Urban Environmental Management Research Articles

Live Intersection Data Acquisition for Traffic Simulators (LIDATS)

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments.

A robust acid-resistant chelating polymer for enhanced stabilization of lead ions in fly ash

Fly ash derived from municipal solid waste incinerators (MSWIs) harbors significant quantities of heavy metals with high leaching toxicity, resulting in detrimental environmental effects. Pb2+ in fly ash is the ion most likely to exceed permissible levels. However, chemical stabilization methods demonstrate poor efficacy in stabilizing Pb2+ under acidic conditions. Herein, we have developed a robust acid-resistant chelating polymer (25DTF) for enhanced stabilization of Pb2+ in fly ash. 25DTF was synthesized through the reaction of formaldehyde with 2,5-dithiourea. 25DTF exhibited remarkable chelation efficiency, nearing 100%, for Pb2+ in fly ash. 25DTF demonstrated exceptional chelation efficiency, surpassing 99.9%, when interacting with Pb2+ in fly ash at pH ≤ 7. Even under acidic conditions, 25DTF effectively prevented the secondary dissolution of Pb2+. Additionally, it indicated outstanding Pb2+ chelation efficiency across diverse regions of China. The 25DTF chelating agent shows considerable potential in alleviating metal ion contamination in soil, wastewater, and urban environmental management, thereby fostering advancements in environmental stewardship.

Advanced Traffic Clearance System for Emergency Vehicles

This paper elaborates development of Traffic Clearance System using camera and mobile Net. In urban areas, navigating through traffic congestion poses a significant challenge for emergency vehicles, often leading to delays that can impact response times and potentially endanger lives. This paper presents an innovative traffic control system leveraging camera technology to prioritize the passage of emergency vehicles. The proposed system utilizes a network of cameras strategically placed at intersections to detect emergency vehicle sirens and flashing lights. Upon detection, the system dynamically adjusts traffic signal timing to create a green corridor, facilitating the swift and safe passage of emergency vehicles. Additionally, the system incorporates machine learning algorithms to optimize traffic flow, minimize congestion, and ensure efficient utilization of road space. Through simulation studies and real-world testing, the effectiveness and reliability of the proposed system are evaluated, demonstrating its potential to enhance emergency response capabilities and improve overall traffic management in urban environments.

Intelligent optimal layout of drainage pipe network monitoring points based on information entropy theory

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

Smart traffic control: machine learning for dynamic road traffic management in urban environments

Smart traffic control: machine learning for dynamic road traffic management in urban environments

Intelligent Waste Management System using IOT

The escalating population density in urban areas has fueled a surge in waste generation, underscoring the urgent need for effective waste management solutions. In response to this challenge this study proposes an innovative approach to waste management through the integration of IOT components. The system focuses on real-time monitoring and segregation of dry and wet waste using a network of sensors. The system also leverages platform to automate data logging and notification processes ,ensuring seamless oversight of waste collection operations. Our Proposed Intelligent Waste Management System employs IOT components such as sensors ,detectors, actuators to optimize waste collection and route planning. This integrated IOT-based approach represents a promising avenue for sustainable waste management in both large and small urban environments , paving the way for enhanced city livability and environmental stewardship.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

Estimation of greenhouse gas emission due to open burning of rice straw using Sentinel data

