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2008 National Emissions Inventory, Version 2
Technical Support Document - June 2012
DRAFT
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EPA-454/D-20-002
July 2012
2008 National Emissions Inventory, Version 2 Technical Support Document - June 2012
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 vi
Acronyms and Chemical Notations viii
1 Introduction 1
1.1 What data are included in the 2008 NEI, version 2 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? 3
1.6 Who are the target audiences for the 2008 NEI? 5
1.7 What are appropriate uses of the 2008 NEI version 2 and what are the caveats about the data? 6
2 2008 inventory contents overview 8
2.1 What are EIS Sectors and what list was used for this document? 8
2.2 What do the data show about the sources of data in the 2008 NEI? 10
2.3 What are the top sources of some key pollutants? 15
2.4 How does this NEI compare to past inventories? 17
2.4.1 Differences in approaches 17
2.4.2 Differences in emissions 19
2.5 How well are tribal data and regions represented in the 2008 NEI? 22
2.6 What does this NEI tell us about mercury? 23
3 Stationary sources 29
3.1 Stationary source approaches 29
3.1.1 Sources of data overview and selection hierarchies 29
3.1.2 Particulate matter augmentation 34
3.1.3 Chromium augmentation 35
3.1.4 Use of the 2008 Toxics Release Inventory 37
3.1.5 HAP augmentation based on emission factor ratios 46
3.1.6 EPA nonpoint data 53
3.1.7 Additional Gap filling efforts targeted at high risk and specific mercury categories 58
3.2 Agriculture - Crops & Livestock Dust 62
3.3 Agriculture - Fertilizer Application 62
3.4 Agriculture - Livestock Waste 62
3.4.1 Sector Description 62
3.4.2 Sources of data overview and selection hierarchy 62
3.4.3 Spatial coverage and data sources for the sector 63
3.4.4 EPA-developed livestock waste emissions data 63
3.4.5 Summary of quality assurance methods 68
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3.5 Bulk Gasoline Terminals 69
3.6 Commercial Cooking 69
3.7 Dust - Construction Dust 69
3.8 Dust - Paved Road Dust 69
3.9 Dust - Unpaved Road Dust 69
3.10 Fuel Combustion - Electric Generation 69
3.10.1 Sector Description 69
3.10.2 Sources of data overview and selection hierarchy 70
3.10.3 Spatial coverage and data sources for the sector 73
3.10.4 Overwrite datasets used for EGUs 73
3.10.5 EPA-developed EGU emissions data 74
3.10.6 Alternative facility and unit IDs needed for matching with other databases 77
3.10.7 Summary of quality assurance methods 77
3.11 Fuel Combustion - Industrial Boilers 77
3.12 Fuel Combustion - Commercial/Institutional 77
3.13 Fuel Combustion - Residential - Natural Gas, Oil, and Other 82
3.14 Fuel Combustion - Residential - Wood 82
3.15 Gas Stations 82
3.16 Industrial Processes - Cement Manufacturing 82
3.17 Industrial Processes - Chemical Manufacturing 82
3.18 Industrial Processes - Ferrous Metals 83
3.19 Industrial Processes - Mining 83
3.20 Industrial Processes - Non-ferrous Metals 83
3.21 Industrial Processes - Oil & Gas Production 83
3.22 Industrial Processes - Petroleum Refineries 83
3.23 Industrial Processes - Pulp & Paper 83
3.24 Industrial Processes - Storage and Transfer 83
3.25 Industrial Processes - NEC (Other) 83
3.26 Miscellaneous Non-industrial NEC (Other) 83
3.27 Solvent - Consumer & Commercial Solvent Use 83
3.28 Solvent - Degreasing, Dry Cleaning, and Graphic Arts 83
3.29 Solvent - Industrial and Non-Industrial Surface Coating 83
3.30 Waste Disposal 83
4 Mobile sources 84
4.1 Mobile sources overview 84
4.2 Aircraft 84
4.2.1 Sector Description 85
4.2.2 Sources of data overview and selection hierarchy 85
4.2.3 Spatial coverage and data sources for the sector 86
4.2.4 Overwrite dataset used for aircraft sector 86
4.2.5 EPA-developed aircraft emissions estimates 87
4.2.6 Summary of quality assurance methods 89
4.3 Commercial Marine Vessels 91
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4.3.1 Sector Description 91
4.3.2 Sources of data overview and selection hierarchy 93
4.3.3 Spatial coverage and data sources for the sector 94
4.3.4 Overwrite datasets used for commercial marine vessel sector 94
4.3.5 EPA-developed commercial marine vessel emissions data 94
4.3.6 Summary of quality assurance methods 96
4.4 Locomotives 99
4.4.1 Sector Description 99
4.4.2 Sources of data overview and selection hierarchy 99
4.4.3 Spatial coverage and data sources for the sector 100
4.4.4 Overwrite datasets used for locomotives sector 100
4.4.5 EPA-developed locomotive emissions data 101
4.4.6 Summary of quality assurance methods 102
4.5 Nonroad Equipment - Diesel, Gasoline, and other 103
4.5.1 Sector Description 103
4.5.2 Sources of data overview and selection hierarchy 103
4.5.3 Spatial coverage and data sources for the sector 105
4.5.4 EPA-developed NMIM-based nonroad emissions data 105
4.5.5 Summary of quality assurance methods 106
4.6 On-road - all Diesel and Gasoline vehicles 109
4.6.1 Sector Description 109
4.6.2 Sources of data overview and selection hierarchy 109
4.6.3 Spatial coverage and data sources for the sector 110
4.6.4 EPA-developed on-road mobile emissions data for the continental U.S 110
4.6.5 EPA-developed on-road mobile emissions data for Alaska, Hawaii, Puerto Rico and the Virgin
Islands 118
4.6.6 Summary of quality assurance methods 119
Fires 121
5.1 Wildfires and Prescribed burning 121
5.1.1 Sector Description 121
5.1.2 Sources of data overview and selection hierarchy 122
5.1.3 Spatial coverage and data sources for the sector 124
5.1.4 EPA-developed fire emissions estimates 125
5.1.5 Wildland Fire HAP Augmentation 130
5.1.6 Summary of quality assurance methods 130
5.2 Fires - Agricultural field burning 131
5.2.1 Sector Description 131
5.2.2 Sources of data overview and selection hierarchy 132
5.2.3 Spatial coverage and data sources for the sector 133
5.2.4 EPA-developed agricultural emissions data 135
5.2.5 Summary of quality assurance methods 136
Biogenics - Vegetation and Soil 136
Quality assessment 137
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7.1 What are the quality criteria used to assess the inventory? 137
7.2 How did the 2008 NEI compare to the quality criteria? 137
7.3 What EIS sectors seem to be incomplete and for which key pollutants? 137
7.4 How can the quality of the emissions data be further evaluated by users? 137
7.5 What improvements in the NEI and EIS submission process are planned for the future? 137
8 Supporting data and summaries 138
8.1 Supporting data 138
8.2 Supporting summaries 141
9 References 143
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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 4
Table 2: Examples of major current uses of the NEI 5
Table 3: EIS sectors and associated emissions categories and document sections 9
Table 4: EIS sectors and associated CAP emissions and total HAP (1000 short tons/year) 16
Table 5: Tribal Participation in the 2008 NEI 22
Table 6: Datasets, groups, and amount of Hg in 2008 NEI from each 24
Table 7: Trends in Mercury Emissions - 1990, 2005, and 2008 25
Table 8: Data sources and selection hierarchy used for point sources 30
Table 9: Data sources and selection hierarchy used for nonpoint sources 33
Table 10: Valid chromium pollutant codes 35
Table 11: Calculations for generating the point chromium augmentation dataset (EPA Chromium Split v2) 37
Table 12: Mapping of TRI Pollutant Codes to EIS Pollutant codes 42
Table 13: Pollutant Groups 45
Table 14: CAP Surrogate assignments to derive HAP-to-CAP Emission Factor Ratios 47
Table 15: Invalid pollutant codes for HAP augmentation 49
Table 16: Conversion factors HAP emission factors for HAP augmentation 50
Table 17: Physical Conversion Factors Used 50
Table 18: EPA-estimated emissions sources expected to be exclusively nonpoint 53
Table 19: Emissions sources not estimated by EPA with potential nonpoint and point contribution 55
Table 20: Solvent sectors nonpoint HAP-VOC and calculated missing HAP-VOC 57
Table 21: Emissions sources using data from former EPA inventories 57
Table 22: Emissions sources not included from EPA data sources 58
Table 23: Hg-emitting Facilities in the S/L/T agency review process with insufficient information to gap fill 59
Table 24: High Risk Facilities in the S/L/T agency review process with insufficient information to gap fill 60
Table 25: Agencies that Submitted Livestock Waste Data 62
Table 26: 2008 NEI agricultural livestock data selection hierarchy 63
Table 27: Source Classification Codes used in the agricultural livestock sector 63
Table 28: Emission Factors for NH3 emissions used for EPA's agricultural livestock data 66
Table 29: Agencies that Submitted EGU data 71
Table 30: 2008 NEI EGU data selection hierarchy by EGU fuel groups from EIS Sectors 73
Table 31: Agency-submitted, PM Augmentation, and total PM10 and PM2.5 emissions for EGU sectors (short
tons/year) 74
Table 32: Agencies that submitted data for the Fuel Combustion - Industrial Boilers, ICEs Sectors 79
Table 33: 2008 NEI selection hierarchy for datasets used by the Fuel Comb - Industrial Boilers, ICEs Sectors 81
Table 34: Source classification codes for the aircraft sector in the 2008 NEI 85
Table 35: Agencies that submitted aircraft emissions data 86
Table 36: 2008 NEI aircraft data selection hierarchy 86
Table 37: Agencies that submitted aircraft activity data for EPA's emissions calculation 87
Table 38: SCCs included in the EPA-created aircraft emissions dataset 88
Table 39: Non-aircraft related SCCs reported by S/L/T agencies to airports 91
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Table 40: Commercial Marine SCCs and Emission Types 92
Table 41: Additional Commercial Marine SCCs used by California and Kentucky 92
Table 42: Agencies that Submitted Commercial Marine Emissions Data 93
Table 43: 2008 NEI commercial marine vehicle selection hierarchy 93
Table 44: Commercial Marine SCCs for which EPA Provided Estimates 94
Table 45: Example of Potential Error in NJ County Diesel CMV Emissions Due to Merge of Inconsistent
County/Shape/SCC/EmissionType/Pollutant combinations 97
Table 46: SCC/Pollutant combinations where State total 2008 NEI is greater than agency or EPA estimates 98
Table 47: Locomotive SCCs, descriptions, and EPA estimation status 99
Table 48: Agencies that submitted Rail Emissions to the 2008 NEI 99
Table 49: 2008 NEI locomotives selection hierarchy 100
Table 50: Agency Submittals of NONROAD inputs and nonroad smissions 104
Table 51: 2008 NEI Non-road equipment selection hierarchy 105
Table 52: Nonroad SCCs included in 2008 NEI that were not in S/L/T agency submittals 108
Table 53: 2008 NEI on-road mobile selection hierarchy 109
Table 54: Characteristics for Representative County Groupings 112
Table 55: Pollutants estimated through national emission factors 118
Table 56: Source classification codes for wildland fires 122
Table 57: Agencies that submitted wildfire and prescribed burning emissions data 122
Table 58: Fire emissions submitted by tribal agencies (short tons/year) 123
Table 59: 2008 NEI wildfire and prescribed fires selection hierarchy 124
Table 60: Pollutants estimated by EPA for wildland fires and HAP emission factors 126
Table 61: Source Classification Codes in the NEI for Agricultural Burning 132
Table 62: Agencies that submitted agricultural fire emissions to the 2008 NEI 132
Table 63: State Emission Estimates for Agricultural Burning using EPA methods (short tons/year) 133
Table 64: 2008 NEI supporting data access information 138
Table 65: 2008 NEI supporting summaries 141
list of Figures
Figure 1: Data sources for point and nonpoint emissions for criteria pollutants 11
Figure 2: Data sources for point and nonpoint emissions for acid gases and HAP VOCs 11
Figure 3: Data sources for point and nonpoint emissions for Pb and HAP metals 12
Figure 4: Point inventory - submission types - includes local agencies 13
Figure 5: Nonpoint inventory - submission types - includes local agencies 13
Figure 6: On-road inventory - submission types - does not include local agencies 14
Figure 7: Nonroad equipment inventory - submission types - does not include local agencies 15
Figure 8: Comparison of 2008 NEI v2 to 2005 NEI v2 CAPs, excluding wildfires 20
Figure 9: Data sources of Hg emissions in the 2008 NEI, by data category 23
Figure 10: States with state- or local-provided Hg emissions in the point data category of the 2008 NEI 25
Figure 11: Proportion of Fires by Type using EPA Methods 128
Figure 12: Acres Burned using EPA Methods 129
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Figure 13: 2008 PM2 5 Emissions using EPA methods 129
Figure 14: 2008 PM25 wild land fire emissions using EPA methods 131
Figure 15: 2008 NEI state-total PM2.5 emissions from agricultural fires 134
Figure 16: Identification of states that submitted agricultural burning emissions to the NEI 135
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Acronyms and Chemical Notations
AERR
Air Emissions Reporting Rule
APU
Auxiliary power unit
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 System
EGU
Electric Generating Utility
EIS
Emission Inventory System
EAF
Electric arc furnace
EF
Emission factor
EMFAC
Emission FACtor (model) - for California
EPA
Environmental Protection Agency
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
HCI
Hydrogen Chloride (Hydrochloric acid)
Hg
Mercury
HMS
Hazard Mapping System
ICR
Information collection request
l/M
Inspection and maintenance
IPM
Integrated Planning Model
KMZ
Keyhole Markup Language, zipped (used for displaying data in Google Earth
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LRTAP Long-range Transboundarv 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 Nitrous oxide
N02 Nitrogen dioxide
NOAA National Oceanic and Atmospheric Administration
NOX Nitrogen oxides
03 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
PM10-PRI Primary PM10
POM Polycyclic Organic Matter
PSC Program system code (in EIS)
RFG Reformulated Gasoline
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RPD Rate per distance
RPP Rate per profile
RPV Rate per vehicle
Rx Prescribed (fire)
SCC 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
SO 2
Sulfur dioxide
S04
Sulfate
TAF
Terminal Area Forecasts
TEISS
Tribal Emissions Inventory Software Solution
TRI
Toxics Release Inventory
UNEP
United Nations Environment Programme
USDA
United States Department of Agriculture
VMT
Vehicle miles traveled
VOC
Volatile organic compounds
WebFIRE
Factor Information Retrieval System
WFU
Wildland fire use
WLF
Wildland fire
WRF
Weather Research and Forecasting Model
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1 Introduction
• hat data are included In th • • ¦ 8 NEI, verslt ! eneral Public release?
The 2008 National Emissions Inventory (NEI), version 2 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 (HCI) 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 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.
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. Sections 3, 0, and 5
provide the sector-by-sector documentation for the stationary, mobile, and fire emissions respectively.
Section 7 provides a quality assessment of the 2008 NEI. Finally, Section 8 provides instructions for accessing
supporting materials, and Section 9 provides the references. A separate document contains the appendices.
1 The current list of HAPs is available at http://www.epa.gov/ttn/atw/188polls.html.
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1.3 Where c btain the 2008 NEI data?
The 2008 NEI data are available in several different ways, as follows. EPA continues to review and streamline
the approach for accessing the NEI data.
1.3.1 Emission Inventory System Gateway
The Emission Inventory System (EIS) Gateway is available to all EPA staff, EIS data partners responsible for
submitting data to EPA (i.e., the state, local, and tribal air agency staff), and contractors working for EPA on
emissions related work. The Gateway can be used to obtain raw input datasets and create summary files from
these datasets as well as the 2008 NEI general public releases. Use the link provided above for more
information about how to obtain an account and to access the gateway itself. Note that if you run facility, unit
or process level reports in EIS, you will get the final 2008 NEI v2 emissions, but the facility inventory, which is
dynamic in EIS, will reflect more current information. For example, if an Agency ID has been changed since the
time we ran the reports for the public website (mid February, 2012), then that new Agency ID will be in the EIS
report but not in the report on the public site.
1.3.2 2008 NEI main webpage query tool
http://www.ep;i.gov/ttn/chie,f/r!et/2U08iriveiitory-htfiil
The 2008 NEI webpage is available from the Clearinghouse for Inventories and Emissions factors (CHIEF)
website. It includes a query tool that allows for summaries by EIS Sector (see Section 2.1) or the more
traditional Tier 1 summary level used in the EPA Trends Report. Summaries from this site include national,
state-, and county-level of CAP and HAP emissions. You can choose which states, EIS Sectors, Tiers, and
pollutants to include in custom-generated reports to download Comma Separated Value (CSV) files to import
into Microsoft® Excel ® or other spreadsheet tools. Biogenic emissions and tribal data are also available from
this tool.
1.3.3 Air Emissions and "Where you live"
The Air Emissions website provides emissions of CAP pollutants except for ammonia using point-and-click maps
and bar charts to provide access to summary and detailed emissions data. The maps, charts, and underlying
data (in CSV format) can be saved from the website and used in documents or spreadsheets.
In addition, the "Where you live" feature of the Air Emissions website allows users to select states and EIS
sectors (see Section 2.1) to create KMZ files used by Google Earth. You must have Google Earth installed on your
computer to open the files. You can customize the maps to select the sectors of interest, and all other sectors
will go into an "Other" category on the maps. The resulting maps allow you to click on the icons for each facility
to get a chart of emissions associated with each facility for all criteria pollutants.
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1.3.4 Modeling files
The modeling files are provided in formats that can be read by the Sparse Matrix Operator Kernel Emissions
(SMOKE, http://www.smoke-model.org). These formats are also CSV formats that can be read by other systems,
such as databases. EPA makes changes to the NEI prior to use in modeling, so both the original 2008 NEI v2 data
as well as the latest available modeling files can be found at this website.
1.4 Why Is the NEI created?
The NEI is created to provide EPA, federal and state decision makers, the U.S. public, and other countries the
U.S.'s best and most complete estimates of CAP and HAP emissions. While EPA is not directly obligated to
create the NEI under the Clean Air Act, the Act authorizes the EPA Administrator to implement data collection
efforts needed to properly administer the NAAQS program. Therefore, the Office of Air Quality Planning and
Standards (OAQPS) maintains the NEI program in support of the NAAQS. Furthermore, the Clean Air Act
requires states to submit emissions to EPA as part of their State Implementation Plans that describe how they
will meet the NAAQS, and the NEI is used as one mechanism for states to meet some of those emissions
requirements, particularly for the 3-year reporting requirements.
