Urban Greenness Research Articles

Assessment of urbanization impacts in Tegucigalpa urban greenness via normalized difference vegetation index

Urban vegetation is essential to develop sustainable, habitable, and healthy cities. Urbanization in large cities may be detrimental to urban vegetation when urban planning is scarce. Tegucigalpa, Honduras's capital city, had an atypical 8-year period of intensive urbanization, which may have modified urban vegetation. Excessive tree removal and reduced urban greenness reports became abundant in local media. This paper aimed to determine if urbanization indeed modified Tegucigalpa's urban greenness to a detrimental level. A Landsat-8 imagery survey was conducted to assess Tegucigalpa's urban vegetation dynamics via the normalized difference vegetation index (NDVI) for the 2013–2021 dry and rainy seasons. The results revealed a stable trend for the dry seasons NDVI remaining in constant proximity to 0.11 while increasing steadily to around 0.17 during the rainy seasons due to precipitation watering vegetation. Negligible fluctuations (<0.1) occurred throughout the study period, suggesting urbanization most likely did not shrink urban vegetation to the extreme extent stated by Honduran media. The NDVI provided a scientific basis for assessing Tegucigalpa's vegetation changes, preventing potential environmental conflicts. Although most researchers have reported that urbanization decreases NDVI in large cities, others found that urbanization does not always produce vegetation degradation and suggested that climatic variables are significant factors determining vegetation growth. Nevertheless, the steadily low NDVI in Tegucigalpa suggests the absence of sustainable urban planning to increase urban vegetation. Urban trees are central to sustainable urban planning; therefore, afforestation policies should be considered by Tegucigalpa's government and policymakers.

Exploring the paradox of densification and greening in China’ s old cities

Urban densification is widely regarded as a key strategy for curbing urban sprawl and optimizing resource efficiency. However, it still threatens urban greenspaces by altering the original land use. The imperative to preserve and restore urban greenspaces has gained increased attention, aligning with the global spotlight on urban resilience. This prompts the crucial question: How will urban densification impact urban greening? Focusing on the old city areas of 159 cities with over one million population in China, we depicted the dynamics of urban greenness using remote sensing data and explored greening trends under urban densification on both horizontal and vertical scales by generalized additive model (GAM). The findings are as follows: (1) As urban horizontal density increased from 68.4 % to 93.1 %, a significant U-shaped trend in urban greenness emerged within the old city areas over the past three decades. Notably, 80 % of these cities experienced turning points before 2010. (2) Almost half of the cities have recovered the urban greenness to 30 years ago, with a growth of 6.58 % on average. While urban densification led to a loss of greenness (-0.0197±0.0182), urban greenspaces countered this with a gain of 0.0049±0.0075, and the original built-up area contributed a gain of 0.0027±0.0284. (3) We decomposed urban densification into horizontal and vertical growth. The horizontal growth coincides with the greenness loss initially, but the rebound occurs when the horizontal density approaches 83 %. Vertical growth, on the other hand, is negatively correlated with urban greenness. Our study depicts the turning point of urban greening in old city areas unveiling the ecological effect of urban densification, which provides a reference for the sustainable development of future urbanization.

Urban residential greenness and cancer mortality: Evaluating the causal mediation role of air pollution in a large cohort

