2017 National Emissions Inventory, August
2019 Point Release
Technical Support Document (DRAFT)
August 2019
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August 2019
2017 National Emissions Inventory, Aug2019PT version
Technical Support Document
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Emissions Inventory and Analysis Group
Research Triangle Park, North Carolina
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Contents
List of Tables ii
Acronyms and Chemical Notations iii
1 Introduction 1-1
1.1 What data are included in the 2017 NEI, Aug 2019 release? 1-1
1.2 What is included in this documentation? 1-1
1.3 Where can I obtain the 2017 NEI PTdata? 1-2
1.3.1 Emission Inventory System Gateway 1-2
1.3.2 NEI main webpage 1-2
1.3.3 Modeling files 1-2
1.4 Why is the NEI created? 1-3
1.5 How is the NEI created? 1-3
1.6 Who are the target audiences for the 2017 NEI? 1-4
1.7 What are appropriate uses of the 2017 NEI and what are the caveats about the data? 1-5
1.8 Known issues in the 2017 NEI point, August 2019 version 1-6
2 2017 NEI contents overview 2-7
2.1 What are EIS sectors? 2-7
2.2 How is the NEI constructed? 2-9
2.2.1 Toxics Release Inventory data 2-10
2.2.2 Chromium speciation 2-10
2.2.3 HAP augmentation 2-12
2.2.4 PM augmentation 2-13
2.2.5 Other EPA datasets 2-13
2.2.6 Data Tagging 2-13
2.2.7 Inventory Selection 2-14
2.3 References for 2017 inventory contents overview 2-14
3 Point sources 3-1
3.1 Point source approach: 2017 3-1
3.1.1 QA review of S/L/T data 3-1
3.1.2 Sources of EPA data and selection hierarchy 3-2
3.1.3 Particulate matter augmentation 3-4
3.1.4 Chromium speciation 3-5
3.1.5 Use of the 2017 Toxics Release Inventory 3-5
3.1.6 HAP augmentation based on emission factor ratios 3-11
3.1.7 Cross-dataset tagging rules for overlapping pollutants 3-12
3.1.8 Additional quality assurance and findings 3-13
3.2 Airports: aircraft-related emissions 3-13
3.2.1 Sector Description 3-13
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3.2.2 Sources aircraft emissions estimates 3-14
3.3 Rail yard-related emissions 3-14
3.4 EGUs 3-14
3.5 Landfills 3-16
3.6 2017EPA_gapfills 3-17
3.7 BOEM 3-18
3.8 PM species 3-18
3.9 References for point sources 3-18
List of Tables
Table 1-1: Point source reporting thresholds (potential to emit) for CAPs in the AERR Error! Bookmark not
defined.
Table 1-2: Examples of major current uses of the NEI 1-5
Table 2-1: EIS sectors/source categories with EIS data category emissions reflected 2-7
Table 2-2: Valid chromium pollutant codes 2-10
Table 3-1: Data sets and selection hierarchy used for 2017vl NEI point source data category 3-3
Table 3-2: Mapping of TRI pollutant codes to EIS pollutant codes 3-6
Table 3-5: Landfill gas emission factors for 29 EIS pollutants 3-16
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Acronyms and Chemical Notations
AERR
Air Emissions Reporting Rule
APU
Auxiliary power unit
BEIS
Biogenics Emissions Inventory System
CI
Category 1 (commercial marine vessels)
C2
Category 2 (commercial marine vessels)
C3
Category 3 (commercial marine vessels)
CAMD
Clean Air Markets Division (of EPA Office of Air and Radiation)
CAP
Criteria Air Pollutant
CBM
Coal bed methane
CDL
Cropland Data Layer
CEC
North American Commission for Environmental Cooperation
CEM
Continuous Emissions Monitoring
CENRAP
Central Regional Air Planning Association
CERR
Consolidated Emissions Reporting Rule
CFR
Code of Federal Regulations
CH4
Methane
CMU
Carnegie Mellon University
CMV
Commercial marine vessels
CNG
Compressed natural gas
CO
Carbon monoxide
CO 2
Carbon dioxide
CSV
Comma Separated Variable
dNBR
Differenced normalized burned ratio
E10
10% ethanol gasoline
EDMS
Emissions and Dispersion Modeling System
EF
emission factor
EGU
Electric Generating Utility
EIS
Emission Inventory System
EAF
Electric arc furnace
EF
Emission factor
El
Emissions Inventory
EIA
Energy Information Administration
EMFAC
Emission FACtor (model) - for California
EPA
Environmental Protection Agencv
ERG
Eastern Research Group
ERTAC
Eastern Regional Technical Advisory Committee
FAA
Federal Aviation Administration
FACTS
Forest Service Activity Tracking System
FCCS
Fuel Characteristic Classification System
FETS
Fire Emissions Tracking System
FWS
United States Fish and Wildlife Service
FRS
Facility Registry System
GHG
Greenhouse gas
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DRAFT
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
LRTAP Long-range Transboundary Air Pollution
LTO Landing and takeoff
LPG Liquified Petroleum Gas
MARAMA Mid-Atlantic Regional Air Management Association
MATS Mercury and Air Toxics Standards
MCIP Meteorology-Chemistry Interface Processor
MMT Manure management train
MOBILE6 Mobile Source Emission Factor Model, version 6
MODIS Moderate Resolution Imaging Spectroradiometer
MOVES Motor Vehicle Emissions Simulator
MW Megawatts
MWC Municipal waste combustors
NAA Nonattainment area
NAAQS National Ambient Air Quality Standards
NAICS North American Industry Classification System
NARAP North American Regional Action Plan
NASF National Association of State Foresters
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
NESCAUM Northeast States for Coordinated Air Use Management
NFEI National Fire Emissions Inventory
NG Natural gas
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)
OEI Office of Environmental Information (of EPA)
ORIS Office of Regulatory Information Systems
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OTAQ Office of Transportation arid 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 PM2.55 (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
POTW Publicly Owned Treatment Works
PSC Program system code (in EIS)
RFG Reformulated gasoline
RPD Rate per distance
RPP Rate per profile
RPV Rate per vehicle
RVP Reid Vapor Pressure
Rx Prescribed (fire)
SCC Source classification code
SEDS State Energy Data System
SFvl SMARTFIRE version 1
SFv2 SMARTFIRE version 2
S/L/T State, local, and tribal (agencies)
SMARTFIRE Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
SMOKE Sparse Matrix Operator Kernel Emissions
S02 Sulfur dioxide
S04 Sulfate
TAF Terminal Area Forecasts
TEISS Tribal Emissions Inventory Software Solution
TRI Toxics Release Inventory
UNEP United Nations Environment Programme
USDA United States Department of Agriculture
VMT Vehicle miles traveled
VOC Volatile organic compounds
USFS United States Forest Service
WebFIRE Factor Information Retrieval System
WFU Wildland fire use
WLF Wildland fire
WRAP Western Regional Air Partnership
WRF Weather Research and Forecasting Model
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1 Introduction
EPA has posted the first part of the 2017 National Emissions Inventory (NEI), which includes the data on
pollutant emissions for individual facilities (or "point sources"). The full 2017 NEI is expected to be completed by
Spring 2020, with data about mobile sources and wild fires being available as soon as Fall 2019. This is the first
time EPA is releasing the point sources data prior to the full NEI release, to provide the data as soon as possible
for public use. This documentation provides an overview of the NEI with a focus on the August 2019 version of
the point data posted on the EPA website.
1.1 What data are included in the 2017 NEI, Aug 2019 release?
The 2017 National Emissions Inventory (NEI), Aug 2019 release, hereafter referred to as the "2017 NEI", is a
national compilation of criteria air pollutant (CAP) and hazardous air pollutant (HAP) emissions for the point
source data category. These data are collected from state, local, and tribal (S/L/T) air agencies and the
Environmental Protection Agency (EPA) emissions programs including the Toxics Release Inventory (TRI), the
Acid Rain Program, and Maximum Achievable Control Technology (MACT) standards development. The 2017 PT
inventory, or more likely minor updates to it, will become a part of the full 2017 NEI to be released later which
will contain emissions from all data categories. This document discusses only the point data category of the NEI.
The NEI program develops datasets, blends data from these multiple sources, and performs data processing
steps that further enhance, quality assure, and augment the compiled data.
The emissions data in the NEI are compiled at different levels of granularity, depending on the data category. For
point sources (in general, large facilities), emissions are inventoried at a process-level within a facility. For
nonpoint sources (typically smaller, yet pervasive sources) and mobile sources (both onroad and nonroad),
emissions are given as county totals. For marine vessel and railroad in-transit sources, emissions are given at the
sub-county polygon shape-level. For wildfires and prescribed burning, the data are compiled as day-specific,
coordinate-specific (similar to point) events in the "event" portion of the inventory, and these emission
estimates are further stratified by smoldering and flaming components.
The pollutants included in the NEI are the pollutants associated with the National Ambient Air Quality Standards
(NAAQS), known as CAPs, as well as 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 (VOCs), sulfur
dioxide (S02), particulate matter 10 microns or less (PM10), particulate matter 2.5 microns or less (PM2.5), and
ammonia (NH3), which is technically not a CAP, but an important PM precursor. The HAP pollutants include the
187 remaining HAP pollutants (methyl ethyl ketone was removed) from the original 188 listed in Section 112(b)
of the 1990 Clean Air Act Amendments1. There are many different types of HAPs. For example, some are acid
gases such as hydrochloric acid (HCI); others are heavy metals such as mercury (Hg), nickel and cadmium; and
others are organic compounds such as benzene, formaldehyde, and acetaldehyde.
1.2 What is included in this documentation?
This technical support document (TSD) provides a reference for the 2017 NEI August 2019 release. The primary
purpose of this document is to explain the sources of information included in the August 2019 version of the
point data category for the 2017 NEI. This includes showing the sources of data and types of sources that are
1 The original of HAPs is available on the EPATechnojogy Transfer Network - Air Toxics Web Site.
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used, and then providing more information about the EPA-created components of the data. Section 2 provides
an overview of the contents of the inventory. Section 3 provides an overview of point sources.
1.3 Where can I obtain the 2017 NEI PTdata?
The 2017 NEI data are available in several different ways listed below. Data are available to the reporting
agencies and EPA staff via the Emission Inventory System (EIS).
1.3.1 Emission Inventory System Gateway
The EIS Gateway is available to all EPA staff, EIS data submitters (i.e., the S/L/T air agency staff), Regional
Planning Organization staff that support state, local and tribal agencies, and contractors working for the EPA on
emissions related work. The EIS reports functions can be used to obtain raw input datasets and create summary
files from these datasets as well as older versions of the NEI such as 2011 and 2008. The 2017 NEI Point dataset
in the EIS is called "2017NEI_Aug2019_PT." Note that if you run facility-, unit- or process-level reports in the EIS,
you will get the 2017 NEI emissions, but the facility inventory, which is dynamic in the 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 (August 2019), then that new Agency ID will be in the Facility Inventory or a Facility Configuration
report in the EIS but not in the report on the public website nor the Facility Emissions Summary reports run on
the "2017NEI_Aug2019_PT" dataset in the EIS.
1.3.2 NEI main webpage
Next, data from the EIS are exported for public release on the 2.017 NEI Data webpage. The 2017 NEI Data page
includes the most recent publicly-available version of the 2017 NEI. The 2017 NEI webpage includes the 2017
NEI plan and schedules, all publicly-available supporting materials by inventory data category (e.g., point for
now, but eventually nonpoint, onroad mobile, nonroad mobile, events) and this TSD.
On the 2017 NEI Data page, two types of point data summaries are available, facility summaries and process-
level summaries. The source classification codes (SCC) data files section of the webpage provides the process
leel summaries. These detailed CSV files (provided in zip files) contain emissions at the process level. Due to
their size, they are broken out into EPA regions. Facility-level by pollutant summaries are also available. These
CSV files must be "linked" (as opposed to imported) to open them with Microsoft® Access®.
The 2017 NEI Documentation page includes links to the NEI TSD and supporting materials referenced in this TSD.
This page is a working page, meaning that content is updated as new products are developed.
1.3.3 Modeling files
The modeling files, provided on the Air Emissions Modeling website, are provided in formats that can be read by
the Sparse Matrix Operator Kernel Emissions (SMOKE). These files are also CSV formats that can be read by
other systems, such as databases. The modeling files provide the process-level emissions apportioned to release
points, and the release parameters for the release points. Release parameters include stack height, stack exit
diameter, exit temperature, exit velocity and flow rate. The EPA may make changes to the NEI modeling files
prior to use. The 2017 modeling platform is based on the 2017 NEI and is under development; it is expected to
be posted in the spring of 2020. Any changes between the NEI and modeling platform data will be described in
an accompanying TSD for the 2017 Emissions Modeling Platform, which would also be posted at the above
website.
