6.2 Sources of data overview and selection hierarchies
Table 6.1 describes the datasets comprising most of the nonpoint inventory, and the hierarchy for combining these datasets in construction of the NEI. While the bulk of these datasets are for stationary sources of emissions, some of these datasets contain mobile sources so that emissions from ports, shipping lanes, rail lines, and in-flight aircraft (lead emissions only) could be included as nonpoint sources. The following table includes the rationale for why each dataset was assigned its position in the hierarchy. We excluded certain pollutants from stationary sources in the 2023 NEI: greenhouse gases for stationary sources and pollutants in the pollutant groups “dioxins/furans” and “radionuclides”4. The EPA has not evaluated the completeness or accuracy of the S/L/T agency dioxin and furan values nor radionuclides and does not have plans to supplement these reported emissions with other data sources to compile a complete estimate for dioxin and furans nor radionuclides as part of the NEI.
| Dataset name | Description and Rationale for the Order of the Selected Datasets | Order |
|---|---|---|
| Responsible Agency Data Set | S/L/T agency submitted data. These data are selected ahead of other datasets. The only other situation where S/L/T agency emissions are not used is where certain records are tagged in the Emissions Inventory System (EIS) (at the specific source/pollutant level). This occurs: 1) for hierarchy purposes to allow EPA nonpoint emissions to be used ahead of S/L/T agency data where states asked for EPA data to be used in place of their data and 2) where S/L/T agency data were suspected outliers, unexpected pollutants for a given process, or submitted for a source category not widely reported or significant. | 1 |
| 2023EPA_Cr_Aug | Hexavalent and trivalent chromium speciated from S/L/T agency reported chromium. EIS augmentation function creates the dataset by applying multiplication factors by SCC, facility, process or North American Industry Classification System (NAICS) code to S/L/T agency total chromium. See Section 2.2.2. | 2 |
| 2023EPA_PMaug | PM components added to gap fill missing S/L/T agency data where S/L/T agency have missing emissions across PM components. Uses ratios of emission factors from the EIS PM Augmentation function for covered source classification codes (SCCs). PM augmentation is discussed in Section 6.3. | 3 |
| 2023EPA_HAPAug | HAP data computed from S/L/T agency criteria pollutant data using ratios of HAP to CAP emission factors. The emission factors used to create the ratios are the same emission factors as are used in creating the EPA estimates (i.e., in the EPA nonpoint emission tools). HAP augmentation is discussed in Section 6.4. | 4 |
| 2023EPA_HAPAug-PMAug | This dataset was created in the same fashion as the 2023EPA_HAPAug dataset above and is a supplement to it. This dataset contains HAPs calculated by applying a ratio to PM25-PRI emissions, for those instances where the S/L/T dataset did not contain any PM25-PRI emissions, but the PM augmentation routine was able to calculate a PM25-PRI value from some PM species that was reported by the S/L/T. | 5 |
| 2023EPA_NONPOINT | All nonpoint EPA estimates are included in this dataset except those listed elsewhere in this table. This dataset includes sources with and without point source subtraction and outputs from most of the EPA tools, including the “Wagon Wheel” with (if provided) SLT-submitted Input Templates (see Section 6.2.2). This dataset also includes EPA-estimated oil and gas production and exploration, biogenics, agricultural and wildland fires, locomotive rail line, and commercial marine vessel emissions. Other sources in this dataset include agricultural fertilizer application, most livestock waste, industrial and commercial/institutional fuel combustion, residential wood combustion, solvent utilization, oil and gas exploration and production, open burning, road and construction dust, and portable fuel containers. | 6 |
| 2020EPA_HAPAug_EPANP | This dataset was created in the same fashion as the 2023EPA_HAPAug dataset above and is a supplement to HAPs not generated in the 2023EPA_NONPOINT dataset via EIS HAP Augmentation computations. | 7 |
| 2023EPA_Airports | 2023 aircraft in-flight emissions (Lead only) | 8 |
The EPA developed all datasets listed above except for the “Responsible Agency Selection,” which contains only S/L/T agency data. We used various methods and databases to compile the EPA generated datasets, which are further described in subsequent subsections. The primary purpose of the EPA datasets is to add or “gap fill” pollutants or sources not provided by S/L/T agencies, to resolve inconsistencies in S/L/T agency-reported pollutant submissions for PM and to speciate S/L/T agency reported total chromium into hexavalent and trivalent forms. The hierarchy or “order” provided in Table 6.1 defines which data are preferentially used when multiple datasets could provide emissions for the same pollutant and emissions process. The dataset with the lowest order on the list is preferentially used over other datasets.
In addition to the order of the datasets, the hierarchy was also influenced by the EIS feature of data tagging. Any data that were tagged by EPA in any of the datasets were not used. S/L/T agency data were tagged for three reasons: 1) S/L/Ts requested that their data not be used, 2) EPA found unexpected pollutants for a source, and 3) sources were submitted that are not widely reported or significant. Due to continued improvements in the new nonpoint survey, there was very little need to tag EPA nonpoint data for 2023. If S/L/T agencies report zero emissions, then backfilling with other datasets will not occur. There are two ways that S/L/T agencies can prevent inappropriately backfilled emissions from being included in the NEI: 1) S/L/T agencies can submit zeros for any pollutant they do not want filled in (the EPA data will otherwise fill in for all pollutants that are on the nonpoint expected pollutant list), 2) S/L/T agencies can complete the nonpoint survey and specify “No…” to prevent any EPA estimates from backfilling where S/L/Ts did not submit data, or 3) the EPA can add tags to backfill datasets that prevent the tagged pollutants from being included in the NEI. The first option is most straightforward, and EPA’s preferred method, and takes care of any possible augmentation from the numerous other datasets in the selection hierarchy. The second option was developed as a quick tool for S/L/Ts to essentially prevent the need to “tag out” EPA data yet achieve the same goal.
6.2.1 Nonpoint Survey updates for the 2023 NEI
The nonpoint survey, first developed for the 2014 NEI, streamlined for the 2017 NEI, underwent minimal changes for the 2020 and 2023NEI. The purpose of the nonpoint survey is to increase the accuracy and transparency in how the nonpoint inventory is built using EPA and S/L/T agency data. The nonpoint inventory includes all nonpoint source categories that EPA generates estimates except for wildland fires, commercial marine vessels, and rail line estimates.
Because each agency has their own universe of sources and inventory development approaches, each agency reports nonpoint estimates a little differently. The nonpoint survey gathers information specifically for each SLT regarding which source categories are covered by point, nonpoint, or both, and about where point source reconciliation needs to be done to nonpoint activity.
For the 2023 NEI, the nonpoint survey was updated to include new source categories (e.g., Motor Vehicle and Structure Fires Tool, other non-agricultural sources of ammonia) and new or revised SCCs (e.g., campfires, many residential wood combustion). Continued from 2020 NEI is a checkbox for SLTs “Did Your Agency Provide an Input Template for this Category?”, and a tagging function for EPA inventory developers to change the Nonpoint Survey response back to the default “Yes – Supplement my data with EPA estimates”.