While the NAAQS program is the basis on which EPA collects CAP emissions from the state, local, and tribal air
agencies, it does not require collection of HAP emissions. For this reason, the HAP reporting requirements are
voluntary. Nevertheless, the HAP emissions are an essential part of the NEI program. These emissions
estimates allow EPA to assess progress in meeting HAP reduction goals described in the Clean Air Act
amendments of 1990. These reductions seek to reduce the negative impacts to people of HAP emissions in the
environment, and the NEI allows EPA to assess how much emissions have been reduced since 1990.
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
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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.
Table 1: Point source reporting thresholds (potential to emit) for criteria
po
lutants 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
03 (moderate) > 100
3 VOC
03 (serious) > 50
4 VOC
03 (severe) > 25
5 VOC
03 (extreme) > 10
6 NOX
> 100
> 100
7 CO
> 1000
03 (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 NH3
> 100
> 100
I
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 (http://www.epa.gov/ttn/chief/eis/
gateway/index.html) that EPA then uses to review and assemble the data from the S/L/T agencies. For the 2008
NEI, these submissions were due to EPA by June 30, 2010 and states continued to revise their submissions
through November 2011. 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).
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 t (18 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 used1
U.S. Public
Learn about sources of air emissions
2008 NEI v2
EPA-NAAQS
Regulatory Impact Analysis - benefits estimates using air quality
modeling
Modified 2005 NEI, v2
for PM NAAQS proposal
S02 NAAQS Monitoring Implementation - Population Weighted
Emissions Index
2008 NEI vl.5
Pb Monitoring Rule
2005 NEI v2
Pb NAAQS final designations
2008 NEI vl.5
PB NAAQS review
Ongoing
Transport Rule air quality modeling (e.g., Clean Air Interstate Rule, Cross-
State Air Pollution Rule)
Modified 2005 NEI, v2
State Implementation Plans - source of emissions data for regions
outside of the state jurisdiction
2008 NEI vl.5
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 vl.5 and vl
EPA - other
Inspector General - review of oil and gas industry
2008 NEI vl.5
NEI booklet - analysis of emissions inventory data
2002 NEI, v3
Report on the Environment
2005 NEI, v2
Air Emissions website for providing graphical access to CAP emissions for
state maps and Google Earth views of facility total emissions
2008 NEI vl.5
Department of Transportation, national transportation sector summaries
of CAPs
2008 NEI vl.5
Black Carbon Report to Congress
Modified 2005 NEI, v2
Other federal or
regional agencies
Western Regional Air Partnership - modeling in support of Regional Haze
SIPs and other air quality issues
Not known
National Oceanic and Atmospheric Administration (NOAA) air quality
forecasting
Not known
International
United Nations Economic Commission for Europe's Convention on Long-
range Transboundary Air Pollution (LRTAP)
2005 NEI vl.5
United Nations Environment Programme (UNEP) - global mercury
program
Modified 2005 NEI, v2
North American Commission for Environmental Cooperation (CEC) -
North American Regional Action Plan (NARAP) on Mercury
Not known
1 These were correct at the time of 2008 NEI v2 release. Some uses of the data will be updated to use the 2008 NEI v2.
5
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hat are appropriate uses of the 2008 NEI versit J .• ttcl 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 v2 is the use of the Motor Vehicle Emissions Simulator (MOVES) model2 for the on-road
data category. 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, and the change of model
has been demonstrated to make significant changes 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 v2 uses updated emissions factors for several metal HAPs and acid gases from coal-fired utility
boilers.
Appendix A of this document provides a detailed listing of the issues that were identified during the course of
the development of the 2008 NEI and the current status of those issues. The Appendix includes all issues
identified as part of the 2008 NEI versions 1, 1.5, and 2. This same information is available in spreadsheet form
from the spreadsheet "2008 NEI v2 issues 24apr2012.xlsx" (see Section 8.1 for access information), and the
spreadsheet will be kept more up to date than the Appendix. If the spreadsheet is newer, it will have a different
date as part of the name, and is also available from the main 2008 NEI data page listed in Section 1.3.2.
In addition to the issues, users should take caution in using the emissions data for filterable and condensible
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
2 See http://www.epa.gov/otaq/models/moves/index.htm
3 See http://www.epa.gov/otaq/m6.htm
6
-------
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.
Also some data summaries may include the pollutant "Chromium." EPA speciated emissions for Chromium
(pollutant code=7440473, see Section 3.1.3) into hexavalent chromium and trivalent chromium (Chromium (VI)
and Chromium III), other than the less than 1 pound of nonpoint emissions inadvertently missed by the
speciation routine. Therefore, 2008 NEI v2 data summaries for "Chromium" will show emissions of 0 or close to
0. Users should use the data for Chromium (VI), Chromium III, Chromic Acid (VI) and Chromium Trioxide, but not
for Chromium.
The primary unresolved issues in the 2008 NEI v2 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.
• 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.
• 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.
• 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 occur with the
possible overlap of EPA data or State data and the Tribal-reported data (see Section 5.1.2). To do this,
accurate shape files for the various Tribal lands would be required.
7
-------
• As described in Section 2.6, 0.5 tons of Hg from boilers were inadvertently left out of the 2008 NEI, but
have been added to summary tables for mercury provided in that section.
• In using the NEI in modeling applications, many unresolved inconsistencies were identified among
reported stack velocities, flows, and diameters.
• 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
("scc_eissector_xwalk_2008neiv2.xlsx") is part of the supporting data (see Section 8.1 for access information).
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
categories in EIS: point, nonpoint, on-road, and nonroad. In EIS, another 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 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. 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
nonpoint category 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. However, NEI users who sum
emissions by EIS category rather than EIS sector should be aware that this change will give differences from
historical summaries of "nonpoint" and "nonroad" data unless care is taken to assign those emissions to the
historical grouping.
8
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Table 3: EIS sectors and associated emissions categories and document sections
Sector name
Point
Nonpoint
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
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
5.2
Fires - Prescribed Burning
0
0
5.1
Fires - Wildfires
0
0
5.1
Fuel Comb - Comm/lnstitutional - Biomass
0
0
0
Fuel Comb - Comm/lnstitutional - Coal
0
0
0
Fuel Comb - Comm/lnstitutional - Natural Gas
0
0
0
Fuel Comb - Comm/lnstitutional - Oil
0
0
0
Fuel Comb - Comm/lnstitutional - 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
Industrial Processes - Oil & Gas Production
0
0
0
9
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Sector name
Point
Nonpoint
On-road
Nonroad
Event
Document
Section
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
0
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 (data sources from on-road, nonroad, and fire sources are described later in this section; these
sectors are different because they each have their own emissions model). 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 Aug" in
the figure, see Section 3.1.2). The data sources shown in the figure are described in more detail in Section 3.
10
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Figure 1: Data sources for point and nonpoint emissions for criteria pollutants
20
18
16
. 14
>
12
o
z 10
i i 11 i n i
EPA Nonpoint
I EPA PM Aug
EPA other
EPAEGU
I EPAAir/Rail/CMV
S/L/T
m
m
¦ ¦
¦
lo
u
¦ ¦
H
m
X
o
lo
Csl
u
u
X
O
T—1
Q.
Q.
i/i
>
NP
NP
NP
NP
NP
NP
NP
PT
PT
PT
PT
PT
PT
PT
In Figure 2, the nonpoint HAP VOCs are evenly split between EPA (250,000 tons) and S/L/T agency (241,000
tons) data sources. The nonpoint acid gases are very small, with 6,700 tons from S/L/T agencies and 3,500 tons
from the EPA nonpoint dataset. For point sources, the bulk of the acid gases emissions (primarily HCI) comes
from two EPA EGU datasets (138,000 tons) in addition to 42,000 tons from S/L/T agencies , while most of the
HAP VOC emissions come from the S/L/T/ agency data (179,000 tons) and just 33,000 tons from TRI.
Figure 2: Data sources for point and nonpoint emissions for acid gases and HAP VOCs
EPA Nonpoint
¦ EPA other
Figure 3 shows emissions sources for Pb and HAP metal emissions. For nonpoint sources, almost all of the
emissions are from the EPA nonroad dataset, which includes emissions from airports, locomotives, and
commercial marine vessels. For point sources, about half of the Pb comes from S/L/T agency data (330 tons),
11
-------
while the EPA nonroad dataset airport emissions makes up a substantial part of the rest (250 tons). For metals,
the point sources data has a significant portion from S/L/T agencies (1,090 tons), with the rest from the EPA EGU
dataset (1,100 tons), TRI (420 tons), and other EPA datasets (250 tons).
Figure 3: Data sources for point and nonpoint emissions for Pb and HAP metals
Pb
NP
HAP-Metals
NP
HAP-Metals
PT
EPA Nonpoint
¦ EPA other
¦ TRI
¦ EPA EGU
¦ EPA Air/Rail/CMV
¦ S/L/T
As shown in the figures above, S/L/T agency data are the bulk of the stationary source emissions in the NEI for
all pollutants except PM10, HAP-VOCs, and HAP-Metals. Nearly all states (and locals responsible for submitting
inventories) submitted data to EPA for the 2008 NEI. The figures below provide more detail about which states
submitted data to the NEI for the stationary and mobile categories. In Sections 3 through 5, we explain more
about what data actually was used by EPA in creating the NEI for each sector. Usually, but not always, EPA uses
the data provided by the states. These figure present the states for which data were available to EPA in
compiling the 2008 NEI.
Figure 4 shows that all states except South Dakota submitted point source CAP emissions and all states but Utah,
South Dakota, Indiana, Georgia, Connecticut, and Alaska submitted point source HAP emissions. Generally,
when states submitted CAP emissions they submitted all of the CAPs, but for HAP emissions there is more
variability in the data provided. S/L/T agencies can only report what they collect, and collection varies
depending on state, local, and tribal reporting regulations. In the case of Indiana, point source HAP data are
collected (voluntarily) but not reported to EPA4.
4 See the Indiana voluntary HAP reporting program at http://www.in.gov/idem/4582.htm.
12
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Figure 4: Point inventory - submission types - includes local agencies
Submission Type
Submission Type
I None
CAP
| CAP_HAP
Figure 5 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.
Submission Type
Figure 5: Nonpoint inventory - submission types - includes local agencies
13
-------
For ori-road mobile sources, emissions in all states except California are based on the MOVES model. California
emissions are estimated by the EMFAC (short for Emission FACtor) model5 and California has provided CAP and
HAP emissions which are used in the 2008 NEI. As shown in Figure 6 below, 30 states accepted EPA estimates
without providing any on-road model inputs. This is a higher number of states than in past inventories because
the NEI timing relative to the release of the MOVES model did not allow for states to submit MOVES-based
emissions. Nineteen states provided some form of either the National Mobile Inventory Mode! (NMIM)' or
MOVES inputs to EPA, including vehicle miles traveled (VMT), which EPA used to prepare inputs to the MOVES
model.
Figure 6: On-road inventory - submission types - does not include local agencies
Submission Type
Accepted EPA Estimates
Hjlnputs
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 model '. Other states emissions come from the NONROAD model8, often through the use
of the NMIM, 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.
5 See "EMFAC Overview" link available at http://www.arb.ca.gov/msei/onroad/background.htm
6 See http://www.epa.gov/otaq/nmim.htm
The OFFROAD model and documentation are available at http://www.arb.ca.gov/msei/offroad/offroad.htm.
The NONROAD model and documentation are available at http://www.epa.gov/otaq/nonrdmdl.htm
14
-------
Figure 7: Nonroad equipment inventory - submission types - does not include local agencies
Submission Type
~ Accepted EPA Estimates
In addition to the maps above, each sector-specific section below has maps that show the distribution of state
and EPA data for CAPs and HAPs. Finally, Appendix B 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?
This section simply 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 includes 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.
15
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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
HAP
Agriculture - Crops & Livestock Dust
923
4,650
1.49E-2
Agriculture - Fertilizer Application
1,190
Agriculture - Livestock Waste
92
7.49
24
2,448
1.92E-5
Bulk Gasoline Terminals
0.777
93
0.392
1.48E-2
8.78E-2
0.101
4.28E-4
2.49E-5
5.45
Commercial Cooking
30
13
6.19E-4
9.52E-5
86
89
5.22
Dust - Construction Dust
0.168
1.66E-2
7.32E-2
1.00E-3
220
2,115
8.34E-4
2.22E-9
3.64E-2
Dust - Paved Road Dust
280
1,539
Dust - Unpaved Road Dust
812
8,104
Fires - Agricultural Field Burning
576
53
25
3.42
66
68
3.88
9.73E-4
6.48
Fires - Prescribed Fires
8,176
1,697
139
65
701
824
119
240
Fires - Wildfires
12,203
2,847
96
70
999
1,178
198
1,051
Fuel Comb - Comm/lnstitutional - Biomass
17
0.541
5.49
1.70
2.52
3.07
0.219
5.80E-4
0.676
Fuel Comb - Comm/lnstitutional - Coal
15
0.418
20
88
2.13
4.65
0.136
3.46E-3
1.88
Fuel Comb - Comm/lnstitutional - Natural Gas
100
8.94
146
1.24
5.94
6.16
1.06
8.91E-4
1.50
Fuel Comb - Comm/lnstitutional - Oil
18
2.62
60
66
3.93
4.78
0.807
9.49E-4
0.824
Fuel Comb - Comm/lnstitutional - Other
12
0.932
9.20
1.22
0.541
0.657
3.64E-2
1.53E-3
8.82E-2
Fuel Comb - Electric Generation - Biomass
21
1.09
10
2.76
1.48
1.80
1.49
1.90E-3
1.38
Fuel Comb - Electric Generation - Coal
573
29
2,813
7,603
276
370
11
4.97E-2
144
Fuel Comb - Electric Generation - Natural Gas
93
9.45
181
15
20
21
11
1.27E-3
2.83
Fuel Comb - Electric Generation - Oil
13
1.95
82
142
9.45
11
1.65
3.14E-3
0.794
Fuel Comb - Electric Generation - Other
27
2.05
27
15
1.93
2.47
2.25
3.76E-3
1.27
Fuel Comb - Industrial Boilers, ICEs - Biomass
192
8.30
80
25
32
39
1.70
1.49E-2
7.34
Fuel Comb - Industrial Boilers, ICEs - Coal
59
2.17
208
663
24
51
0.495
2.08E-2
15
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
367
60
777
38
29
32
6.50
5.29E-3
22
Fuel Comb - Industrial Boilers, ICEs - Oil
26
3.72
93
135
9.16
13
0.955
2.67E-3
0.740
Fuel Comb - Industrial Boilers, ICEs - Other
121
6.33
72
67
31
33
0.741
4.70E-3
2.33
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
2,378
350
35
10
344
345
20
4.62E-4
70
Gas Stations
9.41E-3
643
2.13E-2
2.96E-3
6.16E-3
6.44E-3
3.42E-4
5.24E-4
28
Industrial Processes - Cement Manuf
100
9.19
186
106
13
24
0.889
8.29E-3
3.61
Industrial Processes - Chemical Manuf
205
99
77
196
23
30
19
1.19E-2
31
Industrial Processes - Ferrous Metals
467
19
63
33
36
44
0.625
7.89E-2
2.41
Industrial Processes - Mining
29
1.89
5.93
3.80
106
748
1.88E-3
2.60E-3
0.824
Industrial Processes - NEC
260
217
198
158
120
187
51
4.05E-2
58
Industrial Processes - Non-ferrous Metals
328
16
16
132
20
25
0.994
8.62E-2
9.63
Industrial Processes - Oil & Gas Production
219
1,688
409
61
7.11
10
2.75E-2
5.36E-5
8.33
Industrial Processes - Petroleum Refineries
84
68
93
144
24
27
3.00
5.17E-3
5.87
Industrial Processes - Pulp & Paper
132
130
74
40
40
49
5.95
5.14E-3
54
Industrial Processes - Storage and Transfer
17
238
6.72
5.53
23
53
4.97
9.33E-3
15
Miscellaneous Non-Industrial NEC
29
227
1.81
0.159
3.18
3.73
11
4.02E-4
25
Mobile - Aircraft
458
35
121
13
4.22
10
0.571
8.49
Mobile - Commercial Marine Vessels
143
21
827
150
35
37
0.398
2.90E-3
3.35
Mobile - Locomotives
120
44
846
11
25
28
0.362
2.28E-3
4.09
Mobile - Non-Road Equipment - Diesel
870
166
1,551
32
122
127
1.08
3.27E-5
41
Mobile - Non-Road Equipment - Gasoline
15,462
2,281
250
2.35
55
61
0.837
2.14E-6
539
Mobile - Non-Road Equipment - Other
1,015
47
187
0.741
2.09
2.10
1.07
0.196
16
-------
1,000 short tons/yr
Total
Sector
CO
VOC
NOX
S02
PM2.5
PM10
NH3
Lead
HAP
Mobile - On-road - Diesel Heavy Duty Vehicles
981
238
3,353
85
204
224
6.00
45
Mobile - On-road - Diesel Light Duty Vehicles
47
11
75
2.64
5.64
6.23
0.334
1.91
Mobile - On-road - Gasoline Heavy Duty Vehicles
2,530
166
251
1.48
4.09
6.96
4.71
45
Mobile - On-road - Gasoline Light Duty Vehicles
32,492
2,640
3,455
29
82
138
128
733
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
0.162
198
0.337
0.339
5.49E-2
6.06E-2
5.11E-3
5.74E-4
28
Solvent - Dry Cleaning
8.80E-4
49
5.00E-7
1.58E-2
1.58E-2
1.25E-4
3.86
Solvent - Graphic Arts
3.20
356
3.85
2.67E-2
0.252
0.277
8.84E-2
3.18E-4
24
Solvent - Industrial Surface Coating & Solvent Use
3.35
648
2.76
0.322
3.46
3.93
0.236
4.30E-3
75
Solvent - Non-Industrial Surface Coating
429
1.83E-2
68
Waste Disposal
1,384
180
97
21
205
240
66
1.15E-2
42
Sub Total (no federal waters)
82,552
17,784
17,356
10,369
6,066
21,631
4,366
0.963
3,659
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
61
26
738
457
56
61
0.292
1.60E-3
0.799
Sub Total (federal waters)
143
87
813
458
57
62
0.292
1.60E-3
0.799
Sub Total (all but vegetation and soil)
82,696
17,871
18,168
10,827
6,123
21,693
4,367
0.965
3,660
Biogenics - Vegetation and soil
6,474
31,744
1,078
4,322
Total
89,170
49,615
19,246
10,827
6,123
21,693
4,367
0.965
7,981
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 iocs 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 version2. 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 more lengthy list of caveats identified in Section 1.7 and
Appendix A.
