Lidar Measurements of Aerosols at
Chung-Li, Taiwan (25 N ° ,121E ° )
C. W. Chiang, W. A. Liang, and J. B. Nee
National Central University, Chung-Li, Taiwan 32054
Lidar observations have been carried out for the tropospheric aerosols in the height region 1-5 km during 2002-2003. Lidar measurements revealed the occurrence, height distributions and transport processes of aerosols related to Asian dust and biomass burning aerosols that has been conveyed from China and southeast Asia respectively. The properties of these aerosols are investigated by comparisons with meteorological conditions (Radiosonde), satellite image (NAAPS AOD /
TOMS AI Composite plots), and trajectory studies (HYSPLIT).
The backscattering ratio of the both Asian dust and biomass burning aerosols exhibit the steepest gradient compared with the relative humidity which also show the sharp gradient. It indicates a possibility of modification of aerosol particles which might be mixed with humid air in the transport process. In addition, both of Asian dust and biomass burning aerosols tend to be trapped by temperature inversion layer.
From the analysis of depolarization ratio, we found that the nonspherical particles arrived were predominant below 3 km for dust events and higher altitude for biomass burning events.
Atmospheric aerosols have potential on climate change because of their direct and indirect effects on the Earth’s radiation budget.
One of the important factors is the change of aerosol optical properties in environments with variations in relative humidity. As aerosols are subjected to variations in relative humidity, their size may increase, as well as changes to their refractive index. An increase in aerosol backscattering is then observed. Consequently, more radiative energy may be reflected back to space, creating an overall cooling effect.
( Monitoring efforts for the chemical and radiative effects of dust storms and biomass burning, which are largely uncertain, have been pursued in many places. Research has been emphasized on combining various ground pollution measurements, atmospheric transport models, storm simulations, and satellite images. The ground-based instruments include sun photometer, nephelometer, sky radiometer in invisible or infrared wavelengths, particle counters, etc.[Sokolik et al. 2001].
1

Besides these pollution-based instrumentations, vertical distribution information is required because light scattering and absorption are altitude dependent. Lidar is a power tools for obtaining the vertical profiles of the aerosols and clouds covering altitudes from 0-30 km
[Reagan et al., 1989] and now are monitored by various lidar facilities in
Asian countries through Asian Lidar Network [Murayama et al. 2002].
From 2002, a joint campaign was called to monitor dust storm events arriving in Taiwan. We participated in the observational activities by using the lidar. In the period between February 2002 and
July 2003, clouds associated with arriving dust storm and biomass burning aerosol warnings were detected in many occasions. Besides normal backscattering lidar measurements, the polarization lidar measurements have also been included to strengthen the dust cloud observations. Combined with ground sunphotometer, parcels trajectory and satellite images, we have obtained useful information about the occurrence, height distributions and transport processes of these events.
Currently aerosols are measured mostly by using aerosol lidar systems. An aerosol lidar usually employs a visible laser wavelength at
532 nm, or the 2nd harmonics, of a Nd:YAG laser. Our lidar system consists of a two-wavelength laser transmitter at 532/1064 nm and a
Newtonian telescope and a Schmidt-Cassegrain telescope with a diameter
45 cm and 20 cm respectively as described in previous papers [Nee et al.
1998; Chen et al. 2002]. Figure 1 shows the laser system including the transmitter and receiver systems. A two-wavelength system is useful for particle size measurements, however, in this paper only the 532 nm lidar system and data was used to measure and analyze the vertical profiles of backscattering ratio and depolarization ratio.
Our lidar system was pointing vertically and was operated during various air pollution and meteorological conditions at nighttime, the complete overlap of the laser beam with the telescope field of views are at the ranges of 1500 m and 750m for Newtonian and
Schmidt-Cassegrain telescope respectively. The signals are recorded on a continuous basis by a Multi-channel scaler and transferred into a personal computer automatically, with a vertical resolution of 24 m and a temporal resolution of 5.6 min (10000 shots and 30/s pulse repetition
2

