Aerial Assessment of Methane Emissions from Canadian Landfills
Bibliographic record
Abstract
Canada is committed to reducing methane emissions by 50% below 2020 levels by 2030 in alignment with the Global Methane Pledge. The waste sector accounts for 23% of Canada’s methane emissions, and accurate estimations of current emissions from landfill sites are needed to guide mitigation efforts. In 2022, we conducted a cross-Canada aircraft-based methane measurement campaign in collaboration with Environment and Climate Change Canada (ECCC) and the UNEP’s International Methane Emission Observatory (IMEO). We used a Twin Otter equipped with high-speed gas analyzers and meteorological measurement sensors, which was flown in ascending loops, downwind transects, or both in combination, at 27 active and inactive municipal solid waste landfills in Ontario and Québec, Canada. Mass balance flux estimates were generated using the Top-Down Emission Rate Retrieval Algorithm. Additional mass balance measurements were made by Scientific Aviation using a similar approach based on Gauss’s theorem. A Gaussian dispersion model was used at other sites where conditions were unsuitable for mass balance. We were also able to compare some results to an independent truck-based measurement campaign of the same sites. Most mass balance measurements fell within a factor of ~3 with Greenhouse Gas Reporting Program data submitted by industry operators, showing reasonable correspondence within expected variability to atmospheric pressure changes and other weather variables.The research indicated that aircraft estimates were consistently higher than those derived from trucks. This implies a possible underestimation in truck measurements, particularly during sunny, low-wind conditions when the thermal lift of landfill CH4 plumes is notable. On the other hand, Gaussian dispersion model estimates were higher and more variable than mass balance-based methane emission rate estimates. We also compared our mass balance estimates to a First Order Decay landfill model used by the Environment and Climate Change Canada Waste Reduction for planning purposes, and we found that the model often overestimated emissions. These measurement-based estimates contribute to a refined understanding of methane emissions from Canadian landfills and provide valuable data for regulatory planning purposes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".