Background : Previous research has independently associated air pollution and physical inactivity with increased mortality and morbidity. There is an ongoing debate about whether those factors interact to cause an even higher burden, suggesting potential syndemics. This study aimed to estimate the interaction between air pollution and physical inactivity on cognitive decline in older adults. Methods : The study utilized the Northern Ireland Cohort for the Longitudinal Study of Ageing. The outcome was a ≥3 points decline in the Mini-Mental State Examination score between 2 cohort waves. Exposures were annual mean particulate matter smaller than 2.5 μm (PM 2.5 ) in a 1-km buffer around participants’ residences estimated based on national monitoring and self-reported recreational moderate to vigorous physical activity (MVPA) minutes per week. Logistic regression models were used to estimate additive and multiplicative interactions between exposures adjusting for confounders. Results : Among 2836 participants, 137 (4.8%) had cognitive decline between waves. The median PM 2.5 was 6.6 μg/m 3 (interquartile range: 5.6–7.6), and 50% reported no MVPA in a week (interquartile range: 0–251.2). Models indicated additive (relative excess risk due to interaction = 0.63; 95% CI, −0.98 to 2.24) and multiplicative (synergy factor = 1.76; 95% CI, 0.84 to 3.72) interactions between high PM 2.5 and low MVPA on the risk of cognitive decline; however, estimates were not precise. Conclusions : This study presents a novel quantitative investigation of a potential syndemic focusing on a less-explored outcome of cognitive decline. However, outcome and exposure measurements limited the certainty of our findings. Future studies should include areas with higher variation in air pollution and use more granular exposure and sensitive outcome measures.

This paper examines the nonlinear influences of the quantity and quality of street-level greenery on active travel among older adults. The active travel information was obtained from the Study on Global Ageing and Adult Health conducted in Shanghai, China. Street-level greenery was assessed based on street view data and a deep learning approach, namely street view greenery quantity (SVG-quantity) and quality (SVG-quality). Gradient boosting decision tree models and SHapley Additive exPlanations were applied. The results showed that SVGquantity had a positive and nonlinear link with active travel. However, SVG-quality was positively correlated to the propensity for active travel, but the association became inverse when SVG-quality exceeded a specific cutoff point. SVG-quality also had a nonlinear and positive association with the duration of active travel. This research demonstrates the importance of improving the provision of street-level greenery in urban areas, which is crucial for facilitating active lifestyles among the ageing population.

Introduction There is little evidence investigating the association between green space (exposure and inequality) and active transportation during the COVID-19 pandemic. This study focused on the spatial heterogeneity in trajectories of different transportation modes during the COVID-19 pandemic worldwide, as well as the association between green space exposure and inequality and active transportation during the COVID-19 pandemic from a global perspective.Methods This study was based on an ecological study design and used three different Apple Mobility indices (driving, walking and public transit) to evaluate the trajectories of different transportation modes during the COVID-19 pandemic in 299 cities across 46 countries. Green space exposure was calculated based on fine-resolution population and green space mappings. Green space inequality was calculated by incorporating the Gini index into the green space exposure (green space Gini index). The hot/cold spot analysis was used to explore spatial heterogeneity in trajectories of different transportation modes during the COVID-19 pandemic worldwide, while Gaussian spatial mixed models were used to model the association between green space exposure and inequality and active transportation.Results The hot/cold spot analysis shows that there were spatial inequalities in the trajectories of different transportation modes worldwide during the COVID-19 pandemic. Results from Gaussian spatial mixed models showed that green space exposure was positively associated with the walking index (Coef.=46.82; SE=18.20), while green space inequality was positively associated with the walking index (Coef.=58.88; SE=26.87) and public transit index (Coef.=162.07; SE=80.16). Also, the effect of green space varied across city development levels, the stringency of policy and COVID-19 severity.Conclusions Our findings demonstrate the importance of sufficient city-scale green spaces to support active transportation, with important implications to help cities better prepare for future pandemics and support active transportation during non-pandemic times.

