Technical Support Document (TSD)
Preparation of Emissions Inventories for 2016v1 North American Emissions Modeling Platform

                                September 2020

Contacts:
Alison Eyth, Jeff Vukovich, Caroline Farkas, Madeleine Strum

U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Emissions Inventory and Analysis Group
Research Triangle Park, North Carolina

TABLE OF CONTENTS

List of Tables	iv
List of Figures	vii
List of Appendices	viii
Acronyms	ix
1	Introduction	12
2	Emissions Inventories and Approaches	14
2.1	2016 point sources (ptegu, pt_oilgas, ptnonipm, airports)	18
2.1.1	EGU sector (ptegu)	20
2.1.2	Point source oil and gas sector (pt_oilgas)	21
2.1.3	Non-IPM sector (ptnonipm)	24
2.1.4	Aircraft and ground support equipment (airports)	26
2.2	2016 Nonpoint sources (afdust, ag, np_oilgas, rwc, nonpt)	28
2.2.1	Area fugitive dust sector (afdust)	28
2.2.2	Agriculture Sector (ag)	35
<IP_ADDRESS>	Livestock Waste Emissions	36
<IP_ADDRESS>	Fertilizer Emissions	37
2.2.3	Nonpoint Oil and Gas Sector (np_oilgas)	40
2.2.4	Residential Wood Combustion (rwc)	42
2.2.5	Nonpoint (nonpt)	43
2.3	2016 Onroad Mobile sources (onroad)	47
2.4	2016 Nonroad Mobile sources (cmv, rail, nonroad)	60
2.4.1	Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)	60
2.4.2	Category 3 Commercial Marine Vessels (cmv_c3)	63
2.4.3	Rail Sources (rail)	67
2.4.4	Nonroad Mobile Equipment Sources (nonroad)	76
2.5	2016 Fires (ptfire, ptagfire)	82
2.5.1	Wild and Prescribed Fires (ptfire)	82
2.5.2	Point source Agriculture Fires (ptagfire)	89
2.6	2016 Biogenic Sources (beis)	92
2.7	Sources Outside of the United States	94
2.7.1	Point Sources in Canada and Mexico (othpt)	94
2.7.2	Fugitive Dust Sources in Canada (othafdust, othptdust)	94
2.7.3	Nonpoint and Nonroad Sources in Canada and Mexico (othar)	95
2.7.4	Onroad Sources in Canada and Mexico (onroad_can, onroad_mex)	95
2.7.5	Fires in Canada and Mexico (ptfire_othna)	95
2.7.6	Ocean Chlorine	95
3	Emissions Modeling	96
3.1	Emissions modeling Overview	96
3.2	Chemical Speciation	99
3.2.1	VOC speciation	102
<IP_ADDRESS>	County specific profile combinations	105
<IP_ADDRESS>	Additional sector specific considerations for integrating HAP emissions from inventories into speciation	106
<IP_ADDRESS>	Oil and gas related speciation profiles	109
<IP_ADDRESS>	Mobile source related VOC speciation profiles	110
3.2.2	PM speciation	115
<IP_ADDRESS>	Mobile source related PM2.5 speciation profiles	116
3.2.3	NOX speciation	118
3.2.4	Creation of Sulfuric Acid Vapor (SULF)	118
3.3	Temporal Allocation	120
3.3.1	Use of FF10 format for finer than annual emissions	121
3.3.2	Electric Generating Utility temporal allocation (ptegu)	122
<IP_ADDRESS>	Base year temporal allocation of EGUs	122
3.3.3	Airport Temporal allocation (airports)	124
3.3.4	Residential Wood Combustion Temporal allocation (rwc)	126
3.3.5	Agricultural Ammonia Temporal Profiles (ag)	130
3.3.6	Oil and gas temporal allocation (np_oilgas)	131
3.3.7	Onroad mobile temporal allocation (onroad)	131
3.3.8	Additional sector specific details (afdust, beis, cmv, rail, nonpt, ptnonipm, ptfire)	135
3.4	Spatial Allocation	137
3.4.1	Spatial Surrogates for U.S. emissions	137
3.4.2	Allocation method for airport-related sources in the U.S.	143
3.4.3	Surrogates for Canada and Mexico emission inventories	144
3.5	Preparation of Emissions for the CAMx model	147
3.5.1	Development of CAMx Emissions for Standard CAMx Runs	147
3.5.2	Development of CAMx Emissions for Source Apportionment CAMx Runs	149
4	Development of 2023 and 2028 Emissions	153
4.1	EGU Point Source Projections (ptegu)	156
4.2	Non-EGU Point and Nonpoint Sector Projections	159
4.2.1	Background on the Control Strategy Tool (CoST)	160
4.2.2	CoST Plant CLOSURE Packet (ptnonipm, pt_oilgas)	164
4.2.3	CoST PROJECTION Packets (afdust, ag, cmv, rail, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)	164
<IP_ADDRESS>	Fugitive dust growth (afdust)	165
<IP_ADDRESS>	Livestock population growth (ag)	165
<IP_ADDRESS>	Category 1, Category 2 Commercial Marine Vessels (cmv_c1c2)	166
<IP_ADDRESS>	Category 3 Commercial Marine Vessels (cmv_c3)	167
<IP_ADDRESS>	Oil and Gas Sources (pt_oilgas, np_oilgas)	168
<IP_ADDRESS>	Non-EGU point sources (ptnonipm)	171
<IP_ADDRESS>	Nonpoint Sources (nonpt)	172
<IP_ADDRESS>	Airport sources (airports)	173
<IP_ADDRESS>	Residential Wood Combustion (rwc)	173
4.2.4	CoST CONTROL Packets (nonpt, np_oilgas, ptnonipm, pt_oilgas)	174
<IP_ADDRESS>	Oil and Gas NSPS (np_oilgas, pt_oilgas)	176
<IP_ADDRESS>	RICE NSPS (nonpt, ptnonipm, np_oilgas, pt_oilgas)	178
<IP_ADDRESS>	Fuel Sulfur Rules (nonpt, ptnonipm)	181
<IP_ADDRESS>	Natural Gas Turbines NOx NSPS (ptnonipm, pt_oilgas)	182
<IP_ADDRESS>	Process Heaters NOx NSPS (ptnonipm, pt_oilgas)	184
<IP_ADDRESS>	CISWI (ptnonipm)	187
<IP_ADDRESS>	Petroleum Refineries NSPS Subpart JA (ptnonipm)	188
<IP_ADDRESS>	State-Specific Controls (ptnonipm)	189
4.3	Projections Computed Outside of CoST	190
4.3.1	Nonroad Mobile Equipment Sources (nonroad)	190
4.3.2	Onroad Mobile Sources (onroad)	190
4.3.3	Locomotives (rail)	193
4.3.4	Sources Outside of the United States (onroad_can, onroad_mex, othpt, ptfire_othna, othar, othafdust, othptdust)	194
<IP_ADDRESS>	Canadian fugitive dust sources (othafdust, othptdust)	194
<IP_ADDRESS>	Point Sources in Canada and Mexico (othpt)	195
<IP_ADDRESS>	Nonpoint sources in Canada and Mexico (othar)	196
<IP_ADDRESS>	Onroad sources in Canada and Mexico (onroad_can, onroad_mex)	197
5	Emission Summaries	198
6	References	205

