Introduction
In the last years a lot of effort has been made to characterize the
freezing ability of different aerosol particles, which were thought to
be ice nucleating active. Especially mineral dust particles were
investigated quite intensively, as they were found in ice crystal
residues most frequently . Laboratory measurements indicate that mineral dust
particles are efficient ice nucleating particles in a temperature
range below -15 ∘C or probably only
below -20 ∘C , where the latter
examined droplets in which each contained a single particle
of atmospherically relevant sizes. In contrast,
atmospheric observations with lidar and radar showed that ice
particles also formed at higher temperatures . It has been assumed in the past that the
presence of biological particles like bacteria, fungal spores or pollen is
necessary to explain ice nucleation at higher temperatures, as these
particles show ice nucleation ability up to temperatures of -2 ∘C
. and later
gave detailed overviews over different types of INP and
denominate biological materials as those being ice active at higher
temperatures above about -15 ∘C. Only recently it
became known that biological particles carry small ice nucleating
macromolecules (INMs) which are responsible for their freezing ability. These
INMs could be e.g., proteins in the case of bacteria , fungal
spores and material originating from algae contained in
the sea surface microlayer or polysaccharides in case of
pollen . Furthermore, several studies showed that an INM is
still ice active when the original carrier is detached or in the case of
bacteria non viable . So it is very likely that these INMs are accumulated
in the ground, where they come in contact with mineral dust particles.
suggested that mineral dust particles may act as inert
carriers for biological particles. investigated 46
atmospheric cloud ice crystal residues with ATOFMS (Aerosol Time-Of-Flight
Mass Spectrometer) and found that
60 % of the dust particles were likely a mixture of biological material
and mineral dust. Also , investigating the residual
particles in hail stones, found biological material to be attached to the
surface of mineral dust particles. In that context,
describe that soil organic matter sorbs on mineral surfaces, preserving and
maybe even accumulating INMs when being connected to mineral surfaces.
Therefore, there is some indication that the ascription of mineral dust to
the atmospheric ice nucleating particles (INP) is, at least to a certain
extent, due to unnoticed attached ice nucleating biological material
. The question arises how the freezing ability of a mineral
dust particle changes when there is some biological material attached to its
surface. There are already some laboratory studies which confirm the enhanced
freezing ability of soil dust due to the presence of biological material
. However, the temperature ranges
in which the organic fraction of the examined soil dusts was reported to be
responsible for the ice activity differed between the studies, extending down
to only -15 ∘C for (where ice activation
observed at lower temperatures was ascribed to mineral dust), but down to
even below -30 ∘C for .
Soil dust is a very inhomogeneous substance and it is
very difficult to characterize which of its constituents is responsible for the
freezing initiation, particularly as INMs were found to be on the size of a few
10 nm only . Also, to quantify
the freezing ability of an internal mixture of mineral dust and
biological material it is advantageous to know the freezing ability of
the individual materials. For this reason in the present study we
mixed a well-characterized mineral dust
illite-NX, with well-characterized biological
material birch pollen washing water, and investigated the immersion freezing ability of the
resulting mixed particles, utilizing the Leipzig Aerosol Cloud Interaction
Simulator (LACIS). The knowledge of the mixing state of the produced
particles is essential for the understanding of the observed freezing
abilities. Thus, we applied several methods for characterizing the mixing
state of the generated aerosol: single-particle aerosol mass spectrometry,
Scanning Electron Microscopy (SEM),
Energy Dispersive X-ray analysis
(EDX), and a Volatility–Hygroscopicity Tandem Differential Mobility
Analyser (VH-TDMA). These methods
each use a different approach to characterize the particle mixing state and
can therefore be regarded as complementary. One aspect of this study is to
compare these characterization methods and to assess their ability to
identify the mixing state of a laboratory-generated aerosol. Another
important issue is the adequate interpretation of the freezing behavior of
the mixed particles. For this purpose the Soccerball model
SBM, is used. In the following chapters the basics
of the SBM, the preparation of the mixture as well as the particle generation
will be explained. The applied methods for characterizing the mixing state of
the generated aerosol are also described, together with the measurement
set-up used for the ice nucleation measurements. Afterwards the results are
presented and discussed.
Basics of the Soccerball model
With the help of the SBM it is possible to describe and parameterize the
freezing behavior of different materials. In general it is assumed that
freezing is induced by single ice nucleating entities. These entities can be
e.g., special sites on the surface of a particle (as it is assumed for
mineral dust particles) or, in the case of biological material, single INMs.
