Introduction
Pure water can typically be supercooled to temperatures below
the melting point of ice (0 ∘C at atmospheric pressure) without
freezing (Cantrell and Heymsfield, 2005; Hegg and Baker, 2009; Murray et al.,
2010). In order to freeze, water molecules have to be arranged in an ice-like
pattern and overcome a critical cluster size (Turnball and Fisher, 1949;
Cantrell and Heymsfield, 2005). This freezing mechanism, if happening as a
stochastic process from the pure liquid, and in the absence of catalysing
substances, is called homogeneous ice nucleation (Cantrell and Heymsfield,
2005). In micrometre-sized droplets, this phase change takes place at
temperatures below -35 ∘C (Pruppbacher and Klett, 1997). However,
freezing can also be triggered at higher sub-zero temperatures by foreign
substances (Dorsey, 1948) called ice-nucleating particles (INP, Vali et al.,
2015), which is referred to as heterogeneous freezing. In the atmosphere, INP
can contribute to cloud glaciation and precipitation (Lohmann, 2002). Ice
clouds impact the radiation balance of the Earth and therefore our climate
(Mishchenko et al., 1996; Baker, 1997; Lohmann, 2002; Forster et al., 2007).
Representatives of many different substance classes of aerosols have been
found to act as INP (Hoose and Möhler, 2012; Murray et al., 2012).
Despite this ubiquitous distribution throughout different aerosol species,
ice nucleation active material only represents a small part of total
atmospheric aerosol (Rogers et al., 1998). Typical total aerosol
concentrations range between 102 and 103 cm-3 for free
troposphere and marine boundary layer concentrations, and between 103
and 105 cm-3 for continental boundary layer concentrations
(Spracklen et al., 2010). INP concentrations are much lower and range between
10-1 and 10-4 cm-3 (Rogers et al., 1998; DeMott et al.,
2010).
There are significant gaps in the understanding of heterogeneous ice
nucleation and the contributions of different sources of INP. The role of
biological substances in this process is understudied (Möhler et al.,
2007; Murray et al., 2012). Field studies have demonstrated that the
biosphere acts as an important source of primary aerosol particles (Jaenicke,
2005). Jia et al. (2010) analysed carbon sources of PM2.5 particles
(particulate matter with an aerodynamic diameter of 2.5 µm or
smaller) collected at an urban site and a rural site in Texas, and attributed
5 %–13 % of the particle mass to primary biological sources and
4 %–9 % to secondary organic aerosols. Biological residues can be
adsorbed on dust particles (O'Sullivan et al., 2016). Even small amounts of
adsorbed biological matter can increase nucleation temperatures of less
active INP (Conen et al.,
2011). Several studies point to the importance of biological material in
cloud processes. Precipitation can contain large amounts of INP. Petters and
Wright (2015) combined data from a large number of measurements and found a
high variability in concentration in the range between -5 and
-12 ∘C, which is assumed to be biological, with a maximum of
approx. 500 000 INP L-1 water. Christner et al. (2008) analysed snow
and rain samples from the United States (Montana and Louisiana), the Alps and
the Pyrenees, Antarctica (Ross Island), and Canada (Yukon), where they found
rather low INP concentrations, with biological INP representing the majority
of the INP. Pratt et al. (2009) examined ice crystal residues collected from
ice clouds 8 km over Wyoming, US, and about a third of the collected
material was of biological origin. Moreover, 60 % of the highly abundant
mineral dusts were internally mixed with biological or humic substances.
Kamphus et al. (2010) analysed ice crystal residues from mixed phase clouds
at the Jungfraujoch station in the Swiss Alps, and found that
2 %–3 % of the material at 3500 m could be classified as
biological. Conen et al. (2016) found indications that leaf litter, which
naturally hosts a vast variety of microorganisms, enriches Arctic air with
ice-nucleating particles. Huffman et al. (2013) collected aerosols above
woodlands in Colorado. They observed a burst in biological INP concentrations
in the atmosphere that appeared to be linked to rain events. Since biological
INP are capable of influencing cloud glaciation and precipitation (Sands et
al., 1982; Morris et al., 2014), rain-induced bursts might be important
contributors to atmospheric and hydrological processes.
Biological material from plants could be an abundant source of INP. The
controlled freezing of water within a plant is an important mechanism for
plants to cope with cold climatic conditions. The freezing of water is
challenging for living organisms, since it often leads to lethal injuries
during the process (Storey and Storey, 2004). Some plants that are exposed to
cold stress have developed unique strategies to ensure their survival
(Zachariassen and Kristiansen, 2000). Intracellular freezing can lead to a
disruption of the cell and typically has lethal consequences for the cell and
subsequently for the plants (Mazur, 1969; Burke et al., 1976; Pearce, 2001).
Many plants grow in climatic zones where temperatures regularly fall low
enough to make a complete avoidance of freezing impossible. To avoid cell
damage under such conditions, those plants typically trigger the freezing
process in their extracellular spaces (Burke et al., 1976), a process that
can be achieved by releasing INP in the plant's tissue. This freezing process
leads to a dehydration of the cell, due to the attraction of intracellular
water by extracellular ice (Mazur, 1969). Dehydration induces several changes
inside of cells such as changes in pH, salt concentration, and protein
denaturation. Therefore, frost hardiness is often defined by the degree of
dehydration a plant can survive (Burke et al., 1976). During cell
dehydration, a rapid increase in concentration of ions and small molecules
inside the cell takes place, leading to freezing point depression and thus
hindering intracellular ice formation (Burke et al., 1976). If temperatures
fall too low, the high intracellular salt concentration often promotes glass
formation (Hirsh et al., 1985). Frost hardy plants are able to survive rapid
cooling to liquid nitrogen temperatures, if they are pre-frozen at -15 to
-30 ∘C depending on the plant and time of the year (Sakai, 1973).
