Urban Environment Research Articles

Facilitators or predators? The role of seed-feeding beetles in the germination of an ornamental and invasive plant in an urban environment

Leucaena leucocephala is a forage legume that provides cellulose, firewood, and shade in cities, although it presents a dangerous allelopathic and invasive potential. Its main natural enemy is the endophytic beetle Acanthoscelides macrophthalmus (Chrysomelidae: Bruchinae), whose larvae penetrate seeds and can consume the entire endosperm and embryos. However, there is much to explore on how the abundance of larvae inside the seed affects the plant germination, resulting in an antagonistic (predation) or mutualistic (facilitation) interaction. Our goal was to test how these beetles affect the germination of L. leucocephala seeds from an urban environment in Uberlândia, MG. We hypothesized that a single larva inside the seed facilitates germination since the exit holes left break the physical dormancy and enable water imbibition. On the other hand, two or more larvae inside the same seed result in predation, as the amount of endosperm is not enough for beetle satiation before they attack the seed embryo. We collected 100 pods, dissected them, and quantified and qualified the seeds. In the germination experiment, we used healthy seeds (control), scarified healthy seeds (physical treatment), and attacked seeds (one, two, or more larvae). We found that beetles infested 74% of the sampled seeds. Independently of the abundance of larvae inside the seed, attacked seeds did not germinate. The scarified seeds germinate faster and in greater quantity. Therefore, our hypothesis was rejected for the studied plant population. Acanthoscelides macrophthalmus acts only as a predator in an antagonistic interaction that decreases the germination rate of L. leucocephala.

A spatial analysis of mental health-related calls for service in Atlantic City, New Jersey

ABSTRACT Objective Police officers often find themselves responding to calls that do not involve criminal behavior, but calls related to individuals experiencing emotional distress or mental illness that requires police response. This research aims to identify high-risk places of mental health calls for service (MHCFS) in an urban setting and compare place features of these locations by police response (locations where a CIT-trained officer is dispatched versus a non-trained officer dispatched). Data/Methods Our research uses Atlantic City Police Department’s calls for service data from 1 January 2019 through 30 December 2022, including the incidents of mental incompetence with location components. After removing the calls that were not coded as mental health, a total of 2229 incidents involving perceived mental illness were geocoded and compared. We used RTMDx software to generate risk terrain models for MHCFS in Atlantic City, NJ. Findings The RTM analysis yielded no association between places of interest and MHCFS for calls involving non-CIT-trained officers. Yet, we identified five high-risk activity spaces of MHCFS where officers with CIT training were involved. Grocery stores (RRV = 2.873), places of leisure (RRV = 2.764), Health & Wellness Facilities (RRV = 2.624), Eateries & Restaurants (RRV = 2.547), and Convenience & Variety Stores (RRV = 2.280) are the risk factors identified by the RTM analysis. Given the characteristics of places identified by risk-terrain modeling, we reason that the characteristics of these places indicate that individuals experiencing perceived or actual mental health problems hang out in spaces with ease of access and in spaces where they can stay relatively longer with minimal attention. Conclusion Our research suggests that there is an association between spatial factors and MHCFS that are responded to by CIT-trained officers. City officials should consider deploying additional resources and mental health staff to these places to address the issue of mental health in urban settings. Future research should consider testing whether the places we identified in our analysis are also high-risk places for MHCFS in other urban environments.

Artificial Intelligence for Social Innovation: Beyond the Noise of Algorithms and Datafication

In an era of rapid technological advancement, decisions about the ownership and governance of emerging technologies like Artificial Intelligence will shape the future of both urban and rural environments in the Global North and South. This article explores how AI can move beyond the noise of algorithms by adopting a technological humanistic approach to enable Social Innovation, focusing on global inequalities and digital justice. Using a fieldwork Action Research methodology, based on the Smart Rural Communities project in Colombia and Mozambique, the study develops a framework for integrating AI with SI. Drawing on insights from the AI4SI International Summer School held in Donostia-San Sebastián in 2024, the article examines the role of decentralized Web3 technologies—such as Blockchain, Decentralized Autonomous Organizations, and Data Cooperatives—in enhancing data sovereignty and fostering inclusive and participatory governance. The results demonstrate how decentralization can empower marginalized communities in the Global South by promoting digital justice and addressing the imbalance of power in digital ecosystems. The conclusion emphasizes the potential for AI and decentralized technologies to bridge the digital divide, offering practical recommendations for scaling these innovations to support equitable, community-driven governance and address systemic inequalities across the Global North and South.

