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162 D. Müller et al.

campaigns were carried out in the framework of INDOEX in July and October to

document clean marine conditions. However, even in summer we observed free-

tropospheric particle layers which were advected from Africa and Saudi Arabia

across the North Indian Ocean. The field sites were at Sagres (37.01° N, 8.95° W)

during ACE 2 and in the Maldives (4.1° N, 73.3° E) during INDOEX, respectively.

Table 12.1 compares some characteristic optical features of lofted plumes observed

over Central Europe and at the field sites. For comparison reason we also add some

results on particle properties in the continental boundary layer over Central Europe

(EARLINET) and the marine boundary layer (ACE2 and INDOEX). Details may be

found in the literature cited above. For instance, lowest Ångström exponents are

found for the clean marine boundary layer during ACE 2, and the clean marine condi-

tions that prevailed in the free troposphere in October 1999 during INDOEX.

Anthropogenic pollution from North America and Europe in general is characterized

by higher Ångström exponents than pollution that consists of a mixture of anthropo-

genic pollution and biomass-burning observed during INDOEX. With respect to the

INDOEX area, we found different transport channels of the free-tropospheric plumes,

i.e., pollution was carried from North India, South India, and Southeast Asia to the

field site (Franke et al. 2003). Comparably small Ångström exponents are found for

forest-fire smoke from North America and for dust from the Sahara.

We find lidar ratios at 532 nm ranging from <35 sr for clean marine air to as high

as 80 sr for strongly-light absorbing pollution from North India. On average, the

Table 12.1 Characteristic values of particle Ångström exponent (å) for the wavelength range from

355 to 532 nm and the lidar ratio (S

532

) at 532 nm. Also shown is the spectral dependence of the

lidar ratios measured at 355 nm and 532 nm. The last column shows the ratio of optical depth in

the free troposphere (τ

) to optical depth of the atmospheric column (τ

). The numbers in the last

three rows of the last column describe the ratio of optical depth in the planetary boundary layer to

optical depth in the atmospheric column.

Aerosol type Experiment å S

532

(sr) S

355

532

/τ

Free troposphere

Anthropogenic pollution ACE 2 1.4 35–55 >1 0.63

Anthropogenic pollution EARLINET 1.7 30–50 >1 0.22

from North America

Forest-fire smoke from EARLINET 1.0 40–65 <1 0.3

North America

Saharan dust EARLINET 0.5 50–70 >1 0.5

North Indian pollution INDOEX 1.2 50–80 >1 0.53

South Indian pollution INDOEX 0.9 30–50 >1 0.53

Southeast Asia pollution INDOEX 1.5 30–70 >1 0.31

Clean marine INDOEX 0.1 <35 ∼1 0.33

(October 1999)

Planetary boundary layer

Anthropogenic pollution EARLINET 1.4 40–65 >1 0.22

in the European PBL

Clean marine ACE 2 0.3 20–25 ∼1 0.37

Clean marine INDOEX – 20–30 ∼1 0.23

(mean conditions)

12 Raman Lidar for Monitoring of Aerosol Pollution in the Free Troposphere 163

lidar ratio at 355 nm is larger than the lidar ratio at 532 nm. The exception is forest-

fire smoke for which we find a reversed spectral dependence.

Figure 12.4 finally shows an example of the derived microphysical particle

parameters. Details may be found in Müller et al. (2002, 2003, 2005). Particle

effective radius has been determined for cases of anthropogenic pollution and

forest-fire smoke transported from North America to Central Europe and European

pollution outflow observed during ACE 2. Mixtures of anthropogenic pollution and

biomass-burning aerosols were observed during INDOEX. We find comparably

small particles for anthropogenic pollution. Particles are larger for mixtures of

anthropogenic pollution and biomass-burning smoke. The largest pollution parti-

cles are observed for forest-fire smoke. Sea-salt particles in the marine environment

are at the top end of particle size. As with respect to single-scattering albedo, we

usually observed low light absorption of the particles. Mean values in general were

>0.9 at 532 nm wavelength. However, South Asian pollution often was much more

light-absorbing. For instance, single-scattering albedo was as low as 0.8 for pollu-

tion advected from North India to the Indian Ocean (Müller et al. 2003).

