Impacts of rainfall on PM2.5 concentrations in Ho Chi Minh City, 2016−2022

Air pollution, particularly fine particulate matter (PM2.5), remains a critical issue in urban areas within tropical monsoon regions, such as Ho Chi Minh City (HCMC). This study utilized PM2.5 data the monitoring station at the United States Consulate General HCMC and meteorological variables (rainfall, air temperature, relative humidity, and wind speed) collected by local meteorological agencies between 2016 and 2022. Bayesian linear regression models with variable selection were employed to examine the relationships between meteorological factors and PM2.5 concentrations, focusing on lag effects across various time scales, and comparing seasonal and non-seasonal models. The results revealed a strong inverse relationship between rainfall and PM2.5, with significant reductions observed in the first few hours after rain (lag0 and lag1), particularly within the immediate aftermath of rainfall, where the impact is most pronounced. Temperature and wind speed were identified as critical factors in reducing PM2.5 concentrations, especially during the dry season, where their effects were more substantial and longer-lasting. Seasonal models outperformed non-seasonal models, underscoring the importance of including seasonal variations for accurate PM2.5 predictions. The study also demonstrated the value of Bayesian variable selection in improving regression models by identifying key meteorological factors and their cumulative effects. These findings provide a scientific foundation for developing season-specific air quality management strategies in tropical monsoon regions.