Quantifying and Reducing Uncertainty in Estimated Microcystin Concentrations from the ELISA Method

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• 作者：Song S. Qian ; Justin D. Chaffin ; Mark R. DuFour ; Jessica J. Sherman ; Phoenix C. Golnick ; Christopher D. Collier ; Stephanie A. Nummer ; Michaela G. Margida
• 刊名：Environmental Science & Technology
• 出版年：2015
• 出版时间：December 15, 2015
• 年：2015
• 卷：49
• 期：24
• 页码：14221-14229
• 全文大小：361K
• ISSN：1520-5851

文摘

We discuss the uncertainty associated with a commonly used method for measuring the concentration of microcystin, a group of toxins associated with cyanobacterial blooms. Such uncertainty is rarely reported and accounted for in important drinking water management decisions. Using monitoring data from Ohio Environmental Protection Agency and from City of Toledo, we document the sources of measurement uncertainty and recommend a Bayesian hierarchical modeling approach for reducing the measurement uncertainty. Our analysis suggests that (1) much of the uncertainty is a result of the highly uncertain 鈥渟tandard curve鈥?developed during each test and (2) the uncertainty can be reduced by pooling raw test data from multiple tests. Based on these results, we suggest that estimation uncertainty can be effectively reduced through the effort of either (1) regional regulatory agencies by sharing and combining raw test data from regularly scheduled microcystin monitoring program or (2) the manufacturer of the testing kit by conducting additional tests as part of an effort to improve the testing kit.