Ultra-trace analysis of multiple endocrine-disrupting chemicals in municipal and bleached kraft mill effluents using gas chromatography–high-resolution mass spectrometry
Bibliographic record
Abstract
A comprehensive gas chromatographic-high-resolution mass spectrometric (GC-HRMS)-based method was developed that permitted the simultaneous determination of 30 estrogenic endocrine-disrupting chemicals (EDCs) and related compounds, including surfactants, biogenic and synthetic steroids, fecal sterols, phytoestrogens, and plasticizers, in wastewater. Features of the method include low sample volume (~40 ml), optimized Florisil cleanup to minimize matrix interferences and optimized analyte derivatization to improve sensitivity via GC-HRMS. Detection limits were in the low- to mid-ng/L range, and recoveries were greater than 60% for most target analytes. This new method allows for high throughput analysis of many organic wastewater contaminants in a complex matrix with relative standard deviation of less than 15% for most measurable compounds. The applicability of the method was demonstrated by examining wastewater samples from different origins. Compounds such as di(2-ethylhexyl)phthalate, cholesterol, cholestanol, and other cholesterol derivatives were measured in much higher concentrations in untreated sewage and were reduced substantially in concentration by the treatment process. However, steroidal compounds, particularly estrone (E1), 17beta-estradiol (E2), and estriol (E3), as well as plant sterols (except stigmastanol), were greater in the treated municipal wastewater versus the untreated effluent. Plant and fungi sterols, stigmastanol and ergosterol, were found largely associated with bleached kraft mill effluent (BKME) as compared to the municipal effluents.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".