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In-Situ and Remote Water Quality Monitoring
Importance of Water Quality Monitoring
Detection and identification of algal species in local water supplies provides information
about, and advance warning of, the presence of potentially harmful species. Data on the indicators of health of detected algal
species may also point to other unseen issues in the ecological surroundings. Automated classification to the taxa level would
allow for more timely identification of harmful species, as illustrated in Figure 1: a. Karenia brevis, b. Pfisteria sp., c.
Prorocentrum minimum, d. Chattonella verruculosa, among others, as well as establish their representative ratios with respect
to beneficial algal species.
Occasionally, just the right conditions exist that allow some species to grow rapidly,
producing harmful toxins or consuming natural resources to a degree that they impact the immediate environment. These harmful
algal blooms (HABs) may have serious impacts:
- Economic - a blow to commercial fishing, recreation, and tourism.
- Ecological - wildlife kills from HAB toxins, competition with beneficial algae, oxygen depletion in water.
- Human health - toxicity-related illnesses from indigenous or introduced harmful species.
Michigan Aerospace Corporation's Role
Michigan Aerospace Corporation has the background and expertise to combine automated
water sampling instruments and robust automated classification software to provide early detection of Harmful Algal Blooms
(HAB's). Michigan Aerospace is also experienced in remote sensing, which may be applied to remote species detection based on
species-specific fluorescence spectra.
In-situ Sampling
Michigan Aerospace's Data Exploitation Group has scientists experienced in pattern recognition and
automated learning algorithms, which are directly applicable to the task of automated sampling and
classification.
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In-situ water monitoring to identify
and evaluate aquatic species - for
early HAB warning, or general
environmental health
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Automated recognition of species is achieved using a
powerful machine-learning paradigm. Automation is maintained from the start of sampling to
the end product - the reporting.
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