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Mid-range atmospheric dispersion modelling. Intercomparison of simple models in EMRAS-2 project
Journal of Environmental Radioactivity 162-163 (2016) 225e234
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Mid-range atmospheric dispersion modelling. Intercomparison of simple models in EMRAS-2 project n ~ ez a, *, Kathleen M. Thiessen b, Sohan L. Chouhan c, Francesco Mancini d, Raúl Peria cg Emilie Navarro e, Gert Sdouz f, 1, Dejan Trifunovi a
University of Seville, ETSIA, Ctra Utrera km 1, 41013, Sevilla, Spain Oak Ridge Center for Risk Analysis, 102 Donner Drive, 37830, Oak Ridge, Tennessee, USA Canadian Nuclear Laboratories, K0J 1J0 Chalk River, Ontario, Canada d SOGIN S.p.A., Via Torino, 6, I-00184, Rome, Italy e Institut de Radioprotection et de Sûret e Nucl eaire, 31 Avenue de la division Leclerc BP17, F-922625, Fontenay-aux-Roses, France f Austrian Institute of Technology, A-2444, Seibersdorf, Austria g Federal Authority for Nuclear Regulation, Sheikh Zayed First Street, P.O. Box 112021, Abu Dhabi, United Arab Emirates b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 April 2016 Received in revised form 24 May 2016 Accepted 24 May 2016
An intercomparison of atmospheric dispersion models has been carried out for a hypothetical accident occurring in a nuclear power plant in the center of Spain. The accident consisted of a steam generator tube rupture, and two radionuclides have been considered for the exercise: 137-Cs and 131-I. Meteorological conditions and radionuclide release rates were supplied. Models provided deposition maps, timeintegrated concentrations in air and arrival times of the plumes to specific locations. The effect of the meteorological conditions used in the modelling was clear, with different behavior of the plume with neutral stability vs. stable conditions. The predicted arrival times of the plume at specific locations showed much less variability than deposition and air concentrations. This variability in part reflects the uncertainties inherent in atmospheric dispersion modelling and in the selection of parameter values, such as deposition velocities or diffusivities. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Atmospheric dispersion Nuclear power plant Model 137-Cs 131-I
1. Introduction Environmental assessment models are used for evaluating the radiological impact of actual and potential releases of radionuclides to the environment. They are essential tools for use in the regulatory control of routine discharges to the environment and also in planning measures to be taken in the event of accidental releases. They are also used for predicting the impact of releases which may occur far into the future, for example, from underground radioactive waste repositories. It is important to check, to the extent possible, the reliability of the predictions of such models by comparison with measured values in the environment or by comparing with the predictions of other models. The International Atomic Energy Agency (IAEA) has been organizing programmes of international model testing since the 1980s.
* Corresponding author. ~ ez). E-mail address: [email protected] (R. Peri an 1 Present address: Hockegasse 24/23, A-1180, Vienna, Austria. http://dx.doi.org/10.1016/j.jenvrad.2016.05.027 0265-931X/© 2016 Elsevier Ltd. All rights reserved.
The programmes have contributed to a general improvement in models, in transfer data and in the capabilities of modellers in Member States. The possible benefits of carrying out model validation and testing at an international level were recognized by the Swedish Radiation Protection Institute, which sponsored the Biospheric Model Validation Study (BIOMOVS) and BIOMOVS II programmes starting in 1985 (BIOMOVS II, 1996). BIOMOVS was the first international exercise aimed at the testing and validation of models for the prediction of radionuclide transfer through the environment to humans. The Chernobyl accident in 1986 created a renewed need for reliable assessments in many countries and provided an increased impetus for work in this area. It also originated new data sets that could be put to use for model testing. As a consequence, the IAEA was prompted to start a programme on the Validation of Model Predictions (VAMP) in 1988, which concluded in 1996 (IAEA, 2000). More recently, Environmental Modelling for Radiation Safety (EMRAS) program, running from 2003 to 2007, was launched (IAEA, 2012). From 2009 to 2011, the second stage of EMRAS was organized (EMRAS II). The Urban Areas Working Group was organized
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Fig. 1. Top: General localization of the study area. Bottom: Topography of the domain (elevations in m above sea level).
within this last programme, as part of a theme entitled “Approaches for Assessing Emergency Situations”. The Working Group has been built on the work done by the Urban Remediation Working Group of the first phase of the EMRAS Programme (IAEA, 2012). The goal of the Urban Areas Working Group is to test and improve the capabilities of models used in assessment of radioactive contamination in urban settings, including dispersion and deposition events, short- and long-term contaminant redistribution following deposition events, and potential countermeasures or remediation efforts for reducing human exposures and doses. A mid-range atmospheric dispersion exercise was carried out by the group. It is based on a hypothetical accident at a nuclear power plant and the resulting predicted deposition in urban environments up to 70 km downwind. The scenario assumed a 1-h release from a rupture of a steam generator tube, based on an accident scenario Nucle aire developed by the Institut de Radioprotection et de Sûrete (IRSN), and uses actual geographic and meteorological information for the Trillo nuclear power plant in central Spain. It should be commented that complex atmospheric dispersion models exist now. For instance, WSPEED I-II (Terada et al., 2012)
and LADAS (Suh et al., 2009) were applied in the case of Fukushima accident to evaluate deposition on the sea surface to later compute n ~ ez et al., marine dispersion of these released radionuclides (Peria 2015). Also, atmospheric dispersion models were applied to evaluate the source term from Fukushima (Chino et al., 2011; Kobayashi et al., 2013). These models consist of a meteorological prediction model coupled with an atmospheric dispersion model. Nevertheless, the objective of this work consisted of testing simple atmospheric dispersion models, which do not require the meteorological sub-model, and which can provide a very fast answer in case of an accident. Since the final purpose of the models generally is a dose estimation to the public, models are based on conservative approaches. The purpose of this paper is to present the main results from this exercise. The exercise is described in Section 2. Results are presented in Section 3. 2. Methods The nuclear power plant chosen for the exercise is Trillo (TNPP),
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1200
1100
40.8
• TNPP
40.7
1000
• Guadalajara
Latitude
40.6 900 40.5 800
40.4 MADRID
40.3
700
40.2 −3.8
−3.6
−3.4
−3.2
−3
−2.8
−2.6
600
Longitude 500 1200
1100
40.8
• TNPP
40.7
1000
• Guadalajara
Latitude
40.6 900 40.5 800
40.4 MADRID
40.3
700
40.2 −3.8
−3.6
−3.4
−3.2
−3
−2.8
−2.6
600
Longitude 500 Fig. 2. Wind fields 10 m above the ground provided by WINMOD model for stable (top) and neutral (bottom) stability conditions. Only one of each 16 provided vectors is drawn for clarity. Land elevations (color scale) in m above sea level.
in the central part of Spain, about 70 km northeast from Madrid metropolitan area and 46 km from Guadalajara, which is a smaller town in central Spain located between TNPP and Madrid (Fig. 1). TNPP started operation in 1987. The power is 1043 MW, and the reactor is PW type. Cooling is carried out through two towers. Specific meteorological conditions, which would be representative of a worst-case scenario, were used for the simulations. Simulated wind fields 10 m above the ground were provided. Two situations were considered: one with a stable atmosphere, and one with neutral stability. This allowed assessment of the effects of stability conditions on radionuclide dispersion. In both cases, the same geostrophic wind direction is considered (northeast). Geostrophic wind speeds of 3.0 m/s and 6.0 m/s were used for the
stable and neutral conditions, respectively. The boundary layer height was 1000 m for stable and 1500 m for neutral stability. Wind fields 10 m above the ground were obtained from WINMOD model, developed at the University of North Wales (Jones, 1998). WINMOD calculates such wind fields from the geostrophic wind and atmosphere stability. Essentially, the model diagnoses the local modification of the wind field in regions of complex topography. A geostrophic wind speed and direction, as well as the atmospheric lapse rate and boundary layer height, are specified and the model iterates the horizontal momentum and temperature equations at the surface towards a steady state. Wind fields 10 m above the ground are presented in Fig. 2 for both atmospheric conditions.