List of Tables
Table 2-1.  Platform sectors for the 2016 emissions modeling case	15
Table 2-2. Point source oil and gas sector NAICS Codes	21
Table 2-3. 2014NEIv2-to-2016 projection factors for pt_oilgas sector for 2016v1 inventory	22
Table 2-4. 2016fh pt_oilgas national emissions (excluding offshore) before and after 2014-to-2016 projections (tons/year)	23
Table 2-5. Pennsylvania emissions changes for natural gas transmission sources (tons/year).	23
Table 2-6. SCCs for Census-based growth from 2014 to 2016	24
Table 2-7.  2016v1 platform SCCs for the airports sector	27
Table 2-8. Afdust sector SCCs	28
Table 2-9.  Total impact of fugitive dust adjustments to unadjusted 2016 v1 inventory	32
Table 2-10.  2016v1 platform SCCs for the ag sector	35
Table 2-11.  National back-projection factors for livestock: 2017 to 2016	36
Table 2-12.  Source of input variables for EPIC	39
Table 2-13. 2014NEIv2-to-2016 oil and gas projection factors for CO and OK.	41
Table 2-14. 2016 v1 platform SCCs for RWC sector	42
Table 2-15. Projection factors for RWC by SCC	43
Table 2-16. 2016v1 platform SCCs for Census-based growth	45
Table 2-17. MOVES vehicle (source) types	47
Table 2-18. Submitted data used to prepare onroad activity data	48
Table 2-19. Factors applied to project VMT from 2014 to 2016 to prepare default activity data	49
Table 2-20. Older Vehicle Adjustments Showing the Fraction of IHS Vehicle Populations to Retain for 2016v1 and 2017 NEI	57
Table 2-21. 2016v1 platform SCCs for cmv_c1c2 sector	60
Table 2-22. Vessel groups in the cmv_c1c2 sector	62
Table 2-23. 2016v1 platform SCCs for cmv_c3 sector	64
Table 2-24. 2017 to 2016 projection factors for C3 CMV	67
Table 2-25. 2016v1 SCCs for the Rail Sector	68
Table 2-26. Class I Railroad Reported Locomotive Fuel Use Statistics for 2016	68
Table 2-27. 2016 Line-haul Locomotive Emission Factors by Tier, AAR Fleet Mix (grams/gal)	70
Table 2-28. Surface Transportation Board R-1 Fuel Use Data  -  2016	71
Table 2-29. 2016 Yard Switcher Emission Factors by Tier, AAR Fleet Mix (grams/gal)[4]	71
Table 2-30. Expenditures and fuel use for commuter rail	74
Table 2-31. Submitted nonroad input tables by agency	80
Table 2-32. Alaska counties/census areas for which nonroad equipment sector-specific emissions are removed in 2016v1	81
Table 2-33. SCCs included in the ptfire sector for the 2016v1 inventory	82
Table 2-34. National fire information databases used in 2016v1 ptfire inventory	83
Table 2-35. List of S/L/T agencies that submitted fire data for 2016v1 with types and formats.	85
Table 2-36. Brief description of fire information submitted for 2016v1 inventory use.	85
Table 2-37. SCCs included in the ptagfire sector for the 2016v1 inventory	89
Table 2-38. Assumed field size of agricultural fires per state(acres)	91
Table 2-39. Hourly Meteorological variables required by BEIS 3.61	93
Table 3-1.  Key emissions modeling steps by sector.	97
Table 3-2.  Descriptions of the platform grids	98
Table 3-3. Emission model species produced for CB6 for CMAQ	100
Table 3-4. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM) for each platform sector	104
Table 3-5. Ethanol percentages by volume by Canadian province	106
Table 3-6.  MOVES integrated species in M-profiles	107
Table 3-7.  Basin/Region-specific profiles for oil and gas	109
Table 3-8.  TOG MOVES-SMOKE Speciation for nonroad emissions in MOVES2014a used for the 2016 Platform	110
Table 3-9.  Select mobile-related VOC profiles 2016	111
Table 3-10.  Onroad M-profiles	112
Table 3-11.  MOVES process IDs	113
Table 3-12.  MOVES Fuel subtype IDs	114
Table 3-13.  MOVES regclass IDs	114
Table 3-14.  SPECIATE4.5 brake and tire profiles compared to those used in the 2011v6.3 Platform	117
Table 3-15.  Nonroad PM2.5 profiles	118
Table 3-16.  NOX speciation profiles	118
Table 3-17.  Sulfate split factor computation	119
Table 3-18.  SO2 speciation profiles	119
Table 3-19.  Temporal settings used for the platform sectors in SMOKE	120
Table 3-20.  U.S. Surrogates available for the 2016v1 modeling platforms	138
Table 3-21.  Off-Network Mobile Source Surrogates	140
Table 3-22.  Spatial Surrogates for Oil and Gas Sources	140
Table 3-23. Selected 2016 CAP emissions by sector for U.S. Surrogates (short tons in 12US1)	141
Table 3-24.  Canadian Spatial Surrogates	144
Table 3-25. CAPs Allocated to Mexican and Canadian Spatial Surrogates (short tons in 36US3)	145
Table 3-26. Emission model species mappings for CMAQ and CAMx	148
Table 3-27. State tags for 2023fh1, 2028fh1 USSA modeling	150
Table 4-1.  Overview of projection methods for the 2023 and 2028 regional cases	153
Table 4-2.  EGU sector NOx emissions by State for the 2023 and 2028 regional cases	158
Table 4-3. Subset of CoST Packet Matching Hierarchy	161
Table 4-4. Summary of non-EGU stationary projections subsections	162
Table 4-5. Reductions from all facility/unit/stack-level closures in 2016v1	164
Table 4-6. Increase in total afdust PM2.5 emissions from projections in 2016v1	165
Table 4-7. National projection factors for livestock: 2016 to 2023 and 2028	166
Table 4-8. National projection factors for cmv_c1c2	166
Table 4-9. California projection factors for cmv_c1c2	167
Table 4-10. 2016-to-2023 and 2016-2028 CMV C3 projection factors outside of California	168
Table 4-11. 2016-to-2023 and 2016-2028 CMV C3 projection factors for California	168
Table 4-12.  Year 2014-2017 high-level summary of national oil and gas exploration activity	170
Table 4-13. EIA's 2019 Annual Energy Outlook (AEO) tables used to project industrial sources	171
Table 4-14. Projection factors for RWC	173
Table 4-15. Assumed retirement rates and new source emission factor ratios for NSPS rules	175
Table 4-16. Non-point (np_oilgas) SCCs in 2016v1 modeling platform where Oil and Gas NSPS controls applied	176
Table 4-17. Emissions reductions for np_oilgas sector due to application of Oil and Gas NSPS	177
Table 4-18. Point source SCCs in pt_oilgas sector where Oil and Gas NSPS controls were applied.	177
Table 4-19. VOC reductions (tons/year) for the pt_oilgas sector after application of the Oil and Gas NSPS CONTROL packet for both future years 2023 and 2028.	178
Table 4-20. SCCs and Engine Type in 2016v1 modeling platform where RICE NSPS controls applied for nonpt and ptnonipm sectors.	179
Table 4-21. Non-point Oil and Gas SCCs in 2016v1 modeling platform where RICE NSPS controls applied	179
Table 4-22. Nonpoint Emissions reductions after the application of the RICE NSPS	180
Table 4-23. Ptnonipm Emissions reductions after the application of the RICE NSPS	180
Table 4-24. Oil and Gas Emissions reductions for np_oilgas sector due to application of RICE NSPS	181
Table 4-25. Point source SCCs in pt_oilgas sector where RICE NSPS controls applied.	181
Table 4-26. Emissions reductions (tons/year) in pt_oilgas sector after the application of the RICE NSPS CONTROL packet for future years 2023 and 2028.	181
Table 4-27. Summary of fuel sulfur rule impacts on nonpoint SO2 emissions for 2023 and 2028	182
Table 4-28. Summary of fuel sulfur rule impacts on ptnonipm SO2 emissions for 2023 and 2028	182
Table 4-29. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls	182
Table 4-30. Ptnonipm SCCs in 2016v1 modeling platform where Natural Gas Turbines NSPS controls applied	184
Table 4-31. Ptnonipm emissions reductions after the application of the Natural Gas Turbines NSPS	184
Table 4-32. Point source SCCs in pt_oilgas sector where Natural Gas Turbines NSPS control applied.	184
Table 4-33. Emissions reductions (tons/year) for pt_oilgas after the application of the Natural Gas Turbines NSPS CONTROL packet for future years 2023 and 2028.	184
Table 4-34. Process Heaters NSPS analysis and 2016v1 new emission rates used to estimate controls	185
Table 4-35. Ptnonipm SCCs in 2016v1 modeling platform where Process Heaters NSPS controls applied.	185
Table 4-36. Ptnonipm emissions reductions after the application of the Process Heaters NSPS	186
Table 4-37. Point source SCCs in pt_oilgas sector where Process Heaters NSPS controls were applied	186
Table 4-38.  NOx emissions reductions (tons/year) in pt_oilgas sector after the application of the Process Heaters NSPS CONTROL packet for futures years 2023 and 2028.	187
Table 4-39. Summary of CISWI rule impacts on ptnonipm emissions for 2023 and 2028	188
Table 4-40. Summary of NSPS Subpart JA rule impacts on ptnonipm emissions for 2023 and 2028	188
Table 4-41. Factors used to Project 2016 VMT to 2023 and 2028	191
Table 4-42. Class I Line-haul Fuel Projections based on 2018 AEO Data	193
Table 4-43. Class I Line-haul Historic and Future Year Projected Emissions	194
Table 4-44. AEO growth rates for rail sub-groups	194
Table 5-1. National by-sector CAP emissions summaries for the 2016fh case, 12US1 grid (tons)	198
Table 5-2. National by-sector CAP emissions summaries for the 2023fh1 case, 12US1 grid (tons)	200
Table 5-3. National by-sector CAP emissions summaries for the 2028fh1 case, 12US1 grid (tons)	201
Table 5-4. National by-sector CAP emissions summaries for the 2016fh case, 36US3 grid (tons)	202
Table 5-5. National by-sector CAP emissions summaries for the 2023fh1 case, 36US3 grid (tons)	203
Table 5-6. National by-sector CAP emissions summaries for the 2028fh1 case, 36US3 grid (tons)	204

List of Figures
Figure 2-1.  Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and cumulative	34
Figure 2-2. "Bidi" modeling system used to compute 2016 Fertilizer Application emissions	38
Figure 2-3. Representative Counties in 2016v1	59
Figure 2-4. 2017NEI/2016 platform geographical extent (solid) and U.S. ECA (dashed)	61
Figure 2-5. 2016 US Railroad Traffic Density in Millions of Gross Tons per Route Mile (MGT)	69
Figure 2-6. Class I Railroads in the United States[5]	69
Figure 2-7. 2016-2017 Active Rail Yard Locations in the United States	72
Figure 2-8. Class II and III Railroads in the United States[5]	73
Figure 2-9. Amtrak Routes with Diesel-powered Passenger Trains	75
Figure 2-10. Processing flow for fire emission estimates in the 2016v1 inventory	87
Figure 2-11. Default fire type assignment by state and month in cases where a satellite detect is only source of fire information.	88
Figure 2-12.  Blue Sky Modeling Framework	88
Figure 2-13. Normbeis3 data flows	93
Figure 2-14. Tmpbeis3 data flow diagram.	94
Figure 3-1. Air quality modeling domains	99
Figure 3-2. Process of integrating NBAFM with VOC for use in VOC Speciation	104
Figure 3-3.  Profiles composited for the new PM gas combustion related sources	115
Figure 3-4.  Comparison of PM profiles used for Natural gas combustion related sources	116
Figure 3-5.  Eliminating unmeasured spikes in CEMS data	122
Figure 3-6.  Seasonal diurnal profiles for EGU emissions in a Virginia Region	123
Figure 3-7.  Diurnal Profile for all Airport SCCs	124
Figure 3-8.  Weekly profile for all Airport SCCs	125
Figure 3-9.  Monthly Profile for all Airport SCCs	125
Figure 3-10.  Alaska Seaplane Profile	126
Figure 3-11.  Example of RWC temporal allocation in 2007 using a 50 versus 60 ˚F threshold	127
Figure 3-12.  RWC diurnal temporal profile	128
Figure 3-13.  Data used to produce a diurnal profile for OHH, based on heat load (BTU/hr)	129
Figure 3-14.  Day-of-week temporal profiles for OHH and Recreational RWC	129
Figure 3-15.  Annual-to-month temporal profiles for OHH and recreational RWC	130
Figure 3-16.  Example of animal NH3 emissions temporal allocation approach, summed to daily emissions	131
Figure 3-17.  Example of temporal variability of NOX emissions	132
Figure 3-18.  Sample onroad diurnal profiles for Fulton County, GA	133
Figure 3-19.  Counties for which MOVES Speeds and Temporal Profiles could be Populated	134
Figure 3-20.  Example of Temporal Profiles for Combination Trucks	135
Figure 3-21.  Agricultural burning diurnal temporal profile	136
Figure 3-22.  Prescribed and Wildfire diurnal temporal profiles	137
Figure 4-1.  EIA Oil and Gas Supply Regions as of AEO2019	169

List of Appendices
 Appendix A: CB6 Assignment for New Species
 Appendix B: Profiles (other than onroad) that are new or revised in SPECIATE4.5 that were used in the 2014 v7.2 Platform
Appendix C: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
Acronyms
AADT
Annual average daily traffic
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
AERMOD
American Meteorological Society/Environmental Protection Agency Regulatory Model
AIS
Automated Identification System
APU
Auxiliary power unit
BEIS
Biogenic Emissions Inventory System
BELD
Biogenic Emissions Land use Database
BenMAP
Benefits Mapping and Analysis Program
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAMD
EPA's Clean Air Markets Division
CAMX
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CB6
Version 6 of the Carbon Bond mechanism
CBM
Coal-bed methane
CDB
County database (input to MOVES model)
CEMS
Continuous Emissions Monitoring System
CISWI
Commercial and Industrial Solid Waste Incinerators
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CNG
Compressed natural gas
CO
Carbon monoxide
CONUS
Continental United States
CoST
Control Strategy Tool
CRC
Coordinating Research Council
CSAPR
Cross-State Air Pollution Rule
E0, E10, E85
0%, 10% and 85% Ethanol blend gasoline, respectively
ECA
Emissions Control Area
ECCC
Environment and Climate Change Canada
EF
Emission Factor
EGU
Electric Generating Units
EIA
EIS
Energy Information Administration
Emissions Inventory System
EPA
Environmental Protection Agency
EMFAC
EMission FACtor (California's onroad mobile model)
EPIC
Environmental Policy Integrated Climate modeling system
FAA
Federal Aviation Administration
FCCS
Fuel Characteristic Classification System
FEST-C
Fertilizer Emission Scenario Tool for CMAQ
FF10
Flat File 2010
FINN
Fire Inventory from the National Center for Atmospheric Research
FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPM
Integrated Planning Model
LADCO
Lake Michigan Air Directors Consortium
LDV
Light-Duty Vehicle
LPG
Liquified Petroleum Gas
MACT
Maximum Achievable Control Technology
MARAMA
Mid-Atlantic Regional Air Management Association
MATS
Mercury and Air Toxics Standards
MCIP
Meteorology-Chemistry Interface Processor
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
MTBE
Methyl tert-butyl ether
MWC
Municipal waste combustor
MY
Model year
NAAQS
National Ambient Air Quality Standards
NAICS
North American Industry Classification System
NBAFM
Naphthalene, Benzene, Acetaldehyde, Formaldehyde and Methanol
NCAR
National Center for Atmospheric Research
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESCAUM
Northeast States for Coordinated Air Use Management
NH3
Ammonia
NLCD
National Land Cover Database
NOAA
National Oceanic and Atmospheric Administration
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NOX
Nitrogen oxides
NSPS
New Source Performance Standards
OHH
Outdoor Hydronic Heater
OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
ORD
EPA's Office of Research and Development
OSAT
Ozone Source Apportionment Technology
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
ppm
arts per million
ppmv
Parts per million by volume
PSAT
Particulate Matter Source Apportionment Technology
RACT
Reasonably Available Control Technology
RBT
Refinery to Bulk Terminal
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RWC
Residential Wood Combustion
RPD
Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RPH
Rate-per-hour (emission mode used in SMOKE-MOVES)
RPP
Rate-per-profile (emission mode used in SMOKE-MOVES)
RPV
Rate-per-vehicle (emission mode used in SMOKE-MOVES)
RVP
Reid Vapor Pressure
SCC
Source Classification Code
SMARTFIRE2
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2
SMOKE
Sparse Matrix Operator Kernel Emissions
SO2
Sulfur dioxide
SOA
Secondary Organic Aerosol
SIP
State Implementation Plan
SPDPRO
S/L/T
Hourly Speed Profiles for weekday versus weekend
state, local, and tribal
TAF
Terminal Area Forecast
TCEQ
Texas Commission on Environmental Quality
TOG
Total Organic Gas
TSD
Technical support document
USDA
VIIRS
United States Department of Agriculture
Visible Infrared Imaging Radiometer Suite
VOC
Volatile organic compounds
VMT
Vehicle miles traveled
VPOP
Vehicle Population
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
2014NEIv2
2014 National Emissions Inventory (NEI), version 2

