A Regional Chemical Transport Modeling to Identify the Influences of Biomass Burning during 2006 Base-asia

Atmospheric Chemistry and Physics Discussions This discussion paper is/has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP if available. Abstract To evaluate the impact of biomass burning from Southeast Asia to East Asia, this study conducted numerical simulations during NASA's 2006 Biomass-burning Aerosols in SouthEast Asia: Smoke Impact Assessment (BASE-ASIA). Two typical episode periods (27–28 March and 13–14 April) were examined. Two emission inventories, 5 FLAMBE and GFED, were used in the simulations. The influences during two episodes in the source region (Southeast Asia) contributed to CO, O 3 and PM 2.5 concentrations as high as 400 ppbv, 20 ppbv and 80 µg/m 3 , respectively. The perturbations with and without biomass burning of the above three species were in the range of 10 to 60%, 10 to 20% and 30 to 70%, respectively. The impact due to long-range transport could 10 spread over the southeastern parts of East Asia and could reach about 160 to 360 ppbv, 8 to 18 ppbv and 8 to 64 µg/m 3 on CO, O 3 and PM 2.5 , respectively; the percentage impact could reach 20 to 50% on CO, 10 to 30% on O 3 , and as high as 70% on PM 2.5. An impact pattern can be found in April, while the impact becomes slightly broader and goes up to Yangtze River Delta. 15 Two cross-sections at 15 • N and 20 • N were used to compare the vertical flux of biomass burning. In the source region (Southeast Asia), CO, O 3 and PM 2.5 concentrations had a strong upward tendency from surface to high altitudes. The eastward transport becomes strong from 2 to 8 km in the free troposphere. The subsidence contributed 60 to 70%, 20 to 50%, and 80% on CO, O 3 and PM 2.5 , respectively to surface 20 in the downwind area. The study reveals the significant impact of Southeastern Asia biomass burning on the air quality in both local and downwind areas, particularly during biomass burning episodes. This modeling study might provide constraints of lower limit. An additional study is underway for an active biomass burning year to obtain an upper limit and climate effects.

spread over the southeastern parts of East Asia and could reach about 160 to 360 ppbv, 8 to 18 ppbv and 8 to 64 µg/m 3 on CO, O 3 and PM 2.5 , respectively; the percentage impact could reach 20 to 50% on CO, 10 to 30% on O 3 , and as high as 70% on PM 2. 5 . An impact pattern can be found in April, while the impact becomes slightly broader and goes up to Yangtze River Delta. 15 Two cross-sections at 15 • N and 20 • N were used to compare the vertical flux of biomass burning. In the source region (Southeast Asia), CO, O 3 and PM 2.5 concentrations had a strong upward tendency from surface to high altitudes. The eastward transport becomes strong from 2 to 8 km in the free troposphere. The subsidence contributed 60 to 70%, 20 to 50%, and 80% on CO, O 3 and PM 2.5 , respectively to surface 20 in the downwind area. The study reveals the significant impact of Southeastern Asia biomass burning on the air quality in both local and downwind areas, particularly during biomass burning episodes. This modeling study might provide constraints of lower limit. An additional study is underway for an active biomass burning year to obtain an upper limit and climate effects.

