Micro-meteorological parameters and emission rate for experiments 2 and 3 at third period.

## 1. Introduction

While the renaissance of nuclear power was motivated by the increasing energy demand and the related climate problem, the recent history of nuclear power, more specifically two disastrous accidents have forced focus on nuclear safety. Although, experience gathered along nuclear reactor developments has sharpened the rules and regulations that lead to the commissioning of latest generation nuclear technology, an issue of crucial concern is the environmental monitoring around nuclear power plants. These measures consider principally the dispersion of radioactive material that either may be released in control actions or in accidents, where in the latter knowledge from simulations guide the planning of emergency actions. In this line the following contribution focuses on the question of radioactive material dispersion after discharge from a nuclear power plant.

The atmosphere is considered the principal vehicle by which radioactive materials that are either released from a nuclear power plant in experimental or eventually in accidental events could be dispersed in the environment and result in radiation exposure of plants, animals and last not least humans. Thus, the evaluation of airborne radioactive material transport in the atmosphere is one of the requirements for monitoring and planning safety measures in the environment around the nuclear power plant. In order to analyse the (possible) consequences of radioactive discharge atmospheric dispersion models are of need, which have to be tuned using specific meteorological parameters and conditions in the considered region. Moreover, they shall be subject to the local orography and supply with realistic information on radiological consequences of routine discharges and potential accidental releases of radioactive substances.

The present work provides a model that allows to implement afore mentioned simulations by the use of a hybrid system. In a first step the local meteorological parameters are determined using the next-generation mesoscale numerical weather prediction system “Weather Research and Forecasting” (WRF). The forcasting system contains a three dimensional data assimilation system and is suitable for applications from the meso- down to the micro-scale. The second step plays the role of simulating the dispersion process in a micro-scale, i.e. in the environment within a radius of several tenth kilometers.

## 2. On the advection-diffusion approach

The Eulerian approach is widely used in the field of air pollution studies to model the dispersion properties of the Planetary Boundary Layer (PBL). In this context, the diffusion equation that describes the local mean concentrations

Here

The simplicity of the K-theory has led to the widespread use of this theory as mathematical basis for simulating air pollution phenomena. However, the K-closure has its intrinsic limits: it works well when the dimension of dispersed material is much larger than the size of turbulent eddies involved in the diffusion process. Another crucial point is that the down-gradient transport hypothesis is inconsistent with observed features of turbulent diffusion in the upper portion of the mixed layer (9). Despite these well known limits, the K-closure is largely used in several atmospheric conditions because it describes the diffusive transport in an Eulerian framework where almost all measurements are easily cast into an Eulerian form, it produces results that agree with experimental data as well as any other more complex model, and it is not computationally expensive as higher order closures usually are.

For a time dependent regime considered in the present work, we assume that the associated advection-diffusion equation adequately describes a dispersion process of radioactive material. From applications of the approach to tracer dispersion data we saw that our analytical approach does not only yield a solution for the three dimensional advection-diffusion equation but predicts tracer concentrations closer to observed values compared to other approaches from the literature, which is also manifest in better statistical coefficients.

Approaches to the advection-diffusion problem are not new in the literature, that are either based on numerical schemes, stochastic simulations or (semi-)analytical methods as shown in a selection of articles ([12, 23, 26, 29, 32]). Note, that in these works all solutions are valid for scenarios with strong restrictions with respect to their specific wind and vertical eddy diffusivity profiles. A more general approach, the ADMM (Advection Diffusion Multilayer Method) approach solves the two-dimensional advection-diffusion equation with variable wind profile and eddy diffusivity coefficient ([21]). The main idea here relies on the discretisation of the atmospheric boundary layer in a multi-shell domain, assuming in each layer that eddy diffusivity and wind profile take averaged values. The resulting advection-diffusion equation in each layer is then solved by the Laplace Transform technique. The GIADMT method (Generalized Integral Advection Diffusion Multilayer Technique) ([7]) is a dimensional extension to the previous work, but again assuming the stepwise approximation for the eddy diffusivity coefficient and wind profile. To generalize, a general two-dimensional solution was presented by ([22]). The solving methodology was the Generalized Integral Laplace Transform Technique (GILTT) that is an analytical series solution including the solution of an associate Sturm-Liouville problem, expansion of the pollutant concentration in a series in terms of the attained eigenfunction, replacement of this expansion in the advection-diffusion equation and, finally, taking moments. This procedure leads to a set of differential ordinary equations that is solved analytically by Laplace transform technique. In this work we improve further the solutions of the afore mentioned articles and report on a general analytical solution for the advection-diffusion problem, assuming that eddy diffusivity and wind profiles are arbitrary functions having a continuous dependence on the vertical and longitudinal spatial variables.