The nonpoint survey has an “Accept All Emissions Estimates” button on the home page for S/L/Ts that did not submit emissions for any nonpoint sector. Note, acceptable S/L/T activity inputs provided to EPA were absorbed into EPA tools and therefore became “EPA” estimates. For S/L/Ts that wanted to prevent some EPA data from backfilling, there were options to edit the default responses for each SCC or accept EPA estimates for entire sectors. For each SCC that EPA generates nonpoint emissions, the optional reasons to select “No” are: 1) I do not have this Source, 2) This source is included in my Point Source contributions, 3) My agency uses different SCCs, and 4) My inventory is complete; it does not need to be supplemented. An additional option to select “Yes -Supplement Only for Missing Pollutants at my reported Counties or Tribe” is provided to allow only missing (expected) pollutants to be added for locations where emissions were submitted for at least one pollutant. More information on the nonpoint survey is available in “Section 7.2.6 of the 2023 NEI Plan”. A detailed 2023 NEI nonpoint summary “2023NEI_Nonpoint_Survey_detail_22may2026.xlsx” covering all reporting agencies has been uploaded to the “2023 NEI Supplemental data FTP” site.
6.2.2 Wagon Wheel
A central database, called the “”Wagon Wheel””, first developed for the 2017 NEI, and updated for the 2023 NEI, houses all inputs and calculates emissions for many nonpoint source categories. Prior to the 2017 NEI, EPA shared different tools to S/L/Ts, many with the same inputs; this process was very inefficient and prone to human error as many tools shared similar inputs and different versions of these tools were often used by S/L/Ts vs the “final” versions ultimately regarded as “EPA” for the NEI. The Wagon Wheel links activity input tables to the appropriate sector/module such that refreshing one dataset ensures the next tool iteration captures it for all appropriate sectors. Most wagon wheel sources have input templates for optional controls; those sources that do not have other options for “controlling” emissions (e.g., reducing the number of cords wood burned per campsite). The full list of nonpoint source categories/tools included in the Wagon Wheel is provided in Table 6.2.
| Wagon Wheel Source Category | Point Inventory Subtraction? | Input Template Required? |
|---|---|---|
| Agricultural Livestock Waste | – | – |
| Agricultural Silage | – | – |
| Agricultural Tilling | – | – |
| Asphalt (paving and roofing) | – | – |
| Aviation Gasoline (distribution Stage 1 and 2) | – | – |
| Campfires | – | – |
| Cooking, commercial and residential | – | – |
| Composting | – | – |
| Construction Dust (non-residential, residential, road) | – | – |
| Cremation: Human and Animal | – | – |
| Dust from Hooves | – | – |
| Gasoline Distribution (Stage 1) | Yes | – |
| ICI (fuel combustion) | Yes | Yes |
| Landfills (working face mercury-only) | – | – |
| Mining and Quarrying | – | – |
| Open Burning (land clearing debris, municipal solid waste, residential household waste, yard waste) | – | – |
| Other Ammonia | – | – |
| Other Mercury | – | – |
| POTWs (Publicly-Owned Treatment Works) | Yes | – |
| Residential Heating (non-wood) | – | – |
| Residential Wood Combustion | – | – |
| Road Dust (paved and unpaved) | – | – |
| Solvents | Yes | – |
| Structure and Motor Vehicle Fires | – | – |
6.2.2.1 Wagon Wheel updates for the 2023 NEI
For the 2023 NEI, the wagon wheel had minimal changes, including how default and S/L Input Template data were shared. High-level Wagon Wheel updates include:
• As shown in Table 6.2, all sources now allow S/Ls to submit controls to be applied at the state and county level by SCC and by pollutant for ICI fuel combustion, residential non-wood heating, and POTWs. • The addition of several emissions estimates including new estimates for structure and motor vehicle fires, incorporation of agricultural livestock waste, as well as updated methodology and underlaying EPA default activity data used in Wagon Wheel. • Input Template distribution to and from EPA was streamlined using the EIS Gateway available to S/L inventory developers, contractors, and EPA NEI team members. Input templates were available for download in the EIS Gateway or could be edited manually from within the Gateway. When bulk downloads and edits were completed by SLTs, they were able to reupload to EIS via a convenient upload feature that provided some basic QA of the files.
It is important to stress that the relative changes in emissions between NEI cycles are often more a result of if/how S/Ls choose to submit activity data, accept EPA estimates, or submit direct emissions. A summary of Wagon Wheel tool updates for each tool category between the 2020 NEI and the final version (4.2) used for the 2023 NEI are provided in Table 6.3.
| Tool Category | Summary of Impactful Changes Between 2020 and 2023 |
|---|---|
| Agricultural Livestock Waste | Incorporated into the Wagon Wheel for the first time. |
| Agricultural Silage | No major changes overall; emissions are similar between years |
| Agricultural Tilling | No major changes overall; emissions are similar between years |
| Asphalt | No major changes to asphalt paving; emissions are similar between years. Emissions for asphalt roofing are new for 2023. |
| Aviation Gasoline Distribution | No major changes overall; lead emissions increased slightly from 2020 to 2023 due to 2020 being a COVID year. |
| Campfires | New for 2023, approximately 14,000 tons of PM2.5 nationally |
| Cooking | Emissions factor changes and scaling factors update resulted in significant decrease in emissions across most states. Residential grilling (charcoal) was moved to this module for 2023. There was a small increase seen in TX and PR. |
| Composting | No major changes overall; emissions are similar between years |
| Construction Dust | Emissions increases are mostly driven by an increase in nonresidential construction emissions due to an increase in the value of private nonresidential construction ($347,666 to $471,450). This led to a 35% increase in acres disturbed by nonresidential construction, which are the activity data behind nonresidential construction emissions. |
| Cremation | Human deaths decreased from 2020 to 2023 due to the COVID-19 pandemic in 2020. Cremation rates saw an increase overall. |
| Dust from Hooves | No major changes overall; emissions are similar between years |
| Gasoline Distribution (Stage 1) | Stage 1 Gasoline Distribution emissions increased for most states. The US product supplied of finished motor gasoline (also reported by SEDS), increased between 2020 and 2023, and this data is used for many of the SCCs included in this tool. For some states, emissions increased significantly, driven by increases in service station unloading and breathing and emptying emissions. |
| ICI | No major changes in underlying activity data without point source subtraction. When considering point source data, there are some SCC-dependent changes in emissions. For example, nonpoint coal consumption decreased, so coal emissions decreased. |
| Landfills | No major changes overall; emissions are similar between years |
| Mining and Quarrying | Emissions in many states increased significantly because of increases in mineral, metal, or coal production. Mineral production has a higher EF, so increases in mineral production impact emissions increases the most. |
| Open Burning | Emissions are mostly similar between the years, but some states saw larger increases in emissions due to significant increases in land clearing debris emissions. Acres distributed from nonresidential construction are used to calculate the amount of land clearing debris, and this increased (see construction dust note). |
| Other Ammonia | N/A; new category for 2023 |
| Other Mercury | No major changes overall; emissions are similar between years |
| POTWs | No major changes overall; emissions are similar between years |
| Residential Heating | No major changes overall; emissions are similar between years |
| Residential Wood Combustion | A combination of significant updates to wood stove emission factors, wood stove distributions of catalytic vs non-catalytic stoves, and incorporating Residential Energy Consumption Survey (RECS) microdata analysis to better allocate state level wood consumption from urban to rural counties result in only minor national differences in overall PM emissions. However, intra-state the RECS microdata adjustment significantly reduces urban wood consumption and hence emissions while proportional, but more spread-out rural wood consumption increased. Note, changes in total state SEDS wood consumption determines the overall trend in wood consumption within states and these changes are highly variable spatially. |
| Road Dust | EPA leveraged telematics data in the 2023 to update and better reflect vehicular miles traveled (VMT) on paved and unpaved roads at the county level. For most states, this update led to a significant decline in estimated primary particulate matter emissions from road dust in the 2023 NEI when compared to the 2020 NEI. This reduction is primarily from apportioning more roads as paved vs unpaved. |
| Solvents | No major changes overall; emissions are similar between years |
| Structure Fires | N/A; new category for 2023 |
A complete list of activity data used in the 2023 NEI, including the sources of all data and Wagon Wheel release dates, is provided in the workbook “NEI 2023 Activity Data Tracker.xlsx” on the “2023 Supplemental data FTP site”.
