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 majority of such emissions come from a small fraction of ``super-emitting" sources. We evaluate the emission detection and quantification capabilities of Kairos Aerospace’s airplane-based hyperspectral imaging methane emission detection system for methane fluxes of 18 to 1,025 kilograms per hour of methane (kgh($CH_4$)). 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 15 kgh($CH_4$) per meter per second (mps) of wind and none of the 12 nonzero releases below 8.3 kgh($CH_4$)/mps. 9 of the 26 releases in the partial detection range of 5 to 15 kgh($CH_4$)/mps were detected. There were no false positives: Kairos did not detect methane during any of the 21 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. Performance is comparable even with only modeled wind data. For emissions above 15 kgh/mps, quantification error scales as roughly 30-40\% of emission size, even when using wind reanalysis data instead of ground-based measurements. This reflects both uncertainty in wind measurements and in Kairos' estimates. These findings suggest that at 2 mps winds under favorable environmental conditions in the US, Kairos could detect and quantify over 50\% of total emissions by identifying super-emitting sources.