CHARACTERIZATION OF THE CONSTITUENT MINERAL …

CHARACTERIZATION OF THE CONSTITUENT MINERAL COMPONENTS OF NORTH

AFRICAN SURFACE DUST SAMPLES UTILIZING COMPUTER

CONTROLLED SCANNING ELECTRON MICROSCOPY (CCSEM)

by

ZAKARIAH EDWARD SABATKA

Presented to the Faculty of the Graduate School of

The University of Texas at Arlington in Partial Fulfillment

of the Requirements

for the Degree of

MASTER OF SCIENCE IN EARTH AND ENVIRONMENTAL SCIENCES

THE UNIVERSITY OF TEXAS AT ARLINGTON

December 2015

ii

Copyright © by Zakariah Edward Sabatka 2015

All Rights Reserved

iii

Acknowledgements This thesis could not have been accomplished without the support and guidance of many

individuals. I would like to thank my thesis advisor Dr. Andrew Hunt for his time and mentorship

during this process. A special thanks to my thesis committee Dr. James Grover and Dr. John

Wickham. Thank you Dr. Frank Oldfield and Richard Lyons from the University of Liverpool for

providing the samples which were analyzed. Thank you Zachary Sutton for the guidance and

support with the SEM and for the input into the writing of this thesis. Thank you Roy Yates for

allowing me the flexibility with my professional schedule to meet the demands of this thesis.

Most importantly I must thank my family. I would like to acknowledge and thank my

parents Scott and Wati Sabatka and my brother Rizal Sabatka for their support and

encouragement. Lastly, but certainly not least, my own nuclear family: a loving thank you to my

wife Emily and children Aedan and Alaina for carrying a heavy load on the home front during my

pursuit of higher knowledge. The three of you in my life gives me motivation to be a better person

on a daily basis.

November 17, 2015

iv

Abstract

CHARACTERIZATION OF THE CONSTITUENT MINERAL COMPONENTS OF NORTH

AFRICAN SURFACE DUST SAMPLES UTILIZING COMPUTER

CONTROLLED SCANNING ELECTRON MICROSCOPY (CCSEM)

Zakariah Edward Sabatka, MS

The University of Texas at Arlington, 2015

Supervising Professor: Andrew Hunt

Analysis of the constituent mineral components of surface dusts/soils from Chad-

Niger region, North Africa, was undertaken by computer controlled scanning electron

microscopy (CCSEM) to determine if differences in composition could be used for source

attribution to distinguish windblown material from different contributing source areas. A total

of 11 surficial samples were selected for analysis by computer controlled scanning electron

microscopy (CCSEM). These samples were from two different geographic locations in the

Chad-Niger region. CCSEM analysis generates data on individual dust particles that

includes: particle size, shape, and chemical composition. This type of particle

characterization used a Scanning Electron Microscope (SEM) working in the Backscattered

Electron Imaging (BEI) mode. The BEI mode of imaging of microscopic dust particles

provided information on particle morphology, and average atomic number composition.

Element data on Individual particles was provided by a Silicon Drift X-ray detector with an

ultra-thin window.

The principal goal of this project was to assess whether surface dusts, at the

individual particle level, from two different locations in the Chad-Niger region of North Africa,

could be satisfactorily differentiated. The mineralogy of the individual particles from the dust

samples was determined by CCSEM. CCSEM analysis provides data on thousands of

particles in a time efficient manner. Consequently statistically significant sized data sets can

be evaluated.

v

With surface soils/dust particles it is typically unhelpful in CCSEM analysis to define

specific mineral particle types. Certainly, obdurate minerals such as quartz can often be

recognized in CCSEM data (Particles composed wholly of Si). However, transformation

processes, like the formation of Fe coats on particles, alters the composition of basic

mineral forms in terms of the chemical composition identified in the SEM. To provide a

realistic classification of the particles analyzed here. A reference data set from the analysis

of a North African surface dust sample was used to develop a classification scheme. An

assisted cluster analysis was used to identify homogenous groups of particles within the

reference data set. Homogenous groups of particles were based on associations of the

constituent particle elements. The homogenous groups were assembled into a 59-class

element-based classification scheme. The classes were listed in a linear sorting order that

was used to classify the CCSEM data from the samples investigated in this study. The study

sample CCSEM analysis typically contained element and other data on approximately 4,000

particles per sample.

vi

Table of Contents

Acknowledgements ............................................................................................................ iii

Abstract ............................................................................................................................... iv

List of Illustrations .............................................................................................................. vii

List of Tables ....................................................................................................................... x

Chapter 1 Introduction ........................................................................................................ 1

Chapter 2 Previous Research ............................................................................................ 3

Chapter 3 Objectives and Expected Outcomes ................................................................. 7

Chapter 4 Research Design and Procedures ..................................................................... 8

Chapter 5 Results ............................................................................................................. 13

Chad Niger 2 ................................................................................................................ 17

Chad Niger 3 ................................................................................................................ 18

Chad Niger 4 ................................................................................................................ 19

Chad Niger 2 and 4 Operator assisted scanning electron microscopy analysis .......... 19

Chad Niger 9 ................................................................................................................ 30

Chad Niger 40 .............................................................................................................. 31

Chad Niger 42 .............................................................................................................. 32

Chad Niger 44 .............................................................................................................. 33

Chad Niger 45 .............................................................................................................. 34

Chad Niger 47 .............................................................................................................. 35

Chad Niger 48 .............................................................................................................. 36

Chad Niger 50 .............................................................................................................. 37

Chad Niger 48 and 50 Operator assisted scanning electron microscopy analysis ...... 42

Chapter 6 Conclusions ..................................................................................................... 60

Appendix A Tabulated Results from CCSEM Analysis .................................................... 62

References ....................................................................................................................... 64

Biographical Information ................................................................................................... 68

vii

List of Illustrations

Figure 4-1 (a) Ultrasonic Cleaning System and (b) vacuum and filter system ................................ 8

Figure 4-2 Computer Controlled Scanning Electron Microscope .................................................. 10

Figure 5-1 Chad Niger sample locations ....................................................................................... 14

Figure 5-2 Chad Niger 2 compared to the overall class averages ................................................ 17

Figure 5-3 Chad Niger 3 compared to the overall class averages ................................................ 18

Figure 5-4 Chad Niger 4 compared to overall class averages ...................................................... 19

Figure 5-5 Chad Niger 2 class 37 particle ..................................................................................... 21

Figure 5-6 Chad Niger 2 complex composition unclassified particle ............................................. 21

Figure 5-7 Chad Niger 2 class 40 particle ..................................................................................... 22

Figure 5-8 Chad Niger 2 class 8 particle ....................................................................................... 22

Figure 5-9 Chad Niger 2 complex composition unclassified particle ............................................. 23

Plate 5-10 Chad Niger 2 complex composition unclassified particle ............................................. 23

Figure 5-11 Chad Niger 2 class 47 ................................................................................................ 24

Plate 5-12 Chad Niger 2 class 1 .................................................................................................... 24