Introduction
The U.S. Environmental Protection Agency (EPA), working in conjunction with the National Emissions Inventory Collaborative, developed an air quality modeling platform for criteria air pollutants to represent the years of 2016, 2023 and 2028.  The starting point for the 2016 inventory was the 2014 National Emissions Inventory (NEI), version 2 (2014NEIv2), although many inventory sectors were updated to represent the year 2016 through the incorporation of 2016-specific state and local data along with nationally-applied adjustment methods.  The year 2023 and year 2028 inventories were developed starting with the 2016 inventory using sector-specific methods as described below.  The inventories support several applications, including modeling in support of the Revised Cross State Air Pollution Rule (CSAPR) Update for the 2008 Ozone National Ambient Air Quality Standards (NAAQS).

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 data and techniques including the emission inventories, the ancillary data files, and the approaches used to transform inventories for use in air quality modeling.

The National Emissions Inventory Collaborative is a partnership between state emissions inventory staff, multi-jurisdictional organizations (MJOs), federal land managers (FLMs), EPA, and others to develop a North American air pollution emissions modeling platform with a base year of 2016 for use in air quality planning. The Collaborative planned for three versions of the 2016 platform: alpha, beta, and Version 1.0. This numbering format for this platform is different from previous EPA platforms which had the first number based on the version of the NEI, and the second number as a platform iteration for that NEI year (e.g., 7.3 where 7 represents 2014 NEI-based platforms, and 3 means the third iteration of the platform).  For the emissions modeling documented in this technical support document (TSD), the emissions values for most sectors are the same as those in the Inventory Collaborative 2016v1 Emissions Modeling Platform, available from http://views.cira.colostate.edu/wiki/wiki/10202.  In the file packages for this platform, the platform may sometimes be known as the 2016v7.3 platform. The specification sheets posted on the 2016v1 platform release page on the Wiki provide many details regarding the inventories and emissions modeling techniques in addition to those addressed in this TSD.

Some updates were made to the 2016v1 platform after the fall 2019 release that were included in the Revised CSAPR Update modeling, including some minor revisions to commercial marine vessel (CMV) emissions, and electric generating unit (EGU) emissions developed in January 2020.  Updates to 2016v1 to correct airport emissions and 2016 EGU processing made in June and July of 2020 were not included in the CSAPR Update modeling because the modeling was already complete by that time. The updated data and a description of them are available on the EPA FTP site ftp://newftp.epa.gov/air/emismod/2016/v1/postv1_updates/.

This 2016 emissions modeling platform includes all criteria air pollutants (CAPs) and precursors, and a group of hazardous air pollutants (HAPs).  The group of HAPs are those explicitly used by the chemical mechanism in the Community Multiscale Air Quality (CMAQ) model (Appel et al., 2018) for ozone/particulate matter (PM): chlorine (Cl), hydrogen chloride (HCl), benzene, acetaldehyde, formaldehyde, methanol, naphthalene.  The modeling domain includes the lower 48 states and parts of Canada and Mexico.  The modeling cases for this platform were developed for the Comprehensive Air Quality Model with Extensions (CAMx).  However, the emissions modeling process first prepares outputs in the format used by CMAQ, after which those emissions data are converted to the formats needed by CAMx.

The 2016 platform used in this study consists of a 2016 base case, a 2023 case, and a 2028 case with the abbreviations 2016fh_16j, 2023fh1_16j, and 2028fh1_16j, respectively. Additional cases that included source apportionment by state and in some cases inventory sector were also developed. This platform accounts for atmospheric chemistry and transport within a state-of-the-art photochemical grid model. In the case abbreviation 2016fh_16j, 2016 is the year represented by the emissions; the "f" represents the base year emissions modeling platform iteration, which here shows that it is 2014 NEI-based (whereas for 2011 NEI-based platforms, this letter was "e"); and the "h" stands for the eighth configuration of emissions modeled for a 2014-NEI based modeling platform.  The cases named 2023fh1_16j and 2028fh1_16j are the same as the original 2023 and 2028 future year cases, except that they include EGU emissions that were developed in January 2020 and slightly updated commercial marine vessel emissions.

The 2016v1 emissions modeling platform includes point sources, nonpoint sources, commercial marine vessels (CMV), onroad and nonroad mobile sources, and fires for the U.S., Canada, and Mexico.  Some platform categories use more disaggregated data than are made available in the NEI. For example, in the platform, onroad mobile source emissions are represented as hourly emissions by vehicle type, fuel type process and road type while the NEI emissions are aggregated to vehicle type/fuel type totals and annual temporal resolution.  Temporal, spatial and other changes in emissions between the NEI and the emissions input into the platform are described primarily in the platform specification sheets, although a full NEI was not developed for the year 2016 because only point sources above a certain potential to emit must be submitted for years between the full triennial NEI years (e.g., 2014, 2017, 2020). Emissions from Canada and Mexico are used for the modeling platform but are not part of the NEI.

The primary emissions modeling tool used to create the air quality model-ready emissions was the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (http://www.smoke-model.org/), version 4.7 (SMOKE 4.7) with some updates.  Emissions files were created for a 36-km national grid and for a 12-km national grid, both of which include the contiguous states and parts of Canada and Mexico as shown in Figure 3-1.

The gridded meteorological model used to provide input data for the emissions modeling was developed using the Weather Research and Forecasting Model (WRF, https://ral.ucar.edu/solutions/products/weather-research-and-forecasting-model-wrf ) version 3.8, 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 2016 over a domain covering the continental U.S. at a 12km resolution with 35 vertical layers.  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 "16j."  The full case name includes this abbreviation following the emissions portion of the case name to fully specify the name of the case as "2016fh_16j."

This document contains five sections and several appendices.  Section 2 describes the 2016 and 2028 inventories input to SMOKE.  Section 3 describes the emissions modeling and the ancillary files used with the emission inventories.  Methods to develop future year emissions are described in Section 4. Data summaries are provided in Section 5.  Section 6 provides references.  The Appendices provide additional details about specific technical methods or data.
Emissions Inventories and Approaches
This section summarizes the emissions data that make up the 2016v1 platform.  This section provides details about the data contained in each of the platform sectors for the base year and the future year.
The original starting point for the emission inventories was the 2014NEIv2 although emissions for most sectors have been updated to better represent the year 2016. Documentation for the 2014NEIv2, including a TSD, is available at https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-technical-support-document-tsd Documentation for each 2016v1 emissions sector in the form of specification sheets is available on the 2016v1 page of Inventory Collaborative Wiki (http://views.cira.colostate.edu/wiki/wiki/10202). In addition to the NEI-based data for the broad categories of point, nonpoint, onroad, nonroad, and events (i.e., fires), emissions from the Canadian and Mexican inventories and several other non-NEI data sources are included in the 2016 platform.

The triennial NEI data for CAPs are largely compiled from data submitted by state, local and tribal (S/L/T) air agencies.  HAP emissions data are also from the S/L/T agencies, 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 quality assurance checks to help improve data quality, and also supports tracking release point (e.g., stack) coordinates separately from facility coordinates.  The EPA collaborates extensively with S/L/T agencies to ensure a high quality of data in the NEI.  Using the 2014NEIv2 as a starting point, the National Inventory Collaborative worked to develop a modeling platform that more closely represents the year 2016. All emissions modeling sectors were modified in some way to better represent the year 2016 for the 2016v1 platform.

The point source emission inventories for the platform include partially updated emissions to represent 2016 based on state-submitted data and adjustments to much of the remaining 2014 data to better represent 2016. Agricultural and wildland fire emissions represent the year 2016. Most nonpoint source sectors started with 2014NEIv2 emissions and were adjusted to better represent the year 2016. Fertilizer emissions, nonpoint oil and gas emissions, and onroad and nonroad mobile source emissions represent the year 2016. For CMV emissions, emissions were developed based on 2017 NEI CMV emissions and the sulfur dioxide (SO2) emissions reflect rules that reduced sulfur emissions for CMV that took effect in the year 2015. For fertilizer ammonia emissions, a 2016-specific emissions inventory is used in this platform. Nonpoint oil and gas emissions were developed using 2016-specific data for oil and gas wells and their 2016 production levels.

Onroad and nonroad mobile source emissions were developed using the Motor Vehicle Emission Simulator (MOVES).  Onroad emissions for the platform were developed based on emissions factors output from MOVES2014b for the year 2016, run with inputs derived from the 2014NEIv2 including activity data (e.g., vehicle miles traveled and vehicle populations) provided by state and local agencies or otherwise projected to the year 2016.  MOVES2014b was also used to generate nonroad emissions because it included important updates related to nonroad engine population growth rates and spatial allocation factors.

For the purposes of preparing the air quality model-ready emissions, emissions from the five NEI data categories are 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 the SMOKE programs independently from the other sectors except for the final merge (Mrggrid).  The final merge program combines the sector-specific gridded, speciated, hourly emissions together to create CMAQ-ready emission inputs. For studies that use CAMx, these CMAQ-ready emissions inputs are converted into the file formats needed by CAMx.

