ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-8109-2016Optical and microphysical characterization of aerosol layers over South
Africa by means of multi-wavelength depolarization and Raman lidar
measurementsGiannakakiElinavan ZylPieter G.https://orcid.org/0000-0003-1470-3359MüllerDetlefhttps://orcid.org/0000-0002-0203-7654BalisDimitrishttps://orcid.org/0000-0003-1161-7746KomppulaMikaFinnish Meteorological Institute, P.O. Box 1627, 70211, Kuopio, FinlandUnit for Environmental Sciences and Management, North-West University, Potchefstroom, South AfricaSchool of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield, UKLaboratory of Atmospheric Physics, Thessaloniki, Greeceon leave from: Department of Environmental Physics and Meteorology, Faculty of Physics, University of Athens, Athens, GreeceE. Giannakaki (eleni.giannakaki@fmi.fi)5July201616138109812316November201515December201531May20166June2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/8109/2016/acp-16-8109-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/8109/2016/acp-16-8109-2016.pdf
Optical and microphysical properties of different aerosol types
over South Africa measured with a multi-wavelength polarization Raman lidar
are presented. This study could assist in bridging existing gaps relating to
aerosol properties over South Africa, since limited long-term data of this
type are available for this region. The observations were performed under the
framework of the EUCAARI campaign in Elandsfontein. The multi-wavelength
PollyXT Raman lidar system was used to determine vertical profiles of
the aerosol optical properties, i.e. extinction and backscatter coefficients,
Ångström exponents, lidar ratio and depolarization ratio. The mean
microphysical aerosol properties, i.e. effective radius and single-scattering
albedo, were retrieved with an advanced inversion algorithm. Clear differences
were observed for the intensive optical properties of atmospheric layers of
biomass burning and urban/industrial aerosols. Our results reveal a wide
range of optical and microphysical parameters for biomass burning aerosols.
This indicates probable mixing of biomass burning aerosols with desert dust
particles, as well as the possible continuous influence of urban/industrial
aerosol load in the region. The lidar ratio at 355 nm, the lidar ratio at 532 nm, the linear particle depolarization ratio at 355 nm and the
extinction-related Ångström exponent from 355 to 532 nm were 52 ± 7 sr, 41 ± 13 sr, 0.9 ± 0.4 % and 2.3 ± 0.5,
respectively, for urban/industrial aerosols, while these values were 92 ± 10 sr, 75 ± 14 sr, 3.2 ± 1.3 % and
1.7 ± 0.3,
respectively, for biomass burning aerosol layers. Biomass burning particles
are larger and slightly less absorbing compared to urban/industrial
aerosols. The particle effective radius were found to be 0.10 ± 0.03, 0.17 ± 0.04 and 0.13 ± 0.03 µm for
urban/industrial, biomass burning, and mixed aerosols, respectively, while
the single-scattering albedo at 532 nm was 0.87 ± 0.06, 0.90 ± 0.06, and 0.88 ± 0.07 (at 532 nm), respectively, for these three types
of aerosols. Our results were within the same range of previously reported
values.
Introduction
Atmospheric aerosols of natural and anthropogenic origin contribute
substantially to global climate variability (IPCC, 2013). Currently, the
magnitude of the (anthropogenic) aerosol impact on climate causes the
largest uncertainty in our knowledge of climate change (Forster et al.,
2007). Large uncertainties exist due to the diversity, not only with respect
to aerosol particle size, composition, sources and lifetime variation but
also with regard to the spatial and temporal distributions of aerosols.
Thus, the impacts of aerosols on climate must be understood and quantified
on a regional scale rather than on a global-average basis (Piketh et al.,
2002).
High-quality aerosol measurements in the Southern Hemisphere are rather
limited. South Africa is located at the southernmost tip of the African
continent, extending from 22 to 34∘ S latitude and from
16 to 32∘ E longitude. Previous
studies have indicated that South Africa is one of the countries in the
world that is largely affected by aerosol load, due to various natural and
anthropogenic sources (Piketh et al., 2000, 2002; Formenti et
al., 2002, 2003; Campbell et al., 2003; Eck et al., 2003; Freiman and
Piketh, 2003; Ichoku et al., 2003; Ross et al., 2003; Winkler et al., 2008;
Queface et al., 2011; Tesfaye et al., 2011; Venter et al., 2012; Tiitta et
al., 2014). Intensive efforts have been undertaken during recent years to
characterize aerosol pollution in South Africa. In general, previous studies
pointed at the importance of regional circulation of air masses and seasonal
pollutant variation. The optical properties of aerosols have been studied by
means of sun photometers (e.g. Queface et al., 2011; Eck et al., 2003), in situ
data (e.g. Laakso et al., 2012) and satellite observations (e.g. Tesfaye et
al., 2011) in these studies, which are based on columnar aerosol optical
properties. Ground-based Raman lidars provide vertically resolved
information on the distribution and optical properties of aerosols.
Giannakaki et al. (2015) used Raman lidar data obtained over a 1-year
period at Elandsfontein in South Africa (26∘15′ S,
29∘26′ E, 1745 m above sea level, a.s.l.) to study the
geometrical characteristics and intensive and extensive optical properties of
free-tropospheric aerosol layers. In addition to these characteristics that
can be determined with lidar data, multi-wavelength Raman lidar measurements
can also be used to determine profiles of microphysical particle properties
by using inversion algorithms (Twomey, 1977; Veselovskii et al., 2002;
Müller et al., 2001). In this study we expand our study of aerosols in
South Africa by providing information on the microphysical and optical
properties of aerosol layers. These types of aerosol lidar observations are
valuable for spaceborne lidars such as CALIPSO (Cloud-Aerosol Lidar and
Infrared Pathfinder Satellite Observations) (e.g. Omar et al., 2009), since
lidar ratio values for different aerosol types are required for reliable
aerosol extinction retrievals. Therefore, this study could be useful for
further improving lidar ratio selection-scheme algorithms used in spaceborne
lidar missions.
Four long-term ground-based aerosol measurements were carried out at sites
in economically growing countries in Asia, Africa and South America within
the EUCAARI project (Kulmala et al., 2011), which included Elandsfontein in
South Africa. The aim of EUCAARI was to characterize particles in terms of
physical, optical and chemical aerosol properties. Here we report lidar
observations that were performed at Elandsfontein. In particular, we discuss
the optical and microphysical properties of aerosol layers that are caused
by biomass burning and urban/industrial activities at the site. We present
aerosol lidar ratios, particle linear depolarization ratios and
Ångström exponents for biomass burning and urban/industrial aerosol
layers measured with a multi-wavelength Raman lidar. The possible effect of
desert dust particles on biomass burning aerosol layers in terms of the
intensive optical and microphysical properties is also addressed. In
addition, effective radius and single-scattering albedo are calculated with
an advanced inversion algorithm.
Global map of long-term average tropospheric NO2 column derived
from SCIAMACHY data from August 2002 to March 2012 (Schneider et al., 2015).
The paper is organized as follows. In Sect. 2, the research site, the
methodology used for the retrieval of optical and microphysical properties,
and the aerosol typing are introduced. As a case study, the arrival of a
biomass burning aerosol layer over Elandsfontein is discussed in Sect. 3.
Section 4 presents the main findings of the optical and microphysical
aerosol properties for selected biomass burning, urban/industrial and mixed
aerosol layers. We close our contribution with a summary and conclusion in
Sect. 5.
Number of fire hotspots with confidence levels between 80 and 100 %
averaged in terms of 3 months for the year 2010 in the latitude range between
-40 and 10∘ and longitude range between -20 and 60∘.
Location and methodologyMeasurement site
The measurement site was located on a hilltop at Elandsfontein
(26∘15′ S, 29∘26′ E; 1745 m a.s.l.) in
the Highveld region of South Africa. The station was located approximately
150 km east of the Johannesburg–Pretoria megacity, which is the largest
metropolitan area in South Africa, with a population of more than 10 million
people (Lourens et al., 2012).
In South Africa, anthropogenic atmospheric emissions are predominantly the
product of industrial activities and biomass burning (Ross et al., 2003).
South Africa is the most industrialized country of the continent –
primarily due to the industrialized Highveld region (Freiman and
Piketh, 2003; Wenig et al., 2003). This
region has clusters of industrial complexes and power plants between
25.5∘ S, 27.5∘ E and 27.0∘ S, 30.5∘ E
(Ross et al., 2003), which contributes significantly to aerosol and trace
gases pollution (Freiman and Piketh, 2003).
Tropospheric NO2 distributions derived with SCIAMACHY
(SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) from
August 2002 to March 2012 (Schneider et al., 2015) are presented in Fig. 1.
