Heavy Metal Removal (Copper and Zinc) in Secondary Effluent from Wastewater Treatment Plants by Microalgae
Bibliographic record
Abstract
Microalgae is used for the removal of heavy metals from a wastewater treatment plant discharge. Laboratory-scale experiments are described that characterize the heavy metal uptake of copper and zinc by three microalgae strains: Chlorella vulgaris, Spirulina maxima, and a naturally growing algae sample found in the wastewater from a wastewater treatment plant (containing Synechocystis sp. (dominant) and Chlorella sp. (common) and a few cells of Scenedesmus sp.) Tests were conducted using untreated and autoclaved secondary effluent as a substrate. In the untreated secondary effluent trial, the microalgae removed up to 81.7% of the copper, reaching a lowest final concentration of 7.8 ppb after 10 days. Zinc was reduced by up to 94.1%, reaching 0.6 ppb after 10 days. The removal rates varied significantly with the microalgae strain. Higher heavy metal removal efficiencies were obtained in the autoclaved secondary effluent than the untreated secondary effluent, suggesting microorganisms already present in secondary effluent contribute negatively and compete with microalgae for nutrients, hindering microalgae growth and uptake of heavy metals. Inoculated samples showed decreased heavy metal concentrations within 6 h of initial inoculation, suggesting microalgae do not require long periods of time to achieve biosorption of heavy metals.
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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) | 0.001 | 0.001 |
| 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) | 0.000 | 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".