# Analysis of residual chlorine in simple drinking water distribution system with intermittent water supply

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## Abstract

Knowledge of residual chlorine concentration at various locations in drinking water distribution system is essential final check to the quality of water supplied to the consumers. This paper presents a methodology to find out the residual chlorine concentration at various locations in simple branch network by integrating the hydraulic and water quality model using first-order chlorine decay equation with booster chlorination nodes for intermittent water supply. The explicit equations are developed to compute the residual chlorine in network with a long distribution pipe line at critical nodes. These equations are applicable to Indian conditions where intermittent water supply is the most common system of water supply. It is observed that in intermittent water supply, the residual chlorine at farthest node is sensitive to water supply hours and travelling time of chlorine. Thus, the travelling time of chlorine can be considered to justify the requirement of booster chlorination for intermittent water supply.

## Keywords

Drinking water distribution system (DWDS) Intermittent water supply Residual chlorine Booster chlorination## Introduction

Everywhere in the world, the drinking water utilities face the challenge of providing water of good quality to their consumers as significant water quality changes can occur within drinking water distribution systems due to contamination. Disinfectant like chlorine can control growth of pathogens but it reacts with organic and inorganic matter in water, the chlorine concentration decreases in time called the chlorine decay (Males et al. 1988; Rossman et al. 1994; Clark et al. 1995; Boccelli et al. 2003). Because chlorine is such a strong oxidizer, it reacts with a wide range of chemicals and naturally occurring organic (and/or inorganic) matter (NOM) in the treated and/or distributed water to form potentially harmful disinfection by-products (DBPs). Some of these DBPs are suspected carcinogens and having adverse reproductive and developmental health effects (Krasner et al. 1989; Abdullah et al. 2003, 2009; Rehan Sadiq and Rodriguez 2004; Uyak et al. 2007; Brian Carrico and Singer 2009; Shihab et al. 2009; Shakhawat Chowdhury et al. 2009; Jianrong Wei et al. 2010). Therefore, it is very essential for any water supply authority to manage the chlorine disinfection within lower and upper limit of residual chlorine to safeguard the consumers from water-borne diseases and harmful DBPs simultaneously. Thus, the concentration of residual chlorine at various locations in drinking water distribution system may be considered as the final check to the quality of water supplied to the consumers.

Because of the importance of disinfection, a number of investigators have conducted research for the development of models to predict chlorine decay in drinking water (Feben and Taras 1951; Johnson 1978; Haas and Karra 1984; Biswas et al. 1993; Islam et al. 1997, 1998; Hallam et al. 2002; in Clark 1994, 2012, 1998; Rossman et al. 1994; Rossman and Boulos 1996; Hua et al. 1999; Ozdemir Osman and Alper Ucak 2002; Boccelli et al. 2003; Gibbs et al. 2006; Huang and McBean 2006, 2008). The most popular model is the first-order decay model in which the chlorine concentration is assumed to decay exponentially (Feben and Taras 1951; Johnson 1978; Clark 1994; Rossman et al. 1994; Hua et al. 1999; Boccelli et al. 2003). The performance of six different kinetic models for the decay of free chlorine in over 200 bulk water samples from a number of different sources found that the performance benefit over the simple first-order model was marginal (Powell et al. 2000a, b).

EPANET (Rossman et al. 2000) simulation model which uses first-order chlorine decay for prediction of residual chlorine in drinking water distribution system has been applied by many researchers (Clark et al. 1995; Castro and Neves 2003; Romero Gomez et al. 2006; Toru Nagatani et al. 2008; Shihab et al. 2009; Tomovic et al. 2010). The water quality model can be used as effective tool by water utilities for the predication of residual chlorine and may guide water supply authority for proper maintenance of residual chorine to balance between excessive disinfectant concentration near the source to avoid excessive disinfection by-products and minimum residual chlorine throughout the distribution network to avoid the microbial contamination.

The booster chlorination is found advantageous in maintaining proper balance between the minimum and maximum concentration. Researchers have examined different methods for determining the optimal schedule of disinfection boosters to maintain adequate levels of residual chlorine throughout the distribution system (Boccelli et al. 1998, 2003; Tryby et al. 1999, 2002; Munavalli and Kumar 2003; Ozdemir and Ucaner 2003; Propato and Uber 2004; Parks and Shannon 2009; Ostfeld et al. 2010). Thus, knowledge of residual chlorine concentration throughout the distribution network suggests the water utilities regarding selection of chlorine application strategy i.e. conventional or booster chlorination to avoid the recontamination of water in DWDS.

