## 3 Materials and Methods
### AI-Based Models Employed  
This section examines the AI-based models used to predict, model, and forecast hourly PM concentrations. The selected models include machine learning (ML) and deep learning (DL) approaches tailored for prediction tasks, feature engineering (FS) techniques applied for dimensionality reduction, and statistical AI-based models designed for time series forecasting. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable’s Number** & **Parameter** & **Unit** \\ \hline Variable 1 & Benzene & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 2 & CO & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 3 & Ethylbenzene & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 4 & m-Xylene & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 5 & NO & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 6 & NO\({}_{2}\) & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 7 & NO\({}_{\text{x}}\) & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 8 & o-Xylene & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 9 & Ozone & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 10 & p-Xylene & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 11 & Precipitations & mm \\ Variable 12 & RH & \% \\ Variable 13 & SO\({}_{2}\) & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 14 & Sun Brightness & W/m\({}^{2}\) \\ Variable 15 & T & \({}^{\circ}\)C \\ Variable 16 & Toluene & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Variable 17 & WindDir & degrees \\ Variable 18 & WindVelocity & m/s \\ Output 1 & PM\({}_{2.5}\) & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ Output 2 & PM\({}_{10}\) & \(\text{\mu}\text{g}/\text{m}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The considered predictor variables and output. \begin{table}
\begin{tabular}{c c} \hline \hline
**Station** & **Measured Parameters** \\ \hline Dj-1 & Benzene, CO, Ethylbenzene, m-Xylene, NO\({}_{2}\), NO, NO\({}_{\text{x}}\), o-Xylene, p-Xylene, \\ PM\({}_{10}\), SO\({}_{2}\), Toluene, As, Ni, Cd, Pb \\ \hline DJ2 & Temperature, Wind direction and speed, precipitations, Air pressure, Relative \\ humidity, Sun brightness, Benzene, CO, m-Xylene, NO\({}_{2}\), NO, NO\({}_{\text{x}}\), o-Xylene, \\ p-Xylene, PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), Toluene, As, Ni, Cd, Pb \\ \hline DJ3 & NH\({}_{3}\), NO\({}_{2}\), NO, NO\({}_{\text{x}}\), Ni, O\({}_{3}\), PM\({}_{10}\), SO\({}_{2}\), As, Ni, Cd, Pb \\ \hline DJ5 & CO, NO\({}_{2}\), NO, NOx, O\({}_{3}\), PM\({}_{10}\), SO\({}_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters measured by stations. For the prediction task, 23 ML and DL models are selected, employed, and compared. The models we selected for this first task are Artificial Neural Network (ANN) [48], Decision Tree (DT) [49], Support Vector Machine (SVM) [50], Extreme Learning Machine (ELM) [51], Gradient Boosting Machines (GBoost) [52], Extreme Gradient Boosting (xGBoost) [53], Random Forest (RF) [54], Tree Bagger (TreeBag) [55], Generalized Linear Regression Model (GLR) [56], Gaussian Process Regression (GPR) [57], Linear Regression (LR) [58], Generalized Additive Model (GAM) [59], Kernelized Ridge Regression Model (KRR) [60], K-Nearest Neighbors (KNN) [61], Linear Ridge Regression (LRR) [62], Bayesian Linear Regression (BayesLR) [61], Convolutional Neural Networks (CNNs) [63], Feedforward Neural Network (FNN) [64], Deep Neural Network (DNN) [65], Residual Neural Network (ResNet) [66], Fully Connected Neural Network (FCNN) [67], Long Short-Term Memory (LSTM) [68], and LSTM-based Recurrent Neural (LSTM-RNNs) [69]. Each one of these models has been checked for its best parameter tuning and for its best configuration using grid search algorithms (see Table 3). The best-performing model from this stage will be combined with feature selection (FS) techniques to find the best combination of predictor variables that can enhance the prediction accuracy of the best-performing model. The FS techniques we employed here are summarized in Table 4. In total, we have employed fifty FS techniques.