Microbial Risk Assessment for Recreational Use of the Basin,

By Allison Park

B.S. Civil and Environmental Engineering, 2014 Massachusetts Institute of Technology

Submitted to the Department of Civil and Environmental Engineering in Partial Fulfillment of the Requirements of the Degree of

Master of Engineering in Civil and Environmental Engineering at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY - - INIE OF TECHNOLOGY June 2014 JUN 13 2014 C 2014 Massachusetts Institute of Technology All rights reserved L.RALIBRA RIE S

Signature redacted Signature of Author: Allison Park Department of Civil and Environmental Engineering May 9 , 2014 Signature redacted Certified by: Peter Shanahan Senior Lecturer of Civil and Environmental Engineering (7Thesis/ Adisor

Accepted by: Signature redacted Heidi Nepf Chair, Departmental Committee for Graduate Students

Microbial Risk Assessment for Recreational Use of the , Singapore

By Allison Park

Submitted to the Department of Civil and Environmental Engineering on May 9th 2014 in Partial Fulfillment of the Requirements of the Degree of Master of Engineering in Civil and Environmental Engineering

Abstract

The water quality in the Kallang Basin, Singapore, was analyzed in order to determine how safe the waters are for recreational users, specifically focusing on dragon-boat racers. The Public Utilities Board of Singapore has been managing reservoirs under the "Active, Beautiful, and Clean Waters Programme" in order to help the public recognize the value of their scarce water sources. Therefore, microbial risk assessments were conducted on locations along the Kallang Basin to analyze any diurnal or spatial differences in probabilities of illness, and establish guideline geometric mean concentrations.

Samples were collected at four different locations along the Kallang Basin every four hours during a 48-hour period. Samples were then analyzed for Enterococci and E. coli using most- probable-number methods. Adenovirus was analyzed by Liu (2014) using quantitative polymerase chain reaction. Based on the Wiedenmann et al. (2006) statistics-based risk model, no-observed-adverse-risk levels or guideline geometric-mean levels were established at 128 colony forming units (CFU) / 100 mL for Enterococci and 697 CFU/ 100 mL for E. coli.

Based on these guideline geometric-mean concentrations, all of the stations exceeded the tolerable illness level for indicator bacteria at certain times, with peak concentrations at 7:00 A.M. and 11:00 A.M. However, for adenovirus, the probabilities of illness did not exceed the tolerable level based on appropriate dragon-boat racer ingestion rates. Statistical analysis showed that a high correlation existed between adenovirus concentrations and E. coli concentrations. Future studies should analyze specific locations along the Kallang Basin that contribute to high concentrations of indicator bacteria and viruses.

Thesis Supervisor: Peter Shanahan Title: Senior Lecturer of Civil and Environmental Engineering

Acknowledgements

First, I would like to thank my thesis advisor, Dr. Peter Shanahan, for his support and exceptional amount of technical advice. Without him, this thesis would not be in the shape it is today. Next, I would like to thank my MIT group members who traveled to Singapore with me for their enthusiasm, support, and passion throughout this process: Justin Angeles, Riana Kernan, and Tina Liu.

Additionally, I would like to thank the extremely helpful and experienced members of the National University of Singapore (NUS) for laboratory and field-work assistance. I would especially like to thank Professor Karina Gin and Ginger Vergara for allowing us to work with them.

Finally, I would like to thank my family and friends for their love and support throughout. They provided me with optimism, and taught me the value of hard work and discipline.

Table of Contents

Table of Tables ...... 9 Table of Figures...... 10 1. Introduction...... 11 1.1 Singapore's W ater Sources...... 11 1.1.1 Imported W ater...... 11 1.1.2 Desalination...... 12 1.1.3 N EW ater...... 12 1.1.4 Local Catchment W ater...... 12 1.2 ABC Program m e ...... 13 1.3 K allang River Basin Overview ...... 14 1.4 Current Study ...... 16 2. Quantitative Risk Assessment of Fecal Indicator Microbes for Recreational Use 18 2.1 Fecal Indicators and Viruses ...... 18 2.1.1 Coliphage and Adenovirus ...... 19 2.1.2 Epidem iological Studies...... 20 2.2 Recreational Risk Assessm ent...... 21 2.2.1 Site Characterization ...... 21 2.2.2 Risk Quantification...... 22 2.2.3 Risk M anagement and Communication ...... 22 2.3 US Standards History...... 23 2.3.1 W orld Health Organization and Singaporean Standards ...... 24 3. Models Assessing Risk Associated with Microbes ...... 26 3.1 No-O bserved-Adverse-Effect Levels (NOAELs) ...... 26 3.2 van Heerden et al. (2005) Exponential Dose-Response Risk Model...... 26 3.2.1 Poisson-Distributed Dose-Response M odel ...... 27 3.3 Dufour (1984) Risk M odel ...... 30 3.4 Fleisher (1991) Risk Equations...... 31 3.5 W iedenm ann (2007) Risk M odel...... 31 3.6 Dufour vs. W iedenm ann M odel ...... 35 3.7 Single-Sam ple M axim um Allowable Densities...... 35 4. W ater Sam pling A nalyses and O verview ...... 37 4.1 Field Sam ple Collection ...... 37 4.2 Laboratory Analysis...... 37 4.2.1 Enterococci and E. coli Lab Analysis...... 37 4.2.2 Indicator Bacteria NOAEL and Probability of Illness Derivation...... 38 4.2.3 Quantitative Polymerase Chain Reaction (PCR) Preparation for Adenovirus Analysis ...... 3 9 4.3 W ater Ingestion Calculation...... 40 5. R esults ...... 42

7 5.1 Indicator Bacteria Results ...... 42 5.2 E. coli Results...... 44 5.2.1 E. coli Results for Station 2...... 44 5.2.2 . coli Results for Station 3 ...... 45 5.2.3 E. coli Results for Station 4...... 47 5.2.4 E. coli Results for Station 5 ...... 48 5.2.5 Summary of E. coli Results ...... 50 5.3 Enterococci Results ...... 50 5.3.1 Enterococci Results for Station 2 ...... 50 5.3.2 Enterococci Results for Station 3 ...... 52 5.3.3 Enterococci Results for Station 4 ...... 53 5.3.4 Enterococci Results for Station 5 ...... 55 5.3.5 Summary of Enterococci Results ...... 56 5.4 Adenovirus Results...... 57 5.5 Comparison of Indicator Bacteria and Adenovirus ...... 61 5.5.1 Indicator Bacteria and Adenovirus Relationship...... 61 5.5.2 Causes for High Indicator Bacteria Concentrations ...... 63 6. Conclusion ...... 66 6.1 Guidelines...... 66 6.2 Future Research ...... 66

References...... 68

Appendix A...... 73 List of Adenovirus samples and concentrations in genomic copies/L...... 73 Adenovirus quantitative PCR results in genomic copies/L (GC/L) for Station 2 at each time ...... 7 5 Adenovirus quantitative PCR results in genomic copies/L for Station 3 at each time...... 76 Adenovirus quantitative PCR results in genomic copies/L for Station 4 at each time ...... 77 Adenovirus quantitative PCR results in genomic copies/L for Station 5 at each time ...... 78 Appendix B...... 79

Raw E. coli data for samples collected on January 7th 2014...... 79 Raw E. coli data for samples collected on January 8 th 2014...... 80

Raw E. coli data for samples collected on January 9 1h 2014...... 81

Raw Enterococci data for samples collected on January 7 th 2014 ...... 82 Raw Enterococci data for samples collected on January 8 th 2014 ...... 83

Raw Enterococci data for samples collected on January 9 th 2014 ...... 84

8 Table of Tables Table 1: Indicator Bacteria Density Criteria (USEPA 1986) ...... 24 Table 2: WHO Bacterium Guidelines (WHO 2003)...... 25 Table 3: Findings from van Heerden et al. (2005) to determine concentration of adenovirus (Eq. 4)...... 29 Table 4: Findings from van Heerden et al. (2005) to determine N (Eq. 3)...... 29 Table 5: Wiedenmann (2007) NOAEL risk equation variables ...... 38 Table 6: E. coli and Enterococci geometric mean concentrations of all locations for each sam pling tim e ...... 42 Table 7: Station 2 E. coli concentration and probability of illness (%)...... 44 Table 8: Station 3 E. coli concentration and probability of illness (%)...... 46 Table 9: Station 4 E. coli concentration and probability of illness (%)...... 47 Table 10: Station 5 E. coli concentration and probability of illness (%)...... 49 Table 11: E. coli geometric mean concentrations averaged over all times and probability of illness (%) at each station...... 50 Table 12: Station 2 Enterococci concentration and probability of illness (%)...... 52 Table 13: Station 3 Enterococci concentration and probability of illness (%)...... 52 Table 14: Station 4 Enterococci concentration and probability of illness (%)...... 54 Table 15: Station 5 Enterococci concentration and probability of illness (%)...... 55 Table 16: Enterococci geometric mean concentrations at all times and probability of illness (% ) at each station...... 56 Table 17: Adenovirus concentration calculation at each station...... 58 Table 18: Calculation of N and daily and yearly probability of illness due to adenovirus exposure for an ingestion rate of 6 mL per day, 156 days of the year...... 59 Table 19: Calculation of N and daily and yearly probability of illness due to adenovirus exposure for an ingestion rate of 30 mL per day, 365 days of the year...... 60 Table 20: Daily and yearly probabilities of illness due to adenovirus exposure for each day of sampling, representative of the whole Kallang Basin ...... 60

9 Table of Figures Figure 1: Catchment Areas (light blue) and Reservoirs (dark blue) in Singapore (Joshi et al. 2012)...... 13 Figure 2: Map showing Entire Marina Basin, including the Kallang Basin (PUB 2013a) .15 Figure 3: Detailed View of Kallang Basin (PUB 2013a)...... 15 Figure 4: Sampling Locations along the Basin (Google Maps 2013).....17 Figure 5: Variations in vinJOOmLfrom Wiedenmann (2007) risk equations per Dixon (2009)...... 34 Figure 6: E. coli and Enterococci geometric mean concentrations along the Kallang Basin in MPN/100 mL versus date and time of day...... 43 Figure 7: Station 2 E. coli concentration versus date and time ...... 45 Figure 8: Station 3 E. coli concentration versus date and time ...... 46 Figure 9: Station 4 E. coli concentration versus date and time ...... 48 Figure 10: Station 5 E. coli concentration versus date and time ...... 49 Figure 11: Station 2 Enterococci concentration versus date and time ...... 51 Figure 12: Station 3 Enterococci concentration versus date and time ...... 53 Figure 13: Station 4 Enterococci concentration versus date and time ...... 54 Figure 14: Station 5 Enterococci concentration versus date and time ...... 56 Figure 15: Probability of illness versus concentration of Enterococci curve at different intake rates per day ...... 57 Figure 16: Station 2 Probability of illness per day versus volume consumed (mL) due to adenovirus exposure...... 59 Figure 17: Log Adenovirus concentration (viruses/L) versus Log E. coli Concentration (MPN/ 100 mL) at each station...... 62 Figure 18: Log Adenovirus Concentration (viruses/L) versus Log Enterococci Concentration (MPN/ 100 mL) at each station ...... 62 Figure 19: Map view of Station 3, Kallang Riverside Park, and Station 4, Upper Road, upstream of Station 3 (Google Maps 2014)...... 64 Figure 20: Upstream of Station 4, Upper Boon Keng Road, along the Kallang River (Google M aps 2014)...... 64 Figure 21: Upstream of Station 5, Crawford Street (Google Maps 2014)...... 65

10 1. Introduction Singapore is a densely-populated island nation located in Southeast Asia. Since the 1970s after the end of British military presence in 1971, Singapore began rapid economic growth primarily based on manufacturing and trade. However, Singapore's rapid growth also came with limitations. Because this island nation has no natural aquifers or lakes and little land to collect rainwater, Singapore has been searching for methods to maximize water as much as possible. Currently, Singapore's Public Utilities Board (PUB) creatively manages water supplies and encourages conservation in order to provide the needed 400 million gallons per day (MGD) to its 5.4 million residents (PUB 2013a). Further, the needed water supply is expected to double by 2060. To address this growing demand, Singapore has been increasing supply by tripling water reclamation and increasing desalination capacity tenfold. The augmentation of these supply processes will help meet up to 80% of the water demand in 2060. Due to limited water supplies, under the Active, Beautiful, and Clean Waters Programme discussed in more detail in Section 1.2, the PUB wishes to open up the island's water bodies to recreational use. The PUB hopes to help the public recognize how precious their water sources are in order to conserve and protect their supplies for the future.

The current area of focus for this thesis, the Kallang River Basin (Figure and Figure ), is used for a variety of recreational activities such as dragon- boat racing, water sports, fishing, and picnicking. However, the PUB has concerns that the bacteriological levels in the waters may pose health and safety risks for people coming in contact with it. Because of its intended use for recreational activities, a team of Master of Engineering students from the Massachusetts Institute of Technology visited Singapore in January 2014 in order to evaluate the water quality of the basin.

Section 1 of this thesis was written in collaboration with Justin Angeles, Riana Kernan, and Tina Liu.

1.1 Singapore's Water Sources

Singapore has four water sources: imported water, desalination, reclaimed "NEWater," and local catchment water.

1.1.1 Imported Water Malaysia's Johor State Government and Singapore signed a water agreement in 1961, but it expired on August 31, 2011. Under a separate water agreement created in 1962, Singapore is still allowed to draw up to 250 MGD from the Johor River until 2061 (PUB 2013a). Due to the uncertainty of the future of this agreement and the desire to be water independent, PUB hopes to provide all of its water internally by the expiration of this agreement in 2061.

11 1.1.2 Desalination Singapore's first desalination plant, built and operated since 2005, supplies about 30 MGD. The plant was designed to supply water to PUB for a period of 20 years. With growing demand for water, a second and larger desalination plant, Tuaspring Desalination Plant, opened in September 2013. This plant supplies an additional 70 MGD to Singapore's water supply and together the two desalination plants have been able to supply 25% of Singapore's water needs. However, desalination poses many challenges. Desalination is energy-intensive and more costly than NEWater or conventional water treatment, so PUB strives to develop technologies that will reduce this large energy consumption and cost.

1.1.3 NEWater Since its introduction in 2003, NEWater has been a source of raw water for potable use. The process of NEWater uses advanced membrane technologies (microfiltration, reverse osmosis, and ultraviolet disinfection) in further purifying treated used water. PUB continuously monitors NEWater quality through water sampling and monitoring programs. The National University of Singapore (NUS) discovered through extensive tests that NEWater is of higher purity than PUB water (Soon et al. 2009). Therefore, NEWater can substitute PUB tap water for use in certain manufacturing processes that require ultra-clean water. In addition, PUB has incorporated NEWater into drinking water systems where NEWater is injected into reservoirs, and then the mixed water is further treated through water treatment plants. The largest NEWater plant located in supplies about 50 MGD of water. NEWater can currently meet 30% of Singapore's total water demand with goals of meeting up to 55% by 2060 (PUB 2013a).

1.1.4 Local Catchment Water Singapore's land area is 716 square kilometers, with two-thirds utilized as water catchment. Surface water is collected and stored in 17 reservoirs located throughout the island (Figure 1). Singapore is one of only a few cities around the world that applies urban storm water harvesting on such a large scale. The extensive use of urban runoff necessitates the reduction of non-point source pollution and careful management of surface water quality. PUB's Active, Beautiful, and Clean Waters (ABC Waters) Programme (Section 1.2) seeks to transform the city's concrete channels, drains, and reservoirs into more natural looking and sustainably-managed waterways so that Singapore becomes a "City of Gardens and Water" (PUB 2013a). PUB hopes that these efforts will help increase water conservation and reduce pollution in Singapore's waterways, creating a vitalized community.

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Figure 1 - Catchment Areas (light blue) and Reservoirs (dark blue) in Singapore (Joshi et al. 2012)

Unfortunately, storm water runoff often contains high levels of bacteria and other pathogens that may pose risk to human health. Increased public contact with the water through recreational activities will require extensive monitoring of the water quality. The goal of this study is to perform risk assessments on local catchment water, specifically studying the Marina Reservoir watershed, to gain an understanding of the types of risk posed to recreational users. With these results, the intention is to help PUB achieve their goals of maintaining an active, beautiful, and clean water system for all to enjoy.