6.2.3 Input Templates
EPA strongly encouraged S/L/Ts to provide only inputs to the Wagon Wheel because, often late in the inventory development cycle, EPA finds a need for a tool update (e.g., error, or new, improved information, and so if S/L/Ts submitted emissions (rather than inputs) using an old version of the tool, then their submitted data could be out of date or incorrect.
EPA provided blank input templates for all Wagon Wheel source categories. These blank templates included all default activity data, and as these default activity data were updated, the input templates and the wagon wheel were updated to incorporate it. S/L/Ts then provided their completed input templates back to EPA where their updated data, after rudimentary quality assurance, were used to supersede the default data in the template and ultimately the Wagon Wheel. In this process, all S/L/T-submitted input activity data became “EPA” data. Input activity data also included information on controls and emission factors where provided.
With one key exception, S/L/Ts could opt out of submitting input templates as EPA methods did not need S/L/T inputs to compute reasonable nonpoint estimates. EPA used S/L/T-submitted point emissions as default for nonpoint reconciliation for solvents, stage 2 gasoline distribution, and publicly owned treatment works (POTWs); and little to no overlap with the point inventory would be expected for most other nonpoint source categories in the Wagon Wheel. However, for Industrial and Commercial/Institutional (ICI) nonpoint fuel combustion, we relied on S/L/T-submitted throughput (fuel consumption) data, ideally from their Point inventories. As discussed in Section 25, EPA provided four different options for submitting throughput for the ICI tool. Only three state reporting agencies and two territories did not submit ICI emissions, an input template, or a nonpoint survey indicating there were no nonpoint ICI emissions.
Agencies were strongly encouraged to populate source (Agency Source Description), source year (Agency Source Year), notes (Agency Source Note) for any activity data they provide. EPA’s DEFAULT_VALUE (and associated DEFAULT_SOURCE, DEFAULT_SOURCE_YEAR and DEFAULT_NOTE) were used in the wagon wheel where S/L input data are not provided.
A complete list of all S/L/T-submitted wagon wheel input activity data is provided in Table 6.4.
| S/L Agency | Central Database | ICI Fuel Combustion | POTWs | Solvents | Stage 1 Gas Distribution | Ag Tilling | Landfills | Other Ammonia | Road Dust | Residential Heating | Residential Wood Combustion | Mining and Quarrying | Compost | Asphalt Paving | Campfires | Cremation | Animal Populations | Motor Vehicle and Structure Fires | Construction Dust and Open Burning | County Business Patterns |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alabama | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Alaska | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Maricopa Co, AZ | X | X | – | X | X | X | – | X | – | – | X | X | X | – | X | – | X | X | X | – |
| Arizona | – | X | – | – | – | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Arkansas | – | X | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| California | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Colorado | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Connecticut | – | X | X | X | X | – | – | – | – | – | – | – | – | X | – | – | – | – | – | – |
| Delaware | – | X | – | X | – | – | – | X | – | – | – | X | – | – | X | – | – | X | – | – |
| District of Columbia | – | X | X | X | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Florida | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Georgia | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Hawaii | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Idaho | X | X | – | – | – | – | – | – | – | – | X | – | – | – | – | – | X | – | – | – |
| Illinois | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Indiana | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Iowa | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Kansas | – | X | X | – | X | – | X | – | X | – | – | X | X | X | X | X | – | X | – | X |
| Jefferson Co, KY | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Kentucky | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Louisiana | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Maine | – | X | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Maryland | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Massachusetts | – | X | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Michigan | – | X | X | X | – | – | – | – | – | – | – | X | – | – | X | – | – | – | – | – |
| Minnesota | – | X | – | – | – | – | – | – | – | – | X | – | – | – | X | – | – | – | – | – |
| Mississippi | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Missouri | – | X | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Montana | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Nebraska | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Clark Co, NV | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | X | – |
| Nevada | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| New Hampshire | X | X | – | – | X | – | – | – | – | – | – | – | – | X | – | – | – | X | X | – |
| New Jersey | X | – | X | – | X | – | – | – | X | – | – | – | – | – | – | – | – | – | X | – |
| New Mexico | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| New York | – | X | – | X | – | – | – | – | – | – | – | – | – | X | – | – | – | – | – | – |
| North Carolina | – | X | X | X | X | – | – | – | X | – | – | – | – | – | – | – | – | – | X | – |
| North Dakota | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Ohio | – | X | X | X | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Oklahoma | – | X | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Oregon | – | X | – | X | – | – | – | – | X | – | – | – | – | – | – | – | – | – | – | – |
| Pennsylvania | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Puerto Rico | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Rhode Island | X | X | X | X | X | – | – | – | – | – | – | X | – | – | – | – | – | – | – | X |
| South Carolina | – | X | – | X | – | – | X | – | – | – | – | X | – | – | – | – | – | – | – | – |
| South Dakota | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Knox Co, TN | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | X | – |
| Nashville, TN | – | – | – | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Shelby Co, TN | – | X | – | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Chattanooga, TN | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Tennessee | – | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Texas | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Utah | X | X | X | X | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | X |
| Vermont | – | X | – | – | – | – | – | X | – | X | – | – | X | – | – | X | – | – | X | X |
| Virgin Islands | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Virginia | – | X | – | X | X | – | – | – | – | – | – | – | – | – | – | – | – | – | – | X |
| Washington | X | X | X | – | – | X | – | – | X | – | X | – | – | – | – | – | – | – | X | – |
| West Virginia | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
| Wisconsin | – | X | X | X | – | – | – | – | X | X | – | – | – | X | – | – | – | – | – | – |
| Wyoming | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – | – |
*Input templates needed for point inventory reconciliation
A complete list of the specific S/L-submitted Input Templates for each EPA tool estimate category is provided in the workbook “2023NEI_Nonpoint_Input_Template_Changes_22may2026.xlsx” on the “2023 Supplemental data FTP site”.
