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Water reclamation redesign for reducing Cryptosporidium risksat a recreational spray park using stochastic models

Mark H. Weir a,*, Maria Tereza Pepe Razzolini b, Joan B. Rose a, Yoshifumi Masago c

aDepartment of Fisheries and Wildlife, Michigan State University, 303 Manly Miles Building, 1405 S. Harrison Rd., East Lansing, MI 48824,

USAb School of Public Health, University of Sao Paul, Sao Paulo, BrazilcDepartment of Civil and Environmental Engineering, Tohoku University, Japan

a r t i c l e i n f o

Article history:

Received 30 November 2010

Received in revised form

22 September 2011

Accepted 24 September 2011

Available online 14 October 2011

Keywords:

Risk assessment

Monte Carlo

Markov chains

Water treatment

Cryptosporidium

* Corresponding author. Tel.: þ1 570 460 845E-mail address: weirmarkh@gmail.com (M

0043-1354/$ e see front matter ª 2011 Elsevdoi:10.1016/j.watres.2011.09.047

a b s t r a c t

Recreational outbreaks associated with sprayparks are well recognized, and may be partly

due to the engineering designs used for their water reclamation systems are problematic to

control. This work is based on an outbreak of cryptosporidiosis linked to a spraypark in

New York State, where it was determined, specifically that the spraypad (the main

attraction) was the primary exposure point. We first determined the likely dose the

spraypad users were exposed to, then modeled the efficacy of the treatment system and

used this to inform a Monte Carlo method to estimate the probability of infection and

illness for the users of the spraypad. The current treatment system which consists of; two

holding tanks, a dual media filter and chlorine injection as well as two design change

recommendations were modeled using three independent Markov chain models. Within

the current treatment system design the receiving tank for the treatment train is also

connected with a second pipe to the spraypad used to deliver the return (treated) water,

this return pipe is acting potentially as a bypass for the treatment train. Based on the risk

assessments performed it is recommended that the bypass pipe be removed from the

treatment system since in doing so the probability of infection and illness were reduced

appreciably. Secondarily including an ozone contactor was shown to slightly reduce the

risk further and provide a multiple barrier.

ª 2011 Elsevier Ltd. All rights reserved.

1. Introduction CDC, 33 (56.9%) of the 58 outbreaks of gastroenteritis were

1.1. General overview

In recent years, an increase of waterborne outbreaks associ-

ated with treated recreational waters has been observed. In

the United States from 2005 to 2006, treated water venues

were associated with 58 recreational water outbreaks, with 24

outbreaks reported in 2005 and 34 in 2006, resulting in a total

of 4167 cases of gastroenteritis (CDC, 2008). According to the

9; fax: þ1 517 353 9807..H. Weir).

ier Ltd. All rights reserved

caused by protozoan parasites and of these 33 outbreaks 31

(93.9%) were identified as being caused by Cryptosporidium

(CDC, 2008). Cryptosporidium has been recognized as the most

frequent cause of recreational water-associated outbreaks of

gastroenteritis, including treated and disinfected venues

(CDC, 2007).

Causer et al. (2006) reported an outbreak of Cryptosporidium

infection at an Illinois recreational waterpark in 2001. From 13

August to 30 September a total of 358 cases were identified

.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5e6 5 1 46506

(281 clinical cases and 77 laboratory-confirmed), 77.9% of

them were children (those less than 18 years old). Laboratory

analysis revealed the presence of Cryptosporidium oocysts in

stool specimens from 77 patients, 22 of these specimens

underwent genotypic testing, with ten (45.5%) positive for

Cryptosporidium hominis. Water samples from the toddler/

wading pool and backwash filter system were positive for

Cryptosporidium using USEPAMethod 1622 but all of themwere

PCR negative. According to epidemiological study and envi-

ronmental analysis, the evidence associated attending the

waterpark with the outbreak.

Later, Wheeler et al. (2007) reported an outbreak of cryp-

tosporidiosis at a California waterpark in 2004, where more

than 250 persons were ill due to a common exposure to the

waterpark. Cryptosporidium oocysts were found in stool spec-

imens from 52 persons as well as detected in water samples

from the sand and backwash from the filter. No Cryptospo-

ridium was detected from either the lake or three wells which

supplied the park. According to the authors, both the epide-

miological and environmental investigation supported the

hypothesis that the outbreak was associated with using the

waterslides in the waterpark.

The work presented in this paper is based on a cryptospo-

ridiosis outbreak which occurred at a New York State recrea-

tional spraypark which affected 746 people who reported

gastrointestinal illness after attending the spraypark facility.

