Occupational health and safety, metal exposures and multi-exposures health risk in Canadian electronic waste recycling facilities
Bibliographic record
Abstract
Electronic waste recycling (e-recycling) involves manual operations that expose workers to toxic metals. We aim to describe occupational health and safety practices and workers’ exposures to metals in the Canadian formal e-recycling industry, and to estimate the health risk associated with multiple exposures. This cross-sectional study documented practices through observations and questionnaires, and assessed metal exposures using personal air samples and biomarkers. Health risks were estimated relative to recognised occupational exposure guidelines, and using an additive approach for consideration of multiple exposures. Six e-recycling and one commercial recycling facilities were investigated, and the metal exposures of 99 workers (23 women) were measured. In most facilities, dust control was inadequate and personal protective equipment was improperly worn. In e-recycling, lead was detected in all air samples and in most blood samples, up to 48 µg/m3 and 136 µg/l, respectively. Other quantified metals included beryllium, mercury, arsenic, barium, cadmium, chrome, cobalt, copper, indium, manganese, nickel and yttrium. When handling cathode ray tube screens, workers were 4.9 times and 8.5 times more likely to be exposed to lead and yttrium, respectively, than workers who were not assigned to a specific type of electronics. Overall, exposures were largely associated with facility size and airborne dust concentration. The additive hazard indices for airborne exposures raised concerns for kidney disorders, for peripheral and central nervous systems, and for the male reproductive system. Minimizing airborne dust through collective control methods and adequately using personal protection should reduce metal exposures and associated health risks in this growing industry.
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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 | 0.002 | 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".