Georgia Chapter of the Air & Waste Management Association Annual Conference: Improved Air Quality Modeling for Predicting the Impacts of Controlled Forest.

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Presentation transcript:

Georgia Chapter of the Air & Waste Management Association Annual Conference: Improved Air Quality Modeling for Predicting the Impacts of Controlled Forest Fires Fernando Garcia Menendez Georgia Institute of Technology 10/8/2009 Evaluation of Air Quality Models Applied to Wildland Fire Impact Simulation Fernando Garcia-Menendez Aika Yano Yongtao Hu M. Talat Odman Georgia Institute of Technology 10/19/2010 9th Annual CMAS Conference:

Evaluation of Smoke Models and Sensitivity Analysis for Determining Emissions Related Uncertainties Objective 1: Evaluate performance of a range of AQ models with data from prescribed burn and wildfire events in the Southeast. Objective 2: Quantify the uncertainties in model outputs generated from uncertainties in emission inputs. Episode Selection Meteorology, Fire Data & Emissions Model Simulation:  CMAQ  Daysmoke  Calpuff Diagnostic Model Evaluation

Wildland Fires & Air Quality Wildland fires may have detrimental impacts on air quality, health & visibility. Air Quality Modeling can be used as a basis for decision-making. Informed decisions require knowledge of model’s capabilities & uncertainties. *Bernard & Sabo, 2003

Episode: February 28, 2007

Measurement Sites

Recorded Observations

Air Quality Modeling

Pave Pic

Model Performance Pave Pic

Performance Comparison Pave Pic

Statistical Evaluation

Statistical Comparison Large amount of information is generated but only a small fraction is analyzed! Opportunities for model insight are lost: - Model strengths and weaknesses - Sensitivity to inputs and uncertainty in results - Better guidance for future model development

Model: DAYSMOKE-CMAQ DAYSMOKE: - Developed by US Forest Service - Stochastic plume model - Inert model, PM 2.5 only - Typical scales < 10 km DAYSMOKE-CMAQ: Uses DAYSMOKE as an emissions injector into CMAQ

Model: AG-CMAQ Dynamic, solution-adaptive grid algorithm. Variable t-step algorithm. Conserves original CMAQ grid structure. *Garcia-Menendez, et al. (2010): An Adaptive Grid Version of CMAQ for Improving the Resolution of Plumes, Atmospheric Pollution Research, 1,

Model Evaluation: AG-CMAQ & DAYSMOKE-CMAQ Episode: Feb Fires near Atlanta, GA Fire Emissions: Fire emissions Production Simulator (FEPS) Background Emissions: SMOKE with projected 2002 “typical year” inventory Meteorology: Weather Research & Forecasting model (WRF) Plume Rise & Vertical Profile: DAYSMOKE DAYSMOKE-CMAQ AG- CMAQ

Feb. 28, :30 Z Mar. 1, :00 Z DAYSMOKE-CMAQ AG-CMAQ

Mar. 1, :30 Z Mar. 1, :15 Z DAYSMOKE-CMAQ AG-CMAQ

11 % average improvement in Mean Fractional Error with AG-CMAQ. Better performance in 5 of 6 stations. Much more insight into model performance gained beyond that achieved from statistical comparison only.

CMAQAG-CMAQ CMAQAG-CMAQ Feb. 28, :30 Z Feb. 28, :45 Z DAYSMOKE-CMAQ AG-CMAQ

23:15 Z00:00 Z01:15 Z

23:15 Z00:00 Z01:15 Z 5⁰ Degree Shift in Wind Field

Model: CALPUFF Gaussian dispersion model. Simulates pollution as a series of puffs interacting with terrain & meteorological fields. Typical scales < 100 km

Future Work: Model Evaluations Explore other fire emissions and plume rise options available for CMAQ & SMOKE. Further explore Advanced Plume Treatment (APT) in CMAQ. Future episodes to evaluate: – Prescribed burn episodes at Fort Benning, GA – Florida-Georgia wildfires May-June 2007 Sensitivity analysis with Direct Decoupled Method of fire related parameters.

Adaptive-Grid-Daysmoke CMAQ: – Daysmoke-CMAQ hybrid model – Currently inert Reactive Daysmoke-CMAQ: Future Work: Model Development Chemical treatment of smoke plume will require 3 considerations: 1.Plume Volume 2.Level of Chemical Interaction 3.Reacted Mass Redistribution

Thank you! Acknowledgements: