Distribution and direct radiative forcing of black carbon aerosols over Korean Peninsula

Atmospheric Environment 58 (2012) 45e55

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Distribution and direct radiative forcing of black carbon aerosols over Korean Peninsula Min Young Kim a, b, Seung-Bok Lee c, Gwi-Nam Bae c, Seung Shik Park d, Kyung Man Han e, Rae Seol Park e, Chul Han Song e, Sung Hoon Park a, * a

Department of Environmental Engineering, Sunchon National University, Suncheon, Jeonnam 540-742, South Korea ECO BRAIN Co., Ltd., Seoul 153-768, South Korea Center for Environment, Health and Welfare Research, Korea Institute of Science and Technology, Seoul 136-791, South Korea d Department of Environmental Engineering, Chonnam National University, Gwangju 500-757, South Korea e School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 500-712, South Korea b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 April 2011 Received in revised form 28 March 2012 Accepted 30 March 2012

Regional air quality modelling was used to simulate the distribution of BC aerosol over the Korean Peninsula for four mid-season months of 2009. Compared to ground-based and satellite observations, the model underestimated the average BC burden significantly, which might be attributed to inaccuracy in BC emissions inventories partly due to the neglect of the emissions from biomass burning although it is not possible to rule out inaccurate prediction of meteorology. The model-estimated monthly average BC burden was highest in winter because of the largest emission. When the BC burden was divided by the monthly emission factor, the adjusted BC burden was much higher in spring and fall than in winter and summer due to strong influence of Chinese source conveyed by westerly wind prevailing in spring and fall. Both long-range transport and local sources were shown to contribute to atmospheric BC over the Korean Peninsula. Urban areas were influenced more by local sources while the effect of long-range transport was higher in remote areas. Based on the model simulations, the direct radiative forcing (DRF) of BC was estimated to be 0.1e1.8 W m
2 over the Korean Peninsula with the domain-average value of 0.39 W m
2. Accounting for the model underestimation of absorbing aerosol optical depth by BC by 48% compared to measured monthly averages due to the underestimated emissions inventories, the adjusted average DRF is 0.75 W m
2. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Black carbon Radiative forcing Air quality Climate Modelling Korean Peninsula

1. Introduction Black carbon (BC) refers to a strongly light-absorbing carbonaceous aerosol species (Bond and Bergstrom, 2006) emitted from incomplete combustion of fossil fuel and biomass burning. BCcontaining particles are of particular importance because of their adverse effect on human respiratory and cardiovascular systems (Ibald-Mulli et al., 2002; Oberdorster, 2001; Pope and Dockery, 1996), long-range transport characteristics (Ramanathan et al., 2001b; Rosen et al., 1981), large surface area due to aggregate structure facilitating heterogeneous reactions (Chughtai et al., 2002), and warming climate impact (Chung and Seinfeld, 2005; Jacobson, 2001). BC is the chemical component that imposes large uncertainty in estimating the climate impact of aerosol particles (IPCC, 2007). BC * Corresponding author. E-mail address: [email protected] (S.H. Park). 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2012.03.077

absorbs visible sunlight and heats the atmosphere (Ech et al., 1998; Satheesh and Ramanathan, 2000), exerting positive radiative forcing (direct effect), whereas light-scattering components such as sulphate cool the earth-atmosphere system as long as they are not internally mixed with absorbing aerosol. Jacobson (2000) suggested based on global model simulation results that the direct effect of BC may be larger than the total negative forcing effect contributed by all other anthropogenic aerosol species. The direct effect of BC is different from the warming effect of greenhouse gases in that BC causes atmospheric heating and surface cooling, while greenhouse gases heat both atmosphere and Earth surface. Intense atmospheric heating caused by BC is regarded as an important contributor to the retreat of Himalayan glaciers (Barnett et al., 2005; Ramanathan et al., 2007; Thompson et al., 2003). BC particles mixed with hygroscopic species can take part in cloud processes and affect the aerosol indirect effect (Albrecht,1989; Kaufman et al., 2002; Lohmann and Feichter, 2005; Ramanathan et al., 2001a; Rosenfeld, 2000). BC particles may also act as ice

