New Techniques for Modeling Air Quality Impacts of DoD Activities Talat Odman and Ted Russell Environmental Engineering Department Georgia Institute of Technology

Abstract This is a feasibility study to determine if two recently developed techniques can improve the accuracy of regional-scale air quality models to a level where they can be used to assess the impacts of DoD activities. Current large-scale models cannot differentiate a DoD facility from its surroundings; on the other hand, small-scale models cannot track the long-range impacts. The first technique, adaptive grid, dynamically reduces the grid size to better resolve the evolution of the plumes from the source to the receptor. The second technique, direct sensitivity analysis, efficiently yields the sensitivity of pollutant levels to emissions from various sources; it can accurately detect the effect of even very small sources. In this study, we will target the prescribed burning emissions from Fort Benning and their impact on the air quality of the Columbus metropolitan area. In particular, ozone air quality standards may be exceeded in Columbus if there is significant impact of NO x and VOC emissions from the controlled fires.

Adaptive Grid Modeling and Direct Sensitivity Analysis for Predicting the Air Quality Impacts of DoD Activities Computer Simulation with Air Quality Model Controlled Burning at Military Base Adaptive GridSensitivity Analysis Impact to Downwind City Strategy Design

Adaptive Grid Algorithm Inadequate grid resolution may be an important source of uncertainty in air quality models. Adaptive grids offer an effective and efficient solution. Our adaptive grid technique is a mesh refinement algorithm where the number of grid cells remains constant and the structure (topology) of the grid is preserved. A weight function controls the movement of the grid nodes according to user-defined criteria. It automatically clusters the nodes where resolution is most needed. Grid nodes move continuously as shown in the movie of the simulation. Grid cells are automatically refined/coarsened to reduce the solution error.

Snapshot of the Continuously Adapting Grid

Adaptive Grid Air Quality Model A simulation with the adaptive grid air quality model can be viewed as a sequence of two steps. 1.Adaptation step: The concentration field is frozen in time while the grid nodes are moved. The following tasks are performed: –Computation of the weight function –Repositioning of the grid nodes –Redistribution of the concentration field to new grid node locations 2.Solution step: The grid nodes are held fixed while the concentration field is advanced in time. The tasks are: –Processing of the meteorological and emissions data –Coordinate transformation –Numerical solution The adaptation step takes a fraction of the CPU time required by the solution step.

Ozone Simulation in Tennessee Valley We simulated ozone air quality in the Tennessee Valley for the July 7-17, 1995 period using the same model for physical and chemical processes, but with different grids: –Two static grids with uniform resolutions of 8 km and 4 km, and –An adaptive grid that has the same number of cells as the 8-km resolution static grid. Meteorological inputs were prepared using the Regional Atmospheric Modeling System (RAMS) at 4-km resolution. Emission inputs were prepared using the SAMI emissions inventory. –There are over 9000 point sources in this domain including some of the largest power plants in the Unites States. –Area sources are mapped onto an 8-km emissions grid. The adaptive grid is adapted to surface layer NO concentrations through the simulation. –The cell size may drop to few hundred meters from the original 8 km.

Outstanding Resolution of NO x Plumes Fixed Grid Adaptive Grid

Superior O 3 Predictions with Adaptive Grid Sumner Co., TN Graves Co., KY

Sensitivity Analysis with Direct Decoupled Method (DDM) In DDM, sensitivity is defined as the first derivative (local slope) of the pollutant concentration with respect to an emission. The equations for sensitivities are similar to the equations governing pollutant concentrations. Therefore, they can be solved efficiently. Several sensitivities can be calculated simultaneously in a single model run, along with concentrations. Assuming linear response, the change in concentration resulting from a change in emissions can be approximated as For small changes in emissions, not only this assumption is valid but DDM is more accurate than the brute-force response. So, DDM is ideal for regional impact assessment of smaller sources.

PM 2.5 Simulation in the Southeast Using an integrated air quality model, we simulated the future PM 2.5 levels in the eastern United States focusing on Class I areas of the Southern Appalachian Mountains. Using DDM in these simulations, we calculated the sensitivity of PM 2.5 levels to –SO 2, elevated NO x, ground-level NO x, and NH 3 emissions –from 8 states in the Southeast and 5 surrounding regions. Some of these sensitivities are shown below. DDM sensitivities can be used in designing effective emission control strategies. More information about this study can be found at

SO 4 on July 15, 2010 and its Sensitivity to 10% Reductions of SO 2 Emissions from 8 States KYWVVA TN 2010NC AL GASC 

SO 4 Sensitivity to SO 2 Emissions at 10 Class I Areas

Conclusion Currently, we are merging adaptive grid and sensitivity analysis techniques in a comprehensive air quality model and preparing emissions data for the simulations. Here, we presented results from recent applications of the two techniques. The adaptive grid was used to better simulate the fate of power-plant plumes in the Tennessee Valley during an ozone episode in July, When compared to observations, the adaptive grid produced significantly more accurate results than the classical static-grid models. The direct sensitivity analysis was used to determine the impact of emissions from neighboring states and regions to the air quality in the Southern Appalachian Mountains. For different receptor areas, the adverse contribution of each state or region was ranked. This information can be used in the design of emission control strategies. These results show that both techniques have upside potential for use in impact assessments of DoD facilities and operations.

Related Publications Srivastava, R. K., McRae, D. S. and Odman, M. T., “Simulation of dispersion of a power plant plume using an adaptive grid algorithm,” Atmospheric Environment, vol. 35, no. 28, pp , October Srivastava, R. K., McRae, D. S. and Odman, M. T., “Simulation of a reacting pollutant puff using an adaptive grid algorithm,” Journal of Geophysical Research, vol. 106, no. D20, pp , October Srivastava, R. K., McRae, D. S. and Odman, M. T., “An adaptive grid algorithm for air quality modeling,” Journal of Computational Physics, vol. 165, no. 2, pp , December Yang, Y.-J., Wilkinson, J. G. and Russell, A. G., “Fast, direct sensitivity analysis of multidimensional photochemical models,” Environmental Science and Technology, vol. 31, no. 10, pp , October 1997.