13.5 Emissions methodology and information

13.5.1 Source Classification Codes

Table 13.3 lists the new Source Classification Codes (SCCs) that define the different types of WLFs in the 2023 NEI. There are new SCCs by forest regions (e.g. Eastern, Boreal, Shrubland, etc.) added to this NEI. These new SCCs will enable improved chemical speciation in future Emissions Modeling Platforms (EMPs) used in air quality modeling simulations. The new SCCs also include separate codes for Coarse Woody Debris (CWD) and duff fuels. A new SCC 2811016000 was added to support the new method for estimating emissions from pile burns. Note that we continue to include a specific SCC (2801500170) that houses the grassland fires of “Flint Hills,” which occur over much of eastern Kansas and a small part of eastern Oklahoma. 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 13.3: SCCs for wildland fires
Source Classification Code (SCC) Status SCC Description
2801500171 No change in 2023NEI Grass/Fallow; Flint Hills Prescribed Burning; Flaming
2810070001 New in 2023NEI Other Combustion; Boreal Forest Wildfires; Smoldering + Flaming
2810070002 New in 2023NEI Other Combustion; Boreal Forest Wildfires; CWD Residual Smoldering
2810070003 New in 2023NEI Other Combustion; Boreal Forest Wildfires; Duff Residual Smoldering
2810071001 New in 2023NEI Other Combustion; Eastern Forest Wildfires; Smoldering + Flaming
2810071002 New in 2023NEI Other Combustion; Eastern Forest Wildfires; CWD Residual Smoldering
2810071003 New in 2023NEI Other Combustion; Eastern Forest Wildfires; Duff Residual Smoldering
2810072001 New in 2023NEI Other Combustion; Grassland Wildfires; Smoldering + Flaming
2810073001 New in 2023NEI Other Combustion; Shrubland Wildfires; Smoldering + Flaming
2810073002 New in 2023NEI Other Combustion; Shrubland Wildfires; CWD Residual Smoldering
2810073003 New in 2023NEI Other Combustion; Shrubland Wildfires; Duff Residual Smoldering
2810074001 New in 2023NEI Other Combustion; Western Forest Wildfires; Smoldering + Flaming
2810074002 New in 2023NEI Other Combustion; Western Forest Wildfires; CWD Residual Smoldering
2810074003 New in 2023NEI Other Combustion; Western Forest Wildfires; Duff Residual Smoldering
2810080001 New in 2023NEI Other Combustion; Boreal Forest Prescribed Burning; Smoldering + Flaming
2810080002 New in 2023NEI Other Combustion; Boreal Forest Prescribed Burning; CWD Residual Smoldering
2810080003 New in 2023NEI Other Combustion; Boreal Forest Prescribed Burning; Duff Residual Smoldering
2810081001 New in 2023NEI Other Combustion; Eastern Forest Prescribed Burning; Smoldering + Flaming
2810081002 New in 2023NEI Other Combustion; Eastern Forest Prescribed Burning; CWD Residual Smoldering
2810081003 New in 2023NEI Other Combustion; Eastern Forest Prescribed Burning; Duff Residual Smoldering
2810082001 New in 2023NEI Other Combustion; Grassland Prescribed Burning; Smoldering + Flaming
2810083001 New in 2023NEI Other Combustion; Shrubland Prescribed Burning; Smoldering + Flaming
2810083002 New in 2023NEI Other Combustion; Shrubland Prescribed Burning; CWD Residual Smoldering
2810083003 New in 2023NEI Other Combustion; Shrubland Prescribed Burning; Duff Residual Smoldering
2810084001 New in 2023NEI Other Combustion; Western Forest Prescribed Burning; Smoldering + Flaming
2810084002 New in 2023NEI Other Combustion; Western Forest Prescribed Burning; CWD Residual Smoldering
2810084003 New in 2023NEI Other Combustion; Western Forest Prescribed Burning; Duff Residual Smoldering
2811016000 New in 2023NEI Other Combustion; Prescribed Forest Burning - Pile Burns; Total (Smoldering + Flaming)

Agricultural burning refers to fires that occur over land 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 pasture/grass burning SCCs into this sector. However, for technical reasons, we have moved the grass/pasture burning to the wildland fires category for the 2017, 2020 and 2023 NEIs, thereby causing this sector to once again only house emissions resulting from burning of crops.

Table 13.4 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.” A new SCC was added 2801540000 to support the new method used to estimate emissions from ditch burns on agricultural lands. Previously, these ditch burns were in the prescribed burn sector in the 2020NEI. The assumed area of the ditch burns was reduced to 5 acres per satellite detection based on analysis of active ditch fires.

