Correlation of quantitative PCR for a poultry-specific brevibacterium marker gene with bacterial and chemical indicators of water pollution in a watershed impacted by land application of poultry litter.

The impact of fecal contamination from human and agricultural animal waste on water quality is a major public health concern. Identification of the dominant source(s) of fecal pollution in a watershed is necessary for assessing the safety of recreational water and protecting water resources. A field study was conducted using quantitative PCR (qPCR) for the 16S rRNA gene of Brevibacterium sp. LA35 to track feces-contaminated poultry litter in environmental samples. Based on sensitivity and specificity characteristics of the qPCR method, the Bayesian conditional probability that detection of the LA35 marker gene in a water sample represented a true-positive result was 93%. The marker's covariance with fecal indicator bacteria (FIB) and metals associated with poultry litter was also assessed in litter, runoff, surface water, and groundwater samples. LA35 was detected in water and soil samples collected throughout the watershed, and its concentration covaried with concentrations of Escherichia coli, enterococci, As, Cu, P, and Zn. Significantly greater concentrations of FIB, As, Cu, P, and Zn were observed in edge-of-field runoff samples in which LA35 was detected, compared to samples in which it was not detected. Furthermore, As, Cu, P, and Zn concentrations covaried in environmental samples in which LA35 was detected and typically did not in samples in which the marker gene was not detected. The covariance of the poultry-specific LA35 marker gene with these known contaminants from poultry feces provides further evidence that it is a useful tool for assessing the impact of poultry-derived fecal pollution in environmental waters.

Water quality sample collection, data treatment and results presentation for principal components analysis--literature review and Illinois River Watershed case study.

Comprehensive water quality investigations to characterize large watersheds include collection of surface water samples over time at various locations within the watershed and analyses of the samples for multiple chemical and biological constituents. The size and complexity of the resulting dataset make overall evaluations difficult, and as a result, multivariate statistical methods can be useful to evaluate environmental patterns and sources of contamination. The most commonly applied multivariate method in watershed studies is principal components analysis (PCA), which uses correlation among multiple water quality constituents to effectively reduce the number of variables. The reduced set of variables may assist in the identification and description of spatial patterns in water quality that result from hydrologic and geochemical processes and from sources of contamination. The utility of PCA for identifying important environmental factors in a given study is obviously affected by sampling design, constituents analyzed, data quality, data treatment prior to PCA, methods of interpreting PCA results, and other factors. Unfortunately no comprehensive evaluations have been performed and no standard procedures exist for dealing with these issues. This paper examines and evaluates the current state-of-the-science by review of 49 published papers dealing with multivariate (typically PCA) techniques to evaluate watershed water quality. Additionally an example PCA for a surface water quality study in the Illinois River Watershed (IRW) is provided to illustrate methods to address the above issues and to evaluate the sensitivity of results to alternative methods. The example PCA evaluations were consistent with two dominant sources of surface water contamination in the IRW: 1) discharge to the streams from municipal wastewater treatment plants and 2) runoff and infiltration from fields with land applied poultry waste.