 8.0790E

- 01

PM 10 6.8240E

- 02

PM 2.5 6.8240E

- 02

SO 2 5.9445E

- 03

NH 3 0
Acetaldehyde 2.4783E

- 07

Acrolein 2.1290E

- 07

Benzene 3.2458E

- 08

1,3

- Butadiene 0
Formaldehyde 1.5354E

- 06

Ethanol 0

36 US EPA 2014.Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA

- 420

- RD

- 143

- 0052

. 37 Cook, R. 2014. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for Tier 3 Final

Rule. Memorandum to Docket EPA

- HQ

- OAR

- 20 10

- 0 162.148 Table 4

- 43

. 2018/2025 biodiesel p lant e missions [tons]

Pollutant 20 18

CO 649

NO X 1048

PM 10 89

PM 2.5 89

SO 2 8

VOC 57

4.2.5.3 Cellulosic plants (nonpt)

Ne w Future year inventories : Primary inventory: “2018_cellulosic_inventory” New Iowa inventory: “ cellulosic_new_Iowa_plants_from2018docket_2011v6.2_ff10_28jan2015 ”

Development of primary inventory Depending on available feedstock, cellulosic plants are likel y to produce fuel through either a biochemical process or a thermochemical process. EPA developed county

- level inventories for biochemical and thermochemical cellulosic fuel production for 2018 to reflect AEO2013er renewable fuel volumes.

Emissions factors for each cellulosic biofuel refinery reflect the fuel production technology used rather than the fuel produced. Emission rates in Table 4

- 44 and Table 4

- 45 were used to develop cellulosic plant

inventori es. 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

. Because the future year cellulosic inventory contains ethanol, a VOC E

profile that integrated ethanol was used, see Sections 0 and 3.2.1.3 for more details.

Plants were treated as area sources spread across the entire area of whatever county they we re considered to

be located in. Cellulosic biofuel refinery siting was based on utilizing the lowest cost feedsto ck, accounting for the cost of the feedstock itself as well as feedstock storage and the transportation of the feedstock to the cellulosic biofuel refinery. The total number of cellulosic biofuel refineries was projected using volumes from AEO2013 (e arly r elease). The methodology used to determine most likely plant locations is described in Section 1.8.1.3 of the RFS2 RIA (EPA, 2010a). Table 4

- 46 provides the year 2018 cellulosic plant

emissions estimates. Since biofuels were not p rojected to c hange significantly between 2017 and 2025 the year 2018 inventory was used for both year 2017 and year 2025.

Table 4

- 44

. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/ RIN gal lon )

Cellulosic Plant Type VOC CO NO X PM 10 PM 2.5 SO 2 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 0149 Table 4

- 45

. Toxic Emission Factors for Cellulosic Plants (Tons/ RIN gal lon )

Plant Type Acetaldehyde Acrolein Benzene 1,3

- Butadiene Formaldehyde Ethanol

Thermochemical 2.95E

- 08 1.27E

- 09 9.61E

- 10 0 5.07E

- 09 2.09E

- 07

Biochemical 3.98E

- 07 1.11E

- 08 1.39E

- 08 0 2.28E

- 0 8 6.41E

- 07

Table 4

- 46

. 201 7 /2025 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 54 7

SO 2 819

VOC 414

Development of new Iowa inventory

Iowa DNR (Department of Natural Resources) , via the 2018 NODA comments ( see docket # EPA

- HQ

OAR

- 2013

- 0809 under http://www.regulations.gov

. ), provided inform ation on new cellulosic ethanol
capacity information for three facilities. Emissions for these facilities were computed using the emission
factors previously discussed in Table 4

- 44 and Table 4

- 45 . The re sulting new facilities and NO X emissions

, used for both years 2017 and 2025, are provided in Table 4

- 47

. Note that these facilities are in a nonpoint
inventory because latitude

- longitude coordinates were not available.

Table 4

- 47

. New cellulosic plants NOX emissions provided by Iowa DNR

.

FIPS County Facility Name Approximate

Production Capacity (Mgal/yr) NO X

Emissions

19093 Ida Quad County Corn Processors' Adding Cellulosic Ethanol ( ACE) 2 26

19147 Palo Alto POET

- DSM Project Liberty 25 329

19169 Story DuPont Cellulosic Ethanol 30 394150

4.2.5.4 New cement plants (nonpt, ptnonipm)

Point Inventories: For 2017: “cement_newkilns_year2018_from_ISIS2013_NEI2011v1”

For 2025: “ cement_newkilns_year_2 025_from_ISIS2013_NEI2011v1_08nov2013_v0.csv ”

Nonpoint Inventories:

For 2017: “ cement_newkilns_year_2018_from_ISIS2013_NEI2011v1_NONPOINT _v0.csv”

For 2025: “ cement_newkilns_year_2025_from_ISIS2013_NEI2011v1_NONPOINT_12nov2013_v0.csv ”

As discussed in Sectio n 4.2.3.9 , the ISMP model, was used to project the cement manufacturing sector to future years. This section covers new ISMP

- generated kilns that did not exist in the 2011 NEI. For kilns that were new in 2018 and/or 2025, EPA u sed two different approaches f or modeling. For kilns already permitted, known locations (coordinates) allowed us to p rocess these as point sources. However, the ISMP model also created “generic” kilns in specific geographically strategic locations (countie s) to cover the need for increased produc tion/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 ( ISMP ) artifact in one grid cell. These nonpoint source kilns were then spatially allocated based on industria l land activity in the county. A list of all new point and nonpoint inventory cement kilns in 2018 and 2025 are provided in Table 4

- 48

. Note that as production continues to increase beyond 2018, that additional new kilns are needed in 2025. Not shown here is a cement kiln in Washington (King county) that ISMP generates but according to Washington Department of Ecolo gy, was not correct; th is kiln w as thus removed from our emissions modeling platform.

Table 4

- 48

. Locations of new ISMP

- generated cement kilns

Year(s) ISMP ID Permitted? Facility Name FIPS State County

Both F LNEW2 Y Vulcan 12001 FL Aluchua

2025 FLNEW1 Y American Cement Company 12119 FL

Both GANEW1 Y Houston American Cement 13153 GA Houston

Both NCNEW1 Y Titan America LLC 37129 NC New Hanover

Both NewGA2 N n/a 13153 GA Houston

Both NewPA8 N n/a 42011 PA B erks

Both NewSC1 N n/a 45035 SC Dorchester

Both NewTX1 N n/a 48029 TX Bexar

Both NewTX10 N n/a 48091 TX Comal

2025 NewAZ2 N n/a 04025 AZ Yavapai

2025 NewCO2 N n/a 08043 CO Freemont

2025 NewOK2 N n/a 40123 OK Pontotoc

2025 NewPA8 N n/a 42095 PA North ampton

2025 NewTX4 N n/a 48029 TX Bexar

2025 NewTX5 N n/a 48091 TX Comal

2025 NewTX12 N n/a 48209 TX Hays

For all ISMP future year emissions, PM 10 is assigned as 0.85 of total PM provided by ISMP , and PM 2.5 is assigned as 0.1 5 of total PM. All new ISMP

- generated kilns, point and nonpoint format, are assigned as Precalciner kilns (SCC=30500623). While ISMP provides emissions for mercury, EPA did not retain these in151 our modeling. Table 4

- 49 shows the magnitude of the new ISMP

- bas ed cement kilns

. We split out ISMP

based new kilns in future years with permitted (as of August 2013) kilns modeled as point sources and “generic” ISMP

- generated kilns as nonpoint sources.

Table 4

- 49

. ISMP

- ge nerated new permitted and non

- permitted emissions

New kilns in 2018 New kilns in 2025 Total New ISMP Emissions

Permitted (point) ISMP

generated (nonpoint) Permitted

(point) ISMP

generated (nonpoint) 2018 2025

NO X 3,751 5,697 4,795 13,673 8,546 19,370

PM 2.5 8 24 11 57 19 81

SO 2 1,775 13 2,004 30 3,779 43

VOC 91 2,969 117 7,115 208 10,084



### 4.3 Mobile source projections

Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2017 and 2025 using a combination of the MOVES 20 14 and the NMIM m odels. The 2017 and 2025 onroad emissions account for changes in activity data and the impact of on

- the

- books rules including some of the recent regulations such as the Light Duty Vehicle GHG Rule for Model

- Year 2017

- 2025, and the Tier 3 M otor
Vehicle Emission and Fuel Standards Rule ( 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 Nationa l Low Emissions Vehicle (LEV) and Ozone Transport Commission (OTC) LEV regulations ( http://www.epa.gov/otaq/lev

- nlev.htm ) , local fuel programs, and Stage II refueling control programs

. Table

4

- 1 provides references to many of these programs.

Nonroad mobile emissions reductions for these years include reductions to various nonroad engines such as diesel engines and recreational marine engine types (pleasure craft) , fuel sulfur con tent, and evaporative

emissions standards.

Onroad mobile sources are comprised of several components and are discussed in the next subsection ( 4.3.1 ). Monthly nonroad equipment mobile emission projections are discussed in subse ction 4.4

. Locomotives and Category 1 and Category 2 commercial marine vessel (C1/C2 CMV) projections were discussed in Section

4.2.3.3 , and Category 3 (C3) CMV project ions were discussed in Section 4.2.3.4

.



### 4.3.1 Onroad mobile (onroad)

The onroad emissions for 2017 and 2025 use the same SMOKE

- MOVES system as for the base year (see

Sections 2.3.1 ). Meteorology, speed, spatial surrogates and tempora l profiles , representative counties, and

fuel months were the same as for 20 11

. EPA had already performed many of t he modeling steps to produce

2018 emissions

, when the NRDC v. EPA 38 decision resulted in 2017 being the last full ozone season to
consider f or moderate nonattainment designations with respect to the 2008 ozone NAAQS. To

accommodate this change within the time available , a two

- step process was undertaken for onroad (and nonroad): (1) estimate the emissions for 2018 at a full detailed level (se e Sections 4.3.1.1 and 4.3.1.2 ) , and (2) adjust the 2018 emissions to 2017 (see Section 4.3.1.3 )

.

38 D.C. Circuit Court’s decision in NRDC v. EPA

, 777 F.3dFD 456, 469 (D.C. Cir. 2014).152

4.3.1.1 Future activity data

E stimate s of total national Vehicle Miles Travell ed (VMT) in 20 18 and 2025 came from DOE's Annual Energy Outlook (AEO) 201 4 ( http://www.eia.gov/forecasts/aeo/ ) transportation projections, specifically the

reference case that was released on May 7

, 201 4

. Trends were developed by calculating ratios between 2011

AEO and 2018 AEO 39 estimates and applying the trends to the 2011NEIv 2 VMT

. These ratios were developed for light versus heavy duty and for four fuel types: gasolin e
, diesel , E

- 85, and CNG

. This same method was used to project 2011NEIv 2 VMT to 2025 with the incorporation of 2025 AEO estimates

. The projection factors, the national 2011NEIv 2 VMT (“VMT_2011”) by broad vehicle and fuel type , and the default future VMT (“VMT_2018 ” and VMT_2025” ) are show i n Table 4

- 50

.

