Dependence of column ozone on future ODSs and GHGs in the variability of 500-ensemble members.

State-of-the-art chemistry-climate models (CCMs) have indicated that a future decrease in ozone-depleting substances (ODSs) combined with an increase in greenhouse gases (GHGs) would increase the column ozone amount in most regions except the tropics and Antarctic. However, large Arctic ozone losses have occurred at a frequency of approximately once per decade since the 1990s (1997, 2011 and 2020), despite the ODS concentration peaking in the mid-1990s. To understand this, CCMs were used to conduct 24 experiments with ODS and GHG concentrations set based on predicted values for future years; each experiment consisted of 500-member ensembles.

Validity of the source term for the Fukushima Dai-ichi Nuclear Power Station accident estimated using local-scale atmospheric dispersion simulations to reproduce the large-scale atmospheric dispersion of Cs.

The source term of Cs from the Fukushima Dai-ichi Nuclear Power Station (FDNPS) accident was estimated from the results of local-scale atmospheric dispersion simulations and measurements. To confirm the source term's validity for reproducing the large-scale atmospheric dispersion of Cs, this study conducted hemispheric-scale atmospheric and oceanic dispersion simulations. In the dispersion simulations, the atmospheric-dispersion database system Worldwide version of System for Prediction of Environmental Emergency Dose Information (WSPEEDI)-DB and oceanic dispersion model SEA-GEARN-FDM that were developed by the Japan Atomic Energy Agency were used.

Refinement of source term and atmospheric dispersion simulations of radionuclides during the Fukushima Daiichi Nuclear Power Station accident.

To assess the radiological dose to the public resulting from the Fukushima Daiichi Nuclear Power Station (FDNPS) accident in Japan, especially for the early phase of the accident when no measured data are available for that purpose, the spatial and temporal distributions of radioactive materials in the environment need to be reconstructed through computer simulations using the atmospheric transport, dispersion, and deposition model (ATDM). For the ATDM simulation, the source term of radioactive materials discharged into the atmosphere is essential and has been estimated in many studies. In the present study, we further refined the source term estimated in our previous study and improved the ATDM simulation with an optimization method based on Bayesian inference, which used various measurements such as air concentration, surface deposition, fallout, and newly released hourly air concentrations of Cs derived by analyzing suspended particulate matter (SPM) collected at air pollution monitoring stations.