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2008 National Emissions Inventory, Version 3
Technical Support Document DRAFT
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EPA-454/B-19-018
September 2013
2008 National Emissions Inventory, Version 3 Technical Support Document DRAFT
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Contents
List of Tables v
List of Figures vii
Acronyms and Chemical Notations viii
1 Introduction 1
1.1 What data are included in the 2008 NEI, version 3 General Public release? 1
1.2 What is included in this documentation? 1
1.3 Where can I obtain the 2008 NEI data? 2
1.3.1 Emission Inventory System Gateway 2
1.3.2 2008 NEI main webpage query tool 2
1.3.3 Air Emissions and "Where you live" 2
1.3.4 Modeling files 3
1.4 Why is the NEI created? 3
1.5 How is the NEI created? 4
1.6 Who are the target audiences for the 2008 NEI? 6
1.7 What are appropriate uses of the 2008 NEI version 3 and what are the caveats about the data? 7
2 2008 inventory contents overview 10
2.1 What are EIS Sectors and what list was used for this document? 10
2.2 What do the data show about the sources of data in the 2008 NEI? 13
2.3 What are the top sources of some key pollutants? 21
2.4 How does this NEI compare to past inventories? 25
2.4.1 Differences in approaches 25
2.4.2 Differences in emissions 27
2.5 How well are tribal data and regions represented in the 2008 NEI? 30
2.6 What does this NEI tell us about mercury? 31
3 Stationary Sources 39
3.1 Stationary source approaches 39
3.1.1 Sources of data overview and selection hierarchies 39
3.1.2 Particulate matter augmentation 45
3.1.3 Chromium augmentation 46
3.1.4 Use of the 2008 Toxics Release Inventory 49
3.1.5 HAP augmentation based on emission factor ratios 62
3.1.6 EPA nonpoint data 69
3.1.7 Additional Gap filling efforts targeted at high risk and specific mercury categories 75
3.2 Agriculture - Crops & Livestock Dust 82
3.3 Agriculture - Fertilizer Application 82
3.4 Agriculture - Livestock Waste 82
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3.4.1 Sector Description 82
3.4.2 Sources of data overview and selection hierarchy 82
3.4.3 Spatial coverage and data sources for the sector 84
3.4.4 EPA-developed livestock waste emissions data 84
3.4.5 Summary of quality assurance methods 92
3.5 Bulk Gasoline Terminals 92
3.6 Commercial Cooking 92
3.7 Dust - Construction Dust 92
3.8 Dust - Paved Road Dust 92
3.9 Dust - Unpaved Road Dust 92
3.10 Fuel Combustion - Electric Generation 92
3.10.1 Sector Description 92
3.10.2 Sources of data overview and selection hierarchy 93
3.10.3 Spatial coverage and data sources forthe sector 97
3.10.4 Overwrite datasets used for EGUs 97
3.10.5 EPA-developed EGU emissions data 98
3.10.6 Alternative facility and unit IDs needed for matching with other databases 102
3.10.7 Summary of quality assurance methods 102
3.11 Fuel Combustion - Industrial Boilers 103
3.11.1 Sector Description 103
3.11.2 Sources of data overview and selection hierarchy 104
3.11.3 EPA-developed fuel combustion -Industrial Boilers, ICEs emissions data 110
3.11.4 Summary of quality assurance methods 110
3.12 Fuel Combustion - Commercial/Institutional 110
3.13 Fuel Combustion - Residential - Natural Gas, Oil, and Other 110
3.14 Fuel Combustion - Residential - Wood 110
3.15 Gas Stations Ill
3.16 Industrial Processes - Cement Manufacturing Ill
3.16.1 Sector Description Ill
3.17 Industrial Processes - Chemical Manufacturing Ill
3.18 Industrial Processes - Ferrous Metals Ill
3.19 Industrial Processes - Mining Ill
3.20 Industrial Processes - Non-ferrous Metals Ill
3.21 Industrial Processes - Oil & Gas Production Ill
3.22 Industrial Processes - Petroleum Refineries Ill
3.23 Industrial Processes - Pulp & Paper Ill
3.24 Industrial Processes - Storage and Transfer Ill
3.25 Industrial Processes - NEC (Other) Ill
3.26 Miscellaneous Non-industrial NEC (Other) Ill
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3.27 Solvent - Consumer & Commercial Solvent Use 112
3.28 Solvent - Degreasing, Dry Cleaning, and Graphic Arts 112
3.29 Solvent - Industrial and Non-Industrial Surface Coating 112
3.30 Waste Disposal 112
4 Mobile sources 113
4.1 Mobile sources overview 113
4.2 Aircraft 113
4.2.1 Sector Description 114
4.2.2 Sources of data overview and selection hierarchy 115
4.2.3 Spatial coverage and data sources for the sector 116
4.2.4 Overwrite dataset used for aircraft sector 116
4.2.5 EPA-developed aircraft emissions estimates 116
4.2.6 Summary of quality assurance methods 119
4.3 Commercial Marine Vessels 120
4.3.1 Sector Description 120
4.3.2 Sources of data overview and selection hierarchy 122
4.3.3 Spatial coverage and data sources for the sector 123
4.3.4 EPA-developed commercial marine vessel emissions data 123
4.3.5 Summary of quality assurance methods 125
4.4 Locomotives 127
4.4.1 Sector Description 127
4.4.2 Sources of data overview and selection hierarchy 128
4.4.3 Spatial coverage and data sources for the sector 129
4.4.4 Overwrite datasets used for locomotives sector 129
4.4.5 EPA-developed locomotive emissions data 129
4.4.6 Summary of quality assurance methods 131
4.5 Nonroad Equipment - Diesel, Gasoline, and other 132
4.5.1 Sector Description 132
4.5.2 Sources of data overview and selection hierarchy 132
4.5.3 Spatial coverage and data sources for the sector 134
4.5.4 EPA-developed NMIM-based nonroad emissions data 134
4.5.5 Summary of quality assurance methods 135
4.6 On-road - all Diesel and Gasoline vehicles 137
4.6.1 Sector Description 138
4.6.2 Sources of data overview and selection hierarchy 138
4.6.3 Spatial coverage and data sources for the sector 139
4.6.4 EPA-developed on-road mobile emissions data for the continental U.S 139
4.6.5 EPA-developed on-road mobile emissions data for Alaska, Hawaii, Puerto Rico and the Virgin
Islands 148
4.6.6 Summary of quality assurance methods 148
5 Fires 150
5.1
5.1.1
Wildfires and Prescribed burning
Sector Description
150
150
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5.1.2 Sources of data overview and selection hierarchy 151
5.1.3 Spatial coverage and data sources for the sector 154
5.1.4 EPA-developed fire emissions estimates 154
5.1.5 Wildland Fire HAP Augmentation 159
5.1.6 Summary of quality assurance methods 161
5.2 Fires - Agricultural field burning 162
5.2.1 Sector Description 162
5.2.2 Sources of data overview and selection hierarchy 163
5.2.3 Spatial coverage and data sources for the sector 164
5.2.4 EPA-developed agricultural emissions data 166
5.2.5 Summary of quality assurance methods 167
6 Biogenics - Vegetation and Soil 167
6.1 Biogenic Emission Sources 167
6.1.1 Sector Description 168
6.1.2 Sources of data overview and selection hierarchy 169
6.1.3 Spatial coverage and data sources for the sector 169
7 Quality assessment 172
7.1 What are the quality criteria used to assess the inventory? 172
7.2 How did the 2008 NEI compare to the quality criteria? 172
7.3 What EIS sectors seem to be incomplete and for which key pollutants? 172
7.4 How can the quality of the emissions data be further evaluated by users? 172
7.5 What improvements in the NEI and EIS submission process are planned for the future? 172
8 Supporting data and summaries 173
8.1 Supporting data 173
8.2 Supporting summaries 177
9 References 178
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List of Tables
Table 1: Point source reporting thresholds (potential to emit) for criteria pollutants in the Air Emissions
Reporting Rule 5
Table 2: Examples of major current uses of the NEI 6
Table 3: EIS sectors and associated emissions categories and document sections 12
Table 4: EIS sectors and associated CAP emissions and total HAP (1000 short tons/year) 22
Table 5: Tribal Participation in the 2008 NEI 30
Table 6: Datasets, groups, and amount of Hg in 2008 NEI from each 33
Table 7: Trends in Mercury Emissions - 1990, 2005, and 2008 35
Table 8: Data sources and selection hierarchy used for point sources 40
Table 9: Data sources and selection hierarchy used for nonpoint sources 44
Table 10: Valid chromium pollutant codes 46
Table 11: Calculations for generating the point chromium augmentation dataset (EPA Chromium Split
v2) 48
Table 12: Mapping of TRI Pollutant Codes to EIS Pollutant codes 54
Table 13: Pollutant Groups 60
Table 14: CAP Surrogate assignments to derive HAP-to-CAP Emission Factor Ratios 62
Table 15: Invalid pollutant codes for HAP augmentation 65
Table 16: Conversion factors HAP emission factors for HAP augmentation 66
Table 17: Physical Conversion Factors Used 66
Table 18: EPA-estimated emissions sources expected to be exclusively nonpoint 70
Table 19: Emissions sources not estimated by EPA with potential nonpoint and point contribution.... 72
Table 20: Solvent sectors nonpoint HAP-VOC and calculated missing HAP-VOC 74
Table 21: Emissions sources using data from former EPA inventories 74
Table 22: Emissions sources not included from EPA data sources 75
Table 23: Hg-emitting Facilities in the S/L/T agency review process with insufficient information to
gap fill 77
Table 24: High Risk Facilities in the S/L/T agency review process with insufficient information to gap
fill 78
Table 25: Agencies that Submitted Livestock Waste Data 83
Table 26: 2008 NEI agricultural livestock data selection hierarchy 83
Table 27: Source Classification Codes used in the agricultural livestock sector 84
Table 28: Emission Factors for NH3 emissions used for EPA's agricultural livestock data 88
Table 29: Agencies that Submitted EGU data 94
Table 30: 2008 NEI EGU data selection hierarchy by EGU fuel groups from EIS Sectors 97
Table 31: Agency-submitted, PM Augmentation, and total PM10 and PM2.5 emissions for EGU sectors
(short tons/year) 98
Table 32: Agencies that submitted data for the Fuel Combustion - Industrial Boilers, ICEs Sectors.... 105
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Table 33: 2008 NEI selection hierarchy for datasets used by the Fuel Comb - Industrial Boilers, ICEs
Sectors 109
Table 34: Source classification codes for the aircraft sector in the 2008 NEI 115
Table 35: Agencies that submitted aircraft emissions data 115
Table 36: 2008 NEI aircraft data selection hierarchy 116
Table 37: Agencies that submitted aircraft activity data for EPA's emissions calculation 117
Table 38: SCCs included in the EPA-created aircraft emissions dataset 118
Table 39: Non-aircraft related SCCs reported by S/L/T agencies to airports 120
Table 40: Commercial Marine SCCs and Emission Types 121
Table 41: Additional Commercial Marine SCCs used by California and Kentucky 121
Table 42: Agencies that Submitted Commercial Marine Emissions Data 122
Table 43: 2008 NEI commercial marine vehicle selection hierarchy 123
Table 44: Commercial Marine SCCs for which EPA Provided Estimates 124
Table 45: SCC/Pollutant combinations where State total 2008 NEI is greater than agency or EPA
estimates 127
Table 46: Locomotive SCCs, descriptions, and EPA estimation status 127
Table 47: Agencies that submitted Rail Emissions to the 2008 NEI 128
Table 48: 2008 NEI locomotives selection hierarchy 129
Table 49: Agency Submittals ofNONROAD inputs and nonroad emissions 133
Table 50: 2008 NEI Non-road equipment selection hierarchy 134
Table 51: Nonroad SCCs included in 2008 NEI that were not in S/L/T agency submittals 137
Table 52: 2008 NEI on-road mobile selection hierarchy 138
Table 53: Characteristics for Grouping Counties 141
Table 54: Pollutants estimated through national emission factors 147
Table 55: Source classification codes for wildland fires 151
Table 56: Agencies that submitted wildfire and prescribed burning (Rx) emissions data 151
Table 57: Fire emissions submitted by tribal agencies (short tons/year) 153
Table 58: 2008 NEI wildfire and prescribed fires selection hierarchy 153
Table 59: Pollutants estimated by EPA for wildland fires and HAP emission factors 155
Table 60: Changes in emissions between the 2008 NEI v2 and 2008 NEI v3 due to HAP augmentation
method changes 161
Table 61: Source Classification Codes in the NEI for Agricultural Burning 163
Table 62: Agencies that submitted agricultural fire emissions to the 2008 NEI 163
Table 63: State Emission Estimates for Agricultural Burning using EPA methods (short tons/year) ... 164
Table 64: Source classification codes for Biogenics - Vegetation and Soil 168
Table 65: State Summary of Biogenics - Vegetation and Soil Emissions (short tons/year) 169
Table 66: 2008 NEI supporting data access information 173
Table 67: 2008 NEI supporting summaries 177
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List of Figures
Figure 1: Data sources for point and nonpoint emissions for criteria pollutants1 14
Figure 2: Data sources for onroad and nonroad mobile emissions for criteria pollutants 15
Figure 3: Data sources for point and nonpoint emissions for acid gases and HAP VOCs 16
Figure 4: Data sources for point and nonpoint emissions for Pb and HAP metals 17
Figure 5: Point inventory - submission types - includes local agencies 18
Figure 6: Nonpoint inventory - submission types - includes local agencies 19
Figure 7: On-road inventory - submission types - does not include local agencies 20
Figure 8: Nonroad equipment inventory - submission types - does not include local agencies 21
Figure 9: Comparison of 2008 NEI v3 to 2005 NEI v2 CAPs, excluding wildfires 28
Figure 10: Data sources of Hg emissions in the 2008 NEI, by data category 32
Figure 11: States with state- or local-provided Hg emissions in the point data category of the 2008 NEI
34
Figure 12: Proportion of Fires by Type using EPA Methods 157
Figure 13: Acres Burned using EPA Methods 158
Figure 14: 2008 PM2.5 Emissions using EPA methods 159
Figure 15: 2008 PM2.5 wild land fire emissions using EPA methods 162
Figure 16: 2008 NEI state-total PM2.5 emissions from agricultural fires 165
Figure 17: Identification of states that submitted agricultural burning emissions to the NEI 166
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Acronyms and Chemical Notations
AERR
Air Emissions Reporting Rule
APU
Auxiliary power unit
BEIS
Biogenics Emissions Inventory System
BSO
Benzene Soluble Organics
CI
Category 1 (commercial marine vessels)
C2
Category 2 (commercial marine vessels)
C3
Category 3 (commercial marine vessels)
CAIR
Clean Air Interstate Rule
CAMD
Clean Air Markets Division (of EPA Office of Air and Radiation)
CAP
Criteria Air Pollutant
CEC
North American Commission for Environmental Cooperation
CEM
Continuous Emissions Monitoring
CERR
Consolidated Emissions Reporting Rule
CFR
Code of Federal Regulations
CHIEF
Clearinghouse for Inventories and Emissions Factors
CMU
Carnegie Mellon University
CMV
Commercial marine vessels
CO
Carbon Monoxide
CSV
Comma Separated Variable
E10
10% ethanol gasoline
EDMS
Emissions and Dispersion Modeling Svstem
EGU
Electric Generating Utility
EIS
Emission Inventory Svstem
EAF
Electric arc furnace
EF
Emission factor
EMFAC
Emission FACtor (model) - for California
EPA
Environmental Protection Agencv
ERG
Eastern Research Group
ERTAC
Eastern Regional Technical Advisory Committee
FAA
Federal Aviation Administration
FCCS
Fuel Characteristic Classification System
GIS
Geographic information systems
GPA
Geographic phase-in area
GSE
Ground support equipment
HAP
Hazardous Air Pollutant
HC1
Hydrogen Chloride (Hydrochloric acid)
Hg
Mercury
HMS
Hazard Mapping Svstem
IGCC
Integrated gasification combined cycle
ICR
Information collection request
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I/M Inspection and maintenance
IPM Integrated Planning Model
KMZ Keyhole Markup Language, zipped (used for displaying data in Google Earth
LRTAP Long-range Transboundary Air Pollution
LTO Landing and takeoff
MATS Mercury and Air Toxics Standards
MCIP Meteorology-Chemistry Interface Processor
MMT Manure management train
MOBILE6 Mobile Source Emission Factor Model, version 6
MOVES Motor Vehicle Emissions Simulator
MSO Methylene Chloride Soluble Organics
MW Megawatts
MWC Municipal waste combustors
NAA Nonattainment area
NAAQS National Ambient Air Quality Standards
NARAP North American Regional Action Plan
NASS USDA National Agriculture Statistical Service
NATA National Air Toxics Assessment
NCD National County Database
NEEDS National Electric Energy Data System (database)
NEI National Emissions Inventory
NH3 Ammonia
NMIM National Mobile Inventory Model
NO Nitric oxide
N02 Nitrogen dioxide
NO A A National Oceanic and Atmospheric Administration
NOX Nitrogen oxides
O3 Ozone
OAQPS Office of Air Quality Standards and Planning (of EPA)
ORIS Office of Regulatory Information Systems
OTAQ Office of Transportation and Air Quality (of EPA)
PADD Petroleum Administration for Defense Districts
PAH Polycyclic Aromatic Hydrocarbons
Pb Lead
PCB Polychlorinated Biphenyl
PM Particulate matter
PM25-CON Condensable PM2.5
PM25-FIL Filterable PM2.5
PM25-PRI Primary PM2.5 (condensable plus filterable)
PM2.5 Particulate matter 2.5 microns or less in diameter
PM10 Particular matter 10 microns or less in diameter
PM10-FIL Filterable PM10
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PM10-PRI
Primary PM10
POM
Polycyclic Organic Matter
PSC
Program system code (in EIS)
RFG
Reformulated Gasoline
RPD
Rate per distance
RPP
Rate per profile
RPV
Rate per vehicle
Rx
Prescribed (fire)
see
Source classification code
SFvl
SMARTFIRE version 1
SFv2
SMARTFIRE version 2
S/L/T
State, local, and tribal (agencies)
SMARTFIRE Satellite Mapping Automated Reanalvsis Tool for Fire Incident Reconciliation
SMOKE
Sparse Matrix Operator Kernel Emissions
S02
Sulfur dioxide
S04
Sulfate
TAF
Terminal Area Forecasts
TEISS
Tribal Emissions Inventory Software Solution
TRI
Toxics Release Inventory
UNEP
United Nations Environment Programme
USD A
United States Department of Agriculture
VMT
Vehicle miles traveled
VOC
Volatile organic compounds
WebFIRE
Factor Information Retrieval Svstem
WFU
Wildland fire use
WLF
Wildland fire
WRF
Weather Research and Forecasting Model
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1 Introduction
1.1 What data are included in the 2008 NEI, version 3 General Public release?
The 2008 National Emissions Inventory (NEI), version 3 General Public Release (hereafter referred to
as the 2008 NEI) is a national compilation of emissions sources collected from state, local, and tribal air
agencies as well as from emissions information from the Environmental Protection Agency (EPA)
emissions programs including the Toxics Release Inventory (TRI), emissions trading programs such as
the Acid Rain Program, and data collected as part of EPA regulatory development for reducing
emissions of air toxics. The NEI program develops datasets, blends data from these multiple sources,
and performs quality assurance steps that further enhance and augment the compiled data. The
emissions data in the NEI are compiled for detailed emissions processes within a facility for large
"point" sources or as a county total for smaller "nonpoint" sources and spatially dispersed sources such
as on-road and nonroad mobile sources. For some fires, the data are compiled as day-specific events in
the "event" portion of the inventory.
The pollutants included in the NEI are the pollutants related to implementation of the National Ambient
Air Quality Standards (NAAQS), known as criteria air pollutants (CAPs), as well as hazardous air
pollutants (HAPs) associated with EPA's Air Toxics Program. The CAPs have ambient concentration
limits or are precursors for pollutants with such limits from the NAAQS program. These pollutants
include lead (Pb), carbon monoxide (CO), nitrogen oxides (NOX), volatile organic compounds (VOC),
sulfur dioxide (S02), ammonia (NH3), particulate matter 10 microns or less (PM10) and particulate
matter 2.5 microns or less (PM2.5). The HAP pollutants include the 187 remaining HAP pollutants
from the original 188 listed in Section 112(b) of the 1990 Clean Air Act Amendments1. Key HAP
emissions sources include mercury (Hg), hydrochloric acid (HC1) and other acid gases, heavy metals
such as nickel and cadmium, and hazardous organic compounds such as benzene, formaldehyde, and
acetaldehyde.
1.2 What is included in this documentation?
This document provides a central reference for the 2008 NEI. The primary purpose of this document is
to explain the sources of information included in the inventory. This includes showing which sources of
data are used for each sector, and then providing more information about the EPA-created components
of the data. For each emissions sector, we provide a synopsis of the types of sources that are included in
that sector. Additional analysis of the 2008 NEI is available in the 2008 NEI Report (US EPA, 2013b),
which compares the 2008 NEI (version 2) to previous years and provides graphical summaries of the
data with focus on key sources of emissions for key pollutants.
Section 2 explains the sectors that we use for summarizing the 2008 NEI and organizing this document,
and it provides an overview of the contents of the inventory and a summary of mercury emissions.
1 The current list of HAPs.
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1.5 How is the NEI created?
The NEI is created based on both regulatory and technical components. The Air Emissions Reporting
Rule (AERR) is the rule that requires states to submit emissions of CAP emissions and provides the
framework for voluntary submission of HAP emissions. The 2008 NEI is the first inventory compiled
using the AERR, rather than its predecessor the Consolidated Emissions Reporting Rule (CERR). The
AERR requires agencies to report all sources of emissions, except fires and biogenic sources. Open fire
sources such as wildfires are encouraged but not required. Sources are divided into large categories:
stationary sources are "point" or "nonpoint" (county totals) and mobile sources are either on-road (cars
and trucks driven on paved and unpaved roads) or non-road (locomotives, aircraft, marine, off-road
vehicles and nonroad equipment such as lawn and garden equipment). The AERR has emissions
thresholds that determine whether a state, local, or tribal (S/L/T) agency must report stationary source
emissions as "point" sources or whether the emissions can be lumped together into a county total as
"nonpoint" sources.
The AERR includes emissions thresholds above which states must report stationary emissions as point
sources with the remainder of the stationary emissions reported as nonpoint sources. The AERR
changed the way these reporting thresholds work as compared to the CERR to make these thresholds
"potential to emit" thresholds rather than actual emissions thresholds. In both the CERR and the AERR,
the emissions that are reported are actual emissions, despite that the criteria for which sources to report
is now based on potential emissions. The AERR requires emissions reporting every year, with
additional requirements every third year in the form of lower point source emissions thresholds, and
2008 is one of these third-year inventories.
Table 1 provides the potential-to-emit reporting thresholds that applied for the 2008 NEI cycle. "Type
B" is the terminology in the rule that represents the lower emissions thresholds required for point
sources in the triennial years. The reporting thresholds are sources with potential to emit 100 tons/year
or more for most criteria pollutants with the exceptions of CO (1000 tons/year) and Pb (5 tons/year). As
shown in the table, special requirements apply to nonattainment area (NAA) sources, where even lower
thresholds apply.
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Table 1: Point source reporting thresholds (potential to emit) for criteria
pollutants in the Air Emissions Reporting Rule
Pollutant
2008 NEI thresholds: potential to emit
(tons/yr)
Everywhere
(Type B sources)
NAA sources1
1 SOx
> 100
> 100
2 VOC
> 100
O3 (moderate) >100
3 VOC
O3 (serious) > 50
4 VOC
O3 (severe) >25
5 VOC
O3 (extreme) >10
6 NOX
> 100
> 100
7 CO
> 1000
O3 (all areas) >100
8 CO
CO (all areas) >100
9 Pb
> 5
> 5
10 PM10
> 100
PM10 (moderate) >
100
11 PM10
PM10 (serious) > 70
12 PM2.5
> 100
> 100
13 M 13
> 100
> 100
1 NAA = Nonattainment Area. Special point source reporting thresholds apply for
certain
pollutants by type of nonattainment area. The pollutants by nonattainment area are:
Ozone: VOC, NOX, CO; CO: CO; PM10: PM10
Based on the AERR requirements, S/L/T agencies submit emissions of point, nonpoint, on-road mobile,
nonroad mobile, and fires emissions sources. These submissions are sent to EIS that EPA then uses to
review, assemble and augment the data from the S/L/T agencies. For the 2008 NEI, these submissions
were due to EPA by June 30, 2010. Extra time was allotted to agencies for the 2008 NEI since it was
the first inventory for which EIS was used. Starting with the 2009 inventory, S/L/T agency data are due
by December 31 of the year following the inventory year (e.g., 2009 submissions were due by December
31, 2010).
States continued to revise their submissions of 2008 data through November 2011, which resulted in the
release of the 2008 NEI v2. Other than for Puerto Rico, for which CAP-only emissions were submitted
for the first time in March 2012, state revisions were generally small after the 2008 NEI v2 was released.
The Puerto Rico CAP emission submittal was incorporated into the 2008 NEI v3.
Once the reporting NEI period has closed, EPA works with states to identify any agencies that missed
the reporting window, provide feedback on data quality such as outliers and apparently missing data by
comparing to previously established emissions ranges and past inventories. In addition, EPA works to
augment the HAP inventories with additional data sources such as TRI, data developed by the air toxics
and residual risk programs, and other augmentation procedures. This documentation provides a detailed
account of EPA's quality assurance and augmentation methods.
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1.6 Who are the target audiences for the 2008 NEI?
The comprehensive nature of the NEI allows for many uses and therefore its target audiences include
EPA staff and policy makers, the U.S. public, other federal and state decision makers, and other
countries. Table 2 below lists the major current uses of the NEI and the plans for use of the 2008 NEI in
those efforts. These uses include those by EPA in support of the NAAQS, Air Toxics, and other
programs as well as uses by other federal and regional agencies and international support. In addition to
this list, the NEI is used to respond to Congressional inquiries, provide data that supports university
research, and allow environmental groups to understand sources of air pollution.
Table 2: Examples of major current uses of the NEI
Audience
Purposes
Last NEI
data used
U.S. Public
Learn about sources of air emissions
2008 NEI v3
EPA-
NAAQS
Regulatory Impact Analysis - benefits estimates using air
quality modeling
Modified 2005 NEI
v2, for PM NAAQS
Proposal, Modified
2008 NEI v2, for PM
NAAQS Final
S02 NAAQS Monitoring Implementation - Population
Weighted Emissions Index
2008 NEI v3 with
some 2009
Pb Monitoring Rule
2005 NEI v2
Pb NAAQS final designations
2008 NEI v3
Pb NAAQS Policy Assessment
Modified 2008 NEI v3
Transport Rule air quality modeling (e.g., Clean Air Interstate
Rule, Cross-State Air Pollution Rule)
2005 NEI, v2
State Implementation Plans - source of emissions data for
regions outside of the state jurisdiction
2008 NEI v3
EPA - Air
toxics
National Air Toxics Assessment (NATA)
Modified 2005 NEI,
v2
Mercury and Air Toxics Standard - mercury risk assessment
and Regulatory Impact Assessment
Modified 2005 NEI,
v2
Residual Risk and Technology Review - starting point for
inventory development
2008 NEI v3
EPA - other
Inspector General - review of oil and gas industry
2008 NEIvl.5
NEI Report - analysis of emissions inventory data
2008 NEI, v2
Report on the Environment
2008 NEI, v3
Air Emissions website for providing graphical access to CAP
emissions for state maps and Google Earth views of facility
total emissions
2008 NEIvl.5
Department of Transportation, national transportation sector
summaries of CAPs
2008 NEIvl.5
Black Carbon Report to Congress
Modified 2005 NEI,
v2
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Audience
Purposes
Last NEI
data used
Other federal
or regional
agencies
Western Regional Air Partnership - emissions and air quality
modeling in support of western regional air quality
planning, Regional Haze SIP implementation and other
western air quality issues
Modified 2008 NEI v2
(including different oil
& gas, fire and
biogenic emissions)
International
United Nations Economic Commission for Europe's
Convention on Long-range Transboundary Air Pollution
(LRTAP)
2008 NEI, v2
United Nations Environment Programme (UNEP) - global
mercury program
2008 NEI, v2
North American Commission for Environmental Cooperation
(CEC) - North American Regional Action Plan (NARAP) on
Mercury
Modified 2005 NEI,
v2
1.7 What are appropriate uses of the 2008 NEI version 3 and what are the caveats about the
data?
As shown in the preceding section, the NEI provides a readily-available comprehensive inventory of
both CAP and HAP emissions to meet a variety of users' needs. Although the accuracy of individual
emissions estimates will vary from facility-to-facility or county-to-county, the NEI largely meets the
needs of these users in the aggregate. Some NEI users may wish to evaluate and revise the emission
estimates for specific pollutants from specific source types for either the entire US or for smaller
geographical areas as their particular needs may dictate. Regulatory uses of the NEI by the EPA such as
for the Clean Air Interstate Rule always include a public review and comment period. Large-scale
assessment uses such as the NATA study provide an effective screening tool for identifying potential
risks, the results of which should be reviewed in more detail, including an assessment of the key
emissions and other modeling inputs.
One of the primary goals of the NEI is to provide the best assessment of current emissions levels using
the data, tools and methods currently available. For significant emissions sectors of key pollutants, the
available data, tools and methods typically evolve over time in response to identified deficiencies and
the need to understand the costs and benefits of proposed emissions reductions. As these method
improvements have been made, there have not been consistent efforts to revise previous NEI year
estimates to use the same methods as the current year. Therefore, care must be taken when reviewing
different NEI year publications as a time series with the goal of determining the trend or difference in
emissions from year to year. An example of such a method change in the 2008 NEI v3 is the use of the
Motor Vehicle Emissions Simulator 2010b (MOVES) model2 for the on-road data category. The 2008
NEI v2 used a draft version of MOVES and previous NEI 2008 versions and NEI years had used the
Mobile Source Emission Factor Model, version 6 (MOBILE6)3 and earlier versions of the MOBILE
model for this data category. The change of model has been demonstrated to make significant changes
2 See MOVES
3 See Transportation, Air Pollution, and Climate Change
7
-------
in some pollutants. EPA's rulemaking packages typically provide a consistent trend estimate across
base and future years using the same estimation model or methods, but these efforts do not extend to re-
estimation of past NEI years. Other significant emissions sectors which have seen improvements and
therefore inconsistent trend data through the years include paved and unpaved road PM emissions,
animal waste ammonia emissions, and residential wood combustion emissions. In addition, the 2008
NEI v3 uses updated emissions factors for several metal HAPs and acid gases from coal-fired utility
boilers.
The spreadsheet "2008neiv3 issues.xlsx" (also available from the main 2008 NEI data page listed in
Section 1.3.2) provides a detailed listing of the issues that were identified during the course of the
development of the 2008 NEI, including all issues identified as part of the 2008 NEI versions 1, 1.5, 2
and 3 and the current status of those issues. The spreadsheet will be kept up to date and the date last
updated will be provided in the header.
In addition to the issues, users should take caution in using the emissions data for filterable and
condensable components of particulate matter (PM10-FIL, PM2.5-FIL and PM-CON) which is not
complete and should not be used at any aggregated level. These data are provided for users who wish to
better understand the components of the primary PM species, where they are available, in the
disaggregated, process-specific emissions reports. Where not reported by S/L/T, EPA augments these
components (see Section 3.1.2). However, not all sources are covered by this routine, and in mobile
source models, only the primary particulate species are estimated. Thus, users interested in PM
emissions should use the primary species of particulate matter (PM10-PRI and PM25-PRI), described in
this document simply as PM10 and PM2.5.
The primary unresolved issues in the 2008 NEI v3 are described below:
• The only emissions for any data category in the 2008 NEI for South Dakota are those provided
by EPA. Users of the NEI are encouraged to augment 2008 South Dakota point source emissions
with data from past inventories or other sources.
• It is suspected but not fully confirmed that for a few states, the point source primary PM10 and
primary PM2.5 emissions may be underestimated due to the reporting of only the filterable
portions of particulate matter as the full filterable plus condensable equals "primary" particulate
pollutants by the States. On-road, Nonroad, and probably most of Nonpoint emissions for these
States are expected to accurately reflect the full filterable plus condensable particulate emissions
due to the available models and emission factors for those data categories.
• Hydrogen Cyanide (HCN) emissions from the Mercury and Air Toxics Standard (MATS) dataset
use emission factors which have since been deemed unreliable due to measurement issues.
These data were not used for setting a limit for this pollutant, but, they were used for the NEI
because the issue was not known. The MATS HCN data in the NEI sums to approximately
5,400 tons. In addition, many EGUs have emissions for both HCN and cyanide (CN). The EPA
EGU estimate of CN is from AP-42. EPA staff have since concluded that the CN emission
factor in AP-42 was likely HCN (based on expert inference of the probable test method used,
8
-------
which was not available in the AP-42 references) and therefore would double count any other
HCN estimate at the boilers.
• Landfills have not been estimated by EPA for the 2008 NEI, as had been done in earlier NEI
years. Some States do report some pollutants for some of their larger landfills, and these have
been included in the 2008 NEI. This is expected to be largely an issue for some toxics. The
scope of the underestimate is uncertain, due to an expectation that many landfills have been
adding gas collection systems as a result of various control programs and the value of the
collected gas as a fuel.
• Solvent sectors in the nonpoint data category including consumer & commercial solvent use,
degreasing, dry cleaning, graphic arts, industrial surface coating & solvent use and non-industrial
surface coating were estimated to be missing at least 190,000 tons of HAP VOC because EPA
did not add HAP emissions where S/L/T reported only VOC.
• EPA did not develop default emissions to use for oil and natural gas; where state/local/tribal
agency data are incomplete there were not EPA default data for use in gap-filling. Therefore,
EPA recommends that users of the NEI look to alternative data sources to fill in emissions from
this emissions source, which was in a high growth pattern during calendar year 2008. For future
inventories, EPA is developing a default method to ensure the oil and gas sector has emissions in
future NEIs for all states that have this activity. This issue is further explained in an Inspector
General report released during 2013. (US EPA, 2013a).
• Double counting occurred in Washington (WA) State for agricultural fires. This category was
reported by the state of Washington in the Events data category (which is only for prescribed and
wildfires). EPA added Washington's agricultural fire data to the nonpoint data category (where it
belongs) and EPA inadvertently did not remove it from Events.
• Waste disposal (pile burns) was inadvertently reported in the Events data category by Alaska and
Washington; it should have been in the nonpoint category. No double counting of emissions
occurred.
• Some of the EPA data used in some of the nonpoint data category sectors were carried forward
from previous years including 2002 and 1999 (see Table 21).
• There may be some double-counting of rail switchyard emissions in a small number of locations,
due to State reporting of nonpoint county emissions and EPA reporting of the larger switchyards
as point sources. In the counties where this occurs it is not known if the nonpoint county
emissions reported by the States have been adjusted to exclude the point sources reported by
EPA. See also Section 4.4.6.
• EPA's methods for fires, which rely heavily on satellite data, generally do not capture the smaller
fires (generally not less than 100 acres), and thus the EPA estimates for acres burned and the
emissions are likely low in most cases. The same can be said for interference to remote sensing
caused by excess cloud cover and/or canopy cover.
• In addition to this general underestimation for some fires, there may be minor double-counting in
cases where Tribes also submitted fires data. The Tribal data were included in the 2008 NEI as
submitted by the Tribes. No attempts were made to avoid the possible double count that would
9
-------
occur with the possible overlap of EPA data or State data and the Tribal-reported data (see
Section 5.1.2).
• In using the NEI in modeling applications, inconsistencies were identified among reported stack
velocities, flows, and diameters. While many of have been corrected, there may be others that
remain.
• For future year projections of the 2008 NEI that will substitute Integrated Planning Model (IPM)
results for Electric Generating Utilities (EGUs), the mapping of NEI units to IPM units from the
National Electric Energy Data System (NEEDS) database (used to define the units for IPM) is
somewhat incomplete. Users of the data should confirm that any deficiencies in the mapping are
resolved in 2008 NEI modeling files also provided by EPA.
2 2008 inventory contents overview
2.1 What are EIS Sectors and what list was used for this document?
EIS Sectors are being used for the first time with the release of the 2008 NEI. These sectors have been
developed to better group emissions for both CAP and HAP summary purposes. The sectors are based
simply on grouping the emissions by the emissions process based on the source classification code
(SCC) to the EIS sector. The SCC-EIS Sector cross-walk used for the summaries provided in this
document ("see eissector xwalk 2008neiv3.xlsx") is part of the supporting data listed in Section 8.1.
In building this list, we gave consideration not only to the types of emissions sources our data users most
frequently ask for, but also to the need to have a relatively concise list in which all sectors have a
significant amount of emissions of at least one pollutant. Users of the NEI are free to obtain the SCC-
level data and modify the EIS Sector cross-walk to make custom groupings of their own or to request
assistance from EPA to do so.
Some of the sectors include the nomenclature "NEC", which stands for "not elsewhere classified." This
simply means that those emissions processes were not appropriate to include in another EIS sector and
their emissions were too small individually to include as its own EIS sector.
Traditionally, the inventory has been compiled and considered using four major categories, which are
also data categories in EIS: point, nonpoint, onroad, and nonroad. In EIS, another data category called
"event" has been added and is used to compile day-specific data from fires. While events could be other
non-fire intermittent releases such as chemical spills, these have not been a focus of the NEI creation
effort. Table 3 shows the EIS sectors in the left most column and identifies the EIS category associated
with that sector. It also identifies in the rightmost column the section number of this document that
provides more information about that EIS sector. As the column illustrates, many EIS sectors include
emissions from more than one EIS category because the EIS sectors are compiled based on the type of
emissions sources rather than the category. Also, the right most column is set to zero where the
documentation section has not yet been populated with any information.
10
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One particularly large change from the traditional labeling of sectors and categories is for the EIS
sectors "Mobile - Aircraft", "Mobile - Commercial Marine Vessels", and "Mobile - Locomotives" that
are included in EIS as part of the point and nonpoint data categories4 rather than the nonroad category.
This change is related only to how the data are compiled by EIS and has no other significance for the
emissions values themselves. Another significant change is the inclusion of biogenics emissions,
"Biogenics - Vegetation and Soil", in EIS for the 2008 NEI v3. These county and SCC-level emissions
were incorporated in the nonpoint EIS data category since there was not a separate EIS data category for
biogenic emissions available for 2008 NEI. NEI users who sum emissions by EIS data category rather
than EIS sector should be aware that these changes will give differences from historical summaries of
"nonpoint" and "nonroad" data unless care is taken to assign those emissions to the historical grouping.
4 Mobile- aircraft: aircraft is in point and unpaved air strips and in-flight lead is in nonpoint
Mobile- locomotives: yard locomotives are in point and nonpoint, line haul locomotives are in nonpoint
Mobile- commercial marine: predominantly in nonpoint but some states reported in point due to the existence of point
sees contained in this sector. These point sees were retired after the 2008 inventory cycle.
11
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Table 3: EIS sectors and associated emissions categories and document sections
Sector name
Point
Nonpoin
t
On-road
Nonroad
Event
Document
Section
Agriculture - Crops & Livestock Dust
0
3.2
Agriculture - Fertilizer Application
0
3.3
Agriculture - Livestock Waste
0
0
3.4
Biogenics - Vegetation and Soil
0
6
Bulk Gasoline Terminals
0
0
3.5
Commercial Cooking
0
3.6
Dust - Construction Dust
0
0
3.7
Dust - Paved Road Dust
0
3.8
Dust - Unpaved Road Dust
0
3.9
Fires - Agricultural Field Burning
0
01
5.2
Fires - Prescribed Burning
0
5.1
Fires - Wildfires
0
5.1
Fuel Comb - Comm/Institutional - Biomass
0
0
0
Fuel Comb - Comm/Institutional - Coal
0
0
0
Fuel Comb - Comm/Institutional - Natural Gas
0
0
0
Fuel Comb - Comm/Institutional - Oil
0
0
0
Fuel Comb - Comm/Institutional - Other
0
0
0
Fuel Comb - Electric Generation - Biomass
0
3.10
Fuel Comb - Electric Generation - Coal
0
3.10
Fuel Comb - Electric Generation - Natural Gas
0
3.10
Fuel Comb - Electric Generation - Oil
0
3.10
Fuel Comb - Electric Generation - Other
0
3.10
Fuel Comb - Industrial Boilers, ICEs - Biomass
0
0
3.11
Fuel Comb - Industrial Boilers, ICEs - Coal
0
0
3.11
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
0
0
3.11
Fuel Comb - Industrial Boilers, ICEs - Oil
0
0
3.11
Fuel Comb - Industrial Boilers, ICEs - Other
0
0
3.11
Fuel Comb - Residential - Natural Gas
0
3.13
Fuel Comb - Residential - Oil
0
3.13
Fuel Comb - Residential - Other
0
3.13
Fuel Comb - Residential - Wood
0
0
Gas Stations
0
0
0
Industrial Processes - Cement Manufacturing
0
0
Industrial Processes - Chemical Manufacturing
0
0
0
Industrial Processes - Ferrous Metals
0
0
Industrial Processes - Mining
0
0
0
Industrial Processes - NEC
0
0
0
Industrial Processes - Non-ferrous Metals
0
0
0
12
-------
£
°©
a
¦a
©
•_
¦a
©
Document
Sector name
*©
=
©
a
=
©
>
Section
Plh
Z *
O
Z
W
Industrial Processes - Oil & Gas Production
0
0
0
Industrial Processes - Petroleum Refineries
0
0
0
Industrial Processes - Pulp & Paper
0
0
Industrial Processes - Storage and Transfer
0
0
0
Miscellaneous Non-Industrial NEC
0
0
0
Mobile - Aircraft
0
0
4.2
Mobile - Commercial Marine Vessels
0
0
4.3
Mobile - Locomotives
0
0
4.4
Mobile - Non-Road Equipment - Diesel
0
0
4.5
Mobile - Non-Road Equipment - Gasoline
0
0
4.5
Mobile - Non-Road Equipment - Other
0
0
4.5
Mobile - On-road - Diesel Heavy Duty Vehicles
0
4.6
Mobile - On-road - Diesel Light Duty Vehicles
0
4.6
Mobile - On-road - Gasoline Heavy Duty Vehicles
0
4.6
Mobile - On-road - Gasoline Light Duty Vehicles
0
4.6
Solvent - Consumer & Commercial Solvent Use
0
0
Solvent - Degreasing
0
0
0
Solvent - Dry Cleaning
0
0
0
Solvent - Graphic Arts
0
0
0
Solvent - Industrial Surface Coating & Solvent Use
0
0
0
Solvent - Non-Industrial Surface Coating
0
0
Waste Disposal
0
0
02
0
1 Unintentionally occurs only in Washington. See Section 1.7.
2 Unintentionally occurs only in Alaska and Washington. See Section 1.7.
2.2 What do the data show about the sources of data in the 2008 NEI?
Data in the NEI come from a variety of sources. The emissions are predominantly from S/L/T agencies
for both CAP and HAP emissions. In addition, EPA quality assures and augments the data provided by
states to assist with data completeness, particularly with the HAP emissions since the S/L/T HAP
reporting is voluntary. Additional details on EPA's augmentation datasets are available in the remainder
of this document.
Figure 1 shows the proportion of criteria pollutant emissions from various data sources in the NEI for
point and nonpoint sources. For the nonpoint data in the figure (left 7 bars), most of the emissions come
from EPA sources of data, with S/L/T agency data the majority for VOC and CO. The large "EPA
Nonpoint" bar for PM10 is predominantly dust sources from unpaved roads (7 million tons), agricultural
dust from crop cultivation (3.6 million tons), and construction dust (1.5 million tons). For point data in
the figure (right 7 bars), most of the emissions come from S/L/T agency data, with EPA data making up
a large proportion only for the PM10 and PM2.5 with the EPA PM Augmentation dataset ("EPA PM
13
-------
While not shown in Figure 5, Puerto Rico submitted point source CAP emissions after the release of the
2008 NEI v2 but did not submit HAP emissions. The CAP emissions were incorporated into the 2008
NET v3.
Figure 5: Point inventory - submission types - includes local agencies
Submission Type
Submission Type
| None
H CAP
~ CAP_HAP
Figure 6 shows the states that submitted nonpoint emissions: 41 states submitted CAPs and 32 also
submitted HAPs. Only 7 states did not submit any nonpoint emissions, and at least some of these
notified EPA that EPA's estimates were acceptable for the source types that EPA estimated. Puerto
Rico (not shown) did not submit any nonpoint emissions.
18
-------
Figure 7: On-road inventory - submission types - does not include local agencies
Submission Type
1 [Accepted EPA Estimates
^[inputs
"CAP HAP emissions
Like on-road mobile, the nonroad mobile sector gives a patchwork of scenarios for providing different
data types. Again, California has provided EPA CAP and HAP emissions based on a different model
than the other states - the OFFROAD model8 Other states emissions come from the NONROAD
model9, often through the use of the NMDvl, which drives the NONROAD model. In Idaho, Texas, and
Kansas, state agencies submitted nonroad emissions for CAP and HAP pollutants. For Utah, Illinois,
Pennsylvania, and New York, the states submitted CAP emissions only. Eighteen states submitted
NONROAD model inputs, that EPA could use to generate emissions, and the remaining states accepted
EPA estimates.
8 The OFFROAD model and documentation
9 The NONROAD model and documentation
20
-------
Figure 8: Nonroad equipment inventory - submission types - does not include local agencies
Submission Type
| Accepted EPA Estimates
Appendix A provides a table that shows for each EIS sector whether the data comes from S/L/T
agencies or a selection of EPA created datasets including TRI.
2.3 What are the top sources of some key pollutants?
Table 4 provides a summary of criteria pollutants and total HAP emissions for all of the EIS sectors,
including the biogenic emissions from vegetation and soil. Emissions in federal waters and from
vegetation and soils have been split out and totals both with and without these emissions are included.
Emissions in federal waters include offshore drilling platforms and commercial marine vessel emissions
outside the typical 3-10 nautical mile boundary defining state waters. These emissions values are
subject to change and are bounded by the caveats and methods described by this documentation.
As previously noted, additional analysis of the 2008 NEI is available in the 2008 NEI Report (US EPA,
2013b), which compares the 2008 NEI (version 2) to previous years and provides graphical summaries
of the data with focus on key sources of emissions for key pollutants.
