Influence of Atmospheric Processes on the Solubility and Composition

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Influence of atmospheric processes on the solubility and composition of iron in Saharan dust Amelia F. Longo, Yan Feng, Barry Lai, William M Landing, Rachel U. Shelley, Athanasios Nenes, Nikolaos Mihalopoulos, Kalliopi Violaki, and Ellery D. Ingall Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02605 • Publication Date (Web): 10 Jun 2016 Downloaded from http://pubs.acs.org on June 12, 2016

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Influence of atmospheric processes on the solubility and composition of iron in Saharan dust

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Amelia F. Longo1, Yan Feng2, Barry Lai3, William M. Landing4, Rachel U. Shelley4§, Athanasios Nenes1,5-7, Nikolaos Mihalopoulos7,8, Kalliopi Violaki8, and Ellery D. Ingall1*

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1

Drive, Atlanta, GA 30332-0340, USA

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2

3

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Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA

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Advanced Photon Source, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, USA

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Enviromental Science Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, USA

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School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 Ferst

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School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA 30332-0340, USA.

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Foundation for Research and Technology, Hellas, Patras 70013, Greece.

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National Observatory of Athens, Penteli GR-15236, Greece

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Department of Chemistry, University of Crete, Iraklion 71003, Greece

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§

Present Address: Laboratoire des Sciences de l'Environnement Marin, Institut

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Universitaire Européen de la Mer, Plouzané, 29280, France *Corresponding Author Contact Information:404-894-3883, [email protected]

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21 ACS Paragon Plus Environment

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Keywords Aerosol composition, iron, Saharan dust, GEOTRACES, particle aging

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Abstract Aerosol iron was examined in Saharan dust plumes using a combination of iron near-

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edge X-ray absorption spectroscopy and wet chemical techniques. Aerosol samples were

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collected at three sites located in the Mediterranean, the Atlantic, and Bermuda to characterize

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iron at different atmospheric transport lengths and time scales. Iron(III) oxides were a component

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of aerosols at all sampling sites and dominated the aerosol iron in Mediterranean samples. In

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Atlantic samples, iron(II & III) sulfate, iron(III) phosphate, and iron(II) silicates were also

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contributors to aerosol composition. With increased atmospheric transport time, iron(II) sulfates

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are found to become more abundant, aerosol iron oxidation state became more reduced, and

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aerosol acidity increased. Atmospheric processing including acidic reactions and photo-reduction

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likely influence the form of iron minerals and oxidation state in Saharan dust aerosols and

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contribute to increases in aerosol iron solubility.

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1.0 Introduction

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Iron is a key micronutrient that is vital for all organisms and controls primary productivity in

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approximately 30% of the world’s oceans.1 As a result, the relationship between iron and marine

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microorganisms has been extensively studied most notably through iron fertilization experiments

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in which the sequestration of carbon dioxide was examined.2-5 These studies show that the

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addition of soluble iron to extensive ocean regions increases primary productivity, which is

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reflected in the formation of carbon-sequestering algae blooms.3-7 The solubility of aerosol iron

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is also a key factor in the formation of reactive oxygen species in aqueous phase.8-10 Reactive

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oxygen species are involved in numerous gas and aqueous phase reactions and have been

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associated with negative health effects.11 The potential impacts of soluble aerosol iron on ocean

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nutrient budgets12, 13 and reactive oxygen species formation8-10 underscore the need to understand

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controls and transformations in aerosol iron solubility during atmospheric transport.

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The Sahara Desert contains the largest and most active sources of dust globally.14 The

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Saharan Air Layer, a hot, dry layer of the atmosphere that can overlay dense marine air,

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transports Saharan dust from North Africa, where it is uplifted and entrained as aerosol, to as far

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as North America. The vast transport distances make Saharan dust a key source of nutrients to

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the Mediterranean Sea, much of the North Atlantic Ocean, and even as far as the Gulf of

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Mexico.15, 16 As a result, this influential dust source has been extensively studied as a nutrient

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reservoir.17-19 One trend has emerged for aerosol iron solubility: when total iron is low, the

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relative solubility of iron is high, and when total iron is high, the relative solubility of iron is

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low.20 The factors that control the solubility of iron in mineral dust, however, remain unclear.

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Particle size,21 physical processes,22 compositional, and oxidation state variations as well as

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particle aging processes, including photo-reduction,23-25 organic ligand association,25,

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and


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proton reactions,25, 27-31 have all been proposed as controls of aerosol iron dissolution, but little

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direct evidence for these has been collected for Saharan dust.