Figure 5-13 Chad Niger 2 complex composition unclassified particle ........................................... 25








Figure 5-21 Chad Niger 4 class 1 .................................................................................................. 29

Plate 5-22 Chad Niger 4 class 36 .................................................................................................. 29

Figure 5-23 Chad Niger 9 compared to overall class averages .................................................... 30

Figure 5-24 Chad Niger 40 compared to overall class averages .................................................. 31

viii

Figure 5-25 Chad Niger 42 compared to overall class averages .................................................. 32

Figure 5-26 Chad Niger 44 compared to the overall class averages ............................................ 33





Figure 5-13 Cumulative Results for 31 Chad Niger Samples across 60 classes .......................... 38

Figure 5-32 Results of the samples once a five percent cut-off was in place ............................... 39

Figure 5-33 Chad Niger 9, 40, 42, 44, 45, 47, 48, and 50 cluster ................................................. 40

Figure 5-34 Chad Niger 2 and Chad Niger 4 results with a five percent cut-off ............................ 41

Figure 5-35 Results for Chad Niger 3 using a three percent cut-off .............................................. 42

Figure 5-36 Chad Niger 48 and Chad Niger 50 with five percent cut-off ...................................... 43





Figure 5-41 Chad Niger 44 class 30 .............................................................................................. 46












ix



Figure 5-55 Chad Niger 50 class 47 elongated ............................................................................. 53











Figure 5-66 Niger sample locations ranging from 60 to 250 miles

SE and east of Niamey, Niger ....................................................................................................... 59

Figure 5-67 Chad sample locations ranging from 250 to 500 miles NE

of N’Djamena, Chad ...................................................................................................................... 59

x

List of Tables

Table 3-1 Samples and sample locations .......................................................................... 7

Table 4-1 Linear Classification System and Parameters ................................................. 11

Table 5-1 Relevant Classes to the Chad Niger Region.................................................... 15

1

Chapter 1

Introduction

It is known that trade winds are a major influence upon the deposition of dusts and

other fine sediments. Aeolian transport processes, such as the Trans-Atlantic trade winds,

have deposited fine surface sediments from Africa across the Atlantic Ocean to the Americas.

However, the specific amount or percentage of African surface sediment which has been

transported remains unknown. Furthermore, chemical and physical alterations may have

occurred at some point within the transportation process. Alteration may be affected by the

duration which the sediment spends in the atmosphere and possessed the potential to absorb

pollutants which may include chlorine, sulphates, and organics (Sullivan et al., 2007; Falkovich

et al., 2004; Mamane et al., 1980). Upon deposition, the sediment or dust could initiate allergy

issues for both children and susceptible adults. (Goudie and Midddleton, 2001; Prospero,

1999; Prospero et al., 1981; Mahowald et al., 2005; Mather et al., 2008). To date, it is

uncertain the exact number of asthma hospitalizations which have been the result reactions

with African sediment or dust.

Secondly, atmospheric dusts are believed to contribute nutrients for Central and

South American forests, though the exact amount contributed is unknown at this time

(Bartholet, 2012). Soil within the Amazon basin is constantly bombarded with heavy rainfall

which has depleted soil of nutrients, and thus African dusts are likely a source of

replenishment of lost nutrients. Surface sediments of South America have been considered to

exhibit a relatively high iron content, crucial for vegetation within the region, but the high iron

content may damage to ocean ecosystems due to the addition of anthropogenic pollutants

(Bartholet, 2012). One such study on the effect of iron and anthropogenic pollutants showed

that iron transported in the atmosphere may have the potential to bond with ambient acids in

the atmosphere, and caused iron particles to be more soluble and therefore increased the

amount of available iron in the ocean (Bartholet, 2012).

2

A tertiary effect of African dust would be the impact upon climate as a result of

balance change between solar and thermal radiation (IPCC, 2007). The complete extent of

the effect of aerosolized dust upon chemical reactions in the atmosphere remains (Formenti,

et al., 2011). Due to the effect of atmospheric dusts as a potential hazard, nutrient

replenishment, and effect upon global climate, an accurate characterization and identification

of surface sediment provenance is crucial in understanding the process or sediment creations

as well as the chemical and physical changes which may occur during transportation. The

aforementioned is the cause to develop an initial elemental and chemical analysis.

3

Chapter 2

Previous Research

Previous research indicated that North and Central African surface sediments have

been transported and deposited to the Iberian Peninsula and predominately consist of

calcite-dolomite, silica, but do contain minor amounts of micas, feldspars, gypsum and other

trace minerals (Avila et al., 1997; Coz et al., 2009). Scanning Electron Microscope coupled

with Energy Dispersive X-ray Spectroscopy (SEM/EDS) has been used to characterize 18

basic elements as well as the mineral content of dust derivation via elemental analysis.

Different elements were then clustered in order to generate sample classes which would

segregate elements. Results have shown that samples from Madrid after transport

generally consisted of between 65-85% silica. North African surface sediments

hypothetically cause the relative high abundance of silicates in dust samples collected from

the Iberian Peninsula.

Aspect Ratios (AR) were used, in addition to chemical analysis, to compare particle

morphology to characterize the potential for particle transportation in the atmosphere and to

try to determine the provenance of the dust itself. The determination was reached that

particle deposition occurred as a result of three mechanisms, impaction, sedimentation and

Brownian diffusion (Morman and Plumlee, 2013). For particles with a diameter larger than

0.5 µm, gravitational sedimentation is of significant importance, as the distance of particle

transportation is limited. For particles with a diameter less than 0.5 µm, a particle could be

governed by diffusional transport where minor displacements are the result of the collision

between gas molecules and particles (Shultz et al, 2000). Surface sediments from the Sahel

region were also comprised of an enhancement of secondary ferromagnetic minerals which

led to the conclusion that the concentration of fine-grained clay fraction (<2 µm) had a

4

tenfold difference between the arid north and the humid south of Niger. In addition, there

was also a less significant statistical correlation for hematite concentrations and rainfall

across the transect (Lyons et al, 2010). In some surface sediments from the Benin and

southern Togo region exhibited magnetic properties consistent with the parent rock. The

conclusion drawn by researchers was that climate variations did in fact play an important

role in source locations of African dust. Additional sample collection and characterization,

particle separation, and chemical analysis will be required if a better understanding of how

climate variations effect the distribution of African dusts (Lyons et al, 2010).

Dr. Joseph Prospero began preliminary research at the University of Miami with

samples of dusts collected from the Florida Keys, Bahamas, and the Amazon. It was

determined that any given year the Earth emitted approximately 2 billion metric tons of dust

and more than half of that originated from Africa. Another source stated that approximately

40 million metric tons of the aforementioned dusts consisted of iron and phosphates which

may travel up to 6400 km across the Atlantic Ocean to the Americas, and half of the dusts

were originated from the Bodele depression in Africa (Bartholet, 2012). In order for African

dusts across the Atlantic, a wind speed of approximately 4-12 meters per second was

required. The global transportation of dusts mainly affect the Earth’s climate in two ways.