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M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

nuclei contributing to additional indirect effect (Cozic et al., 2007; DeMott et al., 2010; Liu et al., 2009). Light absorption by atmospheric BC can also affect the cloud cover through various ways. For example, BC contained within or between cloud drops absorbs sunlight increasing the cloud temperature and reducing the cloud cover (Ackerman et al., 2000; Hansen et al., 1997; Lohmann and Feichter, 2001), which imposes additional positive radiative forcing. On the other hand, heating by BC existing below cloud layer can enhance vertical motion and cloud formation (McFarquhar and Wang, 2006). The effect of atmospheric heating by BC on the cloud cover is collectively referred to as the semi-direct effect. Refer to Ramanathan and Carmichael (2008) and Koch and Del Genio (2010) for detailed reviews on the climate impact of BC. Recently, it has been suggested in the literature that BC is the second largest contributor to global warming after carbon dioxide (Jacobson et al., 2007; Ramanathan and Carmichael, 2008), implying that BC emissions control may contribute to slowing global warming significantly (Bond and Sun, 2005; Hansen and Sato, 2001; Jacobson,

2002). Based on global model simulations, Jacobson (2002) argued that removing all BC and organic matter emissions due to fossil fuel combustion may reduce net warming by about 20% within 5 years, while reduction of CO2 emissions by a third will have the same effect in a much longer time (50e200 years). The impact of BC on regional air quality (AQ) and climate is expected to be particularly high over the Korean Peninsula because of large number of vehicles and high fossil fuel consumption as well as trans-boundary transport from Chinese sources. East Asia is known to be one of the most important BC emission source regions in the world (Bond et al., 2004; Ramanathan and Carmichael, 2008; Streets et al., 2003) contributing, together with South Asia, to the highest ground-level BC concentration of Asia in the world (Koch et al., 2009). BC emissions from China and India account for about 30% of global emissions (Ramanathan and Carmichael, 2008). Given that the lifetime of BC is much shorter than those of greenhouse gases such as CO2, the climate impact of BC is inherently more focused over source regions, indicating the regional climate impact

Fig. 1. The spatial domain used in this study: (a) with the annual average BC emission, (b) with the locations of four Korean measurement sites (Seoul (37.57N, 126.95E); Anmyeon (36.32N, 126.55E); Gwangju (35.17N, 126.88E); and Gosan (33.28N, 126.15E)) and seventeen Chinese and one Japanese sites (see the Supplemental Information for their locations). The oval shows the location of the largest BC emission source region in Asia.

M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

of BC may be large in East Asia. Moreover, China and India are still undergoing rapid industrial development implying that BC emissions and burden in East Asia may become even larger in the future without dramatic change in mitigation strategy. There have been a few studies that investigated the impact of BC on regional climate over Asian continent (Lau et al., 2006; Menon et al., 2002; Wu et al., 2008), but investigation on detailed spatial and seasonal distribution of BC over the Korean Peninsula and resulting climate implication is yet to be done. Contrary to the long-lived greenhouse gases, BC, which is an aerosol species, is distributed inhomogeneously in the atmosphere. It is not possible to establish complete knowledge of spatial distribution of BC using the limited database of observations. Therefore, it is required to employ numerical modelling to quantify the impact of BC on AQ and climate. The U.S. EPA Models-3 Community Multiscale Air Quality (CMAQ) Modelling System (Binkowski and Roselle, 2003; Byun and Schere, 2006) is used in this study to simulate the distribution of BC aerosol over the Korean Peninsula in four mid-season months of 2009. Simulated BC concentrations and absorbing aerosol optical depth (AAOD) are evaluated against ground-based and satellite observations available. Spatial and seasonal distributions of BC are discussed and direct radiative forcing (DRF) of BC is estimated based on the results of model simulations. 2. Model description The AQ model used in this study consists of a chemical transport model (CTM) and a meteorological driver. CMAQ version 4.6 was used as the CTM in this study. CMAQ was driven off-line by the meteorological driver, the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) version 3.7.1. This AQ model was used to simulate the physics and dynamics of BC aerosol particles including emission, transport, coagulation, condensation, hygroscopic growth, and wet and dry deposition. Fig. 1 shows the spatial domain used for AQ modelling in this study. The annual average BC emission and the locations of the measurement sites are shown together. The oval appearing in the northeastern China shows the location of the largest BC emission source region in Asia (Jeong et al., 2011). It is noted that this source region is located to the west from the Korean Peninsula implying that its influence on the Korean Peninsula may be strongly dependent on the wind direction. Two-way nesting between Domain 1 (D01) covering East Asia and Domain 2 (D02) covering the Korean Peninsula was used. A 30-km 140  116 and a 10-km 82  127 horizontal grids on a Lambert Conformal map

