## Technical Support Document (TSD)

Preparation of Emissions Inventories for the Version 6.3,

2011 Emissions Modeling Platform

August , 2016

U.S. Environmental Protection Agency

Office of Air and Radiation Office of Air Quality Planning 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

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

ACRONYMS

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

1 INTRODUCTION

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

2 2011 EMISSIO N INVENTORIES AND AP PROACHES

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

2.1 2011 NEI POINT SOURCES ( PTEGU

, PT

_ OILGAS AND PTNONIPM )

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

2.1.1 EGU sector (ptegu)

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

2.1. 2 Point source oil and gas sector (pt_oilgas)

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

2.1.3 Non

- IPM sector (ptnonipm)

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

2.2 2011 NONPOINT SOURCES ( AFDUST

, AG

, AGFIRE

, NP

_ OILGAS

, RWC

, NONPT )

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

2.2.1 Area fugitive dust sector (afdust)

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

2.2.2 Agricultural ammonia sector (ag)

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

2.2.3 Agricultural fires (agfire)

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

2.2.4 Nonpoint source oil and gas sector (np_oilga s)

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

2.2.5 Residential wood combustion sector (rwc)

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

2.2.6 Other nonpoint sources sector (nonpt)

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

2.3 2011 ONROAD MOBILE SOURCE S ( ONROAD )

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

2.3.1 Onroad (onroad)

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

2.4 20 11 NONROAD MOBILE SOURC ES ( CMV

, RAIL

, NONROAD )

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

2.4.1 Category 1, Category 2, Category 3 Commercial Marine Vessels (cmv)

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

2.4.2 Railroad sources: (rail)

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

2.4.3 Nonroad mobile equipment sources: (nonroad)

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

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

- U.S.

SOURCES

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

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

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

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

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

2.5.3 Onroad mobile sources from Canada and Mexico (othon)

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

2.6 F IRES ( PTFIRE )

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

2.7 B IOGEN IC SOURCES ( BEIS )

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

2.8 SMOKE

- READY NON

- ANTHROPOGENIC INVENT ORIES FOR CHLORINE

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

3 EMISSIONS MODELING S UMMARY

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

3.1 E MISSIONS MODELING O VERVIEW

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

3.2 C HEMICAL S PECIATION

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

3.2.1 VOC speciation

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

3.2.1.1 The combination of HAP BAFM (benzene, acetaldehyde, formaldehyde and methanol) and VOC for VOC speciation 45

3.2.1.2 County specific profile combinations (GSPRO_COMBO)

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

3.2.1.3 Additional sector specific details

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

3.2.1.4 Future year speciation

................................ ................................ ................................ ................................ .............. 52
3.2.2 PM speciation

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

3.2.3 NO X speciation

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

3.3 T EMPORAL A LLOCATION

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

3.3.1 Use of FF10 format for finer than annual emissions

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

3.3.2 Electric Generating Utility temporalization (ptegu)

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

3.3.2.1 Base year temporal allocation of EGUs

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

3.3.2.2 Future year temporal allocation of EGUs

................................ ................................ ................................ .................. 63
3.3.3 Residential Wood Combustion Temporalization (rwc)

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

3.3.4 Agricultural Ammonia Temporal Profiles (ag)

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

3.3.5 Onroad mobile temporalization (onroad)

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

3.3.6 Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire, np_oilgas)

........................ 79iii

3.3.7 Time zone corrections

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

3.4 S PATIAL A LLOCATION

................................ ................................ ................................ ................................ ........... 8 2

3.4.1 Spatial Surrogates for U.S. emissions

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

3.4.2 Allocation method for airport

- related sources in the U.S.

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

3.4.3 Surrogates for Canada and Mexico emission inventories

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

4 DEVELOPMENT OF 2017 BASE

- CASE EMISSIONS

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

4.1 EGU SECTOR PROJECTIONS : PTEGU

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

4.2 N ON

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

, AG

, CMV

, RAIL

, NONPT

, NP

_ OILGAS

, PTNONIPM

, PT

_ OILGAS

, RWC

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

4.2.1 CoST Background: Used for NEI non

- EGU Point and Nonpoint sectors

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

4.2.2 CoST Plant CLOSURE packet (ptnonipm)

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

4.2.3 CoST PROJECTION packets (afdust, ag, cmv, rail, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)

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

4.2.3.1 Paved and unpaved roads VMT growth (afdust)

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

4.2.3.2 Livestock population growth (ag)

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

4.2.3.3 Locomotives and Category 1, 2, & 3 commercial marine vessels (cmv, rail, ptnonipm, othpt)

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

4.2.3.4 Upstre am distribution, pipelines and refineries ( nonpt, ptnonipm, pt_oilgas)

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

4.2.3.5 Oil and gas and industrial source growth ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

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

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

Other Fuels

................................ ................................ ................................ ................................ ................................ ........... 121 Oil/Gas Plays

................................ ................................ ................................ ................................ ................................ ....... 122 Remaining Areas

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

4.2.3.6 Data from comments on previous platforms ( nonpt, ptnonipm, pt_oilgas)

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

4.2.3.7 Aircraft (ptnonipm)

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

4.2.3.8 Cement manufacturing (ptnonipm)

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

4.2.3.9 Corn ethanol plants (ptnonipm)

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

4.2.3.10 Residential wood combustion (rwc)

................................ ................................ ................................ ........................ 133
4.2.4 CoST CONTROL packets ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

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

4.2.4.1 Oil and gas NSPS ( np_oilgas, pt_oilgas)

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

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

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

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

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

4.2.4.4 ICI Boilers ( nonpt, ptnonipm, pt_oilgas )

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

4.2.4.5 Fuel sulfur rules ( nonpt, ptnonipm, pt_oilgas )

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

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

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

4.2.4.7 Process heaters NO X NSPS ( ptnonipm, pt_oilgas )

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

4.2.4.8 Arizona Regional Haze controls (ptnonipm)

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

4.2.4.9 CISWI (ptnonipm)

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

4.2.4.10 Data from comments on previous platforms and recent comments ( nonpt, ptnonipm, pt_oilgas )

............................... 150
4.2.5 Stand

- alone future year inventories (nonpt, ptnonipm)

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

4.2.5.1 Portable fuel containers (nonpt)

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

4.2.5.2 Biodiesel plants (ptnonipm)

................................ ................................ ................................ ................................ .... 153

4.2.5.3 Cellulosic plants (nonpt)

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

4.2.5.4 New cement plants (nonpt, ptnonipm)

................................ ................................ ................................ ..................... 156
4.3 M OBILE SOURCE PROJECT IONS

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

4.3.1 Onroad mobile (onroad)

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

4.3.1.1 Future activity data

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

4.3.1.2 Set up and Run MOVES to create EFs

................................ ................................ ................................ .................... 160

4.3.1.3 California and Texas adjustments

................................ ................................ ................................ ........................... 161
4.4 N ONROAD MOBILE SOURCE PROJECTIONS ( NONROAD )

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

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 )

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

5 EMISSION SUMMARIES

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

6 REFERENCES

................................ ................................ ................................ ................................ ...................... 176iv 

## List of Tables

Table 1

- 1. List of cases in the 2011 V ersion 6.3 Emissions Modeling Platform

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

Table 2

- 1. Platform sectors for the 2011v6.3 emissions modeling platform

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

Table 2

- 2. Summary of differences between 2011v6.3 platform and 2011v6.2 emissions 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

Ta ble 2

- 5. Toxic

- to

- VOC Ratios for Corn Ethanol Plants

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

Table 2

- 6. SCCs in the afdust platform sector

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

Table 2

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

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

Table 2

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

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

Table 2

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

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

Table 2

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

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

Table 2

- 11. Onroad emission aggregate processes

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

Table 2

- 12. 2011NEIv2 SCCs extracted for the cmv sector

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

Table 2

- 13. Growth factors to project the 2002 ECA

- IMO inventory to 2011

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

Table 2

- 14. 2011NEIv2 SCCs extracted for the starting point in rail development

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

Table 2

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

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

Table 2

- 16. Large fires apportioned to multiple grid cells

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

Table 2

- 17. Meteorological variable s required by BEIS 3.61

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

Table 3

- 1. Key emissions modeling steps by sector.

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

Table 3

- 2. Descriptions of the platform grids

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

Table 3

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

................................ ................................ ............ 42

Table 3

- 4. Cmaq2camx mapping file

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

Table 3

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

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

Table 3

- 6. MOVES integrated species in M

- profiles

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

Table 3

- 7. VOC profiles for WRAP Phase III basins

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

Table 3

- 8. National VOC profiles for oil and gas

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

Table 3

- 9. Counties included in the WRAP Dataset

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

Table 3

- 10. Select VOC profiles 2011 vs 2017

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

Table 3

- 11. Onroad M

- profiles

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

Table 3

- 12. MOVES Process IDs

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

Table 3

- 13. MOVES Fuel subtype IDs

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

Table 3

- 14. MOVES Regclass IDs

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

Table 3

- 15. PM model species: AE5 versus AE6

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

Table 3

- 16. NO X speciation profiles

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

Table 3

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

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

Table 3

- 18. Time zone cor rections for US counties in 2011v6.3 platform

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

Table 3

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

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

Table 3

- 20

. Off

- Network Mobile Source Surrogates

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

Table 3

- 21. Spatial Surrogates for Oil and Gas Sources

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

Table 3

- 22. Selected 2011 CAP emissions by sector for U.S. Surrogates*

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

Table 3

- 23. Canadian Spatial Surrogates

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

Table 3

- 24. CAPs Allocated to Mexican and Canadian Spatial Surrogates

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

Table 4

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

- case emissions inventories from the

2011 base case

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

Table 4

- 2. Subset of CoST Packet Matching Hierarchy

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

Table 4

- 3. Summary of non

- EGU stationary projections subsections

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

Table 4

- 4. Reductions from all facility/unit/stack

- level closures.

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

Table 4

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

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

Table 4

- 6. Non

- California projection factors for loc omotives and Category 1 and Category 2 Commercial Marine

Vessel Emissions

................................ ................................ ................................ ................................ ............ 107

Table 4

- 7. Difference in Category 1& 2 cmv and rail sector emissions between 2011 and 2017

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

Table 4

- 8. Growth factors to project the 2011 ECA

- IMO inventory to 2017 and 2025

................................ .............. 109v Table 4

- 9. Difference in Category 3 cmv sector and othpt C3 CMV emissions between 2011 and 2017

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

Table 4

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

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

Table 4

- 11. Sources of new industrial source growth factor data from the N ODA and additional corrections implemented for year 2017 in the 2011v6.3 platform

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

Table 4

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

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

Table 4

- 13. NAICS Codes for which NAICS/SCC

- level Growth Factors were developed

................................ ........ 1 19

Table 4

- 14. AEO Oil/Gas Plays

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

Table 4

- 15. Summary of emissions changes in Kansas for year 2017 after projection correction.

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

Table 4

- 16. Industrial source projections net impacts

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

Table 4

- 17. Impact of projection factors for Texas

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

Table 4

- 18. NEI SCC to FAA TAF ITN aircraft categories used for aircraft project ions

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

Table 4

- 19 . National aircraft emission projection summary

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

Table 4

- 20. U.S. Census Division ISMP

- based projection factors for existing kilns

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

Table 4

- 21. ISMP

- based cement industry projected emissions

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

Table 4

- 22. 2011 and 2017/2025 corn ethanol plant emissions [tons]

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

Table 4

- 23. Non

- West Coast RWC projection factors, including NSPS impacts

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

Table 4

- 24

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

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

Table 4

- 25. Assumed retirement rates and new source emission factor ratios for new sources for various NSPS rules

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

Table 4

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

- control 2018 grown values

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

Table 4

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

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

Table 4

- 28. National by

- sector reductions from RICE Reconsideration controls (tons)

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

Table 4

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

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

Table 4

- 30 . National by

- sector reductions from RICE NSPS controls (tons)

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

Table 4

- 31. Facility types potentially subject to Bo iler MACT reductions

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

Table 4

- 32. National

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

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

Table 4

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

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

Table 4

- 34. Summary of ICI Boiler reductions

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

Table 4

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

- VU

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

Table 4

- 36. Summary of fuel su lfur rule impacts on SO 2 emissions

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

Table 4

- 37. Stationary gas turbines NSPS analysis and resulting 2011v6.2 new emission rate s used to compute controls

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

Table 4

- 38. National by

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

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

Table 4

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

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

Table 4

- 40. National by

- sector NO X reductions from Process Heaters NSPS controls

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

Table 4

- 41. Summary of remaining ptnonipm and pt_oilgas reductions

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

Table 4

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

................................ ................................ ...................... 153

Table 4

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

................................ ................................ ............... 153

Table 4

- 44. 2018 biodiesel plant emissions [tons]

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

Table 4

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

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

Table 4

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

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

Table 4

- 47. 2017cellulosic plant emissions [tons]

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

Table 4

- 48. New cellulosic plants NO

x emissions provided by Iowa DNR.

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

Table 4

- 49. Locations of new ISMP

- generated cement kilns

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

Table 4

- 50. ISMP

- generated new permitted and non

- permitted emissions

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

Table 4

- 51. Projection factors for 2017 (in millions of miles)

................................ ................................ ................... 158

Table 4

- 52. Inputs for MOVES runs for 2017

................................ ................................ ................................ .......... 160

Table 4

- 53. CA LEVIII program states

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

Table 5

- 1. National by

- sector CAP emissions summaries for the 2011 evaluation case

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

Table 5

- 2. National by

- sector CAP emissions summaries for the 2017 base case

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

Table 5

- 3. National by

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

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

Table 5

- 4. National by

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

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

Table 5

- 5. National by

- sector NO

x emissions (tons/yr) summaries and percent change

................................ ............. 169vi Table 5

- 6. National by

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

................................ ........... 170

Table 5

- 7. National by

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

................................ ............ 171

Table 5

- 8. National by

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

................................ ............. 172

Table 5

- 9. National by

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

................................ ........... 173

Table 5

- 10. Canadian province emissions changes from 2011 to 2017 for othon sector

................................ ............ 174

Table 5

- 11. Canadian province emissions changes from 2011 to 2017 for othar sector

................................ ............. 174

Table 5

- 12. Canadian province emissions changes from 2011 to 2017 for othpt sector

................................ ............. 175



## List of Figures

Figure 2

- 1. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and cumulati ve 18 Figure 2

- 2. Illustration of regional modeling domains in ECA

- IMO study

................................ ................................ . 28 Figure 2

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

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

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

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

- 1. Air quality modeling domains

................................ ................................ ................................ .................. 40 Figure 3

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

................................ ....................... 46 Figure 3

- 3. Eliminating unmeasured spikes in CEMS data

................................ ................................ ........................ 61 Figure 3

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

................................ .......................... 61 Figure 3

- 5. IPM Regions for EPA Base Case v5.15

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

- 6. Month

- to

- day profiles for different fuels in a West Texas Region

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

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

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

- 8. Excess emissions apportioned to hours less than maximum

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

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

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

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

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

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

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

- 12. RWC diurnal temporal profile

................................ ................................ ................................ ............... 71 Figure 3

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

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

- 14. Day

- of

- week temporal profiles for OHH and Recreational RWC

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

- 15. Annual

- to

- month temporal profiles for OHH and recreational RWC

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

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

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

- 17. Example of SMOKE

- MOVES temporal variability of NO X emissions

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

- 18. Previous onroad diurnal weekday profiles for urban roads

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

- 19. Use of submitted versus new national default profiles

................................ ................................ ........... 76 Figure 3

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

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

- 21

. Updated national default profiles for day of week

................................ ................................ ................. 78 Figure 3

- 22. Combination long

- haul truck restricted and hoteling profile

................................ ................................ .. 79 Figure 3

- 23. Agricultural burning diurnal temporal profile

................................ ................................ ........................ 80 Figure 4

- 1. Oil and gas plays with AEO projection data

................................ ................................ ........................... 122 Figure 4

- 2. Oil and Gas NEMS Regions

................................ ................................ ................................ .................. 123 Figure 4

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

................................ ............ 131 Figure 4

- 4. Light Duty VMT growth rates based on AEO2014

................................ ................................ ................ 160



## List of Appendices

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

## 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) to Pump

C1/C2 Category 1 and 2 commercial marine vessels

C3 Category 3 (commercial marine vessels)

CAEP Committee on Aviation Environmental Protection

CAIR Clean Air Interstate Rule

CAMD EPA’s Clean Air Markets Division

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

CEMS Continuous Emissions Monitoring System

CEPAM California Emissions Projection Analysis Model

CISWI Commercial and Industrial Solid Waste Incinerators

Cl Chlorine CMAQ Community Multiscale Air Quality

CMV Commercial Marine Vessel

CO Carbon monoxide

CSAPR Cross

- State Air Pollution Rule E0, E10, E85 0%, 10% and 85% Ethanol blend gasoline, respectively

EBAFM Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol

ECA Emissions Control Area

EEZ Exclusive Economic Zone

EF Emission Factor

EGU Electric Generating Units

EIS Emissions Inventory System

EISA Energy Independence and Security Act of 2007

EPA Environmental Protection Agency

EMFAC Emission Factor (California’s onroad mobile model)

FAA Federal Aviation Administration

FAPRI Food and Agriculture Policy and Research Institute

FASOM Forest and Agricultural Section Optimization Model

FCCS Fuel Characteristic Classification System

FF10 Flat File 2010

FIPS Federal Information Processing Standards

FHWA Federal Highway Administration

HAP Hazardous Air Pollutant

HCl Hydrochloric acid

HDGHG Heavy

- Duty Vehicle Greenhouse Gasviii

Hg Mercury

HMS Hazard Mapping System

HPMS Highway Performance Monitoring System

HWC Hazardous Waste Combustion

HWI Hazardous Waste Incineration

ICAO International Civil Aviation Organization

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

ICR Information Collection Request

IDA Inventory Data Analyzer

I/M Inspection and Maintenance

IMO International Marine Organization

IPAMS Independent Petroleum Association of Mountain States

IPM Integrated Planning Model

ITN Itinerant

LADCO Lake Michigan Air Directors Consortium

LDGHG Light

- Duty Vehicle Greenhouse Gas

LPG Liquefied Petroleum Gas

MACT Maximum Achievable Control Technology

MARAMA Mid

- Atlantic Regional Air Management Association

MATS Mercury and Air Toxics Standards

MCIP Meteorology

- Chemistry Interface Processor Mgal Million gallons

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

Management, Regulation and Enforcement (BOEMRE)

MOVES Motor Vehicle Emissions Simulator

MSA Metropolitan Statistical Area

MSAT2 Mobile Source Air Toxics Rule

MTBE Methyl tert

- butyl ether

MWRPO Mid

- west Regional Planning Organization

NCD National County Database

NEEDS National Electric Energy Database System

NEI National Emission Inventory

NESCAUM Northeast States for Coordinated Air Use Management

NESHAP National Emission Standards for Hazardous Air Pollutants

NH 3 Ammonia

NIF NEI Input Format

NLCD National Land Cover Database

NLEV National Low Emission Vehicle program

nm nautical mile

NMIM National Mobile Inventory Model

NOAA National Oceanic and Atmospheric Administration

NODA Notice of Data Availability

NONROAD OTAQ’s model for estimation of nonroad mobile emissions

NO X Nitrogen oxides

NSPS New Source Performance Standards

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

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

ORIS Office of Regulatory Information Systemix

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 per billion, parts per million

RBT Refinery to Bulk Terminal

RFS2 Renewable Fuel Standard

RIA Regulatory Impact Analysis

RICE Reciprocating Internal Combustion Engine

RRF Relative Response Factor

RWC Residential Wood Combustion

RPO Regional Planning Organization

RVP Reid Vapor Pressure

SCC Source Classification Code

SEMAP Southeastern Modeling, Analysis, and Planning

SESARM Southeastern States Air Resource Managers SESQ Sesquiterpenes

SMARTFIRE Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation

SMOKE Sparse Matrix Operator Kernel Emissions

SO 2 Sulfur dioxide

SOA Secondary Organic Aerosol

SI Spark

- ignition

SIP State Implementation Plan

SPDPRO Hourly Speed Profiles for weekday versus weekend

SPPD Sector Policies and Programs Division

TAF Terminal Area Forecast TCEQ Texas Commission on Environmental Quality

TOG Total Organic Gas

TSD Technical support document

ULSD Ultra Low Sulfur Diesel

USDA United States Department of Agriculture

VOC Volatile organic compounds

VMT Vehicle miles traveled

VPOP Vehicle Population

WRAP Western Regional Air Partnership

WRF Weather Research 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 Inve ntory (NEI)

, 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 includes the emission inventories, the ancillary data files , and the approaches used to transform inventories for use in air quality modeling. Many emissions inventory components of this air quality modeling platform are based on the 2011NEIv2 , although there are some differences between the platform inventories and the 20 11NEIv2 emissions as a result of addressing public comments and the incorporation of newly available data and improved methods

.

This 2011 modeling platform includes all criteria air pollutants and precursors (CAPs) and the following hazardous air pollutan ts (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 6.3” because it is primarily a CAP platform with BAFM species included. Here, “version 6.3” denotes an evolution from the 2011

- based platform, version 6. 2

. For the rest of this document, the platform that is described is referred to as the “2011 v6.3 platform” or

“2011v6.3

. ”

The 2011 v6.3 platform was used to support the Final Cross

- state Air Pollution Update Rule, the focus of

which is ozone transport modeling for the 2008 National Ambient Air Quality Standards (NAAQS). The air quality model used for this rule is the Comprehensive Ai r Quality Model with Extensions (CAM X ) model ( http://www.camx.com/ ), version 6. 2 0. However, 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 those emissions are

converted to CAM X

- ready format.

Both CAM X and CMAQ support modeling ozone (O 3 ) and particulate matter (PM), and require as input hourly and gridded emissions of chemical species that correspond to CAPs and specific HAPs. The chemical mechanism used by CAM X for this platform is called Carbon Bond version 6 revision 2 (CB6r2)

and in cludes important 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 organic 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 emi ssions modeling 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 6.3 platform consists of two ‘complete’ emissions cases : the 20 11 base case (i.e.,

2011e k _cb6v2 _v6)

, and the 2017 base case (i.e., 201 7 e k _cb6v2 _v6)

. In the case abbreviations, 2011 and

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

- specific

data for fir es and electric generating u nits ( EGUs ) are used ; and the “ k ” represents that this was the

eleventh set of emissions modeled for a 2011

- based modeling platform (i.e., the first case for the 2011 platform was 2011ea, the second was 2011eb, and so on)

. 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 used in corresponding future

- year cases
, including the 2017 future year base case
, as well as any additional future year control and source apportionment cases. This 2011v6.3 TSD is2

available on the EPA’s Air Emissions Modeling website for the version 6 platforms

, https://www.epa.gov/air

- emissions

- modeling/2011

- version

- 6

- air

- emissions

- modeling

- platforms , under

the section entitled “2011 v6.3 Platform

. ”

For regulatory appli cations, the outputs from the 2011 base case are used in conjunction with the outputs from the 2017 base case in the relative response factor (RRF) calculations to identify future areas of nonattainment. For more information on the use of RRFs and air qua lity 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 .3 Emissions Modeling Platform

Case Name Abbreviation Description

2011 base case 2011ek_cb6v2_v6 2011 case relevant for air quality model evaluation purposes and for computing relative response factors with 2017 scenario(s). Uses 2011NEIv2 along with some other inventory data, with hourly 2011 continuous emissions monitoring

s ystem (CEMS) data for Electrical Generating Units (EGUs), hourly onroad mobile emissions, and 2011 day

- specific wild and prescribed fire data.

2017 base case 2017ek_cb6v2_v6 2017 “base case” scenario, representing the best estimate for 2017 that incorpo rates estimates of the impact 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 where “ g ” corresponds to the 7 th version of the 2011 meteorology data

. A special version of the 2017ek_cb6v2_v6 case called 2017ek_ussa_cb6v2_v6_11g was prepared for use with the CAMx OSAT/APCA feature that allowed the

contribution of 2017 base case NO X 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 2017ek_cb6v2_v6 case, except that the emission sources are tagged according to their origin by state or sector. The steps for setting up the 2017ek_ ussa_ cb6v2_v6 source apportionment case include:

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

geographic sector

- s pecific tags for biogenic, fugitive dust, fire, and non

- US emissions); 2) run all sectors in Sparse Matrix Operator Kernel Emissions ( SMOKE ) using the specified source

groups (note that emissions for both source apportionment and for a regular CAMx run can be developed simultaneously) ; 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 sour ce apportionment is available with the scripts provided for the 2011v6.3 platform at ftp://ftp.epa.gov/EmisInventory/2011v6/v 3platform/

.

The EPA has adopted 2017 as the analytic year for the Cross State Air Pollution Update Rule (CSAPR)

and the 2011v6.3 platform as a result of the D.C. Circuit court decision in Natural Resources Defense

Council v. EPA in which it was decided t hat that attainment must be demonstrated using 2015 through

2017 ozone season data

.3

The emissions data in the 2011v6.3 platform are primarily based on the 2011NEIv2 for point 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 a version of the Motor Vehicle Emissions Simulator (MOVES2014 a ) ( http://www.epa.gov/otaq/models/moves/ )

. In contrast, the 2011NEIv2 emissions were generated using

the earlier MOVES2014 and did not include any E

- 85 vehicles or fuels

. The 2011v6.3 platform cases implement comments received on the Federal Register noti ces issued for 2011 and 2018 emissions in the 2011v6.0 platform ( 78 FR 70935 and 79 FR 2437 , respectively) , and comments received on the 2011v6.2 platform 2011 and 2017 emissions through the notice of data availability (NODA) published in 80 FR 46271. Hig h

- level descriptions of implemented comments are included in the relevant subsections. The updates in the 2011v6.3 platform as compared to the 2011v6.2 platform were primarily due to comments

on the 2015 NODA and include the following :

- u se of the Integrat ed Planning Model (IPM) version 5.15 for future year EGU emissions;

- corrected to the cross reference matching NEI EGU units to IPM units;

- updated stack parameters for point sources;

- updated lists of closures for point sources;

- updated nonpoint emissions in Connecticut, Massachusetts, North Carolina;

- updated stack parameters and non

- EGU point and oil and gas emissions in Oklahoma;

- removed plants from lists of new cement plants;

- u pdated year 2017 projection factors for many sectors in North Carolina ;

- updated oil and gas projected emissions for Kansas;

- r evert ed agricultural fires emissions for Missouri back to 2011 NEI v1 levels ;

- used MOVES 2014a (an updated version of MOVES) for base and future year onroad mobile source emissions, along with updated inpu t databases and activity data for North Carolina, Ohio, and New Jersey;

- updated 2011 and 2017 onroad mobile source emissions for California and Texas;

- updated oil and gas and locomotive emissions for Texas;

- u pdated projected 2017 Canada point, onroad

, and nonroad emissions;

- used the newer BELD version 4.1 landuse in Biogenic Emissions Inventory System (BEIS) version 3.61 for biogenic emissions;

- time zone corrections/updates which affects most of Indiana, a few counties in other states along time zone boundaries (KS, KY, ND, SD), Canada (Saskatchewan, Manitoba, far eastern Quebec), Mexico (most of the country needed some kind of correction) ; and

- u pdated projections for non

- EGU point and nonpoint sources due to a correction to the order of precedence for the application of control programs

.

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

- ready emissions was the SMOKE modeling system ( http://www.smoke

- model.org/ ). SMOKE version 3.7 w as used to create

emissions files for a 12

- km national grid that includes all of the contiguous states “12US2

, ” shown in Figure 3

- 1. Electronic copies of the data used as input to SMOKE for the 2011 Platform are available

from the EPA Air Emissions Modeli ng website, https://www.epa.gov/air

- emissions

- modeling/2011

- version

- 6

- air

- emissions

- modeling

- platforms

, under the 2011 v6.3 section.4

The gridded mete orological model used for the emissions modeling was developed using the Weather Research and Forecasting Model (WRF, http://wrf

- model.org ) version 3.4, Advanced Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale numerical weather prediction system developed for both operational forecasting and atmospheric research applications. The WRF was run for 2011 over a domain covering the continental U

. S

. 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 platform 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. 2 platform.

This document contains five sections and several appendices. Section 2 describes the 2011 inventories input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used with the emission inventories. Section 4, describes the develop ment of the 2017 inventory (projected from 2011). Data summaries comparing the 2011 and 2017 base cases are provided in Section 5. Section 6 provides references. The Appendices provide additional details about specific technical methods.5 

## 2 20 11 Emission Inventories and Approaches

This section describes the 2011 emissions data that make up the 2011 platform. The starting point for the 2011 stationary source emission inputs is the 2011NEIv2 , with adjustments made to account for updated

data and m ethods based on public comments and corrections of errors identified by the EPA

. Documentation for the 2011NEIv2, including a Technical Support Document (TSD), is available at https://www.epa.gov/air

- emissions

- inventories/2011

- national

- emissions

- inventory

- nei

- documentation

.

The NEI data for CAPs are largely compiled from data submitted by state, local and tribal (S/L/T) air agencies. HAP emis sions data are also from the state data, but are often augmented by the EPA because they are voluntarily submitted. The EPA uses the Emissions Inventory System (EIS) to compile the NEI.

The EIS includes hundreds of automated QA checks to help improve dat a quality, and also supports tracking release point (e.g., stack) coordinates separately from facility coordinates. The EPA collaborated extensively with S/L/T agencies to ensure a high quality of data in the 2011NEIv2. Tangible benefits of

this collabor ation are seen in improved data quality, improved completeness and reduced duplication between point and nonpoint source categories such as industrial boilers. Onroad mobile source emissions in the 2011NEIv2 were developed using a pre

- release version of M OVES2014; however, the 2011 v6.3 emissions modeling platform used the released version of MOVES2014 a

.

The 2011 NEI includes five data categories: point sources, nonpoint (formerly called “stationary area”)
sources, nonroad mobile sources, onroad mobile sou rces, and events consisting 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 emissions modeling platform. In

add ition to the NEI data, emissions from the Canadian and Mexican inventories and several other non

NEI data sources are included in the 2011 emissions modeling platform.

As explained below, the major differences between the 2011 NEIv2 and the 2011 v6.3 pla tform include: a

different version of MOVES

- based onroad mobile source emissions, meteorologically

- adjusted road dust emissions, CEMS data for EGUs, updates implemented to various sectors as a result of public comment and corrections identified by the EPA, and emissions for areas outside the 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 Reanalys is Tool for Fire Incident Reconciliation (SMARTFIRE) system (Sullivan, et al., 2008). SMARTFIRE2 was the first version of SMARTFIRE to assign all fires as either prescribed burning or wildfire categories. In past inventories, a significant number of fires were published as unclassified, which impacted the emissions values and diurnal emissions pattern. Recent updates to SMARTFIRE include improved emission factors for prescribed burning.

For the purposes of preparing the air quality model

- ready emission s, the 2011NEIv2 was split into finer

grained sectors used for emissions modeling. The significance of an emissions modeling or “platform sector” is that the data are run through all of the SMOKE programs except the final merge (Mrggrid) independently fro m the other sectors. The final merge program then combines the sector

- specific gridded, speciated, hourly emissions together to create CMAQ

- ready emission inputs. For CAMx applications, the CMAQ

- ready emissions are then converted into the format needed b y CAMx using a convertor program.

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 sector6

abbreviations are provided in italics. These abbreviations are use d in the SMOKE modeling scripts, inventory file names, and throughout the remainder of this document.

Table 2

- 1

. Platform sectors for the 20 11 v6.3 emissions modeling platform

Platform Sector:

abbreviation NEI Data

Category Description and resolution of the data input to SMOKE

EGU units:
ptegu Point 2011NEIv2 point source EGUs with specific updates made to stack parameters and emissions based on comments . The 2011NEIv2 emissions are replaced with hourly 2011 CEMS values for NO X and

SO 2, where the units are matched to the NEI. Other pollutants are scaled from 2011NEIv2 using CEMS heat input. Emissions for all

sources not matched to CEMS data come from 2011NEIv2. For future year emissions, these units are map ped to the Integrated Planning Model (IPM) results using a cross reference to the National Electric Energy Database System (NEEDS) version 5.1 5

. Annual resolution

for sources not matched to CEMS data , hourly for CEMS sources.

Point source oil and gas: pt_oilgas Point 2011NEIv2 point sources that include oil and gas production emissions processes with specific updates to emissions and stack parameters based on comments and updates to control program order of precedence . Annual resolution.

Remaining non

EGU point: ptnonipm Point All 2011NEIv2 point source records not matched to the ptegu or pt_oilgas sectors, except for offshore point sources that are in the othpt

sector ; with specific updates to emissions and stack parameters based

on comments and co ntrol program order of precedence . Includes all

aircraft emissions and some rail yard emissions. Annual resolution. Agricultural:

ag Nonpoint NH 3 emissions from 2011NEIv2 nonpoint livestock and fertilizer application, county and annual resolution.

Agricultural fires: agfire Nonpoint 2011NEIv2 agricultural fire sources , except for Missouri

, which

reverted to 2011NEIv1 based on comments . County and monthly

resolution.

Area fugitive dust: afdust Nonpoint PM 10 and PM 2.5 fugitive dust sources from the 2011NEIv2 nonpoint inventory; including building construction, road construction, agricultural dust, and road dust

. H owever, unpaved and paved road

dust emissions differ from the NEI in that they do not have a precipitation adjustment. Instead, the emissions modeling adjustment applies a transport fraction and a meteorology

- based (precipitation and snow/ice cover) zero

- out. County and annual resolution.

Biogenic:

Beis Nonpoint Year 2011, hour

- specific, grid cell

- specifi c emissions generated from the BEIS3.61 model within SMOKE, including emissions in Canada

and Mexico using BELD v4.1 land use data

. Category 1, 2 and

3 CMV:

cmv Nonpoint C ategory 1 (C1), category 2 (C2) and category 3 (C3) commercial marine vessel (CMV) emissions sources from the 2011NEIv2 nonpoint inventory. County and annual resolution; see othpt sector for

all non

- U.S. C3 emissions.

locomotives:
rail Nonpoint Rail l ocomotives emissions from the 2011NEIv2 with specific adjustments based on comments

. Remaining nonpoint:
nonpt Nonpoint 2011NEIv2 nonpoint sources not included in other platform sectors with specific adjustments based on comments

. C ounty and annual

resolution.7

Platform Sector:

abbreviation NEI Data

Category Description and resolution of the data input to SMOKE

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

- related processes with specific adjustments based on comments . County and annual

resolution. I ncludes updates in Utah, and corrects sources in WRAP areas so that they are no

- integrate and use WRAP speciation profiles

. Additionally, new Texas inventory supplied by TCEQ, and

modifications to Oklahoma (VOC

- only changes) and West Virginia (SO2

- only changes) inventories due to NOD A comments.

Residential Wood

Combustion:

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

Nonroad:

nonroad Nonroad 2011NEIv2 nonroad equipment emissions developed with the National Mobile Inventory Model (NMIM) using NONROAD2008 version NR08a. NMIM was used for all states except California and Texas,

which submitted their own emissions for the 2011NEIv2. County and

m onthly resolution.

Onroad:

onroad Onroad 2011 onroad 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 br ake and tire wear. For all states

, except California

and Texas , based on monthly MOVES emissions tables produced by

MOVES2014 a . California emissions are based on Emission Factor (EMFAC). MOVES emissions for Texas provided by TCEQ for year

2012 were back casted to year 2011

. 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: ptfire Fires Point source day

- specific wildfires and prescribed fires for 2011 computed using SMARTFIRE2, except for Georgia and Florida

submitted emissions. Consistent with 2011NEIv2.

Other dust

sources not from

the 2011 NEI:

o thafdust N/A Fugitive dust sources fr om Canada’s 2010 inventory.

Other point

sources not from

the 2011 NEI: othpt N/A Point sources from Canada’s 2010 inventory and Mexico’s 2008 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 Monthly year 2010 Canada (province resolution) and year 2008 Mexico (municipio resolution) nonpoint and nonroad mobile

inventories.

Other non

- NEI

onroad sources:

othon N/A Monthly year 2010 Canada (province resolut ion) and year 2008 Mexico (municipio resolution) onroad mobile inventories, annual

resolution.

Table 2

- 2 provides a brief by

- sector overview of the most significant d ifferences 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 described in greater detail later in this section under each by

- sector subsection

.8

Table 2

- 2

. Summary of differences between 20 11 v6.3 platform and 2011v6.2 emissions by sector

Platform Sector Summary of Significant Inventory Differences of 2011 v6.3 Platform vs.

2011v6.2 platform Agriculture Burning: agfire 1) Reverted back to 2011 NEIv1 values for state of Missouri.

Biogenic:

beis 1) Used Biogenic Emissions Landcover Data (BELD) v4.1 as input into

BEIS3 .61

. Remaining nonpoint sector:
nonpt 1) Connecticut boilers SO2 emissions updated (four SCCs)

.

2) Massachusetts Gas Bulk Plants/Terminals emissions updated

. 3) PFC adjustments

. Nonpoint oil and
gas sector: np_oilgas 1) Emissions updated in Texas, Oklahoma, West Virginia, and 5 Utah counties

.

Onroad sector:
Onroad 1) MOVES 2014a with Colorado I/M fixes and NODA updates to OH and NJ CDBs + activity

. 2) Updates to speciation with MOVES2014a

. 3) CARB submitted new data based on EMFAC2014

. 4) Extended idle updates in Maricopa County, AZ from NATA review: 50% reduction in all Maricopa RPH emissions

. 5) Representative county changes: EFs for Colorado county 08123 were not
available , so that county group was remapped to representative coun ty 08041. Also, 2011ek included a fix in which representative county 48261 (Kenedy, TX) was substitu t ed with 48047 (Brooks, TX) due to negative EFs in 48261

. 6) Using TCEQ

- submitted data (activity and emissions) in Texas. TCEQ submitted 2012 emissions, which we scaled back to 2011 using 2011eh/2012eh ratios by county/SCC (state/SCC for county/SCCs with zero emissions in 2011eh/2012eh). SMOKE

- MOVES emissions in Te xas were then adjusted to match our new 2011 TCEQ

- based inventory by county/SCC, with all non

diesel fuels lumped together. NO X species were adjusted separately, but all PM species were adjusted using PM 2.5 . (Texas’s PM speciation is similar to ours, and Texas did not provide all PM species for 2017). For activity, we used TCEQ

- provided VMT, VPOP, and HOTELLING. For VMT and VPOP, we split out E85 using EPA E85/gas ratios.

EGUs:
ptegu 1) Based on 2011NEIv2 and 2011 CEMS data analysis by the EPA and state s, 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

. 3) Hourly NO X and 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. 4) Some stack parameter and latitude

- longitude updates from NODA comments

. 5) Added sources with new matches to IPM.

