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Atmospheric Environment 44 (2010) 3110e3117

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Atmospheric Environment

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Characteristics of aerosol types from AERONET sunphotometer measurements

J. Lee a,f,g, J. Kim a,f,g,*, C.H. Song b, S.B. Kim c, Y. Chun c, B.J. Sohn d, B.N. Holben e

a Institute of Earth, Astronomy, and Atmosphere, Brain Korea 21 Program, Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of KoreabAdvanced Environmental Monitoring Research Center, Gwangju Institute of Science and Technology, Gwangju, Republic of KoreacNational Institute of Meteorological Research, Seoul, Republic of Koread School of Earth Environmental Sciences, Seoul National University, Seoul, Republic of Koreae Laboratory for Terrestrial Physics, NASA GSFC, Greenbelt, MD, USAfAerosol and Cloud Group, NASA JPL, Pasadena, CA, USAg JIFRESSE, UCLA, Los Angeles, CA, USA

a r t i c l e i n f o

Article history:Received 28 November 2009Received in revised form29 April 2010Accepted 19 May 2010

Keywords:AerosolTypeBlack carbonDust

* Corresponding author. Institute of Earth, AstronKorea 21 Program, Department of Atmospheric ScienRepublic of Korea.

E-mail address: jkim2@yonsei.ac.kr (J. Kim).

1352-2310/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.atmosenv.2010.05.035

a b s t r a c t

By using observations from the Aerosol Robotic Network (AERONET), aerosol types are classifiedaccording to dominant size mode and radiation absorptivity as determined by fine-mode fraction (FMF)and single-scattering albedo (SSA), respectively. The aerosol type from anthropogenic sources is signif-icantly different with regard to location and season, while dust aerosol is observed persistently overNorth Africa and the Arabian Peninsula. For four reference locations where different aerosol types areobserved, time series and optical properties for each aerosol type are investigated. The results show thataerosol types are strongly affected by their sources and partly affected by relative humidity. The analysisand methodology of this study can be used to compare aerosol classification results from satellite andchemical transport models, as well as to analyze aerosol characteristics on a global scale over land forwhich satellite observations need to be improved.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Radiative forcing of aerosol is a key parameter in quantifying theeffect of aerosols on climate change, which depends largely on theoptical properties of aerosol. Direct radiative forcing, inparticular, canbe changed significantly depending on the radiation absorptivity ofaerosol and the underlying surface conditions (e.g. Haywood andShine, 1995; IPCC, 2007). To better understand these factors,numerous studies have classified aerosol types from ground-basedobservations (Eck et al., 1999; Dubovik et al., 2002; Kaskaoutis et al.,2007a; Kim et al., 2008; Kalapureddy et al., 2009; Mielonen et al.,2009) and remote sensing from satellites (Higurashi and Nakajima,2002; Barnaba and Gobbi, 2004; Jeong and Li, 2005; Kaufman et al.,2005; Kaskaoutis et al., 2007b; Kim et al., 2007).

Some of the aforementioned studies classify aerosol types byaerosol optical depth (AOD) and Angstrom exponent (AE). Thismethod sorts aerosol types into dust (high AOD, low AE), marine(low AOD), and anthropogenic aerosols (high AOD, high AE), butcannot subcategorize anthropogenic aerosols into absorbing andnon-absorbing without referring to geolocation information.

omy, and Atmosphere, Brainces, Yonsei University, Seoul,

All rights reserved.

Recently, many satellite algorithms have adopted a procedure forclassifying aerosol types to improve the accuracy of AOD retrieval(Higurashi and Nakajima, 2002; Remer et al., 2005; Kim et al., 2007;Lee et al., 2010). Jeong and Li (2005) used not only AE from theAdvanced Very High Resolution Radiometer (AVHRR) to determinesize, but also employed the aerosol index (AI) from the Total OzoneMapping Spectrometer (TOMS) to determine the radiationabsorptivity of aerosol. A similar methodology and logic can beapplied to ground-based optical measurements, such as those fromthe Aerosol Robotic Network (AERONET) (Holben et al., 1998).Mielonen et al. (2009) classified aerosol types into coarse absorbing(dust), coarse non-absorbing (marine), mixed absorbing (polluteddust), fine absorbing (biomass burning), and fine non-absorbing(polluted continental) via AE and single-scattering albedo (SSA)from AERONET to validate the aerosol types retrieved from theCloud-Aerosol Lidar with Orthogonal Polarization (CALIOP).

