Robust Probabilities of Detection and Quantification Uncertainty for Aerial Methane Detection: Examples for Three Airborne Technologies

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Authors

Bradley Mark Conrad , David R Tyner, Matthew R Johnson

Abstract

Thorough understanding of probabilities of detection (POD) and quantification uncertainties is fundamentally important when applying aerial measurement technologies as part of alternative means of emission limitation (AMEL) or alternate fugitive emissions management programs (Alt-FEMP), as part of monitoring, reporting, and verification (MRV) efforts, and in surveys designed to support measurement-based emissions inventories and mitigation tracking. This paper presents a robust framework for deriving continuous probability of detection functions and quantification uncertainty models for aerial measurement techniques based on controlled release data. Using extensive fully- and semi-blinded controlled release experiments to test Bridger Photonics Inc.’s Gas Mapping LiDAR (GML), as well as available release data for Kairos LeakSurveyor and NASA/JPL AVIRIS-NG technologies, robust POD functions are derived that enable calculation of detection probability for any given source rate, wind speed, and flight altitude. Uncertainty models are separately developed that independently address measurement bias, bias variability, and measurement precision, allowing for a distribution of the true source rate to be directly calculated from the source rate estimated by the technology. Derived results demonstrate the potential of all three technologies in methane detection and mitigation, and the developed methodology can be readily applied to characterize other techniques or update POD and uncertainty models following future controlled release experiments. Finally, the analyzed results also demonstrate the importance of using controlled release data from a range of sites and times to avoid underestimating measurement uncertainties.

DOI

https://doi.org/10.31223/X5S05F

Subjects

Atmospheric Sciences, Climate, Environmental Monitoring, Mechanical Engineering, Oil, Gas, and Energy, Statistical Methodology, Statistical Models

Keywords

methane, remote sensing, aerial detection sensitivity, quantification uncertainty, monitoring, reporting, and verification, MRV, oil and gas, AMEL, Alt-FEMP, measurement-based inventories, fugitive emissions

Dates

Published: 2022-06-17 17:22

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
Non-confidential data will be made available upon request.

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