Table 2-1 presents an overview the sectors in the 2016 platform and how they generally relate to the 2014NEIv2 as their starting point.  The platform sector abbreviations are provided in italics.  These abbreviations are used in the SMOKE modeling scripts, inventory file names, and throughout the remainder of this document. Through the Collaborative workgroups, state and local agencies provided data used in the development of most sectors.
Table 2-1.  Platform sectors for the 2016 emissions modeling case
                         Platform Sector: abbreviation
                               NEI Data Category
             Description and resolution of the data input to SMOKE
EGU units:
Ptegu
                                     Point
Point source electric generating units (EGUs) for 2016 from the Emissions Inventory System (EIS), based on 2014NEIv2 with most sources updated to 2016. Includes some specific S/L/T updates. The inventory emissions are replaced with hourly 2016 Continuous Emissions Monitoring System (CEMS) values for nitrogen oxides (NOX) and SO2 for any units that are matched to the NEI, and other pollutants for matched units are scaled from the 2016 point inventory using CEMS heat input.  Emissions for all sources not matched to CEMS data come from the raw inventory. Annual resolution for sources not matched to CEMS data, hourly for CEMS sources.
Point source oil and gas:
pt_oilgas
                                     Point
Point sources for 2016 including S/L/T updates for oil and gas production and related processes based on facilities with the following NAICS: 2111, 21111, 211111, 211112 (Oil and Gas Extraction); 213111 (Drilling Oil and Gas Wells); 213112 (Support Activities for Oil and Gas Operations); 2212, 22121, 221210 (Natural Gas Distribution); 48611, 486110 (Pipeline Transportation of Crude Oil); 4862, 48621, 486210 (Pipeline Transportation of Natural Gas).  Includes offshore oil and gas platforms in the Gulf of Mexico (FIPS=85). Oil and gas point sources that were not already updated to year 2016 in the baseline inventory were projected from 2014 to 2016. Annual resolution.
Aircraft and ground support equipment: airports
                                     Point
Emissions from aircraft up to 3,000 ft elevation and emissions from ground support equipment based on 2017 NEI data. Note that these emissions were found to be overestimated in June 2020.
Remaining non-EGU point:
ptnonipm
                                     Point
All 2016 point source inventory records not matched to the ptegu, airports, or pt_oilgas sectors, including updates submitted by state and local agencies. Year 2016 rail yard emissions were developed by the rail workgroup.  Annual resolution.
Agricultural:
ag
                                   Nonpoint
Nonpoint livestock and fertilizer application emissions.  Livestock includes ammonia and other pollutants (except PM2.5) and was backcasted from a draft version of 2017NEI based on animal population data from the U.S. Department of Agriculture (USDA) National Agriculture Statistics Service Quick Stats, where available.  Fertilizer includes only ammonia and is estimated for 2016 using the FEST-C model. County and monthly resolution.
Agricultural fires with point resolution: ptagfire
                                   Nonpoint
2016 agricultural fire sources based on EPA-developed data with state updates, represented as point source day-specific emissions. They are in the nonpoint NEI data category, but in the platform, they are treated as point sources.  Mostly at daily resolution with some state-submitted data at monthly resolution.
Area fugitive dust:
afdust
                                   Nonpoint
PM10 and PM2.5 fugitive dust sources from the 2014NEIv2 nonpoint inventory with paved road dust grown to 2016 levels; including building construction, road construction, agricultural dust, and road dust.  The NEI emissions are reduced during modeling according to a transport fraction (newly computed for the 2016 beta platform) and a meteorology-based (precipitation and snow/ice cover) zero-out.  Afdust emissions from the portion of Southeast Alaska inside the 36US3 domain are processed in a separate sector called `afdust_ak'. County and annual resolution.
Biogenic:
beis
                                   Nonpoint
Year 2016, hour-specific, grid cell-specific emissions generated from the BEIS3.61 model within SMOKE, including emissions in Canada and Mexico using BELD v4.1 "water fix" land use data (including improved treatment of water grid cells).
Category 1, 2 CMV:
cmv_c1c2
                                   Nonpoint
Category 1 and category 2 (C1C2) commercial marine vessel (CMV) emissions sources backcast to 2016 from the 2017NEI using a multiplier of 0.98.emissions. Includes C1C2 emissions in U.S. state and Federal waters, and also all non-U.S. C1C2 emissions including those in Canadian waters. Gridded and hourly resolution.
Category 3 CMV:
cmv_c3
                                   Nonpoint
Category 3 (C3) CMV emissions converted to point sources based on the center of the grid cells. Includes C3 emissions in U.S. state and Federal waters, and also all non-U.S. C3 emissions including those in Canadian waters. Emissions are backcast to 2016 from 2017NEI emissions based on factors derived from U.S. Army Corps of Engineers Entrance and Clearance data and information about the ships entering the ports. Gridded and hourly resolution.
Locomotives :
rail
                                   Nonpoint
Line haul rail locomotives emissions developed by the rail workgroup based on 2016 activity and emission factors.  Includes freight and commuter rail emissions and incorporates state and local feedback.  County and annual resolution.
Remaining nonpoint:
nonpt
                                   Nonpoint
2014NEIv2 nonpoint sources not included in other platform sectors with sources proportional to human population activity data grown to year 2016; incorporates state and local feedback. County and annual resolution.
Nonpoint source oil and gas:
np_oilgas
                                   Nonpoint
2016 nonpoint oil and gas emissions output from the NEI oil and gas tool along with state and local feedback. County and annual resolution.
Residential Wood Combustion:
rwc
                                   Nonpoint
2014NEIv2 nonpoint sources from residential wood combustion (RWC) processes projected to the year 2016.  County and annual resolution.
Nonroad:
nonroad
                                    Nonroad
2016 nonroad equipment emissions developed with the MOVES2014b model which incorporates updated equipment growth rates.  MOVES was used for all states except California and Texas, which submitted emissions.  County and monthly resolution.
Onroad:
onroad
                                    Onroad
2016 onroad mobile source gasoline and diesel vehicles from moving and non-moving vehicles that drive on roads, along with vehicle refueling.  Includes the following modes: exhaust, extended idle, auxiliary power units, evaporative, permeation, refueling, and brake and tire wear.  For all states except California, developed using winter and summer MOVES emissions tables produced by MOVES2014b coupled with activity data projected to year 2016 or provided by S/L/T agencies.  SMOKE-MOVES was used to compute emissions from the emission factors and activity data.  Onroad emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands were computed using the same method as the continental U.S.,but are part of the onroad_nonconus sector.
Onroad California:
onroad_ca_adj
                                    Onroad
2016 California-provided CAP onroad mobile source gasoline and diesel vehicles based on the EMFAC model, which ere gridded and temporalized using MOVES2014b results.  Volatile organic compound (VOC) HAP emissions derived from California-provided VOC emissions and MOVES-based speciation.
Point source fires- ptfire
                                    Events
Point source day-specific wildfires and prescribed fires for 2016 computed using Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework (Sullivan, 2008 and Raffuse, 2007) for both flaming and smoldering processes (i.e., SCCs 281XXXX002). Smoldering is forced into layer 1 (by adjusting heat flux). Incorporates state inputs.  Daily resolution.
Non-US. Fires:
ptfire_othna
                                      N/A
Point source day-specific wildfires and prescribed fires for 2016 provided by Environment Canada with data for missing months, and for Mexico and Central America, filled in using fires from the Fire Inventory (FINN) from National Center for Atmospheric Research (NCAR) fires (NCAR, 2016 and Wiedinmyer, C., 2011).   Daily resolution.
Other Area Fugitive dust sources not from the NEI:
othafdust
                                      N/A
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities, from Environment and Climate Change Canada (ECCC) 2015 emission inventory, except that construction dust emissions were reduced to levels compatible with their 2010 inventory.  A transport fraction adjustment is applied along with a meteorology-based (precipitation and snow/ice cover) zero-out. County and annual resolution.
Other Point Fugitive dust sources not from the NEI:
othptdust
                                      N/A
Fugitive dust sources of particulate matter emissions from land tilling from agricultural activities, ECCC 2015 emission inventory, but wind erosion emissions were removed.  A transport fraction adjustment is applied along with a meteorology-based (precipitation and snow/ice cover) zero-out. Data were originally provided on a rotated 10-km grid for beta, but were smoothed so as to avoid the artifact of grid lines in the processed emissions.  Monthly resolution.
Other point sources not from the NEI:
othpt
                                      N/A
Point sources from the ECCC 2015 emission inventory, including agricultural ammonia, along with emissions from Mexico's 2008 inventory projected to 2014 and 2018 and then interpolated to 2016. Agricultural data were originally provided on a rotated 10-km grid for beta, but were smoothed so as to avoid the artifact of grid lines in the processed emissions.  Monthly resolution for Canada agricultural and airport emissions, annual resolution for the remainder of Canada and all of Mexico.
Other non-NEI nonpoint and nonroad:
othar
                                      N/A
Year 2015 Canada (province or sub-province resolution) emissions from the ECCC inventory: monthly for nonroad sources; annual for rail and other nonpoint Canada sectors.  Year 2016 Mexico (municipio resolution) emissions, interpolated from 2014 and 2018 inventories that were projected from their 2008 inventory: annual nonpoint and nonroad mobile inventories.
Other non-NEI onroad sources:
onroad_can
                                      N/A
Monthly year 2015 Canada (province resolution or sub-province resolution, depending on the province) from the ECCC onroad mobile inventory.
Other non-NEI onroad sources:
onroad_mex
                                      N/A
Monthly year 2016 Mexico (municipio resolution) onroad mobile inventory based on MOVES-Mexico runs for 2014 and 2018 then interpolated to 2016.

Other natural emissions are also merged in with the above sectors: ocean chlorine and sea salt. The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cl2) concentrations in oceanic air masses (Bullock and Brehme, 2002).  In CMAQ, the species name is "CL2".  The sea salt emissions were developed with version 4.1 of the OCEANIC pre-processor that comes with the CAMx model. The preprocessor estimates time/space-varying emissions of aerosol sodium, chloride and sulfate; gas-phase chlorine and bromine associated with sea salt; gaseous halo-methanes; and dimethyl sulfide (DMS). These additional oceanic emissions are incorporated into the final model-ready emissions files for CAMx.

The emission inventories in SMOKE input formats for the platform are available from EPA's Air Emissions Modeling website: https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms, under the section entitled "2016v1 Platform".  The platform "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 2016 platform.  The types of reports include state summaries of inventory pollutants and model species by modeling platform sector and county annual totals by modeling platform sector. Additional types of data including outputs from SMOKE and inputs to CAMx are available from the Intermountain West Data Warehouse.
2016 point sources (ptegu, pt_oilgas, ptnonipm, airports)
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 as units such as boilers, reactors, spray booths, kilns, etc.  A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas).  This section describes NEI point sources within the contiguous U.S. and the offshore oil platforms which are processed by SMOKE as point source inventories.  A full NEI is compiled every three years including 2011, 2014 and 2017. In the intervening years, emissions information about point sources that exceed certain potential to emit threshold are required to be submitted to the EIS that is used to compile the NEI.  A comprehensive description of how EGU emissions were characterized and estimated in the 2014 NEI is located in Section 3.4 in the 2014NEIv2 TSD. The methods for emissions estimation are similar for the interim year of 2016, but there is no TSD available specific to the 2016 point source submissions to EIS.  Additional information on state submissions through the collaborative process are available in the collaborative specification sheets.

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/4.7/html/ch08s02s08.html).

For the 2016v1 platform, the export of point source emissions, including stack parameters and locations from EIS, was done on June 12, 2018.  The flat file was modified to remove sources without specific locations (i.e., their FIPS code ends in 777).  Then the point source FF10 was divided into four NEI-based platform point source sectors: the EGU sector (ptegu), point source oil and gas extraction-related emissions (pt_oilgas), airport emissions were put into the airports sector, and the remaining non-EGU sector also called the non-IPM (ptnonipm) sector. The split was done at the unit level for ptegu and facility level for pt_oilgas such that a facility may have units and processes in both ptnonipm and ptegu, but cannot be in both pt_oilgas and any other point sector. Additional information on updates made through the collaborative process is available in the collaborative specification sheets.

The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal processing and future-year projection techniques.  The oil and gas sector emissions (pt_oilgas) were processed separately for summary tracking purposes and distinct future-year projection techniques from the remaining non-EGU emissions (ptnonipm).

