###### Abstract  
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 (NO\({}_{2}\), SO\({}_{2}\), CO, O\({}_{3}\)) 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 \(\pm\) 21.6 \(\upmu\)g m\({}^{-3}\)) at urban/industrial stations and lower (mean of 21.3 \(\pm\) 18.4 \(\upmu\)g 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 R\({}^{2}\) = 0.46-0.76. The validation analysis reveals that the RF model (R\({}^{2}\) = 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. PM\({}_{2.5}\); Himawari-8; random forest; support vector regression; air pollution; Malaysia +
Footnote †: journal: Applied Sciences  
outflow [10] that escalates the pollution levels due to trans-boundary aerosol transport. Air quality in Malaysia is considered rather degraded, as the annual mean PM\({}_{2.5}\) concentration is about 20 ug m\({}^{-3}\) in 2019 [11], thus exceeding the limit set by WHO. Local sources such as traffic and industrial emissions, as well as biomass burning significantly contribute to the local pollution and aerosol loading [12; 13; 14]. According to the Department of Statistics Malaysia [15], chronic lower respiratory diseases (CLRD) is the fifth leading cause of death in Malaysia that was increased from 2.0% to 2.6% between the years 2017 and 2018. In fact, chronic exposure to PM\({}_{2.5}\) has significant impacts on human health by causing asthma, chronic obstructive pulmonary disease, lung cancer, cardiovascular and neurotoxic effects [16; 17]. Therefore, continuous monitoring of the levels and mapping of spatial distribution of PM\({}_{2.5}\) is especially important for taking appropriate actions to maintain a good air quality over Malaysia [18; 19]. Nowadays, many satellites provide reliable AOD (aerosol optical depth) products that can be used through various techniques for PM estimations from space, such as the Advanced Very High Resolution Radiometer (AVHRR) [20; 21], Multiangle Imaging Spectro-radiometer (MISR) [22], Medium Resolution Imaging Spectrometer (MERIS) [23], Spinning Enhanced Visible and Infrared Imager (SEVIRI) [24] and Moderate Resolution Imaging Spectroradiometer (MODIS) [25; 26; 27; 28]. Among the satellite data products, AOD provided by MODIS sensor on board Terra and Aqua satellites has been widely used for many applications due to its high retrieval accuracy over land and near daily global coverage at 10 km and 3 km spatial resolution [26; 27; 29].