RII Track-2 FEC: Computational Methods and Autonomous Robotics Systems for Modeling and Predicting Harmful Cyanobacterial Blooms

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The world's freshwater lakes are a crucial source of water for human use, for drinking, irrigation, cooling, recreation, and food production. However, provision of these essential lake services is threatened by the increased incidence of harmful cyanobacterial blooms in lakes worldwide. Harmful blooms decrease lake water quality, clarity, and aesthetics, negatively impact property values, and can threaten human and animal health through the production of potent toxins that can damage multiple organ systems. This project aims to unravel the drivers of where, when, and how cyanobacterial blooms develop and spread, by combining robotics and big data technologies with traditional water sampling. The project will advance the ability to evaluate and predict cyanobacterial blooms, potentially allowing earlier public health interventions in recreational lakes and in lakes that supply drinking water. Interventions can enable improved water treatment and distribution. The project's workforce development activities will train next generation professionals to work and communicate across disciplines and communities in order to address complex scientific problems that have major societal implications, though use of big data tools and technology. Using the tools of big data jointly with robotics, sensor networks, and limnological sampling, the project develops strategies for real-time, adaptive, autonomous environmental data collection and processing to enhance the ability to predict the development of harmful cyanobacterial blooms in lakes with incipient blooms. Specifically, autonomous surface vehicles equipped with a suite of sensors measuring physical, chemical, and biological parameters and unmanned aerial vehicles equipped with hyper-spectral, multispectral, and visible-light cameras will generate large volumes of data on lakes during the onset and succession of cyanobacterial blooms. Post-acquisition processing and model development will examine controls on the genesis and spread of blooms in near-real time. The project brings together an interdisciplinary group of investigators with expertise in big data, environmental science, ecology, human demography, instrumentation, and robotics from four EPSCoR jurisdictions: Maine, New Hampshire, Rhode Island, and South Carolina. Partnerships with the participating institutions, local lake associations, municipal water providers, and state agencies will produce large-scale datasets of physical, chemical, and biological factors influencing water quality from lakes in all four states, which will then be used to create new models to predict harmful cyanobacterial blooms. Over a dozen early career scientists will be trained in interdisciplinary research. Community partners will be engaged in data collection, data interpretation, and implementation of monitoring and management strategies.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.