UWSpace

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Recent Submissions

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    Utilizing Existing Data to Measure Ecological Connectivity for Planning Southern Ontario’s Urban Growth: A Case Study of the Waterloo Region
    (University of Waterloo, 2026-01-27) Wiens, Cassandra
    Urbanization is an increasing threat to global biodiversity. Urban areas are often thought to preclude native plants and animals but are capable of supporting some species if properly managed. Urban planning tends to focus on maximizing human benefits of the urban landscape; however, urban greenspaces can enhance ecological services for humans and promote natural species diversity. Habitat quantity and quality should be the top priorities when managing urban greenspaces. In urban areas, quantity and quality may be limited by the area available, so other tools are needed to make advancements. Connectivity represents a metric that could help plan urban greenspaces. To explore the utility of connectivity tools for cities in Southern Ontario, resistance maps were developed for Kitchener, Ontario based on four animals (bats, deer, shrews and snakes) using 2019 aerial data. Scenarios were developed based on potential changes to the city by increasing either the number of habitat cells by 5% or 10% (showing potential backyard and small greenspace restorations) or the number of buildings cells to meet projected growth targets. These were created by selecting cells randomly and reassigning values based on desired fragmentation of the land type. The resulting resistance maps were analyzed using an “omnidirectional” method developed for Circuitscape that enabled landscape level analysis of connectivity. Urban connectivity differed for the four species based on the dispersal capability of each species with bats and deer having the most connectivity with maximum resistance values of 0.53 and 0.76 respectively and shrews and snakes the least connectivity with maximum resistance values of 1.07 and 1.33. Connectivity decreased with increasing urbanization, showing a gradient of increasing current as building density increased and urban green spaces decreased. All urban greenspaces, from yards to natural areas, were important for landscape connectivity and need to be maintained if not enhanced. Buildings represented the primary barrier for all species other than bats (due to their ability to fly over them). Roads and paved areas also posed barriers to all species and represented the strongest barrier for bats. Mitigation methods should be considered for these areas, with greenspaces planned through highly built areas. Of the three models, increased building density had the largest effect on habitat connectivity, changing the resistance values by 25-33% for deer, shrew, and snakes. Bats species only had a 5-6% increase in resistance because buildings are less of a barrier to bats. The models with increasing habitat amounts were difficult to visually differentiate from the 2019 baseline, and changes in resistance value were less than 1%. These maps did show some benefits for urban species. This was expected due to the larger number of cells changed in the increased building density scenario. Planning mitigation efforts around densification should be the top priority for maintaining connectivity, but creating and maintaining greenspaces should not be forgotten as increasing habitat provides benefits beyond connectivity. Overall, these results were expected, but this analysis did show the utility of connectivity mapping for Kitchener. Connectivity analysis is potentially a valuable tool for urban planners in Southern Ontario cities if habitat quantity and quality are already being maximized and with the caveat that connectivity planning should not justify the removal of existing habitat patches and care should be taken to avoid undervaluing small patches.
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    Mobility and the Landscape: Investigating mobility of individuals at Wadi Faynan 100 using minimally invasive strontium isotope analysis
    (University of Waterloo, 2026-01-27) Mah, Jessica
    In comparison to other sites in Jordan, life at the Early Bronze Age (~3600-3000BC) site of Wadi Faynan 100 (WF100) is still largely a mystery. To better understand the use of this site in relation to the EBA transformation of social organization toward urbanism, this study explores strontium isotopic variation using laser ablation-multicollector-inductively coupled plasma-mass spectrometry (LA-MC-ICP-MS) to observe movement to and from the local area. Strontium (Sr) isotope ratios throughout the developmental periods of human enamel were used to indicate locality and patterns of mobility at WF100. Thirty-one samples consisting of a collection of permanent incisors, premolars, and first and third molars, in addition to two deciduous molars were analyzed alongside eleven local faunal samples. Initial results indicate variable patterns of mobility throughout the individual’s childhood development, with some increased instances of consistent locality in later childhood. This supports arguments for a more diversified and regionally specific social organization in EBA Jordan and Wadi Faynan, that may embrace a spectrum of sedentism and transhumance in childhood. These results highlight both the potential for, and challenges of conducting further LA-MC-ICP-MS analysis of Sr in the broader Jordan landscape and provide novel insights into EBA mobility using sequential dental sampling.
