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Effects of field storage method on E. coli concentrations measured in storm water runoff

Daren Harmel, Kevin Wagner, Emily Martin, Doug Smith, Pauline Wanjugi, Terry Gentry, Lucas Gregory, Tina Hendon
Environmental monitoring and assessment 2016 v.188 no.3 pp. 170
Escherichia coli, bacterial contamination, die-off, indicator species, quality control, regrowth, samplers, storage temperature, storage time, storms, stormwater, uncertainty analysis, water pollution, watersheds
Storm water runoff is increasingly assessed for fecal indicator organisms (e.g., Escherichia coli, E. coli) and its impact on contact recreation. Concurrently, use of autosamplers along with logistic, economic, technical, and personnel barriers is challenging conventional protocols for sample holding times and storage conditions in the field. A common holding time limit for E. coli is 8 h with a 10 °C storage temperature, but several research studies support longer hold time thresholds. The use of autosamplers to collect E. coli water samples has received little field research attention; thus, this study was implemented to compare refrigerated and unrefrigerated autosamplers and evaluate potential E. coli concentration differences due to field storage temperature (storms with holding times ≤24 h) and due to field storage time and temperature (storms >24 h). Data from 85 runoff events on four diverse watersheds showed that field storage times and temperatures had minor effects on mean and median E. coli concentrations. Graphs and error values did, however, indicate a weak tendency for higher concentrations in the refrigerated samplers, but it is unknown to what extent differing die-off and/or regrowth rates, heterogeneity in concentrations within samples, and laboratory analysis uncertainty contributed to the results. The minimal differences in measured E. coli concentrations cast doubt on the need for utilizing the rigid conventional protocols for field holding time and storage temperature. This is not to say that proper quality assurance and quality control is not important but to emphasize the need to consider the balance between data quality and practical constraints related to logistics, funding, travel time, and autosampler use in storm water studies.