Methane emissions from residential natural gas meter set assemblies
Bibliographic record
Abstract
Residential natural gas meter set assemblies (MSAs) emit methane (CH4), but reported emissions factors vary. To test existing emissions factors, we quantified CH4 emissions from 37 residential MSAs in Calgary, Alberta, Canada. A notable difference with previous studies is the targeted measurement of regulator vents in this study, which were measured with a static chamber, while fugitives were measured with a modified hi-flow sampler. Emissions were dominated by pressure regulator vents (emissions factor = 1.18 g CH4/h/MSA), but 7 fugitives were found (emissions factor = 0.018 g CH4/h/MSA). Six regulator vents were emitting at notably higher rates (≥ 1.79 g CH4/h/MSA). The total empirical emissions factor was 1.20 g CH4/h/MSA (95 % CI, 1.03 to 1.37 g/h/MSA). This is ~7 times higher than the emissions factor for residential MSAs used in the U.S. EPA's Greenhouse Gas Inventory, which may not include emissions from regulator vents. Upscaling to annual CH4 emissions in Calgary indicates 3234.6 t CH4/yr (95 % CI, 2776.4 t to 3692.9 t CH4/yr) could be emitted from MSAs. This is equivalent to 4.1 % (95 % CI, 3.5 % to 4.7 %) of total city-level CH4 emissions as estimated with satellite data. Results suggest residential MSA emissions may be under-estimated and further study isolating root causes of regulator vent emissions is required to guide mitigation and improve emissions modeling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".