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2020 National Emissions Inventory Technical
Support Document: Fires - Wild, Prescribed,
and Agricultural Field Burning
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EP A-454/R-23 -001 g
March 2023
2020 National Emissions Inventory Technical Support Document: Fires - Wild, Prescribed, and
Agricultural Field Burning
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
7 Fires - Wild, Prescribed, and Agricultural Field Burning 7-1
7.1 Sector Descriptions and Overview 7-1
7.2 Sources of year-2020 data 7-3
7.2.1 SLT direct emissions submittals -wildfires and prescribed burning 7-3
7.2.2 SLT direct emissions submittals -agricultural field burning 7-4
7.2.3 SLT activity data and feedback submittals -all fires 7-4
7.3 EPA Methodology 7-7
7.3.1 Wildfires 7-7
7.3.2 Agricultural Field Burning 7-16
7.3.3 PM speciation for all fires 7-19
7.3.4 Quality Assurance (QA) of Final Results 7-20
7.3.5 Agricultural field burning 7-21
7.4 Emissions Summaries 7-21
7.4.1 Wildland fires 7-21
7.4.2 Agricultural field burning 7-30
7.5 References 7-31
7.5.1 Wildfires and prescribed burning 7-31
7.5.2 Agricultural field burning 7-32
List of Tables
Table 7-1: SCCs for wildland fires 7-2
Table 7-2: Nonpoint Agricultural Field Burning SCCs in the 2020 NEI 7-3
Table 7-3: PM2.5 emissions submitted by reporting agency for agricultural field burning 7-4
Table 7-4: SLT fire activity information and feedback submitted for 2020 NEI inventory use 7-5
Table 7-5: National fire information databases used in EPA's 2020 NEI wildland fire emissions estimates
7-8
Table 7-6: 2020 National SmartFire2 Reconciliation Weights 7-12
Table 7-7: Emission factor regions used to assign HAP emission factors for the 2020 NEI 7-13
Table 7-8: Prescribed fire HAP emission factors (lb/ton fuel consumed) for the 2020 NEI 7-13
Table 7-9: Wildfire HAP emission factors (lbs/ton fuel consumed) for the 2020 NEI 7-14
Table 7-10: Assumed agricultural field sizes burned by state in 2020NEI 7-18
Table 7-11: Revised Ag Burning Emission factors (lbs/ton) for VOC 7-19
Table 7-12: Select HAP Emission factors (lb/ton) used in EPA Methods by crop type for entire US 7-19
Table 7-13: PM species for all wildland fires, computed as fraction of total PM2.5 7-20
l
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Table 7-14: CONUS (lower 48 states) and Alaska and Hawaii fire type information for 2020 NEI WLFs 7-23
Table 7-15: Summary of acres burned and PM2.5 emissions by state, fire type, and combustion phase7-25
Table 7-16: Comparison of State vs EPA 2020 PM2.5 emissions (tons) for agencies that submitted 7-31
List of Figures
Figure 7-1: 2020 NEI Wildland Fire Data Sources including S/L/Ts 7-5
Figure 7-2: Processing flow for fire emission estimates in the 2020 NEI inventory 7-11
Figure 7-3: Default fire type assignment by state and month in cases where a satellite detect is only
source of fire information 7-12
Figure 7-4: BlueSky Pipeline modules 7-16
Figure 7-5: Annual comparison of PM2.5 emissions for lower 48 states 7-22
Figure 7-6: Annual comparison of area burned for lower 48 states 7-22
Figure 7-7: Monthly acres burned by fire type for 2020 NEI CONUS Wildland Fires 7-23
Figure 7-8: Monthly PM2.5 by fire type for 2020 NEI CONUS Wildland Fires 7-24
Figure 7-9: Total 2020 NEI area burned by state -wildland fires 7-27
Figure 7-10: Total 2020 NEI PM2.5 wildland fires emissions by state 7-28
Figure 7-11: 2020NEI county PM2.5 wildland fires emissions in tons per square mile 7-29
Figure 7-12: 2020NEI wildland fires county area burned in acres per square mile 7-30
Figure 7-13: Total 2020 NEI Agricultural Burning PM2.5 Emissions by state 7-31
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7 Fires - Wild, Prescribed, and Agricultural Field Burning
7.1 Sector Descriptions and Overview
Wildfires and prescribed burns (Wildland Fires in sum, WLFs) that occur during the inventory year are included
as "non-point" sources beginning with the 2020 NEI. Previous NEIs had wildland fires labeled as "events"
sources. Emissions from these fires, as well as agricultural fires, make up the National Fire Emissions Inventory
(NFEI). Agricultural field burning has been treated as non-point sources in previous NEIs and has had a separate
section in the NEI Technical Support Document. All these fire types will be combined into one section of this TSD
beginning with this 2020NEI.
Estimated emissions from all these fire types in the 2020 NEI are calculated from burned area data. Input data
sets are collected from State/Local/Tribal (S/L/T) agencies and from national agencies and organizations. S/L/T
agencies that provide input data were also asked to complete the NEI Wildland Fire Inventory Database
Questionnaire, which consists of a self-assessment of data completeness. Raw burned area data compiled from
S/L/T agencies and national data sources are cleaned and combined to produce a comprehensive burned area
data set. Emissions are then calculated using fire emission tools/models that rely on burned area as well as fuel
and climatological weather information. These emissions tools/models will be described later in this section. The
resulting emissions are compiled by date and location as day-specific emission estimates.
For purposes of emission inventory preparation, wildland fire (WLF) is defined as "any non-structure fire that
occurs in the wildland (an area in which human activity and development are essentially non-existent, except for
roads, railroads, power lines, and similar transportation facilities). Wildland fire activity is categorized by the
conditions under which the fire occurs. These conditions influence important aspects of fire behavior, including
smoke emissions. In the 2020 NEI, data processing is conducted differently depending on the fire type, as
defined below:
Wildfire (WF): Any fire started by an unplanned ignition caused by lightning; volcanoes; other acts of nature;
unauthorized activity; or accidental, human-caused actions, or a prescribed fire that has developed into a
wildfire.
Prescribed (Rx) fire: Any fire intentionally ignited by management actions in accordance with applicable laws,
policies, and regulations to meet specific land or resource management objectives. Prescribed fire is one type of
fuels treatment. Fuels treatments are vegetation management activities intended to modify or reduce
hazardous fuels. Fuels treatments include prescribed fires, wildland fire use, and mechanical treatment.
Agricultural burning: A type of prescribed fire, specifically used on land used or intended to be used for raising
crops or grazing.
Pile burning is a type of prescribed fire in which fuels are gathered into piles before burning. In this type of
burning, individual piles are ignited separately. Pile burn emissions are not currently included in the NEI due to
lack of usable data and default methods. EPA continues to work to develop methods for estimating emissions of
this source type.
Table 7-1 lists the Source Classification Codes (SCCs) that define the different types of WLFs in the 2020 NEI,
both for EPA data and for S/L/T agency data. The leading SCC description for these SCCs is "Miscellaneous Area
Sources; Other Combustion - as Event". Since the 2014 NEI, the EPA has compiled WLF emissions by smoldering
and flaming phases. The SCCs shown in Table 7-1 are used to denote this differentiation. There are five valid
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SCCs for wildland fires in EIS for the 2020 NEl, and EPA reports estimates into each of these SCCs. One difference
to note for the 2020 NEI is that we have included a specific SCC (2801500170) that houses only the grassland
fires of "Flint Hills/' which occur over much of KS and a small part of OK. In addition, other grassland fires (other
than "Flint Hills" fires) are processed via the SmartFire2/BlueSky Pipeline (SF2/BSP) process described below and
inventoried along with other wildland fires.
Table 7-1: SCCs for wildland fires
SCC
Description
2811020002
Prescribed Rangeland Burning
2811021000
Prescribed Rangeland Burning -Tallgrass Prairie
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland wildfires)
2810001002
Forest Wildfires: Flaming (includes grassland wildfires)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
Agricultural burning refers to fires that occur over lands used for cultivating crops and agriculture. Another term
for this sector is crop residue burning. In past NEIs for this sector, it was exclusively limited to emissions resulting
in the burning of crops. However, in the 2014 NEI, we included grass/pasture burning SCCs into this sector.
