Background: Accurately estimating consumer aggregate exposure to fragrance materials from use of cosmetics and personal care products requires information on habits and practices, along with fragrance use levels in products. This data is available from separate studies and was linked probabilistically using computational models. The use of such models avoids overly conservative deterministic methods permitting a more realistic measure of exposure to fragrance ingredients within the population. Aims: Develop a comprehensive dataset and model for estimating aggregate exposure to fragrance materials in cosmetics and consumer products. Methods: Habits and practices data for over 36,000 consumers in four EU countries and the U.S. was obtained and collated into a comprehensive database. This was created by linking market study data to distributions of amount of product use data obtained from studies conducted in Europe by Colipa (now Cosmetics Europe) and in the US by CTFA (now the Personal Care Products Council). The individual subject data was also linked demographically to distributions of body weights and skin surface areas per body part. Co-use and non-use of different products is built into the data, as well as the relevant biometric parameters per consumer. This was integrated with several novel probabilistic exposure models for fragrance materials. Results: A database and methods for accurately estimating consumer exposure to fragrance materials. Conclusions: Probabilistic modelling, together with concatenated data from appropriate sources can be used to accurately profile fragrance exposure, extensible to the majority of fragrances on the market. The established model provides an accurate estimation of consumer exposure per unit skin surface area (mcg/cm2) and per unit bodyweight (mg/kg) to fragrance materials and allows exposure calculations such as distributions of individual product exposure, total product exposure, as well as exposure per body part.
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