Abstract

This study presents the behavior of residual chlorine using computer modeling for a small water supply system located in La Sirena, Cali, Colombia. The study included field work to calibrate and validate the model together with lab determinations. Results show that the kinetics of first and second order together with the kinetics of mixed order can adequately describe the behavior of residual chlorine in this type of network. The research showed the variables that influence the behavior of residual chlorine in the network are: the quality of the input water, chlorine dosing, the effect of storage that increases the water age, and the presence of dead zones in the tanks. The study revealed that 95% of the reaction occurs in the storage tanks due to the small variation in the water level and the negligible reactions at the pipe walls. This study proved that the residual chlorine modeling in this small network in particular is a valuable tool for monitoring the water quality in the distribution network, which is useful to comply with water quality guidelines.

INTRODUCTION

A water distribution system (WDS) is sensitive, dynamic, and particular, and it can be considered a large-scale chemical and biological reactor with high residence times (Grünwald et al. 2001), so that the quality of drinking water may be affected during its course from the treatment plant to the consumption point (Boulos et al. 1995). Consequently, it is essential to guarantee a certain disinfectant concentration throughout the distribution system, considering that if its concentration is reduced below the guideline value through the network, it can promote bacterial growth with consequent health risks if a minimum concentration is not provided (Vidal et al. 1994). Once chlorinated, water with a short contact time in the supply sources remains a long time (several hours to several days) in the pipelines and tanks before it is used. This time can be considered as a second contact time, also known as water age, which requires a higher chlorine demand compared to the chlorine demand at the point of application. It must be pointed out that water age depends primarily on water demand, operation and system design. Therefore, to track down chlorine decay, it is necessary to know the decay rate and the dependence on its own concentration (Rossman 2000).

This chlorine demand may be due to two reaction mechanisms (Vasconcelos et al. 1997; EPA 2005; Kowalska et al. 2006): one produced in the mass of water due to the reaction of chlorine with oxidizable compounds, particularly dependent on the concentration of total organic carbon (TOC), the initial concentration, and temperature (Hallam et al. 2003); the other due to reactions occurring at the wall of the pipe, which may be due to numerous factors such as age of the pipe, diameter (Ndiongue et al. 2005), surface roughness, and internal corrosion, among others (Munavalli & Kumar 2005; Al-Jasser 2007). Similarly, the decay rate of chlorine may be affected by the microbiological activity, nitrification, exposure to ultraviolet light, and the amount and type of compounds which generate disinfectant demand, such as organic and inorganic compounds (Sánchez et al. 2010). Specifically, in small systems, the behavior of residual chlorine in distribution networks has not been studied sufficiently and there is little evidence of variation and final destination. The aim of this research was to analyze the behavior of residual chlorine in the distribution network of small water supply systems through a full-scale case study using hydraulic and quality modeling, which includes mathematical models and field work to calibrate and validate the model together with lab determinations in order to identify the most influential variables.

Chlorine reaction kinetics in distribution networks

Numerous authors suggest using first-order kinetics (Equation (1)) to determine the decay ratio of chlorine in the mass of water (kb), since it has proven to be consistent enough for most applications in distribution systems (Rossman et al. 1994; Boulos et al. 1995; Alcocer & Tzatchkov 2007).
formula
(1)
where C is the residual chlorine concentration, kb is the decay ratio (with kb < 0); t is the residence time and Co the initial chlorine concentration. Additionally, from the differential equation for advective transport, the mathematical expression of Equation (2) can be obtained, which describes the decay process in the water mass considering the second-order kinetic model with a single constituent (with kb < 0) (Lansey & Boulos 2005).
formula
(2)
On the other hand, Clark & Sivaganesan (2002) claim that the first-order model, although simple to implement, is not able to adequately describe the observed high decay rates in either the initial stages of chlorination or the decay when reaction times are too long. Clark & Sivaganesan (2002), along with Powell & West (2000), argue that occasionally, better adjustments are achieved with models that take two types of constituents into account. For example, the model used by Clark (1998) of chlorine with organic matter, or the model used by Vasconcelos et al. (1997) denominated parallel to the first-order process, used commonly to justify a number of data concerning chlorine decay, which did not fit the first-order model (Rossman 2000). Although the second-order model with two constituents suggests more stability, the difficulty in defining the values of the decay parameter is the main restriction of its use for modeling purposes (Powell & West 2000). Furthermore, Tzatchkov et al. (2004) demonstrated that a better adjustment of the concentration data was estimated with a kinetic equation of mixed order as of (see Equation (3)) with two decay coefficients k1 and k2, whose values depend on the concentration at the beginning of the reaction as opposed to the first-order kinetic model (Alcocer & Tzatchkov 2007).
formula
(3)

