Comprehensive Air Pollution Studies with the Unified Danish Eulerian Model

Air pollution, especially the reduction of the air pollution to some acceptable levels, is a highly relevant environmental problem, which is becoming more and more important. This problem can successfully be studied only when high-resolution comprehensive mathematical models are developed and used on a routinely basis. However, such models are very time-consuming, even when modern high-speed computers are available. The models need a great amount of input data (meteorological, chemical and emission data). Furthermore, the models are producing huge files of output data, which have to be stored for future uses (for visualization and animation of the results). Finally, huge sets of measurement data (normally taken at many stations located in different countries) have to be used in the efforts to validate the model results. The necessity to handle efficiently large-scale air pollution models in order to be able to resolves a series of important environmental tasks is discussed in this paper. The need for parallel runs is emphasized. The particular model used is the Unified Danish Eulerian Model (UNI-DEM), but most of the results can also be applied when other large-scale models are used. The use of UNI-DEM in some comprehensive air pollution studies is discussed in the end of the paper.

[1]  Lawrence F. Shampine,et al.  Solving Index-1 DAEs in MATLAB and Simulink , 1999, SIAM Rev..

[2]  William Gropp,et al.  Skjellum using mpi: portable parallel programming with the message-passing interface , 1994 .

[3]  Zahari Zlatev,et al.  Application of predictor-corrector schemes with several correctors in solving air pollution problems , 1984 .

[4]  Zahari Zlatev,et al.  Parallel matrix computations in air pollution modelling , 2002, Parallel Comput..

[5]  Z. Zlatev Partitioning Ode Systems with an Application to Air Pollution Models , 2001 .

[6]  M. van Loon,et al.  Testing interpolation and filtering techniques in connection with a semi-Lagrangian method , 1993 .

[7]  Zahari Zlatev,et al.  Massive data set issues in air pollution modelling , 2002 .

[8]  Jan Verwer,et al.  An evaluation of explicit pseudo-steady-state approximation schemes for stiff ODE systems from chemical kinetics , 1993 .

[9]  Zahari Zlatev,et al.  Numerical integration of chemical ODE problems arising in air pollution models , 1997 .

[10]  W. P. Crowley,et al.  NUMERICAL ADVECTION EXPERIMENTS1 , 1968 .

[11]  Zahari Zlatev,et al.  A Eulerian air pollution model for Europe with nonlinear chemistry , 1992 .

[12]  R. A. Brost,et al.  The sensitivity to input parameters of atmospheric concentrations simulated by a regional chemical model , 1988 .

[13]  Z. Zlatev,et al.  Calculating losses of crops in Denmark caused by high ozone levels , 2001 .

[14]  Z. Zlatev,et al.  Modeling the long-range transport of air pollutants , 1994, IEEE Computational Science and Engineering.

[15]  Anthony Skjellum,et al.  Using MPI: Portable Programming with the Message-Passing Interface , 1999 .

[16]  Z. Zlatev,et al.  Trends of Hungarian air pollution levels on a long time-scale , 2002 .

[17]  Zahari Zlatev,et al.  Comparison of numerical techniques for use in air pollution models with non-linear chemical reactions , 1989 .

[18]  J. G. Verwer,et al.  Explicit method for stiff ODEs from atmospheric chemistry , 1995 .

[19]  Zahari Zlatev,et al.  Studying high ozone concentrations by using the Danish Eulerian model , 1993 .

[20]  Linda R. Petzold,et al.  Numerical solution of initial-value problems in differential-algebraic equations , 1996, Classics in applied mathematics.

[21]  M. C. Dodge,et al.  A photochemical kinetics mechanism for urban and regional scale computer modeling , 1989 .

[22]  Bruno Sportisse,et al.  Solving reduced chemical models in air pollution modelling , 2003 .

[23]  Z. Zlatev,et al.  Relationships between emission sources and excess ozone concentrations , 1996 .

[24]  C. Molenkamp,et al.  Accuracy of Finite-Difference Methods Applied to the Advection Equation , 1968 .

[25]  Zahari Zlatev,et al.  Studying variations of pollution levels in a given region of Europe during a long time-period , 2000 .

[26]  Peter Deuflhard,et al.  Recent progress in extrapolation methods for ordinary differential equations , 1985 .

[27]  S. Tauber,et al.  Modelling the dispersion of atmospheric pollution using cubic splines and chapeau functions , 1979 .

[28]  Zahari Zlatev,et al.  Running a large air pollution model on an IBM SMP computer , 2001 .

[29]  D. Pepper,et al.  Modeling the dispersion of atmospheric pollution using cubic splines and Chapeau functions. [Environmental transport of chemical and radioactive gaseous wastes at Savannah River Plant] , 1977 .

[30]  Zahari Zlatev,et al.  Parallel Sparse Matrix Algorithms for Air Pollution Models , 1999, Scalable Comput. Pract. Exp..

[31]  William R. Goodin,et al.  Numerical solution of the atmospheric diffusion equation for chemically reacting flows , 1982 .

[32]  J. Butcher Numerical methods for ordinary differential equations , 2003 .

[33]  J. Verwer,et al.  A positive finite-difference advection scheme , 1995 .

[34]  Zahari Zlatev,et al.  Computer Treatment of Large Air Pollution Models , 1995 .

[35]  Z. Zlatev,et al.  Studying cumulative ozone exposures in Europe during a 7‐year period , 1997 .

[36]  I. Isaksen,et al.  Quasi‐steady‐state approximations in air pollution modeling: Comparison of two numerical schemes for oxidant prediction , 1978 .

[37]  A. Bott A positive definite advection scheme obtained by nonlinear renormalization of the advective fluxes , 1989 .

[38]  A. J. Baker,et al.  a Simple One-Dimensional Finite-Element Algorithm with Multidimensional Capabilities , 1979 .