## 5 Results and Discussion
### Models for PM\({}_{2.5}\) Estimation
Both models performed better at urban/industrial sites compared to the suburban sites, while all models underestimated the large PM\({}_{2.5}\) concentrations (PM\({}_{2.5}\) > 60 ug m\({}^{-3}\)). For PM\({}_{2.5}\) below 50 ug m\({}^{-3}\), where the vast majority of the data points lie, the underestimated and overestimated data points are almost equal for all the models and the regression line tends to coincide with the 1-1 line (Figure 4). In addition, four temporal models were also developed for each season in Malaysia, namely, dry season (June-September; Figure 5a,b), wet season (November-March; Figure 5c,d) and two inter-monson seasons (April-May; Figure 5e,f and October; Figure 5g,h). The total number of data for the temporal models is 4223, 5797, 2441 and 915 for the dry season, wet season, April-May and October, respectively. These datasets were randomly divided into calibration and validation groups, and the results of the statistical evaluations of the seasonal PM\({}_{2.5}\) predictions are included in Supplementary Materials Table S2 (models 4-7). The estimation accuracy of PM\({}_{2.5}\) concentrations via the SVR and RF models varied between the seasons, while it was relatively lower in the wet and inter-monson (April-May) seasons for both models. For instance, the R\({}^{2}\) values for the calibration dataset of the SVR model were 0.81, 0.62, 0.58 and 0.74 for the dry, wet seasons, April-May and October, respectively. The RF models exhibited slightly lower \(RMSE\) values compared to the SVR models, as 13.61 ug m\({}^{-3}\), 9.99 ug m\({}^{-3}\), 8.36 ug m\({}^{-3}\) and 12.41 ug m\({}^{-3}\) against 14.0, 10.9, 9.38 and 12.46 ug m\({}^{-3}\) for dry, wet, April-May and October, respectively. Furthermore, RF models displayed higher \(NSE\) values compared to SVR, while the PM\({}_{2.5}\) underestimations from the SVR models (negative \(MBE\) values) are eliminated by using the RF models (Supplementary Materials Table S2; Figure 5). The statistical evaluators of the developed models in Malaysia are mostly comparable to those found from multi-variate models including AOD and several meteorological parameters (Temp, RH, WS, Dew point, mixing height) for PM\({}_{2.5}\) estimations in Indian cities [139]. More recently, [39] developed a deep neural network consisted of recurrent layers for extracting the relationship between high-resolution (1 km) MODIS observations and PM\({}_{10}\), PM\({}_{2.5}\) concentrations in Tehran, Iran. The PM\({}_{2.5}\) and PM\({}_{10}\) estimations resulted in \(RMSE\) values of 11.66 ug m\({}^{-3}\) and 23.79 ug m\({}^{-3}\), respectively, comparable to the current results and previous \(RMSE\) values for PM\({}_{10}\) estimations in Malaysia (11.61 ug m\({}^{-3}\); [18]) and Delhi (18.99 ug m\({}^{-3}\); [140]). MODIS-MAIAC AODs and columnar water vapor (CWV), along with meteorological parameters and land-use data, were included in a linear mixed effect model (LME) and a RF model for daily PM\({}_{2.5}\) estimations at high spatial resolution (1 km \(\times\) 1 km) over the Indo-Gangetic Plains (IGP), India [118]. The RF model exhibited higher accuracy with R\({}^{2}\) = 0.87 and relative \(RMSE\) of 24.5%, compared to LME [118]. The spatial distributions of the R\({}^{2}\) (~0.6 to 0.9) and \(RMSE\) (~20 to 40 ug m\({}^{-3}\)) values from PM\({}_{2.5}\) estimations across the IGP [118] were mostly comparable to those observed over Malaysia. The hourly time series of the measured and predicted PM\({}_{2.5}\) concentrations via the SVR model, separately for the station characteristics and seasons and for the overall model, are shown in Figure 6. The results verify the good performance of the SVR model in predicting the PM\({}_{2.5}\) concentrations across Malaysia--RF performed slightly better with very similar results--also revealing an underestimation at the highest PM\({}_{2.5}\) values.