ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-7175-2017Spectral- and size-resolved mass absorption efficiency of mineral dust aerosols in the shortwave spectrum: a simulation chamber studyCaponiLorenzoFormentiPaolapaola.formenti@lisa.u-pec.frhttps://orcid.org/0000-0002-0372-1351MassabóDarioDi BiagioClaudiahttps://orcid.org/0000-0001-8273-6211CazaunauMathieuPanguiEdouardChevaillierServanneLandrotGautierAndreaeMeinrat O.https://orcid.org/0000-0003-1968-7925KandlerKonradPikethStuartSaeedThurayahttps://orcid.org/0000-0002-6540-4810SeibertDaveWilliamsEarleBalkanskiYveshttps://orcid.org/0000-0001-8241-2858PratiPaolohttps://orcid.org/0000-0002-8097-9460DoussinJean-Françoishttps://orcid.org/0000-0002-8042-7228Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), UMR CNRS 7583, Université Paris-Est Créteil and Université Paris Diderot,
Institut Pierre Simon Laplace, Créteil, FranceDepartment of Physics & INFN, University of Genoa, Genoa, ItalySynchrotron SOLEIL, L'Orme des Merisiers Saint-Aubin, FranceBiogeochemistry Department, Max Planck Institute for Chemistry, P.O. Box 3060, 55020 Mainz, GermanyInstitut für Angewandte Geowissenschaften, Technische Universität Darmstadt, Schnittspahnstr. 9, 64287 Darmstadt, GermanyClimatology Research Group, University of the Witwatersrand, Johannesburg, South AfricaScience Department, College of Basic Education, Public Authority for Applied Education and Training, Al-Ardiya, KuwaitSallyport Global, Phoenix, Arizona, USA Massachusetts Institute of Technology, Cambridge, Massachusetts, USALSCE, CNRS UMR8212, CEA, Université de Versailles Saint-Quentin, Gif-sur-Yvette, FranceGeology and Geophysics Department, King Saud University, Riyadh, Saudi ArabiaPaola Formenti (paola.formenti@lisa.u-pec.fr)16June20171711717571912January201725January20175May201718May2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/7175/2017/acp-17-7175-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/7175/2017/acp-17-7175-2017.pdf
This paper presents new laboratory measurements of the mass
absorption efficiency (MAE) between 375 and 850 nm for 12 individual
samples of mineral dust from different source areas worldwide and in two size
classes: PM10.6 (mass fraction of particles of aerodynamic diameter
lower than 10.6 µm) and PM2.5 (mass fraction of particles of
aerodynamic diameter lower than 2.5 µm). The experiments were
performed in the CESAM simulation chamber using mineral dust generated from
natural parent soils and included optical and gravimetric analyses.
The results show that the MAE values are lower for the PM10.6 mass
fraction (range 37–135 × 10-3 m2 g-1 at 375 nm) than for
the PM2.5 (range 95–711 × 10-3 m2 g-1 at 375 nm) and
decrease with increasing wavelength as λ-AAE, where the
Ångström absorption exponent (AAE) averages between 3.3 and 3.5, regardless of
size. The size independence of AAE suggests that, for a given size
distribution, the dust composition did not vary with size for this set of
samples. Because of its high atmospheric concentration, light absorption by
mineral dust can be competitive with black and brown carbon even during
atmospheric transport over heavy polluted regions, when dust concentrations
are significantly lower than at emission. The AAE values of mineral dust are
higher than for black carbon (∼ 1) but in the same range as
light-absorbing organic (brown) carbon. As a result, depending on the
environment, there can be some ambiguity in apportioning the aerosol
absorption optical depth (AAOD) based on spectral dependence, which is
relevant to the development of remote sensing of light-absorbing aerosols
and their assimilation in climate models. We suggest that the
sample-to-sample variability in our dataset of MAE values is related to
regional differences in the mineralogical composition of the parent soils.
Particularly in the PM2.5 fraction, we found a strong linear
correlation between the dust light-absorption properties and elemental iron
rather than the iron oxide fraction, which could ease the application and
the validation of climate models that now start to include the
representation of the dust composition, as well as for remote sensing of
dust absorption in the UV–vis spectral region.
Introduction
Mineral dust aerosols emitted by wind erosion of arid and semiarid soils
account for about 40 % of the total emitted aerosol mass per year at the
global scale (Knippertz and Stuut, 2014). The episodic but frequent
transport of intense mineral dust plumes is visible from spaceborne sensors because their high concentrations, combined with their ability to scatter and
absorb solar and thermal radiation, give rise to the highest registered
values of aerosol optical depth (AOD) on Earth (Chiapello, 2014). The
instantaneous radiative efficiency of dust particles, that is, their
radiative effect per unit AOD, is of the order of tens to hundreds of
W m-2 AOD-1 in the solar spectrum and of the order of tens of
W m-2 AOD-1 in the thermal infrared (e.g., Haywood et al.,
2003; di Sarra et al., 2011; Slingo et al., 2006; and the compilation of
Highwood and Ryder, 2014). Albeit partially compensated by the radiative
effect in the thermal infrared, the global mean radiative effect of mineral
dust in the shortwave is negative both at the surface and the top of the
atmosphere (TOA) and produces a local warming of the atmosphere (Boucher et
al., 2013). There are numerous impacts of dust on global and regional
climate, which ultimately feed back on wind speed and vegetation and
therefore on dust emission (Tegen and Lacis, 1996; Solmon et al., 2008;
Pérez et al., 2006; Miller et al., 2014). Dust particles perturb the
surface air temperature through their radiative effect at TOA, can increase
the atmospheric stability (e.g., Zhao et al., 2011) and might affect
precipitation at the global and regional scale (Solmon et al., 2008; Xian,
2008; Vinoj et al., 2014; Miller et al., 2014, and references therein).