However, for technical reasons, we have moved the grass/pasture burning to the wildland fires category for the
2017 NEI and 2020 NEI, thereby causing this sector to once again only house emissions resulting from burning of
crops.
Table 7-2 shows, the agricultural field burning SCCs covered by the EPA estimates and by the State/Local and
Tribal agencies that submitted data. The leading SCC description is "Miscellaneous Area Sources; Agriculture
Production - Crops - as nonpoint; Agricultural Field Burning - whole field set on fire;" for all SCCs in the table.
Note that many general crops are included in the SCC 2801500000, and it also is the SCC to report into for "crops
unknown."
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Ta
lie 7-2: Nonpoint Agricultural Field Burning SCCs in the 2020 NEI
see
SCC Description
EPA
S/L/T
2801500000
Unspecified crop type and Burn Method
X
X
2801500112
Field Crop is Alfalfa: Backfire Burning
X
2801500130
Field Crop is Barley: Burning Techniques Not Significant
X
2801500141
Field Crop is Bean (red): Headfire Burning
X
X
2801500142
Field Crop is Bean (red): Backfire Burning
X
2801500150
Field Crop is Corn: Burning Techniques Not Important
X
X
2801500160
Field Crop is Cotton: Burning Techniques Not Important
X
X
2801500171
Fallow
X
X
2801500182
Field Crop is Hay (wild): Backfire Burning
X
2801500192
Field Crop is Oats: Backfire Burning
X
2801500202
Field Crop is Pea: Backfire Burning
X
2801500220
Field Crop is Rice: Burning Techniques Not Significant
X
2801500250
Field Crop is Sugar Cane: Burning Techniques Not Significant
X
X
2801500262
Field Crop is Wheat: Backfire Burning
X
X
2801500264
Double Crop Winter Wheat and Soybeans
X
X
2801500600
Forest Residues Unspecified
X
2801600300
Orchard Crop Other Not Elsewhere Classified
X
2801600320
Orchard Crop is Apple
X
2801600330
Orchard Crop is Apricot
X
2801600350
Orchard Crop is Cherry
X
2801600410
Orchard Crop is Peach
X
2801600420
Orchard Crop is Pear
X
2801600430
Orchard Crop is Prune
X
2801600500
Vine Crop Other Not Elsewhere Classified
X
7.2 Sources of year-2020 data
7.2.1 SLT direct emissions submittals -wildfires and prescribed burning
Only two agencies submitted emissions for wildland fires: Georgia and Washington. These data were formatted
and merged into the EPA the EPA dataset created from SMARTFire version 2 (SF2/BSP), which used available
state inputs, and (discussed in Section 7.3.3) a PM2.5 speciation file that contains the five components of PM2.5
for each fire. This merged information is the basis of the WLF 2020 NEI. The NEI includes only Georgia and
Washington-provided data for that S/L/T; in other words, there were no additions with any EPA-based data
based on the questionnaire GA and WA submitted that indicated their submissions were complete for each of
these states. Both Georgia and Washington were supplied HAP to VOC ratios by EPA, which they used to
estimate HAPs based on their VOC emissions to calculate HAP emissions, so that these emissions calculations
were used consistent with what was used for the remainder of the U.S. via the EPA methods. For other State
and tribal regions, EPA used the nationwide NEI WLF emission estimates and developed tribal land emission
estimates using appropriate shapefiles and GIS. These estimates over tribal lands are available as part of the
public release of 2020 Nonpoint data.
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7.2.2 SLT direct emissions submittals -agricultural field burning
As an example of what agencies submitted, the agencies listed in Table 7-3 submitted PM2.5 emissions for this
sector; agencies not listed used EPA estimates for the entire sector. As we will discuss below, some agencies
provided agricultural field burning activity that was used in estimating emissions using EPA's methodology.
Some agencies submitted emissions for the entire sector while others submitted only a portion of the sector.
When an agency submits less than 100%, their Nonpoint Survey responses, along with other general business
rules for building the NEI, are used to backfill with EPA estimates as appropriate.
Table 7-3: PM2.5 emissions submitted by reporting agency for agricultural field burning
Region
Agency
S/L/T
2
New Jersey Department of Environment Protection
State
4
Georgia Department of Natural Resources
State
9
California Air Resources Board
State
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
10
Washington State Department of Ecology
State
7.2.3 SLT activity data and feedback submittals -all fires
As in previous NEI years and building off the 2016 modeling platform [ref l]collaborative efforts, S/L/Ts were
asked to submit fire occurrence/activity data for the 2020 NEI. A template form containing the desired format
for data submittals was provided to S/L/T air agencies. A map of all states that returned the template form is
shown in Figure 7-1. States that did not return the template form are shown in gray and had emissions based
only on national default data. In total, 23 states returned the template form for the EPA's 2020 NEI wildland fire
emissions estimates processing. The states that returned the forms directly to the EPA are Alaska, Arizona,
California, Delaware, Georgia, Florida, Idaho, Iowa, Kansas, Louisiana, Maine, Massachusetts, Montana, New
Jersey, Nevada (Washoe County only), North Carolina, Oregon, Rhode Island, South Carolina, Utah, Virginia,
Washington, and Wyoming. Texas Parks and Wildlife Department provided prescribed fire activity for their state
lands. In addition to supplying activity data, S/L/Ts that supplied such data were also requested to complete a
questionnaire to help EPA determine how complete their activity data submissions were.
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Figure 7-1: 2020 NEI Wildland Fire Data Sources including S/L/Ts
'<1.1 vi'
WF+RX+AG
WF+RX
RX+AG
WF ONLY
RX ONLY
AG ONLY
States that submitted fire data/feedback by fire type
When fire activity or emissions were provided by S/L/Ts the data were evaluated by EPA and further feedback
on the data submitted by the state was requested at times. Table 7-4 provides a summary of the type of data
submitted by each S/L/T agency and includes spatial, temporal, acres burned, and other information provided by
the agencies.
Table 7-4: SLT fire activity information and feedback submitted for 2020 NEI inventory use
S/L/T name
Fire Types
Description
Alaska
WF/RX
Latitude-longitude, Fuel Characteristic Classification System (FCCS) fuel
beds, and acres burned for wildfire and prescribed burns
Arizona
WF/RX/AG
Day-specific, latitude-longitude, and acres burned for prescribed burns.
Feedback on specific agricultural burns and wildfires in their state.
California
WF/RX
Day-specific, latitude-longitude data for prescribed burns as well as
shapefiles for wildfires.
Delaware
RX/AG
Day-specific, latitude-longitude for prescribed burns and agricultural burns.
Florida
WF/RX/AG
Start and end dates, latitude-longitude, and acres burned for wildfire,
prescribed burns, and agricultural burns.
Georgia
WF/RX/AG
Emissions data submitted included all fire types via EIS. The wildfire and
prescribed burn data were provided as daily, point emissions sources but
were summed by EIS to the county level for 2020NEI use. Also provided
activity data. Provided agricultural burn emissions at county level.
Iowa
WF/RX/AG
Day-specific, latitude-longitude, and acres burned for prescribed burns as
well as feedback on wildfires and agricultural burns in their state.
Idaho
AG
Day-specific, latitude-longitude, acres burned for agricultural burns. This
included activity data from Nez Perce tribe in this state.
Kansas
RX
Day-specific, county-centroid, and acres burned for Flint Hills-only
prescribed grassland burning
Louisiana
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns.
Maine
WF
Day-specific, latitude-longitude, and acres burned for wildfires.
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S/L/T name
Fire Types
Description
Massachusetts
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns.
Montana
WF/RX
Day-specific, latitude-longitude and acres burned information for
prescribed burns. Provided feedback on wildfires in their state.
New Jersey
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns.
North Carolina
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns.
Nevada
(Washoe
County)
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfires and
prescribed burns.
Oklahoma
RX
Day-specific, county-centroid, and acres burned for Flint Hills-only
prescribed grassland burning (thru the Kansas data mentioned in table
above)
Oregon
RX
Day-specific, latitude-longitude, acres burned for prescribed burns.
Rhode Island
WF
Day-specific, latitude-longitude, acres burned for wildfires.
South Carolina
WF/RX/AG
Day-specific, latitude-longitude, and acres burned for wildfire, prescribed
burns, and agricultural burns.
