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Title:
Commensal fecal bacteria population biology, diversity, and usefulness as indicator organisms in reclaimed water
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Book
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English
Creator:
Chivukula, Vasanta Lakshmi
Publisher:
University of South Florida
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Tampa, Fla
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Subjects / Keywords:
Water reclamation
Disinfection
Microbial diversity
Indicator organisms
Water quality
Dissertations, Academic -- Biology -- Doctoral -- USF
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bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract:
ABSTRACT: Water treatment facilities have been relying on indicator bacteria to assess the quality of water for decades. The purpose of this group of studies is to investigate the predictive capabilities of conventional and alternative indicators for pathogenic microorganisms in disinfection processes and treated wastewater effluents. In addition, the possibility that diversity of indicator bacteria, as well as overall bacterial diversity, correlate with fecal contamination in water bodies has been investigated. Indicator organisms (total coliforms, fecal coliforms, enterococci, C. perfringens, and coliphages) as well as pathogens (enteroviruses, Giardia, and Cryptosporidium) were enumerated from six wastewater treatment facilities at various stages of treatment. Statistical analyses were conducted to determine if the indicator organisms (individually or as a set) could predict the presence or absence of pathogens. Single indicator organism analysis failed to correlate with the occu rrence pathogens, thus monitoring a suite of indicator organisms may be a better measure to predict the presence of pathogens. The product of chlorine residual concentration and contact time (CT) was identified as a factor for determining the log10 reduction of enteric viruses in wastewater treatment facilities that used chloramines for disinfection.Samples were also collected from river waters and sediments in watersheds with different human population densities to identify the impact of anthropogenic activities on bacterial diversity. 16S rRNA restriction fragment length polymorphism (RFLP), ribotyping, and denaturing gradient gel electrophoresis (DGGE) were used to determine total coliform, Escherichia coli, and bacterial community population structures, respectively. The concentrations of indicator organisms were significantly different among the river sites in sediments, but not in water column. The population diversity measurements were not significantly different among the river ^sites; while the indicator population and bacterial community structures were dissimilar in water column vs. associated sediment samples. Accumulation curves demonstrated that greater than 20 isolates must be sampled at most of the sites to represent the dominant populations. A better understanding of the relationship between the indicator organisms and pathogens as well as knowledge of the ecology of indicator organisms in pristine and anthropogenically impacted waters may contribute to water quality restoration and public health protection.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2005.
Bibliography:
Includes bibliographical references.
System Details:
System requirements: World Wide Web browser and PDF reader.
System Details:
Mode of access: World Wide Web.
Statement of Responsibility:
by Vasanta Lakshmi Chivukula.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 178 pages.
General Note:
Includes vita.

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aleph - 001912705
oclc - 174040649
usfldc doi - E14-SFE0001334
usfldc handle - e14.1334
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PAGE 1

Commensal Fecal Bacteria: Population Biology, Diversity, and Usefulness as Indicator Organisms in Reclaimed Water by Vasanta Lakshmi Chivukula A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Biology College of Arts and Sciences University of South Florida Major Professor: Valerie J. Harwood, Ph.D. Daniel V. Lim, Ph.D. James D. Garey, Ph.D. Audrey D. Levine, Ph.D. Date of Approval: November 10, 2005 Keywords: water reclamation, disinfection, microbial diversity, indicator organisms, water quality Copyright 2005, Vasanta L. Chivukula

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Dedicated to Lord Sai Baba

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Acknowledgments It gives me great pleasure to thank all th e people who helped made this research possible. I cannot overemphasize my gratitude to my supervisor, Dr. Valerie J. Harwood. I express my sincere thanks to her for believi ng in me, continually pushing me to think and for all the support. Truly, it would all be impossible without her probing questions and candid critiques. Thank you, Dr. Harwood, for guiding me to attain answers for those questions. My appreciation goes to my other committee members, Daniel V. Lim, Ph.D., James D. Garey, Ph.D. and Audrey D. Levine Ph.D., for encouragement, helpful advice and patience. I have been extremely lucky to be surrounded by friendly, helpful and knowledgeable colleagues in Dr. Harwoods lab especially John Whitlock, Robert, Mariya, Katrina, Bina and Matthew. Th ank you everybody in BSF-243 and BSF-244 (Dr. Lims lab) for making it a fun place to wor k. I wish to thank Dr. John Lisle and Dr. Harold May for helping me to co mprehend the intricacies of DGGE. I am especially grateful to Jack Sadowski, John Niles a nd all the plant operators at St. Pete and Diana Biologist at Myakka, for their immense assistance during this project. Also I would like to acknowledge, Ch ristine Smith and her colleagues at Dept. of Biology, USF, for assisting me in many ways. On a different note, I wish to acknowle dge and thank Dr. Rao, Dr. Sekharam, the Parameswarans, Sudersan, Dr. Neeraja Jast hi, Swapna Ravipati, Sireesha Katipalli,

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Bhavana Sirivelu, Alok, Subodh, Sanjit and all my friends in SIA-USF, for reminding me that there is life outside research and keeping me sane. Thank you all very much. These few words of appreciation would do injustice to all the help provided by Arun and Nishanth. Nevertheless, thank you fo r your help and patience in teaching me the nuts and bolts of statistics. I want to thank my parents, Sri Ram achandra Murthy and Subba Lakshmi, not only for having me but for believing in me no matter what I have c hosen to do. Without their support, I would not have persevered in this work. I would also like to express my deep appreciation to my husband and best fri end, Vidura, who has endured with aplomb every emotion I went through in the proce ss. Without his support and companionship which provided necessary strength and persis tence, I cannot imagine completing this work. I also owe a great deal to my in-laws, Jella family, Gouru family and Chivukula family for their love and affection and simp ly believing in me. Without them, I would not be what I am now. Finally to Viji, my six month old daughter, who tossed and turned in my tummy and gave me awesome company during my late night rendezvous with my samples in the lab and giving me much needed relief with her smiles during late night trysts on writing this dissertation.

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i Table of Contents List of Tables................................................................................................................. ....iv List of Figures......................................................................................................................v Abstract....................................................................................................................... .......vi Purpose.................................................................................................................................1 Background..........................................................................................................................3 Study I. Comparison of the efficiency of removal of indicator microorganisms and pathogens from six wastewat er treatment facilities.............................................................3 Indicator organisms........................................................................................................6 Wastewater treatm ent objectives and regulations..........................................................9 Wastewater treatment methods....................................................................................15 Tertiary and adva nced wastewater treatment...............................................................18 Filtration.......................................................................................................................19 Disinfection............................................................................................................. .....22 Reclaimed water.......................................................................................................... .26 Removal of indicator organisms and pathogens through wastewater treatment processes......................................................................................................................29 Study II. Impact of fecal contamination on the diversity of microbial populations in natural waters................................................................................................................. ....32 Diversity and the health of an ecosystem....................................................................32 Species concept in pr okaryotes and diversity indices..................................................33 Restriction fragment length polymorphism (RFLP)....................................................36 Genomic ribotyping.....................................................................................................37 Denaturing gradient gel electrophoresis (DGGE)........................................................38 References....................................................................................................................41 Objectives..........................................................................................................................58 Validity of using indicator organism paradi gm: pathogen reduction and public health protection in reclaimed water.............................................................................................61 Abstract................................................................................................................. .......62 Introduction............................................................................................................. .....64 Materials and Methods.................................................................................................66 Facilities.................................................................................................................66 Sampling................................................................................................................66 Bacterial enumeration............................................................................................67

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ii Bacteriophage analysis...........................................................................................69 Enteric viruses........................................................................................................69 Protozoa.................................................................................................................70 Cryptosporidium infectivity...................................................................................70 Statistical analysis..................................................................................................71 Results.................................................................................................................. ........72 Microbial concentrations through treatment..........................................................72 Predictive relationships between microorganisms.................................................78 Discussion....................................................................................................................85 Detection of microorganisms.................................................................................85 References....................................................................................................................89 Evaluation of microbial data and disinfec tion efficacy in wastewater reclamation facilities..................................................................................................................... .........93 Abstract................................................................................................................. .......94 Introduction............................................................................................................. .....96 Materials and Methods.................................................................................................99 Sampling................................................................................................................99 Enumeration of indicator organisms and pathogens............................................100 Statistical analysis................................................................................................102 Results and discussion...............................................................................................103 Effectiveness of disinf ection processes on micr obial concentrations..................103 Relationship between indicat or organisms and pathogens..................................110 References..................................................................................................................114 Impacts of anthropogenic activities on the dive rsity of indicator organism and bacterial populations in environmental waters...............................................................................118 Abstract................................................................................................................. .....119 Introduction............................................................................................................. ...121 Materials and Methods...............................................................................................124 Sample collection.................................................................................................124 Isolation and enumeration of bacteria..................................................................126 Restriction fragment length polymorphism (RFLP) of total coliform isolates....127 Genomic ribotyping of E.coli ..............................................................................129 Denaturing gr adient gel electrophoresis (DGGE)................................................129 Statistical analysis ...............................................................................................131 Population similarity dendrograms......................................................................132 Results.................................................................................................................. ......132 RFLP of total coliform populations.....................................................................136 Ribotyping patterns..............................................................................................139 RFLP accumulation curves..................................................................................140 E. coli ribotype accumulation curves...................................................................143 Bacterial community structure by DGGE............................................................145 Population similarity dendrograms......................................................................146 Discussion..................................................................................................................149

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iii Effect of ecological distur bance on bacterial diversity........................................150 Population similarity in water column vs. sediments..........................................152 References..................................................................................................................154 Research significance.......................................................................................................161 Appendices.......................................................................................................................163 About the author....................................................................................................End Page

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iv List of Tables Table 1 Comparison of microbiological monitoring requirements for Arizona, California and Florida for the use of reclaimed water for urban applications................................................................................................12 Table 2 Municipal wastewater treatment for reuse guidelines...............................28 Table 3 Comparison of the characteristic s of indicator organisms and pathogens tested in this study......................................................................................60 Table 4 Comparison of wastewater reclam ation facilities sampled for indicator organisms and pathogens in the study.......................................................68 Table 5 Percentage of samples with detectable indicator organisms and pathogens ..................................................................................................76 Table 6 Mean log 10 reduction in the concentration of each microorganism (bacteria, viruses, and protozoa) between filtered effluent and disinfected effluent at all nts calculat ed using detection limits .................................105 Table 7 Pairwise comparison of log 10 reductions for all indicators and pathogens ................................................................................................107 Table 8 Univariate regression analysis computed between the CT and the mean log 10 reduction in the concentra tion of microorganisms .........................109 Table 9 Correlations between indicator or ganisms and each pathogen with respect to mean log 10 reduction determined using detection limits, half the detection limits, and zeros........................................................................112 Table 10 Richness estimators calculated using the number of unique DGGE patterns of bacterial population in wa ter and sediment samples...........................145

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v List of Figures Figure 1 Mean indicator organism and pathogen concentrations in untreated wastewater and disinfected effluent from six wastewater reclamation facilities (n=30) .........................................................................................74 Figure 2 Relationship between detection of individual indicators and accuracy of pathogen detection in disinfected effluents................................................82 Figure 3 Discriminant analysis: results show ing the percentage of samples correctly categorized with respect to pres ence or absence of each pathogen...........84 Figure 4 Mean concentration(log 10 transformed) and standard deviation of indicator organisms and pathogens in filtered effluent and disinfected effluent samples in five treatment facilities .........................................................104 Figure 5 Regression coefficients ( ) between indicators a nd pathogens determined using the mean log 10 reductions calculated with detection limits, half the detection limits, and zeros .......................................................................113 Figure 6 Log 10 transformed concentrations of tota l coliforms, fecal coliforms, and enterococci in A) water and B) se diment samples at each site ...............134 Figure 7 An example of an RFLP patterns for total coliforms...............................137 Figure 8 An example of a dendrogram of th e total coliform isolates collected from Myakka River...........................................................................................138 Figure 9 RFLP accumulation curves for total coliform isolates in A) Water and B) Sediment samples.....................................................................................141 Figure 10 Ribotyping accumulation curves for total coliform isolates in A) Water and B) Sediment samples.........................................................................143 Figure 11 Dendrogram of group similarities across the four samp ling sites calculated based on cluster analysis using UPGM A for A) total coliform RFLP, B) E. coli ribotyping, and C) DGGE of bacterial populations..........................147

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vi Commensal Fecal Bacteria: Population Biol ogy, Diversity, and Usefulness as Indicator Organisms in Reclaimed Water Vasanta L. Chivukula ABSTRACT Water treatment facilities have been relying on indicator bacteria to assess the quality of water for decades. The purpose of this group of studies is to investigate the predictive capabilities of conventional a nd alternative indicators for pathogenic microorganisms in disinfection processes and treated wastewater effluents. In addition, the possibility that diversity of indicator bact eria, as well as overall bacterial diversity, correlate with fecal contamination in water bodies has been investigated. Indicator organisms (total coliforms, f ecal coliforms, enterococci, C. perfringens, and coliphages) as well as pathogens (enteroviruses, Giardia, and Cryptosporidium ) were enumerated from six wastewater treatment facilities at various stages of treatment. Statistical analyses were conducted to determine if the indicator organisms (individually or as a set) could predict the presence or absence of pathogens. Single indicator organism analysis failed to correlate with the occurrence pathogens, thus monitoring a suite of indicator organisms may be a better measure to pr edict the presence of pathogens. The product of chlorine residual concentration a nd contact time (CT) was identifi ed as a factor for determining

PAGE 11

vii the log 10 reduction of enteric viruses in wastew ater treatment facilities that used chloramines for disinfection. Samples were also collected from river wa ters and sediments in watersheds with different human population densities to identif y the impact of anthropogenic activities on bacterial diversity. 16S rRNA restriction fragment length polymorphism (RFLP), ribotyping, and denaturing gradient gel elect rophoresis (DGGE) were used to determine total coliform, Escherichia coli, and bacterial community population structures, respectively. The concentrati ons of indicator organisms we re significantly different among the river sites in sediments, but not in water column. Th e population diversity measurements were not signifi cantly different among the river sites; while the indicator population and bacterial community structures were dissimilar in water column vs. associated sediment samples. Accumulation curves demonstrated that greater than 20 isolates must be sampled at most of the si tes to represent the do minant populations. A better understanding of the relationship between the indicator organisms and pathogens as well as knowledge of th e ecology of indicator or ganisms in pristine and anthropogenically impacted waters may contribut e to water quality restoration and public health protection.

PAGE 12

PURPOSE The broad purpose of this group of studies is to develop a better understanding of the relationship between indicator organisms and pathogens in wastewater and environmental waters, and to determine whether the currently used indicator organisms act as good surrogates for pathogens. In addition, molecular techniques were used to study the ecology of the indicator organisms and the bacterial community to assess the changes occurring in the diversity of these organisms due to anthropogenic impact. The demand for pathogen-free water that could safely be used for nonpotable applications led to the development of wastewater reclamation facilities that treat wastewater using physical, chemical, and biological processes. The treated water, termed reclaimed water, is used for nonpotable purposes such as irrigation, agriculture, industrial water, fire-fighting, and recreational lakes (7, 117). Since the source of reclaimed water is sewage, monitoring for the presence of pathogenic microorganisms is essential to avoid illnesses in humans (and in some cases animals) that are exposed to reclaimed water. Testing for the presence of all possible pathogens in reclaimed water is not feasible due to lack of technology and the high cost involved. Hence as an alternative, indicator organisms are used as surrogates for pathogens to predict their presence in water. The predictive relationship between indicator organisms and pathogens (or human diseases) has varied greatly from one study to the next (12, 53, 57, 73), therefore a better 1

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understanding about the biotic and abiotic factors that influence indicator and pathogen survival outside of the host is needed to protect public health. Another goal of this study is to advance our understanding of the ecology of bacterial populations in undisturbed and disturbed aquatic environments, and to identify the effects of anthropogenic activities on bacterial diversity in natural waters and sediments. This work is divided into two studies focusing on: 1) a comparison of the efficiency of removal of indicator organisms and pathogens through various wastewater treatment processes in facilities that produce reclaimed water (Chapters 2 and 3), and 2) the impact of anthropogenic activity on the diversity of the bacterial populations in aquatic environments (Chapter 4). 2

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Background Study I: Comparison of the Efficiency of Removal of Indicator Microorganisms and Pathogens from Six Wastewater Reclamation Facilities The need to conserve water has resulted in an increase in the use of treated sewage effluent, termed reclaimed water, for nonpotable purposes in the U.S (27, 80). However, if reclaimed water use is to be safe and successful, the potentially harmful contaminants in reclaimed water must be assessed in order to minimize detrimental effects on human health as well as the environment. Pathogenic organisms from fecal material can contaminate water supplies and cause outbreaks of water borne disease (19, 103, 123, 133). Anthropogenic contaminants can compromise human health in coastal waters, estuaries and beaches. The annual risk of enteric illness in US ranges from 1 per 1,000 people to 1 per 100 people, and a significant percentage of those illnesses may be caused by organisms in water (147). Of particular concern is the increased risk to very young, elderly and immunocompromised populations, which are more susceptible to mortality associated with water borne illnesses such as diarrhea than are healthy adults (95). Water borne pathogens of concern include (15): Bacteria, i.e. Salmonella, Shigella, pathogenic Escherichia coli strains such as O157:H7, Vibrio, Klebsiella, Campylobacter, Legionella, and Pseudomonas Viruses, i.e. enteroviruses (poliovirus, echovirus, adenovirus, coxsackievirus), hepatitis A, rotavirus, and noroviruses 3

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Protozoa, i.e. Entamoeba, Giardia, Cryptosporidium, Cyclospora, and Encephalitozoon Other human parasites including flukes and nematodes These organisms are found in the fecal material of infected humans, which may or may not display symptoms of disease. Some of these organisms are also present in the fecal material of other animals (http://www.asm.org/policy/index.asp?bid=29781). Therefore, the direct or indirect contamination of water by fecal material of human or animal origin is a significant factor in the transmission of water borne disease. These organisms typically survive in the gastrointestinal system of the host, are released in the feces, contaminate the water, and reenter the subsequent host by ingestion. Their persistence throughout this cycle not only depends on environmental factors such as pH, temperature, salinity, and ultraviolet radiation (http://www.asm.org/ASM/files/CCPAGECONTENT/docfilename/0000003758/climate2%5B1%5D.pdf) but is also influenced by the phylotype or the strain of the organism (132). Giardia lamblia is the most commonly isolated intestinal parasite (protozoan) in the world, often causing gastrointestinal illnesses in underdeveloped countries (54). Giardia cysts are also present in high numbers in domestic sewage in US (105, 141). Certain members of the protozoan genus Cryptosporidium are human pathogens, and are also found in sewage (13, 188). Cryptosporidium oocysts and Giardia cysts are highly resistant to disinfection processes routinely used in wastewater treatment (66). 4

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Testing for the presence of each possible pathogen in water is very laborious and expensive (49). Tests for many of these organisms have a relatively long incubation period (at least 3-5 days) (84), which hampers efforts to institute public health safety measures such as beach warnings or closures. Monitoring for all potential pathogens is essentially impossible due to limitations in the technology and cost involved in identifying them; therefore the concept of using indicator organisms has been the method of choice for water quality assessment since the early 20 th century (15). An indicator organism is one that can be detected with relative ease and specificity, and whose presence (or absence) has been shown to correlate with a specific condition of interest (4). Indicator organisms for water quality are usually found in the intestines and feces of animals. An ideal indicator organism should show the following characters (15, 62, 129): It should be present where pathogens are present and in greater numbers It should not be able to multiply in the environment The density of indicator should relate to the degree of contamination It should at least be as resistant as the pathogen to environmental stresses and to the treatment processes in a wastewater treatment plant It should be detectable by easy, rapid and inexpensive methods It should not be a pathogen Indicator organisms that are so widely used in the US and in other countries as to be considered conventional include total coliforms, fecal coliforms, Escherichia coli and enterococci. 5

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INDICATOR ORGANISMS Escherichia coli was officially recommended as an indicator organism in 1905 when it was first isolated from human feces (3, 143). Almost a hundred years later, E. coli is still a widely used indicator organism for assessing the quality of both fresh and marine waters. Escherichia and other genera such as Klebsiella, Enterobacter and Citrobacter form the total coliform group. Total coliform bacteria are facultatively anaerobic, gram-negative, non-spore forming, rod-shaped bacteria that ferment lactose with gas production at 37 o C (16). These organisms are released in large numbers in human and animal feces. The source of these organisms in water can be either natural (i.e. animal fecal material in surface water runoff, direct contamination by animals, soil and plant materials) or anthropogenic. Fecal coliform bacteria are a subset of the total coliform group and can be distinguished from the nonthermotolerant coliforms by their ability to grow at elevated temperatures (44.5 o C). This group is composed of organisms such as Escherichia coli and certain Klebsiella spp., Enterobacter spp., and Serratia spp. (http://www.ecy.wa.gov/pubs/99345.pdf). E. coli is a ubiquitous organism among the intestinal microflora and is present in fairly high numbers (up to 10 11 to 10 13 cells per gram feces) hence its presence in water suggests fecal contamination (15, 143). Enterococci, a subset of fecal streptococcus group of bacteria, are gram-positive cocci that contain the group D antigen i.e., the antigen glycerol teichoic acid is found in their cell wall (89). The genus Enterococcus includes (but is not limited to) E. faecalis, E. faecium, E. gallinarum and E. avium. Most members of the genus are primarily found in 6

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feces. The use of fecal streptococci for detecting fecal contamination of water was documented in early 1900s (78). Compared to fecal coliforms, enterococci act as better indicators of fecal contamination as they correlate better with gastrointestinal infections in recreational water users (42). Both E. coli and enterococci show association with gastrointestinal illnesses resulting from recreational water use. E. coli is recommended for use as an indicator organism in fresh water only, while enterococci are recommended in fresh as well as marine water (169). Clostridium perfringens is an obligately anaerobic, gram-positive, endospore-forming rod (77). It is widely distributed in the environment and is frequently found in the intestines of humans and many domestic and wild animals. Endospores persist in soil, sediments, and areas contaminated with fecal pollution (51). This organism has been proposed as an alternative indicator for water quality since it is widely distributed in feces, sewage and polluted water (14). It persists in water long after the event of contamination and survives in the environment longer when compared to pathogens. Hence, its presence may indicate past fecal contamination. Since C. perfringens is present in lower numbers when compared to the coliforms, larger sample volumes may be required for enumeration. Coliphages are bacteriophages that infect E. coli, and can be divided into two groups, somatic phages and F + (F-specific) phages. Somatic coliphages are a heterogeneous group of organisms that contain DNA as genetic material and infect the host by attaching to the cell wall through cell surface receptors. The F + (male-specific) coliphages are RNA or DNA viruses that infect male E. coli strains through the F-pilus 7

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(25, 71). Both somatic and F + specific coliphages have been reported to originate from fecal sources (25, 30, 98, 104, 168) and resemble some of the pathogenic enteric viruses in their morphological and survival characteristics (70, 148). The characteristics that made coliphages good surrogates for viral pathogens are: they are found in the feces of humans and other warm-blooded animals, they rarely or never multiply in the environment, their survival characteristics in environment and in water treatment processes such as filtration, closely resembles that of viruses, and their detection is easy and cost effective (94). Hence, these two groups of phages can serve as indicators of fecal contamination and also the presence of pathogenic viruses (168). Many studies have shown poor correlation between indicator bacteria and pathogenic viruses in the environment (10, 56, 61). Human viruses were detected in drinking water and wastewater where the indicator bacteria were well within the standard (73, 104, 134, 153). Fspecific RNA coliphage are more resistant to processes such as lime flocculation/sedimentation (60) and disinfection processes using chlorine (166) and ozone (154) than enteric viruses. Hence, the presence of these organisms may predict the presence of enteric viruses (86, 166). Studies also showed good correlation between somatic coliphages and enteric viruses in the wastewater treatment processes such as activated sludge treatment or biological treatment (52). For these reasons, and because both groups have similarities in terms of size, structure and morphology (148), coliphages were proposed as surrogates for the prediction of the presence of pathogenic viruses such as enteric viruses. 8

