Construction, monitoring, and efficiency of a biofilter treating a high flow, lean, landfill gas
Bibliographic record
Abstract
The City of Montreal has committed to achieve carbon neutrality by 2050. To meet this commitment, the city has adopted the Climate Plan 2020-2030, which includes the treatment of landfill gas (LFG). Within this framework, this research aimed to investigate the efficiency of a biofilter designed to treat high volumes of low-concentration LFG collected from lateral trenches at the Complexe Environnemental de Saint-Michel (CESM) in Montreal. The methane oxidation layer (MOL) of this biofilter, employed a material composed of 50% compost and 50% wood chips. Over a 54-week monitoring period, the system effectively maintained temperature conditions favorable to bacterial activity and methane oxidation. To assess the accuracy of predicting the hydraulic behavior of a methane oxidation biosystem (MOB) using numerical modeling, the biofilter was designed and constructed with specific features. In particular, the pore voids at the interface between the MOL and the gas distribution layer (GDL) were intentionally blocked along the downstream quarter of the biofilter length. This design ensures that water reaches the occlusion point due to the capillary barrier effect. Moisture content values remained within the expected range for most of the monitoring phase but increased with time. Despite this issue, the biofilter achieved an average efficiency higher than 95%. The findings underscore the capability of biosystems to manage substantial volumes of lean LFG, but also highlight the importance of acclimatizing the compost before exposure to maximum landfill load.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".