## Indian scenario of drinking water supply

Large numbers of households in Indian cities do not have access to one of the most basic of human needs—a safe and reliable supply of drinking water. As per McKenzie-Ray (2009), only half of all Indian urban households have a piped water connection, even those with a connection generally do not receive a regular supply of good quality water. The municipal water supply in most Indian cities is only available for a few hours per day, pressure is irregular, and the water is of questionable quality. No major Indian city has a 24 h supply of water, intermittent supply with 4–5 h of supply per day being the norm as compared to the Asian- Pacific average of 19 h per day supply (McKenzie-Ray 2009). Intermittent supply of water leads to health risks for users due to the higher likelihood of contamination of water pipelines through joints and damaged segments during periods when the system is not pressurized. Due to excessive growth in population, the service area is divided into few zones and each zone is supplied the water for limited hours which leads to the stagnation of water during non-supply hours and decay of chlorine for rest of the hours. Also, there is a problem related to maintenance of pressure at the farthest node in intermittent water supply. To cope up with the decay in chlorine, higher mass rate of chlorine is applied at the source to maintain the minimum residual chlorine up to the farthest end, which results in harmful DBP formation at the nearest locations to the source and less concentration of residual chlorine at farthest location. Thus, the objectives of microbial-free water with proper quantity and pressure is difficult to achieve through conventional water supply networks without targeting continuous water supply and constantly pressurized system (CPHEEO 1999; MoUD 2009) Given the health imperatives and other inconveniences caused by intermittent water supply, it is unfortunate that virtually no city in India has continuous water supply (CPHEEO 1999).

For Indian conditions of intermittent water supply, the use of explicit equations and available water quality model software to find out the residual chlorine is necessity. Booster chlorination is essential as if, supply hours are less than travelling time of chlorine up to the last location, the chances of chlorine decay results in contamination as mass rate of chlorine supplied at source by conventional method may not reach to the farthest node due to less supply hours. In such cases, the adoption of Booster chlorination as well to choose proper supply hours is very essential from health point of view of consumers. The prediction of residual chlorine at various locations can be useful to decide the selection of mode of water supply i.e. Intermittent (Supply hours in intermittent water supply) or continuous 24 × 7 water supply as well as chlorine application strategy i.e. conventional or booster chlorination.

In this study, a sample network is prepared and problem is formulated to find out the residual chlorine concentration at various locations of simple DWDS network with intermittent water supply of 2 h for two different strategies of chlorine applications i.e. of conventional and booster chlorination. The concept is developed to integrate the hydraulic and water quality model using first-order chlorine decay for intermittent water supply with booster nodes. Long travelling time and low velocities of water cause excessive decay of chlorine and the reaction of chlorine with organic and inorganic matter in water forms harmful DBPs. The effect of travelling time on concentration of residual chlorine is checked for both the chlorine application strategy which guides the selection of supply hours of water to achieve the effectiveness of booster chlorination strategy for the intermittent water supply which represents the common scenario of water supply in most of the Indian cities.

## Problem formulation

*R*

_{1}. Case II represents the booster chlorination with chlorine applied at source as well as at nodes 1, 2, and 3. Intermittent water supply with 2 h water supply in a day is considered which represent the general mode of water supply in Indian city. The water remains stagnant for rest of the 22 h during which the decay in chlorine takes place. The initial quality of water at all the nodes is kept as 0.2 mg/l to avoid the contamination of water at various locations.

List of the variables use in model equations

Sr No | Variables | Description |
---|---|---|

1 |
| Mass rate of chlorine applied at source i.e. reservoir |

2 |
| Diameter of Pipe |

3 |
| Length of Pipe |

4 |
| Demand at node 1,2,3,4,5, respectively, m |

5 |
| Flow in pipe |

6 |
| Velocity of flow in pipe |

7 |
| Travelling time of chlorine to reach up to each node 1,2,3,4,5, respectively, from preceding node, days |

8 |
| Travelling time of chlorine to reach up to node 1,2,3,4,5, respectively from the source i.e. reservoir, days |