METHODOLOGY

The research was divided into a hydraulic modeling phase and a water quality phase which included mathematical models and field work for calibration and validation. Lab measurements were performed in order to understand the behavior of residual chlorine in La Sirena WDS, from a set of criteria for the selection of sampling points, as well as for the model calibration and validation. After the modeling stage, variables that helped understand the traceability and decay kinetics of residual chlorine were analyzed for correlation.

Field study

The system has a multi-stage-filtration water treatment plant at an average flow of 12.0 L/s, which supplies 851 subscribers. In Figure 1, the scheme of the distribution network is shown; the total length is 13.5 km, 9.5 of which constitutes the main network. Three main zones were identified: high, medium, and low, where the highest point in the network corresponds to the chlorine contact chamber and the lowest to the end of the network. The difference in elevation between these two points is 170 meters. The distribution network is sectioned from four storage tanks (T1, T2, T3, and T4) and gate valves.

Figure 1

La Sirena's WDS model.

Figure 1

La Sirena's WDS model.

Chlorine application

Chlorine application was done from a plastic tank with a volume of 240 L and a constant-head dose dispenser through which the orifice depth was regulated according to the concentration-in-network needed depending on the variability of the outlet flow given to the network. For the daily preparation of the chlorine solution, 12 L of sodium hypochlorite was used, with a weight percentage of 10.84%, which was determined at the lab under the iodometric method according to the 4500 CL-B guide for standard methods (Eaton & Franson 2005).

Assigning demand to the model

Billing records, which corresponded to the readings of user meters, were used. To determine the demand on consumption nodes, consumption posted was considered (controlled by both value and location) and the unrecorded consumption was considered (that beyond the control of the same operator), such as measurement errors, leaks in the distribution network, losses in deposits, some clandestine water draw-off, and fraud (Cabrera et al. 1999; Farley 2001). Each user was associated with a node in the model and consumption was assigned to that node, always looking for the one in closest proximity or the node that could be considered as the one supplying the user (Cabrera et al. 1996; Alcocer & Tzatchkov 2007).

Construction and calibration of the hydraulic model

The layout of each of the components of the distribution network (elevations, pipe lengths and diameters, storage units, and accessories), were entered into the EPANET computer program since it is login-free and has ease of access for a large number of users. A dynamic model, with a total time of 168 hours, was used given that it allows monitoring the behavior of residual chlorine every day for a week (Sánchez et al. 2010), with a time variation curve of the fractional demand at regular time intervals, specifically for 1 hour according to Alcocer & Tzatchkov (2007). The calibration of the hydraulic model consisted of adjusting the values of the roughness coefficients, behavior patterns, and demands calculated by the system, in order to obtain a satisfactory match between the values of the water levels in the tanks and measurements taken at the control points (demand and pressure), within the specified accuracy levels. Greater detail about the process of hydraulic calibration is available in Méndez et al. (2013).

