Spatial variation of resuspended particulate matter in urban environments and real-world assessment of street sweeping
Bibliographic record
Abstract
Non-tailpipe emissions have gained growing attention as an emerging source of traffic-related air pollution, especially as tailpipe emissions decline. This study conducted extensive mobile measurements in a high-density urban area over two years to investigate the spatial variability of resuspended road dust and evaluate the real-world effectiveness of street sweeping. Resuspended particulate matter (PM), specifically PM 2.5 and PM 10 , was measured alongside pollutants from tailpipe and non-tailpipe sources. The mobile sampling revealed substantial spatial variability in resuspended PM with notably higher concentrations on local streets compared to major roads. This spatial pattern contrasted with tailpipe-related black carbon and ultrafine particles, which were elevated near highways, commercial, and industrial zones. Nearby construction and renovation activities were major contributors to resuspended PM on local streets. As a result, school neighborhoods in residential areas and open spaces may experience acute exposure to road dust PM. Street sweepers equipped with regenerative air systems were generally ineffective in reducing resuspended PM under real-world conditions. This trend was consistent across different types of regenerative sweepers and operational modes, except for one sweeper equipped with an advanced filtration system showing some reduction in resuspended PM. Operational factors, including sweeper speed and water spraying, substantially influenced PM resuspension . While water spraying reduced on-road PM during sweeping, excessive use resulted in unintended increases in both resuspended and on-road PM concentrations afterward. While street sweeping helps to manage debris on road surfaces, these findings underscore the need to optimize sweeping practices and develop strategies to improve near-road air quality .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".