quantification and characterisation of carbon in …...quantification and characterisation of carbon...

Quantification and Characterisation of Carbon

in Deep Kaolinitic Regoliths of South-Western Australia

By

Podjanee Sangmanee

B.Sc. (Hons.) Agriculture (Chiang Mai University)

M.Sc. Soil Science (Khon Kaen University)

The thesis is submitted in fulfillment of the requirements

for the degree of Doctor of Philosophy

School of Veterinary and Life Sciences

Murdoch University, Perth, Western Australia

August 2016

i

Declaration

I hereby declare that the work in this thesis is my own account of my research

and contains as its main content work which has not previously been

submitted for a degree at any tertiary education institution. To the best of my

knowledge, all work performed by others, published or unpublished, has been

acknowledged.

Podjanee Sangmanee

August, 2016

iii

Abstract

Greenhouse gas mitigation is a key element of global climate change

agreements, and soil carbon is a major component of these strategies. However, the

estimation of soil carbon is presently limited to depths of 0.3 m despite some soil

profiles containing carbon at greater depths. Low concentrations (0.01 - 0.5% TOC)

of soil carbon remain even after deforestation in the last 80 years, in deep kaolinitic

soil profiles in south-western Australia but the form, distribution and stability of this

has not been reported. A similar situation pertains elsewhere in the world.

Therefore, using kaolin-based mixtures and soil profiles from this region, the

main aims of this thesis were to (i) expand the methods used for deep soil carbon

quantification (Chapters 3, 4) and (ii) characterise carbon in deep soil (Chapters 4, 5,

6). This laboratory-based study employed standard samples (such as lignin, humic

acid, cellulose and chitin mixed with kaolinite) and their combinations with variation

in concentrations (0.008 - 11.55% TOC) analysed in parallel with field samples.

Quantitatively, the limit of detection (LOD) and limit of quantification (LOQ)

of the Walkley-Black method (0.015 and 0.050% TOC, respectively) were

approximately five times lower than the Heanes method (0.085 and 0.281% TOC,

respectively) evaluated using pure kaolinite as background. Based on the LOQ, the

Walkley-Black method was slightly superior to the Heanes method.

Therefore, the former method was further evaluated against dry combustion

(Leco) using 94 calibration and 27 validation samples from deep soils (1 - 35 m

iv

depth) with a concentration range of 0.01 - 0.536% TOC. The predictive equation

[TOC (%)]actual = 1.66[TOC (%)]WB + 0.018 (R2 = 0.91) obtained from the validation

set agreed well with the benchmark dry combustion values (RMSE = 0.017).

A model for quantification of deep soil carbon using near infrared

spectroscopy (NIR) was tested for sensitivity with standard samples in the

concentration range 0.00 - 1.50% TOC. Positive prediction values were observed

when employing square root of %TOC as input data. Then, models were developed

from the 121 field soil samples previously used in the wet digestion analysis. The first

derivative and standard normal variation (SNV) coupled with exclusion of bands in

the range 5600 - 5000 cm-1

were suitable pre-processing approaches which gave an

LOD of 0.001% TOC, with a very strong correlation (R2 = 0.98, RMSE = 0.0.32) but

less accurate compared to the Walkley-Black method.

For characterisation, the ability of mid infrared spectroscopy (MIR) to

characterise carbon in small concentrations was determined. Diffuse Reflectance

Infrared Fourier Transform spectroscopy (DRIFT) had superior sensitivity to

attenuated total reflectance spectroscopy (ATR). The best LODs were achieved when

using kaolinite as a background. The LODs of DRIFT were 1.92% TOC for lignin or

humic acid and 1.00% TOC for cellulose or chitin. However, key lignin and humic

acid bands were concealed when mixed with cellulose and chitin at the same

proportion. The identification of specific carbon structures from the mid infrared

region was difficult in a mixture of several carbon types due to peaks overlapping.

Therefore, qualification of deep soil samples was hampered by this method.

v

On the other hand, gas chromatography and mass spectrometry (GC/MS) was

a promising approach for deep soil carbon. The concentration of residual carbon,

potential carbon derived from exogeneous organic matter, in the form of low

molecular weight compounds (LMWCs) was determined to be in the range 3.15 -

14.27 µg C g soil-1

. Three compound classes were typically observed from samples

across three different field locations: 1) terpenes, 2) fatty acids, amides and alcohols,

and 3) plant steroids; indicating the influence of above ground input and roots of the

past and present vegetation. In conclusion, (Z)-docos-13-enamide and bis(6-

methylheptyl) phthalate were the main components throughout the soil profiles

representing 53 - 81% of the LMWC, particularly at depths of 18 - 19 m.

Pyrolysis and off-line thermochemolysis using tetramethylammonium

hydroxide (TMAH) were employed to characterise macro organic carbon (MOC) in

deep soil to a depth of 29 m. Pyrolysis using the temperature range 250 - 600 ºC was

suitable to characterise all laboratory standard samples at 3.85% TOC. For TMAH

thermochemolysis, pre-concentration of the sample with 0.1 M NaOH was required

before analysis. Unfortunately, the method was limited to characterisation of soil at 0

- 0.1 m depth where biomarkers such as lignin, polysaccharides, proteins and terpenes

were present. Distribution of carbon species throughout the profile was revealed by

pyrolysis. The coincident evidence from pyrolysis and thermochemolysis implied

influences of vegetation, fire events and traces of microorganisms at 0 - 0.1 m depth,

while lignin compounds were detected consistently down to 29 m by the pyrolysis

method. It is concluded that MOC occurs in multiple chemical forms in surface soil

but only occurs in lignin derived forms in deep soil.

vi

A key issue for global mitigation studies is how deep soil carbon will respond

to both land-use change (deforestation, reforestation) and climate change itself. The

studies presented in this thesis provide a suite of methods suitable for the

quantification and characterisation of deep soil carbon in kaolinitic regoliths and these

can be extended to deep soils in other parts of the world. Identification of LMWC and

MOC shed some light on sources and distributions of carbon throughout a soil profile.

vii

Acknowledgments

I would like to sincerely thank my supervisor, Professor Bernard Dell, for

giving me the opportunity to start my thesis. Your optimism, encouragement, and

tactical advice have been very helpful for my research and my future career.

I also wish to express my deepest appreciation to my co-supervisor, Dr. David

J Henry, for sacrificing his time and providing me with valuable guidance and

inspiration throughout five years of my journey.

Another person I wish to thank is my other co-supervisor, Professor Richard J

Harper who owns the valued deep soil samples and I thank you for the freedom of

thinking and for all my experiences gained from field trips.

My research would not be possible without kind collaborations from Andrew

Foreman, Tina Chaplin, Marc Hampton, David Zeelenberg, Dr. Suman George and

Dr. Paul Greenwood, who let me access all instruments used for the completion of

this work.

I am thankful for the Royal Thai Scholarship for financial support, and to my

workplace, Uttaradit Rajabhat University, for study leave.

Finally, I am grateful to my family for their long lasting emotional support,

unconditional love and for being proud of me. Thank you to many friends for joyful

activities in the past five years, it made my life in Perth truly unforgettable.

ix

Papers arising from the thesis

Journal Articles

Sangmanee P, Dell B, Harper RJ, Henry DJ (2017) Quantification of deep soil carbon

by a wet digestion technique. Soil Research 55, 78 - 85.

Sangmanee P, Dell B, Harper RJ, Henry DJ (2016). Deep Soil Carbon:

Characterisation and Quantification by Vibrational Spectroscopy. In

preparation.

Sangmanee P, Dell B, Harper RJ, Henry DJ (2016). Identification of Low Molecular

Weight Compounds Associated With Deep Soil Carbon. Submitted to

Organic Geochemistry.

Sangmanee P, Dell B, Harper RJ, George S, Henry DJ (2016). Application of

Thermochemolysis and Pyrolysis-GC/MS for Characterisation of Deep Soil

Macromolecular Organic Carbon. Submitted to Organic Geochemistry.

Conference Presentations

Sangmanee P, Harper RJ, Henry DJ, Dell B (2014) Deep soil carbon: Why should we

care? In: 20th

World Congress of Soil Science, 8 - 13 June 2014, Korea.

Sangmanee P, Dell B, Harper RJ, Henry DJ (2015). Deep Soil Carbon: The Insight

into Global Carbon Estimation and Deforestation Impacts. In: General

Assembly 2015 of the European Geosciences Union, 12 - 17 April 2015,

Austria.

xi

List of Abbreviations

ANOVA Analysis of variance

ATR Attenuated Total Reflectance

4C/K Kaolinite mixed with lignin, humic acid, cellulose and chitin or

quaternary mixtures

C/K Kaolinite mixed with cellulose

Ch/K Kaolinite mixed with chitin

CAS Chemical Abstracts Service

CF Correction factor

CRD Completely randomised design

CV Coefficient of variation

DRIFT Diffuse Reflectance Infrared Fourier Transform spectroscopy

et al. et alia

GA Genetic algorithm

GC/MS Gas chromatography/mass spectrometry

H/K Kaolinite mixed with humic acid

HSD Tukey honest significant difference

IHSS International Humic Substances Society

IPCC Intergovernmental Panel of Climate Change

IR Infrared spectroscopy

L/K Kaolinite mixed with lignin

LMWC Low molecular weight compound

LOD Limit of detection

LOQ Limit of quantification

LSD Least significant difference

MIR Mid infrared spectroscopy

MLR Multi linear regression

MOC Macro organic carbon

MSC Multiplicative scatter correction

xii

MSD Mass selective detector

NIR Near infrared spectroscopy

NIST National Institute of Standards and Technology

P Polarity

PC Principle component

PCA Principle component analysis

PLS Partial least squares

PTFE Polytetrafluoroethylene

R2 Correlation coefficient

RC Recovery

RCBD Randomised completed block design

RMSE Root mean square error

RPD Ratio of performance to deviation

RPIQ Performance of inter-quartile distance

RSD Relative standard deviation

SD Standard deviation

SNV Standard normal variation

SOC Soil organic carbon

SOM Soil organic matter

sqrt Square root transformed value

TC Thermochemolysis

TMAH Tetramethylammonium hydroxide

TOC Total organic carbon

xiii

Contents

Abstract ........................................................................................................................ iii

Acknowledgments........................................................................................................ vii

Papers arising from the thesis ....................................................................................... ix

List of Abbreviations .................................................................................................... xi

Chapter 1 General Introduction and Thesis Aims .......................................................... 1

1.1 Introduction ..........................................................................................................1

1.2 Thesis Aims ..........................................................................................................2

1.3 Organisation of the Thesis ....................................................................................3

Chapter 2 Literature Review .......................................................................................... 7

2.1 Introduction ..........................................................................................................7

2.2 Definitions ............................................................................................................7

2.3 Possible sources of organic carbon and its occurrence in deep soils ...................9

2.3.1 Plant roots ...................................................................................................... 9

2.3.2 Other living source of carbon ...................................................................... 10

2.4 Stability of deep soil carbon ...............................................................................12

2.5 Age of deep soil carbon ......................................................................................14

2.6 Methodology to study deep soil carbon .............................................................15

2.6.1 Carbon quantification methods.................................................................... 15

2.6.2 Carbon characterisation methods ................................................................ 17

2.7 Carbon components as a tool for identifying sources of soil organic carbon ....20

2.8 Deep kaolinite profiles in the world ...................................................................23

2.9 Deep soil in south-western Australia .................................................................26

2.9.1 Deep soil ...................................................................................................... 26

2.9.2 Kaolinite clay in soil profile ........................................................................ 26

2.9.3 Deep roots and land use change .................................................................. 27

xiv

2.10 Summary remarks ............................................................................................33

Chapter 3 Quantification of Deep Soil Carbon by a Wet Digestion Technique .......... 35

3.1 Abstract ..............................................................................................................35

3.2 Introduction ........................................................................................................36

3.3 Materials and Methods .......................................................................................38

Experiment 3.1: Validation of methods ................................................................ 38

Experiment 3.2: Determination of deep soil carbon by the Walkley-Black method

– comparison to dry combustion .......................................................................... 41

3.4 Results ................................................................................................................43

Experiment 3.1: Validation of methods ................................................................ 43


–comparison to dry combustion ........................................................................... 50

3.5 Discussion ..........................................................................................................51

3.6 Conclusions ........................................................................................................57

Chapter 4 Characterisation and Quantification of Deep Soil Carbon by Vibrational

Spectroscopy ................................................................................................................ 59

4.1 Abstract ..............................................................................................................59

4.2 Introduction ........................................................................................................60

4.3 Materials and methods .......................................................................................63

Experiment 4.1: Spectroscopic characterisation of carbon substrates.................. 63

Experiment 4.2: Response of NIR spectroscopy to quantify carbon in standard

samples ................................................................................................................. 65

Experiment 4.3: Estimation of deep soil carbon by NIR...................................... 66

4.4 Results ................................................................................................................69

Experiment 4.1: Spectroscopic Characterisation of organic substrates ................ 69

Experiment 4.2: Sensitivity of NIR to quantify carbon in standard samples ....... 79

Experiment 4.3: Estimation of deep soil carbon by NIR spectroscopy ................ 85

4.5 Discussion ..........................................................................................................93

4.6 Conclusions ........................................................................................................99

xv

Chapter 5 Identification of Low Molecular Weight Compounds Associated with Deep

Soil Carbon ................................................................................................................ 101

5.1 Abstract ............................................................................................................101

5.2 Introduction ......................................................................................................102

5.3 Materials and methods .....................................................................................103

Experiment 5.1: Organic solvent selection ......................................................... 103

Experiment 5.2: Identification and quantification of LWMCs .......................... 106

5.4 Results ..............................................................................................................107

Experiment 5.1: Organic solvent selection ......................................................... 107

Experiment 5.2: Identification of LMWCs in deep soil profiles ........................ 120

5.5 Discussion ........................................................................................................127

5.6 Conclusions ......................................................................................................134

Chapter 6 Characterisation of Macromolecular Organic Carbon .............................. 135

6.1 Abstract ............................................................................................................135

6.2 Introduction ......................................................................................................136

6.3 Materials and methods .....................................................................................139

Experiment 6.1: Thermochemolysis with TMAH (TC(TMAH))-GC/MS:

Procedure development for soil macroorganic carbon characterisation ............. 139

Experiment 6.2: TMAH-GC/MS: Procedure application for deep soil carbon .. 141

Experiment 6.3: Pyrolysis-GC/MS for laboratory standard samples and deep soil

carbon ................................................................................................................. 142

6.4 Results ..............................................................................................................143

Experiment 6.1: TMAH-GC/MS: Procedure development for macroorganic

carbon ................................................................................................................. 143

Experiment 6.2: TC(TMAH)-GC/MS : Procedure application for deep soil

carbon ................................................................................................................. 152


carbon characterisation ....................................................................................... 154

6.5 Discussion ........................................................................................................167

xvi

6.6 Conclusions ......................................................................................................177

Chapter 7 General Discussion .................................................................................... 179

7.1 Introduction ......................................................................................................179

7.2 Carbon distribution in deep soil profiles ..........................................................184

7.3 Future direction of analytical techniques for deep soils ...................................187

7.4 Dynamics of deep carbon .................................................................................188

7.5 Conclusions ......................................................................................................190

Appendices ................................................................................................................. 193

References .................................................................................................................. 195

1

Chapter 1

General Introduction and Thesis Aims

1.1 Introduction

Greenhouse gas mitigation is the key commitment of global climate change

agreements. Soil is a component of the terrestrial system that is recognised for its vast

reservoir of carbon. Currently, only the upper soil horizon is used in inventory for

estimating the global soil organic carbon (SOC) stock. Some 684 to 724 Pg has been

estimated for the depth of 0 - 0.3 m according to Intergovernmental Panel of Climate

Change (IPCC) standard sampling (Aalde et al. 2006). However, these values may

double when the first meter is used in estimates (Batjes 1996; Bonfatti et al. 2016;

Callesen et al. 2016). These estimates take no account of soil carbon that may be

stored in deep soils, such as in parts of south-western Australia (Harper and Tibbett

2013). In general, it is clear that the total global carbon stock determined from soil

sequestration is very much underestimated when using the IPPC standard sampling

depth. One of the constraints in estimating carbon storage in deep soils is the low

concentration of carbon. Another is the availability and cost of the certified dry

combustion method (Chatterjee et al. 2009) that requires high levels of expertise. So

2

far, other quantitative methods such as wet digestion, and methods using NIR, have

not been certified for carbon stock accounting.

Despite the development of sophisticated qualitative methods over many

decades for the characterisation of surface soils, little attention has been paid on

carbon attributes in deep soil. Deep soil is beginning to be evaluated as a potential

sink for carbon. However, forms, distribution and stabilities of carbon are poorly

understood in deep soil profiles. Consequently, there is limited available information

on deep soil carbon for formulating strategies to address climate change. This thesis

fills part of this gap in knowledge.

1.2 Thesis Aims

The main aims of this study were to (i) validate alternative methods for deep

soil carbon quantification, and (ii) characterise carbon in deep soil profiles. In order to

achieve these goals, this dissertation employed the use of standard samples (matrix

and carbon substrates) in parallel with field samples. Two main approaches were

used:

Determine the performance of two wet digestion methods (Walkley-Black

method and Heanes‟s method) and the vibrational method using NIR, and

Characterise and identify chemical constituents of deep soil carbon using

GC/MS.

3

1.3 Organisation of the Thesis

In south-western Australia, deep soils store more than five times the carbon

estimated from sampling the first meter (Harper and Tibbett 2013). Soil profiles in

this region may be up to 37 - 41 m deep (Dell et al. 1983; Ward et al. 2015). These

deeply weathered soil profiles have largely resulted from in situ weathering of granite

(Gilkes et al. 1973) resulting in extensive deposits of kaolinite overlain with lateritic

or sandy soils (duplex soil profiles). Therefore, kaolinite was chosen as the ideal

substrate for quantifying known amounts of carbon compounds in this thesis. Samples

with known concentrations of carbon were prepared by using standard carbon

compounds mixed with kaolinite. The prepared samples were first employed to

evaluate limitations of a range of methods before particular methods were selected to

analyse field samples.

The structure of the thesis is shown in Figure 1.1. All techniques used in the

dissertation were categorised into quantitative and qualitative analyses. For the

quantitative analysis, several methods for deep soil carbon were investigated: the wet

digestion methods of Walkley-Black (1934) and Heanes (1984) as well as a model

developed from NIR spectroscopy. Secondly, qualitative methods such as MIR and

GC/MS were employed. Two forms of deep soil carbon were analysed and identified:

macromolecular organic carbon (MOC) consisting of large-non-volatile organic

substances, and low-molecular weight compounds (LMWC) or residual volatile

carbon compounds. Finally, a general discussion of the thesis is presented and

suggestions made for further research.

4

The questions examined in this thesis are:

Q1: What are LOD and LOQ of the Walkley-Black and Heanes methods?

(Chapter 3);

Q2: What is the most suitable wet digestion method considered from recovery

percentage and method performance for deep soil carbon determination? (Chapter 3);

Q3: Is NIR spectroscopy sensitive and able to quantify carbon in deep soil?

(Chapter 4);

Q4: Is MIR spectroscopy able to identify a carbon functional group of

standard samples? (Chapter 4);

Q5: What is a suitable method to identify LMWC of deep soil profiles?

(Chapter 5);

Q6: What compound classes can be identified from soil profiles and do they

differ with depth? (Chapter 5);

Q7: Could thermochemolysis using tetrametyl ammonium hydroxide

[TC(TMAH)-GC/MS] be optimised for deep soil MOC characterisation? (Chapter 6);

and

Q8: What is a suitable pyrolysis procedure for identifying MOC of a whole

soil profile? (Chapter 6).

5

Figure 1.1 Scheme of the relationships between chapters of the thesis.

Introduction (Chapter 1)

Literature Review (Chapter 2)

Quantification

Wet digestion techniques

• Walkley-Black method

• Heanes method

(Chapter 3)

Vibrational spectroscopy

techniques (NIR)

(Chapter 4)

Macroorganic carbon (MOC)

• TC(TMAH)-GC/MS

• Pyrolysis-GC/MS

(Chapter 6)

Characterisation

Vibrational spectroscopy

techniques (MIR)

(Chapter 4)

Low molecular weight

Compounds (LMWC)

Organic solvent extraction

GC/MS (Chapter 5)

General Discussion (Chapter 7)

Deep Soil Carbon

Separation and Identification

Techniques using GC/MS

• Low molecular weight

Compounds (LMWC) (Chapter 5)

• Macroorganic carbon (MOC)

(Chapter 6)

7

Chapter 2

Literature Review

2.1 Introduction

Deep soils with deep roots are located in most continents of the world (Schenk

and Jackson 2005) and aspects of root lengths and terrestrial biomes were reviewed

by Jackson et al. (1996). Plant roots provide the main source of carbon in the entire

deep soil profile (Harper and Tibbett 2013; Wang et al. 2015). In particular, mortality

of trees due to deforestation, commercial logging, land clearing, pests and fires have

left behind tremendous root biomass at the global scale. Estimation of root biomass

and carbon turnover is challenging especially in deep soil. This literature review will

cover deep soil carbon from the perspectives of definition, source, and persistence of

deep soil carbon; methodologies available to study deep soil carbon; and effect of

land use change on deep soil carbon.

2.2 Definitions

According to Ramaan (1928), deep soil is the entire upper weathering layer of

the earth‟s crust which can be tens of meters deep (Glinka 1931; Ramaan 1928 after

Richter and Markewitz 1995). This literature review defines deep soil according to the

8

original work on deep soil carbon in south-western Australia where soils are common

with a profile greater than 5 m in depth with little influence of above ground input of

carbon (Harper and Tibbett 2013). However, soil layers that lie above this arbitrary 5

m can be considered as a transition zone that contributes to the understanding of deep

soils. Therefore, this zone is included in the review for some perspectives in relation

to deep soil.

Historically, soil organic carbon (SOC) has been measured as a proxy for soil

organic matter (SOM) which is complex and may change over time. As a result, SOM

has been defined differently according to methodologies used in order to understand

dynamics of organic matter, and these mostly focus on surface soil (Rosell et al.

1998). For example, organic matter can be defined by physical (size and density),

biological (soil microbial activity, microbial respiration), and chemical (humin, humic

and fulvic acids) fractionations. Nevertheless, SOM is well understood by the general

definition given by the Soil Science Society of America (1979) as “the organic

fraction of soil, including plant, animal and microbial residues, fresh and at all stages

of decomposition, and the relatively resistant soil humus”.

The definition of SOC used in this chapter embraces the carbon components at

the molecular scale, exclusive of decaying tissues, in size greater than 500 µm (more

generally defined in the size range between 100 - 2000 µm). Organic carbon differing

in sizes is partitioned using chemical techniques. For example, low molecular weight

compounds (LMWCs) are apolar to moderately polar volatile compounds that can be

readily extracted using an organic solvent. Compared to LMWC, macromolecular

9

organic carbon (MOC) is higher molecular weight non-volatile compounds requiring

extensive fragmentation before analysis.

2.3 Possible sources of organic carbon and its occurrence in deep

soils

2.3.1 Plant roots

Roots play a key role in organic carbon translocation from top of the

vegetation canopy to the deepest root tips, and roots also take water and nutrient

upwards (Brantley et al. 2011). Carbohydrates synthesized by photosynthesis

facilitate the growth of vegetation as well as organisms in the rhizosphere

communities (Bundt et al. 2001; Leake et al. 2004). Storage of carbon in soil is

mainly contributed by root biomass, root exudates and microbial communities in the

root zone. The main pathways of carbon capture and release in soil have been

reviewed by Kell (2011; 2012).

The study of deep roots has gained more attention after the work of Canadell

et al. (1996). Examples include studies up to 9 m in central Cambodia (Ohnuki et al.

2008); 10 m in south-eastern Brazil (Laclau et al. 2013); and 20 m in central Texas,

USA (Bleby et al. 2010). Root architectures have evolved to exploit deep soils as

adaptations to survive environmental stress. For example, in deep granitic weathering

profiles in Western Australia, sinker roots follow macropores to depths of 40 m (Dell

et al. 1983). These authors observed much higher levels of SOM in the macropores

10

than in the bulk soil. The ecosystem within the macropore sheath was investigated in

a duplex soil between surfaces to 80 cm depth in south-eastern Australia by Pankhurst

et al. (2002). The soil in the macropore sheath zone had higher organic carbon and

microbial population compared to the bulk soil. Moreover, throughout the studied

depth, roots located within the macropore sheath were observed to support microbial

communities, both quantitatively and functionally.

The attributes of deep roots vary with parent materials and land use has been

given particular attention in the literature. Rhizoliths, the calcified roots in loess

deposition above limestones and sandstones, were investigated down to 2.5 m in

Hungary (Huguet et al. 2013); 3 m in Serbia (Gocke et al. 2014); and 9 m in Germany

(Gocke et al. 2011). The inorganic and organic forms were radiocarbon dated to >

3000 years BP (Gocke et al. 2011). Furthermore, lipids and alkanes were identified in

rhizoliths (Huguet et al. 2012; 2013; Gocke et al. 2014). However, the size of the

rhizolith carbon store in subsoil and deep soil has not been evaluated.

Besides rhizoliths, live roots were observed from 2.5 - 18 m deep in the

Chinese loess plateau by Wang et al. (2015). The storage of organic carbon in deep

soil (5 - 21 m) was estimated under forest (47 + 0.43 kg m-2

) and permanent cropland

(38 + 0.47 kg m-2

) but forms of carbon have not been studied (Wang et al. 2015).

2.3.2 Other living source of carbon

Microbial biomass, the living microbial component in the soil ecosystem, is a

labile source of carbon due to its short life cycle. Soil microorganisms responsible for

litter decomposition and organic matter formation in surface soil have been studied

11

intensively over many decades (Prescott 2010). By contrast, the role and abundance of

microorganisms in deep soil have been less well investigated. The implication of deep

soil microorganisms was revealed when fungal remains in the form of glucan were

discovered in paddy soil sampled at a depth of 40 - 43 m in Japan (Kotake et al.

2013).

Microorganisms in groundwater indicate significance for deep soil carbon.

These microorganisms share 6 - 40% of the prokaryotic biomass on earth, but this

biomass hidden within the terrestrial subsurface has only been marginally explored to

date (Griebler and Lueders 2009). Investigation of groundwater at depths of 15 - 90 m

showed that overall bacterial abundance remained high (105 cell ml

-1), even though

the level of organic nutrients varied and dissolved oxygen was very low (< 0.40 µg L-

1) (Roundnew et al. 2012). However, microbial functional diversity is known to be

influenced by land use and season due to water quality, nutrient availability and other

factors (Korbel et al. 2013). For instance, microbial activity determined from carbon

utilisation of groundwater at 10 - 30 m differed among irrigated-cropping, non-

irrigated cropping and grazing land uses (Korbel et al. 2013). In addition to

microorganisms, stygofauna or aquatic animals contribute carbon at depth and their

abundance and richness also varies with different agricultural landscapes (Korbel et

al. 2013). Recently, amino acids of bacterial debris and lignin-derived phenols were

identified in dissolved organic carbon of groundwater collected from 76 m in a

fractured rock zone (Shen et al. 2015).

12

Other living organisms such as earthworms and termites are often overlooked

for deep soil carbon. However, a study on mineral exploration observed that vertical

bioturbation of termites (Tumulitermes tumuli) exceeded 4 m, transporting gold from

the subsoil for nest construction in the Western Australia Goldfield (Stewart et al.

2012). In addition, earthworms play a role in shallower subsoils (Major et al. 2010).

The Giant Gippsland earthworm (Megascolides australis), endemic to an area of

approximately 440 km² of South Gippsland (Victoria, Australia), is an example of soil

megafauna with an average weight of 200 g and length of 3 m that occupies depths of

up to 1.5 m in clay soils. Land use activities have led to vertical migration of the

earthworm but maximum depths have not been recorded (Van Praagh and Yen 2010).

The burrows created by these living organisms facilitate preferential flow of organic

material from surface layers to subsoils or perhaps into deeper soils in some

situations.

2.4 Stability of deep soil carbon

Study into the decomposition of deep roots is limited due to the challenge of

sampling method. Even though deep soil carbon is hard to access, the stability of this

carbon has been postulated from short-time (≤ 1 year) incubation of subsoils. For

example, ancient buried carbon was lost when incubated with fresh plant-derived

carbon (Fontaine et al. 2007). Similarly, Zhang et al. (2015) demonstrated that the

addition of sucrose triggered the degradation of labile and recalcitrant native loess

carbon. A rhizosphere positive priming effect was observed in trees grown under

13

greenhouse condition by Dijkstra and Cheng (2007). The loss of native carbon from

the interactions of soil and tree roots was greater than new carbon formation, resulting

from the interactions of soil and tree roots in this study.

The priming effect is generally promoted by simple compounds which are

favourable substrates for increasing microbial activity. However, the mechanism of

loss of native carbon induced by exudates is more complex. Recently, Keiluweit et al.

(2015) found that the production of root exudates, such as oxalic acid, can cause the

loss of carbon protected by minerals because the exudate, acting as a ligand, liberated

carbon through complexation and dissolution reactions of the protective mineral

phase. As a result, the process promotes microbial access and subsequent loss of

mineral protected carbon. By contrast, addition of pyrogenic carbon materials in the

form of carbon black, charcoal or biochar can influence the stability of native carbon

and this may lead to positive or negative priming effects (Hernandez-Soriano et al.

2015; Keith et al. 2009; McClean et al. 2016; Weng et al. 2015).

The insertion of pyrogenic carbon into deep soil could occur from the in situ

burning of woody roots or leaching of burnt carbon by facilitated transport through

macropores. Burnt roots have been observed down to 3 m in a bauxitic profile at

Weipa, in northern Australia (Eggleton and Taylor 2008). As there is no research on

pyrogenic carbon in deep soil, the interaction between deep soil carbon and carbon

black in relation to their stability should be investigated in future research.

14

2.5 Age of deep soil carbon

Soil organic matter is the product of an ongoing process, and contamination by

recent carbon leads to an underestimation of carbon age when determined by

radiocarbon dating (Trumbore 2000). With less disturbance, the age of deep SOC

tends to be greater than carbon in shallow soil. Understanding the residence time of

carbon in deep soil could help predict the longevity of carbon storage and shed light

on the global carbon balance.

Research has tended to focus on the rapid cycling of fine roots while ignoring

the longevity of large woody root systems in deep soil even though large amounts of

carbon are allocated belowground. For example, Amazon trees aged from 200 - 1400

years old were radiocarbon dated (Chambers et al. 1998) but the residence time of

carbon in roots was not assessed. Recently, recycling of old carbon by live roots was

observed in boreal forests by Helmisaari et al. (2015). Using fine roots that developed

over 3 months in cores, the 14

C was dated at between 1 - 20 years.

The residence time of woody roots has rarely been studied. However,

modeling approaches may be useful to predict the cycling of coarse biomass

(Galbraith et al. 2013). Interestingly, the woody biomass residence time of Australian

tropical forests was highest (104 years) among 177 tropical forests across the world

and the heavily weathered soil was considered to be the main factor contributing to

the long residence time (Galbraith et al. 2013). The range of estimated mean residence

times is very large, ranging from tens to thousands of years, as predicted from three

forest ecosystems of eastern China (Zhang et al. 2010).

15

Furthermore, similar degradation rates of coarse woody roots and stumps were

observed in a field experiment in a spruce forest in Ireland by Olajuyigbe et al.

(2010). The decay constant of these woody roots was approximately 0.393 year-1

.

However, the decay rate determined from carbon respiration varies with microbial

activity and an error of carbon flux from coarse woody debris was demonstrated by

Forrester et al. (2015) due to an inferred flux of microbial CO2 emission.

2.6 Methodology to study deep soil carbon

Many methods have been proposed for carbon quantification and

characterisation. Wet and dry combustions are the ex situ methods commonly used in

many laboratories. However, in situ methods mainly based on remote sensing and

spectroscopic measurements are modern approaches used in the field. Method

principles, including advantages and disadvantages, are summarised by Chatterjee et

al. (2009) and Rosell et al. (1998). This section focuses only on the methods that are

considered to have the most potential for deep soil carbon, and are briefly described

below.

2.6.1 Carbon quantification methods

A: Dry combustion method

The principal steps in the dry combustion method are: (i) the soil carbon is

converted to CO2 by oxidizing the sample at a high temperature, (ii) CO2 gas is

16

separated from other gasses by either a chromatographic system or selective traps, and

(iii) the concentration of CO2 is detected by thermal conductivity, mass spectroscopy

or infrared spectroscopy (Chatterjee et al. 2009). The main advantage for samples that

contain small amounts of carbon is its precision. Therefore, this method is

acknowledged as a benchmark method for carbon determination (Chatterjee et al.

2009; De Vos et al. 2007; Mikhailova et al. 2013). Recently, this method was used to

detect concentrations as low as 0.01% TOC (Harper and Tibbett, 2013).

