United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-01/024
September 1999
Characterizing the Effect of
Chlorine and Chloramines on tl
Formation of Biofilm in a
Simulated Drinking Water
Distribution system
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EPA/600/R-01/024
September 1999
Characterizing the Effect of
Chlorine and Chloramines on the
Formation of Biofilm in a Simulated
Drinking Water Distribution System
by
Robert M. Clark and Mano Sivaganesan
Water Supply and Water Resources Division
National Risk Management Research Laboratory
Cincinnati, OH 45268
National Risk Management Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
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Disclaimer
This report has been subject to the Agency's peer and administrative review, and
it has been approved for publication as an EPA document. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
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Foreword
The U.S. Environmental Protection Agency is charged by Congress with protecting
the Nation's land, air, and water resources. Under a mandate of national environmental
laws, the Agency strives to formulate and implement actions leading to a compatible
balance between human activities and the ability of natural systems to support and nurture
life. To meet this mandate, EPA's research program is providing data and technical
support for solving environmental problems today and building a science knowledge base
necessary to manage our ecological resources wisely, understand how pollutants affect
our health, and prevent or reduce environmental risks in the future.
The National Risk Management Research Laboratory is the Agency's center for
investigation of technological and management approaches for preventing and reducing
risks from pollution that threatens human health and the environment. The focus of the
Laboratory's research program is on methods and their cost-effectiveness for prevention
and control of pollution to air, land, water, and subsurface resources; protection of water
quality in public water systems; remediation of contaminated sites, sediments and ground
water; prevention and control of indoor air pollution; and restoration of ecosystems.
NRMRL collaborates with both public and private sector partners to foster technologies
that reduce the cost of compliance and to anticipate emerging problems. NRMRL's
research provides solutions to environmental problems by: developing and promoting
technologies that protect and improve the environment; advancing scientific and
engineering information to support regulatory and policy decisions; and providing the
technical support and information transfer to ensure implementation of environmental
regulations and strategies at the national, state, and community levels.
This publication has been produced as part of the Laboratory's strategic long-term
research plan. It is published and made available by EPA's Office of Research and
Development to assist the user community and to link researchers with their clients.
E. Timothy Oppelt, Director
National Risk Management Research Laboratory
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Abstract
Drinking water treatment in the United States has played a major role in protecting
public health through the reduction of waterborne disease. However, carcinogenic and
toxic contaminants continue to threaten the quality of surface and ground water in the
United States. The passage of the Safe Drinking Water Act of 1974 and the subsequent
amendments reflect this concern.
The Safe Drinking Water Act and its Amendments have been interpreted as
meaning that some Maximum Contaminant Levels (MCLs) promulgated underthe Act shall
be met at the consumers tap, which in turn, has forced the inclusion of entire distribution
system when considering compliance with a number of the Act's MCLs, Rules and
Regulations. The Surface Water Treatment Rule which was promulgated under the Act
requires that a detectable disinfectant be maintained at representative locations in the
distribution system to provide protection from microbial contamination and to maintain
water quality in the distribution system.
One aspect of maintaining water quality in drinking water distribution systems is
controlling biofilm on distribution system pipe walls. Investigators have demonstrated the
occurrence of high concentrations of bacteria in tubercles that exist in water mains,
especially unlined cast iron mains, and on various types of pipe surfaces.
A study was conducted jointly by the U. S. Environmental Protection Agency and the
University of Nancy in France to examine the control of microorganisms in treated water
and at the pipe wall. A special pilot facility was constructed in which finished water from
parallel water treatment pilot plants was discharged into pipe loops that contained sample
tap locations to facilitate biofilm sampling. The facility was utilized to compare the effects
of post-chlorination and post-chloramination on the concentration of microorganisms in the
bulk phase and at the pipe wall.
The analysis utilized in this study characterizes these effects as measured by direct
count epifluorescence, and cultural techniques. It found that chlorine is as effective or
more effective in reducing the concentration of microorganisms in the bulk phase and in
controlling biofilms at the pipe wall.
IV
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TABLE OF CONTENTS
INTRODUCTION 1
WATER QUALITY DETERIORATION IN DISTRIBUTION SYSTEMS 2
EXPERIMENTAL PILOT FACILITY 3
RESEARCH DESIGN 6
ANALYTICAL METHODS 9
Bacterial Density 9
Bulk Phase Samples 9
Biofilm 9
Bacterial Enumeration 9
THE NETWORK AS A REACTOR 12
DATA ANALYSIS 12
BULK PHASE ANALYSIS 15
EPIFLUORESCENCE DIRECT COUNT 15
Effect of Treatment 16
Effect of Disinfectant 17
CFU15 18
Effect of Treatment 18
Comparison of Disinfectants 19
CFU3 19
Effect of Disinfectant 20
BIOFILM ANALYSIS 20
EPIFLUORESCENCE 20
Chlorine Disinfection 20
Chloramine Disinfection 22
Comparison Disinfectants 22
CFU15 22
Chlorine Disinfection 22
Chloramine Disinfection 24
Comparison of Disinfectants 24
CFU3 24
Chlorine Disinfection 24
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TABLE OF CONTENTS cont.
Chloramine Disinfection 26
Comparison Among Disinfectants 26
PREDICTING BIOFILM DENSITIES 28
SUMMARY AND CONCLUSIONS 36
REFERENCES 38
VI
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1. INTRODUCTION
Drinking water treatment in the United States (U.S.) has played a major role in protecting
public health through the reduction of waterborne disease. For example, in the 1880s for one
year, the typhoid death rate was 158 deaths per 100,000 in Pittsburgh, Pennsylvania but by
1935 the typhoid death rate had declined to 5 per 100,000. The reduction in waterborne
disease outbreaks was brought about by the use of sand filtration, disinfection and the
application of drinking water standards (Clark et al., 1991 a).
Concern over waterborne disease and uncontrolled water pollution resulted in a
dramatic increase in Federal water quality legislation between 1890 and 1970. Even though
significant advances were made in elimination of waterborne disease outbreaks during that
time period, other concerns began to emerge. By the 1970s, more than 12,000 chemical
compounds were known to be in commercial use with many more being added each year.
Many of these chemicals cause contamination of ground and surface water and are known to
be carcinogenic and/or toxic. The passage of the Safe Drinking Water Act of 1974 was a
reflection of this concern.
The Safe Drinking Water Act of 1974 and its Amendments of 1986 (SDWAA) requires
that the U.S. Environmental Protection Agency (U.S. EPA) establish maximum contaminant
level goals (MCLGs) for each contaminant which may have an adverse effect on the health of
persons. Each goal is required to be set at a level at which no known or anticipated adverse
effects on health occur, allowing for an adequate margin of safety (Clark et al., 1987).
Maximum Contaminant Levels (MCLs) must be set as close to MCLGs as feasible. The Safe
Drinking Water Act was amended again in 1996.
Most of the regulations established under the SDWAA have been promulgated with little
consideration of the effect that the distribution system can have on water quality. However,
the SDWAA has been interpreted as meaning that some MCLs shall be met at the
consumer's tap, which in turn, has forced the inclusion of the entire distribution system when
considering compliance with a number of the SDWAA MCLs, Rules and Regulations.
Distribution systems are frequently designed to insure hydraulic reliability, which
includes adequate water quantity and pressure for fire flow as well as domestic and industrial
demand. In order to meet these goals, large amounts of storage are usually incorporated into
system design, resulting in long residence times, which in turn may contribute to water quality
deterioration. In addition, many water distribution systems in this country are approaching
100 years old and an estimated 26 percent of the distribution system pipe is unlined cast iron
and steel and is in poor condition. At current replacement rates for distribution system
components, a utility will replace a pipe every 200 years (Kirmeyer et al., 1994).
