Variation in levels and removal efficiency of heavy and trace metals from wastewater treatment plant effluents in Cape Town and Stellenbosch, South Africa.
Bibliographic record
Abstract
This study focused on one year monitoring campaign to monitor the occurrence and removal of Endocrine Disruptive Metals (EDMs) and trace metals from selected wastewater treatment plants (WWTPs) in Stellenbosch and Cape Town. Composite water samples were collected from the WWTPs from January 2010 to December 2010 on a quarterly basis and concentrations determined using inductively coupled plasma-mass spectrometry (ICP-MS) after open beaker digestion. A total of 432 water samples consisting of raw, primary effluent, secondary effluent and final effluents were collected and analyzed. The general abundance distribution pattern for metals was Zn > Cu > Pb > Cr > Ni > As > Co > Cd > Hg. The removal efficiency ranged from 1.5% for Hg at Zandvliet WWTP plant during winter to 98.27% for Cu at Athlone WWTP treatment plant during summer. The final effluent concentration for most of the metals were within South African water quality guidelines while As, Hg, Cd and Pb concentration were higher than maximum limits set by the Canadian Council of Ministers of the Environment. Potsdam WWTP showed to be the most effective at heavy metals removal as compared with the other five treatment plants investigated in this study. The effluent metal concentration over time could pose health risk if used for agricultural irrigation. Key words: Seasonal variation, endocrine disrupting metals, wastewater treatment plants, effluents, coupled plasma-mass spectrometry (ICP-MS), Cape Town.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".