Equation (1) is considered valid in the domain

Instead of specifying the source term as an inhomogeneity of the partial differential equation, we consider a point source located at an edge of the domain, so that the source position

where

with instantaneous initial condition

where

## 3. A closed form solution

In this section we first introduce the general formalism to solve a general problem and subsequently reduce the problem to a more specific one, that is solved and compared to experimental findings.

### 3.1. The general procedure

In order to solve the problem (1) we reduce the dimensionality by one and thus cast the problem into a form already solved in reference [22]. To this end we apply the integral transform technique in the

where

Upon application of the integral operator

here

Here,

### 3.2. A specific case for application

In order to discuss a specific case we introduce a convention and consider the average wind velocity

The principal aspect of interest in pollution dispersion is the vertical concentration profile, that responds strongly to the atmospheric boundary layer stratification, so that the simplified eddy diffusivity

where

which is equivalent to the problem

by virtue of

The specific form of the eddy diffusivity determines now whether the problem is a linear or non-linear one. In the linear case the

The solution is generated making use of the decomposition method ([1, 2, 3]) which was originally proposed to solve non-linear partial differential equations, followed by the Laplace transform that renders the problem a pseudo-stationary one. Further we rewrite the vertical diffusivity as a time average term

The function

The extension to the closed form recursion is then given by

From the construction of the recursion equation system it is evident that other schemes are possible. The specific choice made here allows us to solve the recursion initialisation using the procedure described in reference [22], where a stationary

### 3.3. Recursion initialisation

The boundary conditions are now used to uniquely determine the solution. In our scheme the initialisation solution that contains

In reference [22] a two dimensional problem with advection in the

where

Replacing equation (21) in equation (20) and using the afore introduced projector (10) now for the

where

A similar procedure leads to the source condition for (22):

Following the reasoning of [22] we solve (22) applying Laplace transform and diagonalisation of the matrix

where

where

The analytical time dependence for the recursion initialisation (20) is obtained upon applying the inverse Laplace transform definition

To overcome the drawback of evaluating the line integral appearing in the above solution, we perform the calculation of this integral by the Gaussian quadrature scheme, which is exact if the integrand is a polynomial of degree

where

## 4. Experimental data and turbulent parameterisation

For model validation we chose a controlled release of radioactive material performed in 1985 at the Itaorna Beach, close to the nuclear reactor site Angra dos Reis in the Rio de Janeiro state, Brazil. Details of the dispersion experiment is described elsewhere ([5]). The experiment consisted in the controlled releases of radioactive tritiated water vapour from the meteorological tower at

Exp | Period | ||||||

The micro-meteorological parameters shown in table 1 are calculated from equations obtained in the literature. The roughness length utilized was

In the atmospheric diffusion problems the choice of a turbulent parameterisation represents a fundamental aspect for contaminant dispersion modelling. From the physical point of view a turbulence parameterisation an approximation for the natural phenomenon, where details are hidden in the parameters used, that have to be adjusted in order to reproduce experimental findings. The reliability of each model strongly depends on the way the turbulent parameters are calculated and related to the current understanding of the planetary boundary layer. In terms of the convective scaling parameters the vertical and lateral eddy diffusivities can be formulated as follows ([11]):

where

The wind speed profile can be described by a power law

Thus, in this study we introduce the vertical and lateral eddy diffusivities (eq. (35) and eq. (29)) and the power law wind profile in the 3D-GILTT model (eq. (16) or equivalently eq. (20)) to calculate the ground-level concentration of emissions released from an elevated continuous source point in an unstable/neutral atmospheric boundary layer.

