Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models

Air pollution assessment in urban and rural areas is really challenging due to high spatio-temporal variability of aerosols and pollutants and the uncertainties in measurements and modelling estimates. Nevertheless, accurate determination of the pollution sources and distribution of PM2.5 concentrations is especially important for source apportionment and mitigation strategies. This study provides estimates of PM2.5 concentrations across Malaysia in high spatial resolution, based on multi-satellite data and machine learning (ML) models, namely Random Forest (RF), Support Vector Regression (SVR) and extreme Gradient Boosting (XGBoost), also covering remote areas without measurement networks. The study aims to develop ML models that are simpler than previous works and demonstrate computational efficiency. Six sub-models were developed to represent different locations and seasons in Malaysia. Model 1 includes all data from 65 air-quality stations, Models 2 and 3 characterize urban/industrial and suburban sites, respectively, while Models 4 to 6 correspond to dry, wet, and inter-monsoon seasons, respectively. The RF technique exhibited slightly better performance compared to the XGBoost and SVR approaches. More specifically, for model 1, it exhibited a high correlation with a coefficient of determination (R2) of 0.64 and RMSE of 12.17 μg m−3, while similar results were obtained for models 3, model 4 and model 5. The lower performance (R2 = 0.16–0.94) observed in the wet and inter-monsoon seasons is due to fewer number of data used in model calibration. Integration of two AOD products from the Advanced Himawari Imager (AHI) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors together with gases pollutants from Sentinel 5P enabled seamless seasonal PM2.5 mapping over Malaysia, even for a short period of time. However, usage of data with insufficient information during the model training procedure, and lack of satellite data due to cloud contamination, can limit the PM2.5 prediction accuracy.