Thoracic Fraction (PM10) of Resuspended Urban Dust: Geochemistry, Particle Size Distribution and Lung Bioaccessibility
Bibliographic record
Abstract
A fluidized bed aerosol generator was connected to a 13-stage cascade impactor (nanoMOUDI) for the size fractionation of urban dust (<10 µm), followed by the gravimetric analysis of loaded PTFE filter samples. This method was used to characterize the PM10 (thoracic) fraction of road dust sampled from expressways, arterial roads and local roads in Toronto, Canada. The fine particle fractions (<1.8 µm) of all the studied samples accounted for 51–72% of the resuspended PM10 (by weight). Elemental analysis using ICP-MS and ICP-OES revealed an overall trend of element enrichment in the <1.8 µm fraction compared to the coarse fraction (1.8–10 µm) of the road dust. By contrast, archived house dust samples displayed the reverse trend for most elements. The lung bioaccessibility of target elements (Al, B, Ba, Co, Cr, Fe, La, Mn, Mo, Sb, Sr, Ti, V and Zn) was assessed for each road dust fraction using 0.1 M ammonium citrate (pH 4.4) to simulate intracellular fluid and Gamble solution (pH 7.2) to simulate interstitial lung fluid. The <1.8 µm fraction of local road dust displayed significantly higher bioaccessibility (p < 0.05) for Zn when using Gamble solution, and for seven out of the 14 target elements when using ammonium citrate. These results show the importance of characterizing the fine fraction of road dust.
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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 | 0.000 | 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) | 0.001 | 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".