ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-11883-2016Porous aerosol in degassing plumes of Mt. Etna and Mt. StromboliShcherbakovValeryv.shcherbakov@opgc.univ-bpclermont.frJourdanOlivierhttps://orcid.org/0000-0003-0890-3784VoigtChristianehttps://orcid.org/0000-0001-8925-7731GayetJean-FrancoisChauvigneAurélienSchwarzenboeckAlfonsMinikinAndreashttps://orcid.org/0000-0003-0999-4657KlingebielMarcusWeigelRalfhttps://orcid.org/0000-0003-1316-0292BorrmannStephanJurkatTinaKaufmannStefanhttps://orcid.org/0000-0002-0767-1996SchlageRomyGourbeyreChristopheFebvreGuyhttps://orcid.org/0000-0001-9655-075XLapyonokTatyanaFreyWiebkehttps://orcid.org/0000-0003-4282-1264MollekerSergejhttps://orcid.org/0000-0002-2980-0330WeinzierlBernadetthttps://orcid.org/0000-0003-4555-5686Laboratoire de Météorologie Physique, UMR 6016
CNRS/Université Clermont Auvergne, Clermont-Ferrand, FranceLaMP, Institut Universitaire de Technologie d'Allier, Montluçon,
FranceInstitut für Physik der Atmosphäre, Deutsches Zentrum für
Luft- und Raumfahrt (DLR), Oberpfaffenhofen, GermanyInstitut für Physik der Atmosphäre, Johannes
Gutenberg-Universität Mainz, Mainz, GermanyMax Planck Institute for Meteorology, Hamburg, GermanyLaboratoire d'Optique Atmosphérique, UMR 8518 CNRS/Université
des Sciences et Technologies de Lille, Villeneuve d'Ascq, FranceMax Planck Institute for Chemistry, Particle Chemistry Department,
Mainz, GermanyUniversity of Vienna, Faculty of Physics, Aerosol Physics and
Environmental Physics, Vienna, Austrianow at: Flugexperimente, Deutsches Zentrum für Luft- und
Raumfahrt (DLR), Oberpfaffenhofen, Germanynow at: The University of Manchester, Centre for Atmospheric
Science, Manchester, UKValery Shcherbakov (v.shcherbakov@opgc.univ-bpclermont.fr)23September2016161811883118971March201631March20161July201611August2016This 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/16/11883/2016/acp-16-11883-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/11883/2016/acp-16-11883-2016.pdf
Aerosols of the volcanic degassing plumes from Mt. Etna
and Mt. Stromboli were probed with in situ instruments on board the Deutsches
Zentrum für Luft- und Raumfahrt research aircraft Falcon during the
contrail, volcano, and cirrus experiment CONCERT in September 2011. Aerosol
properties were analyzed using angular-scattering intensities and particle
size distributions measured simultaneously with the Polar Nephelometer and
the Forward Scattering Spectrometer probes (FSSP series 100 and 300),
respectively. Aerosols of degassing plumes are characterized by low values
of the asymmetry parameter (between 0.6 and 0.75); the effective diameter
was within the range of 1.5–2.8 µm and the maximal diameter was
lower than 20 µm. A principal component analysis applied to the Polar
Nephelometer data indicates that scattering features of volcanic aerosols of
different crater origins are clearly distinctive from angular-scattering
intensities of cirrus and contrails. Retrievals of aerosol properties
revealed that the particles were “optically spherical” and the estimated
values of the real part of the refractive index are within the interval from
1.35 to 1.38. The interpretation of these results leads to the conclusion
that the degassing plume aerosols were porous with air voids. Our estimates
suggest that aerosol particles contained about 18 to 35 % of air voids in
terms of the total volume.
Introduction
The impacts of continuously degassing volcanoes on the environment and human
health are well recognized (Delmelle, 2003; Mather et al., 2004). Fumarolic
plumes of passively degassing volcanos are strongly involved in the
deposition and redistribution of metals and trace elements (Fulignati et
al., 2006). Degassing volcanoes have a potentially large effect on the
natural background aerosol loading and the radiation budget of the
atmosphere (Schmidt et al., 2012; Oppenheimer et al., 2011).
The size-resolved chemical composition, morphology, and particle size
distribution of volcanic aerosols are important in defining their effects on
the atmosphere, environment, and human health. A description of the various
techniques used in the characterization of “near-source” volcanic particles
as well as a review of field campaign results are given by Mather et al. (2004). The morphology of atmospheric and volcanic plume aerosols was
studied mostly by electron microscopy and X-ray spectroscopy (see, e.g.,
Pósfai et al., 1999; Obenholzner et al., 2003; Mather et al., 2004;
Martin et al., 2008). Due to the high spatial resolution of such
instruments, it was evidenced that complex composition, irregular shape,
intricate internal structure, and random surface roughness are the rule
rather than the exception for aerosol particles. At the same time, the
quasi-spherical particles are not uncommon (see, e.g., Obenholzner et al.,
2003).
The overwhelming majority of works dealing with internal structure of
aerosol particles consider a mixture of chemical compounds (see, e.g.,
Nousiainen, 2009; Ulanowski and Schnaiter, 2011, and references therein). To
date, literature on porous aerosol is rather limited. Jeong and Nousiainen (2014) demonstrated, using transmission electron microscopy analysis, that
the internal structures of individual Asian dust particles were formed by
the patterned arrangement of nano-to-micron-sized mineral grains and pores.
Highly porous aerosol particles, obtained from proxies for organic
compounds, were investigated by Adler et al. (2013, 2014) with focused ion
beam – scanning electron microscopy techniques and a cavity ring-down
system. Most of the information on volcanic vesicular, i.e., porous ash,
particles was obtained from samples collected at the ground level. A
compilation of the morphological features of volcanic ash particles derived
through the use of scanning electron microscopy was done by Heiken and
Wohletz (1985).
