Validation of a Lagrangian atmospheric dispersion model against middle-range scale measurements of 85 Kr concentration in Japan

Transcription

1 Journal of Nuclear Science and Technology ISSN: (Print) (Online) Journal homepage: Validation of a Lagrangian atmospheric dispersion model against middle-range scale measurements of 85 Kr concentration in Japan Hiroaki Terada, Haruyasu Nagai & Hiromi Yamazawa To cite this article: Hiroaki Terada, Haruyasu Nagai & Hiromi Yamazawa (2013) Validation of a Lagrangian atmospheric dispersion model against middle-range scale measurements of 85 Kr concentration in Japan, Journal of Nuclear Science and Technology, 50:12, , DOI: / To link to this article: View supplementary material Published online: 04 Oct Submit your article to this journal Article views: 293 Citing articles: 6 View citing articles Full Terms & Conditions of access and use can be found at

2 Journal of Nuclear Science and Technology, 2013 Vol. 50, No. 12, , ARTICLE Validation of a Lagrangian atmospheric dispersion model against middle-range scale measurements of 85 Kr concentration in Japan Hiroaki Terada a, Haruyasu Nagai a and Hiromi Yamazawa b a Nuclear Science and Engineering Directorate, Japan Atomic Energy Agency, 2-4 Shirane, Shirakata, Tokai-mura, Naka-gun, Ibaraki , Japan; b Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya , Japan (Received 24 June 2013; accepted final version for publication 30 August 2013) The Lagrangian atmospheric dispersion model of the computer-based nuclear emergency response system WSPEEDI-II was validated with the measured 85 Kr concentrations in tens-of- to hundreds-of-km (middle-range) scale area by conducting dispersion simulations using the release rate from the nuclear fuel reprocessing plant in Rokkasho, Japan. The calculated weekly concentrations of 85 Kr in two simulation cases during April and September in 2008 agreed with the measurements within a factor of two at the sampling sites 170 to 2000 km away from the plant. However, the sensitivity analysis of horizontal grid resolution of the meteorological model ranging from 2 to 54 km showed that the calculated results had the dependency on the grid resolution, i.e., the calculated concentrations became low compared with the results with the grid resolution of 54 km as the grid resolution became high. An empirical modification of the horizontal diffusion parameter used for long-range dispersions by WSPEEDI-II was attempted based on the sensitivity analysis to reduce the redundant diffusion effect in dispersion simulations with relatively high grid resolution. The modified horizontal diffusion parameter contributed to reduce the dependency of calculated concentrations on the horizontal grid resolution. Keywords: dispersion; numerical simulation; horizontal diffusion; concentration; grid resolution, krypton-85; reprocessing 1. Introduction When radioactive materials are discharged into the atmosphere due to nuclear accidents, computer-based real-time atmospheric dispersion simulation systems are useful tools for providing valuable radionuclide dispersion information. This information is critical for the planning of environmental monitoring and making decisions regarding countermeasures such as evacuation of residents. We have experienced several accidental discharges of radionuclides into the atmosphere, e.g., in the accidents at Three Mile Island, USA in 1979, Chernobyl, former USSR in 1986, Tokai, Japan in 1997 and 1999, Algeciras, Spain in 1998, and Fukushima, Japan in Evacuation was recommended to the residents within 500 m in the accidents at Tokai in 1999, 8 km at Three Mile Island, and 30 km at Chernobyl (during the first week from the beginning of the accident). Air dose rates and concentrations of radionuclides at levels higher than background level were observed by environmental monitoring over a broader area than the recommended evacuation area. This illustrates that atmospheric dispersion of released radionuclides in nuclear emergency needs to be predicted precisely in a wide-range area from the vicinity to the distant area of accident sites depending on accident magnitudes and prediction purposes. To meet this need, we have developed a computerbased nuclear emergency response system, Worldwide version of System for Prediction of Environmental Emergency Dose Information (WSPEEDI-II) [1]. WSPEEDI-II predicts the atmospheric dispersion of radionuclides discharged into the atmosphere from a point source by the combination of a meso-scale meteorological model and a Lagrangian particle dispersion model. Its prediction capability for long-range (thousands-ofkm scale) atmospheric dispersion has been verified by using the data from the European tracer experiment (ETEX) and the Chernobyl accident [2 5]. We continue to improve WSPEEDI-II with the aim of extending its applicability to atmospheric dispersion from the local to global scale. Corresponding author. terada.hiroaki@jaea.go.jp C 2013 Atomic Energy Society of Japan. All rights reserved.

