Characterization of African dust transported to Puerto Rico by individual particle and size segregated bulk analysis



[1] As part of the Puerto Rico Dust Experiment (PRIDE), airborne and surface dust particle samples from Africa were collected and subjected to bulk elemental and single-particle analysis. Airborne samples were collected on polycarbonate filters at various altitudes and underwent single-particle scanning electron microscopy with energy dispersive analysis with X-rays (EDAX) to derive elemental ratios of key soil elements. Particle chemistry was related to size and morphological characteristics. At the principle surface site, particles were collected on a Davis Rotating Drum (DRUM) cascade impactor strips in eight stages from 0.1 to 12 μm at 4 hour time resolution. These samples were subjected to X-ray florescence (XRF) to determine bulk elemental composition from Al through Zn. The elemental data showed good correlation between the DRUM and the aircraft samples. Cluster analysis of single-particle data resulted in 63 statistically significant clusters. Several clusters can be easily related to their parent mineralogical species. However, as dust particles are to a large extent aggregates, most clusters are based on a continuum of varied mineralogical species and cannot be easily categorized. With 60,500 total particles counted from the airborne filters, a statistically significant number of large particles could be analyzed. Estimated mean surface area modal diameter is ∼5 μm, with an average aspect ratio of 1.9. An apparent change in source region is seen in the morphological data and non alumino-silicate minerals but is not seen in the aluminum to silicon ratio. We suspect homogenization during long-range transport.

1. Introduction

[2] Airborne mineral dust particles present special problems in aerosol mechanics and radiation calculations. Dust particles are a significant, if poorly characterized, component of atmospheric aerosols. Irrespective of the intense difficulties in measuring dust particle properties [e.g., Reid et al., 2003b], their range of shapes, sizes and chemistries are difficult to quantify in a consistent and usable statistical manner. Hence their very nature defies easy light scattering and absorption calculations. These issues have in part resulted in airborne dust having the highest uncertainties of any aerosol species in global direct forcing estimates [Intergovernmental Panel on Climate Change (IPCC), 2001; Sokolik et al., 2001].

[3] Recently, single-particle analysis from scanning and transmission electron microscopy methods has become a popular tool for dust particle characterization. Examples of detailed analysis of dust are given by d'Almeida and Schutz [1983], Coude-Gaussen et al. [1987], Reid et al. [1994], Anderson et al. [1996], Gao and Anderson [2001], and Koren et al. [2001]. From these measurements have come a variety of characterizations including particle elemental ratios and shape factors such as particle cross-sectional area, perimeter, major and minor axis. However, these methods have inherent shortcomings that are difficult to overcome. For the most part, analysis is based on a two-dimensional cross section of the particle. Particle chemistry can be ambiguous due to matrix effects, electron scatter, and internal particle in-homogeneity. Hence like so many dust-measuring methods, findings from single-particle analysis must be treated only semiquantitatively.

[4] The Puerto Rico Dust Experiment (PRIDE) was a joint Office of Naval Research (ONR) and NASA funded field campaign to study the properties of African dust advected into the Caribbean region [Reid et al., 2003a]. Conducted at Naval Station Roosevelt Roads on the eastern side of the island of Puerto Rico, participating investigators monitored Saharan dust transport across Northern Tropical Atlantic Ocean between 28 June and 24 July 2000. During the PRIDE campaign, the island of Puerto Rico was near the center of the dust transport plume from Africa. Daily dust optical depths at 500 nm averaged 0.26, with a maximum of 0.52. Dust concentrations at the surface were at times greater than 70 μg m−3.

[5] A goal of PRIDE was to investigate the extent to which the microphysical, chemical and optical properties of dust particles need to be known before remote sensing systems can accurately determine dust optical depth and radiative flux. Direct forcing column closure was not a goal, but rather an attempt was made to find constraints such that particle mircophysical and optical properties can be consistent. As part of this goal, we investigated the extent to which particle size distributions and chemistry can even be characterized and the uncertainties size parameterizations have in model and satellite investigations.

[6] In this manuscript we examine the chemical and microphysical properties of dust particles collected on surface and airborne filter samples using bulk and single-particle analysis methods. We begin with an elemental analysis of cascade impactor data collected at the primary PRIDE field site. This is followed with qualitative and quantitative examinations of single particles collected on the Navajo research aircraft. Particle size, shape, and chemistry functions are presented. A cluster analysis is performed and likely mineralogical speciation is given. We conclude by comparing findings from the bulk and single-particle methods and develop a consistent picture of dust microphysics and chemistry and make recommendations for further modeling studies.

2. Methods

2.1. Sample Collection

[7] Atmospheric dust samples were collected at the surface, and by aircraft at various elevations during the PRIDE field campaign. The surface site utilized a Davis Rotating Drum (DRUM) impactor to collect time resolved aerosol data. The Davis Rotating Drum (DRUM) impactor, owned by investigators from the University of California, Davis (UCD), was deployed at the principal ground site for the PRIDE study; Cabras Island, Puerto Rico. Cabras Island (latitude 18.21°N, longitude 65.60°W) is a small facility on Naval Station Roosevelt Roads several hundred meters offshore of the easternmost portion of Puerto Rico. The sampler was located on the roof of the University of Miami mobile laboratory about 10 m from the shoreline (D. L. Savoie et al., Spectrally-resolved light absorption by Saharan aerosols over the tropical North Atlantic, submitted to Geophysical Research Letters, 2003) (hereinafter referred to as Savoie et al., submitted manuscript, 2003), with a sampling height of ∼10 m.

[8] The DRUM sampler collects particles in eight stages with 50% cut points at: 5 μm, 2.5 μm, 1.1 μm, 0.74 μm, 0.56 μm, 0.34 μm, 0.24 μm, 0.09 μm in diameter, respectively. This sampler is a modified version of the original instrument described by Cahill et al. [1985]. It was recently altered by using a slit jet (instead of a circular jet) for each of the eight stages, and increasing the flow rate from 5 to 10 L min−1. Samples were collected on Apiezon grease coated strips on rotating drums moving at ∼1 mm each 4 hours, giving 4 hour resolution. Sampling was performed continuously from 3 July through 24 July 2000, with minor power outages on 9 and 15 July.

[9] Samples for single-particle analysis were collected with a twin-engine, 8-seat Piper Navajo owned and operated by Gibbs Flite Center and contracted by SSC San Diego. A small inlet mounted on top of the Piper Navajo research aircraft collected aerosol samples in flight. The sampling system pulled a flow rate of 5 L min−1 through a knife-edge inlet for particle collection on a 37 mm, 0.8 micron pore size polycarbonate filter. The sampling regime was nearly isokinetic, with a rough 50% inlet size cut of approximately 6 μm for sea salt and 10 μm for dry dust. We expected that some large particles would be collected, especially at elevation in the low humidity regimes, and indeed we do see a very few particles with sizes ∼20 microns.

[10] Forty aircraft samples were collected during the PRIDE study over fourteen flight days. Of this set nine have been fully analyzed and the data are summarized in Table 1. Sample duration varied from 19 to 72 minutes, with sample elevation ranging from the surface to 5000 m. All samples were collected in cloud free areas. The aircraft elevation during sample collection typically was maintained within the layer being studied: anywhere from 1000 to 4000 m thick. For this analysis we grouped particle collection regimes into three altitude ranges. Particles collected above the trade inversion in the Saharan Air layer are given the SAL designation. As they are above the trade inversion, these particles are almost exclusively dust mostly free of marine influence. Second, particles collected in the marine boundary layer (altitudes <1000 m) are given the MBL designation. These particles are a combination of dust and sea salt. Finally, on one occasion, an integrated sample, designated “integrated” (INT), was collected where sampling occurred from the surface to the top of the dust layer.

Table 1. Summary of Airborne Filter Samples Analyzed by Single-Particle Analysisa
DateDuration, minRegionNumber of Particles AnalyzedHi-Magnification
  • a

    All samples were analyzed for particles >1.5 μm in diameter. Selected samples were also analyzed at higher magnification to examine submicron particles. Sampling region is subcategorized to samples taken in the marine boundary layer (MBL), typically <500 m in altitude; Saharan Air layer (SAL), above the trade inversion; and integrated taken from the surface to the top of the dust layer.

5 July 200054Integrated4504Y
16 July 200033SAL17043Y
16 July 200031MBL2583N
20 July 200072SAL9941N
21 July 200033SAL9348Y
22 July 200019SAL8564N
22 July 200044SAL6038N
24 July 200059MBL2928N

2.2. DRUM Analyses

[11] After collection, DRUM sample strips were subjected to X-Ray Fluorescence (XRF) analysis in beamline 10.3.1 from the synchrotron source at the Advanced Light Source of Lawrence Berkeley National Laboratory. Bulk elemental concentrations of Na through Cu were measured. However, as light elements such as Na emit low energy X-rays, attenuation and interference make detection difficult, and significantly increase the measurement uncertainty. Na, being the lightest element analyzed, had total uncertainties (analytical and sampling) of roughly 45 to 50% for typical DRUM sample loadings. This uncertainty was significantly lower for the heavier elements, such as Si, which had uncertainties in the range of 15–20%. The sampling uncertainty remained stable at roughly 5%, while the analytical uncertainty depended on the sample loading, the particle sizes, the calibration curve fit, and the X-ray spectra peak fitting. For example, the uncertainty in the chlorine measurements increased when sulfur was present, due to difficulties in deconvoluting the spectral peak overlap. While sodium and chlorine correlated very well (r = 0.9) the chlorine to sodium mass ratio was ∼1.9, significantly higher than the nominal value of 1.5 which is more typical for aged sea-salt particles.

[12] As discussed by Reid et al. [2003b], on the basis of comparisons with bulk samples collected by the University of Miami, both the DRUM and a colocated MOUDI underestimated dust concentrations by a third. Preliminarily we suspect inlet losses. Further, Reid et al. [2003b] found mass size distributions from the DRUM sampler were smaller than any other sizing instrument from PRIDE and suspect particle bounce-off and break up to be the cause. In this manuscript we combine stages 1–4 and 5–8 to compare total fine- and coarse-mode particle mass. The stage 4–5 split was chosen as stages 1–4 contain 90% of dust mass. These issues are discussed in section 3 and by Reid et al. [2003b].

