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USING CONTINUOUS MONITORS FOR CONDUCTING TRACER STUDIES IN WATER DISTRIBUTION SYSTEMS
Citation:
PANGULURI, S., R. KRISHNAN, L. M. GARNER, C. L. PATTERSON, Y. LEE, D. HARTMAN, W. M. GRAYMAN, R. M. CLARK, AND H. PIAO. USING CONTINUOUS MONITORS FOR CONDUCTING TRACER STUDIES IN WATER DISTRIBUTION SYSTEMS. In Proceedings, World Water and Environmental Resources Congress 2005: Impacts of Global Climate Change, Anchorage, AL, May 15 - 19, 2005.American Society of Civil Engineers (ASCE), Reston, VA, 53, (2005).
Description:
The use of online monitors for conducting a distribution system tracer study is proving to be an essential tool to accurately understand the flow dynamics in a distribution system. In a series of field testing sponsored by U. S. Environmental Protection Agency (EPA) and Greater Cincinnati Water Works (GCWW) in 2002-2003, food-grade calcium chloride tracer was introduced into a water system network and the movement of the chemical was traced using strategically placed automated online conductivity meters (in conjunction with a limited grab sampling program). Four separate field tests were conducted in the GCWW water system representing 1) a small urbanized residential area, 2) a large urbanized residential area, 3) a small dead-end suburban residential area, and 4) a large suburban residential area pressure zone. Between 20 and 30 continuous monitors were used that covered approximately between 1 and 10 square miles of the water distribution network area. The combined (grab and continuous monitoring) approach yielded detailed data that allowed a thorough analysis. The continuous monitoring instruments provided round-the-clock minute-by-minute information and the grab sampling data allowed for corrections and adjustments of continuous monitoring data (in situations where some of the automated conductivity meters failed to log time correctly).
Many tracer studies of distribution systems have been performed over the past 15 years using grab sampling techniques. The grab sampling techniques work well in many scenarios, but have inherent limitations. For example, in complex looping distribution systems, the water flow may change direction and velocity multiple times depending upon instantaneous demand. Depending upon the size/length of the pipe and demand in a dead-end type situation, it is hard to predict and accurately monitor for tracer arrival. Also, round-the-clock manual monitoring is expensive, unsafe, and may result in missed tracer peaks. In heavy traffic and unsafe areas, frequent monitoring is also difficult. These phenomena make it very difficult to accurately understand the distribution system and calibrate a hydraulic and water quality model using grab sampling for certain networks and locations. Recent technology developments and application of online monitoring technology have improved the efficiency of these studies and provide promise for greatly expanded applications in the future.
With increased availability of these technologies, costs associated with continuous monitoring are expected to decrease so that larger utilities can afford to purchase and routinely use the equipment, and consulting engineers can affordably offer these services to smaller utilities. The application of this technology has the potential for providing new insights on how water distribution systems may be operated and designed to improve water quality.
Continuous monitors result in large streams of data that document minute-by-minute changes in water quality at various points of a network. Therefore, these systems require a relatively high level of sophistication in terms of data management, including the capability to generate real-time reports, graphical and visual representation of information and compliance reports for meeting drinking water standards. Some of these data may provide information on excursions in water quality that constitute violations of current or future drinking water standards. Careful planning and negotiations with appropriate regulatory authorities to define these potential "excursions" and the appropriate corrective action would prevent any misinterpretations of the data.