Introduction
Microorganisms are known to be dispersed into the atmosphere and
disseminated over long distances (e.g., Bovallius et al., 1978; Brodie et
al., 2007; Griffin et al., 2001; Smith et al., 2013, and review by Morris et
al., 2013). This has obvious implications for human, animal and plant
epidemiology as well as microbial ecology (Monteil et al., 2014; Morris et
al., 2007, 2008; Šantl-Temkiv et al., 2013). Moreover, some particular
bacteria notably found in the atmosphere and clouds can induce heterogeneous
ice formation (Cochet and Widehem, 2000; Joly et al., 2013; Lindemann et
al., 1982), which probably affects cloud physics and potentially triggers
precipitation (Möhler et al., 2007). All of these aspects motivated the
development of numerical models intended to describe and predict the aerial
dispersion of microorganisms. For instance, Burrows et al. (2009a, b)
constrained a general atmospheric circulation model using data from the
literature and estimates of concentrations and vertical fluxes of airborne
microorganisms. They estimated that ∼ 1024 bacteria are
emitted into the atmosphere each year at the global scale, with a residence
time aloft between 2 and 10 days (∼ 3 days on average)
depending on emission sources and on meteorological conditions. Such a time
span should allow microbial cells (i.e. particles of ∼ 1 µm) to travel over hundreds or thousands of kilometers. However, it is not
clear what fraction of the aerosolized microorganisms survive over this timescale, and if they maintain properties allowing interactions with
atmospheric water.
Most studies aiming at predicting the death rate of airborne bacteria were
carried out in the late 1960's and early 70's, with particular emphasis on
the influence of temperature and relative humidity (Cox and Goldberg, 1972;
Ehrlich et al., 1970; Lighthart, 1973; Wright et al., 1969). The ability of
bacteria to survive as aerosols and the influence of abiotic parameters on
survival were shown to strongly depend on the microorganism (Marthi et al.,
1990). In experiments at constant temperature ranging from -18 ∘C
to 49 ∘C, the survival rate of Mycoplasma pneumoniae, Serratia marcescens and Escherichia coli decreased with increasing
temperature, while this had little or no effect on the survival of Bacillus subtilis (Ehrlich
et al., 1970; Wright et al., 1969). The highest survival rates were
invariably observed at extreme low and high levels of humidity (Cox and
Goldberg, 1972; Wright et al., 1969). Finally, carbon monoxide concentration
was shown to have variable impacts on the viability of airborne bacteria,
with protective or deleterious effects depending on humidity and on the
species (Lighthart, 1973). Lighthart (1989) compiled these data and others
to build statistical models describing the death rate of airborne bacteria
based on aerosol age, temperature, Gram reaction and humidity. Survival rate
was resolved by aerosol age, i.e. time after aerosolization, at more than
90 %.
In a scientific context motivated by interrogations about cloud-microbes
interactions, we studied bacteria originating from atmospheric samples and
selected for their relevance to atmospheric questions, Pseudomonas syringae and P. fluorescens. Indeed, these
bacteria are among the most frequent species recovered from natural clouds
(Vaïtilingom et al., 2012), some strains are known plant pathogens
(Berge et al., 2014) and some, including those investigated here, are ice
nucleation (IN) active and have potential impacts on cloud microphysics and
precipitation (e.g., Attard et al., 2012; Cochet and Widehem, 2000; Joly et
al., 2013; Möhler et al., 2007; Sands et al., 1982). IN active bacteria
were shown earlier to induce the formation of ice crystals within simulated
clouds (Maki and Willoughby, 1978; Möhler et al., 2008). Here we aimed
at examining the survival and IN activity of such typical bacterial aerosols
in the atmosphere, using the AIDA (Aerosol Interactions and Dynamics in the
Atmosphere) cloud chamber. Cell suspensions were sprayed in the chamber and
the concentrations of airborne micron-sized particles, total and cultivable
cells and ice nucleating particles (INP) were measured over time for up to
several hours after aerosolization. The influence of cloud formation, and
the presence of sulfates as surrogates for the presence of anthropogenic
aerosols were briefly approached and seemed to deeply alter cell survival
and IN activity. The data presented could be used for improving the
parameterization of numerical models describing the atmospheric dispersion
of bacteria.
