In May 2024 the National Emissions Collaborative worked with U.S. EPA to release a 2022v1 emissions modeling platform. This platform will be used to support regulatory air quality modeling for next 2-3 years. Applications of this platform will likely include ozone NAAQS attainment demonstrations, PM2.5 NAAQS transport and attainment modeling, and regional haze progress demonstrations.
LADCO created an interactive industrial point source emissions app to help state planners working on industrial decarbonization projects for Climate Pollution Reduction Grant (CPRG) applications.
The R-Shiny web app allows the filtering of 2022 stationary point source inventory data by state, county, and pollutant. The analysis app can display emissions by either NAICS or Source Classification Code (SCC). The NAICS codes organize the emissions generally by industry classification. SCC codes are more granular and generally organize the emissions by emissions technology (e.g., reciprocating internal combustion engine) and fuel type (e.g., natural gas or coal). Once the user selects the data to display in the table, the top 25 sources in the selected dimensions (e.g. state, CO2, and NAICS) are show. Clicking on a row in the table displays a bubble plot of the emissions in a map below the table, with the bubbles scaled by the size of the emissions (tons/year). Finally, clicking on a bubble on the map displays details about the industrial source.
The data in these charts are from the U.S. EPA 2022v1 draft emissions modeling platform. The specific data file behind these charts is the industrial point (non-IPM) flat file (FF10) inventory file (nonegu_norail_2022_POINT_20240615_stackfix2_23jul2024_v0.csv) located on the EPA FTP site for the 2022v1 platform. Only data for the six LADCO member states is available in this app.
]]>To facilitate review of the draft 2022v1 data, LADCO developed a web application to compare emissions across recent inventories (2016-2022) for different pollutants. A description of the two apps are below, followed by a description of the data sources.
The R-Shiny web apps generate stacked bar charts of inventory data. The segments in each bar show the annual emissions (tons/year) for different inventory sectors. The user interface of the charts allows the selection of multiple inventory pollutants, a single state, and multiple inventory years. Click on each link below to launch the apps.
State Bar Charts and Tables (click to launch)
Each bar shows the total annual emissions for the selected pollutant, state, and inventory year. Users can select multiple pollutants, a single state, and multiple inventory years to populate the chart. A table of data in each chart is included below the charts. The table can be filtered using the “Search” box or sorted by clicking on the header of each column.
State Difference Charts (click to launch)
Each bar shows the difference in annual emissions for the selected pollutant, state, and two inventory years. Users can select multiple pollutants, a single state, and two inventory years to populate the chart. The top chart shows the absolute difference between the inventory years (e.g., 2022 – 2016) and the second chart shows the percent difference between the years (e.g., (2022 – 2016)/2016 * 100). To interpret these difference charts, find the zero line on the y-axis to identify the sectors that increased or decreased across the two selected years.
The data in these charts are from the U.S. EPA 2022v1 draft emissions modeling platform. The specific data file behind these charts is the state-EISSectorGroup-trends spreadsheet that is available from the 2022v1 draft data files and summaries.
]]>Usage Notes
It takes the data a couple of minutes to render on the map, so please be patient. After the data are loaded you can zoom and pan the map, toggle the data sources on/off via check boxes, and if you click on any of the green point source locations or blue warehouse locations you’ll get data about the nature and size of the feature. The point and warehouse icons are scaled based on the size of the source: NOx emissions (tons/year) for the point sources and the number of loading docks for the warehouses.
Data Sources:
2019 NO2 Concentrations: Estimated annual average surface-level NO2 concentrations in 2019 derived from a global 1 km x 1 km land-use regression model detailed in Anenberg, Mohegh et al. (2022) and averaged to underlying census tracts following the methods described in Kerr et al. (2021)
2022 Point Source NOx Emissions: U.S. EPA 2022v1 emissions modeling platform. The data were extracted from the 2022v1 emissions review tool that is accessible from the platform webpage.
Warehouse Locations: Locations of warehouses that are larger than 20,000 sq ft. from CoStar.
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