The validation of the 3D-GILTT model predictions against experimental data from the Angra site together with a two dimensional model (GILTTG) are shown in table 2. While the present approach (3D-GILTT) is based on a genuine three dimensional description an earlier analytical approach (GILTTG) uses a Gaussian assumption for the horizontal transverse direction ([22]). Figure 1 shows the comparison of predicted concentrations against observed ones for the three dimensional approach, which reproduces acceptably the observed concentrations, although this simulation did not make use of the terrain’s realistic complexity.

Exp. | Period | Distance ( | Observed ( | Predictions ( | |

GILTTG | 3D-GILTT | ||||

2 | 3 | 610 | 0.58 | 0.20 | 0.40 |

2 | 3 | 600 | 0.50 | 0.19 | 0.40 |

2 | 3 | 700 | 0.53 | 0.29 | 0.44 |

2 | 3 | 815 | 0.61 | 0.38 | 0.47 |

2 | 3 | 970 | 0.54 | 0.47 | 0.48 |

2 | 3 | 1070 | 0.86 | 0.51 | 0.48 |

2 | 3 | 750 | 0.39 | 0.33 | 0.46 |

2 | 3 | 935 | 0.40 | 0.45 | 0.48 |

3 | 3 | 705 | 38.89 | 47.18 | 31.13 |

3 | 3 | 700 | 24.09 | 46.53 | 31.02 |

3 | 3 | 815 | 48.95 | 59.98 | 32.66 |

3 | 3 | 970 | 36.22 | 73.03 | 32.95 |

3 | 3 | 1070 | 33.50 | 78.65 | 32.44 |

3 | 3 | 500 | 50.26 | 17.74 | 22.58 |

3 | 3 | 375 | 26.86 | 2.57 | 11.67 |

3 | 3 | 960 | 19.61 | 72.35 | 32.97 |

3 | 3 | 915 | 18.02 | 69.04 | 33.03 |

In the further we use the standard statistical indices in order to compare the quality of the two approaches. Note, that we present the two analytical model approaches, since the earlier one was found to be acceptable in comparison to other approaches found in the literature and both give a solution in closed form. The standard statistical indices are NMSE, the normalized mean square error; COR, the correlation coefficient; FA2 and FA5, the fraction of data (in

Statistical Indices | GILTTG | 3D-GILTT |

NMSE = | ||

COR = | ||

FA2 = | ||

FA5 = | ||

FB = | ||

FS = |

In order to validate the two models we fit the predicted versus observed values by a linear regression (see figure 2), where the closer their intersect to the origin and the closer the slope is to unity the better is the approach. The GILTTG approach results in

## 5. Meso-scale simulation for K -closure

The consistency of the K-approach strongly depends on the way the eddy diffusivity is determined on the basis of the turbulence structure of the PBL and on the model ability to reproduce experimental diffusion data. Keeping the K-theory limitations in mind many efforts have been made to develop turbulent parametrisations for practical applications in air pollution modelling which reveals the essential features of turbulent diffusion, but which as far as possible preserves the simplicity and flexibility of the K-theory formulation. The aim of this step is to elaborate parametrisations for the eddy diffusivity coefficients in the PBL based on the micro-meteorological parameters that were extracted from mesoscale WRF simulations. The WRF model is based on the Taylor’s statistical theory and a model for Eulerian spectra ([11, 24]). The main idea of the proposed spectral model relies on considering the turbulent spectra as a superposition of a buoyant produced part (with a convective peak wavelength) and a shear produced part (with a mechanical peak wavelength). By such a model, the plume spreading rate is directly connected with the spectral distribution of eddies in the PBL, that is with the energy containing eddies of the turbulence.

The WRF Simulator is a meso-scale numerical weather prediction system that features multiple dynamical cores and a 3-dimensional variational data assimilation system. The simulator offers multiple physics options that can be combined in various ways. Since this study focusses on the implementation of an interface with a model for the PBL, orography related features of WRF were of importance, more specifically the Land-Surface and PBL physics options were chosen for the present study. In WRF, when a PBL scheme is activated, a specific vertical diffusion is de-activated with the assumption that the PBL scheme will handle this process. The Mellor-Yamada-Janjic PBL scheme derives the eddy diffusivities coefficients and the boundary layer height from the estimations of the Turbulent Kinetic Energy (TKE) through the full range of atmospheric turbulent regimes ([19]).