Material and methods
Experimental setup and particle concentration measurements
The AIDA 84-m3 chamber at the Karlsruhe Institute of Technology was
used in this study both as a static aerosol chamber in order to store and
age the bacterial cell aerosols, and as an expansion cloud chamber in order
to simulate cloud activation events and investigate the impact of fresh and
aged IN active bacterial aerosols on cloud microphysics. The experiments
were conducted during the BIO06 campaign in May 2011. Cell suspensions (see
Sect. 2.2) were sprayed into the chamber at the beginning of the
experiments. The initial relative humidity inside the chamber was around
90 to 95 % with respect to ice, thus the sprayed droplets quickly
evaporated upon entering the chamber. The dried bacterial cell aerosols were
then aged for up to 18 h at the given chamber pressure, temperature and
relative humidity, as summarized in Table 1. Aerosol samples were
collected (see Sect. 2.3) during this step of aerosol ageing in
order to measure the airborne concentrations of total cells, the cultivable
cell number fraction (Sect. 2.4), and the IN activity of the
material collected (Sect. 2.5). Samples were systematically taken
30 min after spraying, and also after 120 min (2 h), 300 min (5 h), 420 min
(7 h), 1020 min (17 h), and 1080 min (18 h).
Detailed cell concentrations and cultivability for the
different experiments carried out in the chamber, expressed as the mean of
triplicate analyses ± standard error from the mean whenever available.
(P.s.: Pseudomonas syringae; P.f.: Pseudomonas fluorescens).
Exp #
AIDA BIO-06
Strain
Initial characteristicsa
Time after
Initial temperature
Airborne in the cloud chamberb
Exp #
CellsSUSP
CFUSUSP
% cultivable
spraying
and conditions
CellsAPS
CellsIMP
CFUIMP
% cultivable
cm-3
cm-3
cellsSUSP
(min)
of experiment
cm-3
cm-3
cm-3
cellsIMP
1c
2
P.s. 32b-74
684 ± 64
3522 ± 2138
515 ± 316 %
30
-5.2 ∘C, no cloud
138
293 ± 5
123 ± 63
42 ± 21 %
2c
4
P.s. 32b-74
530 ± 59
1091 ± 169
206 ± 39 %
30
0.1 ∘C, no cloud
173
279 ± 232
328 ± 34
118 ± 99 %
3c
6
P.s. 13b-2
474 ± 19
694 ± 172
147 ± 37 %
30
-2.3 ∘C, no cloud
216
382 ± 8
314 ± 10
82 ± 3 %
4c
8
P.s. 13b-2
474 ± 19
694 ± 172
147 ± 37 %
30
-17.8 ∘C, no cloud
204
448 ± 5
217 ± 61
49 ± 14 %
5c
10
P.f.CGina-01
217 ± 34
1339 ± 107
616 ± 108 %
30
-2.3 ∘C, no cloud
269
361 ± 49
249 ± 24
69 ± 11 %
13c
29
P.s. 32b-74
491 ± 80
13591 ± 13980
2770 ± 2884 %
30
-7.2 ∘C, no cloud
180
387 ± 22
227 ± 26
59 ± 8 %
14c
32
P.s. 32b-74
491 ± 80
13591 ± 13980
2770 ± 2884 %
30
-13.7 ∘C, no cloud
178
306 ± 19
195 ± 37
64 ± 13 %
7d
15
P.s. 32b-74
437 ± 82
754 ± 96
173 ± 39 %
30
-1.4 ∘C, no cloud
214
436 ± 74
128 ± 21
29 ± 7 %
120
184
291 ± 35
79 ± 11
27 ± 5 %
300
155
171 ± 27
42 ± 4
25 ± 4 %
420
141
143 ± 19
26 ± 3
18 ± 3 %
8d
17
P.s. 32b-74
525 ± 48
1349 ± 326
257 ± 67 %
30
-2 ∘C to -20 ∘C, no cloud
185
349 ± 24
237 ± 69
68 ± 20 %
120
ND
261 ± 11
128 ± 15
49 ± 6 %
300
ND
185 ± 13
69 ± 6
37 ± 4 %
420
139
154 ± 15
46 ± 2
30 ± 3 %
10d
22
P.s. 32b-74
473 ± 90
1567 ± 757
332 ± 172 %
30
-1.3 ∘C, no cloud
172
451 ± 59
276 ± 31
61 ± 11 %
1020
92
101 ± 11
6 ± 1
6 ± 1 %
11d
24
P.f.CGina-01
303 ± 22
10700 ± 12584
3529 ± 4158 %
30
-1.3 ∘C, no cloud
289
321 ± 36
242 ± 43
75 ± 16 %
1080
121
82 ± 2
2 ± 0
3 ± 0 %
6e
12
P.f.CGina-01
217 ± 34
1339 ± 107
616 ± 108 %
30
-16.7 ∘C, before cloud formed
186
282 ± 36
264 ± 92
94 ± 35 %
150
After cloud dissipation
ND
221 ± 19
83 ± 29
38 ± 13 %
9e
19
P.s. 32b-74
529 ± 103
13026 ± 12101
2464 ± 2339 %
30
-19 ∘C, before cloud formed
158
258 ± 24
124 ± 7
48 ± 5 %
150
After cloud dissipation
ND
139 ± 19
8 ± 4
6 ± 3 %
12e
26
P.s. 32b-74
340 ± 107
11161 ± 10312
3285 ± 3207 %
30
-16.7 ∘C, in the presence of
ND
318 ± 24
10 ± 3
3 ± 1 %
ammonium sulfate
a As inferred from the cell suspensions
sprayed.b As measured by aerosol particle sizer and from impinger
samples.c Experiments intended to investigate the impact of fresh IN
active bacterial aerosols on the microphysics of clouds generated in the AIDA
chamber by expansion cooling; no sample for microbiological analyses was
collected aftercloud dissipation.d Experiments intended to investigate the impact
of ageing on the survival and IN activity of bacteria as aerosols; clouds
were generated afterward for investigating their impacts on microphysics; no
sample for microbiological analyseswas collected after cloud dissipation.e Experiments intended to investigate the impact of clouds
or sulfate coating on the survival and IN activity of bacterial aerosols.
