AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-2923-2017Aethalometer multiple scattering correction Cref for mineral dust
aerosolsDi BiagioClaudiacldibiagio@gmail.comhttps://orcid.org/0000-0001-8273-6211FormentiPaolapaola.formenti@lisa.u-pec.frhttps://orcid.org/0000-0002-0372-1351CazaunauMathieuPanguiEdouardMarchandNicolashttps://orcid.org/0000-0001-9745-492XDoussinJean-Françoishttps://orcid.org/0000-0002-8042-7228Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), UMR 7583, CNRS, Université Paris Est Créteil et
Université Paris Diderot, Institut Pierre et Simon Laplace, Créteil, FranceAix Marseille Univ., CNRS, LCE, Marseille, FranceClaudia Di Biagio (cldibiagio@gmail.com) and Paola Formenti (paola.formenti@lisa.u-pec.fr)15August2017108292329399March201718April201711July201712July2017This 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://amt.copernicus.org/articles/10/2923/2017/amt-10-2923-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/2923/2017/amt-10-2923-2017.pdf
In this study we provide a first estimate of the Aethalometer multiple
scattering correction Cref for mineral dust aerosols.
Cref is an empirical constant used to correct the aerosol
absorption coefficient measurements for the multiple scattering artefact of
the Aethalometer; i.e. the filter fibres on which aerosols are deposited
scatter light and this is miscounted as absorption. The Cref at 450 and 660 nm was obtained from the
direct comparison of Aethalometer data (Magee Sci. AE31) with (i) the absorption
coefficient calculated as the difference between the extinction and
scattering coefficients measured by a Cavity Attenuated Phase Shift
Extinction analyser (CAPS PMex) and a nephelometer respectively at 450 nm
and (ii) the absorption coefficient from a MAAP (Multi-Angle Absorption
Photometer) at 660 nm. Measurements were performed on seven dust aerosol samples
generated in the laboratory by the mechanical shaking of natural parent soils
issued from different source regions worldwide. The single scattering albedo
(SSA) at 450 and 660 nm and the size distribution of the aerosols were also
measured.
Cref for mineral dust varies between 1.81 and 2.56 for a SSA of
0.85–0.96 at 450 nm and between 1.75 and 2.28 for a SSA of 0.98–0.99 at
660 nm. The calculated mean for dust is 2.09 (±0.22) at 450 nm and 1.92 (±0.17)
at 660 nm. With this new Cref the dust absorption coefficient by
the Aethalometer is about 2 % (450 nm) and 11 % (660 nm) higher
than that obtained by using Cref= 2.14 at both 450 and
660 nm, as usually assumed in the literature. This difference induces a
change of up to 3 % in the dust SSA at 660 nm. The Cref
seems to be independent of the fine and coarse particle size fractions, and
so the obtained Cref can be applied to dust both close to sources
and following transport. Additional experiments performed with pure kaolinite
minerals and polluted ambient aerosols indicate Cref of 2.49
(±0.02) and 2.32 (±0.01) at 450 and 660 nm (SSA = 0.96–0.97)
for kaolinite, and Cref of 2.32 (±0.36) at 450 nm and 2.32
(±0.35) at 660 nm for pollution aerosols (SSA = 0.62–0.87 at
450 nm and 0.42–0.76 at 660 nm).
Introduction
Mineral dust is abundant and widespread in the atmosphere and strongly contributes
to the global and regional direct radiative effect and climate forcing
(Highwood and Ryder, 2014; Miller et al., 2014). Mineral dust interacts
through processes of scattering and absorption with both incoming shortwave
radiation and outgoing terrestrial longwave radiation (Sokolik and Toon,
1999). Currently, the evaluation of the direct effect of mineral dust and
its climate implications is still limited by knowledge of the intensity
of dust absorption in the shortwave spectral range (Miller et al., 2004;
Balkanski et al., 2007; Solmon et al., 2008; Jin et al., 2016), represented
by the light absorption coefficient (βabs, units of Mm-1).
The absorption coefficient of mineral dust accounts for less than
∼ 10–20 % of its total shortwave extinction, where it shows
a pronounced spectral variation (Cattrall et al., 2003; Redmond et al.,
2010). The highest dust absorption occurs in the UV-VIS region of the
spectrum, while it levels off to null values towards the near IR (Caponi et
al., 2017). As a result, its single scattering albedo (SSA), i.e. the ratio
of the aerosol scattering (βsca) to extinction (βext=βsca+βabs) coefficient, increases from
values of ∼ 0.80–0.90 at 370 nm to values of ∼ 0.95–0.99 at 950 nm (e.g. Schladitz et al., 2009; Redmond et al., 2010;
Formenti et al., 2011; Ryder et al., 2013).
Given its relatively high SSA, mineral dust can be considered as weakly
absorbing in the shortwave. This is particularly true when compared to other
aerosol species, such as soot, for which the SSA in the visible may be as
low as 0.2 (Bergstrom et al., 2007). Nonetheless, because of its elevated
atmospheric concentration (∼ 100–100 000 µg m-3
close to sources and ∼ 0.1–100 µg m-3 after mid-
to intercontinental transport; e.g. Goudie and Middleton, 2006; Kandler et
al., 2009; Querol et al., 2009; Denjean et al., 2016a), light absorption by
mineral dust can be comparable to that of soot both at regional and global
scales (Reddy et al., 2005; Caponi et al., 2017). Under very intense dust
episodes, dust may absorb up to ∼ 150 W m-2 of incoming
solar radiation (Slingo et al., 2006; di Sarra et al., 2011), inducing a
remarkable warming of the atmospheric layer. This strong warming can alter
the atmospheric structure and stability (Heinold et al., 2008), with a
possible influence on the atmospheric dynamics and meteorological fields
(Pérez et al., 2006). By its direct shortwave effect, dust also affects
the position of the Intertropical Convergence Zone, which in turn
influences the West African Monsoon and modifies the pattern and
intensity of rainfall over northern Africa and the Sahel (Yoshioka et al.,
2007). Nonetheless, the extent of the dust effect and its implications
critically depend on the exact amount of absorbed shortwave radiation.
Solmon et al. (2008), for example, showed that a small change (5 %) in the
shortwave SSA of dust may modify the effect of dust on the West African
Monsoon, moving from a reduction to an increase of precipitation over the
Sahel.