Texas
RX
Day-specific, latitude-longitude, acres burned for prescribed burns for
burns located on Texas Parks and Wildlife Department lands only.
Utah
RX
Day-specific, latitude-longitude, and acres burned for prescribed burns
Virginia
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns.
Washington
WF/RX
Emissions data submitted included all fire types via EIS. The wildfire and
prescribed burn data were provided as daily, point emissions sources. EIS
was used to sum to county and annual/monthly scales for nonpoint
category in 2020NEI. County emissions provided for agricultural burns.
Wyoming
WF/RX
Day-specific, latitude-longitude, acres burned for prescribed burns and
wildfires.
In order to develop a format that could be ingested into SMARTFire2 or directly into Bluesky Pipeline certain
preprocessing steps were taken with the S/L/T submitted datasets. The names of columns and formats were
changed to match what the processors required. Additionally, all datasets were reviewed for invalid locations or
those that were spatially identified as occurring outside the submitting state. Obvious location errors, such as
those where the latitude and longitude were swapped or a sign was missing, were fixed. Without additional
information identifying an activity location within the respective state, these records were dropped. Overall, the
records dropped accounted for a very small portion of the total activity.
The temporal approach for the S/L/T varied based on the information provided in the submitted data and
direction from the individual agencies. Some states submitted activity without end dates. Each of these states
provided direction to assume that all fires lasted for a single day. Where a multi-day event could be matched to
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HMS detections the number of HMS detections on each day within the event were used to apportion the total
event activity. When a spatial and temporal match could not be made between the submitted data a flat
approach was used for the multi-day event.
The following states required additional preprocessing steps:
• Kansas and Oklahoma Flint Hills Regions: The activity for the Flint Hills region was spatially
reapportioned from the county-level to 2011 NLCD grass land area at centroids of 4 km grid cells.
Weighting of activity was done using the area of overlap between the grass land grid cells and the
respective county.
7.3 EPA Methodology
7.3.1 Wildfires
Preparation of the EPA WLF emissions begins with raw input data and ends with daily estimates of emissions
from flaming combustion and smoldering combustion phases. The daily estimates were summed to monthly
and annual for use in the NEI. Flaming combustion is combustion that occurs with a flame. Flaming combustion
is more complete combustion and is more prevalent with fuels that have a high surface-to-volume ratio, a low
bulk density, and low moisture content. Smoldering combustion is combustion that occurs without a flame.
Smoldering combustion is less complete and produces some pollutants, such as PM2.5, VOCs, and CO at higher
rates than flaming combustion. Smoldering combustion is more prevalent with fuels that have low surface-to-
volume ratios, high bulk density, and high moisture content. Models sometimes differentiate between
smoldering emissions that are lofted with a smoke plume and those that remain near the ground (residual
emissions). In the 2020 NEI, all flaming emissions are made up of any component that has a flaming component
to it while the smoldering emissions are the residual smoldering component that is generated by the CONSUME
model, as described further below. The emissions estimates were estimated and compiled separately for flaming
and smoldering combustion phases of fire to facilitate air quality modeling and fine-scale research in areas such
as health impacts of smoke emissions, where the known impacts of varying PM and VOC composition by
combustion phase likely play a role.
In the 2020 NEI process, EPA developed draft 2020 emission estimates based just on default information. S/L/Ts
had an opportunity to review these estimates and: 1) accept them as final, 2) submit activity data and a
questionnaire (as detailed below), or 3) provide comments. In developing final 2020 WLF estimates, EPA took
into consideration all 3 of these items. If an S/L/T accepted the draft estimates, those estimates were not
changed in the process to develop final estimates.
7.3.1.1 Na tional Fire In forma tion Da ta
Numerous fire information databases are available from U.S. national government agencies. Some of the
databases are available via the internet while others must be obtained directly from agency staff. Table 7-5
provides the national fire information databases that were used for the EPA's 2020 NEI methods for wildland fire
emissions estimates, including the website where the 2020 data were downloaded.
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Table 7-5: National fire information databases used in EPA's 2020 NEI wildland fire emissions estimates
Dataset
Name
Fire Types
Format
Agency
Coverage
Source
Hazard
Mapping
System
(HMS)
WF/ RX
CSV
NOAA
North
America
Hazard Mapping System Fire and Smoke
Product
National
Incident
Feature
Services
(NIFS)
(formerly
GeoMAC)
wildland fire
perimeter
polygons
WF
SHP
Multi
Entire US
https://data-nifc.opendata.arcgis.com/
Incident
Command
System Form
209: Incident
Status
Summary
(ICS-209)
WF/ RX
CSV
Multi
Entire US
FAMWEB website:
https://famit.nwcg.gov/applications/FAMWeb
Forest
Service
Activity
Tracking
System
(FACTS)
RX
SHP
USFS
Entire US
Hazardous Fuel Treatment Reduction: Polygon
US Fish and
Wildland
Service
(USFWS) fire
database
WF/ RX
CSV
USFWS
Entire US
Direct communication with USFWS
US
Department
of Interior
RX
CSV
DOI
Entire US
Direct communication with DOI
The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and Atmospheric
Administration's (NOAA) National Environmental Satellite and Data Information Service (NESDIS) as a tool to
identify fires over North America in an operational environment. The system utilizes geostationary and polar
orbiting environmental satellites. Automated fire detection algorithms are employed for each of the sensors.
When possible, HMS data analysts apply quality control procedures for the automated fire detections by
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eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via a
thorough examination of the satellite imagery.
The HMS product used for the 2020 NEI inventory consisted of daily comma-delimited files containing fire detect
information including latitude-longitude, satellite used, time detected, and other information. Landcover was
spatially associated with each HMS detects using the Cropland Data Layer (CDL). HMS detects over croplands
were removed from the input files so that only wildland fires are included. All grassland fire HMS satellite
detects were included in the EPA's 2020 NEI wildland fire emissions estimates. These grassland fires were
processed through SmartFire2 and BlueSky Pipeline with the other wildland fires.
National Incident Feature Services (NIFS) (formerly GeoMAC (Geospatial Multi-Agency Coordination)) is an
online wildfire mapping application designed for fire managers to access maps of current U.S. fire locations and
perimeters. The wildfire perimeter data is based upon input from incident intelligence sources from multiple
agencies, GPS data, and infrared (IR) imagery from fixed wing and satellite platforms. Fires in the year-specific
NIFS shapefile with dates outside of 2020 were removed. Some polygons have geometries which cause errors in
SmartFire2 processing. These problematic polygons were simplified using standard GIS methods.
The Incident Status Summary, also known as the "ICS-209" is used for reporting specific information on
significant fire incidents. The ICS-209 report is a critical interagency incident reporting tool giving daily
'snapshots' of the wildland fire management situation and individual incident information which include fire
behavior, size, location, cost, and other information. Data from two tables in the ICS-209 database were merged
and used for the EPA's 2020 NEI wildland fire emissions estimates: the
SIT209_HISTORY_INCIDENT_209_REPORTS table contained daily 209 data records for large fires, and the
SIT209_HISTORY_INCIDENTS table contained summary data for additional smaller fires. Some entries in the ICS-
209 database contained location and date errors. In situations where the errors were obvious in nature, such as
swapped latitude and longitudes or a typo in the year of the data, then appropriate corrections were made.
Fires with unclear location and date issues or those fires without an associated burned area were removed.
Some fires had unreasonable lengths in the reports based on the available fields. Estimated fire lengths were
adjusted based on fire size.
The US Forest Service (USFS) compiles a variety of fire information every year. Year 2020 data from the USFS
Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and used for 2020 NEI
emissions inventory development. This database includes information about activities related to fire/fuels,
silviculture, and invasive species. The FACTS database consists of shapefiles for prescribed burns that provide
acres burned and start and ending time information. As detailed earlier, all fires labeled as pile burns were
removed because the EPA does not currently develop emissions for pile burning.
The US Fish and Wildland Service (USFWS) and Department of Interior (DOI) also compiles wildfire and
prescribed burn activity on their federal lands every year. Year 2020 data were acquired from USFWS and DOI
through direct communication with USFWS and DOI staff and were used for 2020 NEI emissions inventory
development. The USFWS fire information provided fire type, acres burned, latitude-longitude, and start and
ending times. As with the FACTS dataset, fires labeled as pile burns were removed because the EPA does not
currently develop emissions for pile burning.
