Bibliographic record
Abstract
Abstract. Atmospheric methane (CH4) is the second most important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ~60 % of global CH4 emissions, of which 20 % come from oil & gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat’s satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK in Spring/Summer 2023, and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, NAME, to estimate the flux from both satellite and ground-based observation methods and assess the leak’s contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat’s emission estimates to be in broad agreement with those made from the in-situ measurements. During the study period (March–June 2023) GHGSat’s emission estimates are 236–1357 kg CH4 hr-1 whereas the mobile surface measurements are 886–998 kg CH4 hr-1. The large variation is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 hr-1, which are lower than the satellite- and mobile survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat’s observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including for unplanned fugitive releases that are inherently challenging to identify, track and estimate their impact and duration. Rapid access to such evidence to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing the anthropogenic contribution to climate change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.445 | 0.285 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".