2.3. Aircraft Sample Single-Particle Analysis

[13] The aircraft samples underwent individual particle analysis at the University of California, Davis Materials Science Department microscopy lab using a FEI XL-30sFEG scanning electron microscope (SEM). An EDAX Phoenix Energy Dispersive Spectrometer (EDS) system collected the X-ray spectra. The samples were prepared by removing a portion of each total filter and mounting them on aluminum stubs. The mounted stubs were then carbon coated with 30 nm of carbon and previewed to determine optimal settings for automated analysis.

[14] The samples next underwent automated analysis in an FEI SL-30sFEG SEM with an EDAX Phoenix EDS detector with an ultrathin window to derive semiquantitative elemental concentrations for Na through Cu. The microscope operated at 20 keV spot 5 in the EDS imaging mode with a beam size of 5 nm and a beam current of 2.39 nA. An analysis grid ranging from 10 × 10 to 20 × 20 was set up for each sample to ensure that at least 100 separate fields would be analyzed and imaged in backscatter mode at 500X magnification during automated analysis. All nine samples were analyzed at 500X magnification, with a minimum size threshold for analyzed particles of 1.5 μm average diameter. Three selected filters were also analyzed at 2000X to examine characteristics of particles with ∼0.15–3.0 μm average diameters. The volume analyzed in the 500X analysis was roughly 2–3 cubic microns. The EDAX EDS software used gray scale thresholding of the backscatter image to identify particles. Once identified, each particle was characterized by area, longest axis, perimeter, average diameter (diameter of a circle having equivalent area), particle centroid, orientation, aspect ratio (longest projection/average diameter), and shape or roundness (area of circle with equivalent perimeter/measured area). EDS spot analysis was performed on the calculated centroid of each particle with a dwell time of 10 sec to give adequate signal-to-noise ratios.

[15] EDAX Remote Particle/Phase Analysis software was used to fit reference elemental spectra to the particle spectra. The peak to background counts ratio threshold was set to 0.4 for peak identification. Spectral peak values were corrected using the ZAF matrix correction to generate weight percents of the elements for each analyzed particle. From the weight percents, mole fractions of elements were calculated.

[16] Single-particle analysis by nature has large uncertainties. System errors include uncertainties introduced by the three-dimensional analysis surface; as with all standardless quantitation programs, the EDAX program assumes the samples are flat and that the take-off angle is well described. This is not the case for particles deposited on a substrate, so there is some systematic error introduced in the measured elemental quantities, though not in the qualitative presence of the elements. Further error arises for particles having small diameter or thickness (less than 2 microns), and for loose aggregate particles through which the polycarbonate substrate can be imaged.

[17] Functional difficulties include biases introduced by particles nearly touching being counted as single-particles, and by the small beam size of the electron microscope analyzing one side of particle in favor of another for large aggregates (although we do not feel these to be significant uncertainties in this analysis).

3. Results: DRUM Bulk Analysis

[18] Figure 1 shows the time series of key elements from the DRUM sampler for 3 July through 24 July. For comparison, data are segregated into the upper and lower stages (stages 1–4, ∼0.74 μm < dae < 11 μm and stages 5–8, ∼0.09 μm < dae < 0.74 μm, respectively). Reid et al. [2003a] reported that during the PRIDE campaign six significant dust events with aerosol optical depths in excess of 0.3 impacted Puerto Rico. Maximum AOTs for these events were roughly centered on 28–30 June and 5, 9, 15, 21, and 23 July. The impact of these dust events at the surface is clearly visible in the coarse-mode aluminum and silicon data, the two strongest tracers for dust (Figure 1). In addition to the significant dust events, additional peaks in dust not associated with high optical depth days were also found. For example, 13 July showed aluminum and silicon concentrations in excess of those from the 21 and 23 July dust events, even though optical depths were less than 0.2.

Figure 1.

Time series of elemental concentrations from the DRUM sampler at Cabras Island during the PRIDE campaign. (a) Aluminum and silicon concentrations for DRUM stages 1–4 (dae > 0.74 μm). (b) Sulfur, potassium, calcium, and iron concentrations for DRUM stages 1–4 (dae > 0.74 μm). (c) Silicon, sulfur, and potassium, for DRUM stages 5–8 (dae < 0.74 μm). Periods when the DRUM sampler was not operating are blacked out.

[19] Other key crustal elements in the coarse-mode associated with dust events are shown in Figure 1b. Peaks in sulfur, potassium, calcium and iron were coincident with the aluminum and silicon spikes from these dust events. While iron and potassium closely tracked silicon, it is clear that the relative concentration of sulfur and calcium (probably gypsum or anhydrite) is more variable. The 5 and 13 July events showed unusually strong peaks in these elements.

[20] As expected, particles in the accumulation mode (Figure 1c, dae < 0.74 μm) did not significantly correlate with the passing of the dust events with the exception of some enhancement in silicon. Mass concentrations from silicon and other key crustal material were roughly 10% of that from the first four stages. Submicron potassium, a strong indicator for biomass burning, was typically <0.1 μg m−3 and did not vary during the study. The behavior of sulfur, however, was more complicated. Submicron sulfur concentration exceeded values in the coarse mode. Submicron sulfur also tracked the submicron silicon from individual dust events, such as before 10 and 23–24 July. However, additional strong peaks not associated with dust are clearly evident on 14 July and after the dust maximum on 21 July. Much of this anomalous sulfur is on the smallest stages, and is not seen by EDS single-particle analysis of the aircraft filter samples due to the 0.15 μm minimum particle size threshold for analysis.

[21] We can examine the relationship between the key crustal elements through a series of regressions presented in Figure 2. Here we plot the five elements with the highest concentrations versus silicon. Silicon was selected as our prime tracer for dust as the African dust primarily consists of silicates, and because the DRUM data analytical uncertainties are lower for silicon than for lighter elements such as aluminum. Data points only include data for dae > 0.74 μm. As one would expect, after examining Figure 1, aluminum, potassium, and iron had very strong correlations with silicon, with r2 values in excess of 0.9. Calcium had only moderately strong association with silicon with an r2 value of 0.53. Sulfur had the least association with silicon with an r2 value of only 0.25. However, sulfur was much better correlated with calcium (Figure 2f; r2 = 0.75), again suggesting an independent gypsum or anhydrite (CaSO4) component to the dust in addition to the silicates and clays. Strong correlations also existed between silicon and several trace elements such as magnesium, titanium, chromium, manganese, and cobalt (Figure 3).

Figure 2.

Elemental mass ratio regressions for the sum of DRUM stages 1–4 (dae > 0.74 μm) for significant elemental species. (a) Aluminum versus silicon. (b) Sulfur versus silicon, (c) Potassium versus silicon. (d) Calcium versus silicon. (e) Iron versus silicon. (f) Calcium versus sulfur.

Figure 3.

Same as Figure 2 but for trace elements versus silicon. (a) Magnesium, (b) titanium, (c) chromium, (d) manganese, (e) cobalt, and (f) zinc.

[22] Regression statistics for Figures 2 and 3 can be found in Table 2. Included are the mass ratios (i.e., the slopes from the regressions), and computed molar ratios relative to silicon. These regressions indicated that for the most part, the chemical composition of the dust was static during the field campaign. The exception of the enrichment of gypsum or anhydrite did not appear to affect the elemental ratios of other species. Stoichiometry is reasonable for clay minerals from Africa. The roughly 2:1 molar ratio between silicon and aluminum is somewhat consistent with the observation that African dust is primarily illite [K0.6(H3O)0.4Al1.3Mg0.3Fe0.12+Si3.5O10(OH)2 · (H2O)] which has a molar ratio on average of ∼2.7:1. Magnesium and iron are also consistent with the standard model. Other lesser species have molar ratios that are less consistent with illite, suggesting low concentration enrichment by other individual minerals in the dust. Potassium with a molar ratio to silicon of 1:17, is significantly depleted from the standard model with 1:6. Such deviations are indeed expected in bulk analysis, as dust is a heterogeneous mix of hundreds of individual species and variations.

Table 2. Bulk Elemental Mass and Molar Ratios Versus Silicon for DRUM Data Where the Aerodynamic Diameter is >0.74a
ElementsMass RatioMolar Ratior2Percent of Mass
  • a

    These are derived from the scatterplots in Figures 2 and 3. Also shown is the r2 for the regression and the estimated percentage of total dust mass attributed to each element assuming aluminum is 8% of dust total mass.

Total   35.1%

[23] After dust, sea salt was the second largest component to aerosol particle mass at Cabras Island during PRIDE. Regressions between sodium, chlorine, and silicon (as a dust tracer) are presented in Figure 4. Also, in Figure 4 is a time series of sodium and chlorine similar to Figure 1 for the PRIDE study period. Sodium and chlorine values were relatively constant throughout the mission with mean values of 0.9 and 5.2 μg m−3, respectively. Spikes in these values were occasionally seen but were not associated with high winds (perhaps instead these are due to a local breaking wave event).

Figure 4.

Regression of (a) sodium to silicon, (b) chlorine to silicon, (c) chlorine to sodium, (d) same as Figure 4c but with a scale change, and (e) reconstructed mass estimates for chlorine and sodium.

[24] As expected, sodium and chloride are highly correlated, and sea salt was an independent species from dust. No correlations were found between salt and any dust or sulfur related species. Because of the overwhelming presence of sea salt, any trace values of sodium or chloride associated with dust would not be detectable. On the basis of all data points, the sodium to chlorine regression suggests a NaCl mass ratio of 1:1.3 (Figure 4c). This would suggest a molar ratio of 1 to 0.84, a reasonable value for sea salt (chlorine is nominally depleted due to photochemical reactions [Keene et al., 1998, 1999]. However, this regression is strongly affected by a single data point. Removal of this data point increases the mass slope to 1:1.9, or a molar ratio of 1 to 1.24 (Figure 4d). This curvature in the NaCl regression suggests that the XRF analysis systematically underestimated sodium at lower concentrations. As sodium is the lowest energy peak of the spectrum to be analyzed, high minimum detectable limits and uncertainties are not entirely unexpected.