In recent decades, Vietnam has gradually become a critical global rice producer. During that production process, residual straw becomes an environmental pollutant due to open burning, raising greenhouse gas emissions. This study combines the optical images of the Sentinel-2 satellite and the radar images of the Sentinel-1 satellite to estimate the dry biomass of rice and to determine gas emissions due to rice straw burning over the fields in Quoc Oai district, Hanoi city for urban environmental management purposes. Sentinel-2 images have been classified into the land covers, thereby identifying the areas of ​​rice cultivation and the areas of ​​burned straw. Meanwhile, the Sentinel-1 radar image has been used to calculate the dry biomass of rice due to its ability to penetrate clouds, an obstacle to optical images in tropical regions. Furthermore, a field trip during harvesting season allows us to measure aboveground dry biomass. Then, the analysis shows a high correlation between the backscatter V.V. and V.H. of the radar image and the in-situ dry biomass (R=0.923 and R2=0.852), with a relatively low average error (RMSE = 6.58 kg/100 m2). By linear regression method, the study found the total rice dry biomass of 28728.5 tons, which was obtained after the Summer rice crop 2020 for the whole Quoc Oai district, of which 2037.91 tons of rice straw have been burned, releasing a large amount of greenhouse gas emission with 2398.6 tons of CO2, 189.5 tons of CO, 18.8673 tons of PM10 dust, 17.2087 tons of PM2.5 dust and some other gases. The identical procedure has also been applied to the western region of Hanoi city center to estimate the amount of gas emissions. This study has proven the effectiveness of an approach and contributed to supporting urban managers in proposing appropriate policies to monitor and protect the environment.

Enhancing reclaimed water distribution network resilience with cost-effective meshing

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked. Novel mathematical algorithms, including Resilience by Design (RbD) and Resilience-strengthening (RS) algorithms, provide cost-effective and resilient network designs, even with budget constraints. A novel metric, Water Availability (WA), is introduced to offer a comprehensive measure of network resilience, thereby addressing ongoing discrepancies in resilience evaluation methods. Practical benefits are illustrated through a case study in which a resilient-by-design reclaimed water network is created, and an existing equivalent non-resilient network is improved. The resilient-by-design network demonstrates remarkably better results compared to the equivalent non-resilient design, including up to a 36 % reduction in the probability of service disruptions and a nearly 65 % decrease in the annual average unserved water due to service disruptions. These findings underscore the enormous advantages of a resilience-focused network design approach. When compared to the equivalent non-resilient design, the resilient-by-design network generated effectively safeguards up to a significant 91,700m3 of water from the impacts of water disruption events over a 50-year operational period. In addition, the resilient-by-design WDN solution incurs a subtle decrease in overall costs compared to consuming tap water from the drinking WDN baseline over a 50-year operational period. These findings highlight the cost-effectiveness of the approach, even offering financial benefits. This paper builds on our previous research by expanding its scope to include resilience considerations, providing algorithms that can be easily adapted from reclaimed to drinking WDNs. Ultimately, we contribute to the enhancement of water resource management and infrastructure planning in ever-evolving urban environments.

Evolution and determinants of precipitation chemistry in a plateau city, China: Insight from the pollutants source identification and environmental effect

Rainfall-related chemical ions deposition processes have important environment effects on earth-surface, which is also a vital sink of urban air pollutants. Therefore, identifying the rainwater chemical evolution and determinants, and source is essential. The daily-based rainwater samples during 2022– 2023 and the historical rainwater data in a typical plateau city (Kunming) were investigated. The Ca2+ and NH4+, SO42− and NO3− dominated the rainwater cations and anions, with the cationic and anionic percentages of 78% and 10%, 50% and 33%. The historical variations in ions percentages revealed the achievement of environmental protection. The precipitation amount and the seasonal changes of source emission were critical determinants for the variations of rainwater chemistry. Using the stoichiometry and receptor model, the contributions of potential origins to rainwater chemicals were assessed. 86% of NO3− and 40% SO42− were from combustion-related anthropogenic source, volatilization-related source contributed 85% of NH4+ (e.g., agricultural/municipal wastes emissions), whereas marine input was the main contributor of Cl− (59%) and Na+ (52%). Mg2+ (58%) and Ca2+ (41%) were primary from terrestrial input source. The risk of wet deposition-caused acidification was limited due to the high rainwater neutralization level, whereas the impact of nitrogen-related deposition on gross primary productivity (GPP) was non-negligible and further attention was need. Overall, the present study clarified the rainfall chemical features of plateau city region and further provided decision support for more effective urban air environmental management programs.