All models indicate that the effect of mineral dust on climate has great
sensitivity to their shortwave absorption properties (Miller et al., 2004;
Lau et al., 2009; Loeb and Su, 2010; Ming et al., 2010; Perlwitz and Miller,
2010). Absorption by mineral dust started receiving a great deal of interest
when spaceborne and ground-based remote-sensing studies (Dubovik et al.,
2002; Colarco et al., 2002; Sinyuk et al., 2003) suggested that mineral dust
was less absorbing than had been suggested by in situ observations (e.g.,
Patterson et al., 1977; Haywood et al., 2001), particularly at wavelengths
below 600 nm. Balkanski et al. (2007) showed that lowering the dust
absorption properties to an extent that reconciles them both with the
remote-sensing observations and the state of knowledge of the mineralogical
composition allowed calculating the clear-sky shortwave radiative effect of
dust in agreement with satellite-based observations. A significant number of
observations have quantified the shortwave light-absorbing properties of
mineral dust both by direct measurements (Alfaro et al., 2004; Linke et al.,
2006; Osborne et al., 2008; McConnell et al., 2008; Derimian et al., 2008;
Yang et al., 2009; Müller et al., 2009; Petzold et al., 2009; Formenti
et al., 2011; Moosmüller et al., 2012; Wagner et al., 2012; Ryder al.,
2013a; Utry et al., 2014; Denjean et al., 2016a, b) and indirectly by
quantifying the amount and the speciation of the light-absorbing compounds
in mineral dust, principally iron oxides (Lafon et al., 2004, 2006; Lazaro
et al., 2008; Derimian et al., 2008; Zhang et al., 2008; Kandler et al.,
2007, 2009, 2011; Formenti et al., 2014a, b).
However, existing data are often limited to a single wavelength, which
moreover is not the same for all experiments. Also, frequently they do not
represent the possible regional variability of the dust absorption, either
because they are obtained from field measurements integrating the
contributions of different source regions or, conversely, by laboratory
investigations targeting samples from a limited number of locations. This
might lead to biases in the data. Indeed, iron oxides in mineral dust,
mostly in the form of hematite (Fe2O3) and goethite (Fe(O)OH),
have specific absorption bands in the UV–vis spectrum (Bédidi and
Cervelle, 1993) and have a variable content depending on the soil
mineralogy of the source regions (Journet et al., 2014).
In this study, experiments on 12 aerosol samples generated from natural
parent top soils from various source regions worldwide were conducted with a
large atmospheric simulation chamber. We present a new evaluation of the
ultraviolet to near-infrared (375–850 nm) light-absorbing properties of
mineral dust by investigating the size-segregated mass absorption efficiency
(MAE, units of m2 g-1) and its spectral dependence, widely used in
climate models to calculate the direct radiative effect of aerosols.
Instruments and methods
At a given wavelength, λ, MAE is defined as the ratio of the aerosol
light-absorption coefficient babs(λ) (units of m-1)
and its mass concentration (in µg m-3):
MAE(λ)=babs(λ)massconcentration.
MAE values for mineral dust aerosol are expressed in 10-3 m2 g-1.
The spectral dependence of the aerosol absorption coefficient babs(λ)
is described by the power-law relationship
babs(λ)∼λ-AAE,
where the AAE is the Ångström absorption exponent, representing the
negative slope of babs(λ) in a log–log plot (Moosmüller
et al., 2009):
AAE=-dlnbabs(λ)dln(λ).
The CESAM (Experimental Multiphasic
Atmospheric Simulation Chamber)
The experiments in this work have been performed in the 4.2 m3
stainless-steel CESAM (Wang et al., 2011). The
CESAM has been extensively used in recent years to simulate, at sub-
and super-saturated conditions, the formation and properties of aerosols at
concentration levels comparable to those encountered in the atmosphere
(Denjean et al., 2015a, b; Brégonzio-Rozier et al., 2015, 2016; Di Biagio et al., 2014, 2017).
CESAM is a multi-instrumented platform, equipped with 12 circular
flanges to support its analytical environment. Basic instrumentation
comprises sensors to measure the temperature, pressure and relative humidity
within the chamber (two manometers MKS Baratrons® (622A and 626A)
and a HMP234 Vaisala® humidity and temperature sensor). The
particle size distribution is routinely measured by a combination of (i) a
scanning mobility particle sizer (SMPS, mobility diameter range 0.02–0.88 µm),
composed of a differential mobility analyzer (DMA, TSI Inc.
model 3080) and a condensation particle counter (CPC, TSI Inc. model 3772);
(ii) a SkyGrimm optical particle counter (Grimm Inc., model 1.129, optical
equivalent diameter range 0.25–32 µm); and (iii) a WELAS optical
particle counter (PALAS, model 2000, optical equivalent diameter range
0.5–47 µm). Full details of operations and data treatment of the
particle counters are provided in Di Biagio et al. (2017).
Filter sampling
Three filter samples per top-soil sample were collected on different types
of substrate based on the analysis to be performed. Sampling dedicated to
the determination of the aerosol mass concentration by gravimetric analysis
and the measurement of the absorption coefficients by optical analysis was
performed on 47 mm quartz membranes (Pall TissuquartzTM,
2500 QAT-UP). Two samples were collected in parallel. The first quartz membrane
sample (“total”) was collected without a dedicated size cutoff using an
in-house-built stainless-steel sampler operated at 5 L min-1. However,
as detailed in Di Biagio et al. (2017), the length of the sampling line from
the intake point in the chamber to the filter entrance was 50 cm, resulting
in a 50 % cutoff of the transmission efficiency at 10.6 µm
particle aerodynamic diameter. This fraction is therefore indicated as
PM10.6 in the following discussion. The second quartz membrane sample
was collected using a four-stage DEKATI impactor operated at a flow rate of
10 L min-1 to select the aerosol fraction of particles with aerodynamic
diameter smaller than 2.5 µm, indicated as PM2.5. Sampling for
the analysis of the iron oxide content was performed on polycarbonate
filters (47 mm Nuclepore, Whatman; pore size 0.4 µm) using the same
sample holder as used for the total quartz filters and therefore
corresponding to the PM10.6 mass fraction. Samples were collected at a
flow rate of 6 L min-1. All flow rates were monitored by a thermal mass
flowmeter (TSI Inc., model 4140). These samples were also used to determine
the elemental composition (including Fe) and the fraction of iron oxides in
the total mass.
The multi-wavelength absorbance analyzer (MWAA)
The aerosol absorption coefficient, babs(λ), at five wavelengths
(λ= 375, 407, 532, 635 and 850 nm) was measured by in situ analysis of
the quartz filter samples using the MWAA,
described in detail in Massabò et al. (2013, 2015).
