Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Soleimanpour Makuei, Mehrnaz
Doctor of Philosophy - PhD
University of British Columbia
Traditional quality indicators used in water treatment facilities have limitations in assessing particle-related challenges and system performance under challenging conditions. This study seeks to explore the impacts of particle-microorganism interactions on ultraviolet (UV) disinfection performance, identify water quality indicators that best describe performance, and suggest innovative methodologies to mitigate these effects on disinfection performance. Several sampling points of two full-scale treatment plants, receiving surface water without filtration, were monitored to identify the impacts of particulates on UV performance. The research established a link between zeta potential or surface charge and disinfection effectiveness and results suggest that conventional water quality indicators do not adequately capture particle effects on UV disinfection. An examination of methods for detecting particle-microorganism associations was conducted to devise an experimental approach in line with contemporary research directions and supported by a reasonable theoretical foundation. To further explore the impact of challenging source conditions on UV disinfection, the second objective was to study a wider variety of water conditions. Results suggested that disinfection could be compromised even at low turbidities (< 1 NTU), depending on the water matrices. Furthermore, complex interactions with background source water quality and additional particulate and dissolved matters were observed and suggested that non-specific indicators (turbidity or UVT) as opposed to specific indicators (zeta potential) should be used with caution when predicting the UV performance specifically under challenging conditions. Overall, zeta potential enhanced UV disinfection predictability. The observed influence of zeta potential on UV performance led to proposing a UV enhancement strategy in relation to problems regarding particles. A new line of study was started to identify if manipulating particle-microorganism associations through zeta potential modifications was viable as disinfection enhancement strategy. Assessing zeta potential manipulation effects on water quality using bacterial and viral surrogates showed that samples with highly negative zeta potential result in reduced particle-microorganism associations and increasing disinfection efficacy. This research highlights the limitations of conventional indicators in assessing UV disinfection in challenging conditions. Zeta potential emerged as a key factor in improving assessment, leading to exploration of a novel pre-treatment approach for facilities that may experience significant impacts due to particulates.
ULTRAVIOLET DISINFECTION OF UNFILTERED WATER SUPPLIES: EXPLORING EFFECTS OF PARTICLE-MICROORGANISM ASSOCIATION AND EFFICIENCY ENHANCEMENT STRATEGIES by Mehrnaz Soleimanpour Makuei M.Sc., Amirkabir University of Technology, 2019 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) May 2024 © Mehrnaz Soleimanpour Makuei, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a dissertation entitled: ULTRAVIOLET DISINFECTION OF UNFILTERED WATER SUPPLIES: EXPLORING EFFECTS OF PARTICLE-MICROORGANISM ASSOCIATION AND EFFICIENCY ENHANCEMENT STRATEGIES submitted by Mehrnaz Soleimanpour Makuei in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Nicolas Peleato, School of Engineering Supervisor Dr. Deborah Roberts, School of Engineering, University of Northern British Columbia Supervisory Committee Member Dr. Alyse Kiesser, School of Engineering Supervisory Committee Member Dr. Joshua Brinkerhoff , School of Engineering University Examiner Dr. Caetano Dorea, Department of Civil Engineering, University of Victoria External Examiner iii Abstract Traditional quality indicators used in water treatment facilities have limitations in assessing particle-related challenges and system performance under challenging conditions. This study seeks to explore the impacts of particle-microorganism interactions on ultraviolet (UV) disinfection performance, identify water quality indicators that best describe performance, and suggest innovative methodologies to mitigate these effects on disinfection performance. Several sampling points of two full-scale treatment plants, receiving surface water without filtration, were monitored to identify the impacts of particulates on UV performance. The research established a link between zeta potential or surface charge and disinfection effectiveness and results suggest that conventional water quality indicators do not adequately capture particle effects on UV disinfection. An examination of methods for detecting particle-microorganism associations was conducted to devise an experimental approach in line with contemporary research directions and supported by a reasonable theoretical foundation. To further explore the impact of challenging source conditions on UV disinfection, the second objective was to study a wider variety of water conditions. Results suggested that disinfection could be compromised even at low turbidities (< 1 NTU), depending on the water matrices. Furthermore, complex interactions with background source water quality and additional particulate and dissolved matters were observed and suggested that non-specific indicators (turbidity or UVT) as opposed to specific indicators (zeta iv potential) should be used with caution when predicting the UV performance specifically under challenging conditions. Overall, zeta potential enhanced UV disinfection predictability. The observed influence of zeta potential on UV performance led to proposing a UV enhancement strategy in relation to problems regarding particles. A new line of study was started to identify if manipulating particle-microorganism associations through zeta potential modifications was viable as disinfection enhancement strategy. Assessing zeta potential manipulation effects on water quality using bacterial and viral surrogates showed that samples with highly negative zeta potential result in reduced particle-microorganism associations and increasing disinfection efficacy. This research highlights the limitations of conventional indicators in assessing UV disinfection in challenging conditions. Zeta potential emerged as a key factor in improving assessment, leading to exploration of a novel pre-treatment approach for facilities that may experience significant impacts due to particulates. v Lay Summary Disinfection is a crucial step in eliminating disease-causing organisms in water. However, the presence of particles in water can offer protection to these organisms, posing challenges in disinfection systems. Furthermore, the quality standards in water treatment guidelines and regulations often have limitations in representing the impact of particles on the final disinfection delivered. Climate change is affecting the available source water quality, adding stress to treatment facilities that have been designed based on conventional water quality indicators. This research deepens our insight into potential challenges faced by ultraviolet disinfection systems treating non-ideal source waters. It also explores suggestions for disinfection enhancements that would aid in protecting public safety. vi Preface I, Mehrnaz Soleimanpour Makuei, ideated, matured, developed, and wrote this thesis under the supervision of Dr. Nicolas Peleato. Three journal articles, one conference paper, two abstract presentations, and one poster presentation are currently published, accepted, or under review and have been prepared directly from the research presented in this thesis. The first journal paper (chapter 4) explored the impact of water characteristics on UV disinfection of unfiltered water samples. The second article (chapter 6) focused on the important factors affecting particle-microorganism association and UV disinfection with emphasize on the effect of source water, organics, and particle characteristics, while the third article (chapter 7) focused on manipulation of zeta potential as a pre-treatment for UV disinfection. Journal Articles • Soleimanpour M, M., Peleato, N. M., “Manipulation of Particle-Microorganism Association for Improved UV Disinfection of Surface Waters,” Status: Ready to submit. • Soleimanpour M, M., Peleato, N. M., (2023) “Factors Affecting Particle-Microorganism Association and UV disinfection: Effect of source water, organics, and particle characteristics,” Water Process Engineering Journal, Status: Accepted and Published, September 2023. vii • Soleimanpour M, M., Ketabchi, F., Peleato, N. M., (2022) “Impact of Water Characteristics on UV Disinfection of Unfiltered Water Sample,” Water Quality Research Journal, Status: Accepted and Published, June 2022. Conference Paper • Soleimanpour M, M., Peleato, N. M., “The impact of unfiltered source water characteristics on UV-LED disinfection efficiency,” In Canadian Society of Civil Engineering Annual Conference, Springer International Publishing. Status: Accepted, May 2023. Abstract presentation • Soleimanpour M, M., Ketabchi, F., Peleato, N. M., “Impact of Water Characteristics on UV Disinfection of Unfiltered Water Sample,” Presented in May 2022 at the British Columbia Water Works Association Annual Conference, Victoria, BC. • Soleimanpour M, M., Peleato, N. M., “The Impact of Source Water Quality and Particulate Type on UV Disinfection,” Presented in May 2023 at the British Columbia Water Works Association Annual Conference, Penticton, BC. Poster presentation • Soleimanpour M, M., Peleato, N.M., “Surface Charge Control for Improved Unfiltered UV Disinfection Performance,” Accepted in July 2023, American Water Work Association, Water Quality Technology Conference, Dallas, TX. viii Table of Contents Abstract ......................................................................................................................... iii Lay Summary ................................................................................................................. v Preface .......................................................................................................................... vi Table of Contents ....................................................................................................... viii List of Tables ................................................................................................................ xi List of Figures ............................................................................................................. xii Nomenclature ............................................................................................................ xvii Acknowledgements .................................................................................................... xix Dedication .................................................................................................................... xx Chapter 1: Introduction .............................................................................................. 21 1.1 The Challenge and Motivation ....................................................................... 21 1.2 Research Gaps ............................................................................................. 22 1.3 Research Objectives and Hypothesis ........................................................... 29 1.4 Thesis Structure and Organization ................................................................ 31 Chapter 2: Literature Review ..................................................................................... 34 2.1 Contaminants of concern - the need for drinking water treatment ................. 34 2.2 Water Treatment Processes: Disinfection ..................................................... 41 2.3 Water Quality Indicators ................................................................................ 49 2.4 Microorganism Association ........................................................................... 57 2.5 Microorganism Association Detection Methods ............................................. 60 2.6 Strategies to Improve the Efficacy of UV Disinfection ................................... 69 Chapter 3: Material and Methods............................................................................... 74 ix 3.1 Treatment Plants Description ........................................................................... 74 3.2 Water Sample Preparation ............................................................................ 74 3.3 UV Inactivation Kinetics of Microorganisms .................................................. 82 3.4 Water Quality Characterization ..................................................................... 84 3.5 Surrogate Microorganisms Propagation and Enumeration ............................ 85 3.6 Microorganism Association ........................................................................... 90 3.7 UV Reactor....................................................................................................... 94 3.8 Statistical Analysis ........................................................................................ 95 Chapter 4: Water Quality Characteristics of Unfiltered Water and UV Disinfection ... 97 4.1 Water Quality Characterization ..................................................................... 97 4.2 Organism Inactivation ................................................................................. 103 4.3 The Effect of Particles on UV Resistance and Tailing ................................. 109 4.4 Summary ..................................................................................................... 114 Chapter 5: The Analytical Methodology of Particle-Microorganism Association Detection ................................................................................................................. 117 5.1 Results and Discussion ............................................................................... 117 5.2 Summary ..................................................................................................... 128 Chapter 6: Factors Affecting Microorganism Association and UV Disinfection: Effect of Source Water, Dissolved Organics, and Particle Characteristics ............................ 130 6.1 Source Water and Synthetic Sample Characterization ............................... 130 6.2 Microorganism Association ......................................................................... 136 6.3 Impact of Water Quality Characteristics on Microorganism Association ..... 140 6.4 UV Inactivation ............................................................................................ 142 x 6.5 Summary ..................................................................................................... 152 Chapter 7: Manipulation of Microorganism Association with Particulate and Dissolved Matter for Improved UV Disinfection of Surface Waters .......................................... 155 7.1 Sample Characterization ............................................................................. 155 7.2 Particle-Microorganism Association Analysis .............................................. 165 7.3 UV Disinfection Efficiency and Inactivation Rate Constants ........................ 166 7.4 Summary ..................................................................................................... 171 Chapter 8: Conclusion and Recommendations ....................................................... 174 8.1 Summary and Conclusion ........................................................................... 174 8.2 Originality and Contribution ......................................................................... 182 8.3 Limitation ..................................................................................................... 184 8.4 Future Research ......................................................................................... 186 Bibliography .............................................................................................................. 188 Appendix .................................................................................................................... 207 Appendix A: Raw data for each figure presented in the manuscript ..................... 207 xi List of Tables Table 2.1: The review of established particle-microorganism association detection methods ................................................................................................................. 65 Table 4.1: UV disinfection model parameters for water samples and control experiments, the error reported as standard deviation between sampling days, n=8-10, (R2>0.99 for fit in all cases) ............................................................................ 111 Table 6.1: Raw water quality characteristics (pH, Hardness, DOC, alkalinity, UVT, Turbidity, Average Particle Size and Zeta Potential) of the background waters ... 131 Table 6.2: Multiple linear regression models to predict the role of additive characteristics (turbidity and zeta potential), background source water and UV Dose on UV disinfection of MS2. Each particulate additive was modelled separately. Standardized β coefficients are reported with a p-value deemed significant at p-value <0.05. ......................................................................................................... 146 Table 6.3: The analysis of UV disinfection efficiency in terms of the dose required for 3-log inactivation and Log inactivation power of the system at UV dose of 15 mJ/cm2. Doses were corrected to estimate the actual UV dose delivered based on equation (5). Values are linear interpolations between two known data points. .................. 150 Table 7.1: Water quality characteristics of TP1 raw (pH, hardness, DOC, alkalinity, UVT, turbidity, average particle size and zeta potential of the background raw water .. 156 Table 7.2: Overall Model Fit Statistics: R² and Adjusted R² for Double Exponential Model ................................................................................................................... 168 xii List of Figures Figure 1.1: Illustration of a typical UV dose response curve of water [27]. .................... 25 Figure 3.1: A simplified flow diagram of the a) TP1 and b)TP2 [211] ............................ 75 Figure 3.2: Synthetic water samples made with TP1 raw and TP2 Pre-UV waters as background water, a) TP1 raw+ Fe(oH)3, b) TP1 raw+ Humic Acid c) TP1 raw+ Clay, d) TP2 Pre-UV + Fe(oH)3, e) TP2 Pre-UV + Humic Acid, f) TP2 Pre-UV + Clay at 1, 5, 20 NTU from left to right. .................................................................... 79 Figure 3.3: Jar test for optimization of the coagulant (PACI) concentration only for TP1 samples .................................................................................................................. 80 Figure 3.4: Synthetic water sample preparation flow diagram ....................................... 80 Figure 3.5: Zeta potential manipulation experiment’s sample preparation flow diagram 81 Figure 3.6: Example of Dilution Factor (DF) and Plate counts for MS2 enumeration employing a 10-fold serial dilution with double layer agar technique. Plate concentrations are in PFU/ mL. d) Control plate is poured without any MS2 organism (host only) to check for any potential environmental infection or contamination. ........................................................................................................ 87 Figure 3.7 Plate counts for E. coli enumeration employing a 10-fold serial dilution with spreading technique. Plate a and b) results from samples with no zeta potential manipulation (zeta potential=negative). Plate c and d) results from samples with zeta potential manipulation (zeta potential=neutral) ............................................... 89 Figure 3.8: UV device setup used for all biodosimetry experiments [218] ..................... 95 xiii Figure 4.1: Turbidity and bacterial concentration of TP1 raw water during the sampling period. Turbidity measurements were made in triplicates and the error bar represents one standard deviation of replicates. The results for E. coli and coliform samples were communicated from the treatment plant with measurements made in triplicates and an error less than 10 percent. ....................................................... 100 Figure 4.2: a) Turbidity over the one-year sampling period, b) TOC from February to August 2021. c) Average UV absorbance at 254 nm, d) UV absorbance of pre-UV samples, e) Sample date vs. Zeta potential of grab samples. Values are the average of triplicates, and vertical error bars represent one standard deviation. . 101 Figure 4.3: The average dose-response curve of TP2, TP1 pre-UV, and TP1 raw calculated from December 2020 to September 2021. Control tests are representative of MS2 inactivation in reverse osmosis water. Error bars represent one standard deviation (n = 8-11). ....................................................................... 105 Figure 4.4: Average log inactivation normalized by (a) sample turbidity, (b) zeta potential at average pH 7.2, and (c) UVA ........................................................................... 106 Figure 4.5: the Particle Size Distribution (PSD) of a) TP1 raw and b) TP1 pre-UV samples ................................................................................................................ 112 Figure 5.1: a) Initial concentration of MS2 in samples made with natural water matrix and RO water. b) The distinct impact of background water quality on the level of microorganism association ................................................................................... 120 Figure 5.2: The impact of 0.22 µm filtration on samples made with RO water as background. ......................................................................................................... 121 xiv Figure 5.3: Initial concentration of E. coli in samples made with natural water matrix and RO water. ............................................................................................................. 122 Figure 5.4: a) picture of the water samples after coagulation with four doses. b) the picture of the water sample at the optimum dosage with sweep coagulation. c) turbidity level of the supernatant after coagulation (before settling). d) UVT level of the supernatant after coagulation. ........................................................................ 125 Figure 5.5: Impact of settling time on the level of organism concentration, a) E. coli and b) MS2. Sample made with RO water served as control. The values are the average of triplicates. ........................................................................................... 127 Figure 5.6: The percentage of free-floating organisms measured after filtration method (red solid line) and after different times of settling (black dotted line). Samples were prepared with TP1 raw water and MS2 as surrogate organism. The dashed dotted red lines and error bars present the standard deviation of each method (n=3). ... 128 Figure 6.1: Water quality characteristics of the source and synthetic samples versus increased concentration of additives as turbidity. a) ΔUVT levels in TP1, b) ΔUVT levels in TP2, c) the average particle size in TP1, d) the average particle size in TP2, e) zeta potential level in TP1, f) zeta potential level in TP2. Values are the average of triplicates, and vertical error bars represent one standard deviation. . 133 Figure 6.2: % particle-microorganism association versus increased concentration of additives as turbidity in a) TP1 and b) TP2. Values are the average of triplicates, and vertical error bars represent one standard deviation. .................................... 138 Figure 6.3: The average exposure time–UV response curves of TP1 and TP2 calculated under a UV wavelength of 254 nm with four different exposure times. Control tests xv are representative of MS2 inactivation in the background water sample used for each sample set with no additive addition. Error bars represent one standard deviation (n = 3). .................................................................................................. 143 Figure 6.4: The actual UV inactivation in TP1 (n=7) versus predicted values driven from the linear regression models, using pre- and post-addition of zeta potential to the other independent variables (UV dose, turbidity, and UVT) ................................. 148 Figure 6.5. Contour plot illustrating the response surface of acquired log inactivation (color) as a function of dose delivered (mJ/cm2) and turbidity (NTU) for samples with elevated concentration of a) Iron Hydroxide, b) Kaolin Clay and c) Humic Acid. The contour lines represent constant response levels. ........................................ 151 Figure 7.1: The impact of additive dosage on the zeta potential behavior of water (TP1 raw) spiked with MS2 (solid fill) and E. coli (diagonal strips). Error bars represent one standard deviation over replicates (n = 3) ..................................................... 157 Figure 7.2: Water quality characteristics of samples at ranges of zeta potential in samples spiked with MS2 (Solid line) and E. coli. (Dashed line): a) average particle size (µm), b) turbidity (NTU), c) UVT (%), d) mean count rate (kcps), and e) UVA. Samples were made in triplicates and the error bar represents the standard deviation of replicates. The red dots represent the control sample (sample with no zeta potential manipulation). ................................................................................ 159 Figure 7.3: The difference between quality characteristics: a) Turbidity and b) UVA of water samples after settlement period. The negative values represent a drop and positive values represent an increase after settlement. The values represent the xvi difference between the average of three measurements where in all measurements standard deviations were less than 5%. ............................................................... 164 Figure 7.4: particle-microorganism association (%) level of MS2 (solid fill) and E. coli (diagonal strips) sample sets at different zeta potential levels measured by settling separation methods. Error bars are the standard deviation of three replications. 166 Figure 7.5: UV inactivation efficiency of a) E. coli and b) MS2 surrogate microorganisms ............................................................................................................................. 167 Figure 7.6: Double exponential model parameters over zeta potential control. The R-squared values for all regression models were above 0.90. ................................ 169 xvii Nomenclature ATP Adenosine triphosphate AMR Anti-microbial resistance ANOVA Analysis of variance AOP Advanced oxidation processes D Delivered UV Dose (mJ/cm2) DAF Dissolved air floatation DBP Disinfection by product DOC Dissolved organic carbon d Depth of the suspension (cm) Es Average UV intensity (measured before and after irradiating the sample) (mW/cm2) HAA Haloacetic acid k1 First-order inactivation rate constant for UV-susceptible fraction (cm2/mJ) k2 First-order inactivation rate constant for UV-resistance fraction (cm2/mJ) L Distance from lamp centerline (cm) MS2 Male Specific 2 N Residual number of culturable organism after UV exposure in plaque-forming unit (PFU/mL or CFU/mL) xviii No Initial number of culturable organism (PFU/mL or CFU/mL) NIR Near Infrared NOM Natural organic matter Pf Petri Factor (Unitless) PFC Perfluorinated compound PSD Particle size distribution R Reflectance at the air-water interface at 254 nm (Unitless) rpm Rounds per minutes RO Revered osmosis ROS Reactive oxygen species STD Standard Deviation t Exposure time (seconds) TTHM Total trihalomethanes TCC Total Cell Count TOC Total organic carbon UV Ultraviolet UVT Ultraviolet Transmittance (%) UVA UV Adsorption at UV 254 nm (Unitless) β Fraction of UV-resistant phages xix Acknowledgements I would like to express my heartfelt gratitude to the individuals who have contributed to the successful completion of my thesis journey. First and foremost, I am deeply indebted to Dr. Peleato for his unwavering guidance, invaluable insights, and continuous encouragement throughout this research. His expertise and mentorship have been instrumental in shaping the course of my work. I extend my sincere appreciation to Dr. Deborah Roberts and Alyse Kiesser for their dedication as committee members. Their thoughtful feedback and constructive suggestions have significantly enriched the quality of my research. A special note of thanks goes to Tricia Brett, Dustin Heidt, and Taylor Dufaut from Regional District of North Okanagan for their assistance and support. Their contributions have played a pivotal role in shaping the practical aspects of this study. I would like to acknowledge Stephen Horseman from Carola for his invaluable industrial insights that provided a real-world perspective to my research. My heartfelt thanks go out to my fellow team members, Faezeh Ketabchi, Du Ding, Kate Alexander, and Brayden Jones. Their assistance and collaboration in the experimental and lab aspects were invaluable, and I am deeply appreciative of their contributions. I am honored to have been surrounded by such an incredible group of individuals who have made this thesis possible. I would like to acknowledge the financial support from Natural Sciences and Engineering Research Council of Canada (NSERC) and Regional District of North Okanagan. Lastly, I want to express my appreciation to my mother, family, friends for their unwavering love and constant support. xx Dedication To my beloved baba, maman and To Kourosh, the kindest soul on earth. Chapter 1: Introduction In this chapter, the challenges and motivations that shaped this research is discussed. Within the context of water treatment and more specifically Ultraviolet (UV) disinfection, the knowledge gaps are identified and research objectives and hypotheses are listed. The organization of the thesis is provided at the end. 1.1 The Challenge and Motivation Pathogenic microorganisms can be introduced into drinking water sources through a variety of pathways and dictate a need for effective treatment of drinking water for protection of public health [2], [3], [4]. Disinfection, or the inactivation of microorganisms, is often regarded as the most important treatment step, and is historically responsible for significant reductions in water-borne disease outbreaks [4], [5], [6]. UV disinfection has been growing in popularity since the technology avoids the production of harmful disinfection by-products (e.g. from chlorination), has minimal impact on the composition of water, increases operator safety by avoiding usage of oxidants, and is efficient for the inactivation of chlorine-resistant pathogens [3], [6]. However, performance of UV systems is subject to the quality of water supplied to the reactor. Notably, suspended and dissolved substances can interfere with the delivery of UV light to organisms and lead to lower system performance if not adequately accounted for in design and operation [5], [7], [8]. UV disinfection can also be more challenging to monitor since, unlike chlorine, no measurable residual disinfectant is present. Compounding uncertainty in disinfection achieved is the significant influence of 22 dynamic characteristics of the UV lamp, radiation pattern, reactor design, and reactor hydraulics on disinfection performance. Furthermore, the consequential impacts of increased intensity of climate change-driven events such as unpredictable rains, changes in temperature, drought, and floods on water quality have placed more emphasis on the importance of considering worse-case scenarios and short-term upsets occurring in treatment plants [1]. Changes to source water quality are likely to be a more significant challenge for systems with more limited resources that may not have complete or adequate pre-treatment prior to UV disinfection. The motivation behind this study was to expand knowledge related to the effects of particulate matter on UV disinfection performance, consider the adequacy of current monitoring approaches, and offer strategies to effectively mitigate the identified effects. The work presented is focused on water systems that do not have extensive UV pre-treatment or pre-filtration and on particulate matter. 1.2 Research Gaps This research was initiated by inquiries raised by local treatment plant personnel and the health authority who did not have adequate data to assess if their UV disinfection systems were effective without pre-filtration. The questions raised were in regards to the potential impact of particulate matter on disinfection achieved and if current operational approaches were sufficient to meet inactivation requirements. In addition to the need for understanding performance during typical operations, the treatment plant had encountered challenges 23 related to the carryover of particles during water quality events such as rapid source water quality changes, process failure, and high flows. As such, exploring event-based or system upset water quality conditions with a focus on particulate matter and the impact on disinfection was needed. UV disinfection, as a unit process, is usually applied following a pre-treatment process to reduce the load of microorganisms as well as concentrations of particulate and dissolved matter that may interfere with the exposure of pathogens to light. Several types of dissolved matter including metals (e.g. iron) and dissolved NOM can absorb UV light, which will diminish the dose received by pathogens [5]. Particulate matter can also scatter, reflect, or absorb light leading to the loss of disinfection power [9], [10]. Furthermore, microorganisms can be surrounded, enmeshed, or otherwise shielded by particles and dissolved matter [11], [12]. It is generally recommended that UV be applied after filtration to reduce the impact of particulate matter [13]. In some cases, especially in small-scale systems, pre-treatment processes are omitted due to financial constraints, lack of required maintenance, and lack of operator capacities [14]. Guidelines from the USEPA and Health Canada outline criteria to exempt systems using high-quality source waters (either surface or groundwater) from using filtration at any point in treatment [15], [16]. Filtration exemption criteria typically include general guidelines concerning particulate concentrations and pathogen concentrations, such as influent turbidity levels that must be less than 1.0 NTU and 24 consistently low levels of fecal indicator bacteria, such as Escherichia coli (E. Coli), from regular source water quality monitoring. While the criteria aim for low source water particulate concentrations (turbidity < 1.0 NTU), without filtration there is a more direct influence of variability in the source water on treatment. The expected performance of UV disinfection systems under filtration exemption, and potential risks associated with the exemption criteria has not been reported. In 2021, > 5 million Canadians were served by water systems that did not use filtration with approximately 50% of these systems using surface water as a source [17]. Click or tap here to enter text. For full-scale UV disinfection performance monitoring, quantification of dose delivered is based on UV transmittance (UVT) or UV absorbance (UVA) and intensity measurements since the amount of light passing through a reactor should identify the degree to which microorganisms are exposed to UV [18]. While numerous studies have extensively explored the effects of dissolved matter captured through UVT/UVA measurements [19], [20], [21] the assessment of indicators for particle impact remained largely unexplored. Previous work has established only weak correlations between monitored UVT and particle counts [22]. Therefore, there is a need to investigate the capabilities of current monitored parameters on representing changes in UV disinfection performance in the presence of particulate matter. Previous work has identified inactivation of microorganisms by UV typically has an increasing log-linear trend up to a certain dose, after which additional exposure does not 25 result in significantly increased disinfection. The plateau or tailing effect is thought to be due to a fraction of the microorganisms that are resistant to UV, possibly due to being protected by particulate matter [23], [22], aggregation of organisms into clusters providing some degree of protection [24], or the shielding effect resulted from the coating mechanism of natural organic matter (NOM) [12]. Tailing effects are important to consider, since not only does it possibly imply lack of complete disinfection, it also increases energy requirements for high log removal [25]. The composition of the water, including the dissolved and suspended substances, affect the UV dose or light intensity delivered can lead to an overestimation of the delivered UV dose if reflected light is not considered [26], [7]. Figure 1.1 is the schematic of a typical UV dose response curve (DRC) for a treated effluent. This curve consists of an initial steep slope followed by a tailing region where a decrease in the rate of inactivation is observed for the applied UV dose [27]. Figure 1.1: Illustration of a typical UV dose response curve of water [27]. 