# Cloud condensation nuclei activity of internally mixed particle populations at a remote marine free troposphere site in the North Atlantic Ocean

Zezhen Cheng, Megan Morgenstern, Silvia Henning, Bo Zhang, Gregory Roberts, Matthew Fraund, Matthew Marcus, Nurun Nahar Lata, Paulo Fialho, Lynn Mazzoleni, et al.

► To cite this version:

Zezhen Cheng, Megan Morgenstern, Silvia Henning, Bo Zhang, Gregory Roberts, et al. Cloud con- densation nuclei activity of internally mixed particle populations at a remote marine free tropo- sphere site in the North Atlantic Ocean. Science of the Total Environment, 2023, 904, pp.166865. 10.1016/j.scitotenv.2023.166865 . hal-04950255

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Submitted on 16 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Cloud Condensation Nuclei Activity of Internally Mixed Particle Populations at a Remote Marine
Free Troposphere Site in the North Atlantic Ocean

Zezhen Cheng¹, Megan Morgenstern², Silvia Henning³, Bo Zhang⁴, Gregory C. Roberts⁵,⁶, Matthew Fraund⁷, Matthew A. Marcus⁸, Nurun Nahar Lata¹, Paulo Fialho⁹, Lynn Mazzoleni², Birgit Wehner³, Claudio Mazzoleni², Swarup China¹

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¹Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory (PNNL),
Richland, WA 99352, USA

²Atmospheric Sciences Program, Michigan Technological University, Houghton, Michigan, 49921, USA

³ Leibniz Institute for Tropospheric Research, Permoserstraße 15, 04318 Leipzig, Germany

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⁴National Institute of Aerospace, Hampton, VA 23666, USA

⁵Centre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS,
Toulouse, 31400, France

⁶Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92037, USA

⁷Sonoma Technology, Inc., Petaluma, CA 94954, USA

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⁸Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

⁹Institute of Volcanology and Risk Assessment – IVAR, Rua da Mãe de Deus, 9500-321 Ponta Delgada,
Portugal

Correspondence to: Swarup China (swarup.china@pnnl.gov)

Highlights
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* Individual particle composition provides a reliable estimation of the hygroscopicity of ambient particles.

* Particle populations consisting of smaller particles are more homogeneous and internally mixed due to higher degrees of atmospheric aging.

* Particles in air masses with high contribution from the marine boundary layer are more active cloud condensation nuclei.

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## Abstract.

This study reports results from research conducted at the Observatory of Mount Pico (OMP), 2225 m above mean sea level on Pico Island in the Azores archipelago in June and July 2017. We investigated the chemical composition, mixing state, and cloud condensation nuclei (CCN) activities of long-range transported free tropospheric (FT) particles. FLEXible PARTicle Lagrangian particle dispersion model (FLEXPART) simulations reveal that most air masses that arrived at the OMP during the sampling period originated in North America and were highly aged (average plume age >10 days). We probed size-resolved chemical composition, mixing state, and hygroscopicity parameter (κ) of individual particles using computer-controlled scanning electron microscopy with an energy-dispersive X-ray spectrometer (CCSEM-EDX). Based on the estimated individual particle mass from elemental composition, we calculated the mixing state index, χ. During our study, FT particle populations were internally mixed (χ of samples are between 53% and 87%), owing to the long atmospheric aging time. We used data from a miniature Cloud Condensation Nucleus Counter (miniCCNC) to derive the hygroscopicity parameter, κCCNC. Combining κCCNC and FLEXPART, we found that air masses recirculated above the North Atlantic Ocean with lower mean altitude had higher κCCNC due to the higher contribution of sea salt particles. We

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used CCSEM-EDX and phase state measurements to predict single-particle $\kappa$ ($\kappa_{CCSEM-EDX}$) values, which overlap with the lower range of $\kappa_{CCNC}$ measured below 0.15% SS. Therefore, CCSEM-EDX measurements can be useful in predicting the lower bound of $\kappa$, which can be used in climate models to predict CCN activities, especially in remote locations where online CCN measurements are unavailable.

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# 1. Introduction

Atmospheric aerosol particles can affect climate indirectly by serving as cloud condensation nuclei (CCN), altering cloud lifecycle and albedo (Baustian et al., 2012; Bellouin et al., 2020; Bhattu and Tripathi, 2015; Ervens et al., 2011; Fan et al., 2016; Knopf et al., 2018; Myhre et al., 2013; Seinfeld et al., 2016; Sun and Ariya, 2006). However, our knowledge of aerosol-cloud interactions is still limited, leading to significant uncertainties in predicting the Earth's energy balance (Forster et al., 2021). Part of these uncertainties might be due to the persistent gaps in the fundamental understanding of the ambient CCN number concentration ($N_{CCN}$), which depends on the particles' hygroscopicity. Petters and Kreidenweis, 2007 proposed a single parameter, the hygroscopicity parameter ($\kappa$), based on the $\kappa$-Köhler theory to describe particles' hygroscopicity. However, the global distribution of the $\kappa$ is highly variable due to the complex physicochemical and morphology properties of atmospheric particles (Pringle et al., 2010; Riemer et al., 2019), complicating the prediction of CCN activities in climate models. Typically, atmospheric particles contain both carbonaceous (e.g., black carbon and organic carbon) and inorganic (e.g., sulfate, nitrate, ammonium, sea salts, and dust) species depending on the source (Kolb and Worsnop, 2012; Philip et al., 2014; Pöschl, 2005). Particles also come in a wide range of sizes (from nanometer to micrometer scale) and $\kappa$ values (from 0 for hydrophobic species to >1 for hydrophilic species) (Ching et al., 2019; Kristensen et al., 2016; Petters and Kreidenweis, 2013, 2007; Pringle et al., 2010; Schulze et al., 2020). Moreover, these different particle species can be internally (distributed within the same particle) or externally mixed (existing as individual particles) in the particle population (China et al., 2015, 2013; Ching et al., 2019; Laskin et al., 2015; Pham et al., 2017; Riemer et al., 2019; Stevens and Dastoor, 2019). This complexity results in highly variable hygroscopicity values and the abilities of
the particles to act as CCN. For example, Li et al., 2021 show that the hygroscopicity of ammonium sulfate (hygroscopic) decreases with the increasing thickness of α-pinene secondary organic aerosol (SOA, less hygroscopic) coating. Moreover, most model simulations assume a homogenous mixing state in the aerosol particle population and use a single κ value, not capturing the variability in individual particles' κ values (Su et al., 2010). Furthermore, the physicochemical properties of particles are continuously modified by several processes during the atmospheric transportation processes (e.g., atmospheric aging, coagulation, condensation, and heterogeneous reactions), complicating our understanding of their roles as CCN (Bond et al., 2013; Laskin et al., 2015; Riemer et al., 2019; Stevens and Dastoor, 2019). Therefore, to reduce uncertainties when predicting atmospheric particles' CCN activities in climate models, it is essential to have a more comprehensive understanding of the influence of chemical composition and mixing state on the particles' κ values.