Microplastic contamination in urban aquatic environments: Occurrence characteristics in urban streams and stormwater runoff from urban surfaces

This study investigates microplastics in urban environments, focusing on their abundance, types, and relationships with hydrological parameters. Microplastic analyses encompassed two steps: (1) examining urban streams including discharges from a wastewater treatment plant (WWTP) during non-rainy seasons, and (2) analyzing stormwater runoff from urban surfaces for microplastic content during rainy seasons. In urban streams, WWTP discharge exhibited higher microplastic concentrations compared to other streams, indicating WWTP discharge is a dominant source of microplastic pollution. The most prevalent microplastics detected were polypropylene, polyethylene, and their copolymer, although a variety of other types were also found. Concentrations of microplastics were notably influenced by specific urban land uses, as evidenced by a strong correlation (0.95) between microplastic concentrations and areas characterized by industrial and transportation activities. During rainy seasons, microplastics followed the pattern of stormwater runoff, but the highest concentrations, significantly exceeding those in urban streams, were observed before the peak runoff. These maximum concentrations and their timing of occurrence were linked to antecedent dry days, rain intensity, and runoff rate, showing significant statistical correlations. Regardless of their sizes, a diverse range of microplastic types was identified in these conditions, with no consistent pattern across different rain events. This highlights the complex nature of urban microplastic pollution. This study reveals that aquatic ecosystems are significantly affected by two primary factors: (1) the consistent contribution of microplastics from WWTP discharges, and (2) the short-term, but severe, impacts of microplastic pollution associated with stormwater runoff. Furthermore, it suggests the development of alternative strategies to mitigate microplastic pollution in aquatic ecosystems, informed by the findings on the characteristics of microplastics in urban environments. This research underscores the urgent need for integrated urban environmental management strategies, paving the way for future studies to further explore and address the multifaceted challenges posed by microplastic pollution in aquatic ecosystems.

Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces

Conducting precise evaluations and predictions of the environmental conditions for plant growth in green spaces is crucial for ensuring their health and sustainability. Yet, assessing the health of urban greenery and the plant growth environment represents a significant and complex challenge within the fields of urban planning and environmental management. This complexity arises from two main challenges: the limitations in acquiring high-density, high-precision data, and the difficulties traditional methods face in capturing and modeling the complex nonlinear relationships between environmental factors and plant growth. In light of the superior spatial interpolation capabilities of CEDGAN (conditional encoder–decoder generative adversarial neural network), notwithstanding its comparative lack of robustness across different subjects, and the excellent ability of FCNN (fully connected neural network) to fit multiple nonlinear equation models, we have developed two models based on these network structures. One model performs high-precision spatial attribute interpolation for urban green spaces, and the other predicts and evaluates the environmental conditions for plant growth within these areas. Our research has demonstrated that, following training with various samples, the CEDGAN network exhibits satisfactory performance in interpolating soil pH values, with an average pixel error below 0.03. This accuracy in predicting both spatial distribution and feature aspects improves with the increase in sample size and the number of controlled sampling points, offering an advanced method for high-precision spatial attribute interpolation in the planning and routine management of urban green spaces. Similarly, FCNN has shown commendable performance in predicting and evaluating plant growth environments, with prediction errors generally less than 0.1. Comparing different network structures, models with fewer hidden layers and nodes yielded superior training outcomes.

Integrating Tehran metropolitan air pollution into the current transport system and sprawl growth: an emphasis on urban performance and accessibility

