Boosting the circularity of waste management: pretreated mature landfill leachate enhances the anaerobic digestion of market waste
Bibliographic record
Abstract
Adequate waste management is essential not only to ensure healthy living conditions but also to mitigate climate change. Accordingly, the research on developing strategies to boost the circularity of waste management systems is ongoing. In this context, two waste streams are concurrently managed to recover energy and materials in the present study. Specifically, real leachate collected from a full-scale mature landfill site was preliminarily treated through active filtration to remove inhibitory substances partially and then tested, at the laboratory scale, as a nutrient solution for semi-continuous anaerobic digestion of a carbonaceous substrate represented by market waste. The results demonstrate that, at an organic loading rate of 1.0 gVS∙L-1∙d-1, the process was impossible without using the nutrient solution, while the nitrogen present in the pretreated leachate could balance the carbon content of the market waste and provide the system with the necessary buffering capacity, ensuring process stability. The average methane yield (approximately 0.29 NL∙gVS-1) was satisfactory and consistent with the literature. Despite the increases in both the organic loading rate (up to 1.5 gVS∙L-1∙d-1) and volume of added pretreated leachate (up to 100% of the dilution medium), the process remained stable with a slightly lower methane yield of 0.21 NL∙gVS-1, thanks to nitrogen supplementation. The potential use of produced methane as a renewable energy source and residual digestate as fertilizer would close the loop of managing these waste streams.
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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 | 0.001 | 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".