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Quantification of pathogen inactivation efficacy by free
chlorine disinfection of drinking water for QMRA
S. R. Petterson and T. A. Stenström
ABSTRACT
To support the implementation of quantitative microbial risk assessment (QMRA) for managing
infectious risks associated with drinking water systems, a simple modeling approach for quantifying
Log
10
reduction across a free chlorine disinfection contactor was developed. The study was
undertaken in three stages: firstly, review of the laboratory studies published in the literature;
secondly, development of a conceptual approach to apply the laboratory studies to full-scale
conditions; and finally implementation of the calculations for a hypothetical case study system. The
developed model explicitly accounted for variability in residence time and pathogen specific chlorine
sensitivity. Survival functions were constructed for a range of pathogens relying on the upper bound
of the reported data transformed to a common metric. The application of the model within a
hypothetical case study demonstrated the importance of accounting for variable residence time in
QMRA. While the overall Log
10
reduction may appear high, small parcels of water with short
residence time can compromise the overall performance of the barrier. While theoretically simple,
the approach presented is of great value for undertaking an initial assessment of a full-scale
disinfection contactor based on limited site-specific information.
S. R. Petterson (corresponding author)
Water & Health Pty Ltd,
P.O. Box 648,
Salamander Bay 2317,
Australia
and
Department of Mathematical Sciences and
Technology,
Norwegian University of Life Sciences,
Ås,
Norway
E-mail: s.petterson@waterandhealth.com.au
T. A. Stenström
Durban University of Technology, SARChi Chair,
Institute for Water and Wastewater Technology,
Durban University of Technology,
P.O. Box 1334,
Durban 4000,
South Africa
Key words
|
disinfection, drinking water, free chlorine, pathogen inactivation, QMRA
INTRODUCTION
The framework for safe drinking water promotes the man-
agement and mitigation of water-related infectious risks
through systematic assessment of a water supply from catch-
ment to tap (Bartram et al. ; WHO , ).
Quantitative microbial risk assessment (QMRA) is a power-
ful tool for supporting the systematic assessment of drinking
water supplies (Medema & Smeets ; Smeets et al. ).
A key component of this assessment is the evaluation of
drinking water treatment barriers including both their
expected performance (Hijnen et al. ; Teunis et al.
; Hijnen & Medema ), and potential vulnerabilities
(Westrell et al. ; Hunter et al. ).
Chlorination continues to be the most wid ely used
water treatment globally, and is effective at low doses
for a broad range of micro-org anisms. The efficacy
depends on numerous site-specific fact ors related to
water quality (temperature, pH, and org anic constituents) ,
dosage and contact time and d oes vary between micro-
organisms (Haas ; LeChevallier & Au ). Fecal
coliform bacteria, historically used to ensure water
safe ty, are much more readily inactivated by free chlorine
in comparison to some more persistent viruses and proto-
zoa (Ashbolt et al. ). Therefore, absence of indicator
bacteria in finished water is a poor m easure of disinfec-
tion performance.
Measuring the inactivation of pathogens by chlorine at
full scale is difficult if not impossible and therefore modeling
is an essential tool for assessing disinfection performance.
Modeling approaches rely on quantifying the dose of chlor-
ine (defined as Ct: Chlorine concentration (mg L
–1
) × time
(min)) and predicting the pathogen kill associated with
that dose. Modeling approaches vary in their simplicity
from generic point estimates (USEPA a) to detailed
site-specific models (Bellamy et al. ). Simple point
625 © IWA Publishing 2015 Journal of Water and Health
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13.3
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doi: 10.2166/wh.2015.193
estimate models are limited in their capacity to describe
barrier variability, and hence have limited value to support
management of the waterborne risks. Detailed models are
preferred, however they require significant investment of
time and resources to develop and calibrate. There is a
need for a simple modeling approach that can be used to
quantify disinfection efficacy for QMRA as part of a screen-
ing level risk assessment, which allows for the key risk
drivers to be explored. The approach needs to rely on the
best available scientific literature, and require limited site-
specific information.
The objective of this study was therefore as follows:
•
Review the published studies in the literature on patho-
gen and indicator sensitivity to free chlorine in order to
quantitatively compare pathogen sensitivity both within
and between pathogen groups.
•
Develop a model to quantify Log
10
reduction across a
contactor accounting for variability in residence time,
and drawing on the pathogen specific chlorine sensitivity
from the literature. The model should support the needs
of water safety managers at the screening level and rely
on limited site-specific information.
•
Compare the calculations from this new approach with
other comparable screening level methods (Ct calc, con-
tinuously stirred tank reactor (CSTR) equation).
MATERIAL AND METHODS
The study was undertaken in three stages: firstly, review of
the laboratory studies published in the literature; secondly,
development of a conceptual approach to apply the labora-
tory studies to full-scale conditions; and finally,
implementation of the calculations for a hypothetical case
study system and compare with alternate approaches.
Literature review
Peer reviewed published laboratory studies on the sensi-
tivity of human enteric pathogens and indicators to free
chlorine were collated and reviewed. This was based on
a systematic search beginning with the citation list of
selected drinking water treatment reference documents
(USEPA a; LeChevallier & Au ; WHO ),
extended with database searches including Science
Direct, PubMed, Google scholar and selected medical,
and technical journals. Articles were limited to those
addressing free chlorine inactivation only (not in combi-
nation with other agents) in drinking water of enteric
pathogens and selected indicator organisms. When
reviewing the reported data, attention was given to the
experimental conditions including temperature and pH,
chlorine dosage, organism strain, and method of statistical
analysis. For comparison between published studies, it was
necessary to translate reported results into a common
measure. The selected common measure was the required
Ct for a 1, 2, 3, 4, and 5 Log
10
reductions, up to the maxi-
mum demonstrated reduction for each particular study. For
some studies, this required a reanalysis of the reported
results and/or visual reading off published graphs. The
methods used to reach the common metric are summar-
ized in Table 1.
Conceptual model
The conceptual model relied on a simpl e dose-response
relationship to describe pathogen inactivation. The dose
was defi n ed as the Ct delivered across th e chlorine contac-
tor,whichisexpectedtobevariabledependingon
the residence ti me distribut ion an d the chlorine con-
centration. The response was d escribed by a pathogen
specific survival function S(Ct) ( probability of survival as
afunctionofCt).
The conceptual model therefore required quantification
of three components:
1. the residence time distribution of the contactor;
2. the free chlorine concentration across the contactor;
3. pathogen specific survival functions based on the results
of the literature review.
Combining parts (1) and (2) provided a distribution of
the Ct for the full-scale contactor.
The residence time distribution
The residence time di stribution is unique to any particu-
lar disinfection contactor; however, a simple theoretical
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approximation can be made assuming that the contactor
consists of CSTRs in series (Nauman & Buffham ;
Do-Quang et al. a, b). The theoretical distribution
of residence times is represented by E(t), w here t is the
time inside the contactor. For a number of CSTRs in
series E(t)isgivenby
E(t) ¼
t
n1
(n 1)!τ
n
i
e
(t=τ
i
)
(1)
Table 1
|
Method of transformation applied to published data in order to achieve common metric
Adaptation
method
Number Reported results Calculations undertaken References
A Graph of Log
10
percentage surviving
versus contact time (min)
Chlorine concentration at the beginning
and the end of the experiment (mg L
–1
)
Contact time for each Log
10
reduction was read from an
enlarged graph
First-order decay of chlorine was assumed over the duration
of the experiment
Ct was calculated by integrating the chlorine concentration
over the time to the required Log
10
reduction and multiplying
by time
Grabow et al.
