Urinary Metabolites of Organophosphate Esters (OPEs) in Electronic Waste Recycling Workers from the Province of Quebec, Canada
Bibliographic record
Abstract
Background: Organophosphate esters (OPEs), which are used as flame retardants and plasticizers, have been measured in the air of electronic waste recycling (E-recycling) plants. Numerous studies suggest that exposure to OPEs may result in adverse health effects and constitute a potential health risk for E-recycling workers. However, data on urinary OPE metabolites in occupationally exposed workers is scarce.Objective: To measure urinary OPE metabolites in E-recycling workers and assess their relationship with personal air OPE levels.Methods: Levels of 13 OPE metabolites were measured, at the end of the work shift, in urine samples from 56 workers (four facilities in Quebec, Canada). Air levels of 11 OPEs were measured in 8-hour personal samples with OSHA XAD-2 versatile samplers. The relationship between air OPE levels and urinary metabolite levels was determined using Spearman’s rank correlation.Results: Nine of the 13 urinary OPE metabolites were detected in more than 50% of samples. The OPE metabolites with the highest median levels were 1-hydroxy-2-propyl bis (1-chloro-2-propyl) phosphate (OH-BIPCPP) (median: 2.2 ng/mL; maximum: 26), diphenyl phosphate (DPhP) (median: 2.1 ng/mL; maximum: 19), bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) (median: 1.2 ng/mL; maximum: 7.4), and bis(2-chloroethyl) carboxymethyl phosphate (tris(1,3-dichloro-2-propyl) phosphate (BICECMP) (median: 0.5 ng/mL; maximum: 16). Urinary OPE metabolite levels were not significantly correlated with air OPE levels (p values were above 0.05).Discussion: The median urinary levels of BDCIPP and DPhP were higher than those observed in workers from a Chinese E-recycling site, although the timing of sample collection differed. These findings suggest that workers from E-recycling plants are exposed to OPEs, but that exposure through routes other than inhalation, and possibly non-occupational sources, may be more important contributors to the absorbed dose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".