High-Resolution NO2, O3, and PMs Estimation in Puglia: Leveraging AI and Explainability Techniques
Air pollution remains a major environmental challenge, with severe impacts on human health and ecosystems. Recent advances in satellite technology have transformed air quality monitoring by enabling global, continuous observations of atmospheric pollutants. However, satellite data often lack the precision of ground-based stations. This study aims to develop a machine learning model to predict daily surface concentrations of key air pollutants (NO2, O3, PM10, and PM2.5) at high spatial resolution (300 m) in the Apulia region. Using Regional Environmental Protection Agency (ARPA) station data from 2019 to 2022 and meteorological, geographic, land-use, and temporal variables, we trained an XGBoost model on a 300 m grid. Model performance, assessed by repeated cross-validation, showed an average R2 of 0.71, with values of 0.77 for NO2, 0.78 for O3, 0.67 for PM2.5, and 0.64 for PM10. eXplainable AI (XAI) methods confirmed strong alignment with established scientific knowledge, enhancing model reliability and offering insights into pollutant distribution drivers.
Authors
- Mariella Aquilino (ORCID: https://orcid.org/0000-0002-4292-0329)
- R. Bellotti (ORCID: https://orcid.org/0000-0003-3198-2708)
- Maria Adamo (ORCID: https://orcid.org/0000-0003-3030-4884)
- Loredana Bellantuono (ORCID: https://orcid.org/0000-0002-1333-2675)
- Antonio Lacalamita (ORCID: https://orcid.org/0000-0002-1013-3793)
- Alessandra Nocioni
- T. Maggipinto (ORCID: https://orcid.org/0000-0003-4156-5377)
- Roberto Cilli (ORCID: https://orcid.org/0000-0002-4560-3054)
- Alessandro Fania
- Angela Morabito (ORCID: https://orcid.org/0000-0002-0678-5836)
- Alfonso Monaco
- Roberto Primerano
- Nicola Amoroso
- Giovanni Lorusso
- Ester Pantaleo
- Sabina Tangaro
- Marica De De Lucia
- Marianna La La Rocca
Institutions
- Agenzia Regionale per la Protezione Ambientale del Piemonte (IT)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari (IT)
- National Research Council (IT)
- Institute of Atmospheric Pollution Research (IT)
- University of Bari Aldo Moro (IT)
- University of Naples Federico II (IT)
Publication Details
- Journal
- Atmosphere
- Published
- 2026-02-15
- DOI
- https://doi.org/10.3390/atmos17020209
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Dipartimenti di Eccellenza
- European Commission
- Ministero dell’Istruzione, dell’Università e della Ricerca