With the acceleration of urbanization, ensuring equitable access (or accessibility) to peri-urban parks for residents has become a key issue in landscape and urban planning. Traditional studies on peri-urban park accessibility often lack a comprehensive evaluation of the supply and demand for peri-urban parks and traffic conditions. Taking Chengdu as an example, this study develops an improved two-step floating catchment area method that integrates traffic conditions. It dynamically assesses accessibility to peri-urban parks at different times during weekends and spatial inequalities, as well as explores the relationship between these inequalities and traffic conditions. The results indicate that under a 60-minute time threshold, there is significant two-tier differentiation in accessibility to peri-urban parks in Chengdu, with significant differences between different time points. Particularly during periods of traffic congestion, the issue of accessibility inequality becomes more prominent. This phenomenon highlights a strong correlation between congestion levels on routes to parks and inequality in park accessibility. This study provides a novel perspective and methodology for dynamically evaluating and optimizing accessibility to peri-urban parks, providing empirical evidence for urban planners in the planning of peri-urban parks and the design of transportation systems. This study emphasizes the need for comprehensive and proactive measures in the planning process to alleviate the adverse effects of traffic congestion on accessibility to peri-urban parks.

BACKGROUND:Rapid declines in city mobility during the early stages of the COVID-19 pandemic in 2020 resulted in reductions in citizens' exposure to transport-related air pollution and associated health risks as many cities introduced non-pharmaceutical interventions designed to curb the spread of COVID-19. However, these benefits soon reversed during the pandemic's recovery phase (ie, from September, 2020, onwards), especially in cities with designs that afforded mode shifts away from public and active transport in favour of private motor vehicles. The aim of this study was to understand the association between global city designs, transport mode choices, and population-level risk exposure during 2020. METHODS:In this retrospective observational analysis, we assembled and analysed spatial datasets (including historical and predicted pollution levels, mobility indicators, and measures of individual disease transmission) and clustered 507 global cities using a graph neural network approach based on measures of the structural dimensions of each individual city's design and network structures of urban transportation systems. We compared city types on the basis of transportation mode shifts, air pollution levels, and associated health outcomes (ie, cardiovascular disease, ischaemic heart disease, respiratory disease, asthma, and reported COVID-19 cases) throughout 2020. We estimated risk reductions for these health outcomes across four phases of the pandemic, which we defined as the pre-pandemic, entry, mid-crisis, and recovery phases. We also identified city designs showing sustained reductions at the end of 2020 in transport-related air pollution (fine particulate matter [PM2·5] and nitrogen dioxide [NO2]) associated with reduced estimated risk of acute and chronic disease outcomes (ie, all-cause mortality, ischaemic heart disease mortality, cardiovascular disease, respiratory disease, and asthma). FINDINGS:The mean estimated reduction of global NO2 concentrations across the observed cities from the beginning of the entry phase until the mid-crisis phase was 3·76 parts per billion (ppb), calculated as the difference between observed 2020 mean levels of 12·63 ppb and predicted mean levels (if the pandemic and mobility restrictions had not occurred) of 16·39 ppb. The mean estimated reduction of global PM2·5 concentrations across the observed cities was 9·76 μg/m3 (the difference between observed 2020 mean levels [29·03 μg/m3] and predicted mean levels [38·79 μg/m3]). If maintained over the long term, the estimated NO2 reduction could have a substantial effect on reducing health risks for both acute and chronic disease, equating to an estimated overall reduction in all-cause mortality risk of 1·5% (95% CI 2·2-3·0), a reduction in cardiovascular mortality risk of 4·1% (2·6-6·0), and a reduction in respiratory disease mortality risk of 1·9% (0·8-3·0). If the reduction in PM2·5 concentration estimated in this period was maintained over the long term, all-cause mortality risk reductions of 18·9% (95% CI 13·2-25·0), asthma risk reductions of 46·8% (18·7-65·5), and ischaemic heart disease morbidity risk reductions of 0·25% (0·2-0·3) could be achieved. In the later stages of 2020, city designs (primarily in the Americas and Oceania) that afforded a mode shift away from public transit to private motor vehicles during the pandemic's recovery phase tended to show the poorest outcomes across all air pollution and health measures, even increasing risk levels above pre-pandemic baselines in some cases. By contrast, cities located in Japan and South Korea showed little change in pre-crisis and post-crisis transport mode choice, maintaining comparatively low levels of air pollution and associated disease risk, and reduced rates of infectious disease transmission throughout the 2020 observation period. Contrasting experiences of road injury in the post-pandemic phase (ie, post 2020) were also observed between these locations. INTERPRETATION:Our results highlight the transient environmental and health benefits observed during the early stages of the COVID-19 pandemic, driven by substantial reductions in transport-related air pollution and associated health risks due to imposed non-pharmaceutical public health interventions. City design appears to have played a crucial role in observed pollution and health risk differences between cities, with those that afforded a shift away from public and active transport towards private vehicles witnessing a rapid erosion of pollution-related health benefits gained in the entry to mid-crisis phases of the pandemic. These negative effects appear to have also transferred through to increased rates of road trauma in these cities, with a resurgence in road injury above pre-pandemic levels, particularly within countries reliant on private motorised transport. Conversely, cities in Japan, South Korea, and some European regions, which did not experience modal shifts towards cars, sustained their reductions in air pollution and have continued along a trend of declining road transport injuries. These findings underscore city design as a key factor in navigating pandemic-related challenges and suggest that city designs with higher levels of public and mass transit show greater levels of resilience when confronted with infectious disease threats. FUNDING:Australian National Health and Medical Research Council, Australian Research Council, National Institute for Health and Care Research Global Health Research Centre for Non-Communicable Diseases and Environmental Change, UK Prevention Research Partnership, and Economic and Social Research Council.