The Albedo effect is known play a significant role in climate variations. In darker regions,

such as rainforests, higher amounts of thermal energy is absorbed and increases global

temperatures, whereas in highly reflective regions such as the polar ice caps higher

amounts of thermal energy is reflected back. Dusts being transported in the air may also

increase the albedo of the region due to their reflective nature. The second effect of dust

upon climate variation is the role of dust in cloud formation. The formation of clouds require

5

that water droplets form around a condensation nucleus, such as dust, which in turn may

either increase or decrease the climate and amount of rainfall (Bartholet, 2012).

Recent studies on Aeolian transportation and deposition has shown that there is a

link between inorganic mineral dusts and the overall health of the population (Morman and

Plumlee, 2013). There has been limited research on source characterizations and

traditionally caused difficulties in quantifying the health effects of long range transportation

of dust (Morman and Plumlee, 2013). An estimated 1.3 million deaths are the result of

outdoor air pollution (WHO, 2012a). Coarse particles, as defined by the USEPA (2012a),

are those between 2.5-10 µm in diameter with the toxicological effects of dust particles with

less than 5 µm being determined by the chemical stability of the particles. To date, there are

few studies which show a link between inorganic mineral dust and health effects (Morman and

Plumlee, 2013). One of the primary factors which must be determined is whether inorganic

mineral dust does have an effect in the population as well as the physical and chemical

characteristics of the dust (Plumlee et al, 2006). A growing concern of African dusts is high

silica content which may result in pneumoconiosis, also known as Desert Lung.

Pneumoconiosis is a concern for both livestock and humans in arid, dusty regions due to the

increase in desertification from climate change (Kuehn, 2006). Various studies have shown

that regional dust from sources, such as the Sahel/Saharan region of Africa, allude to an

overall increase in hospitalization and mortality in areas such as Europe (Morman and

Plumlee, 2013; Perez et al, 2007). However, contrary studies conducted by Kuehn (2006),

found no evidence which would suggest that increased mortality rates were the result of dusts

which had be deposited in Barbados and increased deposition of African dusts (Bennet et al,

2006; Pospero et al. 2008). Current estimates place approximately half of all African dusts had

a diameter of less than 2.5 µm (Morman and Plumlee, 2013), which would indicate that a

6

significant amount of African dust could be easily aerosolized and transported across the

Atlantic Ocean.

Despite numerous studies that have been conducted on the effects of both short

range and long range dust transportation, inconsistencies and a lack of data have led to a

conclusion that additional research remains needed on sample and source characterization,

model parameters, and susceptibility (Morman and Plumlee, 2013). Morman and Plumlee

(2013) s tated, “Much more information regarding source location, sample

characterization (biological, mineralogy, and chemistry), emission rates and models, and

particle size are needed to understand the implication of exposure and etiological agent(s)

responsible.”

7

Chapter 3

Objectives and Expected Outcomes

This study was conducted with the primary objective of characterizing North African

surface soils/dusts from the Chad-Niger region by their elemental compositions as revealed in

the Scanning Electron Microscope (SEM). By determining a consistent chemical composition

in the soils/dusts at neighboring locations, it was hoped that future studies would be able to

determine the provenance of dust particles from trans-Atlantic winter storms if dust samples

were collected post trans-Atlantic transport. It is posited that the various soil samples from the

Chad-Niger region have unique chemical compositions that can be categorized in this study

and be used as an identifier of sediment provenance in future studies. These samples are

expected to primarily consist of silica, aluminum, iron, and potassium. Data was collected

from 11 samples that were collected between July and September 2007 (Lyons et al. 2011):

Table 3-1 Samples and sample locations

8

Chapter 4

Research Design and Procedures

The mineralogical composition of the 11 dust samples were characterized by

computer controlled SEM analysis (CCSEM). Dust samples were prepared for CCSEM analysis

in the following stages. (i) A subsample of material was placed in a 50 mL test tube containing

distilled water to which a small amount (< 1 ml) of surfactant will be added, and then the

suspension was ultrasonically agitated for 5 minutes. (ii) From a chimney reservoir, an aliquot of

the soil in water suspension was filtered onto a 25 mm diameter 0.4 µm pore size polycarbonate

membrane filter. To ensure an optimal sample preparation for CCSEM, several filters were

prepared. Samples with at least one particle diameter separation between particles were

preferred for analysis. (iii) Prior to analysis, each filter was attached to an SEM mount with

adhesive carbon paint.

(a) (b)

Figure 4-1 (a) Ultrasonic Cleaning System and (b) vacuum and filter system

CCSEM analysis was performed on an ASPEX/FEI personal scanning electron

microscope (PSEM). Specimen images were obtained from the backscatter electron (BE)

collection from an SEM operated in variable pressure mode. Composition of the sample particles

was determined by energy dispersive x-ray spectroscopy (EDS) using an ASPEX/FEI

OmegaMax™ silicon drift detector (SDD) with an ultra-thin window (permitting light element

(Carbon, and Oxygen) detection). Standard operating conditions for the SEM were: an

9

accelerating voltage of 25 keV, a beam current of approximately 1.0 nA, and a working distance

of approximately 16 mm.

The soil/dust individual particle data was collected in an automated mode with the

electron beam and SEM stage being moved under computer control. In order to operate in

automated image analysis mode, the SEM analysis for data collection purposes required specific

software set-up conditions. Firstly, the BE signal threshold was set to separate the soil particles

(generally referred to in the analysis as “features”) on the filter from the filter itself. This

investigation was concerned with the inorganic particles in the soil which have an average atomic

number greater than carbon (atomic number 7). Thus, a binary threshold was set in the software

where the BE signal strength for a feature that was greater than the carbon filter. The low atomic

number for carbon allowed the setting of a binary threshold for feature with relative ease. Having

a detection limit above this threshold allowed for automated analysis when the software was

operating in search and detect mode. This meant that in the automated feature analysis (AFA) all

inorganic particles deposited on the filter were subjected to detection and analysis, and inclusion

in the process was not restricted to features with a specific composition. Thus, for instance,

metal bearing particles in the sample were recorded as a subset of the analyzed particles and

could be subsequently isolated in the CCSEM data.

A minimum of 10 seconds per particle or the acquisition of 10,000 X-ray counts was the

minimum dwell time for the capture of x-ray data which was accomplished at the SEM electron

beam was rastered in chords over the sample feature. During this process, information on the

average composition of the whole particle was collected and then stored. The software vector

editor was used to identify the elements in the feature x-ray spectrum. This allowed for the semi-

quantitative analysis of the elements in the spectrum. Individual elements were determined from

a vector calculation (filter-fit) by the software. Standard spectra for all the elements are used with

these standards collected by the x-ray detector for the machine. This technique assumed that the

unknown spectra could be represented as a weighted sum of the reference spectra. The k-ratio

is constant in the weighted sum is closely related to the weight percentage. During the analysis,

10

samples were run and data stored with the goal of sampling of the order of 4,500 features.

