Digital economy exhibits varying degrees of mitigation of air pollution in China: Total cities-economic subdivisions-urban agglomerations.

Air pollution is a challenge for many cities. The digital economy enhances support for environmental pollution management, while the mechanisms and scaling heterogeneity remain unclear. This study explored the contribution of digital economy development to PM2.5 concentrations control in China and driving mechanisms in different economic subregions and urban agglomerations. Results show that the spillover transfer effect on air pollution mitigation far exceeded the direct effect at different scales. At the national scale, the air pollution mitigation effect of digital economy was mainly through empowering industrial structure optimization and green technology innovation, while it also affected economic subregions and urban agglomerations through varying scenario combinations of pathways with structural optimization, green production, resource allocation, and technology innovation. Research findings provide support for cross-regional joint management strategies of digital economy and air quality and designing regionally differentiated pollution control pathways in the digital economy dimension.

Air quality in a revitalized special economic zone at the center of an urban monocentric agglomeration.

In this study, we have examined the air quality within a revitalized, post-industrial urban area in Lódz, Poland. The use of Dron technology with mobile measurement equipment allowed for accurate assessment of air quality (particulate matter and gaseous pollutants) and factors influencing air quality (wind speed and direction) on a local scale in an area of 0.18 km2 and altitudes from 2 to 50 m. The results show that the revitalization carried out in the Lodz special economic zone area contributed to eliminate internal air pollution emitters through the use of ecological and effective heat sources. The exceedances permissible concentration values were local, and concerned mainly the higher measurement zones of the troposphere (more than 30 m above ground level). In the case of gaseous pollutants, higher wind speeds were associated with a decrease in the concentration of SO2 and an increase in H2S concentration. In both cases, the wind contributed to the occurrence of local areas of accumulation of these gaseous pollutants in the spaces between buildings or wooded areas.

Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach.

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990-2000 to 80.86 km2 in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization.

Urban biodiversity conservation: A framework for ecological network construction and priority areas identification considering habit differences within species.

The construction of ecological networks within the context of urbanization is an effective approach to cope with the challenges of urban biodiversity decline, representing a crucial goal in urban planning and development. However, existing studies often overlook the richness and uniqueness within species communities by homogenizing traits of species in the same class. This study proposes a framework for constructing and optimizing ecological networks focused on differential conservation within the same class. By classifying birds into three groups (specialists of water, forest or urban areas) based on their ecological requirements and urbanization tolerance, we constructed an ecological network tailored to their distinct migratory dispersal patterns. We then identified strategic areas including pinch points, barriers, and breakpoints specific to each bird group. Our findings reveal notable variations in suitable habitat distribution among different bird groups in urban environments. Corridor layouts varied according to habitat preferences and migratory dispersal patterns. Despite these differences, urban built-up areas persist as central hubs for the distribution of suitable habitats for 75% of bird species, with peripheral mountain-plain transition areas constituting 63% of crucial dispersal corridors. This emphasizes the critical role of urban built-up areas in maintaining biodiversity and ecological connectivity. Prioritizing connectivity between central urban areas and distant natural spaces is imperative. Our approach innovatively classifies and constructs networks to identify strategic areas with diverse species-specific attributes, providing valuable spatial information for land planning and guiding solutions to enhance target species. While the primary focus is on bird conservation in Beijing, our framework is broadly applicable to global biodiversity management and green planning under urbanization challenges. Overall, this study offers innovative insights for urban planning development and serves as decision support for prioritizing urban actions.

Visualization analysis of urban planning assistant decision network 3D system based on intelligent computing.

In recent years, with the continuous development of urban construction and the continuous expansion of urban population, the contradiction between spatial planning and actual urban needs in urban construction has become increasingly acute. However, planning decisions based on traditional urban planning renderings and two-dimensional (2D) planar plans are no longer in line with the actual development needs and specific development status of the city. Urban planning 3D auxiliary systems can improve the accuracy and precision of urban planning decisions. By providing planners with detailed 3D spatial models of the urban environment, potential design defects or conflicts can be identified before urban construction starts. This can reduce costs and risks related to urban development and help create a more livable and sustainable urban environment. This article analyzed and studied urban planning problems based on a three-dimensional (3D) auxiliary system of intelligent computing. In order to examine the effectiveness of intelligent computing 3D auxiliary systems in urban planning and construction, three indicators of development sustainability, transportation network convenience, and urban livability were established for effectiveness evaluation. The final experimental results showed that the average score of the 3D urban planning assistance system based on intelligent computing in urban development sustainability was 9.29 points, transportation convenience score was 92.08 points, and urban livability score was 9.249 points.