In 2005 a formal request submitted by Joan B. Rose via the

freedom of information acts, garnered information and data

on the epidemiology, environmental and engineering inves-

tigation and the engineering designs of the spraypark. After

this information was obtained a quantitative microbial risk

assessment (QMRA) was developed in order to evaluate the

probability of infectiondue to exposure to contaminatedwater

withCryptosporidium oocysts, aswell as evaluate the efficacy of

potential combined treatment retrofits aimed at reducing

recreational risks. The goal of this work was first to evaluate

this outbreak of cryptosporidiosis so as to inform the level of

contamination and exposure for the recreating population at

this spraypark. Second, stochastic models of the water recla-

mation treatment system (and design recommendations)

were used as inputs to theMonte Carlomodel,whichwas used

as the means of modeling the risk of infection and illness.

This risk estimate was then used to examine the effective-

ness of engineering design change(s) for the recreational spray

park to reduce the risk of cryptosporidiosis. Two main design

changes were analyzed, first the removal of the pipe, (which

was likely acting as a bypass) from the treatment system, and

the combination of removing this pipe and adding an ozone

contactor. These redesign options were chosen as likely

responses which would ensure use of the current treatment

system (as the entire system could not be bypassed) and

a potential addition to the treatment trainwhich is specifically

able to inactivate oocysts (ozone contactor). The addition of

chlorinewas continued, however for this analysis chlorination

is considered negligibly effective for Cryptosporidium oocysts.

1.2. Cryptosporidium as an environmental Hazard

Cryptosporidium oocysts are environmentally robust, persis-

tent in water and are resistant to common disinfectants

(Smith and Rose, 1998; WHO, 2002, 2006; Carey et al., 2004).

Cryptosporidium transmission occurs by ingestion of food or

water contaminated by oocysts which are relatively potent

even at low doses compared to other pathogens (Smith and

Rose, 1998; WHO, 2006; Haas et al., 1999). The oocysts are

excreted in the feces of the infected host with levels as high

as 107 oocysts per gram of feces and can be shed for as long

as 50 days after the cessation of diarrhea (Chappell et al.,

1996; CDC, 2007). The cryptosporidiosis symptoms are

persistent diarrhea, fever, abdominal cramps, nausea,

appetite loss and vomiting. Cryptosporidium oocysts in recre-

ational water represents a public health concern, consid-

ering, its high inherent risks and persistence in the water

environment (Rose et al., 2002). In recent years QMRAs have

been undertaken for recreational waters (Ashbolt et al., 2010;

Roser et al., 2006), however for drinking water, the proba-

bility of infection is estimated in addition the probability of

illness so as to develop a conservative public health

approach, by using probability of infection, thereby

addressing the initial state of the disease process capturing

all possible outcomes (Regli et al., 1991). Modeling the risk of

infection is also beneficial when considering the more severe

outcomes groups with greater susceptibility such as children

can incur. Typically a conditional dose response model that

estimates the probability of illness given the probability of

infection, however this is not possible for Cryptosporidium

(Teunis et al., 1999). Despite the inability to apply a condi-

tional probability of illness, a probability of illness given

infection can be developed using the morbidity ratio (illness

given infection) recommended by the United States Envi-

ronmental Protection Agency (EPA) (US EPA, 2006a). There-

fore this work examines both the probability of infection and

probability of illness to recreational users of the spraypark

using both infection and illness for evaluation of risk

reduction potential.

1.3. Outbreak description

As discussed earlier this work is based on an outbreak at

a recreational spraypark. Themain attraction at the spraypark

as with most recreational sites of this type is the spraypad.

The spraypad is a large concrete surface, in this case in the

shape of an oval. The spraypad has numerous water spray

attractions that children play with and around. This location

was determined the primary site of exposure by the New York

State Department of Health (NYSDOH). In order to charac-

terize the outbreak better a case control study and a cohort

study were performed by the NYSDOH. The case control study

was used to describe the overall outbreak; and the data from

the cohort study were used to determine the attack rates to

the spraypark users.

In August 2005, the NYSDOHwas notified of an outbreak of

gastrointestinal illness associated with attending this spray-

park facility. The attendance in August 2005, the time frame of

the outbreak, was approximately 30,000 people.

A total of 746 people reported gastrointestinal (GI) symp-

toms, of these; five were hospitalized, with no deaths re-

ported. The reported symptoms were as follows: diarrhea,

nausea, abdominal cramps, vomiting, fever and appetite loss.

The types of cases were defined as follows:

Table 1 e Distribution of recruited subjects for the cohortstudy.

