Remediation of mercury-contaminated soils and sediments using biochar: a critical review
Bibliographic record
Abstract
Abstract The transformation of mercury (Hg) into the more toxic and bioaccumulative form methylmercury (MeHg) in soils and sediments can lead to the biomagnification of MeHg through the food chain, which poses ecological and health risks. In the last decade, biochar application, an in situ remediation technique, has been shown to be effective in mitigating the risks from Hg in soils and sediments. However, uncertainties associated with biochar use and its underlying mechanisms remain. Here, we summarize recent studies on the effects and advantages of biochar amendment related to Hg biogeochemistry and its bioavailability in soils and sediments and systematically analyze the progress made in understanding the underlying mechanisms responsible for reductions in Hg bioaccumulation. The existing literature indicates (1) that biochar application decreases the mobility of inorganic Hg in soils and sediments and (2) that biochar can reduce the bioavailability of MeHg and its accumulation in crops but has a complex effect on net MeHg production. In this review, two main mechanisms, a direct mechanism (e.g., Hg-biochar binding) and an indirect mechanism (e.g., biochar-impacted sulfur cycling and thus Hg-soil binding), that explain the reduction in Hg bioavailability by biochar amendment based on the interactions among biochar, soil and Hg under redox conditions are highlighted. Furthermore, the existing problems with the use of biochar to treat Hg-contaminated soils and sediments, such as the appropriate dose and the long-term effectiveness of biochar, are discussed. Further research involving laboratory tests and field applications is necessary to obtain a mechanistic understanding of the role of biochar in reducing Hg bioavailability in diverse soil types under varying redox conditions and to develop completely green and sustainable biochar-based functional materials for mitigating Hg-related health risks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".