Quality model

Once the hydraulic model was calibrated and validated, the water quality was modeled. For the construction of the quality model, lab data were required as well as field measurements. The water quality sampling was conducted on 1 weekday and 1 weekend day according to Sánchez et al. (2010). Chlorine residual samples were taken at checkpoints every 2 hours throughout the day. The location of the sampling points was carried out according to Table 1.

Table 1

Measuring points and selection criteria for the calibration of the quality model

PointaSamplingCriterionReference
PC1 Residual chlorine Treatment plant outlet Sarbatly & Krishnaiah (2007)  
PC2 Distribute sampling points evenly throughout the system Alcocer et al. (2004) and Alcocer & Tzatchkov (2007
PC3 
PC4 
PC5 Final network point Gibbs et al. (2006)  
PD1 Bottle test Close to the chlorination point Huang & McBean (2007)  
PD2 High initial concentration Alcocer et al. (2004) and Sánchez et al. (2010
PointaSamplingCriterionReference
PC1 Residual chlorine Treatment plant outlet Sarbatly & Krishnaiah (2007)  
PC2 Distribute sampling points evenly throughout the system Alcocer et al. (2004) and Alcocer & Tzatchkov (2007
PC3 
PC4 
PC5 Final network point Gibbs et al. (2006)  
PD1 Bottle test Close to the chlorination point Huang & McBean (2007)  
PD2 High initial concentration Alcocer et al. (2004) and Sánchez et al. (2010

aSee spatial location in Figure 1.

Chlorine sampling from pipes

Chlorine determination was done using the DPD titration method according to the 4500 CI-F guide of standard methods (Eaton & Franson 2005) with an accuracy of ±0.05 ppm (mg/L). The discharge time considered was 5 minutes.

Chlorine decay at pipe walls (kw)

The process of reaction of chlorine at the pipe wall, kw, is a variable, very difficult to estimate in operating water distribution networks. To get the decay constant at pipe walls (kw), the procedure described by Alcocer et al. (2002) was followed. It involves measuring chlorine and flow at both ends of pipe sections without branches. However, it was not possible to detect chlorine decay since the sections with these features were short. Therefore, we chose to assign the minimum value recommended in the literature for PVC equivalent to 0.01 to the model (Lansey & Boulos 2005).

Chlorine decay in the water mass (kb)

For the study of decay kinetics, the method described by Walski et al. (2001) was used. Consequently, successive measurements were made over time in each of the bottles, and chlorine concentration decay curves were generated. To obtain a constant representative of the chlorine reaction with the water mass (kb), the best adjustment regression was considered according to kinetic models (Equations (1)–(3)). These values were used for assignment to the model. Two sample points PD1 and PD2 (see Figure 1) were selected since one had a high initial concentration according to Hallam et al. (2003), Huang & McBean 2007 and Vieira et al. (2004), and the other was close to the chlorination point according to Alcocer et al. (2004) and Sánchez et al. (2010). A total of eight samples were taken and around 12 measurements of chlorine concentration were made for each of them in proportion to the reaction velocity. One hundred-mL Winkler bottles (amber) were used as recommended by Powell & West (2000) and Walski et al. (2001), and a forced convection oven model OFA-110-8 was used to ensure proper temperature. The bottles were washed with a solution of sulfuric acid (20%) and distilled water. Subsequently, they were dried in the oven at 110 °C for 1 hour in order to remove any debris or contaminants that could react with chlorine. Based on an exploratory temperature sampling performed in the network, it was determined that the samples had to be placed in an incubator at an average temperature of 21 °C. Adjustments of each of the curves were made considering the mathematical expressions represented by Equations (1)–(3).

