Estimating ground-level PM<inf>2.5</inf> over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS

Abstract

A number of previous studies have shown that statistical model with a combination of satellite-derived aerosol optical depth (AOD) and PM2.5 measured by the monitoring stations could be applied to predict spatial ground-level PM2.5 concentration, but few studies have been conducted in Thailand. This study aimed to estimate ground-level PM2.5 over the Bangkok Metropolitan Region in 2020 using linear regression model that incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements and other air pollutants, as well as various meteorological factors and greenness indicators into the model. The 12-fold cross-validation technique was used to examine the accuracy of model performance. The annual mean (standard deviation) concentration of observed PM2.5 was 22.37 (± 12.55) µg/m3 and the mean (standard deviation) of PM2.5 during summer, winter, and rainy season was 18.36 (± 7.14) µg/m3, 33.60 (± 14.48) µg/m3, and 15.30 (± 4.78) µg/m3, respectively. The cross-validation yielded R2 of 0.48, 0.55, 0.21, and 0.52 with the average of predicted PM2.5 concentration of 22.25 (± 9.97) µg/m3, 21.68 (± 9.14) µg/m3, 29.43 (± 9.45) µg/m3, and 15.74 (± 5.68) µg/m3 for the year round, summer, winter, and rainy season, respectively. We also observed that integrating NO2 and O3 into the regression model improved the prediction accuracy significantly for a year round, summer, winter, and rainy season over the Bangkok Metropolitan Region. In conclusion, estimating ground-level PM2.5 concentration from the MODIS AOD measurement using linear regression model provided the satisfactory model performance when incorporating many possible predictor variables that would affect the association between MODIS AOD and PM2.5 concentration.