B: Wet digestion method

Wet digestion methods, such as the Walkley-Black method and Heanes

method, are conventional methods that require limited apparatus and are inexpensive

compared to the dry combustion method. Principally, the oxidisable matter is oxidised

by an excess K2Cr2O7 solution and the reaction accelerated by adding H2SO4. The

amount of carbon in the sample is directly related to the amount of dichromate

consumed according to the following equation:

2H2Cr2O7 + 6H2SO4 + 3C 2Cr2(SO4)3 + 3CO2 +8H2O.

This in turn can be determined by ferrous sulphate titration of the excess dichromate.

However, the method generally suffers from variation in recovery due to insufficient

heat being generated during the reactions. Consequently, tube digestion methods have

been developed in a number of laboratories (Bartlett and Ross 1988; Degtjareff 1930;

Edson and Mills 1995; Schollenberger 1927; Tyurin 1931) in which external heat is

applied and the digestion time is extended. Application of external heat (135 ºC for 30

17

min) was found by Heanes (1984) to be optimum and this modification has been

widely employed for analysis of Australian soils.

C: Near Infrared Spectroscopy (NIR)

Infrared spectroscopy (IR) is a non-invasive tool for identifying functional

groups that interact with energy in the infrared region of the wave spectrum. The

infrared spectrum is divided into near (14000 - 4000 cm-1

), mid (4000 - 400 cm-1

) and

far (400 - 10 cm-1

) infrared regions but those regions that can identify functional

groups are restricted to the mid and near infrared. Near Infrared Region Spectroscopy

measures the absorption of the C-H, N-H, O-H, C=O, S-H, CH2 and C-C groups of

organic compound (Berardo et al. 2005; He and Hu 2013). The potential of the

method depends on a developed model for the calibration process which normally

needs chemometric techniques to extract the useful information from NIR spectra

(Viscarra Rossel et al. 2006). The estimation of soil carbon by NIR has been reviewed

by Bellon-Maurel and McBratney (2011) and Reeves (2010).

2.6.2 Carbon characterisation methods

Compared to quantitative approaches, characterisation approaches have been

employed arbitrarily and their limits of detection are not always specified. Functional

and molecular scale characterisation techniques are essential to understand deep soil

carbon. Several methods are currently being employed for characterisation of organic

matter associated with bulk soils and organic carbon fractions.

18

A: Mid Infrared Spectroscopy (MIR)

Mid infrared spectroscopy is being employed for characterisation and

identification of samples because peaks obtained from interaction between functional

groups and energy are more distinct compared to the NIR region. Functional groups

of soil constituents identified by MIR are listed in Table 2.1.

B: Chromatographic technique coupled with mass spectroscopy

Conventional GC/MS is being used to identify organic carbon by many

researchers. Basically, volatile organic carbon compounds are separated over a gas

chromatograph and compounds subsequently identified by a mass spectrometer.

Therefore, fragmentation of non-volatile carbon into a volatile form is required before

GC/MS analysis, and this can be undertaken by thermochemolysis or pyrolysis and is

reviewed by Derenne and Quénéa (2015) and Shadkami and Helleur (2010).

19

Table 2.1 Group frequencies of soil constituents*.

Component Band region (cm-1

)

Organic

O-H stretching of carboxylic acids, phenols,

alcohols

3500 - 3200

N-H stretching of amines, amides 3400 - 3200

Aromatic C-H stretching 3150 - 3000

Aliphatic C-H stretching 2970 - 2820

C = O stretching of carboxylic acids, amides,

ketones

1750 - 1630

Salts of carboxylic acids

Asymmetric COO- stretching

Symmetric COO- stretching

1650 - 1540

1450 - 1360

C-H bending of -CH2- and -CH3 1465 - 1440

C-O stretching, O-H bending of -COOH 1250 - 1200

C-O stretching of polysaccharides 1170 - 950

Inorganic

Clay minerals and oxides

O-H stretching of structural OH

O-H bending of structural OH

Si-O-Si stretching

3750 - 3300

950 - 820

1200 - 970

Sorbed water

O-H stretching

O-H bending

3600 - 3300

1650 - 1620

Carbonates 1600 - 1300, 900 - 670, 1300 - 850

*Johnston and Aochi (1996)

20

2.7 Carbon components as a tool for identifying sources of soil

organic carbon

The GC/MS is a novel instrument for molecular characterisation and

identification. Compounds detected by GC/MS may vary depending on organic

carbon precursors, mainly plant molecules and microbial products. Secondary

variations of compounds are dependent on sample preparation techniques as well as

procedures.

This thesis reviews the soil ecosystem and organic matter sources identified

from carbon components of LMWC and MOC. These forms of carbon are addressed

because carbon observed in kaolinitic regoliths is generally heterogeneous. Table 2.2

provides a summary of compounds derived from SOC, their origin and environmental

significance. Therefore, comprehension of deep soil carbon could be achieved from

interpretation of multiple compounds obtained from chromatographic identification.

21

Table 2.2 Summary SOM compounds, their possible origin and environmental

condition.

Compound Origin/ Environmental condition Reference

Alkane (C7-C14) Lipid, usually microbial,

completely degraded material,

algal-derived from organic matter

in natural water

Almendros et al. (1996);

Buurman et al. (2005; 2007);

Frazier et al. (2003)

6 and 7-Monomethylalkanes,

C17 n-alkane

Cynaobacteria Hoshino and George (2015)

Alkanes and alkenes

(C14-C26)

Lipid, usually plant cutin, suberin

or waxes

Buurman et al. (2005; 2007);

Alkanes and alkenes

(C25-C33 odd)

Higher plant waxes, fungi Otto and Simpson (2007)

Odd or even dominance on the

mid chain and long alkanes and

alkene

Likely to be non-degraded material

rather than microbial material

Buurman et al. (2005; 2007);

Alkanols (C22 - C32 even) Higher plant waxes, suberin van Bergen et al. (1998);

Otto and Simpson (2007)

Alkanol (C26 dominant) Constituent in many grasses van Bergen et al. (1998)

Alkanoic acids Derived directly from plant or

products from oxidation of other

compounds such as alkanes and

alkanols or lipids

van Bergen et al (1998)

Branched-chain alkanoic acids Molecular evidence for microbial

activity

van Bergen et al. (1998)

Fatty alcohols (C7 - C35) Unidentified origin found in soil,

peatland and marine sediment

Huang et al. (2013);

Treignier et al. (2006); Yang

et al. (2014)

Alkyl esters Wax esters van Bergen et al. (1998)

Phytols, phytanols, sterols Polar waxes Franco et al. (2000);

Alkanones In situ microbial oxidation of other

lipid components


β-Sitosterol, stigmasterol,

canpesterol

Steroids of plants Otto and Simpson (2007)

Ergosterol Steroids of fungi Otto and Simpson (2007)

22

Cholesterol Steroids of animals, fungi and

plants

Otto and Simpson (2007)

Cyclopentenone, cyclohexenes Polysaccharide Hatcher et al. (2001); Page et

al. (2002)

Furan, ethanoic acid Polysaccharide Heckman et al. (2014)

Anhydrosugar Polysaccharide Page et al. (2002);

Acetic acid Polysaccharide Hatcher et al. (2001)

Levoglucosan, levomanosan Polysaccharide/ relative

undecomposed cellulose or

microbial polysaccharide

Sollins et al. (1996); Helfrich

et al. (2006)

Alkylbenzene Polysaccharide/ lignin Nierop et al. (2001);

4-Ethenylphenol, 4-ethenyl-2-

methoxyphenol

Angiosperm lignin-cellulose, the

precursors are p-cumaric acid and

ferrulic acid, respectively


Di, trimethoxy benzene,

trimethoxy toluene

Carbohydrates, tannins Frazier et al. (2003)

Methyl phenol, methoxy phenol Lignin Heckman et al. (2014)

Vanillic acid Lignin of grasses Sáiz-Jiménez and de Leeuw

(1986)

Styrene Lignin Ralph and Hatfield (1991)

Methoxy benzene, methoxy

benzoic acid, methyl ester

Proteins Frazier et al. (2003)

Pyridine, methyl pyridine,

benzonitrile, acetobenzonitrile,

indole, methyl indole and

diketodipyrole

Nitrogenous compound different

origins including vegetal and

microbial protein

Heckman et al. (2014); van

Bergen et al. (1998);

Schulten and Schnizer

(1997); van Bergen et al.

(1998)

9-Octadecenamide Compound extracted from polar

wax of non-wetting sand under

eucalyptus trees

Franco et al. (2000)

Glucose, manose, sucrose Carbohydrates of all organisms Otto and Simpson (2007)

Trehalose Carbohydrates of fungi Otto and Simpson (2007)

Galactosamine, glucosamine,

manosamine

Amino sugars of bacteria and fungi Otto and Simpson (2007)

Phenols

Lignin, tannin, protein,

polysaccharide

Nierop et al. (1999); Hatcher

et al. (2001)

23

Benzophenone Found in humic acids in soils under

pine forest

González-Vila et al. (1987)

2.8 Deep kaolinite profiles in the world

Deep soils established by weathering and aeolian processes are ubiquitously

found in many locations. Soils derived from intense weathering processes occur in all

continents except for some parts of Europe and North America (Alavi Nezhad Khalil

Abad et al. 2014; Chevrier et al. 2006; Gaudin et al. 2011; Jones 1985; Modenesi-

Gauttieri et al. 2011), whereas aeolian deposits are found in all continents (Bowler

1976; Bridget and McGowan 2006; Lancaster 2009; Stuut et al. 2009). Terrestrial

sediments such as loess deposits cover vast areas in northwest China (Wang et al.

2013), the great plain of North America (Bettis et al. 2003), the European Loess Belt

(Vasiljević et al. 2014), and parts of Oceania such as New Zealand (Raeside 1964).

This section of the chapter focuses on deep kaolinitic profiles developed from

extended weathering process because this profile type is abundant in south-western

Australia.

Kaolinite genesis can be categorised into 2 groups according to the type of

rocks. Primary kaolins are derived from in situ primary rocks and hydrothermal

alteration of volcanic rocks while, secondary kaolins are associated with sedimentary

rocks (Ekosse 2010). Locations of deep kaolinitic profiles are summarised in Table

2.3. Several deep kaolinitic profiles in Africa, such as in Mozambique (Pekkala et al.

2008) and Sierra Leonne (Warnsloh 2011), have recently been discovered and mined.

24

Table 2.3 Locations of deep kaolinitic profiles in tropical and subtropical regions.

Location Country Parent rock/Genesis Depth

(m) Reference

Asia/Oceania

Darling range,

Western Australia

Australia In situ weathering of granite 20-

150

Anand and Paine

(2002)

Fusui, Guangxi

Province, Youjiang

Basin

China Intense chemical weathering of

sedimentary rock

20 Yu et al. (2014)

Northern Guizhou

Province

China Deposition of limestone 5-17 Gu et al. (2013)

Kerala, southern

India

India Intense weathering of khondalites

and subsequently transported and

deposited input into lakes

30 Nakagawa et al.

(2006)

Johor, southern

Peninsular

Malaysia Weathering of granite 24 Alavi Nezhad

Khalil Abad et al.

(2014)

North/ South America

Georgia Piedmont

Province

USA Weathering of granite 10 White et al. (2001)

Amazon basin Brazil Origin/genesis of kaolin is under

debate between weathering

originating from sediments or

sediment

10-60 Bonotto et al.

(2007)

Costa et al. (2009)

Montes et al.

(2002)

Acoculco Mexico Alteration of the dacitic lavas and

pyroclastic deposits

200 López-Hernández

et al. (2009)

Africa

Cunene complex,

southern Angola

Angola Transformation of basic

anorthosites and gabbros

40 Saviano et al.

(2005)

Djebel Debbagh,

north eastern Algeria

Algeria Burial genesis and deformation of

limestone

60-

200

Renac and Assassi

(2009)

Makoro kaolin

deposit, southeastern

Botswana

Alteration of feldspathic arenites 50 Ekosse (2000)

25

Botswana

Balkouin, central

Burkina Faso

Burkina

Faso

In situ lateritization process of

granitic rocks

12 Giorgis et al.

(2014)

Burundi

Burundi Weathering of basalt 1-3 Schirrmeister and

Störr (1994)

Mayouom, Western

Cameroon

Cameroon

Hydrothermal alteration of

feldspar- and

mica-rich rocks

10-13 Njoya et al. (2006)

Bana, northwest

Cameroon

Cameroon

Weathering of granite 20 Wouatong et al.

(1996)

Nsimi, South

Cameroon

Cameroon

Deep weathering from physical

erosion of granite

38 Braun et al. (2012)

Mada region, south

east Cameroon

Cameroon

The basement rock is constituted

by serpentinites which are

intrusive in micaschists and

quartzites

21 Ndjigui et al.

(2009)

Kombelcha

Ethiopia In situ weathering of granite 10 Fentaw and

Mengistu (1998)

Bombowha Ethiopia Hydrothermal and in situ

weathering of pegmatites and

granites

11 Fentaw and

Mengistu (1998)

Mahlangatsha

Mountains

Swaziland In situ weathering of granites,

gneisses and acid volcanic

18-73 Hunter and Urie

(1966)

Sidi El Bader Tunisia Alteration of sandstone 100 Felhi et al.(2008)

Buwambo deposit,

Kampala

Uganda Weathering of granite 12 Nyakairu et al.

(2001)

26

2.9 Deep soil in south-western Australia

2.9.1 Deep soil

Soils in south-western Australian landscapes have been intensively surveyed

and studied by Anand and Paine (2002). Briefly, this region is characterised by a deep

weathering profile formed on the Archean granites and gneisses of the Yilgarn Craton

(Gilkes et al. 1973). Landscapes having soils that extend up to 150 m deep were

described by Anand and Paine (2002) but typically the soils are shallower than this

(McArthur 1991).

The two deep weathered soil profiles generally found in south-western

Australia are a lateritic profile and a deep sand profile. The lateritic profile is mostly

distributed across the Darling Range, the south-western part of the Yilgarn Craton

where the Jarrah (Eucalyptus marginata) forest ecosystem has evolved. A typical

lateritic profile averages about 20 m in thickness (Hickman 1992) and consists of six

horizons; ferruginous (top soil and duricrust), mottled zone, pallid zone, saprolite and

bedrock. The bedrock is mostly granite but veins of dolerite occasionally occur.

2.9.2 Kaolinite clay in soil profile

Kaolinite is the predominant clay mineral in south-western Australia resulting

from intense weathering of laterite and granite (Viscarra Rossel 2011). Singh and

Gilkes (1992) characterised clays from this region and found that kaolinite contained

approximately 80% of all clay types. Kaolinite occurs in the mottled, pallid and

saprolite zones at depths ranging from 2 to 50 m below the soil surface (Gilkes et al.

27

1973; McArthur 1991) and tends to increase with depth (Sadlier and Gilkes 1976).

Kaolinite interacts with other soil materials leading to the coating of sand-size quartz

and filling of channels with iron oxide, and stained kaolin occurs throughout the

regolith (Kew and Gilkes 2007; Kew et al. 2010).

Chemical and physical attributes of kaolinite are affected by the weathering

process resulting in isomorphic substitution of Fe3+

and Al3+

. Consequently, kaolinite

contains 2.5% of Fe2O3 in its structure and has a poor crystallinity index of 5.4 (Singh

and Gilkes 1992). The kaolinite in south-western Australia is higher in surface area

(35 m2 g

-1) and cation exchange capacity (56.7 mmolc kg

-1) compared to standard

kaolinites that normally have a surface area of 10 m2 g

-1 and cation exchange capacity

of 4.8 mmolc kg-1

(Singh and Gilkes 1992).

2.9.3 Deep roots and land use change

The Jarrah forest is endemic to the Darling Plateau of south-western Australia.

Most woody vegetation in these dry sclerophyll forests exhibit dimorphic root

systems: a shallow lateral root system supplying water and nutrients in the wet season,

with groundwater taken up by the deep tap roots or sinker roots during the dry season

(Dawson and Pate 1996; Dell et al. 1989). The different bedrocks influence the

characteristics of Jarrah sinker root penetration. In doleritic profiles, numerous large

and fine Jarrah roots penetrate into deep clay horizons up to 40 m without root

channels, whereas in the more dense granitic profiles roots access vertical root

channels (recharge channels or macropores) to access water in deep soil profiles (Dell

et al. 1983). These root channels may allow tree roots to grow deeper into the pallid

28

zone of granitic profiles than in doleritic profiles (Johnston et al. 1983) but further

study is needed before a definitive conclusion can be drawn.

Like in many parts of the world, extensive land use changes have taken place

over the last 100 years, from deep-rooted native vegetation to clearing for annual

pastures and crops, and in some areas reforestation with Eucalyptus spp. in the last

two decades. Unlike the deep-rooted perennial native vegetation, the rooting depth of

Gatton panic (Megathyrsus maximus) pasture was 5.3 m after 5 years of plantation and

root depth of 1.5 m was observed in annual crops such as barley (Hordeum vulgare)

and lupin (Lupinus angustifolius) (Ward et al. 2015). By contrast, root systems of 7

year-replanted eucalypts exploited soil water to at least 8 - 10 m depth on several

agricultural soils (Harper et al. 2009). Thus, the change in land use has resulted in a

substantial decrease in the proportion of the soil profile being accessed by roots over

the past century. The impact that this has had on the dynamics of deep soil carbon in

the region is unknown.

In recent decades, extensive areas of forest with lateritic soils rich in

aluminium hydroxide minerals are being mined for bauxite (McArthur 1991). Where

the vertical root channels become occluded during mining, the taproots and sinker

roots of the revegetation species may be unable to penetrate below the depth of

machine ripping (Szota et al. 2007) due to the hostile regolith. It would be interesting

to explore whether root channels also become occluded under agricultural practices

where the soil is annually tilled.

In south-western Australia, the study of carbon in deep soils after land use

change has been overlooked. Deep roots are affected by anthropogenic activities such

29

as mining, road cutting and agricultural activity (Figures 2.1, 2.2 and 2.3).

Opportunistic observations of deep roots reveal that dead deep roots differ in their

extent of decomposition across the landscape. Furthermore, char materials produced

by fire may contribute to the subsoil carbon pool (Figure 2.3b). The persistence of

woody roots at depth and their contribution to global SOC stock accounting has yet to

be investigated for this region.

30

Figure 2.1 Karragullen gravel mine (32º 5′ 51″S, 16º 7′ 14″ E): a) a face in mine floor

approximately 4 m height; b) a bunch of roots penetrated through the duricrust zone;

c) tree roots taken at the bottom of pit (Photographs by P. Sangmanee).

6 cm 8 cm

a

b c

31

Figure 2.2 Roadcutting near Mundaring (32º 53′ 40″ S, 116º 13′ 36″ E): a) profile

approximately 20 m height showing pallid zone; b) dead root fragments with surface

erosion are ubiquitous at the surface; c) fragile woody root; d) presence of woody

roots at surface of cutting, scale in centimeters; e) root (x) surrounded with stain

20 m

b

a

d

c

e

x

32

(arrow) when observed closely, coin for scale (3.15 cm diameter) (Photographs by P.

Sangmanee).

Figure 2.3 Agricultural land, east of Kalamanda (31º 58′ 13″ S, 116º 7′ 38″ E) that

was cleared over 50 years ago: a) grass roots penetrating down to 0.3 m; b) charred

materials 1 cm diameter size were observed at 0.5 m depth; c) woody root located at 1

m, 0.3 m-ruler for scale; d) decaying root of native Jarrah (E. marginata) was found at

1.5 m depth. Scale in centimeters. (Photographs by P. Sangmanee).

a b

c d

33

2.10 Summary remarks

Deep soil is potentially a large reservoir of carbon in terrestrial systems. In

Western Australia, although deep soils are abundant, their carbon content has been

little studied (Harper et al. 2013). In general, the soil carbon content declines with

depth as the influence of above ground vegetation weakens. There is likely to be a

wide range of carbon compounds differing in size and concentration in deep soils and

varying within the soil profile. In order to estimate carbon storage in deep soil,

including understanding its origin and stability, some consideration of methods for the

study of carbon will be necessary. So far, approaches for studying deep soil

quantitatively and qualitatively are poorly certified. Many analytical methods have

not been evaluated for deep soil. The selection of reliable methods should allow data

on carbon in deep soils in Australia to be compared to other parts of the world in the

future.

35

Chapter 3

Quantification of Deep Soil Carbon by

Wet Digestion Techniques

3.1 Abstract

Two wet digestion methods were evaluated using pure kaolinite as background

for quantifying low concentrations of carbon (< 0.05% TOC) in deep kaolinitic

regoliths in south-western Australia. The LOD and LOQ of the Walkley-Black

method (0.015 and 0.050% TOC, respectively) were approximately five times lower

than the Heanes method (0.085 and 0.281% TOC, respectively). Both methods

showed excellent linearity (R2 > 0.99) using prepared standards (lignin, humic acid,

cellulose and chitin mixed with kaolinite and their combinations), in the concentration

range 0.008 - 1.000% TOC. However, the % RCs were underestimated for chitin. The

Walkley-Black method was evaluated with 94 calibration and 27 validation deep soil

samples (1 - 35 m depth) and compared with dry combustion (Leco). The predictive

equation [TOC (%)]actual = 1.66[TOC (%)]WB + 0.018 (R2 = 0.91) obtained from the

calibration set agreed well with the benchmark dry combustion values (RMSE =

0.017) and is recommended for quantification of deep soil carbon in other kaolinitic

regoliths.

36

3.2 Introduction

The wet digestion method, developed by Walkley and Black (1934), has been

widely used for determination of SOC because of its low cost, reliance on simple

laboratory equipment and rapid turnover of results (Chatterjee et al. 2009). However,

the method generally suffers from variation in recovery due to insufficient heat being

generated during the reactions (Chapter 2). Application of external heat (135 ºC for 30

min) was found by Heanes (1984) to be optimum and this modification has been

widely employed for analysis of Australian soils.

Although the Walkley-Black and Heanes wet digestion methods are widely

used, the LOD and LOQ have not been fully validated on a comparative basis. For

example, De Vos et al. (2007) analysed 542 forest soils with diverse textures and from

different plant stands and found that the Walkley-Black method was able to quantify

carbon in the range between 0.42 - 8.00% TOC with 76% recovery (% RC).

Furthermore, Conyers et al. (2011) investigated the accuracy of the Walkley-Black

method by using 26 forms of pure carbon substrates and showed that the method was

most accurate when the absolute quantity of carbon was at least 7 - 10 mg C per

sample size. These studies indicate that large soil sample weights may be required to

accurately quantify the carbon in soil of low carbon content.

Underestimation of carbon obtained from a wet digestion method can be

linked to several factors such as the structure of the organic carbon and the soil parent

materials. A correction factor (CF) is often applied to adjust the measured carbon

content to improve recovery (Chacón et al. 2002; Gillman et al. 1986; Schnitzer and

37

Monreal 2011).

Harper and Tibbett (2013) previously measured deep soil carbon

content by the dry combustion method which is the benchmark method in many

studies (Chatterjee et al. 2009; De Vos et al. 2007; Mikhailova et al. 2003).

Principally, soil carbon is converted to CO2 by oxidizing the sample at a high

temperature. Then, concentration of CO2 is detected by thermal conductivity, mass

spectroscopy or infrared spectroscopy (Chatterjee et al. 2009). However, the dry

combustion method requires specialised equipment not available in all laboratories.

Therefore, it is useful to consider alternative methods that are more readily available.

Although wet digestion methods have been widely used to analyse surface soils they

have not previously been employed for deep soils. Consequently, the main aim of this

study was to quantify deep soil carbon by a wet digestion technique. In order to

achieve the aim, the Walkley-Black and Heanes methods were validated by using

accurately prepared standards. This study focused on assessing the limit of detection

and quantification, evaluating the linearity and the recovery of these methods prior to

the selection of a suitable method to determine deep soil carbon. Two questions are

addressed as detailed in Chapter 1 (page 4): questions 1 and 2.

38

3.3 Materials and Methods

Experiment 3.1: Validation of methods

Wet digestion methods

Total organic carbon (TOC) was measured following a modified Walkley and

Black (1934) technique (Appendix 1). The original method published by Heanes

(1984) was modified after the samples were digested and allowed to cool. Elimination

of solid particle was carried out by filtering the samples through glass filters (10 - 16

µm pore size), into 100 mL volumetric flasks. The solutions were allowed to cool

before being made up to the final volume. Then, the solid fractions were allowed to

precipitate before pouring 10 mL of supernatant into a 15 mL centrifuge tube and

centrifugation at 290 g (3000 rpm) for 15 min. The absorbance of supernatants was

measured at 600 nm using a 10 mm optical path length with syringe sipper operated

by a Shimadzu UV-1601.

To create a standard curve, 2000 mg L-1

of stock solution was obtained by

dissolving 4.754 g purified sucrose (42% carbon) in distilled water and diluted to 1 L.

Sucrose was chosen for the standard curve because 100% RC has been reported

(Conyers et al. 2011) and sucrose represents a similar form to many carbohydrates in

soil (Doutre et al. 1978). The carbon content of each sample was determined from the

standard curve constructed between absorbance and mass of organic carbon present (y

= 0.018x, R2 = 1.00).

39

This study was conducted using kaolinite as the blank because this is the

dominant clay component found in deep soil profiles of south-western Australia

(Chapter 2). Seven sets of kaolinite with 3 replications were used to determine TOC.

Kaolinite was obtained from the same source, Sigma Aldrich (CAS 1318-74-7), and

was oven-dried at 105 ºC overnight before analysis. Each set of kaolinite was

analysed for TOC at different times.

Sample preparation

The study was conducted using 4 substrates [lignin (CAS 471003), humic acid

(CAS 53680), cellulose (CAS 310697), chitin (CAS C7170)] and surface soil mixed

with kaolinite. Cellulose was a microcrystalline powder and chitin was from shrimp

shells.

Decomposed litter mixed with surface soil of the Bassendean sand series from

the Western Australian Swan Coastal Plain (McArthur and Bettenay 1974) was used

as a representative SOC. The sample was collected from a depth of 0 - 10 cm from

natural bushland (32º 4′ 3″ S, 115º 50′ 9″ E). Soil was air dried, sieved to pass a 150

µm mesh and oven-dried at 105 ºC overnight before preparing a mixed substrate.

Chemical analyses of the soil are shown in Appendix 2.

Analysis

The % TOC of the pure organic substrates was determined by dry combustion

(Leco) technique. The % TOC from the pure samples was then used to calculate the

mass of each material to use to prepare standards in a kaolinite matrix. For each

40

organic substrate, 250 g of 1.000% TOC standard was prepared. The lower

concentrations (0.200, 0.040 and 0.008% TOC) were then obtained by serial dilution

of the 1.000% TOC standard with kaolinite. Sixteen substrate combinations were

prepared from the above sets by weighing the required portions and mixing. All

combinations were homogenized using a blender. The standards (Table 3.1) were

oven-dried at 105 ºC before quantifying the carbon.

Recovery of carbon (%)

Four replicates were analysed using the Walkley-Black method and 3

replicates with the Heanes method. The percentage RC of each sample was

calculated:

RC (%) = [TOC (%)measured / TOC (%)known] × 100 (3.1).

Statistical analysis

Simple linear regression, testing of homogeneity of variance and analysis of

variance (ANOVA) were performed using SPSS Inc, version 18.0. ANOVA was

conducted as a completely randomised design (CRD) and randomised completed

block design (RCBD) for testing % RC of different concentrations and different

sample sets, respectively. When ANOVA was significant, the following post hoc tests

(P = 0.05) were employed: Least significant different (LSD) for concentration levels

and Tukey honest significant difference (HSD) for different substrates.

41

Table 3.1 Substrates prepared for analysis with putative values compared with those

derived from the dry combustion (Leco) technique.

Substrate Concentration range of TOC (%)

0.008 0.040 0.200 1.000

1. Lignin 0.008 0.041 0.206 1.031

2. Humic acid 0.010 0.048 0.242 1.208

3. Cellulose 0.008 0.040 0.201 1.005

4. Chitin 0.011 0.056 0.278 1.391

5. Lignin + humic acid 0.009 0.045 0.224 1.120

6. Lignin + cellulose 0.008 0.041 0.204 1.018

7. Lignin + chitin 0.010 0.048 0.242 1.211

8. Humic acid + cellulose 0.009 0.044 0.221 1.107

9. Humic acid + chitin 0.010 0.052 0.260 1.299

10. Cellulose + chitin 0.010 0.048 0.240 1.198

11. Lignin + humic acid + cellulose 0.009 0.043 0.216 1.082

12. Lignin + humic acid + chitin 0.010 0.048 0.242 1.210

13. Lignin + chitin + cellulose 0.009 0.046 0.229 1.142

14. Humic acid + cellulose + chitin 0.010 0.048 0.240 1.201

15. Lignin + humicacid + cellulose + chitin 0.009 0.046 0.232 1.159

16. Soil (0 - 10 cm sample) 0.007 0.034 0.169 0.843


- comparison to dry combustion

Calibration and validation sets of sample

Deep soil samples and a TOC data set determined from a dry combustion

(Leco) method were obtained from a study previously reported by Harper and Tibbett

(2013). In that study the accuracy of the analyser was tested with diluted soil

reference material (net mean = 0.053% TOC, SD = 0.00524, n = 12). The complete

42

set of 409 samples taken from 38 cores from depths of 1 to 35 m had carbon

concentrations of 0.01 to 0.42% TOC. From this sample set, 121 samples were

selected randomly from different depths and locations. This smaller set of samples

was then randomly separated into two sub groups; a calibration set and a validation

set. The calibration set consisted of 94 samples and these were employed to establish

a predictive equation between values of TOC (%) determined by dry combustion and

the Walkley-Black method. The validation set consisted of 27 independent samples.

Validation

Two validation methodologies were compared; a) Walkley-Black values

measured from the samples and b) predicted values (Walkley-Black data applied with

the predictive equation). The root mean square error (RMSE) was used to measure the

error of each validation methodology. The RMSE is defined as:

√

∑

where and are TOC (%) analysed by Walkley-Black method and dry combustion

method, respectively.

Statistical analysis

Constant variance of the validation set was tested using SPSS Inc, Version

18.0. The predictive equation was obtained from the simple linear regression.

(3.2).

43

3.4 Results

Experiment 3.1: Validation of methods

Assessments of the LOD and LOQ for the Walkley-Black and Heanes

methods were carried out from 7 samples (2 g, kaolinite) with 3 replications (n = 21).

The measured/blank TOCs were 0.006 ± 0.003% ( ± SD) (Walkley-Black method)

and 0.031 ± 0.018% ( ± SD) (Heanes method).

Employing the equation XL = Xbi + ksbi , where Xbi is the mean of the n blank

measures, sbi is the standard deviation of the n blank measures, and k is a factor

chosen according to the confidence level desired, commonly set at 3 (Currie 1999; De

Vos et al. 2007), values measured by the Walkley-Black method [xL = 0.006+

3(0.003)], the LOD for kaolinite was determined to be 0.015% TOC. Using the LOD

of 0.015% TOC, the LOQ was calculated to be 0.050% TOC (LOQ = 3.3 LOD). This

indicated that the lowest concentration of carbon that could be determined accurately

was 1 mg C in 2 g of kaolinite (Table 3.2).

Similarly, the LOD for kaolinite by the Heanes method [xL = 0.031+ 3(0.018)]

was determined to be 0.085% TOC. Using the LOD of 0.085% TOC, the LOQ was

calculated to be 0.281% TOC. Hence, the lowest concentration of carbon that could

be determined accurately by the Heanes method was 5.62 mg C in 2 g of kaolinite.

Clearly, the Walkley-Black method was more accurate for quantifying small amounts

of carbon in kaolinite (Table 3.2).

44

Table 3.2 Limit of detection and limit of quantification of Walkley-Black and

Heanes methods.

Method LOD (%TOC) LOQ (%TOC)

Walkley-Black 0.015 0.050

Heanes 0.085 0.281

Substrates and carbon contents

In general, the % RCs of pure carbon substrates were underestimated by the

Walkley-Black method and overestimated by the Heanes method (Table 3.3).

However, % RCs of chitin determined from both wet digestion methods were

underestimated. It was also noticeable that the degree of over- or under-estimation

varied across the different substrates. For the Walkley-Black method, the carbon

contents were ranked as follows: lignin > cellulose > chitin > humic acid > soil, and

in the order lignin > cellulose > humic acid > chitin > soil for the Heanes method.

Table 3.3 Carbon contents (%) and recoveries of substrates determined by three

methods.

Substrate TOC (%)

Dry combustion Walkley-Black Heanes

Lignin 50.15 48.70 (97%)* 62.20 (124%)

Humic acid 32.75 27.14 (83%) 51.09 (156%)

Cellulose 44.69 43.36 (97%) 52.35 (117%)

Chitin 46.86 33.84 (72%) 42.49 (91%)

Soil (0 - 10 cm sample) 16.38 10.08 (62%) 16.63 (102%)

*The % RC values are means (n = 3) relative to dry combustion.