In any case, the ice nucleating entity has a defined two-dimensional surface
area ssite. A specific contact angle θ is assigned to each
ice nucleating entity which determines the ice nucleation ability of this
particular entity in terms of a nucleation rate coefficient jhet
based on classical nucleation theory . The overall contact
angle distribution is described by a Gaussian probability density function
with a mean value μθ and a standard deviation
σθ, with each contact angle θ occurring with a
probability of p(θ).
p(θ)=12πσθexp-(θ-μθ)22σθ2
The probability Punfr of a droplet (which contains an ice
nucleating entity) to be unfrozen at a certain temperature T and a certain
time t is defined as
Punfr(T,μθ,σθ,t)=∫0πpθexp(-jhet(T,θ)ssitet)dθ+∫-∞0pθexp(-jhet(T,θ=0)ssitet)dθ+∫π∞pθexp(-jhet(T,θ=π)ssitet)dθ.
As a next step, we consider a population of droplets, with each droplet
containing a single particle and all particles having the same size.
Naturally, the ice nucleating entities are Poisson distributed over the
particle population. This means that each particle contains one, multiple or
even none ice nucleating entities. Thus the average number of ice nucleating
entities per particle is defined by the expected value of the Poisson
distribution λ . The ice nucleating probability
of each entity is determined by the contact angle distribution. With this,
the probability Punfr,λ for droplets to remain unfrozen is
given by
Punfr,λ(T,μθ,σθ,λ,t)=exp(-λ(1-Punfr(T,μθ,σθ,t))).
Note that Punfr,λ ≠ Punfr. While
Punfr is only valid for droplets which contain an ice nucleating
entity, Punfr,λ is valid for the whole droplet population
i.e. also for those droplets which do not contain an ice nucleating entity.
The ice fraction, which is identical to the freezing probability
Pfr,λ follows with
fice(T,μθ,σθ,λ,t)=1-Punfr,λ.
Equation () represents the combination of the original SBM from
and the CHESS model from and was
derived in detail in . The average number of ice
nucleating entities λ is a material and size depending parameter and
its determination is dependent on the freezing behavior of the investigated
material.
Methods
Materials
To produce particles consisting of both dust and biological material, first
a suspension containing both materials was prepared. For the experiments
presented here illite-NX was chosen as the dust component, as this product
has been used as a proxy for the natural dust composition found in the
atmosphere and references therein. Furthermore, the
freezing ability of pure illite-NX particles was already investigated with
LACIS in a previous study . For the illite-NX suspensions
10 g of illite-NX powder was suspended in 200 mL of MilliQ
water. After shaking the sample it was stored in the refrigerator for about
24 h. During that time large and heavy particles sedimented to the
ground. An Eppendorf pipette was used to sample 50 mL from the top
part of the suspension. To determine the concentration of the illite-NX
suspension, 10 mL of the pipetted suspension was dried in a petri
dish. A precision balance was used to first determine the weight of the empty
petri dish. After drying the suspension the petri dish with the residues was
weighted again. This procedure was repeated three times. The mean
concentration of the illite-NX suspension is about 0.01 gmL-1.
As biological component, washing water from Swedish birch pollen (in the
following BPWW) was used. The INM responsible for the freezing ability of
BPWW is most likely a polysaccharide . The immersion
freezing behavior of these INMs was already investigated with LACIS and
parameterizations are given in . The production of the
BPWW suspension was done similar to that described in
but with a lower concentration of 1 g of pollen grains in
80 mL of MilliQ water. The birch pollen were mixed with the water and the pollen grains were then removed
by filtration (pore size 4–7 µm) of the resulting suspension.
After filtration the concentration of the Swedish birch pollen material in
the suspension was determined with the same procedure that was described for
the illite-NX suspension above, and it was determined to be about
0.004 gmL-1. Thus, after filtration we have about one third of
the original mass of pollen in the washing water. This is the same order of
magnitude than what was observed by .
The illite-NX suspension was mixed with the BPWW using 10 mL of each
of the suspensions, which resulted in a dust-bio-mixture to which we refer to
in the following as illite–BPWW suspension. Concerning this illite–BPWW
suspension, one should be aware of the following facts: a high percentage of
the BPWW consists of soluble material which is released when the pollen
grains are suspended in water. Thus the BPWW can be viewed as a suspension of
INMs (and other larger molecules) in a solution, rather than just
a suspension in water. In contrast to that, the dust contains only a small
amount of soluble material so its suspension consists mainly of solid
particles and dilute water. These facts are important for the resulting
mixing state of the internally mixed illite–BPWW particles and will be
referred to again in the next section.
