The microphysical properties, composition and mixing state of mineral dust,
sea salt and secondary compounds were measured by active and passive aerosol
sampling, followed by electron microscopy and X-ray fluorescence in the
Caribbean marine boundary layer. Measurements were carried out at Ragged
Point, Barbados during June–July 2013 and August 2016. Techniques are
presented and evaluated, which allow for statements on atmospheric aerosol
concentrations and aerosol mixing state based on collected samples. It became
obvious that in the diameter range with the highest dust deposition the
deposition velocity models disagree by more than 2 orders of magnitude.
Aerosol at Ragged Point was dominated by dust, sea salt and soluble sulfates
in varying proportions. The contribution of sea salt was dependent on local wind
speed. Sulfate concentrations were linked to long-range transport from Africa and Europe, and
South America and the southern Atlantic Ocean. Dust sources were
located in western Africa. The dust silicate composition was not
significantly varied. Pure feldspar grains were 3 % of the silicate particles, of which about a third were K-feldspar. The average dust
deposition
observed was 10 mg m
Mineral dust and sea salt are globally the most abundant aerosol types by mass in the atmosphere (Andreae, 1995; Grini et al., 2005). They are considerably affecting the earth's radiation budget (Liao and Seinfeld, 1998; Choobari et al., 2014) by scattering and absorbing solar and terrestrial radiation. Moreover, they modify cloud processes by supplying condensation nuclei and changing the atmospheric stability conditions (Koehler et al., 2009; Tang et al., 2016; Karydis et al., 2017). Over the North Atlantic Ocean, large amounts of dust are transported westwards in the Saharan Air Layer, until they reach the Caribbean (Karyampudi et al., 1999; Prospero et al., 2014). Here, dust usually does not cross the “Central American dust barrier” to the west. Instead, it is mainly removed from the atmosphere, but, to a lesser extent, also transported in meridional directions (Nowottnick et al., 2011). With respect to the removal, dust becomes mixed down into the marine boundary layer by turbulent and convective processes. Here, it is then subject to wet and dry deposition processes, which remove it from the atmosphere. However, these deposition processes are not yet fully understood (Prospero and Arimoto, 2009; Nowottnick et al., 2011).
During its transport, mineral dust may undergo modifications from physical and chemical processing, cloud processing or microphysical effects (Andreae et al., 1986; Falkovich et al., 2001; Matsuki et al., 2005; Sullivan et al., 2007a, b). These processes will change the composition and particle size of dust, and thus modify its radiative properties and cloud impacts. For example, an addition of a soluble compound to an insoluble dust particle may obviously on one hand alter its cloud droplet activation properties (Wurzler et al., 2000; Garimella et al., 2014). On the other hand, it might deactivate the original dust particle for deposition ice nucleation (Cziczo et al., 2009). In addition, the coating may on one hand enhance the scattering of the particle in dry state by adding non-absorbing material and increasing its size (Bauer et al., 2007; Li and Shao, 2009), but, on the other hand, in a deliquesced state the water shell may increase absorption (Bond et al., 2006), enhancing the absorption of corresponding dust components (Lack et al., 2009).
To assess the mixing state of mineral dust, techniques considering single particles are required. While there have been investigations in the past (Zhang and Iwasaka, 1999; Zhang and Iwasaka, 2004; Dall'Osto et al., 2010; Deboudt et al., 2010; Kandler et al., 2011a; Fitzgerald et al., 2015), the data basis is still limited. In particular, previous studies based on electron microscopy did not take into account methodological problems. Also, they observed smaller particle numbers, affecting statistical significance, and used shorter observation periods. Studies based on single-particle mass spectrometry, in contrast, were not able to quantify elemental contributions to single particles and thus could not conclude on material fluxes. In the present work, we present results from two field campaigns in the summers of 2013 and 2016, where the aerosol in the marine boundary layer at Ragged Point on Barbados was collected by active and passive sampling techniques.
A particular challenge for these campaigns was the high wind speed and the high humidity at the sampling site. Therefore, the present publication consists of an extended methodological section in the Supplement with three major topics and methodological as well as atmosphere-related results sections. One methodological section deals with the determination of composition and mixing state of individual particles, taking into account quantification artifacts and modeling the dust and non-dust components as well as their hygroscopic behavior. A second section is on particle collection representativeness and models that relate atmospheric concentration and deposition, taking into account the single particle properties at ambient conditions. Finally, when aerosol mixing state is assessed based on offline aerosol analysis (i.e., analysis of aerosol particles collected on a substrate), considerations on coincidental mixing have to be made to ensure the representativeness of the results for the atmosphere. Therefore, in a third section these fundamental considerations based on model as well as experimental data are presented. In the results section, we report first on these theoretical and experimental methodological aspects, before we then discuss the atmospheric implications of the measurements.
Aerosol was sampled at Ragged Point, Barbados (13
A FWI was constructed for inlet-free collection of particles larger than
5
The DPDS used in the present work is derived from the flat plate sampler of Ott and Peters (2008), which performed best with respect to wind speed dependence in their tests. It consists of two round brass plates (top plate diameter of 203 mm, bottom plate 127 mm, thickness 1 mm each) mounted in a distance of 16 mm. In contrast to the referred design, the one used here has a cylindrical dip in the lower plate, which removes the sampling substrate, a SEM stub with a height of 3.2 mm, from the airflow, reducing the flow disturbance. The dip is larger than the SEM stub and has small holes in the bottom to catch and dispose of droplets creeping across the lower plate due to the wind dynamical pressure. The top surface of the SEM stub is located 5 mm below the lower plate's top surface. Larger droplets (>1 mm) are prevented by this setup from reaching the SEM stub surface at the local wind speeds (Ott and Peters, 2008). A total of 29 samples were collected in 2013 and 22 in 2016 mostly with an exposure time of 1 day.
