Simulating prescribed fire impacts for air quality management
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Transcript of Simulating prescribed fire impacts for air quality management
Simulating prescribed fire impacts for air quality management
Georgia Institute of Technology
M. Talat Odman, Yongtao Hu, Fernando Garcia-Menendez, Aika Yano, and Armistead G. Russell
School of Civil & Environmental Engineering, Georgia Institute of Technology
AQAST Meeting, June 12th, 2012
Improving Operational Regional Air Quality Forecasting Performance through Emissions Correction Using NASA Satellite Retrievals and Surface
MeasurementsPI: Armistead G. Russell1, Co-Is: Yongtao Hu1, M. Talat Odman1, Lorraine
Remer2
1Georgia Institute of Technology, 2 NASA Goddard Space Flight Center Primary Stakeholder Clients: Georgia EPD; Georgia Forestry Commission
Georgia Institute of Technology
Activities Overview of first year AQAST research
Expanded Hi-Res operational forecasting system Forecasting efforts supporting field studies
Discover AQ & Fort Jackson Prescribed burn Simulating biomass burning air quality impacts
Simulating biomass burning using satellite-derived fire emissions Discover AQ and ARCTAS Campaign Evaluation with ground-based and satellite data
Simulating biomass burning Williams, CA prescribed fire Uncertainty and evaluation
Related: Bayesian CMAQ-satellite data assimilation Exposure estimation for epidemiologic studies
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Hi-Res: forecasting ozone and PM2.5 48 hr forecast @ 4-km resolution for Georgia and 12-km for most states of
eastern US Hi-Res Modeling Domains
36-km (148x112)
12-km (123x138)4-km (123x123)
36-km (148x112)
12-km (123x138)4-km (123x123)
Hi-Res forecasting products are in use by Georgia EPD assisting their local AQI forecasts for multiple metro areas
Hi-Res forecasting products are potentially useful for other states
36-km (148x112)
12-km (123x138)
12-km (93x117)4-km (192x210)
4-km (165x141)
4-km (102x108)1-km (120x80)
ARCTASJun15-Jul14,2008
DISCOVER-AQJun27-Aug01,2011
GA-FL WildfiresMay08-Jun01,2007
1-km (112x112)
4-km (108x81)
Williams Burn, November 17, 2009
36-km (148x112)
12-km (123x138)
12-km (93x117)4-km (192x210)
4-km (165x141)
4-km (102x108)1-km (120x80)
ARCTASJun15-Jul14,2008
DISCOVER-AQJun27-Aug01,2011
GA-FL WildfiresMay08-Jun01,2007
1-km (112x112)
4-km (108x81)
Williams Burn, November 17, 2009
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AQAST Modeling Domains
• Bottom-up estimates of fire emissions used for the Williams Burn and GA-FL wildfire simulations. • GOES biomass burning emissions GBBEP used for the ARCTAS and DISCOVER-AQ modeling.
• Provided 48 hour pollutant forecasts during Discover –AQ (with Emory)– Providing spatially more detailed AQ fields for comparison with
observations ( Yang Liu’s poster)• Forecasting for Prescribed Burn Study on October 30, 2011 at
Fort Jackson, SC– Concerned with impacting Columbia
Forecasting in Support of Field Studies
Forecasting with Assimilated PM Fields • Using satellite-data-assimilated PM fields as IC/BC in
forecasting system (with NOAA ARL, Pius Lee’s presentation) – Testing using Discover-AQ campaign period.
Fort Jackson, SC
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CMAQ simulation: DISCOVER-AQ Campaign
12-km 4-km 1-km
O3(40ppb) MNB MNB MNB MNE MNB MNE
16.7 25.3 16.8 23.7 16.8 23.5
24hr PM2.5 FB FB FB FE FB FE
9.1 35.0 10.3 30.1 12.6 26.3
Performance (Surface networks)
Peak hour surface ozoneSurface 24-hr PM2.5
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DISCOVER-AQ Campaign: Comparison with Satellite-derived AOD Fields
CMAQ AOD at 16Z 07022011 CMAQ AOD at 18Z 07022011
MODIS AOD Terra (L2) 16Z 07022011 MODIS AOD Aqua (L2) 18Z 07022011
Simulated AOD is 25% lower in general
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ARCTAS: Northern California Wildfires June 27, 2008
July 8, 2008
12-km 4-km
O3 (40ppb)
MNB MNE MNB MNE
7.4 21.3 3.6 20.0
24h PM2.5 FB FE FB FE
-28.9 51.3 -32.3 48.6
Performance (Surface networks)
Underestimation of surface PM2.5
ARCTAS: CMAQ–Satellite Comparison CMAQ AOD at 21Z 06272008CMAQ surface 24-hr PM2.5 06272008
MODIS AOD Aqua (L2) at 21Z 06272008
Simulated AOD is factor of 10 lower in general, though the maximum is 1.2 versus 4.4 (sim vs. obs)
Simplified treatment of biomass fire plumes may cause issues. There may be missing fires from the GBBEP products.
