CASE STUDY 5:
URBAN WATER DRAINAGE
Quantitative Microbial Risk Assessment Summer School
Delft, The Netherlands
Helena Sales Ortells
Germán Vásquez Niño
The increase of rainfall due to climate change might collapse the sewer systems of cities
that will not be able to collect and transport such amount of water over a short period of
time. Cities in The Netherlands are increasingly designing sustainable urban drainage
systems (SUDS) within urban areas to relieve the burden of the sewer system during such
events. One of the examples is the Westergasfabriek park in Amsterdam, which is a
former gasworks factory on the west edge of Amsterdam and provide space for creative
and cultural businesses (Figure 1). There are trees, meadows and streams, dozens of
offices, spaces for large and small events, bars, restaurants, a cinema, a theatre and much
Figure1. Map of Westergasfabriek park and illustrations of the water source (A and B).
The water source intended for recreation in the park consists of rainwater collected by
runoff. This place has become a popular place for children to play (Fig. 1). However,
since the park is open not only for the people but also to animals, due to runoff of
rainwater the pathogens from animal (dogs, birds etc.) feces might be transported into the
The most common pathogen microbes that can be found in (surface) waters are cited in
Table 1. Most of them are zoonotic, e.g. Campylobacter or Cryptosporidium, but also
Legionella or cyanotoxines produced by Cyanobacteria are common.
Table 1: Waterborne pathogens (adapted from Coulliete et al).
Pathogen Illness Source
E.coli O157:H7 Bloody diarrhea Human and animal feces
Hemorrhagic colitis Manure
Campylobacter Gastrointestinal illness Animal feces
Sallmonella typhimurium Septicemia Human and animal feces
Cryptosporidium Gastrointestinal illness Human and animal feces
Cyclospora Vomiting, fever, flu-like Human feces
Adenovirus Conjunctivitis Human feces
Hepatitis E virus Liver disease Human feces
Norovirus Gastroenteritis Human feces
Polyomaviruses Respiratory, kidney and Human feces and urine
Merkel skin cancer
Legionella Pneumonia Water aerosols
Helicobacter Peptic ulcers Waterborne
In the present study, Cryptosporidium parvum has been selected as a target
microorganism for several reasons:
It is a zoonotic parasite, shed to the environment in the feces of several animals,
including dogs, cats, birds and rodents, which can be easily found in and around
the park (WHO, 2009)
Cryptosporidium is also very prevalent in the human population: 2.6-21.3% of the
population in African countries, 3.2-31.5% in Central and South America, 1.3-
13.1% in Asia, 0.1-14.1% in Europe and 0.3-4.3% in North America (data based
on detection of oocyst in fecal specimens) (Fayer, 2004).
It is present in the environment in the oocyst stage, which is very resistant to
adverse environmental conditions, e.g. several months in cool moist conditions, 6
months between 10 and 20 °C, 3 months between 25 and 30 °C and it is very
resistant to chlorine and other cleaning products (WHO, 2009)
The patent period in animals oscillates between 1 and several weeks, which
means that after infecting an animal, they are shed for a relatively long period
It is highly infective, with an ID50 of 9-1024 oocysts, and is immediately infective
after it is secreted to the environment (Fayer, 2004).
It exist a well-described dose-response relationship model for human
Cryptosporidium infection (WHO, 2009).
2. Problem formulation
Children playing in the pond water may contract cryptosporidiosis. What is the risk of
children playing in these waters?
3. Hazard identification
Cryptosporidium is a protozoan parasite which can infect both animals and humans. The
parasite infects the epithelial cells of the digestive and respiratory tract causing diarrhea.
After ingestion of an oocyst four sporozoites are released and infect the epithelial cells.
Cryptosporidium only survives outside a host in the oocyst state where it can live month
in cold water (Fayer 2004). 49 outbreaks have been reported between 1984 – 1999. The
largest outbreak in Milwaukee (USA) in 1993 (Mac Kenzie 1994) caused infections to
more than 400.000 persons.
C. parvum is the most frequently zoonotically acquired Cryptosporidium (Fayer. 2004).
However, it has been reported that C. baileyi, C. canis, C. felis, C. hominis, C.
meleagridis, C. muris can also infect humans (Fayer 2008).
3.2. Signs and Symptoms in humans
Cryptosporidium cause cryptosporidiosis, which is a self-limiting gastrointestinal illness
in immunocompetent humans. The incubation period lasts around 7 days (range 1-14
days) and the disease has a duration of 6-9 days. Longer duration is often found in AIDS
patient where the effect of the infection can be lethal. All age groups can be infected but
young children are most susceptible. Infection can happen through consumption of water
or food contaminated with animals and humans feces (WHO, 2009, Fayer, 2004 and
Coulliette, in preparation). Diarrhea is the main symptom, and is usually accompanied by
abdominal cramps, anemia, low-grade fever, nausea, vomiting and weight-loss. No
curative therapy exists for individuals with cryptosporidiosis to the date (Coulliette, in
It has been reported that more than 155 different mammals can be infected with
Cryptosporidium. For this case study the excretion of human pathogenic
Cryptosporidium from dogs, birds, ducks and rabbits is regarded. In European countries
the prevalence in humans varies between 0.3 - 4.3% from the population (Fayer, 2004).
