Civil Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10315/34418

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Access status: Open Access ,
Divergent Environmental Behaviour of Imipenem and Fluoxetine: Stability, Interactions, and Removal Process
(2026-07-24) Khurana, Pratishtha; Brar, Satinder Kaur
The occurrence of pharmaceutical residues has long been recognized as a potential health concern; however, the fate of these pharmaceuticals has not been thoroughly investigated. In this regard, the present thesis aims to investigate the environmental behaviour of two model pharmaceuticals: Imipenem (IMP), an antibiotic, and Fluoxetine (FLX), an antidepressant, focusing on stability and metal interactions, and devising removal/degradation strategies where applicable. The findings of the present dissertation indicate that the antibiotic IMP is photolabile and thermosensitive, undergoing rapid degradation under ambient light and temperature. The presence of dissolved organic matter (DOM) and Cu (II) further accelerate its degradation. The drug also exhibits potential to form Imipenem-metal complexes (IMP-Me), exhibiting increased antibacterial activity compared to IMP. Since the drug was observed to be labile, no degradation strategy was proposed. In contrast to IMP, FLX resists natural attenuation, resulting in its persistence in aquatic environments and making its removal challenging. The present dissertation, thereby, investigates physically activated olive-stone-derived biochar for FLX removal. The optimized biochar demonstrated superior FLX adsorption, surpassing previously reported waste-derived biochar. To further improve practical implementation and achieve FLX degradation, an integration of biochar-based adsorption and UVC-based photocatalysis was employed. The proposed CuO-BC/UVC composite photocatalytic system achieved >95% FLX removal under 2 minutes of treatment. The synergistic CuO-BC/UVC system offers a practical, scalable, and safer alternative to more energy-intensive or chemically harsh oxidation processes. Overall, the findings for both drugs represent the two extremes of emerging contaminants and highlight opposite ends of a decision framework. For IMP, intervention was not deemed necessary due to its chemically labile structure and natural attenuation; however, toxicity to bacteria persists both with and without transformation, indicating that risk is chemically dynamic and significant, despite rapid transformation. On the contrary, FLX is chemically stable, exhibits no metal complexation, remains bioavailable, and poses a risk for long-term exposure. This persistence, coupled with chronic effects, makes the invention essential. With these contrasting case studies of IMP and FLX, this dissertation aims to establish that the decision to intervene should be guided by stability, interaction dynamics, and toxicity of the contaminant.
Access status: Open Access ,
Impacts of Geothermal Heating on Groundwater Quality and BTEX Biodegradation
(2026-07-24) Aziz, Ginelle Krystle; Brar, Satinder Kaur; Krol, Magdalena
Benzene, toluene, ethylbenzene, and xylene (BTEX) are common groundwater contaminants that pose environmental and health risks. While bioremediation is a sustainable treatment method, low temperatures in Canada can limit microbial activity. This thesis investigates the coupling of bioremediation with geothermal heat pumps, as there is minimal research on the effects of geothermal heating on groundwater chemistry and microbial degradation in monocultures and a consortium. Laboratory experiments at 15, 28, and 40 °C evaluated BTEX degradation by Bacillus infantis, Microbacterium esteraromaticum, and a consortium. Groundwater parameters were monitored to assess temperature effects. A Triticum aestivum seed germination assay evaluated the soil toxicity post-treatment. Results showed that temperature enhanced microbial activity, with the consortium achieving 42% degradation at 28 °C. Remediated soils supported seed germination, though trace hydrocarbons remained bioavailable and reduced root growth. These findings emphasize integrating chemical and biological assessments to evaluate remediation effectiveness and support sustainable strategies for contaminated subsurface environments.
Access status: Open Access ,
Investigating the Impact of Anaerobic Retention Time and Phosphorus-to-Carbon Influent Ratio on Phosphorus Accumulating Organisms’ Kinetics
(2026-07-24) Hendy, Salma Haitham Elsaid; Eldyasti, Ahmed K.