26 The research focus of this thesis centers on exploring the association between particles and microorganisms, driven by several reasons. Firstly, the understanding and measurement of particle impacts on UV disinfection represents a less understood area, as opposed to the extensively studied impact and established indicators of dissolved matter. There is conflicting evidence of particles having significant [28], [22] or negligible effects [10] , and the context to when particulates may or may not compromise disinfection needs to be better established. Secondly, the omission of filtration stresses the impact on particles rather than the dissolved fraction of solids in water, making particle-microorganism association particularly relevant. Previous studies have shown that the physical and chemical properties (size, concentration, surface charge, hydrophobicity) of particles are crucial factors in particle- microorganism association [10], [11], [29], [30], [31], [8]. Contradictory results were observed by Wua et al. (2018) that reported that solution chemistry has no impact on the inactivation of the target microorganism at neutral pH. However, in acidic pH, there was evidence of microorganism-particle aggregation and adsorption that led to loss of disinfection [32]. Templeton et al. (2005) investigated the particle association for two different bacteriophages (MS2 and T4) and inferred that the characteristics of the organisms are an influencing and controlling parameter in the association and aggregation strength. The predominant cause of the tailing phenomenon could be attributed to resistance that come from microorganism associations, primarily governed by the 27 principles of electrostatic attraction and repulsion forces between particles and microorganisms. Efforts have been made to define relationships between particle size and UV disinfection efficiency. A wide range is reported for the critical particle size that could shield microorganisms from UV light. Some examples of reported influential ranges are 7 to 11 µm [33], [26], 11 to 25 µm [34] and 263 µm and larger [35]. Ultimately, it is expected that the relationship would vary with the size of the microorganism [34], [35], [36]. Tan et al. (2017) developed a comprehensive model for quantification of the impact of dose and UVA on tailing phenomena to indicate the percentage of microorganisms contributing to the tailing effect [37]. Most developed UV disinfection dose-response models account for UVA or UVT as the only and most influential process control parameter. Moreover, UVT is the primary water quality parameter used to assess if UV disinfection will be effective. However, the results presented by Farrel et al. (2018) demonstrated that UVA or UVT alone could not explain the observed dose-response at varying levels of turbidity [30]. The existing literature shows a lack of consistency between studies due to different experimental conditions such as the target organism, particulate type, background water, water quality characteristics measured and the variables considered. Water samples at the same turbidity but different compositions showed unexpected behavior after UV exposure [30]. Turbidity is a general measure that does not offer extensive information on water quality characteristics, such as particle size, dissolved and 28 suspended solid composition and surface charge, that may directly or indirectly impact UV response. Other water quality parameters could be considered to increase the ability to describe variability in dose-response curves between distinct water sources. For instance, if the organism-particle association is a major driver of tailing effects, the difference between the surface charge of the organism and the particle would define the degree of electrostatic attraction and largely dictate the strength and probability of shielded organisms [30]. However, dose-response characteristics in relation to particle surface charge have not been extensively studied, and there are limited results available. Elimination or reduction of particle-microorganism association could potentially enhance UV performance. Water quality characteristics can be deliberately modified to achieve more effective downstream treatment processes. Currently, research on manipulating the water quality characteristics such as surface charge (or zeta potential), to optimize system efficiency is predominantly limited to processes such as coagulation [38], [39], [40], [41], [42] electrocoagulation [43], adsorption [44], membrane filtration [45], and dissolved air floatation (DAF) [46]. There is a notable scarcity of studies exploring manipulation of zeta potential to eliminate microorganism association and enhance UV disinfection. The outcomes of this study are expected to advance water treatment technology toward an adjustable system that could be tailored depending on the water's quality characteristics. 29 It should be noted that most of the studies on the microorganism association are on synthetic water samples, neglecting the impact of real water matrices on process behavior. Moreover, most studies are on UV disinfection's bactericidal efficiency, and fewer studies have been conducted on virus and protozoan indicators. The study on the impact of real water matrices facilitates system optimization for the cases of unexpected spikes during event conditions and potential upstream process failure in treatment facilities. 1.3 Research Objectives and Hypothesis In this thesis, the application of UV disinfection for the treatment of unfiltered water supplies is investigated. The specific objectives followed by the primary hypotheses, are listed below. 1. Investigate the impact of unfiltered water conditions on UV disinfection performance. Hypothesis: The presence of particulate matter will decrease the performance of UV disinfection. It is hypothesized that increasing particulate concentrations will increase the degree of tailing in UV dose-response. It is also hypothesized that the impact of particles will be dependent on their type and characteristics, such as surface charge. Surface charge, turbidity, UV absorption, average particle size impact on UV will be tested. 30 2. Assess the capabilities of current UV performance monitoring metrics (i.e., UVT/UVA) to capture disinfection performance in the presence of particulates. Hypothesis: Turbidity or UVA/UVT levels are not adequate performance indicators for unfiltered UV disinfection performance. The composition of turbidity and UV adsorption-causing compounds, as well as the characteristics of particulates, are powerful controlling factors that are not being captured by traditional indicators. 3. Identify the viability of organisms associated with particles. Hypothesis: The association of organisms to particles does not significantly affect the accuracy of microorganism enumeration methodologies. 4. Develop a methodology for particle-microorganisms association detection tailored to the experimental context. Hypothesis: Incorporating the literature, specific target microorganism considerations, and the characteristics of water quality and treatment processes in the system will lead to the development of a more precise method for detecting particle-microorganism associations. 5. Quantify the relationship between water quality characteristics (particle size, counts, turbidity, UVA/UVT, and zeta potential) and particle-microorganism association. 31 Hypothesis: In addition to the conventional water quality indicators, zeta potential has impact on microorganism-particle association, hence the system efficiency. Inclusion of zeta potential will improve UV dose-response model accuracy. 6. Evaluation of zeta potential control as pre-treatment process for UV. Analysis of the zeta potential control (pH alteration/ coagulant addition) on particle-microorganism association and the impact on UV disinfection efficiency. Hypothesis: Zeta potential serves as a key controller in particle-microorganism association. Decreasing these associations will improve inactivation. Disrupting particle-microorganism association with zeta potential manipulation will render microorganisms free, unprotected, and susceptible to UV inactivation. 7. Compare modelling and experimental methods for particle-microorganism association determination. Hypothesis: The level of particle-microorganism association predicted by the existing UV disinfection model and the experimental measurements will be different. The comparison will suggest insight into the mechanisms driving these associations. 1.4 Thesis Structure and Organization This thesis consists of eight chapters, and the structure of the thesis is as follows: 32 • Chapter 1 introduces the problem, provides a context for this research, and identifies research gaps as well as the motivation behind the study and the primary objectives of the thesis. • Chapter 2 provides a deeper understanding of the required background knowledge delving deep into existing related studies and a review of the literature. • Chapter 3 describes the principal methodology and analytical methods used in this thesis. • Chapter 4 explains the outcomes of the first objective exploring the impact of water characteristics on UV disinfection of unfiltered water samples. This chapter covers objectives 1 and 2. • Chapter 5 elaborates on the necessity of a focused exploration of particle-microorganism association detection methods, emphasizing the need for an experimental approach aligned with current research trends and grounded in a strong theoretical framework. This chapter covers objectives 3 and 4. • Chapter 6 elucidates the outcomes of the fourth objective of the study focused on the important factors affecting particle-microorganism association and UV disinfection with emphasize on the effect of source water, organics, and particle characteristics. This chapter covers objective 5. 33 • Chapter 7 focused on manipulation of zeta potential as UV disinfection pre-treatment for particle-microorganism association. This chapter covers objectives 6 and 7. • Chapter 8 provides the main findings, recommendations, originality, and future research potential. 34 Chapter 2: Literature Review This literature review provides the required background knowledge to understand the following chapters. An analysis of contaminants of concern in drinking water treatment is provided. Then, water treatment processes, emphasizing disinfection methods are explored with a discussion on UV treatment, covering its principles, applications, and the rationale behind considering UV as an alternative to chlorine is addressed. The impact of water quality on UV disinfection is brought in the scope required to support the following chapters. Then, what is currently known about microorganism associations and their detection and enumeration methods is discussed with regards to their impact on UV disinfection performance. Finally potential strategies to improve UV disinfection are overviewed. Click or tap here to enter text. Click or tap here to enter text.Click or tap here to enter text. 2.1 Contaminants of concern - the need for drinking water treatment Any physical, chemical, or biological substance in water present in undesired concentrations is considered contamination according to the US Safe Drinking Water Act. Physical contaminants include non-dissolved matter that primarily impact water appearance such as sediments, some organic compounds, and compounds resulting from soil erosion in water streams. The presence of elements such as nitrogen, along with pesticides, salts, and metals, can act as chemical contaminants in water sources. These 35 contaminants may originate naturally or be introduced into the environment through human activities [47]. Biological contaminants include a wide range of living organisms, such as bacteria, viruses, fungi, and protozoa. These microorganisms have the potential to pose significant health risks when consumed or exposed to humans. Disinfection systems are aimed to inactivate pathogens and reduce the risk associated with biological contamination. It is also important to consider that other contaminants can interfere with the disinfection process and compromise public health. As such, the following section investigates biological contaminants regularly found in water that necessitate disinfection and contaminants or classes of compounds that interfere or compromise disinfection processes and, more specifically, UV disinfection [48]. 2.1.1 Biological Contaminants in Natural Water: Bacteria, Virus, and Protozoa The primary types of microorganisms that can cause waterborne diseases and infections in water are classified into three groups: viruses, bacteria, and protozoa. Concerning microorganisms such as E. Coli, Cryptosporidium parvum, and Campylobacter spp., as well as pathogens that are capable of growing in distribution systems, such as Legionella spp., Mycobacteria, and Aeromonads often originate from fecal sources, e.g., wastewater effluent discharge and animal feces in surface water [49]. Enteric viruses such as Rotavirus, originating from fecal sources, are also of concern in potable water [50]. 36 The presence of protozoa in drinking water sources, most notably Giardia duodenalis and Cryptosporidium spp. are of particular concern for water treatment. Giardia is the most common human enteric parasite, and Cryptosporidium is often considered the most significant public health risk associated with drinking and recreational water use [51], [52]. Outbreaks of Cryptosporidium can impact a large proportion of the population in a short time frame due to its persistence in aquatic environments [53] and high probability of infection at low doses [54]. A review of outbreaks of waterborne protozoa identified 381 outbreaks between 2011 and 2016, with Cryptosporidium identified as the most common agent (63% of cases) [55], [56]. Cryptosporidium is small enough (4-6 µm) to pass through some physical treatment barriers such as filtration and is particularly resistant to disinfection by chlorine [57]. Insufficient treatment efficiency against Cryptosporidium and Giardia spp. has been observed in facilities where chlorination is the only measure taken for disinfection [58]. Therefore, particular attention must be given to Cryptosporidium when setting water treatment objectives. While Cryptosporidium is often a major source of public health risk associated with drinking water [59], information on source water concentrations and facility-specific removal efficiencies are usually unknown. Sampling and measurement challenges due to typically low amount of Cryptosporidium in samples have led to the lack of adequate information on source water concentrations [60]. For example, cyst counts are often reported per 100 L volume to account for low numbers. Lack of information on 37 protozoa is also due to the often poor relationship or correlation between the typical fecal indicator bacteria used for routine monitoring of water quality and the presence of protozoa [2], [61], [62]. Increased levels of cysts may be found in both the source and treated water due to challenges associated with measurement approaches and treatment methods. Studies suggest, measures including augmenting regulations and process upgrades, can reduce reported cases of diseases caused by protozoa in drinking water [63], [64], [65]. The challenges encountered in measuring and sampling protozoan are of less concern for bacteria, with easier enumeration process. The most commonly investigated bacterial groups for assessing water quality and health risks are total coliforms, fecal coliforms, E. coli, and Enterococci spp. These bacteria typically inhabit in the intestines and feces of warm-blooded mammals, including wildlife, livestock, and humans [66]. Major diseases-causing bacteria transmitted through water include Cholera, Salmonella, Shigella, and E. coli. According to WHO, more than 50% of water-associated diseases are attributed to bacterial intestinal infections, prominently cholera [49], [66]. Disinfection studies highlight growing challenges posed by bacteria, such as the disinfection tolerance or resistance profiles exhibited to different disinfection processes often through anti-microbial resistance [67], [68]. While most of the attention regarding unsafe drinking water is centred around bacterial contaminations, the impact of viruses on human health receives less attention [69]. Waterborne viral pathogens of moderate to high health significance include 38 Adenovirus, Astrovirus, Hepatitis A and E viruses, Rotavirus, Norovirus, Caliciviruses, and Enteroviruses. Some viruses excreted through urine, like Polyomaviruses and Cytomegalovirus, also pose transmission risks through water. Detection of viruses in environmental samples can be challenging due to their diverse characteristics as well as low concentrations, but molecular techniques are useful for monitoring their presence. Enteric viruses discharged into water can endure for long periods, affected by factors like UV radiation, temperature, and pH. Despite continuous discharge, surface water concentrations are generally low, necessitating analysis of large volumes [70]. Due to the lower concentrations in water sources (compared to bacteria) and complexity involved in measuring viruses, less frequent monitoring is carried out and it is likely virus presence is underestimated [71], [72]. Detection and removal of viruses in drinking water are important, given their potential health risks, even when concentrations are below detection limits [69]. 2.1.2 Non-Biological Contaminants in Natural Water: Organics Organic matter in water can be natural or synthetic. Natural Organic Matter (NOM) is a complex mixture of carbon-based compounds resulting from the degradation of plants and other organic matter in the watershed and are naturally present in water sources [73]. Per- and polyfluoroalkyl substances (PFAS) on the other hand, are examples of synthetic organic compounds that do not occur naturally in the environment are a result of industrial processes have been widely used in a variety of consumer products [74]. 39 Organic matter in water is a complex and varied grouping of chemically and physically diverse compounds. Organic matter in water can be either dissolved or non-dissolved, as determined by utilizing filters (0.1–0.7 μm) [75], [76]. Compounds within the NOM mixture are also routinely classified into two fractions: hydrophilic and hydrophobic. Humic species, carboxylic acids, amino acids, proteins, and carbohydrates are the primary chemical groups in NOM [77]. NOM reacts readily with chlorine to form various DBPs of health concern and, in the process, interferes with disinfection performance by exerting a chlorine demand [78]. It can also trigger and support bacterial regrowth in the drinking water distribution systems [79]. In UV disinfection, NOM generally interferes by absorbing incident light and reducing the effective dose received by pathogens [5]. While elevated NOM concentrations imply lower effective disinfection by UV, irradiation of NOM can lead to the formation of reactive oxygen species (ROS). Some organic compounds that absorb light will form singlet or triplet excited states, contributing to ROS formation [80]. In particular, humic acid or humic substances have been shown to promote or contribute to the indirect photolysis of antibiotics and other organic compounds [81], [82]. Lester et al. (2013) demonstrated that UV irradiation of dissolved organic matter in surface water accelerated the degradation of organic contaminants [83]. Hence, NOM can theoretically impede UV disinfection by filtering effective light but can also produce ROS that may increase disinfection rates. Further comprehensive studies are imperative to elucidate the relative magnitude of these two effects in specific scenarios. 40 Organic compounds can interact with UV radiation through absorption, scattering, and fluorescence [84]. UV-exposed organic compounds undergo various photochemical reactions, resulting in bond cleavage and the formation of reactive intermediates, depending on their composition [85]. Compounds with unsaturated bonds, such as double bonds, absorb UV light more efficiently due to higher possibility of electron delocalization along the bond, creating regions of electron density that interact with UV light. In contrast, saturated bonds typically absorb UV light less efficiently [86]. The UV absorption properties of organic compounds are directly influenced by their functional groups. For example, aromatic compounds often exhibit stronger UV absorption compared to aliphatic compounds. In NOM, such as humic substances, chromophores are often associated with aromatic rings. The presence of chromophores and the electronic structures of molecules contribute to their UV absorption. Aromatic compounds, with their delocalized π-electron systems, readily undergo electronic transitions upon UV irradiation, leading to enhanced UV absorption [87]. 2.1.3 Non-Biological Contaminants in Natural Water: Inorganics The most common inorganic contaminants of concern in water consist of heavy metals, nutrients such as nitrate, ions (fluoride, sulphate, ammonia, iron, manganese), and natural components such as sediment and particulate matter including clay, silica, and calcite originating from mineral sources in natural waters [88]. Adverse effects of inorganics in water could be aesthetic, such as rise in the cloudiness of water even in low concentrations 41 or harmful impacts on humans and the surrounding environment by providing a transportation vehicle for pathogenic microorganisms and soluble impurities [89]. The negative impacts of poor water quality on different methods of disinfection are correlated with the concentration of the inorganic solids [90]. The UV absorptivity of water can vary considerably, depending on its composition with respect to the presence of inorganic contaminants [91]. The higher the absorption of UV light by water quality, the less of light that reaches microorganisms to disrupt their operations and activities. Depending on electronic properties, chemical structure and specific compounds in inorganic contaminants, the UV absorptivity of water can vary widely. The study by Creighton and Eadon (1991) calculated the absorption spectra of 52 metallic elements [92]. The results indicated that alkali metals such as sodium, magnesium, and calcium generally exhibited low UV absorption compared to transition metals such as iron and copper. With their partially filled d-orbitals, transition metals are known for their significant UV absorptivity characteristics [92]. 2.2 Water Treatment Processes: Disinfection Water treatment facilities consist of various unit treatment processes, including physical, chemical, and biological processes. These technologies are applied either as a single treatment or, most commonly, as part of a multi-barrier treatment system to remove the site-specific contaminants of concern. Disinfection processes are designed to inactivate pathogenic organisms in the source water. Arguably, the disinfection step is the most 42 crucial step to preventing infectious disease from drinking water [5]. Disease outbreaks associated with contaminated drinking water are commonly linked to inadequate treatment [93]. Disinfection causes damage to pathogenic organisms resulting from exposure to chemical agents (such as chlorine and ozone) or by physical processes (such as UV irradiation) [5]. Chlorination (by free/combined chlorine and chlorine dioxide), ozonation, and UV disinfection are considered the most commonly used systems in full-scale treatment plants [5], [94]. Many other disinfection methods can also be utilized including Advanced Oxidation Processes (AOPs) that generate highly reactive radicals that can damage pathogens such as viruses [95] and bacteria [96]. Chlorine-based disinfectants are the most widely used in Canada, with 96.4% of all treatment facilities utilizing chlorine for disinfection in 2015 [97]. Chlorine inactivates most pathogens of concern in water but is mostly ineffective against protozoa such as Cryptosporidium and Giardia [98]. Giardia is the most common human enteric parasite resulting in an average of nine infection cases per 100,000 population per year, and Cryptosporidium resulting in an average of six infections cases per 100,000 population per year often drives, or is the most significant public health risk associated with drinking and recreational water uses [99], [100], [101]. The ineffectiveness of chlorine for protozoa disinfection necessitates the use of alternative disinfection or removal processes, such as physical removal by filtration or disinfection by UV light. 43 Over the last several decades, there has been a growing recognition of the potential risks associated with DBPs produced during disinfection processes (primarily chlorination) [102]. This awareness has driven extensive research into strategies for the removal and mitigation of these substances from water sources [103], [104], [105]. There is also considerable interest in transitioning away from chlorine to systems that are effective against a range of contaminants and do not contribute to DBP formation. Ozone is a powerful disinfection agent with advantages, such as being a wide-spectrum disinfectant with high oxidation power effective on a variety of odor, color and taste causing materials. However, ozone generation and application can be complex and costly [106]. Ozone also requires careful monitoring and control, as excessive exposure can produce harmful DBP [107]. Additionally, the stability of ozone in water is relatively short, necessitating on-site generation. UV disinfection offers advantages over chlorine, ozone, and AOPs. These advantages include chemical-free treatment, absence of byproduct formation, effectiveness against a broad spectrum of microorganisms (protozoan), and rapid treatment [108]. Additionally, UV is safe for sensitive applications, has no impact on taste or odor, and generally incurs lower operating costs compared to ozone. The following sub-sections explore the mechanisms, advantages, challenges, and effectiveness of UV disinfection. 44 2.2.1 Ultraviolet Disinfection UV disinfection has been growing in popularity since the technology is highly effective for disinfection of a wide range of pathogens, avoids the production of harmful disinfection by-products, does not significantly change water composition, increases operator safety by avoiding oxidants, and is efficient for the inactivation of chlorine-resistant pathogens [3]. UV is a particularly attractive technology for small and point-of-use (POU) systems due to low maintenance, compact design and lower operational requirements [109]. Small-scale UV disinfection systems benefit from low maintenance designs due to limited resources, ease of operation, and suitability for remote locations [110]. One of the primary limitations of UV disinfection is the lack of a residual disinfectant. The need for residual disinfection is primarily associated with the characteristics and size of a distribution system in terms of the potential for organism regrowth, repair, and potential for reintroduction of contaminated water (i.e. system integrity) [111]. Maintaining residual disinfectant also contributes to emergency preparedness in cases of unexpected contamination events [112]. To meet regulations, the absence of residual disinfectant from UV may necessitate an additional secondary disinfection process. The rapid development and decreasing costs of ultraviolet light-emitting diodes (UV-LED) are likely to increase the use of UV disinfection in small systems and POU applications due to cost-effectiveness and lower input energy requirements [113]. Additionally, other benefits of UV-LED over conventional UV lamps are the compact size, 45 avoiding use of mercury, longer operational lifespan and less required space and maintenance frequency [6]. Economic and operational requirements associated with pre-treatment technologies, such as filtration, can serve as barriers to small or point-of-use water systems implementing adequate levels of treatment [114]. As such, UV disinfection is expected to be utilized increasingly in scenarios where complete or robust pre-treatment is not practiced or it is highly desirable to avoid this requirement. 2.2.1.1. Mechanism of Disinfection UV light refers to electromagnetic radiation with wavelengths between 100 and 400 nm [5]. For disinfection, the sub-range of 200 to 280 nm (referred to as UVC) is the focus since photons in this range have been demonstrated to cause germicidal effects [115]. UVC light is absorbed efficiently by many unsaturated organic compounds and impacts the stability of the molecule [115]. Generally, it is thought that the primary mechanism of inactivation or disinfection in water is the DNA of microorganisms absorbing UV light and adjacent thymine molecules in the DNA are dimerized. While this is not fatal, it prevents the replication or reproduction process and inactivates the organism’s ability to cause disease [5]. Other organic compounds vital to cell function and metabolism, such as pyrimidines, cytosine, and flavin are also damaged by UVC light [115]. As such, the focus of effective UV disinfection is to maximize the pathogen or microorganism’s exposure to UVC light. 46 2.2.1.2 Effect of water quality on UV disinfection The efficacy of UV disinfection is heavily influenced by the composition of the water being treated [30]. In this section, the impact of water quality on UV disinfection is considered in terms of its dissolved and particulate constituents exclusively. Dissolved effects A spectrum of organic and inorganic compounds can be found within the dissolved constituents of water. Organics include a variety of substances, including NOM, anthropogenic contaminants such as pharmaceuticals, pesticides, and industrial chemicals. Inorganics, consist of minerals, metals, and salts dissolved in water, originating from geological formations, industrial activities, or urban runoff [5]. The dissolved constituents interact with UV through several mechanisms mainly related to reduction of microbial exposure to UV. Organic compounds, particularly those with conjugated double bonds or aromatic structures, can absorb UV photons, leading to the excitation of electrons and subsequent energy dissipation [116]. This absorption competes with the microbial targets for UV photons, reducing the effective UV dose available for disinfection. Inorganic compounds may also influence UV disinfection by altering the optical properties of water, affecting UV transmittance through absorption or scattering, and thus impacting the penetration depth of UV radiation into the water matrix. Particulate effects 47 Particulate matter suspended within water are a diverse array of materials, including soil particles, sediment, algae, and microbial aggregates. These particles vary in size, shape, and composition, ranging from colloidal to larger sedimentary particles [117]. The mechanisms through which particulate matter affect UV disinfection are multifaceted. Firstly, particulates can scatter UV light, redirecting photons away from their intended path and reducing the overall UV intensity experienced by microorganisms [118]. Additionally, larger particles can physically shield microorganisms from UV exposure [22], hindering their inactivation. Moreover, particulate matter can serve as substrates for microbial attachment and biofilm formation, providing protective environments where microorganisms may evade UV disinfection [119]. The association of microorganisms with particulate matter further complicates disinfection processes. Microorganisms may attach themselves to particles, forming aggregates or biofilms that shield them from UV radiation [120], [121]. This association prolongs the exposure time required for effective disinfection and may result in reduced microbial inactivation rates. Furthermore, the carryover of microorganisms associated with particles to downstream treatment processes poses a challenge for overall water quality management [122]. Despite UV disinfection efforts, viable microorganisms may persist in the water supply, potentially compromising process efficiency if not adequately addressed in subsequent treatment stages. 48 2.2.1.3 Challenges with UV disinfection UV disinfection presents several challenges that could compromise its effectiveness and reliability as a disinfection process. Monitoring of effectiveness and determining fluence Accurately monitoring the effectiveness of UV disinfection and determining the UV fluence delivered are crucial for ensuring microbial inactivation. UV fluence, measured in terms of UV dose or energy per unit area, determines the level of disinfection achieved [123]. However, measuring UV fluence accurately in real-time poses technical challenges, particularly in large-scale treatment facilities. Factors such as variations in water quality, flow rates, and lamp aging can affect UV dose delivery [124]. Therefore, developing robust monitoring systems capable of providing accurate and real-time feedback on UV fluence is essential for optimizing disinfection performance and ensuring compliance with regulatory requirements [125]. In theory, within an ideal UV reactor, it's assumed that all microorganisms passing through receive an equal amount of radiation. However, in real-world UV reactors, suspended microorganisms follow diverse paths through the reactor, resulting in varying levels of radiation exposure, creating a distribution of fluences. Hence, a reliable method for determining the fluence distribution is essential [125]. Pre-treatment Pre-treatment of water prior to UV disinfection is often necessary to enhance treatment efficiency and protect UV lamps from fouling and damage [126]. Pre-treatment processes 49 may include filtration to remove particulate matter and turbidity [127], oxidation to break down organic contaminants [128], and chemical addition for disinfection byproduct control [129], [130]. However, selecting appropriate pre-treatment methods tailored to specific water sources and treatment objectives can be challenging. Additionally, pre-treatment processes may introduce additional costs and complexity to UV disinfection systems, requiring careful consideration during system design and operation. Re-activation of organisms One of the challenges associated with UV disinfection is the potential re-activation of microorganisms following treatment [131]. UV radiation can induce sub-lethal damage in microorganisms, leading to temporary loss of viability rather than complete inactivation [132]. Under favourable environmental conditions, such as sufficient light and appropriate pH and temperature, the injured microorganisms may repair UV-damage and regain viability, posing a risk of post-treatment contamination. Furthermore, in UV disinfection systems where residual disinfection is not present, it becomes crucial to implement strategies aimed at preventing microbial re-activation. 2.3 Water Quality Indicators Ideally, the precise contaminants present in both the source and treated water should be known for optimized design and operations of treatment systems. However, water may contain a wide range of pathogens and other compounds of concern, creating a practical challenge for monitoring all potential contaminants directly and individually. As such, a 50 surrogate or indicator approach is taken for routine measurements and monitoring water quality. Ideally, the indicators used are easy to measure, represent the water quality characteristic accurately, and are sensitive to changes in water quality. An example is turbidity, which indicates the concentration of particulate matter through the degree to which light is scattered through a water sample. In this section, indicators used predominantly in the water treatment industry for UV disinfection assessment are described. In the context of the efficiency assessment of UV, surrogate indicators, such as turbidity, UVT, and indicator organisms, E. coli and coliform bacteria, could provide a broader understanding of water quality without relying on measuring direct human health risks. 