Broad Exposure of the North American Environment to Phenolic and Amino Antioxidants and to Ultraviolet Filters
Bibliographic record
Abstract
The present study provides a comprehensive investigation of three suites of commonly used synthetic additives: phenolic and amino antioxidants and ultraviolet filters. The concentrations of 47 such compounds and their transformation products were measured in 20 atmospheric particle samples collected in Chicago, in 21 Canadian e-waste dust samples, in 32 Canadian and United States’ residential dust samples, and in 10 sediment samples collected from the Chicago Sanitary and Ship Canal. Despite their large production volumes in the United States, environmental data on antioxidants and UV filters in North America is limited. These compounds were detected in all the samples, indicating their ubiquitous distribution in the North American environment. The most prevalent compounds were 2,6-di- t -butyl- p -benzoquinone, diphenylamine, 4,4′-di- t -octyl diphenylamine, 2,4-dihydroxybenzophenone, and 2-hydroxy-4-methoxybenzophenone. The e-waste dust contained significantly greater total concentrations of these compounds than the Canadian residential dust, while intermediate levels were detected in the United States residential dust. The sediment samples showed relatively high levels of N, N ′-diphenylbenzidine, the source of which is unclear, and some benzotriazole UV filters. Daily intake rates by dust ingestion for these compounds ranged from 1–10 ng/(kg·day) for adults to 10–100 ng/(kg·day) for toddlers. Due to the wide distribution of these compounds in both the ambient and built environments, future research on their potential toxic effects on people and ecosystems is important.
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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 | 0.001 | 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".