rate).
The lidar polarization measurement is made by detecting the backscattering light in its two components of polarizations, whose parallel or perpendicular directions are defined relative to the outgoing laser beam. Each polarization detector system includes a photomultiplier tube (PMT) combined with a narrow band interference filter centered at 532 nm (FWHM 3 nm).
Normally lidar measures the aerosol backscattering ratio, R, defined as follows:
R=( β a
+ β m
β m
(1) where β a
and β m
are respectively the backscattering coefficients of aerosol and molecular. The extinction coefficient k, optical depth τ d
, and the transmission T are defined as follows:
Extinction k=Sa β (2)
τ d
= ∫ k(z)dz
(3)
Transmission T =exp(τ d
) (4)
Sa is a factor called lidar ratio or extinction to backscatter ratio. In lidar research, Sa is usually determined in calculation or by experimentation
[Chen et al. 2002]. In those cases we used Sa= 60 and 50 for aerosol and dust respectively [ Takamura and Sasano 1987] [ Murayama et al. 1999].
The depolarization ratio measurements are made by detecting the backscattered light with its polarization parallel and perpendicular to the direction of the out going laser light. In a polarization lidar, the depolarization ratio is defined as
δ = I
⊥
/ I
║
(5) where I
║
and I
⊥
are the parallel and the perpendicular intensity relative to the outgoing laser beam. For round particles, such as the stratospheric aerosols, their depolarization ratio is close to zero. For particles with unsymmetrical shapes, the depolarization ratio is nonzero. The depolarization ratio measurement is essential for understanding the composition of cloud as symmetrical water drops or unsymmetrical cloud
3

and dust particles [Sassen 2002].
To use Fernald’s solution [Fernald et al., 1972] one must specify a certain value for backscattering ratio at a designated range, we assumed that an aerosol-free layer exists at a certain level about 35 km, which assumed that scattering occurs from air molecules only. According to this method we must measure the mean backscattering ratio of background aerosol first. Because the effect of saturation and dead time of PMT, we must merge aerosol backscattering ratio measured on different range, one return signal data from 750m to 5 km, one from 3 km to 10 km and another from 9 km to 35 km by changing laser power.
From now on, the averaged backscattering ratio can be used as a boundary to solve Fernald’s equations at a given altitude level.
Figure 2 shows a vertical profiles of mean backscattering ratio and extinction coefficient of background aerosol derived from the lidar data that we used were obtained on days with no clouds, which resulted in a clear-sky aerosol climatology during the period of measurement between
June 1999 and January 2003 at Chung-Li, Taiwan. The solid curve indicates averaged profiles of aerosol backscattering ratio and extinction.
The horizontal bars indicate the magnitudes of the standard deviation.
From the Fig. 2. we can see that most of background aerosols exist below
5 km.
Variations of aerosol optical thickness derived from the lidar measurements from February 2002 to July 2003 are shown in Fig. 3.
Fig. 3. shows that aerosol extinction coefficient profiles derived from our analysis are integrated from 1 to 5 km to yield the optical thickness of tropospheric aerosols and indicate that the optical thickness is at a minimum from autumn to winter and increases in spring and summer.
The observed aerosol optical thickness varies significantly for each season in addition to long-term variation.
4

The large optical thickness observed in sprint and summer might be attributable to spreading and transport of the pollution, in addition to the seasonal variation is also caused by transport system and aerosol itself through its generation and dissipation processes such as gas-to-particle conversion and/or the relative humidity effect. In autumn and winter, on the other hand, predominant wind comes from the north or east, which usually does not generate a hazy atmosphere.
Furthermore, we removed the cloud case, which we defined the backscattering ratio greater than 5, and compared with Sunphotometer data provided by Liu et al.(2002-2003). Because the wavelength of the sun photometer used is different from that of the lidar (532 nm), we corrected the optical thickness measured from the sun photometer by averaging the wavelengths of 440 nm and 670 nm and then multiplying it by 1.04, under an assumption that the aerosol optical thickness is proportional to the inverse of the wavelength. Figure 4 shows the optical thickness throughout the entire measurement region is given in cloud-free and clear atmosphere, we found that lidar derived optical thicknesses show good tendency with the sunphotometer data, which are larger due to accumulate total column, since the altitude range integrated by lidar only covers 1-3 km, since the data quality was not so good in some data, the analysis was made to a 3 km altitude. In addition we also compared the aerosol optical thickness (AOT) between 1-3 km and
5-25km and found that the 1-3 km is 30-100 times AOT of the 5-25km.
Although the daytime and nighttime difference makes direct comparison meaningless, the values show similar tendency and indicated that the main variation of atmosphere are controlled by lower atmosphere.
We compared the probability which cirrus were present, where backscatter ratio is greater than 5. This probability was calculated as cloud days out of measurement days for each month shown in Figure 5.
From Fig.4 and Fig. 5 show the profiles of AOT measured in clear days and the cloud probability that seem to have opposite trend, cloud increases and aerosol declines, maybe indicated that cloud was produced by aerosols adsorbed moisture and then fell to the earth by the attraction of gravity so that the smaller AOT we measured during clear days.
Fig. 3. shows the result of greater AOT in the season of Spring and
Summer, since aerosols in the atmosphere are expected to have various origins and considered to be related to aerosol sources and characteristics, results of lidar measurements were classified into three categories with
5