Calibration of the quality model

Calibration was performed by adjusting the chlorine coefficient (kb), the patterns provided to model the dynamic behavior in the source (change of the initial Co concentration during daytime), and the order of the reaction until the output data approached the real values and reached the desired level of accuracy according to Walski et al. (2001). The model was considered calibrated when the difference between the estimated chlorine concentrations and the actual measurements was less than 0.2 mg/L (Walski et al. 2001; Sánchez et al. 2010). To model the kinetics of order one, two, and mixed, the Multi-Species Extension (MSX) was used (Shang et al. 2011). Then, EPANET and MSX were coupled with PEST software for parameter optimization, given that it is a separate model, thus avoiding changes in the original code, which can be used to estimate parameters and perform different tasks of predictive analysis, including sensitivity analysis, correlation and uncertainty (Méndez et al. 2013). The setting ranges for the chlorine reaction coefficients, in the water water (kb), considered increases of 0.05 (Wu 2006) and variations lower than 0.10 mg/L were allowed for the chlorine injection factor in the source (Jonkergouw et al. 2008).

Calibration parameter for the quality model

Calibration parameters were formulated as an optimization problem where the purpose was to minimize the role of the absolute difference of the responses predicted by the model versus those observed (see Equation (4)) according to Jonkergouw et al. (2008), Munavalli & Kumar (2005), and Wu (2006).
formula
(4)
where MAE refers to the mean absolute error, n is the total number of observations, Oi is the observed value through time i, and Mi is the modeled value as time i passes.

RESULTS AND DISCUSSION

The result analysis contemplated the development of the following topics: (i) chlorine dosing in the system, (ii) evaluation of the reaction kinetics for the decay of chlorine in the distribution system, (iii) construction and calibration of the model quality, and (iv) the identification of the most influential variables for residual chlorine variation.

Chlorine dosing in La Sirena WDS

Originally, the chlorine dosing in the system was done with a constant dosage flow throughout the day, regardless of the outflow into the network. Under the basic principles of mass balance, obviously the dosed chlorine concentration in the network suffered significant changes, especially at hours of minimum night flow, when the inlet flow tends to decrease, so that the residual chlorine concentration increased daily in values ranging from 1.66 to 2.36 mg/L (mean = 1.20, SD = 0.47, and N = 192), exceeding the guideline value under Colombian law, Resolution 2115 (2007) of 2 mg/L. In addition, during an exploratory sampling, chlorine accumulation problems were detected near PD2 (see Figure 1), with values up to 12 mg/L between 3 and 5 am, due to its proximity to the dosing point and its isolation from the rest of the network by two closed valves.

Kinetics model of chlorine decay in the distribution network

The first-order, mixed-order, and second-order kinetics models were analyzed. Good correlations with the three models were obtained (see Table 2), but the best adjustments were for the mixed order. A decay coefficient kb = 0.05 (day−1) for the first-order kinetics with R2 = 0.9 was obtained, while the mixed-order kinetics model yielded kb1 = 0.022 (day−1) and kb2 = 0.930 (day−1) with R2 = 0.98, and for the second-order model kb = 0.117 L/mg·day with R2 = 0.95 similar to that reported by Powell & West (2000) for second-order kinetics under a single component with an average value of R2 = 0.94.

Table 2

Main results of the three kinetics models used

Kinetics modelEquationGoodness of fitDecay coefficient kb
First order  R2 = 0.8992 kb = −0.05 (day−1
Second order  R2 = 0.9471 kb = −0.1169 (L/mg·day) 
Mixed order  R2 = 0.9769 kb1 = 0.022 (day−1); kb2 = 0.930 (day−1
Kinetics modelEquationGoodness of fitDecay coefficient kb
First order  R2 = 0.8992 kb = −0.05 (day−1
Second order  R2 = 0.9471 kb = −0.1169 (L/mg·day) 
Mixed order  R2 = 0.9769 kb1 = 0.022 (day−1); kb2 = 0.930 (day−1

These results suggest that the three models can adequately describe the behavior of chlorine; however, the mixed-order kinetics, characterized by two parameters, k1 and k2, whose values depend on the concentration at the start of the reaction as opposed to the first-order kinetics (Alcocer & Tzatchkov 2007) has better correlation. This confirms the proposal of Tzatchkov et al. (2004) in the sense that the mixed-order kinetics may significantly contribute to the understanding of reaction kinetics in situations where first-order kinetics does not fit the data, with the advantage of easy application.