Optimized Right-Turn Pedestrian Collision Avoidance System Using Intersection LiDAR

The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles.

Sustainable Water Management Through Green Infrastructure

This research explores the implementation of sustainable water management practices through green infrastructure (GI) solutions. As urbanization intensifies, traditional water management systems face challenges such as flooding, water pollution, and reduced biodiversity. Green infrastructure, which integrates natural processes into urban planning, offers innovative strategies for stormwater management, water quality enhancement, and habitat restoration. This study examines various GI techniques, including rain gardens, permeable pavements, and green roofs, assessing their effectiveness in mitigating urban water challenges. Case studies from multiple cities demonstrate the benefits of GI in promoting resilience against climate change while enhancing community well-being. The findings underscore the potential of green infrastructure as a viable alternative to conventional water management methods, advocating for its broader adoption in urban environments. Key Words: Sustainable water management, Green infrastructure, Stormwater Management, Urbanization, Climate resilience, Water quality enhancement

A biosecurity perspective on urban trees in public and private spaces and trees available from nurseries

Urban trees are of high importance for healthy urban ecosystems. However, some trees are invasive plants that may ‘escape’ from urban areas into natural environments. Furthermore, invasive forest pests often arrive and establish in urban environments before they spread to forest ecosystems. Therefore, knowledge of tree species composition in urban areas is important for the biosecurity of cities and surrounding forests. Despite this, urban tree inventories often lack information on trees on private properties. To assess the potential role of urban areas as a source of invasive trees as well as invasive pests of trees, we compared the species composition of urban trees in public and private spaces in several Swiss cities and in tree nurseries (henceforth: ‘nurseries’) in Switzerland. We analysed the various data sets we compiled regarding invasive tree species, and examined how many trees are potential hosts for invasive quarantine pests of trees. We found no statistically significant difference in tree species composition in public and private urban areas, but the proportion of non-native trees was higher in private than in public spaces. Tree species available in nurseries and those present in cities were not significantly different, with larger urban tree inventories having a greater overlap with the species available in nurseries. Five of eight tree species considered invasive whose sale was banned since September 2024 were still on sale ten months before the ban came into force. All eight tree species were present in urban tree inventories. Importantly, between 91 % and 97 % of all urban trees in public and private spaces were suitable hosts for at least one quarantine forest pest. Given the pivotal role that urban trees play for the health of urban and peri-urban ecosystems, knowledge of tree species composition is essential from a biosecurity perspective. Our findings suggest that the combination of public urban tree inventories and trees available from nurseries can provide an estimate of the composition of private urban trees and can be useful for invasive species monitoring.

Optimizing greywater treatment in green walls: A comparative analysis of recycled substrate materials

