Poor sanitation infrastructure in rural areas can often lead to high levels of fecal contamination in local waterbodies and subsequent exposure to waterborne disease can occur. Although standard water quality measures such as quantification of E. coli can reveal relative concentrations of fecal contamination, they do not pinpoint the sources of such contamination. Source assessment in rural areas affected by untreated household waste might be improved with the human-specific, microbial source tracking marker HF183. This study attempted to quantify HF183 in two particular Appalachia streams with known discharges of untreated household waste. Water samples were taken above and at multiple points below these discharges on 29 occasions between August 2012 and April 2016, and tested for both HF183 and E. coli. HF183 was detected consistently in one of the study streams, though the concentrations were generally much lower than those previously reported in raw sewage; in the other watershed, HF183 was never detected. Further analysis via a multiple linear regression model showed a positive correlation between the level of E. coli and the proximity and number of known waste discharge points upstream from each sampling site. Primary conclusions of this study include: 1) HF183 is not always detected, even in watersheds with known sources of human fecal contamination, 2) it may be a useful water quality assessment tool where such contamination is suspected, particularly in cases where contaminant source allocation is necessary for setting mitigation priorities. / Master of Science

Sources of fecal pollution were evaluated as a function of sampling frequency with stream samples from Mill Creek, Montgomery County, VA. Samples were collected monthly for one year, plus weekly for four consecutive weeks during seasonal high flows (March), and seasonal low flows (September-October), plus daily for seven consecutive days within the weekly schedules. Thirty stream samples were collected from each of two sites (60 total) in Mill Creek, and 48 isolates of E. coli per sample (total of 2,880 stream isolates) were classified by source using antibiotic resistance analysis (ARA) and comparing the resulting patterns against a known-source E. coli library (1,158 isolates). The same process was performed with enterococci isolates against an enterococci library (1,182 isolates). The average rate of correct classification (ARCC) for the E. coli library with a three-way split (human, livestock, and wildlife) was 89.0%, and the ARCC of the species-specific E. coli library (cattle, deer, goose, human, misc. wildlife) was 88.9%. The ARCC of the enterococci library with a three-way split was 85.3%, and the ARCC of the species-specific enterococci library was 88.1%. The results did not justify the need for daily or weekly sampling, but indicated that monthly was adequate (quarterly and every-other-month were not). There was a seasonal effect as the human signature was highest during high flow while the livestock signature dominated during low flow. The results also indicated that sampling should be done over a period of time that includes both seasonal wettest and driest periods (at least 8 months). / Ph. D.

Antibiotic resistance analysis (ARA) of Enterococci was used to determine sources of fecal contamination in three District of Columbia waterways: Rock Creek, the Anacostia River, and the Potomac River. These three waterways were identified as exceeding water quality standards set for fecal coliform levels and were designated by the District of Columbia to the Environmental Protection Agency's 303 (d) impaired waters list. A library profile of 1,806 enterococcus isolates from known sources was built based on antibiotic resistance patterns from thirty concentrations of nine antibiotics. These sources included human, cattle, chicken, horse, goat, sheep, deer, raccoon, muskrat, goose, seagull, coyote, duck, wild turkey, dog, and cat. Antibiotic profiles were characterized for 24 unknown enterococci isolates on each of 198 samples (38 samples from the Potomac River, 79 samples from the Anacostia River, and 81 samples from Rock Creek) collected periodically from July 2002 through April 2003. Two major storm events were also sampled during this period. These isolate profiles were compared to the known source library using logistic regression. Three dominant sources of fecal pollution were detected in the Potomac River: livestock (30%), human (29%), and wildlife (22%). Three dominant signatures were also detected in Rock Creek: horse (26%), human (26%), and wildlife (24%). Human was the only dominant source detected in the Anacostia River, averaging 43% over the sampling period. The results of this study indicate that human is a substantial contributor to the fecal contamination problems, especially in the Anacostia River, but there are significant agricultural and wildlife contributions as well. Significant and predictable seasonal variations were also detected, indicating the influence of precipitation on source distributions. The results of this study will aid the Metropolitan Washington D.C. Council of Governments in making important management decisions to help improve the water quality in and around the Washington D.C. area. Expanding the limits of ARA was also an integral part of this research. Three new and even controversial analytical techniques were run on the data collected from this project in an attempt to improve confidence and provide direction to the results of this study. The first was a comparison of the more commonly used statistical analysis model discriminate analysis (DA) with logistic regression (LR). No significant difference was found between the output of the two models for the known source libraries, therefore no suggestion could be made in favor of one model over the other. Another analytical test of the data was the introduction of a standard requiring isolates to meet a minimum of 80% similarity to the known source profiles where it was classified. With the 80% cutoff, between 41% and 44% of the isolates could not be classified to any source and were placed in an unknown category. Based on the remaining isolates, source distributions were recalculated and were not statistically different than those calculated with no restriction for isolate similarity for matching. The last major test of the data was the analysis of the library for representativeness via pulled sample cross validation and the exclusion of all duplicate patterns from the known source library. These analyses did not confirm the representativeness of the databases, but results were further analyzed based on the implications these analyses have on library based methods. / Master of Science