projection were employed for D01 and D02, respectively. The terrain-following vertical coordinate with 14 unevenly spaced vertical levels ranged from the surface to 70 hPa was employed, with 4 levels located in the first 1 km. Emissions were obtained from INTEX-B (Zhang et al., 2009) and REAS (Ohara et al., 2007) inventories for China and Korea and for Japan, respectively, for the year 2006. In the INTEX-B emission inventory (http://mic.greenresource.cn/intex-b2006), emissions are gridded at a spatial resolution of 0.5  0.5 with monthly seasonality. All the anthropogenic sources except open biomass burning were included from four source categories: power plants, industry, residential and transportation. The total anthropogenic BC emission in Asia in the year 2006 was estimated to be 2.97 Tg. The BC emission from China accounted for 1.8 Tg, which represents 14% increase from 2001 (Zhang et al., 2009). The REAS inventory (http:// www.jamstec.go.jp/frcgc/research/p3/emission.htm) includes emissions from anthropogenic combustion and non-combustion (e.g., power generation, industry, and transport) sources except open biomass burning with a spatial resolution of 0.5  0.5 . BC emissions from biomass burning were not taken into account because they could not be gridded and time-stamped properly due to their sporadic and intermittent nature and the lack of knowledge for the year 2009 for which simulations were carried out in this study. The effect of BC emissions from biomass burning will be discussed later in more detail. The annual average BC emission is shown in Fig. 1. The seasonal variation of BC emission was taken into account by adopting the monthly emission factor suggested by Streets et al. (2003) (see the Supplemental Information for details). More detailed model description is provided in the Supplemental Information in terms of size distribution of particulate matters (PMs), mixing state of BC particles, physical and chemical processes, initial and boundary conditions, and model spin-ups. 3. Results and discussion The results of one-month CMAQ simulations for four midseason months, i.e., January, April, July and October, of 2009 and corresponding BC DRF calculations are presented in this section. 3.1. Model evaluation In order to evaluate the model predictions of BC, the concentration of BC contained in ground-level PM2.5 (BC2.5 hereafter) predicted by CMAQ was compared with available observations. The locations of the 4 stations where measurements were conducted 8

8

linear regression: y = 0.53 * x 2 R = 0.71

linear regression: y = 0.53 * x R2 = 0.71

6

3

3

Modelled (µg/m )

6 Modelled (µg/m )

47

4

Gwangju Seoul Anmyeon Gosan

2

4

Jan Apr Jul Oct

2

0

0 0

2

4

6 3

Observed (µ g/m )

8

0

2

4

6

8

3

Observed (µg/m )

Fig. 2. Model-measurement comparison of monthly average BC2.5 (mg m
3) grouped with respect to measurement site (left) and with respect to season (right).