7.3.1.2 Emissions Estima tion Methodology
The national and S/L/T data mentioned earlier were used to estimate daily wildfire and prescribed burn
emissions from flaming combustion and smoldering combustion phases for the 2020 NEI inventory. Flaming
combustion is more complete combustion than smoldering and is more prevalent with fuels that have a high
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surface-to-volume ratio, a low bulk density, and low moisture content. Smoldering combustion occurs without a
flame, is a less complete burn, and produces some pollutants, such as PM2.5, VOCs, and CO, at higher rates than
flaming combustion. Smoldering combustion is more prevalent with fuels that have low surface-to-volume
ratios, high bulk density, and high moisture content. Models sometimes differentiate between smoldering
emissions that are lofted with a smoke plume and those that remain near the ground (residual emissions), but
for the purposes of the 2020 NEI inventory the residual smoldering emissions were allocated to the smoldering
SCCs ending in "1", while the lofted smoldering emissions were assigned to the flaming emissions SCCs ending in
Figure 7-2 is a schematic of the data processing stream for the 2020 NEI inventory for wildfire and prescribe
burn sources. The EPA's 2020 NEI wildland fire emissions estimates were estimated using Satellite Mapping
Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Pipeline (BSP)
system. SMARTFIRE2 is an algorithm and database system that operate within a geographic information system
(GIS). SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified GIS
database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on the
strengths of both data types while avoiding double-counting of fire events. At its core, SMARTFIRE2 is an
association engine that links reports covering the same fire in any number of multiple databases. In this process,
all input information is preserved, and no attempt is made to reconcile conflicting or potentially contradictory
information (for example, the existence of a fire in one database but not another). Further details of the
SMARTFIRE2 process as applied to NEI development can be found in the literature [ref 2],
For the 2020 NEI inventory, the national and S/L/T fire information was input into SMARTFIRE2 and then merged
and reconciled together based on user-defined weights for each fire information dataset. The relative weights
used for the national data stream are shown in Table 7-6. A dataset type with a higher ranking gets preference
for that attribute in the reconciled activity. The output from SMARTFIRE2 was daily acres burned by fire type,
and latitude-longitude coordinates for each fire. The fire type assignments were made using the fire information
datasets. If the only information for a fire was a satellite detect for fire activity, then Figure 7-3 was used to
make fire type assignment by state and by month.
7-10
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Figure 7-2: Processing flow for fire emission estimates in the 2020 NEI inventory
Input Data Sets
(state/local/tribal and national data sets)
% 4 #
Data Preparation
4
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire size and type
Fuel Moisture and
Fuel Loading Data
USFS Bluesky Pipeline
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
7-11
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Figure 7-3: Default fire type assignment by state and month in cases where a satellite detect is only source of
fire information
2020 NEI Draft HMS Default Wildfire Type Months
Table 7-6: 2020 National SmartFire2 Reconciliation Weights
Rank
Location
Weight
Size Weight
Shape
Weight
Growth
Weight
Name
Weight
Fire Type
Weight
1
SLT
Supplementa
1 Data
SLT
Supplementa
1 Data
NIFS
HMS
SLT
Supplementa
1 Data
SLT
Supplemental
Data
2
NIFS
NIFS
FACTS
NIFS
NIFS
NIFS
3
HMS
FACTS
HMS
STL
Supplementa
1 Data
ICS-209
FACTS
4
FACTS
ICS-209
ICS-209
DOI
FACTS
ICS-209
5
ICS-209
DOI
SLT
Supplementa
1 Data
PFIRS
DOI
DOI
6
DOI
PFIRS
DOI
USFWS
PFIRS
PFIRS
7
PFIRS
USFWS
PFIRS
ICS-209
USFWS
USFWS
8
USFWS
HMS
USFWS
FACTS
HMS
HMS
Supplemental S/L/T activity from Arizona, Idaho, Montana, Nevada, Oregon, and Wyoming were incorporated
with the national defaults into the national data reconciliation stream. States that submitted complete activity
datasets were not processed through SmartFire2 with the default national activity. An exception is for those
states that used HMS fire detections for daily apportionment of activity data. Alaska, Florida, and Utah all had
7-12
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their submitted data reconciled against the HMS fire detections. All resulting activity that was identified only
through HMS was removed from the final activity dataset so that only state-submitted event values were used
for emissions estimates. State-submitted activity from Iowa, Kansas, Massachusetts, North Carolina, and South
Carolina were not processed through SmartFire2. Instead, each activity dataset was converted into daily activity
files in a format that can be read directly by Bluesky Pipeline.
The BlueSky Pipeline (BSP version 4.2.13) was used to calculate fuel loading and consumption, and emissions
using various models depending on the available inputs as well as the desired results. BSP is open source at
https://github.com/pnwairfire/bluesky. The contiguous United States and Alaska, where Fuel Characteristic
Classification System (FCCS) fuel loading data are available, were processed using the modeling chain described
in Figure 7-4. The Fire Emissions Production Simulator (FEPS) in the BSP generated all the CAP emission factors
for wildland fires used in the 2020 NEI inventory [ref 3], The HAP emission factors used in this work came from
Urbanski, 2014 [ref 4], These emission factors were regionalized and handled differently by wild and prescribed
fire. Table 7-7 below outlines the regionalization scheme used while Table 7-8 and Table 7-9 show the HAP EFs
employed in this work separately for wild and prescribed fires. Note the differences, in bold in Table 7-7, for
wildfires and prescribed burning region assignments for Alaska and Wisconsin.
Table 7-7: Emission factor regions used to assign HAP emission factors for the 2020 NEI
Region
Wildfires
Prescribed burning
Region 1
AZ, CA, IA, IL, IN, KS, MO, NM, NV, OH, OK, TX
AZ, CA, IA, IL, IN, KS, MO, NM, NV, OH, OK, TX
Region 2
AK, AL, AR, CT, DC, DE, FL, GA, HI, KY, LA, MA,
MD, ME, Ml, MN, MS, NC, NH, NJ, NY, PA, PR,
Rl, SC, TN, VA, VI, VT, Wl, WV
AL, AR, CT, DC, DE, FL, GA, HI, KY, LA, MA, MD,
ME, Ml, MN, MS, NC, NH, NJ, NY, PA, PR, Rl, SC,
TN, VA, VI, VT, WV
Region 3
CO, ID, MT, ND, NE, OR, SD, UT, WA, WY
AK, CO, ID, MT, ND, NE, OR, SD, UT, WA, Wl,
WY
Table 7-8: Prescribed fire HAP emission factors (lb/ton fuel consumed) for the 2020 NEI
HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
1,3-Butadiene (HAP 106990)
0.272326792
0.516619944