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    Filter Performance Optimization for Protozoan Pathogen and Particulate Contaminant Removal During Drinking Water Treatment
    (University of Waterloo, 2026-01-27) De Silva, Kalani
    Physico-chemical filtration (chemically assisted filtration; CAF) remains a critical barrier for the removal of particulate contaminants, including Cryptosporidium oocysts and emerging contaminants such as microplastics, during drinking water treatment. Ensuring consistent CAF performance becomes particularly challenging for systems reliant on high-quality source waters—those with low turbidity and low dissolved organic carbon concentrations—where traditional performance indicators such as turbidity may provide limited insight into the adequacy of coagulation, as source waters often already meet treated-water turbidity criteria prior to coagulation. Under these conditions, coagulant inadequacy or under-dosing may not be readily apparent, potentially resulting in insufficient particle destabilization and overestimation of Cryptosporidium oocyst removal by CAF and associated regulatory treatment credits. The goal of this research was to demonstrate the importance of particle destabilization for achieving reliable removal of protozoan pathogens and other particulate contaminants (including microplastics) by CAF for systems reliant on high-quality source waters. While the importance of coagulation in destabilizing particles for effective CAF is well known, its regulatory and operational relevance—particularly for HQSW—needs to be revisited given the public health importance of drinking water treatment. Pilot-scale performance demonstrations were conducted to: (1) demonstrate the inadequacy of filter effluent turbidity as an indicator of coagulant demand required to achieve ≥3-log protozoan removal by CAF; (2) evaluate zeta potential as an operational tool to indicate the sufficiency of particle destabilization needed to maximize protozoan removal by CAF; (3) investigate direct in-line CAF’s ability to achieve ≥3-log protozoan removal; (4) determine whether removal of microplastics exhibit behavior consistent with other colloidal particles during CAF; and (5) evaluate key methodological factors that contribute to variability and uncertainty in performance demonstrations to enhance confidence in interpreting measured CAF performance. Collectively, the findings reinforce that adequate coagulation and particle destabilization are fundamental drivers of CAF performance across particle types, treatment configurations, and methodological approaches. This work demonstrated that turbidity alone cannot indicate adequacy of coagulant application for high-quality source waters, whereas zeta potential offers a tool to guide coagulant dosing and confirm the particle destabilization needed to achieve ≥3-log protozoan removal by CAF, recognizing that sufficient destabilization range and associated coagulant doses vary with system and water quality specific conditions. At the same time, it also demonstrated performance demonstration methods commonly used to evaluate protozoan removal remain reliable and yield consistent results when particle destabilization is optimized. This work highlights opportunities to strengthen treatment guidance for the removal of protozoan pathogens by CAF for systems reliant on low-turbidity, low-DOC source waters, provides pilot-scale evidence supporting reconsideration of treatment credits assigned to direct in-line CAF, and offers foundational process understanding needed to inform regulatory policy on microplastics.
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    Semantic Segmentation of LiDAR Point Clouds for 3D Mapping of Underground Space
    (University of Waterloo, 2026-01-27) Fatholahi, Sarah N.
    Underground space is among the most challenging environments for 3D mapping because the Global Navigation Satellite Systems (GNSS) signals are often inaccessible. This thesis investigates the use of the LiDAR-based Simultaneous Localization and Mapping (SLAM) technology to map such underground space. Underground parking lots, as an example, offer valuable solutions to the challenges posed by growing populations and urbanization, such as limited surface area, traffic congestion, and environmental concerns. They are GNSS-denied, geometrically repetitive, highly occluded by vehicles and pillars, and contain large, low-texture and specular surfaces that degrade sensing and registration. To support rigorous evaluation under these conditions, this thesis contributes three site-specific underground parking datasets captured using a hand-held LiDAR device, GeoSLAM. Each dataset provides clean point clouds and semantic labels for the core structural and operational classes: wall, pillar, vehicle, and ground, enabling controlled benchmarking. Since low-cost LiDAR scans yield sparse, non-uniform point distributions that omit fine structural features, the first study of the thesis addresses point cloud upsampling, an essential step for creating high-definition maps that preserve fine structural details while ensuring uniform data distribution for downstream tasks. Five deep learning upsampling models including PU-Net, PU-GAN, PU-GCN, PU-Transformer, and RepKPU are trained and tested in a unified pipeline and evaluated with Chamfer Distance for average surface fidelity, Hausdorff Distance for worst-case deviation, and inference time for deployability. RepKPU consistently delivers the best accuracy–latency trade-off in underground setting. Since accurate semantic understanding is crucial for structure-aware mapping and autonomous navigation in complex indoor environments, the second and third studies target semantic segmentation for underground parking spaces, first using Transformer-based backbones and then extending the evaluation to Mamba-based architectures. For Transformer-based methods (PT, PCT, and 3DGTN), the generalization across the three different parking lots is assessed using overall accuracy (OA), mean Intersection over Union (mIoU), and F1-score. The results establish 3DGTN as the most accurate and stable Transformer framework across all three sites. Complementing the Transformer study, Mamba-based methods (PointMamba, PoinTramba, and 3D-UMamba) are compared on the same datasets with 3D-UMamba offering the best overall performance. point cloud upsampling semantic segmentation GNSS-denied environment underground parking lot Transformer-based methods Mamba-based methods
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    UEPVGA: A Novel Unreal Engine 5 Based Methodology for Airport Photovoltaic Glare Assessment
    (University of Waterloo, 2026-01-26) Lyu, Hongliang
    Airports have significant potential for deploying solar photovoltaic (PV) systems because they have large amounts of available land and high energy demands. However, the deployment of PV systems in and around airports in Canada and the United States is constrained by concerns from pilots and ground personnel regarding glare risks and formalized in policy that restricts their deployment without a comprehensive glare risk assessment. To address these issues, we developed a novel Unreal Engine PV Glare Assessment (UEPVGA) framework. The framework uses real-time game engine rendering to create photorealistic, dynamic glare simulations. It employs physically based rendering techniques to model the optical properties of PV modules that accurately reflect the relationship between incident angle and reflectance. Astronomical algorithms precisely simulate the sun's position and trajectory across the sky throughout the year. Simulated glare from the UEPVGA was validated against observational data at different altitudes and angles from real-world PV panels that were acquired by a remotely piloted aircraft. Validation results demonstrated that the simulated solar position and glare intensity of solar panels highly correlate with observational data. The framework was then used to conduct a glare assessment of a study area considering three hypothetical zones for PV panel installations. Results revealed pronounced seasonal risk patterns and identified specific high-risk zones, demonstrating the framework's practical value for operational safety planning. This study suggests the feasibility of using game engines as environmental simulation platforms and highlights their potential to support aviation safety and other fields.