Particle generation and
characterization
In Fig. the generation pathway, starting from a suspension
and ending with dry particles, is shown schematically. First an atomizer
(following the design of TSI 3075) was used to generate droplets from the
illite–BPWW suspension as well as from the pure illite-NX and BPWW
suspensions. As mentioned above, the BPWW contains a soluble fraction which
was distributed in the whole suspension, so it is reasonable to assume that
every droplet generated from the illite–BPWW suspension contained at least
some soluble biological material, although not necessarily any INMs. Most but
maybe not all of these droplets also contained a mineral dust particle. After
the atomization the droplets were dried by passing them through a silica gel
diffusion dryer. The dry particles were passed through a diffusion charger
(85 Kr) to achieve a bipolar charge distribution and afterwards
size-selected using a Differential Mobility Analyzer (DMA; ; type “Vienna medium”). In the
measurements presented here a mobility diameter of 500 nm was
selected. Due to the use of a cyclone (cut off diameter: 500 nm)
upstream of the DMA, the fraction of doubly charged particles contained in
the aerosol is negligible: this was verified through measurements of the
optical particle size distribution done downstream of the DMA using an UHSAS
(Ultra High Sensitivity Aerosol Spectrometer, Droplet Measurement Technologies). After size selection the
aerosol stream was split and one part was always fed into a Condensation
Particle Counter (CPC, TSI 3010) to
measure the total particle number concentration. The remaining aerosol was
then available for particle characterization (e.g., sampling on filters) and
for the Leipzig Aerosol Cloud Interaction Simulator (LACIS). To understand
the observed freezing abilities, the knowledge about the mixing state of the
generated particles is essential. Considering the above mentioned composition
of the generated droplets, we assume that the resulting dry particles were
most likely an internal mixture of illite-NX and BPWW material. There might
have been also some particles consisting only of BPWW material but we expect
no pure illite-NX particles in the generated aerosol. To prove this
hypothesis several aerosol characterization methods were applied which will
be introduced in the following.
Schematic of the particle generation method used in this
study.
Volatility-Hygroscopicity Tandem Differential Mobility
Analyser (VH-TDMA)
The volatility and hygroscopicity of particles from all three suspensions,
the two pure ones and the mixed one were used to infer the particle mixing
state. In contrast to mineral dust, biological material is much more volatile
and hygroscopic. Therefore, treatment with heat or humidity will change the
size of particles consisting of biological material but likely will have
a smaller effect on the mineral dust components. Measurements were performed
with a custom-built VH-TDMA, which was composed of two DMAs and a CPC (TSI
3010) separated by a volatility (V-mode) and a humidity (H-mode) conditioning
device. A mobility diameter of 500 nm, similar to that used for LACIS
measurements, was selected with the first DMA. Monodisperse particles were
then conditioned either in the thermodenuder section at 300 ∘C under
dry conditions (10 % RH) or in the humidity section at 90 % RH
at room temperature (20 ∘C). The residence times in the conditioning
devices were approximately 2 and 4 s for the V-mode and H-mode,
respectively. The resulting particle size distributions obtained after
conditioning were measured by the second DMA coupled to the CPC. The second
DMA and the humidity section were confined in a temperature-controlled box at
20 ∘C. For the H-mode the sheath air of the second DMA was
humidified to 90 % RH. The volatile “growth” factor VGF obtained
from the V-mode is defined as
VGF = Dp(300∘C,10%RH)/Dp(20∘C,10%RH) where
Dp(300∘C,10%RH) is the measured
diameter after heating at 300 ∘C and
Dp(20∘C,10%RH) the selected dry
mobility diameter at ambient temperature. The hygroscopic growth factor HGF
using the H-mode is defined in a similar way:
HGF = Dp(20∘C,90%RH)/Dp(20∘C,10%RH) where Dp(20∘C,90%RH) is the measured diameter at 90 % RH.
In this work, the distribution of growth factors refers to the growth
factor probability density function (GF-PDF) and was fitted as
a superposition of distinct Gaussian modes using the TDMAinv algorithm
developed by . From comparing VGF and HGF
distributions obtained for the pure and mixed particles, the fraction
of internally mixed particles in the aerosol generated from the
illite–BPWW suspensions can be derived.
Scanning Electron Microscope (SEM) and Energy Dispersive X-ray analysis (EDX)
In cooperation with the Leibnitz institute of surface modification in Leipzig
(IOM) and the Technical University Darmstadt filter samples generated from
the illite–BPWW suspension as well as from the pure illite-NX and BPWW
suspensions were imaged by
SEM. Analysis at the IOM was
performed in a ULTRA 55, Carl Zeiss SMT, (Oberkochen, Germany) and in
Darmstadt in a FEI Quanta 200 FEG Environmental Scanning Electron Microscope
(Eindhoven, the Netherlands).
By SEM we intended to examine visual differences between particles generated
from the two pure substances and from the illite–BPWW suspensions to get
more information about the exact mixing-state of the illite–BPWW aerosol,
i.e., if the sample can be described as an external mixture of the two
components, as an agglomeration of the two components or as an internal
mixture. Additionally, at the Technical University Darmstadt the pure samples
and the particles from the illite–BPWW suspension were analyzed by EDX
(Phoenix EDAX, Tilburg, the Netherlands). With this technique the elemental
composition of individual particles is determined. In order to prevent an
influence of the sampling substrate on the chemical results (carbon signal
from the filters) for these analyses the particles were sampled on Boron
substrates.
