Do urinary metabolites reflect occupational exposure to organophosphate flame retardants? A case study in electronic waste recycling workers.
Bibliographic record
Abstract
Organophosphate esters (OPEs) are commonly used in electronic devices to meet safety standards, but electronic-waste recycling (e-recycling) workers may face significant exposure to those potentially hazardous compounds in their workplace. We examined the relationship between urinary OPE metabolites and their parent compounds in the air, in Canadian e-recycling facilities. We collected personal air samples and end-of-shift urine samples from workers at six e-recycling facilities. We employed linear and Tobit regression models to assess associations between air concentrations of triphenyl phosphate (TPhP) and three metabolites, of tris (2-chloroethyl) phosphate (TCEP) and two metabolites, of tris (2-chloroisopropyl) phosphate (TCPP) and two metabolites, of tris (1,3-dichloro-2-propyl) phosphate (TDCPP), and of tris (2-butoxyethyl) phosphate (TBOEP) and one metabolite each. The 85 participants, mostly male (78 %) and aged between 25 and 54, had concentrations of OPEs detected in 90-100 % of air samples, with geometric means of TPhP, TCEP, TBOEP and TDCPP, of 351, 404, 261 and 250 picomoles per cubic metre respectively. The proportion of detection of their corresponding metabolites varied between 32 % and 98 %. Regression models including the urinary flow rate as a covariate showed that a doubling of the air concentration of TCEP was associated with a 42-107 % increase in its metabolites, and a doubling of air concentration of TBOEP, with a 77 % increase. The paucity of data on the toxicokinetics of OPEs limits the determination of appropriate urinary metabolites to monitor OPE occupational exposure. Such additional data, in combination with workplace contextual information, may help clarify the major routes of exposure and the corresponding contributing sources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".