Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation.

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

Characterization of nonpoint source microbial contamination in an urbanizing watershed serving as a municipal water supply.

Inland watersheds in the southeastern United States are transitioning from agricultural and forested land uses to urban and exurban uses at a rate greater than the national average. This study sampled creeks representing a variety of land use factors in a rapidly urbanizing watershed that also serves as a drinking water supply. Samples were collected bimonthly under dry-weather conditions and four times during each of three storm events and assessed for microbial indicators of water quality. Concentrations of fecal indicator bacteria (FIB) including fecal coliforms and Escherichia coli were measured using standard membrane filtration techniques. Results showed that FIB concentrations varied between 10(0) and 10(4) colony forming units (CFU) per 100 mL. An analysis of variance (ANOVA) showed that FIB were generally higher in more developed watersheds (p < 0.01). Concentrations were also significantly greater during storm events than during dry-weather conditions (p < 0.02), although concentrations demonstrated both intra and inter-storm variability. These results indicate that the magnitude of microbial contamination is influenced by intensity of watershed development, streamflow and antecedent precipitation. Dry-weather FIB loads showed considerable seasonal variation, but the average storm event delivered contaminant loads equivalent to months of dry-weather loading. Analysis of intra-storm loading patterns provided little evidence to support "first-flush" loading of either FIB, results that are consistent with environmental reservoirs of FIB. These findings demonstrate that single sampling monitoring efforts are inadequate to capture the variability of microbial contaminants in a watershed, particularly if sampling is conducted during dry weather. This study also helps to identify timing and conditions for public health vulnerabilities, and for effective management interventions.

HuBac and nifH source tracking markers display a relationship to land use but not rainfall.

Identification of the source of fecal pollution is becoming a priority for states and territories in the U.S. in order to meet water quality standards and to develop and implement total maximum daily loads. The goal of this research was to relate microbial source tracking (MST) assay concentrations to land use and levels of impervious surfaces in order to gauge how increasing development is associated with human fecal contamination in inland watersheds. The concentrations of two proposed MST markers, targeting nifH of Methanobrevibacter smithii and HuBac of Bacteroides sp., were positively correlated with increasing anthropogenic development and impervious surfaces. Higher concentrations of these MST markers in more urbanized watersheds suggest that increasing development negatively affects water quality. Neither MST marker concentration was correlated with antecedent rainfall levels, and detection of markers did not differ between dry weather and rain events. Water samples were also analyzed for norovirus and enterovirus, but these enteric viruses were rarely detected. These MST results differ from previous studies that have found correlations between traditional fecal indicator bacteria (FIB) and antecedent rainfall. This difference suggests that the MST markers used in this study may be more specific for recent, land-based contamination events as opposed to resuspension of particle-associated organisms in waterways. HuBac was detected in 98% of samples, correlating with fecal coliform and Escherichia coli concentrations. The ubiquity of the HuBac marker in our samples suggests that this marker does not provide sufficiently different or additional information than FIB, and it is likely this marker was amplifying non-human targets. The nifH marker was detected in 30% of samples. Less than half of the nifH-positive samples contained levels of fecal coliforms or E. coli above regulatory thresholds, suggesting that nifH would be more useful when utilized simultaneously with FIB than in a tiered monitoring strategy. The results of this research suggests that land use factors play an important role in characterizing and mitigating fecal contamination in watersheds.