Relationship between AOT and PM based on multi-measurements
and model simulationsa◆

Makiko Nakata a , Itaru Sano b and Sonoyo Mukai b
Faculty of Sociology, b Graduate School of Science and Technology, Kinki University,
3-4-1 Kowakae, Higashi-Osaka, 577-8502, Japanover the Barbados Island5. Wang and Christopher investigate

Abstract

the relation between satellite based MODIS-AOT and in-situ
The suspending particulate matter (PM 2.5) is a typicalPM2.5 measurements in the Alabama, USA, and obtained good

indicator of small particles in the atmosphere. Accordingly

correlation for monthly even hourly PM2.5 measurements6.

in order to monitor the air quality, sampling of PM 2.5 has

Mukai et al. also investigates the relation in the 2nd large city,

been widely undertaken over the world, especially in the

Osaka, in Japan7. The results showed that a linear correlation

urban cities. On the other hand, it is known that the sun

definitely exists between AOT and PM 2.5, and the value ofphotometry provides us with the aerosol information, e.g.

correlation coefficient for whole data were lower than thataerosol optical thickness (AOT), aerosol size information andin separately considering with two cases as anthropogenic

so on. Simultaneous measurements of PM 2.5 and the AOT

and dust aerosols. This result looks to be showing a fact thathave been performed at a NASA/AERONET (Aerosol Robotics

the atmospheric situation during Asian Dust events, i.e., highNetwork) site in urban city of Higashi-Osaka in Japan since

AOT and low PM2.5, may be

March 2004, and successfully provided a linear correlation

other words, the vertical distribution of atmospheric aerosols,

between PM2.5 and AOT in separately considering with several

namely the height of boundary layer, plays a sufficient role on

cases, e.g. usual, anthropogenic aerosols, dust aerosols and so

the relationship between AOT and PM2.5. Especially our study

on. This fact suggests that the vertical distribution also should

site, Osaka AERONET station, is surrounded by mountains,

be taken into account separately for each aerosol type. In thisand hence the height of boundary layer could have a strong

work, vertical profiles of atmospheric aerosols are considered

influence upon the correlation between AOT and PM2.58. The

different from that as usual. In

based on combination use of photometric data with AERONET,

precise relationship between AOT and PM2.5 is desired. In this

LIDAR (Light Detection and Ranging) measurements andwork, new LIDAR extinction data are examined to improvethe correlation of AOT and PM 2.5 under boundary layer

model simulations.

atmosphere.
Keywords : Aerosols, PM, Aerosol optical depth, LIDAR◆*nakata@socio.kindai.ac.jp

1. INTRODUCTION
It is well known that the increasing emissions of◆

2. MEASUREMENTS◆

2.1 AERONET data

anthropogenic aerosols associated with continuing economic
growth in Asia are causing serious air pollution and climate

The data supplied by the Cimel instrument were analyzed

change. PM 2.5 is sampling widely over the world due towith a standard AERONET processing system9,10. It provides us

monitoring the air quality. Also our group monitors PM2.5 inwith AOT and the Ångström exponent ( α ). The resolution of1

Japan at Higashi-Osaka and Noto sites . Among these sites, thethe AOT is better than 0.01 in all observation wavelengths, andsimultaneous measurements by the sun photometry have been

cloud screening of the obtained data was performed before

undertaken. The sun photometry provides us with the aerosol

aerosol retrieval 11. AOT is the basic parameter describing

information. The world wide NASA/AERONET data are most

atmospheric aerosols and indicates the degree of opacity

2, 3

.