The initial concentration in the bottle test was 1.21 mg/L; after 16 hours, a concentration of 0.9 mg/L was obtained (intense reaction at the onset of the process). Even so, despite having monitored chlorine decay for more than 30 days (see Figure 2), a complete disappearance of chlorine in the bottle was never achieved, where the lowest concentration recorded was 0.2 mg/L.

Figure 2

Curve of chlorine decay in reaction with water using the first-order reaction kinetic model.

Figure 2

Curve of chlorine decay in reaction with water using the first-order reaction kinetic model.

This can be explained because, in the initial phase, chlorine reacts with readily oxidizable substances such as low concentrations of organic matter measured (0.13 to 0.23 mg/L) as well as some possible reactions with inorganic compounds (Tzatchkov et al. 2004; Deborde & von Gunten 2008). In contrast, during the slower and more prolonged phase, the chlorine was consumed possibly by some humic substances (Vieira et al. 2004). With this in mind, research by Vieira et al. (2004) has reported times of up to 104 days, which is higher than that recommended for the duration of the 5–7 day test (Rossman et al. 1994; Vasconcelos et al. 1997; Powell & West 2000; Walski et al. 2001; Alcocer & Tzatchkov 2007). This explains why, with certain compounds, the chlorine reactivity is low, showing small modifications in the structure of the compound under certain characteristics of the treated water (Deborde & von Gunten 2008).

Efficiency of the optimization process in quality calibration

Modeling with first-order, mixed-order, and second-order kinetics was analyzed. Differences in residuals lower than 0.10 mg/L in 98% of the data with a maximum residual difference of 0.12 mg/L were obtained satisfactorily, thus complying with Sánchez et al. (2010), who argued that the difference should be lower than 0.2 mg/L. The model prediction also complies with Walski et al. (2001), who state that the model must be able to reproduce the concentrations of chlorine with an average error of about 0.1 to 0.2 mg/L, which was close to the average error obtained for the scenario using kinetic models of mixed, first, and second order, with values of 0.0513, 0.0507, and 0.0478 mg/L, respectively. These values are in harmony with the MAE obtained by Jonkergouw et al. (2008), which reports 0.059 mg/L, by Wu (2006) with 0.045 mg/L, and by Munavalli & Kumar (2005) with 0.050 mg/L.

Temporal variation in concentrations of residual chlorine

Results show that 98% of the time, concentrations below 1.2 mg/L are presented with an average value of 0.985 mg/L and only 2.41% of the data do not meet the standard because they show concentrations lower than 0.3 mg/L. Hence, the system, despite being run by a community, can adequately meet the standards of water quality guidelines. It was evident that the hydraulic paths and resulting retention times are affected by the configuration of the valves in the water distribution network, where their closing (whether on purpose or accidentally), can lead to a dead point where minimum velocity and chlorine concentrations lower than the reference value predominate. Furthermore, as can be seen in Figure 3, the variation of water level in the tanks is low, which means that some masses of water do not exit the tank at a specific time, thus generating a potential dead zone that explains the increase in water age and chlorine reaction. This proves an inefficient operation and indicates that the tank should be analyzed as a hydraulic reactor, since maybe the complete mix assumption is not met, suggesting further research in the future, both for modeling criteria and for storage systems design.

Figure 3

Correlation between chlorine concentrations with respect to tank levels.

Figure 3

Correlation between chlorine concentrations with respect to tank levels.

