HSE Science and Research Centre, Health and Safety Executive, Buxton SK17 9JN, UK.
Netherlands Organisation for Applied Scientific Research (TNO), Risk Assessment for Products in Development (RAPID), PO Box 360, 3700 AJ Zeist, The Netherlands.
Ann Work Expo Health. 2022 Jun 6;66(5):602-617. doi: 10.1093/annweh/wxab114.
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.
皮肤先进接触评估工具（dART）是一种用于估计非挥发性液体和固液产品对手部（mg min-1）的皮肤接触的二级暴露模型。dART 建立在现有的 ART 框架基础上，描述了三个质量传输过程（沉积（Dhands）、直接排放和直接接触（Ehands）以及接触转移（Thands）），这些过程都可能导致皮肤接触。支撑 dART 的机械模型和机械模型的校准，使得通过将关于任务的上下文信息编码到 dART 的决定因素中而产生的无量纲分数可以转化为暴露（mg min-1）的预测，已在前一篇工作中描述。本文通过将机械模型置于更广泛的统计建模框架内，完成了 dART 模型的方法学框架。在贝叶斯框架内，提出了一个混合效应模型，用于对每分钟活动的皮肤接触率进行建模。特定任务的暴露的中心估计值由经过校准的机械模型提供（因此基于任务的上下文信息）。该模型还描述了皮肤接触中个体间和个体内的变异性来源，方差分量的先验分布基于文献。基于信息先验分布的暴露估计可以使用与任务相关的测量数据进行更新。dART 模型通过三个实际案例进行了演示，其中估计值最初仅基于先验分布获得，然后通过适应任务的测量数据进行了细化。
