Microbial Source Tracking in a Mixed Use Watershed in Northern Virginia

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Virginia Tech

Prince William County, located in the rapidly developing Northern Virginia region, contains watersheds of mixed rural and urban/suburban uses. As part of Virginia regulations, recreational waters must be tested and remain under a certain standard for levels of fecal indicator bacteria (FIB). The sources of fecal pollution in neighboring watersheds within the county were determined over the 12 months previous to this project by performing Antibiotic Resistance Analysis (ARA, a microbial source tracking protocol) on Enterococcus and Escherichia coli (E. coli). This study indicated that multiple sources of pollution were present at all sampling locations and that the dominant sources of contamination were related to the land-use patterns and human activities that were adjacent to each location.

The goal of the current project was to monitor and identify the sources of fecal pollution in eight streams in the Occoquan Basin (OQB) that have been classified as impaired waters due to high E. coli concentrations. Project objectives were i) employ microbial source tracking technology to identify the categories of sources that were responsible for the bacterial impairments; ii) develop and analyze appropriate Known Source Libraries (KSL's) to determine the best design for identifying the sources of water-sample isolates; and iii) evaluate the use of optical brighteners in freshwater by fluorometry as an indicator for human-origin pollution. One site on each of six streams and two sites on the remaining two (ten total) were selected for E. coli and Enterococcus monitoring and microbial source tracking. Repeated sampling of the ten locations for thirteen months assessed the concentrations of the bacteria over time, while comparison of monthly bacterial concentrations to the U.S. standards was used to verify the impaired water designation.

Three thousand, four hundred and eighty-eight Enterococcus and 969 E. coli water-sample isolates were collected and evaluated to determine their sources. These isolates were compared to several known source libraries (KSL's) comprised of host-origin isolates collected from the Northern Virginia region. Linear discriminant analysis (LDA) using a KSL of unique isolates determined wildlife were the dominant source of fecal pollution. Results based on ARA were cross-validated through fluorometry of the water samples (to detect optical brighteners in detergents as human-derived pollution) and pulsed-field gel electrophoresis (PFGE, a DNA fingerprinting technique) of select E. coli isolates. In order to determine the best method to classify the water-sample isolates, variation in antibiotic resistance data representation, known source isolate inclusion, and LDA processing were compared. The KSL that used the most antibiotic resistance datapoints, contained no conflicting data, and performed most of the parameters associated with standard LDA, classified water-sample isolates the most successfully. This project involves the first thorough testing of fluorometry for the detection of human signatures in freshwaters.

Monitoring results showed consistent Enterococcus and E. coli contamination in all eight streams, demonstrating that each had been correctly placed on Virginia's impaired waters list by state regulatory agencies. Counts between Enterococcus and E. coli did not correlate well, although concentrations of both indicator organisms were higher during dry months. Source tracking results determined a dominant wildlife signature at all sites. Few Enterococcus water-source isolates were classified as human and fluorescence at all sites was consistently low. KSL's with antibiotic resistance data represented as binary values classified isolates the best. Removal of conflicting isolates improved the KSL's rate of correct classification (RCC). Creation of an unknown category, clustering of the KSL, and only accepting results above a threshold did not appreciably improve the RCC.

The KSL with the binary representation was not used to classify isolates because it violates the normal distribution assumption of LDA. Differences in the results of Enterococcus and E. coli source classifications indicated that contributing sources vary in frequency. Human fecal matter was shown to be of little concern because both Enterococcus ARA and fluorometry indicated low presence. The positive predictive value (PPV) statistic was found to be preferable to the minimum detectable percentage (MDP) because it does not depend on KSL size. Establishing confidence intervals to determine completeness of KSL allows one to determine whether particular methods to refine the KSL will be helpful.

This project was successfully completed and the monitored streams were correctly identified by state authorities as impaired waters. Source tracking results often conflicted, although wildlife and pets were indicated as the major sources of impairment by ARA. More local source samples need to be taken to verify this result. The best ARA library design used only unique isolates, all pattern data points, and removed conflicting isolates. Continuing examination of the representation of library data as binary is necessary to determine whether the statistical assumptions in LDA prevent meaningful results. Evaluation of fluorometry was partially successful as the absence of "hotspots" of high fluorescent brighteners agreed with ARA results that indicated little contamination form human sources. The fluorometer continues to have potential as a metric of waste in freshwater although more work needs to be done to fully prove its utility.

E. coli, Prince William County, Enterococcus, Microbial Source Tracking, PFGE, Water Quality