1 YR 2 YR 3 YR 5 YR0
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4
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Fuel Loading Estimate Sand Sites
DuffLitterHerbaceousWoody1000hr100hr10hr1hr
Time Since Last Burn
Fuel
Loa
d (to
ns p
er a
cre)
1 YR 2 YR 3 YR 5 YR0
2
4
6
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Fuel Loading Estimate Sand Sites
DuffLitterHerbaceousWoody1000hr100hr10hr
1hr
Time Since Last Burn
Fuel Load (tons per acre)
Estimation of Emissions• Fuel load is estimated using photo-series , if available, or
satellites3 years
Fuel
Loa
d (to
ns p
er a
cre)
• Fuel consumption is calculated by CONSUME 3.0.– Fuel moisture is a key fire parameter.
• Emission Factors (EF) are available from field and/or laboratory studies.– Fire Sciences Lab in Missoula, MT
Fire Progression Model: Rabbit Rules(A cellular automata/free agent model)
12:52:03
12:32:43 12:40:02 12:44:55 12:46:58
12:49:50 12:51:00 12:53:44
12:58:39 13:33:34 Block 703C Fire-Induced Wind Field
Wind symbols: line – less than 1.3 ms-1, short barb: 1.3 – 3.7 ms-1, long barb: 3.7 – 6.3 ms-1.
12:52:03
12:32:43 12:40:02 12:44:55 12:46:58
12:49:50 12:51:00 12:53:44
12:58:39 13:33:34 Block 703C Fire-Induced Wind Field
Wind symbols: line – less than 1.3 ms-1, short barb: 1.3 – 3.7 ms-1, long barb: 3.7 – 6.3 ms-1.
12:52:03
12:32:43 12:40:02 12:44:55 12:46:58
12:49:50 12:51:00 12:53:44
12:58:39 13:33:34 Block 703C Fire-Induced Wind Field
Wind symbols: line – less than 1.3 ms-1, short barb: 1.3 – 3.7 ms-1, long barb: 3.7 – 6.3 ms-1.
12:52:03
12:32:43 12:40:02 12:44:55 12:46:58
12:49:50 12:51:00 12:53:44
12:58:39 13:33:34 Block 703C Fire-Induced Wind Field
Wind symbols: line – less than 1.3 ms-1, short barb: 1.3 – 3.7 ms-1, long barb: 3.7 – 6.3 ms-1.
Fuel Density Map (Satellite –derived)
Fire Induced Winds
Parameters provided by Rabbit Rules
• No. of updraft cores• Vertical velocities• Core diameters• Emissions as f(t)
Block 703 Eglin AFB
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720 730 740 750 760 770 780 790 800
Time (min)
Rel
ativ
e Em
issi
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(%/m
in)
Dispersion and Transport Models• Daysmoke is a dynamic-stochastic Lagrangian particle model
specifically designed for prescribed burn plumes.
• AG-CMAQ is the adaptive grid regional air quality model.
• Daysmoke has been coupled with AG-CMAQ as an inert, subgrid-scale plume model through a process called “handover”.
Williams fire: A chaparral burn in CA• A suite of gases and aerosols and meteorological
parameters were measured aboard an aircraft in the plume of Williams fire on 17 November 2009 (Akagi et al. , ACP, 2012).
• Burn observed by satellites• Fuels/burn information is limited.
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Modeled plume in PBL and Aircraft Track
Unpaired PeaksObserved = 676 mg/m3
Modeled = 508 mg/m3
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0
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PM2.
5 (µ
g/m
3 )
Jefferson St.Benchmark
-10%
-20%
-30%
+10%
+20%
+30%
Potential Sources of Uncertainty
PM2.5 Emissions
Under-predicted by 15%
Field Study at Eglin AFB, FL0
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PBL H
eigh
t (m
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PM2.
5 (µ
g/m
3 )
Jefferson St.
Benchmark
-10%
-20%
-30%
+10%
+20%
+30%
PBL
Sensitivity to PBL Height
Sensitivity to Wind Speed
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Uncertainty in Satellite Data?Modeled PM2.5 and Aircraft Track MODIS Aqua AOD
(regridded from L2 products 10-km resolution at nadir)
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Next StepsEvaluate using airborne measurements and
high resolution, level-3 AOD •Injection heights: MISR multi-angle products•Column information from satellites can provide information
on plume aloft Integrate satellite observations in forecast
system•Data assimilation, potentially using direct sensitivity analysis•Extend 12-km domainKnowledge learned will be applied to inverse
modeling •Improve burn emissions (mass and injection height)
•Better predict impacts from prescribed burns
Georgia Institute of Technology
Acknowledgements• NASA• Georgia EPD• Georgia Forestry Commission• US Forest Service
– Scott Goodrick, Yongqiang Liu, Gary Achtemeier
• Strategic Environmental Research and Development Program
• Joint Fire Science Program (JFSP)• Environmental Protection Agency (EPA)