Introduction Urban green and blue space (UGBS) interventions, such as the development of an urban greenway, have the potential to provide public health benefits and multiple co-benefits in the realms of the environment, economy and society. This paper presents the protocol for a 5-year follow-up evaluation of the public health benefits and co-benefits of an urban greenway in Belfast, UK.Methods and analysis The natural experiment evaluation uses a range of systems-oriented and mixed-method approaches. First, using group model building methods, we codeveloped a causal loop diagram with stakeholders to inform the evaluation framework. We will use other systems methods including viable systems modelling and soft systems methodology to understand the context of the system (ie, the intervention) and the stakeholders involved in the development, implementation and maintenance phases. The effectiveness evaluation includes a repeat cross-sectional household survey with a random sample of 1200 local residents (adults aged ≥16 years old) who live within 1 mile of the greenway. The survey is complemented with administrative data from the National Health Service. For the household survey, outcomes include physical activity, mental well-being, quality of life, social capital, perceptions of environment and biodiversity. From the administrative data, outcomes include prescription medications for a range of non-communicable diseases such as cardiovascular disease, type II diabetes mellitus, chronic respiratory and mental health conditions. We also investigate changes in infectious disease rates, including COVID-19, and maternal and child health outcomes such as birth weight and gestational diabetes. A range of economic evaluation methods, including a cost-effectiveness analysis and social return on investment (SROI), will be employed. Findings from the household survey and administrative data analysis will be further explored in focus groups with a subsample of those who complete the household survey and the local community to explore possible mechanistic pathways and other impacts beyond those measured. Process evaluation methods include intercept surveys and direct observation of the number and type of greenway visitors using the Systems for Observing Play and Recreation in Communities tool. Finally, we will use methods such as weight of evidence, simulation and group model building, each embedding participatory engagement with stakeholders to help us interpret, triangulate and synthesise the findings.Ethics and dissemination To our knowledge, this is one of the first natural experiments with a 5-year follow-up evaluation of an UGBS intervention. The findings will help inform future policy and practice on UGBS interventions intended to bring a range of public health benefits and co-benefits. Ethics approval was obtained from the Medicine, Health and Life Sciences Research Ethics Committee prior to the commencement of the study. All participants in the household survey and focus group workshops will provide written informed consent before taking part in the study. Findings will be reported to (1) participants and stakeholders; (2) funding bodies supporting the research; (3) local, regional and national governments to inform policy; (4) presented at local, national and international conferences and (5) disseminated by peer-review publications.