9 |
| Concentration of chlorine at reservoir R |

10 |
| Concentration of chlorine at outlet of node 1, 2, 3, 4, 5 respectively, mg/l |

11 |
| Constants |

## Computation of residual chlorine

Explicit equations are developed to find out the residual chlorine concentration at inlet and outlet of the nodes 1, 2, 3, 4 and 5 for total 2 h of water supply for two different strategies of application of chorine i.e. case I having conventional chlorination in which the chlorine mass rate of *M*_{0} is applied at only source *R*_{1} and case II is Booster chlorination with mass rates of chlorine applied at source *R*_{1} and at nodes 1, 2 and 3 are *M*_{0}, *M*_{1}, *M*_{2} and *M*_{3}, respectively.

Following assumptions are made for developing the explicit equations for the computations of residual chlorine.

*C* = Concentration of chlorine in the water, mg/l.

*t* = Travelling time, days.

*C*_{o} = Chlorine concentration at the beginning of the transportation, mg/l.

Kb = Bulk decay coefficient, day^{−1}.

(2) Value of bulk decay coefficient Kb is adopted as 0.55 day^{−1} (Rossman et al., 1994).

(3) Flow is steady state for each demand pattern during supply of water for 2 h.

(4) At booster station node, the demand is taken first and then booster dose is applied.

(5) Initial concentration at starting of the day i.e. 0 h is 0.2 mg/l at every node.

The procedure for developing equations to compute residual chlorine at node 1 is described as follows.

## For node 1

### Case I (conventional chlorination)

*M*

_{o}(mg/min) is added at source,

## Case II (booster chlorination)

- (1)
Concentration after addition of and

*M*_{1}at node 1 = \(\frac{{M_{1} }}{{Q_{2} + Q_{3} }} + 0.2\) - (2)
Concentration at the inlet and outlet of the node 1 after end of total travelling time

*T*_{1}=*t*_{1},

Concentration of residual chlorine at various locations at the end of different travelling time

Cases | Concentration of residual chlorine at the end of different travelling time | |||
---|---|---|---|---|

Node I | Inlet of node 1 | Outlet of node 1 | ||

Case I | Concentration at the end of travelling time | \(C_{1i} = M_{0} X_{1}\) | \(C_{10} = M_{0} X_{1}\) | |

Case II | Concentration after addition of and | \(\frac{{M_{1} }}{{Q_{2} + Q_{3} }} + 0.2\) | ||

Concentration at the end of travelling time | \(C_{1i} = M_{0} X_{1}\) | \(C_{10} = M_{0 } X_{1} + \frac{{M_{1} }}{{Q_{2} + Q_{3} }}\) | ||

where, \(X_{1} = \frac{1}{{Q_{1} }} e^{{ - kL_{1} \pi D_{1}^{2} }} /4Q_{1}\) | ||||

Node 2 | Inlet of node 2 | Outlet of node 2 | ||

Case I | Concentration at the end of total travelling time from source, | \(C_{2i} = M_\text {o} X_{1} X_{2}\) | \(C_{20 } = M_\text {o} X_{1} X_{2}\) | |

Case II | Concentration after addition of | \(\frac{{M_{2} }}{{Q_{4} }}\) | ||

Concentration after addition of | \(\frac{{M_{2} }}{{Q_{4} }} + M_{1} X_{3} + 0.2\) | |||

Concentration at the inlet and outlet of node 2 after end of total travelling time from source | \(C_{2i} = M_\text {o} X_{1} X_{2} + M_{1} X_{3}\) | \(C_{20} = M_\text {o} X_{1} X_{2} + M_{1} X_{3} + \frac{{M_{2} }}{{Q_{4} }}\) | ||

where, \(X_{2} = e^{{ - kL_{2} \pi D_{2}^{2} }} /4Q_{2}\), \(\quad X_{3} = \frac{{ X_{2} }}{{{\text{Q}}_{2} + {\text{Q}}_{3} }}\) | ||||

Node 4 | Inlet of node 4 | Outlet of node 4 | ||

Case I | Concentration at inlet and outlet of node 4 after the end of total travelling time from source | \(C_{4i} = M_{0} X_{1} X_{2} X_{4}\) | \(C_{40} = M_{0} X_{1} X_{2} X_{4}\) | |

Case II | Concentration after addition of | \(M_{2} X_{5} + 0.2\) | ||

Concentration after addition of | \(M_{1} X_{3} X_{4} + M_{2} X_{5} + 0.2\) | |||

