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This study explores the estimation of ground-level NO2 concentrations in Mexico City using an integrated approach of machine learning (ML) and remote sensing data. We used the NO2 measurements from the Sentinel-5P satellite, along with ERA5 meteorological data, to evaluate a pre-trained machine learing model. Our findings indicate that the model captures the spatial and temporal variability of NO2 concentrations across the urban landscape. Key meteorological parameters, such as temperature and wind speed, were identified as significant factors influencing NO2 levels. The model’s adaptability was further tested by incorporating additional variables, such as atmospheric boundary layer height. In order to compare the model’s performance to alternative ML models, we estimated the ground-level NO2 using the state-of-the-art TimeGPT. The results demonstrate that our baseline model has the best performance with a mean normalised root mean square error of 84.47%. This research underscores the potential of combining satellite observations with ML for scalable air quality monitoring, particularly in low- and middle-income countries with limited ground-based infrastructure. The study provides critical insights for air quality management and policy-making, aiming to mitigate the adverse health and environmental impacts of NO2 pollution.
Jimenez et al. (Sat,) studied this question.