Innovative bio-covers to mitigate the landfill methane emissions under wide seasonally fluctuating conditions
Bibliographic record
Abstract
Laboratory tests at the University of Manitoba provided the basis for an effective methanotrophic bio-cover design, using a mixture of yard and leaf waste compost and biosolids compost from the City of Winnipeg’s composting facilities. In Step 1 of the current study, a pilot bio-window at a landfill led to observations, including high seasonal variation of ambient temperature causing a thick, solid winter frost cover affecting gas exchange in winter, as well as temperatures above 45ºC in late summer within the bio-window. High fluctuations in the temperature made a shift in methanotrophic populations from mesophiles to thermophiles. Low air diffusion through the bio-window was also observed. Dryness in summer caused low MC at the top layers restricting CH4 oxidation. The effective methanotrophy under favorable environmental conditions was 80%. In Step 2 of the study, based on the findings from the in situ bio-window, interactive effects of critical environmental factors including MC, temperature, and CH4 concentration were investigated through batch incubations. Box–Behnken Design adopting Response Surface Methodology was implemented to develop a statistical model and optimize the conditions for the CH4 oxidation in the compost mixture. The maximum value of CH4 oxidation was obtained under optimum MC of 47.42%, temperature of 32.72℃, and initial CH4 concentration of 23.81%. A parabolic curve for MC and temperature was observed simultaneously, and CH4 concentration was not a significant controlling factor. In Step 3, in-depth column tests were conducted to improve bio-cover performance by increasing aeration capacity in deeper layers through addition of inorganic coarse materials. To increase oxygen (O2) penetration, two sets of columns were packed with compost and two different size of gravel (¼” and ½”) at different gravel:compost mixing ratios (1:1, 1:3, and 1:7), established in three consecutive stages. Column were run for 101 days to find the optimum mixture with maximum CH4 removal. Results showed that the CH4 removal mechanisms in the columns was a combination of adsorption and biological treatment. The highest methanotrophic CH4 removal efficiency was 65% obtained for ¼” gravel to compost (1:7) with the highest portion of compost and the lowest amount of fine gravel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".