Laser diffraction size analysis of loess-paleosol sequences– pretreatment, calculation, interpretation
Von der Fakultät für Georessourcen und Materialtechnik der Rheinisch -Westfälischen Technischen Hochschule Aachen
zur Erlangung des akademischen Grades eines
Doktors der Naturwissenschaften
genehmigte Dissertation
vorgelegt von
M.Sc. Philipp Schulte
aus Lennestadt
Berichter: Univ.-Prof. Dr. rer. nat. Frank Lehmkuhl Prof. Dr. rer. nat. Bernhard Diekmann
Tag der mündlichen Prüfung: 18.04.2017
Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verfügbar
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Zusammenfassung
Die Korngrößenverteilung ist eine der wesentlichsten Eigenschaften von Korngemischen und wird daher seit Jahrhunderten in vielen technischen und wissenschaftlichen Bereichen mit unterschiedlichen Methoden analysiert. In der Industrie werden Korngrößenverteilungen von zum Beispiel Baustoffen, Farbpigmenten, Lebensmitteln oder Medikamenten bestimmt, um die Bestandteile und Eigenschaften der Produkte besser zu verstehen und die Produktionsprozesse zu optimieren (Produkt und Prozessoptimierung) sowie eine gleichbleibende Qualität zu sichern (Compliance).
In den Geowissenschaften werden Korngrößenanalysen angewendet um Sedimente und Böden zu klassifizieren (Granulometrie). Die granulometrischen Eigenschaften (Form und Größe) erlauben Rückschlüsse auf die Entstehungsgeschichte der Boden- und Sedimentpakete. Die gewonnenen Informationen können als Proxy (indirekter Indikator) zur Rekonstruktion der Klima und Umweltbedingungen genutzt werden, welche zur Zeit der Entstehung geherrscht haben. Das Wissen über die känozoische und vor allem über die quartäre Umweltentwicklung hilft uns, aktuelle Prozesse und zukünftige Veränderungen besser einschätzen zu können.
Korngrößenanalysen können mit diversen Methoden (Mikroskopie, Siebanalyse, Sedimentationsverfahren, Laser-Streulicht-Analyse) an unterschiedlichen Archiven (lakustrine, marine, (peri-)glaziale, äolische und kolluviale Sedimente) durchgeführt werden. Aktuell werden häufig Löss-Paläoboden-Sequenzen untersucht, da diese große Bereiche der Erdoberfläche bedecken und teilweise weit in der Zeit zurückreichen. Eine hochaufgelöste Bestimmung der Korngrößenverteilung solcher Sequenzen wurde erst durch die Entwicklung der Laser-Streulicht-Analyse ermöglicht. Obwohl dieses Verfahren deutlich zeit- und kosteneffektiver ist als alle verfügbaren Alternativen, ist es insbesondere in der Bodenkunde bis heute umstritten und wird daher nicht standartmäßig angewendet.
Die vorliegende Dissertation liefert einen Beitrag zum Verständnis der Einschränkungen der Laser- Streulicht-Analyse bezüglich der Erfassung und Interpretation von Korngrößendaten und liefert Ansätze, diesen zu begegnen. Dabei liegt ein besonderer Fokus auf der Bestimmung der vertikalen Korngrößenvariabilität in Löss-Paläoboden-Sequenzen sowie die Etablierung derselben als Proxy zur Rekonstruktion vergangener Klima und Umweltbedingungen.
Im Rahmen einer Literaturübersicht wurden die wichtigsten etablierten Methoden, welche zur Korngrößenbestimmung von Lösssedimenten eingesetzt werden können, zusammengefasst um im Hinblick auf ihre Eignung bewertet. Eine detaillierte Validierung der Laser-Streulicht-Analyse wurde im Rahmen der einzelnen bereits publizierten wissenschaftlichen Artikel durchgeführt.
Bei der Untersuchung eines Schlüsselprofils der Gebirgsregion auf dem nordöstlichen Tibet Plateau wurde eine klassische granulometrische Analyse durchgeführt, um die Korngrößenvariabilität innerhalb dieser Löss-Paläoboden-Sequenz zu bestimmen. Basierend auf einer kombinierten Betrachtung der horizontalen (innerhalb der gemessenen Klassen einer Probe) und vertikalen (über alle gemessenen Proben) Variabilität
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mittels verschiedener etablierter Parameter, konnten Aussagen über holozäne Klima und Umweltveränderungen getroffen werden. Durch die Generalisierung der horizontalen Variabilität in Form eines einzelnen Wertes (z.B. eines Korngrößenverhältnisses) werden geringe aktive Korngrößenänderungen verschleiert. Solche können als Folge leichter klimatischer Veränderungen in schwach frequentierten Korngrößenklassen auftreten und werden durch die relative Veränderungen der hoch frequentierten Klassen maskiert. Dies wurde unter anderem anhand des Vergleichs zweier Fallstudien aus Rumänien und der Ukraine nachgewiesen. Im Rahmen dieser Arbeit wurde die Heatmap als Darstellungsform für hochaufgelöste (große Anzahl an Größenklassen) Korngrößendatensätze von langen und dicht beprobten Sediment Sequenzen vorgeschlagen. Dies ist die einzige Möglichkeit um die Variabilität innerhalb von Löss- Paläoboden-Sequenzen ohne horizontalen oder vertikalen Datenverlust darzustellen.
Mittels einer Versuchsreihe wurde der Einfluss der Probenvorbehandlung, insbesondere der Zugabe von Salzsäure, auf das Ergebnis der Laser-Streulicht-Analyse evaluiert. Es ist festzuhalten, dass mit zunehmender post-depositioneller Veränderung der analysierten Proben die Wirkung der einzelnen Vorbehandlungsschritte undurchsichtiger wird. Besonders die Vorbehandlung mit Salzsäure kann zu missverständlichen Interpretationen führen. Generell ist jede Vorbehandlung eine zum Teil selektive und unergründliche Modifikation der natürlichen Sedimentprobe. Daher sollte die Probenvorbehandlung nur die essentiellsten Schritte umfassen (Oxidation der Organik und Dispergierung).
Nach der eigentlichen Messung wird das detektierte Laser-Streulicht-Muster mithilfe verschiedener optischer Modelle in eine Korngrößenverteilung umgerechnet. Die gängigsten Modelle sind die Fraunhofer Annäherung und das Lorenz-Mie Modell. Obwohl mit letzterem, vor allem für sehr feine Partikel (< 7,8 µm), deutlich validere Ergebnisse berechnet werden können, wird es bis heute nicht standardmäßig angewandt. Der Nachteil dieses Modells besteht darin, dass zur Berechnung der komplexe Brechungsindex, welcher die optischen Eigenschaften der in der Probe enthaltenden Minerale beschreibt, benötigt wird. Da dieser bei polymineralischen Gemischen nicht eindeutig zu bestimmen ist und eine Bestimmung extrem zeit- und kostenintensiv ist, wird in der Regel ein Näherungswert angenommen. Letztendlich liefert keines der beiden Modelle die tatsächliche Größe sehr feiner Partikel. Allerdings konnte nachgewiesen werden, dass die Differenz zweier Korngrößenverteilungen jeweils berechnet mit einem der beiden Modelle von der mineralischen Zusammensetzung der Feinkomponente Probe abhängig ist. Hiervon ausgehend, wurde im Rahmen dieser Arbeit ein neuer Korngrößenparamter, welcher vom Autor als delta grain size distribution (ΔGSD) bezeichnet wird, entwickelt, evaluiert und bereits vielfach zur Charakterisierung von Löss- Paläoboden-Sequenzen angewandt. Der Parameter reagiert sensitiv auf die post-depositionelle Bildung von Tonmineralen als Folge chemischer Verwitterung und kann als Proxy für fossile Bodenbildung und die damit assoziierten Paläoklima- und Paläoumweltbedingungen genutzt werden.