21
-------
Table 4: EIS sectors and associated CAP emissions and total HAP (1000 short tons/year)
1,000 short tons/yr
Sector
CO
voc
NOX
S02
PM2.5
PM10
NH3
Lead
Total
HAP1
Agriculture - Crops & Livestock Dust
923
4,650
0.00E
+0
1.49E-
2
Agriculture - Fertilizer Application
1,183
3.32E-
7
5.83E-
2
Agriculture - Livestock Waste
0.224
93
0.194
1.38E-
2
7.58
25
2,448
Bulk Gasoline Terminals
0.780
93
0.394
1.48E-
2
8.80E-
2
0.101
4.30E-
4
2.49E-
5
5.45
Commercial Cooking
30
12
6.19E-
4
9.52E-
5
78
82
mm#
0.00E
+0
5.15
Dust - Construction Dust
0.176
1.67E-
2
7.69E-
2
1.00E-
3
220
2,115
8.34E-
4
1.99E-
4
3.70E-
2
Dust - Paved Road Dust
280
1,539
0.00E
+0
0.00E+
0
Dust - Unpaved Road Dust
812
8,104
0.00E
+0
0.00E+
0
Fires - Agricultural Field Burning
592
55
25
3.42
68
70
3.88
9.73E-
4
6.48
Fires - Prescribed Fires
8,273
1,693
137
65
696
818
119
207
Fires - Wildfires
12,200
2,847
96
70
999
1,178
198
213
Fuel Comb - Comm/Institutional -
Biomass
17
0.535
5.54
1.69
2.51
3.06
0.199
5.79E-
4
0.663
Fuel Comb - Comm/Institutional - Coal
15
0.423
21
96
2.21
4.73
0.174
3.48E-
3
1.92
Fuel Comb - Comm/Institutional - Natural
Gas
100
9.18
146
1.32
5.92
6.15
1.10
8.94E-
4
1.57
Fuel Comb - Comm/Institutional - Oil
18
2.69
61
66
4.74
6.09
0.821
9.50E-
4
0.840
Fuel Comb - Comm/Institutional - Other
12
0.894
9.29
1.24
0.557
0.678
5.09E-
2
1.53E-
3
8.96E-
2
Fuel Comb - Electric Generation -
Biomass
21
1.04
10
2.72
1.43
1.76
1.51
1.86E-
3
1.34
Fuel Comb - Electric Generation - Coal
574
29
2,810
7,582
275
369
11
4.14E-
2
143
Fuel Comb - Electric Generation - Natural
Gas
91
9.33
181
16
20
21
11
1.42E-
3
3.06
Fuel Comb - Electric Generation - Oil
17
2.57
116
177
11
14
1.99
3.51E-
3
0.824
Fuel Comb - Electric Generation - Other
26
1.99
26
14
1.81
2.38
3.19
3.79E-
3
1.26
Fuel Comb - Industrial Boilers, ICEs -
Biomass
193
8.38
80
25
32
39
1.70
1.53E-
2
7.37
Fuel Comb - Industrial Boilers, ICEs -
58
2.12
209
674
24
51
0.495
2.08E-
15
22
-------
1,000 short tons/yr
Sector
CO
voc
NOX
S02
PM2.5
PM10
NH3
Lead
Total
HAP1
Coal
2
Fuel Comb - Industrial Boilers, ICEs -
Natural Gas
366
59
111
39
29
32
6.46
5.73E-
3
22
Fuel Comb - Industrial Boilers, ICEs - Oil
27
3.84
99
139
7.28
9.97
1.15
2.47E-
3
2.87
Fuel Comb - Industrial Boilers, ICEs -
Other
121
6.37
70
65
31
33
0.711
4.67E-
3
2.27
Fuel Comb - Residential - Natural Gas
94
13
230
1.41
5.06
5.41
38
2.04E-
4
0.920
Fuel Comb - Residential - Oil
14
1.74
57
121
5.87
6.76
2.62
3.80E-
3
0.138
Fuel Comb - Residential - Other
49
2.90
38
8.90
1.07
1.56
0.380
1.33E-
4
7.23E-
2
Fuel Comb - Residential - Wood
2,420
375
35
9.51
353
355
20
4.62E-
4
70
Gas Stations
1.42E-
2
643
2.02E-
2
2.07E-
3
1.05E-
2
2.13E-
2
4.32E-
4
5.24E-
4
28
Industrial Processes - Cement Manuf
102
9.24
191
106
13
24
0.905
8.26E-
3
3.62
Industrial Processes - Chemical Manuf
205
100
77
196
22
29
19
1.10E-
2
32
Industrial Processes - Ferrous Metals
467
19
63
33
36
44
0.623
7.90E-
2
2.41
Industrial Processes - Mining
29
2.01
6.31
3.82
107
749
1.93E-
3
3.07E-
3
0.825
Industrial Processes - NEC
259
215
196
156
117
183
51
5.95E-
2
49
Industrial Processes - Non-ferrous Metals
328
16
16
132
20
25
0.992
8.03E-
2
9.65
Industrial Processes - Oil & Gas
Production
219
1,688
409
61
7.11
10
2.81E-
2
1.42E-
5
8.30
Industrial Processes - Petroleum
Refineries
84
68
92
145
23
27
2.89
5.19E-
3
6.02
Industrial Processes - Pulp & Paper
132
130
75
41
40
50
5.94
5.06E-
3
54
Industrial Processes - Storage and
Transfer
17
240
8.47
6.15
25
55
5.12
9.67E-
3
17
Miscellaneous Non-Industrial NEC
29
227
1.81
0.159
3.18
3.73
11
4.02E-
4
25
Mobile - Aircraft
438
33
121
13
3.66
9.59
0.553
7.66
Mobile - Commercial Marine Vessels
87
14
536
143
25
27
0.261
2.08E-
3
2.13
Mobile - Locomotives
120
44
846
11
25
28
0.362
2.28E-
3
4.09
23
-------
1,000 short tons/yr
Sector
CO
voc
NOX
S02
PM2.5
PM10
NH3
Lead
Total
HAP1
Mobile - Non-Road Equipment - Diesel
860
165
1,546
32
123
129
1.08
8.78E-
6
39
Mobile - Non-Road Equipment - Gasoline
15,367
2,242
250
2.35
57
64
0.837
2.14E-
6
534
Mobile - Non-Road Equipment - Other
1,012
46
186
0.756
2.10
2.11
1.07
0.104
Mobile - On-Road Diesel Heavy Duty
Vehicles
942
213
3,199
5.54
160
179
5.32
0.00E
+0
41
Mobile - On-Road Diesel Light Duty
Vehicles
47
10
75
0.172
4.83
5.39
0.333
0.00E
+0
1.76
Mobile - On-Road Gasoline Heavy Duty
Vehicles
2,584
169
273
1.75
4.21
7.36
4.66
0.00E
+0
45
Mobile - On-Road Gasoline Light Duty
Vehicles
29,583
2,660
3,395
32
83
140
127
0.00E
+0
727
Solvent - Consumer & Commercial
Solvent Use
3.46E-
2
1,619
1.04E-
2
7.62E-
3
1.32E-
2
2.41E-
2
6.89E-
2
5.19E-
6
174
Solvent - Degreasing
0.515
198
6.33E-
2
6.84E-
3
6.88E-
2
8.59E-
2
1.53E-
2
5.75E-
4
28
Solvent - Dry Cleaning
8.80E-
4
49
1.20E-
6
2.25E-
6
1.59E-
2
1.59E-
2
1.25E-
4
2.1E-
11
3.88
Solvent - Graphic Arts
3.20
356
3.86
2.69E-
2
0.255
0.281
0.101
3.18E-
4
24
Solvent - Industrial Surface Coating &
Solvent Use
4.73
648
3.88
1.20
3.83
4.39
0.326
4.82E-
3
63
Solvent - Non-Industrial Surface Coating
#####
#
429
0.00E
+0
0.00E
+0
#####
#
#####
#
1.83E-
2
68
Waste Disposal
1,404
181
98
21
208
240
66
1.06E-
2
38
Sub Total (no federal waters)
79,655
17,759
16,909
10,324
6,014
21,58
0
4,359
0.950
2,749
Fuel Comb - Industrial Boilers, ICEs -
Natural Gas
78
1.42
64
4.02E-
2
0.383
0.383
Fuel Comb - Industrial Boilers, ICEs - Oil
1.83
0.352
7.55
0.715
0.327
0.337
Fuel Comb - Industrial Boilers, ICEs -
Other
5.02E-
3
3.06E-
4
4.47E-
3
2.84E-
5
9.69E-
5
9.69E-
5
Industrial Processes - Oil & Gas
Production
1.85
58
2.31
0.266
5.99E-
2
6.06E-
2
Industrial Processes - Storage and
Transfer
0.909
Mobile - Commercial Marine Vessels
128
33
1,086
477
68
74
0.447
2.76E-
3
2.29
Sub Total (federal waters)
210
94
1,160
478
69
74
0.447
2.76E-
3
2.29
Sub Total (all but vegetation and soil)
79,865
17,853
18,069
10,802
6,083
21,65
4
4,360
0.953
2,751
24
-------
1,000 short tons/yr
Sector
CO
voc
NOX
S02
PM2.5
PM10
NH3
Lead
Total
HAP1
Biogenics - Vegetation and Soil
6,474
38,909
1,078
5,000
Total
86,339
56,762
19,147
10,802
6,083
21,65
4
4,360
0.953
7,750
1 Total HAP does not include diesel PM, which is not a HAP listed by the Clean Air Act
2 Biogenic vegetation and soil emissions excludes emissions from Alaska, Hawaii, and territories
2.4 How does this NEI compare to past inventories?
Many similarities between the 2008 NEI approaches and past NEI approaches exists, notably that the
data are largely compiled from data submitted by S/L/T agencies for CAPs, and that the HAP emissions
have greater augmentation by EPA because they are a voluntary contribution from the partner agencies.
The NEI program continues with the 2008 NEI to work towards a complete compilation of the nation's
CAPs and HAPs. EPA provided feedback to states during the compilation of the data on critical issues
(such as missing Hg data) as has been done in the past, and EPA improved the inventory for the release
of version 3. In addition to these similarities, there are some important differences in how the 2008 NEI
has been created and the resulting emissions, which are described in the following two subsections.
2.4.1 Differences in approaches
The 2008 NEI is the first inventory compiled with the EIS. This new system greatly improved the
collection approach from less structured approaches used in the past. The hundreds of automated QA
checks in EIS have undoubtedly improved the data quality and allowed EPA more time to review the
data prior to publication. One outcome of this additional QA and review is the lengthier list of caveats
identified in Section 1.7 and 2008neiv3 issues.xlsx.
For the inventory in general, but primarily affecting stationary sources, we have consolidated the
number of HAP compounds significantly for metals and cyanides and provided conversion factors to
enable S/L/T agencies to provide them as the metal or cyanide that is important for risk. For all data
categories we provide only speciated chromium and specific allowable chromium species by speciating
agency-reported total chromium (see section 3.1.3). This was done to allow easier toxicity weighting of
the inventories and more streamlined risk modeling.
For point sources, the augmentation approach for using TRI has changed in the 2008 NEI. Since TRI
has facility total emissions and not emissions for each emissions process, EPA needs to assign a process
SCC code to the emissions. In the past, the practice had been to assign a miscellaneous code of
"39999999" to these emissions. This prevents the emissions from being assigned to meaningful
emissions categories (like the EIS sectors) for summaries among other limitations. For the 2008 NEI,
EPA apportioned the HAP emissions to the available processes at the facilities based on CAP emissions
(see Section 3.1.4). For high risk sources (see Section 3.1.7), we used TRI data even if there were no
CAPs to use to apportion them. For other sources, however, the approach did not use TRI emissions
when CAPs were not available for apportioning, resulting in less TRI emissions used overall and
missing emissions in some cases. EPA is currently evaluating the impact of this result and expects to
25
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further revise the TRI augmentation in subsequent NEI years to use more TRI data and also use better
SCC assignments.
Also for point sources, HAP emissions were augmented using some new approaches. EPA used results
from the 2005 NATA to help prioritize review of the highest risk sources for additional review by S/L/T
agencies and EPA (see Section 3.1.7). Additionally, EPA used CAP-HAP emissions ratios to augment
other sources, where HAP emissions were clearly missing (see Section 3.1.5).
Another difference for point sources is related to latitude/longitude coordinates. EIS allows the NEI to
have both facility coordinates as well as release point (e.g., stack) coordinates, whereas previous NEI
databases could store only coordinates at release points10. These two separate sets of values allowed
EPA to assess whether the facility coordinates and the release point coordinates were in the same
vicinity and make adjustments to resolve inconsistencies in collaboration with the S/L/T/ agencies. In
part through this process, we have ensured that priority facilities with high emissions and/or high risk
have accurate coordinates.
In past inventories, the NEI development approach carried forward a larger quantity of older NEI data
for use in the NEI than was done for the 2008 NEI. We changed our approach on widespread use of
prior year emissions both to prevent EPA's creation of faulty data as well as to address state concerns
that EPA overestimated emissions by pulling data forward that was incorrect or duplicative. This
approach prevents double counting and overestimation of emissions at the expense of potentially
missing some emissions.
For nonpoint sources, EPA collaborated with S/L/T agencies to devise a more consistent approach
across states and build tools for states to use for compiling nonpoint emissions (see Section 3.1.6). We
believe that this approach has improved consistency in nonpoint source emission estimates across the
NEI for many sectors. It also resulted in improvements to the approaches (such as updated algorithms or
emission factors) for many sectors. Past feedback on some source categories such as industrial boilers
had been that EPA should not augment the data with its own estimates because emissions were double-
counted with emissions in the point source category. Therefore, as also explained in Section 3.1.6, EPA
did not augment some sectors that had significant potential double-counting concerns between nonpoint
and point sources. EPA still developed estimation methods for S/L/T agency use to help improve
consistency.
For onroad mobile sources, the 2008 NEI uses the MOVES model for the first time. In addition, the
MOVES-based emissions have been compiled using hourly, gridded meteorology data for 2008 rather
than monthly averages used in past approaches, and then summed to an annual value. Section 4.6
10 In past inventories, release point coordinates were sometimes the same for all release points, suggesting that only a
facility latitude/longitude was available. Both the facility coordinates and release point coordinates are available in EIS. For
released 2008 NEI data such as modeling files that are given at the process level, the release point coordinates are used
whenever available, and the facility coordinates are used only when more detailed release point locations are not available.
26
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provides more information on our approaches. Our approach predicts higher NOx and PM emissions
than included in the 2005 NEI, based on the MOBILE6 model.
For nonroad mobile sources, emissions at airports are treated comprehensively as point sources. In past
inventories, some airports were point sources while others were aggregated to a total nonpoint county
estimate. The processes included at each airport are aircraft exhaust, ground support equipment, and
auxiliary power units. The emissions for aircraft ground support equipment and aircraft auxiliary power
units associated with aircraft-specific activity were estimated by the Federal Aviation Administration
(FAA) Emissions and Dispersion Modeling System (EDMS) using the assumptions and defaults
incorporated in the model. This is a significant change from the previous NEI emissions, for which
ground support equipment estimates came from the NONROAD model and auxiliary power unit
emissions were not included in EPA's estimates. In addition, in-flight Pb emissions have been included
in the 2008 NEI for the first time (in the nonpoint data category) and are reflected in the totals for the
"Mobile Sources - Aircraft" sector. Section 4.2.5.2 provides more information.
For fires, EPA has used the SMARTFIRE2 system for the first time in the 2008 NEI v2. This system
eliminated a shortcoming in the 2005 NEI that did not assign all fires to either wildfire or prescribe
burning categories. Another update for HAP augmentation of state emissions has resulted in increases
in HAP VOC emissions, most notably in California. EPA continues to review this method for
subsequent NEI years. In addition, an updated method for agricultural burning has allowed EPA to
include these emissions comprehensively across the US. More information on all fire approaches is
available in Section 5.
2.4.2 Differences in emissions
EPA reviewed the differences in emissions between the 2008 NEI and past inventories and produced a
more complete assessment of the 2008 NEI based on the 2008 NEI v2, called "The 2008 NEI Report"
(US EPA, 2013b). Presented here is a brief comparison of 2005 to 2008 v3 of some selected CAPs
based on seven highly aggregated data categories. Categories not shown here are emissions from
biogenic (natural) sources and wildfires.
Figure 9 illustrates key differences between the 2008 NEI v3 and the 2005 NEI v2, excluding wildfires
and biogenic sources. Emissions of all pollutants, except NH3, have decreased from 2005. There are
some notable increases in particular sectors despite the overall decrease. The following describes that
many of these differences are based on methods changes and do not reflect real differences from 2005 to
2008.
27
-------
in the 2008 NEI, emissions only in state waters (usually 3-10 nm) were allocated to states. The
industrial processes decreased slightly overall from 2005, and the larger decreases in cement
manufacturing (-15%), petroleum refineries (-18%), storage and transport (-48%), and other industrial
processes (-24%) - offset the large increase for oil & gas production (25%).
For PM2.5, 2008 emissions are 5% lower than 2005, partly due again to the attribution of emissions in
the commercial marine portion of the inventory (85% lower than 2005). The increases in the highway
vehicle category are associated with the change to the MOVES model, which has higher PM2.5
emissions than MOBILE6 because of temperature impacts on PM2.5 included in MOVES only and
based on new emissions testing. The increases in the miscellaneous category are related to increases in
dust from agricultural tilling and livestock {61%) and from paved roads (128%). Increases in prescribed
fires are also evident, but these are partly caused by the large number of "unclassified" fires not included
as prescribed fires in our 2005 NEI total (this limitation has been removed in 2008, so more fires have
been classified as prescribed in 2008 simply because of the method change). The decreases in PM2.5
associated with fuel combustion are assumed to be related to co-benefits from S02 controls on EGUs
implemented for CAIR as well as economic throughput. In addition to the nonroad category artifact
reductions in commercial marine vehicles, the aircraft emissions decreased by 62% largely resulting
from the updated estimation approach.
2008 S02 emissions are 31% lower than 2005 emissions, and again an artificial 88% reduction in
commercial marine emissions is a contributor. The primary source of the decreases are emissions
reductions from EGUs as a result of CAIR and additional decreases in other fuel combustion sectors,
perhaps related to decreased throughput and the economy and somewhat from enforcement actions.
For VOC, 2008 emissions are 17% lower than in 2005 based on decreases across all major category
groups shown above. Some decreases are real, while the highway vehicle decreases are largely from
methods changes. For the miscellaneous category, much of the decreases come from bulk gas terminals,
agricultural burning, and nonpoint petroleum product storage. For the fuel combustion category, there
was a general decrease across all combustion sectors. For industrial processes, there was an increase in
some processes, most notably the oil & gas sector and a increase in non-industrial sectors, but
widespread decreases across many other processes including substantial decreases in the solvent surface
coating industrial sectors account for the overall decrease. The nonroad mobile category has decreases
across all components, though the commercial marine decreases are also an artifact of the reallocation
approach in 2008. Finally, the highway vehicle decrease is caused largely by the change to the MOVES
model, for which light duty cars and trucks tend to have similar or lower VOC emissions than in
MOBILE6. This is because new exhaust and evaporative emissions test data has demonstrated that
MOBILE6 was overly pessimistic in estimating how emissions from mid-1990s and later vehicles would
increase with age.
Finally, 2008 CO emissions are 26% lower than in 2005. While the miscellaneous category has a
significant increase in CO from prescribed fires (again due largely to methods changes), this is offset by
significant decreases from miscellaneous non-industrial processes including a 10.5 million ton decrease
in SCC 2810090000 (uncategorized open fires) down to about 7,300 tons in 2008. In 2005, these
29
-------
emissions were included by EPA for 47 states based on the uncategorized fires identified by
SMARTFIRE in the 2005 NEI process. Thus, this difference actually includes differences due to
uncategorized wildfires from 2005 and is an artifact of the methods changes. The fuel combustion
decreases occur in most all sectors and offset the smaller emissions increase in residential fuel
combustion. For nonroad sources, part of this decrease is from the artificial decreases in commercial
marine as described above, with an even larger decrease from gasoline equipment. Finally, the onroad
mobile source model change to MOVES in 2008 drove the CO 15.6 million ton decrease shown above,
with diesel vehicles decreasing 6% and gasoline vehicles decreasing 33%.
2.5 How well are tribal data and regions represented in the 2008 NEI?
The 2008 NEI includes emissions from 20 Tribal regions within the borders of the continental U.S.
Eighteen of them submitted emissions, two (Assiniboine and Sioux Tribes of the Fort Peck Indian
Reservation, Montana and Ute Mountain Tribe of the Ute Mountain Reservation, Colorado, New
Mexico & Utah) include data solely from EPA point datasets (see Table 8). Table 5 summarizes which
Tribal Nations submitted data to the NEI and for which data categories (these categories have been
defined previously in Section 1.5). In this table, a "CAP, HAP" designation indicates that both criteria
and hazardous air pollutants were submitted by the tribe. CAP indicates that only criteria pollutants
were submitted. Facilities on Tribal land were augmented using TRI, HAP augmentation and PM
augmentation in the same manner as facilities under the State jurisdiction, as explained in Section 3.1;
therefore Tribal Nations in Table 5 with just a CAP flag in point will also have some point HAP
emissions in most cases.
During the 2008 submission period, the Tribal Emission Inventory System Software (TEISS) was
undergoing a large upgrade to adjust to the change from the National Inventory Input Format to the new
Consolidated Emissions Reporting structure. TEISS is used by the majority of the Tribes in creating
their emission inventories. This upgrade took much longer than anticipated and prevented many Tribes
from participating in the 2008 National Emission Inventory.
Table 5: Tribal Participation in the 2008 NEI
Tribe
Point
Nonpoint
On-
road
Nonroad
Events
Confederated Tribes of the Colville
Reservation, Washington
CAP,
HAP
Eastern Band of Cherokee Indians
CAP,
HAP
CAP,
HAP
Fond du Lac Band of the Minnesota
Chippewa Tribe
CAP
CAP
Kootenai Tribe of Idaho
CAP,
HAP
CAP
Leech Lake Band of Ojibwe Reservation
CAP
CAP
Little River Band of Ottawa Indians,
Michigan
CAP
CAP
Makah Indian Tribe of the Makah Indian
Reservation
CAP,
HAP
CAP,
HAP
CAP
Navajo Nation
CAP,
30
-------
Tribe
Point
Nonpoint
On-
road
Nonroad
Events
HAP
Nez Perce Tribe
CAP,
HAP
CAP,
HAP
CAP
Northern Cheyenne Tribe
CAP
CAP
Omaha Tribe of Nebraska
CAP
CAP,HAP
CAP
Prairie Band of Potawatomi Indians
CAP
CAP,
HAP
Pueblo of Pojoaque
CAP
CAP,
HAP
Red Lake Band of Chippewa Indians,
Minnesota
CAP,
HAP
Sac and Fox Nation of Missouri in Kansas
and Nebraska Reservation
CAP,
HAP
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
CAP,
HAP
CAP,
HAP
CAP
Southern Ute Indian Tribe
CAP,
HAP
Washoe Tribe of California and Nevada
CAP,
HAP
2.6 What does this NEI tell us about mercury?
This documentation includes this Hg section because of the importance of this pollutant and because the
sectors used to categorize Hg are different than the sectors presented for the other pollutants. The Hg
sectors primarily focus on regulatory categories and categories of interest to the international
community.
Hg emission estimates in the 2008 NEI sum to 61 tons with 59 tons from stationary sources and 2 tons
from mobile sources. Of the stationary source emissions, the inventory shows that 29.6 tons come from
coal- or oil-fired EGUs with units larger than 25 megawatts (MW), with oil-fired units making up just
0.1 ton of that total. The other sources of emissions are summarized below for the special Hg sectors.
We used a variety of data sources to create the 2008 NEI Hg inventory, as shown Figure 10 below. The
datasets are described in more detail starting in Section 3.1.1, and we highlight some key datasets here.
For EGUs, we used an approach developed for the Mercury and Air Toxics Standards (MATS) rule
during 2011u, and used 2008-specific activity. The MATS-based data are labeled "MATS" in the
figure. Also for EGUs, 11% of the Hg data are from S/L/T agency data instead of the MATS-based
data. These data were used for units with continuous emissions monitors (CEMs) for mercury, or where
EPA was aware that the units had been tested in 2008. In addition, S/L/T data were used for 65% of the
11 See "Memorandum: Emissions Overview: Hazardous Air Pollutants in Support of the Final Mercury and Air Toxics
Standard" EPA-454/R-11-014, 12/1/2011, or at Docket number EPA-HQ-QAR-2009-0234
31
-------
Table 6: Datasets, groups, and amount of Hg in 2008 NEI from each
Data
Mercury
Emissions
Grouped Data Source
Category
Dataset name (see section 3.1.1)
(tons/yr)
for Chart
2008 V3 Responsible Agency Selection
1.31
S/L/T
Misc NP Hg Cats
1.26
EPA Other
EIAG all in NP
1.16
EPA Other
Nonpoint
EPA Rail, nonpoint
0.69
EPA Rail
EPA CMV
0.04
EPA Other
EPA Overwrite Nonpoint vl .5
0.02
EPA Other
EPA Possible Pt Source Contrib VI 5
<0.01
EPA Other
2008 MATS-based EGU emissions
26.27
MATS
2008 V3 Responsible Agency Selection
20.09
S/L/T
EPA TRI Augmentation v2
4.35
TRI
EPA NV Gold Mines
1.70
EPA NV Gold Mines
Point
EPA other data developed for using ahead
of SLT for gapfilling
1.27
EPA Other
2008 EPA Rule Data from OAQPS/SPPD
1.25
Other EPA Rule Data
EPA HAP Augmentation v2
0.50
HAP Aug
EPA 2005NATA values pulled forward to
gapfill
0.17
EPA Other
EPA Rail, point
0.05
EPA Rail
EPA EGUvl.5
0.02
EPA Other
Nonroad
Responsible Agency Dataset
0.30
S/L/T
(Section
EPA Nonroad using NCD20100602
0.01
EPA Other
4.5.2)
EPA Nonroad using NCD20101201
<0.01
EPA Other
On-road
(Section
4.6.2)
Responsible Agency Dataset (California
and tribes only)
0.36
S/L/T
2008_EPA_Mobile
0.32
EPA Other
Since mercury is a HAP, it is reported voluntarily by S/L/T agencies. For the 2008 NEI, 42 states
reported point source Hg emissions; Figure 11 identifies the states that included state or local data. Note
that the state of Nevada is shaded because of the Hg reported by the Clark County local agency; Nevada
does not report HAPs though they do collect test data and agency staff helped us use it for the "EPA NV
Gold Mines" dataset. Tribal mercury data are not reflected in this figure. Two tribal agencies reported
point source Hg: Confederated Tribes of the Colville Reservation, Washington and Makah Indian Tribe
of the Makah Indian Reservation.
33
-------
Figure 11: States with state- or local-provided Hg emissions in the point
data category of the 2008 NEI
2008 Mercury Submissions
| No Point Mercury
1 Point Mercury
Table 7 shows the 2008 NEI mercury emi ssions for the key categories of interest in comparison to 1990.
Also shown are the most recent 2005 emissions, which were used in support of the MATS rule. The
MS2007 Access database included in the zip file provides the category assignments at the facility-
process level for point sources, and the county-SCC level for nonpoint, onroad and nonroad data
categories (see Section 8.1 for access information).
34
-------
Table 7: Trends in Mercury
emissions
- 1990, 2005, and 2008
2005
1990
Emission
2008
Emissions
s
Emissio
(tpy)
Baseline
(tpy)
2005
ns
(tpy)
NEI for
MATS
2008
Source Category
HAPs,
11/14/2005
proposal
3/15/2011
NEI v3
Categorization Approach, 2008 NEI
Utility Coal Boilers
58.8
52.2
29.4
Regulatory code, NESHAP: MATS rule.
Plus boiler specific information from
MATS unit-specific emission factor
assignments to distinguish fuels.
Hospital/Medical/
Infectious Waste
Incineration
51
0.2
0.1
Manually assigned based on examination
of facility name and/or unit/process
descriptions
Municipal Waste
Combustors
57.2
2.3
1.3
Regulatory codes: Section 129 rules for
Small Municipal Waste Combustors
(MWC) and Large MWC
Industrial/Commer
SCC list- chose only processes with these
cial/
Institutional Boilers
14.4
6.4
4.2
SCCs that were not already tagged with
rule or via manual approach
and Process
Heaters
Mercury Cell
Chlor-Alkali Plants
Regulatory code: NESHAP, Mercury Cell
Chlor-Alkali Plants. Manually corrected
10
3.1
1.3
a regulatory code assigned to units at a
facility that terminated the chlor-alkali
process but still emitted Hg in 2008 due to
remediation of the equipment and the soil
around the unit.
Electric Arc
Regulatory code: Area Source rule for
Furnaces
7.5
7.0
A Q
"Stainless & Non-stainless Steel
4.o
Manufacturing: Electric Arc Furnaces"
plus 2 major sources that have EAFs
Commercial/Indust
rial Sold Waste
Incineration
Not
available
1.1
0.02
Manually assigned based on examination
of unit/process description and how it was
categorized in 2005
Hazardous Waste
Combination of regulatory code,
Incineration
NESHAP: Hazardous Waste
6.6
3.2
1.3
Incineration, and manual examination
based on examination of unit/process
description and how it was categorized in
2005.
Portland Cement
Regulatory code: NESHAP, Portland
Non-Hazardous
5.0
7.5
4.2
Cement Manufacturing
Waste
35
-------
2005
1990
Emission
2008
Emissions
s
Emissio
(tpy)
Baseline
(tpy)
2005
ns
(tpy)
NEI for
MATS
2008
Source Category
HAPs,
11/14/2005
proposal
3/15/2011
NEI v3
Categorization Approach, 2008 NEI
Gold Mining
4.4
2.5
1.7
Facility Type, SCC, name, dataset
Sewage Sludge
Incineration
2
0.3
0.3
Manually assigned based on examination
of unit/process description, SCC, and how
it was categorized in 2005
Mobile Sources
Not
1.2
1.8
SCC
available
Other Categories
29.5
18
10.7
Total (all
categories)
246
105
61
The top emitting 2008 Mercury categories are: EGUs (rank 1), electric arc furnaces (rank 2), industrial,
commercial and institutional boilers and process heaters and Portland cement excluding hazardous waste
kilns (tied for rank 3), and gold mining (rank 5).
As shown in Table 5, 2008 mercury emissions are 44 tons lower than in the 2005 inventory. Half of this
difference is due to lower mercury emissions from EGUs covered by MATS; the other half is due to
lower emissions from stationary sources other than EGUs. The lower emissions in 2008 are due to a
combination of methodology differences, state rules, consent decrees, activity levels (e.g., lower cement
production in 2008) and reductions that occurred from facilities prior to MACT compliance dates. For
EGUs, the difference in emissions from 2005 to 2008 is due primarily to the installation of Hg controls
to comply with state specific rules and voluntary reductions, and the co-benefits of Hg reductions from
control devices installed for the reduction of S02 and PM as a result of state and federal actions, such as
New Source Review enforcement actions. The MATS rule is expected to reduce mercury by an
additional 23 tons by 2016.
To understand better the differences in emissions from 2005 to 2008, we conducted a detailed analysis
to identify and quantify the differences for the Portland Cement sector. The 2005 emissions for the
Portland Cement industry were largely the same as the emissions developed in support of the Portland
Cement NESHAP whereas the 2008 emissions are from S/L/T agencies (68% of the Hg) and TRI (32%
of the Hg). The Portland Cement NESHAP total of 7.5 tons/yr is 78% higher than the 2008 value of 4.2
tons/yr. After analyzing the underlying data and approach for the NESHAP emissions and conducting
case studies on the NEI emissions, we estimated that about half of the 3.2 ton difference in the estimates
is due to lower actual production at cement facilities in 2008 as compared to production capacity used in
36
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the NESHAP and the other half is due to differences in the emission rates used. We compared the actual
2008 clinker production12 to the production used in the NESHAP and found that the NESHAP
production for non-hazardous waste kilns was 20% higher than 2008. We also evaluated throughput
data supplied by some states to the NEI and found that the throughput was much lower in NEI than that
used for the NESHAP.
For other categories, the difference in emissions from 2005 to 2008 is similarly due to a combination of
methodological differences in the approaches used to develop the two inventories, in addition to
reductions in activity between 2008 and 2005, and reductions implemented by states ahead of Federal
regulations and other factors. For the nonEGU categories, the 2008 NEI uses data primarily submitted
by S/L/T agencies. Where S/L/T agency data are missing, EPA supplemented the information using the
TRI for the year 2008 and other datasets such as the data gathered by EPA for rule development (e.g.,
National Emission Standards for Hazardous Air Pollutants); these data were used for situations in which
S/L/T data were not available. In very few cases where no data were available, but the facility was
believed to be in operation in 2008, data were carried forward from the 2005 inventory at the request of
S/L/T agencies.
Past inventories such as the 2005 NEI have used S/L/T data, but for the key Hg categories, data gathered
for rule development were used ahead of S/L/T agency data. The Portland Cement Hg emissions
discussed above is one such example. For a large number of rules data were developed from
Information Collection Request (ICRs) that for some categories represented years prior to and
subsequent to 2005. In the 2008 NEI, the practice of always using rule data ahead of S/L/T agency data
has not continued. Instead, we reviewed the available data with the states for key Hg categories and
generally allowed the states to choose which value to use (see Sections 3.1.5.4 and 3.1.7). In addition,
the 2005 NEI development approach carried a larger quantity of older NEI data forward for use in the
2005 inventory than was done for the 2008 NEI. We changed our approach on widespread use of prior
year emissions both to prevent EPA's creation of faulty data as well as to address state concerns that
EPA overestimated emissions by pulling data forward that was incorrect or duplicative.
The 2008 NEI is also believed to be lower for some categories due to economic reasons and due to early
reductions for some categories. There were facility shut downs and reduced operations at chemical
manufacturing facilities and in metals industries. For other categories, a combination of voluntary and
state programs has reduced Hg ahead of MACT standards. For gold mines, reductions occurred initially
due to a voluntary program developed by EPA Region 9 and Nevada and then further reductions were
achieved through a Nevada state regulatory program. In the mercury chlor-alkali industry, facilities
have been switching technologies to eliminate Hg emissions from chlorine production. Many switched
prior to 2008 and several switched after; therefore, even more reductions from chlor-alkali facilities are
expected to be seen in the 2011 NEI. For electric arc furnaces, emissions are lower due to methods of
emission estimating.
12 United States Geological Survey, Cement data
37
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The 2008v2 NEI was estimated to be missing some coal fired boiler Hg emissions from industrial,
commercial and institutional boilers. For v3, we made changes to our HAP augmentation that gap filled
more coal and oil fired boilers than had been augmented in v2. However, we did not add the 0.5 tons we
estimated to be missing. This is because after we published version 2, we determined that the gap-fill
estimate for mercury from certain coal-fired boilers was too high. Version 3 uses a better emission
factor, lowering the estimate.
38
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3 Stationary Sources
3.1 Stationary source approaches
Stationary source emissions data are inventoried as point sources or nonpoint sources. These data are
provided by S/L/T agencies, and for certain sectors and/or pollutants, they are supplemented with data
from EPA. This section describes the various sources of data and the priority for each of the datasets for
choosing the data value to use when multiple data sources are available for the same emissions source.
3.1.1 Sources of data overview and selection hierarchies
Table 8 and Table 9 describe the datasets comprising the point and nonpoint inventories, respectively,
and the hierarchy for combining these datasets in construction of the NEI. While the bulk of these
datasets are for stationary sources of emissions, some of these datasets contain mobile sources so that
emissions from airports and rail yards could be included as point sources.
EPA developed all datasets other than those containing S/L/T agency data and the Offshore platform
dataset. We used various methods and databases to compile the EPA generated datasets, which the
tables and subsequent subsections fully describe. The primary purpose of the EPA datasets is to add or
"gap fill" pollutants for sources not provided by S/L/T agencies and to resolve inconsistencies in S/L/T-
reported pollutant submissions for PM (Section 3.1.2) and chromium (Section 3.1.3). EPA also
developed a dataset to overwrite S/L/T agency data where known problems or obvious outliers exist.
Finally, EPA used data from the MATS testing program ahead of S/L/T-reported data in some
circumstances. During the fall of 2011, EPA performed an extensive review of emissions and conducted
a focused data review effort for facilities identified as "high risk" in the 2005 NATA, and facilities in
important Hg emitting source categories (Section 3.1.7). Results of this effort provided additional
emissions estimates in both the S/L/T agency dataset and in EPA datasets. This review also resulted in
emissions data for some facilities being brought forward from the 2005 NATA inventory, resulting in
the dataset called "EPA 2005NATA values pulled forward to gapfill". Many of the EPA datasets used
in the point source data category were developed to include the data and recommendations provided by
S/L/T agencies resulting from this review.
The hierarchy or "order" provided in the tables below defines which data are to be used for situations
where multiple datasets provide emissions for the same pollutant and emissions process. The dataset
with the lowest order on the list is preferentially used over other datasets. The tables include the
rationale for why each dataset was assigned its position in the hierarchy. Two exceptions to the
hierarchy are provided in the last row of the tables. These exceptions do the following: 1) change the
hierarchy for two jurisdictions so that the EPA EGU vl.5 data are selected ahead of the S/L/T agency
data, and 2) exclude any greenhouse gases and pollutants in the pollutant group "dioxins/furans"13 from
the selection.
13 Dioxins/furans include all pollutants with pollutant category name of: Dioxins/Furans as 2,3,7,8-TCDD TEQs,
39
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Table 8: Data sources and selection hierarchy used for point sources
Dataset name
(and Short
Namex)
Description and Rationale for the Order of the Selected Datasets
Orde
r
EPA Overwrite
Point vl.5
(2008EPA
OverPT15)
This dataset addresses three known issues in the S/L/T agency data:
1) All acenaphthylene emissions for the airport SCC of 2275050012
(general aviation turbine) are set to a value of zero since the emission
factor (EF) used in the S/L/T agency datasets was incorrect (see
Section 4.2.4).
2) Some states added airport emissions to new "units" and "processes"
at the EPA airport facilities. To avoid double counting, this dataset
overwrites the state data for these situations with zero values. The
EPA data are used instead and are located at the valid units and
processes as defined by EPA (see Section 4.2.4).
3) PM emissions (all species) for 46 Pennsylvania EGU processes
(based on highest emitting) are set to the values developed in the
EPA EGU vl.5 dataset (9th row in this table) since it was determined
that PA reported the primary PM using the filterable value,
significantly underestimating the total (primary) PM. See Appendix
B for details.
This dataset is first because it serves to overwrite the data in the S/L/T
agency datasets
1
EPA PM
Augmentation,
V2
(2008EPA PM2
5)
PM species added to gap fill missing S/L/T agency data or make
corrections where S/L/T agency have inconsistent PM species'
emissions. Uses speciation factors from the PM Calculator for covered
SCCs. For others, checks/corrects discrepancies or missing PM species
using basic relationships such as ensuring that PM2.5 is less than or
equal PM10 (See Section 3.1.2). This dataset is ahead of the S/L/T
agency data because in addition to filling in missing data, it also corrects
S/L/T agency values based on feedback from the agencies.
2
2008 MATS-
based EGU
emissions
(2008EPA MAT
S)
Lead, mercury, other HAP metal and acid gas HAP emissions developed
from emission factors (including unit specific and average) from a 2010
test program conducted as part of the MATS information collection
request. Emissions utilized the MATS EFs and 2008 throughput. The
dataset excludes MATS Hg emissions for units where EPA knew states
had test data or that the unit had Hg continuous emission monitoring
systems in 2008 (this exclusion allows the S/L/T agency Hg emissions to
be chosen ahead of MATS for such units). These data are selected ahead
of state data because they are expected to be generally more accurate
because they are based on unit specific tests or based on the latest
available EFs derived from testing of similar units, and consistent with
the MATS rule. See Section 3.10 for the methodology. Note that in
2008v2 this dataset was used below the EPA Chromium Split v2 dataset
3
Dioxins/Furans as 2,3,7,8-TCDD TEQs -1/89, Dioxins/Furans as 2,3,7,8-TCDD TEQs - WHO/98, which were valid pollutant
groups for reporting 2008 emissions. The specific compounds and codes are in the pollutant code tables.
40
-------
Dataset name
(and Short
Namex)
Description and Rationale for the Order of the Selected Datasets
Orde
r
resulting in S/L/T chromium being used ahead of MATS chromium
which was not the intent. The order was changed (to fix the issue) to that
shown in this table for the 2008 v3 selection.
EPA Chromium
Split v2
(2008EPA
CHROMv2)
Contains corrected and speciated hexavalent and trivalent chromium
emissions derived from the S/L/T agency data for sources in which S/L/T
agency reports the total (unspeciated) chromium pollutant (See Section
3.1.3). This dataset is ahead of the S/L/T agency data because it replaces
S/L/T agency total chromium with speciated chromium.
4
Other EPA data
(2008EPA OTH
ER)
HAP emissions that S/L/T agencies recommended EPA use as part of the
high risk and NATA2005 review (see 3.1.7). S/L/T agencies could not
submit data themselves for various reasons. Additionally, this dataset
contains Region 2 data for benzene and coke oven emissions for
Tonawanda Coke Corp based on recent testing. This datasets is ahead of
the S/L/T agency data because it changes S/L/T emission values based on
feedback from the agencies.
5
2008 V3
Responsible
Agency
Selection
S/L/T agency submitted data (multiple datasets - one for each reporting
agency). These data are selected ahead of other datasets with the five
exceptions listed above.
6
EPAAirportsl 10
9
(2008EPAAIR)
Emissions of CAP and HAP for aircraft operations including
commercial, general aviation, air taxis and military aircraft, auxiliary
power units and ground support equipment computed by EPA for
approximately 20,000 airports. Methods include the use of the Federal
Aviation Administration's Emissions and Dispersion Modeling System.
See Section 4.2. EPA airport data are selected for a process only if S/L/T
agency data are not, with the exception of airport data contained in the
first dataset discussed above.
7
EPA Rail, point
(2008EPA RAI
L)
Emissions of CAP and HAP for diesel rail yard locomotives at 753 rail
yards. CAP emissions computed using yard-specific emission factors
using yard-specific fleet information, and national fuel values allocated
to rail yards using an approximation of line haul activity within the yard.
HAP emissions computed using HAP-to-CAP emission ratios. See
Section 4.4. EPA Rail data are selected for a county only if S/L/T
agency data are not.
8
2008 Offshore
(2008EPA MM
S)
CAP Emissions from Offshore oil platforms located in Federal Waters in
9
the Gulf of Mexico developed bv the U.S. Department of the Interior,
Bureau of Ocean and Energy Management, Regulation, and Enforcement
(Wilson et. al, 2010) in the National Inventory Input Format and
converted to the CERS format by EPA. The selection order for this
dataset is not important because the data do not overlap with other
datasets.
41
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Dataset name
(and Short
Namex)
Description and Rationale for the Order of the Selected Datasets
Orde
r
EPA EGUvl.5
(2008EPA EGU
15)
Uses Clean Air Markets Division (CAMD) NOX, S02 and other
pollutants (including HAPs) computed using CAMD heat inputs and EFs
(see Section 3.10). These EPA non-MATS EGU data are selected for a
facility only if S/L/T agency data are not present.
10
2008 EPA Rule
Data from
OAQPS/SPPD
(2008EPA_
RuleData)
Mercury emissions from categories for which rule data were used to gap
fill missing S/L/T agency data. Includes: municipal waste combustors,
electric arc furnaces, mercury cell chloralkali plants and industrial,
commercial and institutional boilers. For this latter category, 42 units
from the Boiler MACT information collection request database that were
able to be matched to units in the emissions inventory system were used.
Note that this is greatly increased from the 19 units we used from v2.
These data are selected for a facility only when not included in the S/L/T
agency data.
11
EPA NV Gold
Mines
(2008_NVGLD)
Mercury emissions developed from published results of the Nevada
Mercury Control Program - Annual Emissions Reporting for 2008.
Because of issues with the 2008 testing, data for Homestake Mining Co.
- Ruby Hill and Barrick Goldstrike Mines, Inc. were based on validated
2009 test data provided by Nevada. The Nevada Gold Mine data are
selected for a mine only when alternative emissions are not included in
the S/L/T agency data.
12
EPA coke oven
(2008EPA_CK)
Coke oven emissions computed from AP-42 or updated from 2005
NATA values using 2008 production data. Emissions/approaches
provided by a few states that did not report coke oven emissions in the
S/L/T agency data. These data are selected only for gap-filling missing
data from the S/L/T's.
13
EPA TRI
Augmentation v2
(2008TRI)
TRI data for the year 2008. This dataset includes the TRI data assigned
manually to processes in EIS to facilities in the NATA review (Section
3.1.7) and TRI emissions assigned to processes based on the distribution
of surrogate CAPs via the approach described in Section 3.1.4. These
data are selected for a facility only when alternative emissions are not
included in the S/L/T agency data.
14
EPA HAP
Augmentation v2
(2008EPAHAP
v2)
HAP data computed from S/L/T agency criteria pollutant data using
HAP/CAP emission factor ratios based on the EPA Factor Information
Retrieval System (WebFIRE) database as described in Section 3.1.5.
These data are selected below the TRI data because the TRI data are
expected to be better.
15
EPA 2005NATA
values pulled
forward to
gapfill
(2008EPA
05NATA GAPF
L)
Emissions from the 2005 NATA inventory used as directed by states for
facilities that were part of the NATA review described in Section 3.1.7.
Also includes 2005 NATA Hg emissions from some hazardous waste
incinerators (HWI), where states did not provide Hg data but there were
HWI processes with non-zero emissions of CAPs reported by the agency.
The order for this dataset is unimportant since it does not overlap with
any other datasets.
16
Exceptions to the hierarchy
1. Connecticut and Douglas County, Nebraska: Changed the hierarchy of EGUvl .5 to go ahead of
42
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Dataset name
(and Short
Namex)
Description and Rationale for the Order of the Selected Datasets
Orde
r
2.
state data and EPA PM Augmentation, V2 (EGU vl.5 moved from 10 to 5, PM Aug moved from
2 to 6). These exceptions were made because several of the EGUs reported by CTDEP had much
lower emissions than the EPA EGU vl.5 dataset, even for the S02 and NOX emissions that are
CEM-based in the EPA EGU vl.5 dataset, and because the Douglas County dataset for the one
EGU included in the EPA EGU vl.5 dataset did not contain unit and process specific emissions.
Excluded dioxin/furan individual pollutants and groups and green house gas pollutants.
The dataset short name is the name that EIS will list in its process-level reports
43
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Table 9: Data sources and selection hierarchy used for nonpoint sources
Dataset name
(and Short Name')
Description
Orde
r
2008EPA biogenics
(2008EPA biogenics)
Natural emissions from vegetation and soil, computed using 2007
meteorology and use of the BEIS3.14 model. See Section 6. The
order does not matter because it does not overlap with any other data
used in this selection.
1
EPA PM
Augmentation NP
(PM Aug NP)
Adds PM species to fill in missing S/L/T agency data or make
corrections where S/L/T agency data have inconsistent emissions
across PM species. Uses the PM calculator for processes covered by
that database. For other processes, checks/corrects discrepancies or
missing PM species using basic relationships such as ensuring that
PM2.5 is less than or equal PM10 (See Section 3.1.2).
2
EPA Overwrite
Nonpoint vl.5
(2008EP A_OverNP 15)
Overwrites some unreasonably high values that came in from S/L/T
agencies with zero values to prevent outliers from entering the
released data. Also overwrites submitted total (unspeciated)
chromium for commercial marine vessel (CMV) emissions with zero
value to prevent total chromium from being included in the 2008
NEI
3
Rail EPACorrections
(2008RRCOR)
Overwrite submitted unspeciated chromium and other pollutants that
did not conform to pollutant/SCCs in EPA dataset. Also overwrites
county submittals for counties/SCCs where EPA data exists in shape
files.
4
EPA Chromium
Split v2
(2008EPA CHROMv
2)
Speciated S/L/T agency chromium emissions based on total
chromium provided by S/L/T agencies. Speciation based on SCC
code. See Section 3.1.3.
5
2008 V3 Responsible
Agency Selection
S/L/T agency submitted data
(multiple datasets - one for each reporting agency)
These data are selected ahead of other datasets with the five
exceptions listed above. See also file "matrixsubmittals for Version
2 Feb 13 201 l.xlsx" for a list of submitting agencies and for what
nonpoint sectors they submitted data (see Section 8.2 for access
information).
6
Misc NP Hg Cats
(Misc NP Hg)
Dataset that includes Hg data used in the 2002 NEI for the following
categories: fluorescent light breakage, fluorescent light recycling,
laboratory activities, and dental amalgam. These 2002 NEI data
were not estimated for 2008 but are categories that were largely
unavailable from the S/L/T Agency data.
7
EPA CMV
(2008EPA ERG)
EPA CMV estimates. See Section 4.3.
8
EPA Rail, nonpoint
(2008EPA RAIL)
EPA Rail estimates. See Section 4.4.
9
EIAG all in NP
(2008EPA_NPa)
Contains data for categories for which all of the data should exist in
the nonpoint categories, such as residential heating, consumer
solvent use and paved roads. See Section 3.1.6.
10
44
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2008 EPA Ag Fires
(2008AgFire)
Agricultural fire emissions are estimated by EPA for all agencies that
did not submit them. EPA estimates relied on using satellite data to
identify, by default, lands on which agricultural burning activity
occurred in 2008. Geographic Information Systems (GIS) analysis
was then used to cross-check these lands with those that burn only
crops. These "cropland" activity data were then converted to
emissions based on state- and crop-specific emission factors
(compiled, as available, from the literature) combined with state
usage patterns of these crops. See Section 5.1.4.
11
EPA Possible Pt
Source Contrib VI 5
(2008EPA_NPdl5)
Contains data for categories in which there was the possibility of
point source contribution (or overlap). These categories include
industrial, commercial and institutional emissions that are often
accounted for in the point source inventory. EPA did not want to
add these emissions to the NEI without doing some analysis to
determine if the S/L/T had accounted for them in the point. To do
this, the EPA queried the point source S/L/T datasets to determine if
certain point source SCCs were present. If the point source SCCs
were present, then EPA assumed that the S/L/T agency covered them
in point and the EPA nonpoint data were not included in this dataset.
If the point source SCCs were not present, then the EPA data were
added to this dataset, which means that the data would be in the NEI
provided the S/L/T did not provide nonpoint data (S/L/T agency
dataset is #5 in this hierarchy). EPA did not adjust this nonpoint data
with the point data. See Section 3.1.6.
12
Exceptions to the hierarchy
1) Excluded S/L/T agency data submitted for SCC= 2810015000 (Prescribed Forest Burning) and
2810020000 (Prescribed Rangeland Burning) since these were included in the EVENTS
county-level summary. Prescribed and wildfires are EVENTS categories whereas agricultural
burning and other open burning are in the nonpoint data category.
2) Excluded dioxin/furan individual pollutants and groups and green house gas pollutants, pending
further review of the accuracy and completeness of the data.
1 The dataset short name is the name that EIS will list in its process-level reports
3.1.2 Particulate matter augmentation
The NEI contains 5 particulate matter species:
• primary particulate matter with particle sizes of 10 micrometers and smaller (PM10-PRI),
• primary particulate matter with particle sizes of 2.5 micrometers and smaller (PM25-PRI),
• filterable particulate matter with particle sizes of 10 micrometers and smaller (PM10-FIL),
• filterable particulate matter with particle sizes of 2.5 micrometers and smaller (PM25-FIL) and
• condensable particulate matter (PM-CON).
By definition, primary PM is the sum of filterable PM and condensable PM. Also, PM10 is
inclusive of PM2.5 such that PM10 must be always greater than or equal to PM2.5. EPA strives to
have emissions for all of these species for stationary source in the NEI, where applicable. (Mobile
source models do not separately estimate filterable and condensable.) For the 2008 NEI, EPA
needed to augment the PM components submitted by S/L/T agencies to ensure completeness of the
45
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PM components in the final NEI and to ensure that S/L/T agency data did not contain
inconsistencies. An example of an inconsistency is if the S/L/T agency submitted a primary PM2.5
value that was greater than a primary PM10 value for the same process. Commonly, the
augmentation added condensable PM or PM filterable (PM10-FIL and/or PM25-FIL) where none
was provided or primary PM2.5 where only primary PM10 was provided, or vice versa. Additional
information on the procedure is provided in a memorandum on the 2008 NEI PM augmentation
(Dorn, 2012).
In general, emissions for PM species missing from S/L/T agency inventories were calculated by
applying factors to the PM emissions data supplied by the S/L/T agencies. These conversion factors
were first used in the 1999 NEI's "PM Calculator" and are described in Strait et al. (2003). The
resulting methodology allows EPA to derive missing PM10-FIL or PM25-FIL emissions from
incomplete S/L/T agency submissions based on the SCC and PM controls that describe the emissions
process. In cases where condensable emissions are not reported, conversion factors developed by Strait
et al. (2003) are applied to S/L/T agency reported PM species or species derived from the PM Calculator
databases.
3.1.3 Chromium augmentation
This section describes the procedure we used for augmenting chromium emissions to generate trivalent
chromium and hexavalent chromium from S/L/T agency reported total (unspeciated) chromium.
EPA augmented S/L/T agency-reported chromium emissions through a dataset that splits the S/L/T
agency-reported total chromium (pollutant code 7440473) into trivalent chromium and hexavalent
chromium species. This dataset also computed the trivalent and/or hexavalent species where total
chromium was reported with either hexavalent or trivalent chromium for the same process. This
procedure had no impact on S/L/T agency data that were provided as hexavalent and/or trivalent
chromium or where a S/L/T agency reported chromium trioxide and chromic acid (VI) as long as there
was no total chromium at the same process.
The 2008 reporting cycle has 5 valid pollutant codes for chromium as shown in Table 10.
Table 10: Valid chromium pollutant codes
Pollutant
Code
Description
Pollutant Category Name
1333820
Chromium
Trioxide
Chromium Compounds
16065831
Chromium III
Chromium Compounds
18540299
Chromium (VI)
Chromium Compounds
7440473
Chromium
Chromium Compounds
7738945
Chromic Acid
(VI)
Chromium Compounds
In the above table, all but "chromium" is considered speciated (chromium is referred to as "total
chromium" in the remainder of this section). Total chromium could contain a mixture of chromium with
different valence states. Since one of the main inventory uses is for risk assessment and the valence
46
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states of chromium have very different risks, speciated chromium is the most useful pollutants for the
NEI and why we have included this augmentation.
