physics honors paper - tu nguyen - 2015

Health Risk Assessment of Rainwater Use for

Toilet Flushing

Tu Nguyen

Dr. Sarah Sojka

Presented to the Department of Physics

in partial fulfillment of the requirements

for a Bachelor of Arts degree with Honors

Randolph College

Lynchburg, Virginia

April 30, 2015

HEALTH RISK ASSESSMENT OF RAINWATER USE FOR TOILET FLUSHING 1

Abstract

According to the United States Environmental Protection Agency, the largest use of

household water - 27% - is for toilet flushing. Therefore, if a homeowner implements a rainwater

harvesting system into the house, a significant amount of rainwater collected will be used to

flush toilets. However, given the potential existence of dangerous pathogens, such as

Campylobacter spp., Salmonella spp., and Giardia lamblia, in roof-collected rainwater, it is

important to determine whether there is a potential health risk associated with the use of

rainwater for toilet flushing. In this project, I assessed the risk to human health from using

rainwater to flush toilets. The Quantitative Microbial Risk Assessment (QMRA) was employed

to estimate the microbial risks. The results showed that the risk of infection from inhalation of

Campylobacter and Salmonella after flushing a toilet supplied with rainwater was very low.


Table of Contents

Section I: Introduction …………………………………………………………………………... 3

Section II: Literature Review ……………………………………………………………………. 7

Section III: Methods …………………………………………………………………................ 13

Section IV: Results …………………………………………………………………………….. 30

Section V: Discussion ………………………………………………………………………….. 33

References ……………………………………………………………………………………… 34


I. Introduction

The lack of access to clean water is a profound problem for many people around the

world, especially those from developing countries. According to the WHO/UNICEF Joint

Monitoring Programme for Water Supply and Sanitation (JMP) 2014 Report, 748 million people,

nine percent of the global population, did not have access to improved drinking water in 2012

[1]. An improved drinking-water source is defined by the JMP as one that is protected from

outside contamination, in particular contamination with fecal matter. As a result, researchers

from the World Health Organization (WHO) estimated that 1.5 million people, most of whom

come from African countries, die every year from diarrheal diseases, mainly due to lack of

access to safe drinking water and basic sanitation [2]. Many other severe diseases, such as

trachoma and schistosomiasis, are also attributable to unsafe water, inadequate sanitation or

insufficient hygiene.

Even in the United States, clean water is not guaranteed. A recent article in the USnews

by Simeral and Boardman warned that access to safe water is a growing problem in urban

America. They found that some states in the Midwest and the South are prone to contaminated

water supply troubles due to a high level of agricultural runoff and high water temperatures.

With the use of fertilizers, a considerable amount of nitrogen and phosphorus ends up in the

reservoirs and lakes, creating an ideal environment for algae, which can pollute the drinking

water supply. In addition, older cities are more vulnerable to unclean water supplies because of

their obsolete combined storm system infrastructure. When there is a period of heavy rain,

waterways are contaminated with raw sewage because the combined system causes sewage to

overflow into rivers and streams [3]. As shown above, lack of access to clean water is an

increasing problem not only in developing countries but also in developed countries, including


the United States. With the ever growing global population, freshwater has become more and

more important. It is not something that is unlimited, as it has usually been assumed.

Given the increasing pressures on clean water supplies, developing water conservation

techniques is an urgent task. One of these techniques, rainwater harvesting, the accumulation and

distribution of rainwater for use on-site, has been considered one of the most efficient options to

reduce the demand on domestic water supplies [4]. Rainwater harvesting systems have long been

used in many countries around the world, signifying their importance as an alternative water

supply [5]. By reducing the demand of water for non-potable domestic uses, people can increase

the amount of freshwater available for drinking purposes. Therefore, this helps address the water

shortage issue in the world.