2.3.1 Indicators of Microbial Water Quality and UV Disinfection E. coli and total coliforms are commonly used as surrogate indicators for fecal contamination in water treatment industry. The use of E. coli and total coliforms as surrogate indicators is based on the premise that the presence of these bacteria suggests a higher likelihood of more harmful microorganisms, such as pathogenic bacteria and viruses associated with fecal matter [133], [134]. Due to health risks, low concentrations, and complicated quantification protocols of viruses and protozoa in water, regular and direct monitoring of the levels of these contaminants is challenging [135]. In addressing these challenges, biodosimetry techniques can be used to assess UV disinfection efficacy and microbial inactivation rates. The purpose of this section is to discuss the significance 51 of different surrogates for UV validation and their role in ensuring the safety and reliability of water treatment processes. The challenges associated with direct quantification of viruses, protozoa, and pathogenic bacteria also complicate the validation of disinfection achieved by UV systems. Similar to microbial water quality indicators, surrogate organisms that are easier to enumerate and study are required to establish dose-response relationships between UV and inactivation. The validation process for UV reactors in many developed countries, including the United States, commonly employs MS2 as a surrogate for Cryptosporidium, the targeted regulated pathogen. Though widely accepted, discrepancies in surrogate selection practices are found in the literature [136]. Among all possible surrogate organisms for UV validation, several types of bacteria (aerobic and anaerobic endospores), microbe-sized microspheres, fluorescently labelled bacteria, and protozoa have served as microorganism representatives [137]. Coliphages (bacteriophages that infect coliforms) comply with most of the criteria of an ideal surrogate in terms of characteristics, similar size, structure, and survival mechanism [138]. MS2 is the most prevalent surrogate organism used for UV validation in Europe and North America. MS2 is non-pathogenic to humans and serves as a model virus in water treatment studies to avoid using Cryptosporidium species that can cause gastrointestinal illness and complicate lab-based studies [139], [140]. The action spectra, or relative impact of specific wavelengths on the overall disinfection of MS2 is well known 52 [141], and there is significant compatibility between the UV action spectra of both MS2 and Cryptosporidium [18]. However, it has been noted that MS2 has a different overall sensitivity to UV compared to Cryptosporidium, and the action spectra deviate at the edges of the UVC region [141]. While MS2 offers several practical and safety advantages as a surrogate for Cryptosporidium in water treatment studies, it is essential to recognize that there are differences in size and UV sensitivity between these organisms. MS2 and Cryptosporidium differ in type, genetic material, size, host range, pathogenicity, and environmental stability. MS2 is an RNA bacteriophage that infects E. coli bacteria and is smaller in size (size of MS2: 0.02 to 0.03 µm [142] vs Cryptosporidium: 3 to 5 µm [143]), while Cryptosporidium is a protozoan parasite with DNA as its genetic material, infecting various hosts including humans and exhibiting greater environmental resistance. The continued application of MS2 as a model virus in water treatment studies is mainly due to its safety, reproducibility, historical significance, and regulatory acceptance, especially in the context of UV disinfection. 2.3.2 Non-Microbial Water Quality Parameters The non-microbial content of water is primarily characterized by physical parameters such as turbidity, particle size, particle count, total suspended solids, and UVA/UVT. Turbidity is a measure of the cloudiness of water that is routinely used in water treatment as a widely used indicator for the presence of particulate matter in water [144]. The nephelometric turbidity is measured according to the intensity of scattered light at one or more angles to 53 the incident beam [145]. Turbidity will predominantly represent the concentration of suspended solids, which results in the scattering and absorption of light rays [146]. Turbidity is considered a non-specific measure because it does not provide detailed information about the specific types of particles causing the cloudiness in water [147]. Turbidity is commonly employed as a proxy indicator for microbial contamination and treatment efficacy [148]. However, there is an ongoing debate regarding the appropriateness of routine variations in water turbidity and their potential connection to public health risks. Elevated turbidity levels have been associated with numerous disease outbreaks [149]. However, it is important to note that a direct and proportional relationship between the removal of turbidity through treatment and the reduction of pathogens has not been definitively proven [150], [151]. In a comprehensive investigation by Mann et al. (2007), evidence from the six identified studies regarding the suitability of turbidity as a surrogate was inconclusive. While two studies found no association between effluent water turbidity and relevant health outcomes, others reported increased hospital use and health service calls on specific days following elevated turbidity. Differences in baseline turbidity and seasonal adjustments among the studies may impact comparability. Further research is needed to assess the reliability of turbidity as a surrogate for predicting health impacts [150]. While the direct correlation between turbidity and health risk is not clear, turbidity remains a popular routine measurement that is believed to indicate general changes in water quality and associated risks, such as pollution from environmental 54 events or treatment efficacy [21]. Despite the uncertainty surrounding turbidity, the measurement is simple and can be done at a high-frequency. Total Organic Carbon (TOC) represents the total concentration of organic carbon compounds in water, including dissolved and particulate forms. TOC is generally analyzed as a proxy for the organic portion of the particles present in water sources. TOC analysis involves converting the sample's current carbon content to carbon dioxide, which can be analyzed by Near Infrared spectroscopy. Dissolved Organic Carbon (DOC) refers to the fraction of organic carbon compounds in water that is dissolved or passes through a 0.45 µm filter. These organic carbon compounds can originate from various sources, including decaying plant and animal matter, microbial activity, and anthropogenic inputs. DOC is an essential parameter in water quality assessment, influencing water colour, taste, and odour. Additionally, it plays a crucial role in various water treatment processes and affects the formation of chlorinated DBPs. UV absorbance (UVA254) and Transmittance (UVT) of 0.45 µm filtered samples are other common indicators of water DOM. UVA254 is the amount of light at 254 nm absorbed by a water sample, while UVT refers to the fraction of light passing through a water sample at a wavelength of 254 nm. UV transmittance and UV absorbance can be related by a logarithmic function as equation (1). In this study, all samples are intentionally left unfiltered, and transmittance is measured on the unfiltered samples. This approach is adopted to account for the absorbance influenced by dissolved and particulate organics in the water. In this study, the distinction between the impacts of 55 particulate and dissolved matter was not based on traditional definitions such as turbidity, representing particulate matter, and UVT, representing dissolved matter. Instead, the analysis were based on assessing scattered light intensity as an indicator of particulate matter, while absorbed light intensity was utilized as a measure of dissolved matter. However, it's important to acknowledge that particulate matter can indeed absorb light, and dissolved matter can scatter light. 𝑈𝑉𝐴 = log 10 (1/UVT) equation (1) There are multiple chemical groups on the surface of the particles that could be ionized in water and leave a charge on the surface of the particles. For particles in aqueous solutions, the electrical potential at the interface of mobile fluid from fluid attached to the particle's surface is zeta potential. Particle count and particle size distribution analysis reveal information about water composition and are effective diagnostic indicators, supporting the design, optimization, and operation of water treatment systems [152]. Zeta potential is the electrical potential between the slipping plain of a particle and the bulk water [5]. When particles have a high zeta potential (either positive or negative), they tend to repel each other, resulting in dispersion stability [153]. This electrostatic repulsion prevents particles from aggregating or flocculating, thus maintaining the colloidal system's stability. Conversely, when the absolute value of zeta potential is low (near zero), 56 particles are more likely to aggregate due to weak electrostatic repulsion, leading to the destabilization of the dispersion. Zeta potential measurement is used in various fields, including colloid chemistry, materials science, and environmental engineering, to assess the stability of colloidal dispersions and predict their behavior in various applications such as emulsions, suspensions, and water treatment processes [154]. Zeta potential measurements facilitate the prediction of colloidal behavior under specific conditions. For instance, in water treatment processes, monitoring the zeta potential of particles can help optimize coagulation, flocculation, and sedimentation processes by manipulating the interactions between particles and chemical additives [154]. The interpretation of zeta potential data may be challenging in complex systems with multiple types of particles or interactions. Given the various factors that influence zeta potential, there can be some degree of uncertainty associated with its reliability as an indicator of particle behavior in water. Zeta potential could be influenced by various factors such as the size and shape of particles, the number of charges on particles, the strength of the electric field, and the nature of ions in the diffuse layer [5]. In such cases, complementary techniques and analyses may be necessary to fully understand the factors influencing colloidal stability and behavior. 57 2.4 Microorganism Association Regardless of the disinfection system type, disinfectants must come adequately in contact with microorganisms to result in effective disinfection. Particulate matter can interfere with the disinfection process by acting as carriers for pathogenic microorganism transportation and shielding microorganisms from disinfectants or scattering UV light [32]. Interfering effects are heightened when pathogens are associated with or bonded to particulate matter. Microorganisms in the aquatic environment may also acquire characteristics through interaction with dissolved organic matter that render them more resistant to UV disinfection [12]. The water matrix, including particles, dissolved components, and environmental conditions such as pH and temperature, can significantly impact the associations of organisms [155], [156]. The association of organisms with dissolved organic matter is termed "coating," [12] while associations with particles are referred to as "particle-microorganism associations" [8]. Microorganism interactions with particulates in water are highly dependent on the quality of the water and the type and concentration of organisms. These associations are primarily governed by Van der Waals Forces [157], [158], [159], which arise from temporary fluctuations in electron distribution. Electrostatic interactions occur between charged particles or molecules, where equal charges repel and opposite charges attract each other [160], [161]. Physical adsorption involves the weak attraction of molecules or particles to a surface without the formation of chemical bonds [162], [163], [164]. The 58 balance between these forces and the specific water quality characteristics defines the nature of interactions between particles and microorganisms. The degree and strength of these associations and the consequential impact are influenced by many factors including pathogen and particulate size, morphology, and surface charge. Due to the prevalent presence of dissolved organic substances and their multifunctional molecules in water, these substances may display an affinity to various surfaces through hydrophobic interactions [12] that could lead to association with organism surfaces [165]. In organism association with NOM, a two-step mechanism is likely to be involved [166]. Long-range attraction could be caused by the electrostatic attraction between the opposite charges according to the DLVO theory (Derjaguin-Landau-Verwey-Overbeek) [167], [168] and the bridging effects among organic matter [169]. DLVO theory assumes that particle-particle interaction hinges on the interplay between repulsive and attractive forces exerted on two charged particles as they draw nearer [5]. The second mechanism involved the short-range adhesion accomplished through hydrophobic forces [170]. The interaction between particles and microorganisms is assumed to be linked considerably to their surface charge. When particles are in proximity, the overlap of their diffuse part of double layers initiates attraction or repulsion interactions depending on their charge [171]. pH and the ionic strength of background water are influential factors on 59 surface charge, hence particle and microorganism aggregation. Higher ionic strength and lower pH in water could result in higher levels of aggregation [172]. The surface properties of water constituents and the biology and ecology of the pathogens are critically influential in the subsequent interactions between different types of solids and microorganisms [49]. Most microorganisms have coats composed of different protein polypeptides and amino acids that could drive attraction or repulsion forces and result in aggregation of microorganisms, hence destabilization. Past studies have cited the coating mechanism as the primary mechanism involved in the association of organic matter and water organisms. The surface charge of the solids in water can affect the DLVO electrostatic forces, impacting the hydrophobic, and bridging effects [173]. Organism interactions are also very dependent on the size of the particles and aggregates. Smaller particles with high concentrations will have a substantial effective surface area available for attachment, increasing the potential for higher particle-microorganism association. Interaction occurs more frequently when particulates are relatively close to each other. Usually, the distance should be much less than the particle size to be effective in particle and microorganism association. Based on the net charge on the absorbent, solids with higher isoelectric points are better microorganism adsorbents, meaning metal oxides are stronger as adsorbents than organics for microorganisms [174]. It should be noted that the isoelectric points of targeted microorganisms play an important role in this phenomenon. 60 Microorganism association constitutes a crucial aspect of this thesis. Consequently, the following review has been presented with particular emphasis placed on selecting microorganism association detection methods. 2.5 Microorganism Association Detection Methods A summary of the available research concerning the quantification of microorganism associations is presented in Table 2.1, mainly focusing on the underlying assumptions guiding the methodological selections. Researchers typically adopt one of two core assumptions for quantifying microorganism associations. One assumption suggests that these associations might disrupt the accurate quantification of microorganisms by traditional enumeration approaches, potentially leading to an overestimation of treatment system effectiveness [175], [8], [176], [177]. The second assumption suggests that microorganism associations do not significantly alter the infectivity of microorganisms during the enumeration [178], [179], [180]. Therefore, as associations become more prevalent, the efficiency of inactivation gradually declines but can be directly evaluated from the effluent water quality. The selection of the assumption to form the foundation of the experimental work could be based on the type of organism, enumeration methodology, association level expected, and other factors, such as water quality conditions that lead to potential association mechanisms at play. Results may suggest that either assumption is acceptable, but the context of the experiments is important to consider for method selection. 61 According to the literature, in general, the methods for quantifying particle-microorganism associations can be categorized into three distinct strategies: direct visualization [181], [182], [121], [183], detection after physical separation (to eliminate association by physical force or differentiate the associated organisms with methods such as filtering and settling) [184], [185], [186], [187], and a combined chemical and physical separation techniques (to disrupt association by chemical agent and then eliminate the association with physical force) [175], [8], [176], [177]. Each of the approaches will be explored in the following. Visualization techniques, such as microscopy [182] or fluorescence imaging [121], [183], are excellent at providing qualitative insights into the presence and distribution of particle-microorganism associations. This data can also be valuable for the confirmation of the existence of associations and physical characteristics of them. However, these methods may face challenges regarding quantitative enumeration due to potential issues such as limited field of view, bias in selecting regions of interest, and subjective interpretation [188]. Additionally, visual methods might be very time-consuming for high-throughput analyses, with varying levels of accuracy and precision. Homogenization is a physical separation technique that aims to break particle-microorganism associations through rapid mixing. The typical analysis method is first to enumerate organisms in the original sample, apply rapid mixing (homogenization), and then re-enumerate organisms in the homogenized sample. The process assumes that the 62 original enumeration method will only capture free-floating organisms (associated microorganisms are non-viable). The increase in organisms post-homogenization is, therefore a representation of microorganisms associated with particles in the original sample. The potential damage to the organism cells was investigated by Li et al. (2009) [176], and results showed that the high speed of the homogenizer could be detrimental to organism viability and skew results. Settling and filtration methods align with the assumption that associated organisms can be accurately counted and retain infectivity [8], [176], [177], [175]. In these methods, the portion of associated organisms is separated by settling and filtration methods and enumerated independently. Techniques without vigorous mixing and chemical additives may impose fewer disturbances to the organism’s cell structure, providing a more suitable context for studying microorganism associations. Chemical addition to water in experiments involving organisms and particles, necessitates the consideration of potential adverse effects such as altered behavior, impaired growth, or organism death. The detrimental effects to organism could arise because of the toxicity of chemicals, especially if used in high concentrations or if organisms are particularly sensitive. Additionally, chemical additives can alter the chemical composition of the experimental environment, affecting parameters such as pH, dissolved oxygen levels, and NOM and inorganic concentrations. Such alterations may indirectly impact organism behavior and growth rates, potentially confounding experimental outcomes and requiring careful control 63 measures to ensure accurate interpretation of results [189]. Moreover, chemical additives can also have indirect effects on particle properties within the experimental system. These effects may include changes in particle size, surface charge, or aggregation behavior, which can influence particle interactions with organisms [190]. Furthermore, the presence of chemical additives may mask true biological responses of organisms to the environment, making it challenging to observe the effects of experimental treatments [191]. To address these concerns, a range of controls, including dose-response analyses, negative and positive controls, replication, and randomization could be employed. These controls help mitigate potential biases, account for variability, and ensure the reliability and validity of experimental results in studies involving chemical additives. The choice of filter size is a significant factor in studies utilizing the filtration method for microorganism association analysis. The wide range of the filter size selected in the studies from 1.2 to 80 µm seems to be influenced more by the characteristics of particles present in the sample rather than the actual size of the studied microorganisms [180], [178], [179], [184]. Most studies employing filtration and settling techniques have primarily focused on bacteria, which tend to be larger than virus-sized particles. Limited data is available regarding the behavior of viruses within filtration and settling methodologies. The combined methodology is advantageous since it includes both chemical and physical separation strategies to achieve improved separation efficiency. However, it is essential to note that various factors, including the type of organism under study and the 64 mixing conditions employed, can influence the efficacy and appropriateness of this method. For example, Li et al. (2009) conducted a study where two different rotation speeds were utilized for homogenization after chemical disturbance of interactions. Their results showed that a speed exceeding 20,000 rpm resulted in permanent damage to the organisms [176]. As such, there is a potential risk of overestimating organism inactivation after microorganism association detection. Moreover, the combined method includes the introduction of chemicals such as beef extract [8] or Camper's solution [177], often at relatively high pH levels. The sensitivity of the organisms to such conditions should be considered, as it could influence the association dynamics and subsequent quantification accuracy. Table 2.1: The review of established particle-microorganism association detection methods Detection Strategy Background Water Method of Separation Detection Studied Organism Researcher, Reference Combination of chemical and physical separation Stormwater runoff Homogenization: At 22000 rpm with the addition of the Camper's solution Cultivation analysis: E. Coli, total and fecal coliform, fecal streptococcus Borst et al. (2003), [175] Filtered effluent of two drinking water treatment Homogenization: At 16000 rpm (4°C) with the addition of Tris buffer solution Cultivation analysis: Heterotrophic bacteria Camper et al. (1986), [177] Purified water Homogenization: At 22000 rpm with the addition of Beef extract solution Cultivation analysis: MS2 and T4 bacteriophages Templeton et al. (2005), [8] Wastewater Homogenization: At 13500 rpm with dispersal buffer Cultivation analysis: spore-forming bacteria Li et al. (2009), [176] Physical separation Natural surface water Filtration: With 20 μm nylon filter Cultivation analysis: E. Coli Jameson et al. (2005), [180] Brackish water (mixing of freshwater and saltwater) Filtration: With 3 μm filter Molecular analysis of stable RNA Bidle et al. (1995), [178] Tap water Filtration: With 1.2 μm filter Cultivation-independent analysis: Adenosine triphosphate (ATP) Liu et al. (2013), [179] 66 Table 2.1: The review of established particle-microorganism association detection methods (continued) Detection Strategy Background Water Method of Separation Detection Studied Organism Researcher, Reference Wastewater Filtration: With 80 and 11 μm nylon filters Cultivation analysis: Coliform bacteria Dietrich et al. (2007), [184] Purified water Homogenization: Vortex for 45 seconds Cultivation analysis: E. coli Kollu et al. (2012), [185] Physical separation Grey water Homogenization: Blending at 4000 rpm for 60 seconds Cultivation analysis: Total coliform Winward et al. (2008), [186] Purified water Homogenization: Dispersion by sonication Cultivation analysis: E. coli and Pseudomonas putida Wu et al. (2005), [187] Purified water Settling: For 15 minutes Cultivation analysis: Heterotrophic bacteria Herson et al. (1991), [120] Purified water Settling: For 30 minutes Cultivation analysis: E. coli and faecalis Farrel et al. (2018), [30] 67 Table 2.1: The review of established particle-microorganism association detection methods (continued) Detection Strategy Background Water Method of Separation Detection Studied Organism Researcher, Reference Purified water Settling: For 3 days Cultivation analysis: Bacillus Subtilis Spore Mamane and Linden (2006), [7] Physical separation Purified water Settling: For 20 to 80 hours Cultivation analysis: Cryptosporidium parvum oocysts Searcy et al. (2005), [193] Natural surface water Centrifugation: At 2000 rpm for 10 minute Cultivation analysis: bacterial, protozoan, and viral indicator organisms Characklis et al. (2005), [194] Direct visualization Purified water 30 microscopic fluorescence images Cultivation analysis: Heterotrophic bacteria Krause et al. (2019), [121] Unfiltered drinking water Epifluorescent microscopy Cultivation analysis: E. coli Fiona Lynch (2012), [183] 68 Table 2.1: The review of established particle-microorganism association detection methods (continued) Detection Strategy Background Water Method of Separation Detection Studied Organism Researcher, Reference Direct visualization Wastewater Computer-aided image analysis Cultivation analysis: Coliform bacteria Loge et al. (2002), [181] Brackish water (mixing of freshwater and saltwater) Microscopy Molecular analysis: gene of Rhodopirellula sp. Winkelmann et al. (2009), [182] 2.6 Strategies to Improve the Efficacy of UV Disinfection Several methods are currently being used in treatment facilities or are under investigation to reduce organic/inorganic dissolved solids and suspended particles for higher UV efficiency. It is important to note that increasing efficiency can be achieved by reducing the organism association and increasing UVT. Both objectives can be aimed at and achieved simultaneously as they are interconnected. Distinct mechanisms play a role in achieving higher UV efficiency. For instance, advanced oxidation processes are employed in some systems to elevate the rate of NOM removal or transformation, hence higher UVT and organisms UV exposure. Others achieve higher UV result by reducing the organism association via ultrasonic radiation. The removal of organisms associated (or not) with particles can also be removed by filtration. In the following, different strategies are categorized by their position in the treatment plant, either occurring before or simultaneously with UV disinfection. The studies processes include upstream pre-treatment, upgrading the UV system to an advanced oxidation process, and finally, reducing organism association by adjusting water quality with zeta potential manipulation. 2.6.1 Upstream Pre-Treatment Filtration can be used to reduce particulate concentrations and therefore reducing the risk of particle-microorganism association. However, it has limitations due to its expense, maintenance, and space requirements [127]. Before UV disinfection, various types of filters, including sediment, activated carbon, multi-media, and membrane filters, can be 70 used to reduce turbidity and enhance light transmittance [195]. Sediment filters are effective at removing larger particles like sand and silt, while activated carbon filters could also adsorb organic compounds and chemicals [196]. Multi-media filters can trap a wide range of particles, and microfiltration and ultrafiltration membranes can remove even smaller particles and microorganisms [197], [198]. The target turbidity for water prior to UV treatment typically varies depending on the specific application requirements and regulatory standards. However, as a general guideline, the target turbidity is usually set to be less than 1 NTU aligning with the standard for turbidity in water entering UV reactors [16]. Chemical pre-treatments such as coagulation-flocculation can reduce both dissolved and suspended solids, but involves the introduction of chemical substances to water, necessitating an extra reactor and complications in managing the produced sludge [199]. Activated carbon adsorption effectively removes dissolved organics and enhances UV penetration; however it is costly [200], [201]. Ultrasonic pre-treatment demonstrated higher UV efficacy by reducing the size of agglomerates of particles, reducing the particle size distribution of the samples. The economic viability of this method is constrained by its high energy input, making it impractical as an alternative to conventional disinfection methods [202]. Generally, the resource-intensive nature of these systems poses significant challenges in achieving widespread adoption and economic feasibility. 71 2.6.2 Upgrade to Advanced Oxidation Processes (AOP) There are several UV-based AOP systems, such as ozone-UV and chlorine-UV, as well as photocatalytic processes active under UV light (e.g. TiO2) [5]. Generally, AOPs would result in higher biocidal power in a broad microbial spectrum, the reduction of trace organics in treated water, and in addition to offering taste, discoloration, and odor control. Although AOPs present higher oxidation power, the generation of DBPs such as aldehyde and brominated compounds, is of significant concern [203]. Upgrading a UV disinfection system to a photocatalytic UV system could combine the germicidal effects of UV light with the advanced oxidation capabilities of photocatalysts. This approach could enhance both microbial inactivation and chemical contaminant removal compared to UV alone [106], [204]. Although advanced oxidation processes might mitigate the formation of disinfection by-products, this process is more complex; it may also be impacted by particulates. Therefore, the interaction between the catalyst and UV needs to be optimized. Moreover, this technique is best suited for specific contaminant types and introduces complexities related to catalyst recovery and reuse [205]. 2.6.3 Zeta Potential Adjustment Zeta potential is a key concept in colloid and surface science that plays a significant role in understanding the stability and behaviour of particles suspended in a liquid medium such as water. It has become an invaluable tool for understanding the behaviour of 72 charged particles, including microorganisms since it directly describes possible electrostatic interactions during water treatment [206]. Zeta potential measurements have been applied to enhance various water treatment processes such as coagulation-flocculation [38], [39], [40], [41], [42] electrocoagulation [43], adsorption [44], membrane filtration [45], and DAF [46] systems. However, the application of this diagnostic tool in UV disinfection remains largely unexplored despite the role of zeta potential and particle-microorganism association on UV dose-response [115], [11]. When particles and microorganisms have opposite charges, electrostatic attraction facilitates the association and forms aggregates of different sizes. Conversely, when particles and microorganisms have similar charges, electrostatic repulsion prevents their association and maintains them in a dispersed and stable state [206]. Therefore, control over zeta potential that has evolved into an easy and straightforward tool can significantly contribute to enhanced water quality management by increasing the control of unwanted aggregations or optimizing coagulation [129]. Zeta potential can be effectively manipulated using three main methods: chemical additives, pH adjustment, and external electric fields. pH adjustment can influence the electrostatic interactions and stability of particles by modifying their surface charges. Introducing chemicals, such as polymeric compounds, can change the zeta potential by creating bridges between particles, neutralizing surface charge, or forming three-dimensional networks that trap microorganisms and particles. Applying an external electric 73 field can alter the behaviour and interactions of particles or microorganisms through polarization effects and electrical double-layer compression. These techniques provide powerful tools for controlling zeta potential and subsequently modulating particle behaviour in various systems [209], [210]. The primary knowledge gap that this research aimed to address is the limited understanding of the interplay between particulate matter and microorganisms during UV disinfection. While there is extensive research on the impact and indicators of dissolved matter, the influence of particles on disinfection is less explored and sometimes contradictory. Increasing understanding in this area would pave the way for the development of more accurate UV prediction models, thereby enhancing the efficiency of disinfection processes and developing strategies to ensure safe water supply. 74 Chapter 3: Material and Methods Chapter 3 describes the experimental methodologies and analytical procedures employed in this thesis. The experimental procedures, including sample preparation, data collection, and statistical analyses, are provided to establish the basis for the subsequent discussions presented in Chapters 4 to 7. 3.1 Treatment Plants Description Two drinking water treatment plants without filtration processes served as the model plants for this work. The source water for treatment Plant 1 (TP1) is Duteau Creek and Kalamalka Lake is the source for treatment Plant 2 (TP2). Both plants and the sources are situated in Okanagan Valley, BC. At TP1, raw water is treated by a dissolved air flotation (DAF) process using poly aluminum chloride (PACl) and coagulation and flocculation aid (Epi Amine and Hydrofloc). After DAF, the water is chlorinated to maintain a residual of approximately 0.5 mg/L free chlorine before UV treatment. At TP2, there is no treatment before UV disinfection. 3.2 Water Sample Preparation This study is structured into three distinct projects. In each project, the sample preparation process was tailored to the objectives. Before any sample modification, samples were acquired from three different positions within the treatment plants. It is important to note 75 that the three sources of unfiltered water analyzed in this study had varying treatment levels. Two sample locations (TP1 Pre-UV and TP2 Pre-UV) are directly upstream of UV disinfection. There is no treatment prior to UV at TP2; therefore, the TP2 pre-UV sample also represents unfiltered source water characteristics in Kalamalka Lake. As described above, before UV disinfection at TP1, raw water is treated by a DAF process. Therefore, another sampling point at TP1 was chosen to be the raw water (TP1 raw). Variations in water quality, source water, and treatment levels applied were intended to facilitate exploring the differences in water quality impacts on UV disinfection while representing unfiltered water quality currently disinfected by UV. A simplified flow diagram of the TP1 and TP2 are presented in Figure 3.1. Figure 3.1: A simplified flow diagram of the a) TP1 and b)TP2 [211] 76 After collecting samples from these three sources, they were processed according to the experimental plan. The subsequent sub-sections provide a more detailed explanation of sample preparation specific to each project. 3.2.1 Project 1: Water Sample Preparation Grab samples (20 Litres) were collected monthly from October 2020 to October 2021. The water samples were collected in dark glass bottles to prevent photodegradation and were sanitized and pre-washed with the source water before sampling [212]. 500 µl of Male-Specific 2 (MS2: ATCC 15597-B1) bacteriophage stock (the culturing methods for microorganisms will be elaborated in section 3.5.1 and 3.5.2), at a concentration of 108 PFU/mL, were added to 19.5 mL of grab samples for UV exposure experiments. Then, samples were mixed (magnetic stirrer at 50 rpm) for 30 minutes to encourage microorganism associations with particles or dissolved compounds. The UV dose-response (biodosimetry experiments are discussed in section 3.5.3) experiments were carried out in triplicate for each sample at the time of collection, and the inactivation curves were generated from the average of the replicates. 3.2.2 Project 2: Synthetic Water Sample Preparation Synthetic samples were prepared using additives (kaolin clay, iron hydroxide, and humic acid sodium salt) and raw source waters (background waters). In these experiments, the focus was on investigating the behavior of three commonly found substances (particulate and dissolved) in water which also represent a variety of zeta potential ranges. The 77 selection was based on the results of previous experiments, where zeta potential was found to be effective in predicting the achieved UV disinfection. Humic acid was chosen as it typically exhibits a far negative zeta potential. Kaolin clay was selected to represent additives in the neutral range of zeta potential. Lastly, iron hydroxide was included to represent particles with a positive zeta potential. This selection allowed for the exploration of a diverse range of zeta potential values and the observation of their effects on the behavior of particles in water. The kaolin clay was procured from the VWR brand, while iron hydroxide was sourced from the Fisher brand, and the humic acid sodium salt was obtained from the Aldrich brand. The background water for the synthetic samples was grab raw water samples taken in July 2022 from TP1 and TP2. Additives used are reflective of the common particulates and dissolved materials present in surface and groundwater and they have been utilized as representative components in relevant literature studies [8], [192]. Each additive was added to the background water to reach the desired turbidity level (1, 5, 20 NTU). In this study, turbidity served as the primary indicator for all particulate and dissolved representatives in water for several reasons. Firstly, turbidity is a reliable measure of particles, aligning with the central focus of our investigation on the effects of particles on UV disinfection and microorganism association. Secondly, employing turbidity as a communal indicator was crucial to ensure comparability and relatability of results across different additives that could influence microorganism association. Furthermore, the measurement of UVT, which serves as an indicator of 78 dissolved organic matter, was consistently conducted in all experiments to maintain an understanding of both particulate and dissolved components in the water with their respective commonly used indicator. Turbidity, while commonly associated only with particulates, may not serve as an ideal indicator for dissolved matter. However, even in scenarios involving humic acid additives, the levels of scattered light were observed to correlate with the concentration of additives in the water, suggesting a potential proxy measure for their presence and concentration. The same sample of raw water (background water) was used for all additive experiments. As such, the influence of dissolved and particulate matter in the background water would be equal across all trials and observed changes were expected to be due to the additive. After additives addition and mixing (magnet stirrer at 100 rpm) for 30 minutes, a surrogate microorganism (MS2) was added to samples to reach the final concentration of 108 PFU/mL for UV exposure experiments. Then, samples were mixed (magnetic stirrer at 50 rpm) for 30 minutes to encourage potential microorganism association (Figure 3.2). A coagulation/flocculation/DAF system is implemented before UV disinfection in TP1, while TP2 does not practice any pre-treatment. The pre-treatment in TP1 was simulated at bench scale to achieve realistic water quality conditions that may arise in cases of higher concentrations of particles and dissolved solids in the source water (e.g., weather events). To keep the experimental parameters consistent with the facility, the coagulant (PACl) and coagulation/flocculation aids (Epi Amine and Hydrofloc) were 79 Figure 3.2: Synthetic water samples made with TP1 raw and TP2 Pre-UV waters as background water, a) TP1 raw+ Fe(oH)3, b) TP1 raw+ Humic Acid c) TP1 raw+ Clay, d) TP2 Pre-UV + Fe(oH)3, e) TP2 Pre-UV + Humic Acid, f) TP2 Pre-UV + Clay at 1, 5, 20 NTU from left to right. obtained from the plant’s operational staff. The jar test experiments (Figure 3.3) were carried out for each sample to optimize the coagulation and aid dosage. 