These results show that controlled freezing can be an important mechanism for
plants to cope with cold climatic conditions. Though even controlled freezing
comes with a risk for plants (e.g. cavitation due to bubble formation, Sperry
and Sullivan, 1992), many plants have been found to be ice nucleation active.
Such plants include blueberry (Kishimoto et al., 2014), sea buckthorn (Jann
et al., 1997; Lundheim and Wahlberg, 1998), and winter rye (Brush et al.,
1994). These processes and findings indicate that plants are a viable source
of INP, a topic that requires further study.
Further information on the sampled birches, with sample name, GPS
waypoints, altitude of the growing site, circumference, and description of
the growing site.
ID of
GPS
Circumference at
birch tree
GPS waypoints
altitude
1 m height
Location description
TBA
47.214241, 10.798765
799 m
113 cm
Roadside birch in the valley
TBB
47.221615, 10.829835
799 m
54 cm
Roadside birch in the valley
TBC
47.186231, 10.908341
851 m
75 cm
Riverside birch in the valley
TBD
47.185387, 10.909587
851 m
35 cm
Riverside birch in the valley
TBE
46.973163, 11.010921
1343 m
96 cm
Riverside birch in Sölden next to a road with little traffic
TBF
46.974588, 11.011463
1343 m
61 cm
Riverside birch in Sölden next to a road with little traffic
TBG
46.878959, 11.024441
1925 m
67 cm
Timberline birch, the last birch and one of the last trees in
general we encountered on our way up
TBH
46.873275, 11.026616
1883 m
36 cm
Riverside birch in Obergurgl close to the timberline
TBI
46.873279, 11.026736
1883 m
59 cm
Riverside birch in Obergurgl close to the timberline
VB
48.197796, 16.352189
195 m
86 cm
Located in the centre of a small park in Vienna,
which is surrounded by heavy traffic
Spectroscopic methods are a key instrument in characterizing complex biological
systems. One of the methods typically applied to biological materials is
infrared spectroscopy (Baker et al., 2015), which allows
characterization and discrimination
of plants (Kim et al., 2004; Gorgulu et al., 2007; Anilkumar et al., 2012;
Carballo-Meilan et al., 2014). Further, infrared spectroscopy has already
been shown to respond well to the biochemical features of pollen of different
species (Gottardini et al., 2007; Pummer et al., 2013; Zimmermann and Kohler,
2014; Bağcioğu et al., 2015). It can even be used to gain information
on the environmental conditions (Zimmermann and Kohler, 2014).
In our study,
we examined INP of different parts of birch trees. Birch pollen
are already known to exhibit ice nucleation activity (INA) (Diehl et al.,
2001), and recent research suggests that pollen grains play a role in local
INP concentrations during peak pollen periods (Kohn, 2016). They easily
release their ice nucleation active compounds which are in the macromolecular
size range (Pummer et al., 2012). However, little is known about the
production and release of these ice-nucleating macromolecules (INM) from
other parts of the tree. We hypothesized that the materials throughout birch
trees are ice nucleation active and that the active compound(s) in these
birch materials from different parts of the tree are similar to those in
birch pollen. The specific objectives of this study were to (1) investigate
the INA of the different birch tree samples, especially in regard to
similarities to the INM, which have already been found in birch pollen
(Pummer et al., 2012, 2015), (2) determine the distribution of INM throughout
leaves and branches of birch trees, and (3) compare spectroscopic and ice
nucleation results of different birch trees to establish the variability in
the chemical nature and INA of the different trees.
Materials and methods
Samples
Samples were collected from nine birches in Tyrol, Austria (named TB for
Tyrolian Birch and numbered A to I), and one birch located in an urban park
in Vienna, Austria, in the spring and summer of 2016. Detailed descriptions
of all investigated birches can be found in Table 1. Larger branches were
removed from the lower 3 m of the canopy, and were divided into three sample
groups including leaves, ∼5 cm sections of primary wood (green,
photosynthetically active), and ∼5 cm sections of secondary wood
(brown, no photosynthetic activity). Representative material was combined for
each tree, resulting in 30 bulk samples (1 bulk sample of each category, per
tree) for downstream analyses. All tools used were surface disinfected with
90 % ethanol prior to branch removal. The samples were stored in a cooler
for transport back to the laboratory, and were frozen within a few hours of
collection at -20 ∘C. The Tyrolian samples were collected along an
altitudinal gradient (from altitudes between 799 and 1925 m). The locations
of the Tyrolian birches are shown in Fig. 1. Birch pollen used for FTIR
spectroscopy was Betula pendula pollen from AllergonAB (Thermo
Fisher).
Sampling sites in Tyrol along a valley with an altitudinal gradient
(adapted from Google Maps, https://www.google.com/maps, last access:
18 September 2017). Markings for TBH and TBI, as well as for TBC and TBD,
completely overlap each other due to the close proximity of their growing
sites.
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Sample preparation
Samples were processed using the following milling procedure. Prior to
milling, visible contaminations on the outside of the samples (e.g. lichens)
were removed. A swing mill (Retsch MM400) was used (with a frequency of
25 s-1) to mill each of the samples. We used approx. 20 cm increments
per wood sample (cut into pieces of about 0.5 cm) and two to three leaves
per leaf sample, which were milled and bulked together. In all cases the wood
and leaf samples stemmed from a single branch per tree. Each sample was
cooled with liquid nitrogen between two milling steps. This was
achieved by
immersing the milling container containing the sample and the ball (stainless
steel) in liquid nitrogen. After equilibrium was reached, we remounted the
container on the mill and conducted the next milling step. We milled each
sample four times for 30 s. After the milling process,
the products were
dried in vacuum over silica gel until the weight was constant. All samples
were dried for at least 12 h. Weight consistency was determined by two
weighing steps separated by at least 2 h of drying. Total dry mass of the
sample bulks varied between approx. 100 and 600 mg. Part of the dried bulk
was immersed in ultrapure water (produced with
Millipore® SAS SIMSV0001) (1 mL per 50 mg
of powder). Over a time of 6 h the mixture was shaken two to four times.
Afterwards it was centrifuged (3500 rpm/1123 xg for 5 min) and the supernatant was pressed through a
0.2 µm syringe filter (VWR, cellulose acetate membrane, sterile),
removing all bigger particles, as well as possible impurities, e.g. intact
bacterial cells.
Birch pollen washing water was prepared using 50 mg pollen and adding 1 mL
of ultrapure water. The suspension was treated the same way as the wood and
leaf suspensions, except for centrifuging, which was done for 10 min. Since
we filtered our samples, all data presented refer to INM concentrations in
the submicron size range (per mg sample mass, extractable aqueously with a
50 mg mL-1 sample load within 6 h).
VODCA (Vienna Optical Droplet Crystallization Analyser)
The Vienna Optical Droplet Crystallization Analyser (VODCA) was used to
determine INA as described by Pummer et al. (2012). To monitor freezing of
separated droplets, emulsions were created consisting of an aqueous phase in
paraffin oil containing lanolin as emulsifier. As the aqueous part of the
emulsion, ultrapure water was used for blank measurements and sample extracts
were used for sample measurements. The emulsions were prepared on thin glass
slides via mixing by hand with a pipette tip with oil in small excess,
leading to aqueous droplets in an inert phase (Hauptmann et al., 2016). One
glass slide was then placed on a Peltier element (Quick-cool QC-31-1.4-3.7M)
with a thermocouple on its surface (next to the sample spot). The Peltier
element was mounted on a copper cooling block cooled by an ice water cycle.
The element and the cooling block were situated in an air-tight cell, which
was closed during measurements. To prevent humidity from interfering with
measurements, the cell was flushed with dry nitrogen gas whenever the sample
was changed. To observe the freezing events we used an incident light
microscope (Olympus BX51M) with an attached camera (Hengtech MDC320) linked
to a computer.
Once the sample had been placed on the Peltier element and the cell was
closed, the cooling process was started. All data presented here were
obtained with a cooling rate of 10 ∘C min-1. To evaluate
freezing, photos were taken during the whole process. The first one was
always taken of the unfrozen sample as a blank. For each photo the respective
sample temperature, Tphoto, was recorded. Comparison of different
photos made it possible to evaluate the number of frozen droplets and
therefore the frozen droplet fraction at a certain temperature. Cooling
continued until all droplets were frozen. Only droplets in the size range
between 15 and 40 µm (droplet volume: 1.8–34 pL) were included in
our evaluation.
(a) Mean freezing temperature (MFT) of the different birch
samples. Leaf extracts (L) are marked with green circles, primary wood
extracts (P) with violet stars, and secondary wood extracts (S) with orange
triangles. The solid line is the MFT of birch pollen washing water
(-17.1 ∘C with a standard deviation of ±0.5 ∘C (not
plotted)). The dotted line represents the MFT of a dilution equivalent to
108 INP mg-1 (-17.7 ∘C with a standard deviation of
±1.1 ∘C (not plotted)) and the dashed line refers to the MFT of
a dilution equivalent to 106 INP mg-1 (-23.5 ∘C with a
standard deviation of ±3.6 ∘C (not plotted)). The three values
on the far right side of (a) represent the average of all mean
freezing temperatures for leaves (AVG-L), primary wood (AVG-P), and secondary
wood (AVG-S) with the corresponding standard deviation.
(b) Cumulative nucleus concentration at -34 ∘C
(K(-34 ∘C)) of the different birch samples mg-1 extracted
sample. Assignment of the symbols is similar to the top plot. The solid line
refers to the K(-34 ∘C) of birch pollen washing water mg-1
extracted pollen (1.3×1010 mg-1); the dotted and dashed line
refer to the dilutions from birch pollen washing water introduced
in (a). The three values on the far right side represent the average
of all K(-34 ∘C) values. Error bars point to the area of trust,
ranging from the highest to the lowest measured values.
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Data analysis
Results of the freezing experiments are presented as cumulative nucleus
concentration (see below) and as mean freezing temperature (MFT). The MFT is
the weighted average freezing temperature of all analysed droplets of a
single aqueous sample extract, determined by the following equation:
MFT=∑Ti×nin-35∘C,
with Ti being a recorded temperature, ni being the number of
droplets freezing at this temperature, and
n-35∘C being the number of droplets frozen at
temperatures of -35 ∘C and higher. The formula only accounts for
temperatures of -35 ∘C and higher and consequently only for
droplets frozen at these temperatures. This is done to minimize the risk of
including homogeneous freezing events in our presented data.
The cumulative nucleus concentration K(Tphoto) was used as an
indicator of the number of INM at temperatures above Tphoto
contained in the sample. To determine INP
concentrations, the number of frozen
droplets nfrozen for a given temperature Tphoto were
counted. The droplet volume included in the evaluation was calculated for a
droplet with a diameter of 25 µm (median droplet diameter). To
prevent an underestimation of the concentration of INM freezing at lower
temperatures (Govindarajan and Lindow, 1988), samples showing no homogeneous
freezing in the first measurement were diluted and re-measured. The
measurements of diluted samples were only used for the determination of
K(Tphoto), not for the MFT.
The cumulative nucleus concentration is described as (Vali,
1971; Murray et al., 2012)
KTphoto=-ln(1-fice)V×d,
with fice being the frozen droplet fraction, V the droplet volume
(8.2 pL for 25 µm diameter), and d the dilution
factor.
fice=nfrozenntotal,
with ntotal being the total number of droplets and
nfrozen the number of frozen droplets.
The cumulative nucleus concentrations are given over the whole temperature
range; further, the concentration at -34 ∘C was used to compare
different samples. Since we have never observed homogeneous freezing of
ultrapure water at temperatures of -34 ∘C and higher with our
setup, we attribute these values purely to heterogeneous freezing events.
