Introduction
With global submicron emissions of ∼ 10 ± 5 Tg yr-1
estimated from modelling studies (Gantt and Meskhidze, 2013), primary marine
aerosol (PMA), composed of both sea salt and organic material, is a large
and important component of particulate matter in the atmosphere. To fully
understand its importance for the climate, the physical flux and composition
of the marine aerosol must be well quantified. The aerosol flux properties
are highly dependent on multiple physical and chemical properties of the
seawater.
The current estimates of PMA emissions can vary drastically depending on how
the aerosol flux is parameterized. Size-segregated sea spray source functions
used in modelling studies include physical parameters such as sea surface
water temperature, salinity and wind speed (Grythe et al., 2014). Newer sea
spray source functions have begun to use the wave roughness Reynolds number,
which takes into account the wave development state and implicitly
incorporates water temperature and salinity through a viscosity term (Norris
et al., 2013; Ovadnevaite et al., 2014; Partanen et al., 2014). However, it
has been known for many years that sea spray aerosol also incorporates a
significant fraction of organic matter (Blanchard, 1964), which can impact
aerosol physical and chemical parameters. The most common method of
parameterizing the organic fraction in PMA is to correlate this organic
fraction to the surface water concentration of chlorophyll a (Chl a).
Multiple studies have observed this positive correlation through such methods
as ambient aerosol measurements and chlorophyll a levels detected by
satellite (Ceburnis et al., unpublished data; O'Dowd et al., 2004; Rinaldi et
al., 2013), by modelling the sea spray flux and using satellite
chlorophyll a levels (O'Dowd et al., 2008), and recently by direct PMA
generation and simultaneous seawater chlorophyll a measurements (Schwier et
al., 2015). Rinaldi et al. (2013) observed the strongest correlation using
satellite measurements of chlorophyll a concentrations with a time lag of 8
days between the particulate organic fraction and satellite chlorophyll a
concentration. It is important to note that in situ measurements of ambient
marine aerosol include both primary and secondary aerosol formation, while
marine aerosol source functions focus solely on PMA formation. Modelling
studies have included marine aerosol organic fraction parameterizations to
estimate aerosol emission fluxes (Albert et al., 2012; Vignati et al., 2010)
and cloud condensation nuclei (CCN) concentrations (Westervelt et al., 2012).
Other studies have observed correlations between the aerosol organic fraction
and heterotrophic bacteria abundance (Prather et al., 2013; Schwier et al.,
2015), transparent exopolymer particle (TEP) abundance (Schwier et al., 2015)
or dimethyl-sulfide concentrations (Bates et al., 2012).
However, Ceburnis et al. (2016)
and Rinaldi et al. (2013) observed an anti-correlation between aerosol
organic fraction and wind speed at 10 m above the sea surface at Mace Head,
Ireland, that could indicate a significant contribution of secondary organic
sources to the ambient aerosol organic content.
The organic fraction of PMA affects the aerosol size distribution or its
log-normal mode fitting (Fuentes et al., 2010b; King et al., 2012; Schwier et
al., 2015; Sellegri et al., 2006). In some modified seawater experiments,
the size distributions were fit with a log-normal modal distribution and the
number fraction of each mode was determined; the number concentration of the
Aitken mode increased with changing organic concentrations without affecting
the overall size distributions, indicating a change in the type of particles
dominating the number size distribution (Collins et al., 2013). The organic
fraction also has some impacts on total number concentrations (Fuentes et
al., 2010b), as well as the CCN ability (Collins et al., 2013; Fuentes et
al., 2011; Prather et al., 2013). Ovadnevaite et al. (2011) found a dichotomy
of marine aerosol hygroscopicity and CCN, with organics positively increasing
the CCN activity of marine aerosols. Conversely, other works have
found no noticeable effect of marine organic material on CCN activity (King
et al., 2012; Moore et al., 2011).
Mesocosm experiments are used to study in situ marine aerosol production
based on constrained water parameters. Prather et al. (2013) and Collins et
al. (2013) performed wave channel experiments on seawater by varying
bacteria, phytoplankton and chlorophyll a concentrations. They observed the
largest change of the aerosol activation diameter and the hygroscopicity
parameter during the period of augmented heterotrophic bacteria; however,
these changes did not affect the size distribution of the marine aerosol.
Schwier et al. (2015) performed bubble-bursting experiments on acidified
mesocosm water during pre-bloom and non-bloom (oligotrophic) conditions, and
found that acidification had no strong effect on physical or chemical
parameters. However, pre-bloom conditions (characterized by higher Chl a
concentrations) enhanced the organic fraction over non-bloom conditions
(low Chl a concentrations) and also increased the number fraction of the
Aitken mode (Dp∼37.5 nm) in the aerosol size
distributions. However, this last work that indicates that a direct link
between the primary marine aerosol organic fraction and the seawater Chl a
is based on the data sets issued from two different locations of the
Mediterranean Sea at different seasons, and needs to be confirmed with more
measurements.
Different marine environments can produce drastic seasonal differences in
the aerosol flux and organic concentrations. The Mediterranean Sea is a
highly oligotrophic basin (Bosc et al., 2004; MERMEX group, 2011) with
seasonal blooms in the northwest of the basin (D'Ortenzio and Ribera
d'Alcalà, 2009; Siokou-Frangou et al., 2010) and high UVB solar
radiation in the stratified surface water during summer time (Sempéré et
al., 2015). Additionally, the warm water temperatures of the Mediterranean
Sea (compared to colder locations such as the North Atlantic Ocean) could
also impact the PMA flux (Jaeglé et al., 2011; Long et al., 2014;
Ovadnevaite et al., 2014). In this study, we performed a mesocosm experiment
in the Mediterranean Sea to study an artificial bloom, different from the
natural bloom period studied in the Schwier et al. (2015) study, and tracked
the physical and chemical properties of primary marine aerosol that allows
us to discuss the parameterization of organic material in PMA provided in
earlier works.
