Development of Spectroscopic Methods for Optimizing Drinking Water Treatment Processes

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Access to safe and clean drinking water is recognized as a human right by the United Nations. However, estimates place water sources for approximately 80% of the world's population under high threat levels from stressors such as intensive agriculture, population densification, and climate change. Compromised sources threaten the ability to produce clean drinking water in both developing and affluent nations, driving a need for increasingly sophisticated treatment and monitoring methods. For example, increasingly common extreme weather events (e.g. heavy rainfall) elevate risk of public exposure to pathogens from sewage overflows and pollutants from urban or agricultural run-off. High-risk conditions, which are often event-based (e.g. flooding), are amplified by limitations with our current real-time monitoring technologies. Available measures such as turbidity or conductivity are not sensitive or selective enough to accurately detect adverse conditions and inform treatment adjustments in the short time frame necessary to protect public health.***In response to the need for improved real-time water quality monitoring, fluorescence spectroscopy (FS) is receiving increased attention due to its sensitivity and specificity to many risk indicators and environmental pollutants. FS has shown promise to characterize the chemically diverse mixture of organic matter in water that defines treatment efficiency. Furthermore, FS has an underutilized potential to provide chemical fingerprints of pollutants such as aromatic hydrocarbons present in petrochemical spills and indicators of sewage impacts in source waters. While there is considerable promise of FS monitoring in water treatment, challenges with data analysis limit its use. Superposition of signals, non-linear effects, and natural variations in water quality impede identifying compounds of interest in the high-dimensional spectra. Reasonable approaches to handle and leverage real-time fluorescence data have not been developed.***The goal of this research program is to address the data analysis challenges and realize the potential of FS for water quality monitoring. This program builds on recent interest and my successes in developing machine learning approaches tailored to solve these data challenges. This program addresses a long-term vision of improved resiliency and sustainability of drinking water production in the context of mounting pressures in two ways. First, developing FS to provide real-time and relevant pictures of changing water quality associated with public health risk. Second, leveraging FS for its unprecedented ability to understand interactions of organic matter with treatment processes, informing optimization research and enabling adoption of advanced treatment systems that effectively mitigate challenging source conditions. This research program presents unique opportunities for HQP, providing hands-on training in advanced water treatment, spectroscopy, and machine learning.