Temporal and spatial variability of traffic-related PM2.5 sources: Comparison of exhaust and non-exhaust emissions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The contribution of traffic-related particulate matter (PM 2.5 , particles smaller than 2.5 μm in diameter) sources can vary temporally and spatially, which may disproportionately contribute to health outcomes. Furthermore, non-exhaust emissions are a growing concern due to the high concentrations of redox active metals that can be present. The temporal and spatial variabilities of traffic-related PM 2.5 sources were investigated in this study by comparing source contributions between two near-road sites. In order to identify local PM 2.5 sources with greater temporal and spatial resolution, receptor modeling was performed for hourly-resolved organics, inorganic ions , trace elements, and black carbon in PM 2.5 simultaneously measured at downtown and highway sites located within 15 m of a major roadway and highway, respectively, in Toronto. The source apportionment study revealed that traffic-related PM 2.5 sources were mainly from exhaust emissions (9%–19% of PM 2.5 ) and non-exhaust emissions including brake wear (2%–6%) and resuspension of road dust (3%–4%). The traffic-related sources exhibited strong diurnal and spatial variabilities, whereas no spatial and temporal differences were observed for the largest PM 2.5 contributors, oxidized organic aerosol and secondary sulphate. During morning rush hours, the overall contribution of traffic exhaust and non-exhaust emissions were elevated up to 35%–48% of total PM 2.5 mass, which was found to be the largest PM 2.5 source at the highway site and the second largest contributor in the downtown area. Furthermore, the contribution of traffic-related sources at the highway site was higher than at the downtown site by a factor of 2–3, suggesting that exposure to traffic-related emissions varies greatly in space and time. Nearly one-third of the traffic-related source contributions were associated with non-exhaust emissions from brake wear and road dust resuspension in the urban environment. Elevated levels of non-exhaust sources were correlated with the number of heavy-duty vehicles, rather than total traffic volume. Although the contribution of brake wear and road dust sources to total PM 2.5 mass was relatively low, non-exhaust emissions contributed a substantial fraction of trace elements, especially for Ba (74–79%), Cu (66–71%), and Mn (53–65%) in the urban atmosphere.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it