Methods
Measurement site and mesocosm deployment
A mesocosm experiment was performed from 5 to 23 May 2013 in the northwestern
Mediterranean Sea at the Station de Recherches Sous-marines et
Océanographiques (STARESO), located in the Bay of Calvi, Corsica. The
mesocosms have been described previously in the literature (Vidussi et al.,
2011). Briefly, three mesocosms (1.2 m diameter, 3 m height) were filled
with 2260 L of filtered (< 1000 µm) natural seawater and
deployed in the Mediterranean Sea in the station's bay. They were closed
with UV-transparent ETFE roofs, except for periods of sampling, which
preserved sunlight irradiance of the mesocosm while preventing external air
from entering the mesocosm headspace above the water. In order to test
phytoplankton bloom conditions, one mesocosm remained unchanged as a control
(A) and the other two mesocosms (B and C) were artificially enriched with
nitrates and phosphates, maintaining the Redfield ratio (N : P = 16)
(Takahashi et al., 1985). Mesocosm B and C were enriched with nutrients on
5 May to additional 3 × (PO4-3: 2.7 µM,
NO3-: 42.3 µM) and 1 × (PO4-3: 0.88 µM, NO3-: 14.03 µM) levels, respectively; mesocosm C
was re-enriched with nutrients to 30 × (PO43-: 27 µM,
NO3-: 423 µM) normal levels in the evening of 12 May after
sampling for the day.
In each mesocosm unit, the surface water temperature was monitored frequently
(every 10 min) using thermistor probes (Campbell Scientific 107), and the
average mesocosm surface water temperature over the entire campaign was
17.7 ± 0.5 ∘C. Water sampling was performed daily every
morning (08:00 LT) by pumping 20 L of surface water (50 cm depth) by hand from
each mesocosm into polycarbonate carboys for analysis. The water was stored
in large containers indoors and was covered with black bags to ensure no
additional photochemical reactions took place after sampling. Non-mesocosm
water (outside water) was also sampled every 3 days for a suite of
biogeochemical and primary marine aerosol measurements.
For dissolved organic carbon (DOC) determination, mesocosms were directly
sub-sampled into precombusted glass bottles. Sub-samples were filtrated
through precombusted (450 ∘C, 6 h) GF/F filters and
transferred into 10 mL precombusted (450 ∘C, 6 h) glass
ampoules, immediately acidified with 85 % H3PO4 (final pH ∼ 2), flame sealed, and stored at 4 ∘C in the dark. DOC
concentration was measured by high-temperature combustion on a Shimadzu TOC
5000 analyser, as described in Sohrin and Sempéré (2005). Deep
seawater reference samples (provided by D. Hansell, University of Miami) were run
daily to check the accuracy of the DOC analysis. Nitrates and phosphates
were measured according to Treguer et LeCorre (1975). Chl a
concentrations were determined using a fluorescence technique based on a
methanol extraction procedure (Raimbault et al., 1988). Nitrate and
chlorophyll a concentration time series over the course of the campaign are
shown in Fig. 1.
Temporal trends of (a) chlorophyll a concentrations
measured by fluorometer and (b) nitrate concentrations measured
by Technicon autoanalyser over the time course of the experiment. Panel (b)
clearly shows the enrichment of mesocosm B the first day of the campaign and
the enrichment of mesocosm C on 12 May, compared to the control mesocosm A.
For phytoplankton pigment analysis, samples (0.6–1 L) were vacuum filtered
(< 200 mm Hg), onto precombusted glass-fibre filters (25 mm, 0.7 mm
nominal pore size, GF/F, Wathman), stored in liquid nitrogen and kept at
-80 ∘C until analysis. Pigments were extracted in 2.5 mL of
100 % methanol and analysed by high-performance liquid chromatography
(HPLC) as described by Wright et al. (1991). Moreover, two 1.6 mL aliquots
for microbial plankton analyses were fixed with glutaraldehyde (0.5 %
final concentration, previously filtered with a 0.02 µm swinex),
incubated 15 to 30 min at 4 ∘C, frozen in liquid nitrogen and then
stored at -80 ∘C until analysis. The two aliquots were analysed
with a FacsCanto II cytometer (3-laser, 8-color (4-2-2), BD Biosciences)
equipped with a 20 mW 488 nm coherent sapphire solid-state blue laser to
evaluate the abundance of heterotrophic bacteria (Lebaron et al., 2001),
flagellate abundance (Christaki et al., 2011) and virus (Brussaard, 2004).
Finally, the determination of TEP abundance was performed by microscopic
enumeration following Passow and Alldredge (1994). On each slide, 30 images
were taken, at random, using a Olympus AX-70 microscope with a 400×
objective and equipped with a colour camera. For each image, all TEPs were
counted manually.
Sea spray generation and characterization
The experimental technique has been described previously for similar types of
experiments (Schwier et al., 2015). Briefly, we performed dual
bubble-bursting experiments by splashing mesocosm water through plunging
water jets, separated into eight jets, to generate aerosol. Two square glass
tanks were filled with 3.6 L of seawater each (water depth of 10 cm),
sealed with identical stainless steel lids and flushed with particle-free air
(13.7 L min-1) through a j-shaped tube to mimic the wind-blowing
effect on bubble-bursting. Water was re-circulated via a peristaltic pump at
a flow rate of 1.8 L min-1. The first 10 min of every experiment was
used as a blank measurement to verify that the aerosol concentration was zero
before starting bubbling. All the experimental conditions used (water flow
rates, plunging water depth, etc.) were chosen to follow the work of Fuentes
et al. (2010b). The temperature of the mesocosm water was recorded at the
beginning and end of every experiment.