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In laboratory studies, proton reactions have been shown to enhance solubility of recalcitrant

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mineral phases25,

31, 32

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evidence of acidic reactions is only circumstantial for environmental samples.32,

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atmospheric proton reactions, acidic species may overcome the carbonate buffer capacity of an

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aerosol particle, allowing the pH to reach values of 1 to 2 in aqueous solutions surrounding

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aerosol particles.37,

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particle is rich in calcium carbonate allowing acidic conditions to form more readily in some

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particles.39 Under these acidic conditions, iron is more soluble.36 Models have estimated photo-

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chemical and organic ligand-promoted dissolution to increase solubility of iron by an order of

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magnitude and are predicted to increase total dissolved iron deposition by as much as 75%

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globally.40 Because of the uncertainty surrounding particle aging processes, proton reactions are

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the most common reaction type included in atmospheric models. This variability in modeled

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global soluble iron flux impedes understanding of aerosol iron in marine ecosystems and

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interactions between the atmosphere and ocean biogeochemical cycles.41, 42

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as well as precipitate iron-rich reactive nanoparticles;33,

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however,

35, 36

During

However because of the heterogeneous nature of aerosols, not every

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Observational studies of aerosol composition and aging processes often employ bulk

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measurements of iron composition and oxidation state.24-26, 43 Composition of aerosol iron has

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often been confined to oxidation state44, 45 or sequential extraction procedures, which categorize

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iron as water-exchangeable, easily reducible, or oxidizable.46 Spectroscopic techniques allow for

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more detailed aerosol iron characterization, such as identification of specific compound or

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mineral classes.47-50 Here, bulk chemical characterizations are combined with synchrotron-based

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elemental mapping and spectroscopy to analyze Saharan dust aerosols. The results of these



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experiments are then combined with a thermodynamic analysis of the major soluble ions present

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in the sample for the determination of its acidity.

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2.0 Methods

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2.1 Sample Collection

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Samples were collected at three locations, Greece, Atlantic Ocean, and Bermuda (Figure S1).

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Sampling stations were selected to capture Saharan dust at different transport length and time

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scales but do not represent points along a single atmospheric flow path. Ideally, sampling would

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continuously follow a single plume as it travelled through the atmosphere; however, these

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experiments are cost prohibitive requiring aircraft or multiple ship based sampling sites.

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Therefore, sampling sites were chosen to capture characteristic atmospheric climatology, which

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represents the typical pathways that Saharan dust takes through the atmosphere. These three

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sampling sites are located in the common transport pathway of Saharan dust and receive dust

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laden air masses on seasonal cycles. At Finokalia Research Station, located on the island of

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Crete, Greece, North African dust can be found any time between October and May; however,

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December to April constitutes the primary Saharan dust season.16 Saharan dust is also

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transported episodically across the Atlantic Ocean. These dust events, occurring between May

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and September, originate in North Africa and are transported on the trade winds to Bermuda.51-55

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Although samples were not collected following a single dust plume, this data set examines

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multiple dust events that impact either the Mediterranean Sea or the Atlantic Ocean. These

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sampling sites will be compared, generally, for differences between sampling sites, and general

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characteristics of aerosol iron minerals at individual geographical locations in these Saharan dust

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transport pathways.


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The Greek samples, closest to the Saharan dust source, were collected at the Finokalia

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Research Station (35.32˚N, 25.67˚E), a site isolated from both local and regional influences on

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the island of Crete, Greece, located 70 km from the nearest major city.56 Samples of particulate

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matter with an aerodynamic diameter < 10 µm, PM10, were collected on Teflon filters without

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any pretreatment using a virtual impactor with an operational flow rate of 16.7 L/min. More

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details are reported in Koulouri et al. (2008).57 Seven samples were collected at the Finokalia site

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over a one to three day period from 2009-2011 during which North African air masses

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dominated.

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Twenty-four hour particle collections were completed as part of the 2011 GEOTRACES 58

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Atlantic transect

. Because the GEOTRACES samples were collected over a short period of

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time and relatively near the source, they likely provide the purest sampling of Saharan air

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masses. The GEOTRACES samples are collected from three sites; however, the sampling

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locations are very close together, and the dust was from a single dust event, sampled over three

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days. Therefore, the GEOTRACES sample sites are referred to collectively. The three samples

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were collected at the following locations: Sample 7899, 22.37˚N 35.62˚W; Sample 7946,

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20.88˚N 32.62˚W; Sample 8004, 19.43˚N, 29.38˚W. Total suspended particle (TSP) samples

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were collected using Whatman-41 filters and a Tisch 5170-VBL high volume sampler operated

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on average with a face velocity of ~100 cm/sec.58 The aerosol sampler was wind speed (> 0.5

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m/s) and sector-controlled (+/- 60˚ from bow) to avoid contamination from ship’s exhaust. The

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Whatman 41 (W41) filters were cleaned by (1) soaking in 0.5 M HCl (Optima) for 24 hours, (2)

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rinsing thoroughly with ultra-high purity water (UHP water, 18.2 MΩ*cm), (3) soaking in a

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second 0.5 M HCl (Optima) bath for 24 h, (4) rinsing with UHP water until the pH of the water



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returned to the pH of UHP water (~ pH 5.6), (5) placing on a plastic mesh rack inside a HEPA

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laminar flow bench until dry and (6) storage in two zipper-seal bags until use.

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Weekly samples were collected at the Bermuda Institute of Ocean Sciences (32.24˚N,

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64.87˚W), which represent the longest transport of Saharan dust in our collection. Sampling was

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conducted from June 2011 to June 2012. These samples were collected using a Tisch 5170-

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VBL high-volume Total Suspended Particle (TSP) aerosol sampler using Whatman-41 filters

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and with a face velocity of ~100 cm/sec. The Whatman-41 filters were cleaned as detailed above.

