Optoelectronic and other conventional water quality sensors offer a potential for real-time online detection of
chemical and biological contaminants in a drinking water supply and distribution system. The nature of the application
requires sensors of detection capabilities at low contaminant concentrations, for continuous data acquisition and
management, and with reduced background noise and low false detection rates for a wide spectrum of contaminants. To
meet these application requirements, feasibilities of software-based methods were examined and a novel technique was
developed using adaptive monitoring and contaminant detection methodologies. This new monitoring and early
detection framework relies on the local adaptive and network adaptive sensors in order to reduce background noise
interference and enhance contaminant peak identifications. After "noise" reduction, the sensor measurements can be
assembled and analyzed for temporal, spatial and inter-parameter relationships. Further detection reliability
improvement is accomplished through signal interpretation in term of chemical signatures and in consideration of
contaminant fate and transport in pipe flows. Based on this integrated adaptive approach, a data statistical compression
technique can be used to process and reduce the sensor onboard data for background variations, which frequently
represent a bulk of inflowing data stream.
The adaptive principles and methodology were examined using a pilot-scale distribution simulator at the U.S. EPA
Test & Evaluation facility. Preliminary results indicate the research and development activities on adaptive monitoring
may lead to the emergence of a practical drinking water online detection system.
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