Subjects Total Mean age(range)

years old

Femalegender% (n)

SG exposure% (n)

Primary cases 54 20.1 (1e75) 61 (33) 87 (47)

Secondary cases 8 29.3 (2e55) 50 (4) 50 (4)

Controls 97 38.0 (2e81) 61 (59) 37 (36)

Total/mean 159 31.5 (1e81) 60 (96) 55 (87)

SG e spray ground.

Fig. 1 e Epidemic curve developed from cohort study.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5e6 5 1 4 6507

i. Primary e an individual with GI symptoms within 14

days of visiting the spraypark.

ii. Secondary e an individual with GI symptoms and

a history of visiting the spraypark.

iii. Confirmede a primary or secondary case with a positive

laboratory result from a fecal sample for Cryptosporidium

or other enteric pathogen.

iv. Suspected e a primary or secondary case that did not

have a positive lab result for Cryptosporidium or other

enteric pathogen (generally due to no fecal specimen

being submitted).

For the case control study, case and control subjects were

recruited from the Electronic Clinical Laboratory Reporting

System (ECLRS). Case subjects were people who became ill

within 14 days after visiting the spraypark and had confirmed

cryptosporidiosis, while control subjects were those who

visited the facility, within the same period, but did not develop

cryptosporidiosis. A total of 79 records were entered with 34

being case subjects (43%) where all of them were exposed to

the spraypad and another 45 control subjects (57%) that were

exposed to the spraypad 87% of the time at the spray park

(self-reported as entering the spraypad area to play or play

with their families). The investigation found that the mean

incubation periodwas 4.1days (0e12 d) and themean duration

of symptoms was 11.4 days (4e21 d). The frequency of

symptoms were as follows:

� Diarrhea e 100%

� Bloody diarrhea e 3%

� Nausea e 77%

� Vomiting e 68%

� Abdominal cramps e 82%

� Fever e 77%

� Appetite loss e 88%

Themean age among case subjects was 6.5 years old (range

of 1e46 y/o) and 28.2 years old for control subjects (range of

0e62 y/o). Male gender frequency was 48% of case subjects

and 24% among control subjects. According to this study the

date where the frequency of cases peaked was on August 10th

(29.4% of the cases).

The attack rate was determined using the cohort study, for

which recruitment was done from park attendance lists from

the period of 27 July to 15 August. A total of 159 subjects, cases

and controls, were interviewed by phone using a question-

naire. Within the recruited persons, 54 were considered

primary cases, eight secondary cases and 97 as control cases,

18 subjects were excluded due to visiting the park more than

once in the outbreak period. Table 1 summarizes the distri-

bution of recruited subjects, cases and controls for the case

control study. From the cohort study an epidemic curve and

the attack rate were determined Fig. 1 and Table 2

respectively.

1.4. Site description and current water reclamationtreatment system

The treatment system receiveswater via 10 inch diameter PVC

pipes from two drains in the center of the spraypad (basic

depiction of this can be seen in supplementary information

Fig. S1). Fig. 2a is a flow chart of the Markov chain model for

the current unaltered system which consists of two holding

tanks. Tank-1 is meant to retain water from the spraypad

(water requiring treatment) but tank-1 is also being used to

hold and return treated water to the spraypad which has the

unintended effect of bypassing the treatment system. Tank-2,

is meant to store water after treatment, but only a fraction of

the water delivered for treatment is treated by filtration and is

stored in tank-2. Chlorine is added to the system immediately

after filtration. The volume of water in the spraypad was

estimated based on the area of the elliptical spraypad, and

assumed a consistent minimum water depth of 1 inch on the

surface of the spraypad. The spraypad has an overall slope

from the concrete walk of 2% for sufficient flow to the two

drains.

The spraypark offices use a septic tank approximately 140

feet from the spraypad which serves the restrooms, however

no cross contamination was found and the topography

between the septic tank and the pad allows for a natural

depressionwhich wouldminimize the chances of an overflow

reaching the pad. Also the spraypad is concrete therefore

percolation from this potential source was considered negli-

gible. Since the concrete spraypad is surrounded by a concrete

walk with a six inch riser on the outer edge, the runoff from

the surrounding area is also considered negligible.

The source of Cryptosporidium oocysts is considered to be

associated with infected individuals excreting oocysts and

contaminating the water during recreation. Fecal samples

from cases tested as C. hominis (a human genotype) and thus

the impact on the spraypad water was deemed to be from

some user(s) prior to the outbreak.