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 ptegu sector. The goal is to prevent double counting of EGU emissions in the future years. 2) Some stack parameter and l atitude

- longitude updates from NODA commen t s 3) Some compressor and pump stations were moved to pt_oilgas.

Point oil and gas

sector: pt_oilgas 1) Projected e missions updated in Texas , Kansas and Oklahoma

.9

The emission inventories in SMOKE input format for the 2011 base case are available from the EPA’s Air Emissions Modeling website for the version 6 platforms, https://www.epa.gov/air

- emissions

modeling/2011

- version

- 6

- air

- emissions

- modeling

- platforms , under the section entitled “2011v6.3

Platform

. ” The 2011v6.3 “readme” file indicates the particular zipped files associated with each platform

sector. A number of reports (i.e.

, summaries ) are available with the data files for the 2011 v6.3 platform. The types of reports include 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 list of inventory files, ancillary files, and parameter settings for the 2011v6.3 platform modeling cases is also available.

The remain der of Section 2 provides details about the data contained in each of the 2011 platform sectors. Different levels of detail are provided for different sectors depending on the availability of reference information for the data, the degree of changes or ma nipulation of the data needed to prepare it for input to SMOKE, and whether the 2011 platform emissions are significantly different from the 2011NEIv2.



### 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 facility may have multiple emission release points that may be characterized a s units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas). With a couple of minor exceptions, this section describes only NEI point s ources within the contiguous U

. S. The offshore oil platform (othpt sector) emissions are processed by SMOKE as point source inventories, as described in Section 2.5.1 and Section 2.4.2, respectively. A comprehensive description of how EGU emissions were characterized and estimated in the 2011 NEI is

located in Section 3.10 in the 2011NEIv2 TSD.

The point source file used for the modeling platform is exported from EIS into the Flat File 2010 (FF10) format that is compatible with SMOKE (see https://www.cmascenter.org/smoke/documentation/3.7/html/ch08s02s10.html#d0e44847 ). After moving offshore oil platforms into the othpt sector, and dropping sources witho ut specific locations (i.e., their FIPS code ends in 777), initial versions of the other four platform point source sectors were 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 (ptnonipm) sector. The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal processing an d 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 , ammonia ( NH 3 ) , PM 10 , and PM 2.5 and the following HAPs: HCl (pollutant code = 7647010 ), and

C l (code = 7782505 ). T he inventory BAFM from these sectors was not used, instead VOC was speciated to these pollutants without any use (i.e., integration) of the VOC HAP pollutants from the inventory ( VOC integration is discussed in detail in Section 3.2.1.1 ).

The ptnonipm and pt_oilgas sector emissions were prov ided to SMOKE as annual emissions. For those ptegu sources with CEMS data (that could be matched to the 2011NEIv2), 2011 hourly CEMS NO X and

SO 2 emissions were used rather than NEI emissions, and for all other pollutants

, annual emissions were10

used as

- is from the NEI, but were allocated to hourly values using heat input CEMS data. For the sources in the ptegu sector not matched to CEMS data , daily emissions were created using an approach described in Section 2.1.1, and IPM region

- and pollutant

- specific d iurnal profiles were applied to create hourly emissions.

C hanges made to the point

- based sectors from the 2011NEIv2 for the 20 11 platform involved splitting the stacks, units and facilities into the ptnonipm , pt_oilgas and ptegu sectors. Sources were i ncluded in the pt egu sector only when it was determined that these sources were reflected in the future

- year IPM output data. These changes and other updates to the point source sectors for the 20 11 platform are discussed in the following sections. Speci fic changes to stack parameters and emissions were made

based on comments.



### 2.1.1 EGU s ector (pt egu )

The ptegu sector contains emissions from EGUs in the 2011NEIv2 point inventory that could be

matched to units found in the NEEDS v5.1 5 database. It was necessary to put these EGUs into separate sectors in the platform because IPM projects future emissions for the EGUs defined in the NEEDS database, and emissions for sources in the ptegu sector are replaced with IPM outputs in the future year modeling case. Source s not matched to units found in NEEDS are placed into the pt_oilgas (see Section 2.1.2) or ptnonipm (see Section 2.1.3) sectors and are projected to the future year using projection and

control factors. It is important that the matching between the NEI an d NEEDS database be as complete as possible because there can be double counting of emissions in the future year if emissions for units

that are projected by IPM are not properly mat ched to the units in the NEI.

In the SMOKE point flat file, emission reco rds for sources that have been matched to the NEEDS
database have a value filled into the IPM_YN column. Many of these matches are stored within EIS. In some cases, it was difficult to match the sources between the databases due to different facility nam es 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 la ter in this section. The NEEDS v5.1 5 database can

be found at https://www.epa.gov/airmarkets/power

- sector

- modeling

, along with additional information

about IPM. The cross reference betwe en NEEDS 5.1 5 units and those in the NEI and is used when IPM outputs are postprocessed to create flat files for modeling. It can be found in the reports area of the FTP site for the 2011v6.3 platform: ftp://ftp.epa.gov/EmisInventory/2011v6/v3platform/reports/

.

Some units in the ptegu sector are matched to CEMS data via ORIS facility codes and boiler ID. 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. All stack parameters, stack locations, and SCC codes for these sources come from the NEI. Because these attributes are obtained from the NEI, the chemical speciation of VOC and 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 u nit 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 i n the NEI. The EIS stores many matches from EIS units to the ORIS facility

codes and boiler IDs used to reference the CEMS data. Some additional matches were made in the modeling platform as described later in this section.11

In the SMOKE point flat fi le, emission records for point sources matched to CEMS data have values filled into the ORIS_FACILITY_CODE and ORIS_BOILER_ID columns. The CEMS data in

SMOKE

- ready format is available at http://ampd.epa.gov/amp d/ near the bottom of the “Prepackaged Data” tab. Many smaller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can be matched to the NEI due to inconsistencies in the way a unit is defined between the NEI

and CAMD datas ets, or due to uncertainties in source identification such as inconsistent plant names in the two data systems. Also, the NEEDS database of units modeled by IPM includes many smaller emitting EGUs that are not included in the CAMD hourly CEMS programs. T herefore, there will be

more units in the NEEDS database 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 pr ofiles are used for the remaining units. More detail can be found in

Section 3.3.2.

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 as much as possible for sources that are carried into the future year. This method keeps the temporal behavior of the base and future year cases as consistent as possible. See Section 3.3.2 for more details on the temporalization approach for ptegu

sources.

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

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

- referenced using “ORIS” facility and boiler IDs. The steps described in the 2011NEIv2 TSD provide some detail on how the values in the IPM_YN

column were a ssigned. For the modeling platform, an initial ptegu/ptnonipm split was determined using the values in the SMOKE point source flat file variable “IPM_YN

, ” which is populated based on an EIS alternative facility identifier. Because EIS expects the matches to be one

- to

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

- to

- many or many

- to

- many matches can only be stored in EIS with specified “end dates” and will not export directly to the flat file. However,

one

- to

- ma ny and many

- to

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

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

Ad ditional matches between the NEI and NEEDS were identified by identifying units in IPM outputs that were not yet matched to NEI data, and by looking for units identified in the NEI with facility type codes identifying them as EGUs or facility names that in dicated 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 pla nt 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

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

represented the NEEDS IDs were filled in to the IPM_YN in the modeling platform flat files. Wh en possible, these matches were loaded into EIS.

A similar process was used to identify additional matches between the 2011NEIv2 and CEMS data. To determine whether a NEI unit matched a CEMS unit, the CEMS units were compared to facilities in the

NEI tha t 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

. Additional CEMS matches that were found were applied to the FF10 file by specifying values for ORIS_FACILITY_CODE, ORIS_BOILER _ID. Because IPM uses a concatenation of the ORIS facility code and boiler ID, values were also filled in to the IPM_YN field for

these units.

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

have remai ned in the ptnonipm sector were moved to the ptegu sector. Many new CEMS assignments

were loaded into EIS for use in future inventories. Note that SMOKE can perform matches of CEMS data down to the stack or release point

- level, which is finer than unit

- l evel.



### 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 NAICS

codes shown in Table 2

- 3

. The emissions and other source characteristics in the pt_oilgas sector are submitted by states, while the 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

emissions can be found in the np_oilgas sector. More information on the development of the 2011 oil and gas emissions can be found in Section 3.2 0 of the 2011NEIv2 TSD.

Table 2

- 3

. Point source oil and gas sector NAICS 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 Gas13



### 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 the ptegu or pt_oilgas sectors. For the most part, the ptnonipm sector reflects the

non

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

- emitting EGUs not matched to the NEEDS database or to CEMS data are present in the ptnonipm sector. The sector 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 emissions 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 included 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 f or only a part of the year and are

, therefore

, difficult to allocate to specific places and days as is needed for modeling. Therefore, these sources are dropped from the point

- based sectors in the modeling platform.

EPA estimates for ethanol facilities

As ethanol plants are important facilities for mobile source rules that have impact development work,

the EPA developed a list of corn ethanol facilities for 2011

. E thanol facilities that were not in

2011 NEI v1 were added into 2011NEIv2. Some adjustments were made to these based on comments

. Locations and FIPS codes for these ethanol plants were verified using web searches and Google Earth.

The EPA believes that some of these sources were not originally included in the NEI as point sourc es 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 2017 ) 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 0.00

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 0.00

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 , and were multiplied by facility production estimates for 2011 and 2018 based on analyses performed for the industry characterization described in Chapter 1 of the RFS2 final rule regulatory impact analysis. For14

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)

Several modeling platform sectors were created from the 2011NEIv2 nonpoint inventory. This section

describes the stationary nonpoint sources. Locomotives, C1 and C2 CMV, and C3 CMV are also included the 2011NEIv2 nonpoint data category, but are mobile sources that are described in Sections

2.4.1 and 2.4.2 as the CMV and rail sectors, respectively. The 2011NEIv2 TSD available from https://www.epa.gov/air

- emissions

- inventories/2011

- national

- emissions

- inventory

- nei

- documentation includes documentation for the nonpoin t sector of the 2011NEIv2.

The nonpoint tribal

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

- counting with county

- level emissions and also because spatial surrogates for tribal data are not currently available. These omissions are not expected to have an impact on the results of the air quality modeling

at the 12

- km scales used for this platform.

The following subsectio ns descri be how the sources in the 2011NEIv2 nonpoint inventory 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 (afdus t) sector contains PM 10 and PM 2.5 emission estimates for nonpoint SCCs identified by EPA staff as dust sources. Categories included in the afdust sector are paved roads, unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production, and mining and quarrying. It does not include fugitive dust from grain elevators, coal handling at coal mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as point sources so th ey are properly located.

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

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

land use data used to reduce the NEI emissions determines the amount of emissions that are s ubject 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 “Fugiti ve Dust Modeling for the 2008 Emissions 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 different grid resolutions. A limitation of the transport fraction approach is the lack of monthly variability that would be expected15

with seasonal changes in vegetative cover. While wind speed and direction are not account ed for in the emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is accounted for in the subsequent meteorological adjustment.

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

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

- 6

.
Table 2

- 6

. SCCs in th e afdust platform sector

SCC SCC Description

2275085000 Mobile Sources;Aircraft;Unpaved Airstrips;Total

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

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

- Fugitives

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

2296005000 Mobile Sources;Unpaved Roads;Public Unpaved Roads;Total: Fugitives

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

2311 000000 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;Indu strial/Commercial/Institutional;Total

2311030000 Industrial Processes;Construction: SIC 15

- 17;Road Construction;Total

2311040000 Industrial Processes;Construction: SIC 15

- 17;Spec ial 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 Miscellaneous Area Sources;Agriculture Production

- Livestock;Beef cattle

- finishing

operations on feedlots (drylots);Dust Kicked

- up by Hooves (use 28

- 05

- 020,

- 001,

- 002, or

- 003 for Waste

The dust emiss ions in the modeling platform are not the same as the 2011NEIv2 emissions because the NEI paved and unpaved road dust emissions include a built

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

, data used did not include any precipitation

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

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

- submitted data were not met

adjusted and therefore the meteorological adjustments were sti ll applied. Thus, it is possible that these sources may have been adjusted twice. Even with that possibility, air quality modeling shows that in general, dust is frequently overestimated in the air quality modeling results.

The total impacts of the transport fraction and meteorological adjustments for the 2011NEIv2 are shown

in Table 2

- 7 , where the starting inventory numbers include unadj usted paved and unpaved road dust, so16

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

t he reduction ranges from about 93 % in New Hampshire to about 29 % in Nevada. The afdust emissions did not change much between the 2011v6.2 and 2011v6.3 platforms. The largest change was in Indiana

with a reduction of 2000 tons of PM 10 , and 320 tons of PM 2.5 and was caused by time zone corrections causing different meteorological data to be used. The next largest chan ge was in North Dakota with a

reduction of 115 tons of PM 10 and 20 tons of PM 2.5 . All other changes were 30 tons or smaller.

Figure 2

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

- 1 . The reductions due to the precipitatio n adjustments are shown in the middle of Figure 2

- 1 . The cumulative emission reductions after both transport fraction and meteorological adjustments are shown at the b ottom 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 tr ansport than in many western areas. The middle plot shows how the meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust

emissions.

Table 2

- 7

. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory

State Unadjuste

d PM10 Unadjusted

PM2_5 Change in

PM10 Change in

PM2_5 PM10

Reducti

on PM2_5

Reduction

Alabama 378,874 47,158

- 310,750

- 38,597 82% 82%

Arizona 237,361 30,015

- 78,519

- 9,778 33% 33%

Arkansas 421,958 58,648

- 305,611

- 40,757 72% 69%

California 255,889 38,664

- 119,035

- 17,930 47% 46%

Colorado 244,630 40,421

- 130,598

- 20,991 53% 52%

Connecticut 29,067 4,393

- 25,877

- 3,912 89% 89%

Delaware 11,548 1,968

- 8,219

- 1,396 71% 71%

D.C. 2,115 337

- 1,596

- 254 75% 75%

Florida 292,797 39,637

- 181,017

- 24,333 62% 61%

Georgia 733,478 90,041

- 593,644

- 72,028 81% 80%

Idaho 432,116 49,294

- 291,880

- 32,897 68% 67%

Illinois 763,665 123,680

- 472,806

- 76,086 62% 62%

Indiana 603,152 85,151

- 435,027

- 60,660 72% 72%

Iowa 590,528 96,070

- 339,349

- 54,855 57% 57%

Kansas 747,242 118,726

- 352,559

- 54,760 47% 46%

Kentucky 199,744 29,496

- 160,640

- 23,511 80% 80%

Louisiana 236,787 35,730

- 162,780

- 24,086 69% 67%

Maine 50,547 7,016

- 43,643

- 6,078 86% 87%

Maryland 65,701 10,215

- 49,481

- 7,691 75% 75%

Massachusetts 205,561 22,444

- 177,808

- 19,370 86% 86%

Michigan 462,324 61,969

- 353,225

- 47,137 76% 76%

Minnesota 336,791 64,253

- 217,036

- 41,145 64% 64%17 State Unadjuste

d PM10 Unadjusted

PM2_5 Change in

PM10 Change in

PM2_5 PM10

Reducti

on PM2_5

Reduction

Mississippi 956,702 107,965

- 782,249

- 86,685 82% 80%

Missouri 1,063,992 130,995

- 780,488

- 94,576 73% 72%

Montana 385,541 50,583

- 266,046

- 33,521 69% 66%

Nebraska 591,457 85,206

- 316,918

- 45,198 54% 53%

Nevada 160,699 20,477

- 47,147

- 5,688 29% 28%

New Hampshire 25,540 3,766

- 23,836

- 3,515 93% 93%

New Jersey 24,273 5,412

- 19,215

- 4,255 79% 79%

New Mexico 924,497 95,871

- 352,117

- 36,344 38% 38%

New York 274,114 37,493

- 236,431

- 31,990 86% 85%

North Carolina 186,650 33,409

- 146,918

- 26,184 79% 78%

North Dakota 354,107 59,113

- 218,630

- 36,286 62% 61%

Ohio 414,902 64,609

- 319,831

- 49,298 77% 76%

Oklahoma 733,750 87,864

- 385,344

- 44,585 53% 51%

Oregon 348,093 40,596

- 268,605

- 30,516 77% 75%

Pennsylvania 208,246 30,344

- 179,991

- 26,158 86% 86%

Rhode Island 4,765 731

- 3,628

- 564 76% 77%

South Carolina 259,350 31,494

- 198,175

- 24,002 76% 76%

South Dakota 262,935 44,587

- 155,938

- 26,215 59% 59%

Tennessee 139,731 25,357

- 107,964

- 19,514 77% 77%

Texas 2,573,687 304,551

- 1,278,053

- 146,122 50% 48%

Utah 196,551 21,589

- 113,837

- 12,464 58% 58%

Vermont 67,690 7,563

- 61,423

- 6,855 91% 91%

Virginia 131,798 19,374

- 108,700

- 15,895 82% 82%

Washington 174,969 27,999

- 99,720

- 15,425 57% 55%

West Virginia 85,956 10,652

- 79,745

- 9,888 93% 93%

Wisconsin 239,851 41,669

- 164,113

- 28,542 68% 68%

Wyoming 434,090 45,350

- 264,580

- 27,467 61% 61%

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

- 11,790,743

- 1,566,004 64% 63%18

Figure 2

- 1

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

cumulative19



### 2.2.2 Agricultural ammonia sector (ag)

The agricultural 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 NH 3 emissions, as

there are also a small amount of NH 3 emissions from livestock feedlots in the ptnonipm inventory (as point sources) in California (175 tons) and Wisconsin (125 tons)

. The ag sector in the 2011v6.3 platform is equivalent to the ag sector in the 2011v6.2 platform.

Table 2

- 8

. Livestock SCCs extra cted from the NEI to create the ag sector

SCC SCC Description*

2805001100 Beef cattle

- finishing operations on feedlots (drylots);Confinement

2805001200 Beef cattle

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

2805001300 Beef cattle

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

2805002000 Beef cattle production composite; Not Elsewhere Classified

2805003100 Beef cattle

- finishing operations on pasture/range; Confinement

2805007100 Poultry prod uction

- layers with dry manure management systems;Confinement

2805007300 Poultry production

- layers with dry manure management systems;Land application of

manure 2805008100 Poultry production

- layers with wet manure management systems;Confinement

2805008200 Poultry production

- layers with wet manure management systems;Manure handling and

storage

2805008300 Poultry production

- layers with wet manure management systems;Land application of

manure 2805009100 Poultry production

- broilers;Confinemen t

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

2805020002 Cattle and Calves 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 cattle

- drylot/pasture dairy;Confinement

2805023200 Dairy cattle

- drylot/pasture dairy;Manure handling and storage

2805023300 Dairy cattle

- drylot/pasture dairy;Land application of manure 2805025000 Swine production composite;Not Elsewhere Classified (see also 28

- 05

- 039,

- 047,

- 053)

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

- 05

- 007,

- 008,

- 009)

2805030003 Poultry Waste Emissions;Layers

2805030004 Poultry Waste Emissions;Broilers

2805030007 Poultry Waste Emissions;Ducks

2805030008 Poultry Waste Emissions;Geese20

SCC SCC Description*

2805030009 Poultry Waste Emissions;Turkeys

2805035000 Horses and Ponies Waste Emissions;Not Elsewhere Classified

2805039100 Swine produc tion

- operations with lagoons (unspecified animal age);Confinement

2805039200 Swine production

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

storage

2805039300 Swine production

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

manure 2805040000 Sheep and Lambs Waste Emissions;Total

2805045000 Goats Waste Emissions;Not Elsewhere Classified

2805045002 Goats Waste Emissions;Angora Goats

2805045003 Goats Waste Emissions;Milk Goats

2805047100 Swine production

- deep

- pit house operations (unspecified animal age);Confinement

2805047300 Swine production

- deep

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

manure 2805053100 Swine production

- outdoor operations (unspecified animal a ge);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 Calcium Ammonium Nitrate

2801700012 Potassium Nitrate

2801700013 Diammonium Phosphate

2801700014 Monoammonium Phosphate

2801700015 Liquid Ammonium Polyphosphate

2801700099 Miscellaneous Fertilizers

* All descriptions include “Miscellaneous Area Sources; Agriculture Production

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



### 2.2.3 Agricultural fires (agfire)

The agricultural fire (agfire) sector contains emissions from agricultural fires. These emissions were placed into the sector based on their SCC code. All SCCs starting with 28015 a re included. The first three levels of descriptions for these SCCs are: 1) Fires

- Agricultural Field Burning; Miscellaneous Area Sources; 2) Agriculture Production

- Crops

- as nonpoint; and 3) Agricultural Field Burning

- whole field set on fire. The SCC 2801500000 does not specify the crop type or burn method, while the

mo re specific SCCs specify field or orchard crops and

, in some cases

, the specific crop being grown. The agfire sector in the 2011v6.3 platform is equivalent to that in the 2011NEIv2 and the 2011v6.2 platform, except that the emissions in Missouri were rever ted back to 2011NEIv1 levels based on a21

comment submitted by the Missouri Department of Natural Resources. This resulted in increased CAP

emission s from the 2011v6.2 platform, including 7,42 0 tons PM 2.5 , 3,270 tons of NO
x , and 5,760 tons of

VOC. For more i nformation on how emissions for agricultural 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. The

EPA estimated emissions for 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, pneumatic pumps and other devices, storage tanks, flares, truck loading, compressor engines , and dehydrators

. Nonpoint oil and gas

emissions for most states in the 2011v6.3 platform are consistent with those in the 2011NEIv2. For more information on the development of the oil and gas emissions in the 2011NEIv2 , see Section 3.2 0

of the 2011NEIv2 TSD. The S/L/T agencies that submitted data used in 2011v6.3 include da ta from Texas, Oklahoma and Utah. The changes resulting from these updates include reduction s of 2011 VOC

emissions : 48,700 tons in Oklahoma, 15,900 tons in Texas, and 8,200 tons in Utah ; an increase of 25,500 tons of NO
x in Texas ; and a reducti o n of 5,6 00 tons of NO
x in Utah. Additionally, West Virginia

SO 2 emissions were reduced 1167 tons due to state

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

. See the pt_oilgas sector (section 2.1.2 ) for more information on point source oil and gas sources. Updates were made to the speciation, temporalization, and spatial allocation of sources in the 20 11v6.2 platform based on comments 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 residential wood burning devices such as fireplaces, firepla ces 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 standing woodstoves and inserts are further differentia ted into three categories: 1) conventional (not EPA certified); 2) EPA certified, catalytic; and 3) EPA certified, noncatalytic. Generally speaking, the conventional units were constructed prior to 1988. Units constructed after 1988

had to meet EPA emissi on 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 2011v6.3 platform rwc emissions are equivalent to the 2011v6.2 p latform rwc emissions.

The SCCs in the rwc sector are shown in Table 2

- 10

.

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;Wood;Woodstove: fireplace inserts; EPA certified; non

- catalytic

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

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

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

- EPA certified

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

- catalytic

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

2104008400 SSFC;Residential;Wood;Woodstove: pellet

- fired, general (freestanding or FP insert)22

SCC SCC Description

2104008420 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;Residential;Wood;Hydronic heater: outdoor

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

- pits, chimeas, etc)

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



### 2.2.6 Other nonpoint sources sector (nonpt)

St ationary nonpoint sources that were not subdivided into the afdust, ag, np_oilgas, or rwc sectors were assigned to the “nonpt” sector. Locomotives and CMV mobile sources from the 2011NEIv2 nonpoint inventory are described in Section 2.4.1. T he 2011v6.3 p latform nonpt emissions for the year 2011 differ from the 2011v6.2 platform emissions in Connecticut, which has an increase of 680 tons of SO 2 ; Massachusetts

, which had a decrease of 310 tons of VOC ; New York, which has an increase of 1,070

tons of SO 2 ; and North Carolin a , which has increases of 470 tons of NOx, 2,900 tons of VOC, and some modest changes to other CAPs. There are too many SCCs in the nonpt sector to list all of them individually, but the types of sources in the nonpt sector include:

 stati onary source fuel combustion, including industrial, commercial, and residential;

 chemical manufacturing;

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

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

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

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

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

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

 storage and transport of chemicals;

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

 agricultural burning and orchard heating;

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



### 2.3 2011 onroad mobile sources (onroad)

Onroad mobile sources include emissions from motorized vehicles that are normally operated on public roadways. These include passenger cars, motorcycles, minivans, sport

- utility vehicles, light

- duty trucks, heavy

- duty trucks, and buses. The sources are further divided between diesel, gasoline, E

- 85, and compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle23

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 and Texas , all onroad emissions are generated using the SMOKE

- MOVES emissions modeling framework that leverages MOVES generated outputs ( http://www.epa.gov/otaq/models/moves/index.htm ) and hourly meteorology. The onroad SCCs in the

2011v6.2 and 2011v6.3 platform s are significantly different than those 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 spati al allocation for the onroad sector

were made in the 2011v6.2 and 2011v6.3 platform s as a result of comments received and upgrades to the platform. Sections 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)

, vehicle population (VPOP)

, and hoteling data for all counties, along with tools that integrated the MOVES model with SMOKE. In this way, it was possible to take advantage of the gridded hourly temperature information available from meteorology modeling used for air quality modeling . The “SMOKE

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

.

SMOKE

- MOVES requires that emission rate “lookup” tables be generated by MOVES which differentiate emissions by pro cess (i.e., running, start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of day, etc. To generate the MOVES emission rates that could be applied across the U.S., EPA used an automated process to run MOVES to produce emission fac tors by temperature and speed for a series of “representative counties,” to which every other county is mapped. Representative counties are used because it is impractical to generate a full suite of emission factors for the more than 3,000 counties in the United States. Representative counties, for which emission factors are generated are selected according to their state, elevation, fuels, age distribution, ramp fraction, and inspection & maintenance programs . Each county is then mapped to a representati ve county based on its similarity with the representative county with respect to those attributes. For the 2011v6.3 platform,

there are 285 representative counties.

Once representative counties have been identified, emission factors are generated with M OVES for each representative county and for each “fuel month”

– typically a summer month and a winter month. Using the MOVES emission rates, SMOKE selects appropriate emissions rates for each county, hourly temperature, SCC, and speed bin and multiplie s t he emission rate by activity : VMT (vehicle miles travelled), VPOP (vehicle population)), or HOTELING (hours of extended idle) to produce emissions. These calculations were done for every county and grid cell in the continental U.S. for each hour of the

ye ar.

The SMOKE

- MOVES process for creating the model

- ready emissions consists of the following steps:

1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month’s fuel characteristics.
3) Create MOVES inputs needed only by MOVES. MOVES requires county

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

- maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOV ES and by SMOKE, including temperatures and activity data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.24

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 summaries and quality assurance.

The onroad emissions are processed in four processing streams that are merged together into the onroad

sector emissions after processing:

 rate

- per

- distance (RPD) uses VMT as the activity data plus speed and speed profile information
to compute on

- network emissions from e xhau s t, evaporative, permeation, refueling, and brake

and tire wear processes;

 rate

- per

- vehic le (RPV) uses vehicle population (VPOP) activity data to compute off

- network emissions from exhaust, evaporative, permeation, and refueling processes;

 rate

- per

- profile (RPP) uses VPOP activity data to compute off

- network emissions from evaporative fuel va per venting including hot soak (immediately after a trip) and diurnal (vehicle parked for a long period) emissions; and

 rate

- per

- hour (RPH) uses hoteling hours activity data to compute off

- network emissions for idling of long

- haul trucks from extended idli ng and auxiliary power unit process.

The onroad emissions inputs are similar to the emissions in the onroad data category of the 2011NEIv2, described in more detail in Section 4.6 of the 2011NEIv2 TSD. Specifically the 2011v6.3 platform and

the 2011NEIv2 have nearly identical:

 MOVES County databases (CDBs) including Low Emission Vehicle table dated 20140903

 Representative counties (i.e., 285RepCos2011_M2014_20151208 )

 Fuel months

 Meteorology

 Activity data (VMT, VPOP, speed, HOTELING)

The key differences between the 2011v6.3 platform and the 2011v6.2 platform inventories are:

 A newer version of MOVES (MOVES2014a dated 20151201 ) was used.

 Updates were made to CDBs in Colorado to correct issues with inspection and maintenance

programs ; to Ohio and New Jersey to incorporate updated data submitted in response to the

NODA ; to county 08123 (remapped to 08041) due to missing emission factors ; and to county 48261 (remapped to 48047) due to negative emission factors.

 Activity data updates in O hio and New Jersey.

 Update d emissions provided for California and Texas.

 Extended idle emissions reduced by 50% in Maricopa County, Arizona.

The other key differences between the 2011v6.3 platform onroad emission inventories and the

2011NEIv2 inventories are :

 The 2011 platform used a different post

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

NEI. For example, the NEI needs a much larger set of HAPs and the modeli ng platform requires emissions for the components of PM 2.5

.25

 The 2011 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 NEI CDBs were not generating a complete set of E F for the E

- 85 sources.

 The NEI used a pre

- release version of MOVES2014 , while 2011v6.2 used MOVES2014a

.

 The NEI includes emissions for Alaska, Hawaii, Puerto Rico, and the Virgin Islands, where the modeling platform does not.

 The 2011 v6.3 platform used an updated fuel database that was released with MOVES2014a : M2014a_fuelsupply AND regioncountytrnoda_20151203

.

 The treatment of California 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 aggregated

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

Aggregate process Description MOVES process IDs

40 All brake and tire wear 9;10

53 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 allow 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, evapora tive, or refueling emissions. For more detailed information on methods used to develop the onroad emissions and input data sets and on running

SMOKE

- MOVES, see the 2011NEIv2 TSD.

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

supplied annual emissions with EPA

- developed SMOKE

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

- MOVES. The basic steps involved in temporally allocating onroad emissions from California and Texas based on

SMOKE

- MOVES results were:

1) Run CA and TX using EPA inputs through SMOKE

- MOVES to produce hourly 2011 emissions
hereafter known as “EPA estimates

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

. ”26

2) Calculate ratios between state

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

were calculated for each county/SCC/pollutant combination, except with all road types summed together because California’s emissions did not provide data by road type, and with E

- 85 emissions combined with gasoline because separate emissions were not provided for E

- 85. For Texas, the ratios were calculated for each county/SCC/pollutant combination, including by road type, but also with E

- 85 combined with gasoline.

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

- to

- state estimate ratios.

4) Rerun CA and TX through SM OKE

- MOVES using EPA inputs and the new adjustment factor

file.

Through this process, adjusted model

- ready files were created that sum to annual totals from California and Texas, but have the temporal and spatial patterns reflecting the highly resolved meteorology and

SMOKE

- MOVES. After adjusting the emissions, this s ector is called “onroad_catx_adj

. ” Note that in emission summaries, the emissions from the “onroad” and “onroad_catx_adj” sectors are summed and designated as the emissions for the onroad sector.

An additional step was taken for the refueling emissions. Colorado submitted point emissions for gasoline refueling for some counties 4 . For these counties, the EPA zeroed out the onroad estimates of gasoline refueling (SCC 2201*62) so that the states’ po int emissions would take precedence. The onroad refueling emissions were zeroed out using the adjustment factor file (CFPRO) and Movesmrg.



### 2.4 2011 nonroad mobile sources (c mv, rail, nonroad)

The nonroad mobile source emission modeling sectors consist of nonr oad equipment emissions (nonroad), locomotive (rail) and CMV emissions.



### 2.4.1 Category 1

, Category 2

, Category 3 Commercial Marine Vessels (cmv )

The cmv sector contains Category 1, 2 and 3 CMV emissions. All emissions in this sector are annual

and at the count y

- SCC resolution

. As discussed in Table 2

- 1 and Table 2

- 2 , the mode ling platform emissions for the CMV Category 1 and 2 SCCs were extracted from the 2011NEIv2 nonpoint inventory using the 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 sources in the NEI, see Section 4.3 of the 2011NEIv2 TSD. The 2011v6.3 platform CMV emissions are equivalent to those in the 2011v6.2 platform.

Table 2

- 12

. 2011NEIv2 SCCs extracted for the cmv sector

SCC Sector Description: Mobile Sources prefix for all

2280002100 cmv Marine Vessels; Commercial; Diesel; Port

2280002200 cmv Marine Vessels; Commercial; Diesel; Underway

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 PEC, PNO 3 , POC, PSO 4 , etc. (For

more details on NO X and PM speciation, see Sections 3.2.2, and 3.2.3

. For VOC model

- species, if there was an exact match (e.g., BENZENE), the EPA used that HAP pollutant ratio. For other VOC

- based model

- species that didn’t exist in the NEI inventory, the EPA used VOC ra tios.

4 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 overlap the refueling emissions in onroad.27

SCC Sector Description: Mobile Sources prefix for all

2280003100 cmv Marine Vessels, Commercial;Residual;Port emissions

2280003200 cmv Marine Vessels, Commercial;Residual;Underway emissions

2280004000 cmv Marine Vessels, Commercial;Gasoline;Total, All Vessel Types

The Category 3 (C3) CMV sources in the cmv sector of the 2011v6.3 platform run on residual oil and use the SCCs 2280003100 and 2280003200 for port and underway emissions, respectively, and are

consistent with the 2011NEIv2. Emissions for this sector use s tate

- submitted values and EPA

developed emissions in areas where states did not submit. A change in the 2011v6.2 and 2011v6.3 platform s is to restrict this sector only to include emissions in state

- waters and to treat the emissions as nonpoint sources ins tead of point sources. Thus, the CMV emissions are placed in layer 1 and allocated to grid cells using spatial surrogates. The development of the Emissions Control Area

- International Marine Organization ( ECA

- IMO )

- based C3 CMV inventory is discussed below

. Canadian emissions, C3 CMV emissions outside of state waters, 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 resoluti on ASCII raster format dataset that preserves shipping lanes. This dataset has been used since the 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 diesel engines (at or above 30 liters/cylinder) that

, until recently

, were allowed to meet relatively 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 primari ly foreign

- flagged

ocean

- going vessels, referred to as C3 CMV ships. The cmv inventory sector includes these ships in

several intra

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

- port auxiliary engine emissions.

An overview of the C3 ECA Proposal to the International Maritime Organization project (EPA

- 420

- F

10

- 041, August 2010 ) 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

e ngines with per

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

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

- year ECA

invento ry is 2002 and consists of these CAPs: PM 10 , PM 2.5 , CO, CO 2 , NH 3 , NO X , SO X (assumed to be

SO 2 ), and h ydrocarbons (assumed to be VOC). The EPA developed regional growth (activity

- based)

factors that were applied to create the 20 11 inventory from the 2002 data. Thes e growth factors are 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 Exclusive Economic Zone ( EEZ ) F IPS 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 0.61128 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/mason.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 miles (nm) from the coast, assigned sta ck 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 doma in)

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 nm off sho re. Emissions outside the 3 to 10 mile MMS boundary, but within the approximately 200 nm EEZ

boundaries in Figure 2

- 2 , were projected to year 2011 using the same reg ional 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, exte nding 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 other states such as Pennsylvania and New York. The classification of emissions to U.S. and Canadian FIPS codes is

needed to avoid double

- counting of C3 CMV U.S. emissions in the Great Lakes because, as discussed in the previous section, all CMV emissions in the Midwest RPO are processed in the “ cmv ” sector. 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 1.45729

The SMOKE

- ready data have been cropped from the original ECA

- IMO entire northwestern quarter of the globe to cover only the large continental U.S. 36

- km “36US1” air quality model domain , the largest domain used by EPA in recent years 5

.

The original ECA

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

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

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

See Section 3.2.1.3 for more details on C3 speciation in the cmv sector and Section 3.3.6 for details on temporal allocation.

For Califo rnia, 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 a t: 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 n m of f the California shoreline. These emissions were developed using Version 1 of the California Emissions Projection Analysis Model (CEPAM) that supports v arious 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 cmv and rail sector s data, is provided at: http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles

.



### 2.4.2 Railroad sources: (rail)

The rail sector includes all locomotives except for railway maintenance locomotives. Railway

maintenance emi ssions are included in the nonroad sector. The yard locomotive s are included in the ptnonipm sector. For more information on locomotive sources in the NEI, see Section 4.4 of the

2011NEIv2 TSD.

Table 2

- 14

. 2011NEIv2 SCCs extracted for the starting point in rail development

SCC Sector Description: Mobile Sources prefix for all

2285002007 rail Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations

2285002008 rail Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)

2285002009 rail Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines

2285002010 rail Railroad Equipment;Diesel;Yard Locomotives



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

The nonroad equipment emissions are equivalent to the emissions in the nonroad data category of the 2011NEIv2, 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

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

- 1. Note that this domain is not specifically used in this 2011 platform, although spatial surrogates that can be used with it are provided.30

emissions on a month

- specific basis that accounts for temperature, fuel types, and other variables that vary by month. The nonroad sector includes monthly exhaust, evaporative and refueling emissions from

n onroad engines (not including commercial marine, aircraft, and locomotives) that EPA derived from

NMIM for all states except California and Texas. Additional details on the development of the

2011NEIv2 nonroad emissions are available in Section 4.5 the 20 11NEIv2 TSD. Note that the nonroad emissions for 2011NEIv2 are the same as those in the 2011NEIv1 for all states except Delaware. The nonroad emissions for 2011 in the 2011v6.3 platform are equivalent to those in the 2011v6.2 platform.

California year 2 011 nonroad emissions were submitted to the 2011NEIv2 and are also documented in a staff report (ARB, 2010a). The nonroad sector emissions in California were developed using a modular approach and include all rulemakings and updates in place by December 2 010. These emissions were developed using Version 1 of the CEPAM

, which supports various California off

- road regulations such

as in

- use diesel retrofits (ARB, 2007), Diesel Risk

- Reduction Plan (ARB, 2000) and 2007 State Implementation Plans (SIP s ) for the South Coast and San Joaquin Valley air basins (ARB, 2010b).

The CARB

- supplied nonroad annual inventory emissions values were converted to monthly values by using the aforementioned EPA NMIM monthly inventories to compute monthly ratios by county, SCC7 (f uel, engine type, and equipment type group), mode, and pollutant. The SCC7 ratios were used because the SCCs in the CARB inventory did not align with many of the SCCs in EPA NMIM inventory. By aggregating up to SCC7, the two inventories had a more consis tent 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, aceta ldehyde, and formaldehyde for the specific sources.

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 monthly

inventories to c ompute monthly ratios by county, SCC7 , mode, and poll 6

.

Some updates to spatial surrogate assignments for the nonroad sector we re made in the 2011v6.3 platform in response to comments. Details can be found in Section 3.4.