(and hence the concentration of the aerosol particles) in the

Our group has also managed several AERONET stations as

atmosphere12. Some aerosol events show the characteristic

Shirahama since 2000, Higashi-Osaka since 2002, and Notofeature of dust events of a high AOT and a low α but almost

since 2003 in Japan. Furthermore a standard instrument ofalways the detection of rather high values of

NIES/LIDAR network4, Mie scattering lidar instrument, has

contamination by small anthropogenic particles. Figure 1

been set up since April in 2008 at the Higashi-Osaka site.

indicates the AOT at Higashi-Osaka from 2004 to 2010. It

Smirnov et al. have showed that the relation of total column

shows that high AOT due to dust event is observed frequently

aerosol optical thickness and PM 10 in-situ measurementsin spring.

available as the ground-based sun-photometric products

α indicates17◆

2.2 PM sampling◆

2.3 LIDAR data◆

3. RELATIONSHIP BETWEENMULTI-MEASUREMENTS◆

3.1 Correlation between AOT and PM

An SPM sampler (SPM-613D, Kimoto Electric, Japan) set

Figure 4 presents the measurements with a Mie scattering

up at the same AERONET site on the roof of a building at

LIDAR instrument, which is a part of LIDAR network governed

Kinki University (about 50 m above sea level) can measurewith NIES (National Institute for Environmental Studies) , atthe concentrations of various SPMs (e.g., PM 10 and PM 2.5)

Higashi-Osaka from April in 2008 to June in 2010. The NIES/

Figure 5 shows scatter diagrams of PM2.5 versus AOT(0.675

separately, as well as optical black carbon (OBC). The SPM

LIDAR uses the second harmonics (0.532μm) of a flashlamp-µm) for Cluster-1 plus Cluster-2 and Cluster-3 defined in Fig.2.

sampler makes it possible to determine the relationshippumped Nd:YAG laser as alight source. It is very interesting

The squares in Fig. 5 show a 10-bin data set, because the 10-

between aerosol properties and the particulate mass

to mention that the LIDAR provides the vertical distribution

bin method is a useful procedure for increasing the accuracy

concentration, since it can separate the contributions ofof atmospheric aerosols and the total depolarization ratio ofof correlation coefficients 14. However, the number of datafine particles (PM2.5) and coarse particles(PMC) (defined as

perpendicular component to parallel one of the backscattering

points for Cluster-3 was insufficient for applying the 10-bin

the difference between PM10 and PM2.5), both of which areintensity . The values of LIDAR extinction at a wavelength of

method. Therefore, the regression line was calculated using

abundant when the column AOT is high during dust events.

0.532μm are shown in Fig.4a, where the open and filled circles

the entire data for PM2.5 and AOT in Fig. 5b.

PMC is generally a better indicator of dust events than PM2.5

denote the accumulate values from altitude (h) of 0 to 6 km

Furthermore, correlation between PM2.5 and AOTsurface wasdue to soil dust particles being large. However, it is also

(expressed by Ext0-6km ) and those from 0 to 0.9km (Extsurface),plotted in Fig. 5d. AOTsurface represents the near surface (fromwell known that the size of dust particles decreases duringrespectively. Note that LIDAR signal is rather weak in thethe surface to the height of 0.9km) aerosol optical thickness

long transportation on a westerly wind, with the resulting

higher atmosphere with h > 6km, in other words LIDAR data

estimated by Eqs. (2).

13

dust storm containing fine anthropogenic particles .

seem to be saturated around 6km. Therefore 6km is pretended

Accordingly, the PM2.5 concentration corresponds well to bothto be top of atmosphere with respect to LIDAR measurements.

anthropogenic and dust aerosols. Therefore, PM 2.5 data are

The ratio is available as below.

4

AOTsurface = R(0.9km) × AOT(2)

used for analyzing the correlation between aerosols and PM

The figures show linear correlation between PM2.5 and AOT

even in dust events. Figure 3 shows hourly PM2.5 and PMC data

is different by aerosol type. This fact suggests that the verticalobtained during the same period as in Figs. 1 and 2.

(1)

Figure 1. AOT at a wavelength of 0.675 µm at the Higashi-Osaka site from AERONET
data obtained from 2004 to 2010.

The values of R(0.9km) in Fig.4b represent the efficiency

The aerosol properties at Higashi-Osaka were classified

factor of extinction by aerosols within the near surface layer

by applying a standard statistics software package (SPSS;

against that with total atmosphere.