45

Linearity of wet digestion methods

Highly significant linear relationships (R2

> 0.95) between known and

measured concentrations were obtained for all mixtures analysed within the specified

concentration range (Table 3.4). In all sets of samples, slopes for the linear regression

models obtained from the Walkley-Black method were lower than slopes obtained

from the Heanes method (Table 3.4). However, there were similarities of slope among

sample sets for both methods. For example, slopes obtained from chitin and its

combinations were smaller than other substrates. Furthermore, soil mixed with

kaolinite had the steepest slopes in both methods. The magnitudes of the intercepts

correspond with the magnitude of their LODs. Furthermore, intercepts obtained from

the Walkley-Black method were smaller than from the Heanes method.

Percentage recoveries of the Walkley-Black method

The % RC of the Walkley-Black method was determined at four different

concentrations (Table 3.5). Generally, there was significant overestimation of % TOC

across the standards prepared at the 0.008% TOC concentration. However, the % RC

generally declined to lower than 100% at higher concentrations (Table 3.5). When the

0.008% TOC was excluded, then the highest % RC among substrates was obtained

from soil mixed with kaolinite with 129 and 119% RC at 0.040 and 0.200% TOC,

respectively. On the other hand, the % RCs of chitin were clearly lower than other

single substrates (Table 3.5). Moreover, the effects of chitin also impacted the % RCs

of binary mixtures, especially humic acid + chitin. Smaller effects of chitin were

observed when chitin was present in smaller proportions in ternary and quaternary

46

mixtures.

Percentage recoveries of the Heanes method

Overestimations of % RCs were observed for most concentration levels using

the Heanes method, and this was particularly evident at the lowest concentration

(Table 3.6). The % RCs were most overestimated (224 - 650% RC) at 0.008% TOC.

However, the % RCs were much closer to 100% at higher concentrations except for

substrate 16 (soil mixed with kaolinite) for which the % RCs were 194% and 170% at

0.200 and 1.000% TOC, respectively (Table 3.6). The smallest % RC (93%) was

observed at 1.000% TOC for the chitin standard. A slight underestimation was also

observed in binary and ternary mixtures containing chitin such as lignin + chitin and

lignin + chitin + cellulose. However, the effect of chitin was minimal in quaternary

mixtures of carbon substrates (Table 3.6).

47

Table 3.4 Linear regression models for the relationships between the total organic carbon (%) determined by dry combustion and the

Walkley-Black and Heanes methods.

Substrate Walkley-Black* Heanes

#

Slope P Intercept P R2 Slope P Intercept P R

2

1. Lignin 0.891 0.002 0.004 0.001 1.000 1.165 0.000 0.019 0.000 0.999

2. Humic acid 0.699 0.000 0.006 0.036 0.999 1.223 0.000 0.029 0.000 0.999

3. Cellulose 0.959 0.000 0.001 0.535 1.000 1.081 0.000 0.034 0.000 0.999

4. Chitin 0.672 0.000 0.003 0.447 0.999 0.881 0.000 0.074 0.002 0.984

5. Lignin + humic acid 0.760 0.000 0.008 0.012 0.999 1.191 0.000 0.046 0.000 0.998

6. Lignin + cellulose 0.909 0.000 0.004 0.090 0.999 1.091 0.000 0.042 0.000 0.999

7. Lignin + chitin 0.749 0.000 -0.002 0.585 0.999 0.930 0.000 0.038 0.000 1.000

8. Humic acid + cellulose 0.770 0.000 0.003 0.240 0.999 1.147 0.000 0.059 0.000 0.997

9. Humic acid + chitin 0.595 0.000 0.003 0.556 0.997 1.026 0.000 0.052 0.000 0.997

10. Cellulose + chitin 0.806 0.000 0.005 0.016 1.000 0.927 0.009 0.048 0.005 0.999

11. Lignin + humic acid + cellulose 0.825 0.000 0.004 0.189 0.999 1.166 0.011 0.032 0.006 0.999

12. Lignin + humic acid + chitin 0.738 0.000 0.002 0.487 0.999 1.055 0.000 0.062 0.026 0.977

13. Lignin + chitin + cellulose 0.817 0.000 -0.003 0.624 0.995 0.930 0.000 0.024 0.000 0.999

14. Humic acid + cellulose + chitin 0.710 0.000 0.010 0.000 1.000 1.096 0.000 0.064 0.001 0.992

15. Lignin + humic acid + cellulose + chitin 0.770 0.000 0.006 0.029 0.999 0.994 0.000 0.030 0.000 0.999

16. Soil 1.100 0.000 0.009 0.004 0.999 1.658 0.000 0.034 0.000 1.000

* relationship of %TOC between dry combustion method (x) and Walkley-Black method (y) (n=20),

# relationship of %TOC between dry combustion method (x) and Heanes method (y) (n=15)

48

Table 3.5 Mean recovery of total organic carbon determined by the Walkley-Black method at four concentrations (n = 64).

Substrate Recovery (%)

Mean LSD CV

(%) 0.008% TOC 0.040% TOC 0.200% TOC 1.000% TOC

1. Lignin 137 97 90 90 103 4 19

2. Humic acid 117 91 96 85 97 5 11

3. Cellulose 131 100 99 97 107 20 17

4. Chitin 152 73 65 68 89 42 50

5. Lignin + humic acid 131 99 79 77 96 30 28

6. Lignin + cellulose 146 91 94 92 105 28 27

7. Lignin + chitin 112 69 69 75 81 40 37

8. Humic acid + cellulose 119 80 81 77 89 21 24

9. Humic acid + chitin 112 57 60 60 72 19 36

10. Cellulose + chitin 137 102 79 81 100 15 24

11.Lignin + humic acid + cellulose 143 94 85 83 101 36 32

12.Lignin + humic acid + chitin 127 77 73 74 88 25 31

13.Lignin + chitin + cellulose 97 82 72 82 83 12 14

14.Humic acid + cellulose + chitin 180 99 77 72 107 30 42

15.Lignin + humic acid + cellulose + chitin 115 81 84 77 89 25 23

16. Soil 209 129 119 111 142 28 29

Mean 135 89 82 81

Tukey HSD 83 25 12 5

CV (%) 24 11 6 3

49

Table 3.6 Mean recovery (%) of total organic carbon determined by the Heanes method at four concentrations (n = 48).

Substrate Recovery (%)

Mean LSD CV

(%) 0.008% TOC 0.040% TOC 0.200% TOC 1.000% TOC

1. Lignin 224 156 126 119 156 51 29

2. Humic acid 399 172 138 125 208 157 64

3. Cellulose 552 178 126 111 242 134 79

4. Chitin 457 331 132 93 253 136 56

5. Lignin + humic acid 497 237 148 123 251 71 57

6. Lignin + cellulose 650 192 137 113 273 403 106

7. Lignin + chitin 407 169 114 96 196 76 64

8. Humic acid + cellulose 590 231 154 120 274 47 66

9. Humic acid + chitin 509 179 129 106 231 126 74

10. Cellulose + chitin 466 195 112 97 217 76 68

11.Lignin + humic acid + cellulose 423 171 139 119 213 79 59

12.Lignin + humic acid + chitin 419 290 149 110 242 230 62

13. Lignin + chitin + cellulose 304 159 106 95 166 95 55

14.Humic acid + cellulose + chitin 507 378 146 114 286 87 52

15.Lignin + humic acid + cellulose + chitin 396 167 119 102 196 170 71

16. Soil 547 261 194 170 293 26 50

Mean 459 216 136 113

Tukey HSD 451 210 42 10

CV (%) 32 32 10 3

50


- comparison to dry combustion

Constant variance was observed from whole data sets. A strong linear

relationship (R2 = 0.91) was observed between TOC (%) determined using the

Walkley-Black and dry combustion methods on the calibration set of samples (Figure

3.1). The predictive equation proposed for deep soil carbon is [%TOC]actual =

1.66[%TOC]WB + 0.018.

Figure 3.1 Comparison of TOC determined by the Walkley - Black and the dry

combustion methods (n = 94).

Figure 3.2a presents plot of actual Walkley-Black data from the validation set

compared to values obtained from dry combustion. In Figure 3.2b Walkley-Black data

for the validation set has been transformed using the predictive equation and these

y = 1.66x + 0.018

R² = 0.91

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.00 0.10 0.20 0.30 0.40

TO

C (

%),

dry

co

mb

ust

ion

meth

od

TOC (%), Walkley-Black method

51

values correspond well with the benchmark dry combustion values. As shown earlier,

actual carbon values determined by the Walkley-Black method underestimate the

values from dry combustion. The RMSE of the validation set derived from the

predictive equation was 0.017, while the actual dataset gave a RMSE value of 0.051.

Therefore, the dataset derived from the predictive equation with the smallest RMSE

was better than the Walkley-Black data set.

Figure 3.2 Validation of the data set derived from a) measured values, b) values

applied with predictive equation (n=27).

3.5 Discussion

Walkley-Black method

The limit of detection and the limit of quantification of the Walkley-Black

method were 4 to 10 times lower than values reported previously. For example, De

Vos et al. (2007) reported the LOD and LOQ of lowland forest soils in Belgium to be

1.3 and 4.2 mg C, respectively. Likewise, Conyers et al. (2011) determined the LOQ

y = 0.57x + 0.01

R² = 0.94

RMSE = 0.051

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

Mea

sure

d T

OC

(%

), W

alk

ley

-Bla

ck m

eth

od

TOC (%), dry combustion method

y = 0.95x

R² = 0.94

RMSE = 0.017

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

Pre

dic

ted

TO

C (

%),

Wa

lkle

y-B

lack

m

eth

od

TOC (%), dry combustion method

b) predictive equation applied a) measured values

52

of natural substrates to lie between 7 - 10 mg C. By contrast, the LOD and the LOQ

determined for the 7 sets of samples in the current study were 0.015 and 0.050%, or

absolute quantities of 0.3 and 1.0 mg C in 2 g of kaolinite.

The differences in LOD and LOQ between this current study and the work of

De Vos et al. (2007) may be due to the weight of samples used. This study used 2 g of

pure kaolinite, whereas the study of De Vos et al. (2007) examined the LOD and LOQ

by taking different weights of forest soil containing from 10 - 1000 mg C in order to

vary the carbon content in the wet analysis. The interpolation of LOD was determined

at 33% of relative standard deviation (RSD) using a least-squares curve fit of an

equation obtained from the relationship between carbon concentrations against the

RSD. Their higher LOD could also be attributed to the smaller sample volume used in

their study. Conyers et al. (2011) found that a lower mass of carbon generally gave

higher apparent carbon concentrations. Therefore, variation in sample weight could

affect the derived values of LOD and LOQ in different studies.

When the same methodology was employed in this study using 2 g samples of

kaolinite with different concentrations of a standard (Figure 3.3), an LOD of 0.08 mg

or 0.004% TOC was obtained, which is smaller than the result described above using

kaolinite as a blank. Another reason for the difference in LOD between De Vos et al.

(2007) and this study could be due to the different sample matrices. De Vos et al.

(2007) found that standard deviations (SD) obtained from 3 sets of 10 blanks ranged

from 0.031 - 0.043 mL Fe2SO4. Furthermore, the theoretical LOD was calculated to

be 0.044 + 0.06% TOC for 1 g soil from the blank analysis. By contrast, in the present

study the SD range was smaller, 0.005 - 0.025 mL Fe2SO4 (n = 40), and the

53

theoretical LOD was lower, 0.015 + 0.005% TOC.

Figure 3.3 Empirical determination of the precision versus organic carbon content of

16 sample sets (n=64).

Heanes method

This study performed the modified Heanes method (1984) with a sample size

of 2 g because deep soil samples typically contain less than 0.5% TOC. The measured

LOD and LOQ in the current study, using the Heanes method with a sample size of 2

g, were 1.70 and 5.62 mg C corresponding to 0.085 and 0.281% TOC, respectively.

This can be compared with the study of Bowman (1998) that used 6 different soil

series and a modified Heanes method with only 0.5 g soil. Bowman (1998) showed

that the method was reliable for samples containing 2.0 - 5.5 mg C and reported

recoveries from 89 to 103% RC. Anderson and Ingram (1993) also recommended a

y = 8.36x - 0.55

R² = 0.76

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30

Rel

ati

ve

sta

nd

ard

dev

iati

on

(%

)

Organic carbon (mg)

54

modified Heanes method (0.5 g soil digested at 150 ºC for 30 min) that was suitable

for samples with carbon content of 0.2% TOC or greater.

Linearity of wet digestion methods

The magnitudes of the intercepts independently confirmed the LOD of each

method. The difference in slopes exemplified that carbon concentration determined by

wet digestion methods can be affected by the variety of carbon substrates present in a

sample. Therefore, care is needed for selection of a CF to improve the RC.

Additionally, the main effect of background observed in the Heanes method

could be due to errors associated with light absorption. In particular, kaolinite is not

dissolved and has to be removed. Although filtration of these solutions was

conducted, small particles may remain, which may have contributed to the measured

absorbance and the observed deviations.

Effects of substrate properties on percentage recovery

Differences in carbon substrate affected percentage recovery of both wet

digestion methods and chitin was particularly challenging. Cellulose in this study was

categorised as cellulose I which is the dominant natural cellulose (Salmon and

Hudson 1997). On the other hand, chitin from shrimp used in this experiment was α-

chitin, the most stable form of chitin found in crustaceans and some fungi. The

orientation of the polysaccharides in these compounds is different as chitin has an

antiparallel structure while cellulose has a parallel structure. The former structure has

H-bonding connected stacks along the b-axis and this is likely to be responsible for

55

the resistance to swelling in water (Salmon and Hudson 1997) and hence reduced

oxidation during digestion.

The recoveries of lignin and humic acid did not correspond well to each other.

For example, the % RC of lignin was higher than humic acid when determined by the

Walkley-Black method, while the % RC of lignin was lower than humic acid in the

Heanes method. Schnitzer and Monreal (2011) proposed that only C‟s in the aliphatic

chains of humic acid are oxidized to aliphatic acid while aliphatic C‟s closest to the

aromatic ring are converted to COOH groups, so the C‟s of aromatics are conserved.

Hence, the oxidation of humic acid depends on the number of C‟s in the aliphatic

chain. The aromaticities of humic acid vary widely, ranging from 25 - 40% depending

on the presence of aliphatic structures (Hatcher and Spiker 1988). Therefore, variation

of aromatic structures in humic acid could influence the % RCs.

Effect of carbon substrates on percentage recovery obtained from the Walkley-Black

method

This experiment employed dry combustion as a benchmark method for

comparing two wet digestion methods. The differences in % RC determined from a

pure carbon substrate indicated that the two wet digestion methods performed

differently. In general, the Walkley-Black method underestimated whereas the Heanes

method overestimated total carbon content. However, both wet methods

overestimated the carbon in soil mixed samples relative to values obtaining by dry

combustion.

56

The % RCs of mixed kaolinite substrates agreed with the % RCs of the pure

substrates. However, the % RC of pure chitin was grossly underestimated by both wet

digestion methods. Consequently, the % RCs of chitin mixed samples were also lower

than other mixed samples using both wet digestion methods. Therefore, chitin cannot

be accurately quantified in soil using wet digestion methods.

For the Walkley-Black method, even though pure lignin and cellulose gave the

same % RC of 97, the % RCs of their mixed samples were different. This indicates

that the performance of this method is difficult to predict with mixed samples. In

comparison, for the Heanes method the % RC of mixtures agreed well with the % RC

of pure substrates only at 1.000% TOC.

Generally, carbon concentration is overestimated if the overall mass of sample

analysed is very small (Conyers et al. 2011). For both methods, the most severe

overestimation was observed for soil mixed with kaolinite samples (substrate 16).

This could be due to the four samples having putative concentrations lower than the

desired values.

Performance of the Walkley-Black method in real samples

This study is the first time that the performance of the Walkley-Black method

has been compared to a benchmark method for low soil carbon contents of deep soil

profiles. Based on the constant variance of data sets, the proposed predictive equation

was amenable and has promise for minimising the error in TOC values determined by

the Walkley-Black method. In comparison, actual values determined from samples

lead to underestimation.

57

In general, a CF is applied to provide an improved estimate of % TOC and is

obtained only from the slope of the calibration curve with little consideration of the

intercept. Different CFs have been proposed for different soils including Russian

Chernozem soils (1.63), Belgian agricultural soils (1.91) and soils from each state of

Australia (1.97 - 1.12) (Mikhailova et al. 2003; Skjemstad et al. 2000; Sleutel et al.

2007).

This study, however, further determined the RMSE of the validation set when

adjusted with the slope of the predictive equation (1.66). An RMSE of 0.043 was

obtained which was smaller than the RMSE of the actual value dataset (0.051). This

indicated that applying both the slope and intercept in form of the predictive equation

dramatically minimised the RMSE (0.017) compared to applying the slope alone

(0.043). It is concluded that the Walkley-Black method together with the predictive

equation may have application for the quantification of deep soil carbon.

3.6 Conclusions

The LOD and LOQ (0.015 and 0.050% TOC) of the Walkley-Black method

were clearly smaller than the LOD and LOQ (0.085 and 0.281% TOC) values

obtained from the Heanes method. Both methods showed excellent linearity (R2 >

0.99) when testing with accurately prepared standards in the concentration range

between 0.008 - 1.000% TOC. Nevertheless, the % RCs were generally

underestimated and varied for carbon substrates that contained chitin. The Walkley-

58

Black method performed well on deep soil samples when the predictive equation

[TOC (%)]actual = 1.66[TOC (%)]WB + 0.018 was adopted.

59

Chapter 4

Characterisation and Quantification of Deep Soil Carbon

by Vibrational Spectroscopy

4.1 Abstract

Knowledge of the composition of SOC stored in small concentrations to many

meters depth in deep regolithic soils is crucial to understanding its dynamics. Thus,

the efficacy of mid and near infrared spectroscopy was determined for the

characterisation and quantification of SOC, particularly for small concentrations.

Carbon standards, prepared in a concentration range of 0.008 - 11.55% TOC, were

composed of lignin, humic acid, cellulose and chitin mixed with kaolinite and their

combinations. DRIFT had superior sensitivity to ATR. The best LODs were achieved

when using kaolinite as a background. The LODs of DRIFT were 1.92% TOC for

lignin or humic acid and 1.00% TOC for cellulose or chitin. However, key lignin and

humic acid bands were concealed when mixed with cellulose and chitin at the same

proportion. The identification of specific carbon structures from the mid infrared

region was difficult in a mixture of several carbon types due to peaks overlapping.

A model for quantification of deep soil carbon using NIR was tested for

sensitivity with standard samples in the concentration range 0.00 - 1.50% TOC.

Positive prediction values were observed when employing square root of %TOC as

60

input data. Then, models were developed from 121 real soil samples from depths of 1

- 35 m and concentration range 0.01 - 0.536% TOC. The model was calibrated and

validated using sets of 94 and 27 samples, respectively. The first derivative and

standard SNV coupled with exclusion of bands in the range 5600 - 5000 cm-1

were

suitable pre-processing approaches which gave an LOD of 0.001% TOC, with a very

strong correlation (R2 = 0.981). This approach could be applied to world soils where

deep SOC has been observed.

4.2 Introduction

Deep SOC has been recognised as a potential reservoir of carbon in terrestrial

systems (Chapter 2). Nevertheless, approaches for studying its quantity and quality

are poorly developed. Recently, Harper and Tibbett (2013) measured SOC content to

38 m by the dry combustion method, which is the benchmark method in more shallow

soil horizons (Chatterjee et al. 2009; De Vos et al. 2007; Mikhailova et al. 2003).

However, nothing is known about the composition of this SOC or indeed its wider

distribution across other soils. It is thus important to broaden the number of

techniques for characterisation and quantification of deep SOC. Vibrational

spectroscopy, in mid and near infrared regions, is increasingly being used for soil with

several purposes (Bellon-Maurel and McBratney 2011). However, the technique has

not been proven for small amounts of soil carbon (< 0.05% TOC) in deep soils.

Therefore, characterisation and quantification of deep soils with vibrational

spectroscopy remains challenging.

61

Mid infrared spectroscopy using DRIFT and ATR techniques are increasingly

being used to characterise SOM (Dick et al. 2003; Ge et al. 2014; Madari et al. 2006).

The DRIFT technique collects IR reflected from the soil surface as this is scattered

diffusely and attenuated by absorption of some frequencies by the sample. However,

the intensity of reflected light is not directly related to total absorption and therefore,

it does not directly correspond to the concentration of carbon in the sample. However,

this technique has many advantages for soil analysis including improving the signal of

minor components such as organic matter. The quality of spectra can vary

significantly with the relative proportions of mineral and organic matter as

demonstrated by Nguyen et al. (1991).

In comparison, the ATR technique is used for characterising smooth-surfaced

samples. The reflected light is specular, detected from total internal reflection of the

light at the interface between the sample and the ATR crystal. This technique can

generally be applied without dilution of samples and this reduces peak distortion

problems (Ge et al. 2014).

Identification of SOM determined from MIR spectra can be difficult if there is

spectral overlap with inorganic components of the soil. To address this problem,

characterisation of the SOM must be done by accentuating the organic spectral

signature through spectral subtraction of the mineral signature (Margenot et al. 2015).

Two approaches have previously been employed: (a) whole subtraction of the ashed-

soil spectrum in order to leave only the organic matter signature, and (b) comparison

of the in situ SOM spectrum with the H2O2 oxidised soil spectrum. However, these

approaches gloss over signatures of organic matter and can lead to inaccuracies. For

62

example, clay structure and its functional groups can be changed during ashing

(Reeves 2012). Furthermore, the latter approach was invalidated on control samples

with accurately known SOM levels (Dick et al. 2003).

The advantage of using IR spectroscopic techniques for quantification is that

they are non-destructive, rapid and generally low-cost. The estimation of soil carbon

by NIR has been previously established by several studies as reviewed by Bellon-

Maurel and McBratney (2011) and Reeves (2010). Absorption coefficients are much

smaller in the NIR range so light can penetrate into the sample and diffusion is greater

making NIR spectra appear broad. Therefore, NIR is less suitable for identification of

different organic species but still suitable for quantitative analysis.

Generally, useful information contained in NIR spectra can be extracted by

chemometric techniques such as principle component analysis (PCA), partial least

squares (PLS), genetic algorithm (GA) and multi linear regression (MLR). However,

PLS is the most used technique to develop calibration models because the method

reduces noise, detects unknown samples that are not represented by the calibration

model and obviates wavelength selection (Kang 2002). Furthermore, the PLS method

is usually combined with multi data pre-treatment approaches, such as normalisation,

standard normal variation, multiple scattering correction and, baseline correction of 1st

or 2nd

derivative to obtain the lowest statistical error (Bellon-Maurel and McBratney

2011).

Application of NIR for estimation of soil carbon has been successfully

developed mostly for surface soils (< 40 cm) (Cambule et al. 2012; Dunn et al. 2002;

Gomez et al. 2008; Xie et al. 2011). However, prediction models for subsoil carbon

63

up to 3.2 m were developed by Li et al. (2010) and Pirie et al. (2005). Nevertheless,

an NIR model for estimating deep soil carbon has not yet been developed.

The objectives of this study were to:

(i) determine a suitable background for accentuating the DRIFT spectral

signature of carbon substrates,

(ii) compare the abilities of DRIFT and ATR to detect low levels of carbon in

a kaolinite substrate,

(iii) evaluate the potential of DRIFT for characterisation of a carbon substrate

in accurately prepared standards, and

(iv) develop a model for estimating deep soil carbon in south-western

Australia by employing the NIR spectroscopic technique.

Two questions are addressed as detailed in Chapter 1 (page 4): questions 3 and

4.

4.3 Materials and methods

Experiment 4.1: Spectroscopic characterisation of carbon substrates

Standard sample

Preparation of laboratory standard samples at concentrations of 0.008 - 1.00

%TOC were described in Chapter 3 (Experiment 3.1). Additional samples with

concentrations of 1.92, 3.85 and 11.55 %TOC were prepared individually.

Furthermore, substrate combinations were prepared by weighing the required portion

64

of each laboratory standard sample and mixing. All laboratory standard combinations

were homogenized by using a blender for 15 minutes.

MIR measurements

Mid infrared spectra from 4000 - 400 cm-1

at 4 cm-1

resolution were recorded

on a Perkin Elmer DRIFT Spectrum One version 5.0.1. All spectra were analysed

with and without baseline correction and significant differences were only evident in

the fingerprint region. Therefore, spectra are reported without baseline correction

unless otherwise noted.

Spectra of pure kaolinite and carbon substrates; lignin, humic acid, cellulose

and chitin, were obtained from solid samples diluted with KBr (1% w w-1

) and

recorded from 8 scans. All spectra were compared with published studies (Boeriu et

al. 2004; Fan et al. 2007; Li et al. 2010; Vaculíkova et al. 2001; Zhang et al. 2011).

Preliminary comparison of DRIFT and ATR was performed using kaolinite

mixed with lignin samples (L/K). The ATR spectra were obtained using a Universal

ATR sampling accessory using ZnSe crystal type, with a Perkin Elmer Frontier IR,

Spectrum 400 system version 10.03.09. Samples were analysed in the MIR region

(4000 - 550 cm-1

) with 800 scans and 8 cm-1

resolution and air background. DRIFT

spectra were obtained over the same MIR region, scans and resolution using KBr as

background.

The L/K set was also used for testing subtraction of different backgrounds

including: 1) air, 2) KBr, 3) kaolinite, and 4) 1% kaolinite diluted in KBr (kaolinite +

KBr). Samples without dilution were scanned with air and kaolinite backgrounds,

65

while the KBr background was used for samples diluted with KBr. All spectra were

collected by using a resolution of 4 cm-1

. The spectra were reported in reflectance

percentage. Once a suitable background had been determined all other

carbon/kaolinite mixtures (humic acid, cellulose and chitin) were determined using

the same background.

Experiment 4.2: Response of NIR spectroscopy to quantify carbon in standard

samples

Standard samples

Initially, authentic substrates such as kaolinite, lignin, humic acid, cellulose

and chitin were characterised in the NIR region. Then, a set of laboratory standard

samples was used for testing the possibility of using NIR spectroscopy to develop a

model for quantifying small amounts of carbon in soil. Standard sample

concentrations ranging from 0.008 - 1.00% TOC, as prepared for Experiment 4.1,

were employed. Additional standard samples were prepared in the concentration

range 0.004 - 0.50% TOC. These extra sets were prepared by dilution of the above

sample sets, namely 1) L/K, 2) H/K, 3) C/K, 4) Ch/K, 5) 4C/K and 6) soil/K. Soil

used in this experiment was decomposed litter mixed with surface soil collected from

a depth of 0 - 10 cm from natural bushland. Samples were assigned for the calibration

(n = 65, including pure kaolinite) and validation sets (n = 24).

66

In this study, the spectra of standard samples were assumed to be

homogeneous. Models were further developed without pre-processing approaches

with an input of %TOC and square root transformed values (sqrt %TOC).

NIR measurement

Near infrared spectra from 10000 - 4000 cm-1

at 4 cm-1

resolution with 16

scans were recorded on a Perkin Elmer Frontier IR system, Spectrum 400 series,

version 10.03.09. A spectralon cap was used for background and interleaved, which

means the background was automatically measured before scanning each sample.

Samples of approximately 0.5 g were placed in clear vials and put on the sample

holder for scanning. The spectra were recorded in log-transform of the inverse of

reflectance.

Experiment 4.3: Estimation of deep soil carbon by NIR

Soil samples and chemical analysis

Deep soil samples and a TOC data set used in Experiment 3.2 were employed.

Samples were scanned with NIR as described in Experiment 4.2.

Outlier detection and model development

The spectra of all samples were initially analysed to test homogeneity and

detect outliers using PCA employing sqrt%TOC as an input.

67

The spectra of calibration and validation sets were employed to develop the

models using PLS. The models obtained from trial approaches were initially validated

by the leave-one-out procedure. Finally, models that gave high correlation coefficient

(R2) and low RMSE were chosen. A range of different pre-processing methods were

also explored with various mathematical approaches as shown in Table 4.1.

The analyses were carried out using PCR+ and PLS1 algorithms of the

Spectrum Quant software version 10.4 (Perkin Elmer 2014).

Table 4.1 Pre-processing of NIR spectra for establishment of models.

Model* Pre-processing approach

Weighting Normalisation Baseline correction

a# - - -

b - Multiplicative Scatter

Correction (MSC)

1st derivative,

noise reduction =2

c smooth,

points = 10

- 2nd

derivative,

noise reduction = 2

d - Standard Normal Variation

(SNV) , detrending

1st derivative,

noise reduction =2

e - SNV 1st derivative,

noise reduction =2 #model without pre-processing,*Models b - e pre-processed by automatic blanking, upper threshold =

1.5 and combined with mathematical approaches showed in this table

Calibration and validation analysis

In order to process on a full comparative basis, the sqrt %TOC output datasets

were retransformed to %TOC before all indices were calculated. Selection of models

was based on R2, RMSE, the ratio of performance to deviation (RPD) and ratio of

performance of inter-quartile distance (RPIQ) defined by equations (4.1 - 4.4),

respectively.

68

where y, and are measured values, mean of measured values determined

by dry combustion method and predicted values of %TOC, respectively; n is the

number of predicted or measured values; SD is the standard deviation; and Q1 and Q3

are the lower and upper quartiles of data, respectively.

The developed models were evaluated according to Chang and Laird (2002).

Three classes of model were identified: category A are models that predict accurately

(RPD > 2), category B models have intermediate quality (RPD between 1.4 and 2)

which could possibly be improved, and category C models have no prediction ability

(RPD < 1.4).

The RPIQ was proposed in order to assess a model performance when the

datasets have an asymmetrical distribution (Bellon-Maurel et al. 2010). A model is

considered to perform well if it has large values for R2 and RPIQ and a small RMSE.

∑

(4.1)

√

∑

(4.2)

RPD = SD / RMSE (4.3)

RPIQ = (Q3 – Q1) / RMSE (4.4)

69

4.4 Results

Experiment 4.1: Spectroscopic Characterisation of organic substrates

MIR spectra of standard samples

The IR spectrum of kaolinite was dominated by the strong reflectance between

3694 - 3620 cm-1

which can be attributed to OH stretching vibrations (Figure 4.1a).

Other intense peaks were observed in the fingerprint region including 1113, 1029 and

1006 cm-1

, which indicate the presence of Si-O-Si stretching and bands at 913, 795,

754 and 700 cm-1

which are attributed to OH stretching (Vaculíková et al. 2001;

Zhang et al. 2011).

Infrared spectra of each of the carbon substrates are depicted in Figure 4.1b, c,

d and e. Common bands of all substrates lie at 3400 - 2800 cm-1

arising from CH and

OH stretching and 1600 - 1200 cm-1

from COO- stretching. The spectral bands below

1400 cm-1

arise from several vibrational modes such as C-C, C-O and C-H which are

complex and difficult to analyse. Band regions that identify and reflect the chemistry

of the respective carbon substrates are discussed below.

Lignin had small peaks at 2937 and 2845 cm-1

that arise from OH stretching

(Figure 4.1b). The intense reflectances in the 1590 - 1038 cm-1

range originate from

COO- stretching. Furthermore, the band at 1508 cm

-1 is also a characteristic band of

aromatic C-O stretch of lignin in this region (Boeriu et al. 2004).

The IR spectrum of humic acid (Figure 4.1c) gave a band in the region of 3691

- 3616 cm-1

, due to OH stretching, which overlapped with kaolinite vibrations. There

was also a broad peak attributed to hydroxyl groups at 3223 cm-1

, and bands centred

70

around 2928 and 2840 cm-1

from C-H. In the COO- stretching region, bands were

found at 1581 and 1399 cm-1

. Not surprisingly, there was significant similarity

between the IR spectra of lignin and humic acid.

Cellulose had a broad band from hydroxyl group vibrations at 3337 cm-1

, a

strong band for C-H stretching at 2900 cm-1

and intense bands for COO- stretching at

1646 and 1370 cm-1

(Figure 4.1d). In comparison, chitin had a weak peak at 3263 cm-

1 and a shoulder which was a distinct band corresponding to N-H stretching, and a

band of aliphatic C-H at 2891 cm-1

(Hu et al. 2007; Stawski et al. 2008). Intense peaks

between 1660 and 1031 cm-1

, including the characteristic reflection bands of chitin,

were assigned to amide vibrations at 1660 and 1620 cm-1

(Fan et al. 2012; Li et al.

2010) (Figure 4.1e).

71

Figure 4.1 MIR spectra of a) kaolinite, b) lignin, c) humic acid, d) cellulose and e)

chitin. Characteristic bands of each species are labelled with *.

