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In this paper, we propose parallel graph-grammar-based algorithm for the longest-edge refinements and the pollution simulations in Lesser Poland area. We introduce graph-grammar productions for Rivara’s longest-edged algorithm for the local refinement of unstructured triangular meshes. We utilize the hyper-graph to represent the computational mesh and the graph-grammar productions to express the longest-edge mesh refinement algorithm. The parallelism in the original Rivara’s longest edge refinement algorithm is obtained by processing different longest edge refinement paths in different three ads. Our graph-grammar-based algorithm allows for additional parallelization within a single longest-edge refinement path. The graph-grammar-based algorithm automatically guarantees the validity and conformity of the generated mesh; it prevents the generation of duplicated nodes and edges, elongated elements with Jacobians converging to zero, and removes all the hanging nodes automatically from the mesh. We test the algorithm on generating a surface mesh based on a topographic data of Lesser Poland area. The graph-grammar productions also generate the layers of prismatic three-dimensional elements on top of the triangular mesh, and they break each prismatic element into three tetrahedral elements. Next, we propose graph-grammar productions generating element matrices and right-hand-side vectors for each tetrahedral element. We utilize the Streamline Upwind Petrov–Galerkin (SUPG) stabilization for the pollution propagation simulations in Lesser Poland area. We use the advection–diffusion-reaction model, the Crank–Nicolson time integration scheme, and the graph-grammar-based interface to the GMRES solver.

Air pollution is receiving a lot of interest nowadays. It is visible especially in the Kraków area, as this is one of the most polluted cities in Europe. People living there are more and more aware of the problem, what causes raising of various movements and NGOs that are trying to improve air quality. Unfortunately, this is not as simple as people usually think: air pollution grows because of multiple factors including traffic, climate, heating of buildings in winter, city's architecture, etc. In this paper, we simulate and predict pollution with high resolution, as air quality can vary significantly over a distance of even a few hundreds of meters. Air quality simulation is a multidisplinary endeavor, comprising physical models (meteorological and chemistry) as well as numerical methods (geometry discretization, time and space discretization, etc.). It has been found that there is no proper method for automatic terrain mesh generation, so an algorithm for doing it is presented in this document as its significant part.

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