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
17
-------
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 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 Sl/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 points9. 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
9 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.
18
-------
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 v2 uses the MOVES model for the first time. In addition, the MOVES-
based emissions have been compiled using daily meteorology data for 2008 rather than monthly averages used
in past approaches, and then summed to an annual value. Section 4.6 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 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 continues to review the differences in emissions between the 2008 NEI and past inventories, and we are
working to produce a more complete assessment of the 2008 NEI. In the interim, we have created this brief
comparison of some selected CAPs based on highly aggregated data categories, also known as Tierl.
Figure 8 illustrates key differences between the 2008 NEI v2 and the 2005 NEI v2, excluding wildfires. As shown
in the figure, emissions of all pollutants except NH3 have decreased from 2005, with some notable increases in
particular sectors despite the overall decrease. As explained for each pollutant below, many of these
differences are based on methods changes and do not reflect real differences from 2005 to 2008. In the
following descriptions, all comparison relate to the data shown in
Figure 8, which excludes fires classified as wildfire emissions (most notably for CO, some wildfire emissions are
actually part of the miscellaneous category shown in the figure).
19
-------
Figure 8: Comparison of 2008 NEI v2 to 2005 NEI v2 CAPs, excluding wildfires
NH3
NOX
PM25-PRI
S02
VOC
CO
1,000,000
500,000
0
-500,000
-1,000,000
-1,500,000
-2,000,000
Tons
2008-2005 -2,500,000
-3,000,000
-3,500,000
-4,000,000
-4,500,000
-5,000,000
-5,500,000
B
=
i
—
1
-2,000,000
-4,000,000
-6,000,000
-8,000,000
-10,000,000
-12,000,000
-14,000,000
-16,000,000
-18,000,000
-20,000,000
-22,000,000
IMisc ¦ Fuel Comb ~ Indust Proc ¦ Nonroad Mobile ¦ Highway Vehicle
Emissions Difference from 2008-2005
Source: USEPA NEI 2005 V2, 2008 V2; excludesTribal, PR, VI
2008 NH3 emissions are 3% higher than 2005 emissions. The increase in the miscellaneous category comes from
an increase in prescribed fires and waste disposal, the latter largely due to the addition of municipal/commercial
composting emissions. The decrease in industrial processes is largely from decreases in point sources associated
with food and agricultural product production. The decrease in highway vehicle emissions is mostly caused by
changing to MOVES from MOBILE6, though the VMT did decrease by 0.8% from 2005 to 2008 accounting for a
very small portion of the 54% decrease in highway vehicle NH3.
For NOx, 2008 emissions are 15% lower than 2005, though the overall differences are impacts significantly by
methods differences. Although NOx emissions increase from the highway vehicle and industrial production
sectors, these are offset by significant reductions in fuel combustion and the nonroad mobile categories. The
increase in the highway vehicle emissions is associated with the change to the MOVES model, which is primarily
caused by changes in emission rates from light duty and heavy duty trucks, and a more thorough treatment of
extended idle emissions from heavy duty vehicles. The industrial processes increase is the net result of
increases and decreases within the grouping. Increases are apparent for metals processing (20%) and petroleum
and related industries (20%), which are offset by decreases in chemical manufacturing (-10%), storage and
transport (-46%), waste disposal and recycling (-34%), and other industrial processes (-15%). The decreases in
fuel combustion are primarily related to implementation of the Clean Air Interstate Rule (CAIR) for EGUs and
decreases in non-EGU combustion assumed to be associated with the economy (e.g., facility closures), lower
facility throughputs, and controls for attainment of ozone standards. The large decrease in the nonroad mobile
sector is partly real reductions in railroad emissions (-24%), gas equipment (-45%), and nonroad diesel
equipment (-7%) with a largely artificial decrease in commercial marine (-70%). The commercial marine
decrease is related to the attribution of emissions to states rather than to real decreases. In 2005 NEI, emissions
from vessels out to 200 nautical miles (nm) were allocated to "state" emissions, whereas in the 2008 NEI,
emissions only in state waters (usually 3-10 nm) were allocated to states.
20
-------
For PM2.5, 2008 emissions are 6% lower than 2005, partly due again to the attribution of emissions in the
commercial marine portion of the inventory (79% 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
(67%) 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 56%
largely resulting from the updated estimation approach.
2008 S02 emissions are 35% 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, but widespread decreases across many other processes with substantial decreases in the solvent
surface coating industrial and non-industrial sectors accounting 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 23% lower than in 2005. While the miscellaneous category has an increase in CO
from prescribed fires (again due largely to methods changes), this is greatly offset by decreases from
miscellaneous non-industrial processes including a 10.4 million ton decrease in SCC 2810090000 (uncategorized
open fires) down to about 7,300 tons in 2008, with emissions submitted by just one state (Utah), one tribe, and
one local agency. In 2005, these emissions were included by EPA for 47 states based on the uncategorized fires
identified by SMARTFIRE in the 2005 process. Thus, this difference actually includes differences due to
uncategorized wildfires from 2005 and is an artifact of the methods changes. The fuel combustion decrease are
primarily from industrial boilers in 2008, which could be partly related to EPA not filling in nonpoint industrial
boilers which has caused double counted emissions in past inventories. 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 12
million ton decrease shown above, with diesel vehicles decreasing 17% and gasoline vehicles decreasing 56%.
21
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2.5 How well are tril ta and regions represented in the 2008 NEI?
The 2008 NEI includes emissions from 20 Tribal regions within the borders of the continental U.S. EPA does not
add emissions for Tribal Nations that do not provide data, and so a review of what data were submitted provides
details about what Tribes have emissions included in the 2008 NEI. Table 5 summarizes which Tribal Nations
submitted data to the NEI and for which source 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, HAPs and PM in the same manner as facilities under the State jurisdiction, as
explained in Section 3; therefore Tribal Nations in Table 5 with just a CAP flag will also have some 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
Citizen Potawatami Nation, Oklahoma
CAP, HAP
Confederated Tribes of the Colville Reservation,
Washington
CAP, HAP
Eastern Band of Cherokee Indians
CAP, HAP
CAP, HAP
CAP, HAP
Fond du Lac Band of Lake Superior Chippewa
CAP
CAP
Kootenai Tribe of Idaho
CAP, HAP
CAP, HAP
CAP, HAP
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, HAP
Nez Perce Tribe
CAP, HAP
CAP, HAP
CAP, HAP
CAP
Northern Cheyenne Tribe
CAP
CAP
CAP
Omaha Tribe of Nebraska
CAP
CAP, HAP
CAP
Prairie Band of Potawatomi Indians
CAP
CAP, HAP
Pueblo of Pojoaque
CAP
CAP, HAP
CAP
Red Lake Band of Chippewa Indians, Minnesota
CAP, HAP
Sac and Fox Nation of Missouri in Kansas and
Nebraska Reservation
CAP, HAP
Salt River Pima Maricopa Indian Community
Environmental Protection and Natural Resources
Division
CAP, HAP
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
CAP, HAP
CAP, HAP
CAP, HAP
CAP
Southern Ute Indian Tribe
CAP, HAP
Washoe Tribe of California and Nevada
CAP, HAP
22
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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 9 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 201110, 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 other stationary source emissions, and is represented by
"S/L/T" in the figure. We used several other datasets developed by EPA including TRI (see Section 3.1.4), EPA
HAP Augmentation or "HAP Aug" in the figure (see Section 3.1.5), and other EPA data called "Other EPA rule
data" and "EPA Other" in the figure (see Section 3.1.7). The "Other EPA rule data" is from recent EPA rule
development by the EPA OAQPS Sector Policies and Programs Division (SPPD).
Figure 9: Data sources of Hg emissions in the 2008 NEI, by data category
¦ Other EPA Rule Data
1 HAPAug
EPA Rail
1 EPA Other
EPA NV Gold Mines
¦ TRI
1 MATS
1 S/L/T
1 1 m 1
Nonpoint Point Nonroad On-road
10 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, available at
http://www.epa.gov/ttn/atw/utility/emis_overview_memo_matsfinal.pdf, or at Docket number EPA-HQ-OAR-2009-0234
23
-------
In addition to Figure 9, Table 6 breaks out the emissions data sources further into the amounts of Hg from each
individual dataset used in the selection. More information on these datasets is available in Sections 3.1.1
through 3.1.7 for stationary sources, Sections 4.5.2 through 4.5.5 for nonroad mobile sources, and sections 4.6.2
through 4.6.6 for on-road mobile sources.
Table 6: Datasets, groups, and amount of Hg in 2008 NEI from each
Mercury
Data
Emissions
Grouped Data Source
Category
Dataset name (see section 3.1.1)
(tons/yr) *
for Chart
2008 V2 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
EPACMV
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
26.33
MATS
2008 V2 Responsible Agency Selection
19.94
S/L/T
EPA TRI Augmentation v2
4.33
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
1.18
Other EPA Rule Data
EPA HAP Augmentation v2
0.32
HAP Aug
EPA 2005NATA values pulled forward to gapfill
0.17
EPA Other
EPA Rail, point
0.05
EPA Rail
EPA EGU vl.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 10 identifies the states that included state or local data. 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.
24
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Figure 10: States with state- or local-provided Hg emissions in the point
data category of the 2008 NEI
2008 Mercury Submissions
1 No Point Mercury
| Point Mercury
Table 7 shows the 2008 NEI mercury emissions 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 file
"epa_2008_nei_v2 _hg.accdb" shows the categories assignments at the facility-process level for point sources,
the county-SCC level for the nonpoint data category and the county level for onroad and nonroad data
categories (see Section 8.1 for access information).
Table 7: Trends in Mercury Emissions - 1990, 2005, and 2008
1990
2005
2008
Emissions
Emissions
Emissions
(tpy)
(tpy)
(tpy)
Baseline NEI
2005 MATS
2008 NEI
for HAPs,
proposal
V21
Source Category
11/14/2005
3/15/2011
Categorization Approach, 2008 NEI
Utility Coal Boilers
58.8
52.2
29.5
Regulatory code, NESHAP: MATS rule.
Hospital/Medical/
Manually assigned based on examination of
Infectious Waste
Incineration
51
0.2
0.1
facility name and/or unit/process descriptions
Municipal Waste
Regulatory codes: Section 129 rules for Small
Combustors
57.2
2.3
1.3
Municipal Waste Combustors (MWC) and Large
MWC
Industrial/Commercial/
SCC list- chose only processes with these SCCs
Institutional Boilers
14.4
6.4
4.5 1
that were not already tagged with rule or via
and Process Heaters
manual approach
Mercury Cell Chlor-
Regulatory code: NESHAP, Mercury Cell Chlor-
Alkali Plants
Alkali Plants. Manually corrected a regulatory
10
3.1
1,3
code assigned to units at a 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.
25
-------
1990
2005
2008
Emissions
Emissions
Emissions
(tpy)
(tpy)
(tpy)
Baseline NEI
2005 MATS
2008 NEI
for HAPs,
proposal
V21
Source Category
11/14/2005
3/15/2011
Categorization Approach, 2008 NEI
Electric Arc Furnaces
Regulatory code: Area Source rule for "Stainless &
7.5
7.0
4.7
Non-stainless Steel Manufacturing: Electric Arc
Furnaces" plus 2 major sources that have EAFs
Commercial/Industrial
Manually assigned based on examination of
Sold Waste
Not available
1.1
0.02
unit/process description and how it was
Incineration
categorized in 2005
Hazardous Waste
Combination of regulatory code, NESHAP:
Incineration
Hazardous Waste Incineration, and manual
6.6
3.2
1.3
examination based on examination of
unit/process description and how it was
categorized in 2005.
Portland Cement Non-
5.0
7.5
4.2
Regulatory code: NESHAP, Portland Cement
Hazardous Waste
Manufacturing
Gold Mining
4.4
2.5
1.7
Facility Type
Sewage Sludge
Manually assigned based on examination of
Incineration
2
0.3
0.45
unit/process description, SCC, and how it was
categorized in 2005
Mobile Sources
Not available
1.2
1.7
SCC
Other Categories
29.5
18
10.3
Total (all categories)
246
105
61
1 For Industrial/Commercial/Institutional Boilers and Process Heaters, the 2008 NEI v2 raw data (i.e., in "epa_2008_nei_v2
Hg.accdb" ) will sum to just 4.0 tons, but we have listed the additional known 0.5 tons that should have been included.
The top emitting 2008 Mercury categories are: EGUs (rank 1), electric arc furnaces (rank 2), industrial,
commercial and institutional boilers and process heaters (rank 3), Portland cement, excluding hazardous waste
kilns (rank 4), and gold mining (rank 5). Note that we discovered a large number of coal-fired boilers (industrial,
commercial, institutional) that did not have any Hg emissions and thus believe that emissions for this sector are
underestimated. The missing emissions from boilers are 0.5 tons, which have been added to the totals above;
more details on this are provided below. This addition moves that sector from rank 4 to rank 3.
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
26
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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 the NESHAP and the other half is due to differences
in the emission rates used. We compared the actual 2008 clinker production11 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.
11 United States Geological Survey, Cement data: http://minerals.usgs.gov/minerals/pubs/commoditv/cement/.
27
-------
As stated earlier, the 2008 NEI is missing some coal fired boiler Hg emissions from industrial, commercial and
institutional boilers. For this category, we used the Boiler MACT ICR emissions data to gap fill missing S/L/T
agency data for only the highest emitting Hg units that were able to be matched to processes in the 2008 NEI.
This gap-filling resulted in the use of 19 units (or unit groups) from the Boiler MACT ICR database out of a total
of more than 7700 Boiler MACT units in the Boiler MACT ICR database. The other gap filing approaches were
the use of TRI data (Section 3.1.4) and the HAP augmentation approach (Section 3.1.5).
For the HAP augmentation approach, we applied emission factor ratios of Hg to filterable PM10 to S/L/T-
reported (or EPA augmented) filterable PM10. When analyzing the coal fired boilers without Hg we found 339
coal fired boiler processes with no Hg (163 facilities). Of these units, 231 coal fired boiler processes had nonzero
filterable PM10, but zero Hg. This is because some filterable PM10 augmentation (resulting from quality
assurance of the initial augmentation) occurred after the HAP augmentation was finished. Also, there were
units at facilities with facility types of "EGU" that were not gap-filled because we had expected the EGU
augmentation (Section 3.10.5) fill in for those source, but it did not do so for a few smaller units not regulated by
MATS. We computed that these coal-fired boilers, if gap-filled via the HAP augmentation approach, would have
contributed an additional 0.4 tons of Hg to this category. We also found 108 coal fired boilers that were missing
PM but had other criteria pollutants. Thus, we believe we are missing at least 0.4 tons of Hg from coal-fired
boilers. We also identified 1,739 oil fired boilers with no Hg; 992 of them had PM10-FIL and should have thus
been gap filled, amounting to an additional 0.1 tons. Thus, the total missed emissions are estimated to be 0.5
tons.
28
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3 Stationary sources
ationary source approiich.es
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
or 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. 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 change the hierarchy for two jurisdictions so that the EPA EGU vl.5 data were chosen
ahead of the S/L/T agency data, and to exclude any greenhouse gases and pollutants in the pollutant group
"dioxins/furans"12 from the selection.
12 Dioxins/furans include all pollutants with pollutant category name of: Dioxins/Furans as 2,3,7,8-TCDD TEQs,
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 at:
http://www.epa.gov/ttn/chief/net/neip/appendix 6.mdb.
29
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Table 8: Data sources and selection hierarchy used for point sources
Dataset name
(and Short
Name*)
Description and Rationale for the Order of the Selected Datasets
Order
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 C 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_PM25)
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
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.
3
Other EPA data
(2008EPA_OTHER)
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 ahenda of the S/L/T agency data because it
changes S/L/T emission values based on feedback from the agencies.
4
2008 MATS-based
EGU emissions
(2008EPA_MATS)
Lead, mercury, HAP metal and acid gas HAP emissions from the MATS rule
information collection request, including unit-specific test data and emissions
data derived from EFs from a 2010 testing program 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.
5
30
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Dataset name
(and Short
Name*)
Description and Rationale for the Order of the Selected Datasets
Order
2008 V2
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
EPAAirpo rts 1109
(2008EPA_AIR)
This is a mobile source dataset. 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 county only if S/L/T agency
data are not, with the exception of airport data discussed in the first dataset.
7
EPA Rail, point
(2008EPA_RAIL)
This is a mobile source dataset. Emissions of CAP and HAP for diesel rail vard
locomotives at 753 rail yards. CAP emissions computed using yard-specific
emission factors using yard-specific fleet information and on 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_MMS)
CAP Emissions from Offshore oil platforms located in Federal Waters in the
Gulf of Mexico developed by 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 bv EPA. See also http://www.gomr.boemre.gov/homepg/regulate/
environ/airqualitv/gulfwide emission inventorv/2008GulfwideEmission
9
lnventorv.html. The selection order for this dataset is not important because
the data do not overlap with other datasets.
EPA EGUvl.5
(2008EPA_EGU15)
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_
Rule_Data)
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, 19 units from the Boiler MACT
information collection request database that were able to be matched to units
in the emissions inventory system were used. 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
(http://ndep.nv.gov/bapc/hg/aer.html) for 2008. Because of issues with the
12
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.
31
-------
Dataset name
(and Short
Name*)
Description and Rationale for the Order of the Selected Datasets
Order
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 for a facility only when alternative emissions are not
included in the S/L/T agency data.
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. 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
(2008EPA_HAPv2)
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_GAPFL)
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,
where states did not provide Hg data but there were hazardous waste
incinerator processes with non-zero emissions of criteria pollutants reported
by the agency. These data are selected last because they are the least
preferred method for supplementing HAP emissions, though the way it was
developed should have not caused overlap with other datasets.
