Calibration of the Dermal Advanced REACH Tool (dART) Mechanistic Model

Abstract

Abstract The dermal Advanced REACH Tool (dART) is a Tier 2 exposure modelling tool currently in development for estimating dermal exposure to the hands (mg min−1) for non-volatile liquid and solids-in-liquid products. The dART builds upon the existing ART framework and describes three mass transport processes [deposition (Dhands), direct emission and direct contact (Ehands), and contact transfer (Thands)] that may each contribute to dermal exposure. The mechanistic model that underpins the dART and its applicability domain has already been described in previous work. This paper describes the process of calibrating the mechanistic model such that the dimensionless score that results from encoding contextual information about a task into the determinants of the dART can be converted into a prediction of exposure (mg min−1). Furthermore, as a consequence of calibration, the uncertainty in a dART prediction may be quantified via a confidence interval. Thirty-six experimental studies were identified that satisfied the conditions of: (i) high-quality contextual information that was sufficient to confidently code the dART mechanistic model determinants; (ii) reliable exposure measurement data sets were available. From these studies, 40 exposure scenarios were subsequently developed. A non-linear log-normal mixed-effect model was fitted to the data set of Dhands,   Ehands, and    Thands scores and corresponding measurement data. The dART model was shown to be consistent with activities covering a broad range of tasks [spray applications, activities involving open liquid surfaces (e.g. dipping, mixing), handling of contaminated objects, spreading of liquid products, and transfer of products (e.g. pouring of liquid)]. Exposures resulting from a particular task were each dominated by one or two of the identified mass transport processes. As a consequence of calibration, an estimate of the uncertainty associated with a mechanistic model estimate is available. A 90% multiplicative interval is approximately a factor of six. This represents poorer overall precision than the (inhalation) ART model for dusts and vapours, although better than the ART model for mists. Considering the complexity of the conceptual model compared with the ART, the wide variety of exposure scenarios considered with differing dominant routes, and the particular challenges that result from the consideration of measurement data both above and beneath a protective glove, the precision of the calibrated dART mechanistic model is reasonable for well-documented exposure scenarios coded by experts. However, as the inputs to the model are based upon user judgement, in practical use, the reliability of predictions will be dependent upon both the competence of users and the quality of contextual information available on an exposure scenario.