Typically, κ can be calculated based on in situ CCN counter measurement using the κ-Köhler theory (Petters and Kreidenweis, 2007), which has been widely used for ambient particles (Forestieri et al., 2018; Kawana et al., 2016; Kristensen et al., 2016; Pöhlker et al., 2016) and lab generated particles (Forestieri et al., 2018; Lambe et al., 2011; Petters et al., 2009). However, this method might suffer from limited numbers of supersaturation (SS) ratios and measurements below 0.2% SS due to water depletion and kinetic limitations (Lance et al., 2006; Lathem and Nenes, 2011; Tao et al., 2023; Wang et al., 2019). Another widely used method utilizes aerosol mass spectrometers (AMS) to derive the κ value of particles based on chemical composition (Bhattu et al., 2016; Chang et al., 2010; Fan et al., 2020; Ovadnevaite et al., 2017; Wu et al., 2016). For instance, Ovadnevaite et al., 2017 used AMS to study the influence of liquid-liquid phase separation on the particle's surface tension, which can affect their CCN activation.
Both methods provide an excellent opportunity to reduce uncertainties in CCN prediction related to highly variable κ values, and a combination of both methods can improve our knowledge of the correlation between chemical composition and κ values. Several studies have been conducted at the ground level (Jurányi et al., 2013; Lance et al., 2013; Leck and Svensson, 2015; Vestin et al., 2007; Wang et al., 2010), in the laboratory (Altaf et al., 2018; Fofie et al., 2018; Hatch et al., 2008; R. C. Sullivan et al., 2009), and CCN closure simulation (Bhattu and Tripathi, 2015; Ching et al., 2017; Ren et al., 2018). However, only a limited number of studies have been performed at remote free troposphere (FT) sites (e.g., Schulze et al., 2020) to investigate the relationship between the aerosol chemical composition and mixing state and their CCN roles.

Particles in the FT play an essential role as CCN (Kammermann et al., 2010; Rose et al., 2017; Schulze et al., 2020; Williamson et al., 2019) and can also be transported to the boundary layer via entrainment (De Wekker and Kossmann, 2015; Igel et al., 2017), dry and wet deposition (Igel et al., 2017; Zufall and Davidson, 1998), and dry intrusions (Ilotoviz et al., 2021; Raveh-Rubin, 2017; Raveh-Rubin and Catto, 2019; Tomlin et al., 2021), representing a significant source of CCN in regions with low local emissions such as in the equatorial pacific boundary layer and the arctic (Clarke et al., 2013; Igel et al., 2017). However, it is challenging to perform in-situ CCN measurements at remote FT sites since the deployment of instruments might be difficult and expensive, and the mass concentration of particles is often low (typically ranging from $10^2$ to $10^3$ particles cm$^{-3}$) (Allen et al., 2021; Igel et al., 2017; Rose et al., 2017; Schmeissner et al., 2011; Sun et al., 2021). In such cases, offline single-particle microscopy analysis on aerosol particles collected on substrates is a valuable option for low particle loadings (requires in the order of $10^3$ particles/substrate). One example of such analysis is using Electron Microscopy coupled
with an Energy Dispersive X-Ray Analysis (EDX) to determine the atomic percentage of elements in
individual particles and estimate the average $κ$ values (Ching et al., 2019, 2017). Another example is
using scanning transmission X-ray microscopy with near-edge X-ray absorption fine structure
spectroscopy (STXM- NEXAFS) to calculate $κ$ values of individual particles using their organic volume
fraction (OVF) (Tomlin et al., 2021).

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In this study, we present a single-particle analysis of FT particles collected at the Observatory of Mount
Pico (OMP) in June and July of 2017. Details for the chemical composition of collected samples, the
simulated transport trajectories calculated from the FLEXible PARTicle Lagrangian particle dispersion
model (FLEXPART), atmospheric aging time, and CO source of air mass were discussed in Cheng et al.,
2022. The present study focuses on the influence of chemical composition and mixing state on the $κ$
values for particles sampled from FT environments based on micro-spectroscopy techniques. We also
performed in situ CCN measurements to retrieve $κ$ values. Comparing $κ$ values from offline
measurements with those from in situ measurements is used to estimate the offset of offline $κ$ values,
improving future applications of this method in climate models.

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## 2. Materials and methods

### 2.1. Sampling site, online measurements, and sample collection for offline analysis

The site is located at 2225 m above sea level in the summit caldera of the Pico Volcano on Pico Island in
the Azores archipelago in the North Atlantic. OMP is an excellent location to investigate the long-range
transportation of FT particles above the North Atlantic Ocean during summer due to the negligible
influence of local emissions and the marine boundary layer (China et al., 2015; Dzepina et al., 2015;

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Honrath et al., 2004; Schum et al., 2018; Val Martin et al., 2008). Details of sampling times and conditions can be found in Cheng et al., 2022 and Table S2. Briefly, FT particle samples were collected during June and July 2017 at OMP using a four-stage cascade impactor (MPS-4G1) on TEM B-film grids (300 mesh, Ted Pella, Inc) with a flow rate of ~7 lpm for multi-modal micro-spectroscopy analysis. Particles collected on the third and fourth stages have 50% cutoff diameters of 150 and 50 nm, respectively. Due to the limited transportation events and difficulty of accessing the OMP, coordinating offline sample collection with pollution transportation linked to specific sources is challenging. Thus, in this study, we only collected four Stage 3 samples (S3-1 to S3-4) and four Stage 4 samples (S4-1 to S4-4). From July 5th to 22nd, at OMP, a Scanning Mobility Particle Sizer (SMPS, TROPOS home-built (Wiedensohler et al., 2012)) and a miniature Cloud Condensation Nucleus Counter (miniCCNC) (Roberts and Nenes, 2005; Wex et al., 2016) were also operated to monitor the particle size distribution, total particle concentration, and CCN concentration. The miniCCNC was operated in flow scanning mode, and CCN number concentrations were measured at 10 SS ratios (0.10%, 0.12%, 0.15%, 0.18%, 0.21%, 0.26%, 0.32%, 0.38%, 0.46%, and 0.55%) every 13 mins. The uncertainties in SS ratios is roughly 10% of SS ratios due to a combination of flow, pressure, and temperature control. The miniCCNC was calibrated at each SS ratio, following the procedure in Rose et al., 2008. The SS ratio was controlled by simultaneously changing the flow rate (from 0.09 to 0.22 LPM) through the instrument column and the temperature gradient, allowing a fast response. We also fixed the column's bottom temperature and minimized the temperature difference between that and the optical cavity to avoid evaporating the activated droplets, which caused the CCN instrument to undercount the CCN concentration. Due to the low particle concentration, we minimized the sheath-to-aerosol (SAR) ratio to maximize counting statistics (Lathem and Nenes, 2011). Moreover, since the CCN concentrations during this campaign are well below the
concentrations needed for kinetic limitations (Lathem and Nenes, 2011), particles should be able to activate at their critical supersaturation ratio. Previous studies have shown that the miniCCNC can provide valid data below 0.2% SS (Hammer et al., 2014; Saliba et al., 2021; Ryan C. Sullivan et al., 2009; Sullivan et al., 2010; Wex et al., 2016). Experimental details of the CCN and size distribution measurements were reported elsewhere (Siebert et al., 2021).

## 2.2. Backward trajectory analysis using FLEXPART simulation

The retroplume analysis using FLEXPART (Owen and Honrath, 2009; Seibert and Frank, 2004; Stohl et al., 2005) allow the establishment of the origin and transport trajectories for the air mass arriving at OMP during offline sample collection and CCN measurements. FLEXPART simulation details for the period under study can be found in Cheng et al., 2022. Briefly, the transport trajectories were obtained by integrating the FLEXPART simulated matrices of air mass residence time over time and altitudes, which revealed general transport pathways for analyzed particle samples. Since particles and many of their precursors are usually co-emitted with CO from anthropogenic and wildfire sources, the sources and ages of particle samples were also estimated based on the FLEXPART CO tracer calculation. We estimated the impact of such sources from different continents on our analyzed particle samples by coupling CO emission inventories with FLEXPART simulated residence time. Since aging is critical to particle properties, we also calculated the averaging FLEXPART CO ages (transport time from sources to OMP) for each particle sample approximately as a particle aging indicator (Zhang et al., 2017, 2014).

## 2.3. Microscopy and spectroscopy analysis of individual particles
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The samples collected at OMP were analyzed with computer-controlled scanning electron microscopy with an energy-dispersive X-ray spectrometer (CCSEM-EDX) to probe their physicochemical properties (Laskin et al., 2006, 2005). Details are provided by Cheng et al., 2022. Briefly, an environmental SEM (ESEM) equipped with an FEI Quanta digital field emission gun, operated at 20 kV, 480 pA current, was utilized to analyze samples at 293 K and under vacuum conditions (~2×10⁻⁶ Torr), causing the evaporation of volatile compounds. The individual particles' shape, morphology, and size (projected area equivalent diameter) were retrieved from the ESEM images. The CCSEM was equipped with an EDX spectrometer (EDAX, Inc.), and EDX spectra from individual particles were used to determine the relative atomic percentage of 15 elements (C, N, O, Na, Mg, Al, Si, P, S, Cl, K, Ca, Mn, Fe, and Zn). The relative atomic percentage of all elements was post-corrected as described by Cheng et al., 2022 since lighter elements C, N, and O are considered semi-quantitatively, and there might be considering contributions of C and O from the carbon B-film substrate. Variation of individual particles' aspect ratio (particle width to height ratio) is responsible for uncertainty in the post-correction method. These uncertainties are propagated in all relevant calculations in this study. The validation of the post-correction method is presented in Section S1 in the SI. All samples contain high C, N, and O atomic percentages and low soot fractions, suggesting organic-rich particles. Based on the atomic percentage, each particle was classified as organic (OC), carbonaceous with nitrogen (CNO), carbonaceous with sulfate (CNO with S), sea salt (Na-rich), sea salt with sulfate (Na-rich with S), dust (Al, Si, Ca, and Fe rich), dust with sulfate (Al, Si, Ca, and Fe rich with S), and others (Cheng et al., 2022).

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## 2.4. Mixing state parameters
The particle mixing state defines the different components distributed in the aerosol population (either externally mixed or internally mixed) (Riemer et al., 2019) and the aerosol optical properties (Jacobson, 2001; Lack and Cappa, 2010; Saleh et al., 2015), cloud condensation nuclei activities (Ching et al., 2017; Sánchez Gácita et al., 2017), and wet removal (Koch et al., 2009; Stier et al., 2006) all depend on the mixing state. The mixing state of particle population is calculated from the species mass fractions in a particle and the population (Riemer and West, 2013). The method used to parameterize the mixing state of aerosol samples has been described in detail by Riemer and West, 2013. Briefly, for a given species a ($a=1, 2, ..., A$, with A the total number of species) in the $i^{th}$ particle ($i=1, 2, ..., N$, with N the total number of particles analyzed), the mass of species a is denoted by $\mu_i^a$. From this basic description of the particles' composition, the total mass of each particle ($\mu_i$), the total mass of element a in the sample ($\mu^a$), and the total mass of the sample ($\mu$) are determined:

$$ \sum_{a=1}^{A} \mu_{i}^{a} = \mu_{i}, \sum_{i=1}^{N} \mu_{i}^{a} = \mu^{a}, \sum_{a=1}^{A} \sum_{i=1}^{N} \mu_{i}^{a} = \mu \quad (1) $$

Hence, the mass fraction of species a in particle i ($p_i^a$), the mass fraction of particle i in the sample ($p_i$), and the mass fraction of species a in the sample are defined as:

$$ p_i^a = \frac{\mu_i^a}{\mu_i}, p_i = \frac{\mu_i}{\mu}, p^a = \frac{\mu^a}{\mu} \quad (2) $$

Therefore, the Shannon entropy for the species distribution within the particle i ($H_i$), the average per particle ($H_\alpha$), and the species distribution within a sample ($H_\gamma$) are calculated from the mass fractions (equation (2)):

$$ H_i = \sum_{a=1}^{A} -p_i^a \ln p_i^a, \quad H_\alpha = \sum_{i=1}^{N} p_i H_i, \quad H_\gamma = \sum_{a=1}^{A} -p^a \ln p^a \quad (3) $$
The Shannon entropy quantifies the uniformity of the mass fractions in a given population, which increases when the mass fraction is more uniform across the population. $H_i$, $H_\alpha$ and $H_\gamma$ are converted to the particle diversity of particle $i$ ($D_i$), average particle species diversity ($D_\alpha$), and the bulk population diversity:

$$D_i = e^{H_i}, D_\alpha = e^{H_\alpha}, D_\gamma = e^{H_\gamma} \qquad (4)$$

These diversity values represent the effective number of species within a given population.

