SCC-IRG Track 1: Community Based Approach to Address Contaminants in Drinking Water using Smart Cloud-Connected Electrochemical Sensors

SCC-IRG 第 1 轨道：使用智能云连接的电化学传感器解决饮用水中污染物的基于社区的方法

• 批准号: 2230180
• 负责人: Pradeep Kurup
• 金额: $ 249.5万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230180&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Community; Based

Clean and safe water is a basic necessity for a community to survive and thrive. However, millions of people are exposed to unsafe levels of drinking water contaminants including toxic and persistent heavy metals and ubiquitous “forever chemicals” such as per– and polyfluroalkyl substances (PFAS). Despite strict regulations, and well-established laboratory methods for detecting these widespread and persistent contaminants, these pollutants sometimes go undetected because of infrequent sampling and testing. In this project engineers, computer scientists, and social scientists from the University of Massachusetts Lowell will work closely with community stakeholders (residents, neighborhood groups, nonprofits, drinking water utilities, and regulators) to pilot a smart Internet of Things (IoT) enabled water-quality monitoring and alert system in several socio-economically diverse communities of Massachusetts. Given that drinking water contamination and exposure occurs disproportionately in economically and racially disadvantaged communities with older infrastructure, the proposed technology will empower underprivileged groups to use the data to advocate for remediation efforts. The transdisciplinary sociotechnical systems approach to implement a smart community engaged water-quality monitoring and alert system will be a new paradigm for addressing similar large scale societal and infrastructural problems.In this SCC project, the investigators will (1) deploy citizen-scientist-operated electrochemical electronic tongue (E-Tongue) devices for rapid, onsite, water quality testing of contaminants such as lead and arsenic, (2) co-design with community stakeholders a user-friendly app and cloud-computing platform for data analysis, and (3) foster shared learning and collaboration among community stakeholders to build social cohesion and trust in water testing technologies and the local authorities. Furthermore, this work will develop spatiotemporal machine learning algorithms and a cloud-computing platform that will take the responses from the individual E-Tongue devices and produce predictions of contaminant type, concentration, probable source, and extent of the contamination. This information will be used to quickly notify the public health authorities for intervention and alert affected residents to take appropriate actions. Through the design, development, and testing of a smart sensing and cloud-computing system, the proposed transformative research will contribute to the fundamental understanding and practical design of novel spatiotemporal analytics, mobile computing, and machine learning techniques for real-time water contaminant threat detection and early warning systems. The research will also advance our knowledge and understanding of the technologies, training, and relationships required to facilitate a sustainable, scalable sensor platform for water quality testing and increase awareness and social trust in water testing technologies and local authorities.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.

清洁和安全的水是社区生存和壮成长的基本必需品。但是，数以百万计的人暴露于不安全的饮用水污染物水平，包括有毒和持续的重金属以及无处不在的“永远的化学物质”，例如Per -and Polyfluroalalalyallys（PFAS）。尽管有严格的法规以及良好的实验室方法来检测这些宽度和持续的污染物，但由于不经常进行的采样和测试，这些污染物有时不会被发现。在这位项目工程师中，马萨诸塞大学洛厄尔大学的计算机科学家和社会科学家将与社区利益相关者（居民，邻里团体，非营利组织，饮用水公用事业和监管机构）紧密合作，以促进智能的物联网（IOT），使水质质量的监测和警报在Massachusechusetsets of Massachusetsets of Massachusets of Massionomony Communications中。鉴于饮用水的污染和暴露在经济和大致上受到较旧的基础设施的社区中的不成比例发生，因此拟议的技术将使弱势群体能够使用数据来倡导进行补救工作。 The transdisciplinary sociotechnical systems approach to implement a smart community engaged water-quality monitoring and alert system will be a new paradigm for addressing similar large scale social and infrastructure problems.In this SCC project, the investigators will (1) deploy citizen-scientist-operated electronic chemical electronic tongue (E-Tongue) devices for rapid, onsite, water quality testing of contaminants such as lead and arsenic, （2）与社区利益相关者共同设计一个用户友好的应用程序和云计算平台，用于数据分析，（3）在社区利益相关者之间建立共享的学习和协作，以建立社会凝聚力和信任水测试技术和地方当局。此外，这项工作将开发时空机器学习算法和云计算平台，该平台将从各个电子串联设备中获取响应，并对污染物类型，集中度，有问题的来源和污染的程度产生预测。该信息将用于快速通知公共卫生当局干预措施，并提醒受影响的居民采取适当行动。通过对智能感应和云计算系统的设计，开发和测试，拟议的变革性研究将有助于对新型空间时间分析，移动计算以及实时水污染威胁检测和早期警告系统的基本理解和实践设计。这项研究还将提高我们对促进水质测试可持续，可扩展的传感器平台所需的技术，培训和关系的了解，并提高对水测试技术和地方当局的认识和社会信任。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛的效果来评估，通过评估来诚实地通过评估来诚实地进行评估。