20 mL of the samples were gently taken from the middle of the jar and placed in Petri dishes for the UV disinfection test (in triplicate). a) b) c) d) e) f) 80 Figure 3.3: Jar test for optimization of the coagulant (PACI) concentration only for TP1 samples The UV dose-response experiments at the wavelength of 255 nm were carried out for each sample in triplicate, and the inactivation curves were generated from the average of the replicates. After exposure at four different doses (from approximately 10 to 40 mJ/cm2), each sample was serially diluted and then cultured for enumeration. The preparation flow diagram of the synthetic sample experiments is presented in Figure 3.4. Figure 3.4: Synthetic water sample preparation flow diagram 81 3.2.3 Project 3: Zeta Potential Control Samples were collected from the source at TP1. The sampling point was intentionally selected before all pre-treatment steps to investigate the influence of zeta potential change in a water sample containing natural particulate and dissolved solids. The sample preparation flow diagram of the zeta potential control experiments is presented in Figure 3.5. In these experiments, coagulant was solely introduced to alter the zeta potential of water, without the intention of inducing coagulation. 5 mL of MS2 (ATCC 15597-B1) bacteriophage stock (at an initial concentration of 1010 PFU/mL) and 5 mL of Escherichia coli (E. coli: ATCC 15597) stock (at an initial concentration of 1010 CFU/mL) were added to 495 mL of grab samples of TP1, resulting the initial microorganism concentration of 108 PFU-CFU/mL in samples. The samples were stirred using a magnet stirrer at 50 rpm for 30 minutes to facilitate potential microorganism associations. Figure 3.5: Zeta potential manipulation experiment’s sample preparation flow diagram 82 To establish zeta potential control, a volume of 100 mL of the original water sample, along with microorganisms was distributed in 200 mL beakers. PACl was added to the samples at concentrations ranging from 0 to 30 mg/L to shift zeta potential toward positivity. To shift the zeta potential towards negativity and attain the most negative possible zeta potential (highly negative) value for the samples, a 1M sodium hydroxide solution was prepared and added to the samples at varying doses. In addition to the initial zeta potential of the samples (control sample without additives: negative), three different zeta potentials toward positivity (moderately negative, neutral, and positive) were produced from each sampling set. Finally, the zeta potential was established at 5 data points for each sampling set, comprising viral surrogates in sample set 1 (blue schematic beakers in Figure 3.5) and bacterial surrogates in sample set 2 (yellow schematic beakers in Figure 3.5). The samples were then stirred for 2 hours to ensure thorough mixing, followed by water quality characterization to assess the resulting changes. Then, 20 mL of the stirred samples were conveyed to 70 mm Petri dishes for UV dose-response experiments, and inactivation curves were generated based on the average of the three replicates. 3.3 UV Inactivation Kinetics of Microorganisms The reduction in the abundance of organisms (initially No) due to UV exposure can be described by a double-exponential model (equation(4)) [37]. The model accounts for sub-groups of organisms: UV-susceptible and UV-resistant. The fraction of UV resistant 83 organisms is represented by the 𝛽 parameter, and the kinetic rate constants for each sub-group are represented by 𝑘1 and 𝑘2. The use of a double-exponential model allows for representation of tailing effects to the dose-response model that are commonly observed in UV disinfection. 𝑁 = (1 − 𝛽)𝑁𝑜ⅇ(−𝑘1 𝑈𝑉 𝐷𝑜𝑠𝑒) + 𝛽𝑁𝑜ⅇ(−𝑘2 𝑈𝑉 𝐷𝑜𝑠𝑒) equation (4) Where, N is the residual number of culturable organisms after UV exposure in plaque-forming unit (PFU-CFU/mL), No is the initial number of culturable organisms, UV Dose is the delivered UV Dose in mJ/cm2, 𝛽 is the fraction [0,1] of organisms that are UV-resistant, 𝑘1is the first-order inactivation rate constant for UV-susceptible organisms, 𝑘2 is the first-order inactivation rate constant for UV-resistant organisms, 3.3.1 UV Dose Delivered The actual UV dose delivered to the samples can be calculated by equation (5) [18]. The average UV intensity was calculated from the measured UV intensity incident on the surface of the microbial suspension using the light meter. Reflectance (R) at the air-water interface can be calculated using Fresnel’s Laws as 0.025. In addition, the Petri Factor term compensates for the fact that the irradiance is not uniform over the entire surface area of the sample container. The ratio of the average irradiance to the center irradiance determined the Petri factor, which was measured as 0.99 for the experimental setup used 84 in this work [213]. In the reactor used in this study, the distance from the lamps' centerline (L) and depth of suspension (d) were equal to 9.4 cm and 0.52 cm in the experiments, respectively. 𝑈𝑉 𝐷𝑜𝑠𝑒 = 𝐸𝑠 𝑃𝑓 (1 − 𝑅)𝐿𝑑+𝐿(1−10−𝐴254𝑑)𝐴254 𝑑 𝑙𝑛(10)𝑡 equation (5) Where, Es is the average UV intensity (mW/cm2), Pf is the unitless Petri factor, R is the reflectance at the air-water interface at 254 nm, L is the distance from the lamp centerline (cm), d is the depth of the suspension (cm), A254 is the UV absorbance at 254 nm, t is the exposure time (s) 3.4 Water Quality Characterization A Zetasizer Nano ZS (Malvern Instruments, Ltd., Westborough, MA) measured particle size distribution, mean count rate, and zeta potential. UV light absorbance and transmittance of water samples at 254 nm (UVA and UVT) were measured using HORIBA Scientific Aqualog spectrometer (Piscataway, NJ). The UVT instrument was zeroed with the background water used to see the incremental contribution of each additive to UVT. A VWR Turbidity Meter was used to measure sample turbidities, and pH measurement was carried out using Thermo Scientific Orion 3-Star Benchtop pH Meter. Total Organic Carbon (TOC) was quantified using a Shimadzu TOC-L. The UV light intensity was 85 measured using a ILT2400 Optical Light Meter to determine the UV dose delivered. All measurements were taken in triplicate to check the repeatability of the results obtained. 3.5 Surrogate Microorganisms Propagation and Enumeration In the third project (Chapter 7), E. coli was also utilized as a bacterial surrogate. With limited research in the area of zeta potential manipulation for UV systems, it was deemed important to expand the organisms used in this research. Incorporating several microorganisms would allow for the possible impact of zeta potential variations on behaviour to be elucidated. This approach allowed for an improved understanding of this pre-treatment's effectiveness against various microorganisms. The relative survival rates of surrogates vary, with MS2 demonstrating greater resistance compared to Cryptosporidium and E.coli. This characteristic positions MS2 as a conservative surrogate for water treatment assessment purposes. Factors such as DNA amount and structural features can contribute to Cryptosporidium and MS2's resilience. Conversely, Cryptosporidium and E. coli, may exhibit varied responses due to factors such as the presence of protective structures on their outer membrane. Cryptosporidium, being a protozoan parasite, typically has a larger genome compared to MS2 bacteriophage but smaller than that of E. coli. Therefore, Cryptosporidium generally contains more DNA than MS2 but less than E. coli [214]. Freeze-dried vials of E. coli bacteriophage MS2 (ATCC 15597-B1) and its host E. coli (ATCC 15597) were rehydrated and propagated following their respective ATCC 86 product sheets. To culture E. coli, LB broth (Miller formulation; Fisher Scientific) was chosen as media for host bacteria. 10 mL of LB was inoculated with 20 μL of frozen E. coli and grown overnight at 37ᵒC. 50 mL of fresh LB was then inoculated with 1 mL of overnight cultured E. coli, followed by incubation at 37ᵒC with continuous shaking at 100 rpm to obtain a culture in its log growth phase. The strain utilized was non-motile. The concentration of microorganism in water samples was enumerated before and after treatment to capture the impact of treatment processes on microorganisms and catch the potential detrimental impact of the introduced compositions on microorganisms. All water samples containing MS2 phage and E. coli were serially diluted using LB broth medium. 3.5.1 MS2 Enumeration The Double-Layer technique was used to estimate the concentration of MS2 [215]. The method involves creating a double agar layer system where the bottom layer consists of a nutrient agar medium (solidified LB-1.5% agar (Miller formulation; Fisher Scientific)), and the top layer is made up of a soft agar medium (LB-0.6% agar, 40-48 ᵒC) containing 50 µL of the serially diluted MS2 sample to be tested and 50 µL of the host bacteria (E. coli at concentration of 107 – 109 CFU/ml). After incubation (18 hours, 37ᵒC), observed plaques (Clear circular zones result from MS2 lysing E. coliwere counted, multiplied by dilution factor (the ratio of the final volume of MS2 in the sample to the volume of the original solution used to dilute it) and divided by the sample volume to determine the titer in plaque- 87 forming-unit per mL (PFU/mL). In Figure 3.6, four samples of the cultured MS2 plates are shown with different dilution factors (DFs). In plate counting method, concentrations ranging between 50 and 100 counts per plate were deemed appropriate for accurate enumeration. Concentrations outside of this range were considered too concentrated for reliable counting. Figure 3.6: Example of Dilution Factor (DF) and Plate counts for MS2 enumeration employing a 10-fold serial dilution with double layer agar technique. Plate concentrations are in PFU/ mL. d) Control plate is poured without any MS2 organism (host only) to check for any potential environmental infection or contamination. 3.5.2 E. coli Enumeration The E. coli enumeration was carried out using the Spread Plate Method [215]. To enumerate E. coli, 50 µL of the serially diluted (multiple dilution from 6 to 7 times) water a) DF=106 d) DF=107 b) DF=108 c) Control Plate 88 sample was spread evenly across the surface of the solidified LB with 1.5% agar (Miller formulation; Fisher Scientific) using a sterile spreader, ensuring uniform coverage of the agar surface. After spreading the sample, the agar plates were incubated at 37°C for 18 hours. E. coli colonies appear as distinct, circular, and often slightly raised colonies on the agar surface. These colonies are counted after the incubation period. Considering the dilution factor resulting from the spread and the sample volume, the concentration of E. coli is calculated in colony-forming units per millilitre (CFU/mL). In Figure 3.7 four samples of the cultured E. coli plates are shown with different DFs. The final bacteria titer is calculated by multiplying the number of colonies counted by the dilution factor and then dividing by the volume of the sample plated. In plate counting method, concentrations ranging between 50 and 100 counts per plate were deemed appropriate for accurate enumeration. Concentrations outside of this range were considered too concentrated for reliable counting. 89 Figure 3.7 Plate counts for E. coli enumeration employing a 10-fold serial dilution with spreading technique. Plate a and b) results from samples with no zeta potential manipulation (zeta potential=negative). Plate c and d) results from samples with zeta potential manipulation (zeta potential=neutral) 3.5.3 Biodosimetry Method Development and Optimization Biodosimetry method development refers to the process of creating and refining techniques for assessing the biological effects of UV radiation exposure. Biodosimetry involves quantifying changes in the microorganism population to estimate the amount of radiation they have been exposed to within the system [216]. Biodosimetry experiments are carried out to assess the microbiological concentration of water in a system exposed to various experimental conditions. UV dose-response dose-response curves are developed from the results of biodosimetry experiments, indicating the impact of different particulate matter types and concentrations on UV disinfection efficiency. The curves can a) DF=103 b) DF=105 d) DF=105 c) DF=103 90 be used to establish a relationship between water quality parameters, treatment UV dose, and water quality indicators (UVA, UVT, turbidity, and zeta potential). During the development of the biodosimetry method, a series of iterative steps were undertaken to optimize its various components, focusing on achieving optimum volumes and concentrations for organisms’ enumeration. This process involved refining the stock solution concentrations through repeated trial and error. Additionally, careful calculations were made to determine the precise volumes needed for tasks such as culturing and inoculation. The selection of container types and sizes was also pivotal, ensuring consistency by maintaining the same choices throughout all experiments. Furthermore, the standard operating procedures detailing the propagation and enumeration of organisms underwent continuous refinement. This refinement process was done in response to the specific resources and conditions present within the laboratory environment. This process aimed to establish the most optimal conditions across all aspects of the method, resulting in a robust and reliable biodosimetry approach. 3.6 Microorganism Association Two methods, filtration and settling, were considered throughout this thesis for the experimental detection and quantification of particle-microorganism association (Chapter 6 and Chapter 7). Previous studies have demonstrated the successful application of 0.22 µm filtration method to isolate free-floating or non-particle-associated MS2 organisms from the associated portion [8], [217]. However, in cases where zeta potential manipulation is 91 undertaken (Chapter 7), the 0.22 µm filtration technique proved ineffective in both sample sets (with different organisms) regardless of organism type. Therefore, the analysis in Chapter 7 focused on developing a settling method for the quantification of microorganism association level. It has been determined that dissolved humic acids in water can coat organisms and increase organism survival during UV disinfection [12]. This mechanism involves the deposition of humic acid molecules onto the organism’s surface, rendering the deposited humic substances no longer classified as dissolved. In this study’s experimental design for microorganism association detection and quantification, the focus was on particle-microorganism associations. Consequently, the specific conditions in which ‘coated’ organisms would be counted using the methods applied in this work were in cases where either the coating increased settleability or the coating caused an increase in the diameter of microorganisms beyond 0.22 µm. It is important to note that this work is limited to studying the impact of particulates in water, with these specific constraints affecting the quantification of coated organisms. In the following sub-sections, the two quantification methods adopted are explained in detail. 3.6.1 Filtration Technique Filtration is used to separate organisms associated with particles from the sample based on their size. A three-step process is employed to enumerate the portion of associated MS2 with particles and organic matter, which involves disrupting electrostatic bonds, 92 applying physical force, and finally enumeration [217], [8]. The first two steps are also referred to as the elution step. The percentage of associated microorganisms was calculated using equation (2). After the filtration (0.22 µm, nylon material purchased from Fisher Scientific) step, the concentration of microorganisms was enumerated as free-floating microorganisms. The concentration of all present microorganisms (free-floating and associated) was calculated from the enumeration by eluting organisms in the original sample. Elution was achieved through the addition of 55 mL of 3% (w/v) beef extract (Sigma-Aldrich) solution at pH of 9 (adjusted using NaOH purchased from Fisher Scientific) to 15 mL of sample followed by vigorous mixing for 3 minutes at up to 5000 rpm using Fisher Scientific vortex mixer to break possible aggregates. A previous study by Templeton et al. (2005) concluded that beef extract solution does not harm the viability of MS2 [8]. %𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑖𝑜𝑛 = 𝐶𝑜−𝐶0.22 µ𝑚𝐶𝑜∗ 100 equation (2) Where, Co is the initial concentration of samples enumerated, C0.22 is the concentration of non-eluted samples (10 mL of samples were filtered) after 0.22 µm filtration. The aggregation of MS2 organisms in the stock solution was discouraged through filtration (0.22 µm, nylon) of the stock sample immediately before its addition to the experimental samples. For each experiment, the initial concentration of the stock after filtration was enumerated. However, it is essential to recognize that fouling of the filters 93 could prevent the passage of free-floating phages. To address this concern and maintain a functioning filtration process, the filters were replaced frequently, with each experiment set (approximately 60 mL filtered before replacement). Furthermore, multiple replicates of samples were analyzed to minimize the impact of potential errors and enhance the robustness of the findings. 3.6.2 Settling Technique To enumerate the portion of microorganisms associated with particles and dissolved solids, in experiments with zeta potential adjustments (100 mL of samples in 200 mL beaker), a settling time of 30 minutes was used to differentiate the free-floating MS2 and E. coli from the attached organisms (after gently stirring for 2 hours). The settling method was based on a previous study by Farrel et al. [30]. The settling method used in this study exhibits certain limitations, as there may be remaining microorganisms in the supernatant that are associated and protected. Additionally, there may be free organisms that co-settled by the downward movement of flocs and particles. Nevertheless, when applied correctly and consistently in all experiments, this method can provide a reliable and relative estimate of associated particles in the samples. The percent particle-associated microorganisms were calculated using equation (3). After the settling step, the concentration of microorganisms in the supernatant was enumerated as free-floating microorganisms. The concentration of all present microorganisms (free-floating and particle-associated) was calculated from the enumeration of the sample before settling 94 time. After settling, 10 mL of the supernatant was gently extracted for water quality characterization and microorganism enumeration. %𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑖𝑜𝑛 = 𝐶𝑜−𝐶𝑆𝑒𝑡𝑡𝑙𝑒𝑑𝐶𝑜∗ 100 equation (3) Where, Co is the initial concentration of samples, Csettled is the concentration of the supernatant of samples, after the settling period. 3.7 UV Reactor A UV-LED system consisting of multichip arrays of UV-LEDs enclosed in a glass housing (PearlLab BeamTM, AquiSense Technologies) was used to carry out inactivation experiments (Figure 3.8). Water samples containing MS2 and E. coli were irradiated under LEDs at a wavelength of 255 nm with an average intensity of 478 μW/cm2. A Petri dish with 70 mm diameter containing 20 mL solution (comprising water sample and organisms) was placed under the LEDs, and the complete Petri dish was illuminated. In all experiments, samples were exposed to four different doses from which 20 μL of the sample was taken at each dose level to conduct serial dilution and enumeration. All dose-response measurements were taken in triplicate. 95 Figure 3.8: UV device setup used for all biodosimetry experiments [218] 3.8 Statistical Analysis The statistical significance of the results was determined using Student’s t-test. For kinetic studies, Student’s t-test was performed in pairs for the β, k1, and k2 terms of the double-exponential model at a 95% confidence level. The difference between two samples was deemed to be statistically significant if the calculated p-value was ≤ 0.05. A two-way (with replication) ANOVA at a confidence level of 95% was used to determine the impact of zeta potential change, particle additives, and UV dose on the inactivation of surrogate organisms. To understand the impact of water quality variables on the independent variables (UV log inactivation of MS2), hierarchical multiple linear regression analysis was undertaken separately for each additive particulate. The assumptions of multiple linear 96 regression were met in all cases, and the impact of dependent variables on each independent variable on the same scale was compared by standardized regression coefficients (β coefficient). Step-wise multiple linear regression analysis was used to identify the degree to which various water quality parameters (independent variables: Turbidity, UVT, UVA, average particle size, and zeta potential) describe variability in particle-microorganism association (dependent variable). 97 Chapter 4: Water Quality Characteristics of Unfiltered Water and UV Disinfection The objective of this chapter is to examine the impact of unfiltered water conditions on UV disinfection. The influence of turbidity, UVA, TOC, and zeta potential on UV dose-response curves is analyzed to evaluate relationships between unfiltered water quality and log inactivation of surrogate organisms. The adequacy of common water quality parameters to assess UV disinfection effectiveness in the context of unfiltered water, such as UVA and turbidity, is investigated. The results of this chapter highlight the challenges of UV disinfection of unfiltered supplies concerning both the impacts of unfiltered water quality on UV effectiveness and the ability of conventional measures to capture variability in UV performance in this context. Results can be used to optimize the estimation of inactivation achieved by UV. 4.1 Water Quality Characterization This study used three sources of unfiltered water with varying treatment levels. Two sample locations (TP1 and TP2 pre-VU) are directly upstream of UV disinfection at the two water treatment plants. However, no filtration process has been applied in either case. Before UV disinfection at TP1 (TP1 pre-UV), raw water is treated by DAF using PACl, coagulation aid (Epi Amine), and flocculation aid (Hydrofloc). There is no treatment prior to UV at TP2, and therefore, the TP2 pre-UV sample is also representative of the unfiltered raw source water characteristics of Kalamalka Lake. 98 For this chapter, the experimental design focused on periodic sampling of pre-UV water at two large-scale plants that do not practice filtration. An additional raw water sample (TP1 raw) was included to expand the range of water quality tested and help elucidate the potential effects of water quality on UV disinfection. Sampling was carried out over a year to capture seasonal changes in water quality and provide an overall assessment of the UV disinfection performance at the two unfiltered facilities. Turbidity and bacterial concentration of TP1 raw water during the sampling period were captured in Figure 4.1. Water characteristics (turbidity, zeta potential, TOC, UVA, and pH) were recorded during the sampling period presented in Figure 4.2. Figure 4.2-a shows that pre-UV samples were consistently below 1.0 NTU up to August 2021. An increasing trend in the turbidity of two non-treated water samples (TP2 pre-UV and TP1 raw water) in summertime (July – August 2021) could be attributed to several possible factors, such as recreational activities or the increase in the water temperature causing calcite precipitation and increased biological activity [219], [220], [221]. It should be noted that the risk pathways could be different for the two sources because TP2 is prone to calcite precipitation, and TP1 source water is characterized as an upland watershed influenced by rainfall and runoff and with recreational, forestry and grazing activities within the watershed. Due to the pre-treatments at TP1, the turbidity of pre-UV samples was consistently below 1.0 NTU, while the turbidity of TP1 raw water samples varied between 1.1 and 3.5 NTU. The observation of specific periods of elevated turbidity during the 99 sampling period implies the importance of understanding the impacts of turbidity on UV disinfection and the need for a reliable method to ensure adequate delivery of UV dose. Further to the possibility of turbidity impacting the UV disinfection process in terms of diminishing the exposure of microorganisms to UV light, elevated turbidity is often used as a surrogate for increased microbial contamination of sources. However, microbial contamination in the source water is not always well associated with conventional measures [222], [223]. According to Figure 4.1, establishing control of UV systems based on turbidity does not capture variations in source water microbial quality. Weak relationships between turbidity and coliform counts (R = 0.14), as well as with E. coli (R = 0.17) were observed on sampled days. The peak in total coliform bacteria and E. coli (June 2021) is not concomitant with the peak in turbidity (April 2021). Additionally, fluctuation in the concentration of microorganisms (E. coli concentrations of 0 to 56 MPN with a standard deviation of 22.9 and total coliform concentrations of 14 to 74 MPN with a standard deviation of 25.6) was seen from December 2020 to March 2021, while the turbidity remained constant at 1.3 NTU with a standard deviation of 0.1. It should also be considered that coliforms and E. coli counts are non-precise indicators of the presence of protozoa or viruses [224] 100 Figure 4.1: Turbidity and bacterial concentration of TP1 raw water during the sampling period. Turbidity measurements were made in triplicates and the error bar represents one standard deviation of replicates. The results for E. coli and coliform samples were communicated from the treatment plant with measurements made in triplicates and an error less than 10 percent. UVA at 254 nm is reported at two scales for all water samples in Figures 4.2-c and Figure 4.2-d. In this study, UVA measurements were conducted without prior sample filtration to assess the impact of both dissolved and particulate components of the water on UV absorbance. This approach was intentional, aligning with the focus of this work on unfiltered water supplies. By capturing the complete UV absorbance profile, including contributions from suspended solids, this methodology was aimed to reflect real-world conditions and the UVA that would be measured during the treatment of unfiltered supplies. 0.00.51.01.52.02.53.03.54.00.050.0100.0150.0200.0250.0Dec-20Jan-21Feb-21Mar-21Apr-21May-21Jun-21Jul-21Aug-21Sep-21Turbidity (NTU)Total Coliform Bacteria-E. Coli (MPN)Sampling DateTotal Coliform Bacteria E. coli TP1 raw turbidity 101 Figure 4.2: a) Turbidity over the one-year sampling period, b) TOC from February to August 2021. c) Average UV absorbance at 254 nm, d) UV absorbance of pre-UV samples, e) Sample date vs. Zeta potential of grab samples. Values are the average of triplicates, and vertical error bars represent one standard deviation. Both pre-UV (Figure 4.2-d) samples showed similar trends and consistently low levels of absorbance (0.07 absorbance, 85% transmittance), implying a high degree of UV light penetration through the water. TP1 raw samples were more opaque, reflected by an average UVA of 0.35 with a maximum of 0.50 in April 2021. TOC was recorded over a 0.000.501.001.502.002.503.003.504.00Oct-20Dec-20Feb-21Apr-21Jun-21Aug-21Turbidity (NTU)Sampling Datea)3.003.904.805.706.607.508.409.3010.2011.1012.00Feb-21Mar-21Apr-21May-21Jun-21Jul-21Aug-21TOC(mg/L)Sampling Dateb)0.000.100.200.300.400.500.60Oct-20Dec-20Feb-21Apr-21Jun-21Aug-21UVASampling Datec)0.000.010.020.030.040.050.060.070.08Oct-20Dec-20Feb-21Apr-21Jun-21Aug-21UVASampling Dated)-23.0-21.0-19.0-17.0-15.0-13.0-11.0-9.0-7.0Jan-21Feb-21Mar-21Apr-21May-21Jun-21Jul-21Aug-21Sep-21Zeta Potential (mV)Sampling Datee) 102 subset of the sampling period (February 2021 to August 2021) due to instrument failure. TOC results showed similar trends for all water samples and varied between 7.46-11.83 mg/L, 3.68-4.64 mg/L, and 3.65-4.13 mg/L for TP1 raw, TP1 pre-UV, and TP2 pre-UV, respectively. The large spike in turbidity and TOC in the spring for TP1 pre-UV and raw water could be due to freshet conditions and increased organics in the source water. TOC measurements did not reflect the ascending trend in turbidity for TP2 pre-UV water samples from July 2021 to September 2021. Since TOC would capture changes in both particulate and dissolved organic concentrations, the proportion of particulate matter that resulted in the spike in the turbidity at the last three sets of sampling was more likely due to changes in inorganic particulate material in the TP2 source. Zeta potential was recorded during the sampling period from January 2021 to September 2021. Zeta potential represents the electrostatic potential at the electrical double layer around a particle in solution. As presented in Figures 4.2-e in TP2 pre-UV, particle surface charges were more neutral (zeta potential = −10.4 ± 1.9 mV) than TP1 raw water (zeta potential = −17.2 ± 2.1 mV) and TP1 pre-UV (zeta potential = −20.9 ± 0.9 mV). Zeta potential was constant in TP2 pre-UV samples despite no treatment, indicating that particle surface charge in the source water is consistent. Treatment at TP1 (coagulation/flocculation/DAF) consistently resulted in a more negative average particle surface charge. The difference between TP1 raw and TP1 pre-UV is more pronounced during specific times of the year and this could be due to fluctuations in DAF performance 103 based on coagulant dose, flow rate, contact time in the reservoir, and other operational conditions. Runoff due to snow melt or freshet could also be responsible for the abrupt change to TP1 raw characteristics in May to June 2021. MS2 and Cryptosporidium are expected to be negatively charged in water, with potentials of approximately -27 and -35 mV, respectively [225], [226]. It was hypothesized that if microorganism-particle association drives tailing effects, samples with less negatively charged particles in TP2 pre-UV and TP1 raw water would experience greater tailing effects due to greater attractive forces between MS2 and particulates. 4.2 Organism Inactivation Grab samples were collected once a month at three sampling locations for approximately a year (varying from 8 to 13 months for different locations). The average UV inactivation of each sample from December 2020 to September 2021 is shown in Figure 4.3. The exposure times were selected to be constant during all experiments at 15, 25, 80 and 100 seconds, and doses were corrected based on water quality, as explained in methods using equation (5). The analysis of UV disinfection achieved was conducted for each sampling position individually. From Figure 4.3 tailing effects (reduced increase of inactivation with respect to increase in dose) were observed for all samples. Notably, as the dose increased from approximately 30 mJ/cm2, tailing effects can be seen. Furthermore, in all samples, tailing effects were pronounced enough to cause a plateau in dose-response, or no statistically significant increase in inactivation between doses of 104 approximately 30 mJ/cm2 and > 40 mJ/cm2 (p-value > 0.05 for all comparisons between irradiation of 80 seconds and 100 seconds for each sample type). Control experiments (RO water with no particulates) presented a similar plateau effect at UV doses above 30 mJ/cm2. However, tailing effects occur to some extent, even without particulates, likely due to the aggregation of organisms, resulting in a greater likelihood of survival. The initial phage concentration in the experiments was 108 PFU/mL, and the maximum disinfection achieved was 4.17 log. Thus, the relative plateau in obtained log removal values after approximately 30.0 mJ/cm2 cannot be attributed to the complete inactivation of MS2. It should be noted that the organisms' survival in RO water was not impeded by osmotic challenges. This can be attributed to the presence of the organisms within LB broth, which was introduced alongside them into the RO water. The addition of the medium to the RO water likely created conditions that were tolerable for the organisms' survival.While the operational definition of tailing is not explicitly defined in the literature, it could be characterized as the maximum degree of deviation in slope within specified limits. Where the difference in disinfection rate peaks within a specific range. While in all samples, a reduction in the rate of disinfection was observed, there were statistically significant differences in the absolute log inactivation achieved. Control dose-response showed higher log inactivation than any sampled water and suggests that water quality (dissolved and/or particulate) does play a role in reducing UV effectiveness. Dose-response curves begin to separate based on sample type above 20.0 mJ/cm2. At approximately 30.0 105 mJ/cm2, TP1 pre-UV shows a notable increase in inactivation compared with the other two water samples relative to the lower UV exposure applied. There is a statistically significant improvement in average log removal in TP1 pre-UV samples compared to TP1 raw water and TP2 pre-UV samples at 30 mJ/cm2 (1.2 log increase: p-value < 0.001) and 50 mJ/cm2 (1.1 log increase: p-value < 0.001). As such, it can be concluded that tailing is present in all sample types after approximately 30 mJ/cm2; however, treatment at TP1 does significantly improve average log inactivation at higher UV doses. Figure 4.3: The average dose-response curve of TP2, TP1 pre-UV, and TP1 raw calculated from December 2020 to September 2021. Control tests are representative of MS2 inactivation in reverse osmosis water. Error bars represent one standard deviation (n = 8-11). Pre-treatment at TP1 (coagulation, dissolved air filtration) is likely the cause of the observed difference at high UV doses. Figure 4.4 presents log reduction values -5.0-4.5-4.0-3.5-3.0-2.5-2.0-1.5-1.0-0.50.00 10 20 30 40 50Log N/N0UV Dose (mJ/cm2) 106 normalized by (a) sample turbidity, (b) zeta potential, and (c) UVA. The intention was to identify if dose-response curves would look similar when accounting for the impact of water quality characteristics in different water samples. To analyze the consistency of the values in the transformed dataset, the level of dispersion was also indicated by the coefficients of variance around the mean. Figure 4.4: Average log inactivation normalized by (a) sample turbidity, (b) zeta potential at average pH 7.2, and (c) UVA -35-30-25-20-15-10-5051015200 10 20 30 40 50 60Log N/No / TurbidityUV Dose (mJ/cm2)a)-0.3-0.3-0.2-0.2-0.1-0.10.00 10 20 30 40 50 60Log N/No / Zeta PotentialUV Dose (mJ/cm2)b)-90-80-70-60-50-40-30-20-1000 10 20 30 40 50 60Log N/No /UVAUV Dose (mJ/cm2)c) 107 Figure 4.4 shows that turbidity or UVA independently does not explain the variability in average log reduction identified in Figure 4.3. A successful normalization factor would be expected to result in equal responses between water samples. In all cases, each water source was observed to have a statistically significant difference in normalized log reduction for all exposure times (p-value < 0.001). Cantwell and Hofman (2010) concluded the same results while investigating the absorption properties of suspended particle matter in untreated water [13]. Monitoring the turbidity and UVA of unfiltered waters to determine the disinfection potential may not accurately reflect the actual UV disinfection efficiency for unfiltered samples. When normalized by zeta potential, variability in dose-response curves is minimized. However, significant differences remained between log inactivation for each water source at any given exposure time (p-value < 0.008). It is worth noting that the TP2 surface charge remained relatively constant throughout the study period, whereas the TP1 raw and pre-UV samples varied seasonally as the raw water characteristics changed (zeta potential levels during the sampling period available in Figure 4.4-e). Normalization by zeta potential reduced variability in log inactivation among sampling dates as opposed to normalization by turbidity and UVA. There was a pronounced increase in the dispersion of the data when normalized by turbidity and UVA. For TP1 pre-UV water samples, the average coefficient of variation over sampling days increased from 22.6% (no normalization) to 114.3% when normalized by turbidity and 108 23.3% (normalized by UVA). TP2 pre-UV results showed similar trends, with the average coefficient of variation in monthly data increasing from 22.0% to 51.2% (normalized by turbidity) and 30.5% (normalized by UVA). For both TP1 and TP2 pre-UV samples, normalization by zeta potential decreased variation to 18.8% and 17.3%, respectively. A decrease in month-to-month variation in dose-response when normalized by zeta potential suggests this measure captures some degree of variability and is valuable to an overall assessment of expected UV inactivation levels. Based on the data collected, the particulates' surface charge explains some degree of dose-response variation within the two untreated natural waters that were the most and least turbid samples. However, it did not account for the other water source that experienced treatment (TP1 pre-UV). The increase in negative surface potential of particulates from TP1 raw water to TP1 pre-UV was unexpected since coagulation should cause neutralization of surface charges and shift zeta potentials towards 0 mV. It is possible that through the DAF process, the fraction of particulates that were neutralized were removed in flocs, while another fraction (mainly negatively charged) remained and was not effectively removed by treatment. Due to electrostatic attraction, in the presence of neutral particles and microorganisms with a negative surface charge (-27 to -35 mV), the aggregation of microorganisms and particles was more likely to happen and therefore, microorganisms were trapped and became shielded from UV and contributed to higher levels of tailing. As 109 observed in Figure 4.3, the average inactivation of TP2 pre-UV water samples, containing particulates with the least negative surface potential, reached a plateau after 2.0 log removal as opposed to 2.5 and 3.5 in TP1 raw and pre-UV samples, respectively. This is in agreement with results observed by Farrel et al. (2018) where chalk and Fe3+ with zeta potentials of -3.5 and -3.9 mV (in neutral pH) were associated with higher levels of tailing as opposed to humic acid and clay with zeta potentials of -13.9 and -17.2 mV at pH=7, respectively. Results provided in this study further support the hypothesis that higher levels of particle-microorganism attachment will occur when the surface charge of particulates is closer to neutrality [30]. In general, the dose-response curves acquired using the normalized data by surface charge are more representative based on the lower coefficients of variances of the repetitive measurements. 