Articles | Volume 18, issue 18
https://doi.org/10.5194/gmd-18-6439-2025
https://doi.org/10.5194/gmd-18-6439-2025
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

Related authors

Ground-based monitoring of nitrogen dioxide in Kumasi, Ghana, and its comparison with satellite observations
Bas Mijling, Benjamin Afotey, Tim Henrik Eckert, Magdalena Mairhofer, Águeda Gil Pascual, Emmanuel Yuorkuu, Philip Darko, Phiona Amakah, Klaas Folkert Boersma, and Prince Junior Asilevi
EGUsphere, https://doi.org/10.5194/egusphere-2025-5782,https://doi.org/10.5194/egusphere-2025-5782, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary

Cited articles

Beirle, S., Boersma, K. F., Platt, U., Lawrence, M. G., and Wagner, T.: Megacity emissions and lifetimes of nitrogen oxides probed from space, Science, 333, 1737–1739, https://doi.org/10.1126/science.1207824, 2011 
Boersma, K. F., Jacob, D. J., Trainic, M., Rudich, Y., DeSmedt, I., Dirksen, R., and Eskes, H. J.: Validation of urban NO2 concentrations and their diurnal and seasonal variations observed from the SCIAMACHY and OMI sensors using in situ surface measurements in Israeli cities, Atmos. Chem. Phys., 9, 3867–3879, https://doi.org/10.5194/acp-9-3867-2009, 2009. 
CAMS: Annual report on the evaluation of validated re-analyses for 2019, CAMS2_83_2021SC1_D83.2.2.1-2019_202201_VRA2019 evaluation_v2, issued by INERIS/F. Meleux, date: 16/02/2022, https://atmosphere.copernicus.eu/regional-services (last access: 26 April 2024), 2022. 
Chen, T. and Guestrin, C.: XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD '16, ACM Press, San Francisco, California, USA, 785–794, https://doi.org/10.1145/2939672.2939785, 2016. 
Cimorelli, A. J., Perry, S. G., Venkatram, A., Weil, J. C., Paine, R. J., Wilson, R. B., Lee, R. F., Peters, W. D., and Brode, R. W.: AERMOD: A dispersion model for industrial source applications Part I: General model formulation and boundary layer characterization, J. Appl. Meteor., 44, 682–693, 2004. 
Download
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.
Share