Skip to main navigation Skip to search Skip to main content

Urban green spaces, self-rated air pollution and health: A sensitivity analysis of green space characteristics and proximity in four European cities

  • Delft University of Technology
  • OWL University of Applied Sciences and Arts

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)
191 Downloads (Pure)

Abstract

Exploring the influence of green space characteristics and proximity on health via air pollution mitigation, our study analysed data from 1,365 participants across Porto, Nantes, Sofia, and Høje-Taastrup. Utilizing OpenStreetMap and the AID-PRIGSHARE tool, we generated nine green space indicators around residential addresses at 15 distances, ranging from 100m to 1500m. We performed a mediation analysis for these 135 green space variables and revealed significant associations between self-rated air pollution and self-rated health for specific green space characteristics. In our study, indirect positive effects on health via air pollution were mainly associated with green corridors in intermediate Euclidean distances (800-1,000m) and the amount of accessible green spaces in larger network distances (1,400–1,500m). Our results suggest that the amount of connected green spaces measured in intermediate surroundings seems to be a prime green space characteristic that could drive the air pollution mitigation pathway to health.

Original languageEnglish
Article number103300
JournalHealth and Place
Volume89
DOIs
Publication statusPublished - Sept 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Fingerprint

Dive into the research topics of 'Urban green spaces, self-rated air pollution and health: A sensitivity analysis of green space characteristics and proximity in four European cities'. Together they form a unique fingerprint.

Cite this