3 Journal of Nuclear Science and Technology, Volume 50, No. 12, December Lagrangian particle models typically compute the movement of released radionuclides by a summation of the advection term due to grid-resolved wind fields and the turbulent diffusion term due to subgrid-scale fluctuation of wind field. In this paper, we focus on the parameterization of horizontal diffusion in Lagrangian particle dispersion models. Ishikawa [6] and Desiato et al. [7] have shown that long-range dispersion simulation results by Lagrangian particle dispersion models are influenced by a horizontal diffusion process. Since WSPEEDI-II is designed to be applicable to local- to global-scale atmospheric dispersion, the horizontal diffusion term needs to be parameterized appropriately to represent diffusive effects by subgrid-scale atmospheric turbulence. Several horizontal diffusion parameters have been proposed for atmospheric dispersion simulations. The diffusion parameter from the well-known Pasquill Gifford chart [8] (hereafter referred to as P G ) is often used for short-range dispersion simulations. The curves of horizontal width of plume (σ h ) as a function of downwind distance for different atmospheric stability classes are represented in these charts. The curves were derived based on the dispersion data collected over a flat plain of about 1 km-square area. Portions of the curves beyond this distance scale are extrapolated. Briggs [9]presented different sets of diffusion parameters for country and urban conditions. However, these two parameters are applicable for relatively short downwind distances, typically less than 10 km. The horizontal diffusion parameter by Gifford [10] (hereafter referred to as GIFFORD ) has been used for long-range dispersion simulations using WSPEEDI- II. The formula of mean-square displacement due to the horizontal diffusion σh 2 of GIFFORD is derived theoretically from Langevin s equation for air particle diffusion. Several parameters in this formula were determined empirically from observed dispersion data. This expression is convenient for dispersion calculations in emergency response applications because its functional form varies only with time. Although GIFFORD has been verified for long-range atmospheric dispersion [2 5], there have been few verification studies for the tens-of- to the hundreds-of-km (hereafter referred to as middlerange ) scale atmospheric dispersion because of the quite limited availability of observation data for this purpose. Atmospheric diffusion data used in the derivation of GIFFORD were obtained in the cases where the diffusion of tracers was influenced by atmospheric motions of a wide range of spatial scales from micro-scale turbulence to meso-scale circulations [10]. This fact implies that GIFFORD includes diffusive effects from the phenomena in various scales. When relatively high grid resolution is used for meteorological calculation in atmospheric dispersion simulations, diffusive effects of smallscale atmospheric motions are represented explicitly by grid-resolved wind fields. Thus, horizontal diffusion may be taken into account redundantly when GIFFORD is Figure 1. Temporal variations of the 85 Kr release rate from RRP during the period of (a) 9 JST on September 15 0 JST on September 26 and (b) 18 JST on March 31 0 JST on April 22 in used for short- to middle-range atmospheric dispersion simulations with relatively high grid resolution. Tests of reprocessing, using actual spent fuels have been conducted at the nuclear fuel reprocessing plant in Rokkasho, Japan (Rokkasho Reprocessing Plant, hereafter referred to as RRP ) since When spent fuels are reprocessed, radionuclides, e.g., 85 Kr, 3 H, 14 C, and 129 I, are discharged into the atmosphere under release control. 85 Kr is a radioactive noble gas with a half-life of years. Release rates of 85 Kr from RRP are shown in Figure 1 during the simulation periods in this work (described later). The temporal variation of the release rate in this figure indicates 85 Kr was released intermittently from RRP. Concentrations of 85 Kr have been observed by the Ministry of Education, Culture, Sports Science and Technology (MEXT) at several sampling points in Japan since 2006 to understand the background level [11]. These observations indicate that increases of 85 Kr concentration beyond the background levels were detected 200 to 2000 km away from RRP in The increases are considered due to 85 Kr discharged from RRP because no other major release events were seen during the period. Because concentration observations of airborne materials released from known point sources in the middle-range scale are relatively rare and because of a non-reactive characteristic of 85 Kr, these observational data are particularly useful for the verification of diffusion processes in the middlerange atmospheric dispersion simulations. In this paper, our objective is the verification of the calculation performance of WSPEEDI-II by using the middle-range measurement data of atmospheric 85 Kr concentration. Surface concentrations of 85 Kr simulated by using the release information from RRP

4 1200 H. Terada et al. are compared with the environmental measurement data. On the basis of the dispersion simulation results and a sensitivity analysis to horizontal grid resolutions of the meteorological model, the dependency of 85 Kr concentrations calculated with the diffusion parameter GIFFORD on the grid resolution is examined. A simple empirical modification of GIFFORD is attempted to reduce the redundant diffusion effect in dispersion calculations with relatively high grid resolution. 2. Methods 2.1. Measurement data We used the measured surface air concentrations of 85 Kr in Japan [11] for verification of the simulation results by WSPEEDI-II. The measurement of 85 Kr air concentrations were started at Tsukuba by the Meteorological Research Institute (MRI) of Japan in 1995 [12]. Then, the continuous measurement system of 85 Kr concentration was developed by MRI and Japan Chemical Analysis Center (JCAC) in 2006 [13]. The continuous monitoring of 85 Kr concentration has been carried out at several sampling sites in Japan by JCAC. Air samples are collected at the rate of 1 L min 1 during a week into an adsorbent vessel filled with activated charcoal. Concentrations of 85 Kr in the sampled air are measured by gas chromatography and a gas-flow GM counter. The locations of the sampling sites are shown in Figure 2. Although the air samplings have been conducted normally at Sapporo, Akita, and Chiba, the sampling sites were temporally changed to Dazaifu, Nanjo, and Chiba from July 2007 to June 2008 to understand the latitudinal distribution of the background level. Measured 85 Kr concentration data compared in this study are summarized in Table 1. The background values at each site and period were calculated by averaging 85 Kr concentrations during eight sampling periods (approximately two months), a total of four sampling periods before and after the periods when peak values were measured at each site. The comparisons were done for situations where the increments of the measured 85 Kr concentrations from the backgrounds were more than twice as large as the uncertainties of the backgrounds. Although the increment of the measured 85 Kr concentration at Nanjo (0.06) is slightly less than twice as large as the uncertainties of the background values (0.064), it is also used for comparison. For verification of the accuracy of simulated meteorological fields, we used surface weather charts by Japan Meteorological Agency (JMA) and upper-air observational data from radiosondes. The locations of the upper-air observations used for comparison are shown in Figure 2. The locations of upper-air observation and air sampling for 85 Kr concentration were not co-located at Sapporo and Akita, however, they were close to each other. The radiosonde observation at Misawa was done by Japan Ministry of Defense, and the observations at other sites by JMA. Figure 2. Simulation domains for the Sep-SAP and Apr-DAZ cases, and the locations of air sampling for the measurement of 85 Kr concentration and radiosonde observation.