reference to the HYSPLIT and satellite image data.
1. The Effect of Dust Storm:
Our current understanding is that major dust path occurred in the
Taiwan region during Spring time. This indicates that the main paths of dust are associated with westerly flow, located around 30°N. From the analysis of backward trajectories, one found that the air parcels arrived over Taiwan were transported from the East China Sea at main levels below 3000m, synoptic meteorological analyses of the episode show that the path of the transport of dust is controlled by a high-pressure system located to the west of Japan when the dust storm occurred in north China.
This kind of synoptic system forces the dust to move southeastward from the source region along the western edges of the high-pressure system, making the eastward transport of dust very difficult [T.H. Lin., 2001].
We used HYSPLIT to trace the peak values of our lidar’s measurements, which the backscattering ratio is unusual and the depolarization ratio greater than 0.1, and showed the 5-day isentropic backward air mass trajectories arriving at our lidar site as shown in Fig. 6., and we used the
TOMS satellite image and HYSPLIT to define the dust events. The analysis of backward trajectories of the air masses originating from the north China to the height of measurement of peak values of backscattering ratio in our lidar site (see Fig. 6.) and the individual lidar measurements were averaged as shown in Figure 7.
Figure 7a and Figure 7b showed that dust storm arrived Taiwan and the non-sphereic particles existed mostly below 2500m, in addition, from the relative humidity and the temperature profile of Fig. 7c and Fig. 7d respectively, we found that there are heavier humidity and lower temperature during the events of dust storm and indicated there were strong cold fronts passing over Taiwan. The cold fronts swept out most local pollutants and replaced them with an air mass (with all its dust and pollutants) brought over by long-range transport [Liu et al. 2001].
2. The Effect of Biomass Burning
Extensive biomass burning associated with agricultural practices occurs during the dry season (December to April next year) or/and various anthropogenic sources, such as industrial processes, electricity generation from fossil fuel combustion, and the combustion of fossil fuels from vehicular transportation, across large portions of the southern and eastern Asia each year[Liu et al.]. This burning produces large amounts
6

of smoke aerosol with high optical depth. From the analysis of backward trajectories of our lidar data, which are unusual and the depolarization ratio greater than 0.1, we showed the 5-day isentropic backward air mass trajectories, which came from southeast Asia defined as biomass burning as shown in Figure 8. We averaged and plotted the lidar data of biomass burning events in Figure 9, found that the aerosol distribution of biomass burning are higher than dust events at the level below 4.5 km see Fig. 9a. Also the non-spheric particles existed higher altitude whether that correlated with dried humidity to make depolarization ratio increasingly, we need more investigation. Figure 9 shows the higher temperature is relative to the wind comes from the west and south.
3. The Effect of Local Pollution
Similarly, we used HYSPLIT to track the air masses and found that some cases are different from above discussions, the backward trajectories surround Taiwan and the averaged lidar data were shown in
Figure 10 and 11 respectively. Figure 11 shows the backscattering ratios are large, but the depolarization ratio is smaller than 0.1 that indicated the particles preferred spherical, this result was similar to the urban pollution .
These findings suggest that dust and biomass burning came from north and south China and through our site with lower and higher altitude respectively. In addition, we found that backscattering ratios of the both
Asian dust and biomass burning aerosols exhibit the steepest gradient compared with the relative humidity which also show the sharp gradient.
It indicates a possibility of modification of aerosol particles which might be mixed with humid air in the transport process (eg. some of the mineral dust were coated with solution such as Sulfuric acid) to decline the depolarization ratio.[Iwasaka et al., 1988; Ikegami et al.,1993; Sakai et al.,
2001]. In addition, both of Asian dust and biomass burning aerosols tend to be trapped by temperature inversion layer as shown in Fig. 7d and
Fig.9d. It may be due to radiative heating rates and direct radiative forcing by those aerosols in cloudy atmospheric condition to form the temperature inversion layer.
These aerosols backscattering measurements showed the variation of the structures of the lower troposphere. It is quite probable that aerosols in a layered structure have different characteristic and the advection of air masses of different origin over our site, seems to be the most probable
7

