City and Environment Interactions最新文献

Episodic floods, attributable primarily to climate change, global warming, and sea level rise, have worsened and continue to be a significant threat to life. Building resilience and improving the adaptive capacities of communities under threat will require institutional and cross-agency collaboration. This paper assessed institutional arrangements for mitigating and adapting to climate change-related flood risk in GAMA, Ghana. Using a descriptive cross-sectional survey, the study collected and analysed data from 65 private businesses and governmental, community, and civil society organisations to assess how institutions collaborate in preparing and adapting to flood risks in the study area. The study finds that frequent flooding, destruction of property and lives, and regular community agitations have triggered high-level consciousness, institutional arrangements, and collaboration within and among communities to build capacity and resilience. In addition, organisations have been educating indigenes on flood preparedness, providing relief items to support flood victims, and conducting training and research. Organisations have also supported communities in developing sandbags, planning sea defence mechanisms, providing rescue and evacuation assistance, providing transportation and logistics, and relocating stranded flood victims. Despite these efforts, the lack of adequate funding and logistics, coupled with the absence of proper consultation and involvement of most organisations in making national-level decisions on climate change and flood-related issues, have rendered flood management efforts less effective. This paper makes the case that proactive and collaborative flood responses should replace reactive and individual approaches to improve flood control efficacy and minimise casualties and property losses.

Bangkok, the capital city of Thailand, has experienced a degradation in air quality. In particular, ozone (O₃) concentrations frequently exceed Thailand’s National Ambient Air Quality standards. Various policies have been developed to manage air quality at near-surface levels; however, the assessment of air quality above these levels has been neglected. Analysis of O₃ concentrations and its precursors measured at various altitudes revealed that the vertical structures of the pollutants in Bangkok were stratified. While NO_x concentrations decreased with height, O₃ concentrations increased. These results emphasize the importance of air quality management above ground level, particularly in megacities with high-rise buildings, such as Bangkok. Assuming that the O₃ formation regime over Bangkok is VOC-limited, a simple model was developed based on a linear regression method to estimate the changes in O₃ concentrations with height. We found that the simple model was able to capture the variation in O₃ concentrations at 30, 75, and 110 m above ground level, with an R² of 0.48–0.65. Although the simple model had some difficulty estimating the magnitude of O₃ concentrations, the estimated O₃ values from the model were reasonable for capturing vertical O₃ trends. Using this approach, policymakers can visualize the vertical distribution of pollutants, which will be useful in designing air quality management plans.

In Sub-Saharan Africa, many cities are facing an increased risk of heat due to climate change and rapid urbanization. This poses a particular threat in areas with limited adaptive capacity. However, there is a lack of comprehensive heat risk assessment in the region, possibly due to the absence of high-resolution weather data. This study aims to address this gap by proposing and demonstrating a methodology for mapping high-risk areas in a tropical humid city, specifically focusing on Lagos, Nigeria. The approach utilises advanced numerical modelling techniques and open-source geospatial data.

The urbanised Weather Research and Forecasting (WRF) model is employed to simulate Humidex-based heat stress during a specific heatwave event in March 2020. Open-source high resolution geospatial datasets were used to assess heat exposure and vulnerability. The urban areas were classified based on the Local Climate Zone (LCZ) scheme. Spatial analysis techniques, including Moran’s I test and Optimized Hot Spot Analysis (OHSA), were used to identify spatial clustering patterns and hot spots of heat risk areas.

Moreover, using Gi* statistics in OHSA, the risk layer was categorised into hot, cold, and non-significant spots at various levels of significance (90 %, 95 %, and 99 %). Mapping the hot spots at the highest confidence level of 99 % identified Critical Heat Risk Zones (CHRZ), covering an area of approximately 423 km². The results showed significant heat risk in highly urbanised LCZs. Further investigation indicated that the largest proportion of high-risk zones corresponded to densely populated and highly urbanised LCZs- LCZ3 (59 %), LCZ 6(21 %), and LCZ 7(17 %). Notably, these areas coincide with two well-known slums in Lagos, emphasizing the need for targeted interventions and planning measures in these areas.

The findings highlight the magnitude and extent of heat risk within the city and emphasize the urgent need for targeted climate change adaptation and mitigation strategies in the identified high-risk zones.

Accurate and timely evaluation and assessment of emission data and its impact on environmental status has been a key challenge due to the conventional manual approach utilized for independently computing most emission parameters. To resolve this long-standing issue, we proposed an Artificial Intelligence (AI)-driven Decision Tree model to adequately classify Environmental Protection Agency (EPA) status based on multiple Emission Parameters. The model's performance was systematically evaluated using multiple emission parameters obtained from a two-stroke motorcycle dataset collected in Nigeria across various metrics such as K-S Statistics, Confusion Matrix, Correlation Heat Map, Decision Tree, Validation Curve, and Threshold Plot. The K-S Statistics plot's experimental results showed a considerable correlation between HC, CO, and the target variable, with values ranging from 0.75 to 0.80. At the same time, CO₂ and O₂ do not correlate with the target variable with values between 0.00 and 0.09. The Confusion Matrix revealed that the proposed model has an overall accuracy of 99.9% with 481 true positive predictions and 75 true negative predictions, indicating the effectiveness of the proposed AI-driven model. In conclusion, our proposed AI-driven model can effectively classify EPA status based on multiple emission parameters with high accuracy, which may spur positive advancement in policy enhancement for proper environmental management.

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.