The hazards, treatment measures and sustainable development of electronic waste
Bibliographic record
Abstract
Abstract The faster upgrading and the rising consumer’s demand for electrical and electronic equipment (EEE) results in an ever-increasing number of waste electrical and electronic items (WEEE), which became a severe pollution problem at both local and global scales. This paper presents the current six categories of EEE that are widely used for electronic waste (E-waste) management worldwide and the major component of E-waste: non-hazardous materials, heavy metal, and persistent organic pollutants (POPs). To learn about E-waste’s negative impacts on both environment and the human body, this paper will show the specific polluting route in air, water, and soil. Those pollutants would affect the environment through burning and directly released into the air, penetrating and leaking into water and soil. The polluted environment and the poor sanitary conditions will further destroy people’s health. This paper shows and summarizes the development of current E-waste management, from direct landfill to chemical disposal, to today’s eco-friendly way--bioleaching. Future E-waste disposal technology needs to become more environmentally friendly and more efficient for reusable material recovery. Therefore, we hope that E-waste management can closely connect with Sustainable Development Goals (SDGs). We need to apply SDGs into every aspect of E-waste management, which can significantly reduce the adverse effects of E-waste and be beneficial for the electronic industry.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".