In-Situ Remediation of Heavy Metal–Contaminated Sediments Using the Resuspension Technique
Bibliographic record
Abstract
Heavy metal pollution in sediments and soil is an unavoidable anthropogenic issue with implications for quality of life and is a major long-term remediation challenge. This paper aimed to evaluate an in-situ remediation technique (resuspension) for sediment that may be employed in a variety of contaminated site cleanup programs. Surface sediment samples were obtained from a shallow harbor on the St. Lawrence River, in Canada in 2019. Harbor sediment from the St. Lawrence River in Quebec is anthropogenically polluted by metals. Various experiments were performed using a designed reactor to evaluate sediment resuspension remediation technology. The method is based on sediments with a higher specific surface area that adsorb more metal contaminants. Therefore, the objective was to remove this fraction by the resuspension technique. Results showed that the levels of seven metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) were reduced by removing only 2.63% of the sediment. Removal efficiency values varied from 3.48% for Cd to 32.4% for Cu). The results of the sequential extraction tests imply that the resuspension technique is capable of decreasing the risk of remobilization of heavy metals in the aquatic ecosystem. Therefore, this method could potentially be used to remediate metal–contaminated sediment with minimal sediment removal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".