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Wet scavenging modeling remains a challenge of the atmospheric transport of 137Cs following the Fukushima Daiichi Nuclear Power Plant accident, which significantly influences the detailed spatiotemporal 137Cs distribution. Till now, numerous wet deposition schemes have been proposed for 137Cs, but it is often difficult to evaluate them consistently, due to the limited resolution of meteorological field data and detailed differences in model implementations. This study evaluated the detailed behavior of 25 combinations of in- and below-cloud wet scavenging models in the framework of the Weather Research and Forecasting-Chemistry model, using high-resolution (1 km × 1 km) meteorological input. The above implementation enables consistent evaluation with great details, revealing complex local behaviors of these combinations. The 1-km-resolution simulations were compared with simulations obtained previously using 3-km-resolution meteorological field data, with respect to the rainfall pattern of the east Japan during the accident, atmospheric concentrations acquired at the regional SPM monitoring sites and the total ground deposition. The capability of these models in reproducing local-scale observations were also investigated with a local-scale observations at the Naraha site, which his only 17.5 km from the Fukushima Daiichi Nuclear Power Plant. The performance of the ensemble mean was also evaluated. Results revealed that the 1-km simulations better reproduce the cumulative rainfall pattern during the Fukushima accident than those revealed by the 3-km simulations, but showing with spatiotemporal variability in accuracy. And rainfall below 1 mm/h is critical for the simulation accuracy. Those single-parameter wet deposition models that rely solely on the rainfall showed improvements in performance in the 1-km simulations relative to that in the 3-km simulations, because of the improved rainfall simulation in the 1-km results. Those multiparameter models that rely on both cloud and rainfall showed more robust performance in both the 3-km and -1km simulations, and the Roselle–Mircea model presented the best performance among the 25 models considered. Besides rainfall, wind transport showed substantial influence on the removal process of atmospheric 137Cs, and it was nonnegligible even during periods in which wet deposition was dominant. The ensemble mean of the 1-km simulations better reproduces the high deposition area and the total deposition amount is closer to the observations than the 3-km simulation. At the local scale, the 1-km-resolution simulations effectively reproduced the 137Cs concentrations observed at the Naraha site, but with deviations in peak timing, mainly because of biased wind direction. These findings indicate the necessity of a multi-parameter model for robust regional-scale wet deposition simulation and a refined wind and dispersion model for local-scale simulation of 137Cs concentration.

Wet deposition has been identified as a critical impactor for the modelling of 137Cs in the Fukushima Daiichi Nuclear power plant (FDNPP) accident. However, it is difficult to simulate due to the involvement of close interaction between various complicated meteorological and physical processes during the wet deposition process. The limitation of measurement of the in-cloud and below-cloud scavenging also contribute to the uncertainty in wet deposition modeling, leading to the great variation of 137Cs wet deposition parameterization. These variations can be amplified further by inaccurate meteorological input, making simulation of radionuclide transport sensitive to the choice of wet scavenging parameterization. Moreover, simulations can also be influenced by differences between radionuclide transport models, even if they adopt similar parameterization for wet scavenging. Although intensively investigated, wet deposition simulation is still subject to uncertainties of meteorological inputs and wet scavenging modeling, leading to biased 137Cs transport prediction.To improve modeling of 137Cs transport, both in- and below-cloud wet scavenging schemes were integrated into the Weather Research and Forecasting-Chemistry (WRF-Chem) model, yielding online coupled modeling of meteorology and the two wet scavenging processes. Overall, 25 combinations of different in- and below-cloud scavenging schemes of 137Cs, covering most wet scavenging schemes reported in the literature, were integrated into WRF-Chem. Additionally, two microphysics schemes were compared to improve the simulation of precipitation. These 25 models and the ensemble mean of 9 representative models were systematically compared with a previous below-cloud-only WRF-Chem model, using the cumulative deposition and atmospheric concentrations of 137Cs measurements. The findings could elucidate the range of variation among these schemes both within and across the five in-cloud groups, reveal the behaviors and sensitivities of different schemes in different scenarios.The results revealed that the Morrison's double moment cloud microphysics scheme improves the simulation of rainfall and deposition pattern. Furthermore, the integration of the in-cloud schemes in WRF-Chem substantially reduces the bias in the cumulative deposition simulation, especially in the Nakadori and Tochigi regions where light rain dominated. For atmospheric concentration of 137Cs, those models with in-cloud schemes that consider cloud parameters showed better and more stable performance, among which Hertel-Bakla performed best for atmospheric concentration and Roselle-Apsimon performed best for both deposition and atmospheric concentration. In contrast, the in-cloud schemes that rely solely on rain intensity were found sensitive to the meteorological conditions and showed varied performance in relation to the plume events examined. The analysis based on the spatial pattern shows that the Roselle scheme, which considers cloud liquid water content and depth, can achieve a more balanced allocation of 137Cs between the air and the ground in these two cases than that achieved by the empirical power function scheme Environ. The ensemble mean achieves satisfactory performance except for one plume event, but still outperforms most models. The range of variation of the 25 models covered most of the measurements, reflecting the reasonable capability of WRF-Chem for modeling 137Cs transport.

