Modeling Distribution System Water Quality

MODELING DISTRIBUTION SYSTEM WATER QUALITY: REGULATORY IMPLICATIONS

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By Robert M. Clark,l Member, ASCE, Lewis A. Rossman, 2 Member, ASCE, and Larry J. Wymer3 ABSTRACT: Passage of the Safe Drinking Water Act in 1974 and its Amendments in 1986 (SDWAA) is changing the way water is treated and delivered in the United States. Under the SDWAA the U.S. Environmental Protection Agency (EPA) is required to regulate chemical contaminants and pathogenic microorganisms in drinking water. Emphasis has shifted from a primary concern with treated drinking water to attainment of standards at the point of consumption. Two regulations promulgated under the SDWAA, the Surface Water Treatment Rule (SWTR) and the Total Coliform Rule (TCR) specify treatment and monitoring requirements that must be met by all public water suppliers. This paper will examine the effect of various system variables on chlorine residual propagation. A recently proposed model (EPANET) will be utilized to examine the extent of fluid velocity and pipe radius on chlorine demand. The effect of these variables on the maintenance of chlorine residuals will be demonstrated. It will be shown that the same variables that affect the propagation of chlorine residual levels can potentially affect disinfection efficacy and the formation of disinfection byproducts.

INTRODUCTION

Passage of the Safe Drinking Water Act in 1974 and its Amendments in 1986 (SDWAA) is changing the way water is treated and delivered in the United States. Under the SDWAA the U.S. Environmental Protection Agency (EPA) is required to regulate chemical contaminants and pathogenic microorganisms in drinking water. Emphasis has shifted from a primary concern with treated drinking water to attainment of standards at the point of consumption. Two regulations promulgated under the SDWAA, the Surface Water Treatment Rule (SWTR) and the Total Coliform Rule (TCR) specify treatment and monitoring requirements that must be met by all public water suppliers. The SWTR requires that a detectable disinfectant residual be maintained at representative locations in the distribution system to provide protection from microbial contamination. The TCR regulates coliform bacteria that are used as "surrogate" organisms to indicate whether or not breakdown of primary disinfection or system contamination is occurring. Monitoring for compliance with the Lead and Copper Rule is based primarily on samples taken at the consumer's tap. The current standard for trihalomethanes (THMs) is 0.1 mglL for systems serving more than 10,000 people but the anticipated Disinfection and Disinfection ByProducts (D-DBP) Rule may impose the current (or a reduced) THM level on all systems. This regulation also requires monitoring and compliance at selected monitoring points in the distribution system. Some of these regulations may, however, provide contradictory guidance. For example, the SWTR and TCR recommend the use of chlorine to minimize risk from microbiological contamination. However, chlorine or other disinfectants interact with natural organic matter in treated water to form disinfection by-products. Raising the pH of treated water may assist in controlling corrosion but will increase the formation of trihalomethanes. 'Dir., Drinking Water Res. Div., RREL, U.S. Envir. Protection Agency, Cincinnati, OH 45268. 'Chf., Engrg. and Cost Section, RREL, U.S. Envir. Protection Agency, Cincinnati, OH. 'Sr. Statistician, DynCorp, 26 W. Martin Luther King Dr., Cincinnati, OH 45219. Note. Discussion open until May 1, 1996. To extend the closing date one month, a written request must be filed with the ASCE Manager of Journals. The manuscript for this paper was submitted for review and possible publication on October 5, 1993. This paper is part of the Journal of Water Resources Planning and Management, Vol. 121, No.6, November/December, 1995. ©ASCE, ISSN 0733-9496/95/0006-0423-0428/$2.00 + $.25 per page. Paper No. 7100.

