Single-blind test of airplane-based hyperspectral methane detection via controlled releases

This is a Preprint and has not been peer reviewed. This is version 4 of this Preprint.


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Evan David Sherwin , Yuanlei Chen , Arvind P Ravikumar , Adam R Brandt 


Methane leakage from point sources in the oil and gas industry is a major contributor to global greenhouse gas emissions. The vast majority of such emissions come from a small fraction of "super-emitting" sources which, once detected, can generally be fixed at relatively low cost. We evaluate the emission detection and quantification capabilities of Kairos Aerospace’s airplane-based hyperspectral imaging methane emission detection system. In blinded controlled releases of methane conducted over four days in San Joaquin County, California, USA, Kairos detected 182 of 200 valid nonzero releases, including all 173 over 8 mcfd(CH$_4$) per mile per hour (mph) of wind and none of the 8 nonzero releases below 4 mcfd(CH$_4$)/mph. There were no false positives: Kairos did not detect methane during any of the 19 negative controls. Plume quantification accuracy depends on the wind measurement technique, with a parity slope of 1.15 ($\sigma$=0.037, $R^2$=0.84, N=185) using a cup-based wind meter and 1.45 ($\sigma$=0.059, $R^2$=0.80, N=157) using an ultrasonic anemometer. Quantification error scales roughly as a fixed percentage of emission size. These findings suggest that at 5 mph winds under favorable environmental conditions in the US, Kairos could detect over 50% of total emissions by identifying super-emitting sources.



Education, Engineering, Other Engineering


remote sensing, Hyperspectral Imaging, methane leakage


Published: 2020-01-08 07:56

Last Updated: 2020-06-16 20:01

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CC BY Attribution 4.0 International

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