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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越来越多的水质威胁。营养物污染、污染物和土地利用变化可能导致低氧浓度和藻类大量繁殖，这可能导致金属浓度升高、鱼类和鸟类死亡、厚厚的藻类浮渣、有毒气味和整体有毒的饮用水不安全。如果饮用水管理人员掌握了采取先发制人行动所需的信息，这些不良后果就可以预防。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（SC）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，以减少延迟并加强检测水质退化和采取果断管理行动以减轻此类威胁之间的反馈。这种确保持续水质的能力的提高反过来又可以建立公众的信心，并与饮用水机构进行有意义的接触。该项目将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，研究了SC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。该项目将使用新的传感器技术来监测饮用水供应水库及其集水区，并开发和评估一种新的模型数据融合方法，以推进环境预测领域。这些预测将用于为油藏管理人员创建决策工具，并对其可用性进行评估。此外，还将开发教材，以创建一个课程，使高中生接触到从SC技术中出现的数据，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采用和使用S CC技术改善饮用水质量的看法，以及这种看法、对公用事业的信任和对S CC技术的接受之间的关系。这种增强的理解和信心可能会导致增加社会资本，允许的程度进行评估，其中的气候变化技术可以提高生态系统的恢复力的饮用水质量在供应水库和社区的复原力，通过增加公众的信任，在他们的水系统。

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）

Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates

迭代预测改进了甲烷沸腾率的近期预测

DOI：
10.3389/fenvs.2021.756603
发表时间：
2021
期刊：
Frontiers in Environmental Science
影响因子：
4.6
作者：
McClure, Ryan P.;Thomas, R. Quinn;Lofton, Mary E.;Woelmer, Whitney M.;Carey, Cayelan C.
通讯作者：
Carey, Cayelan C.

The Magnitude and Drivers of Methane Ebullition and Diffusion Vary on a Longitudinal Gradient in a Small Freshwater Reservoir

小型淡水水库中甲烷沸腾和扩散的幅度和驱动因素随纵向梯度的变化而变化

DOI：
10.1029/2019jg005205
发表时间：
2020
期刊：
Journal of Geophysical Research: Biogeosciences
影响因子：
0
作者：
McClure, R. P.;Lofton, M. E.;Chen, S.;Krueger, K. M.;Little, J. C.;Carey, C. C.
通讯作者：
Carey, C. C.

Whole‐ecosystem oxygenation experiments reveal substantially greater hypolimnetic methane concentrations in reservoirs during anoxia

整个生态系统充氧实验揭示了缺氧期间水库中低浅层甲烷浓度显着升高