Environmental Heavy Metal Contamination from Electronic Waste (E-Waste) Recycling Activities Worldwide: A Systematic Review from 2005 to 2017
Bibliographic record
Abstract
The recycling of electronic waste (e-waste) contaminates ecosystems with metals, though a compilation of data from across sites worldwide is lacking, without which evidence-based comparisons and conclusions cannot be realized. As such, here, a systematic review of the literature was conducted to identify peer-reviewed studies concerning e-waste sites (published between 2005 and 2017) that reported on the concentration of heavy metals (Cd, Hg, As, Pb and Cr) in soil, water and sediment. From 3063 papers identified, 59 studies from 11 countries meeting predefined criteria were included. Reported metal concentrations were summarized, and a narrative synthesis was performed. This review summarized 8286 measurements of the aforementioned metals in soils (5836), water (1347) and sediment (1103). More than 70% of the studies were conducted in Asia. In nearly all cases, the average metal concentrations in a particular medium from a given site were above guideline values; suggesting soils, water and sediment at, or near, e-waste recycling sites are contaminated. Across all media, concentrations of Pb were generally highest, followed by Cr, As, Cd and Hg. The synthesized information demonstrates that e-waste sites worldwide are contaminated with metals, that geographic data gaps exist, that the quality of most studies can be improved and that action is needed to help reduce such levels to protect human health and the environment.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".