City and Environment Interactions最新文献

Addis Ababa, a rapidly urbanizing city in Ethiopia, has witnessed a significant transformation in residential forms, with modern housing becoming increasingly prevalent alongside traditional Mud and Wood houses. Despite the widespread belief that modern housings improve the living conditions of residents in African cities, the influence of these residential forms on parameters of livability, here surface temperature, remains understudied. This study aims to determine the change in surface temperature across time and explore the differences in surface temperature between traditional and modern residential forms in Addis Ababa. This research utilizes data on residential forms and remotely sensed surface temperature data to investigate spatio-temporal changes in surface temperature. The analysis involves the use of Welch t-tests to examine the changes over time and Kruskal-Wallis test to compare temperature variations among different residential forms. The findings reveal a significant temperature increase of 2.2 °C in Addis Ababa, with modern residential forms exhibiting the highest surface temperature both in 2006 and 2016. However, when looking at change in surface temperature between 2006 and 2016, traditional residential forms are getting rapidly hotter than their modern counterparts. We concluded that the current boost in modern residential forms exacerbated the surface temperature in the city. To address the issue of increasing surface temperature, land use planning strategies are proposed, including the implementation of well-designed and large-scale infill developments with ample green spaces, the establishment and enforcement of green space ordinances, and halting the regularization of informal settlements.

COVID-19 lockdowns around the world led to significant reductions in anthropogenic emissions, especially in developing countries across Asia. Our study, conducted in Bangkok, Thailand presents a more granular picture; while the annual average fine particulate matter (PM_2.5) decreased by 23.8% during lockdowns, these pandemic restrictions had relatively less impact on particulate matter (PM₁₀) and coarse particles (PM_c). To provide a more comprehensive understanding of the effects of lockdown measures on particulate matter concentrations, this study employed a Land Use Regression (LUR) model. This research confirmed that a reduction in anthropogenic emissions, particularly in commercial and institutional areas could potentially reduce the PM_2.5 concentration. Our results indicated that residential land use in Bangkok was not associated with higher PM_2.5 levels than before the COVID-19 pandemic although Bangkok residents spent more time at home during lockdowns. In addition, Buddhist temple activities did not have a significant impact on PM_2.5 levels. Although deaths due to COVID-19 and air pollutants from furnace cremations increased, the impact of cremations on PM_2.5 was offset by a decrease in normal temple activities, such as burning incense. These evolving changes in anthropogenic emissions specific to different land uses deserve special attention from policymakers, especially as the Thai economy resumes full operations post-pandemic.

Urban heat island (UHI) phenomenon is crucial in the context of climate change. However, while substantial attention has been given to studying UHIs within cities, our understanding at the regional level still needs to be improved. This study delves into the intricate dynamics of the regional heat island (RHI) by examining its relationship with land use/land cover (LULC), vegetation, and elevation. The objective is to enhance our knowledge of RHI to inform effective mitigation strategies. The research employs a data-driven approach, leveraging satellite data and spatial modeling, examining surface and canopy-layer regional heat islands, and considering daytime and nighttime variations. To assess the impact of LULC, the study evaluates three main categories: anthropized (urbanized), agricultural, and wooded/semi-natural environments. Furthermore, it delves into the influence of vegetation on RHI and incorporates elevation data to understand its role in RHI intensity. The findings reveal meaningful variations in heat islands across different LULCs, providing essential insights. Although urbanized areas exhibit the highest RHI intensity, agricultural regions contribute notably to RHI due to land use changes and reduced vegetation cover. This emphasizes the significant impact of human activities. In contrast, wooded and semi-natural environments demonstrate potential for mitigating RHI, owing to their dense vegetation and shading effects. Elevation, while generally associated with reduced heat island, shows variations based on local conditions. Ultimately, this research underscores the complexity of the RHI phenomenon and the importance of considering factors such as different temperatures and their daily variation, landscape heterogeneity, and elevation. Additionally, the study emphasizes the significance of sustainable spatial planning and land management. Targeted efforts to increase vegetation in high daytime land surface temperature areas can reduce heat storage and mitigate RHI. Similarly, planning for agroforestry and green infrastructure in agricultural areas can significantly increase resilience to climate.