Fig. 2 e Markov chain model flow charts of system configurations, a) configuration-1 which is the current unchanged

system, b) configuration-2 which is the first suggested change with the removal of the bypass pipe, c) configuration-3 which

is the last recommended change where the bypass pipe is removed and an O3 contactor is included as well.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5e6 5 1 46508

2. Modeling methods

2.1. Modeling of current and future changes to waterreclamation system

In order to develop amore dynamic understanding of the risks

posed from a Cryptosporidium release onto the spraypad,

a Markov model which describes the current treatment

systemwas developed in the statistical programming package

R. This model was then also adapted to take into account the

removal of the pipe from tank-1 to the spraypad which acted

as a bypass from the reclamation treatment system. A second

Table 2 e Attack rate among exposed and unexposedsubjects of the survey.

Attack rate (%)

Exposed Unexposed

General 51.35 8.96

0e4 years old 53.33 Negligible

5e24 years old 65.52 Negligible

25e54 years old 45.00 9.68

55þ years old Negligible 12.50

Unknown age 66.67 Negligible

adaptation included an ozone contactor in addition to

removing the bypass pipe. These Markov models allow for

a rapid assessment of the current treatment system as well as

the proposed adaptations to the treatment system. Informa-

tion on the spraypark was determined using the design

specifications and schematics, obtained from the freedom of

information act request. The Markov model simulates treat-

ment system operation after a fecal release to the spraypad of

2 (107) oocysts (approximately 2 g of feces). The loss rates used

in the Markov model are shown in Fig. 2 aec. Parameters for

current and recommended system configurations are avail-

able in supplementary information, (i.e. O3 contactor and

bypass removal is shown as the third system configuration in

Table S3).

2.1.1. Markov model statesThe Markov model was first built for the current system

configuration (config-1). Since config-2 is the removal of the

bypass pipe, therefore no additional states are needed, there

are 8 states inwhich the oocysts could reside, during a specific

time step (Dt) for both config-1 and config-2. Each of these

states are shown in Table 3, where the oocysts are transported

in the water through the different treatment system tanks,

entrapped by the filter media (detritus), flowed back into the

spraypad, lost to runoff from the spraypad or succumbed to

the decay rate inherent to the oocysts’ lifecycle. It is assumed

that the filter is operated continuously and properly during

Table 3 e State numbers for treatment system all threeconfigurations (current system configuration andremoval of bypass and removal of bypass with O3

contactor included).

Configurations 1 and 2 Configuration 3

Statenumber

Systemlocation

Statenumber

Systemlocation

State 1 Tank 1 State 1 Tank 1

State 2 Filtration system State 2 Filtration system

State 3 Detritus from filter State 3 Detritus from filter

State 4 Tank 2 State 4 O3 contactor

State 5 Spraypad surface State 5 Cysts inactivated by O3

State 6 Water lost to runoff State 6 Tank 2

State 7 Water lost to

evaporation

State 7 Spraypad surface

State 8 Oocyst death State 8 Water lost to runoff

State 9 Water lost to

evaporation

State 10 Oocyst death

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5e6 5 1 4 6509

the season with a sufficient schmutzdecke developed for

removal of oocysts (Schuler et al., 1991), schmutzdecke being

the complex biological layer formed at the top of a filter,

essentially the main treatment layer of the filter.

In config-3 an O3 contactor is included in the original

treatment train, along with the removal of the bypass pipe as

was done in config-2. Therefore there are 10 states for config-3

(Table 3). Inactivation of the oocysts by O3 was accounted for

as an additional loss mechanism removing oocysts. For all

three configurations inactivation due to chlorine is considered

negligible due to the resistance of Cryptosporidium to chlorine.

2.1.2. Transition probabilities and loss ratesThe term pij is the probability that oocysts in state i will move

to state j during time step Dt. The overall rate at which oocysts

are transported from state i is determined with the sum of the

loss rates for removal from state i, denoted with li. Thus the

probability that oocysts will remain in state i ( pii) within the

time step (Dt), is determined with an exponential survival

probability as seen in equation (1) (Nicas and Sun, 2006; Ross,

2007).

pii ¼ expð�li$DtÞ (1)

In turn the probability of oocysts moving from state i to state j

(equation (2)) is defined as the loss rate from state i to state j

(lij) divided by the sum of the loss rates from state i (li). This

allows for the determination of the unconditional probability

that the oocysts in state i moved to state j during Dt, which is

the complement of the probability of the oocysts in state i

multiplied by the conditional probability (Nicas and Sun, 2006;

Ross, 2007).

pij ¼lij

li$�1� pii

�(2)

Therefore the loss rates are first determined in order to

complete the transition probabilities. Since the decay rate of

oocysts are in units of time�1, all loss rates are first order as

well.