According to the EPANET model results, 94.97% of the reactions occur in the tanks and 5.03% in the mass of water in the network. This behavior of residual chlorine may be related to the age of the water because its decay depends on the time spent in contact with the different substances in the water (Sánchez et al. 2010). It was evident that the chlorine concentrations decrease when water age shows the highest values with full tanks, precisely in the early hours of the morning, when consumption is minimal. This is consistent with reports from EPA (2002) and Sánchez et al. (2010) and suggests that the reaction at the pipe walls may be negligible. Such results are consistent with studies by Lu et al. (1999), which showed that consumption of chlorine, due to the relatively new or in-very-good-condition plastic pipes (PVC), is insignificant compared to the demand in water volume. This fact reveals the good conditions for water treatment through multi-stage filtration; therefore, good treatment is essential for keeping water quality in the network. Despite the variability of Co due to the system dynamics, the chlorine dosing was optimal, because 95% of the time, concentrations were below 1 mg/L in each of the sectors serving subscribers with minimum values of 0.7 mg/L for 25%, improving the values initially found according to the scheme with constant application of chlorine for 24 hours as described in Figure 3.

CONCLUSIONS

This paper presents the behavior of residual chlorine using computer modeling for a small water supply system located in La Sirena, Cali, Colombia. The results show that the kinetics of first and second order together with that of mixed order, can adequately describe the behavior of residual chlorine in this type of water distribution network. It showed that the most influential variables for the behavior of residual chlorine in the network are: the quality of input water, chlorine dosing, the effect of storage that increases water age, and the presence of dead points. It revealed that 95% of the reactions occur in the tanks due to small variations of water level, and the negligible reactions at the pipe walls.

The study found that the modeling of residual chlorine in this small network is a valuable tool for monitoring the water quality in the network, useful for compliance with water quality guidelines.

Similarly, we found that modeling allowed a detailed and comprehensive knowledge of the hydraulic performance and water quality in the distribution network. The disinfection process was optimized because the potentially problematic parts in the system hydraulics were identified, such as the dead zones in pipes due to closed valves, sections directly connected to the injection point where chlorine had accumulated (near PD2), as well as problems in disinfection due to dosing changes. Moreover, it allowed the analysis of the behavior of the disinfectant in the network, determining the optimal chlorine dosage in the plant, thus optimizing the location of chlorine sampling points in the network. This enables better control and consistent operation, constituting a tool for planning processes, design, repair, and redevelopments. Future research should consider the influence of network size through comparative studies with other small networks, as well as storage tank analysis as a hydraulic reactor to ensure that the assumption of complete mixture or piston flow is met, and thereby adjust modeling criteria.

ACKNOWLEDGEMENTS

The authors would like to thank the Administrative Board of La Sirena water supply system, the community of La Sirena, the Puerto Mallarino Research and Technology Transfer Station, Cinara-Universidad del Valle, Dr Velitchko Tzatchkov from the Instituto Mexicano de Tecnología del Agua (IMTA) [Mexican Institute of Water Technology] and Professor Maikel Méndez of the Instituto Tecnológico de Costa Rica (ITCR) [Costa Rica Institute of Technology].

