The focus of this study is on the optimal use of high performance computing in the area of environmental security (air pollution transport, in particular).Contemporary mathematical models of air pollution transport should include a fairly large set of chemical and photochemical reactions to be established as a reliable simulation tool.The investigations and the numerical results reported in this paper have been obtained by using a large-scale mathematical model called the Danish Eulerian Model (DEM).For optimization of some applications of the Danish Eulerian Model in various important scientific, social and economic areas, it is of great importance to simplify the model as much as possible, preserving the high reliability of its output results.A careful sensitivity analysis is needed in order to decide how to do such simplifications.On the other hand, it is important to analyze the influence of variations of the initial conditions, the boundary conditions, the rates of some chemical reactions, etc. on the model results in order to make right assumptions about the possible simplifications, which could be done.The sensitivity analysis version of the Danish Eulerian Model was created for these purposes.Its complexity is of higher order, a real challenge for the top performance supercomputers nowadays.The sensitivity analysis version of DEM (SA-DEM) has been implemented on the new Bulgarian petascale supercomputer DISCOVERER.It is a part of the European High Performance Computing Joint Undertaking (EuroHPC), which is building a network of 8 powerful supercomputers across the European Union (3 pre-exascale and 5 petascale).The results of some scalability experiments with SA-DEM on the new Bulgarian petascale supercomputer DISCOVERER are presented here.They are compared with similar experiments performed on the Mare Nostrum III supercomputer at Barcelona Supercomputing Centre -the most powerful supercomputer in Spain by that time, upgraded currently to the pre-exascale Mare Nostrum V, also part of the EuroHPC JU infrastructure.

This paper introduces a sophisticated multidimensional sensitivity analysis, incorporating cutting-edge stochastic methods for air pollution modeling.The study focuses on a large-scale long-distance transportation model of air pollutants, specifically the Unified Danish Eulerian Model (UNI-DEM).This mathematical model plays a pivotal role in understanding the detrimental impacts of heightened levels of air pollution.With this research, our intent is to employ it to tackle crucial questions related to environmental protection.We suggest advanced Monte Carlo and quasi-Monte Carlo methods, leveraging specific lattice and digital sequences to enhance the computational effectiveness of multi-dimensional numerical integration.Moreover, we further refine the existing stochastic methodologies for digital ecosystem modeling.The main aspect of our investigation is to analyze the sensitivity of the UNI-DEM model output to changes in the input emissions of human-induced pollutants and the rates of a number of chemical reactions.The developed algorithms are utilized to calculate global Sobol sensitivity measures for various input parameters.We also assess their influence on key air pollutant concentrations in different European cities, considering the diverse geographical locations.The overarching goal of this research is to broaden our understanding of the elements influencing air pollution and inform potent strategies to alleviate its negative impacts on the environment.The work is supported by the Project BG05M2OP001-1.001-0004UNITe,

Many difficulties must be overcome when large-scale air pollution models are treated numerically, because the physical and chemical processes in the atmosphere are very fast. This is why it is necessary