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When modelling the dispersion of pollutants in the atmosphere, uncertainty in the simulation results that depend on the available data used as input conditions is a critical issue, particularly in the context of emergency response to the accidental release of harmful substances. In the framework of the UDINEE Project, a Lagrangian particle dispersion model is used to simulate puff emissions in four different test cases, using input wind velocity data from two different datasets, both representative of the flow at the time of the release. The effect of the choice of the input data on the final concentration distribution at ground level is discussed and compared with observations. A statistical analysis is applied to estimate the deviation between the results of the two runs. The bias between the two concentration fields, connected to the variability and the uncertainty in the input flow, is found to be of similar magnitude to the typical bias between model predictions and observations.

Three different modelling techniques to simulate the pollutant dispersion in the atmosphere at the microscale and in presence of obstacles are evaluated and compared. The Eulerian and Lagrangian approaches are discussed, using RAMS6.0 and MicroSpray models respectively. Both prognostic and diagnostic modelling systems are considered for the meteorology as input to the Lagrangian model, their differences and performances are investigated. An experiment from the Mock Urban Setting Test field campaign observed dataset, measured within an idealized urban roughness, is used as reference for the comparison. A case in neutral conditions was chosen among the available ones. The predicted mean flow, turbulence and concentration fields are analysed on the basis of the observed data. The performances of the different modelling approaches are compared and their specific characteristics are addressed. Given the same flow and turbulence input fields, the quality of the Lagrangian particle model is found to be overall comparable to the full-Eulerian approach. The diagnostic approach for the meteorology shows a worse agreement with observations than the prognostic approach but still providing, in a much shorter simulation time, fields that are suitable and reliable for driving the dispersion model.

In this work we simulated the Macdona (USA) chlorine railway accident with the Lagrangian dispersion model MicroSpray, twice: firstly by using the standard version and then by using a recently developed new two-phase module. MicroSpray was coupled with the diagnostic MicroSwift model, which provides the 3-D wind field in presence of obstacles and orography, and was used to test the two options. The results obtained in the two simulations, with and without the new module, are presented and the differences are discussed.

The transfer of a passive tracer in inhomogeneous turbulent flow is investigated. Starting from Lumley's constitutive equation, we derived an expression for the ratio between the effective eddy diffusivity K and eddy diffusivity K as a function of three length scales characterizing the local turbulence structure, flux variations and turbulence inhomogeneities. The theoretical predictions for the one-dimensional case of inhomogeneous symmetric turbulence were validated through a comparison with the numerical results of a Lagrangian particle model simulating a wind tunnel experiment of dispersion in the lee of an idealized two-dimensional hill. A qualitative agreement is reached between the theoretical evaluation of K and the value obtained from the numerical simulation.

Spray, a 3D Lagrangian particle model for the simulation of complex flow dispersion, is presented. Its performance is tested against the Environmental Protection Agency wind tunnel concentration distribution of passive tracer released from elevated point sources, located in the lee region of a two-dimensional schematic hill, in a neutrally stratified boundary layer. Based on the measured values of the first two moments of the turbulent flow velocity, the mean fields are computed over a regular grid using a mass-consistent model, whereas the turbulence structure is simply interpolated. From these fields, trajectories of tracer particles are computed using a linear formulation of the Langevin equation, with a correlated, skewed forcing. The self-consistence test (well-mixed condition), aimed at maintaining an initially well-mixed particle distribution uniform in time, has shown satisfactory results in the region under study. The computed concentration field turns out to be in good agreement with th...

A simple and easy to use method to include Eulerian plume rise in Lagrangian particle models is presented. This approach takes into account the vertical variation of wind and stability. Its ability to realistically simulate plume rise and spread, both through numerical experiments under typical atmospheric conditions and by comparison with actual plumes detected by a Differential Lidar in a case study, is shown. Despite its simplicity, our method proved to describe the main plume rise characteristics in a satisfactory way and to yield a fair agreement among observed and predicted plume centreline heights and standard deviations.