REFERENCES

Alcocer
,
V. H.
&
Tzatchkov
,
V.
2007
Manual de agua potable, alcantarillado y saneamiento. Modelación hidráulica y de calidad del agua en redes de agua potable
.
Comisión Nacional del Agua
,
México, D.F
Alcocer
,
V.
,
Tzatchkov
,
V.
,
Feliciano
,
D.
,
Mejía
,
E.
&
Martínez
,
E.
2002
Implementación y calibración de un modelo de calidad de agua en sistemas de agua potable
.
Informe Final
,
IMTA-CNA
,
Jiutepec, Morelos, México
.
Alcocer
,
V. H.
,
Tzatchkov
,
V.
&
Arreguín-Cortés
,
F. I.
2004
Modelo de calidad del agua en redes de distribución
. In:
Ingeniería Hidráulica en México
.
Instituto Mexicano de Tecnología del Agua
,
Morelos, México
, pp.
77
88
.
Boulos
,
P. F.
,
Altman
,
T.
,
Jarrige
,
P.-A.
&
Collevati
,
F.
1995
Discrete simulation approach for network-water quality models
.
Journal of Water Resources Planning & Management
121
(
1
),
49
.
Cabrera
,
E.
,
Espert
,
V.
,
García
,
J.
,
Martínez
,
F.
,
Andrés
,
M.
&
García
,
M.
1996
Ingeniería Hidráulica Aplicada a los Sistemas de Distribución de Agua
(Hydraulic Engineering Applied to Water Systems)
.
Universidad Politécnica de Valencia
,
Spain
.
Cabrera
,
E.
,
Almandoz
,
J.
,
Arregui
,
F.
&
García-Serra
,
J.
1999
Auditoría de redes de distribución de agua (Audit of wáter distribution networks)
.
Ingeniería del Agua
6
(
4
),
291
303
.
Clark
,
R. M.
1998
Chlorine demand and TTHM formation kinetics: a second-order model
.
Journal of Environmental Engineering
124
(
1
),
16
24
.
Clark
,
R. M.
&
Sivaganesan
,
M.
2002
Predicting chlorine residuals in drinking water: second order model
.
Journal of Water Resources Planning & Management
128
(
2
),
152
.
Eaton
,
A. D.
&
Franson
,
M. A. H.
, (eds)
2005
Standard Methods for the Examination of Water & Wastewater
.
American Public Health Association
,
Washington, DC
;
American Water Works Association
,
Denver, CO
;
Water Environment Federation
,
Alexandria, VA
.
EPA
2002
Effects of Water Age on Distribution System Water Quality
.
US Environmental Protection Agency. Standards and Risk Management Division
,
Washington, DC
.
EPA
2005
Water Distribution System Analysis: Field Studies, Modeling and Management
.
A Reference Guide for Utilities
.
US Environmental Protection Agency. Water Supply and Water Resources Division
,
Cincinnati, OH
.
Farley
,
M.
2001
Leakage Management and Control – A Best Practice Training Manual
.
World Health Organization
,
Geneva
,
Switzerland
.
Gibbs
,
M. S.
,
Morgan
,
N.
,
Maier
,
H. R.
,
Dandy
,
G. C.
,
Nixon
,
J. B.
&
Holmes
,
M.
2006
Investigation into the relationship between chlorine decay and water distribution parameters using data driven methods
.
Mathematical and Computer Modelling
44
(
5–6
),
485
498
.
Grünwald
,
A.
,
Štastný
,
B.
,
Slavíčková
,
K.
&
Slavíček
,
M.
2001
Effect of the distribution system on drinking water quality
.
Acta Polytechnica. Journal of Advanced Engineering
41
(
3
),
1
5
.
Hallam
,
N. B.
,
Hua
,
F.
,
West
,
J. R.
,
Forster
,
C. F.
&
Simms
,
J.
2003
Bulk decay of chlorine in water distribution systems
.
Journal of Water Resources Planning & Management
129
(
1
),
78
.
Huang
,
J. J.
&
McBean
,
E. A.
2007
Use of Bayesian statistics to study chlorine decay within a water distribution system
. In:
8th Annual Water Distribution Systems Analysis Symposium 2006
,
Cincinnati, OH
, p.
148
.
Jonkergouw
,
P. M. R.
,
Khu
,
S. T.
,
Kapelan
,
Z. S.
&
Savić
,
D. A.
2008
Water quality model calibration under unknown demands
.
Journal of Water Resources Planning & Management