The MWAA performs a nondestructive scan of the quartz filters at
64 different points, each ∼ 1 mm2 wide. It measures the
light transmission through the filter as well as backscattering at two
different angles (125 and 165∘). This is necessary to
constrain the multiple scattering effects occurring within the
particle-filter system. The measurements are used as input to a radiative
transfer model (Hänel, 1987, 1994) as implemented by Petzold and
Schönlinner (2004) for the multi-angle absorption photometry (MAAP)
measurements. In this model, a two-stream approximation is applied (Coakley
and Chylek, 1975), in which the fractions of hemispherical backscattered
radiation with respect to the total scattering for collimated and diffuse
incident radiation are approximated on the basis of the Henyey–Greenstein
scattering phase function (Hänel, 1987). This approximation assumes a
wavelength-independent asymmetry parameter (g) set to 0.75, appropriate for
mineral dust (Formenti et al., 2011; Ryder et al., 2013b). The total
uncertainty, including the effects of photon counting and the deposit
inhomogeneity, on the absorption coefficient measurement is estimated at
8 % (Petzold and Schönlinner, 2004; Massabò et al., 2013).
Characteristics of the standards used for the quantification of the
iron oxides in the XAS analysis.
StandardStoichiometric formulaOriginIllite of Puy(Si3.55Al0.45)(Al1.27Fe0.36Mg0.44)O10(OH)2(Ca0.01Na0.01K0.53X(I)0.12)Puy, FranceGoethiteFeO OHMinnesotaHematiteFe2O3NigerMontmorillonite(Na,Ca)0,3(Al,Mg)2Si4O10(OH)2⋅n(H2O)WyomingNontroniteNa0.3Fe2(Si,Al)4O10(OH)2⋅nH2OPennsylvaniaGravimetric analysis
The aerosol mass deposited on the filters (µg) was obtained by
weighing the quartz filter before and after sampling, after a period of 48 h of conditioning in a room with controlled atmospheric conditions
(temperature ∼ 20 ± 1 ∘C; relative
humidity ∼ 50 ± 5 %). Weighing is performed with
an analytical balance (Sartorius model MC5, precision of 1 µg) and
repeated three times to control the statistical variability of the
measurement. Electrostatic effects are removed by exposing the filters,
prior weighing, to a de-ionizer. The error in the measured mass is estimated
at 1 µg, including the repetition variability. The aerosol mass
concentration (µg m-3) is obtained by dividing the mass
deposited on the filter to the total volume of sampled air (m3)
obtained from the mass flowmeter measurements (±5 %). The percent error
on mass concentrations is estimated to 5 %.
Elemental concentrations for the major constituents of mineral dust (Na, Mg,
Al, Si, P, S, Cl, K, Ca, Fe, Ti, Mn) were obtained by wavelength-dispersive
X-ray fluorescence (WD-XRF) of the Nuclepore filters using a PW-2404
spectrometer by Panalytical. Excitation X-rays are produced by a Coolidge
tube (Imax= 125 mA, Vmax= 60 kV) with an Rh anode; the
primary X-ray spectrum can be controlled by inserting filters (Al, at
different thickness) between the anode and the sample. Each element was
analyzed three times, with specific conditions (voltage, tube filter,
collimator, analyzing crystal and detector). Data collection was controlled
by the SuperQ software provided with the instrument. The elemental mass
thickness (µg cm-2), that is, the analyzed elemental mass per
unit surface, was obtained by comparing the elemental yields with a
sensitivity curve measured in the same geometry on a set of certified mono-
or bi-elemental thin layer standards by Micromatter Inc. The certified
uncertainty of the standard deposit (±5 %) determines the lower
limit of the uncertainty of the measured elemental concentrations, which
ranges between 8 and 10 % depending on the element considered. Thanks
to the uniformity of the aerosol deposit on the filters, the atmospheric
elemental concentrations (µg m-3) were calculated by multiplying
the analyzed elemental mass thickness by the ratio between the collection
and analyzed surfaces of each sample (41 and 22 mm, respectively), then
dividing by the total sampled volume (m3). Finally, concentrations of
light-weight elements (atomic number Z< 19) were corrected for the
underestimation induced by the self-absorption of the emitted soft X-rays
inside aerosol particles according to Formenti et al. (2010).
Additional XRF analysis of the quartz filters was performed both in the
PM10.6 and the PM2.5 fractions to verify the absence of biases
between the experiments dedicated to the determination of particle
composition and those where the optical properties were measured.
Iron oxide content
The content and the mineralogical speciation of the iron oxides, also
defined as free iron, i.e., the fraction of iron that is not in the crystal
lattice of silicates (Karickhoff and Bailey, 1973), was determined by XANES
(X-ray absorption near-edge structure) in the Fe K-range (Kα,
7112 eV) at the SAMBA (Spectroscopies Applied to Materials based
on Absorption) beamline at the SOLEIL synchrotron facility in Saclay, France
(Briois et al., 2011). The position and shape of the K pre-edge and edge
peaks were analyzed as they depend on the oxidation state of iron and the
atomic positions of the neighboring ions, mostly O+ and OH-.
As in Formenti et al. (2014b), samples were mounted in an external setup
mode. A Si(220) double-crystal monochromator was used to produce a
monochromatic X-ray beam, which was 3000 × 250 µm2 in size at
the focal point. The energy range was scanned from 6850 to 7800 eV at a
step resolution varying between 0.2 eV in proximity to the Fe K absorption
edge (at 7112 eV) to 2 eV in the extended range. Samples were analyzed in
fluorescence mode without prior preparation. One scan acquisition lasted
approximately 30 min and was repeated three times to improve the signal-to-noise ratio.
The same analytical protocol was applied to five standards of
Fe(III)-bearing minerals (Table 1), including iron oxides (hematite,
goethite) and silicates (illite, montmorillonite, nontronite). The standard
spectra were used to deconvolute the dust sample spectra to quantify the
mineralogical status of iron. The linear deconvolution was performed with
the Athena IFEFFIT freeware analysis program (Ravel and Newville, 2005).
This provided the proportionality factors, αi, representing the
mass fraction of elemental iron to be assigned to the ith standard mineral.
In particular, the values of αhem and αgoe
represent the mass fractions of elemental iron that can be attributed to
hematite and goethite, and αFeox
(αhem+αgoe) is
the mass fraction of elemental iron that can be attributed to iron oxides.
Calculation of the iron oxide content
The measured elemental concentrations obtained by XRF
are expressed in the form of elemental oxides and summed to estimate the
total mineral dust mass concentration MCdust according to the equation
from Lide (1992):
MCdust=1.12×1.658[Mg]+1.889[Al]+2.139[Si]+1.399[Ca]+1.668[Ti]+1.582[Mn]+(0.5×1.286+0.5×1.429+0.47×1.204)[Fe].