Rainwater harvesting systems can be easily implemented. A basic system contains a roof,

gutters or roof drains, and a piping system to transport water to and from a storage tank or

cistern. The catchment area, normally a house or a commercial building’s roof, is the first point

of contact for rainwater. From there, rainfall flows down through the gutters to the cistern. Since

rainfall coming off a roof will contain dust, leaves, and other debris, a filtering system is often

used to filter the water before allowing it to go into the cistern. A cistern can be placed either

inside or outside, and above or below the ground. Stored rainwater will then be pumped up

through a piping system for domestic uses when needed. A sample design of the rainwater

harvesting system is included below.


Figure 1. Rainwater Harvesting Design

Source: http://www.lowenergyhouse.com/rainwater-harvesting.html

Given the benefits of rainwater harvesting as an alternative improved water source and its

relatively simple structure, one might be tempted to start building the system for one’s house.

However, the use of water collected from a rainwater harvesting system poses some potential

health risks. Dangerous pathogens, such as E. coli, Cryptosporidium, Campylobacter, and

Salmonella, have been detected in rainwater cisterns [6]. As a result, there have been cases of

people reporting being sick after consuming collected water [7].

According to the United States Environmental Protection Agency (EPA), the largest use

of household water, 27%, is for toilet flushing. Therefore, if one implements a rainwater

harvesting system in one’s house, the majority of rainwater collected will be used to flush toilets.

However, given the potential existence of dangerous pathogens in roof-collected rainwater [6], it


is important to determine whether there are potential health risks associated with the use of

rainwater for toilet flushing. Hence, in this project, I am going to examine possible health risks

associated with the use of rainwater harvesting, especially for toilet flushing. The Quantitative

Microbial Risk Assessment (QMRA) will be employed to estimate the microbial risks. I will use

data from the literature, from my own designed experiment, and from a theoretical modeling

study. The findings of this study will hopefully help legislators decide whether it is worthwhile

to require disinfection of rainwater before using it for non-potable purposes.


II. Literature Review

A. Potential Pathogens in Harvested Rainwater

A review of the literature suggests that there has been a plethora of studies on the

microbial risks of rainwater. However, there has not been a consensus on the safety of using

rainwater for domestic potable and non-potable purposes. Some researchers found that it is

acceptable to consume roof-harvested rainwater. According to Dillaha and Zolan (1985), the

majority of rainwater catchment systems (RWCS) in Micronesia provided water of satisfactory

quality for domestic uses. Among the 203 catchment systems examined, fifty-seven percent of

the RWCS did not contain fecal coliform bacteria, and sixty-one percent of them had fewer than

ten total coliform bacteria per 100 mL. However, the authors still recommended disinfecting

water before consumption [8]. According to the South African water quality guidelines for

domestic use [9], the amount of total coliform bacteria per 100 mL found in Dillaha and Zolan’s

research poses a risk of infectious disease transmission with continuous exposure and a small

risk with occasional exposure. Similar to Dillaha and Zolan [8], Coombes, Argue, and Kuczera

(2000) sampled from rainwater tanks and hot water systems in 27 residential units in Newcastle,

NSW, Australia, and found the water compliant with the Australian Drinking Water Guidelines

[10]. Moreover, in South Australia, Heyworth, Glonek, Maynard, Baghurst, and Finlay-Jones

(2006) studied 1016 children who drank untreated rainwater or treated water from water mains,

which are principal pipes in a pipeline system used to move water from a treatment plant to

consumers, to study the effect of rainwater on gastroenteritis. The results showed that

consumption of tank rainwater did not increase the chance of being infected with gastroenteritis

relative to public mains water consumption among these children [11]. As has been shown, many


researchers found it acceptable to consume harvested rainwater, implying it is appropriate to be

used for non-potable uses, such as gardening, clothes washing, and toilet flushing.

