O-462 Assessment of overexposure to multiple metals in electronic recycling facilities: using air samples and biomarkers to highlight potential toxicity
Bibliographic record
Abstract
Objective To estimate potential toxicity risks associated with exposure to several metals in electronic waste recycling (e-recycling) facilities in Quebec. Methods In a cross-sectional study, personal air samples were collected on cellulose ester filters from six e-recycling facilities, during an 8-hour work day for 85 workers (66 men, 19 women). Twelve metals were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). End-of-shift blood and urine spot samples were taken; blood cadmium and urinary arsenic were also analyzed by ICP-MS, and blood lead and urinary mercury by atomic absorption spectrometry. Additive hazard indices (HIs) were calculated for organ-specific toxic effects, by adding the ratios of measured concentrations of metals in air or biological fluids, on the threshold limit value (TLV®) or on the biological exposure indices (BEI®). Results All facilities provided workers with some personal protective equipment, with inconsistent wearing of respiratory equipment. They all conducted manual dismantling, and three performed shredding of electronic/plastic residues. Cadmium, copper and lead were found in the highest concentrations in the air, albeit all below the TLVs. Air concentrations of lead showed a strong association with biological levels, indicating an occupational exposure origin. HIs calculated with the biological measures revealed an exceedance of the mixture’s threshold limit for lung toxicity (arsenic, cadmium, cobalt, nickel and chrome) in 95% of the workers, as well as an exceedance for skin irritation (arsenic, mercury, cobalt, nickel) in 19% of them. HIs exceeded the unity as well in some workers for gastrointestinal, peripheral nervous system, and reproductive function toxicity. Conclusions Multi-exposures complicate risk assessment Although individual metals all respected the TLVs, the calculation of hazard indices from both air samples and biomarkers highlighted potentially increased risks of toxicity for several organs or systems in e-recycling workers.
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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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".