Urban Environment Research Articles

Enhancing Bicycle Trajectory Planning in Urban Environments through Complex Network Optimization

Enhancing Bicycle Trajectory Planning in Urban Environments through Complex Network Optimization

Improving Data Quality of Low-Cost Light-Scattering PM Sensors: Toward Automatic Air Quality Monitoring in Urban Environments

Improving Data Quality of Low-Cost Light-Scattering PM Sensors: Toward Automatic Air Quality Monitoring in Urban Environments

Spraint density of the Eurasian otter (Lutra lutra) is an accurate indicator of its population status in urban areas.

The most traditional method used to monitor the distribution of otters is spraint density surveys, but there has been long-standing debate regarding whether this approach is sufficiently accurate. In response to this debate, the effectiveness and reliability of spraint density surveys as a method for assessing the otter distribution within urban areas were evaluated by comparing their results with those achieved using two alternative methods: genetic analysis and camera trap-based density based on the random encounter model. In addition, the spraint density was tested as an indicator of otter habitat preferences by modeling 19 environmental factors (seven associated with prey, nine with land use, and three with the river environment) using a pcount model. Spraint surveys, genetic analysis, and camera trapping were conducted at 36, 22, and 12 sites within Daegu Metropolitan City, respectively. A regression model indicated that the spraint density was strongly associated with the other two methods, while it was also found to accurately represent otter habitat preferences, with otters in urban areas preferring sites with a high density of large fish and river islets. Consequently, spraint density surveys were the most time-effective, cost-effective, and reliable method for investigating the otter distribution in this urban environment. As a result, population status assessment using spraint density surveys can provide important information for the conservation management of urban otters.

Environmental constraints can explain clutch size differences between urban and forest blue tits: Insights from an egg removal experiment.

Urban environments present novel ecological challenges to wild species. In birds, urban populations generally exhibit reduced clutch sizes compared to forest populations. However, whether smaller urban clutches are adaptive or a result of environmental constraints is unclear. To investigate these two hypotheses, we quantified the ability of urban and non-urban blue tits (Cyanistes caeruleus) to lay new eggs after an experimental manipulation aimed to increase egg production. We removed the first four eggs laid by urban and forest birds to test their ability to produce new eggs. If the urban environment imposes constraints on egg production, we predicted that urban birds would not lay new eggs. If the small clutches of urban birds are an adaptive response, we predicted they would lay new eggs to reach the optimal clutch size for the environment. Consistent with the environmental constraint hypothesis, our results suggest that urban females do not lay new eggs to the same extent as forest birds following egg removal. Forest birds laid approximately two new eggs after our experimental manipulation, while urban birds laid approximately 0.36 new eggs following egg removal. Our manipulation resulted in a brood reduction in the urban experimental nests, yet there was no difference in the number of fledged offspring between urban control and experimental nests. This suggests that females might be misjudging urban habitat quality and produce a clutch with more eggs than nestlings they can rear. Overall, our results suggest that environmental constraints could limit the number of eggs that urban females lay, generating urban versus non-urban differences in this trait.

Mapping roof coverings of asbestos-cement, the first step to control the technical condition/threat and establish priorities for replacement in developing countries

This paper presents a systematic and data-driven approach to prioritize interventions in urban areas with asbestos cement (AC) roofs, addressing the urgent need to mitigate asbestos-related risks. The objective is to propose a comprehensive methodology that considers multiple criteria at the neighborhood level, allowing for a nuanced assessment of intervention priorities.The methodology involves the normalization of various parameters, including population density, facility density, and the area covered by asbestos-cement roofs. In addition, an innovative aspect is introduced by incorporating weathering status identification data, represented as an index, validated in previous research, further enriching the evaluation process. The integration of these diverse factors allows for a holistic understanding of the risk landscape associated with AC roofs in urban settings.The cornerstone of the proposed approach is the development of a Priority Intervention Index (PII) at the neighborhood level. This index serves to standardize the assessment of intervention priorities, enabling a fair and transparent comparison across different regions. To enhance practical application, the PII is discretized into three categories, low, mid and high intervention priority. The results obtained are robust, replicable in other scenarios, and practical for decision-makers. The new methodology provides a structured and quantifiable approach to identify and prioritize areas for asbestos-related interventions based on well-defined criteria at the neighborhood level. The resulting prioritization strategy offers urban planners and local officials a clear and evidence-based tool to allocate resources efficiently and effectively manage the inherent risks associated with AC roofs in urban environments. The paper will describe how the prioritization can be applied “at the neighborhood level” by urban planners and local officials.