Table 13.4: Nonpoint Agricultural Field Burning SCCs in the 2023 NEI
SCC EPA default SLT submitted Agricultural burn SCC description
2801500000 X X Unspecified crop type and Burn Method
2801500112 X Field Crop is Alfalfa: Backfire Burning
2801500130 X Field Crop is Barley
2801500141 X X Field Crop is Bean (red): Headfire Burning
2801500142 X Field Crop is Bean (red): Backfire Burning
2801500150 X X Field Crop is Corn
2801500151 X X Double Crop Winter Wheat and Corn
2801500152 X Double Crop Corn and Soybeans
2801500160 X X Field Crop is Cotton
2801500171 X X Fallow
2801500182 X Field Crop is Hay (wild)
2801500220 X X Field Crop is Rice
2801500250 X X Field Crop is Sugar Cane
2801500262 X X Field Crop is Wheat: Backfire Burning
2801500263 X X Double Crop Winter Wheat and Cotton
2801500264 X X Double Crop Winter Wheat and Soybeans
2801500600 X Forest Residues Unspecified
2801540000 X X Ditch Burning (new SCC)
2801600300 X Pile Burning; Orchard Crop Other
2801600320 X Pile Burning; Orchard Crop is Apple
2801600350 X Pile Burning; Orchard Crop is Cherry
2801600410 X Pile Burning; Orchard Crop is Peach
2801600420 X Pile Burning; Orchard Crop is Pear
2801600430 X Pile Burning; Orchard Crop is Prune
2801600500 X Pile Burning; Vine Crop
2811020002 X Rangeland Burning

13.5.2 Wildland Fires Emissions Estimation Methodology

The national and S/L/T activity data mentioned earlier were used to estimate daily wildfire and prescribed burn emissions from flaming combustion and smoldering combustion phases for the 2023 NEI inventory. Flaming combustion is more complete combustion than smoldering and is more prevalent with fuels that have a high 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.

Figure 13.3 is a schematic of data processing stream for the 2023 NEI inventory for wildfire and prescribe burn sources. The EPA’s 2023 NEI wildland fire emissions estimates were estimated using a modified Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (Smartfire2) and US Forest Service’s BlueSky Pipeline (BSP) system. Smartfire2 is an algorithm and database system that operates 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 1].

For the 2023 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 13.5. A dataset type with a higher ranking gets preference for that attribute in the reconciled activity. The uncertainty of each dataset is used to generate date and spatial buffers when reconciling fire activity (13.6). 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 the fire was assumed to be a prescribed burn with the only exceptions being the following month-state assumptions where the fire was then assumed to be a wildfire:

  • Arizona, California, Nevada, and New Mexico: June through August

  • Oregon, Washington, Idaho, Montana, Colorado, Utah and Wyoming: July through September

This figure shows the Processing flow for fire emission estimates in the 2023 NEI inventory

Figure 13.3: Processing flow for fire emission estimates in the 2023 NEI inventory. Input, Data Prep, Data aggregation and reconciliation, USFS Bluesky Pipeline, Emissions Post-Processing, final Inventory

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Table 13.5: 2023 National SmartFire2 Reconciliation Weights
Dataset Name Location Weight Size Weight Shape Weight Growth Weight Name Weight Type Weight
HMS 0.9 0.2 0.4 0.9 0.2 0.2
FACTS 0.4 0.3 0.5 0.6 0.7 0.6
CalMapper 0.6 0.7 0.7 0.4 0.8 0.8
WFIGS 0.7 0.9 0.8 0.3 0.9 0.9
PFIRS 0.3 0.4 0.3 0.2 0.3 0.4
ICS-209 0.2 0.5 0.2 0.5 0.5 0.3
InFORM 0.2 0.5 0.2 0.5 0.5 0.3
NFPORS 0.5 0.6 0.6 0.7 0.6 0.7
CalFIRE 0.65 0.75 0.75 0.4 0.75 0.85
FL/GA/SC/NC 0.9 0.95 0.1 0.8 0.95 0.95
other state data 0.8 0.8 0.1 0.8 0.4 0.95
Table 13.6: 2023 National SmartFire2 Dataset Uncertainty
Dataset Name Detection Rate False Alarm Rate Start Date Uncertainty End Date Uncertainty Location Uncertainty
HMS 0.6 0.3 6 5 1
FACTS 0.5 0.4 3 1 0.2
CalMapper 0.5 0.5 3 1 0.1
WFIGS 0.6 0.1 3 6 0.2
PFIRS 0.5 0.5 3 2 0.5
ICS-209 0.5 0.5 3 5 2
InFORM 0.5 0.5 3 5 2
NFPORS 0.6 0.2 3 1 0.1
CalFIRE 0.7 0.1 3 5 0.1
FL/GA/SC/NC 0.8 0.1 1 1 0.1
other state data 0.8 0.1 4 4 4