4.3 The Effect of Particles on UV Resistance and Tailing The double exponential model, equation (4), was used to describe the UV inactivation kinetics of water samples (R2 > 0.90). This model was initially developed for UV disinfection of bio-flocs and is repurposed in this study under the premise that microorganisms in the presence of particles, flocs, or aggregated organics can be shielded by being embedded in the suspended particles. Numerous studies conclude that the disinfection of microorganisms is comprised of two stages that are attributed to two different groups of microorganisms [37], [227], [228]. The expectation is that the portion of free-floating microorganisms is prone to disinfection, with the remaining portion that is 110 attached to, enmeshed in, or associated with particles, flocs, agglomerations of microorganisms, and organic matter resulting in the majority of tailing effects. Control experiments were also conducted on microorganisms with no particles and reverse osmosis water (Figure 4.3). Complete inactivation is not observed in control samples, which can be connected to the microorganism aggregation. Similar results were observed in previous studies [30]. Average values of k1, k2, and β fitted from inactivation levels are presented in Table 4.1. The degree of tailing is captured in the β term. As expected, the percentage of UV-resistant microorganisms (β) is significantly different in TP1 raw and pre-UV with values of 64% and 19%, respectively (p-value < 0.001). TP1 raw samples had higher organic carbon levels and average turbidity compared to TP1 pre-UV (TP1 Raw TOC = 9.19 mg/L, turbidity = 3.52 NTU; TP1 pre-UV TOC = 4.30 mg/L, turbidity = 0.51 NTU). This suggests that treatment in TP1 (coagulation + flocculation + DAF system) reduced UV resistance and tailing. The particle size distribution (PSD) was also measured to explore possible relationships between size and the obtained results. Two peaks were observed in PSD by volume figures of TP1 raw water at 0.69 and 5.17 µm, while in TP1 pre-UV samples, only one peak at 4.95 µm was reported (Figure 4.5). Data suggested that the treatment applied to TP1 raw removed the portion of particles smaller than 1 µm. The remaining portion of particles within the size range of 2 to 6 µm would carry over to the disinfection stage. 111 Previous studies have found that UV disinfection efficacy is linked to particle size [35]. Therefore, the DAF process may remove particulates and small colloids that could interfere with disinfection and result in better UV disinfection and a lower level of tailing. Table 4.1: UV disinfection model parameters for water samples and control experiments, the error reported as standard deviation between sampling days, n=8-10, (R2>0.99 for fit in all cases) Sample ID β k1 (cm2/mJ) k2 (cm2/mJ) Control 0.18±0.03 0.19±0.07 0.10±0.04 TP1 raw 0.64± 0.11 0.23±0.05 0.14±0.02 TP1 pre-UV 0.17±0.03 0.20±0.07 0.11±0.04 TP2 pre-UV 0.82±0.06 0.20±0.09 0.17±0.07 In previous research by Farrel et al. (2018), the relationship between turbidity levels and UV inactivation of E. coli and E. faecalis was studied. Farrel et al. reported a lower disinfection rate of microorganisms in samples with higher turbidity levels due to increased potential of aggregation and decreased UVT [30]. It is suggested that along with turbidity, other factors could control the behaviour of UV disinfection. Two low turbidity samples, TP1 pre-UV (average turbidity of 0.38 ± 0.09 NTU) and TP2 pre-UV (average turbidity of 0.61 ± 0.33 NTU), showed varying levels of resistant microorganism proportion (𝛽 = 82% and 18%, respectively) and significantly different dose-responses. 112 The initial inactivation slope, k1, is higher than the secondary inactivation slope k2 in all cases. As expected, it indicates that inactivation occurs at a higher rate for the portion of free-floating microorganisms than for associated microorganisms. This is in agreement Figure 4.5: the Particle Size Distribution (PSD) of a) TP1 raw and b) TP1 pre-UV samples with observations from Azimi et al. (2012) [37], where the same parameters were calculated for trickling filter and activated sludge flocs in three different size fractions. Higher particulate matter concentrations enhance the chance of microorganisms a) TP1 raw b) TP1 pre-UV 113 becoming enmeshed into flocs or shielded by particles. Thus, it can be seen that the difference between k1 and k2 is more pronounced in TP1 raw water than in less turbid samples (TP1 and TP2 pre-UV). This is hypothesized due to a lower ratio of non-associated microorganisms in TP1 raw that become inactivated within a short period. k1 and k2 values are significantly different in each sample type (p-value <0.05). There is no significant difference between k1 values for each sample type (p-value =0.6). This supports the idea that the first part of the inactivation curve is associated with free-floating organisms, and the response should be independent of water quality. However, k2 values are different from one another and could be associated directly with particulate characteristics. The higher k2 values are associated with two raw water samples (TP1 raw and TP2 pre-UV). TP1 pre-UV sample demonstrated the most efficient overall inactivation among all sampled water despite having the lowest k2 value. This can be attributed to its correspondingly lowest beta value. It can be inferred that while TP1 before UV treatment has fewer particle-associated microorganisms, those present exhibit higher resistance to inactivation. TP1 pre-UV with the lowest k2 value indicates that the proportion of microorganisms resistant to UV are the least prone to disinfection. This may be related to the introduction of coagulants that are specifically intended to aggregate particulates for removal. There may be a carryover of either particulates or continued effect of the coagulant up to the point of UV disinfection. It can be concluded that the behaviour of UV 114 disinfection in the presence of different particles is complex and highly dependent on several water quality conditions. The significance of correlations between the month-to-month values of modelled parameters (β, k1, k2) and water quality indicators was calculated to investigate relationships between water quality and dose-response further. Generally, there was poor consistency of significant relationships, with no singular modelled parameter and water quality indicator showing significant correlation across all water types. For example, significant correlations were observed between k1 and zeta potential (p-value <0.05) in TP1 raw and TP2 pre-UV samples but not for TP1 pre-UV samples (p-value = 0.46). The month-to-month inconsistency further suggests that dose-response in unfiltered waters can be highly variable, and water quality indicators used in this study are not universally strong indicators of expected performance. Variability in this study also suggests that future work using controlled synthetic water samples where individual parameters can be varied independently would help elucidate potential relationships between water quality and UV performance in unfiltered waters. 4.4 Summary The results of this chapter demonstrated that the UV disinfection response could not be correlated to the conventional treatment indicators such as turbidity, TOC, and UVA at 254 nm. In previous studies, microorganism protection against UV disinfection has been shown by particles with enmeshment or attachment mechanisms. The results of this study 115 supported the theory that even in high-quality unfiltered water supplies (turbidity less than 1 NTU), particulates with more neutral surface charges could offer significant protection to microorganisms from UV light. Samples were taken directly from two drinking water treatment facilities, and therefore, control over the tested conditions was limited. Also, there is potential for cross-correlations between zeta potential and other parameters that could be considered in future studies. Additionally, using a surrogate measure (MS2) when Cryptosporidium inactivation is the true target should be noted. The effect of water quality on the tailing of UV disinfection kinetics of unfiltered water supplies was also investigated. It was shown that zeta potential was correlated with overall disinfection achieved at UV doses > 30 mJ/cm2 and degree of tailing. Water samples with the same turbidity, but differing zeta potential of -11 mv and -21 mv had ~82% and 17% UV-resistant microorganisms, respectively. A higher level of tailing is assumed to be the consequence of more UV-resistant microorganisms, hence more potential for coupling with particles and a shielding effect. Observing two water sources with different zeta potential (-18 and -21 mv at pH=7.2) and different tailing levels (TP1 and TP2 pre-UV samples) confirms that the tailing is attributed to the average surface charge. Tailing was also found to be dependent on the pre-treatment. Particles after coagulation and DAF (TP1 pre-UV) had a lower tailing effect than non-treated water from the same source (TP1 raw) with a more positive average surface charge. 116 The poor representation of dose-response using uniform turbidity level or UVA is more pronounced at higher UV doses (>30 mJ/cm2), and water quality effects are less influential at low doses. The composition of turbidity-causing materials or particulates may be the controlling factor that further needs to be investigated. Although the UV efficiency is highly source-dependent, pre-treatment should be considered even for high-quality surface waters subject to pathogenic contamination to reduce the public health risks. Furthermore, to accurately assess UV disinfection potentials water quality characterization should include zeta potential in addition to turbidity and UVT/UVA. Chapter 5: The Analytical Methodology of Particle-Microorganism Association Detection In continuation of the previous chapter, it became evident that a focused exploration of particle-microorganism association was needed, including developing an experimental methodology to identify the proportion of microorganisms associated with particles. This Chapter provides the progression toward the methodology employed for identifying and analyzing particle-microorganism association for the subsequent chapters. 5.1 Results and Discussion The varying range of available methodologies and levels of detail explored in section 2.5 emphasized the need for method selection based on the particular context (e.g., microorganism type, treatment applied, water quality characteristics and enumeration methodology of the organisms). The upcoming sub-sections will further examine the methodologies adopted for detecting and enumeration of microorganism associations with particles and dissolved organics tailored to the specific treatment context (chapters 6 and 7). The rationale behind the selected experimental parameters will be discussed in two distinct sections: the first focused on experiments conducted without zeta potential manipulation in chapter 6 (section 5.1.1), and the second dedicated to the analysis of data obtained from and experimental methods in the Chapter 7 involving zeta potential manipulation (section 5.1.2). 118 5.1.1 Particle-Microorganism Association Detection in Experiments Without Zeta Potential Manipulation In Chapter 6, MS2 serves as the primary surrogate organism due to its wide recognition as a valuable model organism in UV disinfection research and the high number of comparative studies for our research objectives. By focusing on MS2 as the surrogate organism, methodologies proven effective with bacteria were disregarded. The decision to employ a culturing method for enumeration was based on following the current approaches to validate UV disinfection systems, which led to the exclusion of non-culturing approaches such as microscopic methods. Additionally, microscopic methods face inefficiency in the context of high-throughput applications. Therefore, a filtration method with a 0.22 µm filter with an elution step using beef extract solution was selected to detect particle-microorganism association. In the method selection process, a study carried out by Templeton et al. (2005) that exhibited aligned similarities to the experimental parameters (in terms of surrogate organism and disinfection treatment) outlined in Chapters 4 and 6 was used to establish a relevant point of reference [8]. In addition, the effectiveness of the association detection approach by filtration was demonstrated by Templeton et al. (2005), particularly with the MS2 organism. In the following paragraphs, the foundation of the selected assumption and rationalities behind the experimental parameter are elaborated. Assumption: In this study, it was hypothesized that associated microorganisms would still be viable, and association would not prevent the detection of organisms. Experiments were designed to check the infectivity of associated microorganisms in culturing methods. A known amount of MS2 was added to a natural water matrix (TP1 119 raw) and a control sample of reverse osmosis (RO) water with no, or very low, concentrations of particles or dissolved matter to achieve concentration of 108 PFU/mL. Samples containing MS2 in the natural water matrix (particulates and dissolved matter) were stirred for an hour to encourage potential attachment and enmeshment. An insignificant difference (p-value = 0.15, n=6) between the initial concentration of MS2 in TP1 raw and control (RO) samples was observed, as determined by a culturing method relying on MS2 remaining infective. This observation supported the hypothesis that both associated and free-floating microorganisms could cause infection (Figure 5.1-a). However, it was possible that no particle associations occurred in TP1. To confirm the presence of organism associations, the samples were filtered through 0.22 µm nylon filters after the elution step, as described in section 3.6.1, to quantify the level of microorganism association. Results depicted in Figure 5.1-b confirm that although the initial MS2 concentration in both samples was not significantly different, the association level is significantly different (p-value = 0.002, n = 6). The association level is at an average (arithmetic mean) of 19.8% ±3.1 in samples made with TP1 raw water as opposed to RO samples at 3.13% ±1.9. This result confirms the suitability of the filtration system for experiments solely with MS2 as the target organism. The rationale for selecting the size of filter: For this work, in the process of MS2 propagation and after the phage was cultivated with its appropriate host, the solution containing MS2 and its host was filtered through a 0.22 µm filter. As such, only dispersed microorganisms with a size of less than 0.22 µm (the MS2 only) pass through to the MS2 stock solution. It was assumed that organisms associated with particles or interacted with 120 dissolved organics would exceed 0.22 µm (Although this was not verified), preventing them from passing through the filter. Figure 5.1: a) Initial concentration of MS2 in samples made with natural water matrix and RO water. b) The distinct impact of background water quality on the level of microorganism association Initial experiments on samples with MS2 and RO water as background were conducted to assess the percentage of free-floating MS2 that adsorb or could be retained by the 0.22 µm filter. These experiments were aimed at checking the effect of filtration in cases where no association is expected, but aggregation is possible. An insignificant difference (p-value = 0.29, n = 3) between the concentration of MS2 in filtered RO samples and control (unfiltered MS2+RO water) samples supported the hypothesis that filtration does not significantly capture free-floating MS2 and aggregation was insignificant. The results of the concentration of filtrate and unfiltered RO water sample experiments can be seen in Figure 5.2. Therefore, it was assumed that if the samples a)) b)) 121 were filtered through 0.22 µm, the portion of attached/associated microorganisms would be filtered out with all free-floating MS2 passing through the filter. Figure 5.2: The impact of 0.22 µm filtration on samples made with RO water as background. 5.1.2 Particle-Microorganism Association Detection in Experiments with Zeta Potential Manipulation Chapter 7 of this study investigates the association between components of water and microorganisms across various levels of zeta potentials and the subsequent impact on UV disinfection. An extra element introduced in this Chapter's methodology was the assessment of UV system's effectiveness using two different microorganisms: E. coli as a bacterial surrogate and MS2 as a viral surrogate. The assumption about the infectivity of associated MS2 also holds true in this chapter. However, the hypothesis was also tested for a bacterial surrogate. For this chapter, a settling approach was taken to identify the degree of microorganism association. This chapter improved the applicability of the research findings and explicitly explored differences in microorganism type and 122 microorganism association. In the following paragraphs, the foundation of the selected assumption and rationalities behind the experimental parameter are elaborated on in detail. Assumption: A known amount of E. coli (1010 CFU/mL) was added to a natural water matrix sample (TP1 raw) and a control sample of reverse osmosis (RO) water. Samples containing organisms were stirred for an hour to encourage potential attachment and enmeshment. An insignificant difference (p-value = 0.41, n = 6) between the initial concentration of E. coli in TP1 raw and control (RO) samples was observed and supported the hypothesis that both associated and free-floating microorganisms could remain culturable (Figure 5.3). Figure 5.3: Initial concentration of E. coli in samples made with natural water matrix and RO water. Rationale for Selecting Settling Method: There is a clear relationship between zeta potential levels and microorganism association, as illustrated in the results chapters of this thesis (Chapters 4, 6). Hence, in the study of varying zeta potential, it is vital to 123 select a method for microorganism association detection that has minimal disturbances to the system's charge equilibrium. The methodology consolidated for Chapter 6 could not be reliably used due to the introduction of beef extract in the elution step. Furthermore, preliminary experiments were conducted on the level of microorganism association after zeta potential manipulation revealed that PACl added to adjust zeta potential level induced agglomeration of particles and increased the adhesivity of particulate and dissolved solids in water, which led to the complete entrapment of microorganisms on the filter. This entrapment was due to both microorganism association and the deposition of floc on the filter, which entrapped organisms during the filtration process. Furthermore, the use of E. coli complicated the filtration methodology since bacteria would be easily captured (free-floating or associated with a particle) by a 0.22 µm filter. Due to the lack of consistency in the available data regarding the detection of associated bacteria using filtration (wide range of filter sizes from 1.2 to 80 µm), the prior successful use of the settling method for E. coli in a recent study [30], and to ensure methodological uniformity, the settling method was selected for both organisms. Rationale for Selecting Settling Time: Given the consistent water matrix employed for the experiment sets conducted with two organisms, it was assumed that the particle and floc sizes would generally depend on the original water quality. Therefore, the settling time was considered independent of the specific type of organism. Coagulation/flocculation and other possible water reactions evolve over time, particularly when manipulating zeta potential with chemical additives. Consequently, to ensure a consistent record of coagulation effects or the influence of water composition on particle-microorganism association, each sample should settle for the same duration. 124 In the process of selecting the coagulant dosage for zeta potential manipulation, the goal was to avoid microorganism reduction caused by sweep flocculation and settling. To explore specifically the role of surface charge, the coagulant was used with the intention of predominantly providing charge neutralization and not sweep flocculation. As such, experiments were conducted to determine the dosage range that would minimize microorganism removal due to zeta potential manipulation. The expectation was that when PACI is dosed to water, the turbidity goes up concurrently with the zeta potential. Zeta potential would reach a maximum positive charge, and further addition of PACl would result in turbidity reduction [212]. Similar results were achieved in a study by Wei et al. (2015) and the phenomenon is called sweep coagulation [229]. Coagulation experiments were conducted with seven different dosages of PACl according to the treatment plant’s (TP1) communicated instruction (Figure 5.4). The turbidity of the supernatant after coagulation is presented in Figure 5.4-c. As illustrated in Figure 5.4-b, the sweep coagulation is taking place at the dosage of 30-50 mg/L of PACl and is in the range of usual coagulant concentration used in the treatment plant (this information was acquired through communication with treatment plant staff). At all achieved zeta potential levels in this study, the coagulant dose (4-20 mg/L) remained below the threshold required for sweep coagulation (Figure 5.4-c and d). The prevention of sweep coagulation leads to the conclusion that coagulant addition does not induce substantial floc formation and coagulant addition predominantly results in charge neutralization. Consequently, in the absence of flocculation, the settling velocity of free-floating microorganisms remains unchanged. Therefore, the reduction in turbidity and increase in UVT observed after settling time is attributed to the settling of attached flocs, 125 including any byproducts of coagulant addition to the components of water, with or without microorganism association. Figure 5.4: a) picture of the water samples after coagulation with four doses. b) the picture of the water sample at the optimum dosage with sweep coagulation. c) turbidity level of the supernatant after coagulation (before settling). d) UVT level of the supernatant after coagulation. -30-20-10010203040506005101520250 10 20 30 50 80 100Zeta Potential (mV)Turbidity (NTU) PACl Concentration (mg/L)E. Coli - TurbidityMS2 - TurbidityE. Coli - Zeta PotentialMS2 - Zeta Potentialc))-30-20-1001020304050600204060801001200 10 20 30 50 80 100Zeta Potential (mV)UVT (%)PACl Concentration (mg/L)E. Coli - UVTMS2 - UVTE. Coli - Zeta PotentialMS2 - Zeta Potentiald))b) a) 126 A set of experiments were conducted to determine the optimal settling time. Different settling periods: 0, 15, 30, 60, and 90 minutes were assessed for RO and TP1 raw (natural water) backgrounds. It is important to note that the settling time was selected based on the behaviour of water samples without any zeta potential manipulation to establish a baseline for experiments. Control experiments were conducted using RO water as the background to determine the impact of particulates on microorganism association and to eliminate the potential settling behaviour of microorganisms unaffected by association with particulate matter in water. In previous studies, it has been assumed that free-floating organisms [180] or particulates with a size of less than 5 µm [230] have a negligible settling velocity. A single-factor analysis of variance (ANOVA) with replication was conducted among the five settling time intervals, presenting no significant difference among the control samples (RO+organisms) in both organisms independently (p-value: E. coli= 0.16, MS2= 0.95) after the settling time intervals. This result confirms negligible settling of free-floating organisms in samples with no particulates. On the contrary, as shown in Figure 5.5, significant differences were observed in TP1 samples for both E. coli (p-value =0.006) and MS2 (p-value<0.01), indicating the effect of settling in samples containing particulates. Although the extent of reduction for each organism varied with settling time in TP1, a consistent trend was observed. After 30 minutes, the organism concentration either stabilized or slightly increased for both E. coli (p-value = 0.13) and MS2 (p-value = 0.90). Therefore, a settling time of 30 minutes was identified as the suitable timeframe for both microorganisms. The observed increase in concentration after 60 minutes of settling may be related to organism resuspension. This suggests that factors such as settling 127 distance and resuspension dynamics influenced by potentially agitation, may have contributed to the elevated concentration observed in the supernatant. Figure 5.5: Impact of settling time on the level of organism concentration, a) E. coli and b) MS2. Sample made with RO water served as control. The values are the average of triplicates. MS2 was the shared microorganism used in chapters 4, 6 and 7. A side-by-side comparison between filtration and settling for the percentage of free-floating MS2 in TP1 raw is quantified and presented in Figure 5.6. Results confirm that a settling time of 30 minutes yields the best and most consistent results, ensuring comparability across experiments. While efforts were made to achieve a refined experimental protocol for enhancing the detection of associated organisms, there is potential that microorganisms associated with denser particles settle faster. Those associated with lighter particles, dissolved matter, or organic solids may remain suspended in water [194]. Some level of bias may be introduced by applying settling methods, but the degree of bias can be explored in part by evaluating the alignment of the results with established experimental 0.E+005.E+051.E+062.E+062.E+063.E+063.E+064.E+064.E+065.E+060 10 20 30 40 50 60 70 80 90100E. Coli Concentrtration (CFU/mL)Settling Time (minutes)RO Water TP1 rawa)0.E+002.E+074.E+076.E+078.E+071.E+081.E+081.E+082.E+080 10 20 30 40 50 60 70 80 90100MS2 Concentrtration (PFU/mL)Settling Time (minutes)RO Water TP1 rawb) 128 methodology. Although some residual associated organisms may not settle completely, it is important to highlight that the study's methodology involved maintaining consistency in multiple variables and conducting replicates. Figure 5.6: The percentage of free-floating organisms measured after filtration method (red solid line) and after different times of settling (black dotted line). Samples were prepared with TP1 raw water and MS2 as surrogate organism. The dashed dotted red lines and error bars present the standard deviation of each method (n=3). 5.2 Summary This section examined the procedural process and the underlying rationales that guided the experimental approach to analysis of particle-microorganism association in the coming chapters. In Chapter 6, the filtration method was selected, considering the sole use of MS2 and the absence of coagulation agents in water samples. In Chapter 7 the introduction of coagulants proved the filtration detection method to be inconsistent and 404550556065700 10 20 30 40 50 60 70 80 90 100Free-Floating Microorganism (%)Settling Time (minutes) 129 impractical, leading to the decision to adopt the settling approach to address the identified gaps while maintaining the comparability of the results. 130 Chapter 6: Factors Affecting Microorganism Association and UV Disinfection: Effect of Source Water, Dissolved Organics, and Particle Characteristics Chapter 4 highlighted the need for a more controlled study focusing on the specific characteristics of particles that may impact disinfection and identification of water quality parameters that can define reliable dose-response models. The results thus far noted that differences in UV response could not adequately be accounted for by conventional water quality parameters. Moreover, water samples with low turbidity levels had different microorganism association values that were hypothesized to lead to variable disinfection responses. In this chapter, a range of water quality was produced at bench scale using additives to simulate extreme water conditions. Overall, additive type primarily impacted UV disinfection, particularly at lower turbidity (<5 NTU). The results of this chapter showed that incorporating zeta potential in conjunction with commonly used indicators increased the accuracy of predicting UV disinfection. 6.1 Source Water and Synthetic Sample Characterization In this study, two treatment plants were sampled to be used as background waters. The general water quality characteristics of the natural background waters are provided in Table 6.1. Higher hardness, alkalinity and conductivity levels were found in TP2 raw water compared to TP1. The DOC of water samples was acquired through communication with the treatment plants. The source water for TP1 is considerably higher in organic concentrations (DOC: TP1 = 9.10 mg/L, TP2 = 3.48 mg/L) and a larger average particle size was observed in TP1 pre-UV (2.80 µm) than in TP2 (0.80 µm). The larger sizes were 131 attributed to the pre-treatment processes before UV exposure, which caused the formation of larger particles and flocs in water. Table 6.1: Raw water quality characteristics (pH, Hardness, DOC, alkalinity, UVT, Turbidity, Average Particle Size and Zeta Potential) of the background waters Water quality characteristics Unit TP1 TP2 pH - 7.2 7.4 Conductivity µS/cm 50 520 Hardness mg/L 23.4 176 DOC mg/L 9.1 3.4 Alkalinity mg/L 21.9 163 UVT % 52.03 86.94 Turbidity NTU 1.3 0.7 Average particle size µm 2.8 0.8 Zeta Potential mV -24.2 -17.1 Additives representative of dissolved organic matter (humic acid) and inorganic particulates (iron hydroxide and kaolin clay) were used to simulate changes that may occur to water quality, possibly due to process upsets or weather events such as rainfall or snow melt. These three additives were chosen to both represent realistic scenarios as well as giving variability to characteristics. A spike in humic acid could occur due to failure or ineffectiveness of the DAF system, or rapid changes in the source water. Iron hydroxide and kaolin clay were chosen as two common mineral particulate surrogates that have varying surface charge and size [130], [231], [232]. The introduction of additives to TP1 and TP2 water samples altered the interactions between all the components, and this 132 was adopted deliberately to mimic real-life scenarios where additional contaminant concentrations may change the dynamic of interactions in water. While zeta potential provides valuable insights into particle electrokinetic behaviour, it may not fully capture the complex interactions and changes occurring when different components are mixed. To overcome this limitation, various water quality characteristics such as the average particle size, turbidity, UVT, and pH were analyzed. To measure the particle size of the added additives regardless of the background water matrix and account for potential aggregation in synthetic samples, two concentrations (with turbidity levels of 0.5 and 1 NTU) of particulates were prepared using pure water (RO) as the medium. The results indicated that kaolin clay particles exhibited fine characteristics, with an average particle size ranging from 0.5 to 1 µm. Iron hydroxide particle sizes were within a range of 0.5 to 2 µm in RO water. Humic acid was found to be displaying particle size distributions peaking at 0.5 μm in RO at constant pH. However, humic acid has been reported to show different dissolution rates in the background water depending on the water quality [233]. Therefore, the average particle size was measured in all samples after additive was added. Based on Templeton et al.'s (2006) [165] study on MS2 and Cantwell et al.'s (2008) [12] research on E. coli, it was observed that humic matter induced the formation of distinctive grainy capsules around organisms. This phenomenon was absent in organisms not exposed to humic matter. Furthermore, according to the results, these capsules provided protection and particle-like behaviours, such as protection from UV radiation. Hence, the observed particle-like behaviour of dissolved organic matter can be associated with interactions resembling those between particles and microorganisms, 133 Figure 6.1: Water quality characteristics of the source and synthetic samples versus increased concentration of additives as turbidity. a) ΔUVT levels in TP1, b) ΔUVT levels in TP2, c) the average particle size in TP1, d) the average particle size in TP2, e) zeta potential level in TP1, f) zeta potential level in TP2. Values are the average of triplicates, and vertical error bars represent one standard deviation. 