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The urban environment has changed vastly over past decades, which also has had an impact on our sleep and dietary patterns and possibly health outcomes. Some studies have shown that sleep duration and sleep quality has declined over past decades, especially in children. In parallel, our lifestyle and dietary patterns have also changed including more shift work, more meals outside the home or family setting and more irregular eating patterns, including breakfast skipping and late-night eating. This new area of research in nutritional sciences studying the impact of the timing of eating on health outcomes is called chrono-nutrition, and combines elements from nutritional research with chrono-biology. The objectives of this paper were to discuss secular trends in sleep patterns and related dietary patterns, introduce basic concepts and mechanisms of chrono-nutrition and discuss the evidence for the importance of sleep and chrono-nutrition in relation to health outcomes. Overall, chrono-nutrition could mediate the effects between sleep, diet and urbanisation, and more research is needed to elucidate the importance of chrono-nutrition for metabolic health and its impact on public health.

Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

Over half the world’s population lives in urban regions, and increasingly disasters are of great concern to city dwellers, policymakers, and builders. However, disaster risk is also of great interest to corporations, financiers, and investors. Risky Cities is a critical examination of global urban development, capitalism, and its relationship with environmental hazards. It is about how cities live and profit from the threat of sinkholes, garbage, and fire. Risky Cities is not simply about post-catastrophe profiteering. This book focuses on the way in which disaster capitalism has figured out ways to commodify environmental bads and manage risks. Notably, capitalist city-building results in the physical transformation of nature. This necessitates risk management strategies –such as insurance, environmental assessments, and technocratic mitigation plans. As such capitalists redistribute risk relying on short-term fixes to disaster risk rather than address long-term vulnerabilities. 

Summary Urban transmission of arthropod‐vectored disease has increased in recent decades. Understanding and managing transmission potential in urban landscapes requires integration of sociological and ecological processes that regulate vector population dynamics, feeding behaviour and vector–pathogen interactions in these unique ecosystems. Vectorial capacity is a key metric for generating predictive understanding about transmission potential in systems with obligate vector transmission. This review evaluates how urban conditions, specifically habitat suitability and local temperature regimes, and the heterogeneity of urban landscapes can influence the biologically relevant parameters that define vectorial capacity: vector density, survivorship, biting rate, extrinsic incubation period and vector competence. Incidence of vector‐borne disease in urban host populations is rarely, if ever, evenly distributed across an urban area. The persistence and quality of vector habitat can vary significantly across socio‐economic boundaries to influence vector species composition and abundance, often generating socio‐economically distinct gradients of transmission potential across neighbourhoods. Urban regions often experience unique temperature regimes, broadly termed urban heat islands (UHI). Arthropod vectors are ectothermic organisms, and their growth, survival and behaviour are highly sensitive to environmental temperatures. Vector response to UHI conditions is dependent on regional temperature profiles relative to the vector's thermal performance range. In temperate climates, UHI can facilitate increased vector development rates while having countervailing influence on survival and feeding behaviour. Understanding how urban heat island (UHI) conditions alter thermal and moisture constraints across the vector life cycle to influence transmission processes is an important direction for both empirical and modelling research. There remain persistent gaps in understanding of vital rates and drivers in mosquito‐vectored disease systems, and vast holes in understanding for other arthropod‐vectored diseases. Empirical studies are needed to better understand the physiological constraints and socio‐ecological processes that generate heterogeneity in critical transmission parameters, including vector survival and fitness. Likewise, laboratory experiments and transmission models must evaluate vector response to realistic field conditions, such as variability in sociological and environmental conditions. Lay Summary

Experience is conceptualised in both academic and policy circles as a more-or-less direct effect of the design of the built environment. Drawing on findings from a research project that investigated people’s everyday experiences of designed urban environments in two UK towns, this paper suggests at least two reasons why sensory encounters between individuals and built environments cannot in fact be understood entirely as a consequence of the design features of those environments. Drawing from empirical analysis based on surveys, ethnographic ‘walk-alongs’ and photo-elicitation interviews, we argue that distinct senses of place do depend on the sensory experiencing of built environments. However, that experiencing is significantly mediated in two ways. First, it is mediated by bodily mobility: in particular, the walking practices specific to a particular built environment. Secondly, sensory experiences are intimately intertwined with perceptual memories that mediate the present moment of experience in various ways: by multiplying, judging and dulling the sensory encounter. In conclusion, it is argued that work on sensory urban experiencing needs to address more fully the diversity and paradoxes produced by different forms of mobility through, and perceptual memories of, built environments.

