Estimation of Greenhouse Gas Emissions in Municipal Solid Waste Landfills in Ontario Using Mathematical Models and Direct Measurements
Bibliographic record
Abstract
Waste management is increasingly becoming a serious environmental issue as a result of the associated greenhouse gas emissions from municipal solid waste landfills. In this study, a life-cycle Waste Reduction Model (WARM) has been utilized to evaluate the current municipal solid waste management in the City of Guelph and assesses possible alternative scenarios based on the associated GHG emissions. The results showed that the scenario with enhanced waste-to-energy, reduction at source and recycling has resulted in a high avoided emissions (0.74 kg CO2Eq/kg MSW), whereas the anaerobic Digestion scenario caused the lowest avoided emissions of 0.39 kg CO2Eq/kg MSW. Moreover, this research work has presented quantification techniques of landfill emissions through different models including first order decay rate model (LandGEM) and fuzzy logic model. High correlation was found between actual GHG emissions data from Ontario’s large landfills and LandGEM model data. A methane generation potential (Lo ) of 102 m3/t and a decay rate (k) of 0.037 yr-1 was determined for Ontario landfills. A fuzzy based model was also found to be comparable with the first order decay models in estimation of methane generation. The study also showed that utilization of soil top covers to oxidize methane (use of methanotrophs) has been demonstrated to drastically reduce environmental burdens. For this reason, an independent study was dedicated toward measuring fugitive methane from one of the largest landfills in Ontario (Halton Landfill). A cost-effective method using Flame Ionization Detector was used to measure the actual emissions within the perimeter of the landfills. Fugitive methane concentrations ranged from 0.1 ppm to 63 ppm with the largest emission attributed to spots/areas with malfunctioning gas extraction systems, flooding, or unsealed leachate manholes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".