Articles | Volume 17, issue 3
https://doi.org/10.5194/amt-17-1145-2024
https://doi.org/10.5194/amt-17-1145-2024
Research article
 | 
16 Feb 2024
Research article |  | 16 Feb 2024

A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images

Blanca Fuentes Andrade, Michael Buchwitz, Maximilian Reuter, Heinrich Bovensmann, Andreas Richter, Hartmut Boesch, and John P. Burrows

Related authors

A novel, balloon-borne UV/visible spectrometer for direct sun measurements of stratospheric bromine
Karolin Voss, Philip Holzbeck, Klaus Pfeilsticker, Ralph Kleinschek, Gerald Wetzel, Blanca Fuentes Andrade, Michael Höpfner, Jörn Ungermann, Björn-Martin Sinnhuber, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2023-2912,https://doi.org/10.5194/egusphere-2023-2912, 2024
Short summary
Can a regional-scale reduction of atmospheric CO2 during the COVID-19 pandemic be detected from space? A case study for East China using satellite XCO2 retrievals
Michael Buchwitz, Maximilian Reuter, Stefan Noël, Klaus Bramstedt, Oliver Schneising, Michael Hilker, Blanca Fuentes Andrade, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Hartmut Boesch, Lianghai Wu, Jochen Landgraf, Ilse Aben, Christian Retscher, Christopher W. O'Dell, and David Crisp
Atmos. Meas. Tech., 14, 2141–2166, https://doi.org/10.5194/amt-14-2141-2021,https://doi.org/10.5194/amt-14-2141-2021, 2021
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak
Matthieu Dogniaux, Joannes D. Maasakkers, Daniel J. Varon, and Ilse Aben
Atmos. Meas. Tech., 17, 2777–2787, https://doi.org/10.5194/amt-17-2777-2024,https://doi.org/10.5194/amt-17-2777-2024, 2024
Short summary
U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers
Jack H. Bruno, Dylan Jervis, Daniel J. Varon, and Daniel J. Jacob
Atmos. Meas. Tech., 17, 2625–2636, https://doi.org/10.5194/amt-17-2625-2024,https://doi.org/10.5194/amt-17-2625-2024, 2024
Short summary
CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery
Anna Vaughan, Gonzalo Mateo-García, Luis Gómez-Chova, Vít Růžička, Luis Guanter, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 2583–2593, https://doi.org/10.5194/amt-17-2583-2024,https://doi.org/10.5194/amt-17-2583-2024, 2024
Short summary
Greenhouse gas retrievals for the CO2M mission using the FOCAL method: first performance estimates
Stefan Noël, Michael Buchwitz, Michael Hilker, Maximilian Reuter, Michael Weimer, Heinrich Bovensmann, John P. Burrows, Hartmut Bösch, and Ruediger Lang
Atmos. Meas. Tech., 17, 2317–2334, https://doi.org/10.5194/amt-17-2317-2024,https://doi.org/10.5194/amt-17-2317-2024, 2024
Short summary
Quantitative imaging of carbon dioxide plumes using a ground-based shortwave infrared spectral camera
Marvin Knapp, Ralph Kleinschek, Sanam N. Vardag, Felix Külheim, Helge Haveresch, Moritz Sindram, Tim Siegel, Bruno Burger, and André Butz
Atmos. Meas. Tech., 17, 2257–2275, https://doi.org/10.5194/amt-17-2257-2024,https://doi.org/10.5194/amt-17-2257-2024, 2024
Short summary

Cited articles

Bell, E., O'Dell, C. W., Taylor, T. E., Merrelli, A., Nelson, R. R., Kiel, M., Eldering, A., Rosenberg, R., and Fisher, B.: Exploring bias in the OCO-3 snapshot area mapping mode via geometry, surface, and aerosol effects, Atmos. Meas. Tech., 16, 109–133, https://doi.org/10.5194/amt-16-109-2023, 2023. a
Bergamaschi, P., Danila, A. M., Weiss, R., Ciais, P., Thompson, R. L., Brunner, D., Levin, I., Meijer, Y., Chevallier, F., Janssens-Maenhout, G., Bovensmann, H., Crisp, D., Basu, S., Dlugokencky, E., Engelen, R., Gerbig, C., Günther, D., Hammer, S., Henne, S., Houweling, S., Karstens, U., Kort, E., Maione, M., Manning, A., Miller, J., Montzka, S., Pandey, S., Peters, W., Peylin, P., Pinty, B., Ramonet, M., Reimann, S., Röckmann, T., Schmidt, M., Strogies, M., Sussams, J., Tarasova, O., Van, A. J., Vermeulen, A., and Vogel, F.: Atmospheric monitoring and inverse modelling for verification of greenhouse gas inventories, Publications Office of the European Union, https://doi.org/10.2760/759928, 2018. a
Bézy, J.-L., Sierk, B., Löscher, A., Meijer, Y., Nett, H., and Fernandez, V.: The European Copernicus Anthropogenic CO2 Monitoring Mission, in: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019, IEEE, 8400–8403, ISSN 2153-7003, https://doi.org/10.1109/IGARSS.2019.8899116, 2019. a
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S., Rozanov, V. V., Chance, K. V., and Goede, A. P. H.: SCIAMACHY: Mission Objectives and Measurement Modes, J. Atmos. Sci., 56, 127–150, https://doi.org/10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2, 1999. a
Bovensmann, H., Buchwitz, M., Burrows, J. P., Reuter, M., Krings, T., Gerilowski, K., Schneising, O., Heymann, J., Tretner, A., and Erzinger, J.: A remote sensing technique for global monitoring of power plant CO2 emissions from space and related applications, Atmos. Meas. Tech., 3, 781–811, https://doi.org/10.5194/amt-3-781-2010, 2010. a, b, c, d
Download
Short summary
We developed a method to estimate CO2 emissions from localized sources, such as power plants, using satellite data and applied it to estimate CO2 emissions from the Bełchatów Power Station (Poland). As the detection of CO2 emission plumes from satellite data is difficult, we used observations of co-emitted NO2 to constrain the emission plume region. Our results agree with CO2 emission estimations based on the power-plant-generated power and emission factors.