## 3 Materials and Methods
### Feature Vector Construction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Paper** & **Predicted** & **Predictors** & **Models** & **Metrics** \\ \hline A Machine Learning approach to investigate the build-up of surface ozone in Mexico City (2022) [6]. & \(O_{3}\). & Temperature, Relative humidity, Wind speed and direction, \(NO_{2}\), UV-A and Planetary Boundary Layer Height. & Random Forest, Gradient Boosting, Deep Neural Network. & \(R^{2}\), Index of Agreement (IOA). \\ \hline Prediction of Air Pollutants Using Supervised Machine Learning (2021) [7]. & Air quality index for \(PM_{10}\), \(PM_{2.5}\), \(NO_{2}\), \(NO_{3}\). & Curtry, State, City, Place, Last updated, Min, Max, Average, and Pollutants(\(PM_{10}\), \(PM_{2.5}\), \(SO_{2}\), \(CO\), \(NO_{2}\), \(O_{3}\)). & Decision Tree, Support Vector Machine, Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbor. & Accuracy. \\ \hline Machine Learning-Based Prediction of Air Quality (2020) [8] & Air quality index for 1 h, 8 h, and 24 h. & \(PM_{2.5}\) and \(PM_{10}\) moving average, \(O_{3}\) average of the last eight hours, \(CO\) concentration for the last eight hours, Air quality index based on the maximum concentration of \(PM_{10}\), \(PM_{2.5}\), \(NO_{2}\), \(SO_{2}\), \(O_{3}\) and \(CO\). & Random Forest, AdaBoost, Support Vector Regression, Artificial Neural Network. & Random Forest, AdaBoost, Support Vector Regression, Decision Tree, XGBoost. \\ \hline Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan (2020) [9] & \(O_{3}\). & Ozone, Temperature, Humidity, Wind direction, and speed, Memorable day (week-end, holiday), Day of the year. & Multi-Layer Perceptron, Support Vector Regression, Decision Tree, XGBoost. \\ \hline An ensemble-based model of \(PM_{2.5}\) concentration across the contiguous United States with high spatiotemporal resolution (2019) [10] & \(PM_{2.5}\). & Satellite-derived aerosol optical depth, Satellite-based measurements, Chemical transport model predictions, Land-use variables, and Meteorological variables. & Ensemble Model (Neural Network, Random Forest, Gradient Boosting). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of research on machine learning applications for air quality forecasting models. It's essential to understand that these factors often interact in multifaceted ways. For instance, a hot, calm day might be conducive to ozone buildup due to reduced dispersion and enhanced photochemical reactions. However, if that hot day is also humid with cloud cover, the reduced sunlight might counteract some of the enhanced ozone production. For this reason, it is important to depict the relationships within the process that involves interactions between various precursor pollutants, including nitrogen oxides (\(NO_{x}\), which includes \(NO\) and \(NO_{2}\)) and volatile organic compounds (VOCs), in the presence of sunlight, as shown in the research by [PERSON] and [PERSON] [17], or in that by [PERSON] and [PERSON] [18]. Next, we provide a breakdown of this phenomenon:  
1. Emission of Precursors: Primary pollutants like nitrogen oxides (\(NO_{x}\)) and volatile organic compounds (VOCs) are emitted into the atmosphere. These are typically released from automobile exhaust, industrial processes, power plants, and other combustion processes. 2. Photodissociation of Nitrogen Dioxide (\(NO_{2}\)): \[NO_{2}+hv\to NO+O\] (1)