Urban Planning Management Research Articles

A new model for residential location choice using residential trajectory data

Traditional residential location choice (RLC) models are based on the characteristics of location and demographics, revealing important patterns of RLC, but no RLC models have yet incorporated individual preferences. This study fills this gap by integrating the pattern of home-based travel into the RLC model. Firstly, by analysing residential trajectory data collected from Beijing and Shenzhen, we find that both residents’ commuting time, that is, time spent commuting to work, and home-based non-commuting (HBNC) time, that is, time spent on the consumption of amenities when departing from homes, follow an extreme value distribution (EVT). This indicates that, based on time budget and financial constraints, residents strive to minimise commuting time and maximise HBNC time. Subsequently, by integrating these findings into individual-level RLC analysis, we obtain an RLC model that aligns with the gravity model. Throughout the model training process, we demonstrate that the RLC model exhibits strong robustness by incorporating control variables, changing the spatial scale of the observation unit, testing for endogeneity, and considering historical RLC. Moreover, the model performs well in applications including assessing dynamic changes in RLC behaviours and making predictions based on previous travel behaviours. The RLC model in this study advances our understanding of human habitat selection behaviour and can be utilised by policymakers to develop and implement effective urban planning and epidemic management policies.

How sustainable is OpenStreetMap? Tracking individual trajectories of editing behavior

ABSTRACT OpenStreetMap (OSM) has been playing increasingly important roles in location-based services, urban planning, tourism, and disaster management. In this paper, we examine a fundamental issue: can OSM reliably provide its services in the long run? To address this sustainable issue, we propose (1) inner cycle of career stages (i.e. activity levels) for monitoring the sustainable status of OSM and (2) critical mass theory for identifying driving factors that make OSM sustain. These new tools are used to track individual trajectories of editing behavior in 2005–2021. Our results suggest that OSM has been self-sustaining up to now and does not show a sign of decline yet. This is supported by the ever-decreasing turnover time and stabilized turnover rate. Together with the finding that most contributors transitioned to a higher career stage within a month, we identify an accelerated socialization process within OSM. We find further that it is the critical mass of people rather than content that makes OSM sustain. This contrasts with the common wisdom: recruiting more people with small contributions is far more important to the long-term growth of OSM than relying on a group of core members with large contributions. Implications and recommendations are critically discussed.

How Information and Communications Technology Affects the Micro-Location Choices of Stores on On-Demand Food Delivery Platforms: Evidence from Xinjiekou’s Central Business District in Nanjing

The digitization of consumption, led by information and communications technology (ICT), has reshaped the urban commercial spatial structure (UCSS) of restaurants and retailers. However, the impacts of ICT on UCSS and location selection remain unclear. In this study, based on on-demand food delivery data and real-time traffic data, we used two types of machine learning algorithms, random forest regression (RFR) and the density-based spatial clustering of applications with noise (DBSCAN), to study the spatial distribution patterns, driving factors, and new geographical location phenomena of ‘brick-and-click’ (B&C) stores in Xinjiekou’s central business district (CBD) in Nanjing, China. The results show that the UCSS in the CBD is being decentralized, but the degree of influence is related to the business type. Additionally, the scale of demand and the distance from core commercial nodes greatly affect the scales of B&C stores. Moreover, the agglomeration of high-sales B&C stores seems to indicate a micro-location advantage, characterized by the concentration of delivery riders, which is usually located in the commercial hinterland with dense traffic. This makes stores situated in traditionally advantageous locations more attractive for online sales. Thus, ICT enhances the Matthew effect in business competition. These findings deepen our understanding of urban digital planning management and business systems.