reason to explain the variation of aerosols, but those results are not easy to assess this effect because there has been little data on physical and chemical properties of aerosols in the atmosphere. In order to solve this problem we hope to apply multi-wavelength lidar systems to estimate the size distribution and physical and chemical properties of aerosols to get more information in the future.
.
Furthermore, it’s very difficult to measure the “dry” aerosols at our site in lower atmosphere, since there is heavier relative humidity. We try to analyze the possibility of aerosols adsorbed moisture and condensed into cloud : the statistical data were analyzed that backscattering ratio is greater than 5 and compared with relative humidity simultaneously from
February 2002 to July 2003 in the lower atmosphere (1-5km), we found that most possible clouds were present as the relative humidity greater than 60 % as shown in Figure 12.
That would possibly cause a growth of the aerosol particles by condensation, thus changing their optical properties. This effect is generally more enhanced for relative humidity greater than 70% [Haenel,
1976; Fitzerald , 1982], and at an relative humidity of 30-40%, most ambient aerosol particles will be “dry” or sufficiently close to dryness that no substantial change in particle size would result through further drying of the air [Charlson et al.,1984].
In addition, calcium-rich mineral dust has also been hypothesized to provide a reactive surface in the heterogeneous reaction of SO
2
, oxides of nitrogen, and the photochemical oxidant cycle [Dentener et al.,1996;
Zhang et al., 1994]. The removal of SO
2
by mineral dust depends strongly on the alkalinity and water content on dust particles and the hygroscopic nature of mineral dust is one of the most important factors in determining whether it can effectively serve as a cloud condensation nuclei. [ Gillette et al., 1992; Dentener et al., 1996.].
[Li-Jones and Prospero 1998] also have shown that a substantial fraction of the nss SO
4
= in dust-laden air masses is associated with the supramicrometer size fraction and that the sulfate is presumed to be deposited on the surface of the dust particles. Although the amount of nss
SO
4
= on the dust particles was very low compared to the dust
8

concentration, SO
4
= will adsorb water at high relative humidity. On the basis of their results, at a relative humidity level of about 80%, the amount of water adsorbed is small compared to the dust particle size; consequently, the light-scattering properties of the dust are not appreciably affected. However, because of the coating of soluble material and the large size of the dust, the particles could become active cloud condensation nuclei at relatively low supersaturations [Andreae et al.,
1986; Hobbs,1993]
And then we analyzed the relationship between clouds and relative humidity. Months were arranged into seasons, that is, March,April, and
May for spring; June, July, and August for summer; September, October and November for fall and December, January and February for winter. A two-year average profile was calculated for each season and the number of days selected for each season is listed in Table 1.
Statistical data of clouds were arranged into seasons from February
2002 to July 2003
Seasons Spring Summer Autumn Winter
Days 90 46 29 55
Clouds (%) 49 43 34 42
Our previous measurement showed the cirrus clouds (10-17 km) mostly appeared in summer time [Chen et al.], but from the table 1 found that mostly clouds appeared in Spring time in lower atmosphere. This is different from the idea during the summer, the amount of cloud data is large because cumulus clouds will often form during the daytime because of strong convection. Those phenomena of reduced clouds maybe correlated with higher temperature than the past two years during summer time in Taiwan, of course, we need more data of lower atmosphere to confirm this suggestion.
The two major generative conditions of clouds yielded one is produced locally and another one came from abroad. For the local product suggested to be formed by aerosol adsorbed moisture and existed the suitable temperature. The source of aerosol may come from pollution including industrial pollution and exhaust gas, since our site is close to the air route. The exhaust gas problem has been concerned with scientists, recently [Beck,1990][Kashibhatla, 1993][Johnson and
9