However, many other researchers expressed concern about the safety of using rainwater

for potable and non-potable purposes. Merritt et al. (1999) reported an outbreak of

Campylobacter, a common cause of gastrointestinal illness, in north Queensland, Australia. It

was suggested that untreated rainwater, contaminated by the feces of wild animals, was the most

probable source of the Campylobacter infection [7]. In 2007, an outbreak of gastroenteritis was

found at a rural school camp in Australia. All the fecal specimens tested from the patients were

positive for Salmonella Typhimurium definitive phage type 9 (DT9), and two of the four samples

from the untreated private water supply were positive for DT9. As a result, rainwater collection

tanks, which were contaminated with DT9, were suggested as the cause of the outbreak. The use

of rainwater tanks might, therefore, increases the risk of waterborne disease outbreaks [12]. In

the U.S. Virgin Islands, Cryptosporidium and Giardia were detected in rainwater cisterns at

levels that might pose significant public health risks. Cryptosporidium oocysts and Giardia cysts

were found in 81% of the public cisterns and in 47% of the private cisterns. There were samples

that contained high levels of heterotrophic bacteria (9.9 x 105 CFU/ml) and total coliforms

(>2000 CFU/100 ml) [13].

Regarding the use of rainwater for toilet flushing, there has not been a general consensus

on its potential effects on human health. Fewtrell and Kay (2007) found that the amount of

Campylobacter spp. to which a person can be exposed through toilet flushing is within an

acceptable range for WHO’s drinking water guidelines [14]. On the other hand, Albrechtsen

(2002) investigated seven Danish rainwater systems and found that 12 out of 27 analyzed

samples used for toilet flushing were contaminated with one or more pathogens, such as


Legionella non-pneumophila, Campylobacter jejuni, and Cryptosporidium spp. However, these

pathogens were not found in any of the 32 toilets flushed with water from waterworks. The result

suggested that the use of rainwater introduced new and potentially pathogenic microorganisms

into the households that would not occur in toilets supplied with water from incoming mains

[15].

With the detection of pathogenic microorganisms in samples of rainwater used for toilet

flushing, researchers have attempted to assess whether these pathogens can pose a risk to human

health and if so, through what mechanism. Johnson, Mead, Lynch, and Hirst (2013) did a

literature review and came to the conclusion that previous researchers indicated that toilet plume

could contribute to the transmission of infectious diseases [16]. This finding is supported by

Barker and Jones (2005). In their study, Barker and Jones (2005) conducted an empirical

experiment and concluded that there is potential spread of infection caused by aerosol

contamination after flushing a toilet. They also found that some pathogens could persist in the air

after several flushes and an individual might get infected through inhaling and swallowing [17].

With the growing popularity of rainwater harvesting systems as an alternative source

of water supply and the detection of dangerous pathogens in roof-collected water, legislators

from many states have required in their codes that rainwater be disinfected before use. It is

advisable to purify rainwater prior to consumption. However, it is still debatable whether

rainwater should also be disinfected for non-potable uses because it is still a controversial

question of whether untreated rainwater can cause health issues through non-potable uses. In

addition, it is costly to purify rainwater.


B. Quantitative Microbial Risk Assessment in Rainwater Harvesting

Quantitative Microbial Risk Assessment (QMRA) is a framework that “utilizes estimates

of pathogen density and infectivity information to assess pathogen risk” [18]. There are four

steps to QMRA: hazard identification, dose-response assessment, exposure assessment, and risk

characterization. The first stage, hazard identification, encompasses general information about

the pathogens and the adverse consequences to the host from infection. The second stage, dose

response assessment, describes the relationship between the dose received and the resulting

effects on human health. The third stage, exposure assessment, describes the pathways that allow

a pathogen to reach people and cause infection, and then determines the dose of the pathogen

that a person receives. Finally, the last stage, risk characterization, combines the data and

information from earlier stages into a single mathematical model to calculate pathogen risk.

QMRA has been used frequently by researchers to estimate the microbial risks

associated with the exposure to potential pathogens from harvested rainwater used for potable

and non-potable purposes [14] [18] [19]. For example, Fewtrell and Kay (2007) employed the

QMRA to examine the risk of Campylobacter infection from toilets flushed with roof-collected

rainwater in the United Kingdom. In order to demonstrate how QMRA is used by researchers in

the field, I am going to fully recap Fewtrell and Kay’s study.

