Search

A number of numerical experiments with a large-scale air pollution model (Unified Danish Eulerian Model, UNI-DEM) have been carried out to compute Sobol sensitivity measures. Sensitivity analysis of ozone concentrations according to variations of rates of chemical reactions is developed. The sensitivity study has been done for the areas of four European cities (Genova, Milan, Manchester, and Edinburgh) with different geographical locations. Sensitivity analysis of the output of UNI-DEM according to emission levels is also provided. The influence of emission levels on the concentrations of four important air pollutants (ammonia, ozone, ammonium sulphate and ammonium nitrate) over three European cities (Milan, Manchester, and Edinburgh) is considered. Variance-based methods are most often used for providing sensitivity analysis. Sobol variance-based approach for global sensitivity analysis has been applied to compute the corresponding sensitivity measures. Advanced stochastic methods based on modified Sobol sequences are used for computing the sensitivity measures. The numerical experiments show that the stochastic algorithms for the multidimensional integrals under consideration are one of the best stochastic techniques for computing the small in value sensitivity indices. [ABSTRACT FROM AUTHOR]

In this paper a study the sensitivity of concentration variations of some of the most dangerous air pollutants with respect to the anthropogenic emissions levels and with respect to some chemical reactions rates has been performed. Quasi-Monte Carlo algorithms based on Sobol, Faure, and different lattice rules are used in our sensitivity study of the Unified Danish Eulerian model. Different parts of the large amount of output data, produced by Unified Danish Eulerian model, were used in various practical applications, where the reliability of this data should be properly estimated. Another reason to choose this model as a case study here is its sophisticated chemical scheme, where all relevant chemical processes in the atmosphere are accurately represented. The numerical tests show that the stochastic algorithms under consideration, and especially the optimal lattice rule, are efficient for the multidimensional integrals under consideration and especially for computing small by value sensitivity indices. Clearly, the progress in the area of air pollution modeling, is closely connected with the progress in reliable algorithms for multidimensional integration. [ABSTRACT FROM AUTHOR]

The present study is based on The Unified Danish Eulerian Model (UNI-DEM) as one of the most advanced large- scale mathematical models that describes adequately all physical and chemical processes. One of the most attractive features of UNI-DEM is its advanced chemical scheme the Condensed CBM IV, which consider a large number of chemical species and numerous reactions between them, of which the ozone is one of the most important pollutants for its central role in many practical applications of the results. The calculations are done in a large spatial domain, which covers completely the European region and the Mediterranean, for certain time period. A comprehensive experimental study of Monte Carlo algorithm based on modifications of the Latin Hypercube Sampling Edge and Random Algorithms for multidimensional numerical integration has been done. Samplings with different seeds has been analyzed. This comparison has been made for the first time for sensitivity analysis of UNI-DEM. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the six chemical reactions rates and four different groups of pollutants. [ABSTRACT FROM AUTHOR]

Sensitivity studies are nowadays applied to some of the most complicated mathematical models from various intensively developing areas of application. Such a sophisticated model in the area of air pollution modeling is the Danish Eulerian Model, a powerful large scale air pollution model with a long development history. Over the years it was used successfully in different long-term environmental studies for the European region. In this talk we discuss some performance-critical achievements in the parallel optimization and a systematic approach for sensitivity analysis of the latest version of the Danish Eulerian Model, UNI-DEM. A comprehensive experimental study of Monte Carlo algorithms based on Latin Hypercube Sampling and Adaptive approach for multidimensional numerical integration has been done. The algorithms have been successfully applied to compute global Sobol sensitivity measures, corresponding to the influence of several input parameters on the concentrations of some of the air pollutants of highest importance. The numerical tests show that the Monte Carlo algorithms under consideration are efficient for the multidimensional integrals in this study. [ABSTRACT FROM AUTHOR]

Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is very significant for improving the reliability of these models. Several efficient quasi-Monte Carlo algorithms – the van der Corput sequence and lattice rules based on different generating vectors have been used in our sensitivity studies of the model output results for some air pollutants with respect to the emission levels and some chemical reactions rates. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters (six chemical reactions rates and four different groups of pollutants) on the concentrations of important air pollutants. The numerical tests show that the van der Corput sequences under consideration are efficient for the multidimensional integrals under consideration and especially for computing small by value sensitivity indices. [ABSTRACT FROM AUTHOR]

Air pollution and meteorological models are examples of mathematical models with a lot of natural uncertainties in their input data sets and parameters. Sensitivity analysis is a powerful tool for studying and improving the reliability of such models. In this paper we present the results of a global sensitivity study of the Unified Danish Eulerian Model (UNI-DEM). One of the most important features of UNI-DEM is its advanced chemical scheme the Condensed CBM IV, which considers a large number of chemicals, and various reactions between them, of which the ozone is the most important pollutant because it is used in many practical applications. The stochastic methods based on Faure and Sobol sequences are used for computing the sensitivity measures. The numerical experiments show that the stochastic algorithms for the multidimensional integrals under consideration are efficient methods for computing the small value sensitivity indices. [ABSTRACT FROM AUTHOR]