Finally, we calculated the mixing state index ($\chi$) from the average particle species diversity ($D_\alpha$) and the bulk population diversity ($D_\gamma$):

$$\chi = \frac{D_\alpha - 1}{D_\gamma - 1} \qquad (5)$$

The $\chi$ value indicates a fully externally mixed population ($\chi = 0\%$ and $D_\alpha=1$) to a fully internally mixed population ($\chi = 100\%$ and $D_\alpha=D_\gamma$) (Riemer and West, 2013).

In this study, we retrieved $\chi$ from post-corrected CCSEM-EDX relative atomic percentage data. Therefore, to calculate the $\mu_i$, we retrieved the absolute mass of individual particles. To achieve this goal, we applied a similar method introduced by O'Brien et al., 2015 and Bondy et al., 2018 but with some modifications since these studies did not include C, N, and O from CCSEM-EDX data in the mixing state calculation. Briefly, we assume that each particle is a mixture of the five species commonly observed in the FT particles at the OMP site: carbonaceous, sulfate, sea salt, dust, and anything else (others) (Cheng et al., 2022; China et al., 2017; Dzepina et al., 2015; Lata et al., 2021; Schum et al., 2018). It should be
noticed that this assumption differs from the particle classification introduced in Section 2.3, and assuming only five species in each particle might underestimate the individual particle diversities. Thus, adding more species and precisely assigning elements in each species could improve the accuracy of this method, which might be a task for future studies.

The density and the assumed molecular formula of each species are listed in Table S1. The density of each particle i ($\rho_i$) is calculated as:

$$ \rho_i = \sum_{a=1}^{5} \frac{p_i^a}{\sum_{a=1}^{5} \frac{p_i^a}{\rho_a}} \qquad (6) $$

Where $p_i^a$ is the mass fraction of a$^th$ specie in particle i (total 5 species) retrieved from CCSEM-EDX data, equal to the sum of each element's mass fraction, $\rho_a$ is the density of that species (see Table S1). Since Cheng et al., 2022 have shown that the majority of our particles are in a liquid state with the shape of flat spherical caps upon impaction on the substrate, the volume of each particle ($V_i$) can be calculated as:

$$ V_i = \frac{1}{6} \pi \left( \frac{L_{\text{proj}}}{\overline{\text{AR}}} \right) \left[ \frac{3}{4} L_{\text{proj}}^2 + \left( \frac{L_{\text{proj}}}{\overline{\text{AR}}} \right)^2 \right] \qquad (7) $$

Where $L_{\text{proj}}$ is the longest projected length of each particle retrieved from SEM images and $\overline{\text{AR}}$ is the average aspect ratio (particle width to height ratio) of the sample retrieved from ESEM tilted images (Cheng et al., 2022, 2021). We note that the aspect ratio of individual particles might deviate from the $\overline{\text{AR}}$, leading to either an overestimate or an underestimate of the volume of individual particles. These potential biases have been included while estimating the relevant uncertainty calculation. Moreover, equation 1 needs to be modified to represent different particle shapes. Therefore, the $\mu_i$ is calculated as:
$$245 \quad \mu_i = \rho_i \times V_i \quad (8)$$

## 2.5. $\kappa$ value from miniCCNC measurements

We derived the hygroscopicity parameter ($\kappa$) from the equilibrium saturation ratio of water vapor and particle diameter based on $\kappa$-Köhler theory (Petters and Kreidenweis, 2007), assuming the particles population are homogeneously internally mixed. For the $\kappa$-Köhler theory, the Raoult term in the Köhler equation is parameterized by a single parameter, the hygroscopicity parameter $\kappa$, defined as:

$$\kappa = \frac{4 \left( \frac{4 \sigma M_w}{RT \rho_w} \right)^3}{27 D_{act,d}^3 * \ln^2 SS} \quad (9)$$

where SS is the SS ratio (%), $M_w$ is the molar mass of water (18 g/mol), $\rho_w$ is the density of water (1 g/mL), $\sigma$ is the surface tension of the water/air interface (0.072 J m⁻²), $R$ is the universal gas constant (8.314 J (K mol)⁻¹), and $T$ is the absolute temperature (K).

The equation includes the diameter where a particle is barely large enough to be activated at a given SS and the activation dry diameter ($D_{act,d}$). In general, $\kappa$ quantifies the affinity of a material for water. For atmospheric particles, the range of $\kappa$ values spans roughly from 0 (insoluble particles, e.g., soot) to 1.4 (very hygroscopic, e.g., sodium chloride), as shown in Petters and Kreidenweis (2007). Under the assumption of an internally mixed particle population, $\kappa$ can be calculated by deriving $D_{act,d}$. For that, the measured CCN number concentrations ($N_{CCN}$) and particle number size distribution (PNSD) are used so that the PNSD is integrated until the resulting particle number concentration, $N$, is equal to $N_{CCN}$. The particle diameter at this point is $D_{act,d}$.
## 2.6. Estimation of κ value from CCSEM-EDX measurements

Based on the Zdanovskii-Stokes-Robinson assumption (Petters and Kreidenweis, 2007), the κ value of internally mixed particles (κᵢ) can be estimated by a volume-weighted addition of the κ values of the pure specie:

$$ \kappa_i = \sum_{a=1}^{5} \varepsilon_{i,a} \kappa_{i,a} \qquad (10) $$

Where εₐ and κₐ are the aᵗʰ species' volume fraction and hygroscopicity parameters, respectively. To estimate the κ values for individual particles, we assume each particle is a homogeneous mixture of the five species discussed in Section 2.4, and their κ values are listed in Table S1. This assumption is supported by SEM and TEM images of particles discussed in Cheng et al., 2022, and the mixing state index of each sample (see Section 3.3). εₐ is calculated from the parameters used in the mixing state index calculation in eq. 1, 2, and 6 to 8:

$$ \varepsilon_a = \frac{v_a}{v_i} = \frac{\mu_i^a \rho_i}{\rho_a \mu_i} = p_i^a \frac{\rho_i}{\rho_a} \qquad (11) $$

Then the κ value for the sample is calculated as the mass-weight average of κ values of individual particles in that sample:

$$ \bar{\kappa} = \sum_{i=1}^{i} p_i \times \kappa_i \qquad (12) $$

This method estimates the SS ratio independent κ value based on the aerosol composition, which can be used directly in the climate models.