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review.

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative models. These advancements are reshaping data-driven planning strategies, practices, and approaches, thereby facilitating the achievement of environmental sustainability goals. This transformative wave signals a fundamental shift - marked by the synergistic operation of artificial intelligence (AI), artificial intelligence of things (AIoT), and urban digital twin (UDT) technologies. While previous research has largely explored urban AI, urban AIoT, and UDT in isolation, a significant knowledge gap exists regarding their synergistic interplay, collaborative integration, and collective impact on data-driven environmental planning in the dynamic context of sustainable smart cities. To address this gap, this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies, models, and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart cities. Central to this study are four guiding research questions: 1. What theoretical and practical foundations underpin the convergence of AI, AIoT, UDT, data-driven planning, and environmental sustainability in sustainable smart cities, and how can these components be synthesized into a novel comprehensive framework? 2. How does integrating AI and AIoT reshape the landscape of data-driven planning to improve the environmental performance of sustainable smart cities? 3. How can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities? 4. What challenges and barriers arise in integrating and implementing AI, AIoT, and UDT in data-driven environmental urban planning, and what strategies can be devised to surmount or mitigate them? Methodologically, this study involves a rigorous analysis and synthesis of studies published between January 2019 and December 2023, comprising an extensive body of literature totaling 185 studies. The findings of this study surpass mere interdisciplinary theoretical enrichment, offering valuable insights into the transformative potential of integrating AI, AIoT, and UDT technologies to advance sustainable urban development practices. By enhancing data-driven environmental planning processes, these integrated technologies and models offer innovative solutions to address complex environmental challenges. However, this endeavor is fraught with formidable challenges and complexities that require careful navigation and mitigation to achieve desired outcomes. This study serves as a comprehensive reference guide, spurring groundbreaking research endeavors, stimulating practical implementations, informing strategic initiatives, and shaping policy formulations in sustainable urban development. These insights have profound implications for researchers, practitioners, and policymakers, providing a roadmap for fostering resiliently designed, technologically advanced, and environmentally conscious urban environments.

Combined sewer overflow mitigation through SUDS - A review on modelling practices, scenario elaboration, and related performances.

Hydrologic-hydraulic modelling of urban catchment is an asset for land managers to simulate Sustainable Urban Drainage Systems (SUDS) implementation to fulfil combined sewer overflow (CSO) regulations. This review aims to assess the current practices in modelling SUDS scenarios at large scale for CSO mitigation encompassing every stage of the modelling process from the choice of the equation to the validation of the initial state of the urban system, right through to the elaboration, modelling, and selection of SUDS scenarios to evaluate their performance on CSO. Through a quantitative and qualitative analysis of 50 published studies, we found a diversity of choices when modelling the status quo of the urban system. Authors generally do not explain the modelling processes of slow components (deep infiltration, groundwater infiltration) and interconnexion between SUDS and the sewer system. In addition, only a few authors explain how CSO structures are modelled. Furthermore, the modelling of SUDS implementation at catchment scale is highlighted in the 50 studies retrieved with three different approaches going from simplified to detailed. SUDS modelling choices seem to be consistent with the objectives: studies focusing on dealing with several objectives at the time typically opt for a complex system configuration that includes the surface processes, network, CSO, SUDS, and often the soil and/or groundwater components. Conversely, authors who have selected a basic configuration generally aim to address a single, straightforward question (e.g., which type of SUDS). However, elaboration and selection of scenarios for CSO mitigation is mainly based on local constraints, which does not allow hydrological performance to be directly optimised. In conclusion, to improve current practices in modelling SUDS scenarios at large scale for CSO mitigation, authors suggest to: (i) improve clear practices of CSO modelling, calibration and validation at the urban catchment scale, (ii) develop methods to optimize the performance of scenarios for CSO mitigation using hydrological drivers, and (iii) improve parsimonious and user-friendly models to simulate SUDS scenarios in a context of data scarcity.