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The Point data category SMOKE flat file for the August 2019 version of the 2017 NEI is posted at on the 2017 NE1
Flat Files FTP site.
1.4 Why is the NEI created?
The NEI is created to provide the EPA, federal, state, local and tribal decision makers, and the national and
international public the best and most complete estimates of CAP and HAP emissions. While the EPA is not
directly obligated to create the NEI, the Clean Air 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 the EPA as part of their State Implementation Plans (SIPs) that
describe how they will attain the NAAQS. The NEI is used as a starting point for many SIP inventory development
efforts and for states to obtain emissions from other states needed for their modeled attainment
demonstrations.
While the NAAQS program is the basis on which the EPA collects CAP emissions from the S/L/T 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 the EPA to assess how much emissions have been reduced since 1990.
1.5 How is the NEI created?
The Air Emissions Reporting Rule (AERR) is the regulation that requires states to submit CAP emissions, and the
Emissions Inventory Sytem is the data system used to collect, QA, and compile those submittals as well as EPA
augmentation data. Most S/L/T air agencies also provide voluntary submissions of HAP emissions. The 2008 NEI
was the first inventory compiled using the AERR, rather than its predecessor, the Consolidated Emissions
Reporting Rule (CERR). The 2017 NEI is the fourth AERR-based inventory, and improvements in the 2017 NEI
process reflect lessons learned by the S/L/T air agencies and EPA from the prior NEI efforts. The AERR requires
agencies to report all sources of emissions, except fires and biogenic sources. Reporting of open fire sources,
such as wildfires, is encouraged, but not required. Sources are divided into large groups called "data categories":
stationary sources are "point" or "nonpoint" (county totals) and mobile sources are either onroad (cars and
trucks driven on roads) or nonroad (locomotives, aircraft, marine, off-road vehicles and nonroad equipment
such as lawn and garden equipment).
The AERR has 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, by changing these
thresholds to "potential to emit" thresholds rather than actual emissions thresholds. In both the CERR and the
AERR, the emissions that are reported are actual emissions, despite that the criteria for which sources to report
is now based on potential emissions. The AERR requires emissions reporting for point sources every year, with
additional requirements every third year in the form of lower point source emissions thresholds, and 2017 is one
of these third-year inventories.
Table 1-1 provides the potential-to-emit reporting thresholds that applied for the 2017 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 of 100 tons/year or more for most criteria
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pollutants, with the exceptions of CO (1000 tons/year), and, updated starting with the 2014 inventory, Pb (0.5
tons/year, actual). As shown in the table, special requirements apply to nonattainment area (NAA) sources,
where even lower thresholds apply. The relevant ozone (03), CO, and PM10 nonattainment areas that applied
during the year that the S/L/T agencies submitted their data for the 2017 NEI are available on the
Nonattainment Areas for Criteria Pollutants (Green Book) web site.
Table 1-1: Point source reporting thresholds (potential to emit) for CAPs in the AERR
Pollutant
Triennial reporting thresholds1
Type B Sources
Thresholds within Nonattainment Areas
(1) so2
>100
>100
(2) VOC
>100
03(moderate) > 100
03 (serious) > 50
03 (severe) > 25
03 (extreme) > 10
(3) NOx
>100
>100
(4) CO
>1000
03 (all areas) > 100
CO (all areas) > 100
(5) Lead
>0.5 (actual)
>0.5 (actual)
(6) Primary PMio
>100
PMio(moderate) >100
PMio(serious) >70
(7) Primary PM2.5
>100
>100
(8) NH3
>100
>100
thresholds for point source determination shown in tons per year of potential to emit as
defined in 40 CFR part 70, with the exception of lead.
Based on the AERR requirements, S/L/T air agencies submit emissions or model inputs of point, nonpoint,
onroad mobile, nonroad mobile, and fires emissions sources. With the exception of California, reporting
agencies were required to submit model inputs for onroad and nonroad mobile sources instead of emissions.
For the 2017 NEI, all these emissions and inputs were required to be submitted to the EPA per the AERR by
December 31, 2018 (with an extension given through January 15, 2019). Once the initial reporting NEI period
closed, the EPA provided feedback on data quality such as suspected outliers and missing data by comparing to
previously established emissions ranges and past inventories. In addition, the EPA augmented the S/L/T data
using various sources of data and augmentation procedures. This documentation provides a detailed account of
EPA's quality assurance and augmentation methods.
1.6 Who are the target audiences for the 2017 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 S/L/T decision makers, and other countries. Table 1-2
below lists the major current uses of the NEI and the plans for use of the 2017 NEI in those efforts. These uses
include those by the EPA in support of the NAAQS, Air Toxics, and other programs as well as uses by other
federal and regional agencies and for international needs. 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.
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Table 1-2: Examples of major current uses of the NEI
Audience
Purposes
U.S. Public
Learn about sources of air emissions
EPA - NAAQS
Regulatory Impact Analysis - benefits estimates using air quality modeling
NAAQS Implementations, including State Implementation Plans (SIPs)
Monitoring Rules
Final NAAQS designations
NAAQS Policy Assessments
Integrated Science Assessments
Transport Rule air quality modeling (e.g., Clean Air Interstate Rule, Cross-State Air Pollution Rule)
EPA-Air toxics
National Air Toxics Assessment (NATA)
Mercury and Air Toxics Standard - mercury risk assessment and Regulatory Impact Assessment
National Monitoring Programs Annual Report
Toxicity Weighted emission trends for the Government Performance and Reporting Act (GPRA)
Residual Risk and Technology Review - starting point for inventory development
EPA - other
NEI Reports - analysis of emissions inventory data
Report on the Environment
Air Emissions website for providing graphical access to CAP emissions for state maps and Google
Earth views of facility total emissions
Department of Transportation, national transportation sector summaries of CAPs
Black Carbon Report to Congress
Other federal or
regional agencies
Modeling in support of Regional Haze SIPs and other air quality issues
International
United Nations Environment Programme (UNEP) - global and North American Assessments
The Organization for Economic Co-operation and Development (OECD) - environmental data and
indicators report
UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP) - emission reporting
requirements, air quality modeling, and science assessments
Community Emissions Data System (CEDS) - science network for earth system, climate, and
atmospheric modeling
Commission for Environmental Cooperation (CEC) - North American emissions inventory
improvement and reduction policies
U.S. and Canada Air Quality Reports
Arctic Contaminants Action Program (ACAP) - national environmental and emission reduction
strategy for the Arctic Region
Other outside
parties
Researchers and graduate students
1.7 What are appropriate uses of the 2017 NEI and what are the caveats about the data?
As shown in the preceding section, the NEI provides a readily-available comprehensive inventory of both CAP
and HAP emissions to meet a variety of user 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 U.S. or for smaller geographical areas to meet their needs. Regulatory uses of
the NEI by the EPA, such as for interstate transport, always include a public review and comment period. Large-
scale assessment uses, such as the NATA study, also provide review periods and can serve as an effective
screening tool for identifying potential risks.
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,
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tools and methods typically evolve over time in response to identified deficiencies and the need to understand
the costs and benefits of proposed emissions reductions. As these method improvements have been made,
there have not been consistent efforts to revise previous NEI year estimates to use the same methods as the
current year. Therefore, care must be taken when reviewing different NEI year publications as a time series with
the goal of determining the trend or difference in emissions from year to year. An example of such a method
change in the 2008 NEI v3 and 2011 NEI is the use of the Motor Vehicle Emissions Simulator (MOVES) model for
the onroad data category. Previous NEI years had used the Mobile Source Emission Factor Model, version 6
(MOBILES) and earlier versions of the MOBILE model for this data category. The 2011 NEI (2011v2) also used an
older version of MOVES (2014) that has been updated in the current 2014 NEI (MOVES2014a). The new version
of MOVES (used in both 2014vl and 2014v2) also calculates nonroad equipment emissions, adding VOCs and
toxics, updating the gasoline fuels used for nonroad equipment to be consistent with those used for onroad
vehicles. These changes in MOVES lead to a small increase in nonroad NOx emissions in some locations,
introducing additional uncertainty when comparing 2017 NEI to past inventories.
Other significant emissions sectors have also had improvements and, therefore, trends are also impacted by
inconsistent methods. Examples include paved and unpaved road PM emissions, ammonia fertilizer and animal
waste emissions, oil and gas production, residential wood combustion, solvents, industrial and
commercial/institutional fuel combustion and commercial marine vessel emissions.
Users should take caution in using the emissions data for filterable and condensable components of particulate
matter (PM10-FIL, PM2.5-FIL and PM-CON), which is not complete and should not be used at any aggregated
level. These data are provided for users who wish to better understand the components of the primary PM
species, where they are available, in the disaggregated, process-specific emissions reports. Where not reported
by S/L/T agencies, the EPA augments these components (see Section 2.2.4). However, not all sources are
covered by this routine, and in mobile source and fire 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.
1.8 Known issues in the 2017 NEI point, August 2019 version
Below is a list of issues in the August 2019 that we intend to resolve in the final 2017 NEI:
• Emissions for the off-shore oil&gas platforms in federal waters in the Gulf of Mexico are not included.
Emissions from these sources have been included in the 2011 and 2014 NEIs, and are expected to be
included in a future update of the 2017 NEI;
• Speciated PM2.5 components (EC, OC, S04, N03, PMFINE) and diesel PM are not included in the 2017
NEI PT. These individual component part of PM2.5 were included in the 2014 NEI and are expected to be
included in a future update of the 2017 NEI. Note that the complete aggregate PM2.5-Primary amounts
are included in the 2017 PT.
• A few agencies were late in submitting their 2017 point emissions (ND, CA, NJ, MA). It is not known if
these late submittals have resulted in any issues with the 2017 data, but it should be noted that the
point emissions for these agencies did not receive the same comparison and review OA via the draft
review process as other agencies' data.
• The selection software was enhanced for 2017 NEI to avoid having to tag pollutants belonging to the
same family from different datasets to avoid double-counting of overlapping pollutants. The definition
table implementing this selection rule allowed pollutants CN and HCN to both be selected for a given
process for 2017, whereas in previous NEI years one of these would have been tagged out. However, as
in previous years, HCN from TRI was summed with CN from TRI and was treated as CN.
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2 2017 NEI contents overview
2.1 What are EIS sectors?
First used for the 2008 NEI, EIS Sectors continue to be used for all 2017 NEI data categories, including point
sources. The sectors were 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 as indicated by the SCC to an EIS
sector. 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. The SCC-EIS Sector cross-walk used for the summaries provided in
this document is available for download from the Source Classification Codes (SCCs) website. No changes were
made to the SCC-mapping or sectors used for the 2017 NEI except where SCCs were retired, or new SCCs were
added.
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.
Since the 2008 NEI, the inventory has been reported and compiled in EIS using five major data categories: point,
nonpoint, onroad, nonroad and events. The event category is used to compile day-specific data from prescribed
burning and wildfires. While events could be other intermittent releases such as chemical spills and structure
fires, prescribed burning and wildfires have been a focus of the NEI creation effort and are the only emission
sources contained in the event data category.
Table 2-1 shows the EIS sectors or source category component of the EIS sector in the left most column. EIS data
categories -Point, Nonpoint, Onroad, Nonroad, and Events-that have emissions in these sectors/source
categories are also reflected.
As Table 2-1 illustrates, many EIS sectors include emissions from more than one EIS data category because the
EIS sectors are compiled based on the type of emissions sources rather than the data category. Note that the
emissions summary sector "Mobile - Aircraft" is reported partly to the point and partly to the nonpoint data
categories and "Mobile - Commercial Marine Vessels" and "Mobile - Locomotives" are reported to the nonpoint
data category. We include biogenics emissions, "Biogenics - Vegetation and Soil," in the nonpoint data category
in the EIS; however, we document biogenics in its own Section (8). NEI users who aggregate emissions by EIS
data category rather than EIS sector should be aware that these changes will give differences from historical
summaries of "nonpoint" and "nonroad" data unless care is taken to assign those emissions to the historical
grouping.