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228 9
x 10
2
137
1.8
Cs I
131
Release rate (Bq/s)
1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
0
10
20
30
40
50
60
Time (minutes) Fig. 3. Radionuclide release rates for the steam generator tube rupture accident. The origin of time is arbitrary.
Table 1 Summary of main model characteristics.
Type of model Calculation range Release height Stability class Wind vectors Diffusion coefficients Dry deposition velocity Release time-step Calculation time-step Simulation time Topography Rugosity Reference a b c d e
ADDAM
RASCAL
USev
JRODOS
HOTSPOT
Gaussian user specified radius 50 m E (stable), D (neutral) summed outside the code
Gaussian þ Lagrangian puff 80 km 50 m E, D Limited number used Pasquill-Gifford curvesb
Gaussian þ simplified puff <100 km 10 m E As provided High roughness: Karlsruhe-Jülichc Moderate roughness: Mold
Gaussian >10 m, <100 km 50 m E, D E: 3 m/2; D: 6 m/s
Briggsa Cs: 0.01 m/s I: 0.008 m/s 1h One single step Not applicable Flat 0.40 m Scheier and Chouhan, 2009
Lagrangian <100 km 50 m E, D As provided Horizontal: 60 m2/s Vertical: 30 m2/s
0.003 m/s 1h 15 min 10 h Not used 0.20 m Ramsdell, 2012
Not applicable 1 min 10 s 10 h As provided 0.40 m NPe
Internally determined 0.5 h 1h 7h
Ievdin et al., 2012
Pasquill-Gifford Cs: 0.04 m/s I: 0.22 m/s 1h One single step Not applicable Not applicable City terrain Homann and Aluzzi, 2014
Briggs (1971). Gifford (1968). Geiss et al. (1981). Panitz et al. (1989). Not published.
The same hypothetical accident was considered for both meteorological situations. The radionuclides are assumed to be released as gas, and only dry deposition is considered. Two radionuclides with different half-lives are considered, 137Cs and 131I, with all of the latter in molecular form. Use of these two radionuclides with different half-lives was considered enough for modelling intercomparison purposes, although of course many more radionuclides and in different chemical forms would be released during a real accident. The hypothetical accident considered here consists of a steam generator tube rupture, a scenario which was developed by the Nucle aire (IRSN) of France. Institut de Radioprotection et de Sûrete The duration of the release is 1 h, and the release rate is variable for both radionuclides. Modellers were provided with release data over the 1 h period, shown in Fig. 3. Temporal resolution of the release data is 60 s. Total (integrated) releases are 6.42 1011 Bq and 3.69 1012 Bq for 137Cs and 131I, respectively. An effective release height of 50 m was considered. Modellers were asked to carry out a simulation over 10 h.
Endpoints of simulations were contour maps of deposited activity on the ground and of time-integrated activity concentrations in air at ground level, at the end of the simulation. Modellers were also asked to provide a time series of activity concentrations in air at four selected points (two intermediate point between TNPP and Guadalajara town, denoted as IP1 and IP2, Guadalajara and Madrid downtown). These points are shown in Fig. 1. Table 1provides a summary of the five models used in the exercise. More information about individual models is provided in Appendix A-E and in the references included in the Table. The models represent several different purposes (e.g., emergency assessment and research) and two major types of modelling approaches (Gaussian and Lagrangian). One model (Hotspot) provided results in terms of the distance down the plume centerline, while the others were able to provide results with reference to the local geography. For one model (RASCAL), the range of the model predictions did not extend to the distance of Madrid in this exercise. Four models were used to provide results for both stable atmospheric conditions and neutral stability; the fifth (JRODOS) was
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Fig. 4. Calculated
137
229
Cs deposition by USev model for stable (top) and neutral (bottom) stability conditions.
used only for stable conditions. One model (USev) used timedependent source term information as provided, in 1-min increments. Three models considered the entire release in a single 1h time step, while the other model (JRODOS) considered it in two half-hour time steps. The models also differed in their handling of the information about wind speed and direction and in the dry deposition velocities that were used. 3. Results and discussion Contour maps for predicted deposition of 137Cs are shown in Figs. 4e8. Two sets of predictions (USev and ADDAM, Figs. 4 and 5) clearly show the effect of meteorological conditions: under stable conditions, the predicted plumes intersected Madrid, but with neutral stability, the plume bypassed Madrid. The JRODOS plume
for stable conditions also intersected Madrid (Fig. 6). Predictions with RASCAL did not extend as far as Madrid, but the plots suggest that the plume would have intersected Madrid with stable conditions and bypassed Madrid with neutral stability (Fig. 7). Hotspot provided results in terms of the plume centerline rather than the local geography, but the results do show higher deposition farther downwind for the stable conditions (Fig. 8). Predicted values for deposition of 137Cs and 131I at specific locations are provided in Tables 2 and 3. As expected solely from the distances, the general tendency was for the highest deposition at Intermediate Point-1 (IP1), then IP2, Guadalajara, and Madrid. Predicted differences between both radionuclides reflect both the different source terms and (for some models) different deposition velocities for the two radionuclides. For 137Cs deposition in Madrid, predicted values for stable
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Fig. 5. Calculated
137
Cs deposition by ADDAM model for stable (top) and neutral (bottom) stability conditions.
conditions (4 models) differed by a factor of 28. For neutral stability, all predicted values for Madrid were 0 (USev) or a factor of about 10e100 lower than for stable conditions. For 131I, predicted values in Madrid for stable conditions differed by a factor of about 60. As with 137Cs, predicted values for 131I for neutral stability were 0 or a factor of about 10e100 lower than for stable conditions. These values are consistent with the contour maps, which showed the plume bypassing Madrid in the case of neutral stability. For Guadalajara (5 models), predicted values of deposition with stable conditions were within a factor of 12 for 137Cs and 24 for 131I. For neutral stability, predicted values were within a factor of 165 for 137 Cs and 26 for 131I, indicating more variability among the models for neutral stability. ADDAM and USev predicted higher deposition in Guadalajara for neutral stability than for stable conditions, while Hotspot and RASCAL predicted higher deposition with stable conditions. USev model plume bypassed IP2 with neutral stability. ADDAM predicted deposition at both IP1 and IP2 for both stable conditions
and neutral stability, with a decrease from IP1 to IP2 of about a factor of 9 for stable conditions and a factor of 3 for neutral stability. Surprisingly, RASCAL model plume bypassed IP1 (0 deposition) under stable conditions, and there is not deposition immediately downwind TNPP (Fig. 7). This was attributed to the meteorological preprocessor included in this model (Appendix B): only a part of the provided wind vectors were used. This probably does not allow to describe appropriately the plume behavior in the initial stages of dispersion. Time-integrated concentrations in air are not shown. However, the general tendency was a decrease with distance, as expected. Thus the highest air concentrations were found at Intermediate Point-1 (IP1), then IP2, Guadalajara, and Madrid. One exception was JRODOS, which predicted slightly higher concentrations for parts of Madrid. Also, RASCAL’s predicted plume bypassed IP1 with stable conditions and predicted higher concentrations of 131I in Guadalajara than for IP2 for both sets of meteorological conditions. For Madrid, the results again show a large decrease (a factor of ~10 or
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Fig. 6. Calculated
Fig. 7.
