SCC-IRG Track 2: Resilient Water Systems: Integrating Environmental Sensor Networks and Real-Time Forecasting to Adaptively Manage Drinking Water Quality and Build Social Trust

SCC-IRG 第 2 轨道：弹性水系统：集成环境传感器网络和实时预测，自适应管理饮用水质量并建立社会信任

基本信息

批准号：
1737424
负责人：
Cayelan Carey
金额：
$ 100万
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：
2018
资助国家：
美国
起止时间：
2018-01-01 至 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737424&HistoricalAwards=false
关键词：
SCC IRG Track Resilient Water

项目摘要

1737424 (Carey). The freshwater lakes and reservoirs that provide the majority of Americans with their drinking water face increasing threats to water quality. Nutrient pollution, contaminants, and land use change can lead to low oxygen concentrations and algal blooms, which can result in elevated metal concentrations, fish and bird kills, thick algal scums, noxious odors, and overall toxic water unsafe for drinking. These adverse outcomes may be prevented if drinking water managers have the information needed to act preemptively. To increase the resilience of water supplies, this project will develop a smart water system that integrates smart and connected (S&C) technology and adaptive management to ensure safe drinking water for communities. The smart water system will consist of sensor networks embedded in a drinking water reservoir to reduce delays and enhance feedbacks between the detection of water quality degradation and decisive management action to mitigate such threats. This increased capacity to ensure sustained water quality can in turn build both public confidence and meaningful engagement with drinking water institutions. This project will connect the networks of high-frequency sensors with secure cyberinfrastructure to develop innovative, real-time water quality prediction models and tools for more effective management. Finally, these models will be used to educate local residents and students about the use of S&CC technology to manage their drinking water. By embedding an integrated sensor network in a drinking water reservoir, this project integrates expertise from nine disciplines to study the complete feedback loop of how S&C technologies can improve drinking water management, water quality, and ultimately community well-being. The project will use novel sensor technology to monitor a drinking water supply reservoir and its catchment, and to develop and evaluate a new model-data fusion approach that will advance the field of environmental forecasting. These forecasts will be used to create decision-making tools for reservoir managers that will be evaluated for their usability. In addition, teaching materials will be developed to create a curriculum that exposes high school students to data emerging from S&C technology and increase their interest in and preparedness for careers in STEM. Finally, the project will assess public perception of the adoption and use of S&CC technologies by utilities to improve drinking water quality as well as the relationship between this perception, trust in the utility, and acceptance of the S&CC technology. This enhanced understanding and confidence may lead to increased social capital, permitting an evaluation of the degree to which S&CC technologies can increase both ecosystem resilience of drinking water quality in supply reservoirs and community resilience by increasing the public's trust in their water systems.

1737424（凯里）。为大多数美国人提供饮用水的淡水湖泊和水库面临着日益严重的水质威胁。养分污染、污染物和土地利用变化会导致氧气浓度低和藻类大量繁殖，从而导致金属浓度升高、鱼类和鸟类死亡、浓密的藻类浮渣、有毒气味以及不安全饮用的有毒水。如果饮用水管理者拥有采取先发制人行动所需的信息，则可以避免这些不良后果。为了提高供水的弹性，该项目将开发一个智能供水系统，该系统集成了智能互联（S&C）技术和适应性管理，以确保社区的安全饮用水。智能水系统将由嵌入饮用水水库的传感器网络组成，以减少延迟并增强检测水质恶化和采取果断管理行动以减轻此类威胁之间的反馈。确保持续水质的能力增强反过来可以增强公众信心以及与饮用水机构的有意义的互动。该项目将把高频传感器网络与安全的网络基础设施连接起来，开发创新的实时水质预测模型和工具，以实现更有效的管理。最后，这些模型将用于教育当地居民和学生如何使用 S&CC 技术来管理他们的饮用水。通过在饮用水水库中嵌入集成传感器网络，该项目整合了九个学科的专业知识，研究施恩禧技术如何改善饮用水管理、水质并最终改善社区福祉的完整反馈回路。该项目将使用新型传感器技术来监测饮用水供应水库及其集水区，并开发和评估新的模型数据融合方法，以推动环境预测领域的发展。这些预测将用于为油藏管理者创建决策工具，并对其可用性进行评估。此外，还将开发教材来创建课程，让高中生接触 S&C 技术产生的数据，并提高他们对 STEM 职业的兴趣和准备。最后，该项目将评估公众对公用事业公司采用和使用 S&CC 技术来改善饮用水质量的看法，以及这种看法、对公用事业公司的信任和对 S&CC 技术的接受之间的关系。这种理解和信心的增强可能会导致社会资本的增加，从而可以评估 S&CC 技术可以在多大程度上通过提高公众对其水系统的信任来提高供应水库饮用水质量的生态系统恢复力和社区恢复力。

项目成果

期刊论文数量（39）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Metalimnetic oxygen minima alter the vertical profiles of carbon dioxide and methane in a managed freshwater reservoir

金属金属氧最小值改变了管理淡水水库中二氧化碳和甲烷的垂直剖面

DOI：
10.1016/j.scitotenv.2018.04.255
发表时间：
2018-09
期刊：
Science of The Total Environment
影响因子：
9.8
作者：
McClure, Ryan P.;Hamre, Kathleen D.;Niederlehner, B.R.;Munger, Zackary W.;Chen, Shengyang;Lofton, Mary E.;Schreiber, Madeline E.;Carey, Cayelan C.
通讯作者：
Carey, Cayelan C.

Effectiveness of a bubble-plume mixing system for managing phytoplankton in lakes and reservoirs

气泡羽流混合系统管理湖泊和水库浮游植物的有效性

DOI：
10.1016/j.ecoleng.2018.01.002
发表时间：
2018-04
期刊：
Ecological Engineering
影响因子：
3.8
作者：
Chen, Shengyang;Carey, Cayelan C.;Little, John C.;Lofton, Mary E.;McClure, Ryan P.;Lei, Chengwang
通讯作者：
Lei, Chengwang

Chaoborus spp. Transport CH 4 from the Sediments to the Surface Waters of a Eutrophic Reservoir, But Their Contribution to Water Column CH 4 Concentrations and Diffusive Efflux Is Minor

查博鲁斯属

DOI：
10.1021/acs.est.7b04384
发表时间：
2018-01
期刊：
Environmental Science & Technology
影响因子：
11.4
作者：
Carey, Cayelan C.;McClure, Ryan P.;Doubek, Jonathan P.;Lofton, Mary E.;Ward, Nicole K.;Scott, Durelle T.
通讯作者：
Scott, Durelle T.

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

加强生态学家和计算机科学家之间的合作：经验教训和未来建议

DOI：
10.1002/ecs2.2753
发表时间：
2019-05
期刊：
Ecosphere
影响因子：
2.7
作者：
Carey, Cayelan C.;Ward, Nicole K.;Farrell, Kaitlin J.;Lofton, Mary E.;Krinos, Arianna I.;McClure, Ryan P.;Subratie, Kensworth C.;Figueiredo, Renato J.;Doubek, Jonathan P.;Hanson, Paul C.;et al
通讯作者：
et al

Relative importance of top‐down vs. bottom‐up control of lake phytoplankton vertical distributions varies among fluorescence‐based spectral groups

自上而下与自下而上控制湖泊浮游植物垂直分布的相对重要性在基于荧光的光谱组中有所不同