Fernando Garcia-Menendez

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Presentation transcript:

Fernando Garcia-Menendez Diagnostic Evaluation of a Modeling System for Predicting the Air Quality Impacts of Prescribed Burns Talat Odman Fernando Garcia-Menendez Aika Yano Yongtao Hu CMAS Annual Conference Chapel Hill, North Carolina October 16, 2012

Contributors Scott Goodrick, Yongqiang Liu, Gary Achtemeier (Forest Service) Emissions/Dispersion Modeling, Plume Height Measurements Roby Greenwald (Emory University) Ground-based Smoke Measurements Brian Gullett, Johanna Aurell, William Stevens (EPA) Aerostat-based Emissions and Wind Measurements Roger Ottmar (Forest Service) Fuels/Consumption Measurements Robert Yokelson, Sheryl Akagi (University of Montana) Aircraft-based Smoke/Meteorology Measurements

Motivation: 2007 Atlanta Smoke Incident Two large burns (3000 acres total) were started in the morning of February 28, 2007 under easterly winds about 80 km southeast of Atlanta. Later, the winds shifted to southeasterly and blew the smoke to Atlanta. By late afternoon, PM2.5 levels reached 150 mg/m3 at several Atlanta monitors.

Initial Modeling Results & Potential Sources of Improvement Emissions Fuels, emission factors Meteorology Wind speed/direction Models Plume/PBL height Grid resolution

(empirical stochastic dispersion model) Modeling System Emissions: CONSUME, Fire Emission Production Simulator (FEPS) Meteorology: WRF (MM5) Dispersion and Transport: Daysmoke was coupled with AG-CMAQ as a subgrid-scale plume model. The models used in this research are the Community Multiscale Air Quality (CMAQ) , which is a regional scale model and Daysmoke which is a local scale plume model. We have incorporated the Adaptive Grid methodology in CMAQ to improve its grid resolution and facilitate its coupling with sub-grid scale models. Of course here our target model is Daysmoke but the framework can be used for other models as well. We are also exploring the possibility of using an adaptive grid version of MM5. If this works, the meteorological inputs to CMAQ will be provided on the same grid eliminating the need for interpolations. Because of this exciting opportunity of coupling meteorology models with air quality models we are spending more time on adaptive grid incorporation than originally planned. . Daysmoke (empirical stochastic dispersion model) AG-CMAQ (adaptive grid CMAQ)

Effect of Grid Resolution 4 km ~100 m Mean Fractional Error reduced by 15%

Emission Uncertainties 4 to 6 × Emissions gives the desired level of PM at receptors. But, is such an increase realistic? What are the uncertainties in emissions? Emissions = Fuel Consumption × Emission Factors

Uncertainty in Fuel Consumption Photo Series + CONSUME 3.0 vs. Measurements CONSUME 3.0 calculates the amount of fuel consumption under different fire conditions. Photo series tend to overestimate except for 100hr fuel loading. Ottmar 2.7 vs Scott 3.2 therefore 0.5 tons/acre difference or Fuel load is 20% over-estimated by photo series Consumption is over-estimated by 10%

Emission Factors and Total Emission Uncertainties Emission Factors (EF) are available from field and/or laboratory studies. Mostly on the ground The Fire Sciences Lab in Missoula, MT is equipped to measure emissions from simulated fires. Analytical instrumentation includes GC, MS, FTIR PM2.5 emissions under-predicted by 15%

Uncertainty in Timing of Emission Fire Emission Production Simulator (FEPS) Rabbit Rules

Uncertainties in Winds

Daysmoke Plume Heights vs. Ceilometer WRF (black), MM5 (red) Observed top (circles) and base (triangles)

Ground Measurements Stationary Mobile 2 Daysmoke: stochastic model with different output each run. 2.5 miles or 4km away from fire is stationary site Daysmoke inputs!!!

Aircraft Measurements A suite of gases , aerosols and meteorological parameters were measured in the plume of a chaparral burn near Buelton, CA on November 17, 2009 (Akagi et al. , ACP, 2012).

Sensitivity to Vertical Distribution of Emission (Uniform Temporal Allocation)

Sensitivity to Timing of Emission

Sensitivity to Wind Direction -5⁰

Sensitivity to Wind Speed W/S: Peachtree City Sounding vs. WRF

Final Modeling Emissions: Injection Height: Winds: Models: Total increased by 15% Skewed towards end of burn Injection Height: Plume in PBL at end of burn Winds: WS reduced by 30% WD altered by 5⁰ Models: AGD-CMAQ

Lessons Learned Specific: General: Uncertainties in Rx burn emissions have been reduced. Dispersion and transport models have been significantly improved. Accuracy of smoke impact prediction is limited by the accuracy of WS and WD predictions. General: Using extreme events in model evaluation is beneficial. Uncertainties can best be determined by well designed field studies. Sensitivity analyses can be helpful in setting priorities for future research.

Acknowledgements Thank you! Questions? Strategic Environmental Research and Development Program (SERDP) Joint Fire Science Program (JFSP) Environmental Protection Agency (EPA) Thank you! Questions?