The following subsections list the EIS input template activity data variables that are available for each wagon wheel tool/module. In addition to the tool-specific variables, “Central Database” and “County Business Pattern” templates that impact several tools are also available:
• Central Database - Animal Population (number): Animal populations for each animal type
• Central Database - Coal Distribution Ratios (number <=1): Ratio of anthracite and bituminous to total bituminous/sub-bituminous coal
• Central Database - County Silt Content (percentage): County-specific silt percentage
• Central Database - Population (number): county population
• Central Database - SEDS (number): sector/fuel total fuel consumption
• Central Database - Sulfur and Ash Content (fraction): oil sulfur content, coal sulfur, and coal ash content
• County Business Patterns – County Employment (number): County-level employment by broad NAICS
• County Business Patterns – Employment by SCC (number): County-level employment by nonpoint SCC
• County Business Patterns – State Employment (number): State-level employment by broad NAICS
6.2.3.1 Structure and Motor Vehicle Fires
Three EIS Input Templates were created to allow S/L data submitters to provide more local information on county incidents, fuel loading, and emission factors. Relevant variables for each of these three input templates are provided here; each fire type (Structure Fires, Motor Vehicle Fires) has its own template.
• County Incidents (number): For each fire type, county-level number of fire incidents (INCIDENTS)
• Average fuel load (tons): For each fire type, county-level fuel load (FUEL_LOAD)
• Emission factors (lbs/ton): For each fire type, agency-level emission factor (EMISSION_FACTOR)
6.2.3.2 Campgrounds
Two EIS Input Templates are created to allow S/L data submitters to provide more local information on the camp sites in the county and the cords of wood used per campsite per year.
If the S/L have data on the cords of wood used in campfires per county, they can provide values for the number of sites and the cords per site that when multiplied together, give the cords of wood that match the cords of wood per county. Relevant variables for each the two input templates are provided here.
• Number of Campsites (number): County-level number of campsites
• Cords per campsite per year (cords): County-level number of cords per campsite per year
6.2.3.3 Other NH3
Five EIS Input Templates are available to allow S/L data submitters to provide more local county-level information. Relevant variables for each of these input templates are provided here.
• Other NH3 - Cats: Fraction of homes with cats
• Other NH3 - Deer: Deer population
• Other NH3 - Diapers: Population between 0 and 4 years of age
• Other NH3 - Dogs: Fraction of homes with dogs
• Other NH3 - Smokers: Smoker fraction of population
6.2.3.4 Ag Silage
There is one input template available to S/L to submit county-level animal populations for the nine available animal types for use in the calculation of emissions for agricultural silage sector. Additionally, there is one input template for county-level control factors in this sector. Relevant variables for each of the nine animal populations in the input template are provided here.
• Agricultural Silage - Control Factor: FACTOR
• Central Database - Animal Populations: DAIRY
• Central Database - Animal Populations: LAYERS
• Central Database - Animal Populations: SWINE
• Central Database - Animal Populations: TURKEYS
• Central Database - Animal Populations: BEEF
• Central Database - Animal Populations: GOATS
• Central Database - Animal Populations: HORSES_PONIES
• Central Database - Animal Populations: SHEEP_LAMBS
• Central Database - Animal Populations: BROILERS
Those S/L-submitted activity data are quality assured and used over EPA estimates as appropriate.
6.2.3.5 Ag Livestock Waste
There is one input template available to S/L to submit county-level animal populations for the nine available animal types for use in the calculation of emissions for agricultural livestock waste sector. Relevant variables for each of the nine animal populations in the input template are provided here.
• Central Database - Animal Populations: DAIRY
• Central Database - Animal Populations: LAYERS
• Central Database - Animal Populations: SWINE
• Central Database - Animal Populations: TURKEYS
• Central Database - Animal Populations: BEEF
• Central Database - Animal Populations: GOATS
• Central Database - Animal Populations: HORSES_PONIES
• Central Database - Animal Populations: SHEEP_LAMBS
• Central Database - Animal Populations: BROILERS
6.2.3.6 Ag Tilling
Nine input templates are available to S/L to submit county and or state-level information for fields used in the calculation of emissions for agricultural tilling sector. Additionally, there is one input template for county-level control factors in this sector. State-level data provided in input templates are used to gap-fill missing county-level data. Data provided in the number of tilling passes templates is used to calculate the county-specific PM10 and PM25 emissions factors for agricultural tilling. Relevant variables for each of the ten input templates are provided here.
• Agricultural Tilling - Control Factor: FACTOR
• Agricultural Tilling - Cover Fallow Pasture-State: CROPLAND_ACRES, CROPLAND_SUMMER, PASTURELAND
• Agricultural Tilling - Cover Fallow Pasture-County: CROPLAND_ACRES, CROPLAND_SUMMER, PASTURELAND
• Agricultural Tilling - Field Crops-County: ALFAFA, BARLEY, CANOLA, CORN, COTTON, HAY, OATS, PEANUTS, PEAS, RICE, RYE, SORGHUM, SOYBEANS, SUGARBEETS, SUGARCANE, SUNFLOWER, TOBACCO, WHEAT_DURHAM, WHEAT_SPRING, WHEAT_WINTER
• Agricultural Tilling - Field Crops-State: ALFAFA, BARLEY, CANOLA, CORN, COTTON, HAY, OATS, PEANUTS, PEAS, RICE, RYE, SORGHUM, SOYBEANS, SUGARBEETS, SUGARCANE, SUNFLOWER, TOBACCO, WHEAT_DURHAM, WHEAT_SPRING, WHEAT_WINTER
• Agricultural Tilling - Potato-County: POTATOES
• Agricultural Tilling - Potato-State: POTATOES
• Agricultural Tilling - Tilling Data-County: CONS_TILL, CONV_TILL, NO_TILL
• Agricultural Tilling - Tilling Data-State: CONS_TILL, CONV_TILL, NO_TILL
• Agricultural Tilling - Tilling Passes: BARLEY_CONS, BARLEY_CONV, BARLEY_NO_TILL, BEANS_CONS, BEANS_CONV, BEANS_NO_TILL, CANOLA_CONS, CANOLA_CONV, CANOLA_NO_TILL, CORN_CONS, CORN_CONV, CORN_NO_TILL, COTTON_CONS, COTTON_CONV, COTTON_NO_TILL, COVER_CONS, COVER_CONV, COVER_NO_TILL, FALLOW_CONS, FALLOW_CONV, FALLOW_NO_TILL, FORAGE_CONS, FORAGE_CONV, FORAGE_NO_TILL, HAY_CONS, HAY_CONV, HAY_NO_TILL, OATS_CONS, OATS_CONV, OATS_NO_TILL, PASTURE_CONS, PASTURE_CONV, PASTURE_NO_TILL, PEANUTS_CONS, PEANUTS_CONV, PEANUTS_NO_TILL, PEAS_CONS, PEAS_CONV, PEAS_NO_TILL, PECAN_CONS, PECAN_CONV, PECAN_NO_TILL, POTATOES_CONS, POTATOES_CONV, POTATOES_NO_TILL, RICE_CONS, RICE_CONV, RICE_NO_TILL, RYE_CONS, RYE_CONV, RYE_NO_TILL, SORGHUM_CONS, SORGHUM_CONV, SORGHUM_NO_TILL, SOYBEANS_CONS, SOYBEANS_CONV, SOYBEANS_NO_TILL, SUGARBEETS_CONS, SUGARBEETS_CONV, SUGARBEETS_NO_TILL, SUGARCANE_CONS, SUGARCANE_CONV, SUGARCANE_NO_TILL, SUNFLOWER_CONS, SUNFLOWER_CONV, SUNFLOWER_NO_TILL, TOBACCO_CONS, TOBACCO_CONV, TOBACCO_NO_TILL, WHEAT_FALL_CONS, WHEAT_FALL_CONV, WHEAT_FALL_NO_TILL, WHEAT_SPRING_CONS, WHEAT_SPRING_CONV, WHEAT_SPRING_NO_TILL
6.2.3.7 Asphalt
Four input templates are available for updating county and/or state-level data related to vehicular miles traveled, paved road miles, asphalt roofing usage in tons, asphalt paving in short tons, used in the Asphalt Tool. Additionally, there is one input template for control factors in this sector.