Introduction
The aerosols emitted by biomass burning contain a significant fraction of partially oxidized organic carbon and black carbon or soot, which could have significant climatic implications and uncertain (IPCC, 2007). During the combustion process of biomass, substantial amounts of nitrogen oxides, carbon monoxide, and hydrocarbons 5 are produced, which could be precursors of ozone. A positive link between ozone and smoke aerosol has been identified by satellite observation and modeling (Thompson et al., 2001). Aerosol hygroscopic properties could be changed due to the addition of biomass burning aerosol (Kim et al., 2006;Rissler et al., 2006), which further changes cloud microphysical properties (Guyon et al., 2005) and leads to smaller ice crystals 10 and variations of water vapor budget and distribution in the atmosphere (Kim et al., 2009;Sherwood, 2002). The atmospheric temperature profile is also influenced (Davidi et al., 2009), as is solar irradiation due to the aerosol dimming effect (Winkler et al., 2008). Patra et al. (2005) compared the inversion results with biogeochemical model simulations to provide strong evidence that both natural and anthropogenic biomass 15 burning constitute a major component in land-atmosphere carbon flux anomalies. Potter et al. (2001) reported the estimation of carbon losses by biomass burning on the Brazilian Amazonian region from ecosystem modeling and satellite data analysis. Although biomass burning studies in the last decade have focused on the physical, chemical, and thermodynamic properties of biomass-burning particles (Reid et al., 20 2005), model simulation results of biomass burning aerosol are still limited. Most of recent model studies focus on Mexico, South America and Africa (Alvarado and Prinn, 2009;Fast et al., 2009;Martins et al., 2009;Milton et al. 2008;Moraes et al., 2004;Myhre et al., 2008;Sinha et al., 2004;Staudt et al. 2002;Trentmann et al., 2002;, like several recent field projects including SAFARI 2000 (Swap Introduction  Southeast Asia is one of the major biomass-burning emission source regions in the world (Streets et al., 2004). Both biomass and fossil combustion processes are potential sources of the extensive Asian Brown Clouds (ABC) over South Asia (Gustafsson et al., 2009). The smoke plume from biomass burning generally spreads downwind thousands of kilometers away and affects air quality, human health, and regional climate. 5 To date, information on the regional distribution of biomass-burning aerosols from Asia remains limited, and their regional radiative impact is not well understood .
Early studies showed the springtime high ozone events in the lower troposphere from Southeast Asian biomass burning (Liu et al., 1999). The effects of Southeast Asia 10 biomass burning on aerosols and ozone concentrations over the Pearl River Delta (PRD) region was studied using satellite data, ground measurements, and models; it was suggested that O 3 productivity is reduced due to the reduced UV intensity under the influence of Southeast Asia biomass burning (Deng et al., 2008). Choi and Chang (2006) described the use of MOPITT to evaluate the influence of Siberian 15 biomass burning on CO levels around Korea and Japan. Spatial distributions of black carbon (BC) and organic carbon (OC) aerosols were simulated along with the radiative forcing of the Asian biomass burning . The influence of biomass burning from Southeast Asia on CO, O 3 and radical (OH, HO2) outflow was also simulated by . A new transport mechanism of biomass burning from In-20 dochina was discovered using the WRF/Chem model (Lin et al., 2009). Despite these efforts, however, comprehensive estimates of the impact of Southeast Asia biomass burning on the downstream regions are still lacking.
This study is part of NASA's BASE-ASIA experiment (Biomass-burning Aerosols in South-East Asia: Smoke Impact Assessment; cf. http://smartlabs.gsfc.nasa.gov/) in 25 2006. One of the objectives of BASE-ASIA is to investigate the regional impact of biomass burning on Southeast and East Asia. This study identifies the influences of biomass burning on East Asia during intense burning episodes by employing a regional "one atmosphere" model, the Community Multiscale Air Quality Modeling  (Byun and Schere, 2006;Byun and Ching, 1999). Model simulations were compared with satellite observations and in situ ground measurements to validate the model. Trace gases and particulate matters of biomass burning from model outputs were used to analyze the regional impact of biomass burning on the downstream regions. The goal of this study is to characterize the transport and quantify the impact of 5 biomass burning could from Southeast Asia to East Asia using NASA's 2006 Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) anthropogenic emission inventory (Zhang et al., 2009) and biomass burning emissions (Reid et al., 2009;van der Werf et al., 2006).

Model description
This study uses the Models-3/Community Multiscale Air Quality (CMAQ) modeling system. The model has been widely used to predict atmospheric transport in East Asia (Streets, et al., 2007;Carmichael, et al., 2008;Chuang et al., 2008;Fu et al., 2008Fu et al., , 2009aWang et al, 2008Wang et al, , 2010aXu et al., 2008). The model configuration is 15 described in Table 1. The modeling domain is shown in Fig. 1.