137
231
Cs deposition (Bq/m2) by JRODOS model for stable conditions.
137
Cs deposition contours calculated by RASCAL model for stable (top) and neutral (bottom) stability conditions.
more) from the values predicted for stable conditions to the values predicted for neutral stability, consistent with a predicted plume bypassing Madrid in the case of neutral stability. An important output from atmospheric dispersion models, related to emergency management, is the arrival time of the radioactive plume to a given point. Predicted values for the approximate time to arrival of the predicted plume at specific locations are provided in Table 4. For any given location and stability
class, predicted times to arrival are within a factor of about 2 of each other, for those plumes predicted to reach the location. As described previously, RASCAL predicted that the plume would bypass IP1 under stable conditions, and USev predicted that the plume would bypass both IP2 and Madrid for the case of neutral stability. For plumes predicted to reach a given location, times to arrival were shorter for neutral stability than for stable conditions. In general, ADDAM predicted the longest times to arrival for a given
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Fig. 8. Calculated
Table 2 Comparison of predictions for deposition of Model
IP1
Stable conditions (E) ADDAM 9030 Hotspot NR JRODOS NR RASCAL 0 USev 4348 Neutral conditions (D) ADDAM 4970 Hotspot NR RASCAL 267 USev 7414
IP2
137
Cs deposition contours by Hotspot model for stable (top) and neutral (bottom) stability conditions.
137
Cs (Bq/m2). NR: not reported. Guadalajara
Madrid
964 NR NR 174 233.5
44.8 20 14 166 56.86
1.03 13 28 NR 10.14
1490 NR 69.8 0
381 2.3 122 144.3
0.0165 1.4 NR 0
Table 3 Comparison of predictions for deposition of Model
IP1
Stable conditions (E) ADDAM 43,800 Hotspot NR JRODOS NR RASCAL 0 USev 4312 Neutral conditions (D) ADDAM 23, 400 Hotspot NR RASCAL 1480 USev 7395
IP2 4920 NR NR 964 149.7 7120 NR 389 0
131
I (Bq/m2). NR: not reported. Guadalajara 256 610 110 922 38.98 1880 71 677 146
Madrid 6.66 390 72 NR 11.45 0.0837 45 NR 0
n ~ ez et al. / Journal of Environmental Radioactivity 162-163 (2016) 225e234 R. Peria Table 4 Comparison of predictions of plume arrival times (min). NR: not reported. Model
IP1
Stable conditions (E) ADDAM 67 JRODOS NR RASCAL bypassed USev 40 Neutral conditions (D) ADDAM 33 RASCAL 15 USev 20
(Environmental Modelling for Radiation Safety-2).
IP2
Guadalajara
Madrid
Appendix
150 120 90 100
417 240 210 210
850 420 NR 460
A ADDAM
83 60 bypassed
233 150 130
483 NR bypassed
location and USev and RASCAL the shortest times. 4. Conclusions Participants in this test exercise started with the same information, and while there is general agreement in the results, there are also some obvious differences. It is important to identify and explain the reasons for these differences. As described above, participants varied in their handling of the source term (time-dependent or all at once) and wind field data, and in their selection of parameter values such as deposition velocity (for those models that used a given parameter). The effect of the meteorological conditions used in the modelling is obvious, with different behavior of the plume with neutral stability vs. stable conditions. In this exercise, the most important difference was that the predicted plumes tended to bypass Madrid (the largest city in the test region) with neutral stability but intersected it with stable conditions. Predictions varied as to whether the plume reached or bypassed some of the intermediate points. It is important to note that even when all modellers used the same stability class, the path of the predicted plume could vary among participants. Variability between model predictions was higher for deposition and air concentrations (not shown) than for other endpoints. This variability in part reflects the uncertainties inherent in atmospheric dispersion modelling and in selection of parameter values. For example, for the three models requiring the user to select values for deposition velocity, the selected values ranged from 0.003 to 0.04 m/s for 137Cs and from 0.008 to 0.22 m/s for 131I (Table 1). Models may also have varied in the height used for calculation of the air concentrations. Although uncertainties in individual model predictions were not estimated, the results give an idea of the level of uncertainty that should be acknowledged in dealing with modelling results in important assessments. In this sense, a multimodel approach, as described by Monte et al. (2008), may be of interest when environmental processes are complex. Through this approach, the conclusions that obtain the greatest degree of consensus among modellers are made evident and the aspects that are subject to dispute and which should therefore be handled carefully also become clear. The predicted arrival times of the plume at specific locations showed much less variability, usually less than a factor of 2 among three or four sets of model predictions. In practice, this is an important endpoint, in that it provides an estimate of the time available for evacuating an area or getting people to shelter. The results also show the dependence of this endpoint on meteorological conditions. Acknowledgement Work carried out in the frame of IAEA EMRAS-2 project
233
ADDAM (Ballyk et al., 2003) is a Gaussian plume model which considers the following atmospheric dispersion phenomena (all have been validated): plume rise, downwash, and entrainment (effective release height); fumigation; reflection from an elevated inversion; transport and dispersion (plume broadening and plume diffusion); wet and dry deposition and plume depletion; radioactive decay and build-up; external exposure due to cloudshine (including a finite cloud model) and groundshine; and internal exposure due to inhalation. The model runs stochastically with respect to meteorological data, buoyant releases can be considered and the lateral dispersion coefficient can be calculated from the standard deviation in the wind direction. Meteorological conditions are constant and the terrain is flat. The domain of interest is broken down into 16 sectors and a user-specified number of radial distances. Then, for a given release, dilution factors (concentration divided by release rate) and doses are calculated at each distance in the affected sectors. Some limitations are that the model is not suitable for puff releases (release duration should be greater than the travel time) and that calm winds cannot be treated. B RASCAL RASCAL (Ramsdell, 2012) is a radiological assessment tool for use in emergency response applications. It consists of modules that estimate accident source terms for nuclear power plants and other nuclear fuel cycle facilities; transport, dispersion and deposition of radionuclides; and doses. It also includes a meteorological preprocessor that prepares meteorological data for use by the atmospheric transport modules. RASCAL is a Lagrangian trajectory Gaussian-puff dispersion model. There are two atmospheric stability fields. One consists of Pasquill-Gifford stability classes (Gifford, 1968), and the other consists of the inverse Monin-Obukhov length (Monin and Obukhov, 1954). The Monin-Obukhov length is estimated from the Pasquill-Gifford stability class using a graphical relationship between Monin-Obukhov length, stability class, and surface roughness. RASCAL accepts three precipitation conditions: no precipitation, rain, and snow. Every hour, the precipitation grid is updated using hourly observations. The plume is represented by a series of puffs released at 5-min intervals. Each puff contains the activity released during a 5-min period. The height of release is the sum of the actual release height and final plume rise. The dispersion parameters are a function of distance travelled and atmospheric stability using numerical approximations to the Pasquill-Gifford dispersion curves. Deposition is calculated using a source depletion model with a constant dry deposition velocity. Wet deposition is calculated using a simple washout model with constant washout coefficients. C USev The model, not published, is based on the approach by Elliott (1999). Essentially, it is a Lagrangian three-dimensional particletracking dispersion model. Radioactive decay and turbulent diffusion are simulated using stochastic methods. A horizontal and a vertical diffusion coefficient must be specified. Particles are assumed to be deposited when their height is smaller than 10 cm over the ground level, thus a deposition velocity is not required.