Civil engineering and disaster resilience: A review of innovations in building safe and sustainable communities

Civil engineering plays a pivotal role in ensuring the safety and sustainability of communities, especially in the face of increasing natural and man-made disasters. This paper provides a comprehensive overview of innovations in civil engineering that contribute to disaster resilience and the creation of safe, sustainable communities. As the frequency and intensity of disasters continue to rise globally, adopting advanced engineering solutions that mitigate risks and enhance infrastructure resilience is imperative. This review explores cutting-edge technologies and civil engineering methodologies that address disaster-prone regions' challenges. It encompasses a range of innovations, from resilient structural designs and materials to advanced geotechnical engineering practices. The integration of smart technologies, such as sensors and monitoring systems, is discussed as a means to provide early warning systems and real-time data for disaster response. The paper also delves into sustainable practices within civil engineering, emphasizing the importance of eco-friendly construction materials and energy-efficient designs. The role of urban planning and land-use management in disaster risk reduction is highlighted, emphasizing the need for resilient infrastructure that can withstand diverse environmental threats. Furthermore, the review explores case studies and success stories from around the world, showcasing instances where innovative civil engineering practices have effectively minimized the impact of disasters and contributed to the creation of resilient communities. Lessons learned from these experiences offer valuable insights for future projects and policy development. This paper underscores the critical intersection of civil engineering and disaster resilience, providing a foundation for further research and development in creating safer, sustainable communities. By embracing these innovations, the field of civil engineering can contribute significantly to building a resilient future in the face of an increasingly uncertain world.

Pathways to green urbanism: evaluating Jeddah’s environmental sustainability progress and prospects

Abstract This research provides a comprehensive analysis of environmental sustainability in Jeddah, focusing on the unique challenges and opportunities it faces as a rapidly urbanizing city in an arid region. The study evaluates Jeddah’s performance across multiple environmental areas, including energy and carbon dioxide emissions, land and building use, transportation, waste management, water use, sanitation and air quality. A detailed SWOT analysis identifies the city’s strengths, weaknesses, opportunities and threats in achieving sustainable urban development. The results reveal high per capita carbon dioxide emissions, energy consumption and waste generation, along with challenges in managing water resources and maintaining air quality. The study highlights Jeddah’s efforts in renewable energy policies, urban mass transportation and waste management strategies. The conclusion provides a set of comprehensive recommendations for policy improvement, focusing on the need for sustainable urban planning, energy conservation, improved waste and water management and public engagement. This research contributes significantly to the understanding of urban sustainability in arid environments, providing a valuable framework for policymakers and urban planners.

Machine learning-based techniques for land subsidence simulation in an urban area

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

ДИЗАЙН-КОД КАК СРЕДСТВО РАЗВИТИЯ КОМФОРТНОЙ ГОРОДСКОЙ СРЕДЫ И СПОСОБ ФОРМИРОВАНИЯ ГОРОДСКОЙ АЙДЕНТИКИ

The article is devoted to the study of design code elements as a means of creating a comfortable urban environment in conditions of rapid transformation of spheres of public life, and as a consequence of new relationships between the population and the urban environment. The design code of urban spaces is considered as the main structural element of identity and a change in the vector of urban development in the context of a decrease in the social and investment attractiveness of small and medium-sized cities, the loss of identification and originality of industrial cities and single-industry towns. The necessity of creating a unified stylistic urban environment is substantiated, due to the multiculturalism of architecture and marketing, a multinational population, which leaves its mark on the urban environment and its perception by residents. An analysis of foreign and domestic experience in the use of design code, its impact on architecture, psychological and physical health of a person, economic stimulation and social aspects of public life is carried out, and the problems of modern cities and ways to solve them using design coding are described. Based on the results of the analysis of best practices, the most current trends in the formation of a comfortable urban environment were identified using various design code elements that are most effective and attractive. Recommendations are proposed for the formation of the identity of Russian cities for participants in urban planning and management of architecture and urban planning, as well as a new look at integrated sustainable development

Monitoring bird migration using nocturnal flight calls on the East Asian–Australasian Flyway: a case from Beijing

Summary Migratory birds are experiencing widespread population declines, underscoring the urgency of effective conservation actions. Long-term monitoring of migratory birds, especially during migration, is crucial for such actions yet remains technically challenging. Bioacoustic monitoring of nocturnal flight calls (NFCs) constitutes a promising technique to monitor migratory birds during migration. Such monitoring has increased in North America and Europe, but its application on the East Asian–Australasian Flyway (EAAF) remains limited. Here, we present findings from an NFC monitoring project conducted at a recording station in central Beijing, China over four migration seasons. From around 3,350 hours of recording effort, we manually extracted and identified 84,135 NFCs, involving at least 111 species or species groups that are associated with a wide range of habitat types. We also found that NFCs provided additional information on species’ migration phenology in comparison with citizen science observation data. To our knowledge, this study is the first formal investigation of bird migration using NFC monitoring on the EAAF, serving as a proof-of-concept case for wider, long-term monitoring efforts in this traditionally understudied region. Our findings also highlight the significance of incorporating migratory bird conservation into urban planning and land management practices.

