A data synthesis framework and case study supporting risk-based prioritization of additives and polymer-associated...

Polymers and plastics are widely used to support a range of growing market segments and applications (e.g., packaging, construction, agricultural films) due to functional properties (e.g., durability, versatility) and cost-effectiveness. With continued growing use of polymers and plastics, there is increased interest in health and environmental assessments of these materials, including chemicals associated with their production and use. Additives and polymer-associated chemistries (APAC), as individual chemicals, are subject to regulation by chemical assessment frameworks (e.g., U.S. Toxic Substances Control Act [TSCA], EU REACH [Registration, Evaluation, Authorisation and Restriction of Chemicals]). Finished products and articles are subject to other regulations (e.g., U.S. Food and Drug Administration Federal Food, Drug, and Cosmetic Act). Assessing risk (i.e., hazard × exposure) and making risk-based decisions requires analysis of a complex, dynamic dataset. There is a need for practical screening and prioritization methods to address the large numbers and complexity of APAC. Therefore, a data synthesis framework supporting risk-based screening and prioritization of APAC is proposed to demonstrate where and how to gather, organize, and process data for downstream human health and environmental risk assessment of APAC (i.e., in articles). A case study was performed with the 13,186 APAC identified in the United Nations Environment Programme's Chemicals in Plastics report to evaluate the utility of this data synthesis framework and evaluate how many APAC have sufficient publicly available data to perform downstream risk assessment. Based on application of this framework, it was determined that, at a minimum, Tier 1+ data are readily accessible to support risk-based screening and prioritization of most APAC in the United Nations Environment Programme database for human health (n = 8,819, 66.9%) and environmental (n = 9,068, 68.8%) risk assessment.