WATER QUALITY ASSESSMENT OF THE SANTA CRUZ RIVER IN SOUTHERN ARIZONA By Todd McOmber _________________________________________ A Thesis Submitted to the Faculty of the DEPARTMENT OF SOIL, WATER, AND ENVIRONMENTAL SCIENCE In Partial Fulfillment of the Requirements For the Degree of MASTER OF SCIENCE In the Graduate College THE UNIVERSITY OF ARIZONA 2014 2 STATEMENT BY AUTHOR This thesis has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this thesis are allowable without special permission, provided that an accurate acknowledgement of the source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. SIGNED: Todd McOmber APPROVAL BY THESIS DIRECTOR This thesis has been approved on the date shown below: ______________________ _____________________ Channah Rock Date Professor of Soil, Water and Environmental Science 3 ACKNOWLEDGEMENTS This project was made possible due to the help of family, friends, and mentors throughout the course of my studies. First, I would like to thank my wife Kathryn for supporting me throughout my time in school. I would like to express gratitude to my graduate committee composed of Dr. Jean McLain, Dr. Channah Rock, and Dr. Joan Curry. Thank you to Dr. Channah Rock for affording me the opportunity to be a part of her team and helping me become a more refined scientist. I would also like to thank Dr. Jean McLain for always taking time to assist and support me whenever it was needed. Many thanks to Berenise Rivera for teaching me critical lab techniques to make my study possible. I am also grateful to my friends and colleagues for assisting me perform field work in the hot Arizona sun. Lastly, I would like to thank the Arizona Department of Environmental Quality for funding this project. 4 DEDICATION To my parents: LeGrand and Debbie McOmber 5 TABLE OF CONTENTS LIST OF TABLES…………………………………………………………………………………………………7 LIST OF FIGURES………………………………………………………………………………………………..8 ABSTRACT…………………………………………………………………………………………………………9 CHAPTER 1: LITERATURE REVIEW 1.1 Water Quality History………………………………………………………………………11 1.2 Water Quality Indicators…………………………………………………………………..13 1.2.1 Chemical and Physical Indicators……………………………………...13 1.2.2 Microbial Indicators…………………………………………………………18 1.3 Microbial Source Tracking………………………………………………………………..24 CHAPTER 2: RESEARCH METHODS 2.1 Sampling Locations…………………………………………………………………………31 2.2 Sample Collection……………………………………………………………………………35 2.3 Chemical and Physical Analyses ………………………………………………………37 2.4 Microbial Analyses…………………………………………………………………………..39 2.4.1 Indicator Organisms…………………………………………………………..39 2.4.2 DNA Extraction………………………………………………………………….40 2.4.3 Molecular Methods………………………………………………………….…41 CHAPTER 3: CURRENT STUDY 3.1 Introduction…………………………………………………………………………………...45 3.2 Materials and Methods…………………………………………………………………....47 3.2.1 Sampling Locations and Collection………………………………..……47 3.2.2 Chemical and Physical Analyses…………………………………………52 3.2.3 Microbiological Indicator Tests……………………………………….…53 3.2.4 DNA Extraction…………………………………………………………………54 3.2.5 Molecular Methods……………………………………………………………55 3.3 Results and Discussion………………………………………………………………..….58 3.3.1 Chemical and Physical Conditions…………………………………...…58 3.3.2 Microbial Indicators……………………………………………………….…64 3.3.3 Microbial Source Tracking……………………………………………...…67 3.3.4 Further Analyses and Discussion……………………………………….71 3.4 Conclusions……………………………………………………………………....…………...75 6 APPENDIX A………………………………………………………………………………………………………76 REFERENCES………………………………………………………………………………………………...…..82 7 LIST OF TABLES Table 1. Sampling Sites Overview……………………………………………………………….……….36 Table 2. List of Chemical and Physical Parameters Tested…….....……………….…………38 Table 3. Fecal Indicator Tests Performed……………………………………………………………40 Table 4. Overview of qPCR Markers and Sequences…………………………………….…..…..42 Table 5. Overview of qPCR Temperature Profiles………………………………………….……..44 Table 6. Sampling Sites Overview repeated…………………..……………………………….....…50 Table 7. List of Chemical and Physical Parameters Tested repeated……………….....…53 Table 8. Fecal Indicator Tests Performed repeated…………………………………………..…54 Table 9. Overview of qPCR Markers and Sequences repeated…………………………...…57 Table 10. Table of Chronic Ammonia Data………………………………………………………….62 Table 11. Dissolved Copper Acute Standards and Water Hardness……………………...64 8 LIST OF FIGURES Figure 1. Map of Sampling Sites……………………………………………………………………….…34 Figure 2. Map of Sampling Sites repeated………………………………………………………..….51 Figure 3. Graph of Mean Discharge per Site…………………………………………………….…..59 Figure 4. Graph of Mean pH Level per Site……………………………………………………...……60 Figure 5. Graph of Geometric Mean Total Chlorine Levels per Site…………………….….61 Figure 6. Graph of all Dissolved Copper Levels Observed…………………………………….63 Figure 7. Graph of Mean E. coli Levels Observed per Site……………………………………..65 Figure 8. Graph of Mean Indicator Organism Levels per Site………………………………..66 Figure 9. Graph of Positive Bovine Detections Observed per Site………………………....68 Figure 10. Graph of Mean Counts per 100 mL of Human Marker per Site……………..69 Figure 11. Boxplot of HF183 Counts Observed at Sampling Locations………………….70 9 ABSTRACT Utilization of areas adjacent to rivers for agricultural and industrial purposes can have detrimental effects on water quality and can potentially impact human and ecosystem health downstream. In this study we tested water quality along a stretch of the effluent-­‐dependent Santa Cruz River near Nogales, AZ. This stretch of river has historically been impaired, but prior to upgrades to the Nogales International Wastewater Treatment Plant (NIWTP) in 2009. Our work endeavored to assess water quality according to the Arizona Department of Environmental Quality (ADEQ) standards, and identify sources of pollution entering the river system. Heavy metals were analyzed via ICP. Three IDEXX quantification systems were used to detect E. coli, Enterococcus, and P. aeruginosa as fecal indicators or potential threats to public health. Potential fecal sources were analyzed using quantitative PCR targeting Bacteroides DNA markers for total, human, and bovine feces (Allbac, HF183, and CowM2, respectively). The NIWTP effectively removed chemical and biological contaminants. The lowest E. coli counts occurred at the site located at the NIWTP outfall (mean = 5 MPN/100ml) while the highest counts (mean = 348 MPN/100 ml) came from Nogales Wash, a tributary receiving untreated flow from Nogales, Mexico. The Allbac marker was detected in all samples, while approximately 97% of samples tested positive for HF183 and 33% tested positive for the CowM2 marker. Continued monitoring of effluent effects on river quality and evaluation of water quality degradation will lead to improvements in the management of Arizona’s riparian areas and will ultimately contribute to healthy water bodies. 10 11 CHAPTER 1: LITERATURE REVIEW 1.1 Water Quality History The first congressional action taken in the United States in connection to water quality protection took place in 1948 under the Water Pollution Control Act (WPCA). This was the first of many acts and amendments focused on environmental protection. The WPCA originally provided funds to states to implement water quality protection. The WPCA was reorganized and amended many times and officially became known as the Clean Water Act (CWA) in 1972. The CWA was passed in order to protect both interstate and intrastate waters that included lakes, rivers, streams, estuaries, and wetlands, as well as all waters with a significant nexus to navigable waters. The goal of the CWA is to “restore and maintain the chemical, physical, and biological integrity of that nation’s waters.” (USGPO 2010). The United States Environmental Protection Agency (USEPA) was founded in 1970 in order to implement and enforce environmental regulations and laws at the federal level. In an effort to manage sources of water pollution, the National Pollutant Discharge Elimination System (NPDES) was established as part of the CWA in 1972 under the authority of the USEPA. The NPDES began regulation of identified sources of water pollution through a permitting system. The USEPA has the authority to impose substantial monetary fines and even jail sentences for any 12 persons or entities responsible for noncompliance of NPDES, or any of its many pollution statutes (Cech, 2003). Pollutants are divided into two general categories: (1) point sources, which are defined as contamination discharged through a pipe or other discrete, identifiable location and (2) nonpoint sources, which are generally diffuse sources of pollution that are difficult to identify (Cech, 2003). Point sources regulated by NPDES include but are not limited to, sources such as industrial facilities (i.e. – manufacturers, oil/gas extraction), military bases, and wastewater treatment facilities. Examples of nonpoint sources include natural sediment erosion, as well as agricultural and residential runoff. All forms of storm water runoff were originally considered nonpoint sources of pollution, but runoff from particular man-­‐made activities (i.e. – construction areas, industrial sites, etc) were likewise categorized as point sources under the NPDES in the late 1980s (USEPW, 2002). State environmental agencies have also been created as a means to enforce water quality standards within each state. State water regulations may vary to some degree but state-­‐run water quality standards must be approved by the USEPA (Sullivan & Bell, 2011). This denotes that state standards must meet or exceed those set forth by the USEPA. The CWA requires each state to compile a list of all water bodies that do not meet designated water quality criteria termed a 303(d) list (USEPA, 2012). Applicable standards for all water bodies vary depending on their designated use, but waters found on the 303(d) list require a Total Maximum Daily Load (TMDL) to be performed. A TMDL is designed to assess the quantity of pollutants that a water body can assimilate and still maintain water quality 13 standards. A 303(d) list is submitted by each state to the USEPA every two years, along with a schedule to establish any TMDLs that are needed for those waters (USEPA, 2012). Thus federally mandated water quality standards are generally implemented through individual state environmental agencies. Therefore, state and federal agencies work in collaboration to maintain water quality throughout all fifty states. 1.2 Water Quality Indicators 1.2.1 Chemical and Physical Parameters Water quality can be assessed using a variety of techniques depending on the criteria of the assessment and the category of water to be evaluated. For instance, the factors used to assess water quality can vary when comparing different water bodies such as freshwater lakes and rivers, drinking water, and ocean water. An example of this is observed in USEPA standards set for freshwaters with a partial body contact (PBC) designation compared to a designation as a domestic water source (DWS). The PBC designation for total arsenic permits 280 µg/L, but the DWS standard is set at 6 µg/L. Thus, there is variation of water quality standards as determined by specific uses of a water body. Not all water quality tests are limited to arsenic but can include many physical, chemical, and biological aspects of water. Some common physical water quality parameters for surface waters include turbidity, electrical conductivity, and temperature. Parameters used to measure chemical aspects of water quality commonly include pH, hardness, heavy metal 14 concentrations, and dissolved oxygen. The acceptable limits for many of these parameters can vary regionally and nationally, depending on the surrounding geology and applicable governmental regulations (Sillivan & Bell, 2011). Turbidity is measured as the amount of relative clarity found in a liquid. It is therefore an optical property measured by the amount of light that passes through a liquid without being scattered or absorbed by suspended particles (Swanson and Baldwin, 1965). High turbidity can make the water appear cloudy, muddy, or opaque in nature. This is generally due to silt, clay, chalk, fine organic matter, or other microscopic particles and organisms. Surface runoff to rivers and resuspension of soils and sediments are usually the cause of increased turbidity. High levels of turbidity are not only less visually appealing, but can also provide places of attachment for metals, potential pathogens, and other pollutants by concealing them from drinking water disinfectants (Gerba, 2009). It is therefore important for surface waters to contain low turbidity for drinking water purposes. Electrical conductivity (EC) is a measurement of an aqueous solution to carry an electric current (Standard Methods, 2005). This electrical current is dependent upon the amount of ions in the water, their mobility and valence. Some common cations that contribute to EC are Na+, Mg2+, Ca2+, Fe2+/3+, and Al3+/2+; and common anions include Cl-­‐, SO42-­‐, NO3-­‐, and PO43-­‐ (USEPA, 2012a). EC reflects the geologic region in which tests are performed but EC levels can also vary due to anthropogenic influence. Water run-­‐off typically collects ions from various types of rock and sediment which form natural EC levels, but human influence from such things as agriculture and wastewater can also alter the levels of ions in the water. 15 Increased EC levels can have a negative influence on water quality because high EC levels limit potential water uses. This can be seen when irrigation waters cannot be used to water crops because they are intolerant of high EC water conditions (Swanson and Baldwin, 1965). Temperature is also an important aspect of water quality as some organisms live in a select range of temperatures and altering the water temperatures can affect aquatic life (Swanson and Baldwin, 1965). Water temperature can vary depending on the area, climate, and season from which it is measured. A frequently used chemical test of water quality involves evaluating the acidity or pH. The pH scale ranges from 0-­‐14 and is interpreted in a logarithmic manner. A pH level of 7 corresponds to a neutral measurement, being neither acidic nor basic. As the pH level decreases from 7 a solution becomes more acidic, and as it rises above 7 the solution becomes more basic. A measurement of pH demonstrates a concentration of free hydrogen ions in a solution (i.e.