[25] As gravimetric analysis cannot be performed on DRUM strips, the total dust mass concentration must be estimated based on the available elemental data. The estimated mass percentage of the elements in bulk dust is presented in Table 2 by assuming aluminum is 8% of total bulk dust mass (this aluminum to mass ratio is commonly used in analytical soil studies [Taylor and McLennan, 1995; Maring et al., 2000]). Figure 5 presents estimated mass concentrations from dust, sea salt, and anthropogenics. These are based on the assumption that aluminum is 8% of total dust mass and that chlorine is 58% of the total sea-salt mass. We assume that submicron sulfur is in the form of ammonium sulfate. A ∼15% correction based on a silicon relation is included to account for any submicron sulfur associated with dust (although it is very possible that some of the correlation between submicron sulfur and dust is because of anthropogenic sulfate being advected at the same time as the dust. However, with only a ∼15% correction, this error is not large).

Figure 5.

Reconstructed mass estimates for the DRUM sampler. For comparison, measurements from the University of Miami TEOM are also presented. (a) Reconstructed mass for dust, sea salt, and submicron sulfate and mass concentration from the University of Miami TEOM. (b) Reconstructed mass for dust. TEOM mass concentration estimates for dust where the mean 18 μg m−3 sea-salt mass concentration was subtracted from the total value.

[26] From Figure 5a we find that dust is for the most part the dominant aerosol species at Cabras Island. Submicron sulfate concentrations were typically under 2 μg m−3. However, reconstructed mass from the DRUM sampler significantly underestimated the total aerosol particle mass concentration as determined by the University of Miami Tapered Element Oscillating Microbalance (TEOM) colocated at Cabras Island. While the DRUM follows the TEOM for the episodic dust events, on a whole it underestimates total mass concentrations from 10–50%. Much of the noise in the DRUM reconstructed mass lies in the sea salt. Typically, chlorine is a poor choice as an indicator species for sea salt as its mass ratio varies considerably with age and non-sea-salt sulfate concentration [Keene et al., 1999]. However, as we know that sodium is underestimated in the DRUM analysis, we are left with little choice in this comparison but to use chlorine. However, we know from Reid et al. [2002] and Savoie et al. (submitted manuscript, 2003) that the daily average dry sea-salt concentration in PRIDE varied between 14–22 μg m−3, with a mean value of ∼18 μg m−3. In Figure 5b we compared the DRUM reconstructed dust mass to the TEOM data minus an average value of sea salt of 18 μg m−3. In this case the DRUM and TEOM track considerably better. However, peak dust concentrations are still underestimated in the DRUM sampler. Integrating through each event, on average the DRUM is only reconstructing 60% of the dust mass.

[27] There are several possible reasons why the DRUM is underestimating dust mass. First, we can question whether aluminum is not 8% of dust particle mass, but rather 5%. Such a decrease would more than make up the difference in mass. However, as will be shown in the discussion section, stoichiometry prohibits such a drastic change. Second, we can assume that there is some error in the chemical analysis and that the aluminum mass concentration is underestimated. We know sodium is heavily underestimated, and aluminum is the next element in the X-ray spectrum. But again, such a shift in the silicon-aluminum-iron ratio is unreasonable. After the analysis of Reid et al. [2003a], we suggest that inlet issues may be to blame. With a flow rate of 10 L min−1, it is quite possible that at the 7 m s−1 mean wind speeds, large dust and salt particles could not make the bend in the simple cap inlet. The colocated MOUDI sampler appears to have suffered a similar problem. However, for the purposes of determining bulk chemical ratios in dust, this should not be a significant artifact.

4. Results: Single-Particle Analysis Results

[28] While the DRUM sampler analysis can give us reasonable results for bulk composition, we need to go to single-particle analysis to interpret the results with respect to mineralogy and optical properties. For example, how many mineralogical species are influencing the found aluminum to silicon ratio, and is illite a reasonable model? Are the dust particles aggregates or single particles? Does particle chemistry covary with size or morphology? In the following sections we explore these questions.

4.1. Single-Particle Morphology and Size

[29] As dust is heterogeneous mix of particles of various morphologies, no standard set of size statistics can be easily applied. Examples of secondary electron micrographs for dust collected in the SAL on 21 July 2000 are presented in Figure 6. Four magnifications are shown, depicting particles in 10–20, 5–10, and <5 μm size ranges.

Figure 6.

Secondary electron images of dust particles collected in the Saharan Air Layer near Puerto Rico on 21 July 2000. Because some size segregation occurs on the filter substrate, size and shape distributions from individual images are not representative of the dust as a whole. (a) 1000X, (b) 2000X, (c) 4000X, and (d) 12000X.

[30] Data from this flight was typical for the study. Dust is predominately in the form of large, amorphous alumino-silicate clay particles. These can be relatively large, with particles as large as 30 microns being detected on the filters. Particles can either be free or included in some form of aggregate. Evidence of aggregation can be found by the presence of composite particles that that broke apart on impact.

[31] More detailed images of single particles can be found in Figure 7 (images of large aggregate particles are given by Reid et al. [2003a]). Particles generally fit into three broad categories. Most prevalent were layered silicates (Al-Si clay minerals or feldspars), which qualitatively account for ∼70% of all particles. Dominant species are consistent with illite, kaolinite, and montmorillonite. Examples include Figures 7a (left-hand particle), 7b, 7f, 7g, 7h, and 7j. The second most prevalent (∼20%) are the amorphous silicates, some of which appear to be agglomerates of clay particles and are depicted in Figures 7d, 7e and 7i. Finally there are non silicon trace species such as gypsum and calcium carbonate which account for <10% of all particles (Figures 7a, right-hand side, and 7c). In general, particles were found in some form of aggregate (∼50% of all particles, 70% of particles >3 μm in diameter). Larger clay minerals were usually found to be carrying smaller particles (e.g., Figures 7a, left side, and 7h). More typically, dust particles less than 10 μm in diameter were aggregated in clusters of 2 to 5 components.

Figure 7.

Detailed electron micrographs of individual particles.

[32] As is discussed later in this paper, relating these size statistics to quantities usable in particle models is not straightforward. In this study three principle particle measurements were recorded for each particle from the electron backscatter image. These are cross-sectional area (A), particle perimeter (P), and longest projection (LProj). From these measurements, statistics relating to particle diameter and area can be generated. These include computed average diameter (diameter of a circle with equivalent area), and minor axis (using an ellipse model; assigning the longest projection as the major axis and preserving the particle cross-sectional area to derive a normal minor axis).

[33] Computing particle volume is more uncertain as it requires some estimate of particle thickness (a quantity that usually is not measured). Previously, researchers have used the minimum diameter [e.g., Anderson et al., 1996] or a fraction of the minor projection [Okada et al., 2001] as the best estimate of particle height, and modeled the particles as oblate spheroids. As particles tend to lie flat on the substrate, these values should be considered maximum heights. For example, Okada et al. [2001] measured particle height for Asian dust particles, and determined 1/2 to 1/4 the orthogonal diameter to be a better estimate for particle height for mineral aerosols in the 0.1 to 6 micron diameter range. However, if we are interested in the volume distribution normalized to total volume, as long as there is a consistent trend in the height-width aspect ratio with size these factors are relatively insignificant. As will be shown, particle aspect ratios do not appear to change with size, so this is a reasonable assumption.

[34] Particle area and volume distributions are presented in Figure 8. Distributions are normalized by total area and volume for particles with average diameter >3μm. Here we have grouped size spectra for the two filters collected on 16 July (Figures 8a and 8b), the two filters collected on 22 July (Figures 8c and 8d), and all remaining filter samples (Figures 8e and 8f). All particle distributions share some characteristics. Area distributions are dominated by a lognormal peak with area median diameter (AMD) on the order of 5 to 7 μm and geometric standard deviations of 1.65–1.80. When higher resolution data are available, we consistently find that there is a shoulder that extends to ∼0.7 μm in diameter. Below this point dust particles do not have appreciable surface area. Estimated volume distributions are more lognormal, with volume median diameters (VMD) from 7 to 9 μm, and geometric standard deviations on the order of 1.6 to 1.8.

Figure 8.

Particle normalized cross-sectional area and volume distributions for particles collected on selected aircraft samples. Specified in the key are sample date at altitude region. MBL, marine boundary layer; CBL, convective boundary layer; SAL, Saharan Air Layer.

[35] As was reported in other PRIDE manuscripts [Maring, 2001; Reid et al., 2003a, 2003b] measured particle size distributions were relatively static during the campaign and mostly did not vary with altitude. This can be most clearly seen in the higher moment distributions such as volume. For the 16 July case (at the tail of fairly strong dust event in which mid-visible optical depths were maximum of 0.5, 0.25 during collection), single-particle analysis dust particles collected in the MBL (altitudes <500 m) were only slightly larger than those at 3000 m in the SAL (VMD of 6.8 versus 6.4 μm, respectively). Similarly, for the 22 July case two samples were collected. Particles collected in a region at the bottom of the SAL and in the convective boundary layer (800–2700 m) were only slightly larger than those collected toward the top (2700–5000 m), with VMDs at 6 and 7 μm respectively. Hence, while some enhancement of larger particles can be seen at lower levels (presumably due to settling), significant size separation is for the most part not evident. The one exception to this finding is the case of 5 July. As described by Reid et al. [2003a], the 5 July case was the one case where strong gradients were seen in particle size with altitude. While only one integrated dust sample was collected on the 5 July flight, we can see that the size distribution was like no other with wider distributions and the largest VMD of the study at 10 μm.