Statistical Package for the Social Sciences) to the AERONET

distribution also should be taken into account separately foreach aerosol type. The comparison between Fig.5c and Fig.5d
shows that the correlation between PM2.5 and AOT has been
improved under consideration of near surface aerosol optical
thickness (AOTsurface ).

data shown in Fig.1. The number of clusters and classification
variables were chosen arbitrarily for use with SPSS. The AOT
at a wavelength of 0.675 μ m and the α were examined as
the classification variables. With these two input variables,
SPSS gave the three aerosol clusters shown in Fig. 2 asscatter diagrams of the AOT against α. Cluster-1 appears to
represent a clear atmosphere with small AOT values. Cluster-1
can be attributed to the background aerosols at HigashiOsaka. Namely, the atmosphere over Higashi-Osaka is usuallyc o ve r e d with thesesmall particles evenon ordinary days.
Cluster-2 is associatedwith large values of α
and the AOT, and hence
represents a typicalFigure 5. (a) Scatter diagram of PM2.5 versus AOT(0.675 µm) for Cluster-1+Cluster-2
during the same period as in Fig.1, (b) Same as (a) but for Cluster-3, (c) Same as (a)but during the same period as in Fig.4., (d) Same as (c) but using AOTsurface (0.675µm).

aerosol event involving
small aerosols.
Cluster-3 shows a dust
event with a large AOT
and a small α .

18Figure 2. Scatter diagrams of α against the AOT
at wavelength of 0.675µm for three clusters ofaerosols obtained by SPSS classification for aerosol
data during the same period as in Fig.1.

Figure 3. PM 2.5 and PM C measurements during the same period and the same site as
in Fig.1.

Figure 4. LIDAR extinction at a wavelength of 0.532μm from April of 2008 to June of2010. In Fig. 4a, Ext0-6km and Extsurface represent the accumulate values from altitude of
0 to 6 km and those from 0 to 0.9km, respectively. Fig. 4b shows the ratio R(0.9km)
of Extsurface to Ext0-6km.

19◆

2.2 PM sampling◆

2.3 LIDAR data◆

3. RELATIONSHIP BETWEENMULTI-MEASUREMENTS◆

3.1 Correlation between AOT and PM

An SPM sampler (SPM-613D, Kimoto Electric, Japan) set

Figure 4 presents the measurements with a Mie scattering

up at the same AERONET site on the roof of a building at

LIDAR instrument, which is a part of LIDAR network governed

Kinki University (about 50 m above sea level) can measurewith NIES (National Institute for Environmental Studies) , atthe concentrations of various SPMs (e.g., PM 10 and PM 2.5)

Higashi-Osaka from April in 2008 to June in 2010. The NIES/

Figure 5 shows scatter diagrams of PM2.5 versus AOT(0.675

separately, as well as optical black carbon (OBC). The SPM

LIDAR uses the second harmonics (0.532μm) of a flashlamp-µm) for Cluster-1 plus Cluster-2 and Cluster-3 defined in Fig.2.

sampler makes it possible to determine the relationshippumped Nd:YAG laser as alight source. It is very interesting

The squares in Fig. 5 show a 10-bin data set, because the 10-

between aerosol properties and the particulate mass

to mention that the LIDAR provides the vertical distribution

bin method is a useful procedure for increasing the accuracy

concentration, since it can separate the contributions ofof atmospheric aerosols and the total depolarization ratio ofof correlation coefficients 14. However, the number of datafine particles (PM2.5) and coarse particles(PMC) (defined as

perpendicular component to parallel one of the backscattering

points for Cluster-3 was insufficient for applying the 10-bin

the difference between PM10 and PM2.5), both of which areintensity . The values of LIDAR extinction at a wavelength of

method. Therefore, the regression line was calculated using

abundant when the column AOT is high during dust events.

0.532μm are shown in Fig.4a, where the open and filled circles

the entire data for PM2.5 and AOT in Fig. 5b.

PMC is generally a better indicator of dust events than PM2.5

denote the accumulate values from altitude (h) of 0 to 6 km

Furthermore, correlation between PM2.5 and AOTsurface wasdue to soil dust particles being large. However, it is also

(expressed by Ext0-6km ) and those from 0 to 0.9km (Extsurface),plotted in Fig. 5d. AOTsurface represents the near surface (fromwell known that the size of dust particles decreases duringrespectively. Note that LIDAR signal is rather weak in thethe surface to the height of 0.9km) aerosol optical thickness

long transportation on a westerly wind, with the resulting

higher atmosphere with h > 6km, in other words LIDAR data

estimated by Eqs. (2).