Natural environments have the potential to encourage in-person social interactions, but the extent to which these interactions influence online social networks remains uncertain. This study addresses this gap by examining the relationship between Facebook social connectedness and both the greenness and park coverage of 20,867 zip code tabulated areas ("zip codes") across the continental US. Using linear mixed-effects models adjusted for sociodemographic factors and urbanicity, the findings indicate that residents in greener zip codes are more likely to have online friends within the same zip code. Conversely, residents of zip codes with higher park coverage tend to have fewer online friends within their zip code. However, both measures of natural environments exhibit limited explanatory power regarding social connectedness, highlighting the need for careful interpretation and cautious generalization of these results. The impact of natural environments on online social networks is likely multifaceted and may be better understood through additional measures beyond the mere quantity of natural spaces.

Research on how accessibility to tram stops and proximity to tram tracks affect property prices has been limited. Additionally, the time-dependent effects of the tram system and its effects at different price levels remain underexplored. This study fills these gaps by analyzing the relationship between Chengdu Tram Line 2 and nearby property prices. Using a before-and-after treatment-control design and a dataset of 33,150 property transactions over six years, it applies multilevel hedonic price, difference-in-differences (DID), and quantile regression models to investigate the association between accessibility to tram stops, proximity to tram tracks, and property prices during various phases (e.g., construction and operation phases). Our findings are listed below. First, the positive influence of accessibility to tram stops only becomes significant during the operation phase. Specifically, property prices within 800 m of tram stops are 1.4 % higher than those farther away. Second, price penalties induced by proximity to tram tracks persist throughout the construction and operation phases. Third, the impact of accessibility to tram stops varies significantly across different price levels. Specifically, buyers of low-priced properties are more willing to pay a premium for accessibility to tram stops, whereas purchasers of high-end properties prefer greater distances from tram tracks to avoid nuisances. The results highlight the timedependent accessibility benefits and negative externalities linked to tram services. Finally, policy implications, such as measures to alleviate the disturbances caused by tram tracks, are discussed.

There is a lack of evidence regarding associations of eye-level greenness exposure with blood pressure among children. We aimed to investigate the associations between different types of eye-level greenness and pediatric blood pressure in China. From 2012 to 2013, we recruited 9354 children aged between 5 and 17 years in northeast China. Eye-level of greenness was assessed with Street View Greenness (SVG), derived from Tencent Street View images surrounding participants' schools, utilizing a deep machine learning model. Hypertension was defined as blood pressure above the 95th percentile based on the fourth report's guidelines for children and adolescents. Generalized linear mixed-effects regression models were conducted to estimate adjusted odds ratio (aOR) and estimates of childhood hypertension and pediatric blood pressure per interquartile range (IQR) increase of SVG. Mediation analyses including air pollution and exercise time were also performed. We found the significant association of SVG-total with decreased odds of hypertension in Chinese children (aOR = 0.83, 95% CI: 0.75,0.91), especially with the decrease of SBP (beta = -0.76, 95%CI: 1.09,-0.43). Interestingly, per IQR increase in SVG-tree 800m for trees was associated with lower adjusted odds of pediatric hypertension (aOR = 0.84; 95%CI: 0.76, 0.92), also with the decrease of systolic blood pressure. Mediation analyses showed that hypertension was significantly mitigated by lower levels of air pollutants, including PM2.5, PM10, SO2 and NO2. Results of this study suggested that eye-level greenness, especially trees, were associated with lower prevalence of hypertension in children, with air pollution exhibiting mediating effects. These findings emphasized the importance of incorporating more greenness, especially trees in both urban planning and public health interventions.

