Development of Spectroscopic Methods for Optimizing Drinking Water Treatment Processes

获得安全清洁的饮用水是联合国承认的一项人权。然而，据估计，世界上约80%的人口面临着来自集约农业、人口密集和气候变化等压力因素的高度威胁。受影响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，推动了对日益复杂的处理和监测方法的需求。例如，日益普遍的极端天气事件(如暴雨)增加了公众接触污水溢出病原体和城市或农业径流污染物的风险。高风险条件通常是基于事件的(例如洪水)，由于我们目前的实时监测技术的局限性，这种情况被放大了。现有的措施，如浊度或电导率，不够敏感或不够有选择性，无法准确检测不利条件，并在保护公众健康所需的短时间内通知治疗调整。随着人们对水质实时监测要求的提高，荧光光谱技术因其对许多风险指标和环境污染物的敏感性和特异性而受到越来越多的关注。FS显示出了对水中不同化学成分的有机物混合物进行表征的前景，这种混合物定义了处理效率。此外，FS还具有未得到充分利用的潜力，可以提供污染物的化学指纹，如石化泄漏中存在的芳香烃，以及污水对源水的影响指标。虽然FS监测在水处理中有相当大的希望，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。还没有开发出处理和利用实时荧光数据的合理方法。这项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。这个项目基于最近的兴趣和我在开发机器学习方法方面的成功，这些方法是为解决这些数据挑战而量身定做的。该计划着眼于改善饮用水生产的复原力和可持续性的长期愿景，以两种方式应对不断增加的压力。首先，开发FS以提供与公共健康风险相关的水质变化的实时和相关图片。其次，利用FS前所未有的能力了解有机物与处理过程的相互作用，为优化研究提供信息，并使先进的处理系统能够有效缓解具有挑战性的来源条件。该研究项目为HQP提供了独特的机会，提供高级水处理、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.