SDWAA regulations that emphasize system monitoring include the Surface Water
Treatment Rule (SWTR), the Total Coliform Rule (TCR), the Lead and Copper Rule and the
Total Trihalomethane Regulation. Both the SWTR and the TCR specify treatment and
monitoring requirements that must be met by all public water suppliers.
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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 which are used to indicate the potential
presence of enteric pathogens, as well as efficiency of disinfection. However, some total
coliforms may grow in biofilm under the right conditions and, therefore, do not reasonably
indicate recent contamination in the distribution system. Monitoring for compliance with the
Lead and Copper Rule is based entirely on samples taken at the consumer's tap. The current
standard for trihalomethanes (THMs) is 0.1 mg/L for systems serving more than 10,000
people but the recently promulgated Disinfectant and Disinfection By-Products (D-DBP) rule
will impose a reduced THM level on large 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 may also increase the formation of trihalomethanes (Clark and Sivaganesan,
1998).
One aspect of maintaining water quality in drinking water distribution systems is
controlling biofilm that forms on distribution system pipe walls. A bacterial biofilm can be
defined as a structured community of microorganisms (including protozoa) enclosed in a self-
produced polymeric matrix and adherent to an inert or living surface (Costerton etal., 1999).
There is strong evidence that microorganisms colonize pipe surfaces in drinking water
distribution systems. Investigators have demonstrated the occurrence of high concentrations
of bacteria in tubercles that exist in water mains, especially unlined cast iron mains, and on
various types of pipe surfaces (LeChevallier et al., 1987).
This report characterizes the effect of chlorine and chloramine on the concentration of
biofilm at the pipewall and the concentration of microbes in the bulk phase as measured by
epiflourescence direct count, and cultural techniques. These results are based on data from
a series of experiments conducted in a pilot simulated distribution system located in Nancy,
France.
2. WATER QUALITY DETERIORATION IN DISTRIBUTION SYSTEMS
There are many opportunities for water quality to change as it moves between the
treatment plant and the consumer. Figures 1 and 2 illustrate some of the transformations that
take place in the bulk phase and at the pipe wall. Cross connections, treatment barrier
failures, and transformations in the bulk phase can all degrade water quality. Corrosion,
leaching of pipe material, and biofilm formation and hydraulic scour can occur at the pipe wall
to degrade water quality.
Many investigators have undertaken studies in an attempt to understand the possible
deterioration of water quality once it enters a distribution system. It has been documented that
microbiological changes in water quality may cause aesthetic problems involving taste and
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odor development, discolored water, slime growths, and economic problems including
corrosion of pipes and biodeterioration of materials (Water Research Centre, 1976).
Bacterial numbers tend to increase during distribution and are influenced by a number of
factors including the microbiological quality of the finished water entering the system,
temperature, residence time, presence or absence of a disinfectant residual, construction
materials, and availability of nutrients for growth (Geldreich et al., 1972; LeChevallier et al.,
1987; Maul etal, 1985a,b).
The relationship of microbiological quality to turbidity and particle counts in distribution
waterwas studied by McCoy and Olson (1986). An upstream and a downstream sampling site
in each of three distribution systems (two surface water supplies and a ground water supply)
were sampled twice per month over a one year period. Turbidity was found to be related in
a linear manner to total particle concentration, but not to the number of bacterial cells.
Degradation of microbiological water quality was shown to be the result of unpredictable
intermittent events that occurred within the system.
LeChevallier et al. (1987) conducted a study on the effect of distribution system biofilms
on water quality at a drinking water utility which experienced continuous microbiological
problems. The treatment plant effluent contained concentrations of coliform at <1 /100 mL, but,
based on the total number of gallons produced, it was clear that some total coliforms were
entering the system from the plant. A monitoring program showed increased coliform
densities as the water moved further out into the distribution system. Maintenance of a 1.0
mg/L free chlorine residual was insufficient to control coliform occurrence. This was
considered to be a problem because coliform bacteria growing in distribution system biofilms
may mask the presence of other indicators that might indicate a breakdown in the treatment
barrier.
3.0 EXPERIMENTAL PILOT FACILITY
The pilot facility utilized to generate the data in this paper consisted of two parallel pilot
plants and two sets of three pipe loops in series (Clark et al., 1994b). Each set of loops
received treated water from a pilot plant. The source of water for the pilot plants was a non-
disinfected raw surface water.
The capacity of the 'control' pilot plant was approximately 1 m3/h. Basic operation of the
control consisted of chlorination followed by coagulation with ferric chloride at a rate of 30-50
mg/L depending on influent turbidity. After flocculation and sedimentation, the water was
filtered using European-style sand with a grain diameter of 0.5 mm and a filtration rate of 6
m/h. Back-washing of the sand filter was accomplished by a three-step procedure consisting
of air, air and water, and air for 4-5 min approximately every 18 h depending on head loss.
Post disinfection was accomplished with chlorine or chloramine at concentrations selected
to maintain a free chlorine residual of 0.2-0.5 mg/L or a monochloramine residual of 1 mg/L
after the first 24-h residence time in the experimental distribution system (pipe loops).
Chloramines were generated using an in-line mixer that contained HOCI and NH4CI to obtain
a chloramine solution with a ratio of N:CI2 of 1:5.
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Treated
Drtttrg
Water
SoLbte Compounds
Paniculate Matter
Ch1orh&
Vtabte/Non-Vtable Cete
I Transfonmation I
! atMear hterface I
OUTPUTS
Water
Cross
Connections
Figure 1. Schematic of Chemical and Microbiological Transformations in Drinking Water
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Organic
Compounds
Inorganic
Compounds
Inorganic
Compounds
Inorganic
Corrosion
Products
Sedimentation
of Particulates
Microorganisms Microorganisms
Microbial
Products
Scour / Scour
1 Leaching
of Pipe
Material
t
Corrosion
Pipe Wall
Scale
Formation
Slime
Layer
Corrosion
Figure 2. Schematic of Chemical and Microbiological Transformations at the Pipe Wall
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Other experiments incorporated ozone into the treatment train in various
configurations including ozonation before filtration but after coagulation, and pre-ozonation,
and pre-ozonation coupled with ozonation before GAG filters and after sand filtration.
Finished water from the pilot plant was discharged into a network consisting of three
loops. Each pipe loop was 10 cm in diameter (ID) and 31 m in length. The pipes in the loops
were cement-lined cast iron containing 21 sampling devices per loop for water and biofilm.
Appropriate sample tap locations facilitated removal of water samples. Biofilm formation was
evaluated by placing coupons consisting of pipe material (polyvinyl chloride, polyethylene, or
cement) on the end of the sampling probe which was inserted flush with the pipe wall. A shut-
off valve ensured that pipe material coupons could be removed and changed while water was
flowing through the pipe. Water velocity was 1 m/s with configuration and operation of the
system producing a residence time of 24 h in each loop for a total of 72 h for the system. As
a consequence, only a small portion of water was transferred from a given loop to each
succeeding loop during a given flow cycle. An illustration of the pipe loop system is shown in
Figure 3.
3.1 Research Design
In this research project, the effects studied were:
The physico-chemical, chemical, and microbiological characteristics of the
source and treated water.
The change of these characteristics in the simulated distribution system.
The formation of fixed biofilms on the internal surface of the pipes and its
impact on water quality and network operating conditions.
During the 2-year study, the pilot plants and simulated distribution system were
operated continuously. One pilot plant was used as a control (reference train) with chlorine
added to the raw water and after filtration. Prechlorination was carried to breakpoint which
required an average applied dose of 1.4 mg/L producing an average residual after sand
filtration of approximately 0.1 mg/L. The performance of the chlorine reference pilot plant
(control) was compared against alternative ozone disinfectant schemes used in the second
parallel pilot plant. The three different treatment trains evaluated (T2, T3, T4), as well as the
control (T! ) are described in Table 1. Each treatment train configuration was evaluated using
post-chlorination and post-chloramination.