5001000150020002500300035004000

60

80

100

120

140

160

180

40

5001000150020002500300035004000

60

80

40

30

50

7070

90

5001000150020002500300035004000

45

50

55

60

65

70

75

80

85

5001000150020002500300035004000

0

20

40

60

80

100

5001000150020002500300035004000

0

20

40

60

10

30

50

%R

3694* 3620*

1113*

1029* 1006*

913* 795*

754*

700*

2937*

2845

1590 1508*

1239 1124

1038

3691

3616 3223 2928

2840

1581 1399 1097

1032

913

a) kaolinite

b) lignin

c) humic acid

%R

%R

%R

cm-1

e) chitin

3337

d) cellulose

2900

1646*

1428*

1370*

1013

%R

1031*

1078* 1112

1311*

1378*

1559* 1660*

2891*

3107

3263*

72

Preliminary comparison of ATR and DRIFT

DRIFT requires careful sample preparation in terms of grinding and dilution

with KBr, whereas ATR requires very little sample preparation. Consequently, ATR

allows for rapid analysis and throughput but it is essential to first verify if ATR gives

a similar response to DRIFT across the MIR spectral range. The initial comparison

was performed using an 11.55% TOC-L/K standard and the two spectra are displayed

in Figure 4.2.

The spectrum of 11.55% TOC-L/K obtained using ATR was clearly different

from the DRIFT spectrum. Firstly, the kaolinite peaks in the 3700 - 3600 cm-1

region

were much less intense in the ATR spectrum. Secondly, the kaolinite peaks in the

fingerprint region were better resolved but again generally less intense. Although the

ATR spectrum exhibited excellent baseline resolution, none of the characteristic

vibrations of the organic substrate were visible.

The DRIFT spectrum of the 11.55% TOC-L/K sample also displayed the

characteristic kaolinite vibrations in the 3700 - 3600 cm-1

region and also from 1100 -

400 cm-1

. However, there were other detectable vibrations originating from lignin at

2923, 2851, and 1585 cm-1

.

Although characteristic lignin peaks were observed in the DRIFT spectrum of

the 11.55% TOC-L/K sample, for samples with lower carbon percentage, the organic

substrate was much harder to detect.

73

Figure 4.2 Comparison of DRIFT and ATR spectra for 11.55% TOC-L/K.

Background selection

A range of different background corrections were investigated including air,

KBr, kaolinite and kaolinite + KBr. The IR spectra obtained for different

concentrations of lignin in kaolinite using the different background corrections are

shown in Figure 4.3. Air was an ineffective background for improving resolution of

lignin-derived peaks at all concentrations considered. On the other hand, using KBr,

the traditional background, lignin bands were detected down to 1.92% TOC (Figure

4.3b).

Pleasingly, the kaolinite background resulted in improved spectra even for

0.20% TOC-L/K, the lowest concentration standard (Figure 4.3c). Subtraction of the

KBr + kaolinite backgrounds lead to improvements in the L/K spectra at

concentrations higher than 1.92% TOC-L/K but there was a shift of peaks in the

fingerprint region (Figure 4.3d). The performance of the backgrounds were ranked as

follows; kaolinite > kaolinite + KBr > KBr > air. Therefore, the kaolinite background

1000150020002500300035004000

0

20

40

60

80

100

120

DRIFT ATR

%R

cm-1

2923

28511585

74

was employed as a background for investigation of H/K, C/K, Ch/K and 4C/K

samples.

Figure 4.3 Spectra of L/K when subtracted by a) air, b) KBr, c) kaolinite and d)

kaolinite + KBr.

15002000250030003500

0

1000

2000

3000

4000

5000

kaolinite

1.00%TOC-L/K

1.92%TOC-L/K

3.85%TOC-L/K

15002000250030003500

60

70

80

90

100

110

120

0.20%TOC-L/K 1.00%TOC-L/K 1.92%TOC-L/K 3.85%TOC-L/K

15002000250030003500

60

70

80

90

65

75

85

1.92%TOC-L/K

3.85%TOC-L/K

11.55%TOC-L/K

15002000250030003500

85

90

95

100

105

110

1.92%TOC-L/K 3.85%TOC-L/K 11.55%TOC-L/K

a) air

b) KBr

c) kaolinite

d) kaolinite + KBr

%R

%R

%R

cm-1

%R

2923

2851

1585

2928*

2835

1585

1421

2934

2835

1578

14931404

1267

75

Potential of DRIFT to detect carbon functional groups

The spectra of kaolinite mixed with humic acid (H/K), cellulose (C/K) and

chitin (Ch/K) were generally similar to the L/K mixture. Samples were identified by

their characteristic reflectances, including lignin at 2928 cm-1

, humic acid at 2917,

2846, 1569, 1437 cm-1

, cellulose at 1647, 1429, 1372 cm-1

and chitin at 3267, 2876,

1380 cm-1

. However, detection of these bands occurred at different concentration

levels for different carbon substrates. For example, humic acid was detected down to

1.92% TOC (Figure 4.4a), whereas cellulose and chitin were detected at 1.00% TOC

(Figure 4.4b and 4.4c).

For spectra of the quaternary mixtures (4C/K), lignin and humic acid bands

were completely concealed (Figure 4.4d). Additionally, above 1900 cm-1

, the

spectrum was similar to cellulose (Figure 4.4b) but below 1700 cm-1

, the spectrum

resembled chitin (Figure 4.4c). The results demonstrated that the DRIFT technique

was unable to detect all types of carbon substrate contained in the mixed samples.

Cellulose and chitin interferences on lignin spectrum

As demonstrated in the previous section, there was considerable overlap of

bands arising from the components of the 4C/K. Close inspection of the spectra

indicated that cellulose and / or chitin effectively conceal the presence of lignin or

humic acid. To explore this effect in more detail, the IR spectra were determined for

three additional samples obtained by using equal portions of i) 3.85% TOC-L/K and

3.85% TOC-C/K, ii) 3.85% TOC-L/K and 3.85% TOC-Ch/K, and iii) 3.85% TOC-

76

L/K, 3.85% TOC-C/K and 3.85% TOC-Ch/K. The IR spectra of these three mixtures

are depicted in Figure 4.5a.

When combined with the same proportions of the three carbon substrates

(sample iii), the lignin bands at 2928, 2835, 1585 and 1421 cm-1

were completely

covered by both cellulose and chitin bands (Figure 4.5a). Inspection of Figure 4.5a

also revealed that the spectrum of the ternary mixture (sample iii) was similar to L/K

+ Ch/K (sample ii) in the fingerprint region and similar to L/K + C/K (sample i) in the

region 3500 - 1800 cm-1

, except at 3105 cm-1

. Therefore, these results confirmed that

substrates with strong absorbance such as C/K and Ch/K can conceal L/K, although

they shared the same proportions and concentration levels.

To further explore this effect, additional samples were prepared by mixing

3.85% TOC-L/K with succeeding smaller amounts of C/K and Ch/K. The IR spectra

of these samples are presented in Figure 4.5b and Figure 4.5c, respectively. The

strong interferences of cellulose and chitin over lignin were evidenced by the

appearance of characteristic peaks (Figure 4.5b and Figure 4.5c). Although, the

effects decline with concentration of C/K and Ch/K, the lignin peaks at 2928 and

2835 cm-1

still remained hidden (Figure 4.5b and Figure 4.5c).

77

Figure 4.4 Spectra of a) H/K, b) C/K, c) Ch/K and d) 4C/K subtracted by kaolinite

background, * = characteristic band.

15002000250030003500

60

70

80

90

100

110

120

130

0.20%TOC-H/K 1.00%TOC-H/K 1.92%TOC-H/K 3.85%TOC-H/K

15002000250030003500

40

60

80

100

120

140

0.20%TOC-C/K 1.00%TOC-C/K 1.92%TOC-C/K 3.85%TOC-C/K

15002000250030003500

30

50

70

90

110

130

0.20%TOC-Ch/K 1.00%TOC-Ch/K 1.92%TOC-Ch/K 3.85%TOC-Ch/K

15002000250030003500

40

60

80

100

120

140

0.20%TOC-4C/K 1.00%TOC-4C/K 1.92%TOC-4C/K 3.85%TOC-4C/K

2917

2846

1882

1569 1437a) humic acid / kaolinite

33352899

1647*

1429*1372*

b) cellulose / kaolinite

3267*3099

2876*1661

1556 1380*

1326

c) chitin / kaolinite

3267*3086

2892*

1620

1561

1429

1375*

1656*

d) quaternary carbon substrates / kaolinite

%R

cm-1

%R

%R

%R

78

Figure 4.5 Spectra of a) 3.85% TOC-L/K mixed with 3.85% TOC-C/K and / or 3.85%

TOC-Ch/K, b) 3.85% TOC-L/K combined with smaller concentrations of C/K and c)

Spectra of 3.85% TOC-L/K combined with smaller concentrations of Ch/K, * =

characteristic band.

15002000250030003500

40

60

80

100

120

3.85%TOC-L/K + 3.85%TOC-C/K

3.85%TOC-L/K + 3.85%TOC-Ch/K

3.85%TOC-L/K + 3.85%TOC-C/K + 3.85%TOC-Ch/K

3300

3255 3105

2897

1660

1618

1560

1429

1372

%R

cm-1

15002000250030003500

50

70

90

110

130

3.85%TOC-L/K + 0.008%TOC-C/K

3.85%TOC-L/K + 0.04%TOC-C/K

3.85%TOC-L/K + 0.20%TOC-C/K

3.85%TOC-L/K + 1.00%TOC-C/K

3.85%TOC-L/K + 1.92%TOC-C/K

%R

cm-1

1583

1429*

1375 3300 2897

15002000250030003500

60

80

100

120

3.85%TOC-L/K + 0.008%TOC-Ch/K

3.85%TOC-L/K + 0.04%TOC-Ch/K

3.85%TOC-L/K + 0.20%TOC-Ch/K

3.85%TOC-L/K + 1.00%TOC-Ch/K

3.85%TOC-L/K + 1.92%TOC-Ch/K

1575

1380*

1326

3277*

2878*

%R

cm-1

3099

1665

a) 3.85% TOC-L/K + 3.85% TOC-C/K and/or 3.85% TOC-Ch/K

b) 3.85% TOC-L/K + smaller concentrations of C/K

c) 3.85% TOC-L/K + smaller concentrations of Ch/K

79

Experiment 4.2: Sensitivity of NIR to quantify carbon in standard samples

NIR spectra of authentic kaolinite and carbon substrates

Spectra of pure kaolinite and carbon substrates scanned in the NIR region are

shown in Figure 4.6. Three regions were commonly observed in all substrates. For

example, bands at 7100 - 6700 cm-1

attributed to OH stretch combination in the first

overtone such as 7180 and 7082 cm-1

(kaolinite), 6875 cm-1

(lignin), 7163 cm-1

(humic acid), 6732 cm-1

(cellulose) and 6767 cm-1

(chitin).

Secondly, bands in the region of 5300 - 5100 cm-1

, attributable to OH

stretching, included 5283 cm-1

(kaolinite), 5209 cm-1

(lignin), 5193 cm-1

(humic acid),

5176 cm-1

(cellulose) and 5175 cm-1

(chitin). Additionally, bands in the region of

5000 - 4000 cm-1

, associated with OH stretch combinations in the first overtone,

included 4541 and 4200 cm-1

(kaolinite), 4403cm-1

(lignin), 4527cm-1

(humic acid)

and 4592cm-1

(chitin) (Cambule et al. 2012; He and Hu 2013) (Figure 4.6).

There were also many bands associated with carbon such as bands at 5963 and

4664 cm-1

of lignin that corresponded with CH stretching of aromatic and phenolic

hydroxyl groups, respectively. For humic acid, the band at 7065 cm-1

was attributed to

phenolic groups and bands at 4624 and 4527 cm-1

with carbonyl groups (He and Hu,

2013) (Figure 4.6b, c).

Polysaccharides such as cellulose and chitin can be characterised from the

bands attributed to OH combinations such as 4746 and 4765 cm-1

in cellulose and

chitin. Additionally, CH stretching and deformation was observed at 4395 cm-1

in

cellulose and 4218 cm-1

in chitin (He and Hu 2013; Berardo et al. 2005) (Figure 4.6d,

80

e). Some bands of chitin were unique from cellulose such as 5929 cm-1

, CH stretching

of CH3 group in the third overtone, and 5649 cm-1

, CH in the first overtone. However,

the major differences were bands at 4872 cm-1

of N-H bending in the third overtone of

primary amides and the band at 4218 cm-1

specified as the N-H bending in the second

overtone of primary amides (Berardo et al. 2005) (Figure 4.6e).

81

Figure 4.6 NIR spectra of a) kaolinite, b) lignin, c) humic acid, d) cellulose and e)

chitin.

40005000600070008000900010000

0.1

0.2

0.3

0.4

0.5

0.6

40005000600070008000900010000

0.2

0.3

0.4

0.5

0.6

0.7

0.8

40005000600070008000900010000

0.6

0.7

0.8

0.9

1.0

1.1

40005000600070008000900010000

0.6

0.7

0.2

0.3

0.4

0.5

0.1

40005000600070008000900010000

0.6

0.7

0.2

0.3

0.4

0.5

0.8

log

(1/R

)

b) lignin

c) humic acid

log(

1/R

)lo

g(1

/R)

log(

1/R

)

e) chitin

d) cellulose

log(

1/R

)

7082

5283

4541

68755963

5209

4664

4403

7065

5193

4527

6732

5176

47464395

6767

5929

5649

5175 4872

4765

4592

4218

a) kaolinite

cm-1

4624

7180

7163

4200

82

NIR spectra of standard mixtures and soil samples

The representative NIR spectral features of a laboratory standard sample and

soil samples from the field are shown in Figure 4.7. Although there were similarities

in the spectra of the standard mixture and soil samples, the reflectance unit in terms of

log (1/R) decreased with increasing concentration levels (Figure 4.7a, b).

From close inspection of the laboratory standard sample, the representative

sample of H/K clearly showed that spectral peaks were combined from humic acid

and kaolinite peaks but slightly shifted. For example, peaks of the laboratory standard

sample that resembled humic acid were at 7168, 7065, 4624 and 4527 cm-1

(Figure

4.6c, Figure 4.7a), as well as at 4193 cm-1

that was similar to kaolinite (Figure 4.6a,

Figure 4.7a).

For soil sample spectra, there were some noticeable peaks related to carbon.

For instance, peaks at 4624 and 4533 cm-1

corresponded to carbonyl groups.

Nevertheless, peaks that corresponded to OH stretch combination in the first overtone

(7176, 7082 and 4200 cm-1

) and H2O deformation (5232 cm-1

) were also observed

(Figure 4.7b).

83

Figure 4.7 Spectra of a representative standard and soil sample showing three major

reflection features in NIR region.

Response of NIR to carbon concentration in standard samples

Predictive models obtained from a standard set of samples in kaolinite are

presented in Figure 4.8. The model developed from the standard set showed a high

predictive ability (R2

> 0.89) in both the calibration and validation sets (Figure 4.8).

Generally, the model obtained from transformed data showed all positive validated

values (Figure 4.8b), whereas the untransformed data manifested some small negative

40005000600070008000900010000

0

0.2

0.4

0.6

1.208%TOC-H/K

40005000600070008000900010000

0.6

0.2

0.4

0.8

0.032%TOC-PT06, 28-29m

0.316%TOC-PT08, 1-2m

cm-1

log(1

/R)

7065

4624

4527

a) laboratory standard sample

4193

log(1

/R)

7082

5232

4533

b) soil samples

4200

7168

7176

4624

84

validated values. This experiment demonstrated that NIR spectroscopy is suitable for

quantifying soil carbon concentrations lower than 1.5% TOC.

Figure 4.8 NIR prediction of carbon concentration using standard samples as

calibration and validation data sets.

y = 0.978x + 0.011

R² = 0.893

RMSE = 0.129

PRD = 3.127

RPIQ = 1.187

0.0

0.3

0.6

0.9

1.2

1.5

0.0 0.3 0.6 0.9 1.2 1.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 1.022x + 0.022

R² = 0.891

RMSE = 0.131

RPD = 3.085

RPIQ = 2.127

0.0

0.3

0.6

0.9

1.2

1.5

0.0 0.3 0.6 0.9 1.2 1.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 1.143x + 0.029

R² = 0.945

RMSE = 0.115

RPD = 3.939

RPIQ = 2.015

0.0

0.3

0.6

0.9

1.2

1.5

0.0 0.3 0.6 0.9 1.2 1.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 1.149x + 0.012

R² = 0.973

RMSE = 0.094

RPD = 4.849

RPIQ = 1.283

0.0

0.3

0.6

0.9

1.2

1.5

0.0 0.3 0.6 0.9 1.2 1.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

Calibration sets Validation sets

a) %TOC

b) sqrt %TOC

85

Experiment 4.3: Estimation of deep soil carbon by NIR spectroscopy

Outlier detection and distribution of data set

The first two principle components (PCs) of the NIR spectral variance are

shown in Figure 4.9. The first and second PC accounted for 88 and 8% (Figure 4.9).

No outliers were detected when employing sqrt %TOC as input data. The PC1 and

PC2 score plots obtained from the calibration and validation samples were similar,

indicating that the range of NIR spectra acquired for the calibration samples was

reflective of that obtained for the validation samples.

Figure 4.9 Score plot of the first two principle components of spectra from calibration

and validation sets.

The descriptive statistics of calibration and validation sets are summarised in

Table 4.2. Mean and range values of both sets were close to each other, which

supported the homogeneity of sample spectra analysed by PCA. The data set show

asymmetric distribution or positive skewness (Table 4.2).

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

PC

1 (

88

.70

%)

PC 2 (8.15%)

validation

calibration

86

Table 4.2 The descriptive statistics of soil data sets.

Variable n Mean Medium Range SD Skewness

Calibration set 94 0.270 0.231 0.100 - 0.732 0.129 1.36

Validation set 27 0.246 0.224 0.100 - 0.539 0.107 1.23

Loading spectra

Generally, the term “loading spectra” is a qualitative description of the

spectral data that can indicate the correlation between the NIR frequencies with the

component of interest or carbon concentration in this case. Positive peaks are due to

the amount of carbon, whereas negative peak correspond to interferences that appears

in particular NIR regions.

Visually, the main loading spectra of all models derived from different pre-

processing approaches were quite similar in terms of pattern but different in PC

loading weight. Examples of loading spectra derived from smooth + 2nd

derivative

(model b) and SNV + 1st derivative (model e) are show in Figure 4.10a and b,

respectively. The main loading weights were assigned differently in the PC

components. The PC loading weight accounted for 70% of PC1 in 2nd

derivative and

58% of PC2 in 1st derivative approaches (Figure 4.10a, b).

Positive and negative peaks were observed in both loading spectra (Figure

4.10). From loading spectra, there were three regions that influenced model

performance, identified as i) 7500 - 6500 cm-1

, ii) 5300 - 5100 cm-1

and iii) 5000 -

87

4000 cm-1

. It can be seen that band regions 7500 - 6500 cm-1

(i) and 5000 - 4000 cm-1

(iii) are more intense than region 5300 - 5100 cm-1

(ii) (Figure 4.10).

Figure 4.10 Representative loading spectra of model pre-processed by a) 2nd

derivative and b) 1st derivative.

Number of factors and validation-variance

The numbers of PCs and percentage of variance obtained from pre-processed

models are presented in Table 4.3. In the models without pre-processing (model a)

and treated with MSC + 1st

derivative (model c), there are seven and six PCs,

respectively. These components explain variation of carbon with variances of 61 and

10000 40009000 8000 7000 6000 5000

10

-3

2

-6

-5

-4

-3

-2

-1

0

1

2

cm-1

PC

1 (

70.0

3%

) Loadin

g

10000 40009000 8000 7000 6000 5000

0.33

-0.32-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

cm-1

PC

2 (

58.4

3%

) Loadin

g

a) 2nd derivative

b) 1st derivative

7076

5327

4534

4522 7065

5275

4663

4539

4527

0.30

0.00

0.10

- 0.10

-0.30

0.20

- 0.20

PC

2 (

58.4

3%

)Load

ing

2

-1

0

1

-2

PC

2 (

70.0

3%

)Load

ing

-3

-4

-5

10000 9000 8000 7000 6000 5000 4000

cm-1

88

78%, respectively. However, the last PC was reduced from each model because they

were insignificant in reducing the standard error of prediction.

On the other hand, fitting the model with smooth + 2nd

derivative (model b)

and SNV with/without detrend + 1st derivative (models d and e) required 10 PCs and

higher variances (> 96%) (Table 4.3). All PCs minimised the standard error of

prediction significantly. Thus, in order to compare entire performance of developed

models, this study considered the full number of PCs for all models.

Table 4.3 Number of components and accumulative variances of developed models.

Model

Principle component number (PC)

1 2 3 4 5 6 7 8 9 10

a) without

pre-processing 6.50# 16.37 24.43 35.77 42.67 54.54* 60.68 - - -

b) smooth +

2nd derivative 6.00 29.13 51.68 72.95 86.41 95.64 97.83 99.11 99.44 99.84

c) MSC +

1st derivative 15.46 35.23 51.75 67.68 73.01* 77.84 - - - -

d) SNV, detrend

+ 1st derivative 30.33 43.25 61.87 66.03 71.04 76.58 84.56 91.46 95.24 97.05

e) SNV +

1st derivative 30.45 43.28 62.13 66.95 71.96 77.41 84.90 90.77 94.88 96.64

#variance percentage of validation sets calculated from sqrt%TOC values.

*variance of reduced PCs

Prediction of deep soil carbon from NIR spectra

Predictive models optimised for deep soils are presented in Figure 4.11. The

model obtained without pre-processing is a poor predictive model (model a) due to

noise. Therefore, pre-processing approaches such as weighting, normalisation and

89

baseline correction are needed. The predictive performances of models in which pre-

processing was applied was significantly better (models b - f). For example, except

for model c, calibration models generally showed high predictive ability with high R2

(> 0.97) and small RMSE range (0.003 - 0.015). The model pre-treated by 2nd

derivative (model b) showed a well-fitted relationship with slope close to one,

intercept close to zero, and very high R2 (0.999) (Figure 4.11b). On the other hand,

the model developed by 1st derivative + MSC (model c) provided inferior accuracy

compared to other models (Figure 4.11) (R2 = 0.737, RMSE = 0.048).

The validation set was used to establish the accuracy of the calibration models.

Although the model pre-processed by 2nd

derivative showed very high predictive

abilities in the calibration model, the validation set clearly showed poor model

performances as indicated from R2 and RMSE (model b). Models derived from SNV

+ 1st derivative (models d and e) were more accurate as indicated from RPD and

RPIQ of validation sets. However, the model without detrending (model e) was

slightly superior to the model with detrending (model d) as determined from R2,

RMSE, RPD and RPIQ (Figure 4.11d, e).

As described in the previous section, negative peaks were observed in loading

spectra. Therefore, exclusion of these particular regions could potentially improve the

performance of the predictive models. Unfortunately, the results of this study showed

that intense negative peaks occurred in the same NIR regions as positive peaks

making them difficult to exclude for model development.

From loading spectra and NIR spectra of pure carbon substrates, the band

region of 5300 - 5100 cm-1

seemed to be a less influential band in model processing.

90

Exclusion of several lengths of band in this region were pre-trialled and finally a band

at 5600 - 5000 cm-1

was selected for exclusion. This study considered the

performances of models b, d and e with the exclusion of region 5600 - 5000 cm-1

. The

results showed that all model performances were slightly improved. In particular,

model e gave superior performance but 10 PCs were still obtained. The calibration

and validation sets of this model are depicted in Figure 4.11f.

91

Figure 4.11 NIR prediction of deep soil carbon using calibration and validation data

sets.

y = 1.040x + 0.004

R² = 0.566

RMSE = 0.062

RPD = 1.514

RPIQ = 0.797

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 1.003x

R² = 0.999

RMSE = 0.003

RPD = 31.934

RPIQ = 23.089

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 1.034x + 0.001

R² = 0.737

RMSE = 0.048

RPD = 1.951

RPIQ = 1.359

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 0.911x + 0.001

R² = 0.380

RMSE = 0.052

RPD = 1.286

RPIQ = 0.822

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 0.580x + 0.020

R² = 0.667

RMSE = 0.057

RPD = 1.179

RPIQ = 1.291

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 0.614x + 0.020

R² = 0.516

RMSE = 0.056

RPD = 1.201

RPIQ = 0.761

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

a) non pre-processing

b) smooth + 2nd derivative

c) MSC+ 1st derivative


92

Figure 4.11 (cont.) NIR prediction of deep soil carbon using calibration and validation

data sets.

y = 1.017x + 0.001

R² = 0.973

RMSE = 0.015

RPD = 6.159

RPIQ = 3.986

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 1.020x + 0.001

R² = 0.981

RMSE = 0.013

RPD = 7.309

RPIQ = 4.812

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 1.013x

R² = 0.976

RMSE = 0.015

RPD = 6.493

RPIQ = 3.962

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 0.815x + 0.005

R² = 0.806

RMSE = 0.033

RPD = 2.026

RPIQ = 2.232

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 0.860x + 0.001

R² = 0.836

RMSE = 0.032

RPD = 2.124

RPIQ = 2.097

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

y = 0.578x + 0.020

R² = 0.652

RMSE = 0.058

RPD = 1.168

RPIQ = 2.026

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5

TO

C (

%),

dry

co

mb

ust

ion

Predicted TOC (%)

d) SNV, detrend + 1st derivative

e) SNV+ 1st derivative


f) model e,

5600-5000 cm-1 region excluded

93

4.5 Discussion

The results obtained in this study reveal a number of challenges hampering the

use of IR spectroscopy for qualitative and quantitative analysis of low levels of

organic carbon species in soils.

DRIFT and ATR comparison

Although the ATR technique eliminates the need for diluting samples, this

technique is a poor method to characterise lignin in kaolinite even for high

concentrations (11.55% TOC-L/K). In contrast, KBr-diluted samples scanned by

DRIFT showed some characteristic lignin signals.

In principle, both techniques record the reflectance of IR radiation. However,

there is a significant difference in the degree of penetration of radiation into the

sample. This is important because it contributes to the difference in reflectance

volumes and intensity of bands obtained from both techniques. For ATR, the effective

penetration depth or evanescent wave depends on the ATR crystal type. The ATR

crystal of the instrument used in this experiment was ZnSe, which has an effective

penetration of 1.66 µm (Perkin Elmer 2004), and the smooth surface reflects specula

light. On the other hand, DRIFT measures reflected light from rough surfaces. Light is

partly scattered diffusely and partly penetrates into sample (Khoshesab 2012). The

higher reflectance volume of DRIFT means the low intensity bands are enhanced

(Nguyen et al. 1991).

94

Additionally, the ATR technique exploits a very small sampling volume of

~10-5

ml as estimated by Ge et al. (2014), compared to DRIFT where the volume of

interaction is approximately 6 x 10-2

ml determined by the microcup size used in this

experiment. Therefore, lignin signals could be recorded even when the sample was

diluted with KBr at 1% (w w-1

).

The superiority of DRIFT in terms of accentuated organic carbon signal is also

relevant to quantitative work as shown by the recent study of Ge et al. (2014) who

determined the amount of organic carbon down to 5% TOC by comparing DRIFT and

ATR spectra. The ATR model gave much higher error than DRIFT because ATR

peaks are weak and obscured by the absorption peaks of other chemical groups.

However, the application of ATR may be useful with very small samples that are high

in carbon concentrations such as samples collected from soil fissures and stained root

zones.

Background selection

The degree of kaolinite interference also affected the lignin profile in different

regions of the spectrum. Background of air and KBr were previously compared for

characterisation of Australian soils with ~ 4.4% organic matter. When employing air

as background there was a higher reflectance percentage but organic signals were

distorted compared to sample diluted with KBr (Nguyen et al. 1991).

However, comparing different backgrounds in order to accentuate minor

organic components in soil has not been reported previously. Using only air as a

background, the spectra were saturated with high reflectance of IR, such that no peaks

95

were resolved. On the other hand, diluting the sample with KBr and using KBr as

background, a common technique to reduce absorption of radiation, accentuated

lignin peaks at a concentration of 11.55% TOC-L/K. Further investigation indicated

that KBr is a suitable background for samples with concentrations more than 0.20%

TOC-L/K.

Using kaolinite as a background contributed to the greatest improvement in

lignin bands even at 0.20% TOC-L/K. However, the kaolinite was not a perfect

background due to it still absorbing some light. Nevertheless, using a major

component as a background is an option for characterising low levels of a minor

component in a binary mixture. However, peak shifting was observed because the

background also absorbs some light and bands overlap with carbon peaks. Therefore,

the band shifts could be the result of incomplete background subtraction (Smith

2011).

Peak interference and peak concealment

For binary organic mixtures with kaolinite background, many peaks of the

carbon substrates were obscured, especially in the region of OH and C-O stretching

due to kaolinite partially absorbing in these regions.

However, the organic substrate peaks were not only interfered with kaolinite

absorption but also could be hidden from other types of carbon as shown in the

spectrum of the 4C/K sample (Figure 4.4d). When all four organic substrates were

mixed in the same proportion, the strong signals of cellulose and chitin completely

concealed lignin and humic acid signals. The interferences among carbon substrates

96

are mainly due to different proportions of functional groups in each substrate. For

example, cellulose is 46% TOC, of which roughly 83% is CH2 groups and 17% is CH.

In comparison, lignin contains 9% of carbon as CH3, 10% as CH2 and 19% aliphatic

CH (Shevchenko and Bailey 1996). Therefore, the spectra are manifested by the

dominant functional groups of cellulose, which lead to the concealment of lignin CH2.

Furthermore, the band at 2930 cm-1

, a signature band of lignin, can be

interfered severely from residual water vibrations located on the shoulder of the large

OH band (Tremblay and Gagné 2002). This study indicated that IR spectra are unable

to identify all substrates from their vibrations but exemplify only dominant functional

groups. Therefore, characterisation and identification of low levels of carbon

substrates is very difficult when several types of organic carbon are present.

Sensitivity of NIR to standard samples

Testing the response of NIR to a standard set of carbon/kaolinite samples with

small concentrations of carbon (< 1.50% TOC) has not been demonstrated previously.

The NIR bands of carbon substrates were clearly observed in pure samples.

Furthermore, bands of carbon/kaolinite and a soil sample can be inspected in the NIR

spectra. This study showed that the high sensitivity of NIR, as seen from the

reflectance unit (log 1/R), corresponds very well with concentration levels which

enable a high predictive ability in both calibration and validation sets. Moreover, this

study shows that transformation of values with square root function gives a positive

predictive value, which is recommended for small variable values.

97

Loading spectra and model development

The positive and negative peaks observed nearby the characteristic bands of

pure substrates indicated the important regions for pre-processing the models. These

positive bands are associated with carbon levels such as phenolic compounds (7065,

4539 and 4522 cm-1

) and CH stretching (4663 cm-1

). In contrast, negative bands

indicated interferences such as OH stretching (7070, 4534 and 4527 cm-1

) and H2O

deformation (5327 and 5275 cm-1

) from kaolinite (Figure 4.10).

When bands over the region of 5300 - 5100 cm-1

were excluded for model pre-

processing, this slightly improved the model. This diagnostic study demonstrated the

importance of identifying noise from loading spectra for model improvement. The

H2O deformation band leads to a poor prediction of carbon concentration model,

which has also been noted by Wight et al. (2016). However, this study found that

exclusion of interference bands cannot reduce the number of PCs. It is possible that

all PCs are important in model development because noise was observed throughout

the NIR region.

Distribution of data sets and model development

Asymmetric distribution of the data sets was demonstrated by positive

skewness. The positive skewness pattern of data typically occurs if there are very

small or zero values as is the case for soil carbon concentrations (Baldock et al. 2013;

Li et al. 2015).

Although the calibration models pre-processed by several approaches show

very high R2, the validation set proves that models optimised by 2

nd derivative are

98

over-fitted. The NIR spectral resolution could be improved by applying a derivative.

However, it should be noted that the noise is also increased which could be clearly

observed from the loading magnitude in PC1 of 2nd

derivative loading spectra.

Therefore, 1st derivative is the superior baseline correction for deep soil NIR spectra

compared to 2nd

derivative.

The 1st derivative pre-treated models combined with MSC and SNV provide

accurate predictions according to model evaluation criteria proposed by Chang and

Laird (2002), even though they differ in model performance. The models pre-treated

by SNV minimise not only the RMSE but also give the most linear relationship.

Generally, many baseline correction approaches proposed for elimination of the

multiplicative scatter effect, contribute to similar shape of spectra but different slope,

due to diversity in soil particle size (Barnes et al. 1989).

Several studies have found that MSC performs very well in validation

attributes but often SNV is as good as MSC (Mouazen et al. 2007; Reeves et al.

2006). Mathematically, the SNV corrects each spectrum individually and applies

baseline or detrend optionally depending on the nature of the sample. Detrending is

applied when there is baseline shift due to powdery and packed sample (Barnes et al.

1989). In this case, the model performed well without detrending. The MSC accounts

for baseline effects and corrects the spectrum base on the mean spectrum (Dhanoa et

al. 1994). The validation sets prove that applying the SNV and excluding the 5600 -

5000 cm-1

region is suitable in the case of deep soil carbon.