Existing phosphorus removal technologies face challenges in achieving recent, more stringent regulatory discharge limits on effluent phosphorus levels, necessitating further process optimizations. While anaerobic-phase metabolic activities drive Enhanced Biological Phosphorus Removal (EBPR)’s core mechanism, the design of the aerobic phase dominates the system sizing. This thesis aims to investigate the influence of anaerobic retention time and phosphorus loading on the EBPR’s performance. EBPR performance in bench-scale reactors was compared under baseline conditions and test conditions, which include varying Phosphorus-to-Carbon (P/C = 3:80, 1:80 and 1:40) ratios and anaerobic retention times (60, 90, 120 minutes). The polyphosphate content, release/uptake rates, and maintenance energy demands are evaluated to analyze performance. Polyphosphate content in the biomass was shown to influence P/C ratios (R2= 0.673), directly impacting phosphorus release and uptake rates. Meanwhile, reducing the retention time from 90 mins to 60 mins preserved the specific release rates but significantly lowered uptake and removal efficiency. Extending the anaerobic phase to 120 minutes decreased release rates without affecting uptake. Secondary phosphorus release, indicative of maintenance energy demands, varied across anaerobic retention times. This variation highlights how PAOs adapt polyphosphate hydrolysis rates to sustain baseline metabolism during shortened or extended anaerobic phases, beyond initial substrate-driven release. Results confirm polyphosphate reserves in biomass govern release/uptake dynamics which varies with anaerobic retention time.
Access status: Open Access ,
From Statistical Assessment to Data-Driven Forecasting: Groundwater Level Analysis and Modelling in Southern Ontario
(2026-07-24) Goudarzie, Amirabbas; Krol, Magdalena
Groundwater is a vital resource in Southern Ontario, supplying 90% of rural residents and around 200 municipalities with drinking water and irrigation for agriculture. Accurate groundwater level (GWL) estimation is important for sustainable water management and environmental conservation. However, direct GWL measurements are often costly and spatially limited, underscoring the need for groundwater models for decision-making. Conventional numerical models solve physics-based partial differential equations at every node of a meshed geometry. Conversely, machine learning (ML) algorithms rely on mathematical equations between inputs and outputs by iteratively adjusting the model’s parameters. Each method has its strengths, but a key advantage of ML models is their ability to predict GWLs without requiring the calibration of uncertain boundary conditions, extensive hydrological parameters, and human-induced factors. In this study, Provincial Groundwater Monitoring Network (PGMN) wells, the largest publicly available dataset for GWL data in Southern Ontario, were used to detect long-term trends using the Mann-Kendall test. Results show that 92% of wells exhibit statistically significant trends, indicating the non-stationarity of the GWLs. This study also assessed the effectiveness of ensembles of Artificial Neural Networks (ANNs) – a type of ML model – and compared it to linear regression models to predict monthly GWL in PGMN wells throughout the Barrie-Oro Moraine, an important hydrological area in Southern Ontario. The models incorporated data preprocessing techniques and lagged variables such as temperature, total precipitation, hydrometric levels, and autoregressive GWL data. Results indicate that linear regression is inappropriate since its assumptions, such as linearity and homoscedasticity, are violated, with test R2 less than 0.2 across wells. Conversely, ensembles of ANNs exhibit better performance than linear regression, particularly in single watershed modelling scenarios, capturing the non-stationarity of GWL data, with median R2 values generally ranging between 0.5 and 0.9. In addition, single-well models generally outperformed combined and aggregated modelling configurations, and DMS-based preprocessing generally showed improved performance compared to DSM approaches. These results highlight the usefulness of data-driven models in GWL prediction studies. ML techniques may improve predictive accuracy and help water resource managers develop effective policies for groundwater management.
Access status: Open Access ,
Optimizing drinking water safety in Cambridge Bay, NU using water quality sampling and participatory system dynamics
(2026-07-24) Duncan, Caroline Diana; Gora, Stephanie
Arctic communities face unique challenges in accessing clean, safe drinking water. In Nunavut, these issues are particularly severe due to a lack of water quality data, limited operator capacity, and outdated policies. New strategies are needed to improve drinking water safety in Arctic communities. Water safety planning (WSP) is a risk management approach that considers both quantitative and qualitative hazards related to drinking water and involves stakeholder participation. Participatory systems dynamics (PSD) is a method used to address complex health issues and aligns well with Indigenous knowledge. Additionally, PSD can help evaluate strategies to improve decision-making. The two main objectives of this study were to (1) identify water quality hazards in Cambridge Bay, Nunavut, and (2) assess whether water safety hazards could be identified, characterized, and prioritized using WSP risk matrices and PSD. The study aimed to integrate these tools by using hazard data to develop a model and simulate high-level mitigation strategies for improving drinking water safety. Water sampling identified several water safety hazards, including low chlorine residuals and microbial activity in cisterns and at taps, elevated disinfection byproducts throughout the system, and the presence of lead and copper in some buildings. Data from stakeholder groups revealed concerns about source water quality and quantity, infrastructure condition, and stakeholder-specific issues, such as limited regulatory capacity. Limited data reduced the effectiveness of standard WSP risk assessment methods, which rely on past frequency data to predict future events. Nonetheless, this approach identified potential water safety improvements, including the implementation of treatment barriers and monitoring measures. PSD modelling proved more flexible and adaptable in capturing interactions among infrastructure, operations, the natural environment, governance, and sociocultural factors, but it increased complexity, making it more difficult to implement or use across multiple communities. Despite these individual limitations, by combining WSP and PSD four potential options to improve water safety in Cambridge Bay were identified. This is the first study to collect comprehensive water quality data from source to tap in the Kitikmeot Region of Nunavut and to gather meaningful qualitative information on water safety hazards from community stakeholders. Additionally, it is the first to examine water safety from a systems dynamics perspective.
