Modeling impact of biomass burning on air quality in
Southeast and East Asia
Kan Huang1, Joshua S. Fu1,2, N. Christina Hsu3, Yang Gao1, Xinyi Dong1,
Si-Chee Tsay3, Yun Fat Lam1
1 Department of Civil and Environmental Engineering, The University of Tennessee,
Knoxville, Tennessee, USA
2 UTK-ORNL Center for Interdisciplinary Research and Graduate Education, Knoxville,
Tennessee, USA
3 Goddard Space Flight Center, NASA, Greenbelt, Maryland, USA
11th Annual CMAS Conference
October 16, 2012
Motivations
• Biomass burning in Southeast Asia emits large amounts of trace
gases and particulate matters into the atmosphere, and the long
range transport of biomass burning could have significant impacts
on East Asia.
• Observation in this area is rare, as well as model simulation.
• An quantitative impact assessment of biomass burning from
Southeast Asia on the downstream regions is needed.
• There are large uncertainties in biomass burning emissions
estimations, and which one is better? FLAMBE or GFEDv2.1?
FLAMBE: The joint Navy, NASA, NOAA, and universities Fire Locating and
Modeling of Burning Emissions
http://www.nrlmry.navy.mil/aerosol web/arctas_flambe/data hourly/
GFEDv2.1: The Global Fire Emissions Database, Version 2.1
http://ess1.ess.uci.edu//~jranders/data/GFED2/
WRF-CMAQ Regional Model (schematic framework)
NCEP reanalysis data
Land use
Biogenic Model
(MEGAN)
Emission factors
Biomass carbon emission
(FLAMBE, GFED)
Meteorology Model (WRF)
Plume height
Biogenic Emission
Biomass emission mapping
and vertical allocation
Met-Chem Interface
Processor (MCIP)
+ Anthropogenic emission
(INTEX-B for Asia)
Air Quality Model (CMAQ)
Emission Data
Model evaluation
Model outputs:
Gases and aerosol
Ground and space measurement
Impact assessment
Mapping Biomass Carbon to Species
Emission Factor for Different Land Use types
In order to map carbon emissions to other species (such as CO, CO2, PM2.5,
etc., the following emission factors and land use (next slide) are used
Species
Savanna and
Grassland
Tropical Forest
Extratropical
Forest
Agricultural
Residuals
CO2
1613
1580
1569
1515
CO
65
104
107
92
CH4
2.3
6.8
4.7
2.7
NMHC
3.4
8.1
5.7
7.0
NOx(as NO)
3.9
1.6
3.0
2.5
NH3
1.0
1.3
1.4
1.3
SO2
0.35
0.57
1.0
0.4
PM2.5
5.4
9.1
13.0
3.9
TPM
8.3
6.5
17.6
13
OC
3.4
5.2
8.6
3.3
BC
0.48
0.66
0.56
0.69
Source: Andreae and Merlet (2001)
Mapping Biomass Carbon to Species
Land Use Type from WRF v3.1.1
Inject Height Distributions for Biomass Burning Emission
Source: Air Sciences Inc. 2005. 2002 Fire Emission Inventory for the WRAP Region – Phase II. Draft Report.
Prepared for the Fire Emissions Joint Forum of the Western Regional Air Partnership.
Modeling Domain (27*27km) and Observation Sites
Five Southeast countries:
Burma, Laos, Vietnam, Cambodia, and Thailand
Available observation sites
Carbon Emission Comparison between FLAMBE and GFEDv2.1
GFEDv2
(Tg/m)
FLAMBE
(Tg/m)
FLAMBE/
GFEDv2
JAN
5.54
14.01
2.53
FEB
8.06
35.79
4.44
MAR
28.00
220.90
7.89
APR
13.43
156.22
11.63
MAY
0.90
8.74
9.70
JUN
0.21
1.39
6.72
JUL
0.03
0.12
3.81
AUG
0.02
0.08
3.54
SEP
0.10
0.23
2.30
OCT
0.12
0.47
4.02
Carbon emissions from FLAMBE is about
NOV
0.55
1.92
3.47
7-11 times higher than that from
DEC
2.08
7.60
3.66
sum(Tg/a)
59.04
447.47
7.58
Study period : March to May, 2006
Monthly
carbon emission
FLAMBE
and GFED
Comparison
betweenfrom
FLAMBE
and GFEDv2
carbon emissions(Tg/mon)
250
GFEDv2
FLAMBE
200
150
`
100
50
0
1
3
5
7
Month in 2006
9
GFEDv2.1 in March and April, 2006
11
Evaluation of FLAMBE and GFEDv2.1 in CMAQ
CO is chosen as a tracer for evaluating the
model performance by using FLAMBE and
GFED as emission input, respectively.
Hengchun
Phimai (Obs. data source: NASA)
Hengchun (Obs. data source: Taiwan EPA)
Phimai
The model has better capability to capture the
intensive biomass burning episodes using
FLAMBE than using GFED.
Possible reasons:
1. Time resolution: FLAMBE is hourly while GFED
is monthly or weekly.
2. Different treatment of the fuel type
Fu et al., 2012
Spatial Distribution of Biomass Carbon Emission
Most intense biomass burning occurred in Burma, northern part of
Thailand, Laos, and Vietnam.
Model Validation from Space-based Observation
Huang et al., 2012
Relatively consistent spatial distribution as compared to the satellite observation
Overestimation in Burma: possible overestimation of forest fires
Underestimation in the south: possible underestimation of local anthropogenic emission
Regional Impact from Biomass Burning (Thailand)
North
Northeast
Central
South
Model biased high about 2-5
folds in northern Thailand
Biomass burning contributed
~ 20 – 40% of total CO in the
other regions of Thailand.
