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The Social Impact of Mining on Children: A Case Study in the Gobi Desert of Mongolia
No full textMongolia is considered the most mining-dependent country in Asia. Sandwiched between China and Russia, mining plays a central role in Mongolia’s economy and development strategies. While the country enforces environmental impact assessment law, the absence of a law for social impact assessment in large-scale projects leaves critical gaps in understanding the effects of mining on vulnerable populations, particularly children. The Gobi Desert in Mongolia is rich in minerals such as coal, gold, and copper, yet highly vulnerable to desertification and climate change, and hosts most of the country’s major mining operations. This case study, situated in the Gobi Desert, examines the social impacts of mining on children in two distinct populations: mining employees working under fly-in, fly-out (FIFO) arrangements, and nomadic herders residing in mining host communities. Employing a mixed methods approach consisting of survey questionnaires and semi-structured interviews, this research aims to unpack the lived experiences of these communities and address regulatory and policy gaps. The study is guided by two objectives: (1) to understand the social context and identify the social impacts of mining on the target populations, and (2) to explain these impacts by analyzing the underlying factors that shape them. The findings indicate that the types of social impacts experienced by children of mining employees and those of nomadic herders are distinct, shaped by differing contextual factors. For children of mining employees, impacts are primarily influenced by income and parental absenteeism: the former enables higher wages and access to better education, while the latter leads to challenges such as disrupted parental involvement and communication breakdowns. In contrast, for children of nomadic herders, key shaping factors include: (1) Corporate Social Responsibility - through local consultations, infrastructure development, and support for social services; (2) environmental conditions - manifested in air pollution and groundwater depletion; (3) migration - characterized by population influx, economic stimulation, and increased child safety concerns; and (4) lifestyle - particularly the threats of displacement and restrictions on the traditional mobility of nomadic herders.
These findings contribute to the academic literature on mining and social sustainability while offering practical recommendations for policymakers, highlighting the often overlooked voices of vulnerable communities, such as affected children and families
Towards a Novel Optical Spectroscopy Technique Using Photon Absorption Remote Sensing
No full textOptical spectroscopy has shown great promise in the field of biomedical research. For example, works employing traditional spectroscopy approaches have demonstrated that analyzing a sample’s optical response to incoming light can effectively differentiate between healthy and diseased tissue. However, these techniques suffer from limitations due to the fact that they typically capture signals from only a single light-matter interaction type, such as absorption, scattering or fluorescence. Therefore, many traditional methods are constrained in terms of the types of samples they can feasibly analyze, as well as, potentially, the depth of their sample characterization, as they do not focus on capturing relevant information from other interaction modalities. This work employs photon absorption remote sensing (PARS) to overcome these limitations.
PARS is a novel all-optical imaging technique capable of capturing radiative and non-radiative relaxation processes following electronic photon absorption. This thesis explores the initial development of the first PARS system specifically designed and optimized for optical spectroscopy applications, aimed at studying wavelength-dependent relaxation processes to characterize a wide range of liquid samples.
The first step of this work was to build a non-radiative PARS spectroscopy system capable of accurately capturing the thermal and acoustic relaxation processes that arise from different ultra-violet (UV) excitation wavelengths. These signals were processed and used to construct a non-radiative PARS absorption spectrum for each sample of interest. These spectra were benchmarked against the absorption data collected from a NanoDrop spectrophotometer, which served as the ground truth in this work. This study revealed that for certain samples, such as eumelanin, which is highly absorbent to UV light and relaxes almost all absorbed energy non-radiatively, the non-radiative PARS spectroscopy system is capable of generating highly accurate absorption spectra. However, this system did not generate as close to ground truth spectra for samples that do not have as strong UV absorbing tendencies and are not as non-radiative in nature.
The second step of this work was to integrate a radiative relaxation arm into the developed non-radiative PARS spectroscopy system. This pathway was configured to collect fluorescence emission spectra, which represent radiative sample relaxation, simultaneously with the collected non-radiative data. Radiative PARS absorption spectra were generated for each sample. In this way, the developed PARS system combines absorption (monitoring both relaxation pathways) and fluorescence emission spectroscopy onto a single bench-top system. The radiative PARS absorption spectra were compared to the ground truth, which revealed that molecules that are highly fluorescent in nature are more appropriately studied through the radiative relaxation arm than the non-radiative pathway. Total absorption spectra, which combine the non-radiative and radiative absorption data, were also generated, and it was determined that the absorption profiles of certain samples, such as NADH, are best studied using this approach.