FTIR spectroscopy
FTIR (Fourier-transform-infrared) spectroscopic measurements were conducted
with a Vertex 80v (Bruker, Germany) containing an MCT (mercury cadmium
telluride) detector cooled with liquid nitrogen. The optical bank was
evacuated (2.6 hPa) and had a GladiATR™
single-reflection ATR accessory unit (Pike, USA). The ATR unit contained a
diamond crystal as total reflection window. OPUS 6.5 software was used for
evaluation and instrument control. For each measurement, 128 scans were
accumulated at a resolution of 0.5 cm-1. The crystal surface was
flushed with dry nitrogen to prevent humidity from interfering with the
measurements.
All three extracts of TBA as well as birch pollen washing water were measured
at the same conditions by preparing a thin liquid layer of the extract and
evaporating the contained water with a fan. The temperature on the surface of
the crystal during evaporation was always below 35 ∘C. This process
was repeated until the dried residues of approx. 20 µL of the
sample had been applied. IR spectra of all other extracts can be found in the
supporting information (see Fig. S1–S3).
The results for the Wilcoxon–Mann–Whitney test. All given samples
were shown to match birch pollen washing water or a dilution thereof. Pure
birch pollen washing water is marked with pure, the 1 : 100 dilution
equivalent to 108 INP mg-1 is marked with 1 : 100, and the
1 : 10 000 dilution equivalent to 106 INP mg-1 is marked with
1 : 10 000. n represents the number of data points used for comparison
for each samplea. The used significance level αb was
0.1 %.
Birch pollen washing
Sample
n
water concentration
p-valuec
TBB-S
50
Pure
6.6×10-3
TBD-L
70
Pure
2.8×10-1
TBA-S
48
1 : 100
1.3×10-3
TBA-S2
118
1 : 100
2.7×10-1
TBD-P
28
1 : 10 000
8.3×10-2
TBH-P
117
1 : 10 000
1.9×10-3
TBH-S
102
1 : 10 000
1.3×10-1
TBI-P
43
1 : 10 000
6.6×10-2
a n is equivalent to the number of droplets frozen
homogeneously. n-values of the used standards were (pure washing
water) = 96,
n(1 : 100) = 135, and n(1 : 10 000) = 92.b The significance level marks the probability of falsely assuming
two
populations to differ in their distribution.c The p-value indicates the significance of the result. If the
p-value is higher than the used significance level, the statistics indicate
no difference between two distributions.
Results
Freezing temperature and ice nuclei concentration
All 30 extracts of birch trees were ice nucleation active (Fig. 2). The
highest variation in MFT was found for the extracts from the leaves, which
showed the highest (TBC-L -15.6 ∘C) and lowest (TBI-L
-31.3 ∘C) MFTs amongst all analysed samples (Fig. 2). Of the 10
birch trees, the leaves of only 5 trees (TBC-L, TBD-L, TBF-L, TBG-L, VB-L)
showed freezing temperatures close to the birch pollen line
(-17.1 ∘C; see Fig. 2). Those samples froze between
-15.6 ∘C (TBC-L) and -19.3 ∘C (TBD-L and VB-L). The
remainder of the analysed leaf extracts froze at temperatures of
-25.4 ∘C and below.
(a) The freezing curves of birch pollen
washing water and two
dilutions thereof (the dilution 1 : 100 is equivalent to
108 INP mg-1; the dilution 1 : 10 000 is equivalent to
106 INP mg-1) with the fraction of frozen droplets as a function
of temperature. (b) The MFT with the corresponding standard
deviations of the birch pollen washing water and the two analysed dilutions.
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All primary wood extracts were ice nucleation active, with most MFT values
between -17.5 ∘C (TBE-P) and -22.6 ∘C (TBI-P). Further,
two samples froze at -25.4 ∘C (TBH-P, TBD-P). For most secondary
wood extracts, we found slightly higher MFTs than for the primary wood
samples. The values ranged from -17.2 ∘C (TBB-S) to
-22.8 ∘C (TBH-S). The MFTs of the majority of the wood samples
were close to the birch pollen line (-17.1 ∘C; see Fig. 2).
However, it should be noted that a direct comparison between the different
analysed samples and birch pollen washing water is not straightforward, as
the freezing behaviour of the washing water is highly dependent on its
concentration. We observed freezing events for birch pollen washing water and
its dilutions from -15 ∘C down to temperatures below
-35 ∘C, which marks the same temperature regime as the analysed
birch samples exhibited freezing events. This effect is illustrated in
Fig. 3, showing the freezing curves of pure birch pollen washing water and
two dilutions, as well as their MFT and the corresponding standard deviation.
We compared the heterogeneous freezing regime of our samples (all freezing
events down to -35 ∘C as defined in the data analysis section) to
the heterogeneous freezing regime of pure washing water as well as to the two
dilutions (which correspond to samples with 108 INP mg-1 and
106 INP mg-1) using the Wilcoxon–Mann–Whitney test. This is a
non-parametric test which analyses whether the median of the distribution
function of two populations can be differentiated (DePuy et al., 2005). The
used n-values, which are the same as the number of droplets frozen
heterogeneously during analysis of a sample, ranged between 28 and 118 for
the correlated samples and between 92 and 135 for the standards. The
n-values and gained data can be found in Table 2. It also gives the
calculated p-values as results. The p-values are an indicator of the
significance of the gained results. Calculated p-values above the set
significance level α (0.1 %) indicate that the null hypothesis
cannot be rejected. This means that no significant differences can be found
between two distributions within the set level of significance. Similarities
were shown by the test for the pure washing water with TBB-S and TBD-L. These
two samples belong to the samples tested with the highest activity, which are
similar to pure birch pollen washing water. With the dilution equivalent to
108 INP mg-1, the test shows similarities for both TBA-S and
TBA-S2, and with the dilution equivalent to 106 INP mg-1 the test
shows similarities for TBD-P, TBH-P, TBH-S, and TBI-P. The latter are among
the samples tested with the lowest activity and these samples match a strong
dilution of birch pollen washing water (approx. 1 : 10 000) with only weak
activity left (see Fig. 3).