0.362434922
0.272326792
0.516619944
0.362434922
Acetaldehyde (HAP 75070)
1.678013616
1.283540248
2.240688827
1.678013616
1.283540248
2.240688827
Acetonitrile (HAP 75058)
0.322386864
0.064076892
0.43051662
0.322386864
0.064076892
0.43051662
Acrolein (HAP 107028)
0.512615138
0.646776131
0.684821786
0.512615138
0.646776131
0.684821786
Acrylic Acid (HAP 79107)
0.070084101
0.058069684
0.094112936
0.070084101
0.058069684
0.094112936
Anthracene (HAP 120127)
0.005
0.005
0.005
0.005
0.005
0.005
Benz(a)anthracene (HAP 56553)
0.0062
0.0062
0.0062
0.0062
0.0062
0.0062
Benzene (HAP 71432)
0.450540649
0.566680016
0.600720865
0.450540649
0.566680016
0.600720865
Benzo(a)fluoranthene (HAP
203338)
0.0026
0.0026
0.0026
0.0026
0.0026
0.0026
Benzo(a)pyrene (HAP 50328)
0.00148
0.00148
0.00148
0.00148
0.00148
0.00148
Benzo(c)phenanthrene (HAP
195197)
0.0039
0.0039
0.0039
0.0039
0.0039
0.0039
Benzo(e)pyrene (HAP 192972)
0.00266
0.00266
0.00266
0.00266
0.00266
0.00266
Benzo(ghi)perylene (HAP 191242)
0.00508
0.00508
0.00508
0.00508
0.00508
0.00508
Benzo(k)fluoranthene (HAP
207089)
0.0026
0.0026
0.0026
0.0026
0.0026
0.0026
7-13
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HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
Benzofluoranthenes (HAP
56832736)
0.00514
0.00514
0.00514
0.00514
0.00514
0.00514
Carbonyl Sulfide (HAP 463581)
0.000534
0.000534
0.000534
0.000534
0.000534
0.000534
Chrysene (HAP 218019)
0.0062
0.0062
0.0062
0.0062
0.0062
0.0062
Fluoranthene (HAP 206440)
0.00673
0.00673
0.00673
0.00673
0.00673
0.00673
Formaldehyde (HAP 50000)
2.515018022
3.366039247
4.475370445
2.515018022
3.366039247
4.475370445
lndeno(l,2,3-cd)pyrene (HAP
193395)
0.00341
0.00341
0.00341
0.00341
0.00341
0.00341
m,p-Xylenes (HAP 1330207)
0.216259511
0.160192231
0.288346015
0.216259511
0.160192231
0.288346015
Methanol (HAP 67561)
2.306768122
1.974369243
5.036043252
2.306768122
1.974369243
5.036043252
Methyl Chloride (HAP 74873)
0.128325
0.128325
0.128325
0.128325
0.128325
0.128325
Methylanthracene (HAP
26914181)
0.00823
0.00823
0.00823
0.00823
0.00823
0.00823
Methylbenzopyrenes (HAP
65357699)
0.00296
0.00296
0.00296
0.00296
0.00296
0.00296
Methylchrysene (HAP 41637905)
0.0079
0.0079
0.0079
0.0079
0.0079
0.0079
Methylpyrene, fluoranthene (HAP
2381217)
0.00905
0.00905
0.00905
0.00905
0.00905
0.00905
n-Hexane(HAP 110543)
0.048057669
0.024028835
0.064076892
0.048057669
0.024028835
0.064076892
Naphthalene (HAP 91203)
0.486583901
0.398478174
0.650780937
0.486583901
0.398478174
0.650780937
o-Xylene (HAP 95476)
0.07609131
0.050060072
0.100120144
0.07609131
0.050060072
0.100120144
Perylene (HAP 198550)
0.000856
0.000856
0.000856
0.000856
0.000856
0.000856
Phenanthrene (HAP 85018)
0.005
0.005
0.005
0.005
0.005
0.005
Pyrene (HAP 129000)
0.00929
0.00929
0.00929
0.00929
0.00929
0.00929
Styrene (HAP 100425)
0.10412495
0.080096115
0.138165799
0.10412495
0.080096115
0.138165799
Toluene (HAP 108883)
0.344413296
0.398478174
0.45855026
0.344413296
0.398478174
0.45855026
Table 7-9: Wildfire HAP emission factors (lbs/ton fuel consumed) for the 2020 NEI
HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
1,3-Butadiene (HAP 106990)
0.272326792
0.140168202
0.362434922
0.272326792
0.140168202
0.362434922
Acetaldehyde (HAP 75070)
1.678013616
1.908289948
2.240688827
1.678013616
1.908289948
2.240688827
Acetonitrile (HAP 75058)
0.322386864
0.600720865
0.43051662
0.322386864
0.600720865
0.43051662
Acrolein (HAP 107028)
0.512615138
0.582699239
0.684821786
0.512615138
0.582699239
0.684821786
Acrylic Acid (HAP 79107)
0.070084101
0.080096115
0.094112936
0.070084101
0.080096115
0.094112936
Anthracene (HAP 120127)
0.005
0.005
0.005
0.005
0.005
0.005
benz(a)anthracene (HAP 56553)
0.0062
0.0062
0.0062
0.0062
0.0062
0.0062
Benzene (HAP 71432)
0.450540649
1.101321586
0.600720865
0.450540649
1.101321586
0.600720865
Benzo(a)fluoranthene (HAP
203338)
0.0026
0.0026
0.0026
0.0026
0.0026
0.0026
Benzo(a)pyrene (HAP 50328)
0.00148
0.00148
0.00148
0.00148
0.00148
0.00148
Benzo(c)phenanthrene (HAP
195197)
0.0039
0.0039
0.0039
0.0039
0.0039
0.0039
7-14
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HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
Benzo(e)pyrene (HAP 192972)
0.00266
0.00266
0.00266
0.00266
0.00266
0.00266
Benzo(ghi)perylene (HAP
191242)
0.00508
0.00508
0.00508
0.00508
0.00508
0.00508
Benzo(k)fluoranthene (HAP
207089)
0.0026
0.0026
0.0026
0.0026
0.0026
0.0026
Benzofluoranthenes (HAP
56832736)
0.00514
0.00514
0.00514
0.00514
0.00514
0.00514
Carbonyl Sulfide (HAP 463581)
0.000534
0.000534
0.000534
0.000534
0.000534
0.000534
Chrysene (HAP 218019)
0.0062
0.0062
0.0062
0.0062
0.0062
0.0062
Fluoranthene (HAP 206440)
0.00673
0.00673
0.00673
0.00673
0.00673
0.00673
Formaldehyde (HAP 50000)
2.515018022
3.954745695
4.475370445
2.515018022
3.954745695
4.475370445
lndeno(l,2,3-cd)pyrene (HAP
193395)
0.00341
0.00341
0.00341
0.00341
0.00341
0.00341
m,p-Xylenes (HAP 1330207)
0.216259511
0.120144173
0.288346015
0.216259511
0.120144173
0.288346015
Methanol (HAP 67561)
2.306768122
2.613135763
5.036043252
2.306768122
2.613135763
5.036043252
Methyl Chloride (HAP 74873)
0.128325
0.128325
0.128325
0.128325
0.128325
0.128325
Methylanthracene (HAP
26914181)
0.00823
0.00823
0.00823
0.00823
0.00823
0.00823
Methylbenzopyrenes (HAP
65357699)
0.00296
0.00296
0.00296
0.00296
0.00296
0.00296
Methylchrysene (HAP 41637905)
0.0079
0.0079
0.0079
0.0079
0.0079
0.0079
Methylpyrene,-fluoranthene
(HAP 2381217)
0.00905
0.00905
0.00905
0.00905
0.00905
0.00905
n-Hexane (HAP 110543)
0.048057669
0.054064878
0.064076892
0.048057669
0.054064878
0.064076892
Naphthalene (HAP 91203)
0.486583901
0.554665599
0.650780937
0.486583901
0.554665599
0.650780937
o-Xylene (HAP 95476)
0.07609131
0.054064878
0.100120144
0.07609131
0.054064878
0.100120144
Perylene (HAP 198550)
0.000856
0.000856
0.000856
0.000856
0.000856
0.000856
Phenanthrene (HAP 85018)
0.005
0.005
0.005
0.005
0.005
0.005
Pyrene (HAP 129000)
0.00929
0.00929
0.00929
0.00929
0.00929
0.00929
Styrene (HAP 100425)
0.10412495
0.11814177
0.138165799
0.10412495
0.11814177
0.138165799
Toluene (HAP 108883)
0.344413296
0.480576692
0.45855026
0.344413296
0.480576692
0.45855026
7-15
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Figure 7-4: BlueSky Pipeline modules
For the 2020 NEI inventory, the FCCSv3 spatial vegetation cover was upgraded to the LANDFIRE v2.0 fuel
vegetation cover. The FCCSv3 fuel bed characteristics were implemented along with LANDFIREv2.0 to provide
better fuel classification for the BlueSky Pipeline. The LANDFIREv2.0 raster data were aggregated from the
native resolution and projection to 120-meter resolution using a nearest-neighbor methodology. Aggregation
and reprojection was required to allow these data to work in the BlueSky Pipeline.
Outputs from each BlueSky Pipeline processing stream were aggregated into an annual file. Fires identified as
being over water by FCCS were removed because they produce no fuel consumption in the CONSUME model
and thus no emissions.
