Applied Science, Faculty of
Civil Engineering, Department of
DSpace
UBCV
Jia, Baohui
Doctor of Philosophy - PhD
University of British Columbia
The research investigates the impact of interactions between natural organic matter (NOM), present on membranes as foulants, and hypochlorite, used for chemical cleaning, on the ageing of polyvinylidene fluoride (PVDF) ultrafiltration (UF) membranes. Ageing, defined as the progressive deterioration of physical and chemical characteristics and hydraulic performance of membranes, represents a critical challenge to the sustainable operation of UF membrane facilities. The role of NOM foulant-hypochlorite interactions was poorly understood. Using laboratory accelerated soak and cyclic ageing approaches, and common model NOM components (bovine serum albumin (BSA), sodium alginate (SA), and humic acid (HA)), the research demonstrates that radical oxidants are generated from NOM foulant – hypochlorite interactions under conditions relevant to chemical cleaning, and that these radical oxidants contribute to accelerating membrane ageing. BSA, a model protein type NOM, was identified as the primary contributor to radical oxidant generation when interacting with hypochlorite; other model NOM considered did not interact with hypochlorite. The impact of ageing was quantified based on changes in physical and chemical characteristics (characterized using differential Fourier Transform Infrared (FTIR) spectroscopy, contact angle and scanning electron microscopy (SEM)), and hydraulic performance (characterized using clean water resistance). The research demonstrated that full-scale facilities with feed waters containing higher protein type NOM concentrations are likely to generate more radical oxidants during chemical cleaning, and that these radical oxidants are likely to increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. Results for laboratory accelerated cyclic ageing were similar to those for ageing at full-scale when considered with respect to the loading of protein type NOM from feed waters. Addition of radical scavengers to the chemical cleaning solution effectively mitigated the detrimental effects of radical oxidants on membrane ageing. Permeation of hypochlorite through membranes during chemical cleaning, rather than simply soaking membranes in hypochlorite as typical practice at full-scale, improved hydraulic performance by removing accumulated foulants within membrane pores. The research outcomes advance current understanding of NOM in membrane ageing and offer practical strategies to extend membrane lifespan, consequently improving sustainability of membrane filtration systems in drinking water treatment.
Membrane Ageing in Drinking Water Treatment: Assessing the Impact of Natural Organic Matter Foulants and Hypochlorite Interactions by Baohui Jia M.Sc., Harbin Institute of Technology, 2017 B.Sc., Tongji University, 2015 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2025 © Baohui Jia, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Membrane Ageing in Drinking Water Treatment: Assessing the Impact of Natural Organic Matter Foulants and Hypochlorite Interactions submitted by Baohui Jia in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Civil Engineering Examining Committee: Pierre Bérubé, Department of Civil Engineering Supervisor Robert Andrews, Department of Civil and Mineral Engineering, University of Toronto Co-supervisor Eric Hall, Department of Civil Engineering University Examiner Scott Dunbar, Department of Mining Engineering University Examiner Additional Supervisory Committee Members: Madjid Mohseni, Department of Chemical and Biological Engineering Supervisory Committee Member Jongho Lee, Department of Civil Engineering Supervisory Committee Member iii Abstract The research investigates the impact of interactions between natural organic matter (NOM), present on membranes as foulants, and hypochlorite, used for chemical cleaning, on the ageing of polyvinylidene fluoride (PVDF) ultrafiltration (UF) membranes. Ageing, defined as the progressive deterioration of physical and chemical characteristics and hydraulic performance of membranes, represents a critical challenge to the sustainable operation of UF membrane facilities. The role of NOM foulant-hypochlorite interactions was poorly understood. Using laboratory accelerated soak and cyclic ageing approaches, and common model NOM components (bovine serum albumin (BSA), sodium alginate (SA), and humic acid (HA)), the research demonstrates that radical oxidants are generated from NOM foulant – hypochlorite interactions under conditions relevant to chemical cleaning, and that these radical oxidants contribute to accelerating membrane ageing. BSA, a model protein type NOM, was identified as the primary contributor to radical oxidant generation when interacting with hypochlorite; other model NOM considered did not interact with hypochlorite. The impact of ageing was quantified based on changes in physical and chemical characteristics (characterized using differential Fourier Transform Infrared (FTIR) spectroscopy, contact angle and scanning electron microscopy (SEM)), and hydraulic performance (characterized using clean water resistance). The research demonstrated that full-scale facilities with feed waters containing higher protein type NOM concentrations are likely to generate more radical oxidants during chemical cleaning, and that these radical oxidants are likely to increase the rate at which changes in physical and iv chemical characteristics and hydraulic performance occur during membrane ageing. Results for laboratory accelerated cyclic ageing were similar to those for ageing at full-scale when considered with respect to the loading of protein type NOM from feed waters. Addition of radical scavengers to the chemical cleaning solution effectively mitigated the detrimental effects of radical oxidants on membrane ageing. Permeation of hypochlorite through membranes during chemical cleaning, rather than simply soaking membranes in hypochlorite as typical practice at full-scale, improved hydraulic performance by removing accumulated foulants within membrane pores. The research outcomes advance current understanding of NOM in membrane ageing and offer practical strategies to extend membrane lifespan, consequently improving sustainability of membrane filtration systems in drinking water treatment. v Lay Summary Membrane filtration is widely used in drinking water treatment, however repeated chemical cleaning with chlorine ages the membrane material impacting performance. My research demonstrated that protein-like substances, present in natural water, can react with the chlorine used for cleaning to create highly reactive compounds called radical oxidants, which accelerate membrane ageing. Adding compounds that can scavenge radical oxidants during chemical cleaning significantly reduces the rate of ageing. These findings provide practical strategies to extend membrane life and significantly reduce the cost for drinking water treatment. vi Preface This confirms that Baohui Jia is primarily responsible for the research presented in this dissertation. Manuscripts and conference presentations resulted from this work. Journal Publications: The following journal publications were prepared by Baohui Jia, with the coauthors providing technical and editorial comments. Baohui Jia conducted the literature review, methodology development, experimental work, data analysis, results interpretation, and manuscript writing. • Baohui Jia; Jia Bian; Rahul Dutta; Majid Mohseni; Jongho Lee; Robert C. Andrews; Pierre R. Bérubé. NOM foulant−hypochlorite interactions impact PVDF UF membrane ageing, Separation and Purification Technology, 364 (2025) 132469 • Baohui Jia; Rahul Dutta; Jia Bian; Majid Mohseni; Jongho Lee; Robert C. Andrews; Pierre R. Bérubé. Contributions of Different NOM Foulant - Hypochlorite Interactions to Membrane Ageing (draft completed, being reviewed internally) • Baohui Jia; Jia Bian; Rahul Dutta; Majid Mohseni; Jongho Lee; Robert C. Andrews; Pierre R. Bérubé. Relationship Between Protein Type NOM in Real Feed Water and Membrane Ageing (~90% completed) Conference Presentations: The following conference presentations were prepared by Baohui Jia, with the coauthors provided technical and editorial comments. Baohui Jia conducted the research methodology vii development, experimental work, data analysis, results interpretation, and presentation preparation. • Baohui Jia, Rahul Dutta, Jia Bian, Robert C. Andrews, Madjid Mohseni, Pierre R. Bérubé. Interactions Between Natural Organic Material and Hypochlorite During Chemical Cleaning Accelerates Membrane Ageing. IWA Regional Membrane Technology Conference, Palermo Italy, June 2024. (oral – presented by corresponding author Pierre R. Bérubé) • Baohui Jia, Rahul Dutta, Jia Bian, Robert C. Andrews, Pierre R. Bérubé. Influence Of Organic Foulants to Membrane Ageing, IWA Membrane Technology Conference, St. Louis USA, July 2023. (poster) • Baohui Jia, Rahul Dutta, Jia Bian, Robert C. Andrews, Pierre R. Bérubé. Quantifying Membrane Ageing by Differential ATR-FTIR Spectroscopy: Contribution of Foulants and Chemical Cleaning Agents. AWWA Membrane Technology Conference, Knoxville USA, February 2023. (poster) • Baohui Jia, Rahul Dutta, Jia Bian, Robert C. Andrews, Pierre R. Bérubé. Impact of interaction of foulants and chemical cleaning agents on membrane ageing, Naples Italy, November 2022. (Oral) The use of Generative AI in this dissertation, with the approval of my supervisor, was limited to the use of Claude 3.7 for reviewing sentences and paragraphs for correctness and clarity, and for optimizing Python code for improved algorithmic efficiency. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ................................................................................................................................xv List of Figures ............................................................................................................................. xvi List of Abbreviations ...................................................................................................................xx Acknowledgements ................................................................................................................... xxii Dedication ................................................................................................................................. xxiv Chapter 1: Introduction ................................................................................................................1 1.1 Research Scope and Objectives ...................................................................................... 1 1.2 Organization of the Dissertation ..................................................................................... 4 Chapter 2: Literature Review .......................................................................................................6 2.1 Introduction ..................................................................................................................... 6 2.2 Comparison of Laboratory and Full-Scale Ageing ....................................................... 10 2.2.1 Physical and Chemical Characteristics ................................................................. 11 2.2.1.1 Membrane Chemical Characteristics ................................................................ 11 ix 2.2.1.2 Membrane Surface Hydrophilicity ................................................................... 12 2.2.1.3 Membrane Surface Morphology ....................................................................... 14 2.2.2 Hydraulic Performance ......................................................................................... 15 2.2.3 Summary of Comparison of Laboratory and Full-Scale Ageing .......................... 16 2.3 Role of Hypochlorite in Membrane Ageing ................................................................. 18 2.3.1 Degradation of Hydrophilic Additives.................................................................. 20 2.3.2 Degradation of Bulk PVDF Materials .................................................................. 21 2.4 Role of NOM in Membrane Ageing ............................................................................. 22 2.4.1 Real and Model NOM in Membrane Ageing Studies ........................................... 22 2.4.2 Contribution of NOM-Hypochlorite Interactions to Generation of Radical Oxidants 23 2.4.2.1 Protein Type NOM-Hypochlorite interactions ................................................. 24 2.4.2.2 Other Types of NOM-Hypochlorite interactions .............................................. 27 2.5 Summary ....................................................................................................................... 28 Chapter 3: Materials and Methods ............................................................................................31 3.1 Introduction ................................................................................................................... 31 3.2 Laboratory Investigations ............................................................................................. 35 3.2.1 Material Preparation.............................................................................................. 35 3.2.1.1 Laboratory Test Module Preparation ................................................................ 35 x 3.2.1.2 Chemical Cleaning Solution Preparation .......................................................... 36 3.2.1.3 Model Feed Waters Preparation........................................................................ 36 3.2.2 Laboratory Investigations ..................................................................................... 38 3.2.2.1 Soak ageing ....................................................................................................... 38 3.2.2.2 Cyclic ageing .................................................................................................... 38 3.2.3 Radical Oxidant Screening Experiments for Laboratory Investigations .............. 42 3.2.3.1 Radical Oxidant Screening Experiments Using Backwash as Source of NOM 43 3.2.3.2 Radical Oxidant Screening Experiments Using Solutions Containing Model NOM 44 3.3 Full-scale Investigations ............................................................................................... 44 3.3.1 Material Preparation.............................................................................................. 44 3.3.1.1 Full-Scale Membrane Fiber Harvesting and Test Module Preparation ............ 44 3.3.1.2 Real Feed Water Collection and Characterization ............................................ 47 3.3.2 Radical Oxidant Screening Experiments Using Real Feed Waters from Facilities 47 3.4 Analyses of Membranes ................................................................................................ 48 3.4.1 Membrane Fiber Harvesting during Ageing for Analyses .................................... 48 3.4.1.1 Laboratory Aged Membrane Fiber Harvesting for Membrane Chemical Characteristics, Surface Hydrophilicity and Surface Morphology Analyses ................... 48 xi 3.4.1.2 Laboratory Aged Membrane Fiber Harvesting for Amount of BSA Foulants on Membrane Analyses.......................................................................................................... 49 3.4.2 Membrane Chemical Characteristics .................................................................... 50 3.4.3 Amount of BSA Foulants Present on Membrane ................................................. 52 3.4.4 Membrane Surface Hydrophilicity ....................................................................... 52 3.4.5 Membrane Surface Morphology ........................................................................... 53 3.4.6 Hydraulic Performance ......................................................................................... 54 Chapter 4: NOM Foulant - Hypochlorite Interactions Impact Membrane Ageing ..............57 4.1 Introduction ................................................................................................................... 57 4.2 Summary of Laboratory Investigation Methods ........................................................... 59 4.3 Membrane Ageing Resulting from Interactions Between Model NOM Foulants and Hypochlorite ............................................................................................................................. 62 4.3.1 Changes in Membrane Characteristics.................................................................. 63 4.3.2 Changes in Membrane Hydraulic Performance .................................................... 70 4.4 Assessment of Radical Oxidant Generation from NOM Foulant - Hypochlorite Interactions ................................................................................................................................ 75 4.5 Mitigating The Impact of Radical Oxidants ................................................................. 77 4.6 Conclusions ................................................................................................................... 81 Chapter 5: Specific Types of NOM Components Impact Membrane Ageing ........................83 5.1 Introduction ................................................................................................................... 83 xii 5.2 Summary of Laboratory Investigation Methods ........................................................... 84 5.3 Assessment of Radical Oxidant Generation from Different NOM Foulant - Hypochlorite Interactions.......................................................................................................... 87 5.4 Membrane Ageing Resulting from Different NOM Foulant – Hypochlorite Interactions 94 5.4.1 Changes in Membrane Characteristics.................................................................. 94 5.4.2 Changes in Membrane Hydraulic Performance .................................................. 100 5.5 Mitigating the Impact of Radical Oxidants and Chemically Irreversible Foulants .... 105 5.6 Conclusions ................................................................................................................. 110 Chapter 6: Relationship of Ageing at Laboratory and Full-Scale ........................................112 6.1 Introduction ................................................................................................................. 112 6.2 Summary of Full-Scale Investigation Methods .......................................................... 114 6.3 Assessment of Radical Oxidant Generation for Different Protein Type NOM Concentrations in Real Feed Waters ....................................................................................... 116 6.4 Membrane Ageing at Laboratory (Cyclic) and Full-Scale ......................................... 120 6.4.1 Changes in Membrane Characteristics for Laboratory (Cyclic) and Full-Scale Aged Membranes ................................................................................................................ 122 6.4.2 Changes in Hydraulic Performance for Laboratory (Cyclic) and Full-Scale Aged Membranes .......................................................................................................................... 124 6.4.3 Relationship of Ageing at Laboratory (Cyclic) and Full-Scale .......................... 126 xiii 6.5 Conclusions ................................................................................................................. 127 Chapter 7: Conclusions .............................................................................................................129 7.1 Findings and Contributions ......................................................................................... 129 7.2 Research Gaps and Future Directions ......................................................................... 130 Bibliography ...............................................................................................................................134 Appendices ..................................................................................................................................155 Appendix A Membrane Specification and Preparation Protocols .......................................... 155 A.1 Zeeweed 1000 Membrane ....................................................................................... 155 A.2 Pressure Testing of Test Modules ........................................................................... 155 A.3 Full-Scale Membrane Fiber Harvesting and Transportation Protocol .................... 156 Appendix B Real Feed Water Collection and LC-OCD-OND Analysis (Extended) ............. 159 B.1 Real Feed Water Sample Collection ....................................................................... 159 B.2 LC-OCD-OND Analysis Protocol for Protein Type NOM Quantification ............ 159 Appendix C Cyclic Ageing Apparatus and Operation Setup ................................................. 161 C.1 Design of Cyclic Ageing Apparatus ....................................................................... 161 C.2 Cyclic Ageing Operation Setup .............................................................................. 164 Appendix D Membrane Ageing Metrics Analysis.................................................................. 167 D.1 ATR-FTIR Analysis................................................................................................ 167 D.2 Clean Water Resistance Analysis ........................................................................... 196 xiv D.3 FESEM Analysis ..................................................................................................... 198 D.4 Radical Testing Screening Experiment Analysis .................................................... 212 xv List of Tables Table 2.1 Summary of changes in physical and chemical characteristics and hydraulic performance reported for different ageing approaches. ................................................................ 17 Table 3.1 Rate constants for hydroxyl and chlorine radicals with selected scavengers. .............. 44 Table 3.2 Membrane operational age and corresponding cumulative exposure doses for full-scale aged membranes............................................................................................................................ 46 Table 6.1 Protein type NOM concentrations in real feed waters (mg as C/L) ............................ 117 Table A.1 ZW 1000 membrane specification ............................................................................. 155 Table A.2 ZW 1000 membrane laboratory experiment test module specification ..................... 155 Table A.3 Details of operation for partner full-scale facilities…………………………………158 Table C.1 Timer setup for cyclic ageing cycles. ......................................................................... 164 Table C.2 Control system actuated specific valves and pumps .................................................. 165 Table D.1 Sensitive analysis of pore radius…………………………………………………….209 xvi List of Figures Figure 2.1 Number of publications per year on membrane ageing, illustrating the rising trend in research activity. ............................................................................................................................. 7 Figure 3.1 Experimental approaches and analytical techniques used for the research outlined in the present dissertation. ................................................................................................................. 32 Figure 3.2 (a) Schematic and (b) picture of cyclic ageing apparatus............................................ 41 Figure 3.3 Typical transmembrane pressure (TMP) during cyclic ageing. .................................. 49 Figure 3.4 Typical transmembrane pressure (TMP) during cyclic ageing. .................................. 50 Figure 3.5 Typical transmembrane pressure (TMP) during cyclic ageing. .................................. 55 Figure 4.1 Experimental approaches used to address Objective 1................................................ 61 Figure 4.2 Schematic of main experimental design for laboratory accelerated (a) soak ageing and (b) cyclic ageing. ........................................................................................................................... 62 Figure 4.3 ΔFTIR spectra of soak and cyclic aged membranes at select cumulative exposure doses to hypochlorite. ................................................................................................................... 64 Figure 4.4 Predominant ΔFTIR peak (a) and contact angle (b) for soak and cyclic aged membranes. ................................................................................................................................... 67 Figure 4.5 FESEM images for soak and cyclic aged membranes (a), average pore radius (b), average surface roughness (c) and pore number per unit area (i.e., 2.5 μm2) (d) of soak and cyclic aged membranes............................................................................................................................ 69 Figure 4.6 Clean water resistance (a) and calculated filtration layer pore length (b) for soak and cyclic aged membranes. ................................................................................................................ 71 xvii Figure 4.7 Impact of NOM foulants on the concentration of methylene blue in a solution containing hypochlorite with and without radical scavengers. ..................................................... 76 Figure 4.8 The predominant ΔFTIR peak (a) and clean water resistance (b) for soak and cyclic aged membrane with and without addition of the radical scavenger to the chemical cleaning solution. ......................................................................................................................................... 79 Figure 5.1 Experimental approaches used to address Objective 2................................................ 86 Figure 5.2 Schematic of main experimental design for laboratory accelerated cyclic ageing. ... 87 Figure 5.3 Impact of presence of different model NOM foulants on the concentration of methylene blue in a solution containing hypochlorite. ................................................................. 89 Figure 5.4 Impact of BSA concentration on the methylene blue removal rates for a solution containing hypochlorite and only BSA or mixture of model NOM foulants. .............................. 91 Figure 5.5 Pictorial illustration of potential pathways for hydroxyl radical generation from BSA -- hypochlorite interactions. ........................................................................................................... 93 Figure 5.6 Predominant ΔFTIR peak of cyclic aged membranes when considering feed waters containing (a) only SA and only HA, (b) only BSA and (c) correlation when considering feed waters containing only BSA and mixture of model NOM components. ...................................... 95 Figure 5.7 Correlation of the BSA characteristic peak when considering feed waters containing only BSA and a mixture of model NOM components. ................................................................. 98 Figure 5.8 FESEM images for virgin (0 ppm·h) and cyclic aged (1,300,000 ppm·h) membranes when considering feed waters containing (a) only HA, (b) only SA, (c) only BSA, (d) mixture of model NOM components and (e) no model NOM components. .................................................. 99 Figure 5.9 Clean water resistance for cyclic aged membranes (a) when considering feed waters containing only SA and only HA, (b) when considering feed waters containing only BSA and (c) xviii correlation when considering feed waters containing only BSA and mixture of model NOM components. ................................................................................................................................ 101 Figure 5.10 Predominant ΔFTIR peak (a) and clean water resistance (b) for cyclic aged membrane when considering feed waters containing only BSA with and without addition of the radical scavenger to the chemical cleaning solution, and correlation of clean water resistance when considering feed waters containing only BSA and mixture of model NOM components with addition of radical scavenger (c). ........................................................................................ 107 Figure 6.1 Experimental approaches used for addressing Objective 3. ...................................... 115 Figure 6.2 Impact of NOM in real feed waters from full-scale facilities (a) on the concentration of methylene blue in a solution containing hypochlorite and (b) on the methylene blue removal rate............................................................................................................................................... 118 Figure 6.3 Protein type NOM loading rate for membranes harvested from full-scale facilities and cyclic ageing. .............................................................................................................................. 122 Figure 6.4 Predominant ΔFTIR peak (a) and clean water resistance (b) of membranes from cyclic ageing and full-scale facilities. ................................................................................................... 123 Figure 6.5 Pictorial illustration of changes in the predominant ΔFTIR peak (a) and clean water resistance (b) with increasing exposure dose for cyclic and full-scale aged membranes. .......... 126 Figure B.1 Typical LC-OCD-OND chromatogram of a feed water sample with responses for organic carbon detection (OCD) and organic nitrogen detection (OND). .................................. 160 Figure C.1 Design of (a) membrane tank and (b) details of tank cap.. ....................................... 161 Figure C.2 Connection of components of cyclic ageing apparatus. ........................................... 162 Figure C.3 Schematic of cyclic ageing apparatus (four in parallel) ........................................... 163 Figure C.4 Water level change for each time step in cyclic ageing cycles. ................................ 166 xix Figure D.1 FTIR Peak Identification of typical virgin and aged membranes ............................. 190 Figure D.2 Predominant peaks identification in typical ΔFTIR spectra ..................................... 191 Figure D.3 ΔFTIR Peak area (a) and peak area versus error (max - min) (b) of a typical aged membrane .................................................................................................................................... 192 Figure D.4 Area of the major peaks of the ΔFTIR spectra at 884 cm-1 (a), 1169 cm-1 (b), 1289 cm-1 (c) for soak and cyclic aged membranes. ............................................................................ 193 Figure D.5 Predominant peaks identification in ΔFTIR spectra for aged membrane from (a) Barrie and (b) other partner facilties. .......................................................................................... 194 Figure D.6 Relationship of the predominant peaks in ΔFTIR spectra for mixed model NOM components and individual BSA. ............................................................................................... 195 Figure D.7 Clean water resistance for membranes during cyclic ageing with SA and HA in feed water. ........................................................................................................................................... 197 Figure D.8 Comparison of FESEM images of membrane surface before and after processing . 199 Figure D.9 Typical surface roughness analysis 3D illustration for membrane........................... 211 Figure D.10 Impact of NOM concentration on the concentration of methylene blue. ............... 212 Figure D.11 Methylene blue concentration measured with ambient light. ................................. 213 Figure D.12 Impact of radical scavengers on the concentration of methylene blue. .................. 214 Figure D.13 Impact of (a) polysaccharide type NOM and (b) humic substance type NOM in real feed waters from full-scale facilities on the methylene blue removal rate. ................................ 