Z. Qu

Machine Learning Predictions of PM2.5 and Their Applications to Exposure and Health Assessment in the CONUS

Abstract Atmospheric fine particulate matter (PM2.5) is one of the most important risk factors for various respiratory and cardiovascular diseases. We develop a two‐phase data‐driven framework that uses geostationary Aerosol Optical Depth (AOD) retrievals to predict hourly PM2.5 at 0.01° × 0.01° resolution and support...