Search

A diffusion equation limit derived from the Langevin stochastic particle model is used to study the dispersion of scalars caused by an evolving turbulence in the transition process occurring during the sunset period Therefore the random displacement equation is employed to simulate the cross-wind concentrations of pollutants released from low and high point sources Turbulence inputs are parameterized to describe in a continuous manner the dispersion effects produced by a decaying convective elevated and a shear-dominated stable surface turbulence The simulation results show that for the initial stage of the sunset transition phenomenon the pollutants are transported rapidly to the surface On the other hand for the sunset evolution advanced stages, the dispersion process happens in a deep stable boundary layer (SBL) in which the pollutants can travel long distances practically without reaching the surface The major progress shown in this analysis is the description of the transport properties associated to decaying convective eddies in the residual layer (RL) The study shows that the diffusion effects associated to these decaying convective eddies strongly influence the dispersion of scalars during the sunset transition period (c) 2010 Elsevier BV All rights reserved

Turbulent wind data measured in the atmospheric surface layer and wind tunnels are used to estimate the Kolmogorov constant Ck. The analysis discusses distinct procedures to obtain the mean energy dissipation rate and Ck. We find that for our datasets the most reliable method is the third-order structure function one. The value found for the skewness of longitudinal velocity differences is in accordance with a multifractal approach recently proposed. Despite the limitations of the data discussed in the paper, the results point to a value of Ck independent of the Reynolds number.

An extended validation of the new Lagrangian particle model MicroSpray version for dense gas simulation is proposed. MicroSpray 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 a chlorine railway accident (Macdona), to a field experiment (Kit Fox) and to an atmospheric CFD model.

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).

Generally, both data measured in the air quality stations and numerical model simulations refer to NOX concentration. However, the national and international regulations fix limits for NO2 concentration, thus, to make comparisons with the regulation limits, it is necessary to convert NOX concentration into NO2 concentration. The relationships allowing such a conversion are empirical and are determined on the basis of statistical analyses of measured data and, hence, depend upon on orographic and climatic characteristics of the studied sites. In this work we firstly aimed at verifying the agreement between relationships known in the literature with data gathered by the air quality network of Turin Province. Following the regulations we considered the annual concentration mean and the 99.8 percentile. We found a significant disagreement with measured data. Consequently, our work proceeded to estimate new values of the same empirical conversion relationships, through statistical regressions on the available data, obtaining a noticeable improvement.

An extended validation of the new Lagrangian particle model MicroSpray version for dense gas simulation is proposed. MicroSpray 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 a chlorine railway accident (Macdona), to a field experiment (Kit Fox) and to an atmospheric CFD model.

The simulation of atmospheric dispersion in low wind speed conditions (LW) is still recognized as a challenge for modellers. Recently, a new system of two coupled Langevin equations that explicitly accounts for meandering has been proposed. It is based on the study of turbulence and dispersion properties in LW. The new system was implemented in the Lagrangian stochastic particle models LAMBDA and GRAL. In this paper we present simulations with this new approach applying it to the tracer experiments carried out in LW by INEL (Idaho National Engineering Laboratory, USA) in 1974 and by the Graz University of Technology and CNR-Torino near Graz in 2003. To assess the improvement obtained with the present model with respect to previous models not taking into account the meandering effect, the simulations for the INEL experiments were also performed with the old version of LAMBDA. The results of the comparisons clearly indicate that the new approach improves the simulation results.

Accounting for the current knowledge of the stable atmospheric boundary layer (ABL) turbulence structure and characteristics, a new formulation for the meandering parameters to be used in a Lagrangian stochastic particle turbulent diffusion model has been derived. That is, expressions for the parameters controlling the meandering oscillation frequency in low wind speed stable conditions are proposed. The classical expression for the meandering autocorrelation function, the turbulent statistical diffusion theory and ABL similarity theory are employed to estimate these parameters. In addition, this new parameterization was introduced into a particular Lagrangian stochastic particle model, which is called Iterative Langevin solution for low wind, validated with the data of Idaho National Laboratory experiments, and compared with others diffusion models. The results of this new approach are shown to agree with the measurements of Idaho experiments and also with those of the other atmospheric diffusion models. The major advance shown in this study is the formulation of the meandering parameters expressed in terms of the characteristic scales (velocity and length scales) describing the physical structure of a turbulent stable boundary layer. These similarity formulas can be used to simulate meandering enhanced diffusion of passive scalars in a low wind speed stable ABL.

Lagrangian dispersion models have shown to be effective and reliable tools for simulating the airborne pollutant dispersion. However, the main drawback for their use as regulatory models is the computation demanding. Consequently, in this paper a parallel version of a Lagrangian particle model – LAMBDA – is developed using the MPI message passing communication library. Performance tests were executed in a distributed memory parallel machine, a multicomputer based on IA-32 architecture. Portions of the pollutant in the air emitted from its source are simulated as fictitious particles whose trajectories evolve under stochastic forcing. This yields independent evolution equations for each particle of the model that can be computed by a different processor in a parallel implementation. Speed-up results show that the parallel implementation is suitable for the used architecture.

The transport and diffusion processes of a tracer gas released near the ground in the Rhine valley region, in Central Europe,during the 1992 TRACT field experiment, are simulated by a computational model system for complex terrain. This system (RMS)is composed of the prognostic mesoscale model RAMS, the Lagrangian stochastic dispersion model SPRAY and the interface code MIRS,which links RAMS to SPRAY. Three flow simulations were performed, with different initialisations and the one showing the best agreement with the measured flow was selected for the simulation of the TRACT tracer experiment. Tracer concentrations measured by an array of samplers at ground level and by an airplane aloft, are used to evaluate the 3-D concentration field simulated by the model system. The analysis of the simulation results generated by RMS shows that our model system very well reproduces the general behaviour of the contaminant plume, the temporal and spatial distribution of the concentration and the location of the concentration maxima.