Cloud residues and out-of-cloud aerosol particles with diameters between 150
and 900 nm were analysed by online single particle aerosol mass
spectrometry during the 6-week study Hill Cap Cloud Thuringia (HCCT)-2010 in September–October 2010.
The measurement location was the mountain Schmücke (937 m a.s.l.) in
central Germany. More than 160 000 bipolar mass spectra from out-of-cloud
aerosol particles and more than 13 000 bipolar mass spectra from cloud
residual particles were obtained and were classified using a fuzzy
The results from the ambient aerosol analysis show that 63 % of the analysed particles belong to clusters having a diurnal variation, suggesting that local or regional sources dominate the aerosol, especially for particles containing soot and biomass burning particles. In the cloud residues, the relative percentage of large soot-containing particles and particles containing amines was found to be increased compared to the out-of-cloud aerosol, while, in general, organic particles were less abundant in the cloud residues. In the case of amines, this can be explained by the high solubility of the amines, while the large soot-containing particles were found to be internally mixed with inorganics, which explains their activation as cloud condensation nuclei. Furthermore, the results show that during cloud processing, both sulfate and nitrate are added to the residual particles, thereby changing the mixing state and increasing the fraction of particles with nitrate and/or sulfate. This is expected to lead to higher hygroscopicity after cloud evaporation, and therefore to an increase of the particles' ability to act as cloud condensation nuclei after their cloud passage.
The interaction of aerosol particles and cloud droplets has several aspects:
on the one hand, the presence of a cloud condensation nucleus (CCN) is an
essential prerequisite for the formation of a cloud droplet, and the size
and chemical composition of the aerosol particle determines whether a
particle acts at a certain temperature and supersaturation as a CCN or not
(e.g. Dusek et al., 2006b; Gunthe et al., 2009). On the other hand,
cloud processing alters the chemical composition of the cloud droplet such
that after evaporation of the cloud droplet the remaining aerosol particle
is of a different composition than the original CCN. The uptake of nitric acid
in the aqueous phase of cloud droplets has been observed
(Hayden et al., 2008), but also sulfate is known to
be produced by oxidation of SO
Cloud particle sampling and separation from the not activated interstitial aerosol can be achieved by applying a counterflow virtual impactor (CVI; Ogren et al., 1985; Mertes et al., 2005b; Wendisch and Brenguier, 2013). This technique has been coupled with online aerosol mass spectrometry before, such that the composition of cloud droplets can be measured with high time resolution (Drewnick et al., 2007; Allan et al., 2008; Hayden et al., 2008). The use of single particle mass spectrometry for single cloud residual particle analysis (e.g. Gieray et al., 1997; Kamphus et al., 2010; Pratt et al., 2010; Zelenyuk et al., 2010) gives not only the composition of the bulk residues but also the mixing state of the cloud residues. Comparison with the aerosol observed shortly before cloud formation can give information on the possible addition of chemical compounds in the cloud phase and thereby evidence for cloud processing.
Several previous hill cap cloud experiments – considering the clouds as
“stationary flow processors” – have been conducted and results have been
reported in the literature. One of these is the FEBUKO experiment
(Herrmann et al., 2005), which took place at the same field site as the
Hill Cap Cloud Thuringia (HCCT)-2010 experiment reported here. The results from FEBUKO have shown a
measurable increase of sulfate and ammonium, but only in two of three
investigated cases, and only in the smallest particle size range
(up to 140 nm; Brüggemann et al., 2005).
The mass production in the clouds was about 5 % of upwind aerosol mass
(in a size range between 60 and 300 nm; Mertes et al.,
2005a). Tilgner et al. (2005) found from model
calculations for the same experiment that the mass increase is mainly due to
HNO
The Kleiner Feldberg Cloud Experiment (Fuzzi et al., 1994; Wobrock et
al., 1994) was conducted in 1990. Offline single particle analyses of cloud
residues sampled via a counterflow virtual impactor during this experiment
are reported by Hallberg et al. (1994). It could be
shown that the majority of cloud residues contained soluble compounds
whereas insoluble particles remained in the interstitial air.
