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Record W3016716820 · doi:10.1016/j.envint.2020.105731

Severe dioxin-like compound (DLC) contamination in e-waste recycling areas: An under-recognized threat to local health

2020· review· en· W3016716820 on OpenAlexaff
Qingyuan Dai, Xijin Xu, Brenda Eskenazi, Kwadwo Ansong Asante, Aimin Chen, Julius N. Fobil, Åke Bergman, Lesley Brennan, Peter D. Sly, Innocent C. Nnorom, Antonio Pascale, Qihua Wang, Eddy Y. Zeng, Zhijun Zeng, Philip J. Landrigan, Marie-Noël Bruné Drisse, Xia Huo

Bibliographic record

VenueEnvironment International · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaWorld Health Organization
KeywordsContaminationElectronic wasteHuman healthEnvironmental scienceWaste recyclingWaste managementHousehold wasteEpidemiologyEnvironmental healthMedicineEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.098
GPT teacher head0.370
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations85
Published2020
Admission routes1
Has abstractyes

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