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Of the fifty-two weekly-integrated samples that were collected, eight samples were largely

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influenced by Saharan dust events. All aerosol samples were stored in the dark at -20oC until

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analysis.

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Aerosols sampled from Finokalia Research Station represent particles with an aerodynamic

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diameter of less than 10 µm (PM10). GEOTRACES and BIOS samples represent Total

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Suspended Particles (TSP), meaning they collect particles with an aerodynamic diameter of less

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than 10 µm but also any particles with an aerodynamic diameter of greater than 10 µm. However,

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particles larger than this will often settle out of during long range atmospheric transport.59 So,

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while Finokalia Research Station samples represent a different size fraction from the

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GEOTRACES and BIOS samples, the actual particle size ranges collected are likely similar.

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2.2 HYSPLIT

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Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT)60,

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back

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trajectories were completed for each sample using the GDAS half-degree meteorological archive

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in order to confirm the geographic origin of the air masses sampled (Figure S1). For the

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Bermuda Institute of Ocean Sciences (BIOS) and GEOTRACES samples, HYSPLIT back

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trajectories were calculated at 0 m, 500 m and 1000 m above ground level to confirm the North 27 ACS Paragon Plus Environment

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African provenance. At the Finokalia Research Station, HYSPLIT back trajectories were

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calculated at 1000 m and 3000 m above ground level for five days (approximately atmospheric

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life time of dust aerosols) preceding sample collection. HYSPLIT back trajectories computed at

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1000 m show the origin of air masses within the boundary layer; this height is chosen, rather than

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the more conventional heights of 0 m or 500 m to avoid orographic influences at this site.

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HYSPLIT back trajectories were computed at 3000 m to identify dust events. At Finokalia

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Research Station, dust is either homogeneously distributed from 0 to about 3000 m, during

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spring and autumn dust events, or is found in a layer between 2500 – 4000 m during summer and

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autumn dust events.62 While the HYSPLIT back trajectories do not guarantee that pure end

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members were sampled, they help to demonstrate that most air masses contained North African

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origins during the sampling time periods. Additionally, all samples had the distinctive red-

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orange color of Saharan aerosols.

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The HYSPLIT back trajectories were also used to estimate the residence time of dust

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aerosols in the atmosphere from generation in the Sahara Desert to collection. For this

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calculation, a five day HYSPLIT back trajectory was completed in ensemble mode at 3000 m.

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Since each sample was a multiday collection, HYSPLIT back trajectories were completed for

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each day during the collection period. The mean distance travelled was then determined for each

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sample and taken as the average distance travelled in five days by the air mass. The average

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distance travelled in five days was used to estimate a velocity for each sample (average distance

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travelled in five days divided by five days time). Finally, the distance travelled from source to

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collection was estimated. The source region in the Sahara Desert was taken as a single location

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(30˚N, 8.5˚E). The distances travelled were estimated as follows: Finokalia Research Station,

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1800 km; GEOTRACES, 2100 km; and Bermuda Institute of Ocean Science, 6900 km. Using



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these distances and the estimated velocities, an approximation of travel time was generated for

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each sample. These approximate travel times are reasonable for Saharan aerosols which have

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been known to travel for as many as 30 days before deposition.63

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2.3 Total and soluble iron

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Water soluble iron, hereafter referred to as soluble iron, was determined for all samples using

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the ferrozine technique.64 Handling of the filters was conducted under a HEPA-filter laminar

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flow hood. A quarter filter was extracted into 10 mL of deionized water (>18 MΩ) in a trace

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metal grade centrifuge tube by 30 min of ultrasonication. The extraction was followed by the

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removal of insoluble particles from the extract by a 0.2 µm pore size syringe filter. Any

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dissolved Fe(III) in the sample was reduced to Fe(II) through the addition of 40 µM solution of

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hydroxylamine hydrochloride added in a 1:100 ratio of reagent to filter extract. Samples were

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allowed to react with the hydroxylamine hydrochloride overnight. A 5 mM ferrozine solution

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which forms a colored complex with dissolved Fe(II) was then added in a 1:100 ratio of reagent

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to filter extract. After a ten minute reaction, samples were spectrophotometrically measured at

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562 nm.36 The detection limit for this method (5.6 ng/mL) was two orders of magnitude lower

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than the sample signals. Total iron was measured for GEOTRACES and Bermuda Institute of

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Ocean Science samples using protocols described in Morton et al. (2013).58,

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limits for this methods are between 0.46 – 0.62 ng/m3.66 For BIOS and GEOTRACES samples,

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total iron was also measured via elemental densities collected from X-ray fluorescence maps to

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ensure total iron could be reliably determined through synchrotron-based techniques,47 and for

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Finokalia Research Station samples, total iron was only determined using elemental densities

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collected from X-ray fluorescence maps. The detection limit of this method is approximately 1

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ng/m3.

65, 66

Detection


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2.4 Soluble Ions

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Ion chromatography was used to measure major soluble ions for each aerosol sample

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according to Bardouki et al. (2003).67 Briefly, approximately 1/8th of each filter was extracted

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into 10 mL of nanopure water using an ultrasonic bath for 45 min. The filter extracts were then

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syringe filtered (PALL IC Acrodisc (PES), 0.45 µm, 13 mm) to remove any insoluble species.