The three system configurations used are displayed in

Fig. 2aec for config-1, 2 and 3 respectively. As described above

the (l’s) in the figures are the loss rates of oocysts associated

with moving from one state to another.

The oocysts are delivered through the treatment system

with a volumetric flow rate (Q), and the associated losses are

experienced in the volumes (V) of the compartments, repre-

sented by states in theMarkov chain. The volumetric flow rate

from one state to another (i.e. tank-1 to the filtration tank) is

divided by the volume of the current state which determines

the loss of oocysts from the first state (tank-1) during trans-

port to the next state (current state is symbolized by x) as

shown in equation (3). Therefore in the example of moving

from tank-1 to the filtration tank, the current state volume (Vx)

would be that of tank-1.

lx/y ¼ Qx/y=Vx(3)

The oocysts are also lost during the treatment step(s)

(filtration and ozonation), which is driven by the treatment

efficiency (Elmeko, 2003; Hijnen et al., 2004; Mazounie et al.,

2000), again assuming a schmutzdecke presence for filtra-

tion (Schuler et al., 1991). Equation (4) shows a generalization

of oocyst losses due to treatment. This general form is used

for filtration using a treatment efficiency (h), for filtration

obtained from Schuler et al. (1991) and the O3 contactor

inactivation efficiency for a 2 mg/L dose and contact time of

1 min (Corona-Vaszuez et al., 2002). As can be seen in

equation (4), the physical transport through the treatment

step was considered as well as reduction of oocysts in the

water from treatment, this gave an overall reduction as the

water was transported through the treatment options, rep-

resented as states in the Markov chain. In this general form

a represents the current treatment system state that the

oocysts reside in, they then in turn are transported and

treated in state b.

la/b ¼ Qa/b=Va��Qa/b=Va$hb

�(4)

There are three losses considered constant through the

Markov models, being the decay rate as well as runoff and

evaporation from the spraypad. It is estimated by the spray-

parkmanagers that there is typically a 5% loss ofwater per day

from runoff as well as evaporation (first order loss rate of;

0.0000026 h�1). Decay of Cryptosporidium parvum in neutral

water was considered as well, typical value of 0.000363 h�1

from Robertson et al. (1992) and considered constant for the

simulation. The other loss rate functional forms can be seen in

the supplemental information.

2.1.3. Markov matrixThe loss rates described above allow for the determination of

transition probabilities as described in equations (1) and (2).

These transition probabilities are used to build the Markov

matrix, which is the operational component of the Markov

chain model. For config-1 and config-2 an 8 � 8 matrix was is

for the eight states, and config-3 is a 10� 10 matrix represents

the ten states for this configuration. As discussed earlier pij is

the probability that an oocyst in state-i transitions to state-j,

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5e6 5 1 46510

therefore, p12 is the probability of an oocyst in state-1 tran-

sitioning to state-2, and so forth for each row. Therefore each

of the rows represents each of the states andmovement along

the columns of the matrix represents transitioning from that

state to another.

2.1.4. Code and model verification for Markov modelsSince this is a stochastic system, in this case a discrete time

Markov chain is utilized to model the efficacy of the current

and recommended treatment options. Two verification steps

which included general code verification, which is performed

in two steps, first by using an additional researcher uncon-

nected to the project to visually inspect the code, checking for

errors and a line-by-line debugging of the code. Line-by-line

debugging is accomplished by evaluating each of the lines

(or in the case of loops, individual loops) independently, to

confirm that the intended and required result is gained from

executing that line of code (or that loop).

The second type of verification which is possible for this

type of stochastic model is a check of conservation of mass.

This was performed, where 500 oocysts are entered into each

of the compartments of the model individually and the

outputs from the compartments (states) are accounted for, the

sum total of oocysts lost from the states equals the original

amount going in. Therefore using the filter state as an

example the oocysts can be lost due to entrapment as detritus,

lost to decay or flow through the filter and these three loss

mechanisms are the onlymeans of oocysts being lost from the

filter state. This verification test ensures that oocysts are not

being generated in the states. Then a final conservation of

mass verification is performed, where the entire system is

executed (all states, the entire model executed) and conser-

vation of mass is monitored, again where 500 oocyst were

introduced to the systems and each of the pathways account

for the loss of all 500 oocysts, therefore, mass is not created

and only those loss mechanisms for the states are removing

the oocysts from the individual states.