The final step of this work was to use the collected total absorption and fluorescence emission data from the PARS spectroscopy system to identify the composition of different mixtures of craft red and blue ink samples. Traditional linear and generalized bilinear models were employed to perform this unmixing and the results from this study indicate that the combination of the absorption and fluorescence data collected on this system allows for a more accurate identification of a mixture’s components than either data source individually. This suggests that the PARS spectroscopy system provides an increased level of detail in sample characterization compared single-modality spectroscopy systems.
Ultimately, this research lays the groundwork for the development of a PARS spectroscopy system capable of being deployed in clinical settings to study samples and help inform diagnoses. This work demonstrates the feasibility of leveraging PARS for optical spectroscopy and presents a system design and framework that can be further iterated upon to enhance performance and enable a robust characterization of relevant and complex biological samples
Gait kinematics during walking in children with amblyopia
No full textIntroduction: Coordination between the eyes and body is important for navigating the environment. Children with amblyopia score lower for walking on standardized tests of motor ability. However, standardized tests do not assess gait kinematics during walking. Here, I investigated the development of gait kinematics during walking under natural binocular viewing conditions in children with amblyopia compared to controls.
Methods: A total of 21 children ages 7 to 13 years with amblyogenic factors (14 anisometropia, 7 strabismus) were enrolled in the ‘amblyopia’ group (15∕21 had current amblyopia). An age-similar group of 27 controls were also enrolled. While viewing binocularly, children walked the length of a GAITRite pressure-sensitive walkway and completed 3 conditions of varying complexity: 1) Straight Walk (SW): walk on mat, 2) Isolated Target Walk (IT): walk and step on two-dimensional targets, and 3) Distractor Target Walk (DT): walk and step on two-dimensional targets while avoiding two-dimensional distractors. Gait kinematics were temporal outcomes of normalized velocity (leg lengths/second), cadence (spm), step time (msecs), and stance time, and spatial outcomes of (msecs), step length (cm), step width (cm) and accuracy (%) of stepping on targets or avoiding distractors. Variability in gait kinematics was also examined using the coefficient of variation (COV, %).
Results: Temporal and spatial outcomes of gait kinematics did not differ between children with amblyopia and controls. However, the amblyopia group was less accurate at stepping on targets overall than controls (amblyopia, mean±SD=90.8±8.5% vs control, 96.9±3.8%, p=0.003), with less accuracy found in the IT condition (89.7±9.7% vs 96.5±5.3%, p=0.005), and for the far T2 target (87.4±13.9% vs 97.2±5.4%, p=0.001). Lastly, the amblyopia group showed increased variability (i.e., higher COV) for temporal measures, including normalized velocity (8.9±3.0% vs 6.4±3.0%, p=0.007), stance time (5.7±1.8% vs 4.7±1.8%, p=0.049), cadence (5.0±2.4% vs 3.3±1.7%, p=0.005) and step time (4.9±2.5% vs 3.5±2.1%, p=0.040), and for spatial measures, including step length (8.0±2.4% vs 6.4±2.4%, p=0.024) and step width (7.7±2.2% vs 6.11±2.2%, p=0.014). In the amblyopia group, spatial outcomes were correlated with amblyopic eye visual acuity and temporal measures were correlated with stereoacuity.
Conclusions: This thesis shows that unbalanced visual input early in life from pediatric eye conditions that cause amblyopia results in variable and inaccurate walking patterns compared to children with age-typical visual development. This pattern of findings is similar to younger children, indicating that the typical development of gait is delayed in children with amblyopia, especially when they have poor binocularity outcomes. These findings point to the importance of typical binocular vision for the development of walking
3D printable fungi-based Chitin nanofiber/CNC hydrogels: implication for fabrication of functional cryogels
No full textFungal-derived chitin nanofibers represent a naturally abundant and renewable material with considerable mechanical strength, making them a promising candidate for advanced material applications. However, their application in 3D printing remains in the early stages of research, as pure fungal chitin hydrogels exhibit poor printability that limits their use in additive manufacturing. In our study, we address this challenge by incorporating cellulose nanocrystals (CNCs) into the chitin-based hydrogels. The addition of CNCs effectively fine-tunes the rheological properties of the chitin-based hydrogels, enabling stable extrusion-based 3D printing while preserving the structural integrity of the material. This approach allowed us to formulate a range of high-fidelity printing inks by hybridizing these bio-based nanomaterials, ultimately creating sustainable aerogels that are ideal for divers applications.