215 xx List of Abbreviations AFM - Atomic Force Microscopy ATR-FTIR - Attenuated Total Reflectance Fourier Transform Infrared (Spectroscopy) BSA - Bovine Serum Albumin CEB - Chemical-Enhanced Backwash CIP - Clean-in-Place EPR - Electron Paramagnetic Resonance HA - Hydrophilic Additive / Humic Acid (context dependent) MF - Microfiltration NF - Nanofiltration NOM - Natural Organic Matter PEG - Polyethylene Glycol PES - Polyethersulfone PSf - Polysulfone PVDF - Polyvinylidene Fluoride xxi PVP - Polyvinylpyrrolidone SA - Sodium Alginate SEM - Scanning Electron Microscopy UF – Ultrafiltration xxii Acknowledgements The completion of this dissertation would not have been possible without the support, guidance, and resources provided by many individuals and organizations. I am deeply grateful for their contributions throughout this journey. I would like to acknowledge the financial support provided by the Natural Sciences & Engineering Research Council of Canada (NSERC) and the municipal partners: District of West Vancouver, City of Nanaimo, City of Barrie, and Region of Peel. Their funding made this research possible. I also extend my sincere gratitude to Veolia Water Technologies and Solutions for generously providing the virgin membranes that were essential to this study. My deepest appreciation goes to Dr. Pierre R. Bérubé for his exceptional supervision, constructive criticism, and invaluable mentorship throughout my research. His guidance and encouragement have been instrumental in shaping both this work and my development as a researcher. I am also grateful to my committee members: Dr. Madjid Mohseni, Dr. Jongho Lee, and Dr. Robert C. Andrews for their insightful feedback, expertise, and support. This research required technical knowledge and resources beyond what I possessed alone. I am thankful for the technical assistance provided by Felix Shuen, Simon Lee, Arthur Mak, Scott Jackson, and Doug Hudniuk in setting up experiments and troubleshooting equipment. I also acknowledge Kevin Veltheer, May Liu, and Helen Yau for their administrative support and help navigating paperwork and procedures. xxiii I appreciate the collaborative efforts and intellectual discussions with my laboratory colleagues: Rahul Dutta, Md Nurul Afcher Shishir, Jia Bian, Taylor A. Malkoske, Jess Wei, Binura Senavirathna and Varshaa Kumaran. Their insights, assistance, and camaraderie made the challenging aspects of this research more manageable. Finally, I extend my heartfelt gratitude to my friends and family for their unwavering support throughout this journey. I am especially thankful to my parents, Fuchen Jia and Qingrong Sun, for their endless encouragement and belief in me. Special thanks also to Maverick Mei, Qingyuan Bian, Faxin Chen, Shuhe Tian, Cheng Chen, Tailei Wang, Xuan Lin, and Yanru Wang for their friendship and moral support during the ups and downs of this academic pursuit. xxiv Dedication To - My parents Qingrong Sun and Fuchen Jia1 Chapter 1: Introduction 1.1 Research Scope and Objectives Membrane filtration technologies have emerged as essential components in modern drinking water treatment systems, offering effective removal of particulate contaminants and microbial pathogens [1,2]. Among these technologies, ultrafiltration (UF) membranes, the focus of the present dissertation, have gained widespread adoption in drinking water treatment applications due to their reliable performance and ability to meet increasingly stringent water quality standards. During operation, regular hydraulic and chemical cleanings are required to remove materials that are retained on the membranes [3–5]. While chemical cleaning can generally remove materials that cannot be removed hydraulically, repeated exposure to cleaning agents such as hypochlorite, accelerates membrane ageing - a progressive deterioration of membrane physical and chemical characteristics that compromises long-term performance [4,6–8]. This ageing process represents a critical challenge to maintaining sustainable membrane operations, yet the underlying processes that govern membrane ageing remain poorly understood. Laboratory accelerated ageing approaches are commonly used to investigate changes in membrane physical and chemical characteristics and hydraulic performance over a relatively short time scale. These approaches generally involve exposing (i.e., soaking) membranes in the laboratory to high concentrations of chemical cleaning agents (e.g., 5000 mg/L of hypochlorite at pH 10) to achieve a dose equivalent to what would be anticipated over several years of full-scale 2 operation (e.g., 500,000–1,000,000 ppm·h) in a matter of hours (e.g., 200 hours) [6,7,9]. In previous work, our research group questioned the validity of soak accelerated ageing, suggesting that factors other than exposure to chemical cleaning agents may also contribute to changes in membrane physical and chemical characteristics and hydraulic performance over time [10]. To test this hypothesis, soak accelerated ageing, which considers only interactions between the chemical cleaning agent and the membrane, was compared to cyclic accelerated ageing, which also considers the impact of foulants on these interactions. For cyclic accelerated ageing, membranes were exposed to repeating cycles of filtration with a solution containing model natural organic matter (NOM) components, followed by hydraulic and chemical cleaning using a high-concentration hypochlorite solution (i.e., 5000 ppm, as used for soak ageing). Bovine Serum Albumin (BSA), Sodium Alginate (SA) and Humic Acid (HA) were considered as model NOM components. Both soak and cyclic accelerated ageing were performed for conditions representative of those anticipated during chemical cleaning at full-scale operations (i.e., 0 to 1,000,000 ppm·h of hypochlorite at pH 10). The results from laboratory accelerated ageing were also compared to those obtained for membranes harvested from full-scale facilities after multiple years of operation. Changes in clean water resistance, which was used as a metric of hydraulic performance, presented strikingly differences to laboratory accelerated ageing approaches, with soak accelerated ageing causing a decrease while cyclic accelerated ageing resulted in an increase. Also, the results for cyclic aged membranes more closely resembled those for membranes harvested from full-scale facilities. Based on these findings, it was concluded that the presence of NOM foulants during chemical cleaning may influence membrane ageing [11]. 3 However, at the time, the potential mechanisms responsible for the observed differences were not investigated. Recent investigations outside the field of membrane filtration revealed that interactions between NOM and hypochlorite could generate radical oxidants at pH levels relevant to chemical cleaning practices [12–16]. Radical oxidants, such as hydroxyl and chlorine radicals, are highly reactive species that could significantly accelerate membrane ageing by attacking the chemical structure of membrane surfaces. Radical oxidants can increase oxidative damage of membrane materials, resulting in compromised membrane integrity, altered surface properties, and reduced hydraulic performance over time [17–26]. The extent of radical oxidants generation appears to be influenced by both the specific types of NOM present and the pH conditions [12–16]. However, prior to the present research, no systematic investigation had examined whether such interactions during chemical cleaning influence membrane ageing. The research hypothesizes that radical oxidants generated from interactions between NOM and hypochlorite contribute to accelerating membrane ageing. The hypothesis further proposes that different types of NOM could have distinct effects on generation of radical oxidants, leading to different ageing rates. The hypothesis also suggests that radical scavengers can mitigate these effects. The overall goal of the research outlined in this dissertation is to identify and quantify the impact of interactions between NOM foulants and chemical cleaning agents on membrane ageing. To meet this overall goal, the research was designed to address three objectives. 4 The first objective (Objective 1) was to confirm that NOM foulant – hypochlorite interactions accelerate membrane ageing and that the generation of radical oxidants from these interactions is responsible for the acceleration. The possibility of mitigating the impact of radical oxidants on membrane ageing by adding radical scavengers was also assessed as part of Objective 1. Expanding on Objective 1, Objective 2 was to identify which type(s) of NOM component(s) contributed to the generation of the radical oxidants, and that these can increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. The potential beneficial impact of adding radical scavengers on membrane ageing, as done in Objective 1, was also investigated as part of Objective 2. The third objective (Objective 3) was to assess if the outcomes from addressing Objective 1 and Objective 2, using laboratory accelerated ageing approaches, are representative of those expected at full-scale facilities and therefore can guide the development of strategies to minimize membrane ageing at full-scale. 1.2 Organization of the Dissertation This dissertation is organized into eight chapters. Chapter 1 (Introduction): Presents the research context, overall goals, objectives, and dissertation structure. 5 Chapter 2 (Literature Review): Provides a review of current membrane ageing research, comparing laboratory and full-scale ageing approaches, summarizing the role of chemical cleaning agents and NOM in membrane ageing. Chapter 3 (Materials and Methods): Provides details of the experimental approaches, including laboratory accelerated ageing protocols, analytical methods, and data analysis techniques, used in conducting the research. Chapter 4 (NOM Foulant-Hypochlorite Interactions): Presents the outcomes from addressing Objective 1. Chapter 5 (Specific Types of NOM Components Impact Membrane Ageing): Presents the outcomes from addressing Objective 2. Chapter 6 (Relationship of Ageing at Laboratory and Full-scale): Presents the outcomes from addressing objective 3. Chapter 7 (Conclusions): Synthesizes key findings and presents recommendations for future research and practical applications. Appendices: Provide supplementary information including membrane specifications and preparation protocols, real feed water collection and analysis methods, cyclic accelerated ageing apparatus design and operational setup, and details of membrane ageing metrics analysis. 6 Chapter 2: Literature Review 2.1 Introduction Membranes are widely used in drinking water treatment for the effective removal of particulate contaminants and microbial pathogens [1,2]. In drinking water treatment applications, the buildup of retained materials on membrane surfaces requires frequent hydraulic and chemical cleaning to sustain operational efficiency [3–5]. However, long-term exposure to the chemical cleaning agents, especially hypochlorite, causes membrane ageing, which is a progressive deterioration of the physical and chemical characteristics of membrane, compromising its hydraulic performance [4,6–8]. Scientific interest in membrane ageing has risen over the last two decades (Figure 2.1). This increased scientific interest is one indication of the growing awareness that membrane ageing is an issue of utmost importance to the operational efficiency and economic viability of membrane filtration systems. 7 Figure 2.1 Number of publications per year on membrane ageing, illustrating the rising trend in research activity. Relevant publications in journals were identified using Google Scholar (accessed in Dec 2024) with search criteria “membrane ageing OR aging”. While membrane ageing is relevant across various membrane applications, the present review focuses specifically on PVDF ultrafiltration (UF) membranes used in drinking water treatment applications. PVDF UF membranes are among the most commonly used in drinking water treatment applications. Hereafter, unless otherwise specified, membranes will refer specifically to PVDF UF membranes. The economic implications of ageing are significant for drinking water treatment facilities because the associated deterioration of hydraulic performance impact operational viability and eventually requires that the membranes be replaced. Membrane replacement represents the single largest operational cost component for drinking water treatment facilities [6,8]. 8 Robinson et al. conducted a systematic review of membrane ageing [6]. Their framework categorized membrane ageing research into performance factors (features of membrane performance that change during use), physical and chemical characteristics (membrane properties causing performance changes), and analytical methods (techniques to investigate physical and chemical characteristics). Through mapping relationships between these categories, they have created a valuable foundation for understanding membrane ageing due to chemical cleaning agents [6]. Robinson et al. highlighted that most studies focused solely on interactions between membranes and chemical cleaning agents, without considering the potential role of feed water components [6]. Most of the studies reviewed employed laboratory soak accelerated ageing, which involves exposing membranes to high concentrations of chemical cleaning agent, to achieve an exposure dose to chemical cleaning agents relevant to that anticipated over several years of operation at full-scale, in a short time scale (i.e., in a matter of hours) [6,7,9]. They also noted that strikingly different results have been reported for clean water resistance, used as a metric of hydraulic performance, for studies that considered laboratory soak accelerated aged membranes and studies that considered membranes harvested from full-scale facilities. As an alternative to laboratory soak accelerated ageing, Hajibabania et al. [27] used laboratory cyclic accelerated ageing to also consider the impact of foulants on the interactions between chemical cleaning agents and membranes. Laboratory cyclic accelerated ageing involves exposing membranes to repeating cycles of filtration, with a solution containing model NOM components, and cycles of hydraulic and chemical cleaning, with high concentration of cleaning 9 agent, again to achieve an exposure dose relevant to that anticipated over several years of operation at full-scale in a short time scale (i.e., in a matter of days). The resistance measured for cyclic aged membranes was greater than for soak aged membranes and more closely resembled that of for membranes harvested from full-scale facilities. They hypothesized that the greater increase in resistance observed for laboratory cyclic accelerated ageing than that for laboratory soak accelerated ageing was due to chemically irreversible accumulation of foulants on the membrane [27]. However, they did not observe the presence of model NOM components on the membrane surface when characterizing the membrane, which suggests that an alternative mechanism could be responsible for the greater increase in resistance. Regardless, the inconsistent results reported for laboratory soak accelerated ageing and laboratory cyclic accelerated ageing suggest that factors, beyond direct exposure to chemical cleaning agents, can influence membrane ageing. Recent studies outside the field of membrane filtration have demonstrated that interactions between NOM and hypochlorite can generate radical oxidants at pH levels relevant to chemical cleaning practices of membranes [13,14]. These radical oxidants, if generated, could accelerate membrane ageing by increasing the oxidative degradation rate of the membrane material [13,14,18,24]. However, limited knowledge exists regarding interactions between NOM and hypochlorite during chemical cleaning, and how these can impact membrane ageing. This present review provides a synopsis of membrane ageing knowledge to date. The specific focus of the review is summarized as follows. • Compare results from laboratory accelerated ageing and full-scale ageing studies. 10 • Summarize reported contributions of hypochlorite, as chemical cleaning agents, and NOM, as a foulant, to membrane ageing, highlighting potential impacts of these interactions on membrane ageing. • Identify current knowledge gaps and future research directions to better understand interactions between NOM and hypochlorite during chemical cleaning, and how these can impact membrane ageing. 2.2 Comparison of Laboratory and Full-Scale Ageing Three primary approaches have been considered to investigate membrane ageing: a) laboratory soak accelerated ageing, b) laboratory cyclic accelerated ageing, and c) full-scale ageing observations [10,18,28–30]. Each approach offers distinct advantages and limitations. Laboratory soak accelerated ageing, hereafter referred to as soak ageing, the most commonly used approach, involves exposing virgin membranes to high concentrations of a chemical cleaning agent (typically hypochlorite) to accelerate the ageing process [6,7,9]. This approach allows for precise control of exposure to a chemical cleaning agent but fails to incorporate the effects of any other operational conditions relevant to full-scale facilities. Laboratory cyclic accelerated ageing, hereafter referred to as cyclic ageing, also enables precise control of exposure to a chemical cleaning agent. Cyclic ageing involves exposing membranes to repeated cycles of filtration, hydraulic cleaning and chemical cleaning, providing operational conditions that are more representative of those at full-scale facilities [10,27]. However, cyclic ageing typically uses feed waters containing model NOM components and chemical cleaning solutions containing a high concentration of cleaning agents, conditions that do not fully capture the 11 changes in physical and chemical characteristics and hydraulic performance of full-scale aged membranes [10]. Laboratory studies (i.e., soak and cyclic ageing) nonetheless provide valuable insights into membrane ageing mechanisms [10]. Full-scale ageing studies involve the examination of membranes harvested from facilities over the operational life of a membrane [29–31]. While studies using membranes harvested from full-scale facilities provide the most realistic representation of ageing, they require extensive time periods to complete — often years or decades. In addition, because full-scale ageing studies are not done under controlled conditions (i.e., operating conditions change over time and differ between facilities), the results are often highly variable, making it difficult to draw clear conclusions. 2.2.1 Physical and Chemical Characteristics 2.2.1.1 Membrane Chemical Characteristics Membrane chemical characteristics, typically assessed using Fourier Transform Infrared (FTIR) spectroscopy in the reviewed literature on membrane ageing, provide essential insights into chemical changes during ageing [7,19,26,32,33]. For PVDF membranes, most studies have used FTIR to track changes in hydrophilic additives and bulk PVDF material [23,27,34,35]. For soak ageing, studies consistently report a decrease in the content of hydrophilic additives with increasing hypochlorite exposure [34–36]. This decrease has been attributed to oxidative degradation of hydrophilic additives, such as Polyvinylpyrrolidone (PVP), by hypochlorite 12 during chemical cleaning [34–36]. Changes in the bulk PVDF material have also been observed during soak ageing [23]. For cyclic ageing, decreases in hydrophilic additives [10,27] and changes to the bulk PVDF materials [27], similar to that reported for soak ageing, have been observed with increasing hypochlorite exposure. For full-scale ageing, studies have also consistently reported a decrease in hydrophilic additives with increasing hypochlorite exposure. Robinson and Bérubé [30] observed that hydrophilic additives remained relatively stable during the first 5 years of operation, but decreased after this period, suggesting that significant chemical changes become more apparent after extended operational periods. Similarly, Touffet et al. [29] reported the absence of PVP peaks in FTIR spectra of 5-year-old membranes harvested from a full-scale drinking water treatment facility. The consistent observed changes in chemical characteristics determined using FTIR for soak, cyclic and full-scale ageing suggests that hypochlorite oxidation is a major factor driving changes, regardless of the specific approaches used to investigate ageing [10,27,30,34–36]. 2.2.1.2 Membrane Surface Hydrophilicity Membrane surface hydrophilicity, typically assessed using contact angle measurements in the reviewed literature on membrane ageing, provides essential insights into surface wettability changes during ageing [7,10,27,33,36–39]. A higher contact angle typically indicates lower hydrophilicity (i.e., the membrane becomes more hydrophobic) [7,10,27,33,36–39]. 13 For soak ageing, most studies have reported an increase in contact angle (decreased hydrophilicity) with increasing hypochlorite exposure. This increase is attributed to the removal of hydrophilic additives from the membrane matrix [7,10,27,33,36,38,39]. In contrast, Levitsky et al. [37] observed a decrease in contact angle during soak ageing, which they attributed to the removal of surface preservation residues. For cyclic ageing, inconsistent observations for changes in contact angle have been reported [10,27,38]. Robinson and Bérubé [10] observed increases in contact angle (decreased hydrophilicity) during cyclic ageing, attributing this to the removal of hydrophilic additives, consistent with the most soak ageing studies. In contrast, Hajibabania et al. [27] and Wang et al. [38] reported decreases in contact angle (increased hydrophilicity) for cyclic ageing. They hypothesized that the decreases were due to chemically irreversible accumulation of foulants on the membrane. However, they did not observe the presence of foulants on the membrane surface when characterizing the membrane, which suggests that an alternative mechanism could be responsible for the reported decreases in contact angle. Note that contact angle may be influenced by multiple factors beyond just removal of hydrophilic additives and foulants, including changes in pore size and surface roughness, which might be responsible for the inconsistent observations [27,40]. For full-scale ageing, no publications have directly reported contact angle. However, based on the observed changes in hydrophilic additives and surface chemistry in full-scale aged membranes, Robinson and Bérubé [10] and Touffet et al. [29] hypothesized that an increase in contact angle (decreased hydrophilicity) would be expected as membrane age. 14 The inconsistent changes in hydrophilicity observed for soak, cyclic and full-scale ageing suggest the importance of considering the experimental conditions (i.e., feed water characteristics and operating environments) when interpreting the results from different approaches used to investigate ageing. 2.2.1.3 Membrane Surface Morphology Membrane surface morphology, typically assessed using scanning electron microscopy (SEM) in the reviewed literature on membrane ageing, provides essential insights into surface structural changes during ageing. SEM analysis for PVDF membranes primarily was used to track changes in pore geometry (e.g. pore size) and roughness as membranes age [10,27,36]. For soak ageing, studies consistently report an increase in pore size with increasing hypochlorite exposure [6,7,10,36,41]. This increase is commonly attributed to the removal of hydrophilic additives from the membrane matrix, which creates larger openings in the membrane structure [10,36]. Changes in the roughness are also observed during soak ageing [36,41]. Mustafa et al. [36] and Fu and Zhang [41] hypothesized that the increase in roughness, was due to the removal of hydrophilic additives and changes in bulk PVDF material. For cyclic ageing, inconsistent observations have been reported for changes in pore geometry [10,38]. Robinson and Bérubé [10] observed increases in pore size during cyclic ageing, attributing this to the removal of hydrophilic additives, consistent with the soak ageing studies. In contrast, Wang et al. [38] reported decreases in pore size for cyclic ageing. Similar to membrane surface hydrophilicity, they hypothesized that the decreases observed for cyclic 15 ageing could be attributed to the chemically irreversible accumulation of foulants on the membrane. However, as previously mentioned, they did not observe the presence of foulants on the membrane surface when characterizing the membrane, which suggests that an alternative mechanism could be responsible for the decreases in pore size. For full-scale ageing, He et al. [34] observed an increase in surface roughness with increasing hypochlorite exposure. They hypothesized that the increase in surface roughness was due to the oxidation by hypochlorite exposure and mechanical stress. The inconsistent changes in surface morphology observed for soak, cyclic and full-scale ageing again suggest the importance of considering the experimental conditions (i.e., feed water characteristics and operating environments) when interpreting the results from different approaches used to investigate ageing. 2.2.2 Hydraulic Performance Membrane hydraulic performance provides insight essential to understand the impact of changes to the membrane physical and chemical characteristics on the permeate flow during ageing. Resistance is the most commonly reported metric for hydraulic performance in the reviewed literature on membrane ageing, though some studies report permeability or transmembrane pressure (TMP), which can be converted to resistance using Darcy's law [7,9,10,27,34,36]. For soak ageing, studies consistently report a decrease in resistance with increasing hypochlorite exposure [7,10,27,36,42,43]. This decrease is commonly attributed to the increase in pore size [27,36,42,43]. 16 For cyclic ageing, an increase in resistance has been observed with increasing hypochlorite exposure [10,27]. Hajibabania et al. [27] hypothesized that the increase in resistance observed for cyclic ageing could be attributed to the chemically irreversible accumulation of foulants on the membrane. However, as previously mentioned, they did not observe the presence of foulants on the membrane surface when characterizing the membrane, which suggests that an alternative mechanism could be responsible for the greater increase in resistance. For full-scale ageing, studies consistently report an increase in resistance with increasing hypochlorite exposure [29,30,34]. Robinson and Bérubé [30] observed that the resistance remained relatively stable during the first 5 years of operation, but increased after this period. No explanation was proposed to explain the discordant results. The inconsistent changes in hydraulic performance observed for soak, cyclic and full-scale ageing again suggest the importance of considering the experimental conditions (i.e., feed water characteristics and operating environments) when interpreting the results from different approaches used to investigate ageing. 2.2.3 Summary of Comparison of Laboratory and Full-Scale Ageing A summary of reported changes in physical and chemical characteristics and hydraulic performance in the reviewed literature for the different ageing approaches is presented in Table 2.1. The table highlights similarities and differences between soak, cyclic and full-scale ageing. 17 Table 2.1 Summary of changes in physical and chemical characteristics and hydraulic performance reported for different ageing approaches. Ageing Measurements Soak Ageing Cyclic Ageing Full-scale Ageing Chemical Characteristics (Hydrophilic Additive, Bulk PVDF Material) [10,27,30,34–36] Decrease [34–36] Decrease [10,27] Stable (≤5 years) Then decrease (>5 years) [30] Surface Hydrophilicity (Contact Angle) [7,10,27,33,36–39] Inconsistent (Increase or decrease) [7,10,27,33,36–39] Inconsistent (Increase or decrease) [10,27,38] Not documented Surface Morphology (Pore geometry, roughness) [6,7,10,34,36,38,41] Increase [6,7,10,36,41] Inconsistent (Increase or decrease in pore sizes) [10,38] Increase in roughness [34] Hydraulic Performance (Resistance) [7,10,27,29,30,34,36,42,43] Decrease [7,10,27,36,42,43] Increase [10,27] Stable (≤5 years) Then increase (>5 years) [29,30,34] A comparison of the changes in physical and chemical characteristics and hydraulic performance observed for laboratory and full-scale ageing approaches suggests that cyclic ageing more closely resembles full-scale ageing than soak ageing, particularly with respect to hydraulic performance [10]. While many researchers have attributed the differences between laboratory and full-scale ageing to the accumulation of chemically irreversible foulants [10,27], the evidence for this remains inconclusive and needs further exploration. 18 2.3 Role of Hypochlorite in Membrane Ageing Hypochlorite is extensively used at drinking water treatment facilities as a chemical cleaning agent for membrane due to its relatively low cost, broad commercial availability, proven efficiency in fouling control, and widespread regulatory acceptance [6,8,30]. The strong oxidizing properties of hypochlorite enable effective removal of accumulated NOM foulants and recovery of membrane permeability through oxidative degradation of NOM foulants [33]. The contribution of hypochlorite to membrane ageing has been extensively documented [6,8]. Membrane ageing caused by hypochlorite occurs through oxidation of membrane materials, resulting in changes in physical and chemical characteristics over time [4,8,44]. The oxidative capacity of hypochlorite solutions is affected by the solution pH. Hypochlorite exists in a pH-dependent equilibrium between chlorine (Cl2), hypochlorous acid (HOCl), and hypochlorite ion (OCl-), which predominate under acidic, neutral, and alkaline conditions, respectively [8]. Radical oxidants, generated from hypochlorite, are highly reactive species that exhibit great oxidation potential and could also contribute to membrane ageing [17,18,45,19–26]. Like hypochlorite, radical oxidants can oxidize the membrane material, which changes the physical and chemical characteristics [23,46]. Hypochlorite can generate reactive radical oxidants, such as hydroxyl, chlorine and chloride radicals, over a wide range of pH [17,18,45,19–26]. Two primary pathways have been identified in the literature for radical oxidants generation from hypochlorite. 19 The first pathway was proposed by Wienk et al. [24], involving the interaction between hypochlorous acid and hypochlorite ion. The initial reaction involves electron transfer between these species to generate chlorine oxide and hydroxyl radicals, as presented in equation 2.1. where HClO is hypochlorous acid, OCl- is hypochlorite ion, OCl• is chlorine oxide radical, Cl- is chloride ion, and HO• is hydroxyl radical. The hydroxyl radical can further react with hypochlorite ion to produce additional chlorine oxide radicals, as presented in equation 2.2. where OH⁻ is hydroxide ion produced as a byproduct. The chlorine oxide radicals can undergo reaction with hypochlorite ion and hydroxide ion to generate oxygen and additional hydroxyl radicals, as presented in equation 2.3. where O₂ is molecular oxygen formed during the reaction. The second pathway was proposed by Fukatsu and Kokot [47], involving the decomposition of hypochlorous acid. The initial step involves cleavage of the O-Cl bond in hypochlorous acid to generate hydroxyl and chlorine radicals, as presented in equation 2.4. 𝐻𝐶𝑙𝑂 + 𝑂𝐶𝑙− → 𝐶𝑙− + 𝐻𝑂 ⋅ + 𝑂𝐶𝑙 ⋅ eq. 2.1 𝐻𝑂 ⋅ +𝑂𝐶𝑙− → 𝑂𝐻− + 𝑂𝐶𝑙 ⋅ eq. 2.2 𝑂𝐶𝑙 ⋅ +𝑂𝐶𝑙− + 𝑂𝐻− → 𝐶𝑙− + 𝑂2 + 𝐻𝑂 ⋅ eq. 2.3 20 where Cl• is chlorine radical resulting from bond cleavage. The chlorine radical can react with another hypochlorous acid molecule to generate additional hydroxyl radicals and chlorine gas, as presented in equation 2.5. where Cl₂ is chlorine gas produced as a byproduct. The pH of the hypochlorite affects which radical generation pathway predominates and the types of radical species formed. At pH relevant to chemical cleaning practices for membranes (i.e., pH = 10-11), hypochlorite exists predominantly as the hypochlorite ion rather than hypochlorous acid [19]. The high pH conditions can limit the second pathway due to the reduced availability of hypochlorous acid, while potentially favoring the first pathway when both hypochlorous acid and hypochlorite ion coexist [18,26]. 2.3.1 Degradation of Hydrophilic Additives Hydrophilic additives are commonly blended with PVDF to increase the overall hydrophilicity and porosity of membranes [20,22,24,32,40,42,48–50]. These hydrophilic additives generally have a higher reactivity with hypochlorite than bulk PVDF materials [20,23,46]. The pH can impact the predominant pathway for the oxidation of hydrophilic additives by hypochlorite. Hanafi et al. [19] suggested that hydroxyl radicals generated from hypochlorite, are 𝐻𝐶𝑙𝑂 → 𝐶𝑙 ⋅ + 𝐻𝑂 ⋅ eq. 2.4 𝐻𝐶𝑙𝑂 + 𝐶𝑙 ⋅→ 𝐶𝑙2 + 𝐻𝑂 ⋅ eq. 2.5 21 primarily responsible for the oxidation of hydrophilic additives at neutral pH (i.e., pH = 6.0 – 8.0), while hypochlorite ions are primarily responsible for the oxidation at high pH (i.e., pH = 11.5). However, a number of studies have also reported that radical oxidants could still be responsible for the oxidation of the hydrophilic additives at higher pH, relevant to chemical cleaning practices for membranes [24,26,51]. The degradation of hydrophilic additives was suggested to lead to changes in physical and chemical characteristics and hydraulic performance, and typically results in decrease in membrane hydrophilicity and increase in pore sizes and resistance as discussed in Section 2.2 [7,32,33]. Although oxidation of hydrophilic additives is widely considered a key indicator of membrane ageing, it should be noted that some commonly used hydrophilic additives, such as PVP and Polyethylene Glycol (PEG), are highly soluble in water and may potentially be removed during initial membrane preparation or operational phases rather than through oxidative degradation alone [52,53]. 2.3.2 Degradation of Bulk PVDF Materials The bulk PVDF material is typically oxidized at a slower rate than that for the oxidation of hydrophilic additives [52]. As with hydrophilic additives, pH can impact the predominant pathway for the oxidation of PVDF by hypochlorite. Ravereau et al. [23] observed that PVDF degradation rate was higher at neutral pH (i.e., pH = 6.0 - 7.5) than at high pH (i.e., pH = 11.5). At neutral pH, radical oxidants 22 were primarily responsible for the oxidation of bulk PVDF materials. At high pH, hypochlorite ions dominated the oxidation process via dehydrofluorination [23,52,54]. Similar to the suggested impacts of the oxidation of hydrophilic additives, the degradation of bulk PVDF material has also been suggested to lead to changes in physical and chemical characteristics and hydraulic performance, and typically results in increase in pore sizes and resistance as discussed in Section 2.2 [23,52,54]. 2.4 Role of NOM in Membrane Ageing An understanding of the role of NOM in membrane ageing is important given that membranes are widely used to treat surface waters containing various NOM components. While hydraulic and chemical cleanings are designed to remove accumulated NOM foulants, certain NOM can potentially irreversibly accumulate on membrane after hydraulic and/or chemical cleaning [55]. The irreversible accumulation of NOM foulants can impact the long term performance of membranes and can therefore contribute to membrane ageing [27,30]. Recent studies outside of the field of membrane filtration have also suggested that NOM can interact with hypochlorite to generate radical oxidants, which could change the physical and chemical characteristics of the membranes, impacting long-term hydraulic performance and therefore also contributing to membrane ageing [12–16]. 2.4.1 Real and Model NOM in Membrane Ageing Studies NOM in source waters is a complex mixture of organic compounds including proteins, polysaccharides, humic substances, and other organic constituents [56]. The composition and 23 concentration of NOM varies significantly across different water sources [57,58]. This variability presents challenges for systematic investigation of the role of NOM in membrane ageing studies. For this reason, model NOM components are commonly used to study membrane ageing under controlled conditions [10,59–62]. Model NOM components can be used to generate solutions with targeted concentrations and compositions, essential to investigating mechanisms impacting membrane fouling and cleaning. In addition, the use of standardized model NOM components enables results from different studies to be compared and possibly more importantly, can allow results to be grouped together to gain insights that may not be possible when only considering individual studies. Three model NOM components have been predominantly used in membrane ageing studies: Bovine Serum Albumin (BSA), Sodium Alginate (SA) and Humic Acid (HA) [10,11,27,56,59–63]. These components are used to represent the major NOM fractions typically present in natural waters [11,27]. BSA is typically used as a surrogate of protein type NOM [63]; while SA is typically used as a surrogate of polysaccharide type NOM; both of them are primary constituents of extracellular polymeric substances (EPS) commonly found in surface waters [32,63,64]. HA is typically used as a surrogate of humic substance type NOM, which is generally the most abundant component of NOM [56]. 2.4.2 Contribution of NOM-Hypochlorite Interactions to Generation of Radical Oxidants Recent studies outside of the field of membrane filtration have suggested that interactions between NOM and hypochlorite can generate radical oxidants, which could contribute to 24 membrane ageing [12–14,65]. Among different types of NOM components, protein type NOM appears to play an important role in radical oxidant generation, especially at pH relevant to chemical cleaning practices for membranes (i.e., pH = 10-11) [12]. 