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Robust estimates of human exposure to inhaled air pollutants are necessary for a realistic appraisal of the risks these pollutants pose, and for the design and implementation of strategies to control and limit those risks. However, these tasks are challenging in Bangkok due to few number of official air quality sensors and huge city administrative areas. This research couples data from government official air quality sensors with multiple data sources from ride-hailing, satellite measurement, transportation, official statistics, and meteorological information to infers daily air quality index in three months sample of high, normal, and low seasons for whole Bangkok city at 1km x 1km spatial resolution. The best model shows 0.6 r2 performance using a Land Use Regression (LUR) approach.