0204060801001200 5 10 15 20 25ΔUVT (%)Turbidity (NTU)a) TP10204060801001200 5 10 15 20 25UVT (%)Turbidity (NTU)b) TP2050010001500200025003000350040000 5 10 15 20 25Average Particle Size (d.nm)Turbidity (NTU)c) TP1050010001500200025003000350040000 5 10 15 20 25Average Particle Size (d.nm)Turbidity (NTU)d)TP2-30-25-20-15-10-500 5 10 15 20 25Zeta Potential (mV)Turbidity (NTU)e)TP1-30-25-20-15-10-500 5 10 15 20 25Zeta Potential (mV)Turbidity (NTU)f)TP2 134 and could be referred to as the particle-microorganisms association. The characteristics of all synthetic water samples (zeta potential, UVT and average particle size) and the controls are presented in Figure 6.1. Variations in water quality due to addition of additives were observed to depend on the type of additive and the background water quality. All synthetic waters had additives introduced independently to avoid the compounding effects of interactions between the additives. Furthermore, dissolved and particulate impacts of the natural background water were considered to be constant since the same sample was used for all tests. For UVT measurements, the background water sample without additives was used to zero the instrument. As such, UVT represent the effect of the additives causing the changes in UVT or a ΔUVT, where UVT = 100% signifies no change from the background natural water. A non-linear relationship between the increasing concentration of additive (based on turbidity) and ΔUVT was visually observed for all additive types (Figure 6.1-a and Figure 6.1-b). Increasing humic acid concentrations caused the greatest ΔUVT (UVT from 100.00 to 11.81% for TP1, 30.26% for TP2) in comparison with iron hydroxide (UVT from 100.00 to 86.85% for TP1, 94.42% for TP2) and kaolin clay (UVT from 100.00 to 91.81% for TP1, 85.69% for TP2). The pronounced effect of humic acid on ΔUVT was expected due to the known high absorptivity of humic for UV light [234]. UVT is a simple measure of loss of light across a sample, and the reasons for light loss could be due to absorption or scattering away from the measured light path. As such, UVT measurements may underestimate disinfection performance because scattered light from particles can decrease UVT, yet still contribute to disinfection. On the contrary, light absorbed by dissolved matter or particulate matter would not contribute to disinfection, making 135 predictions of dose-response based on UVT complicated due to the presence of varying additive types. It is possible to capture the amount of light reflected and adjust UVT to more directly represent absorbed light using an integrating sphere. However, integrating spheres are not routinely used in treatment facilities or are considered when setting policies for UV monitoring requirements and would add a layer of complexity to the operation of UV systems. The variability observed in the minimum achieved UVT at 20 NTU turbidity between the two sample sets (TP1 and TP2) highlights the influence of the background composition on the effect of additives on UVT. It is important to note that since background water remained consistent across all experiments (collected on a single sampling date) and was used to zero for UVT (i.e., the background water was set to 100% UVT), any change reflects the impact of the additives introduced. As such, ΔUVT are due to either direct interactions of the additive with UV light or indirect effects caused by the additives interacting with the background water (both dissolved and particulate matter). The connection between turbidity and UVT depends on the nature of interactions between the additives and UV visible light as well as interactions of the additive with background water dissolved and particulate matter. Consequently, the interactions between additives and different wavelengths of light can influence the reduction in UVT observed in response to changes in turbidity except in the case of humic acid additive. In TP2 with no treatment before UV (Figure 6.1-d), a constant increases in particle size were observed for kaolin clay and humic acid additives (1.0 µm and 0.4 µm, respectively) as turbidity increased. However, there was a positive correlation between average particle size and increasing iron hydroxide particle concentration or turbidity. 136 Neutral to positive zeta potential of iron hydroxide particles caused higher levels of aggregation (up to 2.0 µm) in the solution containing higher concentrations of iron hydroxide (Figure 6.1-c and Figure 6.1-d). The low energy barrier due to the relative neutrality of iron hydroxide particles could be the reason behind the relatively higher aggregation of iron hydroxide than other particles (humic acid and kaolin clay) in both sample sets. Higher concentrations led to a higher chance of particulates colliding, hence greater particulate aggregation [30]. 6.2 Microorganism Association Results from chapter 4 highlight that predicted disinfection achieved may be overestimated if the enumeration of particles-associated microorganisms is omitted or not considered. It was hypothesized that particle-associated microorganisms would still be viable, and preliminary experiments were designed to check the infectivity of particle-associated microorganisms in culturing methods. As mentioned in section 5.1.1, a known amount of MS2 was added to a particle-laden sample (synthetic) and a control sample of reverse osmosis (RO) water. An insignificant difference (p-value = 0.15, n= 6) between the initial concentration of MS2 in synthetic and control (RO + phage) samples was observed and supported the hypothesis that both particle-associated and free-floating microorganisms could cause infection (of E. coli) in the enumeration process carried out by culturing methods. MS2 stock solutions were regularly vortexed and filtered through 0.22 µm to eliminate organism aggregation. It was assumed that if the samples were filtered through 0.22 µm, the portion of attached/associated microorganisms would be filtered out with all 137 free-floating MS2 passing through the filter. The average particle sizes of kaolin clay and iron hydroxide in RO water was measured and was predominantly larger than 0.22 µm and it could be concluded that filtered MS2 would not be associated with additives less than 0.22 µm. As such, the microbial concentration of the filtrate represents free-floating bacteria and the initial microbial load accounts for the sum of attached and free microorganisms. Microorganism association with additives increased concurrently with turbidity in both sample sets, as shown in Figure 6.2. However, the interaction between microorganisms and additives differed depending on the additive type and the background zeta potential (Figure 6.2). In general, highest levels of microorganism association can be seen in iron hydroxide samples in TP1 (from 11 to 32% increase in Turbidity of 1 to 20 NTU) and humic acid-containing samples in TP2 (5 to 14% increase in Turbidity of 1 to 20 NTU). Adding kaolin clay had a smaller impact on particle-microorganism association in all sample sets (particle-microorganism increased less than 6% from 1 to 20 NTU in both sample sets). The achieved microorganism association in humic acid samples, coupled with the absence of significant changes in the average particle size, indicates that the humic acid likely remained in a dissolved state and contributed to the coating mechanism of organisms. Additionally, it could also imply that the filtration method retains the coated microorganisms, allowing only the free-floating ones to pass through the filter. However, there are other possibilities, such as organism agglomeration, which could also influence the filtration process. 138 Figure 6.2: % particle-microorganism association versus increased concentration of additives as turbidity in a) TP1 and b) TP2. Values are the average of triplicates, and vertical error bars represent one standard deviation. Water from TP1 had a more negative background zeta potential (-25.5 mV) compared to TP2 (-17.2 mV). This difference in background zeta potential (Figure 6.1-e and f) resulted in more pronounced interactions and microorganism association in TP1. The less negative surface charge of iron hydroxide particulates (zeta potential = -3.9 ± 0.1 mV in RO water at 2 NTU and pH 7) resulted in the average zeta potential of the solution in both sample sets shifting toward neutrality with the addition of iron hydroxide (shifted in TP1: from -25.2 to -14.3 mV, and TP2: from -17.2 to -14.1 mV). Therefore, higher attractive interactions between microorganisms and particles and higher levels of particle-microorganism association. It should be noted that unique factors such as functional groups, surface roughness, and the specific surrounding environment likely influenced the interaction of iron hydroxide with other particles in the background water 051015202530350 10 20 30Particle-Microorganism association (%)Turbidity (NTU)051015202530350 10 20 30Particle-Microorganism association(%)Turbidity (NTU) 139 matrices. These interactions occur due to the distinctive properties of the surrounding environment. Contrasting results were observed with humic acid with negative surface charge (zeta = -13.19 ± 0.1 mV in RO water at 2 NTU and pH 7) in TP1 where the average zeta potential was decreased from -16.5 to -20.2 mV as humic acid concentration increased. The introduction of humic acid, which has a higher neutrality compared to the initial state of TP1, resulted in a decrease in the average zeta potential. This observation suggests that interactions with the background water, whether dissolved or particulate, may be contributing to this phenomenon. Lower microorganism associations were observed in the sample set with the less negative water samples (TP2). Kaolin clay additives caused the lowest levels of particle-microorganism (less than 6%) association in both sample sets. Similar results were reported in previous studies on different microorganisms and water quality composition [30] and were attributed to the highest negativity of kaolin clay in RO water (-17.2 ± 0.3 mV at 2 NTU and pH 7), resulting in the most negative zeta potentials and the least particle-microorganism association level in both sample sets (Figure 6.2). As an organic compound, humic acid has many functional groups along its molecular structure, such as carboxyl, hydroxyl, and phenolic groups [235]. These functional groups contribute to its negatively charged surface, making it highly reactive and capable of forming strong electrostatic interactions with other charged particles including the microorganisms. In contrast, kaolin clay is an inorganic particle with a surface charge that can vary depending on the pH of the water. 140 6.3 Impact of Water Quality Characteristics on Microorganism Association Step-wise multiple linear regression analysis was used to identify the degree to which various water quality parameters describe variability in microorganism association. The microorganism association results were considered as the dependent variable for each sample set. Turbidity, ΔUVT (change in UVT from background water), average particle size, and zeta potential were selected as the independent variables in the models. In step-wise multiple linear regression, multiple variables are regressed while simultaneously removing those that are not significantly important. β in this context defines the relationship between the independent variables and the dependent variable. In TP1, zeta potential (β = 0.855, p-value=0.003) was the only predictive indicator with a significant predictor effect on microorganism association (overall model fit: R2= 0.731, p-value= 0.003). In the model developed for TP2 (overall model fit: R2= 0.901, p-value= <0.001), ΔUVT (β= -0.821, p-value=<0.001) and zeta potential (β= 0.266, p-value= <0.001) were the most important predictive indicators. It is worth noting that contrary to initial expectations based on previous studies on total coliform [236] and MS2 bacteriophage [30] as surrogate microorganisms, the average particle size did not significantly impact the microorganism association. It could be because of the different behaviour of MS2 bacteriophage compared to other microorganisms. Additionally, the experimental methodology used in this study differed from previous studies, leading to other factors having a stronger influence on the microorganism association than the average particle size. Confirming results were observed for aerobic spore-forming bacteria in wastewater samples [176]. Changes in UVT were not significant for microorganism association in TP1, but were significant for TP2. The inclusion of ΔUVT in 141 the model is hypothesized to indirectly relate changes due to additives interacting with the background water matrix of TP2. The significance of changes in UVT with microorganism association with additives being background water specific highlights the complexity of interactions between organic/inorganic compounds, measurements such as UVT, and ultimately system behavior (particle-microorganism association). The inclusion and significance of zeta potential in both models support the hypothesis that zeta potential is an important indicator of microorganism association. However, depending on the background water matrix, the water quality indicators have varying importance for describing variability in microorganism associations. One likely factor differentiating the responses is the background concentration of inorganic ions. The raw water used to synthesize TP2 has higher hardness, alkalinity, and conductivity than TP1 (Table 6.1). According to Figure 6.1-f, the samples made from TP2 showed additional resistance against zeta potential change as the concentration of different additives was elevated. The average surface charge of particulates in water does not shift easily due to more hardness/alkalinity ions, and a greater concentration of additives was needed to see a change. Observations were in line with the results of a study on the effects of variable water chemistry on bacterial transport, where the attachment rate decreased with the decrease in ionic strength of the solution [237], as well as a study on attachment rates of E. coli to particulate matter where the presence of calcium ions resulted in a reduction in the removal of microorganisms [238]. Moreover, the importance of ionic strength in determining the zeta potential of colloidal particles is noteworthy, as it significantly influences the electrostatic interactions between particles and their attachment behaviour. Ionic strength plays a pivotal role in altering the charge distribution at the particle-water 142 interface, which, in turn, impacts the magnitude and stability of the zeta potential, influencing colloidal stability and their propensity for particle aggregation or dispersion in the water matrix. In TP2, the higher ionic strength resulting from increased ion concentrations may lead to a thicker diffuse layer around the iron hydroxide particles than in TP1. Consequently, this altered electric double layer can affect the zeta potential of the particles. A higher ionic strength might result in a smaller absolute zeta potential, as the stronger ion-ion interactions can partially neutralize the surface charge of the particles and the average zeta potential of the water sample. 6.4 UV Inactivation 6.4.1 Impact of Additive Type on Disinfection The results of biodosimetry experiments of all synthetic samples (dose-response curves) are presented in Figure 6.3. As turbidity increased, the overall log inactivation decreased in all samples regardless of the type of particulate additives. Two-factor ANOVA analysis (with replication) was carried out to identify significant differences in dose-response curves at constant turbidity. When ANOVA revealed a significant difference among multiple groups, a post hoc pairwise t-test was employed to compare the means of individual groups to identify where the significant differences lie. At turbidity 1 and 5 NTU in TP2, the dose-response curves for each additive type are significantly different (p-value = 0.007 and 0.049, respectively). On the contrary, there is no significant difference (p-value = 0.064) between samples made with kaolin clay, humic acid, or iron hydroxide at 20 NTU. In TP1, a significant difference was observed at 1 NTU between different additives (p-value = 0.011), while at turbidities of 5 and 20 NTU, no significant differences 143 were observed (p-value = 0.244 and 0.800, respectively). This result implies that the particulate characteristics at low turbidity (1 NTU) significantly alter UV disinfection rates. Figure 6.3: The average exposure time–UV response curves of TP1 and TP2 calculated under a UV wavelength of 254 nm with four different exposure times. Control tests are representative of MS2 inactivation in the background water sample used for each sample set with no additive addition. Error bars represent one standard deviation (n = 3). -7-6-5-4-3-2-100 25 50 75 100Log N/NoExposure Time (s)TP1- 1 NTU-7-6-5-4-3-2-100 25 50 75 100Exposure Time (s)TP1- 5 NTU-7-6-5-4-3-2-100 25 50 75 100TP1-20 NTU-6-5-4-3-2-100 25 50 75 100LOG N/NOTP2- 1NTUExposure Time (s) -6-5-4-3-2-100 25 50 75 100TP2- 5NTUExposure Time (s) -6-5-4-3-2-100 25 50 75 100TP2-20 NTUExposure Time (s) Exposure Time (s) (s) 144 Depending on the background water matrix when turbidity is higher than a specific threshold, turbidity-causing composition has minimal or no impact on the UV disinfection efficiency. Mechanisms of inhibition, such as light scattering, likely become more prominent at high turbidities, and therefore, composition plays a minor role. For all turbidity levels in TP2, an increase in the concentration of kaolin clay particles had the most minimal impact on log inactivation. Kaolin clay particles are reported to have greater light-scattering characteristics compared to humic acid and iron hydroxide [9]. Scattered light could still be available for disinfection, but with a reduced energy due to increased path length of light travelling through water, leading to dissipation of UV energy into the water column [239]. Kaolin clay particles resulted in the lowest level of particle-microorganism association (less than 6% in both sample sets). The same effects were seen in another study on UV disinfection of bacteriophage MS2 by Templeton et al. [8], where minimum shielding effects were observed for synthetic samples made with kaolin clay particulates. Opposite results have been observed in kaolin-containing samples of TP1 at all turbidities, where lower levels of inactivation were observed compared to other particulate additives (iron hydroxide and humic acid). The greater impact in TP1 could be attributed to the applied pre-treatment step, which alters the characteristics of the kaolin. After coagulation, various physical and chemical properties of kaolin particles can change (e.g., size, and surface charge). These changes in the characteristics of kaolin particles can have significant impacts on their functionalities, such as stability, and performance in various applications. Kaolin clay-containing flocs are not associating well with microorganisms (less than 5% in both sample sets). However, due to other possible 145 mechanisms, such as shielding, lower levels of microorganism inactivation were observed. 6.4.2 Modeling UV Response The multiple linear regression method to predict the UV response was used to assess the relative importance of water quality and particulate characteristics on the achieved disinfection. Standardized β coefficients are provided in Table 6.2. UV doses are corrected for the effect of UVT according to the Ultraviolet Disinfection Guidance Manual (UVDGM) document [18]. Zeta potential was considered a significant predictor of the disinfection achieved in all models except for humic acid models. For high concentrations of humic acid, the ΔUVT is significantly larger, and the role of zeta potential and microorganism associations are likely of lesser relative importance. Additionally, humic acid is reported to have a coating mechanism on particles and microorganisms, which is not solely dependent on zeta potential and the particle and dissolved matter type. Solution and humic characteristics will define the level of coating or interaction [12]. Ultimately, due to high levels of humic acid absorbing incident UV light and other potential inhibitory effects, the UV disinfection response is lowest for 20 NTU of humic acid in both sample sets (Figure 6.3). The contribution of zeta potential and turbidity varied between different water samples depending on the background water. Multiple regression analysis showed that zeta potential in the iron hydroxide model is more influential in TP1 (β: 0.563, p-value <0.001) than in TP2 (β: 0.265, p-value: <0.001). The results are parallel with the highest 146 microorganism association (15 versus 32% for TP1 and TP2, respectively) and highest microorganism survival rates observed in previous sections (1.9 versus 3.2-log removal at turbidity of 20 NTU; UV dose of 15 mJ/cm2 for TP1 and TP2, respectively). Table 6.2: Multiple linear regression models to predict the role of additive characteristics (turbidity and zeta potential), background source water and UV Dose on UV disinfection of MS2. Each particulate additive was modelled separately. Standardized β coefficients are reported with a p-value deemed significant at p-value <0.05. Standardized β (p-value) TP1 TP2 Control Model: R2 (p-value) +0.949(<0.001) +0.961(<0.001) UV Dose -0.948 (<0.001) -0.967 (<0.001) Turbidity -0.004 (0.961) +0.073 (0.2960) Iron hydroxide Model: R2 (p-value) +0.863(<0.001) +0.505(<0.001) UV Dose -0.692 (<0.001) -0.903 (<0.001) Turbidity -0.287 (<0.001) -0.115 (0.1200) Zeta potential +0.563 (<0.001) +0.265 (<0.001) Kaolin Clay Model: R2 (p-value) +0.950(<0.001) +0.783(<0.001) UV Dose -0.957 (<0.001) -0.867(<0.001) Turbidity -0.634 (<0.001) -0.436 (0.1660) Zeta potential +0.726 (<0.001) +0.566(<0.005) Humic Acid Model: R2 (p-value): +0.782(<0.001) +0.822(<0.001) UV Dose -0.850 (<0.001) -0.847 (<0.001) Turbidity +0.0240 (0.864) +0.357(<0.001) Zeta potential +0.2240 (0.111) -0.076 (0.3280) 147 NOM present in natural waters can also impact the degree and nature of microorganism association (e.g., polysaccharides, proteins, humic and fulvic acids). These macromolecules can adsorb to surfaces and alter interactions between particles and microorganisms [241], [242]. The potential surface conditioning by present compounds in the background waters could be the reason behind the difference between the two sample sets. TP1 shows that the additive addition changed zeta up to 50%, while TP2 shows less response to increasing turbidity (up to 20%). As such, it is clear that the role any additive or spike would have is dependent on the initial water quality. Factors such as high turbidity and changes in water quality (e.g., alkalinity, temperature, pH, zeta potential) must be considered to accurately evaluate disinfection achieved during abnormal conditions. Previously developed empirical models used in [243], [244] employed only UVT and dose as predictor variables in UV disinfection modeling. This study examines the incorporation of zeta potential as an additional predictor variable in linear models. The water quality characteristics (zeta potential and turbidity) and achieved UV efficiency records of seven sampling data points from TP1 were used as test scenarios. Figure 6.4 compares the actual UV inactivation versus predicted values driven from the linear regression models, using pre- and post-addition of zeta potential to the other independent variables (UV dose, turbidity, and UVT). Generally, the models overestimated log inactivation (Figure 6.4). However, overestimation and error were greater for the model that did not consider zeta potential. 148 Figure 6.4: The actual UV inactivation in TP1 (n=7) versus predicted values driven from the linear regression models, using pre- and post-addition of zeta potential to the other independent variables (UV dose, turbidity, and UVT) The mean squared error (MSE) for the conventional model without zeta potential was 0.94 and improved to 0.43 with the inclusion of zeta potential. Improvements to models that estimate UV disinfection from influent water characteristics enable tighter control of UV dose and could increase efficiency if power were modulated with water quality characteristics. Furthermore, improved modelling through the inclusion of zeta potential would better mitigate risks during process failures or changes in source water conditions. It should be noted that generally, error was high and suggests that modelling and expectations of disinfection achieved for unfiltered systems or during events may be difficult to predict. 6.4.3 Impact of Additives and UV Dose Delivered The actual UV dose delivered to microorganisms is a function of the reactor’s geometry, distance from the lamp, depth of suspension, and UVT of the sample (mentioned in -4.5-4-3.5-3-2.5-2-1.5-4.5 -4 -3.5 -3 -2.5 -2 -1.5Predicted log reductionActual log reductionPost-Zeta Potential Pre-Zeta Potential 149 section 3.3.1: equation (5)). The discussions of this section for log inactivation results are based on calculated UV doses according to the UVDGM document [18]. A common water treatment goal and requirement is 3-log inactivation for Cryptosporidium [245]. Table 6.3 compares the required dose for 3-log removals and the equivalent Cryptosporidium log removal achieved at a delivered UV dose of 15 mJ/cm2. Turbidity less than 1 NTU is the regulatory advice for drinking water treatments without filtration before UV disinfection. At UV doses higher than 15 mJ/cm2, a minimum of 3-log removal was achieved in all 1 NTU samples except for kaolin clay (2.5-log removal) in TP1. As observed in the synthetic water samples, the turbidity level of lower than 1 NTU has limited applicability as a sufficient indication of the achieved minimum required log removal. In synthetic experiments, generally, higher levels of log removal were accomplished at a UV dose of 15 mJ/cm2 in TP2 compared to TP1. This is consistent with the previous results and correlates with the impact of background water matrix [225]. To account for the impact of the background water on additive specific results, the dose required for 3-log inactivation at 5 NTU was divided by the dose required for 3-log inactivation at 1 NTU. The normalized values represent the additional impact that increased concentrations of each additive could have on disinfection. In TP1, samples with iron hydroxide resulted in the highest ratio (5.06), followed by humic acid (1.91) and kaolin clay (1.46). The same trend was observed in TP2, where the ratios were 1.29, 1.24 and 0.52 for humic acid, iron hydroxide and kaolin clay, respectively. The similar impact of raising turbidity on effective disinfection is, to some extent, predictable by the type of particle additive in samples. However, the differences could be attributed to the impacts of the background water matrix. 150 Table 6.3: The analysis of UV disinfection efficiency in terms of the dose required for 3-log inactivation and Log inactivation power of the system at UV dose of 15 mJ/cm2. Doses were corrected to estimate the actual UV dose delivered based on equation (5). Values are linear interpolations between two known data points. The analysis of the results of each sample made with different additives, independent of the background water matrix, was performed using the Response Surface Methodology (RSM). The objective was to determine the range of turbidity, considering a standard UVT of 80% and the average zeta potential of samples, at which the log inactivation would fall below the required level (3-log reduction). Figure 6.5 demonstrates TP1 TP2 Additive Type Turbidity (NTU) UV Dose required for 3-log inactivation (mJ/cm2) Cryptosporidium Log inactivation at 15 mJ/cm2 UV Dose required for 3-log inactivation (mJ/cm2) Cryptosporidium Log inactivation at 15 mJ/cm2 Control TP1: 1.3 TP2: 0.7 4.9 -5.8 7.5 -4.0 Iron Hydroxide 1.0 5.1 -4.1 14.5 -3.4 5.0 25.8 -1.3 18.7 -3.8 20.0 30.0 -1.5 16.2 -3.6 Humic Acid 1.0 13.1 -3.1 12.0 -3.3 5.0 25.0 -1.8 14.9 -3.3 20.0 40.0 -1.4 39.1 -2.4 Kaolin Clay 1.0 16.2 -2.5 14.0 -3.6 5.0 23.8 -1.5 7.3 -4.0 20.0 24.9 -1.5 13.1 -3.7 151 that across all data sets, an increase in turbidity leads to reduced inactivation. The RSM plots visualize the higher sensitivity of samples containing iron hydroxide as seen by a steeper slope in Figure 6.5-a compared to Figures 6.5-b and 6.5-c for kaolin clay and humic acid containing samples, respectively. The RSM plot suggests that 3-log inactivation is not achieved for iron hydroxide samples until a minimum dose of 36 mJ/cm2. Figure 6.5. Contour plot illustrating the response surface of acquired log inactivation (color) as a function of dose delivered (mJ/cm2) and turbidity (NTU) for samples with elevated concentration of a) Iron Hydroxide, b) Kaolin Clay and c) Humic Acid. The contour lines represent constant response levels. Log N/No Log N/No Log N/No 152 Comparing the sample made with kaolin clay and humic acid at the dose of 20 mJ/cm2, 3-log removal was compromised after turbidity surpassed 1 NTU and 0.6 NTU, respectively. This further demonstrates that specific substances in water pose different effects on the removal efficiency and the susceptibility of UV disruption and failure to achieve 3-log disinfection could occur at turbidities as low as 1 NTU at 20 mJ/cm2. Findings of this section have practical implications for treatment utilities to enhance the comprehension of factors affecting the real-world applications. In practical terms, the ability of the developed UV response models to signal the failure to achieve a 3-log reduction is crucial. The sensitivity of various samples to UV disinfection based on specific substances present in water is highlighted. Consequently, these implications advocate for a customized approach that considers the water matrix and contaminants when implementing UV disinfection applications. 6.5 Summary This chapter aimed to quantify relationships between water quality characteristics and their impact on UV disinfection at low and moderate turbidity levels. The motivation was to identify potential challenges for disinfection when source water quality rapidly deteriorates, there is inadequate upstream treatment (e.g., filtration), or there is an upstream process failure. Results from this chapter highlight that associated microorganisms remained infectious and represented a significant proportion of total microorganisms (up to 16% at 1 NTU, 32% at 20 NTU). The highest level of microorganism association was observed when the average zeta potential of the sample shifted toward neutrality regardless of the 153 original zeta potential of water sample depending the composition. For the virus surrogate used (MS2 bacteriophage), average particle size was not observed to impact particle-microorganism association or UV disinfection achieved significantly. As such, results suggest that zeta potential or surface charge is a key particle characteristic that determines variable microorganism association and impact on UV disinfection. Overall, disinfection achieved diminished with increasing turbidity, irrespective of particulate type and background water except for the case of humic acid. However, results indicated that at low and moderate turbidities (< 5 NTU), the type of the water component has a significant impact on microorganism association and disinfection achieved. The developed UV response models indicated failure to achieve 3-log disinfection could occur at turbidities as low as 0.6 NTU at 20 mJ/cm2, depending on the additive type. Furthermore, comparison of additives in two natural background water types indicated that the influence of varying additive types is variable based on background composition. The background water composition was found to directly influence microorganism association with any given additive as well as UV disinfection achieved. Results from this work highlight that the composition of a turbidity spike from rainfall or other disruptions should be considered when assessing potential impacts on UV systems. The results suggest that zeta potential measurements can serve as a valuable operational parameter to consider during the design, assessment, or continuous operation of UV systems. It is suggested that consideration for particle and dissolved composition effects can be captured by zeta potential measurements in designing and operating UV systems for small utilities with limited pre-treatment. Not only should UV system design focus on 154 maintaining high UVT, but the results of this study imply that regulators and utility owners should be aware of zeta potential when considering risk and disinfection performance. One of the limitations of this chapter is the use of only one model organism, MS2, which limits the generalizability of results to other microorganisms or viruses. While MS2 serves as a well-established and commonly used surrogate for viruses, it may not fully represent the diverse range of microorganisms in real-world water systems. Bacteria and protozoa are significantly larger, and it can be hypothesized that there will be a potential difference in the overall impact of organism association. Viruses are more likely to be fully shielded or otherwise protected from incident UV light than larger organisms. However, it is expected that protozoa and MS2 both have negative surface charges in natural water. As such, results of microorganism association trends stemmed from surface charge attractions are likely to be comparable to MS2. 155 Chapter 7: Manipulation of Microorganism Association with Particulate and Dissolved Matter for Improved UV Disinfection of Surface Waters The performance of UV disinfection systems is subject to the quality of water supplied to the reactor. This chapter investigates zeta potential manipulation as a pre-treatment method to improve UV performance. Zeta potential manipulation focused on the control of microorganism association and optimization of zeta potential was hypothesized to minimize the interfering impact of particulate and dissolved matter on UV disinfection. Water samples were collected from a drinking water treatment plant that applies UV for disinfection without pre-filtration. Sample zeta potentials were altered using coagulant (positive) and sodium hydroxide (negative). The results of the study indicated that zeta potential control could effectively enhance water treatment processes with minimal impact on the water quality. 7.1 Sample Characterization Water samples were collected from a treatment plant located in British Columbia, Canada, in November 2022 (TP1). The general quality characteristics of the natural raw water, including pH, hardness, DOC, alkalinity, UVT, turbidity, average particle size and zeta potential used for this study is provided in Table 7.1. This data has been acquired through communication with the treatment plant. PACl coagulant and a 1M sodium hydroxide basic solution were introduced separately to water samples at varying levels to adjust the zeta potential towards positivity and negativity. Figure 7.1 illustrates the relationship between zeta potential of water and the doses of additives. The patterns observed in zeta potential are influenced by the initial 156 quality of the raw water and the specific microorganisms present [246]. Nonetheless, a general and direct parabolic relationship between additive dose and zeta potential can be observed where it reaches its negative peak at 40 mg/L NaOH with a significantly negative value and positive peak at 20 mg/L of coagulant at the far positive zeta potential end. The transition in zeta potential values can be attributed to changes in the energy barrier at the particle surface. Initially, as the pH increases, more charges are introduced on the particle surface, leading to a decrease in the energy barrier and a lower zeta potential. However, when the energy barrier reaches its limit or becomes saturated, further increases in pH may not lead to additional repulsion between particles. This saturation of the energy barrier can result in the neutralization or balancing of charges, leading to a transition from negative to positive zeta potential values [247]. Table 7.1: Water quality characteristics of TP1 raw (pH, hardness, DOC, alkalinity, UVT, turbidity, average particle size and zeta potential of the background raw water Water quality characteristics Unit Sample pH - 7.2 Conductivity µS/cm 50 Hardness mg/L 23.4 DOC mg/L 9.1 Alkalinity mg/L 21.9 UVT % 52.03 Turbidity NTU 1.3 Average particle size µm 2.8 Zeta potential mV -15.8 157 In this study, the primary aim of zeta potential control was to manipulate the dominant electrostatic forces between the particulates, organisms, and dissolved organics as well as to control the potential associations. The usual dose for coagulation and flocculation is 40-70 mg/L at the treatment facility, however they focus on sweep coagulation mechanisms to optimize for NOM removal. To accomplish zeta potential control or charge neutralization, a lower than optimized level of (up to 20 mg/L) coagulant dose was added to the samples to achieve up to +15 mV of zeta potential without Figure 7.1: The impact of additive dosage on the zeta potential behavior of water (TP1 raw) spiked with MS2 (solid fill) and E. coli (diagonal strips). Error bars represent one standard deviation over replicates (n = 3) triggering significant formation of flocs. To cover all potential interactions between microorganisms and the component of water, five final zeta potential levels were selected for the experiments. Due to slight variations in the obtained zeta potentials for both sample sets, the ranges are categorized as highly negative (zeta potential = -26 to most negative achievable), negative (zeta potential = -16 to -25 mV), moderately negative (zeta potential =-3 to -15 mV), neutral (zeta potential = -2 to +2 mV), and positive (zeta potential = +3 to the most positive achievable). The neutral category interval was chosen to have a -40-20020120 mg/L(Na(OH))80 mg/L(Na(OH))40 mg/L(Na(OH))0 4 mg/L(PACl)6 mg/L(PACl)10 mg/L(PACl)12 mg/L(PACl)15 mg/L(PACl)20 mg/L(PACl)Zeta Potential (mV)Concentration(Additive Type)MS2 E.Coli 158 variation of ± 2 mV, consistent with the observed standard deviation of 1.84 (n=3) achieved when applying the same dosage to attain neutrality in samples. The remaining zeta potentials were identified as both achievable and desirable with the selected dosage of the additive. Different microorganisms resulted in varying zeta potential in the final solution. The surface charge characteristics and the interactions between the microorganisms and the water matrix depends on various factors, such as the composition of the microorganism's cell wall, the presence of functional groups [248], the pH and ionic strength of the water [249], [250]. MS2 as a viral surrogate and E. coli as a bacterial surrogate were added to the samples separately to simulate different types of microbes present in source waters and compare the susceptibility and response of the proposed system. The study maintained a constant microorganism concentration across experimental conditions, although not reflective of real-world scenarios. The impacts of additive addition to the level of organism removal served as a variable under investigation, influencing the UV reactor's performance. Figure 7.1 illustrates that MS2, with its distinct composition and the protective protein coating, exhibits a less negative zeta potential compared to E. coli. The observed differences in zeta potential can be attributed to the variations between the two entities. Bacterial cell walls often contain more charged groups, such as negatively charged lipopolysaccharides, which contribute to a more negative zeta potential [251] compared to viruses. In contrast, the viral capsids of bacteriophages such as MS2 have a different structure and composition, resulting in a more neutral zeta potential [252] compared to bacteria. The surrounding water chemistry was kept constant in all experiments. 