Impact of Landfill Gas Exposure on Vegetation in Engineered Landfill Biocover Systems Implemented to Minimize Fugitive Methane Emissions from Landfills
Bibliographic record
Abstract
Engineered landfill biocovers (LBCs) minimize the escape of methane into the atmosphere through biological oxidation. Vegetation plays a crucial role in LBCs and can suffer from hypoxia caused by the displacement of root-zone oxygen due to landfill gas and competition for oxygen with methanotrophic bacteria. To investigate the impact of methane gas on vegetation growth, we conducted an outdoor experiment using eight vegetated flow-through columns filled with a 45 cm mixture of 70% topsoil and 30% compost, planted with three types of vegetation: native grass blend, Japanese millet, and alfalfa. The experiment included three control columns and five columns exposed to methane, as loading rates gradually increased from 75 to 845 gCH4/m2/d over a period of 65 days. At the highest flux, we observed a reduction of 51%, 31%, and 19% in plant height, and 35%, 25%, and 17% in root length in native grass, Japanese millet, and alfalfa, respectively. The column gas profiles indicated that oxygen concentrations were below the levels required for healthy plant growth, which explains the stunted growth observed in the plants used in this experiment. Overall, the experimental results demonstrate that methane gas has a significant impact on the growth of vegetation used in LBCs.
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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 | 0.000 | 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 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".