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Certain pathogenic organisms such as E. coli O157:H7, Cryptosporidium and Giardia can occur in waters from nonhuman sources. The use of fecal organisms as indicators for such pathogenic organisms has been called into question in recent studies (116). There have been studies that show poor correlation between conventional indicator organisms (total coliforms and fecal coliforms), and pathogenic protozoa (17, 18, 68). Hence, alternative indicators as well as physical parameters are being tested to check for their association with pathogenic protozoa (22, 68, 140) The U.S. EPA published its Ambient Water Quality Criteria for bacteria in 1986 for recreational waters based on the levels of E. coli and enterococci. Prior to this, fecal and/or total coliforms were used as indicators of fecal pollution in recreational waters. The EPA now recommends the use of E. coli or enterococci for fresh and enterococci for marine recreational waters (169). The recommended levels were based on geometric means of at least 5 samples over a month of 35 CFU/ 100mL and 33 CFU/ 100mL of entrococci in marine and fresh water respectively, and 126 CFU/ 100mL of E. coli in fresh water. This illness rate was estimated to be approximately 0.8% of swimmers exposed in freshwater and 1.9% of swimmers exposed in marine waters. WASTEWATER TREATMENT OBJECTIVES AND REGULATIONS Wastewater treatment was initially developed to address public health issues arising from its disposal in surface waters. The treatment objectives relied on removal of suspended solids, treatment of biodegradable organics, and removal of pathogens. Knowledge about the effects of wastewater disposal on the environment (58, 137), long9

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term effects of discharging certain compounds found in wastewater (97), and increasing awareness regarding environmental protection lead to the development of more rigorous treatment processes. To monitor for the presence of potential pathogens in treated wastewater effluent, indicator organisms and in some cases certain pathogens such as enteroviruses, Giardia, and Cryptosporidium are employed (136, 148, 150). The Clean Water Act (CWA) framed in 1972 established national objectives in the U.S. regarding wastewater treatment and disposal into environment (32). The CWA imposed certain limitations on the discharge of treated final effluent to surface waters. Its main goal was to restore and maintain the chemical, physical, and biological integrity of the nations water ( http://www.epa.gov/owow/oceans/coral/biocrit/chap2.html ). Reclaimed water is treated wastewater that can be used for beneficial purposes such as irrigating certain plants, landscape irrigation, groundwater recharge, fire protection, toilet flushing, and industrial and commercial process water (6). The first water reuse standards were adopted by California in 1918, where treated wastewater effluent was used for agricultural purposes (36), and over the ensuing century some other states have also set reclaimed water standards, i.e. Arizona and Florida. The standards employed in various states are not consistent: in some cases, total coliform bacteria are used as indicator organisms (26), while in others the effectiveness of treatment is evaluated using fecal coliform bacteria (5, 63). In addition, periodic monitoring of viruses and/or pathogenic protozoa is required in some locations (37, 155). In 1992 the U.S.EPA published guidelines that provide guidance to states that have no established standards or 10

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guidelines (170). A comparison of monitoring requirements for Arizona, California, and Florida for the use of reclaimed water is shown in Table 1. The treatment of wastewater for a particular purpose depends on its physical, chemical, and microbiological quality. Industrial wastes can contain different chemicals such as benzothiazoles, chlorophenols, nitrosodimethylamine, and certain pharmaceuticals such as carbamazepine, gemfibrozil, carisoprodol depending on the type of the industry, which might affect the biological treatment process in a wastewater treatment plant and compromise the quality of the final effluent (116). The storage and distribution systems used in reclaimed water supply sometimes play a role in water quality degradation due to re-growth of microorganisms, especially in the absence of disinfectant residuals (116). Regrowth of microorganisms in distribution systems or in storage systems may occur if there is enough residual organic matter to sustain the microorganisms that survive the disinfection process. Regrowth is a common problem in distribution systems and hence, chlorine residual is maintained in the distribution pipelines to control the after-growth of microorganisms (112). Wastewater treatment processes provide multiple barriers such as physical, chemical, and biological treatments for the removal of pathogens (68). The compliance status of a facility is judged in part based on the assessment of indicator bacteria such as total and fecal coliforms in the final effluent released from the facility (68). Each treatment facility is regulated by the U.S. EPA, which provides the domestic wastewater permits that specify the construction and operation requirements for each facility (i.e., size of the plant, location, type of treatment and disposal). 11

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Table 1. Comparison of microbiological monitoring requirements for Arizona, California and Florida for the use of reclaimed water for urban applications. 1 Parameter Arizona 2 California 3 Florida 4 Microbial monitoring requirement Fecal coliform, ND (7 day median value) 23/100mL maximum (Class A 5 ) Total coliform; 2.2/100mL (7 day median) 23/100mL maximum value in a 30 day period Never exceed 240/100mL Fecal coliform, ND in at least 75% of samples Never exceed 25/100mL Frequency Not specified Daily; compliance is 7 day median value Daily Limits Turbidity <2NTU 24 hour average; Never exceed 5NTU Turbidity <2NTU; daily average; cannot exceed 5NTU more than 5% of time; never exceed 10NTU TSS <5 mg/L CBOD 5 <20 mg/L Other monitoring requirements Filtered effluent turbidity CT 6 450mg-min/L; model contact time of 90 min; or 5 log reduction of Minimum chlorine residual 1 mg/L as Cl 2 after 15 min contact time 12

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MS-2 or poliovirus Periodic testing of effluent Giardia and Cryptosporidium (one sample per 2 years or 5 years depending on plant size) Treatment requirements Biological treatment Yes Yes Yes Coagulation Not required; require chemical feed facilities for coagulant and/or polymer addition in case of filter turbidities over 5NTU (2NTU 24 hour average) Needed if secondary effluent turbidity is >5NTU for a 15 min period or ever >10NTU Needed chemical feed facilities upstream of filtration in case of poor quality secondary effluent Filtration Yes Yes Yes Disinfection Yes Yes Yes 13

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1 Adapted from Crook, 2003 2State of Arizona. 2001. Regulations for the Reuse of Wastewater. Arizona Administratice Code. Chapter 9, Article 7, Arizona Department of Environmental Quality, Phoenix, Arizona. 3State of California. 2000. Water Recycling Criteria. Title 22, Division 4, Chapter 3, California Code of Regulations. California Department of Health services, Drinking water Program, Sacramento, California. 4Florida Department of Environmental Protection. 1999. Reuse of Reclaimed Water and Land Application. Chapter 62-610, Florida Administrative Code. Florida Department of Environmental Protection, Tallahassee, Florida. 5Class A includes open access landscape irrigation (parks, residential, schools), recreational impoundments, food-crop irrigation, closed-loop air conditioning systems, etc. 6CT: Product of contact time in minutes and chlorine residual in mg/L. 14

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WASTEWATER TREATMENT METHODS The different treatment methods utilized in a wastewater treatment plant depend on the type of influent and the intended use of the final effluent (68). Wastewater undergoes physical (screening, and sedimentation), chemical (precipitation, adsorption, disinfection etc.), and biological (removal of organic matter, nitrogen, pathogens and other contaminants) processing in a wastewater treatment plant. The treatment train is classified into primary treatment, secondary treatment, nutrient removal, and tertiary/advanced treatment (114). Primary treatment includes the removal of wastewater particulate material such as debris, coarse suspended material, oil and grease that may create maintenance or operational problems (wear and clogging of equipment). Primary treatment also removes a portion of suspended solids, organic matter, and biochemical oxygen demand (BOD) by screening and sedimentation processes. Biochemical oxygen demand is widely used to measure the amount of organic material that is readily utilized by microorganisms in wastewater and surface waters. It is defined as the amount of oxygen required for the biological decomposition of organic matter under aerobic conditions at a standardized temperature and time of incubation (41). Nutrients (organic nitrogen and phosphorus), metals and microorganisms attached to particulate matter can also be removed by primary treatment. The removal efficiency of primary treatment can be improved by introducing flocculation before sedimentation or filtration after sedimentation (116). Secondary treatment further reduces biodegradable organics and suspended solids, which in turn reduces BOD. Biological treatment involves microbial metabolism 15

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of organic substances in wastewater either by suspended growth or by fixed film process. The suspended growth processes are used in activated sludge processes, lagoon systems, and stabilization ponds while the fixed film reactor types include trickling filters and rotating contactors. All these processes are accompanied by sedimentation regimes for removal of solids (41). Aside from organic molecules, the primary nutrients of concern in wastewater are nitrogen and phosphorus, as these cause eutrophication if discharged in confined water bodies (101). Ground water recharge with water rich in nutrients such as nitrates may lead to the contamination of public water supplies with such chemicals, resulting in public health issues like gastric problems and methemoglobinemia (83, 115). Thus, nutrient removal through tertiary or advanced wastewater treatment is often necessary. Nitrogen can be removed by following the activated sludge process (secondary treatment) with biological nitrification/denitrification processes. This can be achieved by using trickling filters, rotating biological contactors, bioreactors, stabilization (oxidation) ponds, or lagoons (113). Phosphorus removal was initially accomplished by using lime but its use has considerably declined due to the increase in the mass of sludge due to added lime and also operation and maintenance problems involved with the handling, storage, and use (171). Phosphorus is now removed by chemical precipitation using salts of multivalent metal ions such as calcium, aluminum, and iron (111). Florida regulations require that plants that discharge to surface water bodies treat wastewater so that the final concentration of total phosphorus in the discharged effluent is 1 mg/L 16

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(http://www.epa.gov/region4/water/uic/downloads/07-Surface_Water.pdf). In the Tampa Bay area phosphorus is not removed by the treatment plants as the ambient water receiving the treated water contains high amounts of it. The biological processes mentioned above can be used in different combinations to achieve optimum wastewater treatment depending on the type of influent and the final use of the effluent. For many reclamation facilities secondary treatment of water adequately removes organic matter (116). In addition to biological nutrient removal, tertiary/advanced treatment may be required depending on the intended use of the final effluent (59). Tertiary treatment processes include chemical coagulation, flocculation (used to remove colloidal and small particles that settle down slowly), and filtration (separating solids from a liquid by means of a porous substance such as a permeable fabric or membrane or layers of inert media). Advanced treatment involves complete removal of certain compounds like ammonia or nitrate using processes such as nitrification-denitrification, and ammonia stripping. In the nitrification process, nitrifying bacteria (e.g., Nitrosomonas and Nitrobacter) present in the wastewater are allowed to grow by increasing the wastewater detention time and maintaining the required water temperature. These bacteria convert ammonia to nitrate during an aeration activated sludge process or in aeration chambers. Denitrification process then reduces nitrate to nitrite and finally to nitrogen gas. This process is accomplished under anaerobic conditions by facultative anaerobic bacteria in anaerobic ponds or anaerobic sludge systems. In ammonia stripping, lime is added to wastewater to increase the pH of water, to shift the equilibrium between ammonium ions in water and 17

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ammonia gas towards the gaseous form. The gas is then removed from water by stripping with air using an air blower (110). To meet the standards set by the EPA, various states use disinfectants at the end of the treatment train. The various disinfection processes used by the treatment plants is discussed below. In Florida, the final effluent from wastewater treatment plants is used for ground water recharge, discharge into surface waters, ocean outfalls or reuse, which is regulated by the Florida Department of Environmental Protection (http://www.eluls.org/pdf_flyers/2005/education/TPA-1937864-v2-ELULS__Fact_Sheet_Wastewater_Regulation_in_Florida.PDF). Effluent discharged to ground water and to ocean outfalls requires secondary treatment, while reclaimed (reuse) water requires advanced secondary treatment. Each of these discharge practices requires disinfection before the water reaches its final destination. Wastewater must undergo advanced treatment including nutrient removal for discharge into surface waters (http://www.epa.gov/region4/water/uic/downloads/07-Surface_Water.pdf). TERTIARY AND ADVANCED WASTEWATER TREATMENT Advanced wastewater treatment has been developed to meet environmental concerns and treatment requirements such as removal of organic matter, total suspended solids, nutrients, and inorganic matter (110). Removal of colloidal and suspended solids is accomplished using various filtration processes like depth filtration (passing the wastewater through a filter bed), surface filtration (passing the wastewater through a thin filter material followed by mechanical sieving), and membrane filtration (helps in 18

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removing dissolved constituents). Dissolved organic and inorganic substances are removed using carbon adsorption, reverse osmosis, chemical precipitation, oxidation, electrodialysis, and distillation. Some of the above mentioned processes also help in the removal of certain specific microorganisms (bacteria, protozoan cysts and oocysts, and viruses) (110). FILTRATION After biological treatment, water undergoes filtration, which is a process of removing suspended particles from liquids by passing them through a filter medium (4). These suspended particles range from fine, coarse to super coarse sizes of ~ 0.1 30 m diameter (http://www.epa.gov/eogapti1/module3/category/category.htm#total). Filter types can vary depending on the media used: e.g., loose media filters (particles in a bed or loosely packed in a column) or cartridge-type filters (made of porous fibers, ceramic or a combination of materials) (4). In loose media filtration, wastewater is passed through granular media such as diatomaceous earth, granular activated carbon, neutralizing sand, and ion exchange resins, which allows the removal of suspended particles by processes such as physical straining, impaction, and interception (120). The effectiveness of filtration depends largely on the grain size used in the bed or column. During filtration, wastewater is applied at the top of the granular media. The suspended particles in the wastewater are removed by a number of mechanisms such as straining (removal of suspended materials by passage through a straining surface such as a filter cloth), sedimentation (settling of 19

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biological floc or filter material), adhesion (attachment of suspended particles to the surface of the filter medium), flocculation (aggregates of suspended particles known as flocs are trapped in the interstices of filter medium), adsorption (adhesion of the suspended particles to the surface of the filter medium either by physical or chemical mechanisms), and biological growth (growth of organisms in the filter medium, reducing the pore size of the filter and improving the particle removal efficiency) (110). When the filters become clogged due to accumulation of waste material in the interstices of the granules, they are backwashed. Sand filters have generated interest in the wastewater treatment community due to their potential for removal of chlorine-resistant, protozoan parasites (164). Wastewater is applied at a slow rate to the top of the sand filter beds (slow sand filtration) or periodically (intermittent sand filtration) and it percolates down by gravity, which removes particles and microbes from the water by a combination of physical, chemical and biological mechanisms (149, 178, 179). In cartridge-type filters such as membrane filters, the media acts as a selective barrier, allowing only certain type of substances to pass through. Membrane processes include microfiltration, ultrafiltration, hyperfiltration or reverse osmosis, dialysis, and electrodialysis (110). Microfiltration removes suspended solids and particles of the size range 0.08 m to 2 m while ultrafiltration can remove solutes as small as macromolecules (0.05 m to 0.2 m). Reverse osmosis relies upon a semi-permeable membrane that is effective in the removal of dissolved matter and ionic particles (< 0.001 m) from water (110). These processes rely on hydraulic pressure to push the liquid 20

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through the membrane, separating it from the particles. Reverse osmosis has the disadvantage of frequent fouling due to mineral scaling, colloidal fouling or adsorption of organic material. This can be reduced by using microfiltration or ultrafiltration processes ahead of reverse osmosis, as these processes significantly reduce the foulants (8). The concern with using nanofiltration and microfiltration-reverse osmosis treatments is that they cannot efficiently remove polar, low molecular weight organic compounds such as hydrophobic acids and transphilic acids (40). Dialysis is the separation of suspended particles by selective diffusion using a semipermeable membrane (110). In electrodialysis, ion-selective semipermeable membranes are placed alternately and current is passed through the water, allowing cations and anions to migrate to their respective electrodes. This process helps in the removal of nitrate and phosphate ions in wastewater. As suspended organisms tend to be associated with particles, filtration should be an effective process in reducing their concentration. Various pilot and full-scale studies have been conducted to understand the efficiency of removal of protozoan parasites by filtration techniques (9, 152), and found that Giardia is removed more efficiently by filtration processes than Cryptosporidium due to size difference (http://www.who.int/water_sanitation_health/dwq/en/watreatpath4.pdf) (47). The efficiency of the filtration process for removal of microorganisms also depends on the upstream treatment processes and the load on the filter (92). 21

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DISINFECTION Disinfection is a process of destroying or preventing the growth of microorganisms by using chemicals such as chlorine and chloramines, or ultraviolet irradiations or ozone. Disinfection is the final step for the inactivation of microorganisms from wastewater before it is stored or is discharged from the plant. The effectiveness of the disinfection process depends on many factors, including disinfectant concentration, contact time, temperature, and pH. The sensitivity of microorganisms to disinfection is influenced by factors such as their attachment to surfaces, encapsulation (enclosure of particles in a medium or in the organisms capsule), and aggregation (clumping of organisms) (92, 93). Attachment of organisms to various surfaces hinders the disinfectant from reaching their cell membranes (93). Aggregation of organisms protects the embedded organisms from the effect of the disinfectant (93). Pathogen loading to disinfection systems also depends on the effectiveness of upstream processes. Increased knowledge about the transmission of waterborne diseases lead to the use of chlorine for water treatment. Chlorine was first used in England for water treatment in 1890s. Before 1908, disinfection was not used as a means of water treatment in the US (http://c3.org/chlorine_issues/disinfection.html). Chlorine in the form of hypochlorite (H+ OCl-) causes physiological damage to bacterial cell membranes, and decreases the levels of respiration, glucose transport and ATP in cells (67). It can also interrupt metabolic pathways (187) and protein synthesis (138), or modify nucleic acid bases (67). Gram-positive organisms tend to be more resistant to chlorine than gram-negative organisms, as they have thicker cell walls. Bacterial spores are more resistant to 22

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disinfection by chlorine than vegetative cells (126). Certain older cultures of bacteria producing polysaccharide sheath are less sensitive to chlorine as the sheath provides resistance. Thus, in the activated sludge digestion process, the mean cell residence time (time that the sludge stays in the system) has an effect on chlorination (112). Water treatment with 1 mg/L of chlorine for about 30 min is required to efficiently remove bacteria (15). The effectiveness of chlorine on microorganisms depends on the contact time between the organisms and chlorine. This process is most efficient at high temperatures and low pH. E. coli is generally more susceptible to chlorine disinfection than E. faecalis among the indicator bacteria (167). Enteric viruses are generally more resistant to chlorine disinfection than bacteria (92). Higher levels of disinfection are required when viruses are attached to suspended particles compared to freely floating (76). F RNA coliphages (MS2) tend to be more resistant to chlorine when compared to enteric viruses, thus they act as a conservative surrogate for enteric viruses in the chlorine disinfection process (166). Protozoan parasites such as Entamoeba and Giardia and their cysts or oocysts are highly resistant to chlorination and require prolonged contact time for inactivation (92). Chlorine disinfection is particularly ineffective toward Cryptosporidium (11) While chlorine is a very effective disinfectant against most of the microorganisms, and is easy to use and cost effective, it can react with organic matter in water forming disinfection-byproducts. The most common chlorine byproducts are 23

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trihalomethanes which are toxic to humans as well to other animals (174). Chlorine also produces other disinfection byproducts such as bromate, chlorite and haloacetic acids. The effluent from certain wastewater treatment plants can contain nitrogen in the form of ammonia, particularly if the plant achieves nitrification (conversion of NO 3 to NH 3 ). Ammonia reacts with chlorine to form chloramines (112), which is reflected in the biphasic inactivation curve of certain microorganisms with chlorine. The initial rapid phase of reduction, which is due to the action of free chlorine on the organisms, is followed by a slow, continuous reduction as chlorine combines with ammonia to form chloramines. Chloramines were first used to disinfect wastewater in the U.S. in 1917 (http://c3.org/chlorine_issues/disinfection.html). Although they are generally adequate disinfectants, chloramine activity achieves a slow but continuous inactivation of microorganisms (166), in contrast to the initial rapid inactivation phase displayed by chlorine. Hence, addition of free chlorine for short periods before adding ammonia or using alternative disinfectant such as UV or ozone is usually recommended (92). A drawback to chloramines treatment is that their use can result in nitrite formation in distribution systems, as ammonia oxidizing bacteria can nitrify the excess ammonia (92). Presence of nitrite in water allows nitrite oxidizing bacteria to establish which are resistant to chloramines disinfection (142). Chlorine dioxide inactivates microorganisms through oxidation of certain amino acids in membrane proteins or metabolic enzymes (55). It is a strong disinfectant and does not form by-products such as trihalomethanes, which are formed by free chlorine, but forms inorganic by-products when it reacts with organic carbon and inorganic ions 24

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(183). Thus, the use of chlorine dioxide is limited in the US, unlike in European countries where the organic material is removed from the water by granular activated carbon before chlorine dioxide is applied (34). Though chlorine continues to be the most widely used disinfectant in the US, as it is easy to use and is cost-effective, the formation of trihalomethanes as by-products lead to the consideration of other disinfectants such as UV radiation and ozone. Microorganisms react either directly with molecular ozone or indirectly with radicals formed due to ozone decomposition. Ozone and radicals react with the amino acids, proteins, protein functional groups and nucleic acids, thus it acts on the viral capsid, cytoplasmic membrane and nucleic acids in microorganisms (90). Coliforms and other gram negative bacteria are more susceptible to ozone disinfection when compared to gram positive bacteria (92). Viruses are generally more resistant to ozone when compared to bacteria (90). Ozone is more effective against Cryptosporidium than other parasites like Giardia and Naegleria (92). Ultraviolet light has a maximum effect on microorganisms at a wavelength of approximately 265nm. Thymine bases in DNA react with UV light forming thymine dimers, which inhibit replication and transcription processes in organisms. UV was first used in Montana for water treatment in the early 1970s (186). It gained importance in wastewater treatment facilities as it is highly effective in the inactivation of protozoan pathogens (11). In addition, it requires short contact times when compared to chemical disinfectants and does not produce disinfection byproducts (11). UV is effective against most bacteria and phages while double stranded DNA viruses such as adenoviruses are 25

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very resistant to UV inactivation (163). Infectivity studies with mice or with cell cultures infected with Giardia cysts or Cryptosporidium oocysts showed that the chlorine resistant protozoan pathogens are susceptible to UV light (11, 35). UV light is a potential alternative to chemical disinfectants such as chlorine, as the production of toxic byproducts can be avoided (163). But particle association of microorganisms tends to reduce the effectiveness of UV as the radiation is reflected off the surface of the particles (163). RECLAIMED WATER The use of reclaimed water as an alternative water source has gained increasing public acceptability and more widespread use in recent years, as it reduces the demand on available surface and ground waters (http://www.dep.state.fl.us/water/reuse/uses.htm). Though recycling of water for indirect use has been documented as early as the 16th century in Germany, reports on reclaimed water use in Europe and America are found since the mid 1800s (37, 155). It has been reported that developing countries with water shortages use up to 80% of recycled water for irrigation of agricultural land (65). Reclaimed water usage across the US augments natural water resources for non potable purposes. States like California, Florida and Arizona have been in the forefront in implementing reclaimed waste water usage. According to the data released in 1990 by the US Geological Survey, wastewater treatment facilities released about 35,300 Mgal/day of treated wastewater in the US. An average of 1 million to 2 million gallons/ day was returned to surface water bodies and around 928 Mgal/day was reclaimed. Florida alone 26

