Articles | Volume 18, issue 5
https://doi.org/10.5194/amt-18-1301-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-1301-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantitative estimate of several sources of uncertainty in drone-based methane emission measurements
Tannaz H. Mohammadloo
Shell Global Solutions International B.V., Amsterdam, the Netherlands
Matthew Jones
Shell Global Solutions International B.V., Amsterdam, the Netherlands
Bas van de Kerkhof
Shell Global Solutions International B.V., Amsterdam, the Netherlands
Kyle Dawson
SeekOps Inc., Austin, Texas, United States of America
Brendan J. Smith
SeekOps Inc., Austin, Texas, United States of America
Stephen Conley
Scientific Aviation, a Division of ChampionX, Boulder, Colorado, United States of America
Abigail Corbett
SeekOps Inc., Austin, Texas, United States of America
current address: GTI Energy, Chicago, Illinois, United States of America
Rutger IJzermans
CORRESPONDING AUTHOR
Shell Global Solutions International B.V., Amsterdam, the Netherlands
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Short summary
Methane is a potent greenhouse gas. Trustable detection and quantification of methane emissions at facility level are critical for identifying the largest sources and prioritizing them for repair. We provide a systematic analysis of several sources of uncertainty in drone-based methane emission surveys based on theoretical considerations and historical data sets. We provide guidelines for industry on how to avoid or minimize errors in drone-based methane emission quantification surveys.
Methane is a potent greenhouse gas. Trustable detection and quantification of methane emissions...
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