M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

during the temporal domain of this study are shown in D02 of Fig. 1b. Seoul, the capital of South Korea, is the largest city in South Korea with a population of about ten million. Gwangju is the largest city in southwestern part of South Korea with a population of about 1.5 million. On the other hand, Anmyeon and Gosan are remote areas located in Anmyeon Island and Jeju Island, respectively. Refer to the Supplemental Information for more detailed information of these 4 stations and measurement methods. Fig. 2 compares the monthly average ground-level BC concentrations (mg m
3) predicted by CMAQ with the observations. Since the October data measured at Gwangju site were limited to the first 7 days due to the breakdown of the instrument, they were excluded in the data comparison. The measurements made at Gosan from January to April 2009 were not reported in KMA (2010) because of the lack of confidence. It is shown in Fig. 2 that CMAQ underestimated the BC concentrations on average. Refer to the Supplemental Information for model-observation comparison outside Korea. For additional model validation, the model-predicted AAOD was compared with the Aerosol Robotic Network (AERONET) sunphotometer observation data (level 2.0, version 2) and the Ozone Monitoring Instrument (OMI) satellite observation data. The AAOD calculation based on the model prediction was carried out using Eq. (3) at a wavelength of 550 nm as will be discussed later in detail when the DRF is calculated. Within the spatial domain of this study, four AERONET sites, Gosan, Gwangju, Hong Kong, and Shirahama, measured AAOD (at 550 nm) in January, April, and October 2009. Fig. 3 compares the model-predicted monthly average AAODs with the AERONET observations. The monthly averages were calculated only when at least three days of data were available. Similarly to the case of the ground-level BC concentrations (Fig. 2), CMAQ underestimated the AAODs on average except in January. The OMI data used in this study is based on OMAERUVd.003 daily products (at 500 nm), which were available only for 20 days in January (4 Jan 2009e23 Jan 2009) within the temporal domain of this study. All the data values were adjusted to a wavelength of 550 nm before comparison with model prediction, assuming that the crosssection is inversely proportional to wavelength (Bond and Bergstrom, 2006). Fig. 4 compares the 20-day average AAODs predicted by CMAQ with the OMI observations. Comparison was performed only on the grids where there was no missing data for the 20-day period. It is shown in this figure that the OMI-retrieved AAODs were generally lower than model-estimations by a factor of 2.66 on average, which is distinct from the results for the groundlevel BC concentration and the AERONET AAOD that were similar to the model predictions in January. The model high bias against OMI

0.06

0.05 0.04 Modelled

48

0.03 0.02

0.00 0.00

0.02

0.03

0.04

0.05

0.06

Fig. 4. Comparison of model-estimated 20-day average AAODs with OMI observations.

observations is particularly notable when AAOD value is high. AERONET to OMI comparison was possible only for 8 days in January at Hong Kong in this study, where AERONET-observed AAOD was about 6 times higher than the OMI-retrieved value on average. It was previously reported that the OMI-retrieved AAOD is generally smaller than the AERONET value in Asia (Koch et al., 2009). With the limited number of data used in this study, however, it is not possible to infer a firm conclusion. The above-shown model-observation comparisons for groundlevel BC concentration and AAOD indicate that the model simulations tend to underestimate the monthly averages for April, July, and October and the annual average, while the simulated monthly average for January is similar to or higher than the observed values. The reason of the consistently higher model/observation ratio in January than in other months is not clear although one possibility is the inaccuracy of the monthly emission factor suggested by Streets et al. (2003). There are several possible reasons for the general underestimation of BC burden by the model, e.g., inaccurate meteorology prediction, neglect of BC emissions from biomass burning, and inaccurate BC emissions inventory. Biases in the meteorology can cause significant discrepancies in BC concentration. For instance, if the boundary layer height is predicted too large, the concentration may be underestimated. Unfortunately, the effect of meteorology could not be quantified in

0.10 linear regression: y = 0.52 * x 2 R = 0.32

linear regression: y = 0.52 * x 2 R = 0.32 0.08

0.08

0.06

0.06

Modelled

Modelled

0.01

Observed (OMI)

0.10

0.04

Gwangju Hong Kong Shirahama

0.02

0.00 0.00

linear regression: y = 2.66 * x R2 = 0.60

0.01

0.02

0.04

0.06

Observed (AERONET)

0.08

0.10

0.04

Jan Apr Oct

0.02

0.00 0.00

0.02

0.04

0.06

0.08

0.10

Observed (AERONET)

Fig. 3. Comparison of model-estimated monthly average AAODs with AERONET observations, grouped with respect to measurement site (left) and with respect to season (right).