Fuzzi et al. (1994) reported from the
Feldberg Cloud Experiment that a general lack of gaseous NH
During the Great Dun Fell experiment, which took place in 1993, an increased
sulfate concentration of the aerosol was observed after cloud passing
(Laj et al., 1997b). Thereby also the
ammonium concentration increased based on the neutralisation reaction with
ammonia. The increased sulfate concentration could be attributed mainly to
SO
Here we report the results obtained from individual particle chemical analysis by online single particle laser ablation mass spectrometry during the hill cloud experiment HCCT-2010, which was conducted on the mountain site Schmücke in September and October 2010 in central Germany. The analysis includes cloud residual particles that were sampled from the cloud using a CVI and aerosol particles that were measured during cloud-free periods.
The HCCT 2010 experiment took place between 13 September and 25 October 2010, at the mountain ridge Thüringer
Wald in central Germany. The same measurement sites were used as during two
previous experiments (FEBUKO, 2001, 2002; Herrmann et al., 2005): (1) an upwind site (Goldlauter, 10
A detailed description of the mass spectrometer ALABAMA can be found in
Brands et al. (2011). The particles enter the vacuum
chamber via a Liu-type aerodynamic lens (Liu et al., 1995a, b) and are
focused to a narrow particle beam. Due to the pressure drop the particles
are accelerated when exiting the aerodynamic lens. The final particle
velocity depends on their size, shape and density. The particles are
detected by the scattered light of two orthogonal continuous wave
Nd:VO
Measurement set-up and further operated instruments at the summit site Schmücke. Out-of-cloud aerosol was investigated by sampling through the aerosol inlet during cloud free periods while cloud residues were investigated by sampling through the CVI during cloud episodes.
Figure 1 shows the measurement set-up and
additionally operated instruments at the summit site Schmücke. Besides
the ALABAMA, an optical particle counter (OPC; Grimm, model 1.109, time
resolution 6 s) as well as a compact time-of-flight aerosol mass spectrometer (C-ToF-AMS; Aerodyne Research Inc.,
Drewnick et al., 2005) were run simultaneously. Furthermore a high-resolution time-of-flight AMS (HR-ToF-AMS; Aerodyne Research Inc.,
DeCarlo et al., 2006) and a multi-angle absorption photometer (MAAP; Thermo
Scientific, model 5012, time resolution 1 min) were operated continuously at
the aerosol inlet. The MAAP determines the mass concentration of equivalent
black carbon (EBC; Petzold et al., 2013) based on the absorption of
particles sampled on a filter. The results from the C-ToF-AMS and HR-ToF-AMS
measurements are presented in an accompanying paper (Schneider et
al., 2016). Outside of the building, a particle volume monitor (PVM; Gerber
Scientific Inc., model 100, time resolution 1 min) for investigation of the
liquid water content (LWC) and a weather station (Davis Vantage Pro) for
meteorological parameters were installed. Additionally, Caltech active
strand cloud water collectors (one stage, three stage, and five stage) were
mounted. The pH value as well as the content of organic compounds of cloud
water were analysed by offline methods at the Leibniz Institute for
Tropospheric Research. Furthermore, aliphatic amines were analysed from
filtrated cloud water samples (0.45
During the campaign, a measurement period was considered as a full cloud
event (FCE) if the following criteria were fulfilled: LWC of the summit site cloud above 0.1 gm
Overview of the defined FCEs after Tilgner et al. (2014) during HCCT-2010 and the number of obtained single particle mass spectra by ALABAMA. FCE2.1, FCE4.1, FCE5.1, and FCE26.2 are statistically not significant.
Back trajectories for the air masses encountered during HCCT-2010 were calculated using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory; Draxler and Rolph, 2012). The air mass origin was determined 96 h before arriving at Mt. Schmücke with a time resolution of 1 h. The coordinates of the Schmücke at a height of 500 m above the ground were used as an endpoint in the model. 500 m were chosen because the model orography cannot resolve a small-scale mountain range like the Thüringer Wald with sufficient detail. The back trajectories for the whole HCCT-2010 campaign can be found in the Supplement (Fig. S2). Back trajectories for the FCE are discussed in Sect. 3.3.