### 2.5 “Other Emissions”: 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 four emissions modeling s ectors: othpt, othar, othafdust, 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 remaining characters provide the SMOKE source types: “pt” for point, “ar” for “ area and nonroad mobile

, ” “afdust” for area fugitive dust (Canada only), 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 in the othpt sector. These C3 CMV emissions include those assigned to U.S. federal waters,

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

allocated with a similar SCC7.31

Canada, those assigned to the EEZ ( defined as those emissions beyond the U.S. Federal waters approximately 3

- 10 miles offshore, and e xtending 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 cmv sector (see Section 2.4.1 )

. Emissions in U.S. waters are aggrega ted 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 higher than layer 1

.

For Canadi an 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 emissions were also provided. P oint sources in Mexico were compiled based on the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a). The point source emissions in the 2008 inventory were converted to English units and into the FF10 format that could be 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 there are no explicit HAP emissions in this inventory.

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

state

- county boundaries in the Gulf of Mexico. For these offshore emissions, 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 mobile sources, month

- specific year

- 2010 emissions provided by Environment Canada were used, including C3 CMV emissions. An overlap between these emissions

and the U.S. C3 CMV emissions in the St. Lawrence River and Saguenay 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 meteorological

- based adjustments. To p roperly 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 a lso provided.

Area and nonroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a). 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. inventories, 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 Mexic o were compiled from the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a). The 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 sta tes were replaced with values computed based on32

the average per capita emissions for the remaining states. The data were written using English units to the nonpoint FF10 format that could be read by SMOKE.

For Canada, month

- specific 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 (

ptfire )

In the 2011v6.3 platform, both the w ildfire s and prescribed burning emissions are contained in the pt fire

sector. In the 2011v6.2, t he two type of fires 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. Otherwise, the wild and prescribed fire emissions in the 2011v6.2 and 2011v6.3 platforms are equivalent. Fire emissions are specified at geographic coordinates (point locations) and ha ve daily emissions values. The ptfire sector s exclude agricult ural burning and other open burning sources that are included in the nonpt sector. Emissions are day

- specific and include satellite

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

- 15 are treated as point sources and are consistent

with the fires stored i n the Events data category of the 2011NEIv2

. For more information on the development of the 2011NEIv2 fire inventory, see Section 5.1 of the 2011NEIv2 TSD.

Table 2

- 15

. 20 11 Platform SCCs representing emissions in the ptfire 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 2011 fires rely on SMARTFIRE 2 (Sullivan, et al., 2008), which uses the Na tional Oceanic and Atmospheric Administration’s (NOAA’s) Hazard Mapping System (HMS) fire location information as input. Additional inputs include the CONSUMEv3.0 software application (Joint Fire Science Program, 2009http://www.fs.fed.us/pnw/fera/research /smoke/consume/index.shtml) and the Fuel Characteristic Classification System (FCCS) fuel

- loading database to estimate fire emissions from wildfires and prescribed burns on a daily basis. The method involves the reconciliation of ICS

- 209 reports (Incident Status Summary Reports) with satellite

- based fire detections to determine spatial and temporal information about the fires. A functional diagram of the SMARTFIRE 2 process of reconciling fires

with ICS

- 209 reports is available in the documentation (Raffu se, et al., 2007). Once the fire reconciliation process is completed, the emissions are calculated using the U.S. Forest Service’s CONSUMEv3.0 fuel consumption model and the FCCS fuel

- loading database in the BlueSky Framework (Ottmar, et al., 2007).

SMARTFIRE 2 estimates were used directly for all states except Georgia and Florida. For Georgia, the

satellite

- derived emissions were removed from the ptfire inventory and replac ed with a separate state

supplied ptfire inventory. Adjustments were also made to Florida as described in Section 5.1.4 of the

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

2011NEIv2.33

An update originally incorporated 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 from going into a single grid cell when

, in reality

, the fire was mor e 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

- 16

.

Table 2

- 16

. Large f

ires apportioned to multiple grid cells

County FIPS Fire ID

32007 SF11C1774898

32007 SF11C1775252

32013 SF11C1774993

35027 SF11C1760072

35027 SF11C1760460

46065 SF11C1503125

48003 SF11C1718109

48081 SF11C1742329

48125 SF11C1749358

48243 SF11C1738273

48243 SF11C1747162

48353 SF11C1759082

48371 SF11C1750272

48415 SF11C1742358

56013 SF11C1791126



### 2.7 Biogenic sources ( beis )

Biogenic emissions were computed based on the same 11g version of the 2011 meteorology data used for the air quality modeling, and were developed using the Biogenic Emission Inventory System version 3.61 (BEIS3.61) within SMOKE. This was an update from th e 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 emissions from vegetation and soils. It estimates CO, VOC (most notably isoprene, terpen e, and sesquiterpene), and NO emissions for the contiguous U.S. and for portions of Mexico and Canada.

BEIS3.61 has some important updates from BEIS 3.14. These include the incorporation of Version 4 .1 of the Biogenic Emissions Land use Database (BELD4) for the 2011v6.3 platform , and the incorporation of a canopy model to estimate leaf

- level temperatures (Pouliot and Bash, 2015). BEIS3.61 includes a

two

- layer canopy model. Layer structure varies with l ight 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 variables34

output from the Meteorology

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

- 17

.

Table 2

- 17

. Meteorological variables required by BEIS 3.61

Variable Description

LAI leaf

- area index

PRSFC surface pressure
Q2 mixing ratio at 2 m

RC convective precipitation per met TSTEP

RGRND solar rad reaching sfc

RN nonconvective precipitation 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

BELD version 4.1 is based on an updated version of the USDA

- USFS Forest Inventory and Analysis (FIA) vegetation speciation based data from 2001 to 2014 from the FIA version 5.1. Canopy coverage is based on the Landsat satellite National Land Cover Database (NLCD) product from 2011. The F IA includes approximately 250,000 representative plots of species fraction data that are within approximately 75 km of one another in areas identified as forest by the NLCD canopy coverage. The 2011 NLCD provides land cover information with a native data g rid spacing of 30 meters. For land areas outside the conterminous United States, 500 meter grid spacing land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used.

Other improvements to the BELDv4.1 improvements included the fo llowing:

 Used 30 meter NASA's Shuttle Radar Topography Mission (SRTM) elevation data (http://www2.jpl.nasa.gov/srtm/) which will more accurately define the elevation ranges of the vegetation species.

 Used the 2011 30 meter USDA Cropland Data Layer (CDL) data (http://www.nass.usda.gov/research/Cropland/Release/) to the BELD 4 agricultural categories.

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.35 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 201136 

### 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 modeling.37 

## 3 Emissions Modeling S ummary

Both the CMAQ and CAM X models require hourly emissions of specific gas and particle species for the horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To provide emissions in the form and format required by the model, it is necessary to “pre

- process” the “raw” emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2 . In brief, the p rocess 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 in cludes temporal allocation, spatial allocation, and pollutant speciation. In some cases, emissions modeling also include s the vertical allocation of point sources , but many air quality models also perform this task because it greatly reduces the size of

t he 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 individual point sources

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

. 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 inputs for the 2011v6 platform a re available from the CHIEF Emissions Modeling Clearinghouse website (see Section 1 ).

SMOKE version 3. 7 was used to pre

- process the raw emissions inventories into emissions inputs for each modeling sector in a format compatible with CMAQ. For projects that used CAMx, the CMAQ

formatted emissions were converted into the required CAMx formats using CAMx convertor programs. For sectors that have plume rise, the in

- l ine emissions capability of the air quality models was used,

which allows the creation of source

- based and two

- dimensional gridded emissions files that are much

smaller than full three

- dimensional gridded emissions files. For quality assurance of the emis sions modeling steps, emissions totals by specie for the entire model domain are output as reports that are then compared to reports generated by SMOKE on the input inventories to ensure that mass is not lost or gained during the emissions modeling process

.



### 3.1 E missions modeling Overview

When preparing emissions for the air quality model, emissions for 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 : “point” indicates that SMOKE maps the source from a point location (i.e., latitude and longitude) to a grid cell; “surrogates” indicates that some or all of the sources use spatial surrogates to allocate county emissions to grid cells; and “area

- to

- point” indicates that some of the

sources use the SMOKE area

- to

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

, activ ity data and emission factors are used in combination with meteorological data to compute hourly

emissions.38

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

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

- line” means that the plume rise calculations are done inside of the air quality model instead of being computed by SMOKE. The air quality model computes the plume rise using the stack data and the hourly a ir 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 those sectors in the two

- dimensional, layer

- 1 files created by SMOKE. Day

- specific point fires are treated separately. For CMAQ modeling, fire plume rise is done within CMAQ it self, 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 emis sions 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

afdust Surrogates Yes annual

ag Surrogates Yes annual agfire Surrogates Yes monthly

beis Pre

- gridded

land use in BEIS3.61 computed hourly

rail Surrogates Yes annual

cmv Surrogates Yes annual

nonpt Surrogates &

area

- to

- point Yes annual

nonroad Surrogates &

area

- to

- point Yes monthly

np_oilgas Surrogates Yes annual

onroad Surrogates Yes monthly activity, computed hourly

othafdust Surrogates Yes annual

o thar Surrogates Yes annual

othon Surrogates Yes annual othpt Point Yes annual in

- line pt_oilgas Point Yes annual in

- line

ptegu Point Yes daily & hourly in

- line ptfire Point Yes daily in

- line ptnonipm Point Yes annual in

- line

r wc Surrogates Yes annual

SMOKE has the option of grouping sources so that they are treated as a single stack when computing plume rise. For the 2011 platform

, no grouping was performed because grouping combined with “in

line” processing will not give identical results as “offline ” processing (i.e., when SMOKE creates 3

- dimensional files). This occurs when stacks with different stack parameters or lat itudes /lon gitude s are grouped, thereby changing the parameters of one o r more sources

. The most straightforward way to get
the same results between in

- line and offline is to avoid the use of grouping.39

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 invent ories are run through SMOKE programs to read the inventories, speciate, temporalize, 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/3.7/html/ch06s06.html#sect_programs_la ypoint_plume_rise_fires ).

3) The emissions ar e 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 g rid cell centroid represents one stack using the cmaq2uam program. Note that each virtual stack has default stack parameters of 1 m height, 1 m diameter, 273 K temperature, and 1 m/s velocity. Also, an individual virtual stack point (grid cell centroid) w ill 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 PTSO URCE 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 conditi ons were obtained from a 2011 run of GEOS

- Chem.40 Figure 3

- 1

. Air quality modeling domains

Both grids use a Lambert

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

- 97o, with a

center of X =

- 97o and Y = 40o. Table 3

- 2 describes the grids for the t wo domains.

Table 3

- 2

. Descriptions of the platform grids

Comm on Name Grid

Cell Size Description

(see Figure 3

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

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

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

Mexico/Canada 12US1_459X299 ‘LAM_40N97W',

- 2556000,

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

US 12 km or

“smaller”

CONUS

- 12 12 km Smaller 12km CONUS plus some of Mexico/Canada 12US2 ‘LAM_40N97W',

- 2412000 ,

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

Section 3.4 provides the details on the spatial surrogates and area

- to

- point data used to accomplish spatial allocation with SMOKE.41



### 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 ( i.e., “explicit species” ) or groups of specie s ( i.e., “lumped species” )

. The chemical

mechanism used for the 2011 platform is the CB6 mechanism (Yarwood, 2010 ). The 2011v6.2 platform

was the first EPA modeling platform to use CB6 ; previous platforms used CB05 and earlier versions of

the carbon bond mechanism. The key difference in CB6 from CB05 from an emissions modeling perspective is that it has additional lumped and explicit model species. The specific version of CAMx used in applications of this platform include secondary organic aerosol ( SOA) and HONO

enhancements. In addition, this platform generates the PM 2.5 model species associated with the CMAQ Aerosol Module version 6 (AE6)

, though many are not used by CAM X

. Table 3

- 3 lists the model species produced by SMOKE in the 2011v6.2 platform and Table 3

- 4 provides the cmaq2camx mapping

file used to convert the SMOKE gen erated model species to the appropriate inputs for CAM X

.

The TOG and PM 2.5 speciation factors that are the basis of the chemical speciation approach were developed from the SPECIATE 4.4 database ( https://www.epa.gov/air

- emissions

- modeling/speciate

version

- 44

- through

- 32 ), which is the EPA's repository of TOG and PM speciation profiles of air pollution sources. However, a few of the profiles used in the v6. 3 platform will be published in later

versi ons of the SPECIATE database after the release of this documentation. The SPECIATE database development and maintenance is a collaboration involving the EPA’s ORD, OTAQ, and the Office of Air Quality Planning and Standards (OAQPS), in cooperation with Env ironment Canada (EPA, 2006a). The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical compounds, VOC

- to

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

for PM 2.5

.

Some updates to sp eciation made in the 2011v6.2 platform and carried forward into the 2011v6.3 platform include the following (the subsections below contain more details on the specific changes):

 VOC speciation profile cross reference assignments for nonpoint oil and gas so urces were updated based on comments;

 VOC and PM speciation for onroad mobile sources occurs within MOVES2014;

 the latest CB6 chemical mechanism was used;

 only AE6 PM species are included, where previously both AE5 and AE6 species were generated;

 the 2010 Canadian point source inventories in the othpt sector use CB05 speciation as it was provided from Environment Canada; and

 speciation for onroad mobile sources in Mexico was updated to be more consistent with that used

in the United States.

Two additional changes were made for 2011v6.3:

 the speciation within MOVES was updated to use a more updated mapping of the chemical mechanism; and

 the Western Regional Air Partnership (WRAP) speciation profiles ( Table 3

- 7 ) used for the np_oilgas sector are used without HAP integration (discussed in Section 3.2.1 )

.

Speciation profiles and cross

- references for the 2011v6. 3 platform are available in the SMOKE input

files for the 2011v6. 3 platform. Totals of each model species by state and sector can be found in the

state

- sector totals workbooks for the respective cases. In addition, the cou nty

- monthly reports for each42

case include EC and OC, and the 2011e k _county_SCC7_sector_CAP_PM.xlsx workbook contains speciated PM by county and the first seven digits of the SCC code.

Table 3

- 3

. Emission m ode l species produced for CB6 for CAM X *

Inventory Pollutant Model Species Model species description

Cl 2 CL2 Atomic gas

- phase chlorine

HCl HCL Hydrogen Chloride (hydrochloric acid) gas

CO CO Carbon monoxide

NO X NO Nitrogen oxide

NO2 Nitrogen dioxide

HONO Nitrous acid

SO 2 SO2 Sulfur dioxide

SULF Sulfuric acid vapor

NH 3 NH3 Ammonia

VOC ACET Acetone

ALD2 Acetaldehyde

ALDX Propionaldehyde and higher aldehydes

BENZ Benzene

CH4 Methane 7

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 carbon 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

7 Technically, CH 4 is not a VOC but part of TOG. Although emissions of CH 4 are derived, the AQ models do not use these emissions because the anthropogenic emissions are dwarfed by the CH 4 already in the atmosphere.43

Inventory Pollutant Model Species Model species description

PM 2.5 PAL Aluminum

PCA Calcium

PCL Chloride

PEC Particulate elemental carbon  2.5 microns

PFE Iron

PK Potassium

PH2O Water

PMG Magnesium

PMN Manganese

PMOTHR PM 2.5 not in other AE6 species

PNA Sodium

PNCOM Non

- 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

– anthropogenic ) 8 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 CB6 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, CPRM, FCRS, and FPRM, respectively.

8 These emissions a

re created outside of SMOKE44 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 CO 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

- related 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 file 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 compound s) SPECIATE profiles and the chemical mechanism mapping file.

For the 2011v6. 3 platform, an updated CB6 chemical mapping file based on the August 2014

mechanism table for CB05 from Bill Carter was used for all sectors, including onroad mobile sources

.45

T his CB6 mapping file included some corrections to the onroad CB05 profiles used in the 2011v6. 2 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 ombinati on of HAP BAFM (benzene, acetaldehyde, 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 emissions 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 model species excluding the specific HAPs. The EPA believes that the HAP emissions in the NEI are often more representative

of emissions than HAP emissions generated vi a VOC speciation , although this varies by sector

.

T he BAFM HAPs were chosen for in tegration in previous platforms because , with the exception of

BENZENE 9

, they are the only explicit VOC HAPs in the base version of the CMAQ 5.0. 2 (CAPs only

with chlorine c hemistry) model . These remain appropriate for the 2011v6. 3 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 spec ies that participate in the modeled chemistry 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 integrating 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. For example, the E10 headspace gasoline evaporative speciation profile 8763 should be used when ethanol is speciated from VOC, but 8763E should be used when

ethanol is obtained d irectly 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 vi a the INVTABLE and the particular sources to integrate via the NHAPEXCLUDE file (which actually provides the sources to be excluded from integration 10 ). For the “integrate d ” sources, SMOKE subtracts the “integrate d ” HAPs from the VOC (at the source level) to compute emissions for the new pollutant “NONHAPVOC.” The user provides NONHAPVOC

- to

NONHAPTOG factors and NONHAPTOG speciation profiles 11

. SMOKE computes NONHAPTOG

and then applies the speciation profiles to allocate the NONHAPTOG to the other air qual ity model VOC species not including the integrated HAPs. After determining if a sector is to be integrated, if all sources have the appropriate HAP emissions, then the sector is considered fully integrated and does not

9 BENZENE was chosen to keep its emissions consistent between the multi

- pollutant and base versions of CMAQ.

10 In SMOKE version 3.7, the options to specify sources for integration are expanded so that a user can specify the part icular sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector. In addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, b ut it is missing BAFM or VOC, SMOKE will now raise an error.

11 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.46

need a NHAPEXCLUDE file. If

, on the other hand, certain sources do not have the necessary HAPs,

then an NHAPEXCLUDE file must be provided based on the evaluation of each source’s pollutant mix.

The 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, this process is identical to that shown in the figure except for the addition of ethanol (E) to the list of subtracted HAP pollutants. For full integration, the process would be very similar except that the

NHAPEXCLUDE file would not be used and all sources in the sector would be integrated.

Figure 3

- 2

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

In SMOKE, the INVTABLE allows the user to specify both the particular HAPs to i ntegrate. 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 ), the 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 for these species for sectors using th is approach. The second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep the inventory BAFM pollutants and indicates that they are to be 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 integrat e BAFM and another for those that integrate EBAFM.47

Table 3

- 5

. Integration approach for BAFM and EBAFM for each platform sector

Platform

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

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

ptegu No integration ptnonipm No integration ptfire No integration

othafdust No integration

othar No integration

othon No integration

ag N/A

– sector contains no VOC

afdust N/A

– sector contains no VOC biog N/A

– sector contains no inventory pollutant "VOC"; but rather specific VOC species agfire Partial integration (BAFM)

c mv Partial integration (BAFM)

rail Partial integration (BAFM)

nonpt Partial integration (BAFM and EBAFM)

nonroad Partial integration (BAFM) np_oilgas Partial integration (BAFM) pt_oilgas Partial integration (BAFM)

rwc Partial integration (BAFM) othpt Partial integration (BAFM)

onroad Full integration (internal to MOVES) 1

1 For the integration that is internal to MOVES, an extended list of HAPs are integrated , not just BAFM. See 3.2.1.3

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

Section 3.2.1.3

.

3.2.1.2 County specific profile combinations (GSPRO_COMBO)

SMOKE can compute speciation profiles from mixtures of other profiles in user

- specified proportions.

The combinations are specified in the GSPRO_COMBO ancillary file by pollutant (including pollutant mode, e.g., EXH__VOC), state and county (i.e., state/county FIPS code) and time period (i.e., month). This feature was used to speciate nonroad mobile and gasoline

- related stati onary 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), tempo rally (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 integrati on across sectors, sector

- specific GSPRO_COMBO files are used. Different profile combinations are specified by the mode (e.g.

, exhaust, evaporative) and by changing the pollutant name (e.g.

, EXH__NONHAPTOG, EVP__NONHAPTOG). For the nonpt sector, a combin ation 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, the E PA changed the pollutant name for the PFC future year48

inventory so the integration would use EVP__NONHAPVOC to correctly select the E

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

3.2.1.3 Additional sector specific details

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

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 f or 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 c mv and rail sector s
, the EPA integrated BAFM for most sources. There were a few sources that

had zero BAFM and, therefore, they were not integrate d . The CARB inventories (see Section 2.4.1 ) did

not include HAPs and, therefore, all non

- NEI source emis sions in the c mv and rail sector s were not integrated . For California, the CARB inventory TOG was converted to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor.

For the othpt sector, the C3 marine sources (see Section 2.4 .2) are integrated. HAPs in this sector are derived identically to the U.S. C3 in the cmv sector. The rest of the sources in othpt are not integrated, thus the sector is partially 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) 12 . SMOKE essentially calculates

the model

- ready spe cies by using the appropriate emission factor without further speciation 13

. Third, MOVES’ internal speciation us e 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 database.

Fourth , for California and Texas EPA applied adjustmen t factors to SMOKE

- MOVES to produce

California and Texas adjusted model

- ready files (see Section 2.3.1 for details). By applying the ratios through SMOKE

- MOVES, th e CARB and TCEQ inventories are essentially speciated to match EPA estimated speciation.

12 Because the EF table has the sp eciation “baked” into the factors, all counties that are in the county group (i.e., are mapped to that representative county) will have the same speciation.

13 For more details on the use of model

- ready EF, see the SMOKE 3.7 documentation: https://www.cmasc enter.org/smoke/documentation/3. 7 /html/

.49 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 Formaldehyde

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 (SCCs beginning with 2268 or 2267) are not integrated because NMIM computed only VOC and not any HAPs for these SCCs. All other nonroad sources were integrated except in California. For California, the CARB inve ntory TOG was converted to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor. SMOKE later applies the

same VOC

- to

- TOG factor prior to computing speciated emissions. The CARB

- based nonroad data includes exhaust and evaporativ e mode

- specific data for VOC, but does not contain refueling. The CARB inventory does not include HAP estimates for all sources. Therefore, the sources which have VOC but not BAFM, or for which BAFM is greater than VOC, are not integrated and the remaini ng sources are integrated. The future year CARB inventories did not have BAFM so all sources for California were not integrated. The gasoline exhaust profiles were updated to 8750a and 8751a (this is

true nation

- wide).

For the ptnonipm sector, the 2011 and 2017 runs were not integrated . This was an oversight

— it should have been partial integration in 2017 because the biodiesel inventor y (SCC 30125010) provided by OTAQ include s BAFM. 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, the EPA and Rambo ll developed industry

- wide average profiles to improve the speciation of these significant sources of VOC , since a large portion of the SCCs related to these industries used the default profile 00000 . The two new composite profiles are “ Composite Profile

– Chemical Manufacturing (95325) ” and “Composite Profile

– Pulp and Paper Mills” (95326) 14

.

For most sources in the rwc sector, the VOC emissions were greater than or equal to BAFM, and BAFM was not zero, so those sources were integrated, although a few specific sources that did not meet these

14 These profiles are expected to be included in SPECIATE 4.5.50

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 com

position of gas. ENVIRON developed these basin

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

‐ bed methane (CBM) gas sources

. Table

3

- 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 outsid e of the WRAP Phase III basins, the profiles did not 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 Description

DJFLA D

- J Basin Flashing Gas Composition for Condensate

DJVNT D

- J Basin Produced Gas Composition

PNC01 Piceance Basin Gas Composition at Conventional Wells

PNC02 Piceance Basin Gas Composition at Oil Wells

PNC03 Piceance Basin Flashing Gas Composition for Condensate

PRBCO Powder River Basin Produced Gas Composition for Conventional Wells

PRM01 Permian Basin Produced Gas Composition

SSJCO South San Juan Basin Produced Gas Composition for Conventional Wells

SWFLA SW Wyoming Basin Flash Gas Composition

SWVNT SW Wyoming Basin Vented Gas Composition

UNT02 Uinta Basin Gas Composition at Conventional Wells

UNT03 Uinta Basin Flashing Gas Composition for Oil

UNT04 Uinta Basin Flashing Gas Composition for Condensate

WRBCO Wind River Basin Produced Gas Composition for Conventional Wells

Table 3

- 8

. National VOC profiles for oil and gas

P rofile 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

- Crude Oil Production

1001 Internal Combustion Engine

- Natural Gas

1010 Oil and Gas Production

- Fugitives

- Unclassified

1011 Oil and Gas Production

- Fugitives

- Valves and Fittings

- Liquid Service

1012 Oil and Gas Production

- Fugitives

- Valves and Fittings

- Gas Serv ice

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

- 199351

P rofile Description

8489 Natural Gas Production

8950 Natural Gas Transmission

Table 3

- 9

. Counties included in the WRAP Dataset

FIPS State County

08001 CO Adams

08005 CO Arapahoe

08007 CO Archuleta

08013 CO Boulder

08014 CO Broomfield

08029 CO Delta

08031 CO Denver

08039 CO

Elbert

08043 CO

Fremont

08045 CO

Garfield

08051 CO

Gunnison

08059 CO Jefferson

08063 CO

Kit Carson

08067 CO

La Plata

08069 CO

Larimer

08073 CO

Lincoln

08075 CO

Logan

08077 CO

Mesa

08081 CO

Moffat

08087 CO

Morgan

08095 CO

Phillips

08097 CO Pitkin

08103 CO

Rio Blanco

08107 CO

Routt

08115 CO

Sedgwick

08121 CO

Washington

08123 CO

Weld

08125 CO

Yuma

30003 MT

Big Horn

30075 MT Powder River

35005 NM Chaves

35015 NM Eddy

35015 NM Lea

35031 NM Mc Kinley FIPS State County

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

48353 TX Nolan

48371 TX Pecos FIPS State County

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 Campbell

56007 WY Carbon

56009 WY Converse

56011 WY Crook

56013 WY Fremont

56019 WY Johnson

56023 WY Lincoln

56025 WY Natrona

56027 WY Niobrara

56033 WY Sheridan

56035 WY Sublette

56037 WY Sweetwater

56041 WY Uinta

56045 WY Weston52

Everywhere in the WRAP region ( Table 3

- 9 ), WRAP speciation was applied instead of applying BAFM integration. This is a correction from the 2011v6.2 platform, in whic h a SMOKE processing error meant

that for select sources in select counties

, the national VOC profiles for oil and gas were used 15

. VOC

- to

TOG factors for WRAP speciation profiles were also updated for the 2011v6.3 platform. For the biog sector, the speci ation 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 the 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 sour ces were integrated. For portable fuel containers (PFCs) and fuel distribution operations

associated with the b ulk

- plant

- to

- pump (BTP) distribution, ethanol may be mixed into the fuels; therefore,
county

- and month

- specific COMBO speciation was used (via the GSPRO_COMBO file). Refinery to bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation are considered upstream from the introduction of ethanol into the fuel; therefore a single profile is sufficient for these sources. No

refine d information on potential VOC speciation differences between cellulosic diesel and cellulosic

ethanol sources was available ; t herefore cellulosic diesel and cellulosic ethanol sources used the same SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC speciation as was used for corn ethanol plants. For the future year, PFC and the cellulosic sources were integrated EBAFM (i.e.

, used E

- profiles) because ethanol was present in those inventories.

3.2.1.4 Future year speciation

The VOC speciation approach used for the future year case is customized to account for the impact of fuel changes. These changes affect the onroad, 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 actual fuel formulations for 2011 can be found in the 2011NEIv2 TSD

, and for 2017

, see Section 4.3

. For 2011, the EPA used “COMBO” profiles to model combination s of

p rofiles for E0 and E10 fuel use. For 2017

, the EPA assumed E10 fuel use for all nonroad 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 change across years. The speciation changes from fuels in the ptnonipm sector include BTP distribution operations inventoried as point sources. RBT fuel distribution and BPS speciation does not change across the modeling cases because this is considered upstream from the introduction of ethanol into the fuel. For PFCs, ethanol was present in the future inventories and

, therefore

, EBAFM profiles were used to integrate etha nol in the future year speciation. The mapping of fuel distribution SCCs to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix B.

Table 3

- 10 summa rizes 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

15 The sources were accidentally not included in the list of non

- integrated sources/counties. At the time of the 2011v6.2 platform modeling, the 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. This issue was corrected for the 2011v6.3 platform.53

integrated profiles. The term “COMBO” indicates that a combinat ion of the profiles listed was used to speciate that subcategory using the GSPRO_COMBO file.

Table 3

- 10

. Select VOC profiles 2011 vs 2017

S ector Sub

- category 2011 2017

nonroad gasoline exhaust COMBO 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

8753 E0 evap

8754 E10 evap

nonroad gasoline refueling COMBO 8870 E10 Headspace

8869 E0 Headspace

8870 E10 Headspace

nonroad diesel exhaust 8774 Pre

- 2007 MY HDD exhaust 8774 Pre

- 2007 MY HDD exhaust

nonroad diesel evap

orative 4547 Diesel Headspace 4547 Diesel Headspace

nonroad diesel refueling 4547 Diesel Headspace 4547 Diesel Headspace

nonpt/ ptnonipm PFC COMBO

8869 E0 Headspace 8870E E10 Headspace

8870 E10 Headspace

nonpt/ ptnonipm BTP COMBO COMBO

8869 E0 Headspace 8870 E10 Headspace

8870 E10 Headspace 8871 E15 Headspace

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

The speciation of onroad VOC occurs within MOVES. MOVES take s into account fuel type and properties, emission standards as they affect different 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 . F or 2017

, see Section 4.3. Table 3

- 11 describes all of the M

- profiles 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 emi ssions 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 emissions will use a combination of profiles 8756M, 8757M, 8758M, 8750aM, and54

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 s P rocessID F uelSubTypeID R egClassID

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 1,2,15,16,17,90 20, 21, 22 40,41,42,46,47, 48

8774M Pre

- 2007 MY HDD

exhaust 1940

- 2050 91 16 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 1,2,15,16,17,90 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, 42,46,47,48

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 Evap 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, 18 17 10,40,41,42,46,47,48

16 91 is the processed for APUs which are diesel engines not covered by the 2007 Heavey

- Duty Rule, so the older technology applieds to all years.

17 The profile assingments for pre

- 2001 gasoline vehicles fueled on E15/E20 fuels (subtypes 15 and 18) were corrected for MOVES2014a. This model year range, process, fuelsubtype regclass combinate is already assigned to profile 8758.55 Table 3

- 12

. MOVES Process IDs

Process ID Process Name

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 Permeation

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

91 Auxiliary Power Exhaust

Table 3

- 13

. MOVES Fuel subtype IDs

Fuel Subtype ID Fuel Subtype Descriptions

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)56 Table 3

- 14

. MOVES Regclass IDs

Reg. Class 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 with 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 Urban 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 used 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 includ e updated AE6 speciation 18 . The

2011e k _cb6v2 and 2017e k

_ cb6v2 state

- sector totals workbooks include state totals of the PM emissions

for each state for the sectors that include PM.

Table 3

- 15

. PM model species: AE5 versus AE6

Species name Species description AE5 AE6

POC organic carbon Y Y

PEC elemental carbon Y Y

PSO4 Sulfate Y Y

PNO3 Nitrate Y Y

PMFINE unspeciated PM 2.5 Y N

PNH4 Ammonium N Y

PNCOM non

- carbon organic matter N Y

PFE Iron N Y

PAL Aluminum N Y

PSI Silica N Y

PTI Titanium N Y

18 The exceptions are 5674 (Marine Vessel

– Marine Engine

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

– Simplified ) used in nonpt.57 Species name Species description AE5 AE6

PCA Calcium N Y

PMG Magnesium N Y

PK potassium N Y

PMN Manganese N Y

PNA Sodium N Y

PCL Chloride N Y

PH2O Water N Y

PMOTHR PM 2.5 not in other AE6 species N Y

For the onroad sector, for all processes except brake and tire wear, 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 mod el has complete information on the characteristics 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 19

. For onroad brake and tire wear, the PM is speciated in the moves2smk postprocessor that prepares the emission fac tors 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 and Texas onroad emiss ions , adjustment factors were applied to SMOKE

- MOVES to produce California and Texas adjusted model

- ready files (see Section 2.3.1 for details). California did not supply speciated PM, therefore

, the adjustment factors applied to PM2.5 were also applied to the speciated PM components. By applying the ratios through SMOKE

- MOVES, the CARB inventories are essentially speciated to match EPA estimated speciation. Texas did supply speciated PM, but it was determined that Texas’s PM speciation was very similar to the PM speciation from MOVES, so EPA

estimated speciation was preserved in Texas as well as California.



### 3.2.3 NO X 

### speciation

NO X can be speciated into NO, NO 2 , and/or HONO. For the non

- mobile sources, the 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, cmv, rail

, 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 gives 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 SMOKE

- MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NO X . The NO fraction varies by heavy duty versus light duty, fuel type, and model year. The NO 2 fraction = 1

– NO

– HONO.

19 Unlike previous platforms, the PM components (e.g., POC) are now consistently defined between MOVES2014 and CMAQ.

For more details on the use of model

- ready EF, see the SMOKE 3.7 documentation: https://www.cmascenter.org/smoke /documentation/3. 7 /html/

.58

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

Profile pollutant species split factor

HONO NOX NO2 0.092

HONO NOX NO 0.9

HONO NOX HONO 0.008

NHONO NOX NO2 0.1

NHONO NOX NO 0.9



### 3.3 Temporal Allocation

Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer temporal resolution, thereby converting annual emissions to hourly emissions. While the total emissi ons 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 th ese 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 hours 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

2011e k case is available from the reports area of the FTP site for the 2011v6.3 platform ftp://ftp.epa.gov/EmisInventory/2011v6/v3platform/

.

In SMOKE 3. 7 and in the 2011v6. 3 platform, more readable and flexible file formats are used for temporal profiles and cross references

. 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 approaches used for temporal processing across the sectors. In the table, “ Daily temporal approach ” refers to the t emporal 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 fo r the merge step. If this is not “all

, ” then the SMOKE merge step run s only for representative days, which could include holidays as indicated by the right

- most column. The values given are those used for the SMOKE M_TYPE setting (see below for more info rmation)

.

Table 3

- 17

. Temporal settings used for the platform sectors in SMOKE

Platform sector

short name Inventory

resolutions Monthly

profiles

used? Daily

temporal approach Merge

processing approach Process Holidays

as separate days
afdust_adj Annual Yes week all Yes

ag Annual Yes all all Yes
agfire Monthly week week Yes

beis Hourly n/a all Yes

cmv Annual Yes aveday aveday

rail Annual Yes aveday aveday

nonpt Annual Yes week week Yes

nonroad Monthly mwdss mwdss Yes59

Platform sector

short name Inventory

resolutions Monthly

profiles

used? Daily

temporal approach Merge

processing approach Process Holidays

as separate days
np_oilgas Annual yes week week Yes

onroad Annual & monthly 1 all all Yes

onroad_ca_adj Annual & monthly 1 all all Yes
othafdust_adj Annual yes week all

othar Annual & monthly yes week week

othon Annual & monthly 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
ptfire 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 co mputed for every day of the year and that emissions potentially have day

- of

- year variation. The value “week” means that hourly emissions computed for all days in one “representative” week, representing all weeks for each

month. This means emissions have day

- of

- week variation, but not week

- to

- week variation within the month. The value “mwdss” means hourly emissions for one representative Monday, representative weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each mo nth. This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within the month, but not week

- to

- week variation within the month. The value “aveday” means hourly emissions computed for one representative day of each month, meaning emissions for all days within a

month are the same. Special situations with respect to temporalization are described in the following

subsections.

In addition to the resolution, temporal processing includes a ramp

- up period for several days prior to January 1, 2011, which is intended to mitigate the effects of initial condition concentrations. The ramp

- up period was 10 days (December 22

- 31, 2010). For most sectors, emissions from December 2011 were used to fill in surrogate emissions for the end of December 2010. In particular, December 2011 emissions (representative days) were used for December 2010. For biogenic emissions, December 2010 emission s were processed using 2010 meteorology.



### 3.3.1 Use of FF10 format for finer than annual emissions

The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for monthly, daily, and hourly emissions inventories than prior form ats supported. Previously, processing 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 s implify the management of numerous inventories. Similarly, daily and hourly FF10 inventories contain individual records with data for all days in a month and all hours in a day, respectively.

SMOKE prevents the application of temporal profiles on top of the “native” resolution of the inventory. For example, a monthly inventory should not have annual

- to

- month temporalization applied to it; rather,60

it should only have month

- to

- day and diurnal temporalization. This becomes particularly important when

spec ific sectors have a mix of annual, monthly, 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 not matched to CEMS sources 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 and 2011v6.3 pl atform s . 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 erroneously high values in the CEMS data. If the data were not

measured at specific hours , and those values were found to be more th an t hree 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)

. W inter and summer season s are included in the development of the diurnal profiles as opposed to using data for the entire year because a nalysis 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 visible 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 te mporal 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 NO
x and

SO 2 emissions were similar. The EIS stores a base set of previously matched units via alternate facility

and unit IDs. Additions to these matches were made for the 2011v6. 3 platform due to additional specificity available in SMOKE but not in EIS, and also based on comments . For an y 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 for units matched to CEMS data, annual totals of their emissions may be different than the annual values in 2011NEIv2 because the 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 difference between the annual emissions and the total CEMS

emissions are allocated to the non

- summer months.61 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

- specific 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 factors are62

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 used when

there were no CEM S unit s with the specified fuel in the region contain ing 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 monthly emissions

, respectively, while heat input data are used to allocate emissions of all other pollutants and to allocate emissions of all pollutants from monthly to daily emissions

. Figure 3

- 5

. IPM Regions for EPA Base Case v5.1 5

Daily temporal allocation of units matched to CEMS was performed using a pr ocedure 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 using 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

.63

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 NO
x and SO 2 , 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 day

- 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 h eat input data was generally found to be more complete than the pollutant

- specific data. SMOKE 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 F uture year temporal allocation of EGUs

IPM provides unit

- level emission projections of average winter (representing October through April) and average summer (representing May through September) values. 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 h our. CEMS data from the air quality analysis year (e.g., 2011) is used as much as possible for the temporalization of EGUs. An update to previous platforms is that all temporal factors are now expressed in SMOKE temporal profile formats instead of the

ea rlier method of some temporalization being done by SMOKE and some by external programs.