The nuclear emergency response for accidental release around the nuclear power plant site (NPPs) requires a fast and accurate estimate of the influence caused by gaseous hazardous pollutants spreading, which is critical for and preventing protecting lives, creatures, and the environment. However, as usual, the NPPs is consist of dense buildings and multi-type terrain, e.g. river and mountain, which poses challenges to atmospheric dispersion calculation for response tasks. Micro-SWIFT SPRAY (MSS) comprises both the diagnostic wind model and the dispersion model, which enables the airflows and atmospheric dispersion simulation with the meteorological and other inputs. For a small-scale scenario, especially, the separate module for obstacles influence modeling provides the potential capability of precise atmospheric dispersion. But the error behavior of such a scenario around a nuclear power plant site with complex topography remains to be further demonstrated. In this study, MSS is comprehensively evaluated against a wind tunnel experiment with a 1:600 scale for the small-scale (3 km × 3km) atmospheric dispersion modeling. Tens of buildings located in this scenario of a NPPs surrounded by a mountain and river. The evaluations for diagnostic wind modeling include the speed, direction, and distribution of horizontal airflows and vertical profile of speed at a representative site. And for the concentration calculation, horizontal distribution, axis profile, and vertical profile at a representative site. The results demonstrate the MSS can reproduce fine airflows near the buildings but overestimate the wind speed. The maximum deviation of vertical speed is around 2.09 m/s at the representative site. The simulated plume of concentration reproduces the highest concentration place and matches the observations well. The axis profile of concentration is underestimated and the vertical profile displays an increasing deviation with the height increase. Compared with the observations, the FAC5 and FAC2 of concentration simulation reach 0.945 and 0.891 in the entire calculation domain, which convinces the performance of MSS in small-scale modeling.

The atmospheric release of radionuclides is a crucial potential hazard to public health. Its release rate is vital in assessing the international environmental risk of atmospheric radionuclide leaks and conducting nuclear emergency preparedness. However, according to the radionuclide leaks such as the Fukushima Daiichi accident and the recent iodine-131 and ruthenium-106 releases in 2017, the release rate cannot be directly measured or derived in a forward way, but can only be inversely estimated by comparing the environmental measurements with a model-predicted plume, a technique often referred to as source inversion. However, such inversion is vulnerable to the inevitable plume biases, including the plume range (i.e. the area of positive model predictions) and transport pattern in radionuclide transport modeling, leading to inaccurate source estimates and risk assessment.This paper describes an automated method that estimates the release rate while comprehensively correcting plume biases. By using the spatial correlation matrix, the predicted plume can spread over a broader area, thus covering the potential range of the true plume. Then, the difficult task of direct plume adjustment is simplified to tuning the predictions inside a correlation-adjusted plume. Based on this, the previous joint method can work efficiently to estimate the release rate while simultaneously refining the predictions inside the adjusted range, correcting both the plume range and the transport pattern. An ensemble-based algorithm is proposed to automatically calculate the spatial correlation in order to execute this method. With this algorithm, SERACT can accomplish realistic and robust source estimation without manual adjustment on any parameters.The proposed method SERACT is validated with the two wind tunnel experiments based on a real Chinese nuclear power plant site, and the site features highly heterogeneous topography and dense buildings. In this paper, two radionuclide transport models with mild and severe plume biases respectively are used to assess the adjustment efficiency of SERACT, including source estimation and plume distribution. Its performance is compared with that of the standard approach and a recent state-of-the-art method. Its sensitivity to the number and quality of measurements, and the selection of autocorrelation scales is also investigated.The results demonstrate that SERACT corrects the plume biases with high accuracy (Pearson’s Correlation Coefficient=1.0000, Normalized Mean Square Error≤1.03×10−3) and reduces the estimation error by nearly two orders of magnitude at best. In addition, SERACT exhibited stable performance in all the validation tests and gave the lowest error levels with various numbers and quality of measurements. With fully automated parameterization, its performance is close to that obtained with the optimal autocorrelation scale in all test cases. These results indicate that SERACT is robust in various inversion cases and is able to serve as a general remediation to the long-standing imperfect modeling issue in source inversion.

Many efforts have been devoted to estimate the release rate of the radionuclide emission in the Fukushima Daiichi nuclear accident using regional scale observations. Because of the radioactive decay, regional scale observations may not provide information of short-lived radionuclides, which contributes the majority of radiation exposure in the early stage. In this study, the local-scale gamma dose rates data were used to estimate the atmospheric release rates of both long- and short-lived radio nuclides.The proposed method uses reactor physics to obtain an a priori radionuclide composition and a reverse source term estimate as an a priori release rate. A weighted additive model is developed, which uses the local-scale gamma dose rates to handle the conflicts between the two priors and to simultaneously incorporate them into the source inversion. The proposed method is validated against both the local-scale gamma dose rates and the regional concentration measurements of Cs-137. The results prove that the retrieved a posteriori source term combines the advantages of both priors and substantially improves the predictions of the on-site gamma dose rates. Given a detailed priori release rate, this approach also improves the regional predictions of both airborne and deposited Cs-137 concentrations.