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released 1350 Mgal/day of treated effluent into surface waters and reclaimed 174 Mgal/day ( http://water.er.usgs.gov/watuse/wust.html ). In areas with increasing water demand and limited supplies, such as Florida, excess treated wastewater is stored in the aquifer and recovered when needed. Aquifer storage and recovery is a management strategy in which excess surface water is treated and artificially recharged to an aquifer system for later withdrawal when surface water is in short supply (4). This water is locked in the ground and resists any losses due to evaporation, seepage or contamination. Large volumes of water can be stored underground reducing the need for large tanks or reservoirs. Water recharge can also help in the restoration of ground water that has declined due to heavy pumping. Wastewater that has received advanced secondary treatment is injected to the subsurface. This water can later be retrieved for nonpotable purposes or further treated for potable purposes. This practice may also serve to prevent sinkholes as well as controlling intrusion of salt water into fresh water aquifers. The U.S. EPA has classified ASR wells as underground injection control wells and they are subject to regulations under the U.S. Safe Drinking Water Act. Hence, water must be disinfected before injection to meet the Total Coliform Rule of the U.S. Drinking Water Act (0 total coliforms/ 100mL). The primary source of reclaimed water (domestic sewage) demands that stringent control of microbial pathogens must be implemented to protect public health. The level of treatment of wastewater depends on the constituents present in the wastewater and its final use. To protect public health, regulations and guidelines have been established by 27

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many states for reclaimed water which differ from state to state depending on the use of final effluent (table 2). Table 2. Municiple wastewater treatment for reuse guidelines (170) Wastewater reuse Treatment to be achieved Unrestricted urban use, food crops and unrestricted recreational use Secondary treatment, filtration and disinfection BOD 5 = 10mg/L; Turbidity = 2NTU; fecal coliforms = non detects/100mL; Cl 2 residual = 1mg/L; pH 6-9 Restricted urban use, non-food crops and food crops consumed after processing, and restricted recreational use Secondary treatment and disinfection BOD 5 = 30mg/L;TSS = 30mg/L; fecal coliforms = 200/100mL; Cl 2 residual = 1mg/L; pH 6-9 Environmental enhancement Site specific treatment levels Groundwater recharge Site specific 28

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REMOVAL OF INDICATOR ORGANISMS AND PATHOGENS THROUGH WASTEWATER TREATMENT PROCESSES The final effluent from a wastewater treatment plant must be routinely monitored to mitigate risks associated with waterborne pathogens. For example, Cryptosporidium is extremely resistant to routine disinfection procedures and the mortality rate due to Cryptosporidium infections ranges between 50-85% in the immunocompromised group (145). Human enteric viruses may also survive the disinfection processes if not removed in the earlier treatment steps (10). Public health protection is managed based on a relationship between indicator organisms and pathogens in water. Hence, it is very important to have a definitive understanding about the relationship between indicator organisms and pathogens in wastewater treatment processes so as to consider the treated effluent for reuse. Data on microbial concentrations (bacterial indicators, viruses, and protozoa) at various treatment stages through wastewater treatment are available (52, 81, 146, 168), based on which alternative indicators such as enterococci, Clostridium perfringens, and F-specific coliphage have also been proposed as good surrogates to observe the trend of pathogen removal through the different treatment processes. It has been shown that coliform bacteria do not adequately reflect the presence of pathogens in disinfected effluents as they are highly susceptible to chemical disinfection (117) and hence, they do not correlate with the presence of protozoan pathogens and enteric viruses (18, 72). A study on the removal of pathogenic and indicator microorganisms by a full scale water reclamation facility showed that most of the reduction of indicator organisms occurred by disinfection process using chlorine. Twenty 29

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five percent of the samples from the final effluent storage tanks still showed the presence of Giardia cysts and 17% of the samples contained Cryptosporidium oocysts (148). When reduction of Cryptosporidium oocysts, Giardia cysts and indicator organisms through the treatment processes was followed in an aerobic treatment plant, it was observed that the pathogens persisted in the final effluent in very low concentrations. Giardia cysts were inactivated to a lesser extent when compared to Cryptosporidium oocysts and the log 10 reduction of indicator organisms was higher compared to the pathogens (136). In another study that observed the reduction of pathogens and indicator and alternative indicator organisms by wastewater treatment processes, Cryptosporidium oocysts and Giardia cysts were detected in the final effluent samples from three wastewater reclamation facilities even when no indicator organisms were present. There was variability within the three plants as to the microbiological quality of the final effluent which was attributed to the differences in the filter design, operations, and disinfection approaches at the three facilities (150). A study on the correlation between alternative indicators (C. perfringens, somatic and male-specific coliphages) and pathogens (enteric viruses, Giardia and Cryptosporidium) in drinking water showed that C. perfringens is a better indicator for the inactivation of viruses (135). In wastewater, B. fragilis phage correlated better with enteroviruses when compared to somatic coliphages, which were unable to indicate fluctuations in enterovirus concentrations (52). 30

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Although there is data available from previous studies on individual wastewater treatment facilities and the various indicators, there still exists certain concerns regarding water quality and the public health risks associated with reclaimed water for nonpotable reuse. Previous studies also showed varied results regarding the association of indicator organisms and pathogens or the use of indicator organisms to predict the presence of pathogens in wastewater effluents. In addition, very few studies have compared the predictive values of a suite of indicator organisms or alternative indicators to that of pathogens. This study looks at the validity of using indicator organisms (total coliforms, fecal coliforms, and enterococci) and alternative indicators (C. perfringens, somatic and F specific coliphages) to predict the presence of enteric viruses, Giardia, and Cryptosporidium through various stages in six wastewater treatment facilities. 31

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Study II: Impact of fecal contamination on the diversity of microbial populations in natural waters Microorganisms are so versatile in their habitat and metabolism that, as a group, they can utilize virtually every oxidizing and reducing agent on the Earth to produce energy (124). Unfortunately, microbial aspects of ecosystem function have been relatively unexplored, and little is known about the interactions among microbes and between microbes and the environment due to their vast phenotypic, genetic, and metabolic diversity. They play key roles in many biogeochemical processes and hence understanding the connection between the microbial community structure and their functions helps us to identify these roles in ecological processes. DIVERSITY AND THE HEALTH OF AN ECOSYSTEM The health of an ecosystem is dependent in part on the diversity of organisms living in it. External contamination due to various pollutants such as fecal contamination and industrial wastes can disrupt the stability and health of an ecosystem (48). To monitor the health of an ecosystem, researchers have used biological changes at biochemical, cellular, or population levels as indicators. One such indicator that can be used to better understand the health of ecosystems is bacterial diversity (53). Since prokaryotes can survive in a wide range of environments and adapt themselves to various environmental stresses, various groups of organisms work as good indicators of changing conditions in a habitat. Many studies have shown that contamination or disruption of an ecosystem can alter its bacterial community structure (21, 29). Environmental impacts 32

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such as fecal contamination may influence the diversity of microorganisms inhabiting that ecosystem (29), but they have not been studied until recently (29, 165). Knowledge of such impacts on microbial diversity is essential in understanding the interactions between microorganisms as well as with the environments in which they survive. SPECIES CONCEPT IN PROKARYOTES AND DIVERSITY INDICES A diversity index is a parameter used to describe the frequency distribution of species in a given ecosystem (64). Community diversity can be described using species richness in a particular ecological niche (102). One of the aims of conservation biologists is to maximize this species diversity which in turn stabilizes the health of an ecosystem (106). Species diversity is measured using a variety of diversity indices, and the most widely used indices are those that reflect three important characteristics: 1) species richness 2) relative abundance of the species and 3) taxonomic distances between species (177). The predominant indices used in microbial ecology are also used by plant and animal ecologists. The use of such indices has become popular in microbial studies only in the last few decades (12, 88, 91, 119). A basic challenge to applying these indices to prokaryotic studies is that the indices assume an unambiguous identification of species. There is no well established definition for species in the case of prokaryotes, though there are many concepts (31, 87). Ernst Mayr proposed the biological species concept (BSC) and defined species as groups of interbreeding natural populations that are reproductively isolated from other such groups (108).The evolutionary species concept as defined by Simpson states that an 33

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evolutionary species is a lineage (an ancestor-descendant sequence of populations), evolving separately from others and with its own unitary evolutionary role and tendencies (156). The cohesion species concept was developed by Templeton and defines species as the most inclusive population of individuals having the potential for phenotypic cohesion through intrinsic cohesion mechanisms (162). The ecological species concept defines species as a group of organisms that share a distinct ecological niche (172). The phylogenetic species concept defines species as a diagnosable cluster of individuals within which there is a paternal pattern of ancestry and descent and which exhibits a pattern of phylogenetic ancestry and descent among units of like kind (44). The ambiguity in defining species in case of prokaryotes is due in part to horizontal gene transfer (intra-species and inter-species genetic exchange) and asexual reproduction (82, 107), as well as the phenotypic similarity and/or plasticity of many microorganisms. Traditional microbiological techniques such as microscopy and culturing methods do not yield enough useful information regarding the diversity of these organisms, as they frequently cannot be differentiated by microscopy or by morphological characteristics on growth media (176). Furthermore, due to inherent biases in culturing microorganisms, it is impossible to gain a full understanding of population structure in an ecosystem by culture methods alone (24). Because of such problems, scientists have turned their interest towards molecular techniques, which reveal a far greater diversity of microorganisms than was previously found with culture dependent techniques (2, 131). 16S rRNA sequences became the choice for molecular studies, as they provide information useful in phylogenetic analysis (127, 130, 176, 181, 184, 185). The 34

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sequences that code for the 16S rRNA are highly conserved and contain structural domains with some sequence variation (184). The patterns and sequences produced by various molecular techniques using the 16S rRNA gene are now termed operational taxonomic unites (OTU) to avoid any ambiguity in using the term species (79). Molecular techniques can be used to subtype microorganisms in order to determine the population structure in various ecosystems (20, 100). These techniques make direct use of an organisms genetic material (i.e. DNA) in order to generate subtypes. This information can then be analyzed using diversity indices, which take into account the number of different subtypes found within a population and the relative abundance of these subtypes (43, 109). Diversity indices have been used as tools in ecological studies to measure the diversity of animal species within a given area. More recently, microbiological studies have used these indices to measure the diversity of microbial populations in a particular environment (7, 75, 157). Hills diversity indices have been intensively employed by ecologists and other scientists (74). Three different indices that are included in Hills diversity measurements are the richness estimator (S), the Shannon index (H), and the Simpson index (). Shannon's index measures the degree of uncertainty in predicting the species of a random individual from a community. It is a rough measure of the abundant subtypes in a population. This diversity measurement takes into account both the total number of subtypes and their frequency within the population, and is one of the most widely used diversity indices. Simpsons dominance index is a measure of the most abundant subtypes in a population. It represents the probability that two subtypes chosen at 35

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random will be the same. Because it is a probability estimate, the Simpsons value is always between one and zero. Often, the reciprocal of the Simpson value (1/is presented to give a better idea of change in the diversity within a population. When using the reciprocal of Simpsons index, an increasing value shows a higher diversity within the population (99). Some of the molecular techniques used for diversity studies are restriction fragment length polymorphism (RFLP), ribotyping, denaturing gradient gel electrophoresis (DGGE), and DNA sequencing. RESTRICTION FRAGMENT LENGTH POLYMORPHISM (RFLP) This technique uses restriction enzymes that cut amplified 16S rDNA products at specific sequences. The DNA fragments produced can then be separated by electrophoresis. Organisms pertaining to the same species always differ in at least a few nucleotides. This disparity will lead to either production of new restriction sites or removal of existing restriction sites. These differences in restriction sites will produce different banding patterns when cut with specific restriction enzymes. This technique is used in a wide range of fields such as forensics, animal and plant breeding, and cases involving questions about biological parentage. RFLP is a powerful tool in studying differences among organisms at intraspecific levels or among closely related taxa (38, 85). RFLP can be used to understand microbial community structures by PCR amplification of 16S rDNA, and digesting the gene using one or more restriction enzymes 36

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(119). This approach is culture-independent and has revealed higher diversity in an ecosystem when compared to culture-based techniques (23, 50, 119). RFLP analysis using amplified rDNA sequences is termed amplified rDNA restriction analysis (ARDRA) (173). This method does not require the sequence information of the rDNA fragments used in the diversity analysis and is both rapid and simple (180). This method also aids in the differentiation of bacterial species (173). RFLP has been used in a number of studies to identify the microbial community structures in various environments such as microbial mats (119), microbes associated with seagrass (180), and in environments contaminated with fecal or chemical pollutants (29, 39). GENOMIC RIBOTYPING Genomic ribotyping is another molecular method that targets the 16S rRNA gene, in order to find genetic variation within the gene and in surrounding DNA to discriminate between members of the bacterial population (28). Restriction enzymes digest genomic DNA, resulting in many different sized DNA fragments. These fragments are separated by electrophoresis and transferred onto a nylon or cellulose-based membrane. The membrane, along with the adhered DNA fragments, is hybridized with a labeled probe targeting one or more of the genes for rRNA. Any fragment containing a portion of the rRNA gene(s) will hybridize with the probe and can be detected. This results in a fingerprint pattern specific to that particular organism. Ribotyping has been used as an epidemiological tool for many bacteria such as E. coli, Salmonella, and Vibrio cholerae (128, 139, 160). It has also been used to understand 37

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the diversity of microorganisms in various ecosystems (1, 161). Banding patterns obtained by ribotyping techniques can be used to differentiate various strains within a species. DENATURING GRADIENT GEL ELECTROPHORESIS (DGGE) This technique was developed for detection of single base mutations (144). It is a method used to separate PCR-amplified DNA fragments by electrophoresis according to their mobilities under increasing denaturing conditions (usually increasing formamide/urea concentrations). DNA fragments melt/denature in the presence of denaturant or high temperatures (96). The amount of denaturant required to melt the DNA depends on the nucleotide composition of a given sequence, as there is higher bond strength between cyanine and guanine than that of adenine and thymine. As DNA fragments are subjected to electrophoresis in the presence of a linear gradient of increasing denaturant concentration, double stranded DNA partially separates into single strands. The more denatured the DNA fragment, the slower it will migrate through the polyacrylamide gel, allowing for band separation based on nucleotide sequence. One of the primers used in the PCR amplification of the DNA is made with a GC-rich sequence (GC-clamp) of about 40 bases at its 5 end. This clamp does not denature at the conditions chosen for the experiment and allows branching of the double-stranded DNA anchored at the GC-clamp, thus providing melting stability to the PCR product.(121). The branched structure of the single stranded DNA becomes entangled in 38

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the gel matrix and does not move further. These bands can then be analyzed using various software programs to obtain the microbial community structure in various ecosystems. A number of microbial diversity studies have used DGGE as the molecular technique to understand the population community structures (33, 45, 57, 121). Most of these studies identified the organisms present in the community by cloning and sequencing the bands excised from the gel (45, 175). Identification of source of contamination and implementation of remedial measures to prevent the reoccurrence of such contamination are essential to establishing best management practices for water resources. Microbial source tracking (MST) methods are a group of techniques developed for the identification of the source of contamination of water (182). Many phenotypic and genotypic methods are being applied across various water bodies to produce fingerprints for MST (151). However, there is no single MST method that can identify specific sources in all ecological settings. In addition, many of these methods do not accurately identify the contributing sources of fecal contamination in water due to subtype sharing between the sources, inadequate representation of the diversity of source feces, and temporal variability (46, 69, 118, 122, 125, 158, 159). A greater knowledge of the microbial dynamics for a given ecosystem can be useful in determining the most appropriate MST method for any particular set of environmental conditions (158). It is also important to know if the indicator organisms used in the MST methods predict the presence of pathogens, as the final aim of these studies is to be able to know if the contaminated water has pathogens which might cause disease in humans and other animals. A better understanding of the ecology of the 39

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indicator organisms in pristine and anthropogenically impacted waters might improve our knowledge about the source of contamination, allowing restoration of water quality and minimizing human health risks. 40

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158. Stewart, J. R., R. D. Ellender, J. A. Gooch, S. Jiang, S. P. Myoda, and S. B. Weisberg. 2003. Recommendations for microbial source tracking: lessons from a methods comparison study. J Water Health 1:225-31. 159. Stoeckel, D. M., M.V. Mathes, K.E. Hyer, C. Hagedorn, H. Kator, J. Lukasik, T. L. O'Brien, T.W. Fenger, M. Samadpour, K.M. Strickler, B. A. Wiggins. 2004. Comparison of seven protocols to identify fecal contamination sources using Escherichia coli. Environmental Science Technology 38:6109-6117. 160. Stull, T. L., J. J. LiPuma, and T. D. Edlind. 1988. A broad-spectrum probe for molecular epidemiology of bacteria: ribosomal RNA. J Infect Dis 157:280-6. 161. Suihko, M. L., H. Sinkko, L. Partanen, T. Mattila-Sandholm, M. Salkinoja-Salonen, and L. Raaska. 2004. Description of heterotrophic bacteria occurring in paper mills and paper products. J Appl Microbiol 97:1228-35. 162. Templeton, A. R. 1989. The meaning of species and speciation: a genetic perspective. In D. Otte, J.A. Endler (ed.), Speciation and its consequences. Sinauer Associates, Sunderland, Mass. 163. Thompson, S. S., J. L. Jackson, M. Suva-Castillo, W. A. Yanko, Z. El Jack, J. Kuo, C. L. Chen, F. P. Williams, and D. P. Schnurr. 2003. Detection of infectious human adenoviruses in tertiary-treated and ultraviolet-disinfected wastewater. Water Environ Res 75:163-70. 164. Timms, S., J.S. Slade, and C.R. Fricker. 1995. Removal of Cryptosporidium by slow sand filters. Water Sci Technol 31:81-84. 165. Torsvik, V., F. L. Daae, R. A. Sandaa, and L. Ovreas. 1998. Novel techniques for analysing microbial diversity in natural and perturbed environments. J Biotechnol 64:53-62. 166. Tree, J. A., M. R. Adams, and D. N. Lees. 2003. Chlorination of indicator bacteria and viruses in primary sewage effluent. Appl Environ Microbiol 69:2038-43. 167. Tree, J. A., M. R. Adams, and D. N. Lees. 1997. Virus inactivation during disinfection of wastewater by chlorination and UV irradiation and the efficacy of F+ bacteriophage as a viral indicator. Water Sci Technol 35:227-232. 168. Turner, S. J., and G. D. Lewis. 1995. Comparison of F-specific bacteriophage, enterococci, and faecal coliform densities through a wastewater treatment process employing oxidation ponds. Water Science and Technology 31:85-89. 55

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181. Weller, R., J. W. Weller, and D. M. Ward. 1991. 16S rRNA sequences of uncultivated hot spring cyanobacterial mat inhabitants retrieved as randomly primed cDNA. Appl Environ Microbiol 57:1146-51. 182. Whitlock, J. E., D. T. Jones, and V. J. Harwood. 2002. Identification of the sources of fecal coliforms in an urban watershed using antibiotic resistance analysis. Water Res 36:4273-82. 183. WHO. 2000. Disinfectants and disinfectant by-products (Environmental health criteria: 216), World Health Organization, Geneva. 184. Woese, C. R. 1987. Bacterial evolution. Microbiol Rev 51:221-71. 185. Woese, C. R. 1994. There must be a prokaryote somewhere: microbiology's search for itself. Microbiol Rev 58:1-9. 186. Wolfe, R. L. 1990. Ultraviolet disinfection of potable water. Environ Sci Technol 24:768. 187. Wyss, O. 1961. Presented at the Proceedings of the Rudolfs Research Conference, New Brunswick, NJ. 188. Zhou, L., A. Singh, J. Jiang, and L. Xiao. 2003. Molecular surveillance of Cryptosporidium spp. in raw wastewater in Milwaukee: implications for understanding outbreak occurrence and transmission dynamics. J Clin Microbiol 41:5254-7. 57

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Objectives The main objective of the first study was to determine the validity of using indicator organisms in assessing water quality. The levels of conventional and alternative indicator organisms at different stages of the treatment processes were compared with that of pathogens to evaluate the efficiency of the treatment processes in six wastewater treatment facilities. This comparison will also help to investigate the ability of these indicator organisms to predict the presence of pathogens. Six wastewater treatment facilities were evaluated for a suite of indicator organisms and viral and protozoan pathogens over a period of two years. Facilities: 1. Northwest Regional Hillsborough County Facility, Tampa, Florida 2. Northwest St. Petersburg Reclamation Facility, St. Petersburg, Florida 3. El Estero wastewater Reclamation Facility, Santa Barbara, California 4. Eustis wastewater treatment plant, Eustis, Florida 5. Northeast St. Petersburg Water Reclamation Facility, St. Petersburg, Florida 6. Cave Creek Wastewater Reclamation Facility, Phoenix, Arizona In this project, we compared the removal of indicator organisms, total coliforms, fecal coliforms, enterococci with alternative indicators, C. perfringens, coliphage that infects E. coli 15597 host, and coliphage that infects E. coli F amp host 700891, and 58

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pathogens, enteroviruses, Giardia, and Cryptosporidium. A comparison of the characteristics of these organisms is presented in table 3. The main objective of the second study was to determine the effect of anthropogenic impact on the diversity of microorganisms in the water column and sediments of aquatic ecosystems. The diversity of culturable Escherichia coli, culturable total coliform bacteria and bacterial community fingerprints in three types of water and the sediments was compared. Water and sediment samples were collected from Myakka River in the Sarasota County, which is a relatively pristine water source, two Hillsborough River sites (one just upstream of Tampa, and a second within Tampa), and influent from a wastewater treatment plant. Ribotyping (E. coli), restriction fragment length polymorphism (total coliform bacteria), and denaturing gradient gel electrophoresis (bacterial community) techniques was used to ascertain the diversity in each population. The primary hypothesis of this study was that anthropogenic impact on water changes the diversity of the autochthonous bacterial populations. To this effect, various diversity parameters were computed and compared across these samples to study impact of human activities on the ecosystem. 59

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60 Table 3. Comparison of the characteristics of indicat or organisms and pathogens tested in this study Indicator/Pathogen Example of species or description Cell wall Shape Size m Comments Bacterial indicators Total coliform Escherichia, Klebsiella, Citrobacter, Enterobacter Gram negative, nonspore forming Rod 0.5-2 Facultatively anaerobic Fecal coliform Escherichia, Klebsiella, coliforms that are able to grow at 44.5 C Gram negative, nonspore forming Rod 0.5-2 Facultatively anaerobic Enterococci Enterococcus faecalis, Enterococcus faecium Gram positive, nonspore forming Cocci 0.5-1 Aerotolerant anaerobe Clostridium perfringens Opportunistic pathogen; produces enterotoxin Gram positive, spore forming Rod 0.6-1.3by 2.4-19 Obligately anaerobic Coliphages Viruses that infect E. coli and other coliform bacteria 0.025-0.20 Coliphages that infect E.coli F amp host 700891 Male specific (F+) RNA coliphages: can only replicate when bacterial host is in logarithmic growth phase at >30 C No cell wall; coat protein protects RNA Icosahedral protein shell 0.025 Infect hostby attaching to fertility fimbriae Coliphages that infect E. coli 15597 host Male specific (F+) and somatic coliphages that infect E. coli 15597 No cell wall; coat protein protects nucleic acid Icosahedral protein shell 0.025 Somatic coliphagesattach to cell wall, F+ attach to fertility fimbriae Enteroviruses Genus within the family Picornaviridae includes poliovirus, coxsackievirus, echovirus, hepatitis A virus No cell wall; nonenveloped protein coat Icosahedral capsid single stranded RNA genome 0.025-0.03 Infect mammalian cells Protozoan Parasites Complex life cycle. Zoonotic transmission Giardia intestinalis Flagellated protozoan; Phylum Mastigophora Ovoid cyst 8.5 10 Cyst is the infective form Cryptosporidium parvum Coccidian protozoan; Phylum Apicomplexa Ovoid oocysts 4-6 Oocyst is infective form; resistant to disinfection

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61 Validity of the Indicator Organism Paradigm: Pathogen Reduction and Public Health Protection in Reclaimed Water Valerie J. Harwood1, Audrey D. Levine2, Troy M. Scott3, Vasanta Chivukula1, Jerzy Lukasik3, Samuel R. Farrah4 and Joan B. Rose5 1Department of Biology, University of South Florida, Tampa, FL 2Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 3Biological Consulting Services of N. Florida, Inc., Gainesville, FL 4Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 5Department of Fisheries and Wildlife and Crop and Soil Sciences, Michigan State University,East Lansing, MI Running title: Indicator-pathogen rela tionships in reclaimed water (Published in Applied and Environm ental microbiology Vol 71 (6):3163-3170