Elements of interest were sodium (Na), magnesium (Mg), aluminum (Al), silica (Si),

phosphorus(P), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), chromium (Cr),

manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), and zinc (Zn).

Figure 4-2 Computer Controlled Scanning Electron Microscope

Once the mineralogical constituents of a sample had been identified (in the CCSEM

data), they were compared to those of the other samples in the study by sorting the data through

the linear classification system. The class numbers and the class rule of the classification

scheme are set out in Table 4.1. This is the classification scheme through which all the data

were sifted.

11

Table 4-1 Linear Classification System and Parameters

Class Parameters 1 Si > 98% 2 Ca > 98% 3 Fe > 98% 4 Na > 98% 5 Al > 99% 6 Ti > 60% 7 Zn > 4% 8 SiAl > 98% and Si < 50% 9 SiAl > 98% and Si > 75%

10 AlSi > 98% 11 SCa > 98% 12 CaSi > 98% 13 FeS > 98% and S > 3% 14 Ca Mg > 98% and Ca > 3% and Mg > 3% 15 KCl > 98% and K > 3% and Cl > 3% 16 FeP > 98% and Fe > 60% 17 FeSi > 98% 18 MgSi > 98% and Mg > 3% 19 SiNa > 98% 20 NaCl > 98% and Cl > 3% 21 NaFe > 98% 22 FeCa > 98% 23 FePSi > 98% and Fe > 3% and P > 3% and Si > 3% 24 FeNaP > 98% 25 NaMgSi > 98% and Na > 3% and Mg > 3% and Si > 3% 26 NaAlFe > 98% and Na > 3% and Al > 3% and Fe > 3% 27 NaSiFe > 98% and Na > 3% and Si > 3% and Fe > 3% 28 NaSiCa > 98% and Na > 3% and Si > 3% and Ca > 3% 29 MgSiCa > 98% and Mg > 3% and Si > 3% and Ca > 3% 30 AlSiFe > 98% and Al > 3% and Si > 3% and Fe > 3% 31 AlSiK > 98% and Al > 3% and Si > 3% and K > 3% 32 AlSiCa > 98% and Al > 3% and Si > 3% and Ca > 3% 33 AlSiNa > 98% and Al > 3% and Si > 3% and Na > 3% 34 NaAlSiCa > 98% and Na > 3% and Al > 3% and Si > 3 % and Ca > 3% 35 NaAlSiFe > 98% and Na > 3% and Al > 3% and Si > 3 % and Fe > 3% 36 NaAlSiK > 98% and Na > 3% and Al > 3% and Si > 3 % and K > 3% 37 AlSiKFe > 98% 38 AlSiFeMg > 98% 39 AlSiFeP > 98% 40 AlSiFeCa > 98% and Al > 3% and Si > 3% and Fe > 3% and Ca > 3% 41 AlSiKCa > 98% and Al > 3% and Si > 3% and K > 3% and Ca > 3% 42 MgAlSiCa > 98% and Mg> 3% and Al > 3% and Si > 3% and Ca > 3% 43 MgCaSiFe > 98% and Mg> 3% and Ca > 3% and Si > 3% and Fe > 3% 44 Removed from analysis due to redundancy 45 AlSiKFeCa > 98% and Al > 3% and Si > 3% and K > 3% and Fe > 3% and Ca > 3% 46 NaMgAlSiFe > 98% and Na > 3% and Mg > 3% and Al > 3% and Si > 3% and Fe > 3% 47 MgAlSiKFe > 98% and Mg > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% 48 MgAlSiCaFe > 98% and Mg > 3% and Al > 3% and Si > 3% and Ca > 3% and Fe > 3% 49 NaAlSiCaFe > 98% and Na > 3% and Al > 3% and Si > 3% and Ca > 3% and Fe > 3% 50 FePSiAlNa > 98% and Fe > 3% and P > 3% and Si > 3% and Al > 3% and Na > 3% 51 NaAlSiKFe > 98% and Na > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% 52 MgAlSiKFeCa > 98% and Mg > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% and Ca > 3%

12

Table 4-1- continued

53 MgAlSiKFeNa > 98% and Mg > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% and Na > 3% 54 MgAlSiCaFeNa > 98% and Mg > 3% and Al > 3% and Si > 3% and Ca > 3% and Fe > 3% and Na > 3% 55 MgAlSiKFeCa > 98% and Mg > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% and Ca > 3% 56 NaAlSiKFeCaMg > 98% and Na > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% and Ca > 3% and Mg > 3% 57 Ti > 3% and < 60% 58 Mn > 4% 100 Unclassified

13

Chapter 5

Results

A total of 11 samples were analyzed (see Figures 5-1, 5-19, and 5-20). Upon completion

of the experiment, results were tabulated by how the particles were sorted using the linear

classification scheme in Table 4-1. There was an average of approximately 4,000 particles

analyzed per sample with a minimum of 2,455 particles analyzed in Chad Niger 9, and a

maximum of 6,187 particles analyzed in Chad Niger 4.

Several approaches to analyzing the data were undertaken. Firstly, an average

concentration for each class was calculated across the 11 samples analyzed to determine if there

was a trend that could be followed across the Chad Niger samples. Once an average

concentration had been determined for each class, a cut-off of 1% concentration was used

leaving 23 of the original 60 classes. From the classes that remained, a 5% cut-off was used to

determine major constituent classes to begin analyzing a trend amongst the 11 samples.

Of the 23 classes which comprised greater than one percent of the sample composition,

five of the classes were considered major classes with an average concentration above five

percent. The remaining 18 classes were considered minor classes. Table 5-1 summarizes the

classes for the Chad Niger samples with the five major classes being highlighted with bold type,

and the summary of the results for the 23 classes can be found in a table located in Appendix A.

14

Figure 5-1 Chad Niger sample locations

15

Table 5-1 Relevant Classes to the Chad Niger Region

Class Parameters

1 Si > 98%

2 Ca > 98%

3 Fe > 98%

6 Ti > 60%

7 Zn > 4%

8 SiAl > 98% and Si < 50%

9 SiAl > 98% and Si > 75%

30 AlSiFe > 98% and Al > 3% and Si > 3% and Fe > 3%

31 AlSiK > 98% and Al > 3% and Si > 3% and K > 3%

33 AlSiNa > 98% and Al > 3% and Si > 3% and Na > 3%

35 NaAlSiFe > 98% and Na > 3% and Al > 3% and Si > 3 % and Fe > 3%

36 NaAlSiK > 98% and Na > 3% and Al > 3% and Si > 3 % and K > 3%

37 AlSiKFe > 98%

38 AlSiFeMg > 98%

40 AlSiFeCa > 98% and Al > 3% and Si > 3% and Fe > 3% and Ca > 3%

46 NaMgAlSiFe > 98% and Na > 3% and Mg > 3% and Al > 3% and Si > 3% and Fe > 3%

47 MgAlSiKFe > 98% and Mg > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3%

49 NaAlSiCaFe > 98% and Na > 3% and Al > 3% and Si > 3% and Ca > 3% and Fe > 3%

51 NaAlSiKFe > 98% and Na > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3%

53 MgAlSiKFeNa > 98% and Mg > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% and Na > 3%

54 MgAlSiCaFeNa > 98% and Mg > 3% and Al > 3% and Si > 3% and Ca > 3% and Fe > 3% and Na > 3%

55 MgAlSiKFeCa > 98% and Mg > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% and Ca > 3%

56 NaAlSiKFeCaMg > 98% and Na > 3% and Al > 3% and Si > 3% and K > 3% and Fe > 3% and Ca > 3% and Mg > 3%

57 Ti > 3% and < 60%

58 Mn > 4%

59 Si > 40%

100 Unclassified

Classes 1, 9, 30, 35, and 37 were considered major classes with average concentrations

greater than five percent

16

Trends in the data set indicate that the primary elements that comprised the Chad Niger

samples were Al, Si, and Fe from the major classes: 1, 9, 30, 35, and 37. Class 35 also included

greater than three percent Na, and class 37 included traces of K with the primary three elements.