Single Particle Laser Ablation Time-of-flight mass spectrometer (SPLAT)
Finally we also investigated the mixing state of the illite–BPWW aerosol via
single particle mass spectrometry. Here, the instrument SPLAT
() of the Max Planck Institute for Chemistry, Mainz, was
used. For these experiments, particles were generated with the set-up
described above and then examined in the SPLAT immediately after generation.
This was done at first separately for an illite suspension and for BPWW, and
then for the mixed particles generated from the illite–BPWW suspension. In
the SPLAT, single particles are hit by a laser pulse which vaporizes and
ionizes the compounds of the particle. A bipolar time-of-flight mass
spectrometer is used to detect the ions. The composition and thereby the
mixing state of the individual particles can then be inferred from their mass
spectra.
Freezing experiments
For the freezing experiments the laminar flow tube LACIS was utilized. In the
inlet section of LACIS, the aerosol flow is combined isokinetically with
a humidified sheath air flow such that the aerosol forms a beam of
approximately 2 mm in diameter along the center line of the flow
tube. Supersaturated conditions are achieved by cooling the tube walls, and
result in activation of the particles to droplets, with each droplet
containing one size segregated particle. Further downstream of the flow tube,
these droplets then may freeze due to further cooling. For detailed
information on the operation mode of LACIS see and
.
At the outlet of LACIS, TOPS-Ice thermally stabilized optical
particle spectrometer, is used to discriminate between
frozen and unfrozen droplets, and to quantify the fraction of frozen
droplet (fice, number of frozen droplets divided by the
total number of frozen and unfrozen droplets). In the investigations
presented here, fice was determined in the temperature
range between ∼-17 and -40 ∘C.
Measurement results
In the following we will first describe the results concerning the
determination of the mixing state of the generated aerosol (Sect. 4.1).
Afterwards, the results of the immersion freezing experiments will be
discussed (Sect. 4.2).
(a) Probability density function (PDF) of the
hygroscopic growth factor (HGF) determined from the VH-TDMA
measurements at 90 % relative humidity. (b) PDF of
the volatile “growth” factor (VGF) determined from the VH-TDMA
measurements at 300 ∘C.
Characterization of the particle mixing state using different methods
Several methods were applied to characterize the mixing state of the
generated illite–BPWW aerosol. These were already introduced in
Sect. and their results will be discussed in
the following.
VH-TDMA
The HGF and VGF distributions of the different particle types which were
obtained from the VH-TDMA measurements are shown in Fig. . The
values for the mean and standard deviation of the different curves are given
in Table . For the pure illite-NX particles (orange line)
almost no change in size was observed for both treatments, which results in
mean growth factors of approximately 1. This confirms that the illite-NX
contained no or only very little amounts of volatile or soluble material. In
contrast to that, particles generated from the BPWW suspension (green line)
showed a significant change in size for both treatments which lead to HGF and
VGF of 1.38 and 0.56, respectively. The results for the particles
generated from the illite–BPWW suspension (purple line) showed HGF and VGF
values which are between those for the pure substances. It is also obvious
that only one mode is present and that this mode shows no (for the VGF) or
only little (for the HGF) overlap with the pure BPWW material. This suggests
that nearly all particles from the illite–BPWW suspension consisted of both,
illite-NX and BPWW material. In other words: all particles were internally
mixed. Furthermore, the HGF values can be used to determine the
hygroscopicity parameters κ of the different particle types
. These κ values are 0, 0.176 and 0.017 for
pure illite-NX, pure BPWW and internally mixed particles, respectively,
determined from average HGF. For the κ of the internally mixed
particles the simple mixing rule κmix=∑iϵiκi can be applied . Here,
ϵi depicts the amount of material i which has a κ value
of κi. With this it is possible to calculate the average volume
fraction of BPWW material on the internally mixed particles, which was found
to be 9.7 %. Assuming spherical illite-NX particles surrounded by
a smooth layer of BPWW material, the BPWW layer thickness would be
8 nm.
Mean value and standard deviation of the HGF and VGF Gaussian
distribution for the different heated (V) and humidified (H) particle types.
The selected size prior to the treatment was 500 nm.
Suspension
Mean
Standard deviation
illite-NX
0.99
0.01
HGF
BPWW
1.38
0.07
illite–BPWW
1.05
0.05
illite-NX
0.99
0.01
VGF
BPWW
0.56
0.02
illite–BPWW
0.87
0.01
Examples of SEM images of the different
suspensions. (a) BPWW, (b) illite-NX,
(c) illite–BPWW.
SEM and EDX
The typical routine for the EDX based identification of particles as
internal/external mixtures is the use of elemental ratios of main elements of
the pure components (within the individual particles in the mixed sample) as
classification criteria. In this study the classification of the analyzed
particles is based on the determined carbon / silicon (C / Si) ratio.
The choice of the boundary values of the elemental ratios for the
classification as internal respectively external mixture depends on the
detection limit and uncertainty of the EDX measurement. Marginal carbon and
silicon signals (close to detection limit) are often observed in SEM-EDX
measurements. In this way only particles with a C / Si ratio (based on
net count rate) between 0.1 and 10 can be classified as internal
illite/BPWW mixtures. As EDX analysis is limited to major and minor elements,
internal mixtures cannot be identified when one component is only present in
traces.
