Accounting for measurement error in uncertainty modeling and decision‐making using indicator kriging andp‐field simulation: application to a dioxin contaminated site

Abstract

AbstractIn many environmental studies spatial variability is viewed as the only source of uncertainty while measurement errors tend to be ignored. This article presents an indicator kriging‐based approach to account for measurement errors in the modeling of uncertainty prevailing at unsampled locations. Probability field simulation is then used to assess the probability that the average pollutant concentration within remediation units exceeds a regulatory threshold, and probability maps are used to identify hazardous units that need to be remediated. This approach is applied to two types of dioxin data (composite and single spoon samples) with different measurement errors which were collected at the Piazza Road dioxin site, an EPA Superfund site located in Missouri. A validation study shows that the proportion of contaminated soil cores provides a reasonable probability threshold to identify hazardous remediation units. When a lower probability threshold is chosen, the total remediation costs are unreasonably high while false negatives are unacceptably frequent for a higher probability threshold. The choice of this threshold becomes critical as the sampling density decreases. Copyright © 2002 John Wiley & Sons, Ltd.