Monitoring drinking water contamination is vitally important to inform consumers about water safety, identify source water problems, and facilitate discussion of public health and the environment of our drinking water. The overall goal of this project is to develop a framework for reliable and timely detection of drinking water contamination to build sustainable and connected communities. It focuses on communities that use private wells for drinking water without the benefit of a central utility to monitor water quality. It engages the community to participate, leveraging advances in data analytics, exploring the technological and social dimensions to answer a public health question: Is the drinking water in the community safe?
This project advances the role of public participatory scientific research, also referred to as citizen science, in data gathering. It develops new inference models using approaches from machine learning and statistics to improve accuracy, reliability, trustworthiness and value of the data, gathered through public participation. It improves understanding of key socio-demographic factors that influence public participation and data quality in contrasting community types. It demonstrates the potential role of citizen science in eliciting changes in behavior, and how that influences programmatic and regulatory practices, e.g., in this study of groundwater quality for healthy and sustainable communities. This framework, known as the Smart Water Crowdsensing (SWC) framework, developed by this project for communities in Indiana studying water quality, should serve as an exemplar for communities nationwide seeking community public participation in studying local public health questions.
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.