Each treatment train, in parallel with the control, was evaluated at two separate times
of year so that samples were collected under different temperature conditions (Table 2).
Table 2 contains the various combinations examined and the dates of the experiments. For
example, treatment train 1 (T1) was compared against treatment train 2 (T2) using chlorine
as a post disinfectant during late December 1989 and early January 1990. The experiment
was repeated in April and May of 1991.
6
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coipon sarrpkig devices
fHshed water
cootig water Jacket
pressure
measurement eel
pressure! I
regulator
_^ waste
Figure 3, Experimental Distribution Network
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TABLE 1. TREATMENT OPTIONS EVALUATED*
Treatment Train
Designation
Ti
T2
T3
T4
Unit Processes
Prechlorination; coagulation/flocculation/settling;
Coagulation/flocculation/settling; ozonation; sand
Ozonation; coagulation/flocculation/settling; sand
Ozonation; coagulation/flocculation/settling; sand
intermediate ozonation; filtration (GAG)
sand filtration
filtration
filtration
filtration;
* Each treatment train configuration was evaluated using both postchlorination and
postchloramination.
TABLE 2. SEQUENCE OF SELECTED TREATMENT TRAINS
Treatment Trains
Compared
TI vs T2
TI vs T3
TI vs T4
TI vs T3
TI vs T4
TI vs T2
Experiment
1
2
3
4
5
6
7
8
9
10
11
12
Postdisinfection
Scheme
Chlorination
Chloramination
Chlorination
Chloramination
Chlorination
Chloramination
Chlorination
Chloramination
Chlorination
Chloramination
Chlorination
Chloramination
Date of
Experiment
Dec1989/Jan1990
Feb/Mar1990
Apr/May 1 990
May 1 990
June 1990
July 1990
Sept/Oct 1 990
Nov/Dec 1 990
Jan/Feb1991
Feb/Mar1991
Apr/May 1991
June 1991
8
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Each experiment consisted of three phases, a start-up phase, a transition phase, which
permitted the distribution system to acclimate, and a quasi-steady-state phase during which
intense sampling was conducted. The transition phase, which usually lasted 3-5 weeks
depending on the disinfectant, allowed biof ilm to form at the surface of the coupons inside the
pipe loops. After this transition phase, the system was presumed to have attained a quasi-
steady state, and sampling was conducted on three consecutive days. Data collected during
the 3-day intensive portion of the study was utilized in this analysis.
3.2 Analytical Methods
Numerous analytical measurements were made on the treated and distributed water
including: temperature, pH, alkalinity, ammonia, N02, N03, turbidity (NTU), Fe, Mn, particle
counts (1-5, 6-10, 11-40, and >40 um), chlorine (free, combined and total), ozone, total
trihalomethanes (CHCI, CHCI2 Br, CHC1Br2, CHBr3) and total trihalomethane formation
potential, chloral hydrate, total organic carbon (TOC), assimilable organic carbon (AOC),
biodegradable organic carbon (BDOC), dissolved organic carbon (DOC), colony forming
units (CPU) (3 and 15 day incubation time), epifluorescence direct count and total coliforms.
Bacterial Density
Bacteria were enumerated directly from water samples and from the biofilm attached
to cement, polyvinyl chloride (PVC) and polyethylene (PE) coupons. Techniques used to
enumerate these bacteria in both the bulk phase and the pipe walls are described below.
Bulk Phase Samples
Water samples were collected in sterilized bottles, previously rinsed with sterile
distilled water containing sodium thiosulphate at a final concentration of 17.5 mg/L. Cell
densities as determ ined by the direct epifluorescence technique and by cultural methods (pour
plate) were determined at the inlet and outlet of the first and third loops (Block et al., 1993;
Standard Methods 19th Edition, 1995).
Biofilm
The colonized coupons (cement, PVC or PE) were placed in 25 ml of pH 7 bacteria-
free distilled water and the attached bacteria were released from the coupons by sonication
(Vibra Sonic Cells: 10 W, 20 kHz) for 2 min. Preliminary assays showed that these sonication
conditions maintained the viability of bacteria and achieved removal of more than 80% of
attached cells.
Bacterial Enumeration
The total number of bacterial cells were evaluated by direct epifluorescence
microscopic observation. An aliquot of the sonicated biofilm orwater sample was poured into
a sterilized glass test tube and an aqueous solution of acridine orange was added to obtain
a final concentration of 0.01 % (v/v). After 30 minutes of incubation, the sample was filtered
through a black polycarbonate membrane (Nuclepore SN 111156, 0.2 um porosity). The
filters were rinsed with sterile distilled water, a drop of buffered glycerine to reduce
autofluorescence was added and a cover slip placed on the membrane filters. The filter was
9
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then examined using an epifluorescene microscope (Olympus - blue light excitation) with oil
immersion objective (X 1000).
The viable bacteria or colony-forming units (CPU) were enumerated by placing 1 ml
of sample or diluted sample in standard nutrient agar (AFNORNF T90-402). Dilutions were
made in a sterile 0.9% (v/v) NaCI solution. After 3 days and 15 days of incubation at 20-22* C,
the colonies were counted. The results were expressed as viable colony-forming units per ml
(CFU/mL) for water samples or CFU/cm2 for biofilm samples (Clark etal., 1994b). Table 3
shows the average values for several water quality parameters entering the network.
As indicated in Table 3, direct count microscopic techniques (epifluoresence) were
used to count cells on both the pipe wall (coupon samples) and in the bulk phase water
samples. Cultural techniques were also used, however, the use of agar media for estimating
bacterial numbers generally produces a result which underestimates the actual numbers
present (Maul et al., 1991). Within a given population one may find variable numbers of
bacteria which may be cultured in a defined media. Figure 4 illustrates the relationship
between total bacteria as measured by epifluoresence and the number of colony form ing units
obtained by cultivation on agar media (heterotrophic organisms).
TABLE 3. AVERAGE TREATED WATER VALUES FOR
THE PILOT NETWORK
(for all treatment Scenarios)
Item
Temperature(»C)
pH
Turbidity (NTU)
DOC (mg/L)
Logio CFU/mL (3 day)
Log10 CFU/mL (15 day)
Log10 epifluoresence
(Count/mLX103)
Mean
Deviation
17.3
7.5
0.2
1.50
-0.04
1.1
4.1
Standard
3.8
0.2
0.09
0.5
0.4
0.6
0.9
Minimum
11.3
7.2
0.06
0.7
-0.3
0.2
2.6
Maximum
24.1
8.0
0.6
3.6
1.3
3.0
5.7
10
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26 xf)*
Total teetotal
GOUtf
4x10'
CPU
15 days
haiHtian
CPU
72 h
Nuntereof BacteifaperrrlhDfeMBdBdDM^
11
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4.0 THE NETWORK AS A REACTOR
The pilot network operates as a "recycle reactor," in which a certain fraction of the
product stream is separated from the network and returned to the entrance of the reactor. The
recycle ratio R is defined as (Levenspeil, 1972):
R= volume of fluid returned to the reactor as feed
volume entered the system
The value for R for each of the loops in the pilot network was:
R = 2891.85
Assumption of a first order reaction yields the following function:
CAO+RCAr
•= In
R+l
(R
where do = the concentration of the feed going into the loop in mg/L, CAT = the
concentration of the stream leaving the loop in mg/L, k is a reaction rate constant (day1) and
6 is the mean residence time in the pipe loop in days.