Table 4

- 50

. Projection factors for 2018 and 2025 VMT (in millions of miles) 40

Classification MOVES source types VMT 2011 Ratio 2018 VMT 2018 Ratio 2025 VM T 2025

LD gas 11,21,31,32 2,565,979 1.0154 2,605,570 1.0466 2,685,656

HD gas 42,43,51,52,53,54 21,530 1.0598 22,817 1.0880 23,425

HHD gas 61 10 0.7997 8 0.8414 9

LD diesel 21,31,32 41,645 3.0647 127,631 6.3858 265,938

HD diesel 41,42,43,51,52,53,54 58 ,995 1.1959 70,551 1.3621 80,357

HHD diesel 61,62 131,706 1.1709 154,216 1.3296 175,120

Bus CNG 42 303 2.1065 639 3.7099 1,125

LD E

- 85 21,31,32 143,600 1.8800 269,963 2.3570 338,463

Total N/A 2,963,768 N/A 3,251,394 N/A 3,570,094

In the above table, light duty (LD) includes passenger cars, light trucks, and sometimes motorcycles, heavy duty (HD) includes buses and single unit trucks, and heavy

- heavy duty (HHD) includes combination trucks. The specifi c MOVES source type codes are listed above. These national SCC 6 ratios were applied to the

2011NEIv 2 VMT to create an EPA estimate of 2018 and 2025 VMT at the county, SCC level.

Two additional steps were incorporated into the VMT projections. Firs t , a set of states provided 2018 VMT projections 41 : AL, CT, GA, ME, MD, MA, MI, MO, NV, NY, NJ, NC, UT, VT, VA, and WY 42 . The state provided VMT projections were used over the EPA default projections in those specific counties. Second, EPA adjusted the national LD ratios so that it would reflect regional diffe rences in growth rate. EPA analyzed LD VMT and corroborated that it had a high correlation with human population. Therefore, if a region has strong human population growth in the future, it will likely have larger VMT growth than the national average. T o take account of this spatial difference in growth, EPA used human population to adjust

39 By “2011 AEO,” “2018 AEO,” and “2025 AEO,” this refers to the AEO2014’s estimates of national VMT in those specific calendar years.

40 Note: these are the default VMT values. The actual VMT used for 2018 and 2025 has slightly different VMT v alues. This is due to some states providing VMT projections for 2018. In addition, the LD ratios were further adjusted to take into account of high vs low growth of human population (discussed below). On average, the LD ratios match those in this table. For the actual VMT, see the inventory packaged with the cases.

41 Although some states provided 2018 specific VMT projections, these projections were not consistently available for 2025. Therefore, state projections were not incorporated into the 2025 mo deling.

42 For many of these states, we used the county total from the state data and distributed those totals to EPA’s SCCs based on default projected VMT. For MI, SEMCOG provided the Detroit projections and the rest of the counties came from the state. For MO, the state provided the 5 counties around St Louis. For Nevada, EPA received projections only for Clark County. For GA, the state agreed with our default projection method but they wanted to use GA provided human population projections for distrib uting the LD VMT growth rates to counties. They provided the human population for the 21 Atlanta counties. For the remaining counties, GA asked to use EPA defaults.153 the national LD VMT growth rate so that on average the growth rate matched the national average, but any specific county growth rate was adjusted by the human populati on growth for that county:

VMTprojFacto r

sc = AEOprojFacto r s ∗ ( 1 + D ( ( h umanProjFacto r c natl h umanProjFactor )

− 1 ) )

where

◦ s = source type /fuel

◦ c = county

◦ VMTprojFactor = county VMT projection factor (by source/fuel)

◦ AEO projFactor = national VMT projection factor from AEO (by source/fuel)

◦ humanProjFactor = human projection factor for the county (year specific)

◦ natlhumanProjFactor = national human projection factor (year specific)

◦ D = damping factor, 0 = no county adjustme nt, 1 = full county variation

The specific value of D used for EPA projections was 0.5. This was based on an analysis of the growth of LD vehicles over time as compared to human population, which was found to be about 0.5 vehicles per

person. The LD grow th rates will vary by county, fuel, and year. The range of these growth rates are shown

in Figure 4

- 4

.

Vehicle population (VPOP) was developed by creating VMT/VPOP ratios from the 2011NEIv2 VMT and 2011NEIv2 VPOP at the county, f uel and vehicle type (SCC6) level. These ratios were applied to the 2018 VMT to create a 2018 VPOP. This process was repeated using the 2025 VMT to create the 2025 VPOP.

Hoteling (HOTELING) was developed by creating VMT/HOTELING ratios from the 2011 N EIv2 VMT and 2011 NEIv2 HOTELING at the county level. For these ratios, the VMT was limited to combination long

haul trucks (SCC6 220262). The HOTELING was the total of auxiliary power units (APU) and extended idle (EXT). These ratios were applied to th e 2018 VMT to create a 2018 HOTELING. To get the APU split,

16% of HOTELING was assumed to be APU in all counties. This is consistent with MOVES2014 default split for APU for calendar year 2018. This process was repeated using the 2025 VMT to create the 2025 HOTELING. An APU split of 25% was used in 2025.154 Figure 4

- 4

. Light Duty VMT growth rates

4.3.1.2 Set up and Run MOVES to create EF s
Emission factor tables were created by running SMOKE

- MOVES using the sam e procedures and mo dels as

described for 2011 (see the 2011NEIv 2 TSD and Section 2.3 )

. The same meteorology and the same representative counties were used. Changes between 2011 and future years (201 8 or 2025) are predominantly activity data

, fuels , nationa l and local rules, and age distributions

. Age (i.e., model year) distributions were projected forward using the methodology described in the MOVES activity report (EPA, 2015), although

s ome states supplied age distributions in their CDBs. F leet turnover r esult ed in a greater fraction of newer vehicles meeting stricter emission standards. The similarities and differences between the two runs are

described in Table 4

- 51

.

Table 4

- 51

. Inputs for MOVES runs for 2018 and 2025

Element 2017 2025

Code MOVES 20141021cb6 MOVES 20141021cb6

Default

database movesdb20141021cb6 movesdb20141021cb6

VMT and

VPOP 2011 NEIv2 2011 NEIv2

Hydrocarbon speciation Done inside MOVES Done insi de MOVES

Fuels movesdb20141021_fuelsupply movesdb20141021_fuelsupply

CA LEVIII ca_standards_SS_ 20140903

(16 states) ca_standards_SS_ 20140903 (16 states)

The following states were modeled as having adopted the California LEV III program (see Table 4

- 52 ) :

0 1 2 3 4 5 6 7 8 9

2018 gas 2025 gas 2018 diesel 2025 diesel 2018 e85 2025 e85 Growth Rate 

## Range of LD VMT growth rates155 Table 4

- 52

. CA LEVIII program states

FIPS State Name

06 California

09 Connecticut

10 Delaware

23 Maine

24 Maryland

25 Massachusetts

34 New Jersey

36 New Yo rk

41 Oregon

42 Pennsylvania

44 Rhode Island

50 Vermont

53 Washington

Fuels were projected into the future using estimates from the AEO2014 ( http://www.eia.gov/forecasts/aeo/ ), release date May 7 th 2 01 4 , as well as fuel properties changing as part of the Tier 3 Emissions and Fuel Standards Program ( http://www.epa.gov/otaq/tier3.htm ) . The AEO201 4 projection includes market shares of
E10, E15, and E85 in 201 8, as well as biodiesel market shares up to B5 (note that these values do not assume full implementation of the RFS2 program). The regional fuel properties and renewable volumes in 2011 were projected to 201 8 in order to preserve the regional variation present in these fuel supplies, with total fuel volumes aligned to those in the AEO201 4

. Due to similarities between fuel volumes an in order to simplify analysis, t he 2025 fuels were identical to the 201 8 fuels. For details on the 201 8 and 2025 speciat ion of

onroad mobile source emissions , which is dependent on the fuels, see Section 3.2.1.4

.

4.3.1.3 National and California adjustments

A set of adjustments were done in SMOKE

- MOVES to create 201 7 and 2025 emissions: 1) refueling

, 2) California emissions, and 3) 2017 adjustments

.

The first set of adjustment factors w as for refueling. This uses the same approach as was used in 2011 (see

the Section 2.3.1 for details) to take account of the few counties in Colorado that provided point source gas refueling emissions. These adjustment s essentially zero out the MOVES

- based gasoline refueling emissions (SCC 2201*62) in these counties so that the point estimates will be used instead

.

The second set of adjustment factors w as used to incorporate future year emissions provided by Cali fornia

. T he same approach as was used in 2011 was used to match the emissions totals provided by CARB (see

Section 2.3 .1 ). The o nly differences between the 2011 approach and that appli ed for 201 7 are that the latter

uses the 201 7 emissions provided by CARB and the 2018 EPA SMOKE

- MOVES output to apportion and temporalize the emissions

. For 2025, the process was repeated using 2025 emissions provided by CARB a nd

the 2025 SMOKE

- MOVES outpu t for the EPA estimates

. The provided CARB emissions were produced from working draft versions of EMFAC2011

- LD and EMFAC2011

- HD and include the following heavy duty regulations: chip reflash, extended idling, public fleet, trash trucks, drayage trucks, an d trucks and

buses. It does not include the GHG/smartway regulations for trucks, or the low carbon fuel standard. The

California adjustment factors are by county, comparison SCC6 , pollutant and impact all processes (RPD,
RPV, RPH, and RPP ).156 The third set of adjustment factors adjusted 2018 emissions to 2017 levels. EPA ran MOVES2014 in inventory mode at the national level to create a 2017 i nventory and a 2018 inventory. Each inventory was then divided by the respective year’s VMT to create a 2017 emissio ns rate and a 2018 emissions rate. EPA calculated ratios of 2017 to 2018 by SCC and pollutan t and applied them nationally at the SCC and pollutant level to adjust the SMOKE

- MOVES results from 2018 to 2017. Separate ratios were calculated for the

winter f uel months and the summer fuel months. The exception to this adjustment was in California, where CARB emissions were used to adjust the emissions (see above).

Because these adjustment factors are multiplicative, a single set of adjustment factors was c reated by multiplying the three adjustment factors together while taking care to match process (RPD, RPV, RPH, or RPP), pollutant, SCC, and county. Movesmrg applied the composite adjustment factor file (CFPRO) to

compute 201 7 or 2025 emissions that incorpo rated the appropriate adjustments for that case

, county, SCC

, and process

.



### 4.4 Nonroad mobile source projections (nonroad)

The projection of locomotives and Category 1 and 2 commercial marine vessels to 201 7 and 2025 is

described in 4.2.3.3

. The projection of the larger Category 3 commercial marine vessels is described in

Section 4.2.3.4

. Most of the remaining sources in the nonroad sector are projected by running the NMIM model with fuels and vehicle p opulations appropriate to 2018 and 2025 ; this section describes the projection

of these sources. Similar to onroad, EPA adjusted the 2018 modeled emissions to 2017 levels.

This sector includes mont hly exhaust, evaporative and refueling emissions from nonr oad engines (not including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California

and Texas

. NMIM provides nonroad emissions for VOC by three emission modes: exhau st, evaporative and refueling.

With the exception o f 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

u sed for 2011. Specifically, NMIM utilized NONROAD2008a including future

- year equipment population estimates, control programs to the year s 2018 and 2025 , and inputs were either state

- supplied as part of the

2011NEIv1 and 2011NEIv2 process or national level inputs

. Fuels for 2018 and 2025 were assumed to be E10 everywhere for n onroad equipment. The fuels were developed from the MOVES fuels, which in turn were developed to be consistent with AEO2013 projections for 2018 and 2025 . The AEO version is slightly older because nonroad projections were not updated for the 2011NEIv2. Th e databases used in the 2018 run were NMIM county database “ NCD20130731_nei2018dv1 ” and fuels database

“ tier3frm2018ctrlfuels_03152013_ e10fuelsNMIM .” The 2018 and 2025 emissions account for changes in activity data (based on NONROAD model default growth e stimates of future

- year equipment population )

and changes in fuels and engines that reflect implementation of national regulations and local control

programs that impact each year differently due to engine turnover

. For details on the 2018 and 2025 speciat ion of nonroad, see Section 3.2.1.4

.

