Data driven methods to increase the reliability of microplastics hazard assessment

The number of studies testing for adverse effects of micro- and nanoplastic particles (MNP) to organisms has increased substantially over the last years. Deriving generalized conclusions about hazardous effects and establishing reliable effect size estimates is nevertheless still challenging. Studies are often difficult to compare due to differences in experimental design, methodological approaches and study quality. In addition, although studies have been conducted with various types of MNP, it is impossible to test all particle types potentially present in a contaminated environment directly in the lab. MNP are highly complex and each particle has its unique composition of traits including among others particle shape, polymer type, size, surface charge and physical surface properties. Furthermore, these traits change over time when particles are exposed to the environment. Aggregating data from published studies can help approaching both these challenges. Meta-analyses can be used to derive less biased effect size estimates with higher confidence. Predictive modelling approaches based on larger datasets can help to derive effect size estimates for MNP that cannot be tested in the lab and to extrapolate lab outcomes to environmentally relevant MNP mixes. In 2022, the Toxicity of Microplastics Explorer (ToMEx) database has been compiled with an extension of the dataset in 2024 (ToMEx 2.0). It compiles information extracted from the published literature on MNP effects on aquatic organisms and human health related effects. In the present talk, I will show how meta-analyses and the ToMEx dataset can be used to more reliably estimate the hazard of MNP across species and how data aggregation can help us to better understand which MNP traits are associated with particularly toxic outcomes. Also see: https://micro2024.sciencesconf.org/559345/document