Moreover, while CNC aerogels often suffer from insufficient mechanical strength and poor handling characteristics, hybridizing them with chitin nanofibers results in robust, well-structured aerogels. Compression tests confirmed that the mechanical strength of these aerogels is predominantly dictated by chitin network, with CNCs contributing significantly to improved printability and enhanced structural uniformity. To further expand the functional properties of these hybrid aerogels, we incorporated multi-wall carbon nanotubes (MWCNTs) to impart electrical conductivity, thereby enabling their use in electromagnetic interference (EMI) shielding applications. Electrical conductivity measurements demonstrated excellent charge transport capabilities, resulting in a total EMI shielding effectiveness of 34 dB over the X-band frequency range (8–12 GHz). Overall, this study highlights the tremendous potential of fungal-derived, 3D printable chitin aerogels as sustainable, lightweight substrates, offering an eco-friendly alternative to conventional synthetic composites for applications ranging from wound dressings to EMI shielding devices
Techno-economic and Life Cycle Assessment of Airport Hydrogen Production Infrastructure for Future Hydrogen-based Aviation
No full textThe aviation sector faces growing pressure to reduce emissions, as global air travel continues to expand and jet fuel demand rises. Alternatives such as sustainable aviation fuel (SAF), battery electric, and hydrogen have emerged to reduce dependence on fossil fuels. While SAF offers partial emissions reductions, and electric aviation remains constrained by battery weight and power limitations, hydrogen presents a promising long-term solution. Hydrogen can be used to generate power via combustion or fuel cells, offering the potential for zero carbon emissions. However, implementing hydrogen-based aviation fuel faces challenges, particularly in establishing adequate infrastructure for cost-effective hydrogen production and supply. In addition, it is costly to distribute hydrogen, and early adoption is likely constrained by the availability of a reliable fuel supply chain. This study explores the feasibility of achieving low-carbon hydrogen supply through on-site hydrogen production at airports, integrated with renewable energy sources (RES) and the electrical grid, to increase hydrogen independence and avoid long-distance hydrogen distribution. A mixed-integer linear programming (MILP) model is used to determine the optimal component capacity configuration of the on-site hydrogen facility, considering both economic and energy constraints. Under base-case techno-economic assumptions, an optimal configuration results in an annualized total cost of US7.45 per kg of liquid hydrogen (LH2). Sensitivity analyses reveal that the system’s economic performance and operational patterns are significantly affected by variations in component unit costs and efficiencies. Compared to a system powered solely by RES, integrating grid electricity improves both economic viability and energy efficiency. Simulations using projected parameters for 2050 demonstrate the potential for reducing LCOH reduction while increasing RES utilization. Additionally, life cycle assessment (LCA) of the renewable-based hydrogen infrastructure reveals carbon intensities (CI) between 1.47 and 4.17 kg CO₂-eq/kg LH2, which are 3 to 8 times lower than that of fossil jet fuel, highlighting the environmental benefits of renewable hydrogen. The highest emissions are found to be associated with manufacturing RES and storage components due to the use of fossil fuel in material processing. Results also show trade-offs between economic and environmental performance of renewable hydrogen infrastructure in airports: Wind turbine (WT)-powered configuration offer stronger environmental benefits at a higher cost. This underscores the importance of balancing cost and emission reduction in onsite hydrogen infrastructure design for airports
Food Environments Influence on Food Choices Among Different Socioeconomic Groups
No full textThere has been a shifting focus in research within food security studies to food environments as they are proving to be one of the most influential factors in individuals' food and dietary choices. Situated within the second United Nations (UN) Sustainable Development Goal of Zero Hunger, this thesis examines the physical and social food environments of communities in Hamilton, Ontario to determine the influences these environments have on food and dietary choices. Hamilton was chosen due to its unique food landscape; where some communities could be considered living in a food swamp, with little access to healthy food amidst an abundance of convenience stores and fast food. In contrast, other communities within the city are considered a food oasis, with a wide range of high-quality foods readily available. This study employed a survey as its primary research instrument with 204 surveys completed. In addition, follow-up interviews with 20 participants were conducted to provide in-depth context for the survey results. Participants in this study were drawn from areas based on either postal code or income. The study’s findings revealed several similarities irrespective of the postal code or income of the household. Notably, the most popular dietary choice was that the participants did not follow any diet, also called the “house diet.” Moreover, in ranking the most important qualities when choosing a grocery store, price, proximity, and quality always ranked the highest among