The tropospheric NO2 column density of the Highveld region in South
Africa is comparable to that observed over central and northern Europe,
eastern North America and Southeast Asia (Lourens et al., 2012).
In addition, emissions from biomass burning (wild fires) contribute
significantly to regional emission loads (e.g. Giannakaki et al., 2015).
Both natural phenomena (lightning) and human-induced activities are
responsible for biomass burning (Edwards et al., 2006). The number of
hotspots, with confidence levels between 80 and 100 % (http://earthdata.nasa.gov/data/nrt-data/firms/active-fire-data), in the
latitude range between -40 and 10∘ and longitude range
between -20 and 60∘ are plotted in Fig. 2. The number
of hotspots is averaged in terms of 3 months for the year 2010. Wild fires
originate in the subequatorial central African region and progress
southward (Roy et al., 2005). In southern Africa, the fires progress along a
north-west to south-east track.
Description of the lidar system and lidar data processing
The transportable aerosol Raman lidar PollyXT that was operated
remotely at Elandsfontein is described by Althausen et al. (2009) and
Engelmann et al. (2016). PollyXT works with a Nd:YAG laser
emitting at its primary wavelength of 1064 nm, which after frequency
doubling and tripling emits at the wavelengths of 532 and 355 nm,
respectively. The receiver consists of a Newtonian telescope with a diameter
of 300 mm and a field of view of 1 mrad. Photomultiplier tubes are
used for the detection of the elastically backscattered photons at 355, 532
and 1064 nm, as well as the inelastically backscattered photons at 387 and
607 nm that correspond to the Raman shift by nitrogen molecules at 355 and
532 nm, respectively. Additionally, the cross-polarized component at 355 nm
is detected and consequently allows for the determination of the linear
particle depolarization ratio (also called depolarization ratio). To retrieve
the particle depolarization ratio the Rayleigh calibration method was applied
within the data analysis under the assumption of pure Rayleigh depolarization
in an aerosol-free height range (Behrendt and Nakamura, 2002). The vertical
resolution of the signal profiles is 30 m and the raw data are typically
stored as 30 s average values (20 Hz laser frequency). Data were collected
on the web page of PollyNet (http://polly.tropos.de) where the
“quicklooks” of all measurements are available.
Extinction and backscatter coefficient profiles at 355 and 532 nm,
respectively, were obtained with the Raman method (Ansmann et al., 1992). To
vertically retrieve the backscatter coefficient at 1064 nm we use the
Fernald–Klett method (Fernald, 1984; Klett, 1981). With this method the
particle backscatter coefficient is derived applying a backward iteration
starting at a chosen reference height. The method requires independent
information on the lidar ratio and on the reference value of the particle
backscatter coefficient. The cases analysed here are night-time measurements
and the retrieved backscatter at 1064 nm was also evaluated by the Raman
method (Ansmann et al., 1992) using also the signal from the nitrogen Raman
channel at the 607 nm. An overlap correction was applied on the basis of a
simple technique proposed by Wandinger and Ansmann (2002). The depolarization
ratio, i.e. the ratio of the cross-polarized to the parallel-polarized
component of the backscatter coefficient (particles and molecules) at
355 nm, was also calculated. The contribution of the molecules can easily be
calculated, which then provides the linear particle depolarization ratio
(Cairo et al., 1999; Murayama et al., 1999).
The uncertainties affecting the retrieval of extinction and backscatter
coefficients, and thus the calculation of lidar ratio and Ångström
exponents are mainly due to the statistical error owing to signal detection,
the systematic error associated with the estimation of the atmospheric
molecular number density from the pressure and temperature profiles, the
systematic error associated with the evaluation of the aerosol scattering
wavelength dependence, the systematic error for overlap function, the errors
introduced by operation procedure such as signal binning (smoothing) and
averaging accumulating lidar returns. The overall relative errors of the
lidar-derived aerosol properties range between 5 and 15 % for the
backscatter coefficients, 10 and 30% for the extinction coefficients,
20 and 40 % for the Ångström exponents, 15 and 40 % for the lidar
ratios and approximately 5 and 10 % for the linear particle depolarization
ratio (Hänel et al., 2012; Baars et al., 2016; Engelmann et al., 2016). A
detailed discussion on the influence of aerosol optical depth errors to
Ångström exponent errors can be found in Wagner and
Silva (2008).
The layer identification was based on the assumption that the optical
properties should be relatively stable. This means that within a chosen
height layer, the variability in the optical data should be less than the
statistical uncertainty in the individual data points.
In Table 1 we provide information regarding the elevated layers that were
selected for the optical and microphysical aerosol characterization. The
characterization of aerosol types will be discussed in Sect. 2.4.
Retrieval of microphysical properties
Microphysical particle properties are derived with an inversion algorithm
that has been developed at the Leibniz Institute for Tropospheric Research.
A detailed description of the inversion code is given by Müller et al. (1999a, b). A minimum of three backscatter coefficients (355, 532, and
1064 nm) and two extinction coefficients (355 and 532 nm), with measurement
errors less than 30 %, are required as input in order to obtain
microphysical results that have reasonably low uncertainties (Müller et
al., 2001). The selection of the individual inversion solutions is based on
the concept that the back-calculated optical data should agree with the
original data within the limits of the measurement errors, and that a
pre-selected discrepancy level, which is an output parameter of the
inversion algorithm (Müller et al., 1999a), is not exceeded. The mean
particle size in terms of the effective radius is then calculated along with
the standard deviation from these selected individual solutions. One also
obtains a range of complex refractive indexes by applying this method. The
complex refractive index is a wavelength-independent quantity. Therefore,
inversion can only provide a wavelength-independent value that represents
the entire range of wavelengths from 355 to 1064 nm. The single-scattering
albedo can then be calculated from the volume concentration distribution,
which is another data product of the inversion algorithm, and the associated
mean complex refractive index by means of a Mie scattering algorithm.
Uncertainties associated with the retrievals are in general
< 30 % for effective radius. The real part of the complex
refractive index is derived to an accuracy better than ±0.1, while the
imaginary part is obtained for its correct order of magnitude if the value is
< 0.01i (for larger values of the imaginary part the uncertainty
is < 50 %). The single-scattering albedo can be calculated with
an accuracy of ±0.05 if uncertainties of the input optical data are on
average < 10–15%. A detailed error analysis is presented by
Müller et al. (1999b, 2001) and Veselovskii et al. (2002, 2004).
Aerosol type, time and altitude range of aerosol layers used for
optical and microphysical aerosol characterization.
Aerosol sourceDateTime [UTC]eight [m]Extinction coefficient [Mm-1] 355 nm532 nmUrban/industrial25 March 201018:00–19:502100–2670196 ± 1875 ± 1225 March 201018:00–19:502790–3450190 ± 3668 ± 1425 March 201018:00–19:501560–1980260 ± 678 ± 1216 April 201021:20–23:541980–2250147 ± 1358 ± 916 April 201021:20–23:542280–2520129 ± 1039 ± 416 April 201021:20–23:542610–3180196 ± 4381 ± 1414 May 201018:00–00:00930–1360238 ± 37127 ± 2515 May 201018:30–20:201380–1860196 ± 2686 ± 1915 May 201018:30–20:202250–270081 ± 728 ± 330 November 201017:15–18:00960–1300121 ± 644 ± 1330 November 201017:15–18:001350–1920146 ± 2650 ± 1130 June 201017:00–18:001420–1620101 ± 534 ± 530 June 201017:00–18:001650–183071 ± 1137 ± 710 January 201119:15–20:151890–2160303 ± 45146 ± 3113 January 201121:00–22:001200–1800342 ± 24163 ± 1713 January 201121:00–22:001920–2250267 ± 42158 ± 2913 January 201121:00–22:002430–2880199 ± 2368 ± 12Biomass burning1 October 201000:10–01:001090–1900331 ± 9158 ± 85 October 201018:10–23:101115–1750432 ± 62227 ± 375 October 201018:10–23:101980–2700256 ± 18132 ± 156 October 201020:00–00:001175–1540277 ± 27142 ± 56 October 201020:00–00:001565–2160214 ± 14111 ± 116 October 201020:00–00:002190–2520152 ± 685 ± 166 October 201020:00–00:002610–2820121 ± 1980 ± 621 October 201001:30–02:30880–1530261 ± 28131 ± 2021 October 201001:30–02:301685–2280168 ± 766 ± 1621 October 201001:30–02:302400–2880171 ± 3070 ± 1422 August 201000:00–01.001205–1565340 ± 13162 ± 822 August 201000:00–01.001685–1920354 ± 5190 ± 822 August 201002:00–03:001115–1535335 ± 6163 ± 1022 August 201002:00–03:001745–2250331 ± 15170 ± 4Mixed aerosols16 August 201017:00–18:001115–1445316 ± 24151 ± 916 August 201019:00–20:00995–1265296 ± 7157 ± 1118 August 201019:00–20:001175–1355154 ± 975 ± 418 August 201019:00–20:001415–1715174 ± 1166 ± 418 August 201019:00–20:001865–2160184 ± 666 ± 322 August 201017:00–18:001145–1505286 ± 3109 ± 422 August 201017:00–18:001595–2040267 ± 16119 ± 8Aerosol classification
The identification of the source of aerosol particles is possible with the
synergetic use of in situ and satellite measurements, as well as utilizing
model estimations.