During three experiments, aerosol samples for microbiological analyses were
also taken after a cloud activation and evaporation cycle in the AIDA
chamber. Such a cloud cycle in AIDA is initiated by reducing the chamber
pressure within a few minutes from about 1000 to 800 hPa by strong
pumping. This pressure change simulates the conditions of an air parcel
rising in the atmosphere at a vertical updraft velocity of up to a few m s-1, which induces a respective cooling of the air and an increase
in the relative humidity. The expansion run starts at a relative humidity of
about 90 to 95 % with respect to ice, so that at start temperatures below
0 ∘C the air in the cloud chamber first exceeds saturation with
respect to ice, and then saturation with respect to liquid water. Depending
on the temperature and the ice nucleation activity of the bacterial cells,
some ice particles may already be formed in the regime between ice and water
saturation. In all the experiments discussed here, water saturation was
exceeded, so all bacterial cells acted as cloud condensation nuclei and were
first immersed in supercooled cloud droplets before eventually targeting
ice formation. After the pumping stopped at a pressure of about 800 hPa, the
temperature started to increase due to heat flow from the warmer chamber
walls, and the cloud droplets started to evaporate. After full evaporation
of the cloud droplets, the chamber was re-pressurized using particle free
synthetic air to atmospheric pressure. Aerosol samples were collected once
the pressure inside the chamber was returned to ambient pressure. In one of
the three experiments during which aerosol samples were collected for
microbiological analyses after cloud evaporation, bacteria were sprayed as a
suspension in (NH4)2SO4 (50 g L-1, or 0.38 M) (Exp. 12,
Table 1), rather than deionized water, in order to generate sulfate
aerosols and examine competition effects between sulfates and bacteria on
cloud formation and ice nucleation. This also produced preliminary results
about the potential impact of anthropogenic aerosols on the survival of
airborne bacteria.
After each experiment, the chamber was cleaned by deep depressurization, and
refilled with particle free air, so that the chamber was particle free at
the beginning of the next experiment.
Aerosol concentration and size in the chamber were monitored during the
experiments using a combination of a Scanning Mobility Particle Spectrometer
(SMPS) and an Aerodynamic Particle Sizer (APS), both from TSI Incorporated,
USA. The concentration of particles in the size mode around 0.6 µm to
about 5 µm is referred to here as CellsAPS; it
corresponds to single intact bacterial cells and small agglomerates of
cells.
Bacterial strains and preparation of cell suspensions
The following bacterial strains were used: Pseudomonas syringae 13b-2 and P. syringae 32b-74, both isolated
from cloud water samples collected from the puy de Dôme Mountain in
France (GenBank accession numbers of the 16S rRNA gene sequences: DQ512785
and HQ256872, respectively; Amato et al., 2007; Vaïtilingom et al.,
2012), and P. fluorescens CGina-01 isolated from Cotton Glacier in Antarctica (GenBank
accession number FJ152549; Foreman et al., 2013). These were all
previously demonstrated to be IN active by droplet-freezing assays (Attard
et al., 2012; Joly et al., 2013). P. syringae 32b-74 in suspension in deionized water
at the concentration of ∼ 109 cells mL-1 nucleated
ice at -3 ∘C; the frequency of IN active cells was > 2 % at -4 ∘C and > 4 % at -6 ∘C, which
ranks this strain among the most efficient IN active bacteria described so
far. The onset freezing temperature of P. fluorescens CGina-01 at similar cell
concentration was -4 ∘C, with a frequency of IN active cells 3 to
4 orders of magnitude lower than that of 32b-74. P. syringae 13b-2 nucleated ice at
-4 to -5 ∘C, with a much lower activity
(∼ 10-7 IN active cells per cell at -6 ∘C).