()
B Graph of Log
10
percentage surviving
versus contact time (min)
Chlorine concentration at the beginning
of experiment
Contact time for each Log
10
reduction was read from an
enlarged graph
Ct was calculated using contact time and initial chlorine
dose
Lund ()
C Time for 99% inactivation (min)
Concentration of free chlorine during
the experiment (mg L
–1
)
Ct99 calculated from time for 99% reduction and the measured
chlorine concentration at the end of the experiment
Linear interpolation was used to calculate Ct for other Log
10
reductions up to the maximum observed reduction in the
study
Engelbrecht et al.
(); Berman
& Hoff ()
D Graph of Log
10
pfu surviving versus time
Concentration of HOCL at pH 6 and
OCL
–
at pH 10 in uM
Contact time for each Log
10
reduction was read from the
published graphs
Concentration of HOCL or OCL was stoichiometrically
converted to free chlorine concentration mg L
–1
assuming at
pH 6, 95% of free chlorine is HOCl and at pH 10, 99.7% of
free chlorine is OCl
–
Ct was the direct multiplication of chlorine dose and contact
time
Jensen et al.
(); Sharp
& Leong ()
E Percentage surviving after 1, 10, 100, and
1,000 min of contact
Initial chlorine concentration (0.4 mg L
–1
)
and concentration after 16 hours
(0.1 mg L
–1
)
Assumed linear interpolation between each reported point
value
Numerical transformation to calculate min for each Log
10
reduction
Chlorine decay was assumed to follow first-order kinetics
and fit using the two point estimates reported in the paper
Ct calculated by integrating the chlorine concentration over
the exposure time, and multiplying by exposure time (min)
Payment et al.
(b)
F Starting concentration and Log
10
surviving fraction at 0.5, 1, 2 or 5 min
Initial chlorine concentration
Assumed linear interpolation between each reported point
value
Numerical transformation to calculate time for each Log
10
reduction
Ct calculated using initial chlorine concentration over the
exposure time (min)
Blaser et al.
(); Rice &
Clark ()
G Percentage inactivation at 10, 30, and
60 min
Initial chlorine dose
Assumed linear interpolation between each reported point
value
Numerical transformation to calculate min for each Log
10
reduction
Ct calculated using initial dose and multiplying by exposure
time
Li et al. ()
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where n is the n umber of C STRs in series and is se lected to
be equal to the number of cells separated by simple baffles,
and τ is the mean residence time (MRT) of each CSTR.
Optimal hydraulic behavior for a contactor is theoretical
plug flow where all water p arcels achieve an identical con-
tact time (number of CSTRs is assumed to be very high).
The free chlorine concentration
The chlorine concentration across the contactor is influ-
enced by the magnitude, method and location of dosing;
the mixing characteristics of the contactor; the chlorine
demand and the chlorine dissociation rate. A simplified
approach for quantifying chlorine concentration over the
contactor was selected. Following initial losses, the free
chlorine residual was assumed to follow first-order dis-
sociation (C
t
¼ C
0
e
kt
) where k is the chlorine dissociation
rate, C
0
is the initial chlorine residual and C
t
is the chlorine
concentration at time t).
Pathogen specific survival functions
By relying on pathogen sensitivity results reported from lab-
oratory scale experiments in the literature, a survival curve
(probability of survival versus Ct) was constructed for each
pathogen. For each bacteria and virus, required Cts for 1,
2, 3, 4, 5 Log
10
reduction (up to the maximum of reported
observations) were selected based on the most conservative
result over the entire pH and temperature range, rounded up
to one significant figure. The survival function was then con-
structed by linear interpolation between each Ct value (note:
P
Survival
¼ 10
Log
10
reduction
).
Case study: comparison between three approaches
The Log
10
reductions for five selected reference pathogens
(Campylobacter, Escherichia coli O157:H7, rotavirus, noro-
virus, Giardia) were calculated across a hypothetical
chlorine contactor with assumed initial chlorine residuals
of 0.4 and 1.5 mg L
–1
; a MRT of 12 min and a chlorine dis-
sociation rate of 0.1 min
–1
. The hydraulic behavior was
classified as poor, medium or good, and calculations of treat-
ment efficacy were undertaken based on the following three
approaches.
Approach 1: The Ct
calc
method
The Ct
calc
method is a simplified approach recommended by
the USEPA for benchmarking disinfection processes. Con-
tact time was calculated as the MRT × baffling factor (BF)
(USEPA a). BF equaled 0.3 for poor hydraulics, 0.6
for medium hydraulics and 1.0 for perfect plug flow.
Rather than select the chlorine concentration at the outlet
of the contact tank, to compare between approaches, the
chlorine concentration was integrated over the contact
time of the contactor. For the calculated Ct, the k
10
(the
first-order inactivation coefficient for each pathogen on a
Log
10
scale) was used to predict the Log
10
reduction. To
facilitate comparison with approach 3 (see below), the survi-
val functions were used to quantify the inactivation rate on a
Log
10
scale, assuming Log-linear reduction: k
10
¼ (n=Ct
n
)
where Ct
n
was the C
t
required to achieve Log
10
reduction n
(maximum demonstrated Log
10
reduction for each
pathogen).
Approach 2: CSTR approach
The CSTR equation has been applied in drinking water treat-
ment for ozone disinfection contactors (USEPA b;
Smeets et al. ). The Log
10
reduction was quantified
using the following equation:
Log
10
reduction ¼
X
n
i¼1
Log
10
1
1 þ 2:3 × k
10
× Cl
i
× t
i
(2)
where k
10
is the first-order inactivation coefficient for the
pathogen on a Log
10
scale; n is the number of CSTRs; Cl
i
is the chlorine concentration in CSTR i,inmgL
–1
(calcu-
lated assuming first-order inactivation across previous
CSTRs with assumptions consistent with approach 1 and
3), and t
i
is the hydraulic residence time for CSTR i.For
poor hydraulics, two CSTRs were selected, medium-good
hydraulics six CSTRs, and for near perfect plug flow 20
CSTRs.
Approach 3: The method proposed
The residence time distribution based on the theoretical
tanks-in-series Equation (1) for two CSTRs, six CSTRs, and
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20 CSTRs was applied. The Ct distribution for the contactor
was calculated based on a Monte Carlo simulation with
10,000 random samples drawn from the residence time dis-
tribution; for each sample, the chlorine concentration was
integrated over the residence time resulting in a sample of
the Ct. For each sample from the Ct distribution, the Log
10
reduction for each respective pathogen was predicted
using the survival function. Quantiles (0.001, 0.01, 0.05,
0.5, 0.95) of the sample Log
10
reductions were calculated
to show the variability in treatment ef ficacy. In addition,
the overall Log
10
reduction assuming complete mixing of
flow following the disinfection contactor was calculated.
RESULTS
Review
The collation of published laboratory studies on inactivation
of human enteric pathogens and indicators due to free chlor-
ine, translated to the common metric of required Ct for 1, 2,
3, 4, and 5 Log
10
reduction, are summarized in Table 2 for
the bacteria, Table 3 for viruses and Figure 1 for Giardia.
