Thomas Barchyn , Michelle Clements, Tyler Gough, Chris Hugenholtz, Abbey Munn, Joseph Samuel, Clay Wearmouth, Coleman Parker Vollrath

PoMELO Passive is a technology that combines vehicle-based pollution measurements from public roads with cloud-based software to: (i) detect emissions from oil and gas sites, and (ii) quantify emissions rates. Automated attribution and plume modeling algorithms provide results with little human intervention, facilitating large scale monitoring programs. PoMELO Passive is operationally deployed at the University of Calgary as part of its pan-Canadian methane monitoring program.

To evaluate performance, the system underwent a blind test program assessing detection and quantification performance. Tests were administered by the Alberta Methane Emissions Program (AMEP) at the Carbon Management Canada Newell County Test Facility, near Brooks, Alberta, Canada from 23-27 September 2024.

Tests were conducted in a blind configuration where release rates were blind to the University of Calgary. Detections and quantifications were produced by the Passive system, then reported to AMEP. Finally, real release rates were un-blinded, facilitating analysis and reporting. Localization performance was not evaluated. Release rates varied from 0.0 g/s to 2.49 g/s CH4 and were metered with a mass flow controller prior to release from a single stack. A total of 190 independent single-release, single-pass experiments were performed.

Detection results indicate that PoMELO Passive effectively detected 60% to 85% of the releases < 1 g/s, and 88% to 100% of the releases > 1 g/s. There were no false positive detections. Non-detects primarily occurred in situations with low wind speeds (< 3 m/s), suggesting detection was modulated by environmental conditions.

Quantification results were assessed at the single- and multi-pass scales to simulate opportunistic and targeted sampling. Single-pass quantification results had little systematic bias, but some variability (linear model slope = 0.927, r2 = 0.70). Replicates of individual release rates were aggregated to assess quantification improvement with averaging multiple plume passes. Multi-pass results similarly had little systematic bias, but less variability (linear model slope = 1.05, r2 = 0.95).

Broadly, PoMELO Passive can produce high quality data in a low-cost and highly scalable deployment model. However, data users require effective tools to carefully manage uncertainty and make full use of data in assimilation and analysis systems.