PASTOR: Finding the persistency archetypes to stop the biodegradation lottery and move to reliable half-lives

Persistency is a key parameter in risk assessment due to long lasting effects in the environment. There is no reliable, cost-efficient way to determine persistency, which is traditionally evaluated by a single substance biodegradation lab experiment with highly variable outcomes. To date, only 2% of registered chemicals have (inconclusive) persistency data. The proposed research aims to determine persistency rapid and environmentally relevant to avoid future cases of forever chemicals as PFAS. This will be achieved by high‐ throughput biodegradation analysis for mixtures of thousands of chemicals in wastewater treatment plants. This unique dataset will be used for deep learning to 1) identify key chemicals- the archetypes-, which make persistency benchmarked and comparable, and to 2) predict persistency reliably. This project will show how hazard assessment can catch up with the speed of newly introduced chemicals to avoid long lasting regrettable decisions.