Articles | Volume 14, issue 10
https://doi.org/10.5194/amt-14-6469-2021
https://doi.org/10.5194/amt-14-6469-2021
Research article
 | 
08 Oct 2021
Research article |  | 08 Oct 2021

Impact of 3D radiative transfer on airborne NO2 imaging remote sensing over cities with buildings

Marc Schwaerzel, Dominik Brunner, Fabian Jakub, Claudia Emde, Brigitte Buchmann, Alexis Berne, and Gerrit Kuhlmann

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Cited articles

Berchet, A., Zink, K., Muller, C., Oettl, D., Brunner, J., Emmenegger, L., and Brunner, D.: A cost-effective method for simulating city-wide air flow and pollutant dispersion at building resolving scale, Atmos. Environ., 158, 181–196, https://doi.org/10.1016/j.atmosenv.2017.03.030, 2017. a, b, c
Bär, P.: Luftbilanz 2016: Resultate der flächendeckenden Messkampagne, available at: https://www.stadt-zuerich.ch/gud/de/index/umwelt_energie/luftqualitaet/publikationen/luftbilanz-2016.html (last access: 5 October 2021), 2016. a, b
Cracknell, A. P. and Varotsos, C.: Remote sensing and atmospheric ozone: Human activities versus natural variability, Springer Science & Business Media, Berlin/Heidelberg, Germany, https://doi.org/10.1007/978-3-642-10334-6, 2012. a
de Goeij, B. T. G., Otter, G. C. J., van Wakeren, J. M. O., Veefkind, J. P., Vlemmix, T., Ge, X., Levelt, P. F., Dirks, B. P. F., Toet, P. M., van der Wal, L. F., and Jansen, R.: First aircraft test results of a compact, low cost hyperspectral imager for earth observation from space, in: International Conference on Space Optics – ICSO 2016, edited by: Cugny, B., Karafolas, N., and Sodnik, Z., Vol. 10562, International Society for Optics and Photonics, SPIE, Biarritz, France, 465–475, https://doi.org/10.1117/12.2296068, 2017. a
de Graaf, M., Sihler, H., Tilstra, L. G., and Stammes, P.: How big is an OMI pixel?, Atmos. Meas. Tech., 9, 3607–3618, https://doi.org/10.5194/amt-9-3607-2016, 2016. a
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Short summary
NO2 maps from airborne imaging remote sensing often appear much smoother than one would expect from high-resolution model simulations of NO2 over cities, despite the small ground-pixel size of the sensors. Our case study over Zurich, using the newly implemented building module of the MYSTIC radiative transfer solver, shows that the 3D effect can explain part of the smearing and that building shadows cause a noticeable underestimation and noise in the measured NO2 columns.
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