The version of NONROAD used was the current public release, NR08a, which models all in

- force nonroad

controls. The represented 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”).157

 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 even ing refueling on Ozone Action Days

.

To adjust 2018 emissions to 2017 levels, EPA ran NONROAD at the national level to create a 2017 inventory and a 2018 inventory. EPA calculated ratios of 2017 to 2018 by SCC and pollutant. These ratios were applied nati onally at the SCC , mode, and pollutant level to adjust the NONROAD results from 2018 to 2017. Separate ratios were calculated for the winter fuel months versus the summer fuel months. The exception to this adjustment was in California, where CARB emissio ns were used to adjust the emissions (see below).

California and Texas nonroad emissions

Similar to the 2011 base year nonroad mobile, NMIM was not used to generate future

- year nonroad
emissions for California, other than for NH 3

. EPA used NMIM for Califo rnia future nonroad NH 3 emissions

because CARB did not provide these data for any nonroad vehicle types. For the rest of the pollutants, the

CARB

- supplied 2017 and 2025 nonroad annual inventor ies were distributed to monthly emissions values by using the re spective year 2018 and 2025 NMIM monthly inventories to compute monthly ratios by county, SCC7, mode and pollutant, which was consistent with the approach in 2011 (see Section 2.4.3 )

. Some adjustments to the CARB inventory were needed to co nvert 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 ) 43

. The CARB nonroad emissions

include nonroad rules reflected in the December 2010 Rulemaking Inventory ( http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf ) and those in the March 2011 Rule Inventory, the Off

- Road Construction Rule Inventory for “In

- Use Diesel”.

For Texas, EPA combined Texas’ submitted estimates for 2011 with EPA projections of nonroad emissions

into 201 7 and 2025

. Conceptually, EPA used the trend of 2011 to 201 7 or 2025 based on EPA’s estimates to project Texa s’ submitted emissions for 2011. Specifically, projections were based on state

- wide SCC7, mode, poll ratios 44 of 201 7 and 2025 NMIM to 2011 NMIM. These ratios were then applied to Texas’

submitted 2011 nonroad emissions, which had already been distributed t o a monthly inventory (see Section

2.4.3 ), to create 2018 and 2025 monthly nonroad inventor ies

.



### 4.5 “Other Emis s ions”: Offshore Category 3 commercial marine vessels and drilling platforms, Canada and Mexico (othpt, othar, and

otho n)

As described in Section 2.5

, emissions from Canada, Mexico, and non

- U.S. offshore Category 3 Commercial Marine Vessels (C3 CMV) and drilling platforms are included as part of three emissions modeling sectors: othpt, othar, and othon. For oil drilling p latforms, EPA used emissions from the 2011NEIv2 point source inventory for 2011 and both future years. Environment Canada did not provide any future

- year emissions that were consistent with the 2010 base year emissions, therefore e missions for Canada wer e not projected to

43 In addition, airport equipment was removed from CARB’s inventory because these sources were modeled elsewhere.

44 These ratios were initially attempted by county/SCC7/mode/pollutant, but due to significantly different distributions of cert ain source types between EPA and TCEQ’s emissions, this created unreasonable growth in certain areas. Th e above approach was used except in the following, relatively limited conditions. If a state/SCC7/mode/pollutant was in EPA 2017 and 2025 emission s but not in EPA’s 2011 emissions, 2017 and 2025 EPA emissions were used in the final inventory. If a state/S CC7/mode/pollutant was in TCEQ’s 2011 emissions but was not in EPA’s 2017 and 2025 emissions, then state/SCC3/mode/pollutant ratios were used to project to 2017 and 2025.158 future years and are the same as tho se used in the 2011 base case

. E missions for Mexico are based on the Inventario Nacional de Emisiones de Mexico, 2008 projected to year s 2018 and 2025 (ERG, 2014a)

.

As discussed in Section 2.5.1, the ECA

- IMO

- based C3 CMV emissions outside of U.S. state water s are processed in the othpt sector . This enables shipping lanes to be represented and for emissions to be treated

as elevated sources

. These C3 CMV emissions include those assigned to the Exclusi ve 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 emissi ons

. The projection factors for the othpt C3 CMV emissions vary by geographic a nd region as shown in Table 4

- 8

.159 

## 5 Emission Summaries

The following tables summarize emissi ons differences between the 2011 evaluation case

, the 2017 base case

, and the 2025 base case . These summaries are pr ovided at the national level by sector for the contiguous U.S. and for the portions of Canada and Mexico inside the smaller 12km domain (12US2) discussed in Section

3.1 . The afdust sector emissions represent the summaries afte r 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 sector totals are post

- SMOKE

- MOVES totals , representing air quality model

ready emission totals, and include CARB emissions for California. The c3marine sector includes U.S.

emissions with in state waters only; these extend to roughly 3

- 5 miles offshore and include s CMV emissions

at U.S. ports. “ Offshore to EEZ ” represents CMV emissions that are within the (up to) 200 nautical mile Exclusive Economic Zone (EEZ) boundary but are outside of U.S. state waters along with the offshore oil platform emissions from the NEI . Finally, the “ Non

- US SECA C3 ” represents all non

- U.S. and non

- Canada emissions outside of the (up to) 200nm offshore boundary, including all Mexican CMV emissions. Canadian

CMV emissions are included in the othar sector.

National emission totals by air quality model

- ready sector ar e 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”. Table 5

- 2 provides national emissions tot als by sector for C APs in the 2017 base case. Table 5

- 3 provides national emissions totals by sector for CAPs in the 2025 base case.

Table 5

- 4 provides national

- by sector emission summaries for CO for a ll the ca ses: 2011 evaluation case,

2017 base case, and 2025 base case, with pe rcent change from 2011 to 2017 and 2011 to 2025. Table 5

- 5 through Table 5

- 10 provide the same summaries for NH 3 , NO X , PM 2.5 , PM 10 , SO 2 and VOC, resp ectively.

Note that the same fire emissions are used in all cases.160

Table 5

- 1

. National by

- sector CAP emissions summaries for the 2011 evaluation case

Sector CO NH 3 NO X PM 10 PM 2. 5 SO 2 VOC

afdust _adj 6,735,072 923,938

ag 3,515,198
agfire 956,243 3,321 42,767 140,728 93,959 16,224 74,783

c1c2rail 180,579 511 1,075,217 35,359 33,019 12,609 48,281

nonpt 1,645,989 94,242 720,454 491,825 404,258 275,655 3,672,249
np_oilgas 62 7,609 0 647,169 16,843 15,333 18,306 2,556,649

nonroad 13,951,020 2,627 1,630,301 162,417 154,657 4,031 2,024,419

onroad 26,313,827 120,805 5,719,979 367,667 194,690 29,665 2,709,470

c3marine 12,532 68 131,382 10,168 9,043 86,373 5,149

ptprescfire 10,0 63,500 161,999 167,599 1,060,130 900,546 83,082 2,313,404

ptwildfire 10,499,197 167,331 165,799 1,111,857 943,717 82,691 2,374,690

ptegu 777,902 24,925 2,088,233 281,614 206,884 4,662,717 36,336

ptnonipm 2,312,304 66,192 1,222,456 478,885 322,142 1,057, 242 802,988
pt_oilgas 235,162 5,947 509,856 14,585 13,935 66,577 164,098

rwc 2,517,844 19,693 34,436 381,476 381,252 8,954 442,541

Con U.S. Total 70,093,709 4,182,859 14,155,649 11,288,626 4,597,374 6,404,125 17,225,056

Off

- shore to EEZ* 175,353 185 89 9,986 26,247 24,544 139,169 81,602

Non

- US SECA C3 17,184 0 202,432 17,206 15,828 127,579 7,294

Canada othafdust 779,674 112,523

Canada othar 3,015,606 326,610 361,896 158,996 131,114 70,272 886,456

Canada othon 3,032,005 18,653 345,664 17,628 12,216 1,701 178,431 Canada othpt** 496,083 13,069 266,912 70,005 29,165 544,502 129,119

Mexico othar 277,810 163,040 182,869 98,812 50,158 10,679 410,734

Mexico othon 3,361,123 7,978 243,714 2,425 1,624 4,919 319,353 Mexico othpt 153,061 3,706 286,30 3 55,162 42,105 453,466 53,813

Non

- US Total 10,528,225 533,241 2,789,775 1,226,156 419,276 1,352,287 2,066,802 * “Offshore to EEZ” includes both the offshore point emissions, and the “Offshore to EEZ” c3marine emissions161

Table 5

- 2

. National by

- sector CAP emissions summaries for the 2017 base case

Sector CO NH 3 NO X PM 10 PM 2. 5 SO 2 VOC
afdust_adj 7,093,387 964,080

ag 3,577,123

agfire 956,243 3,321 42,767 140,728 93,959 16,224 74,783

c 1c2rail 194,109 513 915,797 25,970 24,245 1,161 34,528

nonpt 1,673,888 94,816 734,072 494,930 416,334 104,585 3,511,392
np_oilgas 677,087 0 687,307 21,112 19,420 37,505 3,164,559

nonroad 12,371,786 2,857 1,129,911 113,568 107,267 1,856 1,422,907

onroad 15,607,769 88,774 3,150,751 302,041 124,930 14,985 1,507,880

c3marine 15,600 68 131,691 1,729 1,531 3,904 6,429

ptprescfire 10,063,500 161,999 167,599 1,060,130 900,546 83,082 2,313,404
ptwildfire 10,499,197 167,331 165,799 1,111,857 943,717 82,691 2,3 74,690

ptegu 820,402 41,275 1,547,987 279,931 212,913 1,462,200 40,433

ptnonipm 2,357,260 66,496 1,192,297 480,114 325,400 807,093 814,400
pt_oilgas 216,480 5,887 411,055 14,013 13,379 69,034 165,559

rwc 2,504,587 19,546 35,507 384,041 383,807 8,814 43 7,984

Con U.S. Total 57,957,907 4,230,006 10,312,541 11,523,551 4,531,527 2,693,133 15,868,951

Off

- shore to EEZ* 186,196 185 855,139 10,237 9,768 9,945 86,093

Non

- US SECA C3 22,335 0 239,528 22,313 20,527 165,925 9,462

Canada othafdust 779,674 11 2,523

Canada othar 3,015,606 326,610 361,896 158,996 131,114 70,272 886,456

Canada othon 3,032,005 18,653 345,664 17,628 12,216 1,701 178,431 Canada othpt** 496,083 13,069 266,912 70,005 29,165 544,502 129,119

Mexico othar 299,511 166,412 199,951 1 03,086 53,005 11,930 470,910

Mexico othon 2,068,792 10,044 164,555 2,565 1,697 1,943 218,848 Mexico othpt 186,436 4,904 348,544 64,263 49,790 363,306 68,970

Non

- US Total 9,306,964 539,877 2,782,187 1,228,767 419,805 1,169,524 2,048,288162

Table 5

- 3

. National by

- sector CAP emissions summaries for the 2025 base case

Sector CO NH 3 NO X PM 10 PM 2. 5 SO 2 VOC
afdust_adj 7,621,008 1,023,169

ag 3,610,283
agfire 956,243 3,321 42,767 140,728 93, 959 16,224 74,783

c1c2rail 215,196 514 679,934 17,679 16,507 888 24,498

nonpt 1,695,259 94,957 746,389 511,976 431,038 95,195 3,426,386
np_oilgas 721,546 0 717,783 25,266 22,439 38,913 3,478,495

nonroad 13,299,524 3,543 794,770 77,615 72,788 2,088 1,17 9,395

onroad 10,444,934 85,521 1,707,692 280,816 83,019 13,759 904,713

c3marine 20,988 68 104,503 2,335 2,075 5,272 8,687

ptprescfire 10,063,500 161,999 167,599 1,060,130 900,546 83,082 2,313,404
ptwildfire 10,499,197 167,331 165,799 1,111,857 943,717 82,691 2,374,690

ptegu 903,766 46,007 1,435,523 274,544 208,678 1,471,933 41,795

ptnonipm 2,440,880 66,698 1,229,333 487,373 333,048 800,678 826,684
pt_oilgas 254,080 5,922 464,256 16,859 16,381 69,560 190,788

rwc 2,323,717 18,150 34,722 355,849 355,59 9 7,606 408,336

Con U.S. Total 53,838,831 4,264,314 8,291,070 11,984,034 4,502,962 2,687,889 15,252,654

Off

- shore to EEZ* 210,270 185 666,757 9,313 8,799 12,165 94,501

Non

- US SECA C3 31,937 0 296,532 7,029 6,407 43,104 13,563

Canada othafdust 779 ,674 112,523

Canada othar 3,015,606 326,610 361,896 158,996 131,114 70,272 886,456

Canada othon 3,032,005 18,653 345,664 17,628 12,216 1,701 178,431 Canada othpt** 496,083 13,069 266,912 70,005 29,165 544,502 129,119

Mexico othar 316,038 168,218 21 8,022 105,952 55,152 13,454 526,197

Mexico othon 2,167,729 13,338 116,490 3,196 2,104 1,351 198,850 Mexico othpt 204,038 5,975 387,578 74,454 57,000 360,401 85,703

Non

- US Total 9,473,705 546,048 2,659,850 1,226,247 414,482 1,046,952 2,112,821163

Table 5

- 4

. National by

- sector CO emissions (tons/yr) summaries and percent change

Sector 2011 CO 2017 CO 2025 CO % change

2011 to 2017 % change

2011 to 2025

afdust_adj 0 0 0 0% 0%

ag 0 0 0 0% 0%

agfire 956,243 95 6,243 956,243 0% 0%

c1c2rail 180,579 194,109 215,196 7% 19%

nonpt 1,645,989 1,673,888 1,695,259 2% 3%

np_oilgas 627,609 677,087 721,546 8% 15%

nonroad 13,951,020 12,371,786 13,299,524

- 11%

- 5%

onroad 26,313,827 15,607,769 10,444,934

- 41%

- 60%

c3marin e 12,532 15,600 20,988 24% 67%

ptprescfire 10,063,500 10,063,500 10,063,500 0% 0%

ptwildfire 10,499,197 10,499,197 10,499,197 0% 0%

ptegu 777,902 820,402 903,766 5% 16%

ptnonipm 2,312,304 2,357,260 2,440,880 2% 6%

pt_oilgas 235,162 216,480 254,080

- 8% 8%

rwc 2,517,844 2,504,587 2,323,717

- 1%

- 8%

Con U.S. Total 70,093,709 57,957,907 53,838,831

- 17%

- 23%

Off

- shore to EEZ* 175,353 186,196 210,270 6% 20%

Non

- US SECA C3 17,184 22,335 31,937 30% 86%

Canada othafdust 0 0 0 0% 0%

Canada othar 3,015,606 3 ,015,606 3,015,606 0% 0%

Canada othon 3,032,005 3,032,005 3,032,005 0% 0% Canada othpt** 496,083 496,083 496,083 0% 0%

Mexico othar 277,810 299,511 316,038 8% 14%

Mexico othon 3,361,123 2,068,792 2,167,729

- 38%

- 36% Mexico othpt 153,061 186,436 204,03 8 22% 33%

Non

- US Total 10,528,225 9,306,964 9,473,705

- 12%

- 10%164

Table 5

- 5

. National by

- sector NH 3 emissions (tons/yr) summaries and percent change

Sector 2011 NH 3 2017 NH 3 2025 NH 3 % change

2011 to 2017 % change

2011 to 2025

afdust_adj 0 0 0 0% 0%

ag 3,515,198 3,577,123 3,610,283 2% 3%

agfire 3,321 3,321 3,321 0% 0%

c1c2rail 511 513 514 0% 1%

nonpt 94,242 94,816 94,957 1% 1%

np_oilgas 0 0 0 0% 0%

nonroad 2,627 2,857 3,543 9% 35%

onroad 120,805 88,7 74 85,521

- 27%

- 29%

c3marine 68 68 68 0% 0%

ptprescfire 161,999 161,999 161,999 0% 0%

ptwildfire 167,331 167,331 167,331 0% 0%

ptegu 24,925 41,275 46,007 66% 85%

ptnonipm 66,192 66,496 66,698 0% 1%

pt_oilgas 5,947 5,887 5,922

- 1% 0%

rwc 19,693 19,54 6 18,150

- 1%

- 8%

Con U.S. Total 4,182,859 4,230,006 4,264,314 1% 2%

Off

- shore to EEZ* 185 185 185 0% 0%

Non

- US SECA C3 0 0 0 0% 0%

Canada othafdust 0 0 0 0% 0%

Canada othar 326,610 326,610 326,610 0% 0%

Canada othon 18,653 18,653 18,653 0% 0%

Canada othpt** 13,069 13,069 13,069 0% 0%

Mexico othar 163,040 166,412 168,218 2% 3%

Mexico othon 7,978 10,044 13,338 26% 67% Mexico othpt 3,706 4,904 5,975 32% 61%

Non

- US Total 533,241 539,877 546,048 1% 2%165

Table 5

- 6

. National by

- sector NO

x emissions (tons/yr) summaries and percent change

Sector 2011 NO X 2017 NO X 2025 NO X % change

2011 to 2017 % change

2011 to 2025

afdust_adj 0 0 0 0% 0%

ag 0 0 0 0% 0%

agfire 42,767 42,767 42,767 0% 0%

c1c2rail 1,075,217 915,797 679,934

- 15%

- 37%

nonpt 720,454 734,072 746,389 2% 4%

np_oilgas 647,169 687,307 717,783 6% 11%

nonroad 1,630,301 1,129,911 794,770

- 31%

- 51%

onroad 5,719,979 3,150,751 1,707,692

- 45%

- 70%

c3marine 131,382 131,691 104,503 0%

- 20%

ptprescfire 167,599 167,599 167,599 0% 0%

ptwildfire 165,799 165,799 165,799 0% 0%

ptegu 2,088,233 1,547,987 1,435,523

- 26%

- 31%

ptnonipm 1,222,456 1,192,297 1,229,333

- 2% 1%

pt_oilgas 509,856 411,055 464,256

- 19%

- 9%

rwc 34,436 35,507 34,722 3% 1%

Con U.S. Tota l 14,155,649 10,312,541 8,291,070

- 27%

- 41%

Off

- shore to EEZ* 899,986 855,139 666,757

- 5%

- 26%

Non

- US SECA C3 202,432 239,528 296,532 18% 46%

Canada othafdust 0 0 0 0% 0%

Canada othar 361,896 361,896 361,896 0% 0%

Canada othon 345,664 345,664 345,664 0% 0% Canada othpt** 266,912 266,912 266,912 0% 0%

Mexico othar 182,869 199,951 218,022 9% 19%

Mexico othon 243,714 164,555 116,490

- 32%

- 52% Mexico othpt 286,303 348,544 387,578 22% 35%

Non

- US Total 2,789,775 2,782,187 2,659,850 0%

- 5%166

Table 5

- 7

. National by

- sector PM 2.5 emissions (tons/yr) summaries and percent change

Sector 2011 PM 2.5 2017 PM 2.5 2025 PM 2.5 % change

2011 to 2017 % change

2011 to 2025

afdust_adj 923,938 964,080 1,023,169 4% 11%

ag 0 0 0 0% 0%

agfire 93,959 93,959 93,959 0% 0%

c1c2rail 33,019 24,245 16,507

- 27%

- 50%

nonpt 404,258 416,334 431,038 3% 7%

np_oilgas 15,333 19,420 22,439 27% 46%

nonroad 154,657 107,267 72,788

- 31%

- 53%

onroad 194,690 124,930 83,019

- 36%

- 57%

c3marine 9,043 1,531 2,075

- 83%

- 77%

ptprescfire 900,546 900,546 900,546 0% 0%

ptwildfire 943,717 943,717 943,717 0% 0%

ptegu 206,884 212,913 208,678 3% 1%

ptnonipm 322,142 325,400 333,048 1% 3%

pt_oilgas 13,935 13,379 16,381

- 4% 18%

rwc 381,252 383,807 355,5 99 1%

- 7%

Con U.S. Total 4,597,374 4,531,527 4,502,962

- 1%

- 2%

Off

- shore to EEZ* 24,544 9,768 8,799

- 60%

- 64%

Non

- US SECA C3 15,828 20,527 6,407 30%

- 60%

Canada othafdust 112,523 112,523 112,523 0% 0%

Canada othar 131,114 131,114 131,114 0% 0%

Canada othon 12,216 12,216 12,216 0% 0% Canada othpt** 29,165 29,165 29,165 0% 0%

Mexico othar 50,158 53,005 55,152 6% 10%

Mexico othon 1,624 1,697 2,104 5% 30% Mexico othpt 42,105 49,790 57,000 18% 35%

Non

- US Total 419,276 419,805 414,482 0%

- 1%167

Table 5

- 8

. National by

- sector PM 10 emissions (tons/yr) summaries and percent change

Sector 2011 PM 10 2017 PM 10 2025 PM 10 % change

2011 to 2017 % change

2011 to 2025

afdust_adj 6,735,072 7,093,387 7,621,008 5% 13%

ag 0 0 0 0% 0%

agfire 140,728 140,728 140,728 0% 0%

c1c2rail 35,359 25,970 17,679

- 27%

- 50%

nonpt 491,825 494,930 511,976 1% 4%

np_oilgas 16,843 21,112 25,266 25% 50%

nonroad 162,417 113,568 77,615

- 30%

- 52%

onroad 367,667 302,041 280,816

- 18%

- 24%

c3marine 10,168 1,729 2,335

- 83%

- 77%

ptprescfire 1,060,130 1,060,130 1,060,130 0% 0%

ptwildfire 1,111,857 1,111,857 1,111,857 0% 0%

ptegu 281,614 279,931 274,544

- 1%

- 3%

ptnonipm 478,885 480,114 487,373 0% 2%

pt_oilgas 14,585 14,013 16,859

- 4% 16%

r wc 381,476 384,041 355,849 1%

- 7%

Con U.S. Total 11,288,626 11,523,551 11,984,034 2% 6%

Off

- shore to EEZ* 26,247 10,237 9,313

- 61%

- 65%

Non

- US SECA C3 17,206 22,313 7,029 30%

- 59%

Canada othafdust 779,674 779,674 779,674 0% 0%

Canada othar 158,996 158 ,996 158,996 0% 0%

Canada othon 17,628 17,628 17,628 0% 0% Canada othpt** 70,005 70,005 70,005 0% 0%

