Frequently, passive dry deposition collectors are used to sample atmospheric dust deposition. However, there exists a multitude of different instruments with different, usually not well-characterized sampling efficiencies. As a result, the acquired data might be considerably biased with respect to their size representativity and, as a consequence, also composition. In this study, individual particle analysis by automated scanning electron microscopy coupled with energy-dispersive X-ray analysis was used to characterize different, commonly used passive samplers with respect to their size-resolved deposition rate and concentration. This study focuses on the microphysical properties, i.e., the aerosol concentration and deposition rates as well as the particle size distributions. In addition, computational fluid dynamics modeling was used in parallel to achieve deposition velocities from a theoretical point of view.
Scanning electron microscopy (SEM)-calculated deposition rate measurements made using different passive
samplers show a disagreement among the samplers. Modified Wilson and Cooke
(MWAC) and Big Spring Number Eight (BSNE) – both horizontal flux samplers –
collect considerably more material than the flat plate and Sigma-2 samplers, which
are vertical flux samplers. The collection efficiency of MWAC increases for
large particles in comparison to Sigma-2 with increasing wind speed, while
such an increase is less observed in the case of BSNE. A positive
correlation is found between deposition rate and PM
Mineral dust aerosol in the climate system has received considerable
scientific attention mainly due to its direct effect on the radiative budget
and indirect one on cloud microphysical properties (Arimoto, 2001;
Huang et al., 2010). Mineral dust particles also play a key part with
respect to gas-phase chemistry by providing a reaction surface, e.g., ozone
depletion (Nicolas et al., 2009; Prospero et al., 1995).
Moreover, dust aerosol also plays an important role in biogeochemical cycles
by supplying important and limiting nutrients to ocean surfaces
(Jickells et al., 2005). Mineral dust is emitted mainly from the arid
and semi-arid regions of the world and believed to have a global source
strength ranging from 1000 to 3000 Tg yr
Deposition measurement data of mineral dust are useful to validate numerical simulation models and to improve our understanding of deposition processes. However, the scarcity and the limited representatively of the deposition measurement data for validation pose a major challenge to assess dust deposition at regional and global scales (Schulz et al., 2012; WMO, 2011). This is in part linked to the uncertainties evolving from the use of different and non-standardized measurement techniques.
Commonly, deposition is measured by passive techniques, which provide an acceptor area for the depositing atmospheric particles. The advantage of these passive samplers is that they operate passively, resulting in simple and thus cheaper instruments, so that many locations can be sampled at a reasonable cost (Goossens and Buck, 2012). The usual lack of a power supply allows also for unattended remote setups. However, the most important disadvantage is that collection efficiency and deposition velocity are determined by the environmental conditions not under operator control and in remote setups also frequently also unknown. That implies, in addition, that the sampler shape can have a strong and variable impact on the collection properties.
While there is previous work describing and modeling single samplers
(Einstein et al., 2012; Wagner and Leith, 2001a, b; Yamamoto et al.,
2006) and a few comparison studies (Goossens and Buck,
2012; Mendez et al., 2016), most previous studies (Goossens and
Buck, 2012; López-García et al., 2013) only compare total mass,
thereby neglecting size dependence and potential comparison biases. Also, a
systematic assessment of the impact of wind conditions is not commonly
carried out, but, for example, Mendez et al. (2016) showed that the
efficiency of the BSNE and MWAC samplers for collecting PM
The purpose of this study is to assess the particle collection properties of
different deposition and other passive samplers based on single-particle
measurements and to assess their agreement with theory. From the available
data, also relations of the collected particle microphysics and composition
homogeneity between the samplers will be presented, which can be used as
estimators for the comparability of previous literature data based on the
different techniques. To the best of our knowledge, this is the first study
to analyze dry deposition measurements collected using passive samplers by
means of a single-particle scanning electron microscopy – energy-dispersive
X-ray analysis (SEM-EDX) analysis approach (particularly in the
size fraction larger than 10
The Sahara and Sahel provide large quantities of soil dust, resulting in a westward flow of mineral dust particles over the North Atlantic Ocean accounting for up to 50 % of global dust budget (Goudie and Middleton, 2001). Due to proximity to the African continent, the Canary Islands are influenced by dust particles transported from the Sahara and Sahel regions. Therefore, Tenerife is one of the best locations to study relevant dust aerosol in a natural environment.
