Operations at military bases create numerous physical disturbances that can damage or eliminate vegetation and/or compact the soil, thereby affecting the rates and extent of surface-water runoff. Changes in vegetation, soil properties, and runoff patterns, in turn, directly or indirectly affect the availability and exchange rates of soil ions. As a result, soil disturbances can be expected to generate characteristic "chemical signatures" in runoff water, which then are transferred to the streams, rivers, or lakes that are the primary recipients of runoff water from these physically disturbed areas.
The objective of this SERDP Exploratory Development (SEED) project was to link and apply two emerging measurement and analysis methods—“lab-on-a-chip” technology and in-situ bioassays—in an effort to generate the data needed to predict damage and recovery of aquatic systems from terrestrial disturbances more rapidly and cost-effectively than is possible using more conventional methods.
Researchers explored the feasibility of using automated "lab-on-a-chip" methods to acquire time-series data that relate to several conservative and semi-conservative properties of water known to be diagnostic of biological activity and anthropogenic disturbance. Efforts were made, using in-situ bioassay tests, to link these time-series measurements to biological responses of organisms that respond rapidly to water-quality changes.
This research verified the utility in further developing, testing, and validating an advanced monitoring technique that combines lab-on-a-chip technology with in-situ bioassays. Through the strategic selection of site-specific test animals as well as sensitive and reliable endpoints for determining biologically significant responses to changes in ions, both techniques can be combined into an approach that has predictive capabilities. "Smart monitoring" techniques that consider chemical and biological variance at shorter-than-conventional time scales can reduce the monitoring costs and time needed to obtain predictive data and increase the accuracy of predictions about environmental damage and ecological recovery. (SEED Project Completed - 2000)