Stevenson, 1996][Brasseur et al., 1996][Schumann, 1997][Meijer et al.,
2000], another one is long-range transport which came from China and southeast Asia and had the similar influence.
The enhanced aerosol backscatter observed could be probably attributed to the presence of convectively driven local aerosol or advectively transported aerosols, through long-range transport mechanisms. This convective activity would also probably lead to an accumulation of water vapor in those zones which revealed indeed an increased relative humidity of 60 %, which would possibly cause a growth of the aerosol particles by condensation.
The change in the magnitude of the aerosol backscattering ratio could be probably again related to a change in the particulates microphysical or chemical properties, which in turn could be probably explained by the advection of aerosol particulates from a different site or vertical air-mass exchanges.
Conclusion:
Our lidar system can provide information on the lower troposphere’s distribution of aerosol, mineral dust, biomass burning, clear air layer, and cloud statistic, etc.
Different types of aerosol with different properties are expected to come from different directions.
The aerosol adsorbed moisture to form clouds and make high backscattering ratio that may consist of aerosols with small water droplets or the effect of multi-scattering [].
Acknowledgements:
The authors thank the Central Weather Bureau of Taiwan for providing the Radiosonde data, the National Science Council of ROC with a grant NSC91-2111-M-008-015, the Environmental Protection
Agency of Taiwan through a contract EPA-91-U1L1-02-109,[Liu et al.] and also the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website
(http://www.arl.noaa.gov/ready.html) used in this publication. .
Plots :
Fig. 1. A schematic diagram of NCU lidar system
10

Fig. 2.
35
30
|---------|
|--------|
25
|------|
20
|--|
15
10
5
|------|
|-------|
|--------------|
0
1 1.1
1.2
Backscatter Ratio
35
30
25
20
15
10
5
1.3
0
10
-5
10
-4
10
-3
10
-2
Extinction Coefficient(1/Km)
10
-1
11

Fig. 3.
1.2
Average of Aerosol Optical Depth of 1-5 km during 2002-2003
2002
2003
1
0.8
0.6
0.4
Fig. 4.
0.2
1 2 3 4 5 6
Month
7 8 9 10 11 12
12

0.8
0.6
1
Compare the optical depth of Sunphotometer and Lidar in 2002-2003
Sunphotometer
Lidar
0.4
0.2
Fig. 5.
60
0
1 2 3 4 5 6
Month
7 8 9 10 11 12
Statistic of cloud during 2002-2003
45
40
35
55
50
Fig. 6.
30
1 2 3 4 5 6
Month
7 8 9 10 11 12
13

Dust Evens
30 
N
45 
N
1.5 km
2.2 km
2 km
1.5 km
15 
N
75 
E
90

E
105

E
120

E
135

E
Fig. 7.
4
3.5
3
4
3.5
3
Dust Events
4
3.5
3
4
3.5
3
2.5
2
1.5
2.5
2
1.5
2.5
2
1.5
2.5
2
1.5
1
0 20 40
Backscattering Ratio
1
0 0.1
0.2
Depolarization Ratio
1
50 100
Relative Humidity %
1
0 5 10
Temperature
℃
14

Fig. 8.
Biomass Burning Evens
30 
N
45 
N
15 
N
75 
E
90

E
105

E
120

E
135

E
Fig. 9.
5
4.5
4
3.5
5
4.5
4
3.5
Biomass Burning Events
5
4.5
4
3.5
5
4.5
4
3.5
3
2.5
2
1.5
3
2.5
2
1.5
3
2.5
2
1.5
3
2.5
2
1.5
1
0 10 20 30
Backscattering Ratio
1
0 0.2
0.4
Depolarization Ratio
1
50 100
Relative Humidity %
1
-5 0 5 10 15
Temperature
℃
15

Fig. 10. pollution Evens
30 
N
45 
N
15 
N
75 
E
90

E
105

E
120

E
135

E
Fig. 11.
16

5
4.5
4
3.5
3
2.5
2
1.5
5
4.5
4
3.5
3
2.5
2
1.5
Pollution Events
5
4.5
4
3.5
3
2.5
2
1.5
5
4.5
4
3.5
3
2.5
2
1.5
1
0 50
Backscattering Ratio
1
0 0.05 0.1
Depolarization Ratio
1
50 100
1
-5 0 5 10 15
Relative Humidity % Temperature
℃
Fig. 12.
35
20
15
10
5
0
30
25
30-40 40-50 50-60 60-70
Relative Humidity
70-80 80-90 90-100
17