The HYSPLIT_4 (Hybrid Single Particle Lagrangian Integrated
Trajectory) model (Draxler and Hess, 1997) was used to compute backward air
mass trajectories employing the kinematic approach and by using the
re-analysed National Oceanic and Atmospheric Administration (NOAA) dataset
with a resolution of 2.5∘× 2.5∘ (latitude, longitude) as input.
Four-day backward trajectories were selected because they extend far enough
back in time and distance to cover the main source regions suspected to
affect the region investigated. The trajectories were calculated for the
centre of the layer observed and for the time of the lidar measurement.
The number of fire hotspots is given by Moderate Resolution Imaging
Spectroradiometer (MODIS) collection-5 active-fire product data (Giglio
et al., 2010). The number of hotspots, obtained from MODIS for 4 days
prior to each of the measurements, was superimposed on the trajectory
analysis map in order to detect the presence of smoke particles over our
site for the cases analysed.
Trace gases were measured as part of routine air quality monitoring at the
site by the national electricity supplier, i.e. Eskom. A Thermo Electron 43C
SO2 analyser and a Thermo Electron 42i NOx analyser were used to
measure SO2 and NOx, respectively. H2S was measured with a
Thermo Electron 43A SO2 analyser with a Thermo Electron 340 converter.
Fifteen-minute data were averaged for the extent of measurement time for each of
the measurements periods (Table 1). For instances where the combined use of
trajectory analysis and fire hotspots did not indicate the presence of
biomass burning aerosols we checked whether the measured NOx, SO2
or H2S concentrations were higher than the seasonal mean values of that
measured for the entire period of the EUCAARI campaign. These seasonal mean
values are presented in Laakso et al. (2012). In addition, when the trace
gases concentrations were lower than the mean seasonal values measured
during the EUCAARI campaign and biomass burning activity or desert dust
advection were absent, we checked whether the daily concentration of the trace
gases exceeded the mean critical values.
There were also cases that indicated desert dust aerosol particles in
addition to the smoke, which originated from either the Kalahari or
Namib desert and could have additionally contributed to the aerosol
loads. Therefore, the measured aerosol optical properties determined for
these cases were attributed to a mixing state where smoke particles were
possible to be mixed with desert dust aerosols. Additional mixing with urban/industrial aerosols is also possible.
An example of a measurement of biomass burning aerosols is discussed in the
subsequent section in order to demonstrate the methodology used to derive
the optical and microphysical aerosol properties.
MODIS fire hotspots for the period 28 September 2010–1 October 2010
and for the latitude range between -35 and -15∘ W and the
longitude range between 10 and 40∘ S (a). Four-day backward
trajectories arriving at Elandsfontein on 1 October 2010 at
00:00 UTC for arrival height of the bottom
(1090 m), centre (1495 m) and top (1900 m) of the aerosol layer
observed (b).
Backscatter coefficients, extinction coefficients, lidar ratios,
Ångstrom exponents and particle depolarization ratio at Elandsfontein on
1 October 2010, 00:10–03:59 UTC.
Biomass burning aerosols on 1 October 2010 at Elandsfontein, South
Africa
In this section we will study a geometrically deep layer that extends up to
2.1 km height above ground level (a.g.l.) as observed on 1 October 2010. The atmospheric structure, in terms of range corrected
signals, is quite stable, which indicates similar optical properties
throughout the layer (http://polly.tropos.de/?p=bilder&lambda=1064&Jahr=2010&Monat=10&Tag=1&Ort=11#bildanker). High backscatter returns are
observed on the day when the measurement is conducted in relation to the
previous and the next day (as can be already seen in Fig. 4a – light
green).
MODIS fire hotspots product reveal that several fires were active during the
period 28 September 2010–1 October 2010 as shown in
Fig. 3a. In Fig. 3b, 4-day backward trajectories arriving at
Elandsfontein on 1 October 2010 at 00:00 UTC are presented. The
trajectories are computed for arrival heights of the bottom, centre and top
of the observed layer. The trajectory analysis along with MODIS fire
hotspots reveals that it is highly possible that the air masses carry smoke
particles at Elandsfontein on the day of the measurement.
In Fig. 4 the optical lidar profiles are presented. The backscatter and
extinction maximum at all three wavelengths were observed within the 0.9 to
1.9 km height range. High values of the lidar ratio of 96 ± 5 sr at
355 nm and 89 ± 5 sr at 532 nm indicate that the smoke particles
inside this layer were most likely highly light-absorbing. The
Ångström exponent, related to extinction between 355 and 532 nm, was
1.8 ± 0.1, which points to comparably small particles and indicative of
fresh smoke (e.g. Müller et al., 2005). A constant particle
depolarization ratio in the order of 4 % is observed at 355 nm
throughout the layer. The lack of significant vertical variability in the
lidar ratio, the Ångström exponent and the particle depolarization
ratio suggests the presence of the same type (biomass burning) of aerosols
throughout the layer.
The mean values of extinction (at 355 and 532 nm) and backscatter
coefficients (at 355, 532 and 1064 nm) were calculated within the defined
layer and were used as input in the inversion algorithm. Effective radius,
complex refractive index and single-scattering albedo were calculated with
the microphysical inversion code. An effective radius of 0.15 ± 0.02 µm was determined, while the single-scattering albedo was approximately
0.86 at 532 nm that indicates relatively strong absorbing aerosols.
Results and discussion
We performed optical lidar data analysis, microphysical retrievals and
aerosol typing for each of the 38 aerosol layers listed in Table 1
in the same way as presented in the example in Sect. 3. Each aerosol layer
in Table 1 was classified into one of the three aerosol types, i.e.
urban/industrial, biomass burning, and mixed aerosols after thorough visual
inspection of the backward trajectories, MODIS hotspot fire products and
in situ aerosol observations, as explained in Sect. 2.4. Table 2
summarizes the mean intensive optical properties (lidar ratio at 355 and 532 nm, depolarization ratio at 355 nm and Ångström exponent related to
extinction between 355 and 532 nm) presented together with the associated
standard deviations, ranges (minimum and maximum values) and medians.
Figure 5 presents the particle lidar ratios at 355 nm vs. the
extinction-related Ångström exponent for urban/industrial (black),
biomass burning (red) aerosol layers and the mixed aerosol layers
(green). Different aerosol types occupy different areas in the
Ångström exponent–lidar ratio plot. Aerosols from urban and
industrial activities are on average characterized by larger Ångström
exponents than (pure or mixed) biomass burning aerosols. The lidar ratios of
biomass burning aerosols are among the highest compared to the literature, with a
mean value of 92 ± 10 sr (e.g. Müller et al., 2007; Nicolae et
al., 2013; Amiridis et al., 2009). Urban/industrial aerosol layers were found
to have lower lidar ratio values in the range between 41 and 59 sr at
355 nm. Our results indicate that biomass burning aerosols have lower lidar
ratios when they are mixed with either desert dust aerosols or
urban/industrial aerosols. This might be due to the non-spherical shape of
desert dust, which may have a significant effect on the lidar ratio. Model
calculations show that a deviation from the spherical shape can efficiently
increase particle backscattering and thus lower the lidar ratio (Mishchenko
et al., 1997), which was also confirmed by Müller et al. (2003).
Ångström exponent values of these aerosols ranged from 1.6 to 2.5,
with a mean value of 2.0 ± 0.4, which is larger (smaller particles)
than the mean value of 1.7 ± 0.3 we observed for “pure” biomass
burning aerosols. The role that hot air close to the surface of the earth
plays in generating these dust size distribution is not well understood
(Nisantzi et al., 2014). Wind stress close to the surface may be very
complex, and the sudden release of all the moisture in the hot soil particles may
strongly influence the breaking of larger particles into smaller ones and
thus lead to a much more complicated size distribution than observed during
desert dust outbreaks (Mamouri and Ansmann,
2014).
Mean value ± standard deviation of aerosol lidar ratio at 355,
particle depolarization ratio and Ångström exponent related to
extinction between 355 and 532 nm for the examined aerosol types, as well as
value of range and median.
Aerosol sourceMean ± SDRangeMedianlidar ratio at 355 nm [sr] Urban/industrial52 ± 741–5954Biomass burning92 ± 1081–11988Mixed aerosols74 ± 1159–9073lidar ratio at 532 nm [sr] Urban/industrial41 ± 1323–7438Biomass burning75 ± 1447–9279Mixed aerosols46 ± 1333–6840Particle depolarization ratio at 355 nm [%] Urban/industrial0.9 ± 0.40.3–1.71.0Biomass burning3.2 ± 1.31.2–5.72.7Mixed aerosols8.3 ± 0.77.3–9.18.1Ångström exponent related to extinction between 355 and 532 nm Urban/industrial2.3 ± 0.51.3–3.02.4Biomass burning1.7 ± 0.31.0–2.41.7Mixed aerosols2.0 ± 0.41.6–2.52.0
Lidar ratio at 355 nm vs. the extinction-related Ångström
exponent from 355 to 532 nm for the three aerosol types investigated in our
study.
Lidar ratio at 355 nm vs. the depolarization ratio at 355 nm for
the three aerosol types investigated in our study.
It is evident from Fig. 5 that Ångström exponent values for the
different aerosol types overlap. Therefore, another intensive aerosol
property, the linear particle depolarization ratio, which is an indicator of
non-spherical particles, was also used. Figure 6 shows the lidar ratio at
355 nm vs. the depolarization ratio at the same wavelength for the three
aerosol types. Different clusters of data pairs can be identified. Lower
depolarization ratio values were found for urban/industrial aerosol layers.
These aerosol layers are also characterized by lower lidar ratios and thus
the data points representing urban/industrial pollution occupy the lower left
region in Fig. 5. Significantly larger particle linear depolarization ratios
with a mean of 8.3 ± 0.7 % were found for mixed aerosols. Typical
desert dust aerosol depolarization ratios determined in field measurements
performed in the north-western corner of the Sahara ranged from 30 to 35%
at 532 nm with a mean value of 31 ± 3% (Freudenthaler et al.,
2009). In addition, particle depolarization ratios ranging between 30 and
35 % were also observed for Asian desert dust (Sugimoto et al., 2003;
Shimizu et al., 2004; Shin et al., 2015) and desert dust originating from
Middle East dust sources (Mamouri et al., 2013). Depolarization ratios of the
mixtures of biomass burning aerosols and desert dust particles determined for
African biomass burning and dust mixtures ranged between 8 and 26 % at
532 nm (Weinzierl et al., 2011; Tesche et al., 2009). Therefore,
depolarization values reported in this study are at the lower end of these
values. This observed difference can be attributed to the different
contribution of desert dust particles to the biomass burning plume. However,
we should also note that the geometrical shape of the dust particles over the
Kalahari desert could be different from the shape of Saharan dust. Also, the
possible influence of the background urban/industrial aerosols in the
mixture should be kept in mind.
A wide range of (lower) depolarization ratios and lidar ratios was found for
biomass burning aerosols. This observed variability can be attributed to
differences in the chemical composition of the particles that depend on the
source region, relative humidity in the atmosphere, the type of fire, or the
combined effect of these factors. In addition, the mixing of the biomass
burning aerosols with maritime or even urban/industrial background aerosols
cannot be excluded as a possible reason for the variability in lidar ratio
and depolarization ratio values.
Several statistics of lidar ratios and Ångström exponents for
different aerosol types in the world are available for comparison. Figure 7
provides some of the general literature with regard to the lidar ratios
values at 355 nm and Ångström exponents of urban/industrial and
biomass burning aerosols, as well as for mixtures of biomass burning and
desert dust aerosols. To interpret the x axis of Fig. 7, one should
also look at Table 4. It is evident from Fig. 7 that intensive aerosol
properties are in good agreement with values found from other studies.
The lidar ratio at 355 nm, in particular, shows similar values for urban/industrial aerosols in various regions of the world. Ångström
exponent values found for urban/industrial particles in this study are at
the upper limit of results previously published for this aerosol type, which
indicates slightly smaller particles at Elandsfontein that can most probably
be ascribed to differences in the emission sources. The depolarization ratio
is at the lower limit indicating spherically shaped anthropogenic particles.
The lidar ratio for biomass burning aerosol layers is within the range of
previously reported values, although the values tend to be more at the upper
limit of the reported values. The Ångström exponents are in very good
agreement with previous studies. Müller et al. (2007) studied the growth
of free-tropospheric forest fire smoke particles and indicated that the
Ångström exponent decreases with the duration of transport. The
Ångström exponent values found in this study (1.7 ± 0.3)
corresponds to travel times of the biomass burning aerosols between 1 and
3 days, which is confirmed by backward trajectory analysis. The
characteristics of biomass burning emissions in the subtropical South African
region vary according to the type of fuel burned (vegetation type),
meteorology and combustion phase (Ross et al., 2003). For example, flaming
grass fires produce smoke with more soot compared to smoke emitted from
smoldering wood and bush fires (Pósfai et al., 2003). Thus, differences in
the chemical composition of the particles might be one of the reasons for the
observed large lidar ratio.
General literature values for lidar ratio at 355 nm,
Ångström exponent and depolarization ratio (355 or 532 nm) for
urban/industrial (black), biomass burning (red) and mixed biomass burning
with desert dust aerosols (green). The x axes are the studies presented in
Table 4. Floating columns refer to range values, while the symbols refer to mean values with 1 standard deviation. The depolarization
values are at 355 nm except for the cases noted with an asterisk
(∗), which refer to visible wavelength (532 or 710 nm).
Effective radius vs. Ångström exponent for the three aerosol
types investigated in our study.
For the mixed aerosols the lidar ratio values reported here are in very good
agreement with previous studies for the mixture of desert dust and biomass
burning aerosols. The contribution of desert dust particles within the
observed biomass burning plumes is probably lower, thus resulting in a lower
depolarization ratio and larger Ångström exponent than what has been
reported in the literature for biomass burning mixed with dust as mentioned
previously. Groß et al. (2011) reported neutral wavelength dependence
of the particle depolarization ratios for mixed dust and smoke layers for
which Ångström exponents varied between 0.12 and 0.16, while Tesche
et al. (2011) reported wavelength-independent linear particle depolarization
ratios of 0.12–0.18 at 355, 532 and 710 nm for mixed dust and smoke layers.
In that sense our results on particle depolarization ratios at 355 nm are
similar to results from these studies reporting linear particle
depolarization ratio at 532 nm.
In Fig. 8 the effective radius against the Ångström exponent is
plotted. In general the plot shows the same features already noted for
Fig. 5. On average the largest aerosols are determined for biomass burning
aerosols (red) with an effective radius of 0.17 ± 0.04 µm.
Particles from anthropogenic pollution (black) are smaller with a mean
effective radius of 0.1 ± 0.03 µm. Our results indicate that the
influence of Kalahari desert dust on biomass burning plumes leads to smaller
particles compared to pure biomass burning aerosols with a mean effective
radius of 0.13 ± 0.03 µm.
Mean microphysical properties i.e. effective radius, single-scattering albedo
and complex refractive index are listed with their associated standard
deviations, ranges (minimum and maximum values) and medians in Table 3. The
particles in the biomass burning aerosol layers show a mean effective radius
of 0.17 ± 0.04 µm, which is within the range of values
reported in previous studies for biomass burning aerosols. Reid and
Hobbs (1998) reported count median diameter
values ranging from 0.12 µm for fresh particles to
0.21 µm for aged particles near rainforest fires in Brazil. Radke
et al. (1988) obtain values of approximately 0.22 µm for particles
from forest fires in North America. Wandinger et al. (2002) found larger
biomass burning aerosols with an effective radius of approximately
0.25 µm. Effective radii in the range between 0.19 and
0.44 µm were found for biomass burning aerosol layers resulting
from long-range transport across Romania (Nicolae et al., 2013). Müller
et al. (2007) presented values ranging between 0.13 and 0.15 nm for plumes
ageing between 1 and 3 days.
Mean value ± standard deviation of effective radius and
single-scattering albedo for the examined aerosol types, as well as range
and median. RRI: real refractive index; IRI: imaginary refractive index.
The code used in Fig. 7 and the respective reference.