Table 2-1: EIS sectors/source categories with EIS data category emissions reflected
Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Event
Agriculture - Crops & Livestock Dust
0
Agriculture - Fertilizer Application
0
Agriculture - Livestock Waste
0
0
Biogenics - Vegetation and Soil
0
Bulk GasolineTerminals
0
0
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Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Event
Commercial Cooking
0
Dust - Construction Dust
0
0
Dust - Paved Road Dust
0
Dust - Unpaved Road Dust
0
Fires - Agricultural Field Burning
0
Fires - Prescribed Burning
0
Fires - Wildfires
0
Fuel Comb - Comm/lnstitutional - Biomass
0
0
Fuel Comb - Comm/lnstitutional - Coal
0
0
Fuel Comb - Comm/lnstitutional - Natural Gas
0
0
Fuel Comb - Comm/lnstitutional - Oil
0
0
Fuel Comb - Comm/lnstitutional - Other
0
0
Fuel Comb - Electric Generation - Biomass
0
Fuel Comb - Electric Generation - Coal
0
Fuel Comb - Electric Generation - Natural Gas
0
Fuel Comb - Electric Generation - Oil
0
Fuel Comb - Electric Generation - Other
0
Fuel Comb - Industrial Boilers, ICEs - Biomass
0
0
Fuel Comb - Industrial Boilers, ICEs - Coal
0
0
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
0
0
Fuel Comb - Industrial Boilers, ICEs - Oil
0
0
Fuel Comb - Industrial Boilers, ICEs - Other
0
0
Fuel Comb - Residential - Natural Gas
0
Fuel Comb - Residential - Oil
0
Fuel Comb - Residential - Other
0
Fuel Comb - Residential - Wood
0
Gas Stations
0
0
0
Industrial Processes - Cement Manufacturing
0
Industrial Processes - Chemical Manufacturing
0
0
Industrial Processes - Ferrous Metals
0
Industrial Processes - Mining
0
0
Industrial Processes - NEC
0
0
Industrial Processes - Non-ferrous Metals
0
0
Industrial Processes - Oil & Gas Production
0
0
Industrial Processes - Petroleum Refineries
0
0
Industrial Processes - Pulp & Paper
0
Industrial Processes - Storage and Transfer
0
0
Miscellaneous Non-Industrial NEC: Residential Charcoal Grilling
0
Miscellaneous Non-Industrial NEC: Portable Gas Cans
0
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Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Event
Miscellaneous Non-Industrial NEC: Nonpoint Hg
0
Miscellaneous Non-Industrial NEC (All other)
0
0
Mobile-Aircraft
0
Mobile - Commercial Marine Vessels
0
Mobile - Locomotives
0
0
Mobile - NonRoad Equipment - Diesel
0
0
Mobile - NonRoad Equipment - Gasoline
0
0
Mobile - NonRoad Equipment - Other
0
0
Mobile - Onroad - Diesel Heavy Duty Vehicles
0
Mobile - Onroad - Diesel Light Duty Vehicles
0
Mobile - Onroad - Gasoline Heavy Duty Vehicles
0
Mobile - Onroad - Gasoline Light Duty Vehicles
0
Solvent - Consumer & Commercial Solvent Use: Agricultural
Pesticides
0
Solvent - Consumer & Commercial Solvent Use: Asphalt Paving
0
Solvent - Consumer & Commercial Solvent Use: All Other Solvents
0
Solvent - Degreasing
0
0
Solvent - Dry Cleaning
0
0
Solvent - Graphic Arts
0
0
Solvent - Industrial Surface Coating & Solvent Use
0
0
Solvent - Non-Industrial Surface Coating
0
Waste Disposal: Open Burning
0
Waste Disposal: Nonpoint POTWs
0
Waste Disposal: Human Cremation
0
Waste Disposal: Nonpoint Hg
0
Waste Disposal (all remaining sources)
0
0
2.2 How is the NEI constructed?
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, the 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.
The NEI is built by data category for point, nonpoint, nonroad mobile, onroad mobile and events. Each data
category contains emissions from various reporters in multiple datasets which are blended to create the final
NEI "selection" for that data category. Each data category selection includes S/L/T data and numerous other
datasets that are discussed in more detail in each of the following sections in this document. In general, S/L/T
data take precedence in the selection hierarchy, which means that it supersedes any other data that may exist
for a specific county/tribe/facility/process/pollutant. In other words, the selection hierarchy is built such that
the preferred source of data, usually S/L/T, is chosen when multiple sources of data are available. There are
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exceptions, to this general rule, which arise based on quality assurance checks and feedback from S/L/Ts that we
will discuss in later sections.
The EPA uses augmentation and additional EPA datasets to create the most complete inventory for
stakeholders, for use in such applications as NATA, air quality modeling, national rule assessments, international
reporting, and other reports and public inquiries. Augmentation to S/L/T data, in addition to EPA datasets, fill in
gaps for sources and/or pollutants often not reported by S/L/T agencies. The basic types of augmentation are
discussed in the following sections.
2.2.1 Toxics Release Inventory data
The EPA used air emissions data from the 2017 Toxics Release Inventory (TRI) to supplement point source HAP
and NH3 emissions provided to EPA by S/L/T agencies. For 2017, all TRI emissions values that could reasonably
be matched to an EIS facility with some certainty and with limited risk of double-counting nonpoint emissions
were loaded into the EIS for viewing and comparison if desired, but only those pollutants that were not reported
anywhere at the EIS facility by the S/L/T agency were included in the 2017 NEI.
The TRI is an EPA database containing data on disposal or other releases including air emissions of over 650 toxic
chemicals from approximately 21,000 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. Section 3 provides more information on how TRI data was used to supplement the point
inventory.
2.2.2 Chromium speciation
The 2017 reporting cycle included 5 valid pollutant codes for chromium, as shown in Table 2-2.
Table 2-2: Valid chromium pollutant codes
Pollutant Code
Description
Pollutant Category Name
Speciated?
1333820
Chromium Trioxide
Chromium Compounds
yes
16065831
Chromium III
Chromium Compounds
yes
18540299
Chromium (VI)
Chromium Compounds
yes
7440473
Chromium
Chromium Compounds
no
7738945
Chromic Acid (VI)
Chromium Compounds
yes
In the above table, all pollutants but "chromium" are considered speciated, and so for clarity, chromium
(pollutant 7440473) 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 key inventory use is for risk assessment,
and since the valence states of chromium have very different risks, speciated chromium pollutants are the most
useful pollutants for the NEI. Therefore, the EPA speciates S/L/T-reported and TRI-based total chromium into
hexavalent chromium and non-hexavalent chromium. Hexavalent chromium, or Chromium (VI), is considered
high risk and other valence states are not. Most of the non-hexavalent chromium is trivalent chromium
(Chromium III); therefore, the EPA characterized all non-hexavalent chromium as trivalent chromium. The 2017
NEI does not contain any total chromium, only the speciated pollutants shown in Table 2-2.
This section describes the procedure we used for speciating chromium emissions from total chromium that was
reported by S/L/T agencies.
We used the EIS augmentation feature to speciate S/L/T agency reported total chromium. For point sources, the
EIS uses the following priority order for applying the factors:
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1) By Process ID
2) By Facility ID
3) By County
4) By State
5) By Emissions Type (for NP only)
6) By SCC
7) By Regulatory Code
8) By NAICS
9) A Default value if none of the others apply
If a particular emission source of total chromium is not covered by the speciation factors specified by any of the
first 8 attributes, a default value of 34 percent hexavalent chromium, 66 percent trivalent chromium is applied.
For the 2017 chromium augmentation, only the "By Facility ID" (2), "By SCC" (6), and "By Default" (9) were used
on S/L/T-reported total chromium values. ForTRI dataset chromium, the "By NAICS" (8) option was primarily
used, although a small number of "By Facility" (2) occurences were used rather than NAICS. The EIS generates
and stores an EPA dataset containing the resultant hexavalent and trivalent chromium species. For all other data
categories (e.g., nonpoint, onroad and nonroad), chromium speciation is performed at the SCC level.
This procedure generated hexavalent chromium (Chromium (VI)) and trivalent chromium (Chromium III), and it
had no impact on S/L/T agency data that were provided as one of the speciated forms of chromium. The sum of
the EPA-computed species (hexavalent and trivalent chromium) equals the mass of the total chromium (i.e.,
pollutant 7440473) submitted by the S/L/T agencies.
The EPA then used this dataset in the 2017 NEI selection by adding it to the data category-specific selection
hierarchy and by excluding the S/L/T agency unspeciated chromium from the selection through a pollutant
exception to the hierarchy.
Most of the speciation factors used in the 2017 NEI are SCC-based and are the same as were used in 2011 and
2014, based on data that have long been used by the EPA for NATA and other risk projects. However, some
values are updated with every inventory cycle. New data may be developed by OAQPS during rule development
or review of NATA data. The speciation factors are accessed in the EIS through the reference data link
"Augmentation Profile Information." A chromium speciation "profile" is a set of output multiplication factors for
a type of emissions source. The profile data for chromium are stored in the same tables as the HAP
augmentation factors described in Section 2.2.3. The speciation factors are a specific case of HAP augmentation
whereby the "output pollutants" are always hexavalent chromium and trivalent chromium, and the "input
pollutant" is always chromium. There are 3 main tables and a summary table. The summary table excludes the
metadata and comments regarding the derivation of the factors and assignment to SCCs; to learn more of the
derivation of the factor or assignment of "profile" to a source, the main tables (not summary table) should be
consulted.
The three main tables are:
• Augmentation Profile Names and Input Pollutants - general information about the profile and source of
the profile names and factors.
• Augmentation Multiplication Factors - provides the output pollutants and multiplication factors
associated with a given Augmentation Profile and input pollutant.
• Augmentation Assignments - provides the assignment of the profile to the data source (the list of 9
items above).
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The summary table is the Augmentation Multiplication Factors and Assignments, a composite table that
provides a view of all the combinations of output pollutants and assignment information associated with a given
profile.
For non-EIS users, the data from the main tables were downloaded and provided as described in Section 3
(3.1.4-S/L/T chromium speciation, 3.1.6 -TRI chromium speciation and 3.1.6, HAP augmentation).
2.2.3 HAP augmentation
The EPA supplements missing HAPs in S/L/T agency-reported data. HAP emissions are calculated by multiplying
appropriate surrogate CAP emissions by an emissions ratio of HAP to CAP emission factors. For the 2017 NEI, we
augmented HAPs for the point and nonpoint data categories. Generally, for point sources, the CAP-to-HAP ratios
were computed using uncontrolled emission factors from the WebPIRE database (which contains primarily
AP-42 emissions factors). For nonpoint sources, the ratios were computed from the EPA-generated nonpoint
data, which contain both CAPs and HAPs where applicable.
HAP augmentation is performed on each emissions source (i.e., specific facility and process for point sources,
county and process level for nonpoint sources) using the same EIS augmentation feature as described in
chromium speciation. However, unlike chromium speciation, there is no default augmentation factor so that not
every process that has S/L/T CAP data will end up with augmented HAP data.
HAP augmentation input pollutants are S/L/T-submitted VOC, PM10-PRI, PM25-PRI, S02, and PM10-FIL. The
resulting output can be a single output pollutant or a full suite of output pollutants. Not every source that has a
CAP undergoes HAP augmentation (i.e., livestock NH3 and fugitive dust PM25-PRI). The sum of the HAP
augmentation factors does not need to equal 1 (100%); however, we try to ensure, for example, that the sum of
HAP-VOC factors is less than 1 for mass balance. HAP augmentation factors are grouped into profiles that
contain unique output pollutant factors related to a type of source. Assigning these profiles to the individual
sources depends on the source attributes, commonly the SCC.
There are business rules specific to each data category discussed in the point (Section 3) and nonpoint (Section
4). The ultimate goal is to prevent double-counting of HAP emissions between S/L/T data and the EPA HAP
augmentation output, and to prevent, where possible, adding HAP emissions to S/L/T-submitted processes that
are not desired. NEI developers use their judgment on how to apply HAP augmentation to the resulting NEI
selection.
Caveats
HAP augmentation does have limitations; HAP and CAP emission factors from WebFIRE do not necessarily use
the same test methods. In some situations, the VOC emission factor is less than the sum of the VOC HAP
emission factors. In those situations, we normalize the HAP ratios so as not to create more VOC HAPs than VOC.
We are also aware that there are many similar SCCs that do not always share the same set of emission
factors/output pollutants. We do not apply ratios based on emission factors from similar SCCs other than for
mercury from combustion SCCs. We would prefer to get HAPs reported from reporting agencies or get the data
from other sources (compliance data from rule), but such data are not always available.