4. Exposure Assessment
4.1. Description of the Westerngasfabriek Park
Figure 2: Area of interest of the Westerngasfabriek Park.
4.2. Cryptosporidium on the ground
The amount of Crypostporidium present in the park depends on the concentration of the
pathogen in animal feces (dogs, rabbits, birds and ducks), the amount of visitors with
dogs and the prevalence of the parasite in the Dutch animals
4.2.1. Excretion in animal feces
The number of animals that visit the park every day, amount of feces that these animals
deposit in the park and concentration of oocysts in those feces, is shown in table 2.
The maximum amount of visitors Westergasfabrik park can hold during events is 18000,
however, the estimated amount of people visiting the park during weekends in summer
months is around 1500 (www.westergasfabriek.nl); due to lower frequency of visits
during the working days, the average frequency of visitors can be assumed to be 700
people per day. Families usually consist of 3 members; therefore it can be assumed that
an average of 200 children are visiting the park daily.
According to the Dutch news (www.dutchnews.nl) the Netherlands has a total population
of 1.8 million dogs therefore an average of 10% of people have dogs.
Table 2: amount of feces per animal, number of animal visiting the park, calculated total amount
of feces and concentration of oocytes in feces.
Amount of Number of Total amount of Concentration
feces animals feces oocytes in feces
(gpapd)* (apd)* (per day) (per g)
Dog 220 70 15400 10-5000 Cox et al.
Cox et al.
Rabbit 100 100 10000 0-77
Gheese 150 50 7500 370+/-197 al.
Kuhn et al.
Duck 153.6 50 7680 50+/-270 Assumption
Birds 151.8 100 15180 210 Assumption
*apd: animal per day
With this data, it is possible to calculate number of Cryptosporidium oocysts that can be
found in the park per day.
4.3. Cryptosporidium in water
4.3.1. Pathogen inactivation and removal
This chapter depicts the pathogen characterization relevant for exposure of children
playing in the described WADI; including the die off rate of Cryptosporidium and the
removal due to the performed reed pond filtration. Cryptosporidium parvum oocysts
follow a first order die off, whereby temperature is believed to be the most lethal factor.
Here, the survival of Cryptosporidum in water and fecal material is of relevance. The
exponential relationship between the die off rate and temperature and parameterization in
water and feces is defined by Peng et al. (2008). Cryptosporidium is known to be very
sensitive to desiccation, and experiments show that they can only survive for about 2 h in
dry state (Fayer, 2004). However, since the moisture content in feces after one week of
storage at ambient temperature is still about 70 % (Ferguson 2007) this factor is
considered as negligible.
To estimate the runoff of rainwater into the WADI data on the rainfall during the summer
months in the area of Amsterdam (meteorological station Schiphol) was collected using
the KNMI website. Data on the daily precipitation events from 1st of June 2009 until the
31st of August was used and it was found that a Weibull distribution fits best. The
duration of precipitation was fitted using a triangular distribution. The average duration
of precipitation (day) is 0.045 and the maximum precipitation is 0.375 mm.
Moreover, the area needs to be defined from which water will flow into the WADI. This
data can be found in the following table:
Table 3: area of the different parts of the WADI
Area Stream 1 Reed
2 2 2
m m m
Concrete 0 2500 0
Grass 10000 2500 12000
Water (m2) 2500 4000 6500
Besides the amount of precipitation and the area, the run off coefficients, which depend
on the kind of soil, need to be considered. We can find two types of surface in the
considered area: Concrete and lawn. The runoff coefficient for concrete is 0.29 and for
lawn (heavy soil average 2-7%) 0.22.
4.3.3. Background concentration
The average concentration of Cryptosporidium oocysts in river water according to
dispersion models developed by Medema and Schijven (2001) ranged from 4.5 – 5.4
oocysts/L, however, the waters where there was no influence of river water (as in
Westergasfabrik park) contained as many as 0.3 – 0.38 oocysts/L.
