The Dermal Advanced REACH Tool (dART): A Bayesian Model for Dermal Exposure Assessment.

The dermal Advanced REACH Tool (dART) is a tier 2 exposure model for estimating dermal exposure to the hands (mg min-1) for non-volatile liquid and solid-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 calibration of 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), have been described in previous work. This paper completes the methodological framework of the dART model through placing the mechanistic model within a wider statistical modelling framework. A mixed-effects model, within a Bayesian framework, is presented for modelling the rate of dermal exposure per minute of activity. The central estimate of exposure for a particular task is provided by a calibrated mechanistic model (and thus based upon contextual information about a task). The model also describes between- and within-worker sources of variability in dermal exposure, with prior distributions for variance components based upon the literature. Estimates of exposure based upon informative prior distributions may be updated using measurement data associated with the task. The dART model is demonstrated using three worked examples, where estimates are initially obtained based upon the prior distributions alone, and then refined through accommodating measurement data on the tasks.

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

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.

Dermal Advanced REACH Tool (dART)-Development of a Dermal Exposure Model for Low-Volatile Liquids.

This article describes the development of a mechanistic model for underpinning the dermal Advanced REACH Tool (dART), an extension of the existing ART model and its software platform. It was developed for hand exposure to low volatile liquids (vapour pressure ≤ 10 Pa at 20°C) including solids-in-liquid products. The model is based on an existing conceptual dermal source-receptor model that has been integrated into the ART framework. A structured taxonomy of workplace activities referred to as activity classes are adopted from ART. Three key processes involved in mass transport associated with dermal exposure are applied, i.e. deposition, direct emission and contact, and transfer. For deposition, the model adopts all the relevant modifying factors (MFs) applied in ART. In terms of direct emission and contact (e.g. splashes) and transfer (e.g. hand-surface contacts), the model defines independent principal MFs, i.e. substance-related factors, activity-related factors, localized- and dispersion control and exposed surface area of the hands. To address event-based exposures as much as possible, the model includes crucial events during an activity (e.g. hand immersions) and translates objective information on tools and equipment (manual or automated) to probable events (e.g. splashes) and worker behaviours (e.g. surface contacts). Based on an extensive review of peer-reviewed literature and unpublished field studies, multipliers were assigned to each determinant and provide an approximated (dimensionless) numerical value. In the absence of (sufficient) evidence, multipliers were assigned to determinants based on assumptions made during discussions by experts in the consortium. A worked example is presented to illustrate the calculation of hand exposure for a specific scenario. The dART model is not yet implemented in the ART software platform, and a robust validation of the model is necessary to determine its predictive ability. With advancing knowledge on dermal exposure and its determinants, this model will require periodic updates and refinements, in addition to further expansion of the applicability domain of the model.