Both of these verification steps were performed and

passed. The external check and debugging of the code passed

andwas verified by the researcher external to this project. The

checks for conservation of mass passed as well, where for

each of the removalmechanisms of themodel, the principle of

conservation of mass held for individual states as well as the

entire model.

2.2. Risk assessment

QMRA is a means of determining the probability of infection

due to exposure to water contaminated with Cryptosporidium

oocysts. The exposure volume was defined using data from

Dufour et al. (2006) as an assumed constant value of 0.108 L/

exposure.

The dose response relationship for probability of infection

(Pi) due to ingestion of Cryptosporidium oocysts was used as

described by the exponential dose response model shown in

equation (5) (Haas et al., 1996). The k parameter used in this

analysis was optimized from human volunteer studies as

recommended in US EPA (2006b), with an optimal value of k

being 0.0907 and bounded on a 95% confidence interval of

0.0074 and 0.3044.

Pi ¼ 1� expð�k$dÞ (5)

If it is desired to estimate the dose from an observed proba-

bility of infection, equation (5) can be rewritten to solve for

dose, since the k parameter is based on human feeding trials

where the observed probability of infection was recorded.

However, since attack rate (AR) was an estimate of the prob-

ability of illness equation (5) will not be as accurate as a dose

response model specific for illness. This could be accom-

plished through a dose responsemodel of probability of illness

conditional on probability of infection. However for Crypto-

sporidium Teunis et al. (1999) in their work developing this type

of conditional dose response model, it was determined that

due to the decreasing trend with dose of the proportion of

infected hosts who became ill such a conditional dose

response model could not be developed. So as to develop

a probability of illness (Pill) via the dose responsemodel which

models probability of infection, the recommended morbidity

ratio (MR) of 0.50 from the US EPA (US EPA, 2006a) is multiplied

by the dose responsemodel in equation (5) (equation (6)). Then

to obtain a dose estimate for the outbreak in question Pill is

substituted with AR after equation (6) is rewritten to solve for

dose (equation (7)).

Pill ¼ ½1� expð�k$doseÞ�$MR (6)

d ¼ ln

��Pill � 1�MR

�.k (7)

The Markov model was executed to simulate 3 h of treatment

system operation after a bolus fecal release of 2(107) oocysts

(approximately 2 g of feces) (Chappell et al., 1996; CDC, 2007).

Thus the scenario being considered is 3 h of spraypad use after

the fecal release. It is assumed that for a trip to the spraypark

which lasts typically five to 6 h that this would give a good

depiction of howmuch the spraypad is used. The Monte Carlo

method models the risks to the users of the spraypad after

a fecal release, using equation (5) to determine the risk of

infection (Pi) from oocyst exposure and equation (6) to esti-

mate the risk of illness (Pill). The Monte Carlo method is

a means of developing a distribution of model results after

repeated random sampling from probability distributions

which describe uncertain variables.

The numerical output of oocyst concentrations from the

Markov models are used to fit probability distributions in

MATLAB for the Monte Carlo model to address the user’s

exposure to oocysts for the QMRA after simulating 3 h of

spraypad use which includes an assumed bolus fecal release

of 2 (107) Cryptosporidium oocysts. The KolmogoroveSmirnov

test statistic was used to determine the goodness of fit for the

distributions. Table 4 shows the probability distributions used

in the Monte Carlo model, which is executed for 10,000 itera-

tions in MATLAB and summarizes the probability distribu-

tions used in this study to estimate the probability of

Cryptosporidium infection (Pi) and illness (Pill) given the

morbidity ratio (US EPA, 2006a). Fitting the probability distri-

butions around the numerical results from the Markov chain

model addresses the first uncertain variable (the concentra-

tion of oocysts for each of the configurations), the second

uncertain variable, the dose response parameter is described

with a triangular distribution, based on the optimal value,

Table 4 e Probability distributions and parameters for the Monte Carlo model.

Uncertainparameter

Scenario Distribution Distribution parameters KolmogrozeSmirnoztest p-value

r unitless NA Triangular 0.00467 (CI95%b: 0.00195; 0.0097) NA, assumed distribution

Cryptosporidium

concentrationa

Filtration w/o bypass

(current system)

Gamma Scale: 7392 0.0061

Shape: 0.432

Cryptosporidium

concentrationa

Filtration w/o bypass Gamma Scale: 6212 0.0075

Shape: 0.442

Cryptosporidium

concentrationa

Filtration þ O3 Gamma Scale: 6110 0.0055

Shape: 0.465

a Data for fitting these distributions from the Markov chain models.

b 95% confidence interval.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5e6 5 1 4 6511

lower and upper 95th confidence intervals of k from the

exponential model fit to human feeding studies (US EPA,

2006a) and the third uncertain variable is MR using a trian-

gular distribution using the likeliest, maximumandminimum

levels (US EPA, 2006b).