Access status: Open Access ,
Evaluating the Operational Impact of Multi-Modal Signal Priority in an Urban Arterial Corridor
(2026-07-24) Yousif, McKeen Mahmood; Park, Peter Y.
Urban growth in the Greater Toronto and Hamilton Area (GTHA) has intensified congestion and reduced the reliability of passenger and freight travel. This study evaluates the operational and environmental impacts of transit signal priority (TSP), freight signal priority (FSP), and a dedicated bus lane (DBL), applied individually and in combination, along a 9.9 km segment of Queen Street in Brampton, Ontario with 17 signalized intersections. A calibrated PTV VISSIM microsimulation model was used to assess changes in average travel time, total person-delay, and emissions relative to a base case. Results show that TSP and FSP increased automobile and freight average travel times by less than 3%, while reducing transit travel times by up to 9%. DBL increased automobile and freight travel times by approximately 5-13% and increased total person-delay by up to 9%, despite reducing transit delay by more than 10%. Emissions and fuel consumption increased by up to 8% under DBL, whereas TSP and FSP resulted in negligible changes (approximately 3%). Combined strategies mitigated some DBL impacts but did not fully offset them, highlighting trade-offs in multimodal corridor prioritization.
Access status: Open Access ,
Biooxidation of Sulfide-Based Refractory Gold Ores at Low Temperature
(2026-07-24) Karimi Darvanjooghi, Mohammad Hossein; Brar, Satinder Kaur
Gold recovery from iron-sulfide-bearing refractory ores presents significant challenges, particularly at low temperatures where traditional pretreatment methods, such as microbial biooxidation, exhibit reduced efficiency. This research systematically investigates and compares two biooxidation strategies—microorganism-assisted biooxidation using Ferroplasma acidiphilum and an enzymatic biooxidation technology (EnBiTe) based on glucose oxidase (GO) immobilization—to enhance gold liberation from refractory ores. Bench-scale experiments demonstrated that microbial biooxidation achieved optimal pyrite dissolution (~75%) under controlled conditions, including a 15-day operating time, a pyrite content of 7.2 wt.%, and a pH of 1.47. However, microbial oxidation efficiency significantly decreased at temperatures below 10°C due to the metabolic limitations of acidophilic microorganisms. To mitigate these limitations, extracellular polymeric substances (EPS) overproduction was induced through monosaccharide supplementation, with D-sucrose yielding the highest ferric ion production and pyrite dissolution. Despite these improvements, the microbial biooxidation approach remained highly sensitive to environmental fluctuations and required precise control of pH, aeration, and nutrient availability to maintain microbial activity, making it less viable for cold-climate applications. Moreover, machine learning models, including Artificial Neural Networks (ANN) and Genetic Programming, were employed to predict biooxidation efficiency based on key parameters such as time, pH, oxidation-reduction potential (ORP), pyrite content, and monosaccharide concentrations. To address the limitations of microbial biooxidation in low-temperature environments, enzymatic biooxidation technology (EnBiTe) was developed as an alternative pretreatment strategy. This method employed glucose oxidase (GO) immobilized on modified sawdust to catalyze the oxidative dissolution of pyrite and arsenopyrite, thereby liberating encapsulated gold. The process optimization study revealed that EnBiTe achieved up to 89.2% pyrite dissolution for high-grade ore and 73.4% for low-grade ore under conditions including a 100 mM glucose substrate concentration, 5 g immobilized enzyme, and a pH range of 5–6. Unlike microbial biooxidation, EnBiTe functioned effectively at low temperatures (~5–10°C) without requiring extensive aeration or pH adjustments. Furthermore, a modular enzyme system incorporating catalase (CAT) was introduced to mitigate oxidative stress and enzyme degradation, extending process stability. Column test evaluations confirmed that EnBiTe not only enhanced pyrite oxidation but also facilitated faster gold liberation, demonstrating a significant advantage over microorganism-assisted methods. The controlled enzymatic approach also reduced operational uncertainties, providing a scalable and environmentally sustainable alternative to conventional biooxidation. A comparative analysis of the two biooxidation strategies indicated that enzymatic biooxidation significantly outperformed microbial biooxidation in low-temperature conditions, achieving higher pyrite oxidation rates and gold recovery efficiencies. Cyanidation trials following biooxidation showed that gold recovery reached 90% after EnBiTe pretreatment, compared to 60% following microbial biooxidation under optimized conditions. The findings of this research establish EnBiTe as a promising alternative to microbial biooxidation for gold recovery from refractory sulfide ores, particularly in cold regions where traditional methods struggle. By offering a more efficient, scalable, and environmentally friendly approach, this research contributes to the advancement of biohydrometallurgical processing and provides a foundation for potential industrial applications of enzymatic biooxidation in the mining sector.