[36] Dust particle morphology statistics were also calculated from the electron backscatter images, and verified by hand calculations for some particles. Two principle quantities, the aspect ratio, (Aspect = π(LProj)2(4A)−1), and shape factor (Shape = P2(4πA)−1) are commonly reported in the literature. The aspect ratio provides a basic approximation of particle roundness and is roughly equivalent to the ratio of major to minor axes of the ellipsoid best fit to the particle. The shape factor, the inverse of particle circularity, is a dimensionless parameter used as an indicator of the complexity of the particle. As the ratio of the square of the perimeter to particle area, the shape factor describes how “jagged” a particle is, by definition equal to 1 for a circle.

[37] Figure 9 presents cumulative probability plots of particle aspect ratio and shape factor as a function of particle average diameter. These plots were derived from the aircraft sampled particles measured during the study (median values of the aspect ratio and shape factors did not significantly vary from sample to sample). As is expected for dust particles, aspect ratios are relatively large and broad based. Median aspect ratios averaged 1.9 with a standard deviation of 0.9. Removal of NaCl-rich particles from the analysis did not significantly change the distributions or the median aspect ratios. The aspect ratio distributions for the dust appear to be independent of size, and the curves do not statistically differ from one another for sizes less 10 μm. The largest particles (dp > 10 μm, representing roughly 3% of the analyzed particles) are slightly more elongated with median aspect ratio values of 2.2 ± 1.2.

Figure 9.

Cumulative probability plots of (a) aspect ratios and (b) shape for variously sized particles collected on aircraft samples. Data from all analyzed aircraft samples are included in these plots.

[38] In contrast to the aspect ratio, shape factors do appear to have a strong dependence on size (Figure 9b). Smaller particles have shape factors near unity while the largest particles have a median shape factor of 3. However, for most particles in the 2 to 10 μm range (where most of the area and volume exists) values are a consistent 1.4. This trend is not unexpected as these are individual minerals. This trend is seen in Asian dust by Okada et al. [2001], and is cited as evidence of increased particle complexity. With increased particle size, there is a higher probability that the particles are aggregates, and hence a substantial increase in perimeter relative to area. This is compounded by the possibility of aggregates flatting on impact with the substrate.

4.2. Semiquantitative Single-Particle Elemental Analysis

[39] While the XRF analysis on DRUM sampling strips provides bulk elemental ratios from which dust chemical stoichiometry can be performed, EDX analysis provides us information on the heterogeneity of individual particles. Particles larger than ∼1.5 μm were individually analyzed for elemental ratios and for morphology. Three of these filters were also analyzed for particles in the range of 0.15 to 3.0 μm diameter. From these results, elemental percentages and molar percentages (relative to analyzed elements) were generated for each particle.

[40] As an example of African dust, electron backscatter images of particles collected in the SAL for the 21 July event are presented in Figure 10 for two size ranges. Associated ternary plots for the two images are also shown. Given are Si-Na-Cl, to differentiate dust from sea salt, Si-Al-Fe, Si-Al-Ca and Si-Al-Mg, to differentiate various clay minerals from amorphous silicon, Si-Na-Mg to differentiate minerals such as feldspar and montmorillonite, and S-Na-Ca to differentiate gypsum. Strong outliers from the analysis are individually labeled. Examination of Figure 10 reveals the basic chemical nature of the dust.

Figure 10.

Production electron backscatter images taken from the 21 July 2000 Saharan Air Layer filter. Shown are (a) course-resolution and (b) fine-resolution images with their associated ternary plots of key elements. More atypical particle types are labeled immediately to the right of the particle or by arrow.

[41] Predominately, dust is made of alumino-silicates and amorphous silicates with the remainder being trace amounts of gypsum, calcium carbonates and other species. Since this sample was taken in the SAL, sodium chloride from sea salt is almost nonexistent. A very strong alumino-silicate population is the most striking feature of these plots with a mass ratio between Al and Si of ∼0.5. Iron, the principle light absorber for dust, was found in >90% of such particles at relatively low concentrations (∼0.08 mass ratio to silicon). Similarly, Mg, Ca and Na (not associated with Cl), other key tracer elements for clay minerals, were also strongly and expectedly associated with aluminum-silicates species. A second population of more pure silicates is also visible. For these particles, while some Al is found, other trace elements such as Mg, Ca, and Na are not present in significant quantities. Finally, a few outlier species are present. Since they are so few, they do not appear clearly in the ternary plots but are labeled in Figure 10. These include; calcium enriched silicate particles (Ca-AlSi; probably clay particles aggregated or layered with calcium carbonate), calcium enriched particles (Ca; probably calcium carbonate particles with some clay particle contamination), gypsum (CaSO4), quartz (SiO2), iron-rich particles (Fe rich; probably clay aggregated with hematite), and titanium enriched silicate particles (Ti-AlSi; probably TiO2 aggregated with clay particles).

[42] While Figure 10 can give us a strong qualitative feel for the chemical nature of the dust, more quantitative descriptions require a thorough cluster analysis. Seven aircraft samples were subjected to unsupervised cluster analysis to group the particles into distinct types. Clusters were identified by average compositions, consisting of molar percentages of the elements analyzed. Species such as oxygen were not included in derivation of the molar percentages. Because many of the particles are aggregates and may contain conversion products, each cluster should be taken to represent a range of species and not a quantified composition.

[43] Table 3 displays the dominant clusters and groups derived from all filter data. Table 4 presents relative cluster scores for the samples analyzed and is discussed further in section 5. Normalized cross-sectional area distributions for these clusters are presented in Figure 11. The clusters fall roughly into 12 compositional groups. These groups were chosen not simply due to chemical composition ratios, but rather how the cluster scores tracked from event to event. Aluminum silicates account for 59% of the particles by number, and roughly 67% of the particle mass. Eight percent of the particles and 17% of the mass belong in the high Si group, which includes SiO2. Most of the remaining particles fall into the following 8 groups (annotated with percentage by count and percentage by mass respectively): calcium-rich particles excluding gypsum and anhydrite (6%, 2%), complex fine-mode aggregates (9%, 3%), NaCl-rich particles (5%, 5%), sulfur- and calcium-rich particles like gypsum or anhydrite (5%, 1%), magnesium silicates (1%, 2%), calcium silicates (1%, 1%), iron-rich particles (0.2%, 0.4%), magnesium and calcium-rich particles (0.5%, 0.4%), titanium-rich particles (0.3%, 0.1%), and sodium-rich particles excluding NaCl (0.7%, 0.1%). We discuss each group under the following headers.

Figure 11.

Particle normalized size distribution plots for cluster analysis groupings; (a) Al- and Si-rich and Mg- and Si-rich clusters, (b) Si-rich and Mg- and Ca-rich clusters, (c) Ca- and Si-rich and Ca-rich clusters, (d) aggregate and NaCl-rich clusters, (e) Ca- and S-rich and Fe-rich clusters, (f) Ti-rich and Na-rich clusters. Data from all analyzed samples are included in these plots.

Table 3. Cluster Analysis Results From an Ensemble of All Filters Collecteda
Particle TypeGroupAbundanceClusterCluster Average Composition
Percent by NumberPercent by Mass
  • a

    Cluster average compositions are listed as molar percentages. Cluster species with <3% molar fractions are not listed in the composition. Clusters are listed by qualitative particle type, followed by group and individual cluster.

