Search

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.

Atmospheric dispersion models (ADMs) have been widely used in simulating the contamination from released pollutants, which supports the emergency response and assist the inverse modeling for unknown source, due to its balance between accuracy and speed of calculation. The Micro-SWIFT-SPRAY modeling system (MSS) is one of the candidates that are able to accurately reproduce the wind and concentration fields with inputs of meteorology, topography, and source information. The obstacle treatments benefit its performance over dense buildings. Applying the optimal parameters to MSS, both the local and small-scale simulations were carried out in the vicinity of the same nuclear power plant (NPP) site with dense buildings and surrounded by mountains and sea. In these scenarios, the airflows came from the NE direction and cross over the sea and buildings to mountains. Both the wind and concentration results were evaluated against the measurements of two wind tunnel experiments. The results demonstrate that MSS can reproduce the variations of wind and concentration towards the changes in terrain elevation or building layout. The local-scale simulation well matches the measurements in the mountain area, whereas the small-scale one better reconstructs those around the buildings. The clusters of wind direction and speed are found that result from the topography of monitoring networks. The high concentration area around the release position is successfully reproduced, which indicates the turbulence is sufficient facing complex obstacles. Besides, MSS outperforms the concentration simulations in the local-scale scenario with a FAC5 of 0.710 and a FB of −0.010. However, the VG of the local-scale scenario reaches 15.510 meaning many extremes are introduced. The small-scale scenario obtains a lower VG of 2.303. Considering different performance dominances of two scales, nesting grids may bring improvement in the case both the simulations in the mountain and building areas are meant for the emergency response.

Temporal absences in observation records lead to release losses during the source term inversions of atmospheric radionuclide emissions. Consequently, objectively-estimated source terms for the Fukushima accident contain fewer release details and present large discrepancies when compared with the expert-judged one. This paper describes an objective method that can adaptively recover the missing releases caused by the temporal absences of observations. The proposed method assumes that the accident releases of radionuclides are piecewise-constant and comprise both peaks and constant releases. The missing releases are adaptively recovered as either peaks or constant releases by minimizing the total variation of the estimated source term. The proposed method is applied to the Fukushima accident and evaluated against regional airborne and deposited

The calculation of three-dimensional dose rate fields plays a key role in radiation dose rate estimation and the service for the nuclear emergency. The recent fast calculation method based on the Fast Fourier Transform (FFT) method can greatly speed up the calculation without losing accuracy, which is promising for operational usage in nuclear emergency response systems. But it can only be used for a uniform grid. Unfortunately, most atmospheric dispersion models use a non-uniform grid, which prevents the direct application of FFT-based calculation. Therefore, interpolation is required beforehand to use the Fourier transform, which may introduce errors and affect computing efficiency. In this paper, an atmospheric dispersion modeling case of a typical nuclear power plant (NPP) is used to investigate the efficiency of different interpolation methods, which are based on a non-uniform grid. These methods are linear interpolation and nearest-neighbor interpolation. The sensitive analysis of grid resolution is investigated in the slices of x, y, and z at typical positions, which confirms the smooth-out and speed-up effects in rough grids. A grid size over 10 m at any slice commonly causes losses of change details of dose rate fields. Given the same resolution of 50 m × 50 m × 50 m, the nearest neighbor performs a 717 times calculation faster than the linear method, which preserves more change details of dose rate fields as well. For complex calculation tasks, e.,g., non-uniform NPP buildings, the nearest neighbor interpolation method is recommended with a resolution of 10 m × 10 m × 10 m to make a good balance between accuracy and speed.

The radiation field measurement and surveying play important roles in optimizing and planning operation work in the radioactive area. In this work, a 3-D radiation field reconstruction method is applied to reconstruct a 3-D radiation field of a facility with two radioactive drums and a shielding, not limited to the existing methods under two-dimensional grids. A numerical scheme of the method is introduced. Monte Carlo simulation of the gamma radiation field was performed to be an original field so that the reconstruction results can be verified. Random and regular sampling ways are taken into consideration while the sampling rates are kept at 4.12%. Quantitative evaluation of the results evaluates MSE (mean relative error) and MRE (mean squared error), MRE is kept at less than 4%, which shows a good reconstruction accuracy of the method in almost all 3D space. The work in this paper has good reference value for applications such as radiation field detection, inversion, and reconstruction, operations under radioactive environments like nuclear waste decommissioning, and nuclear power radiation modeling.