Initial profiles of meteorological variables
Meteorological output is considered an important input for CMAQ. The joint Navy, NASA, NOAA, and universities Fire Locating and Modeling of Burning Emissions (FLAMBE) project was used to investigate a consistent system of emissions (Reid et al., 2009). Hourly emissions from FLAMBE were taken from the 2006 data (http://www.nrlmry.navy.mil/aerosol web/arctas flambe/data hourly/). The fire locations and emissions were computed based on the Global Land Cover Characteri-5 zation Version 2 database (http://edc2.usgs.gov/glcc/glcc.php). This dataset contains hourly biomass burning areas as well as carbon emissions in a certain location with coordinated latitudes and longitudes.
The Global Fire Emissions Database, Version 2 (GFEDv2.1), another biomass burning emission data source, is derived from MODIS fire count data (van der Werf et 10 al., 2006). This dataset comprises eight-day periods throughout 2006 and monthly average emissions. In this study, carbon emissions from FLAMBE and GFED were both allocated to the simulation domain with a resolution of 27 km × 27 km. Since the temporal resolution of GFED data is eight days, the hourly profile in FLAMBE was used to distribute GFED emissions. Quantitative comparisons between the FLAMBE 15 and GFEDv2.1 biomass emission inventories were conducted in this study before one inventory was selected to further estimate the regional impact of Southeast Asian biomass burning.
Both biomass burning emission inventories estimate carbon emissions, which were then converted to other species. Andreae and Merlet (2001) reported emission fac-20 tors (EF) for conversion from carbon emissions to other species (CO, CH 4 , NMHC, NO x , NH 3 , SO 2 , PM 2.5 , TPM, OC and BC) in terms of different land use types, such as tropical forest, extratropical forest, agricultural residues, and savanna and grassland. Based on the 24 land use types (http://www.mmm.ucar.edu/wrf/users/docs/ user guide V3/users guide chap3.htm# Land Use and) and the WRF Preprocessing System (WPS) output, we categorized the land use types into several groups: EF of the grids with land use category 2,3,4 (Dryland Cropland and Pasture, Irrigated Cropland and Pasture, and Mixed Dryland/Irrigated Cropland and Pasture, respectively) were assigned to agricultural residues; EF of the grids with land use category  5,6,7,8,9,10 (Cropland/Grassland Mosaic,Cropland/Woodland Mosaic,Grassland,Shrubland,Mixed Shrubland/Grassland,Savanna,respectively) were assigned to savannas and grasslands; EF of the grids with land use category 11,12,14,15 (Deciduous Broadleaf Forest,Deciduous Needleleaf Forest,Evergreen Needleleaf,and Mixed Forest,respectively) were assigned to extratropical forests; and the grids with land use 5 category 13 (Evergreen Broadleaf ) were assigned to tropical forests. Grids with other land use types usually do not have biomass burnings (van der Werf et al., 2006).

Stratification of biomass burning emission
Determining the inject height is important for the regional chemical model and could significantly affect long-range transport. Leung et al. (2007) used the GEOS-Chem five different scenarios using the University of Maryland CTM (Allen et al., 1996a,b) and found that pressure-weighted injection through the tropospheric column into the midtroposphere agreed the best with observations. In this study, we employed SMOKE 2.6, which is widely used to generate emissions for CMAQ, to calculate the inject height of fires based on three parameters, including the bottom height of the plume (Pbot) and Introduction Troposphere) mobile laboratory. The instrumentation of COMMIT has been discussed in detail elsewhere (Li et al., 2010b) and is only briefly introduced here. CO was measured with a modified Thermo Environmental Instruments (Franklin, MA) Model 48C detector (Dickerson and Delany, 1988 the downwind area of Asia. Continuous operations of precipitation chemistry, aerosol chemistry, trace gases, mercury, atmospheric radiation, and meteorological variables are measured at LABS by various research groups (http://lulin.tw). In this study, CO was measured by HORIBA/APMA-360, with a detection limit of 50 ppb; O 3 was measured by ECOTECH/EC9810, with a detection limit of 0.5 ppb; and PM 10 was measured 5 by Thermo/R&P 1400a with a detection limit of 0.5 µgm −3 . The maximum concentration of pollutants at this site was observed during spring, which corresponds to biomass burning from Southeast Asia (Sheu et al., 2010).

Satellite observation
A number of satellite sensors launched in the past decade have proven valuable for 10 studying anthropogenic pollution in the troposphere (e.g. Martin, 2008;Richter et al., 2005). In this study, we use the tropospheric NO 2 product from the Ozone Monitoring Instrument (OMI) aboard NASA's EOS Aura satellite, and aerosol optical thickness (AOT) retrieved from the MODerate Resolution Imaging Spectrometer (MODIS) instrument aboard the Aqua satellite (for details of the OMI instrument and NO 2 product, 15 see Levelt et al., 2006 andBucsela et al., 2006). MODIS provides remotely sensed aerosol information with a resolution of 10 × 10 km for this study. Detailed information on MODIS sensors and global daily observations of aerosols were used to retrieve aerosol properties over land (Kaufman et al., 1997;Hsu et al., 2004Hsu et al., , 2006Levy et al., 2007) and ocean (Tanré et al., 1997). In this study, we use level 2 collection and Introduction