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Then they do not move any more. On the other hand, if a particle reaches the top of the atmospheric boundary layer, it is reflected there. A standard logarithmic profile is applied to obtain wind speed at any height above the ground from the 10 m above ground provided by WINMOD model. Standard values are used for friction coefficients and ground surface rugosity. In the case of land, friction coefficient and rugosity are, respectively, 0.015 and 40 cm. Particles are released at the accident position and effective height at a constant rate of 200 particles per time step. The number of Bq corresponding to each particle depends on the release data at the considered instant of time. The only free parameters in the model are the horizontal and vertical diffusivities. Values used are 60 and 30 m2/s for the horizontal and vertical respectively. A higher value has been used for the horizontal diffusion coefficient given the larger spatial scale in the horizontal than in the vertical direction (limited by the boundary layer height). D JRODOS ATSTEP (Ievdin et al., 2012) model is integrated within JRODOS for atmospheric dispersion calculations. In ATSTEP the duration of a release step equals the duration of the advection step. The release rate at the source and the wind velocity and direction are assumed to be constant during the time step. The cross wind diffusion is modelled using horizontal and vertical Gaussian distributions normal to the puff axis. The longitudinal diffusion is modelled by error-functions with a parameter sx. The most general case of a spatially inhomogeneous, time dependent windfield can be considered in ATSTEP. The Mol or KarlsruheJülich set of parameters, depending on rugosity (Table 1), are used to define the dispersion parameters when these are considered to be a function of the travelled distance. In the Gaussian modelling of the vertical concentration profile total reflection of the concentration at the ground surface and the top of the mixing layer are assumed. The reflection at ground surface enhances the air concentration close to the surface by a factor of 2. The additional reflection at the top of the mixing layer causes the air contamination to get trapped in the mixing layer. The wind speed profile is generated in the meteorological pre-processor in the form of a power law. Ground contamination is calculated in ATSTEP during each time step from the time-integrated concentration in the air near ground and the deposition velocity. The deposition velocity depends on the degree of vertical turbulent mixing of the air (atmospheric resistance) and on local surface properties. Wet deposition is calculated using a washout model. E HOTSPOT HOTSPOT (Homann and Aluzzi, 2014) uses the well-established Gaussian Plume Model, widely used for an initial emergency assessment or safety analysis planning of a radionuclide release. Virtual source terms are used to model the initial 3D distribution of material associated with an explosive release, fire release, resuspension, or user-input geometry. There are a number of isotopes that can be modelled, and more can be added if the dose factors are known. Only a single wind speed and direction is input into HOTSPOT, thus applicability is limited. In the Gaussian transport and dispersion model, horizontal and vertical dispersion coefficients are typically determined from established curves showing them as a function of atmospheric stability and downwind distance. Atmospheric stability is inferred from measured and/or observed meteorological data. The Pasquill-
Gifford categories for atmospheric dispersion are a simplified way to determine the turbulence intensity level, from which dispersion coefficients are obtained. The wind speed profile is typically considered to follow a power law relationship. The deposition velocity is used to calculate the plume depletion using a source-depletion algorithm to adjust the air concentration in the plume to account for material deposited on the ground. As material is deposited on the ground, the plume above becomes depleted. The plume depletion is determined by multiplying the original source term by a source-term depletion factor. The equation to calculate the source-term depletion factor is described by Van der Hoven (1968).
References BIOMOVS II (Biospheric Model Validation Study, Phase II), 1996. An Overview of the BIOMOVS-ii Study and its Findings. Swedish Radiation Protection Institute, BIOMOVS II Technical Report No. 17 (Stockholm). Briggs, G.A., 1971. Discussion on chimney plumes in neutral and stable surroundings. Atmos. Environ. 6, 507e510. Chino, M., Nakayama, H., Nagai, H., Terada, H., Katata, G., Yamazawa, H., 2011. Preliminary estimation of release amounts of 131I and 137Cs accidentally discharged from the Fukushima Daiichi nuclear power plant into the atmosphere. J. Nucl. Sci. Technol. 48, 1129e1134. Elliott, A.J., 1999. Simulations of atmospheric dispersion following a spillage of petroleum at sea. Spill Sci. Technol. Bull. 5, 39e50. Geiss, H.K., Nester, P., Thomas, P., Vogt, K.T., 1981. In der Bundesrepublik Deutschland experimentell ermittelte Ausbreitungsparameter für 100 m €he. Ber. Kernforsch. Jülich, Nr. 1707, 29 (Also Ber KernforEmissionsho schungszentrum Karlsruhe, Nr. 3095). Gifford, F., 1968. Diffusion in the lower layers of the atmosphere. In: Slade, D.H. (Ed.), Meteorology and Atomic Energy. US Atomic Energy Commision, Washington, pp. 65e116. Homann, S.G., Aluzzi, F., 2014. HotSpot Health Physics Codes, Version 3.0. User’s Guide. Lawrence Livermore National Laboratory. https://narac.llnl.gov/content/ assets/docs/HotSpot-UserGuide-3-0.pdf. IAEA, 2000. Modelling of the Transfer of Radiocaesium from Deposition to Lake Ecosystems. IAEA-TECDOC-1143 (Vienna). IAEA, 2012. Environmental Modelling for Radiation Safety (EMRAS). A Summary Report of the Results of the EMRAS Programme (2003-2007). International Atomic Energy Agency, Vienna. IAEA-TECDOC-1678. Ievdin, I.O., Khalchenkov, O.V., Kovalets, I.V., Rakob, W., Trybushnyi, D., Zheleznyak, M., 2012. Application of decision support system JRODOS for assessments of atmospheric dispersion and deposition from Fukushima Daiichi nuclear power plant accident. Int. J. Energy a Clean Environ. 13, 179e190. Jones, B., 1998. A user guide and model description for WINMOD. unit for coastal and estuarine studies. Marine Science Laboratories. University of Wales, Menai Bridge, Anglesey (UK). Kobayashi, T., Nagai, H., Chino, M., Kawamura, H., 2013. Source term estimation of atmospheric release due to the Fukushima Dai-ichi nuclear power plant accident by atmospheric and oceanic dispersion simulations. J. Nucl. Sci. Technol. 