Systematic Review and Meta-Analysis of Mercury Exposure among Populations and Environments in Contact with Electronic Waste
Bibliographic record
Abstract
Electronic waste (e-waste) recycling releases mercury (Hg) into the environment, though to our knowledge Hg levels at such sites have yet to be examined on a worldwide basis. A systematic review of scientific studies was conducted to extract, analyze, and synthesize data on Hg levels in e-waste products, environments near recycling sites, and in people. Data were extracted from 78 studies from 20 countries, and these included Hg levels in 1103 electrical and electronic products, 2072 environmental samples (soil, air, plant, food, water, dust), and 2330 human biomarkers (blood, hair, urine). The average Hg level in products was 0.65 μg/g, with the highest levels found in lamps (578 μg/g). Average soil and sediment Hg levels (1.86 μg/g) at e-waste sites were at least eight times higher than at control sites. Average urinary Hg levels (0.93 μg/g creatinine) were approximately two-fold higher among e-waste workers versus control groups. Collectively, these findings demonstrate that e-waste recycling may lead to Hg contamination in environments and human populations in close proximity to processing sites. These findings contribute to a growing knowledge base of mercury exposure through diverse source-exposure pathways, and the work has potential policy implications in the context of the Minamata Convention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".