CodeReferenceA05Ansmann et al. (2005)A09aAnsmann et al. (2009)A09bAmiridis et al. (2009)A11Alados Arboledas et al. (2011)B03Balis et al. (2003)B12aBaars et al. (2012)B12bBurton et al. (2012)B13Burton et al. (2013)G10Giannakaki et al. (2010)G11Groß et al. (2011)G13Groß et al. (2013)G16This studyH15Heese et al. (2015)I15Illingworth et al. (2015)K14Kanitz et al. (2014)K12Komppula et al. (2012)M05Müller et al. (2005)M07Müller et al. (2007)M04Murayama et al. (2004)N13Nicolae et al. (2013)P12Preißler et al. (2012)P13Preißler et al. (2013)R98Reid and Hobbs (1998)T11Tesche et al. (2011)W02Wandinger et al. (2002)W11Weinzierl et al. (2011)
The three types of aerosols cover a wide range of single-scattering albedo
values as shown in Table 3. The mean single-scattering albedo for biomass
burning aerosol is 0.90 ± 0.06 (at 532 nm). Lower single-scattering
albedos are reported in the literature for fresh biomass burning particles in
Europe. Nicolae et al. (2013) reported a value of 0.78 ± 0.02, while
Reid and Hobbs (1998) found that single-scattering albedo ranges between 0.74 and 0.77 for fresh smoke. Previous
studies show that aged biomass burning layers are characterized by larger
single-scattering albedos. For example, Murayama et al. (2004) found a value
of 0.95 ± 0.06 at 532 nm, while Noh et al. (2009) reported single-scattering albedos of 0.92 at the same wavelength. Therefore our results
indicate moderately absorbing particles resulting from fresh or medium-aged
(less than 3 days) biomass burning aerosols.
For the mixed aerosols we determined lower mean scattering albedos of
0.88 ± 0.07, which is slightly higher than the mean single-scattering
albedo of 0.87 ± 0.06 determined for urban/industrial aerosol layers.
Laakso et al. (2012) reported values of 0.84 ± 0.08 (637 nm) at ground
level at Elandsfontein, South Africa. Queface et al. (2011) determined
significantly larger values of 0.91 and 0.89 at 440 and 670 nm,
respectively, from AERONET data collected at Skukuza in South Africa. Our
results indicate that elevated anthropogenic aerosol layers from urban and
industrial activities are characterized by stronger light absorption.
Complex refractive indexes are also reported in Table 3. Real parts of the
complex refractive index of these particles are mostly > 1.5,
while imaginary parts vary from 0.007 to 0.04i. Lower real parts of the
refractive index are found for biomass burning aerosols compared to the
urban/industrial particulates, with values ranging from 1.35 to 1.57. The
imaginary parts of the refractive index of biomass burning aerosol layers
are < 0.03i (with the exception of one case that shows an imaginary
refractive index of 0.046i). A large variation of refractive indices for the
real and imaginary parts is observed for mixed aerosols. This might allude
to the different levels of contribution of Kalahari desert dust to biomass
burning aerosol layers.
Summary and conclusions
Thirty-eight aerosol layers of urban/industrial, biomass burning, and mixed
aerosols were studied with regard to their optical and microphysical
properties at Elandsfontein, South Africa. The combination of Raman lidar
observations with backward trajectory analysis, satellite fire observations
and in situ data allowed for source identification of the elevated aerosol layers.
Measurements of the lidar ratios and depolarization ratios are presented in
order to assist in the separation of anthropogenic, biomass burning, and
mixtures of aerosols.
A wide range of optical (lidar ratio and depolarization ratio) and
microphysical (single-scattering albedo, complex refractive index)
properties was determined for biomass burning aerosols, indicating
differences in chemical composition. Aerosols from urban and industrial
activities are on average characterized by larger Ångström exponents
than (pure or mixed) biomass burning aerosols. Lidar ratios for biomass
burning aerosols are among the highest found in the literature, with a mean value
of 92 ± 10 sr, while the anthropogenic aerosols are characterized by
lower lidar ratios in the range between 41 and 59 sr at 355 nm.
Ångström exponents were found to be similar for all types of aerosol
types under study, with slightly larger values determined for anthropogenic
aerosols. Mean effective radii of 0.17 ± 0.04 and 0.1 ± 0.03 µm were calculated for biomass burning and urban/industrial
aerosols, respectively. We have also shown that, in certain instances, biomass
burning aerosols may contain a small number of desert dust particles,
resulting in higher depolarization ratios and lower lidar ratios than the
values reported for pure biomass burning aerosols. Moderately absorbing
particles were found for biomass burning layers with a mean single-scattering albedo of 0.9 ± 0.06. Mixed aerosols were found more
absorbing with a mean single-scattering albedo of 0.88 ± 0.07. A
slightly lower mean single-scattering albedo of 0.87 ± 0.06 was found
for urban/industrial aerosol layers. However, this value was larger than
the values reported for the same site from ground-based in situ
measurements. Our optical and microphysical results for the analysed aerosol
types agreed very well with similar studies reported in the literature.
Ground-based lidar networks provide information on the vertical and
horizontal distribution of optical aerosol properties in a systematic and
statistically significant manner. Different lidar networks that are globally
distributed observe aerosols in Europe, South America, Asia and North
America. The analysis of lidar measurements presented here could assist in
bridging existing gaps with regard to our knowledge of the vertical
distribution of optical and microphysical aerosols in the South African
atmosphere, since limited long-term data of this nature are available for
this region. Our results could also be useful for lidar ratio selection
schemes needed for elastic-backscatter lidars. In that sense our findings
could be used in advancing lidar algorithms used for present and/or future
satellite lidar missions.
Data availability
The data are available upon request (contact mail:
eleni.giannakaki@fmi.fi).
Acknowledgements
This work has been partly supported by the European Commission 6th
Framework Programme under the EUCAARI project (contract no. 036833-2). Elina
Giannakaki acknowledges the support of the Academy of Finland (project no. 270108). The authors acknowledge the staff of the North-West University for
valuable assistance and routine maintenance of the lidar. We also
acknowledge Eskom and Sasol for their logistical support for measurements at
Elandsfontein.
Edited by: M. Tesche
ReferencesAlados Arboledas, L., Müller, D., Guerrero Rascado, J. L., Navas
Guzmán, F., Pérez Ramírez, D., and Olmo, F. J.:, Optical and
microphysical properties of fresh biomass burning aerosol retrieved by Raman
lidar, and star and sun-photometry, Geophys. Res. Lett., 38, L01807,
10.1029/2010GL045999, 2011.Althausen, D., Engelmann, R., Baars, H., Heese, B., Ansmann, A., Müller,
D., and Komppula, M.: Portable Raman Lidar PollyXT for Automated
Profiling of Aerosol Backscatter, Extinction, and Depolarization, J. Atmos.
Ocean. Tech., 26, 2366–2378, 10.1175/2009jtecha1304.1, 2009.Amiridis, V., Balis, D. S., Giannakaki, E., Stohl, A., Kazadzis, S.,
Koukouli, M. E., and Zanis, P.: Optical characteristics of biomass burning
aerosols over Southeastern Europe determined from UV-Raman lidar
measurements, Atmos. Chem. Phys., 9, 2431–2440, 10.5194/acp-9-2431-2009,
2009.Ansmann, A., Wandinger, U., Riebesell, M., Weitkamp, C., and Michaelis, W.:
Independent measurement of extinction and backscatter profiles in cirrus
clouds by using a combined Raman elastic-backscatter lidar, Appl. Optics, 31,
7113–7131, 10.1364/AO.31.007113,1992.Ansmann, A., Engelmann, R., Althausen, D., Wandinger, U., Hu, M., Zhang, Y.,
and He, Q.: High aerosol load over the Pearl River Delta, China, observed
with Raman lidar and Sun photometer, Geophys. Res. Lett., 32, L13815,
10.1029/2005GL023094, 2005.Ansmann A., Baars, H., Tesche, M., Müller, D., Althausen, D., Engelmann,
R., Pauliquevis, T., and Artaxo, P.: Dust and smoke transport from Africa to
South America: Lidar profiling over Cape Verde and the Amazon rainforest,
Geophys. Res. Lett., 36, L11802, 10.1029/2009GL037923, 2009.Baars, H., Ansmann, A., Althausen, D., Engelmann, R., Heese, B., Müller,
D., Artaxo, P., Paixao, M., Pauliquevis, T., and Souza, R.: Aerosol profiling
with lidar in Amazon Basin during the wet and dry season, J. Geophys. Res.,
117, D21201, 10.1029/2012JD018338, 2012.Baars, H., Kanitz, T., Engelmann, R., Althausen, D., Heese, B., Komppula, M.,
Preißler, J., Tesche, M., Ansmann, A., Wandinger, U., Lim, J.-H., Ahn, J.
Y., Stachlewska, I. S., Amiridis, V., Marinou, E., Seifert, P., Hofer, J.,
Skupin, A., Schneider, F., Bohlmann, S., Foth, A., Bley, S., Pfüller, A.,
Giannakaki, E., Lihavainen, H., Viisanen, Y., Hooda, R. K., Pereira, S. N.,
Bortoli, D., Wagner, F., Mattis, I., Janicka, L., Markowicz, K. M., Achtert,
P., Artaxo, P., Pauliquevis, T., Souza, R. A. F., Sharma, V. P., van Zyl, P.