For this study, we conducted a 2-month (July to August 2017) aerosol
collection and dry deposition sampling campaign at Izaña Global
Atmospheric Watch Observatory (Bergamaschi et al., 2000;
Rodríguez et al., 2015) (28.3085
An ultrasonic anemometer (Young model 81000, R. M. Young Company, Traverse City, MI, USA) was installed at approximately 2 m height above the ground to obtain the 3-D wind velocity and direction. It was operated with a time resolution of 10 Hz to get basic information on turbulence structure.
Samples were collected from different, commonly used samplers, namely Big
Spring Number Eight (BSNE) (Fryrear, 1986), modified Wilson and Cooke
(MWAC) (Wilson and Cook, 1980), Sigma-2 (VDI2119, 2013)
and flat plate (UNC derived) (Ott and Peters, 2008). In addition,
the free-wing impactor (FWI) (Kandler et al., 2018) was used to
collect coarser particles. The BSNE, MWAC, FWI and filter samplers were
mounted on wind vanes to align them to the ambient wind direction. Samples
were collected continuously, and substrates were exchanged at intervals of
24 h. The sampling duration for FWI (12 mm Al stub) was 30 min only to
avoid overloading. The sampling duration for filter sampler was set to be
1 h. It has to be noted that the PM
The flat-plate sampler used in this work was taken from the original flat-plate geometry used in Ott and Peters (2008). Briefly, the geometry contains 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. Unlike the original design, the geometry of the current work has a cylindrical dip in the lower plate, which recedes the sampling substrate – a SEM stub with a thickness of 3.2 mm – from the airflow, thereby reducing the flow disturbance. A preliminary study with the modified and original setup side by side in a rural environment had shown that this recession approximately doubles the collection efficiency for large particles. In this design, larger droplets (> 1 mm) are prevented by this setup from reaching the SEM stub surface at the local wind speeds (Ott and Peters, 2008). As described in Wagner and Leith (2001a, b), the main triggers for particle deposition on the substrates for this sampler are diffusion, gravity settling and turbulent inertial forces, of which only the latter two are relevant in our study.
It is important to compare the upward and downward rates to understand the turbulent and the gravitational share in aerosol deposition rate measurement. Following an approach by Noll and Fang (1989), it was assumed that turbulent transport is the main mechanism for upward-directed deposition rate, while turbulent transport and sedimentation are the mechanisms for the downward one. Therefore, a sampler with an upward- and a downward-facing substrate in analogy to the flat-plate sampler was designed. Air is flowing between two circular 1 mm thick steel plates with a diameter of 127 mm. In the centers of the plates, two substrates are mounted opposite to each other. The substrate holders are recessed, so that their adhesive collection surface is in plane with the steel surface. The construction is mounted into a frame with a distance of 16 mm between the plates/substrates.
The Sigma-2 sampling device is described in Dietze et al. (2006),
Schultz (1989) and VDI2119 (2013). Briefly, the geometry consists of a
cylindrical sedimentation tube with a height of about 27 cm made of
antistatic plastic, which is topped by a protective cap with diameter of 158 mm.
At its top, the cap has four rectangular inlet windows (measuring 40 mm
The MWAC sampler is based on an original design developed by Wilson and Cook (1980). The sampler consists of a closed polyethylene bottle, serving as settling chamber, to which an inlet tube and an outlet tube have been added. The MWAC sampling bottles are 95 mm long, with a diameter of 48 mm. The two inlet and outlet plastic tubes with inner and outer diameter 8 and 10 mm, respectively, pass air through the cap into the bottle and then out again. The large volume of the bottle relative to the inlet diameter makes the dust particles entering the bottle to be deposited in the bottle due to the flow deceleration in the total bottle area and due to impaction below the exit of the inlet tube. The air then discharges from the bottle via the outlet tube. MWAC is one of the most commonly used samplers (Goossens and Offer, 2000) and has a high sampling efficiency for large particles (Mendez et al., 2016).
