Estimating Nitrogen Dioxide Levels Using Open Data and Machine Learning: A Comparative Modeling Study - D. Varam; R. Mitra; F. Kamran; D. A. Abuhani; H. Sulieman; I. Zualkernan (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2025)
Nitrogen dioxide (NO2) is a critical pollutant with widespread effects on both air quality and environmental health, recognized as a key concern within the United Nations’ Sustainable Development Goals (SDGs). This study investigates NO2 levels in Italy, analyzing spatial and seasonal variations to better understand pollutant distribution. Using open-source data, we employed machine learning models to estimate NO2 concentrations, achieving strong predictive accuracy based on the mean absolute percentage error and the root mean-squared error. The results reveal that model performance improves significantly when data is segmented based on seasonal and urban development factors. Specifically, predictions for urban, rural, and mixed cities demonstrated that urban areas exhibited higher NO2 concentrations, while rural regions showed comparatively lower levels. The analysis underscores the importance of tailoring models to regional and temporal contexts, affirming that open-source data, combined with machine learning techniques, can effectively estimate NO2 pollution levels across diverse environments.