G., Beukes, J. P., Sun, J., Rohwer, E. G., Deng, R., Mamouri, R.-E., and
Zamorano, F.: An overview of the first decade of PollyNET: an
emerging network of automated Raman-polarization lidars for continuous
aerosol profiling, Atmos. Chem. Phys., 16, 5111–5137,
10.5194/acp-16-5111-2016, 2016.Balis, D. S., Amiridis, V., Zerefos, C., Gerasopoulos, E., Andreae, M.,
Zanis, P., Kazantzidis, A., Kazadzis, S., and Papayannis A.: Raman lidar and
Sun photometric measurements of aerosol optical properties over Thessaloniki,
Greece during a biomass burning episode 2003, Atmos. Environ., 37,
4529–4538, 10.1016/S1352-2310(03)00581-8, 2003.
Behrendt, A. and Nakamura, T.: Calculation of the calibration constant of
polarization lidar and its dependency on atmospheric temperature, Opt.
Express, 10, 805–817, 2002.Burton, S. P., Ferrare, R. A., Hostetler, C. A., Hair, J. W., Rogers, R. R.,
Obland, M. D., Butler, C. F., Cook, A. L., Harper, D. B., and Froyd, K. D.:
Aerosol classification using airborne High Spectral Resolution Lidar
measurements – methodology and examples, Atmos. Meas. Tech., 5, 73–98,
10.5194/amt-5-73-2012, 2012.Burton, S. P., Ferrare, R. A., Vaughan, M. A., Omar, A. H., Rogers, R. R.,
Hostetler, C. A., and Hair, J. W.: Aerosol classification from airborne HSRL
and comparisons with the CALIPSO vertical feature mask, Atmos. Meas. Tech.,
6, 1397–1412, 10.5194/amt-6-1397-2013, 2013.Cairo, F., Di Donfrancesco, G., Adriani, A., Pulvirenti, L., and Fierli, F.:
Comparison of various linear depolarization parameters measured by lidar,
Appl. Optics, 38, 4425–4432, 10.1364/AO.38.004425,1999.Campbell, J. R., Welton, E. J., Spinhirne, J. D., Ji, Q., Tsay, S. C.,
Piketh, S. J., Barenbrug, M., and Holben, B. N.: Micropulse lidar
observations of tropospheric aerosols over northeastern South Africa during
the ARREX and SAFARI 2000 dry season experiments, J. Geophys. Res., 108,
8497, 10.1029/2002jd002563, 2003.
Draxler, R. R. and Hess, G. D.: Description of the HYSPLIT 4 modeling system,
NOAA Tech Memo, ERL ARL-224, 24, NOAA, Silver Spring, Md., USA, 1997.Eck, T. F., Holben, B. N., Ward, D. E., Mukelabai, M. M., Dubovik, O.,
Smirnov, A., Schafer, J. S., Hsu, N. C., Piketh, S. J., Queface, A., Le Roux,
J., Swap, R. J., and Slutsker, I.: Variability of biomass burning aerosol
optical characteristics in southern Africa during the SAFARI 2000 dry season
campaign and a comparison of single scattering albedo estimates from
radiometric measurements, J. Geophys. Res., 108, 8477,
10.1029/2002jd002321, 2003.Edwards, D. P., Emmons, L. K., Gille, J. C., Chu, A., Attie, J. L., Giglio,
L., Wood, S. W., Haywood, J., Deeter, M. N., Massie, S. T., Ziskin, D. C.,
and Drummond, J. R.: Satellite-onserved pollution from Southern Hemisphere
biomass burning, J. Geophys. Res., 111, D14312, 10.1029/2005JD006655,
2006.Engelmann, R., Kanitz, T., Baars, H., Heese, B., Althausen, D., Skupin, A.,
Wandinger, U., Komppula, M., Stachlewska, I. S., Amiridis, V., Marinou, E.,
Mattis, I., Linné, H., and Ansmann, A.: The automated multiwavelength
Raman polarization and water-vapor lidar PollyXT: the neXT
generation, Atmos. Meas. Tech., 9, 1767–1784, 10.5194/amt-9-1767-2016,
2016.Formenti, P., Winkler, H., Fourie, P., Piketh, S., Makgopa, B., Helas, G.,
and Andreae, M. O.: Aerosol optical depth over a remote semi-arid region of
South Africa from spectral measurements of the daytime solar extinction and
the nighttime stellar extinction, Atmos. Res., 62, 11–32,
10.1016/s0169-8095(02)00021-2, 2002.Formenti, P., Elbert, W., Maenhaut, W., Haywood, J., Osborne, S., and
Andreae, M. O.: Inorganic and carbonaceous aerosols during the Southern
African Regional Science Initiative (SAFARI 2000) experiment: Chemical
characteristics, physical properties, and emission data for smoke from
African biomass burning, J. Geophys. Res., 108, 8488,
10.1029/2002jd002408, 2003.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.,
Haywood, J., Lean, J., Lowe, D., Myhre, G., Nganga, J., Prinn, R., Raga, G.,
Schulz, M., and Dorland, R. V.: Changes in atmospheric constituents and in
radiative forcing. Climate Change 2007: The Physical Science Basis.
Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge Univ. Press, Cambridge,
UK and New York, NY, USA, 129–234, 2007.
Fernlad, G. F.: Analysis of atmospheric lidar observations: some comments,
Appl. Optics, 23, 652–653, 1984.Freiman, M. T. and Piketh, S. J.: Air transport into and out of the
industrial Highveld region of South Africa, J. Appl. Meteorol., 42,
994–1002,
10.1175/1520-0450(2003)042<0994:ATIAOO>2.0.CO;2, 2003.Freudenthaler, V., Esselborn, M., Wiegner, M., Heese, B., Tesche, M.,
Ansmann, A., Müller, D., Althausen, D., Wirth, M., Fix, A., Ehret, G.,
Knippertz, P., Toledano, C., Gasteiger, J., Garhammer, M., and Seefeldner,
M.: Depolarization ratio profiling at several wavelengths in pure Saharan
dust during SAMUM 2006, Tellus B, 61, 165–179,
10.1111/j.1600-0889.2008.00396.x, 2009.Giannakaki, E., Balis, D. S., Amiridis, V., and Zerefos, C.: Optical
properties of different aerosol types: seven years of combined Raman-elastic
backscatter lidar measurements in Thessaloniki, Greece, Atmos. Meas. Tech.,
3, 569–578, 10.5194/amt-3-569-2010, 2010.Giannakaki, E., Pfüller, A., Korhonen, K., Mielonen, T., Laakso, L.,
Vakkari, V., Baars, H., Engelmann, R., Beukes, J. P., Van Zyl, P. G.,
Josipovic, M., Tiitta, P., Chiloane, K., Piketh, S., Lihavainen, H.,
Lehtinen, K. E. J., and Komppula, M.: One year of Raman lidar observations of
free-tropospheric aerosol layers over South Africa, Atmos. Chem. Phys., 15,
5429–5442, 10.5194/acp-15-5429-2015, 2015.Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S.,
Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and
long-term trends in burned area by merging multiple satellite fire products,
Biogeosciences, 7, 1171–1186, 10.5194/bg-7-1171-2010, 2010.Groß, S, Tesche, M., Freudenthaler, V., Toledano, C., Wiegner, M.,
Ansmann, A., Althausen, D., and Seefeldner, M.: Characterization of Saharan
dust, marine aerosols and mixtures of biomass-burning aerosols and dust by
means of multi-wavelength depolarization and Raman lidar measurements during
SAMUM 2, Tellus B, 63, 706–724, 10.1111/j.1600-0889.2011.00556.x,
2011.Groß, S., Esselborn, M., Weinzierl, B., Wirth, M., Fix, A., and Petzold,
A.: Aerosol classification by airborne high spectral resolution lidar
observations, Atmos. Chem. Phys., 13, 2487–2505,
10.5194/acp-13-2487-2013, 2013.Hänel, A., Baars, H., Althausen, D., Ansmann, A., Engelmann, R., and
Sun, Y. J.: One-year aerosol profiling with EUCAARI Raman lidar at
Shangdianzi GAW station: Beijing plume and seasonal variation, J. Geophys.
Res., 117, D13201, 10.1029/2012JD017577, 2012.