Evidence linking greenness to all-site and site-specific cancers remains limited, and the complex role of air pollution in this pathway is unclear. We aimed to fill these gaps by using a large cohort in southern China. A total of 654,115 individuals were recruited from 2009 to 2015 and followed-up until December 2020. We calculated the normalized difference vegetation index (NDVI) in a 500-m buffer around the participants’ residences to represent the greenness exposure. Cox proportional-hazards models were used to evaluate the impact of greenness on the risk of all-site and site-specific cancer mortality. Additionally, we assessed both the mediation and interaction roles of air pollution (i.e., PM2.5, PM10, and NO2) in the greenness–cancer association through a causal mediation analysis using a four-way decomposition method. Among the 577,643 participants, 10,088 cancer deaths were recorded. We found a 10% (95% CI: 5–16%) reduction in all-site cancer mortality when the NDVI increased from the lowest to the highest quartile. When stratified by cancer type, our estimates suggested 18% (95% CI: 8–27%) and 51% (95% CI: 16–71%) reductions in mortality due to respiratory system cancer and brain and nervous system cancer, respectively. For the above protective effect, a large proportion could be explained by the mediation (all-site cancer: 1.0–27.7%; respiratory system cancer: 1.2–32.3%; brain and nervous system cancer: 3.6–109.1%) and negative interaction (all-site cancer: 2.1–25.7%; respiratory system cancer: 2.0–25.7%; brain and nervous system cancer: not significant) effects of air pollution. We found that particulate matter (i.e., PM2.5 and PM10) had a stronger causal mediation effect (25.0–109.1%) than NO2 (1.0–3.6%), while NO2 had a stronger interaction effect (25.7%) than particulate matter (2.0–2.8%). In summary, greenness was significantly beneficial in reducing the mortality of all-site, respiratory system, and brain and nervous system cancer in southern China, with the impact being modulated and mediated by air pollution.

Deep learning based automated estimation of urban green space index from satellite image: A case study

The green area factor model is a crucial tool for conserving and creating urban greenery and ecosystem services within neighborhood land. This model serves as a valuable index, streamlining the planning, assessment, and comparison of local-scale green infrastructures. However, conventional on-site measurements of the green area factor are resource intensive. In response, this study pioneers a computational approach that integrates ecological and social dimensions to estimate the green area factor. Employing satellite remote sensing and advanced deep learning techniques, the methodology utilizes satellite orthophotos of urban areas subjected to semantic segmentation, identifying and categorizing diverse green elements. Ground truths are established through on-site measurements of green area factors and satellite orthophotos from benchmarking sites in Örebro, Sweden. Results reveal an 82.0% average F1-score for semantic segmentations, signifying a favourable correlation between computationally estimated and measured green area factors. The proposed methodology is potential for adapting to various urban settings. In essence, this research introduces a promising, cost-effective solution for assessing urban greenness, particularly beneficial for urban administrators and planners aiming for insightful and comprehensive green strategies in city planning.

Efficiency and greenness evaluation of urban land use change in the Yangtze River Economic Belt, China

AbstractImproving urban land use efficiency and greenness is essential for achieving sustainable urban development and improving the well‐being of urban settlements. Using satellite‐based land use data, socioeconomic data, and sustainable development goal (SDG) indicators, we quantified the changes in the urban land use efficiency and greenness under rapid urban expansion in provincial cities in the Yangtze River Economic Belt (YREB) from 1990 to 2020. Over this period, urban area increased significantly, by 254.57%, at a rate of 191.41 km2 per year in the YREB, as national macro‐policies shifted the center of urban expansion westward. Nevertheless, the urban land use efficiency and greenness have both increased. The ratio of land consumption rate to population growth rate increased from 0.55 in 1990–2000 to 0.83 in 2010–2020 in the YREB, accompanied by an increase in the investment output ratio of fixed assets. In addition, the population‐weighted greenspace exposure increased by 5.31%, due to an 8.16% increase in the urban greenspace coverage, which improved the urban living environment amidst substantial urban population growth. We emphasize the importance of fostering coordinated development between central and western cities in the YREB. The results provide an important reference for the rational allocation of land resources, improving land use efficiency, and building resilient cities within global urbanization hotspots under the SDG framework.