In this study, the characteristics of aerosol types from AERONETare analyzed on a global scale to determine the dominant aerosoltype at each location and in each season. Moreover, the opticalproperties of dust, BC, non-absorbing anthropogenic aerosol (NA),and mixed aerosol cases are analyzed at four distinct locations toinvestigate the seasonal variation of aerosol types and opticalproperties. The influence of total precipitable water (TPW) andrelative humidity (RH) on single-scattering albedo (SSA) ofanthropogenic aerosols is investigated.

J. Lee et al. / Atmospheric Environment 44 (2010) 3110e3117 3111

2. Aerosol classification method

Aerosols in the atmosphere can be classified into four majortypes according to their radiation absorptivity and size. These typesare carbonaceous (absorbing fine-mode), soil dust (absorbingcoarse-mode), sulfate (non-absorbing fine-mode), and sea-salt(non-absorbing coarse-mode) particles (Higurashi and Nakajima,2002). Thus, two important tests to determine the radiationabsorptivity and size of aerosol are performed for classifyingaerosols from optical observations. In the current classificationfrom AERONET, the absorbing fine-mode aerosol is classified asblack carbon (BC) instead of carbonaceous, because the opticalproperties of organic carbon (OC) is not well known. Non-absorbing fine-mode aerosol (NA hereafter) includes not onlysulfate, as in the work of Higurashi and Nakajima (2002), but alsoother non-absorbing fine-mode aerosols, such as nitrate (cf. Hesset al., 1998). Aged water-soluble OC is also considered to be NAaerosol in this work.

From the concept of aerosol classification, data products asso-ciated with radiation absorptivity and the size of aerosol fromAERONET can be used to classify aerosol types. In this study, dailyaverage level 2 inversion products are used (Dubovik and King,2000). To classify aerosol types, FMF at 550 nm is used to deter-mine the dominant size mode, and SSA is used to distinguishabsorbing from non-absorbing aerosols. Since the FMF is defined asthe ratio of fine-mode AOD to total AOD, fine-mode AOD at 550 nmis calculated using AE from spectral fine-mode AOD, which is takenfrom AERONET. The reason for using FMF instead of AE is that FMFprovides quantitative information for each fine- and coarse-modeaerosol, whereas AE is a qualitative indicator. Schuster et al. (2006)showed that AE can be changed according to the effective radius ofthe fine-mode aerosol, as well as the ratio of fine-mode to totalvolume concentration, and these influences are even different forAEs calculated from different wavelength pairs. These complexitieslead us to choose FMF instead of AE, although AE is computeddirectly from spectral AOD without any assumptions. The SSA isdefined as the ratio of the scattering coefficient (or scattering AOD)to the extinction coefficient (or total AOD) of total column aerosol,and by definition, it can be used to determine the characteristics ofthe radiation absorptivity of aerosol.

Fig. 1. Flowchart of the aerosol classification algorithm for AERONET. The HA, MA, SA, anabsorbing fine-mode aerosols, respectively.

Fig. 1 shows a flowchart of the aerosol classification algorithmcreated by using FMF and SSA fromAERONET. In order to determinethe dominant size mode, the threshold for FMF needs to be deter-mined. O’Neill et al. (2003) showed that AERONET inversion algo-rithm tends to overestimate fine-mode AOD and underestimate thecoarse-mode because it used a threshold of 0.6 mm to distinguishbetween fine- and coarse-mode aerosols. In this study, instead ofchanging the threshold value, a safety margin is adopted for theFMF threshold in order to minimize the influence of various errors.Consequently, by adopting a safety margin of 0.2, fine-mode aero-sols are defined by FMF to be greater than 0.6, and coarse-modeaerosols are defined by FMF to be less than 0.4. Aerosols in thesafety margin of the thresholds (that is, between 0.4 and 0.6) areclassified as a ‘mixture’ of coarse- and fine-mode aerosols.

Absorptivity of aerosols is then determined by SSAs at 440 nm,which is the shortest wavelength of AERONET channels providingSSAs. In satellite remote sensing, absorbing aerosols are distin-guished from the AI of TOMS, that is calculated from the UVobservations (Jeong and Li, 2005; Kim et al., 2007). Since theinteraction between aerosol and solar radiation is continuous,absorption by aerosol occurs over the wavelength region from UVto shorter visible wavelengths. For example, the radiation absorp-tion of dust is strong in UV and decreases continuously until near-infrared, so that stronger absorption occurs in the shorter visiblewavelengths (blue) than in the longer visible wavelengths (green,red). Similarly, the absorption of BC is strong over both the UV andvisible range (Hess et al., 1998). Consequently, the SSA at 440 nm isused to distinguish absorbing from non-absorbing aerosols.