The goal of the temporal allocation process is to reflect the variability in the unit

- level emissions that can impact air quality over seasonal, daily, or hourly ti me 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 repre sents

two seasons: summer ( May through September ) and winter (October through April)

. IPM unit

- level parsed 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

-64

processing tool 20 based on each unit’s projected fuel use and configuration , coupled with pollutant

specific emission factors 21

. W hen calculating PM emissions , the p ost

- processing tool utilizes specific

data ass umptions such as the ash and sulfur content of the coal projected to be used at the unit

. The tool creates a Flat 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 resulting Flat File contains all of the endogenously

- determined and post

- calculated unit

- level emissions combined with stack parameters (i.e., stack location and other characteristics consistent with information found in the NEI). A cross reference is used to map the units in NEEDS to the stack parameter and facility, unit, release point, and process identifiers used in the NEI. The cross reference also maps sources to the hourly CEMS data used to temporally allocate the emissions in the base year air quality modeling. This cross reference has been updated for the v5.1 5 platform through collaboration

with the EPA, regional planning organizations, and states and is also used to determine which emissions sources in the NEI sources have future year em issions predicted by IPM, and is available in the reports section of the 2011v6.3 platform FTP area: ftp://ftp.epa.gov/EmisInventory/2011v6/v3platform/reports/

.

Emissions from poi nt 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

- counting could result because the fut ure 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. Updates to the cross reference that were reported as comments as part of the 2 015 NODA were incorporated into the 5.15 version of the cross reference.

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 emi ssions 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 Flat File output from the postproc essor 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 not intended to represent an actua l month

- to

- month emission pattern . I nstead, 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 emissions within the Flat File under go 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 unit

- specific CEMS data, the 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 CEMS data are used to allocat e NO
x and SO 2 emissions, while CEMS 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.

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 adjusted CEMS data were used to compute the monthly, daily, and hourly profiles

described below.

20 Documentation of this tool can be found at www.epa.gov/powersectormodeling

. 21 For more information on EPA emission factors see http://www.epa.gov/ttnchie1/ap42/

.65

For units in NEEDS that are matched to units in the NEI, and for which CEMS data are avai lable, th e emissions are tempora lized based 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 generated 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 yea r associated with the air quality modeling analysis being performed, such as 2011. Note that IPM uses load data (reflecting the shape of demand) corresponding to the load in each IPM region that occurred in the base year of the air quality modeling analys is, such as 2011. The fuels used for creating the profiles for a region are coal, natural gas, and 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 composi te 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 previous ly no units with that fuel in the region containing the unit

.

The monthly emission values in the 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 22

. 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 stac k are disaggregated down to the daily level consistent with historic daily emission patterns in the given 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. The EPA uses average emission profiles 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 23 ; (2) CEMS data are only available for a limited number of hours in

tha t base year; (3) units change fuels in the future year; (4) the unit is new in the future year; (5) when there are no CEMS data for one season in the base year but IPM runs the unit during both seasons; or (6 ) units experienced atypical conditions during t he base year, such as lengthy downtimes for maintenance or

installation of 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 fact ors derived from the CEMS data for those fuels and regions using only heat input data for that season

. Only heat

22 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, even though May and July are both in t he 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 season. Therefore, part of the tempo ral allocation process is intended to create month

- specific emissions totals

that reflect this intra

- seasonal variation in unit operation and associated emissions.

23 In such instances, the EPA does not use that unit’s CEMS data for temporal allocation in o rder to avoid assigning large increases in emissions over short time periods in the unit’s hourly emission profile.66

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 emission s 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 of 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 o ther), time of year, pollutant, season (i.e.

, winter versus summer) 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 u nits 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

. The EPA developed seasonal average emission profiles, each derived from base year CEMS data for each season across all units sharing both IPM region and fuel type 24

. Figure 3

- 4 provides an example of seasonal profiles that allocate daily emissions to hours i n one IPM region. These average day

- to

- hour temporal profiles were also used for sources during seasons of the year for which there were no CEMS data available, but for which IPM predicted emissions in that season. This situation can occur for multiple

r easons, 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 are matched to units in the IPM output, the base year CEMS data are scaled so that their seasonal emissions match the IPM

- project ed 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. Any pollutants other than NO
x and SO 2 are temporalized using h eat input as a surrogate. Distinct factors are used for the fuels coal, natural gas, and

“other

. ” Through the temporalization process, the future year emissions have the same temporal pattern as the base year CEMS data while the future

- year seasonal total emissions for each unit match IPM’s

future

- year total projections for each season (see example in Figure 3

- 7 ).

In cases wh en the emissions for a particular unit are projected to be substantially higher in the future year than in the base year, the proportional scaling method to match the emission patterns in the base year described above can yield emissions for a unit that ar e 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 units 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 excess emissions to other hours was repeated until all of the hours had emissions below the maximum, whenever possib le (see example in Figure 3

- 8 ). Note: this reallocation technique was new in the 2011v6.2 platform and was used again in the 2011v6.3 ( 2017e k) case.

24 The EPA also uses a n overall composite profile across all fuels for each IPM region in instances where a unit is projected to

burn a fuel for which the 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 )

.67 Figure 3

- 7

. Future year emissions follow pattern of base year emissions

Figure 3

- 8

. Excess emissions apportioned to hours less than maximum

Using the above app roach, it was not always possible to reallocate excess emissions to hours below the historic maximum, such as when the total seasonal emissions of NO
x or SO 2 for a unit divided by the number of hours of operation are greater than the 2011

- 2014 maximum emis sions level. For these units,68

the regional fuel

- specific average profile was applied to all pollutants, including heat input, for that 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 us ed “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 pollu tants 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 for 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 temp oralization of those,

see https://www.epa.gov/airmarkets/power

- sector

- modeling

- platform

- v515

, in particular the Air Quality Modeling Flat File Documentation and accompanying inputs.

Figure 3

- 9

. Adjustment to Hours Less than Maximum not Possible, Regional Profile Applied69

Figure 3

- 10

. Regional Profile Applied, but Exceeds Maximum in Some Hours



### 3.3.3 Residential Wood Combustion Temporalization (rwc)

There are many factors that impact the timing of when emissions occur, and for some sectors this includes meteorology. The benefits of utilizing meteorology as method for temporal ization are: (1) a meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF); (2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the meteorological variables vary at h ourly resolution and can therefore be translated into hour

- specific temporalization.

The SMOKE program GenTPRO provides a method for developing meteorology

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

- to

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

- to

- hour temporalization for agricultural livestock NH 3 ; and a generic meteorology

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

- based temporalization was used for portions of the rwc sector and for livestock within the ag sector.

GenTPRO reads in gridded meteorological data (output from MCIP) along with spatial surrogates, and uses the specified algorithm to produce a new temporal profile that can be input into SMOKE. The meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend on the selected algorithm and the run parameters. For more details on the development of these algorithms and running GenTPR O, see the GenTPRO documentation and the SMOKE documentation at http://www.cmascenter.org/smoke/documentation/3.1/GenTPRO_TechnicalSummary_Aug20 12_Final.pd

f and https://www.cmascenter.org/smoke/documentation/3.7/html/ch05s03s06.html

, respectively.

As of the 2011v6.2 platform and in SMOKE 3.6.5, the temporal profile format was updated. GenTPRO now produces separate files including the monthly temporal profiles (ATPRO_MONTHLY) and day

- of

month temporal profiles (ATPRO_DAILY), instead of a single ATPRO_DAI LY with day

- of

- year temporal profiles as it did in SMOKE 3.5. The results are the same either way, so the temporal profiles70

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 distribu te annual RWC emissions to the coldest days of the year. On days where the minimum temperature does not drop below a user

- defined threshold, RWC emissions for most sources in the sector are zero. Conversely, the program temporally allocates the largest

p ercentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual emissions do not change, only the distribution of the emissions within the year is affected. The temperature threshold for rwc emissions was 50  ̊F for most of the country, and 60  ̊F for the following states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and

Texas.

Figure 3

- 11 illustrates the impact of changing the temperature threshold for a warm climate county. The plot shows the temporal fraction by day for Duval County, Florida

, for the first four months of 2007

. The

default 50  ̊ F 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 R WC emissions in the morning and the evening when people are typically using these sources. This profile is based on a

2004 MANE

- VU survey based temporal profiles (see http://www.marama.org/publications_folder/ResWoodCombustion/Final_report.pdf ). This profile was created by averaging three indoor and three RWC outdoor temporal profiles from counties in Delaware and aggregating them into a single RWC diurnal profile. This new profile was compared to a concentration based analysis of aethalometer measurements in Rochester, N ew Y ork (Wang et al. 2011) for various seasons and day of the week and found that the new RWC profile generally tracked the

concentration based temporal patterns.71 Figure 3

- 12

. RWC diurnal temporal profile

The temporalization for “Outdoor Hydronic Heaters” (i.e., “OHH

, ” SCC=2104008610) and “Outdoor wood burning device, NEC (fire

- pits, chimn eas, 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 ’s (NYSERDA) “Environmental, Energy Market, and Health Characterization of Wood

- Fired Hydronic Heater Technologies, Final Report ” (NYSERDA, 2012)

, as well as a Northeast States for Coordinated Air Use Management (NESCAUM) report “Assessment of Outdoor Wood

- fired Boilers” (NESCAUM, 2006). A Minnesota 2008 Residential Fuelwood Assessment Survey of individual household responses (MDN R, 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

- w eek profile for recreational RWC follows a typical “recreational” profile with emissions peaked on weekends.

Annual

- to

- month temporalization for OHH as well as recreational RWC were computed from the MDNR

2008 survey and are illustrated in Figure 3

- 15

. The OHH emissions still exhibit strong seasonal variability, but do not drop to zero because many units operate year round for water and pool heating. I n contrast to all other RWC appliances, recreational RWC emissions are used far more frequently during the

warm season.72 Figure 3

- 13

. Diurnal profile for OHH, based 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 Heater73

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 the EPA ’s ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is

based on observations fro m the TES satellite instrument with the GEOS

- Chem model and its adjoint to estimate diurnal NH3 emission variations from livestock as a function of ambient temperature, aerodynamic resistance, and wind speed. The equations are:

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

- 13 80/T i,h ) ] x AR i,h

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

where

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

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

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

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

 AR i,h = Aerodynamic resistance in county i

GenTPRO was run using the “BASH_NH3” profile method to create month

- to

- hour temporal profile s for

these sources. Because these profiles distribute to the hour ba sed on mont hly emissions, the monthly

emissions are obtained from a monthly inventory

, or from an annual inventory that has been temporalized

to the month. Figure 3

- 16 compares the daily emissions for Minnesota from the “old” approach (uniform monthly profile) with the “new” approach (GenTPRO generated month

- to

- hour profiles). Although the GenTPRO profiles show daily (and hourly variability), the monthly total emission s 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 Heater74

Figure 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 combination of more traditional temporal profiles and the influence of meteorology. This section will discuss both the meteorological influences and the diurnal temporal profiles for this platform.

Meteorology is n ot used in the development of the temporal profiles, but rather it impacts the calculation of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the hourly level by grid cell. More specifically, the on

- network (RP D) 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 for the sp ecified SCC/pollutant/mode combination. In the 2011 platform (and for the 2011NEIv2), RPP was updated to use the gridded minimum and maximum temperature for the day. This more spatially resolved temperature range produces more accurate emissions for each grid cell. The combination of these 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 temporalization of the onroad sector and the meteorological influence via

SMOKE

- MOVES. Similar temporalization is done for the VMT in SMOKE

- MOVES, but the meteorologically va rying emission factors add an additional variation o n top of the temporalization.

Figure 3

- 17

. Example of SMOKE

- MOVES temporal variability of NO X emissions75

For the onroad sector, t he “inventories” referred to in Table 3

- 17 actually consist of activity data , not
emissions

. For RPP and RPV processes, the VPOP inventory is annual and does not need temporalization. For RPD, the VMT inventory is monthly and was temporalized to days of the week and then to hourly VMT through temporal profiles. The RPD processes require a speed profile (SPDPRO) that consists of vehicle speed by hour for a typical weekday and weekend day. Unlike other sectors, the temporal profiles and SPDPRO will impact not only the distribution of emissions through time but also

the total emissions. Because SMO KE

- MOVES (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 emission factors

. In other words, two SMOKE

- MOVES runs with

identic al annual VMT, meteorology, and MOVES emission factors , 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 process to RPD except that speed is not included in the calculation of RPH.

In previous platforms, the diurnal profile for VMT 25 varied by road type but not by vehicle type (see Figure 3

- 18 ). These profiles were used throughout the nation.

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 26 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 wit h 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 27 (see the 2011NEIv2 TSD for details on the submittal process for onroad). The EPA mined the state submitted MOVES CDBs for non

- default diurnal profiles 28

. The list

25 These profiles were used in the 2007 platform and proceeding platfo rms.

26 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.

27 The MOVES tables are the hourvmtfraction and the dayvmtfraction.

28 Further QA was done to remove du plicates 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 1.76

of potential diurnal profiles was then analyzed to see whether the profiles varied by vehicle type, road type, weekday v ersus weekend, and by county within a state . For the MOVES diurnal profiles, the 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 state or used in developing default temporal pro files. The Vehicle Travel Information System (VTRIS) is a repository for reported traffic count data to the Federal Highway Administration (FHWA). The 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. The EPA created distinct diurnal profiles for weekdays, Saturday and

S unday along with day of the week profiles 29

.

The 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 profile would be used (see below for description of new profiles). This analysis was done separately for weekdays and for

weekends and , therefore

, some areas had submitted profiles for weekdays but defaults for weekends. The result was a set of profiles that varied geographically depend ing 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 profiles

A new set of diurnal profiles was developed for the 2011v6. 2 platform from the submitted profiles that varied by both vehicle type and road type. For the purposes of constructing the national default diurnal profiles, the EPA created individual profiles for each state (averaging over the counties within) to create a

29 Note, the day of the week profiles (i.e., Monday vs Tuesday vs etc) are only from the VTRIS data. The MOVES CDBs only have weekday versus weekend profiles so they were not included in calculating a new national default day of the week profile.77

single profile by state, vehicle type, road type, and the day (i.e.

, weekday v ersus Saturday v ersu s Sunday)

. The source of the underlying profiles was either MOVES or VTRIS data (see Figure 3

- 19 ). The states individual profiles were averaged together to create a new default profile 30

. 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 (HHDDV, SCC 6 220 262 ) on restricted urban roadways (interstates and freeways )

.

Figure 3

- 20

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

30 Note that the states were weighted equally in the average independent of the size of the state or the variation in submitted county data.78

The blue lines of Figure Figure 3

- 20 indicate the weekday profile, the green the Saturday profile, and the red the Sunday profile. In comparison, the new default profiles for weekdays places more LDGV VMT (upper plot) in the ru sh 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. In addition to creating diurnal profiles, the EPA developed day of week profiles using the VTRIS data. The c reation of the state and national profiles was similar to the diurnal profiles (described above). Figure 3

- 21 shows a set of national default profile s 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 week79

T he EPA also developed a natio nal profile for hote ling by averaging all the combination long

- haul truck profiles on restricted 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 ling irrespective of location.

Figure 3

- 22

. Combination long

- haul truck restricted and hot e ling profile

For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week 31 , and air basin. These CARB specific profiles were used in developing EPA estimates for California. Although

the EPA adjusted the total emissions to match California’s submittal to the 2011NEIv2 , the temporalization 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

, cmv, rail

, 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://www3.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

31 California’s diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles a nd Tuesday, Wednesday, Thursday had the same profile.80

applied to remo ve all emissions for days where measureable rain occurs. Therefore, the afdust emissions vary day

- to

- day based on the precipitation and/or snow cover for that grid cell and day. Both the transport fraction and meteorological adjustments are based on the gridded resolution of the platform; therefore, somewhat different emissions will result from different grid resolutions. Application of the transport fraction and meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as compared to ambient samples.

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
compute d using appropriate emission factors according to the vegetation in each model grid cell, while taking the meteorological data into account.

For the cmv and rail sectors, emissions are allocated with flat monthly and day of week profiles, and most

emissio ns 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 the 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 uniform profile for each day of the week was used for all agricultural burning emissions in all states , except for the following states that the EPA used state

- specific day of we ek profiles: Arkansas, Iowa, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, and Texas.

Figure 3

- 23

. Agricultural burning diurnal temporal profile

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. Industri al process that are not likely to shut down on Sundays such as those at cement plants were assigned to other81

more realistic profiles that included emissions on Sundays. This also affected emissions on holidays because Sunday emissions are also used on hol idays. For the ptfire sectors, the inventories are in the daily point fire format ORL PTDAY. The ptfire sector is used in the model evaluation case (2011ek ) and in the future base case (2017ek). The 2007 and earlier platforms had additional regulatory ca ses that used averaged fires and temporally averaged EGU emissions, but the 2011 platform uses base year

- specific (i.e., 2011) data for both cases.

For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly

inv entories from output from NMIM. For California, a monthly inventory was created from CARB’s annual inventory using EPA

- estimated NMIM monthly results to compute monthly ratios by pollutant and SCC7 and these ratios were applied to the CARB inventory to cr eate a monthly inventory.

Some cross reference updates for temporalization of the np_oilgas sector were made in the 2011v6.2 and

2011v6.3 platform to assign np_oilgas sources to 24 hour per day, 7 days a week based on comments

received.



### 3.3.7 Time zone correcti ons

Various t ime zone corrections/updates were made to the 2011v6.3 platform , which affects the hourly temporalization of emissions

. Table 3

- 18 lists the time zone corrections for U

. S

. counties. Almost the entire country of Mexico needed to be corrected. Most of country is Central time zone with DST, except

for the six northwestern most states. I n the 2011v6.2 platform, most of Mexico was Centr al time without

DST. The time zone corrections made to Canada are the following:

o Quebec: Seven census divisions moved from Atlantic Time to Eastern Time . Only one Quebec

census division remains in Atlantic Time zone.

o Manitoba: Daylight Saving Time (DST) added. (Only affects entire province FIPS; individual census divisions were already correct.)

o Saskatchewan: now Central time without DST; was previously a mix of Central time and

Mountain Time with DST.

o Peace River, BC: changed from Pacific Time with DST t o Mountain Time without DST.

o NW Territories: moved from Pacific Time to Mountain Time . (Only affects entire province FIPS; individual census divisions were already correct.)

Table 3

- 18

. Time zone corrections for US counties in 2011v6.3 platform

FIPS State County 2011eh 2011ek

ALL Indiana ALL some with no daylight saving time implemented (DST) all changed to implementing DST

20093 Kansas Kearny Co MT CT

21087 Kentucky Green Co ET CT

21225 Kentucky Union Co ET CT

21233 Kentucky Webster Co ET CT

38057 North Dakota Mercer Co MT CT

38059 North Dakota Morton Co MT CT

38065 North Dakota Oliver Co MT CT

38085 North Dakota Sioux Co MT CT

46075 South Dakota Jones Co MT CT

46095 South Dakota Mellette Co MT CT

46121 South Dakota Todd Co MT CT82 

### 3.4 Spatial Allocation

The methods used to perform spatial allocation are summarized in this section

. For the modeling platform, spatial factors are typically applied by county and SCC

. As described in Section 3.1

, spatial

allocation was performed for a national 12

- km domain

. To accomplish this, SMOKE used national 12

- km spatial surrogates and a SMOKE area

- to

- point data file. For th e U.S., the 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

.

With the excep tion of some updates to the spatial surrogate cross reference, the spatial surrogates for the U.S. and Mexico used in the 2011v6.3 platform are the same as the surrogates used for the 2011v6.2 platform. The details regarding how the 2011v6.2 platform surr ogates were created are available from ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/spatial_surrogates/ in the files US_SpatialSurrogate_Workbook_v072115.xlsx and US_SpatialSurrogate_Documentation_v070115.pdf

, and SurrogateTools_Scripts_2014.zip available. 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 10 0 s patial surrogates available for spatially allocating U.S. county

- level emissions

to the 12

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

- to

- point approach overrides the use of surrogates for a limited set of sources. Table 3

- 19 lists the codes and descriptions of the surrogates. Surrogate na mes and codes listed in italics are not directly assigned to any
sources for the 2011v6.3 platform, but they are sometimes used to gapfill other surrogates, or as an input for merging two surrogates to create a new surrogate that is used.

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 for railroads and roads ; the

2006 National Land Cover Database ; 2011 gas station and dry cleaner da ta ; 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 2011NEIv2 shapefiles: Ports_032310_wrf and ShippingLanes_111309FINAL_wrf, bu t also included shipping lane data in the Great Lakes and support vessel activity data in the Gulf of Mexico. The creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and documentation for it is available at https://www.cmascenter.org/sa

- tools/documentation/4.2/SurrogateToolUserGuide_4_2.pdf

.

Table 3

- 19

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

Code Surrogate Description Code Surrogate Description

N/A Area

- to

- point approach (see 3.3.1.2) 507 Heavy Light Construction Industrial Land

100 Population 510 Commercial plus Industrial

110 Housing 515 Commercial plus Institutional Land

120 Urban Population 520 Commercial plus Industrial plus Institutional

130 Rural Population 525 Golf Courses + Institutional +Industrial +

Commercial

137 Housing Change 526 Residential Non

- Institutional

140 Housing Change and Population 527 Single Family Residential

150 Residential Heating

- Natural Gas 530 Residential

- High Density83

Code Surrogate Description Code Surrogate Description

160 Residential Heating

– Wood 535 Residential + Commercial + Industrial +

Institutional + Government

165 0.5 Residential Heating

- Wood plus 0.5 Low Intensity Residential 540 Retail Trade

170 Residential Heating

- Distillate Oil 545 Personal Repair

180 Residential Heating

– Coal 550 Retail Trade plus Personal Repair

190 Residential Heating

- LP Gas 555 Professional/Technical plus General

Government

200 Urban Primary Road Miles 560 Hospitals

205 Extended Idle Locations 565 Medical Offices/Clinics

210 Rural Primary Road Miles 570 Heavy and High Tech Industrial

220 Urban Secondary Road Miles 575 Light and High Tech Industrial

221 Urban Unrestricted Roads 580 Food, Drug, Chemical Industrial

230 Rural Secondary Road Miles 585 Metals and Minerals Industrial

231 Rural Unrestricted Roads 590 Heavy Industrial

240 Total Road Miles 595 Light Industrial

250 Urban Primary plus Rural Primary 596 Industrial plus Institutional plus Hospitals

255 0.75 Total Roadway Miles plus 0.25 Population 600 Gas Stations

256 Off

- Network Short

- Haul Trucks 650 Refineries and Tank Farms

257 Off

- Network Long

- Haul Trucks 675 Refineries and Tank Farms and Gas Stations

258 Intercity Bus Terminals 680 Oil & Gas Wells, IHS Energy, Inc. and USGS (see updated surrogates in Table 3

- 19)

259 Transit Bus Terminals 710 Airport Points

260 Total Railroad Miles 711 Airport Areas

261 NTAD Total Railroad Density 720 Military Airports

270 Class 1 Railroad Miles 800 Marine Ports

271 NTAD Class 1, 2, 3 Railroad Density 801 NEI Ports

280 Class 2 and 3 Railroad Miles 802 NEI Shipping Lanes

300 Low Intensity Residential 806 Offshore Shipping NEI NOx

310 Total Agriculture 807 Navigable Waterway Miles

312 Orchards/Vineyards 808 Gulf Tug Zone Area

320 Forest Land 810 Navigable Waterway Activity

330 Strip Mines/Quarries 812 Midwest Shipping Lanes

340 Land 820 Ports NEI NOx

350 Water 850 Golf Courses

400 Rural Land Area 860 Mines

500 Commercial Land 870 Wastewater Treatment Facilities

505 Industrial Land 880 Drycleaners

506 Education 890 Commercial Timber

For the onroad sector, 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 access to 221. Off

- network emissions were spatially allocated according to the mapping in

Table 3

- 20

. Starting with the 2011v6.2 platform, e missions from the extended (i.e., overnight) idling of trucks were assigned to a new surrogate 205 that is based on locations of overnight truck parking spaces.84 Table 3

- 20

. 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 1

- 1 using 2011 data consistent with what was used to develop the 2011NEI 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 the 2011NEIv2 data for the oil and gas sector . The data sources include 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 omp letion 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 identi 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 )

.

Table 3

- 21

. Spatial Surrogates for Oil and Gas Sources

Surrogate Code Surrogate Description

681 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 and CBM Wells

689 Gas production at all wells

692 Spud count

- All Wells

693 Well count

- all wells85 Surrogate Code Surrogate Description

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 update s were made between the 2011v6.2 platform and the

2011v6.3 platform aside from the reworking of the onroad mobile source surrogates described above. These updates included the following:

 Nonroad SCCs using spatial surrogate 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 nonroad SCCs for commercial equipment in New York County had assignments updated to surrogate 34 0;

 Commercial lawn and garden equipment was updated to use surrogate 520; and

 Some county

- specific assignments for RWC were updated to use surrogate 300.

For the 2011v6.3 platform, the CMV underway emissions were change d to use surrogate 802

. RWC firepla ces in all counties, and other RWC emissions in select counties

, were changed 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

- 19 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 surrogates are applied to the

emission invento ries. Table 3

- 22 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 emi ssions

.

Table 3

- 22

. 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 140 Housing Change and Population 0 0 159,485 0 0

afdust 240 Total Road Miles 0 0 286,188 0 0

afdust 310 Total Agriculture 0 0 895,786 0 0

afdust 330 Strip Mines/Quarries 0 0 58,959 0 0

afdust 400 Rural Land Area 0 0 1 0 0

ag 310 Total Agriculture 3,502,246 0 0 0 0

agfire 310 Total Agriculture 3,287 45,594 100,174 17,001 79,615

agfire 312 Orchards/Vineyards 27 432 1,082 753 799

agfire 320 Forest Land 7 8 121 0 124

cmv 801 Port Areas 41 54,101 3,900 36,064 1,998

cmv 802 Shipping Lanes 360 588,291 21,483 57,655 15,444

cmv 820 Ports NEI2011 NOx 19 56,363 1,866 834 1,666

nonpt 100 Population 4,137 0 0 0 1,196,465

nonpt 140 Housing Change and Population 3 23,423 65,897 29 134,88786

Sector ID Description NH3 NOX PM2_5 SO2 VOC

nonpt 150 Residential Heating

- Natural Gas 40,775 217,560 4,785 1,443 13,031

nonpt 170 Residential Heating

- Distillate Oil 2,045 40,842 4,523 88,432 1,394

nonpt 180 Residential Heating

- Coal 247 1,033 605 7,931 1,233

nonpt 190 Residential Heating

- LP Gas 136 38,705 224 705 1,432

nonpt 240 Total Road Miles 0 27 602 0 32,152

nonpt 250 Urban Primary plus Rural Primary 0 0 0 0 102,207

nonpt 260 Total Railroad Miles 0 0 0 0 2,195

nonpt 300 Low Intensity Residential 3,847 18,334 90,706 3,048 40,003

nonpt 310 Total Agriculture 0 0 614 0 363,385

nonpt 312 Orchards/Vineyards 0 441 117 1,806 262

nonpt 320 Forest Land 0 85 287 0 97

nonpt 330 Strip Mines/Quarries 0 4 0 0 48

nonpt 400 Rural Land Area 2,855 0 0 0 0

nonpt 500 Commercial Land 2,367 2 85,404 585 26,183

nonpt 505 Industrial Land 35,360 195,282 124,150 112,01

6 114,391

nonpt 510 Commercial plus Industrial 4 178 27 109 224,110

nonpt 515 Commercial plus Institutional Land 1,408 177,903 18,637 58,798 21,915

nonpt 520 Commercial plus Industrial plus

Institutional 0 0 0 0 14,965

nonpt 527 Single Family Residential 0 0 0 0 153,528

nonpt 535 Residential + Commercial + Industrial +

Institutional + Government 23 366 1,283 0 327,986

nonpt 540 Retail Trade (COM1) 0 0 0 0 1,371

nonpt 545 Personal Repair (COM3) 0 0 93 0 60,289

nonpt 555 Professional/Technical (COM4) plus

General Government (GOV1) 0 0 0 0 2,865

nonpt 560 Hospital (COM6) 0 0 0 0 10

nonpt 575 Light and High Tech Industrial (IND2 +

IND5) 0 0 0 0 2,538

nonpt 580 Food, Drug, Chemical Industrial (IND3) 0 610 313 171 10,535

nonpt 585 Metals and Minerals Industrial (IND4) 0 23 140 8 443

nonpt 590 Heavy Industrial (IND1) 10 4,373 5,419 1,131 138,575

nonpt 595 Light Industrial (IND2) 0 1 244 0 79,169

nonpt 600 Gas Stations 0 0 0 0 416,448

nonpt 650 Refineries and Tank Farms 0 0 0 0 129,221

nonpt 675 Refineries and Tank Farms and Gas

Stations 0 0 0 0 1,203

nonpt 711 Airport Areas 0 0 0 0 1,956

nonpt 801 Port Areas 0 0 0 0 12,469

nonpt 870 Wastewater Treatment Facilities 1,003 0 0 0 4,671

nonpt 880 Drycleaners 0 0 0 0 7,053

nonroad 100 Population 40 39,475 2,824 85 5,030

nonroad 140 Housing Change and Population 554 537,250 45,058 1,255 78,526

nonroad 261 NTAD Total Railroad Density 2 2,673 310 5 568

nonroad 300 Low Intensity Residential 106 26,637 4,324 138 202,92887

Sector ID Description NH3 NOX PM2_5 SO2 VOC

nonroad 310 Total Agriculture 481 488,224 39,037 910 57,473

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 505 Industrial Land 452 146,871 5,809 411 32,978

nonroad 510 Commercial plus Industrial 382 131,572 9,888 348 139,291

nonroad 520 Commercial plus Industrial plus

Institutional 205 70,541 16,361 288 255,836

nonroad 850 Golf Courses 12 2,394 112 17 7,092

nonroad 860 Mines 2 2,931 341 5 594

nonroad 890 Commercial Timber 19 12,979 1,486 38 8,680

np_oilgas 400 Rural Land Area 0 0 0 0 50

np_oilgas 680 Oil and Gas Wells 0 10 0 0 55

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 683 Produced Water at all wells 0 0 0 0 44,772

np_oilgas 684 Completions at Gas and CBM Wells 0 2,579 46 434 11,706

np_oilgas 685 Completions at Oil Wells 0 360 11 376 28,194

np_oilgas 686 Completions at all wells 0 45,044 1,742 106 101,803

np_oilgas 687 Feet drilled at all wells 0 44,820 1,449 119 9,714

np_oilgas 688 Spud count

- Gas and CBM Wells 0 0 0 0 11,322

np_oilgas 689 Gas production at all wells 0 39,184 2,318 224 64,828

np_oilgas 692 Spud count

- all wells 0 30,138 445 502 4,598

np_oilgas 693 Well count

- all wells 0 23,437 436 93 48,205

np_oilgas 694 Oil production at oil wells 0 2,332 0 12,602 729,483

np_oilgas 695 Well count

- oil wells 0 96,244 3,067 88 431,306

np_oilgas 697 Oil production at gas and CBM wells 0 3,579 183 34 465,478

np_oilgas 698 Well count

- gas and CBM wells 0 373,808 6,428 2,644 525,201

onroad 200 Urban Primary Road Miles 24,529 955,745 36,449 5,563 149,459

onroad 205 Extended Idle Locations 802 287,773 6,087 102 68,782

onroad 210 Rural Primary Road Miles 11,799 804,187 24,595 2,609 77,762

onroad 221 Urban Unrestricted Roads 45,231 1,533,073 64,023 11,735 393,928

onroad 231 Rural Unrestricted Roads 29,520 1,245,597 41,956 6,406 221,362

onroad 256 Off

- Network Short

- Haul Trucks 0 11,498 265 10 17,469

onroad 257 Off

- Network Long

- Haul Trucks 0 459 40 2 1,504

onroad 258 Intercity Bus Terminals 0 22 1 0 29

onroad 259 Transit Bus Terminals 0 9 4 0 116

onroad 506 Education 0 344 27 1 1,059

onroad 507 Heavy Light Construction Industrial Land 0 39 2 0 106

onroad 510 Commercial plus Industrial 0 127,442 1,976 140 207,135

onroad 526 Residential

- Non

- Institutional 0 739 18 1 2,258

onroad 535 Residential + Commercial + Industrial +

Institutional + Government 0 641,873 12,424 887 1,320,099

onroad 600 Gas Stations 0 0 0 0 195,830

rail 261 NTAD Total Railroad Density 2 16,536 379 260 92588

Sector ID Description NH3 NOX PM2_5 SO2 VOC

rail 271 NTAD Class 1 2 3 Railroad Density 332 732,956 22,636 7,390 38,304

rail 280 Class 2 and 3 Railroad Miles 13 41,886 948 287 1,622

rwc 165 0.5 Residential Heating

- Wood plus 0.5

Low Intensity Residential 15,162 27,530 318,442 7,900 385,325

rwc 300 Low Intensity Residential 4,520 6,883 62,481 1,049 56,858



### 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 mo deling platform, the EPA used the 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/rocket engine firing and testing). The ARTOPNT approach is described in detail in the 2002 platform documentation: http://www 3.epa.gov/scram001/reports/Emissions%20TSD%20Vol1_02

- 28

- 08.pdf

. The ARTOPNT file that lists the nonpoint sources to locate 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 Tabl e 3

- 23

. Th e leading “9” was added to the surrogate codes to avoid duplicate surrogate numbers with U.S. surrogates. S urrogates for M exico 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

- 24

. 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.

Tabl e 3

- 23

. Canadian Spatial Surrogates

Code Canadian Surrogate Description 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 BUCWHT

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 OATS89

Code Canadian Surrogate Description Code Description

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 wholesaler

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 92446 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 Vessels 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 92432 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 RYEFAL

92406 STEERS 92442 RYESPG

92407 MLKCOW 92443 SOYBNS

92408 MLKHEIF 92444 SUGARB

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 GRWPIG 92451 WHTWIN

92418 NURPIG 92452 BEANS90

Code Canadian Surrogate Description Code Description

92419 SOWS 92453 CARROT

92421 IMPAST 92454 GRPEAS

92422 UNIMPAST 92455 OTHVEG

92423 ALFALFA 92456 SWCORN

92457 TOMATO

Table 3

- 24

. CAPs Allocated to Mexican and Canadian Spatial Surrogates

Code Mexican or Canadian Surrogate Description NH 3 NO X PM 2_5 SO 2 VOC

12 MEX Housing 20,299 37,970 3,076 269 52,351

14 MEX Residential Heating

– Wood 0 974 12,490 149 85,877

16 MEX Residential Heating

- Distillate Oil 0 10 0 3 0

20 MEX Residential Heating

- LP Gas 0 5,042 152 0 86

22 MEX Total Road Miles 7,977 243,883 1,624 4,921 319,740

24 MEX Total Railroads Miles 0 18,946 423 166 738

26 MEX Total Agriculture 141,820 104,270 22,804 5,073 8,876

28 MEX Forest Land 912 2,735 7,792 850 6,352

32 MEX Commercial Land 0 58 1,280 0 19,884

34 MEX Industrial Land 2 1,108 1,527 0 94,034

36 MEX Commercial plus Industrial Land 0 0 0 0 76,677

38 MEX Commercial plus Institutional Land 1 1,243 51 2 32

40 MEX Residential (RES1

- 4)+Comercial+Industrial+Institutional+Government 0 3 8 0 59,870

42 MEX Personal Repair (COM3) 0 0 0 0 4,440

44 MEX Airports Area 0 2,552 68 321 799

46 MEX Marine Ports 0 7,677 487 3,843 78

50 MEX Mobile sources

- Border Crossing

- Mexico 4 142 1 2 262

9100 CAN Population 583 19 607 11 243

9101 CAN total dwelling 265 26,699 6,792 4,937 20,769

9103 CAN rural dwelling 1 426 68 2 2,491

9106 CAN ALL_INDUST 6 8,999 348 8 2,738

9111 CAN Farms 26 27,674 2,409 39 3,212

9113 CAN Forestry and logging 576 6,506 352 632 15,352

9211 CAN Oil and Gas Extraction 1 1,640 98 2 141

9212 CAN Mining except oil and gas 0 0 2,074 0 0

9221 CAN Total Mining 37 11,269 41,316 1,217 987

9222 CAN Utilities 60 3,831 305 652 164

9233 CAN Total Land Development 13 12,742 1,362 20 1,983

9308 CAN Food manufacturing 0 0 4,323 0 7,548

9321 CAN Wood product manufacturing 0 0 537 0 0

9323 CAN Printing and related support activities 0 0 0 0 33,802

9324 CAN Petroleum and coal products manufacturing 0 784 835 410 2,751

9327 CAN Non

- metallic mineral product manufacturing 0 0 4,362 0 0

9331 CAN Primary Metal Manufacturing 0 142 5,279 46 17

9412 CAN Petroleum product wholesaler

- distributors 0 0 0 0 44,248

9448 CAN clothing and clothing accessories stores 0 0 0 0 13291

Code Mexican or Canadian Surrogate Description NH 3 NO X PM 2_5 SO 2 VOC

9481 CAN Air transportation 5 7,692 130 787 6,112

9482 CAN Rail transportation 3 4,247 94 136 94

9562 CAN Waste management and remediation services 1,111 1,497 1,837 2,183 13,868

9921 CAN Commercial Fuel Combustion 467 133,155 11,421 29,102 100,565

9924 CAN Primary Industry 0 0 0 0 220,312

9925 CAN Manufacturing and Assembly 0 0 0 0 71,912

9932 CAN CANRAIL 67 62,931 2,373 1,431 1,846

9941 CAN PAVED ROADS 2 1,261 158,418 2 2,269

9942 CAN UNPAVED ROADS 21 4,245 1,312 26 57,495

9945 CAN Commercial Marine Vessels 30 40,929 3,360 27,659 5,954

9946 CAN Construction and mining 0 1 9 0 78

9950 CAN Combination of Forest and Dwelling 267 2,899 31,312 424 44,339

9955 CAN UNPAVED_ROADS_AND_TRAILS 0 0 242,538 0 0

9990 CAN TOTFERT 0 0 29,266 0 159,858

9996 CAN urban_area 0 0 618 0 0

9997 CAN CHBOISQC 442 4,912 48,652 702 71,050

91201 CAN traffic_bcw 18,654 345,837 12,226 1,702 178,466

92401 CAN BULLS 4,394 0 0 0 0

92402 CAN BFCOWS 46,101 0 0 0 0

92403 CAN BFHEIF 7,398 0 0 0 0

92404 CAN CALFU1 17,987 0 0 0 0

92406 CAN STEERS 24,551 0 0 0 0

92407 CAN MLKCOW 37,603 0 0 0 0

92408 CAN MLKHEIF 2,617 0 0 0 0

92409 CAN MBULLS 35 0 0 0 0

92410 CAN MCALFU1 11,988 0 0 0 0

92412 CAN BROILER 7,049 0 0 0 0

92413 CAN LAYHEN 8,044 0 0 0 0

92414 CAN TURKEY 3,220 0 0 0 0

92416 CAN BOARS 139 0 0 0 0

92417 CAN GRWPIG 51,078 0 0 0 0

92418 CAN NURPIG 13,047 0 0 0 0

92419 CAN SOWS 5,376 0 0 0 0

92421 CAN IMPAST 1,949 0 0 0 0

92422 CAN UNIMPAST 2,081 0 0 0 0

92423 CAN ALFALFA 1,622 0 0 0 0

92424 CAN BARLEY 7,576 0 0 0 0

92425 CAN BUCWHT 21 0 0 0 0

92426 CAN CANARY 282 0 0 0 0

92427 CAN CANOLA 7,280 0 0 0 0

92428 CAN CHICPEA 449 0 0 0 0

92429 CAN CORNGR 15,655 0 0 0 0

92430 CAN CORNSI 2,328 0 0 0 0

92431 CAN DFPEAS 703 0 0 0 0

92432 CAN FLAXSD 1,667 0 0 0 092

Code Mexican or Canadian Surrogate Description NH 3 NO X PM 2_5 SO 2 VOC

92433 CAN FORAGE 526 0 0 0 0

92434 CAN LENTIL 547 0 0 0 0

92435 CAN MUSTSD 722 0 0 0 0

92436 CAN MXDGRN 658 0 0 0 0

92437 CAN OATS 4,452 0 0 0 0

92438 CAN ODFBNS 254 0 0 0 0

92439 CAN OTTAME 5,985 0 0 0 0

92440 CAN POTATS 1,268 0 0 0 0

92441 CAN RYEFAL 153 0 0 0 0

92442 CAN RYESPG 7 0 0 0 0

92443 CAN SOYBNS 1,775 0 0 0 0

92444 CAN SUGARB 30 0 0 0 0

92445 CAN SUNFLS 383 0 0 0 0

92446 CAN TOBACO 72 0 0 0 0

92447 CAN TRITCL 73 0 0 0 0

92448 CAN WHITBN 288 0 0 0 0

92449 CAN WHTDUR 5,524 0 0 0 0

92450 CAN WHTSPG 13,929 0 0 0 0

92451 CAN WHTWIN 2,785 0 0 0 0

92452 CAN BEANS 109 0 0 0 0

92453 CAN CARROT 73 0 0 0 0

92454 CAN GRPEAS 113 0 0 0 0

92455 CAN OTHVEG 294 0 0 0 0

92456 CAN SWCORN 297 0 0 0 0

92457 CAN TOMATO 98 0 0 0 093 

## 4 Development of 201 7 Base

- Case Emissions

The emission inventories for the future year of 2017 ha ve 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 emi ssions from specific 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 occur in the intervening years (e.g ., non

- EGU point and nonpoint sources). For some sectors, the same emissions are used in the base and future years, such as biogenic, fire, and stationary Canadian emissions. For the remaining sectors, rules and specific legal obligations that go into ef fect in the intervening years, along with changes in activity for the sector, are considered when possible. The 2017 base case emission inventory represent s predicted emissions that account for Federal and State measures promulgated or under reconsiderati on by February, 2016

.