While the principal design of the used CI is described by Kandler et al. (2007), a new version with a larger housing but the same collection
characteristics, was deployed in the present work. An omnidirectional inlet
with a central flow deflector cone was used, the transmission of which is discussed
in Sect. 2.4.3. The impactor was operated at a flow rate of 0.48 L min
In 2013, meteorological data was obtained at Ragged Point directly next to the particle sampling devices. In 2016, wind, temperature and relative humidity were measured in parallel at The Barbados Cloud Observatory at Deebles Point, which is located 400 m across a small cove to the southeast (Stevens et al., 2016).
The measurements in 2013 are grouped into two time periods divided by the passage of Tropical Storm Chantal, which changed the atmospheric structure and air mass origin (Weinzierl et al., 2017). The period from 14 June to 8 July will be referred to as pre-storm, the one from 10 to 15 July as post-storm.
Backward trajectories were calculated with Hysplit 4 rev. 761 (Stein et al.,
2015) based on Global Data Assimilation System (GDAS) with 0.5
As a result, a map based on PSCF shows regions with typically high or low values for air masses passing through the boundary layer in according grid cells. Note that by this approach, sources contributing to advected aerosol can be identified, but local sources of course will not provide a usable signal. Also, aerosol from remote sources might be transported inside the boundary layer and, thus, would also be attributed to the transport path in addition to its source.
About 22 000 (FWI), 65 700 (DPDS) and 26 500 (CI) individual particles were
analyzed with a scanning electron microscope (SEM; FEI ESEM Quanta 200 FEG
and 400 FEG, FEI, Eindhoven, The Netherlands) combined with an
energy-dispersive X-ray analysis (EDX; EDAX Phoenix, EDAX, Tilburg, The
Netherlands and Oxford X-Max 120, Oxford Instruments, Abingdon, United
Kingdom). The samples were analyzed under vacuum conditions (approximately
10
The image analysis integrated into the SEM-EDX software determines the
particles size as projected area diameter:
Following Ott et al. (2008), the volume-equivalent diameter is estimated
from the projected area diameter via the volumetric shape factor expressed
by particle projected area and perimeter as follows:
Note that estimating the volume-equivalent diameter by this technique can be source of a bias if the particles are largely non-isometric, e.g., droplets based on soluble material drying into a flat film. As it was observed here that sea salt and sulfate mostly form crystals and clumps, this effect is regarded of minor importance for this work.
In addition, for the assessment of particle coverage homogeneity and size distribution determination series of 1000 to 2700 images were acquired for each sample. They were analyzed by the Software Fiji/ImageJ 1.51d (Rasband, 2015), also using Eq. (2) for particle size determination after application of a “triangle” type auto threshold for particle segmentation (refer to Fiji/ImageJ documentation for further details).
Comparison of element weight ratios for albite and sodium chloride
powder as function of particle size. The nominal ratios for the compounds
are shown as orange lines.
Fully quantitative results in EDX analysis can only be achieved under
specific sample conditions. When the composition of an analyzed spot is
derived from an X-ray spectrum, the sample geometry has to be considered.
Besides assuming perfect smoothness and homogeneity, either
infinite sample depth (i.e., significantly larger than the interaction volume
of a few
Note that the concentrations are always normalized to 100 % of the beam interaction volume. This can include not only the particle, but also the substrate. For this reason, the substrate was chosen to be composed as differently as possible to all expected particle compositions. The correction is identical for atomic and mass concentrations; in the present paper, atomic concentrations are used unless otherwise specified.
The result of the correction as function of particle size is shown in Fig. 1c for 20 kV as demonstration (at 12.5 kV as used for the sample analyses, the problems are obviously less pronounced). It becomes clearly visible that the accuracy of the quantification is strongly improved, while the remaining uncertainty originates mainly from the particle to particle variation. This uncertainty depends on the noise in the analysis system, but is also related to particle surface morphology and its variability. The latter affects the X-ray signal mainly by unknown absorption path lengths, particularly for the lighter elements, as illustrated by Fletcher et al. (2011).
Calculated ion balance for all beam interaction volumes containing
particles dominated by Na and Cl. Particles were collected by the DPDS. The
axes are scaled in arbitrary units of percent
Application to a sample of atmospheric particles is shown in Fig. 2.
Particles dominated by Na and Cl were selected from all DPDS samples, and
the positive and negative ion contributions were calculated for each
particle from the determined concentration. It becomes obvious that for a
wide size range the applied correction works well and thus produces
unbiased relative concentrations for the considered elements. The outliers
may occur due to noise, the negligence of C, N and O compounds or an
internal mixture of sea salt with dust (e.g.,
Mean element index only using Na, Mg, S, Cl and Ca for
normalization, and according standard deviation (1
A typical deposition sample (collected between 21 June 2013, 13:46 and 22 June, 15:02, LT) was analyzed 29 times with a signal collection time
per particle of 16 s. The same 300 particles were analyzed each time. For
illustration of the typical precision, the particles consisting mainly of Na
and Cl were selected. Figure 3 illustrates the average composition and
standard deviation (1
The uncertainty in particle diameter also depends on its size. For particles
with a 2
For assessing the abundances and counting statistics of certain particle types, the particles were classified into different groups and classes. Based on the element index and additional elemental ratios, a set of rules used in former mineral dust investigations in a marine environment was applied.For details, refer to Kandler et al. (2011a).
