Risk assessment of heavy metals in road dust and simulation of pollutant release in Lianyungang city (China)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 |
| 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 it