13

dust storm containing fine anthropogenic particles .

seem to be saturated around 6km. Therefore 6km is pretended

Accordingly, the PM2.5 concentration corresponds well to bothto be top of atmosphere with respect to LIDAR measurements.

anthropogenic and dust aerosols. Therefore, PM 2.5 data are

The ratio is available as below.

4

AOTsurface = R(0.9km) × AOT(2)

used for analyzing the correlation between aerosols and PM

The figures show linear correlation between PM2.5 and AOT

even in dust events. Figure 3 shows hourly PM2.5 and PMC data

is different by aerosol type. This fact suggests that the verticalobtained during the same period as in Figs. 1 and 2.

(1)

Figure 1. AOT at a wavelength of 0.675 µm at the Higashi-Osaka site from AERONET
data obtained from 2004 to 2010.

The values of R(0.9km) in Fig.4b represent the efficiency

The aerosol properties at Higashi-Osaka were classified

factor of extinction by aerosols within the near surface layer

by applying a standard statistics software package (SPSS;

against that with total atmosphere.

Statistical Package for the Social Sciences) to the AERONET

distribution also should be taken into account separately foreach aerosol type. The comparison between Fig.5c and Fig.5d
shows that the correlation between PM2.5 and AOT has been
improved under consideration of near surface aerosol optical
thickness (AOTsurface ).

data shown in Fig.1. The number of clusters and classification
variables were chosen arbitrarily for use with SPSS. The AOT
at a wavelength of 0.675 μ m and the α were examined as
the classification variables. With these two input variables,
SPSS gave the three aerosol clusters shown in Fig. 2 asscatter diagrams of the AOT against α. Cluster-1 appears to
represent a clear atmosphere with small AOT values. Cluster-1
can be attributed to the background aerosols at HigashiOsaka. Namely, the atmosphere over Higashi-Osaka is usuallyc o ve r e d with thesesmall particles evenon ordinary days.
Cluster-2 is associatedwith large values of α
and the AOT, and hence
represents a typicalFigure 5. (a) Scatter diagram of PM2.5 versus AOT(0.675 µm) for Cluster-1+Cluster-2
during the same period as in Fig.1, (b) Same as (a) but for Cluster-3, (c) Same as (a)but during the same period as in Fig.4., (d) Same as (c) but using AOTsurface (0.675µm).

aerosol event involving
small aerosols.
Cluster-3 shows a dust
event with a large AOT
and a small α .

18Figure 2. Scatter diagrams of α against the AOT
at wavelength of 0.675µm for three clusters ofaerosols obtained by SPSS classification for aerosol
data during the same period as in Fig.1.

Figure 3. PM 2.5 and PM C measurements during the same period and the same site as
in Fig.1.

Figure 4. LIDAR extinction at a wavelength of 0.532μm from April of 2008 to June of2010. In Fig. 4a, Ext0-6km and Extsurface represent the accumulate values from altitude of
0 to 6 km and those from 0 to 0.9km, respectively. Fig. 4b shows the ratio R(0.9km)
of Extsurface to Ext0-6km.

19◆

3.2 Vertical distribution of aerosol

the vertical distribution is helpful information to estimate

and Mizobuchi, S., “Suspended Particulate Matter samplingPM2.5 from column AOT. These results show that combining

at an urban AERONET site in Japan Part 1: Clustering

In this section, vertical profiles of atmospheric aerosols

radiometric aerosol information (including space-based data)

analysis of aerosols,” J. Applied Remote Sensing 1, 013518

are considered using model simulation. Figure 6 presents thewith surface-level particulate mass data and LIDAR vertical(2007).

simulated results for the vertical distribution of the annual

profile measurements is a useful approach for improving our[8] Sano, I., Mukai(Nakata), M., Iguchi, N., and Mukai, S.,mean aerosol mass concentration at Higashi-Osaka based

understanding of air quality and the atmospheric environment.

