Prediction of PM2.5 concentrations in Malaysia using machine learning techniques: a review

Abstract

Particulate matter (PM), an air pollutant that is detrimental to breathing, is either emitted or formed ambiently. The exposure of respiratory system towards PM 2.5 , the fine particles of 2.5 micrometres diameter, causes complication for health. Thus, developing pollution control strategies requires the prediction of PM 2.5 concentrations. Advancement of technology and computer science knowledge, machine learning (ML) algorithms are used for highly accurate prediction of air pollutant concentrations. Recently, air quality in Smart Cities of Malaysia has been getting worse due to industrialization, emissions from private motor vehicles, and transboundary haze pollution. Therefore, the forecasting of PM 2.5 emissions to ensure they are within the statutory limits becomes necessary. Several machine learning methods have been implemented in existing research to predict air pollution concentrations in comparison to PM 2.5 . However, very few studies have used ML techniques to predict air quality in Malaysia when compared with global studies. Hence, to create awareness on the ML techniques and promote further research in this area, this study reviews and highlights most of the existing ML techniques for the prediction of PM 2.5 .