Urinary metals and their associations with DNA oxidative damage among e-waste recycling workers in Hong Kong
Bibliographic record
Abstract
Recycling electronic waste (e-waste) poses risks of metal exposure, potentially leading to health impairments. However, no previous study has focused on this issue in Hong Kong. Therefore, from June 2021 to September 2022, this study collected urine samples from 101 e-waste workers and 100 office workers in Hong Kong to compare their urinary levels of metals using ICP-MS. Among the 15 included metals (with detection rates above the 70 % threshold), eight showed significantly higher urinary concentrations (unit: μg/g creatinine) in e-waste workers compared to office workers: Li (25.09 vs. 33.36), Mn (1.78 vs. 4.15), Ni (2.10 vs. 2.77), Cu (5.81 vs. 9.23), Zn (404.35 vs. 431.52), Sr (151.33 vs. 186.26), Tl (0.35 vs. 0.43), and Pb (0.69 vs. 1.16). E-waste workers in Hong Kong generally exhibited lower metal levels than those in developing regions but higher than their counterparts in developed areas. The urine level of 8-hydroxy-2-deoxyguanosine (8-OHdG) was determined by HPLC-MS/MS, and no significant difference was found between the two groups. Multiple linear regression models revealed no significant association between individual metal and urinary 8-OHdG concentrations. However, the metal mixture was identified to marginally elevate the 8-OHdG concentrations (1.12, 95 %CI: 0.04, 2.19) by quantile g‑computation models, with Mn and Cd playing significant roles in such effect. In conclusion, while the metal levels among Hong Kong e-waste workers compared favorably with their counterparts in other regions, their levels were higher than those of local office workers. This underscores the need for policymakers to prioritize attention to this unique industry.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".