Bacteria from stock suspensions were grown on King's medium B agar (King et
al., 1954) for two days at ambient room temperature (i.e. 22–25 ∘C). Cells were then scrapped off agar using sterile plastic
loops, suspended in sterile deionized water at a concentration of
approximatively ∼ 109 mL-1, and incubated overnight
at 4 ∘C. In one experiment, cells were suspended in a solution of
(NH4)2SO4 (50 g L-1, or 0.38 M) in order to examine the
influence of sulfate coating. In each experiment, a volume of
∼ 50 mL of the cell suspension was sprayed into the cloud
simulation chamber (for details see Möhler et al., 2008). The actual
cell concentration in the initial suspensions was later determined by flow
cytometry (total cells) and standard dilution platting (colony forming
units; CFU), as described in Sect. 2.4. These were used for
inferring the initial concentrations of total and cultivable cells airborne
in the AIDA chamber, considering a volume of 84 m3; these are referred
to as CellsSUSP and CFUSUSP,
respectively.
Sampling from the cloud simulation chamber for microbiological analyses
Sampling for microbiological analyses was performed using an ethanol-washed
impinger (SKC Biosampler; Lin et al., 1999) rinsed several times with
sterile deionized water and filled with ∼ 20 mL of sterile
deionized water just prior to use. Unexposed aliquots of the water used as
the impingement liquid served as negative controls for ice nucleation assays
and cell counts. In those controls, no ice nucleation event was detected
within the temperature range investigated, and cell count was < 0.005 % of the cell counts in samples. Sampling operations were performed
at a constant air flow of 12.5 L min-1 for 10 min periods using a
membrane vacuum pump (KFC), with the inlet of the impinger connected to the
inside of the chamber via a stainless steel sampling tube of 4 mm inner
diameter. The exact volume of water contained in the sampler
(∼ 20 mL) before and after sampling was determined by
weighting. It was used to relate the total and cultivable cell
concentrations in the impingement liquid to their respective concentrations
in the air in the AIDA chamber when equilibrated with atmospheric pressure,
considering the volume of the impingement liquid and the sampling rate and
time, and assuming 100 % collection efficiency (Jensen et al., 1992).
These are referred to as CellsIMP and
CFUIMP throughout the manuscript.
Total cells and colony counts
The concentrations of cultivable and total cells in the impingement liquid
were determined by two complementary methods. Cultivable cells were counted
as colony forming units (CFU). Twenty µL of 10-fold serial dilutions
of the impingement liquid were spread on R2A medium (Reasoner and Geldreich,
1985) and incubated at 22–25 ∘C for 2 to 3 days before counting
the colonies formed. Total cells were counted by flow cytometry on
triplicate samples of 450 µL of the impingement liquid mixed with 50 µL of 5 % glutaraldehyde (Sigma) (0.5 % final concentration) and
stored at -20 ∘C. These were then mixed with one volume (500 µL) of Tris-EDTA buffer at pH 8.0 (10 mM Tris; 1 mM EDTA, final
concentrations) and diluted in deionized water to a range of cell
concentrations compatible with the analysis. Finally, 10 µL of the
DNA specific fluorochrome SYBR-Green (100X concentration; Invitrogen) were
added to the samples before incubation in the dark for at least 20 min then
injected into the flow cytometer (Becton-Dickinson FACScalibur). Particles
fluorescing at 530 nm when excited at 488 nm, i.e. labeled with SYBR-Green, were
detected and counted by the cytometer. Counts were performed for 2 min or
100 000 events at a flow rate of about 90 µL min-1. The exact
flow rate was then measured for each series of measurements by weighting a
water sample before and after a 20 to 30 min run in the instrument. All
solutions used for flow cytometry analyses were freshly filtered through
polycarbonate syringe filters (0.22 µm porosity, Whatman) before use
in order to prevent the presence of contaminating particles. In each sample,
a population of particles unambiguously attributed to bacterial cells based
on their intensity of fluorescence and side-scattering was detected.
Finally, cultivability was calculated as the ratio between CFU and total
cells counts.
IN assays
The concentration of ice nucleating particles (CINP) in the collection
liquid was assayed by the drop-freezing method described previously (Vali,
1971). A series of sixteen 0.2 mL microtubes containing 20 µL of the
impingement liquid, undiluted or diluted 10-fold in distilled water, were
placed in a cooling bath (Ecoline Staredition Lauda E200) and exposed to
decreasing temperatures from -2 to -10 ∘C with
1 ∘C steps. The number of tubes containing aliquots still in the
liquid phase was counted after exposition for 8 min at each temperature
step, and CINP was calculated as:
CINP=[ln(Ntotal)-ln(Nliquid)]TV×1Df,
where Ntotal is the total number of tubes tested in a given dilution
series (16), Nliquid the corresponding number of tubes still liquid
after 8 min at temperature T, V the volume of liquid in each tube (0.02 mL)
and Df the dilution factor (1 or 10). CINP were finally normalized
to the corresponding total cell concentrations measured by flow cytometry.
