PM2.5 modeling based on CALIPSO in bangkok

Abstract

Air quality has become a severe issue in Bangkok, mainly due to PM2.5 (fine particulate matter with particle size less than 2.5 μm). Aerosol optical depth (AOD) obtained for active satellite data has been widely used to estimate PM2.5 near the ground. Nevertheless, passive satellite data are rarely used to estimate PM2.5 near the ground. In this study, a total AOD in troposphere data achieved from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) was used to determine PM2.5 with climate parameters (Temperature (TEM), relative humidity (RH), wind speed (WS), boundary layer height (BLH), and the normalized difference vegetation index (NDVI) using Linear Mixed Effect Method (LMEM). It was found that the coefficient (R2) increases from model 1 (0.87) to model 6 (0.99), and the root mean square error (RMSE) reduces from 2.65 to 0.00 μg/m3. The best model gives an R2=0.99 (models 5 and 6). PM2.5 patterns between observed and predicted show similar representative patterns. Therefore, our study provides CALIPSO AOD data with a potentially helpful estimation of PM2.5. GRAPHICAL ABSTRACT HIGHLIGHTS PM2.5 concentrations were obtained using CALOPSO satellite data PM2.5 model based on AOD, climate and other parameters R2 of PM2.5 model obtained from the best model more than 0.99