The accurate estimation of the dust absorption over the whole shortwave
range is therefore necessary to properly assess its direct radiative effect
and climate implications. One instrument used to obtain aerosol–light
absorption from the UV to near-IR range is the Aethalometer (Magee Sci. AE31
model, Hansen et al., 1984; Arnott et al., 2005), operating at seven
wavelengths in the 370–950 nm range. The Aethalometer reports equivalent
black carbon mass concentration but the spectral absorption by aerosols can
be also calculated. Given its large spectral interval, the Aethalometer has
been used in the past to investigate the spectral dependence of dust
absorption (Fialho et al., 2005; Formenti et al., 2011), as well as the
absorption by many aerosol types in different environments (Sandradewi et
al., 2008; Segura et al., 2014; Di Biagio et al., 2016; Backman et
al., 2016). General reviews on aerosol absorption measurements and their
applications are provided by Horvath (1993) and Moosmüller et al. (2009).
The working principle of the Aethalometer, a filter-based instrument,
consists of measuring the attenuation through an aerosol-laden quartz filter
according to the Beer–Lambert law, used then to derive the spectral
attenuation coefficient (βATT) of the deposited particles
(Hansen et al., 1984). The “true” spectral aerosol absorption coefficient
(βabs) is proportional but lower than βATT
(Weingartner et al., 2003; Collaud Coen et al., 2010; hereinafter referred
to as W2003 and C2010), because βATT is enhanced by (i) aerosol
scattering towards directions different from that of the detector
(scattering effect); (ii) gradual accumulation of absorbing particles on the
loaded filter, thus reducing the optical path (loading effect); (iii) multiple scattering of the light beam by the filter fibres, increasing the
optical path (multiple scattering effect).
Empirical formulations of the scattering and loading effects are available
in the literature and permit the correction of Aethalometer data for these
artefacts (W2003; Arnott et al., 2005; Schmid et al., 2006; Virkkula et al.,
2007; C2010). The correction of the multiple scattering effect instead
requires the knowledge of a correction factor Cref, which needs to be
directly estimated by comparison of Aethalometer data against reference
absorption measurements (W2003; C2010).
Experimental set-up used for the Aethalometer intercomparison
experiments.
Currently data for Cref are available for soot particles
(Cref= 2.1–2.2 at 660 nm, W2003), internally and externally
mixed soot particles and organic material (Cref= 2.3–3.9,
W2003), and ambient aerosols collected in Europe and Amazonia
(Cref= 2.6–4.8, C2010; Cref=4.9-6.3, Saturno et
al., 2016) and in the Arctic (Cref= 3.1, Backman et al., 2016).
The value most often used in the literature is 2.14 (±0.21), assumed as
wavelength-independent (e.g. Sandradewi et al., 2008; Formenti et al., 2011;
Di Biagio et al., 2016), which corresponds to the mean of observations at
660 nm for soot aerosols (W2003). Both W2003 and C2010, however, found a
dependence of Cref on the aerosol single scattering albedo, with
Cref decreasing for increasing SSA. Thus, the value of 2.14
obtained for highly absorbing soot (SSA ∼ 0.2 in the visible) may not
be appropriate for weakly absorbing mineral dust.
Specifications and references of instruments used during
experiments.
InstrumentPropertyOperating wavelength (nm)Time resolutionFlow rate (L min-1)Percent uncertaintyReferenceAethalometer (model AE-31, Magee Sci.)Spectral absorption coefficient370, 470, 520, 590, 660, 880, 9502 min8±20 % (attenuation coefficient)Hansen et al. (1984); W2003; C2010Multi-Angle Absorption Photometer (MAAP, model 5012, Thermo Sci.)Single-wavelength absorption coefficient6701 min8±12 %Petzold and Schönlinner (2004); Petzold et al. (2005)Cavity Attenuated Phase Shift Extinction (CAPS PMex, Aerodyne)Spectral extinction coefficient450, 6301 s0.85±5 %Massoli et al. (2010)Nephelometer (model 3563, TSI Inc.)Spectral scattering coefficient450, 550, 7001 s18±∼ 9 %Sherman et al. (2015)SMPS (DMA model 3080, CPC model 3772, TSI Inc.)Number size distribution–3 min2–De Carlo et al. (2004)OPC optical particle counter (model 1.109, Grimm Inc.)Number size distribution6556 s1.2±15 % (diameter optical to geometric conversion); ±10 (concentration)Heim et al. (2008)
Henceforth, in this work we present the experimental estimate of Cref
for mineral dust aerosols at 450 and 660 nm obtained from a laboratory-based
intercomparison study. Experiments were conducted on seven dust aerosol
samples generated by the mechanical shaking of natural parent soils. Control
experiments on pure kaolinite mineral, ambient aerosols sampled in the
polluted environment of the suburbs of Paris, and purely scattering ammonium
sulfate were also performed to investigate the dependence of Cref on
the aerosol single scattering albedo.
Experimental set-up
The experimental set-up used for the intercomparison study is shown in Fig. 1. Instrumental details and uncertainties are summarized in Table 1. The
following measurements were performed from a 8-port glass manifold
(∼ 1 L volume):
The absorption coefficient (βabs) was measured by a 7-wavelength Aethalometer
(Magee Sci., model AE31 working at 370, 470, 520, 590, 660, 880, 950 nm;
flow rate 8 L min-1, 2 min resolution) and a MAAP (Multi-Angle
Absorption Photometer, Thermo Sci., model 5012 working at 670 nm; flow rate
8 L min-1, 1 min resolution). Unlike the Aethalometer, the MAAP
measures the transmitted light from the aerosol-laden filter as well as the
backscattered light at two angles (135 and 165∘) (Petzold et al.,
2005). Backscattering measurements are used to constrain the scattering
fraction of the measured attenuation that would erroneously be interpreted as
absorption. The aerosol absorption coefficient for the MAAP is obtained from
a radiative transfer scheme, taking into account the multiple scattering in
the filter and the scattering effect, without requiring any further
adjustment (Petzold and Schönlinner, 2004). The MAAP is commonly assumed
to provide the most reliable filter-based direct estimate of the aerosol
absorption coefficient at a single wavelength (Andreae and Gelècser,
2006). In this study we assume for the MAAP the manufacturer's reported
wavelength of 670 nm, although Müller et al. (2011) measured a wavelength of 637 nm for this
instrument. An estimate of the change in the obtained
Cref due to the change in MAAP nominal wavelength from 670 to
637 nm is reported in Sect. 4.2.
The scattering coefficient (βsca) was measured by a 3-wavelength nephelometer
(TSI Inc., model 3563 working at
450, 550 and 700 nm; flow rate 18 L min-1, 1 s
resolution).
The extinction coefficient (βext) was measured by two Cavity Attenuated Phase
Shift Extinction analysers (CAPS PMex by Aerodyne; one working at 450 nm and
the other at 630 nm; flow rate 0.85 L min-1, 1 s resolution).
