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Occurrence and Distribution of Microbiological Indicators in Groundwater and Stream Water
Author(s) -
Francy Donna S.,
Helsel Dennis R.,
Nally Rebecca A.
Publication year - 2000
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.2175/106143000x137220
Subject(s) - groundwater , coliphage , environmental science , indicator bacteria , clostridium perfringens , contamination , indicator organism , fecal coliform , hydrology (agriculture) , streams , environmental engineering , sampling (signal processing) , water quality , environmental chemistry , veterinary medicine , escherichia coli , bacteria , ecology , biology , chemistry , bacteriophage , geology , engineering , geotechnical engineering , filter (signal processing) , computer network , computer science , genetics , biochemistry , medicine , electrical engineering , gene
A total of 136 stream water and 143 groundwater samples collected in five important hydrologic systems of the United States were analyzed for microbiological indicators to test monitoring concepts in a nationally consistent program. Total coliforms were found in 99%, Escherichia coli in 97%, and Clostridium perfringens in 73% of stream water samples analyzed for each bacterium. Total coliforms were found in 20%, E. coli in less than 1%, and C. perfringens in none of the groundwater samples analyzed for each bacterium. Although coliphage analyses were performed on many of the samples, contamination in the laboratory and problems discerning discrete plaques precluded quantification. Land use was found to have the most significant effect on concentrations of bacterial indicators in stream water. Presence of septic systems on the property near the sampling site and well depth were found to be related to detection of coliforms in groundwater, although these relationships were not statistically significant. A greater diversity of sites, more detailed information about some factors, and a larger dataset may provide further insight to factors that affect microbiological indicators.