1.2 ABC Programme

In 2006, PUB developed the Active, Beautiful, and Clean Waters (ABC) Programme. As mentioned, PUB is transforming Singapore's utilitarian drains, canals, and reservoirs into streams, rivers, and lakes to be well integrated with the surrounding parks and spaces, while also creating centers for recreational activity. The main intention of this transformation is to allow Singaporeans to appreciate and cherish their limited natural water resources.

13 The objectives of the ABC program are based on its acronyms (PUB 2013a):

Active: Bring people closer to the water through recreational activities. Through these activities, the people will develop a connection with the water to value it and recognize how precious their water sources are.

Beautiful: Make the reservoirs and waterways aesthetically pleasing and well integrated with the local surroundings and residential areas.

Clean: Improve the water quality by incorporating retention ponds, aquatic plants, and fountains to help remove nutrients. Minimize pollution in the waterways through education and close people-water relationships.

1.3 Kallang River Basin Overview

The Kallang River Basin is located in the southeastern part of the country, just northeast of downtown Singapore (Figure 3). The three major tributaries that drain into the basin are the Canal, the Kallang River, and the River. Five main waterways drain into the Marina Basin and include the Singapore River, Stanford Canal, Rochor Canal, Kallang River, and Geylang River. The basin was created in 2008 by the damming of the Marina channel by a 350-meter long barrage. The Marina Bay and Kallang Basin were then converted into freshwater reservoirs. The barrage provides flood protection as well as another source of drinking water for the people of Singapore (Nauta et al. n.d.).

14 Protected Catchment

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Utban 4Sormwater Collecfon

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Figure 2 - Map showing Entire Marina Basin, including the Kallang Basin (PUB 2013a)

Figure 3 - Detailed View of Kallang Basin (PUB 2013a)

15 The Kallang River is the largest river in Singapore, spanning 10 kilometers, and the basin is surrounded by to the north, to the south, the Kallang Stadium area to the east, and the Beach Road area to the west.

Due to extensive recreational usage of the Kallang Basin, the focus of this study is to determine risk towards human health. From the results of this study, continuous monitoring and management of runoff and bacterial concentrations from the basin should be established to evaluate the microbial diversity and determine the risks associated.

1.4 Current Study

The Kallang Basin and the rivers that drain into it have become popular venues for recreational activities such as dragon- boat racing where hundreds of teams race against each other. Within the Kallang River, various dragon- boat racing clubs have practices and competitive sessions during the weekends. It is also an area where leisurely dragon- boat racing can occur for Singaporeans to practice and learn the sport. As mentioned, due to great recreational activity within this basin, it is extremely important to determine health risks associated with such use of the area. Further, analyzing viral pathogens using traditional indicator risk models may pose a challenge, so this study translates extensive field data of viral pathogens and indicator bacterial concentrations into various potential risk models. In addition, spatial and diurnal risk differences may result depending on sampling location and time. Based on the results from this study, the locations and times along the Kallang Basin contributing the highest probabilities of illness could be extremely valuable for regulatory agencies to protect recreational users. The data collected during January 2014 will be used in this study with a statistics-based risk model and exponential dose-response model to determine the probability of illness to recreational users of the Kallang Basin. Additionally, the correlation between indicator bacteria and adenovirus will be determined using linear regression.

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Figure 4 - Sampling Locations along the Kallang River Basin (Google Maps 2013)

17 2. Quantitative Risk Assessment of Fecal Indicator Microbes for Recreational Use

2.1 Fecal Indicators and Viruses

The current criteria for evaluating human risk due to recreational activity are based on water quality measurements of fecal indicator bacterial concentration. Due to the innumerable species of pathogens and the difficulty of detecting them easily, indicator organisms are used as an alternative method for measuring environmental water quality. An indicator organism provides evidence of the presence of a pathogen surviving under similar environments, physically and chemically. According to Wade et al. (2003), studies have shown that Enterococci and . coli are the most effective primary indicators for predicting the presence of pathogens and specifically those causing gastrointestinal illness.

The following characteristics are fundamental in establishing the reliability of an indicator. Specifically for fecal contamination, indicator organisms should be able to do the following (Sloat and Ziel 1992; Thomann and Mueller 1987):

* Be easily detected using simple laboratory tests * Not be present in unpolluted waters * Be present in concentrations related to the extent of contamination * Have a die-off rate not faster than the die-off rate of the pathogens of interest

Unfortunately, many studies have shown that indicators may not accurately predict the presence of waterborne human pathogenic viruses. For instance, preliminary evidence has shown that F coli and Enterococci may be naturally detected in tropical regions without a source of contamination based on a study conducted by researchers at the University of Puerto Rico in 1991 (Hernandez-Delgado et al. 1991). Therefore, van Heerden et al. (2005) evaluated the possibility of directly using human enteric viruses such as human adenovirus as improved fecal contamination indicators. Many different types of adenovirus (51) cause a wide range of infections involving gastrointestinal, respiratory, and urinary tracts. The presence of these viruses in water used for drinking or recreational use pose potential health risks. Adenoviruses also occur in large numbers in many water environments, and these viruses are exceptionally resistant to purification and disinfection processes (USEPA 1998).

Statistical tools have been established to assess the probability of illness constituted by enteric viruses and other pathogens in water used for human consumption. The U.S. Environmental Protection Agency (USEPA 1986) recommended a tolerable risk of one

18 infection per 10,000 consumers per year for drinking water. For recreational waters, the agency has recommended a tolerable risk of one infection per 1000 bathers per day. For fresh water Enterococci and E. coil, the acceptable level of risk or probability of illness is 8 cases of highly credible gastro-intestinal illness per 1000 swimmers. The guideline level for Enterococci is 33 colony- forming units (CFU) per 100 mL, and 126 CFU per 100 mL for E. coli. The USEPA (1986) used the Dufour model discussed in detail in Section 3.3 to establish guideline levels for recreational waters.

A study by Rose et al. (1987) found enteroviruses and rotaviruses in many samples that had been considered acceptable by indicator bacteria standards. The main conclusions from the study were that bacterial indicator occurrence did not correlate well with viral occurrence and that in a majority of the studies that monitored marine waters for both bacterial indicators and pathogenic viruses, viruses were detected when indicator levels were below public health water quality threshold levels.

2.1.1 Coliphage and Adenovirus As discussed in Section 2.1 above, E. coli bacteria and fecal Enterococci are the most common indicators used in water quality testing. In 2006, coliphage, or bacteriophage in the coliform group of bacteria, was considered an equivalent fecal indicator to E. coli according to the Ground Water Rule (GWR) (US Federal Register, 2006). Coliphage effectiveness as a fecal indicator was verified by 20 years of epidemiological data showing that over 50% of waterborne illnesses in the United States were viral in origin (USEPA 2006b). Further, due to the many challenges of using traditional indicators, especially in tropical waters where bacterial indicators may occur naturally, researchers have been searching for more accurate indicators to quantify risk from bacteria and enteric viruses. One of the potential alternatives is coliphage, which are bacterial viruses that attack E. coli. Male-specific coliphage are released through fecal matter and are unable to replicate naturally in the water without the presence of coliform bacteria (USEPA 2001 a). Therefore, coliphages can be useful indicators of water pollution. Unfortunately, coliphage testing is more complicated than E. coli testing. For instance, the official method of coliphage qualitative assessment, EPA Method 1601, uses many steps and reagents. It also takes over 48 hours to complete and obtain results. Therefore, due to the complexity and labor involved, it is very unlikely that regulators and municipalities would perform coliphage tests. Now, with the recognition in the GWR that viral indicators are an equivalent indicator to E. coli, there are now more efforts to simplify processes.

Another alternative is to detect enteric viruses directly using molecular methods such as quantitative polymerase chain reaction (PCR). This alternative is the preferred method because it eliminates any uncertainties of using bacterial fecal indicators and can be used to directly detect viruses, which do not replicate well in cell cultures (Pina et al. 1998). A study conducted at the Nanyang Technological University in Singapore analyzed the

19 prevalence and genotypes of pathogenic viruses in wastewaters in tropical regions (Gin and Aw 2010). Results showed that adenoviruses, astroviruses, and noroviruses were detected in 100% of the sewage and secondary effluent. There was widespread occurrence of tested enteric viruses in urban wastewaters in Singapore.

As mentioned in Section 2.1, adenoviruses occur in many water environments, and these viruses are extremely resistant to disinfection processes (Eischeid et al. 2009 and Nwachuku et al. 2005). Adenovirus types 40 and 41 cause most of adenovirus-associated gastroenteritis and are quite resistant to conventional methods of disinfection, have a high excretion rate in infected individuals, and are extremely persistent in the environment (Enriquez et al. 1995, Kuo et al. 2010 and Rigotto et al. 2011). For these reasons, the presence of adenovirus 40 and 41 in recreational waters poses a large health concern.

A study was conducted on coastal waters in Southern California that analyzed the correlation between human adenovirus and coliphage in urban runoff (Jiang et al. 2001). The results showed that a significant correlation did not exist between adenovirus and somatic coliphage (r=0.32), but a significant correlation did in fact exist between adenovirus and F-specific coliphage (r-0.99). Two types of coliphage, somatic coliphage and male-specific (F+) coliphage, have a subtle difference. Male-specific coliphages are viruses that infect through the F-pilus of male strain E. coli, and somatic coliphages are viruses that infect the outer cell membrane of E. coli host cells (USEPA 2001b).

2.1.2 Epidemiological Studies The primary purpose of conducting epidemiological studies is to determine the causes of diseases and identify methods to prevent and manage them (Fosgate and Cohent 2008). Dufour (1984) and Wiedenmann et al. (2006) sought to determine the relative probabilities of illness associated with recreational use of waters based on chosen indicator bacteria. Dufour (1984) developed a log-linear model analyzing fecal coliforms, E. coli, and fecal streptococci or Enterococci. Wiedenmann et al. (2006) developed a statistics-based risk model analyzing E. co/i, Enterococci, Clostridiumperfingens, and coliphage. Both of these epidemiological studies tested for adverse health effects of gastro-intestinal disease. Dufour's study was a freshwater, prospective-cohort study, where he recruited participants who already used the water for recreational use. Wiedenmann et al. (2006) conducted the first randomized-controlled-trial that looked at freshwater recreational use risks. Measuring the risk associated with recreational water depends on many factors. For instance, sampling locations are chosen based on complete exposure pathways available to recreational users. The choice of indicator bacteria depends on historical regulatory use as well as availability of epidemiological studies. This availability provides a quantitative relationship between the indicator bacteria and probability of illness. The studies by Wiedenmann et al. (2006) took place at five freshwater beaches in Germany. A large group of participants (2,196) was recruited prior to recreational contact, and participation and exposure to water were strictly controlled.

20 The participants were allowed to swim for ten minutes, and were instructed to completely immerse their heads in the water at least three times. Every twenty minutes, samples were taken to analyze the microbial activity within the waters (Wiedenmann et al. 2006). Non- swimmers were not allowed to have any contact with the water. Phone interviews were conducted one week and three weeks after exposure to track illness rates. Wiedenmann et al. (2006) found that E. coli and Enterococci were well correlated with rates of illness. Section 3.3 and Section 3.5 discuss the Dufour (1984) and Wiedenmann et al. (2006) recreational epidemiological studies in more detail and how risk models were derived based on those studies.

2.2 Recreational Risk Assessment

Environmental risk assessment involves the three phases of site characterization, risk quantification, and risk management and communication. Site characterization describes how users are exposed to pathogens within a specific site. Risk quantification, which includes a newer field of quantitative microbial health risk assessment, focuses on the concentration of particular pathogens that humans may become exposed to from recreational activity (USEPA 1998). Quantitative microbial health risk assessment follows the four-step process of hazard identification, exposure assessment, dose- response analysis (probability of illness), and risk characterization (determining how much infection would arise in a population exposed to a distribution of pathogens in the water). Risk management and communication require the participation of a relevant regulatory agency in order to develop any necessary rules or regulations (Dixon 2009).

Dose-response analyses use the terminology "risk of illness" and "probability of illness" interchangeably. Their difference is quite subtle, but should be clarified. Risk is closely related to the probability of illness, except risk incorporates this probability in addition to any consequence of the event (Holton 2004). For instance, the probability of contracting gastrointestinal illness from a certain water body may be slim, but the consequences for the user may be quite severe and harmful to his/her health.

2.2.1 Site Characterization Site characterization involves the development of an exposure model for current or anticipated use of a site. In the exposure model for microbial contamination, the three primary sources are water, sediment, and surficial soil. For each of those sources, three possible exposure routes for pathogens involve dermal contact, inhalation, and ingestion (Haas et al. 1999). There is a fundamental difference between future primary contact (e.g., swimming) and future secondary contact (e.g., boating) for recreational users. Secondary-contact recreational users are exposed to the water and sediment for a shorter duration, so they are exposed to fewer pathogens (Dixon 2009). A complete exposure pathway would include a potential user exposed to pathogenic bacteria through an

21 established exposure route. Specifically for this study along the Kallang Basin, a potential exposure pathway for dragon-boat racers is exposure to pathogens in the water via ingestion.

2.2.2 Risk Quantification Risk quantification involves calculating the dose to which the potential users are exposed. Dose is calculated by analyzing the concentration of pathogens. Since concentrations may differ based on location and time, the geometric mean value of samples must be taken. In addition, dose calculation requires the amount of source medium that the potential user has been exposed to and the concentration of pathogens in the medium. Taking a single event exposure and multiplying by the number of likely exposures can calculate dose over multiple exposures (Dixon 2009).

A relationship exists between the dose of the contaminant and the response of the user through a set of equations. Microbial risk relationships assume that risk increases with increased dose of microbial pathogens that users are exposed to. However, the relationship between the dose and response for indicator bacteria is often not known and has been assumed to follow a log-linear relationship (Dufour 1984), a logistic relationship (Fleisher 1991; Wymer & Dufour 2002), and a statistics-based model (Wiedenmann 2007).

2.2.3 Risk Management and Communication The final step after determining the amount of risk posed to users is risk management and communication. In order to manage the risk and communicate that risk to future users, guidelines and standards of acceptable bacterial concentration must be established. If guidelines are exceeded for certain water bodies, the area must be closed and the risk must be communicated to potential users.

Two types of regulatory criteria exist to assess the water quality within recreational areas. The first is the guideline based on geometric mean concentration. The USEPA uses the geometric mean concentrations and single-sample maximums method (Dixon 2009). The regulation is that if the geometric mean of the last five water sample concentrations exceeds the geometric mean guideline, or if a single-sample concentration exceeds the single-sample maximum, then that body of water should be closed to recreation. The geometric mean guideline indicates the level of bacteria that provides an acceptable level of risk to the public. The second type of criterion is used by the World Health Organization (WHO), which uses a 95th percentile value. A water body is considered safe for recreational use if the 95th percentile value of all samples is below guideline levels (WHO 2003). Singapore currently uses the 95 th percentile value method in evaluating water quality safety for recreational users (SGNEA 2008).

22 This study in Singapore focuses on quantifying risk from waterborne pathogens to recreational users by specifically analyzing traditional indicator bacteria and nontraditional human adenovirus along the Kallang Basin. Indicator bacteria will be analyzed using the relationships found in previous epidemiological studies. Adenovirus concentration will be gathered via quantitative polymerase chain reaction (PCR), and probability of illness will be quantified via an exponential dose-response model. Studies have quantified inhalation adenovirus risk by an exponential dose-response relationship, and inhalation dose-response appears to be a conservative estimator for ingestion (Couch et al. 1969). The primary exposure route to dragon boat racers for this study is ingestion of the water. Section 4.3 explains how this exposure route for these racers was determined.

2.3 US Standards History

Determining guidelines for proper management of the Kallang Basin requires understanding current and historical standards. The American Public Health Association on Bathing Places established the first standard for total coliform counts in the mid- 193 Os with a concentration of 1000 total coliform forming units (CFU) per 100 mL, even without conducting epidemiological studies to support that concentration (APHA 1936). Then, epidemiological studies were conducted in the late 1940s. The United States Public Health Service (USPHS) conducted them with the goals of establishing safe bacterial levels, but the USPHS did not have enough data to accurately predict probabilities of illness from the concentrations of total coliforms (Dufour 1984). In 1968, the National Technical Advisory Committee on Water Quality Criteria (NTAC 1968) recommended using fecal coliforms as opposed to total coliforms as bacterial indicators. The Federal Water Pollution Control Administration then recommended a level of 200 fecal coliforms per 100 mL. In 1972, the USEPA made this standard official based on research that showed reduced numbers of Salmonella infections below that level (USEPA 1972).