-­‐ water) as compared to hydroxyl ions. The higher concentration of hydrogen ions represents higher acidity while higher hydroxyl ions indicates basicity (Swanson and Baldwin, 1965). Natural waters can have a variety of “normal” pH values but with natural unpolluted river water generally between pH 6.5 to 8.5 (Weiner, 2008). Most aquatic organisms live within this range although there are some that can survive more extreme conditions. Anthropogenic influences can alter the pH of surface waters and can be seen as a result of sewage flows, mining operations, and general pollution (Swanson and Baldwin, 1965). Some mining operations create more acidic water conditions through the oxidation of iron pyrite that can lower surface water pH to less than 2 in 16 some cases (Weiner, 2008). It is thus important to maintain surface water pH within the natural range to preserve water quality. Dissolved oxygen (DO) is another parameter that is important to surface water health, especially concerning aquatic organisms. It is a measurement of the amount of gaseous oxygen (O2) in an aqueous solution. Most fish and other aquatic organisms need DO to survive, as low levels of DO can be detrimental to biological health (Hitchman, 1978). Although DO is necessary for some aquatic life, levels of DO can vary greatly depending on factors such as water temperature, location, and biological activity. DO can be in continual flux as there is biochemical depletion as well as reoxygenation by photosynthetic organisms and aeration of moving waters (Hitchman, 1978). Effluent-­‐dominated water presents a different outlook on acceptable levels, as DO is used in the wastewater process by bacteria to break down sewage particles. Levels of acceptable DO in wastewater effluent tend to be lower than those compared to natural waters as some oxygen reduction is an integral part of most wastewater treatment processes. This is reflected in USEPA standards set for A&Ww versus A&Wedw (See Table 1a showing these comparisons). Water hardness is a measurement of the amount of calcium and magnesium dissolved in water. The cations Mg2+ and Ca2+ naturally occur in surface waters throughout the world and their concentrations can be variable even within the continental United States (Swanson and Baldwin, 1965). For example, Arizona is one state that tends to have moderate to high levels of hardness due to the higher concentration of calcium and magnesium-­‐rich geology of the state (Swanson and 17 Baldwin, 1965). Although water hardness does not generally pose a health risk itself, the USEPA published a water quality report in 1986 connecting lower concentrations of total hardness to increased risk of heavy metal bioavailability (Weiner, 2008; USEPA, 1986). Abundances of some metals in surface waters can greatly affect water quality and ecosystem health. Some metals are essential to biological functions (Cu, Fe, Zn) and can be naturally found in low concentrations in surface waters (Weiner, 2008). However, increased concentrations of metals found in surface waters can be linked to anthropogenic sources (Kawata, 2007; Weiner, 2008). Increased metals in surface waters pose a toxicity risk to biological entities. For instance, fish can suffer from metal cations competing for binding sites on their gills, making it difficult for them to breathe properly and can result in death (Pagenkopf, 1983). Humans also experience toxicity to heavy metals that include interference with cellular macromolecules, interference with essential metals in the human body, as well as oxidative stress (Kawata, 2007). The degree to which a metal is found to be harmful is dependent upon its form and speciation. A metal found in water is usually found bound to another molecule, but free metal ions can pose a larger risk to biological functions (Brewer, 2010; Niyogi, 2004). However, in surface waters, metals are more commonly found to bind to such things as dissolved organic matter and naturally occurring anions, which can greatly affect bioavailability, and thus can lower the toxicity of a metal (Niyogi, 2004). Toxicity and bioavailability of metals can be dependent on many factors that include hardness, pH, salinity, and dissolved organic matter 18 concentrations (Niyogi, 2004). However, because water hardness influences toxicity of metals by binding to them (decreasing their bioavailability), total water hardness (total Ca + Mg) is used by the USEPA to better determine acceptable limits for dissolved metals in surface waters. Thus, the acceptable limits for dissolved metals increases as the hardness of the water increases. Dissolved metal concentrations based on hardness are used to provide a more appropriate value (of metal toxicity) based on regional conditions. 1.2.2 Microbial Indicators Near the turn of the twentieth century the concept of using specific microorganisms as a means to estimate possible exposure to pathogens was established. The U.S. Public Health Service began using coliform bacteria as a means of indicating fecal contamination of drinking water in 1914, a method still used in water quality practice today (Gerba, 2009). The concept of using microbial indicators lies in the fact that if certain microorganisms, termed “indicators” are present in a (water or soil) sample, then there is potential for pathogens to also be present, thus posing a risk to human health. Although indicator organisms commonly originate from fecal sources, some have been known to naturally occur and even reproduce in the environment. An example of this is observed by Kim et al. (2009) as E. coli repopulated in compost after the compost had been autoclaved. For such reasons the idea of using indicator organisms to show microbiological health of environmental samples has been further studied (Field and Samadpour, 2007). 19 Total coliforms, for instance, is one such example of a indicator that has been used for decades to indicate microbiological water health but has shown limited correlation to the actual detection of human pathogens (Ashbolt, 2001). This may be due to the difficulty to detect pathogens compared to the relative ease with which indicators are detected. Although the use of some indicator organisms for public health issues has been questioned, they have still been proven effective at reducing the amount of waterborne disease outbreaks caused by enteric bacteria (Osborn, 2004). To date no perfect microbial indicator organism has been recognized that meets all the criteria set by researchers and professionals for pathogen detection. Indicator organisms are used in place of directly testing for pathogens because pathogens are generally more difficult to detect, and direct testing for pathogens is generally less economically feasible than using indicators (Meays et al., 2004). Some criteria that are recommended for an ideal indicator organism are (1) the organism should not grow in water, (2) indicator presence whenever pathogens are present, (3) easy and quick growth on simple media (4) a relative ease of detection, (5) the indicator as a member of the GI tract in all warm-­‐blooded animals, (6) and random indicator distribution throughout the sampled matrix or the ability to easily homogenize the sample (Gerba, 2009; Osborn, 2004). Although no single bacterium or groups of bacteria have been recognized to meet all these criteria, the use of coliform bacteria and others has proven effective to reduce risks to public health (Osborn, 2004). 20 The coliform group is a member of the Enterobacteriaceae family, which includes all Gram-­‐negative, non-­‐spore forming, rod-­‐shaped aerobic and facultatively anaerobic bacteria that can ferment lactose within 48 hours at 35° C (Gerba, 2009). This group includes some strains of E. coli, Enterobacter, Klebsiella, and Cirtobacter that are pathogenic. Though coliform bacteria are typically used to indicate fecal contamination at some level, they are not always derived from fecal sources. Of the above-­‐mentioned members of the coliform group only E. coli possesses definitive fecal origins and is named a fecal coliform (Osborn, 2004). Fecal coliforms originate from the intestinal tract of humans as well as all warm-­‐blooded animals and are differentiated from total coliforms by their ability to ferment lactose with acid and gas production at 44.5° C within 24 hours (Gerba, 2009). The ability for total and fecal coliforms to survive outside of the host intestine for considerable amounts of time has been documented (Gleeson and Gray, 1997). It has therefore been suggested that total coliforms and some fecal coliforms naturally occur in tropical waters due to their ubiquitous nature in unpolluted waters and their environmental survivability (Toranzos, 1991). Despite these suggestions, total and fecal coliforms are still used as indicators today, and E. coli bacteria provide definitive recognition as fecal indicators (Aulenbach 2010). The primary habitat for the fecal coliform E. coli is the lower intestine of warm-­‐blooded animals with secondary habitats that include soil, vegetation, and water (Hagedorn, Blanch, & Harwood, 2011). E. coli has been known to survive in the environment and its regrowth has been identified under various environmental conditions. Due to its durability, E. coli can survive and replicate at lower 21 temperatures (7.5-­‐7.8° C), and has even been found to persist in an ice-­‐covered river in Alaska (Davenport et al., 1976). It is also the most abundant representative of thermo-­‐tolerant coliforms. Despite its ability to survive and potentially regrow outside its host GI tract, E. coli has been widely used as a fecal indicator because it can be easily distinguished from other fecal coliforms by the presence of the specific enzyme β-­‐glucuronidase (Gerba, 2009). β-­‐glucuronidase is found in 94-­‐96% of E. coli strains but has been associated with many other genera of bacteria. Detection of β-­‐glucuronidase alone may lead to false-­‐positive detections of E. coli from environmental samples (McLain et al. 2011). Cultural and molecular methods may be required in order to assure positive identification of E. coli from environmental samples. In spite of possible difficulty in detection from environmental samples, E. coli is an abundant fecal coliform that has been successfully utilized for assessing fecal contamination for decades and is used by the USEPA to assess water quality (Osborn, 2004). Enterococci are Gram positive, facultatively anaerobic, non-­‐spore forming bacteria of the Enterococcaceae family and have long been used as indicator organisms. It was initially thought that these bacteria were only derived from fecal origins as they began to be more frequently detected in some frozen foods (Insalata, 1969). Although many strains of enterococcal bacteria are derived from fecal origins, early studies indicate enterococci can be present in plants that seem to be unaffected by human and animal influence (Mundt, 1961; Mundt, 1962). Enterococcal presence in frozen foods in some instances may therefore be attributable to their natural presence in plants (Mundt, 1961). Enterococcal bacteria 22 are known to be hardy in nature with the ability to survive under diverse environmental conditions. They can grow within a temperature range of 10 -­‐ 45° C, be tolerant to pH ranges from 4.8 – 9.6, and demonstrate resistance to bile salts, azide, detergents, sodium hypochlorite, heavy metals, ethanol, and prolonged desiccation (Huycke, 2002). E. faecalis and E. faecium are predominant constituents found within human and animal small intestines and remain the most predominant enterococcal species found in sewage (Manero, et al. 2002). Bacteriophage are viruses that infect and depend on bacteria for their proliferation and have been utilized as indicators of fecal pollution due to their constant presence and potentially high counts in sewage and polluted waters (Gerba, 2009). Their use as indicators is based on the assumption that their presence in water samples indicates the presence of bacteria capable of supporting phage replication (Gerba, 2009). Two groups of phage have been studied as indicators in particular, somatic coliphage and F-­‐specific RNA coliphage. Both phages infect E. coli host strains although somatic coliphages do so via the outer cell membrane receptors and F-­‐specific phage infect E. coli strains through the F-­‐pilus (USEPA, 2001). Although coliphages can be used in tracking fecal pollution they are not necessarily considered to be strictly of fecal origin. Havelaar et al (1990) reported that F-­‐RNA coliphage were rarely detected in human feces, but somatic coliphage are commonly seen in sewage. Coliphages are therefore used more commonly to distinguish human and non-­‐human sources, and can be used to indicate sewage pollution (Havelaar et al., 1990). 