4.3.4. Pathogen concentration in water
220.127.116.11. Considered parameters and their distributions
In the previous chapters the parameters relevant for the calculation of the final
concentration of oocysts in the water are described. Relevant for the concentration of
oocysts on the ground are: the total amount of feces excreted from the considered animals,
the concentration of oocysts per gram of feces, the prevalence of excretion of oocysts for
these animals and the die-off rate of oocysts in feces. Moreover, we assumed that 90 % of
the dog owners would pick up the dog excrement, meaning that only 10 % will remain on
the lawn. The amount of feces and therewith oocysts transported into the water after a
rainfall event is depended on the run off coefficient and the intensity of the rainfall. The
data for these parameters were fitted to the following distributions (Table 4).
Table 4: Exposure parameters and distributions
Background concentration Single value
Total amount of feces excreted in g Sum of the mean value of the amount of
feces excreted per animal
Oocysts per gram of feces Poisson
Prevalence of excretion of oocysts Poisson
Die-off rate of oocysts in feces Single value
Daily precipitation in m3 Weibull distribution
Duration of precipitation per day Triangular
Run off coefficient Single value (for concrete and lawn)
Die-off rate of oocysts in water Single value
For the data on prevalence and concentration of oocysts per gram of feces, average values
of all animals were necessary to calculate the final concentration of oocysts in the water.
A Poisson distribution was fitted to the data of each animal. The mean of e.g. the oocyst
concentration of each animal was chosen as the describing parameter of the Poisson
distribution. To result in a distribution on the average values of the concentration of
oocysts in the total amount of feces, the distributions on the oocysts concentration per
animal were added and divided by the total number of distributions. A Monte Carlo
simulation with 10.000 repetitions was used to finally result in the distribution of the
average concentration of oocysts for all animals per gram of feces. The same procedure
was performed for the prevalence of oocysts excretion.
18.104.22.168. Equations used to calculate pathogen concentration in water
The pathogen concentration is different in the 3 areas surrounding the main stream of
water. We name those areas Stream 1, Reed and Stream2. Figure 1 represents the water
flow along these 3 areas.
Figure 3:Flow model (for nomenclature see box 1) S1: stream 1, S2: stream 2.
The runoff flow and runoff concentration of C.parvum can be calculated using the
equation 1 and 2, respectively:
P Aland ROc Equation 1
CF M F
C RO kW Equation 2
QRO t P
To calculate the parasite concentration in each area, the following mass balances are
QC Q C RO , S 1 k decay, w
0 RO , S 1
QC Q RO, R C ROR k decay, w 1 Fremoval
QC QRO, S 2 C RO, S 2 k decay, w
Q = Flow Rate (m3/s)
C = Concentration of C.parvum (particles/m3)
Aland = Area (m2)
M = Mass
kdecay = decay constant of cryptosporidium in water
t = time (s)
Fremovla: Fraction of removal
S1 = Stream 1
S2 = Stream 2
R = Reed
RO = Runoff
Box 1: Notation for the equations and figure 3.
4.4. Children exposure
Cryptosporidium oocysts are transmitted via the fecal oral route; the only exposure route
for consumption of the organism was considered the deliberate or accidental ingestion of
contaminated water (WHO 2004). The ingestion routes considered were direct
swallowing of water, swallowing of droplets during splashing and through hand – mouth
The suggested volume of water ingested by non-adults ranges from 30 – 45 ml per 45
minutes of swimming (here playing). For further analyses the mean of 37 ml was used
The mean scenario: A non-adult is assumed to ingest 0.037 L (37 mL) of water
swallowed by a splash in a day. The person is assumed to play for 2 hours a day on the
weekends over a five month period (40 days in a year).
The dose ingested per person (children) per day is equal to the water swallowed times the
concentration of Cyrptosporidium in the water.
5. Hazard Characterization (Dose-Response
The best fit dose-response relation for infection with Cryptosporidium is well described
by Teunis et al. (1999) and it is an exponential model (see equation 6).
P 1 e( rd ) Equation 6
Where P is the probability of infection (in children), r is the probability of a
microorganism to survive inside the host and cause an infection (for Cryptosporidium it
is 0.004005) (Teunis et al., 1999) and d is the dose.
6. Risk characterization
To calculate the dose, three different rainfall scenarios were considered: the falling of
15mm (average precipitation amount), the falling of 40 mm (maximum precipitation) and
a Weibull distribution of the rainfall for the whole period. The risk of infection for
children was calculated separately for stream 1 and 2.
The risk of infection in the different scenarios and streams is shown in figure 4 and table
5. The risk was found to be higher after the 15 mm rainfall event than after the 40mm. It
could be expected a higher risk after higher precipitation, as more water is available to
drag the oocysts present on the ground. However, the more amount of water produces
higher dilution of the parasite in the streams and, therefore, lower concentration and
lower dose. This explanation is supported by figure 5, which shows the sensibility
analysis for stream 2 after 15mm rainfall event. It demonstrates that the duration of
precipitation is indirectly related with the risk of infection. The model should be modified
to include the effect of the amount of water on the amount of oocysts dragged.
