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2020 National Emissions Inventory Technical
Support Document: Overview
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EP A-454/R-23 -001b
March 2023
2020 National Emissions Inventory Technical Support Document: Overview
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Contents
List of Tables i
List of Figures ii
2 2020 NEI contents overview 2-1
2.1 What are EIS sectors? 2-1
2.2 How is the NEI constructed? 2-3
2.2.1 Toxics Release Inventory data 2-4
2.2.2 Chromium speciation 2-4
2.2.3 HAP Augmentation 2-6
2.2.4 Particulate matter augmentation 2-7
2.2.5 Other EPA datasets 2-8
2.2.6 Data Tagging 2-8
2.2.7 Inventory Selection 2-9
2.3 What are the sources of data in the 2020 NEI? 2-9
2.4 What are the top sources of some key pollutants? 2-11
2.5 How does this NEI compare to past inventories? 2-13
2.5.1 Differences in approaches 2-13
2.5.2 Differences in emissions between 2020 and 2017 NEI 2-15
2.6 How well are tribal data and regions represented in the 2020 NEI? 2-17
2.7 What does the 2020 NEI tell us about mercury? 2-18
2.8 References for 2020 inventory contents overview 2-27
List of Tables
Table 2-1: EIS sectors/source categories with EIS data category emissions reflected 2-1
Table 2-2: Valid chromium pollutant codes 2-4
Table 2-3: EIS sectors and associated 2020 CAP and total HAP emissions (thousands of tons/year) 2-11
Table 2-4: 2020 and 2017 NEI CAP emissions and broad sector changes (2020 minus 2017) in tons 2-16
Table 2-5: 2020 and 2017 NEI select HAP emissions and broad sector changes (2020 minus 2017) in tons 2-16
Table 2-6: Tribal participation in the 2020 NEI 2-17
Table 2-7: Facilities on Tribal lands with 2020 NEI emissions from EPA only 2-18
Table 2-8: 2020 NEI Hg emissions (tons) for each dataset type and group 2-20
Table 2-9: Point inventory emissions by reporting agency 2-22
Table 2-10: Trends in NEI mercury emissions - 1990, 2005, 2008 v3, 2011v2, 2014v2 NEI, 2017 NEI, and 2020 NEI
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List of Figures
Figure 2-1: PM Augmentation computations based on S/L/T submitted pollutants 2-8
Figure 2-2: Relative contributions for various data sources of Point emissions for CAPs and select HAPs 2-9
Figure 2-3: Relative contributions for various data sources of Nonpoint emissions for CAPs and select HAPs.. 2-11
Figure 2-4: Data sources of Hg emissions (tons) in the 2020 NEI, by data category 2-19
Figure 2-5: Trends in NEI Mercury emissions 2-27
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2 2020 NEI contents overview
2.1 What are EIS sectors?
First used for the 2008 NEI, EIS Sectors continue to be used for all 2020 NEI data categories. 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 2020 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 had been reported and compiled in EIS using five major data categories: point,
nonpoint, onroad, nonroad and events. The event category was 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 had been a focus of the NEI creation effort and were the only
emission sources contained in the event data category.
For the 2020 NEI, we have aggregated the wildfires and prescribed burning emissions into county-level
estimates and loaded these into the nonpoint 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. 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
Agriculture - Crops & Livestock Dust
0
Agriculture - Fertilizer Application
0
Agriculture - Livestock Waste
0
0
2-1
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Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Biogenics - Vegetation and Soil
0
Bulk Gasoline Terminals
0
0
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
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Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
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
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.
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The NEI is built by data category for point, nonpoint, nonroad mobile, and onroad mobile. 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
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 AirToxScreen, 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 2020 Toxics Release Inventory (TRI) to supplement point source HAP
and NH3 emissions provided to EPA by S/L/T agencies. For 2020, 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 2020 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 (Point Data category) provides more information on how TRI data was used to
supplement the point inventory.
2.2.2 Chromium speciation
The 2020 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 EPAspeciates S/L/T-reported and TRI-based total chromium into
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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 2020
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:
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 emissions 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 2020 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. For TRI dataset chromium, the "By NAICS" (8) option was primarily
used, although a small number of "By Facility" (2) occurrences 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 2020 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 2020 NEI are SCC-based and are the same as were used in 2011
through 2017 NEI, based on data that have long been used by the EPA for NATAand 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 Air Toxics Screening Assessments. 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
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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).
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, 3.1.5 -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 2020 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 WebFIRE 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 typically does not equal 1 (100%) because not all of the VOC or PM mass will be a HAP.
We try to ensure that the sum of HAP-VOC factors is less than 1 because it can't be more but it is sometimes
close or equal to 1. HAP augmentation factors based on PM mass are typically much less than 1 for almost all
SCCs. 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 3.1.6) and nonpoint
sections of the TSD. The ultimate goal is to prevent double-counting of HAP emissions between S/L/T data and
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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 S/L/T agencies or from facility
reports to the Toxics Release Inventory, but HAP augmentation is used as a last available option. Compliance
test data does not usually provide an annual emissions total.
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
NATA and AirToxScreen reviews. More discussion of the underlying data used for the 2020 NEI Point inventory 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 Particulate matter augmentation
Particulate matter (PM) emissions species in the NEI are primary PM10 (pollutant code 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 needs 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. In general, emissions for
PM components missing from S/L/T agency inventories were calculated by applying factors to the PM emissions
data supplied by the S/L/T agencies.
PM Augmentation is only run in EIS for point and nonpoint sources. Unlike the PM calculator/Augmentation tool
used in previous NEIs, EIS PM Augmentation only gap-fills missing PM components, and does not overwrite
existing S/L/T PM data, which already undergoes rudimentary EIS QA checks as the data is being loaded into EIS.
The complete set of conditional logic statement used in EIS PM Augmentation are displayed in Figure 2-1.
