Effects of urban green spaces on human perceived health improvements: Provision of green spaces is not enough but how people use them matters

Understanding the pathways between the use of urban green spaces and self-rated health: A case study in Mexico City

In recent decades, extensive research has demonstrated the positive impact of urban green spaces (UGS) on public health through several pathways. However, in the context of Latin America, particularly Mexico City, there remains a notable scarcity of evidence linking UGS use to health outcomes and an insufficient understanding of the pathways or factors underlying these associations. Therefore, this study employs Structural Equation Modeling (SEM) to investigate the intricate pathways between UGS use and residents' perceived health in Mexico City, a densely populated urban center. The SEM integrates three key mediators: sentiments towards UGS, UGS quality, and time spent within these spaces. Survey data was collected through an online survey distributed via social media in May 2020 (n = 1,707). The findings indicate a minor yet significant direct link between UGS use and self-reported health (0.0427, p < 0.1). Conversely, the indirect pathways through sentiments towards UGS, UGS quality, and time spent in UGS were highly significant (0.1950, p < 0.01), underscoring their substantial role as mediators in the UGS use-health association. While a comprehensive understanding of the mechanisms linking perceived health to UGS use in Mexico City requires further research, this study proposes that fostering positive sentiments towards UGS, enhancing UGS quality, and encouraging extended visits to green areas could potentially amplify the perceived health benefits associated with UGS use among residents. These insights offer valuable inputs for policymaking, emphasizing the importance of integrating public perspectives to optimize nature-based solutions and broaden their positive impact within Mexico City.

The Patterns of Intraspecific Variations in Mass of Nectar Sugar along a Phylogeny Distinguish Native from Non-Native Plants in Urban Greenspaces in Southern England

To serve human needs, non-native species are selected based on an array of functional traits, which generally confer competitive advantages to these species in their recipient environments. Identifying non-obvious functional traits that indirectly inform human selection of non-natives to introduce into urban greenspaces is not yet part of common discussions in invasion biology. We tested whether functional traits integrated within a phylogenetic framework, may reveal those subtle criteria underlying the introduction of non-native plants into urban greenspaces. We found no differences in terms of functional traits between natives and non-natives. We also found no evidence that functional traits predict nectar production, irrespective of how nectar production was measured. Finally, we found that the mean sugar concentration of nectar per flower is evolutionarily shared both within closely related non-native plants as well as within close native plants. However, phylogenetically close species share similar intraspecific variation in mass of nectar sugar per flower, but this is true only for non-native plants, thus revealing a non-obvious selection criteria of non-native plants for urban greenspaces. Our results indicate that the phylogenetic patterns of intraspecific variation in mass of nectar sugar per flower is the major criterion distinguishing non-natives from native plants in urban greenspaces in Southern England.

Predicting self-perceived general health status using machine learning: an external exposome study

BACKGROUND

Self-perceived general health (SPGH) is a general health indicator commonly used in epidemiological research and is associated with a wide range of exposures from different domains. However, most studies on SPGH only investigated a limited set of exposures and did not take the entire external exposome into account. We aimed to develop predictive models for SPGH based on exposome datasets using machine learning techniques and identify the most important predictors of poor SPGH status.

METHODS

Random forest (RF) was used on two datasets based on personal characteristics from the 2012 and 2016 editions of the Dutch national health survey, enriched with environmental and neighborhood characteristics. Model performance was determined using the area under the curve (AUC) score. The most important predictors were identified using a variable importance procedure and individual effects of exposures using partial dependence and accumulated local effect plots. The final 2012 dataset contained information on 199,840 individuals and 81 variables, whereas the final 2016 dataset had 244,557 individuals with 91 variables.

RESULTS

Our RF models had overall good predictive performance (2012: AUC = 0.864 (CI: 0.852-0.876); 2016: AUC = 0.890 (CI: 0.883-0.896)) and the most important predictors were "Control of own life", "Physical activity", "Loneliness" and "Making ends meet". Subjects who felt insufficiently in control of their own life, scored high on the De Jong-Gierveld loneliness scale or had difficulty in making ends meet were more likely to have poor SPGH status, whereas increased physical activity per week reduced the probability of poor SPGH. We observed associations between some neighborhood and environmental characteristics, but these variables did not contribute to the overall predictive strength of the models.

CONCLUSIONS

This study identified that within an external exposome dataset, the most important predictors for SPGH status are related to mental wellbeing, physical exercise, loneliness, and financial status.