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. 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 Georgia and North 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 conterminous United States, Alaska, and Hawaii, where Fuel Characteristic Classification System (FCCS) fuel loading data are available, were processed using the modeling chain described in Figure 13.4. The Smoke Emissions Reference Application (SERA) emissions factors for CAPs are included in the BSP version used in the 2023 NEI. SERA is a searchable online database coordinated by the US Forest Service and University of Washington (https://depts.washington.edu/nwfire/sera/index.php) that consists of existing peer-reviewed emission factors (EFs) of 276 known air pollutants. The SERA database enables the analysis and summaries of existing emissions factors, and creation of average emissions factors to be used in decision support tools for smoke management, including BSP. Fire Emissions Production Simulator (FEPS) emissions factors were used in previous NEIs. The HAP emission factors used in this work came from Urbanski, 2014 [ref 3]. These emission factors were regionalized and handled differently by wild and prescribed fire. Table 13.7 below outlines the regionalization scheme used while Table 13.8 shows the HAP EFs employed in this work for wildland fires.

Table 13.7: Emission factor regions used to assign HAP emission factors for the 2023 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, MI, MN, MS, NC, NH, NJ, NY, PA, PR, RI, SC, TN, VA, VI, VT, WI, WV AL, AR, CT, DC, DE, FL, GA, HI, KY, LA, MA, MD, ME, MI, MN, MS, NC, NH, NJ, NY, PA, PR, RI, 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, WI, WY
Table 13.8: Wildland fire HAP emission factors (lb/ton fuel consumed) for the 2023 NEI
HAP Pollutant ID Flaming Region 1 Flaming Region 2 Flaming Region 3 Smoldering Region 1 Smoldering Region 2 Smoldering Region 3
1,3-Butadiene 106990 0.2723268 0.5166199 0.3624349 0.2723268 0.5166199 0.3624349
Acetaldehyde 75070 1.6780136 1.2835402 2.2406888 1.6780136 1.2835402 2.2406888
Acetonitrile 75058 0.3223869 0.0640769 0.4305166 0.3223869 0.0640769 0.4305166
Acrolein 107028 0.5126151 0.6467761 0.6848218 0.5126151 0.6467761 0.6848218
Acrylic Acid 79107 0.0700841 0.0580697 0.0941129 0.0700841 0.0580697 0.0941129
Anthracene 120127 0.0050000 0.0050000 0.0050000 0.0050000 0.0050000 0.0050000
Benz(a)anthracene 56553 0.0062000 0.0062000 0.0062000 0.0062000 0.0062000 0.0062000
Benzene 71432 0.4505406 0.5666800 0.6007209 0.4505406 0.5666800 0.6007209
Benzo(a)fluoranthene 203338 0.0026000 0.0026000 0.0026000 0.0026000 0.0026000 0.0026000
Benzo(a)pyrene 50328 0.0014800 0.0014800 0.0014800 0.0014800 0.0014800 0.0014800
Benzo(c)phenanthrene 195197 0.0039000 0.0039000 0.0039000 0.0039000 0.0039000 0.0039000
Benzo(e)pyrene 192972 0.0026600 0.0026600 0.0026600 0.0026600 0.0026600 0.0026600
Benzo(ghi)perylene 191242 0.0050800 0.0050800 0.0050800 0.0050800 0.0050800 0.0050800
Benzo(k)fluoranthene 207089 0.0026000 0.0026000 0.0026000 0.0026000 0.0026000 0.0026000
Benzofluoranthenes 56832736 0.0051400 0.0051400 0.0051400 0.0051400 0.0051400 0.0051400
Carbonyl Sulfide 463581 0.0005340 0.0005340 0.0005340 0.0005340 0.0005340 0.0005340
Chrysene 218019 0.0062000 0.0062000 0.0062000 0.0062000 0.0062000 0.0062000
Fluoranthene 206440 0.0067300 0.0067300 0.0067300 0.0067300 0.0067300 0.0067300
Formaldehyde 50000 2.5150180 3.3660392 4.4753704 2.5150180 3.3660392 4.4753704
Indeno(1,2,3-cd)pyrene 193395 0.0034100 0.0034100 0.0034100 0.0034100 0.0034100 0.0034100
m,p-Xylenes 1330207 0.2162595 0.1601922 0.2883460 0.2162595 0.1601922 0.2883460
Methanol 67561 2.3067681 1.9743692 5.0360433 2.3067681 1.9743692 5.0360433
Methyl Chloride 74873 0.1283250 0.1283250 0.1283250 0.1283250 0.1283250 0.1283250
Methylanthracene 26914181 0.0082300 0.0082300 0.0082300 0.0082300 0.0082300 0.0082300
Methylbenzopyrenes 65357699 0.0029600 0.0029600 0.0029600 0.0029600 0.0029600 0.0029600
Methylchrysene 41637905 0.0079000 0.0079000 0.0079000 0.0079000 0.0079000 0.0079000
Methylpyrene, fluoranthene 2381217 0.0090500 0.0090500 0.0090500 0.0090500 0.0090500 0.0090500
n-Hexane 110543 0.0480577 0.0240288 0.0640769 0.0480577 0.0240288 0.0640769
Naphthalene 91203 0.4865839 0.3984782 0.6507809 0.4865839 0.3984782 0.6507809
o-Xylene 95476 0.0760913 0.0500601 0.1001201 0.0760913 0.0500601 0.1001201
Perylene 198550 0.0008560 0.0008560 0.0008560 0.0008560 0.0008560 0.0008560
Phenanthrene 85018 0.0050000 0.0050000 0.0050000 0.0050000 0.0050000 0.0050000
Pyrene 129000 0.0092900 0.0092900 0.0092900 0.0092900 0.0092900 0.0092900
Styrene 100425 0.1041249 0.0800961 0.1381658 0.1041249 0.0800961 0.1381658
Toluene 108883 0.3444133 0.3984782 0.4585503 0.3444133 0.3984782 0.4585503
This figure shows the BlueSky Pipeline Modules

