Creme Global, Ireland
One of the key activities conducted by the task force was to develop an overview of the current human exposure science landscape, from the perspective of what data sources and tools are available for exposure assessment, with a specific focus on consumer exposure (although some occupational sources were also considered). This is with a view to providing a centralised source of information for risk assessors to avail of when carrying out an exposure assessment, to elucidate what the appropriate uses of different data and models are in different contexts, and to identify future opportunities to gather data or develop models to further the field of consumer exposure and risk assessment.
Data sources were categorised into the following categories: Exposure Algorithms, Habits and Practices Data, Co-use Data, Chemical Occurrence Data, and Presence Probability Data. The source or original reference is provided in the landscaping document, as well as some details on the nature of the data. Within the section on tools, a number of additional headings are provided: Product Category, Type of Assessment that can be Performed, Built-in Data/Data Requirements, Regions Covered, Modelling Capabilities, Routes of Exposure Covered, Availability, Occupational or Consumer, and additional Comments. The details of each of the criteria were described and their meanings and interpretation explained.
Finally, identified opportunities for gathering new data and developing new tools, models or analyses were presented, arising out of the landscaping exercise itself. This also touched on some of the learnings from the individual case studies, and the types of data and models that can be introduced to refine exposure assessments for different purposes.
Cian O’Mahony asked participants for their thoughts on the Landscaping document: what are the gaps? What type of guidance is necessary for potential risk assessors using these tools and data? The following three questions should be kept in mind when doing an exposure assessment (see following slide).