Table 5: Risk of infection and probability of risk for the 3 scenarios and the 2 streams.
S1 6.3E-04 0.36
S2 3.1E-04 0.36
S1 8.3E-04 0.35
S2 2.8E-04 0.35
S1 6.3E-04 0.35
S2 2.5E-04 0.35
The sensitivity analysis (figure 5) shows that the prevalence of infection of the
different animals is the factor that contributes more to the uncertainty of the model,
specially the prevalence in ducks. The other contributing factor is the duration of the
precipitation which, as stated before, has an indirect effect on the risk.
Figure 4: Monte Carlo analysis results for the risk of infection for children at
Westergasfabriek Park, summer 2009. A and B show the risk of infection after the 15mm rainfall:
C and D after the 40 mm rainfall; E and F show the results for the whole summer (Weilbull
distribution). A, C and E, represent the risk in stream 1; B, D and F represent the risk in stream 2.
Figure 5: Sensitivity analysis for the risk of infection in stream 1 after the 15mm rainfall scenario.
7. Conclusions and Recommendations
Although the risk could be considered low, not all the possible risk factors were
considered due to data gaps. Several assumptions were done based on literature and some
under common knowledge inside the group; this would give certain bias to the model
design. Further definition of the model would give more specific results.
As recommendations we consider that some measures can be executed to reduce the
quantity of feces dropped in the park and the risk of infection:
Awareness campaigns for people to pick up the feces of dogs and pets brought to
Signalization and campaigns to reduce the feeding of wild animals
Close or signalized areas of risk when sudden and high rainfalls are presented
after dry long periods.
Coulliete, A.D., Alan-Yillmaz, A., Dreelin, E.A., McNinch, R.M.,Fong, T.T. and Rose,
J.B. Drinking Water Safety in the 21st Century, book chapter in preparation).
Cox, P., Griffith M., Angles M., Deere D., Ferguson C. (2005). Concentrations of
Pathogens and Indicators in Animal Feces in the Sydney Watershed. Applied and
Environmental Microbiology, 71 (10), 5929-5934
Graczyk, TK., Fayer R., Trout, J.M., Lewis E.J., Farley, C.A., Sulaiman, I., Lal, A.A.
(1998). Giardia sp. Cysts and Infectious Cryptosporidium parvum Oocysts in the Feces of
Migratory Canada Geese (Branta Canadensis). Applied and Environmental
Microbiology, 64(7), 2736-2738
Kuhn R.C., Rock C.M., Oshima K.H. (2002). Occurrence of Cryptosporidium and
Giardia in Wild Ducks along the Rio Grande River Valley in Southern New Mexico.
Applied and Environmental Microbiology, 68(1), 161-165
Fayer, R. (2004). Cryptosporidium: a Water-borne Zoonotic Parasite. Veterinary
Parasitology 126, 37-56.
WHO/HSE/WHS (2009). Risk Assessment of Criptosporidium in Drinking Water. WHO,
Mac Kenzie, W.R., Hoxie, N.J., Proctor, M.E., Gradus, M.S., Blair, K.A., Peterson, D.E.,
Kazmierczak, J.J., Addiss, D.G., Fox, K.R., Rose, J.B. and Davis J.P. (1994). A Massive
Outbreak in Milwaukee of Cryptosporidium Infection Transmitted through the Public
Water Supply. N Engl J Med, 126(4), 37 - 56.
Fayer, R., Xiao L. (2008). Cryptosporidium and Cryptosporidiosis. Second Edition. CRC
Press, IWA publishing, USA, 545.
Peng, X., Murphy, T. and N. M. Holden. (2008). Evaluation of the Effect of Temperature
on the Die-Off Rate for Cryptosporidium parvum Oocysts in Water, Soils, and Feces.
Appl Environm Microbiol 74(23), 7101–7107.
Dufour, A.P., Evans, O., Behymer, T.D., Cantu, R. (2006) Water ingestion during
swimming activities in a pool: A pilot study. J Water Health 0.4(4), 425 – 430.
Ferguson, C. M., Davies, C.M., Kaucner, C., Krogh, M., Rodehutskors, J., Deere D.A.
and Nicholas J. Ashbolt (2007). Field scale quantification of microbial transport from
bovine faeces under simulated rainfall events. J Water Health 0.5(1), 83-95.
Medema, G.J., Schijven, J.F. (2001) Modelling the sewage discharge and dispersion of
cryptosporidium and giardia in surface water. Water Res, 35(18) 4307-4316.
Teunis, P.F.M., Nagelkerke, N.J.D. and Haas, C.N. (1999). Dose response models for
infectious gastroenteritis. Risk Analysis, 19(6), 1251–1260.
www.dutchnews.nl : (Friday 12 March 2010)
www.knmi.nl: Internet source