# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 2 Materials and Methods
### Data Import Process Description
According to the referenced paper [69], the general form of the \(AQI\) is expressed as mentioned in Equation (1):  
\[AQI=I_{0}+(I_{1}-I_{0})\times\frac{(C-C_{0})}{(C_{1}-C_{0})} \tag{1}\]  
where  
\(AQI\) is the air quality index;  
\(C\)--the pollutant concentration (\(\mu\)g/m\({}^{3}\) for PM\({}_{2.5}\), SO\({}_{2}\) and NO\({}_{2}\), ppm for CO, \({}^{\circ}\)C for temperature, % for humidity, km/h for wind speed and units for vehicular traffic);  
\(C_{0}\) and \(C_{1}\)--the breakpoint concentrations that define the range for a specific \(AQI\) category;  
\(I_{0}\) and \(I_{1}\)--the corresponding \(AQI\) values for those breakpoints. However, the models analyzed in this research include, in addition to pollutants (CO, NO\({}_{2}\), SO\({}_{2}\), and PM\({}_{2.5}\)), other factors (temperature, humidity, wind speed, and vehicular traffic) that were not previously considered in calculating \(AQI\). The authors of this study believe that besides the established factors found in the literature for calculating \(AQI\), weather conditions and traffic are additional factors that should be incorporated into the \(AQI\) formula. Figure 2 illustrates the correlations between \(AQI\) values and their corresponding levels of health concern. It provides a comprehensive overview of how varying \(AQI\) levels relate to potential health effects, highlighting the significance of monitoring AQ for public health awareness [70]. The \(AQI\) equation does not explicitly include meteorological parameters or traffic conditions, which influence AQ and, in particular situations, affect \(AQI\) values. Weather conditions influence the assessment of air pollutant concentration. For instance, humidity can impact the dispersion and retention of PM in the air. The wind can help disperse pollutants, thereby affecting their concentration. Secondly, traffic is a significant source of air pollutants such as NO\({}_{2}\), PM\({}_{2.5}\), and PM\({}_{10}\). Vehicle emissions contribute to elevated levels of these pollutants, especially in urban areas with heavy traffic. Therefore, while the \(AQI\) formula may not directly incor  
Figure 1: A map screenshot containing the contaminants and meteorological station location [68]. porate meteorological and traffic parameters, they should be analyzed to interpret _AQI_ values accurately. The predictive algorithms provide a framework for analyzing AQ dynamics, ensuring informed decision-making in public health interventions. In the following analyzed models, the parameters CO, NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\), temperature, wind speed, humidity, and vehicular traffic are named _features_. The corresponding _AQI_ value is named _label_. Figure 3 presents the BronxStats table that provides the model entries. A total of 1305 records have been imported for the period from 1 January 2020 to 31 December 2023. Figure 3 shows the correlations for each field from the source tables obtained through the imports and the final table BronxStats utilized in the model training tests. These correlations highlight the relationships between the input features and the label, which format impacts model performance. The initial step in the training process is to have the data well-structured and formatted, as it ensures that the algorithms interpret the patterns within the dataset correctly. Properly formatted data allows for better handling of missing values, normalization, and feature engineering as part of the training preparation process, ultimately enhancing the model's predictive capabilities. This is particularly important as it directly affects the accuracy of the predictions made by the customized models. The following sections analyze multiple models from various categories. Initially, algorithms that enable label prediction based on input values were examined. Next, another category of algorithms that facilitate forecasting for the next five days was evaluated. This approach focuses on immediate predictions derived from current conditions and then extends to forecasts anticipating future outcomes.