In addition, a relative ion balance is defined for single particles as follows:
A positive relative ion balance, i.e., an excess of positive ions, would
indicate an undetected presence of negative ions like
For the description of feldspar abundance, we define two index values,
showing the vicinity for a single particle to pure feldspar or pure
K-feldspar composition. These feldspar indices regard the overall
contribution of feldspar-specific elements to the particle and the specific
Al
Sampling was performed in a region where locally emitted sea salt aerosol and other soluble species are mixed with long-range transported mineral dust. In particular, as the mineral dust contribution is of special interest, disentangling the particle populations and considering them separately is an important task.
To calculate the size of a dust inclusion and the according volume fraction for an internally mixed particle from the chemical composition, the different elemental contributions have to be attributed to the dust or non-dust component. This analysis is restricted in the present work to the major compounds. For Al, Si, P, Ti and Fe it can be safely assumed that they belong to a dust component (i.e., an inorganic, thermally stable, oxidized, non-carbonaceous compound), and S and Cl can be attributed to the non-dust component. However, Na, Mg, K and Ca are ambiguous and can be present in fractions. Therefore, a model is needed to estimate the contribution of the ambiguous elements from the dust and non-dust component based on the single particle chemical composition.
A problem arises here from the error in chemical quantification due to
matrix composition and particle geometry. While the correction outlined in
Sect. 2.3.1 adjusts the quantification accuracy of the average particle
composition, for single particles – because of their unknown geometry and
surface orientation angles – a considerable error in element
quantification can still occur. In particular, a bias between lighter and heavier
elements can be introduced by unaccounted X-ray absorption, which can lead
to under- as well as overestimation of the relative contribution of light
elements (Fletcher et al., 2011). As for the present aerosol, the major
cations (
The model outlined here may suffer from systematic errors.
In the presence of larger amount of nitrate and ammonium or organics, the
dust contribution will be overestimated, as the regarded composition is
fitted to the apparent particle volume. However, on Barbados the
concentration of these compounds is usually small in comparison to the dust
(Lepple and Brine, 1976; Savoie et al., 1992; Eglinton et al., 2002;
Prospero and Arimoto, 2009; Zamora et al., 2011). The density values are averages for the assumed components, and the real
density of a particle may be smaller or larger. However, the density range
for the components in question is small (dust: 2300 to 3000 kg m The mass contribution is estimated by ion charge balances. If for the
ambiguous elements an inhomogeneous distribution of univalent and bivalent
elements exists (e.g., univalent as with Na favoring the non-dust component and
bivalent as with Ca favoring the dust component), an error of less than 5 %
in diameter can occur. With an assumption of 5 % iron content in dust,
the maximum error due to the
The upper and lower estimates yield diameters, which differ for the dust
core diameter on average by 25 %; for 75 % of the particles the
difference is less than a factor of two. From the analytical errors in
ratios for major compounds (less than 10 % systematically and 6 %
repetition uncertainty), an dust core size uncertainty of about 6 % is
estimated, as long as the core is larger than 10 % of the particle. An
overall analytical uncertainty of 15 % relative core size is estimated.
In conjunction with the upper or lower limit estimates, an overall core size
error of 25 % is considered appropriate.
Iron bioavailability in general is depending on different chemical and
microphysical parameters as well as residence time in chemically aggressive
environments (Shi et al., 2011a, 2012), e.g., at low pH values
under influence of sulfuric or nitric acid. If considering a homogeneous
iron distribution in larger and smaller particles, it seems plausible that
the distance to the surface, thus the surface to volume ratio,
should have an impact on the short-term iron accessibility (e.g., Baker and
Jickells, 2006; Shi et al., 2011b). Therefore, as a first order estimate we
define a geometrical surface iron availability index SIAI (after virtual
dissolution of the soluble compounds) as follows:
It should be noted that this approach is of a geometrical nature only and does not take into account environmental factors like pH and presence of ligands.
When assessing the uncertainty of values based on counted occurrences, frequently the counting statistics are assumed to follow a Poisson distribution. However, when calculating total aerosol masses or volumes, besides the measurement errors in particular the (usually few) large particles can introduce a considerable statistical uncertainty, which is not necessarily accounted for by the distribution assumption. Therefore, estimates of the statistical uncertainty based on single particle counts for an a priori unknown frequency distribution (i.e., the counting frequency distribution modified by the also unknown particle size distribution), either require reasonable assumptions or distribution-independent estimators. In the present work, the uncertainty is estimated by a bootstrap approach with a Monte Carlo approximation (Efron, 1979). For the bootstrap approach, a considerable number of data replications are necessary (Carpenter and Bithell, 2000; Pattengale et al., 2010). As for the actual number, different recommendations exist, with more than 1000 being among the most common (Carpenter and Bithell, 2000). As higher numbers lead to smaller errors in the uncertainty estimate, 10 000 replications for each sample were performed in the present work. A comparison of the results of the generally robust bootstrap approach (Efron, 2003) to a more simple approach, where the counting statistics is assumed to follow a Poisson distribution, is given in Sect. S3 of the Supplement.