Following this scheme all particles from the illite–BPWW suspension would
have been classified as pure illite samples. Including the morphological data
from the SEM measurements it becomes obvious that the particles from the
illite-BPWW suspension are neither morphologically (secondary electron
images, see Fig. b and c) nor chemically (EDX) discriminable from
the pure illite particles by SEM analysis. Furthermore, nearly none of the
viscous droplets (Fig. a), which would indicate the presence of pure
BPWW particles could be observed in the SEM images of the illite-BPWW
particles (145 particles were counted from which only 1 showed a shape
similar to the pure BPWW particles).
With respect to the limitations of EDX analysis it can be concluded that the
relative abundance of the BPWW residuals within the mixed particles is less
than (maximum) 2 wt %. As the morphological surface features of the illite
particles are still visible in the illite–BPWW suspension, a surface
coating, which is thicker than a few tens of nanometers, can be excluded. This
is consistent with the results from the VH-TDMA measurements, where a layer
thickness of 8 nm was estimated. As the residual carbonaceous
particles (which will contain the INMs) from the BPWW will be sited on the
surface of the illite grains, it is highly likely that they are present
either as very thin film or as small “isles” at the surface of the illite
particles.
(left) Examples of single particle mass spectra of a pure illite
particle, a pure BPWW particle, and a mixed particle. The marker peaks
Na+ (m/z 23) and SiO+ (m/z 44) are highlighted.
The values for Na/(Na+SiO) are given for each
particle. (right) Histograms of the ratio (Na/(Na+SiO)
for all particles. The dashed lines indicate the boundaries for pure illite
(<0.1) and for pure BPWW (>0.65).
Single particle mass spectrometry
For the illite–BPWW suspension 549 mass spectra were measured with the
SPLAT, 150 of which had to be discarded due to insufficient ion count rate or
wrong mass calibration. The remaining 399 mass spectra were used for the
further analysis.
To decide whether a particle is an internal mixture or a pure particle, the
peak intensity ratio of selected marker peaks
(Na/(Na+SiO)) was used (histograms of this ratio as
well as examples of mass spectra for the pure as well as for the mixed
particles are shown in Fig. ).
The experiments with the pure particles showed that values of this ratio
between 0 and 0.1 occurred only for pure illite, while ratios larger than
0.65 were only observed for pure BPWW particles. Thus, particles with ratios
between these threshold values are likely mixtures of both components.
328 (around 82 %) out of the 399 spectra showed a ratio
Na/(Na+SiO) between 0.1 and 0.65, indicating internal
mixtures of illite-NX and BPWW material. Only 59 particles (14 %) appeared
to be pure illite, and 16 (4 %) appeared to be pure BPWW in the SPLAT.
The uncertainties of this method can be estimated as follows: 25 %
uncertainty originates from the amount of useful mass spectra per measurement
(about 25 % of the mass spectra recorded in each experiment had to be
discarded out due to insufficient ion count rate or wrong mass calibration).
Additionally, 37 % of the pure illite values as well as 44 % of the pure
BPWW values have intensity ratios in the intermediate range from 0.1 to 0.65.
These uncertainties result in a possible underestimation of externally mixed
particles of up to about a factor of 1.6 for pure illite and 1.8 for pure
BPWW. This “worst case scenario” would lead to values of 24 % pure illite
particles and 7 % pure BPWW particles, which still means that 69 % of the
particles are internally mixed.
On the other hand, as discussed above, it is rather unlikely to find pure
illite-NX particles in the aerosol generated from the illite–BPWW
suspension. But as the VH-TDMA measurements as well as the SEM images and the
EDX analysis suggest that the amount of BPWW material on the particles might
be rather small, it can not be ruled out that the SPLAT can not detect such
small amounts of biological material and thus the amount of internally mixed
particles is underestimated.
It is obvious that the analysis of the mixing state of the generated
illite-BPWW aerosol is not trivial and that it is bound to the limitations of
the applied instruments. What we learned from these investigations is that,
especially for particles with only small amounts of biological material, the
detection of this biological material is difficult or even not possible.
Nevertheless, all methods showed (albeit some only indirectly) that a
significant fraction, if not all, of the generated particles consisted of
both biological material and dust and are internally mixed particles.
Immersion freezing experiments
In Fig. the results of the immersion freezing experiments
of the particles generated from the illite–BPWW suspension are shown (purple
symbols). For a better understanding of the freezing behavior of the mixed
particles it is necessary to first understand the freezing behavior of the
pure materials. Therefore, results of measurements from the pure
500 nm illite-NX and BPWW particles are shown in
Fig. , as orange and green symbols, respectively. The
freezing ability of illite-NX particles generated from suspension was
compared to the freezing ability of dry generated illite-NX particles of the
same size (500 nm) as presented in (filled and
open orange squares in Fig. , respectively). The ice
nucleation ability of the wet generated particles was only slightly below
that of the dry generated ones. Concerning the BPWW measurements shown here,
we should mention that we had to use another birch pollen batch than in
as the one used in the former study was used up.