The advantages of this algorithm are its simplicity androbustness, but performance depends on threshold values. The SSAat 440 nm of water soluble aerosol, including sulfates, is close tounity (Hess et al., 1998), whereas that for internally or externallymixed BC with sulfate is much less and depends on the RH andmixing ratio (0.91 at 70% RH and 5% BC/sulfate mass ratio forinternally mixed case) (Wang and Martin, 2007). Dubovik et al.(2002) analyzed the SSA of key aerosol types, such as urban/industrial, biomass burning, desert dust, and oceanic aerosol indifferent regions throughout the world. In their study, SSAs at440 nm are in the range of 0.9e0.98 for urban/industrial aerosol,0.89e0.95 for biomass burning, 0.92e0.93 for desert dust, and 0.98

d NA represent highly-absorbing, moderately-absorbing, slightly-absorbing, and non-

J. Lee et al. / Atmospheric Environment 44 (2010) 3110e31173112

for oceanic aerosol. It should be noted that biomass burningaerosol contains BC, whereas urban/industrial aerosol contains BCand/or NA. Thus, it can be inferred that urban/industrial aerosol,which has a higher value of SSA, consists mainly of NA, whereasa lower value of SSA suggests the presence of internally or exter-nally mixed BC. For this reason, an SSA threshold of 0.95, the upperlimit of SSA for biomass burning aerosol, is used to distinguishabsorbing and non-absorbing aerosols. This threshold is alsoacceptable for distinguishing desert dust (SSA ranges 0.92e0.93)and oceanic aerosols (SSA of 0.98) in coarse-mode particles. BCaerosols are further divided into highly-absorbing (HA), moder-ately-absorbing (MA), and slightly-absorbing (SA) depending onthe range of SSAs. The data representing non-absorbing coarse-mode aerosol are not used in this study, since the percentage isonly 0.3%. This is because level 2 AERONET inversion data for SSAis valid only for AOD at 440 nm greater than 0.4, where the typicalAOD of non-absorbing coarse-mode aerosols (sea-salt/marine) isless than that.

3. Results

3.1. Characteristics of aerosol types throughout the world

To investigate the dominant aerosol types for each location andseason, the most frequently detected aerosol types throughout theworld are shown in Fig. 2. Regardless of season, dust aerosol isdetected persistently throughout the source regions, such as NorthAfrica and the Arabian Peninsula. In addition, continuous long-range transport of dust occurred during certain seasons. Dust fromthe Sahara strongly affects the southern part of Europe in MAM andJJA and even affects the Caribbean region in JJA. The western andnorthern part of India is affected by dust from the Arabian Penin-sula and from the north-eastern part of India in MAM and JJA. InEast Asia, dust occurs sporadically in spring and is detected by

Fig. 2. The most frequently detected aerosol type from all AERONET stations in a 5� � 5� grid(green), NA (sky-blue), and BC (red). BC is further categorized into HA (black), MA (gray), anAERONET stations that have more than four data points for each season and year are used tothis figure legend the reader is referred to the web version of this article.)

AERONET observations in MAM. Since transported dust mixes withanthropogenic aerosol in urban/industrial regions, the mixturetype of aerosol is frequently detected in certain locations andseasons.

The distributions and types of anthropogenic aerosols are rathercomplex because of the variety of their sources with respect tolocation and season. Throughout Asia, BC is frequently detectedduring the dry season, whereas NA is the most frequent at certainlocations near the ocean in JJA and SON. The BC observed in Europeand North America shows a relatively high value of SSA (SA), andNA is more frequent than BC in North America. Interestingly, BC ismore prevalent in the western part of the U.S. than in the easternpart in JJA because of wild fires in the west during the summer(Spracklen et al., 2007). A considerable amount of biomass burningoccurs periodically in the spring season in the Southern Hemi-sphere, especially in South America and Southern Africa, which thealgorithm captures well. The biomass burning aerosol is moreabsorbing in Southern Africa than South America because theburning process is different between these regions (Kaufman et al.,2002).

