Kinetics of chlorine decay

LE T T E R S Erratum The biography supplied with my article (“Kinetics of Chlorine Decay,” J.J. Vasconcelos et al) in the July issue implies that I was an independent consultant at the time this project was done. I was, in fact, a project manager–principal investigator employed

by Montgomery Watson. I wish to acknowledge the confidence and support of Montgomery Watson in retaining me as a consultant to complete this interesting and rewarding project after my retirement.

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Copyright (C) 1997 American Water Works Association

DISTRIBUTION SYSTEMS

Kinetics of chlorine decay Models of chlorine kinetics in distribution systems characterize chlorine decay as a combination of first-order decay in the bulk liquid and first-order or zero-order decay reactions at the pipe wall. John J. Vasconcelos, Lewis A. Rossman, Walter M. Grayman, Paul F. Boulos, and Robert M. Clark

S

ince the turn of the century, disinfection of potable water has been routinely carried out to destroy pathogenic organisms and thereby eliminate and prevent waterborne diseases. The most predominantly used water treatment disinfectant is chlorine. The advantages of chlorine disinfection are well known and include such benefits as simplicity, low cost, and a broad range of effectiveness. Proper understanding, characterization, and prediction of water Chlorine is effective in quality behavior in drinking water distribution systems are controlling aesthetic qualcritical to ensure meeting regulatory requirements and customerity; removing iron, manoriented expectations. This article investigates the factors leading ganese, and hydrogen sulto loss of chlorine residual in water distribution systems. Kinetic fide; sterilizing mains and rate equations describing the decay of chlorine were developed, storage tanks; restoring tested, and evaluated using data collected in field-sampling and preserving pipeline studies conducted at several water utility sites. Results indicated capacity; and maintaining that chlorine decay in distribution systems can be characterized distribution system bacteas a combination of first-order reactions in the bulk liquid and rial quality by reducing first-order or zero-order mass transfer–limited reactions at the the growth of micropipe wall. Wall reaction kinetic constants were inversely organisms and slimes. proportional to pipe roughness coefficients. Wide variations in Because of chlorine’s both bulk reaction constants and wall reaction constants were oxidizing potential, minobserved among the sites. imum levels of chlorine residual must be mainCopyright (C) 1997 American Water Works Association

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A modified version of EPANET, the US Environmental Protection Agency’s network simulation program, was used to test alternative kinetic models on the five systems studied.

cost-effective manner. 2–4 These models provide a powerful tool for enhancing engineering insight into the dynamics of water quality variations and the complex processes that occur within the distribution system environment. Nevertheless, the use of water quality models can only be effective and reliable when the mechanisms of chlorine dissipation within water distribution systems are properly defined. This article describes a project undertaken by the AWWA Research Foundation (AWWARF) in collaboration with the US Environmental Protection Agency (USEPA) National Risk Management Research Laboratory, Cincinnati, Ohio, to study the reaction kinetics of chlorine within water distribution systems and to demonstrate how chlorine decay models could be applied to various systems across the country. The project involved extensive laboratory studies on chlorine decay kinetics, field sampling of chlorine levels in several distribution sys-

tained in the distribution system to preserve both chemical and microbial quality of treated water. A minimum chlorine residual level of 0.2 mg/L at the entrance to the distribution system and preservation of a detectable residual level throughout the system are currently required by the Surface Water Treatment Rule. The benefits and the necessity of chlorine residual in water distribution systems have been demonstrated by Snead et al.1 Chlorine is a relatively unstable chemical, however, and readily reacts with a variety of organic and inorganic compounds (e.g., ammonia, sulfides, ferrous iron, manganese, and humic material), thus causing well-calibrated hydraulic model, its gradual dissipation in the distribution system. preferably one based on a tracer study, Reactions of chlorine with is a prerequisite for attempting to model organic matter can produce undesirable by-products, water quality in a distribution system. such as trihalomethanes (THMs). THMs are currently regulated by the Safe Drinking Water Act to a maximum contaminant level tems, and evaluation and testing of alternative of 0.1 mg/L, and more stringent limits may be kinetic formulations in computer models of these imposed by the forthcoming Disinfectants/Disinfection systems. By-products Rule. Excessive chlorine levels may also promote corrosion and cause taste and odor prob- Chlorine decay kinetics lems. Current practice attempts to use the minimum The consumption of residual chlorine in the dischlorine dosage possible to provide effective disin- tribution system is influenced by a number of facfection strength throughout the distribution system. tors. Factors often cited include (1) consumption of The complex pipe geometry in distribution sys- chlorine as it reacts with organic and inorganic chemicals, (2) consumption of chlorine because of reactems, the dynamic flow conditions experienced within them, and the varied nature of chlorine’s reactivity tions with biofilms attached to the distribution pipe make it difficult to predict in advance how chlorine wall, (3) consumption of chlorine in the corrosion will behave throughout a water system. ComputerA full report of this project, Characterization and Modeling of Chlorine based mathematical models of water quality transport Decay in Distribution Systems (order no. 90705), is available from the and fate within distribution systems offer a promising AWWA Bookstore (1-800-926-7337). Reports are free to AWWA Research alternative for predicting disinfectant residuals in a Foundation subscribers by calling 303-347-6121.