In between each experiment, the tanks and tubing were rinsed with ultrapure
ELGA water for 10–15 min. To ensure no experimental biases, experiments
on mesocosm water (A, B and C) were performed in different orders each day.
In order to verify that the sampled water was not altered or affected by
daily storage, we performed a bubble-bursting experiment on water sampled
from one mesocosm as both the first and last experiment of the day. We
observed no significant changes to any of the physical parameters of the
water, verifying that the time necessary to perform all the different
experiments did not affect the experimental outcome.
Two tanks were used simultaneously to perform experiments throughout the
course of the campaign. The aerosol flow from one tank was passed through a
diffusion drier and was sampled by a 3-stage impactor (Dekati) at
10 L min-1 for ∼ 1 h, measuring PM10, PM2.5 and
PM1. Quartz impactor filters also measured the whole PM1 fraction
(Dp<1000 nm). The quartz fibre filters were immediately stored
in a refrigerator after sampling and were later extracted with Milli-Q water
and analysed by ion chromatography (IC) for anions (Cl-, NO3-,
C2O42-, SO42-) and cations (Na+, K+,
NH4+, Mg2+, Ca2+) (Jaffrezo et al., 1998). Analyses for
elemental carbon (EC) and organic carbon (OC) were also performed using the
thermo-optical transmission (TOT) method on a Sunset Lab analyser (Aymoz et
al., 2007) following the EUSAAR2 temperature program proposed in Cavalli et
al. (2010). The aerosol flow from the other tank was passed through a diffusion drier and a soft X-ray aerosol neutralizer (TSI Model 3088) before entering a differential mobility particle analyzer (DMA). Size-selected particles were then detected using simultaneously a condensation particle counter (CPC) and a miniature continuous-flow streamwise thermal-gradient CCN chamber (CCNc) (Roberts and Nenes, 2005) to determine particle CCN activation properties. The relative humidity
of the flow entering all instruments did not exceed 20 %.
For the CCNc–DMPS system, aerosol flow passed first through a TSI-type DMA
(length 44 cm) selecting particle sizes ranging from 10 to 400 nm. The
aerosol flow was then split between the CCNc and a TSI CPC model 3010. The
DMA sheath flow rate was 7.5 L min-1 and the sample flow rate was
1.35 L min-1, with 1 L min-1 to the CPC and 0.35 L min-1
to the CCNc. Multiple charge effects were taken into account, as well as CPC
efficiency curve and DMA transfer function in a raw data inversion procedure
that followed the European recommendations (http://www.actris.eu/).
However, the absence of a PM1 impactor in front of the CCNc–DMPS system
led to sampling large aerosol particles with multiple charges in the
200–400 nm size range. Thus, in the present study we use only the data from
the 10–200 nm size range. In the CCNc, a total aerosol flow rate of
0.1 L m-1 with a sheath-to-aerosol flow ratio of 5 was used. The CCNc tested two
different supersaturations (SSs) by using a temperature gradient of
5∘ (dT5) and 6∘ (dT6) in the column. The top temperature of
the column varied as the ambient temperature changed
(Ttop–Tamb=2 ∘C). Calibrations were
performed with atomized NaCl solutions at the beginning, end and throughout
the campaign. The calibration curves were corrected for doubly charged
particles by removing a fraction determined with the height of the plateau in
calibration curves (Rose et al., 2008). After this, sigmoidal fits were done
separately for each of the curves of activated fraction as a function of
particle diameter, and the obtained activation diameters (Dp50)
were used to calculate the CCNc supersaturations. First, the activation
diameters were corrected for shape using a 1.08 factor. Then, the
corresponding CCNc supersaturation was calculated based on the Köhler
theory (Köhler, 1936) as presented in Seinfeld and Pandis (1998):
Sc=exp256Mwσw27RTρwρwMw3ρsisMs-1Dp50-3,
where Mw and Ms are the molecular weights of water (0.018 kg mol-1) and
solute (0.058 kg mol-1), ρw and ρs are the densities of water
(997 kg m-3) and solute (2165 kg m-3), R is the gas constant (8.314 J mol-1 K-1), T is
the temperature (298 K), σw is the surface
tension of water (0.072 J m-2), Dp50 is the activation
diameter, and is is the van't Hoff factor (Young and Warren,
1992). The van't Hoff factor was estimated to be 2, following the literature
recommendations (e.g. Rose et al., 2008). The temperature differences of 6
and 5 ∘C were deduced to correspond to a supersaturation of 0.30 and
0.15 % respectively.
Results
Size distributions
The submicron size distributions of the aerosol produced from the three
mesocosms were very stable over the entire course of the campaign and were
directly comparable to the non-modified outside water samples. The average
size distributions of the three mesocosms combined and the outside water for
the entire campaign were fit with three log-normal modes (Fig. 2 and Table 1).
Modal diameters of 25.0 ± 1.5, 49.4 ± 1.7 and 105.4 ± 1.4 nm
were observed in the mesocosm samples, and similar sizes were found in the
outside water samples (24.3 ± 0.75, 48.7 ± 0.5 and 104.8 ± 0.4). The size distributions were then normalized with respect to the
maximum total number concentration and the number fractions of each
log-normal mode were calculated. No significant differences were observed
temporally in the control mesocosm or the enriched mesocosms, even after the
second enrichment in mesocosm C (Fig. 3). The average number fractions of
Modes 2 and 3 were similar for the three mesocosms (0.41 ± 0.03 and
0.45 ± 0.04, respectively) throughout the course of the campaign, while
the number fraction of Mode 1 was smaller (0.14 ± 0.03).