Because much of the AP-42 factors are 20+ years old, many incremental edits to these factors have been made
over time. We have removed some factors based on results of NATA reviews. For example, we discovered
ethylene dichloride was being augmented for SCCs related to gasoline distribution. This pollutant was associated
with leaded gasoline which is no longer used. Therefore, we removed it from our HAP augmentation between
2011 NEI v2 and 2014. We also received specific facility and process augmentation factors resulting from the
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NATA reviews. More discussion of the underlying data used for the 2017 NEI August2019 point version is
discussed in Section 3.1.6.
For point sources, HAPs augmentation data are not used when S/L/T air agency data exists at any process at the
facility for the same pollutant. That means that if a S/L/T reports a particular HAP at some processes but misses
others, then those other processes will not be augmented with that HAP.
2.2.4 PM augmentation
Particulate matter (PM) emissions species in the NEI are: primary PM10 (called PM10-PRI in the EIS and NEI) and
primary PM2.5 (PM25-PRI), filterable PM10 and filterable PM2.5 (PM10-FIL and PM25-FIL) and condensable PM
(PM-CON). The EPA needed to augment the S/L/T agency PM components for the point and nonpoint
inventories 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) where none was provided, or primary PM2.5
where only primary PM2.5 was provided.
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" as described in an NEI conference paper [ref 1], The resulting methodology allows the 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 are applied to S/L/T agency reported PM species or species derived from the PM Calculator
databases. The PM Calculator has undergone several edits since 1999; now called the "PM Augmentation Tool,"
this Microsoft ® Access ® database is no longer made available because it should not be run for any purpose
other than gap-filling the final NEI selection.
The PM Augmentation Tool is used only for point and nonpoint sources, and the output from the tool is heavily-
screened prior to use in the NEI. This screening is done to prevent trivial overwriting of S/L/T data from PM
Augmentation Tool calculations, particularly for primary PM submittals by S/L/Ts. More details on the caveats to
using the PM Augmentation Tool are discussed in Section 3 on point sources and Section 4 on nonpoint sources.
2.2.5 Other EPA datasets
In addition to TRI, chromium speciation, HAP and PM augmentation, the EPA generates other data to produce a
complete inventory. Examples of EPA data for point sources, discussed in Section 3, include EPA landfills, electric
generating units (EGUs), and aircraft.
2.2.6 Data Tagging
S/L/T agency data generally is used first when creating the NEI selection. When S/L/T data are used, then the NEI
would not use other data (primarily EPA data from stand-alone datasets or HAP, PM or TRI augmentation) that
also may exist for the same process/pollutant. Thus, in most cases the S/L/T agency data are used; however, for
several reasons, sometimes we need to exclude, or "tag out" S/L/T agency data. Examples of these "S/L/T tags"
are when S/L/T agency staff alert the EPA to exclude their data (because of a mistake or outdated value), or
when EPA staff find problems with submitted data. Another example is when S/L/T emissions data are
significantly less than TRI and are presumed to be incomplete, which can happen for S/L/T that use automated
gap-filling procedures for facilties that do not voluantarily provide HAP emissions. These automated procedures
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gap-fill only for processes that have emission factors and miss proceeses/pollutants for may have been reported
to TRI using other means besides published emisson factors.
In previous NEI years data tagging had also been used to avoid double-counting emissions by using emissions
from more than one dataset because the two datasets were at different levels of granularity and thus not able
to be integrated to the full process level of detail required by the standard selection hierarchy software. The
primary eample of this is the TRI dataset, which provides facility-total emissions rather than individual process-
level emissions. Because the TRI emissions must be stored to a single emission process that is not the same as
that used by the S/L/T agency, the standard hierarchy selction software would use both. Thus, tagging was used
to "block" any TRI values where the S/L/T had reported the same pollutant at any process(es) within the same
facility. For the 2017 NEI, a series of additional rules were added to the selection hierarchy to avoid such
tagging. Point source datasets are now identified as being either Process-level, Unit-level, or Facility-level
granularity, and the selection software now uses those identifications to avoid double-counting, avoiding the
need for those types of tags.
2.2.7 Inventory Selection
Once all S/L/T and EPA data are quality assured in the EIS, and all augmentation and data tagging are complete,
then we use the EIS to create a data category-specific inventory selection. To do this, each EIS dataset is
assigned a priority ranking prior to running the selection with EIS. The EIS then performs the selection at the
most detailed inventory resolution level for each data category. For point sources, this is the process and
pollutant level. For nonpoint sources, it is the process (SCC)/shape ID (i.e., ports) and pollutant level. For onroad
and nonroad sources, it is process/pollutant, and for events it is day/location/process and pollutant. At these
resolutions, the inventory selection process uses data based on highest priority and excludes data where it has
been tagged. The EPA then quality assures this final blended inventory to ensure expected processes/pollutants
are included or excluded. The EIS uses the inventory selection to also create the SMOKE Flat Files, EIS reports
and data that appear on the NEI website.
2.3 References for 2017 inventory contents overview
1. 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.
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3 Point sources
This section provides a description of sources that are in the point data category. Point sources are included in
the inventory as individual facilities, usually at specific latitude/longitude coordinates, rather than as county or
tribal aggregates. These facilities include large energy and industrial sites, such as electric generating utilities
(EGUs), mines and quarries, cement plants, refineries, large gas compressor stations, and facilities that
manufacture pulp and paper, automobiles, machinery, chemicals, fertilizers, pharmaceuticals, glass, food
products, and other products. Additionally, smaller points sources are included voluntarily by S/L/T agencies,
and can include small facilities such as crematoria, dry cleaners, and even gas stations. These smaller sources
may appear in one state but not another due to the voluntary nature of providing smaller sources. There are
also some portable sources in the point source data category, such as hot mix asphalt facilities, which relocate
frequently as a road construction project progress. The point source data category also includes emissions from
the landing and take-off portions of aircraft operations, the ground support equipment at airports, and
locomotive emissions within railyards. Within a point source facility, emissions are estimated and reported for
individual emission units and processes. Those emissions are associated with any number of stack and fugitive
release points that each have parameters needed for atmospheric modeling exercises.
The approach used to build the 2017 National Emissions Inventory (NEI) for all point sources is discussed in
Section 3.1 through Section 3.8. Some changes to aircraft for the 2014v2 NEI are also discussed in Section 3.2,
and revisions to rail yard estimates for 2014v2 are included in Section 3.3..
3.1 Point source approach: 2017
The general approach to building the NEI point source inventory is to use state/local/tribal (S/L/T)-submitted
emissions, locations, and release point parameters wherever possible. Missing emissions values are gap-filled
with EPA data where available. Quality assurance reviews of the emission values, locations, and release point
modeling parameters are done by the EPA on the most significant emission sources and where data does not
pass quality assurance checks.
3.1.1 OA review of S/L/T data
State/local/tribal agency submittals for the 2017 NEI point sources were accepted through January 15, 2019. We
then compared facility-level pollutant sums appearing in either the 2017 NEI S/L/T-submitted values or the
2014v2 NEI. The comparison included all facilities and pollutants, including any missing from the 2017 submittals
(i.e., present in 2014 but not 2017) as well as any that were new in the 2017 submittals and all that were
common to both years. The comparison table also showed the 2017 emission values from the 2017 Toxics
Release Inventory (TRI). We added columns that showed the percent differences between the 2017 S/L/T
agency-submitted facility totals and the 2014 NEIv2 and 2017 TRI datasets. To create a more focused review and
comparison table, we limited these results to include only cases where the 2017 S/L/T agency-submitted facility
total was more than 50 percent different from the 2014 facility total and with an absolute mass value of the
difference greater than a pollutant-specific threshold amount2. When a facility-pollutant combination was new
in 2017 or appeared only in the 2014 NEI v2, we included those values only when they exceeded the absolute
2 These thresholds are dvdildble on the 2014vl Supplements! Data FTP sitB 3S file
"2014_point_pollutant_thresholds_qa_flagl.xlsx"
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mass values greater than the pollutant-specific thresholds because the percent differences were undefined. We
provided3 the resulting table of 3,860 records to S/L/T agencies for review.
State/local/tribal edits to address any emissions values were accepted in the Emissions Inventory System (EIS)
until July 1, 2019. The S/L/T agencies did not change most of the highlighted values. Where the comparisons
were exceptionally suspect, the EPA contacted the agencies by phone or by email if no edits had been made to
obtain confirmation of the reported values. For a small number of cases, neither confirmation nor edits were
obtained, and the value was tagged to be excluded from selection for the NEI. In some but not all of these
instances, a value from TRI or the CAMD data sets was available as a replacement.
Similar to previous NEI years, we quality assured the latitude-longitude coordinates at both the site level and the
release point level. In previous NEI cycles, we had reviewed, verified, and locked (in EIS) approximately 2,500
site-level coordinates of the most significant emitting facilities. For the 2014 NEI coordinate review, we
compared all other site coordinate pairs to the county boundaries for the FIPS county codes reported for those
facilities. We then identified all facilities that met the following criteria: (1) more than 50 tons total criteria
pollutant emissions or more than 20 pounds total hazardous air pollutants (HAPs) for 2014, (2) the coordinates
caused the location of the facility to be more than a half mile outside of its indicated county. For these facilities,
we reviewed the location using Google Earth, edited the location as needed in EIS, and locked the location in EIS.
In addition, we compared the release point coordinates of all release points with any 2017 emissions to their
site level coordinates, whether protected or not. In cases that we found a difference of more than 0.005 degrees
(approximately 0.25 miles) in total latitude plus longitude, we reviewed the release point coordinates in Google
Earth and edited as needed in EIS, and the site-level coordinates were then locked in EIS. This check was able to
find two cases: (1) where the independently-reported release point coordinates may indicate either a suspect
site-level coordinate, even if plotting within the correct county, or (2) an inaccurate release point coordinate.
We also made a third quality assurance check to ensure that the coordinates for any release point that had
emissions greater than 10 pounds for any key high-risk HAP that was within 0.005 degrees of a verified site
coordinate. This check resulted in additional site coordinate reviews and protections. Finally, the site
coordinates as found in the EPA's Facility Registry System were compared to those in EIS. Any facilities where
these coordinates differed by more than 0.01 degrees and with greater than 50 tons criteria emissions or 500
pounds HAP emissions were reviewed, edited, and protected as needed.
3.1.2 Sources of EPA data and selection hierarchy
Table 3-1 lists the datasets that we used to compile the 2017 NEI point inventory and the hierarchy used to
choose which data value to use for the NEI when multiple data sets are available for the same emissions source
(see Section 2.2 for more detail on the EIS selection process).
The EPA developed all datasets other than those containing S/L/T agency data and the dataset containing
emissions from offshore oil and gas platforms in federal waters in the Gulf of Mexico. The primary purpose of
the EPA datasets is to add or "gap fill" pollutants or sources not provided by S/L/T agencies, to resolve
inconsistencies in S/L/T agency-reported pollutant submissions for particulate matter (PM) (Section 3.1.3) and to
speciate S/L/T agency reported total chromium into hexavalent and trivalent forms (Section 3.1.4).
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 number on the list is preferentially used over other datasets. The table includes the rationale for why each
3 We emailed the Emission Inventory System data submitters the table and instructions on March 13, 2019.
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dataset was assigned its position in the hierarchy. In addition to the order of the datasets, the selection also
considers whether individual data values have been tagged (see Section 2.2.6). Any data that were tagged by the
EPA in any of the datasets were not used. State/local/tribal agency data were tagged only if they were deemed
to be likely outliers and were not addressed during the S/L/T agency data reviews. As in earlier NEI years, the
2017vl point source selection also excluded dioxins, furans and radionuclides. The EPA has not evaluated the
completeness or accuracy of the S/L/T agency dioxin and furan values nor radionuclides and does not have plans
to supplement these reported emissions with other data sources to compile a complete and estimate for these
pollutants as part of the NEI. The 2017 NEI point source inventory does include greenhouse gas emissions.
Facility total values for four GHGs (C02, CH4, N20, and SF6) were copied from the U.S. Greenhouse Gas
Inventory Report website and matched to EIS facilities.
Table 3-1: Data sets and selection hierarchy used for 2017 NEI August release point source data category
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2017EPA_GHG
Facility-level emissions for four specific GHGs from the USEPA's Greenhouse
Gas Reporting Program
1
2017EPA_EGUmats
Emission unit level emissions for 29 HAPs from the Mercury and Air Toxics
(MATS) RTR modeling file for electric generating utilities (EGUs)
2
Responsible Agency Data
Set
S/L/T agency submitted data. These data are selected ahead of lower
hierarchy datasets except where individual values in the S/L/T agency
emissions were suspected outliers that were not addressed during the draft
review and therefore tagged by the EPA.
