## 3 Results and Discussion  
The models applied in this work can be grouped into two categories, namely DL models and ML models. A total of six models were applied, specifically four DL models (ANN, GRU, LSTM, and RNN) and two ML models (RF and SVR). DL models are generally more complicated and require more effort to develop. In contrast, ML models are more straightforward and designed for user-friendly applications due to having fewer parameters and hyperparameters. The air pollutants predicted in this work included PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), and CO, which caused the most health concerns and received the most public attention. Table 3 shows the model performance indicator of DL and ML models (average RMSE, MAE, PCC, and KTC) after five runs for each model. The ANN, RF, and SVR models showed the best performance in predicting PM2.5 with the highest PCC and KTC, while the GRU model showed the best performance in forecasting NO\({}_{2}\). Figure 3 shows the measured and predicted PM\({}_{2.5}\) concentrations using ANNs in 2020 and 2021, which offer a high PCC of 0.87 and a KTC of 0.71. Figure 4 shows the measured and predicted PM\({}_{2.5}\) concentration using RF in 2020 and 2021, which provides a high PCC of 0.99 and a KTC of 0.92. Figure 5 shows the SHAP value of the RF model in PM\({}_{2.5}\) prediction, demonstrating that the past days' air pollutants and weather data play a vital role in the prediction. Figure 6 shows the measured and predicted CO concentrations using SVR in 2020 and 2021, which offers a high PCC of 0.72 and a KTC of 0.51. Although the SVR model for CO did not receive the highest PCC and KTC, it provides the lowest RMSE and MAE (0.28 and 0.24, respectively). Figure 7 shows the measured and predicted NO\({}_{2}\) concentrations using GRU in 2020 and 2021, which offers a high PCC of 0.80 and a KTC of 0.62. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Model Parameters and Hyperparameters** \\ \hline \multirow{3}{*}{LSTM} & optimizer & adam \\  & epochs & 20 \\  & batch size & 64 \\ \hline \multirow{3}{*}{RF} & n\_estimators & 100 \\  & criterion & squared error \\ \cline{1-1}  & min\_sample\_split & 2 \\ \cline{1-1}  & max\_depth & none \\ \hline \multirow{3}{*}{RNN} & optimizer & adam \\  & layers & 100 \\ \cline{1-1}  & epochs & 500 \\ \cline{1-1}  & batch size & 32 \\ \hline \multirow{3}{*}{SVR} & kernal & linear \\ \cline{1-1}  & degree & 3 \\ \cline{1-1}  & gamma & scalar \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Pollutant**} & \multicolumn{4}{c}{**Model Performance Indicators**} \\ \cline{4-7}  & & & **RMSE** & **MAE** & **PCC** & **KTC** \\ \hline \multirow{3}{*}{Deep Learning Model} & \multirow{3}{*}{ANN} & PM\({}_{10}\) & 16.15 & 12.63 & 0.84 & 0.67 \\  & & **PM\({}_{\mathbf{2.5}}\)** & 6.94 & 5.13 & **0.87** & **0.71** \\  & & NO\({}_{2}\) & 13.34 & 10.16 & 0.82 & 0.64 \\  & & CO & 0.58 & 0.51 & 0.37 & 0.25 \\ \hline \multirow{3}{*}{Deep Learning Model} & \multirow{3}{*}{GRU} & PM\({}_{10}\) & 24.26 & 19.19 & 0.74 & 0.55 \\  & & PM\({}_{2.5}\) & 12.18 & 9.19 & 0.73 & 0.56 \\ \cline{1-1}  & & **NO\({}_{2}\)** & 15.07 & 12.03 & **0.80** & **0.62** \\ \cline{1-1}  & & CO & 0.38 & 0.30 & 0.61 & 0.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance indicator of DL and ML models in forecasting air pollutants (PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), CO), trained using 2013 to 2019 data and validated with 2020 and 2021 data. Figure 4: Measured and predicted PM\({}_{2.5}\) concentrations using RF for 2020 and 2021. Figure 3: Measured and predicted PM\({}_{2.5}\) concentrations using ANN for 2020 and 2021.