Average size
distributions of the outside water and all mesocosms (A, B and C) at both
supersaturations (SS = 0.30 and 0.15 %) used in this study. The
mesocosms are fit with three different log-normal modes.
Log-normal modal diameter and number fraction for every mode
determined from the average number size distribution. Data from Schwier et
al. (2015) for the pre-bloom period tested at Bay of Villefranche (BV) and
Fuentes et al. (2010) for artificial seawater are also shown.
Mesocosm average
Outside water
Schwier et al. (2015): BV
Fuentes et al. (2010b)
Dp (nm)
Fraction
Dp (nm)
Fraction
Dp (nm)
Fraction
Dp (nm)
Fraction
Mode 1
25.0 ± 1.5
0.14 ± 0.03
24.3 ± 0.75
0.12 ± 0.01
20
0.19
14
0.38
Mode 2
49.4 ± 1.7
0.41 ± 0.03
48.7 ± 0.5
0.40 ± 0.01
37
0.48
48
0.32
Mode 3
105.4 ± 1.4
0.45 ± 0.04
104.8 ± 0.4
0.48 ± 0.02
92
0.24
124
0.17
Mode 4
–
–
–
–
260
0.09
334
0.13
Number and CCN concentrations
The total particle number (condensation nuclei, CN) concentration did not
show different features from one mesocosm to the other over the course of
the campaign (Table 2). The measured water temperatures increased throughout
the course of a 1 h experiment, with an average temperature increase of
5.12 ± 1.8 ∘C h-1. We use the average temperature
recorded over the duration of a given experiment. Larger temperatures are
systematically observed for the experiments for which the lower CCN
supersaturations (SS = 0.15 %) were investigated, because these
experiments were always performed after the SS = 30 % experiments during
which the seawater temperature had increased. We converted our total number
concentrations into fluxes (number of particles produced per second per
water surface area with bubbles) using the air flux in the tank (13.7 L min-1)
and the surface area covered by bubbles, following Fuentes et al. (2010a, 2011) and reported to a whitecap coverage corresponding
to a 9 m s-1 wind speed (i.e. whitecap coverage of 6.89×10-3). No
correlation was found between the CN flux and the seawater biogeochemical
characteristics. However, we see a significant positive linear correlation
(R2=0.35, n=76, p < 0.00001 at 95 % significance) between the total
number flux and the experimental average water temperature (Fig. 4):
CN Number Flux[s-1m-2]=aT∘C+ba=2.99×104±4.8×103;b=-3.43×105±1.21×105.
Note that this relationship is only valid for the range of temperatures
studied (22–32 ∘C). Mårtensson et al. (2003) also observed a
positive linear parameterization for water temperature and particle number
concentration in the particle diameter range 1.4–1.6 µm, but an
anti-correlation for smaller particles (0.0316–0.038 µm). We also
explicitly size segregated the flux (Fig. 5), which was observed to match
previously observed experimental fluxes also shown in Fig. 5 (Clarke et
al., 2006; Fuentes et al., 2010b; Mårtensson et al., 2003).
Average number concentrations, activation diameters, kappa values
(κ) and organic fractions for each mesocosm at both supersaturations tested.
Organic fractions from the filter collections (< 1000 nm) are also
shown for all three mesocosms.
Mesocosm
Number concentration
Activation diameter
κ
OM fraction
(cm-3)
(nm)
SS = 0.30 %
A
2590 ± 710
59.5 ± 2.5
0.74 ± 0.09
0.41 ± 0.075
B
2425 ± 710
60.4 ± 3.9
0.72 ± 0.13
0.43 ± 0.10
C
2580 ± 840
59.5 ± 4.0
0.75 ± 0.13
0.40 ± 0.10
Outside
2300 ± 967
59.5 ± 2.6
0.74 ± 0.095
0.41 ± 0.08
SS = 0.15 %
A
2930 ± 864
93.3 ± 3.9
0.82 ± 0.10
0.35 ± 0.08
B
3400 ± 630
89.9 ± 4.5
0.92 ± 0.15
0.26 ± 0.12
C
3090 ± 540
97.1 ± 4.5
0.73 ± 0.10
0.42 ± 0.08
Filters
A
–
–
–
0.38 ± 0.05
B
–
–
–
0.35 ± 0.11
C
–
–
–
0.34 ± 0.11
Mass fractions of the main inorganic components in sea spray (PM1)
to the total inorganic mass measured from impactor samples on PMA generated
from six samples per mesocosm, and mass fraction of the main inorganic ions in
seawater reported by a Seinfeld and Pandis (1997) and b Pilson (1998).
Mass fractions
Cl-
SO42+
Na+
K+
Mg2-
Ca2+
Cl- / Na+
Meso A
0.47 ± 0.06
0.07 ± 0.02
0.39 ± 0.03
0.00 ± 0.00
0.02 ± 0.00
0.05 ± 0.05
1.21 ± 0.12
Meso B
0.43 ± 0.06
0.07 ± 0.01
0.40 ± 0.03
0.00 ± 0.00
0.02 ± 0.00
0.10 ± 0.06
1.08 ± 0.15
Meso C
0.45 ± 0.02
0.06 ± 0.01
0.40 ± 0.03
0.00 ± 0.00
0.02 ± 0.00
0.08 ± 0.04
1.13 ± 0.17
Seawatera
0.55
0.08
0.31
0.01
0.04
0.01
1.8
Seawaterb
0.55
0.08
0.31
0.01
0.04
0.02
1.8
Number fraction of SMPS log normal modes in mesocosms A, B and C
and outside water at SS = 0.30 % (a) and SS = 0.15 % (b).
Particle flux calculated for a 9 m s-1 wind speed versus average
tank temperature for both supersaturations tested.