Figure 13.4: BlueSky Pipeline Modules

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For the 2023 NEI inventory, the FCCSv4 spatial vegetation cover was upgraded to the LANDFIRE v2.2.0 fuel vegetation cover. The FCCSv4 fuel bed characteristics were implemented along with LANDFIREv2.2.0 to provide better fuel classification for the BlueSky Pipeline. The LANDFIREv2.2.0 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.

In August 2023, a wildfire occurred in Maui County that spread to the nearby city of Lahaina. This fire was a Wildland Urban Interface (WUI) fire where a fire spread across both an urban area and undeveloped land. Thousands of structures and vehicles were burned in Lahaina. The current wildland emissions methodology for the NEI does not include a method for estimating emissions from WUIs. However, for the 2023 NEI the emissions for the wildland part of this WUI fire was estimated in BSP. This consisted of about 2000 acres of shrubland. The emissions from the structures and vehicles burned were estimated for the NEI but are explained in the new Structures and Motor Vehicle Fies section of the 2023 NEI TSD.

13.5.3 Pile burn methodology

For the 2023 NEI, pile burn (PB) emissions were estimated using a combination of federal, state, local, and tribal activity data. This activity data was supplied in the form of daily estimates of area treated, pile volume, pile dimensions, or mass piled by location, varying by data source. As with the prescribed burn and wildfire S/L/T activity data, the pile burn data was imported into Smartfire2 so that it could be reconciled with other data sources to avoid duplication of activity and emissions. HMS satellite detects that reconciled only with the location of the PB activity were removed from the BSP workflow as pile burns. The PB activity data was then directly imported into a calculator script outside of BSP that estimates the amount of biomass consumed at each location and the resulting emissions. The consumption calculations made are consistent with those used in the University of Washington pile burn calculator (https://depts.washington.edu/nwfire/piles/). For activity data where only, a treated area is provided a default fuel loading of 4.5 tons per acre is used based on an analysis of California and Washington historical pile burn permits. A consumption efficiency of 90% is assumed unless otherwise specified in the activity data. Emissions factors averaged over pile burn studies in the SERA database [ref 2] were applied to estimate CAPs from the consumed piled biomass.

13.5.4 Flint Hills prescribed burns methodology

For the 2023 NEI, the emissions from the Flint Hills prescribed burns continued to be calculated outside of BSP. The emissions were calculated for the period of February 3 through May 1, 2023, for the Kansas and Oklahoma counties that make up the Flint Hills region. All “Grass” HMS detects in these counties for this period were used to calculate per county acres per HMS detect. The range for these Flint Hills counties was ~ 50 -160 acres per detect. SERA grass emission factors were used to estimate emissions for all pollutants except PM2.5, which were derived from a field study of Flint Hills prescribed fires [ref 9]. Wildfires did occur during this period in the Flint Hills. Osage County, Oklahoma had about 85,000 acres burned due to wildfires during this time. These wildfires were inserted back into the BSP processing and emissions estimated using the BSP.

