Photochemical Production of Singlet Oxygen by Toronto Road Dust
Bibliographic record
Abstract
Road dust, which consists of brake and tire wear, pavement particles, crustal material, semivolatile vehicle exhaust components, and natural organic matter, can contribute to both airborne particulate matter and urban runoff. To date, research has mainly focused on the health impact of road dust, but little work has been conducted to characterize its role as a reactive surface in the environment. Our group has previously shown that illuminated road dust is a source of singlet oxygen, an important environmental oxidant. Here, we report the singlet oxygen steady state concentration ([ 1 O 2 ] ss ) of illuminated aqueous suspensions and aqueous extracts of road dust samples collected from three different road types (local, arterial, and expressway) in Toronto, Canada. We find that the [ 1 O 2 ] ss generated by aqueous extracts of road dust samples spans less than an order of magnitude and there is no clear trend in the [ 1 O 2 ] ss with road type, but that road dust [ 1 O 2 ] ss depends on organic content and light absorbance. The median singlet oxygen apparent quantum yield of the road dust extracts is 5.4% (UV-A), which is more than double the median reported values (UV-A) for river, lake, soil and wastewater samples. Comparisons of road dust aqueous extracts with aqueous road dust suspensions reveal that suspended road dust has a much higher [ 1 O 2 ] ss, emphasizing the need to examine singlet oxygen production by the insoluble components in particulate matter. Overall, our results highlight the potential of road dust to transform pollutants in the atmosphere and in urban runoff.
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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 | 0.001 | 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".