Access status: Open Access ,
Biofilm Control Inside Secondary Water Storage Containers Using UV LIght
(2026-03-10) Di Falco, Patrick Alexander; Gora, Stephanie
This thesis investigates the feasibility of ultraviolet (UV) light treatment for biofilm control inside secondary storage containers used in humanitarian settings. A scoping literature review investigating real‑world UV light treatment application revealed that, even though it is effective beyond controlled laboratory settings, it is underexplored as a method for biofilm control in humanitarian contexts. To address this gap, a ray tracing model was developed to simulate UV irradiance distribution within a representative jerry can, and experimental validation showed model predictions were within 17% of measured values. Lab-grown biofilms were then treated at locations inside the jerry can receiving moderate and low levels of UV irradiance. At a maximum UV dose of 16 mJ/cm², the location receiving moderate UV irradiance achieved 2.47 ± 0.75 (average ± standard deviation) log-reduction value (LRV) while the location receiving low UV irradiance achieved 2.99 ± 0.07 LRV. Comparisons with other UV/biofilm research showed that similar germicidal thresholds could be achieved even with the lower parameters incorporated into this research. These findings demonstrate that UV light treatment is a technically viable method for biofilm control inside secondary storage containers used in humanitarian settings. Future research investigating this treatment method under real world conditions will prove to be the next key step in determining its feasibility for future implementation in humanitarian contexts.
Access status: Open Access ,
Addressing Zoning Abbreviation Inconsistencies in the Greater Toronto Area (GTA) through a Universal Zoning Ontology
(2026-03-10) Mesbahian, Arian; Jadidi Mardkheh, Amaneh
Urban development in the Greater Toronto Area (GTA) is increasingly constrained by inconsistencies in zoning abbreviations and regulatory terminology across municipal jurisdictions. Zoning labels that appear similar, such as “R2” in the City of Toronto and “R2 S” in the City of Markham, often represent different land use permissions and development standards. These semantic discrepancies create confusion for planners, developers, and regulators, leading to inefficiencies, legal ambiguity, and prolonged approval timelines. At the same time, Ontario faces a significant housing supply crisis. Despite the provincial target of delivering 1.5 million new homes by 2031, housing production continues to lag, with projections estimating only 81,300 units in 2024. Contributing factors include limited servicing capacity for water, wastewater, and stormwater infrastructure, as well as fragmented and slow municipal approval processes. When combined with inconsistent zoning terminology, these challenges further delay housing delivery and undermine coordinated regional planning. To address this systemic issue, this research introduces Zonology, a machine readable, ontology-based framework designed to semantically standardize zoning terminology across municipalities. Using the City of Toronto and the City of Markham as proof of concept case studies, Zonology harmonizes over 60 zoning categories and aligns more than 150 permitted land uses within a shared semantic structure. Zonology formally models zoning designations, permitted land uses, development standards, and spatial relationships by integrating municipal zoning bylaws, planning regulations, and geospatial data. The framework supports semantic querying and interoperability with Geographic Information Systems (GIS), automated planning workflows, and smart city applications. By resolving semantic and regulatory fragmentation, Zonology enhances data driven decision making, improves inter municipal collaboration, and provides a scalable foundation for consistent, future ready zoning governance in the GTA and other multi-jurisdictional regions.
