Exposure Assessment
The Exposure Assessment Core (EAC) aims to provide expertise and services on the use of novel, big geospatial datasets and associated statistical analyses for a variety of climate-health research applications, including epidemiology, risk analysis, disease prediction, hazard forecasting, burden of disease assessment, future projections under emission scenarios, policy analysis, and equity analysis.
The Core’s expertise spans across environmental exposures (e.g., air pollution, extreme heat), novel datasets (e.g., high-resolution models, satellite remote sensing), and methodologies.
Core Services
- Assisting Center Investigators in selecting, integrating, and interpreting novel geospatial datasets that are appropriate for their research questions and needs
- Spatiotemporally aligning geospatial datasets with health data in support of actionable research on climate and health solutions
- Conducting appropriate statistical analyses, responsive to associated limitations and uncertainties of the datasets
- Advising and consulting on data usage
To date, the Core has worked with Center investigators and members of the larger climate and health community to support their research projects, internal and extramural funding applications, and translational research. As examples of this multidisciplinary work, the Core has provided fit-for-use datasets, consultations, and analyses related to (1) extreme precipitation to explore maternal and pediatric health outcomes in Africa; (2) satellite-derived measures of air pollution and heat to understand school absenteeism in the greater Washington, DC region; and (3) high-resolution traffic data to locate diesel hotspots in major U.S. urban areas.
You can request Exposure Assessment Core services by completing the Core Services Request Form.
Core Staff

Pramita Bagchi
Core Co-Investigator | George Washington University

Gaige Kerr
Core Co-Investigator | George Washington University

Daniel Tong
Core Director | George Mason University

Joseph Wilkins
Core Co-Director | Howard University