Satellite Data for Environmental Justice

The SD4EJ team ran from mid-2021-beginning of 2023 and engaged researchers and action-oriented partners in using satellite data for environmental justice applications. This webpage shares some of the work and resources this team developed.

NASA Health and Air Quality Applied Sciences Team (HAQAST) is a collaborative team that works in partnership with public health and air quality agencies to use NASA data and tools for the public benefit. Satellite Data for Environmental Justice (SD4EJ) is a NASA HAQAST Tiger Team whose goal is to integrate satellite data into environmental justice (EJ) screening and mapping tools. Satellite data have strength in spatial coverage to comprehensively identify and target EJ communities for investments and remediation. Through the use of satellite data, we can discern differences in heat, pollution, and other environmental hazards within cities.

Satellite Data for Environmental Justice (SD4EJ) provides satellite data expertise for the following indicators and more: heat, light at night, fine particulate matter, nitrogen dioxide (NO2), ozone (O3), and formaldehyde (HCHO).

SD4EJ Partners

This Tiger Team is engaged with several partners who map EJ indicators, and the team works towards sharing, reformatting, and interpreting satellite data for EJ applications. The content below highlights a few of the team’s many partners exploring how satellite data can complement their existing work to understand and advance environmental justice.

Environmental Defense Fund:
SD4EJ works with the Environmental Defense Fund to integrate satellite data into their climate vulnerability index. The Climate Vulnerability Index integrates existing datasets on climate, health, and the environment and identifies vulnerabilities and factors driving those vulnerabilities on a national scale.

Characterizing vulnerabilities to climate change across the United States

CDC:
The CDC has an Environmental Justice Dashboard. This dashboard uses the Social Vulnerability Index (SVI), calculated based on social factors, including unemployment, minority status, and disability. The SVI illustrates high levels of social vulnerability. This data highlights environmental injustices in communities experiencing human suffering, financial hardships, or public health emergencies.

Consortium for the Valuation of Applications Benefits Linked with Earth Science:
The Consortium for the Valuation of Applications Benefits Linked with Earth Sciences is a cooperative agreement between Resources for the Future and the National Aeronautics and Space Administration (NASA). This program measures how satellite information can benefit people and the environment and inform policy or decision-making.

United States Environmental Protection Agency (EPA) Office of Environmental Justice:
The EPA has an environmental justice screening and mapping tool, EJScreen. SD4EJ is exploring how NO2, an air pollutant recently included in this screening tool, might provide additional insights into air quality and health disparities. Read more here about how the Satellite Data for Environmental Justice team worked to make this crucial inclusion of NO2 possible. Global estimates of surface NO2 concentrations at 1km x 1km are documented in Anenberg, Mohegh, et al. (2022).

Community Engagement, Environmental Justice, and Health (CEEJH) at UMD:
CEEJH has developed public participatory geospatial information science tools for visualizing environmental justice in communities. These tools measure air pollution, chemical releases, food justice, green space, and climate resilience on a map to identify burdens and health disparities in Maryland. Locate the Maryland Environmental Justice Screen Tool here.

The South Coast Air Quality Management District (SCAQMD):
The South Coast Air Quality Management District (SCAQMD) is responsible for controlling emissions from stationary sources of air pollutants. The SCAQMD has instituted several community initiatives to help improve air quality for residents of the south basin, including the Environmental Justice Program. The goals of this program are to advise on environmental justice issues, create and sustain positive and productive relationships between the South Coast AQMD and community members, and contribute to the progress and achievement of environmental justice through decisions and activities. The SCAQMD team is working to understand environmental justice issues related to the Ports of Los Angeles and Long Beach and the growing impact of warehousing and e-commerce in Southern California.

New York State Department of Health:
The NY State Department of Health uses Geographic Information Systems to map potential environmental justice areas here. This map highlights minority groups and percentages of the population with incomes below the federal poverty level. The New York State Department of Health uses data from the NY State Data GIS Clearinghouse and Environmental Resource Mapper to identify potential environmental justice areas based on resources, environmental features that are state or federally protected, areas of conservation or concern, and other factors.

Connecticut Institute for Resilience & Climate Adaptation/University of Connecticut:
Through a partnership forged by HAQAST’s SD4EJ tiger team, a new environmental justice screening tool for the state of Connecticut developed by the University of Connecticut/Connecticut Institute for Resilience and Climate Adaptation features satellite-derived estimates of PM2.5 (from HAQAST PI Randall Martin) and O3. It is an interactive resource that combines both community and data-driven approach that incorporates environmental burdens and demographic indicators. This map allows users to explore the environmental health and the conditions (socioeconomic and or other distinguishing community characteristics) within a specific region, town, city, and or entire state. More information and the EJ screen mapper tool can be found here.

Existing SD4EJ Geographic Information System StoryMaps

This Geographic Information System StoryMap and Dashboard of the United States uses satellite data to map air quality and socioeconomic data around race, ethnicity, poverty, and health status. This data helps identify the most vulnerable communities or individuals and recognize environmental justice issues.

Figure of nitrogen dioxide in DC region.

Figure 1. Satellite-derived nitrogen dioxide from the TROPOMI instrument averaged to underlying census tracts in the Baltimore-Washington, DC region. Red lines denote major roadways.