Air pollution is one of the most challenging issues for urban environment and environmental management. The goal of this study was to determine the impact of Tehran metropolitan's performance and accessibility on urban transportation and air pollution as sprawl grows. Tehran, with a population of 15.98 million people, has many environmental issues, including air pollution. Secondary data were collected from the Tehran Air Quality Control Company as well as Landsat satellite imagery (OLI). The raw data of intra-city and suburban traffic counts for spatial analysis of movements, combined with the raw data of measuring stations, were then used as a sample in the ArcGIS software environment for three selected days in 2013, 2014, and 2016. Following geometric and radiometric correction, programming methods and a multivariate regression algorithm were applied to the images, yielding results in the form of additional stations. According to the results obtained (about 3.29 m) root-mean-square error (RMSE), the Inverse Distance Weighting (IDW) model was used in air pollution maps for better assessment. The findings suggest that Tehran is not the only source of air pollution and that TMA performance and accessibility play a significant role in the amount of air pollution. Furthermore, there is a strong correlation (more than 75%) between air pollution maps and transportation flow maps on specific days (February 23, 2013, February 26, 2015, and December 28, 2016). Finally, the analysis of this situation in three circles showed that the highest volume of traffic was done in the city of Tehran (CBD), suburban area (Suburban), and suburban area (Exurban), respectively, and the pattern of distribution and spatial accumulation of pollution has also been a function of this situation.

Computer-aided resilience: Advanced techniques for disaster management in smart urban environments

This research paper explores innovative contributions to the field of disaster management in smart urban environments, with a particular focus on integrating advanced computer-aided techniques, specifically GRU-CNN. Three key contributions are highlighted: (1) the development of dynamic risk assessment algorithms utilizing GRU-CNN for real-time analysis and predictive modeling, enabling proactive disaster mitigation; (2) the establishment of an integrated sensor network infrastructure for early warning systems, leveraging various sensors and GRU-CNN-based data analytics to detect and respond to potential disasters at their nascent stages; and (3) the implementation of human-centric resilience planning, utilizing GRU-CNN-based computer-aided tools to simulate disaster scenarios and engage communities in preparedness efforts. The dynamic risk assessment algorithms presented in this paper, powered by GRU-CNN, enable continuous monitoring and analysis of diverse data sources, fostering a proactive approach to disaster preparedness. The integrated sensor network architecture enhances early warning capabilities, allowing for timely responses to emerging threats through the application of GRU-CNN methodologies. Furthermore, the human-centric resilience planning approach introduces virtual simulations using GRU-CNN to model disaster scenarios, facilitating the testing and refinement of strategies in a risk-free environment. This approach not only ensures the adaptability of plans but also engages and educates communities, fostering a culture of preparedness, with GRU-CNN playing a pivotal role in the process. Through these contributions, this research paper seeks to advance the discourse on disaster management in smart urban environments, emphasizing the integration and effectiveness of GRU-CNN in enhancing resilience and response strategies. By leveraging cutting-edge GRU-CNN technologies and prioritizing community involvement, the proposed techniques aim to provide a more robust and adaptive response to the challenges posed by natural and man-made disasters in urban settings.

Sustainable energy transition in cities: A deep statistical prediction model for renewable energy sources management for low-carbon urban development

This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments.

Smart City Actions Integrated into Urban Planning: Management of Urban Environments by Thematic Areas

Over the years, the imbalance between population needs and urban spaces’ organized development has been accentuated by increasing urbanization. The implementation of smart city actions began in the 1990s, with the development of integrated solutions in cities, harmonizing social, environmental, and economic aspects. This research measures the impact of thematic areas on smart cities’ management performance. The multi-criteria Decision Making Trial and Evaluation Laboratory (DEMATEL) method was used to identify the interdependent relations between smart cities’ thematic areas, structuring a diagram of cause-and-effect relations using threshold quantification values. Considering their degree of importance in smart city management, the thematic areas of technology and innovation, living environment and infrastructure, education and training, and governance and engagement are highlighted. For the degree of influence, the most influential thematic areas are coexistence and reciprocity, living environment and infrastructure, entrepreneurship, and healthcare. Also, the cause-and-effect analysis identified governance and engagement, education and training, and mobility as central thematic areas for smart city management. Finally, the research construct was developed by ranking the thematic areas’ performance in urban space planning.

