Long-range transport of continental emissions has a far-reaching
influence over remote regions, resulting in substantial change in the size,
morphology, and composition of the local aerosol population and cloud
condensation nuclei (CCN) budget. Here, we investigate the physicochemical
properties of atmospheric particles collected on board a research aircraft
flown over the Azores during the winter 2018 Aerosol and Cloud Experiment in
the Eastern North Atlantic (ACE-ENA) campaign. Particles were collected
within the marine boundary layer (MBL) and free troposphere (FT) after
long-range atmospheric transport episodes facilitated by dry intrusion (DI)
events. Chemical and physical properties of individual particles were
investigated using complementary capabilities of computer-controlled
scanning electron microscopy and X-ray spectromicroscopy to probe particle
external and internal mixing state characteristics. Furthermore, real-time
measurements of aerosol size distribution, cloud condensation nuclei (CCN)
concentration, and back-trajectory calculations were utilized to help bring
into context the findings from offline spectromicroscopy analysis. While
carbonaceous particles were found to be the dominant particle type in the
region, changes in the percent contribution of organics across the particle
population (i.e., external mixing) shifted from 68 % to 43 % in the MBL
and from 92 % to 46 % in FT samples during DI events. This change in
carbonaceous contribution is counterbalanced by the increase in inorganics
from 32 % to 57 % in the MBL and 8 % to 55 % in FT. The
quantification of the organic volume fraction (OVF) of individual particles
derived from X-ray spectromicroscopy, which relates to the multi-component
internal composition of individual particles, showed a factor of 2.06 ± 0.16 and 1.11 ± 0.04 increase in the MBL and FT, respectively, among DI
samples. We show that supplying particle OVF into the κ-Köhler
equation can be used as a good approximation of field-measured in situ CCN
concentrations. We also report changes in the κ values in the MBL from
κMBL, non-DI=0.48 to κMBL, DI=0.41, while changes in the FT result in κFT, non-DI=0.36 to κFT, DI=0.33, which is consistent with enhancements in OVF followed by the DI
episodes. Our observations suggest that entrainment of particles from
long-range continental sources alters the mixing state population and CCN
properties of aerosol in the region. The work presented here provides field
observation data that can inform atmospheric models that simulate sources
and particle composition in the eastern North Atlantic.
Introduction
Marine low clouds play a significant role in the world's climate and energy
balance (Wood et al., 2015). They
are the major factor in increasing the Earth's albedo, which is the fraction of solar
energy reflected back into space, leading to an overall cooling effect
(Wood, 2012; Wood et al., 2015). Marine low clouds represent one of the leading sources
of uncertainty in atmospheric models due to limited observational data,
insufficient understanding of the microphysical changes that regulate these
clouds, and the lack of fine model resolution to account for such processes
(Bony, 2005; Klein et al., 2013). Other
relevant boundary layer processes also contribute to the challenges in
assessing marine low clouds such as turbulent mixing, entrainment, and
emissions of aerosols and their precursors (Pincus
and Baker, 1994; Ackerman et al., 2004). In particular, the response of low-altitude clouds is sensitive to aerosol perturbations, which requires a
greater understanding of the processes that govern regional aerosol budget
and source attribution (Levin
and Cotton, 2009; Altaratz et al., 2014; Rosenfeld et al., 2019; Zheng et
al., 2018, 2021). Source-dependent particle size and composition can lead to
changes in clouds' albedo and precipitation due to their varying
efficiency to act as cloud condensation nuclei (CCN) and ice-nucleating
particles (INPs)
(Johnson
et al., 2004; Hamilton et al., 2014; Zheng et al., 2020a).
Atmospheric particles exhibit complex internal heterogeneity
(Murphy
and Thomson, 1997; Buseck and Posfai, 1999; Prather et al., 2008; Li et al.,
2016; Riemer et al., 2019; Laskin et al., 2019). These particles can come
from direct emissions (i.e., primary particles) or from gas–particle
conversion in atmospheric reactions (i.e., secondary particles)
(Reddington
et al., 2011). Primary particles with complex composition include primary
organic aerosols, elemental carbon (i.e., black carbon and soot), inorganic
species from combustion and biomass burning sources
(Toner
et al., 2006; Souri et al., 2017), and sea spray aerosol with organic
components influenced by ocean biological activity
(Prather
et al., 2013; Pham et al., 2017). On the other hand, secondary organic
aerosol (SOA) is formed from the oxidation products of volatile organic
compounds (VOCs) of either biogenic or anthropogenic origin. Secondary fine
particles of nitrate and sulfate are similarly formed from the oxidation of
their inorganic gaseous precursors NOx and SO2, respectively
(National Research Council, US, 2002). In marine
areas, formation of sulfate aerosol is further influenced by gas-phase
emissions of dimethyl sulfide (DMS) from biota, which upon oxidation yield
low-volatility products such as sulfuric acid (H2SO4)
(Kulmala et al., 2000) and methylsulfonic acid (MSA)
(Andreae et
al., 1985; Hodshire et al., 2019). Physical and chemical characteristics of
individual particles such as morphology, chemical composition,
hygroscopicity, lifetime, and chemical mixing state have a profound effect
on their CCN activity
(Cruz
and Pandis, 1997; VanReken, 2003; King et al., 2012; Schmale et al., 2017;
Riemer et al., 2019). Note that the term “chemical mixing state” refers to
how various chemical species are mixed within individual particles
(Riemer et al., 2019). The chemical mixing
state depends on emission sources and atmospheric aging events, which
include but are not limited to biomass burning influence
(Levin et al., 2010), anthropogenic
emissions (Jacobson, 2001), and large continental dust events
(Fraund et al., 2017;
Adachi et al., 2020). For example, previous studies found that within a few
hours urban non-hygroscopic aerosol (i.e., mixed organic and black carbon
aerosol) can accumulate a sufficient coating of hygroscopic sulfates and
nitrates to increase their hygroscopicity parameter (κ) (Petters and
Kreidenweis, 2007) from 0 to 0.1
(Wang et al.,
2010).
The variability within individual atmospheric particles has been well
documented by both model and field measurements across different regions
worldwide such as urban
(Wang
et al., 2010; Ault et al., 2010, 2012; Wang et al., 2012; Fraund et al.,
2017; Ren et al., 2018), rural
(Vakkari et al., 2018;
Tomlin et al., 2020), remote forests
(Bondy
et al., 2018), Arctic
(Gunsch
et al., 2017; Gonçalves et al., 2021), and marine
(Ault
et al., 2013; Zheng et al., 2020a, b). Long-range transport and
meteorological processes such as dry intrusions (DIs) and vertical mixing of
air also play a significant role in the continuous evolution of particle
composition in the atmosphere
(Raes,
1995; Pratt and Prather, 2010; Cubison et al., 2011; Igel et al., 2017;
Zheng et al., 2020b). DIs are events of dry, slantwise descending airflow
from the upper troposphere in midlatitudes down through the boundary layer
at lower latitudes (Raveh-Rubin, 2017).
Such intrusions of dry air, typically peaking in winter, occur with the
passage of extratropical cyclones and their trailing cold fronts, i.e., in
the post-cold-frontal region
(Wernli, 1997;
Browning, 1997; Catto and Raveh-Rubin, 2019). Events of DI are strongly
coupled to the boundary layer, which cools and deepens during DI, and were
shown to induce enhanced ocean heat fluxes
(Raveh-Rubin
and Catto, 2019; Ilotoviz et al., 2021). DI events are of particular
interest as they can contain air masses with a complex distribution of aged
particles having drastically different size, morphology, and composition
compared to local regional aerosols, leading to changes in the local
aerosol–cloud interactions and cloud lifetimes
(Zheng
et al., 2020b; Wang et al., 2020). For example, it has been shown that the
CCN population in the remote marine boundary layer (MBL) of the eastern North
Atlantic can be influenced by long-range transport of wildfire aerosols
originating from North America
(Zheng
et al., 2020b; Y. Wang et al., 2021). The properties of these wildfire
aerosols, facilitated by long-range transport processes, are altered as they
undergo aging (e.g., multi-phase particle chemistry, photo-bleaching, and
gas–particle partitioning of organics), resulting in changes in both the
optical properties and the cloud-forming potential
(Jacobson,
2001; Levin et al., 2010; Zheng et al., 2020b). In particular, aged wildfire
aerosol is typically dominated by accumulation-mode particles, which readily
serve as CCN in the region despite a substantially lower κ value
(i.e., 0.2 to 0.4) than regional highly hygroscopic aerosol of marine origin
(e.g., sea spray aerosol, κ=1.1)
(Zieger
et al., 2017; Zheng et al., 2020b). Lastly, long-range-transported and
atmospherically aged free-tropospheric particles can contribute to the
ice-nucleating particle population and potentially impact cloud formation
(China et al., 2017).
This paper investigates the physicochemical properties of atmospheric
particles during the Aerosol and Cloud Experiment in the Eastern North
Atlantic (ACE-ENA) field campaign conducted at the Azores in
January–February 2018. Aircraft measurements and onboard sampling of
particles (followed by laboratory-based particle analysis) were utilized to
characterize the difference in the contributions of various sources to FT
and MBL aerosols under representative synoptic conditions (i.e., DI vs.
non-DI periods) in this geographical area. Particle analysis included
particle-type classification with statistical depth provided by
computer-controlled scanning electron microscopy, and a subset of particles
were sampled by X-ray spectromicroscopy to characterize particle chemical
mixing state (internal heterogeneity). The particle-type composition,
chemical mixing state, and morphology from analyzed periods were then
combined with real-time measurement of aerosol size distribution, CCN
concentration, and back-trajectory calculations to obtain the representative
composition of particles present in the MBL during the DI events and
entrainment of particles originating from North America. The data presented
here provide observational input for atmospheric process models to simulate
sources and particle composition in the broader North Atlantic region.
Experimental methodsField campaign and meteorological conditions
Samples of atmospheric particles were collected aboard the U.S. Department
of Energy Gulfstream aircraft (G-1). Flight patterns were flown between
Terceira Island (38∘45′43′′ N, 27∘5′27′′ W) and
Graciosa Island (39∘3′12′′ N, 28∘7′26′′ W), Portugal,
and within 20–30 km of Graciosa Island (J. Wang et al.,
2021). Flight plans were based on the projected meteorological conditions
from various global forecast modes including Monitoring Atmospheric
Composition and Climate, Global Forecast System, and European Centre for
Medium-Range Weather Forecasts (ECMWF). A subset of collected samples was
selected for analysis based on synoptic conditions (identifying DI vs.
non-DI periods) and altitudes (clear MBL and FT layers) for each day.
Samples analyzed were collected during the second intensive operation period
of ACE-ENA, on the dates of 19 January 2018, 21 January 2018, 24 January 2018, 25 January 2018,
26 January 2018, 28 January 2018, 30 January 2018, 1 February 2018, 8 February 2018, 11 February 2018,
15 February 2018, 16 February 2018, and 19 February 2018. These dates were selected due to unique
transport episodes associated with the sampling periods. DI days we
identified objectively using the Lagrangian analysis tool (LAGRANTO) version
2.0 (Sprenger and Wernli, 2015)
and wind field data obtained from the ECMWF interim reanalysis (ERA-Interim)
available 6-hourly, interpolated to 1∘× 1∘
horizontal grid resolution, and at 60 vertical hybrid levels
(Dee
et al., 2011). DIs were identified by a systematic calculation of forward
trajectories at altitudes higher than 600 hPa, while the DI trajectories
were identified based on the vertical descent of the air masses. For a
trajectory to be termed a DI, their pressure must increase (i.e., descend in
altitude) by at least 400 hPa in 48 h
(Raveh-Rubin, 2017). If such a DI
trajectory is found within a 3∘ radius around Graciosa, the
date is considered to be “DI”. In addition, backward trajectories for each
sampling period were calculated for the end points at relevant flight
altitudes (Figs. S1 and S2) using the Hybrid Single-Particle Lagrangian
Integrated Trajectory (HYSPLIT) model
(Stein et al., 2015; Rolph
et al., 2017). Atmospheric data from ERA-Interim are additionally analyzed
for the atmospheric column at Graciosa, namely potential temperature,
equivalent potential temperature, potential vorticity, and boundary layer
height. The latter is diagnosed in ERA-Interim using the critical bulk
Richardson number upon its first passing of the threshold 0.2 when
scanning from the surface upwards (ECMWF, 2007).
Particle collection and in situ measurements of particle and cloud
properties
The G-1 aircraft is equipped with sensor modules to deliver precise
real-time inertial measurement, GPS, meteorological, and turbulence data
such as position, altitude, temperature, pressure relative humidity, and
three-dimensional winds. For particle collection, the G-1 was equipped with
an isokinetic aerosol inlet, from which ambient aerosol was transported to
individual instruments. Particle samples were collected using a custom-built
time-resolved aerosol collector (TRAC) that autonomously collected particles
on substrates at pre-set time intervals (Laskin
et al., 2006). The TRAC is a single-stage impactor with an aerodynamic
cutoff size (D50%) of 0.36 µm (Laskin et al.,
2003) coupled to a rotating disk that can hold up to 160 samples. The disk
was preloaded with microscopy substrates (carbon Type-B film on 400-mesh
copper grids, Ted Pella, Inc.). The sampling was performed at a single spot
on the center of each substrate for 7–10 min, depending upon the flight.
After each flight, sample disks were taken off the TRAC, plated, and
hermetically sealed prior to transport. Once samples were received in the
lab, the sample grids used for the analysis were removed from the sealed
plate and transferred into grid boxes stored at room temperature and dry
conditions in a desiccator cabinet. Online measurements of aerosols aboard
the G-1 include a passive cavity aerosol spectrometer 100X probe (PCASP,
Dp=0.1–3.0 µm, 1 Hz resolution) and a fast integrated
mobility spectrometer (FIMS, Dp=0.01–0.5 µm, 1 Hz
resolution), which provided size distributions and concentrations of ambient
particles (Kulkarni and
Wang, 2006; Wang et al., 2018). During all research flights, a Nafion dryer
reduced the relative humidity of the airstream in the sampling line. A CCN
counter (Droplet Measurement Technologies) measured the concentration of
particles that activate at a supersaturation of 0.14 %. A high-resolution
time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was deployed on board
to characterize bulk non-refractory aerosol composition (i.e., organics,
sulfate, ammonium, and chlorine)
(DeCarlo
et al., 2006; Zawadowicz et al., 2021). The particle size distributions and
CCN concentrations were analyzed when the liquid water content was below
0.001 g m3 to avoid periods when cloud-shattering artifacts could
influence the sampled particles (Korolev et al.,
2011). The liquid water content was obtained by integrating the droplet size
distributions measured by a fast cloud droplet probe (FCDP; Droplet
Measurement Technologies).