16
Exceptions to the hierarchy
1. Connecticut and Douglas County, Nebraska: Changed the hierarchy of EGUvl.5 to go ahead of state data
(moved from 10 to 4). 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. Note that we
mistakenly put the EGUvl.5 data below the PM-Augmentation data so that PM data in the selection (i.e.,
2008v2) were a mix of augmented (from state-reported) and EGUvl.5, instead of a consistent set of
EGUvl.5 emissions for all 5 PM species.
2. Excluded dioxin/furan individual pollutants and groups and green house gas pollutants, pending further
review of the accuracy and completeness of the data.
A The dataset short name is the name that EIS will list in its process-level reports
32
-------
Tab
e 9: Data sources and selection hierarchy used for nonpoint sources
Dataset name
(and Short Name*)
Description
Order
EPA PM
Augmentation NP
(PM AugNP)
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).
1
EPA Overwrite
Nonpoint vl.5
(2008EPA_OverNP15)
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
2
Rail_EPACorrections
(2008RRCOR)
This is a mobile source dataset. 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.
3
EPA Chromium
Split v2
(2008EPA_CHROMv2)
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.
4
2008 V2 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 "matrix_submittals for Version 2 Feb 13 2011.xlsx"
for a list of submitting agencies and for what nonpoint sectors they
submitted data (see Section 8.2 for access information).
5
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.
6
EPA CMV
(2008EPA_ERG)
This is a mobile source dataset. EPA CMV estimates. See Section 4.3.
7
EPA Rail, nonpoint
(2008EPA_RAIL)
This is a mobile source dataset. EPA Rail estimates. See Section 4.4.
8
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.
9
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.
10
33
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EPA Possible Pt
Source Contrib Vl_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). There was no attempt by EPA to adjust nonpoint data with the
point data. See Section 3.1.6.
11
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.
A The dataset short name is the name that EIS will list in its process-level reports
For point sources there are two known issues with the final data selected for version 2. First, when the datasets
were combined, 2008 S/L/T agency emissions data were dropped for processes for which the last process year is
less than 2008. The last process year indicates the last year the process was in operation so the software did not
pick up these emissions for use in the 2008 NEI. In the future, emissions will not be accepted if the last process
year is prior to the inventory year. The amount of emissions dropped is summarized in Appendix A, Table A-6.
The second issue is an error in the order of the point source datasets for Connecticut and Douglas county
Nebraska. The order should have placed the EPA EGU vl.5 dataset ahead of the "EPA PM Augmentation, v2"
dataset since the "EPA PM Augmentation, v2" values are computed based on S/L/T-submitted PM. We intended
to use the complete set of PM from the EGU vl.5 dataset for all PM species for these two jurisdictions. This only
affects EGUs in these two geographic areas, and only affects the PM species that come from the "EPA PM
Augmentation, v2" dataset for these EGUs. Table A-7 of Appendix A shows the magnitude of the error at the
facility-level. In summary, for Douglas Nebraska, the error does not affect primary (i.e., total) PM2.5 or primary
PM10; it affects only the filterable and condensable species. For Connecticut, primary PM2.5 is expected to be
underestimated by 191 tons; the primary PM10 is not affected by the error.
3.1.2 Particulate matter augmentation
S/L/T agency submission of particulate matter (PM) emissions to the NEI are required to include primary PM10
(called PM10-PRI in EIS and NEI outputs) and primary PM2.5 (PM25-PRI). In addition, EPA requests states
provide filterable PM (PM10-FIL and PM25-FIL) along with condensable PM (PM-CON). EPA needed to augment
the PM components submitted by S/L/T agencies to ensure completeness of the 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)
34
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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 the 2008 NEI v2 PM augmentation documentation (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 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"
(3rd row of Table 8 for point and the 4th row of Table 9 for nonpoint).
This augmentation addresses two issues described below.
35
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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" (http://www.epa.gOv/ttn/chief/eidocs/training.html#eis). 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 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.
36
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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/iyT-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)
/- i
Cr
Hex1
Tri1
1
X
Speciate using speciation factors in
"Chromium_speciation_factors.xls"
ECMC = 5 (USEPA Speciation Profile)
EC = "Speciation of reported
chromium via SCC: hex %; tri
%"
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:
then set Cr emissions to 0 and keep Hex and
Trias-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.
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
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 (pollutant code = 7440473); Hex=hexavalent chromium (18540299); Tri = trivalent chromium (16065831).
2 is the value of the agency program system code for the process containing the S/L/T agency data.
3 is the appropriate numerical value of the percent of trivalent or hexavalent chromium.
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. 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
37
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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 and is available from
http://www.epa.gov/tri/tridata/data/basicplus/index.html.
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 the
Emission Inventory System 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 TRI_ID to EISJD 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 (TRIJD) and the Facility Registry System ID (FRSJD). The TRIJD is an
identification number unique to the TRI. The EPA FRSJD is a facility code also used in EPA's Envirofacts
database. The EPA NEI uses the field "EIS Identifier" (EISJD) to uniquely identify facilities. A FRSJD to
EISJD 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 TRIJD 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
38
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information). This crosswalk contains all the potential matches reviewed; the ones we used in the
automated approach have a "Y" in the "MATCH" field.
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 together 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 PMlO-FILfor 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).
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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. 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:
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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.
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 2008EPA_MATS 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 2008EPA_MATS 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.
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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 the value from the EPA
EGU vl.5 dataset, the NEI facility total would equal the TRI facility total.
c. 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.
d. 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
EIS Pollutant
Allocation
TRI CAS
TRI Pollutant Name
Code
EIS Pollutant Name
Surrogate
79345
1,1,2,2-TETRACHLOROETHANE
79345
1,1,2,2-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
96128
1,2-DIBROMO-3-CHLOROPROPANE
96128
1.2-DIBROMO-3-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-DICHLOROPROPYLENE
542756
1,3-DICHLOROPROPENE
voc
1120714
PROPANE SULTONE
1120714
1,3-PROPANESULTONE
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
94757
2,4-DICHLOROPHENOXY ACETIC ACID
94757
2,4-DICHLOROPHENOXY ACETIC ACID
voc
51285
2,4-DINITROPHENOL
51285
2,4-DINITROPHENOL
voc
121142
2,4-DINITROTOLUENE
121142
2,4-DINITROTOLUENE
voc
53963
2-ACETYLAMINOFLUORENE
53963
2-ACETYLAMINOFLUORENE
voc
79469
2-NITROPROPANE
79469
2-NITROPROPANE
voc
119937
3,3'-DIMETHYLBENZIDINE
119937
3,3'-DIMETHYLBENZIDINE
voc
101144
4,4'-METHYLENEBIS(2-CHLOROANILINE)
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
ACETAMIDE
60355
ACETAMIDE
voc
75058
ACETONITRILE
75058
ACETONITRILE
voc
98862
ACETOPHENONE
98862
ACETOPHENONE
voc
107028
ACROLEIN
107028
ACROLEIN
voc
79061
ACRYLAMIDE
79061
ACRYLAMIDE
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
7440360
ANTIMONY
7440360
ANTIMONY
PM10-FIL
N010
ANTIMONY COMPOUNDS
7440360
ANTIMONY
PM10-FIL
7440382
ARSENIC
7440382
ARSENIC
PM10-FIL
N020
ARSENIC COMPOUNDS
7440382
ARSENIC
PM10-FIL
1332214
ASBESTOS (FRIABLE)
1332214
ASBESTOS
PM10-FIL
71432
BENZENE
71432
BENZENE
VOC
92875
BENZIDINE
92875
BENZIDINE
VOC
98077
BENZOIC TRICHLORIDE
98077
BENZOTRICHLORIDE
VOC
100447
BENZYL CHLORIDE
100447
BENZYL CHLORIDE
VOC
7440417
BERYLLIUM
7440417
BERYLLIUM
PM10-FIL
N050
BERYLLIUM COMPOUNDS
7440417
BERYLLIUM
PM10-FIL
92524
BIPHENYL
92524
BIPHENYL
VOC
117817
DI(2-ETHYLHEXYL) PHTHALATE
117817
BIS(2-ETHYLHEXYL)PHTHALATE
VOC
75252
BROMOFORM
75252
BROMOFORM
VOC
7440439
CADMIUM
7440439
CADMIUM
PM10-FIL
N078
CADMIUM COMPOUNDS
7440439
CADMIUM
PM10-FIL
42
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
Allocation
Surrogate
156627
CALCIUM CYANAMIDE
156627
CALCIUM CYANAMIDE
PM10-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
7782505
CHLORINE
7782505
CHLORINE
S02
79118
CHLOROACETIC ACID
79118
CHLOROACETIC ACID
VOC
108907
CHLOROBENZENE
108907
CHLOROBENZENE
VOC
67663
CHLOROFORM
67663
CHLOROFORM
VOC
107302
CHLOROMETHYL METHYL ETHER
107302
CHLOROMETHYL METHYL ETHER
VOC
126998
CHLOROPRENE
126998
CHLOROPRENE
VOC
7440473
CHROMIUM
7440473
CHROMIUM
PM10-FIL
N090
CHROMIUM COMPOUNDS(EXCEPT
CHROMITE ORE MINED IN THE
TRANSVAAL REGION)
7440473
CHROMIUM
PM10-FIL
7440484
COBALT
7440484
COBALT
PM10-FIL
N096
COBALT COMPOUNDS
7440484
COBALT
PM10-FIL
1319773
CRESOL (MIXED ISOMERS)
1319773
CRESOL/CRESYLIC ACID (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
N106
CYANIDE COMPOUNDS
57125
CYANIDE
PM10-FIL
132649
DIBENZOFURAN
132649
DIBENZOFURAN
VOC
84742
DIBUTYL PHTHALATE
84742
DIBUTYL PHTHALATE
PM10-FIL
111444
BIS(2-CHLOROETHYL) ETHER
111444
DICHLOROETHYL ETHER
VOC
62737
DICHLORVOS
62737
DICHLORVOS
VOC
111422
DIETHANOLAMINE
111422
DIETHANOLAMINE
VOC
64675
DIETHYL SULFATE
64675
DIETHYL SULFATE
VOC
131113
DIMETHYL PHTHALATE
131113
DIMETHYL PHTHALATE
VOC
77781
DIMETHYL SULFATE
77781
DIMETHYL SULFATE
VOC
79447
DIMETHYLCARBAMYL CHLORIDE
79447
DIMETHYLCARBAMOYL CHLORIDE
VOC
N120
DIISOCYANATES
NA- pollutant not used
N150
DIOXIN AND DIOXIN-LIKE COMPOUNDS
NA- pollutant not used
106898
EPICHLOROHYDRIN
106898
EPICHLOROHYDRIN
VOC
140885
ETHYL ACRYLATE
140885
ETHYL ACRYLATE
VOC
51796
URETHANE
51796
ETHYL CARBAMATE CHLORIDE
VOC
75003
CHLOROETHANE
75003
ETHYL CHLORIDE
VOC
100414
ETHYLBENZENE
100414
ETHYL BENZENE
VOC
106934
1,2-DIBROMOETHANE
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
87683
HEXACHLORO-1,3-BUTADIENE
87683
HEXACHLOROBUTADIENE
VOC
77474
HEXACHLOROCYCLOPENTADIENE
77474
HEXACHLOROCYCLOPENTADIENE
VOC
67721
HEXACHLOROETHANE
67721
HEXACHLOROETHANE
VOC
110543
N-HEXANE
110543
HEXANE
VOC
302012
HYDRAZINE
302012
HYDRAZINE
VOC
7647010
HYDROCHLORIC ACID (1995 AND AFTER
"ACID AEROSOLS" ONLY)
7647010
HYDROCHLORIC ACID
S02
7664393
HYDROGEN FLUORIDE
7664393
HYDROGEN FLUORIDE
S02
123319
HYDROQUINONE
123319
HYDROQUINONE
VOC
7439921
LEAD
7439921
LEAD
PM10-FIL
N420
LEAD COMPOUNDS
7439921
LEAD
PM10-FIL
58899
LINDANE
58899
1,2,3,4,5,6-HEXACHLOROCYCLOHEXANE
VOC
108316
MALEIC ANHYDRIDE
108316
MALEIC ANHYDRIDE
VOC
43
-------
EIS Pollutant
Allocation
TRI CAS
TRI Pollutant Name
Code
EIS Pollutant Name
Surrogate
7439965
MANGANESE
7439965
MANGANESE
PM10-FIL
N450
MANGANESE COMPOUNDS
7439965
MANGANESE
PM10-FIL
7439976
MERCURY
7439976
MERCURY
PM10-FIL
N458
MERCURY COMPOUNDS
7439976
MERCURY
PM10-FIL
67561
METHANOL
67561
METHANOL
VOC
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
METHYLISOCYANATE
624839
METHYL ISOCYANATE
VOC
80626
METHYL METHACRYLATE
80626
METHYL METHACRYLATE
VOC
1634044
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-DIMETHYLANILINE
121697
N.N-DIMETHYLANILINE
VOC
68122
N.N-DIMETHYLFORMAMIDE
68122
N.N-DIMETHYLFORMAMIDE
VOC
91203
NAPHTHALENE
91203
NAPHTHALENE
VOC
7440020
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-DIMETHYLAMINOAZOBENZENE
60117
4-DIMETHYLAMINOAZOBENZENE
VOC
123911
1,4-DIOXANE
123911
P-DIOXANE
VOC
82688
QUINTOZENE
82688
PENTACHLORONITROBENZENE
VOC
87865
PENTACHLOROPHENOL
87865
PENTACHLOROPHENOL
VOC
108952
PHENOL
108952
PHENOL
VOC
75445
PHOSGENE
75445
PHOSGENE
VOC
7803512
PHOSPHINE
7803512
PHOSPHINE
VOC
7723140
PHOSPHORUS (YELLOW OR WHITE)
7723140
PHOSPHORUS
PM10-FIL
85449
PHTHALIC ANHYDRIDE
85449
PHTHALIC ANHYDRIDE
PM10-FIL
1336363
POLYCHLORINATED BIPHENYLS
1336363
POLYCHLORINATED BIPHENYLS
VOC
191242
BENZO(G,H,l)PERYLENE
191242
BENZO[G,H,l,lPERYLENE
PM10-FIL
85018
PHENANTHRENE
85018
PHENANTHRENE
PM10-FIL
N590
POLYCYCLIC AROMATIC COMPOUNDS
130498292
PAH, total
PM10-FIL
106503
P-PHENYLENEDIAMINE
106503
P-PHENYLENEDIAMINE
VOC
123386
PROPIONALDEHYDE
123386
PROPIONALDEHYDE
VOC
114261
PROPOXUR
114261
PROPOXUR
VOC
78875
1,2-DICHLOROPROPANE
78875
PROPYLENE DICHLORIDE
VOC
75569
PROPYLENE OXIDE
75569
PROPYLENE OXIDE
VOC
91225
QUINOLINE
91225
QUINOLINE
VOC
106514
QUINONE
106514
QUINONE
VOC
7782492
SELENIUM
7782492
SELENIUM
PM10-FIL
N725
SELENIUM COMPOUNDS
7782492
SELENIUM
PM10-FIL
100425
STYRENE
100425
STYRENE
VOC
96093
STYRENE OXIDE
96093
STYRENE OXIDE
VOC
127184
TETRACHLOROETHYLENE
127184
TETRACHLOROETHYLENE
VOC
7550450
TITANIUM TETRACHLORIDE
7550450
TITANIUM TETRACHLORIDE
VOC
108883
TOLUENE
108883
TOLUENE
VOC
95807
2,4-DIAMINOTOLUENE
95807
TOLUENE-2.4-DIAMINE
VOC
8001352
TOXAPHENE
8001352
TOXAPHENE
VOC
79016
TRICHLOROETHYLENE
79016
TRICHLOROETHYLENE
VOC
121448
TRIETHYLAMINE
121448
TRIETHYLAMINE
VOC
1582098
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
1330207
XYLENE (MIXED ISOMERS)
1330207
XYLENES (MIXED ISOMERS)
VOC
44
-------
Ta
lie 13: Pollutant Groups
Group Name
Pollutant 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,l,]Perylene
191300
Dibenzo[a,l]Pyrene
192654
Dibenzo[a,e] Pyrene
192972
Benzo[e]Pyrene
Polycyclic
Organic Matter
(POM)
193395
lndeno[l, 2,3-c,d] Pyrene
194592
7H-Dibenzo[c,g]carbazole
195197
Benzolphenanthrene
198550
Perylene
203123
Benzo(g,h,i)Fluoranthene
203338
Benzo(a)Fluoranthene
205823
Benzo[j]fluoranthene
205992
Benzo[b]Fluoranthene
206440
Fluoranthene
207089
Benzo[k]Fluoranthene
208968
Acenaphthylene
218019
Chrysene
224420
Dibenzo[a,j]Acridine
45
-------
Group Name
Pollutant Code
Pollutant
226368
Dibenz[a,h]acridine
2381217
1-Methylpyrene
2422799
12-Methylbenz(a)Anthracene
250
PAH/POM - Unspecified
26914181
Methylanthracene
3697243
5-Methylchrysene
41637905
Methylchrysene
42397648
1,6-Dinitropyrene
42397659
1,8-Dinitropyrene
50328
Benzo[a]Pyrene
53703
Dibenzo[a,h] Anthracene
5522430
1-Nitropyrene
56495
3-Methylcholanthrene
56553
Benz[a] 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
Cyanide &
57125
Cyanide
Compounds
74908
Hydrogen Cyanide
7440020
Nickel
Nickel &
12035722
Nickel Subsulfide
Compounds
1313991
Nickel Oxide
604
Nickel Refinery Dust
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
(http://www.epa.gov/ttn/chief/webfire/index.html). The spreadsheet "HAP EF Ratios Derived from
WebFIRE.xls" (see Section 8.1 for access information) provides the 2,417 emissions ratios by SCC. For each ratio,
the spreadsheet provides the HAP and CAP Factor Ids for the Efs used to build these ratios. These Factor Ids
46
-------
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 c
erive HAP-to-CAP Emission Factor Ratios
Description
Pollutant
Code
CAP Surrogate
Description
Pollutant
Code
CAP
Surrogate
1,1,2,2-Tetrachloroethane
79345
voc
Ethyl Chloride
75003
VOC
1,1,2-Trichloroethane
79005
voc
Ethylene Dibromide
106934
voc
1,3-Butadiene
106990
voc
Ethylene Dichloride
107062
voc
1,3-Dichloropropene
542756
voc
Ethylidene Dichloride
75343
voc
1,4-Dichlorobenzene
106467
voc
Fluoranthene
206440
PM10-FIL
2,2,4-Trimethylpentane
540841
voc
Fluorene
86737
PM10-FIL
2,4-Dinitrophenol
51285
voc
Formaldehyde
50000
VOC
2-Chloronaphthalene
91587
PM10-FIL
Hexane
110543
VOC
2-Methylnaphthalene
91576
PM10-FIL
Hydrochloric Acid
7647010
S02
4,4'-Methylenediphenyl
Diisocyanate
101688
VOC
Hydrogen Fluoride
7664393
S02
4-Nitrophenol
100027
VOC
Hydroquinone
123319
VOC
Acenaphthene
83329
PM10-FIL
lndeno[l,2,3-c,d]Pyrene
193395
PM10-FIL
Acenaphthylene
208968
PM10-FIL
Isophorone
78591
VOC
Acetaldehyde
75070
VOC
Lead
7439921
PM10-FIL
Acetonitrile
75058
VOC
Manganese
7439965
PM10-FIL
Acetophenone
98862
VOC
Mercury
7439976
PM10-FIL
Acrolein
107028
VOC
Methanol
67561
VOC
Acrylonitrile
107131
VOC
Methyl Bromide
74839
VOC
Anthracene
120127
PM10-FIL
Methyl Chloride
74873
VOC
Antimony
7440360
PM10-FIL
Methyl Chloroform
71556
VOC
Arsenic
7440382
PM10-FIL
Methyl Iodide
74884
VOC
Benz[a]Anthracene
56553
PM10-FIL
Methyl Isobutyl Ketone
108101
VOC
Benzene
71432
VOC
Methyl Tert-Butyl Ether
1634044
VOC
47
-------
Description
Pollutant
Code
CAP Surrogate
Benzo[a]Pyrene
50328
PM10-FIL
Benzo[b]Fluoranthene
205992
PM10-FIL
Benzo[e]Pyrene
192972
PM10-FIL
Benzo[g,h,l,]Perylene
191242
PM10-FIL
Benzo[k]Fluoranthene
207089
PM10-FIL
Beryllium
7440417
PM10-FIL
Biphenyl
92524
VOC
Bis(2-Ethylhexyl)Phthalate
117817
VOC
Cadmium
7440439
PM10-FIL
Carbon Disulfide
75150
VOC
Carbon Tetrachloride
56235
VOC
Chlorine
7782505
S02
Chlorobenzene
108907
VOC
Chloroform
67663
VOC
Chromium
7440473
PM10-FIL
Chromium (VI)
18540299
PM10-FIL
Chromium Trioxide
1333820
PM10-FIL
Chrysene
218019
PM10-FIL
Cobalt
7440484
PM10-FIL
Cumene
98828
VOC
Dibenzo[a,h] Anthracene
53703
PM10-FIL
Dibenzofuran
132649
VOC
Dibutyl Phthalate
84742
PM10-FIL
Dimethyl Phthalate
131113
VOC
Ethyl Benzene
100414
VOC
Description
Pollutant
Code
CAP
Surrogate
Methylene Chloride
75092
VOC
Naphthalene
91203
VOC
Nickel
7440020
PM10-FIL
Nickel Oxide
1313991
PM10-FIL
o-Xylene
95476
VOC
PAH, total
130498292
PM10-FIL
PAH/POM - Unspecified
250
PM10-FIL
Pentachlorophenol
87865
VOC
Perylene
198550
PM10-FIL
Phenanthrene
85018
PM10-FIL
Phenol
108952
VOC
Phosgene
75445
VOC
Phosphorus
7723140
PM10-FIL
Polychlorinated Biphenyls
1336363
VOC
Propionaldehyde
123386
VOC
Propylene Dichloride
78875
VOC
Pyrene
129000
PM10-FIL
Selenium
7782492
PM10-FIL
Styrene
100425
VOC
Tetrachloroethylene
127184
VOC
Toluene
108883
VOC
Trichloroethylene
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 t: 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".