Generally, scattering matrices of irregular and/or heterogeneous particles
have to be modeled on the base of the exact electromagnetic wave theory
(see, e.g., Mishchenko et al., 2000). On the other hand, heterogeneous
materials could be treated similarly to homogeneous substances when the
typical dimension d of their inhomogeneity is much smaller than the
wavelength λ of the considered radiation, d≪λ (Chýlek et
al., 2000), that is, using effective medium approximations (EMAs). The
applicability of EMAs for calculating scattering properties of inhomogeneous
atmospheric particles was verified in the recent work by Liu et al. (2014)
by comparing it with the standards of truth provided by calculations based on
the pseudo-spectral time domain method (PSTD). The exact heterogeneous
mixing structures of nonabsorptive or weakly absorptive particles were
considered in the PSTD calculations. Liu et al. (2014) concluded that
scattering properties of the equivalent homogeneous particles agree well
with those of internally mixed particles. Fundamental aspects and recent
developments of the scattering of electromagnetic radiation by a discrete
random medium as well as applicability of the EMAs are reported in the work
by Mishchenko et al. (2016).
Of course, it is hardly probable that EMAs are able to provide all elements
of a scattering matrix with high accuracy for any value of the scattering
angle θ. But, it is feasible to perform modeling with admissible
errors, especially when one deals with angular-scattering intensities within
a limited interval of θ. For example, the works by Chýlek et al. (1988) and Kolokolova and Gustafson (2001) showed acceptable agreement
between experimental results and EMA calculations.
Remote sensing methods, namely sun photometry, lidar sounding, and satellite
observations, are largely employed for volcanic plume studies. Generally,
such techniques are related to inverse problems, i.e., retrievals of
aerosols characteristics on the base of measured optical signals.
Consequently, values of the aerosol refractive index have to be estimated
with a retrieval code or specified a priori, i.e., preassigned. When values
are preassigned, the refractive index of bulk matter or sulfuric acid
solutions are commonly used (see, e.g., Watson and Oppenheimer, 2000, 2001;
Spinetti et al., 2003; Kahn et al., 2007; Martin et al., 2009; Marenco et
al., 2011; Young et al., 2012). Examples for which the refractive index was
retrieved along with a size distribution can be found in the works by
Toledano et al. (2012), Waquet et al. (2014), and references therein.
Effective medium approximations were used in inverse problems by Abo Riziq
et al. (2007) and Adler et al. (2013, 2014) where refractive indices of
laboratory-generated aerosols were retrieved by comparing the measured
size-resolved extinction efficiency with Mie theory calculations.
This work is devoted to aerosol optical characteristics of degassing plumes, retrievals of size distributions
and refractive index values, and consequent inferences about morphological
properties of aerosols for the volcanoes Mt.
Etna and Mt. Stromboli. In the following, the aircraft instrumentation and
the measurement conditions are outlined first. Section 3 addresses the
outcomes of the principal component analysis in detail. Section 4 describes
a careful approach to derive size distributions and refractive index as well
as the results. Section 5 is devoted to discussion and inferences.
Instrumentation and flight overview
This study addresses in situ measurements in degassing plumes from the volcanoes
Mt. Etna and Mt. Stromboli performed during the CONCERT (contrail, volcano,
and cirrus experiment) campaign in 2011 (Voigt et al., 2014a). Particles and
trace gases of the volcanic plumes were probed with instruments on board the
DLR (Deutsches Zentrum für Luft- und Raumfahrt) research aircraft
Falcon. Descriptions of the instrumentation of the two CONCERT campaigns in
2008 and 2011 are given in Voigt et al. (2010, 2011, 2014a); individual
instruments are discussed in detail by Frey et al. (2011), Gayet et al.,
(2012), Jeßberger et al. (2013), Kaufmann et al. (2014, 2016), and Jurkat et al. (2016). In addition to the instrumentation of
the CONCERT campaign in 2008 (Voigt et al., 2010), the FSSP-100 forward-scattering spectrometer probe was employed during the CONCERT campaign in
2011. Below, we briefly describe the instruments used for our study.
Particle probes
The Polar Nephelometer (PN) (Gayet et al., 1997) measures the angular-scattering intensities (ASIs) of an ensemble of particles ranging from less
than 1 µm to about 1 mm diameter, which intersect a collimated laser
beam (λ= 804 nm) near the focal point of a parabolic mirror.
Observations are usually limited to 32 scattering angles θ
near-uniformly positioned from 15∘to 162∘.
Measurements at nearly forward and backward directions (θ<15∘ and θ>162∘) are not reliable
due to the diffracted light pollution caused by the edges of holes drilled
on the paraboloidal mirror (see, e.g., Jourdan et al., 2010). The sampling
volume is defined by the cross-sectional area of the beam (10 mm long and 5 mm diameter) multiplied by a linear speed of air populated with particles
passing through the instrument. Direct measurement of the ASIs allows
particle types (droplets or nonspherical particles) to be distinguished and
calculation of the optical parameters to be performed (extinction
coefficient Ext and asymmetry parameter g, see Gayet et al., 2002). Generally,
the g value decreases with increasing nonsphericity of the particles, all
other parameters being the same (Gayet et al., 2002, 2012). The
accuracies of the extinction coefficient and asymmetry parameter are 25
and 4 % (Jourdan et al., 2010; Gayet et al., 2012).
The FSSP-300 and FSSP-100 forward-scattering spectrometer probes
(Baumgardner et al., 1992; Petzold et al., 1997; Frey et al., 2011) measure
the intensity of light scattered by single particles in a forward direction at
angles from 6 to 15∘ (Jeßberger et al., 2013),
which is governed mainly by diffraction and therefore depends on (i) the
refractive index of the particles, and (ii) the projected area of the
particle which itself depends on the particle shape. Indeed, the size
calibration for nonspherical particles is expressed in terms of equivalent
surface diameter, i.e., the diameter of a sphere that has the same projected
area (Mishchenko et al., 1997). The FSSP-300 signal is resolved into an
array of 31 channels in the size range of 0.35 to 38.6 µm. The signal
is then converted into a corresponding particle size using water droplet
response (the refractive index of 1.33). Because of the ambiguities in the
Mie scattering curve, the FSSP-100 size distributions have been rebinned to
14 size bins in the diameter range from 1.02 to 47.05 µm (water
droplet).