Since the experimental conditions were variable with
respect to factors including (but not limited to) temperature,
pH, chlorine dose, spiking concentrations, and laboratory
strains, the results for each organism were pooled across
all experimental conditions and plotted against the prob-
ability of survival (Figures 2 and 3) (note the probability of
micro-organism survival is related to the Log
10
reduction
of the microbial population: P
Survival
¼ 10
Log
10
reduction
).
This was done to compare the overall range of expected
sensitivity of different pathogens and indicators (for exper-
iments mainly in demand-free buffer). The results
demonstrate that in general, bacteria are the most sensitive
to chlorine with Ct for 3 Log
10
reduction ranging up to
0.54 min mg L
–1
(E. coli O157:H7 ( Rice & Clark ));
followed by enteric viruses up to 127 min mg L
–1
(Coxsack-
ieB5 (Payment et al. a)); and Giardia were the most
persistent.
Within individual bacterial species, studies were
reviewed that compared the resistance among different
strains. The wild-type strains of E. coli from cattle manure
tested by Rice & Clark () appeared more resistant to
chlorination in comparison to values reported for laboratory
strains (Blaser et al. ; Lund ). Wild-type E. coli
strains required a Ct of 0.52–0.63 min mg L
–1
(pH¼ 7;
T ¼ 5
W
C; Cl ¼ 1.1 mg L
–1
) for 3 Log
10
reduction in compari-
son to 0.21 min mg L
–1
(pH¼ 8; T ¼ 4
W
C; Cl ¼ 0.1 mg L
–1
)
(Blaser et al. ) and 0.073 min mg L
–1
(pH¼ 6.5;
T ¼ 4
W
C; Cl ¼ 0.2 mg L
–1
)(Lund ), however the concen-
tration of chlorine was much higher in the wild-type study.
There was considerable variability in persistence
between virus types (Table 3, Figures 3 and 4). These compi-
lations and illustrations show that adenoviruses, reoviruses,
simian rotaviruses and caliciviruses were susceptible to
chlorine with Ct for 3 Log
10
inactivation typically less than
0.5 min mg L
–1
. Enteroviruses were more resistant with a
required Ct for 3 Log
10
inactivation of ∼20, ∼90, and
∼130 min mg L
–1
for echoviruses, coxsackievirus B4 and
coxsackievirus B5, respectively. The particularly high resist-
ance to chlorine and high variability between strains was
observed by Payment et al. (a) using environmental
strains, with the most resistant strain originating from raw
sewage (all experiments performed in demand free water).
Two later studies on coxsackievirus B5 reported comparable
results with each other, but lower persistence than Payment
et al. (a), for example Ct 3 Log ¼ 5.5 (Black et al. )
and 8.4 min mg L
–1
(Cromeans et al. ) at pH 7 and 7.5,
respectively, using the same laboratory strain (Faulkner
strain). The feline caliciviruses and murine noroviruses
(suggested as surrogates for human noroviruses, since the
latter cannot be cultivated) were easily inactivated by free
chlorine. Of the two bacteriophages included (Table 3),
coliphage V1 was up to an order of magnitude more resist-
ant to free chlorine inactivation than the MS2 phage.
Berman & Hoff () specifically investigated the
impact of cell-association on survival of simian rotavirus,
and observed higher persistence for cell-associated viruses
(Table 3). It is difficult to quantify the magnitude of the pro-
tective effect at pH 6 since the unassociated viruses were so
quickly inactivated; however, if the Ct for 3 Log
10
inacti-
vation was assumed to be at the limit of detection
(0.025 mg L
–1
), then the required Ct for 3 Log
10
inactivation
was increased by an order of magnitude due to cell associ-
ation; at pH 10, the required Ct for 2 Log
10
inactivation
(3 Log
10
inactivation was not observed in the unassociated
virus) was increased by a factor of three.
629 S. R. Petterson & T. A. Stenström
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Table 2
|
Reported studies on susceptibility of bacteria to free chlorine inactivation
Micro-organism pH T
W
C
Approximate
chlorine dose
(mg L
–1
)
Ct for Log
10
reduction (min mg L
–1
)
Max red
Data
transformation
a
Reference12345
E. coli 6 4 0.1 0.019 0.037 0.068 3.89 F Blaser et al. ()
6 25 0.1 0.022 0.044 0.079 3.39 F Blaser et al. ()
6.5 4 0.2 0.028 0.046 0.073 0.16 4.02 5.00 B Lund ()
6.5 10 0.2 0.022 0.026 0.043 0.081 1.32 5.20 B Lund ()
8 4 0.1 0.043 0.079 0.207 0.497 4.01 F Blaser et al. ()
8 25 0.1 0.058 0.183 0.366 3.73 F Blaser et al. ()
E. coli (wild type) 7 5 1.1 0.18 (0.17–0.19)
b
0.35 (0.34–0.38) 0.53 (0.52–0.63) 0.94 (0.83–2.16) NM – 1.94 4.95 (4.74–5.14) F Rice & Clark ()
Pathogenic E. coli
E. coli O157:H7 7 21 0.4–0.5 0.13 Chauret et al. ()
8210.4–0.5 ∼ 9
c
Chauret et al. ()
E. coli O157:H7 7 5 1.1 0.17 (0.17–0.18)
d
0.34 (0.33–0.36) 0.51 (0.50–0.54) 1.06 (0.93–1.36) 2.06 ((2.02–2.11) 4.74 (4.01–5.13) F Rice & Clark ()
Campylobacter
Campylobacter jejuni PEN 1 6 4 0.1 0.012 0.024 0.036 0.049 > 4.12 F Blaser et al. ()
Campylobacter jejuni PEN 2 6 4 0.1 0.014 0.027 0.041 0.086 > 4.60 F Blaser et al. ()
Campylobacter jejuni PEN 3 6 4 0.1 0.011 0.021 0.032 0.043 > 4.70 F Blaser et al. ()
Campylobacter jejuni PEN 1 6 25 0.1 0.035 0.087 2.04 F Blaser et al. ()
Campylobacter jejuni PEN 2 6 25 0.1 0.019 0.039 2.88 F Blaser et al. ()
Campylobacter jejuni PEN 3 6 25 0.1 0.011 0.022 0.033 0.044 > 4.5 F Blaser et al. ()
Campylobacter jejuni 1 6.5 4 0.2 0.024 0.032 0.066 0.28 0.79 5.80 B Lund ()
Campylobacter jejuni 1 6.5 10 0.2 0.027 0.043 0.061 0.15 0.58 5.30 B Lund ()
Campylobacter jejuni PEN 1 8 4 0.1 0.064 0.078 0.092 0.483 > 4.02 F Blaser et al. ()
Campylobacter jejuni PEN 2 8 4 0.1 0.023 0.047 0.073 3.70 F Blaser et al. ()
Campylobacter jejuni PEN 3 8 4 0.1 0.014 0.029 0.043 0.072 > 4.70 F Blaser et al. ()
Campylobacter jejuni
PEN 1 8 25 0.1 0.146 0.474 2.08 F Blaser et al. ()
Campylobacter jejuni PEN 2 8 25 0.1 0.017 0.034 > 2.93 F Blaser et al. ()
Campylobacter jejuni PEN 3 8 25 0.1 0.152 0.294 0.436 3.45 F Blaser et al. ()
All results are from batch studies
a
See Table 1 for transformation method.
b
Range across four different strains.
c
Approximate from published graph.
d
Range across seven different strains.