Al-SiAlSi-13.914.4C1Si.46 Al.34 Mg.05 Na.04 Fe.04
Al-SiAlSi-14.16C2Si.46 Al.27 Mg.08 Na.06 Fe.05
Al-SiAlSi-15.811.4C3Si.49 Al.24 Mg.09 Na.05 Fe.04
Al-SiAlSi-13.215.8C4Si.48 Al.30 Mg.06 Fe.04 Na.04
Al-SiAlSi-12.62.7C5Si.41 Al.33 Na.07 Mg.06 Fe.04
Al-SiAlSi-14.61.8C7Si.40 Al.26 Mg.09 Na.08 Fe.04
Al-SiAlSi-141.9C8Si.41 Al.24 Na.12 Mg.09 Fe.04
Al-SiAlSi-11.12.6C16Si.40 Al.23 Mg.17 Na.06 Fe.06
Al-SiAlSi-12.10.8C18Si.48 Al.19 Na.08 Mg.09 Fe.04
Al-SiAlSi-20.31.1C23Si.35 Al.23 Na.18 Cl.10 Mg.06 Fe.03
Al-SiAlSi-220.7C29Si.32 Al.20 Na.19 Mg.10 Cl.05 Ca.04 Fe.03
Al-SiAlSi-20.40.5C34Na.27 Si.26 Al.18 Cl.13 Mg.06
Al-SiAlSi-20.20.6C38Na.34 Si.22 Cl.18 Al.14 Mg.05
Al-SiAlSi-3(65.50.7C13Si.38 Al.20 Mg.12 Na.11 P.04 Fe.03 S.03
Al-SiAlSi-3(67.10.4C15Si.31 Al.21 Na.13 Mg.12 P.05 S.04 Fe.03
Al-SiAlSi-3(64.60.7C17Si.34 Al.28 Na.10 Mg.09 P.04 S.03
Al-SiAlSi-3(62.70.5C28Si.22 Al.18 Na.17 Mg.15 P.06 S.06 Cl.04 Ca.04
Al-SiAlSi-4(70.50.9C31Si.39 Al.23 Ca.12 Mg.08 Na.05 Fe.04
Al-SiAlSi-5(80.20.5C41Si.38 Al.25 Fe.18 Mg.06 Na.05
Al-SiAlSi-5(80.40.2C42Si.30 Al.24 Fe.23 Mg.07 Na.07
Subtotal 55.365.4  
Si richSi-11.63.5C6Si.79 Al.08 Na.04 Mg.03
Si richSi-11.81.7C11Si.68 Al.12 Na.05 Mg.05
Si richSi-10.84.1C12Si.82 Al.06
Si richSi-22.30.5C24Si.60 Al.11 Na.09 Mg.07 P.03
Si richSi-30.93.3C25Si.54 Al.23 Na.05 Mg.05 K.03
Si richSi-31.34.3C9Si.55 Al.22 K.07 Mg.05 Na.04
Subtotal 8.717.4  
Ca richCa-10.30.3C49Ca.64 Na.08 Mg.07 Si.07 Al.05
Ca richCa-21.10.8C19Ca.43 Si.18 Al.12 Mg.10 Na.09
Ca richCa-20.70.9C20Ca.57 Si.11 Mg.08 Al.08 Na.07
Ca richCa-32.10.1C37Ca.27 Na.18 Mg.14 Si.13 Al.11 P.04 S.04 Cl.03
Ca richCa-32.40.1C40Ca.39 Na.20 Mg.15 Si.11 Al.07
Subtotal 6.62.2  
Fine aggregatesAgg-15.31.4C14Na.23 Mg.18 Al.14 Si.13 P.08 S.07 Cl.05 Ca.03 K.03
Fine aggregatesAgg-10.90.4C45Si.20 Al.15 Mg.12 Na.11 S.10 P.09 Cl.07 Ca.06 K.04 Sn.04 Ti.03
Fine aggregatesAgg-10.30C56Si.24 Al.12 S.10 Cl.10 Ca.10 K.09 Sn.07 Ti.06 Fe.04
Fine aggregatesAgg-10.30C57Si.28 Fe.25 Ca.10 K.10 Al.09 Ti.06 Cl.05 Sn.05 S.03
Fine aggregatesAgg-10.20C58Si.18 Ca.17 K.12 Ti.11 Fe.10 Cl.08 S.07 Al.05
Fine aggregatesAgg-10.20.1C60Si.39 Fe.12 K.10 Al.14 Ca.07 S.04 Cl.04 Ti.03 Sn.03
Fine aggregatesAgg-10.20C65Si.33 Al.12 K.08 Ca.08 S.08 Cl.07 Fe.06 P.06 Sn.05 Ti.05
Fine aggregatesAgg-210.4C39Na.25 Si.20 Al.15 Mg.12 Ca.07 Cl.07 S.06 P.03
Fine aggregatesAgg-20.70.3C46Na.31 S.16 Mg.11 Ca.10 Si.09 Al.08 Cl.08 P.04
Subtotal 9.12.6  
NaCl richNaCl1.40.6C30Na.55 Cl.34 Mg.04
NaCl richNaCl20.5C32Na.51 Cl.29 Si.06 Al.05 Mg.04
NaCl richNaCl0.83C35Na.42 Cl.24 Si.13 Al.09 Mg.05
NaCl richNaCl0.80.4C43Na.40 Cl.16 S.10 Si.07 Mg.07 Ca.07 Al.07
NaCl richNaCl0.10.4C53Na.48 Cl.38 Si.03 Mg.03 Al.03
Subtotal 5.14.9  
Ca, S richCaS-14.40.1C21S.22 Ca.17 Na.16 Si.11 Mg.11 Al.10 P.05 Cl.03
Ca, S richCaS-20.70.9C26S.35 Ca.30 Si.08 Na.08 Al.06 Mg.05
Ca, S richCaS-20.20.3C44Si.24 S.21 Cl.18 Al.14 Na.08 Mg.07
Subtotal 5.31.3  
Mg silicatesMgSi-10.41.1C33Si.43 Mg.26 Al.14 Na.05 Fe.04
Mg silicatesMgSi-20.80.4C36Si.31 Mg.25 Al.20 Na.07 Fe.05
Subtotal 1.21.5  
Ca silicatesCaSi-11.30.6C22Si.29 Ca.24 Al.17 Mg.10 Na.09
Ca silicatesCaSi-10.20.3C52Si.34 Ca.26 Al.14 Mg.08 Na.06 Fe.04
Subtotal 1.50.9  
Fe richFe-10.20.2C48Fe.41 Si.20 Al.17 Na.06 Mg.06
Fe richFe-10.10.2C66Fe.58 Si.13 Al.08 Mg.04 Na.04
Fe richFe-20.7<0.1C51Fe.32 Na.15 Si.15 Al.13 Mg.09 S 06 P.03
Subtotal 1.00.4  
Mg, Ca richMgCa-10.10.2C47Mg.46 Ca.27 Si.09 Al.07 Na.05
Mg, Ca richMgCa-10.10.1C50Mg.36 Ca.20 Fe.18 Al.12 Na.07
Subtotal 0.20.3  
Ti richTi-10.2<0.1C54Ti.39 Si.19 Al.14 Na.09 Mg.07 Fe.04
Ti richTi-20.10.1C55Si.31 Ti.22 Al.21 Mg.07 Na.06 Fe.05
Subtotal 0.30.1  
Na richNa-10.5<0.1C59Na.43 Si.30 Al.15 Mg.05
Na richNa-10.20.1C62Si.36 Na.31 Al.12 Cl.07 Mg.06
Subtotal 0.70.1  
Table 4. Summary of Particle Grouping
 5 July Integrated16 July SAL16 July MBL >3 μm20 July SAL >3 μm21 July SAL22 July SAL24 July MBL >3 μm
<3 μm>3 μm<3 μm>3 μm<3 μm>3 μmSample 1 >3 μmSample 2 >3 μm
Si rich3.58.47.413.312.716.09.718.515.517.511.3
Ca rich3.
Fine aggregates7.
NaCl rich22.317.
Ca and S rich18.
Mg Silicates0.
Ca Silicates0.
Fe rich2.
Mg and Ca rich0.
Ti rich0.
Na rich00.2000.303.10000.6

4.2.1. Alumino-Silicates Group

[44] AlSi, contains the most common particle type seen in the PRIDE dust samples, comprising roughly 55% and 65% of particle number and mass, respectively. There is some separation with size, with 75% of the coarse-particle mass in the SAL samples, and 60% of the fine particles. This group includes particles for which Al and Si are the predominant components of the analyzed composition. Mineralogically, we expect them to be dominated by clay minerals such as illite, kaolinite, chlorite, and montmorillonite. Because they are elementally similar to clay minerals, species such as feldspars and micas will also fall into this group. As these particles make up the bulk of the dust, the area distribution for this cluster (Figure 11a) is nearly identical to that given in Figure 8 with an area modal diameter of 5 μm.

[45] To simplify presentation of the AlSi data, they are sorted into five groups that share common composition and/or occurrence. As most particles are aggregates, and even individual clay minerals vary considerably, they cannot be viewed as individual species. Rather, they should be viewed simply as natural groupings of particles with similar elemental ratios. These groups are described below.

[46] 1. AlSi-1 can be interpreted as clay mineral species and accounts for 30% of all analyzed particles and roughly 60% of the total particle mass. Not surprisingly, The group was strongly associated with samples collected in the SAL dust event (e.g., 21 and 24 July). Si and Al ranged from 40–49% and 19–33% of detectable molar percentages, respectively. Na and Mg molar percentages range from 4–9%, with Fe comprising 4–8% of the analyzed composition. Approximately 80% of Fe, the principal light absorber for dust, is associated with these clay particles. This cluster should be viewed as a continuum of clay minerals rather than an individual species. Stoichiometrically, the average Si to Al ratio of 2:1 is consistent with illite, and excludes such species as kaolinite, having a ratio of 1:1. However, based on individual samples and our ternary analysis, kaolinite is clearly present in the samples (∼20% of these particles). However, because of the aggregate nature of most particles they do not appear in an unsupervised cluster analysis but are rather grouped with other species. This is demonstrated in Figure 12a, which shows the aluminum to silicon ratio distributions for all silicon-rich clusters. For comparison, Figure 12b shows the aluminum to silicon ratio for all analyzed aircraft samples.

Figure 12.

Cumulative probability plots of the molar ratio of aluminum to silicon for all analyzed aircraft sample particles. Specified in the key are sample date and altitude region. MBL, marine boundary layer; CBL, convective boundary layer; SAL, Saharan Air Layer.

[47] 2. AlSi -2, representing 3% of the sample number and mass, is enhanced in both Na and Cl. These likely represent dust particles of similar composition as AlSi-1 that have interacted with sea salt during transport. Not surprisingly, this cluster is more pronounced for samples taken in the marine boundary layer. This group is primarily a coarse-mode feature, accounting for 10% of the 5 July coarse particles, 13% and 25% of the 16 and 24 July MBL coarse particles, respectively.

[48] 3. AlSi -3, representing 20% of the sample number, but only 2.3% of the mass, has lower Si concentrations, and significant levels of P, S, and Cl. This group accounts for 30% of the fine particles on 5 July, and 35% of the fine particles from the 16 July SAL sample. As there are phosphate mines in southwestern Mauritania (a source region), it is possible that these particles represent fine dust from that region. This group is not a significant presence after 16 July, indicating that the later samples may represent different transport or conversion regimes.

[49] 4. AlSi -4 includes calcium-rich Al silicates, and accounts for 0.5% of the analyzed particles. This group is absent in the MBL and integrated samples, and poorly represented in the 16 July fine and coarse SAL samples. However, it accounts for ∼1% of the particles in the later SAL samples, suggesting that the absence of these particles in the MBL and integrated samples may be due to conversion of Ca during transport to anhydrite.

[50] 5. AlSi-5 contains iron-rich Al silicates with average composition of 18–23% Fe. This group is distributed throughout the dusty layers, but only accounts for 0.5% of the samples. This group may simply represent iron-rich silicate minerals, or may indicate Fe contamination of silicate minerals, as suggested by Falkovich et al. [2001].

4.2.2. Si-Rich Group

[51] The Si-rich group is the second largest cluster group, accounts for roughly 9% of the particles analyzed, and is characterized by molar fractions of Si greater than 50%. For the individual samples, 13–18% of the coarse SAL particles, and 3–10% of the fine particles belonged to this group. The derived clusters can be grouped into three distinct subgroups; probable SiO2, fine alumino-silicate, and coarse alumino-silicate particles. Several particles that appear to be diatom skeletons were seen, especially on the 5 July sample, but the SiO2 particles were primarily crystalline fragments, either alone, or as part of larger aggregates. Like other dust particle types, these too have an area median diameter of ∼5 μm, although with a noticeably smaller standard deviation.