Comparison between FLAMBE and GFED emission inventory
We examine biomass burning emissions from five countries in Southeast Asian ( Fig. 1): Burma, Laos, Vietnam, Cambodia, and Thailand. The monthly carbon emissions in Southeast Asia derived from FLAMBE and GFEDv2 emission inventories are shown in Table 3, and the monthly carbon emissions from the rest of the model domain (denoted as East Asia) in the two emission inventories are shown in Table 4. Southeast Asia dominated the total carbon emissions in the study domain in both emission inventories, contributing two to three times more biomass burning carbon emissions than East Asia. Moreover, carbon emissions from FLAMBE were 7.58 and 4.86 times that of GFEDv2 for Southeast Asia and East Asia in 2006, respectively (see Tables 3 and 4). The massive divergences between FLAMBE and GFEDv2 (Reid et al., 2009) were mainly due to the difficulty in estimating the emission inventory from the individual fires. The large uncertainty of the method in estimating the carbon emissions can be attributed to higher emission factors for agricultural fires in FLAMBE (Reid et al., 2009).
Although the two emission inventories differed in quantity, they show similar seasonal patterns, with March and April as the most active months in terms of biomass burning. The two months contributed about 70% to the total carbon emissions in GFEDv2. For FLAMBE, the ratio was even higher at about 84%. In spring, the burning of biomass and biofuels was extensive in Southeast Asia; satellite images show that fire spots 20 frequently occurred in the area during this period (Chang and Song, 2010); this was the main cause for the high carbon emissions in these two months. Thus, this period was our main focus for the evaluation of the impact of biomass burning on local and downwind regions. Given the 8-to 12-fold difference between the two inventories for March and April, we drive our models with both and select the inventory that better 25 matches the measurements for further analysis. Here, we choose Phimai, which is located in central Thailand near the biomass burning source regions, to evaluate the model output using the two emission inventories.  Figure 2 shows the comparisons between the observation and simulation results of carbon monoxide (CO), a typical species or tracer for biomass burning sources. The simulation based on FLAMBE emissions agreed well with the surface measurements and successfully captured the peak values from 27 to 28 March and 13 to 14 April. While the simulation based on GFED emissions seemed to model the low CO periods 5 due to relatively weak biomass burning, it obviously underestimated the CO concentrations during the peak periods by as much as 200 to 450 ppbv. This comparison indicates that the FLAMBE emissions provide a better representation of biomass burning sources in our model than the GFED emissions. Nam et al. (2010) also found underestimation of CO emissions at lower subtropical latitudes over Asia using the GFED emissions. As discussed above, the difference could be attributed to the different methods used to derive the emissions. Additionally, the MODIS satellite observation has limitations in its spatial coverage due to satellite track and clouds; moreover, emissions based on MODIS may not completely cover the study regions at all times. This method also has temporal limitations, as MODIS on 15 board Aqua and Terra could only provide two observations over our study area on a given day. As stated in Sect. 2.3.2, the GFED emissions were derived from the MODIS eightday period fire count data; thus, some biomass burning information could be missing, especially for strong episodes. While the FLAMBE emissions provide hourly resolution 20 based on certain assumptions (Reid et al., 2009) and 75% for PM 2.5 species were used as recommended by Morris (2005), Morris et al. (2006), and Tesche et al. (2006). The observational datasets at all sites were compared with SURFACE and INJECT model output. The results of model performances for O 3 , CO and PM 2.5 are shown in the Supplement. Figure S1 shows the results at the source region, Phimai. Most of the param-20 eters, CO, O 3 and PM 2.5 were within or close to the benchmark according to the USEPA (2007). There was much underestimation for PM 2.5 , which could be related to emissions. As for the four sites in Hong Kong (Fig. S2) and in Lulin (Fig. S3) Overall, the CMAQ model could simulate reasonably well as suggested by the comparison to various datasets. Moreover, there was not much difference between the two allocation methods. n fact, we still see from the supplemental tables that the INJECT method performed slightly better than the SURFACE method. Based on the Air Sciences, Inc., 2005, we believe that the INJECT methods as a better way to allocate the 5 biomass burning emissions in more layers.