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).
48
-------
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: Invalid pollutant codes for HAP augmentationTable 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: Inva
id 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.13
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.
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.
13 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.
49
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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
Ib/k-gal
mg/kLx(3.785L/gal)x(2.2046E-6 Ib/mg)
lb/ton
g/Mgx(lMg/lE6g)x(2000 lb/ton)
Hg/kgx(lkg/lE9ng)x(2000 lb/ton)
lb/1000 barrels
lb/MMBTUx(l40 MMBTU/1000 gallons oil)x(42 gallons/barrel)
Ib/MMBTU
lb/ton woodx(l ton/2000lb)x(llb/5200BTU)x(lE6 BTU/MMBTU)
lb/million cubic feet
ng/Jx(lkg/lE12ng)x(2.204lb/kg)x(1.055E9J/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.
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
50
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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-lo-CAP ratios for Hg from boiler and process heaters
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.
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). 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, Selenium, 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.
51
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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
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 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
52
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previous NEIs (2002,2005). Based on this review, no adjustments were made to the HAP Augmentation dataset
for this SCC.
3.1.6 1EPA 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, http://www.ertac.us/). 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
"ERTAC_state_comparison.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.
Table 18: EPA-estimated emissions sources expected to be exclusively 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
53
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EPA-estimated emissions
source description
Supporting data file name (see also Section 8)
EIS Sector Name
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.zip
Dust - Construction Dust
Dust from Commercial
Institutional
construction_road_res_nonres_rvsd090711.zip
Dust - Construction Dust
Dust from Road
Construction
construction_road_res_nonres_rvsd090711.zip
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 products
consumer_solvents_epa_data.zip
Solvent - Consumer &
Commercial Solvent Use
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
54
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EPA-estimated emissions
source description
Supporting data file name (see also Section 8)
EIS Sector Name
Open Burning - Land
Clearing Debris
ob_land_clearing_debris_rvsd090711.zip
Waste Disposal
Publicly Owned Treatment
Works
potw_epa_data.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.zip
Miscellaneous Non-Industrial
NEC
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
Commercial/Institutional
Fuel Combustion
fuel_comb_ici_epa_data.zip
Fuel Comb -
Comm/lnstitutional - All Fuels
Industrial Surface Coating -
Auto Refinishing
auto_refinishing_epa_data.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
Iarge_appliance_epa_data2.zip
Solvent - Industrial Surface
Coating & Solvent Use
55
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EPA-estimated emissions
source description
Supporting data file name (see also Section 8)
EIS Sector Name
Industrial Surface Coating -
Electronic and other Electric
Coatings
electronic_epa_data.zip
Solvent - Industrial Surface
Coating & Solvent Use
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 -
Miscellaneous
Manufacturing
misc_manufacturing_epa_data.zip
Solvent - Industrial Surface
Coating & Solvent Use
Industrial Maintenance
Coatings
56ndus_maintenance_epa_data.zip
Solvent - Industrial 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
gas_d i stri b_stage_l_u st_breath ing_a n d_
emptying_epa_data2.zip
Gas Stations
Gasoline Distribution - Stage
1 Trucks In Transit
gas_d i stri b_stage_l_ta n k_tru cks _i n_tra nsit_
epa_format.zip
Industrial Processes - 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 "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
56
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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 HAP-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 at ftp://ftp.epa.gov/Emislnventory/finalnei99ver3/
haps/documentation/nonpoint/nonpt99ver3 aug2003.pdf and the 2002 NEI documentation referenced in the
table is available at ftp://ftp.epa.gov/Emislnventorv/2002finalnei/documentation/nonpoint/
2002nei final nonpoint documentation0206version.pdf.
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)
Recycling
Miscellaneous Non-
Industrial NEC
Documentation for the Final 2002 Nonpoint Sector (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
57
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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 "matrix_submittals for Version 2 Feb 13
2011.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 Combustion
Fuel Comb - Comm/lnstitutional - Biomass
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 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", "2008EPA_OTHER", and "2008EPA_05NATA_GAPFL".
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.
58
-------
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.
Table 23: Hg-emitting Facilities in the S/L/T agency review process with insufficient information to gap fill
EIS
FIPS
EIS
Stat
e
EIS
Facility
ID
Category
EIS Facility
Name
EIS
company
name
EIS Address
EIS City
NATA 2005
Hg (lbs) -
facility total
NATA data
source(s) | Year:
42101
PA
4950811
Hazardou
s Waste
Incinerati
on
Sunoco
Chemicals
(Former Allied
Signal)
Na
4700
Bermuda
Street
Philadelphia
5.569941
P | 2005
13245
GA
554311
Hazardou
s Waste
Incinerati
on
DSM Chemicals
North America,
Inc.
Na
1 Columbia
Nitrogen Road
Augusta
2.257605
BOI-AUG | 2005, P
| 2005
22019
LA
6425811
Hazardou
s Waste
Incinerati
on
Olin
Corporation
Lake Charles
Plant
Olin
Corporati
on
900-960
Interstate 10
West
Westlake
3.140196
P | 2005
49045
UT
7199411
Hazardou
s Waste
Incinerati
on
Tooele Army
Depot
Tooele
Army
Depot
Environmental
Management
Division
Tooele
2.48208
BOI-AUG | 2005, P
| 2005
22011
LA
7226211
Hazardou
s Waste
Incinerati
on
MeadWestvaco
South Carolina
LLC-Specialty
Chemicals
Division
MeadWes
tvaco
South
Carolina
LLC
400 Crosby Rd
De Ridder
15.39388
P | 2005
59
-------
EIS
FIPS
EIS
Stat
e
EIS
Facility
ID
Category
EIS Facility
Name
EIS
company
name
EIS Address
EIS City
NATA 2005
Hg (lbs) -
facility total
NATA data
source(s) | Year:
22073
LA
7226711
Hazardou
s Waste
Incinerati
on
Angus Chemical
Co
Angus
Chemical
Co
350 Hwy 2
Sterlington
1.023719
P | 2005
22005
LA
8465311
Hazardou
s Waste
Incinerati
on
Rubicon LLC-
Geismar Plant
Rubicon
LLC
9156 Hwy 75
Geismar
1.726265
P | 2005, S | 2005
22005
LA
8465611
Hazardou
s Waste
Incinerati
on
BASF Corp-
GeismarSite
BASF Corp
8404 River Rd
(Hwy 75)
Geismar
1.298019
P | 2005
*NATA data source code: T=TRI, S=State, L=Local, P is EPA data from rule development, BOI-AUG is boiler augmentation
Table 24: High Risk Facilities in the S/L/T agency review process with insuf
EIS
FIPS
EIS
State
EIS Facility
ID
EIS Facility Name
EIS Company
Name
EIS Address
EIS City
High risk HAP
NATA
Emissions
(2005NATA)
(lbs) -
facility total
NATA data
source(s) |
Year1:
01047
AL
10553911
RENOSOL SEATING L.L.C
6
MEADOWCR
AFT PKWY
SELMA
2,4-TOLUENE
DIISOCYANATE
311.63
T | 2005
01015
AL
10569811
INDUSTRIAL PLATING CO.
INC.
1300
CLYDESDALE
AVE
ANNISTO
N
CHROMIUM (VI)
COMPOUNDS
10
T | 2005
12031
4358511
APAC- SOUTHEAST, INC.
NA
ARSENIC
COMPOUNDS
52.208
N | 2002
21093
KY
5345511
THE GATES CORP
NA
300 COLLEGE
ST RD
ELIZABET
HTOWN
2-
CHLOROACETOPHE
NONE
437.184
N | 2002
22101
LA
5061311
COTE BLANCHE ISLAND
TANK BATTERY # 1
SWIFT ENERGY
OPERATING LLC
10 Ml E
CYPREMO
RTPT
BENZENE
14877.58
R | 2002, R
| 2005
22005
LA
5985911
SCI FABRICATION SHOP
NA
36445 OLD
PERKINS RD.
PRAIRIEVI
LLE
CHROMIUM (VI)
COMPOUNDS
149
N | 2002
22017
LA
6116511
CADDO MANUFACTURING
LLC
VIVIAN
INDUSTRIAL
PLASTICS INC
680 S
PARDUE
VIVIAN
METHYLENE
DIPHENYL
DIISOCYANATE
4285
N | 2002, S
| 2005
25025
MA
3959411
FEDERAL METAL FINISH
FEDERAL
METAL
FINISHING INC
18
DORRANCE
ST
BOSTON
CHROMIC ACID (VI)
400
S | 2005
25025
MA
3959411
FEDERAL METAL FINISH
FEDERAL
METAL
FINISHING INC
18
DORRANCE
ST
BOSTON
CHROMIC ACID (VI)
400
S | 2005
25025
MA
3959411
FEDERAL METAL FINISH
FEDERAL
METAL
FINISHING INC
18
DORRANCE
ST
BOSTON
CHROMIC ACID (VI)
400
S | 2005
25025
MA
3959411
FEDERAL METAL FINISH
FEDERAL
METAL
FINISHING INC
18
DORRANCE
ST
BOSTON
CHROMIC ACID (VI)
400
S | 2005
25013
MA
5922911
SUDDEKOR LLC
NA
240 BOWLES
RD
AGAWAM
CHROMIUM (VI)
COMPOUNDS
146
N | 2002
28035
MS
7071711
MISSISSIPPI TANK AND
MANUFACTURING
COMPANY
AI006151
3000 WEST
SEVENTH
STREET
HATTIESB
URG
4,4'-
METHYLENEDIANILI
NE
280
N | 2002
36103
NY
8535611
WEST BABYLON LANDFILL
NA
125 GLEAM
ST
BABYLON
ACRYLONITRILE
1328.491
N | 1999
39155
OH
7330911
UNITED REFRACTORIES
NA
1929
WARREN
CHROMIUM (VI)
169
N | 2002
icient information to gap fill
60
-------
EIS
FIPS
EIS
State
EIS Facility
ID
EIS Facility Name
EIS Company
Name
EIS Address
EIS City
High risk HAP
NATA
Emissions
(2005NATA)
(lbs) -
facility total
NATA data
source(s) |
Year1:
INC
LARCHMONT
AVE.
COMPOUNDS
39035
OH
7749211
A-BRITE PLATING CO
NA
3000 W. 121
ST.
CLEVELAN
D
CHROMIUM (VI)
COMPOUNDS
255
T | 2005
39035
OH
7783011
ALCON INDS INC
NA
7990 BAKER
AVE.
CLEVELAN
D
CHROMIUM (VI)
COMPOUNDS
250
N | 2002
39035
OH
7783011
ALCON INDS INC
NA
7990 BAKER
AVE.
CLEVELAN
D
NICKEL
COMPOUNDS
250
N | 2002
39049
OH
7788911
CRANE PERFORMANCE
SIDING LLC NORTH
1550
UNIVERSAL
RD.
COLUMB
US
CHROMIUM (VI)
COMPOUNDS
74.3
T | 2005
39169
OH
8425611
PREMIUM BUILDING
PRODS CO
NA
13985
CONGRESS
RD.
WEST
SALEM
CHROMIUM (VI)
COMPOUNDS
255
T | 2005
42029
PA
2983211
TEMTCO STEEL-
PENNSYLVANIA DIV
NA
41 S.
SECOND
AVE.
PHOENIXV
ILLE
CHROMIUM (VI)
COMPOUNDS
1574
T | 2005
42133
PA
3002111
ESAB GROUP INC
NA
801 WILSON
AVENUE
HANOVER
CHROMIUM (VI)
COMPOUNDS
250
T | 2005
42133
PA
3002811
PRECISION COMPONENTS
CORP
NA
500 LINCOLN
ST.
YORK
CHROMIUM (VI)
COMPOUNDS
250
N | 2002
42071
PA
3059311
M H EBY INC
NA
1194 MAIN
ST.
BLUE
BALL
CHROMIUM (VI)
COMPOUNDS
250
N | 2002
42095
PA
3744911
CHRIN BROS SANI
LDFL/CHRIN LDFL
IESI PA
BETHLEHEM
LDFL CORP
635
INDUSTRIAL
DR
EASTON
CADMIUM
COMPOUNDS
691.8
N | 2002
42049
PA
3767111
STERIS CORP
NA
2424 W. 23rd
ST.
ERIE
CHROMIUM (VI)
COMPOUNDS
87
T | 2005
42091
PA
3848711
TUBE METHODS
INC/BRIDGEPORT
GLOBAL PKG
INC
RAMBO &
DEPOT ST
BRIDGEPO
RT
TRICHLOROETHYLE
NE
33940
S | 2005
42121
PA
3893311
JOY TECH INC PLANT #1
NA
325
BUFFALO ST.
FRANKLIN
LEAD COMPOUNDS
1447
T | 2005
42013
PA
4701911
SKF USA INC ALTOONA
PLANT
NA
1000 LOGAN
BLVD.
ALTOONA
CHROMIUM (VI)
COMPOUNDS
250
N | 2002
42081
PA
4952411
LYCOMING
ENGINES/OLIVER ST PLT
TEXTRON
LYCOMING
652 OLIVER
ST
WILLIAMS
PORT
CHROMIUM (VI)
COMPOUNDS
304.8642
BOI-AUG |
2005, R |
2002, R
2006
42041
PA
6464711
AMESTRUETEMPER INC
NA
465
RAILROAD
AVE.
CAMP
HILL
NICKEL
COMPOUNDS
500
N | 2002
42011
PA
7888811
SFS INTEC/WYOMISSING
SFS INTEC INC
SPRING ST &
VAN REED
RD
WYOMISS
ING
CHROMIUM (VI)
COMPOUNDS
2480
N | 2002
42027
PA
7889111
GRAYMONT PA
INC/PLEASANT GAP &
BELLEFONTE PLTS
GRAYMONT PA
INC
N THOMAS
ST
BELLEFON
TE
MANGANESE
COMPOUNDS
1389.6002
BOI-AUG |
2005, S |
2005
42007
PA
8520511
TEGRANT DIVERSIFIED
BRANDS INC/NEW
BRIGHTON FAC
EATON CORP
BLOCKHOUS
E RUN RD
NEW
BRIGHTO
N
CHROMIUM (VI)
COMPOUNDS
500
T | 2005
42007
PA
8520511
TEGRANT DIVERSIFIED
BRANDS INC/NEW
BRIGHTON FAC
EATON CORP
BLOCKHOUS
E RUN RD
NEW
BRIGHTO
N
MANGANESE
COMPOUNDS
500
T | 2005
45045
SC
3965911
STEVENS
AVIATION :DONALDSON
PARK
NA
600
DELAWARE
ST,
DONALDSON
RD
GREENVIL
LE
STRONTIUM
CHROMATE
1061.464
R | 2006, R
| 2002
*NATAdata source code: T=TRI, S=State, L=Local, R,P is EPA data from rule development, BOI-AUG is boiler augmentation
61
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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]
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.