Heese, B., Althausen, D., Baars, H., Bohlmann, S., and Deng, R.: Aerosol
properties over Southeastern China from multiwavelength Raman and
depolarization lidar measurents, in: Reviewed and Revised Papers of 27th ILRC
International Laser Radar Conference, 5–10 July 2015, New York, USA, 2015.Ichoku, C., Remer, L. A., Kaufman, Y. J., Levy, R., Chu, D. A., Tanre, D.,
and Holben, B. N.: MODIS observation of aerosols and estimation of aerosol
radiative forcing over southern Africa during SAFARI 2000, J. Geophys. Res.,
108, 8499, 10.1029/2002jd002366, 2003.Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H.,
Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S.,
Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T.,
Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R.,
Sato, K., Satoh, M., Shephard, M., Wandinger, U., Wehr, T., and Van
Zadelhoff, G.-J.: The EarthCARE Satellite: The next step forward in global
measurements of clouds, aerosols, precipitation and radiation, B. Am.
Meteorol. Soc., 96, 1311–1311, 10.1175/BAMS-D-12-00227.1, 2015.
IPCC: The Physical Science Basis, Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Stocker, T. F., Qin, D., Plattner, G.-K, Tignor, M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge
University Press, Cambridge, UK and New York, NY, USA, 2013.Kanitz, T., Engelmann, R., Heinold, B., Baars, H., Skupin, A., and Ansmann
A.: Tracking the Saharan Air Layer with shipborne lidar across the tropical
Atlantic, Geophys. Res. Lett., 41, 1044–1050, 10.1002/2013GL058780,
2014.Klett, J. D.: Stable analytical inversion solution for processing lidar
returns, Appl. Optics, 20, 211–220, 10.1364/AO.20.000211, 1981.Komppula, M., Mielonen, T., Arola, A., Korhonen, K., Lihavainen, H.,
Hyvärinen, A.-P., Baars, H., Engelmann, R., Althausen, D., Ansmann, A.,
Müller, D., Panwar, T. S., Hooda, R. K., Sharma, V. P., Kerminen, V.-M.,
Lehtinen, K. E. J., and Viisanen, Y.: Technical Note: One year of Raman-lidar
measurements in Gual Pahari EUCAARI site close to New Delhi in India –
Seasonal characteristics of the aerosol vertical structure, Atmos. Chem.
Phys., 12, 4513–4524, 10.5194/acp-12-4513-2012, 2012.Kulmala, M., Asmi, A., Lappalainen, H. K., et al.: General overview: European
Integrated project on Aerosol Cloud Climate and Air Quality interactions
(EUCAARI) – integrating aerosol research from nano to global scales, Atmos.
Chem. Phys., 11, 13061–13143, 10.5194/acp-11-13061-2011, 2011.Laakso, L., Vakkari, V., Virkkula, A., Laakso, H., Backman, J., Kulmala, M.,
Beukes, J. P., van Zyl, P. G., Tiitta, P., Josipovic, M., Pienaar, J. J.,
Chiloane, K., Gilardoni, S., Vignati, E., Wiedensohler, A., Tuch, T.,
Birmili, W., Piketh, S., Collett, K., Fourie, G. D., Komppula, M.,
Lihavainen, H., de Leeuw, G., and Kerminen, V.-M.: South African EUCAARI
measurements: seasonal variation of trace gases and aerosol optical
properties, Atmos. Chem. Phys., 12, 1847–1864, 10.5194/acp-12-1847-2012,
2012.Lourens, A. S. M., Butler, T. M., Beukes, J. P., van Zyl, P. G., Beirle, S.,
Wagner, T. K., Heue, K. P., Pienaar, J. J., Fourie, G. D., and Lawrence, M.
G.: Re-evaluating the NO2 hotspot over the South African Highveld, S.
Afr. J. Sci., 108, 54–59, 10.4102/sajs.v108i11/12.1146, 2012.Mamouri, R. E. and Ansmann, A.: Fine and coarse dust separation with
polarization lidar, Atmos. Meas. Tech., 7, 3717–3735,
10.5194/amt-7-3717-2014, 2014.Mamouri, R. E., Ansmann, A., Nisantzi, A., Kokkalis, P., Schwarz, A. and
Hadjimitsis D.: Low Arabian dust extinction-to-backscatter ratio,
Geophys. Res. Lett., 40, 4762–4766, 10.1002/grl.50898, 2013.
Mishchenko, M., Travis, L. D., Kahn, R. A. and West, R. A.: Modeling phase
functions for dustlike tropospheric aerosols using a shape mixture of
randomly oriented polydisperse spheroids, J. Geophys. Res., 102,
16831–16847, 1997.
Müller, D., Wandinger, U., and Ansmann, A.: Microphysical particle
parameters from extinction and backscatter lidar data by inversion with
regularization: theory, Appl. Optics 38, 2346–2357, 1999a.
Müller, D., Wandinger, U., and Ansmann, A.: Microphysical particle
parameters from extinction and backscatter lidar data by inversion with
regularization: simulation, Appl. Optics 38, 2358–2368, 1999b.Müller, D., Wandinger, U., Althausen, D., and Fiebig, M.: Comprehensive
particle characterization from three-wavelength Raman-lidar observations,
Appl. Optics, 40, 4863–4869, 10.1364/AO.40.004863, 2001.Müller, D., Mattis, I., Wandinger, U., Ansmann, A., Althausen, D.,
Dubovik, O., Eckhardt, S., and Stohl, A.: Saharan dust over a central
European EARLINET-AERONET site: combined observations with Raman lidar and
Sun photometer, J. Geophys. Res., 108, 4345, 10.1029/2002JD002918, 2003.Müller, D., Mattis, I., Wandinger, U., Ansmann, A., Althausen, D., and
Stohl, A.: 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, D17201, 10.1029/2004JD005756,
2005.Müller, D., Ansmann, A., Mattis, I., Tesche, M., Wandinger, U.,
Althausen, D., and Pissani, G.: Aerosol-type dependent lidar raios observed
with Raman lidar, J. Geophys. Res., 12, D16202, 10.1029/2006JD008292,
2007.Murayama, T., Okamoto, H., Kaneyasu, N., Kamataki, H., and Miura, K.:
Application of lidar depolarization measurement in the atmospheric boundary
layer: Effects of dust and sea-salt particles, J. Geophys. Res., 104,
31781–31792, 10.1029/1999JD900503, 1999.Murayama, T., Müller, D., Wada, K., Shimizu, A., Sekigushi, M., and
Tsukamato, T.: Characterization of Asian dust and Siberian smoke with
multi-wavelength Raman lidar over Tokyo, Japan in spring 2003, Geophys. Res.
Lett., 31, L23103, 10.1029/2004GL021105, 2004.Nicolae, D., Nemuc, A., Müller, D., Talianu, C., Vasilescu, J., Belegante
L., and Kolgotin, A.: Characterization of fresh and aged biomass burning
events using multiwavelength Raman lidar and mass spectrometry, J. Geophys.
Res., 118, 2956–2965, 10.1002/jgrd.50324, 2013.Nisantzi, A., Mamouri, R. E., Ansmann, A., and Hadjimitsis, D.: Injection of
mineral dust into the free troposphere during fire events observed with
polarization lidar at Limassol, Cyprus, Atmos. Chem. Phys., 14, 12155–12165,
10.5194/acp-14-12155-2014, 2014.Noh, Y. M., Müller, D., Shin, D. H., Lee, H. L., Jung, J. S., Lee, K.
H., Cribb, M., Li, Z., and Kim Y. J.: Optical and microphysical properties
of severe haze and smoke aerosol measured by integrated remote sensing
techniques in Gwangju, Korea, Atmos. Environ., 43, 879–888,
10.1016/j.atmosenv.2008.10.058, 2009.Omar, A., Winker, D., Vaughan, M., Hu, Y., Trepte, C., Ferrare, R., Lee, K.,
Hostetler, C., Kittaka, C., Rogers, R., Kuehn, R., and Liu, Z.: The CALIPSO
Automated Aerosol Classification and Lidar Ratio Selection Algorithm, J.
Atmos. Ocean. Tech., 26, 1994–2014, 10.1175/2009JTECHA1231.1, 2009.
Piketh, S. J., Tyson, P. D., and Steffen, W.: Aeolian transport from southern
Africa and iron fertilization of marine biota in the South Indian Ocean, S.
Afr. J. Sci., 96, 244–246, 2000.Piketh, S. J., Swap, R. J., Maenhaut, W., Annegarn, H. J., and Formenti, P.:
Chemical evidence of long-range atmospheric transport over southern Africa,
J. Geophys. Res., 107, 4817, 10.1029/2002jd002056, 2002.Pósfai, M., Simonics, R., Li, J., Hobbs, P. V., and Buseck, P. R.:
Individual aerosol particles from biomass burning in southern Africa:
1. Compositions and size distributions of carbonaceous particles, J. Geophys.
Res., 108, 2156–2202, 10.1029/2002JD002291, 2003.