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Copyright (C) 1997 American Water Works Association JULY 1997

J.J. VASCONCELOS ET AL 55

FIGURE 1

k values as a function of pipe diameter, pipe material, and inlet volumetric flow rate. The consumption of chlorine by reaction with organic F014 and inorganic chemicals in the bulk aqueous phase is reasonably well-defined. Certain of the chemicals react rather quickly, and the reaction rate of the remainder is reasonably well-characterized as a simple first-order decay process3, 9–16 or to a much lesser extent, a second-order rate process.10 Biofilms and tubercles that are attached to pipe walls can result in a significant loss of chlorine residual over time.16–19 Biofilms can be very important, particularly in the presence of nitrification, yet they are not adequately understood. Wable et al16 performed measurements of chlorine decay in flasks and compared it with the decay that occurred in the same Waterman water when it was stored in Water Treatment sections of cast-iron and ducPlant tile-iron pipe. They observed that the decay was always more rapid when the water was stored in the pipe than when stored in the flasks, indicating that additional chlorine demand occurs in the pipe. This two-step experimental process allowed the direct calculation of the kinetic constants corresponding to the consumption from both the water and the pipe. The flask experiment gave the first-order kinetic constant for chlorine consumption by the water, whereas the difference among the values determined yielded the chlorine exerted demand at the pipe wall. Trussell19 reported that corrosion might play a dominant role in controlling chlorine residual stability in metallic conduits. He proposed a quantitative relationship between corrosion rate and chlorine consumption in distribution systems. He further tested his model using the experimental data of Wable et al.16 Excellent correlations between the measured values and the model’s predictions were consistently obtained. This model may be expressed as

Network schematic of zone 3 at Fairfield, Calif.

F006

F008

F003

process, and (4) mass transport of chlorine and other reactants between the bulk flow and pipe wall. An overall first-order kinetic model for the disappearance of residual chlorine at different residence times in the network can be expressed as dC = –kC dt

(1)

in which C is the chlorine concentration (mg/L) and k is the first-order decay constant (1/d). The residence time is defined as the pipe length divided by the mean flow velocity in the pipe. Integrating Eq 1 gives C(t) = C0e–kt

(2)

in which C(t) is the chlorine concentration (mg/L) at time t, C0 is the initial chlorine concentration (mg/L), and t is the residence time in the pipe (days). The overall chlorine decay constant k reflects the four factors cited earlier. It is therefore site-specific and must be verified by field measurements. Gotoh 5 reported k values that vary with water quality, water temperature, flow velocity, pipe material, and area of contact with the pipe. Demongeot and Jarrige6 and Saunier and Jarrige7 expressed the value of the decay constant in terms of wetted surface, temperature, and type of inner coating. Sharp et al8 described

dC = – R – kC dt

(3)

in which R is the pipe wall chlorine demand caused by corrosion (mg/L–d). In two studies,20, 21 LeChevallier et al indicated that mass transport between the bulk flow and pipe wall may constitute an important factor affecting the consumption of chlorine in distribution systems.

Copyright (C) 1997 American Water Works Association 56

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FIGURE 2

Comparison of first-order bulk reaction model and sampling results for zone 3 at Fairfield, Calif.

