###### Abstract
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Authors** & **Methods** & **Model, Accuracy** & **Main Conclusions** \\  & & **Data’s Features** & \\ \hline
[1] & [2]: SARIMA, ANN, FTS & [2] ANN & - The most popular statistical method uses (AI) \\ [PERSON], et al. (2018). & [3]: FFMLP, MLP-GA, MLP & MAE: 2.70 & models. \\  & [4]: MLP & MSE: 12.79 & - The accuracy of ANNs is higher than other \\  & [5]: BPNN & RMSE: 3.58 & statistical models, but they are usually be local \\  & [6]: PCA-ANN & \(PM_{10},O_{3},CO_{2},SO_{2}\), & optima. \\  & [7]: GFM-NN & \(NO_{2}\) & - The accuracy of ANNs can be improved by adding data: meteorology, geographic, \\  & & & time-scale, emission pattern of sources. \\  & & & - Some hybrid models can improve the accuracy. \\ \hline
[8] & ANN & [9, 10, 11, 12, 13, 14] DNN & - The most frequently applied input parameter is \\ [PERSON], [PERSON] and [PERSON] (2021). & FUZZY & PM RMSE: 7.27 & API - The best performing AI-based model is the \\  & SVM & PM \(R^{2}\): 0.96 & DNN. \\  & DNN & [10, 14, 15, 16]: DNN & - Fuzzy logic, DNN and SVM are the three \\  & & \(O_{3}\) RMSE: 3.51 & commonly used AI-based techniques \\  & & \(O_{3}\)\(R^{2}\): 0.92 & - DNN, SVM, and Fuzzy techniques showed \\  & & [9, 10]: DNN & better accuracy in forecasting PM concentrations \\  & & CO RMSE: 0.95 & - DNN outperformed the other AI techniques for \\  & & & CO \(R^{2}\): 0.69 \(\times 10^{-5}\) & this pollutant category. DNN improved spatial \\  & & The above RMSE and \(R^{2}\) are & and temporal stability for a multi-step ahead \\  & & mean values of the refs & forecasting of pollutants \\  & & & - Ability to exploit high-level features from raw \\  & & & air quality and meteorological data \\ \hline
[17] & [18] Multi-models Ensemble & [18] OFFS & - Ensemble forecasting has shown significant \\ [PERSON], and [PERSON], [PERSON] (2020). & forecasting: A new Ozone & \(O_{3}\) RMSE: 16.34 & statistical improvements for both O3 and PM2.5 \\ [PERSON], [PERSON] (2020). & Ensemble Forecast System & [19] DLR & forecasts over any individual forecast [21]. \\  & (OFS), & O\({}_{3}\) RMSE: 10.57 & - Multi-model ensemble air quality forecasting \\  & [19] Dynamic Linear Regression & [20] MarcoPo\({}_{0}\) -Panda & has been emerging for AGP on global and \\  & for ensemble (DLR), & \(O_{3}\) RMSE: 32.8 & regional scales \\  & [20] MarcoPo\({}_{0}\)-Panda & \(NO_{2}\) RMSE: 21.8 & - Multi-model ensemble results provide a range \\  & & \(PM_{25}\) RMSE: 30.2 & and an indication of the robustness of the \\  & & & forecasts and help to improve the accuracy of \\  & & & chemical weather and air quality forecasting. \\ \hline
[22] & Ensemble learning (EL) & \(R^{2}\): & - The high accuracies achieved with ML \\ [PERSON], [PERSON], et al. (2019). & Classical Regression (CR) & EL0.79 & algorithms explains it all why these algorithms \\  & NN & CR: 0.74 & are appropriate and should be preferred over \\  & SVM & SVM :0.67 & traditional approaches \\  & Lazy & NN: 0.6 & - Should use critical pollutants (\(NO_{x}\) and \(SO_{2}\)) \\  & & & ensemble learning techniques to improve model \\  & & & accuracy \\ \hline
[23] & Multi-layer neural network & [24] NN & - ANN methods were preferred in studies of PM \\ [PERSON], [PERSON], et al. (2021). & (NN) & \(PM_{25}\) error: 30\%–60\% & and \(O_{3}\) \\  & Land use regression (LUR) & [25] Variety of NN algorithms & - LUR were more widely used in studies of \(NO_{x}\) \\  & Multiple linear regression & \(PM_{10}\) RMSE: 15.700 & - Multi-method hybrid techniques gradually \\  & (MLR) & \(PM_{10}\) MAE: 9.047 & became the most widely used approach between \\  & Hybrid methods & \(PM_{10}\)\(R^{2}\): 0.840 & 2010 and 2018 \\  & & [26] DNN & - The most difficult element of research on air \\  & & \(PM_{25}\) MAPE: 11.93\% & pollution prediction is likely to be the interaction \\  & & & between pollutants, which will be based on a \\  & & & mixed technique to forecast numerous \\  & & & contaminants concurrently in the future. \\ \hline
[27] & Artificial Neural Network & SVR shows better performance & - Linear and Logistic Regression are the choices \\ [PERSON], [PERSON], et al. (2020). & (ANN) & in prediction of AQI while RFR gives the better performance in & of many researchers for the prediction of AQI \\  & Linear and Logistic Regression & gives the better performance in & and air pollutants concentration \\  & & predicting the NOx & - The future scope may include consideration of \\  & & concentration & all parameters that is meteorological parameters, \\  & & air pollutants while predicting AQI or \\  & & forecasting the future concentration level of \\  & & different pollutants. \\ \hline \hline \end{tabular}
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