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Your Environment. Your Health.

University of New Hampshire

Development of novel detection and prediction algorithms for Microcystis blooms

Timothy Moore, Ph.D.
timothy.moore@unh.edu

NIEHS Grant: R01ES021929

Researchers at the University of New Hampshire are working to enhance tools for observing and predicting Microcystis harmful algal blooms (HABs) in the Great Lakes. These HABs pose a serious health and economic risk to people who live around and use the lakes. The investigators are developing a new quantitative detection algorithm that uses remote sensing imagery. They are also working on a model that uses remotely sensed data and environmental measurements to predict the onset of Microcystis HABs. Their work will generate spatial maps that predict the probability of HABs as well as help reveal the ecology of these blooms and provide critical information for informing water quality policy decisions.


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