It is clear that the SOWAA has caused utilities to include the entire distribution system when considering monitoring and compliance with drinking-water standards. Factors that affect water quality in distribution systems include chemical and biological quality of source water; effectiveness and efficiency of treatment processes; adequacy of the treatment facility, storage facilities, and distribution system; age, type, design, and maintenance of the distribution network; and quality of treated water (Clark and Coyle 1990). A factor, infrequently considered, that may influence water quality in a distribution system is the effect of mixing of water from difference sources. Water-distribution systems frequently draw water from multiple sources, such as a combination of wells and/or surface sources. The mixing of waters from different sources takes place within a distribution system and is a function of complex system hydraulics (Clark et al. 1991; Grayman et al. 1988). Another factor that may have a significant effect on distributed water quality is the design philosophy for distribution systems that has been generally adopted in the United States. Distribution systems are frequently designed to ensure hydraulic reliability, which includes adequate water quantity and pressure for fire flow as well as domestic and industrial demand. To meet these goals large amounts of storage are usually incorporated into system design, which results in long residence times, which in turn may contribute to deterioration of water quality. A number of mathematical models have been developed to assess the movement and fate of contaminants within drinking-water-distribution systems. Such models include dynamic and steady-state hydraulic models, which simulate the flow quantity, flow direction, and pressure in the system; steadystate water-quality models and flow-tracing models, which determine the movement of contaminants, their flow paths, and travel times through the network; and dynamic waterquality models, which simulate the movement and transport of substances in water under time-varying conditions. These models are becoming an effective tool for evaluating water quality in distribution systems and have the potential for offering a number of benefits to the water-utility industry. Application of those models includes predicting water-quality-degradation problems, calibrating system hydraulics, designing water-quality-sampling programs, optimizing the disinfection process, and evaluating operational and control strategies and storage reservoir design and the operation of distribution systems. From an economic viewpoint these models

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could be used to plan and design new systems, to evaluate the effect of repairs and rehabilitation prior to construction, and to locate booster chlorination in the system to optimize disinfection. A key factor in using water-quality models is understanding the kinetics associated with water-quality changes in the distribution system. This paper will examine the effect of various system variables on chlorine-residual propagation. A recently proposed model (EPANET) will be utilized to examine the extent of fluid velocity and pipe radius on chlorine demand. The effect of these variables on the maintenance of chlorine residuals will be demonstrated. It will be shown that the same variables that affect the propagation of chlorine-residual levels can potentially affect disinfection efficacy and the formation of disinfection by-products. CHLORINE DECAY STUDIES

Maintenance of chlorine or other disinfectant residual is generally considered to be a water-quality goal in the United States, and most water systems attempt to maintain a detectable residual throughout the distribution system. Before leaving the treatment plant, water is generally chlorinated in a final disinfection step and then stored in a clearwell or basin. When the water is discharged from the cle.arwell it is transported through the distribution system and home plumbing to the consumer. It is presumed that a detectable chlorine residual will minimize the potential for waterborne disease and biofilm growth in the system. As dissolved chlorine travels through the pipes in the network it reacts with natural organic matter in the bulk water and with biofilm and tubercles on the pipe walls or the pipe wall material itself. This reaction results in a decrease in chlorine residual and a corresponding increase in DBP depending on the residence time in the network pipes and holding time in storage facilities. Understanding these reactions will assist water utility managers in delivering high-quality drinking water and in meeting regulatory requirements under the SDWAA. Field Studies

Clark et al. showed how chlorine residuals can vary throughout the day at different locations in a distribution system depending on the flow path and residence time (Clark et al. 1993, 1994). Studies of chlorine decay rates in single lengths of pipe have revealed that the decay rate in the pipe is several times greater than the decay rate of the same water in a flask (Wable et al. 1991). These studies suggest that the pipe wall or material attached to the pipe wall is contributing to the overall chlorine demand observed in distribution systems. Wable et al. (1991) studied the loss of chlorine at three sites in the Paris, France, distribution system. They measured free-chlorine consumption by the network pipes using a Wallace and Tiernan titrator model 790 or a Hach Cl 17 for continuous measurement. A volume of 10 L of water was sampled and at time t = 0, a fixed dose of chlorine was added in order to obtain an instantaneous free-chlorine concentration. Free chlorine was measured as a function of time. It was then possible to calculate the first-order bulk decay constant for chlorine consumption by the water as given by a first-order equation. The ratio between the values from the flask experiments to values in the network showed the effect of pipe wall demand. It was found that the rate of chlorine consumption in the network and the flask ranged from 2.37 to 7.13. Wable et al. also found that the chlorine consumption increased with increasing temperature. Studies by Clark et al. yielded similar results to those of Wable et al. (Clark et al. 1993, 1994). Comparing chlorine-

TABLE 1. Decay Coefficients for Chlorine Demand from Fort Monmouth StUdy Diameter Type of pipe (1 ) Unlined cast iron Unlined cast iron PVC

Length

Flow Rate

Decay coefficient (day-') (Llmin) (7) (8)

(in.)

