Determination of Endocrine Disrupting Chemicals in Water and Wastewater Samples by Liquid Chromatography-Negative Ion Electrospray Ionization-Tandem Mass Spectrometry
Bibliographic record
Abstract
A liquid chromatography-negative ion electrospray ionization-tandem mass spectrometry method was developed for the simultaneous analysis of bisphenol A, 4-octylphenol, 4-nonylphenol, diethylstilbestrol, 17β-estradiol, estriol, estrone, 17α-ethinylestradiol, prednisone, and prednisolone. This method used solid-phase extraction with an elution solvent of acetonitrile to improve the stability of the analytes. To maintain the stability of analytes analyses were completed within five days. The recoveries ranged from 84 to 112% and the relative standard deviation of analysis of duplicate samples was <10%. The limits of quantitation were 1-10 ng/L. Surface water and wastewater were obtained from five wastewater treatment plants in Saskatchewan. Matrix effects were moderate to severe. Using standard addition calibration, all analytes except diethylstilbestrol and 17α-ethinyl estradiol were detected. There was a low frequency of detection of the target analytes in upstream and downstream water, indicating good removal efficiency during the wastewater treatment process. Bisphenol A and 4-nonylphenol were the only analytes detected downstream. Bisphenol A was the most frequently detected in raw wastewater (133 to 403 ng/L). Estriol was detected more often in raw wastewater than estrone or 17β-estradiol. This is the first Canadian study with the detection of prednisone and prednisolone with concentrations at 198-350 ng/L in raw wastewater at 60% of the wastewater treatment plants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".