M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

this study because the relevant meteorological observation data were not available for the spatial and temporal domain of this study. As was mentioned in the previous section, BC emissions from biomass burning were not taken into account because no information on biomass burning emissions for the year 2009 is available for the spatial domain of this study. It should be noted, however, that this can lead to a significant underestimation of BC burden over the Korean Peninsula because the contribution of BC emission from biomass burning in Asia is significant although the local BC emission due to biomass burning within the Korean Peninsula may not be significant (Sahu et al., 2009). For instance, by analyzing the Asian anthropogenic emission inventory of Zhang et al. (2009) and the biomass burning emission inventory of Streets et al. (2003), Kondo et al. (2011) estimated the annual contribution of biomass burning on total BC emissions to be 0.13 in East Asia. In addition, biomass burning in East Asia is most vigorous within 10 N w25 N and 90 E w110 E (Allen et al., 2004), from which the Eastern part of East Asia is little affected (Verma et al., 2011). Based on this reasoning, Kondo et al. (2011) and Verma et al. (2011) presumed that the influence of biomass burning at an observation site located in the East China Sea may not be significant. Table 1 summarizes the monthly fire counts within the spatial domain of this study for recent 5 years, extracted from the overpass- and cloud-corrected Moderate Resolution Imaging Spectroradiometer (MODIS) fire counts data files. It is shown that the biomass burning activity in 2009 was under average. Based on the collected information shown above, we presume the effect of biomass burning on BC burden over the Korean Peninsula in 2009 was less than 10%. While it is very probable that the neglect of biomass burning contributed, to some extent, to the underestimation of BC burden in this study, it is not believed to be the main reason of the whole model-observation discrepancy shown in this study for two reasons. First, the model underestimation was most significant in July (Fig. 2), in which biomass burning activity is smallest in East Asia. Second, the average model underestimation was much larger (w50%) than the expected influence of biomass burning (<10%). The model underestimation for BC burden seems to stem mainly from inaccurate BC emissions inventory. The effect of inaccuracy of the emission inventory on AQ modelling has been discussed by many researchers. The BC emissions estimation in East Asia is known to be particularly uncertain with the overall uncertainty larger than 300% (Huebert et al., 2003; Streets et al., 2003). A comparison of regional AQ model simulation and aircraft measurements over East Asia showed that the model-estimated organic carbon and BC are significantly lower than the observations by a factor of 3e5 (Huebert et al., 2003). The differences were largest at low altitude near Chinese source regions, which was attributed to underestimation of emissions from coal combustion by a factor of 2 or larger (Carmichael et al., 2003). A global-scale model simulation of Jacobson (2002) underpredicted BC on average in many urban areas, where BC concentrations were highest. For example, average BC concentration in Seoul in June was estimated to be 2.34 mg m
3 by the model, while an observation in Table 1 Monthly fire counts for recent 5 years within the spatial domain of this study, extracted from the MODIS fire counts data files.

