Building a quantitative AOP framework for smart remediation of NORM and legacy sites

Effective management of NORM and legacy sites across Europe increasingly depends on our ability to quantify ecological risks from complex mixtures of ionizing radiation, metals, and climate stressors. Current assessment tools rely on simplified benchmarks that treat each stressor separately and focus on short-term, individual-level effects. This leads to conservative assumptions, costly over-remediation, and sometimes misdirected risk management.

This project develops a quantitative Adverse Outcome Pathway (qAOP) toolbox that directly links measurable molecular responses to population-level impacts under realistic, chronic exposure conditions. By integrating these mechanistic insights into process-based population models, we can deliver decision metrics that are both more sensitive (early warning) and more specific (correct attribution of effects), providing regulators and industry with a transparent, science-based framework for prioritizing remediation actions.

To build and demonstrate this approach, rice (Oryza sativa), a monocot model species, is used as a tractable indicator for plant-level energy allocation and stress-response pathways relevant to terrestrial ecosystems. The project combines targeted experiments on ionizing radiation, metals, and drought with quantitative modelling, validated on a NORM/legacy case study. The outcome will be an assessment-ready methodology that supports risk-based, cost-effective remediation and positions SWD at the forefront of next-generation environmental remediation strategies.