The particle number size distribution (dN/dlogD) was measured by a scanning mobility
particle sizer, SMPS (TSI Inc., DMA Model 3080, CPC Model 3772; operated at
2.0/0.2 L min-1 sheath/aerosol flow rates; 3 min resolution) and an
optical particle counter, OPC (Grimm Inc., model 1.109, 655 nm operating
wavelength; flow rate 1.2 L min-1, 6 s resolution). The SMPS measures
the aerosol number concentration in the electrical mobility diameter
(Dm) range 0.019–0.882 µm, and the OPC measures in the optical
equivalent diameter (Dopt) range 0.25–32 µm.
Sampling lines from the manifold to the instruments were made of conductive
silicone tubing (TSI Inc., 6.4×10-3 m diameter) to minimize
particle loss by electrostatic deposition. They were designed to be as
straight and as short as possible. Their length, varying between 0.3 and 0.7 m, was adjusted based on the flow rate of each instrument to ensure an
equivalent particle loss, so that the same aerosol size distribution was
input to the different instruments. Particular care was given to ensure the
same aerosol size at the input of the Aethalometer and the MAAP. To this
end, as illustrated in Fig. 1, the two instruments sampled air from the same
manifold exit line, and the same sampling flow rate was also set for the two
instruments (8 L min-1). Particle loss calculations were performed with
Particle Loss Calculator (PLC) software (von der Weiden et al., 2009).
Aerosols were generated in three ways:
Mineral dust was generated by mechanical shaking as described and
validated in Di Biagio et al. (2014, 2017). About 3 g of soil sample (sieved
at 1000 µm and dried at 100 ∘C) was placed in a
Büchner flask and shaken at 100 Hz by a sieve shaker (Retsch AS200). The
dust was injected in the manifold by a flow of N2 at 3.5 L min-1
through a single-stage impactor used to eliminate particles larger than about
20 µm, which could be preferentially sampled by the instruments
with the highest flow rate. Pure N2 was added to the aerosol flow to
make the injection flow equal to the total sampling flow by instruments
connected to the manifold (about 38 L min-1).
Ammonium sulfate (Sigma-Aldrich 99.999 % purity, 0.03 M solution in
ultrapure water) and kaolinite particles (Source Clay Repository KGa-2, 0.05 M solution in ultrapure water) were generated by a constant flow atomizer
(TSI, model 3075) operated at 3 L min-1 and coupled with a diffusion
drier (TSI, model 3062). As for dust, pure N2 was added to the aerosol
flow to equalize the total sampling flow.
Ambient pollution aerosols were sampled by opening the manifold to the
exterior ambient air. Ambient aerosols were not dried before entering the
manifold. Sampling was performed at the University Paris-Est Creteil, in the
suburbs of Paris, at the ground floor of the University building, which is
close to a main local road (∼ 20 m) and to the A86 highway
(∼ 200 m).
Strategy for data analysis
The Aethalometer spectral attenuation coefficient βATT(λ) is related to the measured attenuation ATT(λ) through the
following formula:
βATT(λ)=ΔATT(λ)ΔtAV,
where A is the area of the aerosol collection spot (0.5 ± 0.1)
cm2 and V the air sample volume (0.016 m3 over 2 min integration
time). ΔATT(λ)/Δt in Eq. (1) can be calculated as
the linear fit of the measured attenuation as a function of time.
The spectral attenuation coefficient βATT(λ) measured
by the Aethalometer is related to the targeted absorption coefficient βabs(λ) using the following formula (C2010):
βabs(λ)=βATT(λ)-α(λ)βsca(λ)R⋅Cref,
where the different terms parameterize different instrument artefacts:
the scattering effect α(λ)βsca(λ),
that is, the amount of scattered radiation by the aerosols deposited on the
filter that is miscounted as absorption, where α(λ) is a
wavelength-dependent proportionality constant and βsca(λ) is the aerosol spectral scattering coefficient;
the loading effect R, representing the artificial flattening of measured
attenuation with time due to the gradual accumulation of absorbing particles
on the loaded filter;
the multiple scattering Cref, representing multiple scattering of the
light beam by the filter fibres.
The α(λ) term and R in Eq. (2) can be calculated through
various empirical formulas reported in the literature (W2003, Arnott et al.,
2005; Virkkula et al., 2007; Schmid et al., 2006; C2010). The determination
of Cref, instead, is the objective of our study.
Scattering effect correction
Arnott et al. (2005) provide for α(λ) the following
formulation:
α(λ)=Ad-1⋅c⋅λ-αS(d-1),
where the A and αS terms are obtained from the power-law fit of
βsca(λ) versus λ, and the c and d terms can be
determined from the power-law fit of the attenuation βATT(λ) versus the scattering βsca(λ)
coefficient as
βsca(λ)=Aλ-αSβATT(λ)=cβscaλd.
Loading effect correction
Two formulations for the loading effect correction R are proposed by W2003
and C2010:
R(W2003)(λ)=1f(λ)-1ln(ATT(λ))-ln(10%)ln(50%)-ln(10%)+1R(C2010)(λ)=1f(λ)-1ATT(λ)50%+1.
The factor f(λ) represents the dependence of the loading effect on
the aerosol absorption. This dependence is parameterized by the aerosol
single scattering albedo SSA(λ) in the form of
f(λ)=a1-SSA(λ)+1,
where a, equal to 0.85 in W2003 and 0.74 in C2010, is obtained as the slope
of the linear fit between the attenuation coefficient βATT
normalized to its value at 10 % attenuation (βATT/β10%) and the natural logarithm of the measured attenuation
ln(ATT(λ)).
Multiple scattering correction
For the determination of Cref only βATT and R are required.
Hence, in this work, attenuation data from the Aethalometer were
corrected for the loading effect R but not for the scattering term α(λ)βsca(λ). Three different formulations of
Cref were therefore considered:
Cref∗(λ)=βATT(λ)βabs-ref(λ)Cref(W2003)(λ)=1βabs-ref(λ)βATT(λ)R(W2003)(λ)Cref(C2010)(λ)=1βabs-ref(λ)βATT(λ)R(C2010)(λ).
The βabs-ref term in Eqs. (8a–c) represents the reference
absorption coefficient estimated from independent measurements.