159 Therefore, the difference in zeta potential between microorganisms is due to the inherent characteristics of each microorganism. Figure 7.2: Water quality characteristics of samples at ranges of zeta potential in samples spiked with MS2 (Solid line) and E. coli. (Dashed line): a) average particle size (µm), b) turbidity (NTU), c) UVT (%), d) mean count rate (kcps), and e) UVA. Samples were made in triplicates and the error bar represents the standard deviation of replicates. The red dots represent the control sample (sample with no zeta potential manipulation). The water quality characteristics (zeta potential, UVT/UVA, particle mean count rate, and average particle size) of both control samples and samples with controlled zeta potential after additive inclusion were monitored (Figure 7.2). The results, revealed 0102030-40 -30 -20 -10 0 10 20Average Particle Size (µm)zeta potential (mV)a)05101520-40 -30 -20 -10 0 10 20Turbidity (NTU)zeta potential (mV)b)60708090100-40 -30 -20 -10 0 10 20UVT (%)zeta potential (mV)c)0100200300400500-40 -30 -20 -10 0 10 20Mean Count Rate (kcps)zeta potential (mV)E.Coli MS2d)00.10.20.30.40.5-40 -30 -20 -10 0 10 20UVAZeta Potential (mV)e) 160 distinct patterns in water quality changes as zeta potential was altered, with variations depending on the additive dose and the type of microorganism present. One important aspect to consider in these experiments is the comparison between adjustments made to pH levels (in a more basic direction) and adjustments made using PACI. The addition of a basic solution to create samples with highly negative zeta potentials has shown limited influence on the monitored quality characteristics in this study. However, manipulation of zeta potential by basic solution significantly increases the pH levels (pH went up to 11). In contrast, when PACI is added to the water, quality characteristics do change, but the pH remains relatively stable. Similar results on pH level of water were observed in study by Wei et al. (2015) when PACI coagulant was added at dosages of 0 to 20 mg/L [229]. The processes and interactions that take place in water in both methods (NaOH and coagulant addition) are interconnected. Changes in the surface properties of the particles due to pH change or coagulant addition can lead to changes in the size, count, and adsorption level of particles in water [253]. Changes in the pH can affect various chemical equilibria at the molecular level and disrupt the stability of water, such as calcium carbonate precipitation and redox potential [254]. Therefore, when discussing manipulation of zeta potential through manipulating pH (NaOH addition), it is important to consider the overall effects high pH levels could have on the water quality. The average particle size (Figure 7.2-a) increased when the zeta potential approached neutrality but decreased as the absolute value of zeta potential increased. The addition of coagulant initiated an immature coagulation and flocculation process by neutralizing charges and promoting particle aggregation. However, the system is 161 underdosed compared to optimal floc formation, which has resulted in weak and poorly formed flocs that have lower structural integrity and are more prone to breakage and dispersion [255]. It was observed that samples with MS2 at moderately negative zeta potential (-3 to -15 mV) resulted in larger particle sizes, which is due to better neuralization (Figure 7.2-a). Bacteria typically range in size from 0.5 to 5 µm, whereas the MS2 bacteriophage is significantly smaller, typically 25-30 nm [256]. Less coagulant was needed to neutralize MS2 compared to E. coli, as their initial zeta potential was less negative. In contrast, at the neutral zeta potential (~0 mV) the highest particle size was seen in E. coli samples with larger size regardless of the initial zeta potential. Despite being at the same zeta potential category, differences were observed between the aggregation level of organisms based on changes of average particle sizes in water. Bacteria, such as E. coli, possess more negative charge and complex cellular structures [257], while viruses like MS2 are less negative, with a simpler structure, consisting of genetic material enclosed in a protein coat [258]. A general trend from previous work suggests larger organisms have a higher likelihood of aggregation with larger size aggregates [259], [260] in neutral samples. Turbidity (Figure 7.2-b) increased with the increase in zeta potential up to neutral and levelled off at positive zeta potential. This can be attributed to the imposed higher interactions that took place by the addition of additives (specifically, coagulation), which is initiated by the coagulant addition. Turbidity did not change considerably as the zeta 162 potential was shifted to highly negative values, which shows that a basic solution does not trigger any chain reactions that could lead to higher light scattering. As the zeta potential moved towards neutrality, UVT experienced a decrease (Figure 7.2-c), while it remained relatively constant from the neutral to the positive side of the zeta potential range. When coagulant is added to the water, hydrolysis occurs, forming positively charged aluminum hydroxide species that can exhibit different optical properties compared to their uncharged counterparts [261]. When coagulant doses are sufficient, these species facilitate the destabilization and aggregation of particles and dissolved organic matter present in the water. Given the low doses of coagulant used, it was not expected that UVT would be reduced significantly. Charge neutralization coagulation regimes are not overly effective for the removal of NOM, which would likely be the major component that contributes to UV absorbance [212]. It should be noted that despite the turbidity levels exceeding the regulated thresholds (recommended turbidity <1.0 NTU) for UV reactors, the UVT remains only slightly lower than the regulated threshold (recommended UVT > 75 %, but dependent on UV reactor manufacturer). The presence of adequate light transmittance is the key factor enabling disinfection to be achieved, even in highly turbid samples [245]. UVA presented in Figure 7.2-e, shows the same trend as UVT. The relationship between UVA and UVT is described in equation (1), section 2.3.2. However, in terms of treatment indicators, they might have different implications. Hence, both UVA and UVT are presented to capture the effects of the treatment. The mean count rate (Figure 7.2-d) exhibited a decreasing trend towards neutrality in both sample sets, followed by an increase as the absolute value of zeta potential 163 increased. The count rate represents the frequency at which particles or microorganisms were detected during the measurement in counts per second. The count rate for E. coli was lower compared to MS2. For the purpose of allowing for biodosimetry tests, the water samples were spiked with high concentrations of microorganisms. The concentration of both microorganisms in the samples was attempted to be kept constant at 108 PFU and CFU/mL. Therefore, samples were saturated with microorganisms that could induce high levels of organism aggregation and particle-microorganism association, especially in E. coli with larger organism sizes that led to lower counts per second. High concentrations of microorganisms allowed for the creation of controlled and reproducible conditions with enhanced sensitivity for the study and resulted in high log removals. However, the results may not directly be applicable to regular operations with expected low concentrations of microorganisms in the source water. To further support the weak interactions of particulates in positive zeta potential, the percentage of the difference between turbidity and UVA after the settling period is presented in Figure 7.3. Samples were settled to allow the portion of associated microorganisms to settle due to gravity forces. The intention here was to highlight the extent of change in the two most common water quality indicators (turbidity and UVA) resulting from the separation of associated organisms. Negative values indicate a decrease, while positive values indicate an increase following the settling process. The highest percentage of change in both turbidity and UVA is observed in the neutral sample with the highest level of particle-microorganism association, which consequently led to their settling. The lowest absolute value of the percentage of change can be seen at the two far ends of the zeta potential axis where neutralization is minimal and the water quality 164 characteristics do not exhibit improvement (decrease in turbidity and in UVA compared to original) after settling time. It's worth to note that settling has demonstrated no effect on the UVA of a sample when there was no manipulation of zeta potential (control sample). However, it does lead to a 10-15% decrease in turbidity. This implies that the associated organisms with particles in the control sample were not contributing to the UVA of the water. However, the manipulation of the zeta potential intrigued the absorbance-causing component of water to enter the equation. Figure 7.3: The difference between quality characteristics: a) Turbidity and b) UVA of water samples after settlement period. The negative values represent a drop and positive values represent an increase after settlement. The values represent the difference -30-25-20-15-10-50HighlyNegative (ZP=-25 to -40 mV)Negative (ZP=-15 to -24 mV)ModeratelyNegative (ZP=-3 to -14 mV)Neutral (ZP= -2 to +2 mV)Positive (ZP=+2 to + 15mV)% of changea)Turbidity (NTU)MS2Ecoli-4.0-2.00.02.04.06.08.010.0HighlyNegative(ZP= -25 to -40 mV)Negative(ZP= -15 to -24 mV)ModeratelyNegative(ZP= -3 to -14mV)Neutral (ZP=-2 to +2 mV)Positive (ZP=+2 to + 15mV)% of changeb) UVAMS2Ecoli 165 between the average of three measurements where in all measurements standard deviations were less than 5%. 7.2 Particle-Microorganism Association Analysis Two approaches to enumerate associated and non-associated organisms were studied: filtration and settling. The settling method was selected as the primary approach for detecting microorganism associations in this chapter. Initial experiments showed that the filtration method trapped a high proportion of MS2 organisms within the filters when the coagulant was introduced, making it unsuitable for the study. Additionally, the filtration method was not suitable for E. coli samples due to their larger size. In contrast, the settling method provided reliable results for both MS2 and E. coli samples associated with particles, making it the preferred choice for analysis (section 5.1.2). A settling time of 30 minutes was selected to differentiate the free-floating MS2 and E. coli from the attached organisms after gently stirring for 2 hours (see section 5.1.2 for more details. The settling process yielded consistent and reproducible results for both organisms (Figure 7.4). Notably, a higher level of particle-microorganism association was observed in E. coli compared to MS2 in all levels of zeta potential. Both samples showed the highest level of microorganism association at neutral zeta potential. These findings are consistent with previous studies, further supporting the correlation between microorganism association and zeta potential levels [246]. 166 Figure 7.4: particle-microorganism association (%) level of MS2 (solid fill) and E. coli (diagonal strips) sample sets at different zeta potential levels measured by settling separation methods. Error bars are the standard deviation of three replications. 7.3 UV Disinfection Efficiency and Inactivation Rate Constants This study examined the efficiency of UV disinfection in terms of log reduction (logarithm of N/No) for both MS2 and E. coli sample sets at various zeta potential levels. All samples were exposed to UV light (254 nm) for up to 150 seconds. The results of the UV experiments are presented in Figure 7.5. Generally, MS2 samples exhibited lower inactivation compared to E. coli across all zeta potential levels. Similar results were observed in previous studies that show higher overall susceptibility of bacterial over viral surrogate [262], [263]. Additionally, the lowest disinfection levels were observed in the samples with neutral and moderately negative zeta potential, consistent for both organisms. This observation can be attributed to the effective association of particle-microorganisms in the neutral and moderately negative state of water, resulting in inadequate UV disinfection confirmed by the results of disinfection kinetics analysis. 0102030405060708090100HighlyNegative(ZP= -25 to-40 mV)Negative(ZP= -15 to-24 mV)ModeratelyNegative(ZP= -3 to -14 mV)Neutral(ZP= -2 to+2 mV)Positive(ZP= +2 to+ 15 mV)Particle-Microorganism Association (%)MS2 E. coli 167 To further understand the disinfection process and identify the fraction of microorganisms contributing to tailing phenomena, the double exponential model (equation (4)) was employed. Implementing this model allowed estimation of the inactivation rate constants associated with each state of disinfection as well as the percent of microorganisms contributing to tailing (β). The first parameter (k1) of the model represents the inactivation Figure 7.5: UV inactivation efficiency of a) E. coli and b) MS2 surrogate microorganisms rate constant of easily disinfected free-floating organisms, while the second parameter (k2) of the model signifies the inactivation rate constant of the particle-associated organisms exhibiting resistance against UV doses. For MS2, the highest levels of β (85%) were observed in moderately negative samples and the lowest level (62%) was observed -9-8-7-6-5-4-3-2-100 50 100 150 200Log N/NoExposure Time (s)a) E. Coli-9-8-7-6-5-4-3-2-100 50 100 150 200Log N/NoExposure Time (s)b) MS2 168 in highly negative samples. For E. coli the lowest β (8%) was also seen in highly negative sample and the highest (74%) was seen in neutral zeta potential. The parameters of the double exponential model are presented in Figure 7.6. In MS2 samples, the 𝛽 level exhibited a parabolic trend (Figure 7.6-a), reaching its peak at moderately negative (zeta potential = -6.2 mV). Meanwhile, in the E. coli samples, the beta level is highest at neutral zeta potential (zeta potential = -1.8 mV). The overall model fit statistics are presented in Table 7.2. Table 7.2: Overall Model Fit Statistics: R² and Adjusted R² for Double Exponential Model Zeta Potential Overall Model Fit Highly Negative (ZP= -26 to -40 mV) Negative (ZP= -16 to -25 mV) Moderately Negative (ZP= -3 to -15 mV) Neutral (ZP= -2 to +2 mV) Positive (ZP= +2 to + 15 mV) MS2 R2 0.99 0.94 0.91 0.95 0.97 Adjusted R2 0.99 0.93 0.90 0.95 0.97 E. Coli R2 0.98 0.99 0.99 0.90 0.99 Adjusted R2 0.98 0.98 0.99 0.85 0.99 The trend of moderately negative to neutral zeta potential resulting in the greatest particle-microorganism association is observed for both modelled particle-microorganism association level (β) (Figure 7.6-a) and experimental measurements (Figure 7.4). However, there are also significant discrepancies including the experimental results showing a consistently lower level of particle-microorganism association in MS2 compared to E. coli, with the opposite trend seen with modelled results. 169 The inconsistency between theoretical and laboratory particle association levels might be due to the different particle-microorganism mechanisms these approaches can cover. The changes to light received by the organisms due to particles and other constituents of water can be influenced by a variety of mechanisms, including even the protective effects that may arise from shadowing organisms without any actual attachment. However, the experimental approach for detecting microorganism- Figure 7.6: Double exponential model parameters over zeta potential control. The R-squared values for all regression models were above 0.90. 020406080100Highly Negative(ZP= -25 to -40mV)Negative (ZP= -15to -24 mV)ModeratelyNegative (ZP= -3to -14 mV)Neutral (ZP= -2 to+2 mV)Positive (ZP= +2to + 15 mV)β(%)a)-1.00-0.80-0.60-0.40-0.200.00Highly Negative(ZP= -25 to -40mV)Negative (ZP= -15to -24 mV)ModeratelyNegative (ZP= -3to -14 mV)Neutral (ZP= -2 to+2 mV)Positive (ZP= +2to + 15 mV)k1 (cm2/mJ)b)-1.00-0.80-0.60-0.40-0.200.00Highly Negative(ZP= -25 to -40mV)Negative (ZP= -15to -24 mV)ModeratelyNegative (ZP= -3to -14 mV)Neutral (ZP= -2 to+2 mV)Positive (ZP= +2to + 15 mV)k2 (cm2/mJ)MS2 E. Colic) 170 associations is limited to instances where organisms have organic material deposition or have physically attached themselves to particles. As a result, the method is effective in capturing scenarios where these microorganisms settle due to the influence of gravitational forces. This phenomenon is more pronounced in the case of MS2 organisms with smaller sizes compared to E. coli. The inactivation rate constant for the portion of free-floating organisms is represented as k1. The lowest k1 level was seen in neutral samples regardless of the type of the organisms (Figure 7.6-b). In theory particle-microorganism association should only impact k2. However, altering zeta potential and particle-microorganism association also affected k1 values. This could be due to partial protection of organisms (even if they were not included in particle-microorganism measurements) or other changes in water quality. In the neutral samples, the particle-microorganism association was most pronounced; therefore, the lower rate of inactivation was achieved where the organism was protected. The high particle-microorganism association indicates strong protection for a significant portion of the organisms present. However, the smaller fraction left free remained vulnerable to inactivation, but with a lower rate, leading to a lower k1 value. Similar findings were observed in a study by Mounaouer et. al (2012), where the effect of particles on the inactivation rate diminishes with increasing initial free-floating concentration [264]. Additionally, E. coli consistently showed higher rates of disinfection than MS2, which is consistent with previous results. k2 accounts for the inactivation rate constant of the portion of the UV-resistant organism in water (Figure 7.6-c). The rate for inactivation of 171 associated particles was different depending on the organism type. k2 value was at its highest in highly negative E. coli and negative MS2 samples. 7.4 Summary This research demonstrated the effectiveness of zeta potential manipulation as a pre-treatment method to enhance UV disinfection in small-scale water systems. The presented approach was investigated to identify pre-treatment methodologies that could be employed by small systems using UV as the primary disinfectant. While other pre-treatment approaches, such as filtration, may provide greater benefits, they are not implementable in many scenarios. Filtration systems demand careful consideration regarding capital costs due to equipment procurement and potential infrastructure costs. Furthermore, ongoing operational aspects such as regular backwashing, filter media replacement, and monitoring contribute to the overall cost considerations associated with filtration systems [265]. Conversely, chemical dosing methods for zeta potential manipulation offer potential advantages in terms of cost, implementation feasibility and space requirements [266]. Chemical dosing, particularly when applied in existing reservoirs or reactors with available mixing, could result in comparatively simpler infrastructure requirements. Moreover, chemical dosing systems generally require less space, presenting an advantage in installations with limited available footprint [267]. However, there are potential drawbacks associated with consistent chemical dosing. These include considerations such as the generation of chemical residuals, chemical handling risks, downstream environmental impact, and the possibility of corrosion in 172 distribution systems. These aspects highlight the necessity for careful evaluation and mitigation strategies when employing chemical dosing methods for water treatment [268]. In summary, zeta potential manipulation was executed practically in raw surface water. The results indicated that the neutral zeta potential led to the highest level of particle-microorganism association, irrespective of the type of microorganism. This suggests that maintaining a neutral zeta potential enhances the attachment of microorganisms to particles, thereby decreasing the effectiveness of UV disinfection. Moreover, the kinetic studies conducted in this research supported the conclusion that water samples with a highly negative zeta potential exhibited reduced tailing phenomena and higher UV efficiency, contributing to improved disinfection outcomes. Importantly, manipulating the zeta potential towards positivity affected water quality observed in high turbidity levels and the formation of aggregates in samples, hence UV efficiency. On the contrary, shifting the zeta potential towards extreme negativity did not compromise water quality in the scope of this study. However, the alteration of water pH can induce various downstream impacts, including increased corrosion of infrastructure, the formation of scale deposits, potential chemical precipitation, and alterations in microbial growth patterns. These consequences can compromise water quality, infrastructure integrity, and system efficiency. However, addressing this challenge is feasible by implementing a neutralization method. In this study, the experiment designs focused on the association of microorganisms and the following impact on UV disinfection. Future studies could focus on developing mitigation strategies to counteract the adverse downstream impacts identified in this research. This finding emphasized the viability of zeta potential control for enhancing water treatment 173 processes, particularly in scenarios where advanced filtration techniques might be impractical or economically prohibitive. The study revealed a direct correlation between zeta potential, turbidity, and average particle size, impacting the level of particle-microorganism association. The interplay of these factors significantly influenced the attachment of microorganisms to particles, thereby affecting the efficiency of the UV disinfection process. However, in instances where samples exhibited high turbidity levels, notably in positive zeta potential samples, the UV disinfection process still demonstrated effective inactivation of microorganisms. This finding highlights the essential role of particle-microorganism association in UV disinfection, surpassing the traditionally emphasized influence of turbidity. 174 Chapter 8: Conclusion and Recommendations This study investigates challenges faced in UV disinfection of unfiltered water. The challenges ranged from the limitations of regulatory indicators for design and performance evaluation to the possible impacts of water quality upsets. Multiple factors impacting UV performance were considered to comprehend UV system's efficiency and further the evolution of this technology by novel strategies. 8.1 Summary and Conclusion The research objectives outlined in Chapter 1 were achieved and are concluded as follows, with each objective followed by its respective concluding statement. 8.1.1 Investigate the impact of unfiltered water conditions on UV disinfection performance. This objective was aimed to fill the knowledge gap about the influence of water quality (measured by conventional water quality indicators such as turbidity, UVT and indicator organisms) on the tailing phenomena observed in UV disinfection kinetics within unfiltered water. The investigation in Chapter 4 explored how water quality affected the tailing phenomena of UV disinfection kinetics in unfiltered water. The results indicated that zeta potential had a more significant impact on tailing than turbidity and UVT. Water samples with the same turbidity but different zeta potentials showed varying degrees of UV resistance. Previous studies considered tailing to be related to microorganisms' resistance to UV, their potential interaction with particles and the water matrix. Particles with negative zeta potentials after coagulation and DAF exhibited less tailing compared 175 to non-treated water from the same source. The study suggested that tailing was influenced by average surface charge and the downstream effects of pre-treatment methods on water quality before UV. 8.1.2 Assess the capabilities of current UV performance monitoring metrics (i.e., UVT/UVA) to capture disinfection performance in the presence of particulates This objective was aimed to fill the knowledge gap about the efficacy of traditional treatment indicators, such as turbidity and ultraviolet absorbance/transmittance, in predicting microbial inactivation achieved by UV disinfection in unfiltered water, while also exploring the utility of non-conventional water quality indicators such as zeta potential. The findings of Chapter 4 substantiated the validity of the hypothesis underlying the first objective of this thesis. Traditional treatment indicators such as turbidity and UVA/UVT did not correlate well with inactivation achieved by UV. This study supported the idea that even in high-quality, unfiltered water with low turbidity (less than 1 NTU), particles with neutral surface charges could protect microorganisms from UV light. These observations implied that using turbidity or UVA/UVT as sole performance indicators for unfiltered UV disinfection may be inadequate in capturing the impact of particles and shielded organisms. These indicators poorly represented dose-response, especially at higher UV doses. Despite being source-dependent, the results suggested the need for pre-treatment even in high-quality surface waters exposed to pathogenic contamination. 176 8.1.3. Identify the viability or infectivity of organisms associated with particles Given the conflicting core assumptions in the literature on viability of associated organisms, this objective was aimed to fill the knowledge gap around the viability of organisms associated with particles. The preliminary experiments assessing the viability of associated organisms validated the hypothesis made for objective three which stated that the association of organisms to particles does not significantly affect the accuracy of microorganism enumeration methodologies. These experiments involved introducing a known quantity of organisms into both real water samples consisting particulate and dissolved matter (TP1 raw) and control samples of RO water. After encouraging attachment through stirring, culturing methods were employed to assess organism viability. The results indicated an insignificant difference in the initial organism concentration between TP1 raw and RO samples, supporting the hypothesis that associated and free-floating microorganisms were viable. Subsequent microorganism association quantification methods were conducted to confirm the presence of association in natural water samples. Results revealed significantly different microorganism association levels between TP1 raw samples and RO, affirming the presence of associations. 8.1.4. Develop a methodology for particle-microorganisms association detection tailored to the experimental context The specific methodology for detecting microorganism associations, even among methodologies with the same baseline assumption, is tailored to the specific treatment 177 context of the experiments in the literature. For the experiments in Chapter 6, the filtration method was selected to detect microorganism associations. A 0.22 µm filtration method was employed to detect microorganism associations. Preliminary experiments verified the viability of associated microorganisms. The results confirmed the filtration system's suitability, as it effectively removed attached/associated microorganisms while allowing free-floating MS2 to pass through. In Chapter 7 the association between particles and microorganisms (virus and bacterial surrogates) at various zeta potentials was investigated. The preliminary experiments confirmed the validity of the same hypothesis as MS2 for the bacterial surrogate. A settling approach was selected to identify microorganism attachment levels. The study selected settling times, with 30 minutes identified as suitable for both microorganisms. Control experiments using RO water confirmed negligible settling of free-floating organisms, while significant concentration reductions were observed in natural water matrixes samples. 8.1.5 Quantify the relationship between water quality characteristics (particle size, counts, turbidity, UVA/UVT, and zeta potential) and particle-microorganism association This objective aimed to address the limited understanding that one-size-fits-all approaches do not suffice for detecting particle-microorganism associations in experimental settings, necessitating tailored methodologies. Chapter 5 aimed to increase the accuracy of the connections found in the previous chapter between water quality characteristics and microorganism association influence on UV disinfection, specifically 178 at low and moderate turbidity levels. The greater our understanding of these connections, especially in cases of unprecedented water quality variation, the stronger our ability to predict UV disinfection outcomes. The objective was to pinpoint potential disinfection challenges from deteriorating source water quality, lack of upstream treatment (such as filtration), or upstream process failures. A method was developed to assess the relative concentration of microorganisms associated, ensuring the consideration of potential shielding effects (tailing effect). This chapter’s experimental results on the microorganism association detection emphasized that associated organisms remained viable and constituted a significant portion of total microorganisms (up to 16% at 1 NTU, 32% at 20 NTU). For the selected surrogate (MS2), average particle size did not significantly impact microorganism association or UV disinfection. This suggested that zeta potential stands as a critical water characteristic determining the extent of microorganism association. While overall disinfection efficacy decreased with rising turbidity across different additive types and background water conditions, results indicated that at low and moderate turbidity levels (< 5 NTU), additive type showed a pronounced impact on microorganism association and achieved disinfection. UV response models developed indicated that failure to achieve 3-log disinfection could occur at turbidities as low as 0.6 NTU at 20 mJ/cm2, dependent on particle type. Furthermore, comparing additives in two distinct natural background water types revealed that the impact of various particle types varies based on background composition. Background water composition directly affected microorganism association for a given additive and the impact of water characteristics on achieved UV disinfection. 179 8.1.6. Evaluation of zeta potential control as pre-treatment process for UV. Analysis of the zeta potential control (pH alteration/ coagulant addition) on particle-microorganism association and the impact on UV disinfection efficiency This chapter addressed the knowledge gap concerning the effectivity of manipulation of zeta potential as a pre-treatment technique to enhance UV disinfection efficiency, particularly in small systems reliant on UV as the primary disinfectant. From the previous Chapters, it became evident that zeta potential plays a crucial role in microorganism association with components of water, thereby influencing UV disinfection. It was also clear that the final microorganism association is highly correlated with the composition of the background water. Therefore, in this chapter, the effectiveness of manipulating zeta potential as a pre-treatment technique to enhance UV disinfection was examined. The objective was to identify simplified pre-treatment method suitable for small systems relying on UV as the primary disinfectant. While other methods, such as filtration, might offer greater efficiencies, there are feasibility and footprint challenges in many situations. In summary, zeta potential manipulation was applied to raw surface water and effectively controlled during the experiments. The results indicated that maintaining a neutral zeta potential led to the highest level of microorganism association, regardless of the type of microorganism. This suggests that a neutral zeta potential enhances the attachment from the reduced repulsive forces around the microorganisms, thereby reducing the effectiveness of UV disinfection. Moreover, kinetic studies conducted in this research supported the conclusion that water samples with a highly negative zeta potential exhibited a reduced tailing phenomena and higher UV efficiency, contributing to improved disinfection outcomes. 180 Manipulating zeta potential towards positivity affected water quality (turbidity, UVT). On the contrary, shifting the zeta potential towards extreme negativity did not compromise water quality within the scope of this study. This finding emphasized the viability of zeta potential control for enhancing treatment processes, particularly in scenarios where advanced filtration techniques might be impractical or economically prohibitive. However, the alteration of water pH can induce various downstream impacts, including increased corrosion of infrastructure, the formation of scale deposits, potential chemical precipitation, and alterations in microbial growth patterns. These consequences can compromise water quality, infrastructure integrity, and system efficiency. However, addressing this challenge is feasible by implementing a neutralization method. In this study, the experiment designs focused on the association of microorganisms and the following impact on UV disinfection. Future studies could focus on developing mitigation strategies to counteract the adverse downstream impacts identified in this research. This chapter aimed to establish a correlation between zeta potential, turbidity, and average particle size. The interplay of these factors significantly influenced the microorganism’s association, thereby affecting the efficiency of the UV disinfection process. However, in instances where samples exhibited high turbidity levels, notably in positive zeta potential samples, the UV disinfection process still demonstrated efficiency against MS2. This finding highlights the essential role of microorganism association in UV disinfection, surpassing the traditionally emphasized influence of turbidity. 181 8.1.7 Compare modelling and experimental methods for particle-microorganism association determination The comparison of modeling and experimental methods for particle-microorganism association determination has not been previously explored in the literature. This study addressed this gap by applying both theoretical and empirical methods to understand microorganism inactivation mechanisms in the presence of particles. In this study, the application of the double exponential model provided a framework for understanding the inactivation mechanisms of microorganisms in the presence of particles. The alignment between the microorganism association calculated via the existing model fitting of UV disinfection and the experimental measurements offered insights into the underlying mechanisms of these associations. The modelled trend of microorganism association in all cases aligned with the experimental measurements but exhibited lower values in MS2 compared to E. coli. The disparity between the theoretical and laboratory levels emphasized the complexity of these interactions. The assumptions and experimental plan of the model only considered the physical attachment of particulates and organisms [27]. However, in the experiments of this study, there is a possibility that some organisms become protected by associations without direct attachment. The difference in experimental outcomes, where organisms are protected without direct physical attachment, doesn't directly affect the data given to the model. The input data includes the total concentration of viable organisms, covering both physically attached and protected ones through various mechanisms. The inconsistency arises during the experiments when non-physically associated organisms are separated from the associated ones. This points to a gap in the model's initial 182 assumptions, emphasizing the need for adjustments to consider the observed variations in protection mechanisms. 