The political price of superblocks. Electoral outcomes of sustainable transport interventions in Barcelona.

Urban and transportation policies are increasingly recognized for their potential to mitigate climate change impacts and address transport externalities. Amidst efforts to shift modal preferences and reduce transport emissions, cities are turning to spatialbased policies, such as Superblocks, to reshape urban mobility. This research examines the electoral outcomes associated with the implementation of Superblocks in Barcelona, focusing on their impact on political support for Barcelona en Comú (BEC) during the local elections of 2015 and 2023. Utilizing a combination of adjusted difference-in-differences and propensity score matching methods, we assessed the public's electoral response to the Superblock initiative amidst a backdrop of declining city-wide support for BEC. Our findings reveal that Superblock areas demonstrated significantly stronger support for BEC, suggesting a political premium for the party responsible for these urban interventions. Specifically, electoral support in Superblock districts saw an increase of 10-14% compared to the rest of the city. This result highlights the potential of urban transformation policies to influence political preferences locally and potentially validate the use of local electoral data as a tool for evaluating public response to highly contested urban policies.

Rethinking the country-level percentage of population residing in urban area with a global harmonized urban definition.

The UN (United Nations) collects global data on the country-level Percentage of Population Residing in Urban Area (PPRUA). However, variations in urban definitions make these data incomparable across countries. This study assesses national defined PPRUA within UN statistics against estimates we derived using global comparable definitions. Refer to the UN's Degree of Urbanization framework, we propose 90 global harmonized methods for estimating PPRUA by combining different configurations of three global population datasets, six urban total population thresholds, and five urban population density thresholds. This approach demonstrated significant variations in country-level PPRUA estimations, with wide 95% confidence intervals using the Z score method. Most national defined PPRUA fall between the upper 95% CI and the median of the estimations, underscoring the need for globally harmonious PPRUA estimates. This study advocates for a reassessment of datasets and thresholds in the future and for investigating urbanization on a scale beyond the country level.

Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts.

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public.

Revolutionizing urban mapping: deep learning and data fusion strategies for accurate building footprint segmentation.

In the dynamic urban landscape, understanding the distribution of buildings is paramount. Extracting and delineating building footprints from high-resolution images, captured by aerial platforms or satellites, is essential but challenging to accomplish manually, due to the abundance of high-resolution data. Automation becomes imperative, yet it introduces complexities related to handling diverse data sources and the computational demands of advanced algorithms. The innovative solution proposed in this paper addresses some intricate challenges occurring when integrating deep learning and data fusion on Earth Observed imagery. By merging RGB orthophotos with Digital Surface Models, deriving from the same aerial high-resolution surveys, an integrated consistent four-band dataset is generated. This unified approach, focused on the extraction of height information through stereoscopy utilizing a singular source, facilitates enhanced pixel-to-pixel data fusion. Employing DeepLabv3 algorithms, a state-of-the-art semantic segmentation network for multi-scale context, pixel-based segmentation on the integrated dataset was performed, excelling in capturing intricate details, particularly when enhanced by the additional height information deriving from the Digital Surface Models acquired over urban landscapes. Evaluation over a 21 km2 area in Turin, Italy, featuring diverse building frameworks, showcases how the proposed approach leads towards superior accuracy levels and building boundary refinement. Notably, the methodology discussed in the present article, significantly reduces training time compared to conventional approaches like U-Net, overcoming inherent challenges in high-resolution data automation. By establishing the effectiveness of leveraging DeepLabv3 algorithms on an integrated dataset for precise building footprint segmentation, the present contribution holds promise for applications in 3D modelling, Change detection and urban planning. An approach favouring the application of deep learning strategies on integrated high-resolution datasets can then guide decision-making processes facilitating urban management tasks.

Urban green spaces and resident health: an empirical analysis from data across 30 provinces in China.