50, 255e264. Monin, A.S., Obukhov, A.M., 1954. Basic laws of turbulent mixing in the ground layer of the atmosphere. Trans. Geophys. Inst. Akad. Nauk. USSR 151 (1), 63e87. Monte, L., Boyer, P., Brittain, J., Goutal, N., Heling, R., Kryshev, A., Kryshev, I., n ~ ez, R., Siclet, F., Zheleznyak, M., 2008. Testing models Laptev, G., Luck, M., Peria for predicting the behaviour of radionuclides in aquatic systems. Appl. Radiat. Isotopes 66, 1736e1740. €sler-Sauer, J., 1989. UFOMOD, Atmospheric Dispersion Panitz, H.J., Matzerath, C., Pa and Deposition, Report KfK-4332. ~ ez, R., Brovchenko, I., Duffa, C., Jung, K.T., Kobayashi, T., Lamego, F., Peri an Maderich, V., Min, B.I., Nies, H., Osvath, I., Psaltaki, M., Suh, K.S., 2015. A new comparison of marine dispersion model performances for Fukushima Dai-ichi releases in the frame of IAEA MODARIA program. J. Environ. Radioact. 150, 247e269. Ramsdell, J.V., 2012. RASCAL 4: Descriptions of Models and Methods. NUREG-1940. U.S. Nuclear Regulatory Commission. Scheier, N.W., Chouhan, S.L., 2009. ADDAM Version 1.4 User’s Manual. Report SQAD-07e5009. CANDU Owners Group Inc, Toronto, ON. Canada. Suh, K.S., Han, M.H., Jung, S.H., Lee, C.W., 2009. Numerical simulation for a longrange dispersion of a pollutant using Chernobyl data. Math. Comput. Model. 49, 337e343. Terada, H., Katata, G., Chino, M., Nagai, H., 2012. Atmospheric discharge and dispersion of radionuclides during the Fukushima Dai-ichi nuclear power plant accident. Part II: verification of the source term and analysis of regional-scale atmospheric dispersion. J. Environ. Radioact. 112, 141e154. Van der Hoven, I., 1968. Deposition of particles and gases. In: Slade, D.H. (Ed.), Meteorology and Atomic Energy. National Technical Information Service, pp. 202e207. U.S. Atomic Energy Commission, Report TID-24190.
Contents lists available at ScienceDirect
Journal of Environmental Radioactivity journal homepage: www.elsevier.com/locate/jenvrad
Mid-range atmospheric dispersion modelling. Intercomparison of simple models in EMRAS-2 project n ~ ez a, *, Kathleen M. Thiessen b, Sohan L. Chouhan c, Francesco Mancini d, Raúl Peria cg Emilie Navarro e, Gert Sdouz f, 1, Dejan Trifunovi a
University of Seville, ETSIA, Ctra Utrera km 1, 41013, Sevilla, Spain Oak Ridge Center for Risk Analysis, 102 Donner Drive, 37830, Oak Ridge, Tennessee, USA Canadian Nuclear Laboratories, K0J 1J0 Chalk River, Ontario, Canada d SOGIN S.p.A., Via Torino, 6, I-00184, Rome, Italy e Institut de Radioprotection et de Sûret e Nucl eaire, 31 Avenue de la division Leclerc BP17, F-922625, Fontenay-aux-Roses, France f Austrian Institute of Technology, A-2444, Seibersdorf, Austria g Federal Authority for Nuclear Regulation, Sheikh Zayed First Street, P.O. Box 112021, Abu Dhabi, United Arab Emirates b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 April 2016 Received in revised form 24 May 2016 Accepted 24 May 2016
An intercomparison of atmospheric dispersion models has been carried out for a hypothetical accident occurring in a nuclear power plant in the center of Spain. The accident consisted of a steam generator tube rupture, and two radionuclides have been considered for the exercise: 137-Cs and 131-I. Meteorological conditions and radionuclide release rates were supplied. Models provided deposition maps, timeintegrated concentrations in air and arrival times of the plumes to specific locations. The effect of the meteorological conditions used in the modelling was clear, with different behavior of the plume with neutral stability vs. stable conditions. The predicted arrival times of the plume at specific locations showed much less variability than deposition and air concentrations. This variability in part reflects the uncertainties inherent in atmospheric dispersion modelling and in the selection of parameter values, such as deposition velocities or diffusivities. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Atmospheric dispersion Nuclear power plant Model 137-Cs 131-I
1. Introduction Environmental assessment models are used for evaluating the radiological impact of actual and potential releases of radionuclides to the environment. They are essential tools for use in the regulatory control of routine discharges to the environment and also in planning measures to be taken in the event of accidental releases. They are also used for predicting the impact of releases which may occur far into the future, for example, from underground radioactive waste repositories. It is important to check, to the extent possible, the reliability of the predictions of such models by comparison with measured values in the environment or by comparing with the predictions of other models. The International Atomic Energy Agency (IAEA) has been organizing programmes of international model testing since the 1980s.
* Corresponding author. ~ ez). E-mail address: [email protected] (R. Peri an 1 Present address: Hockegasse 24/23, A-1180, Vienna, Austria. http://dx.doi.org/10.1016/j.jenvrad.2016.05.027 0265-931X/© 2016 Elsevier Ltd. All rights reserved.
The programmes have contributed to a general improvement in models, in transfer data and in the capabilities of modellers in Member States. The possible benefits of carrying out model validation and testing at an international level were recognized by the Swedish Radiation Protection Institute, which sponsored the Biospheric Model Validation Study (BIOMOVS) and BIOMOVS II programmes starting in 1985 (BIOMOVS II, 1996). BIOMOVS was the first international exercise aimed at the testing and validation of models for the prediction of radionuclide transfer through the environment to humans. The Chernobyl accident in 1986 created a renewed need for reliable assessments in many countries and provided an increased impetus for work in this area. It also originated new data sets that could be put to use for model testing. As a consequence, the IAEA was prompted to start a programme on the Validation of Model Predictions (VAMP) in 1988, which concluded in 1996 (IAEA, 2000). More recently, Environmental Modelling for Radiation Safety (EMRAS) program, running from 2003 to 2007, was launched (IAEA, 2012). From 2009 to 2011, the second stage of EMRAS was organized (EMRAS II). The Urban Areas Working Group was organized
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Fig. 1. Top: General localization of the study area. Bottom: Topography of the domain (elevations in m above sea level).
