Exploring the Influencing Factors of Surface Ozone Variability by Explainable Machine Learning: A Case Study in the Basilicata Region (Southern Italy)

Exposure to high surface ozone (O3) concentrations, which is a major air pollutant and greenhouse gas, constitutes a significant public health concern, especially considering the potential adverse impact of climate change on future O3 values. The implementation of increasingly effective methods to assess the factors determining the formation and variability of O3 is, therefore, of great significance. In this study, a methodological approach combining both supervised and unsupervised machine learning algorithms (MLAs) with the Shapley additive explanations (SHAP) method was used to understand the key factors behind O3 variability and to explore the nonlinear relationships linking O3 to these factors. The SHAP analysis carried out at different event scales indicated (i) the dominant role of the meteorological variables in driving O3 variability, mainly relative humidity, wind speed, and temperature throughout the study period; (ii) an increase in the contribution of temperature, nitrogen oxides, and carbon monoxide to high O3 concentrations during a selected pollution event; (iii) the predominant effect of wind speed and relative humidity in shaping the O3 daily patterns clustered using the k-means technique. The results obtained are expected to be useful for the definition of effective measures to prevent and/or mitigate the health damage associated with ozone exposure.