The relative uncertainty in MCdust, estimated from the analytical error
in the measured concentrations, does not exceed 6 %. As it will be
explained in the Results section (Sect. 3.1), the values of MCdust
estimated from Eq. (4) were found in excellent agreement with the
measured gravimetric mass on the filters.
The fractional mass ratio (in %) of elemental iron (MRFe%) with
respect to the total dust mass concentration, MCdust, is then calculated as
MRFe%=[Fe]MCdust×100.
The mass concentration of iron oxides or free iron (MCFeox),
representing the fraction of elemental iron in the form of hematite and
goethite (Fe2O3 and FeOOH, respectively), is equal to
MCFeox=MChem+MCgoe,
where MChem and MCgoe are the total masses of hematite and goethite.
These can be calculated from the values αhem and αgoe
from XANES analysis, which represent the mass fractions of
elemental iron attributed to hematite and goethite, as
MChem=αhem×[Fe]0.70,MCgoe=αgoe×[Fe]0.63,
where the values of 0.70 and 0.63 represent the mass molar fractions of Fe
in hematite and goethite, respectively. The relative errors of MChem
and MCgoe are obtained from the uncertainties of the values
of αhem and αgoe from XANES analysis (less than 10 %).
The mass ratio of iron oxides (MRFeox%) with respect to the total
dust mass can then be calculated as
MRFeox%=MCFeox×MRFe%.
Experimental protocol
At the beginning of each experiment, the chamber was evacuated to
10-4–10-5 hPa. Then, the reactor was filled with a mixture of
80 % N2 and 20 % O2 at a pressure slightly exceeding the
current atmospheric pressure, in order to avoid contamination from ambient
air. The experiments were conducted at ambient temperature and at a relative
humidity < 2 %. As in Di Biagio et al. (2014, 2017), dust aerosols
were generated by mechanical shaking of the parent soils, previously sieved
to < 1000 µm and dried at 100 ∘C for about 1 h to
remove any residual humidity. About 15 g of soil was placed in a Buchner
flask and shaken for about 30 min at 100 Hz by means of a sieve shaker
(Retsch AS200). The dust particles produced by the mechanical shaking,
mimicking the saltation processing that soils experience when eroded by
strong winds, were injected in the chamber by flushing the flask with
N2 at 10 L min-1 for about 10–15 min, whilst continuing shaking
the soil. Di Biagio et al. (2014, 2017) demonstrated the realism of the
generation system concerning the composition and the size distribution of
the generated dust with respect to the properties of mineral dust in the atmosphere.
The dust remained suspended in the chamber for approximately 120 min thanks
to the four-wheel fan located in the bottom of the chamber body. Previous
measurements at the top and bottom of the chamber showed that the fan
ensures a homogeneous distribution of the dust starting approximately 10 min after the end of the injection (Di Biagio et al., 2014).
To compensate for the air extracted from the chamber by sampling, a
particle-free flow of N2/O2, regulated in real time as a function
of the total volume of sampled air, was re-injected in the chamber. To avoid
excessive dilution the flow was limited to 20 L min-1. Two experiments
per soil type were conducted: a first experiment for sampling on the
Nuclepore polycarbonate filters (determination of the elemental composition
and the iron oxide fraction) and in situ measurements of the infrared optical
constants (Di Biagio et al., 2017), and a second experiment sampling on
total quartz filter and impactor for the study of dust MAE presented in this paper.
Figure 1 illustrates as typical example the time series of the aerosol mass
concentration during the two experiments conducted for the Libyan sample.
The comparison demonstrates the repeatability of the dust concentrations,
both in absolute values and in temporal dynamics. It also shows that the
mass concentrations decreased very rapidly by gravitational settling within
the first 30 min of the experiment (see also the discussion in Di Biagio
et al., 2017), after which concentrations only decrease by dilution. The
filter sampling was started after this transient phase, and then continued
through the end of the experiments, in order to collect enough dust on the
filter membranes for subsequent chemical analysis. Blank samples were
collected before the start of the experiments by placing the holders loaded
with filter membranes in line with the chamber and by flushing them for a
few seconds with air coming from the chamber.
At the end of each experimental series with a given soil sample, the chamber
was manually cleaned in order to remove carry-over caused by resuspension of
particles deposited to the walls. Background concentrations of aerosols in
the chamber vary between 0.5 and 2.0 µg m-3, i.e., a factor
of 500 to 1000 below the operating conditions.
Geographical information on the soil samples used in this work.
GeographicalSampleDesert areaGeographicalareacoordinatesSaharaMoroccoEast of Ksar Sahli31.97∘ N, 3.28∘ WLibyaSebha27.01∘ N, 14.50∘ EAlgeriaTi-n-Tekraouit23.95∘ NN, 5.47∘ ESahelMaliDar el Beida17.62∘ N, 4.29∘ WBodéléBodélé depression17.23∘ N, 19.03∘ EMiddle EastSaudi ArabiaNefud27.49∘ N, 41.98∘ EKuwaitKuwaiti29.42∘ N, 47.69∘ ESouthern AfricaNamibiaNamib21.24∘ S, 14.99∘ EEastern AsiaChinaGobi39.43∘ N, 105.67∘ ENorth AmericaArizonaSonoran33.15∘ N, 112.08∘ WSouth AmericaPatagoniaPatagonia50.26∘ S, 71.50∘ WAustraliaAustraliaStrzelecki31.33∘ S, 140.33∘ E
Time series of aerosol mass concentration in the chamber for two
companion experiments (Libyan dust). Experiment 1 (top panel) was dedicated
to the determination of the chemical composition (including iron oxides) by
sampling on polycarbonate filters. Experiment 2 (bottom panel) was dedicated
to the determination of the absorption optical properties by sampling on
quartz filters.
Locations (red stars) of the soil and sediment samples used to
generate dust aerosols.