Equation (1) was used as the basis for the subsequent analysis for both chlorine and
chloramine decay through the loops. Reformulating equation 1 yields:
CAf = - — r- - (2)
Figures 5 and 6 are examples of fitted curves for the chlorine and chloramine
concentrations in the bulk phase. For the system R = 2891 .85, 6= 24 hr and k = 6.06/day for
chlorine and k = 1 .64/day for chloramines.
5.0 DATA ANALYSIS
Three-dayCFU/ml_(CFU3), 15-day CFU/ml_(CFU1 5) and epifluorescencecounts/mL
X 1 03 (EPI) measurements in the bulk phase and on the pipe wall were evaluated for their
dependency on water quality variables such as Cb (or NH2CI, depending on the final
disinfection method), pH, temperature (Temp), DOC, BDOC, ozone, ammonia, N02, N03, and
fluorescence.
12
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1.50
1.20
T»
0.90
0.60
o
o.oo
0.00
0.50
1.00
1.50
2.00
2.50
3.00
TbiM In DayB
FigureS. Chlorine Decay Curve
13
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1.50
1.20
E
s
•
I
I
5
0.90
0.60
0.30
0.00 0.50 1.00 1.50 2.00 2.50
RMldwice Tim* in Days
3.50
Figure 6. Chloramine Decay Curve
14
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A correlation analysis was also performed to examine the relationship among the
independent variables. It was found that many of the independent variables were correlated
and were linear functions of DOC. Even though the experiment was conducted over a 12-
month period, the loops were inside and the temperature of the water in the loops was
relatively constant. Therefore, temperature was eliminated as an independent variable.
Scatter plots of log EPI vs. CI2 (or NH2CI) in the bulk phase and on the pipe wall
exhibited a clearly decreasing trend with increasing levels of Cb (NH2CI). The scatter plots
of log CFU3 vs. CI2 or NH2CI and log CPU 15 vs. CI2 or NH2CI also showed a decreasing trend
with increasing disinfectant concentration but did not show as definitive a pattern as did
epifluoresence. As a consequence a linear model of log EPI, log CPUS and log CFU15 as
a function of disinfectant concentration was selected for analysis. For example, the model
used for EPI versus chlorine residual is given by equation (3) and for chloramine by equation
(4).
i+ei, i=1,2 n, (3)
b(NH2CI)i+ei, i=1,2 n, (4)
In the above equations, a (intercept) and b (slope) are model parameters, n is the
sample size and e j is the error term. It is assumed that e j's are independent and identically
distributed normal variables with a mean of 0 and the same variance 62. A least-squares
technique was used to estimate the model parameters. The same type of model was used
to characterize log CPUS and log CFU15 versus disinfectant level.
6.0 BULK PHASE ANALYSIS
Using equations 3 and 4 bacterial concentrations in the bulk phase were analyzed as
a function of disinfectant concentration. Effects of both treatment and disinfectant type were
evaluated. A comparison of the effects of chlorine and chloramine on epifluoresence direct
count and organisms cultured for 15 (CFU15) and 3 (CFU3) days was made and will be
discussed in the following sections.
6.1 Epifluorescence Direct Count
Table 4 contains parameter estimates for equations 3 and 4 for both chlorine and
chloramine disinfection in the bulk water phase for all four types of treatment. The p-value for
testing that the parameter b = 0 is given in parenthesis. The slopes aresignificantly negative
for all four treatments, meaning that there is a significant linear correlation between EPI and
the concentration of disinfectant in the water. The smallest R2 occurred in the experiment
involving post-chloramination for treatment T3.
15
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TABLE 4. PARAMETER ESTIMATES FOR CHLORINE AND CHLORAMINE
DISINFECTION REGRESSED AGAINST EPIFLUORESENCE IN THE BULK PHASE
Treat-
ment
Type
T1
T2
T3
T4
Chlorine
a
5.4306
5.4697
5.2598
5.2589
a
-6.556
(0.0001)*
-3.927
(0.0020)*
-2.5876
(0.0004)*
-4.4219
(0.0001)*
62
0.1713
0.6821
0.4201
0.2542
R2
0.8502
(n=51)
0.6324
(n=12)
0.5606
(n=18)
0.7307
(n-18)
Chloramines
a
5.5949
5.4445
5.3793
5.7244
a
-1.314
(0.0001)*
-1.5168
(0.0001)*
-0.6751
(0.0070)*
-1 .4422
(0.0001)*
62
0.0816
0.2565
0.2906
0.2314
R2
0.8298
(n=54)
0.6066
(n=18)
0.3739
(n=18)
0.6164
(n=18)
- At 5% level of significance
Effect of Treatment
For a given disinfection method there are differences among the estimated slopes (as)
for the different treatment trains. Assuming that the error of variance is homogeneous among
treatment groups, a covariance analysis was performed on the pooled data sets to compare
slopes within a disinfection method. The covariate CI2 (or NH2CI) and the effects of the
treatment trains T1, T2, T3 orT4 are included in the covariance model. The following models
were used to compare the effects of chlorine (orchloramine) on EPI for the different treatment
trains:
or
log EP|j = (a +gi) + ( b + d|) (CI2 )N + e,
log EP|j = (a +9i ) + ( b + di ) ( NH2CI)ij + e
i=1 ,2,3,4; j= 1 ,2....,ni,
(5)
(6)
where log EP|j is the jth response for the ith treatment group, a and b are average intercept
and slope for the entire data set QJ and d \ are treatment effect coefficients (differences of
intercept and slope from the corresponding average intercept and slope
for the entire data set). The e\'s are independent and identically distributed normal random
variables with mean 0 of equal variances 62 and n = rii + n2 + n3 + n4 is the pooled sample
size. If the four regression coefficients d.| , d2 , d3 and d4 are not significantly different from
zero then there are no significant differences among the four treatments with respect to the
linear effects of chlorine (or chloramine) on log EPI. If some of the dj's are significantly
different from zero then there is significant difference among the treatments. The hypothesis
that all the dj 's are zero was tested via a covariance analysis. The test revealed that the
regression relationships (slopes) differed among treatment groups (p<0.05). Thus, within
16
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each disinfection method, the linear effects of chlorine (or chloramine) on log EPI are not the
same for each treatment.
For the chlorinated systems, the slope (linear effect) of chlorine on log EPI for treatment
T1 was significantly more negative than the slopes for T2, T3 and T4 (p<0.05 for each
comparison). Treatments T2, T3 and T4 were compared using a covariance analysis and no
significant difference was seen (p = 0.1533). These results imply that for chlorine disinfected
systems the use of ozone in the treatment train resulted in higher levels of bacterial
concentration in the bulk phase than resulted from the use of pre-chlorination alone.
For the chloraminated systems, a covariance analysis showed that the slopes for T1,
T2 and T4 were significantly more negative than the slope for T3 (p<0.05, for each
comparison). No significant differences were seen among the slopes of T1, T2 and T4
(p=0.6792). These results imply that there were no differences in bulk phase bacterial
concentrations between systems that use chloramine even if ozone is used in the treatment
train.
Effect of Disinfectant
The slopes (b's)forall systems using chlorine disinfection were significantly lower than
the slopes for chloramines for all four treatments. Within each treatment group, both
disinfection methods were compared via covariance analysis. The model for the analysis is
given by:
logEPIj =(a+gi) + (b +di)(Xij) + eij, i=1,2; j=1,2, r\, (7)
where log EP|j is the fh response for the ith disinfection method, a and b are intercept and
slope for the pooled data set. The parameters g and dj are treatment effect coefficients
corresponding to the iih disinfection method (difference of intercepts and slope from the
average intercept and slope for the fh disinfection method), es/s are independent and
identically distributed normal random variables with a mean 0 and equal variances 62, n = rii
+ n2 is the pooled sample size, x^ is the jth chlorine value, and x2j is the jth chloramination
value.