99

4.6 Conclusions

This study of SOC using MIR found DRIFT is a more sensitive technique than

ATR for characterisation of organic components in kaolinite. Bands of carbon

substrates can be accentuated by using kaolinite as a background, although some

bands were still obscured. Vibrational bands of lignin and humic acid were detectable

at 1.92% TOC and cellulose and chitin at 1.00% TOC, and these concentration levels

can be considered as the LODs of the DRIFT technique. However, cellulose and

chitin absorptions masked lignin and humic acid bands when these substrates were

mixed at the same proportion. The interferences from cellulose and chitin bands were

diminished at lower concentrations. However, the concealing effect only disappeared

when the concentration of lignin was ~20 times that of cellulose and chitin

concentrations. The identification of organic structures from MIR is possible if a

single substrate is mixed with kaolinite but difficult in a mixture of several organic

substrates due to their peaks overlapping.

The sensitivity of NIR spectroscopy was investigated with a standard set of

samples with concentrations between 0.00 - 1.50% TOC. Positive prediction values

were obtained when using square root of %TOC as input data sets. Consequently, an

NIR model for estimation of deep soil carbon was developed from the 1st derivative

and SNV pre-processing approaches with exclusion of band 5600 - 5000 cm-1

. The

LOD obtained for this model was 0.001% TOC and the predictive equation

[TOC(%)actual = 1.020(TOC (%)predicted) + 0.001], (R2 = 0.981) was derived from the

calibration set.

101

Chapter 5

Identification of Low Molecular Weight Compounds

Associated with Deep Soil Carbon

5.1 Abstract

Residual carbon in the form of LMWC was characterised and quantified from

three deep soil profiles to a depth of 19 m. A protocol was developed for investigating

LMWC qualitatively and quantitatively for a whole profile based on solvent

extraction with ethyl acetate and analysis by GC/MS with an internal standard. The

concentration of LMWC was determined to be in the range 3.15 - 14.27 µg C g soil-1

.

Three compound classes were typically observed from samples from across three

different field locations: 1) terpenes, 2) fatty acids, amides and alcohols, and 3) plant

steroids; indicating the influence of above ground input and roots of the past and

present vegetation. Compounds related to fatty acids such as (Z)-docos-13-enamide,

(9Z)-9-octadecenenitrile, (9Z)-9-octadecenamide, hexadecan-1-ol and (9E)-9-

hexadecen-1-ol were the predominant residual carbon species in deep soils, which

may be derived from plants and microorganisms. In conclusion, (Z)-docos-13-

enamide and bis(6-methylheptyl) phthalate were the main components throughout the

soil profiles representing 53 - 81% of the LMWC, particularly at depths of 18 - 19 m.

102

5.2 Introduction

The study of LMWC will provide an understanding of the sources of SOM,

microbial activity and the pathways of degradation and stabilisation of SOM (Bull et

al. 2000; Feng and Simpson 2007). This will be of particular importance where this

carbon occurs within deep soils, because at present little is know about the

distribution or dynamics of this material.

For instance, components such as aliphatic lipids, steroids, terpenoids,

glycerols and carbohydrates (mono- and disaccharides), extracted sequentially by a

series of dichloromethane and methanol solvent mixtures, were used to study SOM

biomarkers such as plant waxes, bacteria and fungi (Otto and Simpson 2007). In

strongly podsolized laterites, an abundance of free lipids has been interpreted to

indicate the conditions of the area such as combinations of acidity and waterlogging,

complex of elements (Fe, Al and Si) and decreases in microbial activity (Bardy et al.

2008). Furthermore, the water repellency of soil under pine and eucalypt is partly

attributed to lipids and alkanes, which can be extracted with petroleum ether (de Blas

et al. 2013).

However, despite organic carbon occurring in soil profiles to depths of up to

37 m, the LMWC in this OC have not been characterised and quantified. Although

appropriate protocols exist for topsoils and subsoils (e.g. Almendros et al. 1996;

Franco et al. 2000; Otto and Simpson 2007), revised protocols for samples that

contain low carbon concentration are required.

103

This chapter thus aims to:

(i) select a suitable organic solvent for detecting a range of LMWC,

(ii) observe and identify the LMWC in soil profiles from three field

locations, and

(iii) quantify LWMC in deep soil profiles of south-western Australia.


6.


Experiment 5.1: Organic solvent selection

Sample

The samples employed for characterisation were obtained from the study of

Harper and Tibbett (2013). Briefly, the samples were taken from the wheat-belt in

south-western Australia. Vegetation prior to agriculture was deep rooted native plants

which had been cleared 50 - 80 years ago and replaced by shallow rooted annual

plants such as cereal crops or annual pastures. The samples had been stored dry at

room temperature in plastic bags prior to analysis.

Preliminary samples from location CU 03 at depths of 0 - 0.1 (4.68% TOC), 8

- 9 (0.02% TOC), 18 - 19 (0.02% TOC) and 27 - 28 m (0.01% TOC) (35º 20′ 56″ S,

4º 33′ 29″ E) were tested in order to select the appropriate solvent and internal

standard for quantification work. Drill site CU 03 was selected because it had enough

104

samples and material for a pre-trial and for optimising procedures. The depth of 28 -

29 m was the maximum depth of this location that was chosen for characterisation.

Extraction procedure of organic solvent selection

There is no universal technique for the extraction of LMWC, therefore it is

necessary to use organic solvents of different polarity to investigate them. Four

organic solvents with increasing polarity index (P) were chosen for testing extraction

potential; hexane (P = 0.1), dichloromethane (P = 3.1), ethyl acetate (P = 4.4) and

methanol (P = 5.1). Deep soils contain relatively low concentrations (0.01 - 0.02%

TOC) of organic carbon. Therefore, preliminary experiments were performed to

determine the minimum weight of soil needed for extraction of measurable amounts

of carbon. The experiments showed that informative chromatograms could be

obtained with extraction from 50 g samples with 0.01 - 0.02% TOC using 150 ml of

organic solvents. For soil at 0 - 0.1 m, 1 g samples were used with 15 ml of organic

solvents.

Samples were weighed into Erlenmeyer flasks, organic solvents were added

and the flasks closed by glass stoppers. The extraction was carried out for 2 hours

using a flask shaker. The samples were then filtered through glass fibre filters

(Whatman GF/A) and the solutions transferred to 100 ml round bottom flasks (or 50

ml size in case of 0 - 0.1 m soil). The solution was concentrated using a rotary

evaporator, with water bath temperatures of 60 - 65 °C for hexane, ethyl acetate and

methanol and 30 - 35 °C for dichloromethane, until the solution was almost dried to

approximately 1 ml. Then, the solution was transferred to a 5 ml round bottom flask

105

and concentrated until dried. Samples extracted with hexane and dichloromethane

were redissolved with methanol for GC/MS analysis. Methanol and ethyl acetate were

chosen for reconstitution because the boiling points are close to the injection

temperature and are volatile before compounds of interest were separated. Once the

most suitable organic solvent was selected, the identification and quantification of

LMWC was carried out for other locations. Samples were prepared in duplicate for

each organic solvent.

Analytical gas chromatography/mass spectrometry

Samples were analyzed by capillary GC on a Shimadzu GC 2010. The column

used for GC separation was 30 m in length, 0.25 µm in diameter and 0.25 µm thick

(SGE BPX5) and helium was used as the carrier gas. Conditions were used as

follows: the column oven temperature was set to 60 ºC and injection temperature was

310 ºC using splitless injection mode. The column was heated to 60 ºC for 1 min then

ramped at 30 ºC min-1

to 100 ºC, held at this temperature for 1 min before heating at a

rate of 4 ºC min-1

to a final temperature of 300 ºC then held at this temperature for 5

min. Each sample was injected in 3 replicates and compound identification was

achieved by comparison with the National Institute of Standards and Technology

(NIST) database with a threshold match of > 80%.

106

Experiment 5.2: Identification and quantification of LWMCs

Sample and quantification analysis

Samples from three field sites located in south-western Australia were

analysed namely GL03 (36º 46′ 47″ S, 4º 59′ 9″ E), PT06 (34º 50′ 11″ S, 4º 29′ 51″ E)

and ST04 (36º 12′ 9″ S, 4º 15′ 9″ E). The samples were from the study of Harper and

Tibbett (2013). Climate and land use history were reported previously by Harper and

Tibbett (2013). One profile from each location was selected as representative of the

broad study area undertaken by Harper and Tibbett (2013). Three depths were

investigated as representative of the deep profiles. Carbon concentrations of each

profile at depths of 0 - 1, 11 - 12 and 18 - 19 m were 0.55, 0.05, 0.04% TOC (GL03);

0.17, 0.04, 0.03% TOC (PT06) and 0.16, 0.04, 0.04% TOC (ST04). Samples of 50 g

soil were extracted with 150 ml of ethyl acetate and concentrated in a rotary

evaporator as described in Experiment 5.1. In the final step, the sample was

redissolved with 1 ml of internal standard, n-decane in ethyl acetate, before analysis

by GC/MS and identification of compounds. These samples were prepared in

duplicate.

Quantification of compounds was achieved by comparing peak area of a

selected compound to the peak area of an internal standard of known concentration.

The response factor was assumed to be 1 for all compounds. Concentration of

compounds was then normalized to sample weight before the carbon concentration of

each compound was determined.

107

Quality control and reproducibility of procedures

A soil reference sample was prepared by mixing 100 g of sample CU03 at

depth increment of 12 - 13 to 27 - 28 m. The extraction procedure efficiency was

tested by measuring the recovery of a spiked commercial standard, (Z)-docos-13-

enamide from this sample. An average percentage recovery of 95% (SD = 7, n = 3)

was obtained. Furthermore, this reference sample was re-analysed at the same time as

the field samples in order to monitor the quality and reproducibility of the procedure.

5.4 Results

Experiment 5.1: Organic solvent selection

Chromatogram pattern

The main considerations for selecting a suitable solvent for extraction were (a)

the solvent should provide high solubility and compatibility with analytes of interest,

and (b) the solvent should lead to a well separated chromatogram. Chromatograms

obtained from extractions with different solvents are shown in Figures 5.1, 5.2, 5.3

and 5.4. It is clear that there were significant differences between the chromatograms,

related to compatibilities between solvent and LMWC and GC separation efficiencies.

For example, at 0 - 0.1 m, hexane and ethyl acetate were found to be the best solvents

and produced well separated peaks compared to dichloromethane and methanol

(Figure 5.1a, c and Figure 5.1b, d). Unfortunately, hexane chromatograms obtained

from deeper layers gave intense peaks for only a small number of compounds which

108

were also difficult to interpret (Figures 5.2a, 5.3a and 5.4a). In comparison, better

chromatograms were derived from dichloromethane, ethyl acetate and methanol

extractions of deep soils (Figures 5.2b, c, d, 5.3b, c, d and 5.4b, c, d). Consequently,

characterisation of LMWC for the whole profile can be achieved from different

groups of solvents.

Classification of LMWC

Compounds identified from each extract are depicted in Table 5.1. A wide

variety of compound classes was observed from these extractions. Compound classes

and percentage of each are summarised in Table 5.2. At the first meter depth, the

hexane and ethyl acetate extracts exhibited a wider variety of compounds compared to

the dichloromethane and methanol extracts. Alkanes and alkenes were the dominant

groups derived from hexane extraction, while phenols were mainly obtained from

ethyl acetate (Table 5.2). However, both solvents extracted terpenes in about the same

proportions (22 and 28%, respectively). Hexane is an apolar solvent that cannot

extract medium polar compounds in deep soils. In deep soils, dichloromethane, ethyl

acetate and methanol generally exhibited good performance for extraction.

Nevertheless, the relative proportions of each compound class depended on

the polarity of each solvent and the different solvents, apart from hexane, are

complementary to each other. For example, ethyl acetate extracted benzenes, whereas

this was absent in the dichloromethane and methanol extracts (Table 5.2).

Furthermore, steroids could be obtained from both ethyl acetate and dichloromethane

but were not detectable from the methanol extract (Table 5.2).

109

Key compounds identified from all solvents, including terpenes, fatty acids

and plant sterols, correspond to chemical species representative of vegetation and

microbial contributions. However, ethyl acetate was found to be the most appropriate

solvent because it produced well separated peaks in the chromatograms throughout all

soil profiles which could be further investigated for any change in compound

distribution and quantification.

110

Figure 5.1 GC-MS chromatograms of CU 03 soil at depth of 0 - 0.1 m extracted with

a) hexane, b) dichloromethane, c) ethyl acetate and d) methanol. For peak

identifications refer to Table 5.1.

4 6 8 10 12 14 16 18 20 22 24 26

0

2

4

6

8

10

12x 10

6

4 6 8 10 12 14 16 18 20

0

2

4

6x 10

6

4 6 8 10 12 14 16 18 20 22 24

0

1

2

3

4x 10

6

4 6 8 10 12 14 16 18 20 22 24

0

3

6

9x 10

6

Retention time (min)

Abundan

ce

a) hexane

b) dichloromethane

c) ethyl acetate

d) methanol

3

5 10 18

24

26

28 34

35

39

44,45,

47,49

51

53

67

66 61

60

58,

59 68

70

82

3

20 82

3

5 8

12 25

27

29

37

58

62 75

69

82 85 94

96 98

103 106

3

82

111

Figure 5.2 GC-MS chromatograms of CU 03 soil at depth of 8 - 9 m extracted with a)

hexane, b) dichloromethane, c) ethyl acetate and d) methanol. For peak identifications

refer to Table 5.1.

0 10 20 30 40 50 60

0

2

4

6

8x 10

7

0 10 20 30 40 50 60

0

2

4

6

8x 10

6

0 10 20 30 40 50 60

0

4

8

12

16x 10

6

0 10 20 30 40 50 60

0

0.5

1

1.5

2

2.5x 10

7


Ab

un

dan

ce

a) hexane

b) dichloromethane

c) ethyl acetate

d) methanol

4 16

31+

32 36 55

85 90 102

110

116

114

123

118

122

150

3

27 36 69 84

94

100+

101

106

103

125

113

117 131

133 138

140

142

146

17

33

43

55

64

102

110

114

116

121

122

132 133

136

139+

140

146

150

112

Figure 5.3 GC-MS chromatograms of CU 03 soil at depth of 18 - 19 m extracted with




Ab

un

dan

ce

a) hexane

b) dichloromethane

c) ethyl acetate

d) methanol

0 10 20 30 40 50 60

0

1

2

3

4x 10

7

0 10 20 30 40 50 60

0

1

2

3

4x 10

6

0 10 20 30 40 50 60

0

0.5

1

1.5

2x 10

7

0 10 20 30 40 50 60

0

0.5

1

1.5

2

2.5x 10

7

16

43

69

6

42

4

90,93,99,

102,104,

105

110

114

139 146

12

122

136

4

55,60,64,

72,79,85,88

25

31

2

130 125 3

130

94

140

123

74,

77,

78,

79

131

150

84,

88,

89,

91

98,101,103,

106,107,

109,113,114

140

141

11 28

4 16

63

97

42

90

114

122 111

118 108

120

123

131 136

132 145

146

150

a) hexane

b) dichloromethane

c) ethyl acetate

d) methanol

113

Figure 5.4 GC-MS chromatograms of CU 03 soil at depth of 28 - 29 m extracted with



0 10 20 30 40 50 60

0

0.5

1

1.5

2x 10

7

0 10 20 30 40 50 60

0

2

4

6x 10

7

0 10 20 30 40 50 60

0

2

4

6

8x 10

6

0 10 20 30 40 50 60

0

1

2

3x 10

7


Ab

un

dan

ce

a) hexane

b) dichloromethane

c) ethyl acetate

d) methanol

4 43

4

31

122

114

118

2

3,4,6,8,

12,19,

22,23,

25

1

140

27,

29

1

3

64 55 73 68

110,

112

91 102 123

130

132

135+

136

140

146

150

39

40 69 85 91

94

103

106

113,

114

125 130

142

146

151

74,

77,

78,

79

87,

88,

89

98,

100,

101

107,

109,

111

32

16

42

57

63 90 108

111

114

118

120

122

123

131 139 145

146 150

114

Table 5.1 List of compounds and area percentage identified from 4 organic solvent extractions for location CU03 at 4 depths (n = 2).

No.

RT

(min) Compound

0 - 0.1 m 9 - 10 m 18 - 19 m 28 - 29 m

H* D E M H D E M H D E M H D E M

1 3.58 1,2,4-Trimethylbenzene - - - - ? - - - ? - - - ? - 3.5 -

2 4.51 2-Hexyldecan-1-ol - - - - ? - - - - - 8.4 - - - 8.1 -

3 4.65 Phenol 1.7 77.0 27.0 92.1 - - 12.3 - ? - 3.1 - ? - 1.9 -

4 5.08 2-Ethylhexan-1-ol - - - - - 1.0 - - ? 0.8 2.3 0.4 ? 1.2 1.8 -

5 5.20 β-Phellandrene 3.1 - 3.7 - - - - - - - - - - - - -

6 5.61 o-Cresol - - - - - - - - - - 0.4 - - - 1.0 -

7 5.67 Phenyl acetate - - - - - - 0.3 - - - - - - - - -

8 6.08 1-Undecene - - 2.6 - - - - - - - - - - - 4.3 -

10 6.58 6-Methylheptan-1-ol 0.8 - - - - - - - - - - - - - - -

12 6.79 1,2,3,4-Tetramethylbenzene - - 1.0 - - - 0.2 - - - 1.0 - - - 1.8 -

16 6.89 Dodecan-1-ol - - - - - 0.7 - - - 0.9 - 0.4 - 0.7 - 0.6

17 6.91 Dodec-1-ene - - - - - - - 0.2 - - - - - - - -

18 7.15 6-Methyloctan-1-ol 2.0 - - - - - - - - - - - - - - -

19 7.18 3-Ethylphenol - - - - - - 0.2 - - - - - - - 0.4 -

20 7.26 2-Ethylphenol - 1.1 - - - - - - - - - - - - - -

21 7.43 p-Cumenol - - - - - - - - - - - 0.3 - - - -

22 7.50 1,2,3,4-Tetramethyl-5-methylene-1,3-

cyclopentadiene

- - - - - - - - - - - - - - 0.6 -

23 7.64 2,3-Dihydro-1-benzofuran - - - - - - - - - - - - - - 0.3 -

24 7.79 Nonan-1-ol 0.9 - - - - - - - - - - - - - - -

25 7.91 1-(2-Hydroxyphenyl)ethanone - - 2.4 - - - - - - - 2.2 - - - 0.7 -

26 8.06 Tetradecane 1.9 - - - - - - - - - - - - - - -

27 8.22 Dodecane - - 3.9 - - - 0.2 - - - 0.3 - - - 5.1 -

28 8.48 2,6-Dimethylundecane 0.7 - - - - - - - - - - - - - - -

29 8.54 Naphthalene - - 0.6 - - - - - - - - - - - 0.7

31 9.25 (3E)-3-Tetradecene - - - - - 1.0 - - - 0.9 - - - 1.2 - -

115

32 9.32 1-Tridecene - - - - - 0.5 - - - - - 0.9 - - - 1.9

33 9.41 2-Butyloctan-1-ol - - - - - - - 0.8 - - - - - - - -

34 9.80 2,3-Dimethylundecane 0.7 - - - - - - - - - - - - - - -

35 9.96 4,6-Dimethyldodecane 0.9 - - - - - - - - - - - - - - -

36 10.19 Methyl decanoate - - - - - 0.6 0.3 - - - - - - - - -

37 10.35 γ-Elemene - - 4.5 - - - - - - - - - - - - -

39 10.77 Tridecane 3.7 - - - - - - - - - - - - - 6.0 -

40 11.38 1-Methylnaphthalene - - - - - - - - - - - - - - 0.2 -

42 11.84 1-Tetradecene - - - - - - - - - - - 1.4 - - - 3.2

43 11.87 3-Hexadecene - - - - - - - 1.8 - 0.6 - - - 0.8 - -

44 12.19 Decan-4-ylcyclohexane 3.2 - - - - - - - - - - - - - - -

45 12.36 10-Methylnonadecane 0.5 - - - - - - - - - - - - - - -

47 12.53 10-Methylicosane 11.4 - - - - - - - - - - - - - - -

49 12.72 2-Methylnonadecane 1.2 - - - - - - - - - - - - - - -

51 13.11 (12E)-11-Methyl-12-tetradecen-1-yl

acetate

0.6 - - - - - - - - - - - - - - -

53 13.60 Tetradecane 0.4 - - - - - - - - - - - - - - -

55 14.09 2,5-Bis(2-methyl-2-propyl)-1,4-

benzoquinone

- - - - - 0.5 - 0.1 - 0.4 - - - 0.1 - -

57 14.65 9-Octadecene - - - - - - - - - - - - - - - 0.2

58 15.05 β-Aromadendrene 1.0 - 1.6 - - - - - - - - - - - - -

59 15.14 (3β,4α,5α)-4-Methylcholesta-8,24-

dien-3-ol

0.8 - - - - - - - - - - - - - - -

60 15.24 2,6,10,14-Tetramethylhexadecane 2.9 - - - - - - - - 0.1 - - - - - -

61 15.61 Tridecan-3-yl 2-methoxyacetate 2.7 - - - - - - - - - - - - - - -

62 15.69 Isoaromadendrene - - 0.5 - - - - - - - - - - - - -

63 15.71 Methyl laurate - - - - - - - - - - - 0.5 - - - 1.2

64 15.79 Methyl 15-methylhexadecanoate - - - - - - - 1.9 - 0.3 - - - 0.2 - -

66 16.12 Cedrene 8.5 - - - - - - - - - - - - - - -

67 16.48 Pentadecane 0.2 - - - - - - - - - - - - - - -

116

68 16.94 Epiglobulol 1.7 - - - - - - - - - - - - - - -

69 17.00 2,4-Ditert-butylphenol - - 3.7 - - - 0.4 - - - 0.7 - - - 0.4 -

70 17.18 Ethyl iso-allocholate 0.9 - - - - - - - - - - - - - - -

72 17.37 1-o-(2-Methylpropyl) 4-o-propan-2-yl

2,2-dimethyl-3-propan-2-

ylbutanedioate

- - - - - - - - - 0.1 - - - - - -

73 17.41 [2,4,4,-Trimethyl-1-(2-

methylpropanoyloxy)pentan-3-yl] 2-

methylpropanoate

- - - - - - - - - - - - - 0.1 - -

74 17.56 Decan-5-ylbenzene - - - - - - 0.2 - - - 0.3 - - - 0.2 -

75 17.64 Ledol - - 0.1 - - - - - - - - - - - - -

77 17.85 4-Decanylbenzene - - - - - - 0.2 - - - 0.3 - - - 0.2 -

78 18.09 5-Hydroxy-7-methoxy-2-methyl-4H-

chromen-4-one

- - - - - - - - - - 0.8 - - - 0.3 -

79 18.43 3-Decylbenzene - - - - - - 0.3 - - - 0.5 - - - 0.3 -

81 19.33 Hexadecane - - - - - - 0.2 - - - - - - - - -

82 19.43 α-Selinene 0.9 1.6 1.8 0.7 - - - - - - - - - - - -

84 19.52 Undecan-2-ylbenzene - - - - - - 0.7 - - - 0.9 - - - 0.6 -

85 19.63 β-Eudesmol - - 0.1 - - 0.3 - - - 0.2 - - - 0.2 - -


88 20.35 Undecan-5-ylbenzene - - - - - - 1.5 - - 0.2 1.1 - - - 0.8 -


90 21.22 Methyl tetradecanoate - - - - - 0.8 - - - 0.7 - 1.1 - - - 1.1

91 21.27 Methyl heneicosanoate - - - - - - - - - - 1.6 - - 0.2 1.1 -

93 22.12 2,6,10,15-Tetramethylheptadecane - - - - - - 0.3 - - 0.2 0.3 - - - - -

94 22.36 Undecan-2-ylbenzene - - 1.0 - - - 2.9 - - - 2.9 - - - 2.3 -

96 22.64 α-Santalol - - 0.2 - - - - - - - - - - - - -

97 22.86 (E)-Icos-9-ene - - - - - - - - - - - 0.3 - - - -

98 22.93 6-Dodecanylbenzene - - 0.3 - - - 0.9 - - - 0.9 - - - 0.7 -

99 23.01 Heptacosane - - - - - - - - - 0.3 - - - - - -

117

100 23.05 Dodecan-5-ylbenzene - - 0.3 - - - 1.0 - - - - - - - 0.8 -

101 23.41 Dodecan-4-ylbenzene - - 0.3 - - - 1.0 - - - 1.0 - - - 0.9 -

102 23.76 Isopropyl myristate - - - - - 1.2 - 0.5 - 0.4 - - - 0.3 - -


104 24.24 6,10,14-Trimethyl-2-pentadecanone - - - - - - - - - 0.3 - - - - - -

105 24.80 Nonadecane - - - - - - 0.3 - - 0.1 - - - - - -


107 25.71 Tridecan-5-ylbenzene - - - - - - 1.0 - - - 0.7 - - - 0.5 -

108 25.84 Methyl (Z)-7-hexadecenoate - - - - - - - - - - - 0.1 - - - 0.2


110 26.37 Methyl 14-methylpentadecanoate - - - - - 3.1 - 9.4 - 2.1 - - - 1.6 - -

111 26.40 Methyl palmitate - - - - - - - - - - - 5.8 - - - 6.8

112 26.57 Methyl-3-[4-hydroxy-3,5-bis(2-

methyl-2-propanyl)propanoate

- - - - - - - - - - - - - 1.0 - -


114 27.34 Tritetracontane - - - - - 12.7 - 6.9 - 14.4 0.4 2.1 - 7.1 0.3 2.7

116 28.67 Isopropyl palmitate - - - - - 0.6 - 0.7 - - - - - - - -

117 29.07 1-Docosanol - - - - - - 2.6 - - - - - - - - -

118 30.15 Hexadecanol - - - - - 0.6 - - - - - 0.9 - 1.0 - 1.2

120 30.36 Methyl 9,12-octadecadienoate - - - - - - - - - - - 2.1 - - - 2.2

121 30.39 (4Z,13Z)-4,13-Octadecadien-1-ol - - - - - - - 3.4 - - - - - - - -

122 30.49 Methyl 9-Octadecenoate - - - - - 1.7 - 12.8 - 1.9 - 6.3 - 1.9 - 8.3

123 31.10 Methyl stearate - - - - - 0.9 - - - 0.9 - 1.7 - 1.0 - 1.6

125 32.00 Octadecan-1-ol - - - - - - 2.3 - - - 1.9 - - - 1.9 -

130 34.70 Octadecyl acetate - - - - - - 4.0 - 0.3 2.6 - - 0.8 2.1 -

131 34.86 Ethyl (Z,12R)-12-hydroxyoctadec-9-

enoate

- - - - - - - 1.2 - - - 1.7 - - - 1.0

132 36.64 1-Hexacosene - - - - - - 0.4 0.6 - - - 0.5 - 0.2 - -

135 38.87 (9Z)-9-octadecenenitrile (Oleanitrile) 3.0 0.9 0.4 2.3 0.5 1.2 11.7 0.5 2.9

136 39.00 Stearaldehyde - - - - - 1.1 - - - 1.0 - 0.7 - 3.9 0.3 2.1

118

138 40.49 9-Octadecenamide - - - - - 0.1 - - - - - - - - -

139 40.61 9-Icosanylcyclohexane - - - - - - - 0.8 - 0.4 - - - - - 0.8

140 41.49 2-(2-Ethylhexoxycarbonyl)benzoic

acid

- - - - - - 11.8 - - 0.3 7.7 - - 6.3 7.4 -

141 42.06 Ethyl (13Z)-13-docosenoate - - - - - - 0.6 - - - 0.1- - - - 0.2 -

142 42.06 (9Z)-9-Octadecenoic acid - - - - - - 0.6 - - - 0.1 - - - 0.2 -

145 42.91 9-Octadecanone - - - - - - - - - - - 1.1 - - - 1.2

146 44.22 13-Docosenamide - - - - - - 3.4 6.4 - 0.6 - 5.4 - 0.6 3.0 17.5

150 57.05 4,4'-{[4-Hydroxy-5-(2-methyl-2-

propanyl)-1,3-

phenylene]bis(methylene)}bis[2,6-

bis(2-methyl-2-propanyl)phenol]**

- - - - - 8.8 - 3.7 - 18.0 - 13.1 - 18.4 - 6.5

151 57.28 β-Sitosterol - - - - - - - - - - - - - 0.3 0.4 -

Total number of compounds 28 3 21 2 0 18 36 18 3 27 33 22 3 23 43 20

*H = hexane, D = dichloromethane, E = ethyl acetate and M = methanol, ** Tentative identification with 70% match to the NIST database

119

Table 5.2 Percentage (%) of compound class of each solvent extraction determined from area percentage (n = 2).

Compound class

0 - 0.1 m 9 - 10 m 18 - 19 m 28 - 29 m

H D E M H D E M H D E M H D E M

Acids 6 0 0 0 0 16 9 48 0 11 8 24 0 8 5 20

Alcohols 7 0 0 0 0 6 8 10 0 3 25 3 0 5 18 3

Alkanes/alkenes 52 0 12 0 0 36 2 20 0 35 2 11 0 15 23 14

Aromatics (other) 0 0 1 0 0 1 20 0 0 2 16 0 0 11 13 0

Benzenes 0 0 7 0 0 0 30 0 0 0 34 0 0 0 27 0

Fatty acids and amides 0 0 0 0 0 18 9 15 0 12 2 34 0 28 6 53

Phenols 4 98 58 99 0 22 22 7 0 37 13 28 0 32 7 10

Steroids 3 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

Terpenes 28 2 22 1 0 1 0 0 0 0 0 0 0 0 0 0

*H = hexane, D = dichloromethane, E = ethyl acetate and M = methanol

120

Experiment 5.2: Identification of LMWCs in deep soil profiles

Compound identification

Generally, the diversity of compounds declined with depth. However, 3 main

compound classes were identified in the chromatograms 1) terpenes (RT = 6.70 -

21.74 min), 2) fatty acids, amide and alcohol of fatty acids (RT = 22.42 - 44.22 min)

and 3) bioactive compounds and plant sterols (RT = 54.49 - 58.12 min) (Table 5.3).

Chromatograms obtained from profiles GL03, PT06 and ST04 (Figures 5.5,

5.6 and 5.7, respectively) revealed small amounts of terpenes only in the depth of 0 -

1 m. Several terpenes were common between locations such as camphor (9) in

location GL03 and ST04; aromadendrene (46) and epiglobulol (68) in PT06 and

ST04; and α-selinene (82) and cubenol (83) in GL03 and PT06. However, other

terpenes were only detected at a single location. For example, borneol (14) from

GL03, patchoulane (38), globulol (76) and aromadendrene oxide (86) from PT06 and

isothujol (11), isoborneol (15), germacrene (52) and β-eudesmol (85) from ST04.

Fatty acids were commonly observed in all chromatograms (Figures 5.5, 5.6

and 5.7). However, these could be divided into 2 groups according to their

occurrence. The first group were fatty acids such as hexadecanoic acid (115), (Z)-

octadec-9-enoic acid (124) and octadecanoic acid (126) which were commonly

observed in 0 - 1 m. The second group consisted of alcohols, amides and esters of

fatty acids, which were observed throughout the profiles. For example, hexadecan-1-

ol (118), (9E)-9-hexadecen-1-ol (119), (9Z)-9-octadecenamide (138) and (Z)-docos-

13-enamide (146). Furthermore, bis (6-methylheptyl)phthalate (137) was also

121

observed in all depths. In contrast, nitrogen containing compounds such as (9Z)-9-

otadecenenitrile (oleanitrile) (135) were only observed in deeper layers (11-12 and

18-19 m) (Table 5.3).

Steroids, such as pollinastanol (148) and cholesterols (129, 149, 152), were

observed at 0 - 1 m. Another steroid, 4,4'-{[4-Hydroxy-5-(2-methyl-2-propanyl)-1,3-

phenylene]bis(methylene)}bis[2,6-bis(2-methyl-2-propanyl)phenol] (150) was found

throughout most profiles and locations but with only a 70% match to the database

(Table 5.3), so this is only a tentative identification.

122

Figure 5.5 Chromatograms of LMWC of GL 03 profile analysed by GC-MS at depths

of a) 0 - 1 m and b) 10 - 11 m and c) 18 - 19 m. For peak identifications refer to Table

5.3.

0 10 20 30 40 50 60

0

2

4

6

8

10

12

14x 10

6

0 10 20 30 40 50 60

0

2

4

6

8

10

12

14

16x 10

60 10 20 30 40 50 60

0

2

4

6

8

10

12

14

16x 10

6

Retention time

Ab

un

dan

ce

9,

14 30

41

65 83

95

115 118

126

124

137

146

149 147

150

IS

IS

IS

32

65

118

137

146

150

54

135

65

118

137

146

150

54

135

a) 0 - 1 m

b) 10 - 11 m

c) 18 - 19 m

114

138

138

139

123

Figure 5.6 Chromatograms of LMWC of PT 06 profile assigned by GC-MS at depths

of a) 0 - 1 m, b) 10 – 11 m and c) 18 - 19 m. For peak identifications refer to Table

5.3.

