## 5 Conclusions and Future Work  
In this study, ground base measurements of pollutants, meteorological, and predictive data from CMAQ models are concatenated after analyzing the dependent features that affect the concentration of PM\({}_{2.5}\). We estimate the hourly values of PM\({}_{2.5}\) concentration by applying various well-known machine learning models. In our network training process, we input these features to ML models in order to get next 1 h prediction, while the past 24 h data are provided. Due to spatial and temporal constraints, each station gives different prediction results, therefore, average evaluation values are calculated for all sites. The results show that a well-optimized LSTM network performs better than any other models used in the study. Although ML models and specifically RNNs have the ability to map temporal features, it is very important to analyze the data first, which is then followed by optimizing the model. The advantages of pollutants forecasting using ML models include:  
1. The time, effort, and cost to collect and measure the data from ground based stations or from any other sensors are reduced. 2. In the case of any defect or failure of measuring equipment or sensors, there would be missing data that can be generated by ML models in limited resources and time using past data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **XGB** & **LGBM** & **GRU** & **CNNLSTM** & **BiULSTM** & **LSTM** \\ \hline S1 & 4.6443 & 6.8807 & 5.0399 & 4.4284 & 3.9199 & 3.6984 \\ S2 & 5.7573 & 7.7821 & 9.1054 & 5.4990 & 5.2247 & 5.6022 \\ S3 & 5.2859 & 5.1491 & 4.4978 & 4.3430 & 4.0414 & 3.9562 \\ S4 & 6.4752 & 6.8254 & 9.2188 & 7.1382 & 6.1509 & 5.8684 \\ S5 & 5.9329 & 6.0015 & 4.8898 & 5.2103 & 4.8453 & 4.6836 \\ S6 & 6.6210 & 7.0275 & 5.6119 & 5.6495 & 4.9314 & 4.7032 \\ S7 & 6.4090 & 6.4751 & 5.7920 & 5.5931 & 6.0191 & 5.6939 \\ S8 & 6.1043 & 6.1334 & 5.6282 & 5.0527 & 4.4913 & 4.4278 \\ Mean & 5.9037 & 6.5343 & 5.3546 & 5.3643 & 4.9168 & 4.8292 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Models results for _RMSE_ (ug/m\({}^{3}\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **XGB** & **LGBM** & **GRU** & **CNNLSTM** & **BiULSTM** & **LSTM** \\ \hline S1 & 0.8475 & 0.8330 & 0.9008 & 0.8729 & 0.9062 & 0.9155 \\ S2 & 0.8027 & 0.8218 & 0.664 & 0.8415 & 0.8527 & 0.8557 \\ S3 & 0.8369 & 0.8457 & 0.899 & 0.8908 & 0.9066 & 0.9094 \\ S4 & 0.7402 & 0.7091 & 0.5379 & 0.7191 & 0.8119 & 0.827 \\ S5 & 0.8599 & 0.8575 & 0.9099 & 0.8987 & 0.9123 & 0.9151 \\ S6 & 0.8755 & 0.8608 & 0.9262 & 0.9106 & 0.9388 & 0.9328 \\ S7 & 0.8484 & 0.8464 & 0.8957 & 0.8891 & 0.8892 & 0.9036 \\ S8 & 0.8692 & 0.8689 & 0.9102 & 0.9120 & 0.9303 & 0.9324 \\ Mean & 0.8350 & 0.8304 & 0.8640 & 0.8668 & 0.8927 & 0.8989 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Models results for \(R\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **XGB** & **LGBM** & **GRU** & **CNNLSTM** & **BiULSTM** & **LSTM** \\ \hline S1 & 0.9116 & 0.8994 & 0.885 & 0.9114 & 0.9381 & 0.9465 \\ S2 & 0.8803 & 0.8934 & 0.5761 & 0.8892 & 0.9065 & 0.9006 \\ S3 & 0.9074 & 0.9097 & 0.9281 & 0.9357 & 0.9441 & 0.9472 \\ S4 & 0.8495 & 0.8193 & 0.4937 & 0.7678 & 0.8697 & 0.8863 \\ S5 & 0.9171 & 0.9161 & 0.9433 & 0.9363 & 0.949 & 0.9516 \\ S6 & 0.9299 & 0.9186 & 0.9447 & 0.9477 & 0.9616 & 0.9662 \\ S7 & 0.9122 & 0.9124 & 0.9205 & 0.9313 & 0.9232 & 0.9335 \\ S8 & 0.9245 & 0.9249 & 0.9294 & 0.9477 & 0.9617 & 0.9624 \\ Mean & 0.9041 & 0.8992 & 0.8905 & 0.9084 & 0.9334 & 0.9368 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Models results for _IA_. As other pollutants such as NO\({}_{2}\), ozone, and PM\({}_{10}\) are also correlated with the concentration of PM\({}_{2.5}\), ML models can predict their values as well. In a nutshell, ML models can be applied in the development of forecasting systems, especially in weather and pollutants concentration predictions. In the future, we will try to overcome the limitations discussed in Section 4 to get better forecasting results.