Investigating the relationship between particulate matter (PM2.5) concentration and aerosol optical depth (AOD) over Thailand during the dry period

Abstract Air pollution, particularly the presence of Particulate Matter ( PM 2.5), poses significant risks to public health and environmental sustainability. In Thailand, where air quality concerns are prominent, understanding the relationship between PM 2.5 concentration and Aerosol Optical Depth (AOD) during the Dry Period is crucial. This research explores the temporal and spatial variations in PM 2.5 levels and their correlation with AOD data collected from 2016-2021. Utilizing a simple linear regression model, PM 2.5 and AOD data were analysed across five distinct time periods to establish empirical relationships. The analysis revealed varying performance metrics for different time periods and years. R 2 values ranged from 0.43 to 0.79, indicating the extent to which AOD explains PM 2.5 variance. Root Mean Square Error ( RMSE ) values ranged from 7.25 to 22.94, reflecting the average difference between observed and predicted PM 2.5 concentrations. The findings underscored the complexity of the relationship between PM 2.5 and AOD, with model underestimation during certain periods and overestimation during others. Temporal evaluation during the dry seasons of 2019-2020 and 2020-2021 revealed distinct trends in PM 2.5 concentration dynamics. The model consistently underestimated PM 2.5 concentrations during November - January, December - February, and November - April periods, while overestimating during February - April. Spatial assessment indicated that while the model captured overall spatial patterns, it tended to underestimate PM 2.5 levels at stations with high concentrations Furthermore, the research compared PM 2.5 concentrations between the two dry seasons and noted a significant decrease during the 2020-2021 period, possibly attributed to reduced anthropogenic activities during the COVID-19 pandemic. In summary, this research investigates the relationship between PM 2.5 and AOD in Thailand during the dry season, employing remote sensing methods, and highlights significant findings through a simple linear regression analysis. The research contributes to informed decision-making by policymakers and environmental agencies, facilitating the development of effective measures to mitigate air pollution and promote public health and environmental sustainability in future.