The inventory pollutants processed through SMOKE for all point source sectors were:  carbon monoxide (CO), NOX, VOC, SO2, ammonia (NH3), particles less than 10 microns in diameter (PM10), and particles less than 2.5 microns in diameter (PM2.5), and all of the air toxics listed in Table 3-3.  The Naphthalene, Benzene, Acetaldehyde, Formaldehyde, and Methanol (NBAFM) species are explicit in the CB6-CMAQ chemical mechanism and are taken from the HAP emissions in the flat file (if present for a source) as opposed to using emissions generated through VOC speciation, as is normally done for non-toxics modeling applications.  To prevent double counting of mass, NBAFM species are removed from VOC speciation profiles, thus resulting in speciation profiles that may sum to less than 1.  This is called the "no-integrate" VOC speciation case and is discussed in detail in Section <IP_ADDRESS>.  The resulting VOC in the modeling system may be higher or lower than the VOC emissions in the NEI; they would only be the same if the HAP inventory and speciation profiles were exactly consistent.  For HAPs other than those in NBAFM, there is no concern for double-counting since CMAQ handles these outside the CB6 mechanism.

The ptnonipm and pt_oilgas sector emissions were provided to SMOKE as annual emissions.  For those ptegu sources with CEMS data that could be matched to the point inventory from EIS, hourly CEMS NOX and SO2 emissions were used rather than the annual total NEI emissions. For all other pollutants at matched units, the annual emissions were used as-is from the NEI, but were allocated to hourly values using heat input from the 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. For non-CEMS units other than municipal waste combustors and cogeneration units, IPM region- and pollutant-specific diurnal profiles were applied to create hourly emissions.
EGU sector (ptegu)
	The ptegu sector contains emissions from EGUs in the 2016 NEI point inventory that could be matched to units found in the National Electric Energy Data System (NEEDS) v6 database.  The matching was prioritized according to the amount of the emissions produced by the source.  In the SMOKE point flat file, emission records for sources that have been matched to the NEEDS database have a value filled into the IPM_YN column based on the matches stored within EIS. The 2016 NEI point inventory consists of data submitted by S/L/T agencies and EPA to the EIS for Type A (i.e., large) point sources. Those EGU sources in the 2014 NEIv2 inventory that were not submitted or updated for 2016 and not identified as retired were retained. The retained 2014 NEIv2 EGUs in CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV were projected from 2014 to 2016 values using factors provided by the Mid-Atlantic Regional Air Management Association  (MARAMA).

Higher generation capacity units in the ptegu sector are matched to 2016 CEMS data from EPA's Clean Air Markets Division (CAMD) via ORIS facility codes and boiler ID.  For the matched units, SMOKE replaces the 2016 emissions of NOX and SO2 with the CEMS emissions, thereby ignoring the annual values specified in the NEI.  For other pollutants at matched units, the hourly CEMS heat input data are used to allocate the NEI annual emissions to hourly values.  All stack parameters, stack locations, and Source Classification Codes (SCC) for these sources come from the NEI or updates provided by data submitters outside of EIS.  Because these attributes are obtained from the NEI, the chemical speciation of VOC and PM2.5 for the sources is selected based on the SCC or in some cases, based on unit-specific data.  If CEMS data exists for a unit, but the unit is not matched to the NEI, the CEMS data for that unit is not used in the modeling platform.  However, if the source exists in the NEI and is not matched to a CEMS unit, the emissions from that source are still modeled using the annual emission value in the NEI temporally allocated to hourly values.  The EGU flat file inventory is split into a flat file with CEMS matches and a flat file without CEMS matches to support analysis and temporalization.

In the SMOKE point flat file, emission records for point sources matched to CEMS data have values filled into the ORIS_FACILITY_CODE and ORIS_BOILER_ID columns.  The CEMS data in SMOKE-ready format is available at http://ampd.epa.gov/ampd/ near the bottom of the "Prepackaged Data" tab.  Many smaller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can be matched to the NEI due to inconsistencies in the way a unit is defined between the NEI and CEMS datasets, or due to uncertainties in source identification such as inconsistent plant names in the two data systems.  Also, the NEEDS database of units modeled by IPM includes many smaller emitting EGUs that do not have CEMS.  Therefore, there will be more units in the NEEDS database than have CEMS data.  The temporal allocation of EGU units matched to CEMS is based on the CEMS data, whereas regional profiles are used for most of the remaining units.  More detail can be found in Section 3.3.2.

Some EIS units match to multiple CAMD units based on cross-reference information in the EIS alternate identifier table. The multiple matches are used to take advantage of hourly CEMS data when a CAMD unit specific entry is not available in the inventory. Where a multiple match is made the EIS unit is split and the ORIS facility and boiler IDs are replaced with the individual CAMD unit IDs. The split EIS unit NOX and SO2 emissions annual emissions are replaced with the sum of CEMS values for that respective unit. All other pollutants are scaled from the EIS unit into the split CAMD unit using the fraction of annual heat input from the CAMD unit as part of the entire EIS unit. The NEEDS ID in the "ipm_yn" column of the flat file is updated with a "_M_" between the facility and boiler identifiers to signify that the EIS unit had multiple CEMS matches. The inventory records with multiple matches had the EIS unit identifiers appended with the ORIS boiler identifier to distinguish each CEMS record in SMOKE.

For sources not matched to CEMS data, except for municipal waste combustors (MWCs) waste-to-energy and cogeneration units, daily emissions were computed from the NEI annual emissions using average CEMS data profiles specific to fuel type, pollutant, 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.  See Section 3.3.2 for more details on the temporal allocation approach for ptegu sources.  MWC and cogeneration units were specified to use uniform temporal allocation such that the emissions are allocated to constant levels for every hour of the year. These sources do not use hourly CEMs, and instead use a PTDAY file with the same emissions for each day, combined with a uniform hourly temporal profile applied by SMOKE.
Point source oil and gas sector (pt_oilgas)
The pt_oilgas sector consists of point source oil and gas emissions in United States, primarily pipeline-transportation and some upstream exploration and production. Sources in the pt_oilgas sector consist of sources which are not electricity generating units (EGUs) and which have a North American Industry Classification System (NAICS) code corresponding to oil and gas exploration, production, pipeline-transportation or distribution. The pt_oilgas sector was separated from the ptnonipm sector by selecting sources with specific NAICS codes shown in Table 2-2.  The use of NAICS to separate out the point oil and gas emissions forces all sources within a facility to be in this sector, as opposed to ptegu where sources within a facility can be split between ptnonipm and ptegu sectors.

Table 2-2. Point source oil and gas sector NAICS Codes
NAICS
Type of point source
NAICS description
2111, 21111
Production
Oil and Gas Extraction
211111
Production
Crude Petroleum and Natural Gas Extraction
211112
Production
Natural Gas Liquid Extraction
213111
Production
Drilling Oil and Gas Wells
213112
Support
Support Activities for Oil and Gas Operations
2212, 22121, 221210
Distribution
Natural Gas Distribution
4862, 48621, 486210
Transmission
Pipeline Transportation of Natural Gas
48611, 486110
Transmission
Pipeline Transportation of Crude Oil

The starting point for the 2016v1 emissions platform pt_oilgas inventory was the 2016 point source NEI. The 2016 NEI includes data submitted by S/L/T  agencies and EPA to the EIS for Type A (i.e., large) point sources. Point sources in the 2014 NEIv2 not submitted for 2016 were pulled forward from the 2014 NEIv2 unless they had been marked as shut down.  For the federally-owned offshore point inventory of oil and gas platforms, a 2014 inventory was developed by the U.S. Department of the Interior, Bureau of Ocean and Energy Management, Regulation, and Enforcement (BOEM).

The 2016 pt_oilgas inventory includes sources with updated data for 2016 and sources carried forward from the 2014NEIv2 point inventory. Each type of source can be identified based on the calc_year field in the flat file 2010 (FF10) formatted inventory files, which is set to either 2016 or 2014. The pt_oilgas inventory was split into two components: one for 2016 sources, and one for 2014 sources. The 2016 sources were used in 2016v1 platform without further modification.  Updates were made to selected West Virginia Type B facilities based on comments from the state.

For pt_oilgas emissions that were carried forward from the 2014NEIv2, the emissions were projected to represent the year 2016. Each state/ SCC/NAICS combination in the inventory was classified as either an oil source, a natural gas source, a combination of oil and gas, or designated as a "no growth" source. Growth factors were based on historical state production data from the Energy Information Administration (EIA) and are listed in Table 2. National 2016 pt_oilgas emissions before and after application of 2014-to-2016 projections are shown in Table 3. The historical production data for years 2014 and 2016 for oil and natural gas were taken from the following websites:

:: https://www.eia.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_a.htm (Crude production)
:: http://www.eia.gov/dnav/ng/ng_sum_lsum_a_epg0_fgw_mmcf_a.htm (Natural gas production)

The "no growth" sources include all offshore and tribal land emissions, and all emissions with a NAICS code associated with distribution, transportation, or support activities. As there were no 2015 production data in the EIA for Idaho, no growth was assumed for this state; the only pt_oilgas sources in Idaho were pipeline transportation related. Maryland and Oregon had no oil production data on the EIA website. The factors provided in Table 2-8 were applied to sources with NAICS = 2111, 21111, 211111, 211112, and 213111 and with production-related SCC processes.  Table 2-3 provides a national summary of emissions before and after this 2 year projection for these sources in the pt_oilgas sector.  Table 2-4 shows the national emissions for pt_oilgas following the projection to 2016.
Table 2-3. 2014NEIv2-to-2016 projection factors for pt_oilgas sector for 2016v1 inventory
State
                              Natural Gas growth
                                  Oil growth
                          Combination gas/oil growth
Alabama
                                     -9.0%
                                    -17.5%
                                    -13.2%
Alaska
                                     1.9%
                                     -1.1%
                                     0.4%
Arizona
                                    -55.7%
                                    -85.7%
                                    -70.7%
Arkansas
                                    -26.7%
                                     13.6%
                                     -6.6%
California
                                    -14.2%
                                     -9.1%
                                    -11.7%
Colorado
                                     3.5%
                                     22.0%
                                     12.8%
Florida
                                     8.0%
                                    -13.2%
                                     -2.6%
Idaho
                                     0.0%
                                     0.0%
                                     0.0%
Illinois
                                     13.2%
                                     -9.5%
                                     1.8%
Indiana
                                     -6.2%
                                    -27.5%
                                    -16.9%
Kansas
                                    -15.0%
                                    -23.4%
                                    -19.2%
Kentucky
                                     -1.6%
                                    -23.1%
                                    -12.4%
Louisiana
                                    -11.0%
                                    -17.4%
                                    -14.2%
Maryland
                                     70.0%
                                      N/A
                                      N/A
Michigan
                                    -12.6%
                                    -23.4%
                                    -18.0%
Mississippi
                                    -10.9%
                                    -16.3%
                                    -13.6%
Missouri
                                    -66.7%
                                    -37.2%
                                    -52.0%
Montana
                                    -11.9%
                                    -22.5%
                                    -17.2%
Nebraska
                                     27.3%
                                    -25.0%
                                     1.2%
Nevada
                                     0.0%
                                    -12.3%
                                     -6.2%
New Mexico
                                     1.4%
                                     17.4%
                                     9.4%
New York
                                    -33.4%
                                    -36.8%
                                    -35.1%
North Dakota
                                     31.4%
                                     -4.3%
                                     13.6%
Ohio
                                    181.0%
                                     44.4%
                                    112.7%
Oklahoma
                                     5.9%
                                     6.9%
                                     6.4%
Oregon
                                    -18.0%
                                      N/A
                                      N/A
Pennsylvania
                                     24.8%
                                     -7.9%
                                     8.5%
South Dakota
                                    -33.9%
                                    -21.7%
                                    -27.8%
Tennessee
                                    -31.9%
                                    -22.1%
                                    -27.0%
Texas
                                     -6.1%
                                     1.0%
                                     -2.6%
Utah
                                    -19.8%
                                    -25.4%
                                    -22.6%
Virginia
                                    -10.0%
                                    -50.0%
                                    -30.0%
West Virginia
                                     28.9%
                                     0.7%
                                     14.8%
Wyoming
                                     -7.5%
                                     -4.7%
                                     -6.1%