• Asphalt – Control Factor: FACTOR
• Asphalt – County VMT: RINT (Rural Interstate), RLOC (Rural Local), RMJC (Rural Major Collector), RMNA (Rural Minor Arterial), MRNC (Rural Minor Collector), ROFX (Rural Other Freeways and Expressways), ROPA, UINT, ULOC, UMJC, UMNA, UMNC, UOFX, UOPA
• Asphalt – Paved Road Miles: PAV_RINT, PAV_RLOC, PAV_RMJC, PAV_RMNA, PAV_RMNC, PAV_ROFX, PAV_ROPA, PAV_UINT, PAV_ULOC, PAV_UMJC, PAV_UMNA, PAV_UMNC, PAV_UOFX, PAV_UOPA, TOT_RINT, TOT_RLOC, TOT_RMJC, TOT_RMNA, TOT_RMNC, TOT_ROFX, TOT_ROPA, TOT_UINT, TOT_ULOC, TOT_UMJC, TOT_UMNA, TOT_UMNC, TOT_UOFX, TOT_UOPA
• Asphalt – Asphalt Roofing: ASPHALT_ROOF_USAGE
• Asphalt – State Asphalt Use: ASPHALT_HOT, ASPHALT_WARM, CUTBACK, EMULSIFIED
6.2.3.8 Aviation Gasoline
One input template is available for updating the county-level number of landing and take-offs (LTOs) used in the Aviation Gasoline Tool. Additionally, there is one input template for control factors in this sector.
• Aviation Gasoline – County Control Factor: FACTOR
• Aviation Gasoline – County LTOs: LTO
6.2.3.9 Composting
For updating data in the compost tool, input templates are available for state-level amounts of food waste and state-level amounts of yard waste composted. Data is recorded in units of tons. Additionally, there is one input template for county-level control factors in this sector.
• Compost – County Control Factor: FACTOR
• Compost – State-level Food Waste: FOOD-WASTE
• Compost – State-level Yard Waste: YARD-WASTE
6.2.3.10 Construction Dust and Open Burning
There are many options for updating activity data in the construction dust and open burning tool. In total, there are nine available input templates, including two input templates for adjusting factors used to indicate if burn bans are in effect, as well as the amount of forested acres in a county. Activity data values that can be updated in the input templates include county-level numbers of units and/or buildings, state-level annual road construction (in US dollars), precipitation-evaporation values (PE), land clearing debris, and fuel loading factors based on acres of land cover type.
• Construction Dust and Open Burning - Bldgs w 5 or more units: UNITS5
• Construction Dust and Open Burning - Burn Ban Effectiveness: RES_BB_ADJUSTMENT, LCD_BB_ADJUSTMENT
• Construction Dust and Open Burning - County Building Permits: BLDG_PERMITS1, BLDG_PERMITS2, BLDG_PERMITS3, BLDG_PERMITS5, UNITS_PERMITS1, UNITS_PERMITS2, UNITS_PERMITS3, UNITS_PERMITS5
• Construction Dust and Open Burning - County Control Factor: FACTOR
• Construction Dust and Open Burning - County PE values: PE
• Construction Dust and Open Burning - County-Level LCD Burned: LCD_BURNED
• Construction Dust and Open Burning - Land Cover: GRASSES, HARDWOODS, OTHER, SOFTWOODS
• Construction Dust and Open Burning - Road Constr Spending: RURAL_COLLECTOR, RURAL_INTERSTATE, RURAL_OTH_ARTERIALS, URBAND_COLLECTOR, URBAN_INTERSTATE, URBAN_OTH_ARTERIALS
• Construction Dust and Open Burning - Yard Waste Adjustment: ADJ_FACTOR
6.2.3.11 Cooking
Input templates for this sector allow for county-level updates to the number of restaurants by type, as well as a control factor to indicate county-level reductions in cooking emissions to be applied in the cooking tool.
• Cooking – Control Factor: FACTOR
• Cooking – County-level Restaurants: RESTAURANTS
6.2.3.12 Cremation
Input templates available for the cremation tool allow for updates to the county-level population by age group or county-level deaths by age groups. Additionally, S/L may apply a county-level control factor or update the cremation rates for individual counties.
• Cremation – Control Factor: FACTOR
• Cremation – County-level Deaths: POPULATION
• Cremation – County-level Deaths: DEATHS
• Cremation – Cremation Rate: CREMATION
6.2.3.13 Dust from Hooves
Only a county-level control factor input template is available specifically for the Dust from Hooves category. However, the Central Database – Animal Population input templates are also used in the Dust from Hooves Tool.
• Dust from Hooves – Control Factor: FACTOR
6.2.3.14 ICI
The ICI tool includes options for SLT agencies to submit pollutant-, SCC-, and county- or state-specific control factors if needed. These control factors are a number between 0 and 1 that is multiplied by the emissions for that pollutant, SCC, and county. The four ICI – Option templates are used for Point Source subtraction for this sector.