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ABSTRACT The validity of using indicator organisms (total and fecal coliforms, enterococci, Clostridium perfringens and F-specific coliphages) to predict the presence/absence of pathogens (infectious enteric viruses, Cryptosporidium and Giardia) was tested at six wastewater reclamation facilities. Multiple sample events were conducted at each facility over a one-year period. Larger sample volumes for indicators (0.2-0.4 L) and pathogens (30 100L) resulted in more sensitive detection limits than are typical of routine monitoring. Microorganisms were detected in disinfected effluent samples at the following frequencies: total coliforms, 63%; fecal coliforms, 27%; enterococci, 27%; C. perfringens, 61%; F-specific coliphages, ~ 40%; enteric viruses, 31%. Cryptosporidium oocysts and Giardia cysts were detected in 70% and 80%, respectively, of reclaimed water samples. Viable Cryptosporidium, based on cell culture infectivity assays were detected in 20% of the reclaimed water samples. No strong correlation was found between any indicator-pathogen combination. When data for all indicators were tested using discriminant analysis, the presence/absence patterns for Giardia cysts, Cryptosporidium oocysts, infectious Cryptosporidium and infectious enteric viruses were predicted in over 71% of disinfected effluents. The failure of single indicator organism 62

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measurements to correlate with pathogens suggests that public health is not adequately protected by simple monitoring schemes based on detection of a single indicator, particularly at the detection limits routinely employed. Monitoring a suite of indicator organisms in reclaimed effluent is more likely to be predictive of the presence of certain pathogens, and the need for additional pathogen monitoring in reclaimed water in order to protect public health is suggested by this study. 63

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INTRODUCTION Reclaimed water is derived from treated municipal wastewater. The treatment processes used for production of reclaimed water provide multiple barriers (biological treatment, physical removal, and chemical disinfection) for control of pathogens. Reclaimed water is used to provide water for nonpotable applications such as irrigation, cooling water, industrial process water, and environmental enhancement (17). Indirect potable reuse occurs through groundwater recharge or surface water replenishment, and is assuming greater importance with increased production of reclaimed water. As water use in the United States (7) and worldwide increases, the importance of reclaimed water to sustainable water resources will continue to increase (17). A major goal of wastewater reclamation facilities is to reduce pathogen loads in order to decrease public health risks associated with exposure. The effectiveness of pathogen control is indirectly assessed through routine monitoring of the reclaimed water (final effluent) using grab samples to detect standard indicator bacteria such as total or fecal coliforms. Treatment practices for production of reclaimed water vary depending on the ultimate intended use(s) of the water and local regulatory requirements. Currently, there are no universal standards governing the production and quality of reclaimed water, although the World Health Organization (WHO) has developed guidelines for the use of reclaimed water (35) that recommend monitoring fecal coliforms and intestinal nematodes. In the U.S., there are no Federal standards controlling the quality of reclaimed water, and individual States have developed guidelines or implemented specific treatment and monitoring requirements that are intended to protect the public 64

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from exposure to pathogens. Due to the inherent constraints associated with pathogen monitoring, indicator organisms are employed as surrogates for pathogens. In some States, total coliform bacteria are used as the indicator organism (1); however, in the majority of States that have specific regulations, the microbiological safety of reclaimed water is evaluated by daily monitoring of fecal coliform bacteria in the disinfected effluent based on a single, 100 ml grab sample (4). In addition, periodic monitoring of viruses and/or protozoan pathogens has been required by a few States, including Arizona, California and Florida (4). It has been widely demonstrated that coliform bacteria do not adequately reflect the occurence of pathogens in disinfected wastewater effluent due to their relatively high susceptibility to chemical disinfection (18) and failure to correlate with protozoan parasites such as Cryptosporidium (6) and enteric viruses (13). Alternative microbiological indicators have been suggested for evaluation of wastewater, drinking water and environmental waters including Enterococcus spp. (18), Clostridium perfringens (9, 20) and coliphages (8, 10, 20). To date, there have been only a few studies of reclaimed water in which the levels of indicator organisms have been directly compared to viral, bacterial, or protozoan pathogens at each stage of treatment (23, 24). In this work, the validity of using coliform bacteria and alternative microbial indicators to predict the presence or absence of pathogens, and thus assess public health risk, was evaluated in disinfected effluent from six wastewater reclamation facilities in the U.S. The facilities varied in location (Arizona, California, Florida), size, and treatment practices, and were each sampled at least five 65

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times over a one-year period. Each sample was analyzed for a suite of indicator bacteria, coliphages, enteric viruses, and protozoan pathogens, and predictive relationships among the microbial groups were evaluated by several statistical methods, including binary logistic regression and discriminant analysis. MATERIALS AND METHODS Facilities. Six wastewater reclamation facilities in the U.S. were each sampled at least five times over a one-year period. A comparison of the treatment characteristics is given in Table 4. The facilities represent a cross-section of typical treatment approaches that are used for production of reclaimed water. Sampling. All samples were aseptically collected in sterile containers (or sterile filters). Samples were immediately placed on ice in coolers, and kept on ice until processed. At each facility, samples were collected from the influent (untreated wastewater), secondary clarifier (biological treatment), filtered effluent, and disinfected effluent (reclaimed water). Samples were collected under peak flow conditions to provide a worst-case scenario for each facility. Each facility was sampled approximately bimonthly over a one-year period, resulting in at least five sample events per facility. Sample volumes collected for bacterial enumeration were 50 mL of influent, 500 mL from the secondary clarifier, 2 L of effluent from the filtration stage and 2 L of disinfected effluent. Assays were performed in triplicate. Large volumes (up to 53 L) were filtered for protozoan parasite and virus assays. Detection limits for bacterial 66

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indicators in disinfected effluent were 0.2 0.6 CFU mL-1; for coliphages 10 PFU mL-1; for enteric viruses 0.3 1.4 MPN L-1; for Cryptosporidium oocysts 2.0 6.9 oocysts L-1; for infectious Cryptosporidium 0.29 4.1 MPN L-1; for Giardia 1.8 5.2 cysts L-1. Bacterial enumeration. Indicator bacteria were quantified using membrane filtration using 47 mm cellulose acetate filters with a nominal pore size of 0.45 m. Total coliform bacteria were cultured on mEndo LES agar (Difco, Sparks, MD) for 24 h at 37C. Colonies that produced a green sheen were enumerated as total coliforms (3). Fecal coliform bacteria were cultured on mFC agar (Difco, Sparks, MD) for 24 h at 44.5C in a water bath. Blue colonies were enumerated as fecal coliforms (3). Escherichia coli (American Type Culture Collection [ATCC]# 9637) was used as the positive control for all coliform measurements. Enterococci were cultured on mEI agar (31, 32). Plates were incubated at 41C for 24 h, and colonies with a blue halo were enumerated as enterococci. Enterococcus faecalis (ATCC #19433) was used as a positive control. Clostridium perfringens was isolated on mCP agar (Acumedia Manufacturers, Inc) (5). Plates were transferred to gas pack bags (BBL GasPak, Beckton Dickinson) and sealed. After 24 h of incubation at 45C, colonies were exposed to ammonium hydroxide fumes. All of the yellow/straw colored colonies that turned pink/magenta were counted. C. perfringens (ATCC #13124) was used as positive control. 67

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Table 4. Comparison of wastewater reclamation facilities sampled for indicator organisms and pathogens in this study. 1 Facility Averagecapacity (m3s-1) Biological treatment Chemical use prior to filtration Filter composition & depth (m) Backwash frequency (h) Type of disinfection A 0.04 Activated sludge None Fabric (0.02) 24 to 72 Chloraminesa B 0.4 Activated sludge Chlorine Sand (0.3) Automatic (daily) Chloraminesa C 0.4 Activated sludge Cationicpolyelectrolyte Anthracite (1.2 ) 48 Chloraminesa D 0.7 Activated sludge None Anthracite (0.8) Sand (0.25) 48 to 168 Chloramines E 0.08 Nitrification None Sand (1.2)Upflow Continuous Ultravioletlight F 0.13 Biological nutrientremoval None (alum added to secondary clarifier) Anthracite (0.6) Sand (1.2) 48 to 168 Chlorine aChloramines are formed due to the reaction of chlorine with residual ammonia. 2 68

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Bacteriophage analysis. Coliphages were analyzed by the agar overlay method of Adams (2). Two E. coli host strains were used in separate assays: E. coli HS (pFamp) R (ATCC #700891), which infects male-specific (F+) coliphages very efficiently and somatic coliphages poorly (8), and E. coli C3000 (ATCC #15597), which should host both somatic and F+ coliphages (14). Serial dilutions of samples were made in phosphate buffered saline (PBS) according to expected phage concentrations at each treatment step. Five replicate volumes of 0.1 mL to 2 mL were plated for each dilution except in the case of the disinfected effluent samples, for which ten replicates of 2 mL each were plated. Plaque forming units (PFU) mL-1 were calculated after 24 h incubation (3). Enteric viruses. The U.S. EPA methodology (30) was used for the detection of enteric viruses. Influent sample volumes were based on the amount of water that could be processed without clogging the filter. Typically less than 100 L was filtered for each influent sample, depending on water quality (i.e. suspended solids content). Larger sample volumes were used for the other sample locations, i.e. ~190 L samples from the secondary clarifiers, and ~380 L samples from the filtration and disinfection processes. Water samples were pumped through Virusorb 1MDS filters (Cuno, Inc.), which were eluted with 1 L of 1.5% beef extract (BBL V) in 0.05 M glycine (pH 9.5, ~25C) (US EPA/ICR). The eluted sample was concentrated by organic flocculation and assayed for Enterovirus by the observation of cytopathic effects (CPE) on recently passed (<4 days) cell lines. Three cell lines, Buffalo Green Monkey (BGM), Rhabdosarcoma (RD, ATCC# CCL-136), and MA-104 (ATCC# CRL-2378.1) cells were used for this purpose. Positive controls were performed in a separate room using poliovirus I. Cytopathic effects (CPE) 69

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on each cell line were observed, and the most dilute sample showing CPE was recorded. Most probable number (MPN) determinations were performed using EPA released software (Most Probable Number Calculator version 4.04; http://www.epa.gov/microbes/other.htm). Protozoa. For the detection of Giardia and Cryptosporidium, samples were concentrated by filtration using Gelman Envirochek HV cartridge filters and processed according to the manufacturers instructions. Following filtration, samples were processed by immunomagnetic separation (IMS) (Dynal, Inc.) and immunofluorescent antibody detection (Easy Stain, Biotech Frontier, Australia) according to the procedure outlined in USEPA Method 1623 (33). Sample volumes varied depending upon the treatment stage and the amount of water that could be filtered, i.e. 0.5 1.0 L influent, ~19 L secondary effluent, ~38 L effluent from filters, and up to 53 L disinfected effluent. Detection limits varied with the total volume of sample filtered and processed. Each concentrated sample was divided into two aliquots: one for cell culture viability testing and the other for microscopic enumeration. Equivalent volumes were calculated and the results reported as cysts or oocysts L-1. Cryptosporidium infectivity. Concentrates from the IMS procedure were inoculated onto HCT-8 cell monolayers in 8-well chamber glass cell culture slides. The cultures were incubated in a 5% CO 2 atmosphere at 37 C for 48 hours. Infective Cryptosporidium were enumerated by the Foci Detection Method -Most Probable Number (FDM-MPN) assay (27). Results were reported as infectious oocysts L-1. 70

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Statistical analysis. Statistical analyses were conducted using SAS software version 8.2 (SAS Inst., Cary, NC) or SPSS version 12.0. Data distributions were evaluated with the Shapiro-Wilk test, which was conducted on the raw data, log 10 transformed data and square root transformed data. Nonparametric statistical tests were utilized for non-normally distributed data. Parametric tests were used for ANOVA, and the Tukey post-hoc test was used to compare treatment means. The Spearman rank correlation was used to test the relationship between indicator organism and pathogen concentrations in the final effluent. A binary logistic regression model (SPSS 12.0) was utilized to determine whether indicator organism concentrations predicted the probability of the occurrence of pathogens in disinfected effluent samples. The dependent variable (pathogen) was treated as a binary variable, that is, a score of 0 was assigned when the organism was not detected, and a score of 1 was assigned when the organism was detected. The independent variables were continuous, and values for samples in which organisms were not detected were reported as 0. True-positive, true-negative, false-positive and false-negative values were calculated as the number of samples falling into each category divided by the total sample number. Discriminant analysis was performed on data from effluent samples using the DISCRIM procedure of SAS (prior probabilities: equal; covariance matrix: pooled). The results of six assays for indicator organisms (total coliform, fecal coliform, C. perfringens, enterococci, and F-specific coliphage assays on two hosts) were converted into a string of binary variables representing the presence or absence of each indicator. The ability of the indicator data string to predict the presence or absence of each 71

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pathogen (Giardia, Cryptosporidium and enteric viruses) was assessed separately. Results are expressed as the percentage of samples correctly classified into the pathogen present and pathogen absent categories. RESULTS The results presented here represent multiple sample events from six facilities producing reclaimed water, and focus on microbial concentrations in the influent and in the reclaimed water (disinfected effluent), which is distributed to end users. Microbial concentrations through treatment. Concentrations of indicator organisms and pathogens before (untreated wastewater) and after (disinfected effluent) treatment are shown in Figure 1 in a boxplot format. The limit of detection (see Methods) was substituted for measured values for samples in which the organism was not detected, which was rare in influent samples, but common in effluent samples. Total coliform concentrations were the highest of the microbial measurements in influent samples (>107 CFU mL-1), followed by fecal coliforms and enterococci (~106 CFU mL-1) (Figure 1). Clostridium perfringens values ranged from 104 to >106 CFU mL-1. Coliphage levels were highly variable, ranging from 103 108 PFU mL-1. Pathogen concentrations in the influent (Figure 1) were 4-5 orders of magnitude lower than indicator organism concentrations (note that unit for pathogen concentrations is 100L-1). It should be noted that while the enteric virus concentrations represent infectious viruses, Cryptosporidium and Giardia concentrations reflect the total number of cysts or oocysts (infectious and noninfectious) viewed under immunofluorescent microscopy. In the 72

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influent samples, about 40% of the detected Cryptosporidium were infective as defined by the FDM-MPN cell culture assay. Microbial concentrations in disinfected effluents were much lower, as expected (Figure 1) and, in most cases, were near or below the detection limits for each assay. 73

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Figure 1. Mean indicator organism and pathogen concentrations in untreated wastewater and disinfected effluent from six wastewater reclamation facilities (n=30). Log 10 concentrations of bacterial indicators (CFU100 mL-1), coliphages on E. coli 15597 and E. coli 700891 (PFU100 mL-1), enteric viruses (MPN100 L-1) and Giardia total counts (cysts100 L-1) Cryptosporidium total and viable counts (oocysts100 L-1) are shown. Detection limits were used as concentrations for parameters that were nondetectable. The box represents 50% of the data, the vertical line represents the mean, the lines extending from the boxes represent the 95% confidence limits and the individual data points represent outliers. 02468Total ColiformsFecal ColiformsEnterococciC.perfringensColiphage -15597Coliphage -700891EnterovirusGiardia Cryptosporidium (Total)Cryptosporidium (Infective)Indicators#/100 mLPathogens#/100 LLog concentration in plant influent 02468 Indicators#/100 mLPathogens#/100 LLog concentration in disinfected effluent 74

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The percentage of samples from each treatment step that contained detectable levels of each indicator organism and pathogen is summarized in Table 5. Total and fecal coliforms, enterococci, and coliphages were detected in 100% of influent (untreated) wastewater samples, in which detection limits were generally 33.3 CFU or PFU100 mL-1. Three of the 30 untreated wastewater samples were below the detection limit for C. perfringens (33.3 CFU mL-1). Enteric viruses (detection limit 100 MPN100 L-1) and Giardia (detection limit 500 cysts100 L-1) were also found in 100% of untreated wastewater samples. Cryptosporidium oocysts were detected in 74% of the untreated wastewater samples; however, infective oocysts were only identified in 32% of these samples. The detection limit for Cryptosporidium in the influent samples depended upon the volume that could be filtered, and ranged from 300-2100 oocysts100 L-1. Following biological treatment, the concentrations of indicators and pathogens were reduced by about 1 to 2 log 10, thus decreasing the frequency of detection of most organisms, i.e. enteric viruses were detected in only 73% of the secondary effluent samples as compared to 100% of the influent samples. The frequency of detection of Cryptosporidium increased from 75% in the influent samples to 84% in the secondary effluent samples, due to the more sensitive detection limits in secondary effluent (21-94 oocysts100 L-1); however the frequency of detection of infectious oocysts decreased from 32% to 19%. Filtration further decreased the frequency of detection of microorganisms, particularly for enterococci, the coliphages and Giardia (Table 5). 75

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Table 5. Percentage of samples with detectable indicator organisms and pathogens. Data from all sampling events at the six facilities were pooled for each treatment step. Percentage of Samples Positive in Each Stage Indicator or Pathogen Influent Biological Treatment Filter Effluent Disinfected Effluent Indicators Total coliforms 100 100 94 63 Fecal coliforms 100 97 65 27 Enterococci 100 94 84 27 C. perfringens 93 86 79 61 Coliphage on 15597 100 97 83 38 Coliphage on 700891 100 93 80 45 Pathogens Enteric viruses 100 73 58 31 Giardia 100 94 88 80 Cryptosporidium Total oocysts 74 84 71 70 Infectious oocysts 32 19 19 20 76

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In disinfected samples, total coliforms and C. perfringens were detected most frequently, and fecal coliforms and enterococci were least frequently detected (Table 5). While the frequency of detection of fecal coliforms and enterococci in disinfected effluents was similar (27%), they were simultaneously detected in only one sample, whereas either fecal coliforms or enterococci were detected in 50% of the samples. An assessment of the correlation between total residual chlorine and fecal coliform concentrations in treated effluent samples from all the facilities showed no significant relationship between these parameters (data not shown). Pathogens, measured on the scale of 100 L-1, were detected in 80% (Giardia) to 31% (enteric virus) of samples. Both Giardia and Cryptosporidium were detected by microscopy in 60% of disinfected effluent samples. Unlike the trend noted for the other organisms, the percentage of samples in which Cryptosporidium oocysts were detected remained fairly consistent through the treatment stages (71-84%); however, detection limits became progressively more sensitive through the treatment stages, reaching 2.2-6.9 oocysts100 L-1 in the reclaimed water (disinfected effluents). The percent of samples containing detectable levels of infectious oocysts decreased from 32% in the untreated wastewater samples to 20% in the reclaimed water samples. The frequency of detection of the various microorganisms in disinfected effluent samples was compared using Fishers exact test. Total coliforms and C. perfringens were detected in significantly more samples (63% and 61%, respectively) than enterococci or fecal coliforms (both 27%). Other proportional comparisons between indicator organism detects were not significantly different. The protozoan parasites were 77

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detected in significantly more disinfected effluent samples than enteric viruses, but there was no significant difference in the proportion of samples in which Giardia cysts vs. Cryptosporidium oocysts were detected. Infective Cryptosporidium were detected in significantly fewer disinfected effluent samples than total Giardia or Cryptosporidium. Of all the indicator organisms, including the coliphages, the fecal coliforms were found at the lowest concentrations in final effluent samples (Figure 1), and were among the least frequently detected (Table 5). At hypothetical detection limits of 2 CFU.100 mL-1, total coliforms would be detected in 43% of the disinfected effluent samples, whereas fecal coliforms would be detected in only 10% of the samples (n=30). Reducing the detection limit to 0.2 CFU.100 mL-1 (the actual detection limit) increased the frequency of detection of fecal coliforms and total coliforms to 27% and 63%, respectively. The relationship between hypothetical detection limit and detection frequency was loglinear (r2=0.96 for total coliforms; =0.94 for fecal coliforms). Predictive relationships between microorganisms. Data from disinfected effluent samples were analyzed separately (by facility) and as a pooled data set (all facilities) to determine if the concentrations of any of the indicators (total coliforms, fecal coliforms, enterococci, C. perfringens or coliphages) were correlated with each other or with pathogen concentrations (enteric viruses, Giardia or Cryptosporidium). Analysis of results by facility did not yield significant correlations (probably due to small sample size); however, significant correlations between indicator organism concentrations were observed in the pooled datasets: i.e. total coliform and fecal coliform (Spearmans r s = 0.5986, P =0.0005); C. perfringens vs. coliphage on host E. coli 15597 (r s =0.5303, 78

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P=0.0031); C. perfringens vs. coliphage on host E. coli 700891 (r s =0.4981, P=0.0060); and coliphages on the two E. coli hosts (r s =0.7915, P<0.0001). No significant correlation between concentrations of any combination of indicator organism and pathogen was observed. Enteric viruses were above detection limits in 31% of the disinfected effluent samples (n=30); however, coliphage and enteric viruses co-occurred in only 13% of the disinfected effluent samples. Concentrations of coliphage on both E. coli hosts were plotted against enterovirus concentrations using only samples in which coliphage and enteric viruses were detected, but the slopes of the relationships were not significantly different from 0 (data not shown) Binary logistic regression was used to test the hypothesis that indicator organism concentrations were predictive of the presence or absence of pathogens in disinfected effluent. Observations of enteric viruses, Cryptosporidium oocysts and Giardia cysts were converted to binary data, and the relationship between the concentration of each indicator organism and the presence/absence of each pathogen was assessed, as well as the relationships between the pathogens. Nagelkerkes R-square, which can range from 0.0 to 1.0, denotes the strength of the association; stronger associations have values closer to 1.0. Three indicator-pathogen combinations displayed very weak correlations: coliphage concentration (host E. coli 15597) and enteric virus presence-absence (R-square = 0.226), fecal coliform concentrations and Giardia presence/absence (R-square=0.222), and total coliforms and infectious Cryptosporidium presence/absence (R-square=0.241). In each case, the variability in x accounted for only a fraction of the 79

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variability in y (odds that a pathogen would be present). A much tighter association was evidenced, for example, between the two coliphage assays on different hosts (R-square=0.762), as would be expected for the two similar assays. No correlations between indicators and pathogens were found using the Spearman correlation; however, this is not unusual as binary logistic regression relies on maximum likelihood, does not require linear relationships between variables (19), and utilizes a binary (0,1) dependent variable. The analytical consequences of the failure of indicators to correlate with pathogens are shown in Figure 2. True negatives are samples in which neither indicators nor pathogens were detected; true positives: both indicators and pathogens were detected; false negatives: detection of pathogens when indicators were not detected; false positives: detection of indicators when pathogens were not detected. These values add up to 100% for each indicator-pathogen combination. Total coliforms frequently survived the disinfection process, therefore they tended to be present when pathogens were present, resulting in a relatively high true-positive rate compared to the other indicators (Figure 2A-D). However, total coliforms also tended to have a low true-negative rate (which would ideally be high) and a relatively high false-positive rate, particularly in the case of enteric viruses and viable Cryptosporidium. In contrast, fecal coliforms, which were relatively infrequently detected in disinfected effluent, tended to have a high true-negative rate but also a low true-positive rate. The percentage of results in the correct categories (true-positive and true-negative) was not much greater than 50% for any of the indicator-pathogen combinations, although ideally these categories would comprise 80

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100% of observations. Each type of correct and incorrect categorization has distinct implications for public health protection (see Discussion). 81