12.4 Summary

Multiwavelength Raman lidar observations carried out at a Central European site

since 1997 as well as field campaigns carried out in Europe and South Asia provide

us with a comprehensive view on natural and human-made pollution in the free

0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.2

0.4

0.6

0.8

EFFECTIVE RADIUS (µm)

ÅNGSTRÖM EXPONENT

Fig. 12.4 Particle effective radius and Ångström exponent of (diamond) anthropogenic pollution

from North America, (triangle, upward) anthropogenic pollution outflow observed during ACE 2,

(square) anthropogenic pollution/biomass-burning aerosols observed during INDOEX, (triangle,

downward) forest-fire smoke from North America, (circle) marine conditions in October 1999

during INDOEX, and (star) clean marine conditions during ACE 2. Ångström exponents differ in

part from respective numbers given in table 12.1. In this figure we only show results of measure-

ment cases that were used for the retrievel of microphysical particle parameters.

164 D. Müller et al.

troposphere. We observed different aerosol types that vary considerably in mean

particle size, e.g., anthropogenic pollution vs. forest-fire smoke from North

America, and particle single-scattering albedo, e.g., anthropogenic European pollu-

tion vs. South Asian pollution. The results show the merit of multiwavelength Raman

lidar sounding which provides us with a vertically resolved view on aerosol

conditions.

Acknowledgement The observations at the Leipzig EARLINET site presented in this work were

funded by the European Commission under grant EVR1-CT1999-40003. EARLINET is currently

funded by the European Commission under grant RICA-025991. We thank A. Stohl (Norsk institutt

for luftforskning (NILU), Kjeller, Norway) for providing the results from FLEXPART simulations.

The trajectory model HYSPLIT is available at http://www.arl.noaa.gov/ready/hysplit4.html.

References

Althausen D., Müller D., Ansmann A., Wandinger U., Hube H., Clauder E., and Zörner S.

(2000), Scanning 6-wavelength 11-channel aerosol lidar, J. Atmos. Ocean. Technol.,

17, 1469–1482.

Ångström A. (1964), The parameters of atmospheric turbidity, Tellus, 16, 64–75.

Ansmann A. and Müller D. (2005), Lidar and atmospheric aerosol particles. In C. Weitkamp (Ed.),

Lidar. Range-Resolved Optical Remote Sensing of the Atmosphere, Springer, New York,

pp. 105–141.

Ansmann A., Riebesell M., and Weitkamp C. (1990), Measurements of atmospheric aerosol

extinction profiles with a Raman lidar, Opt. Letts., 15, 746–748.

Ansmann A., Wandinger U., Riebesell M., Weitkamp C., and Michaelis W. (1992), Independent

measurement of extinction and backscatter profiles in cirrus clouds by using a combined

Raman elastic-backscatter lidar, Appl. Opt., 31, 7113–7131.

Ansmann A., Wagner F., Althausen D., Müller D., Herber A., and Wandinger U. (2001), European

pollution outbreaks during ACE 2: Lofted aerosol plumes observed with Raman lidar at the

Portuguese coast, J. Geophys. Res., 106, 20,725–20,733.

Ansmann A., Wagner F., Müller D., Althausen D., Herber A., von Hoyningen-Huene W., and

Wandinger U. (2002), European pollution outbreaks during ACE 2: Optical particle properties

inferred from multiwavelength lidar and star/Sun photometry, J. Geophys. Res., 107, EID

4259, DOI 10.1029/2001JD001109.

Bohren C.F. and Huffman D.R. (1983), Absorption and Scattering of Light by Small Particles,

Wiley, New York, NY.

Bösenberg J. et al. (2001), The German aerosol lidar network: Methodology, data, analysis.

Report No. 317. Max Planck Institute for Meteorology, Hamburg, Germany.

Bösenberg J. et al. (2003), EARLINET: A European aerosol research lidar network to establish

an aerosol climatology. Report No. 348. Max Planck Institute for Meteorology, Hamburg,

Germany.

Cairo F., Di Donfrancesco G., Adriani A., Lucio P., and Federico F. (1999), Comparison of vari-

ous linear depolarization parameters measured by lidar. Appl. Opt., 38, 4425–4432.