EPA augmented the emissions by developing datasets containing speciated chromium based on the
S/L/T agency reported total chromium and the process. The resulting chromium augmentation datasets
contain a value of zero for total chromium, which overwrites the S/L/T submitted total chromium so as
not to double count with the EPA dataset speciated chromium. The speciated data are contained in the
dataset "EPA chromium Split v2" (4th row of Table 8 for point and the 5th row of Table 9 for nonpoint).
This augmentation addresses two issues described below.
1. Removes Ambiguity from Overlapping Pollutants: S/L/T agencies sometimes report total
chromium emissions, (pollutant code = 7440473) with hexavalent chromium (18540299) and/or
trivalent chromium (16065831) for the same process. As explained in the HAP reporting
webinar "How to Report HAP Emissions for the 2008 NEI". EPA interprets total chromium to be
the sum of hexavalent chromium and trivalent chromium. Thus, EPA assumes that when a S/L/T
agency submits total chromium emissions as well as hexavalent and/or trivalent chromium, then
the state has submitted emissions mass that is double counted. The Emissions Inventory System
(EIS) does not flag such double counting as an error, and as a result we have received data from
S/L/T agencies that we need to augment to eliminate these apparently overlapping chromium
compounds. Note that it is not double counting to have any form of chromium along with
chromic acid mist (7738945) or chromium trioxide (7738945), which are specific chromium
compounds that we treat as additive to whatever other chromium is already reported for the
process. Users of the NEI data are most interested in hexavalent chromium, chromic acid mist
and chromium trioxide. There may be other chromium ions (such as chromium II); however,
they do not have any risk information associated them and thus we treat them along with
trivalent chromium.
2. Provides a consistent speciated chromium inventory: EPA would like the NEI to only
include speciated chromium emissions consistently throughout the inventory. While total
chromium is a valid pollutant in the NEI, many users of the data request chromium emissions to
be speciated into hexavalent chromium and trivalent chromium in order to estimate health risks.
It is simpler for us and our users to have only the speciated forms in the released data and total
chromium is available by adding the speciated emissions.
For point sources, we used the following sequential hierarchy to perform the speciation. For nonpoint
sources, only the SCC code was used for speciation.
1. Regulatory Code speciation profiles; For pulp and paper (Regulatory Codes R63-0018, R63-
0045 and R63-0018-02), a combination of Regulatory Code and SCC code was used.
2. SCC speciation profiles if Regulatory code speciation profiles are unavailable.
3. If Regulatory code and SCC speciation profiles are unavailable, we used a default to hexavalent
chromium (18540299) percentage of 34%, which is the default value also used starting with the
47
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1996 NATA (US EPA, 2001) and is based on the highest value tested from oil combustion (note
that the average is 18%).
The speciation factors used are provided in the workbook Chromium_speciation_factors.xls (see Section
8.1 for access information). The first tab provides the Regulatory Code/SCC based chromium speciation
profiles. The second tab provides the remaining Regulatory Code chromium speciation profiles. The
third tab provides the SCC-based chromium speciation profiles. The fourth tab provides the SCC-based
Chromium speciation profiles used for the nonpoint data category. We include the Maximum
Achievable Control Technology code "MACT code" in the tables for historical reasons. The speciation
data were initially developed by "MACT" category and we have mapped this to Regulatory Code for use
in the 2008 NEI because MACT code has been replaced by Regulatory code.
Table 11 shows the calculations made for developing the EPA chromium corrections, speciated dataset,
and meta data used for the Emissions Calculation Method Code and the Emissions Comment fields in
EIS. This table does not apply to the nonpoint chromium speciation because it was more
straightforward. The only step taken was to speciate the chromium using the SCC-based profiles
provided in the workbook discussed above.
Table 11: Calculations for generating the point chromium augmentation dataset (EPA Chromium Split
v2)
Case
S/L/T-reported
the pollutants
for a process:
Approach to create emissions for
"EPA Chromium Split v2" dataset
Manipulation
Meta data for
EmissionsCalculationMethodCode
(ECMC) and EmissionsComments
(EC)
Cr1
Hex1
Tri1
1
X
Speciate using speciation factors in
"Chromium_speciation_factors.xls"
ECMC = 5 (USEPA Speciation
Profile)
EC = "Speciation of
reported chromium 4: hex
%; tri %" 2'3
2
X
X
Set Cr emissions to 0, and add Tri to
be computed as follows: Tri = Cr -
Hex. Note: if Tri is <0 it is set to 0.
ECMC=2 (Engineering Judgment)
EC= "Replacement dataset corrects
-reported Cr overlap.
Remove Cr and add Tri computed as
Tri=Cr-Hex" 2
3
X
X
X
If Cr > Hex + Tri:
Set Cr emissions to zero. Subtract:
Cr -(Hex + Tri) and add the
difference to the existing Tri.
Rationale: When total is greater than
hex+tri, we assume total and hex as
valid and re-calculate a new 'Tri'.
This is because we assume that Cr+2
may be the difference that explains
why total Cr is greater than the two
pieces.
If Cr > Hex + Tri:
ECMC=2 (Engineering Judgment)
EC= "Value corrects Chromium (Cr)
overlap. Added difference between
-reported Aggregated Cr
and -reported hexavalent
Cr to -reported trivalent Cr.
Difference assumed to represent
divalent chromium, which we include
with trivalent Cr." 2
If Cr < Hex + Tri
48
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Case
S/L/T-reported
the pollutants
for a process:
Approach to create emissions for
"EPA Chromium Split v2" dataset
Manipulation
Meta data for
EmissionsCalculationMethodCode
(ECMC) and EmissionsComments
If Cr < Hex + Tri:
then set Cr emissions to 0 and keep
Hex and Tri as-is. Rationale: where
total Cr is less than sum of Hex+Tri,
we assume that the hex and tri are
correct, and thus remove the total.
ECMC=2 (Engineering Judgment)
EC= "Replacement dataset corrects
Cr overlap. Remove
reported aggregated chromium
because it is assumed to overlap with
-reported hexavalent and
trivalent chromium." 2
4
X
X
Set Cr emissions to 0, and add Hex
to be computed as follows: Hex =
Cr-Tri
Note: if Hex is <0 it is set to 0.
ECMC=2 (Engineering Judgment)
EC= "Replacement dataset corrects
-reported Cr overlap.
Remove Cr and add Hex computed as
Hex=Cr-Tri"
5
X
X
No augmentation
6
X
No augmentation
7
X
No augmentation
1 Cr=chromium (po
chromium (160658.
2 is the v
lutant code = 7440473); Hex=hexavalent chromium (18540299); Tri = trivalent
51).
alue of the agency program system code for the process containing the S/L/T agency
3 is the appropriate numerical value of the percent of trivalent or hexavalent chromium.
4 is basis for the speciation profile and could have the value of "via see" "via reg code"
"default" depending on how the reg code was assigned. Where both SCC and Reg code were used (for a
single combination), the was "via reg code"
3.1.4 Use of the 2008 Toxics Release Inventory
EPA used 2008 TRI data to supplement point source HAP emissions provided to EPA by S/L/T
agencies. The resulting augmentation dataset is labeled as "EPA TRI Augmentation, v2"in Table 8 and
in EIS. Version 3 of the 2008 NEI added TRI data for sources identified in the issues list for v2 though
the dataset name was kept the same as was used in v2.
This dataset is a combination of 1) TRI data that were assigned to facilities lacking S/L/T agency-
reported HAP and Pb emissions using a mostly automated procedure (roughly 2,400 facilities) and 2)
TRI data that were assigned to a relatively small number of facilities (roughly 200 facilities) using a
more manual approach as a result of the EPA high risk and Hg review. This section describes the
methodology used for the automated procedure.
The basis of the TRI augmentation dataset is the 2008 EPA TRI. TRI is an EPA database containing
data on disposal or other releases including air emissions of over 650 toxic chemicals from thousands of
U.S. facilities. One of TRI's primary purposes is to inform communities about toxic chemical releases
to the environment. Data are submitted annually by U.S. facilities that meet TRI reporting criteria. The
TRI database used in this project was named US_2008_v09.zip downloaded in March 2011.
49
-------
The general approach used to develop the automated portion of the TRI Augmentation file is described
here, followed by a more detailed stepwise list below. In general, we matched TRI facilities with
facilities in EIS and then apportioned TRI emissions to EIS facilities at the process-level using
allocations derived from S/L/T agency-reported CAP surrogate emissions. Apportioning is necessary
because emissions in the TRI database are summed to a facility-wide resolution, whereas the NEI has
process-level (i.e., unit, process) resolution. Where there were no S/L/T agency-reported CAP
emissions, TRI emissions were not used. The following CAP surrogates were used to apportion the
emissions: (1) VOC- used for HAPs that are also VOC, (2) PMlO-filterable - used for particulate HAPs
and Hg, and (3) S02 - used for acid gas HAPs. The use of these S/L/T agency data to assign the TRI
data meant that if a facility did not have S/L/T agency reported emissions for the CAP surrogate, then
TRI emissions for the HAP assigned to that surrogate would not be used. This limitation did not exist in
the manual approach whereby TRI facility-level emissions were manually assigned to processes within
the matched facilities.
The following steps describe in detail the development of automated portion of the TRI Augmentation
database.
1. Create a TRIID to EISID crosswalk (i.e., match TRI facilities to EIS facilities)
The TRI emissions database contains two data elements that are used to uniquely identify a
facility site. These are the TRI Facility ID (TRI ID) and the Facility Registry System ID
(FRSID). The TRI ID is an identification number unique to the TRI. The EPA FRSID is a
facility code also used in EPA's Envirofacts database. The EPA NEI uses the field "EIS
Identifier" (EIS ID) to uniquely identify facilities. A FRS ID to EIS ID crosswalk developed
during the 2008 NEI effort was used as an initial step in linking the TRI emissions to the NEI
facilities.
This crosswalk was supplemented with additional matches from the TRI database that provided
using the TRI ID and FRS ID fields. The crosswalk was also checked to ensure that TRI
facilities matched properly to the EIS facilities using latitude, longitude, street address, facility
name, city, county, and state for both TRI and EIS facilities. 'Hand checks' were performed for
facilities that differed in location by more 0.01 degree longitude or 0.00725 degrees latitude
(since roughly a 0.02 difference in the longitude is 1 mile and a 0.0145 difference in the latitude
is 1 mile; our criteria was to look at 0.5 mile differences and greater) and which did not have
identical facility names, street address, city, county, and state. We also manually removed
matches where it was discovered that one TRI facility represented multiple EIS facilities to
prevent double counting of TRI emissions data. Such differences can happen when the state
inventories a facility in a different manner than the facility itself reports their emissions to TRI.
The resulting TRI to EIS crosswalk file is "TRI to EIS crosswalk.accdb" (see Section 8.1 for
access information). This crosswalk contains all the potential matches reviewed; the ones we
used in the automated approach have a "Y" in the "MATCH" field.
50
-------
2. Combine the TRI data for individual chemicals and chemical groups and create a total air
emissions field
The TRI database organizes the air emissions into "Chemical Groups", and there is some overlap
in these groups that we resolved prior to using the data. The TRI Chemical Group "metals and
metal compounds" includes air releases of both elemental metals and compounds, but the metals
are also included as individual elemental metals. If for the same facility, a metal compound and
the metal group were reported, we summed the emissions together. For example, if a facility
reported chromium compounds and chromium as separate pollutants, we summed these
emissions and assigned them to just chromium emissions. This assumed that the facility would
not intentionally double count mass of a compound. We also combined stack and fugitive air
emissions from the TRI datasets to generate the total air emissions for each pollutant at a facility.
Our allocation method for assigning the TRI stack and fugitive emissions to the NEI emissions
processes did not attempt to allocate using the "stack" or "fugitive" denotation from TRI.
3. Update the 2008 S/L/T submission database with the PM10-FIL Augmentation updates
PM10-FIL is one of the criteria air pollutants used to assign TRI emissions at matching EIS
facilities to the processes within that facility. The PM10-FIL data from the PM Augmentation
dataset was merged with the S/L/T PM10-FIL data to provide a more complete set of PM10-FIL
for use in the allocation of TRI emissions to processes at the facility. This step allowed more
TRI data to be used than if we had used only the S/L/T agency submitted PM10-FIL.
4. Map TRI pollutant codes to valid EIS pollutant codes.
Table 12 provides the pollutant mapping from TRI pollutants to NEI pollutants. Only CAA
pollutants from the TRI are included and even some of these were not used- including ammonia
(our focus was HAPs and lead), dioxins/furans (which we excluded from the inventory) and
others we could not map to specific NEI pollutants (e.g., diisocyanates and certain glycol ethers).
5. Remove TRI records to avoid double counting, as follows:
a. When S/L/T agency submissions contained matching HAPs or HAPs belonging to HAP
groups such as cresols, xylenes and polycyclic organic matter. The pollutant group
assignments are shown in Table 13. For example, if a S/L/T agency-submitted emissions
for any pollutant group member at the facility, we assume that the emissions from that
pollutant group were already provided by the S/L/T agency and did not add emissions of
that HAP or HAP group from the TRI.
b. When emissions records were already submitted in other EPA HAP datasets or for which
TRI emissions were assigned using a manual approach (See Section 3.1.7) such as for
cement and electric arc furnace facilities.
c. When the NEI facility type was "Electricity Generation via Combustion" since this
category is gap filled with two other EPA datasets (MATS and EGU).
6. Calculate the allocation factors to develop process-level emissions.
S/L/T agency CAP emissions reported at the process level were used as surrogates for allocating
the TRI data. The surrogate assignments are shown in Table 12. We computed allocation
factors for the surrogates based on the fraction of surrogate pollutant emissions at each process.
Emissions allocations were limited to processes that contributed to least 1 percent of emissions.
51
-------
The reason is that we did not want to allocate HAPS emissions to processes that had very small
emissions. Where CAP emissions were less than 1 percent, the factor was set to zero and the
allocations were re-normalized in order to use all of the facility-level TRI emissions.
The allocation approach is done to prevent all of the HAP emissions from getting assigned to a
single process, which can cause artifacts in data summaries when the processes are summed to
EIS sectors or other ways. The resulting allocation approach however has the disadvantage of
assigning HAPs to processes that may not actually have those HAP emissions. Thus, at facilities
where TRI data have been used, the process-level HAP emissions should be viewed with this
limitation in mind. Past NEIs have assigned all of these emissions to a default process code SCC
of 39999999, which caused other artifacts, such as a disproportionate amount of HAP emissions
getting summed to "miscellaneous" categories in some instances. While we have not eliminated
the use of this SCC from this version of the NEI, we have reduced its use in hopes of eventual
elimination from future inventories.
7. Calculate process-level emissions by multiplying the TRI facility level emissions with the
allocation factors computed for the surrogate CAPs.
8. Speciate process-level total chromium emissions into hexavalent and trivalent emissions
and remove total chromium emissions.
This followed the procedure described in Section 3.1.3, except that we did not create zero
emissions records for total chromium (we simply did not add total chromium to the dataset) and
we only speciated the total chromium since the TRI does not provide either hexavalent or
trivalent chromium.
The following quality assurance/quality control checks were performed in the development of the data.
1. Review high TRI emissions values for selected and high risk HAPs and for lead; exclude
any data suspected to be outliers.
For the following pollutants, we looked at the highest and sometimes second highest TRI facility
values included in the initial version prior to building the NEI for mercury, lead, chromium,
manganese, nickel, arsenic, 1,3 butadiene, benzene, toluene, ethyl benzene, p-xylene, methanol,
acrolein, carbon tetrachloride, tetrachloroethylene, methylene chloride, acrylonitrile, 1,4-
dichlorobenzene, ethylene oxide, hydrochloric acid, hydrogen fluoride, chlorine, 2,4-toluene
diisocyanate, hexamethylene diisocyanate, and naphthalene. In some cases, we suspected these
highest values to be outliers and excluded them from the augmentation dataset. For lead, we
looked at all facilities with total 2008 TRI emissions greater than 0.5 tons (which will be the new
threshold for reporting lead emissions to the NEI). Where there was no evidence the values were
incorrect, we notified the responsible agency. As a result we changed the following prior to
using the TRI data in the NEI:
a. We did not use TRI lead (pollutant code = 7439921) from PEMCO (TRI ID =
35983CHVTCEWING; EIS Facility ID = 7915711). Rationale: 2008 emissions above
0.5 tons (regulatory threshold) and determine that 2008 was inconsistent with TRI data
from other years, plus this is a coating manufacturer, which is very unlikely to emit lead
at the levels reported to TRI.
52
-------
b. We did not use TRI lead (7439921) from APPLETON COATED L L C. (TRI ID=
54113PPLTN540PR; EIS Facility ID= 6805511).
c. We did not use manganese (7439965) from Orthman Manufacturing Inc (TRI ID =
68850RTHMNRR2; EIS Facility ID = 6702911) - the 2008 value in TRI exceeded 2
million pounds and was very inconsistent with TRI data from other years
d. We changed tetrachloroethylene total emissions (facility wide) (127184) from 19500
pounds to 1815 pounds for Flint Hills Resources LP - Pine Bend (TRI ID =
55164KCHRFPOBOX; EIS Facility ID = 6275811). We called facility and were
informed that the TRI value was an outlier. We received a revised value by email on
12/15/2011 from the plant representative.
2. Excluded the TRI Polycyclic Aromatic Hydrocarbons (PAH) for facilities with coke oven
emissions. Where we found TRI PAH at the same processes as coke oven emissions we did not
use it. The PAH removal was to prevent possible double counting between PAH and the coke
oven emissions pollutants. In the state reported data, if a state reported PAH and coke oven
emissions we did not take any action; but here we chose not to add PAH from TRI from an EPA
dataset to prevent double counting emissions.
3. Check overlaps across TRI and other datasets. As explained in step 5 above, we analyzed
other datasets to make sure we would not be double counting emissions when adding TRI data.
Once we put all of the datasets together, we checked again for overlaps. From this check we
discovered overlaps between the TRI dataset and the 2008EPAJVLATS and EPA EGU vl.5
datasets. These overlaps occurred because for EGUs, we used the EIS "Facility type" field to
identify (and remove from TRI) EGUs rather than comparing the facilities in these datasets to
facilities in the TRI. It would have been a better approach to directly compare the datasets
because there are facilities that do not have a facility type of "Electricity Generation Via
Combustion" in the 2008EPAJVLATS and EPA EGU vl.5 datasets. For these facilities,
emissions for the same pollutant were taken from two separate datasets and assigned to processes
differently such that when combined to generate the NEI, the facility total no longer matched the
TRI. To prevent a double count, we changed some of the emissions in the TRI dataset as
follows:
a. HORSEHEAD CORP/MONACA SMELTER (EIS Facility ID = 7991511). The MATS
data overlap the original TRI dataset. Because the MATS value was greater than the TRI
value, we removed the TRI dataset selenium emissions. We adjusted the cadmium value
in the TRI dataset so that when summed with the MATS value, the facility total would
reflect the original value in 2008 TRI. We did not change chromium due to speciation
issues -, the amount of hexavalent chromium at the coal fired boilers (MATS sources) is
60.5 lbs which is greater than the facility total TRI value of 53.9 lbs. Of the 53.9 lbs total
TRI, 9 lbs was allocated to the MATS sources and the rest to the other industrial
processes. If any double counting did occur, it would have been less than the 44.9 lbs of
TRI allocated to the non-MATS industrial processes.
b. DOMTAR PAPER CO/JOHNSONBURG MILL (EIS Facility ID = 6559611). We
adjusted the TRI phenol value for the non EGU processes such that when summed with
53
-------
the value from the EPA EGU vl.5 dataset, the NEI facility total would equal the TRI
facility total.
CEMEX CNSTRCTION MATERIALS FLORIDA, LLC (EIS Facility ID = 716011). We
allocated TRI nickel emissions to the cement kilns and made sure total emissions match
the original TRI value when the TRI cement kiln value for nickel was summed with
MATS coal-fired boiler value for nickel.
Did not adjust overlaps found at NEW ENERGY CORPORTION (EIS Facility ID =
5552411) and HGLATFELTER CO/SPRING GROVE (EIS Facility ID = 4966111) as
they were determined to be insignificant.
Table 12: Mapping of TRI Pollutant Codes to EIS Pollutant codes
Allocati
on
TRI
CAS
TRI Pollutant Name
EIS Poll.
Code
EIS Pollutant Name
Surroga
te
1,1,2,2-
1,1,2,2-
79345
TETRACHLOROETHANE
79345
TETRACHLOROETHANE
voc
79005
1,1,2-TRICHLOROETHANE
79005
1,1,2-TRICHLOROETHANE
voc
57147
1,1 -DIMETHYL HYDRAZINE
57147
1,1 -DIMETHYL HYDRAZINE
voc
120821
1,2,4-TRICHLOROBENZENE
120821
1,2,4-TRICHLOROBENZENE
voc
l,2-DIBROMO-3-
l,2-DIBROMO-3-
96128
CHLOROPROPANE
96128
CHLOROPROPANE
voc
106887
1,2-BUTYLENE OXIDE
106887
1,2-EPOXYBUTANE
voc
75558
PROPYLENEIMINE
75558
1,2-PROPYLENIMINE
voc
106990
1,3 -BUTADIENE
106990
1,3 -BUTADIENE
voc
542756
1,3 -DICHLOROPROP YLENE
542756
1,3 -DICHLOROPROPENE
voc
112071
4
PROPANE SULTONE
1120714
1,3 -PROP ANESULTONE
voc
106467
1,4-DICHLOROBENZENE
106467
1,4-DICHLOROBENZENE
voc
95954
2,4,5-TRICHLOROPHENOL
95954
2,4,5-TRICHLOROPHENOL
voc
88062
2,4,6-TRICHLOROPHENOL
88062
2,4,6-TRICHLOROPHENOL
voc
2,4-DICHLOROPHENOXY
2,4-DICHLOROPHENOXY
94757
ACETIC ACID
94757
ACETIC ACID
voc
51285
2,4-DINITROPHENOL
51285
2,4-DINITROPHENOL
voc
121142
2,4-DINITROTOLUENE
121142
2,4-DINITROTOLUENE
voc
53963
2-ACETYL AMIN OFLU ORENE
53963
2-ACET YL AMIN OFLU ORENE
voc
79469
2-NITROPROPANE
79469
2-NITROPROPANE
voc
119937
3,3' -DIMETHYLBENZIDINE
119937
3,3 '-DIMETHYLBENZIDINE
voc
101144
4,4'-METHYLENEBIS(2-
CHLORO ANILINE)
101144
4,4'-METHYLENEBIS(2-
CHLORANILINE)
voc
101779
4,4' -METHYLENEDIANILINE
101779
4,4' -METHYLENEDIANILINE
voc
534521
4,6-DINITRO-O-CRESOL
534521
4,6-DINITRO-O-CRESOL
voc
92671
4-AMINOBIPHENYL
92671
4-AMINOBIPHENYL
voc
100027
4-NITROPHENOL
100027
4-NITROPHENOL
voc
75070
ACETALDEHYDE
75070
ACETALDEHYDE
voc
60355
ACET AMIDE
60355
ACET AMIDE
voc
c.
d.
54
-------
Allocati
on
TRI
CAS
TRI Pollutant Name
EIS Poll.
Code
EIS Pollutant Name
Surroga
te
75058
ACET ONITRILE
75058
ACET ONITRILE
voc
98862
ACETOPHENONE
98862
ACETOPHENONE
voc
107028
ACROLEIN
107028
ACROLEIN
voc
79061
ACRYL AMIDE
79061
ACRYL AMIDE
voc
79107
ACRYLIC ACID
79107
ACRYLIC ACID
voc
107131
ACRYLONITRILE
107131
ACRYLONITRILE
voc
107051
ALLYL CHLORIDE
107051
ALLYL CHLORIDE
voc
62533
ANILINE
62533
ANILINE
voc
744036
PM10-
0
ANTIMONY
7440360
ANTIMONY
FIL
PM10-
N010
ANTIMONY COMPOUNDS
7440360
ANTIMONY
FIL
744038
PM10-
2
ARSENIC
7440382
ARSENIC
FIL
PM10-
N020
ARSENIC COMPOUNDS
7440382
ARSENIC
FIL
133221
PM10-
4
ASBESTOS (FRIABLE)
1332214
ASBESTOS
FIL
71432
BENZENE
71432
BENZENE
VOC
92875
BENZIDINE
92875
BENZIDINE
VOC
98077
BENZOIC TRICHLORIDE
98077
BENZOTRICHLORIDE
VOC
100447
BENZYL CHLORIDE
100447
BENZYL CHLORIDE
VOC
744041
PM10-
7
BERYLLIUM
7440417
BERYLLIUM
FIL
PM10-
N050
BERYLLIUM COMPOUNDS
7440417
BERYLLIUM
FIL
92524
BIPHENYL
92524
BIPHENYL
VOC
117817
DI(2-ETHYLHEXYL)
PHTHALATE
117817
BIS(2-
ETHYLHEXYL)PHTHALATE
VOC
75252
BROMOFORM
75252
BROMOFORM
VOC
744043
PM10-
9
CADMIUM
7440439
CADMIUM
FIL
PM10-
N078
CADMIUM COMPOUNDS
7440439
CADMIUM
FIL
PM10-
156627
CALCIUM CYAN AMIDE
156627
CALCIUM CYAN AMIDE
FIL
133062
CAPTAN
133062
CAPTAN
VOC
63252
CARBARYL
63252
CARBARYL
VOC
75150
CARBON DISULFIDE
75150
CARBON DISULFIDE
VOC
56235
CARBON TETRACHLORIDE
56235
CARBON TETRACHLORIDE
VOC
463581
CARBONYL SULFIDE
463581
CARBONYL SULFIDE
VOC
120809
CATECHOL
120809
CATECHOL
VOC
57749
CHLORDANE
57749
CHLORDANE
VOC
55
-------
Allocati
on
TRI
EIS Poll.
Surroga
CAS
TRI Pollutant Name
Code
EIS Pollutant Name
te
778250
5
CHLORINE
7782505
CHLORINE
S02
79118
CHLOROACETIC ACID
79118
CHLORO ACETIC ACID
VOC
108907
CHLOROBENZENE
108907
CHLOROBENZENE
VOC
67663
CHLOROFORM
67663
CHLOROFORM
VOC
CHLOROMETHYL METHYL
CHLOROMETHYL METHYL
107302
ETHER
107302
ETHER
VOC
126998
CHLOROPRENE
126998
CHLOROPRENE
VOC
744047
PM10-
3
CHROMIUM
7440473
CHROMIUM
FIL
CHROMIUM
COMPOUNDS(EXCEPT
CHROMITE ORE MINED IN
PM10-
N090
THE TRANSVAAL REGION)
7440473
CHROMIUM
FIL
744048
PM10-
4
COBALT
7440484
COBALT
FIL
PM10-
N096
COBALT COMPOUNDS
7440484
COBALT
FIL
131977
CRESOL/CRESYLIC ACID
3
CRESOL (MIXED ISOMERS)
1319773
(MIXED ISOMERS)
VOC
108394
M-CRESOL
108394
M-CRESOL
VOC
95487
O-CRESOL
95487
O-CRESOL
VOC
106445
P-CRESOL
106445
P-CRESOL
VOC
98828
CUMENE
98828
CUMENE
VOC
PM10-
N106
CYANIDE COMPOUNDS
57125
CYANIDE
FIL
132649
DIBENZOFURAN
132649
DIBENZOFURAN
VOC
PM10-
84742
DIBUTYL PHTHALATE
84742
DIBUTYL PHTHALATE
FIL
111444
BIS(2-CHLOROETHYL) ETHER
111444
DICHLOROETHYL ETHER
VOC
62737
DICHLORVOS
62737
DICHLORVOS
VOC
111422
DIETHAN OLAMINE
111422
DIETHANOLAMINE
VOC
64675
DIETHYL SULFATE
64675
DIETHYL SULFATE
VOC
131113
DIMETHYL PHTHALATE
131113
DIMETHYL PHTHALATE
VOC
77781
DIMETHYL SULFATE
77781
DIMETHYL SULFATE
VOC
DIMETHYLCARBAMYL
DIMETHYLCARBAMOYL
79447
CHLORIDE
79447
CHLORIDE
VOC
N120
DIISOCYANATES
NA- pollutant not used
DIOXIN AND DIOXIN-LIKE
N150
COMPOUNDS
NA- pollutant not used
106898
EPICHLOROHYDRIN
106898
EPICHLOROHYDRIN
VOC
140885
ETHYL ACRYLATE
140885
ETHYL ACRYLATE
VOC
51796
URETHANE
51796
ETHYL CARBAMATE
VOC
56
-------
Allocati
on
TRI
EIS Poll.
Surroga
CAS
TRI Pollutant Name
Code
EIS Pollutant Name
te
CHLORIDE
75003
CHLOROETHANE
75003
ETHYL CHLORIDE
voc
100414
ETHYLBENZENE
100414
ETHYL BENZENE
voc
106934
1,2-DffiROMOETHANE
106934
ETHYLENE DIBROMIDE
voc
107062
1,2-DICHLOROETHANE
107062
ETHYLENE DICHLORIDE
voc
107211
ETHYLENE GLYCOL
107211
ETHYLENE GLYCOL
voc
75218
ETHYLENE OXIDE
75218
ETHYLENE OXIDE
voc
96457
ETHYLENE THIOUREA
96457
ETHYLENE THIOUREA
voc
75343
ETHYLIDENE DICHLORIDE
75343
ETHYLIDENE DICHLORIDE
voc
50000
FORMALDEHYDE
50000
FORMALDEHYDE
voc
N230
CERTAIN GLYCOL ETHERS
171
N/A Pollutant not used
76448
HEPTACHLOR
76448
HEPTACHLOR
voc
118741
HEXACHLOROBENZENE
118741
HEXACHLOROBENZENE
voc
HEXACHLORO-1,3-
87683
BUTADIENE
87683
HEX ACHLOROBUT ADIENE
voc
HEXACHLOROCYCLOPENTAD
HEXACHLOROCYCLOPENTAD
77474
IENE
77474
IENE
voc
67721
HEXACHLOROETHANE
67721
HEXACHLOROETHANE
voc
110543
N-HEXANE
110543
HEXANE
voc
302012
HYDRAZINE
302012
HYDRAZINE
voc
HYDROCHLORIC ACID (1995
764701
AND AFTER "ACID
0
AEROSOLS" ONLY)
7647010
HYDROCHLORIC ACID
S02
766439
3
HYDROGEN FLUORIDE
7664393
HYDROGEN FLUORIDE
S02
123319
HYDROQUIN ONE
123319
HYDROQUIN ONE
voc
743992
PM10-
1
LEAD
7439921
LEAD
FIL
PM10-
N420
LEAD COMPOUNDS
7439921
LEAD
FIL
1,2,3,4,5,6-
HEXACHLOROCYCLOHEXAN
58899
LINDANE
58899
E
VOC
108316
MALEIC ANHYDRIDE
108316
MALEIC ANHYDRIDE
VOC
743996
PM10-
5
MANGANESE
7439965
MANGANESE
FIL
PM10-
N450
MANGANESE COMPOUNDS
7439965
MANGANESE
FIL
743997
PM10-
6
MERCURY
7439976
MERCURY
FIL
PM10-
N458
MERCURY COMPOUNDS
7439976
MERCURY
FIL
67561
METHANOL
67561
METHANOL
VOC
57
-------
TRI
CAS
TRI Pollutant Name
EIS Poll.
Code
EIS Pollutant Name
Allocati
on
Surroga
te
72435
METHOXYCHLOR
72435
METHOXYCHLOR
voc
74839
BROMOMETHANE
74839
METHYL BROMIDE
voc
74873
CHLOROMETHANE
74873
METHYL CHLORIDE
voc
71556
1,1,1 -TRICHLOROETHANE
71556
METHYL CHLOROFORM
voc
74884
METHYL IODIDE
74884
METHYL IODIDE
voc
108101
METHYL ISOBUTYL KETONE
108101
METHYL ISOBUTYL KETONE
voc
624839
METHYL ISOCYANATE
624839
METHYL ISOCYANATE
voc
80626
METHYL METHACRYLATE
80626
METHYL METHACRYLATE
voc
163404
4
METHYL TERT-BUTYL ETHER
1634044
METHYL TERT-BUTYL ETHER
voc
75092
DICHLOROMETHANE
75092
METHYLENE CHLORIDE
voc
60344
METHYL HYDRAZINE
60344
METHYLHYDRAZINE
voc
121697
N,N-DIMETHYL ANILINE
121697
N,N-DIMETHYL ANILINE
voc
68122
N,N-DIMETHYLF ORMAMIDE
68122
N,N-DIMETHYLF ORMAMIDE
voc
91203
NAPHTHALENE
91203
NAPHTHALENE
voc
744002
0
NICKEL
7440020
NICKEL
PM10-
FIL
N495
NICKEL COMPOUNDS
7440020
NICKEL
PM10-
FIL
98953
NITROBENZENE
98953
NITROBENZENE
VOC
90040
O-ANISIDINE
90040
O-ANISIDINE
VOC
95534
O-TOLUIDINE
95534
O-TOLUIDINE
voc
60117
4-
DIMETHYLAMINOAZOBENZE
NE
60117
4-
DIMETHYLAMINOAZOBENZE
NE
voc
123911
1,4-DIOXANE
123911
P-DIOXANE
voc
82688
QUINTOZENE
82688
PENTACHLORONITROBENZEN
E
voc
87865
PENTACHLOROPHENOL
87865
PENTACHLOROPHENOL
voc
108952
PHENOL
108952
PHENOL
voc
75445
PHOSGENE
75445
PHOSGENE
voc
780351
2
PHOSPHINE
7803512
PHOSPHINE
voc
772314
0
PHOSPHORUS (YELLOW OR
WHITE)
7723140
PHOSPHORUS
PM10-
FIL
85449
PHTHALIC ANHYDRIDE
85449
PHTHALIC ANHYDRIDE
PM10-
FIL
133636
3
POLY CHLORINATED
BIPHENYLS
1336363
POLYCHLORINATED
BIPHENYLS
VOC
191242
BENZO(G,H,I)PERYLENE
191242
BENZO[G,H,I,lPERYLENE
PM10-
FIL
85018
PHENANTHRENE
85018
PHENANTHRENE
PM10-
FIL
58
-------
Allocati
on
TRI
EIS Poll.
Surroga
CAS
TRI Pollutant Name
Code
EIS Pollutant Name
te
POLYCYCLIC AROMATIC
1304982
PM10-
N590
COMPOUNDS
92
PAH, total
FIL
106503
P-PHENYLENEDI AMINE
106503
P-PHENYLENEDI AMINE
VOC
123386
PROPIONALDEHYDE
123386
PROPIONALDEHYDE
VOC
114261
PROPOXUR
114261
PROPOXUR
VOC
78875
1,2-DICHLOROPROP ANE
78875
PROPYLENE DICHLORIDE
VOC
75569
PROPYLENE OXIDE
75569
PROPYLENE OXIDE
VOC
91225
QUINOLINE
91225
QUINOLINE
VOC
106514
QUINONE
106514
QUINONE
VOC
778249
PM10-
2
SELENIUM
7782492
SELENIUM
FIL
PM10-
N725
SELENIUM COMPOUNDS
7782492
SELENIUM
FIL
100425
STYRENE
100425
STYRENE
VOC
96093
STYRENE OXIDE
96093
STYRENE OXIDE
VOC
127184
TETRACHLOROETHYLENE
127184
TETRACHLOROETHYLENE
VOC
755045
0
TITANIUM TETRACHLORIDE
7550450
TITANIUM TETRACHLORIDE
VOC
108883
TOLUENE
108883
TOLUENE
VOC
95807
2,4-DIAMINOTOLUENE
95807
TOLUENE-2,4-DIAMINE
VOC
800135
2
TOXAPHENE
8001352
TOXAPHENE
VOC
79016
TRICHLOROETHYLENE
79016
TRICHLOROETHYLENE
VOC
121448
TRIETHYL AMINE
121448
TRIETHYL AMINE
VOC
158209
8
TRIFLURALIN
1582098
TRIFLURALIN
VOC
108054
VINYL ACETATE
108054
VINYL ACETATE
VOC
75014
VINYL CHLORIDE
75014
VINYL CHLORIDE
VOC
75354
VINYLIDENE CHLORIDE
75354
VINYLIDENE CHLORIDE
VOC
108383
M-XYLENE
108383
M-XYLENE
VOC
95476
O-XYLENE
95476
O-XYLENE
VOC
106423
P-XYLENE
106423
P-XYLENE
VOC
133020
7
XYLENE (MIXED ISOMERS)
1330207
XYLENES (MIXED ISOMERS)
VOC
59
-------
Table 13: Pollutant Groups
Pollutant
Group Name
Code
Pollutant
7440473
Chromium
1333820
Chromium Trioxide
Chromium
7738945
Chromic Acid (VI)
18540299
Chromium (VI)
16065831
Chromium III
1330207
Xylenes (Mixed Isomers)
Xylenes (Mixed
95476
o-Xylene
Isomers)
106423
p-Xylene
108383
m-Xylene
Cresol/Cresylic
Acid (Mixed
Isomers)
1319773
Cresol/Cresylic Acid (Mixed Isomers)
95487
o-Cresol
108394
m-Cresol
106445
p-Cresol
1336363
Polychlorinated Biphenyls (PCBs)
2050682
4,4'-Dichlorobiphenyl (PCB-15)
2051243
Decachlorobiphenyl (PCB-209)
2051607
2-Chlorobiphenyl (PCB-1)
Polychlorinated
Biphenyls
25429292
Pentachlorobiphenyl
26601649
Hexachlorobiphenyl
26914330
Tetrachlorobiphenyl
28655712
Heptachlorobiphenyl
53742077
Nonachlorobiphenyl
55722264
Octachlorobiphenyl
7012375
2,4,4'-Trichlorobiphenyl (PCB-28)
120127
Anthracene
129000
Pyrene
130498292
PAH, total
189559
Dibenzo[ a,i"|Pyrene
189640
Dibenzo[ a,h]Pyrene
191242
Benzo[g,h,I,]Perylene
191300
Dibenzo[ a,l]Pyrene
192654
Dibenzo|"a,e"|Pyrene
Polycyclic
192972
Benzo[e]Pyrene
Organic Matter
193395
Indeno|"l,2,3-c,d"|Pyrene
(POM)
194592
7H-Dib enzo [c, g] carb azol e
195197
Benzolphenanthrene
198550
Perylene
203123
Benzo(g,h,i)Fluoranthene
203338
B enzo( a)Fluoranthene
205823
Benzo[j ]fluoranthene
205992
Benzo[b]Fluoranthene
206440
Fluoranthene
207089
Benzo[k]Fluoranthene
60
-------
Pollutant
Group Name
Code
Pollutant
208968
Acenaphthylene
218019
Chrysene
224420
Dibenzo[ a,] ] Acridine
226368
Dibenz[a,h]acridine
2381217
1-Methylpyrene
2422799
12-Methylbenz(a) Anthracene
250
PAH/POM - Unspecified
26914181
Methyl anthracene
3697243
5-Methyl chrysene
41637905
Methylchrysene
42397648
1,6-Dinitropyrene
42397659
1,8-Dinitropyrene
50328
Benzo[a]Pyrene
53703
Dib enzo [a, h] Anthracene
5522430
1-Nitropyrene
56495
3 -Methyl chol anthrene
56553
B enz [ al Anthracene
56832736
Benzofluoranthenes
57835924
4-Nitropyrene
57976
7,12-Dimethylbenz[a] Anthracene
602879
5 -Nitroacenaphthene
607578
2-Nitrofluorene
65357699
Methylbenzopyrene
7496028
6-Nitrochrysene
779022
9-Methyl Anthracene
8007452
Coal Tar
832699
1 -Methylphenanthrene
83329
Acenaphthene
85018
Phenanthrene
86737
Fluorene
86748
Carbazole
90120
1 -Methylnaphthalene
91576
2-Methylnaphthalene
91587
2-Chloronaphthalene
Cvunide &
57125
Cvanide
Compounds
74l)t>X
1 Ivdroucn Cvanidc
7440020
Nickel
Nickel &
12035722
Nickel Subsulfide
Compounds
1313991
Nickel Oxide
604
Nickel Refinery Dust
61
-------
3.1.5 HAP augmentation based on emission factor ratios
For use in cases where S/L/T agencies did not report HAP emissions and TRI data were not available,
we calculated HAP emissions by multiplying the appropriate surrogate CAP emissions (provided by
S/L/T agencies) by an emissions ratio of HAP to CAP emission factors from WebFIRE.
Several updates were made to the HAP augmentation dataset between the 2008 NEI v2 and v3. The
main goal of these updates was to add missing Hg for boilers combusting coal, wood or oil that had
PM10. The missing Hg from the boiler category was one of the issues on the v2 issues list, and the
update we made for v3 resolved this issue. In the process of adding the missing Hg, we also revised the
Hg ratio approach for boilers where either the Hg or PM10-FIL were missing (see 3.1.5.2) and corrected
the HAP to CAP emission factors for several SCCs.
The spreadsheet "HAP EF Ratios Derived from WebFIRE.xls" (see Section 8.1 for access information)
provides the 2,417 emissions ratios by SCC used for the 2008v2. For each ratio, the spreadsheet
provides the HAP and CAP Factor Ids for the EFs used to build these ratios. These Factor Ids identify
each unique EF in the WebFIRE database. Where the factor Ids in that spreadsheet are null, it means we
used a ratio from a similar WebFIRE SCC. This was only done for Hg from boilers, to allow for a more
complete gap filling of Hg from boilers. Additional ratios were added to allow more complete gap filling
of boilers and process heaters that used fuel types similar to those covered in WebFIRE but are not
explicitly in WebFIRE. Table 14 provides the specific CAPs used for each HAP emission factor
calculated.
A key result of our approach is that the resulting HAP augmentation dataset does not include HAP
emissions for facilities where the HAP was reported by an S/L/T agency at any process at the facility.
For example, if a facility reported formaldehyde at process A only, and the WebFIRE Emission factor
database yields formaldehyde emissions for processes A, B, and C, then the HAP augmentation dataset
would not contain formaldehyde from any processes at the facility. If that facility had no formaldehyde,
then the HAP augmentation dataset would have formaldehyde for processes A, B and C. This approach
was taken to be conservative in our attempt to prevent double counted emissions, which is necessary
because we know that some states aggregate their HAP emissions and assign to fewer or different
processes than their CAP emissions. These types of differences are expected since CAPs are required at
the process level, but HAPs are entirely voluntary.
Table 14: CAP Surrogate assignments to derive HAP-to-CAP Emission Factor Ratios
Polluta
CAP
Description
nt Code
Surrogate
1,1,2,2-
T etrachl oroethane
79345
voc
1,1,2-Trichl oroethane
79005
voc
1,3-Butadiene
106990
voc
1,3 -Di chl oropropene
542756
voc
1,4-Dichlorobenzene
106467
voc
CAP
Pollutan
Surrogat
Description
t Code
e
Ethyl Chloride
75003
VOC
Ethylene Dibromide
106934
VOC
Ethylene Di chloride
107062
VOC
Ethylidene Dichloride
75343
VOC
PM10-
Fluoranthene
206440
FIL
62
-------
Description
Polluta
nt Code
CAP
Surrogate
2 2 4-
Trimethylpentane
540841
VOC
2,4-Dinitrophenol
51285
voc
2-Chloronaphthalene
91587
PM10-FIL
2-Methylnaphthalene
91576
PM10-FIL
4,4'-
Methylenediphenyl
Diisocyanate
101688
VOC
4-Nitrophenol
100027
VOC
Acenaphthene
83329
PM10-FIL
Acenaphthylene
208968
PM10-FIL
Acetaldehyde
75070
VOC
Acetonitrile
75058
VOC
Acetophenone
98862
VOC
Acrolein
107028
VOC
Acrylonitrile
107131
VOC
Anthracene
120127
PM10-FIL
Antimony
744036
0
PM10-FIL
Arsenic
744038
2
PM10-FIL
B enz [ al Anthracene
56553
PM10-FIL
Benzene
71432
VOC
Benzo[alPyrene
50328
PM10-FIL
Benzo[b]Fluoranthen
e
205992
PM10-FIL
Benzo[elPyrene
192972
PM10-FIL
Benzo[g,h,I,]Perylene
191242
PM10-FIL
Benzo[k]Fluoranthen
e
207089
PM10-FIL
Beryllium
744041
7
PM10-FIL
Biphenyl
92524
VOC
Bis(2-
Ethylhexyl)Phthalate
117817
VOC
Cadmium
744043
PM10-FIL
Description
Pollutan
t Code
CAP
Surrogat
e
Fluorene
86737
PM10-
FIL
Formaldehyde
50000
VOC
Hexane
110543
VOC
Hydrochloric Acid
7647010
S02
Hydrogen Fluoride
7664393
S02
Hydroquinone
123319
VOC
Indeno[l,2,3-c,d]Pyrene
193395
PM10-
FIL
Isophorone
78591
VOC
Lead
7439921
PM10-
FIL
Manganese
7439965
PM10-
FIL
Mercury
7439976
PM10-
FIL
Methanol
67561
VOC
Methyl Bromide
74839
VOC
Methyl Chloride
74873
VOC
Methyl Chloroform
71556
VOC
Methyl Iodide
74884
VOC
Methyl Isobutyl Ketone
108101
VOC
Methyl Tert-Butyl Ether
1634044
VOC
Methylene Chloride
75092
VOC
Naphthalene
91203
VOC
Nickel
7440020
PM10-
FIL
Nickel Oxide
1313991
PM10-
FIL
o-Xylene
95476
VOC
PAH, total
1304982
92
PM10-
FIL
PAH/POM-
Unspecified
250
PM10-
FIL
Pentachlorophenol
87865
VOC
Perylene
198550
PM10-
63
-------
Description
Polluta
nt Code
CAP
Surrogate
9
Carbon Disulfide
75150
voc
Carbon Tetrachloride
56235
voc
Chlorine
778250
5
S02
Chlorobenzene
108907
VOC
Chloroform
67663
VOC
Chromium
744047
3
PM10-FIL
Chromium (VI)
185402
99
PM10-FIL
Chromium Trioxide
133382
0
PM10-FIL
Chrysene
218019
PM10-FIL
Cobalt
744048
4
PM10-FIL
Cumene
98828
VOC
Dibenzo[a,h]Anthrace
ne
53703
PM10-FIL
Dibenzofuran
132649
VOC
Dibutyl Phthalate
84742
PM10-FIL
Dimethyl Phthalate
131113
VOC
Ethyl Benzene
100414
VOC
CAP
Description
Pollutan
t Code
Surrogat
e
FIL
PM10-
Phenanthrene
85018
FIL
Phenol
108952
VOC
Phosgene
75445
VOC
PM10-
Phosphorus
7723140
FIL
Polychlorinated
Biphenyls
1336363
VOC
Propionaldehyde
123386
VOC
Propylene Dichloride
78875
VOC
PM10-
Pyrene
129000
FIL
PM10-
Selenium
7782492
FIL
Styrene
100425
VOC
T etrachl oroethyl ene
127184
VOC
Toluene
108883
VOC
Tri chl oroethyl ene
79016
VOC
Vinyl Chloride
75014
VOC
Vinylidene Chloride
75354
VOC
Xylenes (Mixed
Isomers)
1330207
VOC
The HAP Augmentation process consisted of three main steps: (1) calculating HAP-to-CAP ratios from
existing WebFIRE emission factors, (2) adding Hg ratios for boiler and process-heater SCCs using
similar fuels as those covered in step 1, and (3) calculating HAP emissions from these ratios and the
surrogate CAP emissions. In addition, a fourth step was used to perform special quality assurance for
Hg. These steps are described in more detail in the three subsections below.
3.1.5.1 Step 1: Extract and Modify WebFIRE Emission Factors and Calculate HAP-
to-CAP ratios
The following list provides the various parts of Step 1 to extract and modify the WebFIRE emission
factors and calculate the HAP-to-CAP ratios
1. Download latest WebFIRE database from the U.S. EPA: (WebFIREFactors.csv downloaded on
12/19/10). Each separate record in that file is identified with a unique "Factor ID".
64
-------
2. Delete all Revoked and Controlled Emission Factors. This means that only ratios of uncontrolled
emission factors were used in this approach.
3. Change WebFIRE pollcode 246 to 130498292 (PAH).
4. Change WebFIRE pollcode 40 to 250 (unspecified PAH/POM).
5. Change WebFIRE pollcode 102 (Benzo[b+k]Fluoranthene to 205992 (Benzo[b]Fluoranthene).
Although these are not identical compounds, both have the same risk factors.
6. Remove Efs for the pollutants shown in Table 15 because they are not valid pollutant codes in the
2008 NEI and there are no valid pollutant codes that represent these pollutants.
Table 15: Invalid pollutant codes for HAP augmentation
Pollutant
code
Last Valid Year
Pollutant description
37871004
2005
Total Heptachlorodibenzo-p-Dioxin
34465468
2005
Hexachlorodibenzo-p-Dioxin
30402154
2005
Total Pentachlorodibenzofuran
136677093
2005
Dioxins, Total, W/O Individ. Isomers Reported {PCDDS}
136677106
2005
Polychlorinated Dibenzofurans, Total
7. Remove Efs for pollcode 140 (coke oven emissions) since (at the time) we did not have an approach
to map from this code to the benzene soluble organics (BSO) or Methylene Chloride Soluble
Organics (MSO) pollutant codes.14
8. Remove Efs for pollcode 78933 (methyl ethyl ketone) because it is no longer a HAP.
9. Remove Efs for pollcode 123739 (crotonaldehyde) because it is not a HAP.
10. Remove Efs that begin with "<" because these are usually based on minimum detection limits. We
chose to ignore emission factors based on minimum detection limits as a conservative approach to
not adding emissions where they may not exist.
11. Assign the midpoint of emission factor ranges as new emission factor for the situation in which
emission factor is given as a range of values.
12. Multiply the EF for pollcode 1317368 (Lead (II) Oxide) by 0.92832 and rename pollcode to
7439921 (lead). The 0.92832 value is the fraction of lead ion in the total compound.
13. Multiply EF for pollcode 1317346 (Manganese Trioxide) by 0.69599 and rename pollcode to
7439965 (manganese). The 0.69599 value is the fraction of manganese ion in the total compound.
14. Delete PAH, total and PAH/POM-Unspecified factors when the SCC has other specific POM Efs.
This affects FactorlDs: 5530, 5859, 8111, 9741, 11611, 11971, 12109, 12176, 12295, 12651, and
22965.
15. Remove all records for which there is a HAP emission factor but no Surrogate CAP factor.
14 We have since determined that we could have used either of the MSO or BSO codes, since these two methods for
measuring extractable organic matter extract about the same quantity of coke oven pollutant mass.