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The acquired filtered solutions were analyzed for anions (Cl−, Br−, NO3−, SO42-, HPO42-, C2O42-)

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and cations (NH4+, K+, Na+, Mg2+, Ca2+). Anions were determined using a Dionex-500 ion

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chromatograph equipped with an Ion Pac AS4A-SC column and an AG4A-SC precolumn, with

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an ASRS-300 suppressor. Anion separation was conducted with isocratic elution of NaHCO3

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(3.4 mM)/Na2CO3 (3.6 mM) as an eluent and a flow of 1.5 mL/min. For cations, an Ion Pac

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CS12A column and a CG12A guard column were used, with a CSRS-300 suppressor, under

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isocratic elution of 20 mM MSA (methanesulfonic acid) at a flow rate of 1.0 mL/min. The

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detection limit of the analysis was 20, 12, 40, 12 and 40 ppb for NH4+, K+, Na+, Mg2+, and Ca2+,

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respectively, while the corresponding detection limit for all anions (Cl−, Br−, NO3− , SO42-,

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HPO42-, and C2O42-) was 20 ppb.

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2.5 pH modelling

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ISORROPIA was used to estimate the aerosol acidity of each aerosol sample.68,

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This

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thermodynamic model will output the concentration of species in the gas and aerosol

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(solid/liquid) phases, pH, and aerosol water. This approach to aerosol pH modelling will only

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determine whether the samples are acidic or circum neutral. To compute the pH, knowledge of

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the relative humidity and temperature is needed; therefore, values characteristic of a humid

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marine boundary layer were assumed (80-90% Relative humidity; 293K). Input values for major

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aerosol species can be found in Table S1.



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2.6 Synchrotron Analysis

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Aerosol composition was determined using the micro X-ray fluorescence capabilities at

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Argonne National Laboratory, Advanced Photon Source, Station 2-ID-D. In this study X-ray

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fluorescence microscopy and X-ray Absorption Near Edge Structure (XANES) spectroscopy

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were used to examine the iron in aerosol. These techniques work by providing incident X-rays of

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sufficient energy to eject the electrons from the innermost electron shell of atoms. Subsequently,

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an outer shell electron may relax into the vacated position, emitting a characteristic fluorescence

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signal. The type, oxidation state, and structural arrangement of atoms in a particle are revealed as

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the ejected electron interacts with neighboring atoms and are reflected in XANES spectra70.

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Therefore, Fe-XANES spectra provide information on both oxidation state and the composition

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associated with the element of interest.

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An energy dispersive Si-drift detector (Vortex EM, with a 50 mm2 sensitive area, and a 12.5

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µm Be window; SII NanoTechnology, Northridge, CA) was used to measure X-ray fluorescence

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of the samples. All measurements were conducted under a helium atmosphere in order to

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minimize absorption and fluorescence caused by beam interactions with air. A randomly selected

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area of each filter sample (0.5 cm2) was placed over a slot of an aluminum sample mount for

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analysis. The sample was initially analyzed in microscopic X-ray fluorescence mode to identify

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iron-containing particles on the filter. In this mode, the X-ray beam with a diameter of ca. 200

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nm was scanned over a randomly selected filter area (typically 40 to 50 µm2) at a step size of 0.4

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µm and 0.5 s dwell time to produce an elemental distribution map of the filter. The resulting

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elemental data were compiled to identify iron-containing particles (>1 µm diameter) for Fe

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XANES spectroscopy.


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Fe XANES spectroscopy was conducted in bulk and individual particle modes. In bulk Fe

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XANES mode, X-ray focusing optics (zone plate and order sorting aperture) were removed

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allowing characterization of iron present in ca. 0.4 mm2 filter areas. In individual particle mode,

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iron-containing particles identified in elemental distribution maps were characterized with a

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focused beam with a 0.4 µm spot size. Of course, every particle could not be examined because

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of time constraints. Based on elemental maps, particles were selected to attempt to characterize

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as many different elemental iron associations as possible. Individual particle XANES

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spectroscopy represent the composition of an entire particle, which can be a mixture of several

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mineral phases. In both bulk and individual particle modes, XANES scans were collected from

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7100 to 7180 eV in 0.5 eV steps with 0.5−3 s dwell time per step.

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2.7 Data Reduction and Analysis

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Oxidation state was determined for each sample by determining the pre-edge centroid of

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each Fe XANES spectra. The pre-edge feature has been shown to be the most reliable

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indication of oxidation state, and by interpolating between pre-edge centroid position %

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Fe(II) of standards, the relative abundance of Fe(II) can be determined for environmental

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samples.47, 71, 72

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Spectral data were normalized to account for variations in the incoming X-ray flux and

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processed using the Athena software package.73 In total, 105 spectra were collected. In

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order to extract the most useful information from this number of spectra, data reduction

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techniques were employed. A combination of principal component analysis, PCA, and k-

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means clustering were used to group like spectra.74 The PCA algorithm used, built into

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Athena,73 generates a scree plot where the number of principal components can be

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determined. Like spectra were merged73 to create an average spectra representing a cluster,



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and linear combination fitting was then completed for each cluster’s Fe XANES spectra

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using Athena.73 These data reduction techniques were used on each sampling site, with the

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spectra being divided into bulk and individual particle Fe XANES.