Figure of nitrogen dioxide in California region.

Figure 2. Satellite-derived nitrogen dioxide from the TROPOMI instrument averaged to underlying census tracts in the California region.

EJScreen

Viewing nitrogen dioxide pollution in environmental justice screening tools

By Gaige Kerr, Susan Anenberg, and Emily Richardt

This webpage summarizes the importance of considering nitrogen dioxide (NO2) pollution when assessing environmental health-related risks and explains how a high-resolution NO2 dataset can be integrated into EJScreen, the EPA’s environmental justice (EJ) screening and mapping tool. EJScreen is a tool for everyone, and this document will benefit anyone interested in understanding NO2 pollution in their community and screening candidate areas for additional consideration.

Why is it important to include NO2 in EJ mapping tools?

Nitrogen dioxide (NO2) air pollution is pervasive in urban areas and associated with adverse health outcomes. NO2 is formed through fossil fuel combustion and often viewed as an indicator of traffic-related pollution. The short lifetime of NO2 in the atmosphere (i.e., hours) results in high levels where it is emitted and lower levels elsewhere. Often, areas with the greatest emissions and highest levels of NO2 are communities with higher proportions of racial and ethnic minorities and residents with lower socioeconomic status. The resultant inequitable distribution of NO2 presents environmental justice concerns, and NO2 is now included in EPA EJScreen due to the work from the Satellite Data for Environmental Justice Tiger Team.

Is there a high-resolution NO2 concentration dataset that can be used to map disparities?

Yes! The NASA Health and Air Quality Applied Sciences team created a high-resolution dataset of surface-level NO2 concentrations globally (see “How was this NO2 dataset developed?”). It is available at the census block group level, the same resolution used by EJScreen. The figure on the right shows NO2 concentrations in block groups across the United States and in select metropolitan areas. Since traffic and industrial activities are a major source of NO2, urban areas and heavily trafficked corridors show up prominently in this figure.

The figures below depict block group NO2 for select major metropolitan areas alongside two socioeconomic variables currently included in EJScreen: percent minority population and percent low-income population. These maps illustrate intraurban variations of NO2 and highlight potential environmental justice concerns. In New York City, one readily-observable feature is the high NO2 concentrations in the South Bronx, home to a large minority and low income population. In Philadelphia, higher NO2 concentrations do not extend into the white and higher income Northwest Philadelphia; however, concentrations similar to those in Center City are found in minority and low-income Northeast Philadelphia.

Houston

NYC

Philadelphia

How do I add NO2 in EJScreen to see how it impacts specific communities?

This data in now incorporated into EJScreen! Visit the EPA EJScreen webpage and select the NO2 data.

How do I add a custom data to EJScreen to see how it impacts specific communities?

To integrate NO2 or other custom dataset into EJScreen, visit the EPA EJScreen webpage.
Click on the “Tools” icon and then “Add Map Services.”
Under the URL field, paste the following URL, which links to an ArcGIS REST Services directory: https://services.arcgis.com/HRPe58bUyBqyyiCt/arcgis/rest/services/US_NO2_Block_Groups/FeatureServer
Clicking “Add to Map” will add the block group NO2/dataset to the map.

How was this NO2 dataset developed?

Complete global estimates of surface-level NO2 concentrations at 1 km x 1 km grid resolution were described by Anenberg, Mohegh, et al. (2022). These estimates, available annually for 2005-2020, are formed by combining a land use regression model described by Larkin et al. (2017) with satellite-derived tropospheric column NO2 from NASA’s Ozone Mapping Instrument. The spatial patterns in NO2 in this dataset matched the patterns observed by EPA air pollution monitors.

We averaged this dataset to underlying census block groups in the United States using boundaries from the 2010 decadal census, obtained from the U.S. Census Bureau’s TIGER/Line geodatabase, and the methodology described by Kerr et al. (2021). About a fifth of census block groups are smaller than the 1 km x 1 km resolution of the gridded NO2 dataset. For these small units, we apply inverse distance weighting to interpolate NO2 concentrations to the centroid of these units. Interpolating NO2 concentrations to block groups that are much smaller than 1 km2 may smooth over fine scale NO2 gradients. Grid cells over water in the gridded NO2 dataset are assigned a missing value. A small number of block groups are primarily located over water (e.g., estuaries, reservoirs, etc.) and therefore have a missing value for NO2 in the census block group version of this dataset.

Who developed this dataset?

This work was funded through the NASA Health and Air Quality Applied Sciences Team “Satellite Data for Environmental Justice” tiger team, led by Susan Anenberg (George Washington University) and Qian Xiao (University of Texas). Arash Mohegh, Dan Goldberg, and Susan Anenberg (George Washington University) created the global gridded NO2 dataset, working with a team of collaborators from Oregon State University and Institute for Health Metrics and Evaluation. Gaige Kerr (George Washington University) regridded this dataset to census block groups. Colleen Heck (University of Wisconsin-Madison) generated the ArcGIS REST Services for the NO2 dataset to facilitate its integration in EJScreen. Emily Richardt (George Washington University) created the visualizations shown on this page.