Multivariate experimental design provides insights for the optimisation of rechloramination conditions and water age to control disinfectant decay and disinfection by-product formation in treated drinking water.

The stability of drinking water disinfectant residuals is known to be influenced by multiple variables. To evaluate the effects of various influencing variables on disinfectant stability, a multivariate analysis of chloramine decay and associated disinfection by-products (DBPs) formation was investigated in a series of bench-scale experiments. Of nine water quality variables previously identified, monochloramine dose, pH, and bromide concentration were selected as key water quality variables based on previous investigations and modelling. Co-effects of these key variables on monochloramine decay and formation of 33 halogenated and nitrogen-containing DBPs were investigated using response surface experimental design. Rechloramination conditions, including monochloramine dose, pH and bromide concentration, were optimised via a 3-factorial multivariate analysis of monochloramine stability in post-treatment drinking water. Effects of influencing variables on disinfectant decay and DBP formation were assessed and graphically presented as response surfaces with minimal experiments using Doehlert matrix experimental design compared to other multivariate experimental designs. Concentrations of trihalomethanes (THMs), haloacetic acids (HAAs), and N-nitrosamines were found to increase with water age, whereas opposite phenomenon was observed in the net production of haloacetonitriles (HANs). Increasing pH was found to stabilise monochloramine but it could cause DBP speciation to shift. Furthermore, increasing bromide concentration elevated Br-DBP formation. In bromide-containing water, pH = 7.8-8.0 should be considered as higher pH increases Br-THMs formations and lower pH increases formations of Br-HAAs and Br-HANs. However, water age or pH has insignificant impacts on DBP formation after significant monochloramine decay or at low initial monochloramine dose. These findings indicate that effective combined control measures to maintain monochloramine stability should include the application of high monochloramine dose (>1.5 mg-Cl2.L-1) under conditions of moderate to high pH (pH = 7.8-8.0) and minimal bromide concentration. This study provides relevant insights to water utilities aiming to design effective disinfectant residual management strategies for controlling monochloramine decay and DBP formation.

A multivariate Bayesian network analysis of water quality factors influencing trihalomethanes formation in drinking water distribution systems.

Controlling disinfection by-products formation while ensuring effective drinking water disinfection is important for protecting public health. However, understanding and predicting disinfection by-product formation under a variety of conditions in drinking water distribution systems remains challenging as disinfection by-product formation is a multifactorial phenomenon. This study aimed to assess the application of Bayesian Network models to predict the concentration of trihalomethanes, the dominant halogenated disinfection by-product class, using various water quality parameters. Naïve Bayesian and semi-naïve Bayesian models were constructed from Sydney and South East Queensland datasets across 15 drinking water distribution systems in Australia. The targeted variable, total trihalomethanes concentration, was discretised into 3 bins (<0.1 mg L-1, 0.1 - 0.2 mg L-1 and >0.2 mg L-1). The Bayesian network structures were built using water quality parameters including concentrations of individual and total trihalomethanes, disinfectant species (free chlorine, monochloramine, dichloramine, total chlorine), nitrogen species (free ammonia, total ammonia, nitrate, nitrite), and other physical/chemical parameters (temperature, pH, dissolved organic carbon, total dissolved solids, conductivity and turbidity). Seven performance parameters, including predictive accuracy and the rates of true and false positive and negative results, were used to assess the accuracy and precision of the Bayesian network models. After evaluating the model performance, the optimum models were selected to be Bayesian network augmented naïve models. These were observed to have the highest predictive accuracies for Sydney (78%) and South East Queensland (94%). Although disinfectant residuals are among the key variables that lead to trihalomethanes formation, potential concentrations of trihalomethanes in distribution systems can be more confidently predicted, in terms of probability associated with a wider range of water quality variables, using Bayesian networks. The modelling procedure developed in this work can now be applied to develop system-specific Bayesian network models for trihalomethanes prediction in other drinking water distribution systems.

Disinfectant residual stability leading to disinfectant decay and by-product formation in drinking water distribution systems: A systematic review.

Secondary disinfectants, such as chlorine and chloramine, have been widely applied to minimise microbial risks in drinking water during distribution. Key challenges have included the maintenance of stable concentrations of disinfectant residuals and the control of disinfection by-products that may form as a consequence of residual decay processes. Many factors may influence disinfectant residual stability and the consequential formation of by-products. Thus predictions of disinfectant stability and by-product formation are multifactorial problems, complete with numerous complications of parameter co-dependence and feedback amplification of some key parameters. The aim of this review was to derive an understanding of how disinfectant residual stability in drinking water distribution systems is impacted by various influencing factors such as water quality and operational parameters. Factors known to influence disinfectant stability and by-product formation were critically reviewed. A systematic review method was applied to identify 1809 journal articles published in the two decades from January 1998 to December 2017. From the initial screening, 161 papers were selected for detailed assessment. Important factors were identified to include temperature, water age, piping material, corrosion products, pH, hydraulic condition, disinfectant residual type and dosage and microbial activity. Microbial activity is a particularly complex parameter on which to base predictions since many factors are known to influence the degree and nature of such activity. These include temperature, water age, piping material, corrosion products, nutrients, natural organic matter, hydraulic condition and disinfectant residual type and dosage. Disinfectant types and dosages were found to be among the most important factors. Many knowledge gaps and research needs still remain, including the need for a more complete understanding of the factors that influence the production of nitrogenous disinfection by-products.