The integration of text-to-image generation capabilities within GPT-4 allows for the convenient creation of various graphics. However, the proficiency of GPT-4 in crafting challenging scientific visuals remains largely unexplored. In this study, we conduct systematic experiments by employing multiple prompt engineering techniques with various supplementary materials to generate complex scientific illustrations for environmental studies. The locally enhanced electric field treatment for water disinfection is used as an example to illustrate the universal reflection of GPT-4 in graphic creation. From the experiments, we summarize that the existing prompt methods struggle in accuracy, modifiability, and reproducibility for scientific image generation. Based on the findings and insights drawn from the extensive experimental results, we develop GPT4Designer, a framework intended to generate scientific images without tedious prompt modifications. Specifically, a simple but surprisingly effective "envision-first" strategy by combining detailed prompting and guided envisioning is developed in the GPT4Designer framework. This strategy yields images with consistent styles aligned with the initial envisioning, significantly improving modifiability. Besides, by refining the conceptualization phase, we achieve much better control over the output, resulting in both high accuracy and reproducibility. This advancement is not only crucial for environmental scientists seeking to quickly produce engaging and accurate visuals (e.g., with only one step), but also demonstrates the existence "chain-of-thought" in image generation, which can inspire more works on the creative application of text-to-image generation models or tools.

Despite studies investigating the relationships among greenery, physical activity, and sedentary behaviour, it remains unclear whether such associations are nonlinear. Scant attention has also been paid to the role of greenery from a socioeconomic equity perspective. Thus, we assessed possible nonlinear associations between street-level greenery, physical activity, and sedentary behaviour in middle-aged and older adults. We also examined whether and how street-level greenery may increase socioeconomic equity in physical activity and sedentary behaviour. Data on physical activity and sedentary behaviour were obtained from the WHO Study on Global Ageing and Adult Health in Shanghai, China. The quantity and quality of street-level greenery were assessed using segmented street-view images based on deep learning methods. The results of fitting gradient-boosting decision trees showed that street-level greenery quantity was positively related to physical activity. We observed a steep increase in physical activity at low greenery quantity and flattening out at higher quantity. Street-level greenery quality was negatively related to sedentary behaviour. While sedentary behaviour did not change significantly at low greenery quality, the decrease became steep at higher quality. The greenery association was stronger for low incomers and those in deprived neighbourhoods.

In recent years, many researchers have argued that both the availability of green space (GS) and perceived neighbourhood safety may be prerequisites for the use of GS, but empirical findings remain inconsistent. This study explores how perceived neighbourhood safety moderates the associations between the availability of neighbourhood GS and residents’ use of GS, using survey data collected in Guangzhou, China. The Normalised Difference Vegetation Index (overall amount of greenness), park accessibility and a measure of Street View Greenness (eye-level greenness) were used to estimate two types of GS availability (overall vs. eye-level). As shown by the results of the multilevel models, eye-level greenness was positively associated with the use of and perceived comfort of GS for those respondents with a higher level of perceived neighbourhood safety; it was negatively related to the use and perceived comfort of GS in the case of respondents with a lower level of perceived neighbourhood safety. In addition, the overall amount of greenness was positively associated with the use and perceived comfort of GS regardless of the level of perceived neighbourhood safety. Our findings suggest that perceived safety may be a potential prerequisite for positive associations between the availability of GSS at eye level and the use of and perceived comfort of GS.

Urban green spaces are vital for promoting physical activity and have a recognised positive impact on public health. Research consistently shows high levels of physical inactivity among adolescents, with their exercise habits influencing future travel behaviours. While most studies focus on neighbourhood settings, this study explores university campuses in China. Unlike Western campuses, Chinese universities are typically enclosed by physical walls, presenting unique spatial dynamics. This research involved a survey of 811 students across ten universities in Guangzhou, employing the International Physical Activity Questionnaire (IPAQ) to assess their levels of physical activity. To evaluate greenery from a pedestrian's perspective, we applied a deep learning semantic analysis of street-view images. This method is consistent with human visual experience, providing a novel way to analyse campus green spaces. A multilevel linear regression model was used to explore the link between green space exposure and student physical activity within these enclosed environments. The findings indicate a significant positive correlation between accessible green spaces at eye-level and students’ physical activity. In addition, active travel behaviours, such as walking, correlate positively with greater physical activity among students. However, our study found no significant connection between green spaces analysed via remote sensing and student activity levels. These insights underscore the importance of integrating green spaces into urban planning to foster healthier communities.

