Comment on acp-2022-862
Abstract
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.
Authors 18
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GHGSat (Canada) · Space Research Organisation Netherlands
Affiliation as printed
GHGSat Inc, Montréal, Canada
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
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Space Research Organisation Netherlands
Affiliation as printed
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
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Space Research Organisation Netherlands
Affiliation as printed
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
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Space Research Organisation Netherlands
Affiliation as printed
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
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Space Research Organisation Netherlands · Wageningen University & Research
Affiliation as printed
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
now at: Department of Meteorology and Air Quality, Wageningen University, Wageningen, The Netherlands
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California Institute of Technology · Jet Propulsion Laboratory · Space Research Organisation Netherlands
Affiliation as printed
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
now at: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, US
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Space Research Organisation Netherlands
Affiliation as printed
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
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Space Research Organisation Netherlands
Affiliation as printed
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
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Space Research Organisation Netherlands · Vrije Universiteit Amsterdam
Affiliation as printed
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
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Daniel J. Varon corresponding
Harvard University · GHGSat (Canada)
Affiliation as printed
GHGSat Inc, Montréal, Canada
Harvard University, Cambridge, MA, US
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Jason McKeever corresponding
Affiliation as printed
GHGSat Inc, Montréal, Canada
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Dylan Jervis corresponding
Affiliation as printed
GHGSat Inc, Montréal, Canada
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Marianne Girard corresponding
Affiliation as printed
GHGSat Inc, Montréal, Canada
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Itziar Irakulis‐Loitxate corresponding
Universitat Politècnica de València
Affiliation as printed
International Methane Emission Observatory, United Nations Environment Program, Paris, France
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
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Javier Gorroño corresponding
Universitat Politècnica de València
Affiliation as printed
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
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Luis Guanter corresponding
Environmental Defense Fund · Universitat Politècnica de València
Affiliation as printed
Environmental Defense Fund, Amsterdam, The Netherlands
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
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Daniel Cusworth corresponding
Affiliation as printed
Carbon Mapper, Pasadena, CA, US
University of Arizona, Tucson, AZ, US
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Space Research Organisation Netherlands · Vrije Universiteit Amsterdam
Affiliation as printed
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
SRON Netherlands Institute for Space Research, Leiden, The Netherlands
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