Model results

Sampling results Station F006

2.00

2.00

1.80

1.80

1.60

1.60

Free Chlorine—mg/L


Station F003

1.40 1.20 1.00 0.80 0.60 0.40

1.40 1.20 1.00 0.80 0.60 0.40 0.20

0.20

0.00

0.00 0

2

4

6

8

10 12 14 Time—h

16 18

20

0

22 24

2

4

6

8

2.00

1.80

1.80

1.60

1.60



16 18

20

22 24

16 18

20

22 24

Station F014

Station F008 2.00

1.40 1.20 1.00 0.80 0.60 0.40

1.40 1.20 1.00 0.80 0.60 0.40 0.20

0.20

0.00

0.00 0

TABLE 1

10 12 14 Time—h

2

4

6

8

10 12 14 Time—h

16 18

20

0

22 24

2

4

6

8

10 12 14 Time—h

Characteristics of the sampling areas

Characteristic

Bellingham (Dakin-Yew zone)

Fairfield (zone 3)

Harrisburg (Oberlin zone)

North Marin (zone 1)

North Penn (Lansdale low zone)

Miles (km) of pipe Pipe segments in network model Predominant pipe sizes Pipe material* (ages in years) Supply sources

31 (50) 168

11 (18) 126

11 (18) 288

47 (76) 125

99 (160) 399

6–8 in. (150–200 mm) Unlined CI (>40) 1 surface

8–12 in. (200–300 mm) AC (30) 1 surface

12–30 in. (300–760 mm) Unlined CI (>40), AC 2 surface

Sampling locations Tracer chemical

14 None

15 Fluoride

31 Fluoride

15 Sodium

8–16 in. (200–400 mm) Cement-lined DI, unlined CI 2 surface 18 wells 8 Hardness

*AC—asbestos cement, CI—cast iron, DI—ductile iron, GI—galvanized iron

Biswas et al22 developed a rigorous model for chlorine decay within single pipes under steady-state flow conditions that included both bulk flow reaction and radial diffusion and subsequent pipe wall reaction of chlorine. More recently, Rossman et al3 proposed a mass transfer–based model of chlorine decay in pipe networks that applies to nonsteady flow under both turbulent and laminar conditions, with the overall rate of the wall reaction being affected by the rate at

which chlorine can be transported from the bulk flow to the pipe wall.

Field sampling studies Extensive water quality sampling studies were performed at five participating utilities in the United States to collect data on chlorine residuals and other relevant water quality and hydraulic factors. The utilities were the city of Bellingham, Wash.; United Water



TABLE 2

Results of first-order bulk decay tests

Finished Water Source

Temperature oC

TOC mg/L

Free Chlorine mg/L

Bulk Decay Coefficient 1/d

8.05

17.4

0.84

0.72

0.833

8.15

17.9

1.87

1.73

1.16

7.52

16.4

1.73

0.98

0.232

7.42

22.2

0.56

0.31

1.32

8.85

21.9

3.55

0.49

17.7

7.92

22.1

0.40

10.8

pH

Bellingham Watcom Water Treatment Plant Fairfield Waterman Treatment Plant Harrisburg Oberlin Pump Station North Marin Russian River Aqueduct North Marin Stafford Lake Treatment Plant North Marin 50/50 blend of Aqueduct and Stafford Lake water North Penn Keystone tie-in North Penn Forest Park Treatment Plant North Penn 50/50 blend of Keystone and Forest Park water North Penn Well W17 North Penn Well W12

16.2

0.79

1.65*

0.082

13.2

1.64

1.30*

0.767

14.7

1.23

1.38*

0.264

14.8

1.06

0.50*

0.355

18.3

0.52

0.85*

0.102

*Total chlorine

FIGURE 3

Network schematic of Oberlin zone at Harrisburg, Pa. Pump station

OH02

OH12

OH20

Resources (formerly Dauphin Consolidated Water Co.), Harrisburg, Pa.; city of Fairfield, Calif.; North Marin Water District, Novato, Calif.; and North Penn Water Authority, Lansdale, Pa. Field sampling data were used to determine chlorine decay kinetic coefficients and to calibrate a hydraulic and water quality network model of each test network. At four of the five field sampling sites, tracer studies using conservative constituents (e.g., fluoride) were performed as an aid in refining the hydraulic calibration. At each utility, either an easily isolated subnetwork with definable boundary conditions or a long pipeline was selected as a sampling area. Sampling studies were conducted using small sampling areas in order to reduce the level of effort and minimize data requirements. Table 1 describes some pertinent characteristics of each study area. A comprehensive sampling plan was prepared for each site. The sampling plan served as the detailed written description of how the sampling study was to be performed. It contained descriptions of all aspects of the study, including contingency plans addressing unexpected occurrences. Details of the plan can be found elsewhere.23 After each field study was completed, a detailed sampling report was prepared describing all aspects of the field work. The report included all details on the study and the results of all field and laboratory analyses. This document served as the repository of all information on the field study and, as such, provided the basis for the later analysis work.

