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 (D hands), direct emi...

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Published in:Annals of Work Exposures & Health Vol. 66; no. 5; pp. 602 - 618
Main Authors: McNally, Kevin, Goede, Henk A, Schinkel, Jody, Gorce, Jean-Philippe, Warren, Nick
Format: equations & formulas research tables/charts Journal Article
Published: Oxford University Press / USA Jun2022
Online Access:View this record in EBSCOhost
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      dt: Jun2022
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      pub: Oxford University Press / USA
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        10.1093/annweh/wxab114
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        atl: Dermal Advanced REACH Tool (dART): A Bayesian Model for Dermal Exposure Assessment.
      aug:
        au:
          McNally, Kevin
          Goede, Henk A
          Schinkel, Jody
          Gorce, Jean-Philippe
          Warren, Nick
        affil: HSE Science and Research Centre, Health and Safety Executive , Buxton SK17 9JN , UK
      sug:
        subj:
          Occupational Exposure Adverse Effects
          Occupational Exposure Evaluation
          Skin
          Hand
          Chemical Safety
          Occupational Health
          Models, Statistical
          Human
          Calibration
          Inhalation Exposure Adverse Effects
          Chemical Hazard Release Adverse Effects
          Occupational Safety
      ab: 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 (D hands), direct emission and direct contact (E hands), and contact transfer (T hands)) 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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