Mark Pemberton summarised the key learnings from the case studies as follows:
Key learnings on Aggregate Exposure: Some tools are available (PACEM, Creme Care & Cosmetics) for cosmetics and personal care products for example. These require robust data sets (habits, practices, product co-use, chemicals composition and presence probabilities). For some domains, such as household care products, the available data are limited. Approaches are required to indicate when higher tier aggregate assessments might be a priority (information on relative contributions of different sources). Evaluations of total consumer exposure from biomonitoring studies indicate that exposure estimates from higher tier assessments are closer to reality, whereas lower tiers are overly conservative. Model verification with real-life data on a representative range of chemicals would assist to promote use/acceptance of exposure model predictions.
Opportunities for Data Acquisition: How do we determine priorities setting for data acquisition and development? How do we acquire better information on typical concentrations or ranges of a chemical in specific domains? Can we develop representative “default” exposure profiles for product types (concentration, frequency of use etc.)? Can we conduct a sensitivity analysis to determine which exposure data will contribute greatest to refining a risk assessment, in order to prioritise where data acquisition will be of most benefit?
Opportunities for Aggregate Exposure: How do we combine exposure from different domains to better reflect real life exposures i.e. establishing an exposure matrix? The following may be useful to take into account: presence probabilities (proportion of products in a category that contain the chemical); co-exposure to a given chemical from different product types with different profiles of use (consumer preferences, brand loyalty); total exposure (exposure from food, water, consumer products, environment); chemical synergies?