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Summary
Grain size distribution is one of the most important properties of particle mixtures, and, for centuries, it has been analyzed in various technical and scientific fields using different techniques. In industry, grain size distributions of, for example, building materials, color pigments, foodstuffs, or medicines are identified in order to better understand the ingredients and the properties of products, to optimize the production processes (product and process optimization), and to ensure a consistent quality (compliance). In the geosciences, grain size analysis are used to classify sediments and soils (granulometry). The granulometric properties (degree of roundness, shape, and size) allow conclusions about the genesis of the sediment and soil packages. The information obtained can be used as a proxy (indirect indicator) for the reconstruction of climate and environmental conditions which prevailed at the time of its origin. Knowledge about the Cenozoic Era, especially Quaternary period environmental development, helps us to better assess current processes and possible future changes.
Grain size analysis can be conducted using various methods (microscopy, sieve analysis, sedimentation methods, and laser diffraction size analysis) at different archives (lacustrine, marine, (peri-glacial, and aeolian and colluvial sediments). Currently, loess-paleosol sequences are regularly investigated because they cover large areas of the earth’s surface and some date back far in time. Highly resolved determinations of the grain size distribution of such sequences was enabled by the development of laser diffraction size analysis. Although this technique is significantly more time- and cost-effective than all available alternatives, especially in soil sciences, it remains controversial, and was, therefore, not applied by default until recently.
This study intends to disclose and defuse the constraints of the laser diffraction technique concerning grain size date acquisition, subsequent analysis, and interpretation. Especial focus is on the methodological progress of the investigation of vertical grain size variability within loess- paleosol sequences and its validation as a proxy to reconstruct past climate and environmental conditions.
In the literature review, the most important and well-established methods used for grain size analysis of loess sediments were summarized and assessed with regard to their susceptibility. A detailed validation of laser diffraction size analysis was carried out in the framework of the individual previously published chapters.
For the investigation of a key section of the mountain region on the northeastern Tibet plateau, a classical granulometric analysis was conducted to determine grain size variability within this loess- paleosol-sequence. Based on a combined analysis of the horizontal (within the measured classes of
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a sample) and vertical (across all measured samples) variability, using different established parameters, conclusions about the Holocene climate and environmental changes were possible. Due to the generalization of horizontal variability in the form of a single value (e.g., a grain size ratio), small active grain size changes are obscured. Such variations can occur as a result of slight climatic changes in low frequented grain size classes and are masked by the relative changes of the highly frequented classes. This was demonstrated, among other things, by the comparison of two case studies, one from Romania and one from Ukraine. In the framework of this study, the visualization of highly resolved (large number of size classes) grain size data, obtained for long and densely sampled sediment sequences in the form of a heatmap signature, has been established. This is the only way to present the variability within loess-paleosol sequences without horizontal or vertical data loss.
In an analytical test series, the influence of pretreatment agents, particularly hydrochloric acid (HCl), on the result of laser diffraction size analysis was evaluated. It should be noted that with increasing post-depositional alteration of the analyzed sample, the effect of the individual steps of pretreatment becomes more inscrutable. Especially, pretreatment with HCl can result in misleading interpretations. In general, each pretreatment is a partially selective and inscrutable modification of the natural sediment sample. Therefore, sample pretreatment should include only the essential steps (oxidation of the organic matter and dispersion).
After the actual measurement, the detected laser scattering pattern is converted into a grain size distribution by means of various optical models. The most common are the Fraunhofer approximation and the Lorenz-Mie models. Although, by means of the latter, it is possible to calculate more valid results, especially for very fine particles (< 7.8 μm), it has not been used by default until recently. The disadvantage of this model is that the complex refractive index, which describes the optical properties of the minerals contained in the sample, is needed for calculation. Since this cannot be determined clearly in polymineral mixtures and a determination is extremely time-consuming and cost-intensive, an estimated value is generally assumed. Finally, neither of the two models delivers the actual size of very fine particles. However, it was demonstrated that the difference between two grain size distributions, each calculated with one of the two models, is dependent on the mineral composition of the fine component of the sample. Based on this, a new grain size parameter, which was designated by the author as the delta grain size distribution (ΔGSD) signature, was developed, evaluated, and used for the characterization of loess-paleosol sequences. The parameter reacts sensitively to the post-depositional formation of clay minerals as a result of chemical weathering and can be used as a proxy for fossil soil formation and the associated paleoclimatic and paleoenvironmental conditions.
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Table of contents
ZUSAMMENFASSUNG ...... I
SUMMARY ...... III
TABLE OF CONTENTS ...... V
LIST OF FIGURES ...... VIII
LIST OF TABLES ...... XI
INTRODUCTION ...... 1
1.1. FRAMEWORK AND OBJECTIVES ...... 1
1.2. OUTLINE ...... 2
1.3. ASSOCIATED RESEARCH PROJECTS ...... 4
FUNDAMENTALS OF LOESS‐PALEOSOL‐SEQUENCE INVESTIGATION ...... 6
2.1. LOESS AS AN AEOLIAN SEDIMENT ...... 6
2.2. POST‐DEPOSITIONAL ALTERATION OF LOESS SEDIMENTS ...... 7 2.2.1. Grain size reduction ...... 7 2.2.2. Aggregation and agglomeration of loess sediments ...... 8
2.3. GRAIN SIZE PARAMETERS AND RATIOS FOR LOESS‐PALEOSOL SEQUENCES ...... 9
ESTABLISHED METHODS FOR GRAIN SIZE ANALYSIS OF LOESS ...... 12
3.1. MICROSCOPIC ANALYSIS ...... 13
3.2. SIEVE ANALYSIS ...... 15
3.3. SEDIMENTATION / GRAVITATION ANALYSIS ...... 16 3.3.1. Areometer Method ...... 18 3.3.2. Photometer Method ...... 18 3.3.3. Sedimentometer Method (SediGraph) ...... 19 3.3.4. Pipette analysis ...... 20 3.3.5. Atterberg sedimentation ...... 20 3.3.6. Limitations of gravitational techniques ...... 21
LOESS‐PALEOSOL‐SEQUENCES DEVELOPED UNDER DIFFERENT PALEOENVIRONMENTAL CONDITIONS . 22
MANUSCRIPTS ...... 24
5.1. TIMING AND SPATIAL DISTRIBUTION OF LOESS AND LOESS‐LIKE SEDIMENTS IN THE MOUNTAIN AREAS OF THE
NORTHEASTERN TIBETAN PLATEAU ...... 24 Abstract ...... 24 5.1.1. Introduction ...... 25 5.1.2. Regional setting and distribution of loess and loess‐like sediments ...... 27 V
5.1.3. Material and methods ...... 30 5.1.3.1. Geomorphological mapping, site selection and sampling strategy ...... 30 5.1.3.2. Sedimentological and geochemical methods ...... 30 5.1.3.3. Dating methods ...... 31 5.1.4. Results ...... 33 5.1.4.1. Sediments and grain size distribution ...... 34 5.1.4.2. Geochemical results of SHD section ...... 37 5.1.4.3. Geochronological results ...... 39 5.1.5. Discussion ...... 41 5.1.5.1. Late Glacial to early Holocene (15 ka to 8.5 ka) ...... 41 5.1.5.2. Mid‐Holocene (8.5 to 4.0 ka) ...... 42 5.1.5.3. Late Holocene (4.0 – 0 ka) ...... 43 5.1.6. Conclusion ...... 43