Results and discussion
The geographical location of the soil collection sites is shown in Fig. 2,
and the coordinates are summarized in Table 2. The selection of these soils
and sediments was made out of 137 individual top-soil samples collected in
major arid and semiarid regions worldwide and representing the
mineralogical diversity of the soil composition at the global scale. As
discussed in Di Biagio et al. (2017), this large sample set was reduced to a
set of 19 samples representing the mineralogical diversity of the soil
composition at the global scale and based on their availability in sufficient
quantities for injection in the chamber. Because some of the experiments did
not produce enough dust to perform good-quality optical measurements, in
this paper we present a set of 12 samples distributed worldwide but
mostly from northern and western Africa (Libya, Algeria, Mali,
Bodélé) and the Middle East (Saudi Arabia and Kuwait). Individual
samples from the Gobi in Eastern Asia, the Namib, the
Strzelecki desert in Australia, the Patagonian Desert in South America and
the Sonoran Desert in Arizona were also investigated.
Chemical characterization of the dust aerosols in PM10.6
and PM2.5 (in parentheses) size fractions. Columns 3 and 4 give the
Si / Al and Fe / Ca elemental ratios obtained from X-ray fluorescence analysis.
The uncertainty of each individual value is estimated to be 10 %. Column 5
shows MRFe%, the fractional mass of elemental iron with respect to the
total dust mass concentration (uncertainty 10 %). Column 5 reports
MRFe%, the mass fraction of iron oxides with respect to the total dust
mass concentration (uncertainty 15 %). For PM2.5 the determination of
the Si / Al ratio is impossible due to the composition of the filter membranes (quartz).
A total of 41 filters including 15 polycarbonate filters (12 samples and
3 blanks) and 25 quartz filters (12 for the total fraction, 10 for the fine
fraction and 3 blanks) were collected for analysis. The dust mass
concentration found by gravimetric analysis varied between 50 and 5 µg m-3,
in relatively good agreement with the dust mass
concentrations, MCdust, from Eq. (4), based on XRF analysis: the slope
of the linear regression between the calculated and the gravimetric values
of MCdust is 0.90 with R2= 0.86. Di Biagio et al. (2017) showed
that clays are the most abundant mineral phases, together with quartz and
calcite, and that significant variability exists as function of the
compositional heterogeneity of the parent soils. Here we use the Fe / Ca and
Si / Al elemental ratios obtained from XRF analysis to discriminate the origin
of dust samples. These ratios have been extensively used in the past to
discriminate the origin of African dust samples collected in the field
(Chiapello et al., 1997; Formenti et al., 2011, 2014a). The values obtained
during our experiments are reported in Table 3. There is a very good
correspondence between the values obtained for the Mali, Libya, Algeria and
(to a lesser extent) Morocco experiments to values found in environmental
aerosol samples by Chiapello et al. (1997) and Formenti et al. (2011,
2014a). These authors indicate that dust from local erosion of Sahelian
soils, such as from Mali, has Si / Al ratios in the range of 2–2.5 and Fe / Ca
ratios in the range of 3–20, depending on the time proximity to the erosion
event. Dust from sources in the Sahara, such as Libya and Algeria, shows
Si / Al ratios in the range of 2–3 and Fe / Ca ratios in the range of 0.7–3,
whereas dust from Morocco has Si / Al ratios around 3 and Fe / Ca ratios
around 0.4. The only major difference is observed for the Bodélé
experiment, for which the Fe / Ca ratio is enriched by a factor of 6 with
respect to the values of 1 found during the field observations (Formenti et
al., 2011, 2014a). This could reflect the fact that the Bodélé
aerosol in the chamber is generated from a sediment sample and not from a
soil. As a matter of fact, the Bodélé sediment sample consists of a
very fine powder which becomes very easily airborne. This powder is likely
to be injected in the chamber with little or no size fractionation. Hence,
the aerosol generated from it should have a closer composition to the
original powder than the other samples. In contrast, Bristow et al. (2010)
and Moskowitz et al. (2016) showed that the iron content and
speciation of the Bodélé sediments is very heterogeneous at the
source scale. For samples from areas other than northern Africa, the largest
variability is observed for the Fe / Ca values, ranging from 0.1 to 8, whereas
the Si / Al ratio varied only between 2.5 and 4.8. In this case, values are
available in the literature for comparison (e.g., Cornille et al., 1990;
Reid et al., 1994; Eltayeb et al., 2001; Lafon et al., 2006; Shen et al.,
2007; Radhi et al., 2010, 2011; Formenti et al., 2011, 2014a; Scheuvens et
al., 2013, and references within). Values in the PM2.5 fraction are
very consistent with those obtained in the PM10.6: their linear
correlation has a slope of 1.03 (±0.05) and a R2 equal to 0.97,
suggesting that the elemental composition is relatively size independent.
Spectral dependence of the MAE values for the samples investigated
in this study in the PM10.6(a) and in the PM2.5(b)
mass fractions.
The mass fraction of total Fe (MCFe% from Eq. 5), also
reported in Table 3, ranged from 2.8 % (Namibia) to 7.3 % (Australia). These
are in the range of values reported in the literature, taking into account
that differences might be also due to the method (direct
measurement/calculation) and/or the size fraction over which the total dust
mass concentration is estimated (Chiapello et al., 1997; Reid et al., 1994;
2003; Derimian et al., 2008; Formenti et al., 2001, 2011, 2014a; Scheuvens
et al., 2013). The agreement of MCFe% values obtained by the XRF
analysis of polycarbonate filters (Eq. 5) and those obtained from the
XRF analysis of the quartz filters, normalized to the measured gravimetric
mass, is well within 10 % (the percent error of each estimate). Exceptions
are the samples from Bodélé and Algeria, for which the values
obtained from the analysis of the quartz filters are significantly lower
than those obtained from the Nuclepore filters (3.1 % vs. 4.1 % for
Bodélé and 4.3 % vs. 6.8 % for Algeria). We treat that as an
additional source of error in the rest of the analysis and add it to the
total uncertainty. In the PM2.5 fraction, the content of iron is more
variable, ranging from 4.4 % (Morocco) to 33.6 % (Mali), showing a size
dependence. A word of caution to this conclusion is that the two estimates
are not necessarily consistent in the way that the total dust mass is
estimated (from Eq. 4 for the PM10.6 fraction and by gravimetric
weighing for the PM2.5).
