Decision Tree Method for the Classification of Chemical Pollutants: Incorporation of Across-Chemical Variability and Within-Chemical Uncertainty

Abstract

We have developed a decision tree methodology for the classification of chemicals by estimates of potential human exposure. The steps involved in the construction of a decision tree are as follows. Monte Carlo simulations are conducted by randomly sampling chemical and environmental properties, whose range of values represents the variability of parameters across a defined set of chemicals and environmental conditions. The tree structure is then defined by a series of constraints placed on the various chemical and environmental properties using the Clas sification and Regression Tree Algorithm (CART). Each node of the tree is associated with a human exposure value and is considered a bin, which classifies chemicals whose properties are consistent with those parametric constraints associated with the particular node. In addition to being associated with parametric constraints, each bin or tree node is associated with a human exposure level. In this manner, the tree structure functions as a template from wh...