NDLI: Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter $(PM_{2.5}$ and $PM_{10}$) Concentrations

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Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter $(PM_{2.5}$ and $PM_{10}$) Concentrations

Content Provider	MDPI
Author	Hong, Wan Yun Koh, David Yu, Liya E.
Copyright Year	2022
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 PM_2.5 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 ( $R^{2}$ = 0.94 and RMSE = 0.05 µg/m³ for PM_2.5, and $R^{2}$ = 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 ( $R^{2}$ = 0.92 and RMSE = 0.04 µg/m³ for PM_2.5, and $R^{2}$ = 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.
Starting Page	7728
e-ISSN	16604601
DOI	10.3390/ijerph19137728
Journal	International Journal of Environmental Research and Public Health
Issue Number	13
Volume Number	19
Language	English
Publisher	MDPI
Publisher Date	2022-06-23
Access Restriction	Open
Subject Keyword	International Journal of Environmental Research and Public Health Water Science and Technology Pm2.5 Pm10 Statistical Modelling Machine Learning Brunei Darussalam Singapore
Content Type	Text
Resource Type	Article

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