Articles | Volume 23, issue 12
https://doi.org/10.5194/acp-23-6897-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-6897-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jun 2023
Research article |
| 22 Jun 2023
| 22 Jun 2023
Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach
Andreas Forstmaier
CORRESPONDING AUTHOR
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Juan Bettinelli
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Hossein Maazallahi
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University (UU), Utrecht, the Netherlands
Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, the Netherlands
Carsten Schneider
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University (UU), Utrecht, the Netherlands
Institut für Umweltphysik, University of Heidelberg, Heidelberg, Germany
Dominik Winkler
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Xinxu Zhao
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Taylor Jones
Earth and Environment, Boston University, Boston, USA
Carina van der Veen
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University (UU), Utrecht, the Netherlands
Norman Wildmann
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Moritz Makowski
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Aydin Uzun
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Friedrich Klappenbach
Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany
Hugo Denier van der Gon
Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, the Netherlands
Stefan Schwietzke
Environmental Defense Fund, Berlin, Germany
Thomas Röckmann
Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University (UU), Utrecht, the Netherlands
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Cited
3 citations as recorded by crossref.
- Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
- Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning V. Balamurugan et al. 10.5194/acp-23-10267-2023
- Pyra: Automated EM27/SUN Greenhouse Gas Measurement Software P. Aigner et al. 10.21105/joss.05131
2 citations as recorded by crossref.
- Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
- Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning V. Balamurugan et al. 10.5194/acp-23-10267-2023
1 citations as recorded by crossref.
Latest update: 23 Dec 2024
Short summary
Large cities emit greenhouse gases which contribute to global warming. In this study, we measured the release of one important green house gas, methane, in Hamburg. Multiple sources that contribute to methane emissions were located and quantified. Methane sources were found to be mainly caused by human activity (e.g., by release from oil and gas refineries). Moreover, potential natural sources have been located, such as the Elbe River and lakes.
Large cities emit greenhouse gases which contribute to global warming. In this study, we...
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