Collins D.R., Johnsson H.H., Seinfeld J.H., Flagan R.C., Gassó S., Hegg D.A., Russell P.B.,

Schmid B., Livingston J.M., Öström E.K., Noone J., Russell L.M., and Putaud J.P. (2000), In

situ aerosol-size distributions and clear-column radiative closure during ACE 2. Tellus Ser. B,

52, 498–525.

Collins W.J., Rasch P.J., Eaton B.E., Fillmore D.W., Kiehl J.T., Beck C.T., and Zender C.S.

(2002), Simulation of aerosol distributions and radiative forcing for INDOEX: Regional

climate impacts, J. Geophys. Res., 107, EID 8028, DOI 10.1029/2000JD000032.

12 Raman Lidar for Monitoring of Aerosol Pollution in the Free Troposphere 165

Creilson J.K., Fishman J., and Wozniak A.E. (2003), Intercontinental transport of tropospheric

ozone: A study of its seasonal variability across the North Atlantic utilizing tropospheric ozone

residuals and its relationship to the North Atlantic oscillation, Atmos. Chem. Phys., 3,

2053–2066.

Damoah R., Spichtinger N., Forster C., James P., Mattis I., Wandinger U., Beirle S., Wagner T.,

and Stohl A. (2004), Around the world in 17 days—hemispheric-scale transport of forest fire

smoke from Russia in May 2003, Atmos. Chem. Phys., 4, 1311–1321.

Draxler R.R., and Hess G.D. An overview of the HYSPLIT_4 modelling system for trajectories,

dispersion, and deposition, Austral. Meteorol. Magazine, 47, 295–308, 1998.

Franke K., Ansmann A., Müller D., Althausen D., Venkataraman C., Shekar Reddy M., Wagner

F., and Scheele R. (2003), Optical properties of the Indo-Asian haze layer over the tropical

Indian Ocean, J. Geophys. Res., 108, EID 4059, DOI 10.1029/2002JD002473.

Fromm M., Alfred J., Hoppel K., Hornstein J., Bevilacqua R., Shettle E., Servranckx R., Li Z.,

and Stocks B. (2000), Observations of boreal forest fire smoke in the stratosphere by POAM

III, SAGE II, and lidar in 1998, Geophys. Res. Letts., 27, 1407–1410.

Fromm M., Bevilacqua R., Servranckx R., Rosen J., Thayer J.P., Herman J., and Larko D. (2005),

Pyro-cumulonimbus injection of smoke to the stratosphere: Observations and impact of a

super blowup in northwestern Canada on 3–4 August 1998, J. Geophys. Res., 110, EID 08205,

DOI 10.1029/2004JD005350.

Heintzenberg J., Tuch T., Wehner B., Wiedensohler A., Wex H., Ansmann A., Mattis I., Müller

D., Wendisch M., Eckhardt S., and Stohl A. (2003), Arctic haze over Central Europe, Tellus

Ser. B, 55, 796–807.

Jacob D.J., Logan J.A., and Murti P.P. (1999), Effect of rising Asian emissions on surface ozone

in the United States, Geophys. Res. Letts., 26, 2175–2178.

Mattis I., Ansmann A., Wandinger U., and Müller D. (2003), Unexpectedly high aerosol load in

the free troposphere over central Europe in spring/summer 2003, Geophys. Res. Letts., 30, EID

2178, DOI 10.1029/2003GL018442.

Mattis I., Ansmann A., Müller D., Wandinger U., and Althausen D. (2004), Multiyear aerosol

observations with dual-wavelength Raman lidar in the framework of EARLINET, J. Geophys.

Res., 109, EID D13203, DOI 10.1029/2004JD004600.

McKendry I.G., Hacker J.P., Stull R., Sakiyama S., Mignacca D., and Reid K. (2001), Long-range

transport of Asian dust to the Lower Fraser Valley, British Columbia, Canada, J. Geophys.

Res., 106, 18,361–18,370.

Müller D., Ansmann A., Wagner F., and Althausen D. (2002), European pollution outbreaks during

ACE 2: Microphysical particle properties and single-scattering albedo inferred from multiwave-

length lidar observations, J. Geophys. Res., 107, EID 4248, DOI 10.1029/2001JD001110.