Implementation of the concept for forming sustainable development of agricultural land of the region

The problems of sustainable development of agricultural regions cover a number of unresolved issues in the field of land legislation; urban planning and environmental management, determining their purpose in planning the development of territories, preserving the natural re-source potential of agricultural lands. Forming sustainable agricultural land use and prospects for the development of the agro-industrial complex as the basis for ensuring food security of the Russian Federation. In the article, based on the materials of the Tyumen region, scientific approaches, methods and methods of implementing the concept for the formation of integrated sustainable development of the region, agricultural lands and agricultural land use are proposed. The article provides a geoinformation analysis of the distribution of the total area of unused arable land by municipal districts of the Tyumen region using cartodiagrams. The cartographic material shows the reasons for the non-use of arable land in the Tyumen region.

Natural dynamics and watershed approach incorporation in urban water management: A scoping review.

Several cities are facing water emergencies related to urbanization impact and amplified by climate change. Most of the cities have responded to these crises through short-term measures. However, some cities have incorporated a watershed approach to water management in seeking more sustainable solutions. Although the importance of a watershed approach in land management is generally acknowledged, studies on this topic have typically focused on theoretical models, water management in rural areas or single case-studies of cities or countries. In this research, a scoping review of the literature was performed, based on the PRISMA 2020 statement, in three databases: Web of Science, Google Scholar and SciELO. Forty-one studies were identified analyzing 17 city cases implementing urban actions from a watershed approach in water management. These cities were from the Global North and Asian rising world powers. The lack of results of cities from the Global South, based on the research undertaken, was the main limitation and bias identified. Most of the Global South results identified in this research were theoretical models, scenarios and cases of rural areas instead of urban contexts. The results obtained indicate that the main motivations for cities to implement a watershed approach were water scarcity, floods and contamination of water bodies. The implemented actions focused on the shift from gray to green and blue infrastructure and on conservation measures. Lastly, the challenges to introduce those actions were mainly the lack of economic investment, insufficient experience, stakeholder opposition, and regulatory obstacles. Urban water management could be seen as an opportunity to change the way we relate to urban territory. Incorporating a watershed approach into urban planning and water management could promote more sustainable cities.

Application of Autoformer to Short-Term Traffic Flow Prediction

This paper investigates the application of Autoformer model in the field of short-term traffic flow prediction. Traffic flow prediction is crucial for urban planning and traffic management, and is important for relieving traffic congestion and improving traffic efficiency. Autoformer, as a Transformer model based on the self-attention mechanism, provides a powerful modeling capability for the task of traffic flow prediction through its ability to automatically learn feature relationships. The paper first introduces the background and importance of traffic flow prediction and outlines current prediction methods and their limitations. Subsequently, the structure and working principle of Autoformer are described in detail, elucidating the advantages over traditional models. Autoformer’s self-attention mechanism can effectively capture the long-range dependencies in the input sequences and better adapt to the dynamic changes of traffic flow. To verify the performance of Autoformer in short-time traffic flow prediction, experiments are conducted using real traffic flow datasets. The results show that Autoformer significantly improves the prediction accuracy and generalization ability compared to traditional time series models. In addition, model interpretive analysis was conducted, revealing the advantages of Autoformer for automatic extraction of traffic flow features and correlation modeling.