Reaction kinetics Bulk reaction kinetics. Reaction kinetics for chlorine in the bulk liquid were studied Copyright (C) 1997 American Water Works Association

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by analyzing the results of bottle tests that were conducted on treated water at each of the field sites. The methodology for these tests involved measuring the disappearance of chlorine over time in a set of samples stored in nonreacting containers, such as amber glass bottles. Bottle-test results for 11 different treated water sources were available from the field sites. The location and nature of the treated water sources are described in Table 2. These data were then used to estimate the coefficients associated with the kinetics of chlorine decay in the bulk phase liquid. Several different kinetic models for chlorine decay in the bulk liquid were considered. In this article, the authors report results only for the widely used first-order model in which the rate of decay is proportional to the concentration of chlorine remaining: dC | bulk = –kb C dt

unit of time. If this coefficient is denoted as kw,1, then the rate of reaction at the wall in units of concentration per time equals dC kw,1 Cw | wall = – dt rh

(5)

in which Cw is the chlorine concentration at the wall and r h is the hydraulic radius of the pipe (the reciprocal of surface area per unit volume). When there is no mass transfer limitation, Cw is the same as the chlorine concentration in the bulk flow, C. For a zero-order wall reaction, the rate of reaction at the wall is described by a constant, kw,0, with units of mass per area of wall per unit of time (e.g., mg/m2/d). In the absence of mass transfer limitations, the rate of loss of chlorine from the bulk flow to the wall would be dC kw,0 | wall = – dt rh

(6)

The first-order wall model might best represent a process in which chlorine is the limiting reactant, as might be the case with reactions involving complex organic compounds that are found in the exocellular enzymes and metabolic products produced by biofilm on the pipe wall. The zero-order model would better represent the case in which chlorine immediately oxidizes some reductant (such as ferrous compound) and the rate is dependent on how fast the reductant is produced by the pipe. This mechanism would more likely apply to corrosion-induced reactions. No simple laboratory procedure currently exists for estimating pipe wall reaction coefficients throughout a distribution system; instead, field data are used to back-fit assumed coefficient values to the data. Mass transfer kinetics. The apparent wall reaction rate (not the reaction constant) can be influenced by the rate at which chlorine can be transported from the bulk flow to the wall. Following the development in Rossman et al,3 this process can be represented by a film-resistant model of mass transfer which maintains that along a pipe, the rate at which chlorine is hydrodynamically transported to the wall is proportional to the difference between the bulk concentration and the concentration at the wall, i.e.,

Extensive water quality sampling studies were conducted at five participating utilities—here samples are being collected at Bellingham, Wash.

(4)

Nonlinear least-squares regression was used to estimate the first-order bulk decay coefficients kb for the 11 treated water sources. The resulting values (Table 2) show great variation among the various water sources. For example, the decay coefficients ranged from a low of 0.082/d for the Keystone tiein at North Penn to a high of 17.7/d for Stafford Lake water in North Marin. Significant differences can also be observed between coefficients for different sources within the same system (e.g., North Marin and North Penn). Wall reaction kinetics. The approach taken to modeling possible reactions of chlorine with material on or released from the pipe wall assumed that the active reaction zone is located on the wall itself or in an adjacent molecular layer. Free (or combined) chlorine molecules are transported from the bulk flow to the wall and can react according to either first-order or zero-order kinetics. The first-order reaction is characterized by a rate coefficient with units of length divided by time (e.g., m/d), which is equivalent to a ratio of mass per unit of volume to mass per unit of wall surface area per



FIGURE 4

quoted empirical formula for the Sherwood number for turbulent flow in pipes24,25 is

Comparison of first-order bulk reaction model and sampling results for Oberlin zone at Harrisburg, Pa. Model results

Sampling results

Sh = 0.023 Re 0.83Sc 0.33

Station OH02

(9)


1.00 0.80 0.60 0.40 0.20 0.00 0

5

10

15 20 Time—h

25

30

35

in which Re is the Reynolds number and Sc is the Schmidt number (equal to the kinematic viscosity of water divided by D). In fully developed laminar flow, the average Sherwood number along the length of a pipe can be expressed in the form of an infinite series solution of the analogous two-dimensional heat transfer equation with radial heat flux.26 A parametric fit to this solution is given in Edwards et al.25 Sh 3.65