Due to the lack of studies relating the risk of illness to the concentration of indicator bacteria in the water, epidemiological studies were conducted at fresh and marine water swimming areas starting in 1973. The results of these studies produced regression equations relating swimming-associated gastrointestinal symptom rates with the geometric mean E. coli and Enterococci density per 100 mL of freshwater (Dufour 1984). In 1986, the USEPA recommended a probability of illness of 8 illnesses per 1000 swimmers, which means that there is an additional 0.8 percent chance above normal environmental infection rates that a swimmer will contract gastroenteritis from a single swimming event (USEPA 1986).

The USEPA standards summarized in the table below were based on full contact immersion swimming, which is also known as primary-contact recreation (USEPA 1986).

23 Secondary contact activities refer to boating, wading, and fishing. Many US states apply the single-sample maximum allowable density for Moderate Full-Body-Contact Recreation as the standard for secondary recreation. Moderate Full-Body-Contact Recreation refers to recreational waters that are not designated beach waters, but during recreation season are used by about half of the people as a designated beach area (USEPA 2004a). The Designated Beach Area single-sample maximum is commonly used as the standard for primary recreation (Dixon 2009).

Table 1: Indicator Bacteria Density Criteria (USEPA 1986)

Single-sample Maximum Allowable Density (Enterococci/1 00 mL) Steady State Geometric Moderate Lightly Used Infrequently Mean Designated Full-Body- Full-Body- Used Full- Indicator Beach Area Contact Contact Body-Contact Density (upper 75% Recreation Recreation Recreation 95% (Enterococci/ C.L.) (upper 82% (upper 90% (upper 100 mL) C.L.) C.L.) C.L.)

Enterococci 33 61 78 107 151

E. coli 126 235 298 409 575 C.L. = Confidence Limit

Dixon (2009) expressed that the current standards do not adequately account for different usages of recreational waters. However, it is possible to test for many more microbial agents directly instead of relying on indicator bacteria using techniques such as quantitative polymerase chain reaction (PCR).

2.3.1 World Health Organization and Singaporean Standards The World Health Organization (WHO 2003) proposed guidelines dealing with many factors that affect recreational waters, including drowning, bacterial water quality, and dangerous aquatic organisms. The WHO recommendations included guideline indicator bacteria, but instead of using a geometric mean guideline, the WHO used a 9 5 th percentile method for measuring bacterial concentrations (WHO 2003). Under this recommendation, 95% of the water samples taken should fall below the guideline values in order for the waters to be considered safe. Current Singaporean standards for marine and freshwaters are organized based on class levels, which depend on estimated gastrointestinal probability per exposure, respiratory disease, and 9 5 th percentile value of indicator bacteria. Singapore standards follow the WHO recommended guidelines, with goals of achieving at least Class B level for their recreational waters (SGNEA 2008). Following Table 2 below, this goal corresponds to a guideline 9 5th percentile level of 200 Enterococci per 100 mL or less.

24 Table 2: WHO Bacterium Guidelines (WHO 2003)

Class 95th percentile value of Estimated Probability per Exposure Enterococci/100 mL Acute febrile respiratory Gastrointestinal illnessdies disease A <40 <1% <0.3% B 41-200 1-5% 0.3-1.9% C 201-500 5-10% 1.9-3.9% D >500 >10% >3.9%

25 3. Models Assessing Risk Associated with Microbes

Dose-response models are mathematical functions that yield a probability of an adverse health effect as a function of dose. USEPA (2011) described dose-response models for waterborne pathogens. The most common practice of dose-response modeling has been through fitting experimental data to statistical models. The models are almost exclusively focused on the ingestion route of exposure.

3.1 No-Observed-Adverse-Effect Levels (NOAELs)

A no-observed-adverse-effect level (NOAEL) is the bacterial concentration below which probabilities of illness for recreational users are no different than the environmental rate of illness (Dixon 2009). Wiedenmann et al. (2006) determined a NOAEL of 25 Enterococci colony-forming units (CFU) per 100 mL for swimming, and a NOAEL of 100 E. coli CFU per 100 mL. Wade et al. (2003) determined the probability of gastrointestinal (GI) illness in relation to water quality indicator density in various types of water bodies. The results of their fresh water study showed that concentrations below the guideline value or NOAEL for E. coli and Enterococci were not associated with illness. However, exposures to concentrations above the guideline level were. There was evidence that the probability of contracting a GI illness was considerably lower in studies with indicator densities below guidelines proposed by the USEPA. Dufour (1984) recommended a NOAEL of 33 Enterococci CFU per 100 mL and 126 E. coli CFU per 100 mL, which is the current geometric mean concentration guideline used by the USEPA today (Table 1). Section 3.3 and Section 3.5 discuss the Dufour and Wiedenmann models in more detail, with descriptions of how their NOAELs were determined.

The following models utilize various equations that include parameters and constants to describe factors known to influence relations between fecal indicator bacterial organisms and incidence rates of infectious diseases. The dose-response relationships for bacterial indicators are often not known and have been modeled based on the Dufour (1984) and Wiedenmann (2007) models. The dose-response relationship for adenoviruses is known and has been modeled based on studies conducted by van Heerden et al. (2005).

3.2 van Heerden et al. (2005) Exponential Dose-Response Risk Model

Van Heerden et al. (2005) used an exponential dose-response model to assess the risk of infection caused by human adenovirus detected in a river and impoundment used for recreational purposes.

26 3.2.1 Poisson-Distributed Dose-Response Model When the probability of ingesting a dose of pathogens is Poisson-distributed and all of the ingested pathogens have an equal probability of causing illness, the result is an exponential dose-response model of the form:

P (d, r) = 1 - erd (1) Where: P, = probability of illness d= dose or number of pathogens r = probability that an individual pathogen initiates infection

When the probability of ingesting a dose of pathogens is Poisson-distributed and the probability that the individual pathogens initiate infection is beta-distributed, a beta- Poisson model is appropriate:

Pi(d,1a, ) = ,if#>> land#>> a (2)

For risk characterization of E. coil, a beta-Poisson model is commonly used with a and p values of 0.178 and 1.78 x 106 (Haas et al. 1999). The exponential and beta-Poisson models both assume that the number of organisms is Poisson-distributed with a fixed mean (USEPA 2011).

An exponential dose-response model is most commonly used to evaluate the risk associated with exposure to viruses (USEPA 2011). For the exponential model, r is a constant for the interaction between the pathogen and the host. Despite the unrealistic assumption that all individuals within a population have the same probability of illness, the exponential model provides a good fit for a number of human-pathogen data sets (Haas et al. 1999). Once the pathogen is identified in the recreational water, exposure analyses can be completed. An exposure analysis consists of four terms, the average concentration of viruses in the water, the efficiency of the recovery procedure, the viability of the viruses, and the average volume of water consumed per individual. The efficiency of the recovery procedure refers to the efficiency of the method used for the recovery of human adenovirus from water samples. This value was estimated at 40% based on the findings by Grabow and Taylor (1993), Vilagines et al. (1993) and Vilaginds et al. (1997). Viability of the viruses refers to infectivity. For instance, in van Heerden et al. (2005), all of the adenoviruses were considered viable and infectious because they were able to infect their cell culture and at least replicate their nucleic acid. Daily exposure is determined using the following equation (van Heerden et al. 2005):

N = C * 1 *1* V * DR (3) R 1 0

27 Where: C= average concentration of the human adenovirus in the recreational water (viruses L R = efficiency of the recovery method (%) I= fraction of detected human adenovirus that is capable of infection DR = removal efficiency V= volume consumed (L)day van Heerden et al. (2005) assumed that the daily volume of water consumed during swimming in recreational water was 30 mL for healthy adults as supported by Crabtree et al. (1997). The concentration of viruses was determined using PCR, using detected and undetected methods (van Heerden et al. 2005).

The average concentration of human adenovirus was characterized by:

C = A(4) mean volume of water analyzed

Where: X = -ln[P(0)] P(0) = 1-fraction of positives detected by PCR.

Parameters used by van Heerden et al. (2005) are summarized in Table 3 for drinking water, river water, and impoundments from locations in South Africa.

The exponential dose- response model used to assess the risk of human adenovirus is as follows:

Pi = I - e-rN(3

Where: P,= the probability of illness N= number of viruses ingested using (Eq.3) above r = dose-response parameter as seen in (Table 4)

28 Table 3 - Findings from van Heerden et al. (2005) to determine concentration of adenovirus (Eq. 4)

Drinking water supplies Equation or Supply A Supply B River water Impoundment

______Units _____

Poisson parameter (2) X = -ln[P(O)] 0.03 0.06 0.15 0.19

Mean volume (V) (L) 212.7 247.8 27.0 194.7

C= X)V 4 3 4 Human adenovirus 1.40 x 10-4 2.45 x 10- 5.46 x 10- 9.97 x 10~ concentration (C) viruses L

Table 4 - Findings from van Heerden et al. (2005) to determine N (Eq. 3)

Mean value of drinking Mean value Model supplies (river Mean value Units parameters water) (impoundment) Supply A Supply B Human 4 adenovirus 1.40 x 10- 2.45 x 10-4 5.46 x 10-3 9.97 x 10-4 Viruses/L concentration (C) Recovery (R) 0.4 0.4 0.4 0.4 Infectivity (1) 1 1 1 Decimal reduction NA NA NA NA (DR)

Volume 2 2 0.03 0.03 L/day consumed (1/)

Dose-response 0.4172 0.4172 0.4172 0.4172 parameter (r) _ I I A _I

The exponential dose-response model can be modified to determine the probability of illness per year (Haas et al. 1999):

P Pi 365 (6) year day)

A drawback to this exponential dose-response model is that the probabilities of illness calculated may overestimate the actual risk due to unknown inaccuracies in assumed

29 values for variables. For instance, the 30 mL of recreational water ingested per day may be too high for recreational dragon-boat racing, as the racers are not physically swimming in the water while rowing. Ingestion would occur with a volume much less than 30 mL.

3.3 Dufour (1984) Risk Model

The USEPA currently uses the Dufour (1984) risk model to estimate risk for freshwater recreation. Dufour (1984) conducted an epidemiological study at fresh water beaches in Lake Erie, Pennsylvania and Keystone Lake, Oklahoma in order to develop a set of risk equations that linked the concentrations of E. coli or Enterococci in the water to the probability of contracting gastrointestinal illness. The study measured rates of illness among both swimmers and non-swimmers through the use of follow-up surveys conducted by phone eight to ten days after the beach visit. Swimmers were defined as those having complete exposure of the head to the water and non-swimmers were defined as those who did not immerse their heads in the water. Dufour graphed probability of illness per 1,000 people (P) versus Enterococci concentration (CEN in CFU/1 00 mL) and he was able to develop the following relationship between illness rate and bacteria indicator density:

Pi = -4.5 + 14.3 * log(CEN) (7

6 3 9 4 HCGI Pi = - . + . *log10(CEN) (8)

Gastrointestinal illness (GI) refers to symptoms such as vomiting, diarrhea, stomachache, or nausea, whereas highly-credible gastrointestinal illness (HCGI) refers to symptoms such as vomiting, diarrhea with a fever, stomach ache, or nausea occurring together with abdominal cramps. For example, a HCGI symptom is diarrhea and abdominal cramps occurring together or nausea and abdominal cramps occurring together (USEPA 2006).

3.3.1 Criticisms of the Dufour Model Dixon (2009) summarized problems with the Dufour risk equations and the USEPA guidelines derived from them. For instance, the risk equations may not have been accurate due to the calculation of the geometric means of the bacterial concentrations over the span of a whole year. Using yearly geometric mean eliminates many data points that may have influenced the risk equations. Further, the standard deviation was not included in the original analysis and non-swimmer illness rates were taken from many locations.

Additionally, the Dufour studies were prospective-cohort studies, where participants were recruited from people who had already been exposed to recreational waters. These swimmers and non-swimmers were interviewed at specific recreational locations and a

30 follow up survey was conducted 8 to 10 days later to determine illness rates. There were no attempts to control the amount of exposure. However, in a randomized trial conducted by Wiedenmann et al. (2006), participants were recruited before they had any contact with the recreational waters. The locations, the amount of time in the water, and the type of exposure were strictly controlled. Randomized trials are a more accurate way to determine dose-response relationships. Unfortunately, prospective-cohort studies are often used due to the expense and difficulty in controlling and designing randomized trials (Dixon 2009).

3.4 Fleisher (1991) Risk Equations

Fleisher (1991) criticized the Dufour risk equations and developed a logistic regression model that reanalyzed Dufour's 1984 data from studies conducted on marine water. The criticism was that the log-linear model of Dufour's epidemiological data was incorrect. The logistic regression model specifies the probability of illness directly and is generally found to follow an s-shaped curve, with response increasing slowly at low doses, then more quickly at medium doses, and then again more slowly at higher doses. The general formula follows Eq 9 below (Wymer & Dufour 2002):

(1+e-(a+Px))() Where: P = probability of contracting gastrointestinal illness from recreational water use a and 3 = terms that describe the shape of the risk curve and can be found by fitting the risk curve to data from the epidemiological study x = logio of indicator bacterial concentration

When Fleisher constructed his series of risk models, he came to the conclusion that Dufour's general risk equations were inaccurate and needed to be reevaluated. Fleisher constructed his model by separating the data from the three locations used in Dufour's marine studies. The risk varied quite significantly between each location, so Fleisher concluded that the Dufour risk equations were not useful and should be reevaluated (Fleisher 1991).

3.5 Wiedenmann (2007) Risk Model

The Wiedenmann (2007) model will be used in this thesis to assess the probability of illness associated with fecal indicator bacterial organisms. Based on epidemiological studies, this specific model developed by Wiedenmann (2007) describes the probability of acquiring infectious diseases from bathing in recreational waters with increasing levels of fecal indicator organisms. Wiedenmann et al. (2006) conducted the first randomized

31 controlled trial for recreational exposure. Swimmers were allowed to swim for ten minutes and each dunked their heads in the water at least three times. Microbial samples were analyzed every twenty minutes and Wiedenmann found that E. coli and Enterococci were well correlated with rates of illness.

In Wiedenmann's derived risk model, these components were included: (1) a baseline risk, or the risk of acquiring the same kind of disease in an unexposed control group, (2) an attributable risk, or the risk due to exposure, (3) a dose risk, or a risk level that is reached when all susceptible individuals have been infected, (4) a functional form describing the dose-response relationship, (5) a pathogen-indicator ratio, (6) an estimate of the accidental volume intake of water, and (7) an estimate of the probability of illness. The pathogen-indicator ratio multiplied by the concentration of fecal indicator organisms describes the conversion of direct pathogen measurement to fecal indicator bacteria concentrations (Wiedenmann 2007).

The following equations describe risk in terms of the probability of becoming ill:

MR (Maximum Risk Level) = BR + AR (10) Where: BR = baseline risk (%) AR = attributable risk (%)

For gastroenteritis, BR ranges from 0.01 to 0.03 or I to 3% (Pruss 1998).

AR = ARwr + ARdr (11) Where: AR.,= risk attributable to the exposure to water, but is independent of the dose or concentration of the pathogen or fecal indicator organisms ARdr= dose-related attributable risk

ARr is risk that does not depend on the dose or concentration of the pathogenic organisms (POs) or fecal indicator organisms (FIOs). For instance, swallowing water with high salinity content may also cause negative gastrointestinal symptoms. Ardr is the risk dependent on the dose of the POs or FIOs in the water (Wiedenmann 2007).

The probability of illness, Y, is:

Y = BR + ARwr + ARdrmax * f(x)DRR (12) Where: Y= probability of illness x = dose or the intake of a certain number of pathogens ARdrax = dose-related maximal attributable risk

32 f(x)DRR is based on a binomial distribution. If x is the concentration of pathogens per 100 mL of water, then the dose is the concentration, x, multiplied by the average volume intake of water in 100-mL units (vinbooiL). That is, for a swimmer who ingests 30 mL of water, the value of vinOOm,.L = 30 mL/100 mL = 0.3. ARd,,ax is the risk level reached when all susceptible individuals have been infected (Wiedenmann 2007).