23 Other organisms that have been suggested as alternative indicator organisms for water quality include Pseudomonas spp. and Staphylococcus. Pseudomonads are Gram-­‐negative, non-­‐spore forming, aerobic rods. P. aeruginosa strains pose the most risk to public health due to their association with otolayrngological infections (Gerba, 2009). They are also connected to nosocomial infections, an opportunistic pathogens in burn victims and immunocompromised individuals, and have been identified as the causative agent of cystic fibrosis (Bodey et al., 1983). However, P. aeruginosa has been consistently found in high concentrations in sewage and has been suggested as an indicator organism for water quality applications for recreational waters, as well as in hot tubs and swimming pools (Cabelli, 1978). P. aeruginosa has been associated with fecal contamination but is considered a ubiquitous bacterium that can multiply in the environment (Gerba, 2009). Staphylococcus aureus is a Gram-­‐positive bacterium that is commonly found in the human respiratory tract and on the skin. This organism is not always pathogenic but has been associated with skin irritation and infection. Surface and recreational waters may serve as a way to spread skin infections caused by this bacterium. As such, S. aureus has also been suggested as an alternative indicator for the sanitary quality of recreational waters. Its presence in recreational waters has been associated with human activity (Charoenca and Fujioka, 1993). A potential indicator that can be considered of fecal origin is Clostridium perfringes. It is a sulfate-­‐reducing, Gram-­‐positive anaerobic rod that can produce hardy spores resistant to heat of up to 75° C for up to 15 minutes (Gerba, 2009). Not only are their spores resistant to heat, but also to chlorine disinfectants. Although 24 these organisms are originally derived from fecal origins, their spores can survive harsh environmental conditions and, therefore, environmental samples may or may not be indicative of recent fecal pollution. Spore survivability suggests they can serve as indicators of past pollution (Payment and Franco, 1993). Bifidobacterium are another genus of Gram-­‐positive anaerobic bacteria that have been linked to human feces (Hagedorn, Blanch, & Harwood, 2011). Studies have shown that some species of Bifidobacterium are found in wastewater and human feces but are absent in most animal feces (Mara and Oragui, 1983; Resnick and Levin, 1981). Mushi et al. (2010) indicates that Bifidobacterium cultivation may be a viable alternative to molecular methods to detect very recent human fecal contamination in water. However, extra-­‐intestinal survivability of Bifidobacterium is low and more research is needed to develop reliable detection techniques (Mushi et al., 2010). 1.3 Microbial Source Tracking Microbial source tracking (MST) encompasses a series of approaches for tracking sources of fecal contamination based on environmental sampling. It is important to understand that a simple measurement of an FIB, such as E. coli, is used to estimate fecal contamination at some level but does not point to any particular source. MST studies expand FIB data in an attempt to confirm not only the presence of fecal contamination, but also link the fecal contamination to a particular source (animal). The identification of fecal sources is important to protect the public from potential pathogens that can be shed by wild and domestic animals. In 25 accordance with detecting fecal sources from animals, it is equally important to verify human-­‐based fecal pollution, as human sewage generally poses a higher risk to public health than fecal pollution from animal sources (Hagedorn, Blanch, & Harwood, 2011). Being able to understand the origin of fecal pollution is not only beneficial to public health, but can also serve in the determination of necessary actions to remediate environmental waters contaminated by fecal material. MST studies are based on the detection of specific microbes or microbiological components and chemical compounds (i.e. – genes, antibiotics, etc.). They are also divided into two categories of methods called library-­‐dependent and library-­‐independent methods. Library-­‐dependent methods require the utilization of a library, or dataset, that shows known characteristics of fecal isolates from a specific source, to which environmental isolates can then be compared. Thus, library-­‐dependent methods are generally composed of genotypic or phenotypic characteristics of the microbes from specific sources. Library-­‐dependent methods usually require culturing and are time-­‐consuming and labor-­‐intensive (Gourmelon et al., 2007). These approaches are based on a set of three hypotheses (1) certain characteristics of fecal bacteria are associated with specific animals or groups of animals (2) these characteristics in environmental strains are similar to those found in host groups (3) and that these unique characteristics remain relatively constant in the environment over time (Hagedorn, Blanch, & Harwood, 2011). Common phenotypic techniques used in library-­‐dependent methods include antibiotic resistance analysis (ARA) and carbon source utilization (CSU). ARA relies on bacterial resistance to antimicrobials to distinguish various sources of fecal 26 bacteria. The patterns of resistance from environmental samples are compared to isolates from different animals/animal groups to identify sources of fecal pollution. ARA is less technically demanding and was recognized by Price et al (2007) to be 4-­‐
5 times less expensive per isolate than the genotypic method of pulsed-­‐field gel electrophoresis (PFGE). CSU methods are likewise based on phenotypic characterization of environmental isolates in comparison to known isolates. This method relies on the ability of environmental bacterial samples to utilize various carbon substrates. Results are compared to the library dataset to then discriminate sources. The first CSU analysis used in MST characterized enterococci to discriminate between human and non-­‐human isolates and achieved a 92.7% average rate of correct classification (Hagedorn et al., 2003). In both ARA and CSU systems, plate readers connected to computer software are used to better interpret definitive results but concerns for validity still remain. Limited accuracy to identify sources of blind samples has been reported (Moore et al., 2005). Improved geographical and temporal stability for these methods may be needed (Hagedorn, Blanch, & Harwood, 2011). Genotypic techniques for library-­‐dependent methods commonly include PFGE, ribotyping, and rep-­‐PCR. PFGE involves extraction and enzymatic digestion of bacterial DNA, followed by DNA separation using pulsed electrophoresis. Results are then compared to separation patterns from bacterial libraries. This method has been widely used to investigate food-­‐borne outbreaks and has been used by the CDC (USEPA, 2011; Hagedorn, Blanch, & Harwood, 2011). Although PFGE has shown highly discriminative results, it requires technical skill, specific equipment, is time-­‐
27 consuming, and has proven to be expensive (USEPA, 2011). Ribotyping is similar to PFGE but includes a transfer of the digested DNA fragments onto nitrocellulose paper by performing a southern blot. Radioactive probes are used to indicate specific banding patterns from a library. Ribotyping, has been one of the most widely used methods in MST research, and is considered one of the most reproducible molecular methods (Hartel et al., 2003). However, similar to PFGE, ribotyping remains an expensive method that is labor-­‐intensive. Rep-­‐PCR is a faster and easier method that relies on amplification of repetitive segments of DNA. Amplified DNA segments are then electrophoresed through an agarose gel to create banding patterns, and compared to a library dataset (USEPA, 2011). Although rep-­‐
PCR can be much faster and less costly than other library-­‐based methods, temporal and spatial variability have been observed, as has been the case in many library-­‐
based methods (Hansen et al., 2009). The second MST category is composed of library-­‐independent methods, which generally rely on direct detection of source-­‐specific genetic markers unique to an animal or human fecal source (Hagedorn, Blanch, & Harwood, 2011). The source-­‐specific genetic markers are explicit nucleic acid sequences found in the bacterial or viral constituents of feces. In many cases only a particular animal carries the sequence of interest within the microbial constituents of their feces. Nucleic acid sequences are extracted from environmental samples and analyzed via a PCR or quantitative PCR (qPCR) instrument. More recently developed qPCR methods not only identify the presence or absence of the nucleic acid of interest, but can also be used to quantify the relative amount of fecal contributions from identified sources. 28 Bacteria commonly used in library-­‐independent methods include Bacteroides, Enterococcus, and Methanobrevibacter smithii (USEPA, 2011). An understanding of the composition of the bacterial intestinal community is essential to the application of library-­‐independent MST. An example of this is seen in the human intestinal microbiome, which is composed of only nine phyla of bacteria, of which Firmicutes and Bacteroidetes compose over 90 percent (Eckburg et al., 2005). Consequently, the genus Bacteroides is present at higher concentrations in human fecal matter than indicator bacteria (USEPA, 2011). Bacteroides are Gram-­‐
negative non-­‐spore forming obligate anaerobes that demonstrate fecal host specificity in many cases (Hagedorn, Blanch, & Harwood, 2011). Since they are strictly anaerobic they are a commonly used bacteria in library-­‐independent MST, and tend to indicate more recent fecal pollution compared to other FIBs that better survive in the presence of oxygen (Layton et al., 2006). Many genetic markers have been developed to distinguish various sources of fecal pollution using genes found in Bacteroides bacteria. Assays target specific protein-­‐producing genes, toxin genes, and virulence genes, but the most commonly used target is the 16S rRNA gene (Hagedorn, Blanch, & Harwood, 2011). One drawback to using toxin and virulence genes is that the gene is only shed by infected hosts, limiting detection to the prevalence of the disease or the toxin gene concentration in infected hosts (Hagedorn, Blanch, & Harwood, 2011). Protein-­‐
specific assays have been utilized in MST and some studies show good source specificity and sensitivity (Scott et al., 2005; Shanks et al., 2008). However, the 16S rRNA gene is more widely used in MST applications due to its highly conserved 29 regions across many bacteria, as well as the variable regions, which allow discrimination down to the subspecies level in some cases (Hagedorn, Blanch, & Harwood, 2011). A large percentage of library-­‐independent PCR assays target the 16S rRNA gene for these reasons. Library-­‐independent methods rely on the ability of genetic-­‐based assays to discriminate between many animal sources. Specific DNA markers targeting humans, pigs, horses, cats, dogs, cattle and other animals have already been developed (Roslev & Bukh, 2011). The specificity, sensitivity, and reproducibility of any source-­‐based assay are keys to its reliability. A study done by Shanks et al (2010) evaluated the viability of bovine qPCR assays by testing them against fecal samples derived from 24 different animal species. Using the CowM2 and CowM3 molecular markers resulted in non-­‐detection for all animal fecal samples not derived from bovine sources in this study. This shows that the marker proved to be specific for animals tested in this study, but the authors also acknowledge that there is variability in marker abundance and presence between different bovine populations. Not all markers are completely source specific as marker abundance and presence can vary due to such things as geography and animal diets. Another study comparing assays specific to two human molecular markers, HF183 and HF134, showed that both assays consistently detected human markers from sewage samples, but HF134 was also detected in fecal samples from dogs, indicating a lower sensitivity for HF134 (Ahmed et al., 2008). Although many library-­‐independent MST techniques have proven successful, it has been suggested that source-­‐specific molecular markers are better referred to 30 as source “associated” due to a lack of “absolute” host specificity in humans and animals (Roslev & Bukh, 2011). The temporal stability of microbial markers in different host groups, horizontal gene transfer, and low or unknown abundance of microbial markers in some host populations give rise to such concerns (Stewart et al., 2007; Roslev & Bukh, 2011). Continual research and reevaluation of marker specificity and sensitivity will reveal the best available options to track generally source-­‐specific fecal contamination in water. In addition, application of current research and continual study in both library-­‐based and library-­‐independent MST will be keys to discovering the best applicable techniques under various environmental scenarios. 31 CHAPTER 2: RESEARCH METHODS 2.1 Sampling Locations Water quality testing was performed on seven different sites along the Santa Cruz River and its tributaries. This study covered an approximate 25-­‐mile stretch of the Santa Cruz River with a total of five sites located directly on the Santa Cruz River and two sites as tributaries. Figure 1 provides an overview of sampling site locations. See also Table 1 for more site information. Site 1 – Johnson’s Ranch (JR). This site is located approximately ½ mile north of the international border of the US and Mexico and serves as a background site for water quality on the Santa Cruz River. Johnson’s Ranch is located on private land and ADEQ has previously collected samples from this property. Permission was granted from the landowner to collect water samples during this study. As is customary on a ranch, various farm animals free-­‐range graze on this property. Johnson’s Ranch is located approximately 10 miles upstream of the Nogales International Wastewater Treatment Plant (NIWTP) effluent outfall (See site 4) and only contains flowing water surrounding rain events, after which waters quickly recede. No flowing water conditions were ever observed during sampling excursions at Johnson’s Ranch during this study. Due to these no flow conditions, water samples at this site were taken from accumulated pools shortly after precipitation had occurred in the area. 32 Site 2 – Nogales Wash (NW). This is a tributary to the Santa Cruz that converges with Potrero Creek shortly before a confluence into the Santa Cruz River. The Nogales Wash is a tributary that serves as a drainage for the cities of Nogales, Sonora, Mexico, and Nogales, Arizona. It is a concrete ditch for most of its length until its confluence with Potrero Creek. Samples were taken where Highway 82 crosses over the Nogales Wash in Nogales, AZ. Site 3 – Potrero Creek (PC). This is generally a small volume tributary containing a confluence with the Santa Cruz River directly up-­‐stream of the NIWTP outfall. Flow volume is generally low enough that it does not make it to the confluence point except during some rain events and monsoon season. Samples at Potrero Creek were taken as the creek parallels Interstate-­‐19 at exit number 19 off Ruby Road. Site 4 – NIWTP outfall (WO). Although the Santa Cruz River crosses the US-­‐Mexico border at Johnson’s Ranch, it rarely flows the approximate 10-­‐mile stretch to Rio Rico where the wastewater treatment facility is located. The effluent at the wastewater outfall is directed into the Santa Cruz River, at which point the river is predominately effluent-­‐dependent. The exact location from which samples were taken is located in Rio Rico, AZ, approximately 50 feet from the mouth of the outfall. Site 5 – Rio Rico (RR). This site is located just two miles downstream of site four. Samples were taken close to where Rio Rico Drive crosses over the Santa Cruz River. 