To investigate the regional characteristics of aerosol types, thefrequency distribution of aerosol types in each region are repre-sented in Fig. 3 and Table 1. In North America, the most frequentlydetected aerosol type is NA, followed by BC, whereas a mixture ordust is rarely detected. From this result, we conclude that NorthAmerica is affected by aerosols mainly from non-absorbinganthropogenic pollution and wild fires. By contrast, in other activeindustrial regions, such as Central America, Europe, and Asia, themost frequently detected anthropogenic aerosol type is BC fol-lowed by NA, and the dust and mixture are detected morefrequently than in North America.

Differences in the frequency of anthropogenic aerosols (NA andBC) can be explained mainly by the sources of aerosol and partly byRH. Aerosols from different sources show characteristic optical

box throughout the world for each season. Each color represents dust (yellow), mixtured SA (white) as the most frequently detected type of aerosol among BC classifications.calculate the dominant aerosol type. (For the interpretation of the reference to color in

Fig. 3. The frequency distribution of aerosol types from AERONET in each region defined in Table 1. The first, second, and third stages for BC represent SA, MA, and HA, respectively.

J. Lee et al. / Atmospheric Environment 44 (2010) 3110e3117 3113

properties, and the SSA of anthropogenic aerosols can be changedby RH (Shettle and Fenn, 1979; Redemann et al., 2001; Markowiczet al., 2003). The frequency of the dust and mixture in Asia, Europe,and Central America results from the AERONET stations near thedesert and/or from the long-range transport of dust from the sourceregion. Dust from the Sahara and Arabian deserts often affectsCentral America (Colarco et al., 2003) and Europe (Balis et al.,2004), and dust from the Taklamakan and Gobi deserts oftenaffects Asia (Kim et al., 2004). In the northern and southern part ofAsia, the frequency distributions are similar except for the dust

Table 1Percentage of aerosol types in each region. N and Ns represent the total number ofdata points and AERONET stations used in each region, respectively. For the list ofAERONET stations, see (http://aeronet.gsfc.nasa.gov/Site_Lists/site_index.html).

Latitude, �NLongitude, �E

Dust Mixture NA BC

North America (N ¼ 3028, Ns ¼ 97) 30e60 0.2 0.2 74.4 25.3�135 to �45

Central America (N ¼ 556, Ns ¼ 20) 0e30 13.8 1.4 15.1 69.6�135 to �45

South America (N ¼ 1627, Ns ¼ 33) �60e0 0.1 0.2 22.9 76.8�105 to �25

Europe (N ¼ 5582, Ns ¼ 96) 30e60 17.4 8.0 26.5 48.1�20 to 65

North Africa (N ¼ 7654, Ns ¼ 37) 0e30 73.8 19.3 1.1 5.9�20 to 65

South Africa (N ¼ 1250, Ns ¼ 23) �40 to 0 0.4 0.6 4.5 94.5�20 to 65

Asia (North) (N ¼ 5167, Ns ¼ 50) 20e60 8.4 11.2 14.3 66.265e180

Asia (South) (N ¼ 1425, Ns ¼ 16) �10 to 20 0.3 3.9 6.7 89.265e180

frequency due to its pathway. Another feature to note is the BCcontent in urban/industrial regions. The portion ofMA and HA to SAis larger in Asia and Central America than in North America andEurope. Recently, developing countries have become amajor sourceof BC because of fossil fuel combustion for heating and cooking, andunregulated pollution of transportation (Novakov et al., 2003),which leads to a larger proportion of BC (low SSA) in absorbinganthropogenic aerosols in Asia and Central America.

As expected, the most frequently detected aerosol type in NorthAfrica is dust from the Sahara and Arabian deserts throughout theyear. The BC of biomass burning from September to February in thisregion is usually mixed with dust, so that the mixture type isfrequent. In contrast, fine-mode aerosols, such as NA and BC, arerarely detected because dust occurs throughout the year.

The dominance of BC in both South America and Southern Africaarises from periodic biomass burning. The remarkable differencebetween the two regions is evident in the BC content of biomassburning aerosols. The frequency of aerosols with low SSAs is muchlarger in Southern Africa than in South America due to the differ-ences in burning process. More BC is generated from the flamingstage of burning grasses in Africa than the smoldering stage ofburning forests in South America (Kaufman et al., 2002). The higherfrequency of NA in South America and the larger frequency of HAaerosol in Southern Africa can be explained by these differences inburning process.

