Characterization of background concentrations of contaminants using a mixture of normal distributions.
We present a Bayesian approach for characterizing background contaminant concentration distributions using data from sites that may have been contaminated. Our method, focused on estimation, resolves several technical problems of the existing methods sanctioned by the U.S. Environmental Protection Agency (USEPA) (a hypothesis testing based method), resulting in a simple and quick procedure for estimating background contaminant concentrations. The proposed Bayesian method is applied to two data sets from a federal facility regulated under the Resource Conservation and Restoration Act. The results are compared to background distributions identified using existing methods recommended by the USEPA. The two data sets represent low and moderate levels of censorship in the data. Although an unbiased estimator is elusive, we show that the proposed Bayesian estimation method will have a smaller bias than the EPA recommended method.
Qian
Song S
SS
Nicholas School of the Environment and Earth Sciences, Duke University, Durham, North Carolina 27708-0328, USA. song@duke.edu
Lyons
Regan E
RE
D001499:Bayes Theorem; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D006256:Hazardous Waste; D007854:Lead / Q000032:analysis*; D008628:Mercury / Q000032:analysis*; D015233:Models, Statistical; D012016:Reference Values; D012989:Soil Pollutants / Q000032:analysis*; D014481:United States; D014484:United States Environmental Protection Agency
D006256:Hazardous Waste; D012989:Soil Pollutants; D007854:Lead; D008628:Mercury