3
2017EPA_Cr_Aug
Hexavalent and trivalent chromium speciated from S/L/T agency reported
chromium. EIS augmentation function creates the dataset by applying
multiplication factors by SCC, facility, process or North American Industry
Classification System (NAICS) code to S/L/T agency total chromium. See
Section 3.1.4.
4
2017EPA_PM-Aug
PM components added to gap fill missing S/L/T agency data or make
corrections where S/L/T agency have inconsistent emissions across PM
components. Uses ratios of emission factors from the PM Augmentation
Tool for covered source classification codes (SCCs). For SCCs without
emission factors in the tool, checks/corrects discrepancies or missing PM
species using basic relationships such as ensuring that primary PM is
greater than or equal to filterable PM (see Section 3.1.3).
5
2017EPA_EGU
CAP and HAP emission unit level emissions from either the annual sum of
CAMD hourly CEM data for S02 and NOx or from emission factors used in
previous NEI year inventories from AP-42 and other sources multiplied by
2017 CAMD heat input data.
6
2017EPA_TRI
TRI data for the year 2017 (see Section 3.1.5). These data are selected for a
facility only when the S/L/T agency data do not include emissions for a
given pollutant at any process for that facility.
7
2017EPA_TRIcr
TRI data reported as total chromium for the year 2017 speciated into the
chromium III and chromium VI valence amounts, usually by use of a NAICs-
based speciation profile, but possibly by use of a facility-specific profile.
8
3-3
-------
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2017EPA_Airports
CAP and HAP emissions for aircraft operations including commercial,
general aviation, air taxis and military aircraft, auxiliary power units and
ground support equipment computed by the EPA for approximately 20,000
airports. Methods include the use of the Federal Aviation Administration's
(FAA's) Emissions and Dispersion Modeling System (EDMS) (see Section
3.2).
9
2017EPA_LF
Landfill emissions developed by EPA using methane data from the EPA's
GHG reporting rule program.
10
2017EPA_HAPAug
HAP data computed from S/L/T agency criteria pollutant data using
HAP/CAP EF ratios based on the EPA Factor Information Retrieval System
(WebFIRE) database as described in Section 3.1.6. These data are selected
below the TRI data because the TRI data are expected to be better.
11
2017EPA_HAPAug-
PMaug
This dataset was created in the same fashion as the 2017EPA_HAPAug
dataset above and is a supplement to it. This dataset contains HAPs
calculated by applying a ratio to PM10-FIL emissions, for those instances
where the S/L/T dataset did not contain any PM10-FIL emissions, but the
PM augmentation routine was able to calculate a PM10-FIL value from
some PM species that was reported by the S/L/T.
12
2017EPA_gapfills
2014 emissions values for 212 facilities and 12 pollutants not reported in
2017 S/L/T datasets but appear to still be operating and were above CAP
reporting thresholds in 2014. This data set also includes 2017 mercury
emissions for 6 municipal waste combustor facilities that were provided
(outside of EIS) by Maryland and Massachusetts.
13
2017EPA_2016TRI
2016 TRI ethylene oxide emission estimates for 6 facilities that are still
operating but were not reported by S/L/T or are missing from the 2017 TRI.
14
2017EPA_SPPD_PCWP
Subset of the Plywood and Composite Wood Products Manufacture (PCWP)
Risk and Technology Review (RTR) data used for gap filling HAPs at facilities
and updating facility configurations. Facilities were initially selected if either
formaldehyde or benzene were greater than 0.1 tpy. The PCWP rule
information can be found on the Plywood and Composite Wood Products
Manufacture NESHAP weboage.
15
3.1.3 Particulate matter augmentation
Particulate matter emissions components4 in the NEI are: primary PM10 (called PM10-PRI in the EIS and NEI)
and primary PM2.5 (PM25-PRI), filterable PMIO (PMIO-FIL) and filterable PM2.5 (PM25-FIL) and condensable
PM (PM-CON, which is all within the PM2.5 portion on PM, i.e., PM25-PRI = PM25-FIL + PM-CON). The EPA
needed to augment the S/L/T agency PM components 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 PMIO value for the same
process. Commonly, the augmentation added condensable PM or PM filterable (PMIO-FIL and/or PM25-FIL)
where no value was provided, or primary PM2.5 where only primary PMIO was provided. Additional information
on the procedure is provided in the 2008 NEI PM augmentation documentation [ref 1],
4 We use the term "components" here rather than "species" to avoid confusion with the PM2.5 "species" that are used for
air quality modeling (e.g., organic carbon, elemental carbon, sulfate, nitrate, and other PM).
3-4
-------
f
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" as described in an NEI conference paper [ref 2], The resulting methodology allows the 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 are applied to S/L/T agency reported PM species or species derived from the PM
Calculator databases.
3.1.4 Chromium speciation
An overview of chromium speciation, as it impacts both the point and nonpoint data category, is discussed in
Section 2.2.2.
The EIS generates and stores an EPA dataset containing the resultant hexavalent and trivalent chromium
species. The EPA then used this dataset in the 2017 NEI selection by adding it to the selection hierarchy shown in
Table 3-1, excluding the S/L/T agency total chromium from the selection through a pollutant exception to the
hierarchy. This EIS feature does not speciate chromium from any of the EPA datasets because the EPA data
contains only speciated chromium.
For the 2017 NEI, the EPA named this dataset "2017EPA_Cr_Aug." Most of the speciation factors used in the
2017 NEI are SCC-based and are the same as were used for the 2008, 2011 and 2014 NEIs. There are some
facility-specific factors resulting from reviews of previous year (e.g., 2014 and 2011) National Air Toxics
Assessment (NATA) data. Facility-specific factors were also provided for several facilities by the state of Indiana.
The factors "SLT_based_chromium_speciation.zip", based on data that have long been used by the EPA for
NATA and other risk projects, are available on the 2017 Supplemental data FTP site.
3.1.5 Use of the 2017 Toxics Release Inventory
The EPA used air emissions data from the 2017 TRI to supplement point source HAP and ammonia emissions
provided to the EPA by S/L/T agencies. The resulting augmentation dataset is labeled as "2017EPA_TRI" in the
Table 3-1 selection hierarchy shown above. For 2017, all TRI emissions values that could reasonably be matched
to an EIS facility were loaded into the EIS for viewing and comparison if desired, but only those pollutants that
were not reported anywhere at the EIS facility by the S/L/T agency were included in the 2017 NEI. The October
2018 version of these data were used, however, where emissions changes between this version and the April
2019 version of the 2017 TRI data exceeded 2%, the April 2019 version was used.
The basis of the 2017EPA_TRI dataset is the US EPA's 2017 Toxics Release Inventory (TRI) Program. The TRI is an
EPA database containing data on disposal or other releases including air emissions of over 650 toxic chemicals
from approximately 21,000 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 approach used for the 2017 NEI was like that used for the 2014 NEI. The TRI emissions were included in the
EIS (and the NEI) as facility-total stack and facility-total fugitive emissions processes, which matches the
aggregation detail of the TRI database. For the 2017 NEI PT, a change was made in how we avoid double-
counting of TRI and other data sources (primarily the S/L/T data). Rather than tagging each individual TRI facility-
based value for wherever the S/L/T had reported that pollutant at any process(es) within the same facility, we
enhanced the EIS selection software to not use values from a "Facility" level dataset if a more preferred dataset
(the S/L/T datsets) had the pollutant at that facility, (see section 2.2.6). In addition to using this new "facility-
3-5
-------
based rule" in the selection software, we also implemented a new "pollutant family rule" into the selection
software, which prevents pollutants defined as belonging to the same overlapping family of pollutants from
being selected for use if a higher preference dataset has already provided a pollutant value for that family. This
procedure had also been accomplished using tagging in previous NEI years.
The following steps describe in more detail the development of the 2017EPA_TRI dataset.
1. Update the TRIJD to EISJD facility-level crosswalk
For the 2017 NEI, the same crosswalk list of TRI IDs that was used for the 2014 NEI was used as a starting
point. A limited review of the 2017 TRI facilities was conducted to identify new facilities with significant
emissions that had not been previously matched to an EIS facility. A total of approximately 50 additional
TRI facilities were added to the crosswalk for 2017.
2. Map TRI pollutant codes to valid EIS pollutant codes and sum where necessary
Table 3-2 provides the pollutant mapping from TRI pollutants to EIS pollutants. Many of the 650 TRI
pollutants do not have any EIS counterpart, and so are not shown in Table 3-2. In addition, several EIS
pollutants may be reported to TRI as either of two TRI pollutants. For example, both Pb and Pb
compounds may be reported to TRI, and similarly for several other metal and metal compound TRI
pollutants. Table 3-2 shows where such pairs of TRI pollutants both correspond to the same EIS
pollutant. In such cases, we summed the two TRI pollutants together as part of the step of assigning the
TRI emissions to valid EIS pollutant codes. For the 2017 NEI, a total of 197 TRI pollutant codes were
mapped to 185 unique EIS pollutant codes. Similar to the 2011 and 2014 NEIs, we did not use TRI
emissions reported for TRI pollutants: "Certain Glycol Ethers," "Dioxin and Dioxin-like Compounds,"
Dichlorobenzene (mixed isomers)," and "Toluene di-isocyanate (mixed isomers)," because they do not
represent the same scope as the EIS pollutants: "Glycol ethers," "Dioxins/Furans as 2,3,7,8-TCDD TEQs,"
"1,4-Dichlorobenzene," and "2,4-Di-isocyanate," respectively. We maintained TRI stack and fugitive
emissions separately during the summation step and maintained that separation through the storage of
the TRI emissions in the EIS.