Mexico othar 98,812 103,086 105,952 4% 7%

Mexico othon 2,425 2,565 3,196 6% 32% Mexico othpt 55,162 64,263 74,454 16% 35%

Non

- US Total 1,226,156 1,22 8,767 1,226,247 0% 0%168

Table 5

- 9

. National by

- sector SO 2 emissions (tons/yr) summaries and percent change

Sector 2011 SO 2 2017 SO 2 2025 SO 2 % change

2011 to 2017 % change

2011 to 2025

afdust_adj 0 0 0 0% 0 %

ag 0 0 0 0% 0%

agfire 16,224 16,224 16,224 0% 0%

c1c2rail 12,609 1,161 888

- 91%

- 93%

nonpt 275,655 104,585 95,195

- 62%

- 65%

np_oilgas 18,306 37,505 38,913 105% 113%

nonroad 4,031 1,856 2,088

- 54%

- 48%

onroad 29,665 14,985 13,759

- 49%

- 54%

c3marin e 86,373 3,904 5,272

- 95%

- 94%

ptprescfire 83,082 83,082 83,082 0% 0%

ptwildfire 82,691 82,691 82,691 0% 0%

ptegu 4,662,717 1,462,200 1,471,933

- 69%

- 68%

ptnonipm 1,057,242 807,093 800,678

- 24%

- 24%

pt_oilgas 66,577 69,034 69,560 4% 4%

rwc 8,954 8,81 4 7,606

- 2%

- 15%

Con U.S. Total 6,404,125 2,693,133 2,687,889

- 58%

- 58%

Off

- shore to EEZ* 139,169 9,945 12,165

- 93%

- 91%

Non

- US SECA C3 127,579 165,925 43,104 30%

- 66%

Canada othafdust 0 0 0 0% 0%

Canada othar 70,272 70,272 70,272 0% 0%

Canada othon 1,701 1,701 1,701 0% 0% Canada othpt** 544,502 544,502 544,502 0% 0%

Mexico othar 10,679 11,930 13,454 12% 26%

Mexico othon 4,919 1,943 1,351

- 61%

- 73% Mexico othpt 453,466 363,306 360,401

- 20%

- 21%

Non

- US Total 1,352,287 1,169,524 1,046,952

- 14%

- 23%169

Table 5

- 10

. National by

- sector VOC emissions (tons/yr) summaries and percent change

Sector 2011 VOC 2017 VOC 2025 VOC % change

2011 to 2017 % change

2011 to 2025

afdust_adj 0 0 0 0% 0%

ag 0 0 0 0% 0%

a gfire 74,783 74,783 74,783 0% 0%

c1c2rail 48,281 34,528 24,498

- 28%

- 49%

nonpt 3,672,249 3,511,392 3,426,386

- 4%

- 7%

np_oilgas 2,556,649 3,164,559 3,478,495 24% 36%

nonroad 2,024,419 1,422,907 1,179,395

- 30%

- 42%

onroad 2,709,470 1,507,880 904,713

- 44 %

- 67%

c3marine 5,149 6,429 8,687 25% 69%

ptprescfire 2,313,404 2,313,404 2,313,404 0% 0%

ptwildfire 2,374,690 2,374,690 2,374,690 0% 0%

ptegu 36,336 40,433 41,795 11% 15%

ptnonipm 802,988 814,400 826,684 1% 3%

pt_oilgas 164,098 165,559 190,788 1% 16 %

rwc 442,541 437,984 408,336

- 1%

- 8%

Con U.S. Total 17,225,056 15,868,951 15,252,654

- 8%

- 11%

Off

- shore to EEZ* 81,602 86,093 94,501 6% 16%

Non

- US SECA C3 7,294 9,462 13,563 30% 86%

Canada othafdust 0 0 0 0% 0%

Canada othar 886,456 886,456 886,456 0 % 0%

Canada othon 178,431 178,431 178,431 0% 0% Canada othpt** 129,119 129,119 129,119 0% 0%

Mexico othar 410,734 470,910 526,197 15% 28%

Mexico othon 319,353 218,848 198,850

- 31%

- 38% Mexico othpt 53,813 68,970 85,703 28% 59%

Non

- US Total 2,066,802 2,048,288 2,112,821

- 1% 2%170 

## 6 References

Adelman, Z. 2012. Memorandum: Fugitive Dust Modeling for the 2008 Emissions Modeling Platform

. UNC Institute for the Environment, Chapel Hill, NC. September, 28, 2012.

Adelman, Z., M. Omary, Q. He, J. Zhao and D . Yang , J. Boylan, 2012. “ A Detailed Approach for Improving Continuous Emissions Monitoring Data for Regulatory Air Quality Modeling .” Presented at the 2012 International Emission Inventory Conference , Tampa, Florida. Available from http://www.epa.gov/ttn/chief/conference/ei20/index.html#ses

- 5

.

Alpine Geophysics, 2014. Project Technical Memorandum : Future Year Growth and Control Factors. Submitted to Rob Kaleel, Lake Michiga n Air Directors Consortium. Available from http://www.regulations.gov under EPA

- HQ

- OAR

- 2013

- 0809

- 0060 (see ProjectTechMemo_Growth&ControlFactors.docx)

.

Anderson, G.K.; Sandberg, D.V; Norheim, R.A., 2004. Fire Emis sion 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 Emissio ns from Diesel

- Fueled Engines and Vehicles”. California Environmental Protection Agency Air Resources Board, Mobile Source Control
Division, Sacramento, CA. October, 2000. Available at:

h ttp://www.arb.ca.gov/diesel/documents/rrpFinal.pdf

.

ARB, 2007. “Proposed Regulation for In

- Use Off

- Road Diesel Vehicles”. California Environmental Protection Agency Air Resources Board, Mobile Source Control Division, Sacramento, CA. April,

2007. Avai lable at: http://www.arb.ca.gov/regact/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

- Ig nition Fleet Requirements”. California Environmental Protection Agency
Air Resources Board, Mobile Source Control Division, Sacramento, CA. October, 2010. Available

at: http:/ /www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf

.

ARB, 2010b. “Estimate of Premature Deaths Associated with Fine Particle Pollution (PM2.5) in California Using a U.S. Environmental Protection Agency Methodology”. California Environmental Protect ion Agency Air Resources Board, Mobile Source Control Division, Sacramento, CA. August, 2010.

Available at: http://www.arb.ca.gov/research/health/pm

- mort/pm

- report_2010.pdf

.

Bash, J.O., Baker, K.R., Beaver, M.R., Park, J.

- H., Goldstein, A.H., 2015. Evaluation of improved land use and canopy representation in BEIS with biogenic VOC measurements in California (in preparation)

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/E IA

0383(2012), June 2012. Available at: http://www.eia.gov/forecasts/aeo/171 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

. Energy Information Administration. 2010. Annual Energy Outlook 2011 Early Release Overview. U.S. Department of Energy, December 2010. Report # DO E/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

Em issions). 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. Available 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. Office of Transportation and Air Quality, Assessment and Standards Division. Ann Arbor, MI 48105. Available at: http://www.epa.gov/otaq/standards/light

- duty/tier2stds.htm

EPA, 2005. EPA’s National Inventory Model (NMI M), A Consolidated Emissions Modeling System for

MOBILE6 and NONROAD , U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Assessment and Standards Division. Ann Arbor, MI 48105, EPA420

- R

- 05

- 024, December

20 05. Available at http://www.epa.gov/otaq/models/nmim/420r05024.pdf

.

EPA 2006a. SPECIATE 4.0, Speciation Database Development Document, Final Report

, U.S. Environmental Protection Agency, O ffice 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 Particle Pollution. U.S. Environmental Protection Agency, Off ice 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. Available at http://www.regulations.gov/#!documentDetail;D=EPA

- HQ

- OAR

- 2005

- 0036

- 1134

EPA, 2007b. Guidance for Estimating VOC and NO X Emission Changes from MACT Rules, U.S. Environmental Protection Agency Office of Air Q uality 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://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=499572

EPA, 2009a. 2005 National Emissions Inventory, Version 2. Available at: http://www.epa.gov/ttn/ chief/net/2005inventory.html

. EPA. 2009b. “Impact Calculations RFS

- Docket.xls.” Available on the RFS2 Docket EPA

– HQ

– OAR

– 2010

– 0133 at: http://www.regulations.gov

.172 EPA, 2009c. Exhaust Emission Profiles for EPA SP ECIATE 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.

EPA, 2009d. Regulatory Impact Analysis: C ontrol of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder. U.S. Environmental Protection Agency Office of Transportation and Air Quality, Assessment and Standards Division,

Ann Ar bor, MI 48105, EPA420

- R

- 08

- 001a, May 2009. Available at: http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P10024CN.txt

EPA, 2009e. Emission Factors for Locomotives. Office of Transportati on 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. Regul atory 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 Transpor tation 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. Tech nical Support Document: The Industrial Sectors Integrated Solutions (ISIS) Model and the Analysis for the National Emission Standards for Hazardous Air Pollutants and New Source Performance Standards for the Portland Cement Manufacturing Industry, U.S. En vironmental Protection Agency, Sectors Policies and Program Division and 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 F uel 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) Emis sions Inventory for Air Quality Modeling Technical Support Document, Office 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/climate/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#final

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 Li ght

- 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.gov/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

.173 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

. EPA, 2013ci. Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compr ession Ignition (CI) Engines NESHAP: Final Report. 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 Recons ideration of the Existing Stationary Spark Ignition (SI) RICE NESHAP: Final Report. Available from http://www.epa.gov/ttn/ecas/regdata/R IAs/NESHAP_RICE_Spark_Ignition_RIA_finalreconsideratio n2013_EPA.pdf

.

EPA , 2014. Control of Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle Emissions and Fuel Standards Final Rule Regulatory Impact Analysis. EPA

- 420

- R

- 14

- 005. Available at: http://www.epa.gov/otaq/documents/tier3/420r14005.pdf

.

EPA , 2014 b

. Technical Support Document: Preparation of Emissions Inventories for the Version 6.1 Emissions Modeling Platform

. EPA OAQP S, Research Triangle Park, NC . Available at: http://www.epa.gov/ttn/chief/emch/2011v6/2011v6.1_2018_2025_base_EmisMod_TSD_nov2014_v 6.pdf

. EPA, 201 5. Population and Activity of On

- road Vehicles in MOVES2014

– Draft Report. EPA

- 420

- D

- 15

- 001. Available at http://www.epa.gov/otaq/models/moves/documents/420d15001.pdf

. ERG, 2014 a . Develop Mexico Future Year Emissions Final Report. Available at ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/2011emiss ions/Mexico_Emissions_WA%204

- 09_final_report_121814.pdf

. ERG, 2014b. “Technical Memorandum: Modeling Allocation Factors for the 2011 NEI”.

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 Frost & Sullivan, Mountain View, CA 94041.

Hildebrandt Ruiz, L. and Yarwood, G.

, 2013 . Interactions between Organic Aerosol and NOy: Influen ce on Oxidant Production, Final report for AQRP project 12

- 012. Available at http://aqrp.ceer.utexas.edu/projectinfoFY12_13%5C12

- 012%5C12

- 012%20Final%20Report.pdf

Houck, 2011; “Dirty

- vs. Clean

- Burning? What percent of freestanding wood heaters in use in t he 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.shtml

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. “Regional Air Quality Analyses for Ozone, PM2.5, and Regional Haze: Base C Emissions Inventory (September 12, 2011)”. Lake Michig an Air Directors Consortium, Rosemont, IL 60018.174

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 Fuel 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 househo ld responses”. Minnesota Department of Natural Resources. Available from http://files.dnr.state.mn.us/forestry/um/residentialfuelwoodassessment07_08.pdf

.

NESC AUM, 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

report_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

- Fired

- Hydronic

- Heater

- Tech.ashx

. 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 sh rub 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

Classificat ion System

– Quantifying, Classifying, and Creating Fuelbeds for Resource Planning. Canadian Journal of Forest Research. 37(12): 2383

- 2393. FCCS is available at: http://www.fs.fed.us/pnw/fera/f ccs/index.shtml

Parise, T., 2005. Population and Projection of Stationary Engines, Alpha

- Gamma Technologies, Inc., Memorandum to Sims Roy, EPA OAQPS ESD Combustion Group, RTP, NC, June 20, 2005

Pechan, 2001. E.H. Pechan & Associates, Inc., Control Measure Development Support

-- Analysis of Ozone Transport Commission Model Rules, Springfield, VA, prepared for the Ozone Transport Commission, Washington, DC, March 31, 2001.

Pouliot, G., H. Simon, P. Bhave, D. Tong, D. Mobley, T. Pace, and T. Pierce . (2010) “As sessing the Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated175

Speciation of Particulate Matter.” International Emission Inventory Conference, San Antonio, TX.

Available at http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot.pdf

Pouliot, G. and J. Bash, 2015. Updates to Version 3.61 of the Biogenic Emission Inventory System (BEIS). Presented at Air and Waste Management Associatio n conference, Raleigh, NC, 2015.

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, 2007. 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 E nvironment, 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 Infrastructure, 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%2FSESARM%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 Adv anced Research WRF Version 3. NCAR Technical Note. National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder, CO. June 2008.

Available at: http://ww w2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf

SC&A, 2014a. Memorandum from Jim Wilson, SC&A, submitted by Glenn Meganck, EC/R: Documentation of Control Factors for ICI Boilers and Process Heaters, and IC Engines and Gas Turbines, EPA Contract No. EP

- D

- 13

- 001 , WA 1

- 02 . Submitted to Rich Mason and Alison Eyth,

U.S. EPA.

SC&A, 2014b. Memorandum from Andy Bollman and Jackson Schreiber, SC&A, submitted by Glenn Meganck, EC/R: Documentation of Growth Factors for ICI Boilers and Process Heaters, and IC Engines and Gas Turbines, EPA Contract No. EP

- D

- 13

- 001, WA 1

- 02 . Submitted to Rich Mason and Alison Eyth, U.S. EPA.

SRA, 2014. Technical Support Document: Inventory Growth and Control Factors based on EPA 2011 Emissions Modeling Platform. Submitted to Julie McDill, Mi d

- Atlantic Regional Air Management Association, Inc

.

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 modeling

emissions and smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17th176

International Emissions Inventory Conference, Portland, OR, June 2

- 5. Available at: http://www.epa.gov/ttn/chief/co nferences.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 Combusti on Particles Using th e Two

- Wavelength Aethalometer”, Environ. Sci. Technol., 45 (17), pp 7387

– 7393

Yarwood, G., J

. Jung, , G. Whitten, G. Heo, J. Mellberg, and M. Estes, 20 10 : Updates to the Carbon Bond

Chemical Mechanism for Version 6 (CB6). Presented at th e 9 th Annual CMAS Conference, Chapel Hill, NC. Available at https://www.cmascenter.org/conference/2010/abstracts/emery_updates_carbon_2010.pdf

. Zue, Henze, et al, 2013. “Constraining U.S. Ammonia Emissions using TES Remote Sensing Observations

and the GEOS

- Chem adjoint model”, Journal of Geophysical Research: Atmospheres, 118: 1

- 14.177 Appendix A : Nonpoint Oil and Gas (np_oilgas) SCCs

The table below shows the SCCs in the nonpoint oil and gas sector (np_oilgas)

.

SCC SCC description

2310000000 Industrial Processes;Oil and Gas Production: SIC 13;All Processes;Total: All Processes

2310000220 Industrial Processes;Oil and Gas Production: SIC 13; Drill rigs

23 10000230 Industrial Processes;Oil and Gas Exploration and Production;All Processes;Workover Rigs

2310000330 Industrial Processes;Oil and Gas Production: SIC 13; Artificial lift

2310000550 Industrial Processes;Oil and Gas Exploration and Production;All Pr ocesses;Produced Water

2310000660 Industrial Processes;Oil and Gas Exploration and Production;All Processes;Hydraulic Fracturing Engines

2310002000 Industrial Processes;Oil and Gas Production: SIC 13;All Processes : Off

- shore;Total: All Processes

231000 2301 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil And Gas Production;Flares: Continuous Pilot Light

2310002305 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil And Gas Production;Flares: Flaring Operat ions

2310002401 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil And Gas Production;Pneumatic Pumps: Gas And Oil Wells

2310002411 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil And Gas Production;Pressu re/Level Controllers

2310002421 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil And Gas Production;Cold Vents

2310010000 Industrial Processes;Oil and Gas Production: SIC 13;Crude Petroleum;Total: All Processes

2310010100 Indust rial Processes;Oil and Gas Production: SIC 13;Crude Petroleum; Oil well heaters

2310010200 Industrial Processes;Oil and Gas Production: SIC 13;Crude Petroleum; Oil well tanks

- flashing & standing/working/breathing

2310010300 Industrial Processes;Oil and Gas Production: SIC 13;Crude Petroleum; Oil well pneumatic devices

2310010700 Industrial Processes;Oil and Gas Production: SIC 13;Crude Petroleum; Oil well fugitives

2310010800 Industrial Processes;Oil and Gas Production: SIC 13;Crude Petroleum; Oil wel l truck loading

2310011000 Industrial Processes;Oil and Gas Production: SIC 13;Crude Petroleum : On

- shore;Total: All Processes

2310011020 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Storage Tanks: Crude Oil

231001 1100 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Heater Treater

2310011201 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Tank Truck/Railcar Loading: Crude Oil

2310011450 Indust rial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Wellhead

2310011500 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Fugitives: All Processes

2310011501 Industrial Processes;Oil and Gas Exp loration and Production;On

- Shore Oil Production;Fugitives: Connectors

2310011502 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Fugitives: Flanges178

SCC SCC description

2310011503 Industrial Processes;Oil and Gas Exploration and Production ;On

- Shore Oil Production;Fugitives: Open Ended Lines

2310011504 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Fugitives: Pumps

2310011505 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Produ ction;Fugitives: Valves

2310011506 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Fugitives: Other

2310011600 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Artificial Lift Engines

2310012000 Industrial Processes;Oil and Gas Production: SIC 13;Crude Petroleum : Off

- shore;Total: All Processes

2310012020 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Production;Storage Tanks: Crude Oil

2310012511 Industri al Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Production;Fugitives; Connectors: Oil Streams

2310012512 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Production;Fugitives; Flanges: Oil

2310012515 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Production;Fugitives; Valves: Oil

2310012516 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Production;Fugitives; Other: Oil

2310012521 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Production;Fugitives; Connectors: Oil/Water Streams

2310012522 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Production;Fugitives; Flanges: Oil/Water

2310012526 Industrial Processe s;Oil and Gas Exploration and Production;Off

- Shore Oil Production;Fugitives; Other: Oil/Water

2310020000 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas;Total: All Processes

2310020600 Industrial Processes;Oil and Gas Production: SIC 13;N atural Gas; Compressor engines

2310020700 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas; Gas well fugitives

2310020800 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas; Gas well truck loading

2310021010 Industrial Proces ses;Oil and Gas Exploration and Production;On

- Shore Gas Production;Storage Tanks: Condensate

2310021011 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Condensate Tank Flaring

2310021030 Industrial Processes;Oil and Ga s Exploration and Production;On

- Shore Gas Production;Tank Truck/Railcar Loading: Condensate

2310021100 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas; Gas well heaters

2310021101 Industrial Processes;Oil and Gas Exploration and Productio n;On

- Shore Gas Production;Natural Gas Fired 2Cycle Lean Burn

Compressor Engines < 50 HP

2310021102 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Natural Gas Fired 2Cycle Lean Burn

Compressor Engines 50 To 499 HP

2310 021103 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Natural Gas Fired 2Cycle Lean Burn

Compressor Engines 500+ HP

2310021201 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Natural Gas Fired 4Cycle Lean Burn

Compressor Engines <50 HP

2310021202 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Natural Gas Fired 4Cycle Lean Burn

Compressor Engines 50 To 499 HP

2310021203 Industrial Processes;Oil an d Gas Exploration and Production;On

- Shore Gas Production;Natural Gas Fired 4Cycle Lean Burn

Compressor Engines 500+ HP179

SCC SCC description

2310021209

2310021251 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Lateral Compressors 4 Cycle Lean Burn

2310021300 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas; Gas well pneumatic devices

2310021301 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Natural Gas Fired 4Cycle Rich Burn

Compressor Engines <50 HP

2310021302 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Natural Gas Fired 4Cycle Rich Burn

Compressor Engines 50 To 499 HP

2310021303 Industrial Processes;Oil and Gas Exploration and Production;On

- Sho re Gas Production;Natural Gas Fired 4Cycle Rich Burn

Compressor Engines 500+ HP

2310021309

2310021310 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Gas Well Pneumatic Pumps

2310021351 Industrial Processes;Oil and G as Exploration and Production;On

- Shore Gas Production;Lateral Compressors 4 Cycle Rich Burn

2310021400 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas; Gas well dehydrators

2310021401 Industrial Processes;Oil and Gas Exploration and Produ ction;On

- Shore Gas Production;Nat Gas Fired 4Cycle Rich Burn Compressor

Engines <50 HP w/NSCR

2310021402 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Nat Gas Fired 4Cycle Rich Burn Compressor

Engines 50 To 499 HP w/N SCR

2310021403 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Nat Gas Fired 4Cycle Rich Burn Compressor

Engines 500+ HP w/NSCR

2310021411 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Produc tion;Gas Well Dehydrators

- Flaring

2310021500 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas; Gas well completion

- flaring and venting

2310021501 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Fugit ives: Connectors

2310021502 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Fugitives: Flanges

2310021503 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Fugitives: Open Ended Lines

2310021504 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Fugitives: Pumps

2310021505 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Fugitives: Valves

2310021506 Industrial Proces ses;Oil and Gas Exploration and Production;On

- Shore Gas Production;Fugitives: Other

2310021509 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Fugitives: All Processes

2310021600 Industrial Processes;Oil and Gas Produc tion: SIC 13;Natural Gas; Gas well venting

2310021601 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Gas Well Venting

- Initial Completions

2310021602 Industrial Processes;Oil and Gas Exploration and Production;On

- Sho re Gas Production;Gas Well Venting

- Recompletions

2310021603 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Gas Well Venting

- Blowdowns

2310021604 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Gas Well Venting

- Compressor Startups

2310021605 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Gas Well Venting

- Compressor Shutdowns180

SCC SCC description

2310021700 Industrial Processes;Oil and Gas Exploration and Prod uction;On

- Shore Gas Production;Miscellaneous Engines

2310022000 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas : Off

- shore;Total: All Processes

2310022010 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Producti on;Storage Tanks: Condensate

2310022051 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Turbines: Natural Gas

2310022090 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Boilers/Hea ters: Natural Gas

2310022105 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Diesel Engines

2310022410 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Amine Unit

2310022420 Indust rial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Dehydrator

2310022501 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Fugitives; Connectors: Gas Streams

2310022502 Industrial Processes;O il and Gas Exploration and Production;Off

- Shore Gas Production;Fugitives; Flanges: Gas Streams

2310022505 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Fugitives; Valves: Gas

2310022506 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Fugitives; Other: Gas

2310023010 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Storage Tanks: Condensate

2310023030 Industrial Processes;Oil and Gas Explor ation and Production;Coal Bed Methane Natural Gas;Tank Truck/Railcar Loading: Condensate

2310023100 Industrial Processes; Oil and Gas Production; CBM; Dehydrators

2310023102 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Nat ural Gas;CBM Fired 2Cycle Lean Burn Compressor

Engines 50 To 499 HP

2310023202 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Fired 4Cycle Lean Burn Compresso r

Engines 50 To 499 HP

2310023251 Industrial Proce sses;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Lateral Compressors 4 Cycle Lean Burn

2310023300 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Pneumatic Devices Industrial

Processes;Oil a nd Gas Exploration and Production;Coal Bed Methane Natural Gas;Pneumatic Devices

2310023302 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Fired 4Cycle Rich Burn Compresso r

Engines 50 To 499 HP

2310023310 Ind

ustrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Pneumatic Pumps

2310023351 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Lateral Compressors 4 Cycle Rich Bur n