Trace gas instruments
The trace gases SO2, HCl, and HNO3 were measured by the Airborne
chemical Ionization Mass Spectrometer (AIMS) (Voigt et al., 2014a; Jurkat et
al., 2016) equipped with an inlet system and a custom-made ion source
generating SF5- reagent ions (Jurkat et
al., 2010). Air is drawn into the backward-facing perfluoroalkoxy (PFA)
inlet using a pumping system. In the high-voltage gas discharge ion source
SF5- ions are produced from a flow of
SCF8 in N2 at pressures controlled to 40 hPa. The
SF5- ions react selectively with trace
species to form product ions via a fluoride transfer reaction (Jurkat et
al., 2011). The ions pass three differentially pumped vacuum chambers of the
mass spectrometer system, before they are detected with a channeltron
detector. An in-flight calibration is performed by adding a permanent flux
of isotopically labeled 34SO2 to the inlet line. Further,
HNO3 and HCl were calibrated in the laboratory after the flights.
Accuracies of 17–36 % were achieved for SO2, 33–35 % for HCl, and
18–36 % for HNO3, mainly depending on dilution of the inlet flow
(Voigt et al., 2014a, b).
Water vapor was measured with the tunable diode laser system WARAN (WAter
vapoR ANalyzer), which primarily consists of a commercial WVSS-II water
vapor sensor (SpectraSensors Inc.) (Kaufmann et al., 2014) with a
measurement range from 30 to 40 000 ppmv. In the measurement cell, a laser is
tuned over the water vapor absorption line at 1.37 µm and the water
vapor mixing ratio is determined from the absorption signal. The passive
sample flow through the system is realized using a Rosemount inlet. The
water vapor mixing ratio is measured with a relative uncertainty < 6 % at 2.4 s time resolution and for water vapor mixing ratios above 500 ppmv
relevant for the low-altitude flight legs shown here. The instrument is
calibrated before and after the campaign using a frost point hygrometer
(MBW373LX) as the reference. The uncertainty of 0.5 K in the
static air temperature measurement translates to a relative uncertainty of
around 4 % in the saturation pressure over water. Adding both
contributions, the relative humidity with respect to water (RHw) can be
determined with a relative uncertainty of a RHw value generally less than
10 %.
Flight overview
(a) Degassing Etna plumes from Northeast (NEC) and Bocca Nuova
(BN) craters as seen from the DLR Falcon Research aircraft (Photo: Bernadett
Weinzierl); (b) Falcon flight track over Sicily on 30 September 2011.
Degassing plumes from the volcanoes Mt. Stromboli and Mt. Etna were probed
on 30 September 2011 (from 06:50 to 08:40 UTC). Visual observations from the
cockpit of the Falcon showed that there was a single plume degassing from
Mt. Stromboli, whereas there were two distinct degassing plumes from Mt. Etna, see Fig. 1a. The Etna plumes were spreading at two different
altitudes and they originated from at least two craters, namely Northeast
Crater (upper plume) and Bocca Nuova Crater (lower plume) (Voigt et al.,
2014a). From the four main craters at Mt. Etna, these two craters were
identified as the major emitters on that day. The Falcon flight track over
Sicily is shown in Fig. 1b. It was set up to probe the two Etna plumes and
the Stromboli plume.
Aircraft observations of Stromboli and Etna plumes on 30 September 2011. Time series of altitude, air temperature (T), relative humidity with
respect to water (RHw), extinction coefficient (Ext), and asymmetry parameter
(g) are shown by black points. All these parameters are referred to the
left-hand y axes. Time series of HCl, SO2, and HNO3 mixing ratios
are shown by blue points and referred to the right-hand y axes. Shaded areas
labeled SV, BN, and NEC stand for plume samplings related to Stromboli,
Bocca Nuova, and Northeast Etna craters, respectively.
Figure 2 shows time series of trace gases measurements, flight altitude,
extinction coefficient, and asymmetry parameter derived from the Polar
Nephelometer measurements. The very low relative humidity has to be
particularly emphasized. The values of RHw are lower than the
crystallization humidity of most sulfates, nitrates, and chlorides (see,
e.g., Tang and Munkelwitz, 1991). In other words, the plumes were spreading
in the dry troposphere. In addition, the air temperatures within the plumes
were always above the freezing point, i.e., > 273.15 K. The extinction coefficient series reveals several peaks that correspond
to the time intervals when the research aircraft was probing the degassing
plumes (see shaded areas in Fig. 2). The close correspondence between the
extinction coefficient and the time series of the trace gases HNO3,
SO2, HCl is evident in Fig. 2. The trace gas composition of the
volcanic plumes has been investigated by Voigt et al. (2014a). Different
trace gas mixing ratios of CO2/ SO2 and SO2/ HCl have been
identified in the volcanic plumes from the Northeast and Bocca Nuova craters.
Specifically, it is shown that neither SO2 conversion to sulfate nor
HCl uptake in sulfate aerosol play a major role in the aging plume under dry
tropospheric conditions.
PCA analysis of Polar Nephelometer data
In this section, a statistical tool, the principal component analysis (PCA)
is applied to the Polar Nephelometer data obtained during the CONCERT
campaign in 2011. It should be emphasized that the peculiarity of the PN
data recorded within the quiescent degassing volcanic plumes is that the
signals were quite low, at least much lower than those ones registered
within contrails and clouds. We recall that during particle samplings, the
Polar Nephelometer raw signals are superimposed on the background signals
(or zero baselines), which are due to electronic or optical noises. As a
consequence, the accurate subtraction of the background signals was of
utmost importance (see Shcherbakov et al., 2006). Thus, the corresponding
data were preprocessed with special purpose software and operator
supervision of the data treatment quality, i.e., wavelet denoising
technique.
Clustering techniques
We employed the PCA to carry out the cluster analysis (see, e.g., Jolliffe,
2002, chap. 9; Jourdan et al., 2010), that is, to separate the recorded angular-scattering intensities (ASIs) into clusters. The advantage of the PCA
consists of the fact that it is self-sufficient; in other words, no a priori
hypotheses are needed, because the clustering is only based on internal properties
of a data set. In brief, the PCA is an unsupervised method used to explore
the intrinsic variability of the data.
