Modeling of variability and uncertainty in human health risk assessment

Health risk assessments have been carried out worldwide to examine potential health risk due to exposure to toxic contaminants in various environments. In risk assessment, it is most important to know the nature of all available information, data or model parameters. It is observed that available information/data are tainted with uncertainty and variability in the same time, i.e., uncertainty and variability co-exist. In such situation it is important to devise method for processing both uncertainty and variability into same framework and which is an open issue. In this regards, this paper presents an algorithm to combined approach to propagate variability and uncertainty in the same framework. The differences and advantages of this algorithm over the existing methods are presented below: The representation of uncertain model parameters are probabilistic together with generalized fuzzy numbers and normal interval valued fuzzy numbers. The results obtained are then interpreted in terms of p-box and fuzzy numbers. The advantage of this approach over the existing methods is that this approach gives an accurate resultant fuzzy number which is of trapezoidal type generalized fuzzy number that is different from the existing methods. (c) 2017 The Author. Published by Elsevier B.V.