The hypothesis that both djS are zero was tested using a covariance analysis and
shows the regression relationship (slopes) are different among disinfection methods (p<0.01).
Within each treatment group, the linear effects of chlorine on log EPI were significantly
different from the linear effect of chloramine on log EPI. Chlorine disinfection yielded more
negative slopes than chloramine for each of the four treatments (p<0.01). Thus, it could be
concluded that for all the treatments, chlorine was significantly more effective than chloramine
in reducing epifluoresence in the bulk phase.
CFU15
Table 5 contains parameter estimates for log CFU15 (equations 3 and 4) for both
chlorine and chloramine disinfection in the bulk phase for all four treatment conditions. The
17
-------
p-value for testing that the parameter a = 0 is given in parenthesis. The slopes were
significantly negative for all four treatments, meaning that there was a significant linear
correlation between 15-day CPU and the concentrations of disinfectant in the water.
TABLE 5. PARAMETER ESTIMATES FOR CHLORINE AND CHLORAMINE
DISINFECTION REGRESSED AGAINST CFU15 IN THE BULK PHASE
Treat-
ment
Type
T1
T2
T3
T4
Chlorine
a
3.2224
3.2470
3.0404
3.4356
a
-6.9266
(0.0001)*
-4.6909
(0.0003)*
-4.5898
(0.0001)*
-6.8052
(0.0001)*
62
0.7650
0.5860
0.7795
0.2790
R2
0.5866
(n=51)
0.7408
(n=12)
0.6839
(n=18)
0.8541
(n=18)
Chloramines
a
3.4595
3.2243
3.2065
3.6670
a
-2.5716
(0.0001)*
-2.7556
(0.0036)*
-1 .4927
(0.0097)*
-3.3338
(0.0001)*
62
0.4435
0.3058
1.5730
0.2215
R2
0.7745
(n=54)
0.8102
(n=18)
0.3503
(n=18)
0.8997
(n=18)
- At 5% level of significance
Effect of Treatment
For a given disinfection method there are differences among the estimated slopes
(as) for the different treatment trains. Equations 5 and 6 (with log CPU 15 as the
dependent variable) were used to compare the effects of chlorine (or chloramine) on log
CFU15 for the different treatment trains. The hypothesis that all the effects were zero was
tested via a covariance analysis. The test revealed that the regression relationships
(slopes) differed among treatment groups for chloramine disinfection method (p = 0.0035)
and no significant difference was seen among the slopes for chlorine disinfection method
(p = 0.0762).
For the chloraminated systems, a covariance analysis showed that the slopes for
T1, T2 and T4 were significantly more negative than the slope for T3 (p<0.05, for each
comparison). No significant differences were seen among the slopes of T1, T2 and T4 (p
= 0.1728).
Comparison of Disinfectants
18
-------
Within each treatment group, the effects of disinfection methods were compared via
covariance analysis. The model for the analysis is given by equation 7 with log CFU15 as
the dependent variable. The covariance analysis shows that within each treatment group,
the linear effects of chlorine on log CPU 15 were significantly more negative than for
chloramines (p <0.03, for each comparison). Thus, it was concluded that for a given
treatment, chlorine was significantly more effective than chloramine in reducing 15-day
CPU in the bulk phase.
CFU3
Table 6 contains parameter estimates for equations 3 and 4 (with log CPUS as the
dependent variable) for both chlorine and chloramine disinfection in the bulk water phase
for all four treatment conditions. The p-value for testing that the parameter a = 0 is given in
parenthesis. P-values were significant and negative for all four treatments, meaning that
there was a significant linear correlation between log CPUS and the concentrations of
disinfectant in chloramine disinfection (p = 0.0716, 0.0958, respectively). Thus, within
each disinfection method, the linear effects of chlorine (or chloramine) on log CPUS were
not significantly different over the treatment groups.
TABLE 6. PARAMETER ESTIMATES FOR CFU3 VERSUS CHLORINE AND CHLORAMINE
DISINFECTION REGRESSED AGAINST CFU3 IN THE BULK PHASE
Treatment
Type
T1
T2
T3
T4
Chlorine
a
0.930
9
0.588
5
0.432
4
0.577
7
a
-3.639
(0.0001)*
-1 .6238
(0.0061)*
-1 .0852
(0.0393)*
-2.1826
(0.0322)*
62
0.7643
0.1672
0.2994
0.4888
R2
0.2816
(n=51)
0.5454
(n=12)
0.2394
(n=18)
0.2559
(n=18)
Chloramines
a
0.9191
0.3917
0.4243
1 .4938
a
-1 .2260
(0.0001)*
-0.8361
(0.0036)*
-0.6142
(0.0051)*
-1 .8824
(0.0001)*
62
0.809
3
0.165
6
0.219
1
0.396
4
R2
0.2985
(n=54)
0.4206
(n=18)
0.3959
(n=18)
0.6151
(n=18)
* - At 5% level of significance
19
-------
Effect of Disinfectant
The slopes (as) for all systems using chlorine disinfection were more negative than the
slopes for chloramines for all four treatments. Within each treatment group, the effects of
disinfection methods were compared via covariance analysis. The model for the analysis is
given by equation 7 with log CFU3 as the dependent variable. The covariance analysis
showed that within treatment group T1, the linear effect of chlorine on log CFU3 was
significantly more negative than the linear effect of chloramine on log CFU3 (p = 0.0072). For
the other three treatment groups, even though the chlorine disinfection method yielded slopes
that were more negative than for chloramine disinfection method, the linear effects were not
significantly different (p = 0.1478, 0.3484, 0.7587, respectively for T2, T3 and T4).
7.0 BIOFILM ANALYSIS
As with the bulk phase analysis, models of the form shown in equations 3 and 4 were
utilized to evaluate the effect of chlorine and chloramine on biofilm concentrations. The effect
of these disinfectants on EPI, CFU15 and CFU3 were evaluated.
7.1 EPIFLUORESCENCE
In this section the effects of chlorine and chloramine are considered on the
concentrations of epifluorescence and bacteria on cement, PVC and polyethylene.
Chlorine Disinfection
Table 7 contains the estimated model parameters, estimated error variance 62, and
the R2 for each of the four treatments, T1, T2, T3 and T4 and a given pipe material, using
chlorine as a disinfectant. The p-valuesforthe hypothesis that the parametera is negative are
given in parenthesis.
For cement the model parameter a was significantly negative for all treatments
(p<0.05). For all four treatment types, the log EPI on the cement material decreased as
chlorine level increases.
For polyethylene the model parameter a was also significantly negative for all
treatments (p<0.05). Therefore, for all four treatments, log EPI on the wall decreased
significantly as chlorine level increased. However, the slopes for Treatments T2 and T3
were smaller than the slopes for the other two treatments. The model R2s for treatments T2
and T3 are lower than the model R2s for treatment trains T1 and T4.
For PVC the model parameter a was significantly negative for all the treatments except
for T2 (p<0.05). This means that for T1, T3 and T4 the amount of EPI on the PVC wall
decreased significantly as the chlorine level increased. Even though the slopes for T2 and T3
were almost the same, one is significant and the other is not. The error
20
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TABLE 7. MODEL PARAMETERS FOR WALL DENSITIES (EPIFLUORESENCE) USING CHLORINE DISINFECTION
FOR CEMENT, POLYETHYLENE AND PVC
Treatment
Type
T1
T2
T3
T4
Cement
a
6.626
6.643
6.702
6.629
3|
-4.583
(0.0001)*
-3.543
(0.0134)*
-1 .996
(0.0001)*
-3.9448
(0.0001)*
62
0.358
1.062
0.1004
0.3202
R2
0.5736
(n=50)
0.4735
(n=12)
0.7678
(n=17)
0.6316
(n=18)
Polyethylene
a
6.835
6.503
6.730
7.019
a.