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Table 3
|
Reported studies on susceptibility of viruses to free chlorine inactivation
Micro-organism
Batch (B) or
continuous
(C) pH T
W
C
Approximate
chlorine
dose
(mg L
–1
)
Ct for Log
10
reduction (min mg L
–1
)
Max red
Data
transformation
a
Reference12345
Adenoviruses
Adenovirus 2 B 7 5 0.2 0.02 0.06 0.15 4.5 – Cromeans et al. ()
B 8 5 0.2 0.04 0.12 0.27 5 – Cromeans et al. ()
B 7 5 0.2 0.035–0.10 – Kahler et al. ()
B 8 5 0.2 0.037–0.12 – Kahler et al. ()
B 7 15 0.2 <0.040–0.063 – Kahler et al. ()
B 8 15 0.2 <0.02–0.061 – Kahler et al. ()
Adenovirus 40 B 6 5 0.17 0.05 (0.04– 0.13) 0.11 (0.09–0.17) 0.22 (0.17–0.34) – Thurston-Enriquez et al. ()
B 7 5 0.17 0.15 (0.04 –0.17) 0.38 (0.34–0.85) 0.75 – Thurston-Enriquez et al. ()
B750.2 <0.02 <0.02 <0.04 – Cromeans et al. ()
B 8 5 0.31 0.11 (<0.08–0.16) 0.17 (0.16–0.23) 0.24 (0.16–0.23)
– Thurston-Enriquez et al. ()
B850.2 <0.02 <0.02 <0.04 – Cromeans et al. ()
Adenovirus 41 B 7 5 0.2 0.005 0.01 ND – Cromeans et al. ()
B850.2 <0.02 <0.02 <0.03 – Cromeans et al. ()
Reoviruses
Reovirus 3 B 6 25 0.4 0.1 0.16 0.39 1.8 2.3 5.2 A Grabow et al. ()
B 8 25 0.4 0.1 0.16 0.20 0.74 1.3 4.2 A Grabow et al. ()
B 10 25 0.4 0.12 0.2 0.24 0.32 0.40 A Grabow et al. ()
Rotaviruses
SA-11 Simian rotavirus B 6 5 0.1 <0.025 <0.025 <0.025 <0.025 4 C Berman & Hoff ()
B 10 5 0.5 0.45 0.60 2.7 C Berman & Hoff ( )
B 8 25 0.4 0.1 0.12 0.16 0.20 0.30 4.5 A Grabow et al. ()
B 10 25 0.4 0.08 0.20 0.28 0.40 0.70 A Grabow et al. ()
SA-11 Simian rotavirus
– cell associated
B 6 5 0.5 0.25 0.88 2.3 3 C Berman & Hoff ()
B 10 5 0.5 0.15 1.8 2.8 3 C Berman & Hoff ()
Enteroviruses
Coxsackievirus A9 B 6 5 0.5 0.075 0.15 0.22 3 C
Engelbrecht et al. ()
B 10 5 0.5 0.38 0.75 2.5 C Engelbrecht et al. ()
Coxsackievirus B3 C 6 20 0.5 0.02 0.26 0.35 0.43 4 D Jensen et al. ( )
B 7 5 0.2 0.97 1.4 2.9 4.5 – Cromeans et al. ()
B 8 5 0.2 0.65 1.1 1.7 4.5 – Cromeans et al. ()
C 10 20 0.5 2.2 4.5 6.8 3.7 D Jensen et al. ()
Coxsackievirus B4 B 7 5 0.4 0.28–0.30 2.4–26 21–84 3.9 E Payment et al. (a)
Coxsackievirus B5 B 6 5 0.5 0.88 1.8 1.8 C Engelbrecht et al. ()
C 6 20 0.5 0.11 0.22 0.33 0.44 4 D Jensen et al. ( )
B 7 5 0.4 1.3–52 21–59 72–127 101 130 3.1–5E Payment et al. (a)
B 7 5 0.2 3.6 5.5 7.4 3.2 – Cromeans et al. ()
B 7.5 5 1 5.4 8.4 12 – Black et al. ()
B 7.8 5 0.5 1.1 2.3 2.9 C Engelbrecht et al. ()
B 8 5 1 4.7 7.6 10 4.5 – Cromeans et al. ()
B 9 5 1 14 19 23 – Black et al. ()
C 10 20 0.5 1.9 4.0 2.1 D Jensen et al. ()
B1050.5 1734 2.2 C Engelbrecht et al. ()
B 7 5 0.2 3.2–5.2 – Kahler et al. ()
B 8 5 0.2 2.3–7.9 – Kahler et al. ()
B 7 15 0.2 1.0–2.0 – Kahler et al. ()
B 8 15 0.2 0.73–3.6 – Kahler et al. ()
(continued)
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Table 3
|
continued
Micro-organism
Batch (B) or
continuous
(C) pH T
W
C
Approximate
chlorine
dose
(mg L
–1
)
Ct for Log
10
reduction (min mg L
–1
)
Max red
Data
transformation
a
Reference12345
Poliovirus 1 C 6 20 0.3 0.07 0.18 2.2 D Sharp & Leong ()
1.1 0.15 0.27 0.38 0.51 4.2
B 6 5 0.5 0.51 1.0 1.5 3.4 C Engelbrecht et al. ()
B65∼1 0.93
(1.0–2.8)
2.9 (1.0–5.0) 6.7 (<10) – Thurston-Enriquez et al. ()
B 7 5 0.4 0.19–0.47 0.39–3.9 2.1–27 3.9–68 40–130 5.0 E Payment et al. (a)
B 7.5 5 1 1.4 3.0 5.3 – Black et al. ()
B 7.8 5 0.5 0.33 0.66 0.99 nr C Engelbrecht et al. ()
B951 8.2 15 22 – Black et al. ()
C 10 20 0.54 1.1 1.6 3 D Sharp & Leong ()
B 10 5 0.5 5.5 11 16 3.2 C Engelbrecht et al. ()
Poliovirus 2 B 6 5 0.5 0.31 0.61 0.92 3 C Engelbrecht et al. ()
B 6 25 0.4 0.1 0.16 0.20 0.68 1.4 5.2 A Grabow et al. ()
B 7 5 0.4 0.14–0.23 0.28–0.8 0.59–4.0 2.3–71 4.0–130 5.0 E Payment et al. (
a)
B 8 25 0.4 0.1 0.28 0.50 0.74 1.0 4.7 A Grabow et al. ()
B1050.5 1632 2.5 C Engelbrecht et al. ()
B 10 25 0.4 0.16 0.78 3.0 3.8 A Grabow et al. ()
Poliovirus 3 B 7 5 0.4 0.10–0.35 0.20–1.6 0.30–3.1 0.40–71 4.5 E Payment et al. (a)
Echovirus 1 B 6 5 0.5 0.12 0.25 0.37 Nr C Engelbrecht et al. ()
B 7 5 0.2 0.96 1.3 1.5 3.2 – Cromeans et al. ()
B 7.5 5 1 1.6 3.5 6.2 – Black et al. ()
B 7.8 5 0.5 0.29 0.59 0.88 3.2 C Engelbrecht et al. ()
B 8 5 0.2 0.99 1.3 1.6 5.8 – Cromeans et al. ()
B 9 5 0.2 3.3 8.5 17 – Black et al. ()
B 10 5 0.5 25 49 73 3.2 C Engelbrecht et al. ()
B 7 5 0.2 0.49–0.84 – Kahler et al. ()
B 8 5 0.2 0.66–1.2 – Kahler et al. ()
B 7 15 0.2 0.29–
0.48 – Kahler et al. ()
B 8 15 0.2 0.30–0.84 – Kahler et al. ()
Echovirus 5 B 6 5 0.5 0.32 0.64 0.96 nr C Engelbrecht et al. ()
B 7.8 5 0.5 0.47 0.94 1.4 3.5 C Engelbrecht et al. ()
B 10 5 0.5 6.9 14 21 nr C Engelbrecht et al. ()
Echovirus 11 B 7 5 0.2 0.82 1.0 1.1 4 – Cromeans et al. ()
B 8 5 0.2 0.54 0.97 1.4 4 – Cromeans et al. ()
Echovirus 12 B 7.5 5 1 2.1 4.4 7.4 – Black et al. ()
B951 8.4 19 32 – Black et al. ()
Hepatitis A B 6 25 0.4 0.1 0.16 0.2 0.28 0.36 A Grabow et al. ()
B7?5 >300 0.97 G Li et al. ()
10 ∼100 < 300 nr
20 ∼80 < 600 nr
B 8 25 0.4 0.1 0.16 0.2 0.24 0.39 3 A Grabow et al. ()
B 10 25 0.4 0.08 0.2 0.47 0.97 1.5 4 A Grabow et al. ()
(continued)
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Table 3
|
continued
Micro-organism
Batch (B) or
continuous
(C) pH T
W
C
Approximate
chlorine
dose
(mg L
–1
