The development and use of models for predicting exposures are increas
ingly common and are essential for many risk assessments of the United
States Environmental Protection Agency (EPA). Exposure assessments co
nducted by the EPA to assist regulatory or policy decisions are often
challenged to demonstrate their ''scientific validity''. Model validat
ion has thus inevitably become a major concern of both EPA officials a
nd the regulated community, sufficiently so that the EPA's Risk Assess
ment Forum is considering guidance for model validation. The present p
aper seeks to codify the issues and extensive foregoing discussion of
validation with special reference to the development and use of models
for predicting the impact of novel chemicals on the environment. Its
preparation has been part of the process in formulating a White Paper
for the EPA's Risk Assessment Forum. Its subject matter has been drawn
from a variety of fields, including ecosystem analysis, surface water
quality management, the contamination of groundwaters from high-level
nuclear waste, and the control of air quality. The philosophical and
conceptual bases of model validation are reviewed, from which it is ap
parent that validation should be understood as a task of product (or t
ool) design, for which some form of protocol for quality assurance wil
l ultimately be needed. The commonly used procedures and methods of mo
del validation are also reviewed, including the analysis of uncertaint
y. Following a survey of past attempts at resolving the issue of model
validation, we close by introducing the notion of a model having maxi
mum relevance to the performance of a specific task, such as, for exam
ple, a predictive exposure assessment.