Georgia Institute of Technology Adaptive Grid Modeling for Predicting the Air Quality Impacts of Biomass Burning Alper Unal, Talat Odman, Ted Russell School of Civil & Environmental Engineering Georgia Institute of Technology Biomass Burning Impacts on Air Quality, Human Exposure and Health - Exchanging Information for Future Initiatives - Workshop at GIT, ES&T Bldg, L1114 January 22, 2004 My co-authors are Prof. Ted Russell and Dr. Jim Boylan who used to be our student. Now, he works for the Georgia DNR. Georgia Institute of Technology

Georgia Institute of Technology Motivation Endangered Species Act Clean Air Act Georgia Institute of Technology

Georgia Institute of Technology Motivation The endangered Red Cockaded Woodpecker (RCW) resides only in the mature long-leaf pine forests. Most of the forests are on federal and military lands. These ecosystems require periodic burning to maintain health. Prescribed burning is a safe and effective alternative to natural fire regimes. Georgia Institute of Technology

Georgia Institute of Technology Motivation VOCs PM NOx O3 Georgia Institute of Technology

Motivation Gridded Daily Maximum Hourly Averaged Surface Ozone Concentrations for 12-km grid (left) and 4-km grid (right). Georgia Institute of Technology

Computer Simulation with Air Quality Model Impact to Downwind City Objectives Computer Simulation with Air Quality Model Controlled Burning at Military Base Adaptive Grid Sensitivity Analysis Impact to Downwind City Strategy Design To improve the ability to model the air quality impacts of biomass burning on the surrounding environment To develop and apply modeling techniques that allow urban-to-regional scale modeling for small area sources (Adaptive Grid Modeling) To develop and apply techniques that enable to quantify the impact of biomass burning on regional ozone problem (Direct Sensitivity) Georgia Institute of Technology

Georgia Institute of Technology Study Area: Fort Benning, GA Georgia Institute of Technology

Georgia Institute of Technology Methodology Adaptive Grid Modeling Direct Sensitivity Analysis Georgia Institute of Technology

Adaptive Grid Modeling Inadequate grid resolution -- 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 during the simulation. Grid cells are automatically refined/coarsened to reduce the solution error. Georgia Institute of Technology

Adaptive Grid Modeling Georgia Institute of Technology

Sensitivity Analysis with Decoupled Direct Method (DDM) Define first order sensitivities as Take derivatives of Solve sensitivity equations simultaneously Approximate response as Another technique we applied to find the impact of fire emissions is DDM. The alternative would have been the brute-force approach where the model is run twice: once with the fire emissions and the second time without them. The difference in ozone concentrations would yield the impact of the fire. However, the NOx emissions from the fire are a very small fraction of the total NOx emissions. Therefore the difference between the two model runs may be small and it may fall within the range of model errors. On the other hand DDM calculates the sensitivity which is defines as the derivative of concentration with respect to emissions. By definition, it is designed to capture the impact of infinitesimal changes in emissions. The sensitivities are calculated by solving an additional set of equations in the model. Georgia Institute of Technology

Air Quality Simulations Selected Episode: August 15-18, 2000 (Hugh Westburry @ Fort Benning provided the fire data) Meteorology Data: MM5 (FAQS) Base Emissions: FAQS-2000 Inventory Biomass Burning Emissions: FOFEM V5 + Battye and Battye (2002) NET99, CEM, BEIS3 with BELD3 data, MOBILE6 Georgia Institute of Technology

Georgia Institute of Technology Fire Tracer: Adaptive Grid Georgia Institute of Technology

Georgia Institute of Technology O3 Concentration: Adaptive vs. Static Grid August 15, 21:00 (GMT) Georgia Institute of Technology

Georgia Institute of Technology O3 Sensitivity to FIRE: Static + Brute Force At night, there is Ozone titration! Georgia Institute of Technology

Georgia Institute of Technology O3 Sensitivity to FIRE: Adaptive + DDM Georgia Institute of Technology

Georgia Institute of Technology O3 Sensitivity: Adaptive + DDM vs. Static + BF Georgia Institute of Technology

Georgia Institute of Technology O3 Sensitivity: Adaptive + DDM vs. Static + BF August 15, 20:00 GMT Georgia Institute of Technology

Georgia Institute of Technology Conclusions Adaptive Grid Modeling and Direct Sensitivity Analysis were successfully implemented to determine the impact of biomass burning on the surrounding environment The impact of fires at Fort Benning ranged from 16 ppb reduction to 7 ppb increase in O3 concentrations. Impact on Columbus area is minimal due to wind directions Concentration gradients were better resolved by Adaptive Grid Direct Sensitivity compared to Brute Force, better differentiated near and far field impacts Georgia Institute of Technology

Georgia Institute of Technology Future Work Emissions Inventory: Better emissions estimation for biomass burning Plume Rise calculations Comparison with Monitoring Data: “Prediction of Air Quality Impacts from Prescribed Burning: Model Optimization and Validation by Detailed Emissions Characterization “ with Dr. Karsten Baumann Georgia Institute of Technology

Georgia Institute of Technology Acknowledgements Strategic Environmental Research & Development Program (SERDP): Project CP-1249 Study of Air Quality Impacts Resulting from Prescribed Burning on Military Facilities" sponsored by the DOA/CERL in support of the DOD/EPA Region 4 Pollution Prevention Partnership. Georgia Institute of Technology