Remote sensing observations
We also evaluate the model performance against remote sensing data during two biomass burning episodes. As shown in Table 3, the largest CO emission rate occurred in March and April. The TRACE-P Project during the same season  indicated that biomass burning was ubiquitous in Southeast Asia during this period. We found that the peak values did not coincide between days among different years. This is because biomass burning emissions were highly episodic. Due to irregular biomass burning from wildfires or anthropogenic activities, we had to check each case to sort out typical episodes. In March and April, we found that there were sev-15 eral high biomass burning episodes based on the hourly CO emission rate: 26 through 28 March and 11 through 14 April. In the discussions below, we will focus on these specific episodes.
Figures 3 and 4 compare the regional distribution of the observed satellite parameters with the simulated results, including AOT and tropospheric nitrogen dioxide (NO 2 ). 20 The model NO 2 column loading includes a total of 19 layers in CMAQ. The satellite NO 2 product used is Aura/OMI L3e tropospheric NO 2 at 0.25 • ×0.25 resolution, with cloudy pixels (cloud fraction >30%) screened out. The satellite observations on 28 March and 13 April were selected to evaluate the model performance. Gaps in satellite data are mainly due to possible cloud interference and limited satellite swath. 25 The satellite observation and model output demonstrate a similar spatial pattern. During both episodes, hot spots were observed and well simulated in the Southeast Asia region. There were two enhancement regions of NO 2 , one in the Southeast Asia 3084 Introduction region and the other in the industrialized eastern part of mainland China. Although the nitrogen oxides were not the main species emitted from biomass burning, high column concentrations of NO 2 were still present over most of Southeast Asia in both the remote sensing data and model simulation. The areas of high column NO 2 were confined to the source regions in Southeast and East Asia, (Fig. 3), suggesting negligible long-5 range transport of the relatively short-lived NO 2 and different sources of NO 2 in the two regions.
The high column loading of NO 2 in the northeastern part of China and the Pearl River Delta region was observed by various sensors (van der A et al., 2006) and was mainly due to the large consumption of fossil fuels by power plants, industries, and 10 vehicles. AOD indicates the combination of all aerosol sources. The model-simulated AOD in the left panels was calculated based on the method used by Roy et al. (2007), which converts the aerosol dry mass concentration from the CMAQ model to AOD. As shown in Fig. 4, the model generally captured the observed magnitude and distribution of MODIS AOD on 28 March. The gaps in satellite AOD are mainly due to cloudy 15 scenes and sun glint over the ocean.
Both satellite and CMAQ model show heavy aerosol loading over the biomass burning region in Southeast Asia and in the downwind areas, suggesting biomass burning activities and substantial long-range transport during the two episodes. Two high AOD regions were observed and simulated, one in Southeast Asia, which covered large 20 areas extending eastward to the Western Pacific, and the other in a relatively small area located between 25 • N and 30 • N near the Yangtze River Region. On 13 April, the model seemed to predict lower AOD values in the northern part of China than the MODIS observation (Fig. 4). This underestimation was probably due to the lack of a dust module in CMAQ, as dust events derived from the Gobi desert in Northern China As illustrated from the spatial distribution of NO 2 and AOD, the transport pathways of biomass burning plumes could be visualized. There were slightly different transport pathways between the two episodes. On March 28, aerosol originating from Southeast Asia first circulated into the subtropical regions before migrating northward to the Central Pacific. Compared to aerosol, NO 2 transported over much shorter distances. The different transport patterns of these species were probably due to their different lifetimes; that is, NO x (NO + NO 2 ) are highly reactive and quickly convert to other species such as depositions. On 13 April, the simulated plume clearly shifted to higher latitudes 5 near the northeastern parts of China and even Japan, as illustrated by the AOD distribution. The main body of the outflows stayed around 15 • N to 25 • N, and there was an obvious concentration gradient along the transport pathway, although it was not evident in the first episode. In other words, the potential of biomass burning plumes to transport in the second episode was not as strong as in the first; neither was its impact 10 on the downwind regions.
In summary, the model could relatively well simulate the spatial distribution of typical pollutants emitted from biomass burning. In the next section, we will not include the biomass burning emission of Southeast Asia in the model to quantitatively assess the regional influences caused by biomass burning.