Preißler, J., Bravo-Aranda, J., Wagner, F., Granados-Muñoz, M. J.,
Navas-Guzmán, F., Guerrero-Rascado, J. L., Lyamani, H., and
Alados-Arboledas, L.: Optical properties of free tropospheric aerosol from
multi-wavelength Raman lidars over the southern Iberian Peninsula, in:
Proceedings of the 9th International Symposium on Tropospheric Profinling,
3–7 September 2012, l'Aquila, Italy, ISBN 978-90-815839-4-7, 2012.Preißler, J., Wagner, F., Guerrero-Rascado, J. L., and Silva, A. M.: Two
years of free-tropospheric aerosol layers observed over Portugal by lidar, J.
Geophys. Res., 118, 3676–3686, 10.1002/jgrd.50350, 2013.Queface, A. J., Piketh, S. J., Eck, T. F., Tsay, S. C., and Mavume, A. F.:
Climatology of aerosol optical properties in Southern Africa, Atmos.
Environ., 45, 2910–2921, 10.1016/j.atmosenv.2011.01.056, 2011.
Radke, L. F., Heggs, D. A., Lyons, H., Brook, C. A., Hobbs, P. V., Weiss, R.,
and Rasmussen, R.: Airborne measurements on smoke from biomass burning, in:
Aerosols and Climate, edited by: Hobbs, P. V. and McCormick, M. P., Deepak,
A., Hampton, VA, USA, 411–422, 1998.Reid, J. S. and Hobbs, P. V.: Physical and optical properties of smoke from
individual biomass fires in Brazil, J. Geophys. Res., 103, 32013–32031,
10.1029/98JD00159, 1998.Ross, K. E., Piketh, S. J., Bruintjes, R. T., Burger, R. P., Swap, R. J., and
Annegarn, H. J.: Spatial and seasonal variations in CCN distribution and the
aerosol-CCN relationship over southern Africa, J. Geophys. Res., 108, 8481,
10.1029/2002JD002384, 2003.
Roy, D. P., Frost, P. G. H., Justice, C. O., Landmann, T., Le Roux, J. L.,
Gumbo, K., Makungwa, S., Dunham, K., du Toit, R., Mhwandagara, K., Zacarias,
A., Tacheba, B., Dube, O. P., Pereira, J. M. C., Mushove, P., Morisette, J.
T., Santhana-Vannan S. K., and Davies, D.: The Southern Africa Fire Network
(SAFnet) reigional burned-area product-validation protocol, Int. J. Remote
Sens., 26, 4265–4292, 2005.Schneider, P., Lahoz, W. A., and van der A, R.: Recent satellite-based trends
of tropospheric nitrogen dioxide over large urban agglomerations worldwide,
Atmos. Chem. Phys., 15, 1205–1220, 10.5194/acp-15-1205-2015, 2015.Shimizu, A., Sugimoto, N., Matsui, I., Arao, K., Uno, I., Murayama, T.,
Kagawa, N., Aoki, K., Uchiyama, A., and Yamazaki, A.: Continuous
observations of Asian dust and other aerosols by polarization lidars in
China and Japan during ACE-Asia, J. Geophys. Res., 109, D19S17,
10.1029/2002JD003253, 2004.Shin, S.-K., Müller, D., Lee, C., Lee, K. H., Shin, D., Kim, Y. J., and
Noh, Y. M.: Vertical variation of optical properties of mixed Asian
dust/pollution plumes according to pathway of air mass transport over East
Asia, Atmos. Chem. Phys., 15, 6707–6720, 10.5194/acp-15-6707-2015, 2015.Sugimoto, N., Uno, I., Nishikawa, M., Shimizu, A., Matsui, I., Dong, X.,
Chen, Y., and Quan, H.: Record heavy Asian dust in Beijing in 2002:
Observations and model analysis of recent events, Geophys. Res. Lett., 30,
1640, 10.1029/2002GL016349, 2003.Tesche, M., Ansmann, A., Müller, D., Althausen, D., Engelmann, R.,
Freudenthaler, V., and Groß, S.: Vertically resolved separation of dust
and smoke over Cape Verde using multiwavelength Raman and polarization lidars
during Saharan Mineral Dust Experiment 2008, J. Geophys. Res., 114, D13202,
10.1029/2009JD011862, 2009.Tesche, M., Gross, S., Ansmann, A., Müller, D., Althausen, D.,
Freudenthaler, V., and Esselborn, M.: Profiling of Saharan dust and
biomass-burning smoke with multiwavelength polarization Raman lidar at Cape
Verde, Tellus B, 63, 649–676, 10.1111/j.1600-0889.2011.00548.x, 2011.Tesfaye, M., Sivakumar, V., Botai, J., and Tsidu, G. M.: Aerosol climatology
over South Africa based on 10 years of Multiangle Imaging Spectroradiometer
(MISR) data, J. Geophys. Res., 116, D20216, 10.1029/2011jd016023, 2011.Tiitta, P., Vakkari, V., Croteau, P., Beukes, J. P., van Zyl, P. G.,
Josipovic, M., Venter, A. D., Jaars, K., Pienaar, J. J., Ng, N. L.,
Canagaratna, M. R., Jayne, J. T., Kerminen, V.-M., Kokkola, H., Kulmala, M.,
Laaksonen, A., Worsnop, D. R., and Laakso, L.: Chemical composition, main
sources and temporal variability of PM1 aerosols in southern African
grassland, Atmos. Chem. Phys., 14, 1909–1927, 10.5194/acp-14-1909-2014,
2014.
Twomey, S.: Introduction to the Mathematics of Inversion in Remotes Sensing
and Indirect Measurements, Elsevier Scientific, New York, USA, 1977.Venter, A. D., Vakkari, V., Beukes, J. P., van Zyl, P. G., Laakso, H.,
Mabaso, D., Tiitta, P., Josipovic, M., Kulmala, M., Pienaar, J. J., and
Laakso, L.: An air quality assessment in the industrialised western Bushveld
Ignous Complex, South Africa, S. Afr. J. Sci., 108, 1059,
10.4102/sajs.v108i9/10.1059, 2012.
Veselovskii, I., Kolgotin, A., Griaznov, V., Müller, D., Wandinger, U.,
and Whiteman, D. N.: Inversion with regularization for the retrieval of
tropospheric aerosol parameters from multiwavelength lidar sounding, Appl.
Optics, 41, 3685–3699, 2002.
Veselovskii, I., Kolgotin, A., Griaznov, V., Müller, D., Franke, K., and
Whiteman, D. N.: Inversion of multiwavelength Raman lidar data for retrieval
of bimodal aerosol size distribution, Appl. Optics, 43, 1180–1195, 2004.Wagner, F. and Silva, A. M.: Some considerations about Ångström
exponent distributions, Atmos. Chem. Phys., 8, 481–489,
10.5194/acp-8-481-2008, 2008.Wandinger, U. and Ansmann, A.: Experimental determination of the lidar
overlap profile with Raman lidar, Appl. Optics, 41, 511–514,
10.1364/AO.41.000511, 2002.Wandinger, U., Müller, D., Böckmann, C., Althausen, D., Matthias, V.,
Bösenberg, J., Weiß, V., Fiebig, M., Wendisch, M., Stohl, A., and
Ansmann A.: Optical and microphysical characterization of biomassburning and
industrial-pollution aerosols from multiwavelength lidar and aircraft
measurements, J. Geophys. Res., 107, 8125, 10.1029/2000JD000202, 2002.Weinzierl, B., Sauer, D., Esselborn, M., Petzold, A., Veira, A., Rose, M.,
Mund, S., Wirth, M., Ansmann, A., Tesche, M., Gross, S., and Freudenthaler,
V.: Microphysical and optical properties of dust and tropical biomass burning
aerosol layers in the Cape Verde region – an overview of the airborne in
situ and lidar measurements during SAMUM-2, Tellus, 63B, 589–618,
10.1111/j.1600-0889.2011.00566.x, 2011.
Wenig, M., Spichtinger, N., Stohl, A., Held, G., Beirle, S., Wagner, T.,
Jähne, B., and Platt, U.: Intercontinental transport of nitrogen oxide
pollution plumes, Atmos. Chem. Phys., 3, 387–393,
10.5194/acp-3-387-2003, 2003.Winkler, H., Formenti, P., Esterhuyse, D. J., Swap, R. J., Helas, G.,
Annegarn, H. J., and Andreae, M. O.: Evidence for large-scale transport of
biomass burning aerosols from sunphotometry at a remote South African site:
Atmos. Environ., 42, 5569–5578, 10.1016/j.atmosenv.2008.03.031, 2008.