The nutrient enrichment of the mesocosms did not affect the CCN activity.
The ratio of CCN to condensation nuclei at SS = 0.30 % (0.15 %) remains
similar for all three mesocosms over time, with
CCN / CNaverage,A=0.60 ± 0.03 (0.38 ± 0.03),
CCN / CNaverage,B=0.62 ± 0.14 (0.38 ± 0.02) and
CCN / CNaverage,C=0.58 ± 0.06 (0.33 ± 0.03) for mesocosms A,
B and C respectively, indicating no statistical difference between the
control and enriched mesocosms. However, because we observe a correlation
between CN and water temperature, this also indicates that a significant
correlation (R2=0.22, n=56, p < 0.00001 at 95 % significance) exists
between CCN (SS = 0.30 %) and water temperature, shown below:
CCN Number Flux[s-1m-2]=aT∘C+ba=2.00×104±5.1×103;b=-2.51×105±1.24×105
Chemical composition, organic fraction and activation diameters
The average inorganic chemical composition of PM1 resulting from
impactor sampling is given in Table 3, as the mass fractions of individual
components to the total inorganic mass concentration. The average fractions
are calculated from 6 individual samples performed on 6 different sampling
days per mesocosms (18 samples in total). The inorganic composition of sea
spray is very similar from one mesocosm to another and is very similar to the
composition of sea water (Seinfeld and Pandis, 1997; Pilson, 1998), with the
exception of lower fractions of chloride (on average, 45 ± 5 % of the
total inorganic mass in sea spray for all mesocosms compared to 55 % in
typical seawater) and higher concentrations of sodium (on average,
39 ± 3 % of the total inorganic mass in sea spray from the mesocosms
compared to 30.6 % in typical seawater) and calcium (on average,
8 ± 5 % of the total inorganic mass in sea spray from the mesocosms
compared to 1.2 % in typical seawater). As a result, the mass ratio of
chloride to sodium found in the aerosol phase is on average 1.15 ± 0.14
(from all mesocosms), which is substantially different from the ratio of
chloride to sodium reported for seawater (1.8 in Seinfeld and Pandis, 1997).
The depletion of chloride relative to sodium observed in the aerosol phase is
of the order of 36 % compared to the seawater composition. It cannot be
explained by a different relative importance of ions in the Mediterranean
coastal seawater compared to literature values, as this relative abundance
can be considered constant for all oceans (Dittmar, 1884). The composition of
coastal Mediterranean seawater does not deviate from this universal
composition for the major ions reported here (Ramzy et al., 2015). Thus, the
explanation for such a depletion measured for primary sea salt emissions can
be either that a fractionation of chloride relative to sodium happens in the
course of the aerosol emission due to a different composition of the sea
surface microlayer, or that there is already a chloride volatilization
process that takes place during the evaporation of the film drops to aerosol
residuals. In the literature, chloride-to-sodium ratios measured in the
aerosol phase in ambient marine environments have usually been found to be
lower than 1.2 (Bardouki et al., 2003; Koulouri et al., 2008; Pey et al.,
2013), with the chloride loss usually attributed to a loss in the form of HCl
after sodium chloride has reacted with acidic gases such as HNO3 or
H2SO4 during the “aging” process. Our results would call into
question past conclusions about sea salt aging during transport in polluted
air masses.
The enrichment of Ca in the aerosol phase compared to the seawater
composition is of the order of 500 %. A significant enrichment of calcium
in the PM1 primary marine aerosol compared to the composition of the
seawater has also been reported in earlier studies (Keene et al., 2007;
Cochran et al., 2016; Salter et al., 2016), with an increased enrichment with
decreasing aerosol size. The study by Salter et al. (2016) suggests that some
calcium ions are clustering close to the sea surface (probably in the form of
carbonate ions), and that if calcium is complexing to organic matter at the
seawater surface, it is with minor amounts of organic compounds. In our
study, higher chloride deficits and calcium enrichments are observed for
enriched mesocosms compared to the control mesocosm, which suggests that
biological processes may modulate the deficiency or enrichment of these
species in the PMA compared to the seawater. A noticeable portion of free
calcium in seawater is consumed by marine organisms, especially during the
blooming period (Saad and Abdel-Moati,
1991). The photosynthesis process in particular causes the removal of
CO2 from the seawater, causing the precipitation of CaCO3 (Saad and
Hussain, 1978). This process may be enhanced at the sea surface when more
light is available, explaining gradients in the calcium carbonate clusters
and thus in the enrichment of calcium in aerosol particles compared to bulk
seawater composition.
The organic fraction of the primary generated aerosol was addressed using
two different approaches. The first one is the classical chemical analysis
described above. The organic carbon fraction analysed from the 18 quartz
filters collected using impactor sampling for particles Dp<1000 nm was very similar for all three mesocosms with an average value of
0.25 ± 0.06 of the total aerosol PM1 mass, corresponding to an organic
matter (OM) fraction of 0.31 ± 0.07 when using a OM-to-OC conversion
ratio of 1.4 for primary organic aerosol (Turpin and Lim, 2001). Total PM1
fluxes calculated from the chemical analysis of OM and inorganic species was
0.78, 0.58 and 0.64 µg m-2 s-1 for mesocosms A, B and C
respectively. The chemical composition of PMA generated from mesocosm A, B
and C are shown in Fig. 6.
Comparison of size-resolved source flux measurements from
previously published work and this work.
Average chemical composition of the PM1 PMA generated from
seawater of (a) mesocosm A (control), (b) mesocosm B (enriched) and (c)
mesocosm C (enriched).