For the assessment of the confidence interval of relative counting
abundances, a confidence interval based on a binomial distribution is used
as an estimate (Agresti and Coull, 1998), i.e., for a relative number abundance
of a certain particle type class
Obtaining the atmospheric size distribution and representative contributions of particle populations with different hygroscopicity from the FWI requires two corrections. These corrections are applied to each single particle as a function of its size and composition and the thermodynamic and humidity conditions during sampling. First, a window correction is applied, accounting for the exclusion of particles at the analysis image border (Kandler et al., 2009). Second, the FWI collection efficiency is corrected. For the detailed formalism, refer to Sect. S4 of the Supplement.
Potential systematic error sources for this calculation are mainly the uncertainty in collection efficiency, given the considerable spread in data points in the according literature (Golovin and Putnam, 1962; May and Clifford, 1967), and any bias in particle size.
Sampling efficiency considerations are also necessary for the sedimentation sampler. For the supermicron particle size range sedimentation and turbulent impaction dominate the particle deposition velocity (as for example illustrated by Piskunov, 2009).
Deposition velocity to a smooth surface calculated by different
deposition models for the samples of 2013, taking into account the ambient
thermodynamic conditions and the particle composition.
A variety of models estimating the particle deposition speed were published (Sehmel, 1973; Slinn and Slinn, 1980; Noll et al., 2001; Wagner and Leith, 2001; Aluko and Noll, 2006; Piskunov, 2009; Petroff and Zhang, 2010). They yield considerable different results, possibly due to negligence of unaccounted forces (e.g., Lai and Nazaroff, 2005), the way of determining the relevant friction velocity, or other model assumptions. For the present work, the formalism of Piskunov (2009) was selected, as it derives the deposition velocity from physical principles instead of parameterizing a specific measurement setup. Details of the calculation procedure are given in Sect. S5 of the Supplement. The deposition velocity calculated by different formalisms for a series of deposition samples is shown in Fig. 4.
The spread in deposition velocity for each model is caused mainly by the
different wind speeds during exposure, but also by the variation in relative
humidity and, to a lesser extent, by other thermodynamic conditions.
However, it becomes strikingly obvious that in the size range where most of
the atmospheric dust deposition occurs, i.e., between 2 and 50
The impactor sampler was used with two types of inlets. For particles larger
than approximately 2.5 Particles larger than 2.5 Particles smaller than 2.5
For a dry aerosol, these size-selective inlet losses would not considerably bias the relative chemical composition. In the present humid environment with partly soluble species, though, it can lead to an overestimation of non-hygroscopic species for particle sizes in the vicinity of the inlet cut-off if the hygroscopic growth is not explicitly considered. The problem is somewhat diminished by the fact that by water-absorption the density of the particles decreases and, consequently, the Stokes number increases only sub-proportionally to the square of the particle diameter. Nevertheless, the hygroscopic growth should be explicitly accounted for. Therefore, the hygroscopicity model is applied based on the measured geometric diameter and chemical composition, and ambient chemical compositions are computed.
When particles are deposited to a substrate, they might touch each other and form an internal mixture, which is not representative for the atmosphere. While the lower limit of coincidental internal particle mixture on a substrate is easily defined – it equals the ratio of the area covered by particles to the total analyses area for an infinitesimally small depositing particle – the assessment is much more complex for larger particles following a wide size distribution function.
Therefore, in the first step the deposition process was simulated by a
series of Monte Carlo models. For input, the average size distribution
measured at Cape Verde (Kandler et al., 2011b), hereafter CV-ground, and
the median one measured airborne for aged dust (Weinzierl et al., 2011),
hereafter CV-air, were used. These size distributions mainly differ in the
concentration of supermicron particles. The deposition velocity formulation
after Piskunov (2009) was used. The modeled deposition area is 5 mm
To investigate the relevance of mixing artifacts caused by particle
sampling, the sensitivity of SEM/EDX analysis has to be considered. Internal
mixtures can be only detected by SEM/EDX if the minor component exceeds the
limit of detection. At an acceleration voltage of 12.5 kV, the primary X-ray
excitation volume is in the range of 0.5 to 1.5
Besides these fundamental considerations, in the second step, a mixing model was applied to each sample, based on its measured composition. Random particles were virtually selected from the pure components of the measured set of particles and placed at random positions inside a virtual area with the same size as the one analyzed in SEM/EDX, until the same area coverage as of the real sample was reached. Internal mixtures artificially produced on the substrate were counted if their mixing would have been detected by SEM/EDX applying the rules for mixed particle classification. This process was repeated 10 000 times. The upper 95 % confidence interval limit of mixtures modeled by the Monte Carlo simulation was considered as the limit of detection for internal mixtures, and the median of the produced mixtures was regarded as systematic error and was subtracted from the mixtures detected in the real samples.
In the third step, the single mixing probability (SMP) for each binary pure compound combination was calculated by selecting 100 000 random pure-composition particles from the measured data set for each sample, mixing them virtually and determining whether they would be detected as mixed. This was carried out once without any size restrictions and a second time only selecting particles no more than a factor of 3 different in size. The latter was done to account for the fact that in a turbulent environment and in the regarded size range, the collision efficiency is highest for particles of similar size (Pinsky et al., 1999; Wang et al., 2005).
