In nuclear power plant accidents, airborne radioiodine poses major health risks, making reliable reconstructions of its spatiotemporal distribution crucial for emergency management. Current state-of-the-art prognosis systems use atmospheric dispersion modeling but ignore posterior evidence from emergency care centers, comprising movement profiles and thyroid measurements of affected individuals. A first study showed that the AI method Cuboid Splatting can reconstruct iodine air concentrations from such data but it ignores simulations from established prognosis systems. Our multidisciplinary team extends Cuboid Splatting by incorporating these simulations as priors and subsequently correcting them using movement and thyroid data. Several ways to translate and correct priors are developed. The best-performing approaches are combined into a novel Cuboid Splatting-with-prior mechanism, which we evaluate using constructed prior scenarios representing different error types and intensities. Using Cuboid Splatting-with-prior yields more accurate reconstructions than (i) the used dispersion simulations alone and (ii) plain Cuboid Splatting without prior. Across reconstructions, the mean scenario error is 19.61%, improving on (i) by 28.02pp and on (ii) by 89.86pp, the latter with particularly large gains at high spatial resolution. These results demonstrate that combining simulation-based priors with measurement-based posterior inference can substantially improve the reconstruction of iodine air activity concentrations in nuclear emergencies.