Additional information on the sampling conditions is presented in Table S1 of the Supplement and incudes sampling time and date, average sampling
altitude, boundary layer height, particle concentration, and wind speed. The
boundary layer height was calculated based on potential temperature
measurements collected for each flight. The boundary layer is limited by a
well-defined temperature inversion, resulting in a maximum value of the
temperature gradient as a function of height (Stull, 1988).
A summary of each flight (altitude and aerosol particle concentration vs.
time) with the collection times highlighted is shown in Figs. S3 and S4.
Guided by meteorological analysis and wind field data to identify DI
periods, we performed offline microscopy analysis of collected particle
samples across different atmospheric layers and transport episodes during
the ACE-ENA campaign.
Methods of particle analysis
Morphology and elemental analysis of individual particles were performed
using computer-controlled scanning electron microscopy coupled with energy
dispersive X-ray spectroscopy operated at 20 kV (CCSEM/EDX; FEI Quanta 3D,
EDAX Genesis). During CCSEM/EDX analysis particle samples were
systematically imaged, and particles larger than 100 nm are recognized. Of
note is that the particle size reported from CCSEM/EDX analysis is defined as the
area equivalent diameter (AED, µm), which is based on fitting a
circle with an area equivalent to the particle's 2D projected image. This is
followed by an automated acquisition of individual EDX spectra for
each particle (Laskin et al., 2005). EDX spectra
with sufficient X-ray counting statistics (40–1500 photonss-1) were then
processed to quantify relative atomic fractions of 15 elements: C, N, O, Na,
Mg, Al, Si, P, S, Cl, K, Ca, Mn, Fe, and Cu. The EDX peak of Cu is heavily
influenced by a background signal from the copper TEM grid and the sample
holder made of beryllium–copper alloy. Therefore, quantified atomic
fractions of Cu were excluded from particle-type classification of the
analyzed particles. Two independent methods were employed for the
particle-type grouping and classification: (1) k-means clustering and (2)
rule-based particle classification. The k-means clustering is an unsupervised
machine-learning algorithm designed to group similar datasets without user
intervention (Rebotier and
Prather, 2007; Moffet et al., 2012). The second approach for the
categorization of particles utilizes a series of user-defined rules to
separate analyzed particles into groups of typical elemental contribution
(Laskin et al., 2012). For this work, the k-means
clustering was used as a primary method for particle-type classification,
while the rule-based approach was used as a complementary method to build
confidence in the identification of different particle types. Details of the
classification schemes are provided in the Supplement (Figs. S5 and S6) and in previous works
(Moffet et al.,
2012; Tomlin et al., 2020).
Scanning transmission X-ray microscopy with near-edge X-ray absorption fine-structure (STXM/NEXAFS) spectroscopy was used to elucidate the chemical
mixing state of individual particles based on the NEXAFS spectral data
acquired at the carbon K-edge (278–320 eV)
(Hopkins et
al., 2007; Moffet et al., 2010b, c). STXM/NEXAFS was performed at the
synchrotron facilities on beamlines 11.0.2.2 and 5.3.2.2 in the Advanced
Light Source, Lawrence Berkeley National Laboratory, and on beamline 10ID-1
at the University of Saskatchewan Canadian Light Source. STXM instrument
operation is similar in both locations as described elsewhere
(Kilcoyne et al., 2003). Briefly, a set of
raster scan STXM images at each of the pre-set energy levels was acquired
from a synchrotron monochromated incident light focused on the sample using
a Fresnel zone plate. The transmitted light is detected at each of the
energy settings, and spectra of individual particles could then be
reconstructed based on the Beer–Lambert law from the intensity of
transmitted light over the projection area of particles compared to the
particle-free regions. The recorded intensity at each energy setting (E)
across individual pixels was converted into optical density (ODE) as
follows:
ODE=-lnI(E)I0(E)=μρt,
where I(E) is the intensity of light transmitted through a particle,
I0(E) is the intensity of incoming light (determined as intensity of light
in the particle-free areas), μ is the mass absorption coefficient, ρ corresponds to the density, and t is the thickness of a particle. Sequences
of STXM images are acquired at closely spaced energies of I0(E) to record a
“stack” of images. Then, NEXAFS spectra from individual pixels of detected
particles are extracted from the stack (∼96 energies over 278
to 320 eV range, 30–35 nm spatial resolution, 1 ms dwell time).
In addition, faster acquisition of STXM images at four key energies of 278 eV (pre-edge), 285.4 eV (C = C), 288.5 eV (-COOH), and 320 eV (post-edge)
(15 × 15 µm, 30–35 nm spatial resolution, 1 ms dwell time) was
employed to construct “maps” of individual particles using image
processing methods reported in our earlier studies
(Moffet
et al., 2010a, 2013, 2016; Fraund et al., 2017). Briefly, a series of
thresholds were used to identify the mapping components including
inorganics (IN), organic carbon (OC), and soot–elemental carbon
(EC). The total carbon (TC) was calculated as the difference between the
carbon post-edge and pre-edge OD (TC = OD320eV-OD278eV).
IN-rich regions were defined with pixels having an OD278eV/OD320eV ratio greater than 0.5. OC regions are those with
abundant features corresponding to the carboxylic acid functional group (-COOH),
defined by the difference between the intensity of the -COOH peak and carbon
pre-edge peak greater than 0 (i.e., OD288.5eV–OD278eV>0). Finally, EC areas are identified by comparing the value of
the sp2 / total carbon to that of highly oriented pyrolytic graphite (HOPG)
according to (OD285.4eV/TC)×(ODHOPG, TC/ODHOPG,C=C)>0.35, which indicates extensive sp2 bonding
of carbon corresponding to graphitic-like components
(Hopkins et al., 2007).
Results and discussionIdentification of dry intrusion periods
Research flights were conducted under different synoptic conditions to allow
for the characterization of common aerosols, trace gases, clouds, and
precipitation. Figure 1a illustrates the typical flight pattern of the G-1
aircraft, which includes multiple legs at different altitudes while
maneuvering perpendicular and along the wind direction. These patterns
allowed for the full profile of aerosol and cloud layer along the MBL and
lower FT altitudes. Figure 1b shows daily time series from 1 January to 28 February 2018 in relation to DI events identified from ERA-Interim reanalysis.
The marked black dots indicate DI air masses within a 3∘ radius
from 39∘ N, 28∘ W (i.e., the ENA site). The high
frequency of the black dots (i.e., vertical distribution) indicates an
increase in trajectories that satisfy the DI criterion at different pressure
altitudes. For example, on 24 January 2018 in Fig. 1b, we see a series of DI
air parcels (black dots) at different pressure altitudes ranging from 611
to 2360 m a.m.s.l. found to be below or above the boundary layer as indicated by
the dashed red line. Guided by the frequency of the DI air masses, we
selected a subset of the time-tagged particle samples for analysis by the
complementary microscopy techniques as summarized in Table S1. To evaluate
the consistency of the sources and long-range transport trajectories, we
calculated back trajectories using the HYSPLIT model
(Stein et al., 2015; Rolph
et al., 2017). Figure 1c shows results of representative HYSPLIT 72 h
back-trajectory calculations for the research flight on 24 January 2018, which
identifies long-range transport of an air mass originating from North America.
Trajectories were calculated every 6 h from 13:00 UTC on 24 January 2018 to 12:00 UTC on 22 January 2018 at three starting altitudes: 100, 2000, and 3000 m. This
process was repeated with the same HYSPLIT input meteorological parameters
for all research flights utilized in this work, as shown in Figs. S1 and
S2.
(a) A representative flight path of the G-1 aircraft during one of the
DI events (24 January 2018) during ACE-ENA campaign (Azores, Portugal). The size and
color scale correspond to organic concentration provided by onboard Aerodyne
HR-ToF-AMS. (b) A time–height cross section at 39∘ N, 28∘ W using ERA-Interim reanalysis from the ECMWF, showing equivalent potential
temperature (K, shading), potential temperature (black contours), and
boundary layer height (red dashed line). The solid red line is the 2 PVU
contour of potential vorticity, marking the tropopause. The time periods of
DI events (marked by black dots and indicated by yellow arrows) were
identified from calculated forward trajectories based on the wind field data
(ERA-Interim; see text for more details). (c) Calculated HYSPLIT 72 h back
trajectory for 24 January 2018 utilizing GDAS1 archived datasets starting at
three elevations: 100 m (red), 2000 m (blue), 3000 m (green).
Particle-type classification
A total of 38 particle sample grids from 13 (out of 19) research flights
were analyzed. First, CCSEM/EDX analysis was carried out to characterize the
particle-type composition typical of different synoptic scenarios. Figure 2
shows the results of the size-segregated particle-type population (right
column) obtained from k-means clustering analysis of ∼36400
individual particles with the backscattering-mode scanning electron microscope (SEM) imaging of a
representative subset of particles (left column), separated between MBL
versus FT flight altitudes and between synoptic conditions of DI and non-DI
sampling periods. The onboard FIMS instrument measurement provided particle
size distribution data in a range of 0.01–0.5 µm. By superimposing
the CCSEM/EDX particle analysis data with the FIMS size distribution data,
we can approximate the representative composition and number concentration
of potentially CCN active particles (>0.1µm) in the MBL
and FT during non-DI and DI periods, respectively. Note that the error bars
in the particle number concentration indicate variation in the particle size
distribution values averaged across different days and synoptic conditions.
Also, comparison of AED and FIMS sizes needs to be considered with caution
because particle flattering on the substrate results in overestimated
AED sizes compared to more realistic FIMS values. Here, the AED-based
particle distributions are scaled to match the Y axis of FIMS data and therefore
to provide visual illustration of the chemical makeup of CCN particles.
Representative backscattering-mode SEM imaging of particles (left
column) and relative particle-type populations (right column) determined by
CCSEM/EDX and k-means clustering analysis, summarized as a 16-bin per decade
histogram representative of MBL and FT atmospheric layers and DI versus
non-DI synoptic conditions. The compositions of the size-segregated
particle-type population were broken down into carbonaceous and
inorganics (i.e., mixed sea salt + aged sea salt + ammonium
nitrate–sulfate). The average FIMS aerosol size distribution measured
on board G1 is superimposed and anchored at 0.25 µm to facilitate a
visual assessment of particle types and number concentrations for CCN active
particles (>100 nm). Lognormal-mode diameter (Dg) and
standard deviation (σg) were fitted for the FIMS particle size
distribution (gray dashed lines).
The k-means algorithm identified four key clusters termed as
carbonaceous, ammonium nitrates–sulfates, mixed sea salt, and
aged sea salt based on the mean elemental contribution (Fig. S5). Note
that the element fraction values obtained from individual EDX spectra were
filtered to remove values less than 0.5 %. Carbonaceous is the
dominant type and represents the majority of analyzed particles. It is defined
based on the sole contributions of C and O elements in the particle EDX
spectra. The second-most abundant cluster is the ammonium
nitrates–sulfates, to which the contributions of N, O, and S are greater than
1 %. The aged sea salt and mixed sea salt clusters contain similar
elemental signatures, with the latter containing significant amounts of
refractory elements typical for sea salt and mineral dust including Mg, Cl,
K, Ca, Mn, and Fe.
First, we compared the change in particle-type population among samples in
the MBL during non-DI and DI periods. The fraction of carbonaceous
particles within the MBL contributed around 68 % in non-DI samples and
decreased to 43 % in DI samples. Organic aerosol in the remote MBL has
been suggested to originate from VOCs such as isoprenes, monoterpenes,
formic acid, nitrogenized, and aliphatic organics released from biological
activities near the sea surface, which undergo oxidation reactions leading
to SOA formation
(Facchini et
al., 2008; Dall'Osto et al., 2012; Mungall et al., 2017). The lower fraction
of carbonaceous particles during DI periods is counterbalanced by the
increase in inorganics shifting from 32 % (non-DI periods) to 57 %
(DI periods). Here, we operationally defined inorganics as the sum of
mixed sea salt (4 %), aged sea salt (20 %), and ammonium
nitrate–sulfate (33 %), which in fact may also contain organic
carboxylic acids as components of aged sea salt. Regardless, shifting focus
to the comparison of FT samples during non-DI and DI periods, we found that
background carbonaceous particles contribute around 92 % (non-DI
periods), decreasing to 46 % (DI periods). Similar to MBL observations,
the shift in carbonaceous contribution can be attributed to an increase
in inorganic influence during DI events changing from 8 % (non-DI
periods) to 55 % (DI periods). We observed that most of the inorganic
influence originates from ammonium nitrate and sulfate, contributing
between 32 % and 33 % during DI periods regardless of sampling altitude (MBL
vs. FT). Both carbonaceous particles and ammonium nitrate–sulfate
can originate from ocean biological activity or anthropogenic sources.
Typically, over marine areas, sulfate aerosol forms from oxidation of
dimethyl sulfide (DMS), a common gas species emitted by biota. Sulfates are
major components of accumulation-mode particles in the remote marine
environment (Sanchez et al.,
2018; Korhonen et al., 2008). Nitrate in marine particles can also come from
vertical mixing in the ocean that surges nitrate-rich deep waters to the
surface, followed by the aerosolization through wave motion
(Zehr and Ward, 2002). However, the elevated contribution
of ammonium nitrates and sulfates during the DI periods suggests likely
influence from anthropogenic emissions originating from North America.
Inorganic aerosols such as ammonium nitrates and sulfates are predominantly
formed from the condensation of atmospheric precursors such as SO2,
NH3, HOx, and NOx, which are common components of biomass
burning emissions, urban areas, and agriculture activities among others
(Reff et al., 2009). A study utilizing regional
chemical models has found that the mass enhancements in inorganic aerosol
can reach 23 % of carbonaceous enhancements as biomass burning processes
accelerate secondary formation of inorganic aerosols
(Souri
et al., 2017). Uptake of S- and N-containing acidic species, as well as
soluble organic acids, onto the preexisting sea salt particles modifies
their composition through acid-displacement reactions that can be expressed
in the general form of (Finlayson-Pitts, 2003; Laskin
et al., 2012)
*NaCl(aq)+HA(aq,g)→*NaA(aq)+HCl(aq,g),
where *NaCl denotes sea salt, and HA represents atmospheric water-soluble acids
(e.g., HNO3, H2SO4, CH3SO3H, and carboxylic acids).
These reactions release the volatile HCl(g) product, leaving particles
depleted in chloride and enriched in corresponding HA(aq) salts.