A hybrid gradient climbing algorithm for a swarm robot-based gas leak detector

Methane emissions from leak sources can have a negative climate impact, in addition to contributing to the risk of explosions in urban environments. These risks can be minimized by developing systems that provide for an accurate and timely detection and localization of a gas leakage point. This research used a swarm of robots to detect and locate a leakage point. The localization algorithm derives from further optimization of the gradient climbing algorithm using fireflies acting as opportunistic agents. Firefly agents are characterized by their bioluminescent communication which guides them to dynamically adjust their positions and intensities based on the quality of the gradient information available to them. The proposed research focuses on enhancing gas leak detection through the development of a hybrid gradient climbing algorithm. This algorithm integrates gradient climbing techniques with swarm intelligence, utilizing the strengths of both approaches. This simulation resulted in the hybrid algorithm leading to a reduced convergence time and path lengths when compared to the swarm without opportunistic agents. The suggested approach can be important especially in gas distribution systems or in areas where human intervention is considered to be unsafe.

Air Quality Awareness—Empirical Evidence from a Comparative Perspective between Two European Cities

This research, conducted as part of the European project PA-MAP (Participatory Approach to Monitoring Air Quality in Urban Environments), investigates citizens’ perceptions of air pollution in two European cities: Turin (Italy) and Dublin (Ireland). We systematically collected social data through semi-structured surveys distributed via online platforms. Our aim was to gather comprehensive information on the perception of pollution sources and the effectiveness of control systems. We examined citizens’ habits and their suggestions to improve air quality. The results revealed the following: (1) In Turin, the perception of air pollution significantly influences individuals’ self-assessments of health. Consequently, increased vigilance is required to mitigate environmental health risks and to disseminate information on air quality and effective practices for reducing air pollution. (2) The use of sustainable transportation in Turin is hindered due to the inadequate maintenance of cycling infrastructure. Citizens prefer to use cars or trains, which facilitate rapid travel. (3) In Dublin, greater sensitivity to the problem of air pollution has emerged, and the use of sustainable means of transport is facilitated via ad hoc infrastructure. These results provide empirical evidence to policymakers to support the promotion of concrete local actions through which to improve air quality.

Reproducing vortex-induced vibrations of rooftop twin-mast by multi-scale coupled simulation of urban wind fields

The Shenzhen Electronics Group (SEG) Plaza in Shenzhen, China, experienced visible vibrations attributed to significant vortex-induced vibrations of the large spacing twin-mast atop the building. The vibration event occurred under complex urban wind environments. In order to reproduce wind fields around SEG Plaza, a multiscale simulation was carried out by integrating the Weather Research and Forecasting (WRF) and Computational Fluid Dynamics (CFD) models. The aerodynamic characteristics and wake modes of twin-mast are investigated by a downscale CFD simulation in detail. Finally, vortex-induced vibration (VIV) of twin-mast was analyzed considering fluid-structure interaction. This work reveals that wind conditions during the SEG Plaza vibration incident were appropriate for the VIV occurrence of twin-mast, and the results of VIV simulation agree well with the field monitoring results by computer vision identification. This study effectively reproduces the full pictures of VIVs of the rooftop twin-mast during this rare event, offering a critical view for the analysis of this event.