Müller D., Franke K., Ansmann A., and Althausen D. (2003), Indo-Asian pollution during

INDOEX: Microphysical particle properties and single-scattering albedo inferred from multi-

wavelength lidar observations, J. Geophys. Res., 108, EID 4600, DOI

10.1029/2003JD003538.

Müller D., Mattis I., Wehner B., Althausen D., Wandinger U., Ansmann A., and Dubovik O.

(2004), Comprehensive characterization of Arctic haze from combined observations with

Raman lidar and sun photometer, J. Geophys. Res., 109, EID D13206, DOI

10.1029/2003JD004200.

Müller D., Mattis I., Wandinger U., Ansmann A., Althausen D., and Stohl A. (2005), Raman lidar

observations of aged Siberian and Canadian forest-fire smoke in the free troposphere over

Germany in 2003: Microphysical particle characterization, J. Geophys. Res., 110, EID

D17201, DOI 10.1029/2004JD005756.

Müller D., Wandinger U., and Ansmann A. (1999a). Microphysical particle parameters from

extinction and backscatter lidar data by inversion with regularization: Theory, Appl. Opt.,

38, 2346–2357.

Müller D., Wandinger U., and Ansmann A. (1999b). Microphysical particle parameters from

extinction and backscatter lidar data by inversion with regularization: Simulation, Appl. Opt.,

38, 2358–2368.

166 D. Müller et al.

Müller D., Wandinger U., Althausen D., and Fiebig M. (2001), Comprehensive particle charac-

terization from three-wavelength Raman-lidar observations, Appl. Opt., 40, 4863–4869.

Pisani G. (2006), Optical characterization of tropospheric aerosol in the urban area of Napels.

Dissertation. University of Napels, Italy.

Prather M., Gauss M., Berntsen T., Isaksen I., Sundet J., Bey I., Brasseur G., Dentener F., Derwent

R., Stevenson D., Grenfell L., Hauglustaine D., Horowitz L., Jacob D., Mickley L., Lawrence

M., von Kuhlmann R., Muller J.-F., Pitari G., Rogers H., Johnson M., Pyle J., Law K.,

Van Weele M., and Wild O. (2003), Fresh air in the 21st century?. Geophys. Res. Letts., 30,

EID 1100, DOI 10.1029/2002GL016285.

Rodhe H. (1999), Human impact on the atmospheric sulfur balance, Tellus Ser. B, 51, 110–122.

Shaw G.E. (1995), The Arctic haze phenomenon, Bull. Am. Meteorol. Soc., 76, 2403–2413.

Stohl A. and Seibert P. (1998), Accuracy of trajectories as determined from the conservation of

meteorological tracers, Q. J. R. Meteorol. Soc., 125, 1465–1484.

Stohl A., Hittenberger M., and Wotawa G. (1998), Validation of the Lagrangian particle disper-

sion model FLEXPART against large scale tracer experiment data, Atmos. Environ.,

32, 4245–4264.

Veselovskii I., Kolgotin A., Griaznov V., Müller D., Wandinger U., and Whiteman D.N. (2002),

Inversion with regularization for the retrieval of tropospheric aerosol parameters from multi-

wavelength lidar sounding, Appl. Opt., 41, 3685–3699.

Veselovskii I., Kolgotin A., Müller D., and Whiteman D.N. (2005), Information content of multi-

wavelength lidar data with respect to microphysical particle properties derived from eigenvalue

analysis, Appl. Opt., 44, 5292–5303.

Wandinger U. and Ansmann A. (2002), Experimental determination of the lidar overlap profile

with Raman lidar, Appl. Opt., 41, 511–514.

Chapter 13

An Innovative Approach to Optical

Measurement of Atmospheric Aerosols—

Determination of the Size and the Complex

Refractive Index of Single Aerosol Particles

Wladyslaw W. Szymanski

, Artur Golczewski

, Attila Nagy

Peter Gál

, and Aladar Czitrovszky

Abstract As a result of the intrinsic nature of elastic light scattering aerosol

particles, the non-monotonic size dependence of the scattered light intensity

inﬂ uences the function of most single optical particle counters and spectrometers.

