By Dan Kroll
There are a number of potential problems with implementing a monitoring system for source water. There is the problem of diurnal (night and day) as well as seasonal shifts in water quality due to a variety of factors such as aquatic plant respiration and decaying vegetation from autumn leaf falls. Varying amounts of sediments, turbidity and dissolved solids due to precipitation events and spring run-off fluctuations may be problematic. Due to the transient and unexpected nature of the events we are trying to detect, the monitoring systems also need to be on-line and continuous because if we knew when we needed to monitor, we wouldn't need to monitor.
The basic concept here is to actively and continuously monitor a variety of basic water quality parameters and look for significant changes that may be indicative of an event. There are a variety of source water parameters that may find application for this sort of system. Various manufacturers in the environmental market have for many years combined a variety of this sort of instrumentation into self contained data collection bundles that can feed data back to a central location via wireless telemetry or hard wired packages. There are a number of parameters currently available for on-line monitoring. The relative low cost of these instrumentation packages allows monitoring at a variety of sites. The major drawback is the huge amount of data that must be analyzed and correlated to determine if a change is significant or the result of natural variation.
The use of advanced chemometrics techniques have been proven to be effective in recognizing and classifying events in the complex matrix of the drinking water distribution system as demonstrated by the Hach Event Monitor and GaurdianBlue® systems. The use of these same algorithms with a different sensor set to monitor source water is now available for source water deployments. In the system as it is designed, signals from a number of separate orthogonal measurements of water quality contained in the Hach Source Water Panel are processed from a multi-parameter measure into a single scalar trigger signal in an Event Monitor computer system that contains the algorithms modified for source water. The signal then goes through a crucial proprietary baseline estimator. A deviation of the signal from the established baseline is then derived. Then a gain matrix is applied that weights the various parameters based on empirical data. The magnitude of the deviation signal is then compared to a preset threshold level. If the signal exceeds the threshold, the trigger is activated.
The algorithms include an adaptive tuning section that adjusts the "Slow" constants to values that reduce the sensitivity of the Trigger Algorithm to the background noise commonly found at the site. This is done to reduce nuisance triggers. Note that the slow values calculated are not permitted to de-sensitize the algorithm to the point where it cannot recognize serious events. This allows for a simplified method to handle the vast data streams involved in continuous monitoring and provide a simple and easy-to-understand alarm system.
About the Author: Dan Kroll is chief scientist at Hach Company's Homeland Security Technologies division, in Loveland, CO. He has been the lead researcher on a variety of method development projects for the physical, chemical and microbiological quality of water and soils for which he holds several patents.
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