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Figure 2-1: PM Augmentation computations based on S/L/T submitted pollutants
Pollutant
Combo# PM10-PRI
PM25-PRI
PM10-FIL
PM25-FIL
PM-CON
Action
l
Y
Y
Y
Y
Y
No Action
2
Y
Y
Y
10PRI=10FIL+CON; 25PRI=25FIL+CON
3
Y
Y
Y
10FIL=10PRI-CON; 25FIL=25PRI-CON
4
Y
Y
Y
Y
CON=10PRI-10FIL
5
Y
Y
Y
CON=10PRI-10FIL; 25FIL=GREATEST{25PRI-CON, 0)
6
Y
Y
Y
CON=25PRI-25FIL; 10FIL=10PRI-CON
7
Y
Y
Y
CON=10PRI-10FIL; 25PRI=25FIL+CON
8
Y
Y
Y
Y
10FIL=10PRI-CON
9
Y
Y
Y
Y
25FIL=25PRI-CON
10
Y
Y
Y
Y
10PRI=10FIL+CON
11
Y
Y
Y
Y
25PRI=25FIL+CON
12
Y
Y
Y
C0N=25PRI-25FIL; 10PRI=10FIL+CON
13
Y
Y
Y
10FIL=25FIL* RATIO; 10PRI=10FIL+CON
14
Y
Y
Y
25FIL=10FIL*RATIO; 25PRI=25FIL+CON
15
Y
Y
Y
10PRI=10FIL+CON; 25FIL=25PRI-CON
16
Y
Y
Y
10FIL=10PRI-CON; 25PRI=25FIL+CON
17
Y
Y
CON=25PRI* RATIO; 10FIL=10PRI-CON; 25FIL=25PRI-CON
18
Y
Y
CON=25PRI-25FIL; 10FIL=25FIL* RATIO; 10PRI=10FIL+CON
19
Y
Y
25PRI=25FIL+CON; 10FIL=25FIL*RATIO;10PRI=10FIL+CON
20
Y
Y
25FIL=25PRI+CON; 10FIL=25FIL*RATIO;10PRI=10FIL+CON
21
Y
Y
CON=10PRI-10FIL; 25FIL=10FIL*RATIO; 25PRI=25FIL+CON
22
Y
Y
10PRI=10FIL+CON; 25FIL=10FIL*RATIO; 25PRI=25FIL+C0N
23
Y
Y
10FIL=10PRI-CON; 25FIL=10FIL*RATIO; 25PRI=25FIL+C0N
24
Y
Y
CON=10FIL* RATIO; 10PRI=10FIL+CON; 25PRI25=FIL+C0N
25
Y
Y
CON=25PRI*RATIO; 25FIL=25FIL-C0N; 10PRI=10FIL+CON
26
Y
Y
CON=10PRI*RATIO; 10FIL=10PRI-CON; 25PRI=25FIL+CON
27
Y
Each of the 4 missing = 25PRI*Ratio
28
Y
Each of the 4 missing = 10PRI*Ratio
29
Y
Each of the 4 missing = 25FIL*Ratio
30
Y
Each of the 4 missing = 10FIL*Ratio
31
Y
Each of the 4 missing = CON*Ratio
32
No Action
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. New for 2020, as part of the NEI selection process, EIS generates speciated PM2.5
emissions for all sources with PM emissions. These PM species are a result of speciation where the NEI PM25-
PRI emissions are split into five PM2.5 species: elemental (also referred to as "black") carbon (EC), organic
carbon (OC), nitrate (NOB), sulfate (S04), and the remainder of PM25-PRI (PMFINE). In addition, a copy of PM25-
PRI and PM10-PRI from mobile source diesel engines, relabeled as DIESEL-PM25 and DIESEL-PM10, respectively,
are also generated.
Examples of other EPA data for point sources, discussed in Section 3, include commercial sterilizers amended via
AirToxScreen review, landfills, railyards, 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
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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 facilities that do not voluntarily provide HAP emissions. These automated procedures
gap-fill only for processes that have emission factors and miss processes/pollutants that may have been
reported to TRI using other means besides published emission 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 example 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 selection 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. Since the 2017 NEI, a series of additional rules were added to the selection hierarchy to avoid such
tagging. Point source datasets are 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 What are the sources of data in the 2020 NEI?
This section shows the contributions of S/L/T agency data to total emissions for the point and nonpoint data
categories. Figure 2-2 shows the proportion of CAP, select HAPs, and HAP group emissions from various data
sources in the NEI for point data category sources. Except for PM2.5 and PM10, most point CAP emissions come
from S/L/T-submitted data. PM augmentation (see Section 2.2.4), which is based off incomplete S/L/T submittals
of PM, accounts for a significant portion of PM point emissions. The data sources shown in the figure are
described in more detail in Section 3.
Figure 2-2: Relative contributions for various data sources of Point emissions for CAPs and select HAPs
2-9
-------
40%
30%
20%
10%
0%
EPA EGU
EPA HAP & PM Aug
I EPA TRI
IS/LA
0- ^P+
~CV ri.
V K* J* *S'
^ &
Figure 2-3 shows the proportion of CAP, select HAPs, and HAP group emissions from various data sources in the
NEI for nonpoint data category sources. Biogenic sources, all EPA data, are not included in this table. Acid Gases
include the following pollutants: hydrogen cyanide, hydrochloric acid, hydrogen fluoride, and chlorine. HAP VOC
emissions consist of dozens of VOC HAP species, that in-aggregate, should be less than VOC in our QA checks.
HAP metal emissions consist of the following compound groups: Antimony, Arsenic, Beryllium, Cadmium,
Chromium, Cobalt, Lead, Manganese, Mercury, Nickel and Selenium. More than 50% of nonpoint pollutant
totals come from some type of EPA source; however, as discussed in Section 6, S/L/T-submitted nonpoint
activity data is absorbed into EPA nonpoint tools and are therefore classified as "EPA" data. Nonpoint NH3 is
dominated by the agricultural livestock waste and fertilizer application sectors. The large "EPA Nonpoint" bars
for PM10 and PM2.5 are predominantly dust sources from unpaved roads, agricultural dust from crop
cultivation, and construction dust.
2-10
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Figure 2-3: Relative contributions for various data sources of Nonpoint emissions for CAPs and select HAPs
We did not compute relative contributions of emissions from nonroad and onroad data categories because of
the nature in how emissions are created for these sources -via a mix of S/L/T and EPA activity data and
processed through the MOVES model. California, which uses its own onroad and nonroad mobile models, was
the only state that provided emissions rather than inputs for EPA models (this is in accordance with the AERR).
All other states were required to provide inputs to the EPA models. Onroad and nonroad mobile data categories
use the MOVES emissions model, and the EPA primarily collected model inputs from S/L agencies for these
categories and ran the models using these inputs to generate the emissions. The S/L agencies that provided
inputs are presented in the nonroad and onroad portions of the document, Section 4 and Section 5, respectively.
2.4 What are the top sources of some key pollutants?
Table 2-3 provides a summary of CAP and total HAP emissions for all EIS sectors, including the biogenic
emissions from vegetation and soil. Emissions in federal waters and from vegetation and soils have been split
out and totals both with and without these emissions are included. Emissions in federal waters include offshore
drilling platforms and commercial marine vessel emissions outside the typical 3-10 nautical mile boundary
defining state waters. All emissions values are bounded by the caveats and methods described by this
documentation.