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The linear combination fitting algorithm in Athena uses a database of known iron

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mineral standards (e.g., iron oxides, organics, silicates, sulfides, and sulfates)75 run on the

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same synchrotron beamline, in this case, to determine the phases contributing to an

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unknown aerosol Fe XANES spectra. An iterative process was used to refine the standard

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database used to model the sample spectra. First, exotic iron minerals unlikely to be major

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components of aerosol iron were excluded from the database.47 Second, the database was

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narrowed through elimination of standards with low contribution (i.e. less than 10%) or

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poor fit (i.e. high R-factor) during initial linear combination fitting. As the acidic reactions

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and proto-reduction reactions are taking place on the surface of aerosols, very small changes

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in composition may be below the detection limit of these methods. This could lead to

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insufficient quantities of a specific mineral or compound in a sample and an

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underestimation of the specific compound during linear combination fitting;76 thus, iron

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composition was generalized to express broad chemical classes: iron(III) oxides, iron(III)

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sulfates, iron(III) phosphates, iron(II) sulfates, and iron(II) silicates. Different minerals

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within these classes can have widely varying chemical properties. For iron(III) phosphates,

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it is mainly the hydration that differs; however, iron(III) oxide minerals such as hematite

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and goethite are in the same category, but have iron solubilities that differ by an order of

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magnitude.21 Therefore, it is worth noting that in this study that goethite was the iron oxide

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mineral likely present in the aerosol. Athena uses a non-linear, least-squares minimization

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approach to fit spectra of unknown samples with standard material spectra, computing an

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error term, the R-factor, to quantify the goodness of a given linear combination fit. The

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linear combination of standards that yielded the lowest R-factor reflected the best fit.73

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3.0 Results

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To put the results in context, it is important to visualize the typical transport paths of air

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masses originating in the Sahara. Air masses carrying Saharan dust to Finokalia Research

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Station travel a relatively short distance across the Mediterranean (~1800 km). GEOTRACES

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samples collected off the west coast of Africa, near the Cape Verde Islands, also represent a

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relatively short travel distance (~2100 km). The air masses that deliver Saharan dust to the Cape

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Verde islands and surrounding waters may continue their westward journey across the Atlantic

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Ocean, reaching Bermuda after atmospheric transport of approximately 6900 km from source.

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Sampling stations do not represent different points along a single atmospheric flow path; here,

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we use the different sampling sites to explore the effects of a range of atmospheric transport

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times on iron properties in aerosols.

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3.1 Iron Solubility

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Saharan dust solubility varies at the three different sampling sites, with the highest solubility

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at the Bermuda Institute of Ocean Sciences (BIOS) site and the lowest at the GEOTRACES

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collection site (Figure 1).

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concentration of total iron, averaging 196 ng/m3 (Table S2). Both the Finokalia Research Station

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and GEOTRACES sampling sites had significantly higher total aerosol iron concentrations

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(averaging 735 ng/m3 and 1580 ng/m3, respectively). At each collection site, the soluble iron was

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on the same order of magnitude with mean values ranging from 5.0-8.75 ng/m3. Moreover, when

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the soluble iron is normalized to the total iron, the differences between each sampling site

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become apparent. The BIOS contained on average 2.55% soluble iron, with values ranging from

While BIOS exhibited the highest solubility, it had the lowest



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1.7 – 8.9% (Figure 1). In contrast, the GEOTRACES samples had consistently low solubility,

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averaging 0.41%. Samples from the Finokalia Research Station fell between these two locations

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containing on average 1.19% soluble iron; this station also exhibited a wide range of iron

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solubilities from 0.4 – 1.5% (Figure 1). Solubility generally increased with the estimated travel

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time from the source. BIOS samples, which had the longest estimated travel time, had higher

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iron solubility than the GEOTRACES and Finokalia Research Station samples, which both short

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estimated travel times. However, there is not a clear statistically significant relationship between

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estimated travel time and iron solubility or total iron concentrations (Figure 1). This increased

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solubility could be attributed to the decreases seen in pH as the travel time increases (Table S2).

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The pH drops from circum neutral to less than four pH units after approximately 15 days in the

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atmosphere, which corresponds to a marked increase in solubility.

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3.2 Iron Composition

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The results of the k-means cluster analysis and linear combination fitting reveal the aerosol

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iron composition at the Finokalia Research Station to be uniform at the bulk level (Figure 2;

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Table S3). At the GEOTRACES collection sites, each bulk sample was determined to be

327

compositionally distinct despite the fact that the collections sampled three sequential days. At the

328

bulk level, iron from the BIOS collection site was represented by two compositions. Individual

329

particles sampled from Finokalia Research Station all have the same composition; similarly,

330

individual particles sampled from the BIOS collection also exhibited a uniform composition. At

331

the GEOTRACES sample site, the composition of the individual particles sampled was much

332

more varied having one of three different iron compositions (Table S3).