the 13 options. However, when reasoning for rankings were discussed, the higher-income participants expressed maximizing their dollars whereas the lower-income participants preferred stretching their dollars. Moreover, looking at food environments grouped by postal code, 7 out of the 19 participants lived in areas where convenience stores outnumber grocery stores at a ratio of 4:1. In these communities, most participants reported a meat-restricted diet and also ranked accessibility to healthy foods the lowest. Additionally, participants living in areas described as food swamps (high prevalence of low-quality convenience foods), also reported lower than average income compared to participants living in areas with better access to higher-quality food retailers. These finding demonstrate that income plays a consequential role in observed dietary patterns, with 7% of the higher income earners reporting a plant-based diet, compared to 20% of the lower income bracket. In the lower income bracket, 42% reported using alternate modes of transportation to private vehicles, compared to only 8% in the higher income bracket. The findings suggest a chain reaction, where the lower-income earners are more likely to be living in a food environment with low access to healthy foods, and high access to convenience stores. Furthermore, they are less likely to have access to a vehicle, which overall limits their accessibility to healthy, fresh, and sustainable food choices that may be some distance from where they live. These are areas where policies, initiatives, and programs that will promote better accessibility to healthy foods will be the most beneficial, in terms of creating healthy eating patterns that will help achieve Sustainable Development Goal Two
Vulnerabilities in Maximum Entropy Inverse Reinforcement Learning under Adversarial Demonstrations
No full textReinforcement Learning (RL) has emerged as a powerful paradigm for solving complex sequential decision-making problems. However, its effectiveness is fundamentally dependent on the availability of a well-specified reward function, the design of which is often a significant challenge. Inverse Reinforcement Learning (IRL) offers a compelling solution to this problem by enabling an agent to infer an underlying reward function from expert demonstrations. This approach has become a cornerstone of imitation learning, allowing machines to acquire sophisticated behaviors by observing human experts. A critical assumption underpinning most IRL research is that the demonstrators, while potentially suboptimal, are acting in good faith. This thesis challenges that assumption by formally investigating a significant yet underexplored security vulnerability: the susceptibility of IRL algorithms to intentionally malicious demonstrators. We address the scenario where an adversary seeks to corrupt the learning process by strategically injecting a small number of deceptive demonstrations into a training dataset, with the goal of degrading the performance of the final deployed policy.
This research formalizes the problem of adversarial demonstration attacks within the IRL framework. The adversary’s objective is to design a malicious policy that generates trajectories capable of manipulating the inferred reward function. To ensure the attack remains covert, the malicious demonstrations must be statistically similar to the genuine expert demonstrations. We introduce a similarity constraint, based on the expected feature counts of trajectories, that forces the adversarial behavior to remain within a plausible, non detectable margin of the expert’s behavior. The core of our investigation is to determine whether such a constrained, malicious policy can be systematically designed and to quantify the extent of performance degradation it can induce on a policy learned from the corrupted reward function.
To address this problem, we propose a novel optimization-based framework for generating the adversarial policy. The framework models the adversary’s strategy as a constrained optimization problem over the space of state-action occupancy measures. The objective is to find a policy that minimizes the expected cumulative reward according to the true, ground-truth reward function, thereby maximizing the performance loss of the agent that will learn from it. This minimization is subject to two key sets of constraints: (1) the feature-matching similarity constraint that ensures the deceptive nature of the attack, and (2) the standard Bellman flow constraints that ensure the resulting occupancy measure corresponds to a valid policy under the environment’s dynamics. A time-varying stochastic policy is then extracted from the solution to this optimization problem, providing a concrete method for generating the malicious demonstration trajectories.
The effectiveness of this framework is empirically validated through a series of controlled simulation studies targeting the widely-used Maximum Entropy (MaxEnt) IRL algorithm. Our experiments are conducted in two distinct grid-world environments: ‘CliffWorld‘, which represents a safety-critical task with significant negative rewards, and ‘Four Rooms‘, a more complex navigation environment with a larger state space. We systematically evaluate the impact of varying the fraction of injected malicious data and the strictness of the similarity constraint. The performance of our proposed adversarial method is benchmarked against both a baseline of expert-only demonstrations and a scenario where random, non-strategic noise is injected into the dataset.