Emissions inventories for neighboring countries used in our model ing are included in the 2011v6.3 platform, specifically 2008 and 2018 emissions inventories for Mexico, and 2010 emissions inventories for Canada. The meteorological da ta used to create and temporalize emissions for the 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 pre pare 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 5.15 and are reflected in an air quality modeling

- ready flat file tak en from the EPA Base Case v. 5.15

. The NEEDS database 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 includ es basic geographic, operating, air emissions, and other data on these generating units and has been updated for the EPA’s version 5.15 power sector modeling platform based on comments received on the notices of data availability for the 2011 and 2018

emis sions 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 Pl atform v. 5.15 at https://www.epa.gov/airmarkets/power

sector

- modeling

- platform

- v515 . The projected EGU emissions include the Final Mercury and Air Toxics (MATS) rule announ ced on December 21, 2011

, and the Cross

- State Air Pollution Rule (CSAPR) issued July 6,

2011. Note that the Clean Power Plan (CPP) is not included in the 2017 base case.

To project future emissions for onroad and nonroad mobile sources, the EPA used MOVES 2014 a 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, activity 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. In the previous ( 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. For the 2011v6.3 platform

, the

mobile source emissions f or 2017 were generated by using year 2017 activity data couple with emission factors

for a MOVES run that was specifically for th e year 2017.

For non

- EGU point and nonpoint sources, projections of 2017 emissions were developed by starting with the

2011 em issions 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 impacts 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 such94 as oil and gas, residential wood combustion, cement kilns, livestock, aircraft, com mercial 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 control information on stationary non

- EGU sources as described further in

Section 4.2

.

T he following bullets summarize the projection methods used for sources in the various sectors , while additional details and data sources are given in the fo llowing subsections and in Table 4

- 1

.

 EGU sector (ptegu): Unit

- specific estimates from IPM version 5.15 , including CASPR, Final MATS, Regional Haze rule, and Cooling Water Intakes Rule.

 Non

- IPM sector (ptnonipm): Closures, projection factors and percent reductions reflect comments received from the notices of data availability for the 2011 and 2018 emissions modeling platforms, along

with emission reductions due to nat ional and local rules, control programs, plant closures, consent

decrees and settlements. Projection for corn ethanol and biodiesel plants, refineries and upstream impacts take into account Annual Energy Outlook (AEO) fuel volume projections. Airport

- spe cific terminal 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 indi cators using information from AEO 2014 projections to years 2018, as well as comments received on the notices of data availability for the 2011 and 2018 emissions modeling platforms. Co

- benefits of stationary engines CAP

- cobenefit reductions (RICE NESHAP) and controls from New Source Performance Standards (NSPS) are reflected for select source categories.

 Biogenic (beis): 2011 emissions are used for all future

- year scenarios and are computed with the same “11g” meteorology as is used for the air quality m odeling.

 Fires sectors (ptfire, agfire): No growth or control

– 2011 estimates are used directly.

 Agricultural sector (ag): Year 2017 projection factors for livestock estimates based on expected changes in animal population from 2005 Department of Agricult ure data, updated according to EPA experts in July 2012.

 Area fugitive dust sector (afdust): For livestock PM emissions, projection factors for dust categories related to livestock estimates based on expected changes in animal population. For unpaved and paved road dust, county

- level VMT projections to 2017 were considered.

 Remaining Nonpoint sector (nonpt): Projection factors and percent reductions reflect comments received from the notices of data availability for the 2011 and 2018 emissions modeling pla tforms, along with emission reductions due to national and local rules/control programs. PFC projection factors reflecting impact of the final Mobile Source Air Toxics ( MSAT2) rule. Upstream impacts from AEO fuel volume, including cellulosic ethanol plan ts, are reflected. 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 projection fact ors reflect assumed growth of wood burning appliances based on sales data, equipment replacement rates and change outs. These changes

include the 2

- stage NSPS for Residential Wood Heaters, resulting in growth in lower

- emitting stoves

and a reduction in hi gher emitting stoves.

 Locomotive, and non

- Category 3 commercial marine sector ( cmv and rail ): Year 2017 projection factors for Category 1 and Category 2 commercial marine and locomotives reflect final locomotive

- marine

controls.95

 Category 3 commercial marine vessel ( cmv ): Base

- year 2011 emissions grown and controlled to 2017, incorporating controls based on Emissions Control Area (ECA) and International Marine Organization (IMO) global NO X and SO 2 controls.

 Nonroad mobile sector (nonroad): Other than f or 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 years 2018. The year 2018 data were adjusted to 2017 using national adjustment factors.

T he inputs were either state

- supplied as part of the 2011NEIv2 process or using national level inputs, with only minor updates for 2011NEIv2. Final controls from the final locomotive

- marine and small spark ignition rules are included. California data for 2017 were provided by CARB

. For Texas, TCEQ data were projected from 2011 to 2017 using trends based on NMIM data

.

 Onroad mobile (onroad) : MOVES2014

- based emissions factors for year 201 7 were developed using the same representative counties, state

- supplied data, meteorology, and procedures that were used to produce the 2011 emission factors described in Section 2.3.1

. See section 4.3.1.1 for details about future year activity data used in generating emissions estimates.

 Onroad emissions d ata for California were provided by CARB

. Data for Texas were provided by the

Texas Commission of Environmental Quality.

 Other point (othpt), nonpoint/nonroad (othar), onroad (othon): For Canada, year 2010 inventories were projected for the othon and for the nonroad part of the othar sectors using projection factors derived

from U

. S

. emissions changes from 2011 to 2017 by SCC and pollutant. In the othpt sector, the Canadian point sources were modified by removing any remaining EGU facilities using coal. Mexico inventory data were projected from year 2008 to 2018 (used for 2017). C3 CMV data was proj ected using the same methodology as the cmv sector. Offshore oil platform emission s were held constant at 2011

levels.

Table 4

- 1 summarizes the growth and control ass umptions by source type that w ere used to create the U.S. 201 7

base

- case emissions from the 2011v 6. 3 base year inventorie s. T he control, closures and projection packets ( i.e.,

data sets) used to create the 201 7 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 the 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 projection 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 cmv and rail sector s
, 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 strategy.

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 model and onroad mobile uses MOVES), using C oST, or by other mechanisms. The 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 Section 4.2, along with some backgro und on CoST. Onroad and nonroad mobile projections are discussed in Sections 4.3 and 4.4

, respectively. Finally, projections for all “other” sources, primarily outside the U.S., are described in Section

4.5

. Section 5 contains summaries of the 2011 and 2017 emissions the emissions changes between the years for

emissions both within and outside of the United States.96 Table 4

- 1

. Control strategies and growth assumptions for creating the 201 7 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 from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008 ptnonipm, cmv, rail PROJECTION All 4.2.3.3 2,

1

Upstream RFS2/EISA/LDGHG impacts on gas distribution, pipelines and refineries to years 2018 ptnonipm, pt_oilgas,
nonpt PROJECTION All 4.2.3.4 2

AEO growth to 2018: industrial sources, including oil and gas play

- level projections ptnonipm, pt_oilgas,
nonpt, np_oilgas PROJECTION All 4.2.3.5 1
Aircraft growth via Itinerant (ITN) operations at airports to

2017 ptnonipm PROJECTION All 4.2.3.7 1
Corn Ethanol plants adjusted via AEO volume projections to

2017 ptnonipm PROJECTION All 4.2.3.9 1

NESHAP: Portland Cement census

- division level based on Industrial Sector Integrated Solutions policy emissions to the

year 2018. The se resutls are from the Cement 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 4.2.3.8 &

4.2.5.4 1 &

n/a

NESHAP: RICE (reciprocating internal combustion engines)

with re consideration 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

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 + local 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

&

CONTROL All 4.2.3.5

, 4.2.3.6

, 4.2.4.10 1 Commercial and Industrial Solid Waste Incineration (CISWI)

revised NSPS ptnonipm CONTROL 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 4.2.3.1 197

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 ag PROJECTION NH 3 4.2.3.2 1

Upstream RFS2/EISA/LDGHG impacts on gas distribution, pipelines and refineries to years 2018 ptnonipm, pt_oilgas,
nonpt PROJECTION All 4.2.3.4 2

AEO growth to 2018: industrial sources, including oil and gas play

- level projections ptnonipm, pt_oilgas,
nonpt, np_oilgas PROJECTION All 4.2.3.5 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 and change

- outs from year 2011 to years 2018 rwc PROJECTION All 4.2.3.10 1

Industrial/Commercial/Institutional Boiler MACT with Reconsideration Amendments + local 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

&

CONTROL All 4.2.3.5

, 4.2.3.6

, 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 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 Assumptions

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:

onroad 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: October, 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): December,

2010 Light

- Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards;

Final Rule for Model

- Year 2012

- 2016: May, 201098

Description of growth, control, closure data, or, new inventory Sector(s) Packet Type CAPs impacted Section(s) CoST Strategy Final Mobile Source Air Toxics Rule (MSAT2): February,

2007

Local Onroad Programs:
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 equipment)

Stage II refueling control programs

Nonroad Mobile ( cmv, rail , nonroad sectors) Controls and Growth Assumptions

All national in

- force regulations are modeled. The list includes recent key mobile source regulations but is not exhaustive.

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 from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008

Clean Air Nonroad Diesel Final Rule

– Tier 4: May, 2004

Locomotives:
Growth and control to years 2017 from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008 cmv, rail ptnonipm PROJECTION All 4.2.3.3 1,

2

Clean Air Nonroad Diesel Final Rule

– Tier 4: May, 2004 cmv, rail n/a All 4.4 n/a
Commercial Marine:

Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards: April, 2010 cmv PROJECTION All 4.2.3.3 1 Growth and control to years 2017 from Locomotives and Marine Compression

- Ignition Engines Less than 30 Liters per Cylinder: March, 2008 cmv,rail, ptnonipm PROJECTION All 4.2.3.3 1,

2

Clean Air Nonroad Diesel Final Rule

– Tier 4: May, 2004 nonroad n/a All 4.4 n/a



### 4.1 EGU sector projection s : ptegu

The future

- year data for the ptegu sector used in the air quality modeling were created by IPM) version 5.15

. The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector.

IPM version 5.15 reflects state rules, consent decrees and announced shutdowns forecast through calendar year

2017. T he NEEDS database was up dated based on comments received on the notice of data availability for the emissions modeling platform issued prior to the proposal . IPM v ersion 5.15 was update d from the previous

version 5.14 and represents electricity demand projections for the AEO 201 5

. The scenario used for this modeling represents the implementation of the CSAPR, the MATS, and the final actions the EPA has taken to implement the Regional Haze Rule, the Cooling Water Intakes Rule

, and Combustion Residuals from Electric Utilities (CC R) . The Clean Power Plan (CPP) is not included in the 2017 scenario for this rulemaking.

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 s tep also apportions99 the regional emissions down to the unit

- level emissions used for air quality modeling. A single IPM run was postprocessed once for each output year to get results for 2017

.

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 development of the flat file, a cross reference between the 2011NEIv2 and IPM is used to populate stack parameters and other related information fo r matched

sources. The flat file creation methodology is documented in the air quality modeling flat file documentation

available at : https://www.epa.gov /sites/production/files/2015

- 07/documents/flatfile_methodology.pdf

. The

cross reference is available from t he reports directory of the 2011v6.3 platform FTP site : ftp://ftp.epa.gov/EmisInventory/2011v6/v3platform

. The emissions in the flat file created based on the IPM outputs are temporalized into the hourly emissions needed by the air quality model as described in Section

3.3.2.



### 4.2 Non

- EGU Point and NEI Nonpoint sec tor projections : afdust, ag, cmv, rail

, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc

To project all U.S. non

- EGU stationary sources , facility/unit closures information and growth (PROJECTION) factors and/or controls were applied to certain categories within the afdust, ag, cmv, rail , nonpt, np_oilgas, ptnonipm, pt_oilgas and rwc platform sectors. Some facility or sub

- facility

- level closure information was also applied to the point sources. There are also a handful of situations where new inventories were ge nerated 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 details on the data and projection methods used for these sectors.

In recent platforms, the 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” assumption wa s based

on an examination of historical emissions and economic data. Recently however, the 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. The 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)

. The EPA requested comment on the future year growth and control assumptions used to develop

the 2018 inventories

. One of the most frequent comment s the 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 make the projections approach as consistent as possible across all s tates, the EPA decided to expand this effort to all states for some of the most

- significant industrial sources (see Section

4.2.3.5 )

.

Because much of the projections and controls data are developed independently from how the 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, industrial source growth factors are applicable to four 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 inventori es: 1) for point sources, apply plant (facility or sub

- facility

- level) closure information via CoST ; 2) apply all PROJECTION packets via CoST (multiplicative factors that could cause increases or decreases) ; 3) apply all percent reduction

- based CONTROL

pac kets via CoST ; and 4) append all other 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 and do not need to be repeated. Sector names associated with the CoST packets are provided in parentheses.100 

### 4.2.1 CoST Background: Used for NEI non

- EGU Point and Nonpoint sectors

CoST is used to apply most non

- EGU projection/growth fa ctors, controls and facility/unit/stack

- level closures

to the 2011 NEI

- based emissions modeling inventories to create inventories for year 2017 for the following sectors: afdust, ag, cmv, rail , nonpt, np_oilgas, ptnonipm, pt_oilgas and rwc. The CoST trai ning manual is

available at: http://www.cmascenter.org/help/model_docs/cost/2.5/CoST_UsersGuide_2012

- 08

- 01_Final.pdf

. The CoST development document, 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_Developm ent%20Document_2010

- 06

- 09.pdf

.

CoST allows the user to apply projection (growth) factors, controls and closures at various geographic and inventory 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 in cludes all 2011 inventories and applicable CoST packets. For reasons discussed later, some emissions modeling sectors require multiple CoST strategies to account for the compounding of control programs that impact the same type of sources. There are also available linkages to existing and user

- defined control measures databases and it is up to the user to determine how control strategies are developed and applied. The EPA typically creates individual CoST packets that represent specific intended purposes

. For example, aircraft projections for airports are in a separate PROJECTION packet from residential wood combustion sales/appliance turnover

- based projections. CoST uses three packet types as described below:

1. CLOSURE: Applied first in CoST. This packet can be used to zero

- out (close) point source emissions at resolutions as broad as a facility to as specific as a stack. The EPA uses these types of packets for known post

- 2011 controls as well as information on closures provided by states on specific fac ilities,

units or stacks. This packet type is only used in the ptnonipm and pt_oilgas sectors.

2. PROJECTION: This packet allows the user to increase or decrease emissions for virtually any geographic and/or inventory source level. Projection factors are ap plied as multiplicative factors to the 2011 emissions inventories prior to the application of any possible subsequent CONTROLs. A PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable when

information is based more on activity assumptions rather than known control measures. The EPA uses 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 inventory source level application. Control factors are expressed as a percent reduction (0 to 100) and can be applied in addition to any pre

- existing inventory control, or as a replacement con trol 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 and use comma

delimited formats. As mentioned abov e, CoST first applies any/all CLOSURE information for point sources, then applies PROJECTION packet information, followed by CONTROL packets. A hierarchy is used by CoST to separately apply PROJECTION and CONTROL packets. In short, in a separate process for PROJECTION and CONTROL packets, more specific information is applied in lieu of less

- specific information in ANY other packets. For example, a facility

- level PROJECTION factor will be replaced by a unit

- level, or facility and pollutant

- level PROJECTIO N factor. It is important to note that this hierarchy does not apply between packet
types (e.g., CONTROL packet entries are applied irrespective of PROJECTION packet hierarchies )

. A more specific example: a state/SCC

- level PROJECTION factor will be appli ed before a stack/pollutant

- level CONTROL factor that impacts the same inventory record. However, an inventory source that is subject to a CLOSURE packet record is removed from consideration of subsequent PROJECTION and CONTROL packets.101 The implication for this hierarchy and intra

- packet independence is important to understand and quality assure when creating future year strategies. For example, with consent decrees, settlements and state comments, the goal is typically to achieve a targ eted reduction (from the 2011NEI) or a targeted future

- year emissions value. Therefore, as encountered with this 2017 base case, consent decrees and state comments for specific cement kilns (expressed as CONTROL packet entries), needed to be applied instea d of (not in addition to) the more general approach of the PROJECTION packet entries for cement manufacturing. By processing CoST control strategies with PROJECTION and CONTROL packets separated by the type of broad measure/program, it is possible to show actual changes from the 2011 inventory to the 2017 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 potential 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. The refore, when summarizing “emissions reduced” from CONTROL packets, it is important to note that these reductions are not relative to the 2011 inventory, but rather to the intermediate inventory after application of any/ all PROJECTION packet matches (and CL OSURES). 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 FIPS 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 inclu des corrections to matching hierarchy made in 2011v6.3 platform modeling. These corrections did cause emissions changes from the 2011v6.2 platform to 2011v6.3 platform for the np_oilgas, pt_oilgas, ptnonipm and nonpt sectors.

Table 4

- 2

. Subset of CoST Packet Matching Hierarchy

Rank Matching Hierarchy Inventory Type

1 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID, SCC, POLL point

2 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID, POLL point

3 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, POLL point

4 REGION_CD, FACILITY_ID, UNIT_ID, POLL point

5 REGION_CD, FACILITY_ID, SCC, POLL point

6 REGION_CD, FACILITY_ID, POLL point

7 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID, SCC point

8 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID, PROCESS_ID point

9 REGION_CD, FACILITY_ID, UNIT_ID, REL_POINT_ID point

10 REGION_CD, FACILITY_ID, UNIT_ID point

11 REGION_CD, FACILITY_ID, SCC point

12 REGION_CD, FACILITY_ID point

13 REGION_CD, NAICS, SCC, POLL point, nonpoint

14 REGION_CD, NAICS, POLL point, nonpoint

15 STATE, NAICS, SCC, POLL point, nonpoint

16 STATE, NAICS, POLL point, nonpoint

17 NAICS, SCC, POLL point, nonpoint

18 NAICS, POLL point, nonpoint

19 REGION_CD, NAICS, SCC point, nonpoint

20 REGION_CD, NAICS point, nonpoint

21 STATE, NAICS, SCC point, nonpoint

22 STATE, NAICS point, nonpoint

23 NAICS, SCC point, nonpoint

24 NAICS point, nonpoint102 Rank Matching Hierarchy Inventory Type

25 REGION_CD, SCC, POLL point, nonpoint

26 STATE, SCC, POLL point, nonpoint

27 SCC, POLL point, nonpoint

28 REGION_CD, SCC point, nonpoint

29 STATE, SCC point, nonpoint

30 SCC point, nonpoint

31 REGION_CD, POLL point, nonpoint

32 REGION_CD point, nonpoint

33 STATE, POLL point, nonpoint

34 STATE point, nonpoint

35 POLL point, nonpoint

T he contents of the controls, local adjustments and closures for the 201 7 base case s are described in the following subsections

. Year

- specific projection factors (PROJECTION packets) for year s 2017 were used for creating the 201 7 base case s
, unless noted otherwise. The contents of a few 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 ge ographically and their contents could not be provided in the following sections (e.g., facilities and units subject to the Boiler MACT reconsideration has thousands of records). The

remainder of Section 4.2 is divided into several subsections that are sum marized 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 CLOSURE

packet ptnonipm, pt_oilgas All facility/unit/stack closures information,

primarily from Emissions Inventory System (EIS),

but also includes information from states and othe r organizations.

0 CoST PROJECTION

packets All Introduces and summarizes national impacts of all CoST PROJECTION packets to years 2017.

4.2.3.1 Paved and unpaved roads

VMT growth afdust PROJECTION packet: county

- level resolution, based on VMT growth.

4.2.3.2 Livestock population

growth ag PROJECTION packet: national, by

- animal type resolution, based on animal population projections.

4.2.3.3 L ocomotives rail, ptnonipm PROJECTION packet: Rail projections are by

FIPS /SCC/poll for Calif. And SCC/poll for rest of US. NC rail projection packet was added for NODA, by FIPS /SCC/poll.

4.2.3.3 Category 1, 2, and 3

commercial marine vessels cmv PROJECTION packet: Category 1 & 2: CMV uses SCC/poll for all states except Calif. Category 3: region

- level by

- pollutant, based on

cumulative growth and control impacts from rulemaking.

4.2.3.4 OTAQ upstream

distribution, pipelines and

refineries nonpt, ptnonipm, pt_oilgas PROJECTION packet: national, by

- broad source

category, based on upstream impacts from mobile source rulemakings.103 Subsection Title Sector(s) Brief Description

4.2.3.5 Oil and gas and industrial

source growth nonpt, np_oilgas, ptnonipm, pt_oilgas Several PROJECTION packets: varying geographic

resolutions from state, county, to oil/gas play

- level and by

- process/fuel

- type applications. Data derived

from AEO2014 with several modifications.

4.2.3.6 Data from comments on

previous platforms nonpt, ptnonipm Several PROJECTION packets: varying geographic

resolutions and by

- process/fuel

- type applications. Data derived from various sources in response to the previous version of the emissions modeling platform.

4.2.3.7 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.

4.2.3.8 Cement manufacturing ptnonipm PROJECTION packet: by

- kiln projections based on

Indus trial Sectors Integrated Solutions (ISIS) model of demand growth and Portland Cement NESHAP.

4.2.3.9 Corn ethanol plants ptnonipm PROJECTION packet: national, based on 2014 AEO renewable fuel production forecast.

4.2.3.10 Residential 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, ptnonipm, pt_oilgas Introduces and summarizes national impacts of all

CoST CONTROL packets to year 2017.

4.2.4.1 Oil and gas NSPS np_oilgas, pt_oilgas CONTROL packet: national, oil and gas NSPS

impacting VOC only for some 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 s ources,

by

- facility

- type controls impacting Industrial and Commercial/Institutional boilers from rulemaking

and state

- provided information.

4.2.4.5 Fuel sulfur rul es 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.104 Subsection Title Sector(s) Brief Description

4.2.4.7 Process heaters 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

als o equipment retirement assumptions.

4.2.4.8 Arizona 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 Data 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.

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 new 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 ISIS policy case

- derived new cement

kilns, permitted (point) and model

- generated based on shifted capacity from some closed units to open units (no npt)



### 4.2.2 CoST Plant CLOSURE packet ( ptnonipm )

Packet : “ CLOSURES_2011v6_2_v4fix_31aug2015_08jan2016_v5 .txt ” (ptnonipm)

Th e CLOSURES packet contain s 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 November 29, 2014 with status = ‘PS’ ( i.e., post

- 2011 permanent facility /unit

shutdowns known in EIS as of the date of the report)

.
3. Concatenation of all 2011v6.0 closures information; see Section 4.2.11.3 from the 2011v6.0 platform

TSD.

4. Comments from states and regional planning organizations on the 2011v6.2 platform

.

N o te that no pt_oilgas sources are affected by the current CLOSURES packet. 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 used for 2007 air quality modeling. In addition, comments on the 2011v6.0 emissions modeling platform received by states and other agencies indicated that some previously specified closures should remain open

. Ultimately, all data were updated to match the SMOKE FF10 inventory105 key fields, with all duplicates removed, and a single CoST packet was generated. The closures packets include changes to closure dates for North Carolina, West Virginia and Oklahoma fa cilities and other changes received as comments on the NODA for the 2011v6.2 platform . These changes impact sources in the ptnonipm and pt_oilgas sector s

. The cumulative reductions in emissions for ptnonipm are shown in Table 4

- 4

.

Table 4

- 4

. Red uctions from all facility/unit /stack

- level closures

.

Pollutant ptnonipm

CO 13,865

NH3 431

NOX 11,499

PM10 3,980

PM2.5 2,817

SO2 32,360

VOC 14,900



### 4.2.3 CoST PROJECTION packets (afdust, ag, cmv, rail , 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 CoST PROJECTION packets. Regardless of inventory type (point or nonpoint), the PROJECTION packets applied prior to the CoST packets. For several emissions modeling sectors ( i.e., afdust, ag, cmv, rail and rwc), there is only one CoST PROJECTION packet. For all other sectors, there are several different sources of PROJECTIONS dat a 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 PROJECTION (and CONTROL) packets were separated into a few “key” control progra m 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 packets used for oil and gas and industrial source projections; these pack ets are a mix of different sources of data that targeted

similar sources.

4.2.3.1 Paved and unpaved roads VMT growth (afdust)

Packet: “PROJECTION_2011_2017_AFDUST_VMT_2011v6.2_10mar2015 _v0 .txt”

We received comments from the 2018 NODA ( search for ‘EPA

- HQ

- OAR

- 2013

- 0 809’ at www.regulations.gov ) suggesting we grow emissions from paved an d unpaved road dust as a function of VMT. The future year VMT used to project this sector was the 2017 VMT from the 2011v6.2 platform

. The 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, etc.).

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%106

4.2.3.2 Livestock population growth (ag)

Packet: “PROJECTION_2011_2017_ag_2011v6.2_no_RFS2_04feb2015 _v0 .txt”

The EPA estimated animal population growth in NH 3 emissions from livestock in the ag sector. Except for dairy cows and turkey production, the animal projection factors are derived from national

- l evel animal population projections from the U.S. Department of Agriculture (USDA) and the Food and Agriculture Policy and Research Institute (FAPRI). This methodology was initiated in 2005 for the 2005 NEI, but was updated on July 24, 2012

, in support of the 2007v5 platform (EPA, 2012). For dairy cows, the EPA assumed that there would be no growth in emissions based on little change in U.S. dairy cow populations from year s 2011 through

2017

, according to linear regression analyses of the FAPRI projections

. This assumption was based on an analysis of historical trends in the number of such animals compared to production rates. Although productions rates have increased, the number of animals has declined. Based on this analysis, the EPA concluded that

pro duction forecasts do not provide 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 particular, the dairy cow population is projected to d ecrease in the future as it has for the past few decades; however, milk production will be increasing over the same period. Note that the NH 3 emissions from dairies are not directly related to animal population

, but also nitrogen excretion. With the cow numbers going down and the production going up

, the excretion value will change, but no change was assumed because a quantitative

estimate was not available. Appendix C provides the animal population data and regression curves used to derive the growth fa ctors.

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 population and

, therefore

, the projection factor for

these animals is 1.0 (no growth)

. Impacts from the renewable fue ls mandate are not included in projections for

this sector.

Table 4

- 5

. NH 3 projection factors and total impacts to year 2017 for a nimal o perations

Animal Category 2017 Projection Factors

& Total Emissions 2025 Projection Factors

& 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, & 3 commercial marine vessels ( cmv, rail

, ptnonipm , othpt )

Packet s for rail, cmv and ptnonipm : “PROJECTION_2011v6.2_2017_c1c2rail_BASE_06feb2015 _v0 .txt”

“ NCDAQ_PROJECTION_Locomotives_2011_2017_102315 _09dec2015_v0.txt ” “PROJECTION_2011_2017_C3_CMV_ECA_IMO_2011v6.2_10feb2015 _v0 .txt”107 There are two components used to create projection factors for year 2017. The first component of the future

year cmv and rail inventor ies is the non

- California data projected from the 2011 base case. The second component is the CARB

- supplied year 2011 and 2017 data for California.

For all states outside of California, national projection factors by SCC and pollutant betwee n 2011 and future

years reflect the May 2004 “Tier 4 emissions standards and fuel requirements” ( http://www.epa.gov/otaq/documents/nonroad

- diesel/420r04007.pdf ) as well as the March 2008 “Final

locomotive

- marine rule” controls ( http://www.epa.gov/otaq/regs/nonroad/420f08004.pdf )

. The future

- year cmv and rail emissions account for increased fuel consumption based on Energy Information Administration (EIA) fuel consumption projections for freight, and emissions reductions resulting from emissions standards from the

Final Locomotive

- Marine rule (EPA, 2009d) 31

. For locomotives, the EPA applied HAP factors for VOC HAP s by using VOC projection factors 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” (SCC=2285002010) are applied to the ptnonipm (point inventory) “yard locomotives” (SCC=28500201) reported by a couple of states in the 2011 NEI.

Table 4

- 6

. Non

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

Marine Vessel Emissions

SCC Description Poll 2017

Factor 2025

Factor

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 emissions 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 Locomotives: 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 Equipment; 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 Operations 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 Equipment; 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: Passenger 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 Railroad 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 Locomotives: 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; Diesel; 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

31 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

.108 SCC Description Poll 2017

Factor 2025

Factor

2285002010 Railroad Equipment; Diesel; Yard Locomotives SO 2 0. 032 0. 036

2285002010 Railroad Equipment; Diesel; Yard Locomotives VOC 0. 950 0. 767

For California projections, the CARB provided to the EPA the locomotive, and Category 1 and 2 commercial marine emissions used to reflect years 2011 and 2017. These CARB inventories included nonroad rules reflected in the December 2010 Rulemaking Inventory ( http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf ), those in the March 2011 Rule Inventory, the

Off

- Road Construction Rule Inventory for “In

- Use Diesel

, ” cargo handling equipment rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm ), and the 2007 and 2010 regulations to reduce emissions diesel engines on commercial harbor craft operated within California waters and 24 nm of the C alifornia

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 regulation s. The locomotive emissions were obtained from the CARB trains dataset “ARMJ_RF#2002_ANNUAL_TRAINS.txt

. ” Documentation of the CARB off

road methodology, including cmv and rail sector data, is provided here: http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles

. The EPA converted the CARB inventory TOG to VOC by dividing the inventory TOG by the available VOC

- to

- TOG speciation factor. The CARB

year

- 2011 inventory (provided with the 2 017 emissions) did not match the CARB

- submitted inventory in Version 2 of the 2011 NEI; therefore, we used the CARB 2011/2017 data to compute projection ratios that were then applied to the 2011 emissions modeling platform (2011NEIv2). California projecti on factors were “capped” at 2.5 . W e found that those counties/SCCs/pollutants with projection factors greater than 2 (100% increase) were all under 100 tons for any given pollutant. The California projection factors are county

- level and

are, therefore

, not provided here.

The non

- California projection factors were applied to all “offshore” C1 and C2 CMV emissions. These offshore emissions, in the 2011 NEI, start at the end of state waters and extend out to the EEZ. A summary of the national impact for the U

. S

. (including California) and rail and offshore C1 &C2 cmv sector emissions are provided in Table 4

- 7

.
Table 4

- 7

. Difference in Category 1& 2 cmv and rail sector emissions between 2011 and 2017

Region Pollutant 2011 2017 Difference 2017

- 2011

U.S. CMV CO 70,408 72,535 2,127

U.S. CMV NO X 413,314 361,210

- 52,105

U.S. CMV PM 10 19,629 8,522

- 11,106

U.S. CMV PM 2.5 18,099 8,035

- 10,064

U.S. CMV SO 2 91,045 4,777

- 86,268

U.S. CMV VOC 12,578 12,896 318

Offshore CMV CO 66,395 63,515

- 2,880

Offshore CMV NO X 326,631 261,637

- 64,994

Offshore CMV PM 10 10,795 7,687

- 3,108

Offshore CMV PM 2.5 10,471 7,457

- 3,015

Offshore CMV SO 2 4,014 632

- 3,382

Offshore CMV VOC 7,472 6,080

- 1,392109 Region Pollutant 2011 2017 Difference 2017

- 2011

U.S. rail CO 122,703 137,080 14,377

U.S. rail NO X 791,381 683,360

- 108,021

U.S. rail PM 10 25,898 19,137

- 6,762

U.S. rail PM 2.5 23,963 17,704

- 6,259

U.S. rail SO 2 7,936 316

- 7,621

U.S. rail VOC 40,851 28,004

- 12,847

As discussed in Section 2.4.1 , 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 that reflect assumed growth and final ECA

- IMO controls. These emiss ions

estimates reflect the EPA’s coordinated strategy for large marine vessels. More information on the EPA’s coordinated strategy for large marine 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 found at : http://www.epa.gov/otaq/oceanvessels.htm

.

Project ion factors for creating the year 2017 cmv inventor y from the 2011 base case are provided in Table 4

- 8

. Background on the region and EEZ FIPS is provided in the discussion on the cmv inventory for 2011 in Section

2.4.1

. For example, Washington state emissions are grown the same as all No rth P acific offshore emissions.

Table 4

- 8

. Growth factors to project the 20 11 ECA

- IMO inventory to 20 17 and 2025

Region EEZ

(Offshore)

FIPS Year 2017 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 1.266 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

Packet for othpt: “PROJECTION_2011_2017_C3_CMV_ECA_IMO_2011v6.2_10feb2015.txt”

As discussed in Section 2.4.2, e missions outside the 3 to 10 mile coastal boundary

, but within the approximately

200 nm EEZ boundaries

, were projected to year 2017 using the same regional adjustment factors as the U.S. emissions; however, the FIPS codes were assigned as “EEZ” FIP S and these emissions are processed in the “othpt” sector (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 assigned to a U.S.110 county or Ontario. The classification of emissions to U.S. and Canadian FIPS codes 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 ( cmv sector) and

far

- offshore emissions (othpt sector ) in 2017 is provided in Table 4

- 9

.
Table 4

- 9

. Difference in Category 3 cmv sector and othpt C3 CMV emissions between 2011 and 2017

Region Pollutant 2011

emissions 2017

emissions Difference

2017

- 2011

Offshore to EEZ* CO 133,574 150,744 17,170

Offshore to EEZ* NOX 798,258 820,359 22,101

Offshore to EEZ* PM10 28,451 5,514

- 22,937

Offshore to EEZ* PM2_5 26,113 5,077

- 21,036

Offshore to EEZ* SO2 222,113 13,515

- 208,598

Offshore to EEZ* VOC*** 81,593 88,892 7,299

Non

- US SECA C3 CO 187,439 243,239 55,800

Non

- US SECA C3 NOX 2,209,800 2,611,745 401,945

Non

- US SECA C3 PM10 187,587 243,431 55,844

Non

- US SECA C3 PM2_5 172,580 223,957 51,376

Non

- US SECA C3 SO2 1,391,702 1,806,004 414,302

Non

- US SECA C3 VOC*** 79,575 103,264 23,689

*

- Offshore to EEZ includes both c3marine, and the offshore oil rigs/etc from the US point inventory

***

- INCLUDES pre

- speciated inventory VOC in Canada, so post

- SMOKE VOC_INV < VOC

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

Packet: “PROJECTION_2011_2017_OTAQ_upstream_GasDist_pipelines_refineries_2011v6.2_05feb2015 _v0 .txt”

To account for projected increases in renewable fuel volumes due to the Renewab le Fuel Standards (RFS2)/EISA (EPA, 2010a) and decreased gasoline volumes due to RFS2 and light

- duty greenhouse gas standards as quantified in AEO 2014 ( http://www.eia.gov/forecasts/archive/aeo14/ ), the EPA developed county

level inventory adjustments for gasoline and gasoline/ethanol blend transport and distribution. Here, 2018 adjustments are used for 2017. These adjustments account for losses during t ruck, rail and waterways loading/unloading and intermodal transfers such as highway

- to

- rail, highways

- to

- waterways, and all other possible combinations of transfers. Adjustments for 2018 only account for impacts of RFS2. These emissions are entirely evap orative and

, therefore

, limited to VOC.

A 2018 inventory that included impacts of the EISA mandate was developed by applying adjustment factors to

the 2011 NEIv2 inventory

. These adjustments were made using an updated version of the EPA’s model for

upstre am emission impacts, developed for the RFS2 rule 32

. The methodology used to make these adjustments is

described in a 2014 memorandum included in the docket for the EPA Tier 3 rule (EPA, 2014) 33

.