While in the modeling section particles are assumed to be spherical, this is typically not the case for natural aerosol like mineral dust particles. Therefore, a second approach based on particle images was used to estimate the effect of internal particle mixture on the substrate, i.e., taking into account the real particle shapes. Due to the large number of images required, this approach could only be used for assessing the size statistics, but not for the chemical composition. All segmented images of each deposition sample were subject to particle size analysis. In following steps, a number of 2, 3, 5, 10, 15, or 20 segmented images of the same sample were combined into a single image, simulating an extension of exposure time by the according factor. This approach inherently assumes a constant size distribution during exposure and a random particle deposition. The resulting images were then subject to the same particle analysis, yielding apparent size distributions after a coincidental mixing. In contrast to the pure modeling approach, here the true size distribution is not known because even the lowest coverage samples might contain internal mixtures. Certainly though, the lowest coverage sample is closest to the true size distribution and thus will be used as reference.
To assess the homogeneity of particle distribution, for each sample, the
center 80 mm
Similar to above, the DPDS deposition density homogeneity was assessed,
but in this case nearly all of the central 80 mm
Number
Number
Figure 5 shows the apparent number and volume size distributions of particles
deposited from aerosols with CV-ground (a, b) or CV-air (c, d) size
distribution for different area coverages, equaling different exposure
times. As it is to be expected, for short exposure times there is a
considerable counting error, which decreases to less than 10 % for the
smaller particles at area coverages of 0.01 and higher. In the median, no
particle larger than 50
Particle volume per area calculated from single particle
measurements as function of the fractional area coverage. Blue symbols
denote the unmodified samples, red symbols the simulation of higher coverage
by factors of 2, 3, 5, 10, 15 and 20. Error bars denote the two-sided 95 %
confidence interval. The fit function shown as black dashed line is
calculated as
Generally similar but more pronounced effects can be observed if the
second approach, simulating longer exposure times by combining real
microscope images, is used. Figure 6 shows three samples, low (a, b),
medium (c, d) and high (e, f) area coverages, of the evolution of the size
distribution due to simulated longer exposure times. In case of high dust
deposition rates and long exposure times, particles smaller than 10
If total mass deposition is estimated from the microscope images, one can
set up a relation of total volume and apparent area coverage, which might
serve as a quick estimate of total deposited particle mass (Fig. 7). If the
result of the fit function is multiplied with an approximate particle
density, the result gives the deposition in mg m
Upper 95 % quantile of the fractions of internally mixed particles due to coincidental mixing on the substrate (color scale), for a dust–sea salt–sulfate system with measured composition and CV-ground size distribution. Strong mixture refers to a minimum particle volume fraction of the other component of 20 %, detectable mixture refers to 1 %. Mixing compounds are given on top of each graph. Practically, sulfate–sea salt and ternary mixtures do not form coincidentally.
When calculating total mass/volume from small amounts of material, special
attention has to be paid to the errors introduced by counting statistics. To
assess the uncertainty, two size distributions were considered with
different abundance of large particles. Using the CV-ground size
distribution, we observe an uncertainty of a factor of 2 for the total mass
(95 % two-sided confidence interval), when 3000 particles are counted,
which are equivalent to 8
Average atmospheric mass size distribution densities derived from
DPDS and FWI measurements.
When assessing the mixing state of particles from an offline single particle technique, coincidental internal particle mixture has to be taken into account. Higher area coverage, as to be expected, yields higher mixture probability. In particular, if components are present in equal abundances, mixing probabilities already become high for a covered area fraction of a few percent. As an example, Fig. S7 in the Supplement shows the upper 95 % confidence limit, i.e., the detection limit for mixtures, of apparent fractions of internally mixed particles for a two component system as function of source component ratio and area coverage for detectable strong internal mixtures (refer to Sect. 2.5; data are given also in the Supplement, Tables S4 and S5). No significant mixture for submicron particles occurs in these cases. Note also the different size maximum for strong vs. detectable mixture.
Applying the same model type based on the CV-ground size distribution to a ternary modal composition distribution of sulfate, sea salt and dust as described in Sect. 2.5, mixing probabilities for a specific atmospheric composition can be estimated (Fig. 8). It becomes instantly obvious that the mixing probabilities for this atmospherically more relevant aerosol model are much lower than in the homogeneous case. Mixtures between sulfate and sea salt as well as ternary mixture are absent. The relative fraction of internally mixed particles is lower by an order of magnitude. This can be explained by the fact that the defined relative detection limits of 20 % and 1 % restrict the detection of mixing to mixing partners not differing in size by more than a factor of 1.59 (strong mixing) and 4.6 (detectable mixing). However, because different aerosol types are mainly present in different size regimes here (Schladitz et al., 2011), the mixture can only be efficient for size ranges, where these component have an overlap. In general, mixture also increases with particle size.
It can be concluded that mixing studies for large particles are
generally very difficult. Many particles need to be collected in total to
ensure reliable counting statistics, which leads in consequence to high
mixing probabilities. This issue is of less concern for particles smaller
than 10
Dust mass concentration and flux density time series derived from DPDS compared to those obtained from a high-volume sampler (Kristensen et al., 2016). The darker brown bar shows the range from lower to upper estimate, the blue triangles the lower and upper estimate of dust deposition flux density. The date refers to the year 2013.
