An integrated analysis on source-exposure risk of heavy metals in agricultural soils near intense electronic waste recycling activities
Bibliographic record
Abstract
Conducting integrated analysis of the source, exposure and health risk of heavy metals is critical for developing mitigation strategies of soil contamination. Taking the former electronic waste (e-waste) dismantling center in China as an example this study quantitatively apportioned source contribution of soil heavy metals in this area by statistical analysis and positive matrix factorization (PMF) model. Furthermore, the human health risk of identified sources were quantified by combining source profiles and exposure risk assessment. The seven heavy metals investigated were arsenic (As), cadmium (Cd), copper (Cu), chromium (Cr), nickel (Ni), lead (Pb) and Zinc (Zn). Results indicated that agricultural soils were mainly contaminated with Cd and Cu. Parent material and pesticide, fertilizer application, industrial discharge, and vehicle emission accounted for 46.6, 22.2, and 31.2%, respectively, of the accumulation of metals in the soil. Moreover, these sources contributed 52.9, 19.0, and 28.1%, respectively of the total non-cancer risk. For the total cancer risk, the contribution of these three sources was 39.2, 45.3, and 15.5%, respectively. Despite that industrial discharge contributed the least to the accumulation of metals (22.2%), it contributed the most to the total cancer risk (45.3%). Reducing industrial emission was crucial for minimizing the heavy metal input to agricultural soils and preventing potential health hazard. These findings could provide support for environmental protection authority to improve the management and risk prevention of contaminated farmland.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".