5.2. ENVIRONMENTAL CHANGE INDICATED BY GRAIN SIZE VARIATIONS AND TRACE ELEMENTS: EXAMPLES FROM TWO
DIFFERENT SECTIONS ‐ THE SANDY‐LOESS SEDIMENTS FROM THE DOROSHIVTSY SITE (UKRAINE) AND THE LOESS SECTION SEMLAC
(ROMANIA) ...... 45 Abstract ...... 45 5.2.1. Regional Setting ...... 46 5.2.1.1. Western Romania: the section Semlac ...... 46 5.2.1.2. South‐western Ukraine: the section Doroshivtsy ...... 46 5.2.2. Methods ...... 47 5.2.3. Results ...... 47 5.2.3.1. Semlac ...... 47 5.2.3.2. Doroshivtsy ...... 49 5.2.4. Conclusion ...... 52
5.3. INFLUENCE OF HCL PRETREATMENT AND ORGANO‐MINERAL COMPLEXES ON LASER DIFFRACTION MEASUREMENT OF
LOESS‐PALEOSOL SEQUENCES ...... 53 Abstract ...... 53 5.3.1. Introduction ...... 54 5.3.2. Material and methods ...... 58 5.3.2.1. Sample Preparation ...... 59 5.3.2.2. Laser diffraction measurement ...... 60 5.3.2.3. Optical model ...... 60
5.3.2.4. Content of CaCO3 and Corg ...... 61 5.3.2.5. Inductively coupled plasma‐optical emission spectrometry ...... 61 5.3.2.6. Scanning electron microscopy ...... 62 5.3.3. Results ...... 62 5.3.3.1. Effect of acetic acid treatment ...... 62 5.3.3.2. Comparison of HCl treated and untreated samples ...... 62
5.3.3.3. MODGSD dependence on CaCO3 and Corg content ...... 66 5.3.3.4. Element composition of the HCL leachable fraction ...... 67
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5.3.3.5. Scanning electronmicroscopy (SEM) ...... 69
5.3.3.6. MODGSD, CaCO3 and Corg at section DDG ...... 71 5.3.4. Discussion ...... 73 5.3.4.1. MODGSD of the samples from the SHD section ...... 74 5.3.4.2. (a) Disintegration of aggregates and organo‐mineral complexes ...... 75 5.3.4.3. (b) Formation of cSi‐ to fS‐sized aggregates ...... 76 5.3.4.4. Comparison of SHD and DDG results ...... 76 5.3.5. Conclusion ...... 78
5.4. THE DIFFERENCE OF TWO LASER DIFFRACTION PATTERNS AS AN INDICATOR FOR POST‐DEPOSITIONAL GRAIN SIZE
REDUCTION IN LOESS‐PALEOSOL SEQUENCES ...... 80 Abstract ...... 80 5.4.1. Introduction ...... 81 5.4.2. Materials and Methods ...... 82 5.4.2.1. Sample selection ...... 83 5.4.2.2. Laser diffraction measurement ...... 84 5.4.2.3. Optical model ...... 85 5.4.2.4. Data preparation and illustration ...... 87 5.4.3. Results ...... 88 5.4.3.1. Submicron fraction of single samples ...... 88 5.4.3.2. Loess sections ...... 90 5.4.4. Discussion ...... 93 5.4.4.1. Quality check of the clay measurement ...... 93
5.4.4.2. Dependency of the ΔGSDraw on the mineral composition ...... 94
5.4.4.3. ΔGSDclr as proxy in loess research ...... 96
5.4.4.4. ΔGSDclr variability within the loess‐paleosol sequences ...... 97 5.4.4.5. Potential sources of error ...... 99 5.4.5. Conclusion ...... 99
SYNTHESIS ...... 101
6.1. SAMPLE PRETREATMENT PRIOR TO LASER DIFFRACTION SIZE ANALYSIS ...... 101
6.2. GRAIN SIZE MEASUREMENT AND CALCULATION ...... 104
6.3. THE ANALYSIS OF GRAIN SIZE DATA ...... 107
DANKSAGUNG / ACKNOWLEDGMENTS ...... 112
REFERENCES ...... 116
APPENDIX A ...... 140
APPENDIX B ...... 148
VII
List of Figures
Fig. 3.1: Two exemplary samples illustrated as cumulative percentage frequency curves (A and B), as percentage frequency histogram (A), as distribution density curve (B), as colorline signature (C) and as a heatmap signature (D)...... 12
Fig. 5.1.1: Map of the northeastern Tibetan Plateau. The box shows the location of the main section Suohuduo at the southern margin of the Anyemaqen Shan. The numbers indicate further sections mentioned in the text...... 26
Fig. 5.1.2: NW–SE-cross section indicating the distribution of loess and loess-like sediments in eastern Tibet...... 27
Fig. 5.1.3: (A) Situation of the valley close to the settlement Suohuduo. (B) Suohuduo section indicating the two paleosols. Photos: F. Lehmkuhl, August 2006...... 29
Fig. 5.1.4: (A) Mean grain-size distributions of selected units from the Suohuduo on log-normal scale, (B) stratigraphy, (C) depth profiles of selected grain-size fractions (C: 0.04–2 μm; fSi: 2–6.3 μm; mSi: 6.3– 20 μm; cSi b 36: 20–36 μm; cSi > 36: 36–63 μm; fS: 63–200 μm; mS: 200–630 μm; cS: 630–2000 μm) and (D) depth profiles of grain-size mean and mode...... 34
Fig. 5.1.5: Triangle diagram of the textural classes (classification according FAO, 2006) of all Suohuduo samples and of the three comparative sections (Aba, Jiukehe and Dari) as mean values. In addition, the classification of loess, sandy loess and sand from the Donggi Cona catchment are shown...... 36
Fig. 5.1.7: Comparison of paleoenvironmental reconstructions by this study with other results from the Donggi Cona, the Chinese Loess Plateau and the Qinghai Lake...... 41
Fig. 5.2.1: Loess distribution in Europe (modified according to Haase et al., 2007), maximum extent of the Weichselian ice sheet, coastline during LGM and location of the sections Doroshivtsy in the western Ukraine and Semlac in south-western Romania...... 46
Fig. 5.2.2: Lithostratigraphy, sensitive grain size ranges and selected element concentrations of the Semlac section. OSL dating was carried out on silty quartz samples (grain size 40-63 µm) and a standard SAR protocol (Murray and Wintle, 2003). The central age model was used for De calculation...... 48
Fig. 5.2.3: Lithostratigraphy, specific GS-ratio, and U-ratio of the Semlac section...... 49
Fig. 5.2.4: Lithostratigraphy, sensitive grain size ranges, and Al/Zr ratio of the Doroshivtsy section...... 50
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Fig. 5.2.5: Lithostratigraphy, specific GS-ratio, U-ratio, Sedimentology, OSL ages, and Radiocarbon ages of the Doroshivtsy section. OSL dating was carried out using OSL and pIRIR stimulation (Klasen et al., 2015)...... 51
Fig. 5.3.1: Stratigraphy and location of the Suohuduo section at the northeastern margin of the Tibetan Plateau (Lehmkuhl et al., 2014)...... 56
Fig. 5.3.2: Stratigraphy and location of the Düsseldorf-Grafenberg coring situated at on the right side of the Rhine River...... 57
Fig. 5.3.3: Heatmap showing HCl-induced MODGSD (frequency of HCl treated subsamples – frequency of untreated subsamples) of all samples from the Suohuduo section. This difference is shown for each of the 116 grain size classes resulting from the laser diffraction analysis...... 63
Fig. 5.3.4: Depth profiles of the parameters mean, mode and median for the MODGSD of the Suohuduo section...... 64
Fig. 5.3.5: GS distribution curves of untreated samples (top), the HCl-treated samples (upper middle), calculated differences (lower middle), and mean SD of all aliquot measurements (bottom) for three pedogenic units of the Suohuduo section: (A) primary loess, (B) modern soil, (C) paleosols...... 65
Fig. 5.3.6: Dependence of the HCl-induced MODGSD on (A) the content of CaCO3 and on (B) the content
of Corg for the pedogenic units primary loess, modern soil, and paleosol...... 67
Fig. 5.3.7: Two aliquots of the same samples after the first 5 h run of the H2O2 pretreatment cycle. The right glass tube was previously treated with HCl, the attempt in the left glass tube only pre-moistened with aqua dest...... 69
Fig. 5.3.8: SEM images of a sample from paleosol S01 at different magnifications: (A) and (B) show the
untreated sample, (C) and (D) the sample pretreated with H2O2, and (E) and (F) the sample pretreated
with H2O2 und HCl...... 70
Fig. 5.3.9: Heatmap showing HCl-induced MODGSD (frequency of HCl treated subsamples – frequency of untreated subsamples) of all samples from the Düsseldorf-Grafenberg section. This difference is shown for each of the 116 grain size classes resulting from the laser diffraction analysis...... 71
Fig. 5.3.10: Depth profiles of the statistical parameters mean, mode and median of the MODGSD for the Düsseldorf-Grafenberg coring...... 72