Two grids were used for the WRF meso-scale simulation. The outer grid has an extension of the order of half the earth radius so that a significant part of the large scale geological domain of interest is included. The inner grid is centred at the point of interest, i.e. the centre of the power plant where typically the nuclear reactor is located. The simulation may in principal contain a sequence of days or even months. The micro-meteorological data are extracted at the centre point of the inner WRF grid. The spectral model needs these quantities to calculate the eddy diffusivity coefficients.

On the basis of Taylor’s theory, Taylor proposed that under the hypothesis of homogeneous turbulence, the eddy diffusivities may be expressed as

where

where

An analytical form for the dimensional spectra in convective turbulence has been reported in [11]

while for mechanical turbulence ([10])

where

The dimensionless spectrum

The total wind velocity variance is obtained by the sum of mechanical and convective variances

## 6. Application to the Fukushima-Daiichi accident

In order to illustrate the suitability of the discussed formulation to simulate contaminant dispersion in the atmospheric boundary layer, we evaluate the performance of the new solution and simulate radioactive substance dispersion around the Fukushima-Daiichi power plant.

At the 11

In the following we show the results for a sequence of four days from the 12th to the 15th of march. Figure 3 shows some meso-scale meteorological information, that was obtained from WRF. The first plot in fig. 3 corresponds to the situation three hours after the beginning of constant release of radioactive material, the second and third plot correspond to 48 hours and 93 hours after time zero.

From the meso-scale meteorological data one may determine the eddy diffusivity coefficients for each specific hour. In the

In the further we show the radioactive substance concentrations close to the surface around the nuclear power plant. Figures 5 show the distributions for 3 hours, 48 hours and 93 hours after the beginning of the substance release with a logarithmic scale.

The centre of the nuclear power plant is located in the centre of the plot, the cost line is almost in the north south direction, that is parallel to the

## 7. Conclusions

The present work was based on an Eulerian approach to determine dispersion of radioactive contaminants in the PBL. To this end the diffusion equation for the cross-wind integrated concentrations was closed by the relation of the turbulent fluxes to the gradient of the mean concentration by means of eddy diffusivity (K-theory). We are completely aware of the fact that K-closure has its intrinsic limits so that one would like to remove these inconsistencies. However, comparisons of predictions by this approach to experimental data have shown that there are scenarios where this lack is not significantly manifest, which we use as a justification together with its computational simplicity to perform our simulations based on this approach.

Since the consistency of the K-approach depends crucially on the determination of the eddy diffusivity considering the turbulence structure of the PBL in its respective stability regimes, we elaborated parametrisations for the eddy diffusivity coefficients based on the micro-meteorological parameters that were extracted from meso-scale WRF simulations, that allowed to take into account the realistic orography of the larger vicinity of a reactor site in consideration. The approach proposed here for the determination of the eddy-diffusivity coefficient is based on the Taylor statistical diffusion theory and on the spectral properties of turbulence. The assumption of continuous turbulence spectrum and variances, allows the parametrisations to be continuous at all elevations, and in stability conditions ranging from a convective to a neutral condition, and from a neutral to a stable condition so that a simulation of a full diurnal cycle is possible. Simulating micro-meteorology for a short period for the Fukushima Nuclear Power Station Accident may be considered a first step into a direction where the impact of the contamination of radioactive material in the site may be simulated and evaluated for the whole period of the accident until today. Thus the present work may be understood as one tile in a larger program development that simulates radioactive material dispersion using analytical resources, i.e. solutions. In a longer term we intend to build a library that allows to predict radioactive material transport in the planetary boundary layer that extends from the micro- to the meso-scale.