• ICI - Control Factor – County: FACTOR
• ICI - Control Factor – State: FACTOR
• ICI - Boiler - Engine Split: BOILER_COM, BOILER_IND, ENGINES_COM, ENGINES_IND
• ICI - Distillate Stationary Source Assumptions: NO1DIST_INDUSTRIAL, NO2FUELOIL_IND, NO2LOWS_INDUSTRIAL, NO4DIST_IND, NO1DIST_IND_MOBILE, NO2FUELOIL_IND_MOB, NO2LOWS_IND_MOBILE, NO4DIST_IND_MOB, NO1DIST_COMMERCIAL, NO2FUELOIL_COM, NO2LOWS_COMMERCIAL, NO4DIST_COM, NO1DIST_COM_MOBILE, NO2FUELOIL_COM_MOB, NO2LOWS_COM_MOBILE, NO4DIST_COM_MOB, DIESEL_FARM, DIESEL_FARM_MOBILE, OTHERDIST_FARM, OTHERDIST_FARM_MOB, TOTALDIST_OFFHWY, TOTALDIST_OFFHWY_MOB, TOTALDIST_OILCO, TOTALDIST_OILCO_MOB
• ICI - Distillate Sales Data: SALES
• ICI - LPG Stationary Source Assumptions: PCT_MOBILE_COM, PCT_MOBILE_IND, PCT_STATIONARY_COM, PCT_STATIONARY_IND
• ICI - Nonfuel Use: COAL, DISTILLATE, KEROSENE, LPG, NATURAL_GAS, RESIDUAL_OIL
• ICI - Option A: FUEL_CONSUME_XXXX
• ICI - Option B: FUEL_CONSUME_XXXX
• ICI - Option C: FUEL_CONSUME_XXXX
• ICI - Option D: FUEL_CONSUME_XXXX
6.2.3.15 Landfills
Landfill input templates allow S/L to specify the total annual tons of waste in landfills, as well as indicate a control factor to reduce county-level emissions when controls are in use.
• Landfills - Control Factor: FACTOR
• Landfills – County-level waste from landfill: WASTE_DISPOSE
6.2.3.16 Mining and Quarrying
S/L have the option of providing activity day for Coal production and metal or mineral production used in the Mining and Quarrying tool. Additionally, there is one input template for control factors in this sector.
• Mining and Quarrying - Control Factor: FACTOR
• Mining and Quarrying – County Coal Production: COAL
• Mining and Quarrying – Metal Non Metal Production: METAL, MINERAL
6.2.3.17 Other Mercury
Activity data for the Other Mercury Tool can be submitted using input templates related to number of switches available, mercury recovered from switches, number of recyclers of mercury, as well as the number of switches recovered. A control factor input template is also available to S/L for this sector.
• Other Mercury - Control Factor: FACTOR
• Other Mercury – Switches Available: SWITCHES
• Other Mercury – Switches Recovered: HG_RECOVER, RECYCLERS, SWITCHES_RECOVER
6.2.3.18 Publicly Owned Treatment Works (POTWS)
The POTWS Tool has four input templates available covering control factors, point source subtraction and activity data from S/L for county wastewater flow rate in million gallons per year.
• POTWs – Ctrl Factor by County and Pollu: FACTOR
• POTWs – Pt Src Activity – Opt A: FLOW_RATE
• POTWs – Pt Src Activity – Opt B: EMISSIONS
• POTWs – SLT Submitted Flow Rate: FLOW_RATE
6.2.3.19 Residential Heating
The Residential Heating tool uses the number of occupied homes in each county that use a specific fuel type as their primary source of heating. The Heating Fuel Estimates input template allows S/L to update those numbers by fuel type at the county level. Additionally, a control factor can be assigned by county or state using the two control factor input templates.
• Residential Heating - County Control Factor: FACTOR
• Residential Heating - State Control Factor: FACTOR
• Residential Heating - Heating Fuel Estimates: HOME_COAL, HOME_OIL, HOME_LPG, HOME_GAS
6.2.3.20 Road Dust
There are six input templates that can be used by S/L for updates to EPA estimates in the Road Dust tool. These include a control factor, as well as meteorological adjustment factors used to account for the effect of rain and road moisture on dust emissions from paved and unpaved roads. Additionally, vehicle miles traveled, surface material moisture content, and surface material silt content can be updated with S/L values via input templates.
• Road Dust - County Control Factor: FACTOR
• Road Dust – Met Adjustment Factor: PAVED_FACTOR
• Road Dust – Met Adjustment Factor: UNPAVED_FACTOR
• Road Dust - Paved and Unpaved VMT by County: PAVED_VMT, UNPAVED_VMT
• Road Dust - Surface Material Moisture Content: MATERIAL_MOIST
• Road Dust - Surface Material Silt Content: MATERIAL_SILT
6.2.3.21 Residential Wood Combustion (RWC)
For the RWC tool, control factors can be assigned to the county or state-level. For activity data inputs, S/L can submit input templates to adjust appliance fractions for each respective appliance type in each county; change burn rates for cords or tons/appliances, depending on the appliance in each county; update the county-level amount of wood, in tons burned annually; adjust assumptions about profiles of types central heaters and stoves to distribute the total number calculated using the appliance fractions into specific SCCs; update the total number of occupied housing units in each county; and adjust assumptions about wood density (tons/cord).
• RWC - Control Factor – County: FACTOR
• RWC - Control Factor - State: FACTOR
• RWC - Appliance Fractions: FIREPLACE_FRACT, WOODSTOVES_FRACT, FIRE_INSERT_FRACT, CENT_HEATER, OUTDOOR_REC_EQUIP, PELL_STOVE _FRACT, WAX _LOG AP_FRACT
• RWC - Burn Rates: FIREPLACE_BUR_RATE, OODSTOVES_BUR_RATE, FIRE_INSERT_RATES, CENT_HEATER_RATES, OUTDOOR_EQUIP_BURN, PELL_STOVE _RATES, WAX _LOG, BU_RATES
• RWC - County-Level Wood Burned: WOOD_BURNED
• RWC - Distribution - Central Heaters: PELLET_BOILER, PELLET_FURNACE, IN_CORDWOOD_BOILER, IN_CORDWOOD_FURNACE, OUT_CORDWOOD_BOILER
• RWC - Distribution - Stove & Inserts: UNCERT_DIST, CERT_DIST
• RWC - Housing Units: HOMES
• RWC - Wood Density Factors: DENSITY
6.2.3.22 Solvents
Four input templates can be used by S/L for the Solvents tool. One is a county-level control factor template. The other templates allow for adjustments to county-level vehicular lane miles traveled on paved roads, point source emissions for subtraction, and state-level controls for specific solvent SCCs.
• Solvents - Control Factor: FACTOR
• Solvents - Lane Miles: LANE_MILES
• Solvents - Point Source Emissions: SOURCE_EMISS
• Solvents - States with Controls: ARCHITECTURAL_COATINGS_NEW, INDUSTRIAL_MAINTENANCE_NEW, CONSUMER_SOLVENTS_NEW
6.2.3.23 Stage 1 Gasoline Distribution
There are nine input templates that can be used by S/L in the Stage 1 Gasoline Distribution tool, including one template for county-level control factors. This sector uses point source subtraction via input templates to avoid double counting emissions in the point and nonpoint data categories. Other opportunities for S/L to make changes to EPA estimated emissions in this tool are through the templates for benzen emissions factors, county-level bulk plant throughput data, various county-level parameters, county-level fuel delivery technology methods, county-level loading loss of liquid, Reid vapor pressure (RVP) county-level data for fuels, and gas consumption by county for onroad and nonroad uses.