Towards Efficient Hyperspectral Object Detection and Classification using Thermal Optimization Algorithm with Deep Learning

Object detection in remote sensing images (RSI) is a main procedure where the purpose is to automatically recognize and categorize certain objects or features from large-scale, remotely developed images like aerial imagery or satellite. This task role a vital play in extracting appreciated data from massive geographical regions, contributing to various applications under several domains namely environmental monitoring, urban planning, agriculture, and disaster management. Recent developments in deep learning (DL) technologies have significantly enhanced the accuracy and efficacy of object detection systems for RS, enabling more precise and automated analysis of various landscapes and facilitating informed decision-making. DL approaches namely convolutional neural networks (CNNs) are exposed to remarkable abilities in learning intricate patterns and features from difficult spatial data, resulting in enhanced accuracy and effectiveness. In this article, we present a Towards Efficient Hyperspectral Object Detection and Classification using Thermal Optimization Algorithm with Deep Learning (HODC-TOADL) system. The objective of HODC-TOADL algorithm is to identify and categorize distinct types of objects that exist in the RSI. In the HODC-TOADL method, an improved Dense Net model is applied to learn the distinct features of the input RSI. Besides, the TOA has been deployed to boost the hyper parameter choice of the Dense Net method. Furthermore, the classification of objects can be carried out by employing of adaptive neurofuzzy inference system (ANFIS). The experimental evaluation of the HODC-TOADL algorithm can be studied on benchmark databases. The experimental values stated that the HODC-TOADL algorithm reaches effective classification performance compared to recent DL models.

Aproximación conceptual del término “Diseasogenic”, desde una perspectiva urbanística

The rapid urban growth in the Anthropocene poses social challenges for well-being. The term "diseasogenic," originating from the Global North, highlights the relationship between cities and health. The objectives of this study were i) to propose the concept of "pathogenic city" as equivalent to "diseasogenic" based on academic publications from the Global South and ii) to provide a unified operational definition within the framework of urban planning. In this systematic review, using Google Scholar, the content of 7 English-language scientific publications containing "diseasogenic" and 9 Spanish-language publications containing "entornos patógenos" and "ciudad patógena" was analyzed from 27 and 16 initially detected scientific publications, respectively. In English, 3 conference papers, 2 book chapters, 1 scientific article, and 1 review article were analyzed. In Spanish, 9 scientific articles were reviewed. This state-of-the-art review on the application, usage, and operational definition of the term 'diseasogenic' from an urban-architectural perspective is a qualitative, descriptive, and exploratory investigation. The results revealed the relationship between "diseasogenic" and "pathogenic city" and their interaction with urban planning and citizen health management. It is concluded that an emerging definition of urban pathologies fosters identification between the Global North and South, recognizing points of convergence in the impacts of cities on urban and individual health of their occupants regardless of the hemisphere they inhabit.

Urban flood hazard assessment in Awka metropolis using multi-criteria decision process

Urban flooding is an escalating global threat, and Awka Metropolis in Anambra state Nigeria is no exception. The city's rapid urbanization and inadequate infrastructure have left it increasingly vulnerable to flooding, exacerbated by the impacts of climate change. Anticipated flood effects include disruption of daily life, infrastructure damage, and threats to public safety, necessitating urgent attention. This study presents a comprehensive assessment of urban flood hazards in Awka Metropolis, utilizing an integrated approach combining the Analytical Hierarchy Process (AHP) and Flood Hazard Index (FHI) to gauge the city's flood vulnerability. The primary objectives were to identify key geophysical factors contributing to flooding, categorize these factors by their vulnerability, determine their reliability indices, delineate flood-prone zones, and generate a spatial flood extent map. The methodology encompassed data acquisition, pre-processing, Flood Modeling (evaluating slope, Elevation, Drainage Network, and flow accumulation), the application of Analytical Hierarchy Process, and the utilization of the Flood Hazard Index. The results classified Awka Metropolis into three flood risk zones: high (837.84 hectares, 14.70%), moderate (2182.65 hectares, 38.30%), and low (2678.45 hectares, 46.99%). These findings provide indispensable insights into the distribution of flood hazards across the city, enabling urban planners and authorities to strategically allocate resources, prioritize mitigation initiatives, and formulate targeted flood risk reduction strategies. Importantly, the incorporation of stakeholder input enhances the study's practical relevance and applicability in real-world urban planning and disaster management scenarios. In conclusion, this research offers a robust and multidimensional framework for assessing urban flood hazards in Awka Metropolis. The synergy of the Analytical Hierarchy Process and Flood Hazard Index equips urban planners, policymakers, and disaster management agencies to proactively tackle the mounting urban flooding challenges. The study's outcomes contribute to the development of sustainable flood risk reduction measures, ultimately enhancing the resilience of Awka Metropolis to future flood events.

