Insights into Air Quality Index (AQI) Variability with Explainable Machine Learning Techniques

In this study, a combined approach joining the machine learning model Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanation (SHAP) is adopted to simulate the temporal pattern of the air quality index (AQI) and subsequently explore the key factors affecting AQI variability. Based on the analysis of air pollutants and meteorological data acquired from two air quality monitoring stations in Rome (Italy), over the 2018–2022 period, the results demonstrate the effectiveness of the proposed methodological approach in elucidating the role of the main factors driving AQI evolution, and their interaction effects.