Fig. 5.3.11: Comparison of mean MODGSD and standard deviation of both Suohuduo (SHD) and Düsseldorf-Grafenberg (DDG) sequences, showing heterogeneous effects of HCl treatment on different GS spectra...... 77
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Fig. 5.4.1: Grain size distribution curves of four standard mineral samples and two Russian standard soils reduced to the submicron fraction (left column). The specifications of these samples are given in Tab. 5.4.1. The right column shows the results of five natural clay mineral samples...... 89
Fig. 5.4.2: Optical microscope photograph showing the submicron fraction after separation by Atterberg sedimentation of (A) a sample from the S1 palaosol of the Semlac sequence and (B) a pure quartz sample ...... 90
Fig. 5.4.3: Heatmap showing the ΔGSDraw (A) and the ΔGSDclr (B) of all samples from the loess section Semlac (Romania). The vertical curves represent the contents the fraction 15-30 µm and <1µm, the CaCO3 content and the magnetic susceptibility (MS) ...... 91
Fig. 5.4.4: Heatmap showing the ΔGSDclr of all samples from the Düsseldorf-Grafenberg sequence. The vibracore consist of calcareous loess and intercalated interglacial, interstadial and periglacial soils and soil sediments. The vertical curves represent the content of submicron particles...... 92
Fig. 5.4.5: Heatmap showing the ΔGSDclr of all samples from the Suohuoduo section, containing pure loess (white), slightly weathered loess (light grey) and chernozem-like paleosols (S01; S02: dark grey). The
vertical curves represent content of submicron particles and Corg...... 93
Fig. 6.1: GS distribution curves of three loess samples on a different state of pedogenesis. Prior to measuring each sample was dispersed in tetrasodium pyrophosphate solutions of different concentrations (given in g/L)...... 102
Fig. 6.2: GS distribution curves of two silt dominated sediment samples. The samples were measured several times. Prior to each repetition ultrasonic treatment was applied for 120 seconds respectively...... 103
Fig. 6.3: Illustration of different equivalent spheres of the same “submicron” irregularly shaped particle (in accordance to Malvern, 2012)...... 105
Fig. 6.4: Heatmap showing the grain size distribution of all samples from the Suohuoduo section, containing pure loess (white), slightly weathered loess (light grey) and chernozem-like paleosols (dark grey). The vertical curves represent classical grain size parameters and ratios ...... 108
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List of Tables
Tab. 5.1.1: Radionuclide concentrations derived from laboratory high resolution γ-spectrometry, measured water contents, calculated dose rates, equivalent doses and the resulting ages of the 125–180 μm quartz fraction. Ages in italics are calculated with the finite mixture model after Galbraith and Green (1990), all others are with the weighted mean age model including 5% instrumental error and given with 1σ age deviation...... 40
Tab. 5.1.2: Results of the radiocarbon dating of section 2 (2.11), section 3 (3.3) and section 7 (7.11, 7.19)...... 40
Tab. 5.3.1: Precision of particle size measurements of a modern top soil, a paleosol and a loess sample of the SHD section. Precision was assessed by four aliquots of the same bulk sample and is gives as % CV...... 59
Tab 5.3.2: CaCO3 contents [%] and concentrations of HCl-leachable elements [mg/l] within the supernatant after centrifugation ...... 68
Tab 5.3.3: Elemental concentrations [mg/kg] of the stratigraphic units of the SHD section; aqua regia extract
(3:1 concentrated HCl and concentrated HNO3) ...... 68
Tab. 5.4.1: Properties and references of the selected standard material samples...... 83
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Introduction
1.1. Framework and objectives
The application of laser diffraction particle size analysis is widespread in earth sciences (Konert and Vandenberghe, 1997; Pye and Blott, 2004; Blott and Pye, 2006; Machalett et al., 2008; Roberson and Weltje, 2014; Újvári et al., 2016). Large amounts of grain size data can be generated in a short time with satisfying precision and accuracy (Blott and Pye, 2006; McCave et al., 2006; Miller and Schaetzl, 2012). Due to technical advancements since the 1980s, granulometric analysis of sediments from Pleistocene and Holocene loess-paleosol sequences are increasingly used in paleoclimate research (Vandenberghe, 1997; Antoine et al., 2009a; Lehmkuhl et al., 2014). The reconstruction of past environmental and climate conditions is important for the general understanding of climate processes. Loess-paleosol sequences are often investigated since they are one of the most widespread terrestrial archives that record environmental developments (Sun et al., 2006; Antoine et al., 2009a,b; Bokhorst and Vandenberghe, 2009; Antoine et al., 2013; Gocke et al., 2014; Meszner et al., 2014; Vandenberghe et al., 2014; Obreht et al., 2016). Through the investigation of information about the Cenozoic and especially the Quaternary, climate and environmental evolution can be obtained.
Various methods exist to analyze the particle size distribution of natural sediments, including dry or wet sieving, several gravitational techniques, or direct microscopic measurement by microscopy. Each technique is appropriate for a specific grain size range. Sieving is most useful for sand or gravel and gravitational techniques for coarse clay and silt, while a combination of several microscopic techniques allows the entire range from clay to sand to be analyzed. However, the latter technique is very time- and cost-intensive.
The advantage of the laser diffraction technique is that it is suitable for a wide range of grain sizes (fine clay to coarse sand) and is extremely time- and cost-effective. For the investigation of temporal trends within huge sample sets, as is common in loess-paleosol-sequence studies, this method provides grain size results in a high resolution (116 classes in case of the LS 13320) with sufficient precision (Chapter 5.3.) (Blott and Pye, 2006). Recently, laser diffraction has been widely used to characterize fine sediments (< 2 mm). However, especially in soil science, this technique remains controversial since the assumption of a significant underestimation of the clay fraction (in comparison to gravitational techniques) is frequently discussed (Loizeau et al., 1994; Konert and Vandenberghe, 1997; Beuselinck et al., 1998; Roberson and Weltje, 2014; Újvári et al., 2016).
1
This study intends to disclose and defuse the constraints of the laser diffraction technique concerning grain size date acquisition and subsequent analysis and interpretation. For this purpose, the main objectives of this dissertation were the following:
The evaluation of different grain size analysis techniques to characterize loess-
paleosol sequences;
The evaluation of the effect of pretreatment agents on grain size data processed for
samples from loess-paleosol sequences developed under different weathering
conditions. In particular, the influence of HCl treatment should be tested;
The identification of the advantages of grain size calculation by the application of
the Lorenz-Mie theory;
The improvement of the visual presentation of grain size data processed for loess-
paleosol sequences;
The evaluation of commonly used grain size parameters and ratios as proxy data
for paleoenvironmental variations; and,
The introduction and interpretation of a novel reliable grain size parameter.
1.2. Outline
Initially, Chapter 2 presents a brief overview of the state of the research concerning loess-paleosol- sequence investigation. A special focus is on the processes which influence the grain size distribution subsequent to the accumulation of the aeolian dust.
Chapter 3 reviews the most frequently used methods for grain size analysis which have been used through the present time. The respective methods are assessed according to their suitability for the characterization of loess sediments.