The cumulative nucleus concentration K(TPhoto) showed a trend
similar to the MFT (as depicted for -34 ∘C in Fig. 2, and for all
temperatures above -35 ∘C in Fig. 4). Leaf extracts mostly
exhibited cumulative nucleus concentration at -34 ∘C between
2.8×106 mg-1 (TBH-L) and 5.0×109 mg-1
(VB-L), with two outliers exhibiting 4.6×105 mg-1 (TBI-L)
and 6.7×104 mg-1 (TBE-L). However, these two outliers with
the low INM concentration were the two leaf samples exhibiting the lowest MFT
values (TBE-L -30.4 ∘C, TBI -31.3 ∘C). This indicates
that the unusually low MFTs are a result of low concentrations of INM in the
sample. Leaf extracts, which exhibited the highest variation in MFT, also
exhibited the highest variation in INM concentration (see Figs. 2, 4, and 5).
Cumulative nucleus concentration as a function of temperature for
leaf extracts (a), primary wood extracts (b), and secondary
wood extracts (c). The diagram is cut off at -35 ∘C,
since we cannot contribute freezing events below this temperature to
heterogeneous nucleation. The symbols used for the different data points are
grouped. Birches growing in close proximity under similar conditions are
marked with the same symbol (different fillings).
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The dotted line in the lower panel refers to the K(-34 ∘C) value
of birch pollen washing water (1.3×1010 mg-1). Presented
data show that the samples with the highest K(-34 ∘C) values
(TBB-S, and all samples from the Viennese birch) contain similar amounts of
INP mg-1 extracted sample. For primary wood extracts, most values for
K(-34 ∘C) ranged between 1.0×106 mg-1 (TBD-P) and
6.1×109 mg-1 (VB-P). Secondary wood extracts again exhibited
the least variation, which can be seen best in Figs. 2, 4
and 5. Their cumulative nucleus concentrations at
-34 ∘C ranged from 4.6×107 (TBD-S) to 4.6×109 mg-1 (VB-S, TBB-S). Figure 4 shows that this decreased
variation compared to the other samples is not just true for the cumulative
nucleus concentration at -34 ∘C, but also over the whole
temperature regime.
The averages of MFT and cumulative nucleus concentration (Fig. 2) show a
similar trend. Leaves exhibited
the lowest freezing temperature and cumulative
concentration, followed by primary wood, and secondary wood exhibited
the
highest values in both categories. This points towards a relationship between
concentration and freezing temperature, as has already been observed for the
birch pollen extracts.
To examine the INP distribution within a tree, a second branch of TBA was
prepared and measured according to the described protocol. Resulting data are
presented in Fig. 2 and marked with a 2 (TBA-L2, TBA-P2, and TBA-S2). Primary
and secondary wood extracts are well in line regarding their freezing
temperatures (TBA-P -20.4 ∘C, TBA-P2 -19.8 ∘C; TBA-S
-17.8 ∘C, TBA-S2 -16.7 ∘C); however, the primary wood
from the second analysed branch contained higher INP concentrations (TBA-P 2.2×108 mg-1, TBA-P2 1.5×109 mg-1; TBA-S
2.4×108 mg-1, TBA-S2 3.7×108 mg-1). Leaves
varied in their freezing temperatures and cumulative nucleus concentrations
(TBA-L -25.3 ∘C and 3.5×107 mg-1, TBA-L2 -21.8
and 1.0×108 mg-1).
Further, we analysed the relationship of the extractable INM concentration
and the extractable total mass. The total extractable mass (given as dry mass
in Fig. 5) describes the weight of the dry residue of a filtered extract in
mg mL-1. It was highest for leaf extracts and lowest for the secondary
wood extracts. As in the other attributes, leaf extracts exhibited the
highest variations, with dry masses ranging from 11 (TBE-L) to
19 mg mL-1 (TBG-L), followed by primary wood extracts ranging from 7
(VB-S) to 13 mg mL-1 (TBG-P). Dry masses of the secondary wood
extracts ranged from 6 (TBD-S, TBE-S. TBF-S, VB-S) to 11 mg mL-1
(TBH-S). The secondary wood samples tended to exhibit the highest
concentrations of INM mg-1 sample mass; they also had the highest ratio
of INM compared to dry mass (see Fig. 5). The lowest ratio was found for the
leaf extracts.
Scatterplot of dry mass (dry residues of the different filtered
extracts) and cumulative nucleus concentration at -34 ∘C per
sample mass. The dry mass is the mass we were able to extract with the
50 mg mL-1 suspensions. The data show that secondary wood, which
contained mostly the highest INM concentrations and lowest variations between
different samples, also contained the lowest extractable mass. Therefore INM
ratios in the extractable content of the different samples were highest in
secondary wood samples.
![]()
Results of the heat treatment of the different TBA extracts. Leaves
are marked with green circles, primary wood with violet stars, and secondary
wood with orange triangles. The left value belongs to the untreated sample,
the right value to the sample treated with 100 ∘C for 1 h. Filled
symbols represent the mean freezing temperature and correlate with the left
y axis; half-filled symbols represent the cumulative nucleus concentration
as -34 ∘C mg-1 extracted sample and correlate with the right
y axis.
![]()
Band assignment of the IR spectra of TBA extracts (leaves, primary
wood, and secondary wood) and birch pollen washing water (Miyazawa et al.,
1956; Kačuráková et al., 2000; Schulz and Baranska, 2007; Chen et
al., 2010; Pummer et al., 2013).
Band wavenumber
(cm-1)
Assignment of IR spectra
3300
O-H stretch/N-H stretch
2940
C-H stretch
2890
C-H stretch
2700
O-H stretch
1720
C=O, xylan
1650
C=O stretch, C=C, amide I
1600
C=O stretch (lignin), C=C, amide I
1510
C=O stretch (lignin), amide II
1450
CH2 deformation (lignin and xylan)
1425
Aromatic skeletal combined with C-H
1350
C-H deformation (ring)
1300
N-H C-H deformation, amide III
1270
C=O stretch (lignin), amide III
1240
C-O, C-N, C-N-C, C-C-O of phenolic compounds, amide III
1200
Phosphate, C-C-O of phenolic compounds
1140
C-O-C stretching (pyranose rings), C=O stretching (aliphatic groups),
guanine, tyrosine, tryptophane
1110
Sugar skeletal vibration
1070
C-H stretch, C-C stretch
1050
C-H stretch, C-C stretch, guaiacyl units (lignin)
990
OCH3 (polysaccharides)
920
C=C, cellulose P-chains, polysaccharides – β-linkage, phenolic compounds
850
C-O-C skeletal mode (polysaccharides – α-linkage, COPOC RNA, phenolic compounds
810
C=O deformation (polysaccharides), phenolic compounds
770
Phosphate stretch
While our results show that all analysed birch trees were ice nucleation
active, we also found that the trees themselves vary in their activity if
compared to each other. We found the lowest concentrations of INM (if all
samples are regarded) for TBD, TBH, and TBI (see Figs. 2 and 4), all of which
were growing along a riverbank with no traffic next to the trees. Only one
tree with these growing conditions was found to exhibit high INM
concentrations (TBC). Highest concentrations were found in the samples of the
Viennese birch, located in a small park in Vienna, surrounded by heavy
traffic. We found that trees, which were growing in close proximity to each
other (see Fig. 1), often exhibited comparable INA. This is especially true
for TBA and TBB, as well as TBH and TBI. TBE and TBF match each other well
except for the INA of the analysed leaves. TBC and TBD however acted
significantly differently if compared to each other, with TBD showing
decreased INM concentrations.
Heat treatment
To analyse the similarities to birch pollen washing water, all three extracts
of TBA were treated at 100 ∘C following the protocol introduced by
Pummer et al. (2012). Therefore, 100 µL of each extract were
applied on a clean glass slide and put in an oven set to 100 ∘C.
After 1 h the dry residues were resuspended in 100 µL of ultrapure
water each and analysed for INA. The results of this experiment are given in
Fig. 6 as MFT and K(-34 ∘C) values. The corresponding values of
the untreated TBA extracts are plotted for comparison. We find no major
changes in the mean freezing temperatures (TBA-L -25.4 ∘C, TBA-L
treated -26.1 ∘C; TBA-P -20.4 ∘C, TBA-P treated
-20.9 ∘C; TBA-S -17.8 ∘C, TBA-S treated
-18.2 ∘C) or K(-34 ∘C) values (TBA-L 3.5×107 mg-1, TBA-L treated 4.1×107 mg-1; TBA-P
2.2×108 mg-1, TBA-P treated 1.5×108 mg-1;
TBA-S 2.4×108 mg-1, TBA-S treated 1.8×108 mg-1).
FTIR spectra of the TBA extracts (leaves in green, primary wood in
violet, and secondary wood in orange) and birch pollen washing water (black).
(a) The whole spectrum between 3650 and 750 cm-1.
(b) Enlarged right side of the spectrum between 1800 and
750 cm-1. Possible band assignments are given in Table 3.
![]()
FTIR spectroscopy
FTIR spectroscopy was used to
examine similarities in chemical composition between the extracts of TBA
(leaves, primary and secondary wood) and aqueous birch pollen extract. The
normalized FTIR spectra are shown
in Fig. 7. Table 3 contains assignments for the band positions. On the left
side of the spectrum, there is a broad band with a maximum at approx.
3300 cm-1 typical for NH and OH stretching vibrations, and further, a
bisected band with maxima at 2940 and 2890 cm-1, which can be assigned
to aliphatic CH stretching vibrations. All four spectra show a weak shoulder
at approximately 2700 cm-1 which is linked to OH stretching vibrations.
On the low-frequency side (1800–750 cm-1) we find a broad array of
bands. We assigned 19 maxima. Several of these bands are typical for
saccharides as well as for xylan. We also found bands in all three typical
amide regions. All three regions are consistent with other biomolecules (see
Table 3) as well; therefore the presence of peptides is not entirely clear. The spectra of the different
extracts of TBA (Fig. 7) show a strong resemblance to each other, but we find
three main differences. (a) The intensity at 1510 cm-1: while the band
is strongly visible in the spectrum of secondary wood extracts, it is much
less pronounced in the spectra of primary wood and leaf extracts. (b) The
band at 1070 cm-1 is strongest visible for the leaf extract, where it
nearly swallows its neighbour at 1110 cm-1, while it is only present as
a slight shoulder for the wood extracts. (c) The region of 920 cm-1 and
below increases in intensity from leaf extract over primary wood extract to
secondary wood extract.
Comparing the birch pollen washing water to the TBA extracts, we see an
enhancement of the low-frequency side of the spectrum. We find all maxima
present in the pollen washing water spectrum also in the other extracts.
However, some bands, which are clearly pronounced in the pollen spectrum, are
only very weak shoulders in the TBA extract spectra (1350, 1300, 1270, 1200,
1140, 810, and 770 cm-1). Furthermore, we find the maxima of the two
most pronounced bands (3300 and 1050 cm-1 given for the TBA extracts)
to be shifted slightly by approx. 25 cm-1. The spectra of all analysed
samples are given in Figs. S1–S3. They show the same features as the spectra
given for TBA, with varying intensity ratios.
Discussion
We examined the INA of samples from 10 different birch trees (Betula spp.) to extend the knowledge on their freezing behaviour. Samples were
taken from nine birch trees in Tyrol, Austria, and from one tree in a small
urban park in Vienna, Austria. Filtered aqueous extracts of 30 samples of
leaves, primary wood, and secondary wood were analysed for INA using VODCA,
an emulsion technique. All of the samples from milled birch branches
contained INM in the submicron size range. INM of a similar size range were previously found in other
biological material including fungi and leaf litter (Schnell and Vali, 1973;
Fröhlich-Nowoisky et al., 2015; O'Sullivan et al., 2015), as well as
birch pollen (Pummer et al., 2012, 2015). Our results extend these previous
observations and demonstrate that aboveground material from the birch tree
(and not just the pollen) can produce INM.
Several studies have found that organic components can increase the INA of
soil and dust (Conen et al., 2011; O'Sullivan et al., 2014, 2016; Tobo et
al., 2014; Hill et al., 2016). Such organic components could be provided by
INM released by birch trees, which could stick to inactive particles, and
thus enhance their INA. Cracks and wounds on the surface could allow the INP
to be washed off the surface of twigs and leaves into the soil. This marks a
potential to influence the INA of mineral dust and soil particles and act as
INP in the atmosphere. Huffman et al. (2013) observed increased INP
concentrations after rain events related to a burst in concentrations of
biological particles. INM released from plants such as birch play
an important role in this process. Further studies on possible release
pathways of the INP from birches into the surrounding environment are
necessary to quantify such effects.
The freezing temperature observed for the aqueous birch pollen extract
(-17.1 ∘C; see Fig. 2) is in line with values reported in the
literature for aqueous birch pollen extracts (reported freezing events are
generally between -15 and -23 ∘C; Diehl et al., 2001; Pummer et
al., 2012; Augustin et al., 2013; O'Sullivan et al., 2015). Interestingly,
most of our samples froze in the same temperature range between -15 and
-23 ∘C. Half of the leaves (TBC-L, TBD-L, TBF-L, TBG-L, and VB), 8
out of 10 primary wood samples (TBA-P, TBB-P, TBC-P, TBE-P, TBF-P, TBG-P,
TBI-P, and TBV-P), and all secondary wood samples exhibited a mean freezing
temperature in this temperature window. If we broaden the temperature window
by including dilutions of birch pollen washing water, we observe freezing
events happening down to -35 ∘C. Nearly all freezing events
recorded for the presented samples freeze in this temperature window between
-15 and -35 ∘C. Leaves only occasionally show higher freezing
events (see Fig. 2). The Wilcoxon–Mann–Whitney test shows similarities
between more than a quarter of our samples and birch pollen washing water as
well as the respective dilutions (see Table 2). These samples showed some of
the highest and lowest MFT values measured (e.g. TBB-S, the best secondary
wood sample matches pure washing water, while the lowest, TBH-S, matches the
dilution of birch pollen washing water, which is equivalent to
106 INP mg-1). As we were able to match some of the most and
least active samples, we conclude that there is a correlation for each sample
between concentration of INP and freezing temperature relating to the birch
pollen washing water. Moreover, we observed heat resistance at
100 ∘C, similar to the results of Pummer et al. (2012). This
indicates a resemblance between the INM from pollen and those found in the
extracts of leaves, primary wood, and secondary wood. The data show that the
average freezing temperatures of secondary wood, primary wood, and leaves
differ slightly. These differences however follow the same pattern as the INM
concentration. Therefore we assume this to be a concentration effect. Based
on these results, we hypothesize that the INM in birch, which are found in
leaves, primary wood, and secondary wood, behave similarly and can be
statistically related to the INM found in birch pollen. This means that INM
from birch trees are not just relevant during the pollen season but over a
longer period of time, possibly even over the whole year. It is important to
conduct further research on the seasonal dependency of the production of INM
of birch trees.
We observed a high variability of INM in leaves. Even for leaves of two
branches of the same tree, we found differences in their freezing
temperatures. Only 5 out of 10 samples froze at similar temperatures to the
INM from birch pollen. The high variability could be explained by external
impacts, as leaves are easily influenced by their growing conditions. Leaves
growing in the shade exhibit reduced dry masses and nitrogen content
(Eichelmann et al., 2005). Also, their hydrological conductivity is impacted
by radiation (Sellin et al., 2011). Further, the growing site next to a river
typically leads to enhanced water availability, which can cause increased
leaf conductivity and transpiration rate in the lower crown foliage of trees
(Sellin and Kupper, 2007).
Birches are native through most of Europe, even up to central Siberia, and
are capable of growing in boreal regions and at high altitudes (Beck et al.,
2016). Due to climate change and their resistance against cold climate, some
birches can even be found above the tree line (Truong et al., 2007). Due to
this vast distribution area, the growing conditions of birches may vary
greatly. We speculate that environmental conditions may influence the
production and release of INM from birch. Many environmental factors can
affect the plant physiology and growth, e.g. humidity (Sellin et al., 2013),
atmospheric ozone (Maurer and Matyssek, 1997; Harmens et al., 2017),
CO2 (Rey and Jarvis, 1998; Kuokkanen et al., 2001),
NOx and SO2 (Freer-Smith, 1985; Martin et al.,
1988), as well as exposure to light and its wavelength (Eichelmann et al.,
2005; Sellin et al., 2011). All of these factors might also influence the INM
production of birch trees. The extracts of branches showed a systematic
distribution of INM that could relate in part to their growth environment.
Wood samples of the birches' TBD, TBH, and TBI (see Fig. 2), all three of
which were growing next to a river, froze at lower temperatures than the
other birch samples. Moreover, there was a tendency for birch trees located
near roads (TBA, TBB, TBE, TBF, and VB grew directly next to roads) to be
associated with increased INA. TBE and TBF grew next to a road and a river,
but showed comparable INM concentrations to the other roadside birches. If
the INA of birches is based on a stress or defence mechanism, this could be
due to stress caused by the exhaust of traffic, e.g. NOx,
which is an important pollutant released by traffic (Franco et al., 2013). It has the potential to harm
plants, but can also be absorbed by many plants and used as a nitrogen source
(Allen Jr., 1990). Other than roads and rivers in close proximity, the tree
altitude was not correlated with INA. Future investigations of birch trees
located across different altitudes, roads, settlements, and forests are
warranted.
Some investigations on birch stands showed a dry weight of 2 to
25 t ha-1 for twigs and 1 to 8 t ha-1 for leaves (Johansson,
1999; Uri et al., 2007). This leads to estimated INP concentrations of the
order of 1016 to 1019 ha-1 for twigs and 1014 to
1018 ha-1 for leaves. Plant debris can be an important constituent
of ambient particulate matter (Matthias-Maser and Jaenicke, 1995; Andreae,
2007; Winiwarter et al., 2009). However, the underlying processes of the
release of plant debris into the atmosphere are not fully understood, making
predictions of their atmospheric impact hard (Andreae, 2007; Winiwarter et
al., 2009). Sánchez-Ochoa et al. (2007) analysed atmospheric aerosols
collected at various background sites in Europe and used cellulose as a proxy
for plant debris. They found biannual average concentrations of
33.4–363 ng m-3 air. Especially the leaves of birch trees could be an
important source of INP as they are shed and produced annually. Decaying leaf
litter is known to be a good source of INP (Schnell and Vali, 1973). Conen et
al. (2016, 2017) showed that air masses passing over land can be enriched
with INP derived from such leaf litter. Collectively, these studies
underscore the importance of plants as sources of INP.
Since all the analysed materials are of natural origin, we cannot rule out
that some contamination could play a role in the INA of our extracts. Some
bacteria have been found to act as INP (e.g. Pseudomonas syringae,
Maki et al., 1974); however, these bacteria are typically in the size range
>1 µm (Monier and Lindow, 2003) and therefore easily filtered
with the 0.2 µm syringe filter. Further, some lichen are known to
exhibit INA (Kieft, 1988), and some
microorganisms release their small contained INP in the aqueous phase, e.g.
Mortierella alpina (Fröhlich-Nowoisky et al., 2015), which
cannot be filtered with used methods. However, most known ice nucleation
active lichens and microorganisms as well as released INP typically freeze at
significantly higher temperatures (above -10 ∘C, Maki et al.,
1974; Kieft, 1988; Pouleur et al., 1992; Murray et al., 2012;
Fröhlich-Nowoisky et al., 2015) than the freezing temperatures observed
for our samples, with very few exceptions (Iannone et al., 2011). As the
highest onset temperature observed in our measurements was
-14.1 ∘C (TBC-L), the onset temperature of birch pollen washing
water was quite close to this value (-15.1 ∘C), and heat treatment
did not affect the extracts of TBA, we do not suspect significant
contamination of our samples. However, the INA of birches, especially if
growing close to a road or in urban regions, could be affected by soot and
other anthropogenic emissions, as soot can act as INP (DeMott, 1990; Murray
et al., 2012).
The measured FTIR spectra indicate that the birch extracts are chemically
similar to each other, and to pure birch wood. As plants not only contain
polysaccharides but also several soluble carbohydrates (Magel et al., 2000),
we assume those substances to play an important role in the chemical
composition of our extracts. Fitting to this assumption, most of the bands
found in our spectra could be assigned to carbohydrates and polysaccharides.
Presented spectroscopic data matched the literature well (Chen et al., 2010;
Pummer et al., 2013; Dreischmeier et al., 2017); however, intensity ratios
varied. IR spectra from birch pollen and TBA extracts (Fig. 7) show strong
similarities to the spectrum of milled birch wood shown by Chen et al. (2010)
(measured in KBr pellets). In particular, the range between 1150 and
1300 cm-1, i.e. especially the band at 1270 cm-1, was strongly
enhanced compared to our spectra. Also, the band at 1510 cm-1 was very
intense in the pure wood spectrum compared to the extracts. Both bands are
representative of lignin, a main substance in wood that is only weakly
soluble in water. Since the remaining weak bands can be assigned to other
structural elements, our extracts likely did not contain any lignin. Other
than the lignin bands, our aqueous extracts show very similar spectroscopic
features compared to the pure wood samples. These similarities between the
spectra of extracts and the spectrum of pure wood indicate that our extract
method retrieves the majority of components, leading to a similar
distribution of bands, with differing intensities due to differences in
concentration. The IR spectrum of birch pollen washing water (Fig. 7) is in
good agreement with the literature data (Pummer et al., 2013; Dreischmeier et
al., 2017). The extracts of the different TBA samples (leaves, primary wood,
and secondary wood) exhibit similar spectra with no major differences. The
birch spectra of birch pollen washing water and the different wood extracts
match well, showing very similar maxima with mostly minor differences in
intensity ratios.
In the FTIR spectroscopy data, we found strong similarities between birch
pollen washing water and the different aqueous extracts from the TBA samples.
Further comparison with whole pollen grains, as well as with pure wood, as
found in the literature, shows strong similarities in the spectroscopic
features of our different birch samples. As not just the band position, but
also the intensity ratios, agree with each other, we assume this to indicate
that we are able to extract the major components found in wood with our
extraction method and that the pollen and wood sample extracts exhibit
chemical similarities to a certain extent.
Only little INP are known to trigger freezing above -10 ∘C, which
are typically biological substances such as bacteria (Murray et al., 2012).
Below -10 ∘C, birch pollen belong to the group of highest freezing
temperatures, with onset higher than most mineral dusts, ash, and soot
samples (Murray et al., 2012). The vast majority of atmospheric INP and INP
retrieved from precipitation samples exhibit freezing temperatures below
-10 ∘C (DeMott et al., 2010; Petters and Wright, 2015). The
identity of the INP released from birches is still unclear. Pummer et
al. (2013) showed that proteins, saccharides, and lipids are easily extracted
aqueously from birch pollen. While Pummer et al. (2012) and Dreischmeier et
al. (2017) speculate that the responsible molecules are carbohydrates, Tong
et al. (2015) attribute the highest INA to extracted proteins. Hiranuma et
al. (2015) showed that cellulose, a polysaccharide which is ubiquitous in
plants, exhibits INA in the same temperature range. With our spectroscopic
data, we found strong indicators for saccharides being present, including
prominent bands which could be associated with cellulose. Further, we found
bands in the most prominent protein regions, though those could be assigned
to other molecule groups.