Multifactorial influences on land surface temperature within local climate zones of typical global cities

The land surface temperature (LST) of most cities is rising steadily due to both human activities and global climate change, affecting the thermal comfort of cities and threatening the physical health of their inhabitants. Investigating surface temperature features and their affecting elements is therefore essential to improve the thermal environment of cities. Due to differences in surface temperature characteristics in different climatic belts. However, there is still a lack of research on how the local climate zones (LCZs) of different climate belts are affected by natural and social causes. According to the Koppen-Geiger climate classification system, we selected three representative cities from each of the four macroclimatic belts and adopted multiple linear stepwise regression and boosted regression trees (BRT) to systematically explore the linear relationships, relative impacts and marginal effects of normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), population density, and road nuclear density within urban LCZs on LST. The results indicate that (1) LCZs with significant differences in LST among the four global climatic belts account for more than 95 % (P < 0.05), demonstrating that LCZs can effectively differentiate LST based on different land surface cover types. This study can delve into the relationships between the four influencing factors and LST based on LCZs. (2) Primary control factors regulating LST differ in different climate belts. The relative effects of NDVI and MNDWI on LST are greater in the arid and temperate belts, with each 0.1 increase in NDVI and MNDWI producing a cooling effect of more than 0.40 °C and 0.92 °C, respectively. Ventilation corridors created by increased road core density produced a cooling effect of 1 °C or more in the cold belt, with the most pronounced cooling effect. (3) When natural factors are higher and social factors are lower, LST cannot be minimized. It was found that LST was minimized when NDVI, MNDWI, population density and road nuclear density were controlled above 0.4, 0.1–0.2, 10,000–50,000 and 1500–2000 respectively. Our study will provide targeted measures for LCZ-based mitigation of urban thermal environments in various macroclimatic belts.

The effect of urban form parameters on annual and diurnal cycles of land surface temperature with 30-meters hourly resolution

Understanding dynamic changes in urban land surface temperature (U-LST) is vital for improving urban thermal environment. Previous studies noted a close association between two/three-dimensional (2D/3D) urban form parameters (UFPs) and U-LST, but divergent conclusions emerged due to study scale and data spatiotemporal resolution. This study investigated the effect of 2D/3D UFPs on multi-temporal (hourly, diurnal, and annual) U-LSTs with 30-m spatial resolution, which is the first exploration of such dynamics at this fine scale. In addition, the influence of local climate zones (LCZs) was considered. Results showed that 2D/3D vegetation UFPs have the greatest summer cooling effect at 21:00, while 3D building UFP sky view factor (SVF) impacts minimum nighttime temperature more (5:00). The independently total explained variances (R2) of 2D UFPs (0.2-0.7) on seasonal average U-LSTs were higher than that of 3D UFPs (0.1-0.4) in most LCZs. LCZ 3 and LCZ 2 had the highest seasonal and daytime U-LST, respectively. Interestingly, SVF replaced normalized difference vegetation index as the dominant factor in LCZs 4-6 and LCZs A-C after summer. Findings of this study are useful for guiding urban planning, formulating urban heat island mitigation policies, and promoting sustainable development goals (SDGs 11, 13, 15) of cities and communities.

Widespread introduced species dominate the urban tree assemblage on the endemic-rich tropical island of São Tomé.

The Afrotropics are experiencing some of the fastest urbanisation rates on the planet but the impact of city growth on their rich and unique biodiversity remains understudied, especially compared to natural baselines. Little is also known about how introduced species influence β-diversity in these contexts, and how patterns coincide with native ranges of species. Here we investigated how tree assemblages of the endemic-rich Afrotropical island of São Tomé differed between urban, rural and natural zones. These were primarily characterised by urban greenspaces, shade plantations, and old-growth forests, respectively. Based on 81 transects, we assessed biodiversity metrics of endemic, native and introduced species. Tree abundance and species richness were highest in the natural zone, where the composition was most different from the urban zone. The tree community of the rural zone was the most uneven and had the least variation among transects, representing the lowest β-diversity. The urban zone was dominated by introduced species (57.7%), while the natural zone hosted almost exclusively native species (93.3%), including many endemics (26.1%). The biogeographic realms that species originated from were particularly diverse in the urban zone, with few species from the Afrotropics. In contrast to native and endemic trees, introduced trees were clearly associated with urban and rural expansion, as they were much more abundant and species-rich in these zones than in the natural zone, facilitating biotic homogenisation. These findings highlight how urban and rural environments are affecting the native tree flora of São Tomé, and the need for conservation measures geared towards globally threatened and endemic tree species. Importantly, these require the protection of natural forests, despite the rising land demands for settlements and agriculture. Ultimately, such action to conserve endemic trees will contribute to global efforts to prevent further biodiversity declines.