The BSNE sampler, originally designed by Fryrear (1986), is intended
to collect airborne dust particles from the horizontal flux (Goossens
and Offer, 2000). Briefly, the particle-laden air passes through a
rectangular inlet (21 mm wide and 11 mm high, with a total area of 231 mm
A free-rotating wing impactor (Jaenicke and Junge, 1967; Kandler et al.,
2009, 2018) was used to collect particles larger than
approximately 5
A filter sampler with Nuclepore filters (Whatman®
Nuclepore™ track-etched membranes, diameter
of 25 mm, pore size of 0.4
Additional information regarding the aerosol particle size distributions has been obtained by using an optical particle counter (OPC, GRIMM, Ainring, Germany), which is operationally available at the Izaña Global Atmospheric Watch Observatory (Bergamaschi et al., 2000; Rodríguez et al., 2015).
All aerosol samples (except the filter sampler) were collected on pure
carbon adhesive substrates (Spectro Tabs, Plano GmbH, Wetzlar, Germany)
mounted to standard SEM aluminum stubs. The filter samples were stored in
standard “petrislides” (Merck KGaA, Darmstadt, Germany). All adhesive
samples were stored in standard SEM storage boxes (Ted Pella Inc., Redding,
CA, USA) in dry conditions at room temperature. Individual particle analysis
by automated SEM (FEI ESEM Quanta 400 FEG,
FEI, Eindhoven, the Netherlands; operated at 12.5 kV, lateral beam extension
3 nm approximately, spatial resolution of 160 nm) was used to characterize particles
for size and composition. A total of 316 000 particles from six samplers were
analyzed. A total of 26 samples from BSNE (53 000 particles), 23 samples from MWAC
(49 000), 23 samples from Sigma-2 (39 000), 18 samples from the flat plate (12 mm)
(24 000), 22 samples from the flat plate (25 mm) (21 000), 13 samples from the
filter (80 000) and 12 samples from FWI (12 mm) (50 000) were analyzed. Each
sample was characterized in areas selected by a random generator, until a
total of 3000 particles with projected area diameters greater than 1
Chemistry information was derived by EDX (Oxford X-Max 120, Oxford Instruments, Abingdon, United Kingdom). The internal ZAF correction (Z – atomic number, A – absorption, F – fluorescence, accounting for material-dependent efficiencies) of the detector/software system – based on inter-peak background radiation absorption measurements for correction – was used for obtaining quantitative results.
The image analysis integrated into the SEM-EDX software determines the size
of particles as a projected area diameter:
Following Ott et al. (2008), the volumetric shape factor,
The volume-equivalent diameter (sphere with the same volume as the irregular
shaped particle) is then calculated from the projected area diameter via
the volumetric shape factor (Ott et al., 2008) and is
expressed by particle-projected area and perimeter as
The mass deposition rate (MDR) and number deposition rate (NDR) are
calculated from deposited particle numbers per area, individual particle
size and, in the case of MDR, density. The particle density was assumed to be
equal to the bulk material density of the dominating identified compound for
each particle (Kandler et al., 2007). A window correction
(Kandler et al., 2009) was applied to the particle deposition rate as
The MDR of the samples is then determined as
Size distributions for all properties were calculated for the
logarithmic-equidistant intervals of 1–2, 2–4, 4–8, 8–16, 16–32 and 32–64
Concentrations are calculated from the deposition rate using different
deposition velocity models for different samples, namely the models of
Stokes and Piskunov (Piskunov, 2009). The basic relationship between
concentration and deposition rate was already given by Junge (1963) as
the ratio of deposition rate to concentration:
All different approaches now give different formulations for the deposition velocity based on a set of assumptions and neglections.