65
-------
16. Convert HAPs with different EF bases (denominators) as compared to the CAP Efs using the default
heat content by fuel type as shown in Table 16 and other physical conversion factors as shown in
Table 17.
Table 16: Conversion factors HAP emission factors for HAP augmentation
Fuel
Heat Content
Coal
13,000 BTU/lb or 26 mmBTU/ton
Anthracite coal
12,300 BTU/lb or 24.6 mmBTU/ton
lignite coal
7,200 BTU/lb or 14.4 mmBTU/ton
Residual oil
150,000 BTU/gallon
Distillate oil
140,000 BTU/gallon
Diesel
137,000 BTU/gallon
Kerosene
135,000 BTU/gallon
LPG
94,000 BTU/gallon
Natural gas
1,050 BTU/SCF
Coke Oven gas
590 BTU/SCF
Wood
5,200 BTU/lb
Process Gas
not assigned a default heat content
Table 17: Physical Conversion Factors Used
Conversion
Physical factors used
lb/k-gal
mg/kLx(3.785L/gal)x(2.2046E-6 lb/mg)
lb/ton
g/Mg x (1 Mg/1 E6g) x (2000 lb/ton)
|ig/kgx(lkg/lE9|ig)x(2000 lb/ton)
lb/1000 barrels
lb/MMBTUx(140 MMBTU/1000 gallons oil)x(42
gallons/barrel)
lb/MMBTU
lb/ton woodx(l ton/20001b)x(llb/5200BTU)x(lE6
BTU/MMBTU)
lb/million cubic
feet
ng/Jx(lkg/lE12ng)x(2.2041b/kg)x(1.055E9
J/MMBTU)x(1050 MMBTU/million cubic feet NG)
17. Remove all HAP emission factors that cannot be physically converted to the same units as the
associated CAP emission factor units. A ratio will not be valid if it is not in the same units.
18. Remove any CAP emission factors that have formulas that cannot be calculated. In practice, this
step applied only to one natural gas fired ceramic kiln emission factor with a formula in terms of the
sulfur content of the raw material (FactorlD 18899).
19. Calculate all CAP emission factors with formulas, using default ash content of 8% and sulfur content
of 1.7% for coal (bituminous), 0.24% sulfur content for distillate oil, 1.2% sulfur content for residual
oil.
20. Calculate minimum and maximum HAP factors per SCC and pollutant. Delete Factor IDs 12817-
12846 because there were 30 different factors, very different in EF, for different processes not
distinguishable at the SCC level. Delete Factor IDs 13047-13054 because there were 8 different
factors, very different in EF, for different processes not distinguishable at the SCC level.
66
-------
21. Delete HAP factors with multiple unrated factors for an SCC/pollutant combo that are at least an
order of magnitude apart and have no way to be distinguished for accuracy. An unrated factor is one
in which the Webfire database Quality field is "U". FactorlDs affected include: 13444-13446,
13441-13443, 13482-13484, 15222-15224, 22936-22937, 13836-13841, 15890-15891, 12864-
12865, 24974-24977.
22. Speciate total chromium (pollutant code 7440473) WebFIRE emission factors into hexavalent and
trivalent chromium by SCC using the SCC-based speciation factors that were used for developing
the "EPA chromium fix overlaps and speciate" dataset (see Section 3.1.3). SCCs without process-
specific factors were speciated using the default speciation factor of 34% hexavalent chromium.
Where there was an existing WebFIRE factor for hexavalent or trivalent chromium, the WebFIRE
factor took precedence. Afterwards, all total chromium factors are deleted prior to computing HAP
emissions.
23. Calculate dimensionless ratios of HAPs to surrogate CAPs for all HAPs.
24. Delete HAP factors with a HAP to CAP EF ratio greater than 1. This was done because it is not
plausible to have more metal PM than total PM or more VOC HAPs than total VOC. We did not
want to create implausible inconsistencies in the EPA-supplied data.
25. Renormalize HAP to CAP ratios in cases where the SCC-level HAP to CAP ratios exceed 1 (342
ratios affected).
3.1.5.2 Step 2: Add HAP-to-CAP ratios for Hg from boiler and process heaters and
corrections made to this approach in v3
For version 2, we investigated all boiler and process heater SCCs that did not have ratios because they
were missing from WebFIRE. We determined that some of these SCCs were similar to other SCCs
covered in WebFIRE and thus used the ratios from the similar SCCs. We chose the ratio based on fuel
type. If there were multiple WebFIRE SCCs with that fuel type, we chose the lowest ratio. In this step
we also removed ratios associated with Hg emissions from natural gas combustion since there is
uncertainty in the amount of Hg emitted from this process, and we do not compute Hg emissions from
natural gas consumption in the nonpoint data category.
For version 3, we noticed and corrected an error in the EFs we assigned to boiler SCCs that did not have
ratios and corrected them. We had in fact not used the lowest ratio. Instead of using the lowest ratio we
chose the lowest Hg EF and then computed the ratio based on the PM EF for that SCC. Rather than
correcting this by using the lowest Hg EF ratio, we used a different approach to determine ratio for these
missing boiler SCCs. The v3 approach was to use the available WebFIRE factors to compute the ratio
as Hg EF/PM10-FIL EF. If the PM10-FIL EF is not in WebFIRE, then use the lowest ratio for that fuel
type. If the PM10-FIL EF is in WebFIRE, then fill in the Hg EF for that fuel (it is always the same value
for the fuel type) and compute the ratio. The spreadsheet "boiler sees for hg hap augmentation .xlsx"
(see Section 8.1 for access information) provides the revised factors and their derivation.
3.1.5.3 Step 3: Emissions Calculations
The following list provides the steps needed to calculate the HAP emissions to be included in the HAP
Augmentation dataset.
1. Extract the CAP data for VOC, PM-10FIL and S02 from a modified version of the 2008 RAS that
incorporated PM Augmentation updates (PM augmentation is described in Section 3.1.2).
67
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Therefore, VOC and S02 CAP emissions are always from the S/L/T dataset, but PM10-FIL come
from both the S/L/T dataset and from the EPA Augmentation dataset for processes for which S/L/T
data have no PM10-FIL and the PM Augmentation dataset included data. The extraction only
considered annual CAP emissions and all emissions were converted to pounds.
2. Apply ratios to all surrogate emissions data.
3. Keep only HAP emissions for which there are no HAP emissions of that particular HAP at any
process in the facility. The one exception is that we allowed Hg from boilers to be gap filled by the
HAP Augmentation dataset at unmatched processes. As part of this step, we considered overlapping
pollutant groups. For example, we considered that if any PCB was reported at a facility, then no
other PCB's should be allowed. Pollutant groups were created for Chromium, Xylenes (Mixed
Isomers), Cresol/Cresylic Acid (Mixed Isomers), Polychlorinated Biphenyls, Polycyclic Organic
Matter, Cyanide, and Nickel. The one exception to this is that we did not remove Hg from boiler
SCCs (other than boilers at the facilities described in Step 7).
4. Exclude HAP emissions that are higher than the maximum emissions level reported by any S/L/T for
that pollutant and SCC (to avoid producing HAP emissions through HAP augmentation that are
higher than any S/L/T reported value for the SCC/HAP, which could be an outlier). When
determining the maximum reported S/L/T value, we excluded the suspect S/L/T data. For
hexavalent chromium, we excluded emissions from the final HAP Augmentation dataset if the
hexavalent chromium exceeded the maximum S/L/T total chromium multiplied by the default
speciation factor of 0.34; for and trivalent chromium, we excluded emissions from the final HAP
Augmentation dataset if the trivalent chromium exceeded the maximum S/L/T total chromium
multiplied by the default speciation factor of 0.66.
5. Exclude HAP emissions that have no SCC/pollutant match in S/L/T reported data. These were
excluded because there was no comparison dataset to determine whether any of these records could
be outliers, which is a conservative approach to avoid adding erroneous data.
6. Exclude HAP emissions that were included in other EPA datasets that were higher in hierarchy.
7. Exclude HAP emissions from the HAP augmentation dataset for any sources with "Facility Type"
set to "Electricity Generation via Combustion".
3.1.5.4 Step 4: Special QA for Hg (done for 2008v2)
We investigated the SCCs with the greatest Hg emissions in the HAP Augmentation dataset. In
particular, we looked at SCCs where national total augmented Hg emissions exceeded 40 pounds and the
SCC was not coal burning. As a result of that QA, we adjusted the final HAP Augmentation dataset.
The adjustments made were not only for Hg but also for other HAPs since the issues we identified by
looking only at Hg were present for other HAPs as well. The following items describe the results of this
special QA:
Based on a national SCC-level summary of the HAP augmentation dataset, we found that SCC
30600106 (Industrial Processes; Petroleum Industry; Process Heaters; Process Gas-fired) had the highest
augmented Hg emissions of any SCC; in fact, augmented emissions from this SCC was higher than the
next highest SCC by a factor of 3. The cause of this outlier was that this SCC had different units of
measure for the HAP versus CAP emission factor. The units for the Hg EF are pounds per million
68
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BTUs heat input, and the units for the CAP surrogate (PM10-FIL) are pounds per million cubic feet
process gas burned. Although this is a process gas SCC, we had chosen to use the natural gas default
heat content to convert the HAP Efs to the same units as the surrogate CAP EF. We suspect that the
very high HAP/CAP ratios for Hg were a result of the impact of the heat content of process gas being
different from the heat content as natural gas. We presumed this issue would not only be Hg-specific
but would impact all of the HAPs because all had the same discrepancy in the EF units between HAP
and CAP; therefore, we decided to remove all HAP emissions from this SCC from the HAP
augmentation dataset. In addition, this QA prompted our investigation of any other process gas SCC
that had different units of measure and were converted to the same units based on the heat content of
natural gas. We found three additional SCCs where this occurred and removed all HAP emissions from
the HAP augmentation dataset from these SCCs as well. The final result of this check was that we
excluded all HAP emissions from the HAP Augmentation dataset for any process with the following
process gas SCCs: 10200701, 10300701, 30600106, and 30609904.
Also as part of the QA, we found 255 lbs of Hg augmented from 8 processes with SCC= 50100101
(Waste Disp-Govt /Municipal Incineration /Starved Air: Multiple Chamber). This was unexpected
because this SCC represents the municipal waste combustion process for which we had already filled in
Hg emissions from other EPA datasets and the HAP augmentation approach excludes gap filling for
processes covered by other datasets (except for boiler Hg). We discovered that these 8 processes had the
incorrect SCC included in EIS by reviewing other descriptive information on the facilities, units and
processes. Since the basis of the HAP to CAP ratios is the SCC, we chose not to use any augmented
emissions for these 8 processes. We also reviewed EIS emissions processes for SCC 50200501 (Waste
Disposal; Solid Waste Disposal - Commercial/Institutional; Incineration: Special Purpose; Med Waste
Controlled Air Incin-aka Starved air, 2-stg, or Modular comb). These appeared all to be
medical/hospital/infectious waste processes and were missing 2008 Hg emissions that had been present
in previous NEIs (2002,2005). Based on this review, no adjustments were made to the HAP
Augmentation dataset for this SCC.
3.1.6 EPA nonpoint data
For the 2008 NEI, the EPA developed emission estimates for many nonpoint sectors in collaboration
with a consortium of state and regional planning organizations called the Eastern Regional Technical
Advisory Committee (ERTAC), This task is referred to by ERTAC as the "Area Source Comparability"
project on the ERTAC website, and a subgroup was developed to work on this project. The purpose of
the subgroup and project was to agree on methodologies, emission factors, and SCCs for a number of
important nonpoint sectors, and then EPA would prepare the emissions estimates for all states using the
group's final approaches. During the 2008 NEI inventory development cycle while the S/L/T agencies
were submitting emissions data, states could accept the ERTAC estimates or they could go beyond the
"default" methodologies and submit further improved data. The ERTAC process is described in Dorn et
al. (2010) and a spreadsheet showing the sectors, SCCs, emission factors, and a brief description of the
methodologies called "ERTACstatecomparison.xlsx" (see Section 8 for access information). Below
are tables that describe the sectors for which EPA developed emission estimates. Some sectors EPA
expects to be entirely in the nonpoint (and not point source) data category, i.e., residential heating.
These are listed in Table 18.
69
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Table 18: EPA-estimated emissions sources expected to be exc
usively nonpoint
EPA-estimated emissions
source description
Supporting data file name (see also
Section 8)
EIS Sector Name
Residential Heating;
anthracite coal
res_anthra_coal_epa_data.zip
Fuel Comb - Residential -
Other
Residential Heating;
bituminous coal
res_bit_coal_epa_data.zip
Fuel Comb - Residential -
Other
Residential Heating;
distillate oil
res_distillate_fuel_rvsd090711 .zip
Fuel Comb - Residential -
Oil
Residential Heating;
natural gas
res_ng_rvsd090711 .zip
Fuel Comb - Residential -
Natural Gas
Residential Heating;
liquefied petroleum gas
res_lpg_rvsd090711 .zip
Fuel Comb - Residential -
Other
Residential Heating;
Fireplaces
res_wood_comb_epa_data.zip
Fuel Comb - Residential -
Wood
Residential Heating; Free
standing woodstoves
res_wood_comb_epa_data.zip
Fuel Comb - Residential -
Wood
Residential Heating;
Fireplace Inserts
res_wood_comb_epa_data.zip
Fuel Comb - Residential -
Wood
Residential Heating; Pellet
Stoves
res_wood_comb_epa_data.zip
Fuel Comb - Residential -
Wood
Residential Heating;
Indoor Furnaces
res_wood_comb_epa_data.zip
Fuel Comb - Residential -
Wood
Residential Heating;
Outdoor Hydronic Heaters
res_wood_comb_epa_data.zip
Fuel Comb - Residential -
Wood
Residential Heating;
Firelog
res_wood_comb_epa_data.zip
Fuel Comb - Residential -
Wood
Residential Heating;
Kerosene
res kerosene rvsd090711.zip
Fuel Comb - Residential -
Oil
Paved Roads
paved roads rvsd090711.zip
Dust - Paved Road Dust
Unpaved Roads
roads unpaved epa data.zip
Dust - Unpaved Road Dust
Commercial Cooking
commercial cooking rvsd090711.zip
Commercial Cooking
Dust from Residential
Construction
construction_road_res_nonres_rvsd090711 .z
ip
Dust - Construction Dust
Dust from Commercial
Institutional
construction_road_res_nonres_rvsd090711 .z
ip
Dust - Construction Dust
Dust from Road
Construction
construction_road_res_nonres_rvsd090711 .z
ip
Dust - Construction Dust
Mining and Quarrying
mining and quarrying 2008v2.zip
Industrial Processes -
Mining
Architectural Coatings
architectural_coatings_epa_data2. zip
Solvent - Non-Industrial
Surface Coating
Traffic Markings
traffic_paints_eis_format.zip
Solvent - Industrial
Surface Coating & Solvent
Use
Consumer & Commercial
- All personal care
consumer_solvents_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
70
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EPA-estimated emissions
source description
Supporting data file name (see also
Section 8)
EIS Sector Name
products
Consumer & Commercial
- All household products
consumer_solvents_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
Consumer & Commercial
- All coatings and related
products
consumer_solvents_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
Consumer & Commercial
- All adhesives and
sealants
consumer_solvents_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
Consumer & Commercial
- All FIFRA related
products
consumer_solvents_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
Cutback Asphalt Paving
asphalt_paving_cutback_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
Emulsified Asphalt Paving
asphalt_paving_emulsified_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
Consumer Pesticide
Application
pesticides_consumer_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
Commercial Pesticide
Application
ag_pesticide_application_2008v2.zip
Solvent - Consumer &
Commercial Solvent Use
Residential Portable Gas
Cans
portable_fuel_containers_epa_data.zip
Miscellaneous Non-
Industrial NEC
Commercial Portable Gas
Cans
portable_fuel_containers_epa_data.zip
Miscellaneous Non-
Industrial NEC
Aviation Gasoline Stage 1
aviation_gasoline_distribution_stagel_
epa data, zip
Gas Stations
Aviation Gasoline Stage 2
av gas distrib stage2 rsvd090711.zip
Gas Stations
Open Burning - Leaves
ob leaf brush rvsd090711.zip
Waste Disposal
Open Burning - Brush
ob leaf brush rvsd090711.zip
Waste Disposal
Open Burning -
Residential Household
Waste
ob_msw_doc_rvsd090711 .zip
Waste Disposal
Open Burning - Land
Clearing Debris
ob_land_clearing_debris_rvsd090711 .zip
Waste Disposal
Publicly Owned
Treatment Works
potwepadata. zip
Waste Disposal
Agricultural Tilling
ag tilling 2008v2.zip
Agriculture - Crops &
Livestock Dust
Fertilizer Application
fertilizer_application_epa_data.zip
Agriculture - Fertilizer
Application
Animal Husbandry
animal_husbandry_epa_data.zip
Agriculture - Livestock
Waste
Human Cremation
human cremation 2810060100 emissions.zi
P
Miscellaneous Non-
Industrial NEC
71
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There are other sectors for which EPA expects that may overlap with the point source. In other words,
some sources will be submitted as point sources and some sources are submitted as nonpoint, i.e., fuel
combustion at commercial or institutional facilities. In these cases, EPA did not attempt to estimate the
nonpoint emissions because these could cause double-counting with the state-supplied point sources.
Rather, EPA required S/L/T agencies to prevent double-counting of emissions themselves. So, if a
S/L/T agency submitted point sources, they were to also submit nonpoint emissions for which the
emissions were reduced to account for the portion submitted as point sources. Table 19 lists these
emissions sources.
Table 19: Emissions sources not estimated by EPA with potential nonpoint and point contribution
EPA-estimated emissions
source description
Supporting data file name (see also Section 8)
EIS Sector Name
Industrial Fuel
Combustion
fuel_comb_ici_epa_data.zip
Fuel Comb - Industrial
Boilers, ICEs - All Fuels
C ommerci al/Instituti onal
Fuel Combustion
fuel_comb_ici_epa_data.zip
Fuel Comb -
Comm/Institutional - All
Fuels
Industrial Surface Coating
- Auto Refinishing
autorefini shingepadata. zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Factory Finished Wood
factory_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Wood Furniture
wood_furniture_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Metal Furniture
metal_furniture_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Paper Foil and Film
paper_film_foil_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Metal Can Coating
metal_cans_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Sheet Strip and Coil
sheet_strip_coil_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Machinery and
Equipment
machinery_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Large Appliances
1 argeappli anceep a_data2 .zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Electronic and other
Electric Coatings
electronic_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
72
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EPA-estimated emissions
source description
Supporting data file name (see also Section 8)
EIS Sector Name
Industrial Surface Coating
- Motor Vehicles
motorvehicles_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Aircraft
aircraft_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Marine
marine_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
- Railroad
railroads_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Industrial Surface Coating
Solvent - Industrial
- Miscellaneous
Manufacturing
misc_manufacturing_epa_data.zip
Surface Coating &
Solvent Use
Industrial Maintenance
Coatings
Solvent - Industrial
73ndus_maintenance_epa_data.zip
Surface Coating &
Solvent Use
Other Special Purpose
Coatings
other_special_epa_data.zip
Solvent - Industrial
Surface Coating &
Solvent Use
Degreasing
degreasing epa data, zip
Solvent - Degreasing
Graphic Arts
graphic arts epa data.zip
Solvent - Graphic Arts
Dry Cleaning
dry cleaning epa data.zip
Solvent - Dry Cleaning
Gasoline Distribution -
Stage 1 Bulk Plants
gas_distrib_stage_l_bulk_plants_epa_data.zip
Bulk Gasoline Terminals
Gasoline Distribution -
Stage 1 Bulk Terminals
gas_distrib_stage_l_bulk_terminals_epa_data.zip
Bulk Gasoline Terminals
Gasoline Distribution -
Stage 1 Pipelines
gas_distrib_stage_l_pipelines_epa_data.zip
Industrial Processes -
Storage and Transfer
Gasoline Distribution -
Stage 1 Service Station
Unloading
gas_distrib_serv_station_unloading_epa_data.zip
Gas Stations
Gasoline Distribution -
Stage 1 Underground
Storage Tanks
gasdi stribstagel _ust_breathing_and_
emptying_epa_data2. zip
Gas Stations
Gasoline Distribution -
gas distrib stage 1 tank trucks in transit
Industrial Processes -
Stage 1 Trucks In Transit
epa format.zip
Storage and Transfer
Gasoline Distribution -
Stage 2 Refueling at Pump
gas_distrib_stage2_epa_data.zip
Gas Stations
As part of the quality assurance, EPA examined whether some of these categories had VOC but not
HAP VOC. Since many of these sectors are known and important emitters of HAP VOC, when VOC is
provided without HAP VOC this is a clear case of missing emissions. For example, EIS sectors such as
73
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"Solvent - Consumer & Commercial Solvent Use" and "Solvent - Degreasing" are major emitters of
HAP VOCs that are included in Table 19. Since we did not augment these sectors, the HAP VOC is
missing in the released NEI data as well. To estimate the extent of the missing HAP VOC, we
calculated ratios of HAP VOC to VOC for each SCC associated with these categories using data
supplied by the states that did submit HAP VOC for those SCCs. We then applied those ratios by SCC
to the VOC emissions from states and SCCs without associated HAP VOC. We estimated that about
189,900 tons of HAP VOC are clearly missing from the inventory. We believe this to be a conservative
estimate because it does not account for missing glycol ethers, missing PAH/POM or situations where
states submitted only some of the VOC HAPs but not all of them. Note that this calculation of HAP
VOC was made using a chemical definition of HAP VOC and not a regulatory definition, so that
chemicals such as Tetrachloroethylene (a,k.a. PERC) that are not listed as VOCs for regulatory purposes
were included in the mass estimate of missing emissions.
Table 20 below illustrates the breakout by EIS sector of the calculated missing HAP VOC. The largest
estimated sources of missing HAP VOC are in the EIS sectors for consumer and commercial solvent use
and industrial surface coating and solvent use, making up 68% of the total estimated missing HAP VOC.
Table 20: Solvent sectors nonpoint HAP-VOC and calculated missing HA
P-VOC
EIS Sector
2008 NEI
HAP-
VOC
Missing
HAP-VOC
Total
Solvent - Consumer & Commercial Solvent Use
172,443
78,151
250,594
Solvent - Degreasing
24,430
28,587
53,017
Solvent - Dry Cleaning
2,901
16,394
19,294
Solvent - Graphic Arts
18,032
13,606
31,638
Solvent - Industrial Surface Coating & Solvent Use
46,835
51,395
98,230
Solvent - Non-Industrial Surface Coating
58,929
1,793
60,721
Total
323,569
189,926
513,495
For a few emissions sources listed in Table 21, EPA did not create new 2008 estimates. Rather than
have missing emissions where S/L/T agencies did not submit the data, EPA included data from past
inventories. Where S/L/T agencies did submit emissions, these data are included rather than this
fallback data. The 1999 NEI documentation referenced in the table is available and the 2002 NEI
documentation referenced in the table is available.
Table 21: Emissions sources using data from former EPA inventories
Emissions source
EIS Sector Name
Reference
Dental Preparation and
Use
Miscellaneous Non-
Industrial NEC
Documentation for the 1999 Base Year Nonpoint area
source National Emission Inventory for HAPs, page
A-30
General Laboratory
Activities
Miscellaneous Non-
Industrial NEC
Documentation for the Final 2002 Nonpoint Sector
(Feb 06 version) National Emission Inventory for
Criteria and HAPs, page A-106
Lamp (fluorescent)
Miscellaneous Non-
Documentation for the Final 2002 Nonpoint Sector
74
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Recycling
Industrial NEC
(Feb 06 version) National Emission Inventory for
Criteria and HAPs, page A-109
Lamp (fluorescent)
Breakage at Landfills
Miscellaneous Non-
Industrial NEC
Documentation for the Final 2002 Nonpoint Sector
(Feb 06 version) National Emission Inventory for
Criteria and HAPs, page A-107
Finally, there are some emissions sources for which we did not compute 2008 emissions nor use old
inventories to fill in where states did not provide estimates. These sources are listed in Table 22 below.
If a state within the NEI data does not include emissions for these emissions sources, then either that
state does not have such sources, or the state did not send EPA these emissions. The file
"matrixsubmittals for Version 2 Feb 13 201 l.xlsx" has a list of submitting agencies and for what
nonpoint sectors they submitted data (see Section 8.2 for access information).
Table 22: Emissions sources not included from EPA data sources
Emissions source
EIS Sector Name
Cotton Ginning
Agriculture - Crops
Grain Elevators
Agriculture - Crops & Livestock Dust
Commercial/Institutional Wood
Fuel Comb - Comm/Institutional - Biomass
Combustion
Industrial Wood Combustion
Fuel Comb - Industrial Boilers, ICEs -
Biomass
Oil and Gas Production
Industrial Processes - Oil & Gas Production
Animal Cremation
Miscellaneous Non-Industrial NEC
Drum and Barrel Reclamation
Miscellaneous Non-Industrial NEC
Hospital Sterilization
Miscellaneous Non-Industrial NEC
Structure Fires
Miscellaneous Non-Industrial NEC
Swimming Pools
Miscellaneous Non-Industrial NEC
Open Burning - Scrap Tires
Waste Disposal
Of this list, oil and gas production is the most significant source of emissions. EPA recommends that
users of the NEI look to alternative data sources to fill in emissions from this emissions source, which
was in a high growth pattern during calendar year 2008. For future inventories, EPA is developing a
default method to ensure the oil and gas sector has emissions in future NEIs for all states that have this
activity.
3.1.7 Additional Gap filling efforts targeted at high risk and specific mercury categories
EPA performed a targeted review with the help of S/L/T data submitters for facilities that had been
identified as high risk in the 2005 NATA and for facilities in specific mercury source categories. The
"high risk" facilities for our analysis were those that contributed greater than 100 in a million for cancer
risk or produced a noncancer hazard index greater than 5 in the 2005 NATA. We provided to S/L/T
agencies a "high risk" spreadsheet showing facility-level emissions of the risk driver pollutant(s) for
these facilities. We excluded coke oven facilities from this list because they were covered under a
separate review process. As part of the review spreadsheet, we included the emissions values from
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2005NATA, 2008 TRI and 2008 S/L/T emissions (or blanks when not provided). Of the approximately
440 facilities included in the list, approximately 190 had 2008 S/L/T agency-submitted data for the risk
driver pollutant. Where there were no S/L/T agency data, 140 had 2008 TRI data. We requested that
the S/L/T agencies review the emissions, provide feedback, and provide data or their preferred
approaches for gap filling where there was missing S/L/T values. We also requested that the S/L/T
agencies provide the EIS process ID codes to allow us to assign any TRI facility-level emissions to the
EIS/NEI processes. As a result of the review, we added additional data to the NEI through the datasets
described in Table 8 by the following dataset short names: "2008TRI", "2008EPAOTHER", and
"2008EPA05NATAGAPFL". In some situations, states added emission or revised their own data
through EIS, and so these revisions are reflected in the S/L/T datasets in EIS.
For the mercury review, we provided a review package for the following categories: Portland cement
manufacturing, gold mining, electric arc furnaces, hazardous waste incineration, chemical
manufacturing, mercury cell-chloralkali plants, municipal waste combustors, iron and steel foundries,
and integrated iron and steel. In addition to 2005 NATA and 2008 TRI emissions values, we also
included rule data that were available from the OAQPS rule developed. Unlike the high risk package,
we only included facilities for which mercury emissions were missing from the 2008 S/L/T data or for
which the S/L/T data were very different from TRI or the 2005 NATA. Similar to the high risk review,
the mercury review resulted in the added emission data for the following datasets: "2008TRI",
"2008EPA OTHER", and "2008EPA_05NATA_GAPFL", as well as S/L/T agencies revising the data
they provided EPA in EIS.
In most cases, the S/L/T agencies did not provide the allocation method to gap fill the facility emissions
to the appropriate processes. As a result, we used our best judgment to do that, and some examples are
as follows. For cement, we allocated all metal HAPs to the cement kilns. For electric arc furnaces, we
allocated them to the melt shop or furnace. For a number of high risk facilities, it was not obvious how
to allocate the emissions, so EPA used the S/L/T agency-reported CAP emissions (similar to the
automated TRI approach) to allocate the HAPs to the processes. The allocation method is provided in
the emissions comment field in the EIS results.
The review package results can be found in three separate spreadsheets (see Section 8.1 for access
information): high_risk_nata2005_poll_forSLT_reviewed.xlsx (high risk),
Hg EAF forSLT reviewed.xlsx (EAF Hg), and HgFacilities for SLT reviewed.xlsx (Hg other than
EAFs).
In some cases, there was insufficient information to determine how to gap fill the emissions or whether
the facility even operated in 2008. Those facilities are listed in Table 23 (for Hg) and Table 24 (for high
risk). These facilities remain without emissions of Hg or the HAP risk driver pollutant in this version of
the inventory.
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Table 23: Hg-emitting Facilities in the S/L/T agency review process with insufficient information to
gap fill
EIS
FIP
S
EI
S
St
ate
EIS
Facili
ty ID
Categ
ory
EIS
Facility
Name
EIS
compa
ny
name
EIS
Address
EIS City
NATA
2005 Hg
(lbs) -
facility
NATA data
source(s) |
Year:
total
Hazar
Sunoco
421
01
PA
4950
811
dous
Waste
Chemicals
(Former
Na
4700
Bermuda
Philadelp
hi a
5.56994
1
P|2005
Inciner
ation
Allied
Signal)
Street
Hazar
DSM
1
Columbia
Nitrogen
Road
132
45
G
A
5543
11
dous
Waste
Inciner
ation
Chemicals
North
America,
Inc.
Na
Augusta
2.25760
5
BOI-AUG |
2005, P |
2005
Hazar
Olin
220
19
LA
6425
811
dous
Waste
Corporatio
n Lake
Olin
Corpor
900-960
Interstate
Westlake
3.14019
P|2005
Inciner
ation
Charles
Plant
ation
lOWest
o
Hazar
Environ
490
45
7199
411
dous
Tooele
Tooele
mental
BOI-AUG |
UT
Waste
Army
Army
Manage
Tooele
2.48208
2005, P |
Inciner
ation
Depot
Depot
ment
Division
2005
Hazar
MeadWest
vaco
South
Carolina
LLC -
Specialty
Chemicals
Mead
Westv
220
11
LA
7226
211
dous
Waste
Inciner
ation
aco
South
Caroli
na
LLC
400
Crosby
Rd
De
Ridder
15.3938
8
P|2005
Division
Hazar
220
73
LA
7226
711
dous
Waste
Angus
Chemical
Angus
Chemi
350 Hwy
O
Sterlingto
n
1.02371
Q
P|2005
Inciner
ation
Co
cal Co
Z
y
220
LA
8465
Hazar
dous
Waste
Inciner
ation
Rubicon
LLC -
Rubic
on
LLC
9156
Geismar
1.72626
P | 2005, S |
05
311
Geismar
Plant
Hwy 75
5
2005
220
LA
8465
Hazar
BASF
BASF
8404
Geismar
1.29801
P|2005
77
-------
EIS
FIP
S
EI
S
St
ate
EIS
Facili
ty ID
Categ
ory
EIS
Facility
Name
EIS
compa
ny
name
EIS
Address
EIS City
NATA
2005 Hg
(lbs) -
facility
total
NATA data
source(s) |
Year:
05
611
dous
Waste
Inciner
ation
Corp -
Geismar
Site
Corp
River Rd
(Hwy 75)
9
*
B
VATA data source cot
OI-AUG is boiler augi
e: T=TRI, S=State, L=Local, P is EPA data from rule development,
uentation
Table 24: High Risk Facilities in the S/L/T agency review process with insufficient information to gap
fill
EIS
FIP
S
EI
S
Sta
te
EIS
Facility
ID
EIS Facility
Name
EIS
Company
Name
EIS
Address
EIS
City
High risk
HAP
NATA
Emissio
ns
(2005N
ATA)
(lbs) -
facility
total
NATA
data
source(
s) 1
Year*:
0104
7
AL
105539
11
RENOSOL
SEATING L L C
6
MEAD
OWCR
AFT
PKWY
SELM
A
2,4-
TOLUENE
DIISOCYA
NATE
311.63
T |
2005
0101
5
AL
105698
11
INDUSTRIAL
PLATING CO.
INC.
1300
CLYDE
SDALE
AVE
ANNI
STON
CHROMIU
M (VI)
COMPOUN
DS
10
T |
2005
1203
1
435851
1
APAC-
SOUTHEAST,
INC.
NA
ARSENIC
COMPOUN
DS
52.208
N
2002
2109
3
KY
534551
1
THE GATES
CORP
NA
300
COLLE
GE ST
RD
ELIZ
ABET
HTO
WN
2-
CHLOROA
CETOPHEN
ONE
437.184
N
2002
2210
1
LA
506131
1
COTE
BLANCHE
ISLAND TANK
BATTERY #1
SWIFT
ENERGY
OPERATI
NGLLC
10 MI E
CYPR
EMO
RT PT
BENZENE
14877.5
8
R|
2002, R
| 2005
2200
5
LA
598591
1
SCI
FABRICATION
SHOP
NA
36445
OLD
PERKI
PRAI
RIEVI
LLE
CHROMIU
M (VI)
COMPOUN
149
N
2002
78
-------
EIS
FIP
S
EI
S
Sta
te
EIS
Facility
ID
EIS Facility
Name
EIS
Company
Name
EIS
Address
EIS
City
High risk
HAP
NATA
Emissio
ns
(2005N
ATA)
(lbs) -
facility
total
NATA
data
source(
s) 1
Year*:
NSRD.
DS
2201
7
LA
611651
1
CADDO
M ANUF AC TURI
NG LLC
VIVIAN
INDUSTR
IAL
PLASTIC
SINC
680 S
PARDU
E
VIVIA
N
METHYLE
NE
DIPHENYL
DIISOCYA
NATE
4285
N
2002, S
| 2005
2502
5
M
A
395941
1
FEDERAL
METAL FINISH
FEDERA
L METAL
FINISHIN
G INC
18
DORRA
NCE ST
BOST
ON
CHROMIC
ACID (VI)
400
s 1
2005
2502
5
M
A
395941
1
FEDERAL
METAL FINISH
FEDERA
L METAL
FINISHIN
G INC
18
DORRA
NCE ST
BOST
ON
CHROMIC
ACID (VI)
400
s 1
2005
2502
5
M
A
395941
1
FEDERAL
METAL FINISH
FEDERA
L METAL
FINISHIN
G INC
18
DORRA
NCE ST
BOST
ON
CHROMIC
ACID (VI)
400
s 1
2005
2502
5
M
A
395941
1
FEDERAL
METAL FINISH
FEDERA
L METAL
FINISHIN
G INC
18
DORRA
NCE ST
BOST
ON
CHROMIC
ACID (VI)
400
s 1
2005
2501
3
M
A
592291
1
SUDDEKOR
LLC
NA
240
BOWLE
SRD
AGA
WAM
CHROMIU
M (VI)
COMPOUN
DS
146
N
2002
2803
5
MS
707171
1
MISSISSIPPI
TANK AND
M ANUF AC TURI
NG COMPANY
AI006151
3000
WEST
SEVEN
TH
STREE
T
HATT
IE SB
URG
4,4'-
METHYLE
NEDIANILI
NE
280
N
2002
3610
3
NY
853561
1
WEST
BABYLON
LANDFILL
NA
125
GLEAM
ST
BABY
LON
ACRYLONI
TRILE
1328.49
1
N
1999
3915
5
OH
733091
1
UNITED
REFRAC T ORIE
SINC
NA
1929
LARCH
MONT
AVE.
WAR
REN
CHROMIU
M (VI)
COMPOUN
DS
169
N
2002
3903
OH
774921
A-BRITE
NA
3000 W.
CLEV
CHROMIU
255
T I
79
-------
EIS
FIP
S
EI
S
Sta
te
EIS
Facility
ID
EIS Facility
Name
EIS
Company
Name
EIS
Address
EIS
City
High risk
HAP
NATA
Emissio
ns
(2005N
ATA)
(lbs) -
facility
total
NATA
data
source(
s) 1
Year*:
5
1
PLATING CO
121 ST.
ELAN
D
M (VI)
COMPOUN
DS
2005
3903
5
OH
778301
1
ALCON INDS
INC
NA
7990
BAKER
AVE.
CLEV
ELAN
D
CHROMIU
M (VI)
COMPOUN
DS
250
N
2002
3903
5
OH
778301
1
ALCON INDS
INC
NA
7990
BAKER
AVE.
CLEV
ELAN
D
NICKEL
COMPOUN
DS
250
N
2002
3904
9
OH
778891
1
CRANE
PERFORMANC
E SIDING LLC
NORTH
1550
UNIVE
RSAL
RD.
COLU
MBUS
CHROMIU
M (VI)
COMPOUN
DS
74.3
T |
2005
3916
9
OH
842561
1
PREMIUM
BUILDING
PRODS CO
NA
13985
CONGR
ESS
RD.
WEST
SALE
M
CHROMIU
M (VI)
COMPOUN
DS
255
T |
2005
4202
9
PA
298321
1
TEMTCO
STEEL -
PENNSYLVANI
ADIV
NA
41 S.
SECON
DAVE.
PHOE
NIXVI
LLE
CHROMIU
M (VI)
COMPOUN
DS
1574
T |
2005
4213
3
PA
300211
1
ESAB GROUP
INC
NA
801
WILSO
N
AVENU
E
HAN
OVER
CHROMIU
M (VI)
COMPOUN
DS
250
T |
2005
4213
3
PA
300281
1
PRECISION
COMPONENTS
CORP
NA
500
LINCO
LN ST.
YORK
CHROMIU
M (VI)
COMPOUN
DS
250
N
2002
4207
1
PA
305931
1
M H EBY INC
NA
1194
MAIN
ST.
BLUE
BALL
CHROMIU
M (VI)
COMPOUN
DS
250
N
2002
4209
5
PA
374491
1
CHRIN BROS
SANI
LDFL/CHRIN
LDFL
IESIPA
BETHLE
HEM
LDFL
CORP
635
INDUS
TRIAL
DR
EAST
ON
CADMIUM
COMPOUN
DS
691.8
N
2002
80
-------
EIS
FIP
S
EI
S
Sta
te
EIS
Facility
ID
EIS Facility
Name
EIS
Company
Name
EIS
Address
EIS
City
High risk
HAP
NATA
Emissio
ns
(2005N
ATA)
(lbs) -
facility
total
NATA
data
source(
s) 1
Year*:
4204
9
PA
376711
1
STERIS CORP
NA
2424 W.
23rd ST.
ERIE
CHROMIU
M (VI)
COMPOUN
DS
87
T |
2005
4209
1
PA
384871
1
TUBE
METHODS
INC/BRIDGEPO
RT
GLOBAL
PKG INC
RAMB
0&
DEPOT
ST
BRID
GEPO
RT
TRICHLOR
OETHYLEN
E
33940
s 1
2005
4212
1
PA
389331
1
JOY TECH INC
PLANT #1
NA
325
BUFFA
LO ST.
FRAN
KLIN
LEAD
COMPOUN
DS
1447
T |
2005
4201
3
PA
470191
1
SKF USA INC
ALTOONA
PLANT
NA
1000
LOGAN
BLVD.
ALTO
ONA
CHROMIU
M (VI)
COMPOUN
DS
250
N
2002
4208
1
PA
495241
1
LYCOMING
ENGINES/OLIV
ER ST PLT
TEXTRO
N
LYCOMI
NG
652
OLIVE
R ST
WILLI
AMSP
ORT
CHROMIU
M (VI)
COMPOUN
DS
304.864
2
BOI-
AUG|
2005, R
2002,
R-l
2006
4204
1
PA
646471
1
AMES TRUE
TEMPER INC
NA
465
RAILR
OAD
AVE.
CAMP
HILL
NICKEL
COMPOUN
DS
500
N
2002
4201
1
PA
788881
1
SFS
INTEC/WYOMI
SSING
SFS
INTEC
INC
SPRING
ST &
VAN
REED
RD
WYO
MIS SI
NG
CHROMIU
M (VI)
COMPOUN
DS
2480
N
2002
4202
7
PA
788911
1
GRAYMONT PA
INC/PLEASANT
GAP &
BELLEFONTE
PLTS
GRAYM
ONT PA
INC
N
THOM
AS ST
BELL
EFON
TE
MANGANE
SE
COMPOUN
DS
1389.60
02
BOI-
AUG
2005,
2005
S
4200
7
PA
852051
1
TEGRANT
DIVERSIFIED
BRANDS
INC/NEW
BRIGHTON
EATON
CORP
BLOCK
HOUSE
RUN
RD
NEW
BRIG
HTON
CHROMIU
M (VI)
COMPOUN
DS
500
T |
2005
81
-------
EIS
FIP
S
EI
S
Sta
te
EIS
Facility
ID
EIS Facility
Name
EIS
Company
Name
EIS
Address
EIS
City
High risk
HAP
NATA
Emissio
ns
(2005N
ATA)
(lbs) -
facility
total
NATA
data
source(
s) 1
Year*:
FAC
4200
7
PA
852051
1
TEGRANT
DIVERSIFIED
BRANDS
INC/NEW
BRIGHTON
FAC
EATON
CORP
BLOCK
HOUSE
RUN
RD
NEW
BRIG
HTON
MANGANE
SE
COMPOUN
DS
500
T |
2005
4504
5
SC
396591
1
STEVENS
AVIATION :DON
ALDSON PARK
NA
600
DELA
WARE
ST,
DONAL
DSON
RD
GREE
NVIL
LE
STRONTIU
M
CHROMAT
E
1061.46
4
R|
2006, R
| 2002
*NATA data source code: T=TRI, S=State, L=Local, R,P is EPA data from rule development, BOI-AUG
is boiler augmentation
3.2 Agriculture - Crops & Livestock Dust
[Placeholder. See also Section 3.1 and Appendix B]
3.3 Agriculture - Fertilizer Application
[Placeholder. See also Section 3.1 and Appendix B]
3.4 Agriculture - Livestock Waste
3.4.1 Sector Description
Livestock refers to domesticated animals intentionally reared for the production of food, fiber, or other
goods or for the use of their labor. The definition of livestock in this category includes beef cattle, dairy
cattle, ducks, geese, goats, horses, poultry, sheep, and swine.
3.4.2 Sources of data overview and selection hierarchy
The agricultural livestock waste sector includes data from four components: 2 EPA overwrite datasets,
the S/L/T agency submitted data, and the default EPA generated livestock emissions.
The agencies listed in Table 25 submitted emissions for this sector.
82
-------
Table 25: Agencies that Submitted Livestock Waste Data
Agency
Type
Chattanooga Air Pollution Control Bureau
Local
Maricopa County Air Quality Department
Local
Arizona Department of Environmental Quality
State
California Air Resources Board
State
Delaware Department of Natural Resources and Environmental
Control
State
Hawaii Department of Health Clean Air Branch
State
Idaho Department of Environmental Quality
State
Illinois Environmental Protection Agency
State
Kansas Department of Health and Environment
State
Louisiana Department of Environmental Quality
State
Maine Department of Environmental Protection
State
Maryland Department of the Environment
State
New Jersey Department of Environment Protection
State
North Carolina Department of Environment and Natural
Resources
State
Ohio Environmental Protection Agency
State
Tennessee Department of Environmental Conservation
State
Utah Division of Air Quality
State
Kootenai Tribe of Idaho
Tribal
Little River Band of Ottawa Indians, Michigan
Tribal
Nez Perce Tribe of Idaho
Tribal
Omaha Tribe of Nebraska
Tribal
Prairie Band Potawatomi Nation
Tribal
Sac & Fox Nation of Missouri in Kansas and Nebraska
Tribal
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribal
Washoe Tribe of California and Nevada
Tribal
Table 26 shows the selection hierarchy for the agricultural livestock waste sector.
Table 26: 2008 NEI agricultural livestock data selection hierarchy
Priority
Dataset Name
Dataset Content
1
EPA Overwrite Point vl.5
Overwrites NH3 data from this sector in California
to replace with the EPA dataset (see also Section
3.4.5)
2
EPA PM Augmentation, V2
Augments small amounts of PM emissions in
Colorado, Texas, and Wisconsin
3
State/Local/Tribal Data
Agency submitted emissions
4
EIAG all in NP
EPA-generated data, including livestock waste
emissions (see Section 3.4.4)
83
-------
3.4.3 Spatial coverage and data sources for the sector
[Placeholder for maps of CAP and HAP emissions]
3.4.4 EPA-developed livestock waste emissions data
EPA's approach to calculating emissions for this sector consisted of four general steps, as follows:
• Determine county-level activity data, i.e., the population of animals for 2007 (see Section
3.4.4.1).
• For beef, dairy, poultry, and swine, apportion animal populations to a manure management train
(MMT) for each county (see Section 3.4.4.2). Animal populations for ducks, geese, goats,
horses, and sheep were not apportioned to MMTs.
• Modify the emission factor files provided with the Carnegie Mellon University (CMU)
Ammonia Model v. 3.6 (Davidson et al., 2004) to ensure that every county had an assigned
emission factor (see Section 3.4.4.3).
• Use the CMU Ammonia Model v. 3.6 to calculate ammonia emissions based on the updated
county-level animal populations and emission factors (see Sections 3.4.4.4 and 3.4.4.5).
For this source category, EPA computed emissions for the SCCs listed in Table 27. S/L/T submitted
other SCCs in some cases.
Table 27: Source Classification Codes used in the agricultural
ivestock sector
see
SCC Description, level 3
SCC Descriptions, level 4
280500110
0
Beef cattle - finishing operations on feedlots (drylots)
Confinement
280500120
0
Beef cattle - finishing operations on feedlots (drylots)
Manure handling and
storage
280500130
0
Beef cattle - finishing operations on feedlots (drylots)
Land application of
manure
280500200
0
Beef cattle production composite
Not Elsewhere Classified
280500310
0
Beef cattle - finishing operations on pasture/range
Confinement
280500710
0
Poultry production - layers with dry manure management
systems
Confinement
280500730
0
Poultry production - layers with dry manure management
systems
Land application of
manure
280500810
0
Poultry production - layers with wet manure management
systems
Confinement
280500820
0
Poultry production - layers with wet manure management
systems
Manure handling and
storage
280500830
0
Poultry production - layers with wet manure management
systems
Land application of
manure
280500910
0
Poultry production - broilers
Confinement
84
-------
see
SCC Description, level 3
SCC Descriptions, level 4
280500920
0
Poultry production - broilers
Manure handling and
storage
280500930
0
Poultry production - broilers
Land application of
manure
280501010
0
Poultry production - turkeys
Confinement
280501020
0
Poultry production - turkeys
Manure handling and
storage
280501030
0
Poultry production - turkeys
Land application of
manure
280501800
0
Dairy cattle composite
Not Elsewhere Classified
280501910
0
Dairy cattle - flush dairy
Confinement
280501920
0
Dairy cattle - flush dairy
Manure handling and
storage
280501930
0
Dairy cattle - flush dairy
Land application of
manure
280502110
0
Dairy cattle - scrape dairy
Confinement
280502120
0
Dairy cattle - scrape dairy
Manure handling and
storage
280502130
0
Dairy cattle - scrape dairy
Land application of
manure
280502210
0
Dairy cattle - deep pit dairy
Confinement
280502220
0
Dairy cattle - deep pit dairy
Manure handling and
storage
280502230
0
Dairy cattle - deep pit dairy
Land application of
manure
280502310
0
Dairy cattle - drylot/pasture dairy
Confinement
280502320
0
Dairy cattle - drylot/pasture dairy
Manure handling and
storage
280502330
0
Dairy cattle - drylot/pasture dairy
Land application of
manure
280502500
0
Swine production composite
Not Elsewhere Classified
(see also 28-05-039, -047,
-053)
280503000
0
Poultry Waste Emissions
Not Elsewhere Classified
(see also 28-05-007, -008,
-009)
280503000
7
Poultry Waste Emissions
Ducks
280503000
8
Poultry Waste Emissions
Geese
85
-------
see
SCC Description, level 3
SCC Descriptions, level 4
280503500
0
Horses and Ponies Waste Emissions
Not Elsewhere Classified
280503910
0
Swine production - operations with lagoons (unspecified
animal age)
Confinement
280503920
0
Swine production - operations with lagoons (unspecified
animal age)
Manure handling and
storage
280503930
0
Swine production - operations with lagoons (unspecified
animal age)
Land application of
manure
280504000
0
Sheep and Lambs Waste Emissions
Total
280504500
0
Goats Waste Emissions
Not Elsewhere Classified
280504710
0
Swine production - deep-pit house operations (unspecified
animal age)
Confinement
280504730
0
Swine production - deep-pit house operations (unspecified
animal age)
Land application of
manure
280505310
0
Swine production - outdoor operations (unspecified animal
age)
Confinement
3.4.4.1 Activity Data
County-level animal population numbers for 2007 were obtained from the U.S. Department of
Agriculture's 2007 Census of Agriculture report (accessed April 30, 2009). 2007 data were used
because they were the most recent available at the time these estimates were prepared. For Virginia, the
county-level census data includes animal populations from Virginia's 39 independent cities. For some
counties and states, census data were withheld to avoid disclosing data for individual farms. However,
the total national-level animal numbers and most state-level animal numbers for each livestock type
reported in the Census include those animal numbers not disclosed at the county-level. When available,
state-level animal numbers from the United States Department of Agriculture (USDA) National
Agriculture Statistical Service (NASS) online database (accessed 28 January 2010) were used for states
with undisclosed animal numbers in the 2007 Census of Agriculture. To determine the total number of
undisclosed animals, we summed and subtracted disclosed county-level animal numbers for each
livestock type from the total state animal numbers. The total undisclosed animal population for a
specific livestock type was then allocated to those counties reporting undisclosed data proportionally
based on the number of farms raising that livestock in each county. If the state-level data were
undisclosed and not available in the NASS database, then national animal numbers were used to
determine undisclosed state numbers in a manner similar to the case where counties had undisclosed
data. We then summed and subtracted the disclosed county-level data from the state-level data to
determine animal numbers not disclosed at the county-level. We then allocated the difference to those
counties with undisclosed data proportionally based on the number of farms raising that livestock in
each county. States that had undisclosed data at the state level are as follows: for broilers, Massachusetts
and Rhode Island; for layers, Arizona, Connecticut, Delaware, Idaho, Kansas, Maine and New Mexico;
for turkeys, Colorado and Oklahoma; for pullets, Arizona, Connecticut, Delaware, Hawaii, Idaho,
86
-------
Kansas, Massachusetts, New Mexico, North Dakota, and South Dakota; and for ducks, New Jersey and
Utah.
