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Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity
Published on: March 7, 2016
Modeling and testing of reactive contaminant transport in drinking water pipes: chlorine response and implications
Y Jeffrey Yang1, James A Goodrich, Robert M Clark
1Water Supply and Water Resources Division, US EPA National Risk Management Research Laboratory, Cincinnati, OH 45268, USA. yang.jeff@epa.gov
Abstract:
A modified one-dimensional Danckwerts convection-dispersion-reaction (CDR) model is numerically simulated to explain the observed chlorine residual loss for a "slug" of reactive contaminants instantaneously introduced into a drinking water pipe of assumed no or negligible wall demand. In response to longitudinal dispersion, a contaminant propagates into the bulk phase where it reacts with disinfectants in the water. This process generates a U-shaped pattern of chlorine residual loss in a time-series concentration plot. Numerical modeling indicates that the residual loss curve geometry (i.e., slope, depth, and width) is a function of several variables such as axial Péclet number, reaction rate constants, molar fraction of the fast- and slow-reacting contaminants, and the quasi-steady-state chlorine decay inside the "slug" which serves as a boundary condition of the CDR model. Longitudinal dispersion becomes dominant for less reactive contaminants. Pilot-scale pipe flow experiments for a non-reactive sodium fluoride tracer and the fast-reacting aldicarb, a pesticide, were conducted under turbulent flow conditions (Re=9020 and 25,000). Both the experimental results and the CDR modeling are in agreement showing a close relationship among the aldicarb contaminant "slug", chlorine residual loss and its variations, and a concentration increase of chloride as the final reaction product. Based on these findings, the residual loss curve and its geometry are useful tools to identify the presence of a contaminant "slug" and infer its reactive properties in adaptive contaminant detections.