Navigating Planning Crises: Karachi’s Response to COVID-19

The COVID-19 pandemic has invariably highlighted the critical role of spatial planning in managing public health emergencies, particularly with the need for social distancing. Historically, effective spatial planning has been essential for governments in navigating crises such as floods and earthquakes, employing strategies like designating containment zones, repurposing infrastructure, and constructing temporary facilities. The research focuses on Karachi’s response to the pandemic. It examines how the city’s spatial planning strategies were adapted to meet the challenges of its dense urban environment and socio-economic diversity. As Pakistan’s largest city and economic hub, Karachi confronted significant challenges in balancing public health measures with financial activities. The study investigates how state institutions implemented awareness campaigns and emergency spatial planning, including the creation of quarantine centers, isolation zones, inoculation centers, and modifications to public spaces. Similar to other mega-cities, Karachi is vulnerable to natural disasters and emergencies, as it balances a range of issues that require effective spatial and urban planning. The city’s response to COVID-19 reflects broader trends in emergency planning, highlighting its approach to managing crises through strategic interventions. The study aims to uncover the complex relationship between spatial planning and crisis management by illustrating response timelines, temporary structures taxonomy, and infographics. It offers insights into how cities can better prepare for and respond to future crises. It emphasizes the importance of restructuring governance and adaptive and context-specific planning in urban health management.

Urban design and pollution using AI: Implications for urban development in China

The primary aim of this study is to explore the role of AI in urban design and its potential to reduce pollution in Chinese cities. The study investigates how AI-driven urban planning tools can be applied to create more sustainable, efficient, and functional urban environments. PM2.5 and PM10 show high concentrations with peaks between 2014 and 2017, indicating simple pollution actions. Post-2017, there is a noticeable decline in pollution levels, possibly due to improved regulations or the global impact of the COVID-19 pandemic. Specific years, like 2016, show extreme spikes, possibly due to industrial activities or natural events. The overall trend suggests improved air quality and moderate to strong positive correlations exist between PM2.5 and PM10, NO2, SO2, and CO, indicating shared bases or co-occurrence. However, there is no significant correlation between PM2.5 and O3, suggesting different bases and behaviors. Bi-directional causality is observed between PM2.5 and PM10, PM2.5 and O3, PM2.5 and NO2, PM2.5 and SO2, and PM2.5 and CO. This mutual cause suggests interrelated impressive processes and shared bases. The results of the causality analysis suggest the existence of complex interactions, where high levels of pollution can predict changes in others. AI in urban design play vital role for identifying the most effective strategies for reducing pollution and helping to build more sustainable and functional urban environments in China.

Measuring shaded bike lanes for heat stress mitigation with deep learning: A case study in Amsterdam, Netherlands

As global warming intensifies and heatwaves become more frequent, thermal comfort has emerged as a crucial factor in creating habitable and equitable urban environments. Cities worldwide are promoting sustainable transportation and reducing vehicle usage by enhancing cycling infrastructures. However, the thermal comfort of cyclists has often been neglected, with most research focusing on pedestrians. This study introduces a framework for evaluating shade levels along the bike lanes by incorporating a ‘Shade Index’ that quantifies street shadiness. Utilizing deep learning techniques, the index analyzes street view images from various bike lane locations to evaluate the contribution of trees and buildings in providing shade. The application of this framework is a shade rating map of the bike lanes in Amsterdam, providing cyclists with alternatives that prioritize coolness over the shortest routes typically suggested by map applications, particularly useful during warmer weather. The study identifies significant variability in shade provision, with central urban areas typically more shaded than outlying regions. The workflow can be adapted to other cities to enhance shade provision for cycling infrastructures.

