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http://dx.doi.org/10.7780/kjrs.2021.37.6.2.10

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

Son, Sanghun (Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University)
Kim, Jinsoo (Department of Spatial Information Engineering, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.6_2, 2021 , pp. 1881-1890 More about this Journal
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 PM2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM2.5 concentrations 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 PM2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM2.5.
Keywords
$PM_{2.5}$ concentrations; Random forest; Meteorological variables; Chemical variables; Aerosol optical depth;
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