Severe dioxin-like compound (DLC) contamination in e-waste recycling areas: An under-recognized threat to local health
Bibliographic record
Abstract
Electrical and electronic waste (e-waste) burning and recycling activities have become one of the main emission sources of dioxin-like compounds (DLCs). Workers involved in e-waste recycling operations and residents living near e-waste recycling sites (EWRS) are exposed to high levels of DLCs. Epidemiological and experimental in vivo studies have reported a range of interconnected responses in multiple systems with DLC exposure. However, due to the compositional complexity of DLCs and difficulties in assessing mixture effects of the complex mixture of e-waste-related contaminants, there are few studies concerning human health outcomes related to DLC exposure at informal EWRS. In this paper, we have reviewed the environmental levels and body burdens of DLCs at EWRS and compared them with the levels reported to be associated with observable adverse effects to assess the health risks of DLC exposure at EWRS. In general, DLC concentrations at EWRS of many countries have been decreasing in recent years due to stricter regulations on e-waste recycling activities, but the contamination status is still severe. Comparison with available data from industrial sites and well-known highly DLC contaminated areas shows that high levels of DLCs derived from crude e-waste recycling processes lead to elevated body burdens. The DLC levels in human blood and breast milk at EWRS are higher than those reported in some epidemiological studies that are related to various health impacts. The estimated total daily intakes of DLCs for people in EWRS far exceed the WHO recommended total daily intake limit. It can be inferred that people living in EWRS with high DLC contamination have higher health risks. Therefore, more well-designed epidemiological studies are urgently needed to focus on the health effects of DLC pollution in EWRS. Continuous monitoring of the temporal trends of DLC levels in EWRS after actions is of highest importance.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".