Table 2-4. 2016fh pt_oilgas national emissions (excluding offshore) before and after 2014-to-2016 projections (tons/year)
Pollutant
                              Before projections
                               After projections
                             % change 2014 to 2016
CO
                                    175,929
                                    177,690
                                     1.0%
NH3
                                     4,347
                                     4,338
                                     -0.2%
NOX
                                    377,517
                                    379,866
                                     0.6%
PM10-PRI
                                    12,630
                                    12,397
                                     -1.8%
PM25-PRI
                                    11,545
                                    11,286
                                     -2.2%
SO2
                                    35,236
                                    34,881
                                     -1.0%
VOC
                                    127,242
                                    129,253
                                     1.6%

The state of Pennsylvania provided new emissions data for natural gas transmission sources for year 2016. The PA point source data replaced the emissions used in 2016beta. Table 2-5 illustrates the change in emissions with this update.
Table 2-5. Pennsylvania emissions changes for natural gas transmission sources (tons/year).
                                     State
                                  State FIPS
                                     NAICS
                                   Pollutant
                                   2016 beta
                                    2016 v1
                                2016 v1 - beta
                                 Pennsylvania
                                      42
                                    486210
                                      CO
                                     2,787
                                     2,385
                                      403
                                 Pennsylvania
                                      42
                                    486210
                                      NOX
                                     5,737
                                     5,577
                                      160
                                 Pennsylvania
                                      42
                                    486210
                                   PM10-PRI
                                      400
                                      227
                                      173
                                 Pennsylvania
                                      42
                                    486210
                                   PM25-PRI
                                      399
                                      209
                                      191
                                 Pennsylvania
                                      42
                                    486210
                                      SO2
                                      30
                                      33
                                      -3
                                 Pennsylvania
                                      42
                                    486210
                                      VOC
                                     1,221
                                     1,149
                                      71

Non-IPM sector (ptnonipm)
With minor exceptions, the ptnonipm sector contains point sources that are not in the airport, 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 ptnonipm emissions in the 2016v1 platform have been updated from the 2016 NEI point inventory with the following changes.

Non-IPM Projection from 2014 to 2016 inside MARAMA region

2014-to-2016 projection packets for all nonpoint sources were provided by MARAMA for the following states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, NC, PA, RI, VT, VA, and WV.

New Jersey provided their own projection factors for projection from 2014 to 2016 which were mostly the same as those provided by MARAMA, except for three SCCs with differences (SCCs:<PHONE_NUMBER>,<PHONE_NUMBER>,<PHONE_NUMBER>). For those three SCCs, the projection factors provided by New Jersey were used instead of the MARAMA factors.

Non-IPM Projection from 2014 to 2016 outside MARAMA region

In areas outside of the MARAMA states, historical census population, sometimes by county and sometimes by state, was used to project select nonpt sources from the 2014NEIv2 to 2016v1 platform. The population data was downloaded from the US Census Bureau. Specifically, the "Population, Population Change, and Estimated Components of Population Change: April 1, 2010 to July 1, 2017" file (https://www2.census.gov/programs-surveys/popest/datasets/2010-2017/counties/totals/co-est2017-alldata.csv). A ratio of 2016 population to 2014 population was used to create a growth factor that was applied to the 2014NEIv2 emissions with SCCs matching the population-based SCCs listed in Table 2-6 Positive growth factors (from increasing population) were not capped, but negative growth factors (from decreasing population) were flatlined for no growth.
Table 2-6. SCCs for Census-based growth from 2014 to 2016
SCC
Tier 1 Description
Tier 2 Description
Tier 3
Description
Tier 4
Description
2302002100
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Charbroiling
Conveyorized Charbroiling
2302002200
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Charbroiling
Under-fired Charbroiling
2302003000
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Total
2302003100
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Flat Griddle Frying
2302003200
Industrial Processes
Food and Kindred Products: SIC 20
Commercial Deep Fat Frying
Clamshell Griddle Frying
2401001000
Solvent Utilization
Surface Coating
Architectural Coatings
Total: All Solvent Types
2401002000
Solvent Utilization
Surface Coating
Architectural Coatings - Solvent-based
Total: All Solvent Types
2401003000
Solvent Utilization
Surface Coating
Architectural Coatings - Water-based
Total: All Solvent Types
2401100000
Solvent Utilization
Surface Coating
Industrial Maintenance Coatings
Total: All Solvent Types
2401200000
Solvent Utilization
Surface Coating
Other Special Purpose Coatings
Total: All Solvent Types
2425000000
Solvent Utilization
Graphic Arts
All Processes
Total: All Solvent Types
2425010000
Solvent Utilization
Graphic Arts
Lithography
Total: All Solvent Types
2425020000
Solvent Utilization
Graphic Arts
Letterpress
Total: All Solvent Types
2425030000
Solvent Utilization
Graphic Arts
Rotogravure
Total: All Solvent Types
2425040000
Solvent Utilization
Graphic Arts
Flexography
Total: All Solvent Types
2440020000
Solvent Utilization
Miscellaneous Industrial
Adhesive (Industrial) Application
Total: All Solvent Types
2460000000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Processes
Total: All Solvent Types
2460100000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Personal Care Products
Total: All Solvent Types
2460200000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Household Products
Total: All Solvent Types
2460400000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Automotive Aftermarket Products
Total: All Solvent Types
2460500000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Coatings and Related Products
Total: All Solvent Types
2460600000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All Adhesives and Sealants
Total: All Solvent Types
2460800000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
All FIFRA Related Products
Total: All Solvent Types
2460900000
Solvent Utilization
Miscellaneous Non-industrial: Consumer and Commercial
Miscellaneous Products (Not Otherwise Covered)
Total: All Solvent Types
2461800000
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Total: All Solvent Types
2461800001
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Surface Application
2461800002
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: All Processes
Soil Incorporation
2461870999
Solvent Utilization
Miscellaneous Non-industrial: Commercial
Pesticide Application: Non-Agricultural
Not Elsewhere Classified
2465800000
Solvent Utilization
Miscellaneous Non-industrial: Consumer
Pesticide Application
Total: All Solvent Types
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Permeation
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Evaporation (includes Diurnal losses)
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Spillage During Transport
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Refilling at the Pump - Vapor Displacement
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Residential Portable Gas Cans
Refilling at the Pump - Spillage
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Permeation
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Evaporation (includes Diurnal losses)
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Spillage During Transport
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Refilling at the Pump - Vapor Displacement
<PHONE_NUMBER>
Storage and Transport
Petroleum and Petroleum Product Storage
Commercial Portable Gas Cans
Refilling at the Pump - Spillage
<PHONE_NUMBER>
Waste Disposal
Treatment and Recovery
Wastewater Treatment, Public Owned
Total Processed
<PHONE_NUMBER>
Waste Disposal
Treatment and Recovery
TSDFs, All TSDF Types
Total: All Processes
<PHONE_NUMBER>
Miscellane-ous Area Sources
Other Combustion
Residential Grilling
Total
<PHONE_NUMBER>
Miscellane-ous Area Sources
Other Combustion
Cremation
Humans

Other non-IPM updates in 2016v1

In New Jersey, emissions for SCCs for Industrial<PHONE_NUMBER>) and Commercial/Institutional<PHONE_NUMBER>) Distillate Oil, Total: Boilers and Internal Combustion (IC) Engines were removed at that state's request. These emissions were derived from EPA estimates, and double counted emissions that were provided by New Jersey and assigned to other SCCs.

The state of New Jersey also requested that animal waste NH3 emissions from the following SCCs be removed:<PHONE_NUMBER>  -  Cats,<PHONE_NUMBER>  -  Dogs,<PHONE_NUMBER>  -  Black Bears,<PHONE_NUMBER>  -  Grizzly Bears,<PHONE_NUMBER>  -  Elk,<PHONE_NUMBER>  -  Deer, and<PHONE_NUMBER>  -  Human Perspiration and Respiration. These emissions existed in CA, DE, ME, NJ, and UT, and were removed from all states.

The state of Alaska reported several nonpoint sources that were missing in 2014NEIv2. Some of the sources reported by Alaska were identified in our EGU inventory and removed from the new nonpoint inventory. The rest of the stationary sources were converted to an FF10-formatted nonpoint inventory and included in 2016v1 platform in the nonpt sector.

The state of Alabama requested that their Industrial, Commercial, Institutional (ICI) Wood emissions<PHONE_NUMBER>), which totaled more than 32,000 tons/year of PM2.5 emissions in the beta version of this emissions modeling platform and were significantly higher than other states' ICI Wood emissions, be removed from 2016v1 platform.

The state of New York provided a new set of non-residential wood combustion emissions for inclusion in 2016v1 platform. These new combustion emissions replace the emissions derived from the MARAMA projection.
Aircraft and ground support equipment (airports)
The airport sector contains emissions of all pollutants from aircraft, categorized by their itinerant class (i.e., commercial, air taxi, military, or general), as well as emissions from ground support equipment. The starting point for the 2016 version 1 (v1) platform airport inventory is the airport emissions from the 2017 National Emissions Inventory (NEI). The SCCs included in the airport sector are shown in Table 2-7.
Table 2-7.  2016v1 platform SCCs for the airports sector
SCC
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
2265008005
Mobile Sources
Off-highway Vehicle Gasoline, 4-stroke
Airport Ground Support Equipment
Airport Ground Support Equipment
2267008005
Mobile Sources
LPG
Airport Ground Support Equipment
Airport Ground Support Equipment
2268008005
Mobile Sources
compressed natural gas (CNG)
Airport Ground Support Equipment
Airport Ground Support Equipment
2270008005
Mobile Sources
Off-highway Vehicle Diesel
Airport Ground Support Equipment
Airport Ground Support Equipment
2275001000
Mobile Sources
Aircraft
Military Aircraft
Total
2275020000
Mobile Sources
Aircraft
Commercial Aircraft
Total: All Types
2275050011
Mobile Sources
Aircraft
General Aviation
Piston
2275050012
Mobile Sources
Aircraft
General Aviation
Turbine
2275060011
Mobile Sources
Aircraft
Air Taxi
Piston
2275060012
Mobile Sources
Aircraft
Air Taxi
Turbine
2275070000
Mobile Sources
Aircraft
Aircraft Auxiliary Power Units
Total
40600307
Chemical Evaporation
Transportation and Marketing of Petroleum Products
Gasoline Retail Operations  -  Stage I
Underground Tank Breathing and Emptying
20200102
Internal Combustion Engines
Industrial
Distillate Oil (Diesel)
Reciprocating

The 2016v1 airport emissions inventory was created from the 2017NEI airport emissions that were estimated using the Federal Aviation Administration's (FAA's) Aviation Environmental Design Tool (AEDT). Additional information about the 2017NEI airport inventory and the AEDT can be found in the 2017 National Emissions Inventory Technical Support Document (https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-technical-support-document-tsd). The 2017NEI emissions were adjusted from 2017 to represent year 2016 emissions using FAA data. Adjustment factors were created using airport-specific numbers, where available, or the state default by itinerant class (commercial, air taxi, and general) where there were not airport-specific values in the FAA data. Emissions growth for facilities is capped at 500% and the state default growth is capped at 200%. Military state default values were kept flat to reflect uncertainly in the data regarding these sources.

