Examining the effectiveness of municipal street sweeping in removing road-deposited particles and metal(loid)s of respiratory health concern
Bibliographic record
Abstract
Road dust is a demonstrated source of urban air pollution. Given this, the implementation of street sweeping strategies that effectively limit road dust accumulation and resuspension should be a public health priority. Research examining the effectiveness of street sweeping for road dust removal in support of good air quality has been limited to date. To address this, the study aimed to assess the use of a regenerative-air street sweeper to efficiently remove road dust particles and metal(loid)s in size fractions relevant for respiratory exposure in Toronto, Canada. As part of this, the mass amounts, particle size distribution and elemental concentrations of bulk road dust before and after sweeping at five arterial sites were characterized. Sweeping reduced the total mass amount of thoracic-sized (<10 µm) road dust particles by 76 % on average. A shift in the size distribution of remaining particles toward finer fractions was observed in post-sweeping samples, together with an enrichment in many metal(loid)s such as Co, Ti and S. Overall, the mass amounts of metal(loid)s of respiratory health concern like Cu and Zn were greatly reduced with sweeping. Traffic volume and road surface quality were predictors of dust loadings and elemental concentrations. Road surface quality was also found to impact street sweeping efficiencies, with larger mass amounts per unit area collected post-sweeping where street surfaces were distressed. This study demonstrates that street sweeping using advanced technology can be highly effective for road dust removal, highlighting its potential to support air quality improvement efforts. The importance of tailoring sweeping service levels and technologies locally as per the quality of road surface and traffic patterns is emphasized. Continued efforts to mitigate non-exhaust emissions that pose a respiratory health risk at their source is essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 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) | 0.000 | 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 teacher head, 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".