Occurrence of Polycyclic and Nitro Musk Compounds in Canadian Sludge and Wastewater Samples
Bibliographic record
Abstract
Abstract Fragrances such as synthetic musk compounds are commonly used as additives in a wide range of consumer and personal-care products. At the end of their life cycle, most of these compounds will end up in municipal sewage systems. In this work, we report the occurrence of selected polycyclic and nitro musk compounds in sewage sludge, influent, effluent, as well as some industrial wastewater samples collected in Canada. A newly developed supercritical carbon dioxide extraction technique was used for the extraction of residual musk fragrances in the sludge. Final analysis was performed by gas chromatography/ mass spectrometry (GC/MS) using electron-impact and methane negative ion chemical ionization techniques. The results indicated that Galaxolide® (HHCB), Tonalide® (AHTN), musk xylene (MX), and musk ketone (MK) were the most common musk compounds in the Canadian environment, as they were found in every sample in this study. In the same sludge sample, levels of HHCB and AHTN (ranging from 1.3 to 26.7 μg/g) were often found to be about 1000 times higher than those of MX and MK (ranging from 1.4 to 422 ng/g). Similarly, in the sewage influent and effluent collected in Ontario, the levels of HHCB and AHTN (ranging from 159 to 2411 ng/L) were much higher than those of MX and MK (ranging from 1 to 84 ng/L). The levels of musk compounds varied widely in industrial wastewaters. In one sample collected from a detergent manufacturer, the levels of HHCB, AHTN, MX, and MK were found to be 54,200, 13,300, 5480, and 2.2 ng/L, respectively. It was also noted that the levels of MX and MK observed in the samples collected from the commercial laundries in Toronto were significantly higher than those found in domestic sewage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".