Y = BR + ARwr + ARdrmax * f(xpo * vinl1OmL) (13)

If x is not the dose of a certain number of pathogens, but the dose of a certain number of fecal indicator organisms, then the dose is the concentration of the fecal indicator organism per 100 mL, multiplied by the pathogenic organism ratio, multiplied by the volume intake of water in 1 00-mL units.

Y = BR + ARwr + ARdrmax * f (XFIO * PIR * vin100mL) (14)

We can simplify this equation by assuming that the cumulative probability of infection, P(X < x), resulting from the ingestion of x number of pathogenic organisms can be described by the cumulative distribution function of a binomial distribution with:

F(x) = 1 - [1 - P(1)]x (15) Where: P(1) = the probability of illness associated with the single intake of a pathogenic organism

Based on experimental observations between the concentrations of E coli and intestinal Enterococci in fresh and marine water conducted by Borrego et al. (1990), Dizer et al. (2005), WHO (2003), and Wiedenmann et al. (2004), the following equations show the relationship between the number of pathogenic organisms (xpo), the number of fecal indicator organisms (xFIO), and the pathogen-indicator ratio (PIR):

PIR = (16) XFIO

q = log 1 0 ( ) = -0.67 + 0.98 * log 1 0 (') (17)

Ward et al. (1986) modeled experimental dose-response data in human volunteers for rotavirus and suggested a P(1) of 0.17. Wiedenmann (2007) set parameters at baseline risk (BR) = 0.03; maximum risk level (MR) = 0.09; pathogen-indicator ratio (PIR) = 0.1; and ingestion rate per 100 mL (vintoo,, = 0.3. With these values, risk is calculated as:

33 Risk = (MR - BR) * {1 - [1 - P(1)]z} (18) in which z is the number of pathogens ingested defined as:

1 z = PIR * XFIO * v OOmL (19) Where:

MR = 0.09 = maximum risk level BR = 0.03 = baseline risk level PIR = 0.1 = pathogen-indicator ratio VinlOOmL = 0.3 = water ingestion rate as fraction of 100 mL P(J) = 0.17

Dixon found that adjusting the value of PIR by some factor resulted in an approximately proportional inverse change in the NOAEL, Figure 5. Multiplying PIR by 10 decreased the NOAEL by 10 while multiplying the PIR by 1/10 increased the NOAEL by 10. Therefore, due to the lower ingestion rate for dragon-boat racers by a factor of 5 (Section 4.3), a proportional increase in the NOAEL resulted.

I 5%- - Vl

S I 0 I - -NOAEL 2k - I __

2%

Ow' Maa *0

V~ 1225 51 1 10 100 1,000 CRU Enteoccoc/unit of ingstion

Figure 5: Variations in vinllooL (defined as v in Figure 5) from Wiedenmann (2007) risk equations per Dixon (2009). Additional Risk of Gastroenteritis is equivalent to the Probability of Illness evaluated in this study.

34 Fecal indicator organism (XFIO) concentrations were measured along the Kallang Basin, and an appropriate value of viniooml was determined based on dragon- boat racing ingestion rates per event for adults.

Several problems exist with the Wiedenmann model; however, the derivation of the PIR term is the main problem. Wiedenmann (2007) assumed a non-constant pathogen- indicator ratio (PIR) that varies with XFIO. In this derivation, Wiedenmann assumed an ingestion rate of 30 mL for the 10-minute swimming period. The assumed 30-mL ingestion rate seems to be highly conservative because research by Dufour (1984) showed that adult swimmers ingested about 4 mL in a 10-minute period. Therefore, the actual PIR is most likely to be higher than the one calculated by Wiedenmann.

3.6 Dufour vs. Wiedenmann Model

Major differences exist between the Dufour and Wiedenmann models. First, the Dufour model was based on a prospective-cohort epidemiological study, whereas Wiedenmann used a randomized trial. These fundamental differences are significant because Dufour did not strictly control how his users interacted with the water. For instance, Dufour did not control the amount of water ingested by his recruits or the amount of time spent swimming. He also did not control the age of the participant. The Dufour model also did not model the pathogen-indicator ratio. The data collected were averaged, and only two variables were used to model the different factors that influence risk to recreational users. On the other hand, since Wiedenmann controlled for many of the variables that influence users, the values of PIR= 0.1, MR= 0.09, and BR = 0.03 were derived through his epidemiological study (Eq. 18 and Eq. 19). There is variability in the PIR since it may be significantly lower in tropical climates than in temperate climates (Hernandez-Delgado et al. 1991).

Overall, the Wiedenmann model provides a more accurate quantification of the amount of risk to recreational users than the Dufour model. The data used to generate the Wiedenmann model are much more controlled and account for more variables that Dufour ignores. Also, since the Wiedenmann model accounts for different ingestion rates and PIRs, it is much more flexible. Therefore, the Wiedenmann model will be used in this study of the Kallang Basin to analyze indicator bacteria and probabilities of illness to dragon- boat racers.

35 3.7 Single-Sample Maximum Allowable Densities

In addition to determining exposure risk due to recreational activity, single-sample maximum allowable densities can also be constructed for a given water body. Exceeding the value of a single-sample maximum (SSM) will indicate that the mean indicator density is higher than the acceptable risk level (Dixon 2009). The SSM should be customized for the water body of interest, so the statistical distribution of the indicator bacteria or virus must be determined. According to the USEPA (1986), the SSM for a specific water body is the one-sided upper confidence level. USEPA (2002) recommends two methods for calculating the SSM of a given log-normal distribution. While it is recommended that an SSM be calculated for each water body, this is not always done. According to Dixon (2009), none of the states have adopted SSMs that have been adjusted for the different characteristics of each water body due to the large number of recreational water bodies that are regulated. This large number of water bodies makes it difficult to customize regulations for each one.

36 4. Water Sampling Analyses and Overview

4.1 Field Sample Collection

The field sampling process was completed in collaboration with a large team from the National University of Singapore. Water samples were collected from four separate stations along the Kallang Basin and tested for adenoviruses and indicator bacteria. These four stations were Jalan Benaan Kapal, Kallang Riverside Park, Upper Boon Keng Road, and Crawford Street. Water samples were collected over a 48-hour period, at 4-hour intervals starting on January 7th, 2014 at 11 A.M., and ending January 9 th 2014 at 7 A.M. Concentrations of indicator bacteria were analyzed using IDEXX (IDEXX Laboratories, Inc., Westbrook, Maine, USA) MPN trays. The most probable number of colony forming units or CFUs was read from IDEXX-supplied MPN tables. Human adenovirus was identified using quantitative PCR, which was conducted in a separate laboratory setting.

Samples were collected at the four locations by using a bucket and rope to pull up the water. Twenty liters of water were collected at each station during each sampling session. Once the water was gathered, samples were taken into the lab to concentrate further.

4.2 Laboratory Analysis

From each 20-L sample collected at the four sites along the Kallang Basin, a final volume was concentrated to 100 mL via a peristaltic pump and hollow fiber Hemoflow F HF80 hollow-fiber filters (Fresenius Medical Care, Hochtaunuskreis, Hesse, Germany). The 100-mL concentrated sample was eluted using 300 mL of 0.05-M glycine at pH 7. The final volume including the elution was 400 mL, of which 200 mL was used for secondary precipitation for the adenovirus analysis. The remaining 200 mL was used to prepare the Enterococci and E. coli samples.

4.2.1 Enterococci and E. coli Lab Analysis Enterococci and E. coli samples were analyzed using IDEXX Enterolert and Colilert media and Quanti-Tray/2000 MPN trays. Aliquots of concentrated reservoir water samples were diluted with deionized water into 1 00-mL samples. These samples were combined with the Enterolert and Colilert packets and mixed in sterile whirl packs. The samples were poured into a Quanti-Tray/2000 MPN tray, sealed, and labeled with the location, time, and type of microbe. The sealed trays were then placed in an incubator set at 37'C. After 24 hours, the samples were removed and read using a light-box with a 365-nm UV light. The numbers of positives and negatives counted were recorded and then the most probable number of colony forming units (CFU) per 100 mL was read from the IDEXX-provided MPN table (IDEXX Laboratories, Inc., Westbrook, Maine, USA).

37 4.2.2 Indicator Bacteria NOAEL and Probability of Illness Derivation The NOAEL or guideline level for Enterococci concentrations in the basin was calculated using the Wiedenmann (2007) risk equation (Eqs. 18 and 19) and the values of the variables shown in Table 5. These guideline levels will predict whether recreational areas are safe for dragon-boat racers.

Table 5: Wiedenmann (2007) NOAEL risk equation variables:

MR 0.091

BR 0.028

a -0.67

b 0.98

CEN-NOAEI (MPN/1OO mL) 25

Vlf1OOmL 0.3

In Table 5, a and b are constants that were found by curve-fitting the risk data from the epidemiological study conducted by Wiedenmann et al. (2006). The number of pathogens ingested, represented by the variable z in Eqs. 18 and 19, is the tolerable number associated with the NOAEL found by Wiedenmann et al. (2006). Wiedenmann (2007) solved for z in Eq. 19 by substituting the risk equation variable values from Table 5 into the following modified form of Eq. 19. This modified form was derived by substituting Eq. 16 and Eq. 17 into Eq. 19. xFIO in Eq. 17 is equal to CEN-NOAEL in Eq. 20 and Eq. 21.

1 Z = vin OOmL * j ga+b*lo1O(CEN-NAEL) (20) Substituting the values of the risk equation variables from Table 5, including the ingestion rate (vinlfomL) of 0.3 and the NOAEL concentration (CEN-NOAEL) of 25 MPN/100 mL as given by Wiedenmann (2007), z was calculated as 1.5. However, these values are based on Wiedenmann's presumed ingestion rate of 30 mL by swimmers, while for dragon-boat racers an ingestion rate of 6 mL is more appropriate as discussed in Section 4.3. The NOAEL for dragon-boat racers was calculated assuming a constant pathogen ingestion volume, z, but for a different water ingestion rate than that presumed by Wiedenmann. Therefore, keeping the z value constant at 1.5, a NOAEL was calculated by using the new ingestion rate of 0.06. Rearranging Eq. 20 to solve for the NOAEL concentration CEN-NOAEL for dragon-boat racers, we arrive at the equation below:

log1(CEN-NOAEL) = 1-02 * 09 1 0 (15 + 0.68 ()

38 Eq. 21 includes the a and b constants from Table 5. Then, Eq. 18 was used to calculate the probability of becoming ill based on the observed geometric mean concentrations. Eq. 18 is repeated below.

Pi = (MR - BR) * {1 - [1 - P(1)]z} (18) Substituting the values from Table 5, we arrive at the following equation:

Pi = (0. 063) * {1 - (0. 8 3 )1(-0.67098*o10 (CEN *vin00mL} (22) In summary, the NOAEL for Enterococci was calculated using an estimated ingestion rate of 6 mL for dragon-boat racers. The value of the variable vin00L, which represents water ingestion per day as a fraction of 100 mL, was set at 6 mL/1 00 mL or 0.06 and Eq. 21 was solved for CEN-NOAEL. Based on those calculations, the guideline concentration of Enterococci is 128 CFU/100 mL.

Instead of producing new risk equation variables, Wiedenmann (2007) determined the correlation between microbial parameters using linear regression in order to establish the NOAEL for E. coli. The following relationship relates the concentration of Enterococci with the concentration of . coli:

log10(CEN) = 0.836 * lo10(CE.Coli) - 0.270 (23)

Solving Eq. 23 for CE. coli-NOAEL using 128 CFU/100 mL for CEN-NOAEL, CE. coli-NOAEL is determined to be 697 CFU/100 mL.

4.2.3 Quantitative Polymerase Chain Reaction (PCR) Preparation for Adenovirus Analysis Liu (2014) summarized the procedures for quantitative PCR conducted during the January 2014 sampling and microbial analysis period at the National University of Singapore (NUS). The quantitative TaqMan PCR assay developed by Jothikumar et al. (2005) was used to detect adenoviruses in the water samples. After initial RNA extraction, a real-time TaqMan PCR assay was performed using the QuantiTech Probe PCR kit (QIAGEN GmbH, Hilden, Germany) in a R.A.P.I.D. real-time PCR system (BioFire Diagnostics, Inc., Salt Lake City, Utah, USA). Template DNA, forward and reverse primers, and a fluorogenic probe were used to complete the amplification reaction. The complete amplification process took approximately 90 minutes, beginning with a denaturation step at 95'C for 15 minutes. After this initial denaturation step, 45 cycles of a 95'C denaturation step for 10 seconds, a 55 0 C annealing step for 30 seconds, and a 72'C elongation step for 15 seconds completed the amplification procedure (Jothikumar et al. 2005).

Initial RNA extraction required the following steps. Samples of 200- mL created from the original 20 L of bucket water were each transferred to two 50-mL tubes for centrifuging

39 using sterile pipettes. The samples were centrifuged for thirty minutes in order to separate the liquids from the particulate matter, which included the adenovirus as well as other bacteria and pathogens. The liquids were discarded. Polyethylene glycol (PEG) was added to the solids and then mixed thoroughly. Next, chloroform was added in order to kill any bacteria or pathogens remaining in the sample. The goals were to concentrate the sample as much as possible in order to isolate the viral RNA because adenoviruses are resistant to chloroform. The liquids, which contained the adenoviruses, were further filtered using an Amicon Ultra filter unit (EMD Millipore Corporation, Billerica, MA, USA). A QIAmp RNA kit (QIAGEN GmbH, Hilden, Germany) was used to isolate the RNA. Once the samples were concentrated and the RNA isolated, the TaqMan PCR assay process began in order to determine the number of genomic copies/L of adenoviruses based on detected and undetected samples.

4.3 Water Ingestion Calculation

Exposure rates for dragon boat racers were estimated using the ingestion rates for swimming and then adjusted for the relative amounts of water these racers came in contact with. Two dragon- boat racers of the German Dragons in Singapore were interviewed on January 17, 2014 about their dragon-boat racing experiences. The information gathered from these racers was vital towards estimating a water ingestion rate.

Dragon-boat practice sessions last for about two hours and are conducted twice a week. Races are generally once a month and can vary in times from 90 seconds to as long as 50 minutes depending on the race distance. Twenty-two crew members race on each boat. The people in the front keep the pace, while the people in the back are responsible for maintaining the proper direction of the boat. While the boat rarely capsizes, the most common mode of exposure is through splashing from the paddles to the face. Due to rare immersion of their heads during the races or practice sessions, I assumed an average ingestion rate of approximately one-fifth of that for swimming. This approximation results in a per-event ingestion rate of about 6 mL for adults. These numbers are averages because the front rower experiences more splashing to the face than the other members rowing in different parts of the boat. These numbers are also rough estimates, and any guidelines or recommendations derived from these numbers should be confirmed from actual studies conducted on dragon-boat racer exposure.

According to the USEPA Risk Assessment Guidance for Superfund (RAGS) last updated in 2012, an exposure pathway is complete if there is a source or chemical release from a source, an exposure area where contact can occur, and an exposure route where contact can occur (USEPA 2012). Most complete pathways are determined by getting evidence from monitoring human data indicating chemical effects in the site area. RAGS created a population/exposure route matrix that could be used to determine potential exposure

40 pathways at a specific site. For instance, for surface waters and recreational usage, the most common exposure would be through incidental ingestion or dermal contact. Lifetime contact would occur, and exposure in children may be significantly higher than in adults. Because most children do not participate in dragon-boat racing, adult exposure was the focus of this current study.