33 Between sites four and five, the river passes a limited number of agricultural fields, comes in close proximity to the Rio Rico Golf Course, and a small wash converges approximately half way between the two sites. There is an abundance of free-­‐range grazing cattle between the sites. Site 6 – Santa Gertrudis Lane (SGL). The road Santa Gertrudis Lane crosses through the Santa Cruz River, approximately eight miles downstream of site five. It is located on the south end of Tumacacori National Historic Park (TNHP). Vehicles frequently drive through the river to continue on Santa Gertrudis Lane, as flow volumes tend to be low at this point in the river. Samples were collected directly upstream from the crossing point so as not to be affected by the regular traffic. The land between sites five and six is mostly agricultural, with some areas containing grazing animals. Josephine Canyon Wash enters the Santa Cruz River about one mile upstream of the collection location at this site. Site 7 – Tubac Bridge (TB). This site is located approximately four miles downstream from Santa Gertrudis Lane, entering into the small town of Tubac, AZ. After passing the Tumacacori National Historic Park, the Santa Cruz passes through more agricultural fields before entering into Tubac. The stretch of river in Tubac is home to tourists that come to bird watch in riparian habitat zones. As such, areas around the Santa Cruz River at this location contain walking trails and human influence is frequently observed. Samples were obtained close to where Bridge Road crosses the Santa Cruz River. 34 Figure 1. Map of Sampling Sites Source: http://gisweb.azdeq.gov/arcgis/emaps/ 35 2.2 Sample Collection Collection of water samples occurred approximately once every two weeks beginning April 1, 2013 and extending through October 4, 2013. A total of seven collection sites were utilized encompassing an approximate 25-­‐mile stretch of the Santa Cruz River, including two tributaries (Nogales Wash and Potrero Creek). Sampling occurred beginning at the southern-­‐most site (Site 1) located near the U.S./Mexico border, after which the remaining sites where collected in sequential order. Refer to Figure 1 for site locations. This approximate six-­‐month study was executed to capture a large range of hydrologic conditions, taking into account rainy season (monsoon season) that typically occurs during the months of July and August in southern Arizona. This time period also accounted for pre and post monsoon season conditions. Dry seasons in Arizona usually occur during spring and early summer in March-­‐June, and during early fall beginning in September. A total of 67 water samples were collected from all sites with a varying number of samples obtained from each individual site based on water availability conditions. Table 1 below provides further data pertaining to each sampling site. All water samples collected during this study were obtained as grab samples by reach pole using sterilized 1L collection bottles. All bottles were then placed directly on ice while being transported back to a laboratory for remaining analyses. All tests performed for fecal indictor organisms were completed within six hours of collection at University of Arizona laboratories. Microbial source tracking (MST) analyses, pH, and conductivity were also completed at the University of Arizona 36 laboratories in Tucson, Arizona and at the Maricopa Agricultural Center Laboratory in Maricopa, AZ. Laboratory analyses for ammonia, water hardness (Ca + Mg), and metals (dissolved and total cadmium and dissolved copper) were placed in separate collection bottles, stored on ice, and delivered to TestAmerica Laboratories for subsequent testing that was performed within 24 hours of collection. Table 1. Sampling Sites Overview Sampling Site Site Code Johnson’s Ranch Nogales Wash1 Potrero Creek1 JR NW PC NIWTP Waste Water WO Outfall Rio Rico RR GPS Coordinates 30° 120.494’ N 110° 51.035’ W 31° 20.966’ N 110° 55.626’ W Lane Tubac Bridge SGL TB Site Uses Number DWS, FC, AgI, Samples Collected 1 4 2 11 3 11 4 11 5 12 6 10 7 8 AgL A&Ww, PBC A&Ww, FBC, 110° 57.652’ W FC, AgL 31° 27’ 24.12” N A&Wedw, 110° 58’ 5.253” W PBC, AgL 31° 28.193’ N 110° A&Wedw, PBC, AgL 31° 33.738’ N A&Wedw, 111° 02.759’ W PBC, AgL 31° 36.829’ N A&Wedw, 111° 02.467’ W PBC, AgL 1These sites are tributaries to the Santa Cruz River. Number of A&Ww, FBC, 31° 25.823’ N 59.555’ W St. Gertrudis Designated 2.3 37 Chemical and Physical Analyses Chemical tests included pH, total chlorine, dissolved oxygen, ammonia, calcium, magnesium, dissolved copper and cadmium, and total cadmium. Physical parameters tested for included temperature, conductivity, turbidity and water discharge (flow rate). All parameters chosen in these study were deemed the most pertinent based on previous historical data and current circumstances (ADEQ, 2009). Parameters tested for in the field were total chlorine, dissolved oxygen, temperature, conductivity, turbidity, and water discharge. Total chlorine was analyzed in the field using the Hach Colorimeter II (Hach Co., Loveland, CO). Ten milliliters of water sample was measured into a glass cell and a chlorine reagent DPD Powder Pillow was subsequently added to obtain a measurement for total chlorine. Dissolved oxygen was measured using the Hach HQ 40d multi-­‐probe by placing the probe directly into the surface water at each sampling location (Hach Co., Loveland, CO). Air and water temperature were obtained using a simple field thermometer. Turbidity was measured using the Hach 2100P turbidimeter (Hach Co., Loveland, CO). Water discharge was measured at each site by wading rod and flow meter method using the Hach FH950 Portable Velocity Meter (Hach Co., Loveland, CO). Sample pH was tested for upon arrival at University of Arizona laboratories using the Accumet Excel XL20 pH meter (Fisher Scientific, Waltham, MA). All equipment and instrumentation was calibrated and tested before collection of samples occurred during each field excursion. 38 All remaining chemical analyses were performed by TestAmerica Laboratories (Tucson, AZ). TestAmerica provided separate sample collection bottles during each sampling event. All samples submitted to TestAmerica Labs were placed on ice and delivered to TestAmerica Labs in Tucson, AZ. Inductively coupled plasma mass spectrometry (EPA method 200.7 revision 4.4) was used to test for calcium, magnesium, dissolved copper, and dissolved and total cadmium. Ammonia was tested for using an ion selective electrode (SM 4500 NH3 D). A QA/QC report accompanied each chain of custody form to assure accurate measurements. Table 2 provides further information on the chemical and physical parameters tested in this study. Table 2. List of Chemical and Physical Parameters Tested Chemical and Physical Parameters Analyte Detection Limit Method pH pH scale SM 4500 H+ Conductivity 1 uS/cm SM 2510 Turbidity 0.01 NTU SM 2130 Total Chlorine 0.02 mg/L SM 4500-­‐CI G Analytes tested for by TestAmerica Laboratories Analyte Detection Limit Method Dissolved Cadmium 0.0010 mg/L EPA 200.7 Rev. 4.4 Dissolved Copper 0.010 mg/L EPA 200.7 Rev. 4.4 Total Cadmium 0.0010 mg/L EPA 200.7 Rev. 4.4 Ammonia 0.050 mg/L SM 4500 NH3 D Calcium 2 mg/L EPA 200.7 Rev. 4.4 Magnesium 2 mg/L EPA 200.7 Rev. 4.4 2.4 39 Microbiological Analyses 2.4.1 Indicator Organisms Fecal and general water quality indicators are used to measure the quality of surface water. As such, specific organisms have been identified in order to better determine the microbiological health of surface waters. The four specific indicator organisms used in this study to provide a general evaluation of microbiological water quality were total coliforms, E. coli, Pseudomonas, and Enterococcus. A cultural procedure (SM 9223B) was performed for tests on all indicator organisms in this study through the IDEXX quanti-­‐tray system within six hours of sample collection (IDEXX Laboratories, Westbrook, ME). This procedure consisted of 100-­‐mL of water sample being added to 120-­‐mL sample bottle, after which organism-­‐specific substrates were then added for each test. Samples were then thoroughly shaken to ensure all substrate was dissolved. Samples were then poured into 97-­‐well quanti-­‐
trays and sealed using the Quanti-­‐Tray Sealer 2X (IDEXX Laboratories, Westbrook, ME). The trays were subsequently placed in an incubator at 35 ± 0.5°C for approximately 24 hours. Results were then read by counting the number of yellow or fluorescent wells by using the most probable number (MPN) technique. Total coliforms and E. coli tests are executed using the same substrate and quanti-­‐tray, but Pseudomonas and Enterococcus utilize separate substrates and quanti-­‐trays. Table 3 provides an overview of each of these procedures used in this study. 40 Table 3. Fecal Indicator Tests Performed Microbial Target Procedure Name Total coliforms / E. coli Colilert Enterococcus Enterolert Pseudomonas Pseudalert Method IDEXX; SM 9223B IDEXX; ASTM D6503-­‐99 IDEXX 2.4.2 Water Sample Concentration and DNA Extraction Microbial source tracking (MST) methods were employed in this study to determine possible sources of fecal pollution. Water samples were filtered through a Millipore 0.45 µm filter at volumes ranging from 50-­‐500 mL (EMD Millipore, Billerica, MA). Differences in volumes filtered were due to variable levels of turbidity within each sample. Filters were placed on a filtering manifold and water was filtered through the manifold via a vacuum pump and tube connected to the filtering manifold. Sample water was collected into a 4L glass flask to be later discarded. All filters were placed into individual 15 mL conical tubes and stored at 4°C until DNA was extracted. DNA was later eluted from each filter using the MO Bio PowerWater® DNA Isolation Kit as described by the manufacturer protocol (MO Bio, Carlsbad, CA). This yielded approximately 1 mL of DNA concentrate per sample and all DNA was then stored at -­‐80°C until molecular testing began. 41 2.4.3 Molecular Methods Specific molecular markers were selected for quantitative PCR (qPCR) analyses to investigate sources of fecal pollution. The target organism selected in this study for qPCR analyses was Bacteroides spp. Three specific molecular markers were used to quantify fecal contributions from total, human, and bovine Bacteroides. The corresponding markers utilized were Allbac, HF183, and CowM2 respectively. The Allbac and human specific HF183 markers both target the 16S rRNA gene in Bacteroides bacteria (Seurinck, et al. 2005; Layton, et al. 2006). Although the 16S rRNA gene is commonly used in molecular methods due to its robustness in bacteria, an alternative gene was used for the bovine assay, targeting the HDIG domain protein gene for the CowM2 marker. Shanks et al. (2010) reported 100% specificity using the CowM2 marker when tested against 24 different animal fecal sources. Table 4 contains additional information concerning each marker, primer sequence, and amplicon size. 42 Table 4. Overview of qPCR Markers and Sequences Primer DNA Target Allbac 296f Allbac 412r 16S rRNA HF183f Newly Developed Reverse 16S rRNA CowM2f CowM2r CowM2p HDIG domain protein Sequence Amplicon Size qPCR Annealing Temp 5’-­‐ GAGAGGAAGGTCCCCCAC – 3’ 106 60°C 5’-­‐ CGCTACTTGGCTGGTTCAG – 3’ 106 60°C 5’-­‐ ATCATGAGTTCACATGTCCG – 3’ 82 60°C 5’-­‐ TACCCCGCCTACTATCTAATG – 3’ 82 60°C 5’-­‐ CGGCCAAATACTCCTGATCGT– 3’ 92 60°C 5’-­‐ GCTTGTTGCGTTCCTTGAGATAAT– 3’ 92 60°C 6-­‐FAM-­‐AGGCACCTATGTCCTTTACC TCATCAACTACAGACA-­‐TAMRA 92 60°C The Allbac and HF183 assays were performed using SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA). Reaction wells for each individual assay contained a total of 25 µL consisting of 12.5 µL SYBR Green master mix, 1 µL of each assay-­‐specific forward and reverse primer, 2.5 µL bovine serum albumin (BSA), 6 µL RT-­‐grade water, and 2 µL DNA sample. BSA was added to each reaction to stabilize enzymes and neutralize possible nucleases throughout the qPCR process. DNA primers for both assays were at 15 pmol concentrations. Each assay was run through a specific temperature profile for 50 cycles for Allbac, and 40 cycles for HF183. The temperature profiles were followed as was outlined by Seurinck et al. 43 (2005) and Layton et al. (2006). The temperature profiles for each assay are displayed in Table 5. The CowM2 assay was performed using a TaqMan® probe and primers (Applied Biosystems, Foster City, CA). Each 25 µL sample mixture for the CowM2 assay differed slightly from the composition of reaction mixtures mentioned previously. Because the CowM2 assay is probe based, a solution of forward and reverse primers, probe, and RT-­‐grade water was made and added to each reaction well. The resultant primers and probes for the assay consisted of 500 µM and 100 µM concentrations respectively. Each qPCR reaction well contained 3.5 µL of primer/probe mixture, 12.5 µL of 1X TaqMan universal PCR master mix, 2.5 µL BSA, 4.5 µL RT-­‐grade water, and 2 µL of DNA target for a total of 25 µL for each reaction well. The primers, probe and temperature profile was developed based on previous studies from Shanks et al. (2008). The CowM2 assay contained 45 cycles and its temperature profile is listed in Table 5. 