Access status: Open Access ,
A Multi-Stage Optimization Framework for Battery Swapping in Urban E-Bike Sharing Systems
(2026-03-10) Farshchi Heydari, Fatemeh; Nourinejad, Mehdi
Shared electric bicycles (e-bikes) are increasingly central to urban transportation, providing a faster, more accessible alternative to conventional bicycles for short trips. As their use grows, maintaining sufficient battery levels across large fleets becomes critical. Among battery management strategies, battery swapping, which replaces depleted batteries with fully charged ones, offers a scalable solution, especially for dockless systems that lack fixed charging stations. However, it presents logistical challenges related to vehicle routing, battery delivery, and capacity constraints. This thesis introduces an optimization framework that combines dynamic clustering with a dual-objective vehicle routing model. E-bikes requiring service are grouped into van-feasible clusters based on battery level, spatial proximity, and fleet availability. Each cluster is then optimized to minimize travel distance while maximizing economic return. The framework is applied to real-world data from San Francisco’s Bay Wheels system, accessed via the General Bikeshare Feed Specification (GBFS). Results show that the proposed method reduces travel distance and enhances the efficiency of battery-swapping operations.
Access status: Open Access ,
Cyber-security Aware Traffic Flow Modeling and Data Processing Power Optimization
(2026-03-10) Khalajiolyaie, Mahdiye; Nourinejad, Mehdi
The growing deployment of connected and automated vehicles (CAVs) introduces new opportunities and challenges at the intersection of traffic engineering and cybersecurity. While CAVs leverage onboard sensors and computing to navigate their environment, the integration of vehicle to vehicle (V2V) and vehicle-to-infrastructure (V2I) communications has elevated expectations for cooperation, safety, and efficiency. This connectivity, however, introduces system-level vulnerabilities that are not present in isolated autonomous vehicles. In this thesis, we develop a queueing-based analytical framework to examine how cyber-induced communication loads and adversarial message infiltration influence the real-time processing of detection and control signals within CAVs. By formulating and solving optimization problems for resource allocation, we identify how limited computational capacity should be divided between detection and decision-making tasks to maintain traffic performance in adversarial environments. The model derives closed-form relationships between processing rates and macroscopic traffic variables such as delay, flow, and speed. Numerical experiments reveal critical trade-offs, as malicious message share increases, the system must prioritize defensive processing at the cost of responsiveness, leading to changes in traffic efficiency. Our findings emphasize the need for cybersecurity-aware traffic flow models and provide operational insights into the design of resilient and adaptive cooperative driving systems.
Access status: Open Access ,
Coupling Geothermal Heating with BTEX Bioremediation in the Subsurface
(2026-03-10) Kaur, Gurpreet; Brar, Satinder Kaur
There has been a worldwide interest in renewable energy technologies as a means of reducing reliance on fossil fuels, mitigating the effects of climate change, and reducing greenhouse gas emissions. One such technology is geothermal heating, where the constant subsurface temperature is used to cool or heat building interiors via heat pumps. In Canada, the use of geothermal heat pumps (GHPs) has become a popular option for heating and cooling buildings. It is anticipated that, in the near term, most large buildings will incorporate GHPs as part of their climate control strategy. However, little is known about the environmental impacts of geothermal heating on the subsurface environment. The present thesis examined the effect of geothermal heating on groundwater flow and remediation efforts, whereby the heat generated by geothermal systems may aid in addressing urban pollution. "Geothermal remediation" could leverage the subsurface heating resulting from geothermal systems to accelerate biodegradation of certain petroleum-based pollutants at brownfield sites, while providing building(s) with sustainable heating and cooling. This idea coincides with the rising momentum towards sustainable and green remediation in Europe and the United States. To ensure that Geothermal remediation is achievable, the effect of heat on bioremediation needs to be examined. This research investigated the heat effects on the bioremediation potential of pure culture and consortia and their potential for (Benzene, Toluene, Ethylbenzene and Xylene(s)) BTEX degradation as a pollutant. In the present thesis, soil microorganisms with the potential to degrade BTEX were isolated using an enrichment method from soil samples collected at different depths from geothermal boreholes. The microbes were screened and optimized for BTEX degradation at three different temperatures (15, 28 and 40 °C). The bacterial strains Microbacterium esteraromaticum and Bacillus infantis exhibited the highest degradation compared to other isolated strains and the reference strain, Pseudomonas putida. All four BTEX compounds were metabolized 2 times faster at 28 °C and 40 °C. Metabolomics data showed that BTEX was metabolized entirely to acetaldehyde and carbon dioxide by these selected strains. The catechol 1,2-dioxygenases, catechol 2,3-dioxygenases, and toluene monooxygenase enzyme activity confirmed the tol and tod degradation pathways. Furthermore, the present work offers new insights into the responses of soil microbial communities to electron acceptors under anoxic conditions, indicating that intrinsic microorganisms can be successfully stimulated for in-situ bioremediation (ISB) with electron acceptors as a supplement. The investigation revealed a maximum BTEX biodegradation of 57% by B. infantis under sulfate reduction and overall, 98% by M. esteraromaticum in combined nitrate and sulfate reduction. To understand the soil matrix influence and to mimic geothermal heating effects, small-scale soil batch experiments and continuous soil column experiments with cyclic temperature were performed. The results revealed that cyclic temperature of 5 °C to 40 °C (shallow low enthalpy geothermal temperature range) enhanced the BTEX biodegradation by 2-fold in silty loam soil (> 80%) in comparison to constant aquifer temperature (12 °C) (40%). Finally, a metagenomics study was performed on soil samples from different depths at three temperatures (15, 28, and 40 °C) to provide insight into how geothermal heat could impact the soil microbiome and its effect on bioremediation activities. Potential known strains for BTEX biodegradation such as Pseudomonas, Arthrobacter, Bacillus, as well as some novel strains such as Microbacterium, Janthinobacterium, Methylotenera, were found to be dominant at 28 °C and 40 °C. Since microbial abundance and diversity decreased drastically at 15 °C; these findings showed the potential of geothermal heating as a sustainable heat source for ISB of pollutants.