During the whole HCCT-2010 campaign the ALABAMA sampled over 286 000 single
particle mass spectra. The mass spectra were distinguished between
out-of-cloud aerosol and cloud residual particles according to the inlets
and the LWC. For the analysis of out-of-cloud aerosol only measurement
periods with an LWC
The analysis method that is widely used and has become a standard method for
single particle mass spectra data is the clustering of the data set by
similarities of the mass spectra (e.g. Hinz et al., 1999, 2006; Silva and
Prather, 2000; Murphy et al., 2003; Zelenyuk et al.,
2006, 2008; Hinz and Spengler, 2007; Zhao et al., 2008;
Dall'Osto et al., 2009). The analysis presented here was conducted using the
software tool CRISP (version 1.127, 64 bit) that was recently developed at
the Max Planck Institute for Chemistry (Klimach, 2012). It is based on
the programming software IGOR Pro (version 6.3, WaveMetrics). CRISP
facilitates processing and management of large data sets. Data processing
includes mass calibration of the time-of-flight spectra, peak area
integration, and either automated clustering by one of the implemented
algorithms (
Every mass spectrum is compared to the start clusters, calculating
correlation coefficient, distance, and membership coefficient. Afterwards a
mean mass spectrum of every cluster is calculated under consideration of the
membership coefficients. The new mean cluster spectrum serves as reference
for the following run. Again correlation, distance, and memberships are
calculated for every mass spectrum to the new cluster references. This
procedure is repeated until the membership difference of two consecutive
iterations is smaller than a termination threshold (here: 10
The clustering of the current data set resulted in 159 clusters. This number
is smaller than the starting value of 200 clusters, confirming that the
chosen number of 200 starting cluster was large enough and that no particle
types that significantly differ from the others were missed by the
algorithm. About 9 % of all mass spectra were sorted out being represented
by the fraction “others”. According to the fragmentation pattern considering
characteristic peaks for certain particle types, their combination (e.g.
Hinz et al., 1999; Trimborn et al., 2002; Vogt et al., 2003; Dall'Osto and
Harrison, 2006; Pratt and Prather, 2010; Corbin et al., 2012), and relative
peak intensities, every cluster was assigned manually to a certain particle
type. Afterwards the number of obtained clusters was reduced by combining
clusters of the same particle type, if two mean cluster spectra
By manual inspection of the cluster algorithm results, it was found that
occasionally mass spectra were classified falsely by the algorithm,
depending on cluster number and particle type. To take into account
uncertainties of the resulting particle type fractions, the uncertainties
were estimated by means of a reduced, representative data set of the
HCCT-2010 campaign. For this, 1377 single particle mass spectra were
clustered by fuzzy
Overview of particle types and the corresponding determined
uncertainties of the clustering by the fuzzy
Overview of identified particle types and the characteristic peaks used for the assignment of clusters to a particle type. Additionally the observed chemical composition of the particle types and the denotation used in the following (legend) are listed. Secondary inorganic compounds like nitrate and sulfate were present in every particle type and have therefore not been used as characteristic signals for the separation of particle types.
In addition to the clustering method that compares the whole mass spectra of
the individual particles, it is also useful to search for certain marker
peaks, especially in cases when these peaks are of small intensity such that
they do not influence the correlation of two mass spectra and therefore do
not show up in the clustering results. A typical example would be looking
for metals (e.g. lead) or rarely appearing particle types (Dall'Osto et
al., 2004; Tolocka et al., 2004; Snyder et al., 2009). By the marker method
it was possible to identify two different particle types characterised by
the abundance of iron, namely “mineral dust” and “Fe, V” (iron internally
mixed with vanadium). Besides Fe
The method is also suitable for investigating the particle mixing state when looking at the abundance of, e.g. nitrate and sulfate independent of the particle type. Uncertainties for particle types derived by the marker method were estimated using counting statistics (square root of absolute number of counted particles).
