A method for evaluating sensitivity and uncertainty analyses is presented here for use with chemical environmental exposure models. Sensitivity analysis allows one to assess the relationship between a set of potentially important model input variables and the model output. Uncertainty analysis allows one to assess the set of output values obtained and estimate various statistical properties such as the mean, median, standard deviation, and range. Absence of the ability to quantify parameter value uncertainty and model output uncertainty limits the validity of predictive model use. Use of the methods presented in this paper can increase the confidence in predictive environmental chemical fate model use.

The present investigation employed the Latin hypercube sampling method developed by Iman and Shortencarier at Sandia National Laboratories, Albuquerque, NM. It requires that a set of input variables be identified, and the probability distribution of each be specified. The range of each input variable is divided into intervals having equal probabilities, and one value is randomly selected from each interval. Sets of model input variables are then generated which cover the range of each variable. Some model inputs may then be calculated as functions of these key input variables. The computer model is run for each set of input variables and the model output is obtained.

Regression techniques can then be employed to examine the differential sensitivity of the model to the various input variables and functions thereof, for example, cross products of input variables. Since the model input values are chosen based on probabilities, the set of model outputs can also be interpreted on a probabilistic basis.

The techniques discussed in this paper respond to the growing need by chemical exposure model users for better defined sensitivity analysis procedures which can aid in explaining and interpreting results. An example problem data set is presented which highlights the methodological techniques and their utility.