The urban heat island (UHI) effect exacerbates heat stress, energy consumption, and public health challenges in urban environments. Traditional studies often combine vegetated and built-up areas, overlooking their distinct thermal behaviors, and rely on Land Surface Temperature (LST), which does not fully capture the heat exposure perceived by humans. This study addresses these gaps by using 1-m resolution mean radiant temperature (Tmrt) to evaluate heat exposure in Los Angeles, separating vegetated areas (VA) and built-up areas (BA). We developed distinct indicator systems for VA and BA and applied random forest regression, spatial error modeling, and SHapley Additive exPlanations (SHAP) to assess their independent effects on Tmrt. Results indicate that vegetated areas, particularly tree canopies and wetlands, provide significant cooling effects on Tmrt, while impervious surfaces like asphalt roads and bare earth increase heat exposure. In built-up areas, the sky view factor showed the strongest positive correlation with Tmrt, while residential areas demonstrated a negative correlation. The study also highlights the relative importance of variables and nonlinear impacts of indicator thresholds on Tmrt. Notably, increasing albedo in impervious surfaces, a conventional cooling strategy to reduce LST, may elevate perceived heat exposure, challenging its effectiveness. These findings emphasize the need for urban planners to prioritize tree canopy coverage in heat hotspots and develop context-specific strategies that address the nonlinear impacts of urban surfaces on heat exposure. Material selection in built-up areas should carefully consider albedo impacts to balance cooling benefits with potential thermal discomfort, providing actionable insights for sustainable urban design.

Existing studies have highlighted that green space is associated with non-communicable diseases. However, scant attention has been paid to the association between green space quantity and quality with communicable diseases. Here, we explore the relationships between green space and influenza cases in Guangzhou, China, using street-view green (SVG) space quantity and SVG-quality indicators, which offer a better assessment of urban green space than traditional remote sensing metrics. Influenza cases were collected from hospitalization records, while street-level green space was measured by street-view data and deep neural networks. The neighbourhood deprivation index (NDI) was also used as a proxy for neighbourhood-level socio-economic status. We employed the Random Effects-Eigenvector Spatial Filtering (RE-ESF) regression model because of its usefulness in handling spatial dependence. Findings showed that higher levels of SVG-quantity and quality are associated with a lower number of influenza cases, implying a negative relationship. Specifically, the marginal effects for SVG indicate that influenza may decrease by 145 cases for every unit increase in SVG-quantity, and by 11 cases for every unit increase in SVG-quality. In terms of planning, this could mean that though green quality is essential for the aesthetic part of urban life, quantity is much more critical concerning the containment of influenza. In addition, SVG-quantity and quality moderated the positive association between NDI and influenza cases. In other words, people in more deprived neighbourhoods were more influenced by SVG-quantity and quality compared to people living in less deprived areas. This means that more green space should be added to such neighbourhoods. We also observed that the association between SVG-quality and influenza cases was weaker for females, people aged between 18 and 45, and employed people. Because influenza is the most common pandemic worldwide, green space at the street level should be considered when promoting equitable public health and this study provides quantifiable evidence for the negative effect of green space quantity and quality over influenza cases.

