Detection technology benchmarking

Controlled methane release field site experimental set up in Arizona, used to test a variety of detection technologies (El Abbadi, et al., 2023).

Methane detection technologies, both ground-based and remote sensing, are playing an increasing role in voluntary industry and regulatory mechanisms to detect, quantify, and reduce methane. However, rigorous and independent technology testing is needed to grow trust in these capabilities.

We conduct field-based experiments of innovative methane detection technologies ranging from continuous monitors to airplane- and satellite-based platforms. Recent controlled tests have shown that aircraft can reliably detect emissions as small as 5 kg/hour. And satellite-based remote sensing is proving effective in identifying large methane point sources, with controlled release experiments showing comparable accuracy to aircraft systems. Ongoing studies will continue to benchmark performance under varying environmental conditions to grow understanding of how these technologies perform in real-world settings.

Related Publications

Chen, Z., El Abbadi, S., Sherwin, E., Burdeau, P., Rutherford, J., Chen, Y., Zhang, Z., & Brandt, A. (2024). Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study. ACS ES&T Air, 1(8), 871-884. https://doi.org/10.1021/acsestair.4c00015
El Abbadi, S., Chen, Z., Burdeau, P., Rutherford, J., Chen, Y., Zhang, Z., Sherwin, E., & Brandt, A. (2024). Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation. Environmental Science & Technology, 58(22). https://doi.org/10.1021/acs.est.4c02439
Sherwin, E., Rutherford, J., Chen, Y., Aminfard, S., Kort, E., Jackson, R., & Brandt, A. (2023). Single-blind validation of space-based point-source detection and quantification of onshore methane emissions. Scientific Reports, 13, 3836. https://doi.org/10.1038/s41598-023-30761-2
Zhang, Z., Sherwin, E., Varon, D., & Brandt, A. (2022). Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery. Atmospheric Measurement Techniques, 15(23), 7155-7169. https://doi.org/10.5194/amt-15-7155-2022
Bell, C., Rutherford, J., Brandt, A., Sherwin, E., Vaughn, T., & Zimmerle, D. (2022). Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR. Elementa: Science of the Anthropocene, 10(1), 00080. https://doi.org/10.1525/elementa.2022.00080
Wang, J., Ji, J., Ravikumara, A., Savarese, S., & Brandt, A. (2022). VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera. 238, 121516. https://doi.org/10.1016/j.energy.2021.121516
Sherwin, E., Chen, Y., Ravikumar, A., & Brandt, A. (2021). Single-blind test of airplane-based hyperspectral methane detection via controlled releases. Elementa: Science of the Anthropocene, 9(1), 00063. https://doi.org/10.1525/elementa.2021.00063
Klise, K., Nicholson, B., Laird, C., Ravikumar, A., & Brandt, A. (2020). Sensor Placement Optimization Software Applied to Site-Scale Methane-Emissions Monitoring. Journal of Environmental Engineering, 146(7). https://doi.org/10.1061/(ASCE)EE.1943-7870.0001737
Ravikumar, A., Roda-Stuart, D., Liu, R., Bradley, A., Bergerson, J., Nie, Y., Zhang, S., Bi, X., & Brandt, A. (2020). Repeated leak detection and repair surveys reduce methane emissions over scale of years. Environmental Research Letters, 15(3), 034029. https://doi.org/10.1088/1748-9326/ab6ae1
Wang, J., Tchapmi, L., Ravikumar, A., McGuire, M., Bell, C., Zimmerle, D., Savarese, S., & Brandt, A. (2020). Machine vision for natural gas methane emissions detection using an infrared camera. Applied Energy, 257, 113998. https://doi.org/10.1016/j.apenergy.2019.113998
Ravikumar, A., Sreedhara, S., Wang, J., Englander, J., Roda-Stuart, D., Bell, C., Zimmerle, D., Lyon, D., Mogstad, I., Ratner, B., & Brandt, A. (2019). Single-blind inter-comparison of methane detectiontechnologies – results from the Stanford/EDF MobileMonitoring Challenge. Elementa: Science of the Anthropocene, 7(37). https://doi.org/10.1525/elementa.373
Ravikumar, A., Wang, J., McGuire, M., Bell, C., Zimmerle, D., & Brandt, A. (2018). “Good versus Good Enough?” Empirical Tests of Methane Leak Detection Sensitivity of a Commercial Infrared Camera. Environmental Science & Technology, 52(4), 2368–2374. https://doi.org/10.1021/acs.est.7b04945
Ravikumar, A., Wang, J., & Brandt, A. (2016). Are Optical Gas Imaging Technologies Effective For Methane Leak Detection?. Environmental Science & Technology, 51(1), 718–724. https://doi.org/10.1021/acs.est.6b03906