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

32 U.S. EPA. 2013. Spreadsheet “upstream_emissions_rev T3.xls.

33 U. S. EPA. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for the Tier 3 Final Rule. Memorandum from Rich Cook, Margaret Zawacki and Zoltan Jung to the Docket. February 25, 2014. Docket EPA

- HQ

- OAR

2011

- 0135.111 samples from EPA ct exhaust test program 34 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 test program (Environ, 2008) and was also submitted into EPA’s SP ECIATE 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 adjusted 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 the EPA ’s OTAQ and impact p rocesses such as process heaters, catalytic cracking units, blowdown systems, wastewater treatment, condensers, cooling towers, flares and fugitive emissions. Calculation of the emission inventory impacts of decreased gasoline and diesel production, due to renewable fuel volume projections, on nationwide refinery emissions was done in the 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 adjustment factors are estimated relative to that portion of refinery emissions associated with gasoline and diesel fuel production. Production of jet fuel, still gas and other products also produce emissions. If these emissions were

in cluded, the adjustment factors would not be as large.

The resulting 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 bulk terminal to gasoline dispensing pump (BTP) component s

. Emissions for the BTP component are greater than the RBT and BPS c omponents. See Appendix B for the complete cross

- walk between SCC, for all component t ype s of petroleum transport and storage component s

.

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 multiple 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) wit h our first CoST strategy, then applied these “EISA” adjustments on the results of this first CoST strategy. Similarly, we have RICE existing NESHAP

, as well as NSPS

, controls that need to be applied in separate strategies. Alternatively,

we could have m ade “compound” CoST packets that combine these 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 ei ther 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 Factors

2011

Emissions Reduction %

Reduction Pipelines &

Refineries RBT BTP/BPS

CO 2018 0.9896 n/a n/a 53,501 556 1.04%

34 U. S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclos ed Fuel Tanks. Office of Research and Development and Office of Transportation and Air Quality. Report No. EPA

- 420

- R

- 11

- 018. EPA Docket EPA

- HQ

- OAR

- 2011

- 0135.112

NOX 2018 0.9877 n/a n/a 68,354 837 1.23%

PM10 2018 0.9938 n/a n/a 24,484 152 0.62%

PM2.5 2018 0.9940 n/a n/a 21,599 130 0.60%

SO2 2018 0.9909 n/a n/a 78,944 715 0.91%

VOC 2018 0.9934 n/a n/a 750,025 21,616 2.88%

4.2.3.5 Oil and gas and industrial source growth ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

Packets: “PROJECTION_2011v6.2_2018_nonpoint_SCC_SRAcapped_05dec2014 _v0.txt ”

“ PROJECTION_2011v6_2_2018_nonpoint_SCC_SRAcapped_No_NC _v1.txt ” “PROJECTION_2011v6.2_2018_SCC_NONPOINT_LADCO_09dec2014 _v0.txt ” “PROJECTION_2011v6.2_2018_SCC_NONPOINT_SCA_orig_CAPPED_09dec2014 _v0.txt ” “PROJECTION_2011v6.2_2018_SRAcapped_POINT_05dec2014 _v0.txt ” “PROJECTION_2011v6.2_2018_SCC_POINT_LADCO_09dec2014 _v0.txt ” “PROJECTION_2011v6.2_2018_SCC_POINT_SCA_orig_CAPPED_09dec2014 _23feb2016_nf_v3. txt ” “PROJECTION_2011v6.2_2018_NAICS_SCC_SCA_orig_NEI_matched_CAPPED2.5_04dec2014

_ 18feb2016
_v2.txt ”

“ OK_pt_oil_gas_ projection

_ csv

_ 06jan2016_v0.txt ”

“ PROJECTION 2011v6.3: 2017_Oklahoma_source_NODA _11jan2016_v1.txt ”

“ CT_NODA_Projection_2011_2017.csv ”

The EPA provided a NODA ( search for the docket ‘EPA

- HQ

- OAR

- 2013

- 0809’ on reg ulations.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 a pproach for oil and gas sources that was applied similarly to five 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 since

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 platforms suggested that this approach was

insufficient. In addition, the NO
x Budget program, which had a direct impact on post

- 2002 emissions reductions, is in full compliance in the 2011 NEI. This means that additional large

- scale industrial reductions should not be expected beyond 2011 in the absence of on

- the

- books state and federal rules.

In response to the comments about the EPA’s no

- growth approach, the EPA developed industrial sector activity

- based growth factors. In response to the NOD A , many states have additionally provided detailed activity

- based projection factors for industrial sources, including oil and gas sources

. To develop the methods

described here , we have blended the state

- provided growth factors with the EPA

- developed ind ustrial sector growth factors

. This approach has attempted to balance using the specific information that is available with the

EPA’s interest in consistency for a given sector and technical credibility

. Table 4

- 11 lists the resulting data

sources for in dustrial sector non

- EGU growth factors that the EPA applied to this emissions modeling platform.

That additional data were considered and included in our projections as well

, and are discussed separately in

Section 4.2.3.6

.

Ultimately, there were three broad sources of projection information for industrial sources, including oil and gas; these sources are referenced as the following for simplicity (e.g., realizing t hat not all data from Mid

-113 Atlantic Regional Air Management Association (MARAMA) states are limited to MARAMA states):

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 Directors Consortium (LADCO) states (MN, WI, MI, IL, IN, OH). Projection data from this data source are reflected in packet names containing “LADCO” (Alpine Geophysics, 2014).

In the 2011v6.3 platform, the np_oilgas sector ch anges implemented for year 2017 due to comment included implementing a new inventory for Texas, the CoST hierarchy changes mentioned in Section 4.2.1, and a correction to Kansas emissions due to error found in the projection methodology used in previous pl atforms. The year 2011 changes to np_oilgas inventories in UT, OK and WV also resulted in year 2017 emissions changes in the 2011v6.3 platform. For the pt_oilgas sector the CoST hierarchy changes and correction to Kansas projection methodology impacted e missions projected for year 2017.

Table 4

- 11

. Sources of new industrial source growth f actor data f

rom the NODA and additional corrections implemented for year 2017 in the 2011v6.3 platform

Abbrev. Source Geog raphic

Resolution Inventory

Resolution Use/Caveat

EPA 2014 AEO fuel consumption/productio n 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 values (e.g., commercial residual oil)

set to no

- growth,

consistent w ith MARAMA growth factor data (below). Updated Kansas projection factors based

on Kansas lease production data.114 Abbrev. Source Geog raphic

Resolution Inventory

Resolution Use/Caveat

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

inf ormation with minor exceptions (PA drilling,

NY state no

- growth, point source MARAMA data). These data also “cap” growth factors to 2.5 (150% cumulative growth). Correction applied to Kansas projection factors for year 2017 emissions

estimates.

MARAMA MA RAMA/SRA/states State or county for nonpoint and facility and

below for most point

sources Facility and

sub

- facility for point,

SCC

- level for nonpoint Many projection factors

are unchanged from the 2011v6.0 platform; however, many new data provided were

in cluded in our 2011v6.2 platform. However, the provided oil and gas projection data were not used, as we opted for single 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.115 Abbrev. Source Geog raphic

Resolution Inventory

Resolution Use/Caveat

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 NOT 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.

New projection packets for 2011v6.3: CT_NODA_Projection_2011_2017.csv NCDAQ_PROJECTION_NonPtGFs_2011_2017_102315 OK_pt_oil_gas_projection.csv

PROJECTION 2011v6.3: 2017_Oklahoma_source_NODA CT, OK, NC, and other

NODA comments State or

county SCC Various nonpt, oil/gas, ptnonipm state

- specific changes.

A discussion of each projection component in In the 2011v6.3 platform, the np_oilgas sector ch anges implemented for year 2017 due to comment included implementing a new inventory for Texas, the CoST hierarchy changes mentioned in Section 4.2.1, and a correction to Kansas emissions due to error found in the projection methodology used in previous pl atforms. The year 2011 changes to np_oilgas inventories in UT, OK and WV also resulted in year 2017 emissions changes in the 2011v6.3 platform. For the pt_oilgas sector the CoST hierarchy changes and correction to Kansas projection methodology impacted e missions projected for year 2017.

Table 4

- 11 is discussed below. In the 2011v6.3 platform, the np_oilgas sector changes implemented for year 2017 due to comment included implementing a new inventory for Texas, the C oST hierarchy changes mentioned in Section 4.2.1, and a correction to Kansas emissions due to error found in the projection methodology used in previous platforms. The year 2011 changes to np_oilgas inventories in UT, OK and WV also resulted in year 2017 emissions changes in the 2011v6.3 platform. For the pt_oilgas sector the CoST hierarchy changes and correction to Kansas projection methodology impacted emissions projected for year

2017.

“EPA” factors

As previously discussed, the EPA cr eate d a nationall y

- consistent set of industrial source projection factors for several future years. We relied on 2014 AEO fuel consumption/production projections data to develop growth116 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 AEO energy consumption data

sets that reflect the best available i ndicator of ea ch source’s emissions activity. The discussion below provides more details on the approaches summarized in Table 4

- 12

. This discussion is generally or ganized 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 listed 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 fut ure years.

For all applicable commercial/institutional fuel combustion priority category SCCs, we compiled 2014 AEO regional commercial sector energy consumption projections data by fuel type for 2011 and 2018, and computed growth factors for each projecti on year/fuel type as the ratio of each future year’s energy consumption to 2011 energy consumption. The AEO’s commercial sector fuel consumption regions are equivalent to Census Divisions (see www2.census.gov/geo/pdfs/maps

- data/maps/reference/us_regdiv.pdf ). Because Census Divisions are groups of states, commercial/institutional fuel combustion growth factors were developed at the

state

- level, with each state in a gi ven division assigned the same growth factors.117

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 energy 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 output at 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 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 output at 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 oil/gas sector constant dollar output; multiply ratios by AEO Census Division

- level oil/gas sector constant dollar output to estimate Census Division

- 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 in each applicable oil/gas play to yield oil/gas play

- level projections of lease plant fuel + pipeline fuel natural gas consumption; finally, calculate pl ay

- level growth

factors from these estimates.

Rest of Nation : Calculate “rest

- of

- Census Division” consumption 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 of the nine Census Divisions.

All other AEO Oil/Gas Plays : Calculate the national ratio of oil/gas 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 production in each oil/gas play to yield estima tes of oil/gas sector constant dollar output in each play; multiply the oil/gas sector constant dollar out put estimates for each play by ratios of national Mining sector fuel consumption to national oil/gas sector constant dollar output (this procedure develops estimates of mining 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/gas production estimates by subtracting the AEO’s oil/gas production estimates for these plays from AEO’s total oil/gas Supply region production est imates (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, corresponding to each of the AEO’s six oil/gas 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 rail road equipment

- related SCCs.118

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, factors will also be applied to natural gas production SCCs (e.g., 31000203

- Natural Gas Production/Compressors).119

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 Construction

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 Textile 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 Paving, 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, Fertilizer, 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 Preparation 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 Product Manufacturing120

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 Processing

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, and 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 Engine/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

co mpiled growth factors based on the SCC process description. For 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 approach is for consumption of all other individual fuel types. Because the AEO does not have information on the c ounties 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 pl ays, “rest

- of

- Census Division” oil/gas consumption estimates were computed to develop growth factors for projecting energy consumption for all fuel types except natural gas (e.g., residual and distillate oil, coal, LPG, kerosene )

. Details on the oil/gas p roduction sector growth factor development

methods are described below.121 Table 4

- 14

. AEO Oil/Gas Plays

Tight 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 “Other” 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

- permeability that vary widely over relatively short distances.

Natural Gas Consumption and Crude Oil Produ ction

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 36 to the

estimated volum e 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 plant fuel by the estimated 2011 total volume of these fuels.

We calculated “rest

- of

- Census Div ision” consumption estimates for pipeline fuel natural gas + lease and plant fuel by subtracting the oil/gas play consumption estimates from the Census Division estimates published in the AEO. This yielded nine sets of growth factors for each year, corresp onding to each of the nine Census Divisions. These growth factors reflect “rest

- of

- Census Division” estimated consumption of pipeline fuel natural gas + lease and plant fuel, and were applied to 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 as natural gas consumption factors: oil/gas production “play”

- level and rest of Cens us Division resolution. These data are based on AEO reference table “14” Crude Oil Production and Supply projections.

Other Fuels

The AEO prepares fuel consumption projections for the mining 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

36 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.”122 mining sector output (NAICS codes 211, 2121, 2122

- 2123) in t he 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 plays were developed, and assumed that mining sector fuel consumption growth rates approximate growth rates in the oil/gas sector for these plays. Because of concerns that oil/gas production may not represent as significant a contribution to total mining activity in other areas (outside oil/gas plays),

we chose not to use estimated Min ing 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 display ed 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 multiplying 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 ra tios 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 A EO oil/gas play areas.

Figure 4

- 1

. Oil and gas plays with AEO projection data123 Remaining Areas

For all non

- AEO gas/oil play counties, we developed “rest

- of

- Supply Region ” (National Energy Modeling System, o r “NEMS”) oil/gas production estimates as the surrogate growth 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 areas. To accomplish this, we subtracted the AEO’s oil/gas production esti mates 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 implementing 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 related growth factors were capped at 2.5 (150% increase) . Note that r aw AEO growth factors of 4

- 6+ were not uncommon for 2018. The cap addresses

concerns that the uncertainty in these projections was too great to allow such dramatic growth.

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) were not used. Rather, we used the EPA approach for nonpoint oil and gas emissions because it was more comprehensive 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 for124 both point and nonpoint factors at 1.25 (25% cumulative growth) to prevent excessive overestimation of

future

- year emissions.

The MARAMA point inventory projec tion 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 coatin g and degreasing processes, dry cleaners and waste water treatment.

“LADCO” factors

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 Geo physics, 2014)

. We did not use

t he growth data for oil 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

- faci lity growth factors for LADCO’s “priority” sou rce categories 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 oper ations and residential wood combustion (RWC); we split out RWC factors and merged with our “rest of the country” set of projection factors (discussed in

Section 4.2.3.10 )

. 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

.

Correction to Kansas projection factors

The above Oil and Gas Plays methodology generated significant reductions in emissions from year 2011 to

2017 for all pollutants in most Kansas counties in year 2017. Further analysis of this revealed these

reductions are not realistic (60

- 95% reduction in NO
x emissions) . An alternative source of recent historical data for Production from Kansas Oil and Gas Leases ( http://www.kgs.ku.edu/Magellan/Field/lease.html ) was investigated. This historical data contains production levels at the county

- level by month from years 2010

- 2015

. From this data, separate oil and gas production changes from year 2011 to 2015 at the county

- level for all Kansas counties were generated. Two sets of projection factors were generated from this data: 1) oil production and 2) gas production. It was assumed that year 2015 production would be same as y ear 2017 production levels. On average in Kansas, oil production was up about 9

- 10% and gas production was down

about 7

- 8% from year 2011 to 2015. For the np_oilgas sector, the oil production projection factors were applied to the oil production

- related SCCs while the gas production factors were applied to the gas production

- related SCCs. For the pt_oilgas sector, applying county

- level factors to point sources can be problematic. It was decided to keep pt_oilgas emissions in Kansas at year 2011 levels b ut allow controls to be applied where appropriate for year 2017. Table 4

- 15 be low summarizes the Kansas emissions totals in year 2011 ( here 2011eh = 2011ek emissions)

, and 2017eh (from 2011v6.2) vs. 2017ek (from 2011v6.3).125

Table 4

- 15

. Summary of emissions changes in Kansas for year 2017 after projection correction.

State

ID S tate P oll utant S ector 2011eh

and

2011ek 2017eh 2017ek 2017eh

- 2011eh

% diff 2017ek

-

2011ek

% diff

20 Kansas CO np_oilgas 74,716 30,958 80,410

- 58.6% 7.6%

20 Kansas NOX np_oilgas 56,387 22,951 60,660

- 59.3% 7.6%

20 Kansas PM10

- PRI np_oilgas 1,625 570 1,637

- 64.9% 0.8%

20 Kansas PM25

- PRI np_oilgas 1,622 569 1,635

- 64.9% 0.8%

20 Kansas SO2 np_oilgas 151 96 154

- 36.6% 1.9%

20 Kansas VOC np_oilgas 93,310 51,656 114,486

- 44.6% 22.7%

20 Kansas CO pt_oilgas 12,410 799 10,630

- 93.6%

- 14.3%

20 Kansas NH3 pt_oilgas 4 5 4 16.1%

- 0.5%

20 Kansas NOX pt_oilgas 35,886 1,957 33,033

- 94.5%

- 7.9%

20 Kansas PM10

- PRI pt_oilgas 475 76 475

- 84.0% 0.0%

20 Kansas PM25

- PRI pt_oilgas 469 70 469

- 85.1% 0.0%

20 Kansas SO2 pt_oilgas 65 47 65

- 27.7%

- 0.2%

20 Kansas VOC pt_oilgas 4,015 1,992 3,933

- 50.4%

- 2.0%

Net impacts of projection factors

Net i mpacts of these projection packets for each of the modeling sectors is provided in Table 4

- 16

. There are a couple of items to note:

1) All projection factors are for years 2018; we used 2018 factors for year 2017.

2) 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, discussed in 4.2.4

, have not yet been applied in

cr e ating the future

- year emissions values shown in this table

.

Table 4

- 16

. I ndustrial source projections net impa cts

Poll utant Sector 2011 Emissions Subject to

PROJECTION

factors Intermediate

Projected (not yet controlled)

Emissions Difference

(Future

- 2011) %

Difference (Future

- 2011)

For 2017 2017 2017 2017

CO nonpt 663097 689481 26,384 4%

CO np_oilgas 530062 738600 208,538 39%

CO pt_oilgas 229958 247055 17,097 7%

CO ptnonipm 541575 574087 32,512 6%

CO Total 1964692 2249224 284,532 14%

NH 3 nonpt 15722 16230 508 3%126 Poll utant Sector 2011 Emissions Subject to

PROJECTION

factors Intermediate

Projected (not yet controlled)

Emissions Difference

(Future

- 2011) %

Difference (Future

- 2011)

For 2017 2017 2017 2017

NH 3 pt_oilgas 222 164

- 58

- 26%

NH 3 ptnonipm 12158 12717 559 5%

NH 3 Total 28102 29111 1,009 4%

NO X nonpt 466801 486063 19,262 4%

NO X np_oilgas 594443 869814 275,372 46%

NO X pt_oilgas 524303 513919

- 10,385

- 2%

NO X ptnonipm 566086 587182 21,096 4%

NO X Total 2151634 2456979 305,345 14%

PM 10 nonpt 260532 278075 17,543 7%

PM 10 np_oilgas 16211 23023 6,812 42%

PM 10 pt_oilgas 13338 13214

- 124

- 1%

PM 10 ptnonipm 128475 139316 10,841 8%

PM 10 Total 418556 453628 35,072 8%

PM 2.5 nonpt 208997 223606 14,609 7%

PM 2.5 np_oilgas 14761 21374 6,612 45%

PM 2.5 pt_oilgas 13085 12973

- 112

- 1%

PM 2.5 ptnonipm 103571 113105 9,533 9%

PM 2.5 Total 340414 371057 30,643 9%

SO 2 nonpt 244784 246092 1,308 1%

SO 2 np_oilgas 16524 34273 17,748 107%

SO 2 pt_oilgas 51761 54659 2,898 6%

SO 2 ptnonipm 507158 487368

- 19,790

- 4%

SO 2 Total 820227 822392 2,164 0%

VOC nonpt 838548 854413 15,865 2%

VOC np_oilgas 2344824 3785276 1,440,452 61%

VOC pt_oilgas 132764 138931 6,167 5%

VOC ptnonipm 129290 138601 9,311 7%

VOC Total 3,445,426 4,917,221 1,471,795 43%127

4.2.3.6 Data from comments on previous platforms ( nonpt, ptnonipm , pt_oilgas )

2017 Packets: “PROJECTION_2011v6.2_2018_TCEQ_v6_leftovers_NONPOINT_30jan2015 _v0 .txt” “PROJECTION_VA_ME _TCEQ_AL_comments_2011v6_2018_0 4 dec2013 _v0 .txt” “PROJECTION_TCEQ_ptnonipm_NAICS_comments_2011v6_2018

_ 04dec2013_v0.txt ”

These projection packets includes projection factors used in the development of the 2011v6.0 emissions modeling platform, specifically, those discussed in Section 4.2.9 in the 2011v6.1 emissions modeling platform TSD (EPA, 2014b). Most of these data were originally received from the TCEQ.

TCEQ nonpoint projection data Packet: PROJECTION_2011v6.2_20YY_TCEQ_v6_leftovers_NONPOINT_30jan2015

_ v0 .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 b y the 2011v6.0 NODA data , particularly

the SCC

- level growth factors discussed in Section 4.2.3.5

. 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_20YY_0 4 dec2013 _v0 .txt, where “YY” is 18 or 19 for year 2018 (used for 2017).

Th is packet includes comments received prior to 2011v6.0 emissions modeling platform processing from Alabama, Maine, Texas and Virginia. These projections data target specific point sources in each of these states, generally impacting only a couple of facili ties/units in a couple of counties in each state.

TCEQ point county/NAICS projections data Packet: PROJECTION_TCEQ_ptnonipm_NAICS_comments_2011v6_20YY

_ 04dec2013_v0.txt , where “YY” is 18 or 25 for year 2018 (2017 as well).

This packet was provided by TCEQ for minor point s ource 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 approac h

discussed in Section 4.2.3.5

. I n fact, most of these entries are not used because they are lower in the CoST hierarchy tha n the county/NAICS/SCC projection facto rs discussed in the same section.

Summary impacts A summary of the impacts of these three projection factors for the three affected sectors are provided in

Table 4

- 17

. Most of these impacts are in Texas. Note that the 2011 emissions differ for 2017 projection scenarios; one reason for this is that projection factors equal to 1.0 (no change) were dropped from the TCEQ projections data; for some sources, year 2017 (2018) factors were equal to 1.0.128 Table 4

- 17 . Impact of projection factors for Texas

Poll utant Sector 2011 Emissions Subject to

PROJECTION

factors Projected

Emissions Tons Difference

(Future

- 2011) % Difference

(Future

- 2011)

For

2017 For

2025 2017 2025 2017 2025 2017 2025

CO nonpt 61,056 59,457 74,709 75,350 13,653 15,893 22% 27%

CO ptnonipm 54,891 66,329 73,297 100,156 18,406 33,827 34% 51%

CO Total 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 ptnonipm 1,754 2,179 2,259 3,170 505 991 29% 45%

NH 3 Total 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 ptnonipm 54,820 70,536 74,610 104,624 19,790 34,088 36% 48%

NO X Total 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 ptnonipm 16,002 22,293 20,484 30,777 4,482 8,484 28% 38%

PM 10 Total 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 ptnonipm 12,528 18,024 16,237 24,993 3,709 6,969 30% 39%

PM 2.5 Total 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 ptnonipm 22,376 44,050 27,964 56,702 5,588 12,652 25% 29%

SO 2 Total 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 ptnonipm 51,020 70,148 68,586 103,803 17,566 33,655 34% 48%

VOC Total 325,631 345,060 359,424 403,901 33,793 58,841 10% 17%

4.2.3.7 Aircraft (ptnonipm)

Packet: “PROJECTION_2011_2017_aircraft_ST_and_by_airport_ 09mar 2015 _v0 .txt”

Aircraft emissions are contained in the ptnonipm inventory. These 2011 point

- source emissions are projected to future years by applying activity growth using data on ITN operations at airports. The ITN operations are defined as aircraft take

- offs whereby the aircraft leaves the airport vicinity and lands at another airport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. The EPA used projected ITN information available from the Federal Aviation Administration’s (F AA) Terminal Area Forecast (TAF) System: http://www.apo.data.faa.gov/main/taf.asp (publication date March, 2014). This information is available for approximately 3,300 individual airports, for al l 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

.129 None of our aircraft emission projections account for any control programs. The EPA considered the NO
x standard adopted by the International Civil Aviation Organization’s (ICAO) Committee on Aviation Environmental Protection (CAEP ) in February 2004, which is expected to reduce NO
x by approximately 3% by 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule and, therefore, its effects

were not included in the future

- year emissions projections.

The EPA developed two sets of projection factors for aircraft. The first set was a simple state

- level aggregation, used primarily for airports with very little activity, by ITN operation type (commercial, general aviation, military and air taxi) to be used as a default me thod for projecting from 2011 to future years. The second set of projection factors was by airport, where the EPA projects emissions for each individual airport with significant ITN activity.

Where NEI facility identifiers were not matched to FAA airpor t identifiers

, we simply summed the ITN operations to state totals by year and aircraft operation and computed projection factors as future

- year ITN to

year

- 201 1 ITN. The EPA assigned factors to inventory SCCs based on the operation type shown in Table

4

- 18

.
Table 4

- 18

. NEI SCC to FAA TAF ITN aircraft ca 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 Commercial

2268008005 Commercial Aircraft: CNG Airport Ground Support Equipment Commercial

2270008005 Commercial Aircraft: Diesel Airport Ground Support Equipment Commercial

2275000000 All Aircraft Types and Operations Commercial

2275001000 Military Aircraft, Total Military

2275020000 Commercial Aviation, Total Commercial

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 Commercial

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 Commercial

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 largest airports/larger

- emitters had projection factors close to these caps. A national
summary of aircraft emissions between 2011 and future year 2017 are provided in Table 4

- 19

.130 Table 4

- 19

. National aircraft emission projection summary

Emissions Difference % Difference

2011 2017 2017 2017

CO 489,854 509,687 19,833 4.00%

NO X 120,968 131,678 10,710 8.90%

PM 10 9,164 9,373 208 2.30%

PM 2.5 7,891 8,084 194 2.50%

SO 2 14,207 15,256 1,049 7.40%

VOC 32,023 33,601 1,578 4.90%

4.2.3.8 Cement manufacturing (ptn onipm)

Packet: “PROJECTION_2011_2018_ISIS_cement_by_CENSUS_DIVISION_04dec2013

_ 04jan2016_v1 .txt“

As indicated in Table 4

- 1 , the Industrial Sectors Modeling Platform (IS MP ) (EPA, 2010b) was used to project the cement industry component of the ptnonipm emissions mod eling sector to 2018; 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 permitted (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. census 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 are included in the 2018 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 Cemen t NE SHAP reconsideration comments. Updated cement consumption 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.131

Figure 4

- 3

. Cement sector trends in domestic production versus normalized emissions

Multiple regulatory requirements such as the NESHAP and NSPS currently apply to the cement industry to

reduce CAP and HAP emissions. Additionally, state and local regulatory requirements might apply to individual cement facilities depending on their locations relative to ozone and PM 2.5 nonattainment areas. The ISMP model provides the emission reduction strategy that balances: 1) optimal (least cost) industry operation ; 2) cost

- effective controls to meet the demand for cement ; and 3) emission reductio n requirements over the time period of interest.

The first step in using ISMP 2018 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, the EPA cement kilns exper ts preferred that the a gency 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 proj ect existing cement kilns to operate as essentially 0% or 100%
capacity based strictly on ISMP output. Therefore, the EPA developed a U.S. Census Division approach

where ISMP emissions in 2011 and future y ears, that matched the 2011 NEI (e.g., not new ISM P 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 emissions ratios for each pollutant and geographic area. T he projection ratios, provided

in Table 4

- 20 , 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 trend132

Table 4

- 20

. 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

Midwest 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 0.109 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

- 21 shows the magnitude of the ISMP census division based projected cement industry emissions at existing NEI facilities from 2011 to future year 2018 ; we use 2018 projected emissions for year 2017

. Additional new kiln emissions generated by ISMP are discussed in Section 4.2.5.4

. 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

- 21

.

Table 4

- 21

. ISMP

- based 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%

4.2.3.9 Corn ethanol plants (ptnonipm)

Packet: “PROJECTION_2011_2017_Corn_Ethanol_Plants_AEO2014_Table17_2011v6.2_19feb2015 _v0 .txt“

We used the AEO 2014 renewable forecast projections of “From Corn and Other Starch” to compute national year 2017 growth in ethanol plant product ion. Per OTAQ direction, 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 based on AEO 2014 projections

. The projection was applied to all pollutants and all facilities equally. Table 4

- 22 provides the summaries of estimated emissions for the corn etha nol plants in 2011 and future year 2017

.133 Table 4

- 22

. 2011 and 201 7 /2025 corn ethanol plant emissions [tons]

Emissions Difference % Change

2011 2017 2025 2017 2025 2017 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%

4.2.3.10 Residential wood combustion (rwc)

Packet: “PROJECTION_2011_2017_RWC_2011v6.2_03mar2015 _v0 .txt”

The EPA applied the recently

- promulgated national NSPS for wood stoves to the Residential Wood Combustion (RWC) projections methodology for this platform. To learn more about the strengthened NSPS for residential wood heaters, see http://www2.epa.gov/residential

- wood

- heaters/regulatory

- actions

resident ial

- wood

- heaters

. The EPA projected residential wood combustion (RWC) emissions to year 2017 based on expected increases and decreases in various residential wood burning appliances. As newer, cleaner woodstoves replace some older, higher

- polluting wood stoves, there will be an overall reduction of the emissions from older “dirty” stoves but an overall increase in total RWC due to population and sales trends in all other types of wood burning devices such as indoor furnaces and outdoor hydronic heaters (O HH). It is important to note that our RWC projection methodology does not explicitly account for state or local residential wood control programs. There are a number more

- stringent state and local rules in place in 2011, specifically in California, Orego n and Washington. However, at this time, the 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, the EPA is using national level growth rates for each RWC SCC category. After discussions with California air districts, regional office contacts and EPA experts, the EPA decided to hold RWC emissions flat (unchanged) for all SCCs in California, Oregon and Washington.

Assumed Appliance Growth and Replacement Rates

The development of projected growth in RWC emissions to year 2017 starts with the projected growth in RWC appliances derived from year 2012 appliance shipments reported in the Regulatory Impact Analysis (RIA) for Proposed Residential Wood Heaters NSPS Revision Final Report (EPA, 2013b), also available at: http://www2.epa.gov/sites/production/files/2013

- 12/documents/ria

- 20140103.pdf

. The 20 12 shipments are based on 2008 shipment data and revenue forecasts from a Frost & Sullivan Market Report (Frost & Sullivan, 2010). Next, to be consistent with the RIA (EPA, 2013b), growth rates for new appliances for certified wood stoves, pellet stoves, indoor furnaces and OHH were based on forecasted revenue (real GDP) growth rate of 2.0% per year from 2013 through 2017 as predicted by the U.S. Bureau of Economic Analysis (BEA, 2012). While this approach is not perfectly correlated, in the absence of sp ecific shipment projections, the RIA assumes the overall trend in the projection is reasonable. The growth rates for appliances not listed in the RIA (fireplaces, outdoor wood burning devices (not elsewhere classified) and residential fire logs) are estimated based on the average growth in the number of houses between 2002 and 2012, about 1% (U.S. Census, 2012).

In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the replacement of older, existing appliances are needed. Based on long lifetimes, no replacement of fireplaces, outdoor wood burning devices (not elsewhere classified) or residential fire logs is assumed. It is assumed that 95%134 of new woodstoves will replace older non

- EPA certified freestanding stoves (pre

- 1988 NSPS) and 5% will replace existing EPA

- certified catalytic and non

- catalytic stoves that currently meet the 1988 NSPS (Houck, 2011).

The EPA RWC NSPS experts assume that 10% of new pellet stoves and OHH replace older units and that

because of the ir short lifespan, that 10% of indoor furnaces are replaced each year; these are the same assumptions used since the 2007 emissions modeling platform (EPA, 2012d). The resulting growth factors for these appliance types varies by appliance type and also by pollutant because the emission rates, from EPA RWC tool ( EPA, 2013rwc ), vary by appliance type and pollutant. For EPA certified units, the projection factors for PM are lower than those for all other pollutants. The projection factors also vary

because the total number of existing units in 2011 varies greatly between appliance types.

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 stringent emissions standards for new woodstoves, outdoor hydronic heaters and indoor wood

- burning forced air furnaces. New
“Phase 1” less

- polluting heater standards began in 2015, with even more

- stringent Phase 2 standards beginning in 2020. The associated reduced 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 adju stable burn rate stoves, including fireplace 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 limits PM 2.5 emissions for room heaters, which include adjustable and

sin gle burn rate stoves and pellet stoves to 4.5 g/hr in 2015 and 1.3 g/hr in 2020. In addition, the final NSPS

limits PM 2.5 emissions from hydronic heaters to 0.32 lb/MMBtu heat output in 2015, and 0.06 lb/MMBtu in

2020. The final NSPS limits PM 2.5 emissio ns from indoor furnaces to 0.93 lb/MMBtu in 2015 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 wood 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 a ppliances subject to the NSPS, baseline RIA emission factors, when lower than the computed emission factors (2011 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 2011NEI and RIA baseline (2012

- 2014) emission factors vary by pollutant, all RWC appliances subject to the NSPS have pollutant

- specific “projection” factors. We realize that these “projection” factors are a composi te of growth, retirements and potentially emission factors in 4 increments. More detailed documentation on the EPA RWC Projection Tool, including information on baseline, new appliances pre

- NSPS, and Phase 1 and Phase 2 emission factors, is available upon 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 year inventory, the EPA left RWC emissions unchanged in th e future for all three states. The RWC projections factors for states other than California, Oregon and Washington are provided in Table 4

- 23

. VOC HAPs use135 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 subject 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

- 23

. Non

- West Coast RWC projection factors , including NSPS impacts

Default if pollutant no t
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

EPA 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: freestanding, 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

2104008400 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 Outdoor 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 and 2017 are provided in Table 4

- 24 . For direct PM, the NSPS emission factor reductions mostly offset the growth in appliances by year 2017.

Table 4

- 24

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

Pollutant Emissions Difference % Difference

2011 2017 2025 2017

- 2011 2025

- 2011 2017

- 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 2.5 382,591 385,119 356,802 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%136 

### 4.2.4 CoST CONTROL packets ( nonpt, np_oilgas, ptnonipm, pt_oilgas)

The final step in a CoST control strategy, after application 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 rail 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 several different sources of CONTROL data that are concatenated and quality

- assured for duplicates and applicability to the inventories in the CoST strategies. We broke up the CONTROL (and PROJECTION) packets into a few “key” control program type s 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 packets gathered from previous versions of the emissions modeling platform; thes e 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 for 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

where:

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 an d controls for existing sources, and the third term accounts for replacement source controls.

Table 4

- 25 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.137

Table 4

- 25

. 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 Source

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 >6scfm 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: PA, 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

Turbines 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: “CONTROL_2011v6.2_2018_OilGas_VOC_NSPS_12dec2014 _v0 .txt”

For oil and gas NSPS controls

, with the exception of gas well completions (a 95% control) the assumption of no equipment retirements through year 20 17 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

- 25 , 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 of

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,138 VOC NSPS controls will be limited to well compl etions 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 reduct ions are based on year 2018 growth as well.

Table 4

- 26

. NSPS VOC oil and gas reductions from projected pre

- control 2018 grown values

Region Pre

- NSPS

emissions Post

- NSPS

emissions NSPS Reductions NSPS %

reductions

Gulf Coast 75,152 57,787 17,366 23%

Midcontinent 6,048 2,986 3,062 51%

New Mexico/Texas* 128,542 85,647 42,894 33%

Northeast 33,300 14,903 18,397 55%
Rocky Mountains 546,893 326,256 220,637 40%

West Coast 15 1 14 95%

Overall 789,950 487,579 302,370 38%

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

Packet: “CONTROL_2011v6.2_RICE_NESHAP_v2_30jan2015 _v0 .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

- bene fits 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 (propos ed in January, 2012), which result in significantly less stringent NO
x controls (fewer reductions) than the 2010 final rules.

The rules can be found at http://www.epa.gov/ttn/atw/icengines/ and are listed below:

 National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion Engines; Final Rule (FR 9648) published 03/03/10

 National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combu stion Engines; Final Rule (75 FR 51570) published 08/20/2010

The difference among these two 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 horsepow er. In addition, they have different compliance dates, though both are after 2011 and fully implemented prior to 2017. The EPA projects CAPs from the 2011NEIv2 RICE sources, based on the requirements of the rule for existing sources only because the inve ntory includes only existing sources. The EPA estimates the NSPS (new source) impacts 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 Recons ideration of the Existing Stationary Compression Ignition (CI) Engines NESHAP: Final Report (EPA, 2013ci) is available at:139 http://www.epa.gov/ttn/ecas/regdata/RIAs/RICE_NESHAPreconsideration_Compression_Ignition_Engines_ RIA_final2013_EPA.pdf

. The Regulatory Impact Analysis (RIA) for Reconsideration of the Existing Stationary Spark Ignition (SI) RICE NESHAP: Final Report (EPA, 2013si) is available at: http://www.epa.gov/ttn/ecas/regdata/RIAs/NESHAP_RICE_Spark_Ignition_RIA_finalreconsideration2013_ EPA.pdf

. Together, the EPA calls these the RICE NESHAP amendmen t RIA’s for SI and CI engines. From these RICE NESHAP RIA documents, the EPA obtained cumulative RICE reductions for all SCCs

rep resented by CI and SI engines. These aggregate reductions and percen t reductions from baseline emissions (not the 2011NEIv 2 ) are provided in Table 4

- 27

.

Table 4

- 27

. 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 engines 22,211 9,648 9,147 RIA Baseline: CI engines 81,145 19,369 11,053 79,965 RIA Reductions: CI engines 14,238 2,818 5,100 27,142

RIA Cumulative Reductions 36,449 9,638 2,818 5,100 36,289

SI % reduction 3.5% 1.0% n/a n/a 7.2%

CI % reduction 17.5% n/a 14.5% 46.1% 33.9%

These RIA percent reductions were used as an upper
- bound for reducing emissions from RICE SCCs in the 2011NEIv2 point and nonpoint modeling sectors (ptnonipm, nonpt, pt_oilgas and np_oilgas). To begin with, the RIA inventories are based on the 2005 NEI, so the EPA wanted to ensure that our 2011 reductions did

not exceed those in the RICE RIA documents. For the 2011 platform , the EPA worked with EPA RICE NESHAP experts and developed a fairly simple approach to estimate RICE NESHAP reductions. Most SCCs in the inventory are not broken down by horsepower size ra nge, mode of operation (e.g., emergency mode), nor major versus area source type. Therefore, the EPA summed NEI emissions nationally by

- SCC for RICE sources and also for sources that were at least partially IC engines (e.g., “Boiler and IC engines”).

Then

, the EPA applied the RIA percent reductions to the 2011NEIv2 for SCCs where national totals exceeded 100 tons; the EPA chose 100 tons as a threshold, assuming there would be little to no application of RICE NESHAP controls on smaller existing sources.