Creative Strategies for the Development of Modern Cities: Features, Essence of the Concept and Approaches to their Understanding

Introduction. Creative strategies are strategies for activating the development of urban agglomeration, built on innovative projects, usually with an emphasis on the active development of local culture, the preservation of historical heritage and experience, the development of the uniqueness of the agglomeration, and may also include the ideas of a “smart city”. In agglomerations, creative strategies are implemented according to different scenarios, since urban spaces have different cultural and infrastructural contexts. The purpose of this article is to analyze approaches to understanding creative strategies in urban planning and urban space management; to consider the experience of a number of creative cities of the Russian Federation, including the city of Krasnodar, which will allow developing a number of measures within the framework of creative strategy as the most universal and promising approach to urban creativity. Materials and Methods. The presented research is based on the results of the works of authors studying various aspects and approaches to the development and use of creative strategies within the urban environment, engaged in the search for promising universal strategies that contribute to the development of the potential of the cultural, social, economic sphere of the creative city. Diagnostics of the state of the urban cultural environment, assessment of the creative activity of the largest Russian cities, identification of threats and justification of priorities for the development of the urban creative environment are based on the use of retrospective analysis, comparison, synthesis, analogy, generalization. Results. The features of modern creative strategies based on the concept of new urbanism are identified and analyzed. A comparative characteristic of the indices of creativity of urban spaces is given, the problems of their use are analyzed. The experience of creating and implementing projects of creative urban space of Russian cities is analyzed. A comprehensive analysis of internal and external factors, significant challenges and significant threats to the active development of the creative sphere of the city of Krasnodar has been carried out, which makes it possible to develop a number of measures within the framework of the creative strategy as the most promising approach to urban creativity. Discussion and Conclusion. The study allowed us to identify approaches to understanding creative strategies based on innovative ideas, a creative approach to solving problems with an emphasis on the development of art, local culture, preservation of historical heritage and the uniqueness of urban spaces. The conclusions and suggestions of this study can be used as a universal matrix for the development of activities within the framework of a creative strategy for the development of modern urban space.

Extending 3D geometric file formats for geospatial applications

This study addresses the representation and exchange of geospatial geometric 3D models, which is a common requirement in various applications like outdoor mixed reality, urban planning, and disaster risk management. Over the years, multiple file formats have been developed to cater to diverse needs, offering a wide range of supported features and target areas of application. However, classic exchange formats like the JavaScript Object Notation and the Extensible Markup Language have been predominantly favored as a basis for exchanging geospatial information, leaving out common geometric information exchange formats such as Wavefront’s OBJ, Stanford’s PLY, and OFF. To bridge this gap, our research proposes three novel extensions for the mentioned geometric file formats, with a primary focus on minimizing storage requirements while effectively representing geospatial data and also allowing to store semantic meta-information. The extensions, named GeoOBJ, GeoOFF, and GeoPLY, offer significant reductions in storage needs, ranging from 14 to 823% less compared to standard file formats, while retaining support for an adequate number of semantic features. Through extensive evaluations, we demonstrate the suitability of these proposed extensions for geospatial information representation, showcasing their efficacy in delivering low storage overheads and seamless incorporation of critical semantic features. The findings underscore the potential of GeoOBJ, GeoOFF, and GeoPLY as viable solutions for efficient geospatial data representation, empowering various applications to operate optimally with minimal storage constraints.