The dose for equation (5) is determined by multiplying the

concentration of oocysts sampled from the probability

distributions by the volume of water ingested by spraypad

users.

3. Results

Using equation (7), the likeliest morbidity ratio of 0.5 (US EPA,

2006a) and the attack rates in Table 2, this analysis estimates

that the population was exposed to an average dose of 12

oocysts (95th confidence interval of 4e149 oocysts).

Rather than using the estimated risk levels as a compar-

ison to current standards, considering they are associated

with highly credible gastrointestinal illness (HCGI) the overall

risk reductions were highlighted. It can be surmised

however, that since attractions like these are targeted toward

children a very low risk of infection or illness, given their

greater susceptibility, would be considered acceptable by

parents and park owners, however no known studies have

highlighted what acceptable risks might be for such

waterparks.

Fig. 3a and d shows the Monte Carlo simulation results for

Pi and Pill respectively to the population exposed to water

contaminated with Cryptosporidium oocysts with the current

treatment system (config-1). An overall risk to the exposed

population is bimodal and skewed slightly to the higher risk

levels with a low probability of experiencing an infection risk

of less than 0.10 (approximately 18% chance). Infection risks

equal to or greater than 0.9 did occur with a 60% probability

of occurrence, for Cryptosporidium. There is also a 15%

probability of incurring a probability of illness of 0e0.025,

and a 60% probability of incurring a probability of illness of

0.5. This demonstrates the untenable original configuration

with respect to Cryptosporidium risks with respect to the

original configuration. This is attributed to the percentage of

water that received no treatment via the bypass of the filter.

These risk estimates do not include risks from other patho-

gens since risk from Cryptosporidium is being highlighted

here.

When the bypass was eliminated from the water recla-

mation system and all the water was filtered, the overall risk

levels were reduced, Fig. 3b and e for showing Pi and Pillrespectively for config-2. As can be seen there is a 30% prob-

ability of experiencing an infection risk within the range of

0.00e0.01. The high risk level (>w0.9 for infection and >w0.35

for illness) was then reduced to a 44% probability of occurring.

When removing the bypass and including an O3 contactor

(Fig. 3c and f for Pi and Pill respectively for config-3), there was

approximately a 37% probability of the risk being at or less

than 0.01 for infection and 0.025 for illness and a 32% proba-

bility of experiencing a high risk of greater than 0.9 or 0.5 for Piand Pill respectively.

A sensitivity analysis is performed to determine which

uncertain variable is driving the risk. In all conditions

assessed, the greatest contributor to the risk for the exposed

population is the concentration of oocysts (Fig. 4a and b for

illness and infection respectively). The sensitivity analyses for

the other configurations were essentially the same, showing

the concentration of oocysts being the primary contributor to

the estimated risk and associated uncertainty. Thus it can be

recommended that at minimum the removal of the bypass

pipe would be necessary to reduce the risks associated with

waterborne disease. While inclusion of the ozone contactor

reduced the risks further and to an even greater degree from

the unaltered configuration, this was not as a sufficiently

appreciable risk reduction from the simpler retrofit (configu-

ration 2) and may not be justified with the higher costs from

both installation and operation associated with the third

configuration.

4. Discussion

This study addressed an important public health problem,

recreational waterborne disease. Swimming venues that use

chlorine are now known to be at risk from Cryptosporidium

and filtration or other types of disinfection including UV or

ozone are needed to control the parasite. According to the

CDC, recreational illnesses are on the rise. Between 2005 and

2006, 78 outbreaks were reported in 31 states, which is the

largest number of outbreaks ever in a two-year period with

4500 people affected (CDC, 2008). There are over 1000

waterpark facilities in North America (city pools with

waterpark features, independently-owned outdoor and

Fig. 3 e Monte Carlo risk model results for: risk of infection for a) configuration-1, b) configuration-2 and c) configuration-3

respectively, and risk of illness for d) configuration-2, e) configuration-2 and f) configuration-3 respectively. Where the x-

axis is the risk level experienced during the simulations, the primary y-axis is the frequency of observing that risk level

from the 10,000 iterations performed and the secondary y-axis is the probability that each risk level was encountered during

the simulation.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5e6 5 1 46512

indoor waterpark resorts/hotels) and the attendance was

reported at 78 million for 2006 (http://www.waterparks.com/

funfacts.asp; http://www.waterparks.org/otherarticles/

generalfacts.pdf).