A Lagrangian stochastic model (MicroSpray), able to simulate the airborne dispersion in complex terrain and in presence of obstacles, was modified to simulate the dispersion of dense gas clouds. This is accomplished by taking into account the following processes: negative buoyancy, gravity spreading and the particle's reflection at the bottom computational boundary. Elevated and ground level sources, continuous and instantaneous emissions, time varying sources, plumes with initial momentum (horizontal, vertical or oblique in any direction), plumes without initial momentum are considered. MicroSpray is part of the model system MSS, which also includes the diagnostic MicroSwift model for the reconstruction of the 3-D wind field in presence of obstacles and orography. To evaluate the MSS ability to simulate the dispersion of heavy gases, its simulation performances are compared in detail to two field experiments (Thorney Island and Kit Fox) and to a chlorine railway accident (Macdona). Then, a comprehensive analysis considering several experiments of the Modelers Data Archive is presented. The statistical analysis on the overall available data reveals that the performance of the new MicroSpray version for dense gas releases is generally reliable. For instance, the agreement between concentration predictions and observations is within a factor of two in the 72% up to 99% of the occurrences for the case studies considered. The values of other performance measures, such as correlation coefficient, geometric mean bias and geometric variance, mostly set in the ranges indicated as good-model performances in the specialized literature.

A particle model LAMDA suitable for dealing with the atmospheric dispersion is presented. The reliability of the model is tested comparing the results of its simulations to the wind tunnel measurements by Khurshudyanet al. (1981). Two versions of the model, both based on the Langevin equation and askewed distribution of the vertical wind velocity fluctuations, are considered. To develop the second version of the model we derived a proper scheme to produce skewed distributions of particle velocities consistent with the observed first and second moments of turbulent fluctuating velocities, with their horizontal and vertical derivatives, and their cross-correlations. The 2D geometry of the obstacle (in the wind tunnel experiment considered) allowed some simplifications. Due to the lack of some input data (such as the vertical profiles of crosswind standard deviation of wind velocity fluctuations and of the Lagrangian time scales) we looked for three different parametrizations. It was found that particular combinations of measured and parametrized data could give rise to critical vertical regions in which the derived scheme for the generation of random vertical velocity fluctuations cannot be applied. The best vertical distributions of the Lagrangian time scales (third parametrization) were estimated by fitting simple formulations for the average plume height and lateral variances to the measured data. The main results of this work were the model performance in simulating dispersion in shear flow over flat terrain, and its sensivity to the shape of the crosscorrelation term\(\overline {u'w'} \).

A Lagrangian particle model (LAMDA), previously developed and applied to the simulation of atmospheric dispersion in neutral and convective windy conditions, was modified to deal with stable low wind speed conditions. These last are among the most difficult to be treated. In fact, on the one hand, nearly calm situations, associated to strong stability and air stagnation, make the lower layers of the atmosphere poorly diffusive, and, on the other hand, the large fluctuations in the wind direction (meandering), spread the airborne pollutants over wide angular sectors. An ad hoc algorithm to simulate the effect of meadering on the dispersion is proposed. The model is validated by comparing its simulation results to three tracer experiments held in stable low wind speed conditions by the Idaho National Engineering Laboratory (U.S.A.) in 1974. These experiments present plume spread of different width (48, 138 and 360°, respectively, at an arc located 200 m downwind from the source) and are comprehensive of a wide set of conditions, ranging from strong to weak stability and from low wind speed to calm. The results of the comparison are discussed. The ability of the model to simulate the g.l.c. distributions with a good degree of confidence is illustrated.

A new version of the Lagrangian particle model MicroSpray is proposed. It simulates the dense gas dispersion in situations characterized by the presence of buildings, other obstacles, complex terrain, and possible occurrence of low wind speed conditions. Its performances are compared to an atmospheric CFD model output and to a field experiment (Thorney Island).

Conditions of low windspeed associated to strong stability and air stagnation correspond to highly nonstationary and inhomogeneous diffusion situations. In such «poor» diffusion cases, integrated models (like Gaussian models) are no longer valid. New numerical methodologies, like Monte Carlo methods, are needed. Recently a Monte Carlo particle model for airborne pollutant dispersion has been developed by our team. Its ability to predict ground-level concentrations in convective and neutral conditions with a high degree of accuracy was verified against tracer experiments. In the present paper our model is used to simulate the dispersion under low windspeed conditions. Particular emphasis is given to the problem of wind meandering. We have demonstrated the need of recording the wind statistics over averaging periods of the order of a few minutes and anad hoc algorythm to treat low windspeed situation when only hourly average data are available is suggested. The model results are compared to tracer data obtained by the Idaho National Engineering Laboratory (U.S.A.) in 1974.