(cm)

(tt)

(m)

(gpm)

(2)

(3)

(4)

(5)

(6)

6

15.2

1,790

545.6

25

94.6

7.78

12 12

30.5 30.5

3,600 1,097.2 1,770 539.5

150 100

567.8 378.5

5.76 4.89

residual loss data in a network to chlorine loss in a flask yielded ratios of 1.07 to 20.16. Sharp et al. (1991) isolated a section of the Fort Monmouth, New Jersey, water-distribution system. During 2 days of testing in July of 1990, the source water samples averaged a total chlorine residual of 0.90 mg/L and a free-chlorine residual of 0.76 mglL with an average flow rate into the system of 259 gal./min (gpm). These numbers were based on five measurements per day taken consecutively at I-hr intervals for a total of 10 measurements each of free- and total chlorine residuals over the study period (Table 1). Column 8 of Table 1 contains the "total" decay coefficients for the individual pipes. These data suggest that unlined cast-iron pipe has a higher chlorine consumption than polyvinyl chloride (PVC) pipe and that the larger-diameter cast-iron pipe has a lower consumption than the smaller-diameter pipe. These results suggest that an interesting area for additional research would be to conduct studies that differentiate between chlorine demand attributable to reactions with the pipe wall itself versus material accumulated on the pipe wall, such as biofilm and tubercles. MATHEMATICAL MODELS FOR CHLORINE PROPAGATION

Hunt and Kroon (1991) described a network model for chlorine residuals that used a first-order decay reaction with a constant rate coefficient within each pipe and storage facility. In calibrating their model, they noted that smaller pipes off the main transmission lines required larger decay-rate coefficients to match observed chlorine levels. This is consistent with the fact that smaller pipes offer a larger wall surface area per unit of flow volume for reaction to occur. Biswas et al. (1993) developed a model for chlorine decay within single pipes under steady-state laminar and fully turbulent flow that included both bulk flow reaction and radial diffusion and subsequent pipe-wall reaction with chlorine. Building on these prior studies, Rossman et al. (1994) developed a general, mass-transfer-based model of chlorine decay that is particularly well-suited for full network modeling applications. It provides a mechanism for considering the loss (or growth) of a substance by reaction as it travels through the distribution system. Reaction is assumed to occur both within the bulk flow and with pipe wall material based on using first-order kinetics. The general expression for chlorine decay in the bulk flow and at the pipe wall is dc

-

dt

k

= - kbc - - f (c - c w ) r"

(1)

where dcldt = rate of chlorine decay (mg/L/day); c = chlorine concentration in the bulk flow (mg/L); t = time in days; k f = mass-transfer coefficient (ft/day); r" = hydraulic radius of pipe (pipe radius/2) (ft); Cw = chlorine concentration at the pipe wall (mg/L); k b = bulk phase chlorine decay coefficient (day-I). The first term in (1) is the bulk-flow reaction; the second term includes kf and c w , which represent the rate at which material is transported between the bulk flow and re-

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action sites at the pipe wall. If it is assumed that the rate of reaction at the wall is first-order with respect to C w and that it proceeds at the same rate as material is transported to the wall so that no accumulation occurs, the following mass balance at the wall holds: kr(c - cw)

=

(2)

kwc w

where k w = wall reaction constant in ft/day (m/day). Solving for Cw and substituting into (1) yields the following reaction-rate expression: dc kwkf = -k c - -----::~ dt h r,,(k w + kf )

-

(3)


The mass transfer coefficient kf is calculated by using the Sherwood number as follows (Edwards et al. 1976): kf = Sh·D/d

where

Sh = 0.023ROR3SC033 0.0668(d/L)R·Sc

Sh

=

(4)

for

3.65 + 1 + 0.04[(d/L)R'Sc]057

R

fr

~

2,300

(5)

R