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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.
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    Spectra of Translation-Invariant Function Algebras of Compact Groups
    (University of Waterloo, 2026-01-26) Zhang, Zhihao
    Let G be a compact group and let Trig(G) denote the algebra of trigonometric polynomials of G. For a translation-invariant subalgebra A of Trig(G), one can consider the completions of A under the uniform norm and the Fourier norm. We show in Chapter 2 using techniques developed by Gichev that both completions have the same Gelfand spectrum, answering a question posed in a paper of Spronk and Stokke. In the same paper, a theorem describing of the Gelfand spectrum of the Fourier completion of finitely-generated such algebras A was given. In Chapter 3, we extend this theorem to the case of countably-generated, translation-invariant subalgebras, A. In Chapter 4, we give a brief overview of the Beurling--Fourier algebra, a weighted variant of the classical Fourier algebra studied by Ludwig, Spronk, and Turowska. The addition of a weight for these particular algebras invites new spectral data in contrast to its classical counterpart. In Chapter 5, we show for Beurling--Fourier algebras of compact abelian groups G that its weight can be used to construct a seminorm on tensor product of the real numbers with the Pontryagin dual of G that remembers the spectral data of the algebra.
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    Characterization and Comparison of Flammability Properties and Trace Emissions of Select Native and Invasive Canadian Wildland Fire Fuels
    (University of Waterloo, 2026-01-26) Lakhani, Ayaan
    Fire has played an integral role in the evolution, formation, and sustainability of North American forest ecosystems. Historically, Indigenous peoples have employed fire as a deliberate land management tool to maintain forest health, shape landscapes, and achieve early industrial objectives. With the landing of European settlers, and changes in governmental policy, the use of fire as a land/fuel management tool was greatly diminished. In addition to the suppression of fire as a tool, the intentional and accidental introduction of non-native plant species to Canadian forest ecosystems has dramatically altered its structure from the 17th century through to today. In addition, emissions of CO₂ and other greenhouse gases have been rapidly increasing since industrialization, which has warmed the planet, resulting in extreme weather events like droughts and storms that occur at increasing frequency and severity. This has culminated in wildfire conditions that are drastically different to those that shaped the historical evolution of Canadian forests. Key changes in forest fuels include larger spatial distributions of fuel types and moisture content, which affect fire growth and development. Over the past few decades it has become evident that understanding these factors of fuel types, moisture content, and fire growth and development are critical to improve performance of predictive models, as well as our overall understanding of how to combat and minimize the damage caused by these severe wildfire events. Assessment of wildfires has generally taken two approaches: the first being a largescale analysis of a real wildfire event, which characterizes total emissions and bulk burning behaviour, and the second being small-scale studies that often focus only on one specific fire performance metric or a limited set of emissions. While both approaches have yielded significant data in terms of bulk fire performance metrics and separate emissions data, this separation has led to a dearth of integrated, detailed, and comparative data. This comparative data is critically important because the lack of species-specific flammability metrics and associated detailed emissions data under varying conditions hinders the accurate prediction of fire behaviour and the development of effective land management strategies. Furthermore, the absence of data explicitly linking exposure conditions to trace emission profiles (the toxic fraction of smoke) leads to misestimates in both emission inventories and air quality models, potentially compromising environmental safety assessments. In this research, a pair of native species and a pair of invasive species are tested at small scale for their flammability properties, major, and trace emissions. Testing was conducted under two different levels of fuel moisture and two different radiation exposures, with twelve replicates per condition. The native species are trembling aspen and ironwood while the invasive species are buckthorn and barberry. All four species were tested under reference conditions (35 kW m⁻² incident heat flux, and naturally dry conditions), buckthorn and trembling aspen were tested under elevated heat flux (50 kW m⁻² incident heat flux, and naturally dry conditions), and barberry and ironwood were tested at elevated moisture conditions (35 kW m⁻² incident heat flux, and field-tested moisture conditions). Across the tests, flammability properties, such as ignition delay time and heat release rate, were compared as well as real-time concentrations of CO₂, CO, and VOCs. In addition to these three gases, thermal desorption tubes were employed to sample the smoke plume at three phases during a test – pyrolysis, open flaming, and smouldering – and were analyzed using GC-MS to identify and group key emissions, then develop a qualitative sensitivity of trace emissions to species and burning conditions. To properly frame the discussion surrounding the production of trace emissions, the lignocellulosic compositions (% cellulose, % hemicellulose, and % lignin) and the apparent activation energy of each of the species was determined using thermogravimetric analysis. Finally, inductively coupled plasma-optical emission spectroscopy and X-ray diffraction were employed to identify and quantify metallic emission differences in the post-burn particulate matter and the fire smoke plume. A broad summary of the results shows that species composition (lignocellulosic makeup) and intrinsic physical characteristics (sample piece sizes and packing geometry) are the dominant factors driving differences in fire performance and flammability under reference conditions. When exposed to a higher heat flux, the external energy largely overcame the impacts of geometry, allowing compositional differences to become the sole dominant factor dictating distinct species responses in peak heat release rate and emissions. The exposure to the increased heat flux also greatly reduced the ignition delay time and increased the heat release rates for both native and invasive species. Conversely, increased fuel moisture content led to a clear and consequential shift toward less efficient, incomplete combustion processes, resulting in substantial increases ignition delay time, reductions in heat release rate and increases in CO and VOC emission factors during the pyrolysis, flaming, and smouldering phases. Thermogravimetric analysis confirmed a compositional-kinetic relationship where the apparent activation energy varied by up to 24% across species. This kinetic variation, coupled with data from thermal desorption-gas chromatography–mass spectrometry, highlighted the dependent nature of trace species production on the specific species composition and apparent activation energy. Different species produced distinct groups of trace emissions and showed differing responses to both elevated heat flux (where some experienced volatile suppression and others persistent intermediates) and varying moisture conditions (where smouldering emissions were dramatically amplified or altered).