Influence of Conjugation on the Fate of Pharmaceuticals and Hormones in Canadian Wastewater Treatment Plants
Bibliographic record
Abstract
The concentrations of 30 pharmaceuticals and 17 hormones in influent, effluent, and biosolid samples were determined at 19 representative Canadian wastewater treatment plants (WWTPs). Many pharmaceuticals and hormones are excreted by humans in conjugated forms that are not detected by analytical techniques aimed at free analytes. Conjugation can be reversed by naturally occurring enzymes that are found in WWTPs, the collection system, and/or the environment. In this study, we applied deconjugating enzymes (β-glucuronidase/sulfatase) to an aliquot of each sample in order to release target analytes from their glucuronate and sulfate forms. Of the pharmaceuticals evaluated, deconjugation during wastewater treatment only influenced the fate of lamotrigine in lagoons as well as secondary and advanced treatment facilities, but not a primary treatment plant. Several hormones arrived at the WWTPs in part in a conjugated form, and our data indicate that some of the conjugated forms of mestranol and testosterone survived wastewater and/or biosolid treatments. Therefore, mestranol and testosterone may eventually become deconjugated and hence become more prevalent in the natural environment. This study demonstrates the utility of deconjugating enzymes for screening the impact of conjugation and deconjugation processes and more accurately quantifying the occurrence and removal of pharmaceuticals and hormones in WWTPs.
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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 | 0.000 | 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".