2.4.2.1 Protein Type NOM-Hypochlorite interactions Protein type NOM (e.g., BSA) contain multiple reactive sites that can interact with hypochlorite, primarily through their amino groups [12]. The primary initial reaction involves chlorination of amino groups to generate organic chloramines, where one or more hydrogen atoms attached to nitrogen are replaced by chlorine atoms, as presented in equation 2.6. where R is the side chain of organic group within the protein structure, NH₂ is the amino group of the protein, OCl⁻ is hypochlorite ion, RNHCl is the N-chlorinated amino group (organic chloramine), OH⁻ is the hydroxide ion produced as a byproduct of the reaction. Hypochlorite exists predominantly as the hypochlorite ion rather than hypochlorous acid, at pH relevant to chemical cleaning practices for membranes (i.e., pH = 10-11) [66]. Kinetics studies have reported that hypochlorite ion is more reactive toward amino groups than hypochlorous acid to generate organic chloramines [12,66]. The generation of organic chloramines is the critical first step in the pathway toward radical generation, serving as intermediates that can subsequently decompose to form various radical species [65]. 𝑅𝑁𝐻2 + 𝑂𝐶𝑙− → 𝑅𝑁𝐻𝐶𝑙 + 𝑂𝐻− eq. 2.6 25 Hawkins and Davies [12,65] reported that organic chloramines can undergo homolytic cleavage of the N-Cl bond to generate nitrogen-centered and chlorine radicals as presented in equation 2.7. where RNH• is the nitrogen-centered radical generated from N-Cl bond cleavage, Cl• is the chlorine radical generated from homolytic bond cleavage. The nitrogen-centered and chlorine radicals generated from this pathway are highly reactive and can undergo further oxidation reactions, which could contribute to membrane ageing [65–67]. With residual hypochlorite available, organic chloramines may undergo further decomposition to form inorganic chloramines [68,69]. Inorganic chloramine can generate hydroxyl radicals through different pathways [15,70]. Lu et al. [15] identified a pathway where inorganic chloramines decompose to generate nitroxyl (HNO/NO-), which then undergoes dimerization to generate hyponitrous acid (HO-N=N-OH) as an intermediate. This intermediate subsequently undergoes homolytic cleavage to generate hydroxyl radicals as presented in equation 2.8. where NHCl₂ is dichloramine (a type of inorganic chloramines), OH⁻ is hydroxide ion, HNO is nitroxyl (an intermediate in the radical formation pathway), HO-N=N-OH is hyponitrous acid formed through dimerization of nitroxyl species, N₂ is nitrogen gas as a byproduct of the reaction, HO• is hydroxyl radical. 𝑅𝑁𝐻𝐶𝑙 → 𝑅𝑁𝐻 ⋅ + 𝐶𝑙 ⋅ eq. 2.7 𝑁𝐻𝐶𝑙2 + 𝑂𝐻− → 𝐻𝑁𝑂 → 𝐻𝑂 − 𝑁 = 𝑁 − 𝑂𝐻 → 𝐻𝑂 ⋅ + 𝑁2 eq. 2.8 26 The hydroxyl radicals generated can further react with hypochlorite ion to generate chlorine oxide radicals, as previously presented in equation 2.2. The generation of radical oxidant species depends on the mass ratio of chlorine and nitrogen, where excessive free chlorine could shift the generation of radical oxidants toward chlorine oxide radicals [15]. Chuang et al. [70] identified another pathway involving the interaction between two different inorganic chloramine species. They demonstrated the two different inorganic chloramine species (i.e., trichloramine (NCl3) and dichloramine (NHCl2)) can interact to generate tetrachlorohydrazine (Cl₂N−NCl₂) as an intermediate, which undergoes hydrolysis to generate hydroxyl radicals as presented in equations 2.9 and 2.10. where NCl₃ represents trichloramine (a type of inorganic chloramines), Cl₂N-NCl₂ is tetrachlorohydrazine as the intermediate formed in this reaction, H₂O is water, HCl is hydrochloric acid as a byproduct of the reaction, N₂ is nitrogen gas as a byproduct of the reaction, HO• is hydroxyl radical. The hydroxyl radicals generated from these pathways are also highly reactive and can undergo further oxidation reactions at pH relevant to chemical cleaning practices for membranes [15,70]. While these studies focused on inorganic chloramines derived from ammonia rather than organic chloramines from protein type NOM, similar chemical principles may be applicable to protein-derived chloramines. Because protein type NOM contains various amino groups that can interact 𝑁𝐻𝐶𝑙2 + 𝑁𝐶𝑙3 → 𝐶𝑙2𝑁 − 𝑁𝐶𝑙2 + 𝐻𝐶𝑙 eq. 2.9 𝐶𝑙2𝑁 − 𝑁𝐶𝑙2 + 𝐻2𝑂 → 𝐻𝑂 ⋅ + 𝐻𝐶𝑙 + 𝑁2 eq. 2.10 27 with hypochlorite, various chloramine species, including the species discussed, may coexist and potentially facilitate similar pathways. 2.4.2.2 Other Types of NOM-Hypochlorite interactions Humic substance type NOM could also generate radical oxidants when interacting with hypochlorite. Criquet et al. [13] and Rodríguez and von Gunten [14] demonstrated that interaction between humic substance type NOM (i.e., Suwannee River humic acid) and hypochlorite can generate hydroxyl radicals through electron transfer reactions. However, these studies were conducted at neutral pH (i.e., pH = 7), which differs significantly from the high pH relevant to chemical cleaning practices for membranes (i.e., pH = 10-11). At high pH, hypochlorite exists predominantly as hypochlorite ion rather than hypochlorous acid, potentially inhibiting the electron transfer reactions needed for radical oxidants generation from humic substances [71]. In contrast, polysaccharide type NOM (e.g., SA) could consume or scavenge radical oxidants when interacting with hypochlorite [72–75]. Xie et al. [72] demonstrated that polysaccharide type NOM (i.e., chitosan) can effectively scavenge hydroxyl radicals, which could mitigate the impacts of radical oxidants on membrane ageing. Again, these studies were conducted at neutral pH (i.e., pH = 7.8), which differs significantly from the high pH relevant to chemical cleaning practices for membranes (i.e., pH = 10-11). The impacts of polysaccharide type NOM on consuming or scavenging radical oxidants at high pH still remain unknown. 28 The different interactions between various types of NOM (i.e., protein, polysaccharide and humic substance type NOM) and hypochlorite suggest the importance of considering pH conditions and specific type(s) of NOM when considering the generation of radical oxidants. Although the studies outlined above did not focus on membrane filtration, they suggest that NOM foulants, especially protein type NOM, could interact with hypochlorite used for chemical cleaning to generate radical oxidants that may accelerate ageing at a pH relevant to chemical cleaning practices for membranes. The generation of radical oxidants through NOM-hypochlorite interactions and the impact of these radical oxidants on the physical and chemical characteristics of the membrane could be the cause of the observed differences between laboratory and full-scale ageing studies (Section 2.2). 2.5 Summary Comparison of laboratory and full-scale ageing approaches reveals differences in outcomes depending on the investigation approaches used. Outcomes from cyclic ageing more closely resemble those from full-scale ageing investigations than soak ageing investigations, particularly with respect to changes in hydraulic performance: those from both cyclic and full-scale ageing investigations generally reported increases in resistance over time, while soak ageing investigations reported decreases. Although some researchers have attributed the differences observed between laboratory and full-scale ageing to the accumulation of chemically irreversible foulants, there is limited evidence to support this theory. Regardless, interaction between NOM foulants and hypochlorite appears to be an important factor in membrane ageing. Studies outside the field of membrane filtration have suggested that 29 interactions between NOM and hypochlorite can generate radical oxidants at pH levels relevant to chemical cleaning practices for PVDF membranes. These radical oxidants could impact the physical and chemical characteristics of membranes, and therefore membrane ageing. These interactions could be the cause of the observed differences between laboratory and full-scale ageing. Several key research directions emerge from the knowledge gaps identified in the present review. These are listed below. Addressing these research directions would significantly advance our understanding of membrane ageing and provide the insight necessary to develop evidence based chemical cleaning strategies to effectively clean membranes and minimize membrane ageing. 1. NOM Foulant-Hypochlorite Interactions Impact Membrane Ageing: Further research should investigate whether NOM foulant-hypochlorite interactions during chemical cleaning generate radical oxidants that accelerate membrane ageing at a pH relevant to chemical cleaning practices. Also, studies should explore if these interactions could explain the differences observed between soak and cyclic ageing approaches. 2. Specific Types of NOM Components Impact Membrane Ageing: Studies should determine which specific type(s) of NOM components contribute(s) most significantly to radical oxidant generation when interacting with hypochlorite during chemical cleaning of membranes at a pH relevant to chemical cleaning practices. 3. Relationship of Ageing at Laboratory and Full-Scale: Investigations of how outcomes from laboratory accelerated ageing relate to those at full-scale should be performed. This 30 enables us to determine whether outcomes from laboratory investigations can guide the development of strategies to minimize membrane ageing at full-scale. 4. Radical Oxidant Mitigation Strategies: Potential methods for mitigating radical oxidant generation during chemical cleaning, such as the addition of radical scavengers, should be investigated. These could lead to practical solutions for extending the membrane lifespan in drinking water treatment applications. 31 Chapter 3: Materials and Methods 3.1 Introduction The present chapter introduces the experimental approaches and analytical techniques used to address the three objectives of the present research, as outlined in Chapter 1. A graphical summary of the experimental approaches and analytical techniques used is presented in Figure 3.1. The main components of the approaches and techniques are briefly introduced below, and described in detail in Section 3.2, 3.3 and 3.4. 32 Figure 3.1 Experimental approaches and analytical techniques used for the research outlined in the present dissertation. Superscripts 1, 2 and 3 represent which components of the experimental and analytical approaches correspond to Objectives 1, 2 and 3. respectively.33 Laboratory Investigations were performed to address Objectives 1 and 2. Material Preparation involved the preparation of laboratory test module, chemical cleaning solution and model feed waters. Detailed descriptions of material preparation for laboratory investigations are presented in Section 3.2.1. For the Laboratory Accelerated Ageing experiments, two laboratory accelerated ageing approaches were considered: soak ageing and cyclic ageing. Soak ageing was used to assess the impact of hypochlorite on membrane ageing; while cyclic ageing was used to assess the combined impact of model NOM foulants and hypochlorite on membrane ageing. The impact of the model NOM foulants was quantified by comparing the results from both accelerated ageing approaches. Detailed descriptions of laboratory accelerated ageing approaches are presented in Section 3.2.2. To assess if model NOM foulants could potentially have been sheltered and accumulate within the membrane pores, additional laboratory cyclic ageing investigations were performed with the permeation of hypochlorite during chemical cleaning. Also, to gain insight into the potential impact of scavenging radical oxidants formed during chemical cleaning on membrane ageing, further additional cyclic ageing investigations were conducted with the addition of sodium bicarbonate as a radical scavenger to the chemical cleaning solution. Sodium bicarbonate was chosen because it is a non-selective radical scavenger [76,77] , and it is commonly available at drinking water treatment facilities for use in other processes. Detailed descriptions of these additional cyclic ageing investigations are presented in Section 3.2.2. 34 A series of Radical Oxidant Screening Experiments were conducted for laboratory investigations to determine if model NOM foulants impact the oxidation capability of hypochlorite by generating radical oxidants for conditions representative of chemical cleaning of PVDF UF membranes. Methylene blue (Sigma Aldrich) was used as a chemical probe to gauge the generation of radical oxidants, as it is known to be oxidized substantially more rapidly by radical oxidants than by hypochlorite alone [78,79]. Detailed descriptions of radical oxidant screening experiments for laboratory investigations are presented in Section 3.2.3. Full-Scale Investigations were performed to address Objective 3. Material Preparation involved the membrane fiber harvesting from full-scale facilities, the preparation of test modules from full-scale aged membranes and real feed water collection and characterization. Detailed descriptions of material preparations for full-scale investigations are presented in Section 3.3.1. A series of Radical Oxidant Screening Experiments was also conducted for full-scale investigations to determine if NOM components in real feed water impact the oxidation capability of hypochlorite by generating radical oxidants for conditions representative of chemical cleaning practices of PVDF UF membranes. The radical oxidant screening experiments were similar to those used for laboratory investigations. Detailed descriptions of radical oxidant screening experiments for full-scale investigations are presented in Section 3.3.2. A number of Analyses of Membranes were performed to quantify membrane ageing. Changes to the chemical characteristics of the membrane surface were quantified using differential FTIR (ΔFTIR) spectroscopy. The surface hydrophilicity of the membrane was quantified based on contact angle. The surface morphology of the membrane was visualized using a field emission 35 scanning electron microscope (FESEM). The amounts of model NOM foulants present on membrane at the start of chemical cleaning were quantified by ΔFTIR. The hydraulic performance was quantified using clean water resistance. Detailed descriptions of these analyses are presented in Section 3.4. 3.2 Laboratory Investigations 3.2.1 Material Preparation 3.2.1.1 Laboratory Test Module Preparation PVDF UF hollow fiber membranes (ZW 1000, Veolia Water Technologies and Solutions) were used for laboratory accelerated ageing experiments designed to address Objectives 1 and 2 (Chapters 4 and 5). The specifications of the membranes are provided in Appendix A.1. The membranes were assembled into custom test modules, each test module contained five individual membrane fibers. The top ends of the fibers were potted into rigid tubing with epoxy (dev14250, Devcon), ensuring that fiber lumens were not blocked to enable permeate flow; while the bottom ends of the fibers were fully sealed. The total filtration area of each test module was 14.6 ± 0.4 cm2. Prior to use, all test modules were pressure tested at 55 kPa (8 psi) to ensure integrity and thoroughly cleaned to remove any manufacturing residuals and preservatives by soaking in tap water for 3 days (replacing the tap water daily) and then permeating a solution containing hypochlorite at 500 ppm at 50 Lm-2h-1 for 14 hours as described in [10]. 36 The test modules were used for the laboratory accelerated ageing experiments presented in Chapters 4 and 5. 3.2.1.2 Chemical Cleaning Solution Preparation The chemical cleaning solution was prepared by diluting a stock solution of sodium hypochlorite and sodium hydroxide (Lavo Pro 6, Lavo) [80] in distilled water. The pH was adjusted to 10.5 ± 0.2 using sodium hydroxide or hydrochloric acid (both ACS Grade, from Sigma Aldrich), as needed, consistent with typical chemical cleaning practices for PVDF-UF membranes [4,81]. Once prepared, the solution was analyzed to confirm the concentration of hypochlorite measured as free chlorine using N,N-diethyl-p-phenyldiamine (DPD) method [78]. A solution was prepared and used every 2 days to maintain a consistent concentration of hypochlorite of 5000 ± 80 ppm. 3.2.1.3 Model Feed Waters Preparation Three types of model feed waters were considered. 1. The feed waters with no model NOM components consisted of distilled water. The pH of the feed water was adjusted to 7.0 ± 0.2 by adding sodium hydroxide or hydrochloric acid (both ACS grade, from Sigma Aldrich), as needed. The feed waters with no model NOM components were used for the laboratory accelerated ageing experiments described in Chapter 5. 2. The feed waters with a mixture of model NOM components consisted of Bovine Serum Albumin (BSA), Sodium Alginate (SA) and Humic Acid (HA) (all Reagent Grade, from 37 Sigma Aldrich), used as surrogates for the protein, polysaccharide and humic substance type NOM, respectively. Although the fouling potential of these NOM components may differ from that of NOM in real waters [82], they are commonly used to obtain relevant insight into membrane fouling and cleaning [10,59–62]. The model NOM components were added to tap water at a 2:1:1 Total Organic Carbon (TOC) mass ratio to achieve a concentration of 35 mg as TOC/L. This mass ratio was selected to be consistent with that used in the previous studies by our group [10,30], enabling the results from the present research to be comparable to those of the previous studies by our group. A high concentration of model NOM was selected to promote fouling so that a substantial hydraulically and chemically irreversible increase in resistance to permeate flow occurred within a few weeks’ operation. The pH of the feed water was adjusted to 7.0 ± 0.2 by adding sodium hydroxide or hydrochloric acid (both ACS grade, from Sigma Aldrich), as needed. The feed waters with a mixture of model NOM components were used for the laboratory accelerated ageing experiments described in Chapter 4. 3. The feed waters with individual model NOM components consisted of only BSA, only SA or only HA. The individual model NOM components were added to tap water to achieve a concentration of 35 mg as TOC/L; again a high concentration was selected to promote fouling so that a substantial hydraulically and chemically irreversible increase in resistance to permeate flow occurred within a few weeks’ operation. The pH of the feed water was adjusted to 7.0 ± 0.2 by adding sodium hydroxide or hydrochloric acid (both ACS grade, from Sigma Aldrich), as needed. The feed waters with individual model 38 NOM components were used for the laboratory accelerated ageing experiments described in Chapter 5. 3.2.2 Laboratory Investigations Soak and cyclic ageing approaches, as described in [10], were performed to evaluate changes in physical and chemical and hydraulic performance characteristics of membranes as they age. Soak ageing was used to assess the impact of hypochlorite on membrane ageing, while cyclic ageing was used to assess the combined impact of model NOM foulants and hypochlorite on membrane ageing. The impact of the model NOM foulants was quantified by comparing the results from both accelerated ageing approaches. 3.2.2.1 Soak ageing For soak ageing, test modules were submerged in a hypochlorite cleaning solution as described in Section 3.2.1.2, consistent with typical chemical cleaning practices for PVDF-UF membranes at full-scale, at a concentration of 5000 ppm. Soak ageing exposure doses to hypochlorite ranged from 0 to 1,300,000 ppm·h, which includes of doses relevant to full-scale PVDF UF membrane systems over their service life [7]. Soak ageing was used for the laboratory accelerated ageing experiments presented in Chapter 4. 3.2.2.2 Cyclic ageing For cyclic ageing, test modules were subjected to repeated cycles of filtration, hydraulic cleaning and chemical cleaning, to achieve cumulative exposure doses to hypochlorite which ranged from 39 0 to 1,300,000 ppm·h, consistent with the doses considered for soak ageing and again inclusive of doses relevant to full-scale PVDF UF membrane systems over their service life [7]. Four parallel cyclic ageing apparatus, schematic and picture as illustrated in Figures 3.2a and b, were used. Each apparatus consisted of a chemical feed tank (40 L volume; shared by all apparatus), a membrane tank (0.285 L volume) which housed 4 test modules, a filtration and backwash pump (Model Lab V1-II with 4 YZ1515x heads, Shenchen), a waste/overflow tank (20 L volume) and a backwash tank (20 L volume) used to collect the permeate used for backwashing. A control system actuated the feed (Model 0124, Burkert) and waste valves (Model A20, BACOENG) as well as the filtration and backwash pumps. A custom data logging system recorded the transmembrane pressure every 10 seconds during filtration. Each cycle began by filling the membrane tank with 0.25 L of feed water. Filtration was conducted at a flux of 50 Lm-2h-1 for 30 minutes, consistent with typical practices for ZW1000 membranes [30,83]. The resistance to permeate flow was calculated by Darcy’s law using the transmembrane pressure and the temperature of the solution in the membrane tank. Filtration was immediately followed by 10 minutes of hydraulic cleaning, which consisted of an air-scoured backwash, again, consistent with typical hydraulic cleaning practices for ZW1000 membranes [83]. Air for scouring was added at the base of the test modules at a flow of 0.4 m3/h while backwashing with permeate was performed at a flux of 50 Lm-2h-1 [10,83]. Following hydraulic cleaning, the membrane tank was drained and refilled with 0.25 L of hypochlorite cleaning solution. Chemical cleaning was conducted by soaking the test modules in the hypochlorite cleaning solution for 40 minutes to achieve an exposure dose consistent with typical recovery (i.e. extensive) chemical cleaning 40 practices for PVDF UF membranes. The membrane tank was subsequently drained, and a new cycle of filtration, hydraulic cleaning and chemical cleaning was initiated. Cyclic ageing was used for the laboratory accelerated ageing experiments presented in Chapters 4 and 5. 41 a b Figure 3.2 (a) Schematic and (b) picture of cyclic ageing apparatus. 42 To assess if model NOM foulants could potentially have been sheltered and accumulated within the membrane pores, additional cyclic ageing investigations were performed with the permeation of hypochlorite during chemical cleaning. As previously described, cyclic ageing was initially performed by soaking the membranes in hypochlorite during chemical cleaning, consistent with typical chemical cleaning practices for PVDF-UF membranes. It was assumed that by permeating the hypochlorite solution through the membrane during chemical cleaning, sheltering would be reduced. To evaluate the impact of radical oxidants on the physical and chemical characteristics and hydraulic performance of membranes over time, further additional cyclic ageing investigations were performed with and without the addition of radical scavengers to the hypochlorite cleaning solution. For these investigations, sodium bicarbonate (Fischer Bioreagents) was selected as a non-selective radical scavenger [76,77] and added to the cleaning solution at a concentration of 5000 ppm (to fully quench any radical oxidants generated by NOM - hypochlorite interactions) at a pH of 10.5 ± 0.2. As indicated in Table 3.1, bicarbonate reacts (quenches) with multiple types of radical oxidants. 3.2.3 Radical Oxidant Screening Experiments for Laboratory Investigations A series of screening experiments was conducted to determine if model NOM foulants impact the oxidation capability of hypochlorite by generating radical oxidants for conditions representative of chemical cleaning of PVDF UF membranes. Methylene blue (Sigma Aldrich) was used as a chemical probe to gauge the generation of radical oxidants, as it is known to be oxidized substantially more rapidly by radical oxidants than by hypochlorite alone [78,79]. 43 3.2.3.1 Radical Oxidant Screening Experiments Using Backwash as Source of NOM To investigate the impact of model NOM foulants present on membranes, the screening experiments for assessment of radical oxidant generation involved the addition of methylene blue and hypochlorite to 16 mL test tubes which contained either 10 mL of backwash from cyclic ageing or distilled water, to represent conditions with and without NOM foulants, respectively, and the pH was adjusted to 10.5 ± 0.2. Methylene blue and hypochlorite were added to reach concentrations of 50 ppm and 5000 ppm, respectively. The methylene blue concentration was monitored for 240 minutes following hypochlorite addition using a UV-visible spectrophotometer (Model UV 300, Spectronic LNICAM) at 662 nm [78]. To confirm the generation of radical oxidants, as well as identify the specific type(s) produced, additional screening experiments were performed using radical scavengers. Sodium acetate (Sigma Aldrich), a chlorine radical scavenger, or acetone (Sigma Aldrich), a hydroxyl radical scavenger, was added to solutions that contain methylene blue and hypochlorite, with or without model NOM foulants (as previously described) at pH 10.5 ± 0.2 [84]. Each scavenger was added to achieve a concentration of 5000 ppm, a level expected to fully quench any radical oxidants generated by NOM - hypochlorite interactions [84]. The reported rate constants for hydroxyl and chlorine radicals with sodium acetate and acetone are provided in Table 3.1, demonstrating that sodium acetate reacts (quenches) preferentially with chlorine radicals, whereas acetone preferentially reacts with hydroxyl radicals. 44 Table 3.1 Rate constants for hydroxyl and chlorine radicals with selected scavengers Scavenger (S) KCl, S (M-1s-1) KOH, S (M-1s-1) KCl,S/KOH,S Ref. Acetate 37×108 0.75×108 50 [84] Acetone <0.05×108 1.1×108 <0.045 [84] Bicarbonate 2.2×108 1×107-3.9×108 0.56-22 [76,77] 3.2.3.2 Radical Oxidant Screening Experiments Using Solutions Containing Model NOM To investigate the impact of concentration of model NOM foulants, the screening experiments for assessment of radical oxidant generation involved the addition of methylene blue and hypochlorite to 16 mL test tubes which contained either 10 mL of solutions with model NOM components added to achieve a range of concentrations or distilled water, to represent conditions with and without NOM foulants, respectively, and the pH was adjusted to 10.5 ± 0.2. The concentrations of methylene blue and hypochlorite and measurement procedure were as presented in Section 3.2.3. 3.3 Full-scale Investigations 3.3.1 Material Preparation 3.3.1.1 Full-Scale Membrane Fiber Harvesting and Test Module Preparation PVDF UF hollow fiber membranes (ZW 1000, Veolia Water Technologies and Solutions) were harvested from four full-scale drinking water treatment facilities as part of separate studies [80]. The harvested membrane fibers were used to address Objective 3. Note that these membrane 45 fibers are the same type as used for laboratory investigations. The membrane specifications are provided in Appendix A.1. The details of operation (i.e., water treatment capacity, pretreatment and chemical cleaning) for the four full-scale drinking water treatment facilities are summarized in Table A.3. The operational age of the membranes ranged from 0.2 to 8.6 years. The membrane fibers were harvested immediately following an extensive recovery chemical cleaning. Following the harvest, the membranes fibers were preserved in distilled water containing approximately 2 mg/L hypochlorite at 4°C until their assembly into test modules, using the same approach as described in Section 3.2.1.1, prior to analyses. The maximum cumulative exposure dose during preservation (≤500 ppm·h) was negligible compared to experimental exposure dose (up to 1,300,000 ppm·h), representing 0.03% of the total dose and therefore not expected to influence experimental outcomes. The operational age and corresponding cumulative exposure doses for harvested membranes from the different facilities are summarized in Table 3.2. The full-scale cumulative exposure doses were converted to equivalent laboratory accelerated ageing doses based on relationships established in previous studies by our group [7,10]. This conversion was necessary to enable a relevant comparison of results from laboratory and full-scale investigations because previous studies by our group have concluded that the impact of hypochlorite exposure on ageing of PVDF membranes is proportional to 𝐶𝑛 × 𝑡 (with n<1), rather than 𝐶 × 𝑡, where C is hypochlorite concentration, t is exposure time, n is an exponent less than 1. The converted cumulative exposure doses are also summarized in Table 3.2. 46 Table 3.2 Membrane operational age and corresponding cumulative exposure doses for full-scale aged membranes Facility A Facility B Facility C Facility D Operational Age (Year) Cumulative Exposure Dose (ppm·h) Converted Cumulative Exposure Dose (ppm·h) Operational Age (Year) Cumulative Exposure Dose (ppm·h) Converted Cumulative Exposure Dose (ppm·h) Operational Age (Year) Cumulative Exposure Dose (ppm·h) Converted Cumulative Exposure Dose (ppm·h) Operational Age (Year) Cumulative Exposure Dose (ppm·h) Converted Cumulative Exposure Dose (ppm·h) 0.2 11,340 40,156 1.1 34,578 115,094 3.0 91,200 297,686 5.7 204,345 662,552 0.9 49,140 162,053 5.5 168,300 546,316 3.7 111,000 361,537 7.0 250,950 812,843 1.7 93,960 306,587 6.2 189,720 615,390 6.0 180,000 584,046 7.4 265,290 859,086 2.3 122,040 397,138 7.0 214,200 694,333 6.5 195,000 632,417 8.0 286,800 928,451 3.0 159,840 519,034 7.8 238,680 773,275 7.0 210,000 680,789 8.6 308,310 997,815 7.8 358,800 1,160,634 8.6 263,160 852,217 7.6 228,000 738,834 8.5 391,000 1,264,471 Operational age corresponds to the number of years of operation when the membrane fibers were harvested. Cumulative exposure dose estimated based on chemical cleaning practices at each facility. 47 3.3.1.2 Real Feed Water Collection and Characterization Real feed water samples were collected immediately upstream of the membranes from the same four full-scale drinking water treatment facilities from which membranes were harvested. Feed water samples were collected in 500 mL amber bottles, stored in an ice-packed cooler, and immediately transported to the lab for analyses. Liquid chromatography with parallel organic carbon detection (OCD) and organic nitrogen detection (OND) (Model 8, DOC-Labor GmbH) was used to characterize the major NOM fractions in the feed waters (Appendix B.2). Prior to analysis, samples were filtered through 0.45 μm polytetrafluoroethylene membranes (Advantech, Japan) to remove particulates. The concentrations of the different NOM fractions in the feed waters were calculated using ChromCALC software (version 2.4, Germany) based on the OCD and OND measurements from the LC-OCD-OND (Appendix B.2). A phosphate buffer (pH 6.85) was used as the mobile phase and analyses were conducted at 35°C, consistent with standard LC-OCD-OND protocols [80,82,85,86]. The characteristics of the real feed waters are presented in Chapter 6. 3.3.2 Radical Oxidant Screening Experiments Using Real Feed Waters from Facilities To investigate the impact of NOM in real feed waters at full-scale facilities, the screening experiments for assessment of radical oxidant generation involved the addition of methylene blue and hypochlorite to 16 mL test tubes which contained either 10 mL of real feed waters or distilled water, to represent conditions with and without NOM foulants, respectively; and the pH 48 was adjusted to 10.5 ± 0.2. The concentrations of methylene blue and hypochlorite and measurement procedure are as presented in Section 3.2.3. 3.4 Analyses of Membranes 3.4.1 Membrane Fiber Harvesting during Ageing for Analyses Analyses were performed on laboratory aged membrane fibers and full-scale aged membrane fibers. The approach for harvesting full-scale aged membrane fibers is as presented in Section 3.3.1.1. The approaches for harvesting laboratory aged membrane fiber are different based on analysis purposes, as described in the Sections 3.4.1.1 and 3.4.1.2. 3.4.1.1 Laboratory Aged Membrane Fiber Harvesting for Membrane Chemical Characteristics, Surface Hydrophilicity and Surface Morphology Analyses For the analyses relating to membrane chemical characteristics, surface hydrophilicity and surface morphology, membrane fibers were harvested during cyclic and soak ageing. For cyclic ageing, membrane fibers were harvested from the test modules after chemical cleaning at each cumulative exposure dose interval investigated (Figure 3.3). For soak ageing, the membrane fibers were harvested at the same intervals as for cyclic ageing by removing the test module from the soak solution. The fibers were then rinsed with distilled water, cut into coupons (each 8 cm in length), and dried at 50 oC overnight (Model Gravity Convection, Fisher Scientific) prior to characterization. 49 Figure 3.3 Typical transmembrane pressure (TMP) during cyclic ageing. Results presented for typical repeating cycles of filtration, hydraulic and chemical cleaning for cyclic ageing and the timing of a typical membrane harvesting period for chemical characteristics, surface hydrophilicity and morphology analyses is illustrated. 3.4.1.2 Laboratory Aged Membrane Fiber Harvesting for Amount of BSA Foulants on Membrane Analyses For analyses relating to the amount of BSA foulants present on membrane, membrane fibers were harvested during cyclic ageing. Membrane fibers were harvested from test modules after hydraulic cleaning and before the subsequent chemical cleaning of the filtration cycle that preceded each cumulative exposure dose interval investigated (Figure 3.4). The fibers were cut, rinsed, and dried using the same approach as in Section 3.4.1.1. 50 Figure 3.4 Typical transmembrane pressure (TMP) during cyclic ageing. Results presented for typical repeating cycles of filtration, hydraulic and chemical cleaning for cyclic ageing and the timing of a typical membrane harvesting period for amount of BSA foulants present on membrane analyses is illustrated. 