Reducing cardiovascular health impacts from traffic-related noise and air pollution: intervention strategies
Bibliographic record
Abstract
Recent studies suggest that exposure to both traffic-related air pollution (TrAP) and to road traffic noise (RTN) are independent risk factors for cardiovascular disease (CVD). While the exact pathophysiologic mechanisms are not known, plausible biological models exist for both associations. This paper describes interventions and mitigating measures aimed at reducing both air and noise pollution emitted from traffic. Nine types of interventions are examined within the four strategic themes of (i) land-use planning and transportation management, (ii) reduction of vehicle emissions, (iii) modification of existing structures, and (iv) behavioral change. Not all interventions result in concomitant reductions of air and noise pollutant exposures. Most interventions that rely on a scientific basis to reduce CVD are directed at reducing TrAP. Interventions identified with the greatest potential benefits focus on the pollutant source, such as reductions in traffic volume and air pollutant emissions, and are more easily realized, and likely cheaper, if they are considered in the land-use planning stages with less reliance on behavioral changes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".