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

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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.