)
Ct for Log
10
reduction (min mg L
–1
)
Max red
Data
transformation
a
Reference12345
Caliciviruses
Feline Calicivirus B 6 5 0.17 0.02
(<0.04)
0.07 (0.04–0.08) 0.19 (0.11–0.15) – Thurston-Enriquez
et al. ()
B 7 5 0.16 0.05
(<0.08)
0.06 (<0.08) 0.07 (<0.08) – Thurston-Enriquez
et al. ()
B 8 5 0.16 0.18
(<0.32)
0.23 (<0.32) 0.27 (<0.32) – Thurston-Enriquez et al. ()
Murine Norovirus B 7 5 0.2 <0.02 <0.02 <0.07 – Cromeans et al. ()
B 7.2 5 0.19 0.14 0.25 0.31 2.5 – Lim et al. ()
B 7.2 20 0.18 0.061 0.18 0.29 3 – Lim et al. ()
B850.2 <0.02 <0.02 <0.08 – Cromeans et al. ()
B 7 5 0.2 0.016–0.023 – Kahler et al. ()
B 8 5 0.2 0.024–0.034 – Kahler et al. ()
B 7 15 0.2 <0.010–– 0.015 – Kahler et al. ()
B 8 15 0.2 <0.020 –0.021 – Kahler et al. ()
Bacteriophage
Coliphage V1 B 6 25 0.4 1.2 1.9 2.8 3.0 A Grabow et al. ()
B 8 25 0.4 1.0 2.9 2.5 A Grabow et al. ()
B 10 25 0.4 0.24 1.5 3.9 3.4 A Grabow et al. ()
Coliphage MS2 B 6 25 0.4 0.12 0.28 0.57 1.2 1.8 4.7 A Grabow et al. ()
B 7.2 5 0.17 0.24 0.36 0.44 4 – Lim et al. ()
B 7.2 20 0.17 0.080 0.14 0.18 5 – Lim et al. ()
B 8 25 0.4 0.2 0.39 0.70 1.1 1.6 5 A Grabow et al. ()
B 10 25 0.4 0.16 0.32 0.47 0.67 0.90 5.3 A Grabow et al. ()
nr, not reported.
All studies identified infectious viruses by ability to infect host cells in tissue culture/plaque assay procedures.
Except where stated otherwise, all strains used in studies were laboratory strains.
a
Data transformations explained in Table 1, ‘–’ indicates that results are as reported in the original publication.
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Giardia is much more resistant to inactivation by free
chlorine than bacteria and viruses and has therefore been
used as a conservative reference pathogen for disinfection,
most notably in the USEPA long-term 1 surface water treat-
ment rule (LT1SWTR) (USEPA a). As a result, more
detailed research and modeling has been undertaken on Giar-
dia in order to quantify the Ct requirements for 4 Log
10
inactivation under different dosing, temperature, and pH con-
ditions. Clark et al. () fitted a numerical model to two large
datasets from Giardia lamblia (Jarroll et al. ; Hibler et al.
)(thefirst using excystation as an endpoint, the second
using animal infectivity) in order to estimate the model par-
ameters. The resulting equation with best-fitmodel
parameters is given by: Ct ¼ 0:12I
0:27
C
0:19
pH
2:54
temp
0:15
(Clark et al. )whereI is the ratio of organisms remaining
at time t to organisms at time zero, C is the concentration of
chlorine (mg L
–1
), and temp is the temperature (
W
C). To
obtain a conservative level for the surface water treatment
rule, the upper 99% confidence interval at the 4 Log
10
inacti-
vation level was calculated, and then first-order kinetics were
assumed so that the line passed through 1 (no reduction) at
Ct ¼ 0. The best-fit curve and the upper 99% confidence inter-
val used for the LT1SWTR are illustrated in Figure 1.
Results from three other experimental studies on
Giardia (Rice et al. ; Leahy et al. ; Rubin et al.
) were also identified in this review and are also illus-
trated for comparison in Figure 1. Results from Rice et al.
() yielded results consistent with the EPA model that
were above the best-fit line but below the LT1SWTR line.
While Rubin et al. () reported a higher resistance than
other studies, the cysts used in their study were harvested
from gerbils in contrast to previous studies that had used
cysts from humans. The best-fit curve of the Clark et al.
() model was applied in the QMRA calculations.
Figure 1
|
Illustration of published Giardia susceptibility data, and best-fit (solid line)
model from Clark et al. (1990) and upper uncertainty bound (dashed line)
applied in the USEPA long-term surface water treatment rule.
Figure 2
|
Illustration of pooled and transformed data for Campylobacter (above) and
E. coli O157 (below) susceptibility to free chlorine inactivation from the
literature. Lines indicate upper bound of persistence from reported data.
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Figure 3
|
Illustration of pooled and transformed data for human enteric viruses and surrogates susceptibility to free chlorine inactivation from the literature. Lines indicate upper bound of
persistence from reported data. Note: scale of the x-axis varies between figures.
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Conceptual model
The conceptual model accounts for variability in the resi-
dence time of the disinfection contactor, chlorine decay
across the contactor and for pathogen specific survival func-
tions. The residence time distribution represented by the
theoretical tanks in series model (MRT ¼ 12 min) is illus-
trated in Figure 5 with three hydraulic assumptions (two,
six, and 20 CSTRs). With few CSTRs (two) the residence
time is highly variable, with some portions of flow exhibiting
a very short residence time (<1 min). As the hydraulics are
assumed to improve with increasing CSTRs (approaching
plug flow), the variability in residence time is reduced, and
hence the portion of flow that is able to short-circuit the con-
tactor with minimal residence time is reduced.