Terminal settling velocity (
To calculate the turbulent impaction velocity, which depends on the wind
speed, the friction velocity is needed. Friction velocity (
Comparison of the friction velocities obtained from the momentum
flux and the Wood (1981) approaches for different days with different wind
speeds (average wind speeds of 2.9, 2.1 and 3.1 m s
For the current work, the friction velocity is calculation is based on
Wood (1981) approach:
The reason why we opted to use the Wood (1981) over the Ettling (1996) approach is (a) its simplicity, as it requires only average wind speeds instead of 3-D high-resolution ones and therefore will be more commonly applicable; and (b) the fact that the momentum approach yields sometimes uninterpretable data, in particular in the case of buoyancy-driven flow. For some case studies, both approaches are compared below.
There are a variety of models estimating the particles' deposition speed
(Aluko and Noll, 2006; Noll and Fang, 1989; Noll et al., 2001; Piskunov,
2009; Slinn and Slinn, 1980; Wagner and Leith, 2001a) (see
Fig. 2) and these different deposition velocity
models yield different results, which could be due to negligence of
unaccounted forces (Lai and Nazaroff, 2005) or due to how
friction velocity is determined, or can be related to suppositions by
different models (Kandler et al., 2018). Unless otherwise stated, the
particle density used in deposition velocity calculation is 2600 kg m
It can be noted that a particular deposition model therefore may not be suitable in different cases for describing the deposition velocity precisely, so as a result concentrations derived from deposition rate measurements are likely to be biased (Giardina and Buffa, 2018; Kandler et al., 2018).
Deposition velocities for single particles to a smooth surface
(flat-plate sampler) calculated by using a set of different classical
deposition models for the Tenerife samples (9 August 2017; average wind speed of 3.0 m s
Table 1 shows the different deposition velocity models applied to the various samplers. The Piskunov deposition velocity model is made for flat surfaces, and therefore it is applied to the BSNE and flat-plate samplers, where deposition occurs to such surfaces. For the Sigma-2 sampler, it is assumed that each particle settles with the terminal settling velocity (Tian et al., 2017), and therefore Stokes velocity was used for calculation of concentrations. In the case of MWAC, a different approach was required due to its geometry. It is internally in principle an impactor design with the incoming tube pointing at the substrate but is operating at very low flow speed and therefore low Reynolds numbers. As a result, it cannot be described by the impactor theory only. Therefore, we assumed that the deposition velocity cannot become smaller than the one prescribed by the Piskunov model. As a result, we derived a velocity model based on wind speed (or a reduced wind speed) and calculated the collection efficiency assuming MWAC to act as impactor for particles in the range of the cutoff diameter and larger. For smaller particles, we assumed that flow is like a flow over a smooth surface, so the Piskunov deposition velocity model was applied (e.g., as soon as the deposition velocity from impactor considerations becomes smaller than the Piskunov one, the latter was used).
A summary of different deposition velocity models applied to the samplers.
Considering the window correction and the collection efficiency dependence
on the impaction speed and geometry, the overall collection efficiency
Apparent number concentrations are determined from the particle deposition
rate and the volumetric flow rate calculated from the mass flow for ambient
conditions. The inlet efficiency (
Due to the discrete nature of the particle size measurement, the uncertainty coming from counting can pose a significant contribution to the uncertainty of mass deposition rate measurement (Kandler et al., 2018). It is, therefore, important to assess the uncertainties in our mass deposition rate measurements, which is done in accordance with the previous work (Kandler et al., 2018). For the mass deposition rate, the statistical uncertainty is assessed by a bootstrap simulation approach using Monte Carlo approximation (Efron, 1979). In this work, the bootstrap simulations and the two-sided 95 % confidence interval calculation were performed by using Matlab's bootstrap function (MATLAB R2016a; MathWorks, Inc.). Here, MATLAB function uses a non-parametric bootstrap algorithm (Neto, 2015) to compute the 95 % bootstrap confidence interval.
