How human-perceived urban green spaces contribute to mental well-being: a machine learning approach.

Assessing thermal inequities: A spatiotemporal analysis of socio-demographic and urban green space in New York City.

Characterizing the critical role of older people's overall satisfaction with green spaces for their well-being using machine learning methods: Feature extraction and predictive modeling.

To what extent do urban forest and green space plans include housing-related content?

Role of urban green space structure and configuration in regulating land surface temperature in NCT Delhi using explainable artificial intelligence

A supply-demand evaluation framework for uncovering age and gender inequities in urban green space cooling- A case study of Fuzhou

Integrated Assessment of Environmental Sustainability and Urban Green Space Suitability using PCA-AHP with Zonal Prioritization: A Remote Sensing and GIS approach

Darker nights, happier lives? The impact of urban green space night-time accessibility on residents' subjective happiness: A case study of the main urban area of Hangzhou

Beyond the North-South divide: Climatic and socioeconomic interactions shape global patterns of urban greenspace exposure inequality.

Urban green spaces play a critical role in ecological sustainability, public health, and social well-being, especially in rapidly urbanizing regions. This study examines Taman Hutan Pakal, an urban forest in West Surabaya, to explore its potential as a sustainable edu-ecotourism and family recreation destination from a management and innovation perspective. Data were collected through field observations, in-depth interviews with park managers, visitors, local communities, and urban planning experts, as well as document analysis. Thematic analysis identified four key insights. First, Taman Hutan Pakal possesses significant ecological and recreational potential. Second, existing challenges include insufficient family-oriented infrastructure, limited environmental education programs, and low visibility in public engagement. Third, opportunities exist through societal demand and supportive policies to enhance environmental literacy and healthy recreational practices. Fourth, strategic interventions such as branding as an Educational Urban Forest, implementing biodiversity-based environmental education, improving child-friendly facilities, and fostering multi-stakeholder collaboration enable value transformation, innovation in urban park management, and long-term sustainability. The findings highlight the importance of integrating ecological, social, and educational dimensions in urban green space management to generate community well-being and strategic value for regional development.

Urban plant landscapes shape public aesthetic experience and colour preferences, yet existing research relies heavily on image-based or lab studies, lacking real-world validation. This study investigates demographic differences and temporal changes in floral colour preference through a field experiment where 57 participants created flower combinations under controlled conditions, with a follow-up after six months. Results showed 68% initially preferred red and pink, indicating a strong inclination towards warm colours. Significant variations occurred across gender, age, and education (p < .05), accompanied by an exclusivity effect between warm and cool hues. Notably, 26% of participants shifted preference from warm to cool tones over time, underscoring the dynamic nature of colour choice. These findings offer practical insights for human-centred planting design that adapts to diverse and evolving public preferences in urban green spaces.

The ecological benefits of urban green spaces depend on their structure and ecological service function. Evaluation systems used to monitor these characteristics show distinct regional variations. This study analyzed China’s urban green spaces, developed a quantitative ecological benefit evaluation system, and comprehensively evaluated the ecological benefits of green spaces in Xi’an city. Suitable evaluation indexes for Xi’an were selected based on field survey data with large-scale samples and high-resolution remote sensing image data. The results showed that the ecological service function of urban green spaces in Xi’an has been substantially improved by ecological planning. Therefore, it is important to evaluate this function as part of the urban planning and design process. Furthermore, increasing the 3D Green Quantity through urban forests can effectively improve the ecological service function.

Abstract. In April 2024, unexpected heavy rainfall triggered one of the most severe flooding events in Dubai’s recent history, causing widespread concern for urban infrastructure and green spaces. This study evaluates the flood’s impact on urban vegetation in South Dubai using high-resolution PlanetScope satellite imagery and the Normalized Difference Vegetation Index (NDVI). Vegetation conditions before and after the flood (April 14 and April 18–19, 2024) were quantified and compared using NDVI analysis within QGIS to assess changes in vegetation health and coverage. Results indicate significant declines in vegetation health in areas dominated by intensive turf management, such as Damac Hills and Jumeirah Golf Estates, while eco-oriented and sustainably designed communities, including The Sustainable City, displayed stable or slightly improved NDVI values. NDVI difference mapping highlighted both vegetation losses and localized gains, revealing a highly heterogeneous impact across the urban landscape. The study demonstrates the utility of remote sensing and NDVI-based analysis as effective tools for monitoring vegetation response to extreme weather events and provides insights to support urban planning, green infrastructure design, and resilience strategies in rapidly expanding arid cities.

