Cutting-edge approaches in remediating soils co-contaminated by heavy metals and polychlorinated biphenyls: current progress and future directions
Bibliographic record
Abstract
Soil contaminated with heavy metals (HMs) and polychlorinated biphenyls (PCBs) poses significant threats to human health and the ecological environment, attracting widespread attention. Although various methods have been developed to concurrently reduce these pollutants, a critical review of HMs–PCBs co-contaminated soil remediation is still lacking. This study aimed to fill this gap by systematically reviewing the progress made over the past decade. It began with an investigation of the sources and current status of HMs–PCBs co-contaminated soils, summarizing site characteristics and revealing correlation between the concentrations of strongly oxidative/reductive HMs and PCBs. The review then summarized the diverse remediation strategies for soil HMs–PCBs, focusing on the mechanisms of reduction, oxidation, and biological treatments. Particular emphasis was placed on synergistic remediation strategies, including biological, physicchemical-biological and physicchemical complementary approaches, highlighting the advantages of integrated methods, such as combining vegetation and microbial remediation, in minimizing environmental disturbance. Finally, the study identified challenges and future directions, including the development of new materials, the need for practical field applications, and further research into combined technologies and their interactions. This review provides essential insights and technical guidance for addressing the complex issue of HMs–PCBs co-contaminated soil remediation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".