Contaminants of emerging concern in Sharjah wastewater treatment plant, Sharjah, UAE
Bibliographic record
Abstract
The presence and fate of contaminants of emerging concern (CECs) in wastewater have received growing attention due to their potential impacts on human health and the environment. In this study, the authors assessed the presence of CECs in the influent and effluent of the Sharjah wastewater treatment plant (SWWTP), a conventional activated sludge system, over a 1 year period. Sharjah is the capital city of the Emirate of Sharjah in the UAE. Characterisation of the CECs required development of elaborate analytical techniques for detection and quantification of CECs at the nanogram per litre level. A total of 57 CECs were detected and identified, of which ten pharmaceuticals, including seven antibiotics, an analgesic/antipyretic medicine, a β-blocker and an antipsychotic drug were quantified. The measured concentrations of the ten quantified pharmaceuticals were in the tens to hundreds of nanograms per litre, except for acetaminophen, which reached micrograms per litre levels. The quantified CECs in the effluent were 4–69% of their levels in the influent, indicating apparent removals in the range 31–97%. This study is the first one on the presence and removal of CECs in the SWWTP and contributes essential literature on the subject in Sharjah and the region.
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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 | 0.001 | 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) | 0.000 | 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".