3.4.2 Membrane Chemical Characteristics Changes to the chemical characteristics were analyzed for laboratory and full-scale aged membrane fibers. Changes to the chemical characteristics of the membrane surface were quantified using differential FTIR (ΔFTIR) spectroscopy [87]. ΔFTIR was chose to emphasize overall changes in chemical characteristics of the membranes as they age [87,88]. It should be noted that ΔFTIR has an inherent depth limitation, with measurements typically penetrating only a few micrometers into the membrane material, thus primarily characterizing the membrane surface rather than the whole bulk material [89]. However, this limitation is acceptable for the 51 present research as the objective was to identify overall changes in chemical characteristic rather than detailed material-specific changes as membranes age. Absolute FTIR spectra were obtained (Nicolet iS50, Thermo Fisher) for virgin and aged membranes over a wavenumber range from 700 to 2000 cm-1, with a resolution of 2 cm-1 [11,87,90]. ΔFTIR spectra were generated by subtracting the processed absolute FTIR spectra for aged membrane from that of virgin membranes. Processing was applied to correct baselines, normalize data, adjust peak shifts, and reduce scattering effects in the original spectra, as described in [91–93]. Briefly, this involved Savitzky-Golay second derivative smoothing (window width 15 points, third-order polynomial), followed by peak alignment using Moving Window Fast Fourier Transform (MWFFT) to align relevant peaks in the spectra for aged membranes with those of virgin membranes. Extended multiplicative signal correction (EMSC) with second-order polynomial correction was then applied using the mean spectrum of the virgin membrane as described in [91–93]. To validate the use of ΔFTIR, we confirmed that all major peaks in the processed absolute spectra of virgin membranes appeared in aged membranes, supported by second derivative analysis (Appendix D.1). The area of the peak of the ΔFTIR spectra at 1151 cm-1 (Figure 4.1), hereafter referred to as the predominant ΔFTIR peak, was used as an indirect metric for the extent of overall change in the chemical characteristics of the membrane surface. This peak was selected because it consistently was the largest and was among the least variable (Appendix D.1). Note that similar trends with respect to the extent of overall change in the chemical characteristics were observed when considering peaks at other wave numbers (Appendix D.1). FTIR measurements were performed on two membrane coupons for each cumulative exposure dose interval investigated, and replicate measurements were taken for 52 each membrane coupon. Because the focus was on the overall changes in chemical characteristics, a detailed analysis of the functional groups associated with the major peaks in the ΔFTIR spectra was beyond the scope of the present research; additional information on the functional groups corresponding to the observed peaks can be found in the literature [54,94,95]. Results for changes to the chemical characteristics of the membrane surface are presented in Chapters 4, 5 and 6. 3.4.3 Amount of BSA Foulants Present on Membrane The amount of BSA foulants present on membranes was analyzed for cyclic aged membranes. The amount of BSA foulants present on membranes at the start of chemical cleaning was also quantified using ΔFTIR. The area of the peak at 1651 cm⁻¹ characteristic of BSA [96], hereafter referred to as the BSA characteristic peak, was used as an indirect metric of the quantity of BSA present on membrane. The ΔFTIR spectra was generated as outlined in Section 3.4.2. FTIR measurements were performed on two test modules for each cumulative exposure dose investigated. Results for the amount of BSA foulants present on membrane at the start of chemical cleaning are presented in Chapter 5. 3.4.4 Membrane Surface Hydrophilicity The surface hydrophilicity was analyzed for laboratory aged membranes. The surface hydrophilicity of the hollow fiber membrane was quantified based on contact angle [7,43] using 53 a sessile-drop approach as described in [43,97–102]. Membrane coupons were cut into 2 cm sections (three sections per coupon) and straightened by gently stretching the ends of the sections to remove natural curvature, then attached to glass plates using two-sided tape (Model 667, Scotch 3M) to ensure repeatable droplet conformation. A 2 μL sample of distilled water was placed on the membrane surface; images were captured using a goniometer (Model 260, ramé-hart) [11,43]. Contact angle values were calculated using ImageJ (Model V1.54, National Institutes of Health of U.S.) software as described in [103]. Measurements were performed on two membrane coupons for each cumulative exposure dose interval investigated, and triplicate measurements were taken for each coupon. Results for the surface hydrophilicity of the membrane are presented in Chapter 4. 3.4.5 Membrane Surface Morphology The surface morphology was analyzed for laboratory aged membranes. The surface morphology of the membrane was visualized using a field emission scanning electron microscope (FESEM) (Model NanoLab 650, FEI Helios) [104–106]. The pore number and pore radius on the surface of the membrane were estimated from the FESEM images using digital image processing, as outlined in [107,108]. Briefly, the images were enhanced through contrast stretching; adaptive thresholding was used to distinguish pores, and morphological filtering was applied to remove artifacts. Connected regions were labeled to calculate pore number and average pore radius [107,108]. The average surface roughness (RA) was estimated using ImageJ software, as described in [103,109]. The membrane coupons were cut into 1 cm sections, fixed onto stub holders using two-sided tape and coated with iridium to a thickness of approximately 5.0 nm as 54 outlined in [106]. FESEM imaging was performed on one membrane coupon for each cumulative exposure dose interval investigated, and replicate images were taken for each membrane coupon. Although contact angle measurements are influenced by surface roughness [27], and measurements of surface roughness, pore radius, and pore number derived from FESEM images can only be regarded as approximations [110], they nonetheless provide valuable insight to identify trends and assess relative changes in membrane surface characteristics [6]. Results for the surface morphology of the membrane are presented in Chapters 4 and 5. 3.4.6 Hydraulic Performance The hydraulic performance was analyzed for laboratory aged membranes and full-scale aged membranes. The hydraulic performance was quantified using clean water resistance, as is commonly done in membrane ageing studies [10]. For cyclic ageing, clean water resistance was measured after chemical cleaning and before the subsequent filtration cycle, at each cumulative exposure dose interval investigated (Figure 3.5). When measurements were conducted, cyclic ageing was paused and distilled water was filtered through the test modules for 30 minutes at a flux of 50 Lm-2h-1. The membrane tanks were then drained, and cyclic ageing was resumed. 55 Figure 3.5 Typical transmembrane pressure (TMP) during cyclic ageing. Results presented for typical repeating cycles of filtration, hydraulic and chemical cleaning for cyclic ageing and the timing of a typical clean water resistance measurement period is illustrated. For soak ageing, clean water resistance was measured at the same intervals as for cyclic ageing by removing the soak aged test module from the soak solution, rinsing with distilled water, connecting the test modules to the cyclic ageing apparatus, and undertaking clean water resistance measurements as previously described for cyclic ageing. Following the clean water measurement, the test modules were returned to the soak solution and soak ageing was resumed. Clean water resistance measurements were performed on three test modules for each cumulative exposure dose interval investigated. For full-scale ageing, clean water resistance was measured by rinsing the test modules of full-scale aged membranes with distilled water, connecting the test modules to the cyclic ageing apparatus, and undertaking clean water resistance measurements as previously described. Clean 56 water resistance measurements were performed on two test modules for each cumulative exposure dose interval investigated. Results for the hydraulic performance of the membranes are presented in Chapters 4, 5 and 6. 57 Chapter 4: NOM Foulant - Hypochlorite Interactions Impact Membrane Ageing A version of this chapter has been published in a peer-reviewed journal. • Baohui Jia; Jia Bian; Rahul Dutta; Majid Mohseni; Jongho Lee; Robert C. Andrews; Pierre R. Bérubé. NOM foulant−hypochlorite interactions impact PVDF UF membrane ageing, Separation and Purification Technology, 364 (2025) 132469 4.1 Introduction As discussed in Chapter 1, previous studies by our group concluded that the presence of NOM foulants during chemical cleaning may influence membrane ageing [11]. However, at the time, the potential mechanisms responsible for the observation were not investigated. Hajibabania et al. reported similar results to those from previous studies by our group when considering cyclic ageing with and without model NOM components [27]. They hypothesized that the greater increase in resistance observed for cyclic ageing with model NOM components could be attributed to the chemically irreversible accumulation of foulants on the membrane. However, they did not observe the presence of model NOM components on the membrane surface when characterizing the membrane, which suggests that an alternative mechanism could be responsible for the greater increase in resistance. Some studies have suggested that NOM – hypochlorite interaction can generate radical oxidants at pH levels relevant to chemical cleaning practices for PVDF UF membranes [13,14]. Radical 58 oxidants, such as hydroxyl and chlorine radicals, are highly reactive species that could significantly accelerate membrane ageing by attacking the chemical structure of membrane surfaces. Radical oxidants can increase oxidative damage of membrane materials, resulting in compromised membrane integrity, altered surface properties, and reduced hydraulic performance over time [17–26]. Although these studies on NOM – hypochlorite interactions did not focus on membrane filtration, they suggest that NOM retained on membranes as foulants, henceforth referred to as NOM foulants, could interact with hypochlorite used for chemical cleaning to generate radical oxidants that may accelerate membrane ageing. However, prior to the present research, none had investigated whether the generation of radical oxidants from interactions between NOM foulants and hypochlorite during chemical cleaning influences membrane ageing. The research outlined in Chapter 4 aims to address Objective 1 which is to confirm that NOM foulant – hypochlorite interactions accelerate membrane ageing and that the generation of radical oxidants from these interactions are responsible for the acceleration. The approaches used focus on quantifying the impact of interactions between model NOM components, used as surrogates of those commonly found in membrane foulants, and hypochlorite used in chemical cleaning, on changes in the physical and chemical characteristics and hydraulic performance of PVDF UF membranes as they age. BSA, SA and HA were considered as commonly used model NOM components and hypochlorite was selected because it is typically used to clean PVDF UF membranes [7,10,82]. 59 The research also further investigates the generation of radical oxidants from these interactions and evaluates their impact on membrane ageing. Based on the knowledge gained, we aim to propose potential strategies to mitigate membrane ageing. 4.2 Summary of Laboratory Investigation Methods Soak and cyclic ageing experiments were performed to evaluate changes in physical and chemical characteristics and hydraulic performance of membranes as they age. To assess potential impacts of interactions between model NOM foulants and hypochlorite during chemical cleaning on membrane ageing, soak ageing, which considers only interactions between the hypochlorite and the membrane, was compared to cyclic ageing, which also considers the impact of model NOM foulants on these interactions. The feed water used for cyclic ageing contained a mixture of model NOM components (i.e., BSA, SA and HA), as outlined in Section 3.2.1.3. To mitigate the impacts of radical oxidation, further added cyclic ageing investigations were performed with addition of sodium bicarbonate as a radical scavenger, as outlined in Section 3.2.2.2. To investigate the potential generation of radical oxidants from NOM foulant – hypochlorite interactions, a series of screening experiments was performed using methylene blue as a chemical probe to gauge the generation of radical oxidants, as outlined in Section 3.2.3. The physical and chemical characteristics and hydraulic performance for soak and cyclic aged membranes were analyzed at each cumulative exposure dose investigated as outline in Section 3.4. 60 The experimental approaches and main design used are schematically illustrated in Figures 4.1 and 4.2. Details of materials and methods can be found in Chapter 3, and added details of the experimental design are presented in the sections which follow. 61 Figure 4.1 Experimental approaches used to address Objective 1.The approaches not relevant to Objective 1 are shaded (grey). Superscripts 1, 2 and 3 represent which components of the experimental and analytical approaches correspond to Objectives 1, 2 and 3. 62 Figure 4.2 Schematic of main experimental design for laboratory accelerated (a) soak ageing and (b) cyclic ageing. 4.3 Membrane Ageing Resulting from Interactions Between Model NOM Foulants and Hypochlorite Potential impacts of interactions between model NOM foulants and hypochlorite during chemical cleaning, hereafter referred to as NOM foulant - hypochlorite interactions, on membrane ageing were assessed by comparing the changes in membrane characteristics and hydraulic performance of soak and cyclic aged membranes over cumulative exposure doses to hypochlorite, ranging from 0 to 1,300,000 ppm·h. Soak and cyclic ageing were performed without and with model NOM foulants present, respectively. 63 4.3.1 Changes in Membrane Characteristics Changes to the chemical characteristics of the membrane surface were quantified by differential FTIR (ΔFTIR). The areas of the peaks in the ΔFTIR spectra provide a semi-quantifiable measure of the extent of change in the characteristics of the chemicals on the membrane surface [87]. ΔFTIR spectra for soak and cyclic aged membranes at selected cumulative exposure dose intervals, hereafter referred to as exposure doses, considered are presented in Figure 4.3. The peaks associated with the ΔFTIR spectra for soak aged membranes were smaller than those for cyclic aged membranes at lower exposure doses (Figure 4.3a and b), indicating that at these doses, the extent of change was less for soak aged membranes. However, as illustrated in Figure 4.3c, the peaks in the ΔFTIR spectra for soak aged membranes increased to be equivalent to those of cyclic aged membranes at the highest exposure dose considered (i.e., 1,300,000 ppm·h), indicating that at this dose, the extent of change was similar for both soak and cyclic aged membranes. 64 Figure 4.3 ΔFTIR spectra of soak and cyclic aged membranes at select cumulative exposure doses to hypochlorite. (a) 100,000 ppm·h; (b) 500,000 ppm·h; (c) and 1,300,000 ppm·h; results for membranes harvested from full-scale facilities included in b; note that the straight horizontal line at a differential absorbance of zero corresponds to an exposure dose to hypochlorite of 0 ppm·h; results for full-scale system adapted from [80]. a b c 65 The similar ΔFTIR spectra for soak and cyclic aged membranes at the highest exposure dose considered suggest that the observed changes did not result from the irreversible accumulation of foulants on the membrane surface, as suggested by others [6,27,57,81,111,112]. Rather, the changes likely resulted from the transformations of some of the membrane material into new products and the formation of new products [28,113]. Had changes in the chemical characteristics of the membrane surface resulted from the irreversible accumulation of foulants on the membrane, the spectra for soak aged and cyclic aged membranes at all exposure doses greater than zero would have been expected to differ because foulants are not considered (i.e., absent) for soak ageing. Figure 4.3 also includes the ΔFTIR spectra of membranes harvested from a full-scale facility as part of a separate study [80]. When Objective 1 was addressed, only data from one full-scale facility were available; additional data were subsequentially obtained and are presented in Chapter 6. At the exposure dose for laboratory accelerated ageing equivalent to that of the full-scale drinking water treatment facilities (i.e., ~ 500,000 ppm·h), the ΔFTIR spectra of the harvested membranes more closely resembled that of cyclic aged than that of soak aged membranes, consistent with observations from previous studies [10]. Substantive valleys (i.e., differential absorbance < 0) in the spectra were observed for the harvested membranes, which were not observed for soak or cyclic aged membranes, notably at approximately 1500 cm-1 and 1800 cm-1. We hypothesize that this difference resulted from constituents of the feed water of the full-scale facilities that were not considered in the present research. Further research would be required to address this hypothesis. 66 As illustrated in Figure 4.4a, at low exposure doses (< 100,000 ppm·h), the rate of change of the predominant ΔFTIR peak, with increasing dose, was greater for cyclic than soak aged membranes. These results indicate that the presence of model NOM foulants increased the rate of overall change in the chemical characteristics of the membrane surface. The rate of change of the predominant ΔFTIR peak decreased at higher exposure doses for cyclic ageing, eventually becoming zero (i.e., reaching steady state conditions), suggesting that the chemical characteristics of the membrane no longer changed. In contrast, the rate of change of the predominant ΔFTIR peak with increasing exposure dose for soak ageing was relatively constant. Additional soak and cyclic ageing investigations at 2,000,000 ppm·h confirmed that the predominant ΔFTIR peak remained constant at greater exposure doses (i.e. beyond 1,300,000 ppm·h) (Figure 4.4a). As such, these results indicate that although model NOM foulants can increase the rate of overall change in the chemical characteristics of the surface of aged membranes, they did not impact the ultimate overall change. Figure 4.4a also includes the predominant ΔFTIR peak for membranes harvested from the full-scale facility, which again more closely resemble cyclic aged than soak aged membranes [80]. 67 a b Figure 4.4 Predominant ΔFTIR peak (a) and contact angle (b) for soak and cyclic aged membranes. Confidence intervals of the predominant ΔFTIR peak correspond to the maximum and minimum of 4 replicate measurements; confidence intervals of contact angles correspond to the standard deviations of 6 replicate measurements; shaded lines added to illustrate overall trends observed for different conditions investigated; vertical dashed lines correspond to the dose for laboratory accelerated ageing equivalent to approximately 10 years of operation at full-scale facilities [10,30]; results for full-scale system adapted from [80] when available. The characteristics of the membrane surface were also quantified based on changes in contact angle, pore radius, pore number and surface roughness with increasing exposure dose. Overall, similar trends were observed for changes in contact angle, pore radius and surface roughness to those for change in the predominant ΔFTIR peak (Figures 4.4b, 4.5b, 4.5c). At low exposure doses (< 100,000 ppm·h), the rates of change in the contact angle, pore radius and surface roughness were greater for cyclic than for soak ageing, and decreased at higher doses for cyclic ageing, eventually being zero; while the rate of change in these membrane surface characteristics 68 for soak ageing was relatively constant. At the highest exposure dose considered (i.e., 1,300,000 ppm·h), the contact angle, pore radius and surface roughness for soak and cyclic aged membranes were similar. In contrast, a different overall trend was observed for the pore number (Figure 4.5d). For cyclic ageing, the pore number decreased consistently with increasing exposure, suggesting that changes induced by ageing resulted in pores disappearing or being covered. Consistent with our results, studies by others also have reported simultaneous decreases in pore number and increases in pore size during membrane ageing, attributed to pore fusion and structural collapse [39,49]. For soak ageing, however, the pore number remained relatively constant. FESEM images of both soak and cyclic aged membranes at select exposure doses, presented in Figure 4.5a, are consistent with the previously discussed changes in membrane surface characteristics: with increased exposure dose, the membrane surface becomes increasingly heterogeneous and accentuated with crests and crevasses. We hypothesized that model NOM foulants contributed to the generation of highly reactive species, such as radical oxidants, from interactions with hypochlorite [84,114], and that these reactive species increased the rate at which changes to the membrane surface characteristics occurred [17–26]. The potential contribution of radical oxidants to the observed changes is discussed in Section 4.4. 69 a b c d Figure 4.5 FESEM images for soak and cyclic aged membranes (a), average pore radius (b), average surface roughness (c) and pore number per unit area (i.e., 2.5 μm2) (d) of soak and cyclic aged membranes. Confidence intervals for pore radius, surface roughness and pore number correspond to the maximum and minimum of 2 replicate measurements; shaded lines added to illustrate overall trends observed for different conditions investigated; vertical dashed lines correspond to the dose for laboratory accelerated ageing equivalent to approximately 10 years of operation at full-scale facilities [10,30]. 70 4.3.2 Changes in Membrane Hydraulic Performance Changes to the hydraulic performance were quantified based on clean water resistance. The clean water resistances for soak and cyclic aged membranes at selected exposure doses are presented in Figure 4.6a. For cyclic aged membranes, a relatively consistent increase in clean water resistance was observed with respect to the exposure dose; while for soak aged membranes, a slight but significant decrease in clean water resistance was initially observed, followed by a relatively constant clean water resistance. The observed changes were consistent with those reported in previous work by our group [30]. A decrease in clean water resistance with increasing exposure dose has also been reported by others for soak ageing type studies [27,36,42,43]; and has commonly been attributed to an increase in the size of membrane pores with increasing dose [27,36,42,43]. Figure 4.4a also includes the clean water resistance for membranes harvested from the full-scale facility [80], which again (see Section 4.3.1) more closely resemble cyclic aged than soak aged membranes. 71 a b Figure 4.6 Clean water resistance (a) and calculated filtration layer pore length (b) for soak and cyclic aged membranes. Confidence intervals correspond to maximum and minimum of 3 replicate measurements; shaded lines added to illustrate overall trends observed for different conditions investigated; vertical dashed lines correspond to the dose for laboratory accelerated ageing equivalent to approximately 10 years of operation at full-scale facilities [10,30]. As previously discussed, based on the ΔFTIR results, the changes in the membrane surface characteristics with increasing exposure dose likely did not result from the accumulation of foulants on the membrane surface. However, model NOM foulants within the membrane pores may have been sheltered from exposure to hypochlorite during chemical cleaning and as a result have accumulated within membrane pores, affecting clean water resistance. The accumulation of model NOM foulants within the membrane pores cannot be detected using FTIR because of the limited measurement penetration capacity of the method. To assess whether model NOM foulants could potentially have been sheltered and accumulated within the membrane pores, added cyclic ageing investigations were performed with the permeation of hypochlorite during chemical cleaning. As previously described in Section 3.2.2.2, cyclic ageing was initially 72 performed by soaking the membranes in hypochlorite during chemical cleaning, consistent with typical chemical cleaning practices for PVDF-UF membranes. It was assumed that by permeating the hypochlorite solution through the membrane during chemical cleaning, sheltering would be reduced. The clean water resistance was significantly lower for cyclic ageing with permeation of hypochlorite during chemical cleaning (Figure 4.6a). However, the clean water resistance with permeation of hypochlorite remained significantly greater than that observed for soak ageing, suggesting that the observed difference between the clean water resistance for soak and cyclic ageing was not exclusively due to the accumulation of model NOM foulants within the membrane pores. A simple Hagen–Poiseuille type model (equation 4.1) was used to assess if the observed changes in clean water resistance with increasing exposure dose (Figure 4.6a) were consistent with those observed for membrane geometry [115]. Although this type of model is very simplistic, ignoring many of the complex feature characteristics of membrane pore geometry, it can nonetheless provide valuable insight into potential causes of observed changes in clean water resistance [115–117]. where R is the clean water resistance (m-1), A is the membrane surface area (μm2), 𝛿𝐿 is the filtration layer pore length (μm), n is pore number, r is pore radius (nm). 𝑅 =8𝐴(𝛿𝐿)𝑛𝜋𝑟4 eq. 4.1 73 Of the parameters in equation 4.1, only the filtration layer pore length is unknown, and therefore can be calculated from measurements of the other parameters. The filtration layer pore length (𝛿𝐿) represents the effective path length that water must travel through the membrane pores, which differs from the nominal membrane filtration layer thickness due to pore tortuosity and surface roughness [115–117]. Assuming that the filtration layer pore length changed with increasing dose, the overall trends observed for changes in clean water resistance (R) with increasing exposure dose could be effectively modeled using the measured average surface pore radius (r) and pore number (n). Considering that the filtration layer pore length would be influenced by the pore surface roughness [118], it is reasonable to anticipate that the filtration layer pore length may change with increasing exposure dose. The filtration layer pore lengths, calculated by fitting equation 4.1 to the clean water resistance (Figure 4.6a) and using the measured average surface pore radius (Figure 4.5b) and pore number (Figure 4.5d), are illustrated in Figure 4.6b. The overall trends observed for changes in filtration layer pore length are consistent with those observed for changes of the predominant ΔFTIR peak, contact angle, pore radius and surface roughness: at low exposure doses (< 100,000 ppm·h), the rates of change in the filtration layer pore length were greater for cyclic than for soak ageing, and decreased at higher doses for cyclic ageing, eventually being zero; while the rate of change in the filtration layer pore length for soak ageing was relatively constant. Also, at the highest exposure dose considered (i.e., 1,300,000 ppm·h), the filtration layer pore lengths for both soak and cyclic ageing with permeation of hypochlorite during chemical cleaning were similar and less than that for cyclic ageing without permeation of hypochlorite during chemical cleaning. These results 74 suggest that foulants were effectively removed by permeating hypochlorite during chemical cleaning. These results also reaffirm that although model NOM foulants increased the rate of change, they did not impact the eventual extent of change. Note that filtration layer pore lengths calculated for cyclic ageing with permeation were based on pore radius and pore numbers obtained from cyclic ageing without permeation. As previously discussed, based on ΔFTIR results, the changes in membrane surface characteristics likely did not result from accumulation of foulants on the surface. Therefore, it is reasonable to expect that permeation of hypochlorite during chemical cleaning did not impact pore radius and pore number. The overall trends observed for changes in membrane characteristics for soak and cyclic ageing were similar for all parameters considered except for pore number. This unique response of pore number to the exposure dose suggests that it may represent a primary factor underlying the observed differences in clean water resistance between soak and cyclic aged membranes. Further research is needed to elucidate how soak and cyclic ageing impacts clean water resistance. However, regardless of the cause, the extent of the increase in clean water resistance for cyclic ageing remained distinct from, and greater than, that for soak ageing. The overall trends observed in the present research for changes to clean water resistance of cyclic aged membranes are consistent with those reported by others [27]; however, they are not consistent with previous cyclic ageing studies by our group [10]. Robinson and Bérubé reported a decrease in clean water resistance for cyclic aged membranes [10], while the present research observed an increase. This discrepancy is likely due to differences in the membrane chemical formulations and experimental approaches used in the present research and previous research by 75 our group. Robinson and Bérubé considered ZW500 membranes (Veolia Water Technologies and Solutions) [10], the present research considered ZW1000 membranes. Although both are PVDF type membranes from the same manufacturer, the details of their chemical formulations differ. Also, Robinson and Bérubé [10] performed extensive manual physical cleaning of the membrane surface prior to clean water resistance measurements, a practice not considered in the present research, because it is not representative of the typical practices at full-scale. 4.4 Assessment of Radical Oxidant Generation from NOM Foulant - Hypochlorite Interactions To investigate the potential generation of radical oxidants from NOM foulant – hypochlorite interactions, a series of screening experiments was performed where methylene blue was added to solutions containing hypochlorite, with or without model NOM foulants present in the solution at a pH of 10.5 ± 0.2. The concentration of methylene blue in the solutions was monitored over time. Methylene blue is known to be oxidized more rapidly by radical oxidants than by hypochlorite alone [78,79]. The removal of methylene blue during the screening experiments is presented in Figure 4.7. Methylene blue removal rate was relatively low (0.0019 ± 6.8 ×10-5) in the absence of model NOM foulants (i.e., only hypochlorite present) [78,79]. However, in the presence of both model NOM foulants and hypochlorite, methylene blue removal rate was significantly greater at the pH investigated (10.5 ± 0.2) [78,79]. In the absence of hypochlorite, the removal of methylene blue, with and without model NOM foulants, was negligible, (see Appendix D.4). These results 76 indicate that foulants generated from the filtration of a solution containing model NOM components can react with hypochlorite, at a pH relevant to the chemical cleaning practices for PVDF UF membranes (pH ≈10.5), to generate radical oxidants. Figure 4.7 Impact of NOM foulants on the concentration of methylene blue in a solution containing hypochlorite with and without radical scavengers. C0 is the initial methylene blue concentration; C is the concentration at given time; solid lines correspond to linear regression models fitted to the data, k and R2 are the slope and coefficients of determination of the regressions; confidence intervals for concentration data and slopes of linear regression correspond to the maximum and minimum values from 2 replicate experiments. To confirm the generation of radical oxidants, and identify the predominant types, additional screening experiments were performed. For these, either sodium acetate as chlorine radical scavenger or acetone as hydroxyl radical scavenger was added to solutions containing methylene blue and hypochlorite, with or without model NOM foulants present in the solution at pH 77 relevant to chemical cleaning (pH ≈10.5 ± 0.2). Chorine radicals and hydroxyl radicals were considered as potential predominant types of radical oxidants because they were observed by others when investigating NOM - hypochlorite interactions [119,120]. The results from the additional screening experiments are illustrated in Figure 4.7. When acetone was added to a solution containing hypochlorite and model NOM foulants, the removal of methylene blue decreased at a rate equivalent to that in the absence of model NOM foulants. However, the rate of methylene blue removal was not impacted by the presence of sodium acetate in a solution containing hypochlorite and model NOM foulants. These results suggest that the predominant radical oxidant generated from NOM foulant - hypochlorite interactions was likely hydroxyl radicals. 4.5 Mitigating The Impact of Radical Oxidants As previously discussed in Chapter 4.4, scavengers can quench radical oxidants generated from interaction between model NOM foulants and hypochlorite. Therefore, it is reasonable to expect that the rate and extent of membrane ageing could be reduced by adding scavengers to the hypochlorite solution used for chemical cleaning [17–26]. To gain insight into the potential impact of scavenging radical oxidants formed during chemical cleaning on membrane ageing, additional cyclic ageing was conducted with the addition of sodium bicarbonate as a radical scavenger to the chemical cleaning solution. Sodium bicarbonate was chosen because it is a non-selective radical scavenger [76,77], and it is commonly available at drinking water treatment facilities for use in other processes. Changes to the chemical 78 characteristics of the membrane surface and the hydraulic performance were quantified using differential FTIR (ΔFTIR) and clean water resistance, respectively. Differences in membrane characteristics and performance between cyclic ageing conditions with and without the non-selective scavenger were attributed to radical oxidants generated from NOM foulant - hypochlorite interactions during chemical cleaning. The predominant ΔFTIR peak for cyclic aged membranes with and without the addition of sodium bicarbonate are presented in Figure 4.8a. As previously discussed, in the absence of radical scavengers, the rate of change of the predominant ΔFTIR peak was high at low exposure doses, and decreased with increasing doses, eventually being zero. With the addition of the radical scavenger to the chemical cleaning solution, the rate of change of the predominant ΔFTIR peak was much lower, and remained relatively constant with increasing exposure dose, consistent with the results previously reported for soak aged membrane. These results suggest that the changes in chemical characteristics of the membrane surface could be reduced with the addition of a radical scavenger to the chemical cleaning solution, with the benefits most pronounced at lower exposure doses when membrane is in its early ageing stages. 