Practical overview of analytical methods for endocrine-disrupting compounds, pharmaceuticals and personal care products in water and wastewater
Bibliographic record
Abstract
The detection of organic micropollutants, such as endocrine-disrupting compounds, pharmaceuticals and personal care products, in wastewater and the aquatic environment has brought increasing concern over their potential adverse ecological and human impacts. These compounds are generally present at trace levels (ng l(-1)) and in complex water matrices, such as wastewaters and surface waters, making their analysis difficult. Currently, no standardized analytical methods are available for the analysis of organic micropollutants in environmental waters. Owing to the diversity of physico-chemical properties exhibited by the various classes of organic micropollutants, the majority of established analytical methods described in the literature focus on a specific class of compounds, with few methods applicable to multi-class compound analysis. As such, analytical challenges and limitations contribute to the lack of understanding of the effectiveness of drinking water and wastewater treatment processes to remove organic micropollutants. This paper provides a practical overview of current analytical methods that have been developed for the analysis of multiple classes of organic micropollutants from various water matrices and describes the challenges and limitations associated with the development of these methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".