The nuclear fuel that melted during the Fukushima nuclear accident in 2011 is still being cooled by water. In this process, contaminated water containing radioactive substances such as cesium and strontium is generated. The total amount of radioactive pollutants released by the natural environment due to the nuclear accident in Fukushima in 2011 is estimated to be 900 PBq, of which 10 to 37 PBq for cesium. Radioactive cesium (137Cs) is a potassium analog that exists in the water in the form of cations with similar daytime behavior and a small hydration radius and is recognized as a radioactive nuclide that has the greatest impact on the environment due to its long half-life (about 30 years), high solubility and diffusion coefficient, and gamma-ray emission. In this study, alginate beads were designed using Prussian blue, known as a material that selectively adsorbs cesium for removal and detection of cesium. To confirm the adsorption performance of the produced Prussian blue, immersion experiments were conducted using Cs standard solution, and MCNP simulations were performed by modeling 1L reservoir to conduct experiments using radioactive Cs in the future. An adsorption experiment was conducted with water containing standard cesium solution using alginate beads impregnated with Prussian blue. The adsorption experiment tested how much cesium of the same concentration was adsorbed over time. As a result, it was found that Prussian blue beads removed about 80% of cesium within 10-15 minutes. In addition, MCNP simulation was performed using a 1 L reservoir and a 3inch NaI detector to optimize the amount of Prussian blue. The results of comparing the efficiency according to the Prussian volume was shown. It showed that our designed system holds great promise for the cleanup and detection of radioactive cesium contaminated seawater around nuclear plants and/or after nuclear accidents. Thus, this work is expected to provide insights into the fundamental MCNP simulation based optimization of Prussian blue for cesium removal and this work based MCNP simulation will pave the way for various practical applications.

Clogging of the filter media which is brought by physical, chemical, and biological factors tend to reduce the lifespan of filters and remains a challenge. In this study, a laboratory column test method was used to investigate the evolution of physical and biological clogging in a non-vegetated filter media system with layers of sand, gravel, and woodchip. Blank column tests using either sand or gravel were conducted and investigated. Several column setups with varying arrangements and particle sizes of sand and gravel were also prepared to identify the best filter media combination that is least susceptible to clogging without compromising the treatment capacity. Artificial stormwater runoff was introduced in the system at a specific hydraulic loading rate (HLR) and influent characteristics. The degree of clogging was quantified by monitoring the variations in the hydraulic head at different levels of the columns. Water samples were also collected, tested, and analyzed at the end of each test run in order to measure the treatment efficiency of the filter. The insights and results of this study can justify the physical and biological clogging formation in filter media and therefore be used to suggest some filter media particle size modifications that can help to improve the sediment removal and treatment performance. Moreover, it can also aid to reduce the maintenance frequency and costs of a stormwater filter system.