Development of a Resuspension Technique for Heavy Metal Remediation of a Shallow Contaminated Harbour
Bibliographic record
Abstract
Heavy metal pollution is an anthropogenic unavoidable issue with implications for life. Heavy metals are natural occurring elements, having both natural and anthropogenic sources. Sediment and soil contamination with toxic heavy metals, including (Cr, Ni, Cu, Zn, As, Cd and Pb), represents a major long-term remediation challenge. A surface sediment sample (<10 cm) was collected from shallow harbour on the bank of the St. Lawrence River, in Quebec, Canada in 2019 and 2021 for a sediment decontamination study. Harbour sediment from the St. Lawrence River, in Quebec is anthropogenically polluted by heavy metals. To evaluate the metal pollution in this area, the total concentrations of heavy metals in selected stations were analyzed. A series of laboratory-based experiments under various conditions were performed using designed reactor in order to provide information for sediment remediation technology development. The concept of the resuspension method is that finer sediments have a greater tendency to adsorb the contamination. We are therefore developing a sediment-treatment process based on heavy metal removal by resuspension technique. Sequential extraction procedures were used on the sediment to determine the speciation of the heavy metals before remediation and in the suspended particle matter (SPM). The resuspension method was successful in reducing the concentration of seven selected heavy metals (Cr, Ni, Cu, Zn, As, Cd and Pb) by removing just 2.63% of the contaminated sediment (2019). Removal efficiency values on average were positive for all heavy metals (with a minimum 3.48% for Cd and a maximum of 32.4% for Cu). The results of the sequential extraction tests implied that the resuspension technique is capable of decreasing the risk of remobilization of heavy metals in the aquatic ecosystem. The effects of heavy metals on survival and growth of midge Chironomus riparius and Hyalella azteca were investigated. Both larval survival and growth did not show significantly difference between sediment samples (before remediation, after remediation and SPM) with controls. Therefore, exposure of Chironomus riparius and Hyalella azteca larvae to sediments collected in the St. Lawrence River did not have any toxic effects according to the results. \nThe resuspension method showed desirable results for the removal of heavy metals from bottom sediments (2019). A small amount of sediment removed from the system, and no chemical substances were employed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".