## 4 Discussion  
After describing all the selected papers, we created a comparison table by extracting the main features of the papers (Table 2). Table 2 includes _Year_, _Case Study_, _Methods_, _Algorithms_, _Evaluation Metrics_, _Prediction Target_, _Time Granularity_, _Data Rates_, _Dataset Types_, _Open Data_, _Advantages_ and _Limitation/Future Work_. _Year_: as we have already seen (Section 3.1), Figure 2 displays the evolution of the publications over the years. We can see a significant increase since 2014-2015, which can be explained with the appearance of smart cities and open data portals notions in science. _Algorithms_: having information about the distribution of the publications per each machine learning algorithm (Figure 4), it would be interesting to know how the usage of the algorithms changed throughout the years. Figure 5 presents the publications for each machine learning algorithm over the years. It can be noted that, in recent years, the number of publications (used neural network, ensemble and hybrid models) has an increasing trend, which is not applicable to the regression method. The latter one has been applied since 2002 almost with the unchangeable trend, moreover, in recent publications, the regression method was mainly applied along with other algorithms for comparison purposes. Figure 5: Number of publications per machine learning algorithms throughout the years. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Work** & **Year** & **Case Study** & **Methods** & **Algorithms** & **Evaluation Metrics** & **Prediction Target** & **Time Granularity** & **Data Rates** \\ \hline _[121]_ & 2019 & China & SLI-ESN, mRMR & NN & RMSE, NRMSE, MAE, & PM\({}_{2.5}\) & 1 h,5 h, 10 h & Hourly \\ _[131]_ & 2019 & China & IDW-BLSTM & NN & RMSE, MAE, MAPE & PM\({}_{2.5}\) & 1 h,24 h, 1 week & Hourly \\ _[532]_ & 2019 & China & LightGBM & Ensemble & SMAPE, MSE, MAE & PM\({}_{2.5}\) & 24 h & \(N/S\) \\ _[140]_ & 2019 & China & CRCRU & NN & RMSE, MAE, SMAPE & PM\({}_{2.5}\) & 2 h & Hourly \\ _[139]_ & 2019 & China & DTR, RF, MLP, GGR & Ensemble, Regression & MAE, RMSE & PM\({}_{2.5}\) & 1 week & \(N/S\) \\ _[161]_ & 2019 & Italy & ARIMA, SVM, ANN & Hybrid Model & MAE, MAPE & CO & 24 h & Hourly \\ _[177]_ & 2019 & China & AAPAP(tm-step) & NN & MAE, F2 & PM\({}_{2.5}\) & 24 h & Hourly \\ _[163]_ & 2019 & China & ESN-IFSO & Hybrid Model & RMSE, MAE, SMAPE, R & PM\({}_{2.5}\) & 1 h, 10 h & Hourly \\ _[54]_ & 2019 & South Korea & DNING-treas (renes) & Ensemble & IA & O\({}_{3}\) & 24 h & Hourly \\ _[181]_ & 2019 & Italy & ALS-RNN & NN & RMSE, MAE, MAPE & CO(T), NO\({}_{2}\)(GT) & 1 h & Hourly \\ _[165]_ & 2019 & China & SDS-LSTM-RA-LSSVM & Hybrid Model & RMSE, MAE, MAPE, R & \(N/S\) & 24 h & \(N/S\) \\ _[140]_ & 2018 & Spain & RGCRU, RF, DT(MSP), & Ensemble, Regression & MAE, RMSE, R\({}^{2}\) & O\({}_{3}\) & 24 h & Hourly \\ _[167]_ & 2018 & USA & Regularization, & & RMSE & O\({}_{3}\), PM\({}_{2.5}\), SO\({}_{2}\) & 24 h & Hourly \\ _Optimization_ & & & & & & & \\ _[53]_ & 2018 & China & MSSVC & Ensemble & ACC, MSE, WF, WR, WF & PM\({}_{2.5}\) & 1 h, 3 h, 6 h, 9 h, & Hourly \\ _[16]_ & 2018 & China & APNet(CNN-LSTM) & NN & MAE, RMSE, R, IA & PM\({}_{2.5}\) & 1 h & Hourly \\ _[20]_ & 2018 & China & WNN & NN & R\({}^{2}\), RMSE, MAPE & PM\({}_{2.5}\) & 1 h, 3 h, 6 h & Hourly \\ _[21]_ & 2018 & China & DNN & NN & R, MAE & PM\({}_{2.5}\) & 1 h & Daily \\ _[22]_ & 2018 & China & DNN & NN & ACC, MAE & PM\({}_{2.5}\) & 6 h, 12 h, 24 h, 48 h & Hourly \\ _[64]_ & 2018 & China & CEMD-ISOSA-SVR- & Hybrid Model & MAE, MAPE, RMSE, R, IA & PM\({}_{2.5}\) & 24 h & Daily \\ _[15]_ & 2018 & China & STE & Ensemble & RMSE, MAE, ACC & PM\({}_{2.5}\) & 6 h, 12 h, 24 h, 48 h & Hourly \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features of the selected papers. \(N/S\): Not Specified.