Assessing Contributions of Synthetic Musk Compounds from Wastewater Treatment Plants to Atmospheric and Aquatic Environments
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The high environmental concentrations, persistence, and toxicity of synthetic musk compounds (SMCs) necessitate a better grasp of their fate in wastewater treatment plants (WWTPs). To investigate the importance of WWTPs as pathways of SMCs to the environment, air and wastewater samples were collected at four WWTPs in Ontario, Canada. Polycyclic musks (PCMs) were present at higher concentrations than nitro musks (NMs) and macrocyclic musks (MCMs). Three PCMs [galaxolide (HHCB), tonalide (AHTN), and iso-E super (OTNE)] were the most abundant compounds (0.30–680 ng/m 3 in air, 0.40–15 μg/L in influent, and 0.007–6.0 μg/L in effluent). Analyses of multiyear data suggest that risk management measures put in place have been effective in reducing the release of many SMCs into the environment. The highest removal efficiency, up to almost 100% of some SMCs, was observed for the plant with the longest solid retention time. A fugacity-based model was established to simulate the transport and fate of SMCs in the WWTP, and good agreement was obtained between the measured and modeled values. These findings indicate that the levels of certain SMCs discharged into the atmospheric and aquatic environments were substantial, potentially resulting in exposure to both humans and wildlife.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".