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Treatment of Censored Data and Dependency among Sampling Distributions in Probabilistic Human Health Exposure Assessment

Probabilistic human health exposure assessment is commonly challenged by non-detected data in measurement datasets and dependencies among sampling distributions. Methods for dealing with nondetected data, dependencies and associated uncertainty are introduced. An example case study was performed to demonstrate application of the methods and investigate how exposure estimates and associated uncertainty are affected by using different methods. The results show that improper handling of nondetected data may cause bias in the mean or other statistics such as the 97.5 percentile, as well as their uncertainty. A maimum likelihood estimation (MLE)bootstrap approach is suggested for dealing with nondetected data. A modified two-stage Monte Carlo approach, in which sampling distributions obtained from bootstrap simulation are kept in pair-wise data formats, is able to capture complex dependencies among sampling distributions, and thus is recommended as a technique for propagating uncertainty.