Table 2-3: EIS sectors and associated 2020 CAP and total HAP emissions (thousanc
Sector
CO
NH3
NOX
PM2.5
PM10
S02
voc
Black
Carbon
Lead
Total
HAPs1
Agriculture - Crops & Livestock Dust
719
3,669
li
Agriculture - Fertilizer Application
1,834
s of tons/year)
2-11
-------
Sector
CO
NH3
NOX
PM2.5
PM10
S02
voc
Black
Carbon
Lead
Total
HAPs1
Agriculture - Livestock Waste
3.74E-03
2,696
2.23E-03
0.04
0.09
2.67E-05
216
1.95E-03
40
Bulk GasolineTerminals
0.93
0.03
0.37
0.06
0.07
0.02
119
5.48 E-04
4.67E-04
5.58
Commercial Cooking
75
188
202
29
6.44
8.83
Dust - Construction Dust
9.52 E-04
5.90E-07
3.21E-04
125
1,245
3.67E-05
0.06
1.13E-04
1.70E-04
0.07
Dust - Paved Road Dust
194
830
2.02
Dust - Unpaved Road Dust
568
5,709
0.55
Fires - Agricultural Field Burning
676
146
31
67
101
11
106
7.69
19
Fires - Prescribed Fires
8,384
135
149
779
909
71
1,936
38
7.84E-03
402
Fires - Wildfires
19,620
322
246
1,676
1,977
141
4,623
150
8.62E-03
937
Fuel Comb - Comm/lnst
tutional - Biomass
24
0.18
10
16
18
1.10
0.96
0.59
2.71E-04
0.42
Fuel Comb - Comm/lnst
tutional - Coal
1.11
2.10E-03
2.05
0.38
1.21
7.74
0.14
0.02
2.70E-04
0.33
Fuel Comb - Comm/lnst
tutional - Natural Gas
115
1.28
140
4.22
4.47
1.27
9.31
0.29
2.21E-03
1.05
Fuel Comb - Comm/lnst
tutional - Oil
21
0.26
44
2.93
3.21
2.94
3.47
0.44
4.41E-04
0.12
Fuel Comb - Comm/lnst
tutional - Other
12
0.09
16
0.69
0.72
1.07
1.61
0.05
4.29E-04
0.29
Fuel Comb - Electric Generation - Biomass
13
0.38
8.73
1.28
1.43
3.16
0.70
0.05
1.16E-03
0.61
Fuel Comb - Electric Generation - Coal
268
2.28
575
49
62
773
11
1.90
0.02
6.82
Fuel Comb - Electric Generation - Natural Gas
83
15
178
30
31
5.74
12
2.03
1.50E-03
4.32
Fuel Comb - Electric Generation - Oil
6.84
0.55
55
3.31
4.25
38
1.39
1.10
1.40E-03
0.48
Fuel Comb - Electric Generation - Other
28
0.49
23
2.70
2.78
16
3.15
0.15
7.21E-04
1.82
Fuel Comb - Industrial Boilers, ICEs - Biomass
330
3.51
134
195
226
19
11
7.25
4.19E-03
5.66
Fuel Comb - Industrial Boilers, ICEs - Coal
15
0.36
42
4.53
15
112
0.46
0.19
5.74E-03
3.95
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
296
8.39
534
21
23
14
61
1.47
3.47E-03
22
Fuel Comb - Industrial Boilers, ICEs - Oil
24
0.21
70
4.69
5.23
12
5.03
1.20
0.01
0.35
Fuel Comb - Industrial Boilers, ICEs - Other
73
1.67
48
7.74
8.84
33
4.44
0.61
2.23E-03
1.46
Fuel Comb - Residential - Natural Gas
94
45
216
2.67
2.86
1.44
13
0.18
2.70E-06
0.18
Fuel Comb - Residential - Oil
8.37
1.52
28
3.23
3.60
0.63
1.10
0.38
1.92E-03
0.07
Fuel Comb - Residential - Other
10
0.13
37
0.15
0.18
0.17
1.44
0.01
3.68E-07
0.02
Fuel Comb - Residential - Wood
3,159
23
50
485
489
13
460
27
176
Gas Stations
0.03
2.36E-04
0.02
3.07E-03
4.90E-03
5.22E-04
336
1.35E-04
2.12E-04
35
Industrial Processes - Cement Manuf
90
1.33
107
7.14
11
28
5.48
0.21
1.90E-03
1.99
Industrial Processes - Chemical Manuf
138
27
59
21
26
91
90
0.54
3.09E-03
23
Industrial Processes - Ferrous Metals
200
0.19
44
17
22
18
9.25
0.33
0.03
1.53
Industrial Processes - Mining
16
0.05
4.19
49
369
0.67
1.07
0.05
3.53E-03
0.17
Industrial Processes - NEC
128
28
131
70
118
112
193
1.28
0.04
46
Industrial Processes - Non-ferrous Metals
175
0.27
12
8.75
12
22
9.98
0.14
0.03
4.78
Industrial Processes - Oil & Gas Production
673
0.23
612
12
13
165
2,680
0.65
8.64E-05
146
Industrial Processes - Petroleum Refineries
50
2.35
61
14
18
47
48
0.89
1.87E-03
8.64
Industrial Processes - Pulp & Paper
88
4.86
68
30
37
20
127
0.90
3.36E-03
48
Industrial Processes - Storage and Transfer
4.44
0.98
2.07
13
36
0.66
189
0.20
1.91E-03
11
Miscellaneous Non-Industrial NEC
97
1.79 E-04
2.48
13
16
0.19
325
0.53
7.54E-04
20
Mobile - Aircraft
327
84
7.46
8.45
9.17
51
2.57
0.43
11
Mobile - Commercial Marine Vessels
28
0.09
218
4.79
5.03
4.70
8.70
3.64
5.90E-04
0.82
Mobile - Locomotives
98
0.30
463
11
12
0.37
20
8.84
9.00E-07
8.79
Mobile - Non-Road Equipment - Diesel
300
1.19
654
44
45
0.54
57
34
3.39E-07
27
Mobile - Non-Road Equipment - Gasoline
10,727
0.81
187
36
39
0.39
935
4.36
4.78E-12
294
Mobile - Non-Road Equipment - Other
212
0.01
37
2.04
2.04
0.27
7.12
0.75
1.45
Mobile - On-Road Diesel Heavy Duty Vehicles
569
8.97
1,324
40
67
1.59
69
22
13
Mobile - On-Road Diesel Light Duty Vehicles
183
1.43
143
6.53
9.19
0.16
24
4.49
4.26
Mobile - On-Road non-Diesel Heavy Duty Vehicles
538
2.41
31
1.34
3.70
0.28
27
0.22
7.49
Mobile - On-Road non-Diesel Light Duty Vehicles
12,972
77
847
32
110
7.84
835
6.70
229
Solvent - Consumer & Commercial Solvent Use
1,936
202
2-12
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Sector
CO
NH3
NOX
PM2.5
PM10
S02
voc
Black
Carbon
Lead
Total
HAPs1
Solvent - Degreasing
4.64E-03
0.03
3.36E-03
0.05
0.06
1.29E-04
70
3.41E-04
3.21 E-04
6.36
Solvent - Dry Cleaning
3.76E-03
1.00E-07
3.35 E-03
0.04
0.04
5.04E-03
2.30
2.40E-04
1.00E-07
0.76
Solvent - Graphic Arts
0.06
0.04
0.08
0.06
0.06
1.36E-03
170
3.95 E-04
3.56E-08
16
Solvent - Industrial Surface Coating & Solvent Use
5.56
0.30
2.44
3.67
4.06
0.22
381
0.04
1.72E-03
83
Solvent - Non-Industrial Surface Coating
201
73
Waste Disposal
1,479
92
84
227
253
36
191
24
8.83E-03
38
Sub Total (no federal waters)
62,437
5,485
7,816
5,822
16,782
1,845
16,630
377
0.62
2,999
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
49
7.55 E-03
44
0.41
0.41
0.03
1.16
0.03
1.18E-06
1.18E-06
Fuel Comb - Industrial Boilers, ICEs - Oil
1.15
2.83 E-04
4.91
0.21
0.21
0.41
0.24
0.16
2.52E-06
2.52E-06
Fuel Comb - Industrial Boilers, ICEs - Other
4.02 E-04
1.51E-05
4.81 E-04
2.39E-05
2.39E-05
3.30E-06
4.31E-05
1.66E-06
2.36E-09
2.36E-09
Industrial Processes - Oil & Gas Production
1.50
5.42 E-04
0.80
9.13E-03
9.29E-03
0.02
37
2.47E-05
8.46E-08
8.46E-08
Industrial Processes - Storage and Transfer
0.63
Mobile - Commercial Marine Vessels
3.40
0.01
22
0.55
0.57
0.05
0.83
0.43
6.93E-05
0.09
Sub Total (federal waters)
55
0.02
72
1.19
1.21
0.51
40
0.62
7.31E-05
0.09
Sub Total (all but vegetation and soil)