134
(
4
),
326
336
.
Kowalska
,
B.
,
Kowalski
,
D.
&
Musz
,
A.
2006
Chlorine decay in water distribution systems
. In:
Environment Protection Engineering
.
Department of Environmental Engineering, Wroclaw University of Technology
,
Wroclaw, Poland
, pp.
5
16
. .
Lansey
,
K.
&
Boulos
,
P.
2005
Comprehensive Handbook on Water Quality Analysis for Distribution Systems
.
MWH Soft
,
Pasadena, CA
.
Lu
,
W.
,
Kiéné
,
L.
&
Lévi
,
Y.
1999
Chlorine demand of biofilms in water distribution systems
.
Water Research
33
(
3
),
827
835
.
Méndez
,
M.
,
Araya
,
J. A.
&
Sanchez
,
L. D.
2013
Automated parameter optimization of a water distribution system
.
Journal of Hydroinformatics
15
(
1
),
71
85
.
Munavalli
,
G. R.
&
Kumar
,
M. S.
2005
Water quality parameter estimation in a distribution system under dynamic state
.
Water Research
39
(
18
),
4287
4298
.
Powell
,
J. C.
&
West
,
J. R.
2000
Performance of various kinetic models for chlorine decay
.
Journal of Water Resources Planning & Management
126
(
1
),
13
.
Rossman
,
L.
2000
EPANET 2 User's Manual
.
Environmental Protection Agency, National Risk Management Research Laboratory, Office of Research and Development
,
Cincinnati, OH
.
Rossman
,
L. A.
,
Clark
,
R. M.
&
Grayman
,
W. M.
1994
Modeling chlorine residuals in drinking-water distribution systems
.
Journal of Environmental Engineering
120
(
4
),
803
820
.
Sánchez
,
L. D.
,
Rodriguez
,
S.
&
Torres
,
P.
2010
Modelación del cloro residual y subproductos de la desinfección en el sector piloto Nápoles Ciudad Jardín del sistema de distribución de Cali (Modeling of residual chlorine and disinfection byproducts in a pilot sector of the potable wáter distribution system of the city of Cali)
.
Ingeniería y Competitividad
12
(
1
),
127
138
.
Sarbatly
,
R. H.
&
Krishnaiah
,
D.
2007
Free chlorine residual content within the drinking water distribution system
.
International Journal of Physical Sciences
2
(
8
),
196
201
.
Shang
,
F.
,
Uber
,
J. G.
,
Rossman
,
L. A.
,
Janke
,
R.
&
Murray
,
R.
2011
Epanet Multi-Species Extension. User's Manual
.
Risk Management Research Laboratory, US Environmental Protection Agency
,
Cincinnati, OH
.
Tzatchkov
,
V.
,
Alcocer
,
V. H.
&
Arreguín-Cortés
,
F. I.
2004
Decaimiento del cloro por reacción con el agua en redes de distribución (Decay of chlorine by reaction with wáter in distribution networks)
.
Ingeniería Hidráulica en México
XIX
(
1
),
41
51
.
Vasconcelos
,
J. J.
,
Rossman
,
L. A.
,
Grayman
,
W. M.
,
Boulos
,
P. F.
&
Clark
,
R. M.
1997
Kinetics of chlorine decay
.
Journal of the American Water Works Association
89
(
7
),
54
65
.
Vidal
,
R.
,
Martínez
,
F.
&
Ayza
,
M.
1994
Aplicaciones de los modelos de calidad en la simulación de las redes de distribución de agua potable (Applications of quality models in the simulation of drinking water distribution networks)
.
Ingeniería del Agua
1
(
3
),
55
68
.
Vieira
,
P.
,
Coelho
,
S. T.
&
Loureiro
,
D.
2004
Accounting for the influence of initial chlorine concentration, TOC, iron and temperature when modelling chlorine decay in water supply
.
Journal of Water Supply Research and Technology-AQUA
53
(
7
),
453
467
.
Walski
,
T.
,
Chase
,
D.
&
Savic
,
G.
2001
Water Distribution Modeling
, 1st edn.
Haestad Methods
,
Waterbury, CT
.
Wu
,
Z. Y.
2006
Optimal calibration method for water distribution water quality model
.
Journal of Environmental Science and Health, Part A: Toxic/Hazardous Substances and Environmental Engineering
41
(
7
),
1363
1378
.