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, USA2 UTK-ORNL Center for Interdisciplinary Research and Graduate Education, Knoxville, Tennessee, USA3 Goddard Space Flight Center, NASA, Greenbelt, Maryland, USA

11th Annual CMAS Conference

October 16, 2012

• 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/

Motivations

WRF-CMAQ Regional Model (schematic framework)

Meteorology Model (WRF)

NCEP reanalysis data

Met-Chem Interface Processor (MCIP)

Air Quality Model (CMAQ)

Land use Emission factors

Plume height

Biomass emission mapping and vertical allocation

Biogenic Model (MEGAN)

Biogenic Emission

Biomass carbon emission(FLAMBE, GFED)

+ Anthropogenic emission

(INTEX-B for Asia)

Emission Data

Model outputs:Gases and aerosol

Model evaluationGround and space measurement

Impact assessment

Species Savanna and Grassland

Tropical Forest ExtratropicalForest

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)

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

Emission Factor for Different Land Use types

Mapping Biomass Carbon to Species

Land Use Type from WRF v3.1.1

Mapping Biomass Carbon to Species

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.

Inject Height Distributions for Biomass Burning Emission

Modeling Domain (27*27km) and Observation Sites

Five Southeast countries: Burma, Laos, Vietnam, Cambodia, and Thailand Available observation sites

Carbon emissions from FLAMBE is about

7-11 times higher than that from

GFEDv2.1 in March and April, 2006

 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

NOV 0.55 1.92 3.47

DEC 2.08 7.60 3.66

sum(Tg/a) 59.04 447.47 7.58

Comparison between FLAMBE and GFEDv2

0

50

100

150

200

250

1 3 5 7 9 11Month in 2006

carb

on

em

issi

on

s(T

g/m

on

)

GFEDv2FLAMBE

`

Carbon Emission Comparison between FLAMBE and GFEDv2.1

Monthly carbon emission from FLAMBE and GFED

Study period : March to May, 2006

Phimai

Hengchun

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.

Phimai (Obs. data source: NASA)

Hengchun (Obs. data source: Taiwan EPA)

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

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

Huang et al., 2012

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.

Ground CO data: Thailand Pollution Control Department

Huang et al., 2012

Aerosol Chemistry in SE Asia under Influence of BB

Vietnam Thailand Philippines Taiwan

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

Taiwan as a watershed under Influence of BB

Southern Taiwan Northern Taiwan

Strong correlation between CO and OC in southern Taiwan and decreasing trends of OC/EC ratios from March to May.

No correlation between CO and OC in northern Taiwan probably indicated less impact from biomass burning, at least at the surface.

Data source: Taiwan EPA Supersites

The influence of biomass burning ends here and resulted in distinct characteristics of aerosol chemistry in northern and southern Taiwan

Huang et al., 2012

Monthly Surface Impact of Zero Out Biomass Burning Emission

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.

Color contour: Base - Zero Out CaseWhite Arrows: Horizontal WindRed Contours(%): (Base - Zero Out Case)/Base

Fu et al., 2012

Long-range Transport of Biomass Burning Aerosol

AOD AAOD SSA

Vietnam

Thailand

Thailand

Hong Kong

Taiwan

Red dot: AERONET observation, Blue lines: model results

Model generally captured the episodes of column aerosol optical depth at multi-AERONENT sites.

The transport pathway from SE Asia to downwind regions is both illustrated from model and observationHuang et al., 2012

0

2

4

6

8

10

12

14

0 0.1 0.2 0.3

Aerosol extinction (km -1)

He

igh

t (km

)

Lidar

CMAQ

Episodic Vertical Impact from Biomass Burning

Surface to 1km: Local impact dominates1-5km: 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

Deposition around the Taiwan Straits

Comparison to MPL Lidar at Phimai

The discrepancy may come from the injection height method of distributing the emission

Fu et al., 2012

Hanoi

Hong Kong

EPA-NCU

Bac Lieu

Penang

SingaporeSupersite

Kuching

Jambi

Jakarta

AERONET

MPLNET Lidar

AERONET DRAGONRadiation Enhanced Site

MANVASCO

Temporary MPLNET Lidar

Non-NASA Lidars

Kampur

Nepal Llhasa

SEAC4RSAug-Sept 2012 Ground Network

AERONET+MPLNet HQ

VASCO

Future Works

Hal Maring, 2012

Current AERONET Sites and Potential 7SEAS sites

Daughters of Divine Zeal?

Need a site.

General Santos Airport?

Supersite

Supersite

?

Current AERONETSupersite under developmentProposed sites

Kuching?

Neng-Hui Lin, 2012

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.

Conclusions

We thank NASA GSFC on funding support (grant no.: NNX09AG75G). Dataproducts 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.

Acknowledgement

More info

Thanks for your attention!

Questions ?