In the hazard identification stage, the authors reported that bird feces are most likely the

main sources of pathogenic micro-organisms in roof-harvested rainwater. Among those

pathogens, Salmonella spp. and Campylobacter spp. are the two most commonly studied ones.

The authors then decided to use Campylobacter spp. for this project because the infectious dose

from water is lower. Campylobacter infection might result in campylobacteriosis and Guillain-

Barre syndrome [14].


In the dose-response assessment, the authors chose a β-poisson dose-response model for

Campylobacter spp. In addition, among ten infections, 3 were expected to result in illness. There

were two scenarios, uncomplicated and complicated campylobacteriosis. The uncomplicated

scenario had a severity weight of 0.086 with a duration of 6 days compared to 0.28 and 365 days

for the complicated scenario. The severity weight was expressed as disability-adjusted life years

(DALY) with a DALY value of 1 representing death. The complicated forms were expected to

happen 0.5% of the time [14].

For the exposure assessment, Campylobacter concentrations were assumed to be between

0 and 0.56 MPN/100 mL applied throughout the rainwater harvesting system. In addition,

Campylobacter spp. were present between 0 and 10% of the time. Flushing a toilet would eject

water in a range between 0 and 0.25 mL, with a mean of 0.1 mL. It has been further assumed that

4432 people in the defined scenario would use the toilet from three to six times a day, with the

chance of being exposed to aerosol being 5% every time. However, the authors note that several

of these assumptions have not been clarified and may contain a large degree of uncertainty [14].

This motivates me to conduct this study, which aims at providing more reasonable and realistic

assumptions.

Finally, data and estimates from the three earlier stages were combined to estimate the

probability and magnitude of infection for the whole case study population using Monte Carlo

simulations with 5000 iterations. The results showed that the disability-adjusted life years

(DALY) scores satisfied the WHO guidelines for drinking water quality [14].

Similarly, Ahmed et. al (2010) also used the QMRA to quantify the risk of infection

associated with the exposure to potential pathogens through the use of harvested rainwater. In the

hazard identification step, C. jejuni, L. pneumophila, Salmonella spp., G. lamblia and C. parvum


were assessed using binary polymerase chain reaction (PCR) for their presence in harvested

rainwater. In the exposure assessment step, to estimate the pathogen number in the source water,

the number of the genomic copies from the qPCR of each pathogen was converted to bacterial

cells or protozoan cysts; and it was assumed that all the PCR-detected cells and cysts were

capable of causing infections. To estimate the possible pathogen dose received by a person, six

scenarios were chosen. For salmonellosis and giardiasis risk, the scenarios were liquid ingestion

due to drinking rainwater on a daily basis, accidental liquid ingestion due to garden hosing twice

a week, aerosol ingestion due to showering on a daily basis, and aerosol ingestion due to hosing

twice a week. For legionellosis risk, the scenarios were aerosol inhalation due to showering on a

daily basis and aerosol inhalation due to hosing twice a week. In the dose-response assessment,

an exponential dose-response model was used for L. pneumophila and G. lamblia and a beta-

Poisson dose-response model was used for Salmonella. Finally, a Monte Carlo analysis with

500,000 iterations were run to determine the probability of infection for the urban Southeast

Queensland, Australia. The results showed that only those scenarios involving liquid ingestion

via drinking posed an unacceptable level of risk from infection [19].


III. Methods

In this study, I conducted a QMRA to estimate potential risk of infection from inhalation

or ingestion of Campylobacter spp. and Salmonella spp. after flushing a toilet supplied with

harvested rainwater in an office setting. Even though it has been shown that surfaces in a

restroom are often contaminated after flushing a toilet and that the contamination is shared

whenever a person touches those surfaces, in this study I only consider the risk from aerosol

contamination as I assume that people will wash their hands carefully after using the bathroom.

Details for each of QMRA’s four stages are given as follows.

A. Hazard Identification

Campylobacter spp. and Salmonella spp. were selected for this project because these are

the two most commonly studied and most frequently isolated pathogens by scholars [14]. For

example, Ahmed et al. (2010), Albrechtsen (2002), and Franklin et al. (2009) found these two

pathogens in samples of roof-collected rainwater [12] [15] [19]. Therefore, the use of rainwater

for toilet flushing may cause infections related to these pathogens.

Campylobacter can cause campylobacteriosis, an infectious disease. People infected with

campylobacteriosis often develop symptoms such as diarrhea, nausea, abdominal pain, and fever

within two to five days after being exposed to the organism. Campylobacter infection is usually

cured naturally without treatment. Similarly, Salmonella also affects the intestinal tract.

Salmonella infection causes diarrhea, nausea, fever, and dehydration. These symptoms often

appear within 12 to 72 hours after exposure to the bacteria [20].

Both of the pathogens are spread by the fecal-oral route, which is when pathogens in

feces pass from one host to another host’s oral cavity. Following rain events, fecal matter from


birds, which potentially contain these bacteria, might end up in the storage tank via roof runoff.

As a result, after flushing a toilet that uses roof-collected water, there is a risk of bioaerosols

being dispersed in the air, thus causing a risk of infection from inhalation of these bioaerosols

[20].

B. Dose Response Assessment

In the QMRA framework, the dose response assessment stage describes the relationship

between the dose received and the risk of a response, which in this study is the risk of infection.

As suggested by previous researchers, a β-Poisson dose response model was used for both

pathogens.

Haas, Rose, and Gerba (1999) suggests the β-Poisson dose response model for

Salmonella should take the following form [21]:

P=10,000∗[1−(1+ N2884 )

−0.3126] ,

Where P is the expected number of infections per 10,000 exposed persons.

N is the number of infective units ingested.

Similarly, based on experimental data of infection with Campylobacter jejuni, Medema,

Teunis, Havelaar, and Hass (1996) characterizes the dose response model for Campylobacter as

a β-Poisson distribution of the form [22]:

P=10,000∗[1−(1+ N7.59 )

−0.145] .The graphs of these distributions are presented in Figure 2.


0 10000 20000 30000 40000 50000 60000 70000 80000 90000 1000000

1000

2000

3000

4000

5000

6000

7000

8000Beta-Poisson Dose Response Model

Salmonella

Campylobacter

Dose (No. Infective Units)

No.

Infe

ction

s/10

,000

exp

osed

per

sons

Figure 2. Dose Response Models for Salmonella and Campylobacter

As can be seen from the graph, for a low number of infective units, the number of

infections from Campylobacter is high while that from Salmonella is low. As the number of

infective units rises, the risk from Salmonella increases at a much higher rate than the risk from

Campylobacter. Finally, the maximum number of infected people per 10,000 exposed persons

for both of these pathogens is about 7,500.

Since in this study, the number of infective units was fairly small, a graph of the β-

Poisson dose response models for both of these pathogens at low doses is included below.


0.00E+00 2.00E-04 4.00E-04 6.00E-04 8.00E-04 1.00E-03 1.20E-030

0.05

0.1

0.15

0.2

0.25

Beta-Poisson Dose Response Model - Low doses

CampylobacterSalmonella

Dose (No. Infective Units)

No.

Infe

ctio

ns/1

0,00

0 ex

pose

d pe

rson

s

Figure 3. Dose Response Models for Salmonella and Campylobacter at low doses

C. Exposure Assessment

The exposure assessment stage identifies how much a person is exposed to these bacteria.

To be specific, it measures the dose level of the pathogens that a person inhales. This information

will then be fed into the dose-response model. Exposure assessment takes into account the

concentration of bacteria in the rainwater harvesting system and the amount of bioaerosols

inhaled by a person.

In this study, I conducted theoretical and experimental case studies to measure the

exposure to aerosols containing Campylobacter and Salmonella.


