What question did this study set out to answer?

The aim is to create a machine learning framework using satellite and meteorological data to estimate ground-level NO₂ concentrations in Napoli Province.

July 1, 2026Open Access

Environmental Situational Awareness for Ground-Level NO₂ Estimation Using Sentinel-5P TROPOMI and ERA5 Reanalysis Data: A Machine Learning Approach for Napoli Province, Italy

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Authors

MQMuhammad Zaid QamarParthenope University of Naples MAMohammed AjaoudParthenope University of Naples CCCristiano CiccarelliParthenope University of Naples

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Implication

Randomized trial estimates ground-level NO₂ concentrations in Napoli, highlighting an advanced air quality monitoring method.

Key Points

The aim is to create a machine learning framework using satellite and meteorological data to estimate ground-level NO₂ concentrations in Napoli Province.
Developed a Random Forest regression model using 11,325 observations from 16 monitoring stations.
Incorporated 12 predictor variables, including TROPOMI NO₂ column density and various meteorological factors.
Validated the model on an independent test set of 2,832 observations.
Achieved an R² of 0.71 and RMSE of 8.61 μg/m³ on the test set.
Estimated ground-level NO₂ concentration averaged 29.3 ± 16.1 μg/m³, ranging from 0 to 112.8 μg/m³.
Demonstrated effective integration of satellite and meteorological data for high-resolution air quality monitoring.

Cite This Study

Qamar et al. (2026) studied this question.

synapsesocial.com/papers/6a44afdc5cd2549c8bc44c8a https://doi.org/10.5281/zenodo.20407722

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Environmental Situational Awareness for Ground-Level NO₂ Estimation Using Sentinel-5P TROPOMI and ERA5 Reanalysis Data: A Machine Learning Approach for Napoli Province, Italy

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Cite This Study

Environmental Situational Awareness for Ground-Level NO₂ Estimation Using Sentinel-5P TROPOMI and ERA5 Reanalysis Data: A Machine Learning Approach for Napoli Province, Italy

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