GreenR: An open-source framework for quantifying urban greenness

How do we quantify the levels of greenness within urban street networks? Numerous attempts to quantify this factor have been made through survey methodologies, remote sensing data, and street view imagery. The results are promising, but are often limited by scalability constraints, including limited data availability, high requirements of computational power, and validation challenges. This study introduces a comprehensive framework for urban greenness assessment, leveraging open-source data to overcome these limitations. Central to this framework is the development of the greenR R package and an accompanying Shiny app, designed to compute, visualize, and analyze a novel proximity-based green index for individual street segments. Beyond this, the framework innovatively extends its analytical capability by integrating accessibility analysis, Green View Index quantification as well as introducing the Green Space Similarity Index, a novel metric that evaluates and compares the characteristics of urban green spaces across different regions. This extension enriches the proposed framework, providing not just a measure of proximity to green spaces, but also insights into their spatial connectivity and distribution. The efficacy of greenR is demonstrated by studying urban greenness patterns across several cities, highlighting the potential impact of such an open-source framework for citizens, urban planners, and policy-makers. This study not only advances our methodological approach to quantifying urban greenness but also provides practical tools and metrics that can inform sustainable urban planning and policy decisions.

The effects of urban green space and road proximity to indoor traffic-related PM2.5, NO2, and BC exposure in inner-city schools

BackgroundSince there are known adverse health impacts of traffic-related air pollution, while at the same time there are potential health benefits from greenness, it is important to examine more closely the impacts of these factors on indoor air quality in urban schools.ObjectiveThis study investigates the association of road proximity and urban greenness to indoor traffic-related fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in inner-city schools.MethodsPM2.5, NO2, and BC were measured indoors at 74 schools and outdoors at a central urban over a 10-year period. Seasonal urban greenness was estimated using the Normalized Difference Vegetation Index (NDVI) with 270 and 1230 m buffers. The associations between indoor traffic-related air pollution and road proximity and greenness were investigated with mixed-effects models.ResultsThe analysis showed linear decays of indoor traffic-related PM2.5, NO2, and BC by 60%, 35%, and 22%, respectively for schools located at a greater distance from major roads. The results further showed that surrounding school greenness at 270 m buffer was significantly associated (p < 0.05) with lower indoor traffic-related PM2.5: −0.068 (95% CI: −0.124, −0.013), NO2: −0.139 (95% CI: −0.185, −0.092), and BC: −0.060 (95% CI: −0.115, −0.005). These associations were stronger for surrounding greenness at a greater distance from the schools (buffer 1230 m) PM2.5: −0.101 (95% CI: −0.156, −0.046) NO2: −0.122 (95% CI: −0.169, −0.075) BC: −0.080 (95% CI: −0.136, −0.026). These inverse associations were stronger after fully adjusting for regional pollution and meteorological conditions.Impact statementMore than 90% of children under the age of 15 worldwide are exposed to elevated air pollution levels exceeding the WHO’s guidelines. The study investigates the impact that urban infrastructure and greenness, in particular green areas and road proximity, have on indoor exposures to traffic-related PM2.5, NO2, and BC in inner-city schools. By examining a 10-year period the study provides insights for air quality management, into how road proximity and greenness at different buffers from the school locations can affect indoor exposure.

Long-term trends of vegetation greenness under different urban development intensities in 889 global cities

Long-term trends in urban vegetation greenness were systematically analyzed. However, long-term trends in vegetation greenness under different urban development intensities (UDIs) in global cities remain poorly understood. In this study, exurban, suburban, urban and urban core areas were defined according to their UDI in 2001. Subsequently, changes in the UDI and long-term trends in vegetation greenness were analyzed in these four areas in 889 global cities from 2001 to 2019. African and Asian cities have witnessed rapid urbanization, whereas European cities have experienced slow urbanization. The UDI increased significantly in suburban, exurban and urban areas, whereas it remained stable in urban core areas. The enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) increased in all four areas in European cities but decreased in all four areas of the African cities. The EVI and NDVI decreased in suburban and urban areas due to urbanization, but increased in exurban, and urban core areas. Changes in UDI were an important driver of EVI and NDVI trends in exurban and suburban areas. The results of this study enhance our understanding of long-term trends in vegetation greenness in urban and surrounding areas.