[52] 1. Si-1 has average composition of >68% Si with less than 10% Al, Mg and Na, and is probably SiO2 particles. With the exception of the 5 July sample, this group accounts for 8–12% of the coarse particles from each sample, and 3–5% of the fine particles. The 5 July sample contains only 6% coarse and 1% fine Si-rich particles.

[53] 2. Si-2 is primarily fine-mode particles, representing 3% of the fine particles from 5 July, and 4% of the fine particles from 16 July, but only 1% of the fine SAL particles from 21 July. It has average composition of 60% Si, 11% Al, 9% Na and 7% Mg with some trace elements.

[54] 3. Si-3 has an average composition of 55% Si, 23%Al, and 3–7% Na, Mg, and K, indicating that these are probably silicate minerals such as micas or clays. This group is nearly absent in the 5 July samples and the fine 16 July SAL sample. However, it is relevant in the later samples, making up 4–8% of the coarse SAL samples.

4.2.3. Calcium-Rich Group

[55] The calcium-rich group, Ca, excluding probable gypsum and anhydrite particles, represents about 7% of the samples. This group is less prevalent in the MBL samples from 16 July and 24 July but accounts for 3–5% of the coarse particles in the SAL samples, and 2–3% of the fine particles for 5 July and 16 July. In contrast, this group accounts for almost 17% of the 21 July fine SAL sample. The calcium-rich group is the first prevalent cluster to have a size distribution that deviates from the bulk analysis, with an enrichment of submicron particles. Three subgroups simplify the analysis of this cluster.

[56] 1. Ca-1 is primarily Ca (64%), and accounts for 0.3% of the analyzed particles. These are likely calcium carbonate particles.

[57] 2. Ca-2 accounts for 1.8% of the particles analyzed, and ranges in average composition from roughly 40 to 60% Ca, with 10–20% Si, 8–12%Al, 8–10%Mg, and 7–9% Na. This group is primarily comprised of coarse-mode particles. Enrichment in elements like Si and Al are likely due to aggregation with clay particles.

[58] 3. Ca-3 is found almost entirely in the fine mode, and accounts for nearly 5% of the analyzed particles. This group is Ca-rich, but has Na, Mg, and Si as major elemental components. This group accounts for 12% of the fine SAL particles in the 21 July dust layer sample, 3% of the fine integrated particles from 5 July, and 1.5% of the fine SAL particles from 16 July.

4.2.4. Small-Mode Aggregates Group

[59] The small mode aggregates group comprises 9% of the analyzed particles, but is primarily fine particles. These particles are complex and varied combinations of Na, Mg, Al, Si, P, S, Cl, and K, with some Ti and Sn. Size distributions of these particles (Figure 11d) show they are quite clearly unlike other alumino-silcate aggregate particles, having an area median diameter on the order of 2 μm. As these particles appear in the coarse and fine-mode sample populations, we do not think it is an artifact of the analysis. Two subgroups expedite presentation of this group and were found based on sampling location.

[60] 1. Agg-1 is primarily associated with the 16 July dust layer sample, accounting for 18.7% of the fine 16 July SAL particles, though it has a component (∼2%) in the 5 July fine- and coarse-mode samples, and in the coarse 22 July SAL sample aloft.

[61] 2. Agg-2 is primarily in the integrated and MBL samples. In the 5 July sample, it accounts for 5.8% of the fine mode and 2.2% of the coarse-mode particles. It also represents 2.7% of the coarse-mode surface sample from 24 July, and 1.6% of the MBL sample from 16 July. This group is a minor component, accounting for 1.7% of the analyzed particles.

4.2.5. NaCl-Rich Group

[62] The sodium and chlorine-rich group, NaCl, represents 5% of the analyzed particles, and involves integrated and surface sample particles. It includes 22% of the fine and 17% of the coarse particles from 5 July, and 22% of the coarse MBL particles from 24 July. This group makes up only 3% of the 16 July coarse MBL sample when dust was the dominant species near the surface. The size distribution for these particles is surprisingly similar to the aggregate cluster. This group ranges in composition from 40–55% Na, 16–40% Cl, and 3–7% Mg, with some clusters also containing Si, and Al.

4.2.6. Ca- and S-Rich Group

[63] The CaS group includes roughly 5% of the analyzed particles, but is primarily associated with the 5 July fine integrated sample. As discussed in the sections on the DRUM sampler analysis, a Ca and S spike was also observed in the bulk analysis. Particles in this group show the strongest deviation in size from the bulk dust, with a prominent fine-mode component. Two subgroups best characterize these particles.

[64] 1. CaS-1 represents fine particles, many associated with larger branching conglomerate structures, and some nearly circular. This group includes 4.4% of the analyzed particles. It accounts for 18% of the fine-mode particles from 5 July, and 3% of the fine-mode SAL particles from 16 July. This group has an average molar composition of 22% S, 17% Ca, 16% Na, 11% Si, 11% Mg, 10% Al, 5% P, and 3% Cl, and may represent conversion products of Ca-rich particles.

[65] 2. CaS-2 represents 4% of the coarse and 2% of the fine 5 July integrated samples, but also represents about 1.5% of the coarse particles from each of the other samples. With its average composition of 35% S, 30% Ca, 8% Si, 8% Na, 6% Al and 5% Mg, this group probably represents native gypsum or anhydrite from crustal sources. Some characteristic lenticular and spar crystal shapes are seen, but most particles are relatively rounded. The median shape and aspect values reflect the fact that many of these particles are part of loose aggregates.

4.2.7. Mg-Silicate Group

[66] The MgSi group comprises 1% of the analyzed particles. It has a size distribution almost identical to the Al-Si group, suggesting that chemically and morphologically they behave the same. Two subgroups are useful to describe these particles.

[67] 1. MgSi-1 is nearly absent from the 5 and 16 July fine SAL samples. However, it is associated with the SAL and MBL coarse particles from 16, 20, 21, 22, and 24 July samples. This subgroup has an average composition of 43% Si, 26% Mg, 14% Al, 5%Na, 4% Fe.

[68] 2. MgSi-2 is associated with the 5 and 16 July sample periods, but is less prevalent in the later sample periods, perhaps indicating a different source region. This subgroup comprises 0.8% of the analyzed particles and has an average composition of 31% Si, 25% Mg, 20% Al, 7% Na, and 5% Fe.

4.2.8. Ca-Silicate Group

[69] The CaSi group comprises about 1.5% of the analyzed particles, and represents 4.2% of the fine particles from the 21 July SAL sample. The average composition ranges from roughly 29 to 34% Si and 24–26% Ca, with significant Al, Mg and Na. The near absence of CaSi particles from the MBL probably reflects conversion to aggregates or Ca- and S-rich particles, as these groups are enhanced in MBL samples.

4.2.9. Fe-Rich Group

[70] The Fe group, comprises about 1% of the analyzed particles, and accounts for 2.4% of the fine-mode particles from 5 July. The iron group size distribution tracks that of the Ca-S group, another species that was enhanced on 5 July. It is best described by two subgroups.

[71] 1. Fe-1 is found in all samples, but is more prevalent in integrated and SAL coarse-mode samples. This subgroup ranges in average composition from 41–58% Fe, 20–12% Si, 17–8% Al, 6–4% Na and Mg.

[72] 2. Fe-2 consists of fine-mode particles from 5 and 16 July, accounting for 2.4% of the integrated 5 July fine-mode particles, and 0.5% of the 16 July fine-mode particles. This subgroup has an average composition of 32% Fe, 15% Na, 15% Si, 13% Al, 9% Mg, 6% S, and 3% P. The phosphorous could be from ocean aerosols, but may reflect a source region in southern Mauritania where phosphate mining occurs.

4.2.10. Mg- and Ca-Rich Group

[73] MgCa, comprises roughly 0.2% of the analyzed particles and occurs in the coarse-mode dust layer samples from 16 July, 20 July, 21 July, and 22 July. These particles range from 46–36% Mg and 27–20% Ca, and include some Si, Al and Na. These are probably dolomitic minerals with silicate contamination. This cluster is not seen in the 5 July samples and, as the Ca- and S-rich particle groupings have low Mg levels (on average,<10%), the absence cannot be explained by conversion to anhydrite. Size is similar to the Si-rich cluster, with a more pronounced peak in area at 5 μm than other species.

4.2.11. Ti-Rich Group

[74] Ti, comprises 0.3% of the analyzed particles, and contains particles having >20% Ti. These particles are rare, but occur in both the fine and coarse mode, often in small aggregates. Particle size shows more a bi-modal behavior than other species and may suggest two separate mineralogical types. But, as this is a trace species and the modes cannot be cross-verified between the fine- and coarse-mode analysis, we cannot exclude the possibility of artifact.

4.2.12. Na-Rich Group

[75] Na, comprises 0.7% of the analyzed particles and has average composition ranging from 30–45%Na, 30–36%Si, 12–15%Al, with some Mg and Cl. Note that this group is not expected to include many NaSO4 particles; the minimum size range for analysis (0.15 microns) was chosen to exclude the majority of these particles in order to get better statistics on dust particles. Like the Ti-rich group, these particles exhibit bi-modal behavior.

5. Discussion: Bulk Approximations and Estimates of Chemistry

5.1. Derivation of Mineralogical Information

[76] A goal of the PRIDE study was to use bulk and single-particle analysis to help derive mineralogical information to be used in further dust modeling studies. It is first prudent to ensure that the DRUM and single-particle analyses are consistent. For comparison with the bulk DRUM sampler data, the individual particle data masses were approximated by assuming an elliptical volume (with axes of Long Proj, Minor Proj, 1/2 Minor Proj). The particle mass approximations, weighted by the elemental ratios, were used to generate bulk elemental mass relative to Si for comparison (Table 5). The most important ratio, Al to Si, is within 10% with the bulk drum analysis. K and Ti are also well matched. Fe is enriched in the single-particle analysis by 10–30%. Mg is the only element significantly enriched in the single-particle analysis (on the order of 2X). This is not surprising since Mg is a trace species, and in XRF analysis it can be swamped by noise. Conversely S was enriched in the DRUM sample by a similar amount, potentially due to fine sulfates that, while analyzed on the DRUM strips, were smaller than the single-particle analysis size threshold, and so were not analyzed on the aircraft samples. While these differences may appear large, given the relative uncertainties in deriving a mass ratio, we consider the agreement to be quite good.

