Partitioning of Lead in Urban Street Dust Based on the Particle Size Distribution and Chemical Environments
Bibliographic record
Abstract
The objective of the present study was to investigate the distribution of lead among the physical fractions and between the various chemical forms of urban street dust. In order to achieve this aim, street dust samples were collected from three major roads with high traffic volume and one minor road with a low traffic density in urban areas of Az Zarqa City, Jordan. The dust samples (N = 6 for each site) were split into two portions. One part was employed for physical size fractionation and the other portion for chemical and physical analyses. A sequential extraction procedure was used to determine Pb associated with various chemical and physical fractions. It was found that about half of the lead was associated with the carbonate fraction and Fe-Mn oxides were ranked second, followed by the exchangeable and organic fractions. A general trend of increasing lead levels with decreasing particle size in street dust was observed. The current study showed that leaded gasoline was the major source for the elevated lead levels in street dust. Therefore, for those countries still employing leaded gasoline, such as Jordan, substantial reductions in lead contamination of street dust and roadside soils could be achieved by prohibiting the use of Pb additives. Consequently, many health benefits could be expected for the entire population and especially for children. The accuracy of lead results was checked by periodic analysis of SRM Soil 7. Observed concentrations were within 5% of certified value in analyzed SRM for lead.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".