Computational fluid dynamics (CFD) simulations were conducted to predict the deposition of particles onto different passive samplers (MWAC, Sigma-2 and flat plate). A discrete phase model without interaction with continuous phase was used to calculate the trajectories of the particles. The CFD software ANSYS-FLUENT 18.2 was used for performing the numerical simulations.
In a first step, the geometry of samplers was created using ANSYS DesignModeler. In a second step, an enclosure around the geometry was generated. To ensure that there are no large gradients normal to the boundaries at the domain boundary, the domain was created depending on the width, the height and the length of the geometries. The space in front of the geometry is 2 times the height of the sampler, the space behind the sampler is 10 times the height, the spaces left and right of the geometry are 5 times the width of the geometry, and the spaces below and above the sampler are 5 times the height.
Afterwards, a mesh was created using the ANSYS meshing program. For the
enhanced wall treatment, the first near-wall node should be placed at the
dimensionless wall distance of
The turbulence intensity
Details of the sampler construction and geometry are found in the electronic
Supplement (see Figs. S24, S25 and S26). Different cases were calculated
for the flat-plate sampler (deposition areas of 12 and 25 mm), for Sigma-2
and MWAC (Fig. 3). For the flat plate, a mesh with 3 920 000 cells was generated; for Sigma-2, one with
7 600 000 cells; and for MWAC, one with 4 620 000. After the meshing, the
flow fields were calculated. Figure 3 shows, as an
example, the velocity magnitude in the middle of the domain for a velocity of
4 m s
In the last step, particles were injected into the velocity field and their
trajectories computed. For all samplers, the deposition area boundary
condition was set to “trap” and the walls were defined as reflecting
boundaries. Different particle sizes (1, 2.5, 5, 10, 20 and 50
The number of particles trapped in the deposition area was determined. The
deposition velocity
Geometries of the flat-plate sampler
For the flat-plate sampler, stream velocities and turbulence intensities are shown in Fig. 4. The formation of the boundary layer at the wall of the sampler is clearly visible at all velocities. At the central sampling location, the flow between the plates has the same velocity as the free stream, so for the analytical deposition models, the lower plate can be treated as single surface. The highest velocity is found at the sharp edge at the bottom of the sampler. Due to the high velocity gradients, in this part, there is also the highest turbulence intensity in the domain. As expected, the turbulent wake becomes smaller with increasing wind speed.
Flat-plate sampler: velocity magnitude and turbulence intensity at
wind speed of 2 m s
The cross sections of the velocities for Sigma-2 are shown for the 4 m s
Figure S28 shows the cross section of the velocities for MWAC in the
4 m s
Mineral dust was the dominating particle type during this campaign, consisting of different silicates, quartz, calcite, dolomite and gypsum, similar to previous findings for this location (Kandler et al., 2007). Therefore, hygroscopicity was not taken into account, as due to the mostly non-hygroscopic compounds and the moderate humidities their impact was rated low. Details on the composition will be reported in a companion paper.
The mass and number deposition rates (given per unit time and sample surface area) along with daily average temperature and wind speed are presented as daily values. Details for all days and all samplers can be found in the electronic Supplement (see Tables S1, S2, S3 and S4). All data shown in this section are calculated from SEM measurements. Particle sizes are reported as aerodynamic diameter, if not otherwise stated. It is also worth mentioning the plots shown in the paper are a few examples of a comparison, while the bulk of the data are presented in the Supplement.
Figure 5 shows as example mass deposition rates for
different samplers during a dust event and a non-dust event day. For all
samplers, the mass deposition rate size distributions peaked in the 8–16
The campaign maximum, minimum and median mass deposition rates measured by the samplers.
As a consequence, the vertical flux instruments collect much less material than the horizontal flux ones (Table 2), which is in accordance with previous findings (Goossens, 2008). In the present study, horizontal-to-vertical flux mass ratio is approximately between 2.8 and 4.4 (with single size intervals ranging between 2 and 50), while Goossens (2008) reported it to be in between 50 and 160. This difference in the ratio might come from the different approaches. Goossens (2008) used water as a deposition surface, while in our study we used a SEM sampling substrate. Furthermore, from Fig. 5, we can clearly see that there is a strong variation in mass deposition rates between dust event days and non-dust event days (full dataset is shown in Fig. 6). Generally, the temporal variation is higher than the difference between the samplers, so a strict comparison between this and the previous study cannot be done.