Finally, between 11 and 47 % of iron in the samples can be attributed to
iron oxides, in variable proportions between hematite and goethite. The iron
oxide fraction of total Fe in this study is at the lower end of the range
(36–72 %) estimated for field dust samples of Saharan/Sahelian origin
(Formenti et al., 2014b). The highest value of Formenti et al. (2014b),
obtained for a sample of locally emitted dust collected at the Banizoumbou
station in the African Sahel, is anyhow in excellent agreement with the
value of 62 % obtained for an experiment (not shown here) using a soil
collected in the same area. Likewise, the proportions between hematite and
goethite (not shown) are reproduced, showing that goethite is more abundant
than hematite. The mass fraction of iron oxides (MRFeox%), estimated
from Eq. (8) and shown in Table 3, ranges between 0.7 % (Kuwait) and
3.6 % (Australia), which is in the range of available field estimates
(Formenti et al., 2014a; Moskowitz et al., 2016). For China, our value
of MRFeox% is lower by almost a factor of 3 compared to that obtained
for dust of the same origin by Alfaro et al. (2004) (0.9 % against
2.8 %), whereas for a sample from Niger (not considered in this study) our
estimates and that by Alfaro et al. (2004) agree perfectly (5.8 %). A
possible underestimate of the iron oxide fraction for samples other than
those from the Sahara–Sahel area could be due to the fact that – opposite to
the experience of Formenti et al. (2014b) – the linear deconvolutions of the
XANES spectra were not always satisfactory (see Fig. S1 in the
Supplement). This resulted in a significant residual between the observed
and fitted XANES spectra. In fact, the mineralogical reference for hematite
is obtained from a soil from Niger (Table 1) and might not be fully suitable
for representing aerosols of different origins. Additional differences could
arise from differences in the size distributions of the generated aerosol.
As a matter of fact, the number fraction of particles in the size classes
above 0.5 µm in diameter is different in the dust aerosol generated
in the Alfaro et al. (2004) study compared to ours. In the study by Alfaro
et al. (2004), the number fraction of particles is lowest in the 0.5–0.7 size
class and highest between 1 and 5 µm. In contrast, in our study
the number fraction is lowest in the 1–2 µm size range and highest
between 0.5 and 0.7 µm. These differences could be due to
differences either in the chemical composition or in the total mass in the
denominator of Eq. (8).
Mass absorption efficiency (MAE, 10-3 m2 g-1) and
Ångström absorption exponent (AAE) in the PM10.6 and PM2.5
size fractions. Absolute errors are in parentheses.
a Müller et al. (2009).
b Petzold et al. (2009).
c Linke et al. (2006). d Alfaro et al. (2004).
e Fialho et al. (2005). f Denjean et al. (2016b).
g At 528 nm. h At 652 nm. i Yang et
al. (2009).
j At 375 nm. k At 470 nm. l At 590 nm.
m Mossmüller et al. (2012).
Illustration of the links between the MAE values and the dust
chemical composition found in this study. Left column panels, from top to bottom:
linear regression between the MAE values in the range from 375 to 850 nm and
the fraction of elemental iron relative to the total dust mass (MRFe%)
in the PM10.6 fraction; middle column panels: same as left column panels but for the
mass fraction of iron oxides relative to the total dust mass (MRFeox%)
in the PM10.6 size fraction; right column panels: same as left column panels but
in the PM2.5 size fraction.
Spectral and size variability of the mass absorption efficiency
The spectral MAEs at 375, 407, 532, 635 and
850 nm for the PM10.6 and the PM2.5 dust fractions are summarized
in Table 4 and displayed in Fig. 3. Regardless of particle size, the MAE
values decrease with increasing wavelength (almost 1 order of magnitude
between 375 and 850 nm) and display a larger variability at shorter
wavelengths. The MAE values for the PM10.6 range from 37 (±3) × 10-3
to 135 (±11) × 10-3 m2 g-1
at 375 nm and from 1.3 (±0.1) × 10-3
to 15 (±1) × 10-3 m2 g-1 at 850 nm. Maxima
are found for the Australia and Algeria samples, whereas the minima are for
Bodélé and Namibia at, respectively, 375 and 850 nm. In the
PM2.5 fraction, the MAE values range from 95 (±8) × 10-3
to 711 (±70) × 10-3 m2 g-1 at 375 nm
and from 3.2 (±0.3) × 10-3 to
36 (±3) × 10-3 m2 g-1
at 850 nm. Maxima at both 375 and 850 nm are
found for the Morocco sample, whereas the minima are for Algeria and
Namibia, respectively. The MAE values for mineral dust resulting from this
work are relatively in good agreement with the estimates available in the
literature (Alfaro et al., 2004; Linke et al., 2006; Yang et al., 2009;
Denjean et al., 2016b), reported in Table 5. For the China Ulah Buhn sample,
Alfaro et al. (2004) reported 69.1 × 10-3 and
9.8 × 10-3 m2 g-1
at 325 and 660 nm, respectively. The former is lower than the
value of 99 × 10-3 m2 g-1 that we obtain by extrapolating
our measurement at 375 nm. Likewise, our values for the Morocco sample are
higher than reported by Linke et al. (2006) at 266 and 660 nm. Conversely,
the agreement with the estimates of Yang et al. (2009) for mineral dust
locally re-suspended in Xianghe, near Beijing (China), is very good at all
wavelengths between 375 and 880 nm. As expected, the MAE values for mineral
dust resulting from this work are almost 1 order of magnitude smaller than
for other absorbing aerosols. For black carbon, MAE values are in the range
of 6.5–7.5 m2 g-1 at 850 nm (Bond and Bergstrom, 2006;
Massabò et al., 2016) and decrease in a linear way with the logarithm
of the wavelength. For brown carbon, the reported MAE range between
2.3 and 7.0 m2 g-1 at 350 nm (Chen and Bond, 2010; Kirchstetter et al.,
2004; Massabò et al., 2016), 0.05 and 1.2 m2 g-1 at 440 nm
(Wang et al., 2016) and 0.08 and 0.72 m2 g-1 at 550 nm (Chen and Bond, 2010).
The analysis of Table 4 indicates that, at every wavelength, the MAE values
in the PM2.5 fraction are equal or higher than those for PM10.6.