# 3. Results and Discussions

## 3.1. Airmass transport patterns based on FLEXPART simulation
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In an earlier study (Cheng et al., 2022), we discussed in detail the 20 days back trajectory FLEXPART simulation results, including air masses transport and vertical distribution, CO source contribution, and average atmospheric aging time, for the same particle samples analyzed in this study. The same information for air masses that arrived during online miniCCNC measurements (July 5th to July 22nd, 2017) is shown in Figures S1-S3. Air masses were classified into two cases based on their transport pattern, and representative FLEXPART simulations are shown in Figure 1. Case 1 represents air masses that was transported across North America with a relatively higher average height (Figure 1(a)), and Case 2 represents air masses that recirculated over the North Atlantic Ocean for more than ~10 days with a relatively lower mean height (Figure 1(b)). Case 1 represents July 5th to July 10th and July 19th to July 22nd since air masses were mainly transported across North America (up to ~5 days before reaching OMP) (Figures S1 (a-f and o-r)). The air masses for July 5th to 10th and July 19th to July 20th were transported relatively higher in the FT (average height was ~ 8 km) across North America and stayed at approximately the same altitude until they reached OMP ~5-10 days later (Figure S2 (a-f and o-p)). The average height of air masses for July 20th and July 21st was lower (~5-6 km) when transported over North America (Figure S2 (q and r)). Based on the FLEXPART CO tracer analysis, from July 5th to July 7th and July 19th, air masses were mainly influenced by anthropogenic sources in North America (~47-80% CO contribution). From July 8th to July 10th and July 20th to July 22nd, North America's major CO contribution sources were wildfires (~26-90%) (Figure S3). Case 2 includes July 11th to July 18th, where air masses recirculated over the North Atlantic Ocean for more than ~10 days and received air masses from North America and Africa (Figure S1 (g-n)). These air masses traveled relatively higher in the FT (average height was ~8 km) at ~20 days upwind and then continuously decreased in average height to the lower free troposphere across the Northern Atlantic Ocean until reaching OMP (Figure S2 (g-n)). This path
allowed significant contributions from sea sprays through convection and frontal uplift (North et al., 2014).

Besides the contribution from sea spray, CO tracer analysis shows that CO contributions for Case 2 air masses were also influenced by North America anthropogenic emissions (~13.1-85.4 %) and Africa (~0.0-78.9 %). CO tracer analysis also shows that the average CO aging time of all air masses that arrived at OMP during CCN measurements was 14.2±2.3 days (ranging from 9.5 to 18.0 days), indicating they are highly aged.

<figure><img src="image_2.png" /><figcaption>(b) Case 2: Air masses did recirculate above the North Atlantic Ocean with a lower mean altitude</figcaption></figure>

<figure><img src="image_3.png" /></figure>

**Figure 1.** Representative FLEXPART back trajectories of air masses (top panel) and daily vertical distribution of retroplumes (lower panel) for two cases of transport patterns: (a) Case 1, where air masses did not recirculate above the North Atlantic Ocean and had a higher average height (07/07/2017); (b) Case 2, where air masses did recirculate above the North Atlantic Ocean with a lower mean height

(07/11/2017).
The black labels with white numbers on the map views indicate the approximate locations of the center of the plume and the corresponding upwind days. Residence time is color-coded by logarithmic and linear grades in the map and vertical views, respectively, representing the ratios to each view's maximal integrated residence times. The solid black lines in the vertical views show the average height of retroplume air masses during the transport period.

## 3.2. Chemical composition of individual particle

Details of the samples' chemically resolved size distribution have been discussed by Cheng et al., 2022. Briefly, carbonaceous (OC+CNO) (~30-69% by number fraction), Na-rich (~8.8 to 31.6 % by number fraction), and Na-rich with S (~5.2 to 31.5 %) particles are three major particle types in our samples. The source of carbonaceous particles is long-range transported anthropogenic and wildfire emissions and new particle formations in the FT (Bianchi et al., 2016; Cheng et al., 2022). Na-rich with S particles might be products of aqueous-phase processing (i.e., fog and cloud processing). Relatively small Na-rich particles (<0.6 µm) might serve as sulfate nuclei, and large Na-rich particles (>0.6 µm) might serve as condensation nuclei of gaseous hydrogen sulfate (Ervens et al., 2011; Kim et al., 2019; Lee et al., 2012, 2011; Levin and Cotton, 2009; Sorooshian et al., 2007; Yu et al., 2005; Zhou et al., 2019). Moreover, our elemental composition retrieved from CCSEM-EDX analysis shows that sea salt and sea salt with sulfate particles are highly aged due to long atmospheric aging time during long-range transport (Section S2 in SI), suggesting these particles might be internally mixed (Laskin et al., 2012).