2007 2008 2009 2010 2011 Average

Jan

Apr

Jul

Oct

Sum

11,196 8755 7750 8048 7527 8655

21,243 20,598 20,071 18,156 20,200 20,054

5202 4712 3633 4707 7233 5097

2480 2712 2350 2750 3749 2808

40,121 36,777 33,804 33,661 38,709 36,614

49

1999 by Park et al. (2001) recorded 8.40 mg m
3. Koch et al. (2009) compared the simulation results on BC aerosol obtained using a number of global atmospheric models with available observations. Most models underestimated the surface BC concentration in Asia with the average model-to-measurement ratio of 0.5 and its standard deviation of 0.42. A recent modelling study of Jeong et al. (2011) also reported model underestimation of BC concentrations at Korean measurement sites by a factor of 2. Another possible reason for the low bias of model estimation is the contribution of light-absorbing organic carbon aerosol (Park et al., 2010) to BC measurement made by the instrument based on light absorption technique. Therefore, it is important to keep the tendency of model underestimation in mind when the climate impact of BC is inferred from the model simulations. 3.2. Distribution of BC over Korean Peninsula Fig. 5 shows the BC mass column loading (MCL) predicted for the four different months simulated in this study. Note that the contour scale for January (5a) is different from those of the others. The average BC MCL over D02 for January, April, July and October was 2.14, 0.96, 0.73 and 1.10 mg m
2, being highest in January as was expected from large emission in winter. When the simulation was performed without the monthly emission factor taken into account, BC MCL was predicted to be considerably higher in April and in October than in January and in July over D02, implying stronger influence of trans-boundary transport of BC with Chinese origin in April and in October than in January and in July (see the Supplemental Information for details). In order to evaluate the effect of trans-boundary transport of BC more quantitatively, a sensitivity test was carried out, in which BC emission in the Korean Peninsula was removed. The BC loading obtained from this simulation was regarded as the effect of transboundary transport. Fig. 6 shows the fraction of trans-boundary transported BC in total BC column loading. It is clearly shown in this figure that the influence of the trans-boundary transport dominates over that of local emission. The Seoul Metropolitan Area appeared to be the region in which the effect of local emission is strongest. Another possible reason for low BC burden in summer is the seasonal variation of precipitation. The model-estimated average precipitations in D02 for January, April, July and October simulated in this study were 0.050, 0.150, 0.346 and 0.046 mm h
1, respectively. The precipitation in spring was almost the same as the annual average and in summer it exceeded the annual average, while in fall and in winter it was under the annual average. The winter monsoon is characterized by dry cold weather so that the influence of wet deposition in winter is low. In summer, however, precipitation is large due to both summer monsoon and frequent typhoons, which may have caused enhanced washout in July. Seoul Metropolitan Area, having particularly high population density, showed consistently higher BC burden than any other areas in South Korea, which is consistent to the ground-level concentrations as was shown in Fig. 2. Fig. 7 compares the monthly average vertical distributions of BC over the four measurement sites. It is noted that in the middle and upper troposphere (>1 km), Seoul and Anmyeon showed similar BC concentrations while those over Gwangju and Gosan were similar. In the boundary layer, however, Seoul and Gwangju showed much higher BC concentrations than Anmyeon and Gosan, respectively. Similar concentrations in the upper atmosphere can be attributed to the influence of long-range transport from the west: Seoul and Anmyeon, due to their similar latitude, seems to have been influenced by a common air mass, while Gwangju and Gosan, located in southern part of Korea may have been affected by similar remote

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M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

a

b

c

d

Fig. 5. Average BC MCL for each month (mg m
2): (a) January; (b) April; (c) July; (d) October.

sources although some seasonal variations existed. On the other hand, large difference in the boundary layer BC concentration is believed to stem from the local sources: Seoul, a single largest BC source area in Korea, showed the highest concentration followed by Gwangju, a large urban area, whereas two remote areas, Anmyeon and Gosan, showed low concentrations. We conclude from the results shown in Figs. 5 and 7 that atmospheric BC over the Korean Peninsula is influenced by both long-range transport and local sources: urban areas have a larger contribution of local sources while remote areas are dominated by long-range transport. 3.3. Estimation of BC DRF Radiative forcing is defined as the change in net radiative flux at the tropopause caused by an atmospheric component. The average BC burden calculated from model simulations was used to estimate

the DRF of BC over the spatial and temporal domain of this study. Chylek and Wong (1995) derived the following parameterization for the global-average DRF from the radiation transfer equation:

DRFglobal ¼

n o S0 2 Tatm ð1
Fc Þ 4asab
2ð1
aÞ2 bssc 4

(1)

where S0 is the solar irradiance (1370 W m
2), Tatm the atmospheric transmission (0.79), Fc the cloud fraction, a the surface albedo, sab and ssc the absorption and scattering optical depths, respectively, and b the back-scattering fraction. According to Eq. (1), BC DRF increases with surface albedo, which indicates that BC DRF is larger over the land than over the sea. To estimate the BC DRF for particular time and location, a local irradiance value should be used instead of S0 in Eq. (1). A typical way to calculate the local irradiance is to use a radiation transfer model (Meskhidze and Nenes, 2006). In this study, an alternative