Cref∗ does not take into account the loading effect correction
in Aethalometer data, as in Schmid et al. (2006). Cref(W2003) and
Cref(C2010) take this correction into account by using the R(W2003)
and the R(C2010) parameterizations, respectively. The spectral βATT/R(C2010) was used to calculate the absorption Ångström
exponent (αA). Note that in this work we considered, for each
experiment, only data corresponding to ATT < 20 % to calculate
βATT (R2 > 0.99 for the ΔATT/Δt
fits in all cases; see Eq. 1). This threshold was fixed based on two
requirements: first, we limited our data analysis to points with low
attenuation in order to account almost exclusively for the scattering by the
filter fibres in the Cref calculation and not for the scattering from
aerosol particles embedded in the filter. This choice was also made for
consistency with the literature, since both W2003 and C2010 relate Cref
to ATT∼ 10 %. Second, this choice ensured that enough data
points were available for analysis regardless of the aerosol type, in
particular for ambient aerosols, for which attenuation rapidly exceeded
10 %.
Determination of reference absorption coefficient and single scattering
albedo
The reference absorption coefficient βabs-ref in Eqs. (8a–c) was
obtained in different ways depending on wavelength. At 450 nm, βabs-ref was obtained with the “extinction minus scattering” approach
by using the CAPS measurements for extinction and the nephelometer
measurements for scattering. At 660 nm, βabs-ref was
extrapolated from MAAP measurements at 670 nm.
Direct determination of reference absorption coefficient at 660 nm
from the MAAP
The reference absorption coefficient βabs-ref at 660 nm was
obtained by the MAAP measurement at 670 nm. The MAAP attenuation (ATT) at
670 nm is estimated from the measured transmission (T) and retrieved single
scattering albedo of the aerosol-filter layer (SSA0, from the inversion
algorithm) as
ATT(670)=1-SSA0⋅lnT⋅100.
Equation (1) is applied to estimate the absorption coefficient at 670 nm
from ATT(670). The area of the aerosol collection spot is 2 cm2 and the
sampled volume is 0.008 m3 over 1 min integration time. The absorption
coefficient of the MAAP was extrapolated to the 660 nm wavelength by using
the absorption Ångström exponent αA calculated from
Aethalometer data.
Indirect determination of reference absorption coefficient at 450 nm:
extinction minus scattering approach
The reference absorption coefficient βabs-ref at 450 nm was
calculated as the difference between the extinction and scattering
coefficient from the CAPS and the nephelometer.
The extinction coefficient βext at 450 and 630 nm was measured
directly by the two CAPS analysers without additional corrections (Massoli
et al., 2010). The spectral βext was used to calculate the
extinction Ångström exponent (αE), which was then applied to
extrapolate βext at 660 nm.
The scattering coefficient βsca at 450, 550, and 700 nm
measured by the nephelometer between 7 and 170∘ was corrected for the
size-dependent angular truncation of the sensing volume to report it to the
full angular range 0–180∘ (Anderson and Ogren, 1998). Two different
approaches were used: for submicrometric ammonium sulfate, the correction
proposed by Anderson and Ogren (1998) was applied, while for aerosols with a
significant coarse fraction (dust, ambient air and kaolinite), the truncation
correction was estimated by optical calculations according to the Mie theory
for homogeneous spherical particles using the measured number size
distribution as input. In the calculations the real and the imaginary parts
of the complex refractive index m (m=n-ik, where n is the real part
and k is the imaginary part) were varied in the wide range 1.42–1.56 and
0.001–0.025i for dust (Di Biagio et al., 2017), and 1.50–1.72 and
0.001–0.1i for ambient air (Di Biagio et al., 2016), while the value of
1.56–0.001i was assumed for kaolinite (Egan and Hilgeman, 1979; Utry et
al., 2015). Then, n and k were set to the values which reproduced the
measured βsca at 7–170∘. The truncation correction
factor (Ctrunc) was estimated as the ratio of the modelled
βsca at 0–180 and 7–170∘. At the three nephelometer
wavelengths (450, 550, and 700 nm) the correction factor Ctrunc
varied in the range 1.03–1.06 for ammonium sulfate,
1.08–1.6 for dust, 1.03–1.05 for kaolinite, and
1.05–1.25 for ambient air. For both approaches (Anderson and Ogren, 1998
correction and Mie calculations) the uncertainty on the truncation correction
was estimated to be less than 3 %. Once corrected for truncation, the
spectral βsca was used to calculate the scattering
Ångström exponent (αS), which was then applied to
extrapolate βsca at 630 and 660 nm.
Determination of the single scattering albedo (SSA)
The aerosol single scattering albedo (SSA) represents the ratio of
scattering to extinction. At 450 nm, the SSA was estimated by nephelometer
and CAPS data (Eq. 10), while at 660 nm CAPS data were combined with MAAP
observations (Eq. 11):
SSA(450)=βsca(450)nephelometerβext(450)CAPSSSA(660)=βext(660)CAPS-βabs-MAAP(660)βext(660)CAPS.
Number size distribution and effective fine and coarse diameter
The number size distribution was measured by a combination of SMPS and OPC
observations. For the SMPS, corrections for particle loss by diffusion in
the instrument tubing and the contribution of multiple-charged particles
were performed using the SMPS software. The electrical mobility diameter
measured by the SMPS can be converted to a geometrical diameter (Dg) by
taking into account the particle dynamic shape factor (χ;
Dg=Dm/χ). In this study, the SMPS showed good agreement
with OPC data for a shape factor χ=1, which corresponds to spherical
particles.
The OPC optical-equivalent nominal diameters were converted into
sphere-equivalent geometrical diameters (Dg) by taking into account the
aerosol complex refractive index. This consisted of recalculating the OPC
calibration curve for different complex refractive index values. For dust
aerosols the refractive index was varied in the range 1.47–1.53 (n) and
0.001–0.005i (k) following the literature (see Di Biagio et al., 2017) and
Dg was set at the mean ± one standard deviation of the values
obtained for the different n and k. For kaolinite the OPC diameter
conversion was performed by setting the refractive index at 1.56–0.001i. For
ambient air the refractive index was set at 1.60–0.01i, a value that
represents a medium absorbing urban polluted aerosol (see Di Biagio et al.,
2016). The impact of humidity on the refractive index of ambient aerosols
and associated changes OPC response were not taken into account. The
relative humidity was always below 35 % during ambient air measurements,
which implies a very small particle growth. After conversion, the OPC
diameter range became 0.28–18.0 µm for dust (taking into account the
particle cut at ∼ 20 µm due to the use of the
impactor), and 0.27–58.0 µm for kaolinite and 0.28–65.1 µm for
ambient air (the impactor was not used in these cases). The uncertainty was
< 15 % at all diameters.
The aerosol effective fine (Deff,fine) and coarse (Deff,coarse)
diameter were estimated from OPC data as
Deff=∫D1D2Dg3dNdlogDgdlogDg∫D1D2Dg2dNdlogDgdlogDg,
with D1=0.3µm and D2=1µm for the fine mode and
D1=1µm and D2=10µm for the coarse mode.