3.2. Characteristics of aerosol types at four different locations

To investigate the seasonal variation of aerosols and opticalproperties, four AERONET stations, which represent differentaerosol types, are selected, specifically Agoufou (dust), Alta Floresta

J. Lee et al. / Atmospheric Environment 44 (2010) 3110e31173114

(BC from biomass burning), GSFC (NA), and Beijing (mixed). Fig. 4shows a time series of the most frequently detected aerosol typefor each month at each location, together with AOD and FMF. It isclear that dust persistently affects Agoufou, which is located on theborder of the Sahara. (Fig. 4a). The FMF slightly increases in winterbecause of biomass burning in North Africa (Ogunjobi et al., 2008).

Fig. 4b shows a time series at Alta Floresta in Brazil, located nearthe Amazonian forest, which is affected by biomass burning in theSouthern Hemisphere during the spring. Biomass burning in theAmazonian forest usually occurs from August to October, andthe time series represent this feature from 2005 to 2007. The BC inthis location is usually classified as SA (relatively high SSA) becauseof forest fires in the smoldering stage. In the biomass burningseason, AOD abruptly increases, and the mean FMF approachesunity. The monthly mean AOD maximum occurs in September forthe three-year record.

The NA type of aerosol, which is the most frequently detectedaerosol in the U.S., is usually observed at the Goddard Space FlightCenter (GSFC) (Fig. 4c). The SSA of these anthropogenic aerosols canbe determined mainly by the aerosol sources and partly by RH. TheSSAs of internally/externallymixed BCwith non-absorbing aerosolsdecrease with BC content and increase with RH because of thereduced ratio of BC to non-absorbing water soluble aerosol, causedby hygroscopic growth (Wang and Martin, 2007). Consequently, itcan be concluded that the source of aerosols at GSFC containsa small portion of BC. The effect of RH on the optical properties offine-mode aerosols will be investigated further in this article.

Fig. 4d shows a time series of aerosol types at the Beijing station,one of the largest urban areas in the world, located near anindustrial area and a desert. The time series at Beijing shows the

Fig. 4. A time series of the most frequently detected aerosol types in a month for 3 years at (that in Fig. 2, and the number of data points is greater than four for each month. If two or moas different colors in a given month. Each line represents the monthly mean AOD (solid linethe number of data points during the successive three years.

dominance of BC; the dust/mixture and NA are detected in thespring and summer, respectively, under the influence of sporadicdust outflow and seasonal changes in aerosol sources. Sinceindustrial activity in Beijing is extremely high, anthropogenicaerosols are persistently prevalent. The BC content in anthropo-genic aerosols is high (low SSA, MA, HA) from autumn to earlyspring and low (high SSA, SA, NA) from late spring to summer inBeijing because of the increased use of fossil fuel combustion in thecold season.

To estimate the influence of TPW on aerosol optical properties,SSA and FMF is analyzed with respect to TPW observed by theAERONET. Because the TPW is not only a function of RH, whichaffects the hygroscopic growth of aerosols, but also of temperature,the change in TPW is likely caused by seasonal variation and, inpart, by RH. Fig. 5 shows the mean SSA for each bin of FMF and TPWat each AERONET location used in Fig. 4. At Agoufou, the dust-dominant station, no distinct changes of FMF and SSA are foundwith respect to TPW. Notably, the mixed type of aerosol(0.4 � FMF � 0.6) forms only when the TPW is less than 1.75 cm,which occur during the dry and cold season. Ogunjobi et al. (2008)showed that Agoufou is affected by biomass burning aerosol inDecember and January, which results in the mixture type aerosol ofbiomass burning and persistent dust aerosols. The SSA of biomassburning aerosol tends to increase with TPW, because the SSA isaffected by changes in burning type, such as the change fromflaming to smoldering from August to October and also partly bythe hygroscopic growth of water-soluble OC. SSAs greater than 0.95are observed only for TPW greater than 4 cm, which implies thatthere is abundant OC in the wet season and possible hygroscopicgrowth of OC.

a) Agoufou, (b) Alta Floresta, (c) GSFC, and (d) Beijing. The color notation is the same asre aerosol types show the same highest frequency, then these types are plotted togetherwith circle) and FMF (dashed line with triangle). The period is selected by considering

Fig. 5. The mean SSA for each bin of FMF and TPW at each location shown in Fig. 4. Bins with more than two data points are shown.

Fig. 6. A time series of the TPW and SSA of fine-mode aerosols at (a) Alta Floresta, (b)GSFC, and (c) Beijing. The black-circle and gray-triangle represent SSA and TPW,respectively.

