Characterizing the sources, concentrations and resuspension potential of metals and metalloids in the thoracic fraction of urban road dust
Bibliographic record
Abstract
and HCl) and the elemental concentrations measured using ICP-MS. Principal component analysis (PCA) was applied to infer source emissions. Annual elemental loadings to roads were estimated using data on total sweepings collected by the City of Toronto. The mass amounts of metals and metalloids (< 10 μm) available for resuspension were calculated assuming a contribution of 10% to total loadings for this fraction. The median trace element concentrations in city sweepings (n = 64) ranged from highest to lowest as follows: Mn > Zn > Ba > Cr > Cu > Pb > V > Ni > Sn > Mo > Co > As > Sb > Cd. Iron, Cr, Ni, Co, Mo and Cu levels were significantly associated with road class, with the highest concentrations measured for the expressway. Most elements, especially Sb and Zn, were enriched in thoracic sweepings. The PCA results demonstrate the importance of non-fossil fuel, traffic-related elemental emissions. Difficulties in identifying sources, given uncertainties regarding overlapping chemical profiles, are also highlighted. Significant elemental loadings to roads were estimated to occur, with the largest amounts identified for Fe, Al, Mn, Zn, Cr and Cu. Road dust resuspension is predicted to be the most important source of emissions for Fe, Al, Mn, Cr, V, Sn, Mo, Co and Sb.
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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 | 0.001 |
| 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".