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%的人口的水源受到集约化农业、人口密集化和气候变化等压力因素的高度威胁。受损的水源威胁着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，越来越常见的极端天气事件（如暴雨）增加了公众接触污水溢流中的病原体和城市或农业径流中的污染物的风险。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限制而被放大。浊度或电导率等现有测量方法不够敏感或选择性，无法在保护公众健康所需的短时间内准确检测不利条件并告知治疗调整。为了满足实时水质监测的需要，荧光光谱法（FS）由于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已经显示出有希望表征水中有机物的化学多样性混合物，其定义了处理效率。此外，FS在提供污染物的化学指纹方面的潜力未得到充分利用，例如石油化工泄漏中存在的芳烃和污水对源沃茨影响的指标。虽然在水处理中有相当大的前景，但数据分析的挑战限制了其使用。叠加的信号，非线性效应，和水质的自然变化，阻碍了识别化合物的利益在高维光谱。处理和利用实时荧光数据的合理方法尚未开发。该研究计划的目标是解决数据分析的挑战，并实现FS的水质监测的潜力。该计划建立在最近的兴趣和我在开发机器学习方法以解决这些数据挑战方面的成功之上。该计划以两种方式在不断增加的压力背景下解决了提高饮用水生产的弹性和可持续性的长期愿景。首先，开发FS以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与处理过程的相互作用，为优化研究提供信息，并采用先进的处理系统，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.