## 5 Results and Discussion
### Models for PM\({}_{2.5}\) Estimation  
In this study, seven models were developed for PM\({}_{2.5}\) estimations in Malaysia using ML techniques, namely, SVR and RF. These models were developed in order to better capture the remarkable spatial (between stations of different characteristics) and temporal (between seasons) variations of PM\({}_{2.5}\) and to examine the model's capability in representing the levels and evolution of PM\({}_{2.5}\). The developed models are:  
Model 1: Overall model;  
Model 2: Spatial model (urban/industrial);  
Model 3: Spatial model (suburban/rural);  
Model 4: Temporal model (dry season);  
Model 5: Temporal model (wet season);  
Model 6: Temporal model (inter-monsoon, April-May);  
Model 7: Temporal model (inter-monsoon, October). The models' inputs consist of AOD, SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\), WS, WD, TEMP and RH, with the total number of the matching samples for all variables (overall model) to be 13,376 at the 65 monitoring stations from January 2018 to December 2019. The scatter plots between measured and predicted PM\({}_{2.5}\) concentrations via the SVR and RF models are shown in Figures 4 and 5 for the validation of the 7 developed models, while Supplementary Materials Table S2 summarizes the statistical indicators of R\({}^{2}\), \(RMSE\), \(MBE\) and \(NSE\), as well as the importance ranking, for each input variable at the 7 developed models. The statistical indicators correspond to the averaged values of each model for the given number of data (N). Figure 4: Validation of the predicted PM\({}_{2.5}\) concentrations against measured PM\({}_{2.5}\) using SVR (left) and RF (right) for overall Model 1 **(a,b)**, urban/industrial Model 2 **(c,d)**, suburban/rural Model 3 **(e,f)**. Frequency indicates the density of data/count. The statistical indicators are presented as averaged values in each case. The evaluation of the calibration datasets for the overall model 1 showed that the SVR model yielded comparable accuracy (R\({}^{2}\) = 0.69; \(RMSE\) = 10.62 ug m\({}^{-3}\), \(NSE\) = 0.679 and \(MBE\) = \(-\)1.392) to RF (R\({}^{2}\) = 0.66; \(RMSE\) = 11.28 ug m\({}^{-3}\), \(NSE\) = 0.939 and low \(MBE\) = 0.066) (Supplementary Materials Table S2). The validation dataset displayed also small differences between the two models and comparable statistics with the calibration datasets i.e., R\({}^{2}\) = 0.66 and \(RMSE\) = 12.11 ug m\({}^{-3}\) for SVR and R\({}^{2}\) = 0.62, \(RMSE\) = 11.40 ug m\({}^{-3}\) for RF (Figure 4). Figure 5: Validation of the predicted PM\({}_{2.5}\) concentrations against measured PM\({}_{2.5}\) using SVR (left) and RF (right) models in dry season (June–September) (**a**,**b**), wet season (November–March) (**c**,**d**), inter-monsoon (April–May) (**e**,**f**) and inter-monsoon (October) (**g**,**h**). Frequency indicates the density of data/count. The statistical indicators are presented as averaged values in each case. Furthermore, other models for estimating PM\({}_{2.5}\) were also developed by splitting the entire datasets initially into two categories namely urban/industrial (3357 data points) and suburban/rural (\(N\) = 9798). The RF model performed slightly better compared to SVR for urban/industrial (model 2) and suburban (model 3) sites, with R\({}^{2}\) = 0.76, \(RMSE\) = 11.47 ug m\({}^{-3}\), \(NSE\) = 0.735 and R\({}^{2}\) = 0.67, \(RMSE\) = 12.47 ug m\({}^{-3}\), \(NSE\) = 0.661, respectively. Regarding the calibration datasets, the \(NSE\) values of all the RF models were usually above 0.9, indicating an accurate model's performance, compared to the SVR models, which exhibited NSE values in the range of 0.55 to 0.79 for the calibration datasets (Supplementary Materials Table S2). The model validation revealed that RF models performed slightly better than the SVR models for both locations. The validation of SVR and RF models for suburban sites was considered satisfactory with R\({}^{2}\) = 0.61, \(RMSE\) = 11.53 ug m\({}^{-3}\) and R\({}^{2}\) = 0.64, \(RMSE\)= 10.76 ug m\({}^{-3}\), respectively (Figure 4b,c).