The second approach is the retrieval of the organic fraction of the aerosol
at its activation diameter (assuming that organics are hydrophobic) from the
total aerosol hygroscopicity (in the form of the parameter kappa). Kappa (κ)
values were calculated from the activation diameters following Asmi et
al. (2012):
S(Dp)=Dp3-Dp,503Dp3-Dp,503(1-κ)exp4σwMwRTρwDp,
where S is the supersaturation, Dp is the diameter of the
droplet, Dp,50 is the dry diameter, R is the gas constant, T
is temperature, and σw, Mw, and
ρw are the surface tension, molecular weight and density of
water, respectively (Petters and Kreidenweis, 2007). Kappa values were
determined by numerical iteration varying κ until the maximum of the
saturation curve was equal to the saturation used inside the CCNc. Surface
tension ρw was approximated using the surface tension of
water. The aerosol hygroscopicity parameter kappa was measured for two
different temperature gradients (supersaturation) in order to investigate the
organic fraction of two different particle size ranges. Table 4 shows the
total number concentration, activation diameter, kappa and organic fraction
individually for all 3 mesocosms and the outside water; the organic fraction
calculated from filter samples is also included.
R2 values, number of sample points (n) and p values at the
95 % confidence level (p) of correlations between different
biogeochemical parameters and the particle organic fractions calculated from
the CCNc at SS = 0.30 % (representing 60 nm particles) or from a
quartz filter collection (representing < 1000 nm particles) from
averaged data of all mesocosms. The p values are given for significant
correlations at the 95 % confidence level and noted with an asterisk (*)
when significant at the 90 % confidence level. The p significance takes
into account the number of observations.
Organic fraction, Aitken mode
Organic fraction, PM1
R2
n
p
R2
n
p
Total bacteria
0.003
56
0.016
19
Virus
0.13
54
0.007
0.166
17
0.103∗
Heterotrophic flagellates
0.121
56
0.009
0.059
18
Cyanobacteria
0.026
56
0.004
19
Pico-eukaryotes
0.004
56
0.016
19
DOC
0.086
58
0.025
0.05
19
POC
0.012
29
0.024
8
PON
0.006
29
0.013
8
TEPs
0.017
58
0.015
19
Chlorophyll a (fluorometer)
0.022
58
0.073
19
Chlorophyll b
8.10-4
54
0.09
18
Chlorophyll c2
0.026
58
0.077
19
19′-Butafucoxanthin
0.010
54
0.104
18
19′-Hexafucoxanthin
0.011
57
0.001
19
Alloxanthin
1.0-4
51
0.045
17
Diadinoxanthin
0.028
57
0.122
19
Fucoxanthin
0.013
57
0.195
18
0.065∗
Lutein
0.121
29
0.095
8
Peridinin
0.002
34
0.055
9
Pheophorbide-a
0.089
11
0.029
4
Prasinoxanthin
0.077
35
0.333
11
Diatoxanthin
0.036
37
0.41
12
0.023
Beta carotene
0.008
47
0.2
16
0.081∗
Beta epsilon carotene
0.029
38
0.341
12
0.044
Zeaxanthin
0.078
52
0.044
0.018
17
Activation diameters at SS = 0.30 % and SS = 0.15 %. The
shaded and striped areas indicate the NaCl activation diameters at each
given supersaturation as a direct comparison to the seawater samples.
The activation diameters (Fig. 7) at SS = 0.30 % showed little variance
between the control and enriched mesocosms (Dp,50,mesocosm average=59.85 ± 3.52 nm). Larger particle sizes (i.e. 90 nm
particles measured at a lower supersaturation, SS = 0.15 %) had higher
activation diameters (Dp,50,mesocosm average=93.42 ± 5.14 nm),
again with little variance seen between the different mesocosms. The kappa
value at SS = 0.30 % was
κ0.30%=0.74 ± 0.12 and at SS = 0.15 % was
κ0.15%=0.82 ± 0.14, both very similar to the marine aerosol
kappa average, κmarine=0.72 ± 0.24 (Pringle et al., 2010). Using these kappa
values, the organic fraction can then be calculated using the following:
κtotal=εorgκorg+(1-εorg)κinorg,
where
κorg and κinorg are the kappa of the organic and inorganic material,
respectively,
and εorg is the bulk volume fraction of organic material.
The values κinorg=1.25 and
κorg=0.006 were chosen based on Collins et al. (2013) for
marine aerosol. The average OM fraction for SS = 0.30 %
(representing 60 nm particles) was 0.41 ± 0.09, and for SS = 0.15 %
(representing 90 nm particles) it was 0.34 ± 0.11. The organic fraction of
the 90 nm particles, representative of the accumulation mode particles, is
very similar to the OM fraction obtained from the PM1 filter analysis
reported above (0.31 ± 0.07). In comparing the organic fractions
measured by filter collection and those calculated from the activation
diameters, it is clear that the organic concentration decreased with
increasing particle size, following the results of previous studies (O'Dowd
et al., 2004; Keene et al., 2007; Schwier et al., 2015).
Correlations with biogeochemical parameters
The different biogeochemical parameters measured during the experiment were
all compared to the aerosol organic fraction, and only some of these
biogeochemical parameters seem to be connected to the aerosol organic
fraction. Indeed, when combining data from all mesocosms, significant
correlations (at the 95 % level of confidence, Table 4) were seen between
the organic fraction from SS = 0.30 % (Dp=60 nm) and viruses
(R2=0.13, n=54), heterotrophic flagellates
(R2=0.121, n=56), DOC (R2=0.086,
n=58) and the pigment zeaxanthin representative of cyanobacteria
(R2=0.078, n=52). No correlation was observed with
chlorophyll a concentrations (R2=0.022, n=8, Fig. 8).