Drosicha corpulenta (Hemiptera: Monophlebidae) is a major polyphagous pest affecting street and garden trees in arid regions of northern China, causing increasing damage in newly developed cities like Cocodala, Xinjiang. This study was conducted from 2024 to 2025 to investigate this pest's life cycle, key damage periods, and spatial distribution in seven host plants, focusing on nymph emergence, female soil entry, and overwintering egg distribution. The results show that D. corpulenta has one generation per year, which overwinters as eggs. Nymphs emerge in early March, and male pupation occurs from mid-April to early May. Females mate after the third molt in early to mid-May and enter the soil to lay eggs from late May to early June, with consistent timing over two years. The suitability of the host varied significantly: Platanus × hispanica was the most preferred, with the highest daily nymph emergence of 840.8 individuals in 2024 and 1196.0 in 2025, followed by Prunus padus and five other plant species (Populus spp., Fraxinus chinensis, Styphnolobium japonicum, Pinus spp., and Malus spectabilis). Female soil entry reached a maximum on 23 May (979.8 individuals-1 day-1) and gradually decreased with increasing distance from the trunk. Overwintering eggs showed no obvious azimuthal bias, but were mainly concentrated near the trunk (0-30 cm) and in shallow soil (0-10 cm; 179.8 eggs per 100 g composite soil sample per sampling point), decreasing sharply in number with distance and depth. Both Taylor's power law and Iwao's regression confirmed the aggregated distribution. This study identifies key phenological stages, highly susceptible hosts, and the near-trunk shallow soil layer as critical for oviposition and overwintering and provides a basis for precise monitoring and targeted control in urban green spaces.

Pneumonia remains a leading cause of morbidity and hospitalization in sub-Saharan Africa, yet the contributions of long-term environmental and infrastructural factors to its spatial distribution are poorly understood. Kinshasa, a rapidly expanding African megacity, experiences high levels of air pollution, substantial vegetation loss, and marked urban inequalities, potentially exacerbating respiratory vulnerability. We conducted a retrospective ecological spatial study using 484,954 pneumonia hospital admissions recorded across 35 health zones in Kinshasa between 2018 and 2022. Long-term exposure to ambient air pollution (PM₂.₅, NO₂, CO), vegetation cover (NDVI), major road density, climatic indicators, and healthcare infrastructure were assessed via satellite-derived and geospatial data. Bayesian BYM2 negative binomial models were fitted to estimate age-stratified relative risks (RRs), accounting for spatial dependence and overdispersion. Sensitivity analyses were used to evaluate model robustness. Pneumonia incidence showed pronounced spatial heterogeneity, with persistent high-risk zones concentrated in central and northern districts. Children under five years of age accounted for 41% of the cases and presented a substantially higher cumulative incidence than older individuals did. After adjustment for spatial effects and multicollinearity, no air pollutant demonstrated a consistent positive association with pneumonia risk. In contrast, vegetation cover was a robust protective factor across all age groups (RR range: 0.58-0.85). Higher major road density was also associated with reduced risk, likely reflecting improved accessibility and urban infrastructure. Areas combining low vegetation, high climatic stress, and limited infrastructure experienced the highest pneumonia burden. Pneumonia risk in Kinshasa is driven primarily by environmental and infrastructural inequalities rather than by the isolated effects of individual air pollutants. Strengthening urban green infrastructure, reducing environmental stressors, and improving equitable access to healthcare should be central to pneumonia prevention strategies in rapidly urbanizing African cities.

To examine associations between public park characteristics within different walking distances from residential locations and depression, to distinguish between features within parks (e.g. amenities, attractions, facilities, tree cover) and park metrics in the home area (e.g. number of parks, size, and total area), and to employ rigorous geospatial analysis linking the best available objectively measured park and urban green space (UGS) exposures to validated depression outcomes across multiple scales. This population-based cross-sectional study utilised baseline data from 329,363 UK Biobank participants resident in urban areas. Prevalent diagnosed depression was defined as an ICD-10 code of F32 (depressive episode) or F33 (recurrent depressive disorder). Park characteristics and urban green space data were derived from Ordnance Survey Great Britain datasets and spatially linked to participants' residential addresses. Three definitions of Home Catchment Area size were tested for every individual respondent: 400 m (m), 800 m, and 1600 m, as proxies for a 10-,20- and 40-min return walk respectively. Logistic regression models assessed associations with robust statistical approaches including assessment of interaction, correction for multiple testing, confounder adjustment, and sensitivity analyses. Specific park characteristics within 20-min and 40-min catchments were associated with reduced depression likelihood among women only. Within 40-min catchments, protective associations were observed for recreational amenities (cafés: odds ratio (OR) 0.89, 95% confidence interval (CI) 0.85-0.93; toilets: OR 0.85, 95% CI 0.79-0.91), attractions (OR 0.83, 95% CI 0.80-0.87), sports facilities (OR 0.84, 95% CI 0.79-0.90), and tree canopy coverage (e.g. > 20%, OR 0.88, 95% CI 0.85-0.91). In a 20-min catchment, each 1% increase in urban greenspace classified as parks was associated with 11% reduced depression odds among women (OR 0.89, 95% CI 0.82-0.95). No significant protective associations were observed among men, with some paradoxical adverse associations identified. This study provides robust evidence for protective associations between park characteristics and depression among women, but not men. Findings support proximity-based planning concepts but challenge the current policy and practice focus on 20-min neighbourhood and identify park features which optimise preventive potential. Results have direct implications for evidence-based urban planning policy internationally, providing a framework for developing mental health-supporting green infrastructure that recognises sex-based differences.