The survival functions were used to predict the Log
10
reduction for each pathogen, given a quantified chlorine
dose (Ct), as illustrated in Figure 6. The survival functions
constructed for the bacterial and viral reference pathogens
are illustrated in Figure 7. The functions are constructed
from Ct values reported in Table 4 which were selected
from the following publications: Campylobacter (Blaser
et al. ; Lund ); E. coli O157 (Rice & Clark );
rotavirus based on results for non-cell associated SA-11
simian rotavirus (Grabow et al. ; Berman & Hoff
); norovirus based on feline calicivirus (Thurston-
Enriquez et al. ) and murine norovirus (Cromeans
Figure 4
|
Comparative illustration of survival functions from Figure 2 representing (a)
less resistant and (b) more resistant human enteric viruses and surrogates.
Lines indicate upper bound of persistence from reported data.
Figure 5
|
Influence of assumed number of CSTRs on the residence time distribution
applied in the QMRA tool.
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et al. ; Kahler et al. ; Lim et al. ). For Giardia,
the best-fit model parameters published by Clark et al.
() that formed the basis for Ct requirements of the
USEPA surface water treatment rule was used. While not
possible for other pathogens, the pH, dose and temperature
dependence was therefore incorporated into the QMRA
model for Giardia.
Hypothetical case study
A comparison of Log
10
reduction performance for the
hypothetical contactor, using each of the three proposed
approaches is given in Table 5 (initial residual of 0.4 mg L
–1
)
and Table 6 (initial residual of 1.5 mg L
–1
). In each case,
as the hydraulics of the contactor was assumed to be
improved, the predicted Log
10
reduction increased. For
Campylobacter at a dosage of 0.4 mg L
–1
(Table 5), with
approach 1, increasing the BF from 0.3 to 1 increased the
calculated Log
10
reduction from 4.5 to 15.1. For approach
2, increasing the assumed number of CSTRs from 2 to 20
increased the calculated Log
10
reduction from 2.4 to 9.2.
For approach 3, the flow weighted mean Log
10
reduction
was similar and increased from 2.8 to 9.0 Log
10
.
The proposed method (approach 3) for quantifying con-
tactor performance explicitly accounted for variability in
residence time distribution. When hydraulic performance
was assumed to be poor, very low Log
10
reductions were
predicted for a portion of flow. For example, consider the
quantiles of Log
10
reduction of Campylobacter with an
initial chlorine residual of 1.5 mg L
–1
in Table 6. While the
Ct calc method predicted 17 Log
10
reduction, results from
method 3 predicted that 0.1% of flow may only achieve
less than 1.9 Log
10
reduction. Improving the hydraulic
Figure 6
|
Generic example of a survival function S(Ct) illustrating the relationship
between Ct, probability of survival and the Log
10
reduction.
Table 4
|
Selected Ct values for defining the reference pathogen specific survival functions
Reference pathogen
Ct required for reduction
1 Log
10
2 Log
10
3 Log
10
4 Log
10
5 Log
10
S(Ct) 0.1 0.01 0.001 0.0001 0.00001 k
10
Campylobacter 0.2 0.5 0.5 0.5 0.8 2.1
E. coli O157:H7 0.2 0.4 0.6 2 3 0.6
Rotavirus 0.5 0.6 0.6 0.6 0.7 2.4
Norovirus 0.2 0.2 0.3 0.4 ∼ 3.3
Giardia
a
Ct ¼ 0:12I
0:27
Cl
0:19
pH
2:54
temp
0:15
at pH7, 15
W
C, 0.4 mg L
1
17.5 32.6 60.8 113.2 210.7 0.024
at pH7, 15
W
C, 1.5 mg L
1
22.5 42.0 78.1 145.5 270.9 0.018
a
Model developed by Clark et al. (1990).
Figure 7
|
Selected survival functions S(Ct) for reference viruses and bacteria in QMRA
tool, with comparisons to the indicative ranges given in the WHO drinking
water guidelines (2011) (indicated by the arrow bar 2–30 min mg L
–1
) and the
USEPA Ct requirements.
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Table 5
|
Results from three different approaches for quantifying Log
10
reduction efficacy by free chlorine disinfection for QMRA (initial chlorine residual 0.4 mg L
–1
, MRT ¼ 12 min)
1: Ct calc 2: CSTR equation 3: Proposed method with residence time distribution
BF
a
Log
10
reduction n Log
10
reduction n
Quantiles of Log
10
reduction
Overall mean
b
(Log
10
reduction)Reference pathogen 0.001 0.01 0.05 0.50 0.95
Campylobacter 0.3 4.5 2 2.4 2 0.5 1.6 3.6 9.7 13.7 2.8
0.6 9.1 6 5.0 6 4.1 5.3 6.7 10.4 13.1 6.5
1 15.1 20 9.2 20 6.5 7.7 8.6 10.6 12.2 9.0
E. coli O157:H7 0.3 1.2 2 1.4 2 0.5 1.6 3.0 4.2 9.2 2.8
0.6 2.4 6 2.4 6 3.0 3.0 3.1 3.9 9.1 3.7
1 4.0 20 3.6 20 3.0 3.1 3.2 3.8 4.3 3.7
Rotavirus 0.3 5.2 2 2.5 2 0.2 0.7 3.8 22.2 34.0 2.1
0.6 10.3 6 5.3 6 5.4 9.0 13.1 24.3 32.3 7.5
1 17.2 20 9.9 20 12.6 16.2 18.9 24.8 29.6 15.4
Norovirus 0.3 7.2 2 2.7 2 0.5 2.3 5.8 24.2 36.0 2.9
0.6 14.5 6 6.1 6 7.4 11.0 15.1 26.3 34.3 9.5
1 24.1 20 12.0 20 14.6 18.2 20.9 26.8 31.6 17.4
Giardia 0.3 0.02 2 0.05 2 0.01 0.02 0.04 0.14 0.21 0.13
0.6 0.03 6 0.06 6 0.05 0.07 0.09 0.16 0.20 0.15
1 0.06 20 0.06 20 0.09 0.11 0.13 0.16 0.19 0.16
a
BF, baffling factor from (USEPA 2003a).
b
Assuming complete mixing following the contactor.