N ext, the 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 reductions were significantly less than those in the RIA

. The only exception was for SO 2 CI engines, where the EPA scaled the RIA percent reduction from 46.1% to 14.4% 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 non

- 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 percent reduction for these four SCCs resulted in slightly less than 5,100 tons of SO 2 reductions overall from only

RICE NESHAP controls. However, more specific CoST projection packets would later override these RICE

NESHAP reduct ions for SO 2

. Recall the CoST hierarchy discussed earlier; these RICE NESHAP reductions are national by pollutant and SCC and thus easily overridden by more

- specific information such as state

level fuel sulfur rules (discussed in the next section).

Addit ional 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 emiss ions are provided in Table 4

- 28

.140

Table 4

- 28

. National by

- sector reductions from RICE Reconsideration c ontrols (tons)

Pollutant Year Nonpoint

Oil & Gas (np_oilgas) Point Oil

& Gas (pt_oilgas) Nonpoint

(nonpt) Point

(ptnonipm) Total

CO 2017 7,052 4,685 3,217 6,375 21,329

NOX 2017 2,233 1,772 193 81 4,279

PM10 2017 0 9 929 305 1,243

PM2.5 2017 0 8 828 288 1,124

SO2 2017 0 9 71 294 374

VOC 2017 1,552 2,841 587 941 5,921

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

Packet: “CONTROL_2011v6.2_2018_RICE_NSPS_29dec2014 _v0 .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. The EPA emission requirements for stationary engines differ according to whether t he 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. Spark 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 NO
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 p art 63, Sub part ZZZZ ) , NSPS for Stationary Spark Ignition IC engines ( 40 CFR p art 60

, Subpart JJJJ ) , and NSPS for Compression Ignition IC engines ( 40 CFR p art 60

, Subpart IIII )

. SI engine operators were affected by the NSPS if the engine was constructed

after June 1 2, 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 avera ge emission rates for lean burn and rich burn engines was developed using the stationary engine manufacturers data submitted to the EPA for the NSPS analysis (Parise, 2005). Emission factors by pollutant for engines 500

- 1200 horsepower (hp) were used to d evelop the average emission rates. The analysis was organized this way because lean v ersu s 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 emissi on levels lower than 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

- 29

. That information

shows that the P ennsylvania regulations have different emission standards for lean burn v ersu s 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 v ersus rich burn, some SCCs lump these two together. None of the RICE po int source SCCs have information about engine sizes. However, the EPA regulatory impact analysis for the 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. In141 the future, more rigorous analysis can use this table to develop computations of weighted average emission reductions by rated hp to state regulations like Pennsylvania’s.

Table 4

- 29

. RICE NSPS Analysis and resulting 2011v6.2 emission rates used 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 Burn 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 selec ted states for stationary IC engines to determine whether future year emission rates are likely to be significantly lower than for the existing 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 abo ve 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 limit for new RICE engines of 1500 hp until recently (i.e., not engines bigger than 1500 hp can be instal led).

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 than the NSPS). PA expects that the new emission limits will result in an increase in larger engines being insta lled, and bringing the average emission rat e much lower than it is currently. New source Emissions Rate (Fn): Controls % =100 * (1

- Fn) NO X CO VOC
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

All other states NG

- rich burn 0.069 0.250 1.556

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 emissions 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 NO
x , 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

- 29

. 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%.142 P ennsylvania DEP staff note that until recently they have limited RICE engines to a maximum of 1500 hp. That cap is lifted under the new General Permit

- 5 regulations. With that cap lifting, P ennsylvania expects that new applications will choose to install larger engines which have lower emission limits. However, that potential 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 e missions f actors >=1.0 (uncontrolled, as represented by red cells at the bottom o f Table 4

- 29 ), no RICE NSPS controls were computed. National RICE NSPS reductions from projected pre

- NSPS 2018 inventor y are shown in Table

4

- 30

.
Table 4

- 30

. National by

- sector reductions from RICE NSPS controls (tons)

Pollutant Year Nonpoint

Oil & Gas (np_oilgas) Point Oil

& Gas (pt_oilgas) Nonpoint

(nonpt) Point

(ptnonipm) Total NSPS

reductions Pre

NSPS

total

emissions NSPS %

reduction

CO 2017 73,680 20,954 758 862 96,254 458,404 21%

NOX 2017 98,352 47,748 1,688 1,255 149,043 668,504 22%

VOC 2017 1,656 422 0 1 2,079 3,986 52%

4.2.4.4 ICI Boilers ( nonpt, ptnonipm, pt_oilgas )

Packets: CONTROL_2011v6.2_20xx_BoilerMACT_POINT_v2_30jan2015 _v0 .txt CONTROL_2011v6.2_20xx_BoilerMACT_NONPT_08jan2015 _11jan2016_nf_v1 .txt NCDAQ_CONTROL_2011v6_2_2017_BoilerMACT_POINT_revised

_ 07jan2016_v0 .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 reconsideration. Background

information on the Boiler MACT can be found at: http://www.epa.gov/ttn/atw/boiler/boilerpg.html

. The Boiler MACT promulgates national emission standards for the control of HAPs (NESHAP) for new and existing industrial, commercial, and ins titutional (ICI) boilers and process heaters at major sources of HAPs. The expected cobenefit for CAPs at these facilities is significant and greatest for SO 2 with lesser impacts for direct PM, CO and VOC. These packets address only the expected cobenefit s to existing ICI boilers.

Boiler MACT reductions were computed from a non

- NEI database of ICI boilers. As seen in the Boiler MACT Reconsideration RIA ( http://www.epa. gov/ttn/atw/boiler/boilersriaproposalrecon111201.pdf ), this Boiler MACT Information Collection Request (ICR) dataset computed over 558,000 tons of SO 2 reductions by year 2015. However, the Boiler MACT ICR database and reductions are based on the assumptio n that if a

unit could burn oil, it did burn oil, and often to capacity. With high oil prices and many of these units also able to burn cheaper natural gas, the 2011NEIv2 inventory has a lot more gas combustion and a lot less oil

combustion than the boile r MACT database. For this reason, the EPA decided to target units that potentially could be subject to the Boiler MACT and compute preliminary reductions for several CAPs prior to building a control packet.

Step 1: Extract facilities/sources potentially subject to Boiler MACT This step is only applicable to point inventory sources. The EPA did not attempt to map each ICR unit to the NEI units, instead choosing to use a more general approach to extract NEI sources that would be potentially subject to, and hence have emissions reduced by the Boiler MACT. The NEI includes a field that indicates whether a facility is a maj or source of HAPs and/or CAPs. This field in our FF10 point inventory modeling143 file is called “FACIL_CATEGORY_CODE” and the possible valu es for that field are shown in Table 4

- 31

. Because the Boiler MACT rule applies to only major sources of HAPs, the EPA restricted the universe of facilities potentially subject to the Boiler MACT to those classified as HAP major or u nknown (UNK). The third column indicates whether the facility was a candidate for extraction as being potentially subject to the

Boiler MACT.

Table 4

- 31

. Facility types potentially subject to Boiler MACT redu ctions

Code Facility

Category Subject

to Boiler

MACT? Description

CAP CAP Major N Facility is Major based upon 40 CFR 70 Major Source definition paragraph 2 (100 tpy any CAP. Also meets paragraph 3 definition, but NOT paragraph 1 definition).

HAP HAP Major Y Facility is Major based upon only 40 CFR 70 Major Source definition paragraph 1 (10/25 tpy HAPs).

HAPCAP HAP and

CAP Major Y Facility meets both paragraph 1 and 2 of 40 CFR 70 Major Source definitions (10/25 tpy HAPs and 100 tpy any CAP).

HAPOZN HAP and O3

n/a Major Y Facility meets both paragraph 1 and 3 of 40 CFR 70 Major Source definitions (10/25 tpy HAPs and Ozone n/a area lesser tons for NO X or VOC).

NON Non

- Major N Facility's Potential To Emit is below all 40 CFR 70 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) facilities extracted in Step 1, we merged in ICI Boiler controls/adjustments developed under MARAMA in support of the Ozone Transport Commission (OTC) 2007 modeling platform future year analyses. These adjustments are discussed in a w hite p aper
, “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 nonpoint sources due to the Boiler MACT, 1

- hour SO 2 NAAQS and economic factors related to natural gas prices. This MARAMA approach relies on Council of Industrial Boilers (CIBO) analysis of cost

- eff ectiveness of boiler controls and retrofits in order to maintain the Boiler MACT. In short, the CIBO analyses showed that many ICI boilers were converting (or replacing coal units) to natural gas rather than applying more costly controls. Specifically, CI BO 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 r atios of natural gas to non

- natural gas fuel type. Finally, the estimated number of ICI boiler replacements and retrofits were used to create weighted

- average adjustment (“control”) factors

from these normalized emission factor ratios. This methodology ma kes the following assumptions:

 Natural Gas NO X emissions rates: 0.10 lb/MMBtu for new boiler s
, 0.1961 lb/MMBtu for burner retrofits144

 Natural gas emission rates for SO 2 , PM 2.5 and VOC are the same for both boiler replacement and

burner retrofits

 Any un it 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 platform

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 adjus tments to map to those in the 2011 NEI point and nonpoint inventory with non

- zero emissions. The 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 includes adjustments for NO
x , PM and SO 2

. Therefore, the 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 set of facilities (point) and SCCs as those for the pollutants provided by the MARAMA approach.

Some states commented on the 2011v6.0 ICI boile r controls via the 2018 NODA ( docket # EPA

- HQ

- OAR

2013

- 0809 on http://www.regulations.gov ). Wisconsin provided alternative SO 2 , VOC and HCl controls for stoker and pulverized coal fueled units. The national

- leve l 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

- 32 ; note tha t 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 boiler rule NO
x reductions that also supersede these nationally

- applied factors. The N ew J ersey and N ew Y ork factors are provided in Table 4

- 33 ; note that N ew J ersey controls apply only to nonpoint sources and that N ew Y ork controls vary by fuel for point sources

.

The impacts of these ICI boiler reductions are provided in Table 4

- 34

. 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 the esti mated Boiler MACT reductions, likely a result of numerous units undergoing fuel switching from coal or oil to natural gas

.

Table 4

- 32

. National

- level, with Wisconsin exceptions, ICI boiler adjustment factors 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 45145

Table 4

- 33

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

NJ and NY Boiler 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 Rule: 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, < 100 M illion 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

- 34

. Summary of ICI Boiler reductions

Year Pollutant Emissions Eligible for

Control Controlled

(Final)

Emissions Reductions

(tons) %

Reductions

CO 2017 31,637 210 31,427 99.3%

NOX 2017 119,724 61,650 58,074 48.5%

PM10 2017 33,602 7,295 26,308 78.3%

PM2.5 2017 13,731 2,403 11,328 82.5%

SO2 2017 256,746 22,645 234,101 91.2%

VOC 2017 1,727 30 1,697 98.3%

4.2.4.5 Fuel sulfur rules ( nonpt, ptnonipm, pt_oilgas )

Packet: CONTROL_2011v6.2_20xx_Fuel_Sulfur_Rules_09jan2015 _v0 .txt

Fuel sulfur rules, based on web searching and the 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 limi ts for these states are incremental starting after year 2012, but are fully implemented by July 1, 2018

, in all of these states.

A summary of all fuel sulfur rules provided back to the EPA by the 2011 emissions modeling NODA comments is provided in Table 4

- 35

. 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 re sidual oil sulfur contents to calculate a state

- or county

- specific control

factors for residual oil (SRA, 2014)

.146 Table 4

- 35

. State Fuel Oil Sulfur Rules data provided by MANE

- VU

State Reference

Connecticut 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 Statio nary 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. Sulfur cont ent 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: htt p://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 Oil: 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 effe ctive 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 Limitations

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 effect ive 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/governo r_paterson_signs_new_la.html

Pennsylvania § 123.22. Combustion units

Distillate: 500 ppm effective July 1, 2016 Residual: 0.5% effective July 1, 2016 #4 Oil: 0.25% effective July 1, 2016

See: http://www.pacode.com/secure/data/025/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 Oil: 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

- 36

. Most of these reductions (98+%) occur in the nonpt sector; a small amount of reductions occur in the ptnonipm sector (approximately 580 tons in 2017), and a negligible amount of reductions occur in the pt_oilgas sector. Note

that these reductions are base d on intermediate 2017 inventories, those grown from 2011 to the specific future years.147 Table 4

- 36

. Summary of fuel sulfur rule impacts on SO 2 emissions

Year Emissions Eligible

for Control Controlled (Final)

Emissions Reductions % Reductions

2017 76,1 80 13965 62215 81.7 %

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

Packet: “CONTROL_2011v6.2_2018_NOX_GasTurbines_16dec2014 _v0 .txt”

These controls were generated based on examination of emission limits for stationary combustion turbines that are not in the power sector. In 2006, the EPA promulgated standards of performance for new stationary

combustion turbines in 40 CFR part 60

, subpart KKKK. The standards reflect changes in NO
x emission

control techn ologies 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 levels 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

turb ines to reduce their NO
x emissions by 60 percent.

Table 4

- 37 compares the 2006 NSPS emission limits with the NO
x RACT regulations in selected states

within the NO
x SI P Call region. The map showing the states and partial

- states in the NO
x SIP Call Program

can be found at: http://www3.epa.gov/airmarkets/progress/reports/program_basics.ht ml

. We assigned only those counties in Alabama, Michigan and Missouri as NO
x SIP call based 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 t ime.

Table 4

- 37

. Stationary gas t urbines NSPS a nalysis and resulting 2011v6.2 new emission rates used to

compute c ontrols

7 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 Regulations 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 = 0.238 76.2%148 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 U

. S

. MACRS Depreciation Schedule of 15 years (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 a n annual retirement rate of 2.2 %

.

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

limi t to the Federal NSPS. A complicating factor in the above is the lack of size information in the stationary

source SCCs. Plus, the size classifications 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 ratio of 25/42, or 0.595 (40.5% reduction) for states with existing combu stion turbine

emission limits. S tates 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 projections. National Process

Heaters NS PS reductions from projected pre

- NSPS 2018 inventor y are shown in Table 4

- 38

.

Table 4

- 38

. National by

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

Sector Pre

- NSPS Emissions NSPS Reductions NSPS %

Reductions

2018 2018 2018

Non

- EGU Point (ptnonipm) 14,452 2,405 17%

Point Oil & Gas (pt_oilgas) 72,156 9,327 13%

Total 86,609 11,732 14%

4.2.4.7 Process heaters NO X NSPS ( ptnonipm, pt_oilgas )

Packet: “CONTROL_2011v6.2_2018_NOX_Process_heaters_09dec2014 _v0 .txt”

Process heaters are used 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 se nse, process heaters can be considered 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 been 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 areas, and refinery consent decrees.149 The boiler/process h eater NSPS established NO
x emission limits for new and modified process heaters.
These emission limits are displayed in Table 4

- 39

.

In order to develop a relationship between the 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. analysis can be used to establish an effective 2011 process heater NO
x emission rate, although the information that EPA 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 compan y, so it is difficult to make overarching conclusions about when compliance

occurred.

Table 4

- 39

. Process Heaters NSPS a nalysis and 2011v6.2 new emission rates used to compute c ontrols

NO X emission rate Existing (Fe) 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
NSPS Control (%) 61.7 55.4 58.6

The 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

. ” (Scenario 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 (average) 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 reti rement rate. 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 tha n or equal to 1% were retained. National Process Heaters NSPS reductions from projected pre

- NSPS 2018 inventor y are shown in Table 4

- 40

.150

Table 4

- 40

. National by

- sector NO X reductions from Process Heaters NSPS controls

Sector Pre

- NSPS

Emissions NSPS

Reductions NSPS % Reductions

2018 2018 2018

Non

- EGU Point (ptnonipm) 74,131 14,200 19%

Point Oil & Gas (pt_oilgas) 7,034 1,101 16%

Total 81,165 15,301 19%

4.2.4.8 Arizona Regional Haze controls (ptnonipm)

Packet: CONTROL_2011v6.2_20xx_AZ_Regional_Haze_PT_24feb2015 _v0 .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

- 0588;

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 projections.

4.2.4.9 CISWI (ptnonipm)

Packet: CONTROL_CISWI_2011v 6_22nov2013 _v0 .txt

On March 21, 2011, the EPA promulgated the revised NSPS and emission guidelines for Commercial and Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was granted in 2001 and the vacatur a nd 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. The history of the CISWI implementation is documented here: http://www.epa.gov/ttn/atw/129/ciwi/ciwipg.html . Baseline and CISWI rule impacts associated with the CISWI rule are documented here: http://www.epa.gov/ttn/atw/129/ciwi/baseline_emission_reductions_memo.pdf

. The EPA mapped the units

from the CISWI baseline and controlled dataset to the 2011 NEI i nventory and because the baseline CISWI emissions and the 2011 NEI emissions were not the same, the EPA computed percent reductions such that our future year emissions matched the CISWI controlled dataset values. CISWI controls are applied in

Arkansas and Louisiana only, totaling 3,100 of SO 2 reductions in year 2017.

4.2.4.10 Data from comments on previous platforms and recent comments ( nonpt, ptnonipm, pt_oilgas )

Packets: “CONTROL_2011v6.2_20xx_Consent_Decrees_State_comments_2018docket_pt_v2_09mar2015 _v0 .txt”

“CON TROL_2011v6.2_20xx_State_comments_2018docket_nonpt_15jan2015 _v0 .txt”

“ CONTROL_2011v6_2_20xx_CD_St_com_2018docket_pt_15jan2015_fixed _01sep2015_v0.txt ”

“ CT_NODA_Control_2011_2017

_ 13jan2016_ v2.txt ”

All remaining non

- EGU point and nonpoint controls are discu ssed in this section. For the nonpoint sector,

these controls are limited to comments/data

- res ponses on the previous emissions modeling platforms, and the 2018 NODA process. For point sources, controls include data from the 2018 NODA process as well as a151 concatenation of all remaining controls not already discussed. These controls are split into separate packets for point and nonpoint sources.

Nonpoint packet: (CONTROL_2011v6.2_20xx_State_comments_2018docket_nonpt_15jan2015

_ v0 .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, wi th the great majority of these controls restricted to Virginia. These VOC controls cover various state programs and rules such as a uto refinishing, a dhesives and s urface

c oatings. Cumulatively, these VOC controls reduce nonpoint VOC by approximately 3,900 tons in 2017.

Point packet: ( CONTROL_2011v6.2_20xx_Consent_Decrees_State_comments_2018docket_pt_v2_09mar2015 _v0 .txt )

This packet contains all point controls not already discussed in previous sections (e.g., Fuel Sulfur rules, ICI boilers). This packet includ es new controls information provided in response to the 2018 NODA as well as “legacy” controls from the 2011v6.0 emissions modeling platform from numerous sources such as settlement and consent decree data gathering efforts, comments received during the CS APR rulemaking process, regional haze modeling, and stack

- specific control information provided by TCEQ.

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 exi sting 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 previ ous 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 pre

- controlled (projection factors already a pplied) from this CONTROL packet are shown in Table 4

- 41

.

Table 4

- 41

. Summary of remaining ptnonipm and pt_oilgas redu ctions

Year Pollutant Emissions Eligible for

Control Controlled

(Final)

Emissions Reductions %

Reductions

2017 CO 5,801 764 5,019 86.82%

2017 NH3 32 0 32 100.00%

2017 NOX 84,413 42,389 42,401 49.78%

2017 PM10 4,009 1,932 2,075 51.80%

2017 PM2.5 3,584 1,759 1,823 50.92%

2017 SO2 121,735 26,155 95,580 78.51%

2017 VOC 2,742 2,234 507 18.53%

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 changed152 the compliance dates 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 app lied 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 th e pt_oilgas sector; however, this impact is assumed small 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 packets 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), s o it supersedes other possible less

- specifically applied control information 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 invent ories (nonpt, ptnonipm)

This section discusses future year NEI non

- EGU point 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 20 11 (e.g., new cement kilns, biodiesel and cellulosic plants), or are a complete replacement to the year 2011 NEI inventory in the case of portable fuel containers. New non

- EGU facilities 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: “pfc_2018_2011v6.2_ff10_28jan2015_v0.csv”

The EPA used future

- year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and modeled for EPA’s MSAT2 rule (EPA, 2007a). The 6 PFC SCCs are summarized below (not e that the full SCC descriptions for these SCCs include “Storage and Transport; Petroleum and Petroleum Product Storage” as the beginning of the description).

 2501011011 Residential Portable Fuel Containers: Permeation

 2501011012 Residential Portable Fuel Containers: Evaporation

 2501011014 Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement

 2501012011 Commercial Portable Fuel Containers: Permeation

 2501012012 Commercial Portable Fuel Containers: Evaporation

 2501012014 Co mmercial 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 RFS 2 standards on gasoline volatility. The EPA developed year 2018 PFC emissions that include estimated Reid Vapor Pressure (RVP) and oxygenate impacts on VOC emissions, and more importantly, large increases in ethanol emissions from

RFS2. These emission es timates also include gas can vapor displacement, tank permeation and diurnal emissions from evaporation. Because the future year PFC inventories contain ethanol in addition to benzene,

the EPA developed a VOC E

- profile that integrated ethanol and benzene ( see Section 3.2.1.2 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 (se e: http://www.regulations.gov/#!docketDetail;D=EPA

- HQ

- OAR

- 2013

- 0809 ). We used these projection factors to project MARAMA state PFC emissions to year. We used commercial153 s oftware to blend the MARAMA projection factors and existing PFC inventories to create year 2018 PFC inventories for this platform. A summary of the resulting PFC emissions for 2011 and 2018 (used for 2017) are provided in Table 4

- 42

.

Table 4

- 42

. PFC emissions for 2011, 201 8 and 2025 [tons]

Emissions Difference % Change

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”

The EPA ’s OTAQ developed an inventory of biodiesel plants for 2018. Plant location and production

volume data came from the Tier 3 proposed rule 37

, 38

. The total volume of biodiesel came from the AEO 2013 early release, 1.3 BG for 2018. To reach the total volume of b iodiesel, 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 oi l feedstock were applied. These emission factors in Table 4

- 43 are in tons per million gallons (Mgal) and were obtained from the 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

- 44 provides the 2018 biodiesel plant emissions estimates. Since biofuels were not projected to change significantly between 2017

and 2018 the year 2018 inventory was used for ye ar 2017. Emissions in 2011 are assumed to be near zero, and HAP emissions in 2017 are nearly zero. The emission factor for ethanol is 0.

Table 4

- 43

. 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

37 U.S. EPA 2014.Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA

- 420

- RD

- 143

- 0052

. 38 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

- 0 162.154 Table 4

- 44

. 2018 biodiesel plant emissions [tons]

Pollutant 2018

CO 649

NO X 1048

PM 10 89

PM 2.5 89

SO 2 8

VOC 57

4.2.5.3 Cellulosic plants (nonpt)

New 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 likely to produce fuel through either a biochemical process or a thermochemical process. The 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

- 45 and Table 4

- 46 were us ed to develop cellulosic plant inventories. Criteria pollutant emission rates are in tons per RIN gallon. Emission factors from the

cellulosic diesel work in the Tier 3 NPRM were used as the emission factors for the thermochemical plants

. C ellulosic etha nol 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 s pread 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 feedstock, accounting for the cost of the feedstock itself as well as feedstock storage and the transportation of the feedstock to the cellulosic biofuel refinery. The total number of cellulosic biofuel refineries was projected using volumes from AEO2013 (e arly release). The methodology used to determine most likely plant locations is described

in Section 1.8.1.3 of the RFS2 RIA (EPA, 2010a). Table 4

- 47 provides the year 2018 cellulosic plant

emissions estimates.

Table 4

- 45

. 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 0155 Table 4

- 46

. Toxic Emission Factors for Cellulosic Plants (Tons/ RIN gal lon )

Plant Type Acetaldehyde Acrolein Benzene 1,3

- Butadiene Formaldehyde Ethanol

Thermochemical 2.95E

- 08 1.27E

- 09 9.61E

- 10 0 5.07E

- 09 2.09E

- 07

Biochemical 3.98E

- 07 1.11E

- 08 1.39E

- 08 0 2.28E

- 08 6.41E

- 07

Table 4

- 47

. 201 7 cellulosic plant emissions [tons]

Pollutant Emissions

Acrolein 1
Formaldehyde 4

Benzene 1
Acetaldehyde 21

CO 6,088

Ethanol 146

NH 3 0.1

NO X 9,199

PM 10 1,088

PM 2.5 547

SO 2 819

VOC 414

Development of new Iowa inventory

The Iowa DNR (Department of Natural Resources) , via the 2018 NODA comments ( see docket # EPA

- HQ

OAR

- 2013

- 0809 under http://www.regulations.gov ), provided information on new cellulosic ethanol
capacity information for three facilities. Emissions for these facilities were computed using th e emission
factors previously discussed in Table 4

- 45 and Table 4

- 46 . The resu lting new facilities and NO
x emissions

, used for year 2017 are provided in Table 4

- 48

. Note that these facilities are in a nonpoint inventory because

latitude

- longitude coordinates were not available.

Table 4

- 48

. New cellulosic p lants NO
x 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 394156

4.2.5.4 New cement plants (nonpt, ptnonipm)

Point Inventories: “cement_newkilns_year2018_from_ISIS2013_NEI2011v1”

Nonpoint Inventories: “cement_newkilns_year_2018_from_ISIS2013_NEI2011v1_NONPOINT_v0.csv”

As discussed in Section 4.2.3.8 , 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, the EPA used 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 (counties) 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, the EPA decided to model these as county

- level to avoid placing large e missions 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 are provided in Table 4

- 49

. Not shown here is a cement kiln in Washington (King county) that ISMP generates but according to Washington Department of Ecology, was not correct; th is kil n

w as thus removed from our emissions modeling platform.

Table 4

- 49

. Locations of new ISMP

- generated cement kilns

Year(s) ISMP ID Permitted? Facility Name FIPS State County

Both FLNEW2 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 Berks

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 Northampton

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 in our modeling. Ta ble 4

- 50 shows the magnitude of the new ISMP

- based 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.157 Ta ble 4

- 50

. ISMP

- generated new permitted and non

- permitted emissions

New kilns in 2018 Total New

ISMP

Emissions

Permitted (point) ISMP

generated (nonpoint) 2018

NO X 3,751 5,697 8,546

PM 2.5 8 24 19

SO 2 1,775 13 3,779

VOC 91 2,969 208



### 4.3 Mobile source projections

Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2017 using a

combination of the MOVES 2014 and the NMIM m odels. The 2017 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 Motor 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 National 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 content, a nd evaporative

emissions standards.

Onroad mobile sources are comprised of several components and are discussed in Section 4.3.1 . Monthly

nonroad equipment mobile emission projections are discussed in S ection 4.4

. Locomotives an d CMV projections were discussed in Section 4.2.3.3

.



### 4.3.1 Onroad mobile (onroad)

The onroad emissions for 2017 use the same SMOKE

- MOVES system as for the base year (see Section

2.3.1 ). Meteorology, speed, spatial surrogates and temporal profiles , representative counties, a nd fuel

months were the same as for 20 11

. For the 2011v6.3 platform, the EPA developed activity data and emissions factors directly for 2017 and did not need to adjust emissions from 2018 to 2017 as was done for the 2011v6.2 platform.

4.3.1.1 Future activity data

E stimate s of total national VMT in 2017 came from DOE's AEO 201 4 and 2015 ( http://www.eia.gov/forecasts/aeo/ ) transportation projections. Trends were developed by calculating ratios

between 2011 AEO and 201 7 AEO 39 estimates and applying the trends to the 2011NEIv 2 VMT

. Because

39 By “2011 AEO,” “2017 AEO,” and “2025 AEO,” this refers to the AEO2014’s estimates of national VMT in those specific calendar years.158 AEO2015 does not include data for 2011, AEO2014 was used to project from 2011 to 2012, and then AEO2015 to project from 2012 to 2017. These ratios were developed for light versus h eavy duty and for four fuel types: gasolin e
, diesel , E

- 85, and CNG

. The projection factors, the national 2011NEIv 2 VMT (“VMT_2011”) by broad vehicle and fuel type , and the default future VMT (“VMT_201 7 ” ) are show in

Table 4

- 51

.
Table 4

- 51

. Projection factors for 2017 (in millions of miles) 40

Classification MOVES source types VMT 2011 Ratio 2017 VMT 2017 Ratio 2025 VMT 2025

LD gas 11,21,31,32 2,565,979 1.05738 2,713,214 1.0466 2,685,656

HD gas 42,43,51,52,53,54 21,530 0.98057 21,112 1.0880 23,425

HHD gas 61 10 0.72310 7 0.8414 9

LD diesel 21,31,32 41,645 2.74032 114,121 6.3858 265,938

HD diesel 41,42,43,51,52,53,54 58,995 1.20291 70,965 1.3621 80,357

HHD diesel 61,62 131,706 1.16864 153,917 1.3296 175,120

Bus CNG 42 303 1.78937 542 3.7099 1,125

LD E

- 85 21,31,32 143,600 1.95404 280,600 2.3570 338,463

Total N/A 2,963,768 N/A 3,354,478 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 specific MOVES source type codes are listed above. These national SCC6 ratios were applied to the

2011NEIv2 VMT to create an EPA estimate of 2017 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

. This 2018 VMT was also backcasted to 2017 using a simple AEO2014

- based approach: one factor for LD (0.99159391 / 0.84% cut), and one factor for HD (0.98197519 / 1.80% cut)

. 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 differences in growth rate. The EPA analyzed LD VMT and

corrobo rated 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. To take account of this spatial difference in growth, th e EPA used human population to adjust 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 population growth for that county:

40 Note: these are the default VMT values. The actual VMT used for 2018 and 2025 has slightly different VMT values. This is due to some states providing VMT projections for 2018. In addition, t he 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 actu al VMT, see the inventory packaged with the cases.

41 Although some state s provided 2018 specific VMT projections, these projections were not consistently available for 2025. Therefore, state projections were not incorporated into the 2025 modeling.

42 For many of these states, we used the county total from the state data and d istributed 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. Fo r MO, the state provided the 5 counties around St Louis. For Nevada, the EPA receiv ed 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 distributing 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.159

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)

◦ AEOprojFactor = 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 adjustment, 1 = full county variation

The sp ecific 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 growth rates will vary by county, fuel, and year. The range of these growth rates are shown

in F igure 4

- 4

.

Vehicle population (VPOP) was developed by creating VMT/VPOP ratios from the 2011NEIv2 VMT and 2011NEIv2 VPOP at the county, fuel and vehicle type (SCC6) level. These ratios were applied to the 2017

VMT to create a 2017 VPOP.

Hoteling (HOTELING) was de veloped by creating VMT/HOTELING ratios from the 2011 NEIv2 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) an d extended idle (EXT). These ratios were applied to the 2017 VMT to create a 2017 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 2017

.160 F igure 4

- 4

. Light Duty VMT growth rates based on AEO2014

4.3.1.2 Set up and Run MOVES to create EF s Emission factor tables were created by running SMOKE

- MOVES using the same procedures and mo dels as

described fo r 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 ( 2017 ) are predominantly activity

data

, fuels , national 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 result ed in a greater fraction of newer vehicle s meeting stricter emission standards. The similarities and differences between the two runs are described in

Table 4

- 52

.
Table 4

- 52

. Inputs for MOVES runs for 2017

Element 2017

Code MOVES20151201 (MOVES2014a)

Rep. county database 285RepCos2017_M2014_20151218

Default database movesdb 20151028

VMT and VPOP 2017ek

Hydrocarbon speciation CB6v2 d one inside MOVES

Fuels M2014a_fuelsupply AND regioncountytrnoda_20151203

CA LEVIII ca_standards_SS_20140903 (16 states)

The following states were modeled as having adopted the California LEV III program (see Table 4

- 53 ) :

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 rates161 Table 4

- 53

. CA LEVIII program states

FIPS State Name

06 California

09 Connecticut

10 Delaware

23 Maine

24 Maryland

25 Massachusetts

34 New Jersey

36 New York

41 Oregon

42 Pennsylvania

44 Rhode Island

50 Vermont

53 Washington

Fuels were projected into the future using estimates from the AEO2014 ( http://www.eia.gov/forecasts/aeo/ ), release date May 7 th 201 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

pro jected 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

. For details on the 2017 speciation of onroad mobile source

emissions , which is dependent on the fuels, see Section 3.2.1.4

.

4.3.1.3 California and Texas adjustments

A set of adjustments were done in SMOKE

- MOVES to create 2017 emissions: 1) refueling, and 2) California and Texas emissions.

The first set of adjustment fac tors w as for refueling. This uses the same approach as was used in 2011 (see

the Section 2.3.1 for details) to account for the few counties in Colorado that provide d 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 and thus refueling emissions

will not be double

- counted

.

The second set of adjustment factors was used to incorporate future year emissions provided by California

and Texas . The same approach as was used in 2011 was used to match the emissions totals provided by both

CARB and TCEQ (see Section 2.3.1). The only differences between the 2011 approach and that applied for 2017 are that the latter uses the 2017 emissions provide d by CARB /TCEQ and the 2017 EPA SMOKE

MOVES output to apportion and temporalize the emissions.



### 4.4 Nonroad mobile source projections (nonroad)

The projection of locomotive and CMV emissions to 201 7 is described in 4.2.3.3

. Most of the remaining sources in the nonroad sector are projected by running the NMIM model with fuels and vehicle populations appropriate to 2017 ; this section describes the projection of these sourc es.162 The nonroad sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California and Texas. NMIM provides nonroad emis sions for VOC by three emission modes: exhaust, evaporative and refueling.

With the exception of California and Texas , U.S. emissions for the nonroad sector (defined as the equipment types covered by the NONROAD model) were created using a consistent NMIM

- bas ed approach as was

u sed for 2011. Specifically, NMIM version 20090504 utilized NONROAD2008a including future

- year equipment population estimates, control programs to the year s 2017 , and inputs were either state

- supplied as part of the 2011NEIv1 and 2011NEIv2 process or national level inputs

. Fuels for 201 7 were assumed to be E10 everywhere for nonroad equipment. The databases used in the 201 7 run were NMIM county database

“ NCD20151215_nei2017vd1 ” and fuels database “ movesdb20151028_2017nrfuelsNMIM .” The 201 7

emissions account for changes in activity data (based on NONROAD model default growth estimates 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 201 7 speciation of nonroad, see Section 3.2.1.4

.

The version of NONROAD used was the current public release, NR08a, which models all in

- force nonroad controls. 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, 200 2 (“Pentathalon Rule”).

 Small Engine Spark Ignition (“Bond”) Rule, October, 2008: http://www.epa.gov/otaq/smallsi.htm

Not included are voluntary local programs such as encouraging either no refueling o r evening refueling on Ozone Action Days.

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

. The EPA used NMIM for California future nonroad NH 3 emissions because CARB did not provide these data for any nonroad vehicle types. For the rest of the pollutants, the CARB

- supplied 2017 nonroad annual inventor ies were distributed to monthly emissions values by using the respective yea r 201 7 NMIM monthly inventories to compute monthly ratios by county, SCC7, mode and pollutant, which was consistent with the approach in 2011

. 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, the EPA combined Texas’ submitted estimates for 2011 with EPA projections o f nonroad

emissions into 201 7

. The EPA used the trend of 2011 to 201 7 based on EPA’s estimates to project Texas’

43 In addition, airport equipment was removed from CARB’s inventory because these sources were modeled elsewhere.163 submitted emissions for 2011. The projections were based on state

- wide SCC7, mode, poll ratios 44 of 201 7

NMIM to 2011 NMIM. These ratios were then applied to Texas’ submitted 2011 nonroad emissions, which had already been distributed to a monthly inventory (see Section 0 ), to create 201 7 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 othon)

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 plat forms, the EPA used emissions from the 2011NEIv2 point source inventory for 2011 and both future years. T he Canadian onroad (othon) and nonroad emissions in othar sector were projected using US emissions changes by SCC and pollutant (see Tables 5

- 11 and 5

- 12). The Canadian point sources in othpt sectors were modified for 2017 by removing the remaining coal EGU plants (see Table 5

- 13). Emissions for Mexico are based on the Inventario Nacional de Emisiones de Mexico, 2008 projected to years 2018 (ERG, 2014 a).

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 emissi ons include those assigned to the EEZ (defined as those emissions just beyond U.S. waters approximately 3

- 10 miles offshore, extending to about 200 nautical miles from the U.S. coastline), and all other offshore emissi ons

. The projection factors for the ot hpt C3 CMV emissions vary by geographic a nd region as shown in Table 4

- 8

.

44 These ratios were initially attempted by county/SCC7/mod e/pollutant, but due to significantly different distributions of certain source types between the EPA and TCEQ’s emissions, this created unreasonable growth in certain areas. The above approach was used except in the following, relatively limited conditio ns. If a state/SCC7/mode/pollutant was in the EPA’s 2017 and 2025 emissions but not in the EPA’s 2011 emissions, 2017 and 2025 EPA emissions were used in the final inventory. If a state/SCC7/mode/pollutant was in TCEQ’s 2011 emissions but was not in EPA’ s 2017 and 2025 emissions, then state/SCC3/mode/pollutant ratios were used to project to 2017 and 2025.164 

## 5 Emission Summaries

The following tables summarize emissi ons differences between the 2011 evaluation case and the 2017 base

case

. These summaries are provided at the national level by sector for the contiguous U.S. and for the portions of Canada and Mexico inside the smaller 12km domain (12US2) discussed in Section 3.1 . The afdust sector emissions represent the summaries after application of both the land use (transport fraction) and meteorological adjustments (see Section 2.2.1 ); therefore, this sector is called “afdust_ adj” in these summaries. The onroad sector totals are post

- SMOKE

- MOVES totals, representing air quality model

- ready emission totals, and includ e CARB emissions for California. The cmv 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 are provided fo r all CAP emissions for the 2011

evaluation case in Table 5

- 1

. Th e total of all sectors in the 2011 evaluation case are listed as “Con U.S.

Total

. ” Table 5

- 2 pro vides national emissions totals by sector for C APs in the 2017 base case

.