3.4.4.2 Apportion activity data to manure management trains
To run the model using 2007 animal population, it was necessary to match the 2007 animal information
to the CMU model's (v3.6) input files, which were based on 2002 animal population and MMTs. We
apportioned the 2007 county-level animal population data to MMTs based on data available in the
model. AMMT consists of an animal confinement area (e.g., drylot, pasture, flush, scrape); components
used to store, process, or stabilize the manure (e.g., anaerobic lagoons, deep pits); and a land application
site where manure is used as a fertilizer source (US EPA, 2005). It is important to apportion the animal
populations to MMTs because it has a large impact on the emissions estimates in the CMU model for the
animals using that approach. Not all animal types were apportioned to MMTs. MMTs for ducks,
geese, goats, horses, and sheep are not a part of the model. Also, some animal category names did not
match the category names currently in the model. See the example of "Other Cattle" described below.
The apportionment was based on county-level MMT percentages derived from the CMU Ammonia
Model v3.6, which was originally developed for a 2002 inventory year. For each livestock type, we
divided the CMU Model's 2002 county-level number of animals in each MMT by the total county-level
animal population for that livestock type to calculate the percentage of total animals managed by each
MMT. In cases where the county-level numbers were zero in the CMU Ammonia Model and the county
animal population in 2007 for that MMT was not zero, we assigned the county state-level MMT
percentages. We then multiplied the county-level animal population for each livestock type by the
MMT percentages to apportion the 2007 animal populations to each MMT. The result of this approach
is that the proportion of animals in each MMT is unchanged from the CMU model's 2002-based
approach to the 2008 NEI.
Cattle reported as "Other Cattle" in the 2007 Census of Agriculture were divided between dairy cattle
and beef cattle at the county-level using percent allocations derived from county-level dairy and beef
cattle reported in the 2007 Census of Agriculture and corrected for undisclosed data. The animal
numbers from "Other Cattle" apportioned to dairy and beef cattle were used to grow the "Dairy Cattle -
Composite and Beef Cattle - Composite" activity input files from 2002 to 2007 for input to the CMU
Ammonia Model.
County-level pullet numbers reported in the 2007 Census of Agriculture were used to grow the "Poultry
- Composite" activity input file from 2002 to 2007 for input to the CMU Ammonia Model.
3.4.4.3 Emission Factors
Table 28 provides information on emission factors used in the EPA emissions estimate. The table lists
"county" for county-specific emission factors, and "state" for state-specific emission factors. The
emission factor for the poultry composite categories was obtained from an EPA report (US EPA, 2005).
The county-level emission factors for the beef composite and dairy composite categories were
developed using beef and dairy cattle emission factors provided with the CMU Model. Specifically,
weighted average emission factors were calculated based on the number of beef or dairy cattle in each
87
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MMT from the CMU Model's 2002 activity files and the emission factor assigned to each MMT. The
calculations made for the beef composite are available in the file "County-Level Emission Factors for
Beef Composite.xls", and the calculations for the dairy composite are available in the file "County-level
Emission factors for Diary Component.xls" (see Section 8.1 for access information, specifically the
ReadMe.doc file listed in the ag livestock waste folder of Table 66). All other emission factors are
consistent with those included in the CMU Ammonia Model v.3.6.
The emission factors for some counties in the CMU Ammonia Model files were zero. To ensure that all
counties with animal populations were assigned emissions factors, the emission factor input files
provided with the CMU Ammonia Model were modified. For all counties with an emission factor of
zero, the emission factor was replaced with the state average emission factor. If all counties in the state
had emission factors of zero, then the county emission factor was replaced with the national average
emission factor.
The state average emission factor was calculated by summing the counties with non-zero emission
factors in the state and dividing the total by the number of counties in that state with non-zero emission
factors. The national average emission factors listed in the table were calculated by summing the
counties with non-zero emission factors in the nation and dividing the total by the number of counties in
the nation with non-zero emission factors. The final county-specific and state-specific emission factors
are available in the file "Emission Factors for Ag animal husbandry 2008v2.xlsx" (see Section 8.1 for
access information, specifically the ReadMe.doc file listed in the ag livestock waste folder of Table
66).
Table 28: Emission Factors for NH3 emissions used for EPA's agricultural livestock data
Emission
Factor
Reference
Emission
Emission Factor
(see
Description
Factor
Unit
footnotes)
Beef Cattle - Composite
county
kg
NFb/cow/month
2
Beef Cattle - Drylot Operation -
Confinement
9.45E-01
kg
NFb/cow/month
1
Beef Cattle - Drylot Operation - Land
Application
state
kg
NFb/cow/month
1
Beef Cattle - Drylot Operation - Manure
Storage
3.78E-04
kg
NFb/cow/month
1
Beef Cattle - Pasture Operation -
Confinement
county
kg
NFb/cow/month
1
Dairy Cattle - Composite
county
kg
NFb/cow/month
2
Dairy Cattle - Deep Pit Dairy Confinement
2.42E+00
kg
NFb/cow/month
1
Dairy Cattle - Deep Pit Dairy Land
Application
state
kg
NFb/cow/month
1
88
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Emission
Factor
Reference
Emission
Emission Factor
(see
Description
Factor
Unit
footnotes)
Dairy Cattle - Deep Pit Dairy Manure
Storage
1.13E-01
kg
NFb/cow/month
1
Dairy Cattle - Drylot Dairy Confinement
state
kg
NFb/cow/month
1
Dairy Cattle - Drylot Dairy Land
Application
state
kg
NFb/cow/month
1
Dairy Cattle - Drylot Dairy Manure Storage
state
kg
NFb/cow/month
1
Dairy Cattle - Flush Dairy Confinement
2.00E+00
kg
NFb/cow/month
1
Dairy Cattle - Flush Dairy Land Application
state
kg
NFb/cow/month
1
Dairy Cattle - Flush Dairy Manure Storage
state
kg
NFb/cow/month
1
Dairy Cattle - Scrape Dairy Confinement
state
kg
NFb/cow/month
1
Dairy Cattle - Scrape Dairy Land
Application
state
kg
NFb/cow/month
1
Dairy Cattle - Scrape Dairy Manure Storage
state
kg
NFb/cow/month
1
Ducks
7.67E-02
kg
NFb/duck/month
1
Geese
7.67E-02
kg
NFb/goose/month
1
Goats
5.29E-01
kg
NFb/goat/month
1
Horses
1.02E+00
kg
NFb/horse/month
1
Poultry - Broiler Operation - Confinement
8.32E-03
kg
NFb/bird/month
1
Poultry - Broiler Operation - Land
Application
6.80E-03
kg
NFb/bird/month
1
Poultry - Broiler Operation - Manure
Storage
1.51E-03
kg
NFb/bird/month
1
Poultry - Composite
2.00E-02
kg
NFb/bird/month
Poultry - Layers - Dry Manure Operation -
Confinement
3.36E-02
kg
NFb/bird/month
1
Poultry - Layers - Dry Manure Operation -
Land Application
county
kg
NFb/bird/month
1
Poultry - Layers - Wet Manure Operation -
Confinement
9.45E-03
kg
NFb/bird/month
1
89
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Description
Emission
Factor
Emission Factor
Unit
Emission
Factor
Reference
(see
footnotes)
Poultry - Layers - Wet Manure Operation -
Land Application
county
kg
NHa/bird/month
1
Poultry - Layers - Wet Manure Operation -
Manure Storage
county
kg
NHa/bird/month
1
Poultry - Turkey Operation - Confinement
3.78E-02
kg
NHa/bird/month
1
Poultry - Turkey Operation - Land
Application
3.40E-02
kg
NHa/bird/month
1
Poultry - Turkey Operation - Storage
6.80E-03
kg
NHa/bird/month
1
Sheep
2.65E-01
kg
NHa/sheep/month
1
Swine - Composite
county
kg
NHa/pig/month
1
Swine - Deep Pit Operation - Confinement
2.65E-01
kg
NHa/pig/month
1
Swine - Deep Pit Operation - Land
Application
county
kg
NHa/pig/month
1
Swine - Lagoon Operation - Confinement
2.27E-01
kg
NHa/pig/month
1
Swine - Lagoon Operation - Land
Application
county
kg
NHa/pig/month
1
Swine - Lagoon Operation - Manure Storage
county
kg
NHa/pig/month
1
Swine - Outdoor Operation - Confinement
county
kg
NHa/pig/month
1
1 Davidson, et al., 2004
2 Dorn, 2009
3 US EPA, 2005
3.4.4.4 Emissions
The livestock activity files provided with the CMU Ammonia Model v.3.6 were replaced with the
updated county-level animal population files (Sections 3.4.4.1 and 3.4.4.2) and modified emission
factors files. We then ran the CMU Ammonia Model v.3.6 to create county/SCC ammonia emissions.
EPA's county-level emissions can be found in the supporting materials in the file
"animal_husbandry_epa_data.zip" as listed in Table 18, Section 3.1.6. See also Section 8.1 for data
access information.
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3.4.4.5 Sample Calculations
Allocation of Undisclosed Data
From the 2007 Census of Agriculture, the total national number of beef cattle in Alabama is 678,949.
The total number of beef cattle disclosed at the county-level is 388,827.
Total number of beef cattle undisclosed at the county-level = 678,949 - 338,827 = 340,122
From the 2007 Census of Agriculture, the total number of farms in Alabama not disclosing beef cattle
numbers is 10,518.
Average beef cattle per farm not disclosing data = 340,122 / 10,518 = 32.3
For 2007, Baldwin County, Alabama beef cattle data were not disclosed. The total number of farms with
beef cattle in Baldwin County is 343.
Estimated number of beef cattle in Baldwin County = 32.3 x 343 = 11,092
Manure Management Train
From the 2002 CMU Ammonia Model input files, Chilton County, Alabama had 79 beef cattle under
drylot management and 18,900 beef cattle under pasture management in 2002.
Total beef cattle = 79 + 18,900 = 18,979
% of beef cattle under drylot management = 19 I 18,979 = 0.42
% of beef cattle under pasture management = 18,900 / 18,979 = 99.58
The total number of beef cattle for Chilton County reported in the 2007 Census of Agriculture is 7,939.
Number of beef cattle under drylot management in 2007 = 7,939 x 0.0042 = 33
Number of beef cattle under pasture management in 2007 = 7,939 x 0.9958 = 7,906
"Other Cattle"
For Clay County, Alabama, the 2007 Census of Agriculture reports the number of "Other Cattle" as
5,471, the number of dairy cattle as 216, and the number of beef cattle as 9,096.
Total beef and dairy cattle reported = 216 + 9,096 = 9,312
% of other cattle assigned to beef cattle = (9,096/9,312)* 100 = 97.68
% of other cattle assigned to dairy cattle = (216/9,312)* 100 = 2.32
Other cattle allocated to beef cattle = 5,471 x .9768 = 5,344
Other cattle allocated to dairy cattle = 5,471 x 0.0232 = 127
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3.4.5 Summary of quality assurance methods
The EPA data for 2008 and 2005 were compared to the state-submitted data at the state-SCC level and
in the case of local county agencies, at the county-SCC level. Findings are below.
• For Idaho, Illinois, Utah, Kansas, and Maricopa County, double-counting of EPA and state data
occurred. This was corrected by removing the EPA data, thus allowing only agency data to be
selected.
• California data were significantly higher than EPA's and all at one SCC. The state wanted to
submit updated emissions but due to timing issues, was unable to accomplish. EPA chose to
block the state data from being selected and therefore the EPA data were selected. CA agreed
with this approach.
• North Carolina data were about 1/12 of the EPA data. Confirmed with NC staff that their
submittal looked more like monthly data than annual. NC resubmitted correct annual data for
2008 NEI, version 2.
3.5 Bulk Gasoline Terminals
[Placeholder. See also Section 3.1 and Appendix B]
3.6 Commercial Cooking
[Placeholder. See also Section 3.1 and Appendix B]
3.7 Dust - Construction Dust
[Placeholder. See also Section 3.1 and Appendix B]
3.8 Dust - Paved Road Dust
[Placeholder. See also Section 3.1 and Appendix B]
3.9 Dust - Unpaved Road Dust
[Placeholder. See also Section 3.1 and Appendix B]
3.10 Fuel Combustion - Electric Generation
This section includes the description of five EIS sectors:
• Fuel Comb - Electric Generation - Coal
• Fuel Comb - Electric Generation - Oil
• Fuel Comb - Electric Generation - Natural Gas
• Fuel Comb - Electric Generation - Biomass
• Fuel Comb - Electric Generation - Other
They are treated here in a single section because the methods used are the same across all sectors.
3.10.1 Sector Description
These five sectors are defined by the point source SCCs beginning with 1-01 and 2-01. There are no
nonpoint contributions to this sector. These SCCs include boilers, combustion gas turbines, combined
cycle units, and reciprocating engines firing any type of fuel for the purpose of turning a generator
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connected to the electrical grid. The primary fuels used by the boilers are coal and natural gas. A much
smaller number of oil and wood-fired boilers are also included in the oil and natural gas sectors.
Various waste or by-products such as municipal waste, bagasse, petroleum coke, and tires are also used
in some boilers. The primary fuel used by the combustion gas turbines and combined cycle units is
natural gas, although some distillate oil is also used. The reciprocating engines are generally much
smaller in terms of generating capacity and also much less efficient than either the boilers and steam
turbines or the combustion gas turbines. The engines are primarily fired by natural gas or diesel oil, but
there are some which use various available waste gases, such as landfill gas.
The SCC-based EIS sector definitions will cause a different universe of units to be included in these
sectors than would other definitions of EGUs. For example, the EIS sector definitions do not include a
heat input or generator output size threshold. In contrast, some EPA regulatory applications define
EGUs to include only units with capacity greater than 25 MW. Many of the engines and some of the
combustion gas turbines in the EIS sectors for EGUs are well below 25 MW generating capacity. The
boilers and steam turbine-generators, and particularly those fired on coal, are almost always greater than
25 MW capacity, except for some older units.
The use of SCCs in the NEI by S/L/T agencies impacts the units included in these EIS sectors. There
are some boilers and gas turbines in industrial facilities which cogenerate both electricity for distribution
to the public power grid and process steam for their internal use. Some S/L/T agencies reporting to the
NEI use an SCC (1-01 or 2-01) that would include these units in one of the EGU sectors, while others
use an Industrial (1-02 or 2-02) or a Commercial/Institutional (1-03 or 2-03) SCC. This can result in
boilers or gas turbines not connected to the public power grid being included in these EGU sectors, with
the SCC assigned based upon either strictly their large size (some EPA references to utility boilers have
cited them as greater than 100 mmBTU/Hr heat input) or because they may generate electrical power for
internal consumption.
3.10.2 Sources of data overview and selection hierarchy
The EGU sectors includes data from three EPA overwrite datasets, emissions based on data from the
MATS rule development, the S/L/T agency submitted data, and four other EPA generated datasets that
impact this sector.
The agencies listed in Table 29 submitted emissions for these sectors. A box with a "X" means that the
agency submitted data for EGU units included in that EGU fuel group for the individual EIS Sectors.
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Table 29: Agencies that Submitted EGU data
Agency
Type
Coa
1
Oi
1
Natura
1 Gas
Biomas
s
Othe
r
Alabama Department of Environmental Management
State
X
X
X
X
X
Alaska Department of Environmental Conservation
State
X
X
X
X
Allegheny County Health Department
Local
X
X
X
X
Arizona Department of Environmental Quality
State
X
X
X
X
X
Arkansas Department of Environmental Quality
State
X
X
X
X
X
California Air Resources Board
State
X
X
X
X
X
Chattanooga Air Pollution Control Bureau
Local
X
X
City of Albuquerque
Local
X
X
X
Clark County Department of Air Quality and
Environmental Management
Local
X
X
Colorado Department of Public Health and
Environment
State
X
X
X
X
Connecticut Department Of Environmental
Protection
State
X
X
X
DC Department of Health Air Quality Division
State
X
Delaware Department of Natural Resources and
Environmental Control
State
X
X
X
X
Florida Department of Environmental Protection
State
X
X
X
X
X
Forsyth County Environmental Affairs Department
Local
X
X
Georgia Department of Natural Resources
State
X
X
X
X
X
Hawaii Department of Health Clean Air Branch
State
X
X
X
Illinois Environmental Protection Agency
State
X
X
X
X
X
Indiana Department of Environmental Management
State
X
X
X
X
Iowa Department of Natural Resources
State
X
X
X
X
X
Jefferson County (AL) Department of Health
Local
X
X
X
Kansas Department of Health and Environment
State
X
X
X
X
Kentucky Division for Air Quality
State
X
X
X
X
X
Lincoln/Lancaster County Health Department
Local
X
Louisiana Department of Environmental Quality
State
X
X
X
X
Louisville Metro Air Pollution Control District
Local
X
X
X
Maine Department of Environmental Protection
State
X
X
X
X
Maryland Department of the Environment
State
X
X
X
X
Massachusetts Department of Environmental
Protection
State
X
X
X
X
X
Mecklenburg County Air Quality
Local
X
Memphis and Shelby County Health Department -
Pollution Control
Local
X
X
X
X
Metro Public Health of Nashville/Davidson County
Local
X
X
X
Michigan Department of Environmental Quality
State
X
X
X
X
X
Minnesota Pollution Control Agency
State
X
X
X
X
X
Mississippi Dept of Environmental Quality
State
X
X
X
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Agency
Type
Coa
1
Oi
1
Natura
1 Gas
Biomas
s
Othe
r
Missouri Department of Natural Resources
State
X
X
X
X
X
Montana Department of Environmental Quality
State
X
X
X
X
Navajo Nation
Triba
1
X
Nebraska Environmental Quality
State
X
X
X
X
X
Nevada Division of Environmental Protection
State
X
X
X
X
New Hampshire Department of Environmental
Services
State
X
X
X
X
X
New Jersey Department of Environment Protection
State
X
X
X
X
New Mexico Environment Department Air Quality
Bureau
State
X
X
X
New York State Department of Environmental
Conservation
State
X
X
X
X
X
North Carolina Department of Environment and
Natural Resources
State
X
X
X
X
X
North Dakota Department of Health
State
X
X
X
Ohio Environmental Protection Agency
State
X
X
X
X
X
Oklahoma Department of Environmental Quality
State
X
X
X
X
Olympic Region Clean Air Agency
Local
X
X
Omaha Air Quality Control Division
Local
X
Oregon Department of Environmental Quality
State
X
X
X
X
X
Pennsylvania Department of Environmental
Protection
State
X
X
X
X
X
Philadelphia Air Management Services
Local
X
X
X
X
Pinal County
Local
X
X
Puerto Rico
State
X
X
X
X
Puget Sound Clean Air Agency
Local
X
X
X
Rhode Island Department of Environmental
Management
State
X
X
X
South Carolina Department of Health and
Environmental Control
State
X
X
X
X
X
Southern Ute Indian Tribe
Triba
1
X
X
Tennessee Department of Environmental
Conservation
State
X
X
X
X
X
Texas Commission on Environmental Quality
State
X
X
X
X
Utah Division of Air Quality
State
X
X
X
Vermont Department of Environmental Conservation
State
X
Virginia Department of Environmental Quality
State
X
X
X
X
X
Washington State Department of Ecology
State
X
X
X
X
X
Washoe County Health District
Local
X
X
West Virginia Division of Air Quality
State
X
X
X
X
X
Western North Carolina Regional Air Quality
Local
X
X
X
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Agency
Type
Coa
1
Oi
1
Natura
1 Gas
Biomas
s
Othe
r
Agency (Buncombe Co.)
Wisconsin Department of Natural Resources
State
X
X
X
X
X
Wyoming Department of Environmental Quality
State
X
X
X
X
Table 30 shows the selection hierarchy for the EGU sectors. A box with a "X" means that the dataset
contributed to the EGU sector for that fuel group.
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Table 30: 2008 NEIEGU data selection hierarchy by EGU fuel groups from EIS Sectors
Priority
Dataset Name
Dataset Contents and
Impact
Coal
Oil
Natural
Gas
Biomass
Other
1
EPA Overwrite Point vl.5
Overwrites PM
emissions from
Pennsylvania. See also
Table 8 and Appendix
B.
X
X
X
2
EPA PM Augmentation,
V2
Augments PM data in 47
states and some tribes
(see Section 3.1.2)
X
X
X
X
X
3
2008 MATS-based EGU
emissions
(2008EPA_MATS)
Overwrites Hg, other
metals, and acid gases to
use data from the MATS
rule in 49 states and
some tribes (see Section
3.10.5)
X
X
X
X
X
4
EPA Chromium Split v2
Splits total chromium
into speciated chromium
in 37 states (see Section
3.1.3)
X
X
X
X
X
5
State/Local/Tribal Data
Agency submitted
emissions
X
X
X
X
X
6
EPA EGU vl .5
Augments CAP and
HAP emissions in 46
states and some tribes
(see Section 3.10.5).
X
X
X
X
X
7
2008 EPA Rule Data from
OAQPS/SPPD
Adds Hg: 2 lbs in
California, 130 lbs in
Indiana, and 22 lbs in
Missouri
X
X
8
EPA NV Gold Mines
Adds 41 lbs of Hg in
Nevada
X
9
EPA TRI Augmentation
v2
Adds Pb and other HAP
emissions in 26 states
(see Section 3.1.4)
X
X
X
X
X
10
EPA HAP Augmentation
v2
Adds Pb and other HAP
emissions in 46 states
(see Section 3.1.5)
X
X
X
X
X
3.10.3 Spatial coverage and data sources for the sector
[Placeholder for maps of CAP and HAP emissions]
3.10.4 Overwrite datasets used for EGUs
The three overwrite datasets listed in Table 30 include the main overwrite dataset "EPA Overwrite Point
vl.5" used to eliminate problematic or conflicting records from the agency submissions, the "EPA PM
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Augmentation, V2" previously described in Section 3.1.2, and the "EPA Chromium Split v2",
previously described in Section 3.1.3. Of these datasets, the first has very little impact, simply
overwriting some erroneous Pennsylvania PM records. The chromium split only splits the mass of
emissions provided by states rather than add mass, however, this split is important for uses of the
inventory that estimate toxics risk, since the hexavalent portion of the chromium drives the risk.
The PM Augmentation dataset has the most impact on this sector, contributing 36% of the total PM10
mass and 40% of the total PM2.5 to these sectors. Table 31 provides the emissions contribution from all
S/L/T agencies and from the EPA PM augmentation data for each of the EIS sectors associated with
EGUs.
Table 31: Agency-submitted, PM Augmentation, and total PM10 and PM2.5 emissions
for EGU sectors (short tons/year)
PM10
PM10
PM10
PM2.5
PM2.5
PM2.5
EIS Sector
Agency
(tons)
Aug
(tons)
Total
(tons)
Agenc
y
(tons)
Aug
(tons)
Total
(tons)
Fuel Comb - Electric Generation -
1,244
546
1,789
429
1,041
1,469
Biomass
Fuel Comb - Electric Generation - Coal
239,61
130,11
369,730
170,72
104,94
275,66
9
1
0
3
2
Fuel Comb - Electric Generation -
11,950
9,481
21,431
10,464
9,758
20,222
Natural Gas
Fuel Comb - Electric Generation - Oil
4,983
6,312
11,295
4,033
5,416
9,449
Fuel Comb - Electric Generation - Other
1,379
1,106
2,485
890
1,046
1,935
Total
259,17
147,55
406,730
186,53
122,20
308,73
4
6
4
3
8
3.10.5 EPA-developed EGU emissions data
In addition to the S/L/T-reported data, EPA developed two separate emissions datasets specifically for
EGUs. The first EPA dataset developed (EPA EGU vl .5 in EIS) made use of the hourly S02 and NOx
continuous emissions monitoring (CEM) data and hourly heat input values reported by facilities to
EPA's Clean Air Market Division (CAMD). The annual sum of the reported heat input values for 2008
were used to estimate emissions for a set of CAP and HAP pollutants (dependent upon unit type and
primary fuel), and the annual S02 and NOx sums were used directly, for a set of 1984 emission units at
751 different facilities. These units included coal-fired boilers (74 pollutants, including the S02 and
NOx), oil-fired boilers (41 pollutants), gas-fired boilers (39 pollutants), gas-fired simple turbines and
combined cycle units (18 pollutants), and petroleum coke-fired boilers (73 pollutants).
In some applications, the NEI is compared against future-year emissions estimated by the IPM model.
This model predicts S02, NOX, Hg, and HC1 as part of its primary functions and uses emission factors
for these pollutants that reflect the future-year controls associated with the individual units. Other
pollutants such as VOC, PM2.5, PM10, and metal HAPs are estimated using IPM post-processing. The
emission factors used for the EPA EGU vl .5 dataset were consistent with the factors used by the IPM
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post-processing. However, for many of the EGU units for HAPs (including Hg and HC1), the dataset
based on MATS (described below) supersedes this dataset. The starting point for the EPA EGU vl .5
dataset from CAMD is "CAMD08annualallprg_103009.txt", and it is available with the other supporting
materials (see Section 8.1 for access information). More information on the approach used is available
in Rothschild (2010).
In the 2008 NEI v3 selection hierarchy, the EPA EGU vl.5 dataset was used after any S/L/T-reported
emissions for these emission units, except for one State and one local agency. For Connecticut, the
State-reported values for S02 and NOx were noted to be significantly lower than the CEM values
available from the original CAMD data and therefore lower than the EPA EGU vl.5 dataset. For
Douglas County, Nebraska, the emissions had been reported by the local agency as single facility-wide
totals for each facility, rather than the individual unit emissions available in the CEM and heat input
derived dataset. For these two locations only, the S/L data were selected after the EPA EGU vl.5
dataset.
The second EPA EGU emissions dataset (2008EPAMATS in EIS) was developed after vl.5 of the
2008 NEI had been released. This dataset was for a smaller subset of units than covered by the first
dataset, and for only a portion of the HAPs, with no CAPs except for Pb. The emission units included in
the 2008EPA MATS dataset were those electric utility coal and oil-fired units greater than 25 MW
expected to be regulated by the MATS rule finalized by EPA in December 2011. This included 1194
emission units at 491 facilities. The set of pollutants estimated in this dataset included hydrochloric and
hydrofluoric acid gases and hydrogen cyanide, and twelve metal HAPs: antimony, arsenic, beryllium,
cadmium, trivalent chromium, hexavalent chromium, cobalt, lead, manganese, mercury, nickel, and
selenium. In early 2013 it was determined that the emission factors for hydrogen cyanide from the
MATS test program were unreliable15.
15 Email from Barrett Parker, EPA/OAQPS/MPG to Madeleine Strum EPA/OAQPS/EIAG, April 10,
2013: Response to Comments 4 - 5: The EPA does not believe that the results ofHCN testing from the
2010 ICR were consistently reliable. The EPA conditional test method 033 (CTM-033) provided in
accurate results if the tester did not apply some method changes. In particular, maintaining a pH of 12
or greater is critical to HCN sample collection. For the very long test runs necessary for the low
concentrations we expected, testers found that maintaining the high pH was problematic (high CO 2
concentrations depleted 761 the alkaline solutions prematurely). Dropping pH or high sample vacuums
resulting form sludges forming in the impingers required some testers to stop runs before meeting the
minimum sample volume and some ignored the drop in pH. Some testers adjusted the method, but others
did not. Overall, the data we collected during the ICR testing are suspect and thus were not used to set a
HCN emission standard. However, we do believe that acid gas controls represent the best control
technology for HCN. We are not aware of any "HCN specific " control technologies that have been
applied at coal- or oil-fired electric generating units. We believe that HCN will be best controlled due to
its solubility (in a wet scrubber) or due to its acidity (although it is a weak acid). For this reason, the
EPA feels that it is reasonable to include HCN with the acidic gases and assume that it is best controlled
using installed acid gas control technology. "
99
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The 2008 heat input data used for the MATS-based data were related to the MATS non-Hg case studies
and the "current base" inventory development effort described in Houyoux and Strum (2011). The
preferred source of unit-level annual heat input data were CAMD unit-level annual heat input data for
2008, which we downloaded from the CAMD website for all units that report these data. The units
associated with the MATS non-Hg case studies that do not report to CAMD or were missing heat input
for 2008 were contacted directly to obtain actual unit-specific annual heat input data. These plants
included: Spruance Genco (ORIS 54081) Units 1, 2, 3, and 4 (2002 only); Wabash River (ORIS 1010)
Unit PG7221FA; and HECO Waiau (ORIS 766) Units 3, 4, 5, 6, 7, and 8.
For the remaining non-CAMD, non-case study units, annual heat inputs had to be estimated. For many
of these units, the MATS ICR data had obtained the unit-specific maximum hourly heat input capacity
and the actual unit-specific three-year (2007-2009) average capacity factor. These unit-specific data
were used in conjunction with nationwide trends from the CAMD units to estimate annual unit-level
heat inputs for 2008. The specific methodology and an example calculation are available in the tab
"Att_l_ICR_Data" of "2_Attachments_l_and_2_HTIP_Calcs.xls" (see Section 8.1 for access
information). For some units, only the unit-specific maximum hourly heat input rating was available (no
average capacity factor was available). The 2008 unit-level heat input was estimating using the
maximum hourly heat input in conjunction with an assumed capacity factor of 1.0 and nationwide trends
from the CAMD units. The specific methodology and an example calculation are provided the tab
"Attach_2_No_Data" of the spreadsheet just listed.
Annual 2008 heat input values (as well as 2002-2010 values developed for MATS) for the final list of
affected units (boilers) are available through the MATS supporting materials in the "2-Heat Inputs" tab
of the MATS emission inventory workbook".
The emission factors used were those unit-specific and updated average emission factors that had been
developed to support the MATS rule (Houyoux et al., 2011). Because these factors were believed to be
much more up-to-date and more reliable than what EPA had previously made available for S/L/T use,
the 2008EPA MATS emissions dataset was used ahead of S/L/T-reported values for these fifteen
pollutants, with one area of exception. For mercury, there are some units that were already required by
State or local regulations to monitor their emissions using mercury CEMs by 2008. Where EPA could
determine that the S/L/T-reported mercury emissions were based on such CEMs or 2008-specific test
data, EPA removed the emission factor based values from the 2008EPA MATS dataset to allow the
S/L/T-reported CEM values to be selected for the 2008 NEI. As discussed on the previous page, in
April 2013, the HCN EFs were deemed unreliable and this is reported on the issues list. The chromium
EFs from the test program were speciated prior to their use: coal and petroleum coke and gasified coal
(integrated gasification combined cycle—IGCC) fired units used 12% hexavalent chromium, 88%
trivalent; oil units used 18% chromium, 82% hexavalent chromium.
In summary, the 2008 NEI v2 for EGUs is comprised of largely S/L/T-reported data for the CAPs and
any HAPs that the S/L/T agencies reported other than the fifteen MATS-estimated pollutants. For those
fifteen MATS-estimated pollutants, the 2008 NEI v2 is comprised largely of the EPA estimates, except
S/L/T agency data were used for mercury where it was believed to be based upon use of a CEM or unit-
100
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specific test. Other HAPs for the MATS-regulated units, and all HAPs for units not part of MATS,
include S/L/T agency emissions values where they were reported (with PM and Chromium
augmentation, if needed), or include the EPA EGU vl.5 emissions where no S/L/T agency emissions
were reported. Appendix B provides a table summarizing the data sources used in the EGU sectors.
For both of the EPA-created datasets, the emissions were estimated at the unit level, because that is the
level at which the CAMD heat input activity data are available. EPA assumed for both of the EPA
datasets that all heat input came from the primary fuel, and the emission factors used reflected only that
primary fuel. The resultant unit-level estimates had to be loaded into EIS at the process-level to meet
the EIS requirement that emissions can only be associated with the most detailed (process) level, which
includes fuel used. For the EGU sectors, the unit level represents the boiler or gas turbine unit as a
whole, while the process level represents the individual fuels burned within the units. The EPA
emissions were therefore loaded into EIS at the single process for the primary fuel that was used by the
responsible S/L/T agency for reporting their emissions.
As part of our approach, we needed to match the EGU units from the EPA datasets to the process IDs
used by the responsible agencies to ensure that the EIS selection software used only one emissions
estimate for a process-pollutant combination, rather than one estimate from each data supplier. Using
data at a process-pollutant from more than one data supplier would double-count emissions. Because
the EPA EGU vl.5 dataset was only to be used where no S/L/T agency estimate was available for a
given pollutant, it was only necessary to report the EPA estimate to any one of potentially multiple
process IDs reported by the responsible agency for a unit and pollutant, as long as that process was
likely one which would contain at least some of the responsible agency's estimate for the pollutant. If
that primary process contained any portion of the responsible agency's reported emissions for a
pollutant, the EPA estimate would not be selected. But because the 2008EPA MATS estimates were to
be chosen ahead of the responsible agency values, it was necessary to ensure that the MATS dataset
would prevent all process IDs that were reported for a given unit from being selected. For this reason, in
cases where the responsible agency reported a unit's emissions using two different coal processes and a
small oil process, the MATS dataset contain one matching process ID with the actual EPA estimates for
the entire unit, plus two other matching process IDs with zero emissions values for the fifteen pollutants.
This approach prevented double counting. The approach for matching EIS units with the MATS data is
documented in Johnson and Bullock (2012).
The matching of the EPA emissions sets to the responsible agency facility, unit and process IDs was
done largely by using the Office of Regulatory Information Systems (ORIS) plant and CAMD boiler IDs
as found in the original CAMD dataset described in the first paragraph of this section and matching
these to the same two IDs as had been previously stored in EIS. We also compared the facility names
and counties for agreement, and we made manual revisions to the codes in EIS wherever discrepancies
were noted. As a final confirmation that the correct emissions unit and a reasonable process ID in EIS
had been matched to the EPA data, the magnitudes of the S02 and NOx emissions for all preliminary
matches were compared between the S/L/T agency-reported datasets and the EPA datasets. We
identified and resolved several discrepancies from this emissions comparison.
101
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EPA performed these ID matching confirmation step on the 2008 NEI vl data and an EPA EGU vl
dataset, and we repeated the step using the 2008 NEI vl.5 data. Because a few S/L/T agencies had
added new data or revised the unit or process IDs prior to creating the vl.5 data, an EPA EGU vl.5
dataset had to be created for the revised process ID matches. Several vl matches were removed from
the vl .5 dataset due to the uncertainty of some of the matches for some of the smaller emitting units. If
the responsible S/L/T agency did not report some emissions for some of these non-matched units and
processes, no EPA estimates were available in the EPA EGU vl.5 dataset for gap filling. Finally, the
comparison and discrepancy review process was repeated for the 2008EPA MATS dataset prior to
finalizing the 2008 NEI v2.
3.10.6 Alternative facility and unit IDs needed for matching with other databases
The 2008 NEI v2 data contains two sets of alternate unit identifiers related to the ORIS plant and
CAMD boiler IDs (as found in the CAMD heat input activity dataset) for export to the SMOKE
modeling file. The first set is stored in EIS with a Program System Code (PSC) of "EPACAMD". The
alternate unit IDs are stored as a concatenation of the ORIS Plant ID and CAMD boiler ID with
"CAMDUNIT" between the two IDs. These IDs are exported to the SMOKE file in the fields named
ORIS FACILITY CODE and ORIS BOILER ID. These two fields are used by the SMOKE
processing software to replace the annual NEI emissions values with the appropriate hourly CEM values
at model run time. The second set of alternate unit IDs are stored in EIS with a PSC of "EPAIPM" and
are exported to the SMOKE file as a field named "IPMYN". The SMOKE processing software uses
this field to determine if the unit is one that will have future year projections provided by the IPM
model.
The storage format of these alternate unit IDs, in both EIS and in the exported SMOKE file, replicates
the IDs as found in the NEEDS database used as input to the IPM model. The NEEDS IDs are a
concatenation of the ORIS plant ID and the CAMD boiler ID, with either a "_B_" or a "_G_" between
the two IDs, indicating "Boiler" or "Generator". Note that the ORIS Plant IDs and CAMD boiler IDs as
stored in the CAMDBS dataset and in the NEEDS database are almost always the same, but that there
are occasional differences for the same unit.
The "EPACAMD" alternate unit IDs available in EIS are believed to be a complete set of all those that
can safely be used for the purpose of substituting hourly CEM values during SMOKE processing. The
"EPAIPM" alternate unit IDs in EIS are not a complete listing of all the NEEDS/IPM units, although
most of the larger emitters, including all of the EPACAMD CEM units, do have an EPAIPM alternate
unit ID. The NEEDS database includes a larger set of smaller, non-CEM units.
3.10.7 Summary of quality assurance methods
A detailed description of the quality assurance steps used for creating the two EPA EGU emissions
datasets can be found in Rothschild (2010) and for the matching of MATS data to EIS units in Johnson
and Bullock (2012). The S/L/T agency-reported data were subject to the same overall emissions outlier
analysis that was performed on the S/L/T point source emissions datasets as a whole. That outlier
analysis included an initial comparison of the process-level reported emissions values to the established
EIS warning level thresholds specified by SCC and pollutant. The individual values above the threshold
102
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were sorted for each of 30 key pollutants and the largest values were reviewed to identify any unusual
patterns such as all of the largest values being from the same reporting agency or the largest two or three
values being significantly larger than the subsequent values. As a second comparison, facility-level
sums for each of the key pollutants were compared to each other in a similar fashion and were also
compared to the largest facility totals seen in the Toxics Release Inventory reports for 2008, by pollutant
and by facility type. We identified and provided questionable emissions values for S/L/T agency
review. All such flagged values for EGUs were either revised or confirmed as accurate by the
responsible S/L/T agency.
3.11 Fuel Combustion - Industrial Boilers
This section includes the description of five EIS sectors:
• Fuel Comb - Industrial Boilers, ICEs - Coal
• Fuel Comb - Industrial Boilers, ICEs - Oil
• Fuel Comb - Industrial Boilers, ICEs - Natural Gas
• Fuel Comb - Industrial Boilers, ICEs - Biomass
• Fuel Comb - Industrial Boilers, ICEs - Other
They are treated here in a single section because the methods used are the same across all sectors.
3.11.1 Sector Description
These five sectors are defined by the point source SCCs beginning with 1-02, 2-02 and 2-040 and the
nonpoint SCCs 2102 and 280152. These SCCs include boilers, internal combustion engines (ICE),
including reciprocating and turbines, space heaters and orchard heaters firing any type of fuel. The
primary fuels used by the boilers are coal, oil and natural gas. Other fuels used by industrial boilers
include biomass, waste products and process gases. The primary fuels used by the ICE are natural gas
and oil, but there are some which use various available process gases and liquified petroleum gas (LPG).
The SCC-based EIS sector definitions will cause a different universe of units to be included in these
sectors than would other definitions of boilers, turbines or reciprocating internal combustion engines.
For example, the Industrial/Commercial/Institutional Boilers and Process Heaters MACT include 25
MW and smaller boilers used to generate electricity; these boilers are not included in the sectors
described here because they have SCCs beginning with 1-01. Thus the EIS sector definition would put
these units, which are considered industrial boilers for the purpose of the MACT, in the Fuel
Combustion - Electric Generation sector described in 3.10. In addition, while CO Boilers are in this
sector, they are not included in the Industrial/Commercial/Institutional Boilers and Process Heaters
MACT category.
Also as described above in 3.10 the use of SCCs in the NEI by S/L/T agencies impacts the units
included in these EIS sectors. There are some boilers and gas turbines in industrial facilities which
cogenerate electricity for distribution to the public power grid and process steam for their internal use.
Some S/L/T agencies reporting to the NEI use an SCC (1-01 or 2-01) that would include these units in
one of the EGU sectors, while others use an Industrial (1-02 or 2-02) or a Commercial/Institutional (1-
103
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03 or 2-03) SCC. This can result in boilers or gas turbines not connected to the public power grid being
included in these EGU sectors and not the Industrial sectors.
In addition to the potential of ambiguity in assigning SCCs to industrial boiler units that may be used to
generate electricity, there is also mis-assignment where the wrong SCC is applied to clearly defined
units. For this reason, when looking at individual units, other description fields may be useful in
accurately categorizing the unit.
3.11.2 Sources of data overview and selection hierarchy
The industrial fuel combustion sectors include data from S/L/T and 12 EPA datasets that cover both
point and nonpoint data categories. Table 32 shows the agencies that submitted data in each of the data
categories for each of the fuel combustion - industrial boilers and ICE sectors. Where only 0 emissions
were submitted (sum across all pollutants submitted), these are shown as zeroes in the table.
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Table 32: Agencies that submitted data for the Fuel Combustion - Industrial Boilers, ICEs Sectors
Nonpoint
Point
Agency
Typ
e
Bio-
mas
s
Co
al
Nat
Gas
Oil
Oth
er
Bio-
mas
s
Co
al
Nat
Gas
Oil
Oth
er
Alabama Department of Environmental
Management
S
0
0
X
0
X
X
X
X
X
Alaska Department of Environmental
Conservation
S
X
X
X
X
X
X
Allegheny County Health Department
L
X
X
X
X
Arizona Department of Environmental
Quality
S
X
X
X
X
X
X
X
X
Arkansas Department of Environmental
Quality
S
X
X
X
X
X
California Air Resources Board
s
X
X
X
X
X
X
X
X
Chattanooga Air Pollution Control
Bureau (CHCAPCB)
L
0
0
X
X
X
X
X
X
City of Albuquerque
L
X
0
X
X
X
X
X
Clark County Department of Air Quality
and Environmental Management
L
X
0
X
X
X
X
X
Colorado Department of Public Health
and Environment
S
X
X
X
X
X
Confederated Tribes of the Colville
Reservation, Washington
T
X
X
Connecticut Department Of
Environmental Protection
S
X
X
X
DC Department of Health Air Quality
Division
S
0
0
X
X
X
X
Delaware Department of Natural
Resources and Environmental Control
s
0
X
X
X
X
X
X
X
Eastern Band of Cherokee Indians
T
X
Florida Department of Environmental
Protection
s
X
X
X
X
X
Fond du Lac Band of the Minnesota
Chippewa Tribe
T
X
Forsyth County Environmental Affairs
Department
L
X
X
X
X
X
Georgia Department of Natural
Resources
S
X
X
X
X
X
Hawaii Department of Health Clean Air
Branch
s
0
X
X
X
X
X
Idaho Department of Environmental
Quality
s
0
0
X
X
X
X
X
X
X
X
Illinois Environmental Protection
Agency
s
0
0
X
X
0
X
X
X
X
X
Indiana Department of Environmental
Management
s
X
X
X
X
X
X
X
X
105
-------
Nonpoint
Point
Bio-
Bio-
Typ
mas
Co
Nat
Oth
mas
Co
Nat
Oth
Agency
e
s
al
Gas
Oil
er
s
al
Gas
Oil
er
Iowa Department of Natural Resources
S
X
X
X
X
X
Jefferson County (AL) Department of
Health
L
X
X
X
X
Kansas Department of Health and
Environment
S
0
X
X
X
X
X
X
X
Kentucky Division for Air Quality
S
X
X
X
X
X
Kootenai Tribe of Idaho
T
0
0
X
X
X
Lane Regional Air Pollution Authority
L
X
X
X
X
Leech Lake Band of Ojibwe Reservation
T
X
Lincoln/Lancaster County Health
Department
L
X
X
Louisiana Department of Environmental
Quality
S
X
X
X
X
X
X
X
X
X
Louisville Metro Air Pollution Control
District
L
0
X
X
X
X
X
Maine Department of Environmental
Protection
S
X
X
X
X
X
Maricopa County Air Quality
Department
L
X
X
X
X
X
Maryland Department of the
Environment
S
X
X
X
X
Massachusetts Department of
Environmental Protection
S
X
0
X
X
X
X
X
X
X
X
Mecklenburg County Air Quality
L
X
X
X
X
Memphis and Shelby County Health
Department - Pollution Control
L
X
X
X
Metro Public Health of
Nashville/Davidson County
L
0
X
X
X
X
Michigan Department of Environmental
Quality
S
X
X
X
X
X
X
X
X
X
Minnesota Pollution Control Agency
S
X
X
X
X
X
X
X
X
X
X
Mississippi Dept of Environmental
Quality
s
X
X
X
X
X
Missouri Department of Natural
Resources
s
X
X
X
X
0
X
X
X
X
X
Montana Department of Environmental
Quality
s
X
X
X
X
X
Navajo Nation
T
X
Nebraska Environmental Quality
s
X
X
X
X
Nevada Division of Environmental
Protection
s
X
X
X
X
New Hampshire Department of
Environmental Services
s
X
X
X
X
X
X
X
106
-------
Nonpoint
Point
Bio-
Bio-
Typ
mas
Co
Nat
Oth
mas
Co
Nat
Oth
Agency
e
s
al
Gas
Oil
er
s
al
Gas
Oil
er
New Jersey Department of Environment
Protection
S
0
0
X
X
X
X
X
X
New Mexico Environment Department
Air Quality Bureau
S
X
X
X
New York State Department of
Environmental Conservation
S
X
X
0
X
X
X
X
X
X
X
Nez Perce Tribe
T
0
0
X
X
X
X
North Carolina Department of
Environment and Natural Resources
S
X
0
X
X
X
X
X
X
X
X
North Dakota Department of Health
S
X
X
X
X
Ohio Environmental Protection Agency
s
X
0
X
X
X
X
X
X
X
X
Oklahoma Department of Environmental
Quality
s
X
X
X
X
X
X
X
X
X
X
Olympic Region Clean Air Agency
L
X
X
X
X
Oregon Department of Environmental
Quality
s
X
0
X
X
X
Pennsylvania Department of
Environmental Protection
s
X
X
X
X
X
X
X
X
X
X
Philadelphia Air Management Services
L
0
X
X
X
Pinal County
L
X
X
X
Puerto Rico
S
0
X
X
Puget Sound Clean Air Agency
L
X
X
X
X
Rhode Island Department of
Environmental Management
S
X
X
X
Shoshone-Bannock Tribes of the Fort
Hall Reservation of Idaho
T
0
0
X
X
X
South Carolina Department of Health
and Environmental Control
S
X
0
X
X
0
X
X
X
X
X
Southern Ute Indian Tribe
T
X
Tennessee Department of Environmental
Conservation
S
X
X
X
X
X
X
X
X
X
X
Texas Commission on Environmental
Quality
S
X
X
X
X
X
X
Utah Division of Air Quality
s
X
X
X
X
Vermont Department of Environmental
Conservation
s
X
X
X
X
X
X
Virginia Department of Environmental
Quality
s
X
X
X
X
X
X
X
X
X
Washington State Department of
Ecology
s
X
X
X
X
X
West Virginia Division of Air Quality
s
X
X
X
X
X
X
X
X
Western North Carolina Regional Air
Quality Agency (Buncombe Co.)
L
X
X
107
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Nonpoint
Point
Bio-
Bio-
Typ
mas
Co
Nat
Oth
mas
Co
Nat
Oth
Agency
e
s
al
Gas
Oil
er
s
al
Gas
Oil
er
Wisconsin Department of Natural
Resources
S
X
X
X
X
X
X
X
X
Wyoming Department of Environmental
Quality
S
X
X
X
X
Table 33 shows the selection hierarchy for all datasets contributing emissions to the Fuel Comb -
Industrial Boilers, ICEs Sectors. This selection hierarchy combines the S/L/T data with the EPA
datasets. As can be seen, most of the datasets used for this selection have data for the point source data
category only.
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Table 33: 2008 NEI selection hierarchy for datasets used by the Fuel Comb - Industrial Boilers, ICEs
Sectors
DataSetName
Description
Point
Non-
point
EPA Overwrite Point vl .5
Overwrites PM emissions from Pennsylvania. See also
Table 7 and Appendix C. Even though these are EGUs,
some of the SCCs used by PA puts them in the
Industrial sector.
1
EPA PM Augmentation V2
PM species added to gap fill missing S/L/T agency data
or make corrections where S/L/T agency have
inconsistent PM species' emissions. See also Table 7
2
1
EPA PM Augmentation NP
Adds PM species to fill in missing S/L/T agency data or
make corrections where S/L/T agency data have
inconsistent emissions across PM species. See Table 8
2
EPA Chromium Split v2
Contains corrected and speciated hexavalent and
trivalent chromium emissions derived from the S/L/T
agency data for sources in which S/L/T agency reports
the total (unspeciated) chromium pollutant. See also
Table 7.
3
EPA other data developed
for using ahead of SLT for
gapfilling
Data added to boiler and ICE SCCs resulting from the
high risk and Hg review and from the Region 2
Tonawanda facility for the boiler burning coke oven
gas
4
2008EPA MATS
Emissions data for units identified as MATS units
(based on ORIS Ids) but with SCCs (incorrect) that put
these units in the industrial sector (I.e., first 3 digits are
102). Emissions for these are small compared to
MATS units that have fuel combustion - electricity
generation SCCs.
5
S/L/T data
6
2008EPA MMS
Boiler engine and turbine emissions from Offshore oil
platforms located in Federal Waters in the Gulf of
Mexico . See also Table 7.
7
EPA EGUvl.5
EPA non-MATS EGU data developed from CAMD
heat input and EFs. See also Section 3.10.
8
2008 EPA Rule Data from
OAQPS/SPPD
42 units were gap-filled with Hg emissions using the
Boiler MACT rule data. These 42 were among the
highest emissions in the Boiler MACT database for
which no emissions were provided by S/L/T.
9
EPA TRI Augmentation v2
Toxics Release inventory data used for gap-filling.
Some were assigned to industrial fuel combustion
sector SCCs based on the proportion of CAPS at those
SCCs. See Table 7 and Section 3.1.4.
10
EPA HAP Augmentation
v2
HAP data computed from S/L/T agency criteria
pollutant data using HAP/CAP emission factor ratios.
See Table 7 and in Section 3.1.5.
11
109
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EPA 2005NATA values
pulled forward to gapfill
Emissions from the 2005 NATA inventory used as
directed by states for facilities that were part of the
NATA review described in Section 3.1.7. Done for one
facility in WV burning liquid waste in an industrial
boiler.3.1.7
12
3.11.3 EPA-developed fuel combustion -Industrial Boilers, ICEs emissions data
EPA developed data for industrial nonpoint fuel combustion (see Table 19) that was not used in the
2008 NEI. The purpose of the information was to assist S/L/T to develop their own nonpoint estimates
by accounting for the point source contribution that they submitted, and the total fuel available for
combustion tracked by the Energy Information Administration. Year 2006 fuel activity data were used
as it was the latest data available at the time. For point sources, the EPA developed data from various
data sets as listed in Table 33. The rule data (2008 EPA Rule Data from OAQPS/SPPD) consisted of Hg
emissions from the Boiler MACT ICR data. While this database included emissions for thousands of
units, we only used 19 units' emissions due to the difficulty in matching the rule data to the EIS
facilities, units and processes. The 19 units we used were units where emissions were not provided by
S/L/T, were easy to match to EIS based on unit descriptions and were among the top Hg emitters.