5 Journal of Nuclear Science and Technology, Volume 50, No. 12, December Table 1. Measured weekly concentrations of 85 Kr used for comparison with the calculations. Increment of measured Distance Sampling period Measured 85 Kr Background 85 Kr 85 Kr concentration from RRP (month/day time concentration with concentration with from the background Sampling site (km) JST in 2008) uncertainty (Bq m 3 ) uncertainty (Bq m 3 ) (Bq m 3 ) Sapporo 230 (1) 9/8 14:12 9/16 13:55 (2) 9/16 14:33 9/22 12:52 Akita 170 9/18 14:50 9/25 13:27 Dazaifu /7 14:13 4/14 13:20 Chiba 600 (1) 4/7 9:46 4/14 9:10 (2) 4/14 10:25 4/21 9:13 Nanjo /9 13:58 4/16 13: ± ± ± ± ± ± ± ± ± ± ± ± ± ± Numerical models Atmospheric dispersions of 85 Kr discharged from RRP were simulated by using WSPEEDI-II, which uses the off-line combination of a meteorological model MM5 [14] and a Lagrangian particle dispersion model GEARN [5] MM5 MM5 is a non-hydrostatic fully compressible model for meso-scale meteorological prediction. The model predicts three-dimensional meteorological fields (e.g., wind, diffusivity, temperature, precipitation, etc.) by solving atmospheric dynamic equations with a finite difference method in appropriate spatial and temporal resolutions using a domain nesting method. MM5 uses map coordinates for horizontal coordinate system and terrain-following (sigma) coordinate for vertical one. The Lambert conformal map projection was used in this study GEARN GEARN simulates atmospheric dispersions of released radionuclides by tracing the three-dimensional movement of many marker particles following meteorological fields predicted by MM5. GEARN uses the same horizontal coordinates as MM5, thus horizontal resolutions of the two models are the same. The vertical coordinate is a terrain following (z ) coordinate system, z = z z g, h = z t z g, (1) h z t where z is the Cartesian height, z g the terrain height, and z t the height of the top of a model domain. When t is a calculation time step, the position of a particle at the time t + t, (x t+ t, y t+ t, zt+ t ), is calculated as: x t+ t = x t + mu t + mr x, y t+ t = y t + mv t + mr y, z t+ t = z t + w t + R z, where m(x, y) is the map scale factor that is the ratio of transformed distance on a map projection to the true distance. The first term of the right-hand side of the equations represents the position of the particle at the time t. The second term represents the advective displacement of the particle by the grid-resolved mean wind components (u,v,w ) in each direction at the particle position. The third term represents the random displacement of the particle by subgrid-scale turbulent eddy diffusion. The horizontal diffusion terms R x and R y are calculated as: (2) R x = R y = 24K h t (0.5 R(0)), (3) where K h is horizontal diffusivity and R(0) is a uniform random number between 0 and 1 [15]. For long-range atmospheric dispersion calculations, the horizontal diffusion parameter GIFFORD is used in GEARN. The mean-square displacement due to the horizontal diffusion process σh 2 by GIFFORD is expressed as: σh 2 = 2K Lt + v2 0 [1 exp( βt)]2 β2 + K L β [ 3 + 4exp( βt) exp( 2βt)], (4) where t represents the travel time of a particle. According to Gifford [10], large-scale eddy diffusivity K L,the initial speed of a particle v 0 and the inverse of time scale β are (m 2 s 1 ), 0.15 (m s 1 ), and 10 4 (s 1 ),

