Copper hydrophytoremediation by wetland macrophytes in semi-hydroponic and hydroponic mesocosms
Bibliographic record
Abstract
High levels of trace metals such as copper (Cu) can affect water quality and induce toxic effects on living organisms in aquatic ecosystems. This research assesses the potential capacity for Cu phytofiltration by three emergent macrophytes from Cu-contaminated sediments and water containing five concentrations of Cu (0, 50, 100, 150, and 200 µM). We conducted a greenhouse study using semi-hydroponic and hydroponic experimental conditions to simulate a natural wetland system. We selected three plant types that were collected in Quebec (Canada): native Typha latifolia, and native and, exotic Phragmites australis. Under semi-hydroponic, the responses indicated an almost 3-fold higher mean root Cu-accumulation from Cu-0 to Cu-Sediment (80.3–226.1 mg kg−1) and an 8.6-fold increase (122.2–1045.5 mg kg−1) for Cu-0 to Cu-200 µM under hydroponic conditions, resulting in Cu translocation < 1 and BCF >1 under both conditions. We found an inverse correlation between increasing doses of Cu with mean aboveground and belowground biomass together with height, and root length of selected plants under hydroponic conditions. Our results indicate that these wetland macrophytes could be useful in heavy-metal removal from Cu-contaminated sediments and Cu-enriched water. Studies in wetland phytoremediation have focus on either contaminated soil or water. This research highlights the comparison of three emergent macrophytes in removing copper from both soil (a simulated riparian wetland) and water (floating treatment wetland). This study compares the phytoextraction and rhizofiltration capacity of Typha latifolia, with native versus exotic Phragmites australis with a translocation factor for Cu < 1 and bioconcentration factor > 1 in the Cu-Sediment and Cu-enriched water.
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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 | 0.000 | 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".