7.3.2 Agricultural Field Burning
By way of history for this sector, in the 2008 NEI, crop residue emission estimates were developed using satellite
detects occurring over land types classified as "agricultural" and uncertain field sizes or were sporadically
reported by a handful of states. In the 2011 NEI, the method described in McCarty et al. 2009 [ref 6] and
McCarty 2011 [ref 7] was employed to estimate the emissions from this sector with the exception that states
could submit their own estimates. However, this produced significant state to state variability between states
that submitted their own data and states that did not. In addition, we received comments that many false
detects (EPA emission estimates were too high) occurred using this method (due to dark fields resulting from
irrigation) Therefore, a consistent methodology across multiple years for the CONUS has not yet been developed
for this sector. To address these issues, in the 2014 NEI, a simple and efficient method was developed to
estimate emissions from crop residue that can easily be applied across multiple years over the CONUS at
minimal cost. The method was developed by EPA Office of Research and Development and the reader is directed
to a paper in press for details on the methods described below [ref 8], This is the basic method used for the
2020 NEI, with the changes/improvements made as noted below.
The approach developed for use in the 2014 NEI and 2017 NEI, and used again for the 2020 NEI, already
improves on previous estimates [ref 6, ref 7] as follows:
• Multiple satellite detections are used to locate fires using an operational product
7-16
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• Field Size estimates are based on field work studies in multiple states (rather than a one size fits all
approach)
• This method allows for intra-annual as well as annual changes in crop land use
• Additional processing of the HMS data was done to remove 2 types of duplicates
• This method uses USDA NASS Cropland Data Layer (CDL) (USDA, 2015a) [ref 9] information to separate
grass/pasture lands, which include Pasture/Grass, Grassland Herbaceous, and Pasture/Hay lands from all
other agricultural burning and to identify the crop type
• Removal of agricultural fires from the Hazard Mapping System (HMS) dataset before the application of
the SMARTFIRE2 system for wildfires and prescribed fires to eliminate double counting in the NEI and
the use of state information to further identify fires as crop residue burning rather than another type of
fire
• To further identify fires as crop residue burning rather than some type of wildfire. Our approach
complements the method used to estimate emissions from wildfires and prescribed fires because we
use crop level land use information to identify crop residue fires and grassland ('rangeland') fires. The
remaining fire detections are used in SMARTFIRE to estimate emissions in forested areas where fuel
loadings are available from the National Forest Service.
7.3.2.1 Improvements/Changes in the 2020 NEI
For the 2020 NEI, we have made a few revisions to the method used to estimate this sector compared to the
2017 NEI and will summarize them here. For details of the 2017NEI methods see Section 7 of our 2017 NEI
Technical Support Document.
• To avoid double counting with the wildfire inventory, all grassland detections of fires outside of the Flint
Hills in Kansas and Oklahoma have been incorporated into the wildfire and prescribed fire inventory
process and are not part of this database. These fires are included as appropriate in our wildland fire
inventory.
• EPA worked with the state of Iowa to move some fires very near agricultural lands to the prescribed
burn and wildfire types to take into account for the months where agricultural burns were not expected
in the Midwest states.
• In the 2020 NEI, for the first time, we have also included Pb as a pollutant for WLFs. The emission
factors that were used to estimate Pb come from a recently completed EPA test program (Reference =
A. L. Holder, V. Rao, K. Kovalcik, and L. Virtaranta, "Particulate Pb emission factors from wildland fires
in the United States," submitted to Atmospheric Environment X, March 2023. In sum, we find that
WLFs contribute 13 tons of Pb nationwide. The 2020NEI total for Pb emissions from wildland fires is
about 16 tons which about 5% of all Pb emissions in the entire 2020NEI.
7.3.2.2 Activity Data
As with the 2017 process, the HMS satellite product is the main system used for the 2020 NEI. The HMS satellite
product is an operational satellite product showing hot spots and smoke plumes indicative of fire locations. It is
a blended product using algorithms for the Geostationary Operational Environmental Satellite (GOES) Imager,
the Polar Operational Environmental Satellite (POES) Advanced Very High-Resolution Radiometer (AVHRR),
Moderate Resolution Imaging Spectroradiometer (MODIS) and more recently the Visible Infrared Imaging
Radiometer Suite (VIIRS). These satellite detections are provided at 0.001 degrees latitude or longitude, but they
are derived from active fire satellite products ranging in spatial accuracy from 375 m to 4km. To identify the crop
type and to distinguish agricultural fires from all other fires in the HMS product, the USDA Cropland Data Layer
(CDL) (USDA, 2015a) [ref 9] was employed. This dataset is produced annually by the USDA National Agricultural
7-17
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Statistics Service and provides high resolution (30 meter) detailed crop information to accurately identify crop
types for agricultural fires. Based on field reconnaissance of McCarty (2013) [ref 10], a "typical" field size was
assumed for each burn location, which varied by region of the country. The assumed field sizes by state are
shown in Table 7-10. For the S/L/T agencies that submitted agricultural field burning activity that include acres
burned those data were used instead of the data shown in Table 7-10..
Table 7-10: Assumed agricultural field sizes burned by state in 2020NEI
State
FIPS
code
Average Field
Size (Acres)
State
FIPS
code
Average Field
Size (Acres)
AL
1
40
NE
31
60
AZ
4
80
NV
32
40
AR
5
40
NH
33
40
CA
6
120
NJ
34
40
CO
8
80
NM
35
80
CT
9
40
NY
36
40
DE
10
40
NC
37
40
FL
12
60
ND
38
60
GA
13
40
OH
39
40
ID
16
120
OK
40
80
IL
17
60
OR
41
120
IN
18
60
PA
42
40
IA
19
60
Rl
44
40
KS
20
80
SC
45
40
KY
21
40
SD
46
60
LA
22
40
TN
47
40
ME
23
40
TX
48
80
MD
24
40
UT
49
40
MA
25
40
VT
50
40
Ml
26
40
VA
51
40
MN
27
60
WA
53
120
MS
28
40
WV
54
40
MO
29
60
Wl
55
40
MT
30
120
WY
56
80
7.3.2.3 Emission Factors
Emission Factors for CO, NOx, S02, PM2.5 and PM10 were based on Table 1 from McCarty (2011) [ref 7], The
emission factors in McCarty (2011) were based on mean values from all available literature at the time. Emission
Factors for NH3 were derived from the 2002 NEI crop residue emission estimates using the ratio of NH3/NOx and
the NOx emission factor in Table 1 from McCarty (2011). These emission factors are shown in the 2014 NEI TSD.
As discussed above the VOC EFs were improved for the 2017 NEI and 2020NEI, as shown below in Table 7-11.
A subset of the HAP emission factors is shown in Table 7-12. These are based on updated VOC work mentioned
above. The full set of HAP emission factors, available on the 2020 NEI Supplemental data FTP site, also includes
the following HAPs: isopropylbenzene, n-hexane, o-xylene, propionaldehyde, styrene, toluene, 2,2,4-
trimethylpentane, and m, p-xylenes. The sugarcane emissions factors were updated for the 2020NEI.
7-18
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Table 7-11: Revised Ag Burning Emission factors (lbs/ton) for VOC
Crop Type
Emission Factor
Corn
18.47
Wheat
18.69
Soybean
18.47
Cotton
18.47
Fallow
18.47
Rice
18.26
Sugarcane
14.70
All Other crops/Default
18.47
Double Crop Wheat/Soybeans
18.58
Double Crop Corn/Soybeans
18.47
Double Crop Wheat/Cotton
18.58
Table 7-12: Select HAP Emission factors (lb/ton) used in EPA Methods
Crop Type
see
Acetaldehyde
Benzene
1,3-
butadiene
Ethylbenzene
Formaldehyde
Unspecified/General/
Default
2801500000
1.521677
0.227658
0.161739
0.026645
1.025634
Red Bean
2801500141
1.521677
0.227658
0.161739
0.026645
1.025634
Red Bean
2801500142
1.521677
0.227658
0.161739
0.026645
1.025634
Corn
2801500150
1.521677
0.227658
0.161739
0.026645
1.025634
Wheat and Corn
2801500151
1.311003
0.224041
0.144669
0.020768
1.19077
Corn and Soybeans
2801500152
1.521677
0.227658
0.161739
0.026645
1.025634
Cotton
2801500160
1.521677
0.227658
0.161739
0.026645
1.025634
Fallow
2801500171
1.521677
0.227658
0.161739
0.026645
1.025634
Rice
2801500220
1.943024
0.234892
0.195879
0.038401
0.695364
Sugarcane
2801500250
0.238933
0.58
0
0.92
0.8
Wheat
2801500262
1.10033
0.220424
0.127599
0.01489
1.355905
Wheat and Cotton
2801500263
1.311003
0.224041
0.144669
0.020768
1.19077
Wheat and Soybeans
2801500264
1.311003
0.224041
0.144669
0.020768
1.19077
oy crop type for entire US
7.3.3 PM speciation for all fires
The S/L/Ts were not permitted to submit PM2.5 speciated emissions, which are required in the NEI. These PM
species pollutants include EC, OC, S04, N03, and "other" (PMFINE). These were estimated for all nonpoint data
including those states that submitted direct emissions by EPA using the fractions from SPECIATE v5.0 [ref 5]
shown in Table 7-13.
