Prediction of chlorine and trihalomethanes concentration profile in bulk drinking water distribution systems from laboratory data

Nearly all drinking water distribution systems experience a “natural” reduction of disinfection residuals. The most frequently used disinfectant is chlorine, which can decay due to reactions with organic and inorganic compounds in the water and by liquid/solids reaction with the biofilm, pipe walls and sediments. Usually levels of 0.2-0.5 mg/L of free chlorine are required at the point of consumption to maintain bacteriological safety. Higher concentrations are not desirable as they present the problems of taste and odour and increase formation of disinfection by-products. It is usually a considerable concern for the operators of drinking water distribution systems to manage chlorine residuals at the “optimum level”, considering all these issues. This paper describes how the chlorine profile in a drinking water distribution system can be modelled and optimised on the basis of readily and inexpensively available laboratory data. Methods are presented for deriving the laboratory data, fitting a chlorine decay model of bulk water to the data and applying the model, in conjunction with a simplified hydraulic model, to obtain the chlorine profile in a distribution system at steady flow conditions. Two case studies are used to demonstrate the utility of the technique. Melbourne’s Greenvale-Sydenham distribution system is unfiltered and uses chlorination as its only treatment. The chlorine model developed from laboratory data was applied to the whole system and the chlorine profile was shown to be accurately simulated. Biofilm was not found to critically affect chlorine decay. In the other case study, Sydney Water’s Nepean system was modelled from limited hydraulic data. Chlorine decay and trihalomethane (THM) formation in raw and treated water were measured in a laboratory, and a chlorine decay and THM model was derived on the basis of these data. Simulated chlorine and THM profiles agree well with the measured values available. Various applications of this modelling approach are also briefly discussed.