M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

a

b

c

d

51

Fig. 6. Fraction of trans-boundary transported BC in total BC column loading: (a) January; (b) April; (c) July; (d) October.

method to calculate the local irradiance is suggested. To estimate the BC DRF over the Korean Peninsula for different seasons, Eq. (1) was modified into

DRF ¼

o n FSI S0 2 Tatm ð1
Fc Þ 4asab
2ð1
aÞ2 bssc 4

The optical depths appearing in Eq. (2) are calculated by

sab ¼

L X

cBC;n sab;n fmix Dhn

(3)

cBC;n ssc;n fmix Dhn

(4)

n¼1

(2)

where FSI is the solar irradiance factor accounting for the effects of latitude and season on the insolation. The surface-area-weighted average of FSI over the Earth surface is 1 so that Eq. (1) is valid as the global average. The seasonal average values of FSI were computed at each grid by taking into account the effects of latitude and season (see the Supplemental Information for details) and these values at each grid were used for calculation of DRF. The domain-average FSI for January, April, July and October was 0.62, 1.23, 1.41 and 0.83, respectively.

ssc ¼

L X n¼1

where L the number of vertical layers, cBC;n the BC mass concentration at the nth vertical layer (kg m
3), sab;n the specific absorption cross-section (m2 kg
1), ssc;n ð ¼ usab;n =ð1
uÞÞ the specific scattering cross-section (m2 kg
1), u the single scattering albedo, fmix the mixing state effect factor for absorption, and Dhn the thickness of the nth vertical layer (m). Accumulation mode BC was assumed to be internally mixed, while Aitken-mode BC was

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M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

a

b 10000

Seoul Anmyeon Gwangju Gosan

1000

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Height (m)

10000

100

10

Seoul Anmyeon Gwangju Gosan

1000

100

10 0

1

2

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1.5

2.0

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d

c 10000

10000

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1000

Height (m)

Height (m)

1.0

BC concentration (µg/m3)

100

Seoul Anmyeon Gwangju Gosan

1000

100

10

10 0.0

0.5

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2.0

2.5

3.0

BC concentration (µ g/m3)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3

BC concentration ( µ g/m )

Fig. 7. Modelled monthly average vertical distribution of BC over the four measurement sites: (a) January; (b) April; (c) July; (d) October.

assumed to be externally mixed for the purpose of DRF calculation in this study (see the Supplemental Information for details). This assumption on mixing state can influence the DRF calculation because the light absorption by BC can be considerably overestimated if the internal-mixing assumption is made falsely (Bond and Bergstrom, 2006; Jacobson, 2000, 2001; Zaveri et al., 2010). There is another influence of the internal-mixing assumption, however, that is opposite to the enhanced light absorption: enhanced wet deposition (Park et al., 2011). It was reported by Park et al. (2011) that the effect of enhanced wet deposition compensates at least partially for the effect of enhanced absorbance and the overall effect of mixing state on BC direct radiative forcing may be much less significant than is implied by the enhanced absorption effect only. For the input parameters required in Eqs. (2) through (4), the values suggested by Bond and Bergstrom (2006) based on extensive literature survey and credible observations relevant to BC DRF were used (see the Supplemental Information for details). The BC DRF was computed using Eqs. (2) through (4) based on the BC burden predicted by CMAQ simulations. Fig. 8 shows the spatial distribution of BC DRF calculated for four months. It is noted that BC DRF is larger over the land than over the sea with the same BC burden, which is attributed to the difference in surface albedo between land and sea. BC DRF over D02 was in general 0.2e1.8 W m
2 over the land and 0.1e0.5 W m
2 over the sea, being the largest over the Seoul Metropolitan Area and northwestern region around the borderline with China (0.4e1.8 W m
2). The domain-averaged BC DRF over D02 was 0.43, 0.47, 0.38 and 0.30 W m
2 for January, April, July and October, respectively, and the 4-month average was 0.39 W m
2. The domain-average values of BC DRF predicted in this study is comparable to or a little smaller than the global and regional averages estimated in previous studies. Jacobson (2001) suggested the global-average BC DRF of 0.55 W m
2 taking into account the mixing state of BC. Ramanathan and Carmichael (2008) summarized the previous studies on BC