1. Theoretical Framework

The theoretical framework to calculate the amount of aerosols inhaled was based on

Nazaroff (2014). Consider a bathroom stall as in Figure 4.

Figure 4. Bathroom stall

Applying the fundamental principle of material balance, which is a version of the

conservation of matter, we know that in our isolated system the change in mass of aerosols is

equal to the supply minus the removal. As seen from Figure 4, only one process can add

bioaerosol material to the indoor air, namely flushing the toilet, while two processes can remove

it, which are air ventilation out of the room and deposition onto room surfaces. If the rate of

supply exceeds the rate of removal, the amount of bioaerosols accumulated will increase [23].

Nazaroff (2014) suggests that there are more ways that aerosols could be added to or removed

from the indoor environments, such as transmission of bioaerosols from outside air into the


room. However, in this study, the only supply source considered is bioaerosols generated from

flushing a toilet supplied with roof-collected rainwater because that is the main focus of this

project. Other means of transmission either are irrelevant to the research question or are

miniscule compared to those included in the model.

Using this line of thinking, we calculated the amount of bioaerosols in the air through

time. Following is a Matlab code I developed to track the concentration of bioaerosols in the air

every minute in a typical 8-hour work day for 21 days. Values for the rate of air ventilation and

for the settling rate were obtained from Nazaroff’s paper [23]. In addition, the value for the

volume of water droplets produced in each flush was taken from Johnson, Lynch, Marshall,

Mead, and Hirst’s study [24]. These scholars conducted an experiment to study the aerosol

generation by microbe-contaminated toilets. They found that for a flushometer toilet with a flush

volume of 5.3 liters per flush, 145,214 droplets were produced per flush. In addition, 95% of

droplets had diameter less than 2 µm. As a result, in this paper, I assumed that the diameter of all

the droplets produced was 2 µm. In addition, assuming that all the droplets had a spherical shape,

the volume of water droplets produced in each flush was equal to

43

× π ×(10¿¿−6)3× 145,214=6.08× 10−13 m3 .¿


C=10^-19; %% initial volume of bioaerosols (m^3)NV=0.35; %% rate of air ventilation B=0.5; %% rate coefficient for deposition on indoor surfaces NVh=0.3; %% hourly rate (/h)Bh=0.4; %% hourly rate (/h)F=6.08*10e-13; %% Volume of water droplets in each flush (m^3)C_new=0;CMC=zeros(30,480); %% to store the value of C at every iteration

for i=1:30 %% 30 days for t=1:480 %% the toilet is used every minute for a total of 8 hours everyday a=rand; if a<0.01 %% 1% chance of use C_new=C+F; else C_new=C; end C=C_new-C_new*NV/60-C_new*B/60; CMC(i,t)=C; end for k=1:16 %% for the 16 hours that the toilet is not used C=C-C*NVh-C*Bh; endend

As mentioned above, Bh and NVh were the hourly settling rate and hourly air ventilation rate,

respectively, for droplets of small sizes. These values were taken from Nazaroff (2014)’s paper

[23]. In addition, I created B and NV, the settling rate and air ventilation rate every minute, by

slightly modifying Bh and NVh. Finally, the initial volume of bioaerosols was determined by

taking the average value of all the volumes of bioaerosols at the beginning of a workday for 30

days.

Figure 5 shows how the amount of bioaerosols develops over 8 hours in a day.


0 60 120 180 240 300 360 420 4800.00E+00

2.00E-06

4.00E-06

6.00E-06

8.00E-06

1.00E-05

1.20E-05

1.40E-05

1.60E-05

1.80E-05

Amount of Bioaerosols In the Air

Time (minutes)

Amou

nt o

f Bio

aero

sols

(mL)

Figure 5. Amount of bioaerosols accumulated in the air in the first workday.

