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

清洁和安全的水是一个社区生存和繁荣的基本必需品。然而，数百万人暴露在不安全水平的饮用水污染物中，包括有毒和持久性重金属以及无处不在的“永久化学品”，如全氟烷基物质和多氟烷基物质(PFAS)。尽管有严格的法规和完善的实验室方法来检测这些广泛和持久的污染物，但由于采样和测试不频繁，这些污染物有时无法被检测到。在这个项目中，来自马萨诸塞大学洛厄尔分校的工程师、计算机科学家和社会科学家将与社区利益相关者(居民、社区团体、非营利组织、饮用水公用事业公司和监管机构)密切合作，在马萨诸塞州几个社会经济多元化的社区试行启用智能物联网(IoT)的水质监测和警报系统。鉴于饮用水污染和接触的情况在基础设施较旧的经济和种族不利的社区发生得不成比例，拟议中的技术将使贫困群体能够使用数据来倡导补救工作。实施智能社区水质监测和警报系统的跨学科社会技术系统方法将成为解决类似大规模社会和基础设施问题的新范例。在这个SCC项目中，调查人员将(1)部署由公民科学家操作的电化学电子舌(E-Tap)设备，用于快速、现场、水质检测铅和砷等污染物；(2)与社区利益相关者共同设计用户友好的应用程序和云计算平台，用于数据分析；以及(3)促进社区利益相关者之间的共享学习和合作，以建立社会凝聚力和对水测试技术和地方当局的信任。此外，这项工作将开发时空机器学习算法和云计算平台，该平台将从单个电子舌头设备获取响应，并产生对污染物类型、浓度、可能的来源和污染程度的预测。这些信息将被用来迅速通知公共卫生当局进行干预，并提醒受影响的居民采取适当行动。通过智能传感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用于实时水污染威胁检测和预警系统的新型时空分析、移动计算和机器学习技术的基本理解和实际设计。这项研究还将增进我们对促进可持续、可扩展的水质检测传感器平台所需的技术、培训和关系的了解和理解，并增加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的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.