79 a b Figure 4.8 The predominant ΔFTIR peak (a) and clean water resistance (b) for soak and cyclic aged membrane with and without addition of the radical scavenger to the chemical cleaning solution. Confidence intervals of the predominant ΔFTIR peak correspond to the maximum and minimum of 4 replicate measurements; confidence intervals for clean water resistance correspond to the maximum and minimum of 3 replicate measurements; results for soak and cyclic ageing without addition of sodium bicarbonate are the same as those previously presented in Figures 4.1a and 4.4a; shaded lines added to illustrate overall trends observed for different conditions investigated; arrows added to illustrate the extent of the decrease resulting from the addition of sodium bicarbonate; vertical dashed lines correspond to the exposure dose for soak and cyclic ageing equivalent to approximately 10 years of operation at full-scale facilities [10,30]. The clean water resistances for cyclic aged membranes with and without the addition of a radical scavenger are presented in Figure 4.8b. To isolate the impact of radical scavenger on resistance and remove the effects of model NOM foulants within membrane pores (as previously discussed in Section 4.3.2), permeation of hypochlorite during chemical cleaning was performed during cyclic ageing. As previously discussed, with permeation during chemical cleaning but in the 80 absence of a scavenger, the clean water resistance did not increase substantively at lower exposure doses (< 500,000 ppm·h), consistent with the results previously reported for soak aged membrane, but increased significantly at higher exposure doses. Similarly, with the addition of sodium bicarbonate to the permeated chemical cleaning solution, the clean water resistance remained low at lower exposure doses. The clean water resistance did increase at higher exposure doses; however, the magnitude remained consistently lower for cyclic ageing with, than without, the addition of sodium bicarbonate. These results suggest that the hydraulic performance of the membrane could be improved by the addition of sodium bicarbonate as a radical scavenger to the chemical cleaning solution, with the benefits becoming more significant at higher exposure doses when the membrane is in its later ageing stages. While the addition of scavengers appears most beneficial for preserving membrane characteristics at early stages of ageing (lower cumulative exposure doses), the hydraulic performance benefits are more pronounced at later stages (higher cumulative exposure doses). These results suggest that even when chemical characteristics appear to stabilize in aged membranes, the addition of radical scavengers to cleaning solutions could still provide significant benefits to hydraulic performance. Further investigations are required to understand the differences in clean water resistance between soak ageing and cyclic ageing with both radical scavenger addition and cleaning solution permeation, as this insight could inform further improvements to metrics of membrane replacement timing and chemical cleaning protocols. The results for cyclic ageing with addition of sodium bicarbonate as a scavenger support our hypothesis that model NOM foulants interact with hypochlorite during chemical cleaning to 81 generate radical oxidants that increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing (or more accurately, their absence reduces the rate to which these changes occur). These results also suggest that the changes associated with membrane ageing could be mitigated to some extent when treating source waters that naturally contain relatively high concentrations of radical scavengers; and possibly that these changes could be proactively lowered by adding radical scavengers to hypochlorite cleaning solution for source waters that do not naturally contain sufficient scavengers to quench the radical oxidants. Although not a primary focus of the present research, the results also suggest that permeating the chemical cleaning solution through the membrane, rather than simply soaking the membrane in the solution, which is common practice for PVDF UF membranes, would improve the overall hydraulic performance of membranes. 4.6 Conclusions The outcomes outlined in Chapter 4 address Objective 1 regarding interactions between NOM foulants and hypochlorite during chemical cleaning, highlighting the generation of radical oxidants and their impact on the ageing of PVDF UF membranes. By considering radical oxidant screening experiments and both soak and cyclic ageing approaches, we demonstrated that interaction between model NOM foulants and hypochlorite leads to the generation of hydroxyl radicals from hypochlorite under conditions relevant to chemical cleaning. The generation of radical oxidants increased the rate at which changes in 82 physical and chemical characteristics and hydraulic performance occurred during membrane ageing. The addition of radical scavengers to the hypochlorite chemical cleaning solution could reduce the detrimental effects of radical oxidants, suggesting that scavenger addition could be a strategy to reduce ageing of PVDF UF membranes. The research outlined in Chapter 4 provides a deeper understanding of the role of radical oxidants in membrane ageing and offers a potential avenue for improving the long-term performance of membranes. Further studies are needed to identify the specific types of NOM foulants responsible for radical oxidant generation and to consider the use of radical scavengers to improve the long-term performance of PVDF UF membranes. 83 Chapter 5: Specific Types of NOM Components Impact Membrane Ageing A version of this chapter is being reviewed and prepared to be submitted to a peer-reviewed journal. 5.1 Introduction The results presented in Chapter 4 indicate that radical oxidants were generated from model NOM foulant – hypochlorite interactions during chemical cleaning, and that these radical oxidants can increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. While the approach used demonstrated the impact of model NOM components on ageing, it did not enable the identification of the specific type(s) of model NOM component(s) that contributed to accelerating membrane ageing. The generation of radical oxidants from interaction with hypochlorite is expected to be impacted by the type of the NOM components present on the membrane as foulants [13,14]. However, the specific type(s) of NOM components that contribute most to the generation of radical oxidants, and therefore membrane ageing, remains unknown. The research outlined in Chapter 5 expands on that presented in Chapter 4 (Objective 1), aiming to address Objective 2 which is to identify which type(s) of model NOM component(s) contribute to the generation of radical oxidants, and that these can increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. 84 The approaches used were similar to those outlined in Chapter 4, and focused on quantifying the impact of interactions between different model NOM components, used as surrogates of those commonly found in membrane foulants, and hypochlorite used in chemical cleaning, on changes in the physical and chemical characteristics and hydraulic performance of PVDF UF membranes as they age. BSA, SA and HA were considered as commonly used model NOM components and hypochlorite was selected because it is typically used to clean PVDF UF membranes [7,10,82]. The research also further investigated the generation of radical oxidants from these interactions and evaluated their impact on membrane ageing. Based on the knowledge gained, we aimed to propose potential strategies to mitigate membrane ageing. 5.2 Summary of Laboratory Investigation Methods To investigate which type(s) of model NOM foulant(s) contributed to the generation of radical oxidants from interactions with hypochlorite, a series of screening experiments was performed using methylene blue as a chemical probe to gauge the generation of radical oxidants, as outlined in Section 3.2.3. Cyclic ageing with different feed waters was then performed to identify which type(s) of model NOM component(s) is(are) the primary contributor(s) to NOM foulant – hypochlorite interactions and therefore impact ageing as outlined in Section 3.2.2.2. The feed water for cyclic ageing contained either no model NOM components, only BSA, only SA, only HA or a mixture of model NOM components (same as Chapter 4), as outlined in Section 3.2.1.3. To mitigate the 85 impacts of radical oxidation, added cyclic ageing investigations were performed with addition of sodium bicarbonate as a radical scavenger, as outlined in Section 3.2.2.2. The physical and chemical characteristics and hydraulic performance for cyclic aged membranes were analyzed at each cumulative exposure dose investigated as outlined in Section 3.4. The experimental approaches and main design used are schematically illustrated in Figures 5.1 and 5.2. Details of materials and methods can be found in Chapter 3, and added details of the experimental design are presented in the sections which follow. 86 Figure 5.1 Experimental approaches used to address Objective 2.The approaches not relevant to Objective 2 are shaded (grey). Superscripts 1, 2 and 3 represent which components of the experimental and analytical approaches correspond to Objectives 1, 2 and 3. 87 Figure 5.2 Schematic of main experimental design for laboratory accelerated cyclic ageing. 5.3 Assessment of Radical Oxidant Generation from Different NOM Foulant - Hypochlorite Interactions To investigate which type(s) of model NOM foulant(s) contributed to the generation of radical oxidants from NOM foulant – hypochlorite interactions, a series of screening experiments, similar to those outlined in Section 4.4, was performed where methylene blue was added to solutions containing hypochlorite, with or without individual model NOM foulants (i.e., only BSA foulants, only SA foulants, and only HA foulants) present in the solution at a pH of 10.5 ± 0.2. The concentration of methylene blue in the solutions was monitored over time. Methylene blue is known to be oxidized more rapidly by radical oxidants than by hypochlorite [78,79]. The removal of methylene blue during the screening experiments are presented in Figure 5.3. Methylene blue removal rate was relatively low (0.0019 ± 6.8 ×10-5) in the absence of model NOM foulants (i.e., only hypochlorite present) [78,79]. In the presence of only HA or SA 88 foulants and hypochlorite, similar low removal rates were observed, suggesting no significant generation of radical oxidants. However, in the presence of only BSA foulants and hypochlorite, the methylene blue removal rate was significantly greater at the pH investigated (pH = 10.5 ± 0.2). In the absence of hypochlorite, the removal of methylene blue, with and without model NOM foulants, was negligible, as in Section 4.4. These results suggest that only interaction between BSA foulants and hypochlorite, hereafter referred to as BSA foulant – hypochlorite interaction, substantively contributed to the generation of radical oxidants at a pH relevant to the chemical cleaning practices for PVDF UF membranes (pH ≈10.5); and based on the results presented in Chapter 4, these radical oxidants would be expected to increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. 89 Figure 5.3 Impact of presence of different model NOM foulants on the concentration of methylene blue in a solution containing hypochlorite. C0 is the initial methylene blue concentration; C is the concentration at given time; solid lines correspond to linear regression models fitted to the data, k and R2 are the slope and coefficients of determination of the regressions; confidence intervals for concentration data and slopes of linear regression correspond to the maximum and minimum values from 2 replicate experiments; results for the mixture of model NOM foulants are the same as those previously presented in Chapter 4. Also presented in Figure 5.3 are the results from the screening experiments outlined in Chapter 4 (Figure 4.5), which considered a mixture of model NOM foulants (i.e., BSA, SA and HA). Similar methylene blue removal rates were observed for solutions containing only BSA foulants (35 mg as TOC/L BSA in feed water) and the mixture of model NOM foulants (8.75 mg as TOC/L BSA in feed water) despite a 4-fold difference in the concentration of BSA in these feed waters (Figure 5.3). These results suggest that the generation of radical oxidants may not be 90 proportional to BSA concentrations. To investigate the impact of BSA concentration on methylene blue removal rates, further screening experiments were performed where methylene blue was added to solutions containing hypochlorite, with different concentrations of only BSA, and different concentrations of BSA in a mixture of model NOM components, present in the solution. The methylene blue removal rates with respect to BSA concentration for only BSA and a mixture of model NOM components are presented in Figure 5.4. Results indicate that methylene blue removal rate initially increased with BSA concentration; however, above a threshold BSA concentration of approximate 10 mg as TOC/L, the rate remained relatively constant. This diminishing increase in generation of radical oxidants has been observed in studies by others and attributed to the increased self-scavenging [121] and/or self-decay of radical oxidants at higher concentrations [122]. These results suggest that a threshold concentration of BSA is likely to exist, above which the amount of radical oxidants present is relatively constant. Above this concentration, ageing is not further accelerated by additional BSA. The similar results, at a given concentration of BSA, for solutions containing only BSA and solutions containing a mixture of model NOM components also indicate that NOM components other than BSA did not substantively contribute to the generation or the scavenging of radical oxidants. 91 Figure 5.4 Impact of BSA concentration on the methylene blue removal rates for a solution containing hypochlorite and only BSA or mixture of model NOM components. Confidence intervals for methylene blue removal rates correspond to the maximum and minimum values from 2 replicate experiments; shaded grey lines added to illustrate overall trends observed for methylene blue removal rates; vertical dashed lines added to illustrate the threshold concentration of diminishing impact on methylene blue remove rate. The results from screening experiments suggested that the generation of radical oxidants was significantly impacted by the type of model NOM components. In the presence of only HA, no significant radical oxidants were generated, which is not consistent with results of studies by Criquet et al. [13] and Rodríguez and von Gunten [14]. They demonstrated that hydroxyl radicals could be generated from interaction between humic substance type NOM and hypochlorite. However, their work was done at a neutral pH (pH ≈ 7.0) while the present research was performed at a high pH (pH ≈ 10.5), typical of chemical cleaning practices for PVDF 92 membranes. At higher pH, hypochlorite exists predominantly as hypochlorite ions rather than hypochlorous acid, potentially inhibiting the electron transfer reactions needed for radical oxidant generation from humic substances [71]. In presence of only BSA, significant radical oxidants were generated, which is consistent with protein type NOM-hypochlorite interactions at high pH reported in studies by others [15,16,65,68,70]. A pictorial illustration of the potential pathways for hydroxyl radical generation from BSA - hypochlorite interactions is presented in Figure 5.5. As illustrated in the figure, the generation of hydroxyl radicals likely follows multiple pathways initiated by the chlorination of amino groups present in BSA to form organic chloramines [12,67]. These organic chloramines can subsequently generate hydroxyl radicals through two potential pathways based on previous studies [15,16,68–70]. With residual hypochlorite available, organic chloramines may undergo further decomposition to form inorganic chloramines [68,69], which can then generate hydroxyl radicals at high pH (pH ≈ 10.5) through pathways similar to those proposed by Lu et al. [15] and Chuang et al. [70]. Alternatively, organic chloramines formed from BSA can undergo photolytic decomposition to generate hydroxyl radicals directly, as demonstrated by Wang et al. [16]. Although the laboratory investigations were conducted under ambient lighting conditions, trace amounts of UV radiation could potentially initiate photolytic decomposition. Future research would be valuable to identify and confirm this potential pathway. 93 Figure 5.5 Pictorial illustration of potential pathways for hydroxyl radical generation from BSA -- hypochlorite interactions A detailed investigation of the radical oxidant generation pathways was beyond the scope of the present research. Future research would be valuable to identify and quantify the specific radical species formed during BSA-hypochlorite interactions at high pH. Using advanced approaches like Electron Paramagnetic Resonance (EPR) and stopped-flow kinetics, the reaction rates and intermediate species involved in the radical oxidant generation pathway could be further elucidated, providing crucial insights to develop targeted radical scavenging strategies for membrane chemical cleaning protocols. 94 5.4 Membrane Ageing Resulting from Different NOM Foulant – Hypochlorite Interactions To assess if BSA foulant – hypochlorite interactions are predominately responsible for increasing the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing, cyclic ageing experiments, similar to those outlined in Section 4.3, were performed over cumulative exposure doses ranging from 0 to 1,300,000 ppm·h. Changes in physical and chemical characteristics and hydraulic performance during cyclic ageing with feed waters containing individual model NOM components (i.e., only BSA, only SA, and only HA) were compared to those with feed waters containing no model NOM components (i.e., distilled water), and those with feed waters containing a mixture of model NOM components (i.e., results from Section 4.3). 5.4.1 Changes in Membrane Characteristics Changes to the chemical characteristics of the membrane surfaces were quantified by differential FTIR (ΔFTIR) as outlined in Chapter 4. The areas of the peaks in the ΔFTIR spectra provide a semi-quantifiable measure of the extent of change in the characteristics of the chemicals on the membrane surface [87]. The predominant ΔFTIR peak for aged membranes at selected cumulative exposure dose intervals, hereafter referred to as exposure doses, considered are presented in Figures 5.6a-c. 95 Figure 5.6 Predominant ΔFTIR peaks of cyclic aged membranes when considering feed waters containing (a) only SA and only HA, (b) only BSA and (c) correlation when considering feed waters containing only BSA and mixture of model NOM components. Dashed lines correspond to 1:1 relationship of the predominant ΔFTIR peak for feed waters containing only BSA and mixture; confidence intervals of the predominant ΔFTIR peak correspond to the maximum and minimum of 4 replicate measurements; results for the mixture of model NOM components are the same as those previously presented in Chapter 4; shaded dark and light grey lines added to illustrate and contrast overall trends observed for aged membranes; note that results for feed water with no model NOM components are same in both a and b. a b c 96 The rate of change of the predominant ΔFTIR peak with increasing exposure dose for feed waters containing only HA or SA was relatively constant (Figure 5.6a), and similar to that for feed waters containing no model NOM components. These results indicate that the presence of HA and SA in the feed water did not impact the rate of change in the chemical characteristics of the membrane surface during cyclic ageing. In contrast, at low cumulative exposure doses (< 100,000 ppm·h), the rate of change of the predominant ΔFTIR peak was greater for feed waters containing only BSA (Figure 5.6b) than that with feed waters containing only HA or SA or no model NOM components, indicating that the presence of BSA increased the rate of change in the chemical characteristics of the membrane surface during cyclic ageing. The rate of change of the predominant ΔFTIR peak decreased at higher cumulative exposure doses (> 300,000 ppm·h), eventually reaching steady state conditions, suggesting that the chemical characteristics of membrane no longer changed. The predominant ΔFTIR peak was similar for feed waters containing only BSA and no model NOM components at highest exposure dose (i.e., 1,300,000 ppm·h), consistent with results in Section 4.3.1. As such, these results indicate that although BSA can increase the rate of overall change in the chemical characteristics of the surface of aged membranes, it did not impact the ultimate overall change. The rates of change in the predominant ΔFTIR peaks for feed waters containing only BSA were however similar to those for feed waters containing a mixture of model NOM components despite a 4-fold difference in the concentration of BSA in these feed waters; such that a strong correlation was observed when comparing the rates at different cumulative exposure doses 97 investigated (Figure 5.6c). It is possible that for the conditions examined, the concentration of BSA foulants present on the membrane was consistently greater than the threshold concentration of BSA above which the amount of radical oxidants present is relatively constant (see Section 5.3), and therefore ageing would not be impacted by the amount of BSA foulants present on the membrane. It is also possible that similar amounts of BSA foulants were present on the membrane despite the 4-fold difference in the concentration of BSA in these feed waters. The amount of BSA foulants present on the membrane at the start of chemical cleaning was quantified by ΔFTIR (Section 3.4.3). Membrane fibers were harvested from the test modules immediately after hydraulic cleaning and before the subsequent chemical cleaning of filtration cycles that preceded selected exposure dose intervals. The area of the BSA characteristic peak in the ΔFTIR provides a semi-quantifiable measure of the amount of BSA foulants present on the membrane [87,96]. A 1:1 relationship was observed between BSA characteristic peaks for feed waters containing only BSA and feed water containing a mixture of model NOM components at selected exposure dose intervals investigated (Figure 5.7), indicating that similar amounts of BSA foulants present on the membrane despite a 4-fold difference in BSA concentration between these feed waters. The results suggest that NOM components in the mixture, other than BSA, could have enhanced the retention of BSA on the membrane, resulting in similar amounts of BSA foulants present on membrane for both feed waters containing only BSA, at a relatively high concentration, and feed waters containing a mixture of model NOM components, including BSA, but at a relatively low 98 concentration. Studies by others have also reported that retention of BSA on a membrane is greater when other NOM components (e.g., SA) are present [63,123–125]. Investigations into the impact of different NOM components on the membrane fouling are beyond the scope of the present research; nonetheless, the results indicate that the similar rates of change in the predominant ΔFTIR peak for feed waters containing only BSA and those for feed waters containing a mixture of model NOM components was likely due to the similar amounts of BSA foulants present on the membrane at the start of chemical cleaning for both feed waters. Figure 5.7 Correlation of the BSA characteristic peak when considering feed waters containing only BSA and a mixture of model NOM components. Confidence intervals correspond to the maximum and minimum of 2 replicate measurements; dashed lines correspond to 1:1 relationship of the BSA characteristic peak for feed waters containing only BSA and mixture. 99 FESEM images of cyclic aged membranes at select exposure doses, presented in Figure 5.8, are consistent with the previously discussed changes, as in Section 4.3.1, in membrane surface characteristics: with increased exposure dose, the membrane surface becomes increasingly heterogeneous and accentuated with crests and crevasses. Figure 5.8 FESEM images for virgin (0 ppm·h) and cyclic aged (1,300,000 ppm·h) membranes when considering feed waters containing (a) only HA, (b) only SA, (c) only BSA, (d) mixture of model NOM components and (e) no model NOM components. 100 FESEM images were only taken for virgin and extensively aged membranes (i.e., 1,300,000 ppm·h); and therefore, a quantitative analysis of the rate of change in pore radius, pore number and/or surface roughness, as presented in Section 4.3.1, was not possible. However, the FESEM images nonetheless revealed that for feed waters containing only HA and only SA, the pore number remained constant during cyclic ageing, and similar to those for feed waters containing no model NOM components. These results indicate that the presence of HA and SA in the feed water did not impact the pore number during cyclic ageing. For feed waters containing only BSA, the pore number appeared to decrease during cyclic ageing (i.e., pores could not be observed at highest cumulative exposure dose), suggesting that changes induced by ageing resulted in pores being covered. Similar decreases in pore number during membrane ageing have been reported in studies by others, which was attributed to pore fusion and membrane structure collapse [39,49]. 5.4.2 Changes in Membrane Hydraulic Performance Changes to hydraulic performance were quantified based on clean water resistance with increasing cumulative exposure dose. The clean water resistance for aged membranes at selected exposure doses are presented in Figure 5.9a - c. 101 Figure 5.9 Clean water resistance for cyclic aged membranes (a) when considering feed waters containing only SA and only HA, (b) when considering feed waters containing only BSA and (c) correlation when considering feed waters containing only BSA and mixture of model NOM components. Dashed lines correspond to 1:1 relationship of clean water resistances for feed waters containing only BSA and mixture; confidence intervals for clean water resistance correspond to the maximum and minimum of 3 replicate measurements; results for the mixture of model NOM components are the same as those previously presented in Chapter 4; shaded dark and light grey lines added to illustrate and contrast overall trends observed for aged membranes; arrows added to illustrate the extent of the decrease resulting from the permeation of hypochlorite during chemical cleaning; note that results for feed water with no model NOM components are same in both a and b. a b c 102 For feed waters containing no model NOM components, a slight but significant decrease in clean water resistance was initially observed, followed by a relatively constant clean water resistance. The observed changes were consistent with soak ageing as outlined in Section 4.3.2 and previous work by our group [10]. A decrease in clean water resistance with increasing exposure dose has also been reported by others for soak ageing type studies [27,36,42,43]; and has commonly been attributed to an increase in the size of membrane pores with increasing dose [27,36,42,43]. For feed waters containing only HA, the clean water resistance was observed to be similar to that for feed waters containing no model NOM components (Figure 5.9a). These results suggest that the presence of HA in the feed water did not impact the hydraulic performance as membranes aged. As previously discussed in Section 5.3 and 5.4.1, SA was not observed to generate radical oxidants during chemical cleaning or impact chemical characteristics of membrane surface of aged membranes and therefore not expected to impact membrane ageing. However, for feed waters containing only SA, an increase in clean water resistance was nonetheless observed at lower cumulative exposure doses (< 500,000 ppm·h), followed by relatively constant resistance at higher cumulative exposure doses (Figure 5.9a). We hypothesize that the impact of SA on change in clean water resistance was due to sheltering and accumulation within pores. SA within membrane pores may have been sheltered from exposure to hypochlorite during chemical cleaning and as a result may have accumulated within pores, affecting clean water resistance. Recall that results for Chapter 4 also suggested that the accumulation of model NOM foulants within pores can occur during cyclic ageing. 103 To address this hypothesis, added cyclic ageing investigations were performed with permeation of hypochlorite during chemical cleaning, as outlined in Section 3.2.2.2. As presented in Figure 5.9a, the clean water resistance for feed waters containing only SA with permeation of hypochlorite during chemical cleaning was similar to those for feed waters containing no model NOM components and feed waters containing only HA. The results indicate that SA, when effectively removed by chemical cleaning, did not impact hydraulic performance as membrane age. The results also suggest that the model NOM foulants that were observed to be sheltered and accumulated within the membrane pores for feed waters containing a mixture of model NOM components (Section 4.3.2) was likely SA. For feed waters containing only BSA, the clean water resistance did not increase substantively at lower exposure doses (< 300,000 ppm·h), but increased significantly at higher exposure doses, consistent with the results, presented in Section 4.3.2, for feed waters containing a mixture of model NOM components (Figure 5.9b). Results from added cyclic ageing with the permeation of hypochlorite during chemical cleaning suggest no significant accumulation within pores for feed waters containing only BSA. Considering that for the different model NOM components investigated, radial oxidants were only formed from BSA – hypochlorite interactions (Section 5.3), the increase in clean water resistance was likely due to BSA-induced changes to the membrane characteristics. The range of exposure doses over which no increase in clean water resistance was observed (i.e., 0 – ~300,000 ppm·h) for feed waters containing only BSA was narrower than the range (i.e., 0 – ~500,000 ppm·h) for feed waters containing a mixture of model NOM components (Section 104 4.3.2). In addition, a correlation analysis revealed that the clean water resistance observed for feed waters containing only BSA was generally greater than that for feed waters containing a mixture of model NOM components (Figure 5.9c). These results indicate that although similar amounts of BSA foulants were present on the membrane for both feed waters (Figure 5.7), BSA had a greater negative impact on hydraulic performance when no other model NOM components were present. These results suggest that the presence of other model NOM components may be beneficial in mitigating the impact of BSA on clean water resistance. Studies by others have reported that model NOM components, such as SA, could scavenge radical oxidants [72–75]. This scavenging capacity could mitigate the impacts of BSA when in a mixture of model NOM foulants. Although our screening experiment (Section 5.3) did not reveal that SA could substantially scavenge radical oxidants, it could nonetheless have scavenged some trace amounts of radical oxidants, which could have impacted hydraulic performance, over multiple cycles, during cyclic ageing. Further investigations, beyond the scope of the present research, are required to completely assess the scavenging capacities of other model NOM components. As outlined in Section 4.3.2, the increase in clean water resistance may have resulted from changes to the membrane pore number and filtration layer pore length. Unfortunately, for feed waters containing only BSA, pores could not be consistently observed from FESEM images of the membrane surface and therefore an analysis, as presented in Section 4.3.2, could not be performed. Again, the overall trends observed in the present research for changes to clean water resistance for feed waters containing only BSA are consistent with those reported by others [27]; however, 105 they are not consistent with previous cyclic ageing studies by our group [10]. As previously discussed in Section 4.3.2, this discrepancy is likely due to differences in the membrane chemical formulations and experimental approaches used in the present research and previous research by our group. 5.5 Mitigating the Impact of Radical Oxidants and Chemically Irreversible Foulants As discussed in Section 5.4, radical oxidants generated by BSA – hypochlorite interactions are expected to increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. Also, as previously discussed in Section 4.5, scavengers can quench radical oxidants generated from interaction between mixed model NOM components and hypochlorite. Therefore, it is reasonable to expect that the rate and extent of membrane ageing resulted from BSA – hypochlorite interactions could be reduced by adding scavengers to the hypochlorite solution used for chemical cleaning [17–26]. To gain insight into the potential impact of scavenging radical oxidants generated from BSA – hypochlorite interactions during chemical cleaning on membrane ageing, additional cyclic ageing was performed with the addition of sodium bicarbonate to the chemical cleaning solution, consistent with the approach outlined in Section 4.5. Sodium bicarbonate was chosen because it is a non-selective radical scavenger [76,77], and it is commonly available at drinking water treatment facilities for use in other processes. Changes to the chemical characteristics of the membrane surface and the hydraulic performance were quantified using ΔFTIR and clean water resistance, respectively. Differences in membrane characteristics and hydraulic performance 106 between cyclic ageing conditions with and without the non-selective scavenger were attributed to radical oxidants generated from BSA - hypochlorite interactions during chemical cleaning. The predominant ΔFTIR peaks for cyclic aged membranes with and without the addition of sodium bicarbonate to scavenge radical oxidants generated from BSA – hypochlorite interactions, are presented in Figure 5.10a. As discussed in Chapter 5.4.1, in the absence of radical scavengers, the rate of change of the predominant ΔFTIR peak was high at low exposure doses, and decreased with increasing doses, eventually being zero. With the addition of the radical scavenger to the chemical cleaning solution, the rate of change of the predominant ΔFTIR peak was much lower, and remained relatively constant with increasing exposure dose, consistent with the results for feed waters containing no model NOM components. These results suggest that the changes in chemical characteristics of the membrane surface when considering feed waters containing only BSA could be reduced with the addition of a radical scavenger to the chemical cleaning solution, with the benefits most pronounced at lower exposure doses when membrane is in its early ageing stages. 