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 (O<sub>3</sub>) 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 O<sub>3</sub> values. The impleme...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/5/491 |
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| Summary: | Exposure to high surface ozone (O<sub>3</sub>) 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 O<sub>3</sub> values. The implementation of increasingly effective methods to assess the factors determining the formation and variability of O<sub>3</sub> 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 O<sub>3</sub> variability and to explore the nonlinear relationships linking O<sub>3</sub> to these factors. The SHAP analysis carried out at different event scales indicated (i) the dominant role of the meteorological variables in driving O<sub>3</sub> 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 O<sub>3</sub> concentrations during a selected pollution event; (iii) the predominant effect of wind speed and relative humidity in shaping the O<sub>3</sub> daily patterns clustered using the <i>k</i>-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. |
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| ISSN: | 2073-4433 |