Cyclists’ exposure to atmospheric and noise pollution: a systematic literature review
Bibliographic record
Abstract
Cyclists constitute a population particularly exposed to atmospheric and noise pollution in urban environments; at the same time, they contribute to its reduction. For about ten years now, more and more studies have been completed to assess cyclists’ exposure, comparing this mode of transportation with others, quantifying its impacts in term of individual and collective health, understanding cyclists’ perceptions regarding their exposure, etc. Though some literature reviews have examined some of these specific issues, none have yet proposed a general overview of this field of study. Therefore, this mapping literature review fills this gap by jointly analysing 205 articles and identifying elements of consensus and disagreement, as well as existing gaps. Among others, our results indicate that the cities in the South and exposure to noise are under-studied and that cyclists’ ventilation is still too rarely accounted for, regardless of the type of studies. Modelling studies regarding exposure are too heterogeneous methodologically to allow a generalisation of their results. Conversely, intermodal comparison studies clearly indicate overexposure for cyclists compared to other modes. Also, health studies conclude that, either individually or collectively, the benefits of cycling surpass the costs of exposure to atmospheric pollution. The knowledge produced by this research trend remains difficult to exploit by urban planners, but the recent work done seems to offer more practical perspectives to professionals.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".