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Figure 2. Relationship between detection of individual indicators and accuracy of pathogen detection in disinfected effluent. All percentages were calculated out of the total sample number. Detection limits: 0.2 CFU mL-1 for total and fecal coliforms, enterococci, and Clostridium perfringens; 10 PFU mL-1. for coliphages. (A) Enteric viruses (B) Giardia cysts (C) Cryptosporidium oocysts (D) infectious Cryptosporidium A. B. -75-50-250255075TrueNegativeTruePositiveFalseNegativeFalsePositiveEnteric viruses CorrectCategoryIncorrectCategory -75-50-250255075TrueNegativeTruePositiveFalseNegativeFalsePositivePercent indicator detectedPercent indicator nondetected TotalGiardia CorrectCategoryIncorrectCategory -75-50-250255075TrueNegativeTruePositiveFalseNegativeFalsePositive TotalCryptosporidium CorrectCategoryIncorrectCategory -75-50-250255075TrueNegativeTruePositiveFalseNegativeFalsePositivePercent indicator detectedPercent indicator nondetected Viable Cryptosporidium CorrectCategoryIncorrectCategory D. C. Total Coliform Fecal coliform Enterococci Clostridium perfringens Coliphage 15597 Coliphage pF amp 82

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Discriminant analysis (DA) is a multivariate statistical technique that can be used to classify observations into categories based on a series of independent variables. DA was used to test the hypothesis that the presence/absence of indicator organisms in disinfected effluent samples could predict the presence vs. absence of each pathogen (Figure 3). Indicator organism data for each sample was represented as a string of six binary variables (presence/absence of total coliform, fecal coliform, enterococci, C. perfringens, coliphage on E. coli 15597 and coliphage on E. coli 700891). Presence/absence of each of the pathogen measurements were relatively accurately predicted by the suite of indicator organism data in the 29 effluent samples analyzed (Figure 3). The data are presented as (a) the percentage of samples with pathogens actually present, in which pathogen presence was predicted by DA, and (b) the percentage of samples in which pathogens were actually not detected, in which pathogen absence was predicted by DA. When pathogen-positive and pathogen-negative samples were considered together, 72% percent of enteric virus samples, 79% of Giardia samples, 75% of Cryptosporidium oocyst samples and 71% of infectious Cryptosporidium were placed in the correct category (presence or absence of the pathogen) by discriminant analysis. 83

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Figure 3. Discriminant analysis: results showing the percentage of samples correctly categorized with respect to presence or absence of each pathogen. All of the indicators were used as binary dependent variables. Striped bars: Percentage of samples with pathogens were not detected, in which pathogen absence was predicted by DA. Solid bars: Percentage of samples in which pathogens were detected, in which pathogen presence was predicted by DA. Percent samples in which presence/absence is correctly predicted 100 Present Absent 80 60 40 20 0 CryptoOocysts EntericVirus Giardia InfectCrypto 84

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The absence of all pathogens except for Giardia was more accurately predicted than pathogen presence. In most cases, removal of one variable (indicator organism) from the data string caused the correct classification rate to decrease by a few percentage points, as one or two additional observations would be misclassified. No single indicator was most highly predictive of membership in the presence or absence category for pathogens. Interestingly, when coliphage assayed on E. coli 700891 was excluded as a variable, it improved the results of the enteric virus analysis by correctly categorizing one additional presence sample. DISCUSSION The current monitoring approach to assess the microbial safety of reclaimed water is the measurement of total or fecal coliform concentrations in a single daily grab sample. Utilities and regulatory agencies have assumed a predictive relationship between indicator organism and pathogen levels to protect the public from exposure to pathogens; however the imperfect relationship between coliform bacteria and pathogens, such as viruses (12, 13, 25) and protozoa (6), through wastewater treatment has been known for some time; see LeClerc et al (16) for review. A major goal of this work was to examine monitoring strategies and to determine whether any predictive relationship between conventional and alternative indicator organisms and pathogens in reclaimed water could be discerned among a group of treatment facilities producing reclaimed water. Detection of microorganisms. Log 10 reduction of microorganisms through wastewater treatment trains is frequently reported (23, 24), but should not be relied upon 85

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as the sole measurement of treatment efficacy. Organisms with very high initial concentrations may experience large log reductions while maintaining detectable levels in disinfected effluents, as illustrated by the total coliforms in this study. Total coliforms experienced an average log 10 reduction of > 7 from influent to final effluent, but were still detected in 67% of disinfected effluent samples. The linear relationship between hypothetical detection limits and the percentage of samples in which total or fecal coliforms would be detected demonstrates the usefulness of larger sample volumes for detecting indicators, but this ability did not generally translate to a significant predictive relationship between indicators and pathogens. However, if normal volumes (100 ml) had been assayed for fecal coliforms, and we assume that nondetects would have occurred in samples in which <1 CFU/100 ml was detected, the weak correlations between fecal coliforms vs. Giardia presence/absence and total coliforms vs. infectious Cryptosporidium presence/absence would not have been detected (data not shown). Bacteriophages have been suggested as alternative indicator for enteric viruses, as their morphology and survival characteristics resemble some of the enteric viruses (29),(13). This study found a weak, but significant relationship between presence/absence of enteroviruses and coliphage on E. coli 15597 by binary logistic regression. A significant relationship was not found between enteroviruses and coliphage on E. coli 700891. This observation, coupled with the improvement in prediction of enterovirus presence/absence by discriminant analysis when coliphage on E. coli 700891 was 86

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removed as a variable, suggests that the use of other E. coli hosts for coliphage assays should be further explored. Use of USEPA Method 1623 for detection of Cryptosporidium oocysts does not permit determination of oocyst viability or infectivity, which is crucial information for assessment of the human health risk associated with this parasite. The foci detection method of detecting infectious Cryptosporidium (27) has been utilized in a number of studies (11, 15, 21, 22, 26-28, 34), and results coincide well with mouse infectivity assays (15). Approximately one-quarter of the disinfected effluent samples with detected Cryptosporidium oocysts had detectable levels of infectious Cryptosporidium, a disturbing observation in that reclaimed water represents a potential human exposure pathway, depending on how the reclaimed water is used. None of the indicators correlated with Cryptosporidium oocysts or infectious Cryptosporidium. Because indicators were not predictive of pathogen presence, the results yielded a high percentage of false-negative or false-positive results for all indicator-pathogen combinations. The relationship of indicators with pathogens that were detected more frequently, such as Giardia, tended to show a greater frequency of false-negatives (indicators absent; pathogens present). The relationship of indicators with pathogens that were less frequently detected, such as enteric viruses and infectious Cryptosporidium, generally showed a higher frequency of false-positives (indicators present; pathogens absent). False-positive results are undesirable because they represent false alarms. An indicator that is frequently present in the absence of pathogens, such as total coliforms in this study, is not very informative as to the true risk to human health, but is relatively 87

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conservative in terms of human health protection. False negatives, on the other hand, suggest that probable human health risks are not being detected, which certainly compromise efforts to protect public health. This study suggests that choosing one indicator to predict the survival and/or occurrence of a wide variety of microbial pathogens forces a choice between the two types of error. Although individual indicator organisms and pathogens were weakly correlated or uncorrelated, the use of discriminant analysis on the composite data set resulted in the relatively accurate prediction of the presence or absence of enteric viruses, Giardia, Cryptosporidium oocysts and infectious Cryptosporidium. With the exception of Giardia, errors tended to be false-negatives, as the absence of enteric viruses and Cryptosporidium was more accurately predicted than their presence. Further analysis of larger data sets and other indicators, perhaps coupled with measurement of key pathogens, may allow us to refine the predictive capabilities demonstrated by this multivariate analysis. Such a monitoring strategy should better protect public health than the one-indicator system currently used. Acknowledgements We would like to acknowledge the technical help of Molly R. McLaughlin, Stefica Depovic, Angela Gennaccaro and Tracie Jenkins. Funding was provided by the Water Environment Research Foundation, Project 00-PUM-2T. 88

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REFERENCES 1. 1978. Wastewater Reclamation Criteria, California Admistrative Code, vol. Title 22 Division 4. 2. Adams, M. H. 1959. Bacteriophages. Interscience Publishers, New York. 3. American Public Health Association. 1998. Standard Methods for the Examination of Water and Wastewater, 20th ed. American Public Health Association, Inc., Washington DC. 4. Anonymous. 1998. Reuse of reclaimed water and land application State of Florida. 62-610: www.dep.state.fl.us/legal/rules/wastewater/62-610.pdf 5. Bisson, J., and V. Cabelli. 1979. Membrane filtration enumeration method for Clostridium perfringens. Applied and Environmental Microbiology 37:55. 6. Bonadonna, L., R. Briancesco, M. Ottaviani, and E. Veschetti. 2002. Occurrence of Cryptosporidium oocysts in sewage effluents and correlation with microbial, chemical and physical water variables. Environ Monit Assess 75:241-52. 7. Cosgrove, W. J., and F. R. Rijsbermann. 2000. World water vision making water everybody's business. Earthscan Publications, London. 8. Debartolomeis, J., and V. J. Cabelli. 1991. Evaluation of an Escherichia coli host strain for enumeration of F male-specific bacteriophages. Appl Environ Microbiol 57:1301-5. 9. Fujioka, R. S., and L. K. Shizumura. 1985. Clostridium perfringens, a reliable indicator of stream water quality. J Water Pollut Control Fed 57:986-992. 10. Gantzer, C., A. Maul, J. M. Audic, and L. Schwartzbrod. 1998. Detection of infectious enteroviruses, enterovirus genomes, somatic coliphages, and Bacteroides fragilis phages in treated wastewater. Appl Environ Microbiol 64:4307-12. 11. Gennaccaro, A. L., M. R. McLaughlin, W. Quintero-Betancourt, D. E. Huffman, and J. B. Rose. 2003. Infectious Cryptosporidium parvum oocysts in final reclaimed effluent. Appl Environ Microbiol 69:4983-4. 89

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12. Goyal, S. M., W. N. Adams, M. L. O'Malley, and D. W. Lear. 1984. Human pathogenic viruses at sewage sludge disposal sites in the Middle Atlantic region. Appl Environ Microbiol 48:758-63. 13. Havelaar, A. H., M. van Olphen, and Y. C. Drost. 1993. F-specific RNA bacteriophages are adequate model organisms for enteric viruses in fresh water. Appl Environ Microbiol 59:2956-62. 14. Hsu, F.-C., A. Chung, A. Amante, Y.-S. C. Shieh, D. Wait, and M. D. Sobsey. 1996. Presented at the AWWA Water Quality Technology Conference. 15. Joachim, A., E. Eckert, F. Petry, R. Bialek, and A. Daugschies. 2003. Comparison of viability assays for Cryptosporidium parvum oocysts after disinfection. Vet Parasitol 111:47-57. 16. Leclerc, H., D. A. Mossel, S. C. Edberg, and C. B. Struijk. 2001. Advances in the bacteriology of the coliform group: their suitability as markers of microbial water safety. Annu Rev Microbiol 55:201-34. 17. Levine, A. D., and T. Asano. 2004. Recovering sustainable water from wastewater. Environmental Science & Technology 30:201A-208A. 18. Miescier, J. J., and V. J. Cabelli. 1982. Enterococci and other microbial indicators in municipal wastewater effluents. J. Water Pollut. Control. Fed. 54:1599 1606. 19. Motulsky, H. 1995. Intuitive biostatistics. Oxford University Press, New York. 20. Payment, P., and E. Franco. 1993. Clostridium perfringens and somatic coliphages as indicators of the efficiency of drinking water treatment for viruses and protozoan cysts. Applied and Environmental Microbiology 59:2418-2424. 21. Pokorny, N. J., S. C. Weir, R. A. Carreno, J. T. Trevors, and H. Lee. 2002. Influence of temperature on Cryptosporidium parvum oocyst infectivity in river water samples as detected by tissue culture assay. J Parasitol 88:641-3. 22. Quintero-Betancourt, W., A. L. Gennaccaro, T. M. Scott, and J. B. Rose. 2003. Assessment of methods for detection of infectious Cryptosporidium oocysts and Giardia cysts in reclaimed effluents. Appl Environ Microbiol 69:5380-8. 23. Rose, J. B., L. J. Dickson, S. R. Farrah, and R. P. Carnahan. 1996. Removal of pathogenic and indicator microorganisms by a full-scale water reclamation facility. Water Research 30:2785-2797. 90

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24. Rose, J. B., D. E. Huffman, K. Riley, S. R. Farrah, J. O. Lukasik, and C. L. Hamann. 2001. Reduction of enteric microorganisms at the Upper Occoquan Sewage Authority Water Reclamation Plant. Water Environment Research 73:711-20. 25. Simpson, D., J. Jacangelo, P. Loughran, and C. McIlroy. 2003. Investigation of potential surrogate organisms and public health risk in UV irradiated secondary effluent. Water Sci Technol 47:37-43. 26. Slifko, T. R., D. Friedman, J. B. Rose, and W. Jakubowski. 1997. An in vitro method for detecting infectious Cryptosporidium oocysts with cell culture. Appl Environ Microbiol 63:3669-75. 27. Slifko, T. R., D. E. Huffman, and J. B. Rose. 1999. A most-probable-number assay for enumeration of infectious Cryptosporidium parvum oocysts. Appl Environ Microbiol 65:3936-41. 28. Slifko, T. R., E. Raghubeer, and J. B. Rose. 2000. Effect of high hydrostatic pressure on Cryptosporidium parvum infectivity. J Food Prot 63:1262-7. 29. Turner, S. J., and G. D. Lewis. 1995. Comparison of F-specific bacteriophage, enterococci, and faecal coliform densitiesthrough a wastewater treatment process employing oxidation ponds. Water Science and Technology 31:85-89. 30. U.S. Environmental Protection Agency. 1996. ICR Microbial Laboratory Manual EPA/600/R-95/178. U.S. Environmental Protection Agency. 31. U.S. Environmental Protection Agency. 2000. Improved enumeration methods for the recreational water quality indicators:enterococci and Escherichia coli EPA-821/R-97/004. U.S. Environmental Protection Agency. 32. U.S. Environmental Protection Agency. 1997. Method 1600: membrane filter test methods for enterococci in water. EPA-821/R-97/004. U.S. Environmental Protection Agency. 33. U.S. Environmental Protection Agency. 2001. Method 1623: Cryptosporidium and Giardia in water by filtration/IMS/FA EPA-821-R-01-025. U.S. Environmental Protection Agency. 34. Weir, S. C., N. J. Pokorny, R. A. Carreno, J. T. Trevors, and H. Lee. 2001. Improving the rate of infectivity of Cryptosporidium parvum oocysts in cell culture using centrifugation. J Parasitol 87:1502-4. 91

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35. WHO. 1989. Health Guidelines for the Use of Wastewater in Agriculture and Aquaculture. 92

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Evaluation of Microbial Data and Disinfection Efficacy in Wastewater Reclamation Facilities Vasanta L. Chivukula1, Audrey D. Levine2, Joan B. Rose3, and Valerie J. Harwood1,* 1Department of Biology, University of South Florida, Tampa, FL 2Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 3Department of Fisheries and Wildlife and Crop and Soil Sciences, Michigan State University, MI vharwood@cas.usf.edu 93

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ABSTRACT The absence of microbial pathogens in reclaimed water is essential for its widespread use, yet evaluation of its quality is generally performed by measuring one indicator organism. Filtration and disinfection are the final barriers to pathogens in reclaimed water, but microbial concentrations that are too low to detect by standard sampling practices (nondetects) are commonly observed, complicating data analysis. Five wastewater treatment plants (WTPs) producing reclaimed water and using chloramine disinfection were sampled five times each. Log 10 reduction (LTR) from filtered effluent to disinfected effluent was calculated using detection limits, half detection limits, or zeros in place of nondetects, which caused differences in statistical tests of significance. Disinfection significantly reduced the concentration of most microorganisms (total and fecal coliforms, enterococci, Clostridium perfringens, coliphages on E. coli 15597 or 700891, enteric viruses and Cryptosporidium oocysts), but did not reduce the concentration of Giardia cysts or infectious Cryptosporidium. LTR was correlated with disinfection exposure (CT) for total coliforms, C. perfringens, coliphages on E. coli 15597 and enteric viruses. LTR between pathogens and indicators was correlated only for enteric viruses vs. total coliforms, C. perfringens, and coliphages on E. coli 15597. LTR of total coliforms was the best predictor of the LTR of enteric viruses. Thus, CT appeared to be the determining factor for LTR of the chlorine-sensitive enteric viruses in these 94

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WTPs. LTR calculated using detection limits and half the detection limits showed similar results. We recommend the use of detection limits for statistical analyses purposes, as it is the most conservative method in terms of protection of public health. 95

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INTRODUCTION Reclaimed water use for nonpotable purposes (e.g., irrigation, industrial water, landscaping, and agriculture) and indirect potable (e.g., ground water recharge or discharge into surface waters) purposes has gained importance due to increased demand for water and the depletion of water supplies (5, 16, 29). As the source of reclaimed water is sewage, it must be effectively treated and microbiologically monitored for the presence of pathogenic microorganisms to ensure environmental protection and public health safety. Since testing for the presence of all potential pathogens is currently impossible, determining the presence of indicator organisms, which act as surrogates for the presence of pathogens, has been used to protect water quality for over a century (8, 23). However, studies have shown that indicator bacteria do not predict for pathogenic viruses or protozoan parasites (6, 10, 21, 22, 25). Hence, alternative indicators as well as physical parameters are being tested to check for their association with pathogenic viruses and protozoa (12, 24, 36). The treatment methods used in a wastewater treatment facility depend on the type of influent it receives and the intended use of the final effluent. Although primary treatment, secondary treatment and filtration remove most microorganisms, the final disinfection treatment, which destroys or prevents the growth of microorganisms, is essential to improve the quality of treated wastewater before its release for reuse. The California Wastewater Reclamation Criteria (Title 22) for wastewater reuse requires primary treatment, secondary treatment, coagulation and filtration followed by chlorine 96

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disinfection (13). The Florida Department of Environmental Protection stipulates that the wastewater treatment facilities meet basic disinfection or high-level disinfection criteria depending on the final use of the effluent ( http://www.dep.state.fl.us/water/wastewater/dom/domuv.htm ). To meet such criteria set by various states, disinfectants like chlorine, ozone, and UV radiations have been used by the treatment facilities. Chlorination is the most widely used disinfection process for water and wastewater treatment. Chlorination of wastewater that has not been treated for the removal of nitrogenous compounds results in the formation of mono, di or trichloramines depending on the influent characteristics and chlorine contact time (18). Chloramines are less effective than chlorine in the reduction of microorganisms, but they minimize the production of toxic byproducts (35). The effectiveness of a disinfection process also depends on other factors such as disinfection dose, contact time, temperature, pH, and turbidity. CT, the product of contact time (min) and the chlorine residual concentration (mgL-1), is an important factor in the reduction of microorganisms through the disinfection process. The effect of the CT parameter is commonly considered in drinking water treatment (9, 34, 38) but has received minimal attention in the wastewater industry. The United States Environmental Protection Agency (USEPA) has published the Guidance Manual for Compliance with the Filtration and Disinfection Requirements for Public Water Sources (40). The CT values for chloramines in the manual ranged from 214 2,883 for viruses depending on the temperature and log inactivation. In this study, 97

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the effect of CT on indicator organisms and pathogens during wastewater treatment was assessed in order to develop a better understanding of the efficiency of disinfection via chloramination. A recent study found that a suite of indicator organisms can better predict the presence of certain pathogens than a single indicator organism (24). Given that disinfection is the final step in the treatment of wastewater, the effectiveness of this step in the removal of pathogens is crucial to the production of water that is safe for its intended use. Very few studies have compared the reduction of a suite of indicator organisms vs. pathogens during disinfection, rather, most are restricted to enumeration of microorganisms in the final effluent (11, 24, 27). This study examines the relationships among microbial analytes in pre-disinfection (filtered effluent) and post-disinfection processes from five wastewater reclamation plants that use combined chlorine (chloramines) as disinfectant. A suite of conventional indicator organisms (total coliforms, fecal coliforms, and enterococci), alternative indicators (C. perfringens, somatic and F specific coliphages) and pathogens (enteric viruses, Cryptosporidium, and Giardia) was analyzed. Regression models were employed to assess the effectiveness of the disinfection process (CT) on the reduction of these organisms from the filtration step to the disinfection step. Nondetects can occur during sampling events due to limitations in the experimental methods, precluding or complicating statistical analysis. Microbial concentrations tend to be low in the final stages of wastewater treatment; therefore organisms in some percentage of the samples can be below the detection limit of the 98

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assay. In this study, three substitution strategies were employed for nondetects: (1) the detection limit for the assay was utilized as the data point, (2) half the detection limit for the assay, or (3) zero was substituted for nondetects. The data sets were then analyzed using correlation and univariate regression analyses to determine the predictive capabilities of indicator organisms for pathogens. MATERIALS AND METHODS Sampling. Five wastewater reclamation facilities located in two different states in the US (Florida and California) were sampled at least five times over a period of two years. Filtered effluent and disinfected effluent were sampled at or near peak flow conditions at each facility. Microorganisms such as total coliforms, fecal coliforms, enterococci, C. perfringens, somatic and F+ coliphages, enteric viruses, Giardia and Cryptosporidium, were enumerated (see below). Two-liter grab samples were collected from the filtered effluent and disinfected effluent for the enumeration of bacteria. Larger volumes (up to 53 L) were filtered for the enumeration of viruses and protozoan parasites. The detection limits for the filtered effluents for bacterial indicators were 0.2 303 CFU mL-1; for coliphages 5 10 PFU mL-1; for enteric viruses 0.4 8.3 MPN L-1; for Cryptosporidium oocysts 1.0 11.0 oocysts L-1; for Giardia 3.0 10.5 cysts L-1. For disinfected effluents the detection limits for bacterial indicators were 0.2 0.6 CFU mL-1; for coliphages 10 PFU mL-1; for enteric viruses 0.3 1.4 MPN L-1; for Cryptosporidium oocysts 2.0 6.9 oocysts L-1; for Giardia 1.8 5.2 cysts L-1. The number of samples for all the organisms analyzed was 22 except 99

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for infectious Cryptosporidium where the sample size was 20. The correlation between CT and the microorganisms was performed using 20 samples except for infectious Cryptosporidium where the sample size was 17. Enumeration of the indicator organisms and pathogens. Enumeration of bacteria (total coliforms, fecal coliforms, enterococci and C. perfringens), viruses ( F specific coliphage, somatic coliphage and enteric viruses) and protozoan parasites (Giardia and Cryptosporidium) and Cryptosporidium infectivity was performed as previously described (24). Briefly, water samples were filtered by membrane filtration using 47 mm filters with pore size of 0.45 m (Millipore Corporation, Bedford, MA). All membrane filtration assays were performed in triplicate. Total coliform bacteria were cultured on mEndo LES agar (Difco, Sparks, MD) for 24 h at 37C. Colonies that produced a green sheen were enumerated as total coliforms (2). Fecal coliform bacteria were grown on mFC agar (Difco, Sparks, MD) for 24 h at 44.5C in a water bath. Blue colonies were enumerated as fecal coliforms (2). Escherichia coli (American Type Culture Collection [ATCC] # 9637) was used as the positive control for all coliform measurements. For the enumeration of enterococci filters were incubated on mEI agar (42, 43) at 41C for 24 h, and colonies with a blue halo were considered enterococci. Enterococcus faecalis (ATCC #19433) was used as a positive control. Clostridium perfringens was isolated on mCP agar (Acumedia Manufacturers, Inc) under anaerobic conditions (7). Plates were incubated in gas pack bags (BBL GasPak, Beckton Dickinson) and sealed. After 24 h of incubation at 45C, colonies were exposed to ammonium 100