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 Deparment 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
62
-------
Agency
Type
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)
3.4.3 Spatial coverage ant! 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 livestock sector
see
SCC Description, level 3
SCC Descriptions, level 4
2805001100
Beef cattle - finishing operations on feedlots (drylots)
Confinement
2805001200
Beef cattle - finishing operations on feedlots (drylots)
Manure handling and storage
2805001300
Beef cattle - finishing operations on feedlots (drylots)
Land application of manure
2805002000
Beef cattle production composite
Not Elsewhere Classified
2805003100
Beef cattle - finishing operations on pasture/range
Confinement
2805007100
Poultry production - layers with dry manure management systems
Confinement
2805007300
Poultry production - layers with dry manure management systems
Land application of manure
63
-------
see
SCC Description, level 3
SCC Descriptions, level 4
2805008100
Poultry production - layers with wet manure management systems
Confinement
2805008200
Poultry production - layers with wet manure management systems
Manure handling and storage
2805008300
Poultry production - layers with wet manure management systems
Land application of manure
2805009100
Poultry production - broilers
Confinement
2805009200
Poultry production - broilers
Manure handling and storage
2805009300
Poultry production - broilers
Land application of manure
2805010100
Poultry production - turkeys
Confinement
2805010200
Poultry production - turkeys
Manure handling and storage
2805010300
Poultry production - turkeys
Land application of manure
2805018000
Da
ry cattle composite
Not Elsewhere Classified
2805019100
Da
ry cattle-flush dairy
Confinement
2805019200
Da
ry cattle-flush dairy
Manure handling and storage
2805019300
Da
ry cattle-flush dairy
Land application of manure
2805021100
Da
ry cattle - scrape dairy
Confinement
2805021200
Da
ry cattle - scrape dairy
Manure handling and storage
2805021300
Da
ry cattle - scrape dairy
Land application of manure
2805022100
Da
ry cattle - deep pit dairy
Confinement
2805022200
Da
ry cattle - deep pit dairy
Manure handling and storage
2805022300
Da
ry cattle - deep pit dairy
Land application of manure
2805023100
Da
ry cattle - drylot/pasture dairy
Confinement
2805023200
Da
ry cattle - drylot/pasture dairy
Manure handling and storage
2805023300
Da
ry cattle - drylot/pasture dairy
Land application of manure
2805025000
Swine production composite
Not Elsewhere Classified (see
also 28-05-039, -047, -053)
2805030000
Poultry Waste Emissions
Not Elsewhere Classified (see
also 28-05-007, -008, -009)
2805030007
Poultry Waste Emissions
Ducks
2805030008
Poultry Waste Emissions
Geese
2805035000
Horses and Ponies Waste Emissions
Not Elsewhere Classified
2805039100
Swine production - operations with lagoons (unspecified animal age)
Confinement
2805039200
Swine production - operations with lagoons (unspecified animal age)
Manure handling and storage
2805039300
Swine production - operations with lagoons (unspecified animal age)
Land application of manure
2805040000
Sheep and Lambs Waste Emissions
Total
2805045000
Goats Waste Emissions
Not Elsewhere Classified
2805047100
Swine production - deep-pit house operations (unspecified animal age)
Confinement
2805047300
Swine production - deep-pit house operations (unspecified animal age)
Land application of manure
2805053100
Swine production - outdoor operations (unspecified animal 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 (http://www.agcensus.usda.gov/ 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
64
-------
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 (http://www.nass.usda.gov/Data and Statistics/Quick Stats/, 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, 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. A MMT 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 animals 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"
65
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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 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 64). 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 64).
Table 28: Emission Factors for NH3 emissions used for EPA's agricultural livestock data
Emission Factor
Emission
Reference
Description
Factor
Emission Factor Unit
(see footnotes)
Beef Cattle - Composite
county
kg NH3/cow/month
2
Beef Cattle - Drylot Operation - Confinement
9.45E-01
kg NH3/cow/month
1
Beef Cattle - Drylot Operation - Land Application
state
kg NH3/cow/month
1
Beef Cattle - Drylot Operation - Manure Storage
3.78E-04
kg NH3/cow/month
1
Beef Cattle - Pasture Operation - Confinement
county
kg NH3/cow/month
1
Dairy Cattle - Composite
county
kg NH3/cow/month
Dairy Cattle - Deep Pit Dairy Confinement
2.42E+00
kg NH3/cow/month
1
Dairy Cattle - Deep Pit Dairy Land Application
state
kg NH3/cow/month
1
Dairy Cattle - Deep Pit Dairy Manure Storage
1.13E-01
kg NH3/cow/month
1
Dairy Cattle - Drylot Dairy Confinement
state
kg NH3/cow/month
1
Dairy Cattle - Drylot Dairy Land Application
state
kg NH3/cow/month
1
66
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Emission Factor
Emission
Reference
Description
Factor
Emission Factor Unit
(see footnotes)
Dairy Cattle - Drylot Dairy Manure Storage
state
kg NH3/cow/month
1
Dairy Cattle - Flush Dairy Confinement
2.00E+00
kg NH3/cow/month
1
Dairy Cattle - Flush Dairy Land Application
state
kg NH3/cow/month
1
Dairy Cattle - Flush Dairy Manure Storage
state
kg NH3/cow/month
1
Dairy Cattle - Scrape Dairy Confinement
state
kg NH3/cow/month
1
Dairy Cattle - Scrape Dairy Land Application
state
kg NH3/cow/month
1
Dairy Cattle - Scrape Dairy Manure Storage
state
kg NH3/cow/month
1
Ducks
7.67E-02
kg NH3/duck/month
1
Geese
7.67E-02
kg NH3/goose/month
1
Goats
5.29E-01
kg NH3/goat/month
1
Horses
1.02E+00
kg NH3/horse/month
1
Poultry - Broiler Operation - Confinement
8.32E-03
kg NH3/bird/month
1
Poultry - Broiler Operation - Land Application
6.80E-03
kg NH3/bird/month
1
Poultry - Broiler Operation - Manure Storage
1.51E-03
kg NH3/bird/month
1
Poultry - Composite
2.00E-02
kg NH3/bird/month
Poultry - Layers - Dry Manure Operation - Confinement
3.36E-02
kg NH3/bird/month
1
Poultry - Layers - Dry Manure Operation - Land
Application
county
kg NH3/bird/month
1
Poultry - Layers - Wet Manure Operation - Confinement
9.45E-03
kg NH3/bird/month
1
Poultry - Layers - Wet Manure Operation - Land
Application
county
kg NH3/bird/month
1
Poultry - Layers - Wet Manure Operation - Manure
Storage
county
kg NH3/bird/month
1
Poultry - Turkey Operation - Confinement
3.78E-02
kg NH3/bird/month
1
Poultry-Turkey Operation - Land Application
3.40E-02
kg NH3/bird/month
1
Poultry - Turkey Operation - Storage
6.80E-03
kg NH3/bird/month
1
Sheep
2.65E-01
kg NH3/sheep/month
1
Swine - Composite
county
kg NH3/pig/month
1
Swine - Deep Pit Operation - Confinement
2.65E-01
kg NH3/pig/month
1
Swine - Deep Pit Operation - Land Application
county
kg NH3/pig/month
1
Swine - Lagoon Operation - Confinement
2.27E-01
kg NH3/pig/month
1
Swine - Lagoon Operation - Land Application
county
kg NH3/pig/month
1
Swine - Lagoon Operation - Manure Storage
county
kg NH3/pig/month
1
Swine - Outdoor Operation - Confinement
county
kg NH3/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.
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.
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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 = 79 / 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
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.
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• 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 NEl, 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]
• ist - Construct! • ist
[Placeholder. See also Section 3.1 and Appendix B]
! ist - Paved Roa ' t
[Placeholder. See also Section 3.1 and Appendix B]
ist - Unpaved Road Dust
[Placeholder. See also Section 3.1 and Appendix B]
iel Combust;^. ilectric 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 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
69
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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.
70
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Table 29: Agencies that Submitted EGU data
Agency
Type
Coal
Oil
Natural
Gas
Biomass
Other
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 Deparment 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
Missouri Department of Natural Resources
State
X
X
X
X
X
Montana Department of Environmental Quality
State
X
X
X
X
Navajo Nation
Tribal
X
71
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Agency
Type
Coal
Oil
Natural
Gas
Biomass
Other
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
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
Tribal
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 Agency
(Buncombe Co.)
Local
X
X
X
Wisconsin Department of Natural Resources
State
X
X
X
X
X
Wyoming Department of Environmenal 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.
72
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Table 30: 2008 NEI EGU 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 C.
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
EPA Chromium Split v2
Splits total chromium into
speciated chromium in 37
states (see Section 3.1.3)
X
X
X
X
X
4
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
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
EPATRI 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 ant! 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 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.
73
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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)
EIS Sector
PM10
Agency
(tons)
PM10
Aug
(tons)
PM10
Total
(tons)
PM2.5
Agency
(tons)
PM2.5
Aug
(tons)
PM2.5
Total
(tons)
Fuel Comb - Electric Generation - Biomass
1,244
546
1,789
429
1,041
1,469
Fuel Comb - Electric Generation - Coal
239,619
130,111
369,730
170,720
104,943
275,662
Fuel Comb - Electric Generation - Natural Gas
11,950
9,481
21,431
10,464
9,758
20,222
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
259,174
147,556
406,730
186,534
122,203
308,738
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 HCI 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 post-processing. However, for many of
the EGU units for HAPs (including Hg and HCI), 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 v2 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.
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The second EPA EGU emissions dataset (2008EPA_MATS 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.
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_lnputs" tab of the MATS
emission inventory workbook (http://www.epa.gov/ttn/atw/utilitv/mats final current base hap inven.xlsx).
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.
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
75
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used for mercury where it was believed to be based upon use of a CEM or unit-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.
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
76
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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 mutching 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 "IPM_YN". 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 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.
tel Combustion - Industrial Boilers
This section includes the description of five EIS sectors:
77
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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 (1-03 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 ant! selection hierarchy
The industrial fuel combustion sectors include data from S/L/T and 12 EPA datasets that cover both point and
nonpoint datacategories. 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.
78
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Table 32: Agencies that submitted data for the Fuel Combustion - Industrial Boilers, ICEs Sectors
Nonpoint
Point
Agenchy
Type
Bio-
mass
Coal
Nat
Gas
Oil
Other
Bio-
mass
Coal
Nat
Gas
Oil
Other
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 Deparment 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
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
79
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Nonpoint
Point
Agenchy
Type
Bio-
mass
Coal
Nat
Gas
Oil
Other
Bio-
mass
Coal
Nat
Gas
Oil
Other
Nevada Division of Environmental Protection
S
X
X
X
X
New Hampshire Department of Environmental
Services
S
X
X
X
X
X
X
X
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
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
Wisconsin Department of Natural Resources
S
X
X
X
X
X
X
X
X
Wyoming Department of Environmenal 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.
80
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Table 33: 2008 NEI selection hierarchy for datasets used by the Fuel Comb - Industrial Boilers, ICEs Sectors
Non-
DataSetName
Description
Point
point
Overwrites PM emissions from Pennsylvania. See also Table
7 and Appendix C. Even though these are EGUs, some of
EPA Overwrite Point vl.5
the SCCs used by PA puts them in the Industrial sector.
1
PM species added to gap fill missing S/L/T agency data or
make corrections where S/L/T agency have inconsistent PM
EPA PM Augmentation V2
species'emissions. See also Table 7
2
1
Adds PM species to fill in missing S/L/T agency data or make
corrections where S/L/T agency data have inconsistent
EPA PM Augmentation NP
emissions across PM species. See Table 8
2
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
EPA Chromium Split v2
(unspeciated) chromium pollutant. See also Table 7.
3
EPA other data developed for
Data added to boiler and ICE SCCs resulting from from the
using ahead of SLT for
high risk and Hg review and from the Region 2 Tonawanda
gapfilling
facility for the boiler burning coke oven gas
4
Emissions data for units identified as MATS units (based on
ORIS Ids) but with SCCs (incorrect) that put these units in
the industrial sector (l,e., first 3 digits are 102). Emissions
for these are small compared to MATS units that have fuel
2008 EPA_M ATS
combustion - electricity generation SCCs.
5
S/L/T data
6
Boiler engine and turbine emissions from Offshore oil
platforms located in Federal Waters in the Gulf of Mexico .
2008EPA MMS
See also Table 7.
7
EPA non-MATS EGU data developed from CAMD heat input
EPA EGUvl.5
and EFs. See also Section 3.10.
8
19 units were gapfilled with Hg emissions using the Boiler
MACT rule data. These 19 were among the highest
2008 EPA Rule Data from
emissions in the Boiler MACT database for which no
OAQPS/SPPD
emissions were provided by S/L/T.
9
Toxics Release inventory data used for gap-filling. Some
were assinged to industrial fuel combustion sector SCCs
based on the proportion of CAPS at those SCCs. See Table 7
EPATRI Augmentation v2
and Section 3.1.4.
10
HAP data computed from S/L/T agency criteria pollutant
data using HAP/CAP emission factor ratios . See Table 7 and
EPA HAP Augmentation v2
in Section 3.1.5.
11
Emissions from the 2005 NATA inventory used as directed
by states for facilities that were part of the NATA review
EPA 2005NATA values pulled
described in Section 3.1.7. Done for one facility in WV
forward to gapfill
burning liquid waste in an industrial boiler.3.1.7
12
81
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3.11.3 EPA-developed fuel combustion -Industrial Boilers, ICis emissions data
EPA developed data for industrial nonpoint fuel combustion (see Table 19) that was not used in the 2008 NEI
v2. 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. 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,
ftp://ftp.epa.gov/Emislnventorv/2008v2/doc/2008neiv2 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 v2 (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 gapfilling
using TRI and HAP augmentation did not have Hg emitted. We computed that we were missing 0.5 tons of Hg.
Note that this issue included all boilers, not just from the industrial sector.
tel Combustion - Commercial/Institutional
[Placeholder. See also Section 3.1 and Appendix B]
iel Combust' - • • • ..-Meiitlal - Natural Gas, Oil, and Other
[Placeholder. See also Section 3.1 and Appendix B]
iel Combustion - Residential - Wood
[Placeholder. See also Section 3.1 and Appendix B]
is Stations
[Placeholder. See also Section 3.1 and Appendix B]
'v..1 itistrr . icesses - Cement Manufacturing
[Placeholder. See also Section 3.1 and Appendix B]
J.' dustri jcesses - Chemical Manufacturing
[Placeholder. See also Section 3.1 and Appendix B]
82
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ins i ' cesses - Ferrous Metals
[Placeholder. See also Section 3.1 and Appendix B]
ins „! - cesses - Mining
[Placeholder. See also Section 3.1 and Appendix B]
" - dui/i.L'1 ' '.'cesses - Non-ferrous Metals
[Placeholder. See also Section 3.1 and Appendix B]
-itis', */ cesses- "... , ; Production
[Placeholder. See also Section 3.1 and Appendix B]
'v."" ills".-. v: w -V cesses - Petroleum Refineries
[Placeholder. See also Section 3.1 and Appendix B]
3.23 Initis ; 1 cesses - / •, -r;r
[Placeholder. See also Section 3.1 and Appendix B]
i ¦ dm i .: cesses - Storage and Transfer
[Placeholder. See also Section 3.1 and Appendix B]
.> -itis', «• .cesses - NEC (Oth ••
[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]
.J • Jilt - Consumer & Commerc v ;• >lvent Use
[Placeholder. See also Section 3.1 and Appendix B]
3.28 Solvent - Degreaslng, Dry Cleaning, and Graphic Arts
[Placeholder. See also Section 3.1 and Appendix B]
3.29 Solvent - Industrial ani Non-Industrial Surface Coating
[Placeholder. See also Section 3.1 and Appendix B]
aste Disposal
[Placeholder. See also Section 3.1 and Appendix B]
83
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4 Mobile sources
. 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 sources 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 model14. 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 Alrcr
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 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.
14 except for California, which provided emissions from the EMFAC model
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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:
Table 34: Source classification codes for the aircraft sector in the 2008 NEI
see
SCC Description
2275001000
Mobile Sources; Aircraft; Military Aircraft; Total
2275020000
Mobile Sources; Aircraft; Commercial Aircraft; Total: All Types
2275050011
Mobile Sources; Aircraft; General Aviation; Piston
2275050012
Mobile Sources; Aircraft; General Aviation; Turbine
2275085000
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.
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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
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
EPAAirportsll09
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"
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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 (climbout) 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) Climbout.
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 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, t 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
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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.
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.
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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:
LTO Pb (tons) = (piston - engine LTOHavgas Pb g/LTO)(l-Pb retention)
907,180 g/ton
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, 2011a).
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_120211.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
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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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Table 39: Non-aircraft related SCCs reported by S/L/T agencies to airports
EIS
Facility
Identifier
Agency
Facility
Identifier
Agency PSC
Site Name
SCC
Sector
8263311
05
TR405
Cloquet Carlton
County Airport
10300603
Fuel Comb -
Comm/lnstitutional
- Natural Gas
10581911
A141
COHDNREM
Huntsville -
Madison County
Airport Authority
39999999
Industrial
Processes - NEC
12342611
10046
Pinal
Arizona Soaring
40600307
Gas Stations
10026511
401131395
CARB
COUNTY OF SAN
LUIS OBISPO-
OCEANO AIRPORT
20200102
Fuel Comb -
Industrial Boilers,
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 replace) 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.
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.
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 4.3.5).
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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
28000211
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Crew Boats: Main Engine
Exhaust: Idling
CA
28000212
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Crew Boats: Main Engine
Exhaust: Maneuvering
CA, KY
28000213
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Crew Boats: Auxiliary
Generator Exhaust: Hotelling
CA
28000216
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Supply Boats: Main
Engine Exhaust: Idling
CA
28000217
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Supply Boats: Main
Engine Exhaust: Maneuvering
CA, KY
28000218
Internal Combustion Engines; Marine Vessels, Commercial; Diesel; Supply Boats: Auxiliary
Generator Exhaust: Hotelling
CA
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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 see
California Air Resources Board
State
Section 4.3.6
Delaware Department of Natural Resources and Environmental Control
State
Removed from EIS see
Idaho Department of Environmental Quality
State
section 4.3.6
Removed from EIS see
Illinois Environmental Protection Agency
State
section 4.3.6
Removed from EIS see
Kansas Department of Health and Environment
State
section 4.3.6
All emissions records
Kootenai Tribe of Idaho
Tribal
are zero
Louisville Metro Air Pollution Control District
Local
Removed from EIS see
Maryland Department of the Environment
State
section 4.3.6
Removed from EIS see
New Hampshire Department of Environmental Services
State
section 4.3.6
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.