3.11.4 Summary of quality assurance methods
Data analyses involving comparison of emissions between 2008 and 2005 showed large discrepancies in
emissions from this sector between the two years. We determined that some states did not properly
perform the point source reconciliation between nonpoint and point contributions to this sector. This
issue was found early enough in the 2008 NEI development process to fix some data prior to the v2
release (e.g., for Georgia, Virginia and Pennsylvania, as shown by the entries in the issues list,
2008neiv3 issues.xlsx , categorized as "identified in vl_5 and resolved in v2"). However, there were
other situations that did not allow sufficient time and remain as issues for v3 (e.g., Tennessee and
potentially Missouri).
Another quality assurance method conducted for Hg was to look at boiler SCCs and check for Hg
emissions. Other than for natural gas consumption, Hg is expected. As it turned out, some boilers even
after gap filling using TRI and HAP augmentation did not have Hg emitted. We computed that we were
missing 0.5 tons of Hg in v2 and then added the missing boiler hg (which was actually less than 0.5 tons
due to issues noted with the EF we were using for gap filling). Note that this issue included all boilers,
not just from the industrial sector.
3.12 Fuel Combustion - Commercial/Institutional
[Placeholder. See also Section 3.1 and Appendix B]
3.13 Fuel Combustion - Residential - Natural Gas, Oil, and Other
[Placeholder. See also Section 3.1 and Appendix B]
3.14 Fuel Combustion - Residential - Wood
[Placeholder. See also Section 3.1 and Appendix B]
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3.15 Gas Stations
[Placeholder. See also Section 3.1 and Appendix B]
3.16 Industrial Processes - Cement Manufacturing
[Placeholder. See also Section 3.1 and Appendix B]
3.16.1 Sector Description
This sector is defined by some, but not all SCCs beginning with 305006, 305007 plus 39000201 (In-
Process Fuel Use /Bituminous Coal /Cement Kiln/Dryer), 39000402 (In-Process Fuel Use /Residual Oil
/Cement Kiln/Dryer), 39000502 (In-Process Fuel Use /Distillate Oil /Cement Kiln/Dryer) and 39000602
(In-Process Fuel Use /Natural Gas /Cement Kiln/Dryer). The processes associated with this sector from
305006 (dry process) and 305007 (wet process) include the kilns including preheater and pre-calciner
kilns, coal kiln feed units, crushing, screening , raw material grinding and drying, clinker cooler, clinker
grinding,, cement loadout, pre-dryer, and raw mill processes.
3.17 Industrial Processes - Chemical Manufacturing
[Placeholder. See also Section 3.1 and Appendix B]
3.18 Industrial Processes - Ferrous Metals
[Placeholder. See also Section 3.1 and Appendix B]
3.19 Industrial Processes - Mining
[Placeholder. See also Section 3.1 and Appendix B]
3.20 Industrial Processes - Non-ferrous Metals
[Placeholder. See also Section 3.1 and Appendix B]
3.21 Industrial Processes - Oil & Gas Production
[Placeholder. See also Section 3.1 and Appendix B]
3.22 Industrial Processes - Petroleum Refineries
[Placeholder. See also Section 3.1 and Appendix B]
3.23 Industrial Processes - Pulp & Paper
[Placeholder. See also Section 3.1 and Appendix B]
3.24 Industrial Processes - Storage and Transfer
[Placeholder. See also Section 3.1 and Appendix B]
3.25 Industrial Processes - NEC (Other)
[Placeholder. See also Section 3.1 and Appendix B]
3.26 Miscellaneous Non-industrial NEC (Other)
[Placeholder. See also Section 3.1 and Appendix B]
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3.27 Solvent - Consumer & Commercial Solvent Use
[Placeholder. See also Section 3.1 and Appendix B]
3.28 Solvent - Degreasing, Dry Cleaning, and Graphic Arts
[Placeholder. See also Section 3.1 and Appendix B]
3.29 Solvent - Industrial and Non-Industrial Surface Coating
[Placeholder. See also Section 3.1 and Appendix B]
3.30 Waste Disposal
[Placeholder. See also Section 3.1 and Appendix B]
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4 Mobile sources
4.1 Mobile sources overview
Mobile sources are sources of pollution caused by vehicles transporting goods or people (e.g., highway
vehicles, aircraft, rail, and marine vessels) and other nonroad engines and equipment, such as lawn and
garden equipment, construction equipment, engines used in recreational activities, and portable
industrial, commercial, and agricultural engines.
EPA created a comprehensive set of mobile source emissions data for criteria and hazardous air
pollutants for all states, Puerto Rico, and US Virgin Islands as a starting point for the NEI. EPA uses
models to estimate emissions for most of the mobile source categories. During training for their 2008
NEI cycle, EPA encouraged S/L/T/ agencies to submit model inputs, rather than emissions, so that EPA
could use those inputs beyond the 2008 NEI for future year projections. Agencies had the option to
accept EPA's estimates or submit new emissions or emission inputs to replace or enhance EPA's data.
For development and documentation purposes, the major groups of mobile sources are aircraft (Section
4.2), commercial marine vessels (Section 4.3), locomotives (Section 4.4), nonroad equipment (Section
4.5), and on-road vehicles (Section 4.6). In addition, EPA developed nationally consistent datasets for
all of those sectors, though without the benefit of local-specific model inputs in all cases. The sections
below explain how we created the initial estimates, which S/L/T agencies provided model inputs or
emissions data for each sector, and how the EPA data and S/L/T agency data were blended to produce
the NEI.
For on-road vehicles, EPA transitioned from the MOBILE6 model to the MOVES model, and this
transition occurred during the 2008 NEI submission and development process. Thus, S/L/T agencies
submitted inputs and emissions for the on-road sector based on MOBILE6, in the form of inputs to the
NMIM system used to run the MOBILE6 model16. Where agencies submitted model inputs in the form
of NMIM inputs, we used them to generate both nonroad and on-road emissions. For on-road, we
converted the NMIM inputs for input to MOVES, which requires some assumptions and is not as robust
as using state-supplied MOVES inputs. In a limited number of cases, states had and provided MOVES
inputs that we used.
In general, EPA used the data submitted by S/L/T agencies unless EPA determined that the data caused
double counting or invalid pollutant or pollutant/emission type combinations inclusion. For example,
we excluded S/L/T agency-provided estimates for methyl tert-butyl ether, a gas additive no longer used
in US fuel supply. More details are provided in the sections that follow.
4.2 Aircraft
EPA estimated emissions related to aircraft activity for all known airports, including seaplane ports and
heliports, in the 50 states, Puerto Rico, and US Virgin Islands. All of the approximately 20,000
16 except for California, which provided emissions from the EMFAC model
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individual airports are geographically located by latitude/longitude and stored in the NEI as point
sources. As part of the development process, S/L/T agencies had the opportunity to provide both
activity data as well emissions to the NEI. When activity data were provided, EPA used that data to
calculate EPA's emissions estimates.
4.2.1 Sector Description
The aircraft sector includes all aircraft types used for public, private, and military purposes. This
includes four types of aircraft: (1) Commercial, (2) Air Taxis, (3) General Aviation, and (4) Military. A
critical detail about the aircraft is whether each aircraft is turbine- or piston-driven, which allows the
emissions estimation model to assign the fuel used, jet fuel or aviation gas, respectively. The fraction of
turbine- and piston-driven aircraft is either collected or assumed for all aircraft types.
Commercial aircraft include those used for transporting passengers, freight, or both. Commercial
aircraft tend to be larger aircraft powered with jet engines. Air Taxis carry passengers, freight, or both,
but usually are smaller aircraft and operate on a more limited basis than the commercial aircraft.
General Aviation includes most other aircraft used for recreational flying and personal transportation.
Finally, military aircraft are associated with military purposes, and they sometimes have activity at non-
military airports.
The national AT and GA fleet includes both jet and piston-powered aircraft. Most of the Air Taxi and
General Aviation fleet are made up of larger piston-powered aircraft, though smaller business jets can
also be found in these categories. Military aircraft cover a wide range of aircraft types such as training
aircraft, fighter jets, helicopters, and jet-powered and piston-powered planes of varying sizes.
The 2008 NEI also includes emission estimates for aircraft auxiliary power units (APUs) and aircraft
ground support equipment (GSE) typically found at airports, such as aircraft refueling vehicles, baggage
handling vehicles, and equipment, aircraft towing vehicles, and passenger buses. These APUs and GSE
are located at the airport facilities as point sources along with the aircraft exhaust emissions. However,
these emissions are included in the EIS Sectors for Non-road equipment (gasoline, diesel, and other),
described in Section 4.5.
This sector includes the SCCs listed in Table 34 below:
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Table 34: Source classification codes for the aircraft sector in the 2008 NEI
see
SCC Description
227500100
0
Mobile Sources; Aircraft; Military Aircraft; Total
227502000
0
Mobile Sources; Aircraft; Commercial Aircraft; Total: All Types
227505001
1
Mobile Sources; Aircraft; General Aviation; Piston
227505001
2
Mobile Sources; Aircraft; General Aviation; Turbine
227508500
0
Mobile Sources; Aircraft; Unpaved Airstrips; Total
27501014
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Military; Jet Engine:
JP-4
27601014
Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; Military; Jet Engine:
JP-4
27601015
Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; Military; Jet Engine:
JP-5
27602011
Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; Commercial; Jet
Engine: Jet A
4.2.2 Sources of data overview and selection hierarchy
The aircraft sector includes data from three data components: a corrections dataset, S/L/T agency-
provided emissions data, and an EPA dataset that is enhanced with state- and local-provided model
inputs.
The S/L/T agency data were received from agencies listed in Table 35. As described in Section 4.2.4,
all aircraft process emissions submitted by Georgia, Illinois, and Washoe County, NV were excluded by
overwrites in the EPA Overwrite Point vl.5 dataset to prevent double counting with the EPA data.
Table 35: Agencies that submitted aircraft emissions data
Agency
Agency Type
Alabama Department of Environmental Management
State
City of Huntsville Division of Natural Resources and Environmental Mgmt
Local
California Air Resources Board
State
Illinois Environmental Protection Agency
State
North Carolina Department of Environment and Natural Resources
State
Washoe County Health District
Local
Pinal County
Local
Pennsylvania Department of Environmental Protection
State
Texas Commission on Environmental Quality
State
Wisconsin Department of Natural Resources
State
Fond du Lac Band of the Minnesota Chippewa Tribe
Tribal
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The selection hierarchy used for aircraft is shown below in Table 36. This hierarchy pulls the relevant
datasets for this sector from the overall point sources hierarchy listed in Table 8.
Table 36: 2008 NEI aircraft data selection hierarchy
Priority
Dataset Name
Dataset Content
1
EPA Overwrite Point
vl.5
Overwrites some S/L/T emissions data with zeros to prevent use of
invalid acenaphthylene emission factors and to prevent double
counting in the final dataset (Section 4.2.4)
2
State/Local/Tribal Data
Submitted aircraft emissions
3
EP AAirports 1109
EPA data (Section 4.2.5)
4.2.3 Spatial coverage and data sources for the sector
The aircraft sector includes emissions in every state, Puerto Rico, and the US Virgin Islands as well as
six tribes.
[Placeholder for CAP and HAP maps and associated observations]
4.2.4 Overwrite dataset used for aircraft sector
This dataset has two purposes for airport emissions. First, all acenaphthylene emissions for the airport
SCC of 2275050012 (general aviation turbine) are set to zero with this dataset to prevent use of an
incorrect emission factor used in the state-supplied data. The submitted S/L/T estimates appeared
almost identical to EPA's, which were subsequently found to be in error and removed. The states with
records for this correction are Alabama, California, Illinois, North Carolina, and Wisconsin.
Second, some states added airport emissions to new "units" and "processes" at the EPA airport facilities.
If these data had been merged with the EPA data without this overwrite dataset, the emissions at the new
"units" and "processes" would have been added to the units at the EPA "units" and "processes" at these
airports. This situation occurred for all airports in Georgia and Washoe County, NV for CAP emissions
and Illinois for CAP and HAP emissions. To avoid double counting, this corrections dataset overwrites
the all of the state aircraft data with zero values. The NEI selection then includes the EPA emissions
data instead, which are located at the valid units and processes defined by EPA at the start of the NEI
development cycle.
4.2.5 EPA-developed aircraft emissions estimates
EPA developed emissions estimates associated with an aircrafts' landing and takeoff (LTO) cycle. The
cycle begins when the aircraft approaches the airport on its descent from cruising altitude, lands, taxis to
the gate, and idles during passenger deplaning. It continues as the aircraft idles during passenger
boarding, taxis back out onto the runway for subsequent takeoff, and ascent (climb out) to cruising
altitude. Thus, the five specific operating modes in an LTO are (1) Approach, (2) Taxi/idle-in, (3)
Taxi/idle-out, (4) Takeoff, and (5) Climb out.
The LTO cycle provides a basis for calculating aircraft emissions. During each mode of operation, an
aircraft engine operates at a fairly standard power setting for a given aircraft category. Emissions for
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one complete cycle are calculated using emission factors for each operating mode for each specific
aircraft engine combined with the typical period of time the aircraft is in the operating mode.
In spring 2009, the EPA posted preliminary LTO data for review prior to developing the aircraft
inventory. EPA encouraged the S/L/T agencies to review the materials and provide comments on any
necessary corrections to:
1. Airport names and locations for airports to be included in the EIS facility inventory;
2. LTO information that will be used to estimate emissions for each airport;
3. Aircraft/engine combinations to link to FAA LTO data including default assumptions and
AircraftEngineCodeTypes for EIS submittals; and
4. Lead estimates and the lead estimation methodology.
The following S/L/T agencies submitted aircraft activity data that EPA incorporated as inputs to the
final EPA dataset model run.
Table 37: Agencies that submitted aircraft activity data for EPA's emissions calculation
Agency
Agency Type
Connecticut Department of Environmental Protection
State
Minnesota Pollution Control Agency
State
New Jersey Department of Environmental Protection
State
Wisconsin Department of Natural Resources
State
Mecklenburg County North Carolina
Local
Ventura County California Air Pollution Control District
Local
Regional Air Pollution Control Agency (Dayton and Montgomery
County Ohio)
Local
4.2.5.1 Emissions for aircraft with detailed aircraft-specific activity data
For airports where the available LTO, from agencies or FAA data bases, included detailed aircraft-
specific make and model information (e.g., Boeing 747-200 series), EPA used the FAA's EDMS,
Version 5.1 (FAA, 2008a). This type of detail is available for most LTOs at 3410 larger airports that
have commercial air traffic. Smaller, and most general aviation only, airports would not have aircraft
specific activity detail available.
Emissions for GSE and APUs associated with aircraft-specific activity were also estimated by EDMS,
using the assumptions and defaults incorporated in the model. This is significant change from the
previous NEI emissions where GSE estimates came from the NONROAD model and APUs were not
included in EPA's estimates. These emissions are mapped to the EIS Sectors for Non-road equipment
(gasoline, diesel, and other), described in Section 4.5.
EPA estimated aircraft-related emissions for the SCCs identified in Table 38 and associated EIS Sector,
where available.
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Table 38: SCCs included in the EPA-created aircraft emissions dataset
see
Description
Data
Category
EIS Sector
2265008005
Airport Ground Support Equipment, 4-Stroke
Gasoline
Point
Mobile - Non-Road
Equipment - Gasoline
2267008005
Airport Ground Support Equipment, LPG
Point
Mobile - Non-Road
Equipment - Other
2268008005
Airport Ground Support Equipment, CNG
Point
2270008005
Airport Ground Support Equipment, Diesel
Point
Mobile - Non-Road
Equipment - Diesel
2275001000
Aircraft /Military Aircraft /Total
Point
Mobile - Aircraft
2275020000
Aircraft /Commercial Aircraft /Total: All Types
Point
Mobile - Aircraft
2275050011
Aircraft /General Aviation /Piston
Point
Mobile - Aircraft
2275050012
Aircraft /General Aviation /Turbine
Point
Mobile - Aircraft
2275060011
Aircraft /Air Taxi /Piston
Point
Mobile - Aircraft
2275060012
Aircraft /Air Taxi /Turbine
Point
Mobile - Aircraft
2275070000
Aircraft /Aircraft Auxiliary Power Units /Total
Point
Mobile - Non-Road
Equipment - Other
2275087000
Aircraft/In-flight (non-Landing-Takeoff cycle)
Nonpoint
Mobile - Aircraft
4.2.5.2 Emissions for airports without detailed aircraft-specific activity data
EPA estimated emissions for aircraft where detailed aircraft-specific activity data were not available by
combining aircraft operations data from FAA's Terminal Area Forecasts (TAF) and 5010 forms (See
http://www.faa.gov/airports/airport_safety/airportdata_5010/). These sources provide LTO estimates for
general aviation airports. Because the aircraft make and models were not available, EPA used
assumptions regarding the percent of these LTOs that were associated with piston-driven (using aviation
gas) versus turbine-driven (using jet fuel) aircraft. Specifically, EPA assumed that at airports, 72.5% of
all General Aviation and 23.1% of all Air Taxi activity were powered by piston-powered aircraft, with
the remainder powered by turbine aircraft. At heliports, EPA assumed that 36.1%> of all General
Aviation and 2% of all Air Taxi activity were powered by piston-powered, with the remainder powered
by turbine engines. These fractions were developed based on FAA's General Aviation and Part 135
Activity Surveys - CY 2008 (FAA, 2008b). Then EPA estimated emissions based on the percent of
each aircraft type, LTOs, and emission factors.
Pb emission estimates were handled differently from the other pollutants. Lead emissions are associated
with leaded aviation fuel used in piston driven aircraft associated with general aviation. EDMS has a
limited number of piston engine aircraft in its aircraft data and is currently not set up to calculate metal
emissions; therefore, we did not use it to estimate aircraft lead emissions. Lead emissions are instead
based on per-LTO emissions factors, assumptions about lead content in the fuel, and lead retention rates
in the piston engines and oil. The general equation is:
(piston — engine LTO)(avgas PbjjjrQ*)(l — Pb retention)
907,180 g/ton
LTO Pb (tons) =
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The LTO estimate requires assumptions about the number of piston engines per plane, and number of
LTOs necessary to account for US average fuel usage. The assumptions are detailed in a project report
(ERG, 201 la). In addition, a summary of the EPA-only airport lead emissions
"airportlead_20110406.xlsx" is available (see Section 8.2). This summary is not the same as any
summaries of the 2008 NEI, which include about 21 tons of Pb emissions data from S/L/T agencies.
Texas submitted an additional 24.3 tons of Pb at airports for SCC 2275050011. This addition and lower
Pb emissions submitted by other states for some airports result in the 2008 NEI being 21 tons higher
than the EPA-only data for emissions at airports.
In-flight lead emissions, which have not been previously included in the NEI, were calculated based on
national aviation gasoline consumption and similar assumptions noted above about lead fuel content and
retention rates. Lead emissions associated with airport LTO activities were subtracted from the national
fuel based lead emissions to approximate in-flight lead emissions which were allocated to individual
states and noted with the county code 777. This county code is not used to identify any actual counties
across the US, and thus provides a way of uniquely finding all in-flight emissions in the NEI database.
A summary of the EPA in-flight lead emissions "out_of_lto_pb_summary_12021 l.xlsx" is available
(see Section 8.2). This summary is the same as summaries of the 2008 NEI, which do not include data
from S/L/T/ agencies for in-flight Pb emissions.
4.2.6 Summary of quality assurance methods
The Documentation for Aircraft Component of the National Emissions Inventory Methodology
addresses the QA for the EPA estimates. The agency-submitted aircraft emission estimates were
compared to EPA's estimates by pollutant and SCC at the unit (e.g. commercial, general aviation,
military, air taxi) and process (SCC).
• Findings and impacts
o Illinois submitted 35654 records with zero emissions for processes that were not already
populated with EPA data. The result of submitting a zero emissions process where there
is no competing data is the same as no submittal. There is no effect in the 2008 NEI
since Illinois records were overwritten because of the units/process duplication discussed
in section 4.2.4
o 5 agencies (California, Huntsville, Illinois, North Carolina, Wisconsin) reported
pollutants not reported for airports by EPA (PM-CON, PM10-FIL, and
Dibenzo[a,h]Anthracene). The data were not adjusted, thus in the 2008 NEI selection,
only these airports will have emissions from these pollutants,
o 4 agencies reported non-aircraft related SCCs to airport facilities, as shown in Table 39.
Of these, Cloquet Carlton County Airport (EIS Facility ID = 8263311) had no aircraft-
related SCCs reported. No changes were made to these by EPA. However, typically
facilities that are identified as "airport" contain only aircraft-related SCC emissions.
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Ta
)le 39: Non-aircraft relatec
SCCs reported by
S/L/T agencies to airports
EIS
Facility
Identifie
r
Agency
Facility
Identifier
Agency
PSC
Site Name
SCC
Sector
Fuel Comb -
Cloquet Carlton
1030060
C omm/Instituti on
8263311
05
TR405
County Airport
3
al - Natural Gas
Huntsville -
1058191
COHDNR
Madison County
Airport
3999999
Industrial
1
A141
EM
Authority
9
Processes - NEC
1234261
4060030
1
10046
Pinal
Arizona Soaring
7
Gas Stations
COUNTY OF
SAN LUIS
OBISPO-
Fuel Comb -
1002651
40113139
OCEANO
2020010
Industrial Boilers,
1
5
CARB
AIRPORT
2
ICEs - Oil
o Alabama, California, Illinois, North Carolina, and Wisconsin submitted acenaphthalene
from SCC 2275050012 (general aviation turbine). The state estimates were almost
identical to EPA's, which were subsequently found to be in error, since there should be
no acenaphthalene from this SCC. EPA removed these estimates from the EPA data and
the S/L/T agency estimates were overwritten in the EPA Overwrite Point dataset as
described in Section 4.2.4.
o Washoe, Illinois, and Georgia submitted 100% of their aircraft emissions to units and
processes that duplicated ones already present in the airport facility inventory, rather than
using existing units and processes. Using those records in the 2008 NEI would cause the
agency records to add to (instead of replacing) EPA estimates. This finding resulted in
the EPA corrections described as part of the "EPA Overwrite Point vl.5" dataset as
described in Section 4.2.4.
4.3 Commercial Marine Vessels
The 2008 NEI includes emissions from CMV activity in the 50 states, Puerto Rico, and US Virgin Isles,
out to 200 nautical miles from the US coastline. The EPA CMV data changed from 2008v2 to 2008v3.
Read below for details.
4.3.1 Sector Description
The CMV sector includes boats and ships used either directly or indirectly in the conduct of commerce
or military activity. The majority of vessels in this category are powered by diesel engines that are
either fueled with distillate or residual fuel oil blends. For the purpose of this inventory, we assume that
Category 3 (C3) vessels primarily use residual blends while Category 1 and 2 (CI and C2) vessels
typically used distillate fuels.
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The C3 inventory includes vessels which use C3 engines for propulsion. C3 engines are defined as
having displacement above 30 liters per cylinder. The resulting inventory includes emissions from both
propulsion and auxiliary engines used on these vessels, as well as those on gas and steam turbine
vessels. Geographically, the inventories include port and interport emissions that occur within the area
that extends 200 nautical miles (nm) from the official U.S. baseline, which is roughly equivalent to the
border of the U.S. Exclusive Economic Zone. Only some of these emissions are allocated to states
based on official state boundaries that typically extend 3 miles offshore (see Section 4.3.4).
The CI and C2 vessels tend to be smaller ships that operate closer to shore, and along inland and
intercoastal waterways. Naval vessels are not included in this inventory, though Coast Guard vessels are
included as part of the CI and C2 vessels.
The CMV source category does not include recreational marine vessels, which are generally less than
100 feet in length, most being less than 30 feet, and powered by either inboard or outboard. These
emissions are included in those calculated by the NONROAD model in the nonroad category and EIS
sectors of the 2008 NEI.
Each of the commercial marine SCCs requires an appropriate emissions type (M=maneuvering,
H=hotelling, C=cruise, Z=reduced speed zone) because emission factors vary by emission type. Each
SCC and emissions type combination was allocated to a shape file identifier in the nonpoint inventory.
The allowed combinations are shown in Table 40. The default values are those assumed when the actual
emission type may be unknown; for example, emissions that occur in shipping lanes are assumed to be
'cruising' and cannot be 'hotelling', which only occurs at ports.
Table 40: Commercial Marine SCCs and Emission Types
SCC
SCC Description
Allowed
Default
2280002100
Marine Vessels, Commercial Diesel Port
M
M
2280002200
Marine Vessels, Commercial Diesel
Underway
C
C
2280003100
Marine Vessels, Commercial Residual Port
H
H
2280003100
Marine Vessels, Commercial Residual Port
M
H
2280003200
Marine Vessels, Commercial Residual
Underway
C
C
2280003200
Marine Vessels, Commercial Residual
Underway
Z
C
In addition, the additional SCCs in Table 41 were submitted by California and Kentucky (as denoted)
and included in the NEI. We suspect but could not confirm that these emissions double-count emissions
from the EPA shapefile-based datasets.
Table 41: Additional Commercial Marine SCCs used by California and Kentucky
SCC
SCC Description
States
2800021
1
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Crew Boats:
Main Engine Exhaust: Idling
CA
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2800021
2
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Crew Boats:
Main Engine Exhaust: Maneuvering
CA,
KY
2800021
3
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Crew Boats:
Auxiliary Generator Exhaust: Hotelling
CA
2800021
6
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Supply Boats:
Main Engine Exhaust: Idling
CA
2800021
7
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Supply Boats:
Main Engine Exhaust: Maneuvering
CA,
KY
2800021
8
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Supply Boats:
Auxiliary Generator Exhaust: Hotelling
CA
4.3.2 Sources of data overview and selection hierarchy
The commercial marine vessels sector includes data from four data components: two corrections
datasets, S/L/T agency-provided emissions data, and an EPA dataset of CMV emissions.
EPA received emissions data from the agencies identified in Table 42.
Table 42: Agencies that Submitted Commercial Marine Emissions Data
Agency
Agency
Type
Notes
Removed from EIS
California Air Resources Board
State
(See Section 4.3.5)
Delaware Department of Natural Resources and Environmental
Control
State
Removed from EIS
Idaho Department of Environmental Quality
State
(See Section 4.3.5)
Removed from EIS
Illinois Environmental Protection Agency
State
(See Section 4.3.5)
Removed from EIS
Kansas Department of Health and Environment
State
(See Section 4.3.5)
All emissions
Kootenai Tribe of Idaho
Tribal
records are zero
Louisville Metro Air Pollution Control District
Local
Removed from (See
Maryland Department of the Environment
State
Section 4.3.5)
New Hampshire Department of Environmental Services
State
New Jersey Department of Environment Protection
State
Nez Perce Tribe
Tribal
Pennsylvania Department of Environmental Protection
State
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribal
South Carolina Department of Health and Environmental Control
State
Texas Commission on Environmental Quality
State
Table 43 shows the selection hierarchy for the CMV sector.
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Table 43: 2008 NE
commercial marine vehicle selection hierarchy
Priority
Dataset Name
Dataset Content
1
EPA Chromium Split v2
Speciates total chromium in California for SCCs 28000212 and
28000217 (Section Error! Reference source not found.).
2
State/Local/Tribal Data
Submitted commercial marine vessel emissions
3
EPA CMV
EPA data (Section 4.3.4)
4.3.3 Spatial coverage and data sources for the sector
The commercial marine vessel sector includes emissions in every state except Arizona, Colorado,
Montana, Nevada, New Mexico, North Dakota, South Dakota, Utah, Vermont, and Wyoming. It also
includes emissions for Puerto Rico and US Virgin Islands, three tribes, as well as emissions in federal
waters.
4.3.4 EPA-developed commercial marine vessel emissions data
EPA estimated CMV emission estimates17 as a collaborative effort between the Office of Transportation
and Air Quality (OTAQ) and OAQPS. EPA developed the Category 3 commercial marine inventories
for a base year of 2002 and then projected to 2008 by applying regional adjustment factors to account
for growth. In addition, EPA developed and applied NOX adjustment factors to account for
implementation of the NOX Tier 1 standard. The C3 growth factors, NOX adjustment factors by tier and
calendar year, and NOX adjustment factors by engine type and speed are defined in Appendix A of the
2008 NEI CMV documentation (ERG, 2010). For Category 1 and 2 marine diesel engines, the emission
estimates were consistent with the 2008 Locomotive and Marine federal rule making (US EPA, 2003).
EPA derived HAP estimates by applying toxic fractions to VOC or PM estimates.
EPA then allocated these emissions to individual GIS polygons (see Sections 4.3.4.1 and 4.3.4.2) using
appropriate methods that varied by operating mode (i.e., hotelling, maneuvering, reduced speed zone,
and underway). For example, port emissions appear only in port polygons, federal water emissions in
federal waters. HAP emissions were estimated by applying speciation profiles to each polygon's VOC
and PM estimates; see also Appendix B of the 2008 NEI CMV documentation (ERG, 2010).
EPA allocated emissions estimates based on activity to GIS polygons representing port and waterway.
GIS polygons allowed the estimation/allocation of emissions to defined port, waterway, and coastal
areas, leading to improved spatial resolution compared to previous county-level emissions.
Agencies also submitted emissions to this sector. The SCCs for which EPA developed estimates are in
Table 44.
17 While C02 estimates were also developed, the 2008 NEI does not include GHG and so these are not available except
through the EPA-developed dataset included in EIS.
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Table 44: Commercial Marine SCCs for which EPA Provided Estimates
see
Description
Data Category
2280002100
Marine Vessels, Commercial Diesel Port
Nonpoint
2280002200
Marine Vessels, Commercial Diesel
Underway
Nonpoint
2280003100
Marine Vessels, Commercial Residual Port
Nonpoint
2280003200
Marine Vessels, Commercial Residual
Underway
Nonpoint
4.3.4.1 Allocation of Port Emissions
EPA developed port boundaries using a variety of resources to identify the most accurate port
boundaries. First, GIS data or maps provided directly from the port were used. Next, maps or port
descriptions from local port authorities, port districts, etc. were used in combination with existing GIS
data to identify port boundaries. Finally, satellite imagery from tools such as Google Earth and street
layers from StreetMap USA were used to delineate port areas. We placed primary emphasis on mapping
the 117 ports with Category 3 vessel activity using available shape files of the port area. The Port of
Huntington was developed differently given its large extent and limited available map data. The state of
West Virginia provided a revised shape file of US Army Corps of Engineers port terminals reported to
be part of the Port of Huntington-Tristate area. The revised shape that includes a 200 meter buffer of the
water features near these port terminals was created to identify the port area.
In all cases, polygons were created on land, bordering waterways and coastal areas, and were split by
county boundary, such that no shape file crosses county lines and county total emission can be easily
summed. Each polygon was identified by the port name and state and county FIPS in addition to a
unique ShapelD. Smaller ports with Category 1 and 2 activities were mapped as small circles, such that
the port is much like a point source, but without the complication of emissions appearing in both point
and nonpoint inventories. Note that no Category 3 emissions were mapped to small circles. The final
shapefile contained 237 ports and 275 polygons, considering that a single port can cross county
boundaries and thus include multiple polygons. The final shapefile is listed as
"2011_ports_shapefile.zip" in Section 8.1.
To develop emissions for the Category 1 and 2 part of the inventory, EPA started with criteria emissions
and activity as a single national number. We allocated the emissions and activity data to ports based on
total commodity tonnage data obtained from the U.S. Army Corps of Engineers Principal Ports file for
2007 (U.S. Army Corps of Engineers, 2009; see also data file "pport07.xls" listed in Section 8.1).
Emissions were then assigned to polygons within a port based on fraction of the port's area within each
shape.
For the Category 3 activity, EPA developed port-level criteria and C02 emissions for 117 of the largest
U.S. from port activity (maneuvering and hotelling modes) in megawatt hours. We then assigned
emissions to shape file polygons within a port based on fraction of port area. HAP emissions were then
speciated from VOC and PM estimates for each mode, using emission factors for C3 vessels; see also
Appendix A of the 2008 NEI CMV documentation (ERG, 2010).
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4.3.4.2 Allocation of Underway Emissions
Category 1 and 2 criteria emissions were allocated to underway polygons in state waters based on total
commodity movements (in tons) data obtained from US ACE (US ACE, 2001). These data were
waterway-specific, so waterways that crossed into multiple FIPs had emissions assigned by waterway
length in each polygon. HAP emissions were then speciated from VOC and PM estimates using the
methodology described in Section 2.3 of ERG (2010) for each polygon.
For Category 3, EPA/OTAQ developed line shapefiles indicating port-specific approach segment length
and related emissions and activity in the reduced speed zones, the mode when the ship slows to improve
vessel handling near land, on a per-port basis. HAP emissions were then speciated from VOC and PM
estimates using the methodology described in for each polygon as described above. The shapefiles used
for the underway emissions are available in the file "shippinglanes l 12812_shapefile.zip" as listed in
Section 8.1.
For Category 3 Interport emissions, EPA created 4km gridded emissions for interport-only emissions for
CO, C02, HC, NOx, SOx, and PM10, as described in Section 4.3.5. EPA used GIS to overlay the 4-km
grid with county boundaries including state waters to allocate to counties, and the rest of the 4-km data
were allocated to federal waters and labeled with state/county codes starting with 85 in EIS. County
boundaries in the NEI extend to the transition from state to federal waters, typically three miles off
shore. HAP emissions were then speciated from VOC and PM estimates using the methodology
discussed above.
4.3.4.3 2008NEIv3 Reallocation of EPA estimates for Category 1 and 2 vessels
EPA updated the allocation for category 1 and 2 vessels based on activity for the underlying vessel types
(deepwater, ferries, fishing, government, Great Lake, offshore, research, and tugs) available in
" Category 2 Vessel Census, Activity, and Spatial Allocation Assessment and Category 1 and Category 2
In-port/At-sea Splits, " (Census Report) February 16, 2007. This revision described in the August 22,
2012 Memorandum from Eastern Research Group, shifts the distribution of emissions between majority
in ports to majority in underway.
The updates changed the allocation fractions of emissions to underway and port county/shapelD
combinations. Agencies were given an opportunity to resubmit their emissions allocated in proportion
to EPA's. Table 42 and the quality assurance section below were updated to reflect the lates agency
inclusions.
4.3.5 Summary of quality assurance methods
EPA compared shape-, state-, and county-level sums in (1) EPA default data, (2) S/L/T agency
submittals and (3) the resultant 2008 NEI selection by
• Included pollutants, SCCs, SCC-Emission Types
• Emissions summed to agency and SCC level
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Findings prior to corrections and release
• For a given county, the 2008 NEI includes agency emissions only where the
reporting/identification codes used by the state exactly matched the codes used by EPA (i.e., the
shape, SCC, emission type, and pollutant) or where emissions occur in counties with no shape
IDs (i.e., submitted as county totals). When the same codes are used, EIS can replace EPA data.
Several agencies that submitted using shape files included more or fewer shapes (or counties
with no shape files) than the EPA dataset. The result would have been a merging of the agency
and EPA data, which needed to be prevented to avoid double counting. EPA contacted
submitting agencies and provided assistance to those willing to resubmit their data in shape files
or agree to accept EPA's default data. Because the remaining agency data could not be included
in EIS without double counting, it had to be deleted from EIS. This occurred for California,
Idaho, Illinois, Kansas, Maryland. Of these, only Kansas agreed to EPA's data, the others did
not respond to request for resubmittal.
• Most agencies included the same or fewer SCCs than the EPA dataset. However, California,
DC, Delaware, New Hampshire, Texas, and Maricopa included additional SCCs.
o Examples:
¦ California and Louisville Metro Air Pollution Control District included CMV
point source SCCs. These may result in emissions double counting with EPA
shapefile-based data.
• Most agencies either did not submit HAPs or did not submit all the HAPs that EPA estimated. In
this case, EPA data will appear in the 2008 NEI for any HAPs not in the S/L/T agency data. This
can cause problems when the resultant 2008 NEI may have VOC and PM emissions less than the
EPA VOC or PM, and there may be a mathematical inconsistency between VOC HAPs and PM
HAPs with the criteria pollutants. There will also be an inconsistency because of the different
approaches used to compute CAPs and HAPs.
o Example:
¦ New Hampshire submitted CAPs only. For SCC 2280002200, the New
Hampshire total VOC and PM are used in the NEI and are much less than EPA's
VOC and PM estimates, Since the NEI uses EPA's VOC HAPs and PM HAPs,
the sum of these could be greater than the criteria VOC and PM also in the NEI.
This phenomenon occurs for the Rockingham County, NH (FIP= 33015) sum for
VOC, primary PM10 and primary PM2.5, and may occur elsewhere at a shape ID
level.
• The 2008 NEI uses EPA data for any pollutant/SCC/emission type combination that is present in
EPA's dataset and not in the agency.
• 2008 NEI emissions can be greater than both the EPA and the agency estimates when:
o Either the agency or EPA dataset has populated sets of counties or shapes or has different
SCC/emission type, such that the 2008 NEI has more SCCs or SCC/emission types than
either the EPA or agency datasets.
¦ Example: In the following Agency/SCC/CAP combinations, the 2008 NEI
selection total is greater than both the EPA and agency emissions:
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Table 45: SCC/Pollutant combinations where State total 2008 NEI is
State
SCC
Allowed
TX
2280003100
NH3
TX
2280003100
PM10-PRI
TX
2280003100
PM25-PRI
TX
2280003100
S02
TX
2280003200
VOC
TX
2280003200
NOX
TX
2280003100
CO
TX
2280003200
S02
TX
2280003200
NOX
SC
2280003200
PM25-PRI
SC
2280003200
PM10-PRI
SC
2280003200
NH3
SC
2280003200
NOX
SC
2280003200
CO
SC
2280003200
VOC
• EPA estimates for Louisiana diesel CMV emissions (SCC=2280002*) were challenged in similar
previous NEI data as too high. There is also a conference paper from the 2005 EI conference.
The state was contacted 12/2011 and had no other dataset alternatives and agreed users should be
cautioned on this potential over estimate.
• The EPA dataset does not include tribal areas. Therefore the 2008 NEI is equal to the tribal
submission in the three tribal regions that provided data. These tribes used only SCCs
2280002100 and 2280002200.
• All emission records submitted by Kootenai Tribe of Idaho contained zero emission records.
They are included in 2008 NEI, but since they are zero, have no effect.
4.4 Locomotives
4.4.1 Sector Description
The locomotive sector includes railroad locomotives powered by diesel-electric engines. A diesel-
electric locomotive uses 2-stroke or 4-stroke diesel engines and an alternator or a generator to produce
the electricity required to power its traction motors. The locomotive source category is further divided
up into categories: Class I line haul, Class II/III line haul, Passenger, Commuter, and Yard. Table 46
below indicates locomotive SCCs and whether EPA estimated emissions. If EPA did not estimate the
emissions, then all emissions from that SCC that appear in the inventory are from S/L/T agencies.
Table 46: Locomotive SCCs, descriptions, and EPA estimation status
SCC
Description
EPA
/ERTAC
Estimated?
Data
Category
2285002006
Mobile Sources Railroad Equipment Diesel Line Haul
Locomotives: Class I Operations
Yes - in
shape files
Nonpoint
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2285002007
Mobile Sources Railroad Equipment Diesel Line Haul
Locomotives: Class II / III Operations
Yes-in shape
files
Nonpoint
2285002008
Mobile Sources Railroad Equipment Diesel Line Haul
Locomotives: Passenger
no
Nonpoint
2285002009
Mobile Sources Railroad Equipment Diesel Line Haul
Locomotives: Commuter Lines
no
Nonpoint
2285002010
Railroad Equipment Diesel Yard Locomotives
no
Nonpoint
28500201
Internal Combustion Engines Railroad Equipment Diesel
Yard
Yes - as
point sources
Point
4.4.2 Sources of data overview and selection hierarchy
The locomotives sector includes data from five data components: three corrections datasets, S/L/T
agency-provided emissions data, and an EPA dataset of locomotive emissions.
EPA estimated emissions from select locomotive SCCs. The agencies listed in Table 47 also submitted
emissions to the same or other locomotive SCCs.
Table 47: Agencies that submitted Rail Emissions to the 2008 IS
[EI
Agency Organization
Agency Type
Alaska Department of Environmental Conservation
State
California Air Resources Board
State
Connecticut Department Of Environmental Protection
State
DC-District Department of the Environment
Local
Delaware Department of Natural Resources and Environmental Control
State
Idaho Department of Environmental Quality
State
Illinois Environmental Protection Agency
State
Kansas Department of Health and Environment
State
Louisville Metro Air Pollution Control District
Local
Maricopa County Air Quality Department
Local
Maryland Department of the Environment
State
New Hampshire Department of Environmental Services
State
Nez Perce Tribe
Tribal
North Carolina Department of Environment and Natural Resources
State
Omaha Tribe of Nebraska
Tribal
Oregon Department of Environmental Quality
State
Pennsylvania Department of Environmental Protection
State
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Tribal
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribal
Texas Commission on Environmental Quality
State
Utah Division of Air Quality
State
Washoe County Health District
Local
Table 48 shows the selection hierarchy for the locomotive sector.
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Table 48: 2008 NEI locomotives selection hierarchy
Priority
Dataset Name
Dataset Content
1
EPA PM Augmentation, V2 (point)
Zeros out PM species in Texas and Kansas
2
EPA Chromium Split v2 (point)
Zeros out submitted locomotive chromium in Texas
and Kansas.
3
Rail EPACorrections (nonpoint)
Also overwrites county submittals for
counties/SCCs where EPA data exists in shape files
(see Section 4.4.4)
4
Responsible Agency Dataset (point
and nonpoint)
Submitted locomotive emissions
5
EPA Rail (point and nonpoint)
EPA data (see Section 4.4.5)
4.4.3 Spatial coverage and data sources for the sector
The locomotives sector includes emissions in all states, DC, Puerto Rice, and some tribes.
[Placeholder for CAP and HAP maps and associated observations]
4.4.4 Overwrite datasets used for locomotives sector
EPA used three overwrite datasets to make changes to the data provided by S/L/T agencies. The "EPA
PM Augmentation, V2" and "EPA Chromium Split v2" datasets zeroed out small amounts of PM and
unspeciated chromium. The "RailEPACorrections" dataset zeros out agency submissions to prevent
double counting with EPA data. Since EPA's dataset used shapefiles, when agencies submitted without
shapefiles but rather as a county total, EIS was unable to blend the two datasets properly. This
limitation would have resulted in double-counting of the data. Since we knew that EPA data were
complete, but we did not know whether the S/L/T agency data were complete, we overwrote the S/L/T
data with zeros and selected the EPA data for the 2008 NEI. This approach was needed in California,
Connecticut, DC, Idaho, Illinois, Maricopa County, Maryland, North Carolina, Oregon, Louisville and
the Washoe County Health District. In most of these regions, some state data are still used.
4.4.5 EPA-developed locomotive emissions data
EPA's national rail estimates were developed by the Eastern Regional Technical Advisory Committee
hereafter referenced as ERTAC Rail. This group is comprised of eastern states' regulatory agencies in
collaboration with the rail industry. ERTAC Rail developed emissions estimates based on fuel data
obtained from the American Association of Railroads for each subcategory. California locomotive
emission estimates were handled separately from the rest of the United States because of their use of low
sulfur locomotive diesel fuels.
ERTAC Rail used confidential railroad-provided data to generate railroad-specific criteria emission
estimates for line haul and rail yards at the rail segment and rail yard level, respectively. In addition to
the sections below, additional information is available in the project report (ERG, 201 lb).
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4.4.5.1 Line Haul Criteria Emissions Estimates
Criteria pollutant emissions were estimated by applying emission factors to the total amount of distillate
fuel oil used by line haul locomotives. Fuel usage was obtained from publically available Class I
Railroad Annual Reports (Form R-l). The R-l reports are submitted to the Surface Transportation Board
annually and include financial and operations data to be used in monitoring rail industry health and
identifying changes that may affect national transportation policy. Additionally, each railroad provided
fleet mix information that allowed ERTAC Rail to calculate railroad-specific emission factors. Weighted
Efs per pollutant for each gallon of fuel used (gm/gal or lbs/gal) were calculated for each Class I railroad
fleet based on its fraction of line haul locomotives at each regulated Tier level. EPA emission factors
were used for PM2.5, S02, and NH3.
The weighted emission factors were then applied to the link-specific fuel consumption to obtain
emissions for each rail segment. Given the confidentiality of the activity data, emissions for criteria
pollutants were provided to EPA by ERTAC Rail by county for Class I line haul. Class II/III rail was
provided by railroad company and county.
4.4.5.2 Rail Yard Criteria Emissions Estimates
Rail yard locations were identified using a database from the Federal Railroad Administration. Criteria
pollutant emissions were estimated by applying emission factors to the total amount of distillate fuel
used by locomotives. Each railroad provided fleet mix information that allowed ERTAC to calculate
railroad-specific emission factors. The company-specific, system wide fleet mix was used to calculate
weighted average emissions factors for switchers operated by each Class I railroad. EPA emission
factors were used for PM2.5, S02, and NH3.
R-l report-derived fuel use was allocated to rail yards using an approximation of line haul activity data
within the yard. These fuel consumption values were further revised by direct input from the Class I
railroads. The weighted emission factors were then applied to the yard-specific fuel consumption to
obtain emissions for each yard. Since the rail yard inventory was based on publically-available data, the
final criteria emission estimates were provided per rail yard.
4.4.5.3 Hazardous Air Pollutant Emissions Estimates
HAP emissions were estimated by applying speciation profiles to the VOC or PM estimates. Since
California uses low sulfur diesel fuel and emission factors specific for California railroad fuels were
available, calculations of California's emissions were done separately from the other states. HAP
estimates were calculated at the yard and link level, after the criteria emissions had been allocated.
4.4.5.4 Allocation to Rail Segments and Yards
Class I line haul emissions were allocated to rail segments (GIS line shapes) based on segment-specific
railroad traffic data (ton miles) obtained from the Department of Transportation. Because Class II/III
railroads are less likely to use rail segments that are heavily traveled by Class I railroads, the activity-
based approach used for Class I lines was not appropriate. Instead, Class II/III line haul emissions were
allocated to rail segments using segment length as a proxy. The dataset "railway_20110921.zip"
contains the shapefiles used (see Section 8.1 for access information).
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Rail yard point source emissions were developed based on yard name and ownership properties. As a
result, unique yards needed to be identified and emissions summed. 753 unique yards were identified
nationwide. This is known to be an underestimate of the total number of yards due to limited available
data. Once the unique yards were identified and criteria emissions were summed at the yard, the PM and
VOC-based HAP speciation profile was applied to estimate HAP emissions at each yard.
4.4.6 Summary of quality assurance methods
EPA and Agency submitted emissions were compared at shape, state, and county to EPA default values.
All of the EPA rail emissions were allocated to shape files in the EPA dataset. Where agencies
submitted as county-level records in the same counties as the shapes, EIS could not correctly merge the
EPA and agency data. Therefore, agencies were asked to resubmit rail emissions in shapes.
Findings
• The 2008 NEI uses only agency emissions in counties where the agency submissions matched to
the same shape/SCC/pollutant combinations such that they had priority over EPA data, or where
emissions occur in counties with no shape IDs. Several agencies that submitted in shape files
included more or fewer shapes (or counties with no shape files) than the EPA dataset. When
fewer shapes were submitted, the EPA data were still used for those shapes and the state data
were used for the shapes submitted.
• Most agencies included the same or fewer SCCs than the EPA dataset. Several agencies included
passenger and commuter (SCC =2285002008 and 2285002009, respectively), a known omission
in the EPA dataset, but thought to be a far smaller contributor to emissions than line haul. Where
states submitted passenger and commuter rail emissions, they were included in the final NEI.
• New Hampshire submitted CAPs only. For SCC 2285002007, the Sullivan County, NH (FIP=
33019) sum for primary PM10 and primary PM2.5 are about 50% less than EPA's. Since the
NEI uses EPA's PM HAPs, the HAP sum will be greater than the PM also in the NEI. This
phenomenon may occur elsewhere at a shape ID level.
• EPA put rail yards in point format for SCC=28500201. However EPA acknowledges that the
coverage is not complete due to limited activity data available. EPA did not attempt to reconcile
with agency submissions for nonpoint rail yards (SCC= 2285002010). Where agencies
submitted nonpoint rail yards in the same counties as EPA point rail yards, there is a potential for
double counting. This happens in California, DC, Maryland and Oregon. In the counties where
this occurs it is not known if the nonpoint county emissions reported by the States have been
adjusted to exclude the point sources reported by EPA.
• Most agencies either did not submit HAPs or did not submit all the HAPs EPA used, and
therefore EPA data will appear in the NEI for any HAPs in the EPA dataset and not in agency
data.
• Agency rail emissions that were not in shape files but occur in counties with EPA shape
estimates were overwritten with 0 emissions records if the agencies did not resubmit, to avoid
duplication. Submitted rail emission were removed or overwritten for the following agencies:
California, Connecticut, DC, Idaho, Illinois, Maricopa County, Maryland, Oregon, Louisville
and Washoe.
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• An EPA correction file overwrites agency data to 0 tons emissions where unspeciated chromium
(pollutant code = 7440473) were submitted in Texas and Kansas.
• Where agencies submitted CAPs only, EPA data fills in the missing HAP. This is problematic
when the resultant 2008NEI selection may have VOC and PM is less than the EPA VOC or PM,
and there may be a mathematical inconsistency between VOC HAPs and PM HAPs with the
criteria pollutants. There will also be an inconsistency because of the different approaches used
to compute CAPs and HAPs.
• 2008 NEI emissions can be greater than both the EPA and the agency estimates when either the
agency or EPA dataset has populated sets of counties or shapes or has different SCCs, such that
the 2008 NEI has more SCCs or shapes than either the EPA or agency datasets.
• Review of Texas rail data (SCC=2285002006) shows that emissions of all pollutants in all but
the most industrial counties is suspiciously low. Texas was notified 12/2011 and did not choose
to update the data, though they acknowledged the emissions values are low.
• The EPA dataset does not include tribal areas. Therefore the 2008 NEI is equal to the tribal
submission only, and therefore will not have consistent SCCs and pollutants as are present in
counties.
4.5 Nonroad Equipment - Diesel, Gasoline, and other
Although "nonroad" is used to refer to all transportation sources that are not on-highway, these EIS
sectors and this section address nonroad equipment other than locomotives, aircraft, or commercial
marine vehicles.