The utilization of green walls for on-site greywater treatment in urban buildings has garnered significant attention in recent research, with substrates playing a vital role in treatment performance. The incorporation of recycled materials as substrates not only offers a cost-effective solution for greywater treatment, but also enhances sustainability by repurposing wastes. This study investigates the efficacy of three recycled substrates: alum sludge, garden compost, and a mixture of perlite-coconut fiber in three green wall systems for greywater treatment. Each substrate was allocated to a three-pot column. Comparative analysis revealed alum sludge (C3) and the coconut fiber-perlite mix (C1) achieving the highest chemical oxygen demand (COD) removal at 89.45 ± 2.92 % and 89.34 ± 2.47 %, respectively, with garden compost (C2) recording 67.98 ± 7.45 %. For total nitrogen (TN) removal, C2 achieved the lowest efficiency (49.27 ± 9.39 %), while C1 demonstrated 83.79 ± 10.45 %. Notably, C3 exhibited superior total phosphorus (TP) removal at 94.86 ± 1.87 %, compared to 52.07 ± 17.88 % for C1 and 24.90 ± 30.95 % for C2. Our findings demonstrate that substrate selection is crucial for optimizing both pollutant removal and plant health in green walls. The coconut fiber mix emerged as the most effective substrate, supporting robust plant growth and superior pollutant removal, particularly for nitrogen removal. In contrast, garden compost proved unsuitable, with minimal plant survival post-greywater introduction. Alum sludge showed significant promise, particularly for TP removal, reinforcing its effectiveness in green walls. These insights underscore the importance of substrate optimization in greywater treatment systems in urban environments.

A Modeling Framework of Atmospheric CO2 in the Mediterranean Marseille Coastal City Area, France

As atmospheric CO2 emissions and the trend of urbanization both increase, the ability to accurately assess the CO2 budget from urban environments becomes more important for effective CO2 mitigation efforts. This task can be difficult for complex areas such as the urban–coastal Mediterranean region near Marseille, France, which contains the second most populous city in France as well as a broad coastline and nearby mountainous terrain. In this study, we establish a CO2 modeling framework for this region for the first time using WRF-Chem and demonstrate its efficacy through comparisons against cavity-ringdown spectrometer measurements recorded at three sites: one 75 km north of the city in a forested area, one in the city center, and one at the urban/coastal border. A seasonal CO2 analysis compares Summertime 2016 and Wintertime 2017, to which Springtime 2017 is also added due to its noticeably larger vegetation uptake values compared to Summertime. We find that there is a large biogenic signal, even in and around Marseille itself, though this may be a consequence of having limited fine-scale information on vegetation parameterization in the region. We further find that simulations without the urban heat island module had total CO2 values 0.46 ppm closer to the measured enhancement value at the coastal Endoume site during the Summertime 2016 period than with the module turned on. This may indicate that the boundary layer on the coast is less sensitive to urban influences than it is to sea-breeze interactions, which is consistent with previous studies of the region. A back-trajectory analysis with the Lagrangian Particle Dispersion Model found 99.83% of emissions above 100 mol km−2 month−1 captured in Summer 2016 by the three measurement towers, providing evidence of the receptors’ ability to constrain the domain. Finally, a case study showcases the model’s ability to capture the rapid change in CO2 when transitioning between land-breeze and sea-breeze conditions as well as the recirculation of air from the industrial Fos region towards the Marseille metroplex. In total, the presented modeling framework should open the door to future CO2 investigations in the region, which can inform policymakers carrying out CO2 mitigation strategies.

Benidorm’s linguistic landscape: unveiling the multifaceted tapestry of urban expression

ABSTRACT The study of linguistic landscapes offers a captivating and nuanced avenue for understanding the intricate interplay among language, culture, and urban environments. The influence of mass tourism in Benidorm, a vibrant city within the Valencian region, has left an indelible mark on its linguistic landscape, offering a testament to the transformations brought about by the tourism industry. In addition, the coexistence of autochthonous minority languages within this city presents a complex and often precarious situation, where the tensions between cultural preservation and economic prosperity come to the fore. Through our own field methodology, and under the theoretical framework of the Critical Discourse Theory, we will analyse in depth the different aspects of the linguistic landscape of Benidorm, showing the challenges that the city faces to maintain coexistence between all the different languages that populate its streets and that make up a unique linguistic landscape.