Table 43: 2008 NEI commercial marine vehicle selection hierarchy
Priority
Dataset Name
Dataset Content
1
EPA Overwrite Nonpoint vl.5
Overwrites submitted unspeciated chromium with zero value to
prevent unspeciated chromium from being included in the 2008 NEI
(Section 4.3.4)
2
EPA Chromium Split v2
Speciates total chromium in California for SCCs 28000212 and
28000217 (Section 4.3.4).
3
State/Local/Tribal Data
Submitted commercial marine vessel emissions
4
EPA CMV
EPA data (Section 4.3.5)
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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.
[Placeholder for CAP and HAP maps and associated observations]
4.3.4 Overwrite datasets used for commercial marine vessel sector
For commercial marine vessels, the "EPA Overwrite Nonpoint vl.5" dataset contained only records that zeroed
out Texas's unspeciated chromium from this sector. The amount of chromium was not replaced with speciated
chromium data because the amount was trivial and this was a late-breaking finding of our quality assurance
efforts.
4.3.5 EPA-developed commercial marine vessel emissions data
EPA estimated CMV emission estimates15 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 4.3.5.1 and 4.3.5.2) 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.
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
15 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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4.3.5.1 .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 159 ports and 196
polygons, considering that a single port can cross county boundaries and thus include multiple polygons. The
final shapefile is listed as "port_032310.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).
4.3.5.2 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 USACE (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
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the methodology described in for each polygon as described above. The shapefiles used for the underway
emissions are available in the file "shipping_lanes_111309.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.
4,3,6 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
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.
• The 2008 NEI uses EPA data for any County/ShapelD/pollutant/SCC/emission type combination that is
present in EPA's dataset and not in the agency's. This automated merging of EPA and S/L/T data can
result in overestimates where processes are incongruent. For instance, in NJ diesel CMV emissions are
likely overestimated due to process mismatch in EPA and NJ estimates.
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Table 45: Example of Potential Error in NJ County Diesel CMV Emissions Due to Merge of Inconsistent
County/Shape/SCC/EmissionType/Pollutant combinations
FIPS
ShapelD
SCC
EmissionsTypeCode
Pollutant
Code
EPA
NJ_Submittal
2008NEIv2
34017
10085
2280002100
M
NOX
17,616.90
17,616.90
34017
2827
2280002200
C
NOX
236.50
751.88
751.88
34017
2832
2280002200
C
NOX
144.19
458.40
458.40
34017
2829
2280002200
C
NOX
79.82
253.77
253.77
34017
2828
2280002200
C
NOX
43.25
137.49
137.49
34017
2834
2280002200
C
NOX
26.58
84.49
84.49
34017
2831
2280002200
C
NOX
8.88
28.24
28.24
34017
2830
2280002200
C
NOX
6.04
19.20
19.20
Total
18,162.16
1,733.47
19,350.37
• 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's.
• 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
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Rockingham County, NH (FIP= 33015) sum for VOC, PM10-PRI and PM25-PRI, and may
occur elsewhere at a shape ID level.
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:
Table 46: SCC/Pollutant combinations where State total 2008 NEI is
greater than agency or EPA estimates
State
see
Allowed
TX
2280003100
NH3
TX
2280003100
PM10-PRI
TX
2280003100
PM25-PRI
TX
2280003100
SO 2
TX
2280003200
VOC
TX
2280003200
NOX
TX
2280003100
CO
TX
2280003200
SO 2
TX
2280003200
NOX
SC
2280003200
PM25-PRI
SC
2280003200
PM10-PRI
SC
2280003200
NH3
SC
2280003200
NOX
SC
2280003200
CO
SC
2280003200
VOC
NH
2280002200
SO 2
EPA estimates for Louisiana diesel CMV emissions (SCC=2280002*) were challenged in similar previous
NEI data as too high (http://www.dnr.mo.gov/env/apcp/docs/appendixh-7.pdf). There is also a
conference paper from the 2005 El conference:
http://www.epa.gov/ttnchiel/conference/eil4/session8/sullivan.pdf). 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.
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icomotives
ector 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 ll/lll line haul, Passenger, Commuter, and Yard. Table 47 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 47: Locomotive SCCs, descriptions, and EPA estimation status
SCC
Description
EPA/ERTAC
Estimated?
Data
Category
2285002006
Mobile Sources Railroad Equipment Diesel Line Haul
Locomotives: Class 1 Operations
Yes - in shape
files
Nonpoint
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
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 48 also submitted emissions
to the same or other locomotive SCCs.
Table 48: Agencies that submitted Rail Emissions to the 2008 NEI
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
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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 49 shows the selection hierarchy for the locomotive sector.
Table 49: 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)
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]
(verwrite 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 "Rail_EPACorrections" 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.
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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, 2011b).
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 ll/lll 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.
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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 ll/lll 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 ll/lll 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).
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.
ummary 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.
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• 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.
• 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 Dfiosel, 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
(http://www.epa.gov/otaq/nonrdmdl.htm) and the OFFROAD model
(http://www.arb.ca.gov/msei/offroad/offroad.htm) 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 (http://www.epa.gov/otaq/nmim.htm) 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
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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).
Agencies also submitted nonroad emissions. In addition to EPA's estimates, the agencies included in Table 50
submitted inputs and/or emissions to the 2008 NEI.
Table 50: Agency Submittals of NONROAD inputs and nonroad smissions
NONROAD inputs
Submitted CAP
Agency
submitted by
or HAP 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 51. 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 51: 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 discovered a need to update some of the fuel
parameter values from the assumptions used in NCD20090327. Consequently, EPA developed an updated NCD
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reflecting the revised values, which was posted in EIS as "EPA NMIM Activity NCD20090531." This 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 2010.16 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.
o Examples:
¦ VOC in Utah is 3% greater in the 2008 NEI than in the agency submittal, 34% greater in
Texas, 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).
16 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 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 and 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. This was only the case in Texas and Idaho. The possible adverse impacts of adding
emissions to these two states 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 included and the agency
did not are shown in Table 52.
o Example:
¦ NOx in Texas is 4% higher than the agency submittal, primary PM2.5 is 19% higher; due
to 10 additional SCCs in EPA and not in agency submittal. This can be avoided by
agencies in future submissions by submitting zero values for emissions from these SCCs
if that is the agency's intent.
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Table 52: 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 EquipmentOther 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
TX
2270004036
Off-highway Vehicle Diesel Lawn and Garden EquipmentSnowblowers (Commercial)
TX
2260004071
Off-highway Vehicle Gasoline, 2-Stroke Lawn and Garden Equipment Turf
Equipment (Commercial)
TX
2265004036
Off-highway Vehicle Gasoline, 4-Stroke Lawn and Garden Equipment Snowblowers
(Commercial)
TX
2260004036
Off-highway Vehicle Gasoline, 2-Stroke Lawn and Garden Equipment Snowblowers
(Commercial)
TX
2267004066
LPG Lawn and Garden Equipment Chippers/Stump Grinders (Commercial)
TX
2270004056
Off-highway Vehicle Diesel Lawn and Garden EquipmentLawn and Garden Tractors
Commercial)
TX
2268010010
CNG Industrial Equipment Other Oil Field Equipment
TX
2270010010
Off-highway Vehicle Diesel Industrial Equipment Other Oil Field Equipment
TX
2265010010
Off-highway Vehicle Gasoline, 4-Stroke Industrial Equipment Other Oil Field
Equipment
TX
2265004056
Off-highway Vehicle Gasoline, 4-Stroke Lawn and Garden Equipment Lawn and
Garden Tractors (Commercial)
• 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.
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• 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 Examples:
¦ Texas NOx is 20% higher than EPA
¦ Delaware and New York S02 are each about 300% higher than EPA, perhaps indicating
higher sulfur fuel usage than EPA assumed.
4.6 On-roac jsel ami Gasoline vehicles
This section includes the description of four EIS sectors:
• 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.
Prior versions of the 2008 NEI and past NEIs included emissions from the MOBILE6 model. The 2008 NEI v2 is
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 53 shows the
selection hierarchy
Table 53: 2008 NEI on-road mobile selection hierarchy
Priority
Dataset Name
Dataset Content
2008 EPA MOBILE
EPA's draft MOVES2010b-based estimates
Exception: California and tribes
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
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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 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.
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 is 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 IPA-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 county-specific inputs and tools that integrated the MOVES model with the SMOKE17 emission
inventory model to take advantage of the gridded hourly temperature information available from meteorology
modeling used for 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
17 A beta version of SMOKE v3.0 was used for the 2008 NEI v2. The current version is available at: http://www.smoke-
model.org/index.cfm
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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 167 "representative counties," to which every other 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 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, EPA used a modified
version of MOVES2010a (http://www.epa.gov/otaq/models/moves/index.htm). Since the release of
MOVES2010a, EPA has continued to improve MOVES. The NEI was modeled between the release of
MOVES2010a (September 2010) and the release of MOVES2010b (April 2012), and used an intermediate draft
version of the MOVES2010b model. 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.
EPA made other changes to the MOVES2010a model to facilitate the large number of parallel runs that needed
to be done to complete the NEI. Details on the changes to air toxics are detailed in a separate technical report
(US EPA, 2012). Full documentation for MOVES2010b will be available when the model is released.
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 needed by running
the SMOKE-MOVES "Met4moves" tool (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)
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4,6,4,1 Representative counties
Although EPA compiles county-specific databases for all counties in the nation, actual county-specific data are
rare. Instead, much of our "county" data are based on state-wide estimates or national defaults. For the NEI,
rather than explicitly model 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.
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 54 below.
Table 54: Characteristics for Representative County Groupings
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 (l/M) program. All
l/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 167 county groups with similar fuel, emission standards, altitude, l/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). A summary of the representative counties is
available in the spreadsheet included in "MCXREF_2008_summary.zip" and the MOVES County Database
Manager databases are available in the file "RepCounty_Counties.zip" (see Section 8.1 for access information).
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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 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. For the 2008 NEI runs, EPA used January or July to
represent other months. For example, if the grams/mile exhaust emission rates in January were identical to
February's rates for a given reference county, temperature, and other factors, then we used a single fuel month
to represent January and February. In other words, only one of the months was modeled through MOVES. The
hour-specific VMT, temperature and other factors for February were still used to calculate emissions in
February, but the emission factors themselves were not recreated since one month could represent the other
month sufficiently. The fuel months used for each representative county are available in the spreadsheet
included in "MFMREF_2008.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 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 teriary 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 case. The
counties in the GPA are defined in the Code of Federal Regulations (CFR Title 40 Section 80.215).
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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 l/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 data were not available, we used MOVES defaults.
When state-supplied data were not available, 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.
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 (WRF,
http://wrf-model.org). Advanced Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale
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numerical weather prediction system developed for both operational forecasting and atmospheric research
applications. The Meteorology-Chemistry Interface Processor (MCIP) version 3.6
(http://www.cmascenter.org/help/model docs/mcip/3.6/ReleaseNotes) was used as the software for
maintaining dynamic consistency between the meteorological model, the emissions model, and air quality
chemistry model. The hourly gridded meteorological data were post-processed by met4moves to determine the
maximum temperature ranges, average relative humidity, and a series of diurnal temperature profiles. The
hourly gridded meteorological data (output from MCIP) was also used directly by SMOKE (Section 4.6.4.7).
EPA applied the SMOKE-MOVES tool Met4moves to the WRF-based gridded, hourly temperatures to generate a
list of all the possible temperatures 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. In SMOKE-MOVES, the
vapor venting emissions are called "rate-per-profile" processing.
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 temperature profiles using the minimum and maximum
temperatures and 10 degree intervals.
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. 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. 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.
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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 MOVES2010a 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/fueltype 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 MOVES2010a
(MOVESdb20100830) 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 we could determine the
default split of each sourcetype between gasoline and diesel fueled- vehicles. Using this ratio and the
MOVES2010a (MOVESdb20100830) 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 available in the files "VMT_NEI_2008_updated2_18jan2012_v3.zip",
"VPOP_NEI_2008_18jan2012_v3.zip", "SPEED_2008NEI_18nov2011_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. This step resulted in three EF tables created for each
representative county and fuel month: Rate per Distance (RPD), rate per vehicle (RPV), and rate per profile
(RPP).
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4.6.4.8 Run SMOKE to create emissions
The SMOKE-MOVES program that combines activity data and emission factors is "Movesmrg". 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 are
monthly VMT, average monthly speed, and hourly speed profiles for weekday versus weekend (SMOKE
"SPDPRO" file)18. 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 data and uses these values to look-up the appropriate EF from
the representative county's EF table. It then 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 is vehicle population. 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 vehicle
population 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 is vehicle population. Movesmrg reads the county
based diurnal temperature range from met4moves' output for SMOKE. It uses this temperature range to
determine the most 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 vehicle population 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 each
county, rather than just for the representative counties, because Movesmrg has taken the county-specific
activity and combined it with the representative county emission rates to produce emissions.
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.
Metals and dioxins 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
18 If the SPDPRO file is available, the hourly speed takes precedence over the average monthly speed.
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appropriate VMT for a specific county to create annual emissions for that pollutant. Table 55 lists the pollutants
that we estimated using national Efs.
Table 55: Pollutants estimated through national emission factors
NEI pollutant
Description
16065831
Chromium III
1746016
2,3,7,8-Tetrachlorodibenzo-p-Dioxin
18540299
Chromium (VI)
19408743
1,2,3,7,8,9-Hexachlorodibenzo-p-Dioxin
3268879
Octachlorodibenzo-p-Dioxin
35822469
1,2,3,4,6,7,8-Heptachlorodibenzo-p-Dioxin
39001020
Octachlorodibenzofuran
39227286
1,2,3,4,7,8-Hexachlorodibenzo-p-Dioxin
40321764
1,2,3,7,8-Pentachlorodibenzo-p-Dioxin
51207319
2,3,7,8-Tetrachlorodibenzofuran
55673897
1,2,3,4,7,8,9-Heptachlorodibenzofuran
57117314
2,3,4,7,8-Pentachlorodibenzofuran
57117416
1,2,3,7,8-Pentachlorodibenzofuran
57117449
1,2,3,6,7,8-Hexachlorodibenzofuran
57653857
1,2,3,6,7,8-Hexachlorodibenzo-p-Dioxin
60851345
2,3,4,6,7,8-Hexachlorodibenzofuran
67562394
1,2,3,4,6,7,8-Heptachlorodibenzofuran
70648269
1,2,3,4,7,8-Hexachlorodibenzofuran
72918219
1,2,3,7,8,9-Hexachlorodibenzofuran
7439965
Manganese
7439976
Mercury
7440020
Nickel
7440382
Arsenic
N20
Nitrous Oxide
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 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_EPA_MOBILE".
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).
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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.
• 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.
• Identified a problem with the toluene and xylenes HAP emissions for diesel vehicles. Recalculated these
emissions by using a fixed ratio of toluene to VOC (0.00433) and xylenes to VOC (0.003784). Applied
these factors to whole inventory (both portion generated by SMOKE-MOVES and MOVES directly) to
generated new toluene and xylene emissions for diesel SCCs.
• Compared the on-road results to similar results from the previous version of the 2008 NEI. 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
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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.
• The comparisons between Version 1.5 and Version 2 of the 2008 NEI did identify one problem with
Version 2. Sulfur dioxide emissions and sulfate emissions are incorrectly inflated. This also
overestimates emissions of total PM2.5 and total PM10 (since those totals include sulfates). This
overestimate was due to a mistake in the default values for sulfur levels in 2008 diesel fuels. The
magnitude of the error varies by county, depending on the ratio of the modeled and the actual diesel
sulfur levels. Diesel fuel levels should have been closer to 15ppm sulfur because of the Ultra Low Sulfur
Diesel rulemaking phase-in, rather than the average of ~100ppm actually included. This error did not
significantly affect total emissions from diesel vehicles, but it caused the calculated values for S02 and
S04 emissions for diesel vehicles to be too high.
• Compared the 2008 NEI v2 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.
IMfires • escribed 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, and so we combined the data
outside of EIS. The 2008 NEI website (see Section 1.3.2) provides the combined wildfire and prescribed fire data
at the county-SCC resolution, but this same information is not yet available directly through EIS reports.
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 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
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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 56 lists the SCCs that define these three different types of WLFs in the 2008 NEl, 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 56: Source classification codes for wildland fires
Data Origin
Wildfires
Prescribed Burns
Wildland Fire Use
EPA
2810001000
2810015000
2810001001
States/Locals/T ribes
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 three 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), and a HAP
augmentation effort to estimate HAPs from CAP emissions where state data were used (see Section 5.1.5). The
combination of these data are only available as summary information on the 2008 NEI website and not in EIS, as
mentioned above. 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 57. The table notes when the data were
provided as events or as nonpoint data.
Table 57: Agencies that submitted wildfire and prescribed burning emissions data
Agency
Agency
Type
Rx provided
Wildfire
provided
Arizona
State/Local
as nonpoint
as event
California
State
as nonpoint
Delaware
State
as nonpoint
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/Local
as nonpoint
as event
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Agency
Wildfire
Agency
Type
Rx provided
provided
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
Tribe
as nonpoint
Indian Reservation, Montana
Omaha Tribe of Nebraska
Tribe
as nonpoint
Prairie Band Potawatomi Nation
Tribe
as nonpoint
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Tribe
as nonpoint
Idaho
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 56. 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 58 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.
Table 58: Fire emissions submitted by tribal agencies (short tons/year)
Within
Acetalde-
Formalde-
Tribe
State
CO
NOx
VOC
S02
PM2.5
PM10
NH3
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 of the
ID
Fort Hall Reservation of Idaho
Prairie Band Potawatomi Nation
KS
159
3
7
1
13
15
1
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 the
MT
15,608
312
1,070
1,159
Northern Cheyenne Indian
Reservation, Montana
Eastern Band of Cherokee Indians
NC
59
2
10
Omaha Tribe of Nebraska
NE
196
26
2
Citizen Potawatami Nation,
OK
2917
83
500
354
354
Oklahoma
All tribes
20,894
123
899
1
1,577
1,823
14
4
4
1
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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 59 shows the selection hierarchy for the wildfire and Rx burning sectors.