2310023400 Ind

ustrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Dehydrators

2310023509 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives

2310023511 Industrial Processes;Oil and Gas Ex ploration and Production;Coal Bed Methane Natural Gas;Fugitives: Connectors

2310023512 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives: Flanges

2310023513 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives: Open Ended Lines

2310023515 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives: Valves

2310023516 Industrial Processes;Oil and Gas Exploration and Product ion;Coal Bed Methane Natural Gas;Fugitives: Other181

SCC SCC description

2310023600 Industrial Processes; Oil and Gas Production; CBM; Venting

- Compressor Shutdown

2310023601 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Venting

- In itial Completions

2310023602 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Venting

- Recompletions

2310023603 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Venting

- Bl owdowns

2310023606 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Mud Degassing

2310030000 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas Liquids;Total: All Processes

2310030210 Industrial Proce sses;Oil and Gas Production: SIC 13;Natural Gas Liquids; Gas well tanks

- Flashing & Standing/Working/Breathing

2310030300 Industrial Processes;Oil and Gas Exploration and Production;Natural Gas Liquids;Gas Well Water Tank Losses

2310030401 Industrial Pr ocesses;Oil and Gas Exploration and Production;Natural Gas Liquids;Gas Plant Truck Loading

2310111100 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Exploration;Mud Degassing

2310111401 Industrial Processes;Oil and Gas Explorati on and Production;On

- Shore Oil Exploration;Oil Well Pneumatic Pumps

2310111700 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Exploration;Oil Well Completion: All Processes

2310112401 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Exploration;Oil Well Pneumatic Pumps

2310121100 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Exploration;Mud Degassing

2310121401 Industrial Processes;Oil and Gas Exploration and Production;Off

- Sh ore Oil Exploration;Oil Well Pneumatic Pumps

2310121700 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Oil Exploration;Oil Well Completion: All Processes

2310122100 Industrial Processes;Oil and Gas Exploration and Production;Off

- Sh ore Gas Exploration;Mud Degassing182 Appendix B: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT

The table below provides a crosswalk between fuel distribution SCCs and classification type for port able fuel containers (PFC), fuel distribution ope rations associated with the bulk

- plant

- to

- pump (BTP ), r efinery to bulk terminal (RBT ) and bulk plant storage (BPS).

SCC Type Description

40301001 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13: Breathing Loss (67000 Bbl. Tank Size)

40301002 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 10 : Breathing Loss (67000 Bbl. Tank Size)

40301003 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size)

40301004 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refin eries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13: Breathing Loss (250000 Bbl. Tank Size)

40301006 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 7: Breathing Loss ( 250000 Bbl. Tank Size)

40301007 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13 : Working Loss (Tank Diameter Independent)

40301101 RBT Petroleum and Solvent Evaporati on; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 13: Standing Loss (67000 Bbl. Tank Size)

40301102 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varyin g Sizes); Gasoline RVP 10: Standing Loss (67000 Bbl. Tank Size)

40301103 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 7: Standing Loss (67000 Bbl. Tank Size)

4030110 5 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 10: Standing Loss (250000 Bbl. Tank Size)

40301151 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline: Standing

Loss

- Internal

40301202 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor Space; Gasoline RVP 10: Filling Lo ss

40301203 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor Space; Gasoline RVP 7: Filling Loss

40400101 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Breathing L oss (67000 Bbl Capacity)

- Fixed Roof Tank

40400102 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity)

- Fixed Roof Tank

40400103 RBT Petroleum and S olvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity)

- Fixed Roof Tank

40400104 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminal s; Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)

- Fixed Roof Tank

40400105 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss (250000 Bbl Capacity)

- Fixed Roof Tank183

SCC Type Description

4 0400106 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity)

- Fixed Roof Tank

40400107 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Working Loss (Diam. Independent)

- Fixed Roof Tank

40400108 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Working Loss (Diameter Indepe ndent)

- Fixed Roof Tank

40400109 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Working Loss (Diameter Independent)

- Fixed Roof Tank

40400110 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)

- Floating Roof Tank

40400111 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)

- Floating Roof Tank

40400112 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)

- Floating Roof Tank

40400113 RBT Pet roleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss (250000 Bbl Cap.)

- Floating Roof Tank

40400114 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bu lk Terminals; Gasoline RVP 10: Standing Loss (250000 Bbl Cap.)

- Floating Roof Tank

40400115 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss (250000 Bbl Cap.)

- Floating Roo f Tank

40400116 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.)

- Float Rf Tnk

40400117 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Cap.)

- Float Rf Tnk

40400118 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Filling Loss (105 00 Bbl Cap.)

- Variable Vapor Space

40400119 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Filling Loss (10500 Bbl Cap.)

- Variable Vapor Space

40400120 RBT Petroleum and Solvent E vaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Filling Loss (10500 Bbl Cap.)

- Variable Vapor Space

40400130 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Specify Liquid: Standing Loss

- External Floating Roof w/ Primary Seal

40400131 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss

- Ext. Floating Roof w/ Primary Seal

40400132 RB T Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss

- Ext. Floating Roof w/ Primary Seal

40400133 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery) ; Bulk Terminals; Gasoline RVP 7: Standing Loss

- External Floating Roof w/ Primary Seal

40400140 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Specify Liquid: Standing Loss

- Ext. Float

Roof Tank w/ S econdy Seal184

SCC Type Description

40400141 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss

- Ext. Floating Roof w/ Secondary Seal

40400142 RBT Petroleum and Solvent Evaporation; Petroleum Li quids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss

- Ext. Floating Roof w/ Secondary Seal

40400143 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss

- Ext. Floating Roof w/ Secondary Seal

40400148 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss

- Ext. Float Roof (Pri/Sec Seal)

40400149 RBT Petroleum and Solve nt Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Specify Liquid: External Floating Roof (Primary/Secondary Seal)

40400150 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Miscella neous Losses/Leaks: Loading

Racks

40400151 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Valves, Flanges, and Pumps

40400152 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Re finery); Bulk Terminals; Vapor Collection Losses

40400153 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Vapor Control Unit Losses

40400160 RBT Petroleum and Solvent Evaporation; Petroleum Liquids S torage (non

- Refinery); Bulk Terminals; Specify Liquid: Standing Loss

- Internal Floating Roof w/ Primary Seal

40400161 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss

- Int

. Floating Roof w/ Primary Seal

40400162 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss

- Int. Floating Roof w/ Primary Seal

40400163 RBT Petroleum and Solvent Evapora tion; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss

- Internal Floating Roof w/ Primary Seal

40400170 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Specify Liq uid: Standing Loss

- Int. Floating Roof w/ Secondary Seal

40400171 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss

- Int. Floating Roof w/ Secondary Seal

40400172 RBT P etroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss

- Int. Floating Roof w/ Secondary Seal

40400173 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss

- Int. Floating Roof w/ Secondary Seal

40400178 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss

- Int. Float Roof (Pri/ Sec Seal)

40400179 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)

40400199 RBT Petroleum and Solvent Evaporation; Petroleum Liquids St orage (non

- Refinery); Bulk Terminals; See Comment **

40400201 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity)

- Fixed Roof Tank

40400202 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity)

- Fixed Roof Tank185

SCC Type Description

40400203 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery ); Bulk Plants; Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity)

- Fixed Roof Tank

40400204 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Working Loss (67000 Bbl. Capacity)

- Fixe d Roof Tank

40400205 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10: Working Loss (67000 Bbl. Capacity)

- Fixed Roof Tank

40400206 BTP/BPS Petroleum and Solvent Evaporation; Petrole um Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 7: Working Loss (67000 Bbl. Capacity)

- Fixed Roof Tank

40400207 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Standing Lo ss (67000 Bbl Cap.)

- Floating Roof Tank

40400208 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss (67000 Bbl Cap.)

- Floating Roof Tank

40400210 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.)

- Float Rf Tnk

40400211 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Filling Loss (10500 Bbl Cap.)

- Variable Vapor Space

40400212 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10: Filling Loss (10500 Bbl Cap.)

- Variable Vapor Space

40 400213 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 7: Filling Loss (10500 Bbl Cap.)

- Variable Vapor Space

40400230 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storag e (non

- Refinery); Bulk Plants; Specify Liquid: Standing Loss

- External Floating Roof w/ Primary Seal

40400231 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss

- Ext.

Float ing Roof w/ Primary Seal

40400232 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss

- Ext. Floating Roof w/ Primary Seal

40400233 BTP/BPS Petroleum and Solvent Evaporati on; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 7: Standing Loss

- External Floating Roof w/ Primary Seal

40400240 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Specify Liquid: Sta nding Loss

- Ext. Floating Roof w/ Secondary Seal

40400241 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss

- Ext. Floating Roof w/ Secondary Seal

40400248 BTP/BPS Petr oleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10/13/7: Withdrawal Loss

- Ext. Float Roof (Pri/Sec Seal)

40400249 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Specify Liquid: External Floating Roof (Primary/Secondary Seal)

40400250 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Loading Racks

40400251 BTP/BPS Petroleum and Solvent Evaporation; Pet roleum Liquids Storage (non

- Refinery); Bulk Plants; Valves, Flanges, and Pumps

40400252 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Miscellaneous Losses/Leaks: Vapor

Collection Losses186

SCC Type Description

40400253 BT P/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Miscellaneous Losses/Leaks: Vapor Control

Unit Losses

40400260 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants ; Specify Liquid: Standing Loss

- Internal Floating Roof w/ Primary Seal

40400261 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss

- Int. Floating Roof w/ Primary Seal

40 400262 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss

- Int. Floating Roof w/ Primary Seal

40400263 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 7: Standing Loss

- Internal Floating Roof w/ Primary Seal

40400270 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Specify Liquid: Standing Loss

- Int. Floatin g Roof w/ Secondary Seal

40400271 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss

- Int. Floating Roof w/ Secondary Seal

40400272 BTP/BPS Petroleum and Solvent Evapora tion; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss

- Int. Floating Roof w/ Secondary Seal

40400273 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 7 : Standing Loss

- Int. Floating Roof w/ Secondary Seal

40400278 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10/13/7: Withdrawal Loss

- Int. Float Roof (Pri/Sec Seal)

40400279 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Specify Liquid: Internal Floating Roof (Primary/Secondary Seal)

40400401 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Pe troleum Products

- Underground Tanks; Gasoline RVP 13: Breathing Loss

40400402 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks; Gasoline RVP 13: Working Loss

40400403 BTP/BP S Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks; Gasoline RVP 10: Breathing Loss

40400404 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petrol eum Products

- Underground Tanks; Gasoline RVP 10: Working Loss

40400405 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks; Gasoline RVP 7: Breathing Loss

40400406 BTP/BPS Pe troleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum Products

- Underground Tanks; Gasoline RVP 7: Working Loss

40600101 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash Loading **

40600126 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submer ged Loading **

40600131 BTP/BPS Petroleum and Solvent Evapor ation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged Loading (Normal Service)187

SCC Type Description

40600136 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash Loading (Normal Service)

40600141 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submer ged Loading (Balanced Service)

40600144 BTP/BPS Petroleum and So lvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash Loading (Balanced Service)

40600147 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars a nd Trucks; Gasoline: Submerged Loading (Clean Tanks)

40600162 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Loaded with Fuel (Transit Losses)

40600163 BTP/BPS Petroleu m and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Return with Vapor (Transit Losses)

40600199 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Ta nk Cars and Trucks; Not Classified **

40600231 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tank ers: Cleaned and Vapor Free Tanks

40600232 RBT Petroleum and Solvent Eva poration; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers

40600233 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barg es: Cl eaned and Vapor Free Tanks