Abstract Urban planners increasingly recognize the importance of improving access to nature to support human well-being, social inclusion, and urban sustainability. However, assessing accessibility to urban green spaces remains challenging, particularly in rapidly urbanizing cities of the Global South, where mobility constraints and socio-spatial inequalities shape who can effectively benefit from these spaces. Urban wetlands, despite providing multiple ecosystem services, are rarely incorporated into formal green infrastructure networks and are often excluded from accessibility assessments. This study examines how integrating urban wetlands as Nature-Based Solutions (NbS) modifies patterns of access to nature in Concepción, Chile. We combine land-use data on parks and wetlands with accessibility measures and origin–destination travel survey data to model walking accessibility under two scenarios: green spaces excluding wetlands and green spaces including wetlands. A Random Forest model is applied to capture heterogeneity across socio-demographic profiles, including age, gender, employment status, and driver’s license ownership. Results show that incorporating urban wetlands significantly increases the accessible surface of green space across the city, with particularly strong gains for groups with limited mobility options, such as women, older adults, unemployed residents, and individuals without access to private vehicles. In peripheral and underserved areas, wetlands partially compensate for deficits in formal park provision, reducing accessibility gaps. These findings provide empirical evidence that urban wetlands function as grounded NbS by redistributing access to nature and underscore the importance of formally integrating wetlands into urban planning instruments to promote more inclusive and resilient cities.

Decentralized IoT-networks are part of a broader class of decentralized physical infrastructure networks (DePIN), which use blockchain to decentralize, incentivize, and reward the operation and maintenance of technical infrastructure. This study examines how decentralized IoT-networks can address challenges in the design and governance of environmental data streams and reduce adoption barriers for monitoring infrastructure. It draws on the deployment of IoT sensors for measuring environmental variables in a community-based urban restoration project in Nairobi, Kenya. The paper discusses technical features and presents observations on network reliability, reductions in operational costs, accessibility, and community participation. The findings suggest that decentralized IoT systems hold promise for enhancing distributed environmental data collection and transparent multi-party governance, due to significant reductions in operational costs, the potential for fractional income generation, traceable data trails, and inclusive governance mechanisms.

Soil microbiomes in urban green spaces: Foundation of ecosystem functions and human health

The rapid expansion of urban areas has intensified the Urban Heat Island (UHI) effect, particularly in high-density cities characterized by extensive impervious surfaces and limited green spaces. Rising surface temperatures increase energy consumption, reduce outdoor thermal comfort, and pose serious public health risks during extreme heat events. Urban greening is widely recognized as an effective heat mitigation strategy; however, in densely built environments, indiscriminate or uniform distribution of green spaces often fails to achieve optimal cooling benefits. This study proposes an artificial intelligence–based framework for strategically optimizing urban green space placement to maximize heat reduction while accounting for land-use constraints. The proposed approach integrates multisource remote sensing data, vegetation indices, land surface temperature measurements, and urban morphological indicators with machine learning–based thermal modeling. A Random Forest Regression model is employed to capture the nonlinear relationships between vegetation cover, built-up density, and surface temperature, followed by a spatial optimization process to identify priority locations for greening interventions. Experimental results demonstrate a strong negative relationship between vegetation density and land surface temperature, with optimized greening scenarios achieving temperature reductions of up to 2.6°C, significantly outperforming uniform greening strategies with equivalent green area allocation. The findings highlight that the spatial configuration and targeted placement of green spaces are more influential than total green cover alone. By incorporating explainable AI techniques, the framework also provides interpretable insights into the dominant drivers of urban heat, enhancing transparency for planning applications. Overall, this study offers a data-driven and decision-oriented methodology that can support urban planners and policymakers in designing effective, climate-resilient strategies for mitigating heat stress in high-density urban environments.