## 4 Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Work** & **Dataset Type** & **Open Data** & **Advantages** & **Limitation/Future Work** \\ \hline _[_68_]_ & AQ, MET & NO & \(N/S\) & To use SVM, DT and hybrid algorithms to improve the accuracy in existence of imbalanced datasets, to use spatial indexing method. \\ _[_66_]_ & AQ & YES & To eliminate the non-stationary characteristics & \(N/S\) \\ _[_56_]_ & AQ, MET, WFD & YES & \(N/S\) & \(N/S\) \\ _[_14_]_ & AQ, MET, Temporal & NO & \(N/S\) & To forecast other air pollutants. \\ _[_29_]_ & AQ, MET, Temporal & NO & Precision, robustness, generalization & N/S \\ _[_30_]_ & AQ, MET, Social Media & NO & \(N/S\) & Long-term prediction is a challenging task \\ _[_42_]_ & AQ, MET, Spatial, Temporal & Partially & \(N/S\) & Lack of monitors. \\ _[_31_]_ & AQ, MET & YES & \(N/S\) & To extend the algorithm, to include more parameters. \\ _[_33_]_ & AQ, MET, Spatial & NO & \(N/S\) & To improve the precision and reduce the absolute errors. \\ _[_45_]_ & AQ, MET & YES & \(N/S\) & \(N/S\) \\ \hline \multicolumn{4}{l}{MSP outperforms others because of the tree} & To consider data changes over time for real-time structure efficiency and powerful & forecasting. For increasing data seasonality to include \\ _[_34_]_ & AQ, MET, Temporal & NO & generalization ability. & more data. \\ _[_46_]_ & AQ, MET, TIP, Temporal & YES & \(N/S\) & To include local features of the target points. \\ _[_35_]_ & AQ, MET & YES & The method is flexible and reliable. & To use ensemble methods. \\ _[_59_]_ & AQ, WFD, Chemical & Link is not available & Combined model outperforms single model & \(N/S\) \\ _[_60_]_ & AQ, MET & YES & \(N/S\) & \(N/S\) \\ _[_36_]_ & AQ, MET & YES & The short training time, the small model size and managing imbalance dataset. \\ \multicolumn{4}{l}{_[_48_]_} & AQ, MET & NO & QRM captures the contributions of covariates at different quantiles. & To use data from different monitoring sites over a longer period of time, to include traffic characteristics data. \\ _[_49_]_ & AQ, MET & YES & \(N/S\) & \(N/S\) \\ _[_50_]_ & AQ, MET & NO & Online SVM determine dynamically the optimal prediction model. & Computational problem because of dimensionality. \\ _[_38_]_ & AQ, MET & NO & \(N/S\) & To provide more effective and practical models. \\ _[_51_]_ & AQ, MET & NO & SVM is better than RBF. & To improve SVM method. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Evaluation Metrics: are the metrics used in order to measure applied methods. Figure 6 displays for each metric the number of publications where the metric was applied. We can notice that the most used metrics are RMSE (Equation (1)) and MAE (Equation (2)), each of them applied in 24 publications. \[RMSE=\left(\frac{1}{n}\sum_{i=1}^{n}(E_{i}-A_{i})^{2}\right)^{1/2} \tag{1}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|E_{i}-A_{i}| \tag{2}\]  
where \(n\) is the number of instances, \(E_{i}\) and \(A_{i}\) are the estimated and actual values. The lower value of these two metrics corresponds to a better prediction. Prediction Target: is the main pollutant in the case study, for which prediction different techniques were applied in order to monitor, to measure, and in the final step, to predict the concentration of that pollutant. Figure 7 represents the pollutants which were considered as a prediction target in the selected studies. The targets are PM\({}_{2.5}\), NO\({}_{\lambda}\), O\({}_{3}\), PM\({}_{10}\) which in early publications was mentioned as RSP, SO\({}_{2}\), CO, Suspended Particulate Matter (SPM), NMHC, C\({}_{6}\)H\({}_{6}\), black carbon (BC), and \(N/S\) is the number of publications that did not specify the prediction target. It can be noted that PM\({}_{2.5}\) is the principal pollutant, being as the prediction target in 19 studies. Figure 6: Used evaluation metrics. Figure 7: Pollutants as a prediction target. In general, the prediction of particulate matters has always been the main focus of researchers.