44 Table 5. Overview of qPCR Temperature Profiles Assay Allbac Holding Time 50°C for 2 minutes 95°C for 10 minutes HF183 50°C for 2 minutes 95°C for 10 minutes CowM2 50°C for 2 minutes 95°C for 10 minutes Cycling Stage 95°C for 30 seconds 60°C for 45 seconds 95°C for 30 seconds 53°C for 1 minute 60°C for 1 minute 95°C for 15 seconds 60°C for 1 minute Melt Curve 95°C for 15 seconds 60°C for 1 minute 95°C for 15 seconds 95°C for 15 seconds 60°C for 1 minute 95°C for 15 seconds N/A Number of Cycles 50 40 45 All qPCR samples were run in triplicate with a negative control and a set of serially diluted positive controls ranging from 300,000 to 3 copies. Positive controls were obtained by plasmid DNA cloning of each of the three marker sequences. All qPCR analyses were performed using a 96-­‐well qPCR reaction plate and run on the StepOnePlus qPCR instrument (Applied Biosystems, Foster City, CA). Results obtained through qPCR analyses that were not within the limit of quantification (LOQ) of 3 to 300,000 copies required dilution of sample DNA in order to obtain defensible results. Standard curves for all qPCR assays had an r2 value of 0.95 or higher. 45 CHAPTER 3: CURRENT STUDY 3.1 Introduction In recent years many reaches and tributaries of the Santa Cruz River in southern Arizona have been listed for impairments of water quality. As established by the Arizona Department of Environmental Quality (ADEQ), these impairments included exceedances for standards of heavy metals, dissolved oxygen, pH, ammonia, chlorine, and the fecal bacteria Escherichia coli (ADEQ 2009). All water quality standards are established to maintain and protect water quality, public health, and the environment based upon the designated uses assigned to any particular water body. The designated uses for stream reaches in this study and their respective water quality standards are set according to the Arizona Administrative Code Title 18, Chapter 11 (AZSOS 2014). Designated uses of the river vary but include Aquatic and Wildlife warm water (A&Ww), Aquatic and Wildlife effluent dependent water (A&Wedw), Full-­‐body contact (FBC), Partial Body Contact (PBC), Domestic Water Source (DWS), Fish Consumption (FC), Agricultural Irrigation (AgI), and Agricultural Livestock Watering (AgL). When a stream reach has more than one designated use, the use containing the most stringent water quality standard applies. Table 6 provides a comprehensive overview of each sampling site in this study and information concerning their designated uses. 46 The Santa Cruz River begins in the mountains east of Nogales, AZ after which the river flows south crossing into Mexico. After approximately 40 miles, it reenters the U.S. near Nogales and flows north towards Tucson, AZ. The majority of the Santa Cruz River is highly ephemeral and only attains a constant flow due to wastewater effluent being directed into the river at the Nogales International Wastewater Treatment Plant (NIWTP) located in Rio Rico, AZ. Consequently the Santa Cruz River is effluent-­‐dependent from the effluent outfall until the flow dissipates, some 15-­‐20 miles downstream from the wastewater outfall. The NIWTP received technological advancements in late 2009, but E. coli contamination in the Santa Cruz River downstream from the plant raised questions concerning the plant’s effluent quality subsequent to the upgrades. Therefore this study provided an evaluation of water quality pertaining to an approximate 25-­‐mile stretch of the Santa Cruz River both upstream and downstream from the NIWTP, as well as directly at the effluent outfall. In order to assess and improve water quality in the Santa Cruz River a total water quality assessment was needed. This required analyses pertaining to chemical, physical, and biological conditions of the water. This study not only sought to determine water quality conditions within a particular length of the Santa Cruz River, but also serves to aid in the determination of possible point and nonpoint sources of water pollution. This study reevaluated the effects of NIWTP effluent as a possible point source, as well as assisted in evaluating other possible localities of pollutant loading. In addition, the tracking of fecal pollution to a source was of particular interest due to recent concerns of high E. coli in the river. Recently 47 developed methods in microbial source tracking (MST) were employed to identify possible human and non-­‐human sources of fecal pollution based on the fecal associated bacteria Bacteroides. DNA from Bacteroides bacteria was analyzed in this study to indicate possible fecal contributions originating from the NIWTP and alternative sources. Ultimately all combined water quality research will allow ADEQ to apply water quality direction where needed and help to implement best management practices (BMPs) along the Santa Cruz River for its continued water quality improvement and sustainment. 3.2 Materials and Methods 3.2.1 Sampling Locations and Collection The approximate 25-­‐mile stretch of the Santa Cruz River in this study contained a total of seven sampling locations, two of which were tributaries (Nogales Wash and Potrero Creek). The remaining five sites were located directly on the Santa Cruz River. Water samples were collected from each site approximately every two weeks beginning in April 2013, and extending through early October 2013. This approximate six-­‐month collection period provided accountability for pre and post monsoon season (monsoon is generally July-­‐August) dynamics in water quality. See Figure 2 for a map of sampling site locations. 48 Water Sampling Sites: Site 1 – Johnson’s Ranch (JR). Located approximately ½ mile north of the international border of the U.S. and Mexico, site one serves as a background for water quality on the Santa Cruz River. Water samples taken here were from accumulated water pools shortly after precipitation had occurred due to ephemeral conditions and lack of water flow. Site 2 – Nogales Wash (NW). The NW is a tributary that serves as a drainage for the cities of Nogales, Sonora, Mexico, and Nogales, Arizona. It is a concrete ditch for most of its length until its confluence with Potrero Creek. Site 3 – Potrero Creek (PC). This small volume tributary converges with the Nogales Wash after which point it meets its confluence with the Santa Cruz River directly before the NIWTP. Flow volume is generally low enough that it does not reach the confluence point except during some rain events. Site 4 – NIWTP outfall (WO). The effluent directed into the Santa Cruz River at WO provides approximately 25 cubic feet per second (cfs) of water flow near Rio Rico, AZ. The effluent provides a source of year-­‐round water flow to the river for approximately 15-­‐20 miles. 49 Site 5 – Rio Rico (RR). Site five is located just two miles downstream of site four. However, the Santa Cruz River passes through free-­‐range grazed fields and a small wash converges approximately half way between the two sites. Continuous water flow is observed at this site. Site 6 – Santa Gertrudis Lane (SGL). The road SGL crosses through the Santa Cruz River approximately eight miles downstream of site five. Vehicles pass directly into the river to continue onto the other side of SGL because of low volume flow. The land between RR and SGL is agricultural, with some areas having grazing animals. Site 7 – Tubac Bridge (TB). This site is located approximately four miles downstream from SGL, entering into the small town of Tubac, AZ. Agricultural and grazed land, as well as tourism are observed along the river between sites six and seven. Water flow volumes are low to absent depending upon the time of year. All water samples collected from these sites were obtained as grab samples by reach pole and placed into sterilized 1L bottles. Samples were immediately placed on ice for further analyses upon arrival at testing laboratories. Due to variations in water flow at each sampling location, a variable number of water samples were obtained at each sampling site. A total of 67 water samples were collected and analyzed from all sites during the course of this study. See Table 6 for further information regarding samples obtained from each sampling location. 50 Table 6. Sampling Sites Overview Sampling Site Site Code Johnson’s Ranch Nogales Wash1 Potrero Creek1 JR NW PC NIWTP Waste Water WO Outfall Rio Rico RR GPS Coordinates 30° 120.494’ N 110° 51.035’ W 31° 20.966’ N 110° 55.626’ W Lane Tubac Bridge SGL TB Site Uses Number DWS, FC, AgI, Samples Collected 1 4 2 11 3 11 4 11 5 12 6 10 7 8 AgL A&Ww, PBC A&Ww, FBC, 110° 57.652’ W FC, AgL 31° 27’ 24.12” N A&Wedw, 110° 58’ 5.253” W PBC, AgL 31° 28.193’ N 110° A&Wedw, PBC, AgL 31° 33.738’ N A&Wedw, 111° 02.759’ W PBC, AgL 31° 36.829’ N A&Wedw, 111° 02.467’ W PBC, AgL 1These sites are tributaries to the Santa Cruz River Number of A&Ww, FBC, 31° 25.823’ N 59.555’ W St. Gertrudis Designated 51 Figure 2. Map of Sampling Sites
Map showing all sampling locations from the U.S./Mexico border to Tubac, AZ. Source: http://gisweb.azdeq.gov/arcgis/emaps/ 52 Numerical analyses in this study contain means as well as geometric means pertaining to data collected over the course of the study period. Although many scientific studies rely on a typical mean value for parameters tested over time, ADEQ and other regulatory agencies employ the use of a geometric mean for many analyses over a given time interval. For example, the use of a geometric mean (of at least four consecutive samples) is utilized by ADEQ in the determination of chronic water quality standards for pollutants such as E. coli, ammonia, and heavy metals (ADEQ 2009a). In accordance with ADEQ water quality assessments, the use of a geometric mean is used for most parameters evaluated in this study. 3.2.2 Chemical and Physical Analyses A series of chemical and physical parameters were tested for in this study in order to provide a robust assessment of water quality. Chemical analyses included pH, total chlorine, dissolved oxygen, ammonia, hardness, dissolved copper and cadmium, as well as total cadmium. Physical parameters included temperature, conductivity, turbidity and water discharge. Some parameter measurements were obtained in the field, while others were more feasibly performed in a laboratory setting. Parameters tested in the field included total chlorine, dissolved oxygen, water temperature, conductivity, turbidity, and water discharge (water flow). All chemical and elemental analytes, as well as pH, were examined as water samples were transported back to laboratories 53 for testing. Table 7 provides an overview of each parameter with their detection limit and method of detection. Table 7. List of Chemical and Physical Parameters Tested Parameter/Analyte Detection Limit Method pH pH scale SM 4500 H+ Conductivity 1 uS/cm SM 2510 Turbidity 0.01 NTU SM 2130 Total Chlorine 0.02 mg/L SM 4500-­‐CI G Dissolved Cadmium 0.0010 mg/L EPA 200.7 Rev. 4.4 Dissolved Copper 0.010 mg/L EPA 200.7 Rev. 4.4 Total Cadmium 0.0010 mg/L EPA 200.7 Rev. 4.4 Ammonia 0.050 mg/L SM 4500 NH3 D Calcium 2 mg/L EPA 200.7 Rev. 4.4 Magnesium 2 mg/L EPA 200.7 Rev. 4.4 3.2.3 Microbiological Indicator Tests Specific organisms termed “indicators” are utilized in order to determine the microbiological health of surface waters. Indicator organisms used in this study included E. coli, Pseudomonas, and Enterococcus. Analyses were performed via a cultural procedure (SM 9223B) utilizing the IDEXX quanti-­‐tray system (IDEXX Laboratories, Westbrook, ME). This consisted of 100-­‐mL of each water sample being added to 120-­‐mL sample bottles, after which organism-­‐specific substrates were then added to each bottle for cultural procedures. After complete homogenization of water and substrate the samples were poured into 97-­‐well quanti-­‐trays, sealed, and 54 incubated at 35 ± 0.5°C for approximately 24 hours. Results were then read by counting the number of yellow or fluorescent wells under UV light in order to obtain a most probable number (MPN) per 100 mL. Table 8 shows each cultural procedure used in this study. Table 8. Fecal Indicator Tests Performed Microbial Target Procedure Name Total coliforms / E. coli Colilert Enterococcus Enterolert Pseudomonas Pseudalert Method IDEXX; SM 9223B IDEXX; ASTM D6503-­‐99 IDEXX 3.2.4 DNA Extraction and Purification All water samples were filtered through a Millipore 0.45 µm filter (EMD Millipore, Billerica, MA) by placing each filter on a filtering manifold connected to a vacuum tube and collection flask, into which filtered sample water could be deposited and properly discarded. Filtered volumes ranged from 50 to 500 mL. Any differences in volumes filtered were due to variable levels of turbidity within each sample. All filters were then placed into 15 mL conical tubes and stored at 4°C until DNA extraction began. DNA was later extracted from each filter using the MOBio PowerWater® DNA Isolation Kit as described by the manufacturer protocol (MOBio, Carlsbad, CA). Approximately 1 mL of DNA concentrate was obtained from each 55 sample. All DNA concentrates were then stored at -­‐80°C until molecular testing began. 