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Evaluation of multiple laboratory performance and variability in analysis of recreational freshwaters by a rapid Escherichia coli qPCR method (Draft Method C)

Aw, Tiong Gim, Sivaganesan, Mano, Briggs, Shannon, Dreelin, Erin, Aslan, Asli, Dorevitch, Samuel, Shrestha, Abhilasha, Isaacs, Natasha, Kinzelman, Julie, Kleinheinz, Greg, Noble, Rachel, Rediske, Rick, Scull, Brian, Rosenberg, Susan, Weberman, Barbara, Sivy, Tami, Southwell, Ben, Siefring, Shawn, Oshima, Kevin, Haugland, Richard
Water research 2019 v.156 pp. 465-474
Escherichia coli, United States Environmental Protection Agency, data quality, indicator species, models, monitoring, public health, quantitative polymerase chain reaction, surface water, water quality
There is interest in the application of rapid quantitative polymerase chain reaction (qPCR) methods for recreational freshwater quality monitoring of the fecal indicator bacteria Escherichia coli (E. coli). In this study we determined the performance of 21 laboratories in meeting proposed, standardized data quality acceptance (QA) criteria and the variability of target gene copy estimates from these laboratories in analyses of 18 shared surface water samples by a draft qPCR method developed by the U.S. Environmental Protection Agency (EPA) for E. coli. The participating laboratories ranged from academic and government laboratories with more extensive qPCR experience to “new” water quality and public health laboratories with relatively little previous experience in most cases. Failures to meet QA criteria for the method were observed in 24% of the total 376 test sample analyses. Of these failures, 39% came from two of the “new” laboratories. Likely factors contributing to QA failures included deviations in recommended procedures for the storage and preparation of reference and control materials. A master standard curve calibration model was also found to give lower overall variability in log10 target gene copy estimates than the delta-delta Ct (ΔΔCt) calibration model used in previous EPA qPCR methods. However, differences between the mean estimates from the two models were not significant and variability between laboratories was the greatest contributor to overall method variability in either case. Study findings demonstrate the technical feasibility of multiple laboratories implementing this or other qPCR water quality monitoring methods with similar data quality acceptance criteria but suggest that additional practice and/or assistance may be valuable, even for some more generally experienced qPCR laboratories. Special attention should be placed on providing and following explicit guidance on the preparation, storage and handling of reference and control materials.