-3.250
(0.0001)*
-1.142
(0.0470)*
-0.956
(0.0319)*
-2.168
(0.0017)*
62
0.1158
0.1938
0.2119
0.1786
R2
0.6761
0.3389
0.2566
0.4923
PVC
a
6.535
6.565
6.587
6.540
a.
-1 .952
(0.0001)*
-1 .002
(0.1182)
-1 .052
(0.0016)*
-1 .849
(0.0001)*
62
0.1317
0.2617
0.0909
0.0628
R2
0.3986
(n=50)
0.2261
(n=12)
0.4725
(n=18)
0.6575
(n=18)
- At 5% level of significance
21
-------
variability (62) for treatment T2 was higher than for T3, which may explain the insignificance
of the slope for T2. Moreover, R2 for T2 was lower than the R2 for T3.
Chloramine Disinfection
Table 8 shows that the slopes of Treatment T3 were not significantly different from zero
for cement and PE. This means that for treatment T3 and for PE and cement, chloramine had
no significant (linear) effect on the reduction of EPI. Thus, treatment type T3 was dropped
from further analysis. Covariance analysis was used to compare the other three treatments
for each of the three wall types. For cement and PVC, no significant differences were seen
among the three slopes for T1, T2 and T4. The slope of T4 was significantly more negative
than the slope of T1 for the wall type PE (p=0.0347). Moreover, no significant difference was
seen between the slope.
Comparison of Disinfectants
The slopes (b) for all three materials using chlorine disinfection method were more
negative than the slopes of the corresponding materials using chloramines. Covariance
analysis was performed to compare the methods for each of the four treatments (equation 7).
The linear effect of chlorine on log EPI was significantly greater than the linear effect of
chloramine on log EPI for the cement for all treatments (p<0.05 for each comparison). The
effect of chlorine on log EPI was significantly greater than the effect of chloramine on log EPI
for PE and PVC, for treatments T1, T3 and T4. No significant difference was seen between
the disinfection methods for treatment T2.
CFU15
In this section, the effects of chlorine and chloramine are evaluated against the counts
of organisms cultured for 15 days (CPU 15) on cement, polyethylene and PVC.
Chlorine Disinfection
Table 9 contains the estimated model parameters, estimated error variance 62, and
the R2 for each of the four treatments, T1, T2, T3 and T4 and the pipe material, using chlorine
as a disinfectant. The p-values for the hypothesis that the parameter a is zero, are given in
parenthesis.
For cement the model parameter a was significantly negative for T1, T3 and T4
(p<0.05). For these three treatment types, the CFU15 on the cement material decreased as
the chlorine level increased. The slope for treatment T2 was not significantly different from
zero and the model R2 is close to zero.
For polyethylene the model parameter a was significantly negative only for T1
(p<0.05). This means thatforT1,CFU15 on the wall decreased significantly as chlorine levels
increased.
22
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TABLE 8. MODEL PARAMETERS FOR WALL DENSITIES (EPIFLUORESENCE) USING CHLORAMINE DISINFECTION FOR CEMENT,
POLYETHYLENE AND PVC
Treatment
Type
T1
T2
T3
T4
Cement
a
6.694
6.667
6.465
6.618
3|
-0.644
(0.0001)*
-0.807
(0.0014)*
-0.016
(0.8735)
-0.772
(0.0040)*
62
0.0799
0.1063
0.0653
0.1508
R2
0.5523
(n=51)
0.5550
(n=15)
0.0017
(n=17)
0.4137
(n=18)
Polyethylene
a
6.756
6.717
6.479
7.076
3|
-0.568
(0.0001)*
-0.849
(0.0005)*
-0.012
(0.8988)
-0.994
(0.0001)*
62
0.0832
0.1070
0.0504
0.0983
R2
0.4723
0.5367
0.0011
0.6426
PVC
a
7.700
6.582
6.593
6.612
a.
-0.766
(0.0001)*
-0.801
(0.0036)*
-0.425
(0.0059)*
-0.543
(0.0175)*
62
0.0788
0.1393
0.0923
0.1203
R2
0.6316
(n=53)
0.4428
(n=17)
0.4059
(n=17)
0.3050
(n=18)
TABLE 9. MODEL PARAMETERS FOR WALL DENSITIES FOR CHLORINE DISINFECTION REGRESSED AGAINST CFU1 5 FOR CEMENT,
POLYETHYLENE AND PVC
Treatment
Type
T1
T2
T3
T4
Cement
a
5.082
4.562
5.386
5.337
a
-4.612
(0.0001)*
-0.920
(0.6925)
-1 .486
(0.0480)*
-4.246
(0.0092)*
62
1.095
1.699
0.6173
1.161
R2
0.3052
(n=51)
0.0182
(n=11)
0.2227
(n=18)
0.3539
(n=18)
Polyethylene
a
5.447
4.615
5.323
5.219
a
-1 .837
(0.0022)*
-1 .335
(0.4970)
0.3686
(0.3993)
0.4906
(0.8049)
62
0.3546
1.190
0.2321
2.1535
R2
0.1790
(n=50)
0.00527
(n=11)
0.0448
(n=18)
0.0039
(n=18)
PVC
a
5.176
4.791
5.335
5.354
a
-0.8393
(0.2164)
-0.2648
(0.9047)
0.5442
(0.1041)
-0.1861
(0.6654)
62
0.4935
1.155
0.1273
0.1007
R2
0.0317
(n=50)
0.0017
(n=11)
0.1565
(n=18)
0.0120
(n=18)
- At 5% level of significance
23
-------
For PVC the model parameter a was not significantly negative for any of the four treatments
(p>0.10). This means the slopes for T1, T2, T3 and T4 were not significantly different from
zero.
Chloramine Disinfection
Table 10 gives the estimated model parameters, estimated error varianceo2, and the
model R2 for each of the four treatments, T1, T2, T3 and T4 using chloramine disinfectant. For
cement the model parameter a was significantly negative for all the treatments except T3
(p<0.05).
For polyethylene the model parameter a was significantly negative for T1, T2 and T4.
Therefore, for all these treatments, the amount of CFU15 on the polyethylene wall decreased
with increased chloramine level in the water.
For PVC the model parameter a was significantly negative forT1, T2 and T3 (p<0.05).
Therefore, for these three treatments, the amount of CFU15 on the PVC wall decreased with
increased chloramine level in the water. For treatment T4 there was no significant linear effect
of chloramine on 15-day CFU.
As some of the linear effects were not significantly negative for some of the treatments,
comparative analysis among treatments within a disinfection method was not performed for
any of the wall materials.
Comparison of Disinfectants
Most of the slopes (a) for all three materials using chlorine disinfection method were
more negative than the slopes of the corresponding materials of the chloramine disinfection
method.
CFU3
In this section the effects of chlorine and chloramine on biofilm concentrations for CFU3
are evaluated.
Chlorine Disinfection
Table 11 contains the estimated model parameters, estimated error variance s2, and
the R2 for each of the four treatments, T1, T2, T3 and T4 and the pipe material, using chlorine
as a disinfectant. The p-values for the hypothesis that the parameter a is negative are given
in parenthesis.
For cement the model parameter a was significantly negative for T1, T2 and T4
(p<0.05). For these three treatment types, the log CFU3 on the cement material decreased
as chlorine level increased. The slope fortreatmentTS was not significantly different from zero
and the model R2 was close to zero.