Concentration at inlet and outlet of node | \(C_{4i} = M_{0} X_{1} X_{2} X_{4} + M_{1} X_{3} X_{4} + M_{2} X_{5 }\) | \(C_{40} = M_{0} X_{1} X_{2} X_{4 } + M_{1} X_{3} X_{4 } + M_{2} X_{5}\) | ||

If | ||||

where, \(X_{4} = e^{{ - kL_{4} \pi D_{4}^{2} }} /4Q_{4}\), \(\quad X_{5} = \frac{{ X_{4} }}{{Q_{4} }}\) | ||||

Node 3 | Inlet of node 3 | Outlet of node 3 | ||

Case I | Concentration at the end of total travelling time from source | \(C_{3i} = M_\text {o} X_{1} X_{6}\) | \(C_{30} = M_\text {o} X_{1} X_{6}\) | |

Case II | Concentration after addition of | \(\frac{{M_{3} }}{{Q_{5} }} + 0.2\) | ||

Concentration after addition of | \(\frac{{M_{3} }}{{Q_{5} }} + M_{1} X_{7} + 0.2\) | |||

Concentration at inlet and outlet of node 3 after the end of Total travelling time from source | \(C_{3i} = M_\text {o} X_{1} X_{6} + M_{1} X_{7}\) | \(C_{30} = M_\text {o} X_{1 } X_{6} + M_{1} X_{7} + \frac{{M_{3} }}{{Q_{5} }}\) | ||

where, \(X_{6} = e^{{ - kL_{3} \pi D_{3}^{2} }} /4Q_{3}\), \(\quad X_{7} = \frac{{ X_{6} }}{{Q_{2} + Q_{3} }}\) | ||||

Node 5 | Inlet of node 5 | Outlet of node 5 | ||

Case I | Concentration at the end of total travelling time from source, | \(C_{5i} = M_\text {o} X_{1} X_{6} X_{8}\) | \(C_{50 } = M_\text {o} X_{1} X_{6} X_{8}\) | |

Case II | Concentration after addition of | \(M_{3} X_{9} + 0.2\) | ||

Concentration after addition of | \(M_{1} X_{7} X_{8} + M_{3} X_{9} + 0.2\) | |||

Concentration at inlet and outlet of node | \(C_{5i } = M_\text {o} X_{1} X_{6} X_{8} + M_{1} X_{7} X_{8} + M_{3} X_{9}\) | \(C_{50} = M_\text {o} X_{1} X_{6} X_{8} + M_{1} X_{7} X_{8} + M_{3} X_{9}\) | ||

If | ||||

where, \(X_{8} = e^{{ - kL_{5} \pi D_{5}^{2} }} /4Q_{5}\), \(X_{9} = \frac{{ X_{8} }}{{Q_{5} }}\) |

If the distribution network consists of many loops and branches, the development of explicit equations for computing residual chlorine is cumbersome. In such cases, computer-based methods such as EPANET software is resorted to. The governing equations for EPANET’s water quality solver are based on the principles of conservation of mass coupled with reaction kinetics.

## Example problem

Mass rate of chlorine applied at various locations

Cases | Total mass rate applied (gm/day) | Chlorine application period | Source and booster locations/injection rate at | |||
---|---|---|---|---|---|---|

Source | Node 1 | Node 2 | Node 3 | |||

Case I (only source chlorination) | 267.6 | 2 h | 2,230 | – | – | – |

Case II (source and booster chlorination) | 204 (23.78 % reduction in total mass rate of chlorine) | 2 h | 1,300 | 300 | 50 | 50 |

## Analysis and discussion of results

In the Figs. 3, 4, 5, 6, point A indicates initial concentration of chlorine at 0 h i.e. 0.2 mg/l. The observations for both the farthest nodes, node 4 and node 5, for case I and case II are as under.

### Node 4 with case I and case II

As observed from Fig. 3 for node 4, the concentration after 24 h (Point B) is less than 0.2 mg/l as the travelling time of chlorine is greater than water supply hour of 2 h and chlorine decay of initial concentration of chlorine i.e. 0.2 mg/l takes place. If we add more concentration at source then also there will not be any effect on final concentration as the travelling time is greater than supply hours (i.e. 2 h). In such cases, the booster chlorination helps to attain the required minimum concentration of chlorine.