2016 Nonpoint sources (afdust, ag, np_oilgas, rwc, nonpt)
This section describes the stationary nonpoint sources in the NEI nonpoint data category.  Locomotives, C1 and C2 CMV, and C3 CMV are included in the NEI nonpoint data category, but are mobile sources that are described in Section 2.4.

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 resolution used for this platform.

The following subsections describe how the sources in the NEI nonpoint inventory were separated into modeling platform sectors, along with any data that were updated replaced with non-NEI data.
Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PM10 and PM2.5 emission estimates for nonpoint SCCs identified by EPA as dust sources.  Categories included in the afdust sector are paved roads, unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production, and mining and quarrying.  It does not include fugitive dust from grain elevators, coal handling at coal mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as point sources so they are properly located. Table 2-8 is a listing of the Source Classification Codes (SCCs) in the afdust sector.
Table 2-8. Afdust sector SCCs
SCC
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
<PHONE_NUMBER>
Mobile Sources
Aircraft
Unpaved Airstrips
Total
<PHONE_NUMBER>
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
<PHONE_NUMBER>
Mobile Sources
Paved Roads
All Paved Roads
Total: Sanding/Salting - Fugitives
<PHONE_NUMBER>
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
<PHONE_NUMBER>
Industrial Processes
Construction: SIC 15 - 17
All Processes
Total
<PHONE_NUMBER>
Industrial Processes
Construction: SIC 15 - 17
Residential
Total
<PHONE_NUMBER>
Industrial Processes
Construction: SIC 15 - 17
Residential
Vehicle Traffic
<PHONE_NUMBER>
Industrial Processes
Construction: SIC 15 - 17
Industrial/Commercial/ Institutional
Total
<PHONE_NUMBER>
Industrial Processes
Construction: SIC 15 - 17
Road Construction
Total
<PHONE_NUMBER>
Industrial Processes
Mining and Quarrying: SIC 14
All Processes
Total
<PHONE_NUMBER>
Industrial Processes
Mining and Quarrying: SIC 10
Lead Ore Mining and Milling
Total
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture  -  Crops
Total
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture  -  Crops
Tilling
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture  -  Crops
Harvesting
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture  -  Crops
Loading
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Crops
Agriculture - Crops
Transport
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Beef cattle - finishing operations on feedlots (drylots)
Dust Kicked-up by Hooves (use 28-05-020, -001, -002, or -003 for Waste
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Beef cattle - finishing operations on feedlots (drylots)
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Agriculture Production  -  Livestock
Beef cattle - finishing operations on feedlots (drylots)
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Agriculture Production  -  Livestock
Beef cattle - finishing operations on feedlots (drylots)
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Beef cattle production composite
Not Elsewhere Classified
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Beef cattle - finishing operations on pasture/range
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with dry manure management systems
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with dry manure management systems
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with wet manure management systems
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with wet manure management systems
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - layers with wet manure management systems
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - broilers
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - broilers
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - broilers
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - turkeys
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - turkeys
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry production - turkeys
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle composite
Not Elsewhere Classified
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - flush dairy
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - flush dairy
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - flush dairy
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Cattle and Calves Waste Emissions
Beef Cows
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - scrape dairy
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - scrape dairy
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - scrape dairy
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - deep pit dairy
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - deep pit dairy
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - deep pit dairy
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - drylot/pasture dairy
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - drylot/pasture dairy
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Dairy cattle - drylot/pasture dairy
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Swine production composite
Not Elsewhere Classified (see also 28-05-039, -047, -053)
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry Waste Emissions
Not Elsewhere Classified (see also 28-05-007, -008, -009)
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry Waste Emissions
Ducks
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Poultry Waste Emissions
Geese
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Horses and Ponies Waste Emissions
Not Elsewhere Classified
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Swine production - operations with lagoons (unspecified animal age)
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Swine production - operations with lagoons (unspecified animal age)
Manure handling and storage
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Swine production - operations with lagoons (unspecified animal age)
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production - Livestock
Sheep and Lambs Waste Emissions
Total
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production  -  Livestock
Goats Waste Emissions
Not Elsewhere Classified
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production  -  Livestock
Swine production - deep-pit house operations (unspecified animal age)
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production  -  Livestock
Swine production - deep-pit house operations (unspecified animal age)
Land application of manure
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag. Production  -  Livestock
Swine production - outdoor operations (unspecified animal age)
Confinement

The starting point for the afdust emissions is the 2014 National Emissions Inventory version 2.  The methodologies to estimate emissions for each SCC in the preceding table are described in the 2014 NEI version 2 Technical Support Document.   The 2014 emissions were adjusted to better represent 2016 as described below.
MARAMA States area fugitive dust emissions
The MARAMA states include Connecticut, Delaware, the District of Columbia (DC), Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Vermont, Virginia, and West Virginia. MARAMA submitted county-specific projection factors for their states to project afdust emissions from the 2014NEI2 to 2016 for paved roads (SCC<PHONE_NUMBER>), residential construction dust (SCC<PHONE_NUMBER>), industrial/commercial/institutional construction dust (SCC<PHONE_NUMBER>), road construction dust (SCC<PHONE_NUMBER>), dust from mining and quarrying (SCC<PHONE_NUMBER>), agricultural crop tilling dust (SCC<PHONE_NUMBER>), and agricultural dust kick-up from beef cattle hooves (SCC<PHONE_NUMBER>). Other afdust emissions, including unpaved road dust emissions, were held constant at 2014NEIv2 values.

Non-MARAMA States area fugitive dust emissions
For paved roads (SCC<PHONE_NUMBER>) in non-MARAMA states, the 2014NEIv2 paved road emissions in afdust were projected to year 2016 based on differences in county total vehicle miles traveled (VMT) between 2014 and 2016:
2016 afdust paved roads = 2014 afdust paved roads * (2016 county total VMT) / (2014 county total VMT)
The development of the 2016 VMT is described in the onroad documentation. All emissions other than those for paved roads are held constant in the 2016v1 inventory, including unpaved roads for these states.
Area Fugitive Dust Transport Fraction
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 using a script that applies land use-based gridded transport fractions based on landscape roughness, followed by another script that zeroes out emissions for days on which at least 0.01 inches of precipitation occurs or there is snow cover on the ground.  The land use data used to reduce the NEI emissions determines the amount of emissions that are subject to transport.  This methodology is discussed in Pouliot, et al., 2010, and in "Fugitive 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 (i.e., 12km grid cells); therefore, different emissions will result if the process were applied to different grid resolutions.  A limitation of the transport fraction approach is the lack of monthly variability that would be expected with seasonal changes in vegetative cover.  While wind speed and direction are not accounted for in the emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is accounted for in the subsequent meteorological adjustment.

For the data compiled into the 2014NEIv2, meteorological adjustments are applied to paved and unpaved road SCCs but not transport adjustments.  For the 2014NEIv1, the meteorological adjustments were inadvertently not applied. This created a large difference between the 2014NEIv1 and 2014NEIv2 dust emissions which did not impact the modeling platform because the modeling platform applies meteorological adjustments and transport adjustments based on unadjusted NEI values (for both v1 and v2).   Thus, for the 2014NEIv2, the meteorological adjustments that were applied (to paved and unpaved road SCCs) had to be backed out so that the entire sector could be processed consistently in SMOKE and the same grid-specific transport fractions and meteorological adjustments could be applied sector-wide.  Because it was determined that some counties in 2014NEIv2 did not have the adjustment applied, their emissions were used as-is. Thus, the FF10 that is run through SMOKE consists of 100% unadjusted emissions, and after SMOKE all afdust sources have both transport and meteorological adjustments applied.  The total impacts of the transport fraction and meteorological adjustments for 2016v1 are shown in Table 2-9. Note that while totals from AK, HI, PR, and VI are included at the bottom of the table, they are from non-continental U.S. (non-CONUS) modeling domains.