Data integration and error analysis
Aethalometer data were first processed at 2 min resolution to obtain the
time evolution of the attenuation coefficients βATT and βATT/R. Data from the MAAP, CAPS, nephelometer, OPC and SMPS were
averaged over 2 min, so they could be reported as having the same resolution as the
Aethalometer.
The βATT and βATT/R were calculated over the
whole duration of each experiment from Eqs. (1) and (6). Corresponding
averages of the reference absorption coefficient (βabs-ref) were
calculated for each experiment and used to estimate Cref.
Experiment averages of SSA, Deff,fine, and Deff,coarse were also
calculated to be related to the obtained Cref.
Temporal series of experiments showing the measured optical data at
660 nm. The different panels show (from the top to the bottom):
(a) the loading-corrected Aethalometer attenuation at 660 nm (data
corrected with the R formulation by Collaud Coen et al. (2010) (referred to
as R(C2010)) are shown) and the MAAP aerosol absorption coefficient;
(b) the aerosol extinction at 660 nm extrapolated from CAPS PMex
measurements and estimated as the sum of nephelometer scattering and MAAP
absorption; (c) the extinction aerosol Ångström exponent;
(d) the aerosol single scattering albedo at 660 nm. Each point in
the plot corresponds to 2 min average data. The x axis indicates the data
point sequential number. Experiments with dust samples and kaolinite occurred
between the 3 and the 9 November 2016 and lasted between 1 and 2 h each.
Ambient air data were collected at different steps between the 8 and the
14 November 2016 for a total of 7 h of measurements.
The uncertainty of Cref was estimated with the error propagation
formula by taking into account the uncertainties on βATT, βATT/R, and the standard deviation of the averaged βabs-ref
from the CAPS-nephelometer and the MAAP. The uncertainty of βATT
was estimated as the quadratic combination of the uncertainty of the linear
fit of ΔATT with respect to time and the uncertainties on the
surface deposit A. The uncertainty of βATT/R was estimated by
taking into account the uncertainty of βATT and R. Uncertainties
on βATT and βATT/R are both ∼ 20 %.
Summary of experiments and results. The mean and the standard
deviation of Deff,fine, Deff,coarse, SSA at 450 and
660 nm, Cref∗, Cref(W2003), and
Cref(C2010) are reported. As a reminder, Cref∗
is the multiple scattering correction obtained when not taking into account the
loading effect correction in Aethalometer data; Cref(W2003) and
Cref(C2010) take the loading effect correction into account by
using the parameterizations by Weingartner et al. (2003) (referred to as W2003)
and Collaud Coen et al. (2010) (referred to as C2010), respectively. The maximum
of the % difference between Cref∗,
Cref(W2003), and Cref(C2010) is indicated in the
table.
Ammonium sulfate experiment. (a) Temporal evolution of the
extinction and scattering coefficients measured by the CAPS PMex and the
nephelometer at 450 nm (blue scale) and 630 nm (red scale). Each point in
the plot corresponds to 2 min average data. (b) CAPS PMex versus
nephelometer data (10 min averages). The y=x line and the results of the
linear fit between CAPS and nephelometer data are also shown in the plot.
Results
The time series of observations for all the experiments are shown in Fig. 2
as 2 min averages. Seven experiments were performed on mineral dust issued
from six different areas in the Sahel (Niger), eastern Asia (China), North
America (Arizona), northern Africa (Tunisia), Australia, and southern Africa
(Namibia), and on a kaolinite powder. Experiments were performed between the
3 and the 9 November 2016 and lasted between 1 and 2 h
each. The experiments on Niger dust (labelled as Niger 1 and Niger 2) were
duplicated to test the repeatability of the obtained Cref. Ambient air
data were collected between the 8 and the 14 November 2016 for
a total of 7 h of measurements. Eight different periods characterized by
little variation and different levels of SSA were selected in the whole set
of ambient air measurements. These are identified as ambient air 1 to 8. The
summary of information is provided in Table 2. SMPS data were available for
ammonium sulfate and kaolinite experiments, for one of the two Niger dust
experiments (Niger 2), and for some of the ambient air experiments. OPC
measurements were performed for all experiments with the exception of the
ammonium sulfate.
Quality control data
Results of the ammonium sulfate control experiment (24 October 2016) used
to test the performance of the optical instruments are illustrated in Fig. 3.
As expected for this purely scattering aerosol (Toon et al., 1976), the
nephelometer scattering and the CAPS extinction at 450 and 630 nm were in
very good agreement (less than 4 % difference) during the whole duration
of the experiment. This is well below the single instrument uncertainty of
±9 % for the nephelometer (Sherman et al., 2015) and ±5 %
for the CAPS (Massoli et al., 2010). This is further demonstrated by the
scatter plot of their respective 10 min averages, yielding a linear
regression in the form of y=0.95x+5.1 (R2=0.95) at 450 nm and
y=1.01x-1.4 (R2=0.98) at 630 nm. The average βext at
450 and 630 nm from CAPS observations were 913 (±52) and 424 (±33) Mm-1, respectively, while the average βsca was 921
(±36) and 420 (±17). This led to an average SSA of 1.01
(±0.07) at 450 nm and 0.99 (±0.07) at 630 nm.
The absorption coefficient, averaged over the duration of the experiment,
was 0.10 (±0.04) Mm-1 at 450 nm and 0.24 (±0.07) Mm-1 at 660 nm according to the Aethalometer,
and 0.82 (±0.13) Mm-1 at 660 nm according to the MAAP. For the Aethalometer, the
absorption coefficient was calculated from Eq. (2) assuming Cref=2.14
and the R formulation by C2010 (Eq. 6b). The α(λ)
coefficient was calculated from Eq. (3). The c and d terms in Eq. (3) were
determined from the power-law fit of βATT(λ) vs. βsca(λ) and are c= (0.56 ± 0.06) Mm-1 and
d= (0.485 ± 0.09). These values are lower than those reported by
Arnott et al. (2005) (c=0.797, d=0.564). The A and αS
terms, obtained from the power-law fit of βsca(λ) vs.
wavelength (Eq. 3) are A= (4.07 ± 0.49) ×109 Mm-1 and αS= (-2.46 ± 0.12).
Figure 4 shows the extinction coefficient at 660 nm extrapolated from CAPS
observations and calculated as the sum of nephelometer and MAAP data for
dust, kaolinite, and ambient air experiments. The linear regression of the
data yields y=1.03x-0.5 (R2=0.99), indicating the consistency of
optical measurements between the CAPS, nephelometer, and MAAP (less than
3 % difference on average). Based on the success of the optical closure at
660 nm, we therefore assume the “CAPS minus nephelometer” approach, which
is appropriate for estimating the aerosol absorption coefficient at 450 nm.
