Quantitative evaluation of urban green exposure and its impact on human health : A case study on the 3-30-300 green space rule
Copyright © 2024 Elsevier B.V. All rights reserved..
BACKGROUND AND AIMS: Urban green spaces offer various health benefits, yet the impact of comprehensive green exposure criteria on multidimensional health remains unclear. The 3-30-300 green space rule represents the green exposure indicators with specific thresholds. This study aims to quantitatively evaluate urban green exposure in cities and can support investigation of its relationship with human health.
METHODS: We conducted a cross-sectional study based on 902 investigated individuals in 261 residential locations aged 11-95 years from Xiamen City, China. 3-30-300 green exposure was calculated using field surveys, GIS, and Baidu Maps Application Programming Interface (API). Physical health data was based on Occupational Stress Indicator (OSI)-2. Mental health was from the 12-item General Health Questionnaire (GHQ-12). Social health was from a self-constructed evaluation questionnaire. Statistical analyses were conducted using Geographically Weighted Regression and Geographically Weighted Logistic Regression for global and local effects on green exposure and multidimensional health.
RESULT: Among the investigated individuals, only 3.55 % (32/902) fully meet the 3-30-300 rule in Xiamen. Global results show that individuals achieved at least 30 % vegetation coverage (Yes) is associated with better physical (β: 0.76, p < 0.01) and social (β: 0.5, p < 0.01) health. GWLR global results indicate that individuals can "see at least 3 trees from home" meeting one (OR = 0.46, 95%CI: 0.25-0.86, p < 0.05) or two (OR = 0.41, 95%CI: 0.22,0.78, p < 0.01; OR = 0.24, 95%CI: 0.07-0.77, p < 0.05) 3-30-300 rule components are significantly associated with reduced medical visits and hospitalizations refer to not met these criterias. In the GWR local analysis, achieved 30 % vegetation cover is significantly related to improved social health at all locations. Meeting any two indicators also contribute to improved social health (n = 511, β: 0.46-0.51, P < 0.05).
CONCLUSION: Green exposure indicators based on the 3-30-300 rule guiding healthy urban green space development. We observed multidimensional health benefits when 1/3 or 2/3 of the indicators were met.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:924 |
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| Enthalten in: |
The Science of the total environment - 924(2024) vom: 10. Apr., Seite 171461 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zheng, Yicheng [VerfasserIn] |
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| Links: |
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| Themen: |
3–30-300 |
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| Anmerkungen: |
Date Completed 03.04.2024 Date Revised 03.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.scitotenv.2024.171461 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369517105 |
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| 100 | 1 | |a Zheng, Yicheng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Quantitative evaluation of urban green exposure and its impact on human health |b A case study on the 3-30-300 green space rule |
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| 500 | |a Date Revised 03.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND AND AIMS: Urban green spaces offer various health benefits, yet the impact of comprehensive green exposure criteria on multidimensional health remains unclear. The 3-30-300 green space rule represents the green exposure indicators with specific thresholds. This study aims to quantitatively evaluate urban green exposure in cities and can support investigation of its relationship with human health | ||
| 520 | |a METHODS: We conducted a cross-sectional study based on 902 investigated individuals in 261 residential locations aged 11-95 years from Xiamen City, China. 3-30-300 green exposure was calculated using field surveys, GIS, and Baidu Maps Application Programming Interface (API). Physical health data was based on Occupational Stress Indicator (OSI)-2. Mental health was from the 12-item General Health Questionnaire (GHQ-12). Social health was from a self-constructed evaluation questionnaire. Statistical analyses were conducted using Geographically Weighted Regression and Geographically Weighted Logistic Regression for global and local effects on green exposure and multidimensional health | ||
| 520 | |a RESULT: Among the investigated individuals, only 3.55 % (32/902) fully meet the 3-30-300 rule in Xiamen. Global results show that individuals achieved at least 30 % vegetation coverage (Yes) is associated with better physical (β: 0.76, p < 0.01) and social (β: 0.5, p < 0.01) health. GWLR global results indicate that individuals can "see at least 3 trees from home" meeting one (OR = 0.46, 95%CI: 0.25-0.86, p < 0.05) or two (OR = 0.41, 95%CI: 0.22,0.78, p < 0.01; OR = 0.24, 95%CI: 0.07-0.77, p < 0.05) 3-30-300 rule components are significantly associated with reduced medical visits and hospitalizations refer to not met these criterias. In the GWR local analysis, achieved 30 % vegetation cover is significantly related to improved social health at all locations. Meeting any two indicators also contribute to improved social health (n = 511, β: 0.46-0.51, P < 0.05) | ||
| 520 | |a CONCLUSION: Green exposure indicators based on the 3-30-300 rule guiding healthy urban green space development. We observed multidimensional health benefits when 1/3 or 2/3 of the indicators were met | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 3–30-300 | |
| 650 | 4 | |a Green exposure | |
| 650 | 4 | |a Green space | |
| 650 | 4 | |a Mental health | |
| 650 | 4 | |a Physical health | |
| 650 | 4 | |a Social health | |
| 700 | 1 | |a Lin, Tao |e verfasserin |4 aut | |
| 700 | 1 | |a Hamm, Nicholas A S |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jue |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Tongyu |e verfasserin |4 aut | |
| 700 | 1 | |a Geng, Hongkai |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Junmao |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Guoqin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaotong |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Tianyi |e verfasserin |4 aut | |
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