Table 3-2: Mapping of TRI pollutant codes to EIS pollutant codes
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
79345
1,1,2,2-TETRACHLOROETHANE
79345
1,1,2,2-TETRACHLOROETHANE
79005
1,1,2-TRICHLOROETHANE
79005
1,1,2-TRICHLOROETHANE
57147
1,1-DIMETHYL HYDRAZINE
57147
1,1-DIMETHYL HYDRAZINE
120821
1,2,4-TRICHLOROBENZENE
120821
1,2,4-TRICHLOROBENZENE
96128
l,2-DIBROMO-3-CHLOROPROPANE
96128
l,2-DIBROMO-3-CHLOROPROPANE
57147
1,1-DIMETHYL HYDRAZINE
57147
1,1-Dimethyl Hydrazine
106887
1,2-BUTYLENE OXIDE
106887
1,2-EPOXYBUTANE
75558
PROPYLENEIMINE
75558
1,2-PROPYLENIMINE
106990
1,3-BUTADIENE
106990
1,3-BUTADIENE
542756
1,3-DICHLOROPROPYLENE
542756
1,3-DICHLOROPROPENE
1120714
PROPANE SULTONE
1120714
1,3-PROPANESULTONE
106467
1,4-DICHLOROBENZENE
106467
1,4-DICHLOROBENZENE
25321226
DICHLOROBENZENE (MIXED ISOMERS)
NA- pollutant not used
95954
2,4,5-TRICHLOROPHENOL
95954
2,4,5-TRICHLOROPHENOL
88062
2,4,6-TRICHLOROPHENOL
88062
2,4,6-TRICHLOROPHENOL
94757
2,4-DICHLOROPHENOXY ACETIC ACID
94757
2,4-DICHLOROPHENOXY ACETIC ACID
51285
2,4-DINITROPHENOL
51285
2,4-DINITROPHENOL
121142
2,4-DINITROTOLUENE
121142
2,4-DINITROTOLUENE
53963
2-ACETYLAMINOFLUORENE
53963
2-ACETYLAMINOFLUORENE
79469
2-NITRO PROPANE
79469
2-NITROPROPANE
91941
3,3'-DICHL0R0BENZIDINE
91941
3,3'- DICHLOROBENZIDINE
119904
3,3'-DIMETH0XYBENZIDINE
119904
3,3'- DIMETHOXYBENZIDINE
3-6
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
119937
3,3'-DIMETHYLBENZIDINE
119937
3,3'-DIMETHYLBENZIDINE
101144
4,4'-METHYLENEBIS(2-CHLOROANILINE)
101144
4,4'-METHYLENEBIS(2-CHLORANILINE)
101779
4,4'-METHYLEN EDI ANILINE
101779
4,4'-METHYLENE Dl ANILINE
534521
4,6-DINITRO-O-CRESOL
534521
4,6-DINITRO-O-CRESOL
92671
4-AMINOBI PHENYL
92671
4-AMINOBIPHENYL
60117
4-DIMETHYLAMINOAZOBENZENE
60117
4-DIMETHYLAMINOAZOBENZENE
100027
4-NITROPHENOL
100027
4-NITROPHENOL
75070
ACETALDEHYDE
75070
ACETALDEHYDE
60355
ACETAMIDE
60355
ACETAMIDE
75058
ACETONITRILE
75058
ACETONITRILE
98862
ACETOPHENONE
98862
ACETOPHENONE
107028
ACROLEIN
107028
ACROLEIN
79061
ACRYLAMIDE
79061
ACRYLAMIDE
79107
ACRYLIC ACID
79107
ACRYLIC ACID
107131
ACRYLONITRILE
107131
ACRYLONITRILE
107051
ALLYL CHLORIDE
107051
ALLYL CHLORIDE
7664417
AMMONIA
NH3
AMMONIA
62533
ANILINE
62533
ANILINE
7440360
ANTIMONY
7440360
ANTIMONY
N010
ANTIMONY COMPOUNDS
7440360
ANTIMONY
7440382
ARSENIC
7440382
ARSENIC
N020
ARSENIC COMPOUNDS
7440382
ARSENIC
1332214
ASBESTOS (FRIABLE)
1332214
ASBESTOS
71432
BENZENE
71432
BENZENE
92875
BENZIDINE
92875
BENZIDINE
98077
BENZOIC TRICHLORIDE
98077
BENZOTRICHLORIDE
100447
BENZYL CHLORIDE
100447
BENZYL CHLORIDE
7440417
BERYLLIUM
7440417
BERYLLIUM
N050
BERYLLIUM COMPOUNDS
7440417
BERYLLIUM
92524
BIPHENYL
92524
BIPHENYL
117817
DI(2-ETHYLHEXYL) PHTHALATE
117817
BIS(2-ETHYLHEXYL)PHTHALATE
542881
BIS(CHLOROMETHYL) ETHER
542881
BIS(CHLOROMETHYL)ETHER
75252
BROMOFORM
75252
BROMOFORM
7440439
CADMIUM
7440439
CADMIUM
N078
CADMIUM COMPOUNDS
7440439
CADMIUM
156627
CALCIUM CYANAMIDE
156627
CALCIUM CYANAMIDE
133062
CAPTAN
133062
CAPTAN
63252
CARBARYL
63252
CARBARYL
75150
CARBON DISULFIDE
75150
CARBON DISULFIDE
56235
CARBON TETRACHLORIDE
56235
CARBON TETRACHLORIDE
463581
CARBONYL SULFIDE
463581
CARBONYL SULFIDE
120809
CATECHOL
120809
CATECHOL
57749
CHLORDANE
57749
CHLORDANE
7782505
CHLORINE
7782505
CHLORINE
79118
CHLOROACETIC ACID
79118
CHLOROACETIC ACID
108907
CHLOROBENZENE
108907
CHLOROBENZENE
510156
CHLOROBENZILATE
510156
Chlorobenzilate
67663
CHLOROFORM
67663
CHLOROFORM
107302
CHLOROMETHYL METHYL ETHER
107302
CHLOROMETHYL METHYL ETHER
126998
CHLOROPRENE
126998
CHLOROPRENE
7440473
CHROMIUM
7440473
CHROMIUM
N090
CHROMIUM COMPOUNDS (EXCEPT CHROMITE
ORE MINED IN THE TRANSVAAL REGION)
7440473
CHROMIUM
7440484
COBALT
7440484
COBALT
N096
COBALT COMPOUNDS
7440484
COBALT
1319773
CRESOL (MIXED ISOMERS)
1319773
CRESOL/CRESYLIC ACID (MIXED ISOMERS)
108394
M-CRESOL
108394
M-CRESOL
95487
O-CRESOL
95487
O-CRESOL
106445
P-CRESOL
106445
P-CRESOL
98828
CUMENE
98828
CUMENE
N106
CYANIDE COMPOUNDS
57125
CYANIDE
74908
HYDROGEN CYANIDE
57125
CYANIDE
3-7
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
132649
DIBENZOFURAN
132649
DIBENZOFURAN
84742
DIBUTYL PHTHALATE
84742
DIBUTYL PHTHALATE
111444
BIS(2-CHLOROETHYL) ETHER
111444
DICHLOROETHYL ETHER
62737
DICHLORVOS
62737
DICHLORVOS
111422
DIETHANOLAMINE
111422
DIETHANOLAMINE
64675
DIETHYL SULFATE
64675
DIETHYL SULFATE
131113
DIMETHYL PHTHALATE
131113
DIMETHYL PHTHALATE
77781
DIMETHYL SULFATE
77781
DIMETHYL SULFATE
79447
DIMETHYLCARBAMYL CHLORIDE
79447
DIMETHYLCARBAMOYL CHLORIDE
N120
DIISOCYANATES
NA- pollutant not used
26471625
TOLUENE DIISOCYANATE (MIXED ISOMERS)
NA- pollutant not used
584849
TOLUENE-2,4-DIISOCYANATE
584849
2,4-TOLUENE DIISOCYANATE
N150
DIOXIN AND DIOXIN-LIKE COMPOUNDS
NA- pollutant not used
106898
EPICHLOROHYDRIN
106898
EPICHLOROHYDRIN
140885
ETHYL ACRYLATE
140885
ETHYL ACRYLATE
51796
URETHANE
51796
ETHYL CARBAMATE
75003
CHLOROETHANE
75003
ETHYL CHLORIDE
100414
ETHYLBENZENE
100414
ETHYLBENZENE
106934
1,2-DIBROMOETHANE
106934
ETHYLENE DIBROMIDE
107062
1,2-DICHLOROETHANE
107062
ETHYLENE DICHLORIDE
107211
ETHYLENE GLYCOL
107211
ETHYLENE GLYCOL
151564
ETHYLENEIMINE
151564
ETHYLENEIMINE
75218
ETHYLENE OXIDE
75218
ETHYLENE OXIDE
96457
ETHYLENE THIOUREA
96457
ETHYLENE THIOUREA
75343
ETHYLIDENE DICHLORIDE
75343
ETHYLIDENE DICHLORIDE
50000
FORMALDEHYDE
50000
FORMALDEHYDE
N230
CERTAIN GLYCOL ETHERS
171
N/A Pollutant not used
76448
HEPTACHLOR
76448
HEPTACHLOR
118741
HEXACHLOROBENZENE
118741
HEXACHLOROBENZENE
87683
HEXACHLORO-l,3-BUTADIENE
87683
HEXACHLOROBUTADIENE
77474
HEXACHLOROCYCLOPENTADIENE
77474
HEXACHLOROCYCLOPENTADIENE
67721
HEXACHLOROETHANE
67721
HEXACHLOROETHANE
110543
N-HEXANE
110543
HEXANE
302012
HYDRAZINE
302012
HYDRAZINE
7647010
HYDROCHLORIC ACID (1995 AND AFTER "ACID
AEROSOLS" ONLY)
7647010
HYDROCHLORIC ACID
7664393
HYDROGEN FLUORIDE
7664393
HYDROGEN FLUORIDE
123319
HYDROQUINONE
123319
HYDROQUINONE
7439921
LEAD
7439921
LEAD
N420
LEAD COMPOUNDS
7439921
LEAD
58899
LINDANE
58899
1,2,3,4,5,6-HEXACHLOROCYCLOHEXANE
108316
MALEIC ANHYDRIDE
108316
MALEIC ANHYDRIDE
7439965
MANGANESE
7439965
MANGANESE
N450
MANGANESE COMPOUNDS
7439965
MANGANESE
7439976
MERCURY
7439976
MERCURY
N458
MERCURY COMPOUNDS
7439976
MERCURY
67561
METHANOL
67561
METHANOL
72435
METHOXYCHLOR
72435
METHOXYCHLOR
74839
BROMOMETHANE
74839
METHYL BROMIDE
74873
CHLOROMETHANE
74873
METHYL CHLORIDE
71556
1,1,1-TRICHLOROETHANE
71556
METHYL CHLOROFORM
74884
METHYL IODIDE
74884
METHYL IODIDE
108101
METHYL ISOBUTYL KETONE
108101
METHYL ISOBUTYL KETONE
624839
METHYL ISOCYANATE
624839
METHYL ISOCYANATE
80626
METHYL METHACRYLATE
80626
METHYL METHACRYLATE
1634044
METHYL TERT-BUTYL ETHER
1634044
METHYL TERT-BUTYL ETHER
75092
DICHLOROMETHANE
75092
METHYLENE CHLORIDE
60344
METHYL HYDRAZINE
60344
METHYLHYDRAZINE
121697
N,N-DIMETHYLANILINE
121697
N,N-DIMETHYLANILINE
68122
N,N-DIMETHYLFORM AMIDE
68122
N,N-DIMETHYLFORMAMIDE
91203
NAPHTHALENE
91203
NAPHTHALENE
7440020
NICKEL
7440020
NICKEL
3-8
-------
1
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
N495
NICKEL COMPOUNDS
7440020
NICKEL
98953
NITROBENZENE
98953
NITROBENZENE
684935
N-NITROSO-N-METHYLUREA
684935
N-NITROSO-N-METHYLUREA
90040
O-ANISIDINE
90040
O-ANISIDINE
95534
O-TOLUIDINE
95534
O-TOLUIDINE
123911
1,4-DIOXANE
123911
P-DIOXANE
56382
PARATHION
56382
PARATHION
82688
QUINTOZENE
82688
PENTACHLORONITROBENZENE
87865
PENTACHLOROPHENOL
87865
PENTACHLOROPHENOL
108952
PHENOL
108952
PHENOL
75445
PHOSGENE
75445
PHOSGENE
7803512
PHOSPHINE
7803512
PHOSPHINE
7723140
PHOSPHORUS (YELLOW OR WHITE)
7723140
PHOSPHORUS
85449
PHTHALIC ANHYDRIDE
85449
PHTHALIC ANHYDRIDE
1336363
POLYCHLORINATED BIPHENYLS
1336363
POLYCHLORINATED BIPHENYLS
120127
ANTHRACENE
120127
ANTHRACENE
191242
BENZO(G,H,l)PERYLENE
191242
BENZO[G,H,l,]PERYLENE
85018
PHENANTHRENE
85018
PHENANTHRENE
N590
POLYCYCLIC AROMATIC COMPOUNDS
130498292
PAH, TOTAL
106503
P-PHENYLEN EDI AMINE
106503
P-PHENYLENE DIAMINE
123386
PROPION ALDEHYDE
123386
PROPION ALDEHYDE
114261
PROPOXUR
114261
PROPOXUR
78875
1,2-DICHLOROPROPANE
78875
PROPYLENE DICHLORIDE
75569
PROPYLENE OXIDE
75569
PROPYLENE OXIDE
91225
QUIN0UNE
91225
QUINOLINE
106514
QUINONE
106514
QUINONE
7782492
SELENIUM
7782492
SELENIUM
N725
SELENIUM COMPOUNDS
7782492
SELENIUM
100425
STYRENE
100425
STYRENE
96093
STYRENE OXIDE
96093
STYRENE OXIDE
127184
TETRACHLOROETHYLENE
127184
TETRACHLOROETHYLENE
7550450
TITANIUM TETRACHLORIDE
7550450
TITANIUM TETRACHLORIDE
108883
TOLUENE
108883
TOLUENE
95807
2,4-DIAMINOTOLUENE
95807
TOLUENE-2,4-DI AMINE
8001352
TOXAPHENE
8001352
TOXAPHENE
79016
TRICHLOROETHYLENE
79016
TRICHLOROETHYLENE
121448
TRIETHYLAMINE
121448
TRIETHYLAMINE
1582098
TRIFLURALIN
1582098
TRIFLURALIN
108054
VINYL ACETATE
108054
VINYL ACETATE
75014
VINYL CHLORIDE
75014
VINYL CHLORIDE
75354
VINYLIDENE CHLORIDE
75354
VINYLIDENE CHLORIDE
108383
M-XYLENE
108383
M-XYLENE
95476
O-XYLENE
95476
O-XYLENE
106423
P-XYLENE
106423
P-XYLENE
1330207
XYLENE (MIXED ISOMERS)
1330207
XYLENES (MIXED ISOMERS)
An electronic database of the TRI/NEI Pollutant Crosswalk showing NEI and TRI pollutant mappings can
be downloaded from the "State/Local/Tribal (SLT), National Emission Inventory (NEI), Toxic Release
Inventory (TRI) Mapping" portion of the Product Design Team website. It should be noted that while
HCN is in the NEI and the electronic mapping shows NEI HCN to TRI HCN, we brought in both TRI HCN
and TRI CN emissions as NEI CN. We did this to avoid double counting of S/L/T CN with TRI HCN since
some S/L/T include HCN emissions as CN.
