Canadian landfill gas production : improving the model to estimate present and future methane production
Bibliographic record
Abstract
Landfìll gas, consisting primarily of methane and carbon dioxide, negatively affects human and environmental health.Its production and ultimate release into the atmosphere contribute to greenhouse gases and global warming.Accurate portrayals of current and future Canadian landfill-gas production guide appropriate methods for its mitigation.Without adequate knowledge regarding how the landfill environment operates, it is difficult to employ an environmentally friendly and cost effective management strategy.Modeled projections can provide insight to guide current and future policy decisions regarding the control and prevention of methane production.This thesis improves cument and future Canadian landfill methane production models, thereby advancing the knowledge used to select waste management and reduction strategies.Other objectives achieved include: 1) choosing a landfill gas model that best predicts methane production,2) advancing the science of model inputs, and 3) predicting future methane production based on current and prospective waste diversion practices.The German EPE& TNO, Belgium, LandGEM, and Scholl Canyon models were tested for their suitability to model methane production for 35 Canadian landfills.A modified Scholl Canyon/LandGEM model, the "Sawyer Model" best calculated methane production, initially yielding a relative percent error of 19Yo and p:0.2501.Inputs for this model, decay rate (k) and methane generation potential (Lo), were reevaluated to best fit the measured data.Decay rates were trended with annual precipitation, improving the methodology used to calculate site-specific values.Methane generation potentials were assessed using site-speciflrc and provincial waste composition data.Their calculated values were detennined to be I0o/o less than that required to mimic Canadian landfill conditions successfully.Using these modified/improved inputs, the "Sawyer Model" yielded a median of -2%o and p:0.5538.Although this change in Lo increased the percent error, the likeness between the measured and modeled data changed from moderate to high.Other variables not cunently included in the "Sawyer Model" were assessed for their relationship/contribution to methane production.Current waste-in-place amounts (>10 Mt of waste versus <10 Mt of waste) and landfill operating status (closed versus operating landfills) were found to have significant relationships with methane production and should be considered when modeling landfills.Finally, four landfills were modeled to the year 2025 under five different waste diversion scenarios, illustrating that methane recovery projects are viable under avaríety of waste diversion scenarios and in conjunction with recycling/composting initiatives.lll their support and guidance throughout the project.Sincere thanks to Craig Palmer and Paula Critchley for their invaluable landfìll-gas modeling knowledge, experience
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".