Source localization of accidental radionuclide release is crucial to nuclear emergency and decision making, especially when no reliable information about possible release regions can be previously obtained. To rapidly and accurately determine the source location with a limited number of measurements, this paper proposes a two-step data assimilation method based on forward and backward dispersion simulations in the RIMPUFF model. In the first step, meteorological variables such as wind speed are reversed and the measurements are set as sources for several backward RIMPUFF runs. By identifying the overlap of backward plumes originating from different measurement sites, potential regions discretized into grids can be roughly determined. In the second step, the grids selected by the first step are set as sources for forward RIMPUFF runs with unit release rate and the source location will be refined by minimizing a correlation-based function of simulated and measured data. The method is verified by SCK-CEN 41Ar field experiment. Using gamma dose rate data from all measurement sites, source location is retrieved with errors of 22.36m and 30m respectively on the two days of the field experiment. Compared with the direct correlation-based method, the proposed method achieves satisfactory solutions in an obviously shorter time. In particular, there is no need for predetermining the source release rate throughout the process, indicating that the method can conveniently combine with other source term inversion approaches whose source location is viewed as a known quantity. Therefore, the presented two-step data assimilation method here is potentially a useful tool with high accuracy and efficiency for further integration in nuclear emergency response system.

Air dispersion modeling is an important tool for emergency response following a nuclear accident, such as the Fukushima accident. Current researches mainly focus on global- and regional-scale modeling with wind field data derived from different methodological models and observations. However, the capability of the local-scale atmospheric dispersion model hasn’t been discussed in detail for the Fukushima accident. In this paper, the local-scale modeling of radionuclides following the Fukushima accident was investigated with the combination of the wind diagnosed model SWIFT and the radionuclide transport model RIMPUFF. The coarse input wind field of SWIFT is prepared with WRF using the European Centre for Medium-Range Weather Forecasts (ECMWF) Meteorological data. The SWIFT-diagnosed wind field was used to drive RIMPUFF for calculating the dispersion of radionuclides and gamma dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP) site. The diagnostic wind fields were validated to the on-site meteorological observations, whereas the dispersion and gamma dose rates were validated the onsite gamma dose rate monitoring data and the observations of suspended particulate m? near the FDNPP. The plume patterns were also analyzed to help understand the transport behaviour of the radionuclides. The validation demonstrates that, with the 1km-resolved ECMWF meteorological data, SWIFT fairly reproduces the wind field. The speed is slightly overestimated, with a Normalized Mean Squared Error (NMSE) below 6. The wind direction is well simulated at some specific moments, which is critical for reproducing some peaks of the dose rates. However, RIMPUFF underestimates the dose rates around the FDNPP, partly because of the overestimated wind speed. The concentration simulations better agree with observations in the Naraha station than Futaba station, with the Fractional Bias (FB) under 0.3 and NMSE under 6 at the Naraha station.

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 SWIFT-RIMPUFF can provide refined atmospheric dispersion modeling for nuclear emergency response, but its performance for the mesoscale range in a nuclear power plant (NPP) site with highly complex topographies hasn’t been fully investigated. In this study, a validation of SWIFT-RIMPUFF was performed based on a wind tunnel experiment simulating a real China’s multi-reactor NPP site with heterogeneous upwind topography and dense buildings to understand the potential discrepancies or limits. The results demonstrate that the SWIFT-RIMPUFF can reproduce the sharp changes of wind flows for both speed and directions near the buildings, but usually overestimate the wind speed in the complex topography. For vertical wind profiles, the accuracies show high dependencies on the local topography and building layout, and the deviation of those near the building is more obvious. The simulated ground concentrations match the topographic changes of high-altitude mountains. The concentration predictions in the downwind building area are acceptable which displays that the influence of building effects can be well introduced, but the simulations in the building area still show noticeable discrepancies when compared with those in the sea area. However, such deviations do not propagate to the downwind mountainous and sea areas, which the accuracies are quite satisfactory.

Wet scavenging process is critical for air dispersion modeling of Cs-137 in the Fukushima Daiichi Nuclear power plant (FDNPP) accident. Although intensively investigated, wet scavenging simulation is still subject to uncertainties caused by the biases in wet scavenging modeling and meteorological input. To reduce these uncertainties, the on-line coupled modeling feature of the Weather Research and Forecasting-Chemistry (WRF-Chem) model was utilized and both the in-cloud and below-cloud scavenging processes are considered. In this study, the in-cloud scheme Environ and below-cloud scheme Baklanov are combined with each other to form Environ-Bakla to simulate the wet deposition of Cs-137. The model is systematically compared with a previous WRF-Chem model with a single below-cloud scheme Baklanov, based on both the cumulative deposition and ambient concentration of Cs-137 based on the FDNPP accident observation. The results demonstrate that the in-cloud scavenging scheme substantially improves the cumulative deposition simulation in regions with light rain like Tochigi, Nakadori etc. With respect to the atmospheric concentration, the inclusion of in-cloud scavenging doesn’t necessarily improve the performances and the Environ-Bakla only shows fair performance under plume events with no rain or light rain.

Wet deposition, including both in- and below-cloud scavenging, is critical for the atmospheric transport modeling of

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.