The PCA is particularly fruitful when one deals with high-dimensional data.
The PCA provides the possibility of capturing much of the total data variation
in a few dimensions and organizing observed data into meaningful clusters.
That is, with the PCA technique, each vector of the data can be represented
adequately by a few coefficients, usually three, which correspond to
eigenvectors of a covariation matrix. This is especially true when a limited
number of primary physical parameters have a major impact on the measured
functions (in our case, angular-scattering intensities). The obtained
coefficients can be used in visualization of the data by scatter plots.
Algebraically, principal components could be defined as particular linear
combinations of a set of variables. These linear combinations represent the
selection of a new coordinate system obtained by rotating the original
system of coordinates. The new axes correspond to the directions with
maximum variability and provide a simpler description of the covariance
structure of the original set of variables (Johnson and Wichern, 1998).
The peculiarity of our approach is that the log transformation is applied to
the angular-scattering intensities before the PCA is carried out (see
details in Jourdan et al., 2003; Shcherbakov et al., 2005). The reason for
such a preprocessing is the following. In contrast to phase functions, ASIs
are not normalized, e.g., they are proportional to the particle
concentration. In addition, the ASIs values, as well as the variances at
the forward angles, as a rule, are of orders of magnitude higher than that
ones at the sideward and backward angles. In such conditions, the
conventional PCA yields the first principal components that only follow the
variance at the forward angles; they do not reproduce all the ASIs
variability. The preprocessing, that is, the logarithm of ASIs values, makes
the variances comparable at all angles. In the strict sense, the
log transformation leads to the multiplicative model of the variance
analysis, in contrast to the conventional PCA, which is based on the
additive model. At the same time, the variance can be discussed in terms of
the additive model when ASIs are plotted in the log scale.
Expressed mathematically, our approach leads to the following representation
of measured angular-scattering intensities σj(θi) in
terms of the principal components ξl(θi), that is, the
first k eigenvectors of the correlation matrix of the log-transformed
data set:
lnσjθi≈〈ln[σ(θi)]〉+∑l=1kCj,lξlθi,
where θi is the scattering angle, the index i designates the
ith scattering angle and takes values from 1 to 32, the index j refers to the
jth observation of the analyzed flight sequences, 〈…〉 denotes averaging over the total data set (i.e., 1803 PN
measurements). When vector expressions are employed, σj
has the components σj(θi); ξl,
lnσj, and lnσ have the components ξl(θi), lnσjθi, and 〈lnσjθi〉, respectively. The coefficients Cj,l are computed as Cj,l=lnσj-〈lnσ〉T⋅ξl, where T denotes a transposed
matrix. Each vector (i.e., lnσj at each j) is represented by
a few coefficients Cj,l with quite good accuracy.
Clustering results
The PCA results were obtained using the CONCERT data recorded during the
flights on 16 September 2011 (from 14:40 to 17:50 UTC) (Kaufmann et al.,
2014) and on 30 September 2011 (from 06:50 to 08:40 UTC) (Voigt et al.,
2014a). The data set contains the ASIs measured in degassing plumes, cirrus
and contrails. Such a large set was chosen to point out that the scattering
pattern of degassing plumes is clearly distinctive (see below).
(a) Results of the principal component analysis. First three
eigenvectors (ξl stand for ξl) of the
angular-scattering intensities (ASI) of the correlation matrix vs.
measured scattering angles. Values of the first three normalized eigenvalues
λl of the eigenvectors and the remaining variability are
also displayed. (b) Expansion coefficient diagram: third coefficient vs.
second coefficient. Black points for cirrus clouds, blue points for
contrails, olive points for the Northeast Crater plume, violet points for
Stromboli, orange points for BNa, and red points for BNb crater plumes.
(c) Same as (b) but with the focus of attention on the degassing plumes.
Green points for NECa and olive points for NECb crater plumes.
When the PCA is applied to ASIs, the principal components can have a clear
physical meaning. Figure 3a shows the first three principal components
along with the corresponding eigenvalues λl normalized
as a percentage of the total variance. The first vector ξ1
almost does not depend on the scattering angle, it accounts for 96.8 % of
the data variability. This means that it is closely linked to the extinction
coefficient and about 97 % of the ASIs variation is mostly due to
fluctuations of the particle concentration.
It is well known that effects of size distribution, refractive index,
particle shape, and surface roughness are better represented using phase
functions, i.e., normalized ASIs. Thus, not only the other principal
components are of importance, their contribution to the remaining
variability has to be evaluated.
The second vector ξ2 represents 70.4 % of the remaining
variability; it has negative values at sideward angles. Generally, high
negative values of the corresponding coefficients Cj,2 of such a vector
take into account the irregular shape and/or the deep surface roughness of
large particles. All of this leads to lower values of the asymmetry
parameter (see, e.g., Jourdan et al., 2010).
The third vector ξ3 represents 15.6 % of the remaining
variability. Its shape reveals that ξ3 is related to the
forward-/backward-hemisphere partitioning of the scattering. The high
negative values of the corresponding coefficients Cj,3 imply that less
energy is scattered in the forward hemisphere and, thus, more energy is
scattered in the backward hemisphere.
Figure 3b shows the scatter plot of the Cj,3 expansion coefficient
vs. the Cj,2 coefficient. Therefore, such a presentation describes
the features of the PN data set in one of the clearest and most informative ways.
Each point is directly associated with one of the measured ASCs. Three main
distinctive clusters of points can be identified in Fig. 3b. Additional
analysis of the total CONCERT-2011 data set provided the possibility of
associating the clusters with the measurements performed within cirrus clouds,
contrails, and the degassing volcanic plumes (see the notations in Fig. 3b).
The negative Cj,2 values of the cirrus cluster are typical of irregular
large ice particles (see, e.g., Jourdan et al., 2010). The contrail cluster
is characterized by high positive values of Cj,2. This feature means
that the corresponding phase functions are quite similar to those of
ensembles of large spherical particles. The fraction of spherical particles
increases with increasing Cj,2. This is especially true for the
young contrail subcluster. A detailed discussion can be found in Gayet et
al. (2012).
