Nexus between urban green space and adult frequent mental distress
Differentiated non-linear environmental pathways and racial heterogeneity
Dados Bibliográficos
| ID | 21468643 |
|---|---|
| Autores | Peng Chen (0009-0001-5590-8732, The Ohio State University, autor correspondente), Desheng Liu (0000-0002-6088-5985, The Ohio State University), Huicong Han (The Ohio State University) |
| Ano | 2026 |
| Volume | 269 |
| Páginas | 105598 |
| Data de publicação | 2026-05-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Landscape and Urban Planning (JOURNAL) |
| Identificadores do periódico | ISSN: 0169-2046 • E-ISSN: 1872-6062 |
| Editora | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.landurbplan.2026.105598 |
| OpenAlex | W7126200164 |
| Idioma | EN |
| Referências citadas | 77 |
Tree canopy and grass show distinct, non-linear association with mental distress. • Urban heat, air pollution, and noise mediate the UGS-mental health relationship. • The associations between UGS and mental distress vary across racial groups. • The mental health benefits of trees are greater in communities of color. • Planners should prioritize trees over grass in UGS planning. Amid escalating adult mental distress in urban areas, urban green space (UGS) is increasingly recognized as an environmental feature for mitigating distress burden and associated environmental stressors. Effective and equitable UGS planning requires a nuanced understanding of the associations between UGS types and frequent mental distress (FMD), as well as their heterogeneity across racial and ethnic neighborhoods. Using spatial regressions within a Piecewise Structural Equation Modeling (PSEM) framework, this study investigates both direct associations between two UGS types (e.g., trees and grass) and FMD, as well as indirect pathways through land surface temperature (LST), air pollution (PM 2.5 ), and anthropogenic noise. Findings reveal that UGS types have distinct, and often opposing, non-linear associations with FMD and its environmental mediators. Tree canopy exhibits a direct negative association with FMD, with diminishing marginal effects as canopy cover increases, and a U-shaped association with PM 2.5 , while grass shows positive associations with FMD and PM 2.5 concentrations. Although both UGS types are negatively associated with LST and noise levels, trees show a significantly stronger association with temperatures. We also identify significant racial and ethnic heterogeneity in these associations. The overall negative marginal effect of tree canopy on FMD is significant in communities of color but statistically insignificant in predominantly White tracts. This disparity is driven by both direct association with FMD and indirect pathways through LST mitigation, which are significant only in communities of color. These findings challenge one-size-fits-all greening narratives and provide evidence for context-specific, equity-oriented UGS planning aiming at mitigating urban mental distress and advancing restorative environmental justice
Canopy · Distress · Ethnic group · Mental health · Urbanization · Climate Change and Health Impacts · Urban Green Space and Health · Urban Heat Island Mitigation
Spatial Data Science
What is the Best Dose of Nature and Green Exercise for Improving Mental Health? A Multi-Study Analysis
PiecewiseSEM
A dose of nature
The Biggest Myth in Spatial Econometrics
The modifiable areal unit problem and implications for landscape ecology
Association of Urban Green Space With Mental Health and General Health Among Adults in Australia
Racial Disparities in Climate Change-Related Health Effects in the United States
The Health Benefits of Urban Nature
Landscapemetrics
The impact of the physical and urban environment on mental well-being
Does green space matter? Exploring relationships between green space type and health indicators
WHO Environmental Noise Guidelines for the European Region
Neighbourhood greenness and mental wellbeing in Guangzhou, China
Temperature and mental health
Stress recovery during exposure to natural and urban environments
Distribution and Beyond
Urban green space, respiratory health and rising temperatures
Forests are chill
The impacts of racially discriminatory housing policies on the distribution of intra-urban heat and tree canopy
A dose of nature to reduce sexual crimes in public outdoor spaces
Do various dimensions of exposure metrics affect biopsychosocial pathways linking green spaces to mental health? A cross-sectional study in Nanjing, China
Thinking beyond general greenness
Assessment of mediators in the associations between urban green spaces and self-reported health
Association between greenspace morphology and prevalence of non-communicable diseases mediated by air pollution and physical activity
Understanding noise exposure, noise annoyance, and psychological stress
Racial disparities in environmental exposures and Sars-CoV-2 infection rates
Nonlinear relationships and spatial heterogeneity between geographical environment and mental health among middle-aged and older adults in China
Historic Redlining and Urban Health Today in U.S. Cities
Census-Tract-Level Median Household Income and Median Family Income Estimates
Exposure to Neighborhood Green Space and Mental Health
The Built Environment and Mental Health
Connecting climate justice and adaptation planning
Urban green spaces, self-rated air pollution and health
Urban densification in the Netherlands and its impact on mental health
Understanding the relationship between neighbourhood green space and mental wellbeing
Detection of infill development and contributing factors using deep learning and multilevel modeling
Examining the nonlinear relationship between neighborhood environment and residents' health
A Comparative Approach for Environmental Justice Analysis
Inequities in the quality of urban park systems
The restorative benefits of nature
Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits
Neighbourhood effects on health
| Velocidade de citação | historical |
|---|---|
| Altamente citado | Não |