62,493
5,485
7,888
5,823
16,784
1,845
16,670
378
0.62
2,999
Biogenics - Vegetation and Soil
3,660
1,029
29,519
2,968
Total
66,153
5,485
8,916
5,823
16,784
1,845
46,189
378
0.62
5,968
1 Total HAP does not include diesel PM, which is not a HAP listed by the Clean Air Act.
2.5 How does this NEI compare to past inventories?
Many similarities exist between the 2020 NEI approaches and past NEI approaches, notably that the data are
largely compiled from data submitted by S/L/T agencies for CAPs, and that the HAP emissions are augmented by
the EPA to differing degrees depending on geographical jurisdiction because they are a voluntary contribution
from the partner agencies. In 2020, S/L/T participation was again somewhat more comprehensive than the
previous NEI. The NEI program continues with the 2020 NEI to work towards a complete compilation of the
nation's CAPs and HAPs. The EPA provided feedback to S/L/T agencies during the compilation of the data on
critical issues (such as potential outliers, missing SCCs, missing Hg data and coke oven data) as has been done in
the past, collected responses from S/L/T agencies to these issues, and improved the inventory for the release
based on S/L/T agency feedback. In addition to these similarities, there are some important differences in how
the 2020 NEI has been created and the resulting emissions, which are described in the following two
subsections.
2.5.1 Differences in approaches
With any new inventory cycle, changes to approaches are made to improve the process of creating the inventory
and the methods for estimating emissions. The key changes for the 2020 cycle are highlighted here.
To improve the process, we learned from the prior triennial inventories (for 2008, 2011, 2014, and 2017)
compiled with the EIS. We made changes to pollutant, SCC, and NAICS codes, refined quality assurance checks
and features that were used to assist in quality assurance but retained the same Nonpoint Survey functionality
used in the 2017 NEI (introduced for the 2014 NEI) to assist with S/L/T and EPA data reconciliation for the
nonpoint data.
In addition to process changes, we improved emissions estimation methods for all data categories. We
summarize the differences in approaches in the following sections.
2-13
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2.5.1.1 Point da ta ca tegory
For point sources, the only major change for 2020 was our incorporation of the Air Toxics Screening
(AirToxScreen) assessment between the draft NEI and this 2020 NEI release. AirToxScreen provided SLTs a
review of high-risk air toxic facilities. More information on point source improvements is available in Section 3.
2.5.1.2 Non point da ta ca tegory
We made method improvements for several stationary nonpoint sectors (Section 6). The EPA creates and
provides emissions estimation tools for two purposes: 1) as tools for S/L/T agencies to use themselves, and 2) to
backfill emissions values where not provided by S/L/T agencies.
As part of the 2017 NEI development process, we introduced "Input Templates" for S/L/Ts to provide activity
data for several nonpoint data category tools. By allowing a simple template where S/L/Ts can review the
previous year's data, the data source, and easily update values at a county or state level, that then feeds into
EPA's emissions estimation tools, assures that the calculations and methods are identical. For the 2020 NEI, we
centralized the input template download and upload process, and enabled S/L/Ts to directly load their inputs
into EPA tools to generate draft emission estimates prior to submittal to the NEI. EPA provided default Input
Templates to S/L/T inventory developers for them to modify and return to EPA. We encouraged S/L/Ts to submit
inputs rather than direct emission submittals for many nonpoint categories.
We also continued to streamline the Nonpoint Survey (Section 6), first introduced for the 2014 NEI development
cycle, to simplify the options and improve transparency. In particular, we added a button on the NP survey that
indicates whether an agency submitted an input template. This helped us QA our data twofold: 1) did the agency
intend to submit an input template, and 2) did they actually submit a template. By default, all Nonpoint Survey
responses were set to "Yes -Supplement my data with EPA Estimates" to ensure complete coverage in the
absence of S/L/T feedback.
As discussed in Section 25, for the 2020 NEI, we added default fuel consumption data for nonpoint Industrial and
Commercial/Institutional (ICI) fuel combustion, based partially on S/L/T-submitted Point carbon monoxide
emissions; this greatly reduced the potential double-counting of ICI fuel consumption estimates for S/L/Ts that
did not submit direct nonpoint emissions or an input template. Similar to the 2017, we continue to use
estimated point fuel consumption for reconciling the nonpoint component of ICI fuel consumption/emissions -
we no longer allow point emissions subtraction. We provided S/L/Ts with cross-references from point inventory
facilities to existing U.S. Energy Information Administration (EIA) ICI sector assignments and fuel mapping. We
relied on S/L/Ts to provide EPA with these state-level inputs via 4 different Input Template options.
Emissions for residential wood consumption (Section 27) were affected by an updated methodology in the wood
consumption estimates obtained from the State Energy Data System (SEDS), which reflected updated national
survey data and allocation scheme based on heating degree days which distributed emissions from warmer
(southern) states to cooler (northern) states. In addition, we updated to use higher PM emission factors for
certified wood stoves as the old emissions were deemed inappropriate for continued use.
The methods used to estimate nonpoint solvent utilization emissions (Section 32) were updated using a new
emissions model. This model uses national-level product usage estimates to subsequently estimate speciated
emissions, that are further allocated to the county-level using geographically specific sources of data and
modulated if the locality reports control mechanisms for select SCCs. In addition, a new SCC (2460030999) was
2-14
-------
added to this category to reflect emissions from lighter fluids, fuel starters, and other consumer product fuel
sources.
Most states saw a significant increase in CO, PM2.5 and VOC from commercial cooking, a result of an
improvement in the activity data on the number of restaurants. Large decreases in residential fuel combustion
for S02 is a result of a continued decrease in consumption and more significantly, more widespread inclusion of
a lower default sulfur content for distillate fuel oil.
All fires data are now included in the nonpoint data category for the 2020 NEI. This is simply a format issue as
the underlying methodology for computing wildland fires (wildfires and prescribed burning) are still developed
using satellite data for location and day-specific fires, but for 2020 NEI, are subsequently aggregated to the
county-level. Overall, national-level agricultural field burning increased but was mostly offset by corresponding
decreases in prescribed fire estimates.
The 2020 NEI introduces (VOC and associated VOC HAPs) from agricultural silage and new asphalt paving
processes and methodology. Agricultural fertilizer application (NH3) estimates significantly increased due to
several updates: new emission factor measurements, change in how landcover was modeled, improved
meteorological data, and an error correction. Oil and gas production increased significantly in the Permian basin;
otherwise, most VOC changes result from new Solvents methodology (Section 32), which also includes pesticide
application.
For all nonpoint categories, we updated the activity data to use the newest data available, at the time, to
represent the 2020 inventory year; in most cases, this is year-2020 activity data. Most emission changes for all
nonpoint sources not otherwise discussed in this section resulted from these activity data updates -be they from
EPA or new for 2020, provided directly from S/L/Ts.
The Biogenic database incorporated a new version of the Biogenic Emissions Landcover Database (BELD5) and
provides updates for all states, including Alaska, Hawaii, Puerto Rico and the U.S. Virgin Islands.
2.5.1.3 Onroadandnonroaddata categories
For mobile sources, onroad methodology used an updated version of the MOVES model with updated mobile
source activity data such as vehicle miles travelled (VMT), age distributions, and fuel type mix, and improved
idling computations; we also received new telematics data from StreetLight Data, Inc. For both onroad and
nonroad, we relied on model inputs provided by S/L/T agencies and other sources, except for California and
Tribes, who submitted emissions estimates. Sections 5 (nonroad mobile) and 6 (oroad mobile) provide more
detail on these improvements.