333

In contrast to the GEOTRACES and BIOS samples, the Finokalia Research Station samples

334

were on average dominated by iron(III) oxides (>90%) (Figure 2). The GEOTRACES and BIOS


Page 17 of 31


335

samples contained significantly lower fractions of iron(III) oxides (57% and 41%, respectively)

336

on average. In the GEOTRACES and BIOS aerosol samples, iron(III) oxides were found along

337

with lesser portions (70%) with the remaining fraction being iron(II) silicates, and GEOTRACES individual

343

particles contained iron(III) phosphates and iron(II & III) sulfates. (Figure 2)

344

3.3 Oxidation State

345

Iron in Saharan dust exhibits different oxidation states across the different sampling sites.

346

The Finokalia Research Station samples contained the most oxidized iron with more than 85% of

347

the iron as iron(III), while the BIOS and GEOTRACES aerosol samples contained

348

approximately 60% iron(III). The iron oxidation state, shown using the pre-edge centroid

349

position, is negatively correlated (R2=0.54) with estimated travel time, suggesting that the longer

350

an aerosol remains in the atmosphere, the more reduced the iron becomes (Figure 3). Although

351

iron(II) is generally considered more soluble than iron(III), the fractional solubility of iron did

352

not correlate (R2 = 0.14) with oxidation state (Table S2).

353

4.0 Discussion

354

The variation of iron chemistry in Saharan dust during atmospheric transport can be seen

355

most clearly in the oxidation state, with aerosol iron being more reduced with longer transport

356

times. The photo-reduction of iron during atmospheric transport could explain this trend. Photo-

357

reduction pathways for aerosol iron have been demonstrated for both ligand-assisted and



Page 18 of 31

358

unassisted mechanisms in laboratory settings.77, 78 Once in aqueous form, the reducible fraction

359

of iron(III) can be quickly converted to iron(II) by ligand assisted photo-reduction mechanisms,

360

with this reducible fraction having important implications for bioavailability.78, 79 A correlation

361

between the oxidation state and the travel time is evident, but not strong (Figure 3; R2=0.54).

362

This could be attributed to differences within the different mineral classes we have defined. Even

363

within the class of iron(III) oxides, different minerals have variable susceptibilities to

364

photochemical processing.24 Alternatively, reduced iron could be an original component in the

365

BIOS and GEOTRACES samples regardless of travel time, but this would require a significant

366

source of reduced iron in Saharan soil, which is unlikely in this arid environment. Despite

367

iron(II) being more soluble than iron(III), there was no correlation between the oxidation state of

368

iron and the solubility. Although Zhu et al. (1997) did not find iron(II) sulfates, the general lack

369

or correlation between aerosol iron solubility and iron oxidation state is consistent.80 Reductive

370

processes, implied by the change in oxidation state, would require relatively short time frames in

371

these Saharan samples, on the order of one to three days. The short time scale of the reductive

372

processes is consistent with the findings of Majestic et al. (2007),44 although they did not find

373

iron(II) sulfates. If iron phases susceptible to photo-reduction are completely reduced in this

374

short time frame, then other transformation mechanisms, such as proton-induced iron dissolution,

375

may become increasingly important on longer time scales. Nitric acid suppresses the formation

376

of iron(II) at low pH, therefore pH can also act as a control of oxidation state of aerosol iron.81 In

377

these samples, nitrate is negatively correlated with the relative abundance of iron(II) (R2 = 0.75;

378

Table S4), where high concentrations of nitrate correspond to more iron(III). Nitrate has also

379

been suggested to significantly increase the reactivity of iron oxides.82 These complex

380

interactions suggest that the various particle aging mechanisms, such as acidic reactions and


Page 19 of 31


381

photo-reduction, may not be working simultaneously. A shift in mechanisms offers a possible

382

explanation for the lack of a clear correlation between solubility and oxidation state of aerosol

383

iron in the long-range transport of dust aerosols.

384

The changes in oxidation state were accompanied by variations in the composition of iron in

385

Saharan air masses. Bulk aerosol iron from the GEOTRACES and BIOS locations contained on

386

average only ~50% iron(III) oxides. For aerosol samples, the individual particle composition was

387

often different from the bulk composition seen at a sampling site, suggesting that the

388

composition of the large particles (>1µm) is different from the fine particles (1 µm). X-ray fluorescence imaging shows that aerosol phosphorus is sometimes

400

collocated with iron and almost always with calcium (Figure S2). This suggests the iron

401

phosphates are likely formed as a result of the aggregation and subsequent acidification of

402

calcium phosphate containing particles and iron containing particles. At the BIOS site, the iron

403

sulfates account for nearly 50% of the total iron, but appear only in the bulk phases, suggesting



Page 20 of 31

404

that iron sulfates are likely to be found in small particles (15 days), which

408

corresponds to the aerosols with the more soluble iron. This suggests that proton reactions are

409

solubilizing the iron during atmospheric transport. Our observations of iron sulfates36 and

410

phosphates provide further support that acidic reactions are occurring. In these Saharan dust

411

samples, a lower pH is correlated with more iron(II) (R2 = 0.50; Table S4). Because nitrate

412

seems to inhibit the formation of iron(II), other acidic species, such as sulfate, are likely

413

responsible for this correlation. This relationship helps support the hypothesis that iron(II)