Sponet: solve spatial optimization problem using deep reinforcement learning for urban spatial decision analysis

ABSTRACT Urban spatial decision analysis is a critical component of spatial optimization and has profound implications in various fields, such as urban planning, logistics distribution, and emergency management. Existing studies on urban facility location problems are based on heuristic methods. However, few studies have used deep learning to solve this problem. In this study, we introduce a unified framework, SpoNet. It combines the characteristics of location problems with a deep learning model SpoNet can solve spatial optimization problems: p-Median, p-Center, and maximum covering location problem (MCLP). It involves modeling each problem as a Markov Decision Process and using deep reinforcement learning to train the model. To improve the training efficiency and performance, we integrated knowledge SpoNet. The results demonstrated that the proposed method has several advantages. First, it can provide a feasible solution without the need for complex calculations. Second, integrating the knowledge model improved the overall performance of the model. Finally, SpoNet is more accurate than heuristic methods and significantly faster than modern solvers, with a solution time improvement of more than 20 times. Our method has a promising application in urban spatial decision analysis, and further has a positive impact on sustainable cities and communities.

Simulation of Urban Dynamic Traffic Network Based on Greedy Algorithm

This study explores the intricate dynamics of urban traffic flow under varying degrees of intersection and road section carrying capacity. We simulate a city-wide traffic network and examine the impact of extreme situations such as a decline in urban road carrying capacity on the entire traffic system. Our experimental procedure involved simulating traffic flow through intersections and road sections, artificially reducing their maximum carrying capacity, and observing the resultant changes in traffic patterns. Our data reveals a robust correlation between the decrease in carrying capacity and the actual flow percentage across intersections and adjacent sections, with a marked decrease in flow observed when the capacity reduction exceeds 40%. The actual flow percentage of nearby intersections increased significantly under the same conditions, suggesting a degree of redistribution of traffic flow. This study contributes to the broader understanding of urban traffic dynamics, offering valuable insights for urban planning and traffic management strategies.

InSAR Monitoring Using Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) Techniques for Ground Deformation Measurement in Metropolitan Area of Concepción, Chile

InSAR capabilities allow us to understand ground deformations in large metropolitan areas, this is key to assessing site conditions in areas in an inherently expanding context. The multi-temporal interferometry of SAR data records ground surface displacement velocities over large metropolitan areas, identifying anomalous and potential geological hazards. The metropolitan city of Concepción, Chile, is an alluvial basin in one of the world’s most seismically active subduction zones, where many subduction earthquakes have occurred throughout history. In this study, we monitored the deformations of the ground surface in the metropolitan area of Concepción using two interferometric techniques, the first being Persistent Scatterer Interferometry (PSI) and the second, the Small Baseline Subset (SBAS) technique. To do this, we have used the same Sentinel-1 dataset, obtaining ground movement rates between 2019 and 2021. The velocities were aligned with the GNSS station available in the area. Ground deformation patterns show local deformations depending on factors such as soil type and heterogeneity, and regional deformations due to geographical location in the subduction area. Our results highlight the similarity of the deformation rates obtained with different processing techniques and have also allowed us to identify areas of deformation and compare them to site conditions. These results are essential to evaluate ground conditions and contribute to urban planning and risk management in highly seismic areas.

Atmospheric particulate matter retention capacity of bark and leaves of urban tree species

Urban vegetation can effectively filter and adsorb particulate matter (PM). However, limited studies have been conducted on the PM retention capacity of tree barks. This study investigated the ability of five common urban tree species in the Yangtze River Delta region to retain PM through their barks and leaves by conducting a 14-day tree PM retention experiment on the five tree species during autumn and winter. The results showed that (1) the PM retention per unit area of bark was 6.9 times and 11.8 times higher than that of leaves during autumn and winter, respectively; (2) when considering total surface area, bark and leaves exhibited comparable PM retention capacities at the whole-plant scale; (3) the ability of bark to retain PM is species-specific, which can be attributed to different bark morphology among different tree species; and (4) bark and leaves exhibited distinct preferences for retaining PM of different particle sizes, even when exposed to similar environmental conditions. This study highlights the remarkable ability of tree bark to PM removal and provides valuable insights into the role of urban trees in mitigating PM pollution. Furthermore, these findings can provide valuable insights into studies on dry deposition modelling, urban planning, and green space management strategies.