The amount of bioaerosols accumulated during a work day was then used to calculate the

amount of bioaerosols inhaled. Assuming that a person stays in the restroom for 3 minutes and

that the amount of air inhaled in a minute is 10 L, the total amount inhaled is 30 L. In addition,

the volume of the hypothetical bathroom was assumed to be 3.09 m3. Therefore, assuming a

homogenous distribution of droplets in the air, the volume of bioaerosols inhaled was calculated

by multiplying the volume of bioaerosols in the air with 30 L

3090 L . After obtaining data for 30

days, I created a histogram to see the distribution of the amount of bioaerosols inhaled.


Figure 6. Histogram of volume of bioaerosols inhaled.

In Figure 6, the horizontal axis represents different values for the volume of bioaerosols

inhaled and the vertical axis represents their respective probability.

A Beta distribution was selected for this data set because it had the best goodness of fit

statistics.


Figure 7. Beta distribution for the volume of bioaerosols inhaled.

Recall that to conduct the exposure assessment, the concentration of bacteria in the

rainwater harvesting system was also needed. For this variable, three different scenarios were

created: low, medium, and high concentrations.

A review of the literature suggested that the concentrations of Campylobacter and

Salmonella in rainwater harvesting systems vary greatly among different studies. The maximum

concentrations for Salmonella and Campylobacter found were 730 cells/100 mL and 10 cells/100

mL, respectively [25]. Therefore, for the medium concentration scenario, a uniform distribution

with values from 0 to 730 was selected for the concentration of Salmonella and a uniform

distribution with values from 0 to 10 was selected for the concentration of Campylobacter.

Values for the high scenario and for the low scenario were obtained by multiplying and dividing

the above values by 2, respectively.


Multiplying the concentration of bacteria in a rainwater harvesting system by the amount

of aerosols inhaled gave the number of infective units inhaled. Values for the number of infective

units inhaled were given in a distribution format. Examples of these distributions for the medium

concentration scenario are included below.

Figure 8. Number of Campylobacter units inhaled for the medium concentration scenario


Figure 9. Number of Salmonella units inhaled for the medium concentration scenario

The mean number of infective units inhaled for all the risk scenarios is summarized in Table 1.

Concentration Scenarios Pathogens Expected No. of infective units inhaled

LowCampylobacter 0.000000000945132Salmonella 0.000000069108591

MediumCampylobacter 0.000000001897817Salmonella 0.000000138160086

HighCampylobacter 0.000000003788708Salmonella 0.000000276296159

Table 1. Expected number of infective units inhaled.

Therefore, in my theoretical study, it was found that the amounts of infective units inhaled were

really low, being essentially 0 for all of the scenarios.


2. Experimental Study

To verify the results from the theoretical framework, I conducted an experiment. Ideas

for this experiment were inspired by Barker and Jones [17]. A domestic flushometer toilet,

located in a room of size 168 cm x 239 cm x 77 cm, in a Randolph College academic building,

was used throughout the experiment. I chose a flushometer toilet for this study because it has the

highest pressure among different toilet types, which helped represent the worst-case scenario. In

addition, flushometer toilets are fairly common in commercial buildings, which is the setting

being considered. The bacteria used for this experiment was Serratia. There are two reasons for

choosing Serratia instead of the two bacteria being studied. First, Serratia is less dangerous to

human health. Second, it has a distinct bright red color, which makes it easier to identify the

bacteria in a sample. A sample of Serratia is presented below.


Figure 10. A sample of Serratia.

The experiment procedure is outlined as follows:

Before doing the experiment, I disinfected the toilet bowl water with bleach and then

flushed 6 times to clear traces of the cleaning compound. After the sixth flush, I set up control

agar plates around the room and then flushed the toilet. After about 1 minute, I collected the

control plates and placed another set of agar plates. I then seeded the toilet by injecting the

Serratia samples directly into the bowl water. After waiting for the Serratia to spread out in the

toilet bowl for about 2 minutes, I flushed the toilet. I left the plates for 5 minutes, 10 minutes,

and 60 minutes, for the first, second, and third trial respectively and collected them afterwards.


