Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 10
Articles | Volume 26, issue 10
https://doi.org/10.5194/acp-26-7741-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-7741-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 10
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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Short summary
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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