In order to optimise the data analysis and as a consequence of the two preceding sections, we chose to apply a combined method of clustering and marker peaks: after the clustering, the fraction “others” has been additionally investigated by marker peaks of lead, nickel, vanadium, and iron. Also the particle type characterised by iron inferred from the clustering method was analysed further using the marker peak method, resulting in two particle types: one interpreted as mineral dust and the other consisting of iron internally mixed with vanadium (“Fe, V”), belonging probably to an industrial source. Using the combined method of clustering and marker peak analysis, a total of 14 particle types plus “others” were identified. A summary of the resulting particle types, the applied method and their characteristic signals for identification as well as the corresponding chemical composition in the mass spectra are listed in Table 3. Due to the fact that all particle types were internally mixed with secondary inorganic compounds like nitrate and sulfate, these compounds are not explicitly mentioned in the legend. The mean positive and negative mass spectra of the 14 particle types plus the averaged remaining mass spectra (“others”) are shown in Fig. 2. An overview of all cluster types obtained by the clustering method can be found in the Supplement (Figs. S3–S6).
Mean positive (left) and negative (right) mass spectra representative of the particle types “org, K”, “org”, “amines”, “soot”, “soot and org”, “diesel exhaust”, “biomass burning”, “K”, “sea salt”, “Ca”, “mineral dust”, “Fe,V”, “Ni”, “Pb” (with the separation of the Pb isotopes clearly visible), and “others”.
Size-resolved aerosol composition of the resulting particle types detected by the ALABAMA, binned into 50 nm size intervals. The absolute number of analysed particles per size class is given by the grey line.
Figure 3 shows the size-resolved particle
composition for all particles (not separated for out-of-cloud aerosol and
cloud residues). The relative fraction of all particles in the specific size
class is given in order to eliminate the size-dependent detection efficiency
of the ALABAMA (Brands et al., 2011). The total
number of analysed particles per size bin is given by the grey line. The
maximum of the analysed particles lies in the size range between 500 and 550 nm, due to the best detection and ablation efficiency of the ALABAMA in this
size range. The particle types shown in Fig. 3
refer only to the results obtained by the fuzzy
Absolute and relative particle numbers detected by ALABAMA during the HCCT-2010 campaign. The percentage of each particle type is subdivided into the fraction revealing a diurnal trend and into the fraction without diurnal trend. All percentages refer to the total number of 177 752 analysed particles.
Figure 4 shows the number of detected particles as
a function of the local wind direction at the Schmücke. Panel a gives
the standard wind rose for the whole time period. The dominating wind
direction was south-west, with about 50 % probability for wind directions
between 200 and 270
Time series (top) and diurnal variations (bottom, LT) of the particle type “diesel exhaust” during HCCT-2010. Markers denote the mean values, the grey shaded area represents the upper quartile.
Several particle types show a distinct diurnal pattern, indicating a source with a specific emission pattern. The fact that the emission pattern is detectable at the measurement site suggests that the source is not too far away, such that the diurnal pattern is not smoothed by different air mass transport velocities and different wind directions. An example is shown in Fig. 5. The figure shows the complete time series for the particle type “diesel exhaust” (upper panel) and the averaged diurnal pattern (lower panel). The diurnal pattern shows the increased occurrence of particles of this type between 09:00 and 24:00 LT and a decrease of this particle type during the night. This indicates the contribution of traffic emissions from within 1 or 2 hours from the measurement site (local traffic typically starts around 07:00 in the morning). All clusters contained in each particle type were inspected for such a diurnal trend. From this the amount of particle influences by local or regional sources were obtained. Table 4 shows the relative abundance of the particle types during HCCT-2010 along with the percentage of clusters showing diurnal variations and those not showing a diurnal trend. In total, about 63 % of the analysed particles belong to clusters indicating a diurnal variation. This finding implies that the aerosol composition during HCCT-2010 is mainly influenced by local and regional sources.
Aerosol composition of out-of-cloud aerosol (left) and cloud residual particles (right) for the entire HCCT-2010 campaign. Uncertainties of the clustering were estimated according to Sect. 2.4.2. In case of particle types determined by the marker peak method (Sect. 2.4.3) uncertainties are based on Poisson statistic. Number of analysed particles: out-of-cloud aerosol: 164 595, cloud residues: 13 157. Note that the scale is expanded by a factor of 10 below the dashed line (bottom axis), particle abundances above the dashed line refer to the top axis.
