Development of Spectroscopic Methods for Optimizing Drinking Water Treatment Processes

获得安全和清洁的饮用水是联合国承认的一项人权。然而，据估计，世界上大约80%人口的水源受到集约化农业、人口密度和气候变化等压力因素的高度威胁。受到污染的水源威胁到发展中国家和富裕国家生产清洁饮用水的能力，促使人们需要越来越复杂的处理和监测方法。例如，日益常见的极端天气事件（如暴雨）增加了公众接触污水溢出的病原体和城市或农业径流的污染物的风险。由于当前实时监测技术的局限性，通常基于事件的高风险条件（例如洪水）会被放大。现有的浊度或电导率等措施不够敏感或选择性，无法准确发现不利条件，并在短时间内为保护公众健康提供必要的治疗调整信息。由于荧光光谱技术对多种风险指标和环境污染物的敏感性和专一性，越来越受到人们的重视。FS已显示出表征水中有机物化学多样性混合物的前景，这决定了处理效率。此外，FS在提供石化泄漏中存在的芳香烃等污染物的化学指纹和污水对水源影响的指标方面的潜力尚未得到充分利用。虽然FS监测在水处理方面有很大的前景，但数据分析方面的挑战限制了它的使用。信号的叠加、非线性效应和水质的自然变化阻碍了在高维光谱中识别感兴趣的化合物。处理和利用实时荧光数据的合理方法尚未开发。本研究计划的目标是解决数据分析方面的挑战，并实现FS在水质监测方面的潜力。这个项目建立在我最近的兴趣和我在开发专门解决这些数据挑战的机器学习方法方面的成功基础上。本项目着眼于在压力日益增大的背景下，通过两种方式提高饮用水生产的弹性和可持续性。首先，开发FS，提供与公共卫生风险相关的水质变化的实时和相关图片。其次，利用FS前所未有的能力来了解有机物质与处理过程的相互作用，为优化研究提供信息，并使采用先进的处理系统能够有效地缓解具有挑战性的源条件。该研究项目为HQP提供了独特的机会，提供先进水处理、光谱学和机器学习方面的实践培训。

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.