Data analyses
Exponential regression curves of the type y=a×e(-bt) were fitted to
the data. As all the data were normalized to the first time point measured
in the corresponding experiment (i.e. 30 min after spraying, time set as
the time zero for data analysis), a was equal to 1 and the concentration
had its maximum value at t=30 (time t being expressed in minutes). The
time constant of this first-order decay equation is τ=1/b, b being
the decay rate constant, and the half-life time t1/2, at which the
concentration has decreased to half the start value, can be calculated as
t1/2=ln(2)/b.
All statistical analyses were performed using PAST version 2.04 (Hammer et
al., 2001).
Results and discussion
Initial total and cultivable airborne cell concentrations
A total of nine, three and two experiments were carried out in the cloud simulation
chamber with the strains Pseudomonas syringae 32b-74, P. fluorescens CGina-01 and P. syringae 13b-2, respectively. The
initial airborne total and cultivable cell concentrations inferred from in
the initial cell suspensions (SUSP subscript), and the concentrations
measured with the APS (APS subscript) and from impinger samples (IMP
subscript) 30 min and up to 1080 min (18 h) after aerosolization are
presented in Table 1. Fifty mL of cell suspensions at
concentrations ranging from 3.65 × 108 to 1.15 × 109 cells mL-1 were sprayed in the chamber, corresponding to
theoretical initial total airborne cell concentrations (CellsSUSP) of
217 to 684 cells cm-3 in the 84 m3-chamber. The concentrations
actually measured 30 min later by the APS (CellsAPS) and from
impinger samples (CellsIMP) were both significantly lower (t test;
p < 10-6 and p=0.02, respectively; n=13) and ranged in
average between 138 and 289 cells cm-3 and between 258 and 451 cells cm-3, respectively. At this time point, CellsIMP was significantly
higher than CellsAPS by a factor of 1.82 ± 0.40 in average
(t test; p < 0.01; n=13), indicating the presence of cell
aggregates in the ∼ 1 µm aerosol population (it
extended to about 5 µm at the beginning of the experiments). These
were disrupted in the impinger during sampling and counted later as
individual cells by flow cytometry (Terzieva et al., 1996). The presence of
aggregates was also evidenced in the suspensions sprayed by the fact that
the concentration of cultivable cell (CFUSUSP) exceeded that of total
cells (CellsSUSP) (t test; p < 0.01; n=13), with
particularly large deviations on CFU counts between technical replicates,
and resulting in cultivability > 100 %, and at some occasions
> 1000 % (see Table 1). Cell suspensions were prepared
by scratching colonies from the surface of agar plates. Even though care was
taken for homogenizing them, some heterogeneity probably persisted and
resulted in the presence of cell clusters. However, it unintentionally
mimicked bacterial aerosols in natural context, as most cultivable bacteria
in the atmosphere were found associated with particles (Shaffer and
Lighthart, 1997).
Survival rate time dependence
With the intention to take only into account cells already airborne and
avoid any possible impact of the spraying process on cultivability, data
analysis was restricted to t≥ 30 min after aerosolization and data
were normalized to the values measured at this experimental time point. This
normalization also allowed the data to be cleaned by avoiding the large
deviations on cultivable cell concentration and on cultivability rate in the
initial suspensions (CFUSUSP). Each individual absolute value of
cultivability (i.e. not normalized by the cultivability measured at this time
point) is plotted in Fig. S1 in the Supplement. The normalized temporal decay of
airborne micron-sized particles (CellsAPS), total cells (CellsIMP)
and cultivable cells (CFUIMP) concentrations was determined from
experiments #7, #8, #10 and #11 (Fig. 1). The
concentration of particles in the 1 µm-mode (CellsAPS) decreased
exponentially over time with a time constant τ= 1260 ± 170 min (Pearson's r=0.992; n=7). The concentration of airborne
cells (CellsIMP) decreased faster with a time constant τ= 500 ± 120 min (Pearson's r=0.937; n=9). The upper bound
diameter of the CellsAPS size mode, extending to approximately 5 µm
at the beginning of the experiments, decreased to around 3 µm after 7 h, and the cell-to-particle
ratio (CellsIMP/ CellsAPS) decreased from 1.82 ± 0.40 (n=13) to 1.06 ± 0.06 (n=2). These indicated that the cell clusters were progressively
removed from the aerosol population by sedimentation. Cultivable cell
concentration (CFUIMP) decreased with a time constant τ= 230 ± 10 min (Pearson's r=0.990; n=9). This concentration
therefore decreased about twice as fast as that of the concentration of
total cells CellsIMP due to additional temporal loss of cultivability.
The decay rate constant b for cultivability was ∼ 0.28 % min-1, corresponding to a time constant τ= 360 ± 40 min
and a half-life t1/2= 250 ± 30 min (3.5 to 4.5 h) (Pearson's
r=0.911; n=9) (Fig. 2). This has to be regarded as
the most conservative estimate (lower bound) for viability, as viable but
non cultivable (VBNC) state is common in aerosolized cells (Heidelberg et
al., 1997).
