Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data.

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 μm (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.