Similarities and differences between these two batches are addressed further
down in the manuscript.
Results of the immersion freezing experiments of particles
with a mobility diameter of 500 nm generated from the
illite–BPWW-suspension as well as from the pure illite-NX and BPWW
particles (purple, orange and green symbols, respectively). Filled
orange squares: illite-NX particles from suspension, open orange squares:
illite-NX particles from dry generation. Error bars are the standard
deviation of the experiments and were obtained for temperatures with at least
three measurements. The green, the dashed orange and the straight orange line
represent the freezing ability of pure BPWW, dry generated illite-NX and wet
generated illite-NX particles, respectively (500 nm mobility
diameter) based on the SBM parameters determined from measurements with the
pure substances. The grey line represents an extreme case where all the
available BPWW material (9.7 % volume fraction) on the mixed
particles has been dissolved in the droplet. The purple line represents the
freezing behavior of internally mixed particles which was fitted to the
measured data.
The comparison between the freezing behavior of the particles generated from
the illite–BPWW suspension and the pure substances indicates that it is
possible to explain the immersion freezing behavior of the mixture by the
freezing abilities of the pure substances. Down to roughly -30 ∘C,
the temperature range at which the first steep increase is observed, and also
the subsequent course of the ice fraction, are identical to those of pure
BPWW particles. The second increase in the frozen fraction below
-30 ∘C, from roughly 0.2 to above 0.3, occurs in a temperature
range for which the ice nucleation is observed for pure illite-NX particles.
At about -38 ∘C the homogeneous freezing sets in. The solid lines
in Fig. represent modeled ice fractions for the different
particle types. The next section will describe how these curves were obtained
for the pure particles and how they can be combined to describe the ice
nucleation behavior of the internally mixed particles.
Theoretical description and discussion
On the first view it seems to be fairly clear just from the three
experimental data sets in Fig. that the freezing behavior
of the mixed particles can be explained by the freezing behaviors of the pure
substances. Nevertheless, this conclusion has to be checked by adequate
modeling. For this purpose we decided to use the Soccerball model
SBM,see Sect. . We will first
introduce the parameterizations for the pure substances, and afterwards the
ice nucleation of the mixed illite-BPWW particles will be modeled.
Immersion freezing properties of BPWW particles
For the freezing behavior of BPWW (green line in Fig. ) it
was observed that the ice fraction reaches a saturation range below one
, which means that at a certain temperature no further
increase in ice fraction with decreasing temperature is observed. It is known
that single INMs are responsible for the freezing behavior of the BPWW
material . The occurrence of a saturation range below one
implies that not all of the particles generated from a BPWW suspension
contain an INM (a similar behavior was observed for Snomax in
). In those cases the average number of INM per droplet
(λ) can be directly calculated from the ice fraction
fice∗ observed in the saturation range, with: λ=-ln(1-fice∗).
In it was possible to describe the λ value of
the BPWW by both a linear particle volume dependence and a linear particle
surface area dependence, where a surface area dependence yielded a slightly
better regression coefficient (surface area: r=0.9918, and volume:
r=0.9275). This was somehow surprising as it was expected that the BPWW
particles are fully soluble and thus should show a clear linear volume
dependence (as for example in the case of Snomax; ). In
it was speculated that the BPWW contains some slowly
dissolving material and that the generated particles may need more time than
the few seconds they have in LACIS to dissolve completely, which would
explain the surface dependence of λ.
It should be mentioned here that the Swedish birch pollen washing water which
was used in this study contains two different types of INM (called
INM-α and INM-β), which are internally mixed
. So for the BPWW used in this study
Eq. () changes to
Punfr,λBPWW(T,μθ,σθ,λ,t)=exp(-λα(1-Punfr,α)-λβ(1-Punfr,β)).
The two different types of INM are the reason for the shoulder of the BPWW ice
fraction curve at around -20 ∘C, where the increase at
temperatures above and below this shoulder is caused by the more and the less
ice active type of INM being active, respectively. The values for
μθ and σθ for both INM types contained in the BPWW
were taken from and are given in
Table .
The size of a single INM was estimated to be 10 nm
. Assuming spherical INMs this leads to a ssite
of 3.14×10-16 m2. As explained above, the λ
values of INM-α and INM-β can be described by both a linear
volume dependence and a linear surface area dependence. The relations between
λ and particle volume for INM-α and INM-β can be
described as follows:
λα=6.76×1018m-3×Dp3andλβ=1.31×1018m-3×Dp3.
When assuming a surface area dependence the relations are
λα=3.30×1012m-2×Dp2andλβ=6.65×1011m-2×Dp2.
