Articles | Volume 26, issue 10
https://doi.org/10.5194/acp-26-7741-2026
https://doi.org/10.5194/acp-26-7741-2026
Technical note
 | 
01 Jun 2026
Technical note |  | 01 Jun 2026

Technical note: DACNO2 – a multi-constraint deep learning framework for high-resolution 3D NO2 field estimation

Wenfu Sun, Frederik Tack, Lieven Clarisse, and Michel Van Roozendael

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Cited articles

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Bézy, J.-L., Sierk, B., Caron, J., Veihelmann, B., Martin, D., and Langen, J.: The Copernicus Sentinel-5 mission for operational atmospheric monitoring: status and developments, SPIE Remote Sensing, SPIE, https://doi.org/10.1117/12.2068177, 2014. 
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023. 
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Accurate maps of nitrogen dioxide pollution at fine scales are essential for assessing air quality and protecting public health. We developed a machine learning model that produces daily high-resolution 3D nitrogen dioxide fields across Western Europe by combining large-scale atmospheric simulations with ground-based measurements. This approach outperforms traditional methods, especially over cities and complex terrain, and can enhance satellite-based air quality monitoring.
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