These five major classes accounted for 50.1% of the total particles analyzed in this study.

Notable minor classes with concentrations above three percent included classes 31, 46,

47, 53, 57, and 100. Al-Si appear in classes 31, 46, 47 and 53 with Fe, Na, and K in varying

concentrations, but classes 46, 47, and 53 add concentrations of Mg that are greater than three

percent. Classes 57 and 100 are the outliers with class 57 having concentrations of Ti between

three and 60 percent, and class 100 for particles that do not adhere to the criteria of the

established linear classification scheme. The six notable minor classes comprised 22.7% of the

total particles analyzed. The remaining 12 minor classes accounted for 21.0% of the analyzed

particles. The remaining 37 classes in the linear classification scheme comprised of the

remaining 6.2% of the particles are were not considered substantial constituents for this study.

Once the data was tabulated, an average for each class was calculated, and the results

of the individual samples were compared to the regional average for each class. A summary of

this comparison for each sample has been compiled along with a graph that can visually show

individual sample trends against the regional class averages that resulted from this experiment.

Deviations from these averages could potentially help identify any potential unique elemental

markers for each sample.

17

Chad Niger 2

Chad Niger 2 peaked in class 30. Chad Niger 2 had greater than five percent

concentrations of three major and three minor classes. The data tends to follow the overall data

trends of the average of the 23 classes across the 11 samples, but has two notable departures

from the trend at classes 57 and 100. Compared to the sample set average, Chad Niger 2 had

much higher concentrations in classes 57 and 100. This indicated a higher than average of

particles present with higher than average presence Ti. Figure 5-2 demonstrates how

concentrations from Chad Niger 2 compare to the average concentrations from all of the Chad

Niger samples.

Figure 5-2 Chad Niger 2 compared to the overall class averages

18

Chad Niger 3

Chad Niger 3 in general was an outlier to this data set. There was no consistency

between the profile of Chad Niger 3 and the regional averages. Chad Niger 3 peaked in class 37

which was different than most other samples that primarily had a max concentration in class 30.

Chad Niger 3 also had a much higher class 37 concentration when compared to other samples in

the set. The increase in class 30 indicates more K than typically found in Chad Niger samples

while class 37 indicates a Ca concentration not typically seen in other samples. Class 35 is also

much lower than average for this sample set indicating Na is not as prevalent for this sample as

observed in other Chad Niger samples. Overall, Chad Niger 3 appeared to be the biggest outlier

in this batch of samples as it did not fit the averages of the data set with any consistency. Figure

5-3 demonstrates how concentrations from Chad Niger 3 compare to the average concentrations

from all of the Chad Niger samples.


19

Chad Niger 4

Similar to the data set averages, Chad Niger 4 peaked in class 30, but did have a

noticeably higher concentration of classes 6 and 57 indicating there was more Ti in this sample

than the rest of the study. Other classes within Chad Niger 4 generally followed the trend of the

overall data set averages, albeit at generally lower concentrations. However, class 35 was

observed to be much lower than average indication a lower Na concentration than generally

encountered. Figure 5-4 demonstrates how concentrations from Chad Niger 4 compare to the

average concentrations from all of the Chad Niger samples.

Figure 5-4 Chad Niger 4 compared to overall class averages

Chad Niger 2 and 4 Operator assisted scanning electron microscopy analysis

Samples 2 and 4 are from a group of samples collected in the far west of the sampling

area. Both have important particle assignments in class 30. However sample 2 had a significant

percentage of particles that were collected in the unclassified class. This indicates that sample 2

had a significant number of particles with complex compositions that could not be classified by all

the defined classes in the scheme.

20

Part of this study is trying to assess whether there are substantive difference between the

western most samples (samples 2 and 4) and the east most samples (samples 48 and 50). We

are positing that samples from spatially separated areas may be different enough at the individual

particle level to make it possible to distinguish the material from each area in a mixed aerosol

sample. The goal is to determine whether we can attribute particles from a specific source in a

windblown dust distal from the source area(s)

A comparison of samples 2 and 4 vs. samples 48 and 50 will be made later; here we are

simply evaluating particles in samples 2 and 4 by operator evaluation in the SEM. Two SEM fields

of view (FOV), containing several particles, were examined and most of the particles were

examined at high magnification and the X-ray spectrum of each was collected. Some particles

could be clearly attributed to one class in the scheme. For example two Si-only particles (quartz)

were recorded (Figure 5-12 and 5-21). To the common class 37 we assigned particles from

samples 2 and 4 (Figure 5-5, Figure 5-15, Figure 5-17, Figure 5-18, and Figure 5-20), however

they were notably more common in sample 4 (Figure 5-17, Figure 5-18, and Figure 5-19). Clearly

in this limited sampling the particles in this area are defined by class 37 particles. The CCSEM

Class 30 was abundantly populated by particles from samples 2 and 4, however only two

particles attributable Class 30 were observed in the manual analysis (Figure 5-20). Class 51

particles were present in sample 2 (Figure 5-14 and Figure 5-16), but the outstanding feature of

sample 2 was the large number of particles assigned to the unclassified class in the CCSEM.

Assignment to the unclassified class means that the information provided by an individual particle

used to classify it (primarily element concentration data) did not correspond to any the rules

requirements in 58 class scheme. In the linear sort the feature dropped through the scheme not

being assigned to any class. Correspondingly in the operator examination Sample 2 was

characterized by particles with complex compositions (Figure 5-6, Figure 5-9, Figure 5-10, and

Figure 5-13). These particles typically had 8-11 elements in the X-ray spectra, and as yet

unaccountable Zn was found in a couple of these samples.

21

Figure 5-5 Chad Niger 2 class 37 particle

Figure 5-6 Chad Niger 2 complex composition unclassified particle

22



23



24

Figure 5-11 Chad Niger 2 class 47


25



26



27



28



29


Plate 5-22 Chad Niger 4 class 36

30

Chad Niger 9

Chad Niger 9 peaks in class 30 with noticeable concentrations of class 1 and class 37.