Access status: Open Access ,
Production of Volatile Fatty Acids from Food Waste
(2026-03-10) ., Reema; Brar, Satinder K.; Kaur, Guneet
Food waste is a major environmental concern, often ending up in landfills or incinerators, contributing to greenhouse gas emissions and the loss of valuable organic matter. Conventional treatment methods like composting or anaerobic digestion offer limited resource recovery, particularly in colder climates where energy demands for heating remain high. As the demand for sustainable and climate-adaptable solutions grows, volatile fatty acids (VFAs) have emerged as valuable intermediates for bio-based products such as bioplastics and biofuels This research explores the microbial production of VFAs from food waste under psychrophilic conditions (≤20 °C), presenting a low-energy alternative aligned with cold-climate needs. Compared to traditional mesophilic systems, fermentation at 17 °C resulted in slower hydrolysis but showed a distinct shift in the VFA profile, with enhanced butyric acid accumulation. Microbial community analysis revealed the dominance of psychrotolerant genera such as Solibacillus, Sporosarcina, and Paenibacillus, which supported butyrate-producing Clostridium species. These findings highlight the potential for pathway-specific adaptation at low temperatures. To improve process efficiency, substrate solubilization was enhanced using thermal-alkaline pretreatment and rhamnolipid biosurfactants, which led to a twofold increase in VFA yield (up to 4.4 g/L). The addition of rhamnolipids not only improved lipid accessibility but also favored acidogenic microbial populations over lactic acid producers, promoting more efficient fermentation. Further targeted butyric acid was enhanced through bioaugmentation with Clostridium butyricum, a known butyrate producer. Its introduction significantly increased butyric acid concentration by sevenfold (reaching 1.4 g/L), validating the approach of targeted microbial steering even under low-temperature conditions. Overall, this study demonstrates the feasibility of psychrophilic fermentation as a sustainable platform for producing VFAs from food waste. By integrating pretreatment, microbial community insights, and bioaugmentation, the research offers a practical framework for resource recovery in cold regions, advancing circular bioeconomy goals while addressing food waste challenges.
Access status: Open Access ,
Thermal Effects on Concrete Bridges in a Changing Climate
(2026-03-10) Saad, Saad; Bashir, Rashid; Pantazopoulou, Stavroula
The research presented herein aims to: 1) Investigate the suitability of AASHTO Bridge Design Specifications in quantifying the design thermal gradients that account for cold wave events, 2) Study the effect of different climate parameters on thermal gradients to help improve current guidelines and ensure that thermal gradients are derived based on the actual bridge location, 3) Quantify the effect of freezing temperature on the coefficient of thermal expansion (CTE) of concrete, 4) Analyze the structural response of a bridge structure to cold wave events, while considering the effect of temperature on the magnitude and sign of the CTE of concrete, and 5) Investigate the effect of climate change on thermal load. The objectives of this work were achieved mainly using numerical finite element 3D models. Furthermore, the relationship between sub-freezing temperature and thermal strain was studied through experimental testing of concrete cylinders. A weather generator was used to simulate future climate conditions to study the impact of climate change on thermal loads. The findings indicated that current guidelines fail to capture the true thermal load distribution within a bridge superstructure, which leads to an underestimation of the resulting structural implications, particularly the tensile stresses at the bottom of the cross section. It was also determined that a correlation exists between the direct normal irradiance at a specific location and the resulting thermal differential in a bridge. In addition, the effects of subfreezing temperature on the CTE of concrete were found to be significant and to strongly impact the structural behavior of bridges under cold wave events. For instance, a significant increase in tensile stress in both transverse and vertical direction was predicted during a high intensity cold wave event, an issue which can cause concrete cracking. Furthermore, a methodology to model future hourly climate data was developed, through which it was determined that climate change will have considerable effects on thermal loads on bridges in the future. For example, it was determined that climate change can cause an increase of about 5℃ and 6℃ in the absolute maximum positive thermal differential in bridges located in Toronto and Whitehorse respectively.
Access status: Open Access ,
Studying the Effect of Hydraulic Hysteresis with Air Entrapment on Solute Transport and Slope Stability Under Different Climatic Conditions
(2026-03-10) Moustafa, Moamenbellah Mohye Abdelhamid Elsayed; Bashir, Rashid
Hysteresis, a natural soil phenomenon, manifests distinct hydraulic responses influenced by the soil-water characteristic curve (SWCC), reflecting soil pore paths during infiltration and drainage. This study investigates the interplay between hysteresis and air entrapment, essential for accurate hydrological modeling under intermittent water flow. Despite their significance, numerical models often overlook hysteresis, relying on nonhysteretic curves. This research explores the impact of hysteresis with air entrapment on solute transport and slope stability in diverse scenarios. Comparisons between hysteretic and nonhysteretic analyses reveal increased water fluxes and deeper solute migration when considering both hysteresis and air entrapment. Neglecting these factors leads to inaccurate solute fate and slope stability assessments. In slope stability analysis, air entrapment significantly lowers suction strength and factor of safety, potentially triggering slope failures. This thesis establishes the critical importance of considering both hysteresis and air entrapment for robust hydrological and geotechnical assessments.
Access status: Open Access ,
Development, Material and Structural Performance of Tension Hardening Fiber Reinforced Geopolymer Concrete (THFRGC)
(2026-03-10) Ralli, Zoi Georgios; Pantazopoulou, Stavroula
On account of growing environmental and economic concerns, decarbonization of the concrete industry has become a priority with the development of environmentally friendly building materials to attract both research community and industry. A class of advanced eco-friendly building materials is geopolymer concretes. Their production incorporates industrial by-products in lieu of cement which has a double benefit in terms of sustainability: recycling industrial wastes instead of harmful disposal and reducing carbon footprint by eliminating cement. Meanwhile, Tension-Hardening Fiber Reinforced Concrete (THFRC) shows great potential as a structural material for modern infrastructure due its enhanced tensile strength and ductility. Although THFRC is considered a sustainable solution thanks to the tension-hardening delaying the need for retrofits, the high amount of Ordinary Portland Cement (OPC) used as a binder raises concerns regarding the sustainability of the material hindering the widespread application on account of the high cost and carbon footprint. This dissertation aims to advance the knowledge on sustainable and high-performance building materials by developing and characterizing a Tension-Hardening Fiber Reinforced Geopolymer Concrete (THFRGC) in terms of its material and structural behaviour. After a thorough review of the related literature, the experimental stage comprises the characterization of various mineral powders, mix design based on chemical and physical optimization, material identity characterization according to North American Standards prescribed for conventional THFRCs, determination of bond-slip law of reinforcing bar in THFRGC and performance under biaxial stress states. To promote the widespread use of the material, rheology, and fiber orientation in THFRCs are also explored using destructive and non-destructive techniques. Furthermore, this project aims to tackle the conundrum of tensile characterization of THFRC by developing a novel indirect tension technique that combines the simplicity of splitting test with the ability to obtain the whole response in pure tension. The proposed test is numerically validated using advanced Non-Linear Finite Element Analysis. The latter was also employed to investigate the validity of the tensile response obtained using the Inverse Analysis proposed by CSA, S6, Annex 8.1. Finally, self-sensing performance of a multifunctional nanoengineered THFRC is explored to pave the way for smart THFRCs in Structural Health Monitoring (SHM) applications.
Access status: Open Access ,
Subscription & Per-Day Pricing vs Same-Day Service: A Simulation of Temporally Consolidated Delivery Systems
(2025-11-11) Jeoung, Won Mo; Nourinejad, Mehdi
Same day delivery is often viewed as the benchmark for last mile logistics because of its convenience and time savings, but its adoption as a wide spread service is limited by high costs. Orders vary between time and location, restricting economies of scale and often resulting in inefficient routes where multiple vehicles serve the same neighborhood within short intervals. To address these issues, this thesis introduces temporal consolidation, where deliveries are grouped and scheduled for specific days of the week or month. Using simulations of customers with different order behaviors and locations, we evaluate delivery scenarios that include grouping requests, optimizing travel routes, and adjusting delivery frequencies. Results show that consolidated deliveries reduce total travel distance compared with same day service. Customers are generally willing to accept longer consolidation periods when paired with cost savings, while higher delivery prices increase demand for faster service.
Access status: Open Access ,
Data-Driven Bike-Share Ridership Prediction and Network Optimization
(2025-11-11) Mohseni Hosseinabadi, Ghazaleh; Park, Peter; Nourinejad, Mehdi
Shared micro-mobility systems, particularly station-based bike-sharing networks, have become key components of urban transportation, yet their planning remain challenged by spatial and technological complexities. This dissertation develops integrated models for ridership prediction, station placement optimization, and electrification planning to address these challenges. First, a customized Graph Neural Network framework using GraphSAGE is introduced for station-to-station ridership prediction, integrating network topology, sociodemographic features, and station attributes. Applied to Toronto, the model outperforms linear, spatial, and tree-based benchmarks, demonstrating its ability to capture latent dependencies and support demand-responsive planning. Second, a continuum approximation model is proposed for station placement optimization, using a force-based algorithm that balances attraction from demand centers with inter-station forces. This ridership-driven approach departs from conventional accessibility methods by directly aligning locations with demand. Applied to Vancouver, the model reveals optimal spacing patterns and highlights strategies for ridership-driven network expansion under varying demand conditions. Third, the dissertation extends infrastructure planning to electrified systems by introducing a two-dimensional Markovian state-of-charge framework for e-bikes. A heuristic charger deployment algorithm, enhanced by a single-pooling state approximation, maximizes expected ridership and identifies high-impact charging locations, achieving near-optimal performance in case studies from Pittsburgh, Vancouver, and San Francisco. Finally, the models are integrated into a web-based, GIS-enabled decision-support tool that combines predictive, prescriptive, and descriptive analytics to enable scenario-based planning. Demonstrations in Toronto and Vancouver illustrate the tool’s scalability and practical value. This research advances methodological foundations and practical tools for developing resilient, data-driven, and electrified bike-sharing networks across diverse urban environments.
Access status: Open Access ,
Signal timing for LCV trucks on a road network using reinforcement learning
(2025-11-11) Ghanbari Sefiddargoleh, Mohammad; Gingerich, Kevin
Freight activity in urban networks is rising, and jurisdictions such as Ontario are encouraging the use of Long Combination Vehicles (LCVs) to consolidate freight loads. This thesis quantifies the delays and queueing on 16 intersections in the Region of Peel and introduces an adaptive signal-control strategy. Tested scenarios include (1) existing signal timing plans without LCVs and (2) with LCVs, (3) a single-intersection double deep q-network (DDQN) controller without LCVs and (4) with LCVs. Introducing LCVs under existing signal timings raised network-wide delay by 14 % for all vehicles and 22 % for trucks when LCVs comprised just 1.7 % of traffic. The proposed DDQN was found to reduce average delays for all vehicles and trucks based on various conditions. Future work should extend the single intersection approach to a multi-agent framework and explore continuous-time action spaces for even finer control.
Access status: Open Access ,
Surface Infrastructure Improvement for Efficient Long Combination Vehicles and Truck Platooning Operation
(2025-11-11) Akkeh, Jowel; Park, Peter Y.
The growing demand for freight transportation has led to increased congestion on urban arterial roads, requiring innovative solutions such as long combination vehicles (LCVs) and heavy commercial vehicle (HCV) platooning. However, these approaches face challenges at intersections due to limited green time and insufficient lane storage for left turns. This study examines the use of Intelligent Transportation Systems (ITS) to enhance truck travel time, specifically through Freight Signal Priority (FSP) and dedicated truck left-turn lanes (DTLL). A methodology was developed to identify intersections requiring truck priority measures. Using PTV VISSIM, a micro-simulation model was created for a 19.2 km corridor with 32 signalized intersections. Freight vehicle composition included 5% LCVs and single-unit trucks. Eight models were used for comparative analysis. Model 1 represents the 'do-nothing' scenario that includes the assumption of 5% LCVs, Model 2 applies the FSP scenario, Model 3 includes the DTLL scenario, and Model 4 combines Models 2 (FSP) and 3 (DTLL). Model 5 assessed a 5% penetration rate of HCV Platooning under existing conditions. Model 6 adds FSP to Model 5, Model 7 adds DTLL to Model 5, and Model 8 combines Models 6 (FSP) and 7 (DTLL). The study found that implementing both FSP and DTLL together yields better results than applying each individually. This integrated approach demonstrated improvements in efficiency, traffic flow, and sustainability by reducing travel time and greenhouse gas (GHG) emissions for all vehicles.

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