Station OH12


1.00

1 0.04[(d/L)ReSc]2/3

(10)

in which L is the pipe length. Overall rate. The overall rate of chlorine loss in a pipe combines the effects of bulk reaction, wall reaction, and mass transfer. For first-order wall reactions, this overall rate can be expressed as3

0.80 0.60 0.40

kw,1 kf dC – (kb ) C rh (kw,1 kf) dt

0.20 0.00 0

5

10

15 20 Time—h

25

30

35

1.00

(11)

whereas for zero-order wall reactions it is dC kw,0 kf C – kb C – min(,) dt rh r h

Station OH20


0.0668(d/L)ReSc

(12)

These rate expressions can be used in conjunction with a conservation of mass equation to predict chlorine level as a function of time and position within a single pipe or in a network of pipes. With first-order wall reaction, the rate expression (Eq 11) can be reduced to

0.80 0.60 0.40 0.20

dC = – k´ C dt

0.00 0

5

10

15 20 Time—h

dC kf | mass transfer = (C – Cw) dt rh

25

30

(13)

in which k´ is an overall first-order reaction coefficient that incorporates the effect of pipe diameter and flow regime. (7)

in which kf is a mass transfer coefficient with units of length divided by time. Mass transfer coefficients are usually expressed in terms of a dimensionless Sherwood number (Sh): D kf = Sh d

35

(8)

in which D is the molecular diffusivity of the species being transported and d is the pipe diameter. A widely

Analysis of field studies A modified version of EPANET, USEPA’s network simulation program27 was used to test alternative kinetic models on the five water distribution systems where field sampling studies were conducted. EPANET performs extended period hydraulic simulation and dynamic water quality modeling of pressurized pipe networks. It is a public domain package with a modular architecture that facilitates the addition of new features. The original EPANET used first-order kinetics for bulk reactions and first-order, mass transfer–lim-


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Predicted Mean Free Chlorine—mg/L

ited kinetics for wall reactions. FIGURE 5 Comparison of predicted and observed mean chlorine concentrations The modified version used in at sampling locations in the Oberlin zone at Harrisburg, Pa. this study included the following additions: Bulk reaction only Bulk + wall reaction • power law kinetics for 1.00 reactions in the bulk fluid, • zero-order, mass trans0.80 fer–limited wall reaction kinetics, • wall reaction kinetic 0.60 constants that are functions of pipe roughness, and 0.40 • calibration reports that provide statistical measures of goodness-of-fit between 0.20 simulated results and field observations. 0.00 Hydraulic network mod0.00 0.20 0.40 0.60 0.80 1.00 els had been developed preObserved Mean Free Chlorine—mg/L viously at each of the study sites. Therefore information on pipe sizes, lengths, and roughness coefficients as well as baseline water demands were already available. values) and by the following three statistical meaThese data were placed into EPANET data sets, and a sures that were computed by EPANET: (1) the corhydraulic calibration was performed for each site. At relation between the predicted and observed mean sites where tracer compounds were available (fluoride concentrations at all of the sampling stations at a site, at Fairfield and Harrisburg, sodium at North Marin, (2) the average absolute error between the predicted and hardness at North Penn), adjustments were made and observed values for all the samples analyzed, and to baseline demands and temporal demand patterns (3) the average relative error between the predicted until the model gave good agreement with tracer and observed values for all the samples analyzed. concentrations measured during the study period. In The next section provides detailed descriptions of addition, recorded tank water levels and pressures modeling analyses performed at two of the sites, offerwere also used to aid the calibration. ing an overall perspective of the modeling phase of The chlorine decay models tested included com- the project. binations of the following reaction mechanisms: firstRancho Solano zone 3—Fairfield, Calif. The order bulk decay kinetics; first-order, mass transRancho Solano zone 3 of the Fairfield Water System is fer–limited wall decay kinetics; and zero-order, mass a relatively small isolated zone with a single source of transfer–limited wall decay kinetics. In all cases, the supply, the Waterman Water Treatment Plant, a filbulk reaction rate coefficients were taken directly tration plant with preozonation at the headworks. A from the results of the bottle tests (as listed in Table schematic diagram of the system is shown in Figure 1. 2) and were not adjusted during model calibration. At Zone 3 was sampled July 8–9, 1993, for a period sites where multiple water sources were present, the of 24 hours. Chlorine residual leaving the plant is network was divided into zones reflecting the influ- normally about 1.5 mg/L. The normal mode of operence of the different sources, and the measured bulk ation for this zone is to pump from the plant clearwell reaction rate coefficients for the corresponding source to satisfy demands and refill the reservoir between 8 water were used for each zone. p.m. and 12 noon. During the peak energy use period A water quality calibration was then performed of 12 noon to 8 p.m., all pumps are shut off to avoid by sequentially adjusting the chlorine wall reaction energy surcharges, and the zone floats on the resercoefficients to give the best fit between predicted and voir. As a consequence, the chlorine residual in the observed field measurements. Three methods were reservoir decays with time, and chlorine residuals of used for assigning these coefficients to pipes in the less than 0.4 mg/L were observed in the system when network: (1) on a global basis (all pipes given same being supplied from the tank. coefficient), (2) on a zoned basis (all pipes in a specific Examples of the chlorine decay modeling results for zone assigned the same coefficient), and (3) with the Fairfield’s zone 3 are shown in Figure 2. These are coefficient inversely proportional to each pipe’s Hazen time series plots of observed and predicted chlorine Williams roughness coefficient in which the calibration residuals at four selected sampling stations in the sysis made by adjusting the constant of proportionality. tem for a first-order bulk decay model with no wall The suitability of each reaction mechanism tested reaction. Good agreement is seen between observed was determined by visual examination of time series and predicted chlorine residuals. For this system, a plots (comparing model predictions with observed simple first-order bulk decay reaction appears to adeCopyright (C) 1997 American Water Works Association JULY 1997