Table 5

- 3 provides national

- by sector emission summaries for CO for the 2011 evaluation cas e and 2017 base case, along with pe rcent change from 2011 to 2017

. Table 5

- 4 through Table 5

- 9 provide the same

summaries for NH 3 , NO
x , PM 2.5 , PM 10 , SO 2 and VOC, respectively. Note that the same fire emissions are

used in all cases. Tables 5

- 10 through Table 5

- 12 provide summaries of the Canadian emissions used in the

2011 and 2017 base cases for onroad, area, and point source emissions.165

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 6,732,941 923,590

ag 3,515,198

agfire 1,030,817 3,321 46,035 152,837 101,379 17,755 80,540

cmv 70,408 231.5019729 413,314 19,629 18,099 91,045 12,578

nonpt 1,645,989 94,242 720,454 491,825 404,258 276,332 3,671,898

np_oilgas 625,430 0 664,966 17,784 16,333 17,232 2,483,856

nonroad 13,951,020 2,627 1,630,301 162,417 154,657 4,031 2,024,419

onroad 25,369,638 111,881 5,608,800 325,704 187,869 27,458 2,656,899

rail 122,703 347.3226239 791,381 25,898 23,963 7,936 40,851

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 792,397 25,066 2,081,762 283,072 208,129 4,650,841 38,062

ptnonipm 2,297,650 66,051 1,213,528 477,387 320,857 1,049,424 801,188

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 69,221,756 4,173,935 14,048,231 11,257,540 4,598,583 6,383,357 17,105,023

Off

- shore to EEZ* 175,353 185 899,986 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 3,706 286,303 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 emissions166

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 7,091,205 963,726

ag 3,577,123

agfire 1,030,817 3,321 46,035 152,837 101,379 17,755 80,540

cmv 72,535 234 361,210 8,522 8,035 4,777 12,896

nonpt 1,674,862 94,815 734,957 494,835 416,262 106,270 3,513,992

np_oilgas 703,564 0 730,642 16,719 15,108 37,463 2,903,644

nonroad 12,376,430 2,859 1,131,509 113,817 107,508 1,620 1,409,598

onroad 15,600,692 82,295 2,648,665 272,225 117,960 13,000 1,516,369

rail 137,080 347 683,360 19,137 17,704 316 28,004

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 726,315 40,810 1,273,122 243,125 184,042 1,221,285 35,720

ptnonipm 2,331,068 66,702 1,190,849 480,673 325,527 808,482 810,568

pt_oilgas 227,697 5,886 444,604 14,519 13,881 69,264 166,544

rwc 2,504,587 19,546 35,507 384,041 383,807 8,814 437,984

Con U.S. Total 57,948,343 4,223,268 9,613,857 11,463,643 4,499,202 2,454,819 15,603,952

Off

- shore to EEZ* 187,429 189 860,131 10,382 9,909 9,957 86,209

Non

- US SECA C3 21,064 0 225,881 21,041 19,357 156,469 8,923

Canada othafdust 780,456 112,597

Canada othar 2,930,595 326,305 315,634 154,326 126,539 70,121 830,138

Canada othon 1,896,833 13,543 194,111 13,519 7,711 981 102,954

Canada othpt** 489,410 13,060 247,646 68,377 28,291 497,429 129,119

Mexico othar 299,516 166,412 199,951 103,086 53,005 11,930 470,914

Mexico othon 2,068,677 10,044 164,546 2,565 1,697 1,943 218,836

Mexico othpt 186,435 4,904 348,541 64,263 49,790 363,304 68,970

Non

- US Total 8,079,958 534,456 2,556,441 1,218,014 408,896 1,112,133 1,916,063167

Table 5

- 3

. National by

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

Sector 2011 CO 2017 CO % change 2011

to 2017

afdust_adj 0 0 0%

ag 0 0 0%

agfire 1,030,817 1,030,817 0%

cmv 70,408 72,535 3%

nonpt 1,645,989 1,674,862 2%

np_oilgas 625,430 703,564 12%

nonroad 13,951,020 12,376,430

- 11%

onroad 25,369,638 15,600,692

- 39%

rail 122,703 137,080 12%

ptprescfire 10,063,500 10,063,500 0%

ptwildfire 10,499,197 10,499,197 0%

ptegu 792,397 726,315

- 8%

ptnonipm 2,297,650 2,331,068 1%

pt_oilgas 235,162 227,697

- 3%

rwc 2,517,844 2,504,587

- 1%

Con U.S. Total 69,221,756 57,948,343

- 16%

Off

- shore to EEZ* 176,645 187,429 6%

Non

- US SECA C3 16,207 21,064 30%

Canada othafdust 0 0%

Canada othar 3,015,514 2,930,595

- 3%

Canada othon 3,032,193 1,896,833

- 37%

Canada othpt** 496,083 489,410

- 1%

Mexico othar 277,814 299,516 8%

Mexico othon 3,360,930 2,068,677

- 38%

Mexico othpt 153,061 186,435 22%

Non

- US Total 10,528,446 8,079,957

- 23%168

Table 5

- 4

. National by

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

Sector 2011

NH 3 2017 NH 3 % change

2011 to 2017

afdust_adj 0 0 0%

ag 3515198 3577123 2%

agfire 3321 3321 0%

cmv 232 234 1%

nonpt 94242 94815 1%

np_oilgas 0 0 0%

nonroad 2627 2859 9%

onroad 111881 82295

- 26%

rail 347 347 0%

ptprescfire 161999 161999 0%

ptwildfire 167331 167331 0%

ptegu 25066 40810 63%

ptnonipm 66051 66702 1%

pt_oilgas 5947 5886

- 1%

rwc 19745 19546

- 1%

Con U.S. Total 4173987 4223268 1%

Off

- shore to EEZ* 189 189 0%

Non

- US SECA C3 0 0 0%

Canada othafdust 0 0 0%

Canada othar 326281 326305 0%

Canada othon 18655 13543

- 27%

Canada othpt** 13069 13060 0%

Mexico othar 163040 166412 2%

Mexico othon 7977 10044 26%

Mexico othpt 3706 4904 32%

Non

- US Total 532917 534456 0%169

Table 5

- 5

. National by

- sector NO
x emissions (tons/yr) summaries and percent change

Sector 2011 NO X 2017 NO X % change 2011

to 2017

afdust_adj 0%

ag 0%

agfire 46,035 46,035 0%

cmv 413,314 361,210

- 13%

nonpt 720,454 734,957 2%

np_oilgas 664,966 730,642 10%

nonroad 1,630,301 1,131,509

- 31%

onroad 5,608,800 2,648,665

- 53%

rail 791,381 683,360

- 14%

ptprescfire 167,599 167,599 0%

ptwildfire 165,799 165,799 0%

ptegu 2,081,762 1,273,122

- 39%

ptnonipm 1,213,528 1,190,849

- 2%

pt_oilgas 509,856 444,604

- 13%

rwc 34,436 35,507 3%

Con U.S. Total 14,048,231 9,613,857

- 32%

Off

- shore to EEZ* 899,986 860,131

- 4%

Non

- US SECA C3 202,432 225,881 12%

Canada othafdust 0%

Canada othar 361,896 315,634

- 13%

Canada othon 345,664 194,111

- 44%

Canada othpt** 266,912 247,646

- 7%

Mexico othar 182,869 199,951 9%

Mexico othon 243,714 164,546

- 32%

Mexico othpt 286,303 348,541 22%

Non

- US Total 2,789,776 2,556,441

- 8%170

Table 5

- 6

. National by

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

Sector 2011

PM 2.5 2017

PM 2.5 % change

2011 to 2017

afdust_adj 923,590 963,726 4%

ag 0%

agfire 101,379 101,379 0%

cmv 18,099 8,035

- 56%

nonpt 404,258 416,262 3%

np_oilgas 16,333 15,108

- 7%

nonroad 154,657 107,508

- 30%

onroad 187,869 117,960

- 37%

rail 23,963 17,704

- 26%

ptprescfire 900,546 900,546 0%

ptwildfire 943,717 943,717 0%

ptegu 208,129 184,042

- 12%

ptnonipm 320,857 325,527 1%

pt_oilgas 13,935 13,881 0%

rwc 381,252 383,807 1%

Con U.S. Total 4,598,583 4,499,202

- 2%

Off

- shore to EEZ* 24,544 9,909

- 60%

Non

- US SECA C3 15,828 19,357 22%

Canada othafdust 112,523 112,597 0%

Canada othar 131,114 126,539

- 3%

Canada othon 12,216 7,711

- 37%

Canada othpt** 29,165 28,291

- 3%

Mexico othar 50,158 53,005 6%

Mexico othon 1,624 1,697 5%

Mexico othpt 42,105 49,790 18%

Non

- US Total 419,277 408,896

- 2%171

Table 5

- 7

. National by

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

Sector 2011 PM 10 2017 PM 10 % change 2011

to 2017

afdust_adj 6,732,941 7,091,205 5%

ag 0 0 0%

agfire 152,837 152,837 0%

cmv 19,629 8,522

- 57%

nonpt 491,825 494,835 1%

np_oilgas 17,784 16,719

- 6%

nonroad 162,417 113,817

- 30%

onroad 325,704 272,225

- 16%

rail 25,898 19,137

- 26%

ptprescfire 1,060,130 1,060,130 0%

ptwildfire 1,111,857 1,111,857 0%

ptegu 283,072 243,125

- 14%

ptnonipm 477,387 480,673 1%

pt_oilgas 14,585 14,519 0%

rwc 381,476 384,041 1%

Con U.S. Total 11,257,540 11,463,643 2%

Off

- shore to EEZ* 26,247 10,382

- 60%

Non

- US SECA C3 17,206 21,041 22%

Canada othafdust 779,674 780,456 0%

Canada othar 158,996 154,326

- 3%

Canada othon 17,628 13,519

- 23%

Canada othpt** 70,005 68,377

- 2%

Mexico othar 98,812 103,086 4%

Mexico othon 2,425 2,565 6%

Mexico othpt 55,162 64,263 16%

Non

- US Total 1,226,155 1,218,014

- 1%172

Table 5

- 8

. National by

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

Sector 2011 SO 2 2017 SO 2 % change

2011 to 2017

afdust_adj 0 0 0%

ag 0 0 0%

agfire 17,755 17,755 0%

cmv 91,045 4,777

- 95%

nonpt 276,332 106,270

- 62%

np_oilgas 17,232 37,463 117%

nonroad 4,031 1,620

- 60%

onroad 27,458 13,000

- 53%

rail 7,936 316

- 96%

ptprescfire 83,082 83,082 0%

ptwildfire 82,691 82,691 0%

ptegu 4,650,841 1,221,285

- 74%

ptnonipm 1,049,424 808,482

- 23%

pt_oilgas 66,577 69,264 4%

rwc 8,954 8,814

- 2%

Con U.S. Total 6,383,357 2,454,819

- 62%

Off

- shore to EEZ* 139,169 9,957

- 93%

Non

- US SECA C3 127,579 156,469 23%

Canada othafdust 0 0 0%

Canada othar 70,272 70,121 0%

Canada othon 1,701 981

- 42%

Canada othpt** 544,502 497,429

- 9%

Mexico othar 10,679 11,930 12%

Mexico othon 4,919 1,943

- 61%

Mexico othpt 453,466 363,304

- 20%

Non

- US Total 1,352,287 1,112,133

- 18%173

Table 5

- 9

. National by

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

Sector 2011 VOC 2017 VOC % change

2011 to 2017

afdust_adj 0 0 0%

ag 0 0 0%

agfire 80,540 80,540 0%

cmv 12,578 12,896 3%

nonpt 3,671,898 3,513,992

- 4%

np_oilgas 2,483,856 2,903,644 17%

nonroad 2,024,419 1,409,598

- 30%

onroad 2,656,899 1,516,369

- 43%

rail 40,851 28,004

- 31%

ptprescfire 2,313,404 2,313,404 0%

ptwildfire 2,374,690 2,374,690 0%

ptegu 38,062 35,720

- 6%

ptnonipm 801,188 810,568 1%

pt_oilgas 164,098 166,544 1%

rwc 442,541 437,984

- 1%

Con U.S. Total 17,105,023 15,603,952

- 9%

Off

- shore to EEZ* 81,602 86,209 6%

Non

- US SECA C3 7,294 8,923 22%

Canada othafdust 0 0 0%

Canada othar 886,456 830,138

- 6%

Canada othon 178,431 102,954

- 42%

Canada othpt** 129,119 129,119 0%

Mexico othar 410,734 470,914 15%

Mexico othon 319,353 218,836

- 31%

Mexico othpt 53,813 68,970 28%

Non

- US Total 2,066,802 1,916,063

- 7%174

Table 5

- 10

. Canadian province emissions changes from 2011 to 2017 for othon sector

2017 othon emissions (tons) 2011ek 2017ek % diff (2017ek

2011ek) 2011ek 2017ek % diff (2017ek

2011ek) 2011ek 2017ek % diff (2017ek

2011ek)

Province CO CO CO NOX NOX NOX VOC VOC VOC

Newfoundland 70,094 43,564

- 37.8% 7,915 4,453

- 43.7% 3,333 1,903

- 42.9%

Prince Edward Island 24,124 15,080

- 37.5% 3,319 1,920

- 42.2% 1,390 799

- 42.5%

Nova Scotia 119,570 74,765

- 37.5% 13,799 7,822

- 43.3% 6,593 3,791

- 42.5%

New Brunswick 129,867 80,997

- 37.6% 18,604 10,872

- 41.6% 7,621 4,398

- 42.3%

Quebec 885,568 560,768

- 36.7% 106,445 60,922

- 42.8% 48,478 28,249

- 41.7%

Ontario 1,189,550 745,872

- 37.3% 124,063 68,928

- 44.4% 61,637 35,881

- 41.8%

Manitoba 226,661 139,914

- 38.3% 27,249 15,597

- 42.8% 14,285 8,128

- 43.1%

Saskatchewan 353,836 216,675

- 38.8% 41,393 23,548

- 43.1% 25,123 14,153

- 43.7%

Alberta 658,481 406,943

- 38.2% 94,080 53,514

- 43.1% 48,414 27,525

- 43.1%

British Columbia 588,527 363,844

- 38.2% 67,944 36,827

- 45.8% 45,044 25,662

- 43.0%

Yukon 7,590 4,729

- 37.7% 686 376

- 45.3% 476 272

- 42.9%

N W Territories 6,617 4,148

- 37.3% 754 435

- 42.4% 410 235

- 42.6%

Nunavut 1,920 1,158

- 39.7% 155 85

- 45.6% 104 58

- 44.5%

Canada Total 4,262,403 2,658,457

- 37.6% 506,407 285,296

- 43.7% 262,908 151,053

- 42.5%

Table 5

- 11

. Canadian province emissions changes from 2011 to 2017 for othar sector

2017 othar emissions (tons) 2011ek 2017ek % diff (2017ek

2011ek) 2011ek 2017ek % diff (2017ek

2011ek) 2011ek 2017ek % diff (2017ek

2011ek)

Province CO CO CO NOX NOX NOX VOC VOC VOC

Newfoundland 71,720 68,076

- 5.1% 32,106 30,474

- 5.1% 24,884 21,561

- 13.4%

Prince Edward Island 27,420 26,556

- 3.2% 1,309 1,178

- 10.0% 7,459 6,499

- 12.9%

Nova Scotia 108,892 107,023

- 1.7% 34,093 32,667

- 4.2% 31,588 30,261

- 4.2%

New Brunswick 76,757 75,551

- 1.6% 12,057 11,280

- 6.4% 27,446 26,645

- 2.9%

Quebec 923,750 903,823

- 2.2% 96,533 86,703

- 10.2% 274,657 262,286

- 4.5%

Ontario 1,537,669 1,491,730

- 3.0% 169,367 148,282

- 12.4% 388,132 357,527

- 7.9%

Manitoba 153,099 148,358

- 3.1% 16,943 15,647

- 7.6% 67,697 62,482

- 7.7%

Saskatchewan 470,108 450,589

- 4.2% 53,501 43,507

- 18.7% 132,559 116,140

- 12.4%

Alberta 339,458 317,868

- 6.4% 141,209 113,576

- 19.6% 205,096 197,706

- 3.6%

British Columbia 430,751 423,599

- 1.7% 103,465 96,290

- 6.9% 122,900 119,205

- 3.0%

Yukon 1,355 1,281

- 5.5% 524 410

- 21.8% 702 676

- 3.7%

N W Territories 9,214 8,441

- 8.4% 4,736 3,820

- 19.3% 2,199 1,813

- 17.5%

Nunavut 978 831

- 15.0% 1,438 1,124

- 21.8% 658 627

- 4.7%

Canada Total 4,151,170 4,023,726

- 3.1% 667,282 584,958

- 12.3% 1,285,976 1,203,428

- 6.4%175

Table 5

- 12

. Canadian province emissions changes from 2011 to 2017 for othpt sector

2017 othpt emissions (tons) 2011ek 2017ek % diff (2017ek

2011ek) 2011ek 2017ek % diff (2017ek

2011ek) 2011ek 2017ek % diff (2017ek

2011ek)

Province CO CO CO NOX NOX NOX VOC VOC VOC

Newfoundland 13,073 13,073 0.0% 23,646 23,646 0.0% 19,926 19,926 0.0%

Prince Edward Island 49 49 0.0% 321 321 0.0% 417 417 0.0%

Nova Scotia 4,451 4,451 0.0% 25,181 25,181 0.0% 11,346 11,346 0.0%

New Brunswick 28,314 28,310 0.0% 16,900 16,804

- 0.6% 4,691 4,691 0.0%

Quebec 472,250 471,057

- 0.3% 52,177 50,554

- 3.1% 65,053 64,141

- 1.4%

Ontario 85,168 79,696

- 6.4% 90,405 72,773

- 19.5% 121,838 121,747

- 0.1%

Manitoba 2,394 2,394 0.0% 3,822 3,822 0.0% 30,505 30,505 0.0%

Saskatchewan 27,496 27,496 0.0% 65,439 65,439 0.0% 169,269 169,269 0.0%

Alberta 496,794 496,794 0.0% 575,981 575,981 0.0% 498,580 498,580 0.0%

British Columbia 196,308 196,308 0.0% 89,526 89,526 0.0% 56,938 56,938 0.0%

Yukon 50 50 0.0% 135 135 0.0% 5 5 0.0%

N W Territories 1,871 1,871 0.0% 9,107 9,107 0.0% 1,037 1,037 0.0%

Nunavut 817 817 0.0% 5,588 5,588 0.0% 326 326 0.0%

Canada Total 1,329,036 1,322,367

- 0.5% 958,229 938,876

- 2.0% 979,932 978,928

- 0.1%176 

## 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 Appr oach 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 Michigan 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 Emission Production Simulator (FEPS) User's

Gu ide. Available at http://www.fs.fed.us/pnw/fera/feps/FEPS_users_guide.pdf

.

ARB, 2000. “Risk Reduction Plan to Reduce Particulate Matter Emissions from Diesel

- Fueled Engines and

Vehi cles”. California Environmental Protection Agency Air Resources Board, Mobile Source Control
Division, Sacramento, CA. October, 2000. Available at: http://www.arb.ca.gov/diesel/docume nts/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. Available 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

- Ignition Fleet Requirements”. Ca lifornia 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 Protection Agency Air Resources Boa rd, 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., Pa rk, J.

- H., Goldstein, A.H., 2016 . Evaluation of improved land use and canopy representation in BEIS with biogenic VOC measurements in California. Available from http://www.geosci

- model

- dev.net/9/2191/2016/

.

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

– 2146.177 Dep artment of Energy, 2012 Annual Energy Outlook 2012, Early Release. Report No. DOE/EIA

0383(2012), June 2012. Available at: http://www.eia.gov/forecasts/aeo/

. Energy Information Administration. 2006. A nnual 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 # DOE/EIA

- 0383ER (2011). Available at: http://www.eia.doe.gov/forecasts/aeo/tables_ref.cfm

. Environ Corp. 2008. Emission Profiles for EPA SPECIATE Database, Part 2: EPAct Fuels (Evaporative Emissions). Prepared for U. S. EPA, Office of Transportation and Air Quality, September 30, 2008.

EPA, 1994. Onboard Refueling Vapor Recovery for Motor Vehi cles, 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 V ehicle Tier 2 Exhaust

Emission Standards. Office of Transportation and Air Quality, Ann Arbor, MI 48105. Available at: HYPERLINK "http://www.epa.gov/otaq/standards/light

duty/tier2stds.htm" http://www.epa.gov/otaq/standards/light

- du ty/tier2stds.htm

. EPA, 2005. EPA’s National Inventory Model (NMIM), A Consolidated Emissions Modeling System for

MOBILE6 and NONROAD , U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Ann Arbor, MI 4810 5, EPA420

- R

- 05

- 024, December 2005. Available at http://www.epa.gov/otaq/models/nmim/420r05024.pdf

.
EPA 2006a. SPECIATE 4.0, Speciation Database Development Document, Final Report

, U.S

. Environmental Protection Agency, Office of Research and Development

, National Risk Management Research Laboratory, Research Triangle Park, NC 27711, EPA600

- R

- 06

- 161, February 2006.

Available at http://www.epa.gov/ttn/chief/software/speciate/speciate4/documentation/speciatedoc_1206.pdf

. EPA, 2006b. Regulatory Impact Analyses, 2006 National Ambient Air Quality Standards for Particle Pollution. U.S. Environmental Protection Agency, Office of Air Qu ality 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 Quality Planning and Standards, Air Quality Policy Division, Research Triangle Park, NC 27711, EPA

- 457/B

- 07

- 001, May 2007. Available at http://www.epa.gov/ttn/naaqs/ozone/o3imp8hr/documents/guidance/200705_epa457_b

- 07

- 001_emission_changes_mact_rules.pdf

. EPA, 2008. Regulatory Impact Ana lysis: 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,

200 8. 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

.178 EPA. 2009b. “Impact Calculations RFS

- Docket.xls.” Available on the RFS2 Docket EPA

– HQ

– OAR

– 2010

– 0133 at: http://www.regulations.gov

. EPA, 2009c. Exhaust Emission Profiles for EPA SPECIATE Database: Energy Policy Act (EPAct) Low

Level Ethanol Fuel Blends and Tier 2 Light

- Duty Vehicles. Assessment and Standards Division,

Office of Trans portation and Air Quality. Report No. EPA

- 420

- R

- 09

- 002.

EPA, 2009d. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder. U.S. Environmental Protection Agency Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, EPA420

- R

- 08

- 001a, May 2009. Available at: http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P10024CN.txt

. EPA, 2009e. Emission Fa ctors for Locomotives. Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI

. Report No. EPA

- 420

- F

- 09

- 25. Available at: http://www.epa.gov /otaq/regs/nonroad/locomotv/420f09025.pdf

. EPA, 2009f. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Category 3 Marine Diesel Engines. Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI. R eport 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 Transportation and Air Quality, Ann Arbor, MI. Report No. EPA

- 420

- R

- 10

- 006, February, 2010. Available at: http://www.e pa.gov/otaq/fuels/renewablefuels/regulations.htm

. EPA, 2010b. Technical Support Document: The Industrial Sectors Integrated Solutions (ISIS) Model and the Analysis for the National Emission Standards for Hazardous Air Pollutants and New Source

Performanc e Standards for the Portland Cement Manufacturing Industry, U.S. Environmental Protection Agency, Sectors Policies and Program Division and Air Pollution Prevention and Control Division, Research Triangle Park, NC 27711, August 2010.

EPA, 2010c. Light

- Dut y Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards, Final Rule. April, 2010. Available at: http://www.epa.gov/otaq/regs/ld

hwy/greenhouse/l d

- ghg.htm

. EPA, 2011a. Heavy

- Duty Vehicle Greenhouse Gas (HDGHG) Emissions Inventory for Air Quality Modeling Technical Support Document, Office of Air Quality Planning and Standards, Air Quality Assessment Division, Research Triangle Park, NC. Report N o. 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 Emiss ions Inventory, version 2 Technical Support Document. Office of Air Quality Planning and Standards, Air Quality Assessment Division, Research Triangle Park, NC.

Available at: http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc

. EPA, 2012b. Regulatory Impact Analysis: Final Rulemaking for 2017

- 2025 Light

- Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards, Office of Tr ansportation 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

.179 EPA, 2012d. Pre paration of Emission Inventories for the Version 5.0, 2007 Emissions Modeling Platform Technical Support Document. Available from: http://epa.gov/ttn/chief/em ch/2007v5/2007v5_2020base_EmisMod_TSD_13dec2012.pdf

. EPA, 2013ci. Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression 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 Reconsideration of the Existing Stationary Spark Ignition (SI) RICE NESHAP: Final Report. Available fr om http://www.epa.gov/ttn/ecas/regdata/RIAs/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/d ocuments/tier3/420r14005.pdf

. EPA, 2014b. Technical Support Document: Preparation of Emissions Inventories for the Version 6.1 Emissions Modeling Platform. EPA OAQPS, 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, 2015. 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, 2014a. Develop Mexico Future Year Emissions Final Report. Available at ftp://ftp.epa.gov/EmisInventory/2011v6/v2pla tform/2011emissions/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

Heate rs, 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 an d NOy: Influence 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 heat ers in use in the U.S. today are still old, uncertified units?” Hearth and Home, December 2011.

Joint Fire Science Program, 2009. Consume 3.0

-- a software tool for computing fuel consumption. Fire Science Brief. 66, June 2009. Consume 3.0 is available at: http://www.fs.fed.us/pnw/fera/research/smoke/consume/index.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 Michigan Air Directors Consortium, Rosemont, IL 60018.180

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 Su rvey of individual household responses”. Minnesota Department of Natural Resources. Available from http://files.dnr.state.mn.us/forestry/um/residentialfuelw oodassessment07_08.pdf

. NESCAUM, 2006; “Assessment of Outdoor Wood

- fired Boilers” (NESCAUM, 2006). Northeast States for Coordinated Air Use Management (NESCAUM) report. Available from http://www.nescaum.org/documents/assessment

- of

- outdoor

- wood

- fired

- boilers/2006

- 1031

- owb

report_revised

- june2006

- appendix.pdf

. NYSERDA, 2012; “Environmental, Energy Market, and Healt h 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/Environmenta l/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 Resea rch Station.

Ottmar, R.D.; Prichard, S.J.; Vihnanek, R.E.; Sandberg, D.V. 2006. Modification and validation of fuel consumption models for shrub and forested lands in the Southwest, Pacific Northwest, Rockes, Midwest, Southeast, and Alaska. Final report, JFSP Project 98

- 1

- 9

- 06.

Ottmar, R.D.; Sandberg, D.V.; Riccardi, C.L.; Prichard, S.J. 2007. An Overview of the Fuel Characteristic Classification System

– Quantifying, Classifying, and Creating Fuelbeds for Resource Planning.

Canadian Journal of Forest Res earch. 37(12): 2383

- 2393. FCCS is available at: http://www.fs.fed.us/pnw/fera/fccs/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 O zone Transport Commission, Washington, DC, March 31, 2001.

Pouliot, G., H. Simon, P. Bhave, D. Tong, D. Mobley, T. Pace, and T. Pierce. (2010) “Assessing the Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated181

Speciation of Particulate Matter.” International Emission Inventory Conference, San Antonio, TX.

Available at http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot.pdf

.

Pou liot, G. and J. Bash, 2015. Updates to Version 3.61 of the Biogenic Emission Inventory System (BEIS). Presented at Air and Waste Management Association conference, Raleigh, NC, 2015.

Raffuse, S., D. Sullivan, L. Chinkin, S. Larkin, R. Solomon, A. Soja, 200 7. 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 Environment, 20: 2011

- 2025.

SESARM, 2012a. “Development of the 2007 Base Year and Typical Year Fire Em ission 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 Advanced Research WRF Vers ion 3. NCAR Technical Note. National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder, CO. June 2008.

Available at: http://www2.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. Submitte d 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 C ontract 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, Mid

- 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 fir es: the BlueSky smoke modeling framework and SMARTFIRE: 17th182

International Emissions Inventory Conference, Portland, OR, June 2

- 5. Available at: http://www.epa.gov/ttn/chief/conferences.html

.

U.S. Census, 2012; “2012 Statistical Abstract, Construction and Housing: Housing Units and

Characteristics”. U.S. Census Bureau. Available from: http://www.census.gov/compendia/statab/cats/construction_housing/housing_units_and_characteristi

cs.html

.

Wang, Y., P. Hopke, O. V. Rattigan, X. Xia, D. C. Chalupa, M. J. Utell. (2011) “Characterization of

Residential Wood Combustion Particle s Using the Two

- Wavelength Aethalometer”, Environ. Sci. Technol., 45 (17), pp 7387

– 7393

.

Yarwood, G., J. Jung, , G. Whitten, G. Heo, J. Mellberg, and M. Estes,2010: Updates to the Carbon Bond Chemical Mechanismfor Version 6 (CB6). Presented at the 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

- 14.183 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

2310000230 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 Pro duction;All Processes;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

2310002301 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;Flare s: Flaring Operations

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;Pressure/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 Industrial 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 well 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

2310011100 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 Industrial 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 Process es;Oil and Gas Exploration and Production;On

- Shore Oil Production;Fugitives: Connectors

2310011502 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Oil Production;Fugitives: Flanges184

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 Pro duction;On

- Shore Oil Production;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 Producti on;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 Industrial 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; Flange s: 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: Oi l/Water

2310012526 Industrial Processes;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;Natural 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 t ruck loading

2310021010 Industrial Processes;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 Gas 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 Production;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

2310021103 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 and Gas Exploration and Production;On

- Shore Gas Production;Natural Gas Fired 4Cycle Lean Burn

Compressor Engines 500+ HP185

SCC SCC description

2310021209

2310021251 Industrial Processes;Oil and Gas Exploration and Production;On

- Sh ore 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 Industria l Processes;Oil and Gas Exploration and Production;On

- Shore 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 Gas 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

2310021 401 Industrial Processes;Oil and Gas Exploration and Production;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/NSCR

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 Production;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;Fugitives: 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 Pro duction;On

- Shore Gas Production;Fugitives: Valves

2310021506 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Fugitives: Other

2310021509 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Producti on;Fugitives: All Processes

2310021600 Industrial Processes;Oil and Gas Production: 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

- Shore 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 Shutdowns186

SCC SCC description

2310021700 Industrial Processes;Oil and Gas Exploration and Production;On

- Shore Gas Production;Miscellaneous Engines

2310022000 Industrial Processes;Oil and Gas Production: SIC 13;Natural Gas : Off

- shore;Total: All Processes

2310022 010 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;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/Heaters: 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 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore Gas Production;Dehydrator

2310022501 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shor e Gas Production;Fugitives; Connectors: Gas Streams

2310022502 Industrial Processes;Oil 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 B ed Methane Natural Gas;Storage Tanks: Condensate

2310023030 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Tank Truck/Railcar Loading: Condensa te

2310023100 Industrial Processes; Oil and Gas Production; CBM; Dehydrators

2310023102 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;CBM Fired 2Cycle Lean Burn Compresso r

Engines 50 To 499 HP

2310023202 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Met hane Natural Gas;CBM Fired 4Cycle Lean Burn Compressor

Engines 50 To 499 HP

2310023251 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Lateral Compressors 4 Cycle Lean Bur n

2310023300 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Pneumatic Devices Industrial

Processes;Oil and 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 Compressor

Engines 50 To 499 HP

2310023310 Industrial 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 Burn

2310023400 Industrial 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 Exploration 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 Product ion;Coal Bed Methane Natural Gas;Fugitives: Valves

2310023516 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Fugitives: Other187

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

- Initial Completions

2310023602 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Venting

- Recom pletions

2310023603 Industrial Processes;Oil and Gas Exploration and Production;Coal Bed Methane Natural Gas;Venting

- Blowdowns

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 Processes;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 Processes;Oil and Gas Exploration and Production;Natural Gas Liquids;Gas Plant Truck Loading

2310111100 Industrial Processes;Oil and Ga s Exploration and Production;On

- Shore Oil Exploration;Mud Degassing

2310111401 Industrial Processes;Oil and Gas Exploration 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 P roduction;On

- Shore Gas Exploration;Mud Degassing

2310121401 Industrial Processes;Oil and Gas Exploration and Production;Off

- Shore 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

- Shore Gas Exploration;Mud Degassing188 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 portable fuel containers (PFC), fuel distribution operations associated with the bulk

- plant

- to

- pump (BTP), refinery to bulk terminal (RBT) and bulk plant storage (BPS).

SCC Ty pe 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 Refinerie s; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 7: Breathing Loss (67000 Bbl. Tank Size)

40301004 RBT Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13 : Breathing Loss (25000 0 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 Evaporation; Petroleum Product Storage at Refine ries; 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 (Varying Sizes); Gasoline RVP 10: Standing Los s (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)

40301105 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: St anding

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 Loss (67000 Bbl Capacity)

- Fixed Roof Tank

40400102 RBT Petroleum and Solvent Evaporation; Petrol eum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity)

- Fixed Roof Tank

40400103 RBT Petroleum and Solvent 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 Terminals; 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)

- Fix ed Roof Tank189

SCC Ty pe Description

40400106 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 Independent)

- 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 Petroleum 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) ; Bulk 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 Roof 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 (10500 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 Evaporation; 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 RBT 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/ Secondy Seal19 0

SCC Ty pe Description

40400141 RBT Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss

- Ext. Floating Roof w/ Sec ondary Seal

40400142 RBT Petroleum and Solvent Evaporation; Petroleum Liquids 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 Solvent 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; Miscellaneous 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

- Refinery); 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 Storage (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/ Prim ary Seal

40400163 RBT Petroleum and Solvent Evaporation; 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 Liquid: 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 Petroleum 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 (Pr i/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 Storage (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 (670 00 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 Tank191

SCC Ty pe 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

- Refiner y); Bulk Plants; Gasoline RVP 13: Working Loss (67000 Bbl. Capacity)

- Fixed Roof Tank

40400205 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 10: Working Loss (67000 Bbl. Capacity)

- Fixe d Roof Tank

40400206 BTP/BPS Petroleum and Solvent Evaporation; Petroleum 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 Loss (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.)

- Floa t 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

40400213 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 Storage (non

- Refinery); Bulk Plants; Specify Liquid: Standing Loss

- External Floating Roof w/ Pr imary Seal

40400231 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss

- Ext. Floating 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 Evaporation; 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: Standing 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 Petroleum 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; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Valves, Flanges, and Pumps

40400252 B TP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Bulk Plants; Miscellaneous Losses/Leaks: Vapor

Collection Losses192

SCC Ty pe Description

40400253 BTP/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; Spec ify 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

40400262 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

- Refiner y); Bulk Plants; Specify Liquid: Standing Loss

- Int. Floating 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/ Seco ndary Seal

40400272 BTP/BPS Petroleum and Solvent Evaporation; 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 S eal)

40400401 BTP/BPS Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non

- Refinery); Petroleum 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/BPS 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); Petroleum 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 Petroleum 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 Evaporation; Transportation and Marketing of Petroleum Products; Tank Ca rs and Trucks; Gasoline: Submerged Loading (Normal Service)193

SCC Ty pe 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 Solvent Evaporation; Transportation and Marketing of Petroleum P roducts; Tank Cars and Trucks; Gasoline: Splash Loading (Balanced Service)

40600147 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submer ged Loading (Clean Tanks)

4060016 2 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 Petroleum 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 Pro ducts; Tank 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 Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tank ers

40600233 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Ga soline: Loading Barges: Cleaned 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 Solven t Evaporation;Transportation 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: Uncleaned Tanks

40600237 RBT Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum Products;Marine Vessels;Gasoline: Ocean Barges Loading

- Uncleaned Tanks

40600238 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barg es: Uncleaned Tanks

40600239 RBT Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Tankers: Bal lasted

Tank

40600240 RBT Petroleum 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; Marine Vessels; Gasoline: Tanker Balla sting

40600299 RBT Petroleum and Solvent Evaporation; T ransportation and Marketing of Petroleum Products; Marine Vessels; Not Classified **

40600301 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing 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 Controls

40600305 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of P etroleum Products; Gasoline Retail Operations

- Stage I; Unloading

**194

SCC Ty pe Description

40600306 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum 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 and Emptying

40600399 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Market ing of Petroleum Products; Gasoline Retail Operations

- Stage I; Not

Classified **

40600401 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing 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; Pipeline 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 Marketing 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 Products; 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 Evaporation; 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) Fleet 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/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petro leum Products; Consumer (Corporate) Fleet Refueling

- Stage I; Underground Tank Breathing and Emptying

40688801 BTP/BPS Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive Emissions; Specify in Comment s Field

2501050120 RBT Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative Losses; Gasoline

2501055120 BTP/BPS Storage and Transport; Petroleum 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 BT P/BPS Storage and Transport; 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 Submerge d Filling

2501060200 BTP/BPS Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Underground 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 Stor age and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline195

SCC Ty pe 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 Types196

Appendix 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; April 22, 2005), population projections for the beef, dairy, swine, and poultry an imal sectors were developed and used to estimate future ammonia emissions from these animal sectors. To develop the 2005 population projections,

the 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 projections. These data were used to update the 2005 animal inventory project ions. 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 changes in animal populations or regional dislocations associated with the 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 projections 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 populations 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 Agricultural Projections to 2021 (USDAa) provides estimated national ca ttle 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 economic conditions. The pace of the cycle is limited by the reproducti ve 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 time 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 the 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, the EPA assumed 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 the trend line out to 2030

, and adjusted it by the average percenta ge 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 versus other beef cattle (e.g., steers, bulls). The EPA conducted a separate analysis of these animal populations.197 Other beef cattle populations appear to follow similar cycles and were forecasted using the same technique as beef cows (see Figure C

- 3).

Figure C

- 1. Dairy Cow Inventory Projections

Figure C

- 2. Beef Cow In ventory 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 Population198 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 Agricultural 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 p er 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 and market swine population projections and inventory data from the FAPRI report capture the variability of the swine production cycle. Changes in the por k industry in the 1990’s have made recent data atypical and inconsistent. For example, the EPA replaced the 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 esti mated the trend and deviations from the trend as in the beef cattle analysis. However, it was not possible 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 annu al data. The EPA evaluated a 3

- year moving average of the deviation to further reduce the random component. As the smoothed cycle continued to appear irregular,

the EPA assumed that the 2010’s will repeat the pattern of the 1990’s. Breeding hog populati ons were estimated using a similar approach. See Figures C

- 4 and C

- 5 for an illustration of the swine projections 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, the EPA used population and projection data from the annual summary of the USDA/NASS Poultry

– Production and Value reports (USDAb) for broilers and tu rkeys, and the Chickens and Eggs reports (USDAc). With these data, the EPA used a linear regression analysis to predict the number of birds produced in the U.S. for years beyond 2011. 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 Population199 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 Population200 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 Population201 Figure C

- 8. Egg Layer Projection

References Bowerman, Bruce L. and Richard T. O’Connell (1987) Time 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 University and University of Missouri

- Columbia.

htt p://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 Co mmittee

. February 2021. http://www.usda.gov/oce/commodity/archive_projections/USDAAgriculturalProjections2021.pdf

.

USDA (2012b). National Agricult ural Statistics Service (NASS). Poultry

- Production and Value Reports. http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1130

.

USDA (20 12c). NASS. Chickens and Eggs Annual Summary

. http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do;jsessioni d=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