Chapter 4 offers an overview of the locations of the loess-paleosol sequences investigated in this dissertation. The respective sequences have been selected in such a way that they cover a wide spectrum of varying degrees of weathering and pedogenesis. Each sequence consists of loess sediments which experienced different post-depositional alterations as a function of different paleoenvironmental conditions. The Suohuduo sequence (Tibetan Plateau, China) developed 2
during the Holocene. Under predominantly dry and cold environmental conditions, dark and slightly weathered Eurasian steppe soils developed. Under distinctly warmer and moister conditions near Semlac (Arad Plain, Romania), the local loess plateau covers sediments accumulated relatively slowly over the past 350,000 years; therefore, intensively weathered soils were formed and preserved. The Grafenberg sequence (Germany) is located in the oceanic influenced lower Rhine embayment. Due to high humidity and periglacial processes, erosional processes are comparatively enhanced. The Doroshivtsy sequence represents a special case in this selection as it consists of former aeolian sediment accumulated by fluvial and periglacial processes in a foot slope position.
Chapter 5 comprises the manuscripts of three original research articles and one extended abstract showing two case studies. The content of the respective subchapters follows the form as it was published in the corresponding scientific journals. Only editorial modifications have been made to integrate the individual articles into the dissertation.
Chapter 5.1. shows a classic granulometric-based case study focused on the reconstruction of grain size variability within a loess-paleosol-sequence. The paleoclimatic interpretation is based on a combination of different established parameters to visualize the variability within a primary grain size data set. Both the horizontal (over all calculated size classes) variation of grain size distribution curves averaged for the respective sediment units and the vertical variability (over all individual samples of the sediment sequence) of common statistical parameters result in a generalization, and, hence, in a loss of information.
Chapter 5.2. shows the problems occurring when common grain size parameters and ratios of fixed grain size ranges are applied as proxy data. The advantages of individualized grain size ratios, which are independent of firm boundaries and highly sensitive to individual sedimentary or post- sedimentary processes, are discussed. Such ratios react sensitively in individual grain size influencing processes but remain a generalization of the horizontal variability.
Predominantly based on the results of Chapter 5.1., in an analytical test series, the influence of hydrochloric acid treatment prior to laser diffraction size analysis was evaluated. The expertise and interpretations gained from these experiments are described in Chapter 5.3. Additionally, in this chapter, the visualization of grain size data in the form of a heatmap signature has been established. In this way, both primary and differential (e.g., GSD without HCl treatment minus GSD after HCl treatment) grain size data sets of sediment sequences can be visualized without any loss of horizontal or vertical variability.
Chapter 5.4. focusses on the calculation of the primary laser diffraction scattering pattern by the means of two different optical models (Fraunhofer approximation and Lorenz-Mie theory). In this 3
study, a novel grain size parameter, the ΔGSD, has been developed, evaluated, and interpreted. This parameter can be used as a suitable indicator of the degree of post-depositional chemical weathering in loess-paleosol sequences, and, consequently, as a proxy for past climatic and environmental evolution. The ΔGSD signature is unaffected by the constraints outlined in the previous chapters and by other grain size modifying processes, such as cryogenesis, or the input of further synsedimentary deposits like coarse material transported by saltation, reptation, creeping, or sheet wash.
Chapter 6, the final chapter of this dissertation, represents an overall synthesis based on the major findings of the individual chapters.
1.3. Associated research projects
The presented research was conducted as part of two research projects. The first is called Landscape and Lake-System Response to Late Quaternary Monsoon Dynamics on the Tibetan Plateau - Northern Transect and was funded by the Deutsche Forschungsgemeinschaft (DFG) between 2008 and 2014. The research area of the project comprises the two lake catchments on the north-eastern margin of the Tibetan Plateau: These catchments are the Donggi Cona (4100m asl.) in the north-east and the Lake Heihai in the in the Kunlun Mountain range at the northern margin (4420m asl.) of the Plateau. The project aims to reconstruct complete sediment cascades from the high mountain area to the lakes and their relation to the late Quaternary climate evolution (monsoon versus Westerlies). The Sino‐German research group consisted of scientists from the RWTH Aachen University, the Alfred‐Wegener Institute in Potsdam, the Free University of Berlin who collaborated with Nanjing University. The research strategy of the RWTH Aachen team reflects the identification of climato-sensitive geomorphic process regions and the timing of sediment-mobilization and deposition periods of glacial, aeolian and fluvial sediments in the context of the late Quaternary climate and landscape evolution. For the reconstruction detailed sedimentological and geochemical analysis were used. The project results demonstrate that the two study areas have different sediment sources and varying sediment cascades. While the Donggi Cona system is dominated by a large alluvial fan with a strong recycling of older sedimentary deposits (IJmker et al., 2012a; IJmker et al., 2012b; Stauch et al., 2014), in the Heihai catchment the local lithology is the main influencing factor for the sedimentary system. The analysis of some samples which was carried during the first funding period of this project was finished at the beginning of the second phase. The results of the sample set from the Suohuoduo section, mainly processed by the author and his supervisor Frank Lehmkuhl, the full professor for Physical Geography and Geoecology at RWTH Aachen University, are part of this dissertation thesis. 4
The second research project in which framework this dissertation was conducted was the Collaborative Research Center (CRC): OUR WAY TO EUROPE - Culture-Environment Interaction and Human Mobility in the Late Quaternary funded by the Deutsche Forschungsgemeinschaft (DFG) between 2009 and 2017.
The research project deals with the history of mankind and specifically with the understanding of human-environment interaction at the time of Europe’s colonization by first anatomically modern humans and their population mobility. This objective was pursued with a combination of geoscientific and archaeological methods. The chair of Physical Geography and Geoecology at RWTH Aachen University was involved in the subproject B1: The "Eastern Trajectory": Last Glacial Paleogeography and Archaeology of the Eastern Mediterranean and of the Balkan Peninsula. Although a lot of natural factors like the access to raw material, water and food and booty are of importance for a corridor which was preferred during the spread of early modern humans on their way to Western Europe, we believe that local climate acts as one of the main steering factors for human dispersal, presence and absence. A climatically more favorable region with more moisture resulting in a different vegetation pattern close to the border of the Carpathian Arch could be the reason why especially here higher densities of remains from early anatomically modern humans have been found. Further, the chair of Physical Geography and Geoecology investigated the subproject D1: Analysis of Migration Processes due to Environmental Conditions between 40,000 and 14,000 a BP in the Rhine Catchment and Adjacent Areas. Around 40,000 B.P the early modern humans arrives the western central European habitat. After their initial dispersal into Europe they emigrate around 30,000 a BP. Subsequent to the last glacial maximum (LGM), a resettlement by the Homo sapiens started. The combined geoscientific and archaeological research concentrated on the two time slices: MIS 3 and LGM/post-LGM.
One main task of these two subproject was to combine granulometric and geochemical methods for selected key sections to get proxy data to correlate loess-paleosol sequences in different geomorphological positions. In the framework of this dissertation the results of the loess-paleosol sequence Semlac (Arad Plain, Romania) and Düsseldorf Grafenberg (Lower Rhine Embayment, Germany) was used to improve and precise the laser diffraction size analysis.
5
Fundamentals of loess-paleosol-sequence investigation
2.1. Loess as an aeolian sediment
Loess is defined as an aeolian sediment predominantly consisting of silt size particles (Pye, 1995; Stauch et al., 2012; Muhs et al., 2014). Aeolian sediments cover approximately 30% of the earth’s surface (Thomas and Wiggs, 2008).
