Application of Multivariate Statistical Methodology to Model Factors Influencing Fate and Transport of Fecal Pollution in Surface Waters.

摘要

Degraded surface water quality is a growing public health concern. While indicator organisms are frequently used as a surrogate measure of pathogen contamination, poor correlation is often observed between indicators and pathogens. Because of adverse health effects associated with poor water quality, an assessment of the factors influencing the fate and transport of fecal pollution is necessary to identify sources and effectively design and implement Best Management Practices (BMPs) to protect and restore surface water quality. Sinking Creek is listed on the State of Tennessee’s 303D list as impaired due to pathogen contamination. The need to address the listing of this and other water bodies on the 303D list through the Total Maximum Daily Load (TMDL) process has resulted in increased research to find methods that effectively and universally identify sources of fecal pollution. The main objective of this research is to better understand how microbial, chemical, and physical factors influence pathogen fate and transport in Sinking Creek. This increased understanding can be used to improve source identification and remediation. To accomplish this objective, physical, chemical, and microbial water quality parameters were measured and the data were analyzed using multivariate statistical methods to identify those parameters influencing pathogen fate and transport. Physical, chemical, and microbial water and soil properties were also characterized along Sinking Creek to determine their influences on the introduction of fecal pollution to surface water. Results indicate that the 30-day geometric mean of fecal indicator organisms is not representative of true watershed dynamics and that their presence does not correlate with the presence of bacterial, protozoan, or viral pathogens in Sinking Creek. The use of multivariate statistical analyses coupled with a targeted water quality-monitoring program has demonstrated that nonpoint sources of fecal pollution vary spatially and temporally and are related to land use patterns. It is suggested that this data analysis approach can be used to effectively identify nonpoint sources of fecal pollution in surface water.