Size-resolved mass deposition rate measured by different passive
samplers:
Box plots of size-resolved deposition rate (campaign data;
flat-plate, Sigma-2, MWAC and BSNE samplers). On each blue box, the central mark
is the median; the edges of the box are the 25th and 75th percentiles. The
vertical red lines show the standard deviation. The median, percentiles and
standard deviations shown there correspond to the variability of the whole
campaign for each instrument and bin. From the structure of the deposition
models, a wind speed dependency for the deposition velocity should be
expected. The average wind speed during the campaign was about 3.5 m s
Figure 7 shows the mass deposition rate ratio of MWAC, BSNE and flat plate to Sigma-2 as function of wind speed. The Sigma-2 sampler was chosen for comparison, as due to its settling tube design, it is expected to have the least wind sensitivity. The results show highly scattered values. The collection efficiency of MWAC for large particles has an increasing tendency in comparison to Sigma-2 slightly with increasing wind speed, while there is barely a trend visible for BSNE. Both – being horizontal flux samplers – collect considerably more material than Sigma-2. For the flat plate, the deposition velocity in relation to Sigma-2 has a weak decreasing trend for higher wind speeds, but generally, the deposition speed is similar. Overall, the relation of Sigma-2 to BSNE shows the closest agreement, while the scatter is higher for the other combinations. More information on the relation between the other instruments is shown in Figs. S2, S3, S4, S5, S6, S7, S8, S9 and S10.
Deposition rate ratio as function of wind speed for different days:
MWAC/Sigma-2
While without a true reference technique the absolute deposition velocities cannot be determined, their ratio between different instruments can be compared theoretically and by measurement. The deposition velocity ratios for a pair of different samplers are identical to the deposition rate ratios obtained from the corresponding measurements (Eq. 7), as long as the sampling time and the aerosol concentration are the same; the latter condition is achieved by the close and parallel sampling. Therefore, the experimentally determined ratios can now be compared to the deposition velocity ratios derived from the theoretical considerations. Figure 8 shows the corresponding comparison. Note that this consideration allows for the assessment of relative model performance and sampler efficiency, but lacking a “true” reference, it does not allow for determining the most accurate sampler.
While, for BSNE and Sigma-2, observations and model fit comparatively well, the deposition velocity is misestimated for the flat-plate/Sigma-2 pairing for all particle sizes (overestimate for flat-plate deposition velocity or/and underestimate for Sigma-2). For MWAC/Sigma-2, there is a clear size dependency, indicating that probably the impactor model overestimates the deposition velocity; the latter might be due to unaccounted particle losses (e.g., inlet efficiency). MWAC, BSNE and Sigma-2 agree with respect to deposition velocity better based on the measurement data than predicted by the theory. It may be connected to the non-stationarity of the atmosphere, which is not accounted for by the models, i.e., the permanent wind speed fluctuations smoothing out detail differences of a stationary flow. The flat-plate sampler, however, has a lower-than-predicted deposition velocity.
Comparison of geometric mean ratio of deposition velocities for
different sampler pairs derived from measured deposition rates (blue) and
from corresponding deposition models (orange).