Using the FWI sampling efficiencies outlined in Sect. 2.4.1 and the DPDS
deposition velocities from 2.4.2, one can calculate the atmospheric size
distribution derived by the two techniques. Figure 9 shows the average size
distributions for the post- and pre-storm periods based on different
deposition velocity models for total and upper estimate dust mass
concentrations. The lower dust estimate (not shown) exhibits qualitatively
the same behavior. It is evident that there is a large discrepancy between
the different models as well as between the DPDS and FWI measurements. The
discrepancy is clearly larger than the statistical uncertainties. While the
total mass median diameter derived from DPDS (Piskunov model) is around 5
Size dependence of the relative number abundance of major particle types, as derived from single particle analysis of deposited aerosol.
When total mass is calculated from deposition, it can be compared to dust concentration measurements with a high volume filter sampler. Figure 10 shows time series of mass concentrations measured by the high-volume sampler, estimated from dry deposition measurements as well as the raw dry deposition flux densities. For dry deposition, uncertainties derived from the lower or upper estimates as well as from counting statistics are shown. A few things can be learned from this data. With respect to the deposition model, the Piskunov model performs rather well. The average of the high-volume sampler mass concentration time series (see Table 1) is close to the lower estimate of the Piskunov model, while the higher estimate overestimates the mass concentration. The other models deviate considerably more, as to be expected from the deposition velocity differences (Fig. 4). The ratio of the mass concentration estimate to the mass flux density varies over slightly more than an order of magnitude, depending mainly on size distribution and wind conditions. High volume and deposition-estimated mass concentrations as well as the mass flux densities follow qualitatively the same pattern in showing low concentration and high concentration periods. However, the absolute numbers deviate significantly. For sub-periods, the correlation quality seems to be different. For example, starting from 21 June, the correlation of mass flux with high volume mass concentrations seems to be better than the one with deposition estimated concentrations; for the period before 21 June the situation is inverted. No direct link between the correlations and any meteorological variable was found, indicating that the deviations depend in part on erroneous assumptions in the model. For example, tuning other deposition velocity models by arbitrary factors can lead to a better agreement of actively and passively determined mass concentrations for this particular data set (Fig. S10 in the Supplement), but the data basis is too small for a robust tuning without physical backing. Moreover, disagreement might also be caused by physical measurement biases like unknown size-dependent inlet efficiency for the high-volume sampler or angular inflow for the DPDS.
Average dust mass concentrations, estimated from deposited particle mass, applying various deposition models. Lower and upper refer to different dust fraction estimates (see Sect. 2.3.3).
Overall aerosol composition (i.e., the relative number abundance of the different particle groups) was measured by electron microscopy single particle analysis (Fig. 11). The relative abundance of soluble sulfate is highest for the smallest particle sizes, which is in good accordance with previous measurements in the eastern Atlantic Ocean (Kandler et al., 2011a). After the storm's passage, higher sulfate abundances, soluble as well as stable, are observed in 2013, which are similar to those observed in 2016. The sea salt abundance is higher for the pre-storm period in 2013, which is in agreement with the wind speeds observed (see below). In 2016, a much higher abundance of small Fe-rich particles (contained in the oxides/hydroxides class) is observed compared to the pre-storm period in 2013. For the post-storm period in 2013, minor amounts of these particles are visible.
Overall, an average dust deposition of 10 mg m
Number ratio of total feldspar (filled symbols) and K-feldspar (open symbols) to total silicate particles, as a function of particle aerodynamic diameter in dry deposition collected on Barbados. For comparison, data from Tenerife (Kandler et al., 2007) and Morocco (Kandler et al., 2009) are given. Only data points with less than 30 % relative counting error are shown.
Recently, the impact of mineral dust composition on clouds via the ice phase has attracted attention. Feldspar particles and in particular K-feldspars are discussed as most efficient ice nuclei (Atkinson et al., 2013; Augustin-Bauditz et al., 2014; Harrison et al., 2016). Therefore, Fig. 13 shows the total feldspar and K-feldspar number fractions with respect to all silicates as determined by the feldspar indices. In general, approximately 3 % of the silicates are pure feldspar particles and slightly less than 1 % K-feldspars. No significant variation is visible for the different periods and years on Barbados, whereas particles collected in Morocco (Kandler et al., 2009) showed slightly higher values. In this respect, the dust composition on Barbados is constant over time. Note that the bulk feldspar contents of the samples might be higher, as the applied technique only detects pure feldspar grains.
Potential source contribution functions (PSCF) of deposited
material: dust
Ca
The air mass provenance of the sampling periods in 2013 and 2016 is generally similar. The trajectories mostly followed the trade-wind path from northwestern Africa and the eastern Atlantic Ocean to Barbados (Fig. S8 in the Supplement). In 2013, the air was coming more frequently from western Africa than in 2016. After Tropical Storm Chantal in 2013, the air mass origin shifted slightly to more southern regions. In a few cases in 2013, air from the northwestern Atlantic Ocean was recirculated into the trade-wind path.
