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: 2318862
• 负责人: Renato Figueiredo
• 金额: $ 15.47万
• 依托单位: University of Florida
• 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=2318862&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.

通过增加湖泊和水库中的浮游植物盛开，饮用水安全受到威胁，这通过有害毒素，浮渣以及味道和气味的变化对水质构成了主要威胁。为了在面对全球变化的情况下改善饮用水管理，该项目提议开发第一个自动化的实时浮游植物预测系统，以量化水质预测的不确定性。如果经理预测浮游植物盛开，他们可以先发挥作用来减轻水质损害，例如通过调整水处理，从而降低成本并提高饮用水安全性。该项目团队计划将尖端的湖泊生态系统和统计建模与新计算能力相结合，以每天向水管理者提供1至35天的预测，以预测美国几个湖泊的水经理。研究人员打算在预测系统上与水管理人员合作，以产生有关如何最好地进行有效沟通预测以改善水资源决策的宝贵知识。 该项目团队还计划开发有关高中生和社区大学学生在水管理/废水证书计划方面的预测和淡水生态系统的教学模块，从而改善了阿巴拉契亚中部的水质和水工人培训。 作为现有的教育计划的一部分，该教学模块将提供给美国各地的大学，该计划已经达到了100,000多名学生。生命的基本规则控制着这种行为：生态系统规模的出现是在单个生物体上运行的环境动态的函数（例如，温度和光对浮游植物增长率），由人口和社区过程介导的（例如，多种物种相互作用，可以促进浮游植物生物群增加）。该项目将采用生活规则方法，通过将新兴的浮游植物行为实施到预测模型中，以通过云和边缘计算工具生成实时的湖水质量预测来解决重大的社会问题。这项研究是由具有专业知识的跨学科团队独特的，该团队涵盖了生物科学，社会和决策科学，物理科学，计算机和数据科学以及统计学以及与经理，教育者和社区成员的长期合作伙伴关系。这种收敛，使用的研究方法的进步将包括：1）对如何使用生命规则来预测新兴的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的网络基础结构； 3）通过基于个体模型的实时预测的新颖，可削减计算的统计方法； 4）对水管理和生态系统动态如何相互作用以控制浮游植物； 5）创建有效传达预测不确定性的新工具； 6）通过为研究人员，经理和学生提供创新的培训，这些培训可以扩大阿巴拉契亚中部的STEM参与。通过新颖的跨学科整合，该项目旨在开发一种预测系统，该系统将成为全球社区中饮用水系统的模型。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子评估来支持的，并具有更广泛的影响。