Use of Alternative Cover Materials to Control Surface Emissions (H2S and VOCs) at an Engineered Landfill
Bibliographic record
Abstract
Between 2010 and 2015, the Bellechasse Regional County Municipality (Bellechasse RCM) was affected by particularly noxious odors issuing from its Municipal Solid Waste Landfill (Bellechasse RCM MSWL) in Armagh, Canada. A study carried out in 2015-2016 by Centre de recherche industrielle du Québec (CRIQ) confirmed that it was still possible for hydrogen sulfide (H2S) emissions to cause odor issues in and around the site. The experimental project carried out by CRIQ in cooperation with Bellechasse RCM, Englobe, Quebec City and the Regroupement des récupérateurs et des recycleurs de matériaux de construction et de démolition du Québec (represented by AIM Éco-centre) made it possible to test three (3) different industrial residue as an alternative cover materials on site and study how they controlled H2S emissions, volatile organic compounds (VOCs) and odors at the Bellechasse RCM’s landfill. The site was monitored from November 2016 to September 2017 to confirm the effectiveness of alternative biofiltration cover materials (soil + compost), domestic waste incineration bottom ash and 0 to 2.5-inch concrete residues and to compare the results with the sand cover currently used as the cover material. Effectiveness was determined by measuring the Area Source Emission Rate (ASER) with a 3 m x 3 m static flux chamber developed for the project. Methane measurements were concomitantly taken to confirm that the biogas could escape through the cover materials. The monitoring results made it possible to demonstrate that domestic waste incineration bottom ash as well as 0-2.5 in. concrete received the highest load of H2S and showed an H2S capture performance of greater than 83ϿFor volatile organic compounds, materials such as 0-2.5 in. concrete and the alternative biofiltration cover materials were most effective for capture (greater than 73Ͽfor the highest loads. The lowest content of CH4 after covering was measured for the alternative cover materials. The site where the incineration bottom ash was used managed to decrease odors by ±200 odor units. Overall, we have demonstrated in this project, the capacity of different alternative cover materials under real condition for the control of gas emissions from landfill.
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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 | 0.000 |
| 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".