Delay between Sensing and Response in Water Contamination Events Academic Article uri icon

abstract

  • Determining the consequences of a water contamination event is an important concern in the field of water systems security. Morbidity and mortality resulting from such a contamination are influenced in part by the amount of contaminated water consumed and the time between consumption and medical treatment. Water quality sensors in the water distribution network may shorten this time and help the users avoid the contaminant's adverse effects by alerting authorities to unusual water quality parameters; otherwise, the authorities may first suspect contamination when the victims begin seeking treatment. Once the irregular water quality parameters have been detected, some time may still elapse before all users stop consuming the contaminated water. Modeling this additional delay is the focus of this article. This delay has been divided into five independent, sequential processes. The first phase is the amount of time required to transmit the sensed or measured contaminant concentrations to the local authorities. The second process includes the authorities' efforts to verify that there is a genuine contamination event, usually through additional water quality tests. The third stage includes any measures that the authorities take in preparation to alerting the public to the threat including agency coordination, drafting announcements, contacting media, and printing flyers. The fourth phase of the delay is the time required to transmit the news of the contamination to the public. The final period encompasses the time elapsed while the system users, after being informed of the contamination, decide whether or not to comply with instructions on how to avoid the adverse effects. Probability distributions are constructed for the duration of each phase of the delay based on data collected from historical water contamination events and other disasters and characteristics of typical sensor networks. The entire response process is modeled using a Monte Carlo approach to determine probability distributions of response delay. © 2006 ASCE.

author list (cited authors)

  • Bristow, E. C., & Brumbelow, K.

citation count

  • 23

publication date

  • June 2006