Mitigating Methane Emissions: A Comprehensive Measurement Study of Canadian Landfills
Bibliographic record
Abstract
Canada’s waste sector contributes 23% toward national methane emission totals. Recently Canada committed to a 50% reduction in waste sector methane emissions by 2030 from 2020 levels, as part of its Global Methane Pledge action plan. Achieving this ambitious goal will certainty requires that regulators to be armed with an accurate and measurement-informed understanding of landfill methane emissions. In 2022 we carried out a snapshot methane emissions quantification campaign targeting 125 landfills across Canada, followed in 2023 by more detailed source-level measurements across seasons at selected landfills in various climate zones. Snapshot measurements were carried out by vehicle-based surveying coupled with Gaussian and Lagrangian flux inversion, and aircraft mass balance measurements. Repeating source-based measurements were conducted in 2023 across seasons at 12 landfills for 3 climatic regions using vehicle-based surveys, stationary tripod deployments, and drone measurements of plumes, from on-site and off-site locations. Source-specific flux rates were generated based on triangulation, Lagrangian backtrajectory analysis, and a Gaussian dispersion model, and were assessed for magnitude and temporal variability. In snapshot measurement campaigns across the country, we saw generally good agreement between aircraft mass balance and truck measurements, with a moderate but consistent low bias in the truck emission rate estimates. Lagrangian methods to derive flux rate were comparable as long as input data was limited to exclude highly enriched onsite samples. Throughout a varied population of landfills across Canada, we found that emission rate estimates from measurement campaigns were generally in-line with operator-submitted values to the Canadian Greenhouse Gas Reporting Program, whereas a First Order Decay model used by the federal government for planning purposes tended to over-estimate landfill emissions. Climate zone was a clear predictor of methane generated per waste in place. In more detailed source studies, we found that numerous features on landfill operations could emit methane, most expected, but some unexpected. Management practice was a strong predictor of whether source types emitted significantly, or not. Meteorology and seasonal changes in climate were also strong predictor of emissions over time. These large-scale studies provide a wealth of data upon which Canada can base regulatory development and will be beneficial to countries with similar waste sector patterns and climates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".