0 10 20 30 40 50 60

0

2

4

6

8

10

12

14x 10

6

0 10 20 30 40 50 60

0

2

4

6

8

10x 10

6

0 10 20 30 40 50 60

0

2

4

6

8

10

12x 10

6

Retention time

Abundan

ce

a) 0 - 1 m

b) 10 - 11 m

c) 18 - 19 m

137

146

150

135

138 119

IS

IS

IS

137 135

138

133

119

128,

129

146

150

30

70

76 115 126

134 128

137

146

148

152 124

119 82,

86 65

38,

46 13

124

Figure 5.7 Chromatograms of LMWC of ST 04 profile assigned by GC-MS at depths

of a) 0 - 1 m, b) 10 – 11 m and c) 18 - 19 m. For peak identifications refer to Table

5.3.

0 10 20 30 40 50 60

0

1

2

3

4x 10

6

0 10 20 30 40 50 60

0

2

4

6x 10

6

0 10 20 30 40 50 60

0

2

4

6x 10

6

Retention time

Abundan

ce

a) 0 - 1 m

b) 10 - 11 m

c) 18 - 19 m

146

146

146

152

152

IS

IS

IS

138

138

137

137

137

135

135

133

133

133

119

119

119

127

127 71 32

72 56

126,

128 136

115 143

152

150

92

85

75

80 68

52

37

46,

48,

50

9,

11,

15

125

Table 5.3 Concentration of LMWC (µg C g soil-1

) for location GL03, PT06 and ST04 at 3 depths (n = 2).

No. RT

(min) Compound

GL03 PT06 ST04

0 - 1

m

11 - 12

m

18 - 19

m

0 - 1

m

11 - 12

m

18 - 19

m

0 - 1

m

11 - 12

m

18 - 19

m

9 6.40 Camphor 0.02 - - - - - 0.02 - -

11 6.70 Isothujol - - - - - - 0.02 - -

13 6.83 Octanoic acid - - - 0.07 - - - - -

14 6.86 Borneol 0.10 - - - - - - - -

15 6.89 Isoborneol - - - - - - 0.09 - -

30 9.10 Nonanoic acid 0.27 - - 0.22 - - - - -

32 9.32 Tridecene - 0.03 - - - - - - 0.06

37 10.35 γ-Elemene - - - - - - 0.04 - -

38 10.36 Patchoulane - - - 0.04 - - - - -

41 11.45 Decanoic acid 0.08 - - - - - - - -

46 12.41 Aromadendrene - - - 0.01 - - 0.01 - -

48 12.64 Espatulenol - - - - - - 0.02 - -

50 12.84 Caryophyllene - - - - - - 0.03 - -

52 13.13 Germacrene - - - - - - 0.03 - -

54 13.98 2,6-Ditert-butylcyclohexa-2,5-diene-1,4-dione - 0.11 - - - - - - -

56 14.42 1-Heptanol - - - - - - - 0.05 -

65 15.99 Ethyl 4-ethoxybenzoate 0.19 0.32 0.09 0.13 - - - - -

68 16.94 Epiglobulol - - - 0.12 - - 0.07 - -

70 17.18 Ethyl iso-allocholate - - - 0.10 - - - - -

71 17.31 3-Hydroxy-4,4-dimethyldihydro-2(3H)-furanone - - - - - - - - 0.01

72 17.32 1-o-(2-methylpropyl) 4-o-propan-2-yl 2,2-

dimethyl-3-propan-2-ylbutanedioate

- - - - - - - 0.06 -

75 17.64 Ledol - - - - - - 0.29 - -

76 17.67 Globulol - - - 0.45 - - - - -

80 18.90 Eremophylene - - - - - - 0.08 - -

82 19.43 α-Selinene 0.11 - - 0.07 - - - - -

83 19.51 Cubenol 0.11* - - 0.06 - - - - -

85 19.63 β-Eudesmol - - - - - - 0.20 - -

126

86 19.68 Aromadendrene oxide - - - 0.12 - - - - -

92 21.74 α-Limonene diepoxide - - - - - - 0.09 - -

95 22.42 Eicosanoic acid 0.27 - - - - - - - -

114 27.34 Tritetracontane 1.58 - - - - - - - -

115 27.57 Hexadecanoic acid 0.97 - - 1.22 - - 0.26 - -

118 30.15 Hexadecan-1-ol 0.55 0.58 0.44 - - - - - -

119 30.29 (9E)-9-Hexadecen-1-ol - - - 0.36 1.81 0.74 0.17 0.62 0.65

124 31.51 (Z)-Octadec-9-enoic acid 0.33 - - 0.47 - - - - -

126 32.07 Octadecanoic acid 0.49 - - 0.49 0.09 - 0.09 - -

127 32.40 Butyl palmitate - - - - - - - 0.18 0.17

128 32.41 2-Methyl-2-propanyl palmitate - - - - 0.27 - 0.11 - -

129 34.19 4,5-Epoxycholestan-3-one - - - 0.12 0.05 - - - -

133 36.70 Dioctyl adipate - - - - 0.23 - 0.09 0.15 0.11

134 36.74 Hepta decyl heptadecanoate - - - 0.17 - - - - -

135 38.87 (9Z)-9-octadecenenitrile (Oleanitrile) - 0.34 0.29 - 0.50 0.41 - 0.19 0.24

137 39.58 Bis(6-methylheptyl) phthalate 1.42 1.44 3.49 0.27 0.42 0.38 0.19 0.30 0.39

138 40.49 (9Z)-9-Octadecenamide 0.13 0.12 0.09 - 0.23 0.15 - 0.12 0.21

143 42.54 2,6,10-Trimethyldodecane - - - - - - 0.18 - -

144 42.57 Tetratetracontane - - - 0.37 - - - - -

146 44.22 (Z)-Docos-13-enamide 1.31 0.99 1.09 3.72 10.58 7.36 1.00 1.00 3.47

147 54.49 3-Acetoxy-7,8-epoxylanostan-11-ol 0.06 - - - - - - - -

148 54.61 Pollinastanol - - - 0.28 - - - - -

149 54.94 26-(Acetyloxy)- cholest-4-en-3-one 0.06 - - - - - - - -

150 57.05 4,4'-{[4-Hydroxy-5-(2-methyl-2-propanyl)-1,3-

phenylene]bis(methylene)}bis[2,6-bis(2-methyl-2-

propanyl)phenol]*

0.61 0.70 0.62 - 0.10 0.08 0.05 1.02 0.96

152 58.12 14-Methyl-(3β)-cholest-7-en-3-ol - - - 0.06 - - 0.03

Total LMWC (µ C g soil-1

) 8.68 4.63 6.10 8.93 14.27 9.12 3.15 3.69 6.27

* Tentative identification with 70% match to the NIST database

127

Compound quantification

Quantitative analysis revealed that LMWC concentrations ranged from 3.15 -

14.27 µg C g soil-1

. Although fatty acids, amides esters and alcohols were typically

observed in all locations and depths, the dominant compounds differed among

locations. For example, in the first meter of location GL03, fatty acids (115 + 126),

amide of fatty acids (138 + 146), and bis(6-methylheptyl) phthalate (137) had similar

concentrations (1.42, 1.44 and 1.46 µg C g soil-1

, respectively). Bis(6-

methylheptyl) phthalate (137) had a similar concentration (1.44 µg C g soil-1

) at a

depth of 11 - 12 m but a slightly higher concentration (3.49 µg C g soil-1

) at 18 - 19

m. At the PT06 and ST04 locations, (Z)-docos-13-enamide (146) was the most

abundant compound throughout the profiles (Table 5.3). On the other hand, several

terpenes, bioactive compounds and sterols were minor species, sharing approximately

the same magnitude of concentrations in the first meter depths of all locations (Table

5.3).

5.5 Discussion

Solvent selection

Although some compounds were obtained by all solvents, ethyl acetate

appeared to be the most efficient solvent for extraction in all soil bands, with a high

variety of compound classes identified with well separated chromatographic peaks. It

was apparent that LMWCs of surface soil are a mixture of apolar and medium polar

compounds, extractable with hexane and ethyl acetate, while the LMWC of deep soils

128

are predominantly medium polar compounds extractable with dichloromethane, ethyl

acetate and methanol. Though hexane and ethyl acetate could be combined for

coextraction of the whole range of LMWCs in surface soil, hexane did not extract any

compounds from deep soil layers. Furthermore, hexane, dichloromethane and

methanol showed the least variety of compounds extracted at all depths. Therefore,

ethyl acetate was adopted for all further experiments.

Previous solvent studies showed a mixture of compound classes were obtained

from soil with polarity indices similar to the solvents used in this study. For example,

a dominant groups of terpenes, alkanes and fatty acids were extracted with petroleum

ether (P = 0.1) (Almendros et al. 1996; de Blas et al. 2013); fatty acids and esters,

alkanes and sterols were present in chloroform extracts (P = 4.1) (Franco et al. 2000);

and medium polar to polar compound classes such as aliphatic lipids, steroids,

terpenoids, glycerols and carbohydrates were extracted with dichloromethane (P =

3.1) and methanol (P = 5.1) (Otto and Simpson 2007). Some compound classes were

the same as in this study, notably terpenes, fatty acids and esters, and plant steroids.

Identification of LMWCs

For each of the three soil profiles the original land-use had been a eucalyptus

dominated natural vegetation which 50-80 years previously had been replaced with an

annual grass and clover pasture system. In the year prior to sampling, Pinus pinaster

plantations had been established (Harper and Tibbett 2013) with these still containing

the pasture plants within their inter-row areas.

129

Three distinct compound classes, namely 1) terpenes, 2) fatty acids, amide,

alcohol and esters of fatty acids, and 3) plant steroids were obtained from all profiles.

Terpenes are a large and diverse class of compounds produced by all plants. The

previous and current vegetation types are likely to be the main source of the observed

terpene compounds. For example, borneol (14), aromadendrene (46), epiglobulol (68),

globulol (76), α-selinene (82) and β-eudesmol (85) are derived from eucalypts (de

Blas et al. 2013; Song et al. 2009). Eucalypts, rich in terpenes, have dominated the

landscape in south-western Australia for thousands of years (Pearce 1989; Wardell-

Johnson and Williams 1996). Furthermore, caryophyllene (50) and globulol (76) are

derived from pine (de Blas et al. 2013; Liu and Xu 2012).

In addition to the indigenous eucalypts, there are many other genera of native

plants in south-western Australia that contain terpenes in resins or essential oils (Bell

and Heddle 1989). This is in addition to the terpenes that plants produce as

photosynthetic pigments, and hormones. The half-life of soil terpenes originating

from the original endemic vegetation is unknown.

However, the current study suggests that LMWC derived from current above

ground vegetation occurs mainly in the surface horizon and does not percolate to the

deeper layers. This could be due to the hydrophobicity of these compounds such as

borneol (14), caryophyllene (50), epiglobulol (68), α-selinene (82), hexadecanoic acid

(115), (de Blas et al. 2013; Franco et al. 2000) and also the shallow root systems of

both the annual pasture plants and the young Pinus pinaster trees. In contrast, the root

systems of the original natural vegetation are likely to have extended to bedrock (Dell

130

et al. 1983), while leaching may be limited due to the relatively low rainfall (450 mm

year-1

) in the area (Harper and Tibbett 2013).

Similarly, plant steroids were mostly observed at 0 - 1 m depth. The behaviour

of these compounds was also similar to the terpene group as they do not extend into

deeper layers. Plant sterols are highly resistant to biodegradation (Bull et al. 2000)

and are constituents of agricultural crops as well as the native vegetation.

Fatty acids such as hexadecanoic acid (115), (Z)-octadec-9-enoic acid (124)

and octadecanoic acid (126) were dominant in top soil layers and may originate from

plants and microorganisms (Andreetta et al. 2013; Franco et al. 2000; Spielvogel et al.

2014). Fatty acids can occur as cutin monomers in plant waxes and have been

detected in leaves, roots and wood of forest trees (Andreettaa et al. 2013; Martínez-

Íñigo et al. 2000; Spielvogel et al. 2014). Furthermore, fatty acids are abundant in

eucalypt and pine wood (Cruz et al. 2006; Freire et al. 2002a; Liu and Xu 2012;

Silvério et al. 2007a; 2007b; 2008).

Fatty acids can also be an indicator of biological influence in various

environments, such as temperate grassland, lateritic formation processes or even in

deep seas, as reported previously (Bardy et al. 2008; Feng and Simpson 2007; Huang

et al. 2013). The novel finding of this study was that fatty acid amides (138 + 146)

were commonly observed throughout the deep profiles (1.11 - 10.81 µg C g soil-1

)

while fatty acids were not detectable in the deeper layers.

In particular, (Z)-docos-13-enamide (146) occurred throughout these profiles.

This compound has been identified in wood and bark and is the main component of

stem oil of E. urograndis hybrids (Xu et al. 2013). Its presence in these profiles is

131

likely to be a fingerprint for the previous vegetation. Furthermore, the persistence of

(Z)-docos-13-enamide (146) in fire-affected soils was reported by Atanassova et al.

(2012). Their study found that (Z)-docos-13-enamide was still detected even when the

soil was heated up to 300 ºC in air.

Although, (Z)-docos-13-enamide is mostly considered as a compound

extracted from woody parts of eucalyptus trees, it can also be derived from a broad

range of microorganisms including Trichoderma harzianum, a ubiquitous soil fungus

(Siddiquee et al. 2012), Paecilomyces sp., an endophytic fungus of Panex ginseng (Xu

et al. 2009) and Bacillus sp., a halophilic bacteria collected from solar salt works

(Donio et al. 2013).

Fatty alcohols, containing carbon numbers ranging from 7 to 35, have been

discovered in various environments such as soil, peatland and marine sediment

(Huang et al. 2013; Treignier et al. 2006; Yang et al. 2014). However, fatty alcohols

including, hexadecan-1-ol (118) and (9E)-9-hexadecen-1-ol (119), have not

previously been reported in deep soil layers. It is likely that these fatty alcohols were

derived from hexadecanoic acid (115), one of the fatty acids commonly found in

many organisms (Kaneda 1991). Significant amounts of hexadecanoic acid (115) and

(9Z)-hexadecen-9-ol (119) have also been isolated from halophilic bacteria from

extreme environments such a deep-sea carbonate rock at a methane cold seep in Japan

(Hua et al. 2007).

Other long chain compounds including, (9Z)-9-octadecenenitrile (135) and

(9Z)-9-Octadecenamide (138) have also been detected from Paecilomyces sp. and

Bacillus sp., respectively (Donio et al. 2013; Xu et al. 2009). Interestingly, the latter

132

compound is produced by a wide range of microorganisms and has antimicrobial

properties as discussed by Donio et al. (2013). This could be a reason for persistency

of this compound in most of the samples studied (2.25 x 10-4

- 5.75 x 10-4

g C g TOC-

1).

Similarly, (9Z)-9-octadecenenitrile (135), (9Z)-9-octadecenamide (138) and

(Z)-docos-13-enamide (146) were also discovered in extreme habitats such as in

hydrothermal barnacle shells living in marine sediment at a depth 1834 m (Huang et

al. 2013). It can be concluded that fatty acid amides and alcohols including nitrogen

containing compounds present in deep soils of all locations are derived from

microorganisms and plants.

For example, bis(6-methylheptyl) phthalate (137), a compound found

commonly in all locations and depths in this study, is a fatty acid ester present in the

essential oils of many plants including cedar wood (Chamaecyparis sp.). It has been

reported that phthalate compounds are components of wood that resist decomposition

by organisms such as mould, fungi and termites (Xu et al. 2015). This could be a

reason that bis(6-methylheptyl) phthalate (137) was observed abundantly at all

locations and depths in this study. Although bis(6-methylheptyl) phthalate (137) has

not been reported as a compound in eucalypts and pines, several related compounds

have been identified including, phthalic acid, isobutyl tridecyl carbonate, dibutyl

phthalate, bis(2-ethylhexyl)benzene-1,2-dicarboxylate, and bis(2-propylpentyl)

phthalate (Liu and Xu 2012; Xu et al. 2015).

133

Contribution of LMWC in deep soil

Clearly, (Z)-docos-13-enamide (146) made the largest contribution over the

whole depth of profile at locations PT06 and ST04 (Table 5.3). Additionally, the

relative contribution of this compound tended to increase with depth representing

42% of LMWC at 0 - 1 m, 74% at 11 - 12 m and 81% (18 - 19 m) at the PT06 site and

32% of LMWC at 0 - 1 m, 27% at 11 - 12 m and 53% at 18 - 19 m at the ST04 site

(Figure 5.8). However, bis(6-methylheptyl) phthalate (137) was the dominant

compound in profile GL03 (Table 5.3) which represented 16% of LMWC at 0 - 1 m,

31% at 11 - 12 m and 57% at 18 - 19 m (Figure 5.8). The presence of LMWC will be

largely influenced by vegetation and in situ decomposition of plant roots. For

example, terpenes and plant steroids were minor contributions in surface soils.

Figure 5.8 Relative and actual concentrations (µg C g soil-1

) of dominant compounds

in deep soil profiles at 3 locations (GL03, PT06 and ST04) in south-western Australia.

In general the amount of soil carbon arising from LMWC from three profiles

was in the microgram per gram range, which is significantly less than the %TOC

3.72

10.58 7.36

PT06

1.00 1.00

3.47

ST04

1.42

1.44

3.49

0

20

40

60

80

100

Co

mp

ou

nd

(%

rel

ativ

e to

con

cetr

atio

n o

f L

MW

C)

GL03

0-1 m 11-12 m 18-19 m 0-1 m 11-12 m 18-19 m 0-1 m 11-12 m 18-19 m

Bis(6-methylhepthyl)phthalate (Z)-Docos-13-enamide (Z)-Docos-13-enamide

134

determined from dry combustion, wet chemistry or vibrational spectroscopy (Chapters

3 and 4). Even when we factor in LMWC concentrations for species that were below

the detection limit or residual compounds that may not have met the 80% match

criterion of this study, it seems likely that the LMWC represents only a small

percentage (0.92 - 3.56%) of the TOC in deep soil profiles. However, this raises the

question about the contribution of other forms of carbon such as complex and non-

volatile carbon compounds. This question will be addressed in part in the next

chapter.

5.6 Conclusions

The vertical distribution of LMWC was investigated for the first time in three

deep soil profiles in south-western Australia using solvent extraction with ethyl

acetate coupled with GC/MS characterisation. Three common compound classes were

obtained from the first meter of soils 1) terpenes, 2) fatty acids and amides of fatty

acids and 3) steroids but only amides and alcohols of fatty acids were dominant in

deep soils. The concentration of LMWC ranged from 3.15 (0 - 1 m) to 14.27 (11 - 12

m) µg C g soil-1

. The LMWC terpenes and plant steroids in surface soils may result

from vegetation dominated by eucalypts and pine. The compounds typically isolated

from deep soils were (9Z)-9-octadecenenitrile, (9Z)-9-octadecenamide, hexadecan-1-

ol, (9E)-9-hexadecen-1-ol, (Z)-docos-13-enamide and bis(6-methylheptyl) phthalate

might originate from microorganisms and previous native vegetation.

135

Chapter 6

Characterisation of Macromolecular Organic Carbon

6.1 Abstract

Pyrolysis and off-line thermochemolysis using TMAH were employed to

characterise deep soil carbon up to a depth of 29 m. The performance of both methods

was also tested using laboratory standard samples with different concentrations of

lignin, cellulose and chitin in a kaolinite matrix, before analysis of deep soils.

Pyrolysis using the temperature range 250 - 600 ºC was suitable to characterise all

laboratory standard samples at 3.85% TOC. In comparison, thermochemolysis with

TMAH of lignin in kaolinite (3.85% TOC) required pre-concentration of the sample

before analysis. Consequently, a procedure was developed for characterising deep soil

Macromolecular Organic Carbon (MOC) involving sample extraction with 0.1 M

NaOH followed by TMAH thermochemolysis analysis. Distribution of carbon species

throughout the profile was revealed by pyrolysis. Unfortunately, the successful

application of TMAH chemothermolysis was limited to characterisation of soil at 0 -

0.1 m depth. The presence of biomarkers such as lignin, polysaccharides, proteins and

terpenes at 0 - 0.1 m implied influences of vegetation, fire events and traces of

microorganisms. This evidence coincided with the results from pyrolysis that found

polysaccharides were distributed mainly from 0 - 0.1 m, while lignin compounds were

136

detected consistently down to 29 m. It is concluded that MOC occurs in multiple

chemical forms in surface soil but only occurs in lignin derived form in deep soils.

6.2 Introduction

Macromolecular organic carbon (MOC) is usually composed of large-non-

volatile organic substrates that are difficult to analyse directly using conventional

GC/MS. However, pyrolysis and thermochemolysis have been developed for

characterisation of large organic substrates because they lead to extensive

fragmentation, which enables easy separation and identification of fragments by

GC/MS (Kaal et al. 2009; Klingberg et al. 2005; Shadkami and Helleur 2010).

Pyrolysis involves degradation of MOC by elevated temperatures in the absence of

oxygen. Signature fragments of MOC are produced during pyrolysis that identifies the

form(s) of MOC present. Several carbon materials have been qualified by this

technique such as polysaccharides, humic acid, degraded wood or charred materials

(Buurman et al. 2007; Buurman et al. 2009; del Río et al. 2001; Kaal and Rumpel

2009; Kaal et al. 2009).

Thermochemolysis involves assisted fragmentation with an alkylating reagent

at elevated temperatures. TMAH is a common reagent used to assist the

thermochemolysis reactions. This procedure requires lower temperatures than

pyrolysis to degrade MOC because the TMAH cleaves the MOC selectively at ester

and ether bonds (Clifford et al. 1995; Shadkami and Helleur 2010) and the fragment

compounds are easily characterised by GC/MS. Again, the carbon fragments

137

identified by this method can be used to imply the presence of compounds such as

lignin, cellulose and chitin including proteinaceous materials.

Thermochemolysis with TMAH can be performed by on-line and off-line

procedures. The on-line procedure requires a pyrolyser connected with GC/MS

whereas the off-line method can be performed in sealed glass tubes which are heated

in a furnace. The latter approach can achieve results similar to sub-pyrolysis at 300 ºC

for 10 min (McKinney et al. 1995) or 250 ºC for 30 min (del Río et al. 1998; Hatcher

et al. 1995). Off-line thermochemolysis is a practical method to investigate MOC that

does not require pyrolyser equipment or flash pyrolysis. In order to achieve completed

methylation, excess TMAH is generally applied to obtain good contact between

reagent and sample. In fact, the amount of sample should also provide enough

methylated products for a sufficient chromatographic separation (Klingberg et al.

2005).

Nevertheless, some studies have exploited the technique without extraction

(Buurman et al. 2009; Chefetz et al. 2000). In order to enhance recovery, alkali (0.1 M

NaOH) has also been employed to extract carbon in the concentration range 0.03 -

31.9% TOC (Buurman et al. 2009; Chefetz et al. 2002; González-Pérez et al. 2012).

However, different product-patterns were observed between studies with and without

pre-treatment which has led to incomplete understanding and some misinterpretations.

For example, (2E)-3-(4-methoxyphenyl)acrylic acid, methyl benzene and methyl ester

were not observed when soil samples were treated with NaOH relative to an untreated

process (Chefetz et al. 2002). Furthermore, Buurman et al. (2009) noted the

138

abundance of polysaccharide obtained from NaOH extraction of humic acid was five

times higher compared to untreated samples.

Previous work has focused on samples with high amounts of organic carbon,

mostly in surface soils such as peat bog, near shore material and rhizosphere (Chefetz

et al. 2002; González et al. 2003; Hatcher and Clifford 1994; Nierop 1998; Pulchan et

al. 1997; van Bergen et al. 1997). However, pyrolysis and thermochemolysis have not

previously been evaluated to characterise deep soils containing small amounts of

carbon. Furthermore, a systematic study of thermochemolysis to determine the small

concentration of MOC has not been undertaken before.

The main objectives of this experiment were to:

(i) develop the TMAH thermochemolysis methodology for MOC

characterisation using standard samples composed of three carbon species in a

kaolinite substrate,

(ii) apply the developed method to qualify deep soil carbon, and

(iii) employ the pyrolysis technique for characterisation of three carbon

species in a kaolinite substrate and deep soil carbon.


8.

139


Experiment 6.1: Thermochemolysis with TMAH (TC(TMAH))-GC/MS:

Procedure development for soil macroorganic carbon characterisation

Samples

Authentic lignin, cellulose and chitin were analysed by TC(TMAH)-GC/MS to

validate that the procedure can be successfully employed with various types of MOC.

Carbon substrates mixed in kaolinite such as L/K, C/K and Ch/K were prepared at

concentrations 0.20, 3.85, 11.55, 19.25% TOC. Preparation of the standard samples

was as described in Experiments 3.2 and 4.1. Humic acid is composed of a broad

range of carbon substrates and therefore was not considered as a primary standard of

MOC in this study.

Method verification and application

Lignin was treated by TMAH and thermochemolysis as described below.

Approximately 5 mg of the solid extract was placed in glass tubes (1 cm diameter and

18 cm long) with 200 µl of TMAH (25% in methanol) and flushed with nitrogen gas.

Samples were allowed to stand for 30 min then methanol was evaporated completely.

The tubes were sealed under vacuum then placed in an oven at 250 ºC for 30 min.

After cooling to room temperature, condensation of the samples was observed. The

tubes were then opened and extracted with ethyl acetate (3 x 1 ml). The residues were

filtered through 0.45 µm polytetrafluoroethylene (PTFE) membrane before

140

concentrating the samples under a nitrogen stream. Finally, the samples were analysed

by GC/MS.

Extraction

In order to evaluate the potential of extraction, 5 g of laboratory standard

sample, 3.85% TOC-L/K, was placed into a 25 ml plastic tube with 25 ml of 0.1 M

NaOH and flushed with nitrogen gas before extraction. The suspension was shaken

for 24 hours and then centrifuged for 15 min. Following this the solution was

decanted and two further extractions were done by adding 15 ml of 0.1 M NaOH and

shaking the tube by hand for 10 minutes. After each of the extractions the solution

was separated by centrifugation as mentioned above. The combined extracts were

then freeze-dried for 4 days (Hetosicc CD4 freeze dryer) and analysed by TMAH

thermochemolysis.

Extraction without thermochemolysis

To compare the potential of extraction, additional samples of L/K, C/K and

Ch/K at concentrations 0.2% TOC were extracted with 0.1 or 1 M NaOH followed by

freeze drying. A small amount (0.5 g) of each freeze dried extract was then dissolved

with 15 ml methanol. The resulting suspensions were filtered through a 0.45 µm

PTFE membrane and concentrated under a nitrogen stream before analysis with

GC/MS. This experiment provided a guide to the effects of extraction on MOC

without TC(TMAH) treatment.

141

Analytical gas chromatography/mass spectrometry

Samples were analysed with the same GC/MS instrument as described for the

analysis of the MOC in Experiment 5.1. However, different conditions were used as

follows; the column was set to 40 ºC for 1 min then a temperature ramp was applied at

8 ºC/min to a final temperature of 300 ºC which was held for 30 min. Aliquots of 1 µl

were injected at 310 ºC and using splitless injection mode.

Mass spectra were obtained by a Shimadsu QP2012S mass spectrometer with

ion source temperature and interface temperature of 200 ºC. Compounds were

scanned at the rate of 2000 amu/sec and range of 45 - 1000 m/z. Peaks were identified

by comparison with the NIST database. All samples in this experiment were prepared

in duplicate and performed with 3 injections for each sample.

Experiment 6.2: TMAH-GC/MS: Procedure application for deep soil carbon

Samples

The samples employed for this study were obtained previously by Harper and

Tibbett (2013). Sample location CU03 at depths of 0 - 0.1 (4.68% TOC), 1 - 2 (0.23%

TOC), 10 - 11 (0.02% TOC), 19 - 20 (0.02% TOC) and 28 - 29 m (0.01% TOC) were

selected for this experiment (location and details were explained in Experiment 5.1).

The extraction procedure and TC(TMAH)-GC/MS analysis was applied as

described in Experiment 6.1. The GC/MS was performed with 1:5 split injection

mode.

142


carbon

Samples

The same set of standard samples L/K, C/K, Ch/K and H/K at concentrations

of 3.85% TOC was employed for pyrolysis study, followed by analysis of deep soil

samples used in Experiment 6.2.

Pyrolysis-GC/MS

In this study, analysis was conducted on 0.5 mg samples without pre-

treatment. Pyrolysis was performed at two temperature ranges i) 250 - 340 ºC and ii)

250 - 600 ºC, for 10 s with heating rate of 10 ºC/min using a pyrolysis-GC/MS from

an Agilent 6890 GC interfaced to an Agilent 5973b mass selective detector (MSD).

The GC was fitted with a fused silica DB5 phase capillary column (60 m

length x 0.25 mm i.d. x 0.25 μm film thickness) and helium was used as the carrier

gas at a constant flow of 1 ml/min. Split injection modes of 15:1 and 10:1 were

applied for standard and field samples, respectively. The GC oven temperature was

programmed from an initial temperature of 40 °C and held for 2 min, then increased

at a rate of 4 °C/min to a final temperature of 280 °C isothermal for 20 minutes, 70 eV

full scan (50 - 550 amu/sec) and selected ion (m/z 57, 91, 123, 156, 170, 184, 191,

192, 217) analyses were simultaneously recorded at a scan speed of ~2 scans/sec.

Sample analysis was performed with one replication and one injection. The

143

identification of individual compounds was based on comparison of mass spectral and

retention time data to laboratory standards and the NIST mass spectral library.

The relative abundance of each compound was calculated as percentage of

peak area and normalised to 100%. However, absolute quantification was not carried

out because this would require separate verification of each of the compound

identities using pure standards and then determination of the response factors for each

of these compounds. Nevertheless, the relative abundance is suitable for comparing

samples in a semi-quantified way (Buurman et al. 2007; González-Pérez et al. 2012;

Vancampenhout et al. 2012).

6.4 Results

Experiment 6.1: TMAH-GC/MS: Procedure development for macroorganic

carbon

Verification of thermochemolysis condition

A typical chromatogram for TMAH thermochemolysis of pure lignin is shown

in Figure 6.1 and key fragment compounds are listed in Table 6.1. The dominant

peaks were identified as dimethoxy benzenes such as 3,4-dimethoxybenzaldehyde

(13), 1-(2,4-dimethoxyphenyl) ethanone (14), methyl 3,4-dimethoxybenzoate (15),

1,2-dimethoxy-4-[(1E)-3-methoxy-1-propen-1-yl] benzene (16), 1,2-dimethoxy-4-(2-

methoxyvinyl) benzene (17), 1,2-dimethoxybenzene (7), 1,2,4-trimethoxybenzene

(10), vanillinor4-hydroxy-3-methoxybenzaldehyde (11) and 1,2-dimethoxy-4-(2-

144

propenyl)-benzene (18), which are typically obtained from methylation of lignin.

Furthermore, various kinds of alkyl benzenes were also identified from the

chromatogram (Table 6.1).

Figure 6.1 Gas chromatogram of TMAH thermochemolysis products of pure lignin.

For peak identifications refer to Table 6.

Table 6.1 List of compounds identified in pure lignin.

No.

RT

(min) Compounds

1 5.98 1,3-Dimethylbenzene

2 6.44 o-Xylene

3 9.08 1,2,4-Tirmethylbenzene

4 9.76 1,4-Diethylbenzene

5 10.33 1-Isopropyl-4-methylbenzene

6 11.05 1,2,4,5-Tetrametylbenzene

7 11.60 1,2-Dimethoxybenzene

8 12.55 Naphthalene

9 15.64 4-Ethyl-1,2-dimethoxybenzene

10 15.74 1,2,4-Trimethoxybenzene

11 16.40 Vanillin or 4-Hydroxy-3-methoxybenzaldehyde

12 16.97 (3,4-Dimethoxyphenyl)methanol

13 17.61 3,4-Dimethoxybenzaldehyde

5 10 15 20 25

0

2

4

6

8

10

12x 10

5

91 23 4 5

67

8

18

10

1112

13

14

15

16

19

2123+

24

27

17+

20

2225+

26

28

Abun

dan

ce


145

14 18.88 1-(2,4-Dimethoxyphenyl)ethanone

15 19.24 Methyl 3,4-dimethoxybenzoate

16 19.65 1,2-Dimethoxy-4-[(1E)-3-methoxy-1-propen-1-yl]benzene

17 19.95 1,2-Dimethoxy-4-(2-methoxyvinyl)benzene

18 20.19 1,2-Dimethoxy-4-(2-methoxy-1-prophenyl)benzene

19 20.69 2-Undecanylbenzene

20 20.96 6-Dodecanylbenzene

21 21.23 4-Dedecanylbenzene

22 21.55 Dodecan-3-ylbenzene

23 22.05 1,2-Dimethoxy-4-(1,2,3-trimethoxypropyl)benzene

24 22.11 2-Dodecylbenzene

25 22.40 Tridecan-2-ylbenzene

26 22.58 (1-Methyldodecyl)benzene

27 23.30 (1-Pentylheptyl)benzene

28 23.46 Bis(6-methylheptyl)phthalate

Thermochemolysis of cellulose and chitin

Figure 6.2 shows chromatograms obtained via thermochemolysis of pure

cellulose and chitin. The chromatogram for cellulose indicates the presence of

methylated products formed by reaction of TMAH with groups of organic acid (2, 42,

48, 50) (Table 6.2). In contrast to cellulose, the compounds of chitin are quite unique

including nitrogen compounds such as pyrimidine and amines (1, 23, 26, 27). Derived

fatty acid esters and several benzene species were also identified in both cellulose and

chitin thermochemolysis products (Table 6.2).