This project involves developing and applying bacterial source tracking (BST) methodology to determine sources of fecal pollution in water for a rural community (Millwood, VA). Antibiotic resistance analysis (ARA) is the primary BST method for fecal source identification, followed by randomly amplified polymorphic DNA (RAPD) analysis for confirmation. Millwood consists of 66 homes, all served by individual septic systems, and a stream (Spout Run) passes through the center of the community. Spout Run drains a 5,800 ha karst topography watershed that includes large populations of livestock and wildlife. Stream and well samples were collected monthly and analyzed for fecal coliforms and fecal streptococci, starting in 5/99 and ending in 5/00. Twelve percent of the well samples and 92% of the stream samples were positive for fecal coliforms, and 26% of the stream samples exceeded the recreational water standard (1,000 fecal coliforms/100 ml). ARA of fecal streptococci recovered from the stream samples indicated that isolates of human origin appeared throughout the stream as the stream passed through Millwood with a yearly average of (approx. 10% human, 30% wildlife, and 63% livestock), and the percent human origin isolates declined downstream from Millwood. These results were obtained by comparing the antibiotic resistance profiles of stream isolates against a library of 1,174 known source isolates with correct classification rates of 94.6% for human isolates, 93.7% for livestock isolates, and 87.8% for wildlife isolates. There is a human signature in Spout Run, but it is small compared to the proportion of isolates from livestock and wildlife. The sporadic instances where well water samples were positive appeared primarily during very dry periods. Restricting livestock access to streams can dramatically lower fecal coliform counts during the unusually hot and dry periods. Reducing FC counts to below recreational water standards for Virginia (1000 per 100ml for any one sample) may be achievable, however to maintain streams below standards may prove to be difficult, as Spout Run is in an area where there are large populations of Canada geese, deer, and other wildlife, and will be hard to restrict these animals. / Master of Science

