Evaluation of Machine Learning Models for Estimating PM_2.5 Concentrations across Malaysia

Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM_2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO₂, SO₂, CO, O₃) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM_2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM_2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m⁻³) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m⁻³) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM_2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM_2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R² = 0.46–0.76. The validation analysis reveals that the RF model (R² = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM_2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM_2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.