Table 5. Reconstructed Bulk Mass Relative to Si for All Analyzed Particlesa
 DRUM5 July Int16 July MBL16 July SAL20 July SAL21 July SAL22 July SAL24 July MBL24 July SAL
  • a

    Particles are labeled by date and altitude range of samples. Also shown is the average DRUM sampler ratio as given in Table 2. Int, integrated; MBL, marine boundary layer; SAL, Saharan Air Layer.


[77] Given this reasonable comparison, the bulk analysis from the DRUM sampler, and the cluster analysis from the single-particle analysis, we can derive a mineralogical model for the PRIDE field study. With the exception of the 5 July event (to be discussed below), dust at Puerto Rico was remarkably stable. Elemental ratios from the DRUM and single-particle analyses for species with good signal to noise showed high correlation coefficients and little variance. The ratios between magnesium, aluminum, silicon, potassium and iron were nearly static for the entire study period.

[78] As much of the Saharan region overlies ancient mudstone and shale layers, illite, a main constituent of weathered shale and mudstone, is expected in the clay signature. Soils of the more humid Sahel region weather through leaching of soluble cations (Na, Mg, Si, K, Ca, Mn, etc.), producing a material that is relatively enriched in iron oxides, aluminum oxides, and 1:1 Al:Si crystalline clays, such as kaolinite [Food and Agricultural Organization (FAO), 2001]. Feldspar and plagioclase minerals from arid regions, exposed to heat but little water, tend to weather to form chlorites, or smectites such as montmorillonite. Finally, carbonates, gypsum and anhydrite, and various salts found in depressions and seasonal lakes in the Sahara are expected to be mobilized during strong dust events. While many of these minerals cannot be definitively identified without X-ray diffraction analysis, the elemental composition and morphology of the individual particles analyzed from the aircraft samples are not inconsistent with this mineralogy.

[79] From previous X-Ray diffraction (XRD) and optical analysis of Saharan dust [e.g., Falkovich et al., 2001; Anderson et al., 1996; Glaccum and Prospero, 1980; Prospero et al., 1981; Sokolik and Toon, 1999; Caquineau et al., 2002], Saharan dust minerals tend to be comprised primarily of illite, palygorskite and kaolinite, with nonclay minerals including calcite, quartz, dolomite, feldspar (microcline and plagioclase), halite, and gypsum. This is consistent with the elemental data and cluster analysis from PRIDE, except that higher magnesium levels and reduced potassium levels were seen in the particles analyzed in this study. This would suggest that the collected silicates may have included more mafic feldspars, micas, and chlorites (until XRD analysis is performed on these samples, this will remain unresolved). Regardless, ∼60% of dust particle number and 70% of mass can be attributed to alumino-silicates, probably clay and feldspar minerals. A further ∼10% of number and ∼20% of mass can be grouped into Si enriched particles, like quartz or amorphous silicates, thus making a total of 70% and 90% of dust particle mass and volume (respectively), which can be categorized into these two broad groups.

[80] Many of the collected particles visually appear to be mineral aggregates. In fact, the most typical environment for clay minerals is as mixed layers or aggregates of various types of clay particles. Clay particles often coat mineral grains, form aggregates, bridge spaces between mineral grains, or form layered structures. Table 6 lists formulae, density, and optical properties at a standard 589 nm wavelength [Gribble and Hall, 1985] of some of the common minerals expected in Saharan or Sahel soils and dusts. A more in-depth discussion of optical properties, including wavelength dependence of index of refraction for some common Saharan minerals, is given by Sokolik and Toon [1999].

Table 6. Density and Real Index of Refraction of Minerals Found in Saharan Dusta
TypeCommon NameFormulaMean DensityReal Index of Refraction
  • a

    Given is type, common name, formula, mean density (g/cm3), and index of refraction for biaxial minerals (a, b, g), and uniaxial minerals (w,e).

ClayilliteK0.6(H3O)0.4Al1.3Mg0.3Fe0.1Si3.5O10(OH)2 ·(H2O)2.75a = 1.535 − 1.57 b = 1.555 − 1.60 g = 1.565 − 1.605
ClaykaoliniteAl2Si2O5(OH)42.6a = 1.553 − 1.563 b = 1.559 − 1.569 g = 1.56 − 1.57
Claymontmorillonite(Na,Ca)0.5(Al,Mg,Fe)4(Si,Al)8O20(OH)4·n(H2O)2.35a = 1.485 − 1.535 b = 1.504 − 1.55 g = 1.505 − 1.55
Claysmectite(Na,Ca)Al4(Si,Al)8O20(OH)4·2(H2O)2.34a = 1.519 b = 1.55 g = 1.559
ClaychloriteNa0.5(Al,Mg)6(Si,Al)8O18(OH)12·5(H2O)2.42a = 1.542 − 1.564 b = 1.545 − 1.581 g = 1.545 − 1581
Ca richcalciteCaCO32.71e = 1.486 w = 1.64 − 1.66
Ca richdolomiteCaMg(CO3)22.84e = 1.5 w = 1.679 − 1.681
Ca richgypsumCaSO4·2(H2O)2.3a = 1.519 − 1.521 b = 1.522 − 1.523 g = 1.529 − 1.53
Ca richanhydriteCaSO42.97a = 1.569 − 1.573 b = 1.574 − 1.579 g = 1.609 − 1.618
SiO2quartzSiO22.62w = 1.543 − 1.545 e = 1.552 − 1.554
FeldsparsmicroclineKAlSi3O82.56a = 1.518 b = 1.522 g = 1.525
   Plagioclase feldsparVar oligoclase(Na,Ca)(Si,Al)4O82.65a = 1.533 − 1.543 b = 1.537 − 1.548 g = 1.542 − 1.552
   Plagioclase feldsparVar albiteNaAlSi3O82.62a = 1.528 − 1.533 b = 1.532 − 1.537 g = 1.538 − 1.542
   Plagioclase feldsparVar anorthiteCaAl2Si2O82.73a = 1.572 − 1.576 b = 1.578 − 1.583 g = 1.583 − 1.588
OxidesgoethiteFeO(OH)3+a = 2.26 b = 2.393 g = 2.398
OxidesgibbsiteAl(OH)32.34a = 1.568 − 1.57 b = 1.568 − 1.57 g = 1.586 − 1.587
OxidesrutileTiO24.25w = 2.621 e = 2.908
SalthaliteNaCl2.17n = 1.544

[81] In these dust particles, iron is an important component since, in its oxidized form, it is considered the most dominant light absorbing species [Sokolik et al., 1999]. Consequently, the chemistry and morphology of iron in dust is important for radiative transfer modelers. On the basis of the DRUM and single-particle analyses, elemental iron composes ∼2.5–3% of total dust mass (this based on the assumption that aluminum is 8% of total mass). Iron, with a molar ratio to silicon of ∼1:11 from the DRUM sampler, is also close to the empirical illite model [illite = K0.6(H3O)0.4Al1.3Mg0.3Fe0.12+Si3.5O10(OH)2 · (H2O)]. Based on the size partitioning in the DRUM sampler, the Si:Fe mass ratio was fairly consistent as a function of size. A ternary plot of the individual particle analysis data from the aircraft samples, Figure 13, shows the relationship between aluminum, silicon, and iron in all the aircraft samples. The vast majority of the particles have less than 10% Fe (molar), which is normal mineralogy for illite and other silicates. Particles with higher molar percentages of Fe tended toward balanced Al and Si molar percentages, suggesting Sahel origins. Iron-rich particles ranged from 10% Fe (molar) to a high of 90% Fe, though these particles were extremely rare. The cluster analysis gives good average compositions for the Fe-rich particles. The highest relative proportion of iron-rich particles is seen in the 5 July integrated sample, in the fine mode. The fine mode, iron-rich particles are characterized by nearly balanced Al and Si molar percentages, supporting a kaolinite mineralogy. The larger, iron-rich particles show more widely varying Al to Si ratios. This may reflect iron contamination of nonkaolinitic minerals, or may simply indicate aggregation of fine, iron-rich kaolinitic particles with other silicates. Other than the 5 July sample, the samples show relatively consistent percentages of iron-rich particles, though relatively fewer fine-mode iron-rich particles are seen.

Figure 13.

Ternary diagram of molar fractions of iron, aluminum, and silicon for all analyzed aircraft sample particles.

[82] Interestingly, the MgSi-2 group of the Mg enriched silicates also tended to have near 1:1 Al Si molar ratios. These particles were more prevalent in the earlier sampling dates, 5 and 16 July, and were relatively absent from the 21, 22, and 24 July samples, replaced by increased prevalence of the MgSi-1 group particles, having Si:Al ratios closer to 2:1. Without XRD analysis, determining whether this represents a single species, such as amphibole or garnet, or a stable aggregate is not possible, though the apparent shift in mineral type does support a difference in source region.

[83] Bulk mass ratios of Na:Cl, and Ca:S from the aircraft samples suggest that SAL samples are enriched in both sodium and calcium-rich particles. Stoichiometry does not, in general, favor the formation of NaCl or CaSO4 (gypsum). Calcium and sodium are more likely to be in the form of feldspars (e.g., cluster group AlSi-4, Ca-rich, and Na-rich) accounting for ∼8% of particles and 3% of mass.