within this last programme, as part of a theme entitled “Approaches for Assessing Emergency Situations”. The Working Group has been built on the work done by the Urban Remediation Working Group of the first phase of the EMRAS Programme (IAEA, 2012). The goal of the Urban Areas Working Group is to test and improve the capabilities of models used in assessment of radioactive contamination in urban settings, including dispersion and deposition events, short- and long-term contaminant redistribution following deposition events, and potential countermeasures or remediation efforts for reducing human exposures and doses. A mid-range atmospheric dispersion exercise was carried out by the group. It is based on a hypothetical accident at a nuclear power plant and the resulting predicted deposition in urban environments up to 70 km downwind. The scenario assumed a 1-h release from a rupture of a steam generator tube, based on an accident scenario Nucle aire developed by the Institut de Radioprotection et de Sûrete (IRSN), and uses actual geographic and meteorological information for the Trillo nuclear power plant in central Spain. It should be commented that complex atmospheric dispersion models exist now. For instance, WSPEED I-II (Terada et al., 2012)
and LADAS (Suh et al., 2009) were applied in the case of Fukushima accident to evaluate deposition on the sea surface to later compute n ~ ez et al., marine dispersion of these released radionuclides (Peria 2015). Also, atmospheric dispersion models were applied to evaluate the source term from Fukushima (Chino et al., 2011; Kobayashi et al., 2013). These models consist of a meteorological prediction model coupled with an atmospheric dispersion model. Nevertheless, the objective of this work consisted of testing simple atmospheric dispersion models, which do not require the meteorological sub-model, and which can provide a very fast answer in case of an accident. Since the final purpose of the models generally is a dose estimation to the public, models are based on conservative approaches. The purpose of this paper is to present the main results from this exercise. The exercise is described in Section 2. Results are presented in Section 3. 2. Methods The nuclear power plant chosen for the exercise is Trillo (TNPP),
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1200
1100
40.8
• TNPP
40.7
1000
• Guadalajara
Latitude
40.6 900 40.5 800
40.4 MADRID
40.3
700
40.2 −3.8
−3.6
−3.4
−3.2
−3
−2.8
−2.6
600
Longitude 500 1200
1100
40.8
• TNPP
40.7
1000
• Guadalajara
Latitude
40.6 900 40.5 800
40.4 MADRID
40.3
700
40.2 −3.8
−3.6
−3.4
−3.2
−3
−2.8
−2.6
600
Longitude 500 Fig. 2. Wind fields 10 m above the ground provided by WINMOD model for stable (top) and neutral (bottom) stability conditions. Only one of each 16 provided vectors is drawn for clarity. Land elevations (color scale) in m above sea level.
in the central part of Spain, about 70 km northeast from Madrid metropolitan area and 46 km from Guadalajara, which is a smaller town in central Spain located between TNPP and Madrid (Fig. 1). TNPP started operation in 1987. The power is 1043 MW, and the reactor is PW type. Cooling is carried out through two towers. Specific meteorological conditions, which would be representative of a worst-case scenario, were used for the simulations. Simulated wind fields 10 m above the ground were provided. Two situations were considered: one with a stable atmosphere, and one with neutral stability. This allowed assessment of the effects of stability conditions on radionuclide dispersion. In both cases, the same geostrophic wind direction is considered (northeast). Geostrophic wind speeds of 3.0 m/s and 6.0 m/s were used for the
stable and neutral conditions, respectively. The boundary layer height was 1000 m for stable and 1500 m for neutral stability. Wind fields 10 m above the ground were obtained from WINMOD model, developed at the University of North Wales (Jones, 1998). WINMOD calculates such wind fields from the geostrophic wind and atmosphere stability. Essentially, the model diagnoses the local modification of the wind field in regions of complex topography. A geostrophic wind speed and direction, as well as the atmospheric lapse rate and boundary layer height, are specified and the model iterates the horizontal momentum and temperature equations at the surface towards a steady state. Wind fields 10 m above the ground are presented in Fig. 2 for both atmospheric conditions.
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228 9
x 10
2
137
1.8
Cs I
131
Release rate (Bq/s)
1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
0
10
20
30
40
50
60
Time (minutes) Fig. 3. Radionuclide release rates for the steam generator tube rupture accident. The origin of time is arbitrary.
Table 1 Summary of main model characteristics.
Type of model Calculation range Release height Stability class Wind vectors Diffusion coefficients Dry deposition velocity Release time-step Calculation time-step Simulation time Topography Rugosity Reference a b c d e
ADDAM
RASCAL
USev
JRODOS
HOTSPOT
Gaussian user specified radius 50 m E (stable), D (neutral) summed outside the code
Gaussian þ Lagrangian puff 80 km 50 m E, D Limited number used Pasquill-Gifford curvesb
Gaussian þ simplified puff <100 km 10 m E As provided High roughness: Karlsruhe-Jülichc Moderate roughness: Mold
Gaussian >10 m, <100 km 50 m E, D E: 3 m/2; D: 6 m/s
Briggsa Cs: 0.01 m/s I: 0.008 m/s 1h One single step Not applicable Flat 0.40 m Scheier and Chouhan, 2009
Lagrangian <100 km 50 m E, D As provided Horizontal: 60 m2/s Vertical: 30 m2/s
0.003 m/s 1h 15 min 10 h Not used 0.20 m Ramsdell, 2012
Not applicable 1 min 10 s 10 h As provided 0.40 m NPe
Internally determined 0.5 h 1h 7h
Ievdin et al., 2012
Pasquill-Gifford Cs: 0.04 m/s I: 0.22 m/s 1h One single step Not applicable Not applicable City terrain Homann and Aluzzi, 2014
Briggs (1971). Gifford (1968). Geiss et al. (1981). Panitz et al. (1989). Not published.
The same hypothetical accident was considered for both meteorological situations. The radionuclides are assumed to be released as gas, and only dry deposition is considered. Two radionuclides with different half-lives are considered, 137Cs and 131I, with all of the latter in molecular form. Use of these two radionuclides with different half-lives was considered enough for modelling intercomparison purposes, although of course many more radionuclides and in different chemical forms would be released during a real accident. The hypothetical accident considered here consists of a steam generator tube rupture, a scenario which was developed by the Nucle aire (IRSN) of France. Institut de Radioprotection et de Sûrete The duration of the release is 1 h, and the release rate is variable for both radionuclides. Modellers were provided with release data over the 1 h period, shown in Fig. 3. Temporal resolution of the release data is 60 s. Total (integrated) releases are 6.42 1011 Bq and 3.69 1012 Bq for 137Cs and 131I, respectively. An effective release height of 50 m was considered. Modellers were asked to carry out a simulation over 10 h.