The sea salt deposition rates are not linked to air mass provenance (not
shown). The dust provenance for both years (Fig. 14a, b) is, as expected,
pointing to West Africa. This source region is also identified by isotope
measurements in July–August 2013 (Bozlaker et al., 2018). The soluble
sulfate deposition (Fig. 14e, f) is generally linked to three regions, the
Atlantic Ocean, West Africa and southwestern Europe. In 2016 in particular,
the sulfate sources appear to be located more in Europe and less in Africa.
The relative ion balance (Fig. 14g, h) shows mostly slight negative
values indicating presence of
Iron contribution from dust is of particular interest for marine ecosystems. Therefore, Fig. 14c and d show the silicate SIAI as a proxy for quick iron availability. It is obvious, that the iron-containing silicate particle source is located in West Africa. Northern and southern West Africa as source regions can not be distinguished after transatlantic transport, in contrast to investigations close to the source (Kandler et al., 2007). This is consistent with observations based on isotope analysis, where a homogeneous composition has also been observed on Barbados (Bozlaker et al., 2018). A slightly higher SIAI can be observed in 2016 than in 2013, while the dust deposition rates, in contrast, are lower. While the total iron deposition correlates well with dust deposition (not shown), similar to observations by Trapp et al. (2010), for the SIAI an inverse relationship is found on Barbados, with higher dust deposition rates leading to lower ratios of SIAI to total dust. This correlates to previous findings, where iron solubility decreased with increasing dust concentration (Shi et al., 2011b; Sholkovitz et al., 2012), though no direct causal relationship can be derived (Shi et al., 2011a).
When considering sea salt composition, it is generally assumed that except
from the sulfate content, aerosol produced from seawater has a major
composition, resembling the bulk seawater (Lewis and Schwartz, 2004).
However, it was recently shown in the Arctic that a fractionation can also occur with respect to the major composition (Salter et al., 2016). On
Barbados, an increasing positive deviation from the nominal value of 0.022
with decreasing particle size is observed for the Ca
Time series of wind and particle number deposition rates for pure compounds and internally mixed particles for June–July 2013 and August 2016. Particle size ranges are given in the top left of each graph. The limit of detection for the number of internally mixed particles is shown as line in the according color. Where only the detection limit for silicate–sulfate mixtures is visible, both limits are identical.
If we consider the abundance of mixed particles on Barbados, a complex picture emerges as function of particle size, time period and available mixing partners (Fig. 16). It can be observed that the total deposition rate for all particle types is linked to the wind speed, which is to be expected from the physical process (see for example Fig. S9 in the Supplement). The higher sea salt deposition rates and also higher concentrations in 2013 in comparison to 2016 are also linked to the wind speed, showing the local sea salt production. In contrast, the dust concentration is slightly lower for higher wind speeds (Fig. S9) for both years. With increasing particle size, the relative abundance of internal dust–sea salt mixtures increases (Fig. 16), but these mixtures only occur when considerable amounts of sea salt are present. This is different for the internal mixture with sulfate. While there are similar ratios of dust and sulfate particles observed in the second half of the 2013 data as in 2016, in 2013, dust–sulfate mixtures are practically absent. Assuming that higher wind speeds in 2013 should lead to more internal mixing, due to increased turbulence, this is clearly indicating that, in contrast to the sea salt–dust mixture, the sulfate–dust mixture has a non-local origin (e.g., Usher et al., 2002).
Deposition velocities calculated with the Piskunov model for
internal admixture of sea salt
This is corroborated by the dependence of the internal mixtures relative
abundance on the single mixing probability (Fig. S11 in the Supplement). If one considers the binary number fraction of mixed
particles, i.e., ratio of binary mixed particles to pure compounds, as
function of the size-restricted single mixing probability, there is a weak
positive correlation with dust–sea salt mixtures for particles larger than 2
The overall ratio of dust–sea salt internal mixture abundance to all dust
and sea salt particles increases from 0.01–0.03 for 1
If the findings on Barbados are compared to measurements in the eastern Atlantic Ocean (Kandler et al., 2011a), a generally lower abundance of internally mixed particles with respect to dust–sulfate is observed, while comparable abundances of sea salt–dust mixtures are found. While the latter can be explained by similar wind conditions and comparable single mixing probabilities, the former seems to be caused by different aging conditions. Dust arriving over Barbados is transported mostly in the dry Saharan Air Layer (e.g., Schütz, 1980), while dust arriving during winter-time at Cape Verde is transported inside the humid marine boundary layer (Chiapello et al., 1995; Kandler et al., 2011b). Therefore, considerably higher chemical processing rates at Cape Verde due to the higher humidity can be expected (Dlugi et al., 1981; Ullerstam et al., 2002), even though the transport time is most likely shorter. In addition, the boundary layer most probably provides higher concentrations of sulfur compounds for reaction (Davison et al., 1996; Andreae et al., 2000).
Effective deposition velocity for all dust-containing particles observed at Ragged Point. The blue curves take into account internal mixing and hygroscopic growth at ambient conditions, whereas the orange only regards the dry dust fraction of the particles. In addition, cumulative mass distribution is shown on the inverted right axis. Particle size is given as aerodynamic diameter for the dust fraction of a particle. For the ambient deposition velocity, the geometric mean for each size class is shown in conjunction with the 1 geometric standard deviation range.
If dust particles become internally mixed, their mass, size and hygroscopic
behavior change. Therefore, they will have modified deposition velocities as
well as hygroscopic properties. Figure 17 shows the increases in deposition
velocities for mixed particles observed at Ragged Point in 2013 and 2016.
