Four years of mobile monitoring show that urban waste is the primary source of large methane emissions hotspots in Montreal, Canada
Bibliographic record
Abstract
Abstract Urban centers contribute significantly to anthropogenic methane (CH4) emissions, making them key targets for mitigation. This study aimed to map the spatial distribution of CH4 hotspots in Montreal, Canada, identify potential sources, and quantify emissions from key sectors. In over four years, we surveyed over 3,300 km with our mobile monitoring system and detected 3,045 CH4 hotspots, defined as mole fractions exceeding a baseline. Most hotspots were smaller than 1 ppm (85%), while larger hotspots (>1 ppm) were linked to landfills. Three routes were surveyed 10 times each, and within this subset of hotspots, most (89%) were observed only once. Among all detected hotspots, 487 were classified as leak indications, defined as hotspots with narrow widths (<160 m) and distant from known CH4 sources. Leak indications occurred more frequently in densely populated neighborhoods (R2 = 0.48, p = 5.22 × 10−6), with an estimated emission rate of 250–507 kg day−1. Emissions from four major landfills, calculated through a Gaussian plume inversion, were estimated at 10,064–36,410 kg CH4 day−1, with historical landfills alone contributing 6,641–18,467 kg CH4 day−1. These findings confirm the dominant role of landfills to Montreal CH4 emissions and highlight the importance of targeting waste management sites for urban methane mitigation.
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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".