1 Aika Yano, Yongtao Hu, M. Talat Odman, Armistead Russell Georgia Institute of Technology October 15, 2012 11th annual CMAS conference.

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Presentation transcript:

1 Aika Yano, Yongtao Hu, M. Talat Odman, Armistead Russell Georgia Institute of Technology October 15, th annual CMAS conference

2 Fires naturally occur Prescribed burning is used to maintain/improve the ecosystem and to reduce the potential of catastrophic wildfires Land managers must minimize smoke from reaching highly populated areas. Burn strategies and meteorology affect dispersion, long-range transport of plumes, and ground-level concentrations of pollutants downwind. Demand for a tool to assess/predict burn practices Pictures: (left) (right)

3 To evaluate the smoke impact prediction system To assess different burning parameters: frequency, season, size and time of burn, and ignition type Results of different burning strategies are analyzed based on: ● Amounts of pollutants emitted ● How smoke plumes are dispersed ● How pollutants are transported to impact downwind air quality

CMAQ 4.5 Daysmoke Plume dispersion up to a certain distance (8km) from fire Grid Adaptation SCIPROC Fire Emission Production Simulator WRF Generates time- varying emissions Benefit of AG-CMAQ Daysmoke coupling: has the capability to increase grid resolution for tracking biomass burning plumes at the regional scale

BASE CASE 300 acres 3 year-old fuel Location: , Ignition Time: 12:30 pm EDT Ignition Completion: 2:45 pm Wind SE, WRF winds adjusted Mobile 2: 5-6 km away Columbus Airport: 31km

Mean Fractional Error = 63% C max from BG : 1.3% Mean Fractional Bias = - 42%integral C abv BG : -2.5%

8 Scenarios identified by Rx-burn experts: 1. Frequency of burn 25 2 vs 5 year-old 2. Season of burn Winter vs spring 3. Size of burn 300 vs 600 acres 4. Ignition type Hand-lit vs aerial 5. Time of burn Morning vs noon vs afternoon

2 year-old fuel (base case)5 year-old fuel (33.4% more PM 2.5 )

Long range PM 2.5 (~ 31km) 5 vs. 2 years Short range PM 2.5 (~ 5km) 5 year old fuel has 40.2% more emissions Accumulated concentration: increase by 26.0% dC max : 10 vs 15  g/m 3

Winter (RH:42%, 37.3% more PM 2.5 ) Spring (RH: 65%)

Long range PM 2.5 (~ 31km) winter vs. spring Short range PM 2.5 (~ 5km) Lower RH leads to 37% more PM 2.5 emission Accumulated concentration increase by 39.7%

300 acres 600 acres (2 times more emission)

Long range PM 2.5 (~ 31km) 300 vs. 600 acres Short range PM 2.5 (~ 5km) Accumulated concentration increased by 66.1% Emission doubled, C max also doubled, over ambient AQ standard for 24hr avg. PM 2.5

Hand-lit Aerial (faster, shorter helicopter ignition) 35.1% more emission

Long range PM 2.5 (~ 31km) Hand lit vs. Aerial (helicopter) ignition Short range PM 2.5 (~ 5km) 35.1% more emission by aerial ignition Decrease in accumulated concentration by 60.8%

Start at 12:30pm (RH: 65%) Start at 3:30pm (RH: 48%, 23.0% more PM 2.5 ) Start at 10:30am (RH: 77%, 2.0% less PM 2.5 )

Long range PM 2.5 (~ 31km) AM vs. noon vs. PM Short range PM 2.5 (~ 5km) 2% less emission in the morning and 23% more in the afternoon burn C max : 43% &167% increase Accumulated concentration: 13.3% & 9.2% increase

We used AGD-CMAQ (AG-CMAQ coupled with Daysmoke) to simulate different PB scenarios. Frequency: 5 years vs. 2 resulted increased emissions (E) by 33% and concentrations (C) by 26% Season: Winter vs. spring increased E by 37% and C by 40% Size: Doubling acreage increased E by 100% and C by 66% Ignition type: Aerial ignition increased E by 35% and decreased C by 60% Time: RH, temp., and wind speed/direction changes during the day significantly affect the amount of emissions, plume behavior, and maximum concentration observed. The smoke impact prediction system can be used to design burning scenarios with minimal impact to air quality.

20 Acknowledgements Strategic Environmental Research and Development Program Joint Fire Science Program Georgia Department of Natural Resources, Environmental Protection Division US Forest Service, Southern Research Station NASA AQAST

AG-CMAQ coupled with Daysmoke (AGD-CMAQ) used to simulate PB scenarios Frequency: More fuel, more heat flux, same PBL, ground concentration ends up being higher, therefore mixes better, lower ground concentration Emissions increased by 33% increasing the fuel from 2yr old to 5 yr old fuel Season: Fuel sensitive to RH, therefore Lower RH (by 23-24% ) leads to more PM 2.5 emitted (37-39%) Size: total emission increase, increase in maximum concentration Ignition type: higher the plume, less emission at the ground level Time: RH, temp, mostly wind direction changes in one day can significantly affect the amount of emissions, plume behavior, and maximum concentration observed Plumes behave differently case by case and are difficult to predict, the smoke impact prediction system is able to incorporate multiple parameters providing adequate predictions

AGD-CMAQ presented/evaluated at: CMAS 2011 “Coupling a subgrid-scale plume model for biomass burns with the adaptive grid CMAQ model” CMAS 2010 “Modeling biomass burnings by coupling a sub-grid scale plume model with Adaptive Grid CMAQ”