Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley - Meytar Sorek-Hamer; Meredith Franklin; Khang Chau; Michael Garay; Olga Kalashnikova (Remote Sensing, 2020)
Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R2 = 0.35, RMSE = 10.38 μg/m3) were significantly improved when spatial (R2 = 0.45, RMSE = 9.54 μg/m3), temporal (R2 = 0.62, RMSE = 8.30 μg/m3), and spatiotemporal (R2 = 0.65, RMSE = 7.58 μg/m3) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R2 = 0.60, RMSE = 8.42 μg/m3). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications.