449 research outputs found
Comparative influence of active PLA and PP films on the quality of minimally processed cherry tomatoes
No full textMinimally processed fruits and vegetables (F&V) are highly prone to oxidative deterioration and despite many efforts, no tangible solution has been found. Thus, this study was designed to evaluate the influence of antioxidant-releasing PLA (polylactic acid) and PP (polypropylene) films incorporated with orange peel extract (OPE) on the quality of cherry tomatoes during storage. Films were characterized based on color parameters, barrier properties and potential migration of volatile compounds from packaging into the food systems. The success of OPE encapsulation and molecular interactions between extract and polymeric chains was confirmed by FT-IR. The release analysis was performed in terms of DPPH radical scavenging activity and through GC-MS analysis (through liquid injection and SPME). Finally, the influence of the packaging material on the quality of cherry tomatoes was ascertained through oxidative enzyme activity and the production of volatile organic compounds. The effect of the extract on the oxygen permeability depends by the film. There was a significant difference (p < 0.05) in compounds that migrated from the control and active PLA films as observed through GC-MS. Finally, cherry tomatoes packed with active PLA films displayed more total polyphenolic content (TPC) retention and reduced volatile compounds (i.e., hexanal) at the end of storage as compared to PP films. Thus, active PLA films have the potential to be used as a replacement packaging material to PP for cherry tomatoes
Aggregative risk analysis for water quality failure in distribution networks
No full textAggregative risk analysis for water quality failure in distribution networks Sadiq, R., Kleiner, Y., Rajani, B
Evaluating low impact developments (LIDs) beyond storm water management : a framework for the public health, environmental performance, and cost analysis
No full textLow Impact Developments (LIDs) are becoming increasingly popular for their benefits to sustainable and cost-effective stormwater management. LIDs include rain gardens, water ponds, and other nature-based infrastructures. However, both stormwater and LIDs contain microbial pathogens sourced from animal waste because these infrastructures are home to many animals and insects. The widespread use of LIDs is likely to increase human exposure to pathogens and the risk of infection, potentially leading to unexpected disease outbreaks in urban communities. The evaluation of LIDs entails the complexity of stormwater regulations, public health risks, environmental performance, and cost. Currently, an assessment of LIDs that confirms their environmental performance and ability to meet public health targets is not feasible. Therefore, an advanced decision-making framework is required to support risk-based planning of LIDs prior to their installation in urban communities.
This research aims to develop an integrated decision-making framework that facilitates the evaluation of LIDs by considering their microbial risks, environmental performance, and cost. A four-step research approach was adopted to achieve this goal. First, I completed a comparative analysis of Canadian stormwater regulations and guidelines, identifying the use of LIDs in ten provinces. In the second step, I integrated Quantitative Microbial Risk Assessment (QMRA) and predictive modeling to evaluate the public health risks during the planning phase of LIDs. In the third step, I employed the life cycle impact assessment and life cycle cost analysis and later compared the environmental performance and cost of various LIDs. Lastly, I applied Multi-Criteria Decision-Making (MCDM) method to combine public health risks, environmental performance, and life cycle cost of LIDs. The findings indicate that the weightage of these criteria influences the ranking of LIDs. Wetland is the best LID in terms of public health and economic cost, whereas box planter has become a top priority in terms of environmental performance.
The developed framework represents an important step toward addressing microbial risks, environmental performance, and the cost of LIDs in an integrated fashion. The proposed framework offers a unique opportunity to prioritize competing interests and guide actions that ensure well-being and sustainable development. Overall, this framework is valuable for contributing to the UN's 2030 Agenda goals, particularly SDGs 3 and 13.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
An integrated fire risk management of multi-unit residential buildings with smart and green features : machine learning-based framework
No full textThe building industry has been facing significant challenges, including high quality, sustainability, cost-effectiveness and safety. Fire risk poses a significant threat and impacts on public safety, property, and the environment. Canada recorded 0.5 million fires in 10 years, which caused 15,000 fatalities, a direct loss of $7.5 billion, 1,200 children were injured, and 1.2 million Canadians were affected directly. In British Columbia (BC), 55% of the fires were associated with multi-unit residential buildings, which are being increasingly built with smart and green features for a positive impact, however, these features may negatively impact from a fire’s perspective.
This research developed an integrated fire risk management framework using machine learning algorithms for multi-unit residential buildings integrating smart and green features (SG-MURBs). Five phases were incorporated in the framework to enhance fire prevention, protection, and intervention strategies: (I) investigated state-of-the-practice to explore fire risk management practices (II) Identified key potential fire risk and safety-related factors considering relative frequencies and their related impacts (III) Developed benchmarks to set the acceptability levels and determine the critical factors accordingly (IV) Developed models to predict the potential impacts of fire incidents in SG-MURBs, and finally (V) Generated optimal solutions and optimized multidimensional fire impacts.
The developed framework was applied and tested for seven cities in BC, Canada. The results discerned the key potential contributing factors to fire incidents covering common, smart, and green factors, including 40 ignition sources, 28 human errors, and 36 combustible materials. Using the developed benchmarks, Vancouver, Kelowna, and Kamloops were found the top critical cities with 58%, 37%, and 47% of very high levels of ignition sources, human errors, and combustible materials, respectively. The most suitable methods for fire control and fire suppression materials for the top critical combustible materials were determined. The ANN models were found to outperform classifiers with prediction abilities of 65%, 72%, 85%, and 99% in predicting related potential impacts. Moreover, the optimal set of fire safety strategies were determined using genetic algorithms to enhance fire risk management. The developed framework will help decision-makers guide policies and enhance investments for specific fire prevention, protection, and intervention strategies accordingly.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
Optimization of water quality and energy use in large water distribution systems
No full textA water distribution system (WDS) is the most expensive and energy-intensive component of a water supply system. Maintaining acceptable water quality (WQ) and adequate pressure with minimum energy use is important in drinking water management. At the very least, water utilities must maintain a certain residual chlorine level to inactivate the pathogens and minimize human health risks. In terms of energy use, inefficient pump scheduling is a common problem identified in water distribution, leading to high energy costs. Due to increasing urbanization, population growth, high energy costs, and related greenhouse gas emissions, there is an increasing need to improve WQ and minimize energy usage simultaneously. Thus, leading to the development of sustainable water management strategies for large WDS. The optimization in the context of WQ and energy usage for large WDS alone is a challenging task. Until now, no established solution is available, as most of the past studies to find an optimal solution were either having a compromised WQ or unable to deal with the increasing energy demands of a large WDS. This research developed an integrated WQ and energy use optimization framework for an existing large WDS. At first, the water quality index and risk-based fuzzy failure modes effect analysis methods were applied to assess and optimize WQ. Later, optimal pump scheduling was identified to minimize energy use by employing a mixed integer goal programming model. In the last phase, both hydraulics and WQ performances were evaluated by developing a unique optimization approach. The proposed framework was demonstrated using a WDS in Al-Khobar (a large-sized city in Saudi Arabia). The case study results reflected the merits of the developed framework as ~20% energy cost reduction was obtained. Similarly, the use of the proposed optimization framework predicted > 40% WQ improvement. The proposed framework is flexible and can be applied to WDS of different sizes. It can assist water utilities in achieving higher performance with minimal energy use.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review
No full textCOVID-19 is deemed as the most critical world health calamity of the 21st century, leading to dramatic life loss. There is a pressing need to understand the multi-stage dynamics, including transmission routes of the virus and environmental conditions due to the possibility of multiple waves of COVID-19 in the future. In this paper, a systematic examination of the literature is conducted associating the virus-laden-aerosol and transmission of these microparticles into the multimedia environment, including built environments. Particularly, this paper provides a critical review of state-of-the-art modelling tools apt for COVID-19 spread and transmission pathways. GIS-based, risk-based, and artificial intelligence-based tools are discussed for their application in the surveillance and forecasting of COVID-19. Primary environmental factors that act as simulators for the spread of the virus include meteorological variation, low air quality, pollen abundance, and spatial-temporal variation. However, the influence of these environmental factors on COVID-19 spread is still equivocal because of other non-pharmaceutical factors. The limitations of different modelling methods suggest the need for a multidisciplinary approach, including the ‘One-Health’ concept. Extended One-Health-based decision tools would assist policymakers in making informed decisions such as social gatherings, indoor environment improvement, and COVID-19 risk mitigation by adapting the control measurements
Management of unregulated disinfection by-products in water distribution networks : an integrated framework
No full textGood water quality is essential for ecosystem health and human survival. Issues related to deteriorating water quality and its availability with sufficient quantity have become the grand environmental challenges of our times. In an urban environment, water is supplied to the consumers through water distribution networks (WDNs), designed to supply with adequate quantity with acceptable quality. The WDNs performance in terms of service delivery due to internal and external challenges cause water quality failure (WQFs) and can be linked to adverse human health effects. Microbial contamination and the formation of disinfection by-products (DBPs) are a few of the prominent WQF pathways. DBPs in drinking water and their exposure through ingestion and inhalation have been associated with cancer and non-cancer risks. There are numerous regulated and unregulated DBPs (UR-DBPs) present in public water supplies. Water utilities are now increasingly concerned about UR-DBPs because their occurrence has been consistently reported in drinking water supplies. Current research develops an integrated management framework to identify, prioritize, predict, and control the occurrence of UR-DBPs in WDNs. In this study, dichloroacetonitrile (DCAN), trichloropropanone (TCP), and trichloronitromethane (TCNM) were identified as commonly occurring UR-DBPs. The framework is implemented in four phases. Phase 1 identifies and prioritize the commonly occurring UR-DBPs; phase 2 estimates their levels in WDNs through predictive modeling approaches; phase 3 develops a water quality assessment approach to classify water quality and evaluate the impacts; phase 4 prioritizes management alternatives for UR-DBPs control using multi-criteria decision making. The results of this research have the potential to be used at different levels by utility managers, planners, and water purveyors to improve the drinking water quality management in terms of UR-DBPs. Furthermore, the decision-makers can identify and prioritize the UR-DBPs, predict their occurrence in WDNs, and evaluate the impacts of deteriorating drinking water quality on natural water resources. From the management perspective, the applicability of various management strategies in WDNs can be assessed under given settings and criteria. Thus, the water utilities can prioritize investment alternatives by identifying critical contaminants and acknowledging a trade-off between the delivered water quality and associated impacts.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
Ecological risk assessment of tire and road wear particles : a preliminary screening for freshwater sources in Canada
No full textApproximately 1.5 billion tires/year are produced worldwide. Tire tread wear particles due to road abrasion are a major source of environmental microplastic pollution and 5.9 million tonnes/year of tire and road wear particles (TRWP) are emitted globally. Twelve to twenty percent of TRWP are transmitted into surface waters, where they can leach (i.e., release) chemical compounds that can adversely affect aquatic species. The primary objective of this study was to assess the acute, freshwater ecological risk of TRWP emissions. A conceptual ecological risk assessment (ERA) model was developed using ERA frameworks. The model was applied to Kalamalka Lake with respect to British Columbia (BC) Highway 97 (HWY-97) in Western Canada. This was the first ERA of TRWP in Canada and the results and methodology will provide a foundation for future research. This was a screening-level, conceptual ERA, based on secondary data from published scientific studies. Two spatial scenarios were assessed. Spatial scenario 1 assumed annual TRWP emissions from 20 km of HWY-97 equilibrate across the entire lake volume. Spatial scenario 2 assumed TRWP emission from a single storm drain microcatchment (0.875 km of HWY-97) equilibrated adjacent to the shoreline in a smaller water volume in the short-term. For both scenarios, TRWP-derived chemical leachates considered for ERA were aniline, anthracene (ANT), benzo(a)pyrene (B(a)P), fluoranthene (Fl), mercaptobenzothiazole (MBT), and zinc (Zn). An assumed ‘total TRWP-derived leachate set’ was also assessed, based on data representing all compounds present in tire-derived leachate solution tests. The results indicated a potential risk to aquatic species in both spatial scenarios, depending on the contaminant of concern. In Scenario 1, ecotoxicity risk was high from exposure to zinc and the total TRWP-derived leachate set. Scenario 2 results indicated acute risk was high from all TRWP-derived chemicals examined, except MBT. This preliminary screening of ecological risk provides an early signal that freshwater lakes adjacent to busy highways may be at risk from TRWP contamination, demonstrating a need for further research and additional data to verify the results.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
Selection and optimization of marine oil spill response operations using artificial intelligence and soft computing techniques
No full textMarine oil spill incidents are detrimental to both natural environment and human health. Water quality, marine ecosystems, and shoreline conditions can be deteriorated by the spilt oil. Swift and efficient response to an oil spill is crucial to minimize the adverse consequences. However, the oily waste generated from response operations may also become a challenge, requiring careful waste management strategies. Widely used oil spill response methods (OSRMs) include mechanical containment and recovery (MCR), in-situ burning, and the use of chemical dispersants. Choosing the most suitable method is a complex process depending on various factors. Among OSRMs, MCR is the most effective in removal of spilt oil from the marine environment. The management of oily wastewater generated during MCR requires careful attention, as it comprises a significant portion of overall oily waste.
This study developed multiple tools to aid selecting OSRMs in harsh and remote offshore waters. These selection tools employ machine learning techniques and historical response data to predict appropriate OSRMs for new incidents. The tools were developed in MATLABTM using various artificial intelligence and soft computing techniques, such as fuzzy decision tree (FDT), Gaussian process regression (GPR), and artificial neural network, individually or in combination. FDT-based tools were also integrated with regression analysis techniques and an optimization algorithm to enhance their performance. Optimized FDTs integrated with regression analysis and GPR were found to be the most effective techniques based on the prediction power.
Furthermore, this study developed an integrated optimization tool to efficiently manage the mechanical response process. This tool aims to minimize the time and cost associated with MCR and oily wastewater management (OWM) and the volume of weathered oil during the operation. The tool encompasses three components of multi-objective optimization, oil weathering process, and MCR and OWM operational agents, simulating detailed response procedures. Applying the tool to a case study in Canada led to a notable reduction in the time and cost of the entire response, and a considerable increase in the volume of recovered oil. It provides an effective approach to manage response process, and significantly reduces the environmental and socio-economic impacts of oil spill incidents.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
Energy-efficient residential buildings with advanced insulation materials : a lifecycle thinking approach
No full textThe full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
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