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Application of Multivariate Statistical Methodology to Model Factors Influencing Fate and Transport of Fecal Pollution in Surface Waters
Author(s) -
Hall Kimberlee K.,
Evanshen Brian G.,
Maier Kurt J.,
Scheuerman Phillip R.
Publication year - 2014
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2013.05.0190
Subject(s) - watershed , environmental science , pollution , multivariate statistics , hydrology (agriculture) , total maximum daily load , fecal coliform , water quality , prioritization , sampling (signal processing) , streams , water pollution , surface water , water resource management , environmental engineering , ecology , statistics , computer science , biology , mathematics , engineering , computer network , geotechnical engineering , filter (signal processing) , management science , machine learning , computer vision
The increasing number of polluted watersheds and water bodies with total maximum daily loads (TMDLs) has resulted in increased research to find methods that effectively and universally identify fecal pollution sources. A fundamental requirement to identify such methods is understanding the microbial and chemical processes that influence fate and transport of fecal indicators from various sources to receiving streams. Using the Watauga River watershed in northeast Tennessee as a model to better understand these processes, multivariate statistical analyses were conducted on data collected from four creeks that have or are expected to have pathogen TMDLs. The application of canonical correlation and discriminant analyses revealed spatial and temporal variability in the microbial and chemical parameters influencing water quality, suggesting that these creeks differ in terms of the nature and extent of fecal pollution. The identification of creeks within a watershed that have similar sources of fecal pollution using this data analysis approach could change prioritization of best management practices selection and placement. Furthermore, this suggests that TMDL development may require multiyear and multisite data using a targeted sampling approach instead of a 30‐d geometric mean in large, complex watersheds. This technique may facilitate the choice between watershed TMDLs and single segment or stream TMDLs.

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