Potential of biochar and humic substances for phytoremediation of trace metals in oil sands process affected water
Bibliographic record
Abstract
Oil sands process affected water (OSPW) is produced during bitumen extraction and typically contains high concentrations of trace metals. Constructed wetlands have emerged as a cost effective and green technology for the treatment of metals in wastewaters. Whether the addition of amendments to constructed wetlands can improve metal removal efficiency is unknown. We investigated the synergistic effects of carbon based amendments and wetland plant species in removal of arsenic, cadmium, cobalt, chromium, copper, nickel, and selenium from OSPW. Three native wetland species (Carex aquatilis, Juncus balticus, Scirpus validus) and two amendments (canola straw biochar, nano humus) were investigated in constructed wetland mesocosms over 60 days. Amendment effect on metal removal efficiency was not significant, while plant species effect was. Phytoremediation resulted in removal efficiencies of 78.61-96.31 % for arsenic, cadmium, and cobalt. Carex aquatilis had the highest removal efficiencies for all metals. Amendments alone performed well in removing some metals and were comparable to phytoremediation for cadmium, cobalt, copper, and nickel. Metals were primarily distributed in roots with negligible translocation to shoots. Our work provides insights into the role of plants and amendments during metal remediation and their complex interactions in constructed treatment wetlands.
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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 | 0.000 |
| 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".