Correlation curves of the organic fraction from SS = 0.30 %
(representative for the Aitken mode particles) versus heterotrophic bacteria
abundance (a), virus abundance (b) and chlorophyll a
concentrations (c) with R2 values of the fit from all three
mesocosms combined. Data for mesocosm A (green squares), mesocosm B (yellow
triangles) and mesocosm C (red circles) are shown.
However, strong linear correlations between chlorophyll a concentrations
and marine aerosol organic fractions have been observed in previous studies,
both issued from satellite data (O'Dowd et al., 2004; Rinaldi et al., 2013)
and from in situ data (Schwier et al., 2015). We used the organic fractions
of larger particles from the filter collection (< 1000 nm) instead of
those calculated from the CCNc to determine if stronger correlations could be
observed with the measured biogeochemical parameters. When using this organic
fraction, the R2 values of the combined mesocosm data increased, but the
significance of the correlations decreased due to the lower number of points
available (Table 5). The study by Schwier et al. (2015) and the present study
were performed with the same experimental set-up, but still show striking
opposite results, in term of relationships between the organic fraction of
PMA and Chl a. The main difference between both studies is that, in the
Schwier et al. (2015) experiments, Chl a was naturally higher in one
mesocosm field campaign than in the other, while in the present experiment,
Chl a in mesocosms was artificially increased via nutrient addition. One
hypothesis to explain differences is hence that artificial bloom experiments
change biogeochemical equilibrium so that Chl a cannot be taken as a proxy
for predicting the PMA organic fraction, over a 20-day period scale. In line
with this observation, O'Dowd et al. (2015) did not find any relationship
between Chl a concentration and the organic fraction of PMA for laboratory
bubble-bursting experiments using water cultured in
Emiliania huxleyi phytoplankton species. Prather et
al. (2013) also observed no correlation with Chl a, while a correlation was
found between organic fraction and heterotrophic flagellates. O'Dowd et
al. (2015) and Rinaldi et al. (2013) found a time lag of 8 days between
Chl a peak concentrations and the organic fraction of marine aerosols in
ambient air. O'Dowd et al. (2015) suggested that the organic fraction of PMA
is linked to the demise of the bloom due to the release of organic colloids
during grazing or viral infection. The correlations found by Schwier et
al. (2015) were observed over two separate seasons, which would reinforce the
idea that correlations exist on a larger timescale than on a day-to-day
basis. However, Schwier et al. (2015) found higher organic fractions of PMA
during the pre-bloom period, and not during the period of bloom decay.
For the PM1 organic fraction, we do observe a significant correlation (only
at the 90 % level of confidence) with virus abundances (R2=0.166, n=17), while the correlation with heterotrophic flagellates, DOC
and zeaxanthin is lost, but the organic fraction of these larger particles
correlate with diatoxanthin (diatomes and dinoflagellates R2=0.41, n=12) and carotene (cryptophytes, R2=0.341,
n=12) instead.
If we focus on the correlations between different biogeochemical parameters
and the organic fraction for individual mesocosms, we see less significant
correlations than for the combined mesocosm data, except for pigments. Using
the organic fraction from SS = 0.30 % (representing 60 nm particles), we
observed that the only significant correlation at the 95 % confidence
level in mesocosm C is with heterotrophic flagellates (R2=0.272, n=19, p=0.010) and in mesocosm B with viruses
(R2=0.346, n=18, p=0.005). No correlation was found in
mesocosm A, which, as the control, did not experience large variability in
biogeochemical properties over the course of the experiment. At the 90 %
confidence level, we found DOC to be correlated with the organic fraction of
60 nm particles in mesocosm B (R2=0.187, n=19,
p=0.064). More pigments were correlated to the organic fraction of 60 nm
particles for individual mesocosms than for the combined mesocosm data,
especially for mesocosm C (chlorophyll c2, fucoxanthin, diatoxanthin). More
pigments were also correlated to the organic fraction of PM1 particles for
individual mesocosms than for the combined mesocosm data, especially for
mesocosm B (19′ hexafucoxanthin, diadinoxanthin, fucoxanthin, diatoxanthin,
beta-beta-carotene). Different pigmented species are favoured under nutrient
conditions specific to each enriched mesocosm, which may influence the
organic content of aerosol particles in different size ranges. However, the
complexity of the system and relatively low number of data points in each
mesocosm hinder our capability of extracting clear and universal
relationships with specific biological indicators.
Discussion
Based on the consistency of the size distributions and modal diameters
throughout the course of the campaign, we observed few effects on aerosol
physical parameters from the mesocosm enrichments. The modal diameters
observed in this work for the number distributions (25, 49, 105 nm) are
similar to those observed previously using similar bubble production devices.
In acidified seawater mesocosm experiments performed in the Mediterranean,
Schwier et al. (2015) observed the same four log-normal modal diameters
(18.5, 37.5, 91.5, 260 nm) during both pre-bloom and non-bloom
(oligotrophic) conditions. Fuentes et al. (2010a) observed four modes (14,
48, 124, 334 nm) from similar laboratory experiments with artificial
seawater. Modal diameters of 15, 45, 125 and 340 nm were observed with
artificial seawater and western African coastal seawater samples (Fuentes et
al., 2010b). Wave-channel experiments with modified Pacific coastal seawater
showed three modes (∼ 90, 220, 1000 nm) (Collins et al., 2013).
Sellegri et al. (2006) observed three modes (45, 110, 300 nm) by using a
weir to bubble synthetic seawater at 23 ∘C. Hultin et al. (2011)
observed either two log-normal modes (site: Askö, 86, 180 nm) or three
log-normal modes (site: Garpen, 93, 193, 577 nm) with
Baltic seawater. Bubbling systems used in these various studies vary
significantly in terms of the number of jets and distance between them,
height of the jets to the surface of the seawater, and the presence of a
blowing air jet above the surface, etc. The variability of the bubbling
systems can largely explain the discrepancies between the bubble size
distributions within the seawater, and hence the sea spray size distribution.
