Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management

合作研究：URoL:ASC：应用生命规则预测浮游植物的紧急行为并推进水质管理

• 批准号: 2318861
• 负责人: Cayelan Carey
• 金额: $ 207.63万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318861&HistoricalAwards=false
• 关键词: Collaborative; Research; URoL; ASC; Applying

Drinking water safety is threatened globally by increasing phytoplankton blooms in lakes and reservoirs, which pose major threats to water quality via harmful toxins, scums, and changes in taste and odor. To improve drinking water management in the face of global change, this project proposes to develop the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty in water quality predictions. If managers had forecasts of phytoplankton blooms, they could preemptively act to mitigate water quality impairment, such as by adapting water treatment, thereby decreasing costs and improving drinking water safety. The project team plans to integrate cutting-edge lake ecosystem and statistical modeling with new computing capacity to deliver 1 to 35 day-ahead forecasts of phytoplankton blooms to water managers daily for several U.S. lakes. Researchers intend to work with water managers on the forecasting system to generate valuable knowledge about how best to effectively communicate forecasts for improved water resource decision-making. The project team also plans to develop teaching modules on forecasting and freshwater ecosystems for high school students and community college students in water management/wastewater certificate programs, thereby improving both water quality and water worker training in central Appalachia. The teaching modules will be made available to colleges and universities across the U.S. as part of an existing educational program that has reached over 100,000 students to date.Phytoplankton blooms in lakes are a type of emergent behavior that can have ecosystem-scale, societally important consequences by degrading water quality, yet are challenging to predict. A fundamental Rule of Life governs this behavior: ecosystem-scale emergence is a function of environmental dynamics operating on individual organisms (e.g., temperature and light effects on phytoplankton growth rates), mediated by population and community processes (e.g., multi-species interactions that promote increased phytoplankton biomass). This project will apply a Rules of Life approach to solve a major societal problem by implementing emergent phytoplankton behavior into predictive models to generate real-time lake water quality forecasts with cloud and edge computing tools. This research is uniquely enabled by a transdisciplinary team with expertise that spans the biological sciences, social and decision sciences, physical sciences, computer and data sciences, and statistics, as well as long-term partnerships with managers, educators, and community members. Advances from this convergent, use-inspired research approach will include: 1) improved understanding of how a Rule of Life can be used to predict emergent, ecosystem-scale phenomena; 2) new cyberinfrastructure for transferring data from environmental sensors to the cloud; 3) generation of novel, computationally-tractable statistical methods for real-time forecasting with individual-based models; 4) greater understanding of how water management and ecosystem dynamics interact to control phytoplankton; 5) creation of new tools that effectively communicate forecast uncertainty; and 6) capacity-building by providing innovative training for researchers, managers, and students that broadens STEM participation across central Appalachia. Through novel, cross-disciplinary integration, this project aims to develop a forecasting system that will become a model for drinking water systems in communities globally.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.

全球饮用水安全受到湖泊和水库中浮游植物大量繁殖的威胁，这些浮游植物通过有害毒素、浮渣以及味道和气味的变化对水质构成重大威胁。面对全球变化，为了改善饮用水管理，本项目建议开发第一个自动化、实时的湖泊浮游植物预测系统，量化水质预测的不确定性。如果管理人员有浮游植物大量繁殖的预测，他们就可以采取先发制人的行动来减轻水质损害，例如通过调整水处理，从而降低成本并改善饮用水安全。该项目团队计划将先进的湖泊生态系统和统计模型与新的计算能力相结合，每天为美国几个湖泊的水管理者提供1至35天前浮游植物繁殖的预测。研究人员打算在预报系统上与水资源管理人员合作，以产生关于如何最有效地传播预报以改进水资源决策的宝贵知识。项目组还计划在水管理/废水证书课程中为高中生和社区大学生开发预测和淡水生态系统的教学模块，从而改善阿巴拉契亚中部的水质和对水工人的培训。这些教学模块将作为现有教育项目的一部分提供给美国各地的学院和大学，该项目迄今已惠及10万多名学生。湖泊中的浮游植物大量繁殖是一种紧急行为，可以通过降低水质产生生态系统规模的社会重要后果，但很难预测。一个基本的生命法则支配着这种行为：生态系统规模的涌现是一个作用于个体生物的环境动力学函数（例如，温度和光照对浮游植物生长速度的影响），由种群和群落过程（例如，促进浮游植物生物量增加的多物种相互作用）介导。该项目将应用生命法则的方法，通过将突发浮游植物行为引入预测模型，利用云和边缘计算工具生成实时湖泊水质预测，从而解决一个重大的社会问题。这项研究的独特之处在于一个跨学科的团队，他们的专业知识跨越了生物科学、社会和决策科学、物理科学、计算机和数据科学、统计学，并与管理人员、教育工作者和社区成员建立了长期合作伙伴关系。这种融合的、以使用为灵感的研究方法的进展将包括：1)提高对如何使用生命规则来预测新兴生态系统规模现象的理解；2)用于将数据从环境传感器传输到云的新网络基础设施；3)为基于个体的模型的实时预测提供新颖的、计算易于处理的统计方法；4)加深对水管理和生态系统动态如何相互作用以控制浮游植物的理解；5)创建有效沟通预测不确定性的新工具；6)能力建设，为研究人员、管理人员和学生提供创新培训，扩大整个阿巴拉契亚中部地区的STEM参与。通过新颖的跨学科整合，该项目旨在开发一个预测系统，该系统将成为全球社区饮用水系统的模型。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。