Quantification and Characterization of Metals in Ultrafine Road Dust Particles
Bibliographic record
Abstract
Road dust is an important source of resuspended particulate matter (PM) but information is lacking on the chemical composition of the ultrafine particle fraction (UFP; <0.1 µm). This study investigated metal concentrations in UFP isolated from the “dust box” of sweepings collected by the City of Toronto, Canada, using regenerative-air-street sweepers. Dust box samples from expressway, arterial and local roads were aerosolized in the laboratory and were separated into thirteen particle size fractions ranging from 10 nm to 10 µm (PM10). The UFP fraction accounted for about 2% of the total mass of resuspended PM10 (range 0.23–8.36%). Elemental analysis using ICP-MS and ICP-OES revealed a marked enrichment in Cd, Cr, Zn and V concentration in UFP compared to the dust box material (nano to dust box ratio ≥ 2). UFP from arterial roads contained two times more Cd, Zn and V and nine times more Cr than UFP from local roads. The highest median concentration of Zn was observed for the municipal expressway, attributed to greater volumes of traffic, including light to heavy duty vehicles, and higher speeds. The observed elevated concentrations of transition metals in UFP are a human health concern, given their potential to cause oxidative stress in lung cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".