Temporal evolution of total airborne cell concentration
measured with the APS (CellsAPS, open symbols) and total and cultivable
cells concentrations measured from impinger samples (CellsIMP, black
symbols, and CFUIMP, grey symbols, respectively) of P. syringae 32b-74 and P. fluorescens
CGina-01 in the chamber, relative to the concentrations measured 30 min
after spraying cell suspensions. Error bars are standard deviations from the
mean of triplicate samples. The curves show fitted exponential temporal
decays. For total particles: Pearson's r=0.992, n=7; for total cells:
Pearson's r=0.937, n=9; for cultivable cells: Pearson's r=0.990, n=9. Corresponding calculated time constants (τ) and half-life times
(t1/2) are indicated on the right of the figure (mean ± standard
deviation).
Despite the fact that the bacteria investigated here are non-spore-formers,
they lost cultivability only 1.5 to 3 times faster than spores of Bacillus subtilis within
the same temperature range, which decayed at rates of 0.19 % and 0.10 % min-1 at -29 and 4 ∘C, respectively
(Ehrlich et al., 1970). Lighthart (1989) proposed a general time-dependent
model of biological decay (decrease of survival rate) for airborne bacteria
by mixing experimental data from several bacterial strains, including
Pseudomonas species (Fig. 2). This fits our data with a Pearson's r of only
0.517 (n=9), and we observed a much higher cultivability than what would
have been expected from this model, at least for the first 10 h
following aerosolization. This implies that the Pseudomonas strains investigated here,
which were originally isolated from atmospheric samples, are more resistant
as airborne than the average bacterium considered in this model; it
could indicate that these strains are to some extent adapted to atmospheric
transport (e.g., Šantl-Temkiv et al., 2012).
Implications for airborne bacteria dissemination
Assuming that bacteria have an aerodynamic diameter of about 1 µm,
they have a low sedimentation velocity on the order of 10-4 m s-1 (Malcolm and Raupach, 1991). In addition, such particles fall
into the so-called “scavenging gap”, and they have a particularly long
residence time in the atmosphere (Hobbs, 1993). Indeed, residence time was
estimated to be 2.3 to 9.6 days in the case of single bacterial cells
depending on the source ecosystem, with a global mean of 3.4 days (Burrows
et al., 2009a). Under our conditions, after 1 day airborne, 1.7 % of the
cells would still be cultivable. Based on these extreme and mean residence
times, between 0.009 and 1.22 × 10-15 % of aerosolized
cells (0.0001 % in average, i.e. 1/106) would survive the duration of
their atmospheric journey until deposition. Statistically, this implies that
the emission of at least 11 000 cells is necessary, 106 on average, to
assure that one survives the residence time and arrives at its endpoint by
atmospheric dissemination.
Temporal evolution after aerosolization of the proportion of
cultivable cells (cultivability) in impinger samples in P. syringae
32b-74 and P. fluorescens CGina-01, relative to the cultivability
measured 30 min after spraying cell suspensions, in the absence of cloud
(black symbols) or after cloud formation and dissipation (open symbols).
Error bars are standard deviations from the mean of triplicate samples. The
black dashed curve shows fitted exponential temporal decay of cultivability
in the absence of a cloud (Pearson's r=0.911, n=9). The corresponding
calculated time constant (τ) and half-life time (t1/2) are
indicated on the right of the figure (mean ± standard deviation). Data
using the Eq. (5) from Lighthart (1989) are also plotted for comparison
(dashed grey line); this model valid for an “average” bacterial strain fits
our data with a Pearson's r of 0.517 (n=9).
Aerosolization, i.e. the transfer of cells from a solid surface or from a liquid
to the air, is a critical step. In nature, the drag forces created by wind
on surfaces generate aerosols by saltation/blasting phenomena (Grini et al.,
2002) and result in increased amounts of airborne microorganisms during high
wind speed events (e.g., Lindemann and Upper, 1985). Splashing raindrops on
surfaces colonized by microorganisms like plant leaves also lead to the
aerosolization of living bacteria (Graham et al., 1977). From liquids, a
well-known process of aerosolization is bubble-bursting (Blanchard and
Syzdek, 1982). This is actually a phenomenon by which certain types of cells
in a community are preferentially aerosolized, thus adding a new layer of
complexity in the process of bacterial aerosolization as it results in
dissimilarities between the microbial composition in the bulk liquid source
and in the air above (Agogué et al., 2005; Fahlgren et al., 2015). The
complexity of this phenomenon was probably not reflected in our experimental
setup, with bacterial cells being sprayed from liquid suspensions. However,
the results presented here only considered bacteria already aerosolized and
avoided taking into account the aerosolization step. Hence, considering that
the process of aerosolization did not affect subsequent survival rates as
aerosol, we can place our results in natural atmospheric context. Plants are
among the strongest natural sources of airborne bacteria identified, with
emission fluxes around 500 CFU m-2 s-1 measured above bean and
alfalfa fields (Lindemann et al., 1982). At such a rate, each m2 of
crop field would emit 1 cell capable of surviving its atmospheric transport
every 33 min. In other words, 1 cell capable of disseminating alive
would be emitted every second by a field of ∼ 2000 m2.