Regional influences from biomass burning
In order to evaluate the impact of biomass burning on the source and the downstream regions, we performed a numerical experiment without the biomass burning emissions over Southeast Asia in the model to compare with the biomass burning emissions in the previous sections. Figures 5 and 6 illustrate the regional changes in CO, O 3 , and 20 PM 2.5 concentration with and without the biomass burning emissions during the two episodes (27 through 28 March and 13 through 14 April) in 2006. Note that the contours denote the perturbations between the base case and the case without biomass burning emissions in Southeast Asia. Red contoured lines denoted the percentage change of the species, which also represent the percentage contribution from biomass burning in 25 Southeast Asia. The white arrows denote the wind vectors in the 15-th vertical layers at the altitude of 2.4 km. This layer was chosen mainly because of significant long-range transport above it, which would be discussed in the next section. During the first episode on 27 March, the largest impact from the total biomass burning emissions covered areas of 10 to 25 • N and 100 to 130 • E, extending from Southeast Asia to the West Pacific. Corresponding to the satellite-observed spatial distribution in Figs. 3 and 4, the largest changes occurred over the source regions and over the transport pathways. The benefits from the scenario with the total biomass burn-5 ing emissions were most prominent in Southeast Asia and nearby regions such as the Yunnan and Guangxi provinces in Southern China. As illustrated in Fig. 5, without biomass burning in the source regions, concentrations of CO, O 3 , and PM 2.5 were reduced by as much as 400 ppbv, 20 ppbv and 80 µg/m 3 , respectively. The reduction percentages of the above three species were in the range 10 of 10 to 60%, 10 to 20% and 30 to 70%, respectively. It seemed that the contribution percentage of O 3 from biomass burning was relatively small, in agreement with previous results (Zhang et al., 2003).
The downwind areas were also strongly influenced by the long-range transport of biomass burning plumes. On 27 March, the impact spread over the southeastern parts 15 of mainland China, including the Pearl River Delta region and Fujian province. The impact from biomass burning on this region amounts to about 160 to 360 ppbv CO, 8 to 18 ppbv O 3 and 8 to 64 µg/m 3 PM 2.5 . The biomass burning outflows may influence Taiwan and even the West Pacific. The transport impact could reach 20 to 50% on CO, 10 to 30% on O 3 , and 70% on PM 2.5 .

20
On 28 March, the biomass burning outflows transported more southward to the lower latitudes compared to the previous day, and the impact on the southeastern China was almost negligible (Fig. 5). The biomass burning plume was centered over the Western Pacific and the northern part of the Philippines; the magnitude of the biomass burning influence was similar to that on 27 March. The changes between days in these regions 25 were likely related to the meteorological conditions. The northwesterly flow intensified on 28 March compared to the previous day, which pushed the plumes more southward. Moreover, the low-pressure system over Southern China moved over the South China Sea. Introduction