6 1202 H. Terada et al. respectively. For a short-range dispersion calculation, we can use the horizontal diffusion parameter P G alternatively. Regarding P G, variations of σ h with respect to downwind distances for different atmospheric stability classes were derived based on the dispersion experiments in flat grassland. It should be noted that no corrections for surface roughness and release height were made. Atmospheric stability for P G was set to neutral as an average condition in this study because representative stability in the large computational area was difficult to decide. Neutral stability was seen in most hourly meteorological observations near the release point in September and April, 2008 [16]. The horizontal diffusivity K h is calculated by [17]: K h = 1 dσh 2 2 dt. (5) The comparison of horizontal diffusivities by GIF- FORD and P G are shown in Figure 3. The vertical diffusion term R z is calculated as: ( Kz R z = z ) t + 24K z t (0.5 R(0)), (6) where K z is the vertical diffusivity at the particle position calculated by the Planetary Boundary Layer (PBL) parameterization [18] within MM5. The air concentration in each Eulerian cell C ijk (Bq m 3 ) averaged over an output time interval T (s) is calculated as: C ijk = 1 V ijk T [ M N ] (b n,ijk q n t), (7) m=1 n=1 where the suffixes i, j and k represent the cell numbers in the x-, y- andz -directions, respectively, V ijk (m 3 )isthe volume of the Eulerian cell, and q n (Bq) the radioactivity carried by the nth particle. The b n,ijk is the fractional contribution of the nth particle to the (i, j, k)th Eulerian cell. The fractional contribution is defined as the overlap of a Lagrangian cell, whose center is located at the particle position and which has the same volume as the Eulerian cell, with the Eulerian cell. Mand N are the number of time steps during T and the number of particles in the Eulerian cell in question and nearby cells, respectively. GEARN can calculate depositions on the ground surface by turbulence (dry deposition) and precipitation (wet deposition). However, the deposition processes are negligible for 85 Kr because it is a noble gas. The radioactive decay is calculated by multiplying the concentration by correction factors Case studies for dispersion of 85 Kr from RRP To verify the performance of WSPEEDI-II, atmospheric dispersion simulations were carried out using actual 85 Kr releases from RRP. Simulations were done for the following two cases in which 85 Kr was released from RRP and increases of measured 85 Kr concentrations (Table 1) were also detected: m Sep-SAP case: 85 Kr concentration increased at Sapporo and Akita in September 2008, Apr-DAZ case: 85 Kr concentration increased at Dazaifu, Chiba, and Nanjo in April Figure 3. Variations with time after release of horizontal diffusivities of P G for neutral stability and of GIFFORD. The former is plotted for representative wind speeds. Simulation domains are shown in Figure 2. Meteorological fields were predicted simultaneously for Domains 1 and 2 using two-way nesting by MM5 for both the cases. Atmospheric dispersion of 85 Kr in Domains 1 and 2 were computed separately in the Sep-SAP case, and only in Domain 1 in the Apr-DAZ case. Initial and boundary conditions for MM5 were from the grid point values (GPV) of global spectral model (GSM) for the Japan region by JMA ( spatial resolution) and daily real time global sea surface temperature (RTG-SST) by the National Center for Environmental Prediction ( spatial resolution). The GSM is initialized four times daily for GPV and the temporal resolution is 3 h. Analysis nudging was utilized in the MM5 calculations only in Domain 1 at 3-h interval for wind, temperature, and humidity with GPV. Meteorological fields were fed from MM5 to GEARN at

7 Journal of Nuclear Science and Technology, Volume 50, No. 12, December Table 2. Simulation conditions for the Sep-SAP and Apr-DAZ cases. Domain 1 Domain 2 Simulation period Sep-SAP case: 9 JST on September 15 0 JST on September 26, 2008 Apr-DAZ case: 18 JST on March 31 0 JST on April 22, 2008 Horizontal grid number Horizontal grid distance 18 km 6 km Time step 45 s 15 s Vertical level MM5: 23 half-sigma levels from surface up to 100 hpa GEARN: 20 levels from surface up to 10 km (bottom layer with 100 m thickness) Physical model options for MM5 Radiation Cloud-radiation scheme [14] Planetary boundary layer Eta-PBL [18] Cloud microphysics Schultz scheme [19] Cumulus parameterization Grell scheme [14] Land surface Five-layer soil model [20] Sigma values were set as 1.00, 0.99, 0.98, 0.96, 0.93, 0.89, 0.85, 0.80, 0.75, 0.70, 0.65, 0.60, 0.55, 0.50, 0.45, 0.40, 0.35, 0.30, 0.25, 0.20, 0.15, 0.10, 0.05, and h interval. Output time interval from GEARN was set to 3 h. Other settings for MM5 and GEARN are summarized in Table 2. The increment of the time step for each domain was decided based on the upper limit value during which the maximum random displacement due to horizontal diffusion was smaller than each horizontal grid distance, i.e., subgrid-scale movement. Release point in 85 Kr dispersion simulations by GEARN was the main stack of RRP ( N, E; 150 m above ground level). We used the hourly 85 Kr release rate data (Figure 1) provided by Japan Nuclear Fuel Limited (JNFL). 85 Kr had not been released during more than a month prior to the simulation start times (Table 2) of both the simulation cases. This fact indicates that all the 85 Kr releases from RRP necessary to simulate the increases of the measured 85 Kr concentrations were considered in the calculations. The calculations of 85 Kr dispersion using horizontal diffusion parameters shown in Table 3 were done to examine their relative effects. GIFFORD-mod is a diffusion parameter modified from GIFFORD in Section and horizontal diffusivity (K h in Equation (3)) was constantly set to zero in the calculation K h = 0. The Table 3. Combinations of simulation domains and horizontal diffusion parameters for the Sep-SAP and Apr-DAZ cases. Simulation domain Horizontal Simulation for dispersion diffusion case calculations parameter Sep-SAP Domain 1 GIFFORD Domain 2 GIFFORD GIFFORD-mod P G K h = 0 Apr-DAZ Domain 1 GIFFORD GIFP-GFORD-mod P G K h = 0 calculation using GIFFORD for Domain 1 in the Sep- SAP case was done to examine the effect of meteorological grid resolution on the calculated concentration at Sapporo Sensitivity analysis of horizontal grid resolution of the meteorological model To examine the influence of the meteorological grid resolution on the concentration of radionuclides calculated by using GIFFORD, we carried out dispersion calculations of 85 Kr from RRP with a hypothetical release condition by varying horizontal grid distances (hereafter referred to as x ) of MM5 and GEARN for x values of 54, 27, 18, 6, and 2 km. The model domains (Domains A to E) for the sensitivity analysis are shown in Figure 4 and model settings are summarized in Table 4. We used the meteorological conditions from the Apr-DAZ case for the sensitivity analysis because the data were suitable for middleto long-range atmospheric dispersion simulations and the simulated trajectory of 85 Kr plume was straight and less meandering (see Section and Figure S1 of online supplementary material) during the analysis period. The meteorological fields predicted for Domain 1 in the Apr-DAZ case were used for the dispersion calculation of Domain C ( x is 18 km). Meteorological fields for Domains D and E ( x are 6 and 2 km, respectively) were calculated using one-way nesting from the calculation results of Domain C. The coverage of Domains AandB( xare 54 and 27 km, respectively) were set to be almost the same as that of Domain C. Initial and boundary conditions for Domains A and B were generated individually from the GPV data. Analysis nudging in MM5 was utilized for the calculation of Domains A, B, and C with the GPV data. The hypothetical release condition for the sensitivity analysis shown in Table 4 uses the mean 85 Kr release rate of the data provided by JNFL (Figure 1). The selection of the release period for the hypothetical release