The quality of the solution may be estimated by the following considerations. Recalling, that the structure of the pollutant concentration is essentially determined by the mean wind velocity

The square integrable function * Cardinal Theorem of Interpolation Theory* for our problem. Since the cut-off defines some sort of sampling density, its introduction is an approximation and is related to convergence of the approach and Parseval’s theorem may be used to estimate the error. In order to keep the solution error within a prescribed error, the expansion in the region of interest has to contain

Further, the Cauchy-Kowalewski theorem ([8]) guarantees that the proposed solution is a valid solution of the discussed problem, since this problem is a special case of the afore mentioned theorem, so that existence and uniqueness are guaranteed. It remains to justify convergence of the decomposition method. In general convergence by the decomposition method is not guaranteed, so that the solution shall be tested by an appropriate criterion. Since standard convergence criteria do not apply in a straight forward manner for the present case, we resort to a method which is based on the reasoning of Lyapunov ([6]). While Lyapunov introduced this conception in order to test the influence of variations of the initial condition on the solution, we use a similar procedure to test the stability of convergence while starting from an approximate (initial) solution

For model validation one faces the drawback, that the majority of measurements are at ground level, so that one could think that a two dimensional description would suffice, however the present analysis clearly shows the influence of the additional dimension. While in the two dimensional approach the tendency of the predicted concentrations is to overestimate the observed values, this is not the case for the results of the three dimensional description, mainly because it does not assume turbulence to be homogeneous. Moreover the solution of the advection diffusion equation discussed here is more general than shown in the present context, so that a wider range of applications is possible. Especially other assumptions for the velocity field and the diffusion matrix are possible. In a future work we will focus on a variety of applications and introduce a rigorous proof of convergence from a mathematical point of view, which we indicated in sketched form only in our conclusions.

### Acknowledgement

The authors thank Brazilian CNPq and FAPERGS for the partial financial support of thiswork.

