CAREER: Towards Watersheds as Water Treatment Plants through Advances in Distributed Sensing and Control

这个学院早期职业发展（Career）奖支持的研究将探索实时控制水利基础设施的潜力，以减少河网中的营养污染。营养物污染是全球地表水系统面临的最昂贵和最广泛的环境问题之一。来自农业和城市径流的氮和磷促进了河流、湖泊和沿海水域藻类的生长。这些藻华消耗氧气水平，释放对人体健康有害的毒素。该项目将研究如何实时控制水力基础设施，如水坝，可以通过模拟废水处理厂在流域规模的过程来处理营养污染。这种新方法有可能大大减少营养物污染的影响，同时避免对昂贵的基础设施扩建项目的需要。这些研究工作将辅以一项教育计划，旨在通过开发针对各级学生的新的计算机模拟游戏，促进对地表水质量概念的积极学习。研究活动将会很好地融入教学及外展计划，透过积极互动的学习机会，进一步传播研究的影响，让更多学生参与科学、技术、工程及数学（STEM）。该项目将结合计算建模和真实世界的实验评估，揭示关于液压控制对营养动力学影响的新基础知识。首先，将开发一个新的计算水质模型来模拟河网中营养物质的命运和运输，考虑非定常水力学、多物种反应动力学和潜流交换。接下来，将研究新的水质数据同化方法，以提供有效控制所需的水质实时估计。第三，将利用鲁棒模型预测控制方法探索去除营养物和藻类的最佳水库运行策略，并在河网尺度上进行评估。建模、估计和控制营养动态将通过实验室规模的测试平台和系统规模的案例研究来验证，这些案例研究集中在不同大小的真实流域。本研究将描述和量化水库主动控制的潜力，以减轻流域尺度上的营养物污染和藻华。为了促进广泛的教育推广，这项研究的结果将被整合到一个新的“沙盒”电脑游戏中，该游戏将模拟河流网络中营养-生态系统的相互作用。通过鼓励学生在他们自己的虚拟生态系统中管理水生生物，这个模拟游戏将有机地教授富营养化的机制和防止它所需的干预措施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) award supports research that will explore the potential for real-time control of hydraulic infrastructure to reduce nutrient pollution in river networks. Nutrient pollution is one of the most costly and widespread environmental problems facing surface water systems worldwide. Nitrogen and phosphorus from agricultural and urban runoff fuel the growth of algae in rivers, lakes, and coastal waters. These algal blooms deplete oxygen levels and release toxins that are hazardous to human health. This project will investigate how real-time control of hydraulic infrastructure like dams can treat nutrient pollution by emulating processes from wastewater treatment plants at the watershed scale. This new approach has the potential to substantially reduce nutrient pollution impacts while obviating the need for expensive infrastructure expansion projects. These research efforts will be complemented with an educational plan designed to promote active learning of surface water quality concepts through the development of a new computer simulation game targeted at students of all levels. The research activities will be well integrated into teaching and outreach plans, to further disseminate the impacts through active and interactive learning opportunities to broaden participation in science, technology, engineering, and mathematics (STEM). This project will reveal new fundamental knowledge about the effects of hydraulic controls on nutrient dynamics using a combination of computational modeling and real-world experimental assessments. First, a new computational water quality model will be developed to simulate nutrient fate and transport in river networks accounting for unsteady hydraulics, multispecies reaction kinetics, and hyporheic exchange. Next, new methods for water quality data assimilation will be investigated to provide real-time estimates of water quality required for effective control. Third, optimal reservoir operation strategies for nutrient and algae removal will be explored using a robust model-predictive control approach and evaluated at the river network scale. Modeling, estimation, and control of nutrient dynamics will be validated against measured data using both a laboratory-scale testbed and system-scale case studies focused on real-world watersheds of different sizes. This research will characterize and quantify the potential for active control of reservoirs to mitigate nutrient pollution and algal blooms at the watershed scale. To facilitate broad educational outreach, the results of this research will be integrated into a new ‘sandbox’ computer game that will simulate nutrient-ecosystem interactions in river networks. By encouraging students to steward aquatic life in their own virtual ecosystems, this simulation game will organically teach the mechanics of eutrophication and the interventions needed to prevent it.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.