Estimation of hourly one square kilometer fine particulate matter concentration over Thailand using aerosol optical depth - Punpukdee, Pongsakon; Winijkul, Ekbordin; Kyaw, Pyae Phyo; Virdis, Salvatore G. P.; Xue, Wenchao; Nguyen, Thi Phuoc Lai (Frontiers in Environmental Science, 2024)
In the recent years, concentration of fine particulate matter that are 2.5 microns or less in diameter (PM2.5) in Thailand has consistently exceeded the national ambient air quality standard. Currently, the measurement of PM2.5 concentration relies on air quality monitoring stations operated by the Pollution Control Department of Thailand (PCD). However, these stations are insufficient, particularly in rural areas, where agricultural open burning are major sources of pollution after harvesting period. This study aims to enhance the monitoring of PM2.5 concentration by leveraging cost-effective technologies. We propose the integration of satellite data, specifically Aerosol Optical Depth (AOD) from Multi-Angle Atmospheric Correction (MAIAC) product and Himawari-8 satellites, with the Weather Research & Forecasting Model (WRF) data, to provide supplementary data to the ground-based monitoring.Hourly 5×5 km 2 AOD data from Himawari-8 were downscaled to a high-resolution of 1×1 km 2 , leveraging the AOD distribution pattern of the concurrent MAIAC product using eXtreme Gradient Boosting (XGBoost) model. Notably, during Thailand's rainy season (May to August), the study observed a relative reduction in the training model's R-square value. This phenomenon is attributed to temporal discrepancies between Himawari-8 and the MAIAC products during this period. The predictive models of PM2.5 concentrations with the identification of pertinent variables through Pearson's correlation analysis and recursive feature elimination, driven by the robust XGBoost model. Subsequently, the downscaled AOD, wind speed, temperature, and pressure were identified as predictors for the estimation of hourly PM2.5 concentration. This comprehensive approach enabled the projection of PM2.5 levels across Thailand, encompassing over 600,000 grids at 1×1 km 2 resolution.The developed models, thus, offer a valuable tool for robust and high-resolution PM2.5 concentration estimation, presenting significant implications for air quality monitoring and management in Thailand.