The respective values for a 500 nm particle are given in
Table . These values differ a little from the respective
values given in . The reason for that is that a new birch
pollen batch had to be used for the here presented measurements. This
includes both measurements of pure BPWW and those for mixed illite-BPWW
particles. It is not surprising that due to natural variability the number of
INMs produced per pollen grain or per mass of pollen varies. As a result, the
number of INMs per particle also differs from batch to batch. But the ice
nucleation properties (μθ and σθ) which were
determined for the old pollen batch can be used to model the ice nucleation
behavior of particles produced from the new batch, as seen by the good
agreement between measured and modeled data for BPWW shown in
Fig. . This is a strong indication for the fact that the
two types of INMs in the new batch as such are the same than those in the
formerly used batch.
Immersion freezing properties of illite-NX particles
In contrast to the BPWW particles we did not observe a saturation range for
the frozen fraction for the illite-NX particles . But,
due to the lower ice nucleating ability, it is plausible that homogeneous ice
nucleation, which is dominant for T<-38 ∘C, perhaps masks
a potential leveling off of the frozen fraction curves. Therefore, we also
used the presented procedure for representing the ice nucleating ability of
the pure illite-NX particles (orange lines in Fig. ). To
do so, the following assumptions were made. First, the λ value of
illite-NX is assumed to be smaller than the determined λ value for
feldspar given in . This is a reasonable assumption as
K-feldspar was observed to be the most ice active mineral dust found so far
. Second, we assume that λ is
directly correlated to the particle surface area, as for mineral dust it is
assumed that the ice nucleating entities are special sites on the surface of
the particle. This correlation between λ and particle surface area
was already observed for feldspar particles . Due to
these assumptions we could distinctly narrow the range of the possible
λ parameter. For a ssite of 10-14 m2 as
used in , the best fit between measured and modeled
data was obtained for λillite=3.25×1012m-2×Dp2. For a 500 nm
particle the λillite would be 0.813 (see
Tab. ). The resulting μθ and σθ
for both wet and dry generated particles are given in
Table . For the calculations in the next section, the
parameters of the wet generated particles were used, as the illite-BPWW
particles were also generated from a suspension.
Summary of the λ values for 500 nm particles of
different composition. They were calculated with the given relations in
Sect. 5.1 for BPWW particles and Sect. 5.2 for illite-NX particles. For the
calculation of the values of case (b) it was assumed that the soluble volume
fraction of the BPWW material in the internally mixed particle is
approximately 9.7 %. The values for case (c) were estimated by fitting
Eq. () to the measured data with λα,
λβ and λillite being the fit parameters,
where the relation between λα and λβ remains
the same as derived from the measurements of the pure BPWW. The λ
values shown here are only valid for the respective particles generated for
the measurements in this study.
500 nm particle
λillite
λα
λβ
pure illite
0.813
–
–
pure BPWW (surface dependent)
–
0.825
0.166
Case (b) (Fig. 6)
0.759
0.082
0.016
Case (c) (Fig. 6)
0.511
0.206
0.041
Immersion freezing properties of illite–BPWW particles
As already indicated, the freezing behavior of the mixed particles appears as
superposition of the single substances' freezing behaviors, and the most ice
active ice nucleation entity within the particle will dominate the freezing
process. For the 500 nm illite–BPWW particles considered here the
INMs of the BPWW material are the dominant ice nucleation entities in the
temperature range between -17 and -30 ∘C. At this point, it is
worth mentioning that although we assume that every particle contains some
material from the BPWW, not every particle will contain an INM, as already
pure 500 nm BPWW particles did not all contain an INM. Hence in
some illite–BPWW particles, it will be the illite which induces droplet
freezing.
In the following, we model the ice nucleation behavior of the illite–BPWW
particles based on the SBM parameters (μθ and σθ from
Table ) for the pure substances. For the illite-NX
component the parameters of the wet-generated particles were used.
Soccerball model parameters used for the calculations shown in
Fig. . The values for μθ and σθ
are determined from measurements with the pure substances. The BPWW
parameters were directly taken from . The parameters for
illite-NX particles (wet and dry generation) were calculated within this
study.
μθ [rad]
σθ [rad]
pure illite-NX (dry generation)
1.903
0.274
pure illite-NX (wet generation)
2.022
0.315
pure BPWW (INM-α)
1.016
0.0803
pure BPWW (INM-β)
0.834
0.0005
First we consider that all particles are internally mixed (see discussion in
Sect. 4.1). As independent probabilities are multiplicative, the
Punfr, mix is calculated as follows:
Punfr,mix(T,μθ,σθ,λ,t)=Punfr,λillite×Punfr,λBPWW.
With that the ice fraction can be determined as follows:
fice(T,μθ,σθ,λ,t)=1-Punfr,
mix.