In order to tackle the problem of the varying response of single particle spectrometers

when refractive indices of aerosol change, we developed a system utilizing two

laser illumination sources with different wavelengths (533 nm and 685 nm) and four

detectors collecting the forward and backward scattered light from both illuminating

beams. The new method aims to determine the size and refractive index of particles

typically occurring in the atmosphere. We successfully tested this new method

numerically for its capability to simultaneously determine particle size in the range

from 0.1 to 10 µm, the real part of the refractive index spanning from 1.1 to 2,

and the imaginary part of refractive index between 0 and 1. The ﬁ rst experimental

results obtained with the prototype of the spectrometer verify the capability of the

technique for accurate size measurement and real-time differentiation between

non-absorbing and absorbing aerosol particles.

Keywords: Atmospheric aerosols, optical measurement, particle size, refractive index

13.1 Introduction

Aerosol particles affect our environment at the local, regional, and global level.

They are now recognized as a significant environmental problem both in terms of

health and the transfer of radiation through the atmosphere, thus also affecting the

Earth’s thermal budget. Atmospheric aerosols vary widely in space and time and

Faculty of Physics, University of Vienna, Boltzmanngasse 5, A-1090 Vienna, Austria

Research Institute for Solid State Physics and Optics, Dept. of Laser Applications, Hungarian

Academy of Science, H-1525 Budapest, P.O. Box 49, Hungary

167

Y.J. Kim and U. Platt (eds.), Advanced Environmental Monitoring,

167–178.

168 W.W. Szymanski et al.

thus continuous monitoring of their physical and optical properties is crucial.

Single optical particle spectrometry based on elastic light scattering provides a

convenient means of real-time measurement of atmospheric particles above about

100 nm. However, as a result of the intrinsic nature of elastic light scattering by

aerosol particles, the non-monotonic size dependence of the scattered light intensity

and its strong variation with the refractive index of particles influence the function

of most single optical particle counters and spectrometers (Szymanski and Liu

1986; Hanusch and Jaenicke 1993; Czitrovszky and Jani 1993; McMurry et al.

1996; Kerker 1997; Renliang 2000; Chang et al. 2003). Therefore, the major

disadvantage of this widely used type of instrumentation is the very high likelihood

of variability in the size measurement with changing optical properties of the

particles in question. Therefore, through using this instrumentation, particle

sizing information substantially different from the actual values may be obtained,

resulting as a consequence of an erroneous assessment of particle-related

environmental impact.

13.2 Status of the Optical Particle Spectrometry

Optical particle counters and spectrometers measure light scattered from a single

particle as it passes through an illuminated sensing volume of the instrument. The

illumination source is typically monochromatic, either gas laser or semiconductor

laser. In some cases, instruments with an incandescent light source are still being

used, providing the advantage of smoother response characteristics (Cooke and

Kerker 1975; Liu et al. 1985). The scattered light intensity is utilized as a measure

of the particle size. In an ideal instrument, a monotonic relationship between the

particle size and the scattered light intensity independent of the particle material

would allow a unique particle sizing. However, in reality this is usually not the case

(Barnard and Harrison 1988; Liu and Daum 2000). The instruments’ response,

given by the amount of light scattered into a given solid angle vs. particle size, is

essentially a non-monotonic function of particle size. It depends strongly on the

angular range into which the scattered light is collected and in general it also varies

with the refractive index of the particulate material. The response can be calculated

by means of the Mie theory of light scattering (Bohren and Hufman 1983).

Figure 13.1 shows the oscillatory character of response curves calculated by

means of the Mie theory. The horizontal line indicates all particles with different

sizes and refractive indices scattering exactly the same amount of light, hence

yielding exactly the same response. It means that these particles are indistinguishable

by such a spectrometer due to its multi-valued response. An integration of those

intensity functions over an angular range, which corresponds to scattered light

collected in a given optical particle spectrometer, results to a certain degree in

flattening of oscillations, yielding sometimes a nearly single-valued response

curves vs. particle diameter (Barnard and Harrison 1988; Szymanski et al. 2000).

Such behavior can be achieved by assuming particles with a specific refractive

13 An Innovative Approach to Optical Measurement 169

index. In practice, however, the environmental particles and hence their complex

refractive indices are frequently unknown, which may result in incorrect particle

sizing with errors up to an order of magnitude as can be easily deducted from data

shown in Fig. 13.1.