3. Split TRI total chromium emissions into hexavalent and trivalent emissions
The TRI allows facilities to report either "Chromium" or "Chromium compounds," but not the hexavalent
or trivalent chromium species that are needed for the NEI (see Section 3.1.3). Because the only
characterization available for the TRI facilities or their emissions is the facilities' NAICS codes, we created
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f
a NAICS-based set of fractions to split the TRI-reported total chromium emissions into the hexavalent
and trivalent chromium species. A table of Standard Industrial Classification (SlC)-based chromium split
fractions was available from earlier year NEI usage of TRI databases, which had been compiled by SIC
rather than NAICS. The earlier SIC-based fractions were used wherever they could be re-assigned to a
closely matching NAICS description.
Unfortunately, not all SIC-based fractions could be assigned this way, so we computed NAICS-based split
fractions for any NAICS codes in the 2017 TRI data that did not already have an SIC-to-NAICS assigned
split fraction. These factors were used for the remaining TRI-reported chromium. To calculate the NAICS-
based factors, we summed by NAICS the total amounts of chromium III and chromium VI for the entire
U.S. in the 2014 draft NEI data. These 2017 NEI S/L/T emissions were either reported directly by the
S/L/T agencies as chromium III and chromium VI, or they had been split from S/L/T agency-reported
total chromium by the EPA using the procedures described in Section 3.1.4. Those procedures largely
rely on either SCC-based or Regulatory code-based split factors. The derived NAICS split factors,
therefore, represent a weighted average of the SCC and Regulatory code-based split factors, weighted
according to the mass of each chromium valence in the 2017 NEI for that NAICS.
After all TRI facilities with chromium had been assigned a NAICS-based split factor, the factors were
applied separately to both the TRI stack and fugitive total chromium emissions. This resulted in
speciated chromium emissions for each facility's stack and fugitive emissions that were included in the
EIS as part of the 2017EPA_TRI dataset.
Similar to S/L/T chromium speciation data, the TRI chromium speciation data includes some facility-
specific values resulting from 2011 and/or 2014 NATA reviews or provided by S/L/T for use in the 2017
NEI. The TRI-chromium speciation data "TRI_based_chromium_speciation.zip" is available are available
on the 2017 Supplemental d; site.
4. Review high TRI emissions values for and exclude any data suspected to be outliers
A review and comparison of the largest TRI emissions values was conducted for several key high-risk
pollutants. The following pollutants were specifically reviewed, although a few extremely large values
for some of the other TRI pollutants were also noticed and treated in the same manner: Hg, Pb,
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. The review included looking at the largest
10 emitting facilities for each of the pollutants in the 2017 TRI dataset itself to identify large differences
between facilities and unexpected industry types. Comparisons were then made to the 2014 TRI and the
2017 draft NEI emissions values from S/L/T agencies for any suspect facilities identified by that review
(as described above in Section 3.1.1).
5. Write the 2017 TRI emissions to EIS Process IDs with stack and fugitive release points
The total facility stack and total facility fugitive emissions values from the above steps were written to a
set of EIS process IDs created to reflect those facility total type emissions. In most cases, the EIS process
IDs for a given facility already existed in EIS as a result of earlier NEI.
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f
6. Revise SCCs on the EIS Processes used for the TRI emissions
The 2002 and 2005 NEIs had assigned all the TRI emissions to a default process code SCC of 39999999,
which caused a large amount of HAP emissions to be summed to a misleading "miscellaneous" sector.
The 2008 NEI approach reduced this problem somewhat because it apportioned all TRI emissions to the
multiple processes and SCCs that were used by the S/L/T agencies to report their emissions, but this
apportioning created other distortions. The 2011 NEI reverted back to loading the TRI emissions as the
single process stack and fugitive values as reported by facilities to the TRI, but we revised the SCCs on
those single processes to something other than the default 39999999 wherever possible. The purpose of
this is to allow the TRI emissions to map to a more appropriate EIS sector. For the 2017 NEI, we retained
the 2011 approach, process IDs, and SCCs.
On occasion, TRI SCCs are updated where the process is known based on the type of facility or SCCs from
processes for which CAPs were reported. However, there has not been a systematic approach to fill in all
SCCs and for large industrial facilities, it would not be possible due to the variety of different process
operations that can occur at such facilities.
3.1.6 HAP augmentation based on emission factor ratios
The 2017EPA_HAP-augmentation dataset was used for gap filling missing HAPs in the S/L/T agency-reported
data. 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 EFs. For point sources, these EF ratios were largely the same as
were used in the 2008 NEI v3, though additional quality assurance resulted in some changes. The ratios were
computed using the EFs from WebFIRE and are based solely on the SCC code. The computation of these point
HAP to CAP ratios is described in detail in the 2008 NEI documentation, Section 3.1.5.
For pollutants other than Hg, we computed ratios for only the SCCs in WebFIRE that met specific criteria: 1) the
CAP and HAP WebFIRE EFs were both based on uncontrolled emissions and, 2) the units of the EF had to be the
same or be able to be converted to the same units. In addition, for Hg, we added ratios for point SCCs that were
not in WebFIRE for both PM10-FIL (the CAP surrogate for Hg) and Hg by using Hg or PM10-FIL factors for similar
SCCs and computing the resulting ratio. That process is described (and supporting data files provided) in the
2003 NEI documentation (Section 3.1.5.2), since these additional Hg augmentation factors were used in the
2008 NEI v3 as well.
A HAP augmentation feature was built into the EIS for the 2011 cycle, and the HAP EF ratios are available to the
EIS users through the reference data link "Augmentation Profile Information." The same tables provide both the
HAP augmentation factors and chromium speciation factors and were discussed in Section 2.2.2.
Since the initial set of HAP augmentation factors, factors and/or SCC-assignments were added including facility-
specific HAP augmentation factors resulting from NATA reviews. Also new for the 2017 NEI are facility-specific
coke oven to S02 ratios used to compute coke oven emissions for specific facilies with operating coke ovens that
were missing coke oven emissions. We have been also exploring using test-based emission factor ratios in place
of WebFIRE-based ratios where data are sufficient to do so. Users interested the few test-based factors that do
not have access to EIS can download the full set of HAP augmentation factors from the 2017 Supplemental data
FTP site ("HAPaugmentation.zip") and peruse the metadata information (data source and factor comments) to
extract them.
A key facet of our approach is that the resulting HAP augmentation dataset does duplicate HAPs from the S/L/T
agency data or other EPA datasets. The extra step of data tagging of the HAP augmentation dataset was taken to
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ensure the NEI would not use the data from the HAP augmentation dataset for facilities where the HAP was
reported by an S/L/T agency at any process at the facility or where the HAP was included in the EPA TRI dataset.
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 we would not use any records from the HAP
augmentation dataset containing formaldehyde from any processes at the facility. If that facility had no
formaldehyde, but the TRI dataset had formaldehyde for any processes at that facility, then the NEI would still
not use formaldehyde from the HAP augmentation dataset for any of the processes (it would use the TRI data).
If the EPA EGU dataset contained formaldehyde for that facility, we would use the HAP augmentation set but
not for any process at the same unit as EPA EGU dataset. If the EPA EGU dataset contained formaldehyde at
process A or any other process within the same unit as process A, then the HAP augmentation dataset would be
used for processes B and C, but not process A.
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 to be
submitted at the process level, but HAPs are entirely voluntary for the NEI's reporting rule. We used the EIS new
pollutant overlapping business rules (Section 3.3.17) to prevent double counting of pollutants belonging to
pollutant groups that may overlap with other pollutants in that group.
One of the changes we made from previous NEI's is that we no longer tag out point source HAP augmentation
values where the HAP augmentation value exceeded the maximum emissions reported by any S/L/T agency for
the same SCC/pollutant combination, or if no S/L/T agency reported any values for the same SCC/pollutant.
3.1.7 Cross-dataset tagging rules for overlapping pollutants
Several HAPs can be reported as individual chemicals or chemicals that reflect a group which can overlap with
individual chemicals, e.g., o-Xylene and Xylenes (mixed isomers). In previous NEI cycles, we tagged out data to
prevent double counting of pollutants across datasets that overlap one another. For the 2017 NEI, a software
solution that occurs during the blending process was developed so that overlapping pollutants would be
excluded from the selection. The business rules were documented as part of the 2017 NEI plan (see Appendix 5).
One change to these "Proposed" rules that we implemented for the 2017 NEI is that we allow individual xylene
isomers to be reported with Xylenes (mixed isomers) within the same dataset. The cross-data business rules
used are the same as documented the plan.
One issue that came up with these rules regards the hexavalent chromium and trivalent chromium in the
2017EPA_CR_Aug dataset. This dataset, which contains S/L/T speciated chromium (i.e., hexavalent and trivalent
chromim), is separate from the S/L/T datasets but contains data that could be largely characterized as S/L/T
data. While we intended to allow S/L/T to report either unspeciated chromium or hexavalent chromium along
with chromic acid VI ro chromium trioxide at the same process, the software did not allow the hexavalent
chromium in the 2017EPA_CR_Aug dataset to be used with S/L/T chromic acid VI. This occurred only in 2 states,
NC and KY. For KY, the specated chromium was less than 0.1 lb and no corrections were made. In NC, there was
about 500 lbs hex chromium that would have been dropped so we corrected it. The correction was for NC to
incorporate the speciated chromium from2017EPA_CR_Aug into their dataset (instead of unspeciated
chromium) so that both pollutants would be used in the 2017 NEI selection. All records where EPA speciated
chromium data were used include an emissions comment to that effect.
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3.1.8 Additional quality assurance and findings
Prior to the release of the data, we created national summaries of key pollutants and sectors. The list below
provides findings and associated follow-up steps:
• We created a preliminary summary of mercury from point source emissions, even in the absence of the
other sectors that feed the final mercury summary that will be included in Section 2 of the
documentation once the NEI is complete. Such a summary has been included in past documentation for
other inventories. This summary revealed a possible underestimation of mercury from the Commercial
and Industrial Solid Waste Incineration (CISWI) sector. Since not all sources are reported to NEI as point
sources, the NEI may not include all CISWI sources. In addition, the Hg estimates of these sources are
highly uncertain, could be underestimated, and the EPA is currently working to get improved mercury
and other emissions estimates for these sources.
• We summarized hydrazine emissions and found a significantly larger hydrazine estimate in Arkansas
than had been present in past inventories. This makes Hydrazine emissions overall in the NEI increase
since 2014. We contacted the air office of the Arkansas Department of Environmental Quality, and the
inventory staff there confirmed the accuracy of these emissions.
• We summarized ethylene oxide emissions and found that several facilities did not report ethylene oxide
to both the state air agency and to the TRI program in 2017, but those facilities were still operating in
2017. To gap-fill those missing emissions, we used the 2016 TRI data.
• We summarized hexavalent chromium emissions and found a significant increase in emissions since
2014. We identified some missing emissions for sources in NC and worked with NC to include those
chromium emissions. We did not find any errors in hexavalent chromium in the 2017 data, which shows
an increase in these emissions as compared to the 2014 NEI. This could be due to a more complete
inventory or to an actual increase.
3.2 Airports: aircraft-related emissions
The EPA estimated emissions related to aircraft activity for all known U.S. airports, including seaplane ports and
heliports, in the 50 states, Puerto Rico, and U.S. 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, the EPA used that data to calculate the EPA's emissions estimates.
3.2.1 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 (AT), (3) general aviation (GA), 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.
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The national AT and GA fleets include both jet- and piston-powered aircraft. Most of the AT and GA fleets 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-
and piston-powered planes of varying sizes.
The 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.
3.2.2 Sources aircraft emissions estimates
Aircraft exhaust, GSE, and APU emissions estimates are associated with aircrafts' landing and takeoff (LTO) cycle.
LTO data were available from both S/L/T agencies and FAA databases. For airports where the available LTO
included detailed aircraft-specific make and model information (e.g., Boeing 747-200 series), we used the FAA's
Aviation Environmental Design Tool (AEDT) to estimate emissions. Note that this is the first NEI to use this
model. 2008 and 2011 used the FAA's previous model, Emissions and Dispersion Modeling System (EDMS).