## 3.3. Mixing state of particles
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Figure 2 shows the mixing state diagram for all samples (a), histograms of $D_i$, and area equivalent diameters for particles collected on stage 3 (S3-1 to S3-4) (b) and stage 4 (S4-1 to S4-4) (c). Histograms of $D_i$ values for each sample are reported in Figure S5. Figure 2(a) illustrates the relationship between $D_α$, $D_γ$, and $χ$, and their values are listed in Table S2. $D_α$ is smaller than $D_γ$ for all samples, suggesting less inter-particle diversity than diversity between samples, and all samples do not have identical mass fractions (Riemer and West, 2013). The $D_α$ and $D_γ$ range from 1.4-2.8 and 1.5-3.8 (see Table S2), suggesting that $χ$ is almost entirely determined by C, N, and O since these three elements dominate the mass of the individual particles and the bulk samples (Fraund et al., 2017). All samples have values of $χ$ between 53% and 87%, indicating that the particle population were considerably internally mixed as a result of the atmospheric aging process (Riemer and West, 2013). This finding is consistent with direct observations of ESEM images and TEM images (Cheng et al., 2022) and scanning transmission X-ray microscopy (STXM) carbon speciation maps (Figure S6). These results also support our assumption that particles are internally and homogeneously mixed in calculating $κ_{CCSEM-EDX}$. Moreover, Ching et al., 2017 show that the mixing state is not critical for CCN prediction when $χ > 0.75$. Thus, offline CCN prediction in this study might not be impacted by the mixing state. The variations in $χ$ between the samples collected on stages 3 and 4 are small due to low variability in $D_α$ and $D_γ$. Moreover, $χ$ values for samples from stage 4 are higher than those from stage 3 due to a narrow spread in $D_α$ and $D_γ$, suggesting larger particles were fresher. These relatively fresher particles might come from the boundary layer (Knopf et al., 2022), and smaller particles might be more aged due to longer lifetime and/or new particle formation in the FT (Bianchi et al., 2016; Rose et al., 2017). The discrepancy in $χ$ of stage 3 and stage 4 particles suggests that $χ$ might be size-dependent and larger particles have a larger variation in $χ$. This could also be attributed to the higher diversity in the sources during June 2017 (Cheng et al., 2022). Moreover, based
on the FLEXPART CO aging simulations, stage 4 samples have longer atmospheric aging time, suggesting that longer atmospheric aging time might make individual aerosols more homogeneous. This also explains the relatively low χ value of S4-4 than that of other samples from stage 4 since S4-4 has CO aging time of ~11.3 days, lower than others. Furthermore, Cheng et al., 2022 have shown that stage 4 samples have a high abundance of liquid-like particles, which promote faster diffusion and mixing of species in the aerosol, making aerosol population more internally mixed (Riemer et al., 2019; Sharma et al., 2018). A similar observation was reported by (Fraund et al., 2017) on samples collected during the Green Ocean Amazon (GoAmazon2014/5) field campaign conducted in the dry season of 2014 and by O'Brien et al., 2015 based on a field study near the Sierra Nevada Mountains during June 27 and 28, 2010, where more aged particles had smaller variation and larger value of χ.

Figures 2(b) and (c) show an overall trend of increasing average $D_i$ with increasing area equivalent diameter, which has also been reported by (Fraund et al., 2017). O'Brien et al., 2015 also reported a similar trend when using STXM-NEXAFS to probe the mixing state of organic and inorganic. Moreover, particles collected on stage 3 show greater $D_i$ due to a higher fraction of inorganic species and possibly lower degree of atmospheric aging. A higher fraction of inorganics increases individual particle and particle population elemental diversity, while aging makes particles more homogeneous. These two conclusions were also consistent in each sample, but the spread of $D_i$ varied with each sample. As shown in Figure S5, the spread of $D_i$ for S3-2 is wide, while the spread of S4-1 and S4-2 is much narrower. This is also observed in the 2-D histogram plotted for each data set.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (a) Mixing state diagram showing the average particle diversity ($D_α$) and bulk diversity ($D_γ$). Each symbol is colored by its mixing state index ($χ$). The error bar represents the range of uncertainties stemming from particle aspect ratio distribution. The positive and negative error bars of $D_α$ and $D_γ$ are derived from the upper and lower limit of the aspect ratio, respectively. The red dash-dotted line is the linear fitting for stage 3 samples ($D_γ$ = 1.238 $D_α$ - 0.138, $R^2$ = 0.8967), and the brown dash-dotted line is the linear fitting for stage 4 samples ($D_γ$ = 1.082 $D_α$ - 0.059, $R^2$ = 0.9393). (b) and (c) Histograms of</figcaption></figure>
individual particle diversity ($D_i$) and area equivalent diameter values for Stage 3 samples (S3-1 to S3-4, 50% cutoff diameter is 150 nm) and for Stage 4 samples (S4-1 to S4-4, 50% cutoff diameter is 50 nm).

Colorbars in Figures 2 (b) and (c) represent the number of bins.

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## 3.4. Evaluating CCN activity of mixed organic-inorganic particles

### 3.4.1. miniCCNC-derived hygroscopicity parameter ($\kappa_{CCNC}$)

Figure 3 shows temporal CCN concentration (a), CCN fraction (CCN concentration/total particle concentration) (b), activation dry diameter ($D_{act,d}$, above which all of the particles activate into CCN under the assumption of an internally mixed particle population) (c), miniCCNC measurements derived $\kappa_{CCNC}$ values (d), and violin plot of the $\kappa_{CCNC}$ values at 10 different SS ratios (e) from July 5th, 2017 to July 21st, 2017. The solid black lines in Figure 3 (a-d) represent averages across the different SS ratios. Only two samples for offline analysis (narrow grey-shaded areas) (S4-1 and S4-2) overlapped with the CCN measurements. As shown in Figure 3 (a-c), at the same time, a higher SS led to higher CCN and CCN fraction and a lower $D_{act,d}$, which is expected since, at higher SS, small particles can also activate (Petters and Kreidenweis, 2007). The CCN, CCN fraction, and $D_{act,d}$ changes for increasing SS are larger when SS increases from 0.1% to 0.32% than when SS is above 0.32%. This is due to smaller and less hygroscopic particles starting to activate at higher SS, reducing the average $\kappa$ value based on the CCN activation theory. For example, sample S4-2 has a higher fraction of hydrophilic species (sea salt and sea salt with sulfate) and a lower fraction of hydrophobic species (e.g., OC) than S4-1, resulting in S4-2 having higher $\kappa_{CCNC}$ values when SS is less than 0.32% and lower $\kappa_{CCNC}$ values when SS is higher than 0.32%. This finding is further confirmed by the $\kappa_{CCNC}$ trend shown in Figures 3 (d) and (e), which show

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the mean $\kappa_{CCNC}$ value first increases from 0.17 to 0.45 when SS increases from 0.1% to 0.32%, then starts to decrease slightly to 0.39 for SS increase to 0.56%. This phenomenon is due to the fact that ambient particles are a complex mixture of species with different $\kappa$ values which have different critical SS ratios, and their chemical composition is size-dependent (Liu et al., 2018; Petters and Kreidenweis, 2007; Zhao et al., 2015). The dependence of $\kappa_{CCNC}$ with SS that we measured is consistent with our mixing state index ($\chi$) value (~53-87%), indicating a non-homogeneous aerosol population. Therefore, we acknowledge that the homogeneously internal mixing assumption is required for the application of $\kappa$-Köhler theory and its application to calculate $\kappa$ for both homogeneously mixed and not homogeneously mixed particles might introduce bias in the results. A similar trend in $\kappa_{CCNC}$ with increasing SS has been reported by Gaston et al., 2018 and Royer et al., 2023. The $\kappa_{CCNC}$ values at each SS ratio are highly variable due to the low particle concentrations in the free troposphere and infrequent high CCN concentration plume and plume from different sources that arrived at the site during the sampling period, resulting in aerosols with variable properties. Moreover, the site also infrequently receives highly hygroscopic particles (e.g., sea salt) plumes, leading to extreme values of $\kappa_{CCNC}$.