Recently, our group developed a complete 3-D model system aimed to simulate atmospheric pollutant dispersion in complex terrain. The system includes the RAMS model (Pielke et al. 1992) providing the flow field, the Lagrangian Stochastic Models SPRAY (Tinarelli et al., in press; Ferrero and Anfossi 1998) computing the dispersion, and the interface code MIRS (Trini Castelli and Anfossi, 1997). Turbulent quantities can be calculated in MIRS by parameterisations from literature or by the turbulent kinetic energy (E) field, directly extracted by RAMS meteorological output.This model system is applied to the wind tunnel EPA-RUSVAL tracer experiment (Khurshudyan et al. 1990) in which a neutral flow is reproduced on a 2-D cross-wind valley and the source is placed near the bottom of the valley. In the version of RAMS here used, we have replaced the MellorYamada 2.5 turbulence closure with both E-l and closures, where l is the length scale and the mean rate of E dissipation. In a previous work (Trini Castelli et al. 1999) we showed that these last reproduce the flow and the turbulent fields in this wind tunnel experiment better than the Mellor-Yamada 2.5 model, originally implemented in RAMS. The influence of these different turbulent closures on the dispersion process is tested by comparing the SPRAY simulations with the observed tracer concentration fields. We focus our attention on the comparison between the simulations carried out using the Hanna (1982) parameterisation and the turbulent field supplied by the E-l or model. The turbulent quantities needed as input to SPRAY are: the three standard deviations of the wind fluctuation components and the three Lagrangian time scale The turbulent model gives the whilst the are obtained from the relationships

Our joint ENEL-SpA and CNR teams participated in the experimental activity of TRACT in September 1992 at Memmingen-Baden Wurttemberg, Germany, and in the 3 TRANSALP exercises (1989, 1990 and 1991) in Switzerland. Furthermore it performed the simulation of the TRANSALP I (1989) experiment: the transport and diffusion of tracer release inside an Alpine valley has been simulated through the use of a mass-consistent model (for the wind field reconstruction) and of a Lagrangian particle model.

A particle model SPRAY suitable for dealing with the atmospheric dispersion of buoyant emission in complex terrain has been developed. It is an extension of the model LAMBDA (Anfossi et al. 1991, Brusasca et al. 1989 and 1992) based on the Langevin equation, designed to simulate the dispersion on flat terrain, which was validated in various atmospheric conditions. In this last model the vertical profiles of wind and turbulence, defined at the source locations, were kept constant in all the computational domain.

Most of the up to date dispersion simulations are discussed using ground level concentration (g.l.c.) values only. However it is straightforward that the spatial distribution of the concentration should be carefully computed, in order to assess the capability of a given model to correctly simulate the atmospheric dispersion. Therefore, the vertical and horizontal concentration profiles at various downwind distances and at different levels above ground should also be computed and compared to the corresponding observed values if any, even if this is generally thought of as very severe test for any kind of model.

A Lagrangian particle dispersion model for dense gas dispersion is proposed. It is a new version of MicroSpray oriented to simulate dense gas dispersion in urban or industrial environment, and includes the treatment of obstacles, complex terrain and low wind. The dense gas descent is computed by a 3D dynamical plume rise/descent model, the gravity spreading and bottom boundary condition are accounted for by empirical formulations. The differences between simulations obtained with the new model and with the standard one are presented. The model is validated against tracer field data gathered in the Thorney Island experiment 8.

Dispersion in low wind speed conditions is governed by meandering that disperses plumes over wide angular sectors, thus g.l.c. are lower than predicted by Gaussian models. It was proposed to model these dispersion situations in homogeneous conditions with two coupled Langevin equations, based on low wind speed turbulence analysis. Their parameters were derived from the autocorrelation functions of horizontal wind that exhibit an oscillations and large negative lobes. We propose a new equation system for: general case of inhomogeneous turbulence; total velocity; for the windy situations (based on the "Thomson simplest solution") and verify that these new solutions satisfy the "well-mixed condition".

The results of numerical simulations of tracer dispersion in an urban stable boundary layer are presented. Both Gaussian approximation and fourth order closure are considered and a Gram-Charlier probability density function is used in our Lagrangian stochastic model. Different parameterizations for the boundary-layer height and different values of kurtosis are tested. The model is based on the generalized Langevin equation, whose coefficients are solution of the Fokker-Planck equation and plume rise is taken into account. The model accuracy is assessed by means of a model evaluation based on the predicted and observed crosswind integrated concentrations, maximum on the arc and standard deviation of the crosswind concentration distribution on the arc.