The formation of loess is controlled by various factors. The most important are sediment source, chain of transport processes, wind as the final transport agent (Wright, 2001; Smalley et al., 2009), an area favorable for accumulation due to a reduction of wind speed or sediment trapping by vegetation or gravel layer (Bagnold, 1941; Lancaster and Baas, 1998), and an adequate timespan, which is necessary for the initial preservation of the deposited dust particles (Pécsi, 1990; Smalley and Marković, 2014). Generally, aeolian sediment transport occurs during dry and/or glacial periods (Tsoar and Pye, 1987). However, for example, even an increase in moisture availability can result in the accumulation of loess due to the fluvial transport of silty and sandy sediments to storage sites where it is easily available for aeolian deflation (Bullard and McTainsh, 2003; Stevens et al., 2013; Nottebaum et al., 2014; Nie et al., 2015). Further problems in the interpretation of aeolian sediments as paleoproxy are the frequent reworking of primary aeolian sediments and inherited weathering products due to (glacio-) fluvial processes, solifluction, or cryogenic processes (Stevens et al., 2006; Schirmer, 2016; Lehmkuhl et al., 2016) and gaps or discordances in loess sections due to erosional events (Thomas, 2013; Schirmer, 2016; Lehmkuhl et al., 2016).
During the erosion, transport, and accumulation processes, sediment sorting occurs. Variations of the grain size distribution of loess-paleosol sequences are frequently used for the reconstruction of the climatic and environmental conditions during aeolian sedimentation (Ding et al., 1995; Muhs and Bettis, 2003; Nugteren and Vandenberghe, 2004). If the accumulation rate is greatly reduced while moisture availability is increased, soil formation is enhanced (e.g., Stevens et al., 2011; Sprafke and Obreht, 2016). Primary loess consists mainly of coarse silt (Pécsi, 1990; Pye, 1995; Muhs and Bettis, 2003), whereas submicron particles (< 1 µm) are greatly underrepresented during accumulation (Qiang et al., 2010; Újvári et al., 2016). Soil formation results in post-depositional grain size fractionation (e.g., Újvári et al., 2016). As a function of physical and chemical weathering processes, the dominating medium and coarse silt fractions are reduced in favor of the clay and fine silt fractions.
6
2.2. Post-depositional alteration of loess sediments
2.2.1. Grain size reduction After the sedimentation and initial fixation of atmospheric mineral dust particles, a first weak sediment alteration process, often called loessification, begins (Berg, 1916; Pécsi, 1990; Smalley et al., 2011; Svirčev et al., 2013; Smalley and Marković, 2014; Sprafke and Obreht, 2016). Such processes are not sufficiently understood, and it is a matter of debate whether they should be assigned to pedogenic or diagenetic process spheres or to a kind of transition zone (Sprafke and Obreht, 2016). However, there is a consensus that the typical structure of a loess sediment is generated by these loessification processes, whereby the loess is differentiated from primary airborne dust (Pécsi, 1990; Sprafke and Obreht, 2016).
If the sediment accumulation rate is strongly reduced while moisture availability is increased, these initial and weak alteration processes are enhanced and result in actual pedogenesis (e.g., Stevens et al., 2011).
Due to soil formation, the grain size of the non-carbonate part of the loess sediment is reduced (e.g., Újvári et al., 2016). In the near surface range, these post-depositional fractionation processes occur both in carbonate containing and in carbonate-free milieus. If decalcification takes place, grain size reduction follows downwards as much as several meters (Rohdenburg and Meyer, 1966) and results in a reduction of the dominating coarse silt fractions in favor of an increase in the clay fractions (fine and medium silt fractions also increase in most cases). In fact, the post-depositional variation of the sand fraction is negligible.
Generally, there are two possible causes for fractionation: the chemical weathering of silt-sized minerals, such as mica and feldspar, as a result of hydration and hydrolysis (Schaetzl and Thompson, 2015) and the physical breakdown of particles due to cryogenic processes. In contrast to chemical weathering, in physical weathering, all contained mineral types are affected. During the Pleistocene, within the near surface part of loess sequences, both processes emerge in parallel, whereas the physical breakdown decreases with the increasing depth of the weathering front (Rohdenburg and Meyer, 1966).
Due to the relative absence of fine and medium clay during the deposition of loess, the enrichment of these fractions is often used as a proxy for pedogenesis and climate variations (e.g., Nugteren and Vandenberghe, 2004; Antoine et al., 2009b; Terhorst et al., 2012). For example, if physical processes prevail, cryosols or chernozems can be formed within loess sediments. Under dominant chemical weathering conditions, the formation of cambisols, luvisols, and planosols is more likely (Schaetzl and Thompson, 2015).
7
Former soils buried and preserved by overlying sediments are called as paleosols (Sheldon and Tabor, 2009). During their formation, the soils are exposed at the earth’s surface and interact with the contemporary prevailing climatic and environmental conditions. Consequently, they are frequently used as a powerful archive to reconstruct past environmental conditions (Sheldon and Tabor, 2009). To this end, loess-paleosol sequences are increasingly investigated in all regions were loess and loess derivatives are distributed and preserved (Kukla and An, 1989; Muhs and Bettis, 2003; Sun et al., 2006; Smalley et al., 2011; Gocke et al., 2014; Marković et al., 2015; Lehmkuhl et al., 2016).
2.2.2. Aggregation and agglomeration of loess sediments Post-depositional processes not only result in a general size reduction of the sediment components, but, during pedogenesis particularly, the fine materials of loess sediments tend to aggregate into larger flocks with varying grain sizes and resistances (Schulten and Leinweber, 2000; Balabane and Plante, 2004; Six et al., 2004; Lützow et al., 2008; Stamati et al., 2013). These soil aggregates are cemented by agents such as calcium carbonate, clay minerals, organic matter, or metallic oxides and hydroxides (Oades, 1988; Wiseman and Püttmann, 2006; Kögel-Knabner et al., 2008). Especially within temperate soils on loess sediments, various stabilizing mechanisms are evident, reducing or preventing the degradation of the organic matter (Boudot et al., 1989; Lützow et al., 2006; Kögel-Knabner et al., 2008; Lützow et al., 2008).
These mechanisms include: (i) occlusion in aggregates, (ii) aromatic compounds/black carbon, (iii) cation sorption, and (iv) anion sorption.
(i) Within soil aggregates, organic matter (OM) is protected against decomposition by physical separation of the organic material and the decomposer (Oades, 1995; Lützow et al., 2006). The majority of the soil organic carbon (Corg) in silty soils is aggregated with clay minerals and organo- mineral complexes of the 2–20 µm size fraction (Van Gestel et al., 1996; Schulten and Leinweber,
2000; Lützow et al., 2006). The lowest content of Corg is associated with the 20–50 µm fraction which is typically composed predominantly of non-aggregated mineral particles. The largest grain size fraction, 50–200 µm, falls between these extremes regarding its Corg content (Van Gestel et al., 1996). Large fragments of plant residue microaggregates, organo-mineral complexes, and mineral particles are bound together into macroaggregates by transient interaction with fungal hyphae and roots or by polysaccharides (Six et al., 2000, 2004; Stamati et al., 2013). In addition, inorganic binding agents such as iron oxides or carbonates play an important role in the formation of micro- and macroaggregates. During sample pretreatment, the OM in such stable complexes will only be decomposed if the surrounding clay minerals or iron oxides have been destroyed previously. At 8
lower pH values (< 4.8), some clay minerals and iron oxides can be destroyed, and weak clay-humic complexes and microaggregates can be split up (Kögel-Knabner et al., 2008; Vaasma, 2008).
(ii) Particularly in steppe soils, a high proportion of Corg is present as black carbon (highly-dispersed charcoal) (Rodionov, 2010). This is caused by natural vegetation fires and has often been observed in chernozems and similar soils (e.g., Haumaier and Zech, 1995; Glaser et al., 2001). Such black carbon is assumed to form aromatic compounds (Lützow et al., 2006). If present, these occur in different grain size ranges, their detection is complicated (Schmidt et al., 2001), and the compounds are very resistant to degradation (Schmidt and Noack, 2000; Lützow et al., 2008).