This shows as with other Chad Niger samples, that there is a high amount of Si that is a main

component for soil samples from this region. As with other samples, class 37 indicates a K and

Fe concentration above regional averages. Class 35 being lower than average for the region

indicates a small amount of Na being present, but not in the quantities found in the other samples

for this experiment. Classes 46 and 53 also were below expected concentrations indicating Mg

and K are not as abundant in this sample as compared to the rest of the region. Figure 5-23

demonstrates how concentrations from Chad Niger 9 compare to the average concentrations



31

Chad Niger 40

Chad Niger 40 is another sample that did not correspond to class averages across the

data set. Classes 1 and 9 were the primary constituents which indicates the primarily Si and Al

make up with Fe still present, but in slightly lower concentrations than the rest of the samples in

the region. A higher concentration of class 35 shows Na and Fe are present in Chad Niger 40

which can account for some deviation from the data set class averages. Zn was also present in

class 7 at slightly above average concentrations. Figure 5-24 demonstrates how concentrations

from Chad Niger 40 compare to the average concentrations from all of the Chad Niger samples.


32

Chad Niger 42

Chad Niger 42 had a high concentration of Si similar to the other samples, but the low

percentage of class 1 indicated a lower pure Si make up than that of the average Chad Niger

sample. Chad Niger 42 peaked with class 30, similar to other samples, but also saw a high

concentration of class 35 indicating more Na and Fe than normally found in the Chad Niger

region. Chad Niger 42 demonstrated a lower than expected K concentration with a lower than

average class 37 result. Figure 5-25 demonstrates how concentrations from Chad Niger 42

compare to the average concentrations from all of the Chad Niger samples.


33

Chad Niger 44

Chad Niger 44 fit the overall class average profile very well. Class 30 was the primary

component with the normal elements Si, Al, and Fe being a bulk of this sample. Chad Niger 44

did have slightly lower concentrations of Na and K with classes 51 and 53 being lower than the

data set trend. Ti also appeared to be less prevalent in this sample as classes 6 and 57 were

lower than the averages for the Chad Niger region. Figure 5-26 demonstrates how


Niger samples.


34

Chad Niger 45

Chad Niger 45 continued the trend of having class 30 as the primary component which

agreed with the Al, Si, and Fe elements being large constituents of the sediment from this region.

With higher than average concentrations of classes 38 and 47, Mg and K are shown to be

significant components of this sample. Other samples in the Chad Niger region have smaller

quantities of Mg and K, and overall they are not as prevalent in majority of the other samples.

Figure 5-27 demonstrates how concentrations from Chad Niger 45 compare to the average

concentrations from all of the Chad Niger samples.


35

Chad Niger 47

Chad Niger 47, as with the previous samples had high Si content, and was primarily Si,

Al, and Fe as class 30 was the major constituent. Chad Niger 47 like Chad Niger 45 had higher

Mg and K concentrations from classes 46 and 47, and like Chad Niger 40, an above average

class 7 denoted a presence of Zn above average concentrations. A high response in class 53

showed that in addition to Mg, K, and Zn, Chad Niger 47 also had Na in higher concentrations

than the data set normal. Chad Niger 47 appeared to have one of the more diverse make ups of

this sample data set, but managed to follow the average trend with classes 46 and 53 being slight

outliers and showing higher than average Na, Mg, and K. Figure 5-28 demonstrates how


Niger samples.


36

Chad Niger 48

Chad Niger 48 peaks in class 30 and stays consistent to the Chad Niger region primarily

being Si, Al, and Fe. Chad Niger 48 only deviated from the averages in classes 35, 38, and 46,

and it can be shown that it is Mg and Na being present in above average concentrations for this

region. With a slightly higher value for class 53 which includes Mg, Al Si, K, Fe, and Na, it is the

presence of K that is slightly above average for this region as opposed the expected observed Si,

Al, and Fe values, and a slightly less than average concentration of Mg and Na. Figure 5-29

demonstrates how concentrations from Chad Niger 48 compare to the average concentrations



37

Chad Niger 50

Chad Niger 50 had class 30 as the prime constituent. Si, Al, and Fe were the main

elements found in this sample. There were above average readings for classes 46 and 53 and

below average results for class 37 when compared to samples in the Chad Niger region. Classes

46 and 53 indicated that concentrations of Na and Mg were above regional averages, but while K

is present in class 53, the lower concentrations of class 35 should indicate K is not as prevalent in

Chad Niger 50. Figure 5-30 demonstrates how concentrations from Chad Niger 50 compare to

the average concentrations from all of the Chad Niger samples.


38

As expected, the primary elements that comprised the Chad Niger samples were Al, Si,

and Fe. The consistent result of class 30 having the highest concentration in nine of 11 samples

confirms this expectation for the Chad Niger region. To better see any correlation between

samples and class concentrations a five percent cut-off was used to determine major classes.

With the most common class observed being class 30, it can be noted that the Chad

Niger region is primarily composed of alumnosilicate minerals with traces of Fe greater than 3%.

The results from classes 35 and 37 indicated Na and K are also regularly present in this region

with Na greater than three percent in 8.1% of the samples and traces of K found with the three

primary elements, Al, Si, and Fe, 6.4% of the time. Figure 5-31 shows all 11 samples overlaid on

a chart that shows visually the trends with the data, as well as demonstrates that some of the

samples have noticeable deviations from those trends as in the case of Chad Niger 3 and Chad

Niger 45.

Figure 5-13 Cumulative Results for 31 Chad Niger Samples across 60 classes

Once the 5% cut-off is applied in Figure 5-32, clearer trends emerged, and better

conclusions could be drawn. The five major classes stood out with most of the samples being

39

observed in classes 1, 9, 30, 35, and 37, but outliers also became more visible. Chad Niger 3

and Chad Niger 45 had properties that noticeably deviated from the other eight samples.

Figure 5-32 Results of the samples once a five percent cut-off was in place

Most of these samples can be clustered into groups of samples that have similar

concentrations of classes. In Figure 5-33, Chad Niger samples 9, 40, 42, 44, 45, 47, 48, and 50

are clustered. Chad Niger 42, 44, 45, 47, 48, and 50 show peaks in classes 9, 30, and 35

primarily. Chad Niger 9 has a peak in class 37 which indicates a higher presence of K than the

other samples who have Na from class 35. Chad Niger 45 does have an unusual peak in class

47 indicating a higher Mg and K presence, but otherwise fits the cluster well. Chad Niger 40 has

results that can to fit this cluster as a lower class 30 value could be explained by the high class 1

and 9 values indicating a much higher presence of Si and Al relative to other elements, but still

fitting the primarily Si, Al, Fe make-up of the cluster. Chad Niger 40 also having a higher class 31

value helps fit the cluster with class 31 indicating a presence of K which appear in the other

samples in classes 37 and 53.