2.5.2 Differences in emissions between 2020 and 2017 NEI
This section presents a comparison from the 2017 NEI to the 2020 NEI. Table 2-4 compares CAP emissions for
the 2020 minus 2017 NEI for seven highly aggregated emission sectors. Table 2-5 compares emissions for select
HAPs for the 2020 minus 2017 NEI-for the same seven highly aggregated emission sectors. Emissions from the
biogenic (natural) sources are excluded, and the wildfire sector is shown separately for CAPs and HAPs. While Pb
is a CAP for the purposes of the NAAQS, due to toxic attributes and inclusion in previous national air toxics
assessments (NATA) and screenings (Air Toxics Screening) assessements, it is reviewed here with the HAPs. The
HAPs selected for comparison are based on their national scope of interest as defined by Air Toxics Screening
2-15
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Assessments. With a couple notable exceptions, CAP emissions are lower overall in 2020 than in 2017. Some
specific sector/pollutants increased in 2020 from 2017.
The increases in fuel combustion for most pollutants are primarily a result of increases in residential wood
combustion where the underlying source of activity data (fuel consumption) increased significantly via
methodology and geographic distribution changes. Conversely, the significant decrease in electric generating
unit (EGU) emissions account for the decrease in overall NOX and S02 fuel combustion. Increases in
Miscellaneous CO are from increased prescribed and agricultural field burning. Increases in nonroad gasoline
engine lawn and garden and commercial estimates explain the increases in Nonroad Mobile CO. Large increases
in agricultural fertilizer application explain the large Miscellaneous NH3 increase. Large Industrial Processes VOC
increases are primarily from increased oil and gas activity in the Permian Basin.
As expected, the pandemic contributed to significant decreases in 2020 for all Highway Vehicle pollutants. As
discussed in Section 7, there were comparatively more wildfires in 2020 than 2017, explaining the significant
increases in wildfire emissions for 2020. Year 2017 was a generally quiet year for such fires.
Table 2-4: 2020 and 2017 NEI CAP emissions and broad sector changes (2020 minus 2017) in tons
Broad Sector
CO
NH3
NOX
PM10
PM2.5
S02
VOC
Fuel Combustion
649,629
17,300
-340,670
173,719
195,152
-675,635
121,797
Highway Vehicles
-5,250,922
-10,392
-1,149,841
-49,133
-34,808
-15,635
-719,802
Industrial Processes
-125,816
5,662
-84,008
-113,491
-25,872
-7,932
219,865
Miscellaneous
83,207
1,172,618
3,294
-260,334
12,452
10,725
-107,556
Nonroad Mobile
343,796
-21
-467,017
-27,387
-26,135
-11,972
-113,617
Total 2020 NEI,
excluding wildfires
42,817,505
5,163,803
7,569,405
14,805,366
4,146,613
1,703,698
12,007,615
Total 2017 NEI,
excluding wildfires
47,117,611
3,978,637
9,607,648
15,081,992
4,025,823
2,404,147
12,606,929
Total Difference,
excluding wildfires
-4,300,107
1,185,167
-2,038,243
-276,626
120,790
-700,449
-599,313
Total % Difference,
excluding wildfires
-9%
30%
-21%
-2%
3%
-29%
-5%
Wildfires
132,876
2,904
15,702
24,066
20,332
5,510
44,743
Table 2-5: 2020 and 2017 NEI select HAP emissions and broad sector changes (2020 minus 2017) in tons
Broad Sector
Acrolein
Benzene
Ethylene
Oxide
Formaldehyde
Hexavalent
Chromium
Lead
Fuel Combustion
1,064
-4,472
0.40
16,293
-1.58
11
Highway Vehicles
-977
-23,321
-14,619
0.03
Industrial
Processes
1,022
5,109
-17
16,050
-5.41
-23
Miscellaneous
1,468
-2,817
-1.83
1,157
-1.29
1
Nonroad Mobile
-1,013
-1,497
-7,723
-0.02
-41
2-16
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Broad Sector
Acrolein
Benzene
Ethylene
Oxide
Formaldehyde
Hexavalent
Chromium
Lead
Total 2020 NEI,
excluding wildfires
36,331
126,794
92
274,713
25
613
Total 2017 NEI,
excluding wildfires
34,767
153,792
111
263,554
33
665
Total Difference,
excluding wildfires
1,563
-26,998
-19
11,158
-8
-52
Total % Difference,
excluding wildfires
4%
-18%
-17%
4%
-25%
-8%
Wildfires
-1,475
-9,027
-18,559
2.6 How well are tribal data and regions represented in the 2020 NEI?
Nine tribes submitted data to the EIS for 2020 as shown in Table 2-6. In this table, a "CAP, HAP" designation
indicates that both criteria and hazardous air pollutants were submitted by the tribe; "GHG" indicates
greenhouse gases were submitted. CAP indicates that only criteria pollutants were submitted. Facilities on tribal
land were augmented using TRI, HAPs and PM in the same manner as facilities under the state and local
jurisdictions, as explained in Section 3, therefore, Tribal Nations in Table 2-6 with just a CAP flag will also have
some HAP emissions in most cases. Eight additional tribal agencies, shown in Table 2-7, which did not submit
any data, are represented in the point data category of the 2020 NEI due to the emissions added by the EPA. The
emissions for these facilities are from the EPA gap fill datasets for airports, EGUs, and TRI data. Furthermore,
many nonpoint datasets included in the NEI are presumed to include tribal activity. Most notably, the oil and gas
nonpoint emissions have been confirmed to include activity on tribal lands because the underlying database
contained data reported by tribes.
Table 2-6: Tribal participation in the 2020 NEI
Tribal Agency
Point
Nonpoint
Onroad
Nonroad
Coeur d'Alene Tribe
CAP, HAP
CAP, HAP
CAP, HAP
Kootenai Tribe of Idaho
CAP, HAP
CAP, HAP
CAP, HAP
Morongo Band of Cahuilla Mission Indians of the Morongo
Reservation, California
CAP
Nez Perce Tribe
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Northern Cheyenne Tribe
CAP
CAP
CAP
Salt River Pima Maricopa Indian Community (SRPMIC)
EPNR
CAP, HAP,
GHG
CAP
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Idaho
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Southern Ute Indian Tribe
CAP, HAP,
GHG
CAP, HAP,
GHG
Ute Mountain Tribe of the Ute Mountain Reservation
CAP, HAP
2-17
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Table 2-7: Facilities on Tribal lands with 2020 NEI emissions from EPA only
Tribal Agency
EPA data used
Assiniboine and SiouxTribes of the Fort Peck Indian Reservation, Montana
Airports
Coeur d'Alene Tribe
TRI
Confederated Tribes and Bands of the Yakama Nation, Washington
TRI
Fond du Lac Band of Lake Superior Chippewa
Airports
Fort Mojave Indian Tribe of Arizona, California & Nevada
GHG, EGUs
Gila River Indian Community
TRI
Navajo Nation
GHG, EGUs, TRI
Nez Perce Tribe of Idaho
TRI
Northern Cheyenne Tribe of the Northern Cheyenne Indian Reservation, Montana
Airports
Omaha Tribe of Nebraska
Airports
Southern Ute Indian Tribe
GHG, Airports
Tohono O-Odham Nation Reservation
TRI
Ute Indian Tribe of the Uintah & Ouray Reservation, Utah
GHG, EGUs, Airports
2.7 What does the 2020 NEI tell us about mercury?
The NEI documentation includes this Hg section because of the importance of this pollutant and because the
sectors used to categorize Hg are different than the sectors presented for the other pollutants. The Hg sectors
primarily focus on regulatory categories and categories of interest to the international community; emissions
are summarized by these categories at the end of this section, in Table 2-10.
