Particulate matter concentration mapping from MODIS satellite data: a Vietnamese case study

Abstract

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 μ g m ^−3 and RE = 39.789%). Further, validation of satellite-derived 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 μ g m ^−3 , RE = 45.236% and n = 45, r ^2 = 0.444, RMSE = 8.551 μ g 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.

Keywords

• particulate matter concentration
• aerosol optical thickness
• MODIS
• Vietnam
• multiple linear regression