As shown in Fig. 4 for node 4, point B shows the effect of addition of *M*_{2} at node 2 which will reach first to node 4 after travelling time of t_{4}. Peak of point C is the effect of M_{1} added at node 1 which will reach after travelling time of *t*_{4} + *t*_{2}. As travelling time of chlorine is more than 2 h, the effect of *M*_{o} is not felt at node 4. After 2 h, the chlorine decay will take place for rest of 22 h of stagnant period and point D gives the final concentration of chlorine at node 4 after 24 h. As compared to case I due to addition of booster doses at node 1 and 2, chlorine concentration of 0.2 mg/l is achieved after 24 h which was not possible in case I due to less supply hours than travelling time.

### Node 5 with case I and case II

In Fig. 5 for node 5, point B shows the initial decay of initial chlorine concentration of 0.2 mg/l. The peak (point C) is observed due to addition of *M*_{0} at source and it will reach to node 5 as its travelling time is <2 h. After 2 h, the decay of chlorine will take place for 22 h of stagnant period and point D shows the final concentration after 24 h. Here, the supply hours are more than travelling time of chlorine which suggests that conventional chlorination may be effective in maintaining minimum residual chlorine at farthest node in such case.

As shown in Fig. 6 for node 5, point B shows the effect of addition of *M*_{3} at node 3 which will reach first to node 5 after travelling time of *t*_{5}. Point C is the effect of M_{1} added at node 1 which will reach after travelling time of *t*_{3} + *t*_{5}. As travelling time of chlorine is <2 h for node 5, the effect of *M*_{o} is observed at node 5 which gives the peak at point D. After 2 h, the chlorine decay will take place for rest of 22 h of stagnant period and point E gives the final concentration of chlorine at node 5 after 24 h. As time of travelling at node 5 is less than supply hours, there is no major effect of booster chlorination observed on final concentration of chlorine. Thus, Booster chlorination is effective only for the farthest nodes, if the travelling time of chlorine is greater than supply hours as observed for node 4.

## Conclusions

- (1)
For conventional chlorination method if the travelling time of chlorine is greater than supply hours of water, the residual chlorine cannot reach to the farthest node like node 4 after 24 h. In case I, even though high mass rate of chlorine (2,230 mg/min) is supplied, chlorine will not reach to node 4 after 24 h as its travelling time is greater than supply duration of 2 h. In such cases, the selection of the water supply hours may be critical consideration for intermittent water supply system.

- (2)
Provision of booster chlorination is only effective in such conditions where farthest nodes are not receiving minimum desired residual chlorine concentration due to greater travelling time than supply hours.

- (3)
Application of booster chlorination strategy helps to maintain the residual chlorine of 0.2 mg/l at node 4 having travelling time >2 h water supply after 24 h at the same time gives 23.78 % reduction in total mass rate of chlorine application.

Explicit equations based on first-order chlorine decay can provide very useful decision-making tool to justify the chlorine mass injection rate and selection of booster chlorination strategy. These linear equations can be further coupled with optimization technique for further use. It is noted that in this analysis the bulk decay coefficient and roughness values are assumed and minor losses are neglected. The calibration of these parameters with field observations is suggested for the better performances of model application.