4.5.1 Sector Description
This section deals specifically with emissions processes calculated by the EPA's NONROAD model and
the OFFROAD model approved for use by California. They include nonroad engines and equipment,
such as: lawn and garden equipment, construction equipment, engines used in recreational activities,
portable industrial, commercial, and agricultural engines.
The NMIM is EPA's consolidated mobile emissions estimation system that allows EPA to produce
nonroad mobile emissions in a consistent and automated way for the entire country. EPA encouraged
agencies to submit NMIM inputs to the EIS for the 2008 NEI for inclusion in the National County
Database (NCD) . The NCD contains all the county-specific information needed to run NONROAD. It
also contains the ratios that are applied to NONROAD outputs to estimate emissions of HAPs,
dioxins/furans, and some metals. NMIM was run for both on-road and nonroad emissions for the 2008
NEI, but on-road emissions were subsequently replaced by the newer MOVES model estimates
described in section 4.6.
4.5.2 Sources of data overview and selection hierarchy
EPA ran NMIM for nonroad sources twice for estimates used in the final 2008 NEI. EPA developed a
default NCD and replaced its tables and external files with agency data that were submitted by June 1,
2010. Then EPA ran NMIM again to include additional submittals that arrived by December 1, 2010.
For more information on what information agencies submitted in their NCD files and how EPA ran the
NONROAD model, see Section 4.5.4 and the more detailed EPA documentation (E.H. Pechan, 2011).
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Agencies also submitted nonroad emissions. In addition to EPA's estimates, the agencies included in
Table 49 submitted inputs and/or emissions to the 2008 NEI.
Table 49: Agency Submittals of NONROAD inputs and nonroad emissions
NONROAD
Submitted
inputs submitted
CAP or HAP
Agency
by
emissions
Arkansas Department of Environmental Quality
June
DC-District Department of the Environment
June, December
California Air Resources Board
CAP HAP
Delaware Department of Natural Resources and Environmental
Control
CAP
Eastern Band of Cherokee Indians
December
CAP HAP
Georgia Department of Natural Resources
June
Hawaii Department of Health Clean Air Branch
June
Idaho Department of Environmental Quality
CAP HAP
Illinois Environmental Protection Agency
CAP
Kansas Department of Health and Environment
CAP
Kootenai Tribe of Idaho
CAP
Little River Band of Ottawa Indians, Michigan
CAP
Louisville Metro Air Pollution Control District
CAP
Louisiana Department of Environmental Quality
November
Maine Department of Environmental Protection
June
Makah Indian Tribe of the Makah Indian Reservation
CAP
Maricopa County Air Quality Department
CAP
Maryland Department of the Environment
June
Massachusetts Department of Environmental Protection
December
Metro Public Health of Nashville/Davidson County
CAP HAP
Michigan Department of Environmental Quality
December
Minnesota Pollution Control Agency
December
Missouri Department of Natural Resources
June, December
Nevada Division of Environmental Protection
December
New Hampshire Department of Environmental Services
June
Nez Perce Tribe
CAP
New York State Department of Environmental Conservation
CAP
North Carolina Department of Environment and Natural
Resources
June
Ohio Environmental Protection Agency
December
Omaha Tribe of Nebraska
CAP
Pennsylvania Department of Environmental Protection
June
CAP
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
CAP
South Carolina Department of Health and Environmental Control
December
Tennessee Department of Environmental Conservation
December
Texas Commission on Environmental Quality
CAP HAP
Utah Division of Air Quality
CAP
Vermont Department of Environmental Conservation
June
Virginia Department of Environmental Quality
June
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Agency
NONROAD
inputs submitted
by
Submitted
CAP or HAP
emissions
Wisconsin Department of Natural Resources
December
The 2008 NEI merged EPA and agency data according to the hierarchy described by Table 50. Agency
emissions were used except where they were determined to result in double counting or suspect
pollutant inclusion. More detail on this in the sections that follow.
Table 50: 2008 NEI Non-road equipment selection hierarchy
Priority
Dataset Name
Dataset Content
1
EPA Correction Dataset - Nonroad
Overwrites submitted emissions that do not
conform to pollutant and emission types expected
2
Responsible Agency Dataset
Submitted nonroad emissions
3
EPA Nonroad using NCD20101201
Includes NMIM NONROAD inputs received after
June 1 and before November 30, 2010
4
EPA Nonroad using NCD20100602
Includes NMIM NONROAD inputs received
before June 1, 2010 and EPA default inputs for
remaining counties
Exception: California
1
EPA Correction Dataset - Nonroad
Overwrites submitted emissions that do not
conform to pollutant and emission types expected
2
Responsible Agency Dataset
CA Submitted nonroad emissions
4.5.3 Spatial coverage and data sources for the sector
Nonroad equipment emissions are included in every state, DC, Puerto Rice, and the Virgin Islands.
[Placeholder for CAP and HAP maps]
4.5.4 EPA-developed NMIM-based nonroad emissions data
For nonroad equipment, EPA requested that S/L/T agencies submit model inputs for use in running
NMIM to produce NONROAD model emissions for 2008. After EPA completed the NMIM runs for
areas that submitted data, EPA then loaded the resulting data into the EIS for S/L/T agency review.
More information on these emissions is provided below and the full documentation (E.H. Pechan, 2011).
The EPA developed the EPA 2008 nonroad data in multiple phases. In the first phase, EPA ran NMIM
for year 2008 for the entire country. This NMIM run used EPA default modeling inputs incorporated
into "NCD20090327" (the naming convention reflects the NCD's lock-down date). These default inputs
represented EPA's initial assumptions concerning key modeling parameters such as fuel blends, ambient
temperatures, and on-road VMT. The 2008 nonroad source emission estimates from this phase were
listed in the EIS under the dataset descriptions "EPA Nonroad using NCD20090327". The EPA then
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discovered a need to update some of the fuel parameter values from the assumptions used in
NCD20090327. Consequently, EPA developed an updated NCD reflecting the revised values, which
was posted in EIS as "EPA NMIM Activity NCD20090531This NCD was then posted for
review/update by S/L/T agencies.
For the second phase, EPA set a deadline of June 1, 2010 for agencies to submit changes to the
NCD20090531 values for their areas. After obtaining any necessary clarification on these changes from
S/L/T agencies, EPA modified the NCD to reflect S/L/T updates, ran NMIM for 2008 for the entire
country, and processed annual NMIM emissions output for loading into the EIS. This 2008 nonroad
source NEI development phase resulted in the EIS emissions dataset "EPA Nonroad using
NCD20100602".
In the third and final phase, agencies were afforded the opportunity to review EPA's emission estimates
and provide additional revisions to NMIM inputs. After updating the NCD to reflect these revisions,
EPA ran NMIM a final time and produced the EIS emissions dataset "EPA Nonroad using
NCD20101201". This dataset only covers the geographical areas that submitted changes between July
2010 and November 2010.18 The resulting NMIM county database that includes all of the data used to
produce all of the final EPA data used is available in the file "ncd20101201.zip" (see Section 8.1 for
access information).
4.5.5 Summary of quality assurance methods
Quality assurance steps performed on EPA's estimates are described in the documentation (E.H. Pechan,
2011).
EPA also performed QA steps on the agency-submitted data. We compared state and county EPA
defaults, agency submittals and selection results by (1) included pollutants, SCCs, SCC-Emission Types
(nonroad emission types are R=refueling, E=evap, X=exhaust), and (2) emissions summed to agency
level.
Findings
• Although the agency data are assumed to better reflect state- or county-specific inputs, results
can be significantly different for key pollutants, such as NOx, that will have an impact on ozone
and PM formation in and around the state.
• Several agencies had only 1 or 2 of the 3 emission types: X (exhaust), E (evaporative), or R
(refueling). The 2008 NEI selection results in higher emissions than EPA or agency estimates
where SCC/emission type combinations are not congruent, because the remaining EPA estimates
are included for any combinations not already in agency data. This is particularly the case for
VOC and volatile HAPs where all agency emissions are reported as X (exhaust) and EPA
estimates for R (refueling) and E (evaporative) values are added in the 2008 NEI.
18 Although Lincoln County Nebraska data were provided in time for the June submittal deadline, EPA uploaded the NMIM
results in the NCD20101201 dataset rather than the NCD20100602 dataset.
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o Examples:
¦ VOC in Utah is 3% greater in the 2008 NEI than in the agency submittal, and
30% greater in Jefferson Co, Kentucky; due in part to addition of remaining
emission types in EPA dataset.
o Based on EPA analysis of the emissions level, EPA changed every record submitted by
Pennsylvania from emission type "E" (evaporative) to "X" (exhaust).
o The dataset "EPA Correction Dataset - Nonroad" zeroes out agency data where pollutant
code/emission type combinations do not exist in EPA's dataset (e.g., evaporative PM)
because they are not valid combinations in the NONROAD model.
• Some agencies may have overwritten a previous submittal with the resubmission of a single
pollutant.
o Examples
¦ Idaho submittal includes nonzero records only for primary PM10
¦ Louisville Metro submittal only includes S02
o In these cases, the agency-submitted data has been included only for the pollutants
submitted in the last submission, and EPA data were used for the other pollutants
• When either the agency or EPA datasets have different SCCs or more SCC/ emission type
combinations than the other, the 2008 NEI will have more SCCs or SCC/emission types than
either the EPA or agency datasets does alone. While this occurred in both Texas and Idaho in
version 2, it was corrected in version 3 for Texas by a resubmittal of the entire nonroad dataset
between versions 2 and 3. The only EPA gapfilling done in Texas for version 3 was for mercury
and arsenic (162 SCCs), and NH3 (22 SCCs) where not reported by Texas in v3. The possible
adverse impacts of adding emissions due to this issue do not outweigh the benefits of using the
state data, which is often significantly different from EPA data. The SCCs that EPA's dataset
include and Idaho's which did not are shown in Table 51.
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Table 51: Nonroad SCCs included in 2008 NEI that were not in S/L/T agency submittals
State
SCC
Description
ID
2268010010
CNG Industrial Equipment Other Oil Field Equipment
ID
2265007015
Off-highway Vehicle Gasoline, 4-Stroke Logging Equipment Forest Eqp -
Feller/Bunch/Skidder
ID
2270010010
Off-highway Vehicle Diesel Industrial Equipment Other Oil Field
Equipment
ID
2265010010
Off-highway Vehicle Gasoline, 4-Stroke Industrial Equipment Other Oil Field
Equipment
ID
2265007010
Off-highway Vehicle Gasoline, 4-Stroke Logging Equipment Shredders : 6
HP
ID
2270007015
Off-highway Vehicle Diesel Logging Equipment Forest Eqp -
Feller/Bunch/Skidder
ID
2260007005
Off-highway Vehicle Gasoline, 2-Stroke Logging Equipment Chain Saws : 6
HP
• Most agencies did not submit HAPs, and therefore the data in the2008 NEI came from the EPA-
created data. We considered whether including EPA data for HAPs but state and/or state plus
EPA data for CAPs could cause any problems. Since the 2008 NEI for criteria VOC and PM is
always larger than the EPA VOC or PM for any state, we can be assured that the 2008 NEI
criteria VOC will always be larger than the sum of the 2008 NEI VOC HAPs, and that the 2008
NEI criteria PM will always be larger than the sum of the 2008 NEI PM HAPs. Nevertheless,
there is still an inconsistency between CAPs and HAPs because of the different approaches used
to compute each of them.
• The California submittal differed dramatically from EPA dataset in SCC and pollutant coverage
due to being estimated with a different model. The two data sources could not be merged
without numerous double counts. Only California data were used in this case. The 2008 NEI in
California does not agree well with the rest of the country.
o Example:
¦ California nonroad data does not include NH3, and therefore it is missing from
the 2008 NEI as well
• The EPA dataset does not include tribal areas. Therefore the 2008 NEI contains only tribal
submission data and includes only the SCCs and pollutants submitted by tribes, which can be
different from the county data.
• Agencies emissions are likely to capture local scale details that EPA data may not, particularly
because most the agencies submitting emissions did not submit input data. Some agency data
differ significantly from EPA's.
o Example:
¦ Delaware and New York S02 are each about 300% higher than EPA, perhaps
indicating higher sulfur fuel usage than EPA assumed.
4.6 On-road - all Diesel and Gasoline vehicles
This section includes the description of four EIS sectors:
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• Mobile - On-road - Diesel Heavy Duty Vehicles
• Mobile - On-road - Diesel Light Duty Vehicles
• Mobile - On-road - Gasoline Heavy Duty Vehicles
• Mobile - On-road - Gasoline Light Duty Vehicles
They are treated here in a single section because the methods used are the same across all sectors.
4.6.1 Sector Description
The four sectors for on-road mobile sources include emissions from motorized vehicles that are
normally operated on public roadways. This includes passenger cars, motorcycles, minivans, sport-
utility vehicles, light-duty trucks, heavy-duty trucks, and buses. The sectors include emissions from
parking areas as well as emissions while the vehicles are moving.
SCCs starting with 22010 define the light duty gasoline vehicles including motorcycles, with the
exception of SCCs starting with 220107, which define the heavy duty gasoline vehicles. SCCs starting
with 22300 define the light duty diesel vehicles, with the exception of SCCs starting with 223007 that
define the heavy duty diesel vehicles.
The 2008 NEI vl and past NEIs included emissions from the MOBILE6 model. The 2008 NEI v2 and
v3 are the first NEI to include emissions from the MOVES model.
4.6.2 Sources of data overview and selection hierarchy
All 2008 NEI on-road estimates were calculated by EPA using MOVES, except in California. Table 52
shows the selection hierarchy
Table 52: 2008 NEI on-roac
mobile selection hierarchy
Priority
Dataset Name
Dataset Content
1
2008 EPAMOBILE
EPA's MOVES20 lOb-based estimates
Exception: California
1
EPA Correction Dataset - Onroad
Overwrites submitted emissions that do not
conform to pollutant and emissions types expected
2
Responsible Agency Dataset
Submitted on-road emissions
California submitted emissions to the NEI based on the EMFAC model, which is a separately EPA-
approved model to be used only in California. Because California's emissions were calculated with a
different model, the emissions are not congruent with the rest of the country in terms of SCCs used,
pollutants present, and emission type coverage.
During the 2008 NEI development cycle for on-road mobile emissions, EPA requested that S/L/T
agencies submit NMIM inputs for use in an EPA 2008 NEI NMIM run to generate MOBILE6-based
emissions. At the start of the 2008 NEI cycle, the MOVES model had not yet been released for criteria
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pollutants and the input formats were not stable, and so it was not possible for EPA to collect the
MOVES input formats or MOVES-based emissions. A summary of the NMIM input submittals and
EPA's conversion of VMT to inputs is described in Section 3.2.3 of the project documentation for
EPA's mobile data (E.H. Pechan, 2011). EPA used the NMIM inputs to update the EPA NMIM input
database. If an agency submitted on-road emissions (which includes VMT data) rather than NMIM
inputs, then EPA compiled the VMT from this submittal for use in EPA's NMIM run. EPA used the
NMIM database to create 2008 on-road emissions using NMIM, which were used in version 1 and 1.5 of
the 2008 NEI along with any emissions submitted by agencies that did not provide NMIM inputs.
After the formal 2008 NEI submission period had ended, EPA provided S/L/T agencies the opportunity
to provide MOVES inputs. A few states provided these data, which were used in subsequent data
development steps described below. No agencies submitted MOVES-based emissions estimates. EPA
converted the NMIM database for input to MOVES and then overlaid these data with the MOVES
inputs provided by some states. The resulting database was the starting point for the MOVES-based
emissions described below, and as described, EPA continued to make changes to the database prior to
running MOVES for the NEI. The MOVES databases did not change between 2008 v2 and 2008 v3.
Several tribes submitted data based on the MOBILE6 model, but these data were not included in the NEI
selection because of the switch to a MOVES-based inventory. The tribal data are available in EIS.
These tribes were: the Kootenai Tribe of Idaho, the Eastern Band of Cherokee Indians, the Nez Perce
Tribe, the Northern Cheyenne Tribe, the Pueblo of Laguna, New Mexico, and the Shoshone-Bannock
Tribes of the Fort Hall Reservation of Idaho.
4.6.3 Spatial coverage and data sources for the sector
The on-road mobile sectors include emissions in every state, Puerto Rico, and the US Virgin Islands.
[Placeholder for CAP and HAP maps and associated observations]
4.6.4 EPA-developed on-road mobile emissions data for the continental U.S.
For the 2008 NEI, EPA estimated emissions for every county in the U.S. except for California. For the
continental U.S., we used a modeling framework that took into account the strong temperature
sensitivity of the on-road emissions. Specifically, we used county-specific inputs and tools that
integrated the MOVES model with the SMOKE19 emission inventory model to take advantage of the
gridded hourly temperature information available from meteorology and air quality modeling. This
integrated "SMOKE-MOVES" tool was developed by EPA in 2010 and is in use by states and regional
planning organizations for regional air quality modeling. SMOKE-MOVES requires emission rate
"lookup" tables generated by MOVES that differentiate emissions by process (running, start, vapor
venting, etc.), vehicle type, temperature, speed, hour of day, etc. To generate the MOVES emission
rates that could be applied across the U.S., EPA used an automated process to run MOVES to produce
emission factors by temperature and speed for 146 "representative counties," to which every other
19 SMOKE v3.1 was used for the 2008 NEI v3.
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county could be mapped, as detailed below. Using the MOVES emission rates, SMOKE selected
appropriate emissions rates for each county, hourly temperature, SCC, and speed bin and multiplied the
emission rate by activity (VMT (vehicle miles travelled) or vehicle population) to produce emissions.
These calculations were done for every county, grid cell, and hour in the continental U.S. and
aggregated to produce continental U.S. emissions. The MOVES "RunSpec" files (that tells MOVES
what to run for each representative county) are available in the file "RepCounty_Runspecs.zip" (see
Section 8.1 for access information). A full listing of datasets available as supporting information for the
on-road MOVES runs is available in Section 8.1 and these are referenced in the subsections below.
EPA used a different approach for states and territories outside the lower 48 states. For Alaska, Hawaii,
Puerto Rico and the Virgin Islands, EPA ran MOVES in "inventory mode" for each county and month,
using county-specific inputs. More information is provided Section 4.6.5.
SMOKE-MOVES can be used with different versions of the MOVES model. For the 2008 NEI v3,
EPA used the latest publically released version: MOVES2Q10b. This version of the model included
improvements to handling of refueling and extended idle emissions, addressed errors in the
MOVES2010a emission rates for ammonia (NH3), nitrous oxide (NO) and nitrogen dioxide (N02), and
included the capability to model additional hazardous air pollutants. Details on the changes to air toxics
are detailed in a separate technical report (US EPA, 2012). See the MOVES website for full
documentation on MOVES2010b. Using SMOKE-MOVES for creating the NEI requires numerous
steps, as described in the sections below:
• Determine which counties will be used to represent other counties in the MOVES runs (see
Section 4.6.4.1)
• Determine which months will be used to represent other month's fuel characteristics (see
Section 4.6.4.2)
• Create MOVES inputs needed only for MOVES runs (see Sections 4.6.4.3 and 4.6.4.4 ).
MOVES requires county-specific information on vehicle populations, age distributions, and
inspection-maintenance programs for each of the representative counties.
• Create inputs needed both by MOVES and by SMOKE, including a list of temperatures and
activity data (see Sections 4.6.4.5 and 4.6.4.6).
• Run MOVES to create emission factor tables (see Section 4.6.4.7)
• Run SMOKE to apply the emission factors to activities to calculate emissions (see Section
4.6.4.8)
• Aggregate the results at the county-SCC level for the NEI (see Section 4.6.4.9)
4.6.4.1 Representative counties
Although EPA compiles county-specific databases for all counties in the nation, actual county-specific
data is rare. Instead, much of our "county" data is based on state-wide estimates or national defaults.
For the NEI, rather than explicitly modeling every county in the nation, we have done detailed modeling
for some counties and less detailed estimates for the other counties. This approach dramatically reduces
the number of modeling runs required to generate inventories and still takes into account important
differences between counties.
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In this approach, we group counties that have similar properties that would result in similar emission
rates. We explicitly model only one county in the group (the "representative" county) to determine
emission rates. These rates are then used in combination with county specific activity and meteorology
data, to generate inventories for all of the counties in the group. The grouping of counties was based on
several characteristics as summarized in Table 53 below.
Table 53: Characteristics for Grouping Counties
County Grouping Characteristic
Description
PADD
Petroleum Administration for Defense Districts (PADDs).
PADD 1 is divided into three sub-PADD groupings and
each sub-group is treated as a separate PADD (la, lb and
lc). Each state belongs to a PADD and all counties in any
state are within the same PADD.
Fuel Parameters
Weighted average gasoline fuel properties for January and
July 2008, including RVP, sulfur level, ethanol fraction
and percent benzene
Emission Standards
Some states have adopted California highway vehicle
emission standards or plan to adopt them. Since
implementation of the standards varies, each state with
California standards is treated separately.
Inspection/Maintenance Programs
Counties were grouped within a state according to whether
or not they had an inspection/maintenance (I/M) program.
All I/M programs within a state were considered as a
single program, even though each county may be
administered separately and have a different program
design.
Altitude
Counties were categorized as high or low altitude based
on the criteria set forth by EPA certification procedures
(4,000 feet above sea level).
Fleet Age
The weighted average age of passenger cars.
Total VMT
County total vehicle miles traveled.
The result is a set of 146 county groups with similar fuel, emission standards, altitude, I/M programs and
fleet age. For each group, the county with the highest total VMT was chosen as the representative
county for the group (this VMT is not used to calculate the emissions however). The representative
counties for the 2008 NEI v3 match those that were used for the 2007v5 platform, but the v3
representative counties have a different mapping from what was used in the 2008 NEI v2. A summary
of the representative counties is available in the spreadsheet included in "MCXREF_2008v3.zip" and
the MOVES County Database Manager databases are available in the file "RepCounty_Counties.zip"
(see Section 8.1 for access information).
For each county group, SMOKE-MOVES generated a set of emission rates that varied by SCC (vehicle
type and road type), fuel, speed, temperature, and humidity; thus, we did not need to consider the fleet
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mix, fuel, speed, temperature range, or humidity in our grouping characteristics. This greatly increased
the number of counties that can be grouped and reduced the number of MOVES runs required.
4.6.4.2 Fuel months
The concept of a fuel month is used to indicate when a particular set of fuel properties should be used in
a MOVES simulation. Similar to the reference county, the fuel month reduces the computational time of
MOVES by using a single month to represent a set of months. Because there are winter fuels and
summer fuels, EPA used January to represent October through April and July to represent May through
September. For example, if the grams/mile exhaust emission rates in January are identical to February's
rates for a given reference county, and temperature (as well as other factors), then we use a single fuel
month to represent January and February. In other words, only one of the months needs to be modeled
through MOVES. The hour-specific VMT, temperature and other factors for February are still used to
calculate emissions in February, but the emission factors themselves do not need to be created since one
month can represent the other month sufficiently. The fuel months used for each representative county
are available in the spreadsheet included in "MFMREF_2008v3.zip" (see Section 8.1 for access
information).
4.6.4.3 Fuels
Although state-submitted NMIM and MOVES input data may have included information about fuel
properties, the MOVES runs for the 2008 NEI were run using a set of fuel properties for each county in
2008 generated by EPA. We developed these data using a combination of purchased fuel survey data,
proprietary fuel refinery information and known federal and local regulatory constraints.
The 2008 fuels generated by EPA (dated 9/23/2011) were developed by interpolating between a 2005
reference fuel supply and a 2017 fuel supply that had been developed for use in EPA regulation
development, using year-by-year gasoline fuel property regulations (such as sulfur and benzene control)
and projected national ethanol penetration levels per year based on the 2011 Ethanol Industry Outlook
(Renewable Fuels Association, 2011). EPA made adjustments to align 10% ethanol (E10) fuel
properties in interpolated years.
The following list provides a step-by-step outline of the interpolation steps applied to create the 2008
fuel supply database.
1) Methyl tertiary butyl ether, ethyl tert-butyl ether, and tertiary amyl methyl ether fuel blends were
removed from the 2005 fuel supply and replaced with appropriate E10 (a mixture of 10% ethanol
and 90% gasoline) levels and properties found from refinery modeling.
2) Reformulated Gasoline (RFG) areas were adjusted to contain only E10 blends and associated
fuel properties.
3) Ethanol blends from 2005 were removed and replaced with appropriate properties found from
the updated refinery modeling used to generate the 2017 fuel supply.
4) Gasoline sulfur levels were reduced to 30 ppm for all counties outside of the Geographic Phase-
in Area (GPA). Counties within the GPA remain at the sulfur levels found in the 2005 reference
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case. The counties in the GPA are defined in the Code of Federal Regulations (CFR Title 40
Section 80.215).
5) E10 market share was adjusted by county to a minimum market share of 45%. Counties with
market share above 45%, including RFG counties, remain at the higher market share.
6) Diesel fuel was carried over from the 2005 fuel supply.
4.6.4.4 Other local MOVES inputs
In addition to fuels and the information also needed by SMOKE (in the following sections), MOVES
also required inputs such as age distribution and I/M program descriptions for each of the representative
counties. At the county level, these inputs provide an opportunity to assure that the model properly
accounts for the most recent available local data. When these data were available from the state-
supplied NMIM inputs, we converted the NMIM data (version NCD20101201) for use in MOVES.
EPA manually imported the 2008 data from Delaware and Utah into a MOVES format. Only data
related to VMT, vehicle populations, speed distributions and age distributions were imported. Fuel data
submitted by states was not used for the 2008 NEI in order to use the latest EPA estimates and make
selecting representing counties easier. Similarly, meteorological data from states were not used, since
the NEI calculations used the SMOKE generated meteorological data instead. Other state data from the
NMIM data format were not used because of the project schedule and resource constraints.
In the few cases where MOVES input data were provided, we used that data. At their request, we
converted 2007 data (already in MOVES format) submitted by Florida and Shelby County, Tennessee
for use in calendar year 2008, augmenting with 2008 calendar year VMT, population and average speed
estimates. Extensive 2008 data were provided by Texas, but these data were not easily converted to
MOVES format, so EPA did not have time to include these data. EPA also received additional data
from Connecticut, but the data were received too late to be included. When state-supplied MOVES data
were not available, we used MOVES databases created from the NMIM database for 2008 discussed
earlier.
When state-supplied data were not available either in the 2008 NMIM database or from subsequent
submissions, we used MOVES defaults. In the state-provided data, EPA identified errors in age
distributions provided for two counties in Arkansas (FIPS codes 05015, 05143) which resulted in
anomalous results. Those age distributions were replaced with default distributions prior to the final run
of MOVES for the NEI.
For the continental U.S., all of these MOVES inputs were organized by representative counties. This
means that only the counties used to represent other counties had specific information for the MOVES
runs. As listed in Section 8.1, the MOVES input data for the representative counties are available in
several sets of files provided with the supporting data for this documentation.
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4.6.4.5 Temperature and humidity
Ambient temperature can have a large impact on emissions. Low temperatures are associated with high
start emissions for many pollutants. High temperatures are associated with greater running emissions
due to the higher engine load of air conditioning. High temperatures also are associated with higher
evaporative emissions.
The 12-km gridded meteorological input data for the entire year of 2008 covering the continental United
States were derived from simulations of version 3.1 of the Weather Research and Forecasting Model.
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 Meteorology-Chemistry Interface Processor (MOP) version 3.6 was used as the
software for maintaining dynamic consistency between the meteorological model, the emissions model,
and air quality chemistry model.
EPA applied the SMOKE-MOVES tool Met4moves to the gridded, hourly meteorological data (output
from MCIP) to generate a list of the maximum temperature ranges, average relative humidity, and
temperature profiles that are needed for MOVES to create the emission-factor lookup tables.
"Temperature profiles" are arrays of 24 temperatures that describe how temperatures change over a day,
and they are used by MOVES to estimate vapor venting emissions. The hourly gridded meteorological
data (output from MCIP) was also used directly by SMOKE (Section 4.6.4.7).
The temperature lists were organized based on the representative counties and fuel months as described
in Sections 4.6.4.1 and 4.6.4.2, respectively. Temperatures were analyzed for all of the counties that are
mapped to the representative counties, i.e., for the county groups, and for all the months that were
mapped to the fuel months. EPA used Met4moves to determine the minimum and maximum
temperatures in a county group for the January fuel month and for the July fuel month, and the minimum
and maximum temperatures for each hour of the day. Met4moves also generated idealized temperature
profiles using the minimum and maximum temperatures and 10 degree intervals. In addition to the
meteorological data, the representative counties and the fuel months, Met4moves uses spatial surrogates
to determine which grid cells from the meteorological data to collect temperature and relative humidity
statistics. For example, if a county had a mountainous area with no roads, this would be excluded from
the meteorological statistics.
The treatment of humidity was simpler. Met4moves calculated an average day-time (6 am to 6 pm)
relative humidity for the county group for the months mapped to July and for the months mapped to
January. The humidity was also averaged over the grid cells intersecting the counties in the county
group. When the emission factors are applied by SMOKE (Section 4.6.4.7), the appropriate (July or
January) humidity was used for all runs of the county group.
Met4moves can be run in daily or monthly mode for producing SMOKE input. In monthly mode, the
temperature range is determined by looking at the range of temperatures over the whole month for that
specific county. Therefore, there is one temperature range per county per month. While in daily mode,
the temperature range is determined by evaluating the range of temperatures in that county for that day.
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The output for the daily mode is one temperature range per county per day and is a more detailed
approach for modeling the vapor venting emissions. EPA ran Met4moves in daily mode for 2008 NEI.
The resulting temperatures provided to the representative counties are available in the file
"RepCounty_temperatures.zip" (see Section 8.1 for access information). The gridded, hourly
temperature data used are publicly available only upon request and with provision of a disk media to
copy these very large datasets (contact info.chief@epa.gov).
4.6.4.6 VMT, vehicle population, and speed
SMOKE requires county-specific VMT, population, and average speed by SCC to calculate the gridded
or county emissions. Unlike the other inputs that are needed just for the representative counties, these
inputs are needed for every county. When available, VMT and vehicle population estimates were
obtained from data submitted by states. The state submitted input data are discussed in Section 4.6.4.4.
As described above, most of the VMT information used was converted to a MOVES format from data
originally supplied to EPA as NMIM input data. Data obtained from the NCD did not contain vehicle
population data. When population data were not available, the vehicle population data were derived
from the state supplied VMT data using methodologies provided in MOVES guidance for that purpose.
The average speeds provided to SMOKE for each county were derived from the default national average
speed distributions found in the default MOVES2010b database AvgSpeedDistribution table. These
average speeds are the average speeds developed for the previous EPA highway vehicle emission factor
model, MOBILE6. EPA used the MOVES distribution of average speeds for each hour of the day for
each road type to calculate an overall average speed for each hour of the day. These hourly average
speeds were weighted together using the default national average hourly VMT distribution found in the
MOVES default database HourlyVMTFraction table, to calculate an average speed for each road type.
This average speed by road type was provided to SMOKE for each county.
SMOKE requires VMT by county and SCC, but MOVES is not based on the traditional NEI SCCs.
Because the VMT in each MOVES county database is by the broader category of "HPMSVtype", it was
necessary to allocate this VMT to the SCCs. We did this by running MOVES at the national level for
2008 with MOVES defaults. Then we used the activity output to determine default ratio of sourcetype
VMT to HPMSVtype VMT. We also used this output to determine ratios of sourcetype/fuel type to
sourcetype VMT. We used the NCD20110908.baseyearvmt to determine "roadtype ratios" i.e.,
allocation from MOVES roadtypes to SCCroadtypes by county and SCCvtype (same as P5vclass).
Because some ratios were missing, we used ratios for cars (vtype=l) to fill in any missing ratios . Next
we applied these ratios and the MOVES2010b (MOVESdb20121030 sccvtypedistribution for model
year 2008 to allocate VMT to SCCVtype. And we used roadtype ratios previously derived from NCD to
allocate countyroadtype VMT to SCC roadtypes. Finally, we used county-specific monthvmtfractions
to allocate VMT to each month.
Vehicle populations also had to be allocated to SCC. We started with state-provided (or default)
MOVES inputs on vehicle populations by county. These were provided by vehicle sourcetype. We had
to allocate this population by fueltype and to the various SCC categories. To do this, we ran MOVES at
the national level for 2008 with default inputs. This generated activity by sourcetype and fuel type, so
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we could determine the default split of each sourcetype between gasoline and diesel fueled- vehicles.
Using this ratio and the MOVES2010b (MOVESdb20121030 sccvtypedistribution for model year 2008,
we allocated the MOVES default population to SCC.
The MOVES MySQL databases that include the VMT and vehicle population used for the representative
counties are listed in Section 8.1. The SMOKE input VMT, vehicle population, speed data, and hourly
speed profiles used to estimate emissions for every county are the same as what was used in 2008 NEI
v2 and are available in the files "VMT_NEI_2008_updated2_18jan2012_v3.zip",
"VPOP_NEI_2008_18jan2012_v3.zip", "SPEED_2008NEI_18nov201 l_v0.zip", and
"spdpro_2008nei_18nov2011_v0.zip" (see Section 8.1 for access information).
4.6.4.7 Run MOVES to create emission factors
EPA used the SMOKE-MOVES driver scripts to run MOVES for each of the representative counties,
fuel-months, and the listed temperatures and temperature profiles. The runspec generator created a
series of runspecs (MOVES jobs) based on the outputs from Met4moves. Specifically, the script used a
5 degree bin and the minimum and maximum temperature ranges from Met4moves and used the
idealized diurnal profiles from Met4moves to generate a series of MOVES runs that captured the full
range of temperatures for each representative county. The SMOKE-MOVES driver scripts resulted in
three emission factors (EF) tables for each representative county and fuel month: rate per distance
(RPD), rate per vehicle (RPV), and rate per profile (RPP). After the MOVES runs were completed, the
post-processor Moves2smk converted the MySQL tables into EF files that can be read by SMOKE. For
more details, see the SMOKE documentation
4.6.4.8 Run SMOKE to create emissions
Lastly, EPA generated air quality model ready emissions at a gridded and hourly resolution. The
Movemrg SMOKE-MOVES program performs this function by combining activity data, meteorological
data, and emission factors to produce gridded, hourly emissions. EPA ran Movesmrg for each of the
three sets of emission factor tables (RPD, RPV, and RPP). During the Movesmrg run, the program uses
the hourly, gridded temperature (for RPD and RPV) or daily temperature profile (for RPP) to select the
proper emissions rates and compute emissions. These calculations were done for all counties and SCCs
in the SMOKE inputs, covering the continental U.S.
The emissions process RPD is for modeling the on-network emissions. This includes the following
modes: vehicle exhaust, evaporation, evaporative permeation, brake wear, and tire wear. For RPD, the
activity data is monthly VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus
weekend (SPDPRO)20. The SMOKE program Temporal takes vehicle and roadtype specific temporal
profiles and distributes the monthly VMT to day of the week and hour. Movesmrg reads the speed data
for that county and SCC and the temperature from the gridded hourly (MCIP) data and uses these values
to look-up the appropriate emission factors (EFs) from the representative county's EF table. It then
20 If the SPDPRO file is available, the hourly speed takes precedence over the average monthly speed.
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multiplies this EF by temporalized VMT to calculate the emissions for that grid cell and hour. This is
repeated for each pollutant and SCC in that grid cell.
The emission process RPV is for modeling the off-network emissions. This includes the following
modes: vehicle exhaust, evaporative, and evaporative permeation. For RPV, the activity data is vehicle
population (VPOP). Movesmrg reads the temperature from the gridded hourly data and uses the
temperature plus SCC and the hour of the day to look up the appropriate EF from the representative
county's EF table. It then multiplies this EF by the VPOP for that SCC and FIPS to calculate the
emissions for that grid cell and hour. This repeats for each pollutant and SCC in that grid cell.
The emission process RPP is for modeling the off-network emissions for parked vehicles. This includes
the mode vehicle evaporative (fuel vapor venting). For RPP, the activity data is VPOP. Movesmrg
reads the county based diurnal temperature range (Met4moves' output for SMOKE). It uses this
temperature range to determine a similar idealized diurnal profile from the EF table using the
temperature min and max, SCC, and hour of the day. It then multiplies this EF by the VPOP for that
SCC and FIPS to calculate the emissions for that grid cell and hour. This repeats for each pollutant and
SCC within the county.
The result of the Movesmrg processing is hourly, gridded data suitable for use in air quality modeling as
well as daily reports for the three processing streams (RPD, RPV, and RPP). The results include
emissions for every county in the continental U.S., rather than just for the representative counties.
4.6.4.9 Post-Processing to Generate Annual Inventory
For the purposes of the NEI, EPA needed emissions data by county, SCC, pollutant, and emission type
(exhaust, evaporative, brake wear, and tire wear). EPA developed and used a set of scripts to combine
the emissions from the three sets of reports and from all days to create the annual inventory.
A select set of metals were generated through a separate process. Instead of having county, process, and
temperature specific emission factors, a national EF for each pollutant/SCC combination was multiplied
by the appropriate VMT for a specific county to create annual emissions for that pollutant. Table 54
lists the pollutants that we estimated using national EFs.
Table 54: Pollutants estimated through national emission factors
NEI
pollutant
Description
16065831
Chromium III
18540299
Chromium (VI)
7439965
Manganese
7440020
Nickel
7440382
Arsenic
The on-road emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands, which EPA generated via
MOVES in inventory mode (see Section 4.6.5) were appended to the on-road inventory generated from
SMOKE-MOVES. The emissions for metals and dioxins were also appended to the on-road inventory
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to create the final emissions. This complete inventory was submitted to the EIS as the EPA estimates
for the on-road sector. The resulting EIS dataset is named "2008 EPAMOBILE".
4.6.5 EPA-developed on-road mobile emissions data for Alaska, Hawaii, Puerto Rico and
the Virgin Islands
Since the meteorology domain used by EPA for running SMOKE-MOVES covered only the continental
U.S., EPA used the MOVES "inventory mode" to create emissions for Alaska, Hawaii, Puerto Rico and
the Virgin Islands. These runs used the average monthly hourly temperatures and humidity values
available in the MOVES database as derived from the NMIM database described above (see Section
4.6.4.4). These emissions characterized all pollutants including a full set of metals and dioxins.
The MOVES inputs used for these emissions are available as described in Section 8.1. The file
"AKHIPRVI_Counties.zip" contains the MOVES county database manager databases, and the file
"AKHIPRVI_Runspecs.zip" contains the run specifications used to run MOVES. Lastly, the file
"akhiprvi_temperatures.zip" contains the MySQL database containing the tables that describe the
temperatures and relative humidity values used for these states and territories.
4.6.6 Summary of quality assurance methods
EPA did a series of checks and comparisons against both the inputs and the resulting emissions to
quality assure the on-road inventory. The following is a list of the more significant checks and resulting
corrections:
• Checked the VMT data by comparing the 2008 with a 2005 based activity data. Also analyzed
the ratio VMT to vehicle population to look for extreme values. Identified widespread errors in
ID and NV. Found additional problems in two counties in CA and 10 counties in VA. Updated
the VMT in consultation with OTAQ. Reran RPD (the processes that are dependent on VMT)
for the above counties.
• Checked the consistency of VMT with vehicle population and identified counties in which there
was VMT but no vehicle population. Updated the vehicle population in consultation with OTAQ
for the following FIPS (16061, 30069, 31005, 51610, and 51685). Reran RPP and RPV
(processes that are dependent on vehicle population) for these counties.
• Many counties in Texas had identical extremely high populations and VMT. We reran all 254
Texas counties using older data.
• Three counties in Florida and one in Tennessee were missing monthvmt and roadtypedistribution
tables and had to be re-run using MOVES default values.
• The county databases for Norton City VA (51720) listed zero VMT and zero population. We
substituted county data from the 2005 NEI.
• Cottonwood, MN (27033) had an unreasonably low vehicle population of only 82 vehicles.
Instead, we used VMT from NCD 20101201 and Population/VMT ratios from the 2005 NEI.
• Three counties in Florida (12086, 12033, 12057) had populations and VMT that were
inconsistent with independent sources. We substitute more consistent county inputs provided in
spring 2011.
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• Identified a large number of missing SCCs in the activity data for Georgia. Determined that
there was a truncation problem in the conversion of the MOBILE6 activity data submitted by the
states into MOVES activity data. Returned to the original state submitted VMT data and
reprocessed it for SMOKE-MOVES.
• Identified errors in age distributions in two counties in Arkansas (FIPS codes 05015, 05143)
which resulted in anomalous results. The state supplied age distributions were replaced with
default distributions prior to the final run of MOVES for the NEI. Generated new EFs for these
two reference counties and reran RPD, RPP, and RPV for all of Arkansas and Louisiana.
• Compared the on-road results to similar results from the previous version of the 2008 NEI (v2
and vl). The previous version was prepared using MOBILE6. We found numerous differences
between the two sets of results. Detailed comparisons by state, county and SCC vehicle type
showed that most of the differences were due to updated input data from the states, or to
differences between the two emission models. In particular, based on an updated understanding
of vehicle emissions, the MOVES model generally predicts much higher NOx, PM and ammonia
emissions compared to the MOBILE6 model. And, the MOVES model generally predicts lower
emissions of hydrocarbons and carbon monoxide (Beardsley, 2010). These trends were evident
in the comparisons of the two NEI versions.
• Compared the 2008 NEI v3 with a similar run done for 2005 using 2005 inputs. In general, this
comparison indicated the expected growth of emissions over those three years. It also identified
an error in two county-specific age distributions that were fixed before the 2008 NEI was
finalized and identified errors in county VMT and populations that we were able to repair before
finalizing the inventory.
• Air toxic results were quality assured by back-calculating toxics ratios from inventory outputs to
ensure they were consistent with inventory inputs.
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5 Fires
Fire sources in this section are sources of pollution caused by the inadvertent or intentional burning of
biomass including forest, rangeland (e.g., grasses and shrubs), and agricultural vegetative residue. This
section describes the 2008 NEI wildfires (Section 5.1), prescribed burning (also Section 5.1), and
agricultural burning (Section 5.1.4). Other types of fires are included in other EIS sectors, such as "Fuel
Combustion - Residential - Wood" (Section 0), the "Waste Disposal" (Section 0), which includes fires
from burning yard waste, land clearing, residential household waste, logging debris, and commercial,
institutional, industrial, and "open dump" burning of biomass and other refuse; and "Miscellaneous
Non-Industrial NEC" (Section 0), which includes structure fires, firefighting as part of waste disposal,
firefighting training fires, motor vehicle fires, and other open fires.
5.1 Wildfires and Prescribed burning
This section describes the 2008 NEI approach for wildfires, prescribed burning, and wild land fire use,
collectively called "wild land" fires (WLFs). Precise definitions of these types of fires are provided
below in Section 5.1.1. These are included in the same section because the approach used is generally
the same, except with slight differences for the blending of EPA data with data supplied by S/L/T
agencies.
For the 2008 NEI, the EIS database contains wildfires and prescribed fires as both event-based (point
source, day-specific) data and nonpoint data. The EPA dataset for wildfires and prescribed fires used
the event structure, some S/L/T agencies also used this structure, and other S/L/T agencies used the
nonpoint structure (for prescribed fires). Because some EIS features have not yet been built, EPA was
unable to combine these data sources into a single selection for the wildfire and prescribed burning
sectors. Therefore, we combined the data outside of EIS and loaded it into EIS in the EVENT format
using one fire per county per day with daily emissions equal to annual emissions divided by 365. The
2008 NEI website (see Section 1.3.2) provides the combined wildfire and prescribed fire data at the
county-SCC resolution, it can also be obtained in EIS through a summary of the "2008V30 GPR with
Biogenics" EIS selection for the EVENT data category.
5.1.1 Sector Description
WLFs are generally defined as any non-structural fire that occurs in wild lands. Included in WLFs are
the following types of fires:
• Prescribed (Rx) fire: Any fire ignited by management actions to meet specific objectives,
generally related to the reduction of the biomass potentially available for wildfires.
• Wildfire (WF): An unplanned, unwanted WLF including unauthorized human-caused fires,
escaped prescribed fire projects, or other inadvertent fire situation where objective is to put the
fire out.
• Wildland Fire Use (WFU): The application of appropriate management response to naturally-
ignited WLFs to accomplish specific resource mgmt objective in pre-designated areas outlined in
fire management plans. In other words, an unplanned fire that is subsequently controlled and
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used as a Rx fire to meet specific objectives.
A significant improvement to the 2008 NEI over the 2005 NEI and previous data released for 2008 is
that we have eliminated the "unclassified" fires in the EPA dataset as a result of advancements in the
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) approach
by using SMARTFIRE version 2 (SFv2), as described in Section 5.1.4. The unclassified fires had
previously been caused by the satellite-based SMARTFIRE version 1 (SFvl) approach, where no
methods had been implemented to assign a wildfire or prescribed fire status when ground-based
(observational) data were not available for a particular fire. In SFvl, these fires were assigned to an
unclassified status, but that is no longer the case.
Table 55 lists the SCCs that define these three different types of WLFs in the 2008 NEI, both for EPA
data and for S/L/T data. Note that EPA data has only one unique SCC for each of these types of fires.
Data submitted by S/L/T agencies can have several different SCCs that define prescribed fires. As
described below, EPA's approach to combine EPA data with S/L/T data for the 2008 NEI considers all
SCCs that define any one type of fire and appropriately combines emissions from those SCCs.
Table 55: Source classification codes for wild
and fires
Data Origin
Wildfires
Prescribed Burns
Wildland Fire Use
EPA
2810001000
2810015000
2810001001
States/Locals/Tribes
2810001000
2811015000
2810015000
2810020000
2810001001
5.1.2 Sources of data overview and selection hierarchy
The wildfire and Rx fire EIS sectors include data from two components: S/L/T agency-provided
emissions data (event-based and nonpoint county totals), an EPA dataset created from SFv2 (see Section
5.1.4), Only the combination of these data are available- as summary information on the 2008 NEI
website and in EIS, as mentioned above. Unlike other data categories, there is no way to tell which data
came from which of these sources since they were combined outside of EIS. Summaries of the agency-
supplied data are available in the spreadsheets "StateData wildlandFires.xlsx" for the state data and
"TribalData wildlandFires.xlsx" for the tribal data (see Section 8.2).
The S/L/T agency data were received from agencies listed in Table 56. The table notes when the data
were provided as event or as nonpoint data.
Table 56: Agencies that submitted wildfire and prescribed burning (Rx) emissions data
Agency
Agency
Type
Rx provided
Wildfire
provided
Arizona
State/Loca
1
as nonpoint
as event
California
State
as nonpoint
Delaware
State
as nonpoint
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Agency
Agency
Type
Rx provided
Wildfire
provided
Georgia
State
as nonpoint
as event
Idaho
State
as nonpoint
Illinois
State
as nonpoint
Louisiana
State
as nonpoint
Maine
State
as nonpoint
as event
Maryland
State
as nonpoint1
New Mexico
State
as nonpoint1
New York
State
as nonpoint1
Nevada
State
as nonpoint
New Jersey
State
as nonpoint
North Carolina
State/Loca
1
as nonpoint
as event
Utah
State
as nonpoint
Washington
State
as nonpoint
Alaska
State
as event
WFU
as event
Citizen Potawatami Nation, Oklahoma
Tribe
as event
Eastern Band of Cherokee Indians
Tribe
as nonpoint
Fond du Lac Band of the Minnesota Chippewa Tribe
Tribe
as nonpoint
Kootenai Tribe of Idaho
Tribe
as nonpoint
Leech Lake Band of Ojibwe
Tribe
as nonpoint
Nez Perce Tribe of Idaho
Tribe
as nonpoint
Northern Cheyenne Tribe of the Northern Cheyenne
Indian Reservation, Montana
Tribe
as nonpoint
Omaha Tribe of Nebraska
Tribe
as nonpoint
Prairie Band Potawatomi Nation
Tribe
as nonpoint
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
Tribe
as nonpoint
1 Submitted HAP emissions only
As shown in the table above, several tribes submitted both prescribed and wildfire data to the NEI using
the SCCs shown above in Table 55. These data are summarized and reported in the 2008 NEI as
received. EPA did not resolve any double counting that may occur because EPA and State data may
already cover the same areas that these Tribal data encompass. EPA did not augment the tribal fires
with HAP emissions. Updated shapefiles were not available to accurately represent tribal lands to
enable EPA to try and extract out the fires from the NEI estimated by EPA and the states that are
coincident with the fires reported by Tribes. Table 57 summarizes the small amounts emissions
included in the NEI from tribal submissions. These are only double-counted if these emissions were
large enough to have been picked-up by the satellite-based approach with SFv2, and we have not been
able to assess that possibility.
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Table 57: Fire emissions submitted by tribal agencies (short tons/year)
With
Acetald
Formald
in
NO
VO
SO
PM2.
PM1
NH
e-
e-
Tribe
State
CO
X
C
2
5
0
3
hyde
hyde
Toluene
Kootenai Tribe of Idaho
ID
1,032
35
67
134
149
7
4
4
1
Nez Perce Tribe of Idaho
ID
Shoshone-Bannock Tribes
ID
of the Fort Hall Reservation
of Idaho
Prairie Band Potawatomi
KS
159
3
7
1
13
15
1
Nation
Fond du Lac Band of the
MN
923
3
6
120
4
Minnesota Chippewa Tribe
Leech Lake Band of Ojibwe
MN
Northern Cheyenne Tribe of
MT
15,60
312
1,070
1,159
the Northern Cheyenne
8
Indian Reservation,
Montana
Eastern Band of Cherokee
NC
59
2
10
Indians
Omaha Tribe of Nebraska
NE
196
26
2
Citizen Potawatami Nation,
OK
2917
83
500
354
354
Oklahoma
All tribes
20,89
4
123
899
1
1,577
1,823
14
4
4
1
For tribes that did not submit data, EPA did not assign the fires based on the tribal land boundaries.
These fires were assigned to the states within which the tribal lands fall.
Table 58 shows the selection hierarchy for the wildfire and Rx burning sectors.
Table 58: 2008 NEI wildfire and prescribed fires selection hierarchy
Priority
Dataset Name
Dataset Content
Is Dataset in
EIS?
1
State/Local/Tribal Data
Submitted data as listed above. Null values
were filled in with EPA data in the
subsequent datasets, whereas zero estimates
were left as zeros.