The peculiarity of the degassing plume cluster is that the coefficients
Cj,2 are close to zero. Thus, the properties of the degassing plume
ASIs can be discussed just in terms of the vector ξ3. That
fact results in the following. The effective diameter and the asymmetry
parameter of the degassing plume particles have to be smaller, if not much
smaller, compared to those of other clusters in the data set, among
them the young contrails. Another property of the degassing plume cluster
is that it falls into subclusters. Figure 3c repeats Fig. 3b but with
the focus of attention on the degassing plumes. It is seen that the ASIs of
the NECa and NECb plumes are close to each other; they form one cluster. The
BNa and BNb clusters are a little bit separated with the third coefficients,
but belong to the same range of the second coefficient that accounts for
70.4 % of the ASIs shape variability. Other properties of the
degassing plume subclusters will be discussed in detail below.
Number concentrations, effective radii and refractive indices of the
volcanic plume layers
In this work the intervals of the degassing-plume penetration were specified
on the basis of the PN data after subtraction of the background signals,
which correspond to the electronic noise and ASIs of the free atmosphere.
The angular-scattering intensities (ASI) from the Polar Nephelometer were
averaged over the penetration intervals, i.e., over 17 s for Stromboli
(SV) and over 143, 172, 122, 80, and 313 s for Etna's Bocca Nuova (BNa
and BNb) and Northeast (NECa1, NECa2, and NECb) crater plumes, respectively
(see notations in Fig. 1). The averaged ASIs were used to retrieve
microphysical and optical characteristics of degassing plume aerosols.
Retrieval techniques and software
Our retrievals were performed with the software and the pre-calculated
kernels developed by Dubovik et al. (2006). We recall that the software is
employed in the operational processing of AERONET (AErosol RObotic NETwork)
for retrieving detailed properties from observations of ground-based
sun/sky radiometers (Eck et al., 2008). We will briefly describe the main
features of the retrieval algorithm and its implementation on our specific
data set.
The pre-calculated kernels contain optical characteristics of spheroids
mixtures. The surface of particles can be smooth or severely rough (Yang et
al., 2013). A mixture of spheroids of different aspect ratios, sizes and
surface texture (smooth or severely rough) is employed as a generalized
aerosol model (representing spherical, nonspherical, and mixed aerosols).
The aspect ratio values belong to the range (0.3–3.0); the
surface-roughness parameter can take only two values 0.0 or 0.2.
The code offers the possibility of retrieving a complete set of aerosol
parameters, including the complex refractive index and the size
distribution. The aerosol size distribution is presented using size bins;
and the size bins are formed with discrete logarithmically equidistant size
values. As for size distribution retrievals, the undoubted advantage of the
Dubovik code consists in the fact that solutions are constrained to be
nonnegative, which significantly improves the quality of retrievals. The
constraint is imposed through an elegant and well-founded approach, more
specifically, the assumption of the log-normal distribution of measurement
errors (see, e.g., Dubovik et al., 2011).
The code includes a quite large set of input parameters that is very
advantageous for an experienced user. For example, for different hypotheses of
aerosol composition, among others, effects of the ultrafine fraction can be
tested. On the one hand, supervised retrievals are time consuming; on the
other, one has the possibility to assure a high quality of retrievals. We
list some criteria of the retrieval quality in the following. Final results
are scarcely affected by small variations of input parameters. When the
regularization-parameter value is chosen according to the “L-curve” method
(see, e.g., Hansen, 1992), the retrieval residuals correspond to the
measurement errors. In a generally nonlinear case, particular attention
must be given to the verification if the obtained solution corresponds
to the global minimum of an objective function. As for
aerosol characteristic retrievals, different starting values of the
refractive index may be randomly tried.
Summarized, we modeled the degassing plumes as a mixture of two main
particle fractions; one consists of spherical and one of nonspherical
particles. Although aerosol particles were not ellipsoidal, we consider
randomly oriented spheroids as a reasonable approximation of an ensemble of
quite small nonspherical aerosols. In addition, we recall that we
considered smooth and severely rough particles (Yang et al., 2013).
Details of the software package used in this works, as well as the
code application are described in the supplementary material. To summarize
briefly, different initial guesses for the inversion code were performed on
a multidimensional grid of the input parameters using a number of input
files. The minimum and the maximum sizes of particles as well as the
spherical/nonspherical partitioning ratio belong to the set of assessed
parameters in addition to the refractive index and the size distribution.
Retrieval results
Measured (solid lines) and retrieved (solid black circles) aerosol
size distributions (left panels). Measured (solid red circles) and
reconstructed (solid black circles) angular-scattering intensities (right
panels). Stromboli (top), Bocca Nuova (middle), and Northeast (bottom) Etna
craters plums, respectively. (See notations in Fig. 2.)
Three representative examples of retrievals are shown in Fig. 4. The
corresponding time sequences SV, BNa and NECb are identified in the
time series in Fig. 2 by shadowed areas. In Fig. 4a–c, the left panels
display the particle size distributions measured with the FSSP-300 and
FSSP-100 (solid lines) and the retrieved one (solid black circles). It
should be pointed out that the FSSP measurements in Fig. 4 represent
particle size distributions considering the size response to water droplets.
Due to size response uncertainties linked to different refractive indexes
and/or particle shape in plume aerosols (Pinnick and Auvermann, 1979; Dye
and Baumgardner, 1984; Jaenicke and Hanusch, 1993; Febvre et al., 2012), the
comparison with retrieved size distributions should only be considered
qualitative in Fig. 4. Despite these limitations, the FSSP data and the
retrieved distributions reveal very similar features of the degassing plume aerosols, in particular the absence of large particles. The calibrated probe
response of the specific FSSP adjusted to different refractive indexes is
beyond the scope of this paper.
Mean parameter values in the indicated time intervals (see Fig. 1).
