26 Jan 2023
 | 26 Jan 2023
Status: a revised version of this preprint is currently under review for the journal ACP.

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

Abstract. A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit providing daily global coverage of methane mixing ratios at a resolution of up to 7 × 5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a Convolutional Neural Network to detect plume-like structures in the methane data and subsequently apply a Support Vector Classifier to distinguish emission plumes from retrieval artefacts. The models are trained on pre-2021 data, and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021 with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas/landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %) and coal mines (20 %). For twelve (clusters of) TROPOMI detections, we "tip-and-cue" targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, for the latter we find up to ten facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification and monitoring of these methane super-emitters, which is essential for mitigating their emissions.

Berend J. Schuit et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-862', Anonymous Referee #1, 21 Feb 2023
  • RC2: 'Comment on acp-2022-862', Anonymous Referee #2, 06 Mar 2023
  • AC1: 'Comment on acp-2022-862', Berend Schuit, 21 Apr 2023

Berend J. Schuit et al.

Berend J. Schuit et al.


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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/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 zoom-in and identify the exact infrastructure responsible for the emissions.