Greenspace, air pollution, and respiratory health outcomes: A systematic review of cohort studies
Bibliographic record
Abstract
Abstract Exposure to greenspace is associated with improved health outcomes, with proposed mechanisms including improved local air quality and health effects that reduce personal susceptibility to air pollution. This systematic review synthesises cohort study evidence on whether greenspace protects against air pollution effects on respiratory-related health outcomes. We conducted a systematic review of cohort studies, searching Medline, Embase, and Scopus databases published up to December 2024. Study quality was evaluated using the Newcastle-Ottawa Scale. Twelve studies met the inclusion criteria, all of which were rated as “good” quality. Cohorts mostly originated from Europe, with outcomes including respiratory-related mortality, lung function, and non-infectious respiratory diseases. In general, exposure to greenspace was protective against air pollution-associated respiratory mortality, with effects more consistently detected through mediation than moderation. Residential greenspace appears to reduce the harm associated with air pollutants on respiratory health outcomes, including related mortality. The stronger association detected through mediation analysis suggests that the mechanism is through improvements in local air quality rather than the direct effects of greenspace on health to enhance individual resilience to the effects of air pollution. Highlights This systematic review investigated whether greenspace protects against respiratory health harms from air pollution We identified 12 eligible cohort studies, all rated "good" quality Results mixed but generally suggested greenspace is protective More consistent findings for mediated pathways, suggesting greenspace reduces pollution in local environment Visual Abstract:
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".