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One of the key issues of recent research on the dispersion inside complex urban environments is the ability to predict dosage-based parameters from the puff release of an airborne material from a point source in the atmospheric boundary layer inside the built-up area. The present work addresses the question of whether the computational fluid dynamics (CFD)-Reynolds-averaged Navier-Stokes (RANS) methodology can be used to predict ensemble-average dosage-based parameters that are related with the puff dispersion. RANS simulations with the ADREA-HF code were, therefore, performed, where a single puff was released in each case. The present method is validated against the data sets from two wind-tunnel experiments. In each experiment, more than 200 puffs were released from which ensemble-averaged dosage-based parameters were calculated and compared to the model's predictions. The performance of the model was evaluated using scatter plots and three validation metrics: fractional bias, normalized mean square error, and factor of two. The model presented a better performance for the temporal parameters (i.e., ensemble-average times of puff arrival, peak, leaving, duration, ascent, and descent) than for the ensemble-average dosage and peak concentration. The majority of the obtained values of validation metrics were inside established acceptance limits. Based on the obtained model performance indices, the CFD-RANS methodology as implemented in the code ADREA-HF is able to predict the ensemble-average temporal quantities related to transient emissions of airborne material in urban areas within the range of the model performance acceptance criteria established in the literature. The CFD-RANS methodology as implemented in the code ADREA-HF is also able to predict the ensemble-average dosage, but the dosage results should be treated with some caution; as in one case, the observed ensemble-average dosage was under-estimated slightly more than the acceptance criteria. Ensemble-average peak concentration was systematically underpredicted by the model to a degree higher than the allowable by the acceptance criteria, in 1 of the 2 wind-tunnel experiments. The model performance depended on the positions of the examined sensors in relation to the emission source and the buildings configuration. The work presented in this paper was carried out (partly) within the scope of COST Action ES1006 'Evaluation, improvement, and guidance for the use of local-scale emergency prediction and response tools for airborne hazards in built environments'. [ABSTRACT FROM AUTHOR]

One of the key issues of recent research on the dispersion inside complex urban environments is the ability to predict individual exposure (maximum dosages) of an airborne material which is released continuously from a point source. The present work addresses the question whether the computational fluid dynamics (CFD)–Reynolds-averaged Navier–Stokes (RANS) methodology can be used to predict individual exposure for various exposure times. This is feasible by providing the two RANS concentration moments (mean and variance) and a turbulent time scale to a deterministic model. The whole effort is focused on the prediction of individual exposure inside a complex real urban area. The capabilities of the proposed methodology are validated against wind-tunnel data (CUTE experiment). The present simulations were performed ‘blindly’, i.e. the modeller had limited information for the inlet boundary conditions and the results were kept unknown until the end of the COST Action ES1006. Thus, a high uncertainty of the results was expected. The general performance of the methodology due to this ‘blind’ strategy is good. The validation metrics fulfil the acceptance criteria. The effect of the grid and the turbulence model on the model performance is examined. [ABSTRACT FROM PUBLISHER]

The peak values observed in a measured concentration time series of a dispersing gaseous pollutant released continuously from a point source in urban environments, and the hazard level associated with them, demonstrate the necessity of predicting the upper tail of concentration distributions. For the prediction of concentration distributions statistical models are preferably employed which provide information about the probability of occurrence. In this paper a concentration database pertaining to a field experiment is used for the selection of the statistical distribution. The inverses of the gamma cumulative distribution function (cdf) for 75th-99th percentiles of concentration are found to be more consistent with the experimental data than those of the log-normal distribution. The experimental values have been derived from measured high frequency time series by sorting first the concentrations and then finding the concentration which corresponds to each probability. Then the concentration mean and variance that are predicted with Computational Fluid Dynamics-Reynolds Averaged Navier-Stokes (RANS) methodology are used to construct the gamma distribution. The proposed model ('RANS-gamma') is included in the framework of a computational code (ADREA-HF) suitable for simulating the dispersion of airborne pollutants over complex geometries. The methodology is validated by comparing the inverses of the model cdfs with the observed ones from two wind tunnel experiments. The evaluation is performed in the form of validation metrics such as the fractional bias, the normalized mean square error and the factor-of-two percentage. From the above comparisons it is concluded that the overall model performance for the present cases is satisfactory. [ABSTRACT FROM AUTHOR]

The concentration fluctuations of a dispersing hazardous gaseous pollutant in the atmospheric boundary layer, and the hazard associated with short-term concentration levels, demonstrate the necessity of estimating the magnitude of these fluctuations using predicting models. To predict and estimate the maximum expected dosage and the exposure time within which the dosage exceeds certain health limits, the knowledge of the behaviour of concentration fluctuations at the point under consideration is needed. The whole effort is based on the field experiment MUST (Biltoft, 2001) and the computational simulations have been performed with the CFD code ADREA. [ABSTRACT FROM AUTHOR]