“Suspended Particulate Matter sampling at an urban

on a three-dimensional aerosol-transport–radiation model,
SPRINTARS15, which is driven by GCM that was developed

AERONET site in Japan 2. Relationship between column◆

ACKNOWLEDGEMENTS

aerosol optical thickness and PM2.5 mass concentration,” J.

by CCSR (Center for Climate Systems Research), NIES, and
FRCGC (Frontier Research Center for Global Change)

16.

Applied Remote Sensing 4, 403-504 (2010).

The

The authors thank NASA/AERONET and NIES/LIDAR teams[9] Dubovik, O., Smirnov, A., Holben, B. N., King, M. D., Kaufman,model includes carbonaceous, sulfate, mineral dust, andfor providing data. This work was supported in part by Global

Y. J., Eck, T. F. and Slutsker, I., “Accuracy assessments ofsea salt aerosols. The black and gray lines represent the

Change Observation Mission – Climate (GCOM-C) by JAXA andaerosol optical properties retrieved from AERONET sun

mass concentrations of mineral dust and anthropogenic

the Greenhouse Gases Observing Satellite (GOSAT) Scienceand sky-radiaometric measurements,” J. Geophys. Res. 105,

(carbonaceous plus sulfate) aerosols, respectively. Note that

Project by NIES.

9791-9806 (2000) [doi:10.1029/2000JD900040].
[10] Dubovik, O. and King, M. D., “A flexible inversion

the error bars represent the standard deviations generated
in our model simulations. The pie chart in Fig.6 shows the◆algorithm for retrieval of aerosols optical properties from

REFERENCES

sun and sky radiance measurements,” J. Geophys. Res. 105,

relative contributions of the two aerosol types. It shows thatthe two aerosol types exhibit different vertical profiles. The[1] Mukai, S., Sano, I. and Holben, B. N., “Aerosol properties

concentrations of anthropogenic aerosols are maximal atover Japan by sun/sky photometry,” Water, Air and Soil

[11] Smirnov, A., Holben, B. N., Eck, T. F., Dubovik, O. andthe surface and rapidly decrease with height. In particular,

Pollution 5, 133-143 (2005) [doi:10.1007/s11267-005-

Slutsker, I. “Cloud screening and quality control algorithms

carbonaceous and sulfuric aerosols are concentrated in the0731-2].

for the AERONET database,” Remote Sens. Environ. 73, 337-

boundary layer. In contrast, mineral dust is distributed almost[2] Holben, B.N., Eck, T.F., Slutsker, I., Tanré, D., Buis, J.P.,

uniformly from the surface to the higher atmosphere, with a

Setzer, A., Vermote, E., Reagan, J.A., Kaufman, Y., Nakajima,[12] Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov,

moderate peak at a height of 2–4 km. The simulated verticalT., Lavenu, F., Jankowiak, I., and Smirnov, A., “AERONET -A., O’Neill, N. T., Slutsker, I. and Kinne, S., “Wavelength

profile of aerosol is consistent with LIDAR measurement.

A federated instrument network and dataarchive fordependence of the optical depth of biomass burning, urban,

aerosol characterization,” Rem. Sens. Environ. 66, 1-16

and desert dust aerosols,” J. Geophys. Res. 104, 31333-(1998).

Figure 6. Simulated resultsof the vertical distributionof the annual mean aerosol
mass concentration andits standard deviation at
Higashi-Osaka.

◆

4. SUMMARY

349 (2000) [doi:10.1016/S0034-4257(00)00109-7].

31349 (1999) [doi:10.1029/1999JD900923].

[3] Dubovik, O., Holben, B.N., Eck, T.F., Smirnov, A., Kaufman,[13] Mukai (Nakata), M., Nakajima, T. and Takemura, T.

Y.J., King, M.D., Tanré, D. and Slutsker, I., “Variability of“A study of long-term trends in mineral dust aerosol

absorption and optical properties of key aerosol types

distributions in Asia using a general circulation

observed in worldwide locations, “ J. Atmos. Sci. 59, 590-

model,” J. Geophys. Res. 109, D19, D19204 (2004)

608 (2002).

[doi:10.1029/2003JD004270].

