## 2 Related Work  
Time series forecast is the most important part of the ML regression problem; both shallow and DL models have been used for this purpose. Tree-based models such as decision trees, random forests (RF) [7], and gradient tree boosting models are well known to give good performance and have been widely used in supervised ML methods. These can map non-linear relationships among data unlike linear ML models such as linear regression [8] and support vector machine (SVM) [9]. The RF model has been used to study the impact of various factors on pollutants concentrations by utilizing meteorological parameters, pollutants concentration, and traffic flow [10]. XGBoost [11], introduced by [PERSON] and [PERSON], is an ensemble of boosted decision trees that uses gradient descent for model optimization and has been widely used in regression [12], classification [13], and time series forecasting [14]. XGBoost was implemented to predict PM2.5 concentration in [15], where the author analyzed the data of one station in China and compared the results with RF, SVM, Multiple Linear Regression (MLR) [16], and Decision Tree Regression (DTR) algorithms [17]. The dependent variables used in this research were pollutants' concentrations such as PM10, CO, NO2, SO2, and O3; among all the models, XGBoost showed the best results. LightGBM [18] also belongs to the gradient tree boosting models, in which a decision tree is split in leaf-wise with the best fit, thus reducing the loss with better accuracy. Similarly, XGBoost and LightGBM models have been used to predict the thermal power energy development [19] and later showed less Mean Absolute Percentage Error (MAPE%) on their dataset. Along with shallow ML models, DL models are also commonly used these days and have been successfully used for pollutants forecasting [20]. In a recent study [21,22], LSTM model has been used for the prediction of PM\({}_{10}\) and PM\({}_{2.5}\) concentrations by utilizing pollutants concentration and meteorological parameters. The authors compared the results with the Community Multi-scale Air Quality (CMAQ) model [23] and found that DL based model performs better. CNNLSTM is also a variant of LSTM models in which CNN [24] has been used for extracting the features and then fed to the LSTM model to get the forecast; they are being used in various time series prediction problems [25,26]. [PERSON] [27] only used three meteorological parameters (wind speed, wind direction, and precipitation) to predict the PM\({}_{2.5}\) concentrations. Their proposed model, which they named \"APNet\" (a combination of CNN and LSTM), showed good results against SVM, DTR, RF, MLP, CNN, and LSTM. In a recent study [28], the authors proposed a novel CNNLSTM model with attention mechanism. Along with pollutants concentration and meteorological parameters, they also utilized the information of nearest stations to capture the spatial dependencies. GRU [29] is also a type of RNN and a variant of LSTM with fewer gates, making the model faster. It also has been adopted in many time series forecasting problems. In [30], GRU is utilized for estimating primary energy consumption in China and the model results are compared with SVM and MLR, where GRU gives good prediction accuracy. Similarly, a combination of the Bidirectional and Unidirectional LSTM (BiULSTM) model was used for PM\({}_{10}\) forecasting by [PERSON], [PERSON] [31], who tested it with SVM and MLR, with BiULSTM providing better prediction results than the other methods used. In this study, input features used are concentrations of pollutants (SO\({}_{2}\), CO, NO\({}_{2}\), and O\({}_{3}\)), the meteorological parameters, and PM\({}_{10}\) concentration of the nearest stations. The input features play an important role in the prediction of any machine learning model, and, by using background knowledge of the parameters that are vital in the formation of PM particles, the models' performance can be improved.