The CALMET-RIMPUFF is composed of the California Meteorological Model and the Riso Mesoscale PUFF model, which provides refined atmospheric dispersion modeling for nuclear emergency response. Because the performance of an atmospheric dispersion model can be case-sensitive, a multi-scenario validation is important to understand a model's behavior and limits. In this study, a multi-scenario validation of CALMET-RIMPUFF was performed based on six wind tunnel experiments simulating a real China's nuclear powerplant site with complex topographies and dense buildings. The CALMET-RIMPUFF simulations were compared with the measurements of the vertical wind profiles, 2D ground wind and concentration fields, both qualitatively and quantitatively. The results demonstrate that the CALMET-RIMPUFF can simulate the ground-level wind with acceptable accuracies. For vertical wind profiles, the accuracies show high dependencies on the local topography and building layout. The simulated ground concentrations generally agree well with the measurements, though the plume axis showed slight discrepancies from the measurements in three cases. Because the CALMET-RIMPUFF lacks a building effect module, it shows noticeable discrepancies in the building area. However, such discrepancies do not propagate to the downwind mountainous and sea areas, which the accuracies are quite satisfactory. Thus, the CALMET-RIMPUFF is capable for local-scale modeling at this site.

Coagulation/flocculation is one of the most extensive and cost-effective pretreatments to improve the dewaterability of sludge in water treatment plants. In this study, three series of graft cationic starch (St)-based flocculants with distinct structural characteristics including charge density (CD), graft-chain length (L), and graft-chain distribution (N) were synthesized by graft copolymerization of [(2-methacryloyloxy-ethyl) trimethyl ammonium chloride] and acrylamide onto St backbone. The structural effects of these St-based flocculants on the sludge dewaterability have been quantitatively analyzed by using a second-order polynomial model according to phenomenological theory. The predicted dewatering performance and optimal dose were fully consistent with the experimental results. On the basis of this established model, the dewatering mechanisms were discussed in detail by combination of the analysis of the changes in filter cake moisture content, specific resistance of filtration, bound water content, compression coefficient, extracellular polymeric substances fractions and components, spatial distributions of proteins and polysaccharides, microstructures of sludge cake, and flocs properties in the dewatering process. This graft St-based flocculant, with the structural characteristics of high CD, long L, and low N, exhibited superior sludge dewaterability because of the enhanced charge neutralization and bridging flocculation effects. Among these three structural factors, CD played a more important role in improvement of sludge dewaterability than L and N due to the dominant effect of charge neutralization. This study provided a better understanding of structure-activity relationship of these grafting modified flocculants, which was of significant guidance for the exploit and design of novel and efficient flocculants for improvement of sludge dewaterability.

The release rate is vital in assessing the international environmental risk of atmospheric radionuclide leaks, and it usually can only be obtained through inversion. However, such inversion is vulnerable to the inevitable plume biases in radionuclide transport modeling, leading to inaccurate estimates and risk assessment. This paper describes an automated method that estimates the release rate while comprehensively correcting plume biases, including both the plume range and transport pattern. The spatial correlation of predictions is used to simplify the difficult task of direct plume adjustment to that of tuning the predictions inside a correlation-adjusted plume. An ensemble-based algorithm is proposed to automatically calculate the spatial correlation. The proposed method is validated using two radionuclide transport models with mild and severe plume biases and data from two wind tunnel experiments, and its performance is compared with that of the standard approach and a recent state-of-the-art method. The results demonstrate that our method 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. The proposed approach achieves near-optimal performance with fully automated parameterization, keeping the lowest error levels in our validation cases for various measurement sets.

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.

Objective. This study aimed to compare the physical and mental states and the clinical effects of parenteral nutrition combined with enteral nutrition (PN+EN), total enteral nutrition (TEN), and total parenteral nutrition (TPN) after surgery in patients with maxillofacial malignant tumors. Methods. A total of 112 patients were divided into three groups, with 58, 33, and 21 patients in the PN+EN, TPN, and TEN groups, respectively. The psychological survey contained the Faces Pain Scale-Revised (FRS-R), visual analog scale (VAS), numerical rating scale (NRS), Hamilton anxiety rating scale (HAMA), and short-form 36 health survey questionnaire (SF-36). Spirit symptoms, length of hospital stay, nutritional assessments, and related biochemical indices were recorded and compared. Results. The traditional Chinese medicine (TCM) symptoms of anxiety and dysphoria were least frequently identified in the TPN group. The levels of lymphocytes, hemoglobin (HB), albumin (ALB), and prealbumin (PA) were significantly higher in the PE+EN group, whereas white blood cell count, neutrophil count, HB, PA, and ALB were significantly lower in the TPN group. Better psychological scores were observed in the TPN group. The PE+EN group had a shorter length of stay and higher SGA categories. Potassium, sodium, and chlorine levels were significantly lower in the TEN group (all P < 0.05). Conclusions. As an auxiliary method, TCM symptoms can help to identify spirit disequilibrium earlier and are associated with blood indices. Without the consideration of cost and long length of hospital stay, patients in the TPN group had the best mental status, with PN+EN therapy being an alternative.