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement.

Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents the typical performance metrics and explores public datasets for constructing these measures. The results show that accurate (semantic) segmentation is inevitable not only for fine-grained greenness measures but also for the qualitative evaluation of landscape analyses for planning amidst the incomplete explainability of the DL model. Also, the unsupervised domain adaptation (UDA) in aerial images is addressed to overcome the scale changes and lack of labeled data for fine-grained greenness measures. This review contributes to helping researchers understand the recent breakthroughs in DL-based segmentation technology for challenging topics in UGS research.

Spatial patterns and driving forces of urban vegetation greenness in China: A case study comprising 289 cities

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.

Analysis on the impact of smart city construction on urban greenness in China's megacities

Urban greenness serves as a key indicator of sustainable urban development, with smart city construction emerging as a primary strategy for its enhancement. However, there is little empirical evidence considering multi-dimension between urban greenness and smart city construction on the city level. This study focuses on the impact on urban greenness of smart city construction in megacities, using the difference-in-differences regression model to evaluate the impact based on urban development conditions in various aspects from 2010 to 2021 in 10 megacities in China. The results of panel data of different indicator samples show unique conclusions. First, smart city pilot policy in megacities has significant impact on urban greenness, primarily due to demographic and economic developments. Second, the impact is different between the megacity and national level, and different factors of urban greenness have different effects on smart city construction. Third, the effects are time-lagged and lasted for years, and regional heterogeneity divided by building climate zones is existed, where the effect is more obvious in city agglomeration. These findings of smart city construction reveal the unique influences on megacity greenness, and can be generalized to cities with similar characteristics accordingly.

Higher rat abundance in greener urban areas

Urban greening has become an increasingly popular strategy to improve urban life and human health. However, there are indications that the presence and extent of urban greenness may increase the abundance of wild rats. Therefore, we investigated which environmental and socio-economic factors are associated with rat abundance, with a focus on factors related to urban greenness. We systematically trapped rats (222 Rattus norvegicus and 5 Rattus rattus) in parks and residential areas in three cities in the Netherlands. We modelled the relative abundance of rats against various environmental and socio-economic variables. In addition, we compared municipality rat complaint data with our trapping data and analysed trap success over time. We observed positive relationships between the relative abundance of rats and both greenness (NDVI) and different proxies for food resources (restaurants and petting zoos). In addition, there were more municipality rat complaints in residential areas compared to parks, while there was a higher relative abundance of rats in parks. Our findings corroborate that greenness is associated with a higher abundance of wild rats, and that municipality rat complaints may underestimate the abundance of rats in greener urban areas. This study provides new insights on factors affecting relative rat abundance in cities and can guide policy makers and city planners how to minimize rat nuisance in the greener parts of cities. By taking these potential effects of urban greenness on rat abundance into account, measures can be taken that on the one hand maintain the beneficial effects of urban greening, but at the same time reduce the carrying capacity for rats.

Unveiling urban ecological integrity: spatially explicit assessment in contrasting environments

Although much attention has been paid to improve the representation of the urban spatial complexity, most efforts have been limited to the inclusion of green cover data to describe the biological component of cities, leaving the measurement of crucial urban ecological properties out of the picture. To tackle this, the Urban Ecosystem Integrity Index (UEII) was recently proposed to spatially represent the interplay between the intensity of urbanization and the biological components of cities, including urban biodiversity. The UEII has shown to be effective in measuring the ecosystem integrity of a Neotropical city; however its generalized applicability remains to be tested. Thus, here we tested the suitability of the UEII when applied in a boreal city and also evaluated if it adds more valuable information than the NDVI (also contrasting it with a UEII previously published for a tropical city), which is often suggested as a proxy of urban greenness and therefore the ecology of cities. We calculated the UEII by considering land surface temperature, built-up cover, and native plants and bird species richness of the city of Lahti, Finland (Palearctic; boreal). The UEII showed to be robust, comparable, and effective in capturing the intra and inter-urban spatial heterogeneity of both cities. Moreover, although the UEII and NDVI values correlated, the variance in such correlation showed the detail that the UEII brings to the quantificaiton of the ecological integrity of the cities, representing a more integral indicator. Thus, our findings indicate that the UEII is a valuable tool to inform on areas requiring ecological enhancement, as well as identifying those that are thriving, in cities ranging from tropical and highly biodiverse to boreal and with considerable less biodiversity.