Yes
2
EPA event data based on SFv2
CAP and HAP emissions
No
3
EPA HAP augmentation
HAP augmentation for wildfires and
prescribed fires (Section 5.1.5)
No
If a S/L/T agency submitted any type of fire emissions data, it was used as first choice. If a state
submitted data only for some counties, then the counties for which there were null values were filled in
using the EPA data. If any zero values were submitted by states, they were used as zero in contrast to
what was done when a null value was submitted by the state. Several states reported prescribed fire data
to the non point inventory. These data were shifted to the Events inventory and summarized along with
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wildfires in the EPA summaries. It should be noted that when states submitted prescribed fire data to
the nonpoint inventory, they were submitted as a county total for the year 2008. The Event inventory,
on the other hand, is a day- and location-specific inventory. When the prescribed fire data submitted as
non-point were "shifted" to the events inventory for summary purposes, the summaries were all done at
a county level, and as a sum for the total year, so that no attempts were made to assign the county-based
prescribed fires to day-specific events.
Alaska submitted fire emissions, and those were used as reported. There was no backfilling of missing
fires in Alaska, because EPA only estimated fire emissions for the contiguous 48 states for 2008. Since
Hawaii, Puerto Rico, and US Virgin Islands did not report any fire emissions, these regions have no
WLF emissions in the final 2008 NEI.
5.1.3 Spatial coverage and data sources for the sector
The 2008 NEI includes wildfire and Rx fire emissions for all continental US states and Alaska. These
emissions represent a combination of state-submitted information and EPA-estimated emissions from
these fires. The EPA methods are described in Section 5.1.4 below. The way we blended these
emissions to arrive at state totals is summarized in above. Table 56 above shows which states submitted
wildfire, Rx, and WFU emissions to the NEI. A positive entry in this table only indicates that an agency
submitted some data to the NEI; these data were used as supplied as the hierarchy in Table 58 indicates.
In most cases, many counties were null and were therefore filled in using EPA data.
Table 56 shows that the States of AZ, CA, ID, NY, NM, and UT submitted some wildfire, Rx, and WFU
data. Here, counties that had null CAP values were backfilled using EPA data for those counties. For the
States of DE, IL, MD, and NY, only prescribed fire data were received from the States. For these states,
all wildfire data were filled in using the EPA-created data. In addition, prescribed fires that were null
for any counties were also filled in using EPA data for counties that EPA methods found emissions from
prescribed fires. HAP augmentation was also done to fill in HAP emissions for states that submitted
only CAP emissions, as described in Section 5.1.5. For all the other 35 states, no state data were
received, and we used only the EPA data.
As described above, Tribal data were summarized directly from their reporting to the EIS.
5.1.4 EPA-developed fire emissions estimates
For the dataset developed by EPA for the 2008 NEI, we used the following general equation to estimate
wildfires and prescribed fires. Accurate estimates of fire emissions rely on accurate estimates of the
terms in the Equation below.
Emissions = Area burned * Fuel Load Available * Fuel Consumed (Burn Efficiency) * Emission
Factors
Daily CAP emission estimates were prepared using the software SFv2 (Pollard et al., 201 la), which
include fire estimation algorithms and is built within a database. Additional information on the
approaches specific to the NEI are available in Raffuse (2012). SFv2 estimates the "Area burned" term
in the above equation, in conjunction with the Bluesky framework model that estimates the last three
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terms in the above equation. The "fuel load available" term is estimated using the Fuel Characteristic
Classification System (FCCS) maps in the Bluesky model. The "fuel consumed" term is estimated from
Bluesky using the CONSUME3 model, which predicts the fraction of fuel that burns based on many
parameters including fuel moisture. Finally, the "Emission Factors" term is estimated in Bluesky using
the Fire Emissions Prediction Simulator which relies on Efs from the literature apportioned by flaming
and smoldering combustion. Since SFv2 was recently developed, direct references to its development in
conjunction with updated Bluesky methods are not yet available; however, the following reference can
be used in general for past applications of these process models in the SF/Bluesky process.
The EPA data estimate emissions for 38 pollutants. These pollutants are listed in Table 59 below.
CAPs were estimated via SFv2 as just described, while HAPs were estimated using emission factors also
shown in the table, with further information available in (Pace, 2007).
Table 59: Pollutants estimated by EPA for wildland fires and
HAP emission factors
Pollutant
HAP Emission factor
(lb/ton fuel
consumed)
PM2.5
N/A
PM10
CO
C02
CH4
NOx
NH3
S02
VOC
1,3-butadiene
0.405
Acrolein
0.424
Toluene
0.56825
n-hexane
0.0164025
Anthracene
0.005
Pyrene
0.00929
o,m,p-xylene
0.242
benzo(ghi)perlyene
0.00508
benzo(e)pyrene
0.00266
indeno(l ,2,3 -cd)pyrene
0.00341
b enzoOphenanthrene
0.0039
Perylene
0.000856
b enzo(a)fluoranthene
0.0026
Fluoranthene
0.00673
b enzo(k)fluoranthene
0.0026
Chrysene
0.0062
methylpyrene, -fluoranthene
0.00905
methylbenzopyrenes
0.00296
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Pollutant
HAP Emission factor
(lb/ton fuel
consumed)
Methylchrysene
0.0079
Methyl anthracene
0.00823
Carbonylsulfide
0.000534
Formaldehyde
2.575
benzo(a)pyrene
0.00148
benz(a)anthracene
0.0062
Benzofluoranthenes
0.00514
Benzene
1.125
Methylchloride
0.128325
Acetaldehyde
0.40825
Phenanthrene
0.005
SFv2 uses both satellite-detected and ground-reported fires to produce daily fire information (locations
and area burned). Previous versions of the NEI relied on SFvl, which reconciled ICS-209 ground
reports and hot spots from the NOAA Hazard Mapping System (HMS). This reconciliation was
performed using a single algorithm that relies primarily on the HMS data to provide the information
critical for emissions inventories—fire location, daily growth, and final size. In contrast, SFv2, is not a
single algorithm; rather, it is a modular framework for collecting, processing, and reconciling fire
information from a variety of satellite, ground-based, and other sources. Many key updates were made
to the overall SFv2 process, including improvements in (1) identification and sizing of fires needed for
the "Area Burned" term and (2) the burn characteristics needed for the "Fuel Load Available" and "Fuel
Consumed" terms. The key updates include:
• Ability to combine data from many types of fire information sources, including satellite-derived
fire detections, satellite- or helicopter-derived burn scar polygons, and ground-based reports
from federal and state agencies.
• Support for more than one reconciliation algorithm, or "stream."
• Improved and (currently) up-to-date methodologies for determining fire type, fire size, and fire
date.
• Assignment of all fires into one of the three fire types discussed above. This is a significant
improvement from past versions of SMARTFIRE in which many fires in the NEI were left as
"unclassified".
• Use of monitoring trends in burn severity burn scar perimeters in place of the more operational
helicopter-flown perimeters from GeoMac that were used in previous versions of SMARTFIRE
to identify fire sizes.
• An updated fuel bed map, specifically the most recent (at this time) 1-km FCCS fuel bed map21
• Updated Consume 3 Python code for fuel consumption calculations
21 Fuel bed information.
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database named Emissions.mdb (see Section 8.1 for access information and for supporting files that
describe database fields).
Figure 13: Acres Burned using EPA Methods
County Fires in 2008
(Acres Burned)
Legend
CountyFires_
SumOfarea
- 2376
HI 2377 - 6257
¦ 6258- 11260
¦ 11261 -17766
¦ 17767 - 26650
~ 26651 - 39916
Q 39917- 63408
¦ 63409- 93400
¦ 93401 - 181114
¦ 181115- 270964
.AcresBurned
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Figure 14: 2008 PM2.5 Emissions using EPA methods
Legend
CountyFires_2008_PM2.5Emissions
SumOfpm25
¦ 0-200
¦ 201 - 628
¦629-1359
IB 1360- 2516
12517-4606
I 14607 - 8460
Q8461 - 17498
HQ 17499 - 30828
H30829 - 55485
¦ 55486- 131548
County Fires in 2008
PM2.5 Emissions (T/Yr)
5.1.5 Wildland Fire HAP Augmentation
For WLFs, all CAPs and CAP precursor emissions are estimated via the SFv2 approach as described
above. In addition, a set of 29 HAPs are estimated by applying the activity levels estimated from the
methods above with the emission factors in Table 59. These same 29 HAPs have been estimated for
fires over the past 10 years or so for the NEI by EPA.
State data always took precedent over EPA data. However, most states did not submit FLAP data, and
some submitted FIAPs that are not a part of the list in Table 48. We used the following rules to augment
HAP emissions to give a consistent list of HAPs included for fires.
• Only State data were augmented using the approach below, Tribal data were not. Tribal data are
summarized as reported, with the caveat that there may be some double counting with already
State and EPA data.
• If a state reported any of the HAPs in the list above, it was carried through to the 2008 NEI.
• If a state reported any HAPs outside of what is shown in the list of 29 above, it is retained in EIS,
but not released in the 2008 NEI. This approach provides for a nationally consistent dataset with
respect to the pollutants that are included.
• If a state reported a zero value for any of the HAPs, that zero was retained in the 2008 NEI.
• If a state did not report any of the 29 HAPs above, EPA augmented the data estimate each of the
29 HAPs. This was the case for most of the states. The approach used for the 2008 NEI v2 is
159
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described by the first 2 bullets below. This approach and the resultant emissions were changed
for the 2008 NEI v3 as summarized in bullets 3, 4, and 5:
o For the 2008 NEI v2 only: Using summaries of the EPA dataset based on SFv2, we
computed a state-by-state ratio of each of the HAPs to CO emissions. This was done
because most states reported CO emissions. These ratios are available in
"HAP_augmentation_2008neiv2_WLfires_notusedinv3.xlsx" (see Section 8.1 for access
information). EPA had used PM2.5 emissions in the past, but more S/L/T agencies did
not report PM2.5 from fires than CO.
o For the 2008 NEI v2 only: We applied these state-specific ratios (regardless of fire type)
to county-summed estimates of CO emissions supplied by the state (the ratios will be
constant across all counties in a state) to estimate each of the HAPs. These HAPs were
then included in the 2008 NEI (via the website only) as EPA based information,
o For the 2008 NEI v3, we used the HAP estimates provided directly by the SF2 process at
a county-SCC level in lieu of any type of ratio-ing methodology. Thus for all SLTs that
submitted CAP emissions (but none of the HAPs reported by EPA as reported in Table
60), we used the HAP estimates generated by SF2 directly for that county-scc
combination. Note that the CAPs in version 3 are not affected by this process. Emissions
data are aggregated to total (for the 15 states affected) and the differences between v2 and
v3 HAPs for wild land fires are shown in Table 60 below,
o For the total sum of these HAPs, and for the affected states (those that submitted Wild
Land Fire emissions to the NEI in 2008, including an estimate of CO emissions), the
percent reduction in applying the revised method in v3 is about 45%, from about 1.1
million tons total in v2 to about 0.59 tons in v3. Most of this is driven by decreases in
California HAPs in v3, as well as by changes to formaldehyde emissions, which are seen
to be reduced by over 80% in sum in v3.
o EPA did not have estimates for AK data, thus they are not considered here in the changes
made to v3. In addition, tribal data were not altered in going from v2 to v2 as no HAP
augmentation were done on those data. Finally, in going from v2 and v3, two HAPs that
were estimated in v2 for this sector, methylbenzopyrene and methylchrysene, were
omitted due to questions raised about the validity of their emission factors.
160
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Table 60: Changes in emissions between the 2008 NEI v2 and 2008 NEI v3 due to HAP
augmentation method changes
CAS Number HAP Kane lors 2006 v3. Tons »eroent Reduction *n vl
,50000 fetmtteshvdk 465.164 38,421 81
*50328 SenzofajPyrene 263 45 82"
*36553 iemlaj Anthracene 1,100 90? II
?M3Z Bei»en« 199,509 164,570 11
*74873 Methyl Chloride 22,762 18.777 I«
'75070 Acetaldehyde 72,607 59,918 17
85018 Phenanthrerte 887 732 18
*106990 lP3-Iutadien® 69,040 56,462 18
1OT02S Acrolein 75,656 62,475 1?
108883 Toluene 100,802 83,153 18
*110543 Hexsne 2,912 2,402 18-
¦12012? Anthracene Si? 712 18
*129000 Pyrene 1,648 1,359 18
'191242 Benzotgfh^jPerylene 901 743 If
192972 SenzoteJPyrerte 472 3SS W
*193395 indeno[l,2,3-c,d)Pyrene 605 499 18
'195197 Senzo(c}phentnlhrene 692 571 18
198550 Perylene 152 123 18
'203338 aeniefalPfuoranthfifie 461 380 18
*206440 Fluoranthene 1,134 983 il
207089 Benzo{k)Fiuoranthene 461 380 if
118019 Chrysene 1,100 907 II
463581 Carbony! Sulfide 85 78 18
1330207 Xylenes (Mixed Isomers! 42»f25 35,410 II
*2381217 1-Methylpyrene irS5S 1,324 18
26914181 Methyianthracene 1,46® 1,20# 11
'56832136 Serrcofluoranthenes 912 752 IS
Sum Total 1,066,269 513,711 45
5.1.6 Summary of quality assurance methods
• WLFs' emissions developed using the methods above were compared to past EPA efforts to
estimate emissions from these same categories. Some of the spatial patterns were similar, but
since wildfires exhibit great inter-annual variability, it was difficult to make emissions-output or
"area burned" comparisons year-to year. In addition, in the recent past EPA inventories (2003
through an earlier version of 2008) using SFvl, much of the area burned could not be classified
into a type of fires and as such they were labeled as "unclassified" fires. In the 2008 NEI, SFv2
is used, and thus, all fires are classified, which made comparisons of prescribed burning
especially more difficult with previous EPA inventories. For the Eastern states, if the
assumption is made that most of the previously "unclassified" fires were prescribed burns (which
is logical based on the patterns shown in Figure 12 above), then the PM2.5 emission estimates
for those states compare well to the 2008 emissions developed here.
161
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Table 61: Source Classification Codes in the NEI for Agricultural Burning
Data Origin
Agricultural Fires - SCCs used
EPA
2801500000
States/Locals/Tribes
2801500000, 2801500100, 2801500111,2801500130,
2801500150,2801500170, 2801500181, 2801500191, 2801500220,
2801500250, 2801500261, 2801500262, 2801500300, 2801500320,
2801500330, 2801500350, 2801500350, 2801500390, 2801500410,
2801500420, 2801500430, 2801500500, 2801500600, 2801520000
5.2.2 Sources of data overview and selection hierarchy
The agricultural fire sector includes data from three components: S/L/T agency-provided emissions data,
the EPA Chromium Split v2 dataset (see Section 3.1.3), and an EPA dataset created from SFv2 (see
Section 5.1.4).
The chromium augmentation data were used only to speciate California total chromium to hexavalent
and trivalent chromium. The EPA dataset includes emissions from the pollutants VOC, NOx, S02, CO,
PM2.5, C02 and methane because we had emission factors available for these. The C02 and methane
emissions were not included in the final 2008 NEI, but, are available upon request. The state data also
includes HAP emissions (California, Delaware, Idaho, the Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho, the Kootenai Tribe of Idaho, and the Nez Perce Tribe), and in some cases NH3
emissions (California, Hawaii, Idaho, Louisiana, New Jersey, and the Washoe Tribe of California and
Nevada).
Table 62 lists the state and tribal agencies that submitted agricultural fire emissions.
Table 62: Agencies that submitted agricultural fire emissions to the 2008 NEI
Agency
Agency
Type
California Air Resources Board
State
Delaware Department of Natural Resources and Environmental
State
Control
Georgia Department of Natural Resources
State
Hawaii Department of Health Clean Air Branch
State
Idaho Department of Environmental Quality
State
Kootenai Tribe of Idaho
Tribal
Louisiana Department of Environmental Quality
State
New Jersey Department of Environment Protection
State
Nez Perce Tribe
Tribal
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribal
Utah Division of Air Quality
State
Washington State Department of Ecology
State
Washoe Tribe of California and Nevada
Tribal
163
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When we created the 2008 NEI, these data are combined such that in any state that submitted data, only
that data were used to represent that area in the final NEI for the pollutants submitted. As with WLFs,
any counties or pollutants that were null were backfilled with EPA-based county estimates (of criteria
pollutants that we estimated). EPA did not augment HAPs for agricultural fires. Any "zero"
submissions were left as zero in the 2008 NEI for those counties and pollutants.
5.2.3 Spatial coverage and data sources for the sector
Using the methods described above, EPA developed county-by-county agriculture burning estimates for
the contiguous United States. Table 63 summarizes the national EPA estimates for Ag burning for each
State. Figure 16 summarizes, as an example, the PM2.5 emissions data at a state level based on these
EPA data. Total PM2.5 emissions for the 48 contiguous states in the US based on EPA methods is about
50,000 tons.
Table 63: State Emission Estimates for Agricultural Burning using EPA methods (short tons/year)
State1
NOx
SOi
voc
CO
PM2.5
CO2
CH4
Alabama
129.6
17.28
259.2
2980.8
432
92577.6
129.6
Arizona
21.6
3.78
43.2
550.8
59.4
10179
16.2
Arkansas
3654.7
678.73
4698.9
55864.7
7309.4
1836226
1566.3
California
464
92.8
928
11484
1276
245340
348
Colorado
74.5
8.94
163.9
1564.5
268.2
47575.7
74.5
Delaware
8.5
1.02
18.7
178.5
30.6
5428.1
8.5
Florida
1685.6
231.77
3581.9
32658.5
3371.2
915912.9
1053.5
Georgia
836.4
111.52
1533.4
18819
2648.6
552024
697
Iowa
475.3
54.32
950.6
9709.7
1561.7
331487.8
475.3
Idaho
76.2
12.7
228.6
2565.4
279.4
44627.8
101.6
Illinois
468.3
53.52
869.7
9633.6
1471.8
324063.6
468.3
Indiana
213.5
24.4
396.5
4392
671
147376
213.5
Kansas
2065
330.4
3717
49560
6195
1221654
1652
Kentucky
155.4
20.72
310.8
3367
518
106267.7
155.4
Louisiana
1738.1
273.13
2979.6
33023.9
4469.4
1018527
1241.5
Maryland
18.6
2.48
37.2
421.6
58.9
12415.5
18.6
Michigan
15.6
1.82
31.2
322.4
49.4
9960.6
15.6
Minnesota
555
74
1110
12395
1850
387575
555
Missouri
1162.2
154.96
2324.4
25762.1
3680.3
800368.4
1162.2
Mississippi
1032.6
154.89
1893.1
22545.1
3269.9
703372.7
860.5
Montana
45.2
6.78
90.4
1175.2
113
20475.6
33.9
North Carolina
320.4
42.72
587.4
7315.8
1014.6
214935
320.4
North Dakota
568
99.4
1278
16898
1704
350172
568
Nebraska
390
52
780
8710
1300
276510
390
New Jersey
3.6
0.42
7.2
75.6
10.8
2265.6
3
New Mexico
7.6
1.14
15.2
180.5
24.7
3908.3
7.6
Nevada
1.8
0.27
8.1
47.7
9.9
900
2.7
New York
2
0.24
4.4
42
6.8
1226.4
2
Ohio
70.2
9.36
140.4
1579.5
234
49888.8
70.2
Oklahoma
637.6
127.52
1275.2
18171.6
1753.4
361997.4
478.2
164
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Emissions = number of fire pixels identified x 100 x (state-specific, crop-specific, weighted) Emission
Factors
We first assume that each fire pixel (from the satellite images used by SFvl) is equivalent to 100 acres.
We next estimated emissions on a state-by-state basis using crop-burning based emission factors
available in the literature along with state burn-usage patterns (harvesting patterns) of these crops. The
specific crops which are included here based on publicly available Efs include: wheat, sorghum,
sunflower, oats, corn, barley, rice, alfalfa hay, grass seed, and sugarcane. The Efs and usage factors
(crop harvesting) for these crops by state are available in the spreadsheet "Ag Efs for Sat Detects.xlsx"
(see Section 8.1 for access information). Emissions estimates for each county in the US result from
multiplication of the number of pixels by a hundred acres/pixel and then by the appropriately weighted
EF. Efs were available only for certain pollutants: VOC, NOx, S02, CO, PM2.5, C02 and CH4.
PM10 was set equal to PM2.5, since agricultural burning is expected to produce PM that is mostly less
than 2.5 microns. These 8 pollutants are the only ones inventoried by EPA for agricultural fires (though
some states submitted HAP emissions for agricultural fires, which are included in the 2008 NEI).
5.2.5 Summary of quality assurance methods
• We compared state-by-state agricultural burning emissions to peer-reviewed estimates (McCarty,
2011) that do not include the year 2008. Spatial patterns of burning density (and the relative
amounts of the various crops burned) were similar between the NEI and these other data. For
example, the Mississippi Valley, California, Florida, and the Northwest areas showed higher
level of emissions than many other states with both methods. Emissions levels varied due to the
different emission factors and methods used. For example, averaging the years 2003-2007
presented in the McCarty work leads to an estimate of about 25,000 tons of PM2.5 emissions for
the contiguous 48 states; whereas the EPA 2008 methods described here yields about 50,000 tons
of PM2.5.
• For states that submitted agricultural burning data (see map in Figure 17), we compared those
data to EPA estimates in the same counties. The matches between State and EPA data varied,
with Eastern states generally matching better. It is difficult to arrive at major conclusions
because we have limited information on the methods used by states in estimating agricultural
burning emissions.
• It was discovered (2008 NEI v3) that WA submitted a small amount of ag burning data both to
Events and to nonpoint and the values appear to be double counted.
6 Biogenics - Vegetation and Soil
6.1 Biogenic Emission Sources
Biogenic emission sources are emissions that come from natural sources. They need to be accounted for
in photochemical grid models, as most types are widespread and ubiquitous contributors to background
air chemistry. In the NEI, only the emissions from vegetation and soils are included, but other relevant
sources include volcanic emissions, lightning, and sea salt.
167
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• VOC is the sum of all other biogenic species except CO, NO, SESQ.
The BEIS3.14 model
The inputs to BEIS include:
• Temperature data at 2 meters which were obtained from the meteorological input files to the air
quality model,
• Land-use data from the Biogenic Emissions Land use Database, version 3 (BELD3). BELD3
data provides data on the 230 vegetation classes at 1-km resolution over most of North America.
6.1.2 Sources of data overview and selection hierarchy
The only source of data for this sector is the EPA-estimated emissions from BEIS3.14. States are
neither required nor encouraged to report emissions, and no state has done this. The name of the EPA
dataset in EIS is: 2008EPA_biogenics.
6.1.3 Spatial coverage and data sources for the sector
The spatial coverage of the biogenics emissions is governed by the 2007 platform modeling domain
which covers all counties in the lower 48 states.
Table 64 shows state emissions summaries for the biogenic emissions sector and the contribution of
biogenics to the total inventory. Biogenic emissions are a very large fraction of the total NEI VOC,
methanol, formaldehyde and acetaldehyde emissions but a very small fraction of the CO and NOx.
More detailed summaries of the BEIS model species at county level and monthly are available as a
supporting summary (See 8.2).
Table 65: State Summary of Biogenics - Vegetation and Soil Emissions (short tons/year)
State
abbre
v.
Biogeni
c
Formald
-ehyde
Fractio
n of
Total
Biogenic
Methanol
Fract
ion
of
Total
Biogeni
c
Acetald
-ehyde
Fracti
on
of
Total
Biogenic
VOC
Fractio
n of
Total
Bioge
nic
CO
Fractio
n of
Total
Biogeni
cNOx
Fractio
n of
Total
AL
23,395
0.69
80,640
0.91
17,156
0.87
1,552,280
0.80
164,03
9
0.079
12,301
0.031
AR
19,815
0.74
70,827
0.95
14,531
0.90
1,124,476
0.79
138,88
6
0.094
19,752
0.080
AZ
55,771
0.94
244,700
1.00
40,898
0.97
1,920,418
0.90
390,47
4
0.251
19,796
0.063
CA
68,796
0.50
267,742
0.99
50,450
0.75
3,284,154
0.59
481,74
8
0.044
40,242
0.035
CO
22,203
0.85
78,952
0.96
16,282
0.92
865,174
0.79
155,45
4
0.130
27,564
0.091
CT
1,071
0.52
2,808
0.58
786
0.57
48,728
0.36
7,512
0.013
463
0.005
DC
21
0.13
79
0.19
15
0.17
1,348
0.11
146
0.003
16
0.001
DE
511
0.66
1,972
0.80
375
0.71
27,056
0.48
3,581
0.023
813
0.019
FL
32,288
0.70
118,416
0.89
23,678
0.84
1,631,172
0.65
226,20
4
0.048
35,564
0.041
GA
28,077
0.69
103,638
0.92
20,590
0.85
1,817,227
0.80
196,81
0.060
19,515
0.031
169
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State
abbre
v.
Biogeni
c
Formald
-ehyde
Fractio
n of
Total
Biogenic
Methanol
Fract
ion
of
Total
Biogeni
c
Acetald
-ehyde
Fracti
on
of
Total
Biogenic
voc
Fractio
n of
Total
Bioge
nic
CO
Fractio
n of
Total
Biogeni
cNOx
Fractio
n of
Total
6
IA
9,898
0.83
37,916
0.95
7,258
0.84
304,416
0.64
69,350
0.080
35,620
0.118
ID
26,458
0.84
73,007
0.99
19,402
0.92
977,553
0.84
185,22
0
0.200
12,600
0.138
IL
11,799
0.71
45,357
0.82
8,652
0.76
425,333
0.49
82,690
0.039
36,040
0.053
IN
7,807
0.73
29,165
0.86
5,725
0.77
297,968
0.48
54,734
0.032
19,985
0.035
KS
19,051
0.65
80,780
1.00
13,971
0.87
495,009
0.64
133,41
4
0.098
60,081
0.168
KY
11,023
0.75
38,537
0.90
8,084
0.77
540,301
0.70
77,345
0.067
15,154
0.038
LA
20,851
0.70
76,620
0.90
15,290
0.87
1,204,936
0.70
146,15
9
0.071
19,802
0.038
MA
1,622
0.51
4,273
0.96
1,189
0.58
76,410
0.31
11,372
0.014
955
0.006
MD
2,582
0.60
8,924
0.86
1,894
0.67
146,428
0.49
18,108
0.019
2,880
0.014
ME
9,763
0.92
16,844
0.90
7,159
0.93
329,436
0.81
68,389
0.152
1,961
0.027
MI
12,114
0.69
32,760
0.82
8,883
0.74
513,420
0.52
84,910
0.033
14,235
0.022
MN
15,694
0.63
45,395
0.97
11,509
0.77
713,439
0.59
109,99
1
0.044
26,919
0.064
MO
17,663
0.76
63,885
0.94
12,953
0.87
993,544
0.74
123,86
2
0.065
29,967
0.065
MS
21,685
0.80
77,188
0.95
15,902
0.92
1,401,784
0.85
152,03
7
0.122
15,522
0.061
MT
34,226
0.92
104,750
0.99
25,098
0.97
1,197,711
0.93
239,59
0
0.327
44,990
0.267
NC
17,850
0.52
61,766
0.87
13,090
0.76
1,041,979
0.55
125,14
2
0.029
13,273
0.029
ND
9,981
0.90
37,675
0.96
7,319
0.94
251,549
0.82
69,896
0.217
33,582
0.180
NE
14,623
0.90
61,078
0.98
10,723
0.94
432,786
0.82
102,40
4
0.180
47,877
0.172
NH
2,258
0.81
4,810
0.98
1,656
0.84
90,918
0.62
15,824
0.052
460
0.009
NJ
1,975
0.50
6,364
0.97
1,448
0.57
125,144
0.35
13,848
0.012
1,566
0.006
NM
37,163
0.94
153,928
0.99
27,252
0.97
1,209,491
0.92
260,15
1
0.320
29,605
0.150
NV
33,281
0.85
148,901
0.99
24,405
0.95
1,078,754
0.81
232,96
4
0.175
10,549
0.092
NY
9,546
0.71
25,160
0.68
7,001
0.77
333,832
0.39
66,942
0.025
7,613
0.017
OH
8,438
0.66
29,389
0.79
6,187
0.68
304,405
0.42
59,177
0.021
16,924
0.022
OK
20,042
0.69
83,177
0.96
14,698
0.87
861,135
0.63
140,41
7
0.081
43,491
0.094
OR
34,014
0.77
90,953
0.96
24,943
0.91
1,296,968
0.77
238,12
7
0.108
11,987
0.067
PA
9,108
0.68
26,423
0.77
6,679
0.76
420,164
0.49
63,898
0.028
8,305
0.013
RI
242
0.50
631
0.50
178
0.57
12,466
0.34
1,697
0.013
148
0.008
SC
13,511
0.76
48,430
0.85
9,908
0.87
862,672
0.77
94,704
0.068
9,541
0.038
170
-------
State
abbre
Biogeni
c
Formald
Fractio
n of
Total
Biogenic
Methanol
Fract
ion
of
Biogeni
c
Acetald
Fracti
on
of
Biogenic
voc
Fractio
n of
Total
Bioge
nic
CO
Fractio
n of
Total
Biogeni
cNOx
Fractio
n of
Total
V.
-ehyde
Total
-ehyde
Total
SD
13,264
0.91
53,099
0.99
9,727
0.96
414,289
0.87
92,889
0.263
38,551
0.349
TN
13,200
0.77
46,075
0.89
9,680
0.84
786,087
0.72
92,606
0.058
13,682
0.032
TX
115,004
0.88
501,566
0.97
84,335
0.94
3,668,130
0.61
805,35
2
0.144
213,670
0.123
UT
23,816
0.93
98,825
0.98
17,465
0.97
819,842
0.84
166,72
7
0.202
9,353
0.046
VA
12,018
0.72
39,969
0.85
8,813
0.81
731,088
0.68
84,286
0.046
8,049
0.021
VT
2,275
0.84
4,891
0.93
1,668
0.87
74,543
0.72
15,950
0.084
1,001
0.046
WA
24,347
0.81
54,858
0.92
17,854
0.90
748,535
0.70
170,44
5
0.074
13,110
0.042
WI
10,595
0.76
32,959
0.83
7,770
0.80
469,398
0.59
74,278
0.051
18,697
0.057
WV
5,559
0.72
15,848
0.92
4,076
0.86
325,951
0.76
39,036
0.062
2,777
0.013
WY
17,925
0.75
65,636
0.99
13,145
0.92
659,403
0.70
125,48
4
0.134
11,311
0.051
171
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7 Quality assessment
[This section will be included in future versions of this documentation]
7.1 What are the quality criteria used to assess the inventory?
7.2 How did the 2008 NEI compare to the quality criteria?
7.3 What EIS sectors seem to be incomplete and for which key pollutants?
7.4 How can the quality of the emissions data be further evaluated by users?
7.5 What improvements in the NEI and EIS submission process are planned for the future?
172
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8 Supporting data and summaries
The previous sections provide number references to both supporting data and key output summaries.
The following two subsections provide a map to that information. All supporting input data and
summaries referenced in the sections above can be obtained through the CHIEF ftp site
8.1 Supporting data
Table 66 provides information on how to access the supporting data referenced in the preceding
sections. The column at the far left lists the files that can be downloaded from the FTP site listed above.
The "File names included" column of the table lists the file names included in each of the zip files - it is
these file names that are referenced in the other sections of this document. The "Description" column of
this table provides a summary of the purpose of the data file listed on that row.
"able 66: 2008 NEI supporting data access information
File name
File names included
Description
2008neiv3 issues.xlsx
Same
Latest caveats list. May be more up to
date that the list provided in
Appendix A.
see eissector xwalk
Same
Cross-walk between source
classification codes (SCCs) and EIS
sectors. Also shows tiers.
2008neiv3.xlsx
2008 NEI v3 hg
datal4mar2013.accb
section2-mercurv
Access database is within zip file at:
Assignments of mercury-specific
categories used in Table 7 to the 2008
NEI v2 by process (point) and county
(nonpoint, onroad and nonroad).
2008nei supdata 3a.zi
E
section3-
stationary/ag_livestock_waste/
ReadMe.doc
See other data files as explained in
the ReadMe.doc file
Supporting data for EPA agricultural
livestock emissions estimates
including input and output files from
the emissions model used.
Section3-stationary/nonpoint
ERTACstatecomparison.xlsx
For the nonpoint sectors included in
the ERTAC process: provides the
sectors, SCCs, emission factors and
includes a brief description of the
methodologies.
Section3-stationary/point/
2 Attachments 1 and 2 HTIP C
alcs.xls
Example calculations for calculating
unit-level heat input when not
available from CAMD.
Section3-stationary/point/
CAMD08annualallprg 103009.txt
Annual 2008 emissions and heat input
activity data for all units reporting to
the CAMD data system as of Oct 30,
2009
section3 -stationary/point/
Chromium speciation factors.xls
Factors used to speciate total
chromium (Section 3.1.3)
section3 -stationary/point/
EAF ICR Test Data Summary-
Electric Arc Furnace test data
summary
173
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File name
File names included
Description
area ma]or(EPA Rule Data).xls
section3 -stationary/point/
HAP EF Ratios Derived from
WebFIRE.xls
Ratios used in the HAP augmentation
process- 2008 NEI v2
section3-stationary/point/ boiler sees
for hg hap augmentation .xlsx
Revised Hg to PM10-FIL ratios used
in the HAP augmentation process for
the 2008 NEI v3 only. Note: use only
the sheets: "boilersccsfor hg aug",
"pmlO ef uncontolled not revoked",
"hg ef uncontrolled not revoked"
section3 -stationary/point/
Hg EAF forSLT reviewed.xlsx
Data sent to states for review of
electric arc furnace emissions and
results
section3 -stationary/point/
HgFacilitiesforSLTreviewed.xl
sx
Data sent to states for review of high
Hg facilities and results
section3 -stationary/point/
high risk nata2005_poll forSLT
reviewed.xlsx
Data sent to states for reviewed of
high risk facilities and results
section3 -stationary/point/
TRI to EIS crosswalk.accdb
TRI to EIS Facility ID crosswalk
2008nei supdata 3b.zi
E
(nonpoint emissions)
section3-np_emissions/
File names provided in Table 18
(Section 3.1.6)
Data files with EPA nonpoint
emissions data and methods for some
nonpoint categories
2008nei supdata 3c.zi
E
(nonpoint tools)
secti on3 -np_tool s/
File names provided in Table 19
(Section 3.1.6)
Tools with best methods for nonpoint
categories without emissions estimated
2008nei suodata 4a.zi
n
section4-mobile/air loco marine/
pport07.xls
US Army Corps of Engineers
Principal Ports file for 2007.
Section4-mobile/air loco marine/
2011 ports shapefile.zip
Shapefile for allocation of commercial
marine vessel port emissions
section4-mobile/air loco marine/
shippinglanes_112812_shapefile.zi
P
Shapefile for allocation of commercial
marine vessel shipping lane emissions
section4-mobile/air loco marine/
railway 20110921.zip
Shapefile for locomotive emissions
allocation
2008nei supdata 4b.zi
E
section4-mobile/nonroad_equip/
ncd20101201.zip
NMIM county database for EPA
nonroad emissions and earlier versions
(prior to 2008 NEI v2) of on-road
emissions.
2008nei supdata 4c.zi
E
section4-mobile/onroad/
Onroad Read Me.docx
Description of contents of the folder
section4-mobile/onroad/
VPOPNEI 2008 18j an2012_v3 .z
ip
Contains the estimated vehicle
population data used in the SMOKE
run. SMOKE FF10 format - see
SMOKE user manual
174
-------
File name
File names included
Description
section4-mobile/onroad/
VMT_NEI_2008_updated2_
18jan2012 v3.zip
Contains the estimated annual and
monthly vehicle miles traveled used in
the SMOKErun. SMOKE FF10 format
- see SMOKE user manual
section4-mobile/onroad/
AKHIPRVI Counties.zip
Contains the individual MOVES
County Data Manager databases
(folders) in MySQL format for all of
the counties in Alaska, Hawaii, Puerto
Rico and the Virgin Islands.
Section4-mobile/onroad/
AKHIPRVI Runspecs.zip
Contains all of the MOVES run
specifications (ASCII files, XML
format) that were used to run MOVES
to obtain the emission inventories for
the Alaska, Hawaii, Puerto Rico and
the Virgin Island counties.
Section4-mobile/onroad/
akhiprvi_temperatures.zip
Contains the MySQL database (folder)
containing the tables (in MySQL
format) that describe the temperatures
and relative humidity values used for
the Alaska, Hawaii, Puerto Rico and
the Virgin Island counties.
Section4-mobile/onroad/
SPEED_2008NEI_18nov201 l_vO.
zip
Contains the estimated vehicle average
speed data used in the SMOKErun.
SMOKE FF10 format - see SMOKE
user manual
section4-mobile/onroad/
spdpro 2008nei 18nov2011 vO.zi
P
Contains the estimated vehicle average
hourly speed data used in the SMOKE
run. SMOKE SPDPRO format - see
SMOKE user manual
section4-mobile/onroad/
Lev_standards.zip
Contains the MySQL databases
(folders) containing the tables (in
MySQL format) that provide alternate
vehicle emission rates for those states
which have adopted California
emission standards. The appropriate
database is indicated in the run
specification for each county.
Section4-mobile/onroad/
MCXREF_2008v3.zip
CSV file: list of counties selected to be
the representative counties for the
2008 NEI and associated counties
represented.
Section4-mobile/onroad/
MFMREF 2008v3.zip
CSV file: list of the months that are
represented by the January and July
results from the representative
counties.
Section4-mobile/onroad/
RepCounty Counties.zip
Contains the individual MOVES
County Data Manager databases
175
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File name
File names included
Description
(folders) in MySQL format for just the
representing counties.
Section4-mobile/onroad/
RepCounty_Runspecs.zip
Contains all of the MOVES run
specifications (ASCII files, XML
format) that were used to run MOVES
to obtain the emission inventories for
the representing counties.
Section4-mobile/onroad/
RepCounty_temperatures.zip
Contains the MySQL databases
(folders) containing the tables (in
MySQL format) that describe the
temperatures and relative humidity
values used for the representing
counties. These temperature and
humidity values correspond to the
range of meteorology values needed
for the emission rates used by SMOKE
and do not represent daily average
temperature values.
2008nei suodata 5.zid
section5-fires/Ag Fires/
Ag Efs for Sat Detects.xlsx
Emission factors used for agricultural
fires emission factors.
section5-fires/Ag Fires/
rawag activity bystate.xlsx
Agricultural fires activities by state
based on Smartfire vl.
section5-fires/Smartfire2/
AgActivityFieldDescriptions.xlsx
Field descriptions for table
"AgActivityClean" fields in
"Emissions.mdb". This is included in
the Wildland Fires folder because it
goes with the database from Smartfire
version2 processing.
section5-fires/Smartfire2/
Emissions.mdb
2008 daily wild land fire emission
inventory and agriculture fire activity
database based on Smartfire version 2
and Bluesky Framework v3.3.0. The
agricultural fire activity from this
database was not used for the NEI.
Rather, the data from the Smartfire
version 1 was used, as explained in
Section 5.2.
section5-fires/Smartfire2/
EmissionsFieldDescriptions2008.x
Is
Field descriptions for wild land fire
and emissions-related fields in
"Emissions.mdb"
section5-fires/Smartfire2/
HAP augmentation 2008neiv2 W
Lfires_notusedinv3 .xlsx
HAP/CO ratios for states that
submitted wildfire and/or prescribed
fires data. These ratios are based on
EPA estimates and then used to
"augment" and estimate HAPs for
counties in which CO emissions were
reported by the States.
176
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File name
File names included
Description
section5-fires/Smartfire2/
SummaryTables2008.xls
Aggregated data for Smartfire2-based
2008 wild land fire emission inventory
in 'Emissions.mdb'
8.2 Supporting summaries
All supporting summaries listed here are available in the file "2008neiv2 supsumm.zip" included with
the documentation on the 2008 NEI website or are included as separate links from the 2008 NEI
website.
Table 67: 2008 NEI supporting summaries
Section Summary file
No.
Description
Section 1: Introduction
Section 2: Overview
Section 3: Stationary sources
summaries/matrix submittals for
Version 2 Feb 13 2011.xls
Lists which reporting agencies submitted data for
major subcategories of nonpoint emissions (not
organized by EIS sector)
Section 4: Mobile sources
summaries/
out_of_lto_pb_summary_120211 .xls
X
Summary of EPA-generated in-flight lead emissions
summaries/
airportlead 20110406.xlsx
Summary of EPA-generated airport lead emissions
(the NEI includes some EPA data and some S/L/T/
agency data)
Section 5: Fires
summaries/
StateData wildlandFires.xls
All the State data reported for WLFs at the county
level
summaries/
TribalData wildlandFires.xls
Summary of Tribal data (submitted by tribes)
Section 5: Biogenics
Biogenics emissions 2008
Biogenic emissions by model species plus VOC,
various levels of aggregation down to monthly
county emissions
177
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9 References
All references cited in this documentation that have documents associated with them (rather than
websites) are provided in the zipped file "2008nei references.zip". All of the files not listed with a URL
in the right hand column below are included in the "references" folder of this zipped file.
Section Reference
File name or website
Section 1: Introduction
Mason, R., Zubrow, A. and Eyth, A. 2012. Technical
Support Document- Preparation of Emissions
Inventories for the Version 5.0, 2007 Emissions
Modeling platform
http://epa.gov/ttn/chief/emch/2007v5/2007v5 2020b
ase EmisMod TSD 13dec2012.pdf
http://epa.gov/ttn/chief/emch/2007v5/
2007v5 2020base EmisMod TSD 1
3dec2012.pdf
U.S. Environmental Protection Agency, 2013a. EPA
Needs to Improve Air Emissions Data for the Oil and
Natural Gas
Production Sector, Office of Inspector General, 13-P-
0161, February 2013.
document: 20130220-13-P-0161.odf
U.S. Environmental Protection Agency, 2013b. 2008
National Emissions Inventory: Review, Analysis and
Highlights, Office Of Air Quality Planning and
Standards, EPA-454/R-13-005, May, 2013.
document: 2008report.pdf
Section 2: Overview
Section 3: Stationary sources
Davidson, C., Adams, P., Strader, R., Pinder, R.,
Anderson, N., Goebes, M., and Ayers, J., 2004. The
Environmental Institute, Carnegie Mellon
University, CMU Ammonia Model v.3.6. (accessed
April 25, 2009)
Carnesie Mellon Universitv
Dorn, J., 2009. E.H. Pechan & Associates. A weighted
average emission factor calculated using data from
the 2002 CMU Ammonia Model v.3.6.
Results provided in spreadsheets:
"County-Level Emission Factors for
Beef Composite.xls" and
"County-Level Emission Factors for
Dairy Composite.xls" provided in the
subfolder section3-stationary/
ag livestock waste listed in Section
8.1.
Dorn, J, 2012. Memorandum: 2008 NEI Version 2 - PM
Augmentation approach. Memorandum to Roy
Huntley, US EPA.
PM augmt 2008 NEIv2 feb2012.pdf
Dorn, J., Divita, F., Huntley, R., Janssen, M., 2010.
Implementing a Collaborative Process to Improve
the Consistency, Transparency, and Accessibility of
Implementing a Collaborative
Process
178
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Section Reference
File name or website
the Nonpoint Source Emission Estimates in the 2008
National Emissions Inventory, 19th International
Emission Inventory Conference - "Emissions
Inventories - Informing Emerging Issues", San
Antonio, TX, September 27 - 30, 2010.
Houyoux, M., Parker, B., Myers, R., Bullock, D.,
Johnson, S., 2011. Emission Factor Supporting
Documentation for the Final Mercury and Air Toxics
Standards, US EPA, EPA-454/R-11-012, November
2011.
Emission Factor Documentation
Houyoux, M. and Strum, M., 2011. Memorandum:
Emissions Overview: Hazardous Air Pollutants in
Support of the Final Mercury and Air Toxics
Standards, US EPA, EPA-454/R-11-014, November
2011.
Memorandum
Johnson, S. and Bullock, D., 2012. Emission Inventories
for Final Mercury and Air Toxics Standards
National Emission Inventory Matching
Documentation, project memorandum, January 27,
2012.
MATS_NEI2008_Memo.pdf and
Attachment 1:
Attachment 1-Boiler List with EIS
Codes.xlsx
Rothschild, S., 2010. Detailed Plan to Develop 2008
EGUEmissions, project report for work assignment
3-09, contract EP-D-07-097, January 2010.
2008EGU emiss DetailedPlanFinal
012610.pdf
Strait, R.; MacKenzie, D.; and Huntley, R., 2003. PM
Augmentation Procedures for the 1999 Point and
Area Source NEI, 12th International Emission
Inventory Conference - "Emission Inventories -
Applying New Technologies", San Diego, April 29 -
May 1, 2003.
PM Augmentation Documentation
U.S. Environmental Protection Agency, 2001. National-
Scale Air Toxics Assessment for 1996, Office of Air
Quality Planning and Standards, EPA-453/R-01-003,
January 2001 - Appendix G, p 4.
Full reDort:
Appendix G:
U.S. Environmental Protection Agency, 2005. National
Emission Inventory - Ammonia Emissions from
Animal Agricultural Operations, Revised Draft
Report, 22 April 2005, p. 4-6.
accessed 5 Mav 2009
Wilson, D., Billings, R., Oommen, R., Lange, B., Marik,
J., McClutchey, S., Perez, H., 2010. Year 2008
Gulfwide Emission Inventory Study, U.S.
Department of the Interior, Bureau of Ocean Energy
Management, Regulation, and Enforcement,
BOEMRE 2010-045, December, 2010.
2010. Year 2008 Gulfwide Emission
Inventory Study,
Section 4: Mobile sources
Beardsley, M., 2010. MOVES2010: Information for
Transportation Modelers, presentation to
TRB-MOVES2010-Session-
Beardsley.pdf
179
-------
Section Reference
File name or website
Transportation Research Board. January 11, 2010.
Eastern Research Group (ERG), 2010. Project report:
Documentation for the Commercial Marine Vessel
Component of the National Emissions Inventory
Methodology. ERG No. 0245.02.302.001, March 30,
2010.
Cmv_report4.pdf
Eastern Research Group (ERG), 2012. Project report:
Category 1 Category 2 Commercial Marine
Activity Spatial Allocation, August 22, 2012
Catl&2Activity_Spatial_Allocatin_0
82212.docx
Eastern Research Group (ERG), 2007. Project report:
Category 2 Vessel Census, Activity, and Spatial
Allocation Assessment and Category 2 and Category
2 In-port At-Sea Splits, February 16, 2007
Category 2 vessel census.pdf
Eastern Research Group (ERG), 201 la. Project report:
Documentation for Aircraft Component of the
National Emissions Inventory Methodology, ERG
No. 0245.03.402.011, January 27, 2011.
Aircraftreportfinal .pdf
Eastern Research Group (ERG), 201 lb. Project report:
Documentation for Locomotive Component of the
National Emissions Inventory Methodology, ERG
No. 0245.03.402.001, May 3, 2011.
Locomotive_report.pdf
E.H. Pechan & Associates (E.H. Pechan), 2011. Project
report: Documentation for the 2008 Mobile Source
National Emissions Inventory, May 2011.
Nmim_documentation.pdf
Federal Aviation Administration (FAA), 2008a.
Emissions and Dispersion Modeling System,
Version 5.1. September, 2008.
Emissions and Dispersion Modeling
Svstem
Federal Aviation Administration (FAA), 2008b. General
Aviation and Part 135 Activity Survey - Calendar
Year 2008.
General Aviation and Part 135
Activitv Survevs
Renewable Fuels Association, 2011. Building Bridges to
a More Sustainable Future - 2011 Ethanol Industry
Outlook, February, 2011.
Annual Industry Outlook
Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O.,
Barker, D.M., Duda, M.G., Huang, X., Wang, W.,
Powers, J.G., A Description of the Advanced
Research WRF Version 3, NCAR Technical Note
NCAR/TN-475+STR, June 2008.
U.S. Army Corps of Engineers (US ACE), 2001.
Waterway Network Link Commodity Data, Water
Resources Support Center, Fort Belvoir, VA.
Downloaded January 22, 2001.
US Armv Corps of Engineers
Institute for Water Resources
U.S. Army Corps of Engineers (US ACE), 2009.
Principal Ports file for 2007. U.S. Army Corps of
Engineers, Navigation Data Center, Waterborne
Commerce Statistics.
accessed September 10, 2009.
U.S. Environmental Protection Agency (US EPA), 2003.
180
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Section Reference
File name or website
Final Regulatory Support Document: Control of
Emissions from New Marine Compression-Ignition
Engines at or above 30 Liters per Cylinder,
EPA420-R-03 -004, January 2003.
U.S. Environmental Protection Agency (US EPA), 2012.
MOVES2010b: Additional Toxics Added to MOVES,
US EPA, EPA-420-B-12-029, April 2012.
MOVES2010b toxics 420bl2029.pd
f
Section 5: Fires
McCarty, J.L., Remote Sensing-BasedEstimates of
Annual and Seasonal Emissions from Crop Residue
Burning in the Contiguous USA, Journal of the Air
and Waste Management Association, Volume 61,
January 2011.
Pace, T., Attachment 1 in Work Assignment #3-18. Tom
Pace to Sonoma Technologies, Preparation of
Wildland and Agricultural Fire Emission Inventories
for 2003-2006, April 2007
pacememo-2007.pdf
Pollard, E.K., Du, Y., Raffuse, S.M., and Reid, S.B.,
2011a. Sonoma Technology, Inc. Technical
Memorandum: Preparation of Wildland and
Agricultural Fire Emissions Inventories for 2009,
STI-910221-4231, October 6, 2011.
Technical Memo 2009 Wildland
Fires_EmissionsInventory.pdf
Pollard, E.K., Du, Y., and Reid, S.B., 2011b. Sonoma
Technology, Inc. Technical Memorandum:
Estimation of Emissions for Wildfires Wildland Fire
Use, and Agricultural Burning Emissions, STI-
910221-4159, June 2011.
Versionl SMARTFIRE agburning.p
df
Raffuse, S., 2012. Sonoma Technical Inc. Technical
Memorandum: AirFire STI National Wildland Fire
Emission Inventory for 2008, DRAFT, April 2012.
NEIv2_SFv2_Methods.pdf
WITN, 2008. News story on Evans Road Wildfire from
website
http://www.witn.com/home/headlines/19456249.htm
1
evansroadfiremedi areport. pdf
Section 6: Biogenics
Mason, R., Zubrow, A. and Eyth, A. 2012. Technical
Support Document- Preparation of Emissions
Inventories for the Version 5.0, 2007 Emissions
Modeling platform
http://epa.gov/ttn/chief/emch/2007v5/2007v5 2020b
ase EmisMod TSD 13dec2012.pdf
CHIEF
181
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-19-018
Environmental Protection Air Quality Assessment Division September 2013
Agency Research Triangle Park, NC
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