The rate of water ingestion highly impacts estimating health risks of water recreation. Two studies were conducted in the summer of 2009 that estimated water ingestion during limited-contact recreational activities such as canoeing, fishing, kayaking, motor boating, and rowing, as well as during full-contact activities such as swimming in outdoor pools (Dorevitch et al. 2010). In the first study conducted in Chicago area surface waters, survey research methods were used to estimate water ingestion during those activities among 2705 people. In the other survey, conducted on outdoor swimming pools, a tracer was used to estimate water ingestion among 662 people participating in full-contact and limited-contact recreational activities. Results showed that fewer than 2% of canoers and kayakers ingested 5 mL or more. Swimmers in a pool were 25-50 times more likely to report swallowing 5 mL of water than those participating in limited-contact exposure. Dorevitch et al. (2010) found that the mean and upper confidence estimates of water ingestion during limited-contact recreational activities on surface waters were 3-4 mL and 10-15 mL. Therefore, the 6 mL conservative estimation of swimming ingestion rates for dragon- boat racers, taking into account the front-man who ingests more water than those rowing in the middle or back, appears to be a good approximation for this study.

41 5. Results

5.1 Indicator Bacteria Results

Measured concentrations of . coli and Enterococci (Table 6 and Figure 6) were compiled in collaboration with Justin Angeles (Angeles 2014). The numbers for the stations correspond to the order in which samples were taken:

Samples 2 (Jalan Benaan Kapal) 3 (Kallang Riverside Park) 4 (Upper Boon Keng Road) 5 (Crawford Street)

Table 6 and Figure 6 show the geometric mean concentrations at each sampling time averaged over all sampling locations. This data set is representative of the whole Kallang Basin, since geometric mean concentrations were taken for all stations at each time of day. The highest geometric mean concentrations for E coli and Enterococci consistently occurred during mornings: on January 7th at 11:00 A.M., January 8th at 7:00 A.M., and January 9th at 7:00 A.M.

Table 6: E. coli and Enterococci geometric mean concentrations of all locations for each sampling time

E. coli MPN Geometric Mean Enterococci MPN Geometric Mean Date Time (MPN/100 mL) (MPN/100 mL) 7/1/14 11:00 A.M. 3504 562 7/1/14 3:00 P.M. 1720 51 7/1/14 7:00 P.M. 1052 180 7/1/14 11:00 P.M. 1205 88 8/1/14 3:00 A.M. 863 403 8/1/14 7:00 A.M. 1182 1112 8/1/14 11:00 A.M. 1165 69 8/1/14 3:00 P.M. 935 28 8/1/14 7:00 P.M. 234 287 8/1/14 11:00 P.M. 352 32 9/1/14 3:00 A.M. 154 24 9/1/14 7:00 A.M. 1298 210

42 7000

6000

50C0

040( 0

Z 30( 0 E. coli Concentration 20( 10 S-- EnterococciEneooc Concentration 10( 0

0 1/7 6:00 1/7 12:001/7 18:00 1/8 0:00 1/8 6:00 1/8 12:001/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00

Date - Time

Figure 6: E. coli and Enterococci geometric mean concentrations along the Kallang Basin in MPN/100 mL versus date and time of day

The corresponding probabilities of illness were calculated using the Wiedenmann (2007) risk equations. All of the E. coli geometric mean concentrations measured in the field except those at 7:00 P.M. on January 8t, 11:00 P.M. on January 8th, and 3:00 A.M. on January 9th exceeded the calculated NOAEL of 697 CFU/100 mL. Enterococci geometric mean concentrations exceeded the NOAEL of 128 CFU/100 mL on January 7'h at 11:00 A.M. and 7:00 P.M., January 8th at 3:00 A.M., 7:00 A.M., and 7:00 P.M., and on January 9th at 7:00 A.M. For Enterococci concentrations, the geometric mean concentrations measured at the times of 7 A.M., 11 A.M., and 7 P.M. consistently exceeded this guideline level, with peak concentrations occurring in the mornings at 7 A.M. and 11 A.M. This result for Enterococci shows that time of day does impact the probability of illness to recreational users. For instance, 11 A.M., 7 A.M., and 7 P.M. tend to be the times when the most fecal contamination occurs. However, more in-depth studies should be conducted over a longer timescale in order to confirm these findings. For E. coli, most of the concentrations measured exceeded the NOAEL of 697 CFU/100 mL. However, this NOAEL may be inaccurate because it was calculated via a linear regression of Enterococci concentrations, with a correlation of 0.79.

43 5.2 E. coli Results In order to dissect particular stations that may contribute to high geometric mean concentrations in the Kallang Basin, the probability of illness for each station was analyzed. The probability of illness was calculated using Eq. 22; however, Eq. 22 was determined using variables unique to the concentration of Enterococci. Therefore, in order to calculate the probability of illness for E. coli, the E. coli concentrations were translated to equivalent Enterococci concentrations using Eq. 23. The probability of illness was then calculated and is summarized in the sections below.

5.2.1 E. coli Results for Station 2 Results for Station 2 on the Geylang River are shown in Table 7 and Figure 7. Station 2 showed a peak E. coli concentration of 14,300 CFU/l 00 mL and corresponding probability of illness of 6.1% at 11:00 A.M. on January 8th. Further, all of the concentrations exceeded the NOAEL of 697 CFU/100 mL except on January 7th at 7:00 P.M., and on January 9th at 3:00 A.M. A NOAEL of 697 CFU/100 mL corresponds to a probability of illness of 1.5% each time the racers take to the river. This 1.5% was calculated using Eq. 22, solving for the probability of illness by using the concentration of Enterococci associated with the NOAEL of the concentration of E. coli , Eq. 23. Station 2 should be investigated further in order to manage these high probabilities of illness to dragon-boat racers.

Table 7: Station 2 E. coli concentration and probability of illness (%)

Station 2 E. coi Computed Probability Enterococci of Illness Date - Time MPN/100 mL MPN/100 mL (%) 1/7/14 11:00 A.M. 2098 321 3.1% 1/7/14 3:00 P.M. 985 171 1.9% 1/7/14 7:00 P.M. 465 91 1.1% 1/7/14 11:00 P.M. 2224 337 3.2% 1/8/14 3:00 A.M. 1842 288 2.9% 1/8/14 7:00 A.M. 2723 400 3.6% 1/8/14 11:00 A.M. 14300 1601 6.1% 1/8/14 3:00 P.M. 3441 486 4.0% 1/8/14 7:00 P.M. 1133 192 2.1% 1/8/14 11:00 P.M. 1726 273 2.8% 1/9/14 3:00 A.M. 314 66 0.9% 1/9/14 7:00 A.M. 1160 196 2.2%

44 100000

10000

0

1000 697

100 1/7 6:00 1/7 12:00 1/7 18:00 1/8 0:00 1/8 6:00 1/8 12:00 1/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00 Date - Time

Figure 7: Station 2 E. coli concentration versus date and time

5.2.2 E coli Results for Station 3 Results for Station 3, the Kallang River at Kallang Riverside Park just upstream of the Kallang Basin, are shown in Table 8 and Figure 8. Station 3 showed much lower concentrations of E. coli compared to those at Station 2. Peak concentrations occurred at 11:00 A.M. on January 71h with declining concentrations after this peak. Probabilities of illness equivalent to the NOAEL were also exceeded on January 7t at 3 P.M. and 7 P.M. Therefore, because the tolerable illness level was exceeded at Station 3 at certain times of the day, this location should be investigated further. For instance, high concentrations seen on January 7th may have resulted from storm events prior to sampling, or weekend activities that may have impacted the water quality of the basin. In order to properly manage these high probabilities of illness, activity should be analyzed upstream of Station 3. Because dragon-boat racers race during these times of peak concentration, the Kallang Basin should be highly monitored or even closed off to recreation.

45 Table 8: Station 3 E. coli concentration and probability of illness (%)

Station 3 E. coli Computed Probability Enterococci of Illness Date - Time MPN / 100 mL MPN/ 100 mL (%) 1/7/14 11:00 A.M. 1127 191 2.1% 1/7/14 3:00 P.M. 863 153 1.8% 1/7/14 7:00 P.M. 862.5 153 1.8% 1/7/14 11:00 P.M. 572.5 109 1.3% 1/8/14 3:00 A.M. 280.5 60 0.8% 1/8/14 7:00 A.M. 371.5 76 1.0% 1/8/14 11:00 A.M. 226 50 0.7% 1/8/14 3:00 P.M. 281.5 60 0.8% 1/8/14 7:00 P.M. 41.4 12 0.2% 1/8/14 11:00 P.M. 55.6 15 0.2% 1/9/14 3:00 A.M. 103 26 0.4% 1/9/14 7:00 A.M. 443 88 1.1%

10000

1000

0 0100

10 1/7 6:00 1/7 12:00 1/7 18:00 1/8 0:00 1/8 6:00 1/8 12:00 1/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00 Date - Time

Figure 8: Station 3 E. coli concentration versus date and time

46 5.2.3 E. coli Results for Station 4 Results for Station 4, upstream in the Kallang River at Upper Boon Keng Road, are shown in Table 9 and Figure 9. The highest concentrations at Station 4 occurred at 11:00 A.M. and 3:00 P.M. on January 7th and 7 A.M. on January 8th. Station 4 showed a similar trend in E. coli concentration as at Station 3, with declining concentrations after the peak at 11:00 A.M. Because most of the highest probabilities of illness occurred on January 7 th, storm events or weekend activities may have impacted the water quality of Station 4 as well. Studies should be conducted at this location to determine causes of such high concentration, and monitoring programs should be put in place to protect dragon-boat racers during those times.

Table 9: Station 4 E. coli concentration and probability of illness (%)

Station 4 E. coli Computed , Enterococci Probability of Illness Date - Time MPN / 100 mL MPN / 100 mL (%)

1/7/14 11:00 A.M. 4106 563 4.4% 1/7/14 3:00 P.M. 1050 180 2.0% 1/7/14 7:00 P.M. 836 149 1.7% 1/7/14 11:00 P.M. 717 131 1.6% 1/8/14 3:00 A.M. 906 159 1.8% 1/8/14 7:00 A.M. 1046 180 2.0% 1/8/14 11:00 A.M. 420 84 1.1% 1/8/14 3:00 P.M. 591 111 1.4% 1/8/14 7:00 P.M. 269 58 0.8% 1/8/14 11:00 P.M. 233 51 0.7% 1/9/14 3:00 A.M. 134 32 0.4% 1/9/14 7:00 A.M. 278 59 0.8% 1

47 10000

1000

697

100 1/7 6:00 1/7 12:00 1/7 18:00 1/8 0:00 1/8 6:00 1/8 12:00 1/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00 Date - Time

Figure 9: Station 4 E. coli concentration versus date and time

5.2.4 E. coli Results for Station 5 Results for Station 5, the Rochor River at Crawford Street, are shown in Table 10 and Figure 10. Station 5 showed the highest concentrations of E. coli out of all four stations, with similar trends as those of Stations 3 and 4. The highest concentrations at Station 5 occurred on January 71h at 11:00 A.M., 3:00 P.M., and 7:00 P.M., with declining concentrations on January 8th until a sharp peak in concentration at 7:00 A.M. on January

9 th. Since Station 5 showed the highest concentrations out of all the other stations analyzed along the Kallang Basin, this location may significantly contribute high probabilities of illness. In order to determine causes of high concentrations, the areas surrounding Crawford Street are analyzed in greater detail in Section 5.5.2.

48 ...... "" 1111 " I'll------...... -.

Table 10: Station 5 E. coli concentration and probability of illness (%)

Station 5 E. colR Computed Probability Enterococci of Illness Date - Time MPN / 100 mL MPN /100 mL (%) 1/7/14 11:00 A.M. 16000 1710 6.1% 1/7/14 3:00 P.M. 9800 1170 5.7% 1/7/14 7:00 P.M. 3650 511 4.2% 1/7/14 11:00 P.M. 2310 348 3.3% 1/8/14 3:00 A.M. 1190 200 2.2% 1/8/14 7:00 A.M. 1840 289 2.9% 1/8/14 11:00 A.M. 1350 223 2.4% 1/8/14 3:00 P.M. 1330 220 2.4% 1/8/14 7:00 P.M. 240 52 0.7% 1/8/14 11:00 P.M. 690 127 1.5%' 1/9/14 3:00 A.M. 128 31 0.4% 1/9/14 7:00 A.M. 20000 2100 6.2%

100000

10000

0

1000

100 1/7 6:00 1/7 12:00 1/7 18:00 1/8 0:00 1/8 6:00 1/8 12:00 1/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00 Date - Time

Figure 10: Station 5 E. coli concentration versus date and time

49 5.2.5 Summary of E. coli Results Overall, all of the stations showed high concentrations of E. coli, with certain times experiencing particularly high concentrations that exceeded the tolerable illness level for dragon-boat racers. All of the stations except Station 2 showed similar trends with peak concentrations at 11:00 A.M. on January 7 th and declining concentrations on January 8th to January 9 th. Station 2 showed a peak concentration at 11:00 A.M. on January 8 th Overall, 11:00 A.M. seems to be a significant time in the Kallang Basin where particularly high concentrations occur for E. coli. Because these stations all exceeded the tolerable illness level at certain times, they should be investigated further in order to ensure the safety of dragon-boat racers. The locations upstream of these stations are analyzed in greater detail in Section 5.5.2.

Table 11 shows that Station 2 and Station 5 exceeded the tolerable level for E. coli based on the geometric mean concentration averaged over all times sampled. The probability of illness corresponding to the NOAEL for E. coli is 1.5%.

Table 11: E. coli geometric mean concentrations averaged over all times and probability of illness (%) at each station

E. coli Computed Probability Enterococci of Illness (%) Station 2 1600 260 2.7% Station 3 290 60 0.8% Station 4 580 110 1.3% Station 5 1900 290 2.9%

5.3 Enterococci Results

The Enterococci concentrations at each station are similarly analyzed below in order to determine correlations between E. coli and Enterococci concentrations and assess whether consistencies in probabilities of illness exist diurnally and spatially.

5.3.1 Enterococci Results for Station 2 Table 11 and Figure 11 show high concentrations at Station 2 on the Geylang River exceeding the NOAEL of 128 CFU/ 100 mL on January 7th at 11:00 A.M., with a peak concentration on January 8th at 7:00 A.M. Overall, probabilities of illness due to Enterococci did not exceed the tolerable level as frequently as probabilities of illness due to E. coli at Station 2, and the peak Enterococci probability of illness (6.1% at 7 A.M. on

January 8th) was about the same as the peak E. coli probability of illness (6.1% at 11 A.M. on January 8th in Table 7).

50 ...... - --...... I...... 1.11-- -- 11 1111 11 -...... "I'll.." 11,11,1111", ...... 1 1111......

Table 12: Station 2 Enterococci concentration and probability of illness (%)

Station 2 Enterococci Probability of Illness Date - Time MPN / 100 mL (%) 1/7/14 11:00 A.M. 1120 5.7% 1/7/14 3:00 P.M. 22 0.3% 1/7/14 7:00 P.M. 54 0.7% 1/7/14 11:00 P.M. 60 0.8% 1/8/14 3:00 A.M. 1010 5.5% 1/8/14 7:00 A.M. 1730 6.1% 1/8/14 11:00 A.M. 20 0.2% 1/8/14 3:00 P.M. 5 0.1% 1/8/14 7:00 P.M. 70 0.9% 1/8/14 11:00 P.M. 22 0.3% 1/9/14 3:00 A.M. 12 0.2% 1/9/14 7:00 A.M. 60 0.8%

110000

1000

128 100 \VIA

0 r VV

1 1/7 6:00 1/7 12:001/7 18:00 1/8 0:00 1/8 6:00 1/8 12:001/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00 Date - Time

Figure 11: Station 2 Enterococci concentration versus date and time

51 5.3.2 Enterococci Results for Station 3 Table 13 and Figure 12 show concentrations at Station 3 on the Kallang River exceeding the tolerable level of 128 MPN/100 mL at 7:00 A.M. on January 8 th and January 9 th, at 3:00 A.M. on January 8 th, and at 11:00 A.M. on January 7th. These trends are very similar to the high concentrations of Enterococci at Station 2 as well as the peak Enterococci geometric mean concentrations in the Kallang Basin, Figure 6. The times at which concentrations exceeded the tolerable illness level for E. coli at Station 2 correlated well with the times at which concentrations exceeded the tolerable illness level for Enterococci concentrations at Station 2 and Station 3, 11:00 A.M. on January 7 th and 7:00 A.M. on January 8th

Table 13: Station 3 Enterococci concentration and probability of illness (%)

Station 3 Enterococci Probability of Illness Date - Time MPN /100 mL (%) 1/7/14 11:00 A.M. 280 2.8% 1/7/14 3:00 P.M. 30 0.4% 1/7/14 7:00 P.M. 60 0.8% 1/7/14 11:00 P.M. 43 0.6% 1/8/14 3:00 A.M. 88 1.1% 1/8/14 7:00 A.M. 210 2.3% 1/8/14 11:00 A.M. 10 0.2% 1/8/14 3:00 P.M. 17 0.3% 1/8/14 7:00 P.M. 139 1.6% 1/8/14 11:00 P.M. 14 0.2% 1/9/14 3:00 A.M. 12 0.2% 1/9/14 7:00 A.M. 124 1.5%

52 ...... -- .- ----...... -- -...... - ---......