Robust Parking Space Allocation System Using Open CV and Scikit-learn

The proliferation of urbanisation has led to an increased demand for efficient parking management systems. In response, this project presents a Smart Parking Assistant system aimed at providing real-time space availability notifications to users through their smartphones. Leveraging computer vision techniques implemented via Open CV and machine learning algorithms from the Scikit-learn library, the system captures video feed from a webcam to detect and quantify the number of empty parking spaces in a designated area. Upon receiving a request from the user, the system processes the video feed to analyse the occupancy status of parking slots, utilising advanced image processing techniques to accurately identify empty spaces along with mask image of the parking lot. The model built using Scikit-learn efficiently categorises the available slots, enabling the system to relay the precise number of open spaces to the user's smartphone. Furthermore, the Smart Parking Assistant incorporates geo-spatial functionalities to enhance user experience. By integrating the Haversine distance formula, the system calculates the distances between the user's location and nearby parking areas. This information is then displayed to the user, allowing them to conveniently locate and navigate to the nearest available parking facility. The proposed system offers a comprehensive solution to address the challenges associated with parking management in urban environments. By harnessing the power of computer vision, machine learning, and geo-spatial technologies, it provides users with timely and accurate information regarding parking space availability, ultimately improving efficiency and convenience in urban parking scenarios.

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Abstract Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.

Integrating IoT Sensors, Cloud AI, and Satellite Broadband for Enhanced ESG Governance in Smart Cities: A Tripartite Approach to Real-Time Environmental Monitoring

Urbanization is accelerating at an unprecedented rate, pushing the limits of environmental sustainability and demanding innovative solutions for effective management. Traditional methods for monitoring the environment fall short, lacking the scalability, immediacy, and comprehensive coverage required for modern urban settings. Our paper introduces an innovative solution to bridge these gaps, harnessing the power of Internet of Things (IoT) sensors, advanced cloud-based AI through Claude 3 Sonnet models on Amazon Bedrock, and the extensive reach of Project Kuiper's satellite broadband. This threefold integration forms a cutting-edge approach to urban environmental governance. It enables the analysis of environmental data in real time, offers predictive insights, and achieves wide-ranging coverage. The system equips city leaders, policymakers, and citizens with precise, actionable information, fostering decisions that align with Environmental Social Governance (ESG) goals. More than just improving urban life and sustainability, our approach makes environmental data accessible to all, promoting a collaborative and inclusive model of urban environmental management.

Assessing Alien Plant Invasions in Urban Environments: A Case Study of Tshwane University of Technology and Implications for Biodiversity Conservation

Preserving the dwindling native biodiversity in urban settings poses escalating challenges due to the confinement of remaining natural areas to isolated and diminutive patches. Remarkably scarce research has scrutinised the involvement of institutions, particularly universities, in introducing alien plant species in South Africa, thus creating a significant gap in effective monitoring and management. In this study, the Tshwane University of Technology in Tshwane Metropole, South Africa serves as a focal point, where we conducted a comprehensive survey of alien plants both within the university premises and beyond its confines. The investigation involved the classification of invasion status and a meticulous assessment of donor and recipient dynamics. Our findings encompass 876 occurrence records, revealing the presence of 94 alien plant species spanning 44 distinct families. Noteworthy occurrences among the dominant plant families are Asteraceae and Solanaceae. Herbaceous and woody plants emerged as the most prevalent alien species, with common representation across both sampling sites. A substantial majority of recorded species were initially introduced for horticultural purposes (51%) before escaping and establishing self-sustaining populations (62%). Furthermore, 43 species identified are listed in South African invasive species legislation, with some manifesting invasive tendencies and altering the distribution of native species in the remaining natural areas. The notable overlap in species observed between the university premises and adjacent areas provides crucial insights into the influence of institutions on the dynamics of plant invasions within the urban landscape. This underscores the prevailing gaps in the management of invasive alien plants in urban zones and accentuates the imperative of an integrated approach involving collaboration between municipalities and diverse institutions for effective invasive species management in urban environments.