A Monte Carlo model and computer code (MC-LAGPAR) for simulating atmospheric transport and diffusion of plumes are described. The turbulent diffusion is simulated by the semi-random motion of Lagrangian particles. The particles are emitted by a point source and dispersed in a computational domain by pseudo-velocities derived from vertical profiles of meteorological variables. The MC-LAGPAR code includes the implementation of special algorithms for the simulation of a dynamic plume rise, chemical decay, deposition and resuspension effects. Furthermore, computer-graphics displays have been developed. The model, here used in its two dimensional version, is validated in the well-known case of homogeneous and stationary turbulence. In this case, we compared the concentration fields obtained by our model with those calculated by the known analytical solution. In both computations, the standard deviations of wind velocities are calculated according to the Taylor formulas. In the nonhomogeneous case, the vertical structure of turbulence is parameterized according to the scheme suggested by Hanna. As an example of the non-homogeneous case, we present numerical simulations in convective (unstable) conditions in which the influence of updraughts and downdraughts is empirically taken into account.

A Monte Carlo model simulating atmospheric transport and diffusion in the PBL is presented. In a previous study, it was shown that it works well in homogeneous turbulence and fits well the Willis and Deardorff water tank simulations in convective nonhomogeneous conditions. In the present paper a sensitivity analysis aimed at estimating the importance and effectiveness of the model parameters is performed and discussed. Our model makes use of Hanna's scheme for the vertical structure of the turbulent parameters, and of an empirical parametrization of updrafts and downdrafts in convective unstable conditions. Emitting the particles either from a point source or uniformly distributed along the vertical we found that the model avoids the particle accumulation at the top and bottom of the PBL and recovers the Eulerian turbulent statistics. This demonstrates that our numerical scheme is consistent with the physical constraints,i.e. mass and energy are preserved and an initial uniform distribution remains so. Finally it is shown that considering the contribution of the cross-correlation term\(\overline {u'w'} \) does not improve significantly the model performances.

A Lagrangian statistical (Monte Carlo) model for airborne pollutant dispersion is presented. Its ability to simulate the atmospheric dispersion both in homogeneous and inhomogeneous turbulence by comparison with an analytical solution and with the Willis and Deardorff water tank experiments, respectively, has been stated in previous papers. In the present paper the model is used to simulate dispersion in the real atmospheric PBL. The numerical results obtained are verified against experimental data from the Karlsruhe Nuclear Research Center tracer experiments. The model is applied to the problem of predicting the ground level concentration of two different tracers simultaneously released from two heights (160 and 195 m) at the Karlsruhe meteorological tower. Convectively unstable and neutral conditions were prevailing during the two tracer experiments which have been simulated. Model performance was evaluated through two statistical indexes: relative mean bias and normalized mean square error. The cumulative frequency distribution of the point-by-point ratio between observed and predicted ground level concentrations (glcs) was also computed. The simulated concentrations agree very well with observations. The tracer data were also compared to the simulations of 10 Gaussian models. They differed one another for the choice of dispersion sigma curves and for the way to insert the wind speed and direction. None of them proved to perform better than our particle model in all the exercises.

Two Lagrangian particle models, APOLLO and MILORD, were used to simulate the first ETEX experiment. The role played by wind field, mixing height h and horizontal diffusivity KH appeared to be the most important aspects to be studied. The sensitivity to the accuracy of the input advection field was studied through the application of APOLLO using different ECMWF data sets differing in space and time resolution and in being forecasted or analysed, corresponding to the real-time, emergency-like condition, and to the a posteriori benchmark simulation. The role of h and KH was investigated by running both APOLLO and MILORD with different parameterisations, and comparing the model results between them and with the available observations. The model evaluation was carried out through a set of statistical indexes computed on three hourly average concentrations paired in space and time and time-integrated concentrations. It was found that the quality of the input wind field plays a major role in predicting with sufficient accuracy the plume position and extension after the first 24 h from the beginning of the release. The best-model results are obtained with large values of KH (in the range of 2.5×104–4.5×104 m2 s-1), which confirms the need to enhance the horizontal diffusion, in order to include the advection fluctuations unresolved by large-scale meteorological fields. A fixed value of h in the range 1000–1500 m seems to be more efficient than space and time variable h computed with standard algorithms. A reasonable explanation for this result is given, based on the consideration that in the long range, particles diffuse also in the residual layer above the stable nocturnal boundary layer.