FIGURE 6

Model results

Sampling results Station OH02


1.00 0.80 0.60

0.40 0.20 0.00 0

5

10

15 20 Time—h

25

Station OH12


1.00 0.80 0.60

0.40 0.20 0.00 0

5

10

15

20

25

Time—h Station OH20 1.00


risburg, Pa., is an isolated system receiving water from one source, the Oberlin Booster Station fed by the Hummelstown Treatment Plant. There are no storage facilities in the zone. A schematic diagram of the pipe network is shown in Figure 3. The zone is entirely residential and contains many pipes that are unlined galvanized iron or steel, 6 in. (150 mm) or less in diameter, and many are 30 to 50 years old. It was anticipated that these characteristics would contribute to a significant pipe wall demand being exerted on chlorine residuals. Data were collected from a sampling study that was performed Oct. 11–13, 1993. Free chlorine was measured at approximately hourly intervals at 31 30 35 locations over a 35-hour period. In addition, a long-term chlorine decay test was made on the water entering the zone. The hydraulically calibrated Oberlin network model was used to test the suitability of several possible chlorine reaction mechanisms. These included first-order decay within the bulk flow, first-order bulk decay coupled with firstorder decay at the pipe wall, first-order bulk decay coupled with zero-order decay at the pipe wall, and pipe wall reaction rate coefficients as a function of pipe roughness or location. The acceptability of these mechanisms was judged by the 30 35 agreement between the observed and predicted free chlorine concentrations at the 30 sampling locations; (the pump station location served as a boundary condition for the model). The first chlorine reaction mechanism examined was a first-order reaction within the bulk flow. The rate constant over the entire network was set to 0.232/d as determined from the analysis of bottle decay test data on the water entering the pump station. Figure 4 compares chlorine time series at three representative sampling stations, and Figure 5 compares observed and predicted 30 35 mean chlorine residuals at all stations. Clearly, more chlorine decay occurred within the pipe environment than could be explained by reactions within the source water alone. Table 3 compares the results of fitting the various kinetic models to the data from Oberlin. The second reaction model added a first-order surface-type reaction occurring at the pipe wall to the first-order reaction in the bulk flow. This provided a relatively good fit to the data with an average prediction error of less than ±0.11 mg/L. There was, however, a tendency to underpredict concentrations at the top end of the net-

Comparison of first-order bulk plus zero-order wall reaction model and sampling results for Oberlin zone at Harrisburg, Pa.