(iii) Cations are selectively bound to negatively charged humic substances and clay minerals (Edwards and Bremner, 1967; Wiseman and Püttmann, 2006). The tendency to sorb cations increases with higher pH values and cation charge (Na+/K+ < Ca2+/Mg2+ < Al3+/Fe3+). In particular, cations with a higher charge form covalent bonds with an OH- group of adjacent chemical compounds (Lützow et al., 2006; Nieder and Benbi, 2008). Organic compounds form clay-humic complexes (Baldock and Skjemstad, 2000; Laird et al., 2001) with clay minerals and organo-metallic complexes through interaction with iron oxides (Lützow et al., 2006; Lützow et al., 2008).
2- 3- (iv) Humic substances and clay minerals adsorb anions (SO4 , Cl-, NO ) if Fe or Al ions are bound on their surface. The adsorbed amount increases with decreasing pH value (Hingston et al., 1974; Nieder and Benbi, 2008). The sorption increases with the specific surface area of the sorbents (Baldock and Skjemstad, 2000; Kaiser and Guggenberger, 2003). Furthermore, pedogenic chlorites and poorly crystalline pedogenic oxides can be formed owing to pH reduction when sufficient H+ and Cl- ions are supplied. The chlorites and pedogenic (hydro-) oxides tend to coagulate to stable aggregates because of their large surface area and high electrostatic charge (Schulten and Leinweber, 2000).
During sample preparation, aggregates bound by calcium carbonate or clay minerals can be easily decomposed, whereas organic matter can be embedded within stable aggregates, in which it is preserved and protected against chemical decomposition by natural leaching processes or by classic sample preparation techniques (Chapter 5.3.).
2.3. Grain size parameters and ratios for loess-paleosol sequences
9
Over the past decades, several parameters and ratios were introduced and suggested as proxy data for Quaternary paleoclimate and paleoenvironmental evolution. The vertical grain size variability within loess-paleosol sequences serves as a proxy for reconstruction of, e.g., the energy of the wind, moisture availability, distance and direction from the dust source, vegetation cover, and the type of transport process (saltation or suspension) (e.g., Prins et al. 2007, Újvári et al. 2016).
Classical statistic parameters, such as the mean, median, mode, or selected grain size fractions, are calculated from the distribution density curves to function as proxy data (Újvári et al., 2016). Liu et al. (1985) suggested the mean and median as indicators for paleowind strength, while An et al. (1991) concluded that these parameters reflect the aridity of the source region and the frequency of dust storm events. Prins et al. (2007) suggest the median value as a proxy for the dynamic of the East Asian winter monsoon on the Chinese Loess Plateau. In more detail, the variability of the median is interpreted as a function of wind intensity during the winter monsoon, secondary to the distance to the source (Sun et al., 2012). In other studies, specific grain size classes are interpreted as proxy data. For example, the fine sand fraction (63–200 µm) is used as an indicator of the frequency of stronger winds and dust storms (Antoine et al., 2013; Zeeden et al., 2016). Furthermore, the fraction < 20 µm is often suggested as a proportion which was transported as background sedimentation over long distances and was deposited far from the source area (Pye, 1995; Sun et al., 2002). The disadvantage of such single value parameters is their susceptibility to post-depositional or synsedimentary processes (e.g., pedogenesis or aeolian short distance transport) which indirectly affect the primary dust signal.
To avoid this problem, Vandenberghe et al. (1985) introduced the first widely used grain size ratio, the U-Ratio (16–44 µm/5.5–16 µm), and evaluated it for a section of the Chinese Loess Plateau (Luochuan et al., 1997; Nugteren and Vandenberghe, 2004). Machalett et al. (2008) developed a similar ratio using a loess-paleosol-sequence from Kazakhstan (Remisowka) called the Twin-Peak ratio (30.1–63.4/11.8–27.4 µm). Both ratios exclude the fine range where pedogenically formed minerals occur and the sand fraction which is affected by short distance transport (saltation, reptation, creeping, or sheet wash). High values of these ratios indicate relatively coarser, silty material and are interpreted as the influence of higher wind speeds under cold climatic conditions. Conversely, low values are connected to less intense winds and warmer climatic conditions. Today, the most frequently used grain size ratio is the grain size index (GSI), first suggested by Rousseau et al. (2002). The coarse silt fraction (20–50 µm) is divided by the entire finer range, including the pedogenically affected clay. This proxy was modified to adjust to data sets from other measuring techniques (26–52.6 µm/< 26 µm, for the Nussloch section, Germany) (Antoine et al., 2009a) or to other loess regions (20.7-63.4 µm/< 20.7 µm, for the Eustis section, Nebraska, USA) (Rousseau et al., 2007) and (22.73-63 µm/ < 22.73 µm, for the Surduk section, Serbia) (Antoine et al., 2009b). 10
High values are interpreted as a result of a period with a high frequency of intense dust storm events. However, Rousseau et al. (2011) stated that the grain size distribution of loess is the result of a combination of changing wind and moisture conditions, both in the source and the deposition area of the aeolian dust. As the GSI includes the pedogenically affected clay fraction, this proxy is suggested to reflect all these environmental parameters and not just the wind speed (Rousseau et al., 2011; Újvári et al., 2016).
11
Established methods for grain size analysis of loess
The grain size analysis comprises the destruction and dispersion of granular soil sample packages into individual grains and their separation into distinct classes. The percentage of each individual grain size class of the total aliquot volume or weight is determined to present the measurement results as cumulative percentage frequency curves or as particle size distribution density curves (Fig. 3.1).
Fig. 3.1: Two exemplary samples illustrated as cumulative percentage frequency curves (A and B), as percentage frequency histogram (A), as distribution density curve (B), as colorline signature (C) and as a heatmap signature (D).
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There are various grain sizing techniques; the most important can be separated into two main categories (Gee and Orr, 2002; Roberson and Weltje, 2014): (i) Direct measuring of individual grains a. Microscopic analysis b. Sieve analysis (ii) Indirect measuring of collectives or suspensions a. Sedimentation/gravitation analysis b. Laser diffraction size analysis.
The importance of knowledge about the size of individual grains to characterize the properties of a composite particle has been appreciated for a long time. It is said that the Egyptians used sieves to sort metal ores over 4,000 years ago (Miller and Lines, 1988). The technical development of sizing methods continues to the present day. The first technical devices were sieves made of textile fabric (Kinsman, 1978). Since the early 20th century, a combination of sieving and the pipette method is the certified standard for industrial applications (ISO 11277, 2002), and most methods are established in academics (Konert and Vandenberghe, 1997; Buurman, 2001; Müller et al., 2009). Furthermore, sophisticated optical and electron microscopy techniques are used (Allen, 1990). As this procedure is conducted manually and is very time-consuming, cost-intensive, and prone to operator errors (Syvitski et al., 1991), several automated electronic grain sizing techniques which are more rapid, accurate, and require significantly less sample material were developed and established in the previous decades (e.g., Roberson and Weltje, 2014). The first major innovation was a modification of the existing gravitational sedimentation methods in the early 1970s: X-ray granulometry with sedimentometers (Hendrix and Orr, 1972). In the late 1970s, the first authors described the angular dependency of forward scattered coherent light on the size of suspended particles (Cornillault, 1972; Weiss and Frock, 1976; McCave and Syvitski, 1991). The first commercial laser diffraction particle size analyzer was offered by the French company CILAS in 1968 and was followed by instruments developed by Leeds and Northrup, Malvern Instruments, Horiba, Fritsch, and Beckman Coulter (Agrawal et al., 1991).
3.1. Microscopic analysis
Particle sizing by microscopy is often used as an absolute detection of grain size distributions since it is the only method in which the individual particles can be observed directly. Furthermore, microscopic investigation permits observation of the shape and composition of particles and aggregates in a better resolution than any other technique. There are different commonly used microscopic devices: optical microscope, transmission electron microscope (TEM) and scanning 13
electron microscope (SEM). The selection of the most suitable depends on the considered grain size range and the availability of time and money (Allen, 1990).