One of the main objectives of this study was the analysis of cloud residues and the comparison to the aerosol composition under cloud-free conditions. Figure 6 shows the average aerosol particle composition for all out-of-cloud aerosol particles and all cloud residues measured during HCCT-2010, not restricted to the full cloud events. It has to be noted that measurements of cloud residues and out-of-cloud aerosol can by definition not be made simultaneously, such that differences in the meteorological condition influences such a comparison. In the following we will compare the relative abundance of the individual particle types between cloud residues and out-of-cloud aerosol.
For both organic particle types (“org, K” and “org”) the relative abundance
in cloud residues is smaller than in the out-of-cloud aerosol. This
observation may partly be in contradiction with previous measurements
reported in the literature. For example, measurements of cloud residue
composition by Drewnick et al. (2007) using an Aerodyne AMS reported
increased organic mass fractions in cloud residues. However, these
measurements are hard to compare because AMS data are based on average
aerosol mass while the ALABAMA data are based on single particle analysis.
Furthermore, Drewnick et al. (2007) did not consider refractory species
in the aerosol composition and did not separate the organic mass into
different subgroups. For example, in our study the particle types “soot” and
“biomass burning” reveal a significant fraction of the aerosol composition
(see below), and aerosol originating from biomass burning can be a
significant fraction of the “organic” aerosol mass reported by the AMS
(e.g. Lanz et al., 2010; Crippa et al., 2013, 2014). The
AMS data from the HCCT-2010 campaign that are presented in a companion paper
(Schneider et al., 2016) show a slightly lower scavenging
efficiency for organics than for nitrate and sulfate. In-cloud scavenging
of organic particles depends on the solubility of the organic compounds
(Limbeck and Puxbaum, 2000). The slightly lower scavenging efficiency may
therefore be explained by the lower solubility of hydrophobic organic
compounds like aromatics, whose fragments were frequently observed in our single
particle mass spectra (C
Time series (LT) of the amine compounds methylamine (MA), dimethylamine (DMA), and trimethylamine (TMA) from cloud water samples on 2 October 2010 (FCE11.2 and FCE 11.3) compared to the time series (number of mass spectra per hour) of amine-containing cloud residues. The upper panel shows the liquid water content (LWC) and the FCE times.
Several characteristic peaks for amines have been reported in the
literature, the most common appear to be
The results also show an increased fraction of the particle type “soot” in
cloud residues. Freshly emitted soot particles are hydrophobic and do not
serve as CCN at realistic supersaturations (Dusek et al., 2006a; Koehler
et al., 2009). Nevertheless, it was observed in several studies that soot is
more efficiently activated than organic particles (Hitzenberger et al.,
2000; Sellegri et al., 2003). The size-resolved aerosol composition (see
Fig. 3) shows that the observed “soot” particles
were mainly larger than 450 nm, leading to the conclusion that mostly aged
soot particles were analysed by the mass spectrometer. Furthermore, the
“soot” clusters reveal internal mixtures with soluble inorganic compounds
like nitrate or sulfate, which is presumably leading to activation of these
particles at lower critical supersaturation (Dusek et al., 2006a; Henning
et al., 2010). Internally mixed particles can either develop from
condensation of secondary compounds on pre-existing particles or from
coagulation with hygroscopic particles, or cloud droplets. Due to
the fact that even the out-of-cloud aerosol particles that contain soot are
internally mixed with secondary inorganic compounds, the increased fraction
of soot particles in cloud residues can rather be explained by a good CCN
activity of hygroscopic soot particles than caused by in-cloud impaction
scavenging. Aging of atmospheric soot particles by coating with sulfate and
nitrate has been observed using single particle mass spectrometry by
Pratt and Prather (2010) as well as by Moffet and Prather (2009). The authors concluded that such processing of soot particles in the
urban environment of Mexico City takes about 3 hours. Although in a
cleaner environment than Mexico City coating by nitrate and sulfate will
likely be slower, it appears to be a reasonable explanation for the findings
that soot-containing particles internally mixed with nitrate and sulfate
are efficiently activated as CCN and are therefore enhanced in cloud
residues. Similar findings have been reported for growth factors of coated
black carbon particles measured in Paris using a hygroscopic tandem
differential mobility analyser and a single particle mass spectrometer
(Healy et al., 2014b). The mass-based scavenging efficiency of
soot particles in our study was found to be markedly lower than that of
sulfate or nitrate (Schneider et al., 2016), confirming the
assumption that the large soot-containing particles (
The relative percentage of biomass burning particles occurring in the out-of-cloud aerosol does not differ much from that in the cloud residues. In agreement with previous observations (Ross et al., 2003; de Villiers et al., 2010) this implies that aerosol from biomass burning is an effective CCN, resulting from a high content of soluble organic and inorganic compounds in the particles (Silva et al., 1999; Posfai et al., 2003; Andreae and Rosenfeld, 2008; Pratt et al., 2011).