146

Figure 6.2 Gas chromatogram of TC(TMAH) products of pure cellulose (a) and chitin

(b). For peak identifications refer to Table 6.2.

Table 6.2 Identified thermochemolysis products of pure cellulose and chitin.

No.

RT

(min) Compound

Source

Cellulose Chitin

1 3.84 N,N-diethyl-2-pentynamide -

2 4.17 Isobutyl acetate -

3 4.25 Toluene

4 4.51 1,2-Butanediol -

5 4.84 Butyl acetate

6 5.78 Ethylbenzene

7 6.03 m-Xylene -

8 6.12 1-Butoxybutane -

5 10 15 20 25

0

2

4

6

8

10x 10

5

5 10 15 20 25

0

1

2

3

4

5x 10

5

Ab

un

dan

ce


1

3

67-11

27-43

15+16

18

19

21

14

25

32

4

5

6

a) cellulose

b) chitin

12 1315

17

19

20

29-

32 33

34

35- 40

41

42

44

+

4546

5120 22

23

+

24

26

5

131247

147

9 6.48 p-Xylene -

10 6.69 2-Butoxyethanol -

11 8.44 2-Methylnonane -

12 9.07 1,2,3-Trimethylbenzene

13 9.74 (2-Methyl-2-propanyl)benzene

14 10.34 1-Isopropyl-2-methylbenzene -

15 10.97 Undecane

16 11.06 1,2,4,5-Tetramethylbenzene -

17 11.68 1,2,3,4-Tetramethyl-5-methylene-1,2-cyclopentadiene -

18 11.71 1-Isopropyl-4-methylbenzene -

19 12.36 Tridecane

20 12.55 1-Methylene-1H-indene

21 14.18 Hexadecane -

22 14.63 N,N‟-dimethyl-N,N‟-dinitrofuran-3,4-diamine -

23 15.73 1,2,4-Trimethoxybenzene -

24 15.91 Isobutyl pentyl oxalate -

25 18.13 5-Benzyloxypyrimidine-2-carboxylic acid -

26 18.61 Phenyl-2,3-o-ethylboranediyl-4-o-benxyl-β-I-rhamnopyranoside -

27 19.21 1-Methylnonylbenzene -

28 19.57 1-Pentylhexylbenzene -

29 19.62 1-Butylheptylbenzene

30 19.79 1-Propyloctylbenzene

31 20.13 1-Ethylnonylbenzene

32 20.50 2,3,5,8-Tetramethyldecane -

33 20.70 1-Methyldecylbenzene

34 20.97 1-Pentylheptylbenzene

35 21.12 1-Methylundecylbenzene

36 21.23 1-Propylnonylbenzene

37 21.56 1-Ethyldecylbenzene

38 22.31 1-Pentyloctylbenzene -

39 22.41 1-Butylnonylbenzene

40 22.59 1-Propyldecylbenzene

41 22.93 1-Ethylundecylbenzene

42 23.19 Allyl decyl oxalate -

43 23.35 Methyl (9E)-9-dodecenoate -

44 23.47 1-Methyldodecylbenzene

45 23.59 14-Methylpentadecanoic acid

46 25.74 Methyl (9Z)-9-octadecenoate

47 26.01 Methyl stearate

48 26.68 Butyl palmitate -

49 26.72 Pyrene -

50 26.93 Octadecyl acetate -

51 27.99 Methyl (15Z)-15-tetracosenoate -

148

The preliminary experiment investigated thermochemolysis of the 3.85%

TOC-L/K without extraction. Unfortunately, only a limited number of compound

signals were obtained from the chromatogram compared to pure lignin. Therefore, it

is necessary to extract samples that contain carbon concentrations of 3.85% TOC or

lower, including deep soil samples.

Nevertheless, the variety of chromatographic peaks obtained from 3.85%

TOC-L/K extracted with 0.1 M NaOH was still dramatically decreased compared to

pure lignin thermochemolysis. However, the methoxy benzene species which are a

signature of lignin compounds especially 3,4-dimethoxybenzaldehyde were still

clearly visible. Furthermore, methyl 3,4-dimethoxybenzoate, the methylated product

was also observed. Another key compound, bis(6-methylheptyl)phthalate was also

observed (Figure 6.3).

Figure 6.3 Gas chromatogram of TC(TMAH) products of 3.85% TOC-L/K extracted

with 0.1 M NaOH.

16 18 20 22 24 26 28 30 31

0

2

4

6

8

10

12x 10

4

32

3,4

-Dim

etho

xyb

enza

ldeh

yde

Met

hy

l 3,4

-dim

ethox

yben

zoat

e

4-(

Ben

zylo

xy

)ben

zald

ehyde

Met

hy

l 4-m

eth

oxyb

enzo

ate

Bis

(6-m

eth

ylh

epty

l)p

athala

te

Abundance


149

Chromatograms of extracted lignin, cellulose and chitin without thermochemolysis

The effect of the extraction procedure was further tested with low

concentration (0.20% TOC) samples of different carbon substrates such as L/K, C/K

and Ch/K, extracted with NaOH but without thermochemolysis and these are shown

in Figures 6.4 and 6.5.

Generally, the variety of compounds derived from this procedure depended on

the concentration of NaOH (Figures 6.4 and 6.5). For example, the major components

of lignin extracted with 1 M NaOH were mainly benzoic acid and coumarin, a

phenolic compound of lignin (Figure 6.5a). Polysaccharide fragment products were

more varied when extracted with 0.1 M NaOH compared to 1 M NaOH (Figures 6.4b,

6.4c, 6.5b, 6.5c). The signals of pyridine compounds were detected in chitin

chromatograms (Figures 6.4c, 6.5c), while furans were obtained from cellulose

(Figures 6.4b, 6.5c). Discrimination of cellulose and chitin could be achieved by the

presence of an amino group arising from the later. However, once again the number of

peaks derived from this method was low compared to the number of compounds

derived from methylation of pure cellulose and chitin. Although extraction or

TC(TMAH) alone are insufficient to definitely characterise low concentration

samples, when combined they enhance signals for these samples.

150

Figure 6.4 Gas chromatograms of 0.20% TOC of laboratory standard samples L/K (a),

C/K (b), and Ch/K (c) extracted with 0.1 M NaOH.

5 10 15 20 25 30

0

1

2

3

4

5x 10

5

5 10 15 20 25 30

0

2

4

6

8

10x 10

5

10 12 14 16 18 204 6 8

0

1

2

3

4

5x 10

5

Eth

ylh

exanol

1-o

-Do

dec

yl 4

-o-e

thyl

ben

zen

e-1

,4-d

icarb

oxy

late

3,5

-Bis

(2-m

ethy

l-2-p

rop

any

l)p

hen

ol

3-M

eth

yl-

1-b

uta

nol

2,2

-Dim

etho

xyb

uta

ne

5-(

3-F

ury

l)-2

-met

hyl-

1-p

ente

n-3

-one

2,3

-Dim

ethy

lpen

tan

e

a) lignin / kaolinite



Abundance


3-(

2-M

eth

oxy

etho

xym

eth

oxy

)

-2-m

eth

ylp

enta

n-1

-ol

Met

ho

xyb

enze

ne

3-M

eth

yl-

6-(

met

hyla

min

o)-

1-

pro

p-2

-eny

lpy

rim

idin

e-2

,4-

dio

ne

1,1

,3-T

rim

ethylc

ycl

op

enta

ne

5-E

thy

l-4-m

eth

yl-

5-h

epte

n-3

-one

Eth

ylh

exanol

(5Z

)-9

-Met

hy

l-5

-undec

ene

3,5

-Dim

ethy

l-1-h

exen

e

Do

dec

-1-e

ne

Do

dec

-3-e

ne N

,N‟-

bis

(2,4

-dim

ethy

lpen

tan

-3-y

l)

eth

an

e-1

,2-d

iim

ine

Met

hy

l palm

itate

Tra

ns-

cycl

op

rop

anep

enta

noic

aci

d-

un

dec

ylm

ethy

lest

er

Met

hy

l hep

tadec

an

oat

e

151

Figure 6.5 Gas chromatogram of 0.20%TOC of laboratory standard samples L/K (a),

C/K (b), and Ch/K (c) extracted with 1 M NaOH.

5 10 15 20 25 30

0

0.5

1

1.5

2x 10

6

5 10 15 20 25 30

0

1

2

3

4

5x 10

5

10 12 14 16 18 20 22

0

1

2

3

4

5

6

7x 10

5

3-P

hen

ylm

eth

oxy

ben

zald

ehyd

e

(E)-

3-(

3,4

-Dim

ethox

yph

eny

l)p

rop

-2-e

no

ic a

cid

Met

hy

l 2-h

yd

roxyb

enzo

ate

1,4

-Dim

etho

xy-2

-met

hy

lben

zen

e

2-H

yd

roxy

ben

zoic

aci

d

Met

hy

l 3,4

dim

eth

yl-

2-(

3-m

ehy

l-bu

tyly

l)

ben

zoate

Met

hy

l (3

,4-d

imet

hox

yph

eny

l)ac

etate

1-[

4-(

Ben

zylo

xy)-

3-m

etho

xyp

hen

yl]

ethanon

e

Eth

yl o

xo

(ph

eny

l)ac

etate

Co

um

ari

n




Abundance


Eth

ylh

exanol

Eth

ylh

exanol

3,5

-Dim

ethy

l-1H

-py

razo

l-4

-am

ine

1,4

-Dim

ethy

l-2-o

ctadec

ylc

ycl

ohex

ane

(E)-

Tri

cos-

11

-en

e

(2Z

)-6

-Met

hy

l-2

-undec

ene

5-H

epty

l-5

-met

hy

ldih

ydro

-2(3

H)-

fura

non

e

1-o

-Do

dec

yl 4

-o-e

thyl

ben

zen

e-1

,4-d

icarb

oxy

late

1-o

-Do

dec

yl 4

-o-e

thyl

ben

zen

e-1

,4-d

icarb

oxy

late

152

Experiment 6.2: TC(TMAH)-GC/MS: Procedure application for deep soil

carbon

Macromolecular organic carbon (MOC) at different depths

A wide variety of MOC was obtained in the surface layer at 0 - 0.1 m (Figure

6.6) and the analysis indicates the presence of lignin, terpenes, polysaccharides and

proteins. Aromatic compounds of non-unique origin were also identified including

naphthalene, toluene and phenol (Table 6.3). Unfortunately, compounds observed

from the lower depths using this technique were not considered as they were very low

in similarity index.

Figure 6.6 Chromatogram of soil MOC at 0 - 0.1 m assigned by GC/MS. For full list

of peak identifications refers to Table 6.3.

14 16 18 20 22 24 26 28 30 32

0

2

4

6

8

10

12

14

16

18x 10

5

Abundance


2 3 45

6

7

9

10

11

12

13

14-

17

20+

21

22 23

2526

28

30

3118

153

Table 6.3 TMAH products of soil at 0 - 0.1 m depth.

No.

RT

(min) Compound

Lignin

2 15.56 1,2,4-Trimethoxybenzene

3 15.80 4-Hydroxycoumarin

4 16.44 1,2,3-Trimethoxy-5-methylbenzene

5 16.68 1,2,3,4-Tetramethoxybenzene

9 17.82 Methyl 3,4-dimethoxybenzoate

10 18.35 2-Methylcoumarin

13 19.43 3-Methylcoumarin

14 19.83 1,2-Dimethoxy-4-(2-methoxy-1-prophenyl)benzene

19 21.62 3-Methoxybenzoic acid

21 21.88 1,2-Dimethoxy-4-(1,2,3-trimethoxyprophenyl)benzene

25 25.12 Decahydro-1,6-dimethyl-4-(1-methylethyl)naphthalene

29 28.42 3-(3,4-Dimethoxyphenoxy)-4-methoxybenzoic acid

31 31.55 1-[3,4,5-Trimethoxyphenyl]-2-methylnaphth[1,2-d]imidazole-4,5-dione

33 36.93 Octyl methoxyacetate

Polysaccharides

26 26.99 Methyl 2,3,4,6-tetra-o-methyl-α-D-glucopyranoside

28 27.79 Methyl 2,3,5,6-tetra-o-methyl-α-D-glucofuranoside

30 29.02 Methyl 2,3,4,5-tetra-o-methyl-α-D-galactoseptanoside

Protein

15 19.98 9H-xanthene-9-carboxylic acid-(pyridin-4-yl)amide

20 21.70 N‟-hydroxy-N-(3,4,5-trimethoxyphenyl)propanediamide

32 32.36 2,3-Diphenyl-1H-pyrrolo[2,3-b]pyridine-6-amine

Terpenes

6 17.22 Patchouli alcohol

11 19.04 Globulol

22 22.24 Yalangene

Uncertain origin products

1 6.25 Phenyl butyrate

7 17.34 2,4,6-Trimethoxytoluene

8 17.63 3,5-Bis(2-methyl-2-propanyl)phenol

12 19.32 3,3,6,8-Tetramethyl-3,4-dihydro-1(2H)-naphthalenone

16 20.09 2-(4a-Methyl-8-methylidene-1,2,3,4,5,6,7,8a-

octahydronaphthalen-2-yl)propan-2-ol

17 20.20 9H-xanthene

18 21.09 1-(3-Biphenylyl)ethanone

23 23.26 Xanthone

24 23.36 Methyl 8-nonynoate

27 27.40 4-(3-Hydrocyphenyl)-oxobutyric acid

154


carbon characterisation

Pyrolytic products of laboratory standard samples

Compounds derived from pyrolysis of lignin, cellulose and chitin in kaolinite

at temperature ranges of 250 - 340 ºC and 250 - 600 ºC are shown in Tables 6.4 and

6.5, respectively. In general, there were many peaks in the chromatograms derived

from pyrolysis at 250 - 340 ºC but only a small number of these compounds could be

confidently identified compared to the extended temperature range of 250 - 600 ºC. In

particular, 6-methyl-3-pyridinol was obtained uniquely from Ch/K and could

potentially act as a marker for chitin. Three key compounds were identified from C/K,

however, octadec-5-ene was also observed for L/K (Table 6.4).

Table 6.4 Pyrolytic products at temperature range of 250 - 340 ºC identified in lignin,

cellulose and chitin mixed with kaolinite (3.85% TOC) and a soil sample that is not

shown.

No. RT

(min) Compound L/K C/K Ch/K

1 10.86 1,4-Dimethyl-1H-pyrazole - -

2 14.48 5-Methyl-2-furancarboxaldehyde - -

3 25.44 6-Methyl- 3-pyridinol - -

4 33.21 Octadec-5-ene -

5 33.40 15-Heptadecenal - -

By contrast, the products obtained from the extended temperature range of

250-600 ºC were more diverse and distinctly different among the three carbon

substrates. The pyrograms of these carbon substrates are presented in Figure 6.7.

155

The key compounds of each of the three carbon substrates are listed in Table

6.5. Pyrolysis of lignin contributes several groups of compounds including: 1) simple

aromatics such as benzene, toluene, styrene and phenol; 2) methoxy-substituted

phenol and benzene; 3) naphthalenes and phenanthrenes with methyl-substitutents;

and 4) unsaturated hydrocarbons, mainly alkenes (Table 6.5).

Cellulose generates signature products such as 1,4:3,6-dianhydro-α-D-

glucopyranose, furans, ketones, alcohols and acids. On the other hand, chitin showed

several unique compounds containing nitrogen such as pyridine, pyrole, nitriles and

amides.

However, some compound groups were observed for more than one of these

substrates such as 2, 3-dimethyl-2-cyclopenten-1-one, 2-methylbenzofuran and

methyl or ethyl substituted-phenols, obtained from lignin and cellulose. Furthermore,

cyclopentadecane compounds were generated from both cellulose and chitin (Table

6.5).

Table 6.5 List of pyrolytic products at temperature range 250 - 650 ºC identified in

lignin, cellulose and chitin mixed with kaolinite at concentrations of 3.85% TOC.

No. RT

(min) Compound L/K C/K Ch/K

1 7.04 Benzene - -

2 7.59 1,3,5-Heptatriene - -

3 7.99 1-Hydroxy-2-butanone - -

4 8.45 1-Methyl-1,5-dihydro-2H-pyrrol-2-one - -

6 8.78 Pyridine - -

7 9.20 Toluene - -

12 10.44 2-Methylpyridine - -

13 11.53 1,3-Dimethylbenzene - -

14 11.66 Ethylbenzene - -

15 11.69 3-Methylpyridine - -

156

18 12.50 Styrene - -

20 12.55 2-Methyl-2-cyclopenten-1-one - -

21 12.87 Methoxybenzene - -

24 14.01 5-Methyl-2-furancarboxaldehyde - -

25 14.12 3-Methyl-2-cyclopenten-1-one - -

29 14.88 Phenol -

32 15.40 1,2,3-Trimethylbenzene - -

34 15.52 Benzofuran - -

35 15.57 1-Methoxy-2-methylbenzene - -

37 15.95 1-Methoxy-4-methylbenzene - -

38 16.10 3-Methyl-1,2-cyclopentanedione - -

43 16.34 2-Acetyl-1,4,5,6-tetrahydropyridin - -

44 16.42 2,3-Dimethyl-2-cyclopenten-1-one -

45 16.55 1-(2-Pyridinyl)ethanone - -

46 16.73 2-Methylphenol -

47 16.85 2-Hydroxy-3,4 -dimethylcyclopent-2-en-1-one - -

50 17.45 4-Methylphenol - -

51 17.79 2-Methoxyphenol - -

55 18.37 2,6-Dimethylphenol -

56 18.48 7-Methylbenzofuran - -

57 18.51 2-Methylbenzofuran -

58 18.59 3-Ethyl-2-hydroxy-2-cyclopenten-1-one - -

59 18.87 2-Methylbenzoxazole - -

61 19.04 3-Ethylphenol -

62 19.24 1,2-Dimethoxybenzene - -

64 19.36 2,4-Dimethylphenol - -


66 19.57 2-Methylindene - -

67 19.65 1,1a,6,6a-Tetrahydro cycloprop[a]indene - -



73 20.21 4-Methoxy-3-methylphenol - -

74 20.39 2-Ethyl-6-methylphenol - -

75 20.49 Dodec-1-ene - -

76 20.55 3,4-Dimethylphenol - -

77 20.67 1,2-Benzenediol - -

79 20.77 Naphthalene - -

80 20.94 2-Hydroxy-3-propyl-2-cyclopenten-1-one - -

81 21.17 4-Ethoxybenzenamine - -

82 21.20 2-Pyridinemethanol - -

83 21.42 1,4:3,6-Dianhydro-α-D-glucopyranose - -

84 21.59 5-(Hydroxymethyl)-2-furancarboxaldehyde - -

85 21.61 2,3-Dimethoxytoluene - -

86 21.66 2,3,6-Trimethylphenol - -

87 21.78 2-Ethyl-4-methylphenol - -

90 22.24 3-Methyl-1,2-benzenediol - -

157



96 22.55 2,4-Dimethylanisole - -

98 22.65 3-Isopropylthiophenol - -

100 23.01 1-Indanone - -

103 23.44 3-Butyl-2-hydroxy-2-cyclopenten-1-one - -

104 23.46 1-Methylnaphthalene - -

105 23.58 Indolizine - -

106 23.60 5-Methoxy-2,3,4-trimethylphenol - -

107 23.60 2-Methyl-indanone - -

109 23.82 Benzocycloheptatriene - -

110 23.88 2-Methylnaphthalene - -

111 23.89 1-Methy-1,3-dihydro-2H-inden-2-one - -

113 24.41 2-Methyl-1,4-benzenediol - -

115 24.74 3-Methyl-5-(1-methylethyl)- methylcarbamatephenol - -

118 25.17 4,5-Dimethyl-1,3-benzenediol - -

119 25.23 Tetradec-1-ene - -

122 25.64 7-Methyl-1-indanone - -

123 25.75 2,5-Dimethylhydroquinone - -

126 26.26 1,3-Dimethylnaphthalene - -




132 27.05 1,5-Dihydroxy-1,2,3,4-tetrahydronaphthalene - -

133 27.18 1-Vinyl-1H-pyrrole-2-carboxylic acid - -

134 27.38 1-Pentadecanol - -

139 28.64 2,3,6-Trimethylnaphthalene - -

155 33.03 Octadec-9-ene - -

157 33.20 Nonadec-1-ene -

160 33.64 Octadec-5-ene - -

168 35.13 Pentadecanenitrile - -

173 36.05 Tridecanoic acid - -

174 36.18 2-Methylphenanthrene - -

177 38.09 Cyclopentadecane -

178 38.10 1,7-Dimethylphenanthrene - -

182 39.58 Tetradecanamide - -

183 40.59 7-Isopropyl-1-methylphenanthrene - -

187 43.71 4-Hydroxyphenyl-pyrrolidinylthion - -

158

Figure 6.7 Pyrograms at temperature range 250 - 600 ºC of laboratory standard

samples at concentration of 3.85% TOC assigned by GC/MS. For full list of peak


5 10 15 20 25 30 35 40 45

0

1

2

3

4

5

6

7x 10

5

5 10 15 20 25 30 35 40 45

0

2

4

6

8

10

12

14

16x 10

5

5 10 15 20 25 30 35 40

0

0.5

1

1.5

2x 10

5

6

4 12

1543+

45

59

81+

82

105

168 182

29

46

56

24

66

83

84 100

111122

123 133

29

34

4650

51

55

62

69

7386

95104

126 118

157178

183

Ab

un

dan

ce





1

713

18

174

2

3

159

Compound identification of humic acid/kaolinite

A Pyrogram of 3.85% TOC-H/K is depicted in Figure 6.8 and identified

compounds are listed in Table 6.6. Humic acid was employed for testing the

procedure because of its complex and recalcritrant structure. In general pyrolysis

products of humic acid were similar to lignin and cellulose compositions. Compounds

common to lignin were mainly methyl-substituted naphthalenes, whereas several

indene species were common to cellulose. The products of non-specific origin

included simple aromatic and hydrocarbon groups such as phenols, xylenes, pyrenes,

alkanes and alkenes.

Figure 6.8 Pyrogram of laboratory standard 3.85% TOC-H/K assigned by GC/MS.

For full list of peak identifications refer to Table 6.6.

5 10 15 20 25 30 35 40 45 50

0

2

4

6

8

10

12x 10

5

Abundan

ce


10

29

3679

125

146

154

159

66 108

166 177 184185

188

172

116

11

18

5

160

Table 6.6 List of pyrolytic products at temperature range 250 - 650 ºC identified in

humic acid mixed with kaolinite (3.85% TOC-H/K).

No.

RT

(min) Compound

lignin

18 12.52 Styrene

79 20.77 Naphthalene

104 23.46 1-Methylnaphthalene,

110 23.88 2-Methylnaphthalene,

112 24.35 1,4-Bis(1-Methylethenyl)benzene*

125 26.24 1,7-Dimethylnaphthalene*

126 26.26 1,3-Dimethylnaphthalene



138 28.57 4,6,8-Trimethylazulene*

139 28.64 2,3,6-Trimethylnaphthalene

140 28.87 1,4,6-Trimethylnaphthalene*

146 29.77 1,6,7-Trimethylnaphthalene*

147 29.92 3,3'-Dimethylbiphenyl*

152 31.63 7-Ethyl-1,4-dimethylazulene*

154 32.59 1,4,5,8-Tetramethylnaphthalene*

159 33.44 1-Methoxy-4-phenylmethylbenzene*

163 33.90 Phenanthrene*

164 34.35 2-(1-Cyclopenten-1-yl)naphthalene*

170 35.64 1-Methylanthracene*

171 35.75 1-Methyl-phenanthrene*

172 35.93 2-Methyl-anthracene*

176 37.98 3,6-Dimethylphenanthrene*

cellulose

66 19.57 2-Methylindene

89 22.11 4,7-Dimethyl-1H-indene*

91 22.28 1,3-Dimethyl-1H-indene*

108 23.76 2,3-Dihydro-1,1,3-trimethyl-1H-indene*

116 24.94 1,2,3-Trimethylindene*

161 33.67 2,3-Diphenyl-2-cyclopropen-1-one

Uncertain origin products

5 8.77 4-Methylenespiro[2.4]heptane

10 9.70 o-Xylene

11 10.02 p-Xylene

29 14.88 Phenol

36 15.72 3-Methylphenol

50 17.45 4-Methylphenol

57 18.51 2-Methylbenzofuran

65 19.40 2,5-Dimethylphenol

71 20.12 2,3-Dimethylphenol

161

117 25.06 Tetradec-2-ene

120 25.24 Tetradecane

136 27.47 Pentadecane

145 29.53 Hexadecane

149 31.47 Heptadecane

151 31.54 Heptadec-3-ene

153 32.52 4-Phenyl-tetrabicyclo[4.2.1.0(3,7).0(2,9)]non-4-ene*

156 33.19 Octadec-1-ene

158 33.31 Octadecane

160 33.64 Octadec-5-ene

166 35.05 Nonadecane

169 35.26 5-Phenylhexa-1,5-dien-2-ylbenzene

175 37.86 Syn-1,6:8,13-bismethano[14]annulene

177 38.09 Cyclopentadecane

179 38.28 Heneicosane

180 39.38 Pyrene

181 39.51 1-(6-Methyl-2-heptanyl)-4-(4-methylpentyl)cyclohexane

184 41.00 1-Methylpyrene

185 42.65 Tetracosane

186 42.97 Cyclotetracosane

188 47.83 Heptacosane


Compound identification in soil samples

Hexadecanoic acid was the only compound observed from pyrolysis of the 0 -

0.1 m band at 250 - 340 ºC. This is likely to be a natural component of the soil carbon

rather than a pyrolysis product from MOC degradation. No detectible compounds

were obtained from pyrolysis of the deeper samples at 250 - 340 ºC. However, 72

compounds were identified from pyrolysis at 250 - 600 ºC from throughout the profile

(Table 6.7). Representative chromatograms of MOC at 0 - 0.1 m and 28 - 29 m are

given in Figure 6.9.

MOC was identified in these soil samples by comparison with pyrolytic

products of authentic lignin, cellulose and chitin as described in the previous section

and according to the similarity of compound structures (Table 6.7). However,

distribution of carbonaceous MOC changed with profile depth.

162

Generally the identified compounds were most abundant in the 0 - 0.1 m band

and declined in abundance with depth. Lignin derived compounds, in particular

simple aromatic forms such as benzene, toluene, ethylbenzene and styrene, were

observed throughout the profile. By contrast, cellulose and chitin derived compounds

(indene, pyridine and benzonitrile species) were distributed mostly at 0 - 0.1 m (Table

6.7).

Figure 6.9 Chromatogram of macro organic matter at 0 - 0.1 m and 28 - 29 m

assigned by GC/MS. For full list of peak identifications refers to Table 6.7.

5 10 15 20 25 30 35 40

0

1

2

3

4

5x 10

5

5 10 15 20 25 30 35 40

0

1

2

3

4

5

6

7x 10

4

a) 0 – 0.1 m

b) 28 – 29 m

Ab

un

dan

ce


1

7

18

29

31

48

50

5463

88

99110

119 135

142 148 167

14+

16

1

6

14+

16 79

119

19

3052 99 135

143

12

163

Table 6.7 Pyrolytic products and percentage of peak area of soil samples at 5 depths.

No. RT

(min) Compound

Depth (m)

0 - 0.1 1 - 2 10 - 11 19 - 20 28 - 29

lignin

1 7.04 Benzene 6.91 22.32 16.90 12.86 26.81

7 9.20 Toluene 10.73 34.12 28.20 25.39 20.68

14 11.66 Ethylbenzene 1.79 3.85 4.76 4.82 4.43

18 12.50 Styrene 10.33 12.50 31.24 35.94 -

26 14.23 Propylbenzene* 1.11 - - - -

27 14.45 1-Ethyl-3-methylbenzene* 0.51 1.04 - - -

39 16.18 1,3,5-Trimethylbenzene* 0.56 - - - -

40 16.19 1,2,4-Trimethylbenzene* - 1.54 - - -

41 16.23 1-Methyl-3-(1-methylethyl)benzene* 0.32 - - - -

42 16.32 1-Propenylbenzene* 0.60 0.72 - - -

50 17.45 4-Methylphenol 4.66 - - - -

68 19.70 (1-Methyl-2-cyclopropen-1-yl)benzene* 1.99 - - - -

72 20.17 1,4-Dihydronaphthalene* 0.84 - - - -

75 20.49 Dodec-1-ene 1.44 - - - 2.83

79 20.77 Naphthalene 1.99 - 3.35 2.39 2.60

94 22.48 6-Methyl -1,2-dihydronaphthalene* 0.86 - - - -

97 22.58 3-Methyl-1,2-dihydronaphthalene* 0.23 - - - -

104 23.46 1-Methylnaphthalene 1.04 - - - -

110 23.88 2-Methylnaphthalene 1.20 - 0.59 - -

121 25.34 2-Ethenylnaphthalene* 1.04 - - - -

127 26.28 2,6-Dimethylnaphthalene* 0.81 - - - -

128 26.38 1,5-Dimethylnaphthalene 0.44 - - - -

141 29.25 1,6,7-Trimethylnaphthalene* 0.67 - - - -

154 32.60 1,4,5,8-Tetramethylnaphthalene* 0.25 - - - -

50.31 76.10 85.04 81.40 57.35

164

cellulose

48 16.91 Indene 2.60 0.68 1.80 1.51 -

70 19.89 1-Methyl-1H-indene* 1.30 - - - -

3.91 0.68 1.80 1.51 0.00

chitin

12 10.44 2-Methylpyridine 1.18 - - - -

15 11.69 3-Methylpyridine 1.13 - - - -

22 13.64 2,5-Dimethylpyridine 0.40 - - - -

23 13.70 3,4-Dimethylpyridine 0.40 - - - -

31 15.18 Benzonitrile* 5.12 5.41 - - -

54 18.10 2-Methylbenzonitrile* 1.07 - - - -

63 19.32 1-Isocyano-2-methylbenzene* 1.55 - - - -

88 21.96 Benzeneacetonitrile,α-methylene* 0.56 - - - -

102 23.32 (2E)-3-Phenylacrylonitrile 0.88 - - - -

105 23.58 Indolizine 0.74 - - - -

167 35.12 Hexadecanenitrile * 0.67 - - - -

13.70 5.41 0.00 0.00 0.00

unidentified compounds

8 9.49 2-Methylthiophene - 4.52 - - -

9 9.60 2-Octene 1.69 - - - -

16 11.92 p-Xylene 2.20 4.95 5.32 4.34 3.05

17 12.33 Non-1-ene 1.39 - - - -

19 12.52 Benzocyclobutene - - - - 23.31

28 14.55 Benzaldehyde 1.20 - - - -

29 14.88 Phenol 7.67 1.15 - - -

30 15.17 Dec-1-ene - - - - 3.58

33 15.40 1-Propen-1-ylbenzene 1.92 - - - -

34 15.52 Benzofuran 1.46 4.19 - - -

49 17.10 Butylbenzene 0.86 - - - -

52 17.90 Undec-1-ene - - - 3.27 3.16

53 17.90 Dodec-4-ene - - 2.37 - -

57 18.51 2-Methyl-1-benzofuran 0.72 1.51 - - -

165

60 18.96 1-Phenyl-1-butene 0.21 - - - -

78 20.68 Dodecane 0.42 - - - -

92 22.32 Hexylbenzene 0.58 - - - -

99 22.93 Tridec-1-ene 1.74 - 1.75 2.42 3.44

101 23.21 Tetradec-3-ene 0.37 - - - -

114 24.55 8-Prop-1-en-2-ylbicyclo[4.2.1]nona-2,4,7-triene 0.49 - - - -

119 25.23 Tetradec-1-ene 1.37 0.24 1.86 3.01 2.92

124 26.01 Cyclohexa-2,5-dien-1-ylbenzene 0.55 - - - -

131 27.03 Octylbenzene 1.13 - - - -

135 27.39 Hexadecan-1-ol 1.62 - 1.15 2.01 2.10

136 27.47 Pentadecane - 0.88 - - -

137 27.53 Nonadecane 0.44 0.37 - - -

142 29.42 Tetradec-2-ene 1.02 - - - -

143 29.44 Heptadecan-1-ol - - - 1.21 1.08

144 29.44 Dodecan-2-ol - - 0.71 - -

145 29.56 Hexadecane 0.35 - - - -

148 31.37 Eicos-3-ene 0.76 - - - -

150 31.47 Tridecane 0.58 - - - -

157 33.20 Nonadec-1-ene 0.70 - - 0.82 -

162 33.70 Anthracene 0.26 - - - -

165 34.76 Cyclotetradecane 0.39 - - - -

32.08 17.80 13.16 17.09 42.65


167

Quantification of soil pyrolysis products

Relative abundances of compounds at each profile depth showed that the

carbonaceous component of the soil consisted of predominantly lignin derived-

compounds (50 - 80 %) such as toluene, benzene and styrene at all depths (Table 6.7).

