Advanced REACH Tool: Development and Application of the Substance Emission Potential Modifying Factor

摘要

The Advanced REACH Tool (ART) is an exposure assessment tool that combines mechanistically modelled inhalation exposure estimates with available exposure data using a Bayesian approach. The mechanistic model is based on nine independent principal modifying factors (MF). One of these MF is the substance emission potential, which addresses the intrinsic substance properties as determinants of the emission from a source. This paper describes the current knowledge and evidence on intrinsic characteristics of solids and liquids that determine the potential for their release into workplace air. The principal factor determining the release of aerosols from handling or processing powdered, granular, or pelletized materials is the dustiness of the material, as well as the weight fraction of the substance of interest in the powder and the moisture content. The partial vapour pressure is the main intrinsic factor determining the substance emission potential for emission of vapours. For generation of mist, the substance emission potential is determined by the viscosity of the liquid as well as the weight fraction of the substance of interest in the liquid. Within ART release of vapours is considered for substances with a partial vapour pressure at the process temperature of 10 Pa or more, while mist formation is considered for substances with a vapour pressure ≤10 Pa. Relative multipliers are assigned for most of the intrinsic factors, with the exception of the weight fraction and the vapour pressure, which is applied as a continuous variable in the estimation of the substance emission potential. Currently, estimation of substance emission potential is not available for fumes, fibres, and gases. The substance emission potential takes account of the latest thinking on emissions of dusts, mists, and vapours and in our view provides a good balance between theory and pragmatism. Expanding the knowledge base on substance emission potential will improve the predictive power of occupational exposure models and thereby the accuracy and precision of the exposure estimates.