7-19
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Table 7-13: PM species for all wildland fires, computed as fraction of total PM2.5
Species
Fraction
PEC
0.0323
POC
0.4688
PN03
0.0003
PS04
0.0013
PMFINE
0.4973
7.3.4 Quality Assurance (OA) of Final Results
7.3.4.1 Wildfires and prescribed burning
Different types of QA were generally applied with the different parts of the process described above. The
summary below briefly describes the QA checks used in these processes.
7.3.4.1.1 Input Fire Information Data Sets
• Reviewed input data sets to identify data gaps.
• Identified fire incidents that appeared to be double counted in individual data sets and removed
duplicate records.
• Examined fires with long durations or conflicts between date fields such as start date and report date to
identify fires that may have erroneous dates and made necessary corrections.
• Reviewed fire locations to ensure that they fell within the United States. Obvious errors in data entry
such as the reversal of latitude and longitude were corrected where possible.
• Reviewed large and small fires in each data set for validity.
• Modified distant fires (in different states) with the same names to ensure that the events were not
associated.
7.3.4.1.2 Daily Fire Locations from SmartFire2
Quality assurance actions applied to daily fire locations from SmartFire2 included:
• Checked the location, fire type, duration, underlying fire activity input data, final shape, and final size for
large fire events (i.e., area burned >10,000 acres) to ensure that the results were reasonable.
• Checked large fire events by state and by name, removed duplicate events, and renamed fires as
needed.
• Reviewed large fire events with multiple data sources to ensure that SmartFire2 reconciliation rankings
were correct and produced sensible results.
• Identified and removed fire event duplicates incorrectly created by the SmartFire2 reconciliation
process.
• Checked fire events with large differences between the calculated fire area and the geometric fire area.
Since the shape and area are calculated separately in SmartFire2, a large discrepancy can indicate errors
in reconciliation.
7.3.4.1.3 Emissions Estimates
Quality assurance actions applied to resulting emissions estimates included:
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• Checked the location of all final fires and emission estimates. Fires falling outside of the United States
were removed. Some fires near the border were retained if fuel information was available in that
location.
• Identified fire records that were incorrectly associated and adjusted fire event size and emissions
proportionally.
• Produced and reviewed summary tables and plots of the 2020 fire inventory data.
• Compared wildfire acres burned by state to National Interagency Fire Center (NIFC) data to ensure the
summary values were within reasonable range.
7.3.4.1.4 Additional quality assurance on results, and some post-final corrections
WLF emissions developed using the methods described above were compared to 2017NEI estimates, and all the
way back to 2006, since the models used are similar. The spatial (and temporal) patterns seen in the data
correspond to what was expected in 2020. In general, 2020 was a "worse" fire year than many previous years as
more acres were burned, so the emissions are expected to be higher. However, a large portion of the wildfire
acres burned took place in California in 2020 (over 4 million acres burned). The trends graphic shown in Section
7.4 (see Figure 7-5 and Figure 7-6) indicates how the 2020 PM2.5 estimates compare to other years (using
similar methods). These trends represent only the lower 48 states.
These revisions were processed through the Emissions Inventory System (EIS) and summary files were posted on
the 2020 NEI Data website on January 10, 2023.
7.3.5 Agricultural field burning
Review of the quality of EPA's data included addressing of S/L/T comments as we received them during the 2020
NEI process. In addition, the following checks were done on EPA data:
• Comparison to past NEI estimates, and explaining differences noted
• Check of diurnal profile using day specific data generated by EPA methods with existing profiles used for
air quality modeling
• Using past comments received from S/L/Ts for this sector to ground truth estimates
• Ensuring HAPs and VOC speciation line up as expected
The QA of S/L/T-submitted data included checking with EPA estimates, working with S/L/Ts to understand why
differences exist, and making sure pollutant coverage is complete. We do not expect to make any major changes
or improvements (e.g., methodology, pollutants expected) to this sector for the 2023 NEI. We will respond to
specific comments we do receive for this sector.
7.4 Emissions Summaries
7.4.1 Wildland fires
This section shows several graphics and tables that describe emissions of wild and prescribed fires in the 2020
NEI based on the methods discussed above.
In Figure 7-5 and Figure 7-6, the trend in PM2.5 emissions and acres burned is shown from 2006 to 2020. Over
this 15-year time frame similar SF2/BS frameworks were used to estimate these emissions. However, it should
be noted that the estimates are much more robust for NEI years (2008, 2011, 2014, 2017 and 2020) since S/L/T
involvement and data acquisition from S/L/Ts is much higher. In addition, year 2016 was generated with more
S/L/T involvement as part of an Emissions Modeling Platform Collaboration. It can be noted from both these
7-21
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graphics that the year-to-year variability is more controlled by wildfire activity. The amount of prescribed fire
activity does vary from about 8-16 million acres burned as seen in Figure 7-5 and Figure 7-6. At this point, it is
unclear whether this range is due to either the true increases/decreases in prescribed fire activity across the US,
or due to the lack of or changes in prescribed fire activity available for some states for some years or the change
in satellite technology (e.g. Visible Infrared Imaging Radiometer Suite (VIIRS) instrument) used in the FIMS fire
detect methodology or the acres burned assumptions used in SF2 for a fire detect where there is no other fire
activity data available. In the 2020NEI, about 5 million of the 13 million ("40%) estimated acres burned for
prescribed fires did not have any other fire activity data available except HMS to estimate acres burned and
therefore had to use the acres burned assumption method in SF2.
Figure 7-5: Annual comparison of PM2.5 emissions for lower 48 states
Wildland Fire PM2.5 Emissions
___ 3000
V)
o 2500
§ 2000
0
£ 1500
1 1000 ¦ ¦ ¦— "wf
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Table 7-14: CONUS (lower 48 states) and Alaska arid Hawaii fire type information for 2020 NEl WLFs
Fire Type
Millions of Acres
PM2.5 (Tons)
NOx (Tons)
VOC (Tons)
CONUS Wildfires
10.08
1,598,052
239,542
4,399,270
CONUS Prescribed
13.23
681,958
140,719
1,655,184
Alaska All
0.26
172,073
14,287
497,265
Hawaii All
0.02
1,390
380
3,476
Total
23.60
2,453,473
394,929
6,555,195
Figure 7-7 and Figure 7-8 show acres burned and PM2.5 emissions for all fires by month in 2020. The total
emissions that result from month-to-month result from a combination of different fuels that burn in different
fires. It is seen that wildfires are more prevalent in the hotter months, and prescribed fires occur more often in
the cooler months of 2020.
Figure 7-7: Monthly acres burned by fire type for 2020 NEI CONUS Wildland Fires
Monthly acres burned
5,000,000
4,500,000
4,000,000
3,500,000
3,000,000
2,500,000
2,000,000
1,500,000
1,000,000 _
500,000
0 ® ™
123456789 10 11 12
¦ WF area ¦ RX area
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Figure 7-8: Monthly PM2 s by fire type for 2020 NEI CONUS Wildland Fires
PM2.5 monthly emissions (tons)
1,000,000
900,000
800,000
700,000
600,000
500,000
400,000
300,000
200,000
100,000
0
4 5
¦ RX pm25
I
6 7
¦ WF pm25
I
10
11 12
Next, Table 7-15 shows a summary of acres burned and PM2.5 by state, fire type and combustion phase. In
terms of total WLF acres burned, several states are shown to have more than one million acres burned in 2020,
with CA, GA, and KS being the highest acres burned states. However, due to the nature of fuels burned and the
type of fire that occurs in the various States, CA and OR are highest for estimated PM2.5 emissions.