Reference:
[Andreae, M. O., R. J. Charlson, R. Bruynseels H. Storms, R. V. Grieken, and W. Maenhaut, Internal mixture of sea salt, silicates and excess sulfate in marine aerosol, Science, 232, 1620-1623, 1986.]
[Beck, J. P., C. E. Reeves, F. A. A. M. de Leeuw, and S. A. Penkett, The effect of aircraft emissions on atmospheric ozone in the Northern
Hemisphere, report no. 222201001, RIVM, 1990]
[Brasseur, G. P., J. Muller, and C. Granier, Atmospheric impact of NO x emissions by subsonic aircraft: A three-dimensional model study, J.
Geophys. Res., 101, 1423-1428, 1996.]
[ Cairo, F., G. D. Donfrancesco, A. Adriani, L. Pulvirenti, and F. Fierli,
1999: Comparison of Various linear depolarization parameters measured by lidar, Appl. Opt. 38, 4425-4432.].
[Charlson, R. J., D. S. Covert, and T. B. Larson, Observation of the effect of humidity on light scattering by aerosols, in Hygroscopic Aerosols, edited by L. H. Ruhnke and A. Deepak, pp. 35-44, A. Deepak, Hampton,
Va., 1984.]
[ Chen, W. N., C. W. Chiang, and J. B. Nee, 2002: “Lidar ratio and depolarization ratio for cirrus clouds”, Applied Optics, 41,6470].
[Dentener, F. J., G. R. Carmichael, Y. Zhang, J. Lelieveld, and P. J.
Crutzen, Role of mineral aerosol as a reactive surface in the global troposphere, J. Geophys. Res., 101, 22869-22889,1996. ]
[Dentener, F. J., G. R. Carmichael, Y. Zhang, J. Lelieveld, and P. J.
Crutzen, Role of mineral aerosol as a reactive surface in the global troposphere, J. Geophys. Res., 101, 22869-22889,1996.]
[F. G. Fernald, B. M. Herman, and J. A. Reagan, “ Determination of aerosol height distributions by lidar,” J. Appl. Meteorol. 11, 482-489
(1972)]
[Fitzerald, J.W., Hoppel, W. and Vietti, M. (1982) The size and scattering coefficient of urban aerosol particles at Washington DC as a function of relative humidity. Journal of Atmospheric Sciences 39, 1838-1852]
[Hobbs, P. V., Aerosol-cloud interactions, in Aerosol-Cloud-Climate
Interactions, edited by P.V. Hobbs, pp.33-73, Academic, San Diego, Calif.,
1993.].
[Haenel. G. (1976) The properties of atmospheric aerosol particles as a function of the relative humidity at thermodynamic equilibrium with the surrounding moist air. Beitr. Zum. Phys. Atm., 44,137-167.]
18