Nature and Mental Health in Urban Texas: A NatureScore-Based Study.

In this cross-sectional study, we examined the impact of access to nature on mental health utilization in urban neighborhoods using Texas outpatient encounters data merged with NatureScoreTM (0-100; low to high nature levels) and US census data (household income, education, employment, poverty, and insurance coverage) at the zipcode level. Our sample size included 61 million outpatient encounters across 1169 zipcodes, with 63% women and 30% elderly. A total of 369,344 mental health encounters were identified, with anxiety/stress and depression encounters representing 68.3% and 23.6%, respectively. We found that neighborhoods with a NatureScore of 60+ had lower overall mental health utilization than those below 40 (RR 0.51, 95%CI 0.38-0.69). This relationship persisted for depression, bipolar disorder, and anxiety/stress and in neighborhoods with a NatureScore above 80 (p < 0.001). Compared to neighborhoods with a NatureScore below 40, those above 80 had significantly lower depression (aRR 0.68, 95%CI 0.49-0.95) and bipolar (aRR 0.59, 95%CI 0.36-0.99) health encounters after adjusting for demographic and socioeconomic factors. This novel approach, utilizing NatureScore as a proxy for urban greenness, demonstrates the correlation between a higher NatureScore and reduced mental health utilization. Our findings highlight the importance of integrating nature into our healthcare strategies to promote well-being and mental health.

Global distribution of surface soil organic carbon in urban greenspaces

Urban greenspaces continue to grow with global urbanization. The global distribution and stock of soil organic carbon (SOC) in urban greenspaces remain largely undescribed and missing in global carbon (C) budgets. Here, we synthesize data of 420 observations from 257 cities in 52 countries to evaluate the global pattern of surface SOC density (0–20 cm depth) in urban greenspaces. Surface SOC density in urban greenspaces increases significantly at higher latitudes and decreases significantly with higher mean annual temperature, stronger temperature and precipitation seasonality, as well as lower urban greenness index. By mapping surface SOC density using a random forest model, we estimate an average SOC density of 55.2 (51.9–58.6) Mg C ha−1 and a SOC stock of 1.46 (1.37–1.54) Pg C in global urban greenspaces. Our findings present a comprehensive assessment of SOC in global urban greenspaces and provide a baseline for future urban soil C assessment under continuing urbanization.

Residential surrounding greenness is not associated with incident breast cancer in young women in Ontario, Canada

ObjectivesEnvironmental exposures play an important role in the development of breast cancer. The incidence of breast cancer is increasing in young women, and its etiology differs from that of older women. Epidemiological studies have provided mixed evidence about whether proximity to urban greenness reduces the risk of breast cancer, but few studies have evaluated this risk in younger women.MethodsWe investigated associations between residentially-based measures of greenness and breast cancer among participants of the Ontario Environmental Health Study (OEHS). The OEHS was a case–control study of Ontario women, 18–45 years of age, who provided questionnaire data between 2013 and 2015. The study included 465 cases diagnosed with a pathologically confirmed primary diagnosis of breast cancer, and 242 population-based controls. Residentially-based measures of greenness, the Normalized Difference Vegetation Index (NDVI) and tree coverage percentage, at 100-, 250-, 500-, and 1,000-m buffers, were assigned to the residential histories of the women. Odds ratios and their 95% confidence intervals (CIs) were estimated using logistic regression, adjusted for potential confounders including traffic-related air pollution [nitrogen dioxide (NO2)].ResultsWe found no evidence that an increase in NDVI or tree coverage were significantly associated with breast cancer. The adjusted odds ratio of breast cancer in relation to an interquartile range increase (IQR) in the NDVI (500-m buffer) was 0.86 (95% CI = 0.59–1.13). Similarly, the odds ratio of breast cancer among those in the highest quartile of tree coverage (500-m buffer) relative to the lowest was 1.11 (95% CI = 0.59–2.07). Risk estimates for both measures of greenness did not vary substantially across different buffer distances. Exposure to NO2 was an important confounder in these associations.ConclusionsOur findings do not support the hypothesis that residential greenness reduces the risk of breast cancer among young women, while highlighting the importance of adjusting for air pollution.