The parameters are time interval, altitude, air temperature T, relative
humidity with respect to water (RHw); concentration (Conc) of
particles (d>0.9 µm), and effective diameter Deff measured with
the FSSPs; real n and imaginary χ part of the refractive index,
spherical/nonspherical partitioning ratio (SNR), asymmetry parameter g,
extinction coefficients (Ext), and residual (Res) estimated from the
retrievals data. (See notations in Fig. 2.)
The size distribution was retrieved along with the refractive index m=n+iχ and the spherical/nonspherical partitioning ratio (SNR) of aerosol
particles (see Table 1). In the following, the SNR defines the percentage in
number of spherical particles relative to the total number of particles.
Furthermore, the refractive index and the partitioning ratio are assumed to
be constant over the full size range of the retrieved particle size
distribution. We emphasize that the information content of the PN data is
inadequate for retrieving size-dependent values of such parameters. In other
words, variations of the shape and/or the refractive index of small
particles lead to variations of the phase function that are lower than the
PN measurement errors. Therefore, the refractive index and the partitioning
ratio are assumed to be constant over the full size range.
The following results are especially noteworthy. The real part n of the
refractive index belongs to the interval from 1.35 to 1.38; the aerosol
particles were either nonabsorbing or weakly absorbing; the SNR values are
equal to 100 %, i.e., the best fits of the Polar Nephelometer data were
obtained with the model of spherical particles (Table 1).
The assessed value of the maximal particle diameter is about 15 µm
for the all degassing-plume penetrations (Fig. 4). That is, the size
parameter of the probed aerosols is rather small (lower than 60 for the
wavelength of 0.8 µm). This feature can explain the fact that all our
retrieval results were very close for both values of the
surface-roughness parameter. The PN measurements are not sensitive enough to
distinguish whether small particles have smooth or rough surface. In view of
this result and the SNR values, the degassing-plume aerosols are assumed to
be smooth spheres.
The right panels of Fig. 4a–c represent the following data. The average
scattering phase function (without normalization) measured by the Polar
Nephelometer is shown by solid red circles (see the averaging time intervals
in Table 1). The retrieved phase function, shown by solid black circles, was
computed from the retrieved size distribution. The main result of Fig. 4 is
that the retrieved phase functions agree well with the observations. It
should be underscored that there is no systematic bias between them. In
other words, the measured ASIs are well fitted by the retrieved phase
functions. The residuals (Res) were computed with the formula
Res=100⋅1N∑i=1NIθiret-IθimeasIθimeas2,
where Iθi is the light intensity at the scattering
angle θi, and the subscripts “meas” and “ret” refer to the measured
and retrieved values, respectively. The low values of the residuals are
noteworthy as well, see Table 1.
Table 1 summarizes the measured and retrieved characteristics. As mentioned
above, the relative humidity RHw was low, i.e., about 36 % within the
Stromboli plume and near 10 % during the Etna plume samplings. The air
temperature was positive, including at the Etna upper-plume altitude
(0.3 ∘C). The number concentration (Conc) of aerosols with the
diameter d>0.9 µm is estimated within the range of 25–51 cm-3.
The effective diameter (Deff) was low, between 1.5 and 2.8 µm, where both parameters Conc and Deff were deduced from the FSSP data. The
retrieved values of the refractive index belong to the interval from 1.35 to
1.38. Low values [0.65–0.70] of the asymmetry parameter g correspond to
ensembles of small particles, which is in agreement with the Deff values. The
extinction coefficients Ext were computed from the retrieval data and the values
are quite small, confirming that the ASIs were recorded in single-scattering
conditions. We recall that the optical characteristics correspond to the
wavelength of 0.8 µm and that the all parameters were recorded at
distances of more than 6 km from the plume sources.
Probability distribution functions of the asymmetry parameter.
Bocca Nuova (BNa, red) and Northeast (NECb, green) Etna craters plums,
respectively. (See notations in Fig. 2.)
As mentioned above, the ASIs of the degassing plumes are partitioned
into subclusters (see Fig. 3c). The ASIs of the NECa and NECb plumes are
spread within a cluster that is well distinguished from other ones. The
second coefficients Cj,2 of the BNa and BNb data are within the same
range of values. (We recall that the second vector ξ2
represents 70.4 % of the ASIs shape variability.) Consequently, the
asymmetry-parameter values of the Bocca Nuova plumes (BNa and BNb) are a
little bit higher compared to those of the Northeast Crater (NECa1,
NECa2, NECb), see Table 1. The histograms in Fig. 5 show probability
distribution functions of the asymmetry-parameter values computed from the
measured ASIs of the BNa and NECb plumes. These two distributions were
selected only by using larger data sets, i.e., higher statistical
significance. Generally speaking, the BNb and NECa distributions are close
to histograms of the BNa and NECb, respectively. As seen in Fig. 5,
the distributions are narrow, suggesting quite homogeneous aerosol plume
properties. The distinction between optical characteristics of the BN and
NEC plumes could result from the difference in chemical composition of
aerosols (Voigt et al., 2014a). Though completely independent, this
classification of the individual volcanic plumes is similar to results from
trace gas composition measurements of Voigt et al. (2014a) for plumes SV,
BNb, NECa, and NECb. In addition, the difference in the BN and NEC aerosol
emissions was underscored earlier by Allen et al. (2006), who studied
aerosol particles at the summit of Mt. Etna downwind from the degassing
vents, and by Scollo et al. (2012), who reported observations of the
Multiangle Imaging SpectroRadiometer. Martin et al. (2008) evidenced
persistent differences in the size distributions of sulfate aerosols between
the two main Etna summit plumes.