8.2 Originality and Contribution The study evaluated the challenges posed by UV disinfection systems in scenarios of degraded water quality, addressing both regulatory and technical aspects. In summary, this research makes contributions in the following ways. An assessment of commonly used regulatory water quality indicators for UV disinfection: The findings provide insights to consider revising regulatory indicators for UV disinfection, particularly in the context of pre-treatment exemption before UV disinfection. The study initially challenged the guidelines set by the USEPA and Health Canada, outlining criteria for exempting systems that used high-quality source waters (either surface or groundwater) from mandatory filtration throughout the treatment process. The study revealed that, in scenarios of severe and worst-case water quality conditions experienced in previous years, adhering to established guidelines may not be adequate to ensure sufficient disinfection. The outcomes of the initial phase of this research were of originality and were shared with Interior Health. In collaboration with a local water utility in British Columbia, the operational challenges faced by two treatment facilities responsible for providing drinking water to the Okanagan region were examined. Experimentation and analysis of the research outcomes indicated that commonly utilized UV performance indicators, 183 including turbidity and UVA, inadequately assessed water quality in systems lacking pre-filtration. The crucial aspects of background water matrices are acknowledged: In the past, the focus of studies in this area has centered on utilizing simpler water matrices, primarily RO as the background water. The study's results (with real drinking water treatment water as background) revealed the substantial influence of particle type and background composition on the disinfection process, emphasizing the need to account for distinct particle attributes while selecting appropriate disinfection strategies for varying water sources. This finding has the potential to improve the traditional UV dose selection approach utilized in water treatment facilities. Moreover, the impact of the worst-case scenarios and an analysis of variables impacting UV disinfection were investigated. Zeta potential as independent variable significantly enhances the prediction of UV disinfection efficiency: The results of this study proved to be a challenge to most developed models, which typically only account for UVA or UVT along with the delivered UV dose. Furthermore, this research is one of the initial efforts to consider the impact of zeta potential for predicting UV efficiency and showed improvement in the models that did consider zeta potential as a predictor variable. By elucidating the influence of microorganism interactions , this study has opened up new avenues for exploring and improving disinfection procedures. Particle-microorganism association detection methods were studied and built upon: This exploration included a primary focus on microorganism association detection 184 methods. The methodology was developed through an examination of prior studies. Building upon available knowledge, an enhanced version of available detection methods was proposed and employed for each chapter, tailored to the specific context. Zeta potential adjustment is an effective factor in disturbance of microorganism associations: Zeta potential was assessed as a tool to manipulate microorganism associations in a laboratory setting. The results of this section represented the first attempt at manipulating zeta potential to enhance the efficiency of UV disinfection, representing a pioneering approach within this field. 8.3 Limitation Limited generalization due to scale and real-world relevance: Conducting experiments in a Petri dish with a small volume of water has limitations when it comes to replicating real-world conditions accurately. The influence of diverse stressors that organisms encounter and the competition for essential resources remains unexplored. Lack of biological diversity: The applicability of the results is constrained by the limited number of surrogate organisms. Organisms as part of a complex ecosystem have interactions with their surrounding environment and isolating one organism might present limited reflection of the real-world interactions. Experimental Limitations: In zeta potential manipulation experiments, two methods for zeta potential manipulation were employed: adjusting pH levels in a more basic direction and adding PACI to the water. The limitation lies in the fact that these two approaches are not entirely independent, and their effects are interconnected in ways that could 185 influence the interpretation of the experimental results. This interconnectedness of processes makes it challenging to definitively attribute observed effects solely to zeta potential alterations, as pH changes might be confounding factors. Synthetic sample preparation: Creating synthetic samples with increased concentrations of one species at a time provided targeted insights into interactions and effects. However, this approach has limitations. The synergistic effects that different components of water such as certain organics that can exhibit catalytic properties after being exposed to UV radiation remained unexplored. Microorganism associations detection efficiency: Although attempts were made to address systematic biases and refine experimental procedures for detecting associated organisms, limitations still exist. There is no definitive method to calculate the microorganism association detection method’s efficiency, leading to bias in results. Residual Disinfection: One of the most important limitations of UV treatment is the inability to provide residual disinfection power. This study proposes a potential approach for increasing disinfection through the elimination of associations leading to UV resistance. However, it is important to note that residual disinfection plays a crucial role that cannot be entirely replaced. The feasibility of adopting the proposed solution depends on many factors such as the distribution system's scale and the water's intended use. Cost: The economic dimensions of the proposed method have not undergone analysis, highlighting the need for a comprehensive cost-benefit assessment. 186 8.4 Future Research Additional research was deemed essential to facilitate the full-scale commercial utilization of zeta potential manipulation. Outlined below are several recommendations for future research: • For future studies, it is recommended to conduct testing with a more realistic particle and organism count. This would enhance the applicability of the findings to real-world scenarios and provide a better understanding of the behavior and effectiveness of the treatment method under conditions that more closely resemble those encountered in practical settings. • Exploring the effect of water hardness on UV disinfection is important because hard water contains high levels of minerals such as calcium and magnesium, which can interact with UV light and affect its ability to penetrate and inactivate microorganisms effectively. Understanding how water hardness impacts UV disinfection is important for optimizing treatment processes and ensuring reliable pathogen removal in various water treatment applications. • In future work, this research can be extended to address the requirement for large-scale implementation of pre-treatment systems. This can be achieved by designing reactors with continuous flow and conducting comprehensive evaluations of their performance. • Investigating the influence of dark and light repair mechanisms on microorganisms subsequent to zeta potential manipulation would expand the understanding, particularly in cases where no residual disinfection options are considered. 187 • An exploration into the effects of multiple wavelength radiation on particle-microorganism association and tailing levels holds the potential for achieving higher disinfection efficacy and potentially causing lasting harm to present microorganisms. • A study on the impact of different surrogates is crucial to unveil competitive effects when multiple surrogates are concurrently present. • Considering potential correlations between zeta potential and other water quality parameters, such as turbidity, UVT, and particle size, in relation to predicting UV disinfection tailing, could provide valuable insights for future research. • Conducting an in-depth analysis of the impacts of zeta potential manipulation on water composition is essential to mitigate adverse effects stemming from deteriorated water quality. • Further investigation into the roles played by various ROS and their reaction by-products generated by water radiation could contribute to a deeper understanding of the mechanisms behind microbial inactivation. • As a recommendation, exploring the application of zeta potential manipulation in other industrial water treatment systems, such as photocatalytic disinfection, could be investigated to enhance the interaction between microorganisms and catalysts, potentially leading to increased disinfection efficacy.Click or tap here to enter text.Click or tap here to enter text.Click or tap here to enter text.Click or tap here to enter text. 188 Bibliography [1] Ipcc, “Climate change 2014: Impacts, adaptation, and vulnerability. 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Table A 1: Results presented in Figure 4.1: Turbidity and bacterial concentration of TP1 raw water during the sampling period Sampling Date TP1 Raw Water Turbidity (NTU) Total Coliform Bacteria (MPN) E. coli (MPN) Dec-20 1.3 25.4 2.0 Jan-21 1.4 73.8 30.6 Feb-21 1.2 13.7 0.0 Mar-21 1.2 65.9 56.0 Apr-21 3.5 42.9 2.0 May-21 1.6 118.4 2.0 Jun-21 1.5 200.5 94.5 Jul-21 1.3 165.2 5.3 Aug-21 1.7 129.8 17.8 Sep-21 1.6 129.8 2.0 Results presented in Figure 4.2: Water quality characteristics of the grab samples from October 2020 to August 2021 Table A 2: Results presented in Figure 4.2-a: Turbidity (NTU) Oct-20 Nov-20 Dec-20 Jan-21 Feb-21 Mar-21 TP1 raw 1.54 1.11 1.26 1.43 1.15 1.18 TP1-Pre UV 0.35 0.35 0.41 0.32 0.39 0.40 TP2 0.62 0.50 0.35 0.27 0.37 0.57 Table A 2: Results presented in Figure 4.2-a (continued) Turbidity (NTU) Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 TP1 raw 3.52 1.57 1.54 1.32 1.65 1.55 TP1-Pre UV 0.58 0.33 0.51 0.26 0.26 0.30 TP2 0.83 0.37 0.76 0.73 1.38 1.16 Table A 2: Results presented in Figure 4.2-b TOC (mg/L) Feb-21 Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 TP1 raw 7.46 7.64 11.83 8.61 9.64 9.76 9.43 TP1-Pre UV 3.74 3.68 4.35 4.32 4.61 4.65 4.88 TP2 3.92 3.89 3.98 4.02 4.91 4.69 4.13 208 Table A 2: Results presented in Figure 4.2-c and Results presented in Figure 4.2-d UVA Oct-20 Nov-20 Dec-20 Jan-21 Feb-21 Mar-21 Apr-21 TP1 raw 0.32 0.30 0.30 0.29 0.28 0.28 0.47 TP1-Pre UV 0.06 0.04 0.05 0.04 0.06 0.06 0.07 TP2 0.04 0.06 0.04 0.06 0.05 0.05 0.06 Table A 2: Results presented in Figure 4.2-c and Results presented in Figure 4.2-d (continued) UVA May-21 Jun-21 Jul-21 Aug-21 TP1 raw 0.29 0.35 0.35 0.34 TP1-Pre UV 0.06 0.06 0.06 0.06 TP2 0.04 0.04 0.04 0.03 Table A 2: Results presented in Figure 4.2-e Zeta Potential (mV) Jan-21 Feb-21 Mar-21 Apr-21 May-21 Jun-21 Jul-21 TP1 raw -12.58 -12.58 -17.16 -18.43 -9.44 -13.63 -15.8 TP1-Pre UV -15.66 -15.66 -20.66 -20.66 -10.57 -14.53 -20.53 TP2 -9.63 -9.67 -10.36 -10.36 -10.26 -9.93 -10.24 Table A 2: Results presented in Figure 4.2-e (continued) Zeta Potential (mV) Aug-21 Sep-21 TP1 raw -15.88 -15.00 TP1-Pre UV -21 -21.16 TP2 -10.59 -10. 78 Results presented in Figure 4.3: The average dose-response curve of TP2, TP1 pre-UV, and TP1 raw calculated from December 2020 to September 2021 under UV wavelength of 255 nm with four different UV doses. Table A 3: The optical light parameters: Optical Light Meter Reading E_s (mW/cm2) 0.478 P_f 0.99 R 0.025 d (cm) 0.52 L (cm) 9.4 Table A 4: Calculating dose based on TP1 raw water quality parameters: Dose (mJ/cm2) Time Exposure (s) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 25.00 9.10 9.17 9.19 9.23 9.28 50.00 18.20 18.35 18.38 18.45 18.56 80.00 29.13 29.35 29.41 29.52 29.69 120.00 43.69 44.03 44.11 44.28 44.54 209 Table A 4: Calculating dose based on TP1 raw water quality parameters (continued) Dose (mJ/cm2) Time Exposure (s) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 25.00 9.27 8.36 9.25 8.93 8.91 50.00 18.53 16.72 18.50 17.86 17.82 80.00 29.65 26.76 29.60 28.58 28.50 120.00 44.48 40.14 44.40 42.87 42.76 Table A 4: Calculating dose based on TP1 raw water quality parameters (continued) Dose (mJ/cm2) Time Exposure (s) 15-Aug-21 Average 25.00 8.99 9.06 50.00 17.98 18.12 80.00 28.77 29.00 120.00 43.16 43.50 Table A 5: TP1 raw: Log removal values achieved: Log Removal Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 0.00 0.00 0.00 0.00 9.06 -1.26 -1.36 -1.17 -1.93 -2.08 18.12 -3.16 -2.56 -2.28 -2.92 -2.70 29.00 -4.07 -3.80 -3.50 -4.15 -4.38 43.50 -4.97 -4.07 -3.77 -4.57 -4.47 Table A 5: TP1 raw: Log removal values achieved (continued): Log Removal Dose (mJ/cm2) 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 15-Aug-21 0.00 0.00 0.00 0.00 0.00 0.00 9.06 -1.13 -1.10 -1.25 -1.26 -1.26 18.12 -2.34 -2.17 -2.45 -2.08 -2.08 29.00 -4.02 -3.88 -3.78 -4.00 -3.60 43.50 -4.32 -4.50 -4.40 -5.26 -5.26 210 Table A 5: TP1 raw: Log removal values achieved (continued): Log Removal Dose (mJ/cm2) 1-Sep-21 Average without RED Bias Consideration SD 23-Feb-21 0.00 0.00 0.00 0.00 0.00 9.06 -0.95 -0.76 0.20 -1.26 18.12 -1.79 -1.36 0.23 -2.08 29.00 -2.64 -2.15 0.26 -3.60 43.50 -3.51 -2.52 0.31 -5.26 Table A 6: Calculating dose based on TP1 pre-UV water quality parameters: Dose (mJ/cm2) Time Exposure (s) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 25.00 10.52 10.69 10.60 10.66 10.53 50.00 21.04 21.37 21.20 21.31 21.07 80.00 33.66 34.20 33.93 34.10 33.70 120.00 50.50 51.29 50.89 51.15 50.56 Table A 6: Calculating dose based on TP1 pre-UV water quality parameters (continued): Dose (mJ/cm2) Time Exposure (s) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 25.00 10.56 10.48 10.54 10.52 10.53 50.00 21.12 20.95 21.09 21.05 21.07 80.00 33.79 33.53 33.74 33.67 33.71 120.00 50.69 50.29 50.61 50.51 50.56 Table A 7: Calculating dose based on TP1 pre-UV water quality parameters (continued): Time Exposure (s) 15-Aug-21 Average 25.00 10.55 10.56 50.00 21.10 21.12 80.00 33.76 33.80 120.00 50.64 50.70 211 Table A 8: TP1 pre-UV: Log removal values achieved: Log Removal Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 0.00 0.00 0.00 0.00 10.56 -1.08 -1.60 -1.07 -1.51 -0.54 21.12 -2.60 -1.40 -2.30 -2.54 -2.49 33.80 -5.30 -5.18 -4.78 -5.31 -5.86 50.70 - - -4.86 -5.62 -6.56 Table A 8: Log removal values achieved (continued): Log Removal Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 - - 0.00 10.56 -2.41 -1.77 - - -1.55 21.12 -3.34 -3.76 - - -3.01 33.80 -6.71 -5.03 - - -4.59 50.70 -6.79 -5.53 - - -5.54 Table A 8: Log removal values achieved (continued): Log Removal Dose (mJ/cm2) 15-Aug-21 1-Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0.00 10.56 - -1.19 -0.80 0.30 21.12 - -2.36 -1.49 0.38 33.80 - -3.74 -2.92 0.47 50.70 - -5.52 -3.26 0.38 Table A 9: Calculating dose based on TP2 pre-UV water quality parameters: Dose (mJ/cm2) Time Exposure (s) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 25.00 10.67 10.54 10.68 10.57 10.61 50.00 21.34 21.09 21.37 21.15 21.22 80.00 34.14 33.74 34.19 33.83 33.95 212 120.00 51.21 50.61 51.29 50.75 50.92 Table A 9: Calculating dose based on TP2 pre-UV water quality parameters (continued): Dose (mJ/cm2) Time Exposure (s) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 25.00 10.64 10.53 10.65 10.68 10.67 50.00 21.28 21.05 21.30 21.37 21.34 80.00 34.04 33.68 34.08 34.18 34.15 120.00 51.06 50.53 51.12 51.28 51.22 Table A 9: Calculating dose based on TP2 pre-UV water quality parameters (continued): Time Exposure (s) 15-Aug-21 Average 25.00 10.71 10.63 50.00 21.43 21.27 80.00 34.28 34.02 120.00 51.43 51.04 Table A 10: TP2 pre-UV: Log removal values achieved: Log Removal Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 0.00 0.00 0.00 0.00 10.63 -0.40 -0.64 -1.77 -2.08 -0.53 21.27 -1.55 -1.40 -2.66 -2.54 -3.17 34.02 -3.83 -3.83 -3.89 -3.81 -3.71 51.04 - -5.18 -3.89 -4.38 -4.46 Table A 10: Log removal values achieved (continued): Log Removal Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 - 0.00 0.00 10.63 -1.46 -1.59 - -1.10 -1.20 21.27 -2.14 -2.11 - -2.52 -2.64 34.02 -3.95 -3.48 - -3.45 -3.94 51.04 -4.91 -4.16 - -4.53 -4.37 213 Table A 10: Log removal values achieved (continued): Log Removal Dose (mJ/cm2) 15-Aug-21 1-Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0.00 10.63 - -1.72 -0.71 0.33 21.27 - -3.12 -1.35 0.33 34.02 - -4.37 -2.16 0.15 51.04 - -5.33 -2.59 0.27 Results presented in Figure 4.4 Table A 11: TP1 raw log removal values normalized by 1- Turbidity: Normalized Log Removal with Turbidity Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 - 0.00 0.00 0.00 9.06 -0.82 - -0.93 -1.35 -1.81 18.12 -2.05 - -1.81 -2.04 -2.35 29.00 -2.64 - -2.78 -2.90 -3.81 43.50 -3.23 - -3.00 -3.20 -3.88 Turbidity (continued): Normalized Log Removal with Turbidity Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 0.00 0.00 0.00 9.06 -0.96 -0.69 -1.29 -0.74 -3.93 18.12 -1.98 -1.35 -2.53 -1.23 -6.50 29.00 -3.41 -2.43 -3.90 -2.37 -11.25 43.50 -3.66 -2.81 -4.54 -3.11 -16.43 Turbidity (continued): Normalized Log Removal with Turbidity Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 9.06 - -0.61 -0.74 0.559423667 18.12 - -1.15 -1.30 0.872967259 29.00 - -1.70 -2.10 1.542993451 43.50 - -2.27 -2.61 2.371883235 214 2- UVT: Normalized Log Removal with UVT Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 - 0.00 0.00 0.00 9.06 -0.03 - -0.02 -0.04 -0.04 18.12 -0.07 - -0.05 -0.06 -0.05 29.00 -0.08 - -0.07 -0.08 -0.08 43.50 -0.10 - -0.08 -0.09 -0.09 UVT (continued): Normalized Log Removal with UVT Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 0.00 0.00 0.00 9.06 -0.02 -0.03 -0.02 -0.03 -0.03 18.12 -0.04 -0.06 -0.05 -0.05 -0.05 29.00 -0.08 -0.11 -0.07 -0.09 -0.08 43.50 -0.08 -0.13 -0.09 -0.12 -0.12 UVT (continued): Normalized Log Removal with UVT Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 9.06 - -0.02 -0.02 0.003666472 18.12 - -0.04 -0.03 0.004822659 29.00 - -0.06 -0.05 0.008246059 43.50 - -0.08 -0.05 0.011273267 3- Zeta Potential: Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 - - - 0.00 0.00 9.06 - - - 0.15 0.17 18.12 - - - 0.23 0.21 29.00 - - - 0.33 0.35 43.50 - - - 0.36 0.35 215 Zeta Potential (continued): Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 0.00 0.00 0.00 9.06 0.09 0.09 0.10 0.10 0.10 18.12 0.19 0.17 0.19 0.17 0.17 29.00 0.32 0.31 0.30 0.32 0.29 43.50 0.34 0.36 0.35 0.42 0.42 Zeta Potential (continued): Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 9.06 - 0.07 -0.06 0.019144814 18.12 - 0.12 -0.10 0.018909636 29.00 - 0.18 -0.17 0.028713304 43.50 - 0.24 -0.20 0.030853707 Table A 12: TP1 pre-UV log removal values normalized by 1- Turbidity: Normalized Log Removal with Turbidity Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 0.00 0.00 0.00 0.00 10.56 -3.08 -5.52 -3.23 -4.72 -1.36 21.12 -7.43 -4.82 -6.98 -7.94 -6.33 33.80 -15.14 -17.85 -14.48 -16.59 -14.87 50.70 - - -14.72 -17.57 -16.66 Turbidity (continued): Normalized Log Removal with Turbidity Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 - - 0.00 10.56 -6.02 -3.05 - - -26.76 21.12 -8.34 -6.48 - - -51.96 33.80 -16.78 -8.67 - - -79.13 50.70 -16.98 -9.54 - - -95.53 216 Turbidity (continued): Normalized Log Removal with Turbidity Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 10.56 - -4.00 -3.62 4.384514748 21.12 - -7.92 -6.79 8.483602472 33.80 - -12.51 -12.31 12.24746977 50.70 - -18.46 -15.29 17.13795924 2- UVT: Normalized Log Removal with UVT Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 0.00 0.00 0.00 0.00 10.56 -0.01 -0.02 -0.01 -0.02 -0.01 21.12 -0.03 -0.02 -0.03 -0.03 -0.03 33.80 -0.06 -0.06 -0.05 -0.06 -0.07 50.70 - - -0.05 -0.06 -0.08 UVT (continued): Normalized Log Removal with UVT Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 - - 0.00 10.56 -0.03 -0.02 - - -0.02 21.12 -0.04 -0.04 - - -0.03 33.80 -0.08 -0.06 - - -0.05 50.70 -0.08 -0.07 - - -0.06 UVT (continued): Normalized Log Removal with UVT Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 10.56 - -0.01 -0.01 0.003417112 21.12 - -0.03 -0.02 0.004645713 33.80 - -0.04 -0.03 0.005363896 50.70 - -0.06 -0.04 0.00453531 217 3- Zeta Potential: Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 - - - 0.00 0.00 10.56 - - - 0.10 0.03 21.12 - - - 0.16 0.12 33.80 - - - 0.34 0.28 50.70 - - - 0.36 0.32 Zeta Potential (continued): Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 - - - 0.00 10.56 0.12 - - - 0.08 21.12 0.16 - - - 0.15 33.80 0.32 - - - 0.22 50.70 0.33 - - - 0.27 Zeta Potential (continued): Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 10.56 - 0.07 -0.04 0.019177506 21.12 - 0.14 -0.08 0.0098082 33.80 - 0.22 -0.16 0.031512065 50.70 - 0.32 -0.18 0.018110374 Table A 13: TP2 pre-UV log removal values normalized by 1- Turbidity: Normalized Log Removal with Turbidity Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 0.00 0.00 0.00 0.00 10.63 -0.77 -1.27 -3.22 -5.08 -1.41 21.27 -2.99 -2.79 -4.83 -6.20 -8.41 34.02 -7.36 -10.36 -7.08 -9.30 -9.84 51.04 0.00 -7.08 -10.69 -11.83 218 Turbidity (continued): Normalized Log Removal with Turbidity Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 - 0.00 0.00 10.63 -3.77 -1.29 - -1.45 -2.67 21.27 -5.54 -1.72 - -3.31 -5.86 34.02 -10.20 -2.83 - -4.54 -8.76 51.04 -12.70 -3.39 - -5.96 -9.71 Turbidity (continued): Normalized Log Removal with Turbidity Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 10.63 - -1.48 -1.27 0.786304378 21.27 - -2.69 -2.50 1.172931778 34.02 - -3.77 -4.18 1.579348713 51.04 - -4.59 -4.14 2.403157603 2- UVT: Normalized Log Removal with UVT Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 0.00 0.00 0.00 0.00 0.00 10.63 0.00 -0.01 -0.02 -0.02 -0.01 21.27 -0.02 -0.02 -0.03 -0.03 -0.04 34.02 -0.04 -0.06 -0.04 -0.04 -0.04 51.04 0.00 -0.04 -0.05 -0.05 UVT (continued): Normalized Log Removal with UVT Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 0.00 - 0.00 0.00 10.63 -0.02 -0.02 - -0.01 -0.01 21.27 -0.02 -0.02 - -0.03 -0.03 219 34.02 -0.04 -0.04 - -0.04 -0.04 51.04 -0.05 -0.05 - -0.05 -0.05 UVT (continued): Normalized Log Removal with UVT Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 10.63 - -0.02 -0.01 0.003631361 21.27 - -0.03 -0.01 0.003554378 34.02 - -0.05 -0.02 0.003336828 51.04 - -0.06 -0.03 0.009677095 3- Zeta Potential: Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 6-Oct-20 24-Nov-20 12-Dec-20 26-Jan-21 23-Feb-21 0.00 - - - 0.00 0.00 10.63 - - - 0.22 0.05 21.27 - - - 0.26 0.31 34.02 - - - 0.39 0.36 51.04 - - - 0.45 0.43 Zeta Potential (continued): Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 26-Mar_21 29-Apr-21 29-May-21 29-Jun-21 29-Jul-21 0.00 0.00 - - 0.00 0.00 10.63 0.14 - - 0.11 0.12 21.27 0.21 - - 0.25 0.26 34.02 0.38 - - 0.35 0.38 51.04 0.47 - - 0.46 0.43 Zeta Potential (continued): Normalized Log Removal with Zeta Potential Dose (mJ/cm2) 15-Aug-21 Sep-21 Average without RED Bias Consideration SD 0.00 - 0.00 0.00 0 10.63 - 0.17 -0.08 0.031543703 21.27 - 0.31 -0.15 0.021186602 220 34.02 - 0.43 -0.22 0.016633341 51.04 - 0.53 -0.26 0.020407861 Table A 14: Results presented in Figure 5.1: Initial concentration and particle-microorganism association of MS2 in samples made with particle-laden and RO water TP1 raw+ MS2 RO+MS2 TP1 raw+ MS2 (PFU/mL) particle-microorganism association (%) RO+MS2 (PFU/mL) particle-microorganism association (%) 9.00E+07 17.00 4.00E+07 6.00 2.00E+08 38.25 9.00E+09 0.85 2.40E+09 13.68 2.40E+09 0.94 3.00E+08 16.81 2.70E+08 2.00 7.00E+09 24.36 1.00E+09 4.00 5.00E+08 8.53 3.70E+08 5.00 Table A 15: Results presented in Figure 5.2: The impact of 0.22 µm filtration on samples made with RO water as background. Concentration (PFU/mL) Unfiltered MS2 0.22 µm- Filtered MS2 9.00E+06 6.00E+06 2.40E+08 1.60E+08 7.00E+08 6.00E+08 Table A 16: Results presented in Figure 5.3: Initial concentration of E. coli in samples made with particle-laden and RO water. Concentration (CFU/mL) TP1 raw+ E. coli RO+ E. coli 2.69E+10 5.25E+11 7.17E+09 5.11E+11 6.87E+11 1.88E+11 2.16E+12 4.82E+11 1.04E+11 9.18E+12 4.03E+11 2.15E+11 Table A 17: Results presented in Figure 5.4: Turbidity and UVT level of the supernatant after coagulation with four dosages in 1- E. coli E. Coli Turbidity (NTU) UVT (%) PACl Concentration (mg/L) 1 2 3 STD 1 2 3 STD 0.00 2.22 1.89 1.50 0.29 71.33 65.00 63.00 3.55 10.00 19.00 14.00 18.00 2.16 89.92 92.00 85.00 2.94 20.00 22.20 20.09 19.80 1.07 94.57 95.00 97.00 1.06 30.00 9.00 8.00 7.00 0.82 89.92 85.00 82.00 3.26 50.00 6.70 5.40 3.00 1.53 100.00 100.00 100.00 0.00 80.00 5.70 5.00 3.00 1.14 100.00 100.00 100.00 0.00 221 100.00 4.00 3.00 6.00 1.25 100.00 100.00 100.00 0.00 2- MS2 MS2 Turbidity (NTU) UVT (%) PACl Concentration (mg/L) 1 2 3 STD 1 2 3 STD 0.00 0.53 0.50 0.60 0.04 79.61 79.61 76.33 1.55 10.00 14.76 14.50 13.00 0.78 70.37 72.00 87.00 7.48 20.00 5.03 4.00 6.00 0.82 82.00 80.00 91.00 4.78 30.00 2.30 1.90 2.70 0.33 77.21 72.43 71.00 2.66 50.00 2.63 2.50 2.00 0.27 85.41 85.00 87.00 0.86 80.00 3.00 3.76 2.09 0.68 80.88 82.00 90.00 4.06 100.00 4.00 4.34 5.87 0.81 70.00 80.00 76.00 4.11 Table A 18: Results presented in Figure 5.5: Impact of settling time on the level of organism concentration, E. coli and MS2, after coagulation. 1- E. Coli E. coli RO Water TP1 raw Exposure Time (s) 1 2 3 Average STD 1 2 3 Average STD 0 2.2E+06 3.0E+06 3.5E+06 2.9E+06 5.4E+05 2.8E+06 4.0E+06 1.0E+06 2.6E+06 1.2E+06 15 1.6E+06 2.0E+06 3.0E+06 2.2E+06 5.9E+05 7.0E+05 4.0E+05 5.5E+05 5.5E+05 1.2E+05 30 2.8E+06 1.0E+06 9.5E+05 1.6E+06 8.6E+05 2.2E+05 1.0E+05 1.6E+05 1.6E+05 4.9E+04 60 2.8E+06 1.0E+06 3.5E+05 1.4E+06 6.3E+05 4.2E+05 1.0E+05 2.6E+05 2.6E+05 1.3E+05 90 2.1E+06 3.0E+06 3.5E+06 2.9E+06 5.8E+05 4.0E+05 4.0E+05 4.0E+05 4.0E+05 0.0E+00 2- MS2 MS2 RO Water TP1 raw Exposure Time (s) 1 2 3 Average STD 1 2 3 Average STD 0 1.0E+08 8.5E+07 1.5E+08 1.1E+08 2.8E+07 8.5E+07 8.3E+07 1.2E+08 9.6E+07 1.7E+07 15 8.0E+07 6.3E+07 1.2E+08 8.8E+07 2.4E+07 5.0E+07 4.8E+07 5.0E+07 4.9E+07 9.4E+05 30 6.0E+07 8.0E+07 1.4E+08 9.3E+07 3.4E+07 4.5E+07 5.0E+07 4.0E+07 4.5E+07 4.1E+06 60 6.0E+07 8.0E+07 1.2E+08 8.7E+07 2.5E+07 4.5E+07 5.0E+07 4.0E+07 4.5E+07 4.1E+06 90 9.0E+07 8.0E+07 1.2E+08 9.7E+07 1.7E+07 4.5E+07 5.7E+07 3.0E+07 4.4E+07 1.1E+07 222 Table A 19: Results presented in Figure 6.1: water quality characteristics of the source and synthetic samples versus increased concentration of additives as turbidity. Sample ID Average Turbidity (NTU) Average UVT (%) STD Average particle size after pre-treatment (d.nm) STD Average Zetapotential before pre-treatment (mV) STD TP1 Control 1.25 100.00 0.52 200.00 30.17 -24.00 1.82 Iron (III) Hydroxide 1.04 100.55 44.90 1366.67 38.13 -24.27 1.11 5.60 94.15 48.56 1764.33 123.48 -17.80 1.57 20.67 86.85 51.78 3287.40 213.08 -14.57 2.15 Kaolin Clay 1.27 99.25 45.89 1741.33 259.48 -25.50 0.42 7.33 88.74 44.34 2052.33 19.40 -24.63 0.14 20.53 91.81 49.63 2100.33 182.44 -21.57 0.73 Humic Acid 1.28 88.26 5.44 160.30 3.47 -24.57 0.54 5.30 62.75 31.27 275.50 6.20 -17.83 0.65 20.39 11.82 44.10 333.30 34.01 -19.80 0.62 TP2 Control 0.67 100.00 1.80 268.40 10.32 -17.00 0.52 Iron (III) Hydroxide 1.31 83.56 47.20 1301.00 716.62 -14.00 0.28 4.95 92.60 46.28 1028.53 81.44 -15.87 0.94 19.27 94.43 41.74 2118.67 369.80 -16.30 0.58 Kaolin Clay 1.20 84.40 42.68 784.83 126.44 -17.67 1.32 5.60 85.69 42.81 933.57 85.74 -17.73 0.31 19.10 85.43 42.17 1006.80 52.76 -16.13 0.66 Humic Acid 1.42 96.03 14.87 510.30 20.82 -16.70 0.42 5.73 61.96 30.88 298.50 32.98 -12.60 0.16 19.90 30.27 48.01 443.47 15.14 -13.43 0.62 223 Table A 20: Results presented in Figure 6.2: % Particle-microorganism association versus increased concentration of additives as turbidity in TP1 and TP2 Average Particle-microorganism association (%) Turbidity (NTU) TP1 STD TP2 STD Iron (III) Hydroxide ~ 1 16.00 3.00 4.50 1.50 ~ 5 23.50 5.00 3.50 0.50 ~ 20 31.00 1.00 5.00 0.00 Kaolin Clay ~ 1 1.00 1.00 4.50 0.50 ~ 5 1.50 0.50 3.50 2.50 ~ 20 6.00 0.00 5.50 0.50 Humic Acid ~ 1 11.50 0.50 4.00 1.00 ~ 5 17.50 0.50 10.50 0.50 ~ 20 21.50 0.50 13.51 0.03 Table A 21: Results presented in Figure 6.3: The average exposure time–UV response curves of TP1and TP2 calculated under a UV wavelength of 254 nm with four different exposure times. TP1- 1 NTU Exposure Time (s) Iron (III) Hydroxide STD Kaolin Clay STD Humic Acid STD Control STD 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10 -2.50 0.00 -1.06 0.11 -0.74 0.47 -1.54 0.30 30 -3.68 0.01 -2.03 0.24 -2.29 0.02 -4.49 0.38 70 -4.30 0.01 -3.07 0.43 -4.14 0.90 -5.86 0.47 100 -5.38 0.01 -3.64 0.13 -4.32 0.77 -5.96 0.38 TP1- 5 NTU Exposure Time (s) Iron (III) Hydroxide STD Kaolin Clay STD Humic Acid STD Control STD 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.00 -0.89 0.48 -0.09 0.03 -0.37 0.15 -1.54 0.30 30.00 -1.44 0.60 -1.34 0.04 -2.10 0.03 -4.49 0.38 70.00 -1.88 0.67 -2.22 0.02 -2.71 0.41 -5.86 0.47 100.00 -2.92 0.57 -3.33 0.03 -3.50 0.10 -5.96 0.38 TP1- 20 NTU Exposure Time (s) Iron (III) Hydroxide STD Kaolin Clay STD Humic Acid STD Control STD 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 10.00 -1.46 0.04 -0.60 0.00 -0.81 0.37 -1.54 0.30 30.00 -1.60 0.03 -1.30 0.01 -2.04 0.26 -4.49 0.38 70.00 -2.30 0.01 -2.10 0.00 -2.08 0.00 -5.86 0.47 224 100.00 -2.80 0.00 -3.60 0.00 -2.33 0.15 -5.96 0.38 TP2- 1NTU Exposure Time (s) Iron (III) Hydroxide STD Kaolin Clay STD Humic Acid STD Control STD 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.00 -1.68 0.07 -1.06 0.54 -1.24 0.17 -1.72 0.33 30.00 -2.38 0.22 -2.03 0.86 -2.31 0.24 -3.12 0.33 70.00 -3.69 0.17 -3.07 0.19 -3.54 0.39 -4.37 0.15 100.00 -4.03 0.11 -3.64 0.07 -3.89 0.09 -5.33 0.27 TP2- 5 NTU Exposure Time (s) Iron (III) Hydroxide STD Kaolin Clay STD Humic Acid STD Control STD 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.00 -0.25 0.01 -0.09 0.02 -1.58 0.12 -1.72 0.33 30.00 -1.18 0.03 -1.34 0.14 -2.71 0.19 -3.12 0.33 70.00 -3.12 0.10 -2.22 1.02 -3.21 0.01 -4.37 0.15 100.00 -3.27 0.10 -3.33 0.45 -3.48 0.18 -5.33 0.27 TP2- 20 NTU Exposure Time (s) Iron (III) Hydroxide STD Kaolin Clay STD Humic Acid STD Control STD 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.00 -0.64 0.15 -0.60 0.15 -0.71 0.11 -1.72 0.33 30.00 -2.09 0.36 -1.30 0.30 -1.21 0.49 -3.12 0.33 70.00 -3.26 0.36 -2.10 0.21 -1.78 0.17 -4.37 0.15 100.00 -3.79 0.66 -3.60 0.12 -2.74 0.21 -5.33 0.27 Table A 22: Results presented in Figure 7.1: The impact of additive dosage on the zeta potential behavior of water spiked with MS2 and E. coli Zeta Potential (mV) MS2 STD E. coli STD 3 mMol (Na (OH)) -15.0 1.0 -20.5 0.8 2 mMol (Na (OH)) -16.5 1.0 -30.1 0.8 1 mMol (Na (OH)) -20.1 0.8 -36.0 1.3 0 -15.8 0.6 -24.9 1.0 4 mg/L (PACl) -14.0 0.5 -24.0 0.4 6 mg/L (PACl) -10.0 0.3 -22.0 0.3 10 mg/L (PACl) -6.2 0.4 -15.0 1.0 12 mg/L (PACl) 2.8 0.2 -8.3 0.4 15 mg/L (PACl) 15.1 0.3 -1.8 0.3 20 mg/L (PACl) 30.0 0.5 15.3 0.4 225 Table A 23: Results presented in Figure 7.2: Water quality characteristics of samples at ranges of zeta potential in samples spiked with MS2 and E. coli: average particle size (µm), turbidity (NTU), UVT (%), and mean count rate (kcps) MS2 Zeta potential (mV) STD Mean Count Rate (kcps) STD Average Particle Size (d. um) STD pH STD UVT (%) Turbidity (NTU) STD Highly Negative (Zeta Potential= -25 to -40 mV) -20.13 0.76 352.93 9.43 0.19 0.07 11.00 1.93 82.97 0.65 1.22 Negative (Zeta Potential= -15 to -24 mV) -15.80 0.60 230.67 0.82 10.80 4.54 6.60 0.29 83.01 0.70 0.49 Moderately Negative (Zeta Potential= -3 to -14 mV) -6.21 0.40 229.93 13.10 17.68 3.37 7.30 0.24 77.18 10.38 0.53 Neutral (Zeta Potential= -2 to +2 mV) 2.77 0.17 236.13 14.69 15.04 0.44 7.00 0.15 69.01 15.30 0.63 Positive (Zeta Potential= +2 to + 15 mV) 15.10 0.31 380.80 1.12 1.06 0.02 6.70 0.29 68.75 16.56 1.38 226 Table A 24: Results presented in Figure 7.2: Water quality characteristics of samples at ranges of zeta potential in samples spiked with MS2 and E. coli: average particle size (µm), turbidity (NTU), UVT (%), and mean count rate (kcps) (continued) E. coli Zeta potential (mV) STD Mean Count Rate (kcps) STD Average Particle Size (d. um) STD pH STD UVT (%) Turbidity (NTU) STD Highly Negative (Zeta Potential= -25 to -40 mV) -36.03 1.27 287.60 6.67 0.25 0.13 8.60 0.72 78.97 3.16 0.51 Negative (Zeta Potential= -15 to -24 mV) -24.93 1.00 277.10 4.14 1.32 0.07 6.60 0.27 91.00 4.43 0.46 Moderately Negative (Zeta Potential= -3 to -14 mV) -8.25 0.43 151.13 3.24 2.76 1.05 7.30 0.24 85.57 10.55 0.24 Neutral (Zeta Potential= -2 to +2 mV) -1.81 0.26 162.77 9.08 21.31 2.70 7.00 0.15 86.68 15.95 0.37 Positive (Zeta Potential= +2 to + 15 mV) 15.30 0.43 191.17 1.18 0.61 0.01 6.70 0.27 79.78 15.21 0.00 227 Table A 25: Results presented in Figure 7.3: The difference between quality characteristics: Turbidity and UVT% of water samples after settlement period. The negative values represent a drop and positive values represent an increase after settlement. Mean Count Rate (kcps) Average Particle size (µm) UVT (%) Turbidity (NTU) Before Settling After Settling Before Settling After Settling Before Settling After Settling Before Settling After Settling Highly Negative (Zeta Potential= -25 to -40 mV) 352.93 259.30 0.19 1.37 82.97 79.45 0.65 0.35 Negative (Zeta Potential= -15 to -24 mV) 230.67 277.10 10.80 0.11 83.01 75.60 0.70 0.55 Moderately Negative (Zeta Potential= -3 to -14 mV) 229.93 22.00 17.68 10.30 77.18 100.00 10.38 6.55 Neutral (Zeta Potential= -2 to +2 mV) 236.13 53.03 15.04 1.04 69.01 100.00 15.30 5.95 Positive (Zeta Potential= +2 to + 15 mV) 380.80 156.47 1.06 1.30 68.75 84.54 16.56 15.21 MS2 % Of change Highly Negative (Zeta Potential= -25 to -40 mV) Negative (Zeta Potential= -15 to -24 mV) Moderately Negative (Zeta Potential= -3 to -14 mV) Neutral (Zeta Potential= -2 to +2 mV) Positive (Zeta Potential= +2 to + 15 mV) Turbidity (NTU) -46.15 -21.43 -58.47 -61.12 -8.17 UVT (%) -4.24 -8.93 22.82 33.07 22.97 Average Particle size (µm) 606.64 -98.96 -71.63 -93.11 23.56 Mean Count Rate (kcps) -26.53 20.13 -90.43 -77.54 -58.91 E. coli % Of change Highly Negative (Zeta Potential= -25 to -40 mV) Negative (Zeta Potential= -15 to -24 mV) Moderately Negative (Zeta Potential= -3 to -14 mV) Neutral (Zeta Potential= -2 to +2 mV) Positive (Zeta Potential= +2 to + 15 mV) Turbidity (NTU) 6.65 -28.67 -52.61 -62.38 -34.25 UVT (%) 3.84 -2.20 14.43 15.37 6.54 Average Particle size (µm) 350.07 -10.25 44.74 -14.58 3.47 Mean Count Rate (kcps) 39.77 -6.84 -10.39 -22.12 35.71 228 Table A 26: Results presented in Figure 7.3: particle-microorganism association (%) level of MS2 and E. coli sample sets at different zeta potential levels measured by settling separation methods. MS2 - final particle-microorganism association Zeta level 1 2 3 Average STD Highly Negative (Zeta Potential= -25 to -40 mV) 15.00 6.00 10.50 10.50 3.67 Negative (Zeta Potential= -15 to -24 mV) 22.73 15.00 28.00 21.91 5.34 Moderately Negative (Zeta Potential= -3 to -14 mV) 28.57 42.40 35.71 35.56 5.65 Neutral (Zeta Potential= -2 to +2 mV) 40.00 66.00 61.11 55.70 11.28 Positive (Zeta Potential= +2 to + 15 mV) 23.08 33.33 43.40 33.27 8.30 Table A 27: Results presented in Figure 7.3: particle-microorganism association (%) level of MS2 and E. coli sample sets at different zeta potential levels measured by settling separation methods (Continued) E. coli - final particle-microorganism association Zeta level 1 2 3 Average STD Highly Negative (Zeta Potential= -25 to -40 mV) 31.00 38.64 52.00 40.55 8.68 Negative (Zeta Potential= -15 to -24 mV) 40.00 35.65 37.83 37.83 1.78 Moderately Negative (Zeta Potential= -3 to -14 mV) 53.13 37.66 45.39 45.39 6.31 Neutral (Zeta Potential= -2 to +2 mV) 82.00 61.11 71.56 71.56 8.53 Positive (Zeta Potential= +2 to + 15 mV) 34.00 48.35 41.18 41.18 5.86 229 Table A 28: Results presented in Figure 7.5: UV inactiovation efficiency of E. coli and MS2 surrogate microorganisms MS2 E. coli Exposure Time (s) Average Log Removal STD Average Log Removal STD Control Experiment (RO + Organism) 0 0.0 0.0 0.0 0.0 20 -2.0 0.2 -3.0 0.3 40 -4.0 0.4 -6.0 0.3 60 -4.4 0.2 -6.4 0.6 150 -5.3 0.2 -8.3 0.3 Highly Negative (Zeta Potential= -25 to -40 mV) 0 0.0 0.0 0.0 0.0 20 -2.0 0.2 -2.9 0.3 40 -4.3 0.6 -4.7 0.2 60 -4.4 0.0 -5.5 0.2 150 -5.2 0.1 -7.0 0.0 Negative (Zeta Potential= -15 to -24 mV) 0 0.0 0.0 0.0 0.0 20 -1.3 0.4 -1.4 0.4 40 -2.5 0.5 -4.2 0.3 60 -3.6 0.3 -4.3 0.4 150 -4.6 0.3 -7.7 0.5 Moderately Negative (Zeta Potential= -3 to -14 mV) 0 0.0 0.0 0.0 0.0 20 -0.3 0.0 -1.4 0.2 40 -0.8 0.2 -1.8 0.5 60 -2.0 0.1 -2.4 1.2 150 -3.0 0.4 -5.3 0.5 Neutral (Zeta Potential= -2 to +2 mV) 0 0.0 0.0 0.0 0.0 20 -0.5 0.2 -0.5 0.4 40 -1.2 0.4 -1.7 0.2 60 -2.5 0.3 -2.4 1.0 150 -2.7 0.1 -5.3 0.7 Positive (Zeta Potential= +2 to + 15 mV) 0 0.0 0.0 0.0 0.0 20 -0.5 0.2 -0.9 0.2 40 -2.1 0.6 -3.3 0.3 60 -4.1 0.9 -3.7 0.7 150 -5.0 0.9 -7.0 0.0 230 Table A 29: Results presented in Figure 7.6: Double exponential model parameters over zeta potential control Model Parameters Highly Negative (Zeta Potential= -25 to -40 mV) Negative (Zeta Potential= -15 to -24 mV) Moderately Negative (Zeta Potential= -3 to -14 mV) Neutral (Zeta Potential= -2 to +2 mV) Positive (Zeta Potential= +2 to + 15 mV) RMSE MS2 beta-MS2 19.00 61.82 84.94 78.84 71.42 0.02 k1-MS2 -0.90 -0.26 -0.90 -0.15 -0.20 0.10 k2-MS2 -0.21 -0.26 -0.11 -0.15 -0.20 0.12 R-Squared-MS2 1.00 0.94 0.91 0.95 0.97 0.09 Adjusted R-Squared-MS2 1.00 0.94 0.90 0.95 0.97 0.06 E. coli beta-E. Coli 4.00 9.00 8.85 73.65 56.43 0.00 k1-E. Coli -0.89 -0.90 -0.90 -0.18 -0.29 0.02 k2-E. Coli -0.49 -0.17 -0.07 -0.18 -0.29 0.01 R-Squared-E. Coli 1.00 1.00 1.00 0.90 1.00 0.13 Adjusted R-Squared-E. Coli 1.00 1.00 1.00 0.85 0.99 0.02
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Peleato, Nicolas M.
Ultraviolet disinfection of unfiltered water supplies : exploring effects of particle-microorganism association and efficiency enhancement strategies
Text