The persistently high SSA at GSFC suggests the NA type aerosolof anthropogenic origin. The FMF and SSA increase slightly withTPW, implying seasonal variation in the optical properties of NAaerosol. Sincemost of the data are available fromMay to Septemberat GSFC, we conclude that summer is more favorable to generatehigher FMF and SSA at this location. Finally, the SSA and FMF inBeijing show remarkable change over a wide spectrum, ascompared with the other locations. The dust and mixture occuronly when the TPW is less than 1.25 cm, which corresponds to coldand dry season, which, in this location, is winter and spring. TheSSA and FMF of anthropogenic aerosol significantly increases withTPW, mainly because of the decrease in fossil fuel combustion forheating fromwinter to summer (from low to high TPW) and partlybecause of the hygroscopic growth of water soluble anthropogenicaerosol.

To investigate the SSA variation of fine-mode aerosols fromdifferent sources with respect to TPW and RH, a time series of SSAfor fine-mode aerosols and TPW at Alta Floresta, GSFC, and Beijingare shown in Fig. 6, and the Pearson correlation coefficientsbetween SSA and TPW and between SSA and RH are shown inTable 2. The RH is from the National Center for EnvironmentalPrediction (NCEP) reanalysis data (Kanamitsu et al., 2002). TheSSA and TPW show an apparent correlation, especially at AltaFloresta and Beijing. In the sense that TPW variation implies bothchanges of source and RH, the correlation between SSA and TPWand between SSA and RH can be used to determine the influenceof respective source changes and RH on SSA. From the correlationcoefficients in Table 2, both effects are dominant for biomass

Table 2The Pearson correlation coefficient between TPW and SSA and between RH at850 hPa and SSA of fine-mode aerosols from December 2004 to November 2007. Thecoefficient is calculated for the number of data points greater than 44 in each seasonand is represented in boldface if the F-test statistics are statistically significant at the99% confidence level. The period from August to October is used for Alta Floresta toconsider typical biomass burning season. The numbers, read top to bottom and leftto right, represent DJF, MAM, JJA, and SON, respectively. The values in parenthesisrepresent coarse-mode case at Beijing.

AltaFloresta(ASO)

GSFC (JJA) Beijing

TPW vs. SSA e e e e 0.48 0.27 (�0.04)0.51 e 0.37 e 0.54 0.75

RH850 vs. SSA e e e e 0.07 �0.03 (�0.05)0.34 e 0.03 e 0.27 0.07

J. Lee et al. / Atmospheric Environment 44 (2010) 3110e31173116

burning aerosol at Alta Floresta and urban/industrial aerosol atBeijing in JJA (both correlations are significant), whereas onlysource change is significant at GSFC and during other seasons ofBeijing (only TPW vs. SSA is significant). Note that the correlationis very poor for coarse particles of which the SSA is less affectedby TPW and RH. Although the analysis in this section needsfurther work with sophisticated optical and chemical measure-ments, the aerosol classification algorithm for the AERONET allowsus to relate the source of aerosol and RH to the optical propertiesof fine-mode aerosols.

4. Conclusion

From AERONET sunphotometer measurements, aerosol typesare classified on the basis of dominant size mode and radiationabsorptivity determined by the fine-mode fraction (FMF) andsingle-scattering albedo (SSA), respectively. The prevalence of dustin the source region, its long-range transport, and the anthropo-genic aerosols generated in different regions are analyzed bya global map of dominant aerosol types. The absorptivity of urban/industrial aerosol in North America and Europe is lower than thoseof Asia and Central America. Moreover, the SSA of biomass burningaerosol is different for South America and Southern Africa becauseof the different types of burning process in these regions.

For the four reference locations where different aerosol typesare observed, time series and optical properties for each aerosoltype are investigated. The optical properties of biomass burning atAlta Floresta and urban/industrial aerosols at Beijing are affected bysource change and RH. In contrast, the SSA of NA at GSFC showsamoderate correlationwith TPW, but a significant correlation is notobserved with RH. Because of the advantages of the semi-globalcoverage of AERONET stations, this study can be used to compareand validate aerosol types from chemical transport models andsatellites, as well as to analyze the characteristics of aerosol typeson a global scale over land for which satellite observations need tobe improved.

Acknowledgments

We thank the principal investigators and their staff forestablishing and maintaining the AERONET sites used in thisinvestigation. This research was supported by the Eco-technopia21 project under grant 121-071-055 by the Korea Ministry ofEnvironment, Republic of Korea, as well as by the R&D project onthe construction and application of optical observation of dust bythe National Institute of Meteorological Research. This researchwas partially supported by the Brain Korea 21 (BK21) program forJ. Kim and J. Lee.

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