Multi-scale correlation analysis between geometric parameters and solar radiation in high density urban environment——Case study in Nanjing

This study explores the impact of multi-scale urban morphology on solar radiation in high-density environments, focusing on Nanjing as a case study. Using Grasshopper, the research decomposed urban models into four scales—district, block group, block, and plot—and analyzed eight morphological indicators, including site coverage (SC), floor area ratio (FAR), and block surface ratio (BSR). Solar radiation was simulated for the entire year, the summer solstice, and the winter solstice. Key findings reveal that different scales exhibit varying influences on solar radiation. At the district scale, BSR shows the highest correlation with annual solar radiation (R2 = 0.96), while at the neighborhood cluster scale, the minimum distance between buildings (DBmin) is most correlated (R2 = 0.7). At the block scale, BSR significantly correlates with summer solar radiation (R2 = 0.55). At the plot scale, no single indicator strongly correlates with solar radiation, but a combination of indicators is more relevant. These findings enable rapid solar performance assessments in urban design, promoting efficient solar energy use.

LiDAR-Based Semantic Place Recognition in Dynamic Urban Environments

LiDAR-Based Semantic Place Recognition in Dynamic Urban Environments

Configuration optimization and performance analysis of hybrid PV/wind systems in building groups.

Distributed hybrid renewable energy is a promising technology for addressing environmental and energy issues, and its performance and system configuration play an essential role in the application. However, the contribution of energy sharing among buildings in building groups to the matching performance has yet to be fully explored. This study established a joint MATLAB-TRNSYS optimization model for the hybrid PV/wind building system to investigate the matching performance of energy consumption in building groups and energy production in PV/wind systems. The optimal system configuration for three objectives, technical, economic, and environmental, was also studied using the multi-objective particle swarm algorithm. Several factors influencing system performance and configuration were considered: renewable energy hybrid, a hybrid of different buildings, intensity of energy use and energy resource, and economic parameters. The results reveal that a hybrid system outperforms a single system, particularly a PV system with a smaller roof area and less installed capacity. Additionally, wind turbines contribute more to system performance than PV panels. Mixing buildings with different heights and functions could also improve system performance by renewable energy sharing in most instances, except multi-story office buildings. Increasing wind resources improves system performance more than solar resources, and increasing energy use intensity negatively impacts system performance for residential buildings but is beneficial for office buildings. Higher grid prices and feed-in tariffs can bring more economic benefits. This study revealed the contribution of energy sharing among different buildings, which could support better application of distributed hybrid renewable energy systems in complex urban environments.

Seasonal and spatial variations of arsenic and its species in particulate matter in an urban environment of Brno, Czech Republic

The present paper deals with an analysis of total arsenic concentration using ICP-MS/MS and an analysis of concentration of several arsenic species, arsenite (AsIII), arsenate (AsV), monomethylarsonate (MMA), dimethylarsenite (DMA), and trimethylarsine oxide (TMAO), using HPLC-ICP-MS/MS in the PM10 fraction of airborne urban aerosol. The samples were collected during two campaigns, in the autumn of 2022 and in the winter of 2023, at three locations within the central European city of Brno, with the aim to evaluate the seasonal and spatial variations in the PM10 composition. The results confirmed only the seasonal variability in the content of the methylated arsenic species in PM10 influenced by biomethylation processes. To gain better understanding of the possible arsenic origin, a supplementary analysis of the total arsenic concentrations was performed in samples of different size fractions of particulate matter collected using ELPI + . Local emissions, including industrial activities and heating during the winter season, were suggested as the most likely predominant source contributing to the total As content in PM10.Graphical abstract

Automated Parking System using IR Sensor

As the population grows, people face more challenging parking issues that make city life more difficult. However, automated parking systems have become a ray of hope in modern society. Urban living is entering a transformational era with these innovative solutions. Automated parking systems reduce traffic and transform the urban environment by seamlessly incorporating advanced technology into parking management. Therefore, the proposed system utilizes Arduino boards and infrared (IR) sensors to develop an automated parking system (APS) that improves urban parking efficiency, reduces traffic jams, and minimizes environmental harm. The system integrates seamlessly with existing city infrastructure, providing a cost-effective and adaptable solution. The prototype of the APS is developed and tested to ensure its ease of installation and use in urban areas.

A Deep Analysis of Visual SLAM Methods for Highly Automated and Autonomous Vehicles in Complex Urban Environment

A Deep Analysis of Visual SLAM Methods for Highly Automated and Autonomous Vehicles in Complex Urban Environment