## References

- Abdullah MP, Yewa CH, Ramlib MSB (2003) Formation, modeling and validation of trihalomethanes (THM) in Malaysian drinking water: a case study in thedistricts of Tampin, Negeri Sembilan and Sabak Bernam, Selangor, Malaysia. Water Res 37:4637–4644CrossRefGoogle Scholar
- Abdullah MP, Yee LF, Ata S, Abdullah A, Ishak B (2009) The study of interrelationship between raw water quality parameters, chlorine demand and the formation of disinfection by-products, Khairul Nidzham Zainal Abidin. Phys Chem Earth 34:806–811CrossRefGoogle Scholar
- Biswas P, Lu C, Clark RM (1993) Model for chlorine contamination decay in drinking water distribution pipes. Water Res 27(12):1715–1724CrossRefGoogle Scholar
- Boccelli DL, Tryby ME, Uber JG, Rossman LA, Zierolf ML, Polycarpou MM (1998) Optimal scheduling of booster disinfection in water distribution systems. J Water Resour Plan Manag ASCE 124(2):99–111CrossRefGoogle Scholar
- Boccelli DL, Tryby ME, Uber JG, Summers RS (2003) A reactive species model for chlorine decay and THM formation under rechlorination conditions. Water Res 37:2654–2666CrossRefGoogle Scholar
- Carrico B, Singer PC (2009) Impact of booster chlorination on chlorine decay and THM production: simulated analysis. J Environ Eng ASCE 135(10):928–935CrossRefGoogle Scholar
- Castro Pedro, Neves Mario (2003) Chlorine decay in water distribution systems case study- Lousada network. Electron J Environ 2(2):261–266Google Scholar
- Central Public Health and Environmental Engineering Organization (CPHEEO) (1999) Manual on water supply and treatment, Government of IndiaGoogle Scholar
- Chowdhury S, Champagne P, McLellan PJ (2009) Models for predicting disinfection by product (DBP) formation in drinking waters: a chronological review. Elesvier Sci Total Environ 407:4189–4206CrossRefGoogle Scholar
- Clark RM (1994) Managing water quality in distribution systems: simulating TTHM and chlorine residual propagation. J Water Supply Res Technol Aqua 43:182Google Scholar
- Clark RM (1998) Chlorine demand and TTHM formation kinetics: a second-order model. J Environ Eng ASCE 124(1):16CrossRefGoogle Scholar
- Clark RM (2012) Water quality modeling. American Water Works Association, USAGoogle Scholar
- Clark RM, Rossman LA, Wymer LJ (1995) Modeling distribution system water quality: regulatory implications. J Water Resour Plan Mang ASCE 121(6):423–428CrossRefGoogle Scholar
- Feben D, Taras MJ (1951) Studies on chlorine demand constants. J AWWA 43(11):922–932Google Scholar
- Gibbs MS, Morganb N, Maiera HR, Dandya GC, Nixonc JB, Holmes M (2006) Investigation into the relationship between chlorine decay and water distribution parameters using data driven methods. Elsevier Math Comput Model 44:485–498CrossRefGoogle Scholar
- Haas CN, Karra SB (1984) Studies on chlorine demand constants. J WPCF 56(2):170–173Google Scholar
- Hallam NB, West JR, Forster CF, Powell JC, Spencer I (2002) The decay of chlorine associated with the pipe wall in water distribution systems. Water Res 36(13):3479–3488CrossRefGoogle Scholar
- Hua F, West JR, Barker RA, Forster CF (1999) Modelling of chlorine decay in municipal water system. Water Res 12:2735CrossRefGoogle Scholar
- Huang JJ, McBean EA (2006) Using Bayesian Statistics to estimate the coefficients of a two-compartment second order chlroine bulk decay model for a water distribution systems. Water Res 41(2):287–294CrossRefGoogle Scholar
- Huang JJ, McBean EA (2008) Using Bayesian Stastistics to estimate chlorine wall decay coefficients for water supply system. J Water Resour Plan Manag 134(2):287–294CrossRefGoogle Scholar
- Islam MR, Chaudhary MH (1998) Modleing of constituent transport in unsteady flows in pipe networks. J Hydraulic Eng 124(11):1115–1124CrossRefGoogle Scholar
- Islam MR, Chaudhry H, Clark RM (1997) Inverse modeling of chlorine concentration in pipe networks under dynamic condition. J Environ Eng ASCE 123(10)Google Scholar
- Johnson JD (1978) Measurement and persistence of chlorine residuals in natural waters. In: Clark RM (ed) Water Quality Modeling in Distribution Systems. American Water Works Association, USAGoogle Scholar
- Krasner SW, McGuire MJ, Jacangelo JG (1989) The occurence of disinfection by products in US drinking water. J Am Water Works Assoc 81(8):41–53Google Scholar