1000

100

10 1/7 6:00 1/7 12:001/7 18:00 1/8 0:00 1/8 6:00 1/8 12:001/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00 Date - Time

Figure 12: Station 3 Enterococci concentration versus date and time

5.3.3 Enterococci Results for Station 4 Table 14 and Figure 13 depict Station 4 upstream on the Kallang River showing similar trends as those at Station 2 and Station 3, with particularly high concentrations corresponding to a probability of illness exceeding the tolerable illness level on January 7t at 11:00 A.M. and January 8t" at 7:00 A.M. and 7:00 P.M. The highest concentrations occurred on January 7th at 11:00 A.M. and January 8t at 7:00 A.M. The times of peak concentration are consistent throughout Stations 2, 3, and 4. This consistency confirms the need to investigate those stations further, particularly focusing on activities occurring upstream of those locations during the mornings around 7:00 A.M. and 11:00 A.M.

53 Table 14: Station 4 Enterococci concentration and probability of illness (%)

Station 4 Enterococci Probability of Illness Date - Time MPN /100 mL (%) 1/7/14 11:00 A.M. 727 4.9% 1/7/14 3:00 P.M. 101 1.2% 1/7/14 7:00 P.M. 132 1.6% 1/7/14 11:00 P.M. 87 1.1% 1/8/14 3:00 A.M. 122 1.5% 1/8/14 7:00 A.M. 1732 6.1% 1/8/14 11:00 A.M. 52 0.7% 1/8/14 3:00 P.M. 50 0.7% 1/8/14 7:00 P.M. 403 3.6% 1/8/14 11:00 P.M. 29 0.4% 1/9/14 3:00 A.M. 36 0.5% 1/9/14 7:00 A.M. 107 1.3%

10000

1000

1

r - 128 A =mud 100 LI

10 1/7 6:00 1/7 12:001/7 18:00 1/8 0:00 1/8 6:001/8 12:001/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00 Date - Time

Figure 13: Station 4 Enterococci concentration versus date and time

54 5.3.4 Enterococci Results for Station 5 Finally, Table 15 and Figure 14 show the results for Station 5 on the Rochor River. Station 5 had the highest concentrations out of all four stations analyzed. All of the times sampled showed concentrations and corresponding probabilities of illness exceeding the tolerable illness level except for those measured on January 7th at 3:00 P.M., January 8th at 11:00 P.M., and January 9 th at 3:00 A.M.. The highest concentrations corresponding to a probability of illness of 6.3% occurred on January 81h at times 7:00 A.M., 11:00 A.M., 7:00 P.M., and 3:00 A.M.. The concentrations marked as >2420 represent levels that were too high to be measured without further dilution on the IDEXX table used to translate the fraction of positive wells into a concentration of indicator bacteria. Therefore, the probability of illness for these wells is even higher than those shown in Table 15.

Table 15: Station 5 Enterococci concentration and probability of illness (%)

Station 5 Enterococci

Probability Date - Time MPN /100 mL of Illness (%)

1/7/14 11:00 A.M. 435 3.8% 1/7/14 3:00 P.M. 106 1.3% 1/7/14 7:00 P.M. >2420 6.3% 1/7/14 11:00 P.M. 263 2.7% 1/8/14 3:00 A.M. >2420 6.3% 1/8/14 7:00 A.M. >2420 6.3% 1/8/14 11:00 A.M. >2420 6.3% 1/8/14 3:00 P.M. 126 1.5% 1/8/14 7:00 P.M. 1733 6.1% 1/8/14 11:00 P.M. 110 1.3% 1/9/14 3:00 A.M. 62 0.8% 1/9/14 7:00 A.M. 2420 6.3%

55 10000

1000

0

128 100

10 1/7 6:00 1/7 12:001/7 18:00 1/8 0:00 1/8 6:00 1/8 12:001/8 18:00 1/9 0:00 1/9 6:00 1/9 12:00 Date - Time

Figure 14: Station 5 Enterococci concentration versus date and time

5.3.5 Summary of Enterococci Results Overall, the times of 7 A.M., 11 A.M., and 7 P.M. consistently had high concentrations of Enterococci exceeding the tolerable illness level. Table 16 shows that geometric mean concentrations at all sampling times exceeded the tolerable level at Stations 4 and 5. All of the stations that exceeded the tolerable probability of illness should be investigated further in order to develop appropriate regulations to manage these high concentrations. However, because Stations 2, 3, 4, and 5 all showed high probabilities of illness due to E coli and Enterococci at certain times that exceeded the tolerable level, these locations are analyzed in greater detail in Section 5.5.2.

Table 16: Enterococci geometric mean concentrations at all times and probability of illness (%) at each station

Enterococci Probability of Illness (%) Station 2 70 0.9% Station 3 50 0.7% Station 4 130 1.6% Station 5 580 4.4%

56 Figure 15 shows the probability of illness associated with concentrations of Enterococci measured at Station 2 with different curves corresponding to different ingestion rates. As the ingestion rate increases, the probability curve shifts to the left, which suggests that a higher ingestion rate corresponds to a lower NOAEL. This figure shows that at the 6-mL dragon-boat-racer ingestion rate (blue line in Figure 15), the probability of illness of 0.015 or 1.5% is at a concentration of 128 MPN/100 mL or the NOAEL for Enterococci.

MPN/100 mL 0.07 Probability of Illness vs. Enterococci

0.06

An'5

V_intake= 6 mL 0.04 "1_intake =10 mL 0.03 LV_intake = 20 mL

0.02 ------X V intake = 30 mL

0.01 - - -- -

1 10 100 1000 10000 Enterococci MPN/ 100 mL

Figure 15: Station 2 probability of illness versus concentration of Enterococci curve at different intake rates per day. The NOAEL (128 CFU/ 100 mL) is associated with a probability of illness of 1.5% at an intake rate of 6 mL.

5.4 Adenovirus Results

The data presented in this section was compiled in collaboration with Liu (2014) and is presented in Appendix A. The probability of illness associated with exposure to the measured concentrations of adenovirus was compared to the Enterococci and E. coli probabilities of illness in order to determine any diurnal or spatial relationships in the Kallang Basin. Further, linear regression was conducted to determine whether correlations exist between indicator bacteria and adenovirus concentrations.

The following discussion suggests that based on the concentrations of adenovirus at each station for each time sampled, the times of peak concentration were quite inconsistent compared to the times of peak indicator bacteria peak concentration. There do not seem

57 to be specific times of adenovirus concentration that contribute to high probabilities of illness from water in the Kallang Basin.

In order to determine the probability of illness associated with adenovirus exposure, the viral concentrations in terms of genomic copies/L were analyzed. However, since van Heerden et al. (2005) utilized the number of positives detected by PCR in his exponential dose-response model, the genomic copies/L had to be translated into number of positives in order to apply Van Heerden et al.'s model. Following Viau et al. (2011), if one of the two viral replicates was negative, it was assigned a number of 5 genomic copies/L. If both of the replicates were negative, the target was considered "not detected." Therefore, for this current analysis, if one of the replicates produced a negative result, the geometric mean was taken of the two concentrations, assuming that the negative replicate was equivalent to 5 genomic copies/L. Based on van Heerden et al. (2005), the number of "detected" and "not detected" samples was compiled and the fraction of detected samples was calculated in order to compute the concentration in viruses/L. The concentration in viruses/L was calculated using Eq. 4, repeated below, using the original mean sample volume of 20 L:

mean volume of water analyzed Where: A = -ln[P(O)] P(O) = 1-fraction of positives detected by PCR.

Results of this calculation are shown in Table 17.

Table 17: Adenovirus concentration calculation at each station

Number of non-positive Concentration adenovirus i of adenovirus, detections, C P(O) (viruses /L) Station 2 0.33 1.1 0.06 Station 3 0.92 0.09 0.004 Station 4 0.68 0.39 0.02 Station 5 0.33 1.1 0.06

N in the exponential dose-response model was calculated using Eq. 3. The probability of illness was then calculated using Eq. 5, repeated below, with the results shown in Table 18.

Pi = 1 -e -rN (5)

58 Table 18: Calculation of N and daily and yearly probabilities of illness due to adenovirus exposure for an ingestion rate of 6 mL per day, 156 days of the year

N P (%) _year (%) Station 2 0.00083 0.034 5 Station 3 0.000065 0.0027 0.4 Station 4 0.00029 0.012 2 Station 5 0.00083 0.034 5

The probability results in Table 18 show that there is a 3/10,000 probability of illness per day due to adenovirus exposure at Station 2 and Station 5, 1/10,0000 probability at Station 4, and 3/100,000 probability at Station 3. The acceptable or tolerable risk of illness for drinking water is one illness per 10,000 consumers per year with an ingestion rate of 2 L per day. The tolerable risk for recreational waters is one illness per 1000 bathers per day (USEPA 1986) for users consuming 30 mL of water per day of recreation. This adenovirus tolerable illness level is much lower than the indicator bacteria tolerable illness level because adenoviruses are pathogens and pose direct adverse health effects. In contrast, indicator bacteria measure the potential presence of fecal pathogens, and often do not cause illnesses directly (USEPA 1986). The dragon- boat racers are presumed to consume only 6 mL of water per day based on the analysis presented in Section 4.3. Van Heerden et al. (2005) showed that as the volume of water consumed or the exposure to water decreased, the probability of illness also decreased, as shown in Figure 16.

0.002 Cu 0.0018 E.g 0.0016 0.0014 0.0012

T4.g 0.001 0 0.0008 0 1mo 0.0006

Cu 0.0004 0 0.0002 0 5 10 15 20 25 30 Volume Consumed (mL)

Figure 16: Station 2 Probability of illness per day versus volume consumed (mL) due to adenovirus exposure (van Heerden et al. 2005)

59 The probability of contracting a GI illness increases linearly as the volume of consumption increases. At 30 mL, which is the assumed volume of consumption for a swimmer in recreational waters, the probability of contracting an illness due to swimming in the Kallang Basin tributaries (Table 19) exceeds the tolerable level of 0.01%. Adenovirus concentrations at Stations 2 and 5 exceed the tolerable risk when normalized to an ingestion rate of 30 mL per day.

Table 19: Calculation of N and daily and yearly probability of illness due to adenovirus exposure for an ingestion rate of 30 mL per day, 365 days of the year

N P, (%) P, year (%) Station 2 0.0041 0.17 46 Station 3 0.00033 0.014 5 Station 4 0.0015 0.061 20 Station 5 0.0041 0.17 46

The tolerable probability of infection of 1 per 1000 bathers per day (0.1%) translates to a tolerable probability of illness per year of 31% if the users are exposed for 365 days of the year, or 14% for dragon-boat ingestion if ingesting 3 days per week, for a total of 156 days per year, calculated using Eq. 6 repeated below.

Pi _ - - 3 6 5 year day) (6)

For an equivalent ingestion rate of 30 mL per day, Table 19 shows the results. Table 18 shows the yearly probability of illness for an ingestion rate of 6 mL per day for 156 days per year at each station. Finally, Table 20 shows daily and yearly probabilities of illness for each day of sampling, representative of the whole Kallang Basin.

Table 20: Daily and yearly probabilities of illness due to adenovirus exposure for each day of sampling, representative of the whole Kallang Basin

1/7/14 1/8/14 1/9/14 0.58 0.61 0.69 C (viruses/L) 0.029 0.03 0.034 N (viruses) 0.0004 0.00046 0.0005

Pi(6 mL )0 0.018 0.019 0.022

Pvear(6 mL) (%) 2.8 2.9 3.0 P (30 mL) (%) 0.22 0.23 0.26

P year(30 mL)_/0 55 57 61

60 5.5 Comparison of Indicator Bacteria and Adenovirus

5.5.1 Indicator Bacteria and Adenovirus Relationship Kundu et al. (2013) analyzed the probability of adenovirus illness for primary contact by adults, primary contact by children, and secondary contact regardless of age in a multi- use coastal watershed. Results showed that seven of eight virus detections occurred when E. coli concentrations were below the single sample maximum water quality criterion for recreational activity, and five of eight viral detections occurred when fecal coliforms were below the corresponding criterion. However, quantitative microbial risk assessment (QMRA) on adenoviruses showed similar levels of protection to recreational users as risk assessments conducted on fecal indicator bacteria. In contrast, Viau et al. (2011) found that there were no associations between occurrence of viruses and fecal indicator bacterial concentrations. Gastrointestinal risks from viral exposure were generally orders of magnitude greater than those from bacterial exposures. Their conclusions suggested that pathogens could have come from both human and nonhuman nonpoint sources, contributing to the high probabilities of illness.

Results from this study showed that the probability of contracting a gastrointestinal illness from adenovirus exposure was less than the risk associated with indicator bacteria exposure (Table 11 and Table 16 versus Table 18 probabilities of illness at each station). Comparing the probability of illness at each station based on the geometric mean of concentrations at each time for fecal indicator bacteria (Table 11 and Table 16), Stations 2 and 5 for E. coli exceeded the tolerable probability of illness of 1.5%, and Stations 4 and 5 exceeded this tolerable probability for Enterococci. Based on yearly probability of illness for swimmer ingestion rates of adenovirus, Stations 2 and 5 exceeded the tolerable level (Table 19). However, tolerable probabilities of illness were not exceeded when analyzing dragon-boat racer ingestion rates for adenovirus (Table 18). High probabilities of illness for indicator bacteria for dragon-boat racers suggest that all of the stations pose risks to recreational users and thus require proper management.

Further analysis was conducted to determine if there was any correlation between the probability of illness posed by bacteria and that posed by viruses. Figure 17 shows the results of a linear regression of the logarithm of the adenovirus concentration at each station (data from Table 18) with the logarithm of the E coli concentration at each station (Table 11). The concentration of adenovirus correlated well with the concentration of E. coli with an R2 value of 0.95 for each station. However, the concentration of adenovirus did not correlate well with the concentration of Enterococci (Table 16) with an R2 value of 0.36 (Figure 18). The equation relating the concentration of adenovirus with the concentration of F. coli is repeated below:

log1(Cdenovirus) = 1.32 * log1(CE.coli) - 5.52 (24)

61 Log Adenovirus Concentration (viruses/L) vs. Log E. coli Concentration (MPN/100 mL) -0.5 : 2.2 2.4 2.6 2.8 3 3.2 3.4 -0.7

-0.9 0"I -1.1 y = 1.3221x - 5.5177 2 -1.3 ______-___ R =0 ___

-1.5

W -1.7 -1.9

-2.1

-2.3

-2.5 E. coli Concentration (MPN/100 mL)

Figure 17: Log Adenovirus concentration (viruses/L) versus Log E. coli Concentration (MPN/ 100 mL) at each station

Log Adenovirus Concentration (viruses/L) vs. Log Enterococci Concentration (MPN/ 100 mL) -0.5 1.5 2 2.5 3 -0.7 U, -0.9 i -

-1.1 y = U.6665x - 3.U5P SR2 = 0.36 -1.3 -1.5 -1.7 0 -1.9

-2.1 I -2.3 -2.5 Enterococci Concentration (MPN/ 100 mL)

Figure 18: Log Adenovirus Concentration (viruses/L) versus Log Enterococci Concentration (MPN/ 100 mL) at each station

62 5.5.2 Causes for High Indicator Bacteria Concentrations Potential causes for high indicator bacteria concentration are analyzed in this section. The Kallang Basin drains into Station 2, the Geylang River at Jalan Benaan Kapal, seen in Figure 4. The basin contributes nonpoint source runoff and high concentrations of bacterial pollution to this station. In addition, many roads run adjacent to Jalan Benaan Kapal, which may contribute further contamination. Station 3, Kallang Riverside Park is on the Kallang River, which is downstream of Station 4, Upper Boon Keng Road (Figure 19). Table 11, Table 16, and Table 18 all suggest that Station 4 is more contaminated than Station 3. Major sources of contamination may exist upstream of Station 4, and dilution or microbial inactivation during transport downstream to the basin, either through photolysis or bacterial die-off, may explain the lower concentrations at Station 3. Upstream of Station 4 is Bishan Park (Figure 20), which may contribute to additional high concentrations of microbes. This park is 62 hectares and is one of the largest and most popular parks in Singapore. The Kallang River stretches through the park, and the housing estates border it, which may produce high concentrations of fecal bacteria and pathogens during peak activity times.