[84] Although not a dominant species, calcium sulfate, as gypsum or anhydrite, is present in the analyzed samples. The continuous range of the calcium sulfate concentration relative to Si suggests that some of these particles may be conversion products of calcium-rich minerals with SO2 in the atmosphere. The coarse-mode particles tend to have more nearly balanced calcium and sulfur molar fractions that probably represent crustal gypsum or anhydrite, and a range of calcium-rich but sulfur poor particles. The lack of calcium-rich particles in the fine mode supports some of the measured calcium sulfate being due to conversion.

[85] In the MBL, sodium was overwhelmingly in the form of sea salt (see Table 7 and Figure 14). Three clear particle groupings were seen that involve sodium: (1) Particles with clear NaCl X-ray lines (either pure NaCl or NaCl combined with silicate particles, (2) a small sodium-rich silicate group, and (3) an indeterminate population that is either very well aged NaCl-rich particles or high sodium silicates. Particles from group 2 may be Na-feldspars, while particles of group 3 were rare. NaCl was not often found as independent particles; rather it was typically found in combination with silicate particles.

Figure 14.

Ternary diagrams of molar fractions of sodium, chlorine, and silicon for four analyzed aircraft sample dates. The 5 July and 21 July plots also display 5000X magnification particle data (average diameter <2 μm). Note the lack of NaCl in the SAL samples. (a) 5 July 2000 Integrated samples, (b) 21 July 2000 SAL samples, (c) 22 July 2000 SAL samples, and (d) 24 July 2000 MBL surface sample.

Table 7. Molar Ratios of Na and Cl for All Samples of R2 Values for the Na to Cl Regressiona
  • a

    Note the SAL samples show poor R2 values; little of the Na appears to be from NaCl.

5 July1 to 0.620.93−0.04
16 July1 to 0.510.87−0.02
24 July1 to 0.640.93−0.05
16 July1 to 0.370.580
20 July1 to 0.140.150
21 July1 to 0.200.300
22 July1 to 0.130.210
22 July1 to 0.090.230

[86] For the samples re-analyzed at higher magnification, the relationship between the coarse and fine-mode particles is of interest. Three samples were reanalyzed; the 5 July integrated MBL sample, the 16 July SAL sample, and the 21 July SAL sample. For the 5, 16, and 21 July samples, normalized Al/Si ratio histograms were calculated for the coarse and fine-mode particles. The difference plots for the coarse - fine mode Al/Si ratio histograms are shown in Figure 15. The 5 and 16 July samples show similar difference plots with the coarse data slightly enhanced in particles having Al/Si ratio less than 0.8, and the fine mode enhanced for particles with Al/Si ratios greater than 0.8. Two coarse-mode peaks are seen near 0.1 and 0.65, and a fine-mode peak near Al/Si ratio of 1 (possibly kaolinitic clay particles). The 21 July difference plot, in contrast, shows three significant peaks; the coarse-mode data are enhanced for Al/Si ratios of 0.1 and 0.75, and the fine-mode data for particles with Al/Si ratios near 0.55. Little difference is seen between the fine and coarse mode for Al/Si ratios near 1, typical for Kaolinite. The 21 July sample also shows enhanced Al, S, Ca, Mg and Fe levels relative to Si, lending support to the later MBL and SAL samples having a different source or transport regime.

Figure 15.

Normalized difference plot of coarse-fine mode particles versus aluminum/silicon ratio. Specified in the key is sample date.

5.2. The 5 and 16 July Chemistry Anomaly

[87] From the Naval Aerosol Analysis and Prediction System (NAAPS, the primary dust source regions in the Sahara and Sahel shifted from central Saharan Africa toward western Africa during the study period of 25 June to 25 July (Figure 16). The source regions between 25 June and 3 July were located at the edge of the Sahel in the area of the Mali, Niger, and Algeria border, extending northward into Algeria to include a region of seasonal saline lakes to the east of Erg Chech. Soils-rich in salt, gypsum, and calcium are common in the saline lake region of central Algeria near Erg Chech [FAO, 2001]. The source area at the edge of the Sahel centered on 20°N latitude (Figure 16a) is expected to be high in Kaolinite and relatively low in Illite [Caquineau et al., 2002]. We did not see a significant difference in the Al:Si ratio in the DRUM data, though we did see a slight enhancement in particles with Al:Si ratio near 1:1 in the aircraft sample data. The lack of a clear Kaolinite signature, coupled with the observed enhancement in Ca- and S-rich particles from this time period, probably reflects the influence of the more northerly source region, between 25° and 30° latitude near Erg Chech, which is expected to have a higher Illite/Kaolinite ratio [Caquineau et al., 2002]. Beginning 4 July, the source regions shifted west into Mauritania and Western Sahara to include the sand dunes of the southern region of Western Sahara, and the sandy desert areas of Mauritania and northeastern Mali. These soils are exposed to little water, so remain rich in potassium, magnesium, calcium, and other bases. A few later events included minor inputs from the more eastern source regions, occurring 3–4 July, 8–9 July, and 13–14 July, but the main source areas after 3 July were in western regions where Illite is the predominant clay species [Caquineau et al., 2002]. Indeed, the later samples, especially those in the Saharan Air Layer from 20 21, and 22 July, show similar chemistry and morphologies that are distinct from the 5 July sample.

Figure 16.

NAAPS weekly average surface dust emissions for the Sahara and Sahel regions of Africa in year 2000: (a) 26 June to 3 July, (b) 3–10 July, (c) 10–17 July, and (d) 17–24 July.

[88] With an expected ∼7 day transport time from Africa to Puerto Rico, the eastern sources would be responsible for the 5 July anomalous chemistry. Saline lakebeds, calcium-rich, and gypsum-rich soil areas near Erg Chech potentially explain both the high salt and gypsum levels, and the large number of diatom fragments collected. Calcium and Sulfur peaks seen in the DRUM data on 13 July, and in the DRUM and aircraft samples on 22 July may be related to later source events in this area (events seen on 8 July and 16 July). However, concurrent emissions from western regions of the Sahara in Mauritania, which include saline lakes and gypsum-rich playas, cannot be ruled out as significant sources for these later peaks.

6. Summary and Conclusions

[89] In this manuscript we present chemistry and morphology data from Saharan dust particles collected during PRIDE from 3–24 July 2000. Bulk elemental analysis of DRUM impactor strips from a surface site and single-particle analysis of samples collected from an aircraft platform showed significant correlations for those species with good signal-to-noise ratios. The derived molar ratios were consistent with a predominantly illite dust source, though evidence for kaolinite, feldspars and other species is present in the single-particle analysis data. An observed continuum in the Al:SI ratio probably reflects particle agglomeration, mineral layering, analysis uncertainty, and the fact that many mineral species range widely in composition.

[90] During the sampling period, the DRUM impactor recorded significant increases in dust surface concentrations on 5, 10, 15–16, and 21 July 2000 that were coincident with high aerosol optical thicknesses (AOTs). Additional surges of dust at the surface that were not accompanied by high column AOTs occurred on 13 and 24 July. Hence dust surface concentrations did not strongly correlate with dust AOT.

[91] Key elemental ratios from the bulk analysis for the dust were for the most part in agreement with previous findings. Magnesium, aluminum, silicon and iron ratios were consistent with a dust dominated by the clay mineral illite {K0.6(H3O)0.4Al1.3Mg0.3Fe0.12+Si3.5O10(OH)2 ·(H2O)}, although potassium was underrepresented. The DRUM data also showed that while elemental ratios were nearly static for most of the field study, the dust event that occurred on 5 and 16 July were significantly enriched in calcium and sulfur.

[92] Seven of the most heavily loaded filters collected by the research aircraft were analyzed by single-particle analysis. Cross-sectional area distributions showed an area median diameter of ∼6 μm. As particles tend to lie flat on the filter substrate, this value is larger than the true area distribution of the ambient particles. By estimating particle depth, we estimate the volume median diameter to be on the order of 7 μm (again, this is likely to be an over estimate). Average particle aspect ratios, related closely to the average ratio of major to minor axes, were found to have a median value of 1.9. Particle shape factors suggest a higher probability of aggregates for larger dust particles.

[93] Elemental speciation of individual particles was performed on the 60,500 particles for which particle sizes were measured. Ternary and cluster analyses support the stoichiometry of the DRUM impactor samples. More than 70% of dust particle mass can be attributed to alumino-silicate clay minerals such as illite, Kaolinite, and montmorillonite. Silicates with lesser amounts of aluminum (such as amorphous silicon and quartz) make up the next largest group, comprising another 10–15%. Samples collected in the marine boundary layer showed sea-salt particles were typically combined with dust particles. The remaining particles appear to be carbonates, sulfates, salts, and other trace minerals. As seen in the DRUM sampler, the aircraft sample for 5 July also showed enhancement of calcium and sulfur in the form of gypsum or anhydrite (CaSO4).

[94] The ratio of Si to Al remained fairly constant throughout the study, both from the DRUM analysis and the single-particle analysis of the aircraft data. As our dust source regions appeared to shift from the southern central Sahara to the western Sahara during the course of the study, we expected to see some change in the silicates. For the 5 July aircraft sample, we did observe a relative enhancement in particles with 1:1:1 Al to Si to Mg ratios, as well as a substantial enhancement in the number of Ca- and S-rich particles, and of diatom fragments. However, the mean Al to Si ratio remained stable, and relative percentage of kaolinitic particles was only slightly enhanced. Examination of dust source regions in West Africa suggests that the anomalous chemistry associated with the 5 July event is related to a more eastern source region than later events.


[95] We are grateful to the personnel at Naval Station Roosevelt Roads, Daniel Eleuterio, Roger Hahn and the entire staff at North Atlantic Meteorology and Oceanography Detachment, Roosevelt Roads. We also would like to thank the whole Gibbs Flite Center crew, including William “Buzz” Gibbs, Michael Kane, Michael Hubble, and Lyle Richards. Additional thanks for Navajo support are due to Duane Allen, NASA Ames Research Center. We appreciate comments from Richard Paulus, SSC San Diego. PRIDE funding was provided by the Office of Naval Research Code 322, N0001401WX20194, and the NASA Mission to Planet Earth program office.