Time series (local time) of combustion-related parameters
observed during HCCT-2010.
The high percentage of particles originating from combustion processes
(“soot”, “biomass burning”, “diesel exhaust”) of about 43 %
(Fig. 6) is investigated more closely in the
following. Figure 8 shows the time series of the
particle types “soot” (blue, Fig. 8b) and the sum
of all particle types containing elemental carbon (brown,
Fig. 8c) observed by ALABAMA, along with
temperature, concentration of equivalent black carbon (EBC) measured by the
MAAP, and the biomass burning aerosol inferred from AMS data (green,
Fig. 8d). The latter was estimated from the AMS
data based on the marker peak at
Except for the particle type “Ca”, the number fractions of mineral dust and
metallic particle types (“Fe, V”, “Ni”, and “Pb”) are markedly enhanced in
the cloud residues. All these particles contain nitrate and sulfate, too
(see Fig. 2); thus, the good activation or
scavenging efficiency is likely caused by the soluble compounds in these
particles. The presence of metals in cloud droplets has important
implications for the oxidation of sulfur-containing species in the aqueous
phase. Catalytic oxidation of SO
Cloud residue composition during full cloud events (FCE). The number of obtained single particle mass spectra and the mean EBC concentration per event is given below the graph. Only FCE with more than 100 mass spectra are included (see Table 1).
HYSPLIT back trajectories (96 h) for air masses
encountered during the FCE displayed in Fig. 9.
Left: FCE with low soot particle abundance; right: FCE with high soot
particle abundance. Trajectory end point: Schmücke (
In the following the selected FCEs (see
Table 1) will be analysed in more detail. These
cloud events represent a subset of all cloud measurements and are referring
to certain conditions that were given in detail in Tilgner et al.
(2014). The composition of the cloud
residual particles measured during the individual FCE during HCCT-2010 are
shown in Fig. 9. Also given are the number of
analysed mass spectra and the averaged mass concentration of equivalent
black carbon measured in the interstitial aerosol during the events. Only
FCE with sufficient (
As mentioned before, all identified particle types indicate internal
mixtures with nitrate, sulfate, or both species. Therefore, the clustering
algorithm cannot provide information about the mixing state of the particles from
out-of-cloud to inside of the cloud. Therefore, the mixing state of the
particles with nitrate and sulfate was investigated by means of the
characteristic marker peaks
Cloud and out-of-cloud periods (local time) used for the investigation of the mixing state of the particles.
To compare cloud residues and out-of-cloud aerosol, we selected air masses with comparable origins based on HYSPLIT back trajectories for in-cloud and out-of-cloud conditions. As an additional criterion it was required that the local wind direction at the Schmücke was constant. The listed events “I” and “II” in Table 5 fulfilled these criteria. These events differ slightly from the defined FCEs because the criteria for the FCEs were not taken into account here. The cloud sampling phase of event “I” corresponds mostly to FCE1.1, while that of event “II” is a part of FCE24.0. During event “I” the air masses for in-cloud and out-of-cloud conditions both arrived from France, while air masses for both conditions during event “II” passed over England.