To assess the spatial heat resilience of buildings in urban development we test the suitability of a toolchain approach from microscale meteorological simulations, resolving the spatial influences on local urban climate, to building performance simulations, evaluating the indoor overheating risk in buildings. This approach makes it possible to investigate how much microscale effects (e.g. buildings, trees e.g. roads) in open space influence the overheating intensity in a building depending on its location within a district. In this context, the question arises how realistic the microscale meteorological simulation is to be used as input for indoor overheating evaluation. In this context, we applied a 3D urban climate model (ENVI-met) and a 1D boundary layer model (HIRVAC) for two urban districts in Germany as meteorological input for an indoor thermal comfort evaluation of two representative buildings. The results demonstrate that ENVI-met simulations without using measured temperature data create unrealistically low diurnal variations in outdoor air temperature despite an overestimated solar irradiance. The implementation to building simulation leads to a significant underestimation of the heat resilience for both buildings and to wrong conclusions about the efficacy of passive heat adaptation measures. In contrast, the HIRVACsimulations show a more realistic representation of the meteorological variables (when measured data is used for calibration) but are not able to resolve urban 3D structures. Our findings point out that an adjusted boundary layer representation in microscale meteorological simulations is crucial to provide meteorological input suitable for realistic spatially resolved indoor overheating analysis.

Managing the perception of project communities is critical to the success of infrastructure megaprojects. This study focuses on the Nagpur metro rail project in India to understand people's experiences and discourses in the pre-construction, construction, and operation phases. We use qualitative content analysis and open coding of the tweets from five years covering the lifecycle of the project to understand the discourses. The study identifies focus areas such as improving customer experience, sustainability, value for money, and embracing the local community. It also highlights the importance of effective communication and raising awareness to address community concerns throughout the lifecycle. The study provides a framework for using social media for community engagement over the megaproject's lifespan. This research can help megaproject management teams plan efficiently and create a positive perception of their projects.

The 2020 coronavirus pandemic demonstrated that moderation in living and consumption styles can contribute towards a more environmentally sustainable and resilient future. However, such restrained lifestyles are associated with the risk of global economic stagnation. Transitioning from linear to circular economy can potentially provide a framework to decouple environmental benefits from economic stasis. Circular cities are placed in the centre of such efforts to cradle and nurture humanity’s shift towards a sustainable future. However, we investigated the current state of circular cities around the world finding that a bio-based focus is not always prioritised. The lack of emphasis on bio-based solutions could lead to missed economic and environmental opportunities. A symbiosis between “circular”, “regenerative” and “bio-based” is here proposed as the foundation for sustainable societies transitioning to circularity. This Short Communication discusses the vision of circular bio-based cities and proposes a definition based on a reductionist metabolic approach. The aim is to set the basis for an international dialogue on urban development models capable of delivering both human and environmental prosperity beyond the reuse, refurbish and recycle concepts, and towards fully integrated regenerative urban systems which foster symbiotic relationships between urban communities and the natural ecosystem.

Shacks in urban informal settlements will be the most common form of housing in Namibia by 2025. Informal settlements are usually not connected to municipal sewage systems for multiple reasons, including lack of land tenure and lack of official capacity to invest in infrastructure in unplanned spaces. On-site decentralised sanitation is therefore the norm for shack dwellings in Namibia, but any official opposition to this system results in complete absence of sanitation and inadvertently promotes open defecation. This grey zone of urban informality and the gap in sanitation delivery is the focus of this study, which evaluates interactions between local communities, non-government organisations (Clay House Project and Development Workshop Namibia) and an international development agency (GIZ) as they navigate the physical, economic and political landscape of implementing bottom-up sanitation solutions for informal settlements in Namibia. In critical analysis of the three different sanitation delivery models of these organisations, we consider their historical development, underlying philosophies and technical solutions. We also examine how products from different sanitation systems are managed and whether urine source separation could improve their management. Overall, the results provide insights into bridging gaps in sanitation delivery in informal settlements, which are home to more than a billion people worldwide.

