Evaluation of a multivariate analysis modeling approach identifying sources and patterns of nonpoint fecal pollution...

Surface waters listed on impaired waters (303 d) lists due to pathogen contamination pose a significant environmental and public health burden. The need to address impairments through the Total Maximum Daily Load (TMDL) process has resulted in method developments that successfully identify nonpoint fecal pollution sources by maximizing available resources to improve water quality. However, the ability of those methods to effectively and universally identify sources of fecal pollution requires further evaluation. The objective of this research was to assess the usefulness of a previously described multivariate statistical approach to identify common patterns influencing fate and transport of fecal pollutants from sources to receiving streams using the Tuckasegee River watershed in Western North Carolina as a test watershed. Two streams were routinely monitored using a targeted sampling approach to assess fecal pollution extent and identify nonpoint sources using canonical correlation and canonical discriminant analyses. Fecal pollution in the watershed varied spatially and temporally with significantly higher fecal coliform concentrations observed in Scott Creek (f = 9.49, p = 0.002) and during the summer months (f = 14.8, p < 0.0001). Canonical correlations described 62-67% of water quality variability and indicate that fecal pollution in portions of the watershed are influenced by stormwater runoff and fecal indicator bacteria resuspension from sediment, while fecal pollution in other portions are influenced by soil erosion and surface runoff. Canonical discriminant analyses indicate that LULC significantly influences the nature and extent of fecal pollution. These results demonstrate that chemical parameters are useful predictors of fecal pollution and can help identify nonpoint fecal pollution sources in relation to land use patterns and land management practices. This approach to water quality monitoring program design and data analysis may effectively and efficiently identify parameters that best predict fecal pollution to aid in development and implementation of effective TMDLs to remediate impaired waters.