40600234 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tank ers: Ballasted Tank

40600235 BTP/BPS Petroleum and Solvent Evaporation;Transportati on and Marketing of Petroleum Products;Marine Vessels;Gasoline: Ocean Barges Loading

- Ballasted Tank

40600236 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tank ers: Uncleane d Tanks

40600237 RBT Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum Products;Marine Vessels;Gasoline: Ocean Barges Lo ading

- Uncleaned Tanks

40600238 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barges:

Uncleaned Tanks

40600239 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Tankers: Bal lasted

Tank

40600240 RBT Petrol eum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barges: Average Tank Condition

40600241 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Ma rine Vessels; Gasoline: Tanker Ballasting

40600299 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Not Classified **

40600301 BTP/BPS Petroleum and Solvent Evaporation; Transportation and M arketing of Petroleum Products; Gasoline Retail Operations

- Stage I; Splash Filling

40600302 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations

- Stage I; Submerged Filling w/o Con trols

40600305 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations

- Stage I; Unloading

**188

SCC Type Description

40600306 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Pet roleum Products; Gasoline Retail Operations

- Stage I; Balanced Submerged Filling

40600307 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations

- Stage I; Underground Tank Breathing a nd Emptying

40600399 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations

- Stage I; Not

Classified **

40600401 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marke ting of Petroleum Products; Filling Vehicle Gas Tanks

- Stage II; Vapor

Loss w/o Controls

40600501 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport

- General

- All Products; Pipelin e Leaks

40600502 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport

- General

- All Products; Pipeline Venting

40600503 RBT Petroleum and Solvent Evaporation; Transportation and M arketing of Petroleum Products; Pipeline Petroleum Transport

- General

- All Products; Pump Station

40600504 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport

- General

- All

Product s; Pump Station Leaks

40600602 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling

- Stage II; Liquid Spill Loss w/o Controls

40600701 BTP/BPS Petroleum and Solvent Eva poration; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling

- Stage I; Splash Filling

40600702 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fl eet Refueling

- Stage I; Submerged Filling w/o Controls

40600706 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling

- Stage I; Balanced Submerged Filling

40600707 BTP/B PS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling

- Stage I; Underground Tank Breathing and Emptying

40688801 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Ma rketing of Petroleum Products; Fugitive Emissions; Specify in Comments Field

2501050120 RBT Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative Losses; Gasoline

2501055120 BTP/BPS Storage and Transport; P etroleum and Petroleum Product Storage; Bulk Plants: All Evaporative Losses; Gasoline

2501060050 BTP/BPS Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Total

2501060051 BTP/BPS Storage and Transp ort; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Submerged Filling

2501060052 BTP/BPS Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Splash Filling

2501060053 BTP/ BPS Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Balanced Submerged Fillin g

2501060200 BTP/BPS Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Undergroun d Tank: Total

2501060201 BTP/BPS Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Underground Tank: Breathing and Em ptying

2501995000 BTP/BPS Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working Loss; Total: All Products

2505000120 RBT Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline

2505020120 RBT Storage and Transport; Petroleum and Petroleum Product Transport; Mar ine Vessel; Gasoline189

SCC Type Description

2505020121 RBT Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline

- Barge

2505030120 BTP/BPS Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline

2505040120 RBT Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline

2660000000 BTP/BPS Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking Underground Storage Tanks; Total: All Sto rage Types190

A ppendix C: Future Animal Population Projection Methodology , Updated 07/24/12

In the EPA’s ammonia inventory for animal agricultural operations (National Emission Inventory

- Ammonia Emissions from Animal Agricultural Operations; Revised Draft Report; Apri l 22, 2005), population projections for the beef, dairy, swine, and poultry animal sectors were developed and used to estimate future ammonia emissions from these animal sectors. To develop the 2005 population projections, EPA used inventory data from the U.S. Department of Agriculture (USDA) and the Food and Agriculture Policy and Research Institute (FAPRI).

Since completion of the 2005 ammonia emissions inventory, USDA and FAPRI have released updated reports that contain animal population data and proj ections. These data were used to update the 2005 animal inventory projections. The data sources and the methodology used to develop the population projections for each animal type are discussed below. These future projections do not account for any chang es in animal populations or regional dislocations associated with EPA’s revised effluent limitations guidelines and standards for concentrated animal feeding operations promulgated in December 2002 (68 FR 7176, February 12, 2003). Due to insufficient data , animal population projections and future emission estimates were not developed for sheep, goats, and horses.

Dairy Cattle. The 2010 FAPRI U.S. and World Agricultural Outlook (FAPRI 2010) report provides estimated national milk cow inventory data and pr ojections from 2009 through 2019 and shows an overall decline in U.S. dairy cow populations. The FAPRI projections depict an essentially linear relationship between 2001 milk cow populations and subsequent years. The EPA estimated future dairy cattle

pop ulations using a linear regression analysis of the national population data available from the FAPRI report, covering 1982 through 2019. Figure C

- 1 illustrates the linear projection of the U.S. dairy cow population and trend line.

Beef Cattle. The USDA A gricultural Projections to 2021 (USDAa) provides estimated national cattle inventory data and projections from 2010 through 2021. Beef production has a clear cycle generated by producers’ expectations about future prices, grain market cycles, and other ec onomic conditions. The pace of the cycle is limited by the reproductive capacity of the animal. Cattle inventories can expand only as fast as cows can reproduce. This has historically resulted in a 7

- to 12

- year cycle, from peak to peak (Kohls, 1998). Peaks and troughs of the cycle are 5 to 6 percent higher or lower than the general trend in cattle populations so the stage of the cycle can make a significant difference in population at any given future date.

The EPA decomposed the beef cow inventory ti me series into a trend line, a cyclical component, and a random error component (Bowerman, 1987). The trend line was estimated by linear regression of the inventory data from 1990 to 2015 on a time variable. The cyclical component was then estimated as t he percentage deviation from the trend line in the historical data. A graph of that information appeared to show a cyclic trend (trough to peak). The robust U.S. economy of the 1990s may explain the longer than average cycle. With so little data, EPA as sumed the down side of the cycle was symmetrical with the up side, so the data set would contain three values for each stage of the cycle. The average of the absolute value of the three observations represents the cyclical component. The EPA forecasted t he trend line out to 2030 and adjusted it by the average percentage deviation from the trend for that stage of the cycle, as illustrated in Figure C

- 2.

The projection data for the beef cattle inventory show some difference in growth cycle of beef cows ver sus other beef cattle (e.g., steers, bulls). The EPA conducted a separate analysis of these animal populations. Other beef cattle populations appear to follow similar cycles and were forecasted using the same technique as beef cows (see Figure C

- 3).191 Figur e C

- 1. Dairy Cow Inventory Projections

Figure C

- 2. Beef Cow Inventory Projections

Raw data line: y =

- 28.545x + 90437

R2 = 0.0436

Adj. data line: y =

- 26.983x + 88178

R2 = 0.3417

29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 Number Beef Cow (thousand head)

Year U.S. Beef Cow Population192 Figure C

- 3. Non

- cow Beef Inventory Projections

Swine . Annual swine populations are categorized by breeding and market swine. The 2010 FAPRI U.S. and World Agricul tural Outlook (FAPRI 2010) report presents annual inventory data and projections from 2009 through 2019 for breeding swine and market swine inventories (rather than a combined total). The FAPRI data show an overall increase in swine production over time. Due to increasing productivity (i.e., increased number of pigs per litter), the population of breeding swine is expected to decline over the long term.

The EPA estimated future swine populations using a cycle and trend decomposition analysis. Breeding a nd market swine population projections and inventory data from the FAPRI report capture the variability of the swine production cycle. Changes in the pork industry in the 1990’s have made recent data atypical and inconsistent. For example, EPA replaced t he 1996 market hog cyclical deviation with the average of all of

the other data because they were so far out of line with the hog cycle.

The EPA estimated the trend and deviations from the trend as in the beef cattle analysis. However, it was not possibl e to apply the identical technique from the beef cattle industry to the hog industry because a well

defined periodic cycle was not evident in the annual data. The EPA evaluated a 3

- year moving average of the deviation to further reduce the random componen t. As the smoothed cycle continued to appear irregular, EPA assumed that the 2010’s will repeat the pattern of the 1990’s. Breeding hog populations were estimated using a similar approach. See Figures C

- 4 and C

- 5 for an illustration of the swine project ions for the market hog and breeding hog inventories, respectively.

Poultry. Annual poultry populations in the EPA’s ammonia emissions inventory for animal agriculture are presented for broilers, turkeys, and layers. To project poultry populations, EPA u sed population and Raw data line: y =

- 120.28x + 295808

R2 = 0.3119

Adj. data line: y =

- 145.51x + 347612

R2 = 0.9452

50,000 51,000 52,000 53,000 54,000 55,000 56,000 57,000 58,000 59,000 60,000

Non - cow Inventory (thousand head)

Year U.S. Beef Non

- cow Population193 projection data from the annual summary of the USDA/NASS Poultry

– Production and Value reports (USDAb) for broilers and turkeys, and the Chickens and Eggs reports (USDAc). With these data, EPA used a linear regression analysis to predi ct the number of birds produced in the U.S. for years beyond 2011. Figures C

- 6 and C

- 7 present the population projections for broilers and turkeys, respectively. Figure C

- 8 shows the population projections for egg layers.

Figure C

- 4. Market Hog Inventory Projections

Figure C

- 5. Breeding Hog Inventory Projections Raw data line: y = 0.5161x

- 978.44

R2 = 0.9284

Adj. data line: y = 0.5276x

- 1001.5

R2 = 0.982

40 45 50 55 60 65 70 75 Market Hog Inventory (million head)

Year

U.S. Market Hog Population

Raw data line: y =

- 0.0493x + 105.01

R2 = 0.8486

Adj. data line: y =

- 0.0458x + 98.155

R2 = 0.9277

4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 Breeding Hog Inventory (million head)

Year

U.S. Breeding Hog Population194 Figure C

- 6. Broiler Inventory Projection

Figure C

- 7. Turkey Inventory Projection y = 135606x

- 3E+08

R2 = 0.8276

0 2 4 6 8 10 12 14 Million Broilers

Year

U.S. Broiler Population

y =

- 2204.3x + 5E+06

R2 = 0.6536

100,000 150,000 200,000 250,000 300,000 350,000 Number of Turkeys

Year U.S. Turkey Population195 Figure C

- 8. Egg Layer Projection

References Bowerman, Bruce L. and Richard T. O’Connell (1987) T ime Series Forecasting: Unified Concepts and Computer Implementation . Second Edition. Boston, MA. Duxbury Press.

FAPRI (2010). FAPRI 2010 U.S. and World Agricultural Outlook. January 2010 . Food and Agricultural Policy Research Institute. Iowa State Univer sity and University of Missouri

- Columbia. http://www.fapri.iastate.edu/outlook/2010/text/Outlook_2010.pdf

Kohls, Richard L. and Joseph N. Uhl. (1998) Marketing Edition. Upper Saddle River, NJ Prentice Hall. USDA (2012a). USDA Agricultural Projections to 2021. Interagency Agricultural Projections Committee

. February 2021.

htt p://www.usda.gov/oce/commodity/archive_projections/USDAAgriculturalProjections2021.pdf

USDA (2012b). National Agricultural Statistics Service (NASS). Poultry

- Production and Value Reports. http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1130

USDA (2012c). NASS. Chickens and Eggs Annual Summary

. http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do;jsessionid=0AFEA5A73F0092B7498AC

B64B7CABBED?documentID=1509 y = 3,757.24x

- 7,197,845.17
R2 = 0.86

250,000 270,000 290,000 310,000 330,000 350,000 370,000 Number of Layers (1,000)

Year U.S. Egg Layer Population