0.80 0.60

0.40 0.20 0.00 0

5

10

15

20

25

Time—h

quately characterize the chlorine kinetics of the system. Because this system consists of relatively new large-diameter (mostly 8- and 12-in.- [200- and 300mm-] diameter) asbestos–cement pipe, wall uptake in relation to bulk decay is probably insignificant. Oberlin system—Harrisburg, Pa. The Oberlin portion of the United Water Resources (formerly Dauphin Consolidated Water Co.) service area in Har-


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work and overpredict them at TABLE 3 Comparison of alternative chlorine reaction models for Harrisburg the far end. In the third reacsite tion model, the first-order wall reaction was replaced by a Correlation zero-order (i.e., constant rate) Between Wall Coefficient Average Predicted reaction. This type of reaction Bulk m/d (first-order) Absolute and Observed might be more representative Error Means Coefficient or mg/m2/d Reaction Model 1/d (zero-order) mg/L percent of chlorine decay caused primarily by corrosion at the pipe Bulk decay 0.232 0.0 0.363 77 surface. Its mean estimation Bulk + first-order wall 0.232 0.27 0.108 95 Bulk + zero-order wall 0.232 91.5 0.088 97 error was 0.088 mg/L. It also Bulk + first-order wall 0.232 24.4 roughness 0.105 96 showed less tendency to unbased on pipe roughness derpredict near the source and Bulk + zero-order wall 0.232 6,994 roughness 0.086 97 based on pipe roughness overpredict near the far end of the network. The last two reaction models tested assumed that the wall reaction constant was a function of pipe roughaccurate fits were obtained using zero-order wall ness. In the original network data, roughness coeffi- kinetics whose reaction rate constant was tied to pipe cients (Hazen–Williams C-factors) were assigned on roughness coefficient (although in most cases, the the basis of pipe material and age. Having the wall improvement over a first-order wall reaction model was minimal.) reaction constant vary inversely with pipe roughness Table 5 shows the errors and correlations between meant that older, more deteriorated pipes would have observed and predicted chlorine levels at each site higher reactivity. An expression of the form using the zero-order, roughness-based model. In one Wall Rate Constant = Roughness (14) system with relatively new asbestos–cement pipe (Fairfield), first-order bulk decay provided a reasonable prediction of chlorine residuals, indicating minprovided the simplest relationship to test, having only a single parameter () to estimate. Best-fit estimates imal wall uptake. In two other systems (North Marin and Bellingham), only a portion of the system was of were determined for both first-order and zeroorder wall reaction models. The zero-order model assigned a wall demand. The models did a very good job in matching mean produced the slightly better fit, with mean absolute error of 0.086 mg/L for of 6,994 mg/ m2/d. Results chlorine concentrations at the sampling locations. With the exception of one system, correlations between from this model are shown in Figures 5 and 6. observed and predicted mean chlorine concentrations Comparison of all sites. Table 4 compares the estimated coefficients of the chlorine kinetic models exceeded 95 percent. On average, the models were applied to all five sites sampled in this study. In four able to reproduce individual chlorine measurements of the five systems, bulk demand alone was unable to with an absolute error in the range of 0.05 to 0.15 account for all of the chlorine decay observed, thus mg/L and a relative error between 17 and 31 percent. confirming the presence of a chlorine demand exerted A portion of these errors can be attributed to the variby the pipe wall. Both first-order and zero-order wall ability of the method used for measuring chlorine reaction kinetics were able to describe observed chlo- residual. For the DPD colorimetric method used in this rine variations in these systems. In general, the most study, variability can be as high as 15 percent.28

TABLE 4

Summary of chlorine decay kinetics for all sites sampled

Site Bellingham Fairfield Harrisburg North Marin Aqueduct Stafford Lake North Penn Forest Park Keystone Tank

First-Order Bulk Reaction Constant 1/d 0.83 1.16 0.23

First-Order Wall Constants m/d Reaction Constant 0.76 0.0 0.27 1.52

Zero-Order Wall Constants mg/m2/d

Roughness Constant 61 0.0 24.4 198

Reaction Constant

Roughness Constant

Percent of Pipes With Wall Reaction

215 0.0 91.5 215

17,216 0.0 6,994 27,976

95 0 100 13

5,380

100

1.32 17.7 0.03

3.05

53.8

0.77 0.08



TABLE 5

Calibration statistics for chlorine decay using a first-order bulk reaction and a zero-order wall reaction based on pipe roughness

Site Bellingham Fairfield Harrisburg North Marin North Penn

Average Absolute Error mg/L

Average Relative Error percent

Correlation Between Predicted and Observed Means percent

0.11 0.15 0.09 0.05 0.14

28 23 29 31 17

96 96 97 85 98

• Water quality models should be enhanced to better accommodate systems in which blending of different water sources occurs and each source water exhibits different bulk reaction kinetics. • As more systems calibrate chlorine decay models, it may be possible to establish a database relating kinetic parameters to water chemistry and pipe characteristics.