Exploring the impacts of built environment on bike-sharing trips on weekends: The case of Guangzhou

Shared mobility has brought many disruptive changes to urban transportation systems all over the world. Shared bikes have proven to be among the most successful and influential travel tools in attempting to alleviate the last-mile problem – the difficulty in getting people from transportation stations to their final destinations. This study aims to investigate the impacts of built environment factors on bike-sharing trips. Although many studies have explored these impacts, most have focused on the impacts of urban function, and have paid insufficient attention to the cycling environment. This study used multi-source data, including street view images (SVIs), points of interest (POIs), digital elevation models (DEMs), and road networks, to fully identify the influences of the built environment from five dimensions. The multiscale geographically weighted regression (MGWR) method was used to investigate the impacts of the urban built environment on bike-sharing usage. The results found that high-density roads, recreational POIs, and residential POIs all had positive impacts on the volume of bike-sharing trips in residential areas on weekends, whereas urban greenness negatively impacts bike-sharing usage in parks, because of strict regulations promulgated by local governments. Moreover, the impacts of high-density street networks and residential communities had strong spatial non-stationarity, while the influences of other built environment factors, including road gradient, eye-level greenness, and urban function mixture, were demonstrated spatial stationarity. These findings can facilitate local governments’ and enterprises’ efforts to improve the cycling environment and ensure the efficient management of shared bikes.

Spatial and temporal dynamics of urban heat environment at the township scale: A case study in Jinan city, China.

The prolonged dependence on industrial development has accentuated the cumulative effects of pollutants. Simultaneously, influenced by land construction activities and green space depletion, the Urban Heat Island (UHI) effect in cities has intensified year by year, jeopardizing the foundation of sustainable urban development. Prudent urban spatial planning holds the potential to robustly ameliorate the persistent deterioration of the UHI phenomenon. This study selects Jinan City as a case study and employs spatial autocorrelation and spatial regression algorithms to explore the spatiotemporal evolution of urban-rural patterns at the township scale. The aim is to identify key factors driving the spatiotemporal differentiation of Land Surface Temperature (LST) from 2013 to 2022. The research reveals a trend of initially rising and subsequently falling LST in various townships, with low-temperature concentration areas in the southern mountainous region and the northern plain area. The "West-Central-East" main urban axis and the southeast Laiwu District exhibit high-temperature zones. Significant influences on LST are attributed to pollution levels, topographical factors, urbanization levels, and urban greenness. The global Moran's Index for LST exceeds 0.7, indicating a strong positive spatial correlation. Cluster analysis results indicate High-High (HH) clustering in the central Shizhong District and Low-Low (LL) clustering in the northern Shanghe County. Multiscale Geographically Weighted Regression (MGWR) outperforms Geographically Weighted Regression (GWR) and Ordinary Linear Regression (OLR), providing a more accurate reflection of the regression relationships between variables. By investigating the spatiotemporal evolution of LST and its driving factors at the township scale, this study contributes insights for future urban planning and sustainable development.