40

Figure 5-33 Chad Niger 9, 40, 42, 44, 45, 47, 48, and 50 cluster

A second grouping can be made from Chad Niger 2 and Chad Niger 4 are also very

similar to the other cluster, but have a higher presence of Ti than that found in the first cluster of

eight samples. The peaks in classes 6 and 57 show a Ti concentration not typically seen in this

data set of 11 samples. Chad Niger 2 does have a higher percentage of particles that did not fit

the linear classification scheme than all the other 10 samples, and this could account for why its

class 30 result was lower than average as 14.5% of this sample failed to meet the analysis

criteria. Figure 15-34 will show Chad Niger 2 and Chad Niger 4 compared to each other with data

peaks in classes 30 and 57.

41

Figure 5-34 Chad Niger 2 and Chad Niger 4 results with a five percent cut-off

The outlier in this data set was Chad Niger 3. While five of the analyzed classes exceed

the five percent cut-off, only two were found to be major classes for this region. Of the two major

classes that had results over five percent, both had values far different than that of the overall

class averages. Class 30 comprised just 7.2% of Chad Niger 3 while the regional average was

21.2%. Class 37 made up 14.5% of Chad Niger 3 while the regional average was 6.4%.

However, the results from Chad Niger 3 do still agree that Si, Al, and Fe are the prime

components of the sample. Using a three percent cut-off, Figure 15-35 shows the diverse make-

up of Chad Niger 3. Minor classes 32, 40, 45, 47, 57, 59, and 100 all were greater than three

percent of Chad Niger 3. Based on these results it is noted that in addition to the three primary

elements, there is recordable amounts of Ca, K, Mg, and Ti found in Chad Niger 3. Chad Niger 3

also had a higher than average class 100 concentrations where 6.9% of analyzed particles failed

to meet the linear classification parameters. This was over 50% higher than the usual 4.2%

average for class 100 in this data set. Along with Chad Niger 2, there could be possible edge

effects of these samples locations relative to the other samples that could influence the source of

42

the sediment, thus influence the mineralogical constituents of this sample. However, more

samples from this region and adjacent regions would need to be assessed to confirm the

potential influence of distal sediments and source rocks.

Figure 5-35 Results for Chad Niger 3 using a three percent cut-off

Chad Niger 48 and 50 Operator assisted scanning electron microscopy analysis

Samples 48 and 50 are from a group of samples collected in the far east of the sampling

area. One has important particle assignments in class 30 (sample 48). The other sample (sample

50) has important had significant percentages of particles in classes 47, 55, and small

contribution to classes 38 and 31 and others. This indicates that sample 48 was very much

different to 50, and both were different from the far west samples 4 and 2.

For samples 48 and 50, two SEM fields of view (FOV), containing several particles, were

examined and most of the particles were examined at high magnification and the X-ray spectrum

of each was collected. Most particles particles could be clearly attributed to a class in the

scheme. So starting with sample 48 Si-only particles (quartz) were recorded (Figure 5-37, Figure

5-38, and Figure 5-40). But the dominant particle class was 30 (Figure 5-44 and Figure 5-46) this

43

AlSiFe class also dominated in the CCSEM of sample 48. In the case of sample 50 two very

similar particle types recorded similar percentages. Class 47 classified particles with and

MgAlSiKFe composition Class 50 classified particles with MgAlSiKFeCa composition. Clearly the

only difference is the presence of Ca. In the particle assignment to these classes by the operator

the amounts of K and Ca were often low and as the Potassium Kβline sits on top of the Calcium

Kα X-ray line it is sometimes hard to determine if Calcium is present in small amounts if

Potassium is present. As a point of interpretation Class 47 particles shown here sometimes

contain low quantities of Ti. This can be clearly seen in the four morphologically similar particles

termed elongated two of which have Ti two of which do not. It is clear that of the important

classifying elements Ti is of variable importance. In the CCSEM analysis it might be the case that

the Ti content of these particles is high enough to push them out of inclusion in class 47 and force

them to be included in class 57 (Ti-bearing). The decision was taken here that the homogenous

nature of the particles assigned to class 47 was not determined by the Ti content and so Ti was

ignored in the class attribution of the non-CCSEM analyzed particles.

Figure 5-36 Chad Niger 48 and Chad Niger 50 with five percent cut-off

44



45



46



47



48



49



50



51



52



53

Figure 5-55 Chad Niger 50 class 47 elongated


54



55



56

4



57



58


After looking at the data and potential clusters, a geographic analysis was conducted.

While sample clusters could be tied together statistically, the main difference for the regional

samples from the Chad Niger region were that the samples from Niger, samples Chad Niger 2, 3,

4, and 9, contained on average more Ti than samples from Chad which were samples Chad

Niger 40, 42, 44, 45, 47, 48, and 50. Classes 6 and 57 were more prevalent in samples from

Niger than samples from Chad. Thus, Ti could be the key element that differentiates the samples

from the two countries.

59

Figure 5-66 Niger sample locations ranging from 60 to 250 miles SE and east of Niamey, Niger

Figure 5-67 Chad sample locations ranging from 250 to 500 miles NE of N’Djamena, Chad

60

Chapter 6

Conclusions

The results of this investigation demonstrated that there are correlations to be made

between constituent concentrations and samples in the Chad Niger region. The samples

gathered demonstrated that the primary constituents were Si, Al, and Fe. There is varying

amounts of contributing mineralogy including Mg, Ca, Na, K, and Ti. After tabulating the resulting

data, it is apparent that based on the proposed classification system that North African dusts from

the Chad Niger region have several class traits in common while also having potential unique

class identifiers.

Regional analysis of the westernmost samples, Chad Niger 2 and Chad Niger 4, and the

easternmost samples, Chad Niger 48 and Chad Niger 50, show sub-regional uniqueness. Chad

Niger 2 and Chad Niger 4 indicate similarities with Al, Si, and Fe, however can be differentiated

by two classes. Sample 2 has a high percentage of class 100 while Sample 4 had a significant

amount class 37. Chad Niger 48 was dominated by class 30 while Chad Niger 50 had two

classes differentiate it from the other eastern sample with high percentages of classes 47 and 55.

These class differences between the samples demonstrate that these Chad Niger samples are

different at the individual particle level despite some general elemental similarities. This leads to

the conclusions that it is possible to undertake source attribution based on CCSEM to separate

source areas for windblown dust.

To improve upon this data, and better refine both the linear classification system and

correlations for the Chad Niger samples more research is needed. Making multiple runs of each

sample to get an aggregate class average for each sample and compiling a regional class

average from multiple runs of each sample would refine the data and the numbers and would

most likely bring a lot of these individual sample class averages in line with the regional results.

Another recommendation would be to take multiple samples from each location to better

homogenize the samples for each sub-region so that researchers could refine the data as it would

reduce the chances of outliers by reducing the odds of randomly selecting local anomalies and

61

passing the defects through to the analysis. Having multiple samples from each sub-region

would also allow for identification of unique identifiers within the Chad Niger region as consistent

results would confirm or disprove the presence of initial data outliers which would help

researchers create a more accurate elemental/mineralogical “fingerprint” for each sub-region and

region.

