Real-Time Monitoring for Toxicity Caused by Harmful Algal Blooms and Other Water Quality Perturbations
This project, sponsored by EPA's Environmental Monitoring for Public Access and Community Tracking (EMPACT) program, evaluated the ability of an automated biological monitoring system that measures fish ventilatory responses (ventilatory rate, ventilatory depth, and cough rate) to detect developing toxic conditions in water. In laboratory tests, acutely toxic levels of both brevetoxin (PbTx-2) and toxic Pfiesteria piscicida cultures caused fish responses primarily through large increases in cough rate. In the field, the automated biomonitoring system operated continuously for 3 months on Chicamacomico River, a tributary to the Chesapeake Bay that has had a history of intermittent toxic algal blooms. Data gathered through this effort complemented chemical monitoring data collected by the Maryland Department of Natural Resources (DNR) as part of their pfiesteria monitoring program. After evaluation by DNR personnel, the public could access the data at a DNR Internet website, (www.dnr.state.md.us/bay/pfiesteria/00results.html), or receive more detailed information at aquaticpath.umd.edu/empact. The field biomonitor identified five fish response events. Increased conductivity combined with substantial decrease in water temperature was the likely cause of one event, while contaminants (probably surfactants) released from inadequately rinsed particle filters produced another response. The other three events, characterized by greatly increased cough rate (two events) or increased ventilation rate and depth (one event), did not have identified causes. Water quality variations did not correspond to the timing of the three events. Analyses of water taken by an automated sampler were negative for the presence of pfiesteria or chemicals that could be associated with the observed responses, and no fish kills occurred on the Chicamacomico River during the monitoring period. Continuing activities to improve the biomonitoring system include providing a change detection algorithm for fish ventilatory patterns that does not depend on a baseline monitoring period, integrating the fish biomonitor with other automated biomonitoring systems, and developing an expert system to better detect toxic events and distinguish them from fish responses to normal variations in ambient water quality conditions.