13.5.5 Agricultural Field Burning Emissions Methodology

The approach developed for use previous NEIs [ref 6], and was generally used again for the 2023 NEI with some modifications:

  • Multiple satellite detections are used to locate fires using an operational product

  • 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

  • This method uses 2023 USDA NASS Crop Sequence Boundaries (CSB) shapefiles (https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries/index.php) 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. The 2020NEI used Cropland Data Layer (CDL) (USDA, 2015a) information instead of CSB shapefiles.

  • In the 2020NEI, removal of agricultural fires from the Hazard Mapping System (HMS) dataset was done before the application of the Smartfire2 system for wildfires and prescribed fires in an attempt 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. However, this did not eliminate all double counting cases. In the 2023 NEI, HMS detects and SLT agricultural burn activity were allowed as input into Smartfire2. The CSB shapefiles were used post-Smartfire2 to determine which satellite-only detected fires were to be assigned as agricultural burns.

  • EPA designed a new crop burning module for use in BSP for the 2023 NEI. This BSP module calculated consumption and emissions for all agricultural burns except ditch burns which were calculated outside of BSP.

Based on field reconnaissance of McCarty (2013) [ref 8], 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 13.9. 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 13.9.

Table 13.9: Assumed agricultural field sizes burned by state in 2023 NEI
States Average Field Size (Acres)
FL, IL, IN, IA, MN, MO, NE, ND, SD 60
AZ, CO, KS, NM, OK, TX, WY 80
CA, ID, MT, OR, WA 120
All other states 40

Emission Factors for agricultural burns for CO, NOx, SO2, PM2.5 and PM10 were based on Table 1 from McCarty (2011) [ref 5]. 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). The emissions factors used in the 2023 NEI are shown below in Table 13.10.

A subset of the HAP emission factors is shown in Table 13.11. 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 and used again in 2023 NEI.

Table 13.10: 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 Sorghum 18.58
Table 13.11: Select HAP Emission factors (lb/ton) used in EPA Methods by crop type for entire US
Crop Type SCC Acetaldehyde Benzene 1,3-butadiene Ethylbenzene Formaldehyde
Other 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 1.190770
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 0.580000 0.000000 0.920000 0.800000
Wheat 2801500262 1.100330 0.220424 0.127599 0.014890 1.355905
Wheat and Cotton 2801500263 1.311003 0.224041 0.144669 0.020768 1.190770
Wheat and Soybeans 2801500264 1.311003 0.224041 0.144669 0.020768 1.190770

13.5.6 SLT direct emissions submittals -agricultural field burning

S/L/Ts were encouraged only to supply activity for wildland fires for 2023 NEI but could submit agricultural burn emissions. The agencies listed in Table 13.12 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 this 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 13.12: Emissions submitted by reporting agency for agricultural field burning
Region Agency S/L/T
2 New Jersey Department of Environment Protection State
9 Maricopa County, Arizona County
10 Idaho Department of Environmental Quality State
10 Nez Perce Tribe Tribe
10 Washington State Department of Ecology State

13.5.7 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, SO4, NO3, 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 4] shown in 13.13.

Table 13.13: PM species for all wildland fires, computed as fraction of total PM2.5
Species Fraction
PEC 0.0323
POC 0.4688
PNO3 0.0003
PSO4 0.0013
PMFINE 0.4973

13.5.8 Quality Assurance (QA) of Final Results

Different types of QA were generally applied for wildland fires. The summary below briefly describes the QA checks used in these processes:

  • 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 wildfires in each data set for validity.

  • Modified distant fires (in different states) with the same names to ensure that the events were not associated.

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.

Quality assurance actions applied to resulting emissions estimates included:

  • 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 2023 fire inventory data.

  • Compared wildfire acres burned by state and individual major wildfires to National Interagency Fire Center (NIFC) data to ensure the summary values were within reasonable range.

WLF emissions developed using the methods described above were compared to previous years estimates including the 2020NEI and the recent year 2022 inventory generated by EPA, SLTs, MJOs and other federal agencies. The spatial (and temporal) patterns seen in the data correspond to what was expected in 2023. In general, 2023 was a very quiet fire year than many previous years, so the emissions are expected to be lower nationally.

All revisions due to the quality assurance steps were processed through the Emissions Inventory System (EIS) and summary files were posted on the EPA ftp site https://gaftp.epa.gov/Air/nei/2023/doc/supporting_data/events/v1/ on September 4, 2025.

13.5.9 Quality Assurance of Agricultural field burning

Review of the quality of EPA’s data included addressing of S/L/T comments as we received them during the 2023 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.