The results of our investigation reveal a significant vulnerability in MaxEnt IRL. We demonstrate that injecting even a small fraction of malicious demonstrations, as little as 10%, can cause a disproportionately severe degradation in the performance of the deployed policy. This performance drop is substantially greater than that caused by injecting an equivalent amount of random noise, confirming the targeted nature of our adversarial generation framework. The conclusions underscore the need for the development of robust defense mechanisms and adversarially-aware IRL algorithms to ensure the safe and reliable deployment of learning agents in real-world, high-stakes applications
Health System Resilience for Climate Change Adaptation: An Empirical Evaluation of Access and Utilization in Western Province, Zambia
No full textBackground Achieving Universal Health Coverage (UHC) in low- and lower-middle-income countries (LMICs) is jeopardized by the convergence of climate-related shocks and chronic health systems stressors. In Western Province (WP), Zambia, a vast, rural, and remote area characterized by the Barotse Floodplain, progress toward UHC is hindered by the interplay between seasonal flooding variations and pre-existing challenges such as low health facility density and geographic barriers to accessing and utilizing essential primary health care services. A significant gap exists in the empirical evidence necessary to quantify these adverse synergistic interactions, improve routine surveillance systems that currently lack reliable population denominators, and develop dynamic models to assess the impact of shocks and stressors on health service utilization.
Objectives This dissertation develops and applies a comprehensive methodological framework to empirically evaluate access to and utilization of essential health services in WP, Zambia. It endeavors to (1) define and measure the dimensions of access, encompassing supply- and demand-side conditions within the context of spatial and temporal parameters; (2) establish an innovative methodology for generating population denominators to enhance disease surveillance and epidemiological metrics; (3) quantify the synergistic impacts of environmental and systemic challenges on service utilization through a novel econosyndemic framework; and (4) model health system resilience dynamically by forecasting utilization patterns and evaluating the effects of various shocks and stressors over time. Furthermore, this dissertation concludes with a policy brief, representing a preliminary policy assessment (Phase I) that employs a location-allocation model to optimize the current health facility network for geographical efficiency, thereby identifying existing access gaps and redundancies within the system. This initial optimization serves as a foundation for a proposed multi-stage framework designed to generate actionable investment strategies by integrating health system capacity, cost considerations, and evolving population needs into future analyses (Phase II). Ultimately, this work offers an integrated, evidence-based framework aimed at strengthening health system resilience as a vital climate change adaptation strategy, thereby advancing the overarching objective of ensuring equitable access to healthcare for vulnerable populations.
Methods This dissertation, grounded in comprehensive research, comprises four empirical studies in addition to a policy analysis. Study no. 1 employed a cross-sectional design utilizing geospatial analysis of 220 health facilities (centres and posts) to assess access through metrics such as facility density, population growth, travel durations, and personnel distribution. Study no. 2 introduced and applied an innovative Spatially Defined Catchment Area and Population Under Rooftop (SCSO-PUR) methodology, leveraging satellite data to establish denominators for 321 health facilities, as exemplified in a malaria surveillance epidemiological case study spanning 2017 to 2024. Study no. 3 presented and quantified the econosyndemic framework within an ecological longitudinal study of 62 health centres and posts from 2017 to 2023, employing beta regression and structural equation models (SEM) to analyze the interaction between flood exposure and health system capacity concerning maternal and child health, as well as overall general utilization (i.e., outpatient visits). Study no. 4 utilized time-series forecasting and an Interrupted Time Series (ITS) analysis on the same dataset to measure the dynamic effects of three distinct shocks—the 2019 drought, the 2021 COVID-19 pandemic, and the 2023 complex flood event —on overall utilization, namely outpatient visits. The Policy Brief, Preliminary Assessment (Phase I), employed a Set Covering Problem (SCP) model on the network of 321 facilities to optimize geographic coverage and identify system-wide efficiencies.
Findings Geographic access constitutes a primary barrier, with projected declines in facilities per capita and estimated mean travel times ranging from 6.6 to 13.9 hours. A substantial proportion of the population (26.4%, exceeding 322,000 individuals) resides beyond the World Health Organization's recommended two-hour travel time to a comprehensive Health Centre. The SCSO-PUR methodology has demonstrated feasibility in establishing standardized denominators, thereby elucidating previously obscured spatial-demographic disparities in malaria risk. The econosyndemic framework has been empirically validated; notably, the interaction between high flood depths and health system stressors was found to significantly disrupt essential services, including antenatal care (ANC) and facility-based births. The Interrupted Time Series (ITS) analysis indicates that various shocks yield distinct and quantifiable impacts on overall utilization patterns, ranging from immediate declines (drought) to paradoxical increases (COVID-19) and gradual recoveries (i.e., complex flood event). Lastly, the health system optimization analysis uncovers significant spatial redundancy; specifically, while comprehensive geographic coverage could be theoretically achieved with only 46 of the 321 existing facilities, this would entail accepting travel times of up to 7.5 hours, thereby underscoring a crucial trade-off between efficiency and equitable access.