[4] Shimizu, A., Sugimoto, N., Matsui, I., Arao, K., Uno, I.,[14] Kacenelenbogen, M., Léon, J.-F., Chiapello, I. and Tanré,

Murayama, T., Kagawa, N., Aoki, K., Uchiyama, A. andD. “Characterization of aerosol pollution events in France

Yamazaki, A., “Continuous observations of Asian dust andusing ground-based and POLDER-2 satellite data,” Atmos.

ither aerosols by polarization lidars in China and Japan
during ACE-Asia,” J. Geophys. Res. 109, 1-14 (2004)[doi:10.1029/2002JD003253].

Chem. Phys. 6, 4843–4849 (2006).
[15] Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y.
and Nakajima, T., “Simulation of climate response to[5] Smirnov, A., Holben, B. N., Savoie, D., Prospero, J.M.,

aerosol direct and indirect effects with aerosol transport-

Kaufmann, Y. J., Tanré, D., Eck, T.F. and Slutsker, I. ,radiation model,” J. Geophys. Res., 110, D02202 (2005)

The mass concentration of PM2.5 is known as an index of“Relationship between column aerosol optical thickness[doi:10.1029/2004JD005029].

air quality. In order to investigate the relationship between

and in situ ground based dust concentrations over[16] K-1 Model Developers, [K-1 coupled GCM (MIROC)

PM2.5 and AOT, both measurements have been simultaneously

Barbados,” Geophys. Res. Letters 27, 1643-1646 (2000)

description], edited by Hasumi, H. and Emori, S., K-1 Tech.

[doi:10.1029/1999GL011336].

Rep. 1, Cent. For Clim. Syst. Res., Univ. of Tokyo, Tokyo

done at Kinki University Campus, Higashi-Osaka, Japan since
March in 2004 . Furthermore LIDAR instrument has joined[6] Wang, J. and Christopher, S.A. “Intercomparison between

since April in 2008 at the same site. It provided a linear

satellite-derived aerosol optical thickness and PM2.5 mass:

correlation between PM2.5 and AOT in separately considering

Implications for air quality studies,” Geophys. Res. Letterswith several cases. Especially, a stronger linear correlation30, 2095 (2003) [doi:10.1029/2003GL018174].

7exists between PM 2.5 and AOTsurface. This fact suggests that

20

20673-20696 (2000) [doi:10.1029/2000JD900282].

(2004).

[7] Mukai, S., Nishina, M., Sano, I. Mukai(Nakata), M., Iguchi, N.

21

◆

3.2 Vertical distribution of aerosol

the vertical distribution is helpful information to estimate

and Mizobuchi, S., “Suspended Particulate Matter samplingPM2.5 from column AOT. These results show that combining

at an urban AERONET site in Japan Part 1: Clustering

In this section, vertical profiles of atmospheric aerosols

radiometric aerosol information (including space-based data)

analysis of aerosols,” J. Applied Remote Sensing 1, 013518

are considered using model simulation. Figure 6 presents thewith surface-level particulate mass data and LIDAR vertical(2007).

simulated results for the vertical distribution of the annual

profile measurements is a useful approach for improving our[8] Sano, I., Mukai(Nakata), M., Iguchi, N., and Mukai, S.,mean aerosol mass concentration at Higashi-Osaka based

understanding of air quality and the atmospheric environment.

“Suspended Particulate Matter sampling at an urban

on a three-dimensional aerosol-transport–radiation model,
SPRINTARS15, which is driven by GCM that was developed

AERONET site in Japan 2. Relationship between column◆

ACKNOWLEDGEMENTS

aerosol optical thickness and PM2.5 mass concentration,” J.

by CCSR (Center for Climate Systems Research), NIES, and
FRCGC (Frontier Research Center for Global Change)

16.

Applied Remote Sensing 4, 403-504 (2010).

The

The authors thank NASA/AERONET and NIES/LIDAR teams[9] Dubovik, O., Smirnov, A., Holben, B. N., King, M. D., Kaufman,model includes carbonaceous, sulfate, mineral dust, andfor providing data. This work was supported in part by Global

Y. J., Eck, T. F. and Slutsker, I., “Accuracy assessments ofsea salt aerosols. The black and gray lines represent the

Change Observation Mission – Climate (GCOM-C) by JAXA andaerosol optical properties retrieved from AERONET sun

mass concentrations of mineral dust and anthropogenic

the Greenhouse Gases Observing Satellite (GOSAT) Scienceand sky-radiaometric measurements,” J. Geophys. Res. 105,

(carbonaceous plus sulfate) aerosols, respectively. Note that

Project by NIES.