Table 6
|
Results from three different approaches for quantifying Log
10
reduction efficacy by free chlorine disinfection for QMRA (initial chlorine residual 1.5 mg L
–1
, MRT ¼ 12 min)
1: Ct calc 2: CSTR equation 3: Proposed method with residence time distribution
Reference pathogen BF
a
Log
10
reduction n
Log
10
reduction n
Quantiles of Log
10
reduction
Overall mean
b
(Log
10
reduction)0.001 0.01 0.05 0.50 0.95
Campylobacter 0.3 17.0 2 3.5 2 1.9 5.4 9.8 32.8 47.6 4.3
0.6 34.0 6 8.1 6 11.8 16.3 21.5 35.4 45.5 13.6
1 56.6 20 18.0 20 20.9 25.4 28.8 36.0 42.1 23.6
E. coli O157:H7 0.3 4.5 2 2.4 2 1.9 3.2 4.1 10.5 14.9 4.2
0.6 9.1 6 5.0 6 4.1 5.5 7.0 11.2 14.2 6.5
1 15.1 20 9.2 20 6.9 8.2 9.2 11.4 13.2 9.4
Rotavirus 0.3 19.4 2 3.6 2 0.7 9.2 22.3 91.5 135.7 3.3
0.6 38.8 6 8.4 6 28.4 41.8 57.4 99.3 129.4 27.1
1 64.7 20 19.0 20 55.6 69.1 79.3 101.1 119.2 57.8
Norovirus 0.3 27.2 2 3.9 2 2.7 11.2 24.3 93.5 137.7 4.5
0.6 54.3 6 9.3 6 30.4 43.8 59.4 101.3 131.4 29.1
1 90.5 20 21.7 20 57.6 71.1 81.3 103.1 121.2 59.8
Giardia 0.3 0.05 2 0.13 2 0.02 0.05 0.11 0.42 0.62 0.40
0.6 0.10 6 0.17 6 0.14 0.20 0.27 0.45 0.59 0.44
1 0.17 20 0.19 20 0.26 0.32 0.37 0.46 0.54 0.46
a
BF, baffling factor from (USEPA 2003a).
b
Assuming complete mixing following the contactor.
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performance (increasing the assumed CSTRs to 6 and 20)
reduced the impact of this small portion of flow.
DISCUSSION
A large number of studies reporting the sensitivity of human
enteric pathogens to free chlorine inactivation have been
reviewed and compared by transforming reported results
to a common metric. A statistical meta-analysis of all data
was not adopted due to the variation between studies and
the many unquantifiable uncertainties underlying the data,
including the following:
•
The experimental conditions such as temperature, pH,
suspension media, and chlorine dosage. For example,
Blaser et al. () investigated Campylobacter jejuni inacti-
vation at pH 6 and 8, temperatures of 4 and 25
W
C and used
a dosage of 0.1 mg L
–1
;whileLund () investigated
Campylobacter jejuni inactivation at pH 6.5, temperatures
of 4 and 10
W
C and used a dosage of 0.2 mg L
–1
.
•
The selection of experimental strains. Recent indepen-
dent studies on coxsackievirus B5 reported comparable
results, for example Ct
99.9
¼ 5.5 (Black et al. ) and
8.4 mg L
–1
min
–1
(Cromeans et al. ) at pH 7 and
7.5, respectively, using the same laboratory strain
(Faulkner strain). However, older work undertaken by
Payment et al. (a) using environmental strains of cox-
sackievirus B5 reported much higher overall persistence,
and higher variability in persistence between strains, with
the most resistant strain originating from raw sewage
(Ct
99.9
¼ 126 mg L
–1
min
–1
).
•
The interpretation and analysis of experimental data. An
example is the kinetic modeling approaches that
accounted for chlorine decay over the course of the
experiment (Thurston-Enriquez et al. ; Black et al.
; Cromeans et al. ) whereas earlier studies
often only provided data plots, e.g. Sharp & Leong
(), Grabow et al. () and Berman & Hoff ().
Rather than to statistically aggregate all results into a
single estimate for each reference pathogen with some quan-
titative representation of variability and uncertainty, the
results from the literature are tabulated and the upper
bound of the reported range was selected for characterizing
the pathogen specific survival functions. This approach was
deliberately selected to maintain transparency of the under-
lying scientific data. It was concluded that quantifying
uncertainty bounds on these numbers would not provide a
meaningful representation of the true variability and uncer-
tainty underlying the reported data. We expect that for
specific conditions or risk scenarios, and as additional scien-
tific evidence becomes available, risk assessors should and
will select alternative survival functions.
Shielding of micro-organisms due to the presence of
organic matter is expected to lead to a reduction in disinfec-
tion efficacy. Only one study in the review looked
specifically at the difference between free and cell-associ-
ated organisms (Berman & Hoff ), demonstrating
around one order of magnitude difference in required Ct.
Clogging as well as particle association related to chlorine
resistance is poorly characterized and not well accounted
for in QMRA calculations, yet is very important for assessing
the risk associated with full-scale disinfection of drinking
water. In particular, during episodes of poor plant perform-
ance, when conventional treatment may be sub-optimal, the
disinfection barrier is expected to be challenged with micro-
organisms that are associated with organic matter or chemi-
cal floc. Understanding the effectiveness of disinfection
during such events are of critical importance as the need
for the disinfection barrier is increased and the expected
performance of the barrier is reduced. Clearly more quanti-
tative scientific evidence is needed to better understand
drinking water safety during sub-optimal treatment events.
It is very important to recognize that the survival functions
characterized based on the evidence reported in this review
do not account for micro-organism shielding. A safety factor
to account for this shielding could be included in the model.
The requirements of the LT1SWTR (USEPA a) for
enteric viruses are based on Hepatitis A Virus inactivation
studies performed by Sobsey et al. ( ) multiplied by a
safety factor of 3. It may be that to account for shielding,
the safety factor needs to be as high as 10.
Previously published QMRA investigations that have
quantified Log
10
reduction of pathogens by chlorine disinfec-
tion have relied on reported ranges from the literature, and
did not consider site-specific characteristics or operational
factors. Storey & Ashbolt () assumed free chlorine disin-
fection (dose and residence time unspecified) would achieve
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2 Log
10
inactivation of enteric viruses (in general) over the
contact chamber, followed by 0.5 Log
10
reduction in the dis-
tribution system citing (Storey & Ashbolt ; Jacangelo
et al. ; Rose et al. ; Payment ). Westrell et al.
() used triangular distributions (defined by min, mode,
and maximum) to describe Log
10
inactivation (residual con-
centration ¼ 0.2 mg L
–1
and average residence time of 2
days in distribution) for Cryptosporidium (T(0, 0.4, 1.0)
citing results of Korich et al. () and Finch et al. (),
the same triangular distribution was then later applied by
Signor & Ashbolt ()); for rotavirus (T(1.5, 2.0, 3.0)
citing Sobsey ()); and for Campylobacter (T(2.5, 3.5,
5.0) citing Sobsey ()). In these studies, several factors
were unclear. Firstly, the scientific justification for the selec-
tion of reported ranges and parameters of the triangular
distributions were ambiguous. Secondly, the site-specific
characteristics including plant design and contact time
were not explicitly considered. Thirdly, it was not possible
to quantitatively explore the impact of process management
decisions on the consumer safety including a change in
chlorine dose or increase in contact time.
The conceptual model presented overcomes the limit-
ations of previously published methods for quantifying
chlorine disinfection efficacy for QMRA, and has two sig-
nificant additional advantages. Firstly, even within a
screening level QMRA, the model accounts for variability
in residence time of a disinfection contactor. Contact time
is critical for effective chlorine disinfection, and therefore
small fractions of flow that receive a low chlorine dose
may have important implications for public health. It is of
great value to be able to explore this conceptually within
the QMRA model. For the example reported in Table 6 for
Campylobacter, the predicted Log
10
reduction using the
Ct
cal
method would have implied a very high level of consu-
mer protection (17 Log
10
reduction with BF ¼ 0.3).
