Utilizing Routine Water Quality Instruments and Artificial Neural Networks for Monitoring Distribution System Security.

摘要

Drinking water system security concerns have been a considerable issue in the United States in recent years, but in the last two years this issue has risen to new levels of urgency. The tragic events of September 11th highlighted America's vulnerability to terrorism and spurred a domestic security response unprecedented since World War II. Drinking water systems were identified almost immediately as a potential target for future attacks and were urged by the FBI to implement security measures. To address these threats, research and development efforts aimed at new technology for contaminant specific detection have increased significantly. Even though considerable additional research in this area is needed, there are concerns about this technology due to the potential costs, and the specificity of the monitoring being easily defeated. Currently, significant purposeful contamination of a water system won't be properly characterized until post-symptomatic epidemiological events are manifested in the affected community. Most drinking water systems currently monitor a significant number of water quality parameters at the plant. These are required for compliance and maintenance of water quality as the water enters the distribution system. In the distribution system, water quality is usually monitored through grab samples with an analysis turn-around time of hours to days.