Childhood and adolescence are crucial periods for mental and social development. Currently, mental illness among young people is a global epidemic, and rates of disorders such as depression and anxiety are rising. Urban living, compared with rural living, is linked with a higher risk of serious mental illness, which is important because the world is urbanizing faster than ever before. Urban environments and their landscapes, designs, and features influence mental health and well-being. However, no conceptual frameworks to date have detailed the effect of urban environments on young people’s mental health, and few studies have considered the growing role of digital and social media in this relationship, leading to calls for the development of holistic approaches to describe this relationship. This article synthesizes existing knowledge on urban places (both built and natural environments) and mental health in the public health and urban planning literature and examines the emerging field of neurourbanism (a multidisciplinary study of the effect of urban environments on mental health and brain activity) to enhance current practice and research. We developed 2 novel conceptual frameworks (1 research-oriented, 1 practice-oriented), adapted from Bronfenbrenner’s socioecological model, that focus on the relationship between urban environments and young people’s mental health. We added a digital and social media contextual level to the socioecological model, and we applied a multilayer concept to highlight potential cross-field interactions and collaborations. The proposed frameworks can help to guide future practice and research in this area.

Summary Plant–pollinator interactions are affected by global change, with largely negative impacts on pollination and plant reproduction. Urban areas provide a unique and productive study system for understanding the impacts of many global change drivers on plant–pollinator interactions. We review the mechanistic pathways through which urban drivers alter plant–pollinator interactions. The literature on urban drivers of plant–pollinator interactions is small but growing and has already produced exciting insights about how population processes or pollinator behaviour interacts with landscape urban drivers to affect pollination outcomes. Habitat loss and fragmentation can change flower visitation rates and pollination success through changes in pollinator foraging behaviour or through population‐level effects on pollinators. Urban environments, where impermeable surface provides an inhospitable matrix, may allow researchers to identify habitat fragments more clearly than in many other environments. Recent studies have found that non‐native plants are not differently preferred by pollinators relative to native plants, therefore removing the basis for expecting pollinator‐mediated competition between native and non‐native plants in urban habitats. However, non‐native species together with managed vegetation may have powerful effects in urban habitats via changes in community‐level plant phenology and consequent changes in pollinator phenology. The current level of climate warming has not caused plants and pollinators to become detectably temporally separated, although at the same time, diversity among species’ phenological responses could buffer plant–pollinator interactions from climate variation. Due to the urban warming effect, cities provide a promising system for better understanding the warming effects on plant–pollinator interactions. Environmental contaminants such as soil nitrogen and heavy metal pollution have been examined with respect to plant–pollinator interactions in small‐scale, mechanistic studies. The extent to which environmental contaminants drive plant–pollinator interactions in actual urban landscapes is, however, currently unknown. Important knowledge gaps that require research attention include understanding the consequences of plant and pollinator trait filtering on plant–pollinator interactions, and expanding the literature to include underrepresented biomes and pollinator taxa. Lay Summary

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.

Twenty-first century urbanization poses increasing challenges for mental health. Epidemiological studies have shown that mental health problems often accumulate in urban areas, compared to rural areas, and suggested possible underlying causes associated with the social and physical urban environments. Emerging work indicates complex urban effects that depend on many individual and contextual factors at the neighbourhood and country level and novel experimental work is starting to dissect potential underlying mechanisms. This review summarizes findings from epidemiology and population-based studies, neuroscience, experimental and experience-based research and illustrates how a combined approach can move the field towards an increased understanding of the urbanicity-mental health nexus.

SWAT (Soil and Water Assessment Tool) is a continuous-time, semi-distributed, river basin model widely used to evaluate the effects of alternative management decisions on water resources. This study examines the application of the SWAT model for streamflow simulation in an experimental basin with mixed-land-use characteristics (i.e., urban/peri-urban) using daily and hourly rainfall observations. The main objective of the present study was to investigate the influence of rainfall resolution on model performance to analyze the mechanisms governing surface runoff at the catchment scale. The model was calibrated for 2018 and validated for 2019 using the Sequential Uncertainty Fitting (SUFI-2) algorithm in the SWAT-CUP program. Daily surface runoff was estimated using the Curve Number method, and hourly surface runoff was estimated using the Green–Ampt and Mein–Larson method. A sensitivity analysis conducted in this study showed that the parameters related to groundwater flow were more sensitive for daily time intervals, and channel-routing parameters were more influential for hourly time intervals. Model performance statistics and graphical techniques indicated that the daily model performed better than the subdaily model (daily model, with NSE = 0.86, R2 = 0.87, and PBIAS = 4.2 %; subdaily model with NSE = 0.6, R2 = 0.63, and PBIAS = 11.7 %). The Curve Number method produced higher discharge peaks than the Green–Ampt and Mein–Larson method and better estimated the observed values. Overall, the general agreement between observations and simulations in both models suggests that the SWAT model appears to be a reliable tool to predict discharge in a mixed-land-use basin with high complexity and spatial distribution of input data.