Human exposure levels of PAEs in an e-waste recycling area: Get insight into impacts of spatial variation and manipulation mode
Bibliographic record
Abstract
Phthalic acid esters (PAEs) are one important category of additives in plastics, which are ubiquitous products of e-waste recycling areas, where PAEs are released to the environment intensively and higher exposure level is expected for the employees. This study investigated human exposure levels of PAEs in an e-waste recycling area (Ziya Circular Economy Park (ZCEP) in Tianjin, China) with intending to explore the impacts of residence spatial variation and dismantling manipulation mode. We collected 157 urine samples from three sites around ZCEP with different distances from the core dismantling site and urinary phthalate metabolites (mPAEs) concentrations were measured and were compared among these three sites. The exposure levels of PAEs exhibited spatial variation according to the distance from the core dismantling site, and urinary median ∑mPAEs concentrations (389 ng/mL) of the employees in ZCEP were significantly higher than those of residents in Ziya town (285 ng/mL) and the downtown of Jinghai district (207 ng/mL) ( p < 0.05). Moreover, PAEs exposure levels were significantly affected by the manipulation modes in the e-waste recycling area and the urinary median ∑mPAEs concentrations in the employees of family workshops (401 ng/mL) were significantly higher than those in plants with centralized management (298 ng/mL). There were obvious differences on the urinary median mPAEs concentrations between subgroups based on age, BMI, and sex; however, no significant statistical associations were found between PAEs exposure levels and these socio-demographic indices ( p > 0.05). Besides, there was no correlation between exposure levels of different PAEs and their physicochemical parameters like the log K ow ( p > 0.05).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".