Does urban greenness reduce loneliness and social isolation among Canadians? A cross-sectional study of middle-aged and older adults of the Canadian Longitudinal Study on Aging (CLSA).

Urban greenness has been shown to confer many health benefits including reduced risks of chronic disease, depression, anxiety, and, in a limited number of studies, loneliness. In this first Canadian study on this topic, we investigated associations between residential surrounding greenness and loneliness and social isolation among older adults. This cross-sectional analysis of the Canadian Longitudinal Study on Aging included 26,811 urban participants between 45 and 86 years of age. The Normalized Difference Vegetation Index (NDVI), a measure of greenness, was assigned to participants' residential addresses using a buffer distance of 500 m. We evaluated associations between the NDVI and (i) self-reported loneliness using the Center for Epidemiological Studies Depression Scale, (ii) whether participants reported "feeling lonely living in the local area", and (iii) social isolation. Logistic regression models were used to characterize associations between greenness and loneliness/social isolation while adjusting for individual socio-economic and health behaviours. Overall, 10.8% of participants perceived being lonely, while 6.5% reported "feeling lonely in their local area". Furthermore, 16.2% of participants were characterized as being socially isolated. In adjusted models, we observed no statistically significant difference (odds ratio (OR) = 0.99; 95% confidence interval (CI) 0.93-1.04) in self-reported loneliness in relation to an interquartile range (IQR) increase of NDVI (0.06). However, for the same change in greenness, there was a 15% (OR = 0.85; 95% CI 0.72-0.99) reduced risk for participants who strongly agreed with "feeling lonely living in the local area". For social isolation, for an IQR increase in the NDVI, we observed a 7% (OR = 0.93; 95% CI 0.88-0.97) reduction in prevalence. Our findings suggest that urban greenness plays a role in reducing loneliness and social isolation among Canadian urbanites.

Environmental determinants and demographic influences on global urban microbiomes, antimicrobial resistance and pathogenicity

Urban microbiome plays crucial roles in human health and are related to various diseases. The MetaSUB Consortium has conducted the most comprehensive global survey of urban microbiomes to date, profiling microbial taxa/functional genes across 60 cities worldwide. However, the influence of environmental/demographic factors on urban microbiome remains to be elucidated. We collected 35 environmental and demographic characteristics to examine their effects on global urban microbiome diversity/composition by PERMANOVA and regression models. PM10 concentration was the primary determinant factor positively associated with microbial α-diversity (observed species: p = 0.004, β = 1.66, R2 = 0.46; Fisher’s alpha: p = 0.005, β = 0.68, R2 = 0.43), whereas GDP per capita was negatively associated (observed species: p = 0.046, β = −0.70, R2 = 0.10; Fisher’s alpha: p = 0.004, β = −0.34, R2 = 0.22). The β-diversity of urban microbiome was shaped by seven environmental characteristics, including Köppen climate type, vegetation type, greenness fraction, soil type, PM2.5 concentration, annual average precipitation and temperature (PERMANOVA, p < 0.001, R2 = 0.01–0.06), cumulatively accounted for 20.3% of the microbial community variance. Canonical correspondence analysis (CCA) identified microbial species most strongly associated with environmental characteristic variation. Cities in East Asia with higher precipitation showed an increased abundance of Corynebacterium metruchotii, and cities in America with a higher greenness fraction exhibited a higher abundance of Corynebacterium casei. The prevalence of antimicrobial resistance (AMR) genes were negatively associated with GDP per capita and positively associated with solar radiation (p < 0.005). Total pathogens prevalence was positively associated with urban population and negatively associated with average temperature in June (p < 0.05). Our study presents the first comprehensive analysis of the influence of environmental/demographic characteristics on global urban microbiome. Our findings indicate that managing air quality and urban greenness is essential for regulating urban microbial diversity and composition. Meanwhile, socio-economic considerations, particularly reducing antibiotic usage in regions with lower GDP, are paramount in curbing the spread of antimicrobial resistance in urban environments.