A summary of all data sources used to create the 2020 Hg inventory are shown in Figure 2-4.
2-18
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Figure 2-4: Data sources of Hg emissions (tons) in the 2020 NEI, by data category
25
20
u
0)
M—
o
cn
15
10
EPA Mobile
EPA Other
EPA EGU MATS
EPA HAP Augmentation
EPA TRI
EPA Nonpoint
S/L/T
Point
Nonpoint
Onroad
Nonroad
Mercury emission estimates in the 2020 NEI sum to 29.6 tons, with 29.1 tons from stationary sources1 and 0.5
tons from mobile sources (including aircraft, commercial marine vessels and locomotives). In the above figure
the "EPA mobile" accounts for all EPA datasets in the onroad mobile and nonroad mobile data categories:
onroad mobile and nonroad equipment sources; this does not include emissions from commercial marine vessel
and locomotive (also referred to as rail) emissions which reside in the EPA Nonpoint dataset.
Due to large decreases of emissions from sources within the regulated categories, most of the emissions are
from sources other than the regulated categories. The "other" includes includes, but is not limited to, landfills,
primary and secondary metal production, gas turbines, chemical manufacturing processes, production of
gypsum and other mineral products, flash steam geothermal power plants, petroleum refineries, human
cremation, residential fuel combustion, and fluorescent lamp breakage. Of the regulatory categories trended,
the three with highest emissions in the 2020 NEI are: electric arc furnaces (3.8 tons), coal -fired EGU with units
larger than 25 megawatts (MW) (3.6 tons) portland cement production (1.6 tons), and boilers and process
heaters (1.4 tons). Coal-fired EGUs no longer comprise the largest portion of the mercury emissions in NEI.
Most of the mercury emissions from coal and oil-fired electric generating units subject to the Mercury and Air
Toxics Standards (MATS) originate from SLT submitted mercury emissions estimates and from the
1 Outlier Hg emissions at 2 facilities (EIS Faciliy IDs 8542311 and 8452311) were not included for the purpose of this analysis.
2-19
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"2020EPA_EGU" data reported to the Clean Air Markets Division (CAMD). A very small fraction originates from
the TRI dataset. An insignificant fraction is derived from HAP augmentation.
In addition to Figure 2-4, Table 2-8 lists the emissions by data source with the above data sets further broken
out. More information on these datasets is available in Section 3 for point, Section 4 for nonroad mobile, Section
5 for onroad mobile sources, and Section 6 for nonpoint sources.
Table 2-8: 2020 NEI Hg emissions (tons) for each dataset type and group
Data
Category
Data Set
Brief Description
Hg
emissions
(tons)
Point
S/L/T
State, local, tribal agency-submitted
18
2020EPA_TRI
Toxics Release Inventory
3.4
2020EPA_EGU
Mercury and Air Toxics Rule
0.5
2020EPA_HAPAug
Computed based on S/L/T CAPs
0.2
2020 EPA_HAPAug-PM Aug
Computed based on S/L/T Augmented PM
0.08
2020EPA_Rail_HAPAug
Computed based on EPA Rail PM
0.007
2020EPA_LF
Landfills
0.004
2020EPA_ATS_SLT
SLT contributions from Air Toxics Screening review
0.000004
Nonpoint
2020 EPA_NON POINT
All EPA nonpoint tool estimates, including
commercial marine vessels and rail lines
8.7
2020SLT_HAPAug_NP
Computed based on S/L/T CAPs
0.5
S/L/T
State, local, tribal agency-submitted
0.3
2020EPA_HAPAugWWSLIT
Computed based on EPA tool PM
0.13
2020 EPA_HAPAug-PM Aug
Computed based on S/L/T Augmented PM
0.00010
Nonroad
2020EPA_Nonroad
EPA MOVES model
0.04
Onroad
2020EPA_Onroad
EPA MOVES model
0.3
The point and nonpoint data category datasets are described in more detail starting in Sections 3 and 6
respectively, and we highlight some key datasets here.
For point sources, we gap-filled Hg that was not reported by S/L/Ts in the same way as other HAPs - including
use of the TRI (see Section 3), EPA HAP Augmentation or "HAP Aug" in the figure (see Section 2.2.3), and other
EPA data developed for gap filling. Electric arc furnaces (EAFs) were gap filled using HAP aug and TRI only. The
HAP augmentation used facility specific augmentation factors developed so that the resultant emissions would
be the same as was used in 2014 and 2017. This approach was used to provide a more automated approach
than to submit the same emissions year after year, that would (via the use of CAPs) account for changes in
activity. The 2014 estimates were developed by applying a 34% reduction to 2011 NEI emissions (process level).
The 2011 NEI emissions were based on data developed for the National Emission Standards for Hazardous Air
Pollutants (NESHAP) for Area Sources: Electric Arc Furnace Steelmaking Facilities (subpart YYYYY). The 34% value
was the average reduction from a limited 3 facility test program in 2016 (the range was 11-70%) -based on
personal communication with Donna Lee Jones, EPA lead for the NESHAP. The sum of HAP aug mercury for EAFs
is about 0.07 tons. We used the same approach as in 2017 and 2014 for using TRI data associated EAFs in that
we excluded S/L/T estimates at non-EAF processes if they were significantly lower than the TRI Hg value. The
2-20
-------
sum of TRI Hg for EAFs is about 0.65 tons. The largest contribution to total EAF emissions is S/L/T data which
sum to about 3.0 tons.
The nonpoint non-combustion-related and cremation categories used the same or very similar approaches as
were used for the 2014 NEI and 2017 NEl, though activity data was updated. These nonpoint non-combustion
mercury methodologies are described in Section 15. EPA estimates for these categories are included in the
"2020EPA_NONPOINT" (along with other EPA nonpoint category estimates) shown in Figure 2-4 and Table 2-8.
Some of these categories have a point contribution, though the specific categories do not exactly line up
between the nonpoint and point data categories. They are summarized below:
• switches and relays - emissions from the shredding and crushing of cars containing Hg components at
auto crushing yards, SCC = 2650000002: Waste Disposal, Treatment, and Recovery; Scrap and Waste
Materials; Scrap and Waste Materials; Shredding (1.33 tons nonpoint; 4.6 lbs point)
• landfill "working face" emissions associated with the release of mercury via churning/crushing of new
material added to the landfill, SCC= 2620030001: Waste Disposal, Treatment, and Recovery; Landfills;
Municipal; Dumping/Crushing/Spreading of New Materials (working face) (0.511 tons nonpoint, total
point landfill Hg is 0.06 tons)
• thermometers and thermostats - the portion that emit mercury prior to disposal at landfills or
incinerators, SCC=2650000000: Waste Disposal, Treatment, and Recovery; Scrap and Waste Materials;
Scrap and Waste Materials; Total: All Processes (0.117 tons nonpoint)
• dental amalgam - emissions at dentist offices and from evaporation in teeth, SCC=2850001000:
Miscellaneous Area Sources; Health Services; Dental Alloy Production; Overall Process (0.46 tons
nonpoint)
• general laboratory activities, SCC = 2851001000: Miscellaneous Area Sources; Laboratories; Bench Scale
Reagents; Total (0.32 tons nonpoint)
• fluorescent lamp breakage, SCC= 2861000000: Miscellaneous Area Sources; Fluorescent Lamp
Breakage; Non-recycling Related Emissions; Total (0.967 tons nonpoint)
• fluorescent lamp recycling, SCC= 2861000010: Miscellaneous Area Sources; Fluorescent Lamp Breakage;
Recycling Related Emissions; Total (less than 0.1 lb nonpoint, point sum of breakage and recycling = 13
lbs)
• animal cremation, SCC= Miscellaneous Area Sources; Other Combustion; Cremation; Animals (2.4 lbs
nonpoint, 11 lbs point)
• human cremation - emissions primarily due to mercury in dental amalgam, SCC=2810060100:
Miscellaneous Area Sources; Other Combustion; Cremation; Humans (2.33 tons nonpoint, 0.22 tons
point). This is a 31% increase from 2017 emissions.