24
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TABLE 10. MODEL PARAMETERS FOR WALL DENSITIES FOR CHLORAMINE DISINFECTION REGRESSED
AGAINST CFU1 5 FOR CEMENT, POLYETHYLENE AND PVC
Treatment
Type
T1
T2
T3
T4
Cement
a
5.350
4.995
5.272
5.594
a
-1 .059
(0.0001)*
-1 .303
(0.0133)*
-0.5297
(0.2085)
-3.653
(0.0416)*
62
0.5349
0.6030
0.9948
1.417
R2
0.3257
(n=54)
0.3263
(n=18)
0.0970
(n=18)
0.2348
(n=18)
Polyethylene
a
5.591
5.518
5.544
5.920
a
-0.6244
(0.0004)*
-1 .0406
(0.0095)*
-0.2708
(0.1782)
-1.3187
(0.0001)*
62
0.3246
0.3436
0.2253
0.1939
R2
0.2167
(n=54)
0.3514
(n=18)
0.1103
(n=18)
0.6159
(n=18)
PVC
a
5.385
5.194
5.533
5.469
a
-0.6937
(0.0001)*
-0.5268
(0.0103)*
-0.6169
(0.0228)*
-0.6157
(0.1272)
62
0.3263
0.0902
0.3097
0.4188
R2
0.4004
(n=53)
0.0466
(n=17)
0.3863
(n=17)
0.2590
(n=18)
- At 5% level of significance
25
-------
For polyethylene the model parameter a was also significantly negative for T1, T3 and
T4 (p<0.05). This means that for all these three treatments log CFU3 on the pipe wall
decreased significantly as chlorine levels increase. However, the slope for treatment T2 was
not significantly different from zero and the model R2 was close to zero.
For PVC the model parametera was significantly negative for all the treatments except
T2 and T4 (p<0.05). This means that for T1 and T3, the log CFU3 on the PVC wall decreased
significantly as the chlorine level increased.
Chloramine Disinfection
Table 12 gives the estimated model parameters, estimated error varianceo2, and the
model R2 for each of the four treatments, T1, T2, T3 and T4 using chloramine as a disinfectant.
For cement the model parameter a was significantly negative for all the treatments except T2
(p<0.05).
For polyethylene the model parameter a was significantly negative for all the
treatments. Therefore, for all four treatments, the amount of CFU3 on the polyethylene wall
decreased with increasing chloramine levels in the water.
For PVC the model parametera was significantly negative for T1, T3 and T4 (p<0.05).
Therefore, for these three treatments, the concentration of CFU3 on PVC decreased with
increasing chloramine levels in the water.
As some of the linear effects were not significantly negative for some of the treatments,
comparative analysis among treatments within a disinfection method was not performed for
any of the wall materials.
Comparison Among Disinfectants
Most of the slopes (a) for all three materials using chlorine disinfection method were
more negative than the slopes of the corresponding materials using chloramine disinfection.
Covariance analysis was performed to compare the methods for treatments which have
significant linear effects (equation 7). The linear effect of chlorine on log CFU3 was
significantly more negative than the linear effect of chloramine on log CFU3 for cement and
treatments T1 and T4 (p<0.05, for both comparison). For each of the treatments T1, T3 and
T4 and for the wall type PE the disinfection methods were compared and the results showed
no significant difference between methods. The linear effect of chlorine on log CFU3 was
greater than the linear effect of chloramine on log CFU3 for PVC wall types and for treatment
T1 (p<0.05 for each comparison). No significant difference was seen between the methods
for treatment T3.
26
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TABLE 1 1 . MODEL PARAMETERS FOR WALL DENSITIES FOR CHLORINE DISINFECTION REGRESSED AGAINST CFU3 FOR CEMENT,
POLYETHYLENE AND PVC
Treatment
Type
T1
T2
T3
T4
Cement
a
4.209
4.233
3.740
4.258
a
-7.044
(0.0001)*
-2.665
(0.0055)*
-1.313
(0.2171)
-9.0393
(0.0001)*
62
0.1.790
0.4354
2.155
1.139
R2
0.3880
(n=50)
0.5539
(n=12)
0.0990
(n=17)
0.7168
(n=18)
Polyethylene
a
4.691
4.676
4.573
4.515
a
-3.809
(0.0005)*
0.0520
(0.9542)
-3.329
(0.0006)*
-4.294
(0.0122)*
62
1.160
0.5954
0.4306
1.2545
R2
0.2220
(n=50)
0.0003
(n=12)
0.5512
(n=17)
0.3513
(n=17)
PVC
a
4.467
4.599
4.324
4.016
a
-3.916
(0.0001)*
-2.770
(0.6998)
-3.221
(0.0128)*
-2.887
(0.1209)
62
0.9216
0.3708
1.6928
1.753
R2
0.2760
(n=50)
0.0155
(n=12)
0.3291
(n=18)
0.1436
(n=18)
* - At 5% level of significance
TABLE 12. MODEL PARAMETERS FOR WALL DENSITIES USING CHLORAMINE DISINFECTION FOR
CEMENT, POLYETHYLENE AND PVC (CFU3)
Treatment Type
T1
T2
T3
T4
Cement
a
4.286
4.038
3.999
4.562
a
-2.319
(0.0001)*
-0.176
(0.7864)
-1.815
(0.0166)*
-3.653
(0.0008)*
62
1.121
0.9612
2.754
2.226
R2
0.5350
(n=48)
0.0077
(n=12)
0.3264
(n=17)
0.5173
(n=18)
Polyethylene
a
4.634
4.558
4.615
5.249
a
-1.786
(0.0001)*
-1.9652
(0.0154)*
-1.834
(0.0007)*
-2.750
(0.0012)*
62
1.172
1.337
1.088
1.411
R2
0.3843
(n=53)
0.3325
(n=17)
0.5456
(n=17)
0.4895
(n=18)
PVC
a
4.513
4.235
4.527
4.405
a
-1.844
(0.0001)*
-0.2614
(0.4054)
-1.762
(0.0077)*
-2.1652
(0.0310)*
62
1.173
0.2413
1.720
2.397
R2
0.4004
(n=53)
0.0466
(n=17)
0.3863
(n=17)
0.2590
(n=18)
* - At 5% level of significance
27
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8.0 PREDICTING BIOFILM DENSITIES
As the sample sizes for the treatments T2, T3 and T4 are small, only treatment T1 was
considered to predict EPI as a function of chlorine (orchloramine). Because of the low model
R2 for CFU3 and CFU15 no attempt was made to develop a predictive model for these
bacterial concentrations. The observed chlorine (orchloramine) concentration of six randomly
selected EPI values were used in the regression models (equations 3 and 4) to predict the
corresponding EPI values. Tables 13 through 16 give the six observed EPI values along with
the predicted values for bulk phase water and for each of the three wall materials and the two
disinfection methods. In Tables 13 and 14, the first column gives the loop designation, column
two is the experimental run, column three is the free residual chlorine, column four is the log
of the concentration of the organism characterized by epifluoresence, column five is the
predicted value for epifluoresence, and columns six and seven are the upper and lower 95%
confidence intervals respectively. In Tables 15 and 16, column 1 contains the wall material,
column 2 identifies the loop, and column 3 is the run. All of the predicted values fall within the
upper and lower 95% confidence levels. Figures 7-10 give the plots of predicted log EPI
against observed log EPI for bulk phase and for cement wall type. It should be noted that the
predictive models for epifluorescence versus chlorine and chloramine can be used with
confidence over the intervals over which they have been developed.