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hydroxide fumes. Yellow/straw colored colonies that turned pink/magenta after exposure to fumes were counted. C. perfringens (ATCC #13124) was used as positive control. Coliphages were analyzed by the agar overlay method of Adams (1). E. coli HS(pFamp)R (ATCC #700891), which hosts male-specific (F+) coliphages very efficiently and somatic coliphages poorly (14), and E. coli C3000 (ATCC #15597), which can host both somatic and F+ coliphages (26) were used for the isolation of the phages. Samples were serially diluted in phosphate buffered saline (PBS) according to expected phage concentrations. Five replicate volumes of 0.1 mL to 2 mL were plated on trypticase soy agar (DifcoTM, Becton Dickinson, Sparks, MD) for filtered effluent samples and ten replicates of 2 mL each were plated for the disinfected effluent samples. Plaque forming units (PFU) mL-1 were calculated after 24 h incubation (2). For the enumeration of enteric viruses (41) approximately 380 L samples from the filter and disinfected effluent sites were used. Water samples were filtered through Virusorb 1MDS filters (Cuno, Inc.) and the filters were eluted with 1 L of 1.5% beef extract (BBL V) in 0.05 M glycine (pH 9.5, ~25C) (US EPA/ICR). The eluted samples were concentrated by organic flocculation and inoculated on recently passed (<4 days) cell lines. Three cell lines, Buffalo Green Monkey (BGM), Rhabdosarcoma (RD, ATCC# CCL-136), and MA-104 (ATCC# CRL-2378.1) cells were used for this purpose. The cell lines were observed for cytopathic effect (CPE). Positive controls were performed in a separate room using poliovirus I. The most dilute sample showing CPE was recorded. Most probable numbers (MPN) were determined using EPA released software (Most Probable Number Calculator version 4.04; http://www.epa.gov/microbes/other.htm). 101

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For the detection of protozoan pathogens Giardia and Cryptosporidium, samples were filtered through Gelman Envirochek HV cartridge filters and processed according to the manufacturers instructions. Sample volumes depended on the amount of water that could be filtered; i.e., approximately 38 L of sample from filter effluent and 53 L from disinfected effluent site. Following filtration, samples were processed by immunomagnetic separation (Dynal, Inc.) and immunofluorescent antibody detection (Easy Stain, Biotech Frontier, Australia) according to USEPA Method 1623 (44). Equivalent volumes were calculated and the results reported as cysts or oocysts L-1. Detection limits for viruses and protozoan pathogens varied with the total volume of sample filtered and processed. Statistical analysis. Data from the five treatment plants were pooled for the statistical analysis purposes. Data were analyzed using SAS software version 8.2 (SAS Institute, Cary, NC). Data were log 10 transformed to account for skewed and unequal variations in the data. Nondetects were substituted either with detection limits, half the detection limits or zeros. Normality of the data was tested using Shapiro-Wilk test. The data were normally distributed, therefore parametric tests were conducted. Paired t test was conducted to compare the concentrations of organisms between filtered effluent and disinfected effluent. Pairwise comparison of log 10 reductions for all indicators and pathogens was conducted using analysis of variance (ANOVA) followed by Tukey-Kramer multiple comparisons post hoc. Pearsons product moment correlation and univariate regression analysis was conducted to analyze the relationship between CT [the product of contact time (min) and the chlorine residual concentration (mgL-1)] and the 102

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microorganisms and to determine the relationship between indicator organisms and pathogens. RESULTS AND DISCUSSION Effectiveness of disinfection processes on microbial concentrations. The mean concentrations of indicator organisms and pathogens (calculated using detection limits in place of nondetects) in filtered effluent and disinfected effluent are shown (Figure 4). The mean concentration of each microbial analyte was significantly lower in disinfected effluent than in filtered effluent, with the exception of Giardia and infectious Cryptosporidium (Table 6). Similar results were observed when mean concentrations were calculated using half the detection limits except for Cryptosporidium, which did not show a significant difference. When the analysis was conducted using zeros for nondetects, the pathogens (enteric viruses, Giardia, Cryptosporidium, and infectious Cryptosporidium) did not show a significant difference between filtered effluent and disinfected effluent. Note that the mean infectious Cryptosporidium concentration was essentially the same in filtered vs. disinfected effluent; however, this relationship was probably affected by the high percentage of nondetects in disinfected effluent (80%)(24). The mean log 10 concentrations of Giardia and infectious Cryptosporidium were not significantly different using any of the three methods for replacement of nondetects, meaning that no reduction from filtered effluent to disinfectant occurred. Correlations in log reduction of these pathogens compared to indicators are therefore irrelevant, and were not considered in regression analyses. 103

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Figure 4. Mean concentration (log 10 -transormed) and standard deviation of indicator organisms and pathogens in filtered effluent and disinfected effluent samples in five treatment facilities. Units for the Y axis are CFU ml-1 (bacteria), PFU ml-1 (phages), MPN L-1 (enteric viruses), cysts L-1 (Giardia), oocysts L-1 (Cryptosporidium), MPN L-1 (infectious Cryptosporidium) (TC total coliform, FC fecal coliform, Ent enterococci, CP C. perfringens, P1 coliphage on E. coli 15597, P2 coliphage on E. coli 700891, G Giardia, CS Cryptosporidium, ICS infectious Cryptosporidium). 5 Indicators in filtered effluent -2 -1 0 1 2 3 4 T C F C E C C P P1 P2 E V G CS Indicators in disinfected effluent Pathogens in filtered effluent Mean log 10 concentration Pathogens in disinfected effluent I CS 104

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Table 6. Mean log 10 reduction in the concentration of each microorganism (bacteria, viruses and protozoa) between filtered effluent and disinfected effluent at all plants calculated using detection limits. Statistically significant differences in microbial concentrations in filtered effluent vs. disinfected effluent were determined by paired t tests. Microorganisms Mean log 10 reduction Paired t test results (P) % of disinfected effluent samples with detects Total coliform 2.34 <0.0001 64 Fecal coliform 2.04 <0.0001 23 Enterococci 1.96 <0.0001 32 C. perfringens 1.24 <0.0001 64 Coliphage on E. coli 15597 1.02 0.0001 46 Coliphage on E. coli 700891 0.66 0.0003 55 Enteric viruses 0.77 <0.0001 27 Giardia 0.04 0.69 86 Cryptosporidium 0.20 0.03 82 Infectious Cryptosporidium 0.007 0.52 28 105

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The difference between the log 10 reductions of each organism was analyzed using ANOVA (Table 7). Detection limits were substituted for nondetects for calculation of log 10 reduction. As expected the log 10 reduction of bacterial indicators did not show any significant difference with each other except for C. perfringens vs. total coliforms. This might be due to higher loading of total coliforms (9.9 X 103 CFU mL-1) to the disinfection process, and hence higher log 10 reduction in total coliforms when compared to C. perfringens. The log 10 reduction (detection limits replacing nondetects) of total coliforms, fecal coliforms and enterococci was significantly higher than that of coliphages on both E. coli hosts, except enterococci vs. coliphage on E. coli 700891. This observation is in agreement with previous studies, which have shown that chlorination is more efficient in the inactivation of bacteria than phages (4, 15, 17). The log 10 reduction of C. perfringens was not significantly different than that of the coliphages. Previous studies suggest that C. perfringens and coliphages are more resistant to disinfection treatment than the coliforms (32, 39). In addition, the log 10 reductions of coliphages on both E. coli hosts were not significantly different than enteric viruses or protozoan pathogens. The same results were observed when half the detection limits replaced nondetects. When zeros were substituted for nondetects, some differences in the significance of the results were observed in comparisons with respect to fecal coliforms, enterococci, C. perfringens, and coliphage on E. coli 15597 (Table 7), e.g., some comparisons between bacterial analytes became nonsignificant, while coliphages (on E. coli 15597) compared to certain pathogens became significant. 106

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Table 7. Pairwise comparison of log 10 reductions for all indicators and pathogens. S denotes a significant difference (P<0.05), and NS denotes not significantly different when detection limits were substituted for nondetects for calculation of log reduction. Nondetects were also substituted with half detection limits or zeros, and changes in significance are noted with superscripts. (TC total coliform, FC fecal coliform, Ent enterococci, CP C. perfringens, P1 coliphage on E. coli 15597, P2 coliphage on E. coli 700891, G Giardia, CS Cryptosporidium, ICS infectious Cryptosporidium). Organisms FC Ent CP P1 P2 EV G CS ICS TC NS NS S S S S S S S FC NS NS Sa Sa Sa S S S Ent NS NS Sa Sa S S S CP NS NS NS Sa Sa Sa P1 NS NS NSa NS NSa P2 NS NS NS NS EV NS NS NS G NS NS CS NS adifference between detection limits and zeros 107

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In order to assess the effect of disinfectant CT on log 10 reduction of microorganisms, correlation analysis was performed (Table 8). Log 10 reduction of microorganisms between the filtration and disinfection steps was calculated using detection limits, half the detection limits and zeros substituted for nondetects. The correlation between CT and log 10 reduction was statistically significant for total coliforms, C. perfringens, coliphage on E. coli 15597, and enteric viruses (Pearsons r = 0.61, 0.64, 0.57, and 0.70, respectively) using detection limits. Similar results were observed when the correlations were conducted using half the detection limits and zeros. Univariate regression analysis determined that the highest magnitude of log 10 reductions per unit increase in chlorine CT was observed for total coliforms ( = 0.0013). Binary logistic regression was used to determine the relationship between CT and the presence or absence of each microbial analyte (including Giardia and infectious Cryptosporidium) in the disinfected effluents. Fecal coliforms were the only microbial analyte whose presence was correlated with CT. The correlation and regression analyses showed different results with respect to the bacterial indicators. In addition, coliphages on the two E. coli hosts differed in their response to CT. As expected, CT had no effect on the chlorine-resistant protozoan pathogens. 108

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Table 8. Univariate regression analysis computed between the CT and the mean log 10 reduction in the concentration of microorganisms. Only comparisons in which significant correlations were noted are shown. Nondetects were analyzed using detection limits, half the detection limits and zeros (n = 20) Analyte Basis of log reduction calculation Correlation coefficient ( r) Parameter estimate () P-values Total coliforms detection limits 0.61 0.0013 0.0045 Half detection limits 0.61 0.0013 0.0046 Zeros 0.6 0.0013 0.005 C. perfringens Detection limits 0.64 0.0008 0.0025 half detection limits 0.55 0.0007 0.01 Coliphage on E. coli 15597 Detection limits 0.57 0.0008 0.0089 half detection limits 0.55 0.0008 0.01 zeros 0.54 0.001 0.02 Coliphage on E. coli 700891 zeros 0.53 0.0007 0.02 Enteric viruses detection limits 0.7 0.0006 0.0002 half detection limits 0.73 0.0007 0.0003 Zeros 0.66 0.0006 0.0002 109

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Relationship between indicator organisms and pathogens. Log 10 reduction values of indicator organisms vs. pathogens were compared. Significant correlation was observed between enteric viruses vs. total coliforms, C. perfringens, and coliphage on E. coli 15597 using detection limits and half the detection limits. In addition, total coliforms and C. perfringens showed significant correlation with enteric viruses when zeros replaced nondetects (Table 9). The largest value of the parameter estimate was observed for total coliforms vs. enteric viruses, indicating higher magnitude in the reduction of total coliforms with a unit reduction in enteric viruses (Figure 5). Previous studies have suggested the use of C. perfringens as an indicator for fecal pollution, especially for the prediction of enteric viruses and protozoan cysts (20, 32). This study showed a significant correlation between C. perfringens and enteric viruses with a parameter estimate value of approximately 0.7 and r = 0.5. Many studies have focused on determining the reduction of indicator organisms and pathogens in the final effluent samples (24, 28, 30, 33). Lack of correlation between concentrations of single indicator organisms vs. pathogens in the final effluent samples was reported earlier (24). This study shows that log 10 reductions of indicator organisms could be used to predict the log 10 reductions in pathogens in the disinfection process. In addition, the results of the various analyses conducted substituting detection limits for nondetects were similar to using half the detection limits. Analytical measurements that fall below the detection limits are reported either as not detected, less than detection limit, or values such as the limit of detection, a fraction of the detection limit are used (3). Not detected and less than detection limit 110

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cannot be used for statistical analyses purposes which requires some value for calculations. Thus, such data has to be precluded during analysis. In this study, detection limits and half the detection limits provided similar results, and hence, we recommend the use of detection limits to minimize complications during statistical analysis. Generally, pilot or bench-scale studies have been conducted to determine the relationship between indicator organisms and pathogens through wastewater treatment (19, 27). Alternatively, effluent samples from wastewater treatment facilities were analyzed without analysis of samples from any other treatment stage (24, 31, 37). Very few studies have focused on the effect of treatment processes, e.g. CT, on the reduction of microorganisms in wastewater treatment facilities (http://www.werf.org/pdf/00PUM2T.pdf). This study shows that CT is the factor that determines the correlation between enteric viruses and indicator organisms during chloramine disinfection process. Thus, the effect of CT on the reduction of microorganisms must be further evaluated to improve the efficiency of disinfection processes. 111

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Table 9. Correlations between indicator organisms and each pathogen with respect to mean log 10 reduction determined using detection limits, half the detection limits, and zeros (n = 22). Only comparisons in which significant correlations were noted are shown. Correlation with enteric viruses Basis of log reduction calculation Correlation coefficient ( r) P-values Total coliforms detection limits 0.62 0.002 half detection limits 0.66 0.0008 zeros 0.50 0.02 C. perfringens detection limits 0.50 0.002 half detection limits 0.54 0.009 Zeros 0.50 0.02 Coliphage on E. coli 15597 detection limits 0.43 0.04 half detection limits 0.73 0.03 112

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Figure 5. Regression coefficients () between indicators and pathogens determined using mean log 10 reductions calculated with detection limits, half the detection limits and zeros. Only significant correlations are shown. TC total coliforms, CP C. perfringens, P1 coliphage on E. coli 15597, and EV enteric viruses. 00.20.40.60.811.21.41.6EV vs TCEV vs CPEV vs P1Parameter estimates detection limits half the detection limits zeros 113

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24. Harwood, V. J., A. D. Levine, T. M. Scott, V. Chivukula, J. Lukasik, S.R. Farrah and J.B. Rose. 2005. Validity of the indicator organism paradigm:pathogen reduction and public health protection in reclaimed water. Appl Environ Microbiol 71:3163-3170. 25. Havelaar, A. H., M. van Olphen, and Y. C. Drost. 1993. F-specific RNA bacteriophages are adequate model organisms for enteric viruses in fresh water. Appl Environ Microbiol 59:2956-62. 26. Hsu, F.-C., A. Chung, A. Amante, Y.-S. C. Shieh, D. Wait, and M. D. Sobsey. 1996. Presented at the AWWA Water Quality Technology Conference. 27. Jacangelo, J. G., P. Loughran, B. Petrik, D. Simpson, and C. McIlroy. 2003. Removal of enteric viruses and selected microbial indicators by UV irradiation of secondary effluent. Water Sci Technol 47:193-8. 28. Koivunen, J., and H. Heinonen-Tanski. 2005. Inactivation of enteric microorganisms with chemical disinfectants, UV irradiation and combined chemical/UV treatments. Water Res 39:1519-26. 29. Levine, A. D., and T. Asano. 2004. Recovering sustainable water from wastewater. Environ Sci Technol 38:201A-208A. 30. Marzouk, Y., S.M. Goyal, and C.P. Gerba. 1980. Relationship of viruses and indicator bacteria in water and wastewater of Israel. Water Research 14:1585-1590. 31. Miescier, J. J., and V. J. Cabelli. 1982. Enterococci and other microbial indicators in municipal wastewater effluents. J. Water Pollut. Control. Fed. 54:1599-1606. 32. Payment, P., and E. Franco. 1993. Clostridium perfringens and somatic coliphages as indicators of the efficiency of drinking water treatment for viruses and protozoan cysts. Appl Environ Microbiol 59:2418-2424. 33. Payment, P. P., R. and Cejka, P. 2001. Removal of indicator bacteria, human enteric viruses, Giardia cysts, and Cryptosporidium oocysts at a large wastewater primary treatment facility. Can J Microbiol 47:188-193. 34. Peeters, J. E., E. A. Mazas, W. J. Masschelein, I. Villacorta Martiez de Maturana, and E. Debacker. 1989. Effect of disinfection of drinking water with ozone or chlorine dioxide on survival of Cryptosporidium parvum oocysts. Appl Environ Microbiol 55:1519-22. 116

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35. Qi, Y., C. Shang, and I. M. Lo. 2004. Formation of haloacetic acids during monochloramination. Water Res 38:2374-82. 36. Quinonez-Diaz, M. J., M. M. Karpiscak, E. D. Ellman, and C. P. Gerba. 2001. Removal of pathogenic and indicator microorganisms by a constructed wetland receiving untreated domestic wastewater. J Environ Sci Health A Tox Hazard Subst Environ Eng 36:1311-20. 37. Rose, J. B., D.E. Huffman, K. Riley, S.R. Farrah, J.O. Lukasik, C.L. Hamann. 2001. Reduction of enteric microorganisms at the Upper Occoquan Sewage Authority Water Reclamation Plant. Water Environ Res 73:711-720. 38. Stewart, M. H., R. L. Wolfe, and E. G. Means. 1990. Assessment of the bacteriological activity associated with granular activated carbon treatment of drinking water. Appl Environ Microbiol 56:3822-9. 39. Tree, J. A., M. R. Adams, and D. N. Lees. 2003. Chlorination of indicator bacteria and viruses in primary sewage effluent. Appl Environ Microbiol 69:2038-43. 40. U.S. Environmental protection agency. 1989. Guidance manual for compliance with the filtration and disinfection requirements for public water systems using surface water sources. Office of water, U.S. Environmental protection agency, Washington, D.C. 41. U.S. Environmental Protection Agency. 1996. ICR Microbial Laboratory Manual EPA/600/R-95/178. U.S. Environmental Protection Agency. 42. U.S. Environmental Protection Agency. 2000. Improved enumeration methods for the recreational water quality indicators: enterococci and Escherichia coli EPA-821/R-97/004. U.S. Environmental Protection Agency. 43. U.S. Environmental Protection Agency. 1997. Method 1600: membrane filter test methods for enterococci in water. EPA-821/R-97/004. U.S. Environmental Protection Agency. 44. U.S. Environmental Protection Agency. 2001. Method 1623: Cryptosporidium and Giardia in water by filtration/IMS/FA EPA-821-R-01-025. U.S. Environmental Protection Agency. 117

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Impacts of Anthropogenic Activities on the Diversity of Indicator Organisms and Bacterial Populations in Environmental Waters Vasanta L. Chivukula, Mariya J. Dontchev, and Valerie J. Harwood* Department of Biology, SCA 110 University of South Florida 4202 E. Fowler Ave. Tampa, FL 33620 (813) 974-1524 vharwood@cas.usf.edu Running Title: Human Population Effects on Bacterial Diversity (Submitted to Applied and Environmental Microbiology) 118

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ABSTRACT Identification of sources of fecal contamination to water can contribute to accurate total maximum daily load and risk assessments, and water quality restoration. Water quality studies have rarely considered the diversity of indicator organism populations, yet disturbance to an ecosystem (fecal contamination) may impact the diversity and/or community structure of the microbial population, which could in turn affect the performance of microbial source tracking (MST) efforts. The hypothesis that fecal contamination in water bodies affects both indicator organism (IO) diversity and bacterial community structure was investigated in river waters and sediments in watersheds with different human population densities, and also in sewage. 16S rRNA restriction fragment length polymorphism, ribotyping, and denaturing gradient gel electrophoresis determined total coliform, Escherichia coli, and bacterial community population structures, respectively. IO concentrations were significantly different among sites in sediment, but not water samples. Population diversity measurements were not significantly different among the river sites, but tended to be highest in sewage. Accumulation curves indicate that at most sites, more than 20 isolates must be sampled to represent the dominant populations, and many curves did not reach saturation with 30 isolates. Indicator population and bacterial community structures were dissimilar in water column vs. sediment samples, and the E. coli population in unimpacted sediments formed an outgroup, suggesting differential survival of certain subtypes. The relationship between 119

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IO populations in sediment and water column, and differential survival of certain subtypes must be explored to understand IO population biology and implications for their use in monitoring and regulatory applications. 120

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INTRODUCTION Understanding the effects of anthropogenic activities on microbial community structure and microbial diversity in aquatic environments has received limited attention in the field of water quality microbiology (10). It has been noted that many areas including agriculture (30), bioremediation (51), wastewater treatment (48), and medicine (14) can benefit from investigation of microbial community structure using culture-independent methods; however, current water quality standards and monitoring practices rely solely on levels of culturable indicator bacteria (57) (http://www.dep.state.fl.us/legal/rules/shared/62-302t.pdf ). Fecal contamination, disposal of agricultural wastes, and storm water runoff may be environmental stressors, which are generally thought to disrupt the balance of ecosystems (4), and may therefore lead to disruptions in the homeostasis of aquatic ecosystems. The fact that human populations negatively impact water quality in terms of increased indicator bacteria concentrations is well-known (36, 62), but the linkage between culturable indicator bacteria and overall bacterial community structure is poorly understood. Such disruption of aquatic ecosystems may act synergistically with the effect that pathogens contained in human waste have on human health risk. Identifying sources of contamination and implementation of remedial measures to prevent the occurrence of such contamination are central to water safety. Microbial source tracking (MST) methods are being developed for identification of sources of water contamination in surface waters (11, 52, 62-64) and various techniques used for MST [e.g., antibiotic resistance analysis (23, 64), ribotyping (45), and pulsed field gel 121

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electrophoresis (55)] have been developed or adapted to that effect. However, rigorous testing of MST protocols has revealed aspects of the methods that require improvement before confidence in field results can be achieved (15, 24, 39, 41, 43, 53, 55). A greater knowledge of the population dynamics of indicator organisms and other bacteria in aquatic habitats may well be useful for improving MST methods, and for choosing the most appropriate tool(s) for any particular set of environmental conditions (53). The response of microbial communities to anthropogenic impacts in aquatic ecosystems is not only important in applications such as MST; as the human population in the US and in the world increases, it exerts steadily increasing pressure on aquatic ecosystems and food resources (8, 49). Analyzing the community structure of microorganisms will aid in developing and improving techniques for mitigating pollution caused by chemical contaminants and wastes (http://www.asm.org/ASM/files/CCPAGECONTENT/docfilename/0000003770/BasicResearchDecade[1].pdf) Diversity indices are used to describe the frequency distribution of microbial species, or phylotypes, in a given habitat (21, 26). These indices are based on the number of different phylotypes found within a population, and some indices also capture the relative abundance of these phylotypes, providing a snapshot of the community structure in these environments. One of the challenges in applying diversity indices to prokaryotic populations is the delineation of species; whereas the species concept is well-defined among eukaryotes, considerable ambiguity exists with respect to prokaryotic species (28, 31, 37). Operational taxonomic units (OTUs) produced by various molecular techniques 122

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such as restriction fragment length polymorphism (RFLP), denaturing gradient gel electrophoresis (DGGE), ribotyping, and terminal restriction fragment length polymorphism (tRFLP), have been proposed as alternative variables that can be used for measuring the diversity among prokaryotic microorganisms (28). There is no universally accepted diversity index; however, Hills indices have been widely used for measuring prokaryotic diversity (25). Grouped within Hills indices are Shannon Weiners and Simpsons dominance indices. Shannons index gives a measure of the abundant phylotypes in a population, taking into account both the total number of phylotypes and their observed frequency within the population. Simpsons index represents the probability that two phylotypes chosen at random from a population will be the same. Pielous evenness index represents the observed diversity of a population as a proportion of its theoretical maximum diversity. Possible values range from 0 to 1 and are near 1 if the phylotypes are evenly distributed (equal numbers of each phylotype) and near 0 if the phylotypes are unevenly distributed (35). In this study, the diversity of bacterial populations in water bodies that receive varying levels of anthropogenic impact (in terms of human population density in the watershed) was assessed in sediments and in the water column. Bacterial diversity was measured at several taxonomic and phylogenetic levels (i.e., total coliforms, Escherichia coli, and bacterial community). We hypothesized that anthropogenic impact would increase the diversity of total and fecal coliform populations by addition of these fecal-associated bacteria to the ecosystem, but would decrease diversity of the bacterial community due to disruption of the ecosystem. Restriction fragment length 123