Since mercury is a HAP, it is reported voluntarily by S/L/T agencies. For the point data category of the 2020 NEI,
47 states and 2 local agencies reported mercury emissions. Table 2-9 provides the tons of emissions from EPA,
the SLT, and the resulting percent of emissions for the point data category.
2-21
-------
Table 2-9: Point inventory emissions by reporting agency
State
Agency
Type
Agency
From EPA
(tons)
From
Agency
(tons)
Percent
from
Agency
AK
State
Alaska Department of Environmental Conservation
7.89E-02
0.00E+00
0.00%
AL
State
Alabama Department of Environmental Management
2.04E-01
8.42E-01
80.51%
AR
State
Arkansas Department of Environmental Quality
4.69E-01
3.24E-01
40.86%
AZ
State
Arizona Department of Environmental Quality
1.05E-02
1.27E-01
92.38%
AZ
Local
Maricopa County Air Quality Department
8.42E-02
0.00E+00
0.00%
CA
State
California Air Resources Board
2.91E-02
7.95E-01
96.47%
CO
State
Colorado Department of Public Health and Environment
1.24E-01
2.81E-02
18.51%
CT
State
Connecticut Department of Energy and Environmental
Protection
4.11E-05
7.02E-02
99.94%
DC
State
DC-District Department of the Environment
2.40E-03
4.68E-03
66.15%
DE
State
Delaware Department of Natural Resources and
Environmental Control
2.43E-04
9.43E-03
97.49%
FL
State
Florida Department of Environmental Protection
1.02E-01
3.55E-01
77.66%
GA
State
Georgia Department of Natural Resources
1.18E-01
4.14E-05
0.04%
HI
State
Hawaii Department of Health Clean Air Branch
1.54E-02
1.10E-02
41.61%
IA
State
Iowa Department of Natural Resources
2.81E-02
2.78E-01
90.80%
ID
State
Idaho Department of Environmental Quality
4.55E-01
4.10E-03
0.89%
IL
State
Illinois Environmental Protection Agency
1.10E-02
6.84E-01
98.41%
IN
State
Indiana Department of Environmental Management
1.56E-01
6.55E-01
80.80%
KS
State
Kansas Department of Health and Environment
1.21E-02
2.52E-01
95.41%
KY
State
Kentucky Division for Air Quality
1.05E-01
1.62E-01
60.62%
KY
State
Louisville Metro Air Pollution Control District
5.37E-05
4.59E-02
99.88%
LA
State
Louisiana Department of Environmental Quality
2.09E-01
1.07E-01
33.90%
MA
State
Massachusetts Department of Environmental Protection
1.35E-02
0.00E+00
0.00%
MD
State
Maryland Department of the Environment
1.03E-01
0.00E+00
0.00%
ME
State
Maine Department of Environmental Protection
0.00E+00
4.93E-02
100.00%
Ml
State
Michigan Department of Environmental Quality
7.26E-03
2.27E-01
96.91%
MN
State
Minnesota Pollution Control Agency
3.04E-04
4.34E-01
99.93%
MO
State
Missouri Department of Natural Resources
2.34E-02
4.95E-01
95.48%
MS
State
Mississippi Dept of Environmental Quality
2.80E-03
2.28E-01
98.79%
MT
State
Montana Department of Environmental Quality
7.74E-02
4.00E-04
0.51%
NC
State
North Carolina Department of Environmental Quality
6.89E-03
6.02E-01
98.87%
ND
State
North Dakota Department of Health
2.70E-01
2.40E-01
47.02%
NE
State
Nebraska Environmental Quality
2.36E-02
1.31E-01
84.71%
NH
State
New Hampshire Department of Environmental Services
8.83E-05
1.63E-02
99.46%
NJ
State
New Jersey Department of Environment Protection
5.77E-04
6.48E-02
99.12%
NM
Local
City of Albuquerque
7.16E-03
0.00E+00
0.00%
2-22
-------
State
Agency
Type
Agency
From EPA
(tons)
From
Agency
(tons)
Percent
from
Agency
NM
State
New Mexico Environment Department Air Quality Bureau
7.51E-03
0.00E+00
0.00%
NV
Local
Clark County Department of Air Quality and Environmental
Management
1.05E-02
0.00E+00
0.00%
NV
State
Nevada Division of Environmental Protection
3.75E-01
4.75E-01
55.89%
NV
Local
Washoe County Health District
1.31E-05
0.00E+00
0.00%
NY
State
New York State Department of Environmental Conservation
9.17E-04
2.87E+00
99.97%
OH
State
Ohio Environmental Protection Agency
2.39E-01
1.60E+00
86.98%
OK
State
Oklahoma Department of Environmental Quality
7.81E-03
1.74E-01
95.71%
OR
State
Oregon Department of Environmental Quality
3.06E-03
1.07E-01
97.21%
PA
State
Pennsylvania Department of Environmental Protection
2.31E-01
7.76E-01
77.03%
PR
Territory
Puerto Rico
5.22E-02
0.00E+00
0.00%
Rl
State
Rhode Island Department of Environmental Management
7.60E-05
2.31E-02
99.67%
SC
State
South Carolina Department of Health and Environmental
Control
8.65E-05
6.74E-01
99.99%
SD
State
South Dakota Department of Environment and Natural
Resources
1.77E-02
0.00E+00
0.00%
TN
Local
Chattanooga Air Pollution Control Bureau (CHCAPCB)
1.12E-02
6.21E-07
0.01%
TN
Local
Knox County Department of Air Quality Management
1.02E-01
1.20E-02
10.51%
TN
State
Memphis and Shelby County Health Department - Pollution
Control
7.85E-02
1.01E-03
1.28%
TN
Local
Metro Public Health of Nashville/Davidson County
5.05E-05
0.00E+00
0.00%
TN
State
Tennessee Department of Environmental Conservation
7.94E-02
6.37E-02
44.53%
TX
State
Texas Commission on Environmental Quality
9.97E-03
2.34E+00
99.58%
UT
State
Utah Division of Air Quality
3.71E-02
3.66E-01
90.79%
VA
State
Virginia Department of Environmental Quality
2.32E-02
4.46E-01
95.05%
VT
State
Vermont Department of Environmental Conservation
4.63E-04
0.00E+00
0.00%
WA
State
Washington State Department of Ecology
1.32E-01
4.71E-02
26.21%
Wl
State
Wisconsin Department of Natural Resources
5.22E-03
2.77E-01
98.15%
WV
State
West Virginia Division of Air Quality
6.09E-04
2.24E-01
99.73%
WY
State
Wyoming Department of Environmental Quality
1.15E-02
3.99E-01
97.20%
Tribe
Coeur dAlene Tribe
3.65E-04
0.00E+00
0.00%
Tribe
Navajo Nation
9.81E-03
0.00E+00
0.00%
Tribe
Nez Perce Tribe
3.80E-05
0.00E+00
0.00%
Tribe
Southern Ute Indian Tribe
2.19E-06
0.00E+00
0.00%
Tribe
Tohono O-Odham Nation Reservation
4.98E-06
0.00E+00
0.00%
Tribe
Ute Indian Tribe of the Uintah & Ouray Reservation, Utah
2.85E-04
0.00E+00
0.00%
2-23
-------
Eight states (CA, ID, MN, OH, Rl, TX, VA, WV), 2 local agencies (Knox County, TN and Washoe County, NV) and 4
tribal agencies reported Hg to the nonpoint data category. The tribal agencies are Coeur d'Alene Tribe, Kootenai
Tribe of Idaho, Nez PerceTribe, and Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho.