TABLE 13. OBSERVED vs. PREDICTED EPI (Bulk Phase, CI2)
Loop
A1
A1
A1
A2
A1
A1
Run
1
3
5
7
9
11
CL Free
0.35
0.20
0.15
0.00
0.40
0.24
Log EPI
3.677399
2.86362
4.38917
5.41664
2.86362
3.67765
Predicted
3.03060
4.07394
4.42172
5.66506
2.68282
3.79571
U95
3.83488
4.84759
5.19026
6.23431
3.50210
4.57533
L95
2.22631
3.30029
3.65317
4.69581
1.86354
3.01610
28
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TABLE 14. OBSERVED vs. PREDICTED EPI (Bulk Phase, NH2CI)
Loop
A1
A3
A2
A2
A3
A3
Run
2
4
6
8
10
12
CLComb
1.00
0.00
0.10
0.20
0.05
0.04
Log EPI
4.37659
5.60638
5.44716
4.79934
5.55510
5.81023
Predicted
4.26388
5.62113
5.48541
5.34968
5.55327
5.56684
U95
4.84267
6.19888
6.06056
5.92283
6.12964
6.14348
L95
3.68508
5.04339
4.91026
4.77653
4.97690
4.99021
29
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TABLE 15. OBSERVED vs. PREDICTED EPI (Wall, CI2)
Material
Cement
Cement
Cement
Cement
Cement
Cement
PE
PE
PE
PE
PE
PE
PVC
PVC
PVC
PVC
PVC
VC
Loop
A1
A1
A2
A1
A1
A1
A2
A2
A1
A1
A3
A2
A1
A2
A1
A1
A1
A2
Run
1
3
3
7
9
11
1
3
7
9
9
11
1
3
7
9
11
11
CL Free
0.35
0.20
0.00
0.40
0.40
0.24
0.00
0.00
0.35
0.40
0.00
0.01
0.35
0.00
0.40
0.40
0.24
0.00
Log EPI
4.60207
3.90312
6.78533
5.79239
6.38561
5.96848
6.42813
7.06070
5.53148
5.85733
7.39041
6.82866
5.53148
6.84261
6.41996
5.56820
5.86923
6.03342
Predicted
4.89311
5.66201
6.68720
4.63681
4.. 63681
5.45697
6.83527
6.83527
5.77625
5.62496
6.83527
6.80501
5.82793
6.55780
5.72366
5.72366
6.05731
6.55780
U95
5.99369
6.71276
7.72904
5.76143
5.76143
6.51756
7.41540
7.41540
6.37686
6.23602
7.41540
7.38439
6.45532
7.15414
6.36437
6.36437
6.66274
7.15414
L95
3.79253
4.61125
5.64536
3.51220
3.51220
4.39638
6.25514
6.25514
5.17565
5.01391
6.25514
6.22563
5.20053
5.96145
5.08294
5.08294
5.45188
5.96145
30
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TABLE 16. OBSERVED vs. PREDICTED EPI (Wall, NH2CI)
Material
Cement
Cement
Cement
Cement
Cement
Cement
PE
PE
PE
PE
PE
PE
PVC
PVC
PVC
PVC
PVC
PVC
Loop
A2
A1
A1
A2
A1
A3
A2
A1
A3
A1
A1
A2
A1
A3
A1
A2
A1
A2
Run
2
6
8
8
12
12
2
6
6
10
12
12
2
4
6
8
12
12
CL
Comb
0.80
1.20
1.00
0.20
0.98
0.01
0.80
1.20
0.05
1.00
1.01
0.10
0.90
0.00
1.20
0.20
0.98
0.05
Log EPI
5.84510
6.00000
5.59107
6.57171
5.43136
7.06258
5.96379
5.91908
6.80414
5.73239
6.58206
6.93702
6.35411
7.19201
5.90849
6.23805
5.69020
6.89265
Predicte
d
6.25685
604219
6.14952
6.57884
6.16025
6.68080
6.32874
6.13326
6.69525
6.23100
6.22611
6.67082
6.00615
6.68436
5.78008
6.53365
5.94586
6.64668
U95
6.75009
6.54868
6.64830
7.06893
6.65838
7.17417
6.93867
6.75883
7.30475
6.84746
6.84297
7.27916
6.60540
7.28343
6.39125
7.12808
6.54774
7.24434
L95
5.76361
5.53570
5.65073
6.08875
5.66212
6.18743
5.71880
5.50769
6.08575
5.61454
5.60926
6.06248
5.40689
6.08529
5.16890
5.93921
5.34399
6.04902
31
-------
Ill
o
I
I
345
OteervMl LOQ10 (EPI)
Figure 7. Observed vs. Predicted EPI (Chlorine Disinfection, Bulk Phase)
32
-------
I
I
345
ObwrvodLoulO(EPI)
Figure 8. Observed vs. Predicted EPI (Chloramine Disinfection, Bulk Phase)
33
-------
o
9
J | |
456
ObnrvQd LoglD (EPI)
Figure 9. Predicted vs. Observed EPI (Chlorine Disinfection, Cement Wall)
34
-------
456
ObMTVQd LoglO (EPI)
Figure 10. Predicted vs. Observed EPI (Chloramine Disinfection, Cement Wall)
35
-------
9. SUMMARY AND CONCLUSIONS
Four treatment trains (T1, T2 , T3 and T4) were compared in this study. T1 represents
a standard pre-chlorinated treatment process, while T2 , T3 and T4 are different pre-
disinfection methods. Each treatment received a final disinfection with either chlorine (CI2)
or chloramine (NH2CI).
A total of twelve experimental runs were made. Each run lasted approximately one
month and consisted of parallel operation of one of the test treatment trains (T2 , T3 or T4)
and the control treatment train T1. The same final disinfection method, either chlorination or
chloramination, was used for each of the two treatments. Treated water from each of the
treatment processes was distributed through a pipe loop system consisting of two sets of
three pipe loops each, designated loops A and B, such that the output from T1 circulated
through the A loops and that of the test treatment (T2, T3 or T4) through the B loops.
Removable coupons consisting of three different types of pipe wall material, cement,
polyvinyl chloride and polyethylene, were inserted flush with the wall of each pipe loop.
Measurements of bacterial growth on each type of material were made concurrently with
measurements of the distribution water quality bulk parameters. Bacterial measurements
consisted of three-day and fifteen-day CPUs and epifluorescence measurements for bacteria
in the biofilm on the pipe walls and in the bulk phase.
Other water quality variables measured included pH, temperature, dissolved organic
carbon (DOC), biodegradable DOC (BDOC), particle count per ml by four size ranges, total
organic halides (TOX), and trihalomethanes (THM).
Each pipe loop was 10 cm in diameter and 31 m in length. Water velocity was 1 m/s
with configuration and operation of the system producing a residence time of 24 hours in each
loop for a total of 72 hours for the system. As a consequence, only a small portion of water
was transferred from a given loop to each succeeding loop during a given flow cycle. Thus the
water flow entering a pipe loop (A or B) includes both fresh feed and the recycle stream. The
effect of this water flow in the water quality parameters was studied by including the recycle
ratio R, where R = {volume of water returned to a pipe loop entrance /volume leaving the loop}.
The measurements of water quality parameters within the loops were made after a period of
equilibrium was attained in the system.
Both chlorine and chloramine reduced the bacterial growth as measured by
epifluoresence direct count on the pipe wall and in the bulk phase, but chlorine disinfection
was clearly more effective than chloramine disinfection. For both disinfection methods, the
control group T1 was more effective than the other three treatment trains in terms of reducing
the growth of epifluoresence in the bulk phase. Based on epifluoresence direct count as a
measure of biofilm density the results clearly showed that chlorine was much more effective
in reducing biofilm than chloramine. The slopes of the equations were consistently more
negative for chlorine than for chloramine.
For CFU15 chlorine disinfection yielded consistently more negative slopes forall cases
where the slopes were significant for both disinfectants. CFU3 yielded the same results.
36
-------
Based on these results, it can be concluded that chlorine was consistently more effective as
a disinfectant for controlling biofilm than chloramine.
Using epifluoresence direct counts, simple predictive models were developed. In all
cases the predictions were within the 95% confidence intervals for the data. It is the authors'
opinion that these models should be limited to the ranges of data over which the analysis was
conducted.
37
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