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polymorphism, genomic ribotyping and DGGE were applied to estimate the number and distribution of phylotypes or OTUs for each aquatic ecosystem studied. Diversity indices were compared in order to (a) assess the relationship between habitat and microbial diversity, and (b) determine whether diversity co-varied with human population density. Species accumulation curves were used to estimate the sample size needed for adequate representation of the populations (28). MATERIALS AND METHODS Sample collection. Environmental water and sediment samples were collected from three different sites in Florida that differed with respect to human population density in the watershed. A relatively unimpacted, nearly pristine site was chosen in the Myakka River State Park: Deer Prairie Slough in the Myakka River (Sarasota County, FL; GPS N Latitude 27 degrees 10.543' and W Longitude 82 degrees 12.705'). The Hillsborough River at River Front Park was designated Hillsborough Site I (Hillsborough County, FL; GPS N Latitude 28 degrees 04.194 and W Longitude 082 degrees 22.681), which is characterized by low-density housing and light agriculture upstream from the site, and parks on both sides of the river at the site. The Hillsborough site II (Hillsborough County, FL; GPS N Latitude 28 degrees 01.513 and W Longitude 082 degrees 23.833) is located in the city of Tampa, FL, and is characterized by high-density housing and commercial use. Samples were also collected from secondary clarifier and sludge from the anaerobic digest at the Northeast St. Petersburg Wastewater Treatment Plant (Pinellas County, FL; GPS N Latitude 27 deg. 49 42.29" and W Longitude 82 124

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deg. 37 10.50"). Each site represents varying levels of anthropogenic impact; from highest (wastewater) to intermediate (Hillsborough sites) to least (Myakka River). Three replicate samples (true replicates, collected in separate containers) each of water and sediment were collected per site on three sample dates and were analyzed separately. Sample dates were as follows: Myakka River, 8/3/2003, 11/24/2003, 2/16/2004; Hillsborough I, 10/15/2003, 2/28/2004, 4/26/2004; Hillsborough II, 10/15/2003, 12/26/2003, 3/27/2004; wastewater, 9/15/2003, 1/5/2004, 4/2/2004. Water samples were filtered through a 0.45-m pore size membrane filter (47 mm diameter) (Millipore Corporation, Bedford, MA). Volumes filtered for the enumeration of total and fecal coliforms were dependent on the sample site: Myakka River, 1 to 50 ml; Hillsborough River sites I and II, 1ml of 10-1 dilution to 10 ml; wastewater, 1 ml of 10-3 dilution to 1 ml of 10-1 dilution. Sediment samples were collected by scooping approximately 100 g (wet weight) of the sediment into sterile bottles. The sediment samples were processed by first adding 10 g (wet weight) of the sample to 100 ml of phosphate buffered saline (137 mM NaCl, 2.7 mM KCl, 41.4 mM KH 2 PO 4 10.1 mM Na 2 HPO 4, pH 7.0) (1) followed by sonication using an ultrasonic dismembrator (model 100, Fisher Scientific, Pittsburg, PA). The sample was placed on ice, and the sonicator was pulsed three times for 30 sec at 14 W to separate bacteria from sediment particles (3). The sample was allowed to equilibrate at room temperature for 10 min and the supernatant was filtered and processed as was done for water samples, including volumes used. The membranes were then placed on the appropriate medium (see below) and were incubated for 24 h at suitable temperatures (see below) to allow bacterial growth. 125

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Isolation and enumeration of bacteria. Enumeration of total coliforms, fecal coliforms, and enterococci were performed according to standardized membrane filtration protocols (1). All membrane filtration assays were performed in triplicate. Briefly, total coliform bacteria were isolated using mEndo LES agar medium (DifcoTM, Becton Dickinson, Sparks, MD), fecal coliforms on mFC agar medium (DifcoTM, Becton Dickinson, Sparks, MD). mEndo LES agar plates (50mm) were incubated for 24 h at 37oC. Colonies that produced a green sheen were enumerated as total coliforms (58, 59). These colonies were streaked on mEndo LES agar for isolation of pure colonies. For fecal coliform isolation, mFC plates were wrapped in a plastic bag and incubated for 24 h at 44.5oC in a water bath. Blue colonies were enumerated as fecal coliforms (2). Colonies were streaked on trypticase soy agar (TSA) (DifcoTM, Becton Dickinson, Sparks, MD) for isolation of pure colonies. These colonies were transferred with sterile toothpicks into the wells of microtitre plates that contained EC broth amended with 4-methylumbelliferyl--D-glucuronide (MUG) (50g/ml). glucuronidase activity, which is characteristic of E. coli, was assessed by MUG cleavage, which was determined by fluorescence upon excitation with ultraviolet light (6). Ten percent of these isolates were profiled biochemically using API 20E strips, and 94% were identified as E. coli. Isolated colonies from both mEndo LES and TSA plates were transferred into individual wells of a 96-well round bottom micro titer plate (Corning Inc., Corning, NY) containing trypticase soy broth (TSB) (Becton Dickinson, Sparks, MD), each of which was incubated at 37oC for 24 h. Enterococci were cultured on mEI agar (58, 59). Plates were incubated at 41C for 24 h, and colonies with a blue halo were enumerated as enterococci. These colonies were 126

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then transferred into individual wells of a 96-well round bottom micro titer plate containing enterococcosel broth (Becton Dickinson, Sparks, MD) and incubated for 24 h at 37oC. Only cultures that cleaved esculin were processed further. Isolates in the microtiter plates were stored in glycerol (1:1 volume/volume) at -80oC for future use. Restriction fragment length polymorphism (RFLP) of total coliform isolates. Total coliform colonies were inoculated into brain heart infusion (BHI) broth (DifcoTM, Becton Dickinson, Sparks, MD) and incubated overnight at 37oC. The number of isolates processed varied depending on bacterial concentrations in the sample and on the revival rate following cryogenic preservation. Approximately one third of the isolates processed from each sampling event were obtained from each of three replicate samples. E. coli (American Type Culture Collection [ATCC] 9637) was used as a control for RFLP reproducibility. Genomic DNA was isolated from the culture broth using a Qiagen Dneasy Tissue kit (Qiagen, Valencia, CA) following the manufacturers instructions. PCR was performed on the extracted DNA using primers corresponding to E. coli 16S rRNA positions 8-27 and 1492-1510 to amplify the 16S rRNA gene, (forward: 5 AGAGTTTGATCMTGGCTCAG 3 and reverse: 5 GGTTACCTTGTTACGACTT 3) (60). Each 50 l reaction mix consisted of sterile distilled water (adjusted to volume), 2 mM MgCl 2 (4 l of 25mM), buffer B (5 l of 10X concentrate) (Fisher Scientific, Pittsburgh, PA), 160 M dNTP (0.8 l of 10mM) (Promega, Madison, WI), 0.5 M each of the two primers (2.5 l of 10 M) (IDT, Inc., Coralville, IA), 1.25 U Taq polymerase (0.25 l of 5U/l) (Fisher Scientific, Pittsburgh, PA) and the template DNA. The mixture was subjected to the following thermal profile using a thermocycler (T personal, 127

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Biometra, Goettingen): 2 min at 94C (initial denaturation), followed by 35 cycles of 94C for 1 min (denaturation), 50oC for 1 min (primer annealing), 72C for 2 min, and a final extension step at 72C for 7 min. The PCR product was then digested separately with four restriction enzymes: ScaI, BpmI, SexAI, and XcmI (New England Biolabs, Beverly, MA) according to the manufacturers instructions. The digestion reaction consisted of 9 l of water, 2 l of buffer (enzyme dependent), 2 l of bovine serum albumin, 8 l of PCR product (variable concentrations), and 0.5 l of restriction enzyme. The digestion mix was incubated at 37oC for approximately 2.5 h. The digested products were separated by agarose gel electrophoresis (1.0%) in TAE buffer (0.04 M Tris base, 0.02 M glacial acetic acid, 1mM EDTA, pH 8.0) for 3 h at 40 V, stained with ethidium bromide and photographed under UV light. Lambda DNA digested with EcoRI and HindIII enzymes was used as the standard ladder for size reference (band sizes ranging from 21,226 bp to 831 bp) (Promega, Madison, WI). The gels were scanned using fotodyne imaging system (Fotodyne Inc.,) and saved as .tif files. The resulting RFLP profiles were statistically analyzed using BioNumerics Software version 2.5 (Applied Maths, Sint-Martens-Latem, Belgium). Cluster analysis was performed based on maximum similarity using Dice coefficient. Dice binary coefficient was used to measure the similarity based upon common and different bands. The dendrograms were constructed using the UPGMA (unweighted pair group method using arithmetic averaged) algorithm. The software optimization was set at 0.2 % and the position tolerance was set at 0.7 %. Replicate runs (n = 20) of the control E. coli strain were found to be 90% similar, therefore RFLP 128

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patterns were considered the same if they exhibited 90% similarity or greater. The RFLP patterns were analyzed to calculate the diversity indices of total coliforms in water and sediment samples (see below). Genomic ribotyping of E.coli. MUG-positive E. coli isolates from the cryopreserved EC-MUG plate (as previously described) were inoculated in 2 mL of BHI broth and incubated overnight at 37oC, with shaking. The number of isolates processed varied depending on bacterial concentrations in the sample and on the survival rate during cryogenic preservation. Approximately one third of the isolates obtained for each sampling event were taken from each of three replicate samples. E. coli ATCC 9637 was used as a positive control. Ribotyping was performed as described by Parveen et al. (45) with modifications to the probe synthesis protocol (3). The developed membranes were digitally captured as above and imported to the BioNumerics program for analysis. The membrane was dried and laminated for storage. Replicate runs (n = 20) of the control were 90% similar, therefore ribotyping patterns were considered the same, if they were at least this similar. These patterns were used to determine the diversity indices of E. coli in a particular water or sediment sample using various index measurements. Denaturing gradient gel electrophoresis (DGGE). DGGE was used to determine bacterial community structure in sediments and water samples at each site. Water samples were filtered through a 0.45-m-membrane filter (25 mm diameter) for the isolation of genomic DNA from the microbial community. Volumes filtered depended on the sample site: Myakka River, 150 ml; Hillsborough River sites I and II, 100 ml; 129

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wastewater, 10 ml. Genomic DNA was extracted from sediment samples (500 mg wet weight) with an Ultra Clean Soil DNA kit (Mo Bio, Carlsbad, CA) following manufacturers instructions. In case of water samples, the membrane filters were used in place of sediments for DNA extraction using the kit. PCR-DGGE was performed as described by Muyzer et al. (40). The only deviation was in the gradient of denaturant used. Bacterial community DNA extracted as described above was subjected to PCR amplification using a GC-clamped primer set corresponding to the positions 1055-1070 and 1406 1392 to amplify a fragment the 16S rDNA gene, (forward: 5 ATGGCTG TCGTCAGC T 3and reverse: 5 CGCCCGCCGCGCCCCGCGCCCGGCC CGCCGCCCCCGCCCCACGGG CGGTGTGTAC 3), which produced a 322 bp product (12, 40). DGGE of the amplified 16S rDNA was performed (BioRad DCodeTM Universal Mutation Detection System, Hercules, CA). Linear gradient denaturant gels (45 % to 60 %) were formed with urea and formamide according to manufacturers instructions. A 100% denaturing acrylamide solution contains 7 M urea and 40% formamide (40). Therefore, a 45% denaturant solution corresponds to 3.15 M urea and 18% formamide. Similarly, a 65% denaturant solution corresponds to 4.55 M urea and 26% formamide. The gels were stained with 20 l of 10,000X SYBR Green I (Molecular Bio-Probes, Eugene, OR) dissolved in 200 mL of TAE buffer (Molecular Bio-Probes, Eugene, OR) for 20 min at room temperature in the dark with shaking. E. coli ATCC 9637 and Clostridium perfringens D5139 (Sigma, St. Louis, MO) were used as standards to determine the relative electrophoretic mobility of DNA fragments. Replicate runs (n = 12) of the standards were 92% similar, therefore the DGGE patterns were considered 130

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identical if they were at least this similar. The community patterns were analyzed using BioNumerics software as described earlier. Statistical analysis. Statistical analysis was performed using GraphPad InStat version 3.00 (GraphPad Software, San Diego California) and SAS software version 8.2 (SAS Inst., Cary, NC). Differences in the concentrations of total coliforms, fecal coliforms, and enterococci between sampling sites were examined using analysis of variance (ANOVA) and Tukeys post-hoc tests on log 10 -transformed data. Differences between samples collected on different days within each site were examined using non-parametric methods, as the data were not normally distributed. Pearsons product moment correlation coefficient for the concentration of total and fecal coliforms was performed across all four sites to determine if there was any significant correlation between the indicator bacteria. Various diversity indices were used to determine the diversity of bacteria in different water and sediment samples: Shannon Wieners index, Simpsons dominance index, Pielous evenness, and richness estimator. Richness estimators represent all phylotypes within a population, including the rarest phylotypes, and were used to obtain information about the community structure of bacteria. Analysis of variance for the diversity indices (i.e., Shannons, Simpsons and Pielous) was computed from the water and sediment samples using non-parametric method (Krukal-Wallis ANOVA). Accumulation curves were constructed to compare total coliform and E. coli population structure by estimating the number of new patterns observed as a function of sampling effort for each site. Species accumulation curves were produced in Excel using average diversity values obtained with EcoSim software (19). Each data point used to 131

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generate the species accumulation curve represents an average of three phylotype richness values corresponding to the three sampling events at each site. Population similarity dendrograms. To determine the similarities among the patterns of the bacterial populations from the four sampling sites, pair-wise comparisons of the patterns (RFLP, ribotyping, and DGGE) between the sites and the sampling events were performed using BioNumerics software. The number of matching patterns was determined for each group-wise comparison, e.g. all RFLP patterns from wastewater compared to all RFLP patterns from Hillsborough River site II water column. A matrix with similarity values was computed using Dice coefficient. All dendrograms were constructed with the unweighted pair group method using arithmetic averages (UPGMA) algorithm. RESULTS Concentrations of each indicator bacteria group (total coliforms, fecal coliforms, enterococci) were not significantly different between sites in the water column (Figure 6A) although the trend was for lowest indicator bacteria concentrations at Myakka River (the pristine site). The concentrations were significantly different among the sites for the three bacterial groups in the sediments (Figure 6B) of the river samples (total coliforms P <0.0001, fecal coliforms P = 0.0009, enterococci P = 0.0123). As expected, all indicator bacteria concentrations in wastewater samples from the secondary clarifier were significantly higher than those in river samples for both water column and sediments (P 132

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<0.001). The secondary clarifier, rather than primary influent was sampled due (1) to availability of access and (2) because overgrowth of coliform plates by nontarget organisms was less problematic. A previous study showed similar diversity from both raw and treated (activated sludge treatment followed by chemical precipitation or chemical flocculation) sewage samples (61). In addition, ribotype fingerprints of E. coli from raw sewage samples analyzed in our laboratory showed similar diversities when compared with the samples from the secondary clarifier (data not shown). Total coliform concentrations were significantly correlated with fecal coliform concentrations when data from the four sites was pooled (r = 0.8520, P<0.0001 for water; r = 0.8812, P<0.0001 for sediment). In addition, total coliform and fecal coliform concentrations were significantly correlated with Enterococcus concentrations for the pooled data (P<0.001 for all comparisons). Pearsons product moment correlation for total coliforms vs. Enterococcus yielded the following values: r = 0.9170 for water; r = 0.8991 for sediment. The analysis for fecal coliforms vs Enterococcus yielded r = 0.9365 for water; r = 0.9198 for sediment). 133

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Figure 6. Log 10 -transformed concentrations of total coliforms, fecal coliforms and enterococci in the A) water (CFU100 mL-1) and B) sediment (CFU g-1) samples at each site (n = 3). 6A) 0.001.002.003.004.005.006.007.00MyakkaHBR IHBR IIWastewaterSite total coliform fecal coliform enterococciMean log10 value (CFU/100ml) 0.001.002.003.004.005.006.007.00MyakkaHBR IHBR IIWastewaterSite total coliform fecal coliform enterococci 0.001.002.003.004.005.006.007.00MyakkaHBR IHBR IIWastewaterSite total coliform fecal coliform enterococciMean log10 value (CFU/100ml) 134

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6B) 0123456789MyakkaHBR IHBR IIWasteSiteMean log10 values (CFU/100g) total coliform fecal coliform enterococci 0123456789MyakkaHBR IHBR IIWasteSiteMean log10 values (CFU/100g) total coliform fecal coliform enterococci total coliform fecal coliform enterococci 135

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RFLP of total coliform population. 154 total coliform isolates from Myakka River, 105 isolates from Hillsborough site I, 142 isolates from Hillsborough Site II, and 132 isolates from wastewater were typed by RFLP from water column (or wastewater) samples. A representative set of RFLP patterns using the four restriction enzymes (Figure 7) and an example of a dendrogram of the total coliform isolates (Figure 8) are shown. Shannons diversity index for the total coliform RFLP data set ranged from 2.08 for Hillsborough site I to 3.30 for Hillsborough site II, while the Simpsons dominance values ranged from 0.04 for Hillsborough site II to 0.23 for wastewater. Pielous evenness values averaged around 0.88, with little variability among the sites. Sixty-five isolates from Myakka River sediment samples, 54 isolates from Hillsborough site I, 132 isolates from Hillsborough site II and 139 isolates from waste activated sludge were processed from sediments. Shannons index was lowest at Hillsborough site II (1.82) and highest in waste activated sludge (2.67), and. Simpsons dominance index ranged from 0.10 for wastewater to 0.35 for Hillsborough site II. Pielous evenness for all the sites averaged around 0.88. Mean diversity indices were not significantly different among sites for water or sediment samples, which may be due in part to the small sample size (n = 3 sample events). Interestingly, total coliform diversity was relatively high in the Myakka River samples (Shannons index of 3.05 for water and 2.12 for sediment samples), even though it is a relatively unimpacted site. The mean diversity indices compared between water and sediment samples showed no significant difference for the three river samples. 136

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Figure 7. An example of RFLP patterns for total coliforms. Each number corresponds to a particular isolate, whose amplified 16S rDNA was digested using four different restriction enzymes (BpmI, ScaI, SexAI, and XcmI). ScaI 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Bpm I 1 2 3 4 5 6 7 8 9 10 1 1 12 13 14 Sex AI 1 2 3 4 5 6 7 8 9 10 1 1 12 13 14 X c m I 1 2 3 4 5 6 7 8 9 10 11 12 13 14 137

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Figure 8. An example of a dendrogram of the RFLP patterns of total coliform isolates collected from Myakka River. The dendrogram is produced by cluster analysis using Dice Coefficient to determine maximum similarity. 138

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Ribotyping patterns. Diversity indices were also calculated based on phylotypes determined by E. coli ribotyping. One hundred sixty-seven isolates from the Myakka River, 102 isolates from Hillsborough site I, 146 isolates from Hillsborough site II, and 173 isolates from wastewater were processed from the water column samples. Shannons index was lowest for Hillsborough site I (2.36), and highest for wastewater (3.13). Hillsborough site I had the highest dominance index of 0.15 compared to wastewater, which had the lowest value of 0.06. The evenness value averaged around 0.89, and was similar for all sites. Seventy-nine isolates from Myakka sediment samples, 53 isolates from Hillsborough site I, 95 isolates from Hillsborough site II and 134 E. coli isolates from the activated sludge samples were processed using ribotyping. No fecal coliforms were isolated from Hillsborough site I sediment samples during the second sampling event, therefore the total number of isolates and patterns analyzed from this site are from two sampling events. Hillsborough site II had the lowest Shannons index (2.19) and highest dominance index (0.17), while waste activated sludge samples had the highest Shannons index (3.07) and lowest dominance index (0.06). The E .coli samples in the sediments were more evenly distributed than water column samples (Pielous evenness e 0.93). As expected, the wastewater and activated sludge samples showed the highest diversity among the E. coli populations; however, no significant differences in diversity indices were found, either among sites in water column and sediment samples, or between water column vs. sediment samples at all the four sites. 139

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RFLP accumulation curves. RFLP accumulation curves showed that the dominant total coliform populations in Hillsborough site I water samples tended to be represented by fewer unique phylotypes than the total coliform populations at other sites (Figure 9A). Hillsborough site II water samples had the highest phylotype richness. Thus, the accumulation curves reflect the differences in diversity indices noted above. The slope of the accumulation curves did not reach 0 for any of the sites; however, it began to level off in the case of Hillsborough site I. The accumulation curves indicate that the number of isolates needed to adequately represent the dominant total coliform populations at most of the sites is > ~ 21 25 isolates. Total coliform phylotype richness in waste activated sludge samples was greater than that in any of the sediment sample from rivers (Figure 9B), and the sampling effort of 25 isolates was not enough to capture the population diversity. Phylotype richness was lower at other sites, but the accumulation curve for the site with lowest diversity (Hillsborough II) did not reach saturation at 27 isolates. Disparities in the number of isolates obtained at each site during each sampling event are indicated by the difference in sampling effort. 140

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Figure 9. RFLP accumulation curves for total coliform isolates in A) water and B) sediment samples. Each data point used to generate the curves represents an average of three phylotype richness values at each site. 9A) 05101520253035135791113151719212325Sampling effort# of new subtypes Myakka Hillsborough site I Hillsborough site II Wastewater 141

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9B) 051015202513579111315171921232527Sampling effort# of new subtypes Myakka Hillsborough site I Hillsborough site II Waste 142

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E. coli ribotype accumulation curves. Richness in E. coli populations was highest in wastewater and waste activated sludge samples (Figures 10A and 10B). The sampling effort for Myakka and Hillsborough site I sediment samples (21 and 23 isolates respectively) appeared to be adequate, as the slopes of the curves approached 0 (Figure 10B). This is in contrast to results for water samples (Figure 10A); slopes did not approach 0 with a sampling effort of 29-30 isolates. Figure 10. Ribotyping accumulation curves for E. coli isolates in A) water and B) sediment samples. Each data point used to generate the curves represents an average of three phylotype richness values at each site 10A) 051015202530351357911131517192123252729Sampling effort# of new subtypes Myakka Hillsborough site I Hillsborough site II Wastewater 143

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10B) 051015202530135791113151719212325272931Sampling effort# of new subtypes Myakka Hillsborough site I Hillsborough site II Waste Each data point used to generate the curve represents an average of three richness values at each site (except Hillsborough site I where the data points are an average of two richness values). 144

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Bacterial community structure by DGGE. Bacterial community richness was assessed by DGGE analysis. The richness values for water samples indicate that the wastewater had the highest diversity followed by Hillsborough site II, Hillsborough site I, and Myakka water samples (Table 10). Among the water samples, Myakka and Hillsborough site I were significantly different from wastewater (Table 10). Among sediment samples, the highest diversity was observed in waste activated sludge and lowest in the Myakka samples. Myakka and wastewater sludge/sediment samples were significantly different (Table 10). At all sites except Myakka, richness was significantly higher in sediments/sludge than in the water column. Table 10. Richness estimators calculated using the number of unique DGGE patterns of bacterial population in water and sediment samples. Values that share superscripts are not significantly different. Site Average Richness in water column1 Average Richness in sediments1 Myakka 12.1b 15.7 b,d Hillsborough Site I 13.7b 23d,e Hillsborough Site II 15.8b,c 24.7d,e Sewage 27.1c 33e 1 Each richness value represents an average of measurements obtained from at least two samples in each sampling event. 145

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Population similarity dendrograms. Similarities among the bacterial populations from the four sampling sites were determined using pair-wise comparisons of the genotypic or community structure patterns (Figures 11A 11C). Total coliform populations (RFLP) were most similar in wastewater and waste activated sludge samples (Figure 11A), while E. coli populations (ribotypes) were most similar in the wastewater and Hillsborough site II water samples (Figure 11B). Bacterial community structure (DGGE) was most similar in the sediments of Hillsborough sites I and II, and these two populations were more similar than any other comparison (85%) (Figure 11C). Myakka River sediments also grouped closely with the other river sediment populations. In contrast, the total coliform and E. coli populations in sediments of the relatively unimpacted Myakka River were outgroups compared to populations at the other sites. When replicate samples (water and sediment) obtained on the same day from each site were compared, it was found that both total coliform RFLP and E. coli ribotype patterns showed low similarity (averaged around 30%), although replicate DGGE patterns for the bacterial communities were nearly identical (data not shown). Both patchy distribution and undersampling probably contributed to the lack of similarity in replicate samples of total coliform and E. coli populations. 146