• Stage I Gasoline Distribution - Benzene Speciation: FACTOR
• Stage I Gasoline Distribution - County Bulk Plant Throughput: BULK_THROU
• Stage I Gasoline Distribution - County Control Factor: FACTOR
• Stage I Gasoline Distribution - County Parameters: SLOPE, TEMPERATURE, RANKINE, EFFICIENCY
• Stage I Gasoline Distribution - Filling Technology: SPLASH_PCT, SUBMERGED_PCT, BALANCE_PCT
• Stage I Gasoline Distribution - Loading Loss: LOADING_LOSS
• Stage I Gasoline Distribution - Nonroad Gas Consumption: RVP, NR_FUEL_CONSUME
• Stage I Gasoline Distribution - Onroad Gas Consumption: ON_FUEL_CONSUME
• Stage I Gasoline Distribution - Point Source Emissions: EMISSIONS
6.2.4 SLT-submitted emissions
A complete list of S/L/T agencies that submitted 2023 nonpoint emissions for source categories that EPA also estimates are provided in Table 6.5. It is important to note that this does not provide a single indication on whether some/all S/L/T data or some EPA data are included in the 2023 nonpoint NEI selection for these agencies and categories. Factors that could result S/L/T data not being in the NEI, or EPA data appearing in the NEI for these agencies/categories include:
• Completeness of S/L/T data: complete geographic and expected pollutant coverage
• Outlier values resulting in tagging out of S/L/T data
• Nonpoint Survey responses set to (Yes) allow EPA data to supplement any missing S/L/T data
• Decision to use only EPA data for a particular source category (e.g., Biogenics)
| EPA Estimate Category | TSD Section | State Agencies | Local Agencies | Tribal Agencies |
|---|---|---|---|---|
| Biogenics - Vegetation and Soil | 7 | CA | – | Nez Perce Tribe |
| Agriculture - Fertilizer Application | 8 | CA | – | Nez Perce Tribe |
| Mobile - Commercial Marine Vessels | 9 | CA, TX | – | Nez Perce Tribe |
| Mobile - Locomotives | 10 | AK, CA, TX, VA | – | Nez Perce Tribe |
| Industrial Processes - Oil & Gas Production | 11 | AK, CA, CO, NJ, OH, OK, TX, UT, WV | Maricopa County Air Quality Department | Southern Ute Indian Tribe |
| Portable Fuel Containers | 12 | CA, DE, MD, NJ | – | Nez Perce Tribe |
| Fires - Agricultural Field Burning | 13 | ID, NJ, WA | Maricopa County Air Quality Department | Nez Perce Tribe |
| Fires - Prescribed Fires | 13 | CA, WA | – | Nez Perce Tribe |
| Fires - Wildfires | 13 | CA | – | Nez Perce Tribe |
| Motor Vehicle and Structure Fires | 14 | CA, MD, MN, TX | Maricopa County Air Quality Department | – |
| Non-combustion Mercury: Dental Amalgam | 15 | MN | – | – |
| Non-combustion Mercury: Fluorescent Lamp Recycling | 15 | – | – | – |
| Non-combustion Mercury: Lamp Breakage (Landfill emissions) | 15 | MN | – | – |
| Non-combustion Mercury: Laboratory Activities | 15 | MN | Washoe County Health District | – |
| Non-combustion Mercury: Switches + Relays | 15 | – | – | – |
| Non-combustion Mercury: Thermostats + Thermometers | 15 | – | – | – |
| Gas Stations: Aviation Gasoline | 16 | DE, MD, NJ | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Gas Stations: Stage 1 Gasoline Distribution | 16 | CA, DE, MD, NJ | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe, Southern Ute Indian Tribe |
| Storage + Transport: Stage 1 Gasoline Distribution | 16 | AK, CA, DE, MD, NJ, VA | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Bulk Gasoline Terminals | 18 | AK, CA, CT, TX | Knox County Department of Air Quality Management, Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Agriculture - Livestock Waste | 17 | CA | Maricopa County Air Quality Department | Nez Perce Tribe |
| Agricultural Silage | 18 | CA | Maricopa County Air Quality Department | Nez Perce Tribe |
| Agricultural Dust: Ag Tilling | 19 | CA, DE, MD | – | Nez Perce Tribe |
| Agricultural Dust: Animal Hooves | 19 | – | Maricopa County Air Quality Department | Nez Perce Tribe |
| Dust: Residential Construction | 20 | CA, DC, DE, MD, NJ, UT | Maricopa County Air Quality Department, Washoe County Health District | Nez Perce Tribe |
| Dust: Non-Residential Construction | 21 | AK, CA, DC, DE, MD, NJ, UT | Maricopa County Air Quality Department, Washoe County Health District | Nez Perce Tribe |
| Dust: Road Construction | 22 | AK, CA, DE, MD, NJ, TX, UT | Maricopa County Air Quality Department, Washoe County Health District | Nez Perce Tribe |
| Dust - Paved Roads | 23 | CA, DE, ID, MD, TX | – | Nez Perce Tribe |
| Dust - Unpaved Roads | 23 | AK, CA, MD, TX | Maricopa County Air Quality Department | Nez Perce Tribe |
| Dust: Mining and Quarrying | 24 | AK, CA, MD, NJ, TX | Clark County Department of Air Quality and Environmental Management, Maricopa County Air Quality Department, Washoe County Health District | Nez Perce Tribe |
| Commercial and Residential Cooking | 25 | CA, TX, UT | Maricopa County Air Quality Department, Washoe County Health District | Nez Perce Tribe |
| Fuel Comb - Comm/Institutional - Biomass | 26 | MD, WA | – | Nez Perce Tribe |
| Fuel Comb - Comm/Institutional - Coal | 26 | MD, NJ | – | – |
| Fuel Comb - Comm/Institutional - Natural Gas | 26 | AK, CA, MD, NJ, TX, UT | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control, Washoe County Health District | Nez Perce Tribe |
| Fuel Comb - Comm/Institutional - Oil | 26 | AK, CA, MD, NJ, PR, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Fuel Comb - Comm/Institutional - Other | 26 | AK, CA, MD, NJ, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Fuel Comb - Industrial Boilers, ICEs - Biomass | 26 | MD, WA | – | Nez Perce Tribe |
| Fuel Comb - Industrial Boilers, ICEs - Coal | 26 | AK, CA, MD, NJ | – | – |
| Fuel Comb - Industrial Boilers, ICEs - Natural Gas | 26 | AK, CA, MD, NJ, TX, UT | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control, Washoe County Health District | Nez Perce Tribe |
| Fuel Comb - Industrial Boilers, ICEs - Oil | 26 | AK, CA, MD, NJ, PR, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Fuel Comb - Industrial Boilers, ICEs - Other | 26 | AK, CA, MD, NJ, PR, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe, Southern Ute Indian Tribe |
| Fuel Comb - Residential - Natural Gas | 27 | CA, MD, NJ, TX, UT | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control, Washoe County Health District | Nez Perce Tribe, Southern Ute Indian Tribe |
| Fuel Comb - Residential - Oil | 27 | AK, CA, MD, NJ, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Fuel Comb - Residential - Other | 27 | AK, CA, MD, NJ, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe, Southern Ute Indian Tribe |
| Residential Wood Combustion | 28 | AK, CA, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control, Washoe County Health District | Nez Perce Tribe, Southern Ute Indian Tribe |
| Campfires | 29 | MD | Maricopa County Air Quality Department | – |
| Cremation: Human and Animal | 30 | ID | Knox County Department of Air Quality Management, Maricopa County Air Quality Department, Washoe County Health District | Nez Perce Tribe |
| Asphalt Paving and Roofing Asphalt | 31 | CA, DE, MD, NJ, TX | Maricopa County Air Quality Department | Nez Perce Tribe |