However, there was also a large proportion of compounds of unknown origin (13 – 42

%) such as p-xylene. Polysaccharides were present in smaller proportions near the

surface (17% in surface soil and 6% at 1 - 2 m) but declined in deeper layers. Clearly,

lignin was the main carbon source throughout the soil profile.

6.5 Discussion

Verification of method

Lignin is a naturally occurring complex polymer found in the cell walls of

plants and is a key component of wood. Although the composition of lignin varies

from species to species, the main components of the polymers are aromatic alcohols

with common characteristics (Clifford et al. 1995; Klingberg et al. 2005; McKinney et

al. 1995). Therefore, a commercially available standard lignin was selected for

optimising the pyrolysis conditions.

Treatment of pure lignin with TC(TMAH) mainly resulted in derivatives that

were di-and tri-methoxybenzenes with 1-3 phenyl, prophenyl or ethenyl side chains,

which is in close agreement with previous studies (Clifford et al. 1995; del Río et al.

1998; Klingberg et al. 2005; McKinney et al. 1995). In particular, the presence of

168

methyl 3, 4-dimethoxy benzoate, a key methylated derivative of lignin, confirmed that

the thermochemolysis was completed.

The major derivatives observed from TC(TMAH) treatment of cellulose and

chitin included acetic acid, furan and N-containing compounds (in case of chitin)

which corresponded well with previous studies (Bierstedt et al.1998; Fabbri and

Helleur 1999; Marbot 1997; Schwarzinger et al. 2001, 2002). The presence of the

fatty acid ester group was listed by Hartgers et al. (1995). These results confirmed the

suitability of the reported reaction conditions for this analysis, as the signature

compounds relevant to lignin, cellulose and chitin could be discriminated.

Nevertheless, akyl benzenes were produced from TC(TMAH) pyrolysis of all three

pure carbon substrates in this study. Sáiz-Jiménez (1995) suggests this is due to

incomplete methylation as a result of insufficient amount of TMAH.

Importance of extraction prior to methylation

Extraction has previously been employed on samples with a wide range of

concentrations (0.03 - 31.9% TOC). However, these studies have not established a

concentration limit below which extraction is essential (Buurman et al. 2009; Chefetz

et al. 2002; González-Pérez et al. 2012). This study has demonstrated that extraction

is the key step to enhance analysis for samples that contained less than 3.50% TOC.

Two different concentrations of NaOH were used for extraction and signature

compounds of lignin, cellulose and chitin were observed from both concentration

levels.

169

An extraction methodology for humic acid was established by the

International Humic Substances Society (IHSS) (Buurman et al. 2009; Chefetz et al.

2002; González-Pérez et al. 2012). Briefly, samples are treated with 0.1 M NaOH

before being precipitated with 6 M HCl. The humic acid fraction is re-dissolved by

0.1 M KOH followed by centrifugation and then treated with a mixture of HCl / HF to

remove solid particles and ash before dialysis against distilled water. In comparison to

the conventional methodology, the current study does not consider acid hydrolysis

because it has been shown that esters, amides, carbohydrates and some N-containing

compounds are removed by the HCl treated procedure (Chefetz et al. 2002).

Thermochemolysis with TMAH of extracted lignin

The number of derivatives obtained from extracted lignin, treated with

TC(TMAH), were dramatically decreased compared to methylation of pure lignin.

Although the same weight of carbon in these samples was the same as used in pure

samples, the freeze-dried extracts also contained some residual NaOH. Therefore, the

actual mass of lignin reacted was smaller than that of the previous pure lignin

experiment. This would partially account for the decrease in the concentration of

compounds obtained from extracted lignin. However, the method was still considered

to be suitable to identify lignin extracted from simulated soil samples.

The results derived from different concentrations of NaOH indicated that

lignin remnants could be obtained with stronger NaOH. However, the method

employing lower concentration of NaOH followed by methylation was selected for

further study for two reasons. Firstly, the key compounds, methoxy benzoic acid

170

species were obtained. Secondly, there was a lower rate of explosive pressure failure

of the thermochemolysis‟s tubes. This was most common for samples that were

extracted initially using 1 M NaOH. The presence of the NaOH led to very high

production of water and carbon dioxide by-products, causing a build-up of pressure,

which has also been noted previously by Tanczoc et al. (2003).

TC(TMAH) of extracted polysaccharides was not done because a number of

laboratories have demonstrated that polysaccharides and other carbon substrates can

be methylated simultaneously (Chefetz et al. 2000, 2002; del Río et al. 1998; Frezier

et al. 2003; Pulchan et al. 1997). Thus, it should be possible to analyse the extracted

cellulose and chitin by this procedure.

Thermochemolysis of soil profile from field samples

An abundance of MOC was identified in the surface soil layer. Various

polysaccharide compounds indicated the decomposition of organic material produced

by recent vegetation. For example, furan and pyran are released from carbohydrates

by wildfire or biodegradation (Atanassova et al. 2012; Schellekens et al. 2009). The

contribution from vegetation could also be proposed from the presence of 1,2,3-

trimethoxy-5-methylbenzene which could be the syringyl unit of lignin. This lignin

subunit could represent the residue of grass (Buurman et al. 2009). It is likely some of

the syringyl measured was the result of the recent degradation from current agricultual

practices: annual pasture and crop rotation.

Terpene species such as globulol and yalangene are constituents of the

essential oils of native eucalypts and exotic pines, respectively (Dob et al. 2005; Tan

171

et al. 2008; Xu et al. 2013) and provide evidence of the link to vegetation in previous

and present times. Furthermore, patchouli alcohol, a compound common to the

Lamiaceae plant family whose members include wild flowers of south-western

Australia, was also found in this study and is consistent with the research of Corrick

and Fuhrer (2009).

The finding of globulol in deep profiles but an absence of yalangene and

patchouli at depth, suggests that the carbon in the deep soil was contributed by exotic

pines and the previous primary vegetation, for over a hundred years. Pines have been

established in plantations over 85000 ha in south-western Australia since 1897

(Dunstan et al. 1998). Also, it was common for farmers to plant avenues of pines as

windbreaks in some areas or as individual trees near homesteads and gateways.

Furthermore, condensed aromatic rings such as naphthalene observed from surface

soil are the signature compounds of fire events (del Río et al. 2001; 2002). Fire is

widely used for prescribed burning and land clearing, and wildfires are common

where fuel loads are high enough (Boer et al. 2009; Burrows 2008).

Effect of Pyrolysis temperature

This study was carried out on mixtures of kaolinite with authentic carbon

species; lignin, cellulose and chitin at 3.85% TOC. It was assumed that there is no

reaction between a carbon substrate and kaolinite during pyrolysis.

The extended temperature range (250 - 600 ºC) was more effective at

decomposing carbon structures in pure standards and soil samples compared to the

lower pyrolysis temperature range of 250 - 340 ºC.

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Different thermal stability ranges of carbon substrates have been investigated

in the past for example, from 150 - 900 ºC for lignin, from 315 - 400 ºC for cellulose

and from 200 - 360 ºC for chitin (Chen et al. 1998; Lu et al. 2011; Yang et al. 2007;

Zeng et al. 2015). The thermal stability of lignin was slightly greater than

polysaccharides because of its complex composition and structure, which is also

influenced by its moisture content. Cellulose and chitin are homopolysaccharides that

differ from lignin in term of linkages (Brebu and Vasile 2010). Lignin is a co-polymer

of three basic building blocks; p-coumaryl alcohol, coniferyl alcohol and sinaphyl

alcohol with various C-O and C-C linkages between the different units. The C-C

linkages of lignin need quite high temperatures to cleave and produce volatile

fragments. In cellulose and chitin, there are only C-O linkages between the sugar

building blocks and these require lower temperature to fragment.

Pyrolytic products of lignin, cellulose and chitin

Signature compounds and pyrolysis pathways of carbon substrates have been

investigated previously (Asmadi et al. 2011; Jiang et al. 2010; Lu et al. 2011). In

general, the degree of polymerisation is decreased when the pyrolysis temperature is

increased and the number of pyrolysis products depends on the pyrolysis conditions.

The main pyrolysis products of lignin identified in this study are methyl or

methoxy substituted phenols, which agrees well with the study of Jiang et al. (2010).

Naphthalenes and phenanthrenes were also obtained from this study which has

previously been reported in the study of Asmadi et al. (2011).

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The different aromatic compounds result from different pyrolysis pathways for

lignin. Asmadi et al. (2011) demonstrated that the degradation pathway occurs after

the ether bonds of lignin are broken. Consequently, the aromatic compounds are

released and react via two main pathways. The first pathway is radical induced

rearrangement followed by decomposition to cresols and xylenols, and subsequently

phenol and benzofuran are generated. Secondly, aromatics are further reacted via

homolysis of the O-CH3 bonds to produce catechols and pyrogallols before these

intermediates are decomposed to produce naphthalenes and phenanthrenes.

For cellulose, a key pyrolysis product was 1,4:3,6-dianhydro-α-D-

glucopyranose, which corresponds with the studies of Lu et al. (2011) and Stefanidis

et al. (2014). Although the signature compounds are mainly furans and

cyclopentanones, there is no evidence of levoglucosan, which may be influenced by

the pyrolysis temperature.

Levoglucosan is the primary product of cellulose that can be either an

intermediate or a product resulting from pyrolysis (Zhang et al. 2013). Generally,

levoglucosan is produced at < 350 ºC and declines when the temperature is raised to >

600 ºC (Lu et al. 2011) when furan derivatives are preferentially formed. However,

levoglucosan was not detected in either temperature range. Lin et al. (2009) proposed

that levoglucosan is initially formed from pyrolysis of cellulose and then undergoes

dehydration and isomerisation reactions to form other anhydro-monosaccharides such

as 1,4:3,6-dianhydro-β-D-glucopyranose, levoglucosenone(6,8-dioxabicyclo

[3.2.1]oct-2-en-4-one) and 1,6-anhydro-β-D-glucofuranose(2,8-dioxabicyclo

[3.2.1]octane-4,6,7-triol). In this case, levoglucosan might be only an unstable

174

intermediate leading to anhydro-monosaccharides which would explain its absence in

the analysis.

An early study also showed that several derivatives of furans, cyclopentanones

and phenols were produced at 700 ºC (Schlotzhauer et al. 1985). Moreover, some

studies noted that phenols were produced from cellulose at 510 ºC (Sakuma et al.

1981) or in the temperature range of 450 - 800 ºC, with a maximum yield generated at

temperatures above 750 ºC (Pouwels et al. 1989).

Simple aromatic hydrocarbons and phenols are not derived from primary

pyrolysis of cellulose but are formed from secondary reactions. These reactions have

been studied by Evans and Milne (1987) who demonstrated that aromatic

hydrocarbons and phenols are formed from the gas phase polymerization of

unsaturated species such as propylene, butadiene and butene.

The pyrolytic characterisation of chitin has been far less studied compared to

lignin and cellulose. Recently, Zeng et al. (2015) characterised volatile gas produced

from pyrolysis of chitin at 260 - 360 ºC in the presence of oxygen. Their results

showed that acetamine, furan-2-ylmethanol, 1H-pyrrole-2-carbaldehyde, N-(furan-3-

yl)acetamide and 2-fufaldehyde were the major products. However, in the present

study 6-methyl-3-pyridinol was obtained from pyrolysis at 250 - 340 ºC. A diversity

of N-containing pyrolytic products such as pyridines, amines, nitriles and amides

were obtained when the expanded temperature range (250 - 600 ºC) was used.

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Compound characterisation and distribution in a soil profile

Characterisation of deep soil carbon in profiles to a depth of 29 m has not

previously been studied. This study exemplified the persistence and distribution of

different carbon compounds over the entire depth. It is highly possible that many of

the compounds identified are root-derived as evidenced by the early studies (Carbon

et al. 1980; Dell et al. 1983) together with the field observations of this study, as

described in Chapter 2.

The detection of lignin-derived compounds over the entire depth implies that

lignin is a recalcitrant biopolymer compared to cellulose and chitin. On the basis of

TMAH/thermochemolysis analysis, cellulose and chitin appears to be mostly

distributed in the surface soil at 0 - 0.1 m, which is obviously linked to above ground

inputs. The results also suggest that these materials are decomposed before leaching

to greater depths. On the other hand, the second most abundant group were

compounds of unidentified origin, which suggests that soil carbon includes many

residual compounds from different stages of degradation.

This is consistent with the observation that lignin comprises a major portion of

root tissue and has a low decay rate (Kögel-Knabner 2002; Rasse et al. 2005).

Chemical recalcitrance of plant litter material is generally attributed to the aromatic

compounds of lignin that require strong oxidation agents to be degraded. There are

limited numbers of organisms, mostly the wood-rotting fungi from the groups of

ascomycetes, basidiomycetes and deuteromycetes, that are able to decompose lignin

(del Río et al. 2001).

176

Although these fungi are generally expected near the surface, there is a

substantial level of oxygen in the deep soil and ground water environments

(Roundnew et al. 2012). This ecosystem supports living soil microorganisms

including aquatic animals evidenced by finding of fungi remnants in the form of

glucan molecules at 40 - 43 m in paddy soil (Kotake et al. 2013) and microbial and

aquatic animal activities in soil at depth of 10 - 30 m in different agricultural land uses

(Korbel et al. 2013).

Performance of thermochemolysis and pyrolysis methods

Thermochemolysis with TMAH and pyrolysis methods were found to be

robust for characterisation of carbon determined from pure carbon substrate products.

However, the performances of these methods when dealing with low concentration

samples were different.

The procedure developed involving extraction with NaOH followed by

TMAH thermochemolysis of 3.85% TOC-L/K resulted in a dramatically reduced

number of products compared to the list of species determined from pure lignin

without pre-treatment. Moreover, extraction of the sample with NaOH and analysed

with TMAH was limited to analysis of deep soil samples with concentrations of

>0.23% TOC. On the other hand, the pyrolysis technique was able to deal with deep

soil samples at concentrations down to 0.01% TOC.

When comparing both methods applied to the same surface soil sample,

TMAH/thermochemolysis resulted in selective fragmentation of MOC resulting in

177

products with complex structures. In comparison, pyrolysis gave high numbers of

simple compounds.

Secondly, terpene compounds, indicative of plant species were observed when

samples were analysed using TC(TMAH) but unfortunately were lost during heating

in the pyrolysis technique. The difference demonstrates the advantage of using both

techniques to obtain compound information about soil organic components. However,

TC(TMAH) is unsuitable for samples with low amounts of carbon (< 0.23% TOC).

6.6 Conclusions

This study is a first contribution to understand the distribution of MOC

vertically throughout a deep soil profile using TMAH thermochemolysis and

pyrolysis techniques. The procedure developed for characterising the deep soil MOC

involved sample extraction with 0.1 M NaOH followed by thermochemolysis with

TMAH. The developed method allows extraction of carbon substrates in kaolinite

(3.85 - 0.23% TOC) and soil matrixes at 0 - 0.1 m, while the pyrolysis technique

revealed the origins of soil carbon throughout the profile down to 29 m without pre-

treatment.

The TMAH thermochemolysis results suggest that when the land use changed

from forest to annual pasture, the MOC associated with forest also degraded. The

present vegetation influences only the surface soil, which is revealed by the

biomarkers of terpenes, lignin, polysaccharides, proteins and the occurrence of

yalangene and patchouli alcohol. The presence of soil biota such as bacteria and

178

fungi was revealed by distribution of N-containing compounds in the profile.

Furthermore, napthalene compounds disclosed fire incidents in this agricultural area.

On the other hand, the pyrolysis results indicated that lignin, cellulose and chitin are

distributed differently in the profile. Lignin was observed throughout the profile and

mainly contributed to deep soil carbon concentration, while polysaccharides were

present only in surface soil layers, which is consistent with the TC(TMAH) results.

This study deduced that carbon in the form of lignin was stabilised in deep soil

whereas other polysaccharides such as cellulose and chitin were confined to the

surface soil.

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Chapter 7

General Discussion

7.1 Introduction

The objectives of the research reported in this thesis were to (i) validate select

methods for quantifying deep soil carbon, and (ii) characterise carbon in deep soil

profiles in south-western Australia. To the author‟s knowledge, this is the first study

to systematically evaluate the performance of methods based on a series of carbon

laboratory standards before analysis of field samples. From the series of laboratory

studies, the major findings in relation to the research questions posed in Chapter 1 are

summarised in Table 7.1.

Overall, the performance of methods was quite variable and some were of

limited application due to their low sensitivity. Examples in this category were the

wet digestion method for carbon quantification developed by Heanes (1984) (Chapter

3), and vibrational spectroscopy in the mid-infrared region for characterisation of

carbon (Chapter 4).

Nevertheless, the classical wet digestion method of Walkley-Black was

successfully verified for quantification of organic carbon in deep soils (Chapter 3).

Furthermore, a model was developed for NIR spectroscopy that also provided reliable

quantification of TOC in deep soils (Chapter 4). This research provides an

180

opportunity for the application of the methods for deep soil carbon accounting in

other parts of the world.

Analysis of real soils in this study was performed on core samples taken from

bulk soil or root zone area. No root fragments were observed in these bulk samples

(Harper and Tibbett 2013). Nevertheless, the methods developed in this study could

be applied to provide even greater detail of the spatial distribution of carbon, e.g.

around root channels in kaolinitic regolith (Figure 7.1). Recommended methods are

summarised in Table 7.2.

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Table 7.1 Research questions and results obtained in this study.

No. Question Result

1 What are the LOD and LOQ of the

Walkley-Black and Heanes methods?

(Chapter 3)

LOD and LOQ of the Walkley-Black method were 0.015 and 0.050% TOC, while

the values were five times higher for the Heanes method with LOD and LOQ of

0.085 and 0.281% TOC, respectively.

2 What is the most suitable wet digestion

method considered from %RC and method

performance for deep soil carbon

determination?

(Chapter 3)

Though, the %RC of both methods were uncertain and dependended on the type of

carbon substrates. However, based on performance, the Walkley-Black method is

superior to the Heanes method. When the Walkley-Black was implemented for deep

soil samples, the predictive equation [TOC (%)]actual = 1.66[TOC (%)]WB + 0.018

(R2 = 0.91) was obtained with an RMSE of 0.017.

3 Is NIR spectroscopy sufficiently sensitive to

quantify carbon in deep soil? (Chapter 4)

Sensitivity of NIR spectroscopy was tested with standard samples in the

concentration range 0.00 - 1.50% TOC. The predictive equation [TOC(%)actual =

1.020(TOC(%)predicted) + 0.001], (R2 = 0.981) was obtained from the calibration set,

while the validation set exhibited an RMSE of 0.032. Therefore, this method is less

accurate compared to the Walkley-Black method.

4 Is MIR spectroscopy using DRIFT able to

identify the functional groups of standard

carbon samples? (chapter 4)

The LODs of DRIFT were 1.92% TOC for lignin or humic acid and 1.00% TOC for

cellulose or chitin. Unfortunately, functional groups of a mixture of various carbon

types were not fully detected by DRIFT, as only the dominant functional group

appeared. Therefore, DRIFT is not suitable for characterisation of deep soil carbon.

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5 What is a suitable method to identify

LMWC of deep soil profiles? (Chapter 5)

LMW compounds are residual carbon that could be extracted by ethyl acetate before

GC/MS characterisation. The method was able to reveal source and environment of

LMWC for three deep soil profiles down to 28 m.

6 What compound classes can be identified

from soil profiles and do they differ with

depth? (Chapter 5)

Three compound classes were identified from chromatograms of the first meter of

soil: 1) terpenes, 2) fatty acids, including amides and alcohols of fatty acids, and 3)

bioactive compounds and plant sterols. In comparison, compounds related to fatty

acids such as (Z)-docos-13-enamide, (9Z)-9-octadecenenitrile, (9Z)-9-

octadecenamide, hexadecan-1-ol and (9E)-9-hexadecen-1-ol were the predominant

residual carbon species in deep soils.

7 Could thermo-chemolysis using tetramethyl

ammonium hydroxide [TC(TMAH)-

GC/MS] be optimised for deep soil MOC

characterisation? (Chapter 6)

Characterisation of deep soil carbon using TC(TMAH)-GC/MS required pre-

concentration of samples with 0.1 M NaOH. In the surface soil at 0 - 0.1 m, this

method revealed several biomarkers such as lignin, polysaccharides and protein

indicating the influence of vegetation, microorganisms and fire events.

Unfortunately, characterizing the deeper layers with concentrations ≤ 0.23% TOC

was limited by this method.

8 What is a suitable pyrolysis temperature for

identifying MOC of a whole soil profile?

(Chapter 6)

Pyrolysis-GC/MS with a temperature range of 250 - 600 ºC was suitable for

characterization of standard samples at concentration of 3.85% TOC and field

samples.

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Figure 7.1 Heterogeneous staining of soil around root channels of a deep kaolinitic

regolith, coin for scale (3.15 cm diameter).

Table 7.2 Methods recommended for analysis of deep carbon in kaolinitic regolith.

Method Root

area

Diffused

zone

Bulk

soil

Remark

Quantification

Dry combustion

Walkley-Black

method

? Able to analyse but not 100% recovery

NIR ? Highest concentration used in this

study was 1.5% TOC, optimisation of

a new model may be required for root

samples.

Root

Diffused zone

Bulk soil

184

Characterisation

MIR ? Partly characterised if concentrations >

LOD

NIR ? ? Some peaks were noticeable

TC(TMAH)-

GC/MS

? Method can be applied when

concentration > 0.23% TOC

Pyrolysis GC/MS

Organic solvent

extraction

7.2 Carbon distribution in deep soil profiles

A qualitative study was also conducted to shed some light on the composition

of deep soil carbon in kaolinitic regoliths of south-western Australia. The

characterisation of deep soil carbon was achieved by partitioning carbon into two

forms: LMWC and MOC. Both forms of carbon were characterized and semi-

quantified by using the same sample, a 29 m profile called CU03 taken from the

previous study of Harper and Tibbett (2013).

The LMWC can be readily released from soil samples by solvent extraction.

Organic solvent extraction (hexane, dichloromethane, ethyl acetate and methanol)

indicated that LMWC was composed of a mixture of apolar and moderately polar

compounds in the surface soils (0 - 0.1 m) but the deep soil layers (9 - 10, 18 - 19 and

27 - 28 m) contained only moderately polar compounds. The LMWC was further

investigated at three depths (0 - 1, 11 - 12 and 18 - 19 m) from three locations (GL03,

185

PT06 and ST04). A number of residual carbon species were identified in these deep

soil profiles. The most widely identified compounds were amides and alcohols of

fatty acids, fatty acid esters and nitriles, implying that these are the most persistent

compounds in deep soils which are derived from tree roots and microorganisms.

Analysis of LMWC in these profiles indicated that the influence of the current

vegetation was limited to the first meter of soil, which was evident from terpenes and

plant sterols (Chapter 5).

Hydrophobicity, or water repellency, is a key property of compounds that

contribute to the stabilisation of lipids in surface soils (Bachmann et al. 2008) and is a

common feature of surface soils in this region (Harper et al. 2000; Walden et al.

2015). Soil water repellency has been associated with a coating of fatty acid

molecules on soil particles (McKissock et al. 2003). The authors explained that the

head group of fatty acid affixes with the polar surface of soil particles and turns the

hydrophobic tail group opposite to the surface soil. Another mechanism was

suggested in that soil water repellency was introduced by the accumulation of

hydrophobic material within soil pores (Doerr et al. 2000). Compounds associated

with water repellency were identified from the first meter of all profiles (Chapter 5).

The stability of terpenes and plant sterols in surface soils may be due to their

resistance to decomposition by organisms such as insects and microorganisms (Xu et

al. 2013; Xu et al. 2015).

Quantitative analysis for the three profiles showed the LMWC concentration

ranged from 3.15 - 14.27 µg C g soil-1

. Concentrations of LMWC in surface soil (0 - 1

m) (3.85 - 8.93 µg C g soil-1

) were only slightly higher than values at lower depths

186

(0.92 - 3.56 %TOC). Overall, this concentration range was very low relative to the

total organic carbon content of deep soil samples (Chapters 3 and 4). This is likely to

be due to the bulk of carbon compounds remaining in the form of undegraded MOC.

A significant shift of MOC compounds was distinctly observed between

surface (0 - 0.1 m) and deep (10 - 29 m) soils. Analysis of surface soils by TMAH-

GC/MS and Py-GC/MS revealed compounds derived from pyrolysis of lignin,

polysaccharides, proteins and terpenes. Cellulose and chitin were identified further

down in the 1 - 2 m band. These biomarkers demonstrated the influence of vegetation,

fire events and microorganisms. However, lignin was the only MOC detected

throughout the full depth of these cores. This suggests that the non-lignin MOCs may

have been depleted in deep soils and concentrations were under the detection limit.

This implies that discontinuous carbon supply to deep soil has significantly

affected the detectable MOCs. This corresponds with a change in land use, since

European settlement, from deep-rooted vegetation to annual pasture over the past 50 -

150 years, followed by some reforestation. In comparison, in fields in Japan where

land has been cultivated for rice for 70 - 100 years, cellulose biomarkers such as

polysaccharides and fungal debris were observed in a 43 m deep profile (Kotake et al.

2013). The authors also claimed that there were hemicellulose biomarkers but these

were unable to be detected because of the limitations of the method (T. Kotake

personal communication). Furthermore, changes in land use have led to substantial

changes in groundwater (Peck and Hatton 2003) but the consequences on dynamics of

deep soil carbon are unknown as the biota associated with these groundwater systems

is totally uncharacterized. In very different agricultural environments some studies are

187

beginning to unravel the biological components. For example, groundwater

ecosystems under three agricultural land uses (irrigated, non-irrigated and grazing

sites) transformed from cotton farming in the last 30 years in New South Wales were

monitored by Kobel et al. (2013). Compared among the three sites, aquatic animals

were more abundant and diverse in irrigated sites and the lowest microbial functional

diversity indicated by the Biolog system was observed in the non-irrigated site in

winter (Kobel et al. 2013). This indicated that sensitive groundwater-biota were

disturbed by short term changes in land use. Our study in south-western Australia

investigated rain-fed agricultural lands where traces of microorganisms in LMWC

signals indicated that degradation had occurred at depth. It would be informative to

determine the types of microorganisms that persist in deep root channels and whether

changes in land use and groundwater levels have disturbed the dynamics of deep soil

carbon in this region.

7.3 Future direction of analytical techniques for deep soils

This thesis focused on characterising the LMWC and MOC distributed in the

whole profile as a first priority followed by quantification and partitioning of carbon.

Thus, the absolute quantification of LMWC based on the ethyl acetate extraction was

determined. In fact, three other solvents also showed potential for isolation of

different compound classes (Chapter 5). In order to gain the absolute quantification of

the whole LMWC, it would be necessary to reappraise the extraction procedure by

188

considering solvent combinations for co-extraction, which is necessary to maximise

the concentration and variety of LMWC.

The thesis explored the use of TMAH-GC/MS and Py-GC/MS to gain more

details of MOC in deep soils. Unfortunately, the detection was limited by the small

concentrations of carbon present at these depths. For example, off-line TMAH-

GC/MS was limited to concentrations >0.23 % TOC. Although, some volatile carbon

species may be lost when opening reaction tubes, quite complex information could be

gained compared to the Py-GC/MS procedure (Chapter 6). Analysis of the cores by

Py-GC/MS showed thermal stability of deep soil carbon in the range 250 - 340 °C

because only hexadecanoic acid was observed. In comparison, 72 compounds were

identified from pyrolysis in the temperature range 250 - 600 °C.

To achieve a greater understanding of MOC in deep soil profiles, at least three

complementary improvements are recommended. Firstly, pre-concentration of

samples with 0.1 M NaOH before analysis with Py-GC/MS. Secondly, employing

multi-shot Py-GC/MS in the temperature range 250 - 600°C. Thirdly, exploiting

selective fragmentation with TMAH combined with Py-GC/MS, at temperatures

lower than thermal degradation, might resolve loss of compound information.

7.4 Dynamics of deep carbon

It is well acknowledged that lignin is the most abundant and rigid biopolymer

compared to other macromolecular organic compounds (Kögel-Knabner 2002). Many

studies have demonstrated that lignin or stabilised carbon degradation is hampered if

189

the easily decomposable carbon forms are exhausted, as there is no fuel for microbial

decomposers (Fontaine et al. 2007; Klotzbücher et al. 2011; Marschner et al. 2008).

This thesis highlights that identification of MOC and LWMC carbon in a

whole profile indicates the different stages of decomposition that have occurred in soil

profiles. For example, the LMWC indicated the compounds already degraded into

very small portions, namely 0.92 - 3.56 % relative to total organic carbon. In

comparison to MOC, the organic material that has not been decomposed, particularly

lignin, constitutes the main component of organic carbon in a full profile.

Although, LMWC concentrations were of a similar magnitude between

subsoils (< 2 m) and deep soils (> 9 m), there were different systems of carbon

degradation apparent as interpreted from various kinds of MOCs observed. Clearly,

subsoils had been influenced by above ground input from past deep-rooted vegetation,

however this had been removed some 50 - 80 years previously (Harper and Tibbett

2013). The more recent agricultural land-use comprised shallow-rooted crop and

pasture plants, and the maritime pine (P. pinaster) was only 10 years old (Harper et al.

2009), and its roots would have unlikely penetrated to depth. If the above hypothesis

is true, carbon in deep soil might be relatively inert compared to subsoils because the

degradation of carbon in subsoils was promoted by easily decomposable carbon, such

as proteins and polysaccharides, from above ground, while these MOCs were

exhausted in deep soils.

Land use practices, such as applying organic materials, tillage or fire

prescriptions, would obviously affect carbon stores in surface and subsoils (< 5 m)

(Eggleton and Taylor 2008). On the other hand, reforestation with deep-rooted

190

vegetation seems to be a certain way to promote carbon in deep soils. Certainly, for

eucalypts in this environment, roots appear to follow old root channels and have been

reported to a depth of 8 m within 3 years of establishment (Harper et al. 2014).

However, it is not known whether native lignin degradation is triggered by the carbon

generated from root exudations as plant roots penetrate into deep soils. Nor is it

known how new and old carbon interacts in deep soils.

Unfortunately, the amount of carbon lost since land has been deforested has

never been estimated. Identification of LMWC and MOC might help to investigate

the half-life of carbon in deep soil and to parameterise a model. In addition, other

precise methods for monitoring the loss of deep soil carbon are required for carbon

accounting and this is an area for further study.

7.5 Conclusions

In conclusion, this thesis provides significant insights into the composition and

quantitation of deep soil carbon. Until recently this has been overlooked in carbon

accounting procedures, despite soil carbon representing the largest global store of

carbon and its possible role in mitigation strategies. Methods were carefully validated

for deep soil in south-western Australia and these can be extended to deep soils in

other parts of the world. Although knowledge of deep soil carbon was projected from

studies of subsoils (≤ 2 m) (Chabbi et al. 2009; Fontaine et al. 2007; Rumpel 2014;

Sanaullah et al. 2011), several hypotheses require further testing. Clearly, knowledge

of deep soils is still far less than that of surface soils and many questions remain

191

unanswered. Increasing our knowledge of deep soil properties will lead to better

understanding and management of global terrestrial carbon stores.

193

Appendices

Appendix 1. Comparison of the Walkley-Black method (1934) and the modified

method used in this study#.

Reagent Walkley-Black (1934) This study

sample size (µm) 149 500

sample weight (g) - 2

concentration range 20-25 mg C -

K2Cr2O7 1M, 10 mL 1M, 10 mL

H2SO4 20 mL 10 mL

Distilled water 120 mL 20 mL

indicator 0.5% diphenylamine, 1 mL 1.48% o-phenanthroline ferrous

sulfate complex, 5 drops

Fe2SO4 0.4 M 0.5 M

NaF 5 g -

# From preliminary work, there is no difference when changing H2SO4 and distilled

water volumes or Fe2SO4 concentration. The volume of H2SO4 generates enough heat

for a sample with less effect on the environment when disposed also the % RC is not

enhanced. The volume of water also helps to make a clear colour when the end point

is reached and is suitable for 250 ml conical flasks. Concentration of Fe2SO4 is in

accordance to the level of oxidation which must be standardised each time.

194

Appendix 2. Chemical properties of a representative soil (0 - 10 cm, Bassendean

Sand).

Property Value

pH (water) 6.2

pH (CaCl2) 5.8

EC (dS/m) 0.206

Leco total N (%) 0.82

Colwell (bicarbonate) P (mg/kg)# 160

Colwell (bicarbonate) K (mg/kg)# 335

Exchangeable Ca (cmolc/kg)*

Exchangeable Mg (cmolc/kg) *

Exchangeable Na (cmolc/kg) *

Exchangeable K (cmolc/kg) *

21.98

0.34

2.94

<0.10

# Colwell (1965)

* Rayment and Lyons (2011)

195

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