107 a b c Figure 5.10 Predominant ΔFTIR peak (a) and clean water resistance (b) for cyclic aged membrane when considering feed waters containing only BSA with and without addition of the radical scavenger to the chemical cleaning solution, and correlation of clean water resistance when considering feed waters containing only BSA and mixture of model NOM components with addition of radical scavenger (c). Dashed lines correspond to 1:1 relationship of clean water resistances for feed waters containing only BSA and mixture; confidence intervals of the predominant ΔFTIR peak correspond to the maximum and minimum of 4 replicate measurements; confidence intervals for clean water resistance correspond to the maximum and minimum of 3 replicate measurements; results for cyclic ageing without addition of sodium bicarbonate are the same as those previously presented in Figures 5.6b and 5.9b; shaded lines added to illustrate overall trends observed for different conditions investigated; arrows added to illustrate the extent of the decrease resulting from the addition of sodium bicarbonate.108 The clean water resistances for cyclic aged membranes for feed waters containing only BSA with and without the addition of sodium bicarbonate to scavenge radical oxidants generated from BSA – hypochlorite interactions, are presented in Figure 5.10b. As previously discussed in Section 5.4.2, in the absence of a scavenger, the clean water resistance did not increase substantively at lower exposure doses (< 300,000 ppm·h), consistent with the results for feed waters containing no model NOM components, but increased significantly at higher exposure doses. The range of exposure doses over which no increase in clean water resistance was observed (i.e., 0 – ~500,000 ppm·h) was wider for cyclic ageing with, than without, the addition of sodium bicarbonate. The clean water resistance did increase at higher exposure doses; however, the magnitude remained consistently lower for cyclic ageing with, than without, the addition of sodium bicarbonate. These results suggest that the hydraulic performance of the membrane could be improved by the addition of sodium bicarbonate as a radical scavenger to chemical cleaning solution for feed waters containing only BSA, with the benefits becoming more significant at higher exposure doses when the membrane is in its later ageing stages. While the addition of scavengers appears most beneficial for preserving membrane characteristics at early stages of ageing (lower cumulative exposure doses), the hydraulic performance benefits are more pronounced at later stages (higher cumulative exposure doses). The results suggest that even when chemical characteristics appear to stabilize in aged membranes, the addition of radical scavengers to cleaning solutions could still provide significant benefits to hydraulic performance. Further investigations are required to understand 109 the differences between changes in membrane characteristics and hydraulic performance with the addition of radical scavengers, as this insight could inform further improvements to metrics of membrane replacement timing and chemical cleaning protocols. A correlation analysis of the clean water resistance for cyclic ageing with the addition of sodium bicarbonate revealed greater values for feed waters containing only BSA than that for feed waters containing a mixture of model NOM components (Figure 5.10c). These results indicate that although similar amounts of BSA are expected to be present on membrane as foulants for both types of feed waters (Figure 5.7), and radical oxidants would be expected to be absent (i.e., quenched) for both conditions, BSA nonetheless had a greater negative impact on hydraulic performance when no other model NOM components were present, consistent with the results presented in Section 5.4. The results for cyclic ageing with addition of sodium bicarbonate as a scavenger to the chemical cleaning solution, support our hypothesis that BSA interacts with hypochlorite during chemical cleaning to generate radical oxidants that accelerate the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing (or more accurately, their absence reduces the rate at which these changes occur). These results also suggest that the changes associated with membrane ageing could be mitigated, to some extent, when treating source waters that naturally contain relatively high concentrations of radical scavengers; and possibly that these changes could be proactively lowered by adding radical scavengers to hypochlorite cleaning solution for source waters that do not naturally contain 110 sufficient scavengers to quench the radical oxidants. Alternatively, BSA-like substances (i.e., protein type NOM) could be removed during pretreatment, such that they would not be present, or present a lower concentration, on the membrane as foulants. 5.6 Conclusions The outcomes outlined in Chapter 5 address Objective 2 regarding the role of different model NOM components and their interactions with hypochlorite during chemical cleaning. By considering cyclic ageing approaches with feed waters containing only BSA, SA, HA and a mixture of model NOM foulants, we demonstrated that BSA – hypochlorite interactions were responsible for the generation of radical oxidants during chemical cleaning, and that these radical oxidants can increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. The addition of radical scavengers to the chemical cleaning solution could reduce the detrimental effects of BSA-hypochlorite interactions, suggesting that scavenger addition could be a strategy to reduce ageing of PVDF UF membrane. The research outlined in Chapter 5 provides a deeper understanding of the specific roles of different model NOM components in membrane ageing and offers potential avenues for improving the long-term performance of membranes based on source water characteristics. 111 Further studies are needed to investigate the mechanisms of BSA-induced radical generation and to optimize scavenger applications across various operational conditions. 112 Chapter 6: Relationship of Ageing at Laboratory and Full-Scale A version of this chapter is being reviewed and prepared to be submitted to a peer-reviewed journal. 6.1 Introduction The results presented in Chapters 4 and 5 indicate that BSA is the primary contributor to the generation of radical oxidants from model NOM foulant - hypochlorite interactions during chemical cleaning, and that these radical oxidants can increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. BSA was selected as a surrogate of protein type NOM in real feed waters at full-scale membrane facilities [123,126]. Therefore, it is reasonable to expect that the presence of protein type NOM in real feed waters could also impact membrane ageing at full-scale facilities. Investigations into membrane ageing at full-scale facilities is challenging, requiring monitoring over multiple years of operation [7]. Laboratory accelerated ageing approaches are commonly used to investigate changes in membrane physical and chemical characteristics and hydraulic performance over a relatively short time scale. As previously discussed in Chapter 2, soak ageing, the most commonly used accelerated ageing approach, only considers the effects of chemical cleaning agents on membrane ageing, ignoring the contribution of other factors, relevant to full-scale facilities, on membrane ageing. Cyclic ageing was developed to investigate membrane ageing under conditions that are more representative of full-scale facilities [10,27]. 113 Although more representative than soak ageing, cyclic ageing still does not consider the contribution of all factors, relevant to full-scale facilities, on membrane ageing [10]. Therefore, it is possible that outcomes from laboratory accelerated ageing approaches, such as those outlined in Chapters 4 and 5, although insightful, may not be useful to guide the development of strategies to minimize membrane ageing at full-scale. The research outlined in Chapter 6 aims to address Objective 3, which is to assess if the outcomes from addressing Objective 1 and Objective 2, using laboratory accelerated ageing approaches are representative of those expected at full-scale facilities and therefore can guide the development of strategies to minimize membrane ageing at full-scale. Because BSA, a model protein type NOM, was identified as the primary contributor to the generation of radical oxidants that accelerated ageing during chemical cleaning, the assessment considers changes in physical and chemical characteristics and hydraulic performance of membranes with respect to concentration of protein type NOM in the feed waters from full-scale facilities. The approach used focuses on first confirming the generation of radical oxidants from interactions between protein type NOM, present in real feed waters from full-scale facilities, and hypochlorite used in chemical cleaning, hereafter referred to as real protein type NOM – hypochlorite interactions. The changes in physical and chemical characteristics and hydraulic performance observed for full-scale aged membranes with feed waters containing different concentrations of real protein type NOM were then compared to those observed for cyclic aged membranes at similar concentrations of model protein type NOM. 114 6.2 Summary of Full-Scale Investigation Methods To investigate the generation of radical oxidant from real protein type NOM – hypochlorite interactions, a series of screening experiments was performed using methylene blue as a chemical probe to gauge the generation of radical oxidants, as outlined in Section 3.2.3. The physical and chemical characteristics and hydraulic performance of membranes harvested from full-scale facilities over an 8+ year period were analyzed as outline in Section 3.4. The concentrations of the different NOM components in the feed waters for cyclic ageing and those for the full-scale facilities were quantified using LC-OCD-OND as outlined in Section 3.3.1.2. The experimental approach is schematically illustrated in Figure 6.1. Details of materials and methods can be found in Chapter 3, and added details of the experimental design are presented in the sections which follow. 115 Figure 6.1 Experimental approaches used for addressing Objective 3.The approaches not relevant to Objective 3 are shaded (grey). Superscripts 1, 2 and 3 represent which components of the experimental and analytical approaches correspond to Objectives 1, 2 and 3. 116 6.3 Assessment of Radical Oxidant Generation for Different Protein Type NOM Concentrations in Real Feed Waters To investigate the generation of radical oxidants from real protein type NOM – hypochlorite interactions, a series of screening experiments was performed where methylene blue and hypochlorite were added to real feed waters or distilled waters at a pH of 10.5 ± 0.2. The concentration of methylene blue in the solutions was monitored over time. Methylene blue is known to be oxidized more rapidly by radical oxidants than by hypochlorite [78,79]. As discussed in Chapters 4 and 5, the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing would be expected to be greater for the feed waters containing more protein type NOM. The protein type NOM concentrations in the real feed waters from the four full-scale facilities considered, hereafter referred to as feed waters from facilities A, B, C and D, are presented in Table 6.1. The feed waters from facilities A and B had relatively low protein type NOM concentrations, while feed waters from facilities C and D had relatively high protein type NOM concentrations. For reference, other types of NOM component are also presented in Table 6.1. 117 Table 6.1 Protein type NOM concentrations in real feed waters (mg as C/L) Facility Concentration of NOM Component Protein Type Polysaccharide Type Humic Substance Type Total A 0.0086 ± 0.00067 0.028 ± 0.0022 1.00 ± 0.023 1.04 ± 0.024 B 0.013 ± 0 0.023 ± 0 1.26 ± 0.0087 1.30 ± 0.087 C 0.051 ± 0 0.27 ± 0 1.12 ± 0 1.44 ± 0 D 0.080 ± 0 0.27 ± 0 1.08 ± 0 1.43 ± 0 Confidence intervals correspond to the maximum and minimum of 2 replicate measurements The removal of methylene blue during the screening experiments for feed waters from facilities A, B, C and D are presented in Figure 6.2a. Consistent with the results in Sections 4.4 and 5.3, methylene blue removal rate was relatively low (0.0019 ± 6.8 ×10-5) in the absence of NOM (i.e., only hypochlorite present) [78,79]. For feed waters from facilities A and B, increased removal rates were observed, suggesting the generation of some of radical oxidants. For feed waters from facilities C and D, the methylene blue removal rates were highest (i.e., among feed waters from facilities A, B, C and D). In the absence of hypochlorite, the removal of methylene blue, for all feed waters, was negligible, as in Section 4.4. 118 a b Figure 6.2 Impact of NOM in real feed waters from full-scale facilities (a) on the concentration of methylene blue in a solution containing hypochlorite and (b) on the methylene blue removal rate.C0 is the initial methylene blue concentration; C is the concentration at given time; solid lines correspond to linear regression models fitted to the data, k and R2 are the slope and coefficients of determination of the regressions; confidence intervals for concentration data and rates obtained from linear regression correspond to the maximum and minimum values from 2 replicate experiments. The methylene blue removal rates with respect to protein type NOM concentrations in feed waters from facilities A, B, C and D are presented in Figure 6.2b. Feed waters from facilities A and B had relatively low protein type NOM concentrations and correspondingly relatively low methylene blue removal rates, while feed waters from facilities C and D had relatively high protein type NOM concentrations and relatively higher methylene blue removal rates. These results suggest that real protein type NOM – hypochlorite interaction, could substantively 119 contribute to the generation of radical oxidants, consistent with results in Section 5.3. In addition, the results suggest that the extent of generation of radical oxidants is greater for feed waters that contain more protein type NOM, again consistent with results in Section 5.3. Protein type NOM in real feed waters likely contributes to the generation of radical oxidants, which is consistent with protein type NOM-hypochlorite interactions reported in studies by others [15,16,65,70]. As previously discussed in Section 5.3, the pathway for the generation of radical oxidants is likely similar to that proposed by Lu et al. [15], Chuang et al. [70] or Wang et al. [16], where chloramines, formed from protein type NOM, decompose to hydroxyl radicals at high pH, as described in Section 2.4.2. A detailed investigation of the radical oxidant generation pathways was beyond the scope of the present research. As expected, based on the results from Section 5.3, the methylene blue removal rates were not correlated to the concentration of humic substance type NOM in feed waters from facilities A, B, C and D; the lack of a correlation (Appendix D.4) supports our contention that humic substance type NOM in the feed waters does not contribute to the generation of radical oxidants. However, a correlation was observed between methylene blue removal rates and the concentration of polysaccharide type NOM (Appendix D.4). The occurrence of a correlation was likely due to the fact that, for the feed waters considered, the concentration of polysaccharide type NOM was also correlated to the concentration of protein type NOM in the feed waters. As discussed in Section 5.3, no radical oxidants are expected to be generated from polysaccharide type NOM. 120 6.4 Membrane Ageing at Laboratory (Cyclic) and Full-Scale As discussed in Section 5.4, membrane ageing is expected to be impacted by the amount of protein type NOM present on the membrane as foulants at the start of chemical cleaning. Unfortunately, this amount cannot be directly measured because the membranes from facilities A, B, C and D were harvested after chemical cleaning; when properly performed, chemical cleaning removes NOM foulants from the membrane [9,33]. However, it is reasonable to expect that the amount of protein type NOM present on the membrane as foulants at the start of chemical cleaning would be greater for membranes filtering feed waters with higher loading of protein type NOM (i.e., higher concentrations and volumes). For this reason, in the discussions which follow, changes in physical and chemical characteristics and hydraulic performance of the membranes were assessed relative to the loading of protein type NOM. The protein type NOM loading rate was defined as the amount of protein type NOM filtered per unit membrane area per unit exposure dose of hypochlorite (mg/m²/ppm·h). In the discussion which follows, cyclic ageing with feed waters containing no model NOM is referred to as cyclic ageing with no protein type NOM; while cyclic ageing with feed waters containing only BSA, for which the concentration of protein type NOM was relatively high, is referred to as cyclic ageing with high protein type NOM. Similarly, ageing at full-scale for facilities A and B, with feed waters containing relatively low protein type NOM concentrations, is referred to as full-scale ageing with low protein type NOM; while ageing at full-scale for facilities C and D, with feed waters 121 containing relatively high protein type NOM concentrations, is referred to as full-scale ageing with high protein type NOM. For membranes from cyclic ageing and full-scale facilities, the protein type NOM loading rates from feed waters are illustrated in Figure 6.2. Based on the results presented in Chapters 4, 5 and Section 6.2, full-scale ageing with low protein type NOM (facilities A and B) would be expected to be more similar to cyclic ageing with no protein type NOM, while full-scale ageing with high protein type NOM (facilities C and D) would be expected to be more similar to cyclic ageing with high protein type NOM. 122 Figure 6.3 Protein type NOM loading rate for membranes harvested from full-scale facilities and cyclic ageing. Confidence intervals for protein type NOM loading rate from feed waters correspond to the maximum and minimum of 2 replicate measurements; results for cyclic ageing with and without protein type NOM are the same as those with and without BSA as previously presented in Chapter 5. 6.4.1 Changes in Membrane Characteristics for Laboratory (Cyclic) and Full-Scale Aged Membranes Changes to the chemical characteristics of the membrane surface were quantified by differential FTIR (ΔFTIR) as outlined in Chapter 4. The area of the peaks in the ΔFTIR spectra provides a semi-quantifiable measure of the extent of change in the characteristics of the chemicals on the 123 membrane surface [87]. The predominant ΔFTIR peak for membranes from cyclic ageing and full-scale facilities at the relevant cumulative exposure dose intervals, hereafter referred to as exposure doses, considered are presented in Figure 6.3. For cyclic ageing, the exposure doses correspond to those selected to undertake the investigations outlined in Chapters 4 and 5. For ageing at full-scale, the exposure dose corresponds to the exposure dose at the time of harvesting, converted so that it can be compared directly to the cumulative exposure dose for cyclic ageing (see Section 3.3.1.1). a b Figure 6.4 Predominant ΔFTIR peak (a) and clean water resistance (b) of membranes from cyclic ageing and full-scale facilities. Confidence intervals for predominant ΔFTIR peak and clean water resistance data correspond to the maximum and minimum of 2 replicate measurements; shaded dark and light grey lines added to illustrate and contrast overall trends observed for aged membranes. 124 The rate of change of the predominant ΔFTIR peak with increasing exposure dose for membranes from facilities A and B was relatively constant (Figure 5.3a), and similar to those from cyclic ageing with no protein type NOM. These results suggest that the low protein type NOM loading rate for facilities A and B did not significantly contribute to increasing the rate of change in the chemical characteristics of the membrane surface during full-scale ageing. In contrast, the rate of change of the predominant ΔFTIR peak for membranes from facilities C and D was relatively steady and similar to those from cyclic ageing with high protein type NOM. Also, the predominant ΔFTIR peaks for membranes from facilities C and D were consistently greater than those from facilities A and B and cyclic ageing with no protein type NOM. Unfortunately, no results are available for membranes from full-scale facilities at lower exposure doses (i.e., < 500,000 ppm·h); however, the similarity between membranes from full-scale and those from cyclic ageing at higher exposure doses, suggests that similarities would also be expected for membranes from full-scale facilities, and those from cyclic ageing, at lower exposure doses. 6.4.2 Changes in Hydraulic Performance for Laboratory (Cyclic) and Full-Scale Aged Membranes Changes to hydraulic performance were quantified based on clean water resistance with increasing cumulative exposure dose. The clean water resistances for both membranes from cyclic ageing and full-scale facilities were normalized to the clean water resistance of virgin 125 membranes. The normalized clean water resistances for membranes from cyclic ageing and full-scale facilities at exposure doses considered are presented in Figure 6.4b. For membranes from facilities A and B, the clean water resistance remained relatively constant at lower exposure doses (< 500,000 ppm·h), and increased slightly at higher exposure doses. The clean water resistance was observed to be between that for membranes from cyclic ageing with no protein type NOM and cyclic ageing with high protein type NOM. These results suggest the low protein type NOM loading rate for facilities A and B contributed, but only slightly, to change in the hydraulic performance of the membranes during full-scale ageing. For membranes from facilities C and D, the clean water resistance also remained relatively constant at lower exposure doses (< 300,000 ppm·h), however then increased significantly at higher exposure doses, consistent with results for membranes from cyclic ageing with high protein type NOM. These results suggest that the high protein type NOM loading rates for facilities C and D significantly contributed to change in the hydraulic performance of the membranes during full-scale ageing. Also, the range of exposure doses over which the clean water resistance remained relatively constant was narrower (i.e., 0 – ~300,000 ppm·h) for membranes from facilities C and D than the range observed (i.e., 0 – ~500,000 ppm·h) for membranes from facilities A and B. These results also suggest that the high protein type NOM loading rate for facilities C and D could effectively shorten the range of exposure doses for which hydraulic performance would be expected to remain relatively constant, before significant changes. 126 6.4.3 Relationship of Ageing at Laboratory (Cyclic) and Full-Scale Based on the results outlined in Sections 6.4.1 and 6.4.2, we concluded that changes in physical and chemical characteristics and hydraulic performance of membranes from cyclic ageing are similar to those from ageing at full-scale facilities A, B, C and D when considered with respect to protein type NOM loading rates, with the extent of the changes being impacted by the magnitude of the protein type NOM loading rate. The extent of change, as they relate to the loading rates, are pictorially summarized in Figures 6.5a and b. a b Figure 6.5 Pictorial illustration of changes in the predominant ΔFTIR peak (a) and clean water resistance (b) with increasing exposure dose for cyclic and full-scale aged membranes. Solid and dashed lines in black represent the changes for membranes from cyclic ageing with no protein type NOM and high protein type NOM, respectively; solid and dashed lines in orange represent the changes for membranes from full-scale facilities A and B, respectively; solid and dashed lines in red represent the changes for membranes from facilities C and D, respectively; arrows represent overall increasing changes with increasing protein type NOM loading rate; circles indicate threshold doses above which clean water resistance begins to increase. 127 However, it is not yet possible to confirm that laboratory accelerated ageing approaches are representative of those expected at full-scale facilities. Added investigations are needed to establish a clear relationship between protein type NOM loading rate, for multiple increments over a wide range, and the extent of change in physical and chemical characteristics and hydraulic performance as membrane age. The present research nonetheless suggests the followings. 1. The rate at which physical and chemical characteristics change is expected to be greater for feed waters containing greater concentrations of protein type NOM. 2. A decrease in the timeline prior to which a change in hydraulic performance is expected to occur, and an increase in the rate at which the change occurs is expected with respect to the concentrations of protein type NOM in the feed waters. The results outlined in Sections 4.5 and 5.5 also indicate that laboratory accelerated ageing can guide the development of strategies to minimize membrane ageing at full-scale; one of the major objectives of the present research. 6.5 Conclusions The outcomes outlined in Chapter 6 address Objective 3 regarding the roles of protein type NOM in feed waters on membrane ageing at full-scale. 128 The research demonstrates that facilities with feed waters containing higher protein-type NOM concentrations are likely to generate more radical oxidants during chemical cleaning, and that these radical oxidants are likely to increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. The changes in physical and chemical characteristics and hydraulic performance for laboratory accelerated cyclic ageing were similar to those for ageing at full-scale when considered with respect to the loading of protein type NOM from feed waters. 129 Chapter 7: Conclusions This research contributed to improving the understanding of membrane ageing in drinking water treatment systems, providing insight into the impact of NOM foulant - hypochlorite interactions during chemical cleaning on ageing. 7.1 Findings and Contributions The outcomes from this dissertation establish that interactions between NOM foulants and hypochlorite during chemical cleaning play a crucial role in membrane ageing through the generation of radical oxidants. By addressing Objective 1 (Chapter 4), the research confirmed that interactions between model NOM foulants (mixture of BSA, SA, and HA) and hypochlorite during chemical cleaning generate radical oxidants, primarily hydroxyl radicals, and that these increase the rate at which changes in membrane physical and chemical characteristics and hydraulic performance occur, as membrane age. The research was also able to identify potential evidence based strategies to mitigate the impact of radical oxidants on membrane ageing. By addressing Objective 2 (Chapter 5), the research identified that BSA, a protein type component in our model NOM mixture, was the primary contributor to the generation of radical oxidants when interacting with hypochlorite during chemical cleaning. The other model NOM components had negligible impacts on radical oxidant generation and membrane ageing. 130 By addressing Objective 3 (Chapter 6), the research demonstrated that feed waters that contain higher protein type NOM concentrations are likely to generate more radical oxidants during chemical cleaning, and that these radical oxidants are likely to increase the rate at which changes in physical and chemical characteristics and hydraulic performance occur during membrane ageing. The results for laboratory accelerated cyclic ageing were similar to those for ageing at full-scale when considered with respect to the loading of protein type NOM from feed waters. 7.2 Research Gaps and Future Directions The research outcomes outlined in this dissertation have advanced our understanding of membrane ageing in drinking waters treatment applications by identifying and characterizing the role of NOM foulant – hypochlorite interactions. The research is the first to identify the predominant type of NOM components in feed water that accelerate ageing through interactions with hypochlorite, offering insight that explains differences in ageing observed at different full-scale facilities. The research is also the first to identify approaches to minimize the impacts of NOM foulant – hypochlorite interactions, potentially enabling extending membrane operational lifespan. Although the research outcomes begin to shed light into the specific mechanisms responsible for membrane ageing, because of the overall goal (i.e., to identify and quantify the impact of interactions between NOM foulants and chemical cleaning agents on membrane ageing), the research outcomes are largely observational. To fully realize the potential of the outcomes from 131 the present research, future research should focus on the mechanisms governing the observations from the present research. Listed below are some areas of future study that would complement and expand on the outcomes from the present research, so that its full potential can be realized. • Assess protein type NOM accumulation on the membrane and identify the conditions and mechanisms that promote the accumulation. This is needed to understand the extent to which protein NOM is present on the membrane as foulants. • Identify conditions and mechanisms governing protein type NOM - hypochlorite interactions that lead to the generation of radical oxidants and changes to physical and chemical characteristics and hydraulic performance as membranes age. This is needed to enable a quantitative analysis of the potential of these interactions to accelerate ageing. • Explore advanced characterization techniques to monitor membrane material changes during ageing. Current methods provide limited insight into the progressive overall change of membrane physical and chemical characteristics. Advanced characterization approaches are needed to track specific material changes. • Establish how different membrane components are impacted by radical oxidants. Although research in this area has already been undertaken, a compilation of this past research, and additional new research, is needed to assess the potential of NOM – hypochlorite interaction for accelerated ageing for different membrane types. • Investigate pore geometry changes throughout the ageing process. The present research primarily focused on overall changes in physical and chemical characteristics and 132 hydraulic performance, but detailed understanding of relationship between pore geometry changes and membrane ageing is critical, which would enable more precise predictions of membrane lifespan and performance degradation. • Quantify the impact and benefit of strategies to minimize membrane ageing. The research has proposed strategies to mitigate the impact of radical oxidants from NOM - hypochlorite interactions, but the long-term impact and robustness of these need to be assessed. Potential unintended consequences of strategies also need to be identified and considered in the evaluation of the benefits. • Enhance chemical cleaning protocols. The research suggests that the efficiency of commonly used chemical cleaning approach at full scale facilities (i.e., by simply soaking the membrane in the chemical cleaning solution) may not enable efficient cleaning. • Evaluate pretreatment strategies for protein type NOM removal. Given the predominant role of protein type NOM in accelerating membrane ageing observed in the present research, future research is needed into pretreatment technologies specifically designed to remove protein type NOM in feed water as a complementary approach to radical scavenging. • Develop predictive models for membrane ageing. 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Markowska, K. Karakulski, The influence of polypropylene degradation on the membrane wettability during membrane distillation, J. Memb. Sci. 326 (2009) 493–502. https://doi.org/10.1016/j.memsci.2008.10.022. 155 Appendices Appendix A Membrane Specification and Preparation Protocols A.1 Zeeweed 1000 Membrane Zeeweed (ZW) 1000 A membrane was used for soak and cyclic ageing in this dissertation, the specification of this membrane is present below: Table A.1 ZW 1000 membrane specification Material Nominal Pore Size (nm) Filtration Mode Fiber Diameter (mm) Flow Range (m3/day) TMP Range (kPa) PVDF 20 Outside-in 0.95/0.47 (OD/ID) 55 – 110 0 – 90 Table A.2 ZW 1000 membrane laboratory experiment test module specification Number of fibers Fiber length (cm) Total surface area (cm2) Operating Flux (Lm-2h-1) 5 10 14.9 50 A.2 Pressure Testing of Test Modules Prior to experimental use, the integrity of test modules was validated through pressure decay testing conducted at 55 kPa (8 psi). The pressure decay test (PDT) are a standardized, pressure-driven methodology widely implemented in membrane filtration applications for non-destructive 156 integrity verification [127]. The testing protocol involves submerging the test module in water and applying pressure (i.e., 8 psi) as outlined in [127]. A.3 Full-Scale Membrane Fiber Harvesting and Transportation Protocol The protocol for harvesting and transporting membrane fibers from full-scale drinking water treatment facilities was adapted from Bian [80]. This standardized protocol was developed to ensure sample integrity and experimental reproducibility. A.3.1 Harvest Timing and Conditions Membrane fibers were harvested immediately following chemical-in-place (CIP) cleaning to maintain consistent chemical exposure history across samples. The CIP procedure consisted of treatment with heated sodium hypochlorite solution, performed after citric acid recovery cleaning when operationally feasible. A.3.2 Spatial Sampling Strategy Test module Selection: Two discrete test modules were randomly selected from each treatment facility for comparative analysis. Vertical Distribution: Within each test module, fibers were harvested from three predetermined vertical positions (top, middle, and bottom sections) to account for potential spatial heterogeneity in membrane degradation. 