The PM2.5/ PM10.6 MAE ratios reach values of 6 for the Mali sample
but are mostly in the range 1.5–3 for the other aerosols. The values
decrease with wavelength up to 635 nm, whereas at 850 nm they have values
comparable to those at 375 nm. The observed size dependence of the MAE
values is consistent with the expected behavior of light absorption of
particles in the Mie and geometric optical regimes that are relevant for the
two size fractions. Light absorption of particles of sizes smaller or
equivalent to the wavelength is proportional to their bulk volume, whereas
for larger particles absorption occurs on their surface only (Bohren and
Huffman, 1983). In contrast, the size-resolved measurements of Lafon
et al. (2006) show that the proportion (by volume) of iron oxides might be
higher in the coarse than in the fine fraction, which would counteract the
size-dependence behavior of MAE. To validate the observations, we calculated
the spectrally resolved MAE values in the two size fractions using the Mie
code for homogeneous spherical particles (Bohren and Huffman, 1983) and the
number size distribution estimated by Di Biagio et al. (2017) and averaged
over the duration of filter sampling. We estimated the dust complex
refractive index as a volume-weighted average of a non-absorbing dust
fraction having the refractive index of kaolinite, the dominant mineral in
our samples (see Di Biagio et al., 2017), from Egan et Hilgeman (1979) and
an absorbing fraction estimated from the mass fraction of iron oxides and
having the refractive index of hematite (Bédidi and Cervelle, 1993). The
results of this calculation indicate that the observed size-dependent
behavior is well reproduced at all wavelengths, even in the basic hypothesis
that the mineralogical composition does not change with size. The only
exception is 850 nm, where at times the PM2.5/ PM10.6 MAE ratio is
much higher than expected theoretically. We attribute that to the relatively
high uncertainty affecting the absorbance measurements at this wavelength,
where the signal-to-noise ratio is low. Indeed, the two sets of values (MAE
in the PM2.5 fraction and MAE in the PM10.6 fraction) are not
statistically different according to a two-pair t test (0.01 and 0.05 level
of confidence), confirming that any attempt of differentiation of the size
dependence at this wavelength would require a stronger optical signal.
The analysis of the spectral dependence, using the power-law function fit
(Eq. 2), provides the values of the AAE,
also reported in Table 4. Contrary to the MAE values, there is no
statistically significant size dependence of the AAE values, ranging
from 2.5 (±0.2) to 4.1 (±0.3), with an average of 3.3 (±0.7),
for the PM10.6 size fraction and between 2.6 (±0.2)
and 5.1 (±0.4), with an average of 3.5 (±0.8), for the PM2.5
fraction. Our values are in the range of those published in the literature
(Fialho et al., 2005; Linke et al., 2006; Müller et al., 2009; Petzold
et al., 2009; Yang et al., 2009; Weinzierl et al., 2011; Moosmüller et
al., 2012; Denjean et al., 2016b), shown in Table 5. AAE values close to 1.0
are found for urban aerosols where fossil fuel combustion is dominant, while
AAE values for brown carbon from incomplete combustion are in the
range of 3.5–4.2 (Yang et al., 2009; Chen et al., 2015; Massabò et al., 2016).
Finally, Fig. 4 shows correlations between the MAE values in the
PM10.6 fraction (Fig. 4a) and in the PM2.5 fraction (Fig. 4b)
and the estimated percent mass fraction of iron and iron oxides
(MCFe% and MCFeox%), respectively. Regardless of the size
fraction, the correlation between the MAE values and the percent mass of
total elemental iron are higher at 375, 407 and 532 nm. Best correlations
are obtained when forcing the intercept to zero, indicating that elemental
iron fully accounts for the measured absorption. At these wavelengths,
linear correlations with the mass fraction of iron oxides are low in the
PM10.6 mass fraction (R2 up to 0.38–0.62) but higher in the
PM2.5 fraction (R2 up to 0.83–0.99), where, however, one should
keep in mind that they have been established only indirectly by considering
the ratio of iron oxides to elemental iron independent of size. At 660 and
850 nm, little or no robust correlations are obtained, often based on very
few data points and with very low MAE values. It is noteworthy that, in both
size fractions, the linear correlation yields a nonzero intercept,
indicating a contribution from minerals other than iron oxides to the measured absorption.
Conclusive remarks
In this paper, we report new laboratory measurements of the shortwave MAE of mineral dust of different origins and as a
function of size and wavelength in the 375–850 nm range. Our results were
obtained in the CESAM using mineral dust generated from
natural parent soils, in combination with optical and gravimetric analysis
on extracted samples.
Our results can be summarized as follows: at 375 nm, the MAE values are
lower for the PM10.6 mass fraction (range 37–135 × 10-3 m2 g-1)
than for the PM2.5 fraction (range 95–711 × 10-3 m2 g-1),
and they vary opposite to wavelength as λ-AAE, where
AAE averages between 3.3 and 3.5 regardless of
size fraction. These results deserve some concluding comments:
The size dependence, characterized by significantly higher MAE values in the
fine fraction (PM2.5) than in the bulk (PM10.6) aerosol, indicates
that light absorption by mineral dust can be important even during
atmospheric transport over heavily polluted regions, where dust
concentrations are significantly lower than at emission. This can be shown
by comparing the aerosol absorption optical depth (AAOD) at 440 nm for
China, a well-known mixing region of mineral dust and pollution (e.g., Yang
et al., 2009; Laskin et al., 2014), as well as offshore
western Africa where large urban centers are downwind of dust transport
areas (Petzold et al., 2011). Laskin et al. (2014) report that the average
AAOD in China is of the order of 0.1 for carbonaceous absorbing aerosols
(sum of black and brown carbon; Andreae and Gelencsér, 2006). This is
lower or comparable to the AAOD of 0.17 and 0.11 at 407 nm (total and fine
mass fractions, respectively) that we derive by a simple calculation
(AAOD = MAE × MCdust×H) from MAE values estimated in this
study;
MCdust, the dust mass concentrations typically observed in urban Beijing
during dust storms (Sun et al., 2005); and H, a scale height factor of 1 km.
The spectral variability of the dust MAE values, represented by the AAE
parameter, is equal in the PM2.5 and PM10.6 mass fractions. This
suggests that, for a given size distribution, the possible variation of dust
composition with size does not affect in a significant way the spectral
behavior of the absorption properties. Our average value for AAE is 3.3 ± 0.7,
higher than for black carbon, but in the same range as
light-absorbing organic (brown) carbon. As a result, depending on the
environment, there can be some ambiguity in apportioning the AAOD based on
spectral dependence. Bahadur et al. (2012) and Chung et al. (2012) couple
the AAE and the spectral dependence of the total AOD (and/or its scattering
fraction only) to overcome this problem. Still, Bahadur et al. (2012) show
that there is an overlap in the scatterplots of the spectral dependence of
the scattering and absorption fractions of the AOD based on an analysis of
ground-based remote-sensing data for mineral dust, urban and non-urban
fossil fuel over California. A closer look should be taken at observations
in mixing areas where biomass burning aerosols may have different chemical
composition and/or mineral dust has heavy loadings in order to generalize
the clear separation observed in the spectral dependences of mineral dust
and biomass burning (Bahadur et al., 2012). This aspect is relevant to the
development of remote-sensing retrievals of light absorption by aerosols
from space and their assimilation in climate models (Torres et al., 2007;
Buchard et al., 2015; Hammer et al., 2016).