Theoretical forms of mixing of a spherical internally mixed
illite–BPWW particle. (a) The illite-NX particle is
completely covered with BPWW material which does not dissolve in the
droplet. (b) The BPWW material is completely dissolved in
the droplet. (c) The BPWW material is partly dissolved and
partly still on the illite-NX particle.
Equation () now represents the freezing behavior of particles,
which consist of both illite-NX and BPWW material, assuming that the freezing
behavior of the pure substances remains unchanged, even when they are mixed.
As already mentioned, the μθ and σθ values of the
pure substances are independent of the particle size. In contrast to that the
parameters λα, λβ and
λillite change when the particle size changes.
From the VH-TDMA measurements we know that the soluble volume fraction of the
BPWW material in the internally mixed particle is approximately 9.7 %.
As mentioned above it might be that the BPWW material on the illite-NX
material does not fully dissolve during the few seconds in LACIS, where the
particle is immersed in a droplet. In the following we want to discuss three
different behaviors which the internally mixed particles may show during the
freezing experiments (Fig. ). Case (a) depicts an extreme case,
where the illite-NX particle is fully covered with BPWW material and the BPWW
material does not dissolve at all. Only the BPWW material is exposed to the
water and can trigger the freezing process. In this case the internally mixed
particles would behave exactly like pure 500 nm BPWW particle
(green line in Fig. ). Obviously this case overestimates
the measured ice fractions of the illite–BPWW particles. Case (b) shows
another extreme case. Here the whole BPWW material is dissolved in the
droplet. Assuming the volume fraction of BPWW material on the illite–BPWW
particles to be 9.7 %, we can calculate the λ values of the
BPWW material (assuming λ to be volume dependent) as well as
λillite for the remaining spherical illite-NX particle. The
values are given in Table . The resulting ice fraction curve
is shown in Fig. as a grey line. It is obvious that in
this case the ice fraction of the illite–BPWW particles is underestimated.
We can conclude from this that the real case is an intermediate case of
(a) and (b) (panel c in Fig. ). The BPWW material dissolves
partly, as it was already suggested in . For this case the
size parameters can not be calculated directly as we have no information of
how much of the BPWW material will dissolve and how much will still cover the
illite-NX core. So we fit Eq. () to the measured data with
λα, λβ and λillite being the
fit parameters, where the relation between λα and
λβ remains the same as derived from the measurements of the
pure BPWW. This leads to the following results:
λα=0.206,λβ=0.041,λillite=0.511.
This is depicted as purple line in Fig. , and fits the
course of the measured data well over the whole temperature range. It should
be noted at this point that in the model the ice nucleation properties
(μθ and σθ) of both materials have not been changed
and still are the same as for the pure materials. So with the modeled ice
fraction curves we could show that the course of the immersion freezing
behavior of the mixture can be explained by the freezing abilities of the
pure substances. This can be interpreted as a confirmation of our assumption
that it is the ice nucleating active biological fraction which determines the
INM containing mixed particles' ice nucleation ability.
Conclusions
Several studies showed that mineral dust particles can act as carriers for
biological material. Up to now it was not shown clearly how a single particle
which consists of both mineral dust and biological material behaves in terms
of ice nucleation.
In this study we showed that it is possible to quantitatively describe the
freezing behavior of particles generated from an illite–BPWW suspension,
based on parameters (mean and standard deviation of the contact angle
distribution) of the pure substances. In other words, the freezing behavior
of the mixed particles appears as superposition of the single substances'
freezing behaviors and for a droplet containing such a mixed particle, the
most active freezing entity in the droplet will control the freezing process.
For the internally mixed particles presented here, this means that if there
is an INM located on the surface of an illite-NX particle, this INM will
initiate the freezing of the droplet at much higher temperatures than the
pure illite-NX particle.
This study also indicates that it is fairly difficult to determine the
mixing state even already of a laboratory generated aerosol. The VH-TDMA
measurements strongly suggest that the generated particles are internally
mixed. However, the amount of biological material on these particles was
estimated to be less than 10 % of the whole particle mass. The microscope
techniques (SEM and EDX) apparently did not detect this small biological
fraction of a mixed bio-dust particle because of their limited analytical
possibilities. Also the single particle mass spectrometry (SPLAT) did not
detect such small amounts of biological material on all particles. As a
result the amount of internally mixed particles appears to be underestimated.
This may also be the case for atmospheric measurements. Therefore it can not
be ruled out that ice nucleation attributed to mineral dust in the past
occasionally might have been due to an undetected biological component.
Based on our results the following advice can be given for the modeling of
atmospheric ice nucleation: on the one hand it is not necessary to define new
parameterizations for dust-bio-mixtures as these particles, depending on
actual composition, induce freezing similar to the pure substances. On the
other hand, this implies that for proper modeling, the knowledge concerning
the number of mineral and biological INP and their mixing state is highly
desirable. Finding the respective atmospherically relevant values is still
a big challenge as the particle characterization methods are limited in their
ability to detect small amounts of certain substances.