Proportion of cultivable airborne cells associated with
the distance reached from their emission source for typical horizontal wind
velocities (2. 5. 10 and 30 m s-1, i.e. 7.2, 18, 36 and 108 km h-1,
respectively), relative to the respective initial cultivability, as inferred
from the data presented in Fig. 2. The proportion of 0.0001 %
is reached in 3.4 days, the mean residence time of bacteria in the
atmosphere estimated by Burrows et al. (2009a).
Once airborne, as a first approximation bacteria are passively transported
horizontally at the speed of horizontal wind. So, for typical horizontal
winds in the troposphere, i.e. ∼ 2 to ∼ 30 m s-1
(not considering extreme events such as storms or cyclones), at the survival
rate measured here, 50 % of the cells emitted alive from a source would be
transported about 30 to 600 km away, and 1 % would reach the ground up to
4000 km away (Fig. 3). There are indeed many observations of such
long distance transport of living bacteria between distant ecosystems in
nature (Bovallius et al., 1978; Hervàs et al., 2009; Hervàs and
Casamayor, 2009; Comte et al., 2014).
Impact of cloud processing
The conditions investigated here (temperature between -20 and
0 ∘C and absence of light) can be considered relatively close to
the conditions encountered in the high atmosphere during the night. It is
probable that in nature, during the day, UV light has deleterious effect and
increases mortality rates (Tong and Lighthart, 1997). In addition, cloud
formation can alter viability, as shown in samples collected after expansion
cooling (i.e. depressurization) experiments (experiments #6 and #9). Even
though it is not statistically testable here, we noticed a strong decrease
in the cultivability of P.s. 32b-74 and CGina-01 cells exposed to a cloud (see
Table 1, Figs. 2 and S1). Fractions of only about
∼ 12 and ∼ 40 % of the cells cultivable
before expansion cooling remained cultivable after cloud dissipation for
32b-74 and CGina-01, respectively, compared to ∼ 70 % when
the pressure was maintained constant. For cloud formation in the AIDA
chamber, pressure was typically decreased at rates of 30 to 50 hPa min-1 during expansion, and the associated cooling rates were typically
2 K min-1 at the beginning of an expansion and below 0.5 K min-1
towards the end of the expansion. Considering pressure and temperature
changes with altitude of 10 hPa and 1 K every 100 m, these roughly
correspond to uplifts of air masses of around 100 to 500 m min-1 (1.7
to 8.3 m s-1), which falls within the range of observations for
convective precipitating clouds (Balsley et al., 1988). Our results suggest
that the shifts in environmental conditions encountered by living cells
transported upward, along with the osmotic shock and free radicals generated
by water condensation and freezing (e.g., Stead and Park, 2000; Tanghe et
al., 2003) probably alter airborne cell survival in clouds to a larger
extent compared with non-convective situations.
Ice nucleation activity
Figure 4 shows freezing profiles of air samples collected by
impingement from the cloud chamber at different times after injection of P. syringae
32b-74 suspensions. Thirty minutes after aerosolization, there were
∼ 2 × 10-5 INP cell-1 at -3 ∘C (1 INP every ∼ 50 000 cells)
and ∼ 3 × 10-3 INP cell-1 at -5 ∘C (1 INP every ∼ 333 cells) on a per-total-cells (CellsIMP) basis. This is about one
tenth the IN activity of cells in suspension for this strain (Joly et al.,
2013). Decreased IN activity in airborne bacteria compared with suspension
was expected from previous observations in cloud simulation chamber
involving P. syringae (Maki and Willoughby, 1978). No further significant loss of
activity over time was observed at temperatures ≤-4 ∘C in
aerosolized cells (ANOVA, 5 % confidence level), i.e. the frequency of INP cell-1 did not vary with time after aerosolization. This confirmed that
non-viable cells retained IN activity, as previously reported (Kozloff et
al., 1991).
Cumulative frequencies of INP per airborne cell in P. syringae
32b-74 within the AIDA chamber 30 min, 7 h and 17 h after aerosolization in
the absence of cloud (black symbols), 30 min after aerosolization in the
presence of ammonium sulfate (grey symbols), and when the pressure inside
the chamber was returned to ambient after cloud formation by expansion
cooling (open triangles). Error bars are standard deviations from the mean
of independent experiments, when available.