There is an important sample-to-sample variability in our dataset of MAE
values for mineral dust aerosols. At 532 nm, our average MAE values are
34 ± 14 and 78 ± 70 m2 g-1 in the
PM10.6 and PM2.5 mass fractions, respectively. Figure 3, showing
the correlation with the estimated mass fraction of elemental iron and iron
oxides, suggests that this variability could be related to the regional
differences of the mineralogical composition of the parent soils. These
observations lead to further conclusions. To start with, our study
reinforces the need for regionally resolved representation of the light-absorption properties of mineral dust in order to improve the representation
of its effect on climate. As a matter of fact, the natural variability of
the absorption properties that we obtain from our study is in the range
50–100 %, even when we limit ourselves to smaller spatial scales, for
example those from northern Africa (samples from Libya, Algeria, Mali and
Bodélé). As a comparison, Solmon et al. (2008) showed that varying
the single scattering albedo of mineral dust over western Africa by
±5 %, that is, varying the co-albedo (or absorption) by
45 % (0.1 ± 0.045), could drastically change the climate response in the region.
The question is then “how to represent this regional variability?” Like
Moosmüller et al. (2012) and Engelbrecht et al. (2016), we found that
elemental iron is a very good proxy for the MAE, especially in the
PM2.5 fraction, where iron-bearing absorbing minerals (hematite,
goethite, illite, smectite clays) are more concentrated. In the coarse
fraction, Ca-rich minerals, quartz and feldspars could also play a role,
and that could result in the observed lower correlation (although adding a
term proportional to elemental Ca does not improve the correlation in the
present study). The correlation of the spectral MAE values with the iron
oxide fraction is satisfactory but rather noisy, also owing to some
uncertainty in the quantification of iron oxides from X-ray absorption
measurements. In this case, the intercept is significantly different from
zero, again indicating that a small but distinct fraction of absorption is
due to minerals other than iron oxides. There are contrasting results on
this topic: Alfaro et al. (2004) found an excellent correlation between MAE
and the iron oxide content, whereas Klaver et al. (2011) found that the
single scattering albedo (representing the capacity of an aerosol population
to absorb light in relation to extinction) was almost independent on the
mass fraction of iron oxides. Moosmüller et al. (2012) disagreed,
pointing out the uncertainty in the correction procedure of the measurement
of absorption by Klaver et al. (2011). As a matter of fact, Klaver et al. (2011)
and Alfaro et al. (2004) used the same correction procedure. It is
more likely that the lack of correlation found in Klaver et al. (2011) is
due to the fact that minerals other than iron oxides contribute to
absorption, in particular at their working wavelength (567 nm), where the
absorption efficiency of iron oxides starts to weaken. Clearly, the linear
correlation between elemental iron in mineral dust and its light-absorption
properties could ease the application and validation of climate models that
are now starting to include the representation of the mineralogy (Perlwitz
et al., 2015a, b; Scanza et al., 2015). Also, this would facilitate
detecting source regions based on remote sensing of dust absorption in the
UV–vis spectral region (e.g., Hsu et al., 2004). However, such a
quantitative relationship cannot be uniquely determined from these studies,
including the present one, which use different ways of estimating elemental
iron, iron oxides and the total dust mass. A more robust estimate should be
obtained from the imaginary parts of the complex refractive indices
associated with the measurements of absorption and their dependence on the
mineralogical composition.
Experimental and processed data are immediately available
upon request to the contact author. They will also soon be made available through
the EROCHAMP-2020 data center.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-7175-2017-supplement.
Lorenzo Caponi, Paola Formenti, Dario Massabò, Paolo Prati, Claudia Di Biagio and
Jean-François Doussin designed the chamber experiments and discussed the results.
Lorenzo Caponi and Claudia Di Biagio conducted the experiments with contributions by
Mathieu Cazaunau, Edouard Pangui, Paola Formenti and Jean-François Doussin. Lorenzo Caponi,
Dario Massabò and Paola Formenti performed the full data analysis with
contributions by Claudia Di Biagio, Paolo Prati and Jean-François Doussin. Lorenzo Caponi,
Paola Formenti and Servanne Chevaillier performed the XRF measurements. Paola Formenti and
Gautier Landrot performed the XAS measurements. Dario Massabò performed the MWAA
and the gravimetric measurements. Meinrat O. Andreae, Konrad Kandler, Thuraya Saeed,
Stuart Piketh, Dave Seibert and Earle Williams collected the soil samples used for
experiments. Lorenzo Caponi, Paola Formenti, Dario Massabò and Paolo Prati wrote the
manuscript with comments from all co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
This work has received funding from the European Union's Horizon 2020
research and innovation programme through the EUROCHAMP-2020 Infrastructure
Activity under grant agreement no. 730997. It was supported by the French
national programme LEFE/INSU, by the OSU-EFLUVE (Observatoire des Sciences
de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie) through
dedicated research funding, by the CNRS-INSU by supporting CESAM as national
facility and by the project of the TOSCA program of the CNES (Centre
National des Études Spatiales). Claudia Di Biagio was supported by the CNRS via
the Labex L-IPSL. Meinrat O. Andreae was supported by funding from King Saud
University and the Max Planck Society. The mobility of researchers between
Italy and France was supported by the PICS programme MedMEx of the
CNRS-INSU. The authors acknowledge the CNRS-INSU for supporting CESAM as a
national facility. Konrad Kandler acknowledges support from the Deutsche
Forschungsgemeinschaft (DFG grant KA 2280/2-1). The authors strongly thank
the LISA staff who participated in the collection of the soil samples from
Patagonia and the Gobi used in this study and the two anonymous
reviewers for useful comments on the manuscript. Paola Formenti thanks
Hans Moosmüller (Desert Research Institute, Reno, Nevada) for providing
fruitful suggestions for improvement and discussion to the paper.
Edited by: T. Takemura
Reviewed by: two anonymous referees
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