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

1737424(凯莉)。为大多数美国人提供饮用水的淡水湖和水库面临着日益严重的水质威胁。营养物污染、污染物和土地利用变化可导致低氧浓度和藻华，这可能导致金属浓度升高、鱼类和鸟类死亡、厚厚的藻渣、有毒气味和整体有毒的水，不适合饮用。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，这些不利后果是可以预防的。为了提高供水的弹性，该项目将开发一个智能供水系统，将智能互联技术与适应性管理相结合，确保社区饮用水安全。智能供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，增强检测水质退化和果断管理行动之间的反馈，以减轻此类威胁。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和与饮用水机构的有意义的参与。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如何使用S&amp；CC技术管理他们的饮用水。通过在饮用水水库中嵌入集成传感器网络，该项目整合了来自九个学科的专业知识，以研究S&amp；C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，这将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，创建一门课程，让高中生接触到s&p C技术产生的数据，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污水处理技术以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污水处理技术的接受程度之间的关系。这种增强的理解和信心可能会导致社会资本的增加，从而可以评估S&amp；CC技术通过增加公众对其供水系统的信任来提高供水水库饮用水质量的生态系统恢复能力和社区恢复能力的程度。

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.