Discussion and inferences
Our results in Table 1 show that the spherical/nonspherical partitioning
ratio (SNR) value is of 100 % for the all considered cases of degassing
plumes. In other words, the best fits of the Polar Nephelometer data were
obtained with the model of spherical aerosol particles. It must be
emphasized that this result does not mean that aerosols were perfect
spheres. Since (i) the aerosol particles were quite small with respect to
the wavelength of 0.8 µm (see the effective diameter values in Table 1), (ii) the angular-scattering intensities (ASIs) were measured within the
limited range of angles for the relatively small number of θi,
(iii) the ASIs were averaged over an ensemble of particles, and (iv) the PN
data were affected by measurement errors, we only can conclude that the
aerosols of the degassing plumes were optically spherical (see, e.g.,
Dick et al., 1998). The term “optically spherical” should be considered
within the context of optical instrumentation. For example, the model of
spherical particles matches well the PN experimental data, and we believe
that the asymmetry-parameter estimates are trustworthy. At the same time, it
might be that polarization measurements, particularly in the backward
hemisphere, will reveal nonspherical features of degassing plume aerosols.
The retrievals indicate that the aerosol particles were either nonabsorbing
or weakly absorbing with an upper bound for the imaginary part of the
refractive index of 10-4. Variations of the phase functions for the
imaginary part within the interval [0; 10-4] are smaller than the
measurement errors (Verhaege et al., 2008).
The most important and somewhat unexpected result of this work is the fact
that the retrieved values of the real part n of the refractive index belong
to the interval from 1.35 to 1.38 (Table 1).
Degassing plume aerosols of Mt. Etna were probed at distances greater than
6 km from the sources, i.e., the volcanic craters. The recorded values of
the horizontal air speed at the flight altitudes were 5.9 m s-1 or lower.
Consequently, our measurements are related to aerosols that were generated
more than 16.5 min earlier. Large aerosols tend to settle quickly out of
the atmosphere. High initial concentrations of fine particles decay rapidly
in the atmosphere due to coagulation and dilution, so that measurements
depend on the distance from the source and the wind conditions (Ammann and
Burtscher, 1990). Thus, it is hardly possible to directly compare our in situ data
and characteristics of particles sampled on filters at the ground.
Nevertheless, some ideas on the chemical composition of degassing plume aerosols can be drawn from published results.
Electron microscopy analysis of particles in the range of diameters 5–100 nm showed significant levels of silicate nanoparticles in Mt. Etna
plumes (Ammann and Burtscher, 1990; Martin et al., 2008). As
mentioned above, the Dubovik code provides the possibility of evaluating the effects
of the ultrafine fraction, i.e., nanoparticles, on retrieval results. Our
simulations and tests led us to the following conclusions. The information
content of the PN data is inadequate for a correct estimation of the
ultrafine-fraction number concentration. Variations of that number
concentration within a large range of values do not affect the retrieved
values of the refractive index.
According to chromatographic analysis of ionic species of soluble particles
sampled on filters from degassing plumes near the crater rims of Mt. Etna
(Allen et al., 2006; Martin et al., 2008), aerosols are mainly composed of
sulfates. Fluorides, chlorides, and nitrates are present as well. Besides,
silicates were observed in Etna degassing plumes (Lefevre et al., 1986;
Martin et al., 2008). Generally, the values of the bulk refractive index at
λ=0.8 µm of sulfates, and other soluble inorganic species
presumed to form the degassing plumes belong to the interval 1.48–1.58 or
higher (see, e.g., Toon et al., 1976; Tang, 1996; Lide, 2010). The exception
is some alkali halides with n about 1.40 or lower. For example, the
refractive index values at λ=0.8 µm of NaF, KF, and MgF2
are 1.323, 1.36, and 1.375, respectively (Li, 1976). As for silicate
particles, they have quite large values of the bulk refractive index, e.g.,
n is about 1.46 for amorphous silica glass (see, e.g., Kitamura et al.,
2007), 1.52–1.59 or higher for feldspars (Lide, 2010), and about 1.56–1.99 or higher for garnets and other silicate minerals (Lide, 2010).
Refractive index dependence of phase functions. The range of the
Polar Nephelometer operating angles is indicated by the gray bar.
It is obvious that the retrieved values n (Table 1) are substantially lower
than the bulk refractive index of sulfates and other inorganic matter
presumed to form the degassing plumes. That result was thoroughly verified.
The Dubovik code provides the possibility of holding a fixed refractive index value
nfix while a size distribution is retrieved, and to compute scattering
characteristics for given optical constants and the retrieved size
distribution. As expected, the residuals Res increased when the fixed
value nfix of the refractive index deviated from the retrieved one. Two
important points are to be underscored. When nfix was higher than 1.40,
(i) the Res values significantly exceeded the measurement error level, (ii) the plots of the reconstructed angular-scattering coefficients, that is,
computed for nfix and the corresponding size distribution, clearly
manifested a systematic deviation from the plots of the measured ASIs. An
illustration of the systematic deviation is given in Fig. 6. The phase
functions, i.e., the normalized ASIs, were computed for the retrieved size
distribution of the NECb case (see Fig. 4) and a set of values of the real
part n of the refractive index. It is seen that the behavior of the curves
within the scattering-angles interval of 15–150∘ is sensitive to
the n value. Not only the slopes of the curves are different but the local
minimum shifts monotonically from 114∘ (n=1.33) to 124∘
(n=1.58). The gray bar in Fig. 6 indicates the range of the PN operating
angles. Thus, we can say that the accuracy of the refractive index
estimation is based on the functional behavior of measured ASIs. That
conclusion corroborates the results of sensitivity tests performed by
Verhaege et al. (2008) for ASIs measured within a limited range of
scattering angles. It was shown that despite the absence of aureole and
backward measurements the real part of the refractive index and the
microphysical parameters can be retrieved in the case of the low absorbing
particles.
It is unlikely that the relative concentration of alkali halides increased
at distances greater than 6 km from the sources so that their optical
properties became dominant. In our particular case, the hypothesis of
aqueous solutions or suspensions in the Mt. Etna plumes has to be rejected
as well because of the very low relative humidity at the flight altitudes
(Table 1). The measured RHw values (about 10 %) are much lower than the
efflorescence RHw of the overwhelming majority of the abovementioned
chemical species (see, e.g., Table 3, Martin, 2000). We recall that
efflorescence is a specific process in the more encompassing concept of
crystallization; it specifically involves water vapor (Martin, 2000). In
other words, the conditions were favorable even for homogeneous nucleation.
