Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches

Mano Sivaganesan; Tiong Gim Aw; Shannon Briggs; Erin Dreelin; Asli Aslan; Samuel Dorevitch; Abhilasha Shrestha; Natasha Isaacs; Julie Kinzelman; Greg Kleinheinz; Rachel Noble; Rick Rediske; Brian Scull; Susan Rosenberg; Barbara Weberman; Tami Sivy; Ben Southwell; Shawn Siefring; Kevin Oshima; Richard Haugland

doi:10.1016/j.watres.2019.03.011

Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches

Water Res. 2019 Jun 1:156:456-464. doi: 10.1016/j.watres.2019.03.011. Epub 2019 Mar 15.

Authors

Mano Sivaganesan¹, Tiong Gim Aw², Shannon Briggs³, Erin Dreelin⁴, Asli Aslan⁵, Samuel Dorevitch⁶, Abhilasha Shrestha⁶, Natasha Isaacs⁷, Julie Kinzelman⁸, Greg Kleinheinz⁹, Rachel Noble¹⁰, Rick Rediske¹¹, Brian Scull¹¹, Susan Rosenberg¹², Barbara Weberman¹², Tami Sivy¹³, Ben Southwell¹⁴, Shawn Siefring¹⁵, Kevin Oshima¹⁵, Richard Haugland¹⁶

Affiliations

¹ U.S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.
² Department of Global Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA, 70112, USA.
³ Water Resources Division, Michigan Department of Environmental Quality, P. O. Box 30458, 525 West Allegan Street, Lansing, MI, 48909, USA.
⁴ Center for Water Sciences, Michigan State University, 1405 South Harrison Road, East Lansing, MI, 48823, USA.
⁵ Georgia Southern University, Department of Environmental Health Sciences, 501 Forest Drive, Statesboro, GA, 30458, USA.
⁶ University of Illinois at Chicago, School of Public Health, 2121 W. Taylor Street, Chicago, IL, 60612, USA.
⁷ U.S. Geological Survey, Upper Midwest Water Science Center, 6520 Mercantile Way, Ste 5, Lansing, MI, 48911, USA.
⁸ City of Racine Public Health Department, 730 Washington Ave, Racine, WI, 53403, USA.
⁹ University of Wisconsin-Oshkosh, Environmental Research Laboratory, 800 Algoma Boulevard, Oshkosh, WI, 54901, USA.
¹⁰ Institute of Marine Sciences, University of North Carolina at Chapel Hill, 3431 Arendell Street, Morehead City, NC, 28557, USA.
¹¹ Annis Water Resources Institute, Lake Michigan Center, 740 W. Shoreline Dr, Muskegon, MI, 49441, USA.
¹² Oakland County Health Division Laboratory, 1200 N. Telegraph, Pontiac, MI, 48341, USA.
¹³ Saginaw Valley State University, Department of Chemistry, 7400 Bay Road, University Center, MI, 48710, USA.
¹⁴ Lake Superior State University, Environmental Analysis Laboratory, 650 W. Easterday Ave, Sault Ste Marie, MI, 49783, USA.
¹⁵ U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.
¹⁶ U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA. Electronic address: haugland.rich@epa.gov.

Abstract

There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made.

Keywords: Data quality criteria; E. coli; EPA method C; Water quality criteria; qPCR.

Published by Elsevier Ltd.

MeSH terms

Bathing Beaches
Bayes Theorem
Data Accuracy
Environmental Monitoring
Escherichia coli*
Feces
Water
Water Microbiology
Water Quality*

Substances

Water

Grants and funding

EPA999999/ImEPA/Intramural EPA/United States