Therefore, comparisons of aircraft emissions output may be a function of model revisions, rather than an actual
trend in emissions. For airports where FAA databases do not include such detail, the EPA used assumptions
regarding the percent of LTOs that were associated with piston-driven (using aviation gas) versus turbine-driven
(using jet fuel) aircraft. Then, the EPA estimated emissions based on the percent of each aircraft type, LTOs, and
EFs The emissions factors used, as well as the complete methodology for estimating aircraft exhaust from LTOs
is in the aircraft documentation available in the document "2017Aircraft_main_19aug2019.pdf" on the 2017
Supplemental da ite. Only Texas and California submitted aircraft emissions.
In addition to airport facility point, the EPA also estimated in-flight Pb (from aviation gas) emissions that are
allocated to counties in the nonpoint inventory. Details about EPA's estimates
(2017Aircraft_lnflightLead_19aug2019.pdf), including a summary of state-level in-flight lead estimates
"2017Aircraft_lnflightLeadByState_19aug2019.csv" can be found on the 2017 Supplemental data FTP site.
3.3 Rail yard-related emissions
The 2017 NEI PT includes estimates compiled by the Eastern Regional Technical Advisory Committee (ERTAC) for
most rail yards in the US. The ERTAC effort was comprised of a collaborative of state/local agencies, rail
companies, and the Federal Rail Administration. Yard emissions are associated with the operation of switcher
engines at each yard. The project is documented in a report" 2017Rail_main_21aug2019.pdf" on the 2017
Supplemental data FTP site. S/L/Ts submitted point rail yard emissions were given priority over the ERTAC
estimates when present.
3.4 EGUs
The EPA developed a single combined dataset of emission estimates for EGUs to be used to fill gaps for
pollutants and emission units not reported by S/L/T agencies. For the 2017EPA_EGU dataset, the emissions were
estimated at the unit level, because that is the level at which the CAMD heat input activity data and the MATS-
based emissions factors and the CAMD CEM data are available. The 2017EPA_EGU dataset was developed from
three separate estimation sources. The three sources were the 2010 MATS rule development testing program
EFs for 15 HAPs; annual sums of S02 and NOx emissions based on the hourly CEM emissions reported to the
EPA's CAMD database; and heat-input based EFs that were built from AP-42 EFs and fuel heat and sulfur
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contents as part of the 2008 NEI development effort. We used the 2014 annual throughputs in BTUs from the
CAMD database with the two EF sets to derive annual emissions for 2017. A small number of the AP-42-based
estimates were discarded because the fuels or control configurations were found to be different than what they
were during the 2008 development effort that provided the heat-input based EFs that were available.
As shown above in Table 3-1, the selection hierarchy was set such that S/L/T-submitted data was used ahead of
the values in the 2017EPA_EGU dataset. In the 2011 NEI, the EPA EGU estimated emissions that were derived
from the MATS testing program were used ahead of the S/L/T values, unless the S/L/T submittal indicated that
the value was from either a CEM or a recent stack test. For the 2017 NEI, we used the S/L/T-reported values
wherever they were reported (unless they were tagged out as an outlier), including where a MATS-based value
existed in the 2017EPA EGU dataset. In addition, we made the MATS emission factors available to S/L/T agencies
far in advance of the data being submitted so that facilities and/or S/L/T agencies could choose to use that
information to compute emissions if it was most applicable.
We assumed that all heat input came from the primary fuel, and the EFs used reflected only that primary fuel.
This introduces a small amount of uncertainty as many EGU units use a small amount of alternative fuels. 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 level. To do this for the EGU sectors, we needed to
bridge the unit level (i.e., the boiler or gas turbine unit as a whole) to the process level (i.e., the individual fuels
burned within the units). So, the EPA emissions were assigned to a single process for the primary fuel that was
used by the responsible S/L/T agency for reporting the largest portion of their emissions. The EPA emissions
were then "tagged out" wherever the S/L/T agency had reported the same pollutant at any process within the
same emission unit. This approach prevented double counting of a portion of the S/L/T-reported emissions in
cases where the S/L/T agency may have reported a unit's emissions using two different coal processes and a
small oil process, for example.
The matching of the 2017EPA_EGU dataset to the responsible agency facility, unit and process IDs was done
largely by using the ORIS plant and CAMD boiler IDs as found in the CAMD heat input activity dataset and linking
these to the same two IDs as had been stored in EIS. We also compared the facility names and counties for
agreement between the S/L/T-reported values and those in CAMD, and we revised the matches 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 dataset. We identified and
resolved several discrepancies from this emissions comparison.
Alternative facility and unit IDs needed for matching with other databases
The 2017 NEI 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 Sparse Matrix Operator Kernel Emissions
(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 integrated planning model (IPM). The storage format of these alternate EPAIPM unit IDs, in both
EIS and in the exported SMOKE file, replicates the IDs as found in the National Electric Energy Data
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System (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." The ORIS Plant IDs and CAMD boiler IDs as stored in the CAMD Business System (CAMDBS) dataset
and in the NEEDS database are almost always the same, but there are occasional differences for the same unit.
The EPACAMD alternate unit IDs available in the 2017 NEI are believed to be a complete set of all those that can
safely be used for the purpose of substituting hourly CEM values without double-counting during SMOKE
processing. The EPAIPM alternate unit IDs in the 2017 NEI are not a complete listing of all the NEEDS/IPM units,
although most of the larger emitters do have an EPAIPM alternate unit ID. The NEEDS database includes a much
larger set of smaller, non-CEM units.
3.5 Landfills
The point source emissions in the EPA's Landfill dataset includes CO and 28 HAPs, as shown in Table 3-5. This set
of pollutants was included in the 1999 NEI, and we continue to use the same set of pollutants each year for a
consistent time series. To estimate emissions, we used the methane emissions reported by landfill operators in
compliance with Subpart HH of the Greenhouse Gas Reporting Program (GHGRP) as a "surrogate" activity
indicator. We converted the methane as reported in Mg C02 equivalent to Mg as actual methane emitted by
dividing by 23 (the Global Warming Potential of methane believed to be used in the version of the 2017 GHGRP
facility inventory) to get MG methane emitted, and then multiplied by 1.1023 to get tons methane emitted5. We
created emission factors for CO and the 28 HAPs on a per ton of methane emitted basis using the default
concentrations (ppmv) in AP-42 Section 2.4 (final section dated Jan 1998), Table 2.4-1. The concentrations for
toluene and benzene were taken from Table 2.4-2 of AP-42, for the case of "no or unknown" co-disposal history.
Per Equation 4 of that AP-42 section, Mp=Qp x MWp x constant (at any given temperature). Writing this
equation twice, for the mass of any pollutant and for methane (CH4), and dividing Mp by McH4 yields:
Mp / MCH4 = (Qp x MWp x k) / QCH4 x MWCH4 x k) = (Qp/QcH4) x (MWp/MWcH4)
in units of pounds pollutant per pound CH4.
A rearrangement of Equation 3 of that AP-42 section provides Qp/ QcH4 = 1-82 x Cp/1000000, where the 1.82 is
based upon a default methane concentration of 55 % (550,000 ppm). Plugging this expression for Qp/ QcH4 into
the first expression yields:
Mp / McH4 = (1.82 x Cp/1000000) x (MWp/ MWCH4) x 2000, units of pounds p/ton CH4
Mp / MCH4 = (1-82 x Cp/1000000) x (MWp/16) x 2000 = Cp x MWp / 4395.6
Table 3-3: Landfill gas emission factors for 29 EIS pollutants
Pollutant
code
Pollutant description
MW
ppmv
MW x
ppmv
lbs/Ton
ch4
CO
Carbon monoxide
28.01
141
3949.41
0.89849
108883
toluene
92.13
39.3
3620.709
0.82371
1330207
Xylenes
106.16
12.1
1284.536
0.29223
75092
Dichloromethane (methylene chloride)
84.94
14.3
1214.642
0.27633
5 For more information on C02 equivalent and global warming potential, please refer to EPA's page "Understanding Global
Warming Potentials".
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Pollutant
code
Pollutant description
MW
ppmv
MW x
ppmv
lbs/Ton
ch4
7783064
Hydrogen sulfide
34.08
35.5
1209.84
0.27524
127184
Perchloroethylene (tetrachloroethylene)
165.83
3.73
618.5459
0.14072
110543
Hexane
86.18
6.57
566.2026
0.12881
100414
Ethylbenzene
106.16
4.61
489.3976
0.11134
75014
Vinyl chloride
62.5
7.34
458.75
0.10437
79016
Trichloroethylene (trichloroethene)
131.4
2.82
370.548
0.08430
107131
Acrylonitrile
53.06
6.33
335.8698
0.07641
75343
1,1-Dichloroethane (ethylidene dichloride)
98.97
2.35
232.5795
0.05291
108101
Methyl isobutyl ketone
100.16
1.87
187.2992
0.04261
79345
1,1,2,2-Tetrachloroethane
167.85
1.11
186.3135
0.04239
71432
benzene
78.11
1.91
149.1901
0.03394
75003
Chloroethane (ethyl chloride)
64.52
1.25
80.65
0.01835
71556
1,1,1-Trichloroethane (methyl chloroform)
133.41
0.48
64.0368
0.01457
74873
Chloromethane
50.49
1.21
61.0929
0.01390
75150
Carbon disulfide
76.13
0.58
44.1554
0.01005
107062
1,2-Dichloroethane (ethylene dichloride)
98.96
0.41
40.5736
0.00923
106467
Dichlorobenzene
147
0.21
30.87
0.00702
463581
Carbonyl sulfide
60.07
0.49
29.4343
0.00670
108907
Chlorobenzene
112.56
0.25
28.14
0.00640
78875
1,2-Dichloropropane (propylene dichloride)
112.99
0.18
20.3382
0.00463
75354
1,1-Dichloroethene (vinylidene chloride)
96.94
0.2
19.388
0.00441
67663
Chloroform
119.39
0.03
3.5817
0.00081
56235
Carbon tetrachloride
153.84
0.004
0.61536
0.00014
106934
Ethylene dibromide
187.88
0.001
0.18788
0.00004
7439976
Mercury (total)
200.61
0.000292
0.05857812
0.00001
3.6 2017EPA_gapfills
This EPA dataset is used to fill in miscellaneous emissions which were not reported by S/L/T agencies for 2017,
and for which no EPA dataset has 2017 emissions, but which are believed to exist in 2017. These unreported
facilities and pollutants were identified as part of the QA review steps performed on the S/L/T data (see Section
3.1.1). A total of 95 unique facilities across 4 different States and 88 different pollutants are represented in this
dataset. Most of the additions were for Indiana (73 facilities), which did not submit emissions for all of their
operating facilities for 2017. 2016 NEI emissions were copied into the gapfills dataset for those facilities. Nine
facilities in Pinal County, AZ were also added using 2016 NEI emissions. NOx emissions only were added for
eleven coal mines in Wyoming which have significant emissions from trucks and other mobile equipment which
were not included in WYDEQ's point source dataset, and which are not expected to be adequately covered in
EPA's nonroad emission estimates. WYDEQ sent 2017 facility totals for these facilities mobile emissions to be
added to the 2017 NEI PT. Lastly, mercury emissions for two municipal waste combustors in Maryland and four
municipal waste combustors in Massachusetts were added.
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3.7 BOEM
The U.S. Department of the Interior, Bureau of Ocean and Energy Management (BOEM) estimates emissions of
CAPs in the Gulf of Mexico from offshore oil platforms in Federal waters, and these data have been previously
incorporated into the NEI. The 2017 offshore data were not available in time for inclusion in the 2017 NEI August
release. They will be added into a future version of the 2017 NEI.
3.8 PM species
For the 2017 NEI PT inventory, the five species (EC, OC, S04, N03, and other) of PM2.5-PRI and diesel PM (which
are estimated for diesel mobile engines such as locomotives and diesel-fueled ground support equipment) were
not included. These species will be generated as in earlier NEI years by using the PM speciation ratios as found
on the Air Emissions Modeling website.
3.9 References for point sources
1. Dorn, J, 2012. Memorandum: 2011 NEI Version 2 - PM Augmentation approach. Memorandum to Roy
Huntley, US EPA. (PM augmt 2011 NEIv2 feb2012.pdf, accessible in the file "2008nei_reference.zip" on
the 2QQ8v3 IS ijte.
2. Strait et al. (2003). 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.
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