Huang et al., 2012
Ground CO data: Thailand Pollution Control Department
Aerosol Chemistry in SE Asia under Influence of BB
Data source: EANET & Taiwan EPA
Hanoi was most polluted in SE Asia
from the observation. Model
underestimated sulfate, nitrate and
ammonium by a factor of 5–9, 3–10,
and 1.5–2.5, respectively.
Possible reasons:


Local emission may be not
good enough.
Heterogeneous reaction is not
treated well in the model.
Observation found a
considerable amount of CaSO4
and Ca(NO3)2.
Underestimation also occurred in
downwind regions, e.g. southern
Taiwan. While model performed well
in northern Taiwan.
Huang et al., 2012
Vietnam
Thailand
Philippines
Taiwan
Taiwan as a watershed under Influence of BB
Data source: Taiwan EPA Supersites
Southern Taiwan
Northern Taiwan
No correlation between CO and
OC in northern Taiwan probably
indicated less impact from
biomass burning, at least at the
surface.
Huang et al., 2012 The influence of biomass burning
ends here and resulted in distinct
Strong correlation between CO and OC in southern Taiwan
characteristics of aerosol chemistry
and decreasing trends of OC/EC ratios from March to May.
in northern and southern Taiwan
Monthly Surface Impact of Zero Out Biomass Burning Emission
Color contour: Base - Zero Out Case
White Arrows: Horizontal Wind
Red Contours(%): (Base - Zero Out Case)/Base
In March, Southeast Asia biomass burning
mainly affects southern part of East Asia,
while in April, the impact could reach Yangtze
River Delta region.
Local impact of Southeast Asia biomass
burning contributes about 30-60% to CO and
PM2.5, and 10-20% to O3, while the transport
impact could reach 20-40% to CO, 10-20% to
O3 and 30-60% to PM2.5 in southern of East
Asia.
Fu et al., 2012
Long-range Transport of Biomass Burning Aerosol
AOD
AAOD
SSA
Vietnam
Model generally captured the
episodes of column aerosol
optical depth at multiAERONENT sites.
Thailand
Thailand
Hong Kong
Taiwan
Huang et al., 2012
Red dot: AERONET observation, Blue lines: model results
The transport pathway from
SE Asia to downwind regions
is both illustrated from model
and observation
Episodic Vertical Impact from Biomass Burning
Comparison to
MPL Lidar at Phimai
14
Lidar
CMAQ
12
Height (km)
10
8
6
4
2
0
0
0.1
0.2
0.3
-1
Aerosol extinction (km )
The discrepancy may
come from the injection
height method of
distributing the
emission
Surface to 1km:
Local impact
dominates
1-5km:
Transport could
be very fast due to
strong horizontal
west wind;
Pollutants start to
downwash after
long range
transport, which
is an important
factor on surface
impact. The long
range transport
could contribute
70% to CO and
80% to PM2.5.
5km-14km:
Not much impact
in both local and
transport cases
Fu et al., 2012
Deposition around the
Taiwan Straits
Nepal
Llhasa
Future Works
Kampur
EPA-NCU
Hong Kong
Hanoi
SEAC4RS
Aug-Sept 2012
Ground Network
VASCO
AERONET+
MPLNet HQ
Bac Lieu
Penang
AERONET
AERONET DRAGON
Radiation Enhanced Site
MPLNET Lidar
Kuching
Singapore
Supersite
Temporary MPLNET Lidar
Jambi
Non-NASA Lidars
Hal Maring, 2012
Jakarta
MAN
VASCO
Current AERONET
Sites and Potential
7SEAS sites
Supersite
Daughters of Divine Zeal?
?
General Santos
Airport?
Kuching?
Supersite
Current AERONET
Supersite under development
Proposed sites
Need a site.
Neng-Hui Lin, 2012
Conclusions
1) FLAMBE biomass emissions are about 7-11 times higher than GFEDv2 in March
and April 2006, and through the comparison in Phimai and Hengchun, FLAMBE
shows better consistence with observational data.
2) Overall, CMAQ predicts similar spatial distributions as compared to various
satellite sensors; Model comparison to various ground measurements
suggested underestimation, which was attributed to the underestimated
emission (local and/or biomass burning). The long-range transport pattern
from the source region to downwind areas was well illustrated.
3) Monthly average impacts from biomass burning in Southeast Asia on East Asia
could reach 4-6ppbv for O3, 40-120ppbv for CO, and 10-80 ug/m3 for PM2.5.
Local impact of SA BB contributes about 30-60% to CO and PM2.5, and 10-20%
to O3, while the transport impact could reach 20-40% to CO, 10-20% to O3 and
30-60% to PM2.5 in southern of East Asia.
4) Gases and aerosol had a strong upward transport from surface to high
altitudes. The eastward transport becomes strong from 2 to 8 km in the free
troposphere. The subsidence process during the long-range transport
contributed 60 to 70%, 20 to 50%, and 80% on CO, O3 and PM2.5, respectively.
Acknowledgement
We thank NASA GSFC on funding support (grant no.: NNX09AG75G). Data
products from SMART-COMMIT and Deep Blue groups of NASA GSFC are funded
by the NASA Radiation Sciences Program, managed by Dr. Hal Maring. We
thank Dr. Edward J. Hyer for providing FLAMBE biomass burning emission data.
We thank Thailand PCD, EANET, Taiwan EPA, Hong Kong EPD and AERONET for
proving the measurement data. We would also like to thank Dr. Can Li from
NASA for providing satellite products and observational data in Phimai, Dr.
Carlo Wang for providing Lulin Mountain data and Dr. Hsin-Chih Lai for
providing monitoring data in Taiwan.
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