Related to this acid-displacement chemistry, mixed sea salt and aged
sea salt particle types were identified by the k-clustering analysis as
illustrated in Fig. S5. The mixed sea salt particles contain key
components of seawater (i.e., Na, Mg, and Cl; atomic fractions of Na and Cl >10 % with characteristic ratio of Cl/Na∼0.6)
and minor fractions (<2 %) of additional elements (e.g., Ca, Mn,
Fe, Al, and Si), suggesting internal mixing of relatively fresh sea salt with
other inorganic components without extensive chloride depletion. The other
cluster of aged sea salt particles shows significant fractions of Na
(∼10 %) but with substantially lower ratios of
Cl/Na<0.1, which indicates chloride depletion (Fig. S5) due to
atmospheric aging. Atomic fractions of C and N elements in this type of
particle are much higher than those in the mixed sea salt cluster,
while the fraction of S is much smaller. These observations suggest that in
this geographical region acid-displacement reactions in the aged sea
salt particles are mostly driven by water-soluble carboxylic acids (common
components of SOA) (Laskin et al., 2012) and nitric
acid (Finlayson-Pitts, 2003), while contributions by sulfonic or
sulfuric acids are minor during the wintertime. Based on the k-means
clustering, fractions of mixed sea salt range from 0.5 % to 3 %, while
fractions of aged sea salt are overall more populous and range between
0.1 % and 20 % across all investigated samples. Additionally, both the aged
sea salt and mixed sea salt cluster groups include minor contributions
of Al and Si, indicative of possible mixing with mineral dust transported
from the long-range continental sources.
To better discriminate particle-type groups according to their composition
and the acid-displacement chemistry identified through the k-means
clustering, a supplemental rule-based classification was performed using
previously published definitions of particle-type classes common in marine
environments (Laskin
et al., 2012; Tomlin et al., 2020). Results of the particle-type
characterization utilizing the rule-based assessment of their elemental
composition (assigned into five major classes) are presented in Fig. S6. The
applied rule-based classification scheme distinguishes among particle types
common in the remote marine environment of sea salt, sea
salt–sulfate, carbonaceous–sulfate, carbonaceous, and other
(Fig. S6). For each sample, 600–3000 particles were analyzed, depending
on particle loading on the substrates. The size-resolved particle-type
classification identified using the rule-based schematic was overlaid on the
acquired FIMS size distribution as shown in Fig. S7. Similar to the k-means
clustering breakdown, we first compared the impact of DI events in MBL
samples. Significant fractions of carbonaceous and
carbonaceous–sulfate particles were identified in the background MBL
samples, amounting to 86 % (non-DI periods) and decreasing to 49 % (DI
period). Furthermore, the combined fraction of sea salt and mixed sea
salt–sulfate is substantially smaller at around 10 % (non-DI periods) to
21 % (DI period). Fractions of uncategorized “other” particles
contribute around 30 % (DI period), while only having a minimal
contribution of 4 % during non-DI events. In contrast, background FT
samples were dominated by carbonaceous and carbonaceous–sulfate,
contributing as much as 95 % (non-DI periods) then decreasing to 55 %
(DI period). Unlike the MBL samples, there was only minimal change in
larger sea salt + mixed sea salt–sulfate from 2 % (non-DI
period) to 4 % (DI period). However, the reduction in the carbonaceous and
carbonaceous–sulfate contribution among FT samples during DI periods is
associated with the large change in the “other” fraction shifting from
4 % (non-DI period) to 41 % (DI period). Based on the mean elemental
composition of the “other” category, this group contains a combination of
dust, sea salt, and carbonaceous components, suggesting extensive internal
mixing of particles consistent with long-range transport
(Froyd et al., 2019). This finding is
also consistent with the k-means clustering results that indicated elevated
contributions of particles with inorganic components during the DI periods.
Overall, the particle-type fractions identified by both the k-means clustering
and the rule-based classification schemes are consistent across all samples,
suggesting that the mixing state population significantly changes from
heavily organic-dominated to a mixture of inorganic–organic particle-type
distribution, resulting in the observation of more complex particle
compositions during DI periods.
Relative contributions of the particle-type fractions among separate DI
events show substantial variability between different flights and MBL versus
FT altitudes (Fig. S8). Furthermore, the dominant carbonaceous
particle-type groups identified by CCSEM/EDX elemental analysis may exhibit
significant differences in the spectral characteristics of carbon bonding,
indicative of its long-range transport from North America during the DI
periods. Furthermore, a previous study tracked the origin of air masses
transported over long distances across the Atlantic Ocean to the Azores
utilizing the Lagrangian Flexible Particle (FLEXPART) dispersion model to show
detailed spatial resolution of air masses across different locations and
altitudes (China et al., 2017). The
influence of North American emissions on distant remote regions is well
documented, with occurrences of continental pollutant transport events
accompanied by strong influence from urban city emissions from
Boston, Toronto, Detroit, and Chicago (Owen et al.,
2006). On the other hand, extensive boreal wildfires in northern North
America release large amounts of trace gases and aerosols into the
atmosphere, which then can be transported to other remote regions including
North America (Val Martín et al., 2006). In
particular, boreal wildfires emit around 10 % of the annual anthropogenic
aerosol black carbon in the Northern Hemisphere (Bond
et al., 2004). The eastward transport of North American emissions begins as
hot plumes of biomass burning emissions from wildfires rapidly rise to high
altitudes (∼8 to 13 km a.g.l.) under favorable conditions
(Zhu
et al., 2018; Yu et al., 2019; Kloss et al., 2019). These plumes can be
lofted into a warm conveyor belt preceding a cold front from an associated
cyclone, which is followed by the entrainment of a cold descending airstream (from the same cyclone) that ultimately results in the air parcels
containing continental emissions reaching the lower altitudes of the eastern
North Atlantic
(Owen
et al., 2006; Zheng et al., 2020b). The transported aerosol undergoes
substantial atmospheric aging through photochemical reactions
(Hems et al., 2021), gas–particle partitioning
(Vakkari et al., 2018), and coagulation
(Ramnarine et al., 2019) processes as it
travels across the Atlantic Ocean and descends into the MBL during the DI
events.
Internal mixing of individual particles
Results of the elemental microanalysis of particles presented above provide
statistics on broad particle classes identified and show the
significant contribution of organic-dominated particles in the region well.
However, CCSEM/EDX analysis is limited in providing detailed information on
the carbon speciation within individual particles and other metrics of
particle internal composition (chemical mixing state). To investigate
chemical differences in the carbon components of particles we employed
STXM/NEXAFS spectromicroscopy methods, which provide spatially resolved
carbon bonding speciation and differentiate between EC and OC regions within
individual particles (Moffet et al., 2010a,
c). It is also worth mentioning that the definitions of carbonaceous
particles identified by CCSEM/EDX and described in the previous section is
somewhat different from OC particles defined by STXM/NEXAFS. The former
corresponds to the distribution of organics across a population of particles
(i.e., external mixing), while the latter is related to the multi-component
internal heterogeneity of individual particles (i.e., internal mixing).
Figure 3a shows an illustrative carbon K-edge map of individual particles
from one of the DI period samples (the cumulative map of all ∼4300 particles from all samples analyzed in this study is included in the Supplement,
Fig. S9). The carbon K-edge composition map distinguishes three main
components based on the spectral information (Moffet et
al., 2010a) as described earlier: IN (blue), OC (green), and EC (red). Each
pixel within an individual particle may contain either single or multiple
components (i.e., components can overlap) that are grouped to yield five
typical classes based on the internal mixing between OC, EC, and IN
components: (1) IN, (2) OC-EC-IN, (3) OC-EC, (4) OC-IN, and (5) OC. The
size-resolved histograms of these five classes superimposed with the onboard
particle size distribution data measured by FIMS are shown in Fig. 3b to
highlight the organic and inorganic contributions within individual particles as
a function of particle size. A mixture of organic and inorganic particles
(OC-IN) appears to be the dominant class across all samples, contributing
40 %–76 % to the total particle population. Furthermore, the consideration
of multiple sources of EC from wildfires
(Park et al.,
2007), residential wood smoke
(Allen and Rector, 2020),
agricultural burning
(Liu
et al., 2016; Holder et al., 2017), and urban emissions
(Paredes-Miranda et al., 2013) in North
America led us to expect a large contribution of EC within our sample.
However, OC-EC and OC-EC-IN particles contributed only 0.4 %–1.3 % to the
total particle population. EC–soot lifetime is primarily governed by its wet
deposition rate, which is dependent on the particle's affinity to absorb
water (Barrett et al.,
2019). Freshly emitted soot particles are hydrophobic; however, atmospheric
processes can increase the hydroscopicity properties of soot particles
through the accumulation of OH-initiated oxidation of organics during
long-range transport and atmospheric aging
(Dzepina
et al., 2015), leading to decreased atmospheric lifetime of EC regardless of
initial composition
(Khalizov
et al., 2013; Browne et al., 2015; China et al., 2015). IN particles (i.e.,
inorganics such as sea salt and sulfates) appear to be consistent with the
particle-type observations inferred from CCSEM/EDX data. Singe-component IN
particles contribute up to 15 % in the MBL at the time of non-DI periods,
while their contribution during DI decreases to ∼0.8 %.
Subject to long-range transport, IN-dominant particles also accumulate
substantial OC components when encountering DI, and as they entrain into the
MBL and create ensembles of ambient particles with complex multi-component
internal mixing states through different atmospheric processes such as
condensation (Mozurkewich, 1986) and coagulation
(Holmes, 2007). Consistently, fractions of
single-component OC particles within the MBL during DI periods increased
(from 7 % to 22 %) and slightly decreased in the FT layer (from 26 %
to 20 %). These observations suggest that entrainment of aerosols with
higher extents of internal mixing (from long-range transport) are present in
the MBL and can contribute to the regional aerosol composition, which in
turn may modify aerosol–cloud interactions typical for the area.
(a) Carbon speciation map of a subset of particles acquired by STXM
from DI periods. Note that components can overlap, and each pixel can
contain a different combination of the individual components: EC + IN
constituents as purple; OC + EC as yellow; OC + IN as cyan. (b) Size
distribution of analyzed particles identified via STXM/NEXAFS shown as an 8-bin per decade histogram to compare particle multi-component internal mixing
state between atmospheric transport events. FIMS particle size distribution
is overlaid to facilitate a visual comparison from the same atmospheric
episodes. Abbreviations are as follows: IN – inorganics, OC – organic carbon
(i.e., COOH), EC – elemental carbon (i.e., sp2 C = C carbon).
NEXAFS spectra (285–294 eV) of individual particles were used to assess
the carbon chemical bonding environment allowing, us to identify representative
types of OC-containing particles (Moffet et al., 2010a).
Figure 4 shows the representative NEXAFS spectra acquired for 103
individual carbon-containing particles. This resulted in the identification
of six carbon “types”, as shown along with their illustrative secondary
electron-mode SEM imaging. Each carbon type is classified based on
characteristic spectral features such as peak positions and relative
intensities. For all spectra shown in Fig. 4a, the individual contribution
of carbon energy transitions was quantified via spectral deconvolution.
Details on the deconvolution process are described in previous works
(Moffet
et al., 2010b, 2013; Tomlin et al., 2020). Figure 5a shows the deconvolution
fit of the averaged NEXAFS spectra for each carbon type identified
across different sampling conditions, with Fig. 5b illustrating the
contribution of each functional group based on the individual peak area. It
is worth noting that the difference in absorption between the post-edge
(OD320eV) and pre-edge (OD278eV) energies is a measure of
the amount of total carbonaceous material in the particles.
(a) Individual NEXAFS spectra showing differences in carbon content
of representative particles collected at MBL and FT altitudes under
different synoptic conditions. Identified carbon types are the following: type
1 – biological (green), type 2 – homogeneous organic particles (orange),
type 3 – soot (red), type 4 – fresh sea salt–organics (blue), type
5 – aged sea salt–organics (pink), and type 6 – K-dominated salt (teal).
Dashed lines correspond to the transition energies: 285.4 eV (C*= C), 286.7 eV (C*= O), 287.7 eV (C*–H), 288.3 eV (R-NH(C*= O)R), 288.5 eV
(R(C*= O)OH), 289.5 eV (RC*–OH), 290.0 eV (C edge step), 290.4 eV
(C*O3), 297.1 eV (KL2*), and 299.7 eV (KL3*). (b)
Representative secondary electron-mode SEM imaging of particles
corresponding to the different carbon types identified with the STXM/NEXAFS
analysis.
(a) Carbon K-edge NEXAFS spectra of six carbon types identified in
individual particles: type 1 – biological (green), type 2 –
homogeneous organic particles (orange), type 3 – soot (red), type 4
– fresh sea salt–organics (blue), type 5 – aged sea salt–organics
(pink), type 6 – K-dominated salt (teal). (b) The contributions of the
different carbon functional groups are reported as a percentage of the total
peak area.
The type 1 (biological) class has some contribution from alkene groups
(C*= C at 285.4 eV) with significant enhancement of aliphatic hydrocarbons
(C*–H at 287.7 eV) and alcohol groups (C*–OH at 289.5 eV). These spectra
appear to be similar to the reported NEXAFS spectrum for phospholipids, a
constituent of cell walls
(Lawrence et al.,
2003; Nováková et al., 2008). Lipid material is concentrated in the
sea surface microlayer through the rupturing of phytoplankton cell membranes
(i.e., cell lysis) (X. Wang et
al., 2015). A majority of lipid compounds produced by phytoplankton in
seawater include glyceroglycolipids, phospholipids, and triacylglycerols
containing significant amounts of aliphatic and alcohol groups
(Harwood and Guschina, 2009). The transition of
aliphatic-rich organic species into the aerosol phase is governed by the
bursting of bubble films (Blanchard, 1989) enriched in
lipid organic species found on the surface of seawater
(X. Wang et al., 2015). The type 2
(homogeneous organic particles) class has almost equivalent peak contributions
from each reported functional group as shown in Fig. 5b. The NEXAFS
spectrum for type 2 is quantitatively similar to those reported for organic
particles from anthropogenic emissions in urban areas of Mexico City
(Moffet et al., 2010b) and central California
(Moffet et
al., 2013). As the aerosol plume is transported away from the source of
emission, organic mass increases, while the fraction of C = C decreases
(Doran et al., 2007; Kleinman et al., 2008;
Moffet et al., 2010b). As a result, organic functional groups build up with
particle age such as carboxylic acids, carbonyl, alcohol, and other
carbon–oxygen functional groups. It has been suggested that formation of
these homogeneous organic particles likely results from the accumulation
growth of primary emitted particles as they travel further away from their
emission source (Moffet et al., 2010b). Type 3 (soot)
had the largest contribution of C*= C at 285.4 eV spectral feature (42 %
of peak area contribution). Based on reported literature, this spectrum is
comparable with atmospheric particles collected during various field studies
of biomass burning emissions (Hopkins
et al., 2007). Interestingly, particles collected during aircraft measurements
during the Aerosol Characterization Experiment in Asia (ACE-Asia) campaign
(Maria et al., 2004) from emissions over mixed
combustion sources had nearly identical percentage of sp2 values of around 41 %
(Hopkins et al., 2007).
Field and laboratory studies showed that sea salt particles can react with
atmospheric water-soluble organic acids, leading to chloride depletion within
particles (Laskin et al., 2012; B. Wang et al.,
2015). Consistent with these previous studies, fresh sea salt typically has
an intact rectangular inorganic core with a carbon outer shell arising from
a thin layer of carboxylic acid coating as indicated by the peak for
R(C*= O)OH at 288.5 eV. Accordingly, type 4 is referred to as “fresh
sea salt” in this work. In addition, the minor quantity of carbonaceous
material in type 4, as inferred from the small difference between the post-
and pre-edge energies (OD320eV–OD278eV) apparent from Fig. 5a, further supports the observation of freshly
emitted sea salt particles. In contrast, type 5 (aged sea
salt–organics) is made up of sea salt particles that have reacted with carboxylic
acid components of organic aerosol condensate, which results in a substantial
contribution of the R(C*= O)OH at 288.5 eV peak while retaining the
carbonate peak C*O3 at 290.4 eV. Of note is that type 5 (aged sea
salt–organics) contains significantly more carbon mass than type 4 (fresh
sea salt–organics), as indicated by the NEXAFS spectrum. Finally, the type
6 (K-dominated) class is identified based on the appearance of
characteristic potassium peaks at 297.1 eV (K*L2) and 299.7 eV
(K*L3), with a percent contribution of ∼51 % relative
to the total peak area. Potassium-salt particles are common markers of
biomass burning smoke (Andreae,
1983; Li et al., 2003). Large fractions of KCl particles are commonly
emitted from both flaming and smoldering fires, while atmospheric aging can
transform them into K2SO4 and KNO3 through multi-phase acid-displacement reactions similar to those of NaCl (Li et
al., 2003). However, these K-dominated particles can also be released as
mixed secondary particles containing fractions of organic species,
methylsulfonic acid, trimethylamine, SO42-, NH4+, and K
from potential biogenic sources in oceans
(Willis et al., 2017).
Organic volume fraction of individual mixed organic–inorganic particles
Organic volume fraction (OVF) is a practical parameter to assess reactivity
(Worsnop et al., 2002; Folkers et
al., 2003) and hygroscopicity
(Wang et al.,
2008; Schill et al., 2015; Ruehl et al., 2016) of mixed inorganic–organic
particles. Based on the STXM/NEXAFS measurements of individual particles,
OVF is defined as a ratio of the optical thickness of the organic components
(torg) divided by the total optical thickness of the particle
(torg+tinorg)
(Moffet
et al., 2010a; Pham et al., 2017; Fraund et al., 2019). STXM images
collected at the carbon K-edge were used to calculate the OVF. The values of
absorbance at the pre-edge (278 eV) and the post-edge (320 eV) energies are
related to the inorganic mass and the sum of inorganic + organic mass,
respectively. Assuming specific values for densities (ρ) and mass
absorption coefficients (μ) for the organic and inorganic components,
values of torg and tinorg can be determined, allowing OVF
calculation (Fraund et al.,
2019). For this study, we assumed that the inorganic component of particles
corresponds to (NH4)2SO4 based on the particle elemental
composition identified by CCSEM/EDX analysis, while oxalic acid
(C2H2O4) is used as a proxy for the organic component. Oxalic
acid was chosen to represent biomass burning (Yamasoe et
al., 2000) and vehicular exhaust (Kawamura and Kaplan,
1987). Of note, based on previous reported studies, is that assumptions of
chemically different organic components have a minor effect on the resulting
OVF values, while choice of the inorganic components resulted in a larger
variation in the OVF calculations
(Pham
et al., 2017; Fraund et al., 2019). Here, we estimate the systematic error
in OVF when assuming different inorganic–organic components, as shown in
Table S2. Assuming NaCl to be the inorganic component instead of
(NH4)2SO4 yields a difference of ∼35 %. On
the other hand, assuming the organic component to be oxalic acid yields a
∼5 %–30 % difference in OVF when compared to other organics
such as sucrose, adipic acid, and glucose.
Figure 6 shows representative chemical mixing state maps and OVF values of
particles sampled during different atmospheric transport episodes during
this study. Particles appear to have a varying amount of organic coating for
different sampling episodes as shown on the OVF maps. The comparison of the
OVF map and the carbon speciation map illustrates overlap between the two
mapping schemes. Finally, histograms show particle fractions at varying OVF
values during different atmospheric transport episodes. Layers of organics
are seen encapsulating inorganic cores. As expected, background particles
collected in the MBL show inorganic NaCl cores (as indicated by a
rectangular core morphology) with modest organic coating (OVF <30 %), consistent with a previous report
(Chi et al., 2015).
However, during the DI periods, the majority of particles have equal or
greater fractions of organic-to-inorganic components (40 %–60 % OVF), while
only a few particles exhibit core–shell morphology typical for background
particles (i.e., non-DI periods). Furthermore, FT particles during non-DI
periods have OVF <10 % when compared to FT samples during DI
periods (10 %–20 % OVF). In general, samples collected at the FT altitudes
show reduced OVF values compared to the MBL samples regardless of the
occurrence of DIs. Core–shell particle morphologies were also observed in FT
samples, although not frequently (see Fig. S9). FT samples were dominated by
inorganic–organic particles in the size range of 0.20–0.25 µm, which
are likely mixed sulfate–organic particles based on the size-resolved
particle-type datasets obtained from CCSEM/EDX analysis. A recent study
conducted in central Oregon found that the organic mass fraction from FT
samples was between 27 % and 84 %, while sulfate mass fractions ranged
from 39 %–50 %
(Zhou
et al., 2019). Based on these reported studies the elevated contributions of
organics and sulfate in the FT may be attributed to the enrichment of
organonitrates and organosulfate compounds originating from biogenic sources
in the absence of wildfire influence. However, FT organic and sulfate
aerosol mass is also known to be associated with urban and biomass burning
emissions
(Bahreini,
2003; Dunlea et al., 2009; Roberts et al., 2010; Y. Wang et al., 2021).
Studies in the northeastern Pacific found that submicron aerosol mass was
dominated by sulfate and organic components originating from aged Asian
pollution plumes (Dunlea et al., 2009). FT organic and sulfate
particles can then experience long-range transport and aging as the air
parcels are carried across the Atlantic and descend into the MBL of the ENA
site (China et al., 2017). To summarize,
we observe enhancements in the OVF values of individual particles during the
DI periods, quantified as 2.06 ± 0.16- and 1.11 ± 0.04-fold
increases in OVF for the MBL and FT samples, respectively, assuming
(NH4)2SO4–oxalic acid components. The larger total OVF in
the MBL (relative to FT samples) regardless of DI events is most likely due
to the additional contribution of marine organic sources within the boundary
layer. The background organic concentration in the MBL is different than FT
due to other sources of organics such as dissolved organic matter on the
seawater surface
(Doval et al., 2001;
Miyazaki et al., 2018). The transport of organics from the ocean surface
directly into the atmosphere is primarily driven by turbulent winds
(O'Dowd
et al., 2004; Prather et al., 2013), resulting in the enhancement of the
background organic concentration in the MBL. Furthermore, the observed
enhancements in OVF in the MBL during DI periods could be the result of
organic-rich air parcels (originating from North America) descending from
the FT into the MBL, leading to changes in the total organic concentration
(Zheng
et al., 2020b; Y. Wang et al., 2021).
(a) Representative organic volume fraction (OVF) maps of individual
particles. (a) Carbon speciation maps of the identical particles; teal –
inorganic dominant regions; green – organic dominant regions (i.e., COOH);
red – elemental carbon (i.e., sp2 carbon). (c) Histogram of particle
fractions as a function of their OVF values with average OVF (red dashed
line). The rows correspond to the different atmospheric layers and synoptic
conditions to highlight the differences in organic–inorganic composition and
multi-component internal mixing state of particles identified in this study.
Evaluating CCN activity of mixed organic–inorganic particles
CCN activity of individual particles is governed by both their size and
chemical composition. In particular, condensation of organic carbon onto
atmospheric inorganic particles can impact the efficiency at which particles
of mixed organic–inorganic composition can act as CCN and INPs due to changes
in particles' hygroscopicity and viscosity
(Beydoun et al.,
2017; Ovadnevaite et al., 2017; Altaf et al., 2018). To account for the
effects of organics on aerosol hygroscopicity, we use the κ-Köhler
equation (Petters and Kreidenweis, 2007) to
estimate the hygroscopicity parameter κ corresponding to mixed
inorganic–organic particles:
κ=(1-forg)κinorg+forgκorg,
where forg is the OVF value derived from the STXM data, κorg=0.1 is the hygroscopicity of the organic component, and
κinorg=0.6 is that of (NH4)2SO4
(Petters and Kreidenweis, 2007). We derived
κ values for different synoptic and atmospheric layer conditions
using the size-resolved OVF ratio shown in Fig. S10, and we found that
κMBL, DI=0.41 and κFT, DI=0.33 for DI
periods and that κMBL, non-DI=0.48 and κFT, non-DI=0.36 for non-DI periods. The lower κ values under DI
periods are consistent with enhancements in the organic contribution. Of
note is that the values of κ obtained here using Eq. (2) need to be
considered the low-limit values, which might be somewhat higher
considering possible contributions from more hygroscopic components of
particles related to original and aged sea salt (κNaCl=1.3 and κNa2SO4=0.8).
Using κ, we can calculate the critical size of a dry particle
(Fig. S11) that can be activated under the supersaturation of 0.14 %
(setting of the CCN counter deployed on G-1) (Petters and Kreidenweis,
2007). The theoretical CCN number concentrations are then estimated by
integrating the FIMS-measured aerosol size distributions above the critical
dry particle diameter. Figure 7 shows the results for the OVF-based
calculations of theoretical CCN concentrations compared to the onboard CCN
measurements at 0.14 % supersaturation. There is general agreement
between calculated and measured CCN concentration, but FT cases appear to
have better agreement. The uncertainty in the calculated CCN is due to the
supersaturation fluctuation of the CCN counter (0.13 %–0.15 %), as shown in
Fig. S11. The large error bars in the measured CCN are a result of the
variability of the measured CCN during different sampling periods. We also
note that the exact value of κorg may play a role in affecting
the CCN calculation. So, theoretical CCN concentrations were also calculated
using κorg=0, and the results are compared against the
measured CCN concentrations in Fig. 7b. However, the impact of this change
in κorg does not significantly change the agreement between the
calculated and measured CCN concentrations. This result shows that
calculating the CCN concentration using OVF values derived from the STXM
data and the κ-Köhler theory can be a good estimate of the
actual CCN concentrations.
Comparison of the CCN concentration predicted from the particle
size distribution and OVF with field-measured CCN by onboard instruments
across the different atmospheric layer and transport event. (a)κorg=0.1; (b)κorg=0.0. The gray dashed line
corresponds to the 1:1 calculated CCN to measured CCN.
Conclusion
Here, we presented detailed chemical imaging of individual atmospheric
particles collected over the Azores during long-range transport events. Air
mass back-trajectory calculations suggest that air parcels in the ENA region
can be traced from more than 4000 km away from North America within a span
of 48–72 h. During these long-range transport episodes, aerosols undergo
substantial changes in size, morphology, and chemical composition among
others as they are carried across the Atlantic Ocean and descend from the FT
into the MBL altitudes over the ENA region. Chemical compositions of elements
of individual particles (∼36400) were quantified using
CCSEM/EDX, while a subset of particles (∼4300) was analyzed
using STXM/NEXAFS to determine the particle internal mixing state and
organic spatial distribution. Based on CCSEM/EDX analysis, we observe a
substantial contribution of carbonaceous particles, which are the
dominant particle type across all samples. The fraction of externally mixed
carbonaceous particles decreases during the DI periods, compensated for by
the increase in the ammonium nitrate–sulfate fraction. The elevated
contribution of atmospheric nitrate suggests influence from anthropogenic
and biomass burning emissions (Reff et al., 2009).
This observation is consistent with the DI periods, suggesting that air masses
originating from North America descend from FT to MBL over the ENA region.
Interestingly, there is also an increase in particle-type diversity in the
FT during DI periods, most likely due to significant mixing during DI
episodes based on measured particle number concentrations. Among these
identified carbonaceous particles, the OVF across individual particles
derived from STXM measurements is enhanced in DI samples. Aged aerosols
accumulate organics through condensation of secondary semi-volatile species,
resulting in an increase in the organic contribution among individual particles.
We utilize the STXM-derived OVF values and implement them in the
calculation of particle hygroscopicity using κ-Köhler theory
(Petters and Kreidenweis, 2007). Particles
collected during DI periods resulted in lower κ values with respect
to background marine aerosols common in the ENA region, resulting in reduced
CCN propensity. We calculated κ values between ∼0.29
and ∼0.44, corresponding to mixed organic–inorganic aerosol in
the FT and MBL, respectively. These values are consistent with previous
studies on mixed organic particles
(Petters
and Kreidenweis, 2007; Schmale et al., 2018; Zheng et al., 2020b).
Current atmospheric models lack the representation of aerosol mixing states,
limiting studies to only simple assumptions and leading to high uncertainty of the aerosol
impact on the Earth system. It is traditionally assumed that sulfate
particles dictate particle growth over remote ocean regions, while
the influence of organic particles on the CCN activity over
remote oceans is underestimated. We have shown that particles transported from North America
can have a substantial impact on the aerosol mixing state and aerosol
population over the region of study, as organic contribution and
particle-type diversity are significantly enhanced during the DI periods.
These observations need to be considered in current atmospheric models to
have a better predictive understanding of the impact of long-range transport
episodes on the source apportionment of specific aerosol particle types and
the extent of particle internal heterogeneity.
Code availability
The scripts used in this work to process STXM/NEXAFS datasets are available at https://github.com/MFraund/OrganicVolumeFraction_StandardAerosols (last access: 8 December 2021) (https://doi.org/10.5194/amt-12-1619-2019, Fraund et al., 2019) and at https://www.mathworks.com/matlabcentral/fileexchange/29085-stxm-spectromicroscopy-particle-analysis-routines (last access: 8 December 2021) (https://doi.org/10.1021/ac1012909, Moffet et al., 2010a).
Data availability
The dataset used for this work is available for download as a .zip file from https://doi.org/10.4231/6CT5-3R55 (Tomlin et al., 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-18123-2021-supplement.
Author contributions
DPV, SC, DAK, RCM, JWa, and AL designed the study. DPV and
JWa executed sample collection and data acquisition during field
deployment. SRR performed modeling tasks of the study. JMT, KAJ, DPV,
SC, PW, MF, JWe, FRA, DAK, RCM, and MKG performed chemical
imaging experiments and analyzed associated data. GZ, YW, and JWa
analyzed real-time data from G-1. JMT and AL wrote the paper with
contributions from all coauthors.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Marine aerosols, trace gases, and clouds over the North Atlantic (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We would like to thank the ACE-ENA campaign team for their help and support. The Purdue University, Washington University, Stonybrook University, and STI groups gratefully acknowledge support from the U.S. Department of Energy’s Atmospheric System Research (ASR) program, Office of Biological and Environmental Research (OBER). Shira Raveh-Rubin acknowledges support from the Israel Science Foundation. The research used STXM/NEXAFS instruments at beamline 5.3.2.2 and 11.0.2 at the Advanced Light Source at Lawrence Berkeley National Laboratory with guidance from David Kilcoyne, Matthew Markus, Hendrik Ohldag, and David Shapiro. In addition, the soft X-ray spectromicroscopy 10ID-1 beamline at the Canadian Light Source was also used in this study, with assistance from beamline scientist Jian Wang. We used the CCSEM/EDX instrument at the Environmental Molecular Sciences Laboratory located at the Pacific Northwest National Laboratory. We thank John Shilling for providing the data collected from the Aerodyne HR-ToF-AMS on board the G-1 aircraft during the ACE-ENA campaign. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model as well as the READY website (https://www.ready.noaa.gov, last access: 31 August 2020) used in this publication.
Financial support
This research has been supported by the Office of Biological and Environmental Research (grant no. DE-SC0018948, Purdue/STI group; grant no. DE-SC0020259, Washington University; and grant nos. SC0016370 and SC0021034, Stonybrook University) and the Israel Science Foundation (grant no. 1347/18, Weizmann Institute).
Review statement
This paper was edited by Armin Sorooshian and reviewed by two anonymous referees.
ReferencesAckerman, A. S., Kirkpatrick, M. P., Stevens, D. E., and Toon, O. B.: The
impact of humidity above stratiform clouds on indirect aerosol climate
forcing, Nature, 432, 1014–1017, 10.1038/nature03174, 2004.Adachi, K., Oshima, N., Gong, Z., de Sá, S., Bateman, A. P., Martin, S. T., de Brito, J. F., Artaxo, P., Cirino, G. G., Sedlacek III, A. J., and Buseck, P. R.: Mixing states of Amazon basin aerosol particles transported over long distances using transmission electron microscopy, Atmos. Chem. Phys., 20, 11923–11939, 10.5194/acp-20-11923-2020, 2020.Allen, G. and Rector, L.: Characterization of Residential Woodsmoke PM2.5 in
the Adirondacks of New York, Aerosol Air Qual. Res., 20, 2419–2432,
10.4209/aaqr.2020.01.0005, 2020.Altaf, M. B., Dutcher, D. D., Raymond, T. M., and Freedman, M. A.: Effect of
Particle Morphology on Cloud Condensation Nuclei Activity, ACS Earth Space
Chem., 2, 634–639, 10.1021/acsearthspacechem.7b00146, 2018.Altaratz, O., Koren, I., Remer, L. A., and Hirsch, E.: Review: Cloud
invigoration by aerosols – Coupling between microphysics and dynamics,
Atmos. Res., 140–141, 38–60,
10.1016/j.atmosres.2014.01.009, 2014.Andreae, M. O.: Soot Carbon and Excess Fine Potassium: Long-Range Transport
of Combustion-Derived Aerosols, Science, 220, 1148–1151,
10.1126/science.220.4602.1148, 1983.Andreae, M. O., Ferek, R. J., Bermond, F., Byrd, K. P., Engstrom, R. T.,
Hardin, S., Houmere, P. D., LeMarrec, F., Raemdonck, H., and Chatfield, R.
B.: Dimethyl sulfide in the marine atmosphere, J. Geophys. Res., 90, 12891,
10.1029/JD090iD07p12891, 1985.Ault, A. P., Gaston, C. J., Wang, Y., Dominguez, G., Thiemens, M. H., and
Prather, K. A.: Characterization of the Single Particle Mixing State of
Individual Ship Plume Events Measured at the Port of Los Angeles, Environ.
Sci. Technol., 44, 1954–1961, 10.1021/es902985h, 2010.Ault, A. P., Peters, T. M., Sawvel, E. J., Casuccio, G. S., Willis, R. D.,
Norris, G. A., and Grassian, V. H.: Single-Particle SEM-EDX Analysis of
Iron-Containing Coarse Particulate Matter in an Urban Environment: Sources
and Distribution of Iron within Cleveland, Ohio, Environ. Sci. Technol., 46,
4331–4339, 10.1021/es204006k, 2012.Ault, A. P., Moffet, R. C., Baltrusaitis, J., Collins, D. B., Ruppel, M. J.,
Cuadra-Rodriguez, L. A., Zhao, D., Guasco, T. L., Ebben, C. J., Geiger, F.
M., Bertram, T. H., Prather, K. A., and Grassian, V. H.: Size-Dependent
Changes in Sea Spray Aerosol Composition and Properties with Different
Seawater Conditions, Environ. Sci. Technol., 47, 5603–5612,
10.1021/es400416g, 2013.Bahreini, R.: Aircraft-based aerosol size and composition measurements
during ACE-Asia using an Aerodyne aerosol mass spectrometer, J. Geophys.
Res., 108, 8645–8658, 10.1029/2002JD003226, 2003.Barrett, T. E., Ponette-González, A. G., Rindy, J. E., and Weathers, K.
C.: Wet deposition of black carbon: A synthesis, Atmos. Environ., 213,
558–567, 10.1016/j.atmosenv.2019.06.033, 2019.Beydoun, H., Polen, M., and Sullivan, R. C.: A new multicomponent heterogeneous ice nucleation model and its application to Snomax bacterial particles and a Snomax–illite mineral particle mixture, Atmos. Chem. Phys., 17, 13545–13557, 10.5194/acp-17-13545-2017, 2017.Blanchard, D. C.: The Ejection of Drops from the Sea and Their Enrichment
with Bacteria and Other Materials: A Review, Estuaries, 12, 127–137,
10.2307/1351816, 1989.Bond, T. C., Streets, D. G., Yarber, K. F., Nelson, S. M., Woo, J.-H., and
Klimont, Z.: A technology-based global inventory of black and organic carbon
emissions from combustion, J. Geophys. Res., 109, D14203,
10.1029/2003JD003697, 2004.Bondy, A. L., Bonanno, D., Moffet, R. C., Wang, B., Laskin, A., and Ault, A. P.: The diverse chemical mixing state of aerosol particles in the southeastern United States, Atmos. Chem. Phys., 18, 12595–12612, 10.5194/acp-18-12595-2018, 2018.Bony, S.: Marine boundary layer clouds at the heart of tropical cloud
feedback uncertainties in climate models, Geophys. Res. Lett., 32, L20806,
10.1029/2005GL023851, 2005.Browne, E. C., Franklin, J. P., Canagaratna, M. R., Massoli, P.,
Kirchstetter, T. W., Worsnop, D. R., Wilson, K. R., and Kroll, J. H.:
Changes to the Chemical Composition of Soot from Heterogeneous Oxidation
Reactions, J. Phys. Chem. A, 119, 1154–1163,
10.1021/jp511507d, 2015.Browning, K. A.: The dry intrusion perspective of extra-tropical cyclone
development, Meteorol. Appl., 4, 317–324,
10.1017/S1350482797000613, 1997.Buseck, P. R. and Posfai, M.: Airborne minerals and related aerosol
particles: Effects on climate and the environment, P. Natl. Acad. Sci. USA,
96, 3372–3379, 10.1073/pnas.96.7.3372, 1999.Catto, J. L. and Raveh-Rubin, S.: Climatology and dynamics of the link
between dry intrusions and cold fronts during winter. Part I: global
climatology, Clim. Dynam., 53, 1873–1892,
10.1007/s00382-019-04745-w, 2019.Chi, J. W., Li, W. J., Zhang, D. Z., Zhang, J. C., Lin, Y. T., Shen, X. J., Sun, J. Y., Chen, J. M., Zhang, X. Y., Zhang, Y. M., and Wang, W. X.: Sea salt aerosols as a reactive surface for inorganic and organic acidic gases in the Arctic troposphere, Atmos. Chem. Phys., 15, 11341–11353, 10.5194/acp-15-11341-2015, 2015.China, S., Scarnato, B., Owen, R. C., Zhang, B., Ampadu, M. T., Kumar, S.,
Dzepina, K., Dziobak, M. P., Fialho, P., Perlinger, J. A., Hueber, J.,
Helmig, D., Mazzoleni, L. R., and Mazzoleni, C.: Morphology and mixing state
of aged soot particles at a remote marine free troposphere site:
Implications for optical properties, Geophys. Res. Lett., 42, 1243–1250,
10.1002/2014GL062404, 2015.China, S., Alpert, P. A., Zhang, B., Schum, S., Dzepina, K., Wright, K.,
Owen, R. C., Fialho, P., Mazzoleni, L. R., Mazzoleni, C., and Knopf, D. A.:
Ice cloud formation potential by free tropospheric particles from long-range
transport over the Northern Atlantic Ocean: Ice cloud formation by aged
particles, J. Geophys. Res.-Atmos., 122, 3065–3079,
10.1002/2016JD025817, 2017.Cruz, C. N. and Pandis, S. N.: A study of the ability of pure secondary
organic aerosol to act as cloud condensation nuclei, Atmos. Environ., 31,
2205–2214, 10.1016/S1352-2310(97)00054-X, 1997.Cubison, M. J., Ortega, A. M., Hayes, P. L., Farmer, D. K., Day, D., Lechner, M. J., Brune, W. H., Apel, E., Diskin, G. S., Fisher, J. A., Fuelberg, H. E., Hecobian, A., Knapp, D. J., Mikoviny, T., Riemer, D., Sachse, G. W., Sessions, W., Weber, R. J., Weinheimer, A. J., Wisthaler, A., and Jimenez, J. L.: Effects of aging on organic aerosol from open biomass burning smoke in aircraft and laboratory studies, Atmos. Chem. Phys., 11, 12049–12064, 10.5194/acp-11-12049-2011, 2011.Dall'Osto, M., Ceburnis, D., Monahan, C., Worsnop, D. R., Bialek, J.,
Kulmala, M., Kurtén, T., Ehn, M., Wenger, J., Sodeau, J., Healy, R., and
O'Dowd, C.: Nitrogenated and aliphatic organic vapors as possible drivers
for marine secondary organic aerosol growth: Marine secondary organic
aerosol growth, J. Geophys. Res.-Atmos., 117, D12311,
10.1029/2012JD017522, 2012.DeCarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T.,
Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K. S., Worsnop,
D. R., and Jimenez, J. L.: Field-Deployable, High-Resolution, Time-of-Flight
Aerosol Mass Spectrometer, Anal. Chem., 78, 8281–8289,
10.1021/ac061249n, 2006.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy.
Meteor. Soc., 137, 553–597, 10.1002/qj.828, 2011.Doran, J. C., Barnard, J. C., Arnott, W. P., Cary, R., Coulter, R., Fast, J. D., Kassianov, E. I., Kleinman, L., Laulainen, N. S., Martin, T., Paredes-Miranda, G., Pekour, M. S., Shaw, W. J., Smith, D. F., Springston, S. R., and Yu, X.-Y.: The T1-T2 study: evolution of aerosol properties downwind of Mexico City, Atmos. Chem. Phys., 7, 1585–1598, 10.5194/acp-7-1585-2007, 2007.Doval, M. D., Álvarez-Salgado, X. A., and Pérez, F. F.: Organic
matter distributions in the Eastern North Atlantic–Azores Front region, J.
Mar. Syst., 30, 33–49, 10.1016/S0924-7963(01)00036-7, 2001.Dunlea, E. J., DeCarlo, P. F., Aiken, A. C., Kimmel, J. R., Peltier, R. E., Weber, R. J., Tomlinson, J., Collins, D. R., Shinozuka, Y., McNaughton, C. S., Howell, S. G., Clarke, A. D., Emmons, L. K., Apel, E. C., Pfister, G. G., van Donkelaar, A., Martin, R. V., Millet, D. B., Heald, C. L., and Jimenez, J. L.: Evolution of Asian aerosols during transpacific transport in INTEX-B, Atmos. Chem. Phys., 9, 7257–7287, 10.5194/acp-9-7257-2009, 2009.Dzepina, K., Mazzoleni, C., Fialho, P., China, S., Zhang, B., Owen, R. C., Helmig, D., Hueber, J., Kumar, S., Perlinger, J. A., Kramer, L. J., Dziobak, M. P., Ampadu, M. T., Olsen, S., Wuebbles, D. J., and Mazzoleni, L. R.: Molecular characterization of free tropospheric aerosol collected at the Pico Mountain Observatory: a case study with a long-range transported biomass burning plume, Atmos. Chem. Phys., 15, 5047–5068, 10.5194/acp-15-5047-2015, 2015.ECMWF: Integrated forecasting system's documentation, Part IV: Physical
processes, (IFS Documentation CY31R1), 10.21957/4whwo8jw0, 2007.Facchini, M. C., Rinaldi, M., Decesari, S., Carbone, C., Finessi, E.,
Mircea, M., Fuzzi, S., Ceburnis, D., Flanagan, R., Nilsson, E. D., de Leeuw,
G., Martino, M., Woeltjen, J., and O'Dowd, C. D.: Primary submicron marine
aerosol dominated by insoluble organic colloids and aggregates, Geophys.
Res. Lett., 35, L17814, 10.1029/2008GL034210, 2008.Finlayson-Pitts, B. J.: The Tropospheric Chemistry of Sea Salt: A
Molecular-Level View of the Chemistry of NaCl and NaBr, Chem. Rev., 103,
4801–4822, 10.1021/cr020653t, 2003.Folkers, M., Mentel, Th. F., and Wahner, A.: Influence of an organic coating
on the reactivity of aqueous aerosols probed by the heterogeneous hydrolysis
of N2O5: Organic coatings and aerosol reactivity, Geophys. Res.
Lett., 30, 1644, 10.1029/2003GL017168, 2003.Fraund, M., Pham, D., Bonanno, D., Harder, T., Wang, B., Brito, J., de
Sá, S., Carbone, S., China, S., Artaxo, P., Martin, S., Pöhlker, C.,
Andreae, M., Laskin, A., Gilles, M., and Moffet, R.: Elemental Mixing State
of Aerosol Particles Collected in Central Amazonia during GoAmazon2014/15,
Atmosphere, 8, 173–201, 10.3390/atmos8090173, 2017.Fraund, M., Park, T., Yao, L., Bonanno, D., Pham, D. Q., and Moffet, R. C.: Quantitative capabilities of STXM to measure spatially resolved organic volume fractions of mixed organic/inorganic particles, Atmos. Meas. Tech., 12, 1619–1633, 10.5194/amt-12-1619-2019, 2019 (data available at: https://github.com/MFraund/OrganicVolumeFraction_StandardAerosols, last access: 8 December 2021).Froyd, K. D., Murphy, D. M., Brock, C. A., Campuzano-Jost, P., Dibb, J. E., Jimenez, J.-L., Kupc, A., Middlebrook, A. M., Schill, G. P., Thornhill, K. L., Williamson, C. J., Wilson, J. C., and Ziemba, L. D.: A new method to quantify mineral dust and other aerosol species from aircraft platforms using single-particle mass spectrometry, Atmos. Meas. Tech., 12, 6209–6239, 10.5194/amt-12-6209-2019, 2019.Gonçalves, S. J., Weis, J., China, S., Evangelista, H., Harder, T. H.,
Müller, S., Sampaio, M., Laskin, A., Gilles, M. K., and Godoi, R. H. M.:
Photochemical reactions on aerosols at West Antarctica: A molecular
case-study of nitrate formation among sea salt aerosols, Sci. Total
Environ., 758, 143586, 10.1016/j.scitotenv.2020.143586,
2021.Gunsch, M. J., Kirpes, R. M., Kolesar, K. R., Barrett, T. E., China, S., Sheesley, R. J., Laskin, A., Wiedensohler, A., Tuch, T., and Pratt, K. A.: Contributions of transported Prudhoe Bay oil field emissions to the aerosol population in Utqiaġvik, Alaska, Atmos. Chem. Phys., 17, 10879–10892, 10.5194/acp-17-10879-2017, 2017.Hamilton, D. S., Lee, L. A., Pringle, K. J., Reddington, C. L., Spracklen,
D. V., and Carslaw, K. S.: Occurrence of pristine aerosol environments on a
polluted planet, P. Natl. Acad. Sci. USA, 111, 18466–18471,
10.1073/pnas.1415440111, 2014.Harwood, J. L. and Guschina, I. A.: The versatility of algae and their lipid
metabolism, Biochimie, 91, 679–684,
10.1016/j.biochi.2008.11.004, 2009.Hems, R. F., Schnitzler, E. G., Liu-Kang, C., Cappa, C. D., and Abbatt, J.
P. D.: Aging of Atmospheric Brown Carbon Aerosol, ACS Earth Space Chem., 5,
722–748, 10.1021/acsearthspacechem.0c00346, 2021.Hodshire, A. L., Campuzano-Jost, P., Kodros, J. K., Croft, B., Nault, B. A., Schroder, J. C., Jimenez, J. L., and Pierce, J. R.: The potential role of methanesulfonic acid (MSA) in aerosol formation and growth and the associated radiative forcings, Atmos. Chem. Phys., 19, 3137–3160, 10.5194/acp-19-3137-2019, 2019.Holder, A. L., Gullett, B. K., Urbanski, S. P., Elleman, R., O'Neill, S.,
Tabor, D., Mitchell, W., and Baker, K. R.: Emissions from prescribed burning
of agricultural fields in the Pacific Northwest, Atmos. Environ., 166,
22–33, 10.1016/j.atmosenv.2017.06.043, 2017.Holmes, N. S.: A review of particle formation events and growth in the
atmosphere in the various environments and discussion of mechanistic
implications, Atmos. Environ., 41, 2183–2201,
10.1016/j.atmosenv.2006.10.058, 2007.Hopkins, R. J., Tivanski, A. V., Marten, B. D., and Gilles, M. K.: Chemical
bonding and structure of black carbon reference materials and individual
carbonaceous atmospheric aerosols, J. Aerosol Sci., 38, 573–591,
10.1016/j.jaerosci.2007.03.009, 2007.Igel, A. L., Ekman, A. M. L., Leck, C., Tjernström, M., Savre, J., and
Sedlar, J.: The free troposphere as a potential source of arctic boundary
layer aerosol particles: Free Troposphere and Boundary Layer Arctic Aerosol,
Geophys. Res. Lett., 44, 7053–7060, 10.1002/2017GL073808,
2017.Ilotoviz, E., Ghate, V. P., and Raveh-Rubin, S.: The Impact of Slantwise
Descending Dry Intrusions on the Marine Boundary Layer and Air-Sea Interface
Over the ARM Eastern North Atlantic Site, J. Geophys. Res.-Atmos., 126, e2020JD033879,
10.1029/2020JD033879, 2021.Jacobson, M. Z.: Strong radiative heating due to the mixing state of black
carbon in atmospheric aerosols, Nature, 409, 695–697,
10.1038/35055518, 2001.Johnson, B. T., Shine, K. P., and Forster, P. M.: The semi-direct aerosol
effect: Impact of absorbing aerosols on marine stratocumulus, Q. J. Roy.
Meteor. Soc., 130, 1407–1422, 10.1256/qj.03.61, 2004.Kawamura, K. and Kaplan, I. R.: Motor exhaust emissions as a primary
source for dicarboxylic acids in Los Angeles ambient air, Environ. Sci.
Technol., 21, 105–110, 10.1021/es00155a014, 1987.Khalizov, A. F., Lin, Y., Qiu, C., Guo, S., Collins, D., and Zhang, R.: Role
of OH-Initiated Oxidation of Isoprene in Aging of Combustion Soot, Environ.
Sci. Technol., 47, 2254–2263, 10.1021/es3045339, 2013.Kilcoyne, A. L. D., Tyliszczak, T., Steele, W. F., Fakra, S., Hitchcock, P.,
Franck, K., Anderson, E., Harteneck, B., Rightor, E. G., Mitchell, G. E.,
Hitchcock, A. P., Yang, L., Warwick, T., and Ade, H.:
Interferometer-controlled scanning transmission X-ray microscopes at the
Advanced Light Source, J. Synchrotron Radiat., 10, 125–136,
10.1107/S0909049502017739, 2003.King, S. M., Butcher, A. C., Rosenoern, T., Coz, E., Lieke, K. I., de Leeuw,
G., Nilsson, E. D., and Bilde, M.: Investigating Primary Marine Aerosol
Properties: CCN Activity of Sea Salt and Mixed Inorganic–Organic Particles,
Environ. Sci. Technol., 46, 10405–10412, 10.1021/es300574u,
2012.Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and
Gleckler, P. J.: Are climate model simulations of clouds improving? An
evaluation using the ISCCP simulator: Evaluating clouds in climate models,
J. Geophys. Res.-Atmos., 118, 1329–1342,
10.1002/jgrd.50141, 2013.Kleinman, L. I., Springston, S. R., Daum, P. H., Lee, Y.-N., Nunnermacker, L. J., Senum, G. I., Wang, J., Weinstein-Lloyd, J., Alexander, M. L., Hubbe, J., Ortega, J., Canagaratna, M. R., and Jayne, J.: The time evolution of aerosol composition over the Mexico City plateau, Atmos. Chem. Phys., 8, 1559–1575, 10.5194/acp-8-1559-2008, 2008.Kloss, C., Berthet, G., Sellitto, P., Ploeger, F., Bucci, S., Khaykin, S., Jégou, F., Taha, G., Thomason, L. W., Barret, B., Le Flochmoen, E., von Hobe, M., Bossolasco, A., Bègue, N., and Legras, B.: Transport of the 2017 Canadian wildfire plume to the tropics via the Asian monsoon circulation, Atmos. Chem. Phys., 19, 13547–13567, 10.5194/acp-19-13547-2019, 2019.Korhonen, H., Carslaw, K. S., Spracklen, D. V., Mann, G. W., and Woodhouse,
M. T.: Influence of oceanic dimethyl sulfide emissions on cloud condensation
nuclei concentrations and seasonality over the remote Southern Hemisphere
oceans: A global model study, J. Geophys. Res., 113, D15204,
10.1029/2007JD009718, 2008.Korolev, A. V., Emery, E. F., Strapp, J. W., Cober, S. G., Isaac, G. A.,
Wasey, M., and Marcotte, D.: Small Ice Particles in Tropospheric Clouds:
Fact or Artifact? Airborne Icing Instrumentation Evaluation Experiment,
B. Am. Meteorol. Soc., 92, 967–973,
10.1175/2010BAMS3141.1, 2011.Kulkarni, P. and Wang, J.: New fast integrated mobility spectrometer for
real-time measurement of aerosol size distribution – I: Concept and theory,
J. Aerosol Sci., 37, 1303–1325,
10.1016/j.jaerosci.2006.01.005, 2006.Kulmala, M., Pirjola, L., and Mäkelä, J. M.: Stable sulphate
clusters as a source of new atmospheric particles, Nature, 404, 66–69,
10.1038/35003550, 2000.Laskin, A., Iedema, M. J., and Cowin, J. P.: Time-Resolved Aerosol Collector
for CCSEM/EDX Single-Particle Analysis, Aerosol Sci. Technol., 37, 246–260,
10.1080/02786820300945, 2003.Laskin, A., Wietsma, T. W., Krueger, B. J., and Grassian, V. H.:
Heterogeneous chemistry of individual mineral dust particles with nitric
acid: A combined CCSEM/EDX, ESEM, and ICP-MS study, J. Geophys. Res., 110,
D10208, 10.1029/2004JD005206, 2005.Laskin, A., Cowin, J. P., and Iedema, M. J.: Analysis of individual
environmental particles using modern methods of electron microscopy and
X-ray microanalysis, J. Electron Spectrosc. Relat. Phenom., 150, 260–274,
10.1016/j.elspec.2005.06.008, 2006.Laskin, A., Moffet, R. C., Gilles, M. K., Fast, J. D., Zaveri, R. A., Wang,
B., Nigge, P., and Shutthanandan, J.: Tropospheric chemistry of internally
mixed sea salt and organic particles: Surprising reactivity of NaCl with
weak organic acids: Mixed Sea Salt/Organics Particles, J. Geophys. Res.-Atmos., 117, D15302, 10.1029/2012JD017743, 2012.Laskin, A., Moffet, R. C., and Gilles, M. K.: Chemical Imaging of
Atmospheric Particles, Acc. Chem. Res., 52, 3419–3431,
10.1021/acs.accounts.9b00396, 2019.Lawrence, J. R., Swerhone, G. D. W., Leppard, G. G., Araki, T., Zhang, X.,
West, M. M., and Hitchcock, A. P.: Scanning Transmission X-Ray, Laser
Scanning, and Transmission Electron Microscopy Mapping of the Exopolymeric
Matrix of Microbial Biofilms, Appl. Environ. Microbiol., 69, 5543–5554,
10.1128/AEM.69.9.5543-5554.2003, 2003.Levin, E. J. T., McMeeking, G. R., Carrico, C. M., Mack, L. E., Kreidenweis,
S. M., Wold, C. E., Moosmüller, H., Arnott, W. P., Hao, W. M., Collett,
J. L., and Malm, W. C.: Biomass burning smoke aerosol properties measured
during Fire Laboratory at Missoula Experiments (FLAME), J. Geophys. Res.,
115, D18210, 10.1029/2009JD013601, 2010.Levin, Z. and Cotton, W. R. (Eds.): Aerosol pollution impact on
precipitation: a scientific review, Springer, Dordrechet, the Netherlands, 18–19
pp., IBSN 978-1-4020-8690-8, 10.1007/978-1-4020-8690-8, 2009.Li, J., Pósfai, M., Hobbs, P. V., and Buseck, P. R.: Individual aerosol
particles from biomass burning in southern Africa: 2, Compositions and aging
of inorganic particles: Composition and Aging of Inorganic Particles, J.
Geophys. Res.-Atmos., 108, 8484–8496,
10.1029/2002JD002310, 2003.Li, W., Sun, J., Xu, L., Shi, Z., Riemer, N., Sun, Y., Fu, P., Zhang, J.,
Lin, Y., Wang, X., Shao, L., Chen, J., Zhang, X., Wang, Z., and Wang, W.: A
conceptual framework for mixing structures in individual aerosol particles:
Individual Aerosol Mixing Structure, J. Geophys. Res.-Atmos., 121,
13784–13798, 10.1002/2016JD025252, 2016.Liu, X., Zhang, Y., Huey, L. G., Yokelson, R. J., Wang, Y., Jimenez, J. L.,
Campuzano-Jost, P., Beyersdorf, A. J., Blake, D. R., Choi, Y., St. Clair, J.
M., Crounse, J. D., Day, D. A., Diskin, G. S., Fried, A., Hall, S. R.,
Hanisco, T. F., King, L. E., Meinardi, S., Mikoviny, T., Palm, B. B.,
Peischl, J., Perring, A. E., Pollack, I. B., Ryerson, T. B., Sachse, G.,
Schwarz, J. P., Simpson, I. J., Tanner, D. J., Thornhill, K. L., Ullmann,
K., Weber, R. J., Wennberg, P. O., Wisthaler, A., Wolfe, G. M., and Ziemba,
L. D.: Agricultural fires in the southeastern U.S. during SEAC4RS:
Emissions of trace gases and particles and evolution of ozone, reactive
nitrogen, and organic aerosol: Agricultural Fires in the SE US, J. Geophys.
Res.-Atmos., 121, 7383–7414, 10.1002/2016JD025040,
2016.Maria, S. F., Lynn, R. M., Gilles, M. K., and Myneni, S. C. B.: Organic
Aerosol Growth Mechanisms and Their Climate-Forcing Implications, Science,
306, 1921–1924, 10.1126/science.1103491, 2004.Miyazaki, Y., Yamashita, Y., Kawana, K., Tachibana, E., Kagami, S., Mochida,
M., Suzuki, K., and Nishioka, J.: Chemical transfer of dissolved organic
matter from surface seawater to sea spray water-soluble organic aerosol in
the marine atmosphere, Sci. Rep., 8, 14861,
10.1038/s41598-018-32864-7, 2018.Moffet, R. C., Henn, T., Laskin, A., and Gilles, M. K.: Automated Chemical
Analysis of Internally Mixed Aerosol Particles Using X-ray Spectromicroscopy
at the Carbon K-Edge, Anal. Chem., 82, 7906–7914,
10.1021/ac1012909, 2010a (data available at: https://www.mathworks.com/matlabcentral/fileexchange/29085-stxm-spectromicroscopy-particle-analysis-routines, last access: 8 December 2021).Moffet, R. C., Henn, T. R., Tivanski, A. V., Hopkins, R. J., Desyaterik, Y., Kilcoyne, A. L. D., Tyliszczak, T., Fast, J., Barnard, J., Shutthanandan, V., Cliff, S. S., Perry, K. D., Laskin, A., and Gilles, M. K.: Microscopic characterization of carbonaceous aerosol particle aging in the outflow from Mexico City, Atmos. Chem. Phys., 10, 961–976, 10.5194/acp-10-961-2010, 2010b.Moffet, R. C., Tivanski, A. V., and Gilles, M. K. (Eds.): Scanning
Transmission X-ray Microscopy: Applications in Atmospheric Aerosol Research,
in: Fundamentals and Applications in Aerosol Spectroscopy, Taylor and
Francis Books, New York, 438–481, 10.1201/b10417-21, 2010c.Moffet, R. C., Furutani, H., Rödel, T. C., Henn, T. R., Sprau, P. O.,
Laskin, A., Uematsu, M., and Gilles, M. K.: Iron speciation and mixing in
single aerosol particles from the Asian continental outflow: Aerosol Iron
Speciation in Asian Outflow, J. Geophys. Res.-Atmos., 117, D07204,
10.1029/2011JD016746, 2012.Moffet, R. C., Rödel, T. C., Kelly, S. T., Yu, X. Y., Carroll, G. T., Fast, J., Zaveri, R. A., Laskin, A., and Gilles, M. K.: Spectro-microscopic measurements of carbonaceous aerosol aging in Central California, Atmos. Chem. Phys., 13, 10445–10459, 10.5194/acp-13-10445-2013, 2013.Moffet, R. C., O'Brien, R. E., Alpert, P. A., Kelly, S. T., Pham, D. Q., Gilles, M. K., Knopf, D. A., and Laskin, A.: Morphology and mixing of black carbon particles collected in central California during the CARES field study, Atmos. Chem. Phys., 16, 14515–14525, 10.5194/acp-16-14515-2016, 2016.Mozurkewich, M.: Aerosol Growth and the Condensation Coefficient for Water:
A Review, Aerosol Sci. Technol., 5, 223–236,
10.1080/02786828608959089, 1986.Mungall, E. L., Abbatt, J. P. D., Wentzell, J. J. B., Lee, A. K. Y., Thomas,
J. L., Blais, M., Gosselin, M., Miller, L. A., Papakyriakou, T., Willis, M.
D., and Liggio, J.: Microlayer source of oxygenated volatile organic
compounds in the summertime marine Arctic boundary layer, P. Natl. Acad.
Sci. USA, 114, 6203–6208, 10.1073/pnas.1620571114, 2017.Murphy, D. M. and Thomson, D. S.: Chemical composition of single aerosol
particles at Idaho Hill: Negative ion measurements, J. Geophys. Res.-Atmos., 102, 6353–6368, 10.1029/96JD00859, 1997.National Research Council (U.S.) (Ed.): The Congestion Mitigation and Air
Quality Improvement Program: assessing 10 years of experience,
Transportation Research Board, National Research Council: National Academy
Press, Washington, D.C, 175–176 pp., 2002.Nováková, E., Mitrea, G., Peth, C., Thieme, J., Mann, K., and
Salditt, T.: Solid supported multicomponent lipid membranes studied by x-ray
spectromicroscopy, Biointerphases, 3, FB44–FB54,
10.1116/1.2976445, 2008.O'Dowd, C. D., Facchini, M. C., Cavalli, F., Ceburnis, D., Mircea, M.,
Decesari, S., Fuzzi, S., Yoon, Y. J., and Putaud, J.-P.: Biogenically driven
organic contribution to marine aerosol, Nature, 431, 676–680,
10.1038/nature02959, 2004.Ovadnevaite, J., Zuend, A., Laaksonen, A., Sanchez, K. J., Roberts, G.,
Ceburnis, D., Decesari, S., Rinaldi, M., Hodas, N., Facchini, M. C.,
Seinfeld, J. H., and O' Dowd, C.: Surface tension prevails over solute
effect in organic-influenced cloud droplet activation, Nature, 546,
637–641, 10.1038/nature22806, 2017.Owen, R. C., Cooper, O. R., Stohl, A., and Honrath, R. E.: An analysis of
the mechanisms of North American pollutant transport to the central North
Atlantic lower free troposphere, J. Geophys. Res.-Atmos., 111, D23S58, 10.1029/2006JD007062, 2006.Paredes-Miranda, G., Arnott, W. P., Moosmüller, H., Green, M. C., and
Gyawali, M.: Black Carbon Aerosol Concentration in Five Cities and Its
Scaling with City Population, B. Am. Meteorol. Soc., 94, 41–50,
10.1175/BAMS-D-11-00225.1, 2013.Park, R. J., Jacob, D. J., and Logan, J. A.: Fire and biofuel contributions
to annual mean aerosol mass concentrations in the United States, Atmos.
Environ., 41, 7389–7400, 10.1016/j.atmosenv.2007.05.061,
2007.Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, 10.5194/acp-7-1961-2007, 2007.Pham, D. Q., O'Brien, R., Fraund, M., Bonanno, D., Laskina, O., Beall, C.,
Moore, K. A., Forestieri, S., Wang, X., Lee, C., Sultana, C., Grassian, V.,
Cappa, C. D., Prather, K. A., and Moffet, R. C.: Biological Impacts on
Carbon Speciation and Morphology of Sea Spray Aerosol, ACS Earth Space
Chem., 1, 551–561, 10.1021/acsearthspacechem.7b00069, 2017.Pincus, R. and Baker, M. B.: Effect of precipitation on the albedo
susceptibility of clouds in the marine boundary layer, Nature, 372,
250–252, 10.1038/372250a0, 1994.Prather, K. A., Hatch, C. D., and Grassian, V. H.: Analysis of Atmospheric
Aerosols, Annu. Rev. Anal. Chem., 1, 485–514,
10.1146/annurev.anchem.1.031207.113030, 2008.Prather, K. A., Bertram, T. H., Grassian, V. H., Deane, G. B., Stokes, M.
D., DeMott, P. J., Aluwihare, L. I., Palenik, B. P., Azam, F., Seinfeld, J.
H., Moffet, R. C., Molina, M. J., Cappa, C. D., Geiger, F. M., Roberts, G.
C., Russell, L. M., Ault, A. P., Baltrusaitis, J., Collins, D. B., Corrigan,
C. E., Cuadra-Rodriguez, L. A., Ebben, C. J., Forestieri, S. D., Guasco, T.
L., Hersey, S. P., Kim, M. J., Lambert, W. F., Modini, R. L., Mui, W.,
Pedler, B. E., Ruppel, M. J., Ryder, O. S., Schoepp, N. G., Sullivan, R. C.,
and Zhao, D.: Bringing the ocean into the laboratory to probe the chemical
complexity of sea spray aerosol, P. Natl. Acad. Sci. USA, 110, 7550–7555,
10.1073/pnas.1300262110, 2013.Pratt, K. A. and Prather, K. A.: Aircraft measurements of vertical profiles
of aerosol mixing states, J. Geophys. Res., 115, D11305,
10.1029/2009JD013150, 2010.Raes, F.: Entrainment of free tropospheric aerosols as a regulating
mechanism for cloud condensation nuclei in the remote marine boundary layer,
J. Geophys. Res., 100, 2893, 10.1029/94JD02832, 1995.Ramnarine, E., Kodros, J. K., Hodshire, A. L., Lonsdale, C. R., Alvarado, M. J., and Pierce, J. R.: Effects of near-source coagulation of biomass burning aerosols on global predictions of aerosol size distributions and implications for aerosol radiative effects, Atmos. Chem. Phys., 19, 6561–6577, 10.5194/acp-19-6561-2019, 2019.Raveh-Rubin, S.: Dry Intrusions: Lagrangian Climatology and Dynamical Impact
on the Planetary Boundary Layer, J. Climate, 30, 6661–6682,
10.1175/JCLI-D-16-0782.1, 2017.Raveh-Rubin, S. and Catto, J. L.: Climatology and dynamics of the link
between dry intrusions and cold fronts during winter, Part II: Front-centred
perspective, Clim. Dynam., 53, 1893–1909,
10.1007/s00382-019-04793-2, 2019.Rebotier, T. P. and Prather, K. A.: Aerosol time-of-flight mass spectrometry
data analysis: A benchmark of clustering algorithms, Anal. Chim. Acta, 585,
38–54, 10.1016/j.aca.2006.12.009, 2007.Reddington, C. L., Carslaw, K. S., Spracklen, D. V., Frontoso, M. G., Collins, L., Merikanto, J., Minikin, A., Hamburger, T., Coe, H., Kulmala, M., Aalto, P., Flentje, H., Plass-Dülmer, C., Birmili, W., Wiedensohler, A., Wehner, B., Tuch, T., Sonntag, A., O'Dowd, C. D., Jennings, S. G., Dupuy, R., Baltensperger, U., Weingartner, E., Hansson, H.-C., Tunved, P., Laj, P., Sellegri, K., Boulon, J., Putaud, J.-P., Gruening, C., Swietlicki, E., Roldin, P., Henzing, J. S., Moerman, M., Mihalopoulos, N., Kouvarakis, G., Ždímal, V., Zíková, N., Marinoni, A., Bonasoni, P., and Duchi, R.: Primary versus secondary contributions to particle number concentrations in the European boundary layer, Atmos. Chem. Phys., 11, 12007–12036, 10.5194/acp-11-12007-2011, 2011.Reff, A., Bhave, P. V., Simon, H., Pace, T. G., Pouliot, G. A., Mobley, J.
D., and Houyoux, M.: Emissions Inventory of PM 2.5 Trace Elements
across the United States, Environ. Sci. Technol., 43, 5790–5796,
10.1021/es802930x, 2009.Ren, J., Zhang, F., Wang, Y., Collins, D., Fan, X., Jin, X., Xu, W., Sun, Y., Cribb, M., and Li, Z.: Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing, Atmos. Chem. Phys., 18, 6907–6921, 10.5194/acp-18-6907-2018, 2018.Riemer, N., Ault, A. P., West, M., Craig, R. L., and Curtis, J. H.: Aerosol
Mixing State: Measurements, Modeling, and Impacts, Rev. Geophys., 57,
187–249, 10.1029/2018RG000615, 2019.Roberts, G. C., Day, D. A., Russell, L. M., Dunlea, E. J., Jimenez, J. L., Tomlinson, J. M., Collins, D. R., Shinozuka, Y., and Clarke, A. D.: Characterization of particle cloud droplet activity and composition in the free troposphere and the boundary layer during INTEX-B, Atmos. Chem. Phys., 10, 6627–6644, 10.5194/acp-10-6627-2010, 2010.Rolph, G., Stein, A., and Stunder, B.: Real-time Environmental Applications
and Display sYstem: READY, Environ. Model. Softw., 95, 210–228,
10.1016/j.envsoft.2017.06.025, 2017.Rosenfeld, D., Zhu, Y., Wang, M., Zheng, Y., Goren, T., and Yu, S.:
Aerosol-driven droplet concentrations dominate coverage and water of oceanic
low-level clouds, Science, 363, eaav0566,
10.1126/science.aav0566, 2019.Ruehl, C. R., Davies, J. F., and Wilson, K. R.: An interfacial mechanism for
cloud droplet formation on organic aerosols, Science, 351, 1447–1450,
10.1126/science.aad4889, 2016.Sanchez, K. J., Chen, C.-L., Russell, L. M., Betha, R., Liu, J., Price, D.
J., Massoli, P., Ziemba, L. D., Crosbie, E. C., Moore, R. H., Müller,
M., Schiller, S. A., Wisthaler, A., Lee, A. K. Y., Quinn, P. K., Bates, T.