Endpoints of simulations were contour maps of deposited activity on the ground and of time-integrated activity concentrations in air at ground level, at the end of the simulation. Modellers were also asked to provide a time series of activity concentrations in air at four selected points (two intermediate point between TNPP and Guadalajara town, denoted as IP1 and IP2, Guadalajara and Madrid downtown). These points are shown in Fig. 1. Table 1provides a summary of the five models used in the exercise. More information about individual models is provided in Appendix A-E and in the references included in the Table. The models represent several different purposes (e.g., emergency assessment and research) and two major types of modelling approaches (Gaussian and Lagrangian). One model (Hotspot) provided results in terms of the distance down the plume centerline, while the others were able to provide results with reference to the local geography. For one model (RASCAL), the range of the model predictions did not extend to the distance of Madrid in this exercise. Four models were used to provide results for both stable atmospheric conditions and neutral stability; the fifth (JRODOS) was
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Fig. 4. Calculated
137
229
Cs deposition by USev model for stable (top) and neutral (bottom) stability conditions.
used only for stable conditions. One model (USev) used timedependent source term information as provided, in 1-min increments. Three models considered the entire release in a single 1h time step, while the other model (JRODOS) considered it in two half-hour time steps. The models also differed in their handling of the information about wind speed and direction and in the dry deposition velocities that were used. 3. Results and discussion Contour maps for predicted deposition of 137Cs are shown in Figs. 4e8. Two sets of predictions (USev and ADDAM, Figs. 4 and 5) clearly show the effect of meteorological conditions: under stable conditions, the predicted plumes intersected Madrid, but with neutral stability, the plume bypassed Madrid. The JRODOS plume
for stable conditions also intersected Madrid (Fig. 6). Predictions with RASCAL did not extend as far as Madrid, but the plots suggest that the plume would have intersected Madrid with stable conditions and bypassed Madrid with neutral stability (Fig. 7). Hotspot provided results in terms of the plume centerline rather than the local geography, but the results do show higher deposition farther downwind for the stable conditions (Fig. 8). Predicted values for deposition of 137Cs and 131I at specific locations are provided in Tables 2 and 3. As expected solely from the distances, the general tendency was for the highest deposition at Intermediate Point-1 (IP1), then IP2, Guadalajara, and Madrid. Predicted differences between both radionuclides reflect both the different source terms and (for some models) different deposition velocities for the two radionuclides. For 137Cs deposition in Madrid, predicted values for stable
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Fig. 5. Calculated
137
Cs deposition by ADDAM model for stable (top) and neutral (bottom) stability conditions.
conditions (4 models) differed by a factor of 28. For neutral stability, all predicted values for Madrid were 0 (USev) or a factor of about 10e100 lower than for stable conditions. For 131I, predicted values in Madrid for stable conditions differed by a factor of about 60. As with 137Cs, predicted values for 131I for neutral stability were 0 or a factor of about 10e100 lower than for stable conditions. These values are consistent with the contour maps, which showed the plume bypassing Madrid in the case of neutral stability. For Guadalajara (5 models), predicted values of deposition with stable conditions were within a factor of 12 for 137Cs and 24 for 131I. For neutral stability, predicted values were within a factor of 165 for 137 Cs and 26 for 131I, indicating more variability among the models for neutral stability. ADDAM and USev predicted higher deposition in Guadalajara for neutral stability than for stable conditions, while Hotspot and RASCAL predicted higher deposition with stable conditions. USev model plume bypassed IP2 with neutral stability. ADDAM predicted deposition at both IP1 and IP2 for both stable conditions
and neutral stability, with a decrease from IP1 to IP2 of about a factor of 9 for stable conditions and a factor of 3 for neutral stability. Surprisingly, RASCAL model plume bypassed IP1 (0 deposition) under stable conditions, and there is not deposition immediately downwind TNPP (Fig. 7). This was attributed to the meteorological preprocessor included in this model (Appendix B): only a part of the provided wind vectors were used. This probably does not allow to describe appropriately the plume behavior in the initial stages of dispersion. Time-integrated concentrations in air are not shown. However, the general tendency was a decrease with distance, as expected. Thus the highest air concentrations were found at Intermediate Point-1 (IP1), then IP2, Guadalajara, and Madrid. One exception was JRODOS, which predicted slightly higher concentrations for parts of Madrid. Also, RASCAL’s predicted plume bypassed IP1 with stable conditions and predicted higher concentrations of 131I in Guadalajara than for IP2 for both sets of meteorological conditions. For Madrid, the results again show a large decrease (a factor of ~10 or
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Fig. 6. Calculated
Fig. 7.
137
231
Cs deposition (Bq/m2) by JRODOS model for stable conditions.
137
Cs deposition contours calculated by RASCAL model for stable (top) and neutral (bottom) stability conditions.
more) from the values predicted for stable conditions to the values predicted for neutral stability, consistent with a predicted plume bypassing Madrid in the case of neutral stability. An important output from atmospheric dispersion models, related to emergency management, is the arrival time of the radioactive plume to a given point. Predicted values for the approximate time to arrival of the predicted plume at specific locations are provided in Table 4. For any given location and stability
class, predicted times to arrival are within a factor of about 2 of each other, for those plumes predicted to reach the location. As described previously, RASCAL predicted that the plume would bypass IP1 under stable conditions, and USev predicted that the plume would bypass both IP2 and Madrid for the case of neutral stability. For plumes predicted to reach a given location, times to arrival were shorter for neutral stability than for stable conditions. In general, ADDAM predicted the longest times to arrival for a given
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Fig. 8. Calculated
Table 2 Comparison of predictions for deposition of Model
IP1
Stable conditions (E) ADDAM 9030 Hotspot NR JRODOS NR RASCAL 0 USev 4348 Neutral conditions (D) ADDAM 4970 Hotspot NR RASCAL 267 USev 7414
IP2
137
Cs deposition contours by Hotspot model for stable (top) and neutral (bottom) stability conditions.
137
Cs (Bq/m2). NR: not reported. Guadalajara
Madrid
964 NR NR 174 233.5
44.8 20 14 166 56.86
1.03 13 28 NR 10.14
1490 NR 69.8 0
381 2.3 122 144.3
0.0165 1.4 NR 0
Table 3 Comparison of predictions for deposition of Model
IP1
Stable conditions (E) ADDAM 43,800 Hotspot NR JRODOS NR RASCAL 0 USev 4312 Neutral conditions (D) ADDAM 23, 400 Hotspot NR RASCAL 1480 USev 7395
IP2 4920 NR NR 964 149.7 7120 NR 389 0
131
I (Bq/m2). NR: not reported. Guadalajara 256 610 110 922 38.98 1880 71 677 146
Madrid 6.66 390 72 NR 11.45 0.0837 45 NR 0
n ~ ez et al. / Journal of Environmental Radioactivity 162-163 (2016) 225e234 R. Peria Table 4 Comparison of predictions of plume arrival times (min). NR: not reported. Model
IP1
Stable conditions (E) ADDAM 67 JRODOS NR RASCAL bypassed USev 40 Neutral conditions (D) ADDAM 33 RASCAL 15 USev 20
(Environmental Modelling for Radiation Safety-2).
