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Zheng Y, Lin T, Hamm NAS, Liu J, Zhou T, Geng H, Zhang J, Ye H, Zhang G, Wang X, Chen T. Quantitative evaluation of urban green exposure and its impact on human health: A case study on the 3-30-300 green space rule. Sci Total Environ 2024; 924:171461. [PMID: 38461976 DOI: 10.1016/j.scitotenv.2024.171461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 12/12/2023] [Accepted: 03/01/2024] [Indexed: 03/12/2024]
Abstract
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.
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Affiliation(s)
- Yicheng Zheng
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; School of Geographical Sciences, Faculty of Science and Engineering, University of Nottingham, Ningbo 315100, China.
| | - Tao Lin
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China; CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315800, China.
| | - Nicholas A S Hamm
- School of Geographical Sciences, Faculty of Science and Engineering, University of Nottingham, Ningbo 315100, China.
| | - Jue Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100191, China; Department of Global Health and Population, Harvard TH Chan School of Public Health, 677 Huntington Avenue Boston, Boston, MA 02115, USA.
| | - Tongyu Zhou
- Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo 315100, China.
| | - Hongkai Geng
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Junmao Zhang
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Hong Ye
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China.
| | - Guoqin Zhang
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China.
| | - Xiaotong Wang
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; School of Geographical Sciences, Faculty of Science and Engineering, University of Nottingham, Ningbo 315100, China.
| | - Tianyi Chen
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Digital Technology for Territorial Space Analysis and Simulation, Fuzhou 350108, China.
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