Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations

Abstract

Despite extensive research on air pollution estimation/prediction, inter-country models for estimating air pollutant concentrations in Southeast Asia have not yet been fully developed and validated owing to the lack of air quality (AQ), emission inventory and meteorological data from different countries in the region. The purpose of this study is to develop and evaluate two machine learning (ML)-based models (i.e., analysis of covariance (ANCOVA) and random forest regression (RFR)) for estimating daily PM2.5 and PM10 concentrations in Brunei Darussalam. These models were first derived from past AQ and meteorological measurements in Singapore and then tested with AQ and meteorological data from Brunei Darussalam. The results show that the ANCOVA model (R2 = 0.94 and RMSE = 0.05 µg/m3 for PM2.5, and R2 = 0.72 and RMSE = 0.09 µg/m3 for PM10) could describe daily PM concentrations over 18 µg/m3 in Brunei Darussalam much better than the RFR model (R2 = 0.92 and RMSE = 0.04 µg/m3 for PM2.5, and R2 = 0.86 and RMSE = 0.08 µg/m3 for PM10). In conclusion, the derived models provide a satisfactory estimation of PM concentrations for both countries despite some limitations. This study shows the potential of the models for inter-country PM estimations in Southeast Asia.