Table 2-9.  Total impact of fugitive dust adjustments to unadjusted 2016 v1 inventory
State
                                Unadjusted PM10
                               Unadjusted PM2.5
                                Change in PM10
                                Change in PM2.5
                                PM10 Reduction
                                PM2.5 Reduction
Alabama
                                                                        535,218
                                                                         63,682
                                                                       -372,853
                                                                        -44,336
                                      70%
                                      70%
Arizona
                                                                        264,628
                                                                         32,808
                                                                        -96,814
                                                                        -11,809
                                      37%
                                      36%
Arkansas
                                                                        321,488
                                                                         49,397
                                                                       -211,050
                                                                        -31,802
                                      66%
                                      64%
California
                                                                        314,917
                                                                         41,395
                                                                       -134,347
                                                                        -17,059
                                      43%
                                      41%
Colorado
                                                                        242,327
                                                                         36,848
                                                                       -121,263
                                                                        -17,718
                                      50%
                                      48%
Connecticut
                                                                         23,740
                                                                          3,385
                                                                        -17,548
                                                                         -2,510
                                      74%
                                      74%
Delaware
                                                                         14,566
                                                                          2,502
                                                                         -8,843
                                                                         -1,533
                                      61%
                                      61%
District of Columbia
                                                                          2,619
                                                                            378
                                                                         -1,627
                                                                           -236
                                      62%
                                      62%
Florida
                                                                        721,379
                                                                         82,397
                                                                       -412,621
                                                                        -46,899
                                      57%
                                      57%
Georgia
                                                                        557,354
                                                                         66,609
                                                                       -389,482
                                                                        -46,272
                                      70%
                                      69%
Idaho
                                                                        454,301
                                                                         55,978
                                                                       -241,373
                                                                        -28,363
                                      53%
                                      51%
Illinois
                                                                        997,748
                                                                        143,992
                                                                       -619,594
                                                                        -88,735
                                      62%
                                      62%
Indiana
                                                                        718,027
                                                                         84,663
                                                                       -498,442
                                                                        -58,430
                                      69%
                                      69%
Iowa
                                                                        387,029
                                                                         60,253
                                                                       -222,941
                                                                        -34,557
                                      58%
                                      57%
Kansas
                                                                        613,183
                                                                         99,486
                                                                       -277,007
                                                                        -44,234
                                      45%
                                      44%
Kentucky
                                                                        312,872
                                                                         42,952
                                                                       -233,163
                                                                        -31,762
                                      75%
                                      74%
Louisiana
                                                                        266,812
                                                                         35,788
                                                                       -172,875
                                                                        -22,923
                                      65%
                                      64%
Maine
                                                                         38,345
                                                                          5,963
                                                                        -31,893
                                                                         -4,978
                                      83%
                                      83%
Maryland
                                                                        105,892
                                                                         16,672
                                                                        -68,246
                                                                        -10,824
                                      64%
                                      65%
Massachusetts
                                                                        148,284
                                                                         18,297
                                                                       -112,998
                                                                        -13,852
                                      76%
                                      76%
Michigan
                                                                        390,994
                                                                         48,838
                                                                       -286,999
                                                                        -35,560
                                      73%
                                      73%
Minnesota
                                                                        405,052
                                                                         61,723
                                                                       -250,646
                                                                        -37,609
                                      62%
                                      61%
Mississippi
                                                                        434,575
                                                                         53,546
                                                                       -299,888
                                                                        -36,494
                                      69%
                                      68%
Missouri
                                                                      1,604,501
                                                                        185,103
                                                                     -1,084,830
                                                                       -124,078
                                      68%
                                      67%
Montana
                                                                        432,844
                                                                         62,062
                                                                       -236,341
                                                                        -32,695
                                      55%
                                      53%
Nebraska
                                                                        349,373
                                                                         55,303
                                                                       -165,083
                                                                        -25,739
                                      47%
                                      47%
Nevada
                                                                        161,820
                                                                         23,360
                                                                        -54,899
                                                                         -7,953
                                      34%
                                      34%
New Hampshire
                                                                         22,330
                                                                          4,607
                                                                        -18,436
                                                                         -3,803
                                      83%
                                      83%
New Jersey
                                                                         40,336
                                                                          9,118
                                                                        -26,776
                                                                         -6,035
                                      66%
                                      66%
New Mexico
                                                                        490,617
                                                                         54,236
                                                                       -200,695
                                                                        -22,038
                                      41%
                                      41%
New York
                                                                        264,041
                                                                         44,137
                                                                       -196,162
                                                                        -32,785
                                      74%
                                      74%
North Carolina
                                                                        206,465
                                                                         30,017
                                                                       -141,501
                                                                        -20,610
                                      69%
                                      69%
North Dakota
                                                                        473,241
                                                                         82,478
                                                                       -249,646
                                                                        -43,138
                                      53%
                                      52%
Ohio
                                                                        931,847
                                                                        116,560
                                                                       -638,127
                                                                        -79,098
                                      68%
                                      68%
Oklahoma
                                                                        450,904
                                                                         67,915
                                                                       -232,046
                                                                        -33,983
                                      51%
                                      50%
Oregon
                                                                        659,099
                                                                         73,832
                                                                       -456,949
                                                                        -49,830
                                      69%
                                      67%
Pennsylvania
                                                                        242,608
                                                                         37,707
                                                                       -179,647
                                                                        -27,959
                                      74%
                                      74%
Rhode Island
                                                                          4,935
                                                                            785
                                                                         -3,503
                                                                           -556
                                      71%
                                      71%
South Carolina
                                                                        164,477
                                                                         22,016
                                                                       -110,278
                                                                        -14,795
                                      67%
                                      67%
South Dakota
                                                                        339,195
                                                                         63,248
                                                                       -169,300
                                                                        -31,302
                                      50%
                                      49%
Tennessee
                                                                        295,092
                                                                         43,414
                                                                       -204,746
                                                                        -29,995
                                      69%
                                      69%
Texas
                                                                      1,264,131
                                                                        180,314
                                                                       -636,591
                                                                        -87,931
                                      50%
                                      49%
Utah
                                                                        209,800
                                                                         26,453
                                                                       -111,587
                                                                        -13,771
                                      53%
                                      52%
Vermont
                                                                         22,437
                                                                          3,275
                                                                        -18,644
                                                                         -2,699
                                      83%
                                      82%
Virginia
                                                                        286,237
                                                                         37,007
                                                                       -211,882
                                                                        -27,348
                                      74%
                                      74%
Washington
                                                                        242,907
                                                                         41,851
                                                                       -135,713
                                                                        -23,281
                                      56%
                                      56%
West Virginia
                                                                        123,003
                                                                         15,127
                                                                       -105,093
                                                                        -12,911
                                      85%
                                      85%
Wisconsin
                                                                        690,830
                                                                         89,899
                                                                       -486,508
                                                                        -62,683
                                      70%
                                      70%
Wyoming
                                                                        240,156
                                                                         29,140
                                                                       -123,388
                                                                        -14,561
                                      51%
                                      50%
Domain Total (12km CONUS)
                                                                     18,484,575
                                                                      2,506,516
                                                                    -11,280,883
                                                                     -1,500,070
                                      61%
                                      60%
Alaska
                                                                        112,025
                                                                         11,562
                                                                       -101,822
                                                                        -10,508
                                      91%
                                      91%
Hawaii
                                                                        109,120
                                                                         11,438
                                                                        -73,612
                                                                         -7,673
                                      67%
                                      67%
Puerto Rico
                                                                          5,889
                                                                          1,313
                                                                         -4,355
                                                                           -984
                                      74%
                                      75%
Virgin Islands
                                                                          3,493
                                                                            467
                                                                         -1,477
                                                                           -195
                                      42%
                                      42%

Figure 2-1 illustrates the impact of each step of the adjustment.  The reductions due to the transport fraction adjustments alone are shown at the top of the figure.  The reductions due to the precipitation adjustments alone are shown in the middle of the figure.  The cumulative emission reductions after both transport fraction and meteorological adjustments are shown at the bottom of the figure.  The top plot shows how the transport fraction has a larger reduction effect in the east, where forested areas are more effective at reducing PM transport than in many western areas.  The middle plot shows how the meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust emissions.

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

Agriculture Sector (ag)
The ag sector includes NH3 emissions from fertilizer and emissions of all pollutants other than PM2.5 from livestock in the nonpoint (county-level) data category of the 2017NEI. PM2.5 from livestock are in the Area Fugitive Dust (afdust) sector. Combustion emissions from agricultural equipment, such as tractors, are in the Nonroad sector.  The sector now includes VOC and HAP VOC in addition to NH3. The 2016 version 1 (v1) platform uses a 2016-specific fertilizer inventory from the USDA's Environmental Policy Integrated Climate (EPIC) model combined with a 2016 USDA-based county-level back-projection of 2017NEI livestock emissions. The SCCs included in the ag sector are shown in Table 2-10.
Table 2-10.  2016v1 platform SCCs for the ag sector
SCC
Tier 1 description
Tier 2 description
Tier 3 description
Tier 4 description
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Crops
Fertilizer Application
Miscellaneous Fertilizers
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Beef cattle production composite
Not Elsewhere Classified
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Poultry production - layers with dry manure management systems
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Poultry production - broilers
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Poultry production - turkeys
Confinement
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Dairy cattle composite
Not Elsewhere Classified
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Swine production composite
Not Elsewhere Classified (see also 28-05-039, -047, -053)
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Horses and Ponies Waste Emissions
Not Elsewhere Classified
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Sheep and Lambs Waste Emissions
Total
<PHONE_NUMBER>
Miscellaneous Area Sources
Ag.  Production - Livestock
Goats Waste Emissions
Not Elsewhere Classified

 Livestock Waste Emissions
The 2016v1 platform livestock emissions consist of a back-projection of 2017NEI livestock emissions to the year 2016 and include NH3 and VOC. The livestock waste emissions from 2017NEI contain emissions for beef cattle, dairy cattle, goats, horses, poultry, sheep, and swine. The data come from both state-submitted emissions and EPA-calculated emission estimates. Further information about the 2017NEI emissions can be found in the 2017 National Emissions Inventory Technical Support Document (https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-technical-support-document-tsd). Back-projection factors for 2016 emission estimates are based on animal population data from the USDA National Agriculture Statistics Service Quick Stats (https://www.nass.usda.gov/Quick_Stats/). These estimates are developed by data collected from annual agriculture surveys and the Census of Agriculture that is completed every five years. These data include estimates for beef, layers, broilers, turkeys, dairy, swine, and sheep. Each SCC in the 2017NEI livestock inventory, except for<PHONE_NUMBER> (horses and ponies) and<PHONE_NUMBER> (goats), was mapped to one of these USDA categories. Then, back-projection factors were calculated based on USDA animal populations for 2016 and 2017.  Emissions for animal categories for which population data were not available (e.g. horses, goats) were held constant in the projection.

Back-projection factors were calculated at the county level, but only where county-level data was available for a specific animal category. County-level factors were limited to a range of 0.8 to 1.2. Data were not available for every animal category in every county. State-wide back-projection factors based on state total animal populations were calculated and applied to counties where county-specific data was not available for a given animal category. However, data were often not available for every animal category in every state. For categories other than beef and dairy, data are not available for most states. In cases of missing state-level data, a national back-projection factor was applied. Back-projection factors were not pollutant-specific and were applied to all pollutants. The national back-projection factors, which were only used when county or state data were not available, are shown in Table 2-11. The national factors were created using a ratio between animal inventory counts for 2017 and 2016 from the USDA National livestock inventory projections published in February 2018 (https://www.ers.usda.gov/webdocs/outlooks/87459/oce-2018-1.pdf?v=7587.1).
Table 2-11.  National back-projection factors for livestock: 2017 to 2016
beef
-1.8%
swine
-3.6%
broilers
-2.0%
turkeys
-0.3%
layers
-2.3%
dairy
-0.4%
sheep
+0.4%

 Fertilizer Emissions
Fertilizer emissions for 2016 are based on the Fertilizer Emission Scenario Tool for CMAQ (FEST-C) model (https://www.cmascenter.org/fest-c/). The bidirectional version of CMAQ (v5.3) and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C (v1.3) were used to estimate ammonia (NH3) emissions from agricultural soils. The approach to estimate year-specific fertilizer emissions consists of these steps:
 Run FEST-C to produce nitrate (NO3), Ammonium (NH4+, including Urea), and organic (manure) nitrogen (N) fertilizer usage estimates
 Use USDA Economic Research Services crop specific fertilizer use data and state submitted data to adjust the FEST-C fertilizer totals to match the USDA and State submitted.
 Run the CMAQ model with bidirectional ("bidi") NH3 exchange to generate gaseous ammonia NH3 emission estimates.
 Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertilizer emissions to FEST-C total N fertilizer application.
 Assign the NH3 emissions to one SCC: "...Miscellaneous Fertilizers"<PHONE_NUMBER>).

FEST-C is the software program that processes land use and agricultural activity data to develop inputs for the CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the Biogenic Emissions Landuse Dataset (BELD), meteorological variables from the Weather Research and Forecasting model, and nitrogen deposition data from a previous or historical average CMAQ simulation. FEST-C, then uses the EPIC modeling system (https://epicapex.tamu.edu/epic/) to simulate the agricultural practices and soil biogeochemistry and provides information regarding fertilizer timing, composition, application method and amount.

An iterative calculation was applied to estimate fertilizer emissions for the 2016 platform.   We first estimate fertilizer application by crop type using FEST-C modeled data. After receipt and addressing of comments to the extent possible, we then adjusted the fertilizer application estimates using state submitted data, (currently only Iowa), and USDA Economic Research Service state and crop specific survey data. The USDA and state submitted annual fertilizer data was used to estimate the ratio of UDSA/state fertilizer use to FEST-C annual total fertilizer estimates for each state and crop with USDA or state data. This ratio is then applied to the FEST-C fertilizer application rates for each state and crop with data.  A maximum annual fertilization rate was estimated from the FEST-C simulation and annual adjusted totals were limited to this rate to prevent unrealistically higher fertilization rates. Then we ran the CMAQ v5.3 model with the Surface Tiled Aerosol and Gaseous Exchange (STAGE) deposition option with bidirectional exchange to estimate fertilizer and biogenic NH3 emissions.  We use this approach for three reasons: (1) FEST-C estimates fertilizer applications based on crop nutrient needs which is typically lower than real world fertilization rates; (2) FEST-C fertilizer timing and application methods are assumed to be correct; and (3) We desired a method to incorporate state submitted and USDA reported data into the final fertilization emission estimates.

Example Calculation:
Adjustment of FEST-C fertilizer rates using state or USDA data:
Fertajusted,crop=maxFertsubmitted,crop1ncropFertFEST-C,cropFertFEST-C,crop, Fertmax,crop