Vulnerability Assessment for Fine Particulate Matter (PM2.5) in the Schools of the Seoul Metropolitan Area, Korea: Part I - Predicting Daily PM2.5Concentrations

Abstract

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM2.5concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM2.5concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM2.5concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R2, RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R2, RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM2.5concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM2.5concentrations predictions, which can then be used to assess the vulnerability of schools to PM2.5