Antibiotic resistance analysis (ARA) was used to determine sources of fecal pollution in the Blackwater River in South-central Virginia. The Department of Environmental Quality designated six segments as impaired due to high fecal coliform concentrations with non-point source (NPS) agriculture the suspected source of impairment. The Blackwater River watershed encompasses 72,000 ha of dairy, beef, and intensive production agriculture, abundant wildlife populations and many homes with onsite septic systems. A library of antibiotic resistance profiles based on 30 concentrations of 9 antibiotics was developed for 1,451 enterococci isolates from human, cattle, chicken, horse, goat, sheep, deer, raccoon, muskrat, goose, duck, coyote, and wild turkey fecal samples. Each isolate was classified as human, wildlife or livestock. Correct classification rates were 82.3% for human, 86.2% for livestock and 87.4% for wildlife isolates when profiles were analyzed with discriminant analysis. Profiles were also determined for 48 isolates from 128 stream samples collected periodically from August 1999 thru April 2001 and compared to the known sources using discriminate analysis. A human signature was found at each site at least once during the year, ranging from 0.0% to 85.0% of the sample isolates. The livestock signature varied from 2.3% to 100% over sites and months, and the wildlife signature varied from 0.0% to 79.5%. The results indicate that both humans and wildlife contribute to fecal pollution in addition to the suspected source, livestock, and reducing fecal pollution will require consideration of all three sources. The results from this research are being used to develop a total maximum daily load (TMDL) project allocations for fecal coliforms in the Blackwater River. Isolates identified by ARA were also profiled using the Biolog metabolic identification system. A library of metabolic profiles was constructed from known source isolates. Stream isolates were identified by Biolog and the metabolic profile was compared to the Biolog library. Of ten stream isolates identified by ARA as human, the Biolog library identified one as human, four as livestock, and five as wildlife. Of ten isolates identified by ARA as livestock, the Biolog library identified seven as livestock and three as wildlife. Of ten isolates identified by ARA as wildlife, one was identified as human, three as livestock and six as wildlife. The overall correct classification of Blackwater isolates in the Biolog library was 14 of 30 isolates, or 47%. Although the Biolog library was constructed with some isolates from the Blackwater basin, there may not be enough isolates in the Biolog library to adequately represent the variability shown by the Blackwater isolates, resulting in lower than expected correct classifications. In spite of these results, Biolog remains promising as one of several tools with potential as a bacterial source tracking method. / Master of Science

No description available.

Bacterial water pollution is a significant problem because it is associated with reduction in the ‘quality’ of water systems with a potential impact on human health. Faecal indicator bacteria (FIB) are usually used to monitor the quality of water, and to indicate the presence of pathogens in water bodies. However, enumeration alone does not enable identification of the precise origin of these pathogens. This study aimed to monitor the quality of bathing water and associated fresh water in and out of the ‘bathing season’ in the UK, and to evaluate the use of microbial source tracking (MST) such as the host-specific based polymerase chain reaction (PCR) and quantitative PCR (qPCR) to recognize human and other animal sources of faecal pollution. The culture-dependent EU method of estimating FIB in water and sediment samples was performed on beach in the South Sands, Kingsbridge estuary, Devon, UK- a previously ‘problematic’ site. FIB were present at significant levels in the sediments, especially mud, as well as fresh water from the stream and pond flowing onto South Sands beach. However, the quality of bathing water was deemed to be ‘good’ and met with the EU bathing water directive 2006. Using MST it was possible to successfully classify the nature of the source from which the bacteria came. PCR was applied to detect the Bacteroides species 16S rRNA genetic markers from human sewage and animal faeces. All water and sediment samples displayed positive results with a general Bacteroides marker indicating the presence of Bacteroides species. Host-specific PCR showed the human Bacteroides genetic marker only in the sediment of the stream. However, limitations in the ‘types’ of probes available and in the persistence of these markers were identified. Thus, novel dog-specific Bacteroides conventional PCR and qPCR primer sets were developed to amplify a section of the 16S rRNA gene unique to the Bacteroides genetic marker from domestic dog faeces, and these were successfully used to quantify those markers in water samples at a ‘dog permitted’ and ‘dog banned’ beach (Bigbury-on-Sea, Devon, UK). Generic, human and dog Bacteroides PCR primer sets were also used to evaluate the persistence of Bacteroides genetic markers in controlled microcosms of water and sediment at differing salinities (< 0.5 and 34 psu) and temperature (10 and 17 ºC). The rates of decline were found did not differ significantly over 14 and 16 days for the water and sediment microcosms, respectively. Beach sediments which were studied in this project may act as a reservoir for adhesive FIB, and this was confirmed using fluorescence in situ hybridisation (FISH). The similarity in the persistence of these Bacteroides 16S rRNA genetic markers in environmental water and sediment suggests that viable but non-culturable (VBNC) Bacteroides spp. do not persist in the natural environment for long. Therefore, 16S rRNA genetic markers can be of value as additional faecal indicators of bathing water pollution and in source tracking. Thus, in this study MST methods were successfully used and in future applications, dog-specific primer sets can be added to the suite of host-specific Bacteroides genetic markers available to identify the source(s) of problem bacteria found on failing beaches.

No description available.