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Figure 11. Dendrogram of group similarities across the four sampling sites calculated based on cluster analysis using UPGMA for A) total coliform RFLP, B) E. coli ribotyping, and C) DGGE of bacterial populations. 11A) 20 ..........Wastewater ..Waste activated slud .............. .............. g e Hillsborou g h II Sediment Hillsborou g h I wate r Myakka water Hillsborou g h I Sediment Hillsborough II Water M y akka Sediment 100 80 60 40 147

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11B) 80 100 20 40 60 Hillsborou g h I Sediment Hillsborough II Sediment ............ Myakka Water Hillsborough I water ....... Wastewater Hillsborough II Water Waste activated slud g e 11C) ............ Myakka Sediment 100 80 60 40 20 0 Hillsborough I Sediment Hillsborou g h II Sediment Myakka Sediment .. Waste activated sludge Myakka Water .. Hillsborough I Water .. Hillsborough II Water .. Wastewater 148

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DISCUSSION This study assessed the population diversity of culturable fecal indicator bacteria and overall bacterial community structure by DGGE in aquatic habitats and sewage in Florida. Microbial diversity was compared at three sites that differed with respect to human population density in the surrounding watershed, and in one wastewater treatment plant. Measurements of indicator bacteria concentrations were conducted to confirm that the human population density did, in fact, impact the water quality in terms of indicator bacteria loads. Surprisingly, E. coli, total coliform and Enterococcus concentrations in the water column were not significantly different among the three river sites. The geomeans of fecal coliform concentrations for the river sites were well within the Florida standard for recreational waters (200 CFU/100mL) (http://www.dep.state.fl.us/legal/rules/shared/62-302t.pdf), while the Enterococcus concentrations exceeded the EPA and Florida standards (35 CFU/100mL) at each of the three sites (57). The presence of total coliforms, fecal coliforms and enterococci in the pristine waters at levels similar to those observed in water bodies in urban watersheds calls into question the reliability of these organisms as indicators for fecal pollution, particularly in subtropical/tropical waters. Other studies have identified similar problems in using these organisms as indicators of water quality and hence the validity of using these organisms as indicators should be reevaluated, as previously suggested (20, 22, 33, 47). Significant differences were observed in the sediment samples among the river sites for the three indicator bacteria groups. The pristine Myakka River site had lower 149

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concentrations of indicator bacteria compared to the impacted river sites. Furthermore, indicator bacterial levels in the sediments were consistently higher than those in the water column at all sites, which supports the previously published contention that sediments can act as a reservoir of indicator bacteria in aquatic ecosystems (16, 42, 44). This phenomenon may well be due to increased persistence of these organisms when they are protected from environmental stressors (13, 32, 50). Hence, monitoring of indicator microorganisms in the sediments might provide a better long-term assessment of water quality than water column monitoring. Effect of ecological disturbance on bacterial diversity. The major hypothesis explored in this study was that increasing anthropogenic impact (in terms of human population density) would result in increased indicator bacteria diversity and decreased diversity of the total bacterial community. The genetic diversity of total coliforms and E. coli populations did not show any specific trend among the three river sites, although E. coli diversity was greatest in the sewage samples. The high diversity observed in the E. coli populations in this study was comparable to data from other studies on human sewage (3, 54), bovine feedlot sewage (27) and water samples (7, 34) The high E. coli diversity observed in this study is all the more notable because ribotyping using one enzyme is a relatively conservative method for estimating genetic diversity, particularly when compared to methods such as rep-PCR or PFGE (41). The high genetic diversity of E. coli in all of these environments implies that the sampling effort required for population studies of these organisms is considerable, i.e., greater than 20 isolates per sample for all sites in this study. 150

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The bacterial community diversity measured by DGGE was higher in both the water column and sediments in the impacted river sites (Hillsborough River site I and Hillsborough River site II) compared to the less impacted site (Myakka River), which also argues against the original hypothesis. One possible explanation for this observation is that new phylotypes are periodically added to the existing community as a result of storm water runoff and other inputs in anthropogenically impacted waters. In addition, contamination may supplement the water bodies with nutrients that support the growth of organisms, allowing survival and/or proliferation of phylotypes that would otherwise be so rare as to be undetectable. Previous studies have found varying impacts of contamination on bacterial community structure. Higher bacterial diversity was found in a coastal lagoon contaminated with sewage when compared to a relatively less impacted lagoon by sequence analysis of 16S rRNA genes (5). A similar trend was observed in groundwater samples contaminated with livestock wastewater using RFLP patterns of cloned 16S rRNA gene sequences (10). In contrast, when anthropogenic pollution introduces toxic substances into the ecosystem, diversity tends to decrease. The introduction of heavy metals via application of sewage sludge decreased bacterial diversity in amended soils compared to control soil (38), and diversity in soils managed for agriculture was lower than that of pristine soils (56). A reduction in the microbial diversity was also observed in an aquifer where phenol, toluene and chlorinated aliphatic hydrocarbons were added as substrates to stimulate trichloroethene bioremediation (17). Amendment of soils with benzene decreased bacterial diversity; however, community structure in soils with higher 151

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initial diversity were less affected than soils with comparatively lower initial diversity (18). Population similarity in water column vs. sediments. Population similarity dendrograms of phylotypes (RFLP, ribotype, or DGGE) revealed that population structures in the water column were quite dissimilar from those in associated sediments at all three river sites. E. coli (ribotypes) and bacterial community (DGGE) populations were also dissimilar in wastewater vs. associated sludge, although total coliforms populations were relatively similar (~50%). The E. coli population structure in Myakka River sediments was notably different than that of other river sites/habitats. The dissimilarity in overall bacterial community structure (DGGE) between water and sediments is to be expected, as many prokaryotic ecotypes are better adapted for survival and growth in one habitat or the other. This argument could also be extended to the total coliform group, which includes several bacterial genera and many species. The dissimilarity in E. coli phylotypes in the two habitats suggests that some E. coli subtypes persist and/or grow better in the sediments than others. A previous study found that certain E. coli subytpes consistently dominated the population after incubation under ambient environmental conditions, which was termed differential survival (3). At least one other study demonstrated the growth of a particular E. coli strain under environmental conditions (46). Previous studies have also investigated the possibility that resuspension of microorganisms from sediments increases indicator bacteria concentrations (9, 29, 44), resulting in false-positive indications of poor water quality. Furthermore, given that 152

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indicator bacteria populations in the water column and sediment are different, resuspension would influence the distribution of specific indicator bacteria subtypes (e.g., E. coli ribotypes) in the water column. Changes in the sub-population distribution of indicator bacteria such as E. coli and enterococci that may occur with changes in rainfall and/or flow rates have implications toward microbial source tracking studies, thus, sampling both water column and sediments is necessary to understand the ecology of indicator bacteria, both from a basic science standpoint and so that the accuracy of MST studies can be can be improved. Acknowledgments This study was funded by STAR grant no. R828829 from the United States Environmental Protection Agency to V.J.H. This study was also funded by the Water Environment Research Foundation, project 00-PUM-2T. 153

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159 53. Stewart, J. R., R. D. Ellender, J. A. Gooch, S. Jiang, S. P. Myoda, and S. B. Weisberg. 2003. Recommendations for microbial source tracking: lessons from a methods comparison study. J Water Health 1: 225-31. 54. Stoeckel, D. M., C.M. Kephart, V.J. Harwood, M.A. Anderson and M. Dontchev. 2004. Diversity of fecal indicator bacteria subtypes: implications for construction of microbial source tracki ng libraries. 104th Gen. Meet. Am. Soc. Microbiol., Q-245. 55. Stoeckel, D. M., M.V. Mathes, K.E. Hyer, C. Hagedorn, H. Kator, J. Lukasik, T. L. O'Brien, T.W. Fenger, M. Sama dpour, K.M. Strickler, B. A. Wiggins. 2004. Comparison of seven protocols to identify fecal contamination sources using Escherichia coli Environ Sci Technol 38: 6109-6117. 56. Torsvik, V., F. L. Daae, R. A. Sandaa, and L. Ovreas. 1998. Novel techniques for analysing microbial diversity in natural and perturbed environments. J Biotechnol 64: 53-62. 57. U.S. Environmental Protection Agency. 1986. Ambient water quality criteria for bacteria-1986 EPA-440/5-84/002. U.S. Environmental Protection Agency. 58. U.S. Environmental Protection Agency. 2000. Improved enumeration methods for the recreational water quality indicators: enterococci and Escherichia coli EPA-821/R-97/004. U.S. Environmental Protection Agency. 59. U.S. Environmental Protection Agency. 1997. Method 1600: membrane filter test methods for enterococci in wate r. EPA-821/R-97/004. U.S. Environmental Protection Agency. 60. Urakawa, H., K. Kita-Tsukamoto, and K. Ohwada. 1999. 16S rDNA restriction fragment length polymorphism an alysis of psychrotrophic vibrios from Japanese coastal water. Can J Microbiol 45: 1001-1007. 61. Vilanova, X., A. Manero, M. Cerda-Cuellar, and A. R. Blanch. 2004. The composition and persistence of faecal co liforms and enterococcal populations in sewage treatment plants. J Appl Microbiol 96: 279-88. 62. Whitlock, J. E., D.T. Jones, and V.J. Harwood. 2002. Identification of the sources of fecal coliforms in an urba n watershed using antibiotic resitance analysis. Water Res 36: 4273-4282. 63. Wiggins, B. A. 1996. Discriminant analysis of antibiotic resist ance patterns in fecal streptococci, a method to differentia te human and animal sources of fecal pollution in natural waters Appl Environ Microbiol 62: 3997-4002.

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160 64. Wiggins, B. A., R. W. Andrews, R. A. Co nway, C. L. Corr, E. J. Dobratz, D. P. Dougherty, J. R. Eppard, S. R. Knupp, M. C. Limjoco, J. M. Mettenburg, J. M. Rinehardt, J. Sonsino, R. L. Torrijos, and M. E. Zimmerman. 1999. Use of antibiotic resistance analysis to identify nonpoint sources of fecal pollution. Appl Environ Microbiol 65: 3483-6.

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161 RESEARCH SIGNIFICANCE The supply and demand of water is controlled by factors such as climatic changes, population growth and human activities. To c ope with the increased demand for water and acute shortages, water reuse is one re latively economical way of increasing water supply for potable and non-potable purposes. Reclaimed wastewater is a good example of reused water, which not only helps free up fr esh water for potable purposes but also reduces discharge of polluted water into rece iving waters. Since the source of reclaimed water is sewage, effective treatment pro cesses are required for safe public use. Wastewater reclaimed facilities rely on indicator organisms such as total coliforms and fecal coliforms (especially Escherichia coli ), to assess the quality of water. Care must be taken while drawing conclusions about the water quality using indicator organisms as these organisms are not reliable predictors of the presence or fate of all pathogens. Regulatory agencies should consider alternat ives such as use of a suite of indicator organisms or chemicals found in human wast ewater to assess the quality of treated wastewater effluents. Escherichia coli is also an important organi sm in microbial source tracking studies, whose goal is to identifiy the source of fecal contamination in water bodies, which in turn can aid in water quality restor ation and prevention of further contamination. However, studies have shown that source tr acking methods do not accurately identify the contributing sources of fecal contamination in all ecological settings. Most source tracking studies have isolated indicator organisms from th e water column, while ignoring the sediment populations. A greater know ledge about dynamics of the indicator

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162 organisms in both water column and sedime nts can help us understand the population biology of these organisms and explore their implications for mon itoring and regulatory purposes.

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Appendices

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Appendix A: Wastewater Reclamation Facilities Study Table 1. Mean log 10 reduction in the concentrations of each of the microorganism across the filtration effluent and the disinfection effluent for six wastewater treatment plants Analyte Mean Log10 reduction (95% CI) Standard error P-value Total coliform 2.32 (1.73-2.91) 0.29 <0.0001 Fecal coliform 2.16 (1.56-2.77) 0.3 <0.0001 Enterococci 2.12 (1.53-2.70) 0.28 <0.0001 C.perfringens 1.3 (0.96-1.64) 0.16 <0.0001 Coliphage on E. coli 15597 1.03 (0.68-1.39) 0.17 <0.0001 Coliphage on E. coli 700891 0.73 (0.47-0.99) 0.13 <0.0001 Enteric viruses 0.65 (0.41-0.90) 0.12 <0.0001 Giardia 0.1 (-0.10-0.30) 0.1 0.33 Cryptosporidium 0.18 (0.01-0.35) 0.08 0.03 Infectious Cryptosporidium -0.0005 (-0.19-0.19) 0.09 0.99 164

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Table 2. Differences in the mean log 10 concentrations of microorganisms in disinfected effluents between plants utilizing chlorine-based and ultraviolet radiations (nondetects replaced with detection limits) Analyte Mean log 10 concentration in plant using chlorine-based, n=24 (SD) Mean log 10 concentration in plant using UV, n=4* (SD) P value % of non-detects in the disinfected effluent samples Total coliform 0.25 (0.98) 0.59 (0.88) 0.39 32.14 Fecal coliform 0.32 (0.93) 0.29 (0.24) 0.0503 71.43 Enterococci 0.04 (1.15) 0.15 (0.98) 0.82 71.43 C. perfingens 0.23 (0.78) 0.7 (0) NA 40.74 Coliphage on E. coli 15597 1.12 (0.59) 1 (0) NA 57.14 Coliphage on E. coli 700891 1.32 (0.81) 1 (0) NA 46.43 Enteric viruses 0.30 (0.39) 0.29 (0.23) 0.36 71.43 Giardia 1.3 (0.77) 0.89 (0.53) 0.36 17.86 Cryptosporidium 0.99 (0.65 0.57 (0.25) 0.26 32.14 Infectious Cryptosporidium 0.55 (0.43) 0.2 (0.15) 0.0599 73.91 165

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Table 3. Differences in the mean log 10 concentrations of microorganisms in disinfected effluents between plants utilizing chlorine-based and ultraviolet radiations (nondetects replaced with zeros) Analyte Mean concentration in plant using chlorine-based, n=24 (SD) Mean concentration in plant using UV, n=4* (SD) P value % of non-detects in the disinfected effluent samples Total coliform 0.66 (0.96) 0.99 (0.46) 0.36 32.14 Fecal coliform 0.91 (1.59) 0.22 (0.24) 0.57 71.43 Enterococci 1.6 (0.78) 1.32 (0) NA 71.43 C. perfingens 0.65 (0.59) 0 NA 40.74 Coliphage on E. coli 15597 1.43 (0.62) 0 NA 57.14 Coliphage on E. coli 700891 1.66 (0.80) 0 NA 46.43 Enteric viruses 0.24 (0.58) 0 NA 71.43 Giardia 1.49 (0.70) 1.11 (0.37) 0.46 17.86 Cryptosporidium 1.12 (0.67) 0 NA 32.14 Infectious Cryptosporidium 0.79 (0.76) 0 NA 73.91 166

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Table 4. Univariate regression analysis between the CT and the mean log 10 reduction in the concentration of microorganisms in the disinfected effluent for chlorine based disinfection plants (# of plants = 5) Analyte (n = 22)* Pearsons Correlation Coefficient ( r ) Parameter estimate () P-values Total coliform 0.58 0.0014 0.0049 Fecal coliform 0.27 0.0006 0.23 Enterococci 0.26 0.0005 0.25 C. perfringens 0.63 0.0008 0.0016 Coliphage on E. coli 15597 0.57 0.0008 0.0054 Coliphage on E. coli 700891 0.36 0.0003 0.09 Enteric viruses 0.68 0.0006 0.0005 Giardia -0.46 -0.0003 0.03 Cryptosporidium -0.19 -0.0001 0.4 Infectious Cryptosporidium 0.08 0.0001 0.74 n = 18 in case of infectious Cryptosporidium 167

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Table 5. Pearsons product moment correlation coefficients computed between the log10 transformed concentrations of indicator organisms and the pathogens in the disinfected effluent in six wastewater treatment plants Giardia Crypto sporidium Analyte (# of samples) Enteric viruses (P-value) (P-value) (P-value) Infectious Crypto sporidium* (P-value) Total coliform (28) 0.46 (0.015) 0.14 (0.47) 0.012 (0.95) 0.14 (0.53) Fecal coliform (28) 0.18 (0.35) 0.076 (0.70) 0.05 (0.79) 0.07 (0.76) Enterococci (28) 0.20 (0.32) 0.14 (0.46) 0.04 (0.82) 0.04 (0.84) C. perfringens (27) 0.33 (0.09) 0.13 (0.51) 0.14 (0.47) 0.15 (0.50) Coliphage on E. coli 15597 (28) 0.31 (0.11) 0.41 (0.03) 0.01 (0.95) 0.19 (0.38) Coliphage on E. coli 700891 (28) 0.08 (0.68) 0.36 (0.06) 0.008 (0.97) 0.20 (0.35) In case of infectious Cryptosporidium n = 23 168

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Table 6. Univariate regression analysis between mean log 10 of enteric viruses and the indicator microorganisms between filtered effluent and disinfected effluent samples for six wastewater treatment plants Analyte (n = 28)* Regression Coefficient () Standard error P value Total coliform 0.19 0.07 0.015 Fecal coliform 0.07 0.08 0.35 Enterococci 0.08 0.08 0.32 C. perfringens 0.25 0.14 0.09 Coliphage on E. coli 15597 0.22 0.13 0.11 coliphage on E. coli 700891 0.08 0.19 0.68 In case of C. perfringens n = 27 169

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Table 7. Univariate regression analysis between mean log 10 of Giardia and the indicator microorganisms between filtered effluent and disinfected effluent samples for six wastewater treatment plants Analyte (n = 28)* Regression Coefficient () Standard error P value Total coliform -0.05 0.07 0.47 Fecal coliform 0.03 0.06 0.7 Enterococci -0.05 0.07 0.46 C. perfringens -0.08 0.12 0.51 Coliphage on E. coli 15597 -0.23 0.1 0.03 Coliphage on E. coli 700891 -0.28 0.14 0.058 In case of C. perfringens n = 27 170

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Table 8. Univariate regression analysis between mean log 10 of Cryptosporidium and the indicator microorganisms between filtered effluent and disinfected effluent samples for six wastewater treatment plants Analyte (n = 28)* Regression Coefficient () Standard error P value Total coliform -0.003 0.06 0.95 Fecal coliform 0.01 0.05 0.79 Enterococci 0.01 0.06 0.82 C. perfringens 0.08 0.1 0.47 Coliphage on E. coli 15597 0.006 0.09 0.95 Coliphage on E. coli 700891 -0.005 0.13 0.97 In case of C. perfringens n = 27 171

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Table 9. Univariate regression analysis between mean log 10 of infectious Cryptosporidium and the indicator microorganisms between filtered effluent and disinfected effluent samples for six wastewater treatment plants Analyte (n = 23) Regression Coefficient () Standard error P value Total coliform -0.05 0.07 0.53 Fecal coliform 0.02 0.06 0.76 Enterococci -0.01 0.07 0.84 C. perfringens -0.08 0.12 0.5 Coliphage on E. coli 15597 0.1 0.11 0.38 Coliphage on E. coli 700891 0.14 0.14 0.35 172

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Figure 1. Percent of disinfected effluent samples with detectable levels of total or fecal coliform as a function of detection limit (n=30), correlation coefficients, r2, are 0.96 for total coliform and 0.94 for fecal coliform. 02550751000.010.1110Detection limit, cfu/100 mLPercent of disinfected effluent samples above detection limitTotal coliformFecal coliform 173

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Figure 2. Detected coliphage concentrations (PFU100 mL-1) vs. detected enteric virus concentrations (MPN100 L-1) in the final effluent from six wastewater treatment plants (n=7). ( Coliphage with E. coli ATCC 15597 as host and Coliphage with E. coli ATCC 700891 as host). 0.11101101001,00010,000Detected coliphage concentration, pfu/100 mLDetected enteric virus concentration, MPN/100 L 15597 host 700891 host 174

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Table 10. Primer sets used to amplify the enteric virus RNA by RT-PCR Virus Primer set Reference Pan enterovirus RT primer 5-ATTGTCACCATAAGCAGCCA-3 PCR primer 5-CGGTACCTTTGTACGCCTGT-3 Nested Fwd 5-TCCGGCCCCTGAATGCGGCTA-3 Nested Rev 5-GAAACACGGACACCCAAAGTA-3 Chapron et al 2000 Rotavirus RT primer 5-GGCTTTAAAAGAGAGAATTTCCGTCTGG-3 PCR primer 5-GATCCTGTTGGCCATCC-3 Nested Fwd 5-GTATGGTATTGAATATACCAC-3 PCR primer 5-TCCATTGATCCTGTTATTGG-3 Le Guyader et al., (1994) Reovirus Fwd 5-CAGTCGACACATTTGTGGTC-3 Rev 5-GCGTACTGACGTGGATCATA-3 Spinner and Di Giovanni, (2001) 175

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Appendix B: Microbial Diversity Study Table 11. Diversity indices calculated using total coliform RFLP patterns from water and sediment isolates (HBR I Hillsborough site I, HBR II Hillsborough site II, WWwastewater for water column and waste activated sludge for sediment). Indices Site Isolates typed (n) Number unique patterns Shannon's (H)1 Simpson's (d)1 Pielou's (e)1 Water Column Myakka 154 87 3.05 0.07 0.93 HBR I 105 43 2.08 0.22 0.83 HBR II 142 97 3.3 0.04 0.94 WW 132 55 2.12 0.23 0.84 Sediment Myakka 65 38 2.12 0.17 0.91 HBR I 54 40 2.06 0.21 0.94 HBR II 132 49 1.82 0.35 0.74 WW 139 61 2.67 0.1 0.92 1All diversity index values represent a mean of three measurements obtained from three sampling events 176

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Table 12. Diversity indices based on E. coli ribotypes (HBR I Hillsborough site I, HBR II Hillsborough site II, WWwastewater for water column and waste activated sludge for sediment). Indices Site Isolates typed (n) Number of unique patterns Shannon's (H)1 Simpson's (d)1 Pielou's (e)1 Water Column Myakka 167 79 2.8 0.1 0.86 HBR I 102 50 2.36 0.15 0.86 HBR II 146 79 2.94 0.08 0.92 WW 173 93 3.13 0.06 0.91 Sediment Myakka 79 41 2.3 0.14 0.91 HBR I 53 31 2.57 0.09 0.94 HBR II 95 50 2.19 0.17 0.91 WW 134 87 3.07 0.06 0.95 1 All values represent a mean of measurements obtained from at least two sampling events 36 isolates from the MUG positive plates were analyzed using API 20 E system. 34 of these isolates were identified as E. coli and the remaining two as Aeromonas. 177

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About the Author Vasanta Chivukula received her Masters Degree in Coastal Aquaculture and Marine Biotechnology from Andhra University, India in 1999. She came to the U.S. in 2000 in pursuit of higher education. She entered the Ph.D. program in the Department of Biology at the University of South Florida in Fall 2000. While in the Ph.D. program, Ms. Chivukula became the president of the Students of India Association (2003-04), a cultural organization on campus. Under her leadership, the association received an award for the best organization from USF among all other student organizations. She also joined the Gurukulam of Tampa Bay, Sunday school at USF a voluntary organization and taught Biology to high school students. She was a member of the graduate council at USF (Fall 2003) and worked actively on the policy sub committee and fellowship and scholarship ad-hoc committee. She was very philanthropic and provided voluntary assistance in major investigations for Florida Law Enforcement agencies. She received the Best Graduate Oral Presentation Award in Environmental Microbiology during the Southeastern/South Carolina Branch of the American Society of Microbiology Branch meeting in Oct 2003 in Athens, Georgia. She also received the Summer Tharpe fellowship from USF in 2004. 178