An Approach to Evaluate Risks of Radiation Exposure Using Long-Term Meteorological Data for Urban Setting

A risk analysis framework is proposed to evaluate radiation exposure risks in an urban environment using long-term meteorological data. An in-house Gaussian plume dispersion model has been developed to incorporate the high-resolution subzone and population information in Singapore. This model enables the presentation of radiation risks across subzones. Risk index (RI), defined as the product of activity concentration and population, is used to quantify the radiation risks. Meteorological data utilised in the study includes ten years’ worth of surface wind observations collected by MSS across five different source locations, and the atmospheric stability statistics derived from the ERA5 reanalysis data. A series of dispersion simulations are performed using these source locations for different meteorological conditions. The computed RIs are then summed across the subzones for each month. Subsequently, the radiation exposure risks for all the source locations are ranked. The ranking derived from using the “atmospheric pollution coefficient” approach is also examined and compared with the proposed approach. The disparities between the two underscore the necessity of considering the population distribution when evaluating radiation exposure risks. Leveraging long-term meteorological data and high-resolution information on subzones and population distribution, the proposed approach can provide more reliable results for radiation exposure risk analysis.