157 A.3.3 Fiber Harvesting and Storage Procedure Fiber Harvesting: Membrane fibers were harvested by first moistening lint-free towels with distilled water and placing them on a clean tray. The module shroud was removed to gain access to the fiber bundle. From the middle of the module, 14 full-length fibers (approximately 50cm each) were harvested using a two-point cutting technique. This technique involved cutting at one end, carefully pulling the fiber away from the module, then cutting at the other end. All harvested fibers were thoroughly rinsed with distilled water to remove any residual constituents. Fiber Storage: The harvested fibers were placed on the pre-moistened towels and the towels were folded over to maintain fiber moisture. These wrapped fibers were sealed in labeled Ziploc bags, with each bag clearly marked with the date, train ID (T1, T2, etc.), cassette ID (C1, C2, etc.), and module ID (serial number). The sealed samples were stored in a cooler for shipment. After receiving samples from shipment, the membranes were rinsed thoroughly with distilled water. Holes were cut in the Ziploc bags to allow soaking in a hypochlorite bath. The membranes were stored in a refrigerator while soaking in a 2-5 mg/L NaOCl solution, which was replaced bimonthly to maintain effectiveness. 158 A.3.4 Details of operation for partner full-scale facilities Table A.3 Details of operation for partner full-scale facilities Facility A Facility B Facility C Facility D Max Capacity (MLD) 20 116 363 400 Avg Capacity (MLD) 8-16 83 180 200 Pretreatment Pre-chlorination Yes Yes Yes Yes Ozonation No No Yes Yes UV No No No Yes Coagulant Aluminium Chlorohydrate Aluminium Chlorohydrate No Coagulation No Coagulation Coagulant Dosage (mg/L) 4-5 mg/L 1-2 mg/L Membrane Maintenance Clean Chemical Hypochlorite Hypochlorite Hypochlorite Hypochlorite Concentration (mg/L) 200 200 200 250 Soak Duration (hours) 20 20 20 15 Frequency Every 48 hours Every 48 hours Every 48 hours Every 48 hours Membrane Recovery Clean Chemical Type A: Hypochlorite Type B: Citric Acid Type A: Hypochlorite Type B: Citric Acid Type A: Hypochlorite Type B: Citric Acid Type A: Hypochlorite Type B: Citric Acid Concentration (mg/L) Type A: 1000 Type B: 500 Type A: 400 Type B: 2000 Type A: 500 Type B: 2000 Type A: 500 Type B: 1000 Soak Duration (hours) Type A and B: 5 Type A and B: 5 Type A and B: 6 Type A and B: 8 Frequency Type A and B: Once per month Type A and B: Once per month Type A and B: Once per month Type A and B: Once per month Heated Clean No No Yes (>30℃) Yes (>30℃) Types A and B alternate monthly. Operation details are adapted from previous work by our group as out lined in [83]. 159 Appendix B Real Feed Water Collection and LC-OCD-OND Analysis (Extended) B.1 Real Feed Water Sample Collection Real feed water samples were collected from four different drinking water treatment facilities. Water samples were collected in 500 mL amber bottles, stored in an ice-packed cooler, and immediately transported to the lab for analysis. B.2 LC-OCD-OND Analysis Protocol for Protein Type NOM Quantification The liquid chromatography with parallel organic carbon detection and organic nitrogen detection (LC-OCD-OND) system enabled comprehensive characterization of natural organic matter (NOM) fractions, particularly biopolymers (i.e., protein and polysaccharides) and humic substances, in water samples. The analysis protocol was adapted from [82,86]. Prior to analysis, samples were filtered through 0.45-μm polytetrafluoroethylene membranes (Advantec, Japan) to remove particulates. The LC-OCD-OND system utilized both an organic carbon detector (OCD) for quantifying carbon concentrations (reported in mg C/L) and an organic nitrogen detector (OND) for quantifying nitrogen concentration (reported in mg N/L). During LC-OCD-OND analysis, biopolymers, which include proteins and polysaccharides, eluted between retention times of 30-40 minutes, corresponding to molecular weights between 20,000 and 1,000,000 Da, while humic substances and building blocks were detected at retention times of approximately 50-60 minutes [86]. Both OCD and OND spectra are presented in Figure 160 B.1. The protein type NOM within the biopolymer fraction was determined based on the nitrogen signal from the OND, as proteins contain nitrogen while polysaccharides typically do not. Protein type NOM concentration was obtained from % protein in biopolymer calculated by ChromCALC software (version 2.4, Germany) [86]. The analysis temperature was maintained at 35°C and a phosphate buffer (pH 6.85) served as the mobile phase, consistent with standard LC-OCD-OND protocols [86]. Figure B.1 Typical LC-OCD-OND chromatogram of a feed water sample with responses for organic carbon detection (OCD) and organic nitrogen detection (OND). 161 Appendix C Cyclic Ageing Apparatus and Operation Setup C.1 Design of Cyclic Ageing Apparatus Membrane tank was designed to house the test modules described in Section 3.2.1, as presented in Figures C.1a and b. The different system components of the cyclic ageing apparatus are presented in Figures C.2 and C.3. a b Figure C.1 Design of (a) membrane tank and (b) details of tank cap.162 Figure C.2 Connection of components of cyclic ageing apparatus. 163 Figure C.3 Schematic of cyclic ageing apparatus (four in parallel) 164 C.2 Cyclic Ageing Operation Setup The cyclic ageing apparatus was operated via an automated control system that executed pre-programmed sequences of filtration, hydraulic cleaning, and chemical cleaning operations. The detailed operational sequence and corresponding time intervals are presented in Table C.1. The control system actuated specific valves and pumps according to the timing protocol outlined in Table C.2, which defines the operational state (open/closed for valves, on/off for pumps) of each component during sequential stages of the ageing cycle. The change of water levels within the membrane tank during a complete operational cycle is illustrated in Figure C.4, demonstrating the volumetric changes associated with each process stage. This automated control strategy ensured consistent and reproducible experimental conditions throughout the cyclic ageing by maintaining precise control over hydraulic parameters and chemical exposure dose. Table C.1 Control system actuated specific valves and pumps Timer ON OFF T1 (on/off) valve V1A/V1B/V1C/V1D open (ctrl by L1A/L1B/L1C/L1D) valve V1A/V1B/V1C/V1D off (ctrl by L1A/L1B/L1C/L1D) T2 (on/off) valve V2 open (ctrl by L1A/L1B/L1C/L1D) valve V2 off (ctrl by L1A/L1B/L1C/L1D) T3 (on/off) valve V3A/V3B/V3C/V3D open valve V3A/V3B/V3C/V3D off T4 (on/off) Air pump AR on Air pump AR off T5 (on/off) pump P5A/P5B/P5C/P5D on pump P5A/P5B/P5C/P5D off T6 (on/off) pump P6A/P6B/P6C/P6D on pump P6A/P6B/P6C/P6D off 165 Table C.2 Timer setup for cyclic ageing cycles. Operation sequences (Filtration/ Hydraulic cleaning/ chemical cleaning) Time step Timer ID Initiates action Step duration T3 T4 T5 T6 T1 T2 1 on off off off off off Fill membrane tank with feed T1 off if Level switchs reach high level ( L1A/L1B/L1C/L1D); T1 on if level swiths reach low level (L1A/L1B/L1C/L1D) 30s 2 on off off off on off Permeate through membranes T1 off if Level switchs reach high level ( L1A/L1B/L1C/L1D); T1 on if level swiths reach low level (L1A/L1B/L1C/L1D) 30 minutes 3 off off off on off on Backwash with permeate water coupled with aeration 10 minutes 4 off off on off off off drain waste water 30s 5 off on off off off off fill membrane tank with hypo T2 off if Level switchs reach high level ( L1A/L1B/L1C/L1D); T2 on if level swiths reach low level (L1A/L1B/L1C/L1D) 120s 6 off off off off off off CIP 40 minutes 7 off off on off off off drain hypo 30s 8 on off on off off off feed during draining 5s 8 = 1 on off off off off off Fill membrane tank with feed T1 off if Level switchs reach high level ( L1A/L1B/L1C/L1D); T1 on if level swiths reach low level (L1A/L1B/L1C/L1D) 0.5 minutes 166 Figure C.4 Water level changes for each time step in cyclic ageing cycles. 167 Appendix D Membrane Ageing Metrics Analysis D.1 ATR-FTIR Analysis D.1.1 ATR-FTIR Sample Preparation and Measurement Preparation and measurement were the same as [80]. Before ATR-FTIR measurements, membrane coupons were harvested (8 cm length) from test modules. The specimens were dried overnight at 50°C in a laboratory drying oven. The dried samples were stored in labeled 15 mL centrifuge tubes until analysis. ATR-FTIR spectra were collected using a Thermo-Nicolet iS50FTIR spectrometer (Thermo Scientific) equipped with a diamond crystal ATR accessory. The ATR-FTIR analysis was conducted with 32 scans accumulated per spectrum at a spectral resolution of 2 cm-1. The wavenumber range was set to 700-2000 cm-1, Air was collected as a background before each sample set. During measurement, the ATR crystal and pressure tip were cleaned thoroughly with isopropanol-soaked lint-free wipes. A background spectrum was collected with no sample present. The hollow fiber membrane sample was cut and flattened to maximize surface contact area. The flattened membrane was positioned centered on the ATR crystal surface. The pressure tip was lowered and locked to ensure consistent sample contact. The crystal and pressure tip were cleaned between samples. 168 D.1.2 ATR-FTIR Data Process for Differential FTIR The differential FTIR (ΔFTIR) spectra were generated through sequential processing steps. Initially, raw spectra data for virgin and aged membranes were imported, transmittance values were converted to absorbance units, and manual baseline offset correction was applied if needed. Savitzky-Golay smoothing was then applied using a second derivative smoothing algorithm with a window width of 15 data points and polynomial order of 3, which reduced noise while preserving spectral features. Peak alignment was achieved via Moving Window Fast Fourier Transform (MWFFT), which aligned characteristic peaks of aged membrane spectra with corresponding virgin membrane peaks. The window size was determined by peak width (approximately 20-30 data points) with a shift tolerance of ±5 cm-1. Extended Multiplicative Signal Correction (EMSC) was applied using the mean spectrum of virgin membrane as reference with a second-order polynomial correction. Finally, the differential spectrum was generated by subtracting the processed aged membrane spectrum from the processed virgin membrane spectrum. This produced a ΔFTIR spectrum showing chemical changes due to ageing. The differential spectra were normalized to a common scale for comparative analysis. 169 D.1.3 Python Code for FTIR data processing: 1. Normalization of FTIR spectra using EMSC: the following code takes raw spectra from both virgin and aged membranes and applies several data normalization techniques to make spectra of aged membranes be normalized to virgin membrane. The main processing steps include interpolating spectra to a common wavenumber range, applying Extended Multiplicative Signal Correction (EMSC) to remove baseline variations and scaling differences, aligning peaks using Moving Window Fast Fourier Transform (MWFFT) to account for peak shifts, and calculating second derivatives using Savitzky-Golay filtering to enhance peak detection. 1. import numpy as np 2. import pandas as pd 3. from pybaselines import Baseline 4. from scipy.signal import savgol_filter, find_peaks 5. from scipy.interpolate import interp1d 6. from scipy.linalg import lstsq 7. import glob 8. import os 9. from scipy.fftpack import fft, ifft 10. 11. 12. # Interpolate spectrum to match a target set of wavenumbers 13. def interpolate_spectrum(target_wavenumbers, source_wavenumbers, source_spectrum): 14. interp_func = np.interp(target_wavenumbers, source_wavenumbers, source_spectrum) 15. return interp_func 16. 17. 18. # Function to apply EMSC 19. def emsc(spectra, reference=None, degree=2): 20. num_points, num_spectra = spectra.shape 21. spectra = np.nan_to_num(spectra, nan=np.nanmean(spectra)) 22. 23. if reference is None: 24. reference = np.mean(spectra, axis=1) 25. 170 26. design_matrix = [np.ones(num_points), reference] 27. x = np.linspace(-1, 1, num_points) # Scaled wavenumber axis 28. for d in range(1, degree + 1): 29. design_matrix.append(x ** d) 30. 31. design_matrix = np.vstack(design_matrix).T 32. corrected_spectra = np.zeros_like(spectra) 33. 34. for i in range(num_spectra): 35. y = spectra[:, i] 36. coeffs, _, _, _ = lstsq(design_matrix, y) 37. baseline = design_matrix[:, 2:] @ coeffs[2:] 38. corrected = (y - coeffs[0] - baseline) / coeffs[1] 39. corrected_spectra[:, i] = corrected 40. 41. return corrected_spectra 42. 43. 44. # Align peaks in the corrected spectrum to match reference peaks 45. def align_peaks(reference_spectrum, corrected_spectrum, wavenumbers, tolerance=5): 46. """ 47. Align peaks in the corrected spectrum to match reference peaks. 48. 49. Parameters: 50. - reference_spectrum: 1D array of the reference (virgin membrane) spectrum. 51. - corrected_spectrum: 1D array of the corrected spectrum. 52. - wavenumbers: 1D array of wavenumbers. 53. - tolerance: Maximum allowed shift in wavenumber to align peaks (default is 5 cm⁻¹). 54. 55. Returns: 56. - aligned_spectrum: Corrected spectrum with shifted peaks. 57. """ 58. aligned_spectrum = np.copy(corrected_spectrum) 59. 60. # Find peaks in both reference and corrected spectra 61. ref_peaks, _ = find_peaks(reference_spectrum) 62. corr_peaks, _ = find_peaks(corrected_spectrum) 63. 64. # Align peaks by shifting corrected peaks to reference peaks within tolerance 65. for ref_peak in ref_peaks: 66. # Find the closest peak in the corrected spectrum within the tolerance 67. closest_peak = [peak for peak in corr_peaks if abs(wavenumbers[peak] - wavenumbers[ref_peak]) <= tolerance] 68. 171 69. if closest_peak: 70. closest_peak = closest_peak[0] 71. peak_shift = wavenumbers[closest_peak] - wavenumbers[ref_peak] 72. 73. # Shift the corrected peak back to the reference peak position 74. aligned_spectrum[ref_peak] = corrected_spectrum[closest_peak] 75. 76. # Smoothly adjust the neighboring points instead of zeroing the shifted peak 77. window_size = 3 # Define a small window size around the peak for smoothing 78. for offset in range(-window_size, window_size + 1): 79. neighbor_index = closest_peak + offset 80. if 0 <= neighbor_index < len(aligned_spectrum): 81. aligned_spectrum[neighbor_index] = (aligned_spectrum[neighbor_index] + corrected_spectrum[ 82. closest_peak]) / 2 83. 84. return aligned_spectrum 85. 86. 87. # Function to apply MWFFT for peak alignment 88. def mwfft_align_peaks(reference_spectrum, corrected_spectrum, window_size=50): 89. """ 90. Align peaks in the corrected spectrum to match the reference spectrum using MWFFT. 91. 92. Parameters: 93. - reference_spectrum: 1D array of the reference (virgin membrane) spectrum. 94. - corrected_spectrum: 1D array of the corrected spectrum. 95. - window_size: Size of the moving window for FFT cross-correlation. 96. 97. Returns: 98. - aligned_spectrum: Corrected spectrum with aligned peaks. 99. """ 100. aligned_spectrum = np.zeros_like(corrected_spectrum) 101. num_points = len(reference_spectrum) 102. 103. # Apply moving window FFT to correct shifts in the spectrum 104. for i in range(0, num_points - window_size + 1, window_size): 105. # Extract the windowed segments 106. ref_segment = reference_spectrum[i:i + window_size] 107. corr_segment = corrected_spectrum[i:i + window_size] 108. 109. # Perform FFT cross-correlation 110. corr_fft = fft(corr_segment) 111. ref_fft = fft(ref_segment) 172 112. cross_corr = ifft(corr_fft * np.conj(ref_fft)).real 113. 114. # Find the shift that maximizes the correlation 115. shift = np.argmax(cross_corr) - (window_size // 2) 116. 117. # Apply the shift to the corrected segment 118. aligned_spectrum[i:i + window_size] = np.roll(corr_segment, shift) 119. 120. return aligned_spectrum 121. 122. 123. # Apply second derivative and Savitzky-Golay filter for peak detection 124. def smooth_and_find_peaks(spectrum, window_length=15, polyorder=2): 125. # Apply Savitzky-Golay smoothing and calculate second derivative 126. second_derivative = savgol_filter(spectrum, window_length=window_length, polyorder=polyorder, deriv=2) 127. return second_derivative 128. 129. 130. # Save corrected spectra and second derivatives to CSV 131. def save_spectra_and_derivatives(wavenumbers, corrected_spectra, second_derivatives, file_list, corrected_output_dir, 132. second_derivative_output_dir): 133. if not os.path.exists(corrected_output_dir): 134. os.makedirs(corrected_output_dir) 135. 136. if not os.path.exists(second_derivative_output_dir): 137. os.makedirs(second_derivative_output_dir) 138. 139. for i, file_path in enumerate(file_list): 140. base_name = os.path.splitext(os.path.basename(file_path))[0] 141. 142. # Save corrected spectra 143. output_file_corrected = os.path.join(corrected_output_dir, f"{base_name}_corrected.csv") 144. df_corrected = pd.DataFrame({ 145. 'Wavenumber': wavenumbers, 146. 'Corrected Absorbance': corrected_spectra[:, i] 147. }) 148. df_corrected.to_csv(output_file_corrected, index=False) 149. 150. # Save second derivatives 151. output_file_derivative = os.path.join(second_derivative_output_dir, f"{base_name}_corrected_second_derivative.csv") 152. df_derivative = pd.DataFrame({ 153. 'Wavenumber': wavenumbers, 173 154. 'Second Derivative': second_derivatives[:, i] 155. }) 156. df_derivative.to_csv(output_file_derivative, index=False) 157. 158. print(f"Saved corrected spectrum to: {output_file_corrected}") 159. print(f"Saved second derivative to: {output_file_derivative}") 160. 161. 162. # Load FTIR spectra from CSV files 163. def load_ftir_spectra(file_pattern, target_wavenumbers): 164. file_list = glob.glob(file_pattern) 165. interpolated_spectra = [] 166. for file in file_list: 167. data = pd.read_csv(file, header=None) 168. source_wavenumbers = data[0].values # First column is wavenumber 169. source_spectrum = data[1].values # Convert to absorbance by applying 100 - value 170. 171. # Interpolate the spectrum to the target wavenumbers 172. interpolated_spectrum = interpolate_spectrum(target_wavenumbers, source_wavenumbers, source_spectrum) 173. interpolated_spectra.append(interpolated_spectrum) 174. 175. interpolated_spectra = np.array(interpolated_spectra).T # Shape: (num_points, number_of_spectra) 176. return target_wavenumbers, interpolated_spectra, file_list 177. 178. 179. # Load the virgin membrane spectrum 180. def load_virgin_spectrum(file_path): 181. data = pd.read_csv(file_path, header=None) 182. wavenumbers = data[0].values # First column is wavenumber 183. virgin_spectrum = data[1].values # Second column is absorbance 184. return wavenumbers, virgin_spectrum 185. 186. baseline_obj = Baseline() 187. 188. def baseline_correction_asls(spectrum, lam=1e6, p=0.01): 189. baseline, _ = baseline_obj.asls(spectrum, lam=lam, p=p) 190. return spectrum - baseline 191. 192. # Main script to apply EMSC, align peaks, and save corrected spectra and second derivatives 193. if __name__ == "__main__": 194. current_directory = os.path.dirname(os.path.abspath(__file__)) 195. 196. # Folder for original spectra 174 197. original_spectra_folder = os.path.join(current_directory, 'original_spectra') 198. 199. # Path to the virgin membrane spectrum file 200. virgin_file_path = os.path.join(current_directory, 'virgin_spectrum.csv') 201. 202. # Folder to save corrected spectra and second derivative spectra 203. corrected_output_dir = os.path.join(current_directory, 'corrected_spectra') 204. second_derivative_output_dir = os.path.join(current_directory, 'second_derivative_spectra') 205. 206. # File pattern to load all CSV files in the original spectra folder 207. file_pattern = os.path.join(original_spectra_folder, '*.csv') 208. 209. # Load all FTIR spectra and the virgin spectrum 210. virgin_wavenumbers, virgin_spectrum = load_virgin_spectrum(virgin_file_path) 211. 212. # Set a target wavenumber range and number of points (e.g., match the virgin membrane's range) 213. target_wavenumbers = np.linspace(700, 2000, 1000) # Example of resampling to 1000 points in range 700-2000 cm⁻¹ 214. 215. wavenumbers, spectra, file_list = load_ftir_spectra(file_pattern, target_wavenumbers) 216. 217. # Interpolate the virgin membrane spectrum to the target wavenumber range 218. resampled_virgin_spectrum = interpolate_spectrum(target_wavenumbers, virgin_wavenumbers, virgin_spectrum) 219. 220. # Apply EMSC correction using the resampled virgin membrane spectrum as the reference 221. corrected_spectra = emsc(spectra, reference=resampled_virgin_spectrum, degree=2) 222. 223. # Prepare arrays to store second derivatives 224. second_derivative_spectra = np.zeros_like(corrected_spectra) 225. 226. # Process and save each spectrum 227. for i in range(corrected_spectra.shape[1]): 228. corrected_spectrum = corrected_spectra[:, i] 229. 230. # Align peaks in the corrected spectrum to match the reference spectrum 175 231. aligned_spectrum = mwfft_align_peaks(resampled_virgin_spectrum, corrected_spectrum, window_size=50) 232. 233. # Compute second derivative for the aligned spectrum 234. second_derivative = smooth_and_find_peaks(aligned_spectrum) 235. second_derivative_spectra[:, i] = second_derivative 236. 237. # Save corrected spectra and second derivatives 238. save_spectra_and_derivatives(target_wavenumbers, corrected_spectra, second_derivative_spectra, file_list, 239. corrected_output_dir, second_derivative_output_dir) 2. Generation of differential spectra: The following code generates differential FTIR spectra by calculating the differences between aged and virgin membrane samples. It loads previously normalized spectra from CSV files, interpolates the virgin membrane spectrum to match the same wavenumber range, and calculates delta spectra by subtracting the virgin spectrum from each normalized spectrum. These delta spectra directly highlight chemical changes that occurred during membrane aging. The code then applies Savitzky-Golay filtering to calculate second derivatives of the delta spectra, which enhances subtle peak differences and improves feature detection. Finally, it saves both the delta spectra and their second derivatives to separate CSV files for further analysis. 1. import numpy as np 2. import pandas as pd 3. import os 4. import glob 5. from scipy.signal import savgol_filter 6. from scipy.interpolate import interp1d 7. 8. # Load corrected spectra from CSV files 9. def load_corrected_spectra(file_pattern): 10. """ 176 11. Load corrected spectra from CSV files. 12. """ 13. file_list = glob.glob(file_pattern) 14. wavenumbers = None 15. corrected_spectra = [] 16. 17. for file in file_list: 18. data = pd.read_csv(file) 19. if wavenumbers is None: 20. wavenumbers = data['Wavenumber'].values # First column is wavenumber 21. corrected_spectra.append(data['Corrected Absorbance'].values) # Second column is absorbance 22. 23. corrected_spectra = np.array(corrected_spectra).T # Shape: (num_points, number_of_spectra) 24. return wavenumbers, corrected_spectra, file_list 25. 26. # Load and interpolate virgin membrane spectrum 27. def load_and_interpolate_virgin_spectrum(file_path, target_wavenumbers): 28. """ 29. Load the virgin membrane spectrum and interpolate it to match target wavenumbers. 30. """ 31. data = pd.read_csv(file_path, header=None) 32. source_wavenumbers = data.iloc[:, 0].values # First column is wavenumber 33. virgin_spectrum = data.iloc[:, 1].values # Second column is absorbance 34. interp_func = interp1d(source_wavenumbers, virgin_spectrum, kind='linear', fill_value='extrapolate') 35. interpolated_virgin_spectrum = interp_func(target_wavenumbers) 36. return interpolated_virgin_spectrum 37. 38. # Calculate delta spectra (corrected spectra - virgin membrane spectrum) 39. def calculate_delta_spectra(wavenumbers, corrected_spectra, virgin_spectrum): 40. """ 41. Calculate delta spectra by subtracting the virgin membrane spectrum from corrected spectra. 42. """ 177 43. virgin_spectrum_absorbance = virgin_spectrum 44. delta_spectra = corrected_spectra - virgin_spectrum_absorbance[:, np.newaxis] 45. return delta_spectra 46. 47. # Calculate second derivative using Savitzky-Golay filter 48. def calculate_second_derivative(delta_spectra, window_length=15, polyorder=2): 49. """ 50. Calculate the second derivative of the delta spectra using Savitzky-Golay filter. 51. """ 52. second_derivative = savgol_filter(delta_spectra, window_length=window_length, polyorder=polyorder, deriv=2, axis=0) 53. return second_derivative 54. 55. # Save delta and second derivative spectra to CSV files 56. def save_spectra(wavenumbers, spectra, file_list, output_dir, suffix): 57. """ 58. Save spectra (delta or second derivative) to individual CSV files. 59. """ 60. if not os.path.exists(output_dir): 61. os.makedirs(output_dir) 62. 63. for i, file_path in enumerate(file_list): 64. base_name = os.path.splitext(os.path.basename(file_path))[0] 65. output_file = os.path.join(output_dir, f"{base_name}_{suffix}.csv") 66. df = pd.DataFrame({ 67. 'Wavenumber': wavenumbers, 68. f'{suffix}': spectra[:, i] 69. }) 70. df.to_csv(output_file, index=False) 71. print(f"Saved {suffix} to: {output_file}") 72. 73. # Example usage 74. if __name__ == "__main__": 75. current_directory = os.path.dirname(os.path.abspath(__file__)) 76. 77. # Folder for corrected spectra 78. corrected_spectra_folder = os.path.join(current_directory, 'corrected_spectra') 178 79. 80. # Path to the virgin membrane spectrum file 81. virgin_file_path = os.path.join(current_directory, 'virgin_spectrum.csv') 82. 83. # Folder to save delta spectra and second derivative spectra 84. delta_spectra_folder = os.path.join(current_directory, 'delta_spectra') 85. second_derivative_folder = os.path.join(current_directory, 'delta_second_derivative_spectra') # Change folder name to distinguish 86. 87. # File pattern to load all corrected CSV files 88. file_pattern = os.path.join(corrected_spectra_folder, '*.csv') 89. 90. # Load all corrected spectra and the virgin spectrum 91. wavenumbers, corrected_spectra, file_list = load_corrected_spectra(file_pattern) 92. virgin_spectrum_interpolated = load_and_interpolate_virgin_spectrum(virgin_file_path, wavenumbers) 93. 94. # Calculate delta spectra 95. delta_spectra = calculate_delta_spectra(wavenumbers, corrected_spectra, virgin_spectrum_interpolated) 96. 97. # Calculate second derivative of delta spectra 98. second_derivative_spectra = calculate_second_derivative(delta_spectra) 99. 100. # Save the delta spectra to CSV files in the delta spectra folder 101. save_spectra(wavenumbers, delta_spectra, file_list, delta_spectra_folder, 'delta') 102. 103. # Save the second derivative spectra to CSV files in the second derivative folder 104. save_spectra(wavenumbers, second_derivative_spectra, file_list, second_derivative_folder, 'delta_second_derivative') 3. Peak analysis of differential FTIR spectra: The following code performs peak analysis and visualization of differential FTIR spectral data to identify significant chemical changes in membrane samples. It processes previously normalized spectra to detect 179 characteristic peaks, calculate their areas, and generate informative visualizations. The code first applies Savitzky-Golay smoothing to enhance signal quality, then finds common peaks between the aged membrane spectra and their differential (delta) spectra using peak detection algorithms. For each identified peak, it calculates the area under the curve, representing the magnitude of chemical change at that wavenumber. The code generates plots showing the relationships between virgin and aged membrane spectra, delta spectra, and their second derivatives with highlighted peaks. Additionally, it creates statistical visualizations that plot peak areas and identifies the most reliable peaks as indicator of ageing (those with high area and low variability). 1. import re 2. 3. import numpy as np 4. import pandas as pd 5. from scipy.signal import savgol_filter, find_peaks 6. from scipy.integrate import simpson 7. import matplotlib.pyplot as plt 8. import glob 9. import os 10. 11. 12. # Smooth the data using Savitzky-Golay filter 13. def smooth_data(spectrum, window_length=15, polyorder=3): 14. return savgol_filter(spectrum, window_length=window_length, polyorder=polyorder) 15. 16. 17. # Load virgin membrane spectrum and adjust values for absorbance (100 - value) 18. def load_virgin_spectrum(file_path, target_wavenumbers): 19. data = pd.read_csv(file_path, header=None) 20. source_wavenumbers = data[0].values # First column is wavenumber 21. virgin_spectrum = data[1].values # Second column is absorbance (100 - value) 22. 23. # Interpolate virgin spectrum to target wavenumbers 180 24. interp_func = np.interp(target_wavenumbers, source_wavenumbers, virgin_spectrum) 25. return interp_func 26. 27. 28. def find_and_refine_peaks(wavenumbers, second_derivative, min_prominence=0.01, min_distance=5, max_distance_combine=5): 29. # Find peaks with specified prominence and minimum distance 30. peaks, properties = find_peaks(np.abs(second_derivative), prominence=min_prominence, distance=min_distance) 31. 32. # Optionally combine peaks that are too close 33. refined_peaks = [] 34. for i, peak in enumerate(peaks): 35. if i == 0 or wavenumbers[peak] - wavenumbers[peaks[i - 1]] > max_distance_combine: 36. refined_peaks.append(peak) 37. 38. return np.array(refined_peaks) 39. 40. # Find common peaks in both second derivatives with highest absolute values 41. def find_common_peaks(wavenumbers, corrected_second_derivative, delta_second_derivative, tolerance=5, num_peaks=10): 42. corrected_peaks = find_and_refine_peaks(wavenumbers, corrected_second_derivative, min_prominence=0.01, 43. min_distance=5) 44. delta_peaks = find_and_refine_peaks(wavenumbers, delta_second_derivative, min_prominence=0.01, min_distance=5) 45. 46. common_peaks = [] 47. for corr_peak in corrected_peaks: 48. closest_peak = [delta_peak for delta_peak in delta_peaks if 49. abs(wavenumbers[corr_peak] - wavenumbers[delta_peak]) <= tolerance] 50. if closest_peak: 51. common_peaks.append((corr_peak, closest_peak[0])) 52. 53. common_peaks = sorted(common_peaks, key=lambda p: abs(delta_second_derivative[p[1]]), reverse=True)[:num_peaks] 54. 55. 56. return common_peaks 57. 58. 59. # Calculate peak areas for the matched common peaks in delta spectra 60. def calculate_peak_areas(wavenumbers, delta_spectrum, common_peaks): 61. peak_areas = {} 62. for corr_peak, delta_peak in common_peaks: 181 63. left = max(0, delta_peak - 10) 64. right = min(len(delta_spectrum) - 1, delta_peak + 10) 65. area = simpson(y=np.abs(delta_spectrum[left:right]), x=wavenumbers[left:right]) 66. peak_areas[wavenumbers[delta_peak]] = area 67. return peak_areas 68. 69. # Save peak areas to a CSV file 70. def save_peak_areas(peak_areas, file_path): 71. # Sort the peak areas dictionary by wavenumber (key) 72. sorted_peak_areas = dict(sorted(peak_areas.items())) 73. df = pd.DataFrame(list(sorted_peak_areas.items()), columns=['Wavenumber', 'Area']) 74. df.to_csv(file_path, index=False) 75. print(f"Saved peak areas to: {file_path}") 76. 77. # Adjust global x and y axis range 78. def find_global_axis_ranges(corrected_spectra_folder, delta_spectra_folder, second_deriv_folder, delta_second_folder): 79. corrected_x_range, corrected_y_range = [np.inf, -np.inf], [np.inf, -np.inf] 80. delta_x_range, delta_y_range = [np.inf, -np.inf], [np.inf, -np.inf] 81. second_deriv_y_range = [np.inf, -np.inf] 82. 83. corrected_files = glob.glob(os.path.join(corrected_spectra_folder, '*.csv')) 84. delta_files = glob.glob(os.path.join(delta_spectra_folder, '*.csv')) 85. 86. for corrected_file, delta_file in zip(corrected_files, delta_files): 87. corrected_data = pd.read_csv(corrected_file) 88. delta_data = pd.read_csv(delta_file) 89. corrected_spectrum = corrected_data['Corrected Absorbance'].values 90. delta_spectrum = delta_data['delta'].values 91. 92. corrected_wavenumbers = corrected_data['Wavenumber'].values 93. delta_wavenumbers = delta_data['Wavenumber'].values 94. 95. corrected_x_range = [min(corrected_x_range[0], corrected_wavenumbers.min()), 96. max(corrected_x_range[1], corrected_wavenumbers.max())] 97. delta_x_range = [min(delta_x_range[0], delta_wavenumbers.min()), max(delta_x_range[1], delta_wavenumbers.max())] 98. 99. corrected_y_range = [min(corrected_y_range[0], corrected_spectrum.min()), 100. max(corrected_y_range[1], corrected_spectrum.max())] 182 101. delta_y_range = [min(delta_y_range[0], delta_spectrum.min()), max(delta_y_range[1], delta_spectrum.max())] 102. 103. corrected_second_deriv_file = os.path.join(second_deriv_folder, os.path.basename(corrected_file).replace('.csv', 104. '_second_derivative.csv')) 105. delta_second_deriv_file = os.path.join(delta_second_folder, 106. os.path.basename(delta_file).replace('.csv', '_second_derivative.csv')) 107. 108. corrected_second_deriv = pd.read_csv(corrected_second_deriv_file)['Second Derivative'].values 109. delta_second_deriv = pd.read_csv(delta_second_deriv_file)['delta_second_derivative'].values 110. 111. second_deriv_y_range = [min(second_deriv_y_range[0], corrected_second_deriv.min(), delta_second_deriv.min()), 112. max(second_deriv_y_range[1], corrected_second_deriv.max(), delta_second_deriv.max())] 113. 114. return corrected_x_range, corrected_y_range, delta_x_range, delta_y_range, second_deriv_y_range 115. 116. 117. # Plot spectra and highlight common peaks with adjusted axis range and smoothing 118. def plot_spectra(wavenumbers, corrected_spectrum, virgin_spectrum, delta_spectrum, corrected_second_derivative, 119. delta_second_derivative, common_peaks, peak_areas, output_file, axis_ranges, apply_smoothing=True): 120. corrected_x_range, corrected_y_range, delta_x_range, delta_y_range, second_deriv_y_range = axis_ranges 121. 122. if apply_smoothing: 123. corrected_spectrum = smooth_data(corrected_spectrum) 124. delta_spectrum = smooth_data(delta_spectrum) 125. corrected_second_derivative = smooth_data(corrected_second_derivative) 126. delta_second_derivative = smooth_data(delta_second_derivative) 127. 128. plt.figure(figsize=(12, 10)) 129. 130. # Plot 1: Corrected spectrum and virgin membrane 131. plt.subplot(4, 1, 1) 132. plt.plot(wavenumbers, corrected_spectrum, label='Aged Membrane Spectra (After Normalization)', color='green') 183 133. plt.plot(wavenumbers, virgin_spectrum, label='Virgin Membrane (Reference)', linestyle='--', color='blue') 134. plt.title('Aged and Virgin Membrane FTIR Spectra') 135. plt.xlabel('Wavenumber (cm⁻¹)') 136. plt.ylabel('Absorbance') 137. plt.xlim(corrected_x_range) 138. plt.ylim(corrected_y_range) 139. plt.gca().invert_xaxis() 140. plt.legend() 141. 142. # Plot 2: Delta spectrum with peak areas 143. plt.subplot(4, 1, 2) 144. plt.plot(wavenumbers, delta_spectrum, label='Delta Spectra', color='orange') 145. for corr_peak, delta_peak in common_peaks: 146. plt.plot(wavenumbers[delta_peak], delta_spectrum[delta_peak], 'ro') 147. plt.text(wavenumbers[delta_peak], delta_spectrum[delta_peak], 148. f"Area: {peak_areas[wavenumbers[delta_peak]]:.2f}", fontsize=8, color='red') 149. plt.title('Delta Spectra of Aged Membrane') 150. plt.xlabel('Wavenumber (cm⁻¹)') 151. plt.ylabel('Delta Absorbance') 152. plt.xlim(delta_x_range) 153. plt.ylim(delta_y_range) 154. plt.gca().invert_xaxis() 155. plt.legend() 156. 157. # Plot 3: Second derivative of corrected spectrum 158. plt.subplot(4, 1, 3) 159. plt.plot(wavenumbers, corrected_second_derivative, label='Second Derivative (Aged Membrane Spectra)', color='red') 160. plt.title('Second Derivative of Aged Membrane Spectra') 161. plt.xlabel('Wavenumber (cm⁻¹)') 162. plt.ylabel('Second Derivative') 163. plt.xlim(corrected_x_range) 164. plt.ylim(second_deriv_y_range) 165. plt.gca().invert_xaxis() 166. plt.legend() 167. 