Articles | Volume 15, issue 6
https://doi.org/10.5194/amt-15-1609-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-1609-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Mar 2022
Research article |
| 21 Mar 2022
| 21 Mar 2022
Mapping the spatial distribution of NO2 with in situ and remote sensing instruments during the Munich NO2 imaging campaign
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Ka Lok Chan
Meteorological Institute, Ludwig Maximilian University (LMU), Munich, Germany
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, Germany
now at: Rutherford Appleton Laboratory Space, Harwell Oxford, United Kingdom
Sebastian Donner
Max Planck Institute for Chemistry (MPIC), Mainz, Germany
Ying Zhu
Meteorological Institute, Ludwig Maximilian University (LMU), Munich, Germany
Marc Schwaerzel
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Environmental Remote Sensing Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Steffen Dörner
Max Planck Institute for Chemistry (MPIC), Mainz, Germany
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Andreas Hueni
Department of Geography, University of Zurich (UZH), Zurich, Switzerland
Duc Hai Nguyen
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
now at: Leibniz Supercomputing Centre (LRZ), Garching bei München, Germany
Alexander Damm
Department of Geography, University of Zurich (UZH), Zurich, Switzerland
Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland
Annette Schütt
Meteorological Institute, Ludwig Maximilian University (LMU), Munich, Germany
Florian Dietrich
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Dominik Brunner
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Cheng Liu
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC), Hefei, China
Brigitte Buchmann
Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland
Thomas Wagner
Max Planck Institute for Chemistry (MPIC), Mainz, Germany
Mark Wenig
Meteorological Institute, Ludwig Maximilian University (LMU), Munich, Germany
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Cited
2 citations as recorded by crossref.
- Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO2) with hyperspectral imagers and reduce noise in spectral fitting J. Joiner et al. 10.5194/amt-16-481-2023
- Simultaneous Retrieval of Trace Gases, Aerosols, and Cirrus Using RemoTAP—The Global Orbit Ensemble Study for the CO2M Mission S. Lu et al. 10.3389/frsen.2022.914378
Latest update: 19 Apr 2024
Short summary
Nitrogen dioxide (NO2) is an air pollutant whose concentration often exceeds air quality guideline values, especially in urban areas. To map the spatial distribution of NO2 in Munich, we conducted the Munich NO2 Imaging Campaign (MuNIC), where NO2 was measured with stationary, mobile, and airborne in situ and remote sensing instruments. The campaign provides a unique dataset that has been used to compare the different instruments and to study the spatial variability of NO2 and its sources.
Nitrogen dioxide (NO2) is an air pollutant whose concentration often exceeds air quality...
