The article reports the results of different methods of modelling releases and dispersion of dangerous gases or vapours in cases of major accidents from road and rail transportation in urban zones. Transport accidents of dangerous substances are increasingly frequent and can cause serious injuries in densely inhabited areas or pollution of the environment. For quantitative risk assessment and mitigation planning, consequence modelling is necessary.
The modelling of dangerous substance dispersion by standard methods does not fully represent the behaviour of toxic or flammable clouds in obstructed areas such as street canyons. Therefore the predictions from common software packages as ALOHA, EFFECTS, TerEx should be augmented with computational fluid dynamics (CFD) models or physical modelling in aerodynamic tunnels, and further studies are planned to do this.
The goal of this article is to present the results of the first approach of modelling using these standard methods and to demonstrate the importance of the next development stage in the area of transport accident modelling of releases and dispersions of dangerous substances in urban zones in cases of major accident or terrorist attacks. 相似文献
Industrial activities produce vast amounts of weakly contaminated materials which are commonly reused as filling materials on natural ground. There is a strong demand to define guidelines for the application of these materials, to estimate the leaching potential of contaminants from the materials, and to assess the potential hazard for groundwater pollution. We present a multiple batch experiment, where measurements of liquid-phase concentrations at varying liquid/solid ratios are used to estimate the total mass of contaminant that can be extracted from a contaminated material with a mild extractant like water. Furthermore, the experiment yields estimates of the isotherm describing the partitioning of the contaminant between the solid and liquid phases, and a concentration that might be expected under soil hydraulic conditions representative for the field situation. Model parameters are estimated from liquid-phase concentrations within a Bayesian framework by applying the Shuffled Complex Evolution Metropolis Algorithm (SCEM-UA), an efficient Markov Chain Monte Carlo sampler. A sensitivity analysis and inversions of synthetically generated data corrupted with noise show the general suitability of the proposed method. An uncertainty analysis for model parameters and model predictions shows the expected accuracy of the estimates. An application to concentration measurements obtained from a multiple batch extraction test illustrates the applicability of the approach for a real situation. 相似文献