There are two main objectives to the proposed research: 1) to develop a new quantitative detection algorithm for Harmful Algal Blooms (HABs) in the Great Lakes based on remote sensing imagery by refining and augmenting currently qualitative algorithms;and 2) to develop an ecological niche model to predict the onset of HABs in the Great Lakes with inputs from remotely-sensed data products and state-of-the-art in-situ environmental measurements. Cyanobacteria-dominated HABs currently pose a serious health and economic risk to inhabitants and users of the Great Lakes, often producing toxins in sufficient quantity to cause severe health problems to humans and animals. The investigators'proposed research will benefit the human health and livelihood by providing tools to better manage the human health risks associated with HABs in the future based on a better understanding of the underlying ecology of HABs and improved synoptic detection methods. One component of the investigators'research will consist of implementing a monitoring program consisting of autonomous measurements - combined with ship measurements - of environmental parameters in western Lake Erie and Saginaw Bay critical to HAB initiation and promotion. These measurements will be used in the development of new satellite products that can provide synoptic observation of environmental signatures of these toxic algal blooms. The investigators will combine these new satellite products with other synoptic satellite environmental products and available in-situ environmental measurements to construct an historical data set that characterizes HAB events in the context of associated environmental conditions that cultivate the blooms. This data set will guide the development of an innovative ecological niche model based on multivariate analysis that will predict the spatial and temporal distribution of imminent HAB events from real-time environmental inputs. The investigators'team consists of seasoned oceanographers with many years of experience in instrumentation, bio-optics, remote sensing, phytoplankton and HAB ecology, and algorithm development, bringing a complimentary and comprehensive set of expertise to the problem of HAB detection and prediction. Their ultimate aim is to develop a critical set of tools for effectively managing human health risks associated with cyanobacteria blooms in water bodies around the globe. Public Health Relevance: Cyanobacteria HABs that occur in the Great Lakes can produce a wide range of toxins with severe health impacts on humans and animals, and also cause significant economic disruption. During HAB events, toxin concentration limits in the Great Lakes drinking water supply are often exceeded. The investigators'proposed research will improve current detection and prediction capabilities of HABs in the Great Lakes region, and will be directly beneficial to health and economic aspects of the many U.S. citizens who live in the region. The investigators bring a broad range of experience in remote sensing, in situ observational technology, and phytoplankton ecology that can help address the health and science issues related to toxic blooms in the Great Lakes.
Cyanobacteria HABs that occur in the Great Lakes can produce a wide range of toxins with severe health impacts on humans and animals, and also cause significant economic disruption. During HAB events, toxin concentration limits in the Great Lakes drinking water supply are often exceeded. The investigators'proposed research will improve current detection and prediction capabilities of HABs in the Great Lakes region, and will be directly beneficial to health and economic aspects of the many U.S. citizens who live in the region. The investigators bring a broad range of experience in remote sensing, in situ observational technology, and phytoplankton ecology that can help address the health and science issues related to toxic blooms in the Great Lakes.
| Moore, Timothy S; Dowell, Mark D; Bradt, Shane et al. (2014) An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. Remote Sens Environ 143:97-111 |