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Table 7-15: Summary of acres burned and PM2.5 emissions by state, fire type, and combustion phase
State
Area (acres)
PM2.5 Emissions (tons)
Prescribed
Wildfire
Total
Prescribed
Wildfire
Flaming
Smoldering
Total
Flaming
Smoldering
Total
Alabama
673,865
3,065
676,930
40,557
4,289
44,846
255
25
280
Alaska
79,966
178,948
258,913
68,041
27,336
95,377
47,135
29,560
76,696
Arizona
20,816
990,864
1,011,680
522
112
634
40,454
8,188
48,642
Arkansas
410,213
1,634
411,847
35,324
5,809
41,133
294
47
341
California
159,448
4,124,077
4,283,525
7,800
1,602
9,402
448,245
114,212
562,456
Colorado
12,212
707,261
719,473
284
88
371
95,767
59,323
155,091
Connecticut
468
-
468
32
6
39
-
-
-
Delaware
1,736
-
1,736
171
89
260
-
-
-
Florida
1,386,808
113,112
1,499,920
85,495
10,221
95,716
7,006
400
7,407
Georgia
2,254,166
8,320
2,262,486
42,985
5,551
48,536
427
44
471
Hawaii
9,362
14,166
23,528
583
14
598
787
5
792
Idaho
46,108
337,629
383,737
2,536
897
3,433
37,136
21,389
58,525
Illinois
95,592
5,135
100,727
5,151
786
5,937
113
1
114
Indiana
30,175
1,713
31,888
1,391
198
1,588
88
10
98
Iowa
164,596
12,100
176,696
6,496
907
7,403
470
76
546
Kansas
2,653,259
30,925
2,684,184
61,909
2,531
64,440
897
40
937
Kentucky
89,909
2,481
92,391
5,917
692
6,609
402
47
448
Louisiana
389,916
7,306
397,222
28,015
4,090
32,105
691
82
774
Maine
2,361
837
3,198
199
65
264
124
30
154
Maryland
10,576
6
10,582
812
133
945
1
0
1
Massachusetts
1,236
410
1,646
103
18
121
57
10
67
Michigan
13,974
892
14,866
763
175
938
62
12
74
Minnesota
75,374
8,853
84,227
3,587
1,506
5,093
439
131
570
Mississippi
334,932
18,411
353,344
17,954
1,775
19,729
1,221
95
1,316
Missouri
405,937
12,501
418,437
31,021
4,957
35,979
633
71
704
Montana
79,835
342,310
422,145
4,392
1,775
6,167
21,663
5,704
27,367
Nebraska
129,177
15,658
144,834
6,447
1,393
7,840
889
233
1,122
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Area (acres)
PM2.5 Emissions (tons)
State
Prescribed
Wildfire
Total
Prescribed
Wildfire
Flaming
Smoldering
Total
Flaming
Smoldering
Total
Nevada
4,690
254,927
259,617
77
7
84
5,763
761
6,524
New Hampshire
1,818
14
1,832
111
26
138
2
0
3
New Jersey
26,139
4,780
30,919
2,170
460
2,629
604
128
732
New Mexico
39,355
155,002
194,357
659
190
849
9,577
3,236
12,812
New York
24,057
162
24,219
1,678
410
2,088
28
5
33
North Carolina
100,715
11,476
112,191
5,113
713
5,826
611
60
671
North Dakota
67,348
2,779
70,126
2,330
457
2,786
144
25
170
Ohio
19,446
888
20,334
1,093
185
1,279
51
5
56
Oklahoma
1,039,072
87,444
1,126,516
44,796
3,998
48,794
5,208
519
5,727
Oregon
162,152
1,101,771
1,263,923
32,988
13,131
46,119
414,480
155,367
569,847
Pennsylvania
26,364
929
27,293
1,733
296
2,030
138
20
158
Rhode Island
39
84
123
4
0
4
10
2
11
South Carolina
314,497
2,761
317,258
16,972
2,211
19,183
199
22
221
South Dakota
37,381
16,297
53,678
1,717
377
2,094
2,127
587
2,714
Tennessee
128,903
908
129,811
8,622
1,108
9,731
143
15
158
Texas
1,530,896
209,545
1,740,442
66,020
17,104
83,124
6,406
927
7,332
Utah
17,208
310,404
327,612
756
479
1,235
24,528
16,738
41,266
Vermont
1,638
4
1,642
91
19
110
1
0
1
Virginia
98,344
7,279
105,623
6,350
909
7,259
1,025
115
1,140
Washington
91,447
843,176
934,623
2,152
-
2,152
36,255
8,577
44,832
West Virginia
29,232
385
29,616
2,507
411
2,918
80
10
90
Wisconsin
18,858
2,631
21,488
1,187
338
1,525
167
24
191
Wyoming
9,984
323,747
333,731
310
164
473
25,522
10,337
35,860
Grand Total
13,321,598
10,276,007
23,597,606
657,922
120,012
777,934
1,238,325
437,215
1,675,540
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Figure 7-9 and Figure 7-10 show 2020 total area (acres) burned and PM2.5 emissions by state,
respectively. It summarizes the data in Table 7-15 in map format. The Southeast states are seen to be
dominated by prescribed fires and the western states by wildfires. This is a typical pattern we see from
NEI-to-NEI, In addition, for acres burned, CA and KS are seen to dominate and for PM2.5 emissions, CA
and OR and other western states are seen to be dominant.
Figure 7-9: Total 2020 NEI area burned by state -wildland fires
Wildfire
1,000,000 acres
7-27
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Figure 7-10: Total 2020 NEI PM2.5 wildland fires emissions by state
Fire Types
Prescribed
Wildfire
- 50000 tons
PM2.5 emissions per square mile are shown in Figure 7-11 and acres burned per square mile are shown
in Figure 7-12. The patterns seen correspond to the other graphics and tables shown in this section and
are fairly typical of a given NEI for WLFs.
7-28
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Figure 7-11: 2020NEI county PM2 s wildland fires emissions in tons per square mile
7-29
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Figure 7-12: 2020NEI wildland fires county area burned in acres per square mile
2020 NEI Wildland Fire Area Burned
acres/square mile
I I 0 - 0.05
I I 0.05 - 2
I I 2-5
I I 5-20
¦I 20-50
¦I 50 - 450
7.4.2 Agricultural field burning
Figure 7-13 summarizes 2020 NEI PM2.5 emission estimates by state, sorted from largest to smallest,
based on the 2020 NEI Florida, Washington, California, Georgia, and North Dakota are the top emitters.
Some of these emissions come from S/L/T submissions, and some from EPA estimates. Tribal emissions
are not shown here. A total of about 30,000 tons of PM2.5 are estimated to be emitted by this sector.
Shown in Table 7-16 are comparisons of PM2.5 emissions for those states that submitted PM2.5 vs EPA
estimates. Only a few states submitted. Of those states that submitted to EIS, only 3 states (GA, ID, IL)
and tribes included HAPS in their ag burning emission submittals. Only Idaho indicated to supplement
their data with EPA estimates via the Nonpoint Survey. A total of about 33,000 tons of PM2.5 are
estimated to be emitted for this sector using EPA methods alone, compared to about 30,000 when these
SLT emissions are also factored into the final NEI.
7-30
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Figure 7-13: Total 2020 NEI Agricultural Burning PM2.5 Emissions by state
2020NEI ?Mi ns)
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7.5.2 Agricultural field burning
6. McCarty, J.L., S. Korontzi, C. O. Justice, and T. Loboda. 2009. The spatial and temporal distribution of
crop residue burning in the contiguous United States. Science of the Total Environment 407 (21),
5701-5712.
7. McCarty, J. L. 2011. Remote Sensing-Based Estimates of Annual and Seasonal Emissions from Crop
Residue Burning in the Contiguous United States. Journal of the Air & Waste Management
Association 61 (1), 22-34.
8. Pouliot, G., Rao, V., McCarty, J. L., and A. Soja. 2017. Development of the crop residue and rangeland
burning in the 2014 National Emissions Inventory using information from multiple sources. Journal of
the Air & Waste Management Association Vol. 67, Issue 5.
9. United States Department of Agriculture. 2015a. USDA National Agricultural Statistics Service
Cropland Data Layer for 2015.
10. Personal communication with DrJ. McCarty, 2013, Michigan Technological Institute.
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-23-001g
Environmental Protection Air Quality Assessment Division March 2023
Agency Research Triangle Park, NC
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