9791-9806 (2000) [doi:10.1029/2000JD900040].
[10] Dubovik, O. and King, M. D., “A flexible inversion

the error bars represent the standard deviations generated
in our model simulations. The pie chart in Fig.6 shows the◆algorithm for retrieval of aerosols optical properties from

REFERENCES

sun and sky radiance measurements,” J. Geophys. Res. 105,

relative contributions of the two aerosol types. It shows thatthe two aerosol types exhibit different vertical profiles. The[1] Mukai, S., Sano, I. and Holben, B. N., “Aerosol properties

concentrations of anthropogenic aerosols are maximal atover Japan by sun/sky photometry,” Water, Air and Soil

[11] Smirnov, A., Holben, B. N., Eck, T. F., Dubovik, O. andthe surface and rapidly decrease with height. In particular,

Pollution 5, 133-143 (2005) [doi:10.1007/s11267-005-

Slutsker, I. “Cloud screening and quality control algorithms

carbonaceous and sulfuric aerosols are concentrated in the0731-2].

for the AERONET database,” Remote Sens. Environ. 73, 337-

boundary layer. In contrast, mineral dust is distributed almost[2] Holben, B.N., Eck, T.F., Slutsker, I., Tanré, D., Buis, J.P.,

uniformly from the surface to the higher atmosphere, with a

Setzer, A., Vermote, E., Reagan, J.A., Kaufman, Y., Nakajima,[12] Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov,

moderate peak at a height of 2–4 km. The simulated verticalT., Lavenu, F., Jankowiak, I., and Smirnov, A., “AERONET -A., O’Neill, N. T., Slutsker, I. and Kinne, S., “Wavelength

profile of aerosol is consistent with LIDAR measurement.

A federated instrument network and dataarchive fordependence of the optical depth of biomass burning, urban,

aerosol characterization,” Rem. Sens. Environ. 66, 1-16

and desert dust aerosols,” J. Geophys. Res. 104, 31333-(1998).

Figure 6. Simulated resultsof the vertical distributionof the annual mean aerosol
mass concentration andits standard deviation at
Higashi-Osaka.

◆

4. SUMMARY

349 (2000) [doi:10.1016/S0034-4257(00)00109-7].

31349 (1999) [doi:10.1029/1999JD900923].

[3] Dubovik, O., Holben, B.N., Eck, T.F., Smirnov, A., Kaufman,[13] Mukai (Nakata), M., Nakajima, T. and Takemura, T.

Y.J., King, M.D., Tanré, D. and Slutsker, I., “Variability of“A study of long-term trends in mineral dust aerosol

absorption and optical properties of key aerosol types

distributions in Asia using a general circulation

observed in worldwide locations, “ J. Atmos. Sci. 59, 590-

model,” J. Geophys. Res. 109, D19, D19204 (2004)

608 (2002).

[doi:10.1029/2003JD004270].

[4] Shimizu, A., Sugimoto, N., Matsui, I., Arao, K., Uno, I.,[14] Kacenelenbogen, M., Léon, J.-F., Chiapello, I. and Tanré,

Murayama, T., Kagawa, N., Aoki, K., Uchiyama, A. andD. “Characterization of aerosol pollution events in France

Yamazaki, A., “Continuous observations of Asian dust andusing ground-based and POLDER-2 satellite data,” Atmos.

ither aerosols by polarization lidars in China and Japan
during ACE-Asia,” J. Geophys. Res. 109, 1-14 (2004)[doi:10.1029/2002JD003253].

Chem. Phys. 6, 4843–4849 (2006).
[15] Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y.
and Nakajima, T., “Simulation of climate response to[5] Smirnov, A., Holben, B. N., Savoie, D., Prospero, J.M.,

aerosol direct and indirect effects with aerosol transport-

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