We discuss an adaptive resolution system for modeling regional air pollution based on the chemical transport model STEM. The grid adaptivity is implemented using the generic adaptive mesh refinement tool Paramesh, which enables the grid management operations while harnessing the power of parallel computers. The computational algorithm is based on a decomposition of the domain, with the solution in different subdomains being computed with different spatial resolutions. Various refinement criteria that adaptively control the fine grid placement are analyzed to maximize the solution accuracy while maintaining an acceptable computational cost. Numerical experiments in a large-scale parallel setting (∼0.5 billion variables) confirm that adaptive resolution, based on a well-chosen refinement criterion, leads to the decrease in spatial error with an acceptable increase in computational time. Fully dynamic grid adaptivity for air quality models is relatively new. We extend previous work on chemical and transport modeling by using dynamically adaptive grid resolution. Advantages and shortcomings of the present approach are also discussed.