In the natural atmosphere, phenomena such as coating may affect bacterial IN
activity. For example coating with sulfate was reported to decrease the IN
activity of soot or Arizona Test Dust particles, a material widely used in
laboratory ice nucleation studies as a surrogate for natural mineral
aerosols (Cziczo et al., 2009; Möhler et al., 2005). However, sulfate
coating had no detectable impact on the IN activity of the commercial powder
of lyophilized IN active P. syringae cells Snomax (Chernoff and Bertram, 2010). In
order to further investigate the influence of sulfate coating, cells were
suspended in a solution of ammonium sulfate instead of water before being
sprayed into the chamber (experiment #12, Table 1). Thirty
minutes after spraying, we found that the frequency of INP per cell had
decreased markedly compared to cells sprayed from water suspensions,
especially at the warmest temperatures of activity: the frequency of INP per
cell was decreased by about 98.5, 91 and 34 % at -4,
-5, and -7 ∘C, respectively (Fig. 4). In
this particular bacterial strain, pH at values typical for cloud water
influenced by anthropogenic emissions (pH ∼ 4) were also shown
to be responsible for a significant decrease in INA (Attard et al., 2012).
Such observations show that the IN activity of bacteria is clearly modulated
by abiotic factors, and this must be kept in mind when replacing
experimentations into environmental context.
The capacity of cells of nucleating ice in the atmosphere is particularly
relevant where condensed water is present, i.e. in clouds. Using the AIDA
chamber, it was shown previously that some strains of P. viridiflava and P. syringae can act as INP
in clouds at temperatures around -10 ∘C in the immersion-freezing
mode (Möhler et al., 2008). Here, clouds were formed in the chamber by
expansion cooling in two experiments (experiments #6 and #9), and
aerosol samples were collected by impingement after cloud dissipation, when
the pressure inside the chamber was back to ambient pressure (Table 1). The onset ice formation temperature of the impingement liquid was
-6 ∘C, compared to -3 ∘C in samples not exposed to
cloud, and the frequency of INP per cell was decreased by three orders of
magnitude (Fig. 4). A possible deactivation effect of the IN
activity of bacteria was already suggested from equivalent experiments
(Möhler et al., 2008). However, our results expressed on a per-cell
basis suggest that, more likely, IN active cells among a population of
airborne bacteria were more efficiently precipitated than others. This could
explain the observed distribution of IN active bacteria in natural air,
clouds and precipitation: Stephanie and Waturangi (2011) observed that the proportion of IN active bacterial strains was higher
in falling rain water than in the air at the same location. In addition,
whereas only 50 % of the P. syringae strains isolated from non-precipitating cloud
water were IN active (eight strains) (Joly et al., 2013), those isolated from
freshly fallen snow by Morris et al. (2008) all had this
capacity (47 strains).
Conclusions
In this work, we observed that the concentration and cultivability of cells
aerosolized in the AIDA cloud chamber decreased exponentially over time at
constant rates. Aggregation seemed to favor cell survival, but this was of
course at the cost of the time span as airborne and so, in nature, of the
potential distance of dispersion. Hence, for bacteria, aerial dissemination
is clearly a compromise between the distance traveled (which decreases for
large aggregates) and the chances of successful dissemination (which
increases for large aggregates).
The survival rate determined here should provide a basis to the existing
numerical models describing the aerial dispersion of bacteria (Burrows et
al., 2009a; Sesartic et al., 2012), in order to better predict their
atmospheric transport as living entities. By focusing on time as the only
explicative variable, we were able to explain quite well (Pearson's r=0.911) the decrease of cultivability observed for Pseudomonas syringae and P. fluorescens in the AIDA
chamber, although adjustments of the predictions in an environmental context
could be made by integrating viability parameters as needed, like
temperature, humidity, UV, or phenotypic traits. Some work in this direction
has already been carried out (Attard et al., 2012; Lighthart, 1973;
Lighthart et al., 1971; Smith et al., 2011; Tong and Lighthart, 1997), but
more experiments would help build a more mechanistic viability model. In
addition, these models are still weakened by the large uncertainties that
remain concerning the input to be used, as there are still very few data
available about the sources of airborne bacteria and the associated emission
fluxes (e.g. Lindemann et al., 1982). These need to be documented for
different surface types and meteorological situations.
Numerical simulations demonstrated that the impact of IN active bacteria on
precipitation is probably negligible at the scale of the planet (Hoose et
al., 2010; Sesartic et al., 2012). However, precipitation patterns at
regional scales have important socio-economic impacts and the underlying
processes still need to be elucidated. We observed that the IN activity of
airborne bacteria did not change over time for at least several hours after
aerosolization. In nature, this is enough time for an IN active cell to be
transported to high altitudes and get incorporated into a cloud. Then, as
suggested by others (Constantinidou et al., 1990; Möhler et al., 2008;
Morris et al., 2008, 2014), they can induce freezing of supercooled
droplets, trigger precipitation and thus selectively prime their own
redeposition. For a complete and accurate description of the transport of
bacteria in the atmosphere, the partitioning of cells and in particular of
IN active cells, between air, clouds and precipitation should be determined.