168. # Plot 4: Second derivative of delta spectrum with common peaks 169. plt.subplot(4, 1, 4) 170. plt.plot(wavenumbers, delta_second_derivative, label='Second Derivative (Delta Spectra of Aged Membrane)', color='purple') 171. for corr_peak, delta_peak in common_peaks: 172. plt.plot(wavenumbers[delta_peak], delta_second_derivative[delta_peak], 'ro') 173. plt.title('Second Derivative of Delta Spectra of Aged Membrane') 184 174. plt.xlabel('Wavenumber (cm⁻¹)') 175. plt.ylabel('Second Derivative') 176. plt.xlim(delta_x_range) 177. plt.ylim(second_deriv_y_range) 178. plt.gca().invert_xaxis() 179. plt.legend() 180. 181. plt.tight_layout() 182. plt.savefig(output_file) 183. plt.close() 184. print(f"Saved plot to: {output_file}") 185. 186. 187. # Process each spectrum and identify common peaks 188. def process_spectra(corrected_spectra_folder, second_deriv_folder, delta_spectra_folder, delta_second_folder, 189. output_folder, virgin_file_path, peak_locations): 190. if not os.path.exists(output_folder): 191. os.makedirs(output_folder) 192. 193. corrected_x_range, corrected_y_range, delta_x_range, delta_y_range, second_deriv_y_range = find_global_axis_ranges( 194. corrected_spectra_folder, delta_spectra_folder, second_deriv_folder, delta_second_folder) 195. 196. axis_ranges = (corrected_x_range, corrected_y_range, delta_x_range, delta_y_range, second_deriv_y_range) 197. 198. corrected_files = glob.glob(os.path.join(corrected_spectra_folder, '*.csv')) 199. delta_files = glob.glob(os.path.join(delta_spectra_folder, '*.csv')) 200. 201. # Define the target wavenumber range (e.g., 1000 points between 700-2000 cm⁻¹) 202. target_wavenumbers = np.linspace(700, 2000, 1000) 203. 204. # Load the virgin membrane spectrum and resample it to 1000 points 205. virgin_spectrum = load_virgin_spectrum(virgin_file_path, target_wavenumbers) 206. 207. for corrected_file, delta_file in zip(corrected_files, delta_files): 208. base_name = os.path.splitext(os.path.basename(corrected_file))[0] 209. delta_base_name = os.path.splitext(os.path.basename(delta_file))[0] 210. 211. # Load corrected spectra, second derivative, delta, and delta second derivative 185 212. corrected_data = pd.read_csv(corrected_file) 213. delta_data = pd.read_csv(delta_file) 214. 215. # Load second derivative files 216. corrected_second_deriv = pd.read_csv(os.path.join(second_deriv_folder, f"{base_name}_second_derivative.csv")) 217. delta_second_deriv = pd.read_csv(os.path.join(delta_second_folder, f"{delta_base_name}_second_derivative.csv")) 218. 219. wavenumbers = corrected_data['Wavenumber'].values 220. corrected_spectrum = corrected_data['Corrected Absorbance'].values 221. delta_spectrum = delta_data['delta'].values 222. corrected_second_derivative = corrected_second_deriv['Second Derivative'].values 223. delta_second_derivative = delta_second_deriv['delta_second_derivative'].values 224. 225. # Find common peaks in corrected second derivative and delta second derivative 226. common_peaks = find_common_peaks(wavenumbers, corrected_second_derivative, delta_second_derivative, tolerance=10, num_peaks=30) 227. #peaks_barrie = find_and_refine_peaks(wavenumbers, corrected_second_derivative, min_prominence=0.01, 228. #min_distance=5) 229. 230. # Calculate peak areas in delta spectra 231. peak_areas = calculate_peak_areas(wavenumbers, delta_spectrum, common_peaks) 232. 233. # Save peak areas to a CSV file 234. peak_areas_file = os.path.join(output_folder, f"{base_name}_peak_areas.csv") 235. save_peak_areas(peak_areas, peak_areas_file) 236. 237. # Plot and save results 238. output_plot_file = os.path.join(output_folder, f"{base_name}_peak_analysis.png") 239. plot_spectra(wavenumbers, corrected_spectrum, virgin_spectrum, delta_spectrum, 240. corrected_second_derivative, delta_second_derivative, common_peaks, peak_areas, 241. output_plot_file, axis_ranges, apply_smoothing=True) 242. 186 243. 244. # Function to extract the base name without numeric suffix 245. def extract_base_name(file_name): 246. print(f"Processing file: {file_name}") # Debugging statement 247. # Updated regex to correctly handle both cases with and without the numeric suffix 248. match = re.match(r'(.+?)_([\d.]+)(?:_\d+)?_corrected_peak_areas\.csv', file_name) 249. if match: 250. base_name = match.group(1) + "_" + match.group(2) 251. print(f"Extracted base name: {base_name}") # Debugging statement 252. return base_name 253. else: 254. print("No match found") # Debugging statement 255. return None 256. 257. 258. # Function to generate scatter plot with peak areas and error bars 259. def generate_scatter_plot_from_duplicates(folder_path, output_folder): 260. peak_files = glob.glob(os.path.join(folder_path, '*_peak_areas.csv')) 261. 262. # Group files by their base name (ignoring numeric suffixes) 263. peak_groups = {} 264. for file in peak_files: 265. base_name = extract_base_name(os.path.basename(file)) 266. if base_name: 267. if base_name not in peak_groups: 268. peak_groups[base_name] = [] 269. peak_groups[base_name].append(file) 270. 271. common_peaks_data = [] 272. 273. # Iterate through each group of duplicate files 274. for base_name, files in peak_groups.items(): 275. if len(files) < 2: 276. continue # Skip if there are no duplicates 277. 278. peak_dfs = [pd.read_csv(file) for file in files] 279. 280. # Find common wavenumbers across all duplicates 281. common_wavenumbers = set(peak_dfs[0]['Wavenumber']) 282. for df in peak_dfs[1:]: 283. common_wavenumbers &= set(df['Wavenumber']) 284. 285. if not common_wavenumbers: 286. continue 187 287. 288. common_wavenumbers = sorted(list(common_wavenumbers)) 289. 290. peak_areas = [] 291. for df in peak_dfs: 292. areas = df[df['Wavenumber'].isin(common_wavenumbers)]['Area'].values 293. peak_areas.append(areas) 294. 295. peak_areas = np.array(peak_areas) 296. mean_peak_areas = np.mean(peak_areas, axis=0) 297. error_bars = np.std(peak_areas, axis=0) # Standard deviation for error bars 298. 299. common_peaks_data.append((common_wavenumbers, mean_peak_areas, error_bars)) 300. 301. # Scatter plot for common peaks with error bars (std deviation) 302. plt.figure(figsize=(8, 6)) 303. plt.errorbar(common_wavenumbers, mean_peak_areas, yerr=error_bars, fmt='o', capsize=5, label='Peak Areas') 304. 305. # Set y-axis to logarithmic scale 306. #plt.yscale('log') 307. #plt.yscale('function', functions=(np.power, lambda y: np.power(y, 1 / 2))) 308. 309. plt.title(f'ΔFTIR Peak Area', fontsize= 20) 310. plt.xlabel('Wavenumber (cm⁻¹)', fontsize= 15) 311. plt.ylabel('Peak Area', fontsize= 15) 312. plt.gca().invert_xaxis() 313. plt.legend(fontsize=15) 314. plt.tight_layout() 315. 316. # Save scatter plot 317. scatter_plot_file = os.path.join(output_folder, f'{base_name}_scatter_peak_areas.png') 318. plt.savefig(scatter_plot_file) 319. plt.close() 320. print(f'Saved scatter plot to: {scatter_plot_file}') 321. 322. ### New Plot: Peak Area vs Error Plot (Four Quadrants) ### 323. 324. # Define thresholds to divide the data into four sections 325. area_threshold = np.median(mean_peak_areas) # Median or mean could be used here 326. error_threshold = np.median(error_bars) # Median or mean for errors 188 327. 328. # Plot peak area vs error scatter 329. plt.figure(figsize=(8, 6)) 330. plt.scatter(mean_peak_areas, error_bars, label='Peak Area vs Error', color='blue') 331. 332. # Add vertical and horizontal lines to create the four quadrants 333. plt.axvline(x=area_threshold, color='red', linestyle='--', label='Area Threshold') 334. plt.axhline(y=error_threshold, color='green', linestyle='--', label='Error Threshold') 335. 336. # Highlight peaks with high area and low error 337. high_area_low_error = (mean_peak_areas > area_threshold) & (error_bars < error_threshold) 338. plt.scatter(mean_peak_areas[high_area_low_error], error_bars[high_area_low_error], color='orange', 339. label='High Area, Low Error') 340. 341. # Highlight selected peaks in the scatter plot 342. selected_wavenumbers = np.array(common_wavenumbers)[high_area_low_error] 343. for i, (area, error, wavenumber) in enumerate( 344. zip(mean_peak_areas[high_area_low_error], error_bars[high_area_low_error], selected_wavenumbers)): 345. plt.annotate(f'{int(wavenumber)} cm⁻¹', (area, error), textcoords="offset points", xytext=(0, 5), ha='center', 346. fontsize=12, color='black') 347. 348. plt.title(f'ΔFTIR Peak Area vs Error', fontsize = 20) 349. plt.xlabel('Peak Area', fontsize = 15) 350. plt.ylabel('Error (Max-Min)', fontsize = 15) 351. plt.legend(fontsize=15) 352. plt.tight_layout() 353. 354. # Save the Peak Area vs Error plot 355. scatter_plot_2_file = os.path.join(output_folder, f'{base_name}_scatter_area_vs_error.png') 356. plt.savefig(scatter_plot_2_file) 357. plt.close() 358. print(f'Saved scatter plot to: {scatter_plot_2_file}') 359. 360. 361. # Example usage 362. if __name__ == "__main__": 363. current_directory = os.path.dirname(os.path.abspath(__file__)) 364. 189 365. corrected_spectra_folder = os.path.join(current_directory, 'corrected_spectra') 366. second_deriv_folder = os.path.join(current_directory, 'second_derivative_spectra') 367. delta_spectra_folder = os.path.join(current_directory, 'delta_spectra') 368. delta_second_folder = os.path.join(current_directory, 'delta_second_derivative_spectra') 369. output_folder = os.path.join(current_directory, 'peak_analysis_output') 370. peak_areas_folder = os.path.join(current_directory, 'peak_analysis_output') 371. 372. # Path to the virgin membrane spectrum 373. virgin_file_path = os.path.join(current_directory, 'virgin_spectrum.csv') 374. 375. # Run the process_spectra function to handle peak analysis 376. process_spectra(corrected_spectra_folder, second_deriv_folder, delta_spectra_folder, delta_second_folder, 377. output_folder, virgin_file_path, peak_locations=None) 378. 379. # Run the function to generate scatter plots from duplicate files 380. generate_scatter_plot_from_duplicates(peak_areas_folder, output_folder) 190 FTIR Peak Identifications for virgin and aged membrane Figure D.1 FTIR Peak Identification of typical virgin and aged membranes ; results presented for cyclic aged membrane at a cumulative dose of 100,000 ppm·h (similar results observed for other conditions investigated). The spectra (second derivative) analysis identifies 39 major peaks (marked with red dots) for the virgin membrane, all of which are consistently present for aged membrane spectra. 191 ΔFTIR Peak Identification for aged membranes Figure D.2 Predominant peaks identification in typical ΔFTIR spectra ; results presented for cyclic aged membrane at a cumulative dose of 500,000 ppm·h (similar results observed for other conditions investigated). The spectral (second derivative) analysis identifies peaks (marked with red dots) in the ΔFTIR spectra of aged membrane. 192 a b Figure D.3 ΔFTIR Peak area (a) and peak area versus error (max - min) (b) of a typical aged membrane; results presented for cyclic aged membrane at a cumulative dose of 100,000 ppm·h (similar results observed for other conditions investigated). Peak areas calculated using numerical integration within a fixed range for each peak; peak area vs. error plot divided into four quadrants based on median thresholds for peak area and error to identify major peaks with high area and low error (orange markers at wavenumbers 884, 1151, 1169, and 1289 cm⁻¹). The major peaks at 1151, 1169, and 1289 cm⁻¹ are commonly associated with the stretching of C-O bonds [95], which were attributed to the oxidation of hypochlorite or attacked by hydroxide ion at high pH, similar to conditions used in the present research [128,129]. The major peak at 884 cm⁻¹ is commonly associated with the bending of C=C bonds [95], where was attributed to the oxidation of hypochlorite ion via dehydrofluorination at high pH, similar to conditions used in the present research [23,54]. 193 Figure D.4 Area of the major peaks of the ΔFTIR spectra at 884 cm-1 (a), 1169 cm-1 (b), 1289 cm-1 (c) for soak and cyclic aged membranes. Confidence intervals for area of major peaks of ΔFTIR spectra correspond to the maximum and minimum of 4 replicate measurements; shaded lines added to illustrate overall trends observed for different conditions investigated; note similar overall trends for all major peaks. a b c 194 ΔFTIR Peak Identification for full-scale aged membranes a b Figure D.5 Predominant peaks identification in ΔFTIR spectra for aged membrane from (a) Barrie and (b) other partner facilities. The spectral (second derivative) analysis identifies peaks (marked with red dots) in the ΔFTIR spectra of aged membrane. 195 Figure D.6 Relationship of the predominant peaks in ΔFTIR spectra for mixed model NOM components and individual BSA. No statistical correlation was observed between mixed model NOM components and individual BSA on change of the predominant peaks in ΔFTIR spectra. 196 D.2 Clean Water Resistance Analysis D.2.1 Calculation of Clean Water Resistance Transmembrane pressure (TMP) was monitored using calibrated pressure transducers, with monthly calibration procedures implemented to ensure measurement accuracy. The voltage outputs were converted to TMP values using Equation D.1, though actual conversion factors were periodically adjusted based on calibration data specific to each transducer. Membrane resistance (R), a key parameter for evaluating hydraulic performance, was calculated using Equation D.2, where R is the clean water membrane resistance, μ represents dynamic viscosity, and J denotes permeate flux. 𝑇𝑀𝑃 (𝑝𝑠𝑖) = 𝑉𝑜𝑙𝑡𝑎𝑔𝑒 (𝑚𝑉) × 200 Eq D.1 𝑅 =𝑇𝑀𝑃𝜇𝐽 Eq D.2 197 D.2.2 Clean Water Resistance Supplement Data Figure D.7 Clean water resistance for membranes during cyclic ageing with SA and HA in feed water. Confidence intervals of clean water resistance correspond to maximum and minimum of 3 replicate measurements. 198 D.3 FESEM Analysis D.3.1 Pore Radius and Surface Roughness Analysis Pore radius measurements were conducted through processing FESEM images using a custom-developed image processing algorithm. The analytical procedure employed contrast stretching with percentile-based intensity rescaling (1st to 15th percentile) to enhance image features, followed by adaptive thresholding using a block size of 19 pixels to segment porous regions. Small artifacts and noise were systematically removed through morphological operations, with minimum size thresholds of 20 pixels for object removal and 200 pixels for hole filling. To ensure measurement robustness, systematic sensitivity analyses were performed by varying key processing parameters including block size (±20%), offset values (±50%), minimum object size (±25%), and area threshold for hole filling (±25%) within reasonable ranges. The segmented binary images were then processed using connected component labeling to identify individual pores, and the area of each pore was calculated (Figure D.8). Assuming circular pore geometry, which is consistent with the observed membrane morphology, the average pore radius was determined by converting individual pore areas to radii using the fundamental relationship between circular area and radius (r = sqrt(A/π)), where A represents the measured pore area. Individual image scales were calibrated using scale bars present in each SEM image to convert pixel measurements to nanometers, ensuring accurate physical dimensions. This methodology enables systematic characterization of membrane surface morphology while maintaining 199 analytical rigor through automated image processing protocols and quantified measurement uncertainty. Figure D.8 Comparison of FESEM images of membrane surface before and after processing Code for FESEM process to get pore radius: The following code analyzes FESEM images to quantify pore geometry. It processes grayscale images by enhancing contrast, applying adaptive thresholding to identify pores, and cleaning the result by removing noise. The algorithm incorporates automated sensitivity analysis by systematically varying processing parameters across predefined ranges to assess measurement robustness. The code then calculates key porosity metrics including overall porosity percentage, total pore count, and average pore radius. For the radius calculation specifically, it identifies individual pores through connected 200 component labeling, computes the area of each pore, converts these areas to equivalent circular radius using the formula 𝑟 = √𝐴/𝜋, where r is pore radius, A is pore area. Each image is processed with its individual scale calibration (nm/pixel) determined from scale bar measurements to provide accurate physical dimensions. These radius values are averaged to determine the mean pore radius, with coefficient of variation (CV) calculated from sensitivity analysis to quantify measurement uncertainty. Results are compiled into CSV files for further analysis, including standard measurements, sensitivity analysis results, and summary statistics. 1. import pandas as pd 2. from PIL import Image 3. import numpy as np 4. from matplotlib import pyplot as plt 5. from scipy import ndimage 6. import os 7. from skimage import exposure, filters, morphology 8. from skimage.filters import threshold_otsu, threshold_local 9. from math import sqrt, pi 10. 11. 12. class SEMImageProcessor: 13. def __init__(self, image_path, scale_nm_per_pixel): 14. """ 15. Initialize with image path and specific scale calibration 16. 17. Parameters: 18. - image_path: path to SEM image 19. - scale_nm_per_pixel: nanometers per pixel for this specific image 20. """ 21. self.image_path = image_path 22. self.image = Image.open(image_path) 23. self.gray_image = self.image.convert("L") 24. self.gray_array = np.array(self.gray_image) 25. self.scale_nm_per_pixel = scale_nm_per_pixel 26. 27. def apply_contrast_stretching(self): 28. p1, p15 = np.percentile(self.gray_array, (1, 15)) 29. self.contrast_stretched = exposure.rescale_intensity(self.gray_array, in_range=(p1, p15)) 201 30. 31. def calculate_porosity_with_params(self, block_size=19, offset=10, min_size=20, area_threshold=200): 32. """Calculate porosity with specified parameters for sensitivity analysis""" 33. self.apply_contrast_stretching() 34. smoothed_image = self.contrast_stretched 35. 36. # Apply adaptive thresholding with variable parameters 37. adaptive_thresh = threshold_local(smoothed_image, block_size, offset=offset) 38. binary_image = smoothed_image < adaptive_thresh 39. 40. # Remove small noise and large artifacts with variable parameters 41. binary_image_cleaned = morphology.remove_small_objects(binary_image, min_size=min_size) 42. binary_image_cleaned = morphology.remove_small_holes(binary_image_cleaned, area_threshold=area_threshold) 43. 44. porous_area = np.sum(binary_image_cleaned) 45. total_area = self.contrast_stretched.size 46. 47. return porous_area / total_area, binary_image_cleaned 48. 49. def calculate_number_of_pores(self, binary_image): 50. labeled_array, num_pores = ndimage.label(binary_image) 51. return num_pores, labeled_array 52. 53. def calculate_average_pore_radius(self, binary_image, labeled_array, num_pores): 54. """ 55. Calculate average pore radius in both pixels and nanometers 56. """ 57. if num_pores == 0: 58. return 0, 0 59. 60. # Calculate areas for each labeled region (in pixels²) 61. pore_areas_pixels = ndimage.sum(binary_image, labeled_array, range(1, num_pores + 1)) 62. 63. # Convert areas to radii assuming circular pores: A = πr² 64. pore_radii_pixels = np.sqrt(pore_areas_pixels / pi) 65. avg_radius_pixels = np.mean(pore_radii_pixels) 66. 67. # Convert to nanometers using specific scale for this image 68. avg_radius_nm = avg_radius_pixels * self.scale_nm_per_pixel 69. 202 70. return avg_radius_pixels, avg_radius_nm 71. 72. def sensitivity_analysis(self, block_size_range=[15, 17, 19, 21, 23], 73. offset_range=[5, 8, 10, 12, 15], 74. min_size_range=[15, 20, 25], 75. area_threshold_range=[150, 200, 250]): 76. """ 77. Perform sensitivity analysis by varying key processing parameters 78. Returns results with both pixel and nanometer measurements 79. """ 80. results = [] 81. 82. print( 83. f"Running sensitivity analysis with {len(block_size_range) * len(offset_range) * len(min_size_range) * len(area_threshold_range)} parameter combinations...") 84. 85. for block_size in block_size_range: 86. for offset in offset_range: 87. for min_size in min_size_range: 88. for area_threshold in area_threshold_range: 89. try: 90. # Calculate with varied parameters 91. porosity, binary_image = self.calculate_porosity_with_params( 92. block_size, offset, min_size, area_threshold) 93. 94. num_pores, labeled_array = self.calculate_number_of_pores(binary_image) 95. avg_radius_px, avg_radius_nm = self.calculate_average_pore_radius( 96. binary_image, labeled_array, num_pores) 97. 98. results.append({ 99. 'block_size': block_size, 100. 'offset': offset, 101. 'min_size': min_size, 102. 'area_threshold': area_threshold, 103. 'porosity': porosity, 104. 'num_pores': num_pores, 105. 'avg_pore_radius_px': avg_radius_px, 106. 'avg_pore_radius_nm': avg_radius_nm 107. }) 108. except Exception as e: 109. # Skip parameter combinations that cause errors 110. continue 111. 203 112. # Calculate statistics for both pixel and nanometer measurements 113. if results: 114. radii_px = [r['avg_pore_radius_px'] for r in results if r['avg_pore_radius_px'] > 0] 115. radii_nm = [r['avg_pore_radius_nm'] for r in results if r['avg_pore_radius_nm'] > 0] 116. porosities = [r['porosity'] for r in results] 117. pore_counts = [r['num_pores'] for r in results] 118. 119. stats = {} 120. 121. if radii_px: 122. stats['radius_px'] = { 123. 'mean': np.mean(radii_px), 124. 'std': np.std(radii_px), 125. 'cv_percent': (np.std(radii_px) / np.mean(radii_px)) * 100, 126. 'min': np.min(radii_px), 127. 'max': np.max(radii_px), 128. 'range': np.max(radii_px) - np.min(radii_px) 129. } 130. 131. if radii_nm: 132. stats['radius_nm'] = { 133. 'mean': np.mean(radii_nm), 134. 'std': np.std(radii_nm), 135. 'cv_percent': (np.std(radii_nm) / np.mean(radii_nm)) * 100, 136. 'min': np.min(radii_nm), 137. 'max': np.max(radii_nm), 138. 'range': np.max(radii_nm) - np.min(radii_nm) 139. } 140. 141. stats['porosity'] = { 142. 'mean': np.mean(porosities), 143. 'std': np.std(porosities), 144. 'cv_percent': (np.std(porosities) / np.mean(porosities)) * 100 145. } 146. 147. stats['pore_count'] = { 148. 'mean': np.mean(pore_counts), 149. 'std': np.std(pore_counts), 150. 'cv_percent': (np.std(pore_counts) / np.mean(pore_counts)) * 100 151. } 152. 153. return results, stats 204 154. 155. return [], {} 156. 157. def process_image(self, perform_sensitivity=False): 158. # Standard processing with default parameters 159. porosity, binary_image = self.calculate_porosity_with_params() 160. num_pores, labeled_array = self.calculate_number_of_pores(binary_image) 161. avg_radius_px, avg_radius_nm = self.calculate_average_pore_radius(binary_image, labeled_array, num_pores) 162. 163. # Display the images 164. plt.figure(figsize=(15, 6)) 165. plt.subplot(1, 3, 1) 166. plt.imshow(self.contrast_stretched, cmap='gray') 167. plt.title('Contrast Stretched Image') 168. plt.axis('off') 169. 170. plt.subplot(1, 3, 2) 171. plt.imshow(binary_image, cmap='gray') 172. plt.title(f'Binary Image\nPores: {num_pores}\nRadius: {avg_radius_nm:.1f} nm') 173. plt.axis('off') 174. 175. # Add measurement information 176. plt.subplot(1, 3, 3) 177. plt.text(0.1, 0.9, f'Image: {os.path.basename(self.image_path)}', fontsize=10, fontweight='bold', 178. transform=plt.gca().transAxes) 179. plt.text(0.1, 0.8, f'Scale: {self.scale_nm_per_pixel:.3f} nm/pixel', fontsize=12, transform=plt.gca().transAxes) 180. plt.text(0.1, 0.65, f'Measurements:', fontsize=12, fontweight='bold', transform=plt.gca().transAxes) 181. plt.text(0.1, 0.55, f'• Radius (pixels): {avg_radius_px:.2f}', fontsize=11, transform=plt.gca().transAxes) 182. plt.text(0.1, 0.45, f'• Radius (nm): {avg_radius_nm:.2f}', fontsize=11, transform=plt.gca().transAxes) 183. plt.text(0.1, 0.35, f'• Number of pores: {num_pores}', fontsize=11, transform=plt.gca().transAxes) 184. plt.text(0.1, 0.25, f'• Porosity: {porosity:.3f}', fontsize=11, transform=plt.gca().transAxes) 185. plt.axis('off') 186. plt.title('Analysis Results') 187. 188. plt.tight_layout() 189. plt.show() 190. 191. result = { 205 192. 'porosity': porosity, 193. 'num_pores': num_pores, 194. 'avg_pore_radius_px': avg_radius_px, 195. 'avg_pore_radius_nm': avg_radius_nm, 196. 'scale_nm_per_pixel': self.scale_nm_per_pixel 197. } 198. 199. # Perform sensitivity analysis if requested 200. if perform_sensitivity: 201. print(f"\nPerforming sensitivity analysis for {os.path.basename(self.image_path)}...") 202. sens_results, stats = self.sensitivity_analysis() 203. 204. if stats and 'radius_nm' in stats: 205. print(f"Pore Radius Sensitivity Analysis Results:") 206. print(f" Mean radius: {stats['radius_nm']['mean']:.2f} ± {stats['radius_nm']['std']:.2f} nm") 207. print(f" Coefficient of Variation: {stats['radius_nm']['cv_percent']:.1f}%") 208. print(f" Range: {stats['radius_nm']['min']:.2f} - {stats['radius_nm']['max']:.2f} nm") 209. print(f" Absolute range: {stats['radius_nm']['range']:.2f} nm") 210. 211. result['sensitivity_stats'] = stats 212. result['sensitivity_results'] = sens_results 213. else: 214. print(" Warning: No valid sensitivity results obtained") 215. 216. return result 217. 218. 219. def create_your_scale_calibration_dict(): 220. """ 221. Define the specific nm/pixel scale for each of your images 222. REPLACE THE VALUES BELOW WITH YOUR ACTUAL MEASUREMENTS 223. """ 224. scale_dict = { 225. } 226. 227. print("Scale calibration loaded:") 228. for image, scale in scale_dict.items(): 229. print(f" {image}: {scale:.3f} nm/pixel") 230. 231. return scale_dict 232. 233. 206 234. def process_images_and_save_to_csv(image_paths, output_base_name, scale_dict, perform_sensitivity=False): 235. """ 236. Process images and save results to CSV files 237. Creates separate CSV files for standard results and sensitivity analysis 238. """ 239. data = [] 240. sensitivity_data = [] 241. 242. print(f"Processing {len(image_paths)} images...") 243. print(f"Sensitivity analysis: {'Enabled' if perform_sensitivity else 'Disabled'}") 244. print("-" * 60) 245. 246. for i, path in enumerate(image_paths, 1): 247. image_name = os.path.basename(path) 248. print(f"\n[{i}/{len(image_paths)}] Processing {image_name}...") 249. 250. # Get scale for this specific image 251. if image_name not in scale_dict: 252. print(f" Warning: No scale found for {image_name}, skipping...") 253. continue 254. 255. scale = scale_dict[image_name] 256. print(f" Using scale: {scale:.3f} nm/pixel") 257. 258. try: 259. processor = SEMImageProcessor(path, scale_nm_per_pixel=scale) 260. result = processor.process_image(perform_sensitivity=perform_sensitivity) 261. 262. # Standard data 263. data.append({ 264. "Image Name": image_name, 265. "Scale (nm/px)": result['scale_nm_per_pixel'], 266. "Porosity": result['porosity'], 267. "Number of Pores": result['num_pores'], 268. "Avg Pore Radius (px)": result['avg_pore_radius_px'], 269. "Avg Pore Radius (nm)": result['avg_pore_radius_nm'] 270. }) 271. 272. # Add sensitivity data if available 273. if perform_sensitivity and 'sensitivity_stats' in result: 274. stats = result['sensitivity_stats'] 275. if 'radius_nm' in stats: 207 276. sensitivity_entry = { 277. "Image Name": image_name, 278. "Scale (nm/px)": result['scale_nm_per_pixel'], 279. "Radius Mean (nm)": stats['radius_nm']['mean'], 280. "Radius Std (nm)": stats['radius_nm']['std'], 281. "Radius CV (%)": stats['radius_nm']['cv_percent'], 282. "Radius Min (nm)": stats['radius_nm']['min'], 283. "Radius Max (nm)": stats['radius_nm']['max'], 284. "Radius Range (nm)": stats['radius_nm']['range'], 285. "Porosity CV (%)": stats['porosity']['cv_percent'], 286. "Pore Count CV (%)": stats['pore_count']['cv_percent'] 287. } 288. sensitivity_data.append(sensitivity_entry) 289. 290. except Exception as e: 291. print(f" Error processing {image_name}: {str(e)}") 292. continue 293. 294. # Save results to CSV files 295. if not data: 296. print("No data to save!") 297. return 298. 299. # Save standard results 300. df = pd.DataFrame(data) 301. standard_output = f"{output_base_name}_standard_results.csv" 302. df.to_csv(standard_output, index=False) 303. print(f"\nStandard results saved to: {standard_output}") 304. 305. if perform_sensitivity and sensitivity_data: 306. # Save sensitivity analysis results 307. sens_df = pd.DataFrame(sensitivity_data) 308. sensitivity_output = f"{output_base_name}_sensitivity_analysis.csv" 309. sens_df.to_csv(sensitivity_output, index=False) 310. print(f"Sensitivity analysis saved to: {sensitivity_output}") 311. 312. # Create and save summary statistics 313. cv_values = [entry['Radius CV (%)'] for entry in sensitivity_data] 314. summary_data = [{ 315. 'Metric': 'Radius CV (%)', 316. 'Mean': np.mean(cv_values), 317. 'Std': np.std(cv_values), 208 318. 'Min': np.min(cv_values), 319. 'Max': np.max(cv_values), 320. 'Count': len(cv_values) 321. }] 322. 323. summary_df = pd.DataFrame(summary_data) 324. summary_output = f"{output_base_name}_summary_statistics.csv" 325. summary_df.to_csv(summary_output, index=False) 326. print(f"Summary statistics saved to: {summary_output}") 327. 328. # Print overall summary to console 329. print(f"\nSensitivity Analysis Summary:") 330. print(f" Average CV across all images: {np.mean(cv_values):.1f} ± {np.std(cv_values):.1f}%") 331. print(f" CV range: {np.min(cv_values):.1f}% - {np.max(cv_values):.1f}%") 332. print(f" Images with CV < 5%: {sum(1 for cv in cv_values if cv < 5.0)}/{len(cv_values)}") 333. print(f" Images with CV < 10%: {sum(1 for cv in cv_values if cv < 10.0)}/{len(cv_values)}") 334. 335. # Save detailed console output to text file 336. console_output = f"{output_base_name}_console_summary.txt" 337. with open(console_output, 'w') as f: 338. f.write("Sensitivity Analysis Summary\n") 339. f.write("=" * 40 + "\n\n") 340. f.write(f"Average CV across all images: {np.mean(cv_values):.1f} ± {np.std(cv_values):.1f}%\n") 341. f.write(f"CV range: {np.min(cv_values):.1f}% - {np.max(cv_values):.1f}%\n") 342. f.write(f"Images with CV < 5%: {sum(1 for cv in cv_values if cv < 5.0)}/{len(cv_values)}\n") 343. f.write(f"Images with CV < 10%: {sum(1 for cv in cv_values if cv < 10.0)}/{len(cv_values)}\n\n") 344. 345. f.write("Individual Image Results:\n") 346. f.write("-" * 30 + "\n") 347. for entry in sensitivity_data: 348. f.write(f"{entry['Image Name']}: CV = {entry['Radius CV (%)']:.1f}%, ") 349. f.write(f"Mean = {entry['Radius Mean (nm)']:.2f} nm, ") 350. f.write(f"Range = {entry['Radius Range (nm)']:.2f} nm\n") 351. 352. print(f"Console summary saved to: {console_output}") 353. 354. print(f"\nProcessing complete! Files created:") 355. print(f" • {standard_output}") 209 356. if perform_sensitivity and sensitivity_data: 357. print(f" • {sensitivity_output}") 358. print(f" • {summary_output}") 359. print(f" • {console_output}") 360. 361. 362. if __name__ == '__main__': 363. # List of image file paths 364. image_paths = [ 365. ] 366. 367. # IMPORTANT: Replace the values in this function with your actual measurements 368. scale_dict = create_your_scale_calibration_dict() 369. 370. output_base_name = 'pore_analysis_results' 371. 372. # Process images with sensitivity analysis and save to CSV 373. process_images_and_save_to_csv(image_paths, output_base_name, 374. scale_dict=scale_dict, 375. perform_sensitivity=True) Table D.1 Sensitive analysis of pore radius Cyclic/ Soak Ageing/ Virgin Cumulative Dose (ppm·h) Mean Pore Radius (nm) Pore Radius Coefficient of Variance Pore Radius Min (nm) Pore Radius Max (nm) Virgin 0 16.03252 0.078 14.62 16.66 Cyclic Ageing 100,000 19.59095 0.081 16.65 20.01 200,000 20.88447 0.091 17.23 21.06 500,000 21.26508 0.097 21.7 25.85 1,000,000 21.42495 0.074 20.25 23.39 1,300,000 21.67428 0.052 20.74 23.03 Soak Ageing 100,000 17.53023 0.083 14.69 17.98 200,000 19.27515 0.084 16.74 20.54 500,000 20.58604 0.094 18.72 22.72 1,000,000 21.49969 0.075 20.49 23.89 1,300,000 21.5966 0.071 19.77 23 Coefficient of variance calculated as the ratio of standard deviation to mean (CV = σ/μ) from 255 parameter combinations including contrast threshold settings, background correction values, noise removal criteria, and artifact filtering thresholds. 210 Surface roughness analysis was conducted using the Fiji variant of ImageJ software, following established protocols for FESEM images. The analytical procedure employed the Extended Depth of Field and SurfCharJ plugins to generate height profile maps and calculate surface roughness parameters. Initially, image preprocessing was performed through contrast enhancement and noise reduction using the de-speckle filter to minimize artifacts from electron scattering. The Extended Depth of Field plugin was utilized in medium quality mode with enabled height map computation to construct topographical representations of the membrane surface. Subsequently, the SurfCharJ plugin processed these height maps to determine quantitative roughness parameters, including arithmetic average roughness (Ra), root mean square roughness (Rq), and peak-to-valley height (Rz). This methodology enabled systematic characterization of membrane surface topography while maintaining analytical consistency across multiple sample regions. The process was standardized across all membrane samples to ensure comparative validity of the roughness measurements, with careful consideration given to maintaining consistent imaging parameters and analysis settings throughout the investigation. 211 Figure D.9 Typical surface roughness analysis 3D illustration for membrane 212 D.4 Radical Testing Screening Experiment Analysis Impacts of NOM concentration on Methylene blue concentration Figure D.10 Impact of NOM concentration on the concentration of methylene blue. C0 is the initial methylene blue concentration; C is the concentration at given time; confidence intervals for concentration data correspond to the maximum and minimum values from 2 replicate experiments. 213 Methylene Blue concentration measured with ambient light Figure D.11 Methylene blue concentration measured with ambient light. C0 is the initial methylene blue concentration; C is the concentration at given time; confidence intervals for concentration data correspond to the maximum and minimum values from 2 replicate experiments. Methylene blue concentration did not change with time and was not impacted by ambient light. 214 Impact of radical scavengers on methylene blue concentration Figure D.12 Impact of radical scavengers on the concentration of methylene blue. C0 is the initial methylene blue concentration; C is the concentration at given time; confidence intervals for concentration data correspond to the maximum and minimum values from 2 replicate experiments. Presence of radical scavengers (i.e. Acetate and Acetone) did not impact the concentration of methylene blue. 215 Impact of NOM in real feed waters from full-scale facilities on methylene blue removal rate a b Figure D.13 Impact of (a) polysaccharide type NOM and (b) humic substance type NOM in real feed waters from full-scale facilities on the methylene blue removal rate. Confidence intervals rates correspond to the maximum and minimum values from 2 replicate experiments.
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-ShareAlike 4.0 International
Graduate
Bérubé, Pierre R.
Andrews, Robert C. (Robert Carl), 1954-
Membrane ageing in drinking water treatment : assessing the impact of natural organic matter foulants and hypochlorite interactions
Text