Estimate of Cref
The Cref∗, Cref(W2003) and Cref(C2010) at 450 and
660 nm obtained for all different experiments and the corresponding aerosol
SSA, Deff,fine, and Deff,coarse are summarized in Table 2.
CAPS PMex extinction coefficient extrapolated at 660 nm versus
nephelometer and MAAP calculated extinction at 660 nm for all experiments
(dust, kaolinite, ambient air). Each point in the plot corresponds to 10 min
average data. The y=x line and the results of the linear fit between CAPS
and nephelometer and MAAP data are also shown in the plot.
Cref for mineral dust varied between 1.81 and 2.56 for a SSA of
0.85–0.96 at 450 nm and between 1.75 and 2.28 for a SSA of 0.98–0.99 at 660 nm. The estimate for Niger 1 and 2 samples agreed within 4.9 %, which
suggests a good repeatability of the Cref estimate. For kaolinite
Cref was 2.47–2.51 and 2.31–2.34 at 450 and 660 nm, respectively, with
an associated SSA of 0.96 and 0.97 at the two wavelengths. For ambient air
Cref varied in the range 1.91–4.35 for a SSA of 0.62–0.87 at 450 nm and
1.66–2.96 for and SSA of 0.42–0.76 at 660 nm. For samples 6 and 8 the
Cref at 450 was lower than at 660 nm. Otherwise, for all other cases,
the Cref was larger at 450 nm than at 660 nm.
Differences within 2.8 % were obtained between Cref∗,
Cref(W2003) and Cref(C2010) at 450 and 660 nm for weakly absorbing
dust and kaolinite. In contrast, for more absorbing ambient air aerosols the
differences between Cref∗, Cref(W2003) and
Cref(C2010) were in the range 2.7 to 24.3 %. The different ATT
threshold, assumed here to be 20 % compared to W2003 and C2010 (10 %), has a
negligible impact (less than 1 % difference) on the results. In some cases
(ambient air 1–2 and Niger 1 samples), however, we obtained
Cref(C2010) > Cref(W2003); these cases correspond to a
mean Aethalometer measured ATT < 10 %, for which
R(W2003) > R(C2010), and this explains the larger
Cref(C2010). Conversely, Cref(C2010) < Cref(W2003)
when the measured ATT was ∼ 15–20 %, yielding
R(W2003) < R(C2010). The percent difference between the obtained
Cref(W2003) and Cref(C2010) increased for decreasing SSA due to
the increase of the R(W2003) to R(C2010) absolute difference for decreasing
SSA. When averaging data for all ambient air samples, the two formulations
yield very similar values. For example, at 660 nm the mean Cref(W2003)
was 2.44 (±0.38), less than 2 % larger than the mean
Cref(C2010) of 2.39 (±0.35).
The mean and standard deviation of the multiple scattering correction at 450
and 660 nm for dust, kaolinite, and ambient air calculated as the mean of
the Cref∗, Cref(W2003), and Cref(C2010) are reported
in Table 3. The mean Cref at 450 and 660 nm is 2.09 (±0.22) and
1.92 (±0.17) for dust, 2.49 (±0.02) and 2.31 (±0.02)
for kaolinite, and 2.32 (±0.36) and 2.32 (±0.35) for pollution
aerosols. If the wavelength of 637 nm is assumed for the MAAP instead of 670 nm, as suggested by Müller et al. (2011),
the average Cref at 660 nm would increase by up to ∼ 15 % for dust and ambient air
(2.17 ± 0.19 and 2.48 ± 0.41, respectively) and ∼ 3 % for kaolinite (2.40 ± 0.02).
(a) Estimated f values versus (1-SSA) at
660 nm for dust aerosols. Different symbols are used to distinguish between
dust from different sources. The uncertainty of (1-SSA) is the
standard deviation over 2 min data, while that of f is calculated with the
error propagation formula taking into account the uncertainty of a
(±0.14) and that of (1-SSA). (b)f versus SSA at
660 nm for all experiments. Different symbols are used to distinguish
between different aerosol types. The results of the linear fit between f
and (1-SSA) are also reported. Data from Weingartner et al. (2003)
(W2003) (extracted from their Fig. 4) are also shown in the plot for
comparison.
Dependence of Cref on SSA
As reported in Table 2, very different SSA values at 450 and 660 nm were
obtained for the various cases. For dust aerosols, the measured SSA values
were larger than 0.85 at 450 nm and close to unity (> 0.98) at
660 nm, in line with field observations of dust from different sources
(Schladitz et al., 2009; Formenti et al., 2011; Ryder et al., 2013). In
particular, our results for China, Arizona, and Australia samples are in
line with published values by Engelbrecht et al. (2016), who used a
photoacoustic instrument to measure absorption of resuspended dust
aerosols. This would suggest the similar performances of the Aethalometer
compared to the photoacoustic technique. The SSA for kaolinite was 0.96–0.97
at 450 and 660 nm, in agreement with Utry et al. (2017), also using a
photoacoustic method to measure absorption (0.97 and 0.99 (±0.04) at
450 and 635 nm, respectively). Both at 450 and 660 nm, the single scattering
albedo for ambient air varied in the wide range of 0.2 to 0.9 during the whole
measurement period (see Fig. 2 for measurements at 660 nm). The average
values obtained for air samples 1–8 were 0.62–0.87 at 450 and 0.42–0.76 at
660 nm. The SSA decreased with increasing wavelength, as expected for
pollution aerosols (e.g. Bergstrom et al., 2007; Di Biagio et al., 2016).
The wide range of values indicates the occurrence of particles with very
different absorption properties, hence chemical composition (or complex
refractive index) and/or different size distribution (e.g. Moosmüller
and Arnott, 2009). For instance, in urban environments, Bergstrom et al. (2007) reported SSA in the range 0.2–1.0 at 550 nm, with lowest values
observed for soot-dominated air masses and highest values for urban
pollution dominated by low-absorbing organic components.
Mean and standard deviation multiple scattering correction
Cref‾ at 450 and 660 nm for dust, kaolinite, and
ambient air. The Cref‾ was calculated as the mean of
the Cref∗, Cref(W2003), and
Cref(C2010) obtained at each wavelength for the different aerosol
types. As a reminder, Cref∗ is the multiple scattering
correction obtained when not taking into account the loading effect correction in
Aethalometer data; Cref(W2003) and Cref(C2010) take
the loading effect correction into account by using the parameterizations by
Weingartner et al. (2003) and Collaud Coen et al. (2010), respectively.
