Spatial optimal design of networks for monitoring atmospheric radioactive releases in the UAE

Monitoring accidental radioactive releases into the atmosphere is strategic for public safety and environmental protection. In the initial phase of a radiological event involving the release of radioactive material into the atmosphere, emergency monitoring data are essential for understanding the accident's dynamics, thus delivering crucial information across time and space to guide decisions on implementing suitable countermeasures. The Federal Authority for Nuclear Regulation in the UAE (FANR) operates a national gamma-dose monitoring network to automatically sample radioactivity levels over the UAE territory. The primary purpose of this monitoring network is to notify authorities of any potential threat arising from the dispersion of radionuclides in the atmosphere. This study seeks to offer recommendations on how to optimize the spatial patterns of networks used to monitor radioactive releases from the UAE and adjacent countries. To achieve this goal, we computed a state-of-the-art database of short-term dispersion accidents for hypothetical radioactive releases from potential nuclear sites. These simulations were conducted using the Lagrangian dispersion model HYSPLIT, driven by meteorological data from the Weather Research and Forecasting (WRF) model at a 5 km spatial resolution. This comprehensive approach ensures accurate modeling of dispersion patterns, considering various atmospheric conditions throughout the year. Based on the database of dispersion simulations, a dual-objective criterion considering both network emergency detection and prediction capabilities is thoroughly investigated. This criterion ensures that the network is not only capable of detecting radioactive releases promptly but also accurately predicting their dispersion patterns. A simulated annealing optimization algorithm was used to determine the most effective set of optimal locations for monitoring stations. This algorithm efficiently searches for the best station spatial arrangement to maximize the network's emergency detection and prediction capabilities.