Tracking the Global Distribution of Persistent Organic Pollutants Accounting for E-Waste Exports to Developing Regions
Bibliographic record
Abstract
Elevated concentrations of various industrial-use Persistent Organic Pollutants (POPs), such as polychlorinated biphenyls (PCBs), have been reported in some developing areas in subtropical and tropical regions known to be destinations of e-waste. We used a recent inventory of the global generation and exports of e-waste to develop various global scale emission scenarios for industrial-use organic contaminants (IUOCs). For representative IUOCs (RIUOCs), only hypothetical emissions via passive volatilization from e-waste were considered whereas for PCBs, historical emissions throughout the chemical life-cycle (i.e., manufacturing, use, disposal) were included. The environmental transport and fate of RIUOCs and PCBs were then simulated using the BETR Global 2.0 model. Export of e-waste is expected to increase and sustain global emissions beyond the baseline scenario, which assumes no export. A comparison between model predictions and observations for PCBs in selected recipient regions generally suggests a better agreement when exports are accounted for. This study may be the first to integrate the global transport of IUOCs in waste with their long-range transport in air and water. The results call for integrated chemical management strategies on a global scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".