3.2.5 Molecular Methods MST methods were used in this study to identify possible sources of fecal pollution based on Bacteroides bacteria. Analyses were performed via quantitative PCR (qPCR) analyses utilizing three specific DNA markers to detect contributions from total Bacteroides (generated from multiple fecal sources), humans, and bovine. These corresponding markers were Allbac, HF183, and the CowM2 respectively (Seurinck, et al. 2005; Layton, et al. 2006; Shanks et al. 2010). All DNA markers targeted genes identified in Bacteroides, however the Allbac and HF183 markers target the 16S rRNA gene, while the CowM2 marker targets the HDIG domain protein gene. The Allbac and HF183 assays were performed using SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA). Reaction wells for each individual assay contained a total of 25 µL consisting of 12.5 µL SYBR Green master mix, 1 µL of each assay-­‐specific forward and reverse primer, 2.5 µL bovine serum albumin (BSA), 6 µL RT-­‐grade water, and 2 µL DNA sample. BSA (Fisher Scientific, Waltham, MA) was added to each reaction to stabilize enzymes and neutralize possible nucleases throughout the qPCR process. DNA primers for both assays were at 15 pmol concentrations. Each temperature profile was followed as outlined by Seurinck et al. 56 (2005) and Layton et al. (2006). The temperature profiles for each assay are displayed in Table 9. The CowM2 assay was performed using a TaqMan® probe and primers (Applied Biosystems, Foster City, CA). Each 25 µL sample mixture for the CowM2 assay differed slightly from the composition of reaction mixtures for Allbac and HF183. Because the CowM2 assay is probe based, a solution of forward and reverse primers, probe, and RT-­‐grade water was made and added to each reaction well. The resultant primers and probes for the assay consisted of 500 µM and 100 µM concentrations respectively. Each qPCR reaction well contained 3.5 µL of primer/probe mixture, 12.5 µL of 1X TaqMan universal PCR master mix, 2.5 µL BSA, 4.5 µL RT-­‐grade water, and 2 µL of DNA target for a total of 25 µL. The CowM2 assay for this study followed Shanks et al. (2008) and can be seen in Table 9. 57 Table 9. Overview of qPCR Markers and Temperature Profiles Assay Allbac Holding Time 50°C for 2 minutes 95°C for 10 minutes HF183 50°C for 2 minutes 95°C for 10 minutes CowM2 50°C for 2 minutes 95°C for 10 minutes Cycling Stage 95°C for 30 seconds 60°C for 45 seconds 95°C for 30 seconds 53°C for 1 minute 60°C for 1 minute 95°C for 15 seconds 60°C for 1 minute Melt Curve 95°C for 15 seconds 60°C for 1 minute 95°C for 15 seconds 95°C for 15 seconds 60°C for 1 minute 95°C for 15 seconds N/A Number of Cycles 50 40 45 All DNA samples were run in triplicate with a negative control and a set of serially diluted positive controls. Positive controls were obtained by plasmid DNA cloning of each of the three marker sequences. The limit of quantification (LOQ) for all qPCR analyses was 3 to 300,000 copies. Assays were performed using 96-­‐well qPCR reaction plates and run on the StepOnePlus qPCR instrument (Applied Biosystems, Foster City, CA). Results obtained through qPCR analyses that were not within the LOQ required dilution of sample DNA in order to obtain defensible results. Standard curves for all qPCR assays had an r2 value of 0.95 or higher. 3.3 58 Results and Discussion 3.3.1 Chemical and Physical Conditions Water discharge measurements yielded variable flow regimes for some sites, while others remained more constant. The sites with the most consistent flows were NW, PC, WO, and RR. Sites NW and PC are low volume sites (mean ≤ 3 cfs) with flow coming directly from Mexico (NW), or coming in from surrounding foothills (PC). Site WO contained the highest mean flow volume due to the constant effluent be directed into the river at this site (mean ~25 cfs). Water volumes at RR remained consistent (mean ~18 cfs), as it is only located two miles downstream of WO. Sites JR, SGL, and TB all had more variable flow patterns. Site JR never yielded any flowing water although flowing conditions momentarily occurred after rain events (water samples were obtained from pooled water after flowing water had depleted). Sites SGL and TB are located near the end of the sampling area, where the effluent flow from the NIWTP tends to dissipate. Accordingly, these sites show lower flow volumes. Sites NW and PC are tributaries that inconsistently reach their confluence with the Santa Cruz River allowing the NIWTP effluent to provide the only consistent source of water flow for the Santa Cruz River in this study. Figure 3 shows mean water discharge at each sampling location (JR was omitted due to lack of discharge). 59 Figure 3. Graph of Mean Discharge per Site Mean Discharge per Site (ft.3/second ) 30.0 25.0 NW 20.0 PC 15.0 WO RR 10.0 SGL 5.0 TB 0.0 The pH levels per A&Ww/A&Wedw standards state that the pH must remain within the specified range of 6.5 to 9 (ADEQ, 2009a). Results indicate that 92% of the samples tested remained within this range throughout the study period. The mean pH level at each site remains within the specified range as is shown in Figure 4. 60 Figure 4. Graph of Mean pH Level per Site pH level Mean pH 9.50 JR 9.00 NW 8.50 PC 8.00 WO 7.50 7.00 RR SGL 6.50 6.00 TB Dissolved oxygen standards are set at 6.0 mg/L for A&Ww, and 3.0 mg/L for A&Wedw (ADEQ, 2009a). In order to achieve this standard the dissolved oxygen levels must be equal to or higher than the limits as set for each designated use. The only sites to render exceedances, and thereby go below the limits, occurred at JR and PC. These exccedances occurred three times at each site. However, dissolved oxygen at JR was obtained from idle pools and flow volumes at PC were consistently low. All other measurements for dissolved oxygen at JR and PC, as well as those taken at all remaining sites, met dissolved oxygen standards per their designated uses. Total chlorine levels observed in this study were compared to A&Ww/A&Wedw standards that correspond to chronic and acute standards of 11 and 19 µg/L respectively. Total chlorine levels observed in this study exceeded 61 chronic standards at every site and over 90% of single samples exceeded the acute standard. The limit of detection for the total chlorine method used in this study was 20 µg/L. Therefore chlorine levels below this limit were generally not detected. Figure 5 shows geometric mean chlorine levels observed at each site throughout the full study period. The dotted line represents the chronic total chlorine limit for A&Ww/A&Wedw standards (11 µg/L). Figure 5. Graph of Geometric Mean Total Chlorine Levels per Site Geometric Mean for Total Chlorine 500 450 Total Cl (µg /L) 400 350 300 250 Geometric mean Cl 200 Chronic Cl limit 150 100 50 0 JR NW PC WO RR SGL TB Further investigation into background chlorine levels in surface waters revealed that levels observed during this study appear typical of levels observed in natural waters (Weiner, 2008; Faust & Aly, 1981). Chlorine is a natural constituent of many igneous rocks and is dissolved into surface waters through natural 62 weathering processes. Total chlorine levels observed in surface water can exceed 1000 µg/L without anthropogenic influences (Weiner, 2008; Faust & Aly, 1981). Total chlorine levels in this study appear to be typical of natural surface waters, but may need to be further investigated in the future. Ammonia standards are divided into acute and chronic categories for all A&W designations. Acute limits vary with the pH of the water, while chronic limits vary with pH and temperature (ADEQ, 2009a). From all samples collected, only one acute exceedance occurred at NW on 7/31/13 with an observed value of 6.6 mg/L (limit of 4.71 mg/L at pH = 8.3). No other acute exceedances were observed throughout the study. Chronic limits become more stringent as temperatures and pH levels increase. The only site to exceed chronic standards (based on the geometric mean of any four consecutive samples) occurred at NW from samples taken in July through August. Table 10 shows this chronic exceedance at NW based on its corresponding mean temperature and pH level during the same time period. Ammonia levels observed at all other sites fell within the designated limits for acute and chronic categories throughout the study period. Table 10. Table of Chronic Ammonia Data (reference: ADEQ, 2009a) Site Code Mean pH Approx. Mean Temp (°C) Chronic Ammonia Limit (mg/L) NW 7.9 30 1.03 Geometric Mean Ammonia Observed (mg/L) 1.41 Over Chronic Limit YES 63 Heavy metals tested in this study were dissolved and total cadmium and dissolved copper. The allowable limits for dissolved metals in surface waters are dependent upon water hardness and are likewise divided into acute and chronic categories. However, no chronic exceedances occurred due to the lack of metals detected throughout the study. Total and dissolved cadmium were never detected throughout the course of the study. Dissolved copper was only detected at sites NW, WO, and RR during two consecutive sampling dates in July. Each site yielded two detections. NW yielded dissolved copper in notably higher concentrations compared to the other sites, but detections at all sites were in exceedance of acute limits. Figure 6 shows all dissolved copper levels observed throughout the study. Table 11 shows applicable acute standards for each instance of dissolved copper detected, along with site-­‐specific corresponding levels of water hardness. Figure 6. Graph of Dissolved Copper Levels Observed Dissolved Copper 90 81 Diss. Cu (µg /L) 80 70 60 NW 50 40 30 20 WO 27 RR 12 10 13 9.9 9.8 0 7/18/13 7/31/13 64 Table 11. Dissolved Copper Acute Standards and Water Hardness Site Observed Hardness on 7/18/13 (mg/L) NW 67.3 WO 52.7 RR 55.2 *(ADEQ, 2009a) Allowable Acute Standard* (µg/L) 9.22 7.39 7.65 Observed Hardness on 7/31/13 (mg/L) 70 54.4 54.4 Allowable Acute Standard* (µg/L) 9.6 7.52 7.52 3.3.2 Microbial Indicators Tests for all microbial indicators were counted in MPN/100 mL of water. Although multiple indicator organisms were tested, only E. coli contains a designated standard for surface water in this study. The applicable standard for E. coli per FBC/PBC designations corresponds to a geometric mean of 126 MPN/100 mL of water (ADEQ, 2009a). Figure 7 shows the geometric mean of E. coli observed at each site throughout the study period. The dotted horizontal line represents the 126 MPN/100 mL E. coli standard. 65 Figure 7. Graph of Mean E. coli Levels Observed per Site Geometric Mean for E. coli 1000 348 195 155 114 222 157 E. coli FBC/PBC limit MPN/100 mL 100 10 5 1 JR NW PC WO RR SGL TB Only two sites did not exceed the 126 MPN/100 mL standard (WO and JR) over the course of the study. WO had lower counts of E. coli by an order of magnitude compared to all other sites, while NW contained the highest counts. WO possessed the lowest (geometric mean) counts of all indicator organisms tested in this study, indicating that NIWTP is effectively removing fecal contamination from their influent and clearly meeting the 126 MPN/100 mL standard for E. coli. An abrupt increase in E. coli counts was observed downstream from WO beginning at RR, which levels persisted throughout the remaining sites. This sudden increase in fecal contamination was not limited to E. coli, but was similarly observed in all indicator organisms tested. Figure 8 illustrates the geometric mean MPN/100 mL at each site for Enterococcus and Pseudomonas throughout the study period. 66 Figure 8. Graph of Mean Indicator Organism Levels per Site Indicator Organism Geometric Means 10000 MPN/100 mL 1000 Pseudomonas 100 Enterococcus 10 1 JR NW PC WO RR SGL TB The trends in Figure 8 similarly show moderate to high bacterial counts observed in sites JR, NW, and PC with a notable decrease at WO for both organisms. A sharp increase began at RR and continued to SGL and TB. This follows similar patterns as is observed in E. coli from Figure 7. Also, NW contained the highest counts for Pseudomonas, while WO possessed the lowest counts for both Pseudomonas and Enterococcus. Although the Enterococcus counts are relatively high at NW, the highest Enterococcus counts are observed at JR. The general trend for all indicator organisms showed that consistent fecal pollution occurred at NW and that additional fecal inputs likely occurred downstream of WO. 67 3.3.3 Microbial Source Tracking Results obtained through qPCR analysis of Bacteroides bacteria indicate fecal pollution observed in the Santa Cruz River and its tributaries originates from multiple sources. The Allbac marker yielded positive detections for all samples collected, while the human marker HF183 possessed similar results with positive detection in approximately 97% of all samples. However, the bovine marker CowM2 only produced positive detection for approximately 33% of samples. The positive detection rate for the Allbac and HF183 markers (100% and 97%, respectively) are similar but the resultant concentrations of each respective marker varied. Allbac marker concentrations were higher than HF183 (and CowM2) concentrations by at least an order of magnitude in over 98% of samples. DNA was serially diluted for Allbac assays in order for detection levels to fall within the LOQ of 3 to 300,000 copies. All concentrations obtained using HF183 and CowM2 markers fell within the same LOQ and did not undergo serial dilutions. qPCR assay results from all markers were converted to concentrations (gene copies) per 100 mL of water in order to more feasibly assess fecal contributions in this study. These concentrations are shown in Table(s) 1a, 2a, and 3a (see Appendix A). Data from qPCR analyses suggests that fecal contamination occurred at all sites. However it should be noted that the Allbac marker accounts for fecal contamination originating from any mammalian source, and only two source-­‐