Locations of the agar plates are outlined in Figure 11.

Figure 11. Agar plates locations.

Plates 1 and 2 were placed on graduated cylinders at a distance of 44 cm above the

ground. Plates 3 and 4 were placed at a distance of 74 cm above the floor. In addition, for the last

trial, plates 5 and 6, at 118.5 cm above the floor, were added.


An image of the 6 agar plates collected from the last trial is given as an example.

Figure 12. Treatment agar plates from the third trial.

Even though a high concentration of Serratia was injected into the toilet bowl in all three

of the trials, I could hardly find any Serratia colonies in the agar plates collected after each trial.

This result confirms the low exposure risk as suggested by the theoretical study.

D. Risk Characterization

In the QMRA framework, the last stage, Risk Characterization, integrates information

from the previous stages into a single mathematical model to calculate the risk of infection from


inhalation of Campylobacter and Salmonella. The result shows a full range of possible risks,

including average and worst-case scenarios. A Monte Carlo analysis was performed using input

parameter distributions as described in the previous sections. The Monte Carlo simulation was

used because it is a standard technique that takes into account the variation and uncertainty in

some variables in the model to estimate the amount of risk in the model, which in this case is the

risk of infection. The Crystal Ball software was used to run the Monte Carlo simulation.


IV. Results

Following are the sample results after running 100,000 Monte Carlo Simulation

iterations.

Figure 13. Number of people infected from inhalation of Salmonella – Medium scenario.


Figure 14. Number of people infected from inhalation of Campylobacter – Medium scenario.

The full results for all the concentration scenarios are outlined in Table 2.

Concentration Scenarios Pathogens Expected number of infections per 10,000 people




Table 2. Results for all risk scenarios – 1% chance of use


As can be seen from Table 2, the risk of infection from inhalation of Campylobacter and

Salmonella was really low, being essentially 0. As a result, I decided to increase the frequency of

use to see how it would influence the results. I expect that increasing the frequency of use will

increase the aerosol concentration, thus resulting in a higher risk of infection. Table 3 and Table

4 summarized the results of the Monte Carlo simulation when I modified the frequency of use to

6.67% (meaning the toilet is used every 15 minutes) and to 20% (meaning the toilet is used every

5 minutes), respectively.





Table 3. Results for all concentration scenarios – 6.67% chance of use





Table 4. Results for all concentration scenarios – 20% chance of use


V. Discussion

The risk of infection from inhalation of Campylobacter and Salmonella in this study is

really low due to the extremely low volumes of bioaerosols inhaled. The findings are comparable

to what is typically found in the literature [14] [19]. In particular, these risk values are much

lower than the acceptable risk level of one extra infection per 10,000 persons per year as

indicated by the U.S. Environmental Protection Agency [26]. Given the fact that this research

project aims to describe a worst-case scenario for the risk of infection from inhalation of

Campylobacter and Salmonella as values for many of the parameters were chosen to represent

the highest risk found in the literature, the results found indicate that there is no justification to

require disinfection of rainwater for toilet flushing.

In this study, I made some assumptions that might not be realistic and practical in real

life. Future research should attempt to correct these assumptions. First, even though there can

only be one cell or no cell at all, I also accepted non-integer values of a cell; for example, 0.5 cell

is also included. Second, the death and growth rates of bacteria were not included in the model. It

will be interesting to see how the incorporation of these rates influences the results. Third, the

distribution of droplets in the air was assumed to be homogenous, which might not be the case in

real life.

In addition, this project only assessed the risk of infection from Salmonella and

Campylobacter. However, it is found in the literature that many other pathogens, such as Giardia

and Legionella have been detected in rainwater harvesting systems [19]. Therefore, future

researchers should conduct a health risk assessment with respect to these pathogens.

Finally, it might be worthwhile to dig into the sensitivity analysis for the Monte Carlo

simulation to see how a change in one of the input parameters will influence the final results.


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physics honors paper - tu nguyen - 2015

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