Single- and multi-objective sensor network designs have relied on water quality models that assume instantaneous and complete mixing of species at junctions. However, recent findings show that the perfect mixing assumption at pipe junctions potentially results in erroneous outcomes in predicting water quality in pipe networks. The latest studies, through a series of computational and experimental approaches, provide a higher-accuracy water quality model. In the present study, sensor network designs in water distribution networks are reexamined using both the perfect mixing and non-perfect mixing assumptions. The optimization algorithm minimizes the number of sensors needed for detecting potential contaminant intrusions at all the nodes (100% detection coverage), while maximizing the redundancy of sensor coverage. Extended-period simulations of a set of contamination events were performed on two water quality models and resulted in two distinct contamination-event matrices. Comparisons of the required number of sensors and corresponding locations indicate that incomplete mixing at pipe junctions has a significant impact on the optimal sensor placement. Therefore, the improvement of water quality modeling will improve the effectiveness of early warning detection systems in the event of accidental ordeliberate contamination.