Findings and recommendations

Conclusions

This study uncovered a number of findings that increase understanding of the mechanisms of chlorine decay and the kinetic models that describe it. This understanding can lead to improved ability to model the fate of chlorine residuals in distribution systems. The most significant findings of this study included the following: • Chlorine decay in distribution systems can occur because of reactions within the bulk fluid and from reactions with materials associated with the pipe wall. • The rate of reaction of chlorine at the pipe wall is inversely related to pipe diameter and can be limited by the rate of mass transfer of chlorine to the wall. • There is currently no established method for directly determining the kinetics of chlorine decay attributable to pipe wall reactions, and calibration against field data must be used instead. • A well-calibrated hydraulic model, preferably one based on a tracer study, is a prerequisite for attempting to model water quality in a distribution system. • Calibration of network chlorine decay models can be based on a first-order kinetic constant for bulk reactions derived from bottle tests and either firstorder or zero-order kinetics for wall reactions, in which the wall kinetic constant is inversely related to the pipe roughness coefficient. Chlorine decay models calibrated in this way can have an average error of 0.05–0.15 mg/L, compared with individual field measurements, and correlations of 85–98 percent, compared with mean chlorine levels. Based on the experience gained during this study, the following recommendations for future work are offered: • A safe, effective, and nonreacting chemical should be found to serve as a tracer in field studies used for model calibration. • More direct methods of estimating pipe wall–related chlorine reaction constants are needed (e.g., relating chlorine decay to corrosion rates). • Use of strategically placed sensors in the distribution system, coupled with remote telemetry, might offer a way to perform continuous on-line calibration of network chlorine decay models as conditions change over time.

Maintaining adequate levels of disinfectant residual in water distribution systems is of vital importance to ensure delivery of high-quality water and compliance with water quality regulations. This requires a thorough understanding and characterization of the factors leading to loss of disinfectant residual within the distribution system environment. The rigorous models of chlorine decay kinetics in water distribution systems presented in this article consider reactions to occur both in the bulk flow and at the pipe wall. The models were tested and evaluated with data collected from five water distribution systems. The results of this study indicate that chlorine kinetics can be effectively characterized as a combination of first-order decay in the bulk liquid and first-order or zero-order decay reactions at the pipe wall. The developed models provide a powerful tool to assist water utilities in selecting operational strategies and establishing capital improvement priorities to optimize chlorine disinfection practices.

Acknowledgment The authors thank the AWWA Research Foundation for financial support of this research under contract number 815-92, Project Officer Albert Ilges for his support, guidance, and patience; project advisory committee members Anne Camper, Charles Haas, William Hunt, and Eric Lehan for their advice and support; and technical advisory committee members Gilbert Gordon, Yves Levi, C.P. Liou, Issam Najm, Charlotte Smith, and R. Rhodes Trussell for their contributions. The authors also gratefully acknowledge the participation and logistical support of Lyonnaise des Eaux-Dumez, Le Pecq, France; the US Environmental Protection Agency (USEPA) National Risk Management Research Laboratory, Cincinnati, Ohio; Montgomery Watson, Pasadena, Calif.; the cities of Bellingham, Wash., and Fairfield, Calif.; the North Marin Water District, Novato, Calif.; United Water Resources (formerly Dauphin Consolidated Water Co.), Harrisburg, Pa.; and the North Penn Water Authority, Lansdale, Pa. This material is based on work partially funded by USEPA. It has not been subject to USEPA review and does not necessarily reflect the agency’s views.


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JOURNAL AWWA

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About the authors: John J. Vasconcelos is an independent consulting engineer and may be reached at 1010 East Union St., Suite 101, Pasadena, CA 91106-1756. He has a BS from California State University in Fresno and MS and PhD degrees from the University of California at Berkeley. He has been involved in the modeling, design, and research of water distribution systems for more than 20 years. Lewis A. Rossman is an environmental scientist with the US Environmental Protection Agency, Water Supply and Water Resources Division, 26 West Martin Luther King Dr., Cincinnati, OH 45268. Walter M. Grayman, a consulting engineer, may be contacted at 730 Avon Fields Lane, Cincinnati, OH 45229. Paul F. Boulos is vice-president of MW Soft, 300 North Lake Ave., Suite 1200, Pasadena, CA 91101. Robert M. Clark is the director of USEPA’s Water Supply and Water Resources Division in Cincinnati.