8 1204 H. Terada et al. the resolution of x ref. A slight horizontal displacement was allowed for each concentration field to align the location of maximum concentration with that of the reference. This procedure was aimed to reduce the effect of discrepancy in wind fields calculated in different domains, which made small differences in the location of plumes between the domains. (2) Scatter diagrams of calculated concentrations in each domain comparing with those in Domain E with x ref of 54 km were created using the data greater than the lower limit of concentration, 0.01 Bq m 3. This lower limit corresponds to the concentration level at which statistical error in the dispersion calculations is approximately 15% at the maximum, and to the detection limit of the measurements of the 85 Kr concentration. (3) Linear regression was made to determine slope and determination coefficient R 2, which is the square of correlation coefficient, by using the least-squares method for each scatter diagram. Figure 4. Simulation domains for sensitivity analysis. condition was based on a parametric study that used changing release periods at 6-hour interval for the Apr- DAZ case (not shown). The release period had the most critical contribution to the increase of 85 Kr concentration at Dazaifu. Other calculation settings for MM5 and GEARN were set to the same as those for the case studies described in Section 2.3. The results of the above dispersion calculations were analyzed by the following procedure: (1) To compare with the calculated concentration using a reference grid distance (hereafter referred to as x ref, set to as 54 km), concentrations calculated in Domains A to D with x from 2 to 27 km were averaged to be concentrations with 3. Results and discussion 3.1. Dispersion simulations of 85 Kr for the Sep-SAP and Apr-DAZ cases Validity of meteorological simulation and overview of 85 Kr dispersion The wind field patterns at the lowest MM5 model layer were compared with the JMA surface weather charts at 9 JST of each day to confirm the validity of meteorological simulation. Through this qualitative validation, we found that MM5 generally reproduced the distribution of high- and low-pressure systems, weather fronts, and corresponding wind fields. Two representative comparison examples are shown in Figure 5. Synoptic meteorology analyzed from the surface weather charts and the movement of calculated 85 Kr plume was as follows (see Figure S1 of online supplementary material): (1) Sep-SAP case: A high pressure system moved from northern Japan to the south of the Kamchatka Peninsula and a low pressure system Table 4. Calculation conditions for sensitivity analysis. Domain A Domain B Domain C Domain D Domain E Simulation period 0 JST on April 10 0 JST on April 14, 2008 Horizontal grid number Horizontal grid distance 54 km 27 km 18 km 6 km 2 km Time step 150 s 80 s 45 s 15 s 10 s Cumulus parameterization for MM5 Grell scheme [14] (not used for Domain E) Hypothetical release condition of 85 Kr Release rate Bq h 1 (constant) Release period 0 6 JST on April 10, 2008

9 Journal of Nuclear Science and Technology, Volume 50, No. 12, December Figure 5. Comparisons between surface weather charts (left) and simulated wind fields at the lowest model layer (right, vectors) at (a) 9 JST on September 18 and (b) 9 JST on April 10, moved from northeastern China to the Sea of Okhotsk during the period September 15 to 18. A typhoon approaching from the East China Sea also passed through southern Japan during the period September 17 to 20. The 85 Kr plume was transported mainly to the western part of Hokkaido, fluctuating from the northwest to the east of RRP from the afternoon on September 15 to the night on September 18. Then, the plume dispersed over the area from the southeast to the southwest of RRP due to the wind turning clockwise from northwesterly to southeasterly during the period from September 19 to September 20. No concentrations greater than 1 Bq m 3 were calculated from 9 JST on September 20 to 12 JST on September 25 except at the vicinity of RRP because of low or zero release rate of 85 Kr. (2) Apr-DAZ case: During this simulation period, several highs and lows passed through the simulation domain from the East China Sea to the Pacific Ocean along western and southern Japan. Because of the predominance of westerly winds, the plume was transported east from RRP from the calculation start through April 6. The plume started to change its direction anticlockwise to the northwest of RRP from April 7, and then, the plume dispersed widely over the