However, when considering the variability in residence
time (approach 3), 0.1% of flow was calculated to achieve
less than 1.9 Log
10
reduction. For a 1 ML per day drinking
water treatment plant, this equates to 1,000 L per day of
water. For water treatment plants with limited clear water
tank storage, and for consumers located close to the water
treatment plant, this water could be directly distributed to
the consumer. The model results demonstrate that charac-
terizing the hydraulic performance of the disinfection
contactor and storage tank are critical for ensuring that
the minimal Log
10
reductions are adequate to ensure
safety for the entire population.
The model describes the residence time distribution
using the theoretical tanks-in-series model. The residence
time distribution of any real full-scale contactor will how-
ever deviate (at least to some extent) from this theoretical
distribution. For a more detailed site-specific study the resi-
dence time distribution should be locally quantified using
tracer testing (Haas et al. ; Peplinski & Ducoste ))
and/or computational fluid dynamics (Greene et al. ).
In addition to residence time, mixing behavior within con-
tactors has also been shown to affect disinfection efficacy
and needs to be considered for a detailed analysis (Haas
). The theoretical model presented is not an alternative
to these approaches, but rather provides a generic approach
to be applied as a first screening step, allowing the residence
time distribution to be predicted based on general inputs
such as MRT and baffle structure.
Validation (how well do the predicted Log
10
reductions
reflect actual full-scale plant performance?) is an important
consideration of any proposed modeling approach. In the
case of full-scale disinfection performance of human enteric
pathogens, direct validation is typically not possible.
Previous studies have compared the predicted Log
10
reductions by alternative modelling methods with those cal-
culated from measured E. coli concentration at bench scale
(Smeets et al. ) and pilot scale (Pfeiffer & Barbeau
). In each case, conclusions regarding the preferred
modeling method were based on which method predicted
a Log
10
reduction closest to those calculated from obser-
vations. This apparently logical approach does not give
adequate consideration to the mechanistic assumptions
behind each approach and why each may lead to different
predictions. The CSTR equation (approach 2) resulted in
similar Log
10
predictions to the flow weighted mean result
achieved with approach 3. While both approach 2 and
approach 3 consider the contactor as a series of CSTRs in
series, they are based on different assumptions. Approach
2 (a widely applied equation in many fields of environmental
and engineering processes) applies a materials balance
approach and simpli fies exponential inactivation as a
single step for each CSTR. The reduction in predicted
Log
10
inactivation between approach 1 and approach 2 is
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caused by the limited number of steps of inactivation that
are quantified when the CSTR equation is applied. In con-
trast, approach 3 applies the theoretical residence time
distribution for a series of CSTRs and therefore explicitly
accounts for the impact of variability in residence time on
the overall Log
10
reduction. Approach 3 does not simplify
the microbial inactivation as a step function, but quantifies
inactivation according to a defined survival function.
Approach 3 should therefore be preferred for QMRA
because the impact of contact time on consumer risk can
be explicitly explored. When more detailed site-specific
information on the residence time distribution is available,
the theoretical distribution can be replaced in the model
with the site-specific residence time distribution.
Secondly, the model accounted for reference pathogen
specific survival functions. Reported ranges in review and
guidance documents are typically intended to be indicative
only, referring broadly to the performance across each of
the pathogen groups: viruses, bacteria and protozoa. For
example, the WHO drinking water guidelines report a Ct
99
for viruses to be 2–30 min mg L
–1
at 0–10
W
C and pH 7–9;
for bacteria to be 0.04–0.08 min mg L
–1
at 5
W
C and pH
6–7; and for protozoa (identified as mainly Giardia)tobe
25–245 min mg L
–1
at 0–25
W
C and pH 7–8. Slightly broader
but similar ranges were reported by LeChevallier & Au
(). The literature review data summarized in Tables 2
and 3 show the documented considerable variability
between pathogens in their sensitivity to chlorine. Reference
pathogens are selected for QMRA under the assumption
that they are conservative models for all pathogens in their
respective microbial group. If the water treatment operation
is designed to protect the consumer from the reference
pathogen, then it is assumed that the consumer will also
be protected from all pathogens within that group. The refer-
ence pathogens used in the model were not conservative
choices for chlorine disinfection, but were selected due to
their high prevalence in the community, and high infectivity.
When undertaking QMRA of systems that use free chlorine
disinfection, it may be more reasonable to consider the
resistance of enteroviruses such as echovirus or coxsackie-
virus. While less prevalent and (assumed to be) less
infectious, enteroviruses can lead to very significant health
outcomes including meningitis and myocarditis. Some
dose-response data are available for Echovirus 12 (Schiff
et al. ) and mouse infectivity data for coxsackievirus
B4 (Suptel ), both of which have been previously
applied for water-related QMRA (Mena et al. ; Åström
et al. ; van Lieverloo et al. ).
The approach for quantifying chlorine concentration
across the contactor was very simplistic. Different
approaches for modeling chlorine decay have been reviewed
by Clark & Sivaganesan (). Particularly in the initial
stages of disinfection, the first-order relationship has been
shown to be appropriate; however, the first-order model is
not capable of reproducing the higher decay rates often
observed in the initial stages of chlorination nor the
slow tailing off of the decay at very long reaction times
(Haas & Karra a, b). The residence times of disinfection
contactors are in practice relatively short (<30 min) in
comparison to long-term deviations (>4 h) from linear inac-
tivation kinetics, and therefore first-order decay may be
appropriate for considerations within the contact itself.
Better predictions of required chlorine dosage would require
a site-specific appreciation of the mixing characteristics of
the contact chamber, the compositions of the reactive com-
pounds in the water, and factors such as temperature that
drive the reaction kinetics. In addition, longer term chlorine
decay rates within the distribution network combined with
travel time to consumers would provide a better overall
appreciation of Log
10
reductions beyond the contact
chamber itself.
CONCLUSIONS
This paper presents a screening level approach for quantify-
ing the efficacy for free chlorine disinfection across a full-
scale contactor taking into consideration the variability in
residence time and the pathogen specific chlorine sensi-
tivity. Quantitative review of the breadth of laboratory
studies on pathogen and indicator sensitivity to free chlorine
was a challenge due to differences in experimental con-
ditions (pH, temperature, dosing), micro-organism strains
and spiking density, and data reporting protocols, however
survival functions were constructed for a range of pathogens
relying on the upper bound of the reported data. The resi-
dence time distribution for a full-scale contactor was
quantified based on limited local information regarding
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the geometry and flow rate. The application of the model
within a hypothetical case study demonstrated the impor-
tance of accounting for variable residence time in QMRA.
While the overall Log
10
reduction may appear high, small
parcels of water with short residence time can compromise
the overall performance of the barrier. QMRA is an
emerging tool for the assessment, management and com-
munication of water-related infectious risks. While
theoretically simple, the approach presented is of great
value for undertaking an initial assessment of a full-scale
disinfection contactor based on limited site-specific
information.
ACKNOWLEDGEMENTS
The tool was partly developed under the EU HiWATE
project (www.hiwate.org) with the Swedish Institute for
Preventive Disease Control as project partner. The work is
(partly) funded by HIWATE; a Specific Targeted Research
Project (STREP) under the EU Sixth Framework Program
for Research and Technological Development by the
Research Directorate-Biotechnology, Agriculture and Food
Research Unit (Contract no Food-Ct-2006-036224).
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