Source category attribution of methane emissions in Calgary using Positive Matrix Factorization (PMF)
Bibliographic record
Abstract
Cities emit methane (CH4) and have a role to play in mitigating the climate impacts of their emissions. Research suggests that CH4 emissions from most North American cities have large contributions from natural gas distribution and end use. In this work, we examine trace gas measurements from a central air monitoring station in Calgary, Alberta, Canada to attribute the city’s CH4 emissions to major source categories. Using Positive Matrix Factorization (PMF), we identified four primary CH4 emissions source categories: natural gas – fugitives, natural gas – incomplete combustion, waste/biogenic, and petroleum product processing. Results from PMF modeling indicate that the bulk of CH4 emissions in Calgary are from natural gas fugitives and incomplete combustion (81% ± 35%). This is much higher than the proportion derived from available bottom-up emissions inventories. The CH4 emissions from natural gas sources increase in winter and may be related to increased natural gas use for space heating. Emissions from waste/biogenic sources were the next largest contributor, which doubled in warmer months, consistent with temperature-driven microbial activity. Though relatively small, CH4 emissions from petroleum product processing are non-negligible and consistent. Overall, these findings underscore the need for targeted mitigation strategies focused on the natural gas supply chain, while also highlighting the influence of seasonal dynamics on urban CH4 emissions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".