Monitoring for contaminants of emerging concern in drinking water using POCIS passive samplers
Bibliographic record
Abstract
Contaminants of emerging concern (CEC) have been detected in drinking water world-wide. The source of most of these compounds is generally attributed to contamination from municipal wastewater. Traditional water sampling methods (grab or composite) often require the concentration of large amounts of water in order to detect trace levels of these contaminants. The Polar Organic Compounds Integrative Sampler (POCIS) is a passive sampling technology that has been developed to concentrate trace levels of CEC to provide time-weighted average concentrations for individual compounds in water. However, few studies to date have evaluated whether POCIS is suitable for monitoring contaminants in drinking water. In this study, the POCIS was evaluated as a monitoring tool for CEC in drinking water over a period of 2 and 4 weeks with comparisons to typical grab samples. Seven "indicator compounds" which included carbamazepine, trimethoprim, sulfamethoxazole, ibuprofen, gemfibrozil, estrone and sucralose, were monitored in five drinking water treatment plants (DWTPs) in Ontario. All indicator compounds were detected in raw water samples from the POCIS in comparison to six from grab samples. Similarly, four compounds were detected in grab samples of treated drinking water, whereas six were detected in the POCIS. Sucralose was the only compound that was detected consistently at all five plants. The POCIS technique provided integrative exposures of CECs in drinking water at lower detection limits, while episodic events were captured via traditional sampling methods. There was evidence that the accumulation of target compounds by POCIS is a dynamic process, with adsorption and desorption on the sorbent occurring in response to ambient levels of the target compounds in water. CECs in treated drinking water were present at low ng L(-1) concentrations, which are not considered to be a threat to human health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".