Simulating prescribed fire impacts for air quality management Georgia Institute of Technology M. Talat Odman, Yongtao Hu, Fernando Garcia-Menendez, Aika

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


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


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


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


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


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


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 YR

0

2

4

6

8

10

12

14

Fuel Loading Estimate Sand Sites

Duff

Litter

Herbaceous

Woody

1000hr

100hr

10hr

1hr

Time Since Last Burn

Fue

l Loa

d (t

ons

per

acre

)

1 YR 2 YR 3 YR 5 YR

0

2

4

6

8

10

12

14

Fuel Loading Estimate Sand Sites

Duff

Litter

Herbaceous

Woody

1000hr

100hr

10hr

1hr

Time Since Last Burn

Fuel Lo

ad (ton

s per a

cre)

Estimation of Emissions• Fuel load is estimated using photo-series , if available, or

satellites3 years

Fu

el L

oad

(to

ns

per

acr

e)

• 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 13:33:34Block 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



12:52:03

12:32:43 12:40:02 12:44:55 12:46:58

12:49:50 12:51:00 12:53:44



12:52:03

12:32:43 12:40:02 12:44:55 12:46:58

12:49:50 12:51:00 12:53:44



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

0

1

2

3

4

5

720 730 740 750 760 770 780 790 800

Time (min)

Re

lati

ve

Em

iss

ion

s

(%/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.


Modeled plume in PBL and Aircraft Track

Unpaired PeaksObserved = 676 mg/m3

Modeled = 508 mg/m3


0

20

40

60

80

100

120

PM

2.5

(µ

g/m3 )

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

200

400

600

800

1000

1200

0

10

20

30

40

50

60

PB

L H

eig

ht

(m)

PM

2.5

(µ

g/m3 )

Jefferson St.

Benchmark

-10%

-20%

-30%

+10%

+20%

+30%

PBL

Sensitivity to PBL Height

Sensitivity to Wind Speed


Uncertainty in Satellite Data?Modeled PM2.5 and Aircraft Track MODIS Aqua AOD

(regridded from L2 products 10-km resolution at nadir)


Next Steps

Evaluate 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 domain

Knowledge learned will be applied to inverse modeling

•Improve burn emissions (mass and injection height)•Better predict impacts from prescribed burns


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)

Documents

Simulating prescribed fire impacts for air quality management Georgia Institute of Technology M. Talat Odman, Yongtao Hu, Fernando Garcia-Menendez, Aika