In situations where radiological risk does not originate from a single ongoing accident but from a widespread degradation of the operational context conditions affecting several nuclear installations, traditional event-oriented emergency preparedness may become insufficient. Under such conflict-related circumstances, protective planning requires a broader framework capable of capturing the aggregated and latent risk arising from multiple facilities and locations. This paper presents the Diagnosis And Prognosis of Hazards in Nuclear Emergencies (DAPHNE) methodology (developed by the European Commission's Joint Research Centre), designed to support European Commission services and EU Member States in nuclear emergency preparedness and response. The methodology integrates MAAP, HYSPLIT, and the JRODOS decision-support system—together with dedicated in-house release models where appropriate—to provide a coherent assessment of both onsite accident progression and offsite radiological consequences. Building on the conceptual framework introduced in Part I, this paper applies DAPHNE to a conflict-scenario case study to quantify integrated latent radiological risk and illustrate how the methodology enhances the identification of geographical areas where risk accumulates. By providing a harmonised numerical workflow for scenario selection, source-term calculation, dispersion modelling, and risk mapping, DAPHNE improves upon other methodologies and offers a scientifically grounded basis for designing conflict-related Emergency Planning Zones that complement existing site-specific arrangements. Through its capacity to integrate multiple risk sources and their aggregated contribution to radiological exposure, DAPHNE strengthens preparedness planning under complex conflict scenarios and supports more informed decision-making across affected territories. • Applies the DAPHNE methodology to assess radiological risk in conflict scenarios. • Integrates MAAP, HYSPLIT and JRODOS for end-to-end accident and dispersion analysis. • Produces radiological risk maps to identify and prioritize high-risk areas. • Quantifies EPZ distance adaptation through probabilistic-based risk mapping. • Demonstrates practical implementation of the conceptual framework from Part I.
Blul et al. (Sat,) studied this question.