specific markers accounting for human and bovine fecal contributions were utilized in this study. Fecal inputs from bovine species were detected but at low 68 concentrations and relatively infrequently (approx. 33% positive detection rate). However, RR yielded the highest number of bovine positive results with a 66% positive detection rate. RR also possessed the single highest concentration of bovine marker at a level of 8.07E+03/100 mL. Figure 9 illustrates the percentage of positive bovine marker detections that occurred at each sampling location. Additionally, Figure 10 shows the corresponding mean counts for the CowM2 marker at each sampling location. Figure 9. Graph of Positive Bovine Detections Observed per Site Percent of Positive CowM2 Results per Site 100% 90% 80% 66% 70% 60% 50% 40% 33% 50% 50% SGL TB 33% 30% 20% 9% 8% 10% 0% JR NW PC WO RR 69 Figure 10. Mean CowM2 Markers Observed per Site Mean CowM2 Counts per Site CowM2 counts/100 mL 10000 1000 100 10 1 JR NW PC WO RR SGL TB The positive detections and resulting concentrations for CowM2 marker observed in Figures 9 and 10 indicate bovine fecal pollution occurred more frequently and in higher mean concentrations after WO. This trend of low counts at WO, followed by noted increases thereafter mirrors the trend of fecal indicators illustrated previously. CowM2 concentrations appear to gradually decrease at each site after RR, but still maintained higher mean concentrations than what was observed at WO. Conversely, NW possessed the lowest mean counts for CowM2 marker. Human marker HF183 concentrations are higher than bovine concentrations and detected more frequently, suggesting human fecal contamination is present at all sites. However, fecal contamination observed from the human marker at sites 70 downstream of WO appear to be at similar or lower levels than is observed at WO. A boxplot of HF183 counts observed at each site helps to illustrate these findings in Figure 11. However, it is important to note that genes detected from each marker cannot be compared at a 1:1 ratio because they are not proportionally found in the Bacteroides genomes. Figure 11. Boxplot of HF183 Counts Observed at Sampling Locations Boxplot for HF183 Counts per Site 1.00E+07 HF183 counts/100 mL 1.00E+06 1.00E+05 1.00E+04 1.00E+03 1.00E+02 1.00E+01 1.00E+00 JR NW PC WO RR SGL TB *Each boxplot depicts median (line in each box), first and third quartiles (top and bottom of each box), and the maximum and minimum values for each site. The general trend for the last four sites shows HF183 counts appear to have marginally decreased after WO, beginning at RR, and then more significantly at SGL and TB. One outlier sample from RR yielded the highest maximum count for HF183. However, a geometric mean analysis of HF183 markers (not shown) also revealed a decrease in HF183 levels at all sites downstream of WO. Boxplot and geometric 71 mean data combined suggest that no additional human fecal inputs consistently occurred downstream from the NIWTP outfall. Therefore, human fecal contributions derived from NIWTP or other sources are unlikely to be significant factors pertaining to exceedances of E. coli observed after WO. 3.3.4 Further Analyses and Discussion Each sampling location was strategically chosen to be able to best assess water quality conditions throughout each reach of river in this study. However, due to variable water flow conditions at each site, a limited number of water samples were obtained from JR and TB. In particular, JR served as a background of natural water flows coming from Mexico into the United States, but only four total water samples were obtained from this site. In order to better assess and compare water quality conditions downstream, more samples should be obtained from JR or another background site for comparisons of water samples to background levels in future studies. NW is a tributary that crosses into the U.S. from Nogales, Sonora, Mexico and has been listed as an impaired water body for multiple water quality parameters (E. coli, ammonia, chlorine, and dissolved copper) in past years (ADEQ, 2009). It is a relatively low volume tributary ultimately converging with the Santa Cruz River directly before WO. NW contained the highest geometric mean fecal indicator counts for E. coli and Pseudomonas. The human marker HF183 was also detected in highest concentrations at this site per geometric mean analysis, suggesting that human fecal 72 inputs are consistently occurring at this location (See also Figure 11). NW also exceeded chronic limits for ammonia in addition to one acute ammonia exceedance during the month of July. The highest dissolved copper levels were also observed at NW, as well as sudden spikes in total chlorine, all of which also occurred during July. These abrupt increases in pollutants may indicate possible pollutant dumping into NW is (infrequently) occurring in Mexico and being fed downstream into the United States. Efforts were made to investigate if these increased pollutant levels observed in July had possible links to increased water discharge. Linear regression analyses were used to indicate possible correlations between water discharge and pollutant levels. These analyses yielded no significant correlations (p-­‐values > 0.1). This indicates that increased water flow may have no significant impacts with the increased pollutant levels observed. It further suggests that pollutant surges at NW observed during the monsoon season may be due to variables other than increased water flow (i.e. – pollutant dumping). The dates during which dissolved copper was detected at NW, it was also detected at WO and RR. However, higher concentrations of dissolved copper were observed at NW as compared to WO and RR (WO and RR were sequential sampling locations in this study). The higher concentrations observed at NW may represent raw concentrations that NIWTP was receiving from influent during the same time period, but resulting dissolved copper levels in the effluent at WO may have been lower due to the wastewater treatment process. Dissolved copper levels at RR were very similar to those observed at WO, indicating no other inputs occurred in 73 between WO and RR. Dissolved copper was never detected downstream of RR, possibly due to undetectable levels as water flow began to dissipate. Further investigation into dissolved copper at the NIWTP influent should be addressed as well as sources of dissolved copper observed in NW. Site WO served as a crucial site in this study due to the consistent water discharge it provides into the Santa Cruz River. Water samples at WO were obtained approximately 50 feet from the outfall directly before the effluent reached the Santa Cruz River. This represented freshly produced effluent for analyses. Water flow averaged approximately 25 cfs at WO, which provided a continual water source for the river and riparian areas downstream. WO contained the lowest mean bacterial counts for all indicator organisms tested in this study. In particular, E. coli averaged 5 MPN/100 mL and fell well within the regulated standard. WO was also among sites with the lowest concentrations of ammonia and never exceeded designated standards for this pollutant. WO did exceed total chlorine limits (as did all sites), but was among sites with lower geometric mean levels of total chlorine (90 µg/L). Sites downstream from WO contained higher levels of total chlorine but are less directly influenced by NIWTP effluent. However, as indicated earlier, applicable total chlorine limits may need to be readdressed in the future to account for background levels in natural waters as well as possible problems with the instrument used in this evaluation (Weiner, 2008; Faust & Aly, 1981). Overall, the NIWTP provided a clean and consistent source of water for the Santa Cruz River that met surface water quality standards tested in this study. 74 Fecal contamination appears to be occurring at elevated levels at tributaries NW and PC, as well as in the Santa Cruz River at sites downstream of WO (RR, SGL, and TB). E. coli exceeded surface water standards for all five of these sites. Water pollution from Mexico is a probable source of fecal pollution for NW and possibly PC. Analyses from human marker HF183 and CowM2 suggest high human and low bovine influences at NW. This would suggest that the high E. coli observed at NW is (at least) in part due to human inputs occurring in NW. In contrast, HF183 counts at WO were higher than counts observed at downstream sites, but E. coli counts at WO were significantly lower compared to downstream sites. This suggests that human fecal inputs are unlikely to be the source of heightened E. coli observed downstream from WO. In addition, CowM2 data suggests that cattle have low influence concerning fecal contamination at sites NW, PC, and WO but more significantly contribute to fecal contamination at RR, SGL and TB. The similarity in E. coli and CowM2 results observed after WO suggests fecal inputs from cattle are likely contributing to sharp increases in fecal pollution downstream of WO. However, any future MST studies in this region should address other sources of fecal inputs occurring after the NIWTP outfall that could also be potential contributing factors to the elevated E. coli levels observed in this stretch of the Santa Cruz River. 75 3.4 Conclusions •
The NIWTP effluent met ADEQ water quality criteria for ammonia, dissolved oxygen, pH, and heavy metals in most instances. In addition it met ADEQ standards for E coli and contained the lowest mean counts of all indicator organisms tested in this study. The NIWTP appears to have a positive contribution to the Santa Cruz River and its riparian areas due to its constant flow of clean effluent. •
The Nogales Wash (NW) contains consistently high levels of E. coli and also possesses increased levels of ammonia, chlorine, and dissolved copper on an irregular basis. Further investigation and collaboration with Mexican officials may yield water quality improvements coming from this tributary. •
All sites located downstream of the NIWTP contained E. coli concentrations in excess of ADEQ standards. MST studies indicated that human and bovine influences are detected downstream of WO. However, bovine markers were detected more frequently downstream at sites RR, SGL and TB. Fecal indicator counts suggest that bovine fecal inputs may be partially responsible for higher counts in E. coli after WO. Implementation of BMPs for the riparian areas of the Santa Cruz River after WO may render improvements to meet ADEQ standards. 76 APPENDIX A Table 1a. qPCR Results for Allbac Sample Sampling Target Name Date Name JR-­‐01 4/1/13 Allbac NW-­‐01 4/1/13 Allbac PC-­‐01 4/1/13 Allbac WO-­‐01 4/1/13 Allbac RR-­‐01 4/1/13 Allbac SGL-­‐01 4/1/13 Allbac TB-­‐01 4/1/13 Allbac WO-­‐03 5/2/13 Allbac RR-­‐03 5/2/13 Allbac SGL-­‐03 5/2/13 Allbac TB-­‐03 5/2/13 Allbac NW-­‐04 5/15/13 Allbac PC-­‐04 5/15/13 Allbac WO-­‐04 5/15/13 Allbac RR-­‐04 5/15/13 Allbac SGL-­‐04 5/15/13 Allbac TB-­‐04 5/15/13 Allbac NW-­‐05 5/29/13 Allbac PC-­‐05 5/29/13 Allbac WO-­‐05 5/29/13 Allbac RR-­‐05 5/29/13 Allbac SGL-­‐05 5/29/13 Allbac TB-­‐05 5/29/13 Allbac NW-­‐06 6/17/13 Allbac PC-­‐06 6/17/13 Allbac WO-­‐06 6/17/13 Allbac RR-­‐06 6/17/13 Allbac NW-­‐07 7/1/13 Allbac PC-­‐07 7/1/13 Allbac WO-­‐07 7/1/13 Allbac RR-­‐07 7/1/13 Allbac NW-­‐08 7/18/13 Allbac PC-­‐08 7/18/13 Allbac WO-­‐08 7/18/13 Allbac Result Concentration1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 2.97E+09 4.14E+08 1.40E+07 1.79E+08 8.93E+06 3.70E+06 9.10E+05 3.00E+06 3.31E+06 1.52E+03 1.85E+06 6.31E+06 2.15E+06 5.83E+06 3.54E+07 2.76E+06 8.06E+06 1.18E+07 4.75E+06 1.57E+07 9.02E+06 2.86E+07 2.00E+07 3.02E+07 1.08E+07 3.15E+07 1.92E+07 1.42E+09 6.41E+09 6.87E+06 6.80E+06 1.57E+07 1.63E+07 1.26E+09 RR-­‐08 7/18/13 SGL-­‐08 7/18/13 JR-­‐09 7/31/13 NW-­‐09 7/31/13 PC-­‐09 7/31/13 WO-­‐09 7/31/13 RR-­‐09 7/31/13 SGL-­‐09 7/31/13 TB-­‐09 7/31/13 JR-­‐10 8/15/13 NW-­‐10 8/15/13 PC-­‐10 8/15/13 WO-­‐10 8/15/13 RR-­‐10 8/15/13 SGL-­‐10 8/15/13 TB-­‐10 8/15/13 JR-­‐11 8/31/13 NW-­‐11 8/31/13 PC-­‐11 8/31/13 RR-­‐11 8/31/13 SGL-­‐11 8/31/13 TB-­‐11 8/31/13 NW-­‐12 9/18/13 PC-­‐12 9/18/13 WO-­‐12 9/18/13 RR-­‐12 9/18/13 SGL-­‐12 9/18/13 TB-­‐12 9/18/13 NW-­‐13 10/4/13 PC-­‐13 10/4/13 WO-­‐13 10/4/13 RR-­‐13 10/4/13 SGL-­‐13 10/4/13 1Concentrations are per 100mL 77 Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac Allbac + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 7.27E+06 3.81E+07 1.33E+07 3.51E+09 1.17E+07 1.33E+07 9.77E+07 1.53E+07 1.94E+07 1.02E+08 2.01E+07 1.09E+08 9.12E+06 4.43E+07 8.76E+06 5.37E+07 2.13E+06 7.88E+06 2.19E+07 1.50E+07 7.33E+06 1.29E+08 1.74E+08 9.86E+06 1.97E+06 1.59E+07 2.58E+06 3.08E+06 1.46E+08 2.84E+05 4.05E+06 1.62E+07 3.80E+06 Table 2a. qPCR Results for HF183 Sample Sampling Target Name Date Name JR-­‐01 4/1/13 HF183 NW-­‐01 4/1/13 HF183 PC-­‐01 4/1/13 HF183 WO-­‐01 4/1/13 HF183 RR-­‐01 4/1/13 HF183 SGL-­‐01 4/1/13 HF183 TB-­‐01 4/1/13 HF183 WO-­‐03 5/2/13 HF183 RR-­‐03 5/2/13 HF183 SGL-­‐03 5/2/13 HF183 TB-­‐03 5/2/13 HF183 NW-­‐04 5/15/13 HF183 PC-­‐04 5/15/13 HF183 WO-­‐04 5/15/13 HF183 RR-­‐04 5/15/13 HF183 SGL-­‐04 5/15/13 HF183 TB-­‐04 5/15/13 HF183 NW-­‐05 5/29/13 HF183 PC-­‐05 5/29/13 HF183 WO-­‐05 5/29/13 HF183 RR-­‐05 5/29/13 HF183 SGL-­‐05 5/29/13 HF183 TB-­‐05 5/29/13 HF183 NW-­‐06 6/17/13 HF183 PC-­‐06 6/17/13 HF183 WO-­‐06 6/17/13 HF183 RR-­‐06 6/17/13 HF183 NW-­‐07 7/1/13 HF183 PC-­‐07 7/1/13 HF183 WO-­‐07 7/1/13 HF183 RR-­‐07 7/1/13 HF183 NW-­‐08 7/18/13 HF183 PC-­‐08 7/18/13 HF183 WO-­‐08 7/18/13 HF183 RR-­‐08 7/18/13 HF183 SGL-­‐08 7/18/13 HF183 JR-­‐09 7/31/13 HF183 78 Result Concentration1 + + + + + + + + + -­‐ + + + + + + + + + + + + + + + + + + + + + + + + + + + 1.57E+02 6.22E+04 6.15E+03 1.80E+04 3.77E+03 5.65E+02 1.03E+01 2.68E+03 2.27E+03 0.00E+00 3.01E+01 1.27E+02 4.78E+01 3.99E+03 3.11E+03 2.62E+01 6.24E+01 8.36E+01 6.16E+01 1.36E+02 7.04E+01 5.36E+00 5.81E+01 1.26E+04 3.27E+04 7.86E+04 3.96E+04 6.35E+04 1.62E+05 4.37E+05 7.35E+06 2.88E+06 3.18E+05 2.21E+05 2.62E+05 1.04E+05 9.79E+04 NW-­‐09 7/31/13 PC-­‐09 7/31/13 WO-­‐09 7/31/13 RR-­‐09 7/31/13 SGL-­‐09 7/31/13 TB-­‐09 7/31/13 JR-­‐10 8/15/13 NW-­‐10 8/15/13 PC-­‐10 8/15/13 WO-­‐10 8/15/13 RR-­‐10 8/15/13 SGL-­‐10 8/15/13 TB-­‐10 8/15/13 JR-­‐11 8/31/13 NW-­‐11 8/31/13 PC-­‐11 8/31/13 RR-­‐11 8/31/13 SGL-­‐11 8/31/13 TB-­‐11 8/31/13 NW-­‐12 9/18/13 PC-­‐12 9/18/13 WO-­‐12 9/18/13 RR-­‐12 9/18/13 SGL-­‐12 9/18/13 TB-­‐12 9/18/13 NW-­‐13 10/4/13 PC-­‐13 10/4/13 WO-­‐13 10/4/13 RR-­‐13 10/4/13 SGL-­‐13 10/4/13 1Concentrations are per 100mL 79 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 HF183 + + + + + + + + + + + + + + + + + + + + + + + + + + -­‐ + + + 4.20E+06 6.87E+04 1.31E+05 1.85E+05 6.03E+04 8.15E+04 2.44E+03 