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This paper examines the uses and limits of empirical data in evaluating measurement and modeling approaches to human lead exposure. Empirical data from experiment or observation or both have been used in studies of lead exposure. For example, experimental studies have elucidated and quantified physiologic or biokinetic parameters of lead exposure under controlled conditions. Observation, i.e., epidemiology, has been widely applied to study population exposures to lead. There is growing interest in the use of lead exposure prediction models and their evaluation before use in risk assessment. Empirical studies of lead exposure must be fully understood, especially their limits, before they are applied as "standards" or reference information for evaluation of exposure models, especially the U.S. Environmental Protection Agency's lead biokinetic model that is a focus of this article. Empirical and modeled datasets for lead exposure may not agree due to a) problems with the observational data or b) problems with the model; caution should be exercised before either a model or observational data are rejected. There are at least three sources of discordance in cases where there is lack of agreement: a) empirical data are accurate but the model is flawed; b) the model is valid but reference empirical data are inaccurate; or c) neither empirical data nor model is accurate, and each is inaccurate in different ways. This paper evaluates some of the critical empirical input to biokinetic models, especially lead bioavailability.

Uses and limits of empirical data in measuring and modeling human lead exposure.