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Interactive Discussion
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Along the major export pathway of the plumes, biomass burning derived CO and O 3 were spatially correlated with each other, probably indicating their common source. The transport pathways of biomass burning derived particles (PM 2.5 ) differed from CO and O 3 , diffused quickly, and covered relatively short distances. On March 27, PM 2.5 from biomass burning decreased from 40 to 80 µg/m 3 over the continent to 8 to 20 µg/m 3 over the ocean; thus, over 70% of the particles were scavenged during the transport. Compared to the gaseous pollutants, particles were more easily subject to scavenge through the wet/dry deposition.
As for the second episode on 13 and 14 April (Fig. 6), the impacts from biomass burning were not as widespread or intense. The effects of biomass burning centered over 10 the source region areas of Southeast Asia, Southern China, and the regions between Southern China and the South China Sea. Beyond the Pearl River Delta region, the impact becomes less significant, although weak influence of transported plume may exist over oceanic areas as far as Japan (biomass burning derived CO and O 3 <100 ppbv and 6 ppbv, respectively with a negligible effect on PM 2.5 ). Figure 7 shows the monthly average impact of biomass burning and wind patterns in March and April. In March, Southeast Asia biomass burning mainly affected southern parts of East Asia. Biomass burning contributed about 30 to 60%, 10 to 20%, and 20 to 70% of the total CO, O 3 and PM 2.5 concentrations, respectively. The long-range transport had a significant impact over the Yunnan and Guangxi provinces in China 20 and over the South China Sea around Hainan Island, with 140 to 180 ppbv CO derived from biomass burning. The transported CO extended over broader areas such as the Fujian, Jiangxi, and Hunan provinces in China and the South China Sea. However, this effect was relatively insignificant, with less than 100 ppbv from biomass burning. As for O 3 , the area influenced was broader than that of CO, with considerable biomass There was a slight difference between the two months in that the impact from biomass burning reached the Yangtze River Delta region, as shown in Fig. 7. This difference is attributed to the change in wind patterns. In April, the easterly flows at the low latitudes turned to the southeasterly flows above 15 • N, which could push the Southeast Asia outflows northward. The biomass burning impact on CO emissions 5 remained in Southeast Asia and southern parts of China. The CO concentration that reached the Yangtze River Delta region was less than 60 ppbv, which contributed only a small percentage of the total concentration.
In April, ozone contributed by biomass burning plumes covered a region broader than that in March. Over most of the Pearl River Delta region, the Guangxi province, 10 and large areas of South China Sea, the O 3 contribution by biomass burning plumes reached 9 to 11 ppbv. Additionally, the impact of biomass burning on the O 3 concentrations in parts of the Fujian, Jiangxi, and Hunan provinces reached about 8 ppbv, about 4 ppbv higher than in March. Another obvious difference was a high O 3 concentration belt extending from the East China Sea to the regions below Japan. The fast disper-15 sion of O 3 was probably related to the prevailing wind pattern during this period. As for PM 2.5 , its transport was also more widespread and influenced major areas of southern China; the particulate contribution from biomass burning ranged from 10 to 30%. Figures 8 and 9 show the simulated altitude-longitude structure of CO, O 3 , and PM 2.5 20 by biomass emission. Note that the contours denote the perturbations between the base case and the case without biomass burning emissions in Southeast Asia. Red contoured lines denote the percentage change of the species, which also represent the percentage contribution from biomass burning in Southeast Asia. The white arrows denote the wind vectors in the different vertical layers. range transport contributed 60 to 70% to CO, 20 to 50% to O 3 , and 80% to PM 2.5 , respectively. On 13 April, the transport of the biomass burning plumes was not as strong as on 27 March (Fig. 9), which is also consistent with our previous findings. On the crosssection of 15 • N, the main body of pollutants was located between 98 • E and 110 • E. 20 At the altitudes between 1km and 5km, there also existed a plume layer with a short tail that transported to around 120

Vertical distribution of biomass plumes
• E for CO and O 3 . At the cross-section of 20 • N, the biomass burning emission intensified. The subsidence of pollutants was found at 105-115 • N, which was located at the junction of Vietnam and Guangxi province of China.
The air pollutants could be depleted by various pathways during the subsidence. 25 As illustrated from the vertical structure of the biomass burning derived species, CO decreased from 400 ppbv at the top to about 160 to 200 ppv, with a depletion percentage of 50 to 60%. O 3 decreased from 20 ppbv to 8 to 10 ppbv, also with a depletion percentage of 50 to 60%, while PM 2. 5  20%. It seemed that the long-range transport of the biomass plumes exerted greater influence in the free troposphere than in the boundary layer.

Conclusions
In this paper, we evaluate the impact from Southeast Asia to East Asia during high biomass burning emissions periods. Through comparisons, we find that biomass burn- 10 ing plays a significant role in air quality in both local and downwind areas.
During the first episode on 27 March, the influence of biomass burning in the source region contributed to CO, O 3, and PM 2.5 concentrations as high as 400 ppbv, 20 ppbv, and 80 µg/m 3 , respectively. The reduction percentages of the concentrations on the above three species without biomass burning were in the range of 10 to 60%, 10 to 15 20% and 30 to 70%, respectively. Also, the impacts due to long-range transport could spread over the southeastern parts of mainland China, including the Pearl River Delta region and the Fujian province in China. the Guangxi province in China, and large areas of the South China Sea. As for PM 2.5 , its transport was also more widespread and influenced major areas of southern China, with the particulate contribution from biomass burning ranged from 10 to 30%. Two cross-sections at 15 • N and 20 • N were selected to compare the vertical flux of biomass burning. In the source region (Southeast Asia), CO, O 3, and PM 2.5 concen-10 trations had a strong upward tendency from surface to high altitudes. The transport became strong from 2 to 8 km in the free troposphere, and the pollutants were quickly transported eastward due to a strong western wind. The subsidence contributed 60 to 70% CO, 20 to 50% O 3 , and 80% PM 2.5 , respectively, to surface in the downwind area. Though NASA's BASE-ASIA conducted this biomass burning measurement, it 15 might be less active in biomass burning in 2006. This modeling study might provide constraints of lower limit. An additional study is underway for an active biomass burning year to obtain an upper limit. Introduction

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