title: Quantifying and Reducing Uncertainty in Estimated Microcystin Concentrations from the ELISA Method.
abstract: 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 "standard curve" 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.
authors.affiliation: Department of Environmental Sciences, The University of Toledo , Toledo, Ohio 43606, United States.
authors.forename: Song S
authors.initials: SS
authors.lastname: Qian
authors.affiliation: Franz Theodore Stone Laboratory, The Ohio State University , Put-in-Bay, Ohio 43456, United States.
authors.forename: Justin D
authors.initials: JD
authors.lastname: Chaffin
authors.affiliation: Department of Environmental Sciences, The University of Toledo , Toledo, Ohio 43606, United States.
authors.forename: Mark R
authors.initials: MR
authors.lastname: DuFour
authors.affiliation: Department of Environmental Sciences, The University of Toledo , Toledo, Ohio 43606, United States.
authors.forename: Jessica J
authors.initials: JJ
authors.lastname: Sherman
authors.affiliation: Department of Environmental Sciences, The University of Toledo , Toledo, Ohio 43606, United States.
authors.forename: Phoenix C
authors.initials: PC
authors.lastname: Golnick
authors.affiliation: Department of Environmental Sciences, The University of Toledo , Toledo, Ohio 43606, United States.
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Collier
authors.affiliation: Department of Environmental Sciences, The University of Toledo , Toledo, Ohio 43606, United States.
authors.forename: Stephanie A
authors.initials: SA
authors.lastname: Nummer
authors.affiliation: Department of Environmental Sciences, The University of Toledo , Toledo, Ohio 43606, United States.
authors.forename: Michaela G
authors.initials: MG
authors.lastname: Margida
mesh_terms: D001499:Bayes Theorem; D000458:Cyanobacteria; D060766:Drinking Water / Q000032:analysis* / Q000382:microbiology*; D004784:Environmental Monitoring / Q000379:methods; D004797:Enzyme-Linked Immunosorbent Assay / Q000379:methods* / Q000706:statistics & numerical data; D057097:Harmful Algal Bloom; D052998:Microcystins / Q000032:analysis*; D015233:Models, Statistical; D009820:Ohio; D035501:Uncertainty; D014481:United States; D014484:United States Environmental Protection Agency; D014871:Water Microbiology
chemical_list: D060766:Drinking Water; D052998:Microcystins; C078588:microcystin