Table 2-10 and Figure 2-5 show the 2020 NEI mercury emissions for the key categories of interest in comparison
to other triennial inventory years and the baseline HAP inventory of 1990. The 2005 data are from the MATS
2005 modeling platform. Two comma-separated values included in the zip file (to be posted in early April 2023),
2020nei supdata mercury.zip, provide the category assignments at the facility-process level for point sources,
and the county-SCC level for nonpoint, onroad and nonroad sources. Individual point source processes were
matched to categories based on the process-level or unit-level category assignments used in the previous
triennial NEI (2017 NEI) as a starting point, and then supplemented with manual assignments considering SCC,
NAICS, facility category codes, emission factor information (e.g., fuel combusted) and facility names.
Table 2-10: Trends in NEI mercury emissions - 1990, 2005, 2008 v3, 2011v2, 2014v2 NEI, 2017 NEI, and 2020 NEI
Source Category
1990
(tpy)
Baseline
11/2005
2005
(tpy)
MATS
3/2011
2008
(tpy)
2008v3
2011
(tpy)
2014
(tpy)
2017
(tpy)
2020
(tpy)
Notes
Utility Coal Boilers
(Electricity
Generation Units -
EGUs, combusting
coal)
58.8
52.2
29.4
26.8
22.9
4.4
3.6
This category includes coal-fired
utility boilers and integrated
gasified coal combustion units
greater than 25 MW, excluding
small Hg estimated for startup
or cofired gas/oil.
The following utility and
independant power plant units
are included in the "Other"
category: non-coal fired boilers,
coal fired boilers <25MW, gas
turbines, geothermal units, and
combined cycle units).
Hospital/Medical/
Infectious Waste
Incineration
51
0.2
0.1
0.1
0.02
0.003
0.010
Municipal Waste
Combustors
57.2
2.3
1.3
1.0
0.6
0.4
0.3
2-24
-------
Source Category
1990
(tpy)
Baseline
11/2005
2005
(tpy)
MATS
3/2011
2008
(tpy)
2008v3
2011
(tpy)
2014
(tpy)
2017
(tpy)
2020
(tpy)
Notes
Industrial,
Commercial/lnstitut
ional (ICI) Boilers
and Process
Heaters
14.4
6.4
4.2
3.6
3.2
2.5
1.4
Sum of nonpoint ICI boiler and
point emissions. Change in
category: Previously included
some electricity generating
units less than 25 MW.
Currently includes strictly
industrial units and industrial
cogenerating units. Electricity
generating units other than
those in the Utility Coal Boilers
category are now included in
the "Other" category along with
large non-coal fired electric
generating units. Decrease from
2017 is due in part to this
change in category definition.
Mercury Cell Chlor-
Alkali Plants
10
3.1
1.3
0.5
0.1
0.1
0.1
Electric Arc
Furnaces
7.5
7.0
4.8
5.4
5.0
4.7
3.8
Commercial/lndustr
ial Sold Waste
Incineration
Not
available
1.1
0.02
0.01
0.01
0.06
0.03
Possibly an underestimate due
to missing sources and overlap
in categorization of cement kilns
and hazardous waste
incineration in facilities that can
burn multiple fuels
Hazardous Waste
Incineration
6.6
3.2
1.3
0.7
0.8
1.0
0.2
Possibly an underestimate due
to missing sources and overlap
in categorization of cement kilns
and commercial/industrial solid
waste incineration in facilities
that can burn multiple fuels
Portland Cement
Non-Hazardous
Waste
5.0
7.5
4.2
2.9
3.2
1.7
1.6
Gold Mining
4.4
2.5
1.7
0.8
0.6
0.9
0.9
Includes fugitive emissions at
mines such as TRI emissions at
fugitive release points that were
not reported by S/L/T
Sewage Sludge
Incineration
2
0.3
0.3
0.3
0.3
0.4
0.2
2-25
-------
Source Category
1990
(tpy)
Baseline
11/2005
2005
(tpy)
MATS
3/2011
2008
(tpy)
2008v3
2011
(tpy)
2014
(tpy)
2017
(tpy)
2020
(tpy)
Notes
Mobile Sources
Not
available
1.2
1.8
1.3
1.0
0.6
0.5
Sum of all onroad, nonroad,
locomotives and commercial
marine vessels. Decrease likely
due to decrease in mobile
source activity in 2020.
Other Categories
29.5
18
10.7
13
14.0
16.0
16.9
Sum of nonpoint {ICI fuel
combustion other than boilers,
residential fuel combustion,
industrial processes, cremation,
dental alloy production,
fluorescent lamp breakage} and
point emissions. Increase due in
part to inclusion of electric
generating units previously
included in the ICI Boilers and
Process Heaters Category.
Total (all
categories)
246
105
61
56
52
33
30
2-26
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Figure 2-5: Trends in NEI Mercury emissions
300
250
200
150
100
50
I
Other
Portland Cement Manufacturing
Electric Arc Furnaces
Industrial, Commercial, Institutional Boilers
Medical Waste Combustors
Municipal Waste Combustors
Utility Coal Boilers
1990 2005 2008 2011 2014 2017 2020
As shown in Table 2-10, 2020 Hg emissions are 3 tons lower than in the 2017. This difference is primarily due to
lower Hg emissions from EGUs covered by MATS; industrial, commercial/institutional boilers and process
heaters; and Electric Arc Furnaces. For EGUs, the decrease is a combination of fuel switching to natural gasthe
installation of Fig controls to comply and the co-benefits of Fig reductions from control devices installed for the
reduction of S02 and PM. For industrial and commercial/institutional boilers, there appears to be fewer boilers
using coal.
2.8 References for 2020 inventory contents overview
1. Strait, R.; MacKenzie, D.; and Fluntley, 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.
2. U.S. Environmental Protection Agency, 2018. Residual Risk Assessment for the Coal- and Oil-Fired EGU
Source Category in Support of the 2019 Risk and Technology Review Proposed Rule. Office of Air Quality
Planning and Standards, Docket No. EPA-FIQ-OAR-2018-0794-0070, December 2018.
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3. Email from Nabanita Modak, EPA, to Janice Godfrey, EPA (cc: Madeleine Strum, EPA and Eric Goehl, EPA)
with attached spreadsheet "Facility FRS_NEI IDS For CISWI Units030917.xlsx" emailed 9/6/2019.
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-23-001b
Environmental Protection Air Quality Assessment Division March 2023
Agency Research Triangle Park, NC
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