Overall, however, most sea spray size distributions show an Aitken mode
around 40–50 nm and a small accumulation mode around 100 nm. Similar
systems such as the ones used in Sellegri et al. (2006), Fuentes et
al. (2010a, b) and Schwier et al. (2015) show very similar size distributions
to the ones reported in the present study, with an additional ultra-fine mode
around 15–25 nm. In the present study, we did not detect the larger
accumulation mode at 300 nm, due to the smaller upper-size cut used in our
DMPS system.
No change in the modal fraction was observed after either nutrient
enrichment to the mesocosms. Other studies have shown an increasing number
fraction of the Aitken mode with increasing heterotrophic bacteria
concentration (Collins et al., 2013) and during pre-bloom periods (Schwier
et al., 2015). In Schwier et al. (2015), the increase in the Aitken mode was
positively correlated with viruses, heterotrophic prokaryotes, TEPs,
chlorophyll a and other pigments. In the present study, correlations were
significant between the organic fraction of the Aitken mode and
heterotrophic flagellates, viruses and with DOC, whereas it was not the case
with Chl a, for the combined data set or for the individual mesocosms. This
result is different from previous observations (Schwier et al., 2015), in
which a clear correlation was observed between Chl a and the organic
fraction in the Aitken and accumulation modes, suggesting differences in the
speciation of organic material between natural and artificial blooms, which
are correlated to the particle size.
The organic mass fraction calculated for ∼ 60 nm particles in
this work (0.41 ± 0.075) in the unperturbed mesocosm A is intermediate
from organic fractions observed during a natural Mediterranean pre-bloom
period (60 nm, 0.64 ± 0.11) and from the same experiments during
oligotrophic seawater conditions (47 nm, 0.24 ± 0.14) (Schwier et al.,
2015), indicating that only the correlation in mesocosm A between Chl a and
60 nm particles' organic fraction would be in line with previous
observations. The organic fraction measured in PM1 does fall within the
observed range of 30–80 % organic mass fractions from sea spray aerosol
studies (Collins et al., 2013; Facchini et al., 2008; Keene et al., 2007);
however, marine organic mass fractions as low as 4 % (8 % volume) have
also been observed (Modini et al., 2010), and the relevant biogeochemical
parameters of the seawater influencing the organic fraction of sea spray are
still debatable.
No clear impacts of mesocosm enrichment were observed on the total particle
concentrations, which is consistent with past literature. However, the
experimental temperature affected the number concentration. Hultin et
al. (2011) and Zábori et al. (2012a, b) previously observed that particle
number concentration decreased with increasing water temperatures in the
range 10–14 ∘C, whereas it remained constant at higher
temperatures. Salter et al. (2014) found that particle concentrations at
larger sizes (dry diameter > ∼ 0.3 µm) increased slightly
at temperatures between 9 and 30 ∘C while smaller particle
concentrations (dry diameter > 0.01 µm) remained constant. More
recently, Salter et al. (2015) confirm the different behaviour of particles
with diameters larger than 1 µm from the ones for which diameter
was smaller than 1 µm. They also show a rupture in the temperature
dependence of submicron PMA, with a strong decrease of their number
concentration with increasing temperature for temperatures below
13 ∘C, and a very moderate decrease of number concentrations with
increasing temperature for temperatures above 13 ∘C. These results
are different from the ones observed in our study. Here we observe that
within the temperature range studied (22–32 ∘C), higher
temperatures favoured higher particle aerosol and CCN fluxes, for all
particle submicron sizes. This result could be specific to oligotrophic
waters. The experiments described in Salter et al. (2014, 2015) were
performed with synthetic seawater, and it can not be excluded that organic
matter present in natural seawater influence the temperature dependence of
the aerosol number emission flux. Also, while our temperatures are closer to
those tested in Salter et al. (2014), their experiments were controlled at
the same temperatures for up to 5 h, while our experimental
temperatures increased over the course of 1 h. However, the number
concentration throughout the course of an individual experiment most often
either remained constant or increased slightly at all sizes with increasing
temperature, and the shape of the size distribution remained the same
regardless of the temperature. Previous studies have incorporated water
temperature effects into marine flux parameterizations. As mentioned,
Mårtensson et al. (2003) observed a positive linear relationship between
water temperature and 1.4–1.6 µm diameter particles, but an
anti-correlation for smaller particles of 0.0316–0.038 µm
diameter. Jaeglé et al. (2011) empirically derived a third-order
polynomial relationship between particle concentrations and water
temperatures, also observing a positive correlation between the parameters.
Partanen et al. (2014) and Ovadnevaite et al. (2014) used the Reynolds number
to implicitly incorporate the surface water temperature in global models.
Partanen et al. (2014) observed that seasonal wind speed was more important
than seasonal water temperature changes in determining the sea spray aerosol
flux. In a global modelling study, Ovadnevaite et al. (2014) observed that
warmer water temperatures enhanced particle flux over colder water. The
size-resolved flux we observed was similar to those found in most previous
studies (Clarke et al., 2006; Fuentes et al., 2010b; Mårtensson et al.,
2003), although the experimental apparatus tested were different
(Mårtensson: sintered glass filter with synthetic seawater, Clarke:
breaking wave ambient measurements, Fuentes: plunging water jet on collected
seawater samples). Finally, Jaeglé et al. (2011) observed strong marine
aerosol modelling underestimates in warm waters (> 25 ∘C) and
overestimates in colder waters (< 10 ∘C) when compared to
observations, showcasing the need for better marine aerosol flux
parameterization, possibly adapted for different oceanic parts of the world.