Rochor Canal leads to the Rochor River, Station 5, which then empties into the Kallang Basin (Figure 21). The Rochor River is integrated with surrounding developments to allow the people to enjoy the waterway. Rain gardens are also incorporated into the Rochor Canal. Station 5 contributed the highest concentrations, but Angeles (2014) showed that the Rochor River had the lowest flow rate out of the rivers tributary to the Kallang Basin. Therefore, the high microbial concentration seen at Station 5 may not be due to any single source, but may more result from the relatively stagnant conditions associated with this low flow rate. Upstream of the Kallang River, Station 4 had the largest flow rate, which may also explain high microbial contamination at Station 4. Overall, regulators should analyze the quality of the water, primarily focusing on the Kallang River, before allowing further recreational use of the basin, especially during these peak activity times.

63 VL

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Sr Manmatha Karuneshverar (Sivan) Temple

der W. K Lavender Kaang River KaHang Riverside Park 00004. oKilo

Rochor River

Figure 19: Map view of Station 3, Kallang Riverside Park, and Station 4, Upper Boon Keng Road, upstream of Station 3 (Google Maps 2014)

Yahoo 8usiness AnU

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Figure 20: Upstream of Station 4 - Upper Boon Keng Road, along the Kallang River. (Google Maps 2014)

64 7 VHoteliidend The Music Cbinic Pie

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MyDrumscpo 't Sgfnaloan Neia ~r~aIRc. Kong PeN Chang le

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SantaGrd HotelBugis A Bar On 0oe SPORTS CT Mectcal r,,Mh( n il Store Pie I1 Figure 21: Upstream of Station 5, Crawford Street (Google Maps 2014)

65 6. Conclusion In order to ensure the safety of recreational users of the Kallang Basin, the PUB must determine appropriate guidelines. Therefore, microbial risk assessments of indicator bacteria and adenovirus were conducted on locations along the Kallang Basin. Because probabilities of illness may vary diurnally and spatially, the risk assessments were based on concentrations measured at locations along the Kallang Basin during a 48-hour time period, with samples taken every four hours. Based on guidelines calculated from statistics-based and exponential dose-response models using appropriate ingestion rates for dragon-boat racers, Station 2 on the Geylang River; Station 3 on the Kallang River; Station 4 on the Kallang River upstream; and Station 5 on the Rochor River all exceeded the tolerable levels of illness for indicator bacteria, especially at 7:00 A.M. and 11:00 A.M. The probabilities of illness for dragon-boat racers did not exceed the tolerable illness level for exposure to adenoviruses. However, tolerable illness levels were exceeded when considering swimmer exposure to adenovirus at ingestion rates of 30 mL. While probabilities of illness were inconsistent among indicator bacteria and adenovirus, because the stations all exceeded the tolerable levels of illness at certain times of the day, the PUB should investigate those locations further.

6.1 Guidelines

Based on the statistics-based risk model by Wiedenmann (2007), the guideline geometric mean concentration for Enterococci is 128 CFU/100 mL and for E. coli, 697 CFU/ 100 mL, corresponding to a probability of illness of 1.5% per recreational day for dragon-boat racers. Based on the van Heerden et al. (2005) exponential dose-response model for adenovirus, the tolerable probability of illness per day of 0.1% was not exceeded for dragon-boat racer ingestion rates of 6 mL per day. However, for swimmer ingestion rates of 30 mL per day, probabilities of illness exceeded this level at Station 2 and Station 5. Analyzing the probabilities of illness for each day within the Kallang Basin, the basin is safe for dragon-boat racers ingesting adenovirus, but not for indicator bacteria, especially during peak bacterial concentrations at 7:00 A.M. and 11:00 A.M.

6.2 Future Research

Liu (2014) determined the correlation between human adenovirus and coliphage (male- specific and somatic). She analyzed whether viral indicators were effective at specific sampling locations in the Kallang River Basin, focusing on the detection of coliphages and adenovirus. Because microbial risk assessments have not been conducted on coliphages, a future study should investigate potential models based on the correlation between adenovirus and coliphage. The results of derived risk equations based on the

66 prevalence of coliphage would be vital towards calculating a more accurate risk level for recreational users of the Kallang Basin.

Also, the locations that exceeded the tolerable levels of illness along the Kallang Basin should be explored more deeply to determine causes for such high concentrations. Studies could explore public activities during peak times of high bacterial concentration in order to determine specific times and locations when the Kallang Basin should be closely monitored or even closed to recreational use.

Dixon (2009) recommended sampling at various locations along the Reservoir during storm events. Dragon-boat racers still race during storms, so a study of the water quality after a storm event should be conducted to ensure that particularly high microbial concentrations do not exist during this time in the Kallang Basin. A study conducted by the Nanyang Technological University (2008) showed that the Kranji Reservoir experienced significantly higher bacteria densities after storm events than during dry periods. Further, if probabilities of illness are significantly higher during storms, a study should be conducted analyzing when dragon-boat racers can safely use the Kallang Basin after the storms have cleared.

67 References

Angeles, J.V.V. (2014). Water Quality Modelling for Recreational Use in the Kallang River Basin, Singapore. Master of Engineering thesis. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Aw, T.G. and Gin, K.Y.H. "Prevalence and genetic diversity of waterborne pathogenic viruses in surface waters of tropical urban catchments." Journal of Applied Microbiology, 110(4): 903- 914 (2010).

Borrego, J. J., Cornax, R., Moriffigo, M. A., Martinez-Manzanares, E., and Romero, P. (1990). "Coliphages as an indicator of a faecal pollution in water. Their survivial and productive infectivity in natural aquatic environments." Water Research, 24(1), 111-116.

Couch, R.B. and Knight V. (1969). "The minimal infectious dose of adenovirus type 4; the case for natural transmission by viral aerosol." Transactions of the American and Climatological Assocation. 80: 205-211.

Crabtree, K. D., Gerba, C. P., Rose, J. B., and Haas, C. N. (1997). "Waterborne adenoviruses: a risk assessment." Water Science and Technology, 35, 1-6.

Dixon, C. C. (2009). "Microbial Risk Assessment for Recreational Use of the Kranji Reservoir, Singapore." Master of Engineering thesis. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Dizer, H., Wolf, S., Fischer, M., Lopez-Pila, J. M., Roske, I., and Schmidt, R. (2005). "Die Novelle der EU-Badegewasserrichtlinie - Aspekte der Risikobewertung bei der Grenzwertsetzung." BundesgesundheitsblattGesundheitsforsch Gesundheitsschutz. 48, 607-614.

Dorevitch, S., Panthi, S., Huang, Y., Li, H., Michalek, A.M., Pratap, P., Wroblewski, M., Liu, L., Scheff, P.A., Li, A. (2010). "Water Ingestion during Water Recreation." Water Research, 45(5), 2020-2028.

Dufour, A. P. (1984). Health Effects CriteriaforFresh Recreational Waters. No. EPA-600/1-84- 004. United States Environmental Protection Agency, Research Triangle Park, NC.

Eischeid, A.C., Meyer, J.N., and Linden, K.G. (2009) "UV disinfection of adenovirus: Molecular indications of DNA damage efficiency." Applied and Environmental Microbiology. 75(1):23-28.

Enriquez, E.C., Hurst, C.H., Gerba, C.P., 1995. "Survival of enteric adenoviruses 40 and 41 in tap, sea and wastewater." Water Research 29, 2548-2553.

Fleisher, J. M. (1991). "A Reanalysis of Data Supporting U.S. Federal Bacteriological Water Quality Criteria Governing Marine Recreational Waters." Research Hournal of the Water Pollution Control Federation, 63(3 ), 259.

68 Fosgate, G.T. and Cohent, N.D., (2008). "Epidemiological study design and the advancement of equine health." Equine Veterinary Journal,40(7): 693-700.

Grabow, W. 0. K., and Taylor, M. B. (1993). "New methods for the virological analysis of drinking water supplies." Biennial Conference and Exhibition of the Water Institute of Southern Africa, 259-264.

Haas, C. N., Rose, J. B., and Gerba, C. P. (1999). Quantitative MicrobialRisk Assessment. John Wiley & Sons Inc., New York, N.Y.

Hernandez-Delgado, E. A., Sierra, M. L., & Toranzos, G.A. (1991). "Coliphages as Alternate Indicators of Fecal Contamination in Tropical Waters." Environmental Toxicology and Water Quality, 6(2), 131-143.

Holton, Glyn A. (2004). "Defining Risk." FinancialAnalysts Journal,60(6), 19-25.

IDEXX Laboratory Inc. (2013b). "Water Testing Solutions: Product Catalog."

Jiang, S., Noble, R. and Chu, W. (200 1). "Human adenoviruses and coliphages in urban runoff- impacted coastal waters of Southern California." Applied and Environmental Microbiology, 67, 179-184.

Joshi, Y. K., Tortajada, C., and Biswas, A. K. (2012). "Cleaning of the Singapore River and Kallang Basin in Singapore: Economic, Social, and Environmental Dimensions." InternationalJournal of Water Resources Development, 28(4), 647-658.

Jothikumar, N., Cromeans, T. L., Hill, V. R., Lu, X., Sobsey, M. D., and Erdman, D. D. (2005). "Quantitative Real-Time PCR Assays for Detection of Human Adenoviruses and Identification of Serotypes 40 and 4 1." Applied and Environmental Microbiology, 71(6), 3 13 1-3 136.

Kundu, A., McBride, G., Wuertz, S. (2013). "Adenovirus-associated health risks for recreational activities in multi-use coastal watershed based on site-specific quantitative microbial risk assessment." Water Research, 47(16):6309-25.

Kuo, D. H. W., Simmons, F.J., Blair, S., Hart, E., Rose, J.B. & Zagoraraki, 1. (2010). "Assessment of human adenovirus removal in a full-scale membrane bioreactor treating municipal wastewater." Water Research, 44, 1520-1530.

Liu, T.Y. (2014). Enteric Adenovirus and Coliphage as Microbial Indicators in Singapore's Kallang Basin. Master of Engineering thesis. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Nauta, T., Wui, C. C., Smits, J., and Lee, E. (n.d.). OperationalWater Quality Managementjor Marina Reservoir, Singapore. .

69 Nwachuku, N., Gerba C.P., Oswald, A, and Mashadi, F.D. (2005). "Comparative Inactivation of Adenovirus Serotypes by UV Light Disinfection." Applied and Environmental Microbiology, 71(9): 5633-5636.

Pina, S., Puig, M., Lucena, F., Jofre, J., and Girones, R. (1998). "Viral pollution in the environment and in shellfish: human adenovirus detection by PCR as an index of human viruses." Applied and EnvironmentalMicrobiology, 64(9), 3 3 76-82.

PUB. (2013). "Our Water, Our Future." Public Utilities Board,Singapore. .

Rigotto, C., Hanley, K., Rochelle, P.A., De Leon, R., Barardi, C.R.M, Yates M.V. (2011). "Survival of Adenovirus Types 2 and 41 in Surface and Ground Waters Measured by a Plaque Assay." Environmental Science Technology, 45, 4145-4150.

Rose, J. B., Mullinax, R. L., Singh, S. N., Yates, M. V., and Gerba, C. P. (1987). "Occurrence of rotaviruses and enteroviruses in recreational waters of Oak Creek, Arizona." Water Research, I I, 1375-1381.

SGNEA (2008). "Recreational Water Quality Guidelines for Recreational Beaches and Fresh Water Bodies." Singapore National EnvironmentAgency. , (Jan. 4, 2014)

Sloat, S., and Ziel, C. (1992). The Use ofIndicator Organisms to Assess Public Water Safety. Hach Company, Loveland, CO.

Soon, T.Y., Jean, L.T., Tan, K. (2009). Clean, green and blue. Singapore'sjounrey towards environmentaland water sustainabilitv. ISEAS Publishing, Singapore.

Thomann, R. V., and Mueller, J. A. (1987). Principlesof Surface Water Quality Modeling and Control. Harper and Row, New York.

USEPA. (1986). Ambient Water Quality Criteriafor Bacteria.No. EPA440/5-84-002, United States Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC.

USEPA. (1998). Guidelinesfor EcologicalRisk Assessment. No. EPA/630/R-95/002F. Risk Assessment Forum, U.S. Environmental Protection Agency, Washington, D.C.

USEPA. (200 1 a). "Manual of Methods for Virology: Chapter 16." Report Number EPA 600/4- 84/013 (N 16). Office of Research and Development, U.S. Environmental Protection Agency, Washington, D.C.

USEPA. (2001 b). "Method 1601 : Male-specific ( F + ) and Somatic Coliphage in Water by Two- step Enrichment Procedure." Report Number EPA 821-R-01-030. Office of Water, U.S. Environmental Protection Agency, Washington, D.C.

70 USEPA. (2002). "Calculating Upper Confidence Limits for Exposure Point Concentrations at Hazardous Waste Sites." United States Environmental ProtectionAgency, (Jan, 2014)

USEPA. (2006b). "National Primary Drinking Water Regulations: Ground Water Rule; Final Rule. 40 CFR Parts 9, 141, and 142. FederalRegister, 71:216:65574. . (Jan, 2014)

USEPA. (2011). "Revisions to the Unregulated Contaminant Monitoring Regulation (UCMR 3) for public water systems." FederalRegister, 76(42):11713-11737. van Heerden, J., Ehlers, M.M., Heim, A. and Grabow, W.O. (2005) "Prevalence, quantification and typing of adenoviruses detected in river and treated drinking water in South Africa." Journal ofApplied Microbiology, 99, 234-242.

Vilagines, P., Sarrette, B., Champsaur, H., Hugues, B., Dubrou, S., Joret, J.-C., Laveran, H., J., L., Paquin, J. L., Delattre, J. M., Oger, C. Alame, J., Grateloup, I. Perrollet, H., Serceau, R. Sinegre, F., and Vilagines, R. (1997). "Round robin investigation of glass wool method for poliovirus recovery from drinking water and sea water." Water Science and Technology, 35, 445-449.

Viau, E.J., Lee, D., and Boehm, A.B. (2011). "Swimmer Risk of Gastrointestinal Illness from Exposure to Tropical Coastal Waters Impacted by Terrestrial Dry-Weather Runoff." EnvironmentalScience Technology, 45(17) 7158-7165.

Vilagines, P., Sarrette, B., Husson, G., and Vilagines, R. (1993). "Glass wool for virus concentration at ambient water pH level." Water Science and Technology, 27, 299-306.

Wade, T.J., Pai, N., Eisenberg, J. N. S., & Colford, J.M. (2003). "Do US Environmental Protection Agency water quality guidelines for recreational waters prevent gastrointestinal illness? A systematic review and meta-analysis." Environmental Health Perspectives, 111(8), 1102-1 109.

Ward, R. L., Bernstein, D. I., Young, E. C., Sherwood, J. R., Knowlton, D. R., and Schiff, G. M. (1986). "Human rotavirus studies in volunteers: determination of infectious dose and serological response to infection." Journal of Infections Diseases, 154, 871-880.

WHO. (2003). "Guidelines for safe recreational waters: Volume 1 - Coastal and fresh waters. Water Sanitation Health." World Health Organization, Geneva, Switzerland. http://www.who.int/watersanitationhealth/bathing/srwg 1.pdf

Wiedenmann, A. (2007). "A plausible model to explain concentration-response relationships in randomized controlled trials assessing infectious disease risks from exposure to recreational waters." In: StatisticalFramework for Recreational Water Quality Criteriaand Monitoring,L. Wymer (ed.), John Wiley and Sons Ltd., West Sussex, 153-179.