2.37E+04 3.69E+04 1.76E+03 5.97E+03 5.20E+03 3.61E+04 6.97E+02 8.15E+03 1.88E+03 1.25E+03 2.17E+03 1.10E+04 1.15E+04 2.40E+00 5.63E+02 3.16E+02 3.78E+01 2.04E+02 1.59E+03 0.00E+00 5.51E+03 2.30E+03 4.32E+00 Table 3a. qPCR Results for CowM2 Sample Sampling Target Name Date Name JR-­‐01 4/1/13 CowM2 NW-­‐01 4/1/13 CowM2 PC-­‐01 4/1/13 CowM2 WO-­‐01 4/1/13 CowM2 RR-­‐01 4/1/13 CowM2 SGL-­‐01 4/1/13 CowM2 TB-­‐01 4/1/13 CowM2 WO-­‐03 5/2/13 CowM2 RR-­‐03 5/2/13 CowM2 SGL-­‐03 5/2/13 CowM2 TB-­‐03 5/2/13 CowM2 NW-­‐04 5/15/13 CowM2 PC-­‐04 5/15/13 CowM2 WO-­‐04 5/15/13 CowM2 RR-­‐04 5/15/13 CowM2 SGL-­‐04 5/15/13 CowM2 TB-­‐04 5/15/13 CowM2 NW-­‐05 5/29/13 CowM2 PC-­‐05 5/29/13 CowM2 WO-­‐05 5/29/13 CowM2 RR-­‐05 5/29/13 CowM2 SGL-­‐05 5/29/13 CowM2 TB-­‐05 5/29/13 CowM2 NW-­‐06 6/17/13 CowM2 PC-­‐06 6/17/13 CowM2 WO-­‐06 6/17/13 CowM2 RR-­‐06 6/17/13 CowM2 NW-­‐07 7/1/13 CowM2 PC-­‐07 7/1/13 CowM2 WO-­‐07 7/1/13 CowM2 RR-­‐07 7/1/13 CowM2 NW-­‐08 7/18/13 CowM2 PC-­‐08 7/18/13 CowM2 WO-­‐08 7/18/13 CowM2 RR-­‐08 7/18/13 CowM2 SGL-­‐08 7/18/13 CowM2 JR-­‐09 7/31/13 CowM2 80 Result Concentration1 -­‐ + + -­‐ -­‐ -­‐ -­‐ -­‐ -­‐ -­‐ -­‐ -­‐ -­‐ -­‐ + + -­‐ -­‐ + + + + -­‐ -­‐ -­‐ -­‐ + -­‐ + -­‐ -­‐ -­‐ -­‐ -­‐ + -­‐ -­‐ 0.00E+00 5.80E+01 2.21E+02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.37E+03 2.37E+02 0.00E+00 0.00E+00 1.17E+02 1.10E+02 3.00E+02 1.48E+03 0.00E+00 0.00E+00 0.00E+00 0.00E+00 3.53E+02 0.00E+00 2.07E+02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.94E+02 0.00E+00 0.00E+00 NW-­‐09 7/31/13 PC-­‐09 7/31/13 WO-­‐09 7/31/13 RR-­‐09 7/31/13 SGL-­‐09 7/31/13 TB-­‐09 7/31/13 JR-­‐10 8/15/13 NW-­‐10 8/15/13 PC-­‐10 8/15/13 WO-­‐10 8/15/13 RR-­‐10 8/15/13 SGL-­‐10 8/15/13 TB-­‐10 8/15/13 JR-­‐11 8/31/13 NW-­‐11 8/31/13 PC-­‐11 8/31/13 RR-­‐11 8/31/13 SGL-­‐11 8/31/13 TB-­‐11 8/31/13 NW-­‐12 9/18/13 PC-­‐12 9/18/13 WO-­‐12 9/18/13 RR-­‐12 9/18/13 SGL-­‐12 9/18/13 TB-­‐12 9/18/13 NW-­‐13 10/4/13 PC-­‐13 10/4/13 WO-­‐13 10/4/13 RR-­‐13 10/4/13 SGL-­‐13 10/4/13 1Concentrations are per 100mL 81 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 CowM2 -­‐ -­‐ -­‐ + + + + -­‐ -­‐ -­‐ -­‐ + + -­‐ -­‐ -­‐ + + -­‐ -­‐ + -­‐ + + -­‐ -­‐ -­‐ -­‐ + -­‐ 0.00E+00 0.00E+00 0.00E+00 8.07E+03 4.00E+01 2.30E+02 4.25E+02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 6.50E+01 2.77E+02 0.00E+00 0.00E+00 0.00E+00 2.98E+02 2.07E+03 0.00E+00 0.00E+00 2.86E+02 0.00E+00 1.05E+02 4.02E+02 0.00E+00 0.00E+00 0.00E+00 0.00E+00 2.60E+01 0.00E+00 82 REFERENCES ADEQ, Arizona Department of Environmental Quality. (2009) Santa Cruz River Watershed [Online]. Available from: http://www.azdeq.gov/environ/water/assessment/tmdl_status-­‐scr.html [Accessed 21 January 2014]. ADEQ, Arizona Department of Environmental Quality. (2009a) Water Quality Standards for Surface Waters [Online]. Available from: http://www.azdeq.gov/environ/water/standards/download/SWQ_Stand
ards-­‐1-­‐09-­‐unofficial.pdf [Accessed 21 January 2014]. Ahmed, W., Stewart, J., Powell, D., & Gardner, T. (2008). Evaluation of Bacteroides markers for the detection of human faecal pollution. Letters in Applied Microbiology, 46(2), 237-­‐242. Ashbolt, N.J., Grabow, W. O. K., and Snozzi, M. (2001). Indicators of microbial water quality. In “Water Quality: Guidelines, Standards, and Health” IWA publishing, London, pp. 289-­‐315 Aulenbach, B. T. (2010). Bacteria holding times for fecal coliform by mFC agar method and total coliform and Escherichia coli by Colilert(A (R))-­‐18 Quanti-­‐Tray(A (R)) method. Environmental Monitoring and Assessment, 161(1-­‐4): 147-­‐159. Bodey, G. P., Bolivar, R., Fainstein, V., & Jadeja, L. (1983). Infections Caused By Pseudomonas-­‐Aeruginosa. Reviews of Infectious Diseases, 5(2), 279-­‐313. Brewer, G. J. (2010). Risks of Copper and Iron Toxicity during Aging in Humans. Chemical Research in Toxicology, 23(2): 319-­‐326. Cabelli, V. J. (1978). Swimming Associated Disease Outbreaks. Journal Water Pollution Control Federation, 50(6), 1374-­‐1377. Cech, Thomas, V. 2003 Principles of Water Resources: History, Development, Management, and Policy. John Wiley and Sons, USA Charoenca, N., & Fujioka, R. S. (1993). Assessment Of Staphylococcus Bacteria In Hawaii Marine Recreational Waters. Water Science and Technology, 27(3-­‐
4), 283-­‐289. Davenport, C. V., Sparrow, E. B., & Gordon, R. C. (1976). Fecal Indicator Bacteria Persistence Under Natural Conditions In An Ice-­‐Covered River. Applied and Environmental Microbiology, 32(4), 527-­‐536. 83 Eckburg, P. B., Bik, E. M., Bernstein, C. N., Purdom, E., Dethlefsen, L., Sargent, M., . . . Relman, D. A. (2005). Diversity of the human intestinal microbial flora. Science, 308(5728), 1635-­‐1638. Faust, S. D., & Aly, O. M. (1981). Chemistry of Natural Waters. Ann Arbor, Mich: Ann Arbor Science Publishers. Field, K. G., & Samadpour, M. (2007). Fecal source tracking, the indicator paradigm, and managing water quality. Water Research, 41(16), 3517-­‐3538. Gerba, C.P., (2009) Indicator Microorganisms, “Environmental Microbiology”. Edited by R.M. Maier, I.L. Pepper, and C.P. Gerba. Academic Press, San Diego, CA, USA. pp. 485-­‐499. Gleeson, C., and Gray, N. (1997) “The Coliform Index and Waterborne Disease,” E and FN Spon, London, UK Gourmelon, M., Caprais, M. P., Segura, R., Le Mennec, C., Lozach, S., Piriou, J. Y., & Rince, A. (2007). Evaluation of two library-­‐independent microbial source tracking methods to identify sources of fecal contamination in french estuaries. Applied and Environmental Microbiology, 73(15), 4857-­‐4866. Hagedorn, C., Crozier, J. B., Mentz, K. A., Booth, A. M., Graves, A. K., Nelson, N. J., & Reneau, R. B. (2003). Carbon source utilization profiles as a method to identify sources of faecal pollution in water. Journal of Applied Microbiology, 94(5), 792-­‐799. Hagedorn, C., Blanch, A. R., & Harwood, V. J. (2011). Microbial source tracking: Methods, applications, and case studies. New York: Springer. Hansen, D. L., Ishii, S., Sadowsky, M. J., & Hicks, R. E. (2009). Escherichia coli Populations in Great Lakes Waterfowl Exhibit Spatial Stability and Temporal Shifting. Applied and Environmental Microbiology, 75(6), 1546-­‐
1551. Hartel, P. G., J. D. Summer and W. I. Segars (2003). Deer diet affects ribotype diversity of Escherichia coli for bacterial source tracking. Water Research 37(13): 3263-­‐3268. Havelaar, A. H., Pothogeboom, W. M., Furuse, K., Pot, R., & Hormann, M. P. (1990). F-­‐
Specific Rna Bacteriophages And Sensitive Host Strains In Feces And Waste-­‐Water Of Human And Animal Origin. Journal of Applied Bacteriology, 69(1), 30-­‐37. Hitchman, Michael, L. (1978). Measurement of Dissolved Oxygen. John Wiley and Sons, New York 84 Huycke, M. M. (2002). Physiology of enterococci. Enterococci: Pathogenesis, Molecular Biology, and Antibiotic Resistance, 133-­‐175. Insalata, N. F., J. S. Witzeman and F. C. A. Sunga (1969). Fecal Streptococci in Industrially Processed Foods -­‐ An Incidence Study. Food Technology 23(10): 1316-­‐&. Kawata, K., Yokoo, H., Shimazaki, R., & Okabe, S. (2007). Classification of heavy-­‐
metal toxicity by human DNA microarray analysis. Environmental Science & Technology, 41(10), 3769-­‐3774. Layton, A., McKay, L., Williams, D., Garrett, V., Gentry, R., & Sayler, G. (2006). Development of Bacteroides 16S rRNA gene TaqMan-­‐based real-­‐time PCR assays for estimation of total, human, and bovine fecal pollution in water. Applied and Environmental Microbiology, 72(6), 4214-­‐4224. Manero, A., Vilanova, X., Cerda-­‐Cuellar, M., & Blanch, A. R. (2002). Characterization of sewage waters by biochemical fingerprinting of Enterococci. Water Research, 36(11), 2831-­‐2835. Mara, D. D. and J. I. Oragui (1983). Sorbitol-­‐fermenting bifidobacteria as specific indicators of human fecal pollution. Journal of Applied Bacteriology 55(2): 349-­‐357. McLain, J. E. T., C. M. Rock, K. Lohse and J. Walworth (2011). False-­‐positive identification of Escherichia coli in treated municipal wastewater and wastewater-­‐irrigated soils. Canadian Journal of Microbiology 57(10): 775-­‐
784. Meays, C. L., Broersma, K., Nordin, R., & Mazumder, A. (2004). Source tracking fecal bacteria in water: a critical review of current methods. Journal of Environmental Management, 73(1), 71-­‐79. Moore, D. F., Harwood, V. J., Ferguson, D. M., Lukasik, J., Hannah, P., Getrich, M., & Brownell, M. (2005). Evaluation of antibiotic resistance analysis and ribotyping for identification of faecal pollution sources in an urban watershed. Journal of Applied Microbiology, 99(3), 618-­‐628. Mundt, J. O. (1961). Occurrence Of Enterococci -­‐ Bud, Blossom, And Soil Studies. Applied Microbiology 9(6): 541-­‐&. Mundt, J. O., Coggin, J. H., & Johnson, L. F. (1962). Growth of streptococcus faecalis var. Liquefaciens on plants. Applied Microbiology, 10(6), 552 85 Mushi, D., Byamukama, D., Kivaisi, A. K., Mach, R. L., & Farnleitner, A. H. (2010). Sorbitol-­‐fermenting Bifidobacteria are indicators of very recent human faecal pollution in streams and groundwater habitats in urban tropical lowlands. Journal of Water and Health, 8(3), 466-­‐478. Niyogi, S., & Wood, C. M. (2004). Biotic ligand model, a flexible tool for developing site-­‐specific water quality guidelines for metals. Environmental Science & Technology, 38(23), 6177-­‐6192. Osborn, M.J., (2004). Introduction and Historical Background. In: “Indicators of Waterborne Pathogens”. The National Academies Press, Washington DC, pp. 1-­‐47 Pagenkopf, G. K. (1983). Gill Surface Interaction-­‐Model For Trace-­‐Metal Toxicity To Fishes -­‐ Role Of Complexation, Ph, And Water Hardness. Environmental Science & Technology, 17(6), 342-­‐347. Payment, P., & Franco, E. (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(8), 2418-­‐2424. Price, B., Venso, E., Frana, M., Greenberg, J., & Ware, A. (2007). A comparison of ARA and DNA data for microbial source tracking based on source-­‐classification models developed using classification trees. Water Research, 41(16), 3575-­‐3584. Resnick, I. G. and M. A. Levin (1981). Assessment Of Bifidobacteria As Indicators Of Human Fecal Pollution. Applied and Environmental Microbiology 42(3): 433-­‐438. Roslev, P., & Bukh, A. S. (2011). State of the art molecular markers for fecal pollution source tracking in water. Applied Microbiology and Biotechnology, 89(5), 1341-­‐1355. Scott, T. M., Jenkins, T. M., Lukasik, J., & Rose, J. B. (2005). Potential use of a host associated molecular marker in Enterococcus faecium as an index of human fecal pollution. Environmental Science & Technology, 39(1), 283-­‐
287. Seurinck, S., T. Defoirdt, W. Verstraete and S. D. Siciliano (2005). Detection and quantification of the human-­‐specific HF183 Bacteroides 16S rRNA genetic marker with real-­‐time PCR for assessment of human faecal pollution in freshwater. Environmental Microbiology 7(2): 249-­‐259. 86 Shanks, O. C., E. Atikovic, A. D. Blackwood, J. R. Lu, R. T. Noble, J. S. Domingo, S. Seifring, M. Sivaganesan and R. A. Haugland (2008). Quantitative PCR for detection and enumeration of genetic markers of bovine fecal pollution. Applied and Environmental Microbiology 74(3): 745-­‐752. Shanks, O. C., White, K., Kelty, C. A., Hayes, S., Sivaganesan, M., Jenkins, M., . . . Haugland, R. A. (2010). Performance Assessment PCR-­‐Based Assays Targeting Bacteroidales Genetic Markers of Bovine Fecal Pollution. Applied and Environmental Microbiology, 76(5), 1359-­‐1366. Standard Methods, (2005) American Public Health Association, American Water Works Association, Water Environmental Federation. In “Standard Methods for the Examination of Water and Wastewater”. 21st Edition. American Public Health Association, Washington DC. Stewart, J. R., J. W. S. Domingo and T. J. Wade (2007). Fecal Pollution, Public Health, and Microbial Source Tracking. Microbial Source Tracking: 1-­‐32. Sullivan, T. F. P., & Bell, C. L. (2011). Environmental Law Handbook. Lanham, Md: Government Institutes. Swanson, H.A., and Baldwin, H.L. (1965). A primer on Water Quality: Water Properties and Measurements. US Geological Survey. Available at http://water.usgs.gov/edu/characteristics.html. [Accessed Jan. 13, 2014]. Toranzos, G. A. (1991) Current and possible alternative indicators of fecal contamination in tropical waters: A short review. Environmental Toxicology and Water Quality. 6, 121-­‐130 USEPA (US Environmental Protection Agency). (2001) Method 1601: Male Specific (F+) and Somatic Coliphage in Water by two-­‐step Enrichment Procedure [Online]. Available from http://water.epa.gov/scitech/methods/cwa/bioindicators/upload/2008_
11_25_methods_method_biological_1601.pdf. [Accessed 5 February 2014]. USEPA (US Environmental Protection Agency). (2011) Using Microbial Source Tracking to Support TMDL Development and Implementation [Online]. Available from http://www.epa.gov/region10/pdf/tmdl/mst_for_tmdls_guide_04_22_11.
pdf. [Accessed 2 February 2014]. USEPA (US Environmental Protection Agency). (2012) Overview of Impaired Waters and Total Maximum Daily Load Program [Online]. Available from http://water.epa.gov/lawsregs/lawsguidance/cwa/tmdl/overview.cfm. [Accessed 17 January 2014]. 87 USEPA (US Environmental Protection Agency). (2012a) What is conductivity and why is it important? [Online]. Available from http://water.epa.gov/type/rsl/monitoring/vms59.cfm. [Accessed 12 January 2014]. USEPA (US Environmental Protection Agency). (1986) Quality Criteria for Water [Online]. Available from http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/uplo
ad/2009_01_13_criteria_goldbook.pdf. [Accessed 2 March 2014]. USEPW (US Senate Committee on Environment and Public Works). (2002) Federal Water Pollution Control Act [Online]. Available from http://www.epw.senate.gov/water.pdf. [Accessed 18 February 2014]. USGPO (US Government Printing Office) (2010). Title 33 – Navigation and Navigable Waters [online]. Available from: http://www.gpo.gov/fdsys/pkg/USCODE-­‐2010-­‐title33/pdf/USCODE-­‐
2010-­‐title33-­‐chap26.pdf. [accessed 7 February 2014]. Weiner, E. R. (2008). Applications of environmental aquatic chemistry: A practical guide. Boca Raton, FL: CRC Press.