Phylogenetics and species delimitation of the recluse spider, Loxosceles rufescens (Araneae: Sicariidae) populations invading Bangkok, Thailand

The Mediterranean recluse spider, Loxosceles rufescens, has been discovered for the first time inhabiting human dwellings in Bangkok, Thailand. Expeditions across 39 localities revealed five establishments with L. rufescens populations. The highest density was recorded in a storage house on Yaowarat Road, located in the heart of Bangkok's Chinatown, where 315 individuals were found, including adults, juveniles, and spiderlings. This medically significant spider's presence in such a densely populated urban area raises concerns about potential envenomation risks. Thirteen specimens of L. rufescens were extracted for DNA and sequenced for molecular phylogenetic analyses. COI and ITS2 markers were used to investigate relationships within L. rufescens and across available Loxosceles species sequences. Results indicate COI is superior for resolving species-level genetic clusters compared to ITS2. Surprisingly, L. rufescens individuals from the same house were found in significantly distant COI lineages, suggesting mtDNA may not be suitable for studying intra-specific phylogeography in this case. Species delimitation methods ABGD and ASAP demonstrated promising results for both COI and ITS2, while bPTP and GMYC tended to overestimate species numbers. ITS2 exhibited high sequence similarity in L. rufescens, suggesting potential utility as a barcoding marker for identification of this globally distributed species. Genetic distance analyses revealed a potential barcoding gap (K2P) of 8–9 % for COI and <2 % for ITS2 in Loxosceles. This study contributes valuable sequence data for the medically important genus Loxosceles and highlights the need for integrative approaches in understanding its evolution and spread. The findings have important implications for pest management strategies and public health in urban environments.

Three-Dimensional Reconstruction of Partially Coherent Scatterers Using Iterative Sub-Network Generation Method

Synthetic aperture radar tomography (TomoSAR) has gained significant attention for three-dimensional (3D) imaging in urban environments. A notable limitation of traditional TomoSAR approaches is their primary focus on persistent scatterers (PSs), disregarding targets with temporal decorrelated characteristics. Temporal variations in coherence, especially in urban areas due to the dense population of buildings and artificial structures, can lead to a reduction in detectable PSs and suboptimal 3D reconstruction performance. The concept of partially coherent scatterers (PCSs) has been proven effective by capturing the partial temporal coherence of targets across the entire time baseline. In this study, an novel approach based on an iterative sub-network generation method is introduced to leverage PCSs for enhanced 3D reconstruction in dynamic environments. We propose a coherence constraint iterative variance analysis approach to determine the optimal temporal baseline range that accurately reflects the interferometric coherence of PCSs. Utilizing the selected PCSs, a 3D imaging technique that incorporates the iterative generation of sub-networks into the SAR tomography process is developed. By employing the PS reference network as a foundation, we accurately invert PCSs through the iterative generation of local star-shaped networks, ensuring a comprehensive coverage of PCSs in study areas. The effectiveness of this method for the height estimation of PCSs is validated using the TerraSAR-X dataset. Compared with traditional PS-based TomoSAR, the proposed approach demonstrates that PCS-based elevation results complement those from PSs, significantly improving 3D reconstruction in evolving urban settings.

Estimating daily road traffic pollution in Hong Kong using CFD modelling: Validation and application

Road traffic is a major source of urban air pollution, exposing residents to harmful levels of pollutants, particularly in densely populated urban centers. In this study, Computational Fluid Dynamics (CFD) modelling is applied to investigate the impact of road traffic emissions on air pollution within a dense urban environment. The model, applied in Mong Kok, Hong Kong, incorporates high-resolution traffic activity, meteorological, and background concentration data. It demonstrates strong agreement with roadside measurements, deviating by 18.8 % for NOx, 15.5 % for CO, and 0.7 % for PM10, with potential underestimation linked to unmodeled NOx-O3 photochemistry during midday hours (12:00–17:00). Results reveal that local road traffic significantly increases background NOx levels (41 % average increase), highlighting the importance of dispersion modelling for accurate pollution assessments. Traffic-induced increases in CO and PM10 background levels are less pronounced. Two applications demonstrate the model's utility. Firstly, analysis of daily pollution distribution reveals that approximately 10 % of the studied area experiences moderate to very high pollution levels during peak hours. Secondly, an investigation into pollution sources within a leisure park identifies heavy-duty and light-duty vehicles as the primary NOx polluters. This modelling approach provides a valuable decision-making tool for policymakers and local authorities. It enables the assessment of urban design changes, strategic placement of sensitive structures, and future traffic fleet composition choices to mitigate pollution exposure. As air quality regulations tighten, this study offers a robust method for guiding impactful mitigation strategies.

Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment by Artificial Intelligence Driven Carbon Foot-print Forecasting

Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.

Noise hindering sound complexity in urban environments: Towards a novel evaluation criterion for urban sustainability

Urban environments are complex adaptive systems, wherein changes in one area can lead to unforeseen consequences in others. Environmental noise emerges as a multifaced issue influencing various receivers across different spatial and temporal scales. The perception of noise as a high-intensity or unwanted sound has defined the concept of quietness accordingly overlooking sustainability co-benefits. This study proposes an alternative evaluation criterion for urban planning and design, focusing on the sound complexity of public spaces that are vulnerable to environmental noise fluctuations. To determine the sensitivity of sound complexity to environmental noise, sound level measurements and sound recordings were conducted along the waterfront of a Mediterranean metropolis during the pandemic lockdowns (April 2021). Additionally, the bird dawn chorus phenomenon was studied. The analysis of collected data revealed an inverse relationship between complexity and noise, highlighting an additional impact on the urban sound environment. The promotion of sound complexity as a strategy for livable and sustainable cities can guide urban planners and designers in developing quiet and ecologically resilient urban environments.

Development of low-cost MEMS microphone array for sound localization in urban environments

Understanding noise pollution and mitigating its impact on the quality of life in urban environment is a major challenge from a societal and health point of view. In order to monitor and ultimately understand these noisy environments a process of long term acoustic measurements and analysis is required. These urban environments are noisy and obtaining a clear recording of a desired acoustic signal is difficult hence an effective approach is the use of beamforming theory coupled with a microphone array. In this paper a novel approach utilizing compact low-cost microphone arrays equipped with MEMS sensors for continuous sound localization in urban areas is proposed. This approach focuses on leveraging the affordability and scalability of MEMS sensors to deploy distributed acoustic sensor networks across diverse urban locations. The aim is to capture real-time acoustic data, enabling comprehensive monitoring of sound sources and their spatial distribution within the urban environment. This sensor unit can be upgraded with a camera, i.e. acoustic camera, which will provide information on the conditions of the environment where the measurements are performed. This low-cost solution facilitates long-term monitoring, providing valuable insights into the dynamic nature of urban soundscapes.

Eight years of thin noise reducing asphalt layer monitoring in an urban environment

The City of Antwerp initiated a pilot project on thin noise reducing asphalt layers in an urban environment with the aim to investigate the behavior of the asphalt layers from a noise perspective with respect to urban traffic without neglecting the mechanical durability. Five thin asphalt layers were selected and compared with a reference section. The pavements were constructed in October 2016 on two different test sites. The construction was followed up extensively. The acoustical quality of the thin asphalt layers is followed up in time. Close-ProXimity (CPX) measurements are performed according to ISO 11819-2 within certain time intervals after construction to follow up the evolution. Texture measurements according to ISO 13473 are linked to the noise measurement results. Visual inspections are performed at regular intervals. The results of eight years of monitoring are discussed. While some test sections are already partially replaced, others are still in acceptable condition. The major concern in the STOLA project is to learn about a good balance between the noise reducing capacity of the chosen thin asphalt layers, a limited degradation of the noise performance during its lifetime and a good mechanical performance delaying ravelling and texture changes as long as possible.