10 1206 H. Terada et al. northern Japan Sea until the morning of April 10. After April 10, the plume flowed straight to western Japan, including Kyushu and Okinawa due to the northeasterly wind at the northwest side of the low-pressure system migrating over Japan. Whereas, 85 Kr plume from RRP flowed over the Pacific Ocean due to the westerly wind from the evening of April 11 to April 12. Then, on April 13, it passed over eastern and northern Japan and eventually over the Japan Sea due to the easterly wind. From April 14 to 15, the plume dispersed over eastern and northern Japan stretching in the north south direction. To discuss the accuracy of calculated results from the viewpoint of horizontal diffusion, it is necessary for both the wind fields and vertical diffusion to be simulated appropriately. Vertical distributions of 85 Kr concentration and accuracy of vertical profiles of calculated potential temperature were examined along the pathways of the plume that influenced on the increases of 85 Kr concentration at Sapporo and Dazaifu (Figure S2 of online supplementary material). In both the cases, the calculation generally reproduced the observed vertical profiles of potential temperature Calculation performance of 85 Kr concentration 85 Kr concentrations averaged over one-week sampling periods (hereafter referred to as weekly concentration ) from calculated three-hourly surface concentrations were compared with the measurements (Figure 6). In this figure, for example, Sapporo (1) means the 85 Kr concentration at Sapporo during sampling period (1) in Table 1. The weekly concentrations, calculated by using GIFFORD, generally agreed well with the measured ones although the calculated result was underestimated for Sapporo (2). The calculated Figure 6. Comparisons of weekly concentrations of 85 Kr calculated using different diffusion parameters with the measurements (OBS) in the (a) Sep-SAP and (b) Apr-DAZ cases. The numbers in parentheses after the name of sampling sites of Sapporo and Chiba correspond to the sampling periods in Table 1.

11 Journal of Nuclear Science and Technology, Volume 50, No. 12, December Figure 7. Plot of calculated-to-observed ratios of weekly 85 Kr concentrations for the results using different diffusion parameters against downwind distance from RRP. results using P G and K h = 0 were underestimated for Sapporo (1) and Akita, and overestimated for most of the other comparisons. Calculated-to-observed ratios of 85 Kr concentrations are plotted against downwind distance from the release point in Figure 7. The calculation results using GIFFORD showed generally higher accuracy than those using P G and K h = 0 over all distances and agreed with the measurements within a factor of two at all the sites. This result indicates GIFFORD has higher applicability than P G over a wide-range scale. Temporal variations of the calculated three-hourly surface concentrations of 85 Kr at the sampling sites are shown in Figure 8. This figure indicates that the small increases in weekly concentrations of Sapporo (1) and Akita were due to the increases of three-hourly concentrations during the morning of September 16 and 20, respectively. From the analysis of horizontal distributions of 85 Kr concentrations in the Sep-SAP case (Figure S1a of online supplementary material), the increases of concentration were due to the passages of the plume edge over the sites during the periods. The substantial underestimation of concentrations by P G and K h = 0atthese sites shown in Figure 6 can be attributed to the narrower horizontal widths of the distribution due to smaller horizontal diffusivity by P G during the period ranging from several hours to days after the release (Figure 3) and constantly null diffusivity by K h = 0, respectively. These results suggest the advantage of GIFFORD in reproducing the horizontal diffusion. Figure 8. Temporal variations of three-hourly surface concentration of 85 Kr calculated by different diffusion parameters at (a) Sapporo, (b) Akita, (c) Dazaifu, (d) Chiba, and (e) Nanjo. Dashed lines with arrow heads below the horizontal axes represent the sampling periods of the measurements in Table 1.

12 1208 H. Terada et al. Figure 9. Comparison of temporal variations of three-hourly surface concentration of 85 Kr at Sapporo calculated using GIFFORD between the results in Domain 1 with x of 6 km (solid line) and those in Domain 2 with x of 18 km (dashed line). However, the accuracy of the calculation using GIF- FORD was not satisfactory at several sites. From the point of view of the effect of horizontal diffusion and grid resolution, we focus on the comparison for Sapporo (2) and Dazaifu, the sites where the increases in concentration were due to the passage of the central part of plume via straight pathways from RRP (Figure S1 of online supplementary material). The calculated concentration using GIFFORD was slightly underestimated at Sapporo (2) in Domain 2 with x of 6 km, but agreed well with the measurement at Dazaifu calculated in Domain 1 with x of 18 km (Figure 6). We also compared the temporal variation of threehourly 85 Kr concentration at Sapporo calculated in Domain 2 with x of 6 km with the calculated results in Domain 1 with x of 18 km in the Sep-SAP case (Figure 9). In this comparison, we calculated average 85 Kr concentrations in the nine cells of Domain 2, centering the cell where the sampling site of Sapporo was located. Figure 9 indicates that the temporal variation patterns of the two results are similar; however, steeper peaks of concentration were calculated by the simulation with x of 18 km than with x of 6 km. The above comparison results suggest that horizontal grid resolution of the meteorological model influences concentrations calculated by using GIFFORD Sensitivity analysis of horizontal grid resolution of the meteorological model Effects of horizontal grid resolution of wind on calculated concentrations The analysis results for the calculated concentrations at 12, 24, 36, and 48 h after the release start are shown in Figure 10. In general, good correlations are seen with R 2 greater than 0.64, corresponding to correlation coefficients greater than 0.8 with a few exceptions. In addition, intercepts of regression lines of all the scatter diagrams were smaller than 10% of the maximum concentrations of each comparison result. Figure 11 represents the relationship between the slopes of regression lines in the scatter diagrams and x. The slopes should be unity in all the results with different x if the calculation results have no dependency on the horizontal grid resolution. However, the slope values decreased as x became small, implying that the concentrations calculated by using GIFFORD with x smaller than x ref of 54 km were underestimated compared with the calculated results with x ref. This variation of the slopes with x in Figure 11 can be approximated by the following expression as a function of x (km): f ( x) = 1 1 ( 2 exp x ), (8) A where A is a parameter from 5 to 30 as shown by lines in Figure Modification of Gifford s diffusion parameter From the analysis in the previous subsection, we tentatively modify the mean-square displacement by GIF- FORD as: σh 2 = [1 12 ( exp x )] σh 2 A, (9) where σh 2 is the modified mean-square displacement, and A has a value ranging from 5 to 30 as in Equation (8). In the following discussion, we use the well-known Gaussian plume model to express the relationship between concentration and diffusion parameter. Although there are limitations in applying it to middle- to longrange dispersion phenomena, we can still expect that the fundamental relationship between concentration and diffusion parameter substantially holds. Concentration distribution in the Gaussian plume model can be

