NSF Convergence Accelerator Track L: Innovative chemical microsensor development for in situ, real-time monitoring of priority water pollutants to protect water quality

Clean water is essential for the health and wellbeing of human life and of nature. Human activities, however, have far-reaching impacts on water quality, often leading to a decline in human and ecosystem health, reduction in food production, and escalation of poverty throughout the United States and globally. The increasingly high levels of water pollutants, most of which are not adequately monitored, are creating an “invisible water crisis” that disproportionately impacts low-income communities, tribal nations, and communities of color. This project aims to develop a compact, customizable chemical sensing system integrated with new microsensors and data management and analytic technologies to accurately quantify, and make visible, high-priority pollutants that threaten human and ecosystem health of freshwater and managed water systems. This collaboration of expertise and emerging technologies in engineering, environmental chemistry, artificial intelligence, and water resource management enhances the innovation of these real-time, cost-effective water-monitoring technologies. Their data outputs will ultimately improve capabilities for making timely, informed decisions and spurring progressive actions that ensure societal and planetary health. Engagement with diverse stakeholders (water advocacy non-profits, resource managers, tribal and state government groups, and environmental engineers) will ensure that these microsensors and data are accessible and user-friendly to benefit high-risk and underserved populations. Using microelectromechanical systems-based technologies through advanced microfabrication techniques, this project will develop miniaturized, portable chemical sensors with exceptional sensitivity (sub parts-per-billion level) and specificity of multiple target pollutants (specifically, nutrients and metals) in freshwater and managed water environments. Remote deployment of sensor modules and sensor arrays will be enabled by leveraging embedded systems design and ultra-low-power systems advances to extend the lifetime of deployments and enable wireless communications for real-time data transfer. Big Data technologies will be used to ensure that the data obtained from these sensor arrays are readily accessible, accurate, well-organized, and interpretable by end-users with wide-ranging expertise and needs, and spatial artificial intelligence methods will facilitate water quality prediction and forecasting. This innovative chemical sensing and data analytics platform will provide sensitive, selective, real-time, reliable, and cost-effective water quality monitoring for broad applications.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.