Barriers Affecting Women’s Access to Urban Green Spaces during the COVID-19 Pandemic

During the COVID-19 pandemic, urban green spaces (UGS) have gained relevance as a resilience tool that can sustain or increase well-being and public health in cities. However, several cities in Latin America have seen a decrease in their UGS use rates during the health emergency, particularly among vulnerable groups such as women. Using Mexico City as a case study, this research examines the main barriers affecting women’s access to UGS during the COVID-19 pandemic in Latin America. We applied a sequential mixed-methods approach in which the results of a survey distributed via social media in June 2020 to women aged 18 and older were used to develop semi-structured interviews with 12 women during October 2020. One year later, in November 2021, the continuity of the themes was evaluated through focus groups with the same group of women who participated in the interviews. Our results suggest that (1) prohibiting access to some UGS during the first months of the pandemic negatively impacted UGS access for women in marginalized neighborhoods; (2) for women, the concept of UGS quality and safety are intertwined, including the security level of the surrounding streets; and (3) women who live in socially cohesive neighborhoods indicated using UGS to a greater extent. Our findings highlight that while design interventions can affect women’s willingness to use UGS by improving their perceived safety and comfort, they remain insufficient to fully achieve equity in access to UGS.

Ecosystem Services Provided by Urban Forests in the Southern Caucasus Region: A Modeling Study in Tbilisi, Georgia

All cities globally are growing considerably as they are experiencing an intensive urbanization process that leads to high soil consumption and pollution of environmental components. For this reason, cities are required to adopt measures to reduce these impacts and tree planting has been suggested as a cost-effective strategy. In our study, we implemented for the first time in a Southern Caucasus city the i-Tree Eco model to quantify the main ecosystem services provided by urban forests. Trees in two parks in Tbilisi, EXPO Park (694 trees) and RED Park (1030 trees), have been measured, and a model simulation was performed for the year 2018. These green infrastructures store large amounts of carbon in their woody tissues (198.4 t for EXPO Park and 126.5 t for RED Park) and each year they sequester 4.6 and 4.7 t of CO2 for EXPO Park and RED Park. They also remove 119.6 and 90.3 kg of pollutants (CO, NO2, O3, PM2.5, SO2), and reduce water runoff of 269.5 and 200.5 m3, respectively. This analysis highlights the key role of urban forests in improving the environmental sustainability of the city of Tbilisi and provides important decision support for tree species selection in this geographic area.

Assessing Emotional Responses to the Spatial Quality of Urban Green Spaces through Self-Report and Face Recognition Measures

Although creating a high-quality urban green space (UGS) is of considerable importance in public health, few studies have used individuals' emotions to evaluate the UGS quality. This study aims to conduct a multidimensional emotional assessment method of UGS from the perspective of spatial quality. Panoramic videos of 15 scenes in the West Lake Scenic Area were displayed to 34 participants. For each scene, 12 attributes regarding spatial quality were quantified, including perceived plant attributes, spatial structure attributes, and experiences of UGS. Then, the Self-Assessment-Manikin (SAM) scale and face recognition model were used to measure people's valence-arousal emotion values. Among all the predictors, the percentages of water and plants were the most predictive indicators of emotional responses measured by SAM scale, while the interpretation rate of the model measured by face recognition was insufficiently high. Concerning gender differences, women experienced a significantly higher valence than men. Higher percentages of water and plants, larger sizes, approximate shape index, and lower canopy densities were often related to positive emotions. Hence, designers must consider all structural attributes of green spaces, as well as enrich visual perception and provide various activities while creating a UGS. In addition, we suggest combining both physiological and psychological methods to assess emotional responses in future studies. Because the face recognition model can provide objective measurement of emotional responses, and the self-report questionnaire is much easier to administer and can be used as a supplement.

Performance Assessment Indicators for Comparing Recreational Services of Urban Parks

Parks can offer varied services to human well-being, including recreational services (RS); however, there is insufficient understanding of park differences concerning the actual performance of their varied RS. To fill this gap, this study aimed to develop a set of performance indicators as a tool for comparing the functional efficacy of RS among different parks. The indicators covered three aspects of RS: recreational usage of various physical activities, their recreational satisfaction level and the collective performance rating. These indicators were applied in a case study of four parks in Guangzhou, China, based upon on-site observation and a questionnaire survey. The functional difference of these indicators was compared and the collective indicator was found to be able to effectively capture different efficacies of urban parks in supporting varied RS. Results show all the parks were far from reaching the maximum performance of designed RS, so it is worthy of consideration by urban managers to further improve their RS efficacy. In addition, the overall spatial design and configuration were inferred to be essential for improving the RS efficacy of urban parks, but not park size nor ornamental vegetation. The findings offered valuable evidence for the optimal spatial design and management of urban parks.