414

sulfates are formed as a result of acidic reactions in the atmosphere. Sulfates have also been

415

shown to preferentially accumulate on aluminosilicate-rich dust if the dust air mass contacts a

416

sulfate-rich air mass during transport.35 Correlations between non-seasalt sulfate and soluble Fe

417

have been suggested as evidence for sulfuric acid reactions that solubilize iron.41,

418

correlations were not significant in our samples (R2 = 0.22); however, the absence of correlation

419

does not exclude sulfuric acid as a means of iron solubilization in our samples because non-

420

seasalt sulfate concentrations may not be an adequate indicator of aerosol acidity.37,

421

solubilization of iron by sulfuric acid might explain the presence of iron sulfates, which could

422

precipitate out of solution as these acidic reactions occur. However, these reactions can be

423

buffered by minerals such as carbonates found in Saharan dust,30, 85 so the buffer capacity of the

424

particles must be overcome for these reactions to proceed. The buffering effect of minerals

425

present in Saharan dust helps explain the finding of secondary phosphates from acidic reactions

83

These

84

The


Page 21 of 31


426

as well32 as the iron(III) oxides in particles where acids were not able to overcome the buffering

427

capacity.

428

As the largest source of dust to the Atlantic Ocean, it is important to consider iron inputs

429

from the Sahara Desert. The variations in oxidation state suggest that a reductive process is

430

taking place during atmospheric transport while formation of iron phosphate and sulfate phases

431

offer possible support for proton reactions. However, these two together are not fully sufficient

432

to explain differences in aerosol iron solubility seen at the short and long range sampling

433

locations. For example, iron sulfate phases are generally considered a soluble form of iron,

434

however samples which contained significant iron sulfates never contain more than 10% soluble

435

iron, suggesting that the iron sulfates are not fully dissolved. This could be a result of the

436

heterogeneous nature of aerosols, where the most labile phases may not remain on the surface of

437

the particle where they can be readily dissolved. Furthermore, physical processes,22 particle

438

size,21 and ligand promoted iron dissolution,86 not explored in this study, are also factors that

439

may contribute to the variations in aerosol iron solubility that were observed at the different

440

sampling locations. Overall, these findings suggest that a combination of factors affects aerosol

441

iron solubility during long-distance atmospheric transport and emphasize the need to consider

442

reductive mechanisms as well as proton-induced solubilization of aerosol iron in modeling

443

studies.



Page 22 of 31

444

Acknowledgments This material is based upon work supported by the National Science

445

Foundation under Grants OCE-1357375 (EDI), OCE-0929919 (WML), and OCE-1034764

446

(WML). The data used to produce these results is available upon request to the corresponding

447

author. Any opinions, findings, and conclusions or recommendations expressed in this material

448

are those of the authors and do not necessarily reflect the views of the National Science

449

Foundation. Use of the Advanced Photon Source and support to YF and BL are provided by

450

Argonne National Laboratory under the U.S. Department of Energy contract No. DE-AC02-

451

06CH11357. NM and KV acknowledge support from European Union (European Social Fund)

452

and Greek national funds through the Operational Program "Education and Lifelong Learning"

453

of the National Strategic Reference Framework Research Funding Program, ARISTEIA.

454

Supporting Information This article is accompanied by additional tables and figures available

455

in the Supporting Information. This information is available free of charge via the Internet at

456

http://pubs.acs.org/.