Table 59: 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 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 57 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 59 indicates. In most cases, many counties were null and were therefore
filled in using EPA data.
Table 57 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
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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., 2011a), 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 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: http://getbluesky.org/smartfire/
The EPA data estimate emissions for 38 pollutants. These pollutants are listed in Table 60 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).
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Table 60: 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
CO 2
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
benzoOphenanthrene
0.0039
Perylene
0.000856
benzo(a)fluoranthene
0.0026
Fluoranthene
0.00673
benzo(k)fluoranthene
0.0026
Chrysene
0.0062
methylpyrene, -fluoranthene
0.00905
methylbenzopyrenes
0.00296
Methylchrysene
0.0079
Methylanthracene
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
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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 map19
• Updated Consume 3 Python code for fuel consumption calculations
Thus, SFv2 represents a significant step forward in the use of multiple fire information data sources for the
development of fire emissions inventory activity data. More extensive details can be found in the project
documentation (Pollard et al., 2011a and Raffuse, 2012).
Using the SFv2 approach, some of EPA's 2008 emissions data are shown in several summary maps below. First
shown is the proportion of each type of fire by state in Figure 11. In the West, there are more wildfires than in
the East, where most of the burning is seen to be from prescribed burning.
19 Fuel bed information is available at http://www.fs.fed.us/pnw/fera/fccs/.
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Figure 11: Proportion of Fires by Type using EPA Methods
Weighted Distribution of Acres Burned by Fire Type
Legend
Distribution AcresBurned by Fire Type
Sum of Fields
870,000
| WF_Wildfire_AcresBumed
| RX_PrescribedBurn_AcresBurned
¦ WFU_WildfireUse_AcresBurned
Lots of Rx burning in the East
Wildfires in West
SE US, CA. TX have a lot of acres burned
Then, Figure 12 shows the total acres burned on a county-by-county basis. Active areas are seen in northern
California and in some southeastern parts of the US. Shown immediately below the "acres burned" map is
Figure 13, which shows PM2.5 emissions. For emissions, the pattern is based on not only on acres burned, but
also on fuel consumption, fuel loading, and how emission factors vary by fire type and other dynamics that occur
in a given type of fire. Certain areas in the country (eastern NC, northern MN, northern CA) stand out for
emissions but not necessarily for acres burned. This is likely due to the relationship between fire characteristics
and emission factors: prescribed fires likely have lower amounts of emissions due to flame being cooler
compared to wildfires; extensive smoldering causing emissions to accumulate over time; peat type fires burning
extensive duff; wildfires burning very hot and for a long duration causing higher emissions. For example, in
eastern NC, there is seen to be a 'hotspot' of PM2.5 emissions though the acres burned do not stand out. This is
due to the Evans road fires, which was a peat fire, and which lasted over a month in June 2008, and caused
extensive smoldering and burning of duff. More information on this fire can be found at (WITN, 2008). All of
EPA's data using the SFv2 approach on a daily basis by county and fire type can be found in the access database
named Emissions.mdb (see Section 8.1 for access information and for supporting files that describe database
fields).
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Figure 12: Acres Burned using EPA Methods
County Fires in 2008
(Acres Burned)
Legend
CountyFires_2008_AcresBurned
SumOfarea
I -2376
¦ 2377 - 6257
H 6258- 11260
¦11261 - 17766
¦ l7767- 26650
AH 26651 - 39916
[T~]39917 - 63408
Q63409- 93400
¦ 93401 - 181114
¦ 181115- 270964
Figure 13: 2008 PM2 5 Emissions using EPA methods
Legend
CountyFires_2008_PM2.5Emissions
SumOfpm25
¦ 0-200
¦ 201 - 628
¦ 629- 1359
¦ 1360- 2516
¦ 2517 - 4606
I 14607 - 8460
Q8461 - 17498
¦ l7499- 30828
¦ 30829- 55485
¦ 55486- 131548
County Fires in 2008
PM2.5 Emissions (T/Yr)
129
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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 60. 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 HAP data, and some
submitted HAPs that are not a part of the list in Table 49. 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. This was done as follows:
o 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_augmenatation_2008neiv2_Wlfires.xls" (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 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.
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 wild fires
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 11 above), then the
PM2.5 emission estimates for those states compare well to the 2008 emissions developed here.
• Where states submitted data, we compared them to EPA estimates in those same counties. Some
matches were good (e.g., Georgia, Arizona were within 10%), while some were from 15% less than EPA
130
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to 75% more than EPA estimates depending on the state and pollutant. The state agencies were not
required to provide (and did not provide) documentation of their methods for identification,
classification, and quantification of emissions from fires, which makes comparisons more difficult.
• We compared total mass of emissions (the sum of all WLFs) to past EPA inventories, which generally
showed that all pollutants were in a reasonable range given the year to year variability that would be
expected from these types of fires. This is shown in Figure 14 below, which shows SF-based PM2.5
emissions from 2003 to 2008. As mentioned previously the estimates for 2003-2007 reflect use of SFvl,
whereas our 2008NEI relies on use of SFv2 for EPA-based data, so that caveat should be considered
when looking at this time series. However, the overall model is the same and, as such, the agreement
across years for total emissions is still relevant. As shown in the figure, the total of 1.7 million tons of
PM2.5 estimated in 2008 is in line with past estimates.
Figure 14: 2008 PM2.5 wild land fire emissions using EPA methods
Annual Wildland Fire Emissions of PM2.5 (lower 48 only)
3.000
m 2.500
C
o
^ 2,000
d
o
» 1.500
e
HI
L'-< 1.000
CM
CL
500
0
2003 200-1 2005 2006 2007 2008
5.2 Fires - Agricultural field burning
EPA's approach to estimate agricultural fire emissions was done for the very first time in the 2008 NEI. In
addition to the data submitted by S/L/T agencies, EPA developed a nationally consistent agricultural fires
estimate that relies on SFvl for fire and activity level identification (acres burned). Then, EPA converted these
activity levels into emissions using emission factors and crop-usage patterns on a state-by-state basis. These
annual agricultural fire estimates reside in the EPA's non-point inventory, which are county based totals for
2008. They are also available outside of EIS as monthly totals upon request.
5.2.1 Sector Description
Agricultural burning refers to fires that occur over lands used for cultivating crops and agriculture. The SCCs that
pertain to this source in the NEI are listed below. EPA data are all put into one SCC, while state-submitted data
are entered into one of 24 different SCCs as shown in Table 61.
131
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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/T ribes
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 132peciated 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 Control
State
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
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.
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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
15 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
S02
VOC
CO
PM2.5
C02
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
Oregon
78.4
11.76
176.4
2077.6
235.2
39160.8
78.4
Pennsylvania
5.5
0.66
13.2
128.7
19.8
3822.5
5.5
South Carolina
121.8
16.24
243.6
2821.7
406
85706.6
121.8
South Dakota
102.5
14.35
205
2398.5
328
66030.5
102.5
Tennessee
207
27.6
414
4795.5
690
148729.5
207
Texas
453
81.54
815.4
10962.6
1540.2
282943.8
362.4
Utah
2.7
0.36
8.1
61.2
9.9
1168.2
2.7
Virginia
32
4.48
70.4
819.2
115.2
23225.6
32
Washington
152.8
26.74
305.6
4240.2
420.2
78042.6
114.6
Wisconsin
60.5
8.47
145.2
1415.7
229.9
43935.1
72.6
Wyoming
4.2
0.56
12.6
99.4
16.8
1961.4
5.6
US 2008 Totals
18,118
2,836
32,672
381,815
49,653
10,869,964
13,794
1 No agricultural fires identified through satellite detection methods in Connecticut, West Virginia, or Rhode Island
133
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As an example, the PM2.5 emissions data in Table 1 are summarized in Figure 15 below. It is apparent that
EPA's methods for estimating emissions from Agricultural fires show higher levels in the Mississippi Valley States
and some states in the West.
Figure 15: 2008 NEI state-total PM2.5 emissions from agricultural fires
Agricultural Fires in 2008
PM2.5 Emissions (T/Yr)
Legend
AgFiresByState_2008_PM2.5
PM2_5
>7-31
I 132 - 59
060- 115
Q116"328
[ 1329 - 518
~ 519-1015
lU 1016 -1850
>1851 - 3680
>3681 - 4469
¦ 4470-7309
Figure 16 below shows states that submitted agricultural burning data to the NEI. As with other fire data, any
state that submitted data, that data were used to represent that area in the final NEI. And as always for fires,
any data that were null (missing counties) were backfilled with EPA-based county estimates. Any "zero"
submissions were left as zero in the final NEI for those areas. Unlike with wild land fires, no efforts were made
to augment pollutants. EPA's list of pollutants for agricultural fires is listed above in Table 63. States may or
may not have submitted Ag fire data for those same pollutants, and the final NEI reflects only what the States
have submitted.
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Figure 16: Identification of states that submitted agricultural burning emissions to the NEI
Submittals / Estimates for AgFires
Legend
AgFires_EPA/State
AgFires_EPA/State
5.2.4 EPA-developed agricultural emissions data
EPA's emission estimates for Agricultural Fires begin with SFvl, and are described more fully in the EPA project
documentation (Pollard et al., 2011b). This is the older version of SMARTFIRE and we did not use this for the
wildfires or prescribed fires as described in Section 5.1. We do not believe that using SFvl for agricultural
burning emissions caused significant uncertainties because the enhancements made to SFv2 are not expected to
have significant changes for agricultural fires.
To compile the agricultural fire emissions, the fire locations from SFvl were spatially overlaid with the fuel
loading data from the FCCS module. The result is a FCCS code assigned to all fire records and locations from
SFvl. We assumed that those prescribed and unclassified fires with a FCCS code of 0 were agricultural fires.
These fires were extracted from the 2008 SFvl result to make an agricultural fire database table. Then using
ARCGIS, we further categorized fires as having occurred on "rangeland," "cropland/' or "other" land use using
the USGS 2006 National Land Cover database (http://www.mrlc.gov/nlcd2006.php). EPA only retained the
"cropland" fires in its agricultural fire inventory, since the Emission Factors EPA had available reflect crop
burning only. These raw "activity" for a count of cropland fires are available on a state-by-state basis from the
spreadsheet "rawag_activity_bystate.xlsx" (see Section 8.1 for access information).
We next converted these activity levels to emission estimates. This is done using the equation below, which is
very similar to the equation used for the Wild land fire emissions in the NEI.
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
135
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literature along with state burn-usage patterns (harvesting patterns) of these crops. The specific crops which
are included here based on publically 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 16), 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.
6 Biogenics - Vegetation and. Soil
This section is a placeholder for future additional documentation.
136
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7 Quality assessment
[This section will be included in future versions of this documentation]
hat are the quality criteria us - ! .< assess the Inventory?
7.2 How' e 2008 NEI compare to the quality criteria?
7.3 What EIS sectors seem to be Incomplete and for which key pollutants?
can the quality of the emissions data be further evaluated by users?
7.5 What Improvements In the NEI ani EIS submission process are planned for the
future?
137
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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 at:
ftp://ftp.epa.gov/Emislnventory/2008v2/doc/
8.1 Supporting data
Table 64 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.
Table 64: 2008 NEI supporting data access information
File name
File names included
Description
2008neiv2 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
2008neiv2.xlsx
codes (SCCs) and EIS sectors.
2008nei supdata 2.zip
section2-mercury
Assignments of mercury-specific
epa_2008_nei_v2_hg.accdb
categories used in Table 7 to the 2008 NEI
v2 by process (point) and county
(nonpoint, onroad and nonroad).
2008nei supdata 3a.zip
section3-
Supporting data for EPA agricultural
stationary/ag_livestock_waste/
livestock emissions estimates including
ReadMe.doc
input and output files from the emissions
See other data files as explained in
model used.
the ReadMe.doc file
Section3-stationary/nonpoint
For the nonpoint sectors included in the
ERTAC_state_comparison.xlsx
ERTAC process: provides the sectors,
SCCs, emission factors and includes a
brief description of the methodologies.
Section3-stationary/point/
Example calculations for calculating unit-
2 Attachments 1 and 2 HTIP Calcs.
level heat input when not available from
xls
CAMD.
Section3-stationary/point/
Annual 2008 emissions and heat input
CAMD08annualallprg_103009.txt
activity data for all units reporting to the
CAMD data system as of Oct 30, 2009
section3-stationary/point/
Factors used to speciate total chromium
Chromium_speciation_factors.xls
(Section 3.1.3)
section3-stationary/point/
Electric Arc Furnace test data summary
EAF ICR Test Data Summary-
area_major(EPA Rule Data).xls
section3-stationary/point/
Ratios used in the HAP augmentation
HAP EF Ratios Derived from
process
138
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File name
File names included
Description
WebFIRE.xls
section3-stationary/point/
Hg_EAF_forSLT_reviewed.xlsx
Data sent to states for review of electric
arc furnace emissions and results
section3-stationary/point/
HgFacilities_for_SLT_reviewed.xlsx
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.zip
(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.zip
(nonpoint tools)
section3-np_tools/
File names provided in Table 19
(Section 3.1.6)
Tools with best methods for nonpoint
categories without emissions estimated
2008nei supdata 4a.zip
section4-mobile/air_loco_marine/
pport07.xls
US Army Corps of Engineers Principal
Ports file for 2007.
Section4-mobile/air_loco_marine/
port_032310.zip
Shapefile for allocation of commercial
marine vessel port emissions
section4-mobile/air_loco_marine/
shipping_lanes_111309.zip
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.zip
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.zip
section4-mobile/onroad/
Onroad Read Me.docx
Description of contents of the folder
section4-mobile/onroad/
VPOP_NEI_2008_18jan2012_v3.zip
Contains the estimated vehicle
population data used in the SMOKE run.
SMOKE FF10 format - see SMOKE user
manual: www.smoke-model.org
section4-mobile/onroad/
VMT_N E l_2008_u pdated2_
18jan2012_v3.zip
Contains the estimated annual and
monthly vehicle miles traveled used in
the SMOKErun. SMOKE FF10 format - see
SMOKE user manual: www.smoke-
model.org
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,
139
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File name
File names included
Description
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_18nov2011_v0.zip
Contains the estimated vehicle average
speed data used in the SMOKErun.
SMOKE FF10 format - see SMOKE user
manual: www.smoke-model.org
section4-mobile/onroad/
spdpro_2008nei_18nov2011_v0.zip
Contains the estimated vehicle average
hourly speed data used in the SMOKE
run.SMOKE SPDPRO format - see SMOKE
user manual: www.smoke-model.org
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_2008_summary.zip
MS Excel spreadsheet: list of counties
selected to be the representative
counties for the 2008 NEI and associated
counties represented.
Section4-mobile/onroad/
MFMREF_2008.zip
MS Excel spreadsheet: 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 (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
140
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File name
File names included
Description
used by SMOKE and do not represent
daily average temperature values.
2008nei supdata 5.zip
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.xIsx
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.xls
Field descriptions for wild land fire and
emissions-related fields in
"Emissions.mdb"
section5-fires/Smartfire2/
HAP_augmentation_2008neiv2_
Wlfires.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.
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.
Table 65: 2008 NEI supporting summaries
Section No. Summary file
Description
Section 1: Introduction
Section 2: Overview
Section 3: Stationary sources
141
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Section No. Summary file
Description
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.xlsx
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)
142
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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
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)
http://www. cmu.edu/ammonia/
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 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.
http://www.epa.gov/ttn/chief/
conference/eil9/session7/huntlev.pdf
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.
http://www.epa.gov/ttn/atw/utilitv/
mats efs casestudies currentbaseei.pdf
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.
http://www.epa.gov/airtoxics/utilitv/
emis overview memo matsfinal.pdf
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
143
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Section Reference
File name or website
Rothschild, S., 2010. Detailed Plan to Develop 2008 EGU
Emissions, 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.
http://www.epa.gov/ttn/chief/
conference/eil2/point/strait.pdf
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 report:
http://www.epa.gov/ttn/atw/sab/
sabrev.html
Appendix G:
http://www.epa.gov/ttn/atw/sab/
appendix-g.pdf
U.S. Environmental Protection Agency, 2005. National
Emission Inventory - Ammonia Emissions from Animal
Agricultural Operations, Revised Draft Report, 22 April
2005, p. 4-6.
http://www.epa.gov/ttn/chief/net/
2002inventorv.html, 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.
http://www.gomr.boemre.gov/PI/
PDFImages/ESPIS/4/5056.pdf
Section 4: Mobile sources
Beardsley, M., 2010. MOVES2010: Information for
Transportation Modelers, presentation to Transportation
Research Board. January 11, 2010.
TRB-MOVES2010-Session-Beardsley.pdf
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), 2011a. Project report:
Documentation for Aircraft Component of the National
Emissions Inventory Methodology, ERG No.
0245.03.402.011, January 27, 2011.
Ai rcraft_re po rt_f i n a 1. pdf
Eastern Research Group (ERG), 2011b. 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.
http://www.faa.gov/about/office org/
headauarters offices/aep/models/edms
model/
144
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Section Reference
File name or website
Federal Aviation Administration (FAA), 2008b. General
Aviation and Part 135 Activity Survey - Calendar Year
2008.
http://www.faa.gov/data research/
aviation data statistics/
general aviation/
Renewable Fuels Association, 2011. Building Bridges to a
More Sustainable Future - 2011 Ethanol Industry
Outlook, February, 2011.
http://ethanolrfa.3cdn.net/lace47565fa
bba5d3f ifm6iskwq.pdf
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.
http://www.iwr.usace.armv.mil/ndc/db/
waternet/tons/dbf/linktons07.xls
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.
http://www.iwr.usace.armv.mil,
accessed September 10, 2009.
U.S. Environmental Protection Agency (US EPA), 2003. 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.pdf
Section 5: Fires
McCarty, J.L., Remote Sensing-Based Estimates 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 ofWildlandand
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_Emissionslnventory.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.pdf
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.html
evans_road_fire_media_report.pdf
145
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/D-20-002
Environmental Protection Air Quality Assessment Division July 2012
Agency Research Triangle Park, NC
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