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The range of $\kappa_{CCNC}$ values at each SS (0.17±0.09 – 0.44±0.16, see Table S3) is in good agreement with literature reported $\kappa$ values of particles collected over the North Atlantic Ocean and overall in the free troposphere (0.05-0.88) (Kammermann et al., 2010; Pringle et al., 2010; Roberts et al., 2010; Schulze et al., 2020; Zheng et al., 2020). July 11th to 18th have lower average $D_{act,d}$ and higher $\kappa_{CCNC}$ overall SS ratios (~90.5±46.1 and ~0.45±0.16, respectively, where the value after the ± represents one standard deviation) than other days (~105.1±53.8 and ~0.32±0.12, respectively). Based on the FLEXPART simulations (Figure S1 and S2), air masses that arrived at OMP these days likely had higher contributions from sea salt particles, which are very hygroscopic. Moreover, from July 20th to July 22nd, $D_{act,d}$ and $\kappa_{CCNC}$ values
(~111.5±60.0 nm and ~0.29±0.34, respectively) are more variable (higher standard deviation) than those for other days across all SS ratios (~98.0±47.7 nm and ~0.35±0.38, respectively). It has been shown in Section 3.1 that these days had a higher CO contribution from wildfires. Thus, we suggest that wildfires might lead to particles with more complex hygroscopicity, which is worth further study. This complex hygroscopicity might be partly due to the diversity of particle types generated during the wildfire, mostly carbonaceous with a wide variety of chemical compositions, leading to variable hygroscopicities.

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<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>
**Figure 3.** Time series of (a) CCN concentration, (b) CCN fraction (CCN concentration/total particle concentration), (c) activation dry diameter ($D_{act,d}$, above which all of the particles activate), and (d) hygroscopicity parameter ($\kappa_{CCNC}$), from July 5th to July 21st, 2017. The solid black lines in (a-d) are the averages across all SS (SS) ratios, and the narrow gray shaded areas represent the sample collection periods for S4-1 (40 minutes) and S4-2 (70 minutes) (50% cutoff diameter is 150 nm). (e) Violin plots of $\kappa_{CCNC}$ values in ten SS bins. The red lines indicate the means, the black dots are the medians, the gray rectangles are the interquartile ranges, the gray verticle lines are the 95% confidence intervals, and the violin-shaded areas show the data distribution.

### 3.4.2. Individual particle chemical composition derived hygroscopicity parameter ($\kappa_{CCSEM-EDX}$)

<figure><img src="image_7.png" /></figure>

**Figure 4.** Violin plot of $\kappa$ values derived from the CCSEM-EDX chemical composition analysis. The red lines indicate the means, the black dots are the medians, the gray rectangles are the interquartile ranges,
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the gray verticle lines are the 95% confidence intervals, and the violin-shaded areas show the data
distribution. The large shaded areas represent the range of κ values derived from CCN measurements.
S3-1 to S3-4 were collected on stage 3 (50% cutoff diameter: 150 nm), and S4-1 to S4-4 were collected
on stage 4 (50% cutoff diameter: 50 nm).

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The mass-weighted average κ values (κ̄<sub>CCSEM-EDX</sub>) and their uncertainties range derived from CCSEM-EDX
chemical composition are listed in Table S2, and their distributions are shown in Figure 4 in the form of
violin plots. The predicted κ<sub>CCSEM-EDX</sub> values ranged from 0.10-0.98, in good agreement with literature
reported κ values for FT particles and particles collected over the North Atlantic Ocean (0.05-0.88)
(Kammermann et al., 2010; Pringle et al., 2010; Roberts et al., 2010; Schulze et al., 2020; Zheng et al.,
2020). Predicted κ<sub>CCSEM-EDX</sub> values have a wide range due to variations in individual particles' elemental
composition and morphology. Stage 3 samples (S3-1 to S3-4) have slightly higher average κ<sub>CCSEM-EDX</sub>
values than stage 4 samples (S4-1 to S4-4) due to the larger 50% cutoff diameter of stage 3 than stage
4 (150 vs. 50 nm, respectively). These larger particles contain a higher volume fraction of sea salts and
sulfates (see Figure S7), which are hydrophilic and have larger sizes to be better activated as CCN. The
shaded large rectangle areas in Figure 4 represent the range of κ<sub>CCNC</sub> values during the sampling periods
of S4-1 and S4-2 (κ<sub>CCNC</sub> ranges from 0.08 to 0.54 and 0.15 to 0.48, respectively). The κ̄<sub>CCSEM-EDX</sub> values for
S4-1 and S4-2 are 0.14±0.03 and 0.15±0.04, respectively, within the lower range of κ<sub>CCNC</sub> (SS < 0.15%).
The primary reason is that S4-1 and S4-2 were collected on stage 4, whose cutoff diameter is 50 nm,
smaller than the *D*<sub>act,d</sub> from the miniCCNC measurements until SS reached ~0.46%. Moreover, particle
volume derived from CCSEM-EDX measurements might be lower than the actual particles in the
atmosphere due to liquid water and volatile species losses inside the SEM chamber. Therefore, particles

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in S4-1 and S4-2 might not activate as CCN efficiently until the SS is larger than 0.46%. On the other hand, the $κ_{CCSEM-EDX}$ value of more than 99% of particles in S4-1 and S4-2 were within the range of $κ_{CCNC}$ values during the sampling periods, suggesting offline CCSEM-EDX method could be a potentially valuable tool to predict individual particle's $κ$ values.

