Ágnes Havasi

Advances in air pollution modeling for environmental security

Preface. Acknowledgements. Mathematical modeling of the regional-scale variability of gaseous species and aerosols in the atmosphere A. Aloyan, V. Arutyunyan. Air pollution modeling in action K. Balla et al. Advances in urban meteorology modelling E. Batchvarova, S.-E. Gryning. Modelling studies on the concentration and deposition of air pollutants in East-Central Europe L. Bozo. Estimation of the exchange of sulphur pollution in Southeast Europe H. Chervenkov. Implementing the trajectory-grid transport algorithm in an air quality model D.P. Chock et al. Estimation of air pollution parameters using artificial neural networks H.K. Cigizoglu et al. Some aspects of interaction between operator splitting procedures and numerical methods P. Csomo. Mathematical aspects of data assimilation for atmospheric chemistry models G. Dimitriu, R. Cuciureanu. Fighting the great challenges in large-scale environmental modelling I. Dimov et al. Challenges in using splitting techniques for large-scale environmental modeling I. Dimov et al. Simulation of liberation and dispersion of radon from a waste disposal M. de Lurdes Dinis, A. Fiuza. Methods of efficient modeling and forecasting regional atmospheric processes A.Yu. Doroshenko, V.A. Prusov. Numerical forecast of air pollution - Advances and problems A. Ebel et al. Alternative techniques for studying / modeling the air pollution level L-D. Galatchi. Application of functions of influence in air pollution problems K. Ganev et al. Long-term calculations with a comprehensive nested hemispheric air pollution transport model C. Geels et al. Dispersion modelling for environmental security: principles and their application in the Russian regulatory guideline on accidental releases E. Genikhovich. Higher order non-conforming FEM up-winding K. Georgiev, S. Margenov. Emission control in single species air pollution problems K. Georgiev et al. A new operator splitting method and its numerical investigation B.Gnandt. Advances in urban dispersion modelling S.-E. Gryning, E. Batchvarova. Internet-based management of environmental simulation tasks K. Karatzas. Air pollution assessment inside and around iron ore quarries M. Kharytonov et al. Data assimilation of radionuclides at small and regional scale M. Krysta et al. The impact of sea breeze on air quality in Athens area D. Melas et al. Developments and applications in urban air pollution modelling C. Mensink et al. Demands for modelling by forecasting ozone concentration in Western Slovenia A. Planinsek. A pilot system for environmental impact assessment of pollution caused by urban development and urban air pollution forecast I. Sandu et al. The use of MM5-CMAQ air pollution modelling system for real-time and forecasted air quality impact of industrial emissions R. San Jose et al. Regulatory modelling activity in Hungary R. Steib. Creation and testing of flux-type advection schemes for air pollution modeling application D. Syrakov et al. Bulgarian emergency response system: description and ensemble performance D. Syrakov et al. Global and regional aerosol modelling: a picture over Europe E. Vignati et al. The ABL models Yordan and Yorcon - top-down and bottom-up approaches for air pollution applications D. Yordanov et al. Major conclusions from the discussions Z. Zlatev et al. List of participants. Subject index.

Efficient algorithms for large scale scientific computations: Introduction

Complex mathematical models are extensively used in our modern computer age to handle many difficult problems which arise in different fields of science and engineering. These models are typically described by time-dependent systems of partial differential equations (PDEs) and lead after appropriate discretization of the spatial derivatives to the solution of huge systems of ordinary differential equations (ODEs). The number of equations in these systems of ODEs is very often greater than one million. The discretization of the time derivative in the systems of ODEs leads to the treatment of algebraic equationswhich are as a rule also very large, non-linear and have to be handled by applying iterativemethods (very often the well-known quasi-Newton iterative procedure is used). Then huge systems of linear algebraic equations have to be solved during the iterative process. It should also be stressed that the mathematical models have normally to be run (1) many times, (2) on long time-intervals and (3) with different scenarios.