Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 18
Articles | Volume 18, issue 18
https://doi.org/10.5194/gmd-18-6439-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-6439-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 18
Development and technical paper
 | 
25 Sep 2025
Development and technical paper |  | 25 Sep 2025

High-resolution mapping of urban NO2 concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in Madrid

Bas Mijling, Henk Eskes, Sascha Hofmann, Pau Moreno, David García Falin, and María Encarnación de Vega Pastor

Supplement

https://doi.org/10.5194/gmd-18-6439-2025-supplement

Data sets

High-resolution mapping of urban NO$_{2}$ concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in Madrid (v1.0) Bas Mijling https://doi.org/10.5281/zenodo.15096617

Model code and software

High-resolution mapping of urban NO$_{2}$ concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in Madrid (v1.0) Bas Mijling https://doi.org/10.5281/zenodo.15096617

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Short summary
Given the serious health risks of urban air pollution, monitoring local pollution levels is crucial. The Retina v2 algorithm creates high-resolution pollution maps by integrating satellite and local measurements with an air quality model. Easily portable to other cities, it balances accuracy with low computational demands, matching or outperforming complex dispersion models and data-heavy machine learning. Satellite data proves especially valuable in cities with sparse or no monitoring networks.
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