Besides, discrepancies between $κ_{CCNC}$ and $κ_{CCSEM-EDX}$ values might partially arise from limitations in measurement techniques and the associated biases and uncertainties. In this study, we could underestimate the $κ$ value of carbonaceous species, which can range over an order of magnitude due to variable carbon chain length and functional groups (Petters et al., 2017) and oxidation levels (Chang et al., 2010; Lambe et al., 2011). We have shown in Cheng et al., 2022 that our samples are highly aged and potentially oxidized during long-range transport. An improved estimate of the $κ$ values for carbonaceous species could be obtained from mass spectrometry since the $κ$ value of carbonaceous species is positively correlated with the O/C ratio (Lambe et al., 2011). Moreover, we might overestimate the carbonaceous weight fraction since we assume all C belongs to organics, neglecting C, N, and O inorganic species (e.g., $CO_3^{2-}$). Furthermore, we might underestimate the weight fraction of sea salt species by not including some elemental fractions (e.g., metals). The interactions between species can alter the $κ$ values. For instance, the CCN activation of sea salt particles can be enhanced by surfactant coatings (e.g., oleic acid) due to the reduction of the surface tension (Bramblett and Frossard, 2022; Forestieri et al., 2018; Nguyen et al., 2017). However, we cannot estimate these interactions due to a lack of molecular data. Last, due to the limited instrument available time and limitation and challenges of access to the OMP, we have only two samples during the CCN measurements, making the comparison less statistically significant. Therefore, to better understand the effects of chemical composition and
mixing state on aerosol CCN activities, it is critical to collect more data sets and samples at remote sites
and to apply mass spectrometry analyses to constrain the organic composition of particles better.

4. Conclusions.

This study investigated mixing state and CCN activities of free tropospheric particles. Multi-modal micro-
spectroscopy techniques (CCSEM-EDX and STXM-NEXAFS) were used to probe the chemical
composition, mixing state, and hygroscopicity parameters (κ) of these free-tropospheric, long-range
transported particles. κ derived from miniCCNC measurements (κCCNC), ranging from 0.17±0.09 to
0.44±0.16, is within the range reported in the literature for particles collected over the North Atlantic
Ocean. Combining miniCCNC measurements with FLEXPART simulation, we found air masses with lower
mean height and recirculating over the North Atlantic Ocean for a longer period present higher mean
κCCNC values due to the contribution of sea spray aerosols (e.g., sea salt). CCSEM-EDX analysis indicated
that all samples were dominated by carbonaceous aerosols (~30-69% by number) but still contained a
substantial fraction of sea salt particles (~8.8 to 31.6% by number) and sea salt with sulfate (~5.2 to 31.5
%) particles since the air masses were transported over the North Atlantic Ocean. The mixing state index
(χ) analysis revealed that all samples were internally mixed particle population (0.53-0.87), but larger
particles (stage 3 samples) have lower values (0.53-0.69) in the mixing stage index than smaller particles
.stage 4 samples) (0.78-0.87), suggesting that the mixing state depends on size with large particles being
fresher. Our estimated κCCSEM-EDX values are within the lower range of the measured κCCNC values,
suggesting that CCSEM-EDX combined with phase state measurements can be used to predict κ values
of ambient particles. However, we still found a discrepancy between κCCSEM-EDX and κCCNC, which might be
due to 1) differences in particle size ranges between the stages of the impactor and the CCN counter; 2)
lack of detailed knowledge on the molecular composition of organics; 3) lack of detailed knowledge on the mass fraction and spatial distribution (single particle mixing state) of different species within each particle which hampers our ability to account for interactions among different species; and 4) limited number of samples. The first aspect can be resolved by collecting a wider size range of particles in the future. The second can be improved by applying molecular analysis using mass spectrometry. The third can be addressed by combining higher spatial resolution CCSEM-EDX with STXM-NEXAFS measurements on the same particles to obtain single-particle molecular information. The last limitation could be improved by collecting and analyzing more samples; however, this limitation should also be put in the context of the remoteness and difficult access to OMP, which makes a collection of samples more challenging than at most other atmospheric monitoring sites but concurrently provides opportunities for more unique sampling.

Our results are critical for understanding the CCN properties when deployment of online CCN and chemical composition measurement instruments (e.g., CCNC and AMS) is not feasible (e.g., onboard tethered balloon system) to study aerosol vertical distribution or at remote sites that do not have CCN data but have samples for single particle or off-line chemical analysis. Moreover, our study provides opportunities for probing ambient particles' CCN activities using chemical composition data (e.g., from mass spectrometry) without CCNC measurements. Widespread estimates of CCN activities are crucial for climate models to predict the indirect radiative forcing of ambient particles based on the particle's chemical composition. Moreover, our offline single-particle method provides $\kappa$ value at the individual particle level, which can help reduce uncertainties associated with the particle-resolved mixing state in the climate model predicted CCN (Fierce et al., 2013; Healy et al., 2014). Climate models typically
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assume particles are homogeneously mixed and use a single $κ$, which might underrepresent the variation between individual particles. Future studies should aim to develop a parameterization to incorporate the mixing state index in aerosol population average $κ$ calculation based on individual particle measurements. Finally, our study underscores the urgent need to collect additional field data on FT aerosols to improve our understanding of aerosol-cloud interactions and enhance the accuracy of climate models. Our proposed method works well with FT particles since they are usually long-range transported and well-mixed. However, the proposed method might incorporate relatively higher uncertainties for relatively less atmospheric aged (e.g., urban and fresh biomass burning particles) particles where particles might have different morphological configurations (e.g., core-shell and partially engulfed) (Lingaswamy et al., 2022; Unga et al., 2018). Future studies should investigate the spatial distribution of different species in the particles to improve the hygroscopicity parameter estimation.

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## Acknowledgments.

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A portion of this research was performed on a project award (10.46936/lser.proj.2019.50835/60000118) from the Environmental Molecular Sciences Laboratory (EMSL). EMSL is a US Department of Energy (DOE) Office of Science User Facility sponsored by the Biological and Environmental Research (BER) program under Contract No. DE-AC05-76RL01830. STXM/NEXAFS analysis was performed at the Advanced Light Source (beamline 5.3.2.2) at Lawrence Berkeley National Laboratory. ALS is supported by the Office of Basic Energy Sciences of the U.S. DOE under Contract No. DE-AC02-05CH11231. We thank support from the U.S. DOE-BER, Atmospheric System Research. Some of the sample collection and data analysis were supported with funding from NSF (AGS-1110059), DOE (DE-SC0006941 and DE-SC0020053), and NASA's Earth and Space Science Graduate Fellowships (NNX12AN97H, and
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NNX13AN68H). We thank the Regional Government of the Azores for providing logistic support for the operation of the site through the Regional Secretary for Science and the Pico Island Natural Park. We thank Andrea Baccarini, Mike Dziobak, Detlev Helmig, Jacques Huber, Sumit Kumar, Kendra Wright, Stefano Viviani, and Oliver Welz, for their help in the field. Aerosol measurements in 2017 were supported by the German Science Foundation (Deutsche Forschungsgemeinschaft, DFG) under S1 1543/4-1, WE 2757/2-1, and HE 6770/2-1.

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