Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/110162
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Type: Journal article
Title: Chloramine demand estimation using surrogate chemical and microbiological parameters
Author: Moradi, S.
Liu, S.
Chow, C.
van Leeuwen, J.
Cook, D.
Drikas, M.
Amal, R.
Citation: Journal of Environmental Biology: an international research journal of environmental sciences and toxicology, 2017; 57:1-7
Publisher: Elsevier
Issue Date: 2017
ISSN: 0254-8704
1878-7320
Statement of
Responsibility: 
Sina Moradi, Sanly Liu, Christopher W.K. Chow, John van Leeuwen, David Cook, Mary Drikas, Rose Amal
Abstract: A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (Fm) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection.
Keywords: Chloramine demand; drinking water treatment plants; modelling
Rights: © 2017 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
RMID: 0030064974
DOI: 10.1016/j.jes.2017.01.010
Appears in Collections:Earth and Environmental Sciences publications

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