S., Porter, J., Bell, T. G., Saltzman, E. S., Vaillancourt, R. D., and
Behrenfeld, M. J.: Substantial Seasonal Contribution of Observed Biogenic
Sulfate Particles to Cloud Condensation Nuclei, Sci. Rep., 8, 3235,
10.1038/s41598-018-21590-9, 2018.Schill, S. R., Collins, D. B., Lee, C., Morris, H. S., Novak, G. A.,
Prather, K. A., Quinn, P. K., Sultana, C. M., Tivanski, A. V., Zimmermann,
K., Cappa, C. D., and Bertram, T. H.: The Impact of Aerosol Particle Mixing
State on the Hygroscopicity of Sea Spray Aerosol, ACS Cent. Sci., 1,
132–141, 10.1021/acscentsci.5b00174, 2015.Schmale, J., Henning, S., Henzing, B., Keskinen, H., Sellegri, K.,
Ovadnevaite, J., Bougiatioti, A., Kalivitis, N., Stavroulas, I., Jefferson,
A., Park, M., Schlag, P., Kristensson, A., Iwamoto, Y., Pringle, K.,
Reddington, C., Aalto, P., Äijälä, M., Baltensperger, U.,
Bialek, J., Birmili, W., Bukowiecki, N., Ehn, M., Fjæraa, A. M., Fiebig,
M., Frank, G., Fröhlich, R., Frumau, A., Furuya, M., Hammer, E.,
Heikkinen, L., Herrmann, E., Holzinger, R., Hyono, H., Kanakidou, M.,
Kiendler-Scharr, A., Kinouchi, K., Kos, G., Kulmala, M., Mihalopoulos, N.,
Motos, G., Nenes, A., O'Dowd, C., Paramonov, M., Petäjä, T., Picard,
D., Poulain, L., Prévôt, A. S. H., Slowik, J., Sonntag, A.,
Swietlicki, E., Svenningsson, B., Tsurumaru, H., Wiedensohler, A., Wittbom,
C., Ogren, J. A., Matsuki, A., Yum, S. S., Myhre, C. L., Carslaw, K.,
Stratmann, F., and Gysel, M.: Collocated observations of cloud condensation
nuclei, particle size distributions, and chemical composition, Sci. Data, 4,
170003, 10.1038/sdata.2017.3, 2017.Schmale, J., Henning, S., Decesari, S., Henzing, B., Keskinen, H., Sellegri, K., Ovadnevaite, J., Pöhlker, M. L., Brito, J., Bougiatioti, A., Kristensson, A., Kalivitis, N., Stavroulas, I., Carbone, S., Jefferson, A., Park, M., Schlag, P., Iwamoto, Y., Aalto, P., Äijälä, M., Bukowiecki, N., Ehn, M., Frank, G., Fröhlich, R., Frumau, A., Herrmann, E., Herrmann, H., Holzinger, R., Kos, G., Kulmala, M., Mihalopoulos, N., Nenes, A., O'Dowd, C., Petäjä, T., Picard, D., Pöhlker, C., Pöschl, U., Poulain, L., Prévôt, A. S. H., Swietlicki, E., Andreae, M. O., Artaxo, P., Wiedensohler, A., Ogren, J., Matsuki, A., Yum, S. S., Stratmann, F., Baltensperger, U., and Gysel, M.: Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories, Atmos. Chem. Phys., 18, 2853–2881, 10.5194/acp-18-2853-2018, 2018.Souri, A. H., Choi, Y., Jeon, W., Kochanski, A. K., Diao, L., Mandel, J.,
Bhave, P. V., and Pan, S.: Quantifying the Impact of Biomass Burning
Emissions on Major Inorganic Aerosols and Their Precursors in the U.S.:
Burning Impact on Inorganic Aerosols, J. Geophys. Res.-Atmos., 122,
12020–12041, 10.1002/2017JD026788, 2017.Sprenger, M. and Wernli, H.: The LAGRANTO Lagrangian analysis tool – version 2.0, Geosci. Model Dev., 8, 2569–2586, 10.5194/gmd-8-2569-2015, 2015.Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D.,
and Ngan, F.: NOAA's HYSPLIT Atmospheric Transport and Dispersion Modeling
System, B. Am. Meteorol. Soc., 96, 2059–2077,
10.1175/BAMS-D-14-00110.1, 2015.Stull, R. B.: Mean Boundary Layer Characteristics, in: An Introduction to
Boundary Layer Meteorology, Springer Netherlands, Dordrecht, 1–27,
10.1007/978-94-009-3027-8_1, 1988.Tomlin, J. M., Jankowski, K. A., Rivera-Adorno, F. A., Fraund, M., China,
S., Stirm, B. H., Kaeser, R., Eakins, G. S., Moffet, R. C., Shepson, P. B.,
and Laskin, A.: Chemical Imaging of Fine Mode Atmospheric Particles
Collected from a Research Aircraft over Agricultural Fields, ACS Earth Space
Chem., 4, 2171–2184, 10.1021/acsearthspacechem.0c00172,
2020.Tomlin, J. M., Jankowski, K., Veghte, D., China, S., Wang, P., Fraund, M., Weis, J., Zheng, G., Wang, Y., Rivera-Adorno, F., Raveh-Rubin, S., Knopf, D., Wang, J., Gilles, M., Moffet, R., and Laskin, A.: Impact of dry intrusion events on the composition and mixing state of particles during the winter Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA), Purdue University Research Repository [data set], 10.4231/6CT5-3R55 2021.Toner, S. M., Sodeman, D. A., and Prather, K. A.: Single Particle
Characterization of Ultrafine and Accumulation Mode Particles from Heavy
Duty Diesel Vehicles Using Aerosol Time-of-Flight Mass Spectrometry,
Environ. Sci. Technol., 40, 3912–3921, 10.1021/es051455x,
2006.Vakkari, V., Beukes, J. P., Dal Maso, M., Aurela, M., Josipovic, M., and van
Zyl, P. G.: Major secondary aerosol formation in southern African open
biomass burning plumes, Nat. Geosci., 11, 580–583,
10.1038/s41561-018-0170-0, 2018.Val Martín, M., Honrath, R. E., Owen, R. C., Pfister, G., Fialho, P.,
and Barata, F.: Significant enhancements of nitrogen oxides, black carbon,
and ozone in the North Atlantic lower free troposphere resulting from North
American boreal wildfires, J. Geophys. Res.-Atmos., 111, D23S60,
10.1029/2006JD007530, 2006.VanReken, T. M.: Toward aerosol/cloud condensation nuclei (CCN) closure
during CRYSTAL-FACE, J. Geophys. Res., 108, 4633,
10.1029/2003JD003582, 2003.Wang, B., Lambe, A. T., Massoli, P., Onasch, T. B., Davidovits, P., Worsnop,
D. R., and Knopf, D. A.: The deposition ice nucleation and immersion
freezing potential of amorphous secondary organic aerosol: Pathways for ice
and mixed-phase cloud formation, J. Geophys. Res.-Atmos., 117, D16209,
10.1029/2012JD018063, 2012.Wang, B., Gilles, M. K., and Laskin, A.: Reactivity of Liquid and Semisolid
Secondary Organic Carbon with Chloride and Nitrate in Atmospheric Aerosols,
J. Phys. Chem. A, 119, 4498–4508, 10.1021/jp510336q,
2015.Wang, J., Lee, Y.-N., Daum, P. H., Jayne, J., and Alexander, M. L.: Effects of aerosol organics on cloud condensation nucleus (CCN) concentration and first indirect aerosol effect, Atmos. Chem. Phys., 8, 6325–6339, 10.5194/acp-8-6325-2008, 2008.Wang, J., Cubison, M. J., Aiken, A. C., Jimenez, J. L., and Collins, D. R.: The importance of aerosol mixing state and size-resolved composition on CCN concentration and the variation of the importance with atmospheric aging of aerosols, Atmos. Chem. Phys., 10, 7267–7283, 10.5194/acp-10-7267-2010, 2010.Wang, J., Wood, R., Jensen, M. P., Chiu, J. C., Liu, Y., Lamer, K., Desai, N., Giangrande, S. E., Knopf, D. A., Kollias, P., Laskin, A., Liu, X., Lu, C., Mechem, D., Mei, F., Starzec, M., Tomlinson, J., Wang, Y., Yum, S. S., Zheng, G., Aiken, A. C., Azevedo, E. B., Blanchard, Y., China, S., Dong, X., Gallo, F., Gao, S., Ghate, V. P., Glienke, S., Goldberger, L., Hardin, J. C., Kuang, C., Luke, E. P., Matthews, A. A., Miller, M. A., Moffet, R., Pekour, M., Schmid, B., Sedlacek, A. J., Shaw, R. A., Shilling, J. E., Sullivan, A., Suski, K., Veghte, D. P., Weber, R., Wyant, M., Yeom, J., Zawadowicz, M., and Zhang, Z.: Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA), B. Am. Meteorol. Soc., 1–51, 10.1175/BAMS-D-19-0220.1, in press, 2021.Wang, X., Sultana, C. M., Trueblood, J., Hill, T. C. J., Malfatti, F., Lee,
C., Laskina, O., Moore, K. A., Beall, C. M., McCluskey, C. S., Cornwell, G.
C., Zhou, Y., Cox, J. L., Pendergraft, M. A., Santander, M. V., Bertram, T.
H., Cappa, C. D., Azam, F., DeMott, P. J., Grassian, V. H., and Prather, K.
A.: Microbial Control of Sea Spray Aerosol Composition: A Tale of Two
Blooms, ACS Cent. Sci., 1, 124–131,
10.1021/acscentsci.5b00148, 2015.Wang, Y., Pinterich, T., and Wang, J.: Rapid measurement of sub-micrometer
aerosol size distribution using a fast integrated mobility spectrometer, J.
Aerosol Sci., 121, 12–20, 10.1016/j.jaerosci.2018.03.006,
2018.Wang, Y., Zheng, X., Dong, X., Xi, B., Wu, P., Logan, T., and Yung, Y. L.: Impacts of long-range transport of aerosols on marine-boundary-layer clouds in the eastern North Atlantic, Atmos. Chem. Phys., 20, 14741–14755, 10.5194/acp-20-14741-2020, 2020.Wang, Y., Zheng, G., Jensen, M. P., Knopf, D. A., Laskin, A., Matthews, A. A., Mechem, D., Mei, F., Moffet, R., Sedlacek, A. J., Shilling, J. E., Springston, S., Sullivan, A., Tomlinson, J., Veghte, D., Weber, R., Wood, R., Zawadowicz, M. A., and Wang, J.: Vertical profiles of trace gas and aerosol properties over the eastern North Atlantic: variations with season and synoptic condition, Atmos. Chem. Phys., 21, 11079–11098, 10.5194/acp-21-11079-2021, 2021.Wernli, H.: A Lagrangian-based analysis of extratropical cyclones. II: A
detailed case-study, Q. J. Roy. Meteor. Soc., 123, 1677–1706,
10.1002/qj.49712354211, 1997.Willis, M. D., Köllner, F., Burkart, J., Bozem, H., Thomas, J. L.,
Schneider, J., Aliabadi, A. A., Hoor, P. M., Schulz, H., Herber, A. B.,
Leaitch, W. R., and Abbatt, J. P. D.: Evidence for marine biogenic influence
on summertime Arctic aerosol, Geophys. Res. Lett., 44, 6460–6470,
10.1002/2017GL073359, 2017.Wood, R.: Stratocumulus Clouds, Mon. Weather Rev., 140, 2373–2423,
10.1175/MWR-D-11-00121.1, 2012.Wood, R., Wyant, M., Bretherton, C. S., Rémillard, J., Kollias, P.,
Fletcher, J., Stemmler, J., de Szoeke, S., Yuter, S., Miller, M., Mechem,
D., Tselioudis, G., Chiu, J. C., Mann, J. A. L., O'Connor, E. J., Hogan, R.
J., Dong, X., Miller, M., Ghate, V., Jefferson, A., Min, Q., Minnis, P.,
Palikonda, R., Albrecht, B., Luke, E., Hannay, C., and Lin, Y.: Clouds,
Aerosols, and Precipitation in the Marine Boundary Layer: An Arm Mobile
Facility Deployment, B. Am. Meteorol. Soc., 96, 419–440,
10.1175/BAMS-D-13-00180.1, 2015.Worsnop, D. R., Morris, J. W., Shi, Q., Davidovits, P., and Kolb, C. E.: A
chemical kinetic model for reactive transformations of aerosol particles:
Reactive transformation of aerosol particles, Geophys. Res. Lett., 29,
1996, 10.1029/2002GL015542, 2002.Yamasoe, M. A., Artaxo, P., Miguel, A. H., and Allen, A. G.: Chemical
composition of aerosol particles from direct emissions of vegetation fires
in the Amazon Basin: water-soluble species and trace elements, Atmos.
Environ., 34, 1641–1653, 10.1016/S1352-2310(99)00329-5,
2000.Yu, P., Toon, O. B., Bardeen, C. G., Zhu, Y., Rosenlof, K. H., Portmann, R.
W., Thornberry, T. D., Gao, R.-S., Davis, S. M., Wolf, E. T., de Gouw, J.,
Peterson, D. A., Fromm, M. D., and Robock, A.: Black carbon lofts wildfire
smoke high into the stratosphere to form a persistent plume, Science, 365,
587–590, 10.1126/science.aax1748, 2019.Zawadowicz, M. A., Suski, K., Liu, J., Pekour, M., Fast, J., Mei, F., Sedlacek, A. J., Springston, S., Wang, Y., Zaveri, R. A., Wood, R., Wang, J., and Shilling, J. E.: Aircraft measurements of aerosol and trace gas chemistry in the eastern North Atlantic, Atmos. Chem. Phys., 21, 7983–8002, 10.5194/acp-21-7983-2021, 2021.Zehr, J. P. and Ward, B. B.: Nitrogen Cycling in the Ocean: New Perspectives
on Processes and Paradigms, Appl. Environ. Microbiol., 68, 1015–1024,
10.1128/AEM.68.3.1015-1024.2002, 2002.Zheng, G., Wang, Y., Aiken, A. C., Gallo, F., Jensen, M. P., Kollias, P., Kuang, C., Luke, E., Springston, S., Uin, J., Wood, R., and Wang, J.: Marine boundary layer aerosol in the eastern North Atlantic: seasonal variations and key controlling processes, Atmos. Chem. Phys., 18, 17615–17635, 10.5194/acp-18-17615-2018, 2018.Zheng, G., Kuang, C., Uin, J., Watson, T., and Wang, J.: Large contribution of organics to condensational growth and formation of cloud condensation nuclei (CCN) in the remote marine boundary layer, Atmos. Chem. Phys., 20, 12515–12525, 10.5194/acp-20-12515-2020, 2020a.Zheng, G., Sedlacek, A. J., Aiken, A. C., Feng, Y., Watson, T. B.,
Raveh-Rubin, S., Uin, J., Lewis, E. R., and Wang, J.: Long-range transported
North American wildfire aerosols observed in marine boundary layer of
eastern North Atlantic, Environ. Int., 139, 105680,
10.1016/j.envint.2020.105680, 2020b.Zheng, G., Wang, Y., Wood, R., Jensen, M. P., Kuang, C., McCoy, I. L.,
Matthews, A., Mei, F., Tomlinson, J. M., Shilling, J. E., Zawadowicz, M. A.,
Crosbie, E., Moore, R., Ziemba, L., Andreae, M. O., and Wang, J.: New
particle formation in the remote marine boundary layer, Nat. Commun., 12,
527, 10.1038/s41467-020-20773-1, 2021.
Zhou, S., Collier, S., Jaffe, D. A., and Zhang, Q.: Free tropospheric aerosols at the Mt. Bachelor Observatory: more oxidized and higher sulfate content compared to boundary layer aerosols, Atmos. Chem. Phys., 19, 1571–1585, 10.5194/acp-19-1571-2019, 2019.Zhu, L., Val Martin, M., Gatti, L. V., Kahn, R., Hecobian, A., and Fischer, E. V.: Development and implementation of a new biomass burning emissions injection height scheme (BBEIH v1.0) for the GEOS-Chem model (v9-01-01), Geosci. Model Dev., 11, 4103–4116, 10.5194/gmd-11-4103-2018, 2018.Zieger, P., Väisänen, O., Corbin, J. C., Partridge, D. G.,
Bastelberger, S., Mousavi-Fard, M., Rosati, B., Gysel, M., Krieger, U. K.,
Leck, C., Nenes, A., Riipinen, I., Virtanen, A., and Salter, M. E.: Revising
the hygroscopicity of inorganic sea salt particles, Nat. Commun., 8, 15883,
10.1038/ncomms15883, 2017.