62

Appendix A

Tabulated Results from CCSEM Analysis

64

References

Avila, A., Queralt-Mitjans, I., Alarcon, M. 1997. Mineralogical Composition of African Dust

Delivered by Red Rains Over the Northeastern Spain, J. Geophys. Res, 102, 21 977–21

996. Bartholet, J. 2012. Swept From Africa to the Amazon, Scientific American, 45-49. Bennet, C.M., McKendry, I.G., Kelly, S., Denike, K., Koch, T. 2006. Impact of the 1998

Gobi Dust Event on Hospital Admission in the Lower Fraser Valley, British Columbia,

Sci. Tot. Environ. 366, 918-925.

Blanco, A., De Tomasi, F., Fillippo, E., Manno, D., Perrone, M.R., Serra, A., Tafuro,

A.M., Tepore, A. 2003. Characterization of African Dust Over Southern Italy, Atmos.

Chem. Phys. Discuss, 3, pp. 1-38.

Coz, A., Andrés, A., Soriano, S., Viguri, J. R., Ruiz, M. C., Irabien, J. A. 2009.

Influence of Commercial and Residual Sorbents and Silicates as Additives on the

Stabilisation/Solidification of Organic and Inorganic Industrial Waste, Journal of

Hazardous Materials, 164, 755–761.

Cos, E., Moreno, T., Artinano, B. 2011. Variations on the Mineralogical Particle

Properties of Atmospheric Saharan Dust from Different Source Regions. Revista de

la Sociedad Espanola de Mineralogia, Macla 15, pp. 69-70.

“Dust to Gust”. EurekAlert!. AAAS. 28 December 2006. Retrieved 17 August 2014.

65

Falkovich, A.H., Schkolnik, G., Ganor, E., Rudich, Y., 2004. Adsorption of Organic

Compounds Pertinent to Urban Environments onto Mineral Dust Particles.

J. Geophys. Res. 109, D02208. doi:10.1029/2003JD003919. Formenti, P., Shutz, Y., Desboeufs, K., Ebert, M., Kandler, K., Petzold, A., Scheuvens,

D., Weinbruch, S., Zhang, D. 2011. Recent Progress in Understanding Physical and

Chemical Properties of African and Asian Mineral Dust. Atmos. Chem. Phys., 11,

8231-

8256, doi: 10.5194/acp-11-8231-2011.

Goudie, A.S., Middleton, N.J., 2001. Saharan Dust Storms: Nature and Consequences,

Earth-Sci. Rev., 179-204. Intergovernmental Panel on Climate Change (IPCC). 2007. Available from:

<http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf>.

Krech, S., McNeill, J.R., Merchant, C., Jump Up. Encyclopedia of World Environmental

History. Routledgw. Pp. 748-749. ISBN 0-415-93734-5 Lyons, R., Oldfield, F., Williams, E. 2010. Mineral Magnetic Properties of Surface

Soils/Sands Across Four North African Transects and Links to Climatic Gradients,

Geochemistry, Geophysics, Geosystems, vol 11, issue 8, doi:

10.1029/2010GC003183.

http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf

http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf

66

Lyons, R., Oldfield, F., Williams, E. 2011. The Possible Role of Magnetic Measurements

in the Discrimination of Sahara/Sahel Dust Sources, Earth Surface Processes and

Landforms, vol 37, doi: 10.1002/esp.2268.

Mahowald, N.M., Baker, A.R., Bergametti, G., Brooks, N., Duce, R.A., Jickells, T.D.,

Kubilay, N., Prospero, J.M., Tegen, I. 2005. Atmospheric Global Dust Cycle and Iron

Inputs to the Ocean, Global Biogeochem. Cy., 19, GB4025, doi:

10.1029/2004GB002402.

Mamane, Y., Ganor, E., Donagi, A.E., 1980. Aerosol Composition of Urban and Desert

Origin in the Eastern Mediterranean I: Individual Particle Analysis. Water Air Soil Pollut.

14, 29–43. Mather, R.L., Reynolds, S.E., Wolff, G.A., Williams, R.G., Torre-Valdes, S., Woodward,

E.M.S., Landolfi, A., Pan, X., Sanders, R., Achterberg, E.P. 2008. Phosphorus Cycling

in the North and South Atlantic Ocean Subtropical Gyres, Nature Geoscience, 1, 439-

443.

Mormon, S., Plumlee, G. 2013. The Role of Airborne Mineral Dusts in Human Disease,

Aeolian Research 9, 203-212.

Plumlee, G.S., Morman, S.A., Ziegler, T.L. 2006. The Medical Geochemistry of Dusts,

Soils, and Other Earth Materials: Invited. In: Lollar, G.S. (Ed.), Treatise on

Geochemistry, vol. 9, Elsevier, pp. 1-61.

Prospero, J.M., Glaccum, R.A., Nees, R.T. 1981. Atmospheric Transport of Soil

Dust from Africa to South America, Nature, 289, 570-572.

67

Prospero, J.M., 1999. Long-Range Transport of Mineral Dust in the Global Atmosphere:

Impact of African Dust on the Environment of the Southeastern United States, Proc.

Natl. Acad. Sci. USA, 96, 3396-3403.

Shultz, H., Brand, P., Heyder, J. 2000. Particle Deposition in the Respiratory tract. In:

Gehr, Peter, Heyder, Joachim (Eds.), Particle-Lung Interactions. Lung Biology in Health

and Disease, vol. 143. Marcel Dekker, Inc., New York, pp. 229-277.

Sullivan, R.C., Guazzotti, S.A., Sodeman, D.A., Tang, Y., Carmichael, G.R., Prather,

K.A., 2007. Mineral Dust is a Sink for Chlorine in the Marine Boundary Layer. Atmos.

Environ. 41, 7166–7179.

United States Enviromental Protection Agency (USEPA). 2012a. Particulate Matter.

Available from: http://www.epa.gove/airquality/particlepollution/

World Health Organization (WHO). 2012a. Available from:

http://www.who.int/mediacentre/factsseet/fs313/en/

http://www.epa.gove/airquality/particlepollution/

http://www.who.int/mediacentre/factsseet/fs313/en/

68

Biographical Information

Zakariah Sabatka is currently a geologist with MD America Energy in Fort Worth, Texas.

He has a Master of Science degree in Earth and Environmental Sciences from the University of

Texas at Arlington as well as two Bachelor of Science degrees, one from Texas A&M University

in Renewable Natural Resources, and one from the University of Texas at Arlington in Geology.

Mr. Sabatka has worked professionally in multiple geoscience roles including time as an

environmental geoscientist working on site investigation and remediation projects in the United

States and Australia, and he has also worked as a petroleum geologist developing the Eaglebine

formation in East Texas. Mr. Sabatka also holds a Professional Geoscientist License in Geology

from the State of Texas Board of Professional Geoscientists. His future plans include continuing

to grow personally and professionally in a range of geoscience roles with the main goal of

increasing his technical abilities so that he may mentor younger professionals and students

interested in a wide variety of geological topics.

CHARACTERIZATION OF THE CONSTITUENT MINERAL …

Documents

Transcript of CHARACTERIZATION OF THE CONSTITUENT MINERAL …