Figure S22 and Table 3 display for the approximate
PM
Summary of the regression analysis for the correlations between the
dust deposition rate and the atmospheric concentrations (PM
In a second step, it was tested whether the application of each sampler's
assigned deposition model can increase the correlation between the
measurements by the deposition samplers and the OPC observations, i.e.,
whether the meteorological parameters accounted for in the models can
decrease the deviation. Therefore, in analogy to the previous correlation,
the concentrations modeled from each sampler's SEM data were correlated with
the OPC data for the size range between 1 and 10
From the correlation relations in Table 3, it can be
seen that MWAC is least suitable for estimating PM
Figure 9 displays the apparent deposition velocity
(calculated as the ratio of the number deposition rate to the concentration
of the OPC) as function of the wind speed. Obviously, here, there is also no
clear trend. The apparent deposition velocities range between
Apparent deposition velocity: ratio of number deposition rate
determined from SEM measurements to the number concentration observed by the
OPC as function of wind speed. For the consideration, only the overlapping
size range (approximately 1–10
Figure 10 compares a mass deposition rate size distribution with the corresponding concentrations derived by the modeled deposition velocities. Calculating the mass concentrations from different passive samplers with different models leads in most cases to a better agreement between the measurements, taking into account the statistical uncertainties (see Fig. S11). This indicates that the deposition velocity models selected for the samplers are generally suitable, despite the deviations in single cases.
Comparing different samplers with respect to
The calculated number concentrations in the size interval between 1 and
10
Figure 11 (see also Figs. S14, S15, S16 and S17)
shows a comparison of number concentration size distributions calculated
from deposition rate measurements of the flat-plate, Sigma-2, BSNE and MWAC
samplers with the number size distributions measured by the OPC for
different days. Overall, most of the time, the number concentrations
obtained from OPC measurements are slightly higher than the ones from the
deposition rates for the size range 2–5
In this context, Fig. 11 shows also the low
influence of the two techniques used for
Comparison of the number concentrations calculated from the
deposition measurements with the number concentrations measured by the OPC.
Number size distributions are obtained by converting the SEM number
deposition rates to number concentrations using the different deposition
velocity models (Table 1), in analogy to the mass
size distributions. For the concentrations obtained from the number
deposition rates, two different approaches for the friction velocity are
shown. The black curve shows the concentration curve calculated using the
momentum flux approach without PM
Figure 12 (see also Fig. S12) shows the
comparisons for the larger particles between the deposition-derived number
concentrations and the ones from the FWI. Here, a significant inconsistency
occurs between the mass size distributions from passive samplers and the
ones from FWI. In particular, the size range larger than 10
Daily average mass size distributions obtained from the passive
sampler techniques in comparison to an active sampler (FWI). Mass
concentration size distributions were calculated from the SEM mass flux
measurements using the corresponding deposition velocity models. Samples
were collected on 26 July
In a last step, the deposition-derived concentrations are compared to those
determined from the isoaxial filter sampler. Figure 13 shows that, while the calculated size distributions are in good
agreement with the OPC ones, the filter-derived ones seem to relatively
underestimate the concentrations. A correlation analysis (
Average size distributions obtained from the SEM analysis of the
filter sampler, in comparison to BSNE and OPC for different measurement days:
The size-resolved upward and downward deposition rates were derived from the
upward-/downward-facing deposition sampler by the same type of SEM analyses.
Results of the size-resolved mass and number deposition rate measurements
along with daily average temperatures and wind speeds are given in the
electronic Supplement (see Tables S5 and S6). The upward deposition rate is
always less than the downward deposition rate. This is expected because the
upward-facing substrate (i.e., measuring the downward-directed deposition
rate) collects particles deposited by gravitational settling and turbulent
inertial impaction, while the downward-facing substrate (for the
upward-directed deposition rate) collects particles only by means of
turbulent impaction. Figure 14 shows the ratio of
upward to downward mass deposition rate as function of particle size. The
deviation is greatest for the particle size range around 8
Ratio of upward- to downward-directed mass deposition rate as function of particle size. The deposition rate is measured using the upward–downward flat-plate sampler (with 25 mm stub).
