Resolving the flow and pollutant concentrations at the highest possible resolution is of paramount importance in atmospheric chemistry calculations especially when dealing with chemical reactions in plumes. The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is used to explore the modeling of pollutant plumes. OMEGA is built upon an unstructured adaptive grid made up of triangular prisms. OMEGA has an embedded Atmospheric Dispersion Model (ADM), which uses a hybrid approach to disperse the pollutants. The particles are treated as centroids of growing puffs, with the growth determined by the ambient turbulent characteristics. The ADM uses a particle diffusion algorithm using a Monte Carlo method and a receptor-oriented concentration calculation algorithm. The basic motivation of grid adaptivity is the ability to increase resolution just in the area of interest and keep the domain coarse elsewhere to maximize computational efficiency. The OMEGA grid adapts to the model solution via a sequence of grid refinement and coarsening functions. These are controlled by a cost function built from criteria imposed on the various model variables. Thus it is possible to adapt easily to more than one variable simultaneously. For the work discussed in this paper, the adaptivity criteria are set to dynamically adapt the grid around the predicted plume during the simulation. The cost function is built on the location of the puff centroids. This allows the plume to ‘feel’ the terrain and other environmental parameters at a higher resolution without adding high resolution elsewhere, and without any a priori knowledge of the solution. In this paper, we will present results of several runs using the solution adaptive refinement and compare them with ‘traditional’ fixed-resolution results. We will also present implications of the results on plume chemistry calculations.