Abstract
The predictions from air quality models are subject to many sources of uncertainty; among them, grid resolution has been viewed as one that is limited by the availability of computational resources. A large grid size can lead to unacceptable errors for many pollutants formed via nonlinear chemical reactions. Further, insufficient grid resolution limits the ability to perform accurate exposure assessments. To address this issue in parallel to increasing computational power, modeling techniques that apply finer grids to areas of interest and coarser grids elsewhere have been developed. Techniques using multiple grid sizes are called nested grid or multiscale modeling techniques. These approaches are limited by uncertainty in the placement of finer grids since pertinent locations may not be known a priori, loss in solution accuracy due to grid boundary interface problems, and inability to adjust to changes in grid resolution requirements. A different approach to achieve local resolution involves using dynamic adaptive grids. Various adaptive mesh refinement techniques using structured grids as well as mesh enrichment techniques on unstructured grids have been explored in atmospheric modeling. Recently, some of these techniques have been applied to full blown air quality models. In this paper, adaptive grid methods used in air quality modeling are reviewed and categorized. The advantages and disadvantages of each adaptive grid method are discussed. Recent advances made in air quality simulation owing to the use of adaptive grids are summarized. Relevant connections to adaptive grid modeling in weather and climate modeling are also described.
Highlights
Air quality modeling is the computational science of developing mathematical models that describe the behavior of pollutants in the atmosphere
All adaptive grid air quality modeling applications previously described find that dynamic mesh refinement significantly increases the accuracy of results
Observed differences in simulated concentration fields using adaptive and static grids demonstrate a legitimate need for increased resolution in air quality modeling
Summary
Air quality modeling is the computational science of developing mathematical models that describe the behavior of pollutants in the atmosphere. Pollutants are emitted from various sources, including natural sources It is the fate of emissions resulting from human activity that is typically the focus of air quality modeling. For models that use implicit schemes, the computational resource demand grows more rapidly with increasing resolution. Doubling the resolution, which increases the number of grid cells by a factor of 8 as described above, may result in an operation count 64 times larger. It may still be desirable to reduce the time steps, as characteristic times of certain processes are shortened (e.g., the time it takes to advect emissions by the winds over the length scale ∆x) This obviously increases the computational cost even further. The paper will focus on the adaptive grid method and continue with a review of its use in air quality modeling. It will end with remarks on the prospect of growing use of adaptive grids in atmospheric modeling
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