| dc.description.abstract | Climate change poses significant challenges for drinking water treatment plants (DWTPs), with extreme weather events increasingly impacting water quality. While various aspects of climate adaptation, such as source water protection and demand management, are crucial, managing water quality is paramount for consumer safety. Understanding the effects of climate-driven water quality exacerbations on DWTPs is essential, including identifying watershed risk factors and their impact on treatment processes. Quantifying and substantiating a treatment system's capability and vulnerability to different raw water quality scenarios, especially for turbidity and Natural Organic Matter (NOM) is crucial for performance assessment and preparing for future extreme weather events. In this context, enhancing robustness, which is the ability of a DWTP to maintain desired drinking water quality even during raw water quality disturbances, is key to safeguarding treatment processes against sudden and long-term changes in surface water quality. However, existing tools for assessing vulnerabilities and improving resilience in critical infrastructure lack a focus on water quality management and specifically on evaluating and enhancing treatment process robustness. A comprehensive framework is needed to guide utilities in determining the robustness of their systems and devising strategies to address deficiencies. There is also a need for robustness metrics specifically addressing NOM reduction/removal which is still an unexplored area.
This thesis contributes to understanding climate impacts on drinking water treatment processes, focusing on two critical water quality parameters (WQPs) - turbidity and NOM. It explores the implications of turbidity and NOM variations on treatment processes within the context of watershed changes due to climate change. Additionally, this thesis examines the robustness of DWTPs as a vital climate adaptation strategy, synthesizing related concepts like resilience, reliability, risk, and vulnerability to present a comprehensive understanding within the context of DWTPs.
The most significant contribution of this research is the development and introduction of three robustness frameworks tailored to DWTPs. The general framework outlines systematic steps for assessing and improving overall robustness, while the parameter-specific framework applies this methodology to specific water quality parameters (WQPs). A plant-specific framework then tailors the parameter-specific approach to individual DWTPs. The thesis proposes a parameter-specific framework for turbidity, utilizing the turbidity robustness index (TRI) for evaluation of individual treatment processes and the overall robustness index (ORI) for the overall assessment of the plant. This framework is applied to two full-scale DWTPs, Plants A and B in Ontario, Canada, using historical plant data and bench-scale experimental data simulating extreme high-turbidity scenarios. The application identifies less robust processes vulnerable to climate extremes, operational responses for short-term robustness, and critical WQP thresholds necessitating capital improvements. This framework serves as a valuable tool for assessing and enhancing the robustness of DWTPs, offering insights into their current state, and aiding in climate adaptation planning.
Another notable contribution of this research lies in the comparative analysis of outcomes from four DWTPs where the turbidity robustness framework was implemented. This comparison not only underscores the versatility of the framework, but also offers valuable insights into the performance of four distinct plants using standardized metrics. It also introduces a decision-tree for charting out the next steps for utilities based on the robustness assessment and shows examples tailored to each plant, enhancing the practical applicability of the framework.
This thesis also addresses the lack of NOM-related robustness metrics and introduces a novel index, Organic Matter Robustness Index (OMRI) to quantify the robustness of critical treatment processes. This index aims to incorporate the complexity of NOM in natural waters and the variety of surrogate parameters used for measurement at DWTPs in the NOM robustness quantification. Apart from the OMRI, this research also proposes another quantitative method for evaluating NOM robustness by extending the use of TRI to appropriate NOM surrogate parameters. This index was incorporated in the NOM robustness framework, which was applied to plant B, with a focus on historical plant data as well as experimental data simulating extremely high NOM scenarios. Short-term adaptation options in the form of operational changes were identified to improve the removal of NOM in such adverse source water situations. The results from this study show the applicability and ease of use of the OMRI to a full-scale DWTP and offer insights into the current operational robustness with respect to NOM of plant B. | en |