BACKGROUND:The world faces increasing risk from more frequent and larger scale natural hazards, including infectious disease outbreaks (IDOs) and climate change-related extreme weather events (EWEs). These natural hazards are expected to have adverse mobility and public health impacts, with people living in cities especially vulnerable. Little is known about how transport systems can be optimally designed to make cities more resilient to these hazards. Our aim was to investigate how cities' transport systems, and their resulting mobility patterns, affect their capabilities to mitigate mobility and health impacts of future large-scale IDOs and EWEs. METHODS:System dynamics modelling was used to investigate how different city mobility scenarios can affect the health and mobility impacts of four plausible future IDO and EWE (flooding) shocks in three cities: Belfast, UK; Belo Horizonte, Brazil; and Delhi, India. Three city mobility scenarios with incremental degrees of modal shift towards active travel (private motor vehicle volume reduced to 50% and 20% of total road trip volume in vision 1 and 2, and motor vehicle volume [including buses] reduced to 20% of total road trip volume in vision 3) were tested. For each city and each IDO and EWE shock, we estimated the percentage of deaths prevented in visions 1, 2, and 3, relative to the reference scenario, as well as changes in mode share over time. FINDINGS:In all scenarios, all cities showed reduced susceptibility to flooding, with 4-50% of deaths potentially prevented, depending on case city, city mobility, and EWE scenario. The more ambitious the transition towards healthier city mobility patterns, the greater the resilience against flooding. Only vision 3 (the most ambitious transition) showed reduced vulnerability to IDOs, with 6-19% of deaths potentially prevented. Evolution of mode shares varied greatly across cities and mobility scenarios under the IDO shocks. INTERPRETATION:Our results emphasise the importance of well designed, forward-thinking urban transport systems that make cities more resilient and reduce the impact of future public health-related and climate-related threats. FUNDING:UK Prevention Research Partnership, UK Economic and Social Research Council, UK Medical Research Council, UK National Institute for Health and Care Research, Australian Research Council, Australian National Health and Medical Research Council, and Health and Social Care Research and Development Office Northern Ireland.

OBJECTIVES:To examine (1) how visual green space quantity and quality affect depression among older adults; (2) whether and how the links may be mediated by perceived stress, physical activity, neighbourhood social cohesion, and air pollution (PM2.5); and (3) whether there are differences in the mediation across visual green space quantity and quality. METHOD:We used older adults samples (aged over 65) from the WHO Study on Global Ageing and Adult Health in Shanghai, China. Depression was quantified by two self-reported questions related to the diagnosis of depression and medications or other treatments for depression. Visual green space quantity and quality were calculated using street view images and machine learning methods (street view green space = SVG). Mediators included perceived stress, social cohesion, physical activity, and PM2.5. Multilevel logistic and linear regression models were applied to understand the mediating roles of the above mediators in the link between visual green space quantity and quality and depression in older adults. RESULTS:SVG quantity and quality were negatively related to depression. Significant partial mediators for SVG quality were social cohesion and perceived stress. For SVG quantity, there was no evidence that any of the above mediators mediated the association. CONCLUSION:Our results indicated that visual green space quantity and quality may be related to depression in older adults through different mechanisms.

The benefits of green spaces on individuals’ health have been widely acknowledged due to their inherent natural qualities. Currently, university students are experiencing significantly higher levels of mental health problems than other social groups. There is a scarcity of studies examining the association between built environment factors and mental health issues among university students, particularly in the Chinese context. University campuses in China are physically isolated, secluded communities, and in this respect, they differ markedly from the spatial organisation patterns of Western universities. Therefore, this study focuses on the correlation between the extent of green space exposure within closed university campuses and the occurrence of mental health issues among resident students. A deep-learning methodology incorporating streetscape images, remote sensing data, and multilevel linear modelling is employed in order to facilitate a comprehensive analysis. The results demonstrate a negative correlation between green space exposure on campus and the level of mental health issues among university students. Individual socio-demographic characteristics, such as whether a person has a partner, are also found to influence the level of mental health issues that they experience. In addition, a significant relationship is found between travel patterns and mental health issues, with students who walked regularly having a lower incidence of mental health issues than those who drove. Our research indicates that, in order to foster healthier communities and enhance social inclusion, urban planners should prioritise the development of greener campuses and urban transport services to improve accessibility to green spaces. This study investigates the impacts of campus green space exposure on mental health issues. The study participants comprised 811 students from 10 universities in Guangzhou, China. An inverse correlation exists between exposure to green space within closed university campuses and the prevalence of mental health issues. Personal characteristics and travel patterns have significant impacts on mental health.