For the both mixtures (dust–sea salt and dust–sulfate), an increase at
ambient conditions of a factor of 2–3 is observed for submicron dust
particles, which rises to a factor of 5–10 for particles of 3
Aerosol deposition measurements by means of passive samplers were carried out on a daily basis at Ragged Point, Barbados in June–July 2013 and August 2016. In addition, active aerosol collection was performed with a cascade and a novel free-wing impactor. Size, shape and composition of about 110 000 particles were determined by electron microscopy. Focus was placed in this work on measurement accuracy of chemical composition and mixing state determination for individual particles.
Ragged Point is a high-wind and high-humidity environment (in 2013 in particular), which considerably influences representativeness and accuracy of the different sampling techniques. A deposition model including chemistry-dependent hygroscopic growth was adapted to the sampling situation to assess atmospheric concentration of large particles. Fair agreement was reached between passive and active techniques regarding mass concentration, but clear discrepancies were observed for particle size distribution.
Special attention was paid to the mixing state of dust particles. A model
was developed to assess the mixing state of airborne particles by correcting
for sampling artifacts due to particle overload, leading to coincidental
internal mixing of particles on the substrate (i.e., not representative for
the airborne state). Different approaches were tested based on model size
distributions and observed particle deposition images. It was found that the
size distribution is only weakly affected for substrate area coverages with
particles below 10 %. However, the chemical composition of mixtures is
already affected at much lower area coverages of
During our measurement campaigns, the aerosol was dominated by dust, sea salt and sulfate in changing proportions. The sea salt concentration at Ragged Point is mainly dependent on wind speed. Back trajectory analysis showed that dust is originating from the usual sources in West Africa, and the dust composition with respect to different silicate phases was not varying. Sulfate showed three major potential source areas, Africa, Europe and the Atlantic Ocean. In 2013 in particular, sulfate was more linked to the African source, while in 2016 southwestern Europe occurred as a potential source, with a possible contribution of nitrate.
It was further found that internal mixing of dust and sea salt depends on local wind speed, and we thus hypothesize that it is produced locally, most likely by turbulent processes. In contrast, mixtures of dust and soluble sulfates are presumably not produced locally, but may have formed during the intercontinental transport. Even though the overall amount of internally mixed particles is comparatively low, a considerable impact on total dust deposition velocity is estimated. In addition, a pathway is hypothesized by which the ice-nucleation efficiency of dust can be increased by mixing with soluble compounds during or after the long-range transport.
For future work, the following conclusions can be drawn from our
observations.
If different techniques for deposition and/or atmospheric concentration
measurements are compared, it is crucial to measure particle size
distributions. We observed in some cases that total mass concentration can
compare rather well, even though size distributions, and thus
collection efficiencies, are considerably different. A better understanding, in theory as well as in experimental use, of
particle deposition and collection efficiencies is required in particular
under high wind-situations, where turbulent transport has a considerable
impact. This most probably applies to a wide range of deposition samplers,
not limited to those used in this work. When mixing state investigations are done based on collected aerosol
particles, the impact of coincidental mixtures on the substrate must be
assessed, unless the area coverage with particles is very low ( Internal particle mixing most likely has a considerable influence on dust
deposition speed. Future models regarding dust dry deposition should take a
deposition speed enhancement by internal mixing into account. The internal
mixing also likely increases its efficiency to be activated into cloud
droplets (Kelly et al., 2007; Kumar et al., 2011). As a result internally
mixed dust particles may be subject to preferential removal by wet
deposition. However, more systematic investigations are needed to better
understand the mixing processes. The intensity of chemical processing might also be affected by the internal
mixing, when the particles are activated more efficiently into cloud
droplets. For example, the iron solubility for these particles might
increase (Shi et al., 2009). With respect to a potential cloud impact, the observed fraction of dust
mixed with soluble species can be used as input parameters for cloud
condensation nuclei parameterizations. Regarding the impact of mixing on
dust ice nucleation activity, on one hand studies show a deactivation of
dust for high solute concentrations (Zobrist et al., 2008; Iwata and
Matsuki, 2018). On the other hand, the more efficient activation into cloud
droplets might increase the overall availability of dust for immersion
freezing. Further studies are needed to assess and constrain the effects of atmospheric mixing state on ice nucleation in clouds.
The data sets of all particles used for this investigation including particle size, shape and composition are given as text tables in the Supplement along with a data overview.
The supplement related to this article is available online at:
KK designed the experiment. KK and MH carried out field work in 2013. MP and CP carried out the field work in 2016. KK and KS analyzed the samples. KK programmed the models and data processing code. KK, SW and ME analyzed data and prepared the manuscript. All authors contributed in data discussion and manuscript finalization.
The authors declare that they have no conflict of interest.
This article is part of the special issue “The Saharan Aerosol Long-range Transport and Aerosol–Cloud-interaction Experiment (SALTRACE) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
We acknowledge financial support from the German Research Foundation (DFG
grant KA 2280/2-1 and KA 2280/3-1). We thank Joseph Prospero for his
valuable comments on the manuscript and discussion; his wind and mass
concentration data were obtained under National Science Foundation (NSF)
grant AGS-0962256. The authors gratefully acknowledge the NOAA Air Resources
Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion
model and/or READY website (