Wiedenmann, A., Kruger, P., Dietz, K., Lopez-Pila, J.M., Szewzyk, R., & Botzenhart, K. (2006). "A Randomized controlled trial assessing infectious disease risks from bathing in fresh recreational waters in relation to the concentration of Escherichia coli, intestinal

71 enterococci, Clostridium perfringens, and somatic coliphages." Environmental Health Perspectives, 114(2), 228-236.

Wiedenmann, A., Kruger, P., Gommel, S., Eissler, M., Hirlinger, M., Paul, A., Junst, K., Sieben, E., and Dietz, K. (2004). Epidemiologicaldetermination of disease risks from bathing. Report No. UFOPLAN 298 61 503. Federal Environmental Agency (UBA), Berlin.

Wymer, L.J., & Dufour, A.P. (2002). "A model for estimating the incidence of swimming-related gastrointestinal illness as a function of water quality indicators." Environmetrics, 13(5-6), 669-678.

72 Appendix A

List of Adenovirus samples and concentrations in genomic copies/L

GC/L dilutions Well Sample Name CT CT Mean GC GC/L

Al 4-4 Undetermined A2 4-8 Undetermined A3 4-12 Undetermined AIO 5-4 Undetermined All 5-8 34.38 34.38 89.67 389.91 3899 A12 5-12 36.20 35.17 28.12 122.2 1222 B 1 4-4 Undetermined B2 4-8 Undetermined B3 4-12 Undetermined BlO 5-4 Undetermined B1I1 5-8 Undetermined 34.38 B12 5-12 34.15 35.17 103.99 452.1 4521 C1 4-3 Undetermined 36.85 C2 4-7 Undetermined C3 4-11 Undetermined CIO 5-3 Undetermined 38.30 CII 5-7 Undetermined C12 5-11 36.25 36.68 27.22 118.38 1183 D1 4-3 36.85 36.85 18.52 80.51 805. D2 4-7 Undetermined D3 4-11 Undetermined D1O 5-3 38.30 38.30 7.36 32.01 320 DII 5-7 Undetermined D12 5-11 37.11 36.68 15.70 68.29 682 El 4-2 33.86 33.86 125.51 545.7 5457 E2 4-6 Undetermined 38.95 E3 4-10 Undetermined El0 5-2 Undetermined Eli 5-6 Undetermined 39.44 E12 5-10 Undetermined 35.92 F1 4-2 Undetermined 33.86 F2 4-6 38.95 38.95 4.85 21.10 211.06 F3 4-10 Undetermined FIO 5-2 Undetermined F1 5-6 39.44 39.44 3.54 15.43 154.34 F12 5-10 35.92 35.92 33.58 146 1460 Gl 4-1 Undetermined G2 4-5 Undetermined 36.49 G3 4-9 Undetermined GIO 5-1 35.66 35.23 39.65 172.4 1724 G1 5-5 Undetermined 38.13 G12 5-9 36.24 35.66 27.36 118.9 1189 H1 4-1 Undetermined H2 4-5 36.49 36.49 23.33 101.4 1014

73 Well Sample Name CT CT Mean GC GC/L GC/L*dilutions

H3 4-9 Undetermined H7 Pos 29.54 29.54 1980 8610 86110 HlO 5-1 34.81 35.23 68.37 297 2972 HII 5-5 38.13 38.13 8.19 35.61 356 H12 5-9 35.08 35.66 57.48 249 2499

74 Adenovirus quantitative PCR results in genomic copies/L (GC/L) for Station 2 at each time

Station 2 1 2 Geometric Mean Date - Time GC/L GC/L GC/L 1/7/14 11:00 A.M. 2500 1780 2100 1/7/14 3:00 P.M. 940 X 690 1/7/14 7:00 P.M. 520 1500 890 1/7/14 11:00 P.M. X X X 1/8/14 3:00 A.M. 790 1210 980 1/8/14 7:00 A.M. 690 X 60 1/8/14 11:00 A.M. 450 X 50 1/8/14 3:00 P.M. 910 X 70 1/8/14 7:00 P.M. 2420 X 110 1/8/14 11:00 P.M. X X X 1/9/14 3:00 A.M. X X X 1/9/14 7:00 A.M. X X X

2500

2000

8 1500

1000

500

0 1/7/14 0:00 1/7/14 12:00 1/8/14 0:00 1/8/14 12:00 1/9/14 0:00 1/9/14 12:00 Date-Time

75 Adenovirus quantitative PCR results in genomic copies/L for Station 3 at each time

Station 3 2 Geometric Mean Date - Time GC/L GC/L GC/L 1/7/14 11:00 A.M. X X X 1/7/14 3:00 P.M. X X X 1/7/14 7:00 P.M. X X X 1/7/14 11:00 P.M. X X X 1/8/14 3:00 A.M. X X X 1/8/14 7:00 A.M. X X X 1/8/14 11:00 A.M. X X X 1/8/14 3:00 P.M. X X X 1/8/14 7:00 P.M. X X X 1/8/14 11:00 P.M. X X X 1/9/14 3:00 A.M. 190 X 30 1/9/14 7:00 A.M. 1200 X 80

90

80

70

,60

S50

o40

o30

20 10 10 I

1/7/14 0:00 1/7/14 12:00 1/8/14 0:00 1/8/14 12:00 1/9/14 0:00 1/9/14 12:00 Date-Time

76 Adenovirus quantitative PCR results in genomic copies/L for Station 4 at each time

Station 4 1 2 Geometric Mean Date - Time GC/L GC/L GC/L 1/7/14 11:00 A.M. X X X 1/7/14 3:00 P.M. 5460 X 165 1/7/14 7:00 P.M. X 805 60 1/7/14 11:00 P.M. X X X 1/8/14 3:00 A.M. X 1000 70 1/8/14 7:00 A.M. X 210 30 1/8/14 11:00 A.M. X X X 1/8/14 3:00 P.M. X X X 1/8/14 7:00 P.M. X X X 1/8/14 11:00 P.M. X X X 1/9/14 3:00 A.M. X X X 1/9/14 7:00 A.M. X X X

180

160 - 9

140

120

100 I---- -

80 +-

60

40 - 20 I I 0 II I I 1/7/14 0:00 1/7/14 12:00 1/8/14 0:00 1/8/14 12:00 1/9/14 0:00 1/9/14 12:00 Date-Time

77 Adenovirus quantitative PCR results in genomic copies/L for Station 5 at each time

Station 5 1 2 Geometric Mean Date - Time GC/L GC/L GC/L 1/7/14 11:00 A.M. 1720 3000 2260 1/7/14 3:00 P.M. X X X 1/7/14 7:00 P.M. X 320 40 1/7/14 11:00 P.M. X X X 1/8/14 3:00 A.M. X 360 40 1/8/14 7:00 A.M. X 150 30 1/8/14 11:00 A.M. X X X 1/8/14 3:00 P.M. X X X 1/8/14 7:00 P.M. 1190 2500 1700 1/8/14 11:00 P.M. X 1460 85 1/9/14 3:00 A.M. 1180 680 900 1/9/14 7:00 A.M. X 4520 150

2500

2000

1500 4 2 U,

0 1000 +---

500 -

A L - -- I -. ~--- 1/7/14 0:00 1/7/14 12:00 1/8/14 0:00 1/8/14 12:00 1/9/14 0:00 1/9/14 12:00 Date-Time

78 Appendix B

Raw E. coli data for samples collected on January 7th, 2014

MPN MPN MPN MPN MPN MPN Date Time Samples I Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 7/l/ 11:00 2 10 145.5 209.8 301.1 1455 2098 3011 14 A.M. ______3 5 169.5 225.4 296.2 847.5 1127 1481 4 10 260.6 410.6 618.9 2606 4106 6189 5 10 1016.2 1553.1 2353.1 10162 15531 23531 Average 3504.18

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER

7/1/1 3:00 2 10 72.2 98.5 132.1 722 985 1321 4 P.M. ______3 5 126.4 172.6 228.5 632 863 1142.5 4 10 74.8 105 143.9 748 1050 1439 5 10 660.6 980.4 1410.2 6606 9804 14102 Average 1719.92

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER

7/1/1 7:00 2 10 32.3 46.5 64.7 323 465 647 4 P.M. 3 5 123 172.5 235.5 615 862.5 1177.5 4 10 59.6 83.6 113.8 596 836 1138 5 10 231.9 365.4 555.5 2319 3654 5555 Average 1052.07

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 7/1/1 11:00 2 10 158.5 222.4 303.3 1585 2224 3033 4 P.M. 3 5 81.6 114.5 155.5 408 572.5 777.5 4 10 51.1 71.7 97.5 511 717 975 5 10 169.2 231 315.5 1692 2310 3155 Average 1205.06

79 Raw E. coli data for samples collected on January 8 th 2014

Dat MPN MPN MPN MPN MPN MPN Time Samples Dilution e LOWER ACTUAL UPPER LOWER ACTUAL UPPER

8/1 A:. 2 10 134.9 184.2 251.4 1349 1842 2514

3 5 40 56.1 75.9 200 280.5 379.5 4 10 64.6 90.6 124.1 646 906 1241 5 10 84.7 118.7 162.7 847 1187 1627 Average 863.38

DatT MPN MPN MPN MPN MPN MPN e rime Samples LOWER ACTUAL UPPER LOWER ACTUAL UPPER

8/1 A.. 2 10 183.5 272.3 382.9 1835 2723 3829

3 5 53 74.3 98.8 265 371.5 494 4 10 74.6 104.6 142.1 746 1046 1421 5 10 134.9 184.2 251.4 1349 1842 2514 Average 1181.5

Dat T MPN MPN MPN MPN MPN MPN e rime Samples LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1 11:00 2 10 924.9 1431.6 2101.6 9249 14316 21016 /14 A.M. ______3 5 31.3 45.2 62.5 156.5 226 312.5 4 10 28.3 42 59.7 283 420 597 5 10 96.5 135.4 184 965 1354 1840 Average 1164.6

Dat Time Samples Dilution MPN MPN MPN MPN MPN MPN e rime LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1 3:00 /14 P.M. 2 10 245.3 344.1 472.5 2453 3441 4725 3 5 39.1 56.3 77.6 195.5 281.5 388 4 10 42.1 59.1 81.2 421 591 812 5 10 95.1 133.4 177.9 951 1334 1779 Average 934.81

Dat MPN MPN MPN MPN MPN MPN Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1 7:00 /14 P.M. 2 10 87.4 113.3 144.2 874 1133 1442

80 I I I 3 1 1 27.9 41.4 58.6 27.9 41.4 58.6 4 10 17.1 26.9 39.8 171 269 398 5 10 15.5 23.7 35 155 237 350 Average 233.85

Dat MPN MPN MPN MPN MPN MPN e Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1 11:00 2 10 126.4 172.6 228.5 1264 1726 2285 /14 .P. M. ______3 1 38.5 55.6 77.2 38.5 55.6 77.2 4 10 14.4 23.3 36.1 144 233 361 5 10 50.5 68.9 93.8 505 689 938 Average 352.31

Raw E. coli data for samples collected on January 91, 2014

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWE ACTUAL UPPER

9/1/ 3:00 2 10 21.8 31.4 44.2 218 314 442 14 A. M. 3 5 12.7 20.6 31.8 63.5 103 159 4 10 7.4 13.4 22.3 74 134 223 5 10 6.9 12.8 21.7 69 128 217 Average 153.47

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUA UPPER L

9/1/ 7:00 2 10 87.2 116 152.6 872 1160 1526 14 A.M. 3 5 64.9 88.6 118.3 324.5 443 591.5 4 10 18.2 27.8 40.6 182 278 406 5 10 1222 1986.6 3300.2 12220 19866 33002 Average 1297.9

81 Raw Enterococci data for samples collected on January 7 t, 2014

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWE LOWER ACTUAL UPPER ACTUAL UPPER R 7/l/ 11:00 2 1 754.6 1119.9 1614 754.6 1119.9 1614 14 A.M. ______3 1 205.8 280.9 378.3 205.8 280.9 378.3 4 1 475.7 727 1048.9 475.7 727 1048.9 5 1 276.2 435.2 650 276.2 435.2 650 Average 561.67

. MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER

14l/ P. 2 1 12.9 21.6 33.7 12.9 21.6 33.7

3 1 19.7 30.1 44.2 19.7 30.1 44.2 4 1 74 101 133.7 74 101 133.7 5 1 79.7 105.9 137 79.7 105.9 137 Average 51.35

MPN MPN Date Time Samples Dilution MPN MPN MPN MPN LOWER ACTUAL UPPER LOWER ACTUAL UPPER

14l/ P. 2 1 39.7 54.3 72.4 39.7 54.3 72.4

3 1 43.4 60.9 83.3 43.4 60.9 83.3 4 1 102 132.2 168 102 132.2 168 5 1 1439.5 2419.6 lx^OA7 1439.5 2419.6 lx^OA7 Average 180.3

MPN MPN Date Time Samples Dilution MPN MPN MPN MPN LOWER ACTUAL UPPER LOWER ACTUAL UPPER 7/1/ 11:00 2 1 44.1 60.2 80 44.1 60.2 80 14 P.M. ______3 1 29.9 43.1 59.8 29.9 43.1 59.8 4 1 65.5 87.1 112.8 65.5 87.1 112.8 5 1 218.4 263.1 314.5 218.4 263.1 314.5 Average 87.81

82 Raw Enterococci data for samples collected on January 8 h 2014

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1/ 3:00 14 A.M. 2 1 740.6 1011.2 1323.5 740.6 1011.2 1323.5 3 1 62.9 88.2 120.2 62.9 88.2 120.2 4 1 91.9 122.2 159.8 91.9 122.2 159.8 5 1 1439.5 2419.6 108 1439.5 2419.6 108 Average 402.97

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1/ 7:00 14 A.M. 2 1 1167.7 1732.9 2709.5 1167.7 1732.9 2709.5 3 1 154.2 210.5 281 154.2 210.5 281 4 1 1167.7 1732.9 2709.5 1167.7 1732.9 2709.5 5 1 1439.5 2419.6 108 1439.5 2419.6 108 Average 1112.07

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1/ 11:00 14 A.M. 2 1 9.7 16.8 26.8 9.7 16.8 26.8 3 1 5.1 10.7 18.5 5.1 10.7 18.5 4 1 37.1 52 71 37.1 52 71 5 1 1439.5 2419.6 108 1439.5 2419.6 108 Average 68.96

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1/ 3:00 14 P.M. 2 1 2.3 5.2 11.9 2.3 5.2 11.9 3 1 10.7 17.9 28.2 10.7 17.9 28.2 4 1 35.4 49.6 67.8 35.4 49.6 67.8 5 1 104.2 125.6 149.8 104.2 125.6 149.8 Average 27.59

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1/ 7:00 14 P.M. 2 1 49.7 69.7 95.3 49.7 69.7 95.3 3 1 101.9 139.1 182 101.9 139.1 182 4 1 295.5 403.4 526.2 295.5 403.4 526.2 5 1 1167.7 1732.9 2709.5 1167.7 1732.9 2709.5

83 Average 286.92

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 8/1/ 11:00 14 P.M. 2 1 13.4 21.8 33.1 13.4 21.8 33.1 3 1 7.6 14.2 23.4 7.6 14.2 23.4 4 1 18.8 28.7 41.6 18.8 28.7 41.6 5 1 82 109 142.2 82 109 142.2 Average 31.36

Raw Enterococci data for samples collected on January 9 th, 2014

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 9/1/ 14 3:00 A.M. 2 1 6 11.6 20.1 6 11.6 20.1 3 1 6.5 12.1 21.1 6.5 12.1 21.1 4 1 24.2 35.9 51 24.2 35.9 51 5 1 43.9 61.6 83.3 43.9 61.6 83.3 Average 23.60

MPN MPN MPN MPN MPN MPN Date Time Samples Dilution LOWER ACTUAL UPPER LOWER ACTUAL UPPER 7:00 9/1/14 A.M. 2 1 42.9 60.2 83 42.9 60.2 83 3 1 91.2 124.6 167.8 91.2 124.6 167.8 4 1 78.2 106.7 140.4 78.2 106.7 140.4 5 1 1630.4 2419.6 4716.1 1630.4 2419.6 4716.1 Average 209.78

84