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457

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67. Bardouki, H.; Liakakou, H.; Economou, C.; Sciare, J.; Smolik, J.; Zdimal, V.; Eleftheriadis, K.; Ladaridis, M.; Dye, C.; Mihalopoulos, N., Chemical composition of size-resolved atmospheric aerosols in the eastern Mediterranean during summer and winter. Atmospheric Environment 2003, 37, (2), 195-208. 68. Fountoukis, C.; Nenes, A., ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+-Ca2+-Mg2+-Nh(4)(+)-Na+-SO42--NO3--Cl--H2O aerosols. Atmospheric Chemistry and Physics 2007, 7, (17), 4639-4659. 69. Nenes, A.; Pandis, S. N.; Pilinis, C., ISORROPIA: A new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquat. Geochem. 1998, 4, (1), 123-152. 70. Ingall, E.; Brandes, J.; Diaz, J.; de Jonge, M.; Paterson, D.; McNulty, I.; Elliott, W.; Northrup, P., Phosphorus K-edge XANES spectroscopy of mineral standards. Journal of Synchrotron Radiation 2011, 18, 189-197. 71. Wilke, M.; Farges, F.; Petit, P. E.; Brown, G. E.; Martin, F., Oxidation state and coordination of Fe in minerals: An FeK-XANES spectroscopic study. American Mineralogist 2001, 86, (5-6), 714-730. 72. Bajt, S.; Sutton, S. R.; Delaney, J. S., X-ray microprobe analysis of iron oxidation-states in silicates and oxides using X-ray -absorption near-edge structure (XANES). Geochimica Et Cosmochimica Acta 1994, 58, (23), 5209-5214. 73. Ravel, B.; Newville, M., ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X-ray absorption spectroscopy using IFEFFIT. Journal of Synchrotron Radiation 2005, 12, 537-541. 74. Marcus, M. A.; Lam, P. J., Visualising Fe speciation diversity in ocean particulate samples by micro X-ray absorption near-edge spectroscopy. Environmental Chemistry 2014, 11, (1), 10-17. 75. Ingall, E.; Diaz, J.; Longo, A.; Oakes, M.; Finney, L.; Vogt, S.; Lai, B.; Yager, P. L.; Twining, B. S.; Brandes, J., Role of biogenic silica in the removal of iron from Antarctic Seas. Nature Commun. 2013, 4:1981 76. Hesterberg, D., Chapter 11 - Macroscale Chemical Properties and X-Ray Absorption Spectroscopy of Soil Phosphorus. In Developments in Soil Science, Balwant, S.; Markus, G., Eds. Elsevier: 2010; Vol. Volume 34, pp 313-356. 77. Siefert, R. L.; Webb, S. M.; Hoffmann, M. R., Determination of photochemically available iron in ambient aerosols. Journal of Geophysical Research 1996, 10, (D9), 14441-14449. 78. Siefert, R. L.; Pehkonen, S. O.; Erel, Y.; Hoffmann, M. R., Iron photochemistry of aqueous suspensions of ambient aerosol with added organic acids. Geochimica Et Cosmochimica Acta 1994, 58, (15), 3217-3279. 79. Chen, Y.; Siefert, R. L., Determination of various types of labile atmospheric iron over remote oceans. Journal of Geophysical Research-Atmospheres 2003, 108, (D24). 80. Zhu, X. R.; Prospero, J. M.; Millero, F. J., Diel variability of soluble Fe (II) and soluble total Fe in North African dust in the trade winds at Barbados. Journal of Geophysical Research 1997, 102, (D17), 21297 - 21305. 81. Cwiertny, D. M.; Baltrusaitis, J.; Hunter, G. J.; Laskin, A.; Scherer, M. M.; Grassian, V. H., Characterization and acid-mobilization study of iron-containing mineral dust source materials. Journal of Geophysical Research 2008, 113, (D05202). 82. Kong, L. D.; Zhao, X.; Sun, Z. Y.; Yang, Y. W.; Fu, H., B.; Zhang, S. C.; Cheng, T. T.; Yang, X.; Want, L.; Chen, J. M., The effects of nitrate on the heterogeneous uptake of sulfur dioxide on hematite. Atmospheric Chemistry and Physics 2014, 14, 9451-9467. 83. Srinivas, B.; Sarin, M. M.; Kumar, A. K., Impact of anthropogenic sources on aerosol iron solubility over the Bay of Bengal and the Arabian Sea. Biogeochemistry 2012, 110, 257-268. 84. Hennigan, C. J.; Izumi, J.; Sullivan, A. P.; Weber, R. J.; Nenes, A., A critical evaluation of proxy methods use to estimate the acidity of atmospheric particles. Atmospheric Chemistry and Physics 2015, 15, 2775-2790.



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698 699 700


Page 29 of 31


701 10%

2000

8%

1500

6%

1000

4%

500

2%

0

0%

Soluble Fe (%)

Total Fe (ng/m3)

2500

Travel Time (days) Total Fe (ng/m3)

Fe Solubility (%)

702 703

Figure 1: Aerosol samples from the Finokalia Research Station ( ) had the shortest estimated

704

travel times, followed by the GEOTRACES samples ( ), and the BIOS samples ( ). While the

705

BIOS sample consistently had the highest aerosol iron solubility, there was no significant

706

correlation between the estimated travel time and either total iron (R2 = 0.35) or fractional iron

707

solubility (R2 = 0.18).

708 709



Page 30 of 31

710 1

Factional Relative Abundance

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

711

Finokalia Bulk

Finokalia Particle

GEOTRACES GEOTRACES Bulk Particle

BIOS Bulk

BIOS Particle

712

Figure 2: The mean bulk and individual particle iron composition of samples from each

713

sampling site. Iron(III) oxides ( ) dominate in the Finokalia Research Station samples, with

714

traces of iron(II) silicates ( ) and iron (II) sulfates ( ) in the bulk and individual particles,

715

respectively (7 bulk sample spectra; 11 individual particle spectra). In the GEOTRACES

716

samples, iron(III) sulfates ( ) and iron(III) phosphates ( ) appeared (3 bulk sample spectra; 17

717

individual particle sample spectra). The bulk samples from the BIOS site contained significant

718

contributions from iron sulfates; while individual particles were a mix of iron(II) silicates and

719

iron(III) oxides (8 bulk sample spectra; 18 individual particle spectra).

720 721


Page 31 of 31


722 30.0

R2 = 0.54

Travel Time (days)

25.0 20.0 15.0 10.0 5.0 0.0 7.1134 7.1136 7.1138

7.114

7.1142 7.1144 7.1146

PrePre-Edge Centroid (keV)

723 724

Figure 3: Travel time, estimated using HYSPLIT back trajectories, is negatively correlated with

725

the pre-edge centroid, a reliable indicator of oxidation state that increases as iron transitions from

726

iron(II) to iron(III).

727 728


Influence of Atmospheric Processes on the Solubility and Composition

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