climate impact available in the literature and concluded that the best global-average BC DRF estimation is 0.9 W m
2, which is as much as 55% of the radiative forcing of CO2 (1.6 W m
2). Moffet and Prather (2009) estimated the global-average BC DRF to be 0.2e1.2 W m
2 based on global-scale modelling and observations taking uncertainties into account. Wu et al. (2008) estimated the spatial distribution of BC DRF over Asian Continent using regional climate modelling with relatively coarse grid (90 km). The DRF value over the Korean Peninsula was estimated to be 1.2e1.5 W m
2. In Seoul Metropolitan Area, over which BC burden was highest in South Korea, the DRF of BC was comparable (in October) to or considerably larger (in all the other months) than that of methane, 0.48 W m
2 (IPCC, 2007), but was consistently smaller than that of CO2, 1.66  0.17 W m
2 (IPCC, 2007). Based on the model-observation comparison results of this study and relevant reports available in the literature, the estimation of BC DRF made in this study is very likely to be an underestimation although the uncertainty is pretty large. If we assume that CMAQ underestimated the BC loading by 48% based on the result shown in Fig. 3, for example, the domain-average BC DRF over D02 would be 0.75 W m
2. In order to reduce the uncertainty of BC DRF estimation, efforts for continuous improvement of the emissions inventory over East Asia domain is required. Another potential error stems from the adaptation of simplified parameterization, Eq. (2), for calculating DRF. The DRF value calculated by using Eq. (2) is known to be smaller than that calculated by climate radiation models based on sophisticated microphysics although it is reasonably accurate for low BC burden (Bond and Bergstrom, 2006). Taking into account all the possible underestimations discussed above, it is likely that the BC DRF over the Korean Peninsula is larger than that of methane though smaller than that of CO2. Based on the results presented in this paper, controlling BC emissions seems to be a very attractive measure in Korea to counteract the global warming. Given its much shorter lifetime

M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

a

b

c

d

53

Fig. 8. Spatial distribution of average BC DRF for each month simulated (W m
2): (a) January; (b) April; (c) July; (d) October.

than those of greenhouse gases, controlling BC is particularly effective in the short term. It is important to note, however, that the reduction of BC emission may cause a decrease in cloud condensation nuclei concentrations and a decrease in indirect aerosol effect, compensating for the decrease in BC direct effect (Chen et al., 2010). Additional studies are required for more quantitative assessment of the effect of BC emission mitigation. It should also be noted that only controlling the greenhouse gas emissions can “stop” the global warming although controlling BC emissions may “slow down” the warming. Another benefit of controlling BC which can never be overemphasized is that it can also improve air quality. 4. Conclusions Distribution of BC aerosol over the Korean Peninsula was simulated using a regional air quality model for four mid-season months of 2009. Simulated BC concentration and AAOD were

evaluated against ground-based and satellite observations available. While the correlation between the model-estimated and measured monthly average BC burden was reasonable, the model tended to underestimate BC burden significantly. While it is not possible to rule out inaccurate prediction of meteorology, the main reason for the model underestimation is believed to be inaccurate BC emissions inventories partly due to the neglect of the emissions from biomass burning. The model-estimated BC MCL was highest in January due to high emission in winter. When divided by the monthly emission factor, however, the adjusted MCL was considerably higher in April and in October than in January and in July indicating strong impact of trans-boundary transportation of BC with Chinese origin. Atmospheric BC over the Korean Peninsula was shown to be influenced by both long-range transport and local sources; urban areas have a larger contribution of local sources while remote areas are dominated by long-range transport. DRF of BC was estimated over the Korean Peninsula using a simple

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M.Y. Kim et al. / Atmospheric Environment 58 (2012) 45e55

parameterization based on the model simulations. BC DRF over the Korean Peninsula was estimated to be 0.1e1.8 W m
2 with the domain-average value of 0.39 W m
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