UN Sustainable Development Goals (e.g., Goal 16) have highlighted the importance of using policy tools (e.g., through urban planning) to prevent crimes. Existing evidence of the association between green space and crime is mixed. Some studies indicate that the inconsistencies may be due to the variance in types of vegetation and the rates of crime reported across regions and countries. This study aims to assess the conditional association between green space and crime by considering the influence of vegetation type (e.g., grassland, woodland), crime type (e.g., violence, theft) and rates of crime reported in Northern Ireland (NI), United Kingdom. Crime data were obtained from the Police Service NI and green space was determined by Land Cover Map at the Super Output Area (SOA) level provided by the UK Centre for Ecology & Hydrology. Spatial quantile regressions were used to model the adjusted association between green space and crime across areas with different rates of crime. The results showed that more grassland may be associated with lower crime rates, but only in areas with relatively low crime rates. More woodland may also be associated with lower crime rates, but only for areas with relatively high crime rates. Also, we found that associations between green space and crime varied by type of crime. In summary, policymakers and planners should consider green space as a potential crime reduction intervention, factoring in the heterogeneous effects of vegetation type, crime type and crime rate.

BACKGROUND:During the COVID-19 pandemic, changes were seen in city mobility patterns around the world, including in active transportation (walking, cycling, micromobility, and public transit use), creating a unique opportunity for global public health lessons and action. We aimed to analyse a global natural experiment exploring city mobility patterns during the pandemic and how they related to the implementation of COVID-19-related policies. METHODS:We obtained data from Apple's Mobility Trends Reports on city mobility indexes for 296 cities from Jan 13, 2020 to Feb 4, 2022. Mobility indexes represented the frequency of Apple Maps queries for driving, walking, and public transit journeys relative to a baseline value of 100 for the pre-pandemic period (defined as Jan 13, 2020). City mobility index trajectories were plotted with stratification by country income level, transportation-related city type, population density, and COVID-19 pandemic severity (SARS-CoV-2 infection rate). We also synthesised global pandemic policies and recovery actions that promoted or restricted city mobility and active transportation (walking, cycling and micromobility, and public transit) using the Shifting Streets dataset. Additionally, a natural experiment on a global scale evaluated the effects of new active transportation policies on walking and public transit use in cities around the world. We used multivariable regression with a difference-in-difference (DID) analysis to explore whether the implementation of walking or public transit promotion policies affected mobility indexes, comparing cities with and without implementation of these policies in the pre-intervention period (Jan 27 to April 12, 2020) and post-intervention period (April 13 to June 28, 2020). FINDINGS:Based on city mobility index trajectories, we observed an overall decline in mobility indexes for walking, driving, and public transit at the beginning of the pandemic, but these values began to increase in April, 2020. Cities with lower population densities generally had higher driving and walking indexes than cities with higher population density, while cities with higher population densities had higher public transit indexes. Cities with higher pandemic severity generally had higher driving and walking indexes than cities with lower pandemic severity, while cities with lower pandemic severity had higher public transit indexes than other cities. We identified 587 policies in the dataset that had known implementation dates and were relevant to active transportation, which included 305 policies on walking, 321 on cycling and micromobility, and 143 on public transit, across 230 cities within 33 countries (19 high-income, 11 middle-income, and three low-income countries). In the global natural experiment (including 39 cities), implementation of policy interventions promoting walking was significantly associated with a higher absolute value of the walking index (DID coefficient 20·675 [95% CI 8·778-32·572]), whereas no such effect was seen for public transit-promoting policies (0·600 [-13·293 to 14·494]). INTERPRETATION:Our results suggest that the policies implemented to mitigate the COVID-19 pandemic were effective in changing city mobility patterns, especially increasing active transportation. Given the known benefits of active transportation, such policies could be maintained, expanded, and evaluated post pandemic. The discrepancy in the interventions between countries of different incomes highlights that changes to the infrastructure to prioritise safe walking, cycling, and easy access to public transit use could help with the future-proofing of cities in low-income and middle-income countries. FUNDING:None.