Abstract
Understanding the potential health impacts that can occur from contamination events in drinking water distribution systems has been the subject of much research in recent years. Both the characterization of these impacts on distribution systems and the design of contamination warning system (CWS) sensor networks rely on models that vary widely in detail and representation of the actual system. A complete representation of the distribution system for even a medium-sized city can be enormously complex. Macro skeletonization is used to describe the aggregation of the network’s nodes and pipes. Micro skeletonization is used to describe the aggregation of all post-service connection piping and system detail to the model node. Models typically do not include information (pipe detail) beyond the service connection. In water systems dominated by multi-story buildings, such as commercial and residential high-rise buildings, considerable infrastructure (pipes, pumps, and tanks) are absent from the model. Previous work has evaluated the effects that varying levels of macro skeletonization have on the performance of sensor network monitoring designs. However, research investigating the impact to sensor network design performance due to the lack of post service connection piping and system detail is lacking. This paper reports on the modeling of CWS sensor network designs for two real water systems. The first system is small and simple and the model has been artificially modified to include multi-story buildings. The second water system is actually dominated by multistory buildings but its base model does not contain any micro-level detail. Geographical information system (GIS) tools and site-specific information are used to construct and arrange reasonable representations of the high rise buildings into the model. For each water system, EPANET simulations are performed along with public health impacts assessments and sensor network design optimizations. Evaluations are made between the skeletonized models (base models without post-service connection detail) and the multistory building-enhanced models to assess the performance of sensor networks to reduce public health impacts.
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