Conclusion This dissertation provides a comprehensive, empirically grounded framework for understanding and strengthening health system resilience in a climate-vulnerable setting. It demonstrates that the adverse synergistic interaction between environmental shocks and systemic supply- and demand-side stressors creates an econosyndemic that dynamically and inequitably disrupts access to and utilization of essential health services. The novel methodologies developed offer scalable, data-driven tools for ministries of health to transition from reactive to proactive, evidence-based planning. The findings provide a clear policy directive: building health system resilience for climate adaptation requires targeted, context-specific interventions that address underlying vulnerabilities in infrastructure and geographic accessibility
Path Reduction and Coverage Complexity for Fuzzing
No full textCoverage-guided fuzzing is one of the most effective approaches to automated software
testing, yet its performance depends critically on the coverage metric that guides input
generation. It is widely assumed that finer metrics —especially path coverage, which cap-
tures complete control-flow information— should lead to more effective fuzzing. However,
practical realizations of path coverage have been limited to restricted forms due to path
explosion.
In this work, we introduce a path reduction algorithm that bounds loop iterations in
execution paths, enabling a practical form of path coverage that preserves essential control-
flow information. Despite this advancement, we find that path coverage performs no better
than existing metrics such as edge coverage.
To understand this phenomenon, we establish the concept of coverage complexity—a
quantitative measure of the granularity of coverage metrics. Analogous to complexity
and the Big-Onotation in algorithm analysis, coverage complexity classifies metrics into
asymptotic complexity classes such as linear, polynomial, and exponential. This framework
provides a structured overview of the entire space of coverage metrics, and guides the design
of new coverage metrics.
Our complexity analysis and empirical evaluation on the MAGMA benchmark reveals a
consistent pattern: metrics within the same complexity class tend to exhibit similar fuzzing
performance, where linear-complexity metrics consistently outperform more complex met-
rics. This suggests a simple but powerful principle: when designing a new coverage metric,
the first step is to determine its complexity class, which serves as an early predictor of
its potential performance. Since higher-complexity metrics consistently underperform, our
results imply that the family of linear metrics may already represent the optimal fron-
tier of coverage-guided fuzzing, offering—for the first time—a structured overview of the
landscape of coverage metrics
Liminal Habitats: An Investigation of Stormwater Management Facilities in Urban and Suburban Kitchener-Waterloo
No full textStormwater Management Ponds (SWMPs) are a tool to protect neighbourhoods from floods and collect pollutants before they enter the natural environment. Despite these facilities being infrastructure, they inadvertently become habitat for an array of taxa, notably macroinvertebrates. My research has three primary goals: to understand the language used in municipal documents to inform how SWMPs are viewed by the municipal governments in the Kitchener-Waterloo Region; to understand the broader implications of SWMP research; and to investigate the drivers of biodiversity in SWMPs. Firstly, I confirm that SWMPs are predominantly seen as pieces of infrastructure rather than habitat by the municipalities in the Kitchener-Waterloo region. Secondly, I found that SWMPs harbour similar levels of biodiversity compared to control ponds. The majority of the literature focuses on single-taxa investigations, predominantly those of odonates and plant diversity. Thirdly, I investigated biodiversity in SWMPs in the Kitchener-Waterloo area. I investigated the attributes of the ponds and discovered that turbidity and the surrounding land cover impact biodiversity within the ponds. Additionally, I found that facility type impacts biodiversity, where engineered wetlands harboured higher diversity and evenness of macroinvertebrate communities. I compared Kitchener-Waterloo’s SWMPs to other lentic systems across the province, finding that the macroinvertebrate communities in Kitchener-Waterloo’s SWMPs are similar. Based on my research, I recommend implementing engineered wetlands in place of traditional wet ponds in urbanized areas. As well, I recommend municipalities incorporate biodiversity initiatives in their design manuals for SMWP infrastructure. Finally, I advocate for a re-signification of the SWMP and acknowledgement that SWMPs provide habitat in urban areas for macroinvertebrates