Removal efficiencies of wastewater treatment technologies for top pharmaceuticals
Bibliographic record
Abstract
The presence of pharmaceuticals in wastewater, and their subsequent release into the environment have attracted growing public concern. Although studies have been performed to detect pharmaceuticals in wastewater effluents, little research has been conducted to assess the efficiency of pharmaceutical removal using different treatment technologies. To assess the removal efficiencies of pharmaceuticals from wastewater, samples taken from wastewater treatment plants in Nova Scotia and New Brunswick were analysed for 12 of the top 20 pharmaceuticals sold in Canada, and 2 metabolites. Pharmaceutical concentrations were quantified, and average removal efficiencies of pharmaceuticals were calculated at the 95% confidence level. The average pharmaceutical removal efficiencies for 9 technologies: aerated lagoon, extended aeration, facultative lagoon, membrane bioreactor, modified secondary, oxidation ditch, primary treatment, rotating biological contactor and sequencing batch reactor technologies, were determined to be 95.1±0.3%, 87±1%, 94±4%, 97%, 82±4%, 93.8±0.6%, -23±6%, 87.4±0.4%, and 75±4% respectively. The negative removal efficiency of primary treatment was due to sampling uncertainty introduced by several hours of retention time. Further experiments were performed to assess dissolved oxygen, chemical oxygen demand, total suspended solids, colour, and turbidity of the samples. The results help establish fundamental knowledge for studying pharmaceuticals in wastewater treatment, where improvements need to be made for better preventing pharmaceuticals from releasing into the environment
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".