As for the chemical systems NaNO3/ H2O, NH4HSO4/ H2O,
and NH4NO3/ H2O that do not readily crystallize at the lowest
RHw values (Martin, 2000), the conditions were favorable for
heterogeneous nucleation due to significant levels of silicate nanoparticles
in the Mt. Etna plumes (Martin et al., 2008). The hypothesis of unsteady
state conditions was discarded in view of the data of the PCA analysis. For
example, the NECb plume was probed at distances from the craters between 5.9 and
47.7 km, respectively. Such distances correspond to the aerosol age between
16.5 min and 2 h. There is no trend in g values of the NECa plume.
A slight rise at the end of the NECb g series may be due to increased
measurement errors at low aerosol concentrations.
In view of the results above, the most reasonable conclusion to make is that
the aerosol particles of the degassing plumes were porous with air voids.
Therefore, the retrieved values n (Table 1) correspond to the refractive
index of the effective medium instead of the bulk one.
That conclusion is in agreement with data of transmission electron
microscopy of aerosol particles collected in the remote marine troposphere
(Pósfai et al., 1999). Figure 3a by Pósfai et al. (1999) is
especially noteworthy. It shows a quasi-spherical ammonium-sulfate aerosol
particle of about 1 µm diameter. The particle has the onion-like
structure with soot inclusions. Obenholzner et al. (2003) investigated the
micromorphology of aerosol particles from the passively degassing plume of
Popocatepetl volcano using the Field emission scanning electron microscopy
(FESEM). A large set of spherical particles were observed, including spongy,
i.e., porous aerosols and spheres, enclosing a dozen small crystals
Obenholzner et al. (2003). Moreover, porosity could be the cause of the
quite low values of the refractive index of the Eyjafjallajökull
volcanic aerosol retrieved from POLDER/PARASOL measurements (Waquet et al.,
2014).
Microwave analog experiments performed by Kolokolova and Gustafson (2001)
corroborate our conclusion as well. The measured ASIs of inhomogeneous
particles show reasonable agreement with effective medium approximations
(Kolokolova and Gustafson, 2001). The effective medium approximation (EMA),
along with the Maxwell Garnett mixing rule, has already been used in a
number of works to calculate optical properties of porous particles (see,
e.g., Voshchinnikov et al., 2007; Kylling et al., 2014). The question of EMA
applicability is discussed in detail by Mishchenko et al. (2016).
At a wavelength of 0.8 µm, the real part of the bulk refractive
index of sulfates, nitrates, and other inorganic matter presumed to form the
degassing plumes belongs to the range of about 1.48–1.58. As for the
imaginary part χ, it is very small, i.e., lower than 10-6 at the
wavelength of 0.8 µm (see, e.g., Gosse et al., 1997). We recall that
the aerosol particles are found to be either nonabsorbing or weakly
absorbing according to our retrievals. Thus, we assumed that χ=0.0 in the following estimations.
Considering the range of [1.48–1.58], we employed the Maxwell Garnett
mixing rule (Maxwell Garnett, 1904; see also Kolokolova and Gustafson, 2001)
to estimate the volume fraction f of the inorganic matter that leads to the
refractive index of the mixture about [1.35–1.38]. In other words, the
inorganic matter and the air voids were taken as the matrix and the
inclusions, respectively. We have obtained the interval of [0.65–0.82] as
the estimate for the volume fraction value. This means that aerosol
particles of the degassing plumes contained 18–35 % of air voids (in
terms of the total volume). The estimated volume fraction f and the
bulk-refractive index range of inorganic matter would lead to the refractive
index of aerosols about [1.43–1.53] if pores were filled up with water.
The last assessment is in agreement with the results by Waquet et al. (2014).
Our finding has the following consequences. (i) Climate models, optical
methods of remote sensing and optical instruments of particles counting have
to consider not the bulk- but the effective refractive index of volcanic
particles and aerosols in general. (ii) The volume fraction f of bulk matter
has to be taken into account in assessing the volcanic ash spreading and
loading. (iii) The volume fraction f has to be considered in aerosol sizing
instrumentation based on the inertial separation. (iv) Aerosols porosity
could be of importance for condensation and freezing processes.
Conclusions
Volcanic degassing plumes were probed on 30 September 2011 with in situ instruments
onboard the DLR Falcon research aircraft during the CONCERT experiment. The
plumes were spreading in the dry troposphere at temperatures above the
freezing point.
Aerosols of degassing plumes from the volcanoes Mt. Etna and Mt. Stromboli
are characterized by quite low values of the asymmetry parameter (between
0.6 and 0.75); their scattering features are clearly distinctive from
angular-scattering intensities of cirrus and contrails.
The measured and the retrieved size distributions of the degassing plume aerosols are in good agreement. The effective diameter of the aerosols was
within the range of 1.5–2.8 µm, the maximal diameter of particles
was lower than 20 µm.
Retrievals of aerosol properties revealed that the particles were
optically spherical; the estimated values of the real part of the
refractive index belong to the interval from 1.35 to 1.38, which is
substantially lower than the bulk refractive index of sulfates, nitrates, and
other inorganic matter presumed to form the degassing plumes.
That property leads to the conclusion that the aerosol particles of the
degassing plumes were porous with air voids. Our estimates, based on the
Maxwell Garnett mixing rule, suggest that aerosol particles of the degassing
plumes contained 18–35 % of air voids in terms of the total volume.
Data availability
The data from this study can be obtained by contacting the
corresponding author of this article.
The Supplement related to this article is available online at doi:10.5194/acp-16-11883-2016-supplement.
Acknowledgements
We thank the DLR flight department for excellent support during the CONCERT
campaign. The campaign was organized by the Research Group AEROTROP under
HGF contract VH-NG-309. Part of this work is funded by the Research Cluster
VAMOS at Johannes Gutenberg University, Mainz and by the German Science
Foundation DFG within SPP1294 HALO. J.-F. Gayet is grateful to DLR for
having provided a guest scientist opportunity at the Institut für Physik
der Atmosphäre during this study.Edited by: A. Petzold
Reviewed by: two anonymous referees
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