Optical microscopes are suitable for a grain size range from 0.8 to 150 µm (Allen, 1990). Possible measuring parameters are Feret’s diameter (distance between two parallel tangents at opposite particle edges), Martin’s diameter (length of a line dividing the particle in a fixed direction into two segments with same area), equivalent projected area (and the related diameter) and longest dimension (Kinsman, 1978; Allen, 1990). Generally, the projected area diameter is the most suitable estimator of the true cross-sectional area of the particle (Allen, 1990). It is the most suitable for comparison with sieving results, whereas for comparison with laser diffraction size analysis an average of all possible Feret’s diameters is best suited.
Submircon particles (colloides) cannot be detected directly using optical microscope. Siedentopf and Zsigmondy (1903) demonstrated that such small particles can be made visible by lateral lighting, as a light scattering cone spreads out from them. In contrast to laser diffraction techniques, the size and angle of theses diffraction cone is not indicative for the size of the particles (Lagaly et al., 1997).
Scanning electron microscopy (SEM) is suitable for particle characterization in a size range of 0.1 to 15 µm. The sample is scanned line by line by an electron beam. The secondary electrons emitted by atoms excited by the electron beam are collected by a detector. Due to the low emitted energy, only atoms located near the sample surface are exited and thus provide an image of the sample topography (Goldstein et al., 2007). The advantage of SEM is the high optical depth, so that the images of particles can be sharp focused from the center to the edges. The disadvantage for particle sizing of loess is the charging effect due to the low electric conductivity of dry terrestrial sediments. To avoid electric charge, the top of the sample surface has to be connected to sample holder. To achieve this, the electrically non-conducting material such as carbon, Au-Pb or Pt-Pd mixtures is dusted with a thin layer (Goldstein et al., 2007, Halfpenny, 2010). However, for layer silicates with a porous structure and a large inside surface the coating can be is partially incomplete (see Chapter 5.3.). Modern techniques as low vacuum method (Halfpenny, 2010), applying of biasing voltage to the sample surface or surface charge neutralization by an ion gun (Titze, 2013) allow the microscopy of non-conducting materials without coatings.
Using Transmission electron microscopes (TEM) a resolution from 0.001 to 5 µm can be achieved (Allen, 1990; Reimer, 2013). The sample is set within a thin membrane on a grid made usually by copper. Electrons are transmitted throw the sample and provide an image on a photographic plate or on a fluorescent screen. This technique is expensive, time consuming and rarely statistically significant (Kinsman, 1978; Allen, 1990; Reimer, 2013). 14
Grain size analysis by any of these microscopic techniques require a careful slide preparation, which include the selection of a representative aliquot, a proper dispersion and a minimum of particle stacks and overlaps. Generally, the higher the resolution of the microscopic technique, the lower is the representativeness of the investigated aliquot, since sample quantities are extremely small compared to other techniques. For adequate particle separation a wet dispersed suspension can be transferred onto a microscopic slide. After evaporation of the fluid the dry dispersed particles can be investigated. Alternatively, the dispersion can be fixed by very fast freezing (Lagaly et al., 1997).
Beside the slide preparation, the statistical significance of the counted particle amount is most important to obtain an accurate grain size distribution representing the investigated sediment sample. Kinsman (1978) stated that a minimum of 2000 counts are required for narrow size distributions or previously fractionated subsamples and accordingly more for broader distributions as loess sediments. Manually microscopic investigations are time consuming and prone to deviations due to different operating persons. Modern automated image scanning techniques have been developed to increase the speed and accuracy of microscopic grain size characterization (Kinsman, 1978; Allen, 1990; Holdich, 2002; Francus, 2005; Jouve et al., 2013).
3.2. Sieve analysis
Particles that pass through a standardized sieve are defined as particles having a sieve diameter just below the length of a square hole (Konert and Vandenberghe, 1997). The sieve diameter is defined as the width of the minimum square aperture through which the particle will pass (Allen, 1990). Since Egyptian times different types of sieves have been used. The first known sieves were made of woven material, but the ancient Egyptians already used perforated plate sieves and woven wire sieves, as they still in use nowadays in a similar form (Allen, 1990). The first sieves with standardized meshes were proposed by P. Ritter von Rittinger (1967). From this point sieves were used for particle classification and test sieving. Following, an increasing number of different classification systems were developed (Blott and Pye, 2012). The technical committee ISO/TC 24 “Particle characterization including sieving” of the international standard organization (ISO) had the nominal sizes of sieve apertures committed to 125 to 0.02 mm in the ISO 565 (1983). In this standard the gradation of the sieve meshes is derived from the preferred numbers R20/3 (Renard series) which is based on a sieve opening of 1 mm in a twenties root of ten progression. The 3 after the slash means a reduction of the entire series to every third value. For more narrow size distributions there is an intermediate series based on R20 numbers in the same standard. In Germany the corresponding standard is the DIN 4188-1 which defines the grading of analytic sieves openings since 1977. Here the main scale is defined as the R10 number series which is closer 15
to the Atterberg size scale (Atterberg, 1905). In particle size measurements by sieve analysis the produced grain size classes are constrained by sieves with opening sizes which are produced by the manufactures. Today, the most widely used sieves refer to the standard DIN ISO 3310-1 (2001) which has evolved from the DIN 4188-1 or the Atterberg scale which is incorporate in the international standard DIN EN ISO 14688-1 (2016). The sieves are stacked and sorted with opening size ascending from bottom to top. After shaking over a defined intensity and time each sieve with the oversize grain is weighed. The mass of each grain size fraction is calculated by subtracting the weight of the empty sieve respectively. The result of an individual fraction is expressed as the percentage of each fraction of the total sample mass. The percentage mass fractions of the sediment sample can be visualized as a histogram. By interpolation between the measurement points a cumulative distribution curve can be calculated (see. Fig. 3.1) (Allen, 1990; Retsch 2015). Depending on the general fineness of the sediment sample, different sieving techniques should be preferred: dry sieving is used for particles between 40 µm and 125 mm in size, wet sieving is appropriate in a size range between 20 µm and 20 mm and air jet sieving should be used in a range between 10 µm and 200 µm if dry measuring conditions are essential (Retsch, 2015). The resultant size values are to be considered as the minimum square opening through which the particle can pass, named sieve diameter. This value is only a function of the two shortest dimensions, whereas the longest dimension is not crucial for the passage of the particle (Allen, 1990).
Several limitations in sieving methods have been known for many years. One problem is that for an individual particle the probability of passing through a sieve opening depends on the size distribution of the loaded sample (especially the content of very fine particles), the number of particles which are close to the opening size, the physical properties of the particles (especially the shape) and the geometry of the sieve (relation of openings to woven metal mesh) (Gee and Or, 2002). Since not all openings have the defined size, but are slightly smaller, and irregular shaped particles can pass only in one orientation through the meshes, particularly the particles with sizes near the opening size have a limited chance of passage (Gee and Or, 2002).
Nevertheless, sieve analysis is one of the most widely used methods of particle size analysis since particles are fractionated just on the basis of size, independently of their density and surface properties and the results are quite reproducible even when different sets of sieves are used (Allen, 1990).
3.3. Sedimentation / Gravitation analysis
There are several sedimentation techniques which are all based on Stokes’ Law (Kinsman, 1978; Allen, 1990; Újvári et al., 2016), which is used to calculate the sedimentation velocity of spherical 16
bodies in a fluid. If gravitational forces prevail, the operating temperature remain constant and vibrations are avoided, it is assumed that the terminal velocity of a particle depends on the mass of the particle and the viscosity of the fluid (Stokes 1851). The distance (or height h) which a particle settles over time t is used to determine the terminal velocity V: