EGU22-11548, updated on 28 Mar 2022
https://doi.org/10.5194/egusphere-egu22-11548
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Multi-scale measurements combined with inverse modeling for assessing methane emissions of Hamburg

Jia Chen1, Florian Dietrich1, Andreas Forstmaier1, Juan Bettinelli1, Hossein Maazallahi2, Carsten Schneider2,8, Thomas Röckmann2, Dominik Winkler1, Xinxu Zhao1, Moritz Makowski1, Friedrich Klappenbach1, Carina van der Veen2, Norman Wildmann3, Taylor Jones4, Felix Ament5, Ingo Lange5, Hugo Denier van der Gon6, and Stefan Schwietzke7
Jia Chen et al.
  • 1Environmental Sensing and Modeling, Technical University of Munich, Munich, Germany (jia.chen@tum.de)
  • 2Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht University (UU), Utrecht, the Netherlands
  • 3Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 4Earth and Environment, Boston University, Boston, United States
  • 5Universität Hamburg, Hamburg, Germany
  • 6Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, the Netherlands
  • 7Environmental Defense Fund, Germany
  • 8Institut für Umweltphysik, University of Heidelberg, Heidelberg, Germany

Urban areas are hotspots for greenhouse gas emissions. The short-lived greenhouse gas methane is the second-most prevalent greenhouse gas emitted by human activities, and its reduction will help mitigate climate change effectively. However, the source strengths and locations of methane emitters in the urban areas are highly uncertain.

Here we present a multi-scale measurement campaign for assessing methane emissions in Hamburg. Hamburg is the second largest city in Germany with a population of about 1.8 million, and an important international harbor city. It has an interesting mixture of methane sources caused by anthropogenic emitters such as refineries and biogenic emitters such as wetlands associated with the strong tide of the Elbe River. Commissioned by UNEP, we conducted a campaign using remote sensing instruments and mobile surveys to investigate methane emissions of Hamburg. We deployed four automated solar-tracking Fourier transform spectrometer systems (Dietrich et al. 2021), one in the west, south, east and center of Hamburg to capture the total city emissions using a Bayesian inversion framework (Jones et al. 2021). Mobile measurements with a Picarro laser spectrometer in a car and a boat were performed to refine the spatial pattern of the emission inventory that is used as a prior for the inversion. We also deployed a wind LiDAR instrument to measure the 3D wind field that provides constraints to the transport model. In addition, an isotope ratio mass spectrometer was installed on a rooftop in the city center to distinguish anthropogenic and biogenic sources.

Using the column measurements and inverse modelling, we are able to determine the total city emissions and have found a major natural source, whose emissions are not yet included in the standard emission inventories. This dominant biogenic source is also indicated by the stationary isotopic measurements of δ13C and δD. Nevertheless, more than half of the city emissions are attributed to anthropogenic emissions, indicating the importance of reducing these emissions. With our study, we show that the combination of mobile measurements and column measurements is a powerful technique to correct for the strength and spatial distribution of urban greenhouse gas emission inventories.

Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: MUCCnet: Munich Urban Carbon Column network, Atmos. Meas. Tech., 14, 1111–1126, https://doi.org/10.5194/amt-14-1111-2021, 2021.

Jones, T. S., Franklin, J. E., Chen, J., Dietrich, F., Hajny, K. D., Paetzold, J. C., Wenzel, A., Gately, C., Gottlieb, E., Parker, H., Dubey, M., Hase, F., Shepson, P. B., Mielke, L. H., and Wofsy, S. C.: Assessing urban methane emissions using column-observing portable Fourier transform infrared (FTIR) spectrometers and a novel Bayesian inversion framework, Atmos. Chem. Phys., 21, 13131–13147, https://doi.org/10.5194/acp-21-13131-2021, 2021.

How to cite: Chen, J., Dietrich, F., Forstmaier, A., Bettinelli, J., Maazallahi, H., Schneider, C., Röckmann, T., Winkler, D., Zhao, X., Makowski, M., Klappenbach, F., van der Veen, C., Wildmann, N., Jones, T., Ament, F., Lange, I., Denier van der Gon, H., and Schwietzke, S.: Multi-scale measurements combined with inverse modeling for assessing methane emissions of Hamburg, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11548, https://doi.org/10.5194/egusphere-egu22-11548, 2022.

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