Bibliographic record
Abstract
Abstract. Oil and gas facilities are responsible for approximately 35 % of global emissions of methane (CH4), a potent greenhouse gas, posing significant environmental and regulatory challenges. While Continuous Emission Monitoring Systems (CEMS) are widely implemented to track real-time emissions, their effectiveness in localizing specific CH4 emission sources remains limited, particularly under complex environmental conditions. This study integrates CEMS technologies with a Lagrangian Stochastic Back-Trajectory Model, along with an automated Gradient Indicator (GI) tool to improve methane source localization accuracy in oil and gas settings. The model was then applied to a real-world gas distribution site to validate its performance in accurately localizing methane emissions under operational conditions. Using synthetic data simulations, we evaluated the performance of this integrated system under various atmospheric stability conditions, sensor-source height differences, and source proximity. Our results indicate that this combined approach significantly enhances localization performance, achieving a 90 % probability of detection (POD) within a 25–75 meter source-sensor distance under optimal conditions. However, detection performance varied across configurations, with false positive rates (FPF) ranging from 22 % to 86 %, and localization accuracy (LA) ranging from 14 % to 78 %, depending on atmospheric stability, source-sensor geometry, and height differences. The Localization Accuracy (LA) improves when sensor placements are exactly downwind of the emission sources (alignment). The system meets Canadian regulatory requirements for CEMS applications by maintaining localization accuracy above 90 % for unstable and slightly neutral atmospheric conditions, ensuring that emissions are correctly attributed. However, neutral atmospheric conditions and large height differentials between sensors and sources reduce localization accuracy, making optimized sensor configurations important. Findings of the research can be useful for upgrading CEMS systems and help them to overcome some difficulties from regulations associated with methane emission reporting and mitigation efforts.
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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 | 0.011 |
| 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 | 0.008 |
| Insufficient payload (model declined to judge) | 0.412 | 0.271 |
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".