(a, b)Cref(W2003) (multiple scattering
correction obtained by taking into account the loading effect correction
using the parameterizations by Weingartner et al., 2003) versus SSA at 450 and
660 nm for mineral dust samples analysed in this study. Different symbols
are used to distinguish between dust from different sources. As indicated in
Table 2, the difference between Cref∗,
Cref(W2003), and Cref(C2010) is very low for mineral
dust aerosols. The uncertainty of SSA is the standard deviation over 2 min
data, while that of Cref(W2003) is calculated with the error
propagation formula taking into account the uncertainty of
βabs,ref and that of βATT/R(W2003).
(c, d)Cref versus SSA at 450 and 660 nm for the
different aerosol samples analysed in this study. Different symbols are used
to distinguish between different aerosol types. Data for both
Cref(W2003) and Cref∗ (multiple scattering
correction obtained while not taking into account the loading effect correction in
Aethalometer data) are shown for ambient air aerosols, while for dust and
kaolinite, for which the difference between the different formulations is
very low, only Cref(W2003) is reported. Data from Weigartner et
al. (2003) (W2003) (Cref from their Table 3, and SSA extracted
from their Fig. 4) and Collaud Coen et al. (2010) (C2010) (extracted from
their Fig. 5) at 660 nm are also shown in the plot for comparison. The
results of the linear fits between Cref and SSA for mineral dust
and for the entire data set are also shown in the plot.
The experimental SSA values serve two purposes. Firstly, as shown in Fig. 5,
they are linearly related to the factor f in the loading effect correction
term R in Eqs. (6a–b) as f=a(1-SSA)+1. The linear regression of our
data yields a slope a= (1.48 ± 0.14), which is larger than the value of 0.85
reported in W2003 (f data from W2003 are also shown in Fig. 5) and 0.76 in
C2010.
Secondly, SSA data serve to investigate the dependence of Cref on
relative amounts of particle absorption for mineral dust. As shown in Fig. 6
(top panel), Cref for dust seems to be independent of SSA at
660 nm, whereas it decreases for increasing SSA at 450 nm. This trend is
statistically significant (correlation coefficient of R2=0.85). The
relationship between Cref and SSA is also investigated in Fig. 6
(bottom panel) for all aerosol samples. Globally, Fig. 6 suggests a decrease
of Cref for increasing SSA, in particular at 450 nm, albeit with
a poorer statistical significance at both wavelengths (R2=0.35 and
0.59). Data are also compared to those reported in W2003 and C2010 at 660 nm
for different aerosol types. Diesel soot and soot mixed with ammonium sulfate
were investigated in W2003, while C2010 reported data for ambient aerosols
sampled at different locations in Europe and in Amazonia. W2003 also reported
the Cref for soot particles at 450 nm (not shown in Fig. 6),
with values between 2.08 and 3.64; these values are in line with our
observations at 450 nm for ambient air. However, as illustrated in Fig. 6,
both W2003 and C2010 found a relationship between Cref and SSA at
660 nm. Contrasting results are obtained when plotting the two data sets
together. C2010 obtained a sharp and almost linear decrease of
Cref with increasing SSA (Cref∼ 5–2.5 for
SSA ∼ 0.65–0.9), while W2003 data showed a pronounced decrease of
Cref (∼ 2–4) for increasing SSA in the range 0.5 and 0.7
and low Cref values (∼ 2) at SSA ∼ 0.2. Our data for
dust and kaolinite at high SSA (> 0.97) seem to follow the same
linear relationship as C2010. However at lower SSA, our data for ambient
aerosols are closer to W2003 results at 660 nm. These differences between
W2003 and C2010 data, and also with our results, are quite difficult to
explain. The main difference between W2003 compared to C2010 is that W2003
performed measurements in a simulation chamber, while C2010 was a field
study. Working in ambient conditions may influence the retrieved
Cref. In fact, volatile-organic compounds or water vapour present
in the atmosphere may condense on the filter (Lack et al., 2008), thus
enhancing the scattering from the filter fibres and leading to higher
Cref. This could explain the higher Cref obtained in
C2010 compared to W2003. Our results for ambient air particles, however, are
in agreement with W2003 chamber results. Differences in the size
distributions of the investigated aerosols are also expected to possibly
affect the comparison; however, no detailed information on the size of
investigated aerosols is provided in W2003 and C2010. Another source of
discrepancy may be in the fact that, in contrast to W2003 and our study,
where the Aethalometer and MAAP were compared at 660 nm, Cref in
C2010 was estimated by comparing Aethalometer data at 660 nm with MAAP
observations at 630 nm. As aerosol absorption increases with decreasing
wavelength, this wavelength difference may induce an underestimation of
Cref in C2010.
Dependence of Cref on particles size
Examples of the number size distribution measured by the SMPS and OPC for
ammonium sulfate, Niger dust, kaolinite, and ambient air are shown in Fig. 7. Ammonium sulfate had mostly a submicron distribution, while dust aerosols
presented the largest fraction over the whole supermicron range up to about
10–20 µm. Dust particles larger than 20 µm were completely
suppressed by the impactor system and were not detected by the OPC. The
coarse component, up to about 10 µm, was also identified in the
kaolinite and ambient air samples. In particular, a defined mode at
∼ 4 µm was detected in the number distribution of
ambient air particles, and may be linked to the presence of soot aggregates,
tire abrasions, resuspended road dust, or bioaerosols (Harrison et al.,
2001; Bauer et al., 2008; Pakbin et al., 2010; Liu and Harrison, 2011). The
Deff,fine varied between 0.24 and 0.62 µm and the
Deff,coarse between 2.3 and 6.2 µm for the different cases
(Table 2). For mineral dust, Deff,coarse ranged between 2.3 and 3.6 µm, encompassing the
value of Deff,coarse∼ 3 µm reported by Denjean et al. (2016b) in their Fig. 11 for Saharan
dust, both close to sources and during transport over the Atlantic.
These observations are consistent with the extinction (αE) and
the absorption (αA) Ångström exponent measured during the
experiments. The αE (shown in Fig. 2) was ∼ 0 for
kaolinite, varied between about 0 and 2 for mineral dust aerosols, and
between 0.5 and 2.5 for ambient air, indicating particles with variable
sizes, both the submicron and the supermicron fractions. The absorption
Ångström coefficient αA obtained from Aethalometer data was
between 2.2 and 4 for dust, between 1 and 1.5 for kaolinite and between 0.5
and 1.5 for ambient air aerosols.
Examples of number size distribution (normalized to the total number
concentration) for ammonium sulfate, dust (Niger sample), kaolinite, and
ambient air aerosols. Data refer to the mean over each experiment as measured
from the SMPS and the OPC. Error bars (standard deviations) have been omitted
for the sake of clarity.
