Risk assessment of heavy metals in road dust and simulation of pollutant release in Lianyungang city (China)
Bibliographic record
Abstract
This study focuses on road dust in Haizhou District, Lianyungang City, and analyses the content and distribution characteristics of six pollutants (Cu, Pb, Zn, Cd, Nitrogen and phosphorus pollutants) in the road dust. The study employed three evaluation methods and a health risk assessment to evaluate the risk of pollutants in road dust. Additionally, the impact of different rainfall intensities on the turbidity of initial rainwater and the release of road dust pollutants was explored, and the mechanisms of pollutant's migration and release were analysed. The results indicated that road dust contamination by Cu, Cd, and TN is more severe, with the average contents of Pb and Zn (72.11 mg/kg, 701.33 mg/kg). TP (433.75 mg/kg) is slightly below the limit set by the Canadian Department of Environment and Energy guidelines. Additionally, under simulated external disturbances, the release of pollutants (heavy metals, TN, and TP) is significantly positively correlated with the turbidity of the overlying water and significantly negatively correlated with the particle size of road dust, suggesting that different rainfall intensities affect pollutant's migration and release by influencing water turbidity and road dust particle size. This study provides theoretical support for improving the urban environmental quality.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".