[Iwasaka, Y., Yamato, M., Imasu, R., Ono, A., 1988. Transport of Asian dust (KOSA) particles; importance of weak KOSA events on the geochemical cycle of soil particles. Tellus 40B,494-503.]
[Gillette, D. A., G. J. Stensland, A. L. Williams, W. Barnard, D. Gatz, P. C.
Sinclair, and T. C. Johnson, Emissions of alkaline elements calcium, magnesium, potassium, and sodium from open sources in the contiguous
United States, Global Biogeochem. Cycles, 6,437-457, 1992.][ Dentener et al.,1996; Zhang et al., 1994]
[Ikegami,M., Okada,K., Zaizen, Y., Makino,Y.,1993. Aerosol particles in the middle troposphere over the northern Pacific. Journal of the
Meteorological Society of Japan 71,517-528.]
[Johnson, C. E. and D. S. Stevenson, Effect on tropospheric oxidant concentrations resulting from aircraft NOx emissions, Air pollution
Research Report 58, Pollution from Aircraft emissions in the North
Atlantic Flight Corridor (POLINAT), 175-202, 1996]
[Kasibhatla, P. S., NOy from sub-sonic aircraft emissions: A global three-dimensional model study, Geophys. Res. Let., 20, 1707-1710,1993]
[Li-Jones, X., and J. M. Prospero, Variations in the size distribution of non-sea-salt sulfate aerosol in the marine boundary layer at Barbados:
Impact of African dust, J. Geophys. Res., 103, 16073-16084,1998.]
[Murayama T., Nobuo Sugimoto, Itsushi Uno, Kisei Kinoshita, Kazuma
Aoki, Naseru Hagiwara, Zhaoyan Liu, Ichiro Matsui, Tetsu Sakai,
Takashi Shibata, Kimio Arao, Byung-Ju Sohn, Jae-Gwang Won,
Soon-Chang Yoon, Tao Li, Jun Zhou, Huanling Hu, Makoto Abo, Kengo
Iokibe, Ryuji Koga, and Yasunobu Iwasaka, 2001: Ground-based network observation of Asian dust events of April 1998 in east Asian, J. Geophys.
Res., 106, 18,345-18,359.]
[Meijer, E. W., P. F. J. van Velthoven, A. M. Thompson, L. Pfister, H.
Schlager, P. Schulte, and H. Kelder, Model calculations of the impacts of
NOx from air traffic, Lightning, and surface emissions, compared with measurements, J. Geophys. Res., 105, 3833-3850, 2000]
[ Nee, J. B., C. N. Lien, W. N. Chen, and C. I. Lin, 1998: “Lidar detection of cirrus cloud in Chung-Li (24.96’ N, 121.19’ E),” J. Atmos. Sci. 55,
2249-2257.]
[Reagan, J. A., M. P. Mc Cormick, and J. d. Spinhirne, 1989: Lidar sensing ofaerosols and clouds in the troposphere and strtosphere, Proc.
IEEE 77 433-448.]
[Sokolik, I. N., D. M. Winker, G. Bergametti, D. A. Gillette, G.
19

Carmichael, Y. J. Kaufman, L.Gomes, L. Schuetz, and J. E. Penner, 2001:
Introduction to special section: Outstanding problems in quantifying the radiative impacts of mineral dust, J. Geophys. Res. 106 (D16),
18015-18027.]
[Sakai, T., Shibata, T., Hara, K., Kido, M., Osada, K., Hayashi,M.,
Matsunaga, K., Iwasaka, Y.,2001. Raman lidar and insitu aircraft measurements of free tropospheric aerosol particles over Japan. In: Singh,
U.N., Itabe, T., Sugimoto,N.(Eds.), Lidar Remote Sensing for Industry and Environment Monitoring, Sendai, Proceedings of SPIE 4153, pp.547-558.]
[ Shaw Chen Liu, Chung-De Lee, Man-Ting Cheng, Chein-Jung Shiu, and Chea-Yuan Young, “ On the sources of pollutants in the April 12-13,
2001 Asian Dust Storm over Taiwan” Seventh National Conference on
Atmospheric Sciences in Taiwan, p163-168]
[Schumann, U., The impact of nitrogen oxides emissions from aircraft upon the atmosphere at flight altitudes-Results from the Aeronox project,
Atmos. Environ, 31, 1723-1733, 1997]
[ Sassen, K., 2002: Polarization LIDAR evidence at FARS for indirect climate forcing from asian dust stroms, LIDAR remote sensing in atmospheric and earth sciences 21th conference, 483-484.].
[T. H. Lin., Atmospheric Environment 35 (2001) 5873-5882]
[ T. Murayama, H. Okamoto, N. Kaneyasu, H. Kamataki, and K. Miura,
1999 : Application of lidar depolarization measurement in the atmospheric boundary layer : Effects of dust and sea-salt particles, J.
Geophys. Res., 104, D24, 31,781-31,792.].
[T. Takamura and Y. Sasano,”Ratio of aerosol backscatter to extinction coefficients as determined from angular scattering measurements for use in atmospheric lidar applications,”Opt. Quantum Electron. 19, 293-302,
1987.]
[Zhang, Y., Y. Sunwoo, V. Kotamarthi and G. R. Carmichael,
Photochemical oxidant processes in the presence of dust; An evaluation of the impact of dust on particulate nitrate and ozone formation, J.Appl.
Meteorol., 33, 813-824,1994.]
20