Source: STATE UNIV OF NEW YORK submitted to NRP
DETECTION AND MONITORING OF HARMFUL ALGAL BLOOMS
Sponsoring Institution
Other Cooperating Institutions
Project Status
COMPLETE
Funding Source
Reporting Frequency
Annual
Accession No.
0205812
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Jul 1, 2003
Project End Date
Jun 30, 2013
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
STATE UNIV OF NEW YORK
(N/A)
SYRACUSE,NY 13210
Performing Department
Chemistry
Non Technical Summary
The occurrence of harmful algal blooms in marine and freshwater environments can significantly impair the beneficial usage of this water through their high biomass or the production of toxic metabolites. These projects use a combination of chemical, molecular and biochemical techniques to develop the needed prediction, monitoring and alert strategies necessary to protect both ecosystem and human health. Basic information on ecosystem function, including occurrence, physiological ecology and transfer through the food chain, as required to predict both the onset and threat proposed by these blooms will also be obtained.
Animal Health Component
(N/A)
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
7230210100050%
7237210100050%
Goals / Objectives
(1) Develop and implement monitoring activities to assess and safeguard recreational and potable water supplies; (2) Evaluate the relationship between environmental variables and the occurrence of harmful algal blooms; (3) Develop new monitoring strategies that may detect impaired water use.
Project Methods
Collection of water, sediment and tissue samples; analysis of samples for nutrient, genetic and biochemical parameters including enzymes, genes and toxins; identification of samples for phytoplankton abundance and enumeration; preparation of new biological and chemical sensors using antibody and molecular approaches to be incorporated onto autonomous sampling platforms.