Can Silicone Passive Samplers be Used for Measuring Exposure of e-Waste Workers to Flame Retardants?
Bibliographic record
Abstract
Abstract Silicone passive samplers were assessed for measuring personal exposure to 37 flame retardants at three Québec e-waste recycling facilities. Silicone brooches (n = 45), wristbands (n = 28), and armbands (n = 9) worn during a ∼8 h work shift accumulated detectable amounts of 95–100% of the target compounds. Brooch concentrations were significantly correlated with those from active air samplers from which we conclude that the brooches could be used to approximate inhalation exposure and other exposures related to air concentrations such as dermal exposure. The generic sampling rate of the brooch (19 ± 11 m3 day–1 dm–2) was 13 and 22 times greater than estimated for home and office environments, respectively, likely because of the dusty work environment and greater movement of e-waste workers. BDE-209 concentrations in brooches and wristbands were moderately and significantly (p < 0.05) correlated with levels in blood plasma; organophosphorus esters in brooches and wristbands were weakly and insignificantly correlated with their metabolite biomarkers in post-shift spot urine samples. Silicone brooches and wristbands deployed over a single shift in a dusty occupational setting can be useful for indicating the internal exposure to compounds with relatively long biological half-lives, but their use for compounds with relatively short half-lives is not clear and may require either a longer deployment time or an integrated biomarker measure.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".