A comparison of methane source localization methods in landfills across Canada using truck-based measurement, Lagrangian stochastic back trajectory modeling, and Landsat thermal images
Bibliographic record
Abstract
Canada is a signatory to the Global Methane Pledge and is aiming to achieve a 75% cut in methane from 2030 levels from oil and gas production through regulatory updates and a 50% cut in waste sector emissions using new regulations. Despite numerous large-scale studies that have measured and identified emission sources from Canada's oil and gas sector, there are virtually no measurements of emissions from landfills in Canada. As such, inventory values for landfill emissions are based on a combination of industry-submitted data and emission factors. Canada could design better policies and regulations if policymakers had access to actual emission rates and source types. Therefore, we designed and carried out a large-scale measurement campaign targeting minimally 125 landfills across Canada, using aircraft mass balance measurements and truck-based measurements (i.e., downwind and onsite transects) coupled with Gaussian inversions to determine emission rate. This study focuses on methodologies used to determine source locations, or methane hotspots, on the landfill surface. In Particular, source attribution methods included a Lagrangian back trajectory footprint analysis of mobile surveys, onsite mixing ratios and winds measured with the truck, as well as Landsat thermal retrievals that have been shown in prior studies to correlate with methane hotspots. Measurements were carried out between June and December 2022 using one or more measurement methodologies for a total of 143 sites. We performed truck-based measurements of mixing ratios across navigable portions of 59 landfills. All indicators showed some correlation to mixing ratios collected onsite, although Lagrangian analysis products from downwind measurements were somewhat more reliable in flagging hotspots than the satellite thermal indices. The indicators often highlighted the active disposal face, leachate impoundment ponds, or compost areas, as the active source area. This study will help contribute much-needed source information for solid waste sector regulatory design in Canada and has the potential to help improve models of landfill methane generation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".