## 6 Conclusions  
The current study developed new machine-learning models, namely, Random Forest (RF) and Support Vector Regression (SVR), to estimate PM\({}_{2.5}\) concentrations across Malaysia for the first time covering the years 2018 and 2019. Satellite (Himawari-8) AOD, ground-measured air pollutants (NO\({}_{2}\), CO, SO\({}_{2}\), O\({}_{3}\)) and meteorological parameters (temperature, relative humidity, wind speed and direction) were used as input variables. Due to the high spatial (between stations with different characteristics like urban/industrial and suburban/rural) and temporal (between seasons) evolution of the PM\({}_{2.5}\) levels across Malaysia, seven sub-models were developed separately for the different sites (urban/industrial, suburban/rural) and seasons (dry, wet and two inter-monsoons (April-May and October)), and one overall model. Of the available dataset, 70% was randomly selected for the model calibration, and the remaining 30% for the model validation. The PM\({}_{2.5}\) predictions of each model are compared to those measured at 65 air pollution monitoring stations, using standard statistical estimators. Figure 7: Variable importance analysis for the RF Models 1–7 (**a**–**g**). Y-axis indicates the predictor for PM\({}_{2.5}\) estimations, and x-axis indicates the importance scores between 0 and 100. For the overall model, SVR calibration performed slightly better than RF with R\({}^{2}\) = 0.69 and \(RMSE\) = 10.62 u\({}_{2}\) m\({}^{-3}\) against measured PM\({}_{2.5}\) concentrations. Whilst for the spatial models, the RF validation performed slightly better than SVR, with statistical indicators of R\({}^{2}\) = 0.76, \(RMSE\) = 11.47 u\({}_{2}\) m\({}^{-3}\) for urban/industrial, and R\({}^{2}\) = 0.64, \(RMSE\) = 10.76 u\({}_{2}\) m\({}^{-3}\) for the suburban/rural sites. Therefore, both RF and SVR models displayed slightly higher performance for PM\({}_{2.5}\) estimations at urban/industrial sites with higher levels of AOD and air pollutants. Furthermore, the estimation accuracy of SVR and RF models was lower in the wet (November-March) and inter-monsoon (April-May) seasons compared to the dry (June-September) season. Based on the model accuracy and variable importance analysis, CO was always the most influential predictor variable for PM\({}_{2.5}\) estimations in Malaysia, followed by AOD, O\({}_{3}\), NO\({}_{2}\), SO\({}_{2}\) and meteorological parameters but with different order depending on the dataset and model. An important finding was the very weak correlation and contribution of the meteorological variables to PM\({}_{2.5}\) estimations. Furthermore, very low correlation was found between PM\({}_{2.5}\) and columnar AOD, indicating that surface pollution followed a different temporal pattern than AOD and the presence of a significant aerosol layer aloft due to transported smoke plumes from wildfires in southeast Asia. The current results showed that the use of machine-learning techniques for PM\({}_{2.5}\) estimations over Malaysia was promising as these models can satisfactorily represent the values and temporal evolution of PM\({}_{2.5}\) concentrations over both urban/industrial and suburban/rural sites although the underestimation of the highest PM\({}_{2.5}\) levels. In a next step, gaseous pollutants from satellite remote sensing observations will be included in ML approaches in order to estimate PM\({}_{2.5}\) concentrations over large areas, aiming to cover the whole Malaysian territory. The following are available online at [[https://www.mdpi.com/article/10.3390/app11167326/s1](https://www.mdpi.com/article/10.3390/app11167326/s1)]([https://www.mdpi.com/article/10.3390/app11167326/s1](https://www.mdpi.com/article/10.3390/app11167326/s1)), Figure S1: Residual analysis (residuals = predicted PM\({}_{2.5}\)--measured PM\({}_{2.5}\)) from SVR and RF for the developed Models 1-7.