Using CFD, deposition velocities of particles
for different passive samplers were predicted and compared to the analytical
deposition velocity models used for the different samplers (see
Figs. 15 and S31). While for the flat-plate
and MWAC samplers, the curves agree qualitatively (i.e., showing deposition
speeds higher than Stokes velocity at particle sizes 4–16
Deposition velocities calculated for different samplers by
analytical and CFD approaches. The red curve shows the deposition velocity
calculated using the Piskunov model, the dotted red curve shows the
combination of the Piskunov and the impaction curve model, the black curve
shows the Stokes deposition velocity, the blue curve shows the Noll and Fang
model, the cyan curve shows the Zhang model, and the green curve shows the deposition
velocity from CFD. Panels
As there is no reference instrument for dry deposition sampling, the separate approaches are compared in a relative way. Figure 16a–c show comparisons of the deposition velocity ratios derived from the analytical models with the corresponding measured deposition velocity ratios (equalling the corresponding deposition rate ratios), Fig. 16d–f the respective correlation of the ratios derived from CFD modeling with the measurement. As the CFD models could only be calculated for a limited number of flow velocities, deposition velocity values were interpolated between the calculated cases. Generally, the agreement is very poor. Practically, no variation observed in the measurement data can be explained by model variation, independently of the type of model. While this might be explained to a smaller extent by the propagating measurement uncertainties for the largest particles with low counting statistics, for the smaller ones, this systematic deviation must have other reasons.
Comparison of the observed deposition velocity ratios with
modeled ones by the analytical deposition models
Parallel dust aerosol deposition measurements by means of deposition and other passive samplers were conducted at Izaña Global Atmospheric Watch Observatory continuously from 14 July to 24 August 2017. In addition, active aerosol collection was done with a free-wing impactor and an isoaxial filter sampler. Additional information regarding the aerosol particle size distributions has been obtained by an OPC. Overall, 316 000 single particles from six different samplers were analyzed by SEM-EDX, yielding size-resolved deposition rates.
As known from previous studies, the total deposition rate was dominated by
coarse particles (8–16
In the PM
Deposition velocities from different analytical deposition models are compared to ones calculated using computational fluid dynamics simulations for different samplers. The comparison shows that two methods largely disagree. Moreover, all theory-based deposition velocities (analytical as well as CFD approaches) fail to represent the observed measurement differences between the samplers. This obviously points to the need for better understanding the physics of dry deposition in general.
The correlation analysis between dust deposition rate, dust concentrations
and wind speed reveals that the variation in deposition rate is mainly
controlled by changes in concentration; variations in wind speed play a
minor role for wind speeds lower than 6 m s
The correlation analysis between deposition rates and OPC measurements
demonstrated that BSNE and Sigma-2 can be a good option for PM
Moreover, as the results show that the different samplers cannot deliver consistent results between the sampler types, a recommendation must be that if a certain sampler type is chosen for a study, it should not be modified or replaced by another one for sake of consistency of results, even if it was shown that the results do not agree well, for example, with active sampling. The results show, nevertheless, that passive sampling techniques coupled with an automated single-particle analysis provide insights into the variation of size distribution, deposition rate and concentration of atmospheric particles.
The datasets used for this publication are available from the Pangaea
repository free of charge (
The supplement related to this article is available online at:
AW conducted the field measurements, and conducted data evaluation and interpretation. KS helped with the field measurements, carried out the SEM analyses and conducted the data processing. JM and BE executed the CFD model setup and calculations. SR operated the OPC, including the data processing and the meteorological base measurements. KK designed the experiment, designed and prepared the sampling equipment and conducted the data processing and interpretation. All authors contributed to the data discussion and manuscript preparation.
The authors declare that they have no conflict of interest.
We are grateful for the financial support by the DFG in the framework of the Excellence Initiative, Darmstadt Graduate School of Excellence Energy Science and Engineering (GSC 1070). We thank our colleagues Thomas Dirsch and Conrad Ballschmiede. We are grateful to all staff members of Izaña Global Atmospheric Watch Observatory for helping us with maintenance of the sampling equipment. We are especially indebted to Dr. Roger Funk from the Leibniz Centre for Agricultural Landscape Research, Institute of Soil Landscape Research, for providing us with some of the passive samplers.
This research has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (grant nos. 264907654, 264912134 and 416816480 (KA 2280)).
This paper was edited by Mingjin Tang and reviewed by Mingjin Tang and two anonymous referees.