13 Journal of Nuclear Science and Technology, Volume 50, No. 12, December Figure 10. Scatter diagrams of surface concentration of 85 Kr comparing the calculated results with x ref of 54 km and each result with x from 2 to 27 km. The results for x of 2 to 27 km are arranged horizontally (left to right). The results at 12 to 48 h after the release start are arranged vertically (top to bottom). Solid lines in each scatter diagram represent linear regression lines. expressed as: χ(x, y, z) = [ ( )] Q 2πσ y σ z U exp 1 y 2 + z2, (10) 2 σy 2 σz 2 The modified standard deviation of horizontal plume distribution σy can be expressed from Equation (9) as: σy = ασ y, (11) where χ(x, y, z)(bqm 3 ) is the concentration at the location (x, y, z), Q (Bq s 1 ) is the constant release rate, σ y and σ z (m) are standard deviations of plume distribution in the y- andz-directions, respectively, and U (m s 1 )is mean wind speed in the x-direction. where α = 1 1 ( 2 exp x ). A

14 1210 H. Terada et al. Figure 12. Distribution of the indicator I in Equation (13) when A and β are 20 and 0.89, respectively. Figure 11. Plot of the slopes of the regression lines in the scatter diagrams in Figure 10 against x. The equation for χ, which is the concentration by the modified diffusion parameter σy, can be obtained from Equation (10) by using σy instead of σ y. Substitution of Equation (11) into the equation of χ and its division by Equation (10) yields χ χ = 1 [ α exp β (1 1α )], (12) 2 where α = 1 1 ( 2 exp x ), β = y2 A 2σ 2 y Here, on the basis of the idea that χ /χ in Equation (12) corresponds to the slopes of regression lines of scatter diagrams in Figure 10, we introduce the following indicator for the effect of the modification of the diffusion parameter: I = Slope/ 1 [β (1 α exp 1α )]. (13) 2. slopes of regression lines using GIFFORD-mod are shown in Figure 13. Some refinements are seen from the comparison with Figure 11, although the improvement is insufficient for the slopes with low R 2 values smaller than 0.64 (indicated in Figure 13)andwith x of 2 km. By using GIFFORD-mod, the dispersion simulations were carried out for the Sep-SAP and Apr-DAZ cases with the actual release rate from RRP. The three-hourly concentrations calculated by using GIFFORD-mod showed slightly higher peaks than those by using GIFFORD at Sapporo (Figure 8a) and Dazaifu (Figure 8c). The weekly concentrations calculated by using GIFFORD-mod increased by 5% and 10% at Sapporo (2) and Dazaifu, respectively, and agreed better with the measurements compared with those by using GIFFORD (Figures 6 and 7). Small increases in the weekly concentrations at Sapporo (1) and The indicator I is close to unity when the modification effect to reduce the grid resolution dependency of calculated concentration is high. If we assume A = 20 in Equation (11) based on its range from 5 to 30, the summation of absolute values of differences between unity and I for the slopes except those with low R 2 less than 0.64 (Figure 11) was smallest when β = Figure 12 is the plot of I when A and β are 20 and 0.89, respectively. We executed the sensitivity analysis using the GIFFORD diffusion parameter modified by Equation (9) with A = 20 (hereafter referred to as GIFFORD-mod ). The Figure 13. As in Figure 11, but for the calculation results using GIFFORD-mod.