Indications for a change of the mixing state of the particles in the cloud (for details of event I and II see Table 5). Left: percentage of out-of-cloud aerosol particles and cloud residues containing either nitrate (blue) or sulfate (red). Right: particles containing only nitrate but no sulfate (blue), only sulfate but no nitrate (red), and particles containing both nitrate and sulfate (purple).
The characteristic marker peaks
Histograms of particles analysed by ALABAMA during event I and event II (Table 5). In both cases the histograms are shifted to larger sizes for the cloud residues, indicating the uptake of gaseous compounds by the cloud droplets leading to an increased size of the cloud residual particles compared to the out-of-cloud particles measured shortly before cloud formation.
These two case studies demonstrate the change of the mixing state of the particles
by chemical processes inside the cloud liquid phase. Similar observations
were found earlier in numerous studies (e.g. Laj et al., 1997b; Sellegri
et al., 2003; Brüggemann et al., 2005; McFiggans et al., 2006; Hayden et
al., 2008; Zelenyuk et al., 2010). The enrichment of nitrate was also
observed by simultaneous measurements with an AMS providing evidence of an
increased mass concentration of nitrate in cloud residues compared to
interstitial and out-of-cloud aerosol (Schneider et al., 2016).
Such an enhancement of nitrate in cloud droplets can be explained by the
uptake of gaseous nitric acid into the cloud droplets (Tilgner et al.,
2005; Hayden et al., 2008). Enrichment of sulfate in cloud droplets can
occur via different pathways. Besides the uptake of gaseous H
Furthermore, the aerosol hygroscopicity was investigated in the same field experiment before and after cloud formation at the valley sites. In agreement with the described results, the hygroscopicity of the particles was found to be increased after passing the cloud (up to 50 %, see Henning et al. (2014). By means of the above described processes, water-soluble material is enriched inside the particles while being processed by the cloud. After evaporation of the cloud the water-soluble material it is likely to remain in the particles, thereby increasing their hygroscopicity. This process will occur in all cloud droplets formed from all CCN sizes, and therefore also influence the CCN properties of aerosol particles smaller than analysed here. For small aerosol particles that are in the size range of the activation diameter for a specific supersaturation the chemical composition plays an important role for the activation.
During the HCCT-2010 campaign, more than 170 000 aerosol particles and more
than 14 000 cloud residual particles were analysed by single particle mass
spectrometry. The data evaluation was done by a combination of the
clustering algorithm fuzzy
Analysis of the cloud residues revealed that the relative percentage of soot and amines is increased compared to out-of-cloud aerosol. Analysis of cloud water samples by ion chromatography showed that amines were mainly found in the form of trimethylamine. The increased fraction of soot can be explained by processing of soot particles leading to coating by nitrate and sulfate, which is known to occur in a few hours. In addition the size-resolved aerosol composition reveals that the detected particles containing soot are larger than 450 nm. Both facts suggest that such processed soot particles are good cloud condensation nuclei.
All observed particle types show internal mixtures with the secondary
inorganic compounds nitrate and/or sulfate. By means of the characteristic
marker peaks
Such a cloud processing of aerosol particles has important implications for the hygroscopic properties of the aerosol particles after cloud passage. An increase of soluble compounds in the particles, together with the involved growth of the particle size, will lead to an enhanced number of CCN that are available in the air mass after evaporation of the cloud. Additionally the modified chemical composition can lead to altered radiation properties concerning light scattering and absorption. Especially internal mixed soot particles indicate a higher absorption than pure soot particles (Jacobson, 2001) and could therefore counteract the cooling effect of clouds.
The German Research Foundation DFG funded the participation of S. Mertes (grant HE 939/25-1 and ME 3534/1-2).
We thank Thomas Böttger, Wilhelm Schneider, Paul Reitz, Jovana Diesch, Sarah-Lena von der Weiden-Reinmüller, and Friederike Freutel for the support at the measurement site as well as Frank Helleis for electrical and technical advices and the whole HCCT-2010 team.The article processing charges for this open-access publication were covered by the Max Planck Society Edited by: M. C. Facchini