Background: This study aims to explore the correlation between urban green space coverage and resident health, and to analyze its underlying mechanisms. Methods: Using panel data from 30 provinces in China from 2006 to 2022, which mainly includes urban green space coverage, general health of the population, air quality, and social connectivity. This research constructed a fixed effects model to perform baseline regression analysis. A series of robustness tests, including variable substitution, controlling for geographical differences, regional robustness tests, and shortening the time span of the study, further verified the robustness of the results. Additionally, mechanism tests were conducted to examine the positive impacts of urban green spaces on resident health by improving air quality and enhancing social connectivity. Results: The findings indicate a significant positive correlation between urban green space coverage and resident health levels. That is, the greater the area covered with urban green space, the healthier the residents of the area will be. Robustness tests support the reliability of this finding, while mechanism analysis reveals that urban green spaces have a positive impact on the health of the population by improving air quality and increasing social connectivity. Discussion: This study underscores the importance of urban green space planning in improving resident health and quality of life, providing urban planners with scientific evidence to optimize urban green systems for broader health objectives.

Impact of urban space on PM2.5 distribution: A multiscale and seasonal study in the Yangtze River Delta urban agglomeration.

Despite concerted efforts in emission control, air pollution control remains challenging. Urban planning has emerged as a crucial strategy for mitigating PM2.5 pollution. What remains unclear is the impact of urban form and their interactions with seasonal changes. In this study, base on the air quality monitoring stations in the Yangtze River Delta urban agglomeration, the relationship between urban spatial indicators (building morphology and land use) and PM2.5 concentrations was investigated using full subset regression and variance partitioning analysis, and seasonal differences were further analysed. Our findings reveal that PM2.5 pollution exhibits different sensitivities to spatial scales, with higher sensitivity to the local microclimate formed by the three-dimensional structure of buildings at the local scale, while land use exerts greater influence at larger scales. Specifically, land use indicators contributed sustantially more to the PM2.5 prediction model as buffer zone expand (from an average of 2.41% at 100 m range to 47.30% at 5000 m range), whereas building morphology indicators display an inverse trend (from an average of 13.84% at 100 m range to 1.88% at 5000 m range). These results enderscore the importance of considering building morphology in local-scale urban planning, where the increasing building height can significantly enhance the disperion of PM2.5 pollution. Conversely, large-scale urban planning should prioritize the mixed use of green spaces and construction lands to mitigate PM2.5 pollution. Moreover, the significant seasonal differences in the ralationship between urban spatical indicatiors and PM2.5 pollution were observed. Particularly moteworthy is the heightened association between forest, water indicators and PM2.5 concentrations in summer, indicating the urban forests may facilitate the formation of volatile compunds, exacerbating the PM2.5 pollution. Our study provides a theoretical basis for addressing scale-related challenges in urban spatial planning, thereby forstering the sustainable development of cities.

The barriers to uptake of disaster risk management science in urban planning: A political economy analysis.

There is increasing effort in science to support disaster risk management (DRM) and climate change adaptation in urban environments. It is now common for research calls and projects to reference coproduction methods and science uptake goals. This paper identifies lessons for researchers, research funders, and research users wishing to enable useful, useable, and used science based on the perspectives of research users in urban planning from low- and middle-income countries. DRM-supporting science is viewed by policy actors as: complicated and poorly communicated; presenting inadequate, partial, and outdated information; misaligned with policy cycles; and costly to access and inadequately positioned to overcome the policy barriers that hinder integration of DRM into urban planning. Addressing these specific concerns points to more systematic collection and organisation of data and enhancement of supporting administrative structures to facilitate better sight of human vulnerability and its link to development decision-making and wider processes of urban risk creation.

Assessing Community Vulnerability to Extreme Events in the Presence of Contaminated Sites and Waste Management Facilities: An Indicator Approach.

Communities across the world are facing extreme events, such as excessive heat, droughts, floods, and wildfires. In the presence of contaminated sites and waste management facilities, communities must consider the impacts of potential releases from these sites due to such events. Impacts of extreme events on sites and consequently on surrounding, often disadvantaged communities result from complex interactions between natural, physical, and social factors. A conceptual framework was developed to identify and provide a shared understanding of key vulnerabilities and pathways that transcend disciplines. A transparent and replicable method was developed to create mappable indicators that represent contaminated sites, waste facilities, contaminant transport via air and water, and population sensitivities. This method can be applied as a screening step to assist states and local communities in prioritizing targeted strategies and resources and determining where in-depth assessments are needed. These indicators can facilitate communication with a broad audience more easily than complex modeling approaches or aggregated indices. Case study results demonstrate the importance of considering indicators in conjunction with each other. The indicator method was developed together with U.S.-based partners, but can be adapted for other countries seeking to understand the potential impacts of extreme events on contaminated sites and communities.

