E-waste: Growing environmental and health problems and its management alternatives in developing countries
Bibliographic record
Abstract
The management of hazardous municipal waste is a challenge. Added to this burden is the management of huge and growing quantities of electrical and electronic waste, which is emerging as one of the most important environmental challenges and health problems in developing countries, particularly in Africa. This has been accelerated by rapid growth and development in the information and communication technology (ICT) industries. The growth in waste electrical and electronic equipment (e-waste) has brought several challenges including introducing effective management practices that are environmentally sound to reduce the negative impacts on human health and the environment. This review aimed to show the extent of e-waste as a growing issue to the environment and human health in developing countries where waste management problems pose immense challenges. Seven electronic databases (Scopus, Web of Science, PubMed, ScienceDirect, DOAJ, JSTOR, and Google Scholar) were used to access published scientific articles. Systematic reviews, case studies, analytical cross-sectional studies, policy review papers, and available relevant studies were considered. The findings of this review show that the volume of electronic waste destined for developing countries is increasing from year to year. Most countries did not have specific policies on e-waste but relied on hazardous waste policies. Dumping and improper recycling and handling of e-waste cause problems such as contamination of soil and water, depletion of grazing land, health problems such as respiratory infections, various cancers, congenital disabilities, and other health issues that affect the brain and other vital organs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".