So Fresh, So FAIR: Updateable and FAIR Geostatistical Air Pollution Exposure Models

This sub product is an abstract for the ISEE-ISES 2025 Annual Meeting describing the creation of an exposure model suitable for evaluating health effects using EHRs. Electronic health records and modern epidemiological cohorts offer opportunities to conduct large-scale epidemiological studies focused on spatiotemporal exposures in recent years.  However, the lack of high-resolution environmental exposure data at recent temporal scales often precludes this opportunity. Continued efficacy of environment and health studies will require geospatial exposure models that are rapidly (< 1 year) updatable in a transparent, flexible, and timely manner. This study develops a new, regularly updating (So Fresh), open source (So FAIR), high-resolution, daily model for air pollution, which meets the timeliness, reproducibility, and extensibility needs of the next generation of environmental health studies

Impact/Purpose

This is an abstract for the ISEE-ISES 2025 Annual Meeting which describes the development of exposure models suitable for EHRs.

Citation

Manware, M., I. Song, E. Marques, M. Alifa, A. Singh, L. Clark, C. Ward-Caviness, AND K. Messier. So Fresh, So FAIR: Updateable and FAIR Geostatistical Air Pollution Exposure Models. ISEE-ISES Annual Meeting, Atlanta, GA, August 17 - 20, 2025.