CDC recently reported on violations during inspections of

111,487 swimming venues. Immediate closure was war-

ranted for 12% due to a lack of appropriate disinfection.

Violations for the circulation and filtration systems were

found to range from 24 to 38% and 35% of the waterparks

specifically were found to have violations in the filtration

system (CDC, 2010).

These water venues and water/sprayparks in particular

use large volumes of water and thus as pool codes are re-

examined throughout the US, appropriate engineering

controls for pathogenic organisms which enter the system via

infected swimmers will need to be implemented.

It was found that use of epidemiological, environmental

and engineering investigations of this particular NY spraypark

outbreak could be used to develop an appropriate exposure

and riskmodel. For this incident, this study estimated that the

population was likely exposed to a small dose of Cryptospo-

ridium oocysts resulting in risk of infection and disease.

The current configuration (config-1) was compared to the

potential remediation options, of removing the bypass pipe

from tank-1 to the spraypad and removing the bypass pipe as

well as including an O3 contactor (config-2 and config-3

respectively). Risk reductions were observed in conjunction

with further improvements to the engineering design and

Fig. 4 e Sensitivity chart demonstrating the concentration

of oocysts being the largest contributor to the risks of

infection, and driver of uncertainty. This sensitivity chart

is for one of the Monte Carlo models however, the other

two showed nearly identical results.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 0 5e6 5 1 4 6513

disinfection process. For the unaltered configuration the high

risk range of infection and risk of illness of 0.9e1.0 and

0.475e0.5 respectively occur with a 61% probability of

occurrence, and an 18% probability of incurring a risk of

infection and illness ranging from 0 to 0.10 and 0e0.05

respectively (Fig. 3a and d). The first of the recommended

configurations reduced the risk of infection and illness for the

same risk ranges to a probability of 45% for the high range

and 30% probability of occurrence for the low range. This risk

reduction was enhanced further by including the additional

disinfection step for config-3, where for the same risk range,

the probabilities of occurrence drop again to 32% for the high

risk range and the low risk range just overcame the high with

a probability of occurrence at 37%. The main treatment steps

have an overall retention time that is designed to be between

1 and 2 min, a combination of increasing the ozone dose and

contact time for that treatment step will increase the prob-

ability of incurring each person’s acceptable risk level (i.e.

parents do not want any children to be taken ill). Should

these recommendations have been implemented for this

outbreak, the overall impact from the outbreak could have

been reduced.

This demonstrates the need to understand the risks asso-

ciated with a fecal release of a number of pathogens and not

just indicators like E. coli for recreational treatment systems.

In the case of the spraypark the treatment system was

designed to combat E. coli from a fecal release.

The sensitivity analysis showed that the greatest uncer-

tainty in estimating the risk was linked to the concentration

of oocysts. Excretion of pathogens and release rates during

swimming are not well known. Excretion estimates vary

widely and it is clear that even asymptomatic infections

prior to and after noticeable symptoms signify that enteric

pathogens could end up in recreational waters.

5. Conclusions

� Using the QMRA framework, the level of contamination

from a Cryptosporidium recreational outbreak was deter-

mined. This dose estimation was also bounded by confi-

dence intervals to account for the uncertainty of exposure

after a fecal release at the spray park.

� >A stochastic model was constructed to simulate the

treatment system. This model was used to simulate the

effectiveness of the current and recommended adaptations

to the current system. The recommendations were pre-

sented in the context of a risk reduction potential by

coupling the stochastic model with a Monte Carlo risk

model.

� An overview of the treatment system layout, such as

recognizing an inadvertent bypass pipe, is a simple and

lower cost means of risk mitigation. And including an

additional step of examining the risks to the recreating

population can be enacted and serve as a quantitative

overview of the treatment system.

� Given the high relative risks still related to this spray park

and the exposure scenario, a risk communication strategy

may be a simple and very cost effective means of mitigating

risk to users. Something as simple as a pamphlets available

for users to be warned of potential pathogen exposure, or

a staff member who can be consulted with questions users

may have based on the potential risks from spray park

recreation.

� Water treatment for recreational venues such as sprayparks

demands more attention to protect human health.

Acknowledgments

Funding for this work is partially supported by the Center for

Advancing Microbial Risk Assessment (CAMRA) under STAR

grant #R83236201. The financial support of CNPq e Conselho

Nacional de Desenvolvimento Cientifico e Tecnologico

(200007/2009-2) is also appreciated.

Appendix. Supplementary material

Supplementary data related to this article can be found online

at doi:10.1016/j.watres.2011.09.047.

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