Using Parallel Monte Carlo Methods in Large-Scale Air Pollution Modelling

Abstract

Large-scale air pollution models can successfully be used in different environmental studies. These models are described mathematically by systems of partial differential equations. Splitting procedures followed by discretization of the spatial derivatives lead to several large systems of ordinary differential equations of order up to 80 millions. These systems have to be handled numerically at up to 250 000 time-steps. Furthermore, many scenarios are often to be run in order to study the dependence of the model results on the variation of some key parameters (as, for example, the emissions). Such huge computational tasks can successfully be treated only if (i) fast and sufficiently

accurate numerical methods are used and (ii) the models can efficiently be run on parallel computers. Efficient Monte Carlo methods for some subproblems will be presented and applications of the model in the solution of some environmental tasks will also be made.

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.