15 Journal of Nuclear Science and Technology, Volume 50, No. 12, December Akita were still reproduced by using GIFFORD-mod, although the values were smaller than those by using GIFFORD (Figure 6a). 4. Conclusions The atmospheric dispersion simulations of 85 Kr discharged from the Rokkasho reprocessing plant (RRP) in Japan were conducted by using the computer-based nuclear emergency response system WSPEEDI-II for the cases with actual atmospheric discharges during April and September in 2008 to verify the calculation performance of WSPEEDI-II. The calculated weekly concentrations of 85 Kr by using the horizontal diffusion parameter GIFFORD by Gifford [10] agreed with the measurements within a factor of two at all the sampling sites from 170 to 2000 km away from RRP and had generally higher accuracy than those by using P G. However, the concentration calculated by using GIFFORD with the horizontal grid resolution of 6 km for the meteorological model was underestimated at Sapporo (230 km away from RRP) compared with the calculation with the grid resolution of 18 km. To examine the dependency of calculated concentrations using GIFFORD on the horizontal grid resolution of the meteorological model, a sensitivity analysis of the grid resolution ranging from 2 to 54 km was carried out with a hypothetical release condition and the meteorological condition of the simulation case in April, From the comparison of the slopes of regression lines in the scatter diagrams between the calculated results with the grid resolution of 54 km and those with each grid resolution ranging from 2 to 27 km, it was found that calculated concentrations became low compared with the results with the 54 km resolution as the grid resolution became high. On the basis of the sensitivity analysis, an empirical modification of GIFFORD was attempted to reduce horizontal diffusivity when high grid resolution was used. The simulation with the actual release rates from RRP using the modified GIFFORD parameter showed that the accuracy of weekly 85 Kr concentrations were improved at Sapporo and Dazaifu (1200 km away from RRP) compared with the calculated results using the original diffusion parameter GIFFORD. The improvements represent that the modification of the horizontal diffusion parameter in this study can contribute to reduce the dependency of calculated concentrations on horizontal grid resolution of the meteorological model. Acknowledgements The authors would like to thank Dr Masamichi Chino of Japan Atomic Energy Agency (JAEA) for valuable comments. We would like to express gratitude to Dr Paul Bieringer of National Center for Atmospheric Research for helpful comments in the preparation of the manuscript. We used the GPV data and surface weather charts by JMA, release rate of 85 Kr by JNFL, and measurement data of 85 Kr concentration by JCAC. We are grateful for their provision of the valuable and essential data. We are also indebted to the Center for Computational Science and e-systems in JAEA for their support and providing the resources of supercomputer systems in JAEA. Supplemental data Supplemental data for this article can be accessed at References [1] Terada H, Nagai H, Furuno A, Kakefuda T, Harayama T, Chino M. Development of Worldwide version of system for prediction of environmental emergency dose information: WSPEEDI 2nd version. Trans At Energy Soc Jpn. 2008;7: Japanese. [2] Furuno A, Terada H, Chino M, Yamazawa H. Experimental verification for real-time environmental emergency response system: WSPEEDI by European tracer experiment. Atmos Environ. 2004;38: [3] Terada H, Furuno A, Chino M. Improvement of Worldwide Version of System for Prediction of Environmental Emergency Dose Information (WSPEEDI), (I) New combination of models, atmospheric dynamic model MM5 and particle random walk model GEARN-new. J Nucl Sci Technol. 2004;41: [4] Terada H, Chino M. Improvement of Worldwide Version of System for Prediction of Environmental Emergency Dose Information (WSPEEDI), (II) Evaluation of numerical models by 137 Cs deposition due to the Chernobyl nuclear accident. J Nucl Sci Technol. 2005;42: [5] Terada H, Chino M. Development of an atmospheric dispersion model for accidental discharge of radionuclides with the function of simultaneous prediction for multiple domains and its evaluation by application to the Chernobyl nuclear accident. J Nucl Sci Technol. 2008;45: [6] Ishikawa H. Evaluation of the effect of horizontal diffusion on the long-range atmospheric transport simulation with Chernobyl data. J Appl Meteor. 1995;34: [7] Desiato F, Anfossi D, Castelli ST, Ferrero E, Tinarelli G. The role of wind field, mixing height and horizontal diffusivity investigated through two Lagrangian particle models. Atmos Environ. 1998;32: [8] Gifford FA. Atmospheric dispersion models for environmental pollution applications. Lectures on air pollution and environmental impact analyses, Boston, MA: American Meteorological Society. 1975; [9] Briggs, GA. Diffusion estimation for small emissions. ATDL Contribution File No. 79, Oak Ridge, TN: Air Resources Atmospheric Turbulence and Diffusion Laboratory, National Oceanic and Atmospheric Administration; [10] Gifford, FA. Horizontal diffusion in the atmosphere: a Lagrangian-dynamic theory. Atmos. Environ. 1982;16: [11] MEXT (Ministry of Education, Culture, Sports Science and Technology). Environmental Radiation Database Japanese. Available from [12] Igarashi Y, Sartorius H, Miyao T, Weiss W, Fushimi K, Aoyama M, Hirose K, Inoue HY. 85 Kr and 133 Xe monitoring at MRI, Tsukuba and its importance. J Environ Radioact. 2000;48: [13] Aoyama M, Fujii K, Hirose K, Igarashi Y, Isogai K, Nitta W, Sartorius H, Schlosser C, Weiss W. Establishment of a cold charcoal trap-gas chromatography-gas counting system for 85 Kr measurements in Japan and results from 1995 to Technical reports of the

16 1212 H. Terada et al. Meteorological Research Institute No. 54, Tsukuba: Meteorological Research Institute; Available from 54/54 en.html. [14] Grell GA, Dudhia J, Stauffer DR. A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR/TN-398+STR, Boulder, CO: National Center for Atmospheric Research; Available from [15] Ahlstrom SW, Foote HP, Arnett RC, Cole CR, Serne RJ. Multicomponent mass transport model: theory and numerical implementation (discrete-parcel-random-walk version). BNWL-2127, Richland, WA: Battelle Northwest Laboratory; [16] Aomori Prefecture. Report of environmental radiation monitoring around nuclear facilities (fiscal year 2008); Japanese. Available from chousahoukokusho.pdf.] [17] Pasquill F. Atmospheric diffusion. New York: John Wiley & Sons; p [18] Janjic, ZI. The step-mountain Eta coordinate model: further development of the convection, viscous sublayer, and turbulent closure schemes. Mon Wea Rev. 1994;122: [19] Schultz, P. An explicit cloud physics parameterization for operational numerical weather prediction. Mon Wea Rev. 1995;123: [20] Dudhia, J. A multi-layer soil temperature model for MM5. Preprints of the Sixth PSU/NCAR Mesoscale Model Users Workshop; 1996 July 22 24; Boulder, CO Available from mm5/lsm/soil.pdf.