Biodiversity and Health in the Urban Environment.

PURPOSE OF REVIEW: Biodiversity underpins urban ecosystem functions that are essential for human health and well-being. Understanding how biodiversity relates to human health is a developing frontier for science, policy and practice. This article describes the beneficial, as well as harmful, aspects of biodiversity to human health in urban environments. RECENT FINDINGS: Recent research shows that contact with biodiversity of natural environments within towns and cities can be both positive and negative to human physical, mental and social health and well-being. For example, while viruses or pollen can be seriously harmful to human health, biodiverse ecosystems can promote positive health and well-being. On balance, these influences are positive. As biodiversity is declining at an unprecedented rate, research suggests that its loss could threaten the quality of life of all humans. A key research gap is to understand-and evidence-the specific causal pathways through which biodiversity affects human health. A mechanistic understanding of pathways linking biodiversity to human health can facilitate the application of nature-based solutions in public health and influence policy. Research integration as well as cross-sector urban policy and planning development should harness opportunities to better identify linkages between biodiversity, climate and human health. Given its importance for human health, urban biodiversity conservation should be considered as public health investment.

Relationships between health outcomes in older populations and urban green infrastructure size, quality and proximity.

BACKGROUND: There is a growing body of literature supporting positive associations between natural environments and better health. The type, quality and quantity of green and blue space ('green-space') in proximity to the home might be particularly important for less mobile populations, such as for some older people. However, considerations of measurement and definition of green-space, beyond single aggregated metrics, are rare. This constitutes a major source of uncertainty in current understanding of public health benefits derived from natural environments. We aimed to improve our understanding of how such benefits are conferred to different demographic groups through a comprehensive evaluation of the physical and spatial characteristics of urban green infrastructure. METHODS: We employed a green infrastructure (GI) approach combining a high-resolution spatial dataset of land-cover and function with area-level demographic and socio-economic data. This allowed for a comprehensive characterization of a densely populated, polycentric city-region. We produced multiple GI attributes including, for example, urban vegetation health. We used a series of step-wise multi-level regression analyses to test associations between population chronic morbidity and the functional, physical and spatial components of GI across an urban socio-demographic gradient. RESULTS: GI attributes demonstrated associations with health in all socio-demographic contexts even where associations between health and overall green cover were non-significant. Associations varied by urban socio-demographic group. For areas characterised by having higher proportions of older people ('older neighbourhoods'), associations with better health were exhibited by land-cover diversity, informal greenery and patch size in high income areas and by proximity to public parks and recreation land in low income areas. Quality of GI was a significant predictor of good health in areas of low income and low GI cover. Proximity of publicly accessible GI was also significant. CONCLUSIONS: The influence of urban GI on population health is mediated by green-space form, quantity, accessibility, and vegetation health. People in urban neighbourhoods that are characterised by lower income and older age populations are disproportionately healthy if their neighbourhoods contain accessible, good quality public green-space. This has implications for strategies to decrease health inequalities and inform international initiatives, such as the World Health Organisation's Age-Friendly Cities programme.

Evaluating the impact of improvements in urban green space on older adults' physical activity and wellbeing: protocol for a natural experimental study.

BACKGROUND: Creating or improving urban green space has the potential to be an effective, sustainable and far-reaching way to increase physical activity and improve other aspects of wellbeing in the population. However, there is a dearth of well-conducted natural experimental studies examining the causal effect of changing urban green space on physical activity and wellbeing. This is especially true in older adults and in the United Kingdom. This paper describes a natural experimental study to evaluate the effect of four small-scale urban street greening interventions on older adults' physical activity and wellbeing over a 1-year period, relative to eight matched comparison sites. All sites are located in deprived urban neighbourhoods in Greater Manchester, United Kingdom. METHODS: Components of the interventions include tree and flower planting, and artificial tree decorations. Eight unimproved comparison sites were selected based on a systematic process of matching using several known objective and subjective environmental correlates of physical activity in older adults. The outcome measures are physical activity and two other behavioural indicators of wellbeing (Connect: connecting with other people; and Take Notice: taking notice of the environment), collected using a newly developed observation tool. The primary outcome is Take Notice behaviour due to largest effects on this behaviour being anticipated from improvements in the aesthetic quality of green space at the intervention sites. Baseline data collection occurred in September 2017 before the interventions were installed in November 2017. Follow-up data collection will be repeated in February/ March 2018 (6 months) and September 2018 (12 months). DISCUSSION: The present study permits a rare opportunity to evaluate the causal effects of small-scale changes in urban green space in an understudied population and setting. Although the interventions are expected to have small effects on the outcomes, the present study contributes to developing natural experiment methodology in this field by addressing key methodological weaknesses causing high risk of bias in previous natural experimental studies. Key improvements to reduce risk of bias in the present study are rigorous matching of multiple comparison sites and appropriate statistical control of key confounders. TRIAL REGISTRATION: Retrospectively registered with study ID NCT03575923 . Date of registration: 3 July 2018.

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data.

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR2: 0.33-0.38). For NO2 CTM improved prediction modestly (adjR2: 0.58) compared to models without SAT and CTM (adjR2: 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.