Streets classification models by urban features for road traffic noise estimation.

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

Locust behavior and city topology: A biodynamic approach for assessing urban flows.

A city's economic growth and the inhabitants' wellbeing are highly affected by its topology and connecting networks, which, in turn, influence movement and flows in the city. Flow relates to how a city is developed, organized, managed, and built. The analysis of flow in cities is challenging but essential. In this study, the fields of urban design and animal science are combined, and a new approach for exploring the relationships between urban topology and physical flow is developed. Specifically, we establish an interdisciplinary methodology to evaluate mobility performance in various urban settings, utilizing experimental observations of the dynamic behavior of natural-biological agents, i.e., locusts, within physical city models. Our novel approach enriches the currently available toolbox by using living organisms as indicators for flow in physical city models. Our findings improve our understanding of the intricate flow interactions in urban settings.

Does the morphology of residential greenspaces contribute to the development of a cardiovascular-healthy city?

BACKGROUNDS: Greenspaces are indispensable for the construction of a healthy city. Research has shown that greenspaces contribute to the reduction of cardiovascular risks. However, the role of greenspace morphology in the development of a healthy city is not well understood. METHODS: Our study utilized data from a cardiovascular disease screening cohort comprising 106,238 residents in Anhui Province, China, aged between 35 and 75 years. We calculated landscape indices of each participant using high-resolution land cover data to measure the greenness, fragmentation, connectivity, aggregation, and shape of greenspaces. We used a multivariate linear regression model to assess the associations between these landscape indices and triglyceride risk, and employed a structural equation model to explore the potential contributions of heatwaves and fine particulate matter (PM2.5) to this association. RESULTS: Overall, triglyceride was expected to increase by 0.046% (95% CI: 0.040%, 0.052%) with a 1% increase in the percentage of built-up area. Conversely, an increase in the percentage of greenspace was associated with a 0.270% (95% CI: 0.337%, -0.202%) decrease in triglyceride levels. Furthermore, when the total greenspace was held constant, the shape, connectedness, and aggregation of greenspace were inversely correlated with triglyceride levels, with effects of -0.605% (95% CI: 1.012%, -0.198%), -0.031% (95% CI: 0.039%, -0.022%), and -0.049% (95% CI: 0.058%, -0.039%), respectively. Likewise, the protective effect of the area-weighed mean shape index was higher than that of the total amount of greenspace. The stratification results showed that urban residents benefited more from greenspace exposure. Greenspace morphology can minimize triglyceride risk by reducing pollutant and heatwaves, with aggregation having the greatest effect on reducing pollutants whereas fragmentation is more efficient at reducing heatwaves. CONCLUSION: Exposure to the greenspaces morphology is associated with a reduction in triglyceride risk. The study has important practical and policy implications for early health monitoring and the spatial layout of greenspace and will provide scientific information for healthy urban planning by reducing unfavorable health consequences.

Association of transportation noise with cardiovascular diseases.

This comprehensive article delves into the intricate and multifaceted issue of noise pollution, shedding light on its diverse sources, profound health implications, and the economic burden it imposes on societies. Noise pollution is an increasingly prevalent environmental challenge, impacting millions of people worldwide, often without their full awareness of its adverse effects. Drawing from a wealth of scientific research, the article underscores the well-established links between noise pollution and a spectrum of health issues, including cardiovascular diseases, sleep disturbances, and psychological stress. While exploring the sources and consequences of noise pollution, the article highlights the urgent need for a holistic and collaborative approach to mitigate its impact. This entails a combination of regulatory measures, technological innovations, urban planning strategies, and public education campaigns. It is increasingly evident that the detrimental effects of noise pollution extend beyond physical health, encompassing mental and social well-being. The article also addresses the synergistic relationship between noise pollution and other environmental stressors, emphasizing the importance of considering noise in conjunction with factors like air pollution and access to green spaces. It examines the potential of green spaces to mitigate the effects of noise pollution and enhance overall health.

Reliability and Detectability of Emergency Management Systems in Smart Cities under Common Cause Failures.

Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings.