## 5 Results and Discussion
### Models for PM\({}_{2.5}\) Estimation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Study Area**} & \multicolumn{2}{c}{**Input Data**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**R/R2**} & \multirow{2}{*}{**Accuracy**} \\ \cline{3-4} \cline{6-8}  & & **Source  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Study Area**} & \multicolumn{2}{c}{**Input Data**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**R\(\mathbf{\text{R}}^{2}\)**} & \multirow{2}{*}{**Accuracy**} \\ \cline{3-4} \cline{6-7}  & & **Source of AOD** & & & & & MLR & \(\text{R}^{2}=0.733\) & RMSE = 33.016 ug m\({}^{-3}\) \\ \hline
[141] & BTH, China & MODIS AOD 10 km (Aqua) & AT, RH, WS, WD and \(P\) & PM\({}_{2.5}\) & MARS & \(\text{R}^{2}=0.776\) & RMSE = 30.180 ug m\({}^{-3}\) \\  & & (Aqua) & & SVR & \(\text{R}^{2}=0.850\) & RMSE = 24.748 ug m\({}^{-3}\) \\  & & & & RSV & \(\text{R}^{2}=0.843\) & RMSE = 25.320 ug m\({}^{-3}\) \\ \hline
[149] & Wuhan, China & Himawari’s AOD 13 & MODIS NDVI, RH, AT, WS, SP, & PBL, DEM & PM\({}_{2.5}\) & DL & \(\text{R}^{2}=0.850\) & RMSE = 39.303 ug m\({}^{-3}\) \\ \hline
[144] & China & MAIAC AOD 1 km & TEMP, SPI competition, & & & & \\  & & avg, PM, RD, RD, SD, SD, SD, SD, SD, SD, SD, SD, SD, SD, SD, SD  
Determining the strength of the correlation between PM\({}_{2.5}\) and all the parameters used for its prediction is very important because it can indirectly portray the pollution process and the source of the pollution. The results of the variable importance analysis for the RF model have been included in Supplementary Materials Table S2 and are shown in Figure 7. For the overall model 1, CO is the highest contributor to the PM\({}_{2.5}\) estimations, similar to the other models, as discussed above, and is followed by AOD, O\({}_{3}\), NO\({}_{2}\), SO\({}_{2}\) and the meteorological parameters. This is because both PM\({}_{2.5}\) and CO are originated from common sources in Malaysia like biomass burning and traffic which enhance the CO emissions [86; 138]. Besides that, CO may stay in the atmosphere for a long period (weeks or months), being able to get transported in high concentrations from biomass-burning areas in Indonesia [152]. Parameters with the least importance in PM\({}_{2.5}\) estimations are RH, WD, TEMP and finally the WS with a zero score (Figure 7a). Similar to the overall model (model 1), the contributions of the meteorological parameters were relatively weak in the spatial models as well, which also revealed CO, AOD and O\({}_{3}\) as the most important variables (Figure 7b,c). CO remains the most important predictor in the seasonal models as well, with minimal contributions from the meteorological variables (Figure 7d-g). The WS and WD have minimal contributions, in agreement with [81], who found that both parameters were not significantly correlated with PM\({}_{2.5}\) in the Klang Valley region in Peninsula Malaysia. Although AOD usually exhibited a high correlation with PM\({}_{2.5}\)[153], in our case, there was not a direct association with PM\({}_{2.5}\) concentrations, implying complicated pollution conditions in the vertical layer over Malaysia [141]. Generally, PM\({}_{2.5}\) is affected more by gaseous pollutants and not so much by the columnar AOD (missing values due to cloud cover and elevated aerosol layers) and meteorological conditions since Malaysia has rather uniform weather conditions throughout the year. Therefore, influence of the meteorological parameters is minimal towards seasonally changing PM\({}_{2.5}\) concentrations. However, previous studies in Malaysia showed that the meteorological parameters affected the coarse particles, e.g., PM\({}_{10}\) concentrations [81], indicating a meteorological-dependent character of the coarse-mode aerosols. In our previous study [18], we found that the estimations of PM\({}_{10}\) concentrations based on satellite AODs were significantly improved after inclusion of the meteorological parameters in the model.