Using satellites to uncover large methane emissions from landfills Article Swipe
Joannes D. Maasakkers
,
Daniel J. Varon
,
Aldís Elfarsdóttir
,
Jason McKeever
,
Dylan Jervis
,
Gourav Mahapatra
,
Sudhanshu Pandey
,
Alba Lorente
,
Tobias Borsdorff
,
Lodwijck Foorthuis
,
Berend J. Schuit
,
Paul Tol
,
T. A. van Kempen
,
Richard M. van Hees
,
Ilse Aben
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.31223/x5n33g
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.31223/x5n33g
As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Landfills are responsible for large methane emissions that can be readily abated but have been sparsely observed. Here we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfill facilities. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots, and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this ‘tip and cue’ approach, we detect and analyze strongly emitting landfills (3-29 t hr−1) in Buenos Aires (Argentina), Delhi (India), Lahore (Pakistan), and Mumbai (India). We find that city-level emissions are 1.6-2.8 times larger than reported in commonly used emission inventories and that the landfills contribute 5-47% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane super-emitters at the facility-level.
Related Topics
Concepts
Methane
Environmental science
Methane emissions
Emission inventory
Greenhouse gas
Leverage (statistics)
Atmospheric methane
Satellite
Work (physics)
Remote sensing
Meteorology
Air quality index
Geography
Engineering
Computer science
Geology
Oceanography
Aerospace engineering
Mechanical engineering
Biology
Machine learning
Ecology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31223/x5n33g
- http://eartharxiv.org/repository/object/2985/download/6039/
- OA Status
- gold
- Cited By
- 21
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200244887
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200244887Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31223/x5n33gDigital Object Identifier
- Title
-
Using satellites to uncover large methane emissions from landfillsWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-12-28Full publication date if available
- Authors
-
Joannes D. Maasakkers, Daniel J. Varon, Aldís Elfarsdóttir, Jason McKeever, Dylan Jervis, Gourav Mahapatra, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Lodwijck Foorthuis, Berend J. Schuit, Paul Tol, T. A. van Kempen, Richard M. van Hees, Ilse AbenList of authors in order
- Landing page
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https://doi.org/10.31223/x5n33gPublisher landing page
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https://eartharxiv.org/repository/object/2985/download/6039/Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://eartharxiv.org/repository/object/2985/download/6039/Direct OA link when available
- Concepts
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Methane, Environmental science, Methane emissions, Emission inventory, Greenhouse gas, Leverage (statistics), Atmospheric methane, Satellite, Work (physics), Remote sensing, Meteorology, Air quality index, Geography, Engineering, Computer science, Geology, Oceanography, Aerospace engineering, Mechanical engineering, Biology, Machine learning, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 7, 2022: 12, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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