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Source Water Quality Monitoring Networks
Citation:
Allen, Joel, M. Elovitz, C. Nietch, AND Jim Lazorchak. Source Water Quality Monitoring Networks. Source Water Quality Monitoring Networks WEBINAR IEPA#8825 , NA, IL, October 29, 2014.
Impact/Purpose:
The prupuse of this presentation is to inform stakeholders of the Illinois Section of the American Water Works Association of potential avenues for Harmful Algal Bloom monitoring in the context of a source water monitoring program.
Description:
Harmful Algal Blooms (HABs) are increasingly impacting aquatic systems, reducing provided ecological services and requiring expensive engineered solutions. HABs, particularly those dominated by cyanobacteria (cyanoHABs) are a public health, ecologic, and economic concern. Characteristically, cyanoHABs produce large amounts of biomass and have the potential for producing taste and odor compounds and potent cyanotoxins. Cyanotoxins are now under review for regulation on the Contaminant Candidate List 3. The primary objective of this project is to develop empirical relationships between algal community composition, toxicity, and cyanotoxin concentrations with temporally dense data. This information can be used by water quality managers to optimize responses to cyanobacterial blooms and their toxins. Lopez et al. noted the following in the Interagency Working Group on Harmful Algal Blooms, Hypoxia, and Human Health of the Joint Subcommittee on Ocean Science and Technology's report, "An ideal monitoring system would allow for real-time, highly automated, accurate bloom detection that in the short run could provide early warning of impending toxic events and will lead to better predictive capability in the long run.” This project uses currently available water quality monitoring technologies to collect high frequency data on algal population dynamics at the division level, whole-organism responses to cyanoHABs and their toxins, and abiotic parameters that influence bloom occurrence and cyanotoxin production. These data will form the basis of data driven predictive models to achieve the goal of a ``real-time, highly automated, accurate bloom detection'' system.
