Heavy metal transformation in livestock manure (co-)pyrolysis: pathways toward safe biochar and sustainable agriculture
Bibliographic record
Abstract
Livestock manure is enriched with heavy metals such as copper, zinc, and cadmium due to feed additives and intensive farming practices. Inadequate management can lead to soil accumulation, nutrient cycle disruption, and ecosystem risks. Pyrolysis, as a versatile thermochemical process, simultaneously enables pollutant control, energy recovery, nutrient recycling, and heavy metal stabilization. This review integrates mechanistic insights with sustainability-oriented evaluation, linking thermochemical transformations to agricultural applications and policy frameworks. We examine thermal-induced changes in heavy metal speciation and mobility, highlighting stabilization through encapsulation, complexation, and mineralization, while also critically assessing sequential extraction methods. The synergistic effects of co-pyrolysis and mineral additives are further discussed. By bridging molecular-scale mechanisms with sustainable resource management, this work provides a cross-disciplinary perspective to guide safe biochar reuse, integrated manure management, and broader sustainability goals. • Heavy metal transformation pathways during manure pyrolysis are systematically reviewed. • Speciation dynamics determine ecological risk and stabilization mechanisms. • Sequential extraction methods for manure-derived biochar are critically assessed. • Co-pyrolysis and additives enhance metal immobilization and reduce leaching.
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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 | 0.001 |
| 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".