(a, b)Cref(W2003) (multiple scattering
correction obtained by taking into account the loading effect correction
using the parameterizations by Weingartner et al., 2003) at 450 and 660 nm
versus the effective diameter coarse Deff,coarse. for mineral
dust samples analysed in this study. Different symbols are used to
distinguish between dust from different sources. The uncertainty of
Deff,coarse is the standard deviation over 2 min data, while
that of Cref(W2003) is calculated with the error propagation
formula taking into account the uncertainty of βabs,ref and
that of βATT/R(W2003). (c, d)Cref at
450 and 660 nm versus the effective diameter coarse Deff,coarse
for the different aerosol samples analysed in this study. Different symbols
are used to distinguish between different aerosol types. Data for both
Cref(W2003) and Cref∗ (multiple scattering
correction obtained while not taking into account the loading effect correction in
Aethalometer data) are shown for ambient air aerosols, while for dust and
kaolinite, for which the difference between the different formulations is
very low, only Cref(W2003) is reported. The results of the linear
fits between Cref and Deff,coarse for mineral dust
and for the entire data set are also shown in the plot.
The dependence of Cref at 450 and 660 nm on the effective fine
Deff,fine and coarse Deff,coarse
diameters as a measure of particle size was investigated. The scatter plot of
Cref versus Deff,coarse is shown in Fig. 8 and
indicates that the Cref does not have any statistically
significant dependence on the particle size for mineral dust at both
wavelengths and for all data at 660 nm (R2≤0.40). Conversely, a
slight increase of Cref for increasing Deff,coarse is
obtained at 450 nm when all aerosol samples are considered (R2=0.70).
In contrast, no dependence of Cref on Deff,fine is
found (R2≤0.44, not shown).
Conclusions
In this paper we presented an intercomparison study between an Aethalometer
and a MAAP, a nephelometer, and two CAPS with the aim of determining a
two-wavelength multiple scattering correction (Cref) for Aethalometer
measurements for weakly absorbing mineral dust aerosols. Mineral dust
aerosols investigated here were generated from natural parent soils
collected in desert areas, both in the northern and southern
hemispheres (Di Biagio et al., 2014, 2017). The size distribution of the
generated dust included both the submicron and the supermicron fractions,
with an effective fine and coarse diameter between 0.32–0.55 and 2.3–3.6 µm, respectively.
The estimated Cref was in the range 1.81–2.56 at 450 nm and
1.75–2.28 at 660 nm for the different dust samples, with mean
Cref values of 2.09 (±0.22) and 1.92 (±0.17),
respectively. Using these values of Cref, the dust absorption
coefficient estimated by the Aethalometer will be about 2 % (450 nm) and
11 % (660 nm) higher than obtained by using the wavelength-independent
value of 2.14, which is commonly used in the literature (e.g. Sandradewi et
al., 2008; Formenti et al., 2011; Di Biagio et al., 2016). The new estimate
of Cref has a negligible impact on the dust SSA at 450 nm (less
than 0.5 % difference between the value obtained for
Cref=2.09 or 2.14), but affects the estimate of SSA by up to
∼ 3 % at 660 nm.
Given that the median of the solar spectrum occurs at about 700 nm, the
expected change in the dust SSA at 660 nm may significantly affect the
impact of dust on radiation. Mallet et al. (2009) estimated that about a
3 % change in the visible SSA of dust may determine up to a 10 % change
in the radiative effect of dust at the surface, and up to 20 % change at
the top of the atmosphere, with a net ∼ 25 % increase of
dust absorption in the atmosphere. Given the strong sensitivity of the dust
direct effect to particle absorption (Solmon et al., 2008; Mallet et al.,
2009; Di Biagio et al., 2010; Jin et al., 2016, among others), we recommend
this new Cref value at 660 nm to be used when analysing Aethalometer
data for mineral dust aerosols.
The analysis performed in this study indicates that there is no dependence of
Cref on the coarse component of the particle size distribution
for dust. This suggests that the Cref obtained here can be used
to correct Aethalometer data for dust at the time of emission, when the coarse fraction dominates the
dust size distribution, as well as after long-range transport, when the
coarsest component of dust has preferentially settled out.
Finally, our body of observations, spanning a wide range of SSA values from
0.96–0.97 (kaolinite) to ∼ 0.4–0.8 (ambient urban aerosols),
indicates that Cref decreases for increasing SSA, both at 450 and 660 nm. This is generally consistent with the results of
W2003 and C2010 at 660 nm. However, a unique relationship cannot be established. At high SSA
(> 0.90), our data, as well as those of C2010, suggest a sharper
decrease than at SSA in the range 0.4–0.8, whereas our data are more
consistent with those of W2003. Differences in aerosol sampling conditions
and in the exact analysed wavelengths from the three studies may be the
cause of such a discrepancy, but clear conclusions, as well as an explicit
relationship between Cref and SSA, are still difficult to state.
Similarly, our observations seem to indicate that Cref increases for
increasing Deff,coarse at 450 nm. This trend was only observed when
the entire data set was considered, and not when the data set was limited to
just the dust observations, making it difficult to draw clear conclusions.
A more extensive characterization of Cref is required to provide an
appropriate correction of Aethalometer data under the wide range of
atmospheric conditions.
Experimental and processed data are available upon request to the contact
author.
CDB and PF designed the experiments, discussed the
results, and wrote the manuscript with comments from all co-authors. NM provided the
MAAP used in the experiments. CDB, MC, and EP performed the experiments. CDB performed
the data analysis.
The authors declare that they have no conflict of interest.
Acknowledgements
The RED-DUST project was supported by the French national programme
LEFE/INSU, by the Institut Pierre Simon Laplace (IPSL), and by OSU-EFLUVE
(Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à
l'Exobiologie) through dedicated research funding. Claudia Di Biagio was
supported by the CNRS via the Labex L-IPSL, which is funded by the ANR
(grant no. ANR-10-LABX-0018). This work has also received funding from the
European Union's Horizon 2020 (H2020) research and innovation programme
through the EUROCHAMP-2020 Infrastructure Activity under grant agreement No
730997. The authors thank Konrad Kandler, Dave Seibert, and the LISA staff who
collected the soil samples used in this study, Emilie Journet who provided the
kaolinite sample, Andreas Petzold for helpful discussions on the Aethalometer
multiple scattering effects, and Benjamin Tamime-Roussel for logistic help with
the MAAP. The three anonymous reviewers are also gratefully acknowledged for
their helpful comments which allowed us to improve and clarify the
manuscript.
Edited by: Pierre Herckes
Reviewed by: three anonymous referees
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