- Males RM, Grayman WM, Clark RM (1988) Modeling water quality in distribution systems. ASCE J Water Resour Plan Manag 114(2)Google Scholar
- McKenzie-Ray (2009) Urban water supply in India: status reform options and possible lessons. Water Policy 11(4):442–460CrossRefGoogle Scholar
- Ministry of Urban Development (MoUD), Government of India (2009) Guidance notes for continuous water supply (24 × 7 supply). www.urbanindia.nic.in. Accessed 7 Aug 2013
- Munavalli G, Kumar M (2003) Optimal scheduling of multiple chlorine sources in water distribution systems. J Water Resour Plan Manag 129(6):493–504CrossRefGoogle Scholar
- Osman, Ozdemir, Alper U (2002) Simulation of chlorine decay in drinking water distribution system. J Environ Eng ASCE 128(1):31–39CrossRefGoogle Scholar
- Ostfeld, Oha Z, Avi (2010) Alternative formulation for DBP’s minimization by optimal design of booster chlorination stations. World Environmental and Water Resources Congress 2010. Providence, Rhode Island, United States: American Society of Civil Engineers pp. 4260–4269Google Scholar
- Ozdemir, Ucaner M (2003) Application of Genetic Algorithms for Booster Chlorination in Water Supply Networks. World Water and Environmental Resources Congress 2003. Philadelphia, Pennsylvania, United States: American Society of Civil Engineers pp. 1–10Google Scholar
- Parks, Shannon L, Isovitsch (2009) Booster disinfection for response to contamination in a drinking water distribution system. ASCE J Water Resour Plan Manag 135(6):502–511CrossRefGoogle Scholar
- Powell JC, West JR, Hallam NB, Forster CF, Simm J (2000a) Performance of various kinetic models for chlorine decay. J Water Resour Plan Manag ASCE 126(1):13–20CrossRefGoogle Scholar
- Powell JC, Hallam NB, West JR, Forester CF, Simms J (2000b) Factors which control bulk chlorine decay rates. Water Res 34(1):117–126CrossRefGoogle Scholar
- Propato M, Uber JG (2004) Linear least-squares formulation for operation of booster disinfection system. J Water Resour Plan Manag ASCE 130(1):53–62CrossRefGoogle Scholar
- Romero Gomez P, Choi CY, Van Bloemen Waanders B, McKenna S (2006) Transport Phenomena at intersections of pressurized pipe systems. 8th Annual Water Distribution Systems Analysis Symposium. Cincinnati, Ohio, USA, pp. 27–30Google Scholar
- Rossman LA, Boulos PF (1996) Numerical methods for modeling water quality in distribution systems: a comparison. J Water Resour Plan Manag ASCE 122(2):137–146CrossRefGoogle Scholar
- Rossman LA, EPANET—Users manual (2000) US Environmental Protection Agency, Risk Reduction Engineering Laboratory, CincinnatiGoogle Scholar
- Rossman LA, Clark RM, Grayman WM (1994) Modelling chlorine residuals in drinking water distribution systems. J Environ Eng ASCE 120(4):803CrossRefGoogle Scholar
- Sadiq R, Rodriguez MJ (2004) Disinfection by-products (DBPs) in drinking water and predictive models for their occurrence: a review. Sci Total Environ 321(1–3):21–46CrossRefGoogle Scholar
- Shihab MS, Alhyaly AI, Mohammad MH (2009) Simulation of Chlorine concentrations in Mosul University’s Distribution Network using EPANET program. J Al-Rafidain Eng 17(6):28–41Google Scholar
- Tomovic S, Nikolic V, Milicevic D (2010) Modelling of water distribution system of city of Budva. BALWOIS. Ohrid, Republic of MacedoniaGoogle Scholar
- Toru Nagatani, Koji Yasuh,Toshiko Nakamura. (2008) Residual chlorine decay simulation in water distribution system. The 7th International Symposium on Water Supply Technology. Yokohama, Japan, pp. 1–11Google Scholar
- Tryby ME, Boccelli DL, Koechling MT, Uber JG, Summers RS, Rossman LA (1999) Booster Chlorination for managing disinfectant residuals. J Am Water Works Assoc 9(1):95–108Google Scholar
- Tryby ME, Boccelli DL, Uber JG, Rossman LA (2002) Facility location model for booster disinfection of water supply networks. J Water Resour Plan Manag ASCE 128(5):322CrossRefGoogle Scholar
- Uyak V, Ozdemir K, Toroz I (2007) Multiple linear regression modeling of disinfection by-products formation in Istanbul drinking water reservoirs. Sci Total Environ 378:269–280CrossRefGoogle Scholar
- Wei J, Ye B, Wang W, Yang L, Tao J, Hang Z (2010) Spatial and temporal evaluations of disinfection by-products in drinking water distribution systems in China. Sci Total Environ 408(20):4600–4606CrossRefGoogle Scholar

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