During the 1980’s millions of households in Bangladesh switched from drinking surface water to private groundwater wells to reduce their exposure to fecal microorganisms. Sadly, this switch to shallow groundwater resulted in the largest example of drinking water poisoning in history, with approximately 100 million people exposed to high concentrations of naturally occurring Arsenic in the groundwater. Spatial distribution of Arsenic in the shallow aquifers tends to be patchy, so the most economical mitigation option has been lateral switching from high Arsenic wells to nearby low Arsenic wells. The recently developed Arsenic flushing conceptual model, which explains the spatial distribution of Arsenic throughout the shallow aquifers in Bangladesh, suggests however, that low Arsenic zones are recharged via coarse-grained, rapid flow pathways and therefore represent a higher risk for waterborne pathogens. The objectives of this dissertation are to evaluate new methods for sampling and detection of waterborne pathogens, while also identifying sources of fecal contamination and transport pathway(s) to private wells emplaced within the shallow aquifers. It was demonstrated that private wells are broadly contaminated with E. coli, with prevalence ranging from 30 to 70%. The fact that E. coli was detected more frequently in private wells than sealed monitoring wells (p<0.05) suggests that well construction and/or daily pumping contribute to fecal contamination of the private wells. Using DNA-based molecular fecal source tracking, contamination was demonstrated to originate from human fecal waste. Unsanitary latrines, which spill effluent onto the open ground, were demonstrated to cause elevated levels of fecal bacteria in ponds, found in every village. These ponds were demonstrated to have an influence on concentrations of fecal bacteria to at least distances of 12m into the adjacent aquifer. In a culture where latrines, private wells and ponds are frequently clustered closely together, these findings suggest that improvements in the management of human fecal waste changes in placement and construction of private wells could substantially reduce exposure of people to fecal pathogens. Fecal contamination was found to be pervasive in low Arsenic, unconfined, shallow aquifers, and therefore gains from well switching to avoid Arsenic need to be balanced with the risk of consuming waterborne pathogens.

Microbial water quality is generally assessed using fecal indictor organisms; however host-specific microbial source tracking (MST) methodologies can be employed to differentiate sources of fecal pollution. The central goal of this research was to develop and validate a QPCR assay for the quantification of two human-specific polyomaviruses (HPyVs) in environmental water samples. These viruses are prevalent worldwide and produce lifelong, asymptomatic viruria in immunocompetent individuals. A Taqman® quantitative PCR (QPCR) assay based on the conserved T-antigen of two HPyVs (JCV and BKV) was developed and optimized (Chapter 2). HPyVs were detected in a high proportion of human-associated waste samples (e.g. sewage) and were not detected in animal excrement samples (Chapter 2). The effects of ultraviolet radiation, temperature, and salinity on the persistence of HPyVs in water were reported in Chapter 3. Laboratory studies analyzing the effects of various UV doses, temperatures, and/or salinities demonstrated high doses of UV were required to significantly decrease the detection of HPyVs DNA and salinity stabilized pure cultures of HPyVs virus particles at high temperatures (25°C and 35°C). Solar radiation as well as potential predation from microorganisms in sewage significantly reduced the persistence of HPyVs DNA in outdoor mesocosm studies (Chapter 3). An improved method to extract human polyomavirus (HPyVs) DNA from environmental water samples was developed, and the recoveries were larger and more consistent over a range of DNA concentrations as compared to the standard protocol (Chapter 4). In the California beaches study (Chapter 4), the presence of HPyVs by either QPCR or PCR had a high degree of matching results with the adenoviruses (83-91%), Methanobrevibacter smithii marker (82-92%) and moderate degree of matching results with the human-associated Bacteroidales spp. marker (57-80%) (Chapter 4). HPyVs were detected in the presence of various pathogens including: Giardia spp., Cryptosporidium spp., Vibrio spp., enteroviruses, and noroviruses (Chapter 5). The presence of HPyVs in relatively high concentrations of sewage and the specificity of HPyVs combined with the relatively conservative persistence of HPyVs when exposed to environmental conditions and the correlation of HPyVs with pathogens demonstrates that this assay is a useful MST method to detect human sewage.