Effect of Aeration on Fresh and Aged Municipal Solid Waste in a Simulated Landfill Bioreactor
Bibliographic record
Abstract
Abstract Municipal solid waste (MSW) is slow to stabilize under conventional anaerobic landfill conditions, demanding long-term monitoring and pollution control. Provision of aerobic conditions offers several advantages including accelerated leachate stabilization, increased landfill airspace recovery and a reduction in greenhouse gas emissions. Air injection was applied over 130 days to bench-scale bioreactors containing fresh and aged MSW representative of newly constructed and pre-existing landfill conditions. In the fresh MSW simulation bioreactors, aeration reduced the average time to stabilization of leachate pH by 46 days, TSS by 42 days, TDS by 84 days, BOD5 by 46 days and COD by 32 days. In addition, final leachate concentrations were consistently lower in aerated test cells. There was no indication of a gradual decrease in the concentration of ammonia, and it is likely this high ammonia concentration would continue to be problematic in bioreactor landfill applications. This study focussed only on biodegradability of organics in the solid waste. The concentrations of the nonreactive or conservative substances such as chloride and/or heavy metals remain in the bioreactor landfills due to the continuous recirculation of leachate. The results of this study demonstrate the potential for air injection to accelerate stabilization of municipal solid waste, with greatest influence on fresh waste with a high biodegradable organic fraction.
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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 | 0.001 |
| 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 | 0.001 | 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".