IP2
Guadalajara
Madrid
Appendix
150 120 90 100
417 240 210 210
850 420 NR 460
A ADDAM
83 60 bypassed
233 150 130
483 NR bypassed
location and USev and RASCAL the shortest times. 4. Conclusions Participants in this test exercise started with the same information, and while there is general agreement in the results, there are also some obvious differences. It is important to identify and explain the reasons for these differences. As described above, participants varied in their handling of the source term (time-dependent or all at once) and wind field data, and in their selection of parameter values such as deposition velocity (for those models that used a given parameter). The effect of the meteorological conditions used in the modelling is obvious, with different behavior of the plume with neutral stability vs. stable conditions. In this exercise, the most important difference was that the predicted plumes tended to bypass Madrid (the largest city in the test region) with neutral stability but intersected it with stable conditions. Predictions varied as to whether the plume reached or bypassed some of the intermediate points. It is important to note that even when all modellers used the same stability class, the path of the predicted plume could vary among participants. Variability between model predictions was higher for deposition and air concentrations (not shown) than for other endpoints. This variability in part reflects the uncertainties inherent in atmospheric dispersion modelling and in selection of parameter values. For example, for the three models requiring the user to select values for deposition velocity, the selected values ranged from 0.003 to 0.04 m/s for 137Cs and from 0.008 to 0.22 m/s for 131I (Table 1). Models may also have varied in the height used for calculation of the air concentrations. Although uncertainties in individual model predictions were not estimated, the results give an idea of the level of uncertainty that should be acknowledged in dealing with modelling results in important assessments. In this sense, a multimodel approach, as described by Monte et al. (2008), may be of interest when environmental processes are complex. Through this approach, the conclusions that obtain the greatest degree of consensus among modellers are made evident and the aspects that are subject to dispute and which should therefore be handled carefully also become clear. The predicted arrival times of the plume at specific locations showed much less variability, usually less than a factor of 2 among three or four sets of model predictions. In practice, this is an important endpoint, in that it provides an estimate of the time available for evacuating an area or getting people to shelter. The results also show the dependence of this endpoint on meteorological conditions. Acknowledgement Work carried out in the frame of IAEA EMRAS-2 project
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ADDAM (Ballyk et al., 2003) is a Gaussian plume model which considers the following atmospheric dispersion phenomena (all have been validated): plume rise, downwash, and entrainment (effective release height); fumigation; reflection from an elevated inversion; transport and dispersion (plume broadening and plume diffusion); wet and dry deposition and plume depletion; radioactive decay and build-up; external exposure due to cloudshine (including a finite cloud model) and groundshine; and internal exposure due to inhalation. The model runs stochastically with respect to meteorological data, buoyant releases can be considered and the lateral dispersion coefficient can be calculated from the standard deviation in the wind direction. Meteorological conditions are constant and the terrain is flat. The domain of interest is broken down into 16 sectors and a user-specified number of radial distances. Then, for a given release, dilution factors (concentration divided by release rate) and doses are calculated at each distance in the affected sectors. Some limitations are that the model is not suitable for puff releases (release duration should be greater than the travel time) and that calm winds cannot be treated. B RASCAL RASCAL (Ramsdell, 2012) is a radiological assessment tool for use in emergency response applications. It consists of modules that estimate accident source terms for nuclear power plants and other nuclear fuel cycle facilities; transport, dispersion and deposition of radionuclides; and doses. It also includes a meteorological preprocessor that prepares meteorological data for use by the atmospheric transport modules. RASCAL is a Lagrangian trajectory Gaussian-puff dispersion model. There are two atmospheric stability fields. One consists of Pasquill-Gifford stability classes (Gifford, 1968), and the other consists of the inverse Monin-Obukhov length (Monin and Obukhov, 1954). The Monin-Obukhov length is estimated from the Pasquill-Gifford stability class using a graphical relationship between Monin-Obukhov length, stability class, and surface roughness. RASCAL accepts three precipitation conditions: no precipitation, rain, and snow. Every hour, the precipitation grid is updated using hourly observations. The plume is represented by a series of puffs released at 5-min intervals. Each puff contains the activity released during a 5-min period. The height of release is the sum of the actual release height and final plume rise. The dispersion parameters are a function of distance travelled and atmospheric stability using numerical approximations to the Pasquill-Gifford dispersion curves. Deposition is calculated using a source depletion model with a constant dry deposition velocity. Wet deposition is calculated using a simple washout model with constant washout coefficients. C USev The model, not published, is based on the approach by Elliott (1999). Essentially, it is a Lagrangian three-dimensional particletracking dispersion model. Radioactive decay and turbulent diffusion are simulated using stochastic methods. A horizontal and a vertical diffusion coefficient must be specified. Particles are assumed to be deposited when their height is smaller than 10 cm over the ground level, thus a deposition velocity is not required.
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Then they do not move any more. On the other hand, if a particle reaches the top of the atmospheric boundary layer, it is reflected there. A standard logarithmic profile is applied to obtain wind speed at any height above the ground from the 10 m above ground provided by WINMOD model. Standard values are used for friction coefficients and ground surface rugosity. In the case of land, friction coefficient and rugosity are, respectively, 0.015 and 40 cm. Particles are released at the accident position and effective height at a constant rate of 200 particles per time step. The number of Bq corresponding to each particle depends on the release data at the considered instant of time. The only free parameters in the model are the horizontal and vertical diffusivities. Values used are 60 and 30 m2/s for the horizontal and vertical respectively. A higher value has been used for the horizontal diffusion coefficient given the larger spatial scale in the horizontal than in the vertical direction (limited by the boundary layer height). D JRODOS ATSTEP (Ievdin et al., 2012) model is integrated within JRODOS for atmospheric dispersion calculations. In ATSTEP the duration of a release step equals the duration of the advection step. The release rate at the source and the wind velocity and direction are assumed to be constant during the time step. The cross wind diffusion is modelled using horizontal and vertical Gaussian distributions normal to the puff axis. The longitudinal diffusion is modelled by error-functions with a parameter sx. The most general case of a spatially inhomogeneous, time dependent windfield can be considered in ATSTEP. The Mol or KarlsruheJülich set of parameters, depending on rugosity (Table 1), are used to define the dispersion parameters when these are considered to be a function of the travelled distance. In the Gaussian modelling of the vertical concentration profile total reflection of the concentration at the ground surface and the top of the mixing layer are assumed. The reflection at ground surface enhances the air concentration close to the surface by a factor of 2. The additional reflection at the top of the mixing layer causes the air contamination to get trapped in the mixing layer. The wind speed profile is generated in the meteorological pre-processor in the form of a power law. Ground contamination is calculated in ATSTEP during each time step from the time-integrated concentration in the air near ground and the deposition velocity. The deposition velocity depends on the degree of vertical turbulent mixing of the air (atmospheric resistance) and on local surface properties. Wet deposition is calculated using a washout model. E HOTSPOT HOTSPOT (Homann and Aluzzi, 2014) uses the well-established Gaussian Plume Model, widely used for an initial emergency assessment or safety analysis planning of a radionuclide release. Virtual source terms are used to model the initial 3D distribution of material associated with an explosive release, fire release, resuspension, or user-input geometry. There are a number of isotopes that can be modelled, and more can be added if the dose factors are known. Only a single wind speed and direction is input into HOTSPOT, thus applicability is limited. In the Gaussian transport and dispersion model, horizontal and vertical dispersion coefficients are typically determined from established curves showing them as a function of atmospheric stability and downwind distance. Atmospheric stability is inferred from measured and/or observed meteorological data. The Pasquill-
Gifford categories for atmospheric dispersion are a simplified way to determine the turbulence intensity level, from which dispersion coefficients are obtained. The wind speed profile is typically considered to follow a power law relationship. The deposition velocity is used to calculate the plume depletion using a source-depletion algorithm to adjust the air concentration in the plume to account for material deposited on the ground. As material is deposited on the ground, the plume above becomes depleted. The plume depletion is determined by multiplying the original source term by a source-term depletion factor. The equation to calculate the source-term depletion factor is described by Van der Hoven (1968).
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