Adaptive Grid Use in Air Quality Modeling

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. OPEN ACCESSAtmosphere 2011, 2 485

Sub-Grid Scale Plume Modeling

“…However, the approach has not yet been routinely applied in an operational mode for long-term multi-pollutant air quality simulations over large domains and with hundreds of sub-grid scale plumes. Garcia-Menendez and Odman [48] provide additional information on the state-of-the-science of adaptive grid modeling and what the future holds.…”

Dispersion modeling of air pollutants in the atmosphere: a review

“…(20), where second order differences are used for the spatial approximation and the time integration is executed using the so-called Crank-Nicolson method: and -refinement (right). Reprinted with permission from [76].…”

“…All of the methods above can be further developed by applying a time-dependent discretization [76]. In practice, one uses a variable grid with finer resolution at locations where the numerical error is high (usually where the concentration gradient is large); this is called adaptive gridding.…”

Influence of Grid Resolution in Modeling of Air Pollution from Open Burning

“…However, some studies suggested that results of modeled concentrations were also significantly sensitive to grid resolution [14][15][16][17][18]. Jang et al [14] used a high-resolution version of the regional acid deposition model (HR-RADM) to simulate ozone (O 3 ) formation at different grid resolutions (20,40 and 80 km) and found that the use of coarser grid spacing tended to underpredict O 3 maxima because of the of O 3 precursor dilution and to overpredict O 3 minima because of the NO titration effect.…”

“…A better correlation of observations compared to predictions are shown when using fine resolution, and the influence of model resolution was more significant for air quality than for meteorology simulation [17]. Using a coarse grid resolution may produce large discrepancies in the results compared to a fine grid resolution since it cannot capture inhomogeneities in emission rates, meteorology and land cover, while using a fine grid resolution may cause the simulation to be inefficient because it can be considerably limited by calculation time [15,18]. Therefore, model simulation should be performed using the appropriate grid resolution to obtain reliable and acceptable predictions in terms of both accuracy and computational time.…”