Estimating Exposure Intensity Based on Odor

Abstract

Abstract The odor detection threshold for a chemical typically varies widely across population members, and the values can be described by a lognormal distribution. Assuming that the parameters of a chemical’s odor detection threshold distribution can be estimated, a method is presented to retrospectively estimate exposure intensity based on detecting the chemical’s odor. The context is a single exposure period involving a single air contaminant. In brief, where k out of n identically exposed persons detect the chemical’s odor, the best estimate of the chemical’s concentration in air corresponds to the k ÷ n fractile of the odor detection threshold distribution. Where n is small and/or k ÷ n is close to zero or one, exact 100×(1−α)% confidence bounds for the fractile estimate can be computed without using the normal distribution assumption. In addition, statistical uncertainty in the parameter estimates of the chemical’s odor detection threshold distribution can be considered via a parametric bootstrap procedure, such that an overall 100×(1−α)% confidence interval on the chemical’s concentration in air is obtained. The method is illustrated for benzene. Analysis of the available benzene odor detection threshold literature provides best estimates of a population geometric mean equal to 37.8 ppm and geometric standard deviation equal to 1.92. In a hypothetical example in which two out of six workers detect the odor of benzene, the point estimate of the benzene exposure level is 28.5 ppm, and the approximate one-sided lower 95% confidence limit is 8.2 ppm. The present analysis also makes clear that odor does not provide an adequate warning of excessive levels of airborne benzene.