Urban greenspace under a changing climate: Benefit or harm for allergies and respiratory health?
Bibliographic record
Abstract
An increasing proportion of the world's population lives in urban settings that have limited greenspace. Urbanization puts pressure on existing greenspace and reduces its access. Climate impacts, including increased temperature and extreme weather events, challenge the maintenance of urban vegetation, reducing its ecosystem services and benefits for human health. Although urban greenspace has been positively associated with numerous health indicators, the evidence for allergies and respiratory health is much less clear and mixed. To address these uncertainties, a workshop with 20 global participants was held in Munich, Germany, in May 2024, focusing on the impact of greenspace-related co-exposures on allergies and respiratory health. This narrative review captures key insights from the workshop, including the roles of urban greenspace in (1) climate change mitigation, (2) interactions with pollen, and (3) emissions of biogenic volatile organic compounds and their byproducts, such as ozone. Additionally, it presents research and stakeholder recommendations from the workshop. Future studies that integrate advanced greenspace exposure assessments and consider the interplay of greenspace with pollen and biogenic volatile organic compounds, along with their relevant byproducts are needed. Increased public awareness and policy actions will also be essential for developing urban greenspace that maximizes health benefits, minimizes risks, and ensures resilience amid a changing climate and rapid urbanization.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".