[To join the working group, please first become a member at ISES Europe]
The Exposure Models Working Group, established under the auspices of the International Society of Exposure Science, European Chapter (ISES Europe), has the overarching aim to establish within the exposure science scientific and regulatory community a common understanding of use, documentation, validity and limitations of the models and tools for exposure assessment. This working group addresses the need to have guidance to enhance transparency of choices made in the selection of models, tools and exposure-related input data, and to better understanding the quality aspects of model results, since these issues were expressed by multiple actors in the field of exposure science during the first European exposure science strategy workshop from 19th - 20th June 2018 at BAuA in Dortmund, Germany.
In two of the outbreak sessions (Regulatory exposure assessment and Exposure assessment and tools) it was concretely proposed to initiate a working group within ISES Europe that should address different aspects of modelling of exposure. The first goal of the exposure models working group therefore is to build a framework until 2022 that allows the creation of mechanisms to develop new models and use existing models across various domains and regulations.
As next steps towards this goal, we will take the first set of activities and milestones outlined from the Dortmund workshop as a point of departure and discuss with the working group priorities and timelines.
Point of departure: possible short-time milestones and long-term visions.
1. Develop guidance on what tool/model to use in different situations ensuring the complexity for being used in regulatory context is not too high
2. Identification of (applicability) gaps and inform on needs for new data generation / model developments. Further develop models/methods for
3. Development of innovative methods/approaches in exposure modelling (e.g. big data, machine learning, 3D input data, artificial intelligence, neural networks etc.)
4. Advancement of dissemination and training of modelling