Although these effects were addressed in previous studies (Cooke and Kerker

1975; Hinds and Kraske 1986; Liu et al. 1983; Szymanski and Liu 1986; Hering

and McMurry 1991; Pinnick et al. 2000), it should be pointed out that the deficien-

cies of the single optical particle spectrometry technique have not so far been

solved in a satisfactory manner. Regardless of the actual optical design, in a

conventional optical particle spectrometer (OPS) light scattered from a single

particle is collected in one given angular range. This scattered light is detected and

converted into a voltage pulse. Its magnitude relates to particle size but varies

basically in the same way as does the scattered light shown in Fig. 13.1. It means

that data inversion in such a case is basically an “ill-posed” problem, because the

measurable quantity depends in a nonlinear way on three parameters—size, and the

real and imaginary parts of the refractive index.

To quantify the possible sizing errors which may occur when measuring

environmental particles by means of conventional optical particle spectrometers,

sizing performance with scattered light collection in the angular range from 10 to

30° was simulated using a stochastic method. Assuming that the OPS in question

was calibrated with non-absorbing particles having a complex refractive index of

m = 1.45 + 0i, size measurement simulations were carried out in the size range

Fig. 13.1 Response curves for different complex refractive indices of particles in the size range

0.1–10 µm. The scattered light is collected in the solid angle enclosing q = 10°–30° with respect

to the laser beam propagation direction (0°)

170 W.W. Szymanski et al.

from 0.1 to 10 µm for particles having different complex refractive indices indicated

in Fig. 13.2.

The quality of sizing, given here by the ratio of the difference between the

numerically retrieved and actual size to the actual particle size, is called here the

relative error as indicated in Fig. 13.2. Results of these numerically simulated

measurements for randomly selected particle sizes and complex refractive indi-

ces are shown in Fig. 13.2. It is evident that depending on the refractive indices,

either under-sizing or over-sizing of measured particles takes place except for

particles with the same refractive index as calibration particles. It can be seen,

that for all absorbing particles a systematic under-sizing occurs, which is nearly

independent of the absorption of a particle given by the imaginary part of the

refractive index.

An interesting approach to the particle sizing problem based on multi-angle

differential light scattering was explored previously (Wyatt et al. 1988). Later some

progress towards more complete optical particle analysis was achieved; however, a

real breakthrough was not accomplished (Dick et al. 1994, 1996).

13.3 The Dual Wavelength Optical Particle Spectrometer

In order to overcome the problem of the varying responses of single optical particle

spectrometers when refractive indices of aerosol change, we developed a system

utilizing two illumination laser sources with different wavelengths of 533 nm and

Fig. 13.2 Results of sizing simulations in particle range from 0.1 to 10 µm with an optical particle

spectrometer collecting scattered light in the angular range from 10° to 30°

13 An Innovative Approach to Optical Measurement 171

685 nm. Four sensors collect forwards and backwards scattered light from each

detected particle. We named this device the dual wavelength optical particle

spectrometer (DWOPS). The concept of this technique was introduced earlier

(Szymanski et al. 2002). Here we show the results of computational examination of

its measuring performance, as well as the first experimental verification of the

prototype. The schematic diagram of the DWOPS is shown in Fig. 13.3.

An aerosol particle passing the instrument’s sensing volume scatters simultane-

ously both green and red light, which then is redirected by a parabolic mirror

towards dichromatic beam splitters, which transmit the red and reflect the green

light. In this way, separated monochromatic scattered light information is then

registered by four sensors. This method delivers four independent scattered light

fluxes, yielding four pulses from each detected aerosol particle.

Three parameters of a particle—its size, and the real and imaginary parts of the

refractive index allow to exactly determine the amount of light scattered into a

given angular range and hence the resulting pulse height. Providing that the parti-

cles in question are spherical, the described instrument directly yields the

information about particle size and its complex refractive index based on the signal

evaluation procedure which will be discussed here. The assumption of the sphericity

of measured particles need not always be the case; however, experimental evidence

shows that a substantial portion of atmospheric aerosols, especially in the sub-

micrometer size range, are spherical, or nearly spherical (McMurry et al. 1996).

Fig. 13.3 The principle of the DWOPS. Utilization of two lasers and an appropriate scattering

geometry ensures four independent signals from each aerosol particle. The aerosol flow is perpen-

dicular to the plane of the diagram