Conscious walk: integrating soundscape, biodiversity, and air quality metrics for holistic urban environmental assessment

The impact of environmental noise, poor biodiversity, and air pollution on urban populations poses a significant challenge to public health. Addressing these concerns requires a comprehensive approach that considers the perceptual experiences of citizens and objective environmental data. The Conscious Walks introduce a hybrid methodology designed to address the perceptive impact of citizens on their environment. Conscious Walks are citizen-science participative projects that combine the active involvement of citizens with scientific data collection. Implemented and tested in Barcelona, Conscious Walks provide a unique opportunity to assess urban soundscapes, and this contribution specifically evaluates the soundscapes of the Ciutadella Park in Barcelona for three consecutive years. The gathered data is carefully cross-referenced with information on air quality and biodiversity obtained during the walks. The novelty of this research lies in the development of integrated indicators that comprehensively capture the interplay between biodiversity, noise pollution, and air pollution. These indicators provide an understanding of urban environmental quality, offering valuable insights for sustainable urban planning and policy-making. The results presented in this contribution underscore the significance of adopting holistic methodologies to address the complex challenges posed by urban environments, emphasizing the need for integrated indicators in assessing the multifaceted aspects of urban sustainability.

Railway curve squeal detection and tonal analysis

Railway curve squeal is a loud tonal noise generated by instabilities in the wheel-rail contact of railway vehicles while negotiating tight curves. The emitted noise is perceived as highly disturbing and contributes to noise problems in urban environments. Traditional monitoring of squeal occurrence involves analyzing train pass-by sound measurements. Such monitoring system is sensitive to high background noise which can pose a problem in urban environments with limited possibilities for suitable microphone placement. To overcome these issues, an alternative approach to detect curve squeal from rail vibration measurements is investigated. This comprises a simple and practical monitoring solution virtually unaffected by sound disturbances from the surroundings. Furthermore, this method can identify whether squeal noise occurs on the low or high rail. Field measurements involve a two-part analysis: 1) the development and validation of a method for detecting squealing events from rail vibrations, and 2) identifying tonal components from the measured signals. Results from the first part suggest that curve squeal can be detected from rail vibrations and the specific rail where squeal is generated can be identified. In the second part, specific frequency regions where squeal tonal components tend to emerge are recognized.

Personal noise exposure patterns and health implications in urban work environments

Noise-induced hearing loss remains a preventable occupational health concern, contingent on timely detection and mitigation of excessive workplace noise. However, the lack of effective identification of at-risk workers often results in the neglect of safety measures that could prevent hearing loss. To address this, personal noise exposure measurements, using noise dosimeters, were conducted on 50 individuals across diverse urban work environments, including work from home, offices, outdoor settings, and mixed-use spaces. The results demonstrated significant variations in noise exposure levels among different environments. Outdoor and mixed-use environments exhibited the highest average dose percentages, while home-based workers had the lowest exposure. A strong correlation between noise levels above 80 dB and increased dose percentages emphasized the need to reduce high-noise exposure times. Additionally, the study integrated quantitative dosimeter measurements with qualitative data from a questionnaire survey to provide a holistic understanding of the participants' perception on noise pollution. Traffic emerged as the primary source of noise pollution, with a substantial number of respondents experiencing stress and ear infections attributed to noise exposure. The research underscores the importance of personalized noise dosimetry in assessing health implications, emphasizing the need for awareness and preventative measures to mitigate health risks in urban work environments.

Design and analysis of a periodic metamaterial barrier for mitigating urban-induced vibrations

Urban areas are increasingly subjected to vibrations from various sources within the urban environment. Therefore, there is an urgent necessity to find measures that can minimize their negative effects. This pa-per investigates the concept of metamaterial and analyzes the impact of its inclusion on the range and span of band-gaps at low frequencies (up to 80 Hz), which characterize the vibrational waves in urban areas. This is achieved by developing a finite element model of the relative unit-cell and conducting a parametric investigation on the geometrical characteristics and material properties of the inclusion. Furthermore, the depth of the embedded inclusion is also examined to understand the transition of metamaterials from seismic (with the inclusion fully embedded) to resonant ones, where the inclusion is on the surface. Finally insights are given in order to help on the selection of the correct parameters to minimize a certain range of frequencies.