## References

- 1.
Adomian G. 1984 A New Approach to Nonlinear Partial Differential Equations J. Math. Anal. Appl.,102 page numbers (420 EOF 434 EOF - 2.
Adomian G. 1988 A Review of the DecompositionMethod in AppliedMathematics. J. Math. Anal. Appl.,135 page numbers (501-544). - 3.
Adomian G. 1994 Solving Frontier Problems of Physics: The Decomposition Method, Kluwer, Boston, MA. - 4.
Batchelor G. K. 1950 The application of the similarity theory of turbulence to atmospheric diffusion Quart. J. Royal Meteor. Soc.,76 328 page numbers (133 EOF 146 EOF - 5.
Biagio R. Godoy G. Nicoli I. Nicolli D. Thomas P. 1985 First atmospheric diffusion experiment campaign at the Angra site.- KfK 3936, Kar lsruhe, and CNEN 1201, Rio de Janeiro. - 6.
Boichenko V. A. Leonov G. A. Reitmann V. 2005 NDimension theory for ordinary equations, Teubner, Stuttgart. - 7.
Costa C. P. Vilhena M. T. Moreira D. M. Tirabassi T. 2006 Semi-analytical solution of the steady three-dimensional advection-diffusion equation in the planetary boundary layer Atmos. Environ.,40 29 page numbers (5659 EOF - 8.
Courant R. Hilbert D. (1989 1989 Methods ofMathematical Physics. JohnWiley&Sons, New York. - 9.
Deardoff J. W. . Willis G. E. 1975 A parameterization of diffusion into the mixed layer. J. Applied Meteor.,14 page numbers (1451-1458). - 10.
Degrazia G. A. Moraes O. L. L. 1992 A model for eddy diffusivity in a stable boundary layer Bound. Layer Meteor.,58 page numbers (205 EOF 214 EOF - 11.
Degrazia G. A. Campos Velho. H. F. Carvalho J. C. 1997 Nonlocal exchange coefficients for the convective boundary layer derived from spectral properties Contr. Atmos. Phys.,70 page numbers (57 EOF 64 EOF - 12.
Demuth C. 1978 A contribution to the analytical steady solution of the diffusion equation for line sources Atmos. Environ.,12 page numbers (1255 EOF 1258 EOF - 13.
Djolov G. D. Yordanov D. L. Syrakov D. E. 2004 Baroclinic planetary boundary layer model for neutral and stable stratification conditions Bound. Layer Meteor.,111 page numbers (467 EOF 490 EOF - 14.
Hanna S. R. 1989 Confidence limit for air quality models as estimated by bootstrap and jacknife resampling methods. Atmos. Environ.,23 page numbers (1385-1395). - 15.
Hjstrup J. H. 1982 Velocity spectra in the unstable boundary layer. J. Atmos. Sci.,39 page numbers (2239-2248). - 16.
Irwin J. S. 1979 A theoretical variation of the wind profile power-low exponent as a function of surface roughness and stability. Atmos. Environ.,13 page numbers (191-194). - 17.
Mangia C. Degrazia G. A. Rizza U. 2000 An integral formulation for the dispersion parameters in a shear/buoyancy driven planetary boundary layer for use in a Gaussian model for tall stacks J. Applied Meteor.,39 page numbers (1913 EOF 1922 EOF - 18.
Mangia C. Moreira D. M. Schipa I. Degrazia G. A. Tirabassi T. Rizza U. 2002 Evaluation of a new eddy diffusivity parameterisation fromturbulent eulerian spectra in different stability conditions. Atmos. Environ.,36 34 page numbers (67-76). - 19.
Mellor G. L. Yamada T. 1982 Development of a Turbulence Closure Model for Geophysical Fluid Problems Reviews of Geo. and Space Phys.,20 page numbers (851 EOF - 20.
Moeng C. H. Sullivan P. P. 1994 A comparison of shear-and buoyancy-driven planetary boundary layer flows. J. Atmos. Sci.,51 page numbers (999 EOF 1022 EOF - 21.
Moreira D. M. Vilhena M. T. Tirabassi T. Costa C. Bodmann B. 2006 Simulation of pollutant dispersion in atmosphere by the Laplace transform: the ADMM approach. - 22.
Moreira D. M. Vilhena M. T. Buske D. Tirabassi T. 2009 The state-of-art of the GILTT method to simulate pollutant dispersion in the atmosphere Atmos. Research,92 page numbers (1 EOF 17 EOF - 23.
Nieuwstadt F.T.M. & de Haan B.J. 1981 An analytical solution of one-dimensional diffusion equation in a nonstationary boundary layer with an application to inversion rise fumigation. Atmos. Environ.,15 page numbers (845-851). - 24.
Olesen H. R. Larsen S. E. Højstrup J. 1984 Modelling velocity spectra in the lower part of the planetary boundary layer. Bound. Layer Meteor.,29 page numbers (285-312). - 25.
Panofsky A. H. Dutton J. A. 1988 Atmospheric Turbulence. John Wiley & Sons, New York. - 26.
Sharan M. Singh M. P. Yadav A. K. 1996 Amathematical model for the atmospheric dispersion in low winds with eddy diffusivities as linear functions of downwind dist ance. Atmos. Environ.,30 7 page numbers (1137 EOF - 27.
Stroud A. H. Secrest D. 1966 Gaussian quadrature formulas Prentice Hall Inc., Englewood Cliffs, N.J.. - 28.
Taylor G. I. 1921 Diffusion by continuous movement. Proc. Lond.Math. Soc.,2 page numbers (196-211). - 29.
Tirabassi T. 2003 Operational advanced air pollution modeling PAGEOPH,160 1-2 page numbers (05 EOF 16 EOF - 30.
Torres R. H. 1991 Spaces of sequences, sampling theorem, and functions of exponential type. Studia Mathematica,100 1 page numbers (51-74). - 31.
Ulke A. G. 2000 New turbulent parameterisation for a dispersion model in the atmospheric boundary layer. Atmos. Environ.,34 page numbers (1029-1042). - 32.
van Ulden A. P. 1978 Simple estimates for vertical diffusion from sources near the ground. Atmos. Environ.,12 page numbers (2125-2129). - 33.
Wandel C. F. Kofoed-Hansen O. 1962 On the Eulerian-Lagrangian Transform in the Statistical Theory of Turbulence J. Geo. Research,67 page numbers (3089 EOF 3093 EOF - 34.
Yordanov D. Syrakov D. Kolarova M. 1997 On the Parameterization of the Planetary Boundary Layer of the Atmosphere: The Determination of the Mixing Height, In: Current Progress and Problems. EURASAPWorkshop Proc.. - 35.
(Zanetti P. 1990 ). Air Pollution Modeling. Comp. Mech. Publications, Southampton (UK).