Particulate Matter (PM) pollution is one of the most important air quality concerns in Vietnam. In this study, we integrate ground-based measurements, meteorological and satellite data to map temporal PM concentrations at a 10×10 km grid for the entire of Vietnam. We specifically used MODIS Aqua and Terra data and developed statistically-significant regression models to map and extend the ground-based PM concentrations. We validated our models over diverse geographic provinces i.e., North East, Red River Delta, North Central Coast and South Central Coast in Vietnam. Validation suggested good results for satellite-derived PM 2.5 data compared to ground-based PM 2.5 (n=285, r 2 =0.411, RMSE=20.299 μg m −3 and RE=39.789%). Further, validation of satellitederived PM 2.5 on two independent datasets for North East and South Central Coast suggested similar results (n=40, r 2 =0.455, RMSE=21.512 μg m −3 , RE=45.236% and n=45, r 2 =0.444, RMSE=8.551 μg m −3 , RE=46.446% respectively). Also, our satellite-derived PM 2.5 maps were able to replicate seasonal and spatial trends of ground-based measurements in four different regions. Our results highlight the potential use of MODIS datasets for PM estimation at a regional scale in Vietnam. However, model limitation in capturing maximal or minimal PM 2.5 peaks needs further investigations on ground data, atmospheric conditions and physical aspects. OPEN ACCESS RECEIVED

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