Large-scale, centralised sanitation systems provide safe, reliable, affordable and dignified sanitation to those located within the waterborne sewerage network. However, publicly managed hydro-modernist sanitation systems are not available to all, with many households and communities across the world having to access basic off-grid, state-provided sanitation services, such as pit latrines or Urine Diversion Dehydration Toilets, or having to provide their own solutions to sanitation waste. Urban green sanitation, which is non-sewered and off-grid, represents a new form of hydro-social and socio-technical relations. It aims to address sanitation backlogs and provide an alternative to centralised sanitation systems using ecological circularity. This paper categorises the social acceptability of urban green sanitation technologies into four main dimensions: social concerns, environmental concerns, the right to sanitation, and making a contribution to change. It draws on social assessments undertaken between 2016 and 2023, for the testing of sanitation technologies in Durban, South Africa, as part of the Engineering Field Testing Platform funded by the Bill and Melinda Gates Foundation’s Reinvent the Toilet Challenge and the Water Research Commission, South Africa, to reflect on the social dimensions of urban green sanitation. It argues that transdisciplinary research approaches and the co-production of knowledge are essential in understanding the social acceptability of green sanitation.

Modeling and prediction of mode choice are essential to support more sustainable and safer transportation decisions. There is plenty of literature in this decade showing that machine learning (ML) models have been effective predicting techniques, although not easily interpretable. When these techniques are used, there is a lack of connection with the data-gathering step, which is crucial to the technique selection and appropriate analysis of results. Based on a systematic literature review on mode choice studies, we present a methodology that interconnects the data-gathering process as a fundamental part of the descriptive phase when ML classification methods are used to predict mode choice preferences. The case study presented occurs in a university context whose descriptive phase shows interesting behavior patterns and highly imbalanced data in terms of mode choice. We show how decision tree methods allow us to tackle this issue in a contextualized manner and permit sensitivity analysis to test policies promoting changes in the modal split that aim for more sustainable mobility for the community of the university.

Downscaling is a particularly needed process in many environmental, social and governance applications at the fine scale. The need for an automated and reliable very high spatial resolution downscaling approach is then required. In this paper, a fully-automated open-access downscaling approach was proposed, named HSR-LST. It is based on the High Spatial Resolution (HSR) Red, Green and Blue (RGB) bands collected from commercial and free-to-access satellite images, generating LST values lower than 2-m spatial resolutions. This is based on the Landsat-8 thermal datasets and while implementing a fully-automated Ordinary Least Squares (OLS) approach. HSR-LST was implemented over Beirut, Boston and Dubai between 2016 and 2018. In comparison to an airborne LST image captured over ElKhorn River in Nebraska, USA, HSR-LST showed an AME of 0.88 °C and a R-squared value of 86.33%. Main results showed the variability of LST based on the sensed land features’ type. Different LST distribution footprints (i.e., irregular in Beirut, intermitted in Boston, systematic in Dubai) were highlighted depicting a characteristic urban configuration in each city. This latter along buildings’ material, density and height appear also to show a different effect on the local and surrounding LST values. By implementing the automated HSR-LST model in cities around the Globe, urban planners, policy makers and inhabitants can acquire improved information to assess urban heat islands, to propose more adequate planning policies, but more importantly to tackle urban heat and thermal comfort at the finest scales. HST-LST will effectively address the low spatial resolution of thermal bands. As HSR-LST is both automated and dynamic, it can be portable to other urban areas with diverse climatic regions.