| Solvent - Consumer & Commercial Solvent Use | 32 | AK, CA, DE, MD, NJ, TX, UT | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Solvent - Degreasing | 32 | AK, CA, DE, MD, NJ, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Solvent - Dry Cleaning | 32 | CA, MD, NJ, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control, Washoe County Health District | – |
| Solvent - Graphic Arts | 32 | CA, MD, NJ, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control, Washoe County Health District | Nez Perce Tribe |
| Solvent - Industrial Surface Coating & Solvent Use | 32 | AK, CA, DE, MD, MN, NJ, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control, Washoe County Health District | Nez Perce Tribe |
| Solvent - Non-Industrial Surface Coating | 32 | CA, DE, MD, NJ, TX | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Composting | 33 | CA, NC | Maricopa County Air Quality Department | Nez Perce Tribe |
| Open Burning: Land Clearing Debris | 34 | DE, NJ | Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control | Nez Perce Tribe |
| Open Burning: Household Waste | 35 | AK, CA, DE, MN, NC, NJ, TX | Maricopa County Air Quality Department | Nez Perce Tribe |
| Open Burning: Yard Waste | 36 | CA, DE, NJ, TX | Maricopa County Air Quality Department | Nez Perce Tribe |
| Publicly-owned Treatment Works | 37 | CA, MD, TX | Knox County Department of Air Quality Management, Maricopa County Air Quality Department, Memphis and Shelby County Health Department - Pollution Control, Washoe County Health District | Nez Perce Tribe |
| Other Ammonia Sources | 38 | CA, NJ, UT | Maricopa County Air Quality Department | – |
6.2.5 Data selection rules: cross-dataset tagging
We compiled the 2023 nonpoint inventory using much of the same EIS automated data selection rules first implemented for the 2017 NEI: Nonpoint Survey Rule, Pollutant Grouping Rule, and the Option Group/Option Set Rule. In addition, the PM speciation rule has since been automated to run as part of the NEI selection, rather than a separate EIS processing step (and input/output data). When applied, these rules greatly minimized the need to “tag” out data that would otherwise be needed to prevent double counting of emissions across pollutant groups, SCCs, and from different data submittal sources.
6.2.5.1 Nonpoint Survey rule
The 2023 nonpoint survey responses were directly applied to the nonpoint selection in the EIS. All S/L/Ts that completed the nonpoint surveys (green status button on the home screen for the nonpoint survey), had their responses directly applied in the NEI selection. For each “EPA Tool Estimate Category”, nonpoint survey responses were applied if the “Category Complete?” column was saved and submitted as “Yes”. By default, all nonpoint survey responses were defaulted to “Yes -Supplement my data with EPA estimates. This simply means that if S/L/T data was not submitted, and EPA data exists (for that process/pollutant), then EPA data will be selected for the NEI with a caveat to the 2 rules discussed in the next two sections. S/L/Ts were strongly encouraged to leave the SCCs as default (yes) if they were submitting nonpoint inputs, because S/L/T inputs were absorbed into EPA tools and became “EPA” data, the Nonpoint Survey includes a checkbox for each tool category “Did Your Agency Provide an Input Template for this Category?” to help with quality assurance, particularly for tool categories that are limited to a single SCC.
A detailed 2023 NEI nonpoint summary “2023NEI_Nonpoint_Survey_detail_22may2026.xlsx” covering all reporting agencies has been uploaded to the “2023 NEI Supplemental data FTP site”.
6.2.5.2 Pollutant grouping rule
In previous NEI cycles, we tagged out data to prevent double counting of pollutants across datasets that overlap one another. Starting with the 2017 NEI and continued for the 2023 NEI, a software solution that occurs during the blending process was developed so that overlapping pollutants would be excluded from the selection. Business rules were developed to select data with overlapping pollutants across datasets, to allow different datasets included in a selection to be blended together in a way that avoids double counting due to overlapping pollutants. Because there are several HAPs that belong to pollutant groups or represent a pollutant group themselves, these rules are needed to prevent both a group and individual pollutant in that group from being used for the same process or facility. The implementation of these rules is automated in the EIS. These rules are applied at the process level (location and SCC) for nonpoint sources and prevent lower-hierarchy dataset pollutants/pollutant groups from possible double-counts. For example, if an S/L/T reports “Xylenes (Mixed Isomers), then any EPA (lower hierarchy) -generated individual (or mixed) isomers will not make it into the NEI. Rules for the following pollutant groups were applied: xylenes, cresols, polychlorinated biphenyls (PCBs), glycol ethers, chromium, nickel, and PAHs. A complete discussion of the cross dataset tagging proposed rules, applied to the 2023 NEI nonpoint inventory selection are available in ”Appendix 5” of the ”2017 NEI Plan”. One change to these “Proposed” rules that we implemented for the 2017 and continued for the 2023 NEI is that we allow individual xylene isomers to be reported with Xylenes (mixed isomers) within the same dataset.
6.2.5.3 Option Group/Option Set rule
We applied the EIS Option Group/Option Set (OGOS) feature for the first time in the 2017 nonpoint NEI and continued with the same application for the 2023 NEI. In the Source Classification Code table, we can define SCCs that have a hierarchical nature. That is, there may be a “general” group, as well as more specific SCCs within the same group. These relationships are defined by the “Option Group / Option Set” (OGOS) fields in the SCC table. When EPA and SLT datasets are placed in an NEI selection, there is the potential for double counting of data sources (emissions) across these data sources. For example, the EPA may report emissions to a “general” SCC while SLTs report data to detailed SCCs. Without OGOS evaluation, both sets of data would be included in the NEI selection. The current OGOS rules employed in the Selection assumes that if a SLT submits data, they are summitting data for the entire group and no additional data sets are to be used to “back-fill” any SCCs within the same option set. The desired function is for the selection to back-fill any SCCs within the same option set. Refer to “”Appendix 6 - Option Group Option Set Enhancement EIS Requirements.pdf”” on the “2017 National Emissions Inventory Plan website” for a comprehensive discussion on the OGOS business rules used in EIS for the 2023 nonpoint NEI. A complete list of OGOS assignments can be obtained by downloading the complete SCC table (Bulk Download Options) from the “SCC search site”, and filtering on columns where Option Group is populated.
Dioxins/furans include all pollutants with pollutant category name of: Dioxins/Furans as 2,3,7,8-TCDD TEQs, or Dioxins/Furans as 2,3,7,8-TCDD TEQs – WHO2005. Radionuclides have the pollutant category name of “radionuclides” The specific compounds and codes are in the pollutant code tables in EIS.↩︎