Automated detection and monitoring of methane super-emitters using satellite data

Schuit, Berend J.; Maasakkers, Joannes D.; Bijl, Pieter; Mahapatra, Gourav; van den Berg, Anne-Wil; Pandey, Sudhanshu; Lorente, Alba; Borsdorff, Tobias; Houweling, Sander; Varon, Daniel J.; McKeever, Jason; Jervis, Dylan; Girard, Marianne; Irakulis-Loitxate, Itziar; Gorroño, Javier; Guanter, Luis; Cusworth, Daniel H.; Aben, Ilse

doi:https://doi.org/10.5194/acp-23-9071-2023

Articles | Volume 23, issue 16

https://doi.org/10.5194/acp-23-9071-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-9071-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 16

Research article

|

19 Sep 2023

Research article |

| 19 Sep 2023

Automated detection and monitoring of methane super-emitters using satellite data

Berend J. Schuit, Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Sander Houweling, Daniel J. Varon, Jason McKeever, Dylan Jervis, Marianne Girard, Itziar Irakulis-Loitxate, Javier Gorroño, Luis Guanter, Daniel H. Cusworth, and Ilse Aben

Data sets

Dataset: all TROPOMI detected plumes for 2021. [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data] (1.0) B. J. Schuit, J. D. Maasakkers, P. Bijl, G. Mahapatra, A.-W. Van den Berg, S. Pandey, A. Lorente, T. Borsdorff, S. Houweling, D. J. Varon, J. McKeever, D. Jervis, M. Girard, I. Irakulis-Loitxate, J. Gorroño, L. Guanter, D. H. Cusworth, and I. Aben https://doi.org/10.5281/zenodo.8087134

Interactive map with TROPOMI and high-resolution scenes [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data] (1.0.1) B. J. Schuit, J. D. Maasakkers, P. Bijl, G. Mahapatra, A.-W. Van den Berg, S. Pandey, A. Lorente, T. Borsdorff, S. Houweling, D. J. Varon, J. McKeever, D. Jervis, M. Girard, I. Irakulis-Loitxate, J. Gorroño, L. Guanter, D. H. Cusworth, and I. Aben https://doi.org/10.5281/zenodo.8355808

Replication Data for: Automated detection and monitoring of methane super-emitters using satellite data D. Varon https://doi.org/10.7910/DVN/QQQ9IU

CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.1) A. Bloom, K. Bowman, M. Lee, A. Turner, R. Schroeder, J. Worden, R. Weidner, K. McDonald, and D. Jacob https://doi.org/10.3334/ORNLDAAC/1915

EDGAR v6.0 Greenhouse Gas Emissions [Dataset] M. Crippa, D. Guizzardi, M. Muntean, E. Schaaf, E. Lo Vullo, E. Solazzo, F. Monforti-Ferrario, J. Olivier, and E. Vignati http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b

Global Fuel Exploitation Inventory (GFEI) T. R. Scarpelli and D. J. Jacob https://doi.org/10.7910/DVN/HH4EUM

TROPOMI scientific XCH4 data product, version 18_17 A. Lorente, T. Borsdorff, J. Landgraf, and SRON L2 team https://ftp.sron.nl/open-access-data-2/TROPOMI/tropomi/ch4/18_17/

Model code and software

The official repository for the Weather Research and Forecasting (WRF) model Contributors to the WRF repository https://github.com/wrf-model/WRF/releases/

Short summary

Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.