Identification and quantification of trace metal(loid)s in water-extractable road dust nanoparticles using SP-ICP-MS
Bibliographic record
Abstract
Resuspension of road dust is a major source of airborne particulate matter (PM) in urban environments. Inhalation of ultrafine particles (UFP; < 0.1 μm) represents a health concern due to their ability to reach the alveoli and be translocated into the blood stream. It is therefore important to characterize chemical properties of UFPs associated with vehicle emissions. We investigated the capability of Single-Particle ICP-MS (SP-ICP-MS) to quantify key metal(loid)s in nanoparticles (NPs; < 0.1 μm) isolated from road dust collected in Toronto, Canada. Water extraction was performed to separate the <1-μm fraction from two different road dust samples (local road vs. arterial road) and a multi-element SP-ICP-MS analysis was then conducted on the samples' supernatants. Based on the particle number concentrations obtained for both supernatants, the metal(loid)-containing NPs could be grouped in the following categories: high (Cu and Zn, > 1.3 × 1011 particles/L), medium (V, Cr, Ba, Pb, Sb, Ce, La), low (As, Co, Ni, < 4.6 × 109 particles/L). The limit of detection for particle number concentration was below 5.5 × 106 particles/L for most elements, except for Cu, Co, Ni, Cr, and V (between 0.9 and 7.7 × 107 particles/L). The results demonstrate that road dust contains a wide range of readily mobilizable metal(loid)-bearing NPs and that NP numbers may vary as a function of road type. These findings have important